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Genetic parameters, physiological and molecular analysis of root and

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GENETIC PARAMETERS, PHYSIOLOGICAL AND MOLECULAR ANALYSIS OF ROOT AND SHOOT TRAITS RELATED TO DROUGHT TOLERANCE IN RICE (Oryza sativa L.)

VEERESH GOWDA R.P. PAK 50 45

DEPARTMENT OF GENETICS AND PLANT BREEDING UNIVERSITY OF AGRICULTURAL SCIENCES BENGALURU

2010

GENETIC PARAMETERS, PHYSIOLOGICAL AND MOLECULAR ANALYSIS OF ROOT AND SHOOT TRAITS RELATED TO DROUGHT TOLERANCE IN RICE (Oryza sativa L.)

VEERESH GOWDA R.P. PAK 50 45

Thesis submitted to the

University of Agricultural Sciences, Bengaluru In partial fulfillment of the requirements For the award of the degree of

Doctor of Philosophy (Agriculture) in

Genetics and Plant Breeding BENGALURU

JULY, 2010

Affectionately Dedicated to My Beloved Parents Smt. Laxmi Devi Sri. Po mpana Gowda R. and Friend, Kalmesh

DEPARTMENT OF GENETICS AND PLANT BREEDING UNIVERSITY OF AGRICULTURAL SCIENCES G.K.V.K. BENGALURU - 560 065 CERTIFICATE This is to certify that the thesis entitled “Genetic parameters, physiological and molecular analysis of root and shoot traits related to drought tolerance in rice (Oryza sativa L.)” submitted by Mr. VEERESH GOWDA R.P., ID No. PAK 5045 in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY (AGRICULTURE) in Genetics and Plant Breeding to the University of Agricultural Sciences, GKVK, Bengaluru is a record of research work done by him during the period of his study in this university under my guidance and supervision, and the thesis has not previously been formed the basis for the award of any degree, diploma, associateship, fellowship or other similar titles.

Bangalore

(Dr. H.E. SHASHIDHAR) Professor, Plant Biotechnology, UAS, GKVK, Bengaluru

July, 2008

APPROVED BY Chairman

Members

:

:

___________________________ (Dr. H.E. SHASHIDHAR))

: 1. ___________________________ (Dr. K. P. VISHWANATHA)

2.

___________ ________________ (Dr. D. L. SAVITHRAMMA)

3.

__________________________ (Dr. N. B. PRAKASH) __________________________ (Dr. VIJAYALAKSHMI)

4.

ACKNOWLEDGEMENT It is my heart’s turn to express my deepest sense of gratitude to all of those who directly and indirectly helped me in this endeavour. At the very outset, I fell inadequacy of words to express my profound indebtedness

and

deep

sense

of

gratitude

to

my

esteemed

chairman

Dr. Shashidhar H.E., Professor, Department of Plant Biotechnology, GKVK, University of Agricultural Sciences, Bengaluru for his esteemed stewardship, enabling guidance, cherishable counselling and personal affection for which I am greatly indebted to him. It was really a great pleasure and privilege for me to be associated with him during my M. Sc. Degree Programme. It gives me immense pleasure to express my heartfelt thanks to the supervisor’s of my research work Dr. S. Rachid Serraj, Senior Scientist, CESD, IRRI, Philippines. Dr. Ajay Kohli, Senior Scientist, CESD, IRRI, Philippines. Dr. Vincent Vadez, Senior Scientist, ICRISAT, India. Dr. Ram T, Senior Scientist DRR, India. for their valuable counsel, note-worthy guidance and cordial co-operation during the course of investigation. I would like to thank Dr. Shailaja hittalmani, Professor and Head, Department of Genetics and Plant Breeding, GKVK, University of Agricultural Sciences, Bangalore who provided all kind of support to me in completion of Doctoral study. I feel no words to express my heartfelt gratitude and respect to all his kindness. It also gives me immense pleasure to express my heartfelt thanks to the members of my advisory committee Dr. K. P. Vishwanath, Professor, GKVK, University of Agricultural Sciences, Bangalore. Dr. D. L. Savithramma, Professor, department of Soil

Science, and Dr. N. B. Prakash, Professor for their valuable counsel, note-worthy guidance and cordial co-operation during the course of investigation. I owe a lot to my parents, brother and sisters, without whose affection, support and sacrifice, this study would scarcely have been accomplished. My diction is poor to translate into words the sense of gratitude, heartfelt respect and affection to my father Mallappa Patne mother Kalavati M. Patne and sisters Anita, Sunita and Sangeeta and my brother Revansiddappa Patne and my grand mother Shantveeramma for their moral support, boundless love and building unshakable confidence in me which motivated as a force to fulfill this long cherished ambition. I bow my head with overwhelming respect and thanks to all people in ZARS, Hiriyur, Chitradurga especially to Field supervisors Basavaraj, Kumar and Basavaraj who helped me lot during my stay at Hiriyur. I use this opportunity to sincerely thank my dearest classmate’s Abdul, Rahamani, Chetan, Dhanajaya, Gopal, Madan, Matruthi, Nandhini, Prasanna, Praveen and Suresh, for their lovely friendship, help and care and for making the two year study very much enjoyable and memorable. Words could not help me when I need to thank my dear friends, Patil, Jaba, Kallihal, Mahesh Salimath, Sashi, Vishnu Vardhan, M. P, Yankareddy, Sharanappa, Gundappa, Sangappa and Naveen Dhama, for the great support they gave me. I fondly thank my senior friends Dr. Kalmeshwer Gouda Patil, Ramesh Patil, Kamala Kant and Muniraj who provided me their valuable guidance and to my room mates, Akhter and Jai Kumar for all their help. I am overwhelmed with gratitude to all my respondents, without whose whole hearted co-operation, this study would not have been fruitful.

Bangalore July, 2008

(Nagesh)

CONTENTS CHAPTER NO.

TITLE

I

INTRODUCTION

II

REVIEW OF LITERATURE

III

MATERIAL AND METHODS

IV

EXPERIMENTAL RESULTS

V

DISCUSSION

VI

SUMMARY AND CONCLUSION

VII

REFERENCES

PAGE NO.

LIST OF TABLES

TABLE NO.

TITLE

1.

List of OryzaSNP panel rice genotypes used for real time water uptake rates, root and grain yield parameters under both field and lysimetric experiments.

2.

Climatic conditions at experimental sites during 1-112 days after transplanting.

3.

Soil properties of experimental site used for screening OryzaSNP panel rice accessions during DS2008.

4.

Mean climatic conditions at experimental site during 1-112 days after transplanting

5.

List of parents of mapping population, donors and advanced breeding lines, of IRRI-India drought breeding network used for analyzing real time water uptake rates, root distribution and grain yield under both field and lysimetric experiments

6.

List of NILs and parents used for both gene expression and lysimetric experiments.

7.

List of LEA primers used for gene expression study and their sequence

8.

Means of root number (RN), root to shoot ratio (RSR) and root length density (RLD, cm cm-3) across different soil depths of OryzaSNP panel rice accessions under both drought stress and well- watered treatment in field experiment during DS 2008

9.

Means of root surface area (RSA, cm2) across different soil depths of OryzaSNP panel rice accessions under both drought stress and well-watered treatment in field experiment during DS 2008

10.

Means of root volume (RV, cm3) across different soil depths of OryzaSNP panel rice accessions under both drought stress and well watered treatment in field experiment during DS 2008 Means of root dry weight (RDW, g) across different soil depths of OryzaSNP panel rice accessions under both drought stress and well-watered treatment in field experiment during DS 2008

11.

PAGE NO.

LIST OF TABLES

TABLE NO.

TITLE

12.

Means of physiological character of OryzaSNP panel rice accessions measured under drought stress treatment in field experiment during DS 2008.

13.

Means of shoot parameters of OryzaSNP panel rice accessions under both drought stress and well-watered treatments in field experiment during DS 2008

14.

Means of grain yield (GY g/m2), straw biomass (SB, g/m2) and harvest index (HI) of OryzaSNP panel rice accessions under both well watered and drought stress treatments in field experiment during DS 2008

15.

Means of shoot and grain yield parameters of OryzaSNP panel rice accessions under both drought stress and well-watered in field experiment during DS 2009.

16.

Analysis of variance for root and shoot characters of OryzaSNP rice panel accessions under drought stress and well-watered condition in field experiment during DS 2008

17.

Analysis of variance for physiological characters of OryzaSNP rice panel accessions under drought stress condition in field experiment during DS 2008

18.

Analysis of variance for shoot and grain yield characters of OryzaSNP rice panel accessions under drought stress and well-watered condition in field experiment during DS 2009

19.

Mean, range and genetic parameters for shoot and physiological characters of OryzaSNP panel rice accessions under drought stress and well watered condition in field experiment during DS 2008.

20.

Mean, range and genetic parameters for root characters of OryzaSNP panel rice accessions under both drought stress and well-watered in field experiment during DS 2008.

PAGE NO.

LIST OF TABLES

TABLE NO.

TITLE

21.

Mean, range and genetic parameters for shoot and grain yield characters of OryzaSNP panel rice accessions measured under both drought stress and well-watered condition in field experiment during DS 2009.

22.

Phenotypic correlation co-efficients among root, shoot and grain yield traits of OryzaSNP panel rice accessions under drought stress in field experiment during DS 2008.

23.

Phenotypic correlation co-efficients among root distribution shoot and grain yield traits of OryzaSNP panel rice accessions under well watered condition in field experiment during DS 2008.

24.

Water uptake rates (g/plant) measured at different intervals during drought stress period using OryzaSNP panel accessions in lysimetric experiment during WS 2008

25.

Means of maximum root length (MRL, cm)), root number (RN), and root length density(RLD, cm/cm3) across different soil depths of OryzaSNP panel rice accessions under both drought stress and well-watered treatment in lysimetric experiment during WS 2008.

26.

Means of root surface area (RSA, cm2) across different soil depths of OryzaSNP panel rice accessions measured under both drought stress and well-watered treatment in lysimetric experiment during WS2008

27.

Means of root volume (RV, cm3) across different soil depths of OryzaSNP panel rice accessions measured under both drought stress and well-watered treatment in lysimetric experiment during WS 2008

PAGE NO.

LIST OF TABLES

TABLE NO.

TITLE

28.

Means of root dry weight (RDW, g) across different soil depths of OryzaSNP panel rice accessions measured under both drought stress and well-watered treatment in lysimetric experiment during WS 2008

29.

Means of root and shoot traits of OryzaSNP panel accessions under both drought stress and well-watered conditions in lysimetric experiment during WS 2008.

30.

Water uptake rate (g) measured at different intervals using OryzaSNP panel accessions under drought stress condition in lysimetric experiment during DS 2009.

31.

Means of root parameters of OryzaSNP panel rice accessions under both drought stress and well-watered conditions in lysimetric experiment during DS 2009.

32.

Means of shoot parameters of OryzaSNP panel accessions under drought stress and well-watered environment in lysimetric experiment during DS 2009.

33.

Water uptake rate (g/plant) measured at different intervals using OryzaSNP panel rice accessions under drought stress treatment in lysimetric experiment during WS 2009.

34.

Mean total water uptake (TWU, g/plant) during stress period under drought stress condition using OryzaSNP panel rice genotypes belonging to different rice types in three lysimetric experiments (WS2008 /IRRI; DS 2009 /ICRISAT; WS 2009/ IRRI).

35.

Analysis of variance for real time water uptake rates of OryzaSNP rice panel accessions under drought stress condition in three lysimetric experiments (WS 2008, DS 2009 and WS 2009)

PAGE NO.

LIST OF TABLES

TABLE NO.

TITLE

36.

Analysis of variance for root and shoot characters of OryzaSNP rice panel accessions under drought stress and well-watered condition in lysimetric experiment during WS 2008

37.

Analysis of variance root and shoot characters of OryzaSNP rice panel accessions under drought stress and well-watered condition in lysimetric experiment during DS 2009

38.

39.

Mean, range and genetic parameters for root characters of OryzaSNP panel rice accessions under drought stress and well-watered conditions in lysimetric trial during WS 2008. Mean, range and genetic parameters for shoot characters of OryzaSNP panel rice accessions under drought stress and well- watered conditions in lysimetric experiment during WS 2008.

40.

Mean, range and genetic parameters for root and shoot characters of OryzaSNP panel rice accessions under both drought stress and well-watered condition in lysimetric experiment during DS 2009.

41.

Phenotypic correlation co-efficients among water uptake, root distribution and shoot traits of OryzaSNP panel rice accessions under drought stress in lysimetric experiment during WS 2008

42.

Phenotypic correlation co-efficients among water uptake, root distribution and shoot traits of OryzaSNP panel rice accessions under well watered condition in lysimetric experiment during WS 2008.

PAGE NO.

LIST OF TABLES TABLE NO.

TITLE

43.

Phenotypic correlation co-efficients among root and shoot traits of OryzaSNP panel rice accessions under drought stress in lysimetric trial during DS 2009.

44.

Phenotypic correlation co-efficients among root and shoot traits of OryzaSNP panel rice accessions under well-watered condition in lysimetric experiment during DS 2009.

45.

Path analysis (phenotypic) indicating direct and indirect effects of component characters on shoot dry weight (SDW, g/plant) measured under drought stress condition using OryzaSNP panel rice accessions in lysimetric experiment during WS 2008

46.

Path analysis (phenotypic) indicating direct and indirect effects of component characters on shoot dry weight (g/plant) measured under well-watered condition using OryzaSNP panel rice accessions in lysimetric experiment during WS 2008.

47.

Grouping of OryzaSNP panel rice accessions based on D2 analysis using root and shoot traits measured in drought stress treatment during WS2008

48.

49.

50.

Grouping of OryzaSNP panel rice accessions based on D2 analysis using root and shoot traits measured in well watered treatment during WS 2008 Average intra and intercluster D2 values using root and shoot traits measured in drought stress treatment during WS 2008 Average intra and intercluster D2 values using root and shoot traits measured in well-watered treatment during WS 2008

PAGE NO.

LIST OF TABLES TABLE NO.

TITLE

51.

The nearest and farthest clusters from each cluster based on D2 values using root and shoot traits measured in drought stress treatment during WS 2008

52.

The nearest and farthest clusters from each cluster based on D2 values using root and shoot traits measured in well watered treatment during WS 2008

53.

Per cent contributions of twelve root and shoot characters towards diversity in drought stress and wellwatered condition.

54.

The mean values of clusters for twelve root and shoot characters measured under drought stress treatment in lysimetric experiment during WS 2008

55.

The mean values of clusters for root and shoot characters using root and shoot traits measured in wellwatered treatment during WS 2008

56.

Means of shoot and grain yield parameters of parents of mapping population, donors and advanced breeding lines of IRRI-India drought breeding network under both drought stress and well-watered condition in field experiment during DS 2009

57.

Analysis of variance for shoot characters under wellwatered and drought stress condition using parents of mapping population, donors and breeding lies of IRRIIndia drought breeding network in field experiment during DS 2009

58.

Mean, range and genetic parameters for shoot and grain yield characters of contrasting breeding lines and parents of mapping population measured under both drought stress and well-watered condition in field experiment during DS 2009.

PAGE NO.

LIST OF TABLES TABLE NO.

TITLE

59.

Water uptake rates (g/plant) of parents of mapping population, donors and advanced breeding lines of IRRIIndia drought breeding network in lysimetric experiment during DS 2009.

60.

Means of root parameters of contrasting parents of mapping population, donors and advanced breeding lines of IRRI-India drought breeding network under drought stress and well-watered conditions in lysimetric experiment during DS 2009

61.

Means of shoot parameters of parents of mapping population, donors and advanced breeding lines of IRRIIndia drought breeding network under both drought stress and well-watered condition in lysimetric experiment during DS 2009

62.

63.

64.

Analysis of variance for real time water uptake rates under drought stress condition using parents of mapping population, donors and breeding lies of IRRI-India drought breeding network in lysimetric experiment during DS2009 Analysis of variance for root and shoot characters under drought stress and well-watered condition using parents of mapping population, donors and breeding lies of IRRIIndia drought breeding network in lysimetric experiment during DS2009 Mean, range and genetic parameters for root and shoot characters of contrasting donors, parents of mapping population and breeding lines of IRRI –India measured under both drought stress and well-watered condition in lysimetric experiment during DS 2009.

PAGE NO.

LIST OF TABLES TABLE NO.

TITLE

65.

Phenotypic correlation co-efficients among root and shoot traits measured under drought stress in lysimetric trial during DS 2009 using contrasting parents of mapping population, donors and breeding lies of IRRIIndia drought breeding network.

66.

Phenotypic correlation co-efficients among root and shoot traits measured under well watered during DS 2009 using contrasting parents of mapping population, donors and breeding lies of IRRI-India drought breeding network.

67.

Water uptake rates at different time intervals using NILs and its parents in lysimetric experiment during WS 2008

68.

Means of root number (RN), maximum root length (MRL,cm) and root length density (RLD,cm cm-3) across different soil depth of Adeysel NILs along with parents under both drought stress and well-watered treatment in lysimetric experiment during WS 2008

69.

Means of root surface area (cm2) across different soil depth of Adeysel NILs along with parents under both drought stress and well watered treatment in lysimetric experiment during WS 2008

70.

Means of root volume (cm3) across different soil depth of Adeysel NILs along with parents under both drought stress and well watered treatment in lysimetric experiment during WS 2008

71.

Means of root dry weight (mg) across different soil depth of Adeysel NILs along with parents under both drought stress and well watered treatment in lysimetric experiment during WS 2008

PAGE NO.

LIST OF TABLES TABLE NO. 72.

73.

74.

75.

TITLE Means of shoot parameters of NILs and parents under both drought stress and well watered treatment in lysimetric experiment during WS 2008 Mean gene expression pattern under well water (1.0 FTSW) and severe drought stress (0.2 FTSW) conditions in different zones of shoot using IR64 and Dular. Mean gene expression pattern under well water (1.0 FTSW) and severe drought stress (0.2 FTSW) conditions using top zone of root in NILs of Adeysel X IR64 with IR64 and Dular. Mean gene expression pattern under well water (1.0 FTSW) and severe drought stress (0.2 FTSW) conditions using deep zone of root in NILs of Adeysel X IR64 with IR64 and Dular.

PAGE NO.

LIST OF FIGURES FIGURE NO.

TITLE

1.

Soil water potential measured by mercury-manometer tensiometers at soil depths of 15 and 30 cm. Values shown are mean ± s.e., n = 3. Measurements were not taken from days 72-94 as the field was irrigated for root sampling.

2.

Volumetric soil moisture at a soil depth of 70 cm, measured by diviner. Values shown mean ± s.e. , n = 6. Measurements were not taken from days 72-94 as the field was irrigated for root sampling.

3.

Rainfall pattern during stress period at experimental site during DS 2008, IRRI, Philippines.

4.

Volumetric soil moisture at a soil depth of 45 cm, measured by TDR. Values shown mean ± s.e. , n = 3. Measurements were not taken from days 22-24 as the field was irrigated.

5.

Rainfall pattern during stress period at experimental site during DS 2009, ICRISAT, India.

6.

Percent total root length distribution with depth for genotypes of Oryza SNP panel under drought stress treatment measured during DS 2008.

7.

Drought induced root growth at depth (30-45cm) in all OryzaSNP rice panel accessions

BETWEEN PAGES

LIST OF PLATES PLATE NO.

TITLE

1.

Overview of well- watered treatment during DS 2008 at IRRI, Philippines

2.

Overview of drought stress treatment during DS 2008 at IRRI, Philippines

3.

Method of root sampling under field condition using monolith sampler during DS 2008 at IRRI, Philippines

4.

Overview of well- watered treatment during DS 2009 at ICRISAT, India

5.

Overview of well- watered treatment during DS 2009 at ICRISAT, India

6.

Overview of lysimetric experiment during WS 2008 at IRRI, Philippines

7.

Lysimetric system used for real- time water uptake measurements during WS 2008 and WS 2009 at IRRI, Philippines

8.

Overview of lysimetric experiment during WS 2008 at IRRI, Philippines

9.

Method of root washing during DS 2009 at ICRISAT, India.

10.

Overview of pot experiment during WS 2009 at IRRI, Philippines

11.

Expression pattern revealed by semi quantitative RTPCR of LEA genes in top root tissues of drought resistant and susceptible checks

BETWEEN PAGES

LIST OF PLATES PLATE NO.

TITLE

12.

Expression pattern revealed by semi quantitative RTPCR of LEA genes in deep root tissues of drought resistant and susceptible checks

13.

Expression pattern revealed by semi quantitative RTPCR of LEA genes in top root tissues of drought resistant and susceptible NILs.

14.

Expression pattern revealed by semi quantitative RTPCR of LEA genes in deep root tissues of drought resistant and susceptible NILs.

15.

Expression pattern revealed by semi quantitative RTPCR of LEA genes in top root tissues of drought resistant and susceptible NILs

16.

Expression pattern revealed by semi quantitative RTPCR of LEA genes in deep root tissues of drought resistant and susceptible NILs.

BETWEEN PAGES

I. INTRODUCTION Rice is the world’s single most important food crop and a primary food for more than a one third of the world’s population. Rainfed lowland rice accounts 28% of the world’s rice growing area and it forms 18 per cent of world rice supply. More than 90% of the world’s total rainfed lowland area is in Asia. India and Bangladesh in south Asia and Indonesia, Thailand and Myanmar in south East Asia together accounts more than 80 per cent of the total area and production in Asia (IRRI, 1993). Despites its importance, rainfed lowland rice received very little attention from rice research community. Overall average yield of rainfed rice for Asia is 2.3 t/ha. In eastern India itself 13 million ha is grown to rice in the rainfed lowland condition. Even a small increase in the yield of these regions would add significantly to global rice production (Mackill et al 1996). Drought is a major abiotic stress, strongly limiting rice production system in several parts of the world. Drought resistance is genetically and physiologically complex. The plant uses different mechanisms to cope with drought stress namely, drought escape, drought tolerance, drought recovery and drought avoidance mechanisms (Blum, 1988). Achieving drought resistance in rice will be necessary for meeting the growing water shortage of the world and it will require deeper understanding of the possible mechanisms available for drought resistance. Drought stress needs more time to develop in lowlands than in uplands. But when stress occurs, it causes more loss of yield in lowland varieties than upland varieties because lowland varieties are not conditioned for such kind of situations. The drought resistant mechanisms, which are appropriate for upland system, may not be suitable for rainfed lowland and vice versa (Mackill et al 1996). Both systems even require different root phenotyping methods. Both theoretical and experimental studies illustrated that root system have role in water uptake and nutrient uptake (Andrews and Newman, 1970). Extensive reports are available, describing the role of roots in upland condition. However, information regarding

their development, distribution and role in drought resistance under lowland conditions is limited. Till today, there is no clear evidence of their contribution to grain yield under drought stress. Keeping this in mind, we have conducted a series of field and lysimetric experiments across years, using diverse genotypes of rice along with advanced breeding lines, donors, parents of mapping population and near-isogenic lines. LEA proteins play a special role in protecting cytoplasm from dehydration and storage of seeds and in whole-plant stress resistance to drought, salt, and cold. LEA proteins are expressed through all the developmental stages with different expression levels and no tissue specificity (Bo et al 2005). Gene expression analysis helps in identifying functionally important genes and pathways involved in root architecture under drought stress condition (Breyne et al. 2003). Keeping these points in view, the present investigation was taken up with the following objectives. 1. Characterization of OryzaSNP panel accessions for plant water uptake, root distribution and yield under rainfed lowland ecosystem, 2. Genetic parameters for root and shoot traits under irrigated control and stress, 3. Association between root and yield morphological traits under different moisture regimes, 4. Diversity studies using OryzaSNP panel accessions under well-watered and drought stressed conditions, 5. Screening of parents of mapping population, donors and advanced breeding lines with improved yield under drought for root and shoot characteristics under field and controlled conditions, 6. Dissection of drought tolerance mechanism using NILs of grain yield for drought stress and

7. Comparative expression of LEA genes in different zones of root and shoot under different soil water levels.

II REVIEW OF LITERATURE The literature pertaining to the present study in rice is reviewed and resented under the following headings. 1. Rainfed lowland ecosystem and drought 2. Genetic variation for root traits and their role in drought resistance 3. Role of physiological traits in drought tolerance 4. Genetic variability parameters, correlation, path coefficient and genetic divergence studies for root and shoot traits in rice 5. Genes conferring drought tolerance 6. Gene expression studies in rice 1. Rainfed Lowland Ecosystem and Drought Rice is produced in a wide range of locations under a variety of climatic conditions, occupying one-tenth of the global agricultural land. Rice is grown in four main ecosystems; irrigated lowland, rainfed lowland, deep-water and upland. As described by IRRI (IRRI 2000), in rainfed lowland, rice is transplanted or direct seeded in puddled soil, on level slightly sloping, bunded or dyked fields with variable depth and duration of flooding, depending of rainfall. Soils alternate from flooded to non flooded; yields vary depending on rainfall, cultivation practices and use of fertilizer. Most of rainfed lowlands are located in south and Southeast Asia. Modern high yielding rice varieties do not adapt well to these ecosystems, so farmers grow traditional varieties that yield about 2.3 t/ha, which is less than half of the irrigated rice. Garrity et al (1986) estimated that more than 50 per cent of rainfed lowland area could be classified as drought prone or highly drought prone. These areas may experience frequent and severe drought stress at any time during rice growth and may be subjected to uncertain rainfall distribution patterns. But in spite of those difficulties, around 20 per cent of global rice production occurs in rainfed land (Pandey et al., 2007). Rainfed lowland rice is grown on 46 M.ha out of the 132 M.ha of world rice area (MacLean et al., 2002).Yield improvements in this ecosystem not only make large

impact on world rice production but also on rural poverty in Asia as most of these areas are located in Asia (Garrity et al 1986; Khush, 1984). Drought is the most important abiotic stress limiting rice production in rainfed lowland condition (Widawsky and O’Toole 1990). Drought stress occurs when the combination of rainfall and soil water supply are inadequate to meet the demands of the crop with reference to transpiration from areal plant parts and evaporation from soil surface. Drought is highly heterogeneous in time (over seasons and years) and space (between and within locations) in most of the rainfed agricultural areas of world. The level of damage to crops due to drought stress depends on the genotypes ability to withstand drought, growth stage of the crop when it impacts, duration and intensity (which in turn depends several edaphic factors). 2. Genetic Variation for Root Traits and Their Role in Drought Resistance Roots are the principle plant organ for anchorage, nutrient and water uptake. Further, roots also influence shoot growth, water use efficiency, and overall productivity of the crop, as the roots constantly communicate with shoots and vice versa.

Studies on genetic variation for root traits in rice have been

ongoing for several decades. Only important and recent ones are reviewed hereunder. In upland conditions, Mambani and Lal (1983) reported a significant positive correlation between deep root growth and grain yield, and the authors clearly demonstrated that deep roots had a role in water uptake. O’Toole and Bland (1987) reviewed the genotypic variations in root systems and reported that plant root systems have capability of coping with the changes in environmental factors such as water status and temperature. A significant genotypic variation for root penetration ability was reported by Yu et al (1995) by using the wax layer method. Using the same method, Babu et al (2001) found that japonica accessions have a higher root penetration index (number roots penetrating the wax layer/ total number nodal and seminal roots)

than indica types, and these were used to develop double haploid mapping populations (CT9993/IR62266 and IR58821/IR52561) for mapping and tagging root traits, over the next decades. Sarkarung and Pantuwan (1999) reported a role of rooting depth and root thickness in determining drought tolerance of rice varieties under rainfed lowland conditions. In experiments simulating rainfed lowland conditions using pot plants, Wade (1999) noticed genotypic difference for water extraction and their relation with root distribution under drought stress. Genotypic differences for root mass, root length and distribution across lowland rice varieties were reported by Azhiri-Sigari et al (2000). Similarly Thanh et al (1999) and Kondo et al (2003) noticed large genotypic variation for root traits among upland varieties. The genotypic variation for root traits in different types of rice were studied by Lafitte et al (2001) and reported that Indica rice types had thin, highly branched superficial roots with narrow vessels and low root to shoot ratio, whereas japonica types had coarse roots with wider vessel, less branched long roots and a large root to shoot ratio, and aus types had intermediate root diameter, with a root distribution profile similar to that of japonica but with thin roots. Toorchi et al (2002) evaluated root morphology and related characters at three different stages and different moisture regimes using PVC cylinders. They notices significant genotypic differences for all root and shoot traits measured across different sampling. Venuprasad et al (2002), in a study involving simultaneous evaluation of root character and grain yield of under stress, and control conditions concluded that roots, that a rice plant produced prior to onset of stress, will enable a plant to tide through the stress situation and also produce better yield that a genotype that did not have the capacity to produce roots prior to the onset of stress. In a subsequent study Toorchi (2006) and Kanbar et al (2009), based on canonical correlation studies conducted under contrasting moisture regimes,

suggested that maximum root length, ration of root to shoot by weight and length, and

root dry weight (even when evaluated under well watered conditions)

conferred an advantage to grain yield under stress. 3. Role of Physiological Traits in Drought Tolerance Plant water status is estimated by several major variables such as water potential, turgor potential, and relative water content. Generally, maintenance of high relative water content has been considered to be a drought-resistance rather than drought-escape mechanism, and it is a consequence of adaptive characteristics such as osmotic adjustment and/or bulk modulus of elasticity. Leaf water potential is recognized as an index for whole plant water status (Turner, 1982) and maintenance of high leaf water potential is considered to be associated with dehydration avoidance mechanisms (Levitt, 1980). The maintenance of leaf water potential is determined by the interaction of numerous mechanisms. These include access to soil water and the pattern of soil water uptake by roots, loss to atmosphere controlled by stomatal conductance, canopy size, leaf rolling and death, and possible internal resistance to water transport. The maintenance of high leaf water potential minimized the effects of water deficit on spikelet sterility and consequently grain yield. Stomatal closure is the immediate response of plants to water stress in order to avoid the tissue dehydration during drought stress. Stomata regulate the flow of carbon dioxide and water between the dry atmosphere and the wet leaf interior. This method of water reservation helps maintain plant water potential and has been associated with reduced spikelet sterility and increased grain yield under flowering stage drought conditions in rice (Pantuwan et al., 2002). Various stomata characteristics such as low conductance, high sensitivity to leaf water status and ABA accumulation have been suggested as desirable traits in crop improvement for water-limited environment (Turner et al., 1986). However, with these traits the concurrent reduction in carbon dioxide movement,

photosynthesis and dry matter production is unavoidable, (Turner 1982) which in turn has a negative impact on yield. The sensitivity of stomata to leaf water status has been shown to have significant genetic variation in rice (Tuner et al., 1986; Dingkuhn et al., 1989; Dingkuhn et al., 1991 and Price et al., 1997). O'Toole and Chang (1979) observed that, leaf rolling under controlled conditions is related with the stomatal closure and decreases transpiration from rice leaves. In rice, cultivars with greater leaf rolling maintained higher leaf water potentials but this had no detectable effect on water transpired or dry matter produced over a ten-day period (Turner et al., 1986). Heritability of leaf rolling has been identified as moderate to high (Price et al., 2002). 4. Genetic Variability Parameters, Correlation, Path Coefficient and Genetic Divergence Studies for Root and Shoot Traits in Rice 4.1 Coefficients of Variation, Heritability and Genetic Advance There are several reports published for these genetic parameters, heritability and advance in rice. Only important and recent ones are reviewed hereunder. Armenta-Soto et al (1983) reported higher narrow sense heritability estimates for root thickness (62 per cent), root length (60 per cent), and root number (44 per cent). Similarly Mao (1984) reported that broad sense heritability for root length and thickness were high, root number and dry weight were moderately high and low respectively. Ekanayake et al., (1985a), using F1, F2 and F3 populations of cross between IR 20 (shallow, thin root system) and MGL-2 ( deep, thick root system), reported that root thickness, root dry weight and root length are polygenic traits with substantial proportions of additive variation and with narrow sense heritability’s greater than 50 per cent. They suggested that selection for these root traits based individual plant performance could be successful in early segregating generations. In another study, Ekanayake et al., (1985b) noticed low inheritance for root pulling force under lowland rice.

Chang et al., (1986) reported moderate to high heritability for maximum root length (61 per cent), root tip and root base diameters (62 per cent) by using aeroponics. They found that dominant genes controls root numbers, root depth and root mass where as root thickness is controlled by both dominant and recessive genes. Shashidhar et al (1990) reported high heritability estimates for five root traits in a study on twenty- four rice genotypes. High heritability was also reported for root length and root thickness (Das et al 1991). They also reported high environmental coefficient of variability for root length, root to shoot ratio, root dry weight, shoot dry weight and tiller number. In an F2 population, high phenotypic variability was observed for grain yield, panicle weight, number of productive tillers and total tillers. High heritability (broad sense) values were recorded for plant height and days to maturity in both direct sown rainfed (aerobic) and irrigated (anaerobic) conditions. The expected genetic advance was also high for panicle weight and productive tillers (Venkataravana 1991). Hemamalini (1997) reported moderate to high heritability to maximum root length, root number and root weight, highest expected genetic advance as per cent of mean for root volume and lowest for root diameter. She reported high and low heritability for root number and root thickness respectively. Latha (1996) reported high heritability for root dry weight (94.05 per cent), shoot dry weight (87.26 per cent), root number (86.52 per cent), root volume (80.50 per cent), number of tillers (77.63 per cent) and root length (72.35 per cent) and moderate heritability for other traits. She also reported high and low expected genetic advance as per cent of mean for rot dry weight and root thickness respectively. High heritability for root thickness and moderate heritability for root volume was reported by Price et al (1997). They also reported moderately high heritability for root length.

Vaithiyalingan and Nadarajan (2006) reported significant differences among the F2 populations for all the characters studied. Among the characters, grain yield showed high genotypic co-efficient of variation, heritability along with genetic advance as per cent of mean, followed by the characters viz., spikelet fertility per cent, productive tillers per plant and number of grains per panicle. These traits are highly amenable for selection while going for the crop improvement program of rice through inter sub-specific hybridization. High genotypic and phenotypic co-efficients of variability was reported for straw yield per plant, total biological yield per plant, number of fertile florets per panicle and number of branches per panicle (Panwar et al. 2007). The heritability estimates were highest for days to fifty per cent flowering, days to maturity and thousand grain weight. The genetic advance as per cent of mean were higher for number of branches per panicle, straw yield per plant, total biological yield per plant and grain yield per plant. Estimates of variability, heritability and genetic advance as per cent of mean were worked out in twenty four aromatic rice genotypes by Patil (2009) for yield and its attributing characters. They observed higher per cent of genetic and phenotypic co-efficient of variability for iron content, zinc content, test weight and length/ breadth ratio while, plant height, grain length, grain yield per plant, number of productive tillers per plant. Number of tillers per plant recorded moderate PCV and GCV values in the studied genotypes. The heritability values coupled with high genetic advance as per cent of mean were recorded for zinc content, iron content, test weight, length/breadth ratio, grain length, plant height and grain yield per plant. 4.2 Correlation and Path Co-efficient Analysis The literature regarding association and path co-efficient analysis in rice have been reviewed and presented in the following paragraphs. Ekanayake et al (1985a) reported that root thickness and root numbers were correlated with plant height, tiller number and shoot weight. They also found

that root length, thickness and root volume were significantly correlated with drought recovery. They observed positive association amongst root characters and reported significant correlation between plant height and root characters. Cruz et al (1986) reported strong linear relationship between total root dry mass and total root length and also between total plant dry mass and root dry mass. Shashidhar (1990) reported significant association of root weight with root length and root volume. Shahid et al (1994) reported positive correlations between root length, root dry weight, shoot dry weight, stomatal frequency and drought tolerance. Latha (1996) reported highest association between shoot dry weight and total dry weight. They also reported significant association amongst root traits. Hemamalini (1997) found positive correlation between all root characters under well watered conditions. They recorded highest correlation between root dry weight and root volume. Yadav et al (1997) also reported positive correlation among studied root traits. Thanh et al (1999) studied thirty three upland rice cultivars and observed significant correlation among all root traits except root number. Highest correlation among root characters was observed between maximum root length and total root dry weight. Plant height was also correlated with root thickness, maximum root length and total root dry weight. Venuprasad (1999) reported significant and positive association of grain yield with plant height, productive tiller number, panicle length, straw yield, total dry matter, harvest index and dry matter per day per plant at both phenotypic and genotypic levels. Gireesha (1999) found significant and positive correlation of plant height with total root number, root length, root dry weight, shoot dry weight and total dry weight. Kanbar (2002) reported significant positive correlation between plant height and root characters. Maximum root length, number of roots, root volume, root dry weight and number of tillers were observed to be interrelated. Prabuddha

(2002) reported root length showed significant positive association with root number, root volume, root dry weight, root thickness, plant height and shoot dry weight at both phenotypic and genotypic level. Significant positive association of grain yield with plant height, productive tillers per plant, dry matter per plant, leaf weight and harvest index was reported by Shashidhar et al. (2005). While, stem weight per plant, number of grains per panicle and flag leaf area showed significant positive association at genotypic level. Path co-efficient analysis revealed that, dry matter per plant had maximum positive direct effect followed by harvest index and plant height at phenotypic level. Kanbar et al (2009), based on canonical correlation studies conducted under contrasting moisture regimes, suggested that maximum root length, ration of Root to Shoot by weight and length, and root dry weight (even when evaluated under well watered conditions) conferred an advantage to grain yield under stress. 4.3 Genetic Diversity in Rice Genetic divergence was estimated by Biswas and Sasmal (1990) using Mahalanobis D2 statistic in seven rice varieties and their twenty one F1 hybrids. The twenty eight genotypes were grouped into six clusters but the grouping of parental genotypes did not follow geographical pattern. Jha et al. (1999) grouped twenty accessions of wild rice genotypes of Uttar Pradesh based on Mahalanobis D2 statistics into three clusters in which cluster I represented Oryza nivara with fourteen accessions, cluster II comprised of five accessions of Oryza sativa and cluster III with only one Oryza rifipogon accession. Das et al (2004) evaluated fifty land race collections of rice for genetic distance. The genotypes were grouped in ten clusters. Intra-cluster distance was highest in cluster IX followed by cluster I which included twelve genotypes of diverse origin. The maximum inter-cluster D2 value was recorded between clusters IV and IX. This was followed by cluster VIII and IX. The clustering pattern indicated that, the geographic diversity was not necessarily related with genetic

diversity. Days to fifty per cent flowering, grain yield per plant, grain length, kernel breadth and hundred-kernel weight were identified as potential characters that can be used as parameters while selecting diverse parents in the hybridization programme for yield and quality improvement. Assessment of genetic divergence using Mahalanobis D2 statistics was carried out on forty one high yielding and local genotypes of rice by Bhutia et al. (2005). The genotypes were grouped into six clusters. Cluster I had the highest number of genotypes (twenty seven) followed by cluster II with eight, and cluster III with three genotypes, respectively. An experiment was conducted with fifteen modern rice cultivars to estimate the contribution of different characters to the total divergence and the pair wise distance for each case to identify the right pair to be used in hybridization programme. Days to fifty per cent flowering had the greatest contribution to the total divergence, followed by thousand-grain weight and plant height. According to the distance matrix tables of Mahalanobis D2 analysis, BR-10 and BRRE - dhan 30 was the closest pair and BR-5 and BRRI-dhan 33 the most distant pair. The grain yield, tiller number per hill and filled grains per panicle were the least contributing characters towards the total divergence (Zaman et al., 2005). Patil (2009) evaluated genetic diversity in twenty four aromatic rice genotypes using D2 statistics. The varieties belonging to diverse ecological regions clustered together whereas, genotypes of the same region have entered widely into separate groups. Contribution of each character towards genetic divergence indicated that grain yield per plant contributed maximum towards the genetic divergence followed by grain length, zinc content and test weight. 5. Genes Conferring Drought Tolerance Many of the drought stress related genes have been isolated and characterized in the last two decades in a variety of crop species (Ramanjulu and Bartels 2002; Cattivelli et al., 2008). Most of the QTL studies have been undertaken in rice but in spite of such great effort on roots, no single QTL cloning has been achieved so

far in rice root. But recently in Arabidopsis one gene controlling root morphology/ architecture QTL has been identified by map- based cloning (Sergeeva et al 2006). In any study once a major QTL is identified and validated, positional cloning is the approach most commonly utilized to close the genotype-phenotype gap although alternative approaches based on candidate genes and linkage disequilibrium may represent an interesting shortcut to QTL cloning (Salvi and Tuberose 2005). Numerous transcription factors have been reported across crops and are responsible for the regulation of signal transduction and the expression of stress related genes which import stress resistance to plants. Transgenic rice with the transcription factor AtDREB1A or its orthologue OsDREB1A (DehydrationResponsive Element Binding gene) tested in pots which demonstrated improved resistance to simulated drought, high salt and low temperature stresses (Yamaguchi-Shinozaki and Shinozaki, 2004). In spite of their evident role in water uptake, so far roots have been less targeted in genetic engineering strategies to improve the performance of crops under water deficit conditions. Xiao et al (2007) have showed that over accumulation of LEA genes increases drought tolerance without yield penalty in field conditions. Recent work of Park et al (2005) demonstrated increase of root size by single gene transformation. AVP1 plays role in root development through the facilitation of auxin fluxes. Vinod et al (2006) identified candidate genes for root traits related to morphology, physiology. These were validated in the CT9993/IR62266 mapping population which was evaluated for roots traits under contrasting moisture regimes. Another work on peanut reported that the DREB1A transcription factor had a significant effect on root growth under drought stress conditions. In this case, transgenic plants showed twenty to thirty per cent higher water uptake than wild types and this water uptake was highly correlated with deep root dry weight (Vadez et al 2008).

Recently Norton et al (2008) proposed a novel approach for identification of positional candidate genes. This approach has three steps viz., initial meta analysis, then transcriptomic analysis and finally gene expression analysis. By using this approach in Bala x Azucena population they identified a reasonably good number of candidate genes with differential gene expression among genotypes. Although this approach is less accurate than fine mapping but it helps in identify positional candidate genes for small effect QTL with less cost and time. Prabuddha et al (2008) identified near isogenic lines for several root traits adopting an innovative strategy. The lines differing for the particular root trait also differed at the molecular marker loci cross validating the result. The candidate genes that were found to be associated with the particular root trait also validated with the isogenic lines. Improved performance under drought was observed in transgenic plants of the gene OsMT1a (metallothionein) that is predominantly expressed in roots and is induced by dehydration (Yang et al 2009). Another transgenic ONAC045 for transcription factor, NAC (NAM, ATAF1/2, CUC2), whose functions include a role in the development of lateral roots, was reported to have a greater survival rate in rice after drought and salt treatments (Zheng et al 2009). 6. Gene Expression Studies in Rice LEA proteins mainly play functions in dehydration tolerance and storage of seeds and in whole-plant stress resistance to drought, salt, and cold. LEA proteins are expressed through all the developmental stages with different expression levels and no tissue specificity. For instance, Em, RAB21 and dehydrins in seeds can be found in the root, stem, leaf, callus and suspension cultures of higher plants under ABA or/and NaCl induction (Federspiel, 2003). Gene expression analysis helps in identifying functionally important genes and pathways involved in root architecture under water deficit condition (Breyne et al. 2003). LEA protein gene expression in terms of time course starts from the late period of maturation and initiation period of drying reaches its peak in progressive

dehydration and sharply decreases after some hours of germination (Brands and David 2002). Many reports show that LEA protein gene expression has no tissuespecificity at the levels of tissues and organs as the gene can express in cotyledons, panicles of seeds and also in stems, leaves and roots (Federspiel, 2003). In fact, various factors and conditions and processes influence LEA protein gene expression, among which ABA is considered the most important, especially in reducing the harm caused by drought and is connected directly or indirectly with other regulatory circuits (Chaves et al. 2003; Shao et al.2005). Four steps at least are involved in LEA protein gene expression and regulation induced by drought: signal recognition, signal transduction, signal amplification and integration. Recently a few studies have been conducted in rice on tissue-specific gene expression patterns in different parts of the root system under drought stress condition. Yang et al. (2004) identified and cloned sixty six transcripts that were differentially responded in different types of roots tissue of Azucena under drought stress. Besides, they mapped four transcripts within interval containing QTL for root growth under water deficit in Azucena/IR1552 population. In another study, Wang et al 2007 noticed that the majority of genes expressed in upland rice and lowland rice are almost identical and Student’s t test showed that thirteen per cent of all the ESTs detected in leaves and seven per cent of that in roots expressed differentially in transcripts abundance between the two genotypes.

III. MATERIAL AND METHODS The particulars of the material used, methods and protocols followed and statistical tools used for analysis, in different experiments are presented under the respective experiments separately. 3.1 Experiment I Genetic Diversity and Assessment of OryzaSNP Panel Rice Accessions for Drought Tolerance Based on Water Uptake, Root Distribution and Shoot Characters under Different Moisture Regimes. 3.1.1 Field Experiment - Dry Season 2008 (DS 2008) 3.1.1.1 Plant Material The material for the study was obtained from gene bank, International Rice Research Institute (IRRI), Philippines. The genetic material used for this work was the OryzaSNP panel (McNally et al., 2009), which comprising 20 genotypes from the indica, japonica, and aus groups that have been completely mapped for SNP markers and selected as a mini-core collection representing genomic diversity of Oryza sativa (Table 1). 3.1.1.2 Experimental Site The experiment was conducted in lowland condition at the experimental farm of the IRRI, Los Baños, Philippines (14° 30’ N, 121° 15’E) during the 2008 dry season. The study included an irrigated control and a drained drought stress treatments. 3.1.1.3 Experimental Design and Crop Management Out of twenty genotypes only eighteen genotypes were evaluated due to poor germination. One seedling per hill was transplanted with 0.2m between hills and 0.25m between rows of 3m in length. The experimental design used was alpha lattice with three replications per treatment. Three rows per plot were used in both

the well-watered and drought treatments. The well-watered treatment was planted in a field neighboring the drought stress treatment (slightly far). Randomization and field layout for alpha lattice design was prepared using the CROPSTAT software. Soil was maintained saturated but without standing water for the first two weeks after transplanting to reduce risk of pest infestation. Two weeks after transplanting, both treatments were flooded to a standing water level of about 10 cm. This water level was maintained in the well-watered treatment by flood irrigation until one week before harvest. The drought treatment was drained at thirty days after transplanting, and no further irrigation was applied, except at seventy two days after sowing when drought plots were flooded to facilitate root sampling.

The fields were hand weeded two-three times.

Basal fertilizer

applications equivalent to 40 kg P and 40 kg K ha-1 were applied in the form of single super phosphate and potassium chloride, and 120 kg ha-1 of N in the form of ammonium sulphate was applied in three even splits around 21, 42 and 61 DAS. 3.1.1.4 Environmental Characterization Solar radiation, rainfall, and pan evaporation were recorded at agrometeorological stations operated by the IRRI Climate Unit located nearby (within 1.5km) of the experimental fields (Table 2 and Figure 3). The soil of the IRRI lowland soil was Aquandic epiaquall. General soil characteristics are presented in Table 3. Mercury manometer-type tensiometers (15 and 30cm soil depth) and poly vinyl chloride (PVC) tubes (4cm diameter, 1m long) for frequency domain reflectometry readings were installed at three locations within drought treatment field, as soon as the soil had solidified after draining Soil water potential based on height of the mercury column, and volumetric soil moisture at 10cm increments to a depth of 70cm (Diviner 2000, Sentek Sensor Technologies, Stepney SA, Australia) were monitored up-to three times weekly thereafter. Diviner readings were converted to volumetric soil moisture content (Ѳv) according to a calibration curve of Ѳv = 0.6007x + 35.66 (Figure 1 and 2).

3.1.1.5 Method of Root Sampling and Root Measurements Soil samples for root measurements were acquired with a 20cm x 20cm monolith sampler in all plots of the drought and well-watered treatment to a depth of 45cm at seventy two days after sowing. Before root sampling, the shoot parameters like, tiller number and plant height was measured. The monolith sampler was made of iron sheet with 20 X 20 X 50cm internal dimensions and these were fabricated at the IRRI. To ease the method of sampling, one day before root sampling, all the water was removed in well-watered treatment plots whereas, in stress treatment plots shallow irrigation was given. A rice plant observed to be healthy and representative of respective genotype was sampled in each replication. Root sampler was centered over the rice hill and then pushed into soil to a 45cm depth by hammering on a block of wood placed on top of the sampler. The surrounding soil was removed to dig out the sampler and then soil was sectioned with sharp cutting blade into 0-10, 10-20, 20-30 and 30-45cm depths. Roots were either washed from the soil immediately after sampling or stored at -4ºC until washing (within three weeks). Roots were washed by placing each soil sample on a 1mm screen and running water over the sample to remove the soil. All samples were stored in 50 per cent ethanol until scanning. Root samples were scanned at 400 dpi (Epson V700, California, USA). Scanned images were analyzed for architectural attributes using WinRhizo v. 2007d (Régent Instruments, Québec, Canada). A pixel threshold value of 150-175 was set for the analysis. 3.1.1.6 Observations Recorded 3.1.1.6.1 Physiological Traits 3.1.1.6.1.1 Leaf Water Potential (LWP, MPa) LWP (MPa) was measured at midday using pressure chamber. The uppermost fully expanded leaf (flag leaf) was cut approximately 2.0 cm below the leaf collar and placed in pressure chamber with the cut portion just protruding

through the seal on the top of the chamber into the atmosphere. Pressure was applied slowly to the leaf blade until a drop of water appears from the cut surface. The equivalent pressure was recorded from the gauge and this gave the approximate leaf water potential. The LWP measurements were taken twenty days after stress imposition. 3.1.1.6.1.2 Stomatal Conductance (SC, mol m-2 s-1) SC (mol m-2 s-1) was measured twenty days after stress imposition using a Li-1600 steady-state porometer (Li-Cor Biosciences, USA). Porometer readings were conducted mid-morning on the youngest fully expanded leaf blade of a main culm or primary tiller. 3.1.1.6.1.4 Photosynthesis Rate (PR, μmol mol-2 s-1) PR (μmol mol-2 s-1) was measured twenty days after stress imposition using a Li-1600 steady-state porometer (Li-Cor Biosciences, USA). Porometer readings were conducted mid-morning on the youngest fully expanded leaf blade of a main culm or primary tiller. 3.1.1.6.1.5 Transpiration Rate (TR, mmol mol-2 s-1) TR (mmol mol-2 s-1) was measured twenty days after stress imposition using a Li-1600 steady-state porometer (Li-Cor Biosciences, USA). Porometer readings were conducted mid-morning on the youngest fully expanded leaf blade of a main culm or primary tiller. 3.1.1.6.1.6 Relative Water Content (RWC, %) RWC was measured twice during the stress period. Leaves were sampled midday after the dew had dried. One uppermost fully expanded leaf per plot was sampled and placed in pre-weighed centrifuge tubes. Samples were stored on ice and weighed immediately upon return to the lab for the fresh weight. Tubes were then filled and stored overnight in the dark at 4°C. The next morning, leaves were blotted dry with paper towels using the standard procedure that required about 30s

per sample, and were weighed immediately. After recording fully turgid weight, leaves were dried at 70 °C to constant weight. 3.1.1.6.1.7 Leaf Rolling Score (LRS) One week after the start of drought, leaf rolling score were recorded based on standard evaluation system for rice. Leaf rolling scores ranged from 1 (no leaf rolling) to 9 (leaves completely rolled). Leaf rolling score measurements were taken between 11 am to 12 am at twenty days after stress imposition. 3.1.1.6.2 Root Traits 3.1.1.6.2.1 Root Number Number of all developed roots present at crown region was counted for root number. 3.1.1.6.2.2 Root Length (cm) Root length (cm) at different soil depths was measured using WinRhizo software. 3.1.1.6.2.3 Root Volume (RV, cm3) Root volume at different soil depths was measured using WinRhizo software. 3.1.1.6.2.4 Root Surface Area (RSA, cm2) Root surface area at different soil depths was measured using WinRhizo software. 3.1.1.6.2.5 Root Length Density (RLD, cm/cm3) Root length density (cm/cm3) was calculated by dividing the root length by the soil volume at each soil section.

3.1.1.6.2.6 Root Dry Weight (RDW, g) Root dry weight per plant (g) at different soil depths were recorded after oven drying for 72 hours 3.1.1.6.2.7 Root: Shoot Ratio (RSR) Root to shoot ratio was calculated by dividing the total root dry weight (g) with shoot dry weight (g). 3.1.1.6.2.8 Drought Induced Root Growth at Depth Drought induced root growth at 30-45cm was calculated by the difference of total root length (cm) in drought and well-watered conditions at depth. 3.1.1.6.3 Agronomic Traits 3.1.1.6.3.1 Shoot Dry Weight (SDW, g/plant) Shoot dry weight per plant was recorded after oven drying for 72 hours for the same plant which was used for root sampling 3.1.1.6.3.2 Plant Height (PH, cm) Plant height was measured in three plants randomly in each plot at maturity. The height of the plant was measured from base of main tiller to the tip of the panicle at the time of harvest and expressed in centimeter (cm). 3.1.1.6.3.3 Tiller Number (TN) Total number of tillers (both productive and non productive) was counted per plant at the time of harvest. 3.1.1.6.3.4 Straw Biomass (g/m2) Five plants from each replication were harvested from the middle 2 meters of all three rows, leaving 0.5m of the row at each ends. Harvested plants were oven dried for three days and used for straw biomass measurements (g/m2).

3.1.1.6.3.5 Grain Yield (g/m2) Ten plants from each replication were harvested from the middle two meters of all three rows, leaving 0.5m of the row at each ends. Harvested plants were threshed and air dried. Total grain weight for ten plants was measured in grams and converted to g/m2 at fourteen percent moisture content. 3.1.1.6.3.8 Harvest Index (HI) The ratio of the grain yield and the total dry matter of the plant computed. Grain yield (g/m2) Harvest index =

x 100 Total dry matter weight (g/m2)

3.1.2 Field Experiment – Dry Season 2009 (DS 2009) 3.1.2.1 Experimental Site The experiment was conducted at the Directorate of Rice Research field, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India during the DS 2009. ICRISAT is located at 17°30′N latitude, 78°16′E longitude and at an elevation of about 549m above mean sea level. 3.1.2.2 Experimental Design and Crop Management Due to poor germination, only 13 genotypes were evaluated. One seedling per hill was transplanted with 0.2m between hills and 0.25m between rows of 4m in length. The experimental design used was alpha lattice with four replications in stress treatment and three replications in well-watered treatment. Three rows per plot were used in both the well-watered and drought treatments. The well-watered treatment was planted in a field neighboring the drought stress treatment. The drought treatment was drained at forty one days after transplanting, and further irrigation was applied after noticing sever rolling. Rests of crop management practices were same as in 3.1.1.3.

3.1.2.3 Environmental Characterization Soil moisture content was monitored by using Time Domain Reflectometry (TDR) (Tektronix Inc., Wilsonville, Oregon, USA) which gives the soil moisture in percent volume at different depths (10 to 40cm) (Figure 4). TDR readings were taken once in three days during drought stress in stress treatment.

Rainfall,

temperature, solar radiation and relative humidity of farm, during the stress period were obtained from ICRISAT website (Table 4 and Figure 5). The temperature recorded was also quite high (average maximum temperature was 39.57oc). Both drought and heat stress affected crop drastically. Because of this we were able to record only biomass in stress treatment. But in well-watered treatment, both grain yield and biomass was recorded. 3.1.2.4 Observations Recorded Ten plants were selected at random from each replication for recording plant height, number of tiller per plant, grain yield, biomass and harvest index observations. The averages of ten plants were considered for analysis. 3.1.3 Lysimetric Experiment- Wet Season 2008 (WS 2008) 3.1.3.1 Experimental Site The experiment was conducted in greenhouse (BG02) at IRRI, Los Baños, Philippines, during wet season 2008. 3.1.3.2 Experimental Design All genotypes were grown under two environments (well-watered and drought stress). The experimental design used was split-plot with five replications per treatment. Basal fertilizer applications equivalent to 40 kg P and 40 kg K ha_1 were applied in the form of single super phosphate and potassium chloride, and 80 kg ha_1 of N in the form of ammonium sulphate was applied in two even splits around 21 and 61 DAS. Irrigation was applied once in two days.

3.1.3.3 Method of Simulation of Lowland Condition. The experiment was conducted in PVC cylinder with a diameter of 19cm and height of 105cm. The cylinders (attached with plastic membrane inside) were filled with 29kg of sieved air dried upland sandy loam soil (bulk density of 1.28g of dry soil cm-3) to a level up to 80cm from the bottom of cylinder. The soil was shaken and compacted with circular metal plate (diameter 17cm) to bring soil to pre marked level in pipes i.e. 80cm. To achieve a uniform bulk density throughout the soil column, the compaction was carried out in increments of 5cm. The top 20cm of cylinder was filled with puddled lowland soil. The upland and lowland soil used in this experiment were collected from the top layer of upland and lowland fields of IRRI. Cylinders were painted with white to avoid the soil temperature and were placed in cement pits under greenhouse to mimic field growth condition. Two seedlings were transplanted per cylinder but after one week, one plant having uniform growth with those in the other cylinders was maintained. This method was intended to simulate lowland field conditions as closely as possible. Drought stress was imposed at thirty days after transplanting by opening the drainage at bottom. From then onwards no water was added. To prevent evaporation, each cylinder of drought stress was covered with two layer of plastic sheet. 3.1.3.4 Method of Root Sampling and Root Measurements For root sampling, plastic bags were pulled out of the cylinders and then soil columns were separated into four sections (0-30, 30-45, 45-60 and 60-100cm). Roots were separated from soil by gently spraying tap water on 1-mm screen until all soil washed through the screen. After cleaning, roots were stored in an alcohol solution (50 per cent isopropanol) in a refrigerator at 40C for later analyses. Measurements of roots were performed as mentioned in 3.1.1.5 except a pixel threshold value of 190 was set for the analysis.

3.1.3.5 Observations Recorded 3.1.3.5.1 Physiological Traits 3.1.3.5.1.1 SC (mol m-2 s-1) SC (mol m-2 s-1) was measured twice during stress using a Li-1600 steadystate porometer (Li-Cor Biosciences, USA). Porometer readings were conducted mid-morning on the youngest fully expanded leaf blade of a main culm or primary tiller. 3.1.3.5.1.2 Water Uptake (g/plant) Water uptake (g/plant) was calculated as the difference of the initial cylinder weight and the cylinder weight once in every week (7, 14, 21 and 28 days after stress) 3.1.3.5.2 Root Traits Root traits measured in previous experiment (3.1.1.5) were also measured in this experiment along with below mentioned trait. 3.1.3.5.2.1 Maximum Root Length (MRL, cm) The lowest visible root at the bottom, after removing the plastic bag 3.1.3.5.3 Agronomic Traits 3.1.3.5.3.1 PH (cm) One day before root sampling, plant height was measured from base of stem to top most leaves. 3.1.3.5.3.2 TN Total number of tillers (both productive and non productive) was counted per plant one day before sampling. 3.1.3.5.3.3 SDW (g/plant)

Plants were cut at crown level and shoot dry weight (g) were recorded after oven drying for 72 hours. 3.1.4 Lysimetric Experiment- DS 2009 3.1.4.1 Experimental Site The experiment was conducted under rainout shelter at ICRISAT, Patancheru, India during the dry season 2009. 3.1.4.2 Experimental Design The experimental design of this experiment was same as that of previous lysimetric experiment (3.1.3.2). 3.1.4.3 Experimental Method The light- gray PVC cylinder was 20cm diameter and 120cm in length with a PVC plate at the bottom. Initially bottom of cylinders were sealed with silicone adhesive.

The vertisol was collected from fields of ICRISAT. The

collected soil sample was air dried in sunlight, and big clods were broken and sieved with 2mm sieve to get uniform fine soil particles. Cylinders were filled with 44 kg of vertisol leaving the top 5cm empty. The soil was compacted with circular metal plate (diameter 19cm) to get bulk density of 1.22 g of dry soil cm-3. To achieve a uniform bulk density throughout the soil column, the compaction was carried out by evenly compacting the soil every 5kg while filling the cylinders. Cylinders were placed in cement pits to mimic field growth condition. Twenty-nine days old four seedlings were transplanted in each of cylinder. Plants were thinned to two individuals per cylinder at seven days after transplanting. All the plants were fully irrigated by watering every other day until the drought stress treatment. Drought stress was imposed after forty one days of transplanting by opening the silicone adhesive present all along the border of PVC plate at the bottom of cylinder. To prevent evaporation each cylinder of drought stress were covered 400g of plastic beads (low-density plastic beads from Reliance

Ltd). At the end of stress period i.e., thirty days after stress, plants were used for root phenotyping. 3.1.4.4 Method of Root Sampling and Root Measurements Root washing and root trait measurements were carried out in the same manner as in above Lysimetric experiment. Unlike in above cylinder trial, here entire root system was collected. Root measurements are same like previous experiment (3.1.1.5) except a pixel threshold value of 190 was set for the analysis. 3.1.4.5 Observations Recorded 3.1.4.5.1 Physiological Traits 3.1.4.5.1.1 Water Uptake (g/plant) Water uptake (kg/plant) was measured in same way like previous experiment but measured once in every three days in this experiment (5, 8, 12, 15, 18 and 23). 3.1.4.5.2 Root Traits Root traits measured in previous experiment (3.1.1.5) were also measured in this experiment. 3.1.4.5.3 Agronomic Traits Agronomic traits measured in previous experiment (3.1.3.5.3) were also measured in this experiment. 3.1.5 Lysimetric Experiment- Wet Season 2009 (WS 2009) 3.1.5.1 Experimental Site The experimental site of this experiment was same as that of previous lysimetric experiment (3.1.3.1). 3.1.5.2 Experimental Design

The experimental design of this experiment was same as that of previous lysimetric experiment (3.1.3.2). 3.1.5.3 Method of Simulation of Lowland Condition. The method was same as that of previous lysimetric experiment (3.1.3.3). 3.1.5.4 Observations Recorded 3.1.5.4.1 Physiological Traits 3.1.5.4.1.1 Water Uptake (g/plant) Water uptake (g/plant) was calculated as the difference of the initial cylinder weight and the cylinder weight once in every week (7, 14, 21, 25, 28, 33 and 35 days after stress imposition) 3.2 Experiment II Variation in Water Uptake, Root and Shoot Characters and Their Association with Drought Tolerance in Parents of Mapping Population, Donors and Advanced Breeding Lines of IRRI-India Drought Breeding Network. 3.2.1 Field Experiment - Dry Season 2009 3.2.1.1 Plant Material The IRRI-India drought breeding network has identified a large number of promising drought resistant breeding lines developed using diverse donors at IRRI and eight national institutes in India. Breeding lines with consistent superior performance over popular checks under severe and moderate stress and with no yield penalty under irrigated conditions, along with donors and popular checks, were selected for this experiment. This advanced breeding line’s along with donors was obtained from Dr. O.N. Singh, Director, Central Rice Research Institute, Cuttack, India. Whereas parents of mapping populations were collected from gene bank, IRRI, Philippines (Table 5).

3.2.1.2 Experimental Site The experimental site of this experiment was same as in previous experiment (3.1.2.1) 3.2.1.3 Experimental Design and Crop Management The experimental design and crop management was same as in previous experiment (3.1.2.2) 3.2.1.4 Environmental Characterization The environmental characterization of this experiment was same as in previous experiment (3.1.2.3) 3.2.1.5 Observations Recorded The observations recorded this experiment was same as in previous experiment (3.1.2.4) 3.2.2 Lysimetric Experiment- Dry Season 2009 3.2.2.1 Experimental Site The experimental site of this experiment was same as in previous experiment (3.1.4.1) 3.2.2.2 Experimental Design Forty nine advanced breeding lines, donors and parents of mapping population were grown under two environments (well-watered and drought stress). The experimental design of this experiment is same as in previous experiment (3.1.4.2). 3.2.2.3 Experimental Method The experimental method of this experiment was same as in previous experiment (3.1.4.3) 3.2.2.4 Method of Root Sampling and Root Measurements

The method of root sampling and root measurements of this experiment is same as in previous experiment (3.1.4.4) 3.2.2.5 Observations Recorded All physiological, root and agronomic traits measured in previous experiment (3.1.4.5) were also measured in this experiment. 3.3 Experiment III Physiological and Molecular Mechanism of Drought Avoidance Root Mechanisms in Adeysel NILs. 3.3.1 Studies on Water Uptake, Root Distribution under Different Water Levels 3.3.1.1 Plant Materials Two pairs of NILs [IR77298-14-1-2-10 (+), IR77298-14-1-2-13 (-), IR77298-5-6-18 (+) and IR77298-5-6-11(-)] along with their parents [IR64 and Adeysel] were used in our experiment. The material was obtained from Dr Arvind Kumar, IRRI, Philippines (Table 6). This experiment was conducted using lysimeters in wet season 2008 under greenhouse condition. Experimental site, experimental design, experimental method, method of root sampling and root measurements, and observations recorded are same as mentioned in experiment 3.1.3. 3.3.2 Comparative Expression of Four LEA Genes under Different Water Levels 3.3.2.1 Experimental Site The experiment was conducted in greenhouse (BG11) at IRRI, Los Baños, Philippines, during wet season 2009. 3.3.2.2 Experimental Design

Two pairs of NILs [IR77298-14-1-2-10 (+), IR77298-14-1-2-13 (-), IR77298-5-6-18 (+) and IR77298-5-6-11(-)] plus its recurrent parent (IR64) and drought resistance genotype (Dular) were grown under two environments (drought stress and well-watered). The experimental design used was randomized block design with eight replications per treatment. Four to five seeds of each entry were hand dibbled in the PVC pot filled with a mixture of sand and soil in 1:2 proportions. After germination, one healthy seedling was allowed to grow in each pot. Basal fertilizer applications equivalent to 40 kg ha_1 of N, 40 kg P and 40 kg K ha_1 were applied in the form of of ammonium sulphate, single super phosphate and potassium chloride respectively 3.3.2.3 Experimental Method and Drought Treatment Two kilograms of ground, air-dried soil mixture (2 soil: 1 sand) was placed in a PVC pot. Each pot was adequately fertilized and grown under greenhouse condition. All pots were watered to soil saturation. Ten to twelve seeds were sowed per pot and later thinned to 1 plant per pot after one week of sowing. All pots were irrigated twice daily to maintain the soil at saturation. Stress was imposed by initiating soil dry down protocol starting 25 days after sowing. The day before the start of progressive soil drying, all pots were fully watered (saturation). All pots were allowed to drain overnight by loosening the stoppers and were weighed early next morning to get the saturated weight. Then the pots were enclosed in white plastic bags, around the stem of the plant to prevent direct soil-evaporation. Thereafter, the pots were weighed every morning around 7.30 AM to know the soil moisture status. Daily transpiration (T) was computed as the difference in weight on successive days and fraction of transpirable soil water (FTSW) based on the proportion of soil water remaining in the pot. Well-watered control plants of each genotype are maintained at initial target weight by adding the daily water loss back to the pot. Feeder pipe will be inserted in all 1.0 FTSW treatment pots for

watering. The water stress will be continued, until each pot reaches the respective 0.2 FTSW. Lower limit for the same soil mixture was got from other experiment and it is 13 percent. Lower limit is the soil weight at 0 FTSW or when transpiration is minimal. Calculation of FTSW goes like this (below). TTSW (total transpirable soil water) = saturated soil weight- (total soil weight x 0.13 + dry soil weight) Pot weight at targeted 0.2FTSW = Pot weight at lower limit + TTSW x 0.2 3.3.2.4 Method of Root Sampling for RNA Isolation Five replications were used for root sampling. After removing the roots from pots, each root samples was washed quickly in a series of trays filling with clean tap water. The excess of moisture was removed by wiping with dry paper towel. After washing and cleaning all samples were stored in liquid nitrogen and later stored at -80oC for RNA extraction. -From each plant, 4 different tissue samples were collected. Out of 4 samples, 2 samples were collected from leaves (0-20cm, >20cm) and rest 2 samples were from roots (0-15cm, >15cm). All these process of sampling was done quickly to avoid degradation of RNA (less than one minute for each plant). 3.3.2.5 RNA Extraction and Gene Expression Analysis RNA is sensitive to ubiquitously present RNase in the surrounding environment so extreme care was taken to avoid RNase contamination. Total RNA was extracted from rice tissues using the TRIzol protocol, according to the instructions of the manufacturer (Invitrogen, UK). Tissues were ground using a chilled mortar and pestle. 100 mg of the homogenized tissue was transferred in 1 ml of TRIZOL Reagent (Invitrogen, Life technologies, Carlsbad, CA, USA) and the mixture incubated at room temperature for 10 min to allow complete dissociation of the nucleoprotein complexes. After the addition of 0.2 ml of chloroform, the tubes were shaken vigorously by hand for 15 sec and eventually

left for incubation for ~3 min at room temperature. Following the centrifugation at 12,000x g for 15 min at 4ºC, the aqueous-upper phase was transferred into a fresh tube and the RNA was precipitated using 0.5 ml of isopropyl alcohol. The mixture was centrifuged at 12,000x g for 10 min at 4ºC and the pellet formed was washed once with 75 per cent ethanol. Additional centrifugation at 7,500x g for 5 min at 4ºC lead to further formation of a pellet that was left to dry at room temperature for 10 min. Eventually the pellet was re-suspended in 20-50 µL of DEPC-water and the RNA concentrations were determined by nanodrop. RNA quality was assessed by fractionation on 1.5 per cent (w/v) agarose gel [1x Tris-Borate-EDTA (TBE)]. RNA was visualized by SyBR safe (5 µl/50ml) staining under UV illumination. The extracted RNA was treated with DNase I, Amplification Grade (Deoxyribonuclease I; Invitrogen, UK or Promega Corporation, Madison, WI, USA), for digestion of the DNA. The first strand of cDNA was synthesized as described in the SUPERSCRIPT II RNase H-Reverse Transcriptase kit (Invitrogen, UK), using 6 µg of total RNA and the oligo(dT) primer. RT-PCR was performed in a 50 µl reaction volume according to the instructions of the manufacturer (Invitrogen) using recombinant U-Taq DNA polymerase (SBS Genetech). The gene specific primers listed in Table 7 were used for amplification of LEA genes. As positive control the product of 18S ribosomal RNA was used (F: 5’– AAA CGG CTA CCA CAT CCA AG –3’ and R: 5’– TCA TTA CTC CGA TCC CGA AG –3’). The cycle conditions were as follows: preamplification denaturation at 94ºC for 3 min, 32 cycles of denaturation at 94ºC for 30sec, primer annealing at 57 ºC for 45 sec, and primer extension at 72ºC for 30 sec, and a final extension of RT-PCR products at 72ºC for 7 min. RT-PCR products were fractionated on 1.2

per cent agarose gels (TAE), and were

visualized by SYBR® Safe DNA Gel Stain (Invitrogen) staining under UV illumination.

3.4. Statistical Analysis 3.4.1 Analysis of Variance The analysis of variance for different characters was carried out using mean data in order to assess the genetic variability among genotypes as given by Cochran and Cox (1957). The level of significance was tested at 5 per cent and 1 per cent using F test. The model of ANOVA used is presented below.

Sl. No.

Source of variation

DF

M.S.S.

Expected M.S.S.

1

Replication

r-1

Mr

gσ2r + σ2e

2

Genotypes

g-1

Mt

σ2e + σ2g

3

Error

(r-1)(g-1)

Me

σ2e

Total

rg-1

Mr+Mt+Me

Where, r = number of replication g = number of treatments (genotypes) 3.4.2 Estimation of Genetic Variability Parameters 3.4.2.1 Phenotypic and Genotypic Variance Phenotypic and genotypic components of variance were estimated by using the formula given by Cochran and Cox (1957). MSS due to genotypes – MSS due to error (σ 2e) Genotypic variance (σ g2) = --------------------------------------------------------------r Phenotypic variance (σ p2) = Genotypic variance (σ 2g) + Environmental variance (σ 2e)

3.4.2.2 Co-efficient of Variability Both phenotypic and genotypic co-efficient of variability for all the characters were estimated using the formulae of Burton and De Vane (1953). Genotypic Co-efficient of Variability (GCV): Genotypic variance GCV per cent = ----------------------------------- x 100 Grand mean Phenotypic Co- efficient of Variability (PCV): Phenotypic variance PCV per cent = ----------------------------------- x 100 Grand mean PCV and GCV were classified as per Sivasubramanian and Menon (1973) and as shown below: 0-10 per cent - Low;

10 -20 per cent - Moderate; >20 per cent - High

3.4.2.3 Heritability in Broad Sense (h2) Heritability (broad sense) was estimated for all the characters as the ratio of genotypic variance to the total variance as suggested Lush, (1949) and Hanson et al. (1956). σ g2 h = --------σ p2 2

According to Robinson (1966) heritability estimates in cultivated plants can be placed in following categorizes. 5-10 per cent- Low;

10-30 per cent- Moderate;

30-60 per cent - High

3.4.2.4 Genetic Advance (GA) Genetic advance for each character was estimated by using the following formula of Johnson et al. (1955) GA = h2 x K x σ p Where, h2 = heritability estimate K = selection differential which is equal to 2.06 at 5 per cent intensity of selection. σ p = phenotypic standard deviation Further the genetic advance as per cent of mean was computed by using the following formula GA GA as per cent of mean = ---------------- x 100 Grand mean Genetic advance as per cent of mean was categorized according to Johnson et al. (1955), as given below. 0 -10 per cent = Low; 10 - 20 per cent = Moderate;

>20 per cent = High

3.4.3 Correlation Studies To determine the degree of association of characters with yield and also among the yield components, the correlation co-efficients were calculated. Phenotypic co-efficients of correlation between two characters were determined by using variance and covariance components as suggested by Al-Jibourie et al. (1958). Covp (xy) rp (xy)= ------------------------

σ2p (x) . σ2p (y) Where, rp (xy) is the phenotypic correlation co-efficient Covp is phenotypic co-variances of xy. σ2p phenotypic variance of x and y, The calculated value of ‘r’ was compared with table ‘r’ value with n-2 degree of freedom at 5 per cent and 1 per cent level of significance, where n refers to number of pairs of observations. 3.4.4 Path Co-efficient Analysis Path-co-efficient analysis was carried out using phenotypic correlation values of yield components on yield as suggested by Wright (1921) and illustrated by Dewey and Lu (1959). Standard path co-efficients which are the standardized partial regression co-efficients were obtained using statistical software package SPAR 1. These values were obtained by solving the following set of ‘P’ simultaneous equations by using the above package. P01 + P02 r12 + -----------+ P0P r1P = r 01 P01 + P12 r02 + -----------+ P0P r2P = r 02

P01 + r1P+ P02 r2 P -----------+ P0P = r 0P Where P01, P02, ---------------------------P0P are the direct effects of variables 1,2, -------------p on the dependent variable 0 and r12, r13 ,-------r1P------ rP(P-1 ) are the possible correlation co-efficients between various independent variables and r01, r02, r03 ------- r0P are the correlations between dependent and independent variables. The indirect effect of the ith variable via jth variable is attained as (Poj x rij). The contribution of remaining unknown factor is measured as the residual factor, which is calculated and given below.

P2ox = 1- [P2 01 + 2P01 P02 r12 + 2 P01 P03 r13 +----------+ P202 + 2P02 P03 r13 +……..+P20P] Residual factor = (P2ox)½. 3.4.5 Genetic Divergence Analysis The genetic divergence between genotypes was estimated using Mahalanobis’s D2 statistic (1936). The distance D from the sample was computed using the formula. D2p = d1 S-1d Where, D2p = Square of distance considering ‘p’ variables d =Vector observed differences of the mean values of all the characters (xi1- xi2) S-1 = inverse of variance and covariance matrix 3.4.5.1 Clustering of the D2 Values All the genotypes used were clustered into different groups following Tocher’s method (Rao, 1952). The intra and inter-distance were also computed the criterion used in clustering to the same cluster should atleast on the average, show a smaller D2 values than those belonging to different clusters. The device suggested by Tocher (Rao, 1952) was started with two closely associated populations and find a third population which had the smallest average of D2 from the first two. Similarly, the fourth was chosen to have a smallest average D2 value from the first three and so on. The permissible increase in D2 value shown by a population to the nearest population. If at any stage increase in average D2 value exceeded the average of already included, because of the addition of a new genotypes, than that genotypes was deleted. The genotypes that are included already in that group were considered as the first cluster. This procedure was repeated till D2 values of the other genotypes were exhausted

omitting those that were already included in the former cluster and grouping them into different cluster. 3.4.5.2 Intra-Cluster Distance The average intra-cluster distances were calculated by the formula given by Singh and Chaudhary (1977). Square of intra-cluster distance = ΣDi2 / n Where, ΣDi2 = sum of distance between all possible combinations. n = number of all possible combinations 3.4.5.3 Inter-Cluster Distance The average inter-cluster distance was calculated by the formulae described by Singh and Chaudhary (1977). Square of inter-cluster distance = ΣDi2 / ni nj Where, ΣDi2 = sum of distances between all possible combinations (ninj) of the entries included in the cluster study. ni = number of entries in cluster i nj = number of entries in cluster j 3.4.5.4 Contribution of Individual Characters towards Genetic Divergence The character contribution towards genetic divergence was computed using method given by Singh and Chaudhary (1977). In all the combination, each character is ranked on the basis of di = yij – yik values. Where, di = mean deviation

yij = mean value of the jth genotype for the ith character and yik = mean value of the kth genotype for the ith character. Rank ‘I’ is given to the highest mean difference and rank p is given to the lowest mean difference Where, P is the total number of characters. Finally, another table giving information on, number of times that each character appeared in the first rank is prepared and per cent contribution of characters towards divergence was calculated.

IV. EXPERIMENTAL RESULTS The results obtained from the present investigation on are presented under the following subheadings. 4.1 Genetic Diversity and Assessment of OryzaSNP Panel Rice Accessions for Drought Tolerance Based on Water Uptake, Root Distribution, Shoot and Yield Parameters under Different Moisture Regimes. 4.1.1 Field Experiments – Dry Season 2008 and 2009 4.1.1.1 Mean Performance The mean performance of all OryzaSNP panel rice accessions in respect of phenology, physiological, root, shoot and yield traits are briefly presented below. 4.1.1.1.1 Field Experiment – Dry Season 2008 4.1.1.1.1.1 Root Traits The mean performance of all OryzaSNP panel rice accessions in respect of root traits (RN, RSR, RLD, RSA, RV and RDW) across different soil depths under both drought stress and well-watered condition are presented below (Table 8-11). 4.1.1.1.1.1.1 RN Comparison of means (Table 8) showed that, Minghui 63 had highest RN (259.7) followed by SHZ 2 (243.5) and Dular (231.0) while, Azucena had the lowest RN (113.0) followed by N22 (115.67) and Cypress (120.5) under drought stress condition. Whereas, FR13A had highest RN (412.0) followed Sadu Cho (400.3) and Dom Sufid (357.5) while, LTH had the lowest RN (60.5) followed by N22 (132.5) and Moroberekan (153.0) under well-watered condition. 4.1.1.1.1.1.2 RSR

Under drought stress condition, Moroberekan and Swarna had highest RSR (0.15) followed by Minghui 63 and SHZ 2 (0.11). On the other hand, Pokkali recorded lowest RSR (0.05) followed by Sadu Cho and LTH (0.06). Whereas, under well-watered condition FR13A had highest RSR (0.34) followed by Azucena (0.25) and Tainung 67 (0.18). On the other hand, LTH and Pokkali recorded lowest RSR (0.05) followed by Zhenshan 97B (0.06) (Table 8). 4.1.1.1.1.1.3 RLD (cm/cm3) Means of RLD across different soil depths under both drought stress and well-watered are presented in Table 8. The highest RLD at 0-10cm soil layer under drought stress condition was recorded by IR64 (1.89 cm/cm3) followed by Dom Sufid (1.73 cm/cm3) and Aswina (1.51 cm/cm3). On the other hand, Azucena recorded lowest RLD (0.49 cm/cm3) followed by Tainung 67 (0.85 cm/cm3) and Cypress (0.87 cm/cm3). Under well-watered condition highest RLD

were

recorded by Dom Sufid (3.95 cm/cm3) followed by Sadu Cho (3.84 cm/cm3) and Swarna (3.64 cm/cm3). Whereas LTH was recorded lowest RLD (1.21 cm/cm3) followed by M 202 (1.23 cm/cm3) and Moroberekan (1.63 cm/cm3). Under drought stress condition, the highest RLD at 10-20cm soil layer was recorded by Swarna (1.45 cm/cm3) followed by SHZ 2 (1.02 cm/cm3) and Dom Sufid (0.99 cm/cm3). On the other hand, N22 recorded lowest RLD (0.16 cm/cm3) followed by Tainung 67 and LTH (0.29 cm/cm3). However in well-watered condition, the highest RLD at 10-20 cm soil layers was recorded by Minghui 63 (1.25 cm/cm3) followed Azucena (1.19 cm/cm3) and Swarna (0.71 cm/cm3). Whereas Tainung 67 was recorded lowest RLD (0.21 cm/cm3) followed by M 202 (0.22 cm/cm3) and IR64 (0.24 cm/cm3). The highest RLD at 20-30 cm soil layer was recorded by Swarna (1.21 cm/cm3) followed by M 202 (0.87 cm/cm3) and Azucena (0.67 cm/cm3) under drought stress condition. On the other hand, Cypress recorded lowest RLD (0.11 cm/cm3) followed by Zhenshan 97B (0.15 cm/cm3), LTH and Tainung 67 (0.17

cm/cm3). Under well-watered condition, the highest RLD at 20-30 cm soil layer was recorded by Minghui 63 (0.87 cm/cm3) followed SHZ 2 (0.35 cm/cm3) and Azucena (0.29 cm/cm3). Whereas M 202 was recorded lowest RLD (0.06 cm/cm3) followed by Tainung 67 (0.07 cm/cm3) and FR 13A (0.08 cm/cm3). Large variation was recorded for RLD at depth only under drought stress condition. At 30-45cm soil layer, highest RLD was recorded by Dular (0.24 cm/cm3) followed by SHZ 2 (0.20 cm/cm3) and Dom Sufid (0.17 cm/cm3). On the other hand, IR64 recorded lowest RLD (0.02 cm/cm3) followed by M 202 and Zhenshan 97B (0.03 cm/cm3). Under well-watered condition, all genotypes recorded lowest RLD at 30-45cm soil layer. Among them, Dular and Minghui 63 recorded highest RLD (0.06 cm/cm3) followed N22 (0.05 cm/cm3). Aswina, Swarna, M202, and Tainung 67 recorded lowest RLD (0.01 cm/cm3). 4.1.1.1.1.1.4 RSA (cm2) Means of RSA across different soil depths under both drought stress and well-watered condition are presented in Table 9. At 0-10cm soil layer in drought stress condition the highest RSA was recorded by IR64 (1036.77 cm2) followed by Dom Sufid (923.21 cm2) and FR13A (851.43 cm2). On the other hand, Azucena recorded lowest RSA (377.03 cm2) followed by Tainung 67 (453.10 cm2) and Cypress (555.67 cm2). Whereas under well-watered condition, Dom Sufid was recorded highest RSA (2053.87 cm2) followed by Sadu Cho (1958.67 cm2) and FR13A (1924.03 cm2). LTH was recorded lowest RSA (599.39 cm2) followed by M 202 (859.49 cm2) and Cypress (1012.98 cm2). Under drought stress condition, the highest RSA at 10-20cm soil layer was recorded by Swarna (587.52 cm2) followed by SHZ 2 (469.09 cm2) and Dom Sufid (430.62 cm2). On the other hand, N22 recorded lowest RSA (114.66 cm2) followed by M 202 (128.38 cm2) and Tainung 67(149.21 cm2). The highest RSA at 1020cm soil layer under well-watered condition was recorded by Azucena (658.87 cm2) followed Minghui 63 (545.84 cm2) and Aswina (497.05 cm2). Whereas

Cypress was recorded lowest RSA (152.02 cm2) followed by Tainung 67 (154.23 cm2) and M 202 (160.55 cm2). At 20-30cm soil layer in drought stress condition, the highest RSA was recorded by Swarna (372.33 cm2) followed by M 202 (315.04 cm2) and Dom Sufid (254.88 cm2). On the other hand, Cypress recorded lowest RSA (45.25) followed by LTH (59.33 cm2), and IR64 (59.77 cm2). Under well-watered condition, the highest RSA at 20-30 cm soil layer was recorded by Minghui 63 (361.92 cm2) followed SHZ 2 (290.50 cm2) and N22 (213.66 cm2). Whereas M 202 was recorded lowest RSA (41.97 cm2) followed by FR 13A (53.54 cm2) and Tainung 67 (54.50 cm2). Highest RSA at depth (30-45cm) under drought stress condition was recorded by Dular (123.73 cm2) followed by SHZ 2 (97.03 cm2) and FR13A (88.67 cm2). On the other hand, IR64 recorded lowest RSA (10.44 cm2) followed by M 202 (15.31 cm2) and Zhenshan 97B (16.57 cm2). All genotypes recorded drastically lower RSA at 30-45cm soil layer under well-watered condition. Among them Dular (57.55 cm2) and recorded highest RSA (0.06 cm2) followed Minghui 63 (54.96 cm2) and N22 (46.49 cm2). On the other hand, Tainung 67 recorded lowest RSA (7.68) followed by Swarna (9.43 cm2) and M 202 (10.27 cm2). 4.1.1.1.1.1.5 RV (cm3) Means of RV across different soil depths under both drought stress and well-watered are presented in Table 10. Under drought stress condition, the highest RV at 0-10cm soil layer was recorded by FR13A (14.28 cm3) followed by IR64 (11.46 cm3) and Dom Sufid (10.15 cm3). On the other hand, Tainung 67 recorded lowest RV (4.90 cm3) followed by Azucena (5.82 cm3) and M 202 (6.36 cm3). The highest RV at 0-10 cm soil layer under well-watered condition, was recorded by FR13A (25.00 cm3) followed by Aswina (22.19 cm3) and Dom Sufid (22.01 cm3). Whereas LTH was recorded lowest RV (5.90 cm3) followed by Azucena (11.41 cm3) and Cypress (11.82 cm3).

The highest RV at 10-20cm soil layer under drought stress condition was recorded by Swarna (4.88 cm3) followed by Aswina (4.52 cm3) and SHZ 2 (4.50 cm3). On the other hand, M 202 recorded lowest RV (1.19 cm3) followed by Tainung 67 (1.59 cm3) and LTH (1.62 cm3). Whereas the highest RV at 10-20cm soil layer under well-watered condition was recorded by Aswina (9.83 cm3) followed Azucena (7.94 cm3) and Moroberekan (5.33 cm3). Whereas Cypress was recorded lowest RV (2.04 cm3) followed by Tainung 67 (2.24 cm3) and M 202 (2.32 cm3). Under drought stress condition, the highest RV at 20-30 cm soil layer was recorded by Swarna (2.32 cm3) followed by Aswina (2.31 cm3) and M 202 (2.28 cm3). On the other hand, Cypress recorded lowest RV (0.39 cm3) followed by IR64 (0.41 cm3) and LTH (0.42 cm3). Under well-watered condition, the highest RV at 20-30 cm soil layer was recorded by SHZ 2 (4.98 cm3) followed N22 (3.91 cm3) and Dular (3.80 cm3). Whereas M 202 was recorded lowest RV (0.58 cm3) followed by FR13A (0.69 cm3) and Tainung 67 (0.82 cm3). Considerable variation was recorded under drought stress condition for RV at 30-45cm soil layer. Highest RV was recorded by Dular (0.86) followed by FR 13A (0.79 cm3) and Moroberekan (0.64 cm3). On the other hand, LTH, M 202 and Zhenshan 97B recorded lowest RV (0.11 cm3). Whereas under well-watered condition, all genotypes recorded lower RV at 30-45cm soil layer. Among them Minghui 63 recorded highest RV at 30-45 cm soil layer followed by Dular (0.71 cm3) and N22 (0.62 cm3). On the other hand, Tainung 67 recorded lowest RV (0.09 cm3) followed by Swarna and Sadu Cho (0.10 cm3). 4.1.1.1.1.1.6 RDW (g) Means of RDW across different soil depths under both drought stress and well-watered are presented in Table 11. Under drought stress condition, the highest RDW at 0-10cm soil layer was recorded by Azucena (1.97g) followed by FR13A (1.28g) and IR64 (1.25g). On the other hand, LTH recorded lowest RDW

(0.63g) followed by M 202 (0.66g) and N22 (0.72g). Under well-watered condition, at 0-10cm soil layer, FR13A was recorded highest RDW (2.88g) followed by Sadu Cho (2.19g) and Dom Sufid (1.80g). Whereas LTH was recorded lowest RDW (0.46g) followed by M 202 (0.82g) and Zhenshan 97B (1.08g). The highest RDW at 10-20cm soil layer under drought stress condition was recorded by Swarna and Moroberekan (0.56g) followed by Azucena (0.42g). On the other hand, N22 and M 202 recorded lowest RDW (0.13g) followed by IR64 and Tainung 67(0.17g). The highest RDW at 10-20cm soil layer under wellwatered condition was recorded by Azucena (0.66g) followed Aswina (0.50g) and Minghui 63 (0.48g). Whereas M 202 was recorded lowest RDW (0.12g) followed by Cypress (0.13g) and Tainung 67 (0.15g). Under drought stress condition, the highest RDW at 20-30 cm soil layer was recorded by M 202 (0.24g) followed by Aswina (0.22g) and Swarna (0.20g). On the other hand, LTH recorded lowest RDW (0.03g) followed by Cypress and IR64 (0.04g). Under well-watered condition, the highest RDW at 20-30 cm soil layer was recorded by Minghui 63 (0.29g) followed SHZ 2 (0.24g) and N22 (0.20g). Whereas M 202 was recorded lowest RDW (0.03g) followed by FR 13A and Tainung 67 (0.04g). At deep soil layer (30-45cm), Minghui 63 recorded highest RDW (0.05g) followed Dular, SHZ 2 and N22 (0.03g). Apart from these genotypes in wellwatered condition, most of other genotypes recorded lowest RDW. On the other hand considerable variation was recorded under drought stress condition for RDW at 30-45cm soil layer. Highest RDW was recorded by Aswina (0.11g) followed by Dular (0.07g). On the other hand, LTH, Zhenshan 97B and M202 recorded lowest RDW (0.01g).

4.1.1.1.1.2 Physiological Traits The mean performance of all OryzaSNP panel rice accessions in respect of PR (µmol mol-2 s-1), TR, SC, RWC, and LWP under both drought stress and wellwatered condition are presented below (Table 12). Azucena had highest PR (25.20 µmol mol-2 s-1) followed by Moroberekan (21.95 µmol mol-2 s-1) and M202 (21.05 µmol mol-2 s-1). On the other hand, FR13A recorded lowest PR (13.30 µmol mol-2 s-1) followed by SHZ2 (14.90 µmol mol-2 s-1) and IR64 (15.05 µmol mol-2 s-1). Highest TR was recorded in Azucena (3.20 m mol mol-2 s-1) followed by Moroberekan (2.97 m mol mol-2 s-1) and Dular (2.59 m mol mol-2 s-1). Whereas Pokkali recorded lowest TR (1.62 m mol mol-2 s-1) followed by SHZ 2 (1.69 m mol mol-2 s-1) and Tainung 67 (1.70 m mol mol-2 s-1). Among the genotypes evaluated, Aswina recorded lowest RWC of 60.83 % followed by SHZ 2 (61.85 %) and Sadu Cho (66.82 %). On the other hand, M 202 recorded highest RWC (83.21 %) followed by Dular (79.53 %) and IR64 (77.06 %) which were on par with one another. FR13A had highest SC (701.67 mol m-2 s-1) followed by Moroberekan (673.33 mol m-2 s-1) and N22 (651.67 mol m-2 s-1). On the other hand, Pokkali recorded lowest SC (376.33 mol m-2 s-1) followed by Aswina (383.33 mol m-2 s-1) and Zhenshan 97B (447.67 mol m-2 s-1). Dular recorded lowest LWP of -3.35 MPa followed by Moroberekan (-3.27 MPa) and Azucena (-3.08 MPa). On the other hand, Dom Sufid recorded highest LWP (-1.27 MPa) followed by Minghui 63 (-1.87 MPa) and IR64 (-2.17 MPa) Lowest LRS (1.0) was recorded for Aswina, Azucena, Moroberekan and Minghui 63 under drought stress condition. Whereas highest LRS was recorded in cypress (6.33) followed by LTH and Dular (5.67). 4.1.1.1.1.3 Phenology

The mean performance of all OryzaSNP panel rice accessions in respect of PH, TN and SDW under both drought stress and well-watered condition are presented below (Table 13). In drought stress condition, Swarna (39.17 cm) was the shortest followed by Minghui 63 (50.57 cm) and Zhenshan 97B (62.17 cm) while, Azucena was the tallest (129.78 cm) followed by Dom Sufid (113.89 cm) and Aswina (112.94 cm). Whereas under well-watered condition, M 202 (62.78 cm) was the shortest followed by Zhenshan 97B (64.61 cm) and Minghui 63 (75.00 cm) while, Azucena was the tallest (123.61 cm) followed by Aswina (118.94 cm) and Dom Sufid (117.78 cm). SHZ 2 had highest TN (26.33) followed by N22 (24.50) and Swarna (21.50) under drought stress condition while, Moroberekan had the lowest TN (7.00) followed by Azucena (9.00) and Dular (11.00). On the other hand Swarna had highest TN (38.00) followed by Minghui 63 (29.00) and SHZ 2(24.50) while, Azucena had the lowest TN (9.33) followed by Moroberekan (9.33) and Cypress (10.00) under well-watered condition. Pokkali had maximum SDW (29.51 g/plant) followed by FR 13A (24.79 g/plant) and Sadu Cho (22.06 g/plant) under drought stress condition. On the other hand, Tainung 67 recorded lowest SDW (10.27 g/plant) followed by Swarna (10.35 g/plant) and Azucena (11.43 g/plant). Whereas under well-watered condition, Sadu Cho had maximum SDW (38.47 g/plant) followed by Pokkali (34.54 g/plant) and Zhenshan 97B (23.97 g/plant). On the other hand, Azucena recorded minimum SDW (9.79 g/plant) followed by FR 13A (10.08 g/plant) and Tainung 67 (11.37 g/plant). 4.1.1.1.1.4 Agronomic Traits Means of all agronomical traits measured under both drought stress and well-watered are presented in Table 14.

4.1.1.1.1.4.1 SB (g/m2) Moroberekan had highest SB (4090.27g/m2) followed by FR13A (905.69 g/m2) and Azucena (852.44 g/m2) under drought stress condition. On the other hand, LTH recorded lowest SB (173.93 g/m2) followed by Zhenshan 97B (255.79 g/m2) and M 202 (289.40 g/m2). Cypress recorded lowest SB (224.68 g/m2) followed by LTH (272.57 g/m2) and Zhenshan 97B (304.60 g/m2) in well-watered condition. On the other hand, Swarna recorded highest SB (1470.30 g/m2) followed by IR 64 (881.68 g/m2) and FR13A (798.50 g/m2). 4.1.1.1.1.4.2 GY (g/m2) Under drought stress, Dular recorded highest GY of 254.05 g/m2 followed by N22 (252.13 g/m2) and Zhenshan 97B (166.58 g/m2). On the other hand, Minghui 63 recorded lowest GY (13.04 g/m2g) followed by Aswina (43.27 g/m2) and LTH (43.71 g/m2). Under well-watered condition, Cypress recorded lowest GY of 83.62 g/m2 followed by Aswina (85.30 g/m2) and Moroberekan (91.56 g/m2). On the other hand, IR64 recorded highest GY (409.93 g/ m2) followed by SHZ 2 (371.76 g/m2) and Minghui 63 (273.20 g/m2). 4.1.1.1.1.4.3 HI Zhenshan 97 B recorded highest HI (0.38) followed by N22 (0.34) and Dular (0.33) under drought stress condition. On the other hand, Moroberekan and Minghui 63 recorded lowest HI (0.03) followed by Aswina (0.06). SHZ 2 recorded highest HI (0.38) followed by Sadu Cho (0.34) in well-watered condition. Whereas, Aswina recorded lowest HI of 0.13 followed by Moroberekan (0.14) and Azucena (0.15). 4.1.1.1.2 Field Experiment – Dry Season 2009 4.1.1.1.2.1 Phenology and Grain Yield Parameters The mean performance of all OryzaSNP panel rice accessions under both drought stress and well-watered condition are presented below (Table 15).

4.1.1.1.2.1 PH (cm) In drought stress condition, Swarna (44.33 cm) was the shortest followed by IR64 (52.67 cm) and Zhenshan 97B (55.89 cm) while, Pokkali was the tallest (89.44 cm) followed by Dom Sufid (86.00 cm) and Sadu Cho (85.33 cm). Pokkali (103.78 cm) was the tallest followed by Dom Sufid (100.78cm) and Aswina (99.89 cm) under well-watered condition while, Zhenshan 97B was the shortest (70.44 cm) followed by Swarna (79.78 cm) and SHZ 2 (80.44 cm). 4.1.1.1.2.2 TN Rayada had highest TN (21.44) followed by Zhenshan 97B (20.44) and IR64 (20.22) in drought stress condition while, Moroberekan had the lowest TN (8.22) followed by Azucena (8.67) and Aswina (13.89). Highest TN was recorded by FR13A (21.33) followed by IR64 (20.89) and Rayada (20.11) in well-watered condition while, Dom Sufid had the lowest TN (10.89) followed by Zhenshan 97B (14.56), Sadu Cho and Aswina (15.11). 4.1.1.1.2.3 SB (g/m2) Under drought stress condition, Pokkali had maximum SB (555.00 g/m2) followed by FR13A (484.00 g/m2) and Azucena (450.67 g/m2). On the other hand, Dular recorded minimum SB (218.67 g/m2) followed by Sadu Cho (240.67 g/m2) and IR 64 (292.00 g/m2). Under well-watered condition, FR13A had maximum SB (1257.33 g/m2) followed by Dom Sufid (1050.67 g/m2) and Aswina (940.00 g/m2). On the other hand, Dular recorded minimum SB (480.00 g/m2) followed by Sadu Cho (574.00 g/m2) and Zhenshan 97B (578.67 g/m2). 4.1.1.1.2.4 GY (g/m2)) Among the genotypes evaluated, Swarna recorded highest GY of 473.33 g/m2 followed by FR13A (357.33 g/m2) and Zhenshan 97B (327.33 g/m2). On the other hand, SHZ 2 recorded lowest GY (126.67 g) followed by Dom Sufid (140.67 g/m2) and Rayada (148.67 g/m2) which were on par with one another. Most of the

entries did not produce any GYs in stress condition because of high temperature during grain filling and also due to severe drought stress. Because of this problem we measured GY only in well-watered condition. 4.1.1.1.2.5 HI As indicated above, HI was also calculated only in well-watered condition. Swarna and Zhenshan 97B had highest HI (0.36) followed by Sadu Cho (0.35). Dom Sufid recorded lowest HI of 0.12 followed by Rayada, SHZ 2 and Aswina with 0.16. 4.1.1.2 Analysis of Variance The mean sum of squares for root, shoot characters of both drought stress and well-watered condition (Table 16) and physiological traits of drought stress (Table 17) recorded in DS 2008 field experiment are presented in tabular form. Similarly mean sum of squares for all shoot and GY parameters recorded in DS 2009 field experiment are presented in Table 18. Highly significant differences among the genotypes were observed for all the characters in both experiments under both drought stress and well-watered condition (except RWC and SC under drought stress condition during DS 2008 experiment). 4.1.1.3 Variability Parameters Variability in respect of all the characters in all OryzaSNP panel accessions of rice are presented in tabular form and briefly described below. 4.1.1.3.1 Field Experiment – Dry Season 2008 4.1.1.3.1.1 Phenology, Physiological and Grain Yield Traits Variability parameters in respect of all shoot and physiological characters in all OryzaSNP panel accessions of rice under drought stress and well-watered are presented Table 19.

The overall mean PH of the genotypes was 83.65 cm with a range of 39.17 to 129.78cm under drought stress condition. High PCV and GCV values of 29.20 and 28.02 per cent respectively with high h2 estimate (92.10 %) and GA (55.39) as per cent of mean were recorded for this trait. PH ranged from 62.78 to 123.61cm with a mean value of 92.03 cm under well-watered condition. The genotypes showed high PCV (20.96 %) and GCV (20.30 %) values accompanied with high h2 (93.73%) and GA (40.48) as per cent of mean. TN varied from 10.27 to 28.84 with a mean value of 16.67 under drought stress condition. The genotypes showed high PCV (32.03 %) and GCV (30.71 %) values accompanied with high h2 (91.98 %) and GA (60.68) as per cent of mean. However genotypes varied largely (7.00-26.67) under well-watered condition with regard to TN and the overall mean for this trait was 15.74. High PCV (33.54 %) and GCV (32.38 %) were recorded for this trait with high h2 and GA as per cent mean of 92.23 % and 64.41 respectively. Under drought stress condition, the overall mean SDW of the genotypes was 10.00g/plant with a range of 5.00 to 24.00g/plant. High PCV and GCV values of 48.88 and 47.36 per cent respectively with high h2 estimate (93.90 %) and GA (94.95) as per cent of mean were recorded for this trait. SDW ranged between 9.79 to 38.47 g/plant with a mean value of 18.55g under well-watered condition. The PCV (43.20 %) and GCV (42.43%) co-efficients of variation were high accompanied with high h2 of 96.47 per cent and GA of 85.85 as per cent of mean. Genotypes exhibited a wide range of PR (13.30-25.20) with an average of 17.66 under drought stress condition. The values of PCV (19.26 %) and GCV (16.20 %) were moderate coupled with high h2 (70.74 %) and GA as per cent of mean (28.07) for this trait. TR ranged from 1.62 to 3.20 g with a mean value of 2.13 g under drought stress condition. The PCV (23.80 %) and GCV (20.90 %) values were high. This

trait also recorded high h2 (77.13 %) coupled with high GA (37.81) as per cent of mean. In drought stress condition, RWC varied from 60.83 to 83.21 with a mean value of 72.45. Moderate PCV (13.97%) with low GCV (2.30%) coupled with low h2 estimate (2.70 %) and GA (0.78) as per cent of mean were recorded for this trait. Genotypes exhibited a wide range of SC (376.33-701.67 mol m-2 s-1) with an average of 557.26 mol m-2 s-1under drought stress condition. High PCV (30.80%) with low GCV (7.09%) coupled with low h2 estimate (-5.30 %) and GA (-3.36) as per cent of mean were recorded for this trait. LWP ranged from -1.27 to -3.35 with a mean value of -2.64 under drought stress condition. High PCV (26.04%) with moderate GCV (16.45%) were recorded for this trait. Although this trait was recorded high h2 (39.90 %) coupled with high GA (21.40) as per cent of mean. Under drought stress condition, the overall mean LRS of the genotypes was 3.33 with a range of 1.00 to 6.33. High PCV and GCV values of 60.62 and 49.86 per cent respectively with high h2 estimate (67.66 %) and GA (84.49) as per cent of mean were recorded for this trait. The overall mean SB of the genotypes was 525.75g/m2 with a range of 173.93 to 905.69g/m2 under drought stress condition. High PCV and GCV values of 39.64 and 38.57 per cent respectively with high h2 estimate (94.66 %) and GA (77.30) as per cent of mean were recorded for this trait. SB ranged between 224.69 to 1470.30 g/m2 with a mean value of 576.77g/m2 under well-watered condition. The genotypes showed high PCV (49.01 %) and GCV (47.52 %) values accompanied with high h2 (94.03 %) and GA (94.93) as per cent of mean. Under drought stress condition, the overall mean GY of the genotypes was 122.30g/m2 with a range of 13.04-254.05g/m2. High PCV and GCV values of

53.54 and 52.56 per cent respectively with high h2 estimate (96.75 %) and GA (106.50) as per cent of mean were recorded for this trait. GY ranged between 83.62-618.16g/m2 with a mean value of 212.29g/m2 under well-watered condition. The PCV (65.42 %) and GCV (64.39%) co-efficients of variation were high accompanied with high h2 of 96.88 per cent and GA of 130.55 as per cent of mean. HI varied from 0.03 to 0.38 with a mean value of 0.20 under drought stress condition. The genotypes showed high PCV (57.96 %) and GCV (56.56 %) values accompanied with high h2 (95.23 %) and GA (113.71) as per cent of mean. The genotypes also varied largely (0.13-0.38) under well-watered condition with regard to HI and the overall mean for this trait was 0.27. High PCV (28.44 %) and GCV (25.13 %) were recorded for this trait with high h2 and GA as per cent mean of 78.05 % and 45.73 respectively. 4.1.1.3.1.2 Root Traits Variability parameters in respect of all root characters in all OryzaSNP panel accessions of rice under both drought stress and well-watered conditions are presented Table 20. 4.1.1.3.1.2.1 RLD (cm/cm3) In drought stress condition, genotypes exhibited wide range of RLD at 010cm soil layer (0.49-1.89 cm/cm3) with an average of 1.16 cm/cm3. High PCV (34.74 %) and GCV (24.25 %) coupled with high h2 (48.74 %) and GA as per cent of mean (34.88) were recorded for this trait. However genotypes varied largely (1.21-3.95 cm/cm3) under well-watered condition with regard to RLD at 0-10cm soil layer and the overall mean for this trait were 2.43 cm/cm3. High PCV (38.86 %) and GCV (33.45%) were recorded for this trait with high h2 and GA as per cent mean of 74.10 % and 59.33 respectively.

RLD at 10-20 cm soil layer ranged widely (0.16-1.45 cm/cm3) with a mean value of 0.63 cm/cm3 under drought stress condition. High PCV (53.57 %) and GCV (49.82%) were recorded for this trait with high h2 and GA as per cent mean of 85.92 % and 95.13 respectively. In well-watered condition, RLD at 10-20cm soil layer varied from 0.21 to 1.25 cm/cm3 with a mean value of 0.50 cm/cm3. The genotypes showed high PCV (66.14%) and GCV (59.64 %) values accompanied with high h2 (81.29 %) and GA (110.76) as per cent of mean. RLD at 20-30cm soil layer varied from 0.11 to 1.34 cm/cm3 with a mean value of 0.45 cm/cm3 under drought stress condition. The genotypes showed high PCV (73.53 %) and GCV (65.64 %) values accompanied with high h2 (79.69 %) and GA (120.71) as per cent of mean. Whereas, under well-watered condition genotypes varied from 0.06 to 0.87 cm/cm3 with regard to RLD at 20-30cm soil layer and the overall mean for this trait was 0.22 cm/cm3. High PCV (84.89 %) and GCV (81.16 %) were recorded for this trait with high h2 and GA as per cent mean of 91.40 % and 159.84 respectively. Genotypes varied largely from a range of 0.02 to 0.24 cm/cm3 with regard to RLD at 30-45cm soil layer and the overall mean for this trait was 0.09 cm/cm3. PCV (74.03 %) and GCV (72.20 %) values were high accompanied with high h2 (95.12 %) and GA as per cent of mean (145.06). However genotypes varied narrowly (0.01-0.06 cm/cm3) under well-watered condition with regard to RLD at 30-45cm soil layer and the overall mean for this trait was 0.03 cm/cm3. High PCV (65.62 %) and GCV (58.73 %) were recorded for this trait with high h2 and GA as per cent mean of 80.10 % and 108.10 respectively. RSA at 0-10cm soil layer ranged widely (377.03-1036.77 cm2) with a mean value of 686.36 cm2 under drought stress condition. High PCV (26.87 %) and GCV (23.05 %) were recorded for this trait coupled with high h2 and GA as per cent mean of 73.59 % and 40.74 respectively. RSA at 0-10cm soil layer also varied widely from 599.39-2053.86 cm2 with a mean value of 1389.05 cm2 in

well-watered condition. The genotypes showed high PCV (31.47 %) and GCV (27.51 %) values accompanied with high h2 (76.38 %) and GA (49.52) as per cent of mean. In drought stress condition, genotypes exhibited wide range of RSA at 1020cm soil layer (114.66-587.52 cm2) with an average of 296.15 cm2. High PCV (45.77 %) and GCV (40.58 %) coupled with high h2 (78.60 %) and GA as per cent of mean (74.12) were recorded for this trait. However genotypes also varied largely (152.02-658.87 cm2) under well-watered condition with regard to RSA at 10-20cm soil layer and the overall mean for this trait was 317.89 cm2. High PCV (45.46 %) and GCV (42.82%) were recorded for this trait with high h2 and GA as per cent mean of 88.74 % and 83.11 respectively. RSA at 20-30cm soil layer varied from 45.25 to 372.34 cm2 with a mean value of 164.46 cm2 under drought stress condition. The genotypes showed high PCV (59.47 %) and (56.88 %) values accompanied with high h2 (91.47%) and GA (112.06 cm2) as per cent of mean. Whereas, under well-watered condition genotypes varied from 41.97 to 361.92 cm2 with regard to RSA at 20-30cm soil layer and the overall mean for this trait was 143.58 cm2. High PCV (62.67 %) and GCV (56.33 %) were recorded for this trait with high h2 and GA as per cent mean of 80.78 % and 104.29 respectively. RSA at 30-45cm soil layer ranged widely (10.45 – 123.73 cm2) with a mean value of 49.75 cm2 under drought stress condition. High PCV (72.41 %) and GCV (68.28 %) were recorded for this trait coupled with high h2 and GA as per cent mean of 88.93 % and 132.65 respectively. RSA at 30-45cm soil layer also varied widely from 7.08 to 57.55 cm2 with a mean value of 23.77 cm2 in well-watered condition. The genotypes showed high PCV (66.72 %) and GCV (63.58 %) values accompanied with high h2 (90.82 %) and GA (124.82) as per cent of mean. Genotypes exhibited wide range of RV at 0-10cm soil layer (4.90-14.16 cm3) with an average of 8.50 cm3 under drought stress condition. High PCV

(29.46 %) and GCV (24.47 %) coupled with high h2 (69.03 %) and GA as per cent of mean (41.89) were recorded for this trait. Genotypes also varied largely (5.9025.00 cm3) under well-watered condition with regard to RV at 0-10cm soil layer and the overall mean for this trait was 16.84 cm3. High PCV (30.48 %) and GCV (27.21%) were recorded for this trait with high h2 and GA as per cent mean of 79.67 % and 50.03 respectively. RV at 10-20 cm soil layer ranged widely (1.20-4.89 cm3) with a mean value of 2.93 cm3 under drought stress condition. High PCV (42.67 %) and GCV (40.70 %) were recorded for this trait with high h2 and GA as per cent mean of 90.97 % and 79.97 respectively. In well-watered condition, RV at 10-20cm soil layer varied from 2.03 to 9.83 cm3 with a mean value of 4.44 cm3. The genotypes showed high PCV (45.83 %) and GCV (42.88 %) values accompanied with high h2 (87.51 %) and GA (82.63) as per cent of mean. RV at 20-30cm soil layer varied from 0.39 to 2.33 cm3 with a mean value of 1.29 cm3 under drought stress condition. The genotypes showed high PCV (55.86 %) and GCV (51.64 %) values accompanied with high h2 (85.48 %) and GA (98.36) as per cent of mean. Whereas, under well-watered condition genotypes varied from 0.58 to 4.65 cm3 with regard to RV at 20-30cm soil layer and the overall mean for this trait was 2.06 cm3. High PCV (60.98 %) and GCV (56.56 %) were recorded for this trait with high h2 and GA as per cent mean of 86.04 % and 108.08 respectively. Genotypes varied largely from a range of 0.11 to 0.86 cm3 with regard to RV at 30-45cm soil layer and the overall mean for this trait was 0.38.cm3 PCV (70.87 %) and GCV (66.08 %) values were high accompanied with high h2 (86.95 %) and GA as per cent of mean (126.94). However genotypes varied narrowly (0.09-0.83 cm3) under well-watered condition with regard to RV at 30-45cm soil layer and the overall mean for this trait was 0.29 cm3. High PCV (77.10 %) and

GCV (72.32 %) were recorded for this trait with high h2 and GA as per cent mean of 87.98 % and 139.75 respectively. RDW at 0-10cm soil layer ranged widely (0.63-1.97g) with a mean value of 0.94g under drought stress condition. High PCV (33.54 %) and GCV (31.80 %) were recorded for this trait coupled with high h2 and GA as per cent mean of 89.87 % and 62.10 respectively. RDW at 0-10cm soil layer also varied widely from 0.46 to 2.89g with a mean value of 1.44g in well-watered condition. The genotypes showed high PCV (37.19 %) and GCV (35.83 %) values accompanied with high h2 (92.84 %) and GA (71.12) as per cent of mean. Genotypes exhibited wide range of RDW at 10-20cm soil layer (0.13 – 0.56g) with an average of 0.30g under drought stress condition. High PCV (45.35 %) and GCV (42.65 %) coupled with high h2 (88.47 %) and GA as per cent of mean (82.65) were recorded for this trait. However genotypes also varied largely (0.12-0.66g) under well-watered condition with regard to RDW at 10-20cm soil layer and the overall mean for this trait was 0.31g. High PCV (50.94 %) and GCV (48.54%) were recorded for this trait with high h2 and GA as per cent mean of 90.82 % and 95.30 respectively. Under drought stress condition, RDW at 20-30cm soil layer varied from 0.03 to 0.35g with a mean value of 0.13g. The genotypes showed high PCV (65.53 %) and (62.46 %) values accompanied with high h2 (90.85%) and GA (122.65) as per cent of mean. Whereas, under well-watered condition genotypes varied from 0.03 to 0.29g with regard to RDW at 20-30cm soil layer and the overall mean for this trait was 0.12g. High PCV (61.60 %) and GCV (59.44 %) were recorded for this trait with high h2 and GA as per cent mean of 93.10 % and 118.15 respectively. At depth (30-45cm), RDW ranged widely under drought stress condition (0.01-0.11g) with a mean value of 0.04g. High PCV (76.89 %) and GCV (71.69 %) were recorded for this trait coupled with high h2 and GA as per cent mean of

86.93 % and 137.65 respectively. RDW at 30-45cm soil layer varied from 0.00 to 0.06g with a mean value of 0.02g in well-watered condition. The genotypes showed high PCV (80.10 %) and GCV (72.59 %) values accompanied with high h2 (82.12 %) and GA (135.50) as per cent of mean. Under drought stress condition, the overall mean RN of the genotypes was 187.34 with a range of 113.00 -259.67. High PCV and moderate GCV values of 32.88 and 19.59 per cent respectively with high h2 estimate (35.45 %) and GA (24.04) as per cent of mean were recorded for this trait. Under well-watered condition, RN ranged from 60.50 to 412.00 with a mean value of 231.18. The PCV (40.25 %) and GCV (38.71 %) were high accompanied with high h2 of 92.50 per cent and GA of 76.70 as per cent of mean. Genotypes exhibited wide range of RSR (9.33-38.00) with an average of 19.20 under drought stress condition. High PCV (38.00 %) and GCV (37.95 %) coupled with high h2 (37.14 %) and GA as per cent of mean (95.78) were recorded for this trait. However genotypes also varied largely (0.05-0.34) under wellwatered condition with regard to RSR and the overall mean for this trait was 0.12. High PCV (63.79 %) and GCV (60.48 %) were recorded for this trait with high h2 and GA as per cent mean of 89.91 % and 118.15 respectively.

4.1.1.3.2 Field Experiment – Dry Season 2009 4.1.1.3.2.1 Phenology and Grain Yield Parameters Variability parameters in respect of all shoot and physiological characters in all OryzaSNP panel accessions of rice under drought stress and well-watered are presented Table 21. The overall mean PH of the genotypes was 69.46 cm with a range of 44.33 to 89.44 cm under drought stress condition. High PCV and GCV values of 21.90 and 19.82 per cent respectively with high h2 estimate (81.89 %) and GA (36.94) as

per cent of mean were recorded for this trait. PH ranged from 70.44 to 103.78cm with a mean value of 91.85 under well-watered condition. The genotypes showed moderate PCV (19.83 %) and low GCV (4.57 %) values accompanied with low h2 (-5.33%) and GA (-2.17) as per cent of mean. TN varied from 8.22 to 21.44 with a mean value of 16.39 under drought stress condition. The genotypes showed high PCV (28.83 %) and GCV (24.32 %) values accompanied with high h2 (71.17 %) and GA (42.27) as per cent of mean. However genotypes varied largely (10.89-21.33) under well-watered condition with regard to TN and the overall mean for this trait was 16.56. High PCV (22.99 %) and moderate GCV (14.33 %) were recorded for this trait with high h2 and moderate GA as per cent mean of 38.85 % and 18.40 respectively. The overall mean SB of the genotypes was 360.67g/m2 with a range of 218.67-550.00 g/m2 under drought stress condition. High PCV and GCV values of 31.88 and 23.98 per cent respectively with high h2 estimate (56.59 %) and GA (37.16) as per cent of mean were recorded for this trait. Under well-watered condition, SB ranged between 480.00 to 1257.33 g/m2 with a mean value of 776.15g/m2. The genotypes showed high PCV (30.58 %) and GCV (25.17 %) values accompanied with high h2 (67.78 %) and GA (42.70) as per cent of mean. Under well-watered condition, GY ranged between 126.66-473.33g/m2 with a mean value of 244.41g/m2. The PCV (45.66 %) and GCV (40.70%) were high accompanied with high h2 of 79.46 per cent and GA of 74.75 as per cent of mean. HI varied from 0.12 to 0.36 with a mean value of 0.24 under well-watered condition. The genotypes showed high PCV (31.34 %) and GCV (25.84 %) values accompanied with high h2 (68.02 %) and GA (43.91) as per cent of mean. 4.1.1.4 Correlation Studies 4.1.1.4.1 Associations of Root and Shoot Characters with SDW and GY

Under drought stress condition, GY is positively correlated with deep root related traits such as RLD at 30-45cm, RSA at 30-45cm, RV at 30-45cm but was not significant. Similarly positive correlation of SDW per plant with deep root related traits i.e., RLD at 30-45cm, RSA at 30-45cm, RV at 30-45cm and RDW at 30-45cm also recorded along with its significant and positive correlation with RSA at 0-10cm and RDW at 0-10cm. On contrary GY is positively correlated with top root related traits and negatively related with deep root related traits under well-watered condition but was non significant in both the cases. Highly significant positive correlation is recorded between SB and GY (0.79) (Table 22 and Table 23). 4.1.1.4.2 Association among Root and Shoot Characters RLD at 0-10cm had highly significant and positive association with RSA at 0-10cm (0.74) under drought stress condition and with RSA at 0-10cm (0.77), RV at 0-10cm (0.59) and RDW at 0-10cm (0.59) and RN (0.73) under well-watered condition. RLD at 0-10cm also significantly correlated with RN (0.48) and RV at 0-10cm (0.55) under drought stress condition but at 5% significance level. Highly significant positive association of RLD at 10-20cm with RSA at 1020cm (0.87), RV at 10-20cm (0.65), RDW at 10-20cm (0.73) under drought stress and with RLD at 20-30cm (0.65), RSA at 10-20cm (0.84), RDW at 10-20cm (0.76) under well-watered condition were recorded. RLD at 10-20cm also significantly correlated with RLD at 20-30cm (0.53), RSA at 20-30cm (0.55), and RV at 20-30cm (0.51) under drought stress condition and with RSA at 20-30cm (0.50), RV at 10-20cm (0.54), and RDW at 20-30cm (0.51) under well-watered condition but at 5% significance level. Highly significant and positive correlation of RLD at 20-30cm with RSA at 20-30cm (0.88) and RV at 20-30cm (0.79) under drought stress condition and with RSA at 20-30cm (0.84), RV at 30-45cm (0.60), RDW at 20-30cm (0.81) and RDW at 30-45cm (0.65) under well-watered condition were recorded. At 5%

significance level, RLD at 20-30cm correlated with RSA at 10-20cm (0.46) under drought stress condition and with RLD at 30-45cm (0.51), RSA at10-20cm (0.55), RSA at 30-45cm (0.54) and RV at 20-30cm (0.50) under well-watered condition. At depth (30-45cm) RLD had highly significant and positive association with RSA at 30-45cm (0.92), RV at 30-45cm (0.83) and RDW at 30-45cm (0.69) under drought stress condition and with RSA at 30-45cm (0.92), RV at 30-45cm (0.89), RDW at 20-30cm (0.62) and RDW at 30-45cm (0.81) under well-watered condition. RLD at 30-45cm also significantly correlated with RV at 10-20cm (0.48) under drought stress condition and with RSA at 20-30cm (0.55) and RV at 20-30cm (0.55) under well-watered condition but at 5% significance level. Highly significant and positive correlation of RSA at 0-10cm with RV at 010cm (0.75) under drought stress condition and with RV at 0-10cm (0.81), RDW at 0-10cm (0.72) and RN (0.68) under well-watered condition was recorded. Highly significant positive association of RSA at 10-20cm with RV at 1020cm (0.83) and RDW at 10-20cm (0.82) under drought stress and with RV at 1020cm (0.83) and RDW at 10-20cm (0.90) under well-watered condition were recorded. RSA at 10-20cm also significantly correlated with RSA at 20-30cm (0.49) and RV at 20-30cm (0.49) under drought stress condition but at 5% significance level. RSA at 20-30cm had highly significant and positive association with RV 20-30cm (0.89) under drought stress condition and with RSA at 30-45cm (0.59), RV at 20-30cm (0.75), RV at 30-45cm (0.59), RDW at 20-30cm (0.93) and RDW at 30-45cm (0.66) under well-watered condition. RSA at 20-30cm also significantly correlated with RV at 10-20cm (0.46), RDW at 10-20cm (0.46) and RDW at 20-30cm (0.47) under drought stress condition but at 5% significance level.

At deeper soil layer (30-45cm) RSA had highly significant and positive association with RV at 30-45cm (0.90) and RDW at 30-45cm (0.79) under drought stress condition and with RV at 20-30cm (0.65), RV at 30-45cm (0.95) RDW at 20-30cm (0.67) and RDW at 30-45cm (0.80) under well-watered condition. RSA at 30-45cm also significantly correlated with RV at 10-20cm (0.48) and SB (0.50) under drought stress condition but at 5% significance level. At top soil layer (0-10cm) RV had significant and positive association with RV at 30-45cm (0.46, p<0.05) under drought stress condition and with RDW at 010cm (0.70, p<0.01) and RN (0.60, p<0.01) under well-watered condition. Significant positive association of RV at 10-20cm with RDW at 10-20cm (0.83, p<0.01), RV at 20-30cm (0.50, p<0.05) and RDW at 30-45cm (0.52, p<0.05) under drought stress and with RDW at 20-30cm (0.47, p<0.05) under well-watered condition were recorded. Significant and positive correlation of RV at 20-30cm with RDW at 1020cm (0.49, p<0.05) and RDW at 30-45cm (0.48, p<0.05) under drought stress condition and with RV at 30-45cm (0.59, p<0.01), RDW at 20-30cm (0.81, p<0.01) and RDW at 30-45cm (0.54, p<0.05) under well-watered condition were recorded. At depth (30-45cm) RV had highly significant and positive association with RDW at 30-45cm (0.86) and SB (0.60) under drought stress condition and with RDW at 20-30cm (0.68) and RDW at 30-45cm (0.87) under well-watered condition. At 0-10cm soil layer, RDW had significant and positive association with SB (0.54, p<0.05) under drought stress condition and with RN (0.68, p<0.01) under well-watered condition. Similarly at 10-20cm soil layer RDW had significant and positive correlation with SB (0.48, p<0.05) under drought stress condition.

Highly significant and positive correlation of RDW at 20-30cm with RDW at 30-45cm (0.68) under well-watered condition was recorded. At depth (30-45cm) RDW had highly significant and positive association with SB (0.60) under drought stress condition. 4.1.2 Lysimetric Experiments – Wet Season 2008, Dry Season 2009 and Wet Season 2009 4.1.2.1 Mean Performance The mean performance of all OryzaSNP panel rice accessions in respect of water uptake, phenology, physiological, root, and agronomical traits are briefly presented below. 4.1.2.1.1 Lysimetric Experiment- Wet Season 2008 4.1.2.1.1.1 Water Uptake (g/plant) Mean performance of all genotypes for water uptake rates measured at different intervals under drought stress are presented in Table 24. At 14DAS, N22 was recorded highest water uptake (1868.0 g/plant) followed by Aswina 1767.0 g/plant) and Azucena (1580.0 g/plant). Whereas IR64 was recorded lowest water uptake (794.0 g/plant) followed by Tainung 67 (1026.0 g/plant) and Swarna (1042.0 g/plant). The highest water uptake at 21DAS was recorded by Aswina (1566.0 g/plant) followed by Dular (1491.0 g/plant) and Azucena (1489.0 g/plant) On the other hand, Tainung 67 recorded lowest water uptake (808.0 g/plant) followed by IR64 (840.0 g/plant) and Zhenshan 97B (868.0 g/plant). At the end of stress (28DAS), Aswina recorded highest water uptake (1725.0 g/plant) followed Azucena (1720.0 g/plant) and Dular (1547.0 g/plant). Whereas Zhenshan 97B was recorded lowest water uptake (710.0 g/plant) followed by LTH (732.0 g/plant) and IR64 (872.0 g/plant).

A phenotypic difference for TWU during entire drought period was noticed among the entries. Genotype such as Aswina, Azucena, N22 and Dular extracted more than 4500.0 g/plant of water during drought stress period. On contrary mega varieties such as IR64 and Swarna recorded only 2506.0 g/plant and 3203.0 g/plant respectively. 4.1.2.1.1.2 Root Traits 4.1.2.1.1.2.1 MRL (cm) Higher MRL with the mean of more than 85 cm was observed among the 11 genotype in drought stress condition. Lower MRL was recorded by LTH (64.6cm) followed by SHZ 2 and Zhenshan 97B (70.6cm). Whereas under wellwatered condition, higher MRL was recorded for Dular (56.7cm) followed by Aswina (56.0cm) and N22 (53.5cm) (Table 25). 4.1.2.1.1.2.2 RN Comparison of means in drought stress condition showed that, Dom Sufid had highest RN (160.3) followed by Dular (141.3) and Tainung 67 (138.0) while, Azucena had the lowest RN (49.0) followed by Swarna (70.6) and Moroberekan (71.7) (Table 25). Whereas in well watered condition, Rayada had highest RN (457.3) followed Tainung 67 (418.3) and FR13A (400.0) while, Azucena had the lowest RN (114.8) followed by Cypress (164.7) and Moroberekan (192.0). 4.1.2.1.1.2.3 RLD (cm/cm3) Mean performance of all genotypes for RLD across different soil depth under both drought stress and well-watered were presented in Table 25. At 0-30cm soil layer, Rayada was recorded highest RLD (1.95 cm/cm3) followed by Pokkali (1.60 cm/cm3) and IR64 (1.43 cm/cm3) under drought stress condition. Whereas Moroberekan was recorded lowest RLD (0.39 cm/cm3) followed by Nipponbare (0.45 cm/cm3) and SHZ 2 (0.46 cm/cm3). Under wellwatered condition, highest RLD was recorded by Rayada (12.87 cm/cm3) followed

by N22 (5.01 cm/cm3) and Sadu Cho (4.90 cm/cm3).

On the other hand,

Nipponbare recorded lowest RLD (1.12 cm/cm3) followed by Minghui 63 (1.54 cm/cm3) and Aswina (1.91 cm/cm3). Rayada (3.77 cm/cm3) recorded highest RLD at 30-45cm soil layer followed by Aswina (2.41 cm/cm3) and N22 (2.27 cm/cm3) under drought stress condition. On the other hand, SHZ2 recorded lowest RLD (0.23 cm/cm3) followed by Sadu Cho (0.50 cm/cm3) and Moroberekan (0.52 cm/cm3). Among the genotypes under well-watered condition, N22 recorded highest RLD (2.03 cm/cm3) followed Pokkali (1.81 cm/cm3) and FR13A (1.58 cm/cm3). Whereas Nipponbare was recorded lowest RLD (0.00 cm/cm3) followed by IR64 (0.07 cm/cm3) and LTH (0.12 cm/cm3). At 45-60cm soil layer, Rayada recorded highest RLD (1.84 cm/cm3) followed N22 (1.72 cm/cm3) and Aswina (1.56 cm/cm3) under drought stress condition. Whereas SHZ 2 was recorded lowest RLD (0.04 cm/cm3) followed by Nipponbare (0.14 cm/cm3) and Moroberekan (0.19 cm/cm3). Most of the genotypes recorded very less or zero RLD at both 45-60cm and 60-100cm soil layer under well-watered condition. Among all, Azucena (0.77 cm/cm3) and Don Sufid (0.02 cm/cm3) had significantly higher RLD at 45-60cm and 60-100cm soil layers respectively.. The highest RLD at 60-100cm soil layer under drought stress was recorded by Aswina (0.42 cm/cm3) followed by FR13A (0.40 cm/cm3) and Rayada (0.37 cm/cm3). On the other hand, SHZ2 recorded lowest RLD (0.00 cm/cm3) followed by Nipponbare (0.01 cm/cm3) and LTH (0.02 cm/cm3). 4.1.2.1.1.2.4 RSA (cm2), RV (cm3) and RDW (g) Mean performance of all genotypes for RSA, RV and RDW across different soil depth under both drought stress and well-watered are presented in Table 26, Table 27 and Table 28 respectively. At 0-30cm soil layer, considerable variation

was noted in drought stress condition for RSA (1583.1-455.2 cm2), RV (12.684.44 cm3), and RDW (0.86-0.26g). Among them, Rayada produced significantly higher RSA (1583.1) and RV (12.68 cm3), whereas N22 produced higher RDW (0.86g) than others. At 30-45cm soil layer, Rayada had significantly higher RSA (1150.00 cm2), RV (6.58 cm3) and RDW (0.37 g) than others. SHZ 2 recorded lowest RSA (62.10 cm2 at 30-45cm, 14.70 cm2 at 45-60 and 2.10 cm2 at 60100cm), RV (0.31 cm3at 30-45cm, 0.10 cm3 at 45-60 and 0.02 cm3 at 60-100cm) and RDW (0.03 g at 30-45cm, 0.01 g at 45-60 and 0.00 g at 60-100cm) at deeper soil layers. Among them, Rayada (584.4 cm2) and N22 (3.55 cm3) had higher RSA and RV at 45-60cm soil layer. Whereas at 60-100cm soil layer, Aswina had highest RSA at 45-60cm (381.3 cm2) and 60-100cm (2.47 cm3) soil layers respectively. Dular had highest RDW (0.25g at 45-60 cm and 0.29 g at 60 -100cm) at deeper soil layer. 4.1.2.1.1.3 Phenology and Physiological Traits Mean performance of all genotypes for all phenology and physiological traits under both drought stress and well-watered conditions are presented in Table 29. In drought stress condition, Azucena (144.50 cm) was the tallest followed by Aswina (140.40cm) and Pokkali (127.20 cm) while, Nipponbare was the shortest (76.80 cm) followed by Swarna (78.40 cm) and IR 64 (89.30 cm). Whereas, under well-watered condition, Pokkali (165.70 cm) was the tallest followed by Aswina (165.00 cm) and Azucena (152.50 cm) while, Nipponbare was the shortest (78.30 cm) followed by Swarna (86.20 cm) and Zhenshan 97B (100.70 cm). Rayada had highest TN (27.00) followed by Swarna (21.60) and Minghui 63 (18.80) under drought stress condition while, Moroberekan had the lowest TN (5.33) followed by Azucena (6.50) and Tainung 67 (7.60). Whereas under wellwatered condition, Rayada had highest TN (40.67) followed by Minghui 63 (30.00) and IR64 (29.00). While, Moroberekan had the lowest TN (8.33) followed by Azucena (13.00) and Cypress (13.67).

Nipponbare had higher SC (190.33 mol m-2 s-1) followed by Moroberekan (181.33 mol m-2 s-1) and Rayada (138.53 mol m-2 s-1) under drought stress condition. On the other hand, Zhenshan 97B recorded lower SC (41.33 mol m-2 s) followed by Aswina (48.60 mol m-2 s-1) and Minghui 63 (56.67 mol m-2 s-1).

1

Under well-watered condition, Zhenshan 97B had higher SC (590.00 mol m-2 s-1) followed by IR64 (577.50 mol m-2 s-1) and Swarna (563.33 mol m-2 s-1). On the other hand, Azucena recorded lower SC (233.25 mol m-2 s-1) followed by LTH (353.00 mol m-2 s-1) and N22 (357.50 mol m-2 s-1). Drought stress reduced SB by 49.7 per cent (data not shown). Under wellwatered condition, Pokkali had maximum SB (55.61 g) followed by Rayada (55.30 g) and Dular (52.26 g). On the other hand, Nipponbare recorded minimum SB (13.70 g) followed by M202 (21.79 g) and LTH (24.48 g). Whereas under drought stress condition, Dular had maximum SB (28.47 g) followed by N22 (25.07 g) and FR13A (25.04 g). On the other hand, IR64 recorded lowest SB (10.70 g) followed by Tainung 67 (13.73 g) and SHZ 2 (13.83 g). 4.1.2.1.2 Lysimetric Experiment- Dry Season 2009 4.1.2.1.2.1 Water Uptake (g/ plant) Mean performance of all genotypes for water uptake rates measured at different intervals under drought stress are presented in Table 30. At 5DAS, LTH was recorded highest water uptake (990.0 g) followed by N22 (784.0g) and Dular (732.0g). Whereas, Dom Sufid was recorded lowest water uptake (405.0g) followed by Sadu Cho (437.5g) and Nipponbare (452.5g). The highest water uptake at 8DAS was recorded by LTH (1053.0g) followed by Dular (796.0g) and SHZ 2 (730.0g) On the other hand, Cypress recorded lowest water uptake (330.0g) followed by Moroberekan (447.5g) and Nipponbare (452.5g).

At 12DAS, N22 recorded highest water uptake (806.0g) followed Dular (702.0g) and LTH (686.6g). Whereas Nipponbare was recorded lowest water uptake (360.0g) followed by IR64 (430.0g) and Dom Sufid (435.0g). The highest water uptake at 15DAS was recorded by Dular (902.0g) followed by Azucena (873.3g) and N22 (814.0g) On the other hand, Cypress recorded lowest water uptake (506.0g) followed by Nipponbare (537.5g) and IR 64 (544.0g). At 18DAS, Azucena recorded highest water uptake (640.0g) followed Dular (622.0g) and N22 (594.6g). Whereas Dom Sufid was recorded lowest water uptake (365.0g) followed by IR64 (370.0g) and Nipponbare (370.0g). The highest water uptake at last measurement (23DAS) was recorded by Dular (844.0g) followed by Azucena (593.3g) and N22 (710.0g) On the other hand, Cypress recorded lowest water uptake (418.0g) followed by IR 64 (424.0g) and Zhenshan 97B (434.0g). A phenotypic difference for TWU during entire drought period was noticed among the entries. Genotype such as Aswina, Azucena, LTH, N22 and Dular extracted more than 4000.0 g of water during drought stress period. On contrary mega varieties such as IR64 and Swarna recorded only 2914.0 g and 3702.0 g respectively. 4.1.2.1.2.2 Root Traits Mean performance of all genotypes for RLD, RSA, RV and RDW under drought stress and well-watered were presented in Table 31. In drought stress condition RLD ranges from 0.29 to 1.32 cm/cm3, whereas in well-watered conditions it ranges from 0.25 to 1.34 cm/cm3. Dular was recorded highest RLD in both well-watered (1.34 cm/cm3) and drought stress condition (1.32 cm/cm3) followed by Swarna (1.25 cm/cm3 in well-watered and 0.95 cm/cm3 in drought stress). Cypress and Azucena was recorded lowest RLD (0.25 cm/cm3) in well-

watered condition followed by Nipponbare (0.36 cm/cm3). Whereas in drought stress condition, Nipponbare was recorded lowest RLD (0.29 cm/cm3) followed by Cypress (0.34 cm/cm3) and N22 (0.60 cm/cm3). Under drought stress condition, the genotypes varied greatly in RSA (mean values ranging from 1362.74 to 6697.4 cm2), RV (14.68–78.79 cm3), and RDW (1.31-2.91g). Considerable variation was also noted in well-watered condition for RSA (1382.91-7483.93 cm2), RV (14.63-98.64 cm3), and RDW (0.42-3.33g). Dular was recorded higher RSA (7483.93 cm2 in well-watered and 6697.40 cm2 in drought stress), RV (97.30 cm3 in well-watered and 78.79 cm3 in drought stress), and RDW (3.33g in well-watered and 2.91g in drought stress) in both the conditions. Whereas, Nipponbare (1486.85 cm2, 14.63 cm3 and 0.73g of RSA, RV, RDW respectively in well-watered and 1362.74 cm2, 14.68 cm3, and 0.71g of RSA, RV, RDW respectively in drought stress respectively) and Cypress (1382.91 cm2, 17.80 cm3 and 0.42g of RSA, RV, RDW respectively in well-watered and 1735.85 cm2, 17.80 cm3 and 0.80g of RSA, RV, RDW respectively in drought stress condition) was recorded lowest RSA, RV and RDW in both the condition. 4.1.2.1.2.3 Phenology Mean performance of all genotypes for PH, TN and SDW under both drought stress and well-watered were presented in Table 32. In drought stress condition, Pokkali (93.00 cm) was the tallest followed by Dular (86.20 cm) and LTH (79.67 cm) while, Nipponbare was the shortest (39.60 cm) followed by Swarna (46.40 cm) and IR 64 (48.80 cm). Under well-watered condition, Dom Sufid (115.80 cm) was the tallest followed by Dular (109.80 cm) and Pokkali (105.20 cm) while, Nipponbare was the shortest (54.80 cm) followed by Swarna (63.50 cm) and Minghui 63 (65.60 cm). Swarna had highest TN (18.40) followed by Rayada (17.20) and LTH (17.20) under drought stress condition while, Moroberekan had the lowest TN (2.40) followed by Azucena (3.50) and Dom Sufid (4.40).Rayada had highest TN

(39.00) followed by Nipponbare (16.00) and SHZ2 (15.25) under well-watered condition while, Cypress had the lowest TN (3.75) followed by Moroberekan (4.33) and Tainung 67 (4.40). Drought stress reduced SDW by 58.90 per cent (data not shown). Under well-watered condition, Rayada had maximum SB (21.40 g/plant) followed by Sadu Cho (20.49g/plant) and Dular (20.46g/plant). On the other hand, LTH recorded minimum SB (6.81 g/plant) followed by Azucena (8.00 g/plant) and Tainung 67 (8.10 g/plant). Whereas under drought stress condition, LTH had maximum SDW (14.14 g/plant) followed by Dular (10.30 g/plant) and N22 (7.41 g/plant). On the other hand, Cypress recorded lowest SB (2.58 g/plant) followed by Moroberekan (3.09 g/plant) and Zhenshan 97B (3.36 g/plant). 4.1.2.1.3 Lysimetric Experiment – Wet Season 2009 4.1.2.1.3.1 Water Uptake (g/plant) Mean performance of all genotypes for real-time water uptake rates measured at different intervals under drought stress are presented in Table 33. The highest water uptake at 7DAS was recorded by Sadu Cho (1191.3 g) followed by N22 (1129.0 g) and Dular (1046.0 g). On the other hand, Nipponbare recorded lowest water uptake (589.0 g) followed by FR13A (693.0 g) and IR64 (786.0 g). At 14DAS, Rayada was recorded highest water uptake (1674.5 g) followed by Aswina (1608.2g) and Dular (1598.2g).

Whereas Nipponbare was recorded

lowest water uptake (824.8g) followed by Tainung 67 (943.6g) and M202 (1044.6g). The highest water uptake at 21DAS was recorded by Dular (1360.4g) followed by Pokkali (1286.4g) and Azucena (1194.5g). On the other hand, Nipponbare recorded lowest water uptake (470.6g) followed by M 202 (625.0g) and Swarna (692.2).

At 25DAS, Dular recorded highest water uptake (1043.2g) followed Pokkali (884.8g) and Azucena (879.7g). Whereas Nipponbare was recorded lowest water uptake (402.8g) followed by LTH (443.3g) and Tainung 67 (508.0g). The highest water uptake at 28DAS was recorded by Dular (636.6g) followed by Aswina (636.2g) and Rayada (582.2g). On the other hand, LTH recorded lowest water uptake (269.4g) followed by Nipponbare (292.4g) and M202 (316.6g). At 33DAS, Pokkali recorded highest water uptake (1137.0 g) followed Dular (1074.8 g) and Azucena (986.0 g). Whereas, LTH recorded lowest water uptake (331.2 g) followed by Nipponbare (397.4 g) and M202 (537.2 g). The highest water uptake at 35DAS was recorded by SHZ2 (397.4 g) followed by FR13A (347.2 g) and Rayada (312.2 g). On the other hand, Zhenshan 97B recorded lowest water uptake (162.4 g) followed by Azucena (164.8 g) and M202 (175.6 g). Some of genotypes such as Dular, Aswina, Pokkali, and Rayada, extracted more than 6000.0 g of water during drought stress period which is considerably higher than IR64 (4624.0 g). Although there are only few entries in each rice type but among the rice types studied (Table 34), Aus (4530.0 g in WS 2008 lysimetric experiment, 4100.0 g DS 2009 in lysimetric experiment and 6030.0 g in WS 2009 lysimetric experiment) and deep water accessions (4650.0 g in WS 2008 lysimetric experiment, 3810g in DS 2009 lysimetric experiment and 6450.0 g in WS 2009 lysimetric experiment) are having higher water uptake ability than other rice types such as temperate japonica (3390.0 g in WS 2008 lysimetric experiment 1, 3460.0 g in DS 2009 lysimetric experiment 2 and 3460.0 g in WS 2009 lysimetric experiment), tropical japonica (3920.0 g in WS 2008 lysimetric experiment, 3370.0 g in DS 2009 lysimetric experiment and 5420.0 g in WS 2009 lysimetric experiment), indica

(3070.0 g in WS 2008 lysimetric experiment, 3360.0 g in DS 2009 lysimetric experiment

and 5390.0 g in WS 2009 lysimetric experiment) and aromatic

accessions (3960.0 g in WS 2008 lysimetric experiment, 2740.0 g in DS 2009 lysimetric experiment and 4750.0 g WS 2009 lysimetric experiment) (Table 34). 4.1.2.2 Analysis of Variance The mean sum of squares for real-time water uptake rates measured in all three lysimetric trials were presented in Table 35. Whereas, the mean sum of squares for root and shoot traits recorded under both drought stress and wellwatered condition during WS 2008 and DS 2009 lysimetric experiment are presented in Table 36 and Table 37 respectively. Highly significant differences among the genotypes were observed for all the characters in all experiments under both drought stress and well-watered condition. 4.1.2.3 Variability Parameters 4.1.2.3.1 Lysimetric Experiment- Wet Season 2008 4.1.2.3.1.1 Root Traits Variability parameters in respect of all root characters using OryzaSNP panel accessions of rice under both drought stress and well-watered are tabulated in Table 38 and briefly described below 4.1.2.3.1.1.1 RLD (cm/cm3) In drought stress condition, genotypes varied from a range of 0.39 to 1.95 cm/cm3 with regard to RLD at 0-30cm soil layer and the overall mean for this trait was 0.93 cm/cm3. High PCV (47.13 %) and GCV (46.72 %) values were recorded coupled with high h2 (98.28 %) and GA as per cent of mean (95.42). However genotypes varied largely (1.12-5.01 cm/cm3) under well-watered condition with regard to RLD at 0-30cm soil layer and the overall mean for this trait were 3.36 cm/cm3. High PCV (74.03 %) and GCV (73.42 %) were recorded for this trait with high h2 and GA as per cent mean of 98.38 % and 150.02 respectively.

RLD at 30-45 cm soil layer ranged widely under drought stress condition (0.23-3.77 cm/cm3) with a mean value of 1.23 cm/cm3. High PCV (70.42 %) and GCV (64.12 %) were recorded for this trait with high h2 and GA as per cent mean of 82.91 % and 120.27 respectively. Under drought stress condition RLD at 45-60cm soil layer varied from 0.04 to1.84 cm/cm3 with a mean value of 0.76 cm/cm3. The genotypes showed high PCV (77.59 %) and GCV (65.64 %) values accompanied with high h2 (79.69 %) and GA (120.71) as per cent of mean. Genotypes varied largely from a range of 0.00 to 0.42 cm/cm3 with regard to RLD at 60-100 cm soil layer and the overall mean for this trait was 0.15 cm/cm3. High PCV (103.15 %) and GCV (98.14 %) values were recorded accompanied with high h2 (90.53 %) and GA as per cent of mean (192.37). In well-watered condition, RLD at 30-100 cm soil layer varied from 0.21 to 1.25 cm/cm3 with a mean value of 0.50 cm/cm3. The genotypes showed high PCV (85.18%) and GCV (84.13 %) values accompanied with high h2 (97.57 %) and GA (171.19) as per cent of mean. 4.1.2.3.1.1.2 RSA (cm2) RSA at 0-30cm soil layer ranged widely (455.19-1583.12 cm2) with a mean value of 890.82 cm2 under drought stress condition. High PCV (54.66 %) and low GCV (32.02%) were recorded for this trait with high h2 and GA as per cent mean of 34.43 % and 38.77 respectively. In well-watered condition, RSA at 0-30cm soil layer varied from 948.99 to 4965.17 cm2 with a mean value of 3299.63 cm2. The genotypes showed high PCV (63.89 %) and GCV (62.71 %) values accompanied with high h2 (96.34 %) and GA (126.80) as per cent of mean. Under drought stress condition, genotypes exhibited wide range of RSA at 30-45cm soil layer (62.11-1150.21 cm2) with an average of 394.59 cm2. High PCV (62.82 %) and GCV (61.74 %) coupled with high h2 (96.58 %) and GA as per cent

of mean (124.99) were recorded for this trait. RSA at 45-60cm soil layer varied from 14.69 to 584.37 cm2 with a mean value of 243.62 cm2 under drought stress condition. The genotypes showed high PCV (75.85 %) and (73.66 %) values accompanied with high h2 (94.29%) and GA (147.34) as per cent of mean. RSA at 60-100cm soil layer ranged widely (2.14-381.29 cm2) with a mean value of 137.82 cm2 under drought stress condition. High PCV (100.45 %) and GCV (97.93 %) were recorded for this trait coupled with high h2 and GA as per cent mean of 95.05 % and 196.68 respectively. However genotypes also varied largely (152.02-658.87 cm2) under wellwatered condition with regard to RSA at 30-100cm soil layer and the overall mean for this trait was 317.89 cm2. High PCV (81.54 %) and GCV (80.90%) were recorded for this trait with high h2 and GA as per cent mean of 98.42 % and 165.32 respectively. 4.1.2.3.1.1.3 RV (cm3) RV at 0-30cm soil layer varied from 4.44 to 12.68 cm3 with a mean value of 8.34 cm3 under drought stress condition. The genotypes showed high PCV (50.43 %) and low GCV (23.51 %) values accompanied with low h2 (21.73 %) and GA (22.57) as per cent of mean. However genotypes varied largely (7.62-89.78 cm3) under well-watered condition with regard to RV at 0-30cm soil layer and the overall mean for this trait was 31.45 cm3. High PCV (54.35 %) and GCV (53.49 %) were recorded for this trait with high h2 and GA as per cent mean of 96.87 % and 108.47 respectively. Under drought stress condition RV at 30-45 cm soil layer ranged widely (0.31-6.58 cm3) with a mean value of 2.41 cm3. High PCV (60.72 %) and GCV (59.03 %) were recorded for this trait with high h2 and GA as per cent mean of 94.51 % and 118.21 respectively.

RV at 45-60cm soil layer varied from 0.10 to 3.55 cm3 with a mean value of 1.48 cm3 under drought stress condition. The genotypes showed high PCV (74.48 %) and GCV (72.62%) values accompanied with high h2 (95.05 %) and GA (145.84) as per cent of mean. Genotypes varied largely from a range of 0.02 to 2.47 cm3 with regard to RV at 60-100cm soil layer and the overall mean for this trait was 0.91 cm3. PCV (97.96 %) and GCV (96.23 %) values were high accompanied with high h2 (96.49 %) and GA as per cent of mean (194.72). In well-watered condition, RV at 30-100cm soil layer varied from 2.03 to 9.83 cm3 with a mean value of 4.44 cm3. The genotypes showed high PCV (83.45 %) and GCV (82.92 %) values accompanied with high h2 (98.73 %) and GA (168.73) as per cent of mean. 4.1.2.3.1.1.4 RDW (g) Genotypes varied from a range of 0.26 to 0.86g with regard to RDW at 030cm soil layer and the overall mean for this trait was 0.55g under drought stress condition. PCV (46.30 %) and GCV (22.69 %) values were moderate with high h2 (23.96 %) and moderate GA as per cent of mean (22.84). However genotypes varied largely (0.39-5.84g) under well-watered condition with regard to RDW at 0-30cm soil layer and the overall mean for this trait was 1.97g. High PCV (63.34 %) and GCV (61.92 %) were recorded for this trait with high h2 and GA as per cent mean of 95.59 % and 124.71 respectively. Genotypes exhibited wide range of RDW at 30-45cm soil layer (0.030.37g) with an average of 0.13g under drought stress condition. High PCV (65.41 %) and GCV (63.26 %) coupled with high h2 (93.53 %) and GA as per cent of mean (126.04) were recorded for this trait. Under drought stress condition, RDW at 45-60cm soil layer varied from 0.01 to 0.25 g with a mean value of 0.08g. The genotypes showed high PCV

(86.17 %) and (84.86 %) values accompanied with high h2 (96.98%) and GA (172.15) as per cent of mean. At depth (60-100cm), RDW ranged widely under drought stress condition (0.00-0.29g) with a mean value of 0.06g. High PCV (135.79 %) and GCV (133.74 %) were recorded for this trait coupled with high h2 and GA as per cent mean of 97.00 % and 271.34 respectively Genotypes exhibited wide range of RDW at 30-100cm soil layer (0.030.37g) with an average of 0.13g under well-watered condition. High PCV (102.66 %) and GCV (102.06 %) coupled with high h2 (98.83 %) and GA as per cent of mean (209.00) were recorded for this trait. 4.1.2.3.1.1.5 MRL (cm) Genotypes exhibited wide range of MRL (64.60-94.60cm) with an average of 84.52cm under drought stress condition. Moderate PCV (12.06 %) and low GCV (9.71 %) coupled with high h2 (64.79 %) and moderate GA as per cent of mean (16.10) were recorded for this trait. However genotypes also varied (27.5056.75cm) under well-watered condition with regard to MRL and the overall mean for this trait was 44.95cm. Moderate PCV (17.06 %) and GCV (15.70 %) were recorded for this trait with high h2 and GA as per cent mean of 84.71 % and 29.77 respectively. 4.1.2.3.1.1.6 RN Under drought stress condition, the overall mean RN of the genotypes was 106.97 with a range of 49.00-160.25. High PCV and GCV values of 27.79 and 25.46 per cent respectively with low h2 estimate (83.91 %) and GA (48.03) as per cent of mean were recorded for this trait. Whereas under well-watered condition, RN ranged between 114.75-457.25 with a mean value of 293.14, indicating presence of enough variation for this trait. The PCV (31.05 %) and GCV (29.91

%) co-efficients of variation were high accompanied with high h2 of 92.78 per cent and moderate GA of 59.34 as per cent of mean. 4.1.2.3.1.2 Phenology and Physiological Traits Variability parameters in respect of shoot and physiological characters using OryzaSNP panel accessions of rice under both drought stress and wellwatered are tabulated in Table 39 and briefly described below The overall mean PH of the genotypes was 108.11 cm with a range of 76.80 -144.50cm under drought stress condition. Moderate PCV and GCV values of 19.78 and 19.11 per cent respectively with high h2 estimate (93.34 %) and GA (38.04) as per cent of mean were recorded for this trait. PH ranged from 78.27 to 165.67 cm with a mean value of 127.20 under well-watered condition. The genotypes showed high PCV (20.96 %) and moderate GCV (19.06 %) values accompanied with high h2 (85.91%) and GA (36.38) as per cent of mean. TN varied from 5.33 to 27.00 with a mean value of 14.04 under drought stress condition. The genotypes showed high PCV (37.31 %) and GCV (34.94 %) values accompanied with high h2 (87.71 %) and GA (67.42) as per cent of mean. However genotypes varied largely (8.00-40.67) under well-watered condition with regard to TN and the overall mean for this trait was 20.88. High PCV (38.28 %) and GCV (37.87 %) were recorded for this trait with high h2 and GA as per cent mean of 97.86 % and 77.17 respectively. Genotypes exhibited a wide range of SC (41.33-190.33 mol m-2 s-1) with an average of 93.92 mol m-2 s-1 under drought stress condition. High PCV (52.27%) with GCV (41.47%) coupled with high h2 estimate (62.95 %) and GA (67.79) as per cent of mean were recorded for this trait. Whereas genotypes varied largely (233.25-590.00 mol m-2 s-1) under well-watered condition with regard to SC and the overall mean for this trait was 453.98 mol m-2 s-1. High PCV (27.78 %) and

moderate GCV (15.12%) were recorded for this trait with moderate h2 and GA as per cent mean of 29.63 % and 16.95 respectively. Under drought stress condition, the overall mean SDW of the genotypes was 19.09 g/plant with a range of 10.70 to 28.47g/plant. High PCV and GCV values of 25.72 and 24.79 per cent respectively with high h2 estimate (92.90 %) and GA (49.22) as per cent of mean were recorded for this trait. SDW ranged between 13.70 to 55.61 g/plant with a mean value of 37.79 g/plant under wellwatered condition. The PCV (34.57 %) and GCV (34.09 %) were high accompanied with high h2 of 97.27 per cent and GA of 69.27 as per cent of mean. 4.1.2.3.2 Lysimetric Experiment- Dry Season 2009 Variability parameters in respect of root and shoot characters using OryzaSNP panel accessions of rice under both drought stress and well-watered are tabulated in Table 40 and briefly described below 4.1.2.3.2.1 Phenology and Root Traits The mean PH of the genotypes was 65.04 cm with a range of 39.60-93.00 cm under drought stress condition. High PCV and GCV values of 25.53 and 20.20 per cent respectively with high h2 estimate (62.60 %) and GA (32.93) as per cent of mean were recorded for this trait. PH ranged from 54.80 to 115.80 cm with a mean value of 84.05cm under well-watered condition. The genotypes showed high PCV (22.73 %) and moderate GCV (19.13 %) values accompanied with high h2 (70.80 %) and GA (33.15) as per cent of mean. TN varied from 2.40-18.40 with a mean value of 9.44 under drought stress condition. The genotypes showed high PCV (49.57 %) and GCV (48.03 %) values accompanied with high h2 (93.89 %) and GA (95.88) as per cent of mean. However genotypes varied largely (3.75-39.00) under well-watered condition with regard to TN and the overall mean for this trait was 11.65. High PCV (66.78 %)

and GCV (65.16 %) were recorded for this trait with high h2 and GA as per cent mean of 95.21 % and 130.98 respectively. Under drought stress condition, the overall mean SDW of the genotypes was 5.92 g with a range of 2.58 to 14.14 g. High PCV and GCV values of 46.88 and 45.06 per cent respectively with high h2 estimate (92.38 %) and GA (89.22) as per cent of mean were recorded for this trait. Whereas SDW ranged between 6.8021.24 g with a mean value of 14.44 g under well-watered condition, The PCV (37.15%) and GCV (34.83) were high accompanied with high h2 of 87.89 per cent and GA of 67.27 as per cent of mean. In drought stress condition, genotypes varied from a range of 0.29 -1.32 cm/cm3 with regard to RLD at 0-100cm soil layer and the overall mean for this trait was 0.72 cm/cm3. High PCV (44.13 %) and GCV (42.72 %) values were recorded coupled with high h2 (93.73 %) and GA as per cent of mean (85.20). Genotypes varied (0.25-1.34 cm/cm3) under well-watered condition with regard to RLD at 0-100cm soil layer and the overall mean for this trait was 0.78 cm/cm3. High PCV (64.55 %) and GCV (58.88 %) were recorded for this trait with high h2 and GA as per cent mean of 83.20 % and 110.65 respectively. RSA at 0-100cm soil layer ranged widely (1362.74-6697.40 cm2) with a mean value of 3682.44 cm2 under drought stress condition. High PCV (43.72 %) and low GCV (42.02%) were recorded for this trait with high h2 and GA as per cent mean of 92.48 % and 83.29 respectively. In well-watered condition, RSA at 0-100cm soil layer varied from 1382.91-7483.93 cm2 with a mean value of 4258.14 cm2. The genotypes showed high PCV (67.05 %) and GCV (61.89 %) values accompanied with high h2 (96.55 %) and GA (117.67) as per cent of mean. RV at 0-100cm soil layer varied from 14.68 to 78.79 cm3 with a mean value of 43.66 cm3 under drought stress condition. The genotypes showed high PCV (47.93 %) and low GCV (46.15 %) values accompanied with low h2 (92.73 %) and GA (91.55) as per cent of mean. However genotypes varied largely (14.63-

98.64cm3) under well-watered condition with regard to RV at 0-100cm soil layer and the overall mean for this trait was 55.08 cm3. High PCV (67.29 %) and GCV (66.12 %) were recorded for this trait with high h2 and GA as per cent mean of 81.36 % and 133.83 respectively. Genotypes varied from a range of 0.78-2.91g with regard to RDW at 0100cm soil layer and the overall mean for this trait was 1.45g under drought stress condition. PCV (46.95 %) and GCV (44.61 %) values were moderate with high h2 (90.25 %) and GA as per cent of mean (87.30). However genotypes varied largely (0.41-3.33g) under well-watered condition with regard to RDW at 0-100cm soil layer and the overall mean for this trait was 1.77g. High PCV (62.53 %) and GCV (55.96 %) were recorded for this trait with high h2 and GA as per cent mean of 80.08 % and 110.65 respectively. TWU varied from 2655.00-4598.00g with a mean value of 3511.00 under drought stress condition. The genotypes showed high PCV (24.39 %) and moderate GCV (14.05 %) values accompanied with high h2 (33.17%) and moderate GA (16.66) as per cent of mean 4.1.2.4 Correlation Studies 4.1.2.4.1 Lysimetric Experiment-WS 2008 The PCV correlation co-efficients among all the characters were estimated under both drought stress and well-watered condition and the results are presented in Table 41 and Table 42 respectively. 4.1.2.4.1.1 Associations of Root and Shoot Characters with SDW Under drought stress condition, SDW had highly significant and positive association with root traits such as RLD at 45-60cm (0.65), RLD at 60-100cm (0.74), RSA at 30-45cm (0.56), RSA at 45-60cm (0.69), RSA at 60-100cm (0.74), RV at 30-45cm (0.58), RV at 45-60cm (0.70), RV at 60-100cm (0.74), RDW at 30-45cm (0.62), RDW at 45-60cm (0.79), RDW at 60-100cm (0.75), PH (0.72)

and TWU (0.69). SDW also significantly correlated with RLD at 30-45cm (0.52) and MRL (0.55) but at 5% significance level. Whereas under well-watered condition SDW is highly significantly correlated with RLD at 30-100cm (0.66), RSA at 30-100cm (0.72), RV at 0-30cm (0.58), RV at 30-100cm (0.74), RDW at 30-100cm (0.69) and MRL (0.58). At 5 per cent level of significance, SDW significantly correlated with RSA at 0-30 (0.48), RDW at 0-30cm (0.55), PH (0.45) and TN (0.45). 4.1.2.4.1.2 Association among Root and Shoot Characters RLD at 0-30cm had highly significant and positive association with RSA at 0-30cm (0.69), RSA at 30-45cm (0.57) and RV at 0-30cm (0.57) under drought stress condition and with RSA at 0-30 (0.98), RV at 0-30cm (0.93), RDW at 030cm (0.88), and RN (0.59) under well-watered condition. RLD at 0-30cm also significantly correlated with RLD at 30-45cm (0.54), RV at 30-45cm (0.55), RDW at 0-30cm (0.48) and RDW at 30-45cm (0.45) under drought stress condition but at 5% significance level. Highly significant positive association of RLD at 30-45cm with RLD at 45-60cm (0.78), RLD at 60-100cm (0.73), RSA at 30-45cm (0.91), RSA at 4560cm (0.78), RSA at 60-100cm (0.64), RV at 30-45cm (0.87), RV at 45-60cm (0.79), RV at 60-100cm (0.62), RDW at 30-45cm (0.85) and RDW at 45-60cm (0.65) under drought stress condition and with RSA at 30-100cm (0.97), RV at 30100cm (0.90) and RDW at 30-100cm (0.93) under well-watered condition. RLD at 30-45cm also significantly correlated with RSA at 0-30 (0.47), RDW at 60-100cm (0.49) and TWU (0.46) under drought stress condition and with MRL (0.49) and PH (0.50) under well-watered condition but at 5% significance level. Highly significant and positive correlation of RLD at 45-60cm with RLD at 30-45cm (0.87), RSA at 30-45cm (0.84), RSA at 45-60cm (0.96), RSA at 60100cm (0.86), RV at 30-45cm (0.83), RV at 45-60cm (0.95), RV at 60-100cm (0.82), RDW at 30-45cm (0.86), RDW at 45-60cm (0.86) and RDW at 60-100cm

(0.73) under drought stress condition were recorded. At 5% significance level, RLD at 45-60cm correlated with PH (0.44) and TWU (0.57) under drought stress condition. At depth (60-100cm) RLD had highly significant and positive association with RSA at 30-45cm (0.75), RSA at 45-60cm (0.89), RSA at 60-100cm (0.94), RV at 30-45cm (0.74), RV at 45-60cm (0.90), RV at 60-100cm (0.92), RDW at 30-45cm (0.80), RDW at 45-60cm (0.86), RDW at 60-100cm (0.81) and TWU (0.59) under drought stress condition. RLD at 60-100cm also significantly correlated with MRL (0.48) and PH (0.48) under drought stress condition but at 5% significance level. Highly significant and positive correlation of RSA at 0-30cm with RV at 030cm (0.92) and RDW at 0-30cm (0.71) under drought stress condition and with RV at 0-30cm (0.96), RDW at 0-30cm (0.91) and RN (0.58) under well-watered condition were recorded. RSA at 0-30cm also significantly correlated with TN (0.49) under well-watered condition but at 5% significance level. Highly significant positive association of RSA at 30-45cm with RSA at 4560cm (0.86), RSA at 60-100cm (0.71), RV at 30-45cm (0.96), RV at 45-60cm (0.85), RV at 60-100cm (0.67), RDW at 30-45cm (0.91) and RDW at 45-60cm (0.74) under drought stress condition and with RV at 30-100cm (0.97) and RDW at 30-100cm (0.93) under well-watered condition were recorded. RSA at 30-45cm also significantly correlated with RDW at 60-100cm (0.54) and TWU (0.49) under drought stress condition and with MRL (0.53), PH (0.53) and RV at 0-30cm (0.45) but at 5% significance level. RSA at 45-60cm had highly significant and positive association with RSA at 60-100cm (0.89), RV at 30-45cm (0.84), RV at 45-60cm (0.96), RV at 60100cm (0.85), RDW at 30-45cm (0.89), RDW at 45-60cm (0.89), RDW at 60100cm (0.74) and TWU (0.59) under drought stress condition. RSA at 45-60cm

also significantly correlated with PH (0.49) under drought stress condition but at 5% significance level. At depth (60-100cm), RSA had highly significant and positive association with RV at 30-45cm (0.72), RV at 45-60cm (0.89), RV at 60-100cm (0.97), RDW at 30-45cm (0.78), RDW at 45-60cm (0.87), RDW at 60-100cm (0.85) and TWU (0.62) under drought stress condition were recorded. RSA at 30-45cm also significantly correlated with MRL (0.50) and PH (0.52) under drought stress condition but at 5% significance level. At top soil layer (0-30cm), RV had significant and positive association with RDW at 0-30cm (0.73, p<0.05) under drought stress condition and with RDW at 0-30cm (0.94, p<0.01), RV at 30-100cm (0.45, p<0.05), RN (0.54, p<0.05) and TN (0.46, p<0.05) under well-watered condition. Significant and positive correlation of RV at 30-45cm with RV at 45-60cm (0.84, p<0.01), RV at 60-100cm (0.69, p<0.01), RDW at 30-45cm (0.90, p<0.01), RDW at 45-60cm (0.76, p<0.01), RDW at 60-100cm (0.55, p<0.05) and TWU (0.51, p<0.05) under drought stress condition and with RDW at 30-100cm (0.90, p<0.01), PH (0.56, p<0.01), RDW 0-30cm (0.51, p<0.05) and MRL (0.52, p<0.05) were recorded. Highly significant positive association of RV at 45-60cm with RV at 60100cm (0.87), RDW at 30-45cm (0.89), RDW at 45-60cm (0.90), RDW at 60100cm (0.76) and TWU (0.59) under drought stress condition were recorded. RV at 45-60cm also significantly correlated with MRL (0.44) and PH (0.49) under drought stress condition but at 5% significance level. At deeper soil layer (60-100cm), RV had highly significant and positive association with RDW at 30-45cm (0.74), RDW at 45-60cm (0.85), RDW at 60100cm (0.88) and TWU (0.63) under drought stress condition. RV at depth also

significantly correlated with MRL (0.52) and PH (0.53) under drought stress condition but at 5% significance level. At top soil layer (0-30cm), RDW had significant and positive association with only RN (0.59) under well-watered condition. Significant and positive correlation of RDW at 30-45cm with RDW at 45-60cm (0.83, p<0.01), RDW at 60-100cm (0.65, p<0.01) and TWU (0.59, p<0.01) under drought stress condition and with RDW at 30-100cm (0.68, p<0.01) and PH (0.54, p<0.05) under wellwatered condition were recorded. Highly significant positive association of RDW at 45-60cm with RDW at 60-100cm (0.87) and TWU (0.62) under drought stress condition was recorded. RDW at 45-60cm also significantly correlated with MRL (0.48) and PH (0.53) under drought stress condition but at 5% significance level. At depth (60-100cm) RDW had significant and positive association with TWU (0.65, p<0.01), MRL (0.50, p<0.05) and PH (0.59, p<0.05) under drought stress condition. Significant and positive (p<0.05) correlation was recorded between MRL with PH (0.49) and TWU (0.52), PH with TWU (0.65) under drought stress condition and RN with TN (0.51) under well-watered condition. 4.1.2.4.2 Lysimetric Experiment - DS 2009 The PCV correlation co-efficients among all the characters were estimated under both drought stress and well-watered condition and the results are presented in Table 43 and Table 44 respectively. 4.1.2.4 .2.1 Associations of Root and Shoot Characters with SDW Under drought stress condition, SDW had positive association with all root and shoot traits but was not significant except RDW at 0-100cm. (0.72, p<0.05). On contrary, SDW under well-watered condition is significantly (p<0.05) correlated with RLD at 0-100cm (0.76), RSA at 0-100cm (0.75), RV at 0-100cm (0.74) and RDW at 10-100cm (0.75).

4.1.2.4.2.2 Association among Root and Shoot Characters RDW at 0-100cm had highly significant and positive association with RLD at 0-100cm (0.75, p<0.05), RSA at 0-100cm (0.84, p<0.01), and RV at 0-100cm (0.81, p<0.01) under drought stress condition and with RLD at 0-100cm (0.88, p<0.01), RSA at 0-100cm (0.93, p<0.01), and RV at 0-100cm (0.90, p<0.01) under well-watered condition. Highly significant positive association of RLD at 0-100cm with RSA at 0100cm (0.92), and RV at 0-100cm (0.89) under drought stress condition and with RSA at 0-100cm (0.95), and RV at 0-100cm (0.92) under well-watered condition. Highly significant and positive correlation of RSA at 0-100cm with RV at 0100cm (0.97) was recorded under both drought stress and well-watered condition. 4.1.2.5 Path Co-efficient Analysis Path analysis was carried out at PCV level considering SDW per plant recorded under drought stress condition as dependent character while, RLD at 30100cm, RSA at 30-100cm, RDW at 30-100cm, MRL and PH recorded under drought stress condition as independent characters (Table 45). Among the characters studied under drought stress condition RLD at 60-100cm had the highest positive direct effect of 0.432 followed by PH (0.396) whereas, TN (0.521) and RLD at 30-100cm (0.506) had highest direct effect of towards SDW per plant in well-watered condition (Table 46). Under drought stress condition RLD at 45-60cm (0.374), RLD at 30-45cm (0.317), MRL (0.295) and TWU (0.254) contributed to SDW indirectly through RLD at 60-100cm. TWU also contributed to SDW indirectly through PH (0.257). Under well-watered condition RLD at 0-30cm contributed to SDW through TN (0.262) whereas MRL (0.250) and PH (0.251) contributed through RLD at 30-100cm 4.1.2.6 Genetic Divergence Studies 4.1.2.6.1 Mahalanobis’s Generalized Distance (D2)

The D2 value between any two genotypes was calculated as sum of squares of the differences between the mean values of all characters (twelve in drought stress and nine in well-watered condition) and used for the final grouping of the genotypes. 4.1.2.6.2 Clustering of D2 Values Procedure suggested by Tocher’s (Rao, 1952) has been used to group 20 genotypes into clusters (five in drought stress and eight in well-watered condition) by treating the estimated D2 values as the square of the generalized distance. Under drought stress condition, Cluster II was the biggest cluster consisting of seven genotypes viz., Dular, N22, FR13A, Aswina, Azucena, Cypress and Dom Sufid followed by cluster III, comprised of six genotypes viz., Zhenshan 97B, IR64, Sadu Cho, Minghui 63, M202 and LTH. The pattern of distribution of genotypes into various clusters is presented in Table 47. Whereas under wellwatered condition, Cluster III and IV are bigger cluster consisting of five genotypes in each cluster viz., Dular, FR13A, Azucena, Pokkali and Dom Sufid; IR64, LTH, Minghui 63, M202 and Tainung 67 respectively followed by cluster I, comprised of three genotypes viz., Zhenshan 97B, SHZ2 and Aswina. The pattern of distribution of genotypes into various clusters under well-watered condition is presented in Table 48. 4.1.2.6.3 Inter Relation of Cluster The intra and inter-cluster D2 and D values among clusters in drought stress and well-watered conditions are given in the Table 49 and Table 50 respectively. Under drought stress condition the intra-cluster D2 and D exhibited that, cluster V (D2 = 229.90, D= 15.16) had the maximum genetic diversity followed by cluster II (D2 = 121.43, D=11.02) and cluster III (D2=121.26, D=11.01). Whereas under well-watered condition cluster V (D2 = 277.52, D= 16.65) had the maximum

genetic diversity followed by cluster III (D2 = 197.05, D=14.03) and cluster IV (D2=192.27, D=13.86). The inter-cluster D2 values under drought stress condition revealed that highest inter-cluster generalized distance (D2= 669.21) was between cluster IV and cluster V while, the lowest (D2 = 103.71) was between cluster I and cluster IV. On the other hand highest inter-cluster generalized distance (D2= 2846.87) was recorded between cluster VII and cluster VIII in well-watered condition while, the lowest (D2 = 149.75) was between cluster I and cluster V. The nearest and distant clusters from each of the cluster based on D values under drought stress and well-watered conditions are presented in Table 51 and Table 52 respectively. Under drought stress condition Cluster IV was nearest to cluster I (10.18), II (14.37) and III (14.61) and distant from cluster V (25.87). Cluster III exhibited close proximity with cluster IV (14.61) and V (17.83). Cluster V was exhibited wide diversity with cluster I (25.53), II (21.28), III (17.83) and IV (25.87). Whereas in well-watered condition, cluster V was nearest to cluster I (12.23) and VII (34.98) and cluster I was nearest to cluster II (17.15), IV (13.05) and V (12.23). Similarly cluster III exhibited close proximity with cluster VI (18.44) and VIII (31.04). Cluster VII exhibited wide diversity with cluster I (38.84), II (44.18), III (37.18), IV (42.33), V (34.98) and VIII (53.35). Nearest and farthest clusters for cluster VI were III (18.44) and VIII (39.45) clusters respectively. Similarly nearest and farthest clusters for cluster VII were V (34.98) and VIII (53.35) clusters respectively 4.1.2.6.4 Contribution of Different Characters towards Divergence The per cent contribution of each character towards divergence in drought stress and well-watered condition are presented in Table 53. It was observed that RLD at 0-30cm was the largest contributor (24.21%) towards divergence followed by SDW (18.94%), RSR (17.89%), PH (17.89%), SC (5.78%), TN (4.73%) and RLD at 60-100cm (3.68%) under drought stress condition. The remaining

characters did not contribute significantly to the total divergence. On the other hand under well-watered condition RSR was the single largest contributor (42.63%) towards divergence followed by TN (20.00%), RLD at 30-100cm (18.95%), SDW (9.47%), PH (4.21%), and RLD at 0-30cm (3.16%). 4.1.2.6.5 Performance of Characters in Clusters The mean values of characters studied in both drought stress and wellwatered conditions are presented in Table 54 and Table 55 respectively. The cluster V genotypes had lower RN (97.87) whereas; cluster I genotypes had high RN (113.50) under drought stress condition. Whereas under well-watered condition cluster II and cluster I genotypes had lower (178.00) and higher RN (457.25). The genotypes of cluster I (45.45cm/cm3) in drought tress condition had lowest RLD at 0-30cm while, genotypes of cluster V had highest RLD at 0-30cm compared to other clusters. On the other hand under well-watered condition, cluster VIII and VII had lowest (1.12 cm/cm3) and highest (12.87 cm/cm3) RLD at 0-30cm respectively. Cluster I under drought stress condition had the genotypes with lowest RLD at 30-45cm (0.54 cm/cm3), on contrary, the cluster V had the genotypes with highest RLD at 30-45cm (2.05 cm/cm3). Similarly cluster I under drought stress condition also had the genotypes with lowest, RLD at 45-60cm (0.09 cm/cm3) and RLD at 60-100cm (0.01 cm/cm3) on contrary, the cluster II had the genotypes with highest RLD at 45-60cm (1.26 cm/cm3) and RLD at 60-100cm (0.03 cm/cm3).At depth (30-100cm) cluster VIII and VI had lowest (0.00 cm/cm3) and highest (2.19 cm/cm3) RLD respectively in well-watered condition. The genotypes lying in the cluster I and II in drought stress condition and cluster VIII and VI in well-watered condition had shortest (76.10cm and 27.50cm respectively) and longest (92.06cm and 53.50cm respectively) rooting depth respectively. Genotypes in cluster III and IV under drought stress condition and in cluster VI and V under well-watered condition had lowest (69.31 and 328.33 in drought stress and well-watered

condition respectively) and highest (148.50 and 513.83 in drought stress and wellwatered condition respectively) SC respectively. Cluster I and II genotypes under drought stress condition and cluster VIII and III genotypes under well-watered condition are shortest (82.85cm and 78.27cm in drought stress and well-watered condition respectively) and tallest (127.81cm and 150.71cm in drought stress and well-watered condition respectively) respectively among other cluster genotypes. The genotypes lying in the cluster IV and V in drought stress condition and cluster VI and VII in wellwatered condition had lowest (6.47 and 8.00 in drought stress and well-watered condition respectively) and highest (21.20 and 40.67 in drought stress and wellwatered condition respectively) number of TN respectively. Cluster III under drought stress condition had the genotypes with lowest TWU (3.14), on contrary; the cluster II had the genotypes with TWU (4.30). Genotypes in cluster I and II under drought stress condition and in cluster VII and VIII under well-watered condition had lowest (13.88 and 13.70 in drought stress and well-watered condition respectively) and highest (24.08 and 55.30 in drought stress and well-watered condition respectively) SDW respectively. The genotypes lying in the cluster I and III in drought stress condition and in cluster VII and VIII in well-watered condition had lowest (22.93 and 30.09 in drought stress and well-watered condition respectively) and highest (49.38 and 100.28 in drought stress and well-watered condition respectively) RSR respectively. 4.2 Variation in Water Uptake, Root and Shoot Characters and Their Association with Drought Tolerance in Parents of Mapping Population, Donors and Advanced Breeding Lines of IRRI-India Drought Breeding Network. 4.2.1 Field Experiment – Dry Season 2009 4.2.1.1 Mean Performance 4.2.1.1.1 Phenology and Grain Yield Parameters

Mean performance of all genotypes for all phenology and GY characters under both drought stress and well-watered are presented in Table 56. Swarna (44.33 cm) was the shortest followed by IR1552 (48.22cm) and Samba Masasuri (49.33cm) while, Vandana was the tallest (87.22 cm) followed by IR76569-259-11-3 (86.67 cm) and Kalinga III (84.44 cm). In well-watered condition, IR62266 was the shortest (70.67cm) followed by DGI 307 (73.33 cm) and CT9993 (76.11cm) while, IR 78908-80-B-3-B was the tallest (108.22 cm) followed by IR52561-UBN-1-1-2 (106.56cm) and Kalinga III (102.89 cm). Comparison of means in well-watered condition showed that, the breeding line IR74371-46-1-1 had highest TN (21.78) followed by IRRI 123 (21.11) and IR64 (20.89) while, Birsa gora had the lowest TN (8.67) followed by Anjali (11.00) and CO39 (12.78). Whereas in drought stress condition, Kalinga III had highest TN (21.11) followed by Thara (20.78) and IR64 (20.22) while, Moroberekan had the lowest TN (8.22) followed by Azucena (8.67) and Labelle (11.67). Under well-watered condition, Brown Gora had minimum SB (180.67 g/m2) followed by Anjali (215.33 g/m2) and CBT 306 (256.67 g/m2). On the other hand, IR62266 recorded maximum SB (905.33 g/m2) followed by CT9993 (840.00 g/m2) and Moroberekan (754.67 g/m2). Whereas under drought stress condition, Azucena had highest SB (450.67 g/m2) followed by IR70213-10-CPA-4-2-2-2 (406.67 g/m2) and IR 74371-3-1-1 (390.00 g/m2). On the other hand, Birsa Gora recorded lowest SB (195.33 g/m2) followed by IAC165 (227.00 g/m2) and Anjali (244.67 g/m2). Among the genotypes evaluated, CT9993 recorded lowest GY of 106.67g/m2 followed by Samba Mahsuri (150.00 g/m2) and Labelle (150.67 g/m2) under wellwatered condition. On the other hand, Swarna recorded highest GY (473.3 g) followed by IR 74371-70-1-1 (436.6 g/m2) and MTU 1010 (412.6 g/m2) which were on par with one another. ARB 6 and Anjali had highest HI (0.56) followed

by Swarna and IR 78937-B-4-B-B-B (0.53). Whereas, CT9993 recorded lowest HI of 0.11 followed by Labelle (0.18) and IR52561-UBN-1-1-2 (0.19). 4.2.1.2 Analysis of Variance The mean sums of squares for shoot and yield traits recorded under both drought stress and well-watered condition in DS 2009 in field experiment are presented in Table 57. Highly significant differences among the genotypes were observed for all the characters under both drought stress and well-watered condition 4.2.1.3 Variability Parameters Variability parameters in respect of shoot and GY characters using OryzaSNP panel accessions of rice under both drought stress and well-watered are tabulated in Table 58 and briefly described below 4.2.1.3.1 Phenology and Grain Yield Parameters PH of the genotypes varied from 44.33 to 87.22 cm with a mean of to 69.14 cm under drought stress condition. Moderate PCV and GCV values of 15.98 and 13.70 per cent respectively with high h2 estimate (73.00 %) and GA (24.21) as per cent of mean were recorded for this trait. PH ranged from 70.66 to 108.22 cm with a mean value of 89.72 cm under well-watered condition. The genotypes showed moderate PCV (15.00 %) and low GCV (4.50 %) values accompanied with low h2 (9.00%) and GA (2.79) as per cent of mean. TN varied from 8.22 to 21.11 with a mean value of 17.19 under drought stress condition. The genotypes showed high PCV (22.13 %) and moderate GCV (11.61 %) values accompanied with moderate h2 (27.00 %) and GA (12.54) as per cent of mean. However genotypes varied largely (8.66-21.77) under well-watered condition with regard to TN and the overall mean for this trait was 16.34. High PCV (22.89 %) and moderate GCV (11.33 %) were recorded for this trait with moderate h2 and GA as per cent mean of 24.00 % and 11.61 respectively.

The overall mean SB of the genotypes was 335.34 g/plant with a range of 195.33-450.66 g/plant under drought stress condition. Moderate PCV and GCV values of 19.17 and 10.35 % respectively with moderate h2 estimate (29.00 %) and GA (11.56) as per cent of mean were recorded for this trait. Under well-watered condition, SB ranged between 180.00-840.00 g/plant with a mean value of 421.60 g/plant. The genotypes showed high PCV (39.63 %) and GCV (37.41 %) values accompanied with high h2 (89.00 %) and GA (72.77) as per cent of mean. Under well-watered condition, GY ranged between 106.67-473.33 g/plant with a mean value of 287.43 g/plant. The PCV (33.11 %) and GCV (29.51 %) coefficients of variation were high accompanied with high h2 of 79.00 per cent and GA of 55.18 as per cent of mean. HI varied from 0.11 to 0.56 with a mean value of 0.40 under well-watered condition. The genotypes showed high PCV (30.11 %) and GCV (28.50 %) values accompanied with high h2 (89.00 %) and GA (55.59) as per cent of mean. 4.2.2 Lysimetric Experiment-Dry Season 2009 4.2.2.1 Mean Performance 4.2.2.1.1 Water Uptake (g/plant) Mean performance of all genotypes for water uptake rates at different intervals under both drought stress condition is presented in Table 59. At 5 DAS, MTU1010 was recorded highest water uptake (800.0 g/plant) followed by DGI 307 (778.0 g/plant) and IR78908-80-B-3-B.

Whereas

Kinandang Patong was recorded lowest water uptake (384.0 g/plant) followed by IR 74371-46-1-1(392.0 g/plant) and IR69515-6-KKN-4-UBN-4-2-1-1-1 (396.0 g/plant). The highest water uptake at 8DAS was recorded by line Brown Gora (833.0 g/plant) followed by CO 39 (810.0 g/plant) and ARB6 (775.0 g/plant). On the other hand, IR69515-6-KKN-4-UBN-4-2-1-1-1 recorded lowest water uptake

(346.0 g/plant) followed by IR123 (383.0 g/plant) and IR 70213-10-CPA-4-2-2-2 (398.0 g/plant). The highest water uptake at 12DAS was recorded by DGI 307 (695.0 g/plant) followed by CO39 (680.0 g/plant) and IR 74371-54-1-1 (674.0 g/plant). On the other hand, IR 74371-46-1-1 recorded lowest water uptake (334.0 g/plant) followed by IR69515-6-KKN-4-UBN-4-2-1-1-1(358.0 g/plant) and Kinandang Patong (390.0 g/plant). At 15DAS, Azucena was recorded highest water uptake (873.0 g/plant) followed by IR 74371-54-1-1 (848.0 g/plant) and ARB4 (825.0 g/plant). Whereas IRRI123 was recorded lowest water uptake (450.0 g/plant) followed by IR 7437146-1-1 (472.0 g/plant), IR69515-6-KKN-4-UBN-4-2-1-1-1 and Kinandang Patong (500.0 g/plant). The highest water uptake at 18DAS was recorded by line Azucena (640.0 g/plant) followed by IRAT109 (620.0 g/plant) and IAC165 (610.0 g/plant). On the other hand, IR 74371-46-1-1(360.0 g/plant) recorded lowest water uptake (360.0 g/plant) followed by IR64 (370.0 g/plant) and IR123 (385.0 g/plant). At the end of stress (23DAS), Azucena recorded highest water uptake (793.0 g/plant) followed by ARB8 (747.0 g/plant) and ARB4 (745.0 g/plant). On the other hand, IR 74371-46-1-1(374.0 g/plant) recorded lowest water uptake followed by Kinandang Patong and IR64 (424.0 g/plant). Out of forty nine genotypes studied, DG 307, IR74371-54-1-1, ARB 4, IR 78908-80-B-3-B, and ARB 3 recorded TWU more than 4000 g per plant. Apart from Brown Gora, some of the other donors such as Vandana and Budda recorded higher TWU of 3858.0 g per plant and 3.822.0 g per plant respectively. 4.2.2.1.2 Root Traits Mean performance of all genotypes for root characters under both drought stress and well-watered are presented in Table 60.

Under drought stress condition, Brown Gora recorded highest RDW (2.42g) followed by CT9993 (2.29g) and ARB 4 (1.97g). On the other hand, IR74371-46-1-1 recorded lowest RDW (0.49g) followed by Black Gora (0.65g) and Kinandang Patong (0.66g). Whereas under well-watered condition, Anjali recorded highest RDW (2.77g) followed Kalinga III (2.71g) and Brown Gora (2.68g). Black Gora was recorded lowest RDW (0.63g) followed by Azucena (0.83g) and CO39 (1.22g). ARB 4 was recorded highest RLD (0.92 cm/cm3) followed by ARB 3 (0.84 cm/cm3) and Brown Gora (0.83 cm/cm3) under drought stress condition. Whereas IR74371-46-1-1 was recorded lowest RLD (0.17 cm/cm3) followed by Kinandang Patong (0.28 cm/cm3) and Black Gora (0.38 cm/cm3). Under well-watered condition highest RLD was recorded by Kinandang Patong (1.57 cm/cm3) followed by Anjali (1.22 cm/cm3) and Swarna (1.20 cm/cm3). On the other hand, Azucena recorded lowest RLD (0.25 cm/cm3) followed by Black Gora (0.43 cm/cm3) and Moroberekan (0.46 cm/cm3). Among the studied entries, Moroberekan recorded RSA (4650.10) followed ARB4 (4537.35 cm2) and DGI 307 (4365.99 cm2) under drought stress condition. Whereas IR74371-46-1-1 was recorded lowest RSA (755.84 cm2) followed by Kinandang Patong (1664.40 cm2) and Black Gora (1921.00 cm2). The highest RSA under well-watered condition was recorded by Kinandang Patong (8872.89 cm2) followed by Swarna (7146.52 cm2) and IR64 (6707.94 cm2). On the other hand, Azucena recorded lowest RSA (1469.36 cm2) followed by Black Gora (2265.62 cm2) and Moroberekan (2301.06 cm2). Moroberekan was recorded highest RV (62.45 cm3) followed by DGI307 (52.59 cm3) and Brown Gora (49.79 cm3) under drought stress condition. Whereas IR74371-46-1-1 was recorded lowest RV (7.40 cm3) followed by Anjali (20.31 cm3) and RR 345-2 (21.71 cm3). Under well-watered condition, highest RV was recorded by Kinandang Patong (112.40 cm3) followed by Swarna (98.64 cm3) and

IR64 (91.99 cm3). On the other hand, Azucena recorded lowest RV (19.74 cm3) followed by Black Gora (26.76 cm3) and Moroberekan (26.79 cm3). 4.2.2.1.3 Phenology Mean performance of all genotypes under both drought stress and wellwatered conditions are presented in Table 61. In drought stress condition, IAC165 (89.2 cm) was the tallest followed by Kalinga III and CBT306 (79.20 cm) while, IR64 was the shortest (45.60 cm) followed by Samba Mahsuri (49.60 cm) and IR69502-6-SRN-3-UBN-2-B-2-2-2 (50.20 cm). Under well-watered condition, IR78908-80-B-3-B (110.60 cm) was the tallest followed by Birsa Gora (108.50 cm) and Vandana (107.80 cm) while, Bala was the shortest (56.00 cm) followed by IR1552 (63.00 cm) and Swarna (63.50 cm). Swarna had highest TN (16.80) followed by Samba Mahsuri (14.00) and IR69502-6-SRN-3-UBN-2-B-2-2-2 (12.60) under drought stress condition, while, Kinandang Patong and Moroberekan had the lowest TN (2.40) followed by Azucena (6.00). Whereas, Birsa Gora had highest TN (16.00) followed by IR 76569-259-1-1-3 and Kalinga III (15.75) under well-watered condition. While, Labelle had the lowest TN (4.20) followed by Moroberekan (4.33) and Azucena (6.00). Under drought stress condition, ARB 3 had maximum SDW (8.27 g/plant) followed by DGI 307 (8.25 g/plant), and CT9993 (8.13 g/plant). On the other hand, IRRI123 recorded lowest SDW (2.32 g/plant) followed by IR69515-6-KKN4-UBN-4-2-1-1-1 (2.61 g/plant) and Kinandang Patong (2.63 g/plant). Under well-watered condition, Birsa Gora had maximum SDW (22.26 g/plant) followed by IR52561-UBN-1-1-2 (21.70 g/plant) and Vandana (20.13 g/plant). On the other hand, Azucena recorded minimum SDW (8.00 g/plant) followed by Moroberekan (9.05 g/plant) and Bala (10.51 g/plant).

4.2.2.2 Analysis of Variance The mean sum of squares for real-time water uptakes at different intervals under drought stress in lysimetric experiment during DS 2009 are presented in Table 62. Similarly the mean sum of squares for root and shoot characters under both drought stress and well-watered in lysimetric experiment during DS 2009 are presented in Table 63. Highly significant differences among the genotypes were observed for all the characters under both drought stress and well-watered condition. 4.2.2.3 Variability Parameters Variability parameters in respect of root and shoot characters under both drought stress and well-watered are presented in Table 64 and briefly described below In drought stress condition, genotypes varied from a range of 0.17-0.92 cm/cm3 with regard to RLD at 0-100cm soil layer and the overall mean for this trait was 0.69 cm/cm3. High PCV (32.84 %) and GCV (28.80 %) values were recorded coupled with high h2 (76.00 %) and GA as per cent of mean (52.03). Genotypes varied (0.25-1.22 cm/cm3) under well-watered condition with regard to RLD at 0-100cm soil layer and the overall mean for this trait was 0.86 cm/cm3. High PCV (42.18 %) and GCV (31.47 %) were recorded for this trait with high h2 and GA as per cent mean of 55.00 % and 48.37 respectively. RSA at 0-100cm soil layer ranged widely (755.84-4650.10 cm2) with a mean value of 3430.91 cm2 under drought stress condition. High PCV (34.03 %) and GCV (30.88%) were recorded for this trait with high h2 and GA as per cent mean of 82.00 % and 57.72 respectively. In well-watered condition, RSA at 0100cm soil layer varied from 1469.36 to 7146.52 cm2 with a mean value of 4629.87 cm2. The genotypes showed high PCV (43.97 %) and GCV (35.01 %) values accompanied with high h2 (63.00 %) and GA (57.42) as per cent of mean.

RV at 0-100cm soil layer varied from 7.40 to 62.45 cm3 with a mean value of 38.33 cm3 under drought stress condition. The genotypes showed high PCV (37.38 %) and GCV (35.25 %) values accompanied with high h2 (88.00 %) and GA (68.50) as per cent of mean. However genotypes varied largely (19.74-98.65 cm3) under well-watered condition with regard to RV at 0-100cm soil layer and the overall mean for this trait was 56.49 cm3 under well-watered condition. High PCV (47.59 %) and GCV (38.73 %) were recorded for this trait with high h2 and GA as per cent mean of 66.00 % and 64.93 respectively. Genotypes varied from a range of 0.49-2.42 g with regard to RDW at 0100cm soil layer and the overall mean for this trait was 1.43 g under drought stress condition. PCV (39.21 %) and GCV (36.99 %) values were high accompanied with high h2 (88.00 %) and GA as per cent of mean (71.88). However genotypes varied largely (0.83-2.77g) under well-watered condition with regard to RDW at 0-100cm soil layer and the overall mean for this trait was 1.97 g. High PCV (39.74 %) and GCV (30.86 %) were recorded for this trait with high h2 and GA as per cent mean of 60.00 % and 49.38 respectively. TWU varied from 2334.00-4300.00g with a mean value of 3766.06 g/plant under drought stress condition. The genotypes showed high PCV (23.41 %) and moderate GCV (11.48 %) values accompanied with high h2 (24.00%) and moderate GA (11.60) as per cent of mean. The mean PH of the genotypes was 66.37 cm with a range of 45.60-79.20 cm under drought stress condition. Moderate PCV and GCV values of 19.28 and 12.60 per cent respectively with high h2 estimate (43.00 %) and low GA (0.16) as per cent of mean were recorded for this trait. Whereas under well- watered condition, PH ranged from 63.50 to 110.60 cm with a mean value of 87.99 cm. The genotypes showed moderate PCV (18.65 %) and moderate GCV (14.26 %) values accompanied with high h2 (58.00%) and GA (22.46) as per cent of mean.

TN varied from 2.40- 16.80 with a mean value of 8.33 under drought stress condition. The genotypes showed high PCV (37.90 %) and GCV (30.80 %) values accompanied with high h2 (66.00 %) and GA (51.58) as per cent of mean. However genotypes varied largely (4.33-15.25) under well-watered condition with regard to TN and the overall mean for this trait was 10.48. High PCV (27.00 %) and GCV (22.54 %) were recorded for this trait with high h2 and GA as per cent mean of 69.00 % and 38.75 respectively. Under drought stress condition, the overall mean SDW of the genotypes was 5.96 g/plant with a range of 2.96 to 8.27 g/plant. High PCV and GCV values of 32.09 and 28.68 per cent respectively with high h2 estimate (79.00 %) and GA (52.81) as per cent of mean were recorded for this trait. SDW ranged between 8.00-19.80 g/plant with a mean value of 15.25 g/plant under well-watered condition. High PCV and moderate GCV values of 27.54 and 19.87 % respectively with high h2 estimate (52.00 %) and GA (29.54) as per cent of mean were recorded for this trait. 4.2.2.4 Correlation Studies 4.2.2.4 1Associations of Root and Shoot Characters with SDW Highly significant and positive correlation of SDW with RDW at 0-100cm (0.58), RLD at 0-100cm (0.60), RSA at 0-100cm (0.56), RV at 0-100cm (0.54) and TN (0.55) under drought stress condition was recorded (Table 65). Under well-watered condition SDW is significantly and positively correlated (p<0.05) with RDW at 0-100cm (0.50), RLD at 0-100cm (0.43) and TN (0.46) (Table 66). 4.1.2.4.2 Association among Root and Shoot Characters RDW at 0-100cm had highly significant and positive association with RLD at 0-100cm (0.68), RSA at 0-100cm (0.72), and RV at 0-100cm (0.69) under drought stress condition and with RLD at 0-100cm (0.68), RSA at 0-100cm (0.73), and RV at 0-100cm (0.71) under well-watered condition.

Highly significant positive association of RLD at 0-100cm with RSA at 0100cm (0.87) and RV at 0-100cm (0.82) under drought stress condition and with RSA at 0-100cm (0.96) and RV at 0-100cm (0.88) under well-watered condition. Significant and positive correlation of RSA at 0-100cm with RV at 0-100cm (0.89 and 0.97, p<0.01 under both drought stress and well-watered condition respectively) and TN (0.42, p<0.05 under well-watered condition) were recorded. RV at 0-100cm is significantly (p<0.05) with TN (0.42) under well-watered condition. 4.3 Physiological and Molecular Dissection of Drought Avoidance Root Mechanisms in Adeysel NILs. 4.3.1. Studies on Water Uptake, Root Distribution under Well-Watered and Drought Stress Condition 4.3.1.1 Water Uptake (g/plant) Mean performance of parents i.e., IR 64 and Adeysel and two pairs of NILs for real-time water uptake rates under drought stress are presented in Table 67 At 14DAS, NIL18 was recorded highest water uptake (1713.75 g/plant) followed by NIL10 (1526.25 g/plant). Whereas IR64 was recorded lowest water uptake (794.0 g/plant) followed by Adeysel (970.00 g/plant). The highest water uptake at 21DAS was recorded by NIL18 (1672.50 g/plant) followed by NIL 10 (1347.50 g/plant). On the other hand, IR 64 recorded lowest water uptake (840.00 g/plant) followed by NIL11 (1192.50 g/plant). At the end of stress period (28DAS), Adeysel recorded highest water uptake (1613.75 g/plant) followed NIL10 (1260.00 g/plant). Whereas IR64 was recorded lowest water uptake (872.00 g/plant) followed by NIL11 (962.00 g/plant). A GCV difference for TWU during entire drought period was noticed among the NILs and parents. NIL10 and NIL18 extracted more than 4100.0

g/plant of water during drought stress period. On contrary parents such as IR64 and Adeysel recorded only 2506.0 g/plant and 3845.0 g/plant respectively. 4.3.1.2 Root Traits Mean performance of parents and NILs for RN, MRL and RLD across different soil depths under both drought stress and well-watered are presented in Table 68. 4.3.1.2 1 RN Comparison of means in drought stress condition showed that, Adeysel had highest RN (183.20) followed NIL 18 (148.30) while, IR64 had the lowest RN (92.50) followed by NIL11 (125.60). Whereas in well-watered condition NIL11 had highest RN (366.60) followed by NIL10 (335.60) while, NIL13 had the lowest RN (160.00) followed by IR64 (230.60) 4.3.1.2.2 MRL (cm) NIL13 (90.50 cm) recorded highest MRL followed by Adeysel (86.50 cm) under drought stress condition. On the other hand, NIL11 recorded lowest MRL (75.00 cm) followed by IR64 (76.70 cm). Whereas under well-watered condition NIL18 recorded highest MRL (51.00 cm) followed NIL10 (49.30 cm). Whereas Adeysel was recorded lowest MRL (35.00 cm) followed by IR64 (35.50 cm). 4.3.1.2.3 RLD (cm/cm3) At 0-30cm soil layer, NIL11 was recorded highest RLD (2.58 cm/cm3) followed by NIL13 (2.34 cm/cm3) under drought stress condition.

Whereas

Adeysel was recorded lowest RLD (1.11 cm/cm3) followed by IR64 (1.43 cm/cm3). Under well-watered, highest RLD was recorded by NIL11 (8.91 cm/cm3) followed by Adeysel (4.42 cm/cm3). On the other hand, NIL13 recorded lowest RLD (2.45 cm/cm3) followed by IR64 (2.79 cm/cm3). NIL18 (2.28 cm/cm3) recorded highest RLD at 30-45cm soil layer followed by NIL11 (1.99 cm/cm3) under drought stress condition. On the other hand,

Adeysel recorded lowest RLD (0.6 cm/cm33) followed by IR64 (0.99 cm/cm3). Under well-watered, NIL18 recorded highest RLD (1.18 cm/cm3) followed NIL10 (1.16 cm/cm3). Whereas IR64 was recorded lowest RLD (0.08 cm/cm3) followed by Adeysel (0.17 cm/cm3). At 45-60cm soil layer, NIL18 recorded RLD (1.58 cm/cm3) followed Adeysel (0.58 cm/cm3) under drought stress condition. Whereas NIL11 was recorded lowest RLD (0.33 cm/cm3) followed by NIL10 (0.39 cm/cm3). Under well-watered condition, NIL18 (0.12 cm/cm3) had highest RLD at 45-60cm followed by NIL10 (0.10 cm/cm3). Most of other entries recorded very less or zero RLD at 45-60cm soil layer. The highest RLD at 60-100cm soil layer under drought stress was recorded by NIL18 (0.16 cm/cm3) followed by NIL13 and NIL10 (0.04 cm/cm3). Most of other entries recorded very less or zero RLD. Similarly under well-watered condition all entries recorded zero RLD at deeper level. 4.1.2.1.2.4 RSA (cm2) Mean performance of parents and NILs for RSA across different soil depths under both drought stress and well-watered condition are presented in Table 69. NIL11 was recorded highest RSA (1782.60 cm2) followed by NIL13 (1602.50 cm2) under drought stress condition at 0-30cm soil layer. Whereas Adeysel was recorded lowest RSA (840.10 cm2) followed by NIL10 (1096.60 cm2). Under well-watered, highest RSA was recorded by NIL11 (6479.40 cm2) followed by NIL10 (3882.60 cm2). On the other hand, NIL13 recorded lowest RSA (1967.90 cm2) followed by IR64 (3015.00 cm2). NIL18 (597.20 cm2) recorded highest RSA at 30-45cm soil layer followed by NIL11 (522.00 cm2) under drought stress condition.

On the other hand,

Adeysel recorded lowest RSA (175.90 cm2) followed by IR64 (303.50 cm2). Under well-watered, NIL18 recorded highest RSA (511.00 cm2) followed NIL10

(459.80 cm2). Whereas IR64 was recorded lowest RSA (46.70 cm2) followed by Adeysel (82.30 cm2). At 45-60cm soil layer, NIL18 recorded RSA (423.10 cm2) followed IR64 (195.10 cm2) under drought stress condition. Whereas NIL11 was recorded lowest RSA (77.10 cm2) followed by NIL10 (103.60 cm2). Under well-watered condition, NIL18 (55.06 cm2) had highest RSA at 45-60cm followed by NIL10 (43.96 cm2). Most of other entries recorded very less or zero RSA at 45-60cm soil layer. The highest RSA at 60-100cm soil layer under drought stress was recorded by NIL18 (128.00 cm2) followed by NIL10 (36.10 cm2). Most of other entries recorded very less or zero RSA. Similarly under well-watered condition all entries recorded zero RSA at deeper level. 4.1.2.1.2.5 RV (cm3) Mean performance under both drought stress and well-watered for RV at different soil depths were presented in Table 70. NIL11 (11.89 cm3) recorded highest RV at 0-30cm soil layer followed by NIL13 (10.66 cm3) under drought stress condition. On the other hand, Adeysel recorded lowest RV (6.11 cm3) followed by NIL10 (8.07 cm3). Under wellwatered, NIL11 recorded highest RV (45.01 cm3) followed NIL10 (34.58 cm3). Whereas NIL13 was recorded lowest RV (14.89 cm3) followed by Adeysel (27.29 cm3). At 30-45cm soil layer, NIL18 was recorded highest RV (2.94 cm3) followed by NIL11 (2.63 cm3) under drought stress condition. Whereas Adeysel was recorded lowest RV (0.92 cm3) followed by NIL10 (1.58 cm3). Under wellwatered, highest RV was recorded by NIL18 (4.28 cm3) followed by NIL10 (3.45 cm3). On the other hand, IR64 recorded lowest RV (0.54 cm3) followed by Adeysel (0.75 cm3).

NIL18 recorded RV (2.12 cm3) followed IR64 (0.98 cm3) under drought stress condition at 45-60cm soil layer. Whereas NIL11 was recorded lowest RV (0.34 cm3) followed by NIL10 (0.53 cm3). Under well-watered condition, NIL18 (0.48 cm3) had highest RV at 45-60cm followed by NIL10 (0.38 cm3). Like RLD and RSA remaining entries recorded very less or zero RV at 45-60cm soil layer. The highest RV at 60-100cm soil layer under drought stress was recorded by NIL18 (0.72 cm3) followed by NIL10 (0.21 cm3). Whereas Adeysel was recorded lowest RV (0.08 cm3) followed by NIL11 (0.11cm3). On the other hand under well-watered condition all entries recorded zero RV at depth (60-100cm). 4.1.2.1.2.5 RDW (mg) Mean performance of parents and NILs for RDW across different soil depths under both drought stress and well-watered are presented in Table 71. At 0-30cm soil layer, NIL11 was recorded highest RDW (990.00 mg) followed by NIL18 (927.00 mg) under drought stress condition. Whereas Adeysel was recorded lowest RDW (440.00 mg) followed by NIL10 (710.00 mg). Under well-watered, highest RDW was recorded by NIL11 (3651.60 mg) followed by NIL10 (2510.00 mg). On the other hand, NIL13 recorded lowest RDW (970.00 mg) followed by IR64 (1786.60 mg). NIL18 recorded highest RDW (185.00 mg) at 30-45cm soil layer followed by NIL11 (143.33 mg) under drought stress condition. On the other hand, Adeysel recorded lowest RDW (54.50 mg) followed by IR64 (80.00 mg). Under wellwatered, NIL18 recorded highest RDW (213.30 mg) followed NIL10 (193.30 mg). Whereas IR64 was recorded lowest RDW (9.50 mg) followed by NIL13 (30.60 mg). At 45-60cm soil layer, NIL18 recorded RDW (116.40 mg) followed NIL10 (34.10 mg) under drought stress condition. Whereas IR64 was recorded lowest RDW (19.30 mg) followed by NIL11 (27.50 mg). Under well-watered condition,

NIL18 (22.43 mg) had highest RDW at 45-60cm followed by NIL10 (16.66 mg). Most of other entries recorded very less or zero RDW at 45-60cm soil layer. At depth (60-100cm) the highest RDW under drought stress was recorded by NIL18 (42.80 mg) followed by NIL10 (14.20 mg).

Whereas IR64 was

recorded lowest RDW (5.40 mg) followed by NIL11 (7.40 mg). Most of entries under well-watered condition recorded zero RDW at deeper level. 4.1.2.1.3 Phenology and Physiological Traits Mean performance of parents and NILs for all phenological and physiological traits under both drought stress and well-watered condition were presented in Table 72. In drought stress condition, NIL13 and Adeysel (98.50 cm) were the tallest while, IR64 was the shortest (86.50 cm) followed by NIL10 (87.75 cm. On the other hand under well-watered condition, NIL10 (122.27 cm) was the tallest followed by NIL11 (116.33cm) while, IR64 was the shortest (111.00 cm) followed by NIL13 (111.70 cm) which are on par with each other. NIL18 had highest TN (25.00) followed by NIL13 (22.25) under drought stress condition. While, IR64 had the lowest TN (11.50) followed by Adeysel (15.75). Whereas in well-watered condition, Adeysel had highest TN (33.50) followed by NIL11 (32.33) while, NIL13 had the lowest TN (25.00) followed by IR64 (29.00). Under drought stress condition, NIL18 had maximum SDW (34.65 g/plant) followed by NIL10 (24.04 g/plant). On the other hand, both the parents, IR64 and Adeysel recorded minimum SDW of 10.70 and 14.55 g/plant respectively. Whereas under well-watered condition, NIL10 had maximum SDW (54.87 g/plant) followed by NIL11 (54.70 g/plant). On the other hand, both the parents, Adeysel and IR64 recorded minimum SB of 30.57 and 37.90 g/plant respectively

NIL10 had higher SC (75.25 mol m-2 s-1) followed by Adeysel (72.25 mol m-2 s-1) under drought stress condition. On the other hand, NIL11 and IR64 recorded lower SC 46.85 and 50.93 mol m-2 s-1 respectively. Whereas under wellwatered condition, NIL10 had higher SC (1145.00 mol m-2 s-1) followed by NIL11 (726.67 mol m-2 s-1). On the other hand, NIL18 and Adeysel recorded lower SC of 462.50 and 522.50 mol m-2 s-1respectively. 4.3.2. Comparative Expression of Four LEA Genes in Different Zones of Shoot and Roots under Different Soil Water Levels 4.3.2.1 Shoot Tissue: Mean gene expression of, IR 64 and Dular in different zones of shoot under both drought stress and well-watered are presented in Table 73 and plate . 4.3.2.1.1 Top Shoot Tissue (>20cm) In well-watered condition, expression of Lea 1a, Lea 2h and Lea 4d genes were not recorded in both IR64 and Dular. Whereas HVA 1 gene was expressed only in Dular. Like well-watered condition Lea 1a and Lea 2h gene were also not expressed under drought stress condition in both IR 64 and Dular. However Lea 4d and HVA 1 was expressed in both IR 64 and Dular. Among all four genes only HVA 1 gene was upregulated more in drought stress condition. 4.3.2.1.2 Bottom Shoot Tissue (0-20cm) Lea 1a expression was not observed in both IR64 and Dular under wellwatered condition. Lea 2h, Lea 4d and HVA 1 were expressed only in Dular. However, under drought stress condition, expression of Lea2h, Lea 4d and HVA 1 in IR64 and Lea 1a, Lea 4d and HVA 1 in Dular were recorded. Among all four genes Lea 4d and HVA 1 were upregulated more under drought stress condition. 4.3.2.2 Root Tissue

Mean gene expression of IR 64, Dular and two pairs of NIL’s in top and deep zones of root under both drought stress and well-watered are presented in Table 74 and Table 75 respectively. 4.3.2.2.1 Top Root Tissue (0-15cm) Under well-watered condition, Lea 1a and Lea 2h expression was noticed only in Dular and IR 64 respectively. Lea 4d expression was recorded in all genotypes except Dular and NIL18. Whereas HVA 1 gene was expressed in all the genotypes. On the other hand under drought stress condition all four genes were expressed in all six genotypes except gene Lea 2h in Dular. Results have clearly indicated that all the four LEA genes were upregulated by water stress in most of the genotypes studied. 4.3.2.2.2 Deep Root Tissue (>15m) Under well-watered condition, expression of Lea 1a was not recorded in all the genotypes. Similarly, Lea 4d gene expression was also not recorded in most of the entries except IR 64 and NIL10. Expression of Lea 4d gene was recorded only in NIL10. However, expression of HVA 1 gene was recorded in all the genotypes except NIL10 and NIL11. Under drought stress condition, expression of Lea 1a was recorded in IR64, NIL 13 and NIL18. Lea 2h gene expression was recorded in NIL10 and NIL11. Expression of Lea 4d gene was recorded in all the genotypes except NIL10. However expression of HVA 1 gene was recorded in all the genotypes. Lea 4d and HVA 1 was upregulated by water stress in most of the genotypes.

V. DISCUSSION Rice (Oryza sativa (L.) is the most important stable food crop for over half of the world’s population (Narciso and Hossain 2002). Drought is the major constraint for rice production. It affects 14 m ha of upland rice and 19 m ha of lowland rice production (Pandey et al., 2007). Crop species have changed several mechanisms that enable them to acclimatize to a limited water supply. Several physiological and morphological characters contributing resistance to drought in rice have been identified. However, most of them are difficult to study because they are diverse and genotypic and / or environment specific. Among all physiological and morphological characters, most studied and believed are root related attributes. Among roots, most of research has focused on its morphology and ecology. High water uptake is important factor for plant growth under drought stress condition (Lilley and Fukai 1994). But, little is known about water uptake ability among rice genotypes. Water uptake depends on RLD or root mass density and its distribution. In rice, extensive root morphological variation has been reported but only few attempts have been made to exploit them fully in crop improvement programmes. In upland rice, drought resistance has been found to be related with deep root growth (Yoshida and Hasegawa 1982) and its ability to uptake more water (Lilley and Fukai 1994). Under rainfed lowland condition, occurrence of drought stress is very slow but it will be very severe once it occurs. Under severe drought stress, top layer of lowland soil breakdown and it exposes most of it to direct sunlight. In such circumstances amount of available water to plant growth in that layer is very scanty. But at deeper layer of soil, large amount of water are accumulated and are less utilized in most of the situations. The effective utilization of deep water is possible with existence of deep root traits (Clark et al 2002). In upland condition it has been clearly demonstrated that water extraction has been associated with RLD (Lilley and Fukai 1994) and root depth (Puckridge and

O’Toole 1981). However, limited work has been performed to demonstrate significant genotypic variation for root traits in lowland conditions (Fukai and Cooper 1995, Pantuwan et al 1997) and its relation with water uptake. The phenotypic variance measures the magnitude of variation arising out of difference in phenotypic values while, the genotypic variance measures the magnitudes of variation due to differences in genotypic values. The absolute values of phenotypic and genotypic variances can not be used for comparing the magnitude of variability for different characters since the mean and units of measurement of the characters may be different. Hence, the co-efficients of variation expressed at phenotypic and genotypic levels have been used to compare the variability observed among different characters. While, genotypic co-efficient of variation indicates the amount of genetic variability present in the character, the heritability estimates aid in determining the relative amount of heritable portion of variation. However, heritability values itself provides no indication of the amount of genetic progress that would result from selecting the best individuals. Ramanujam and Tirumalachar (1967) while studying the genetic variability in redpepper discussed the limitation of estimating the heritability in broad sense as it included both additive and non-additive genetic effects. According to them heritability estimates in broad sense would be reliable if accompanied by high GA. It would be more meaningful if the structure of complex traits is probed through its components rather than per se. The phenotypic correlations reveal the extent of association between characters. Thus, it helps to base selection procedure to a required balance, when two opposite desirable characters affecting the principal characters are being selected. It also helps to improve different characters simultaneously (Falconer, 1981). The other genetic parameter commonly used is the path analysis given by Dewey and Lu (1959). Path analysis gives a cause and effect relationship. It critically breaks up different direct and indirect effects that finally make up correlation co-efficient.

In recent years, the cognizance of genetic diversity and the evolutionary history of crop plants yielded major advances in crop improvement. Measure of genetic divergence reveals the differences in gene frequencies. Mahalanobis’s generalized distance estimated by D2 statistic (Rao, 1952) is a unique tool for discriminating populations by considering a set of parameters together. In addition to estimation of variability, cognizance of the genetic diversity of the germplasm is necessary for effective choice of parent in hybridization. The knowledge about the amount of genetic variability present in a crop species with respect to root and shoot attributes and their association, which reflects the nature and degree of relationship between any two measurable characters is of great importance in achieving improvement in that crop. Therefore, in the present investigation, variability parameters viz., range, PCV, GCV, h2 and GA as per cent of mean as well as correlation co-efficients and path co-efficients among root and shoot characters were estimated. 5.1 Genetic Diversity and Assessment of OryzaSNP Panel Rice Accessions for Drought Tolerance based on Water Uptake, Root Distribution, Shoot and Yield Parameters under Different Moisture Regimes. The study of the genetic variability of key shoot and root attributes associated with GY under drought stress conditions, and subsequent selection of such attributes, are important in a crop improvement programme intended to develop drought tolerant genotypes. Rice genotypic differences in length and weight of root systems under well-watered situations and drought stress in controlled and under upland conditions have been well demonstrated (Fukai and Inthupan, 1988 and Kondo et al 2003). However, documentation of differences in root distribution in different soil layers under lowland conditions is less available in literature. The genetic material used for this work was the OryzaSNP panel (McNally et al., 2009), which is comprised of 20 diverse rice germplasm accessions from the

indica, japonica, deep water aromatic and aus groups that have been completely mapped for SNP markers and selected as a mini-core collection representing diversity in Oryza sativa. The present study was aimed at assessing variation in root characters and to relate their variability to water uptake, SB and GY under drought and well-watered condition. In order to investigate such variations and relationships, a series of field and lysimetric experiments were conducted. 5.1.1 Mean Performances Considerable variation in root distribution across different soil layers in both drought stress and well-watered condition was observed. In most of the genotypes, under both drought stress and well-watered conditions, RLD, RSA, RV and RDW were highest in the top soil layer (0-10cm) and they gradually reduced with increasing soil depths. Under drought stress condition, most of the genotypes had showed lesser RLD, RSA, RV and RDW at top soil layer and higher RLD, RSA, RV and RDW at deeper soil layer than in well-watered condition. An increase in RLD due to drought stress in different soil layers as reported by O’Toole (1982) was not confirmed by our experiment for any genotype. Pandey et al (1984) reported that drought stress increased RLD in the lower soil profile of a peanut genotype. The soil moisture and rainfall data indicates the severity of drought stress imposed in our study. Drought stress severely reduced the PH, TN, SB and GY in both the field experiments. In the DS 2008 field experiment, drought stress decreased GY about 43 per cent. Dular a deep rooted traditional accession from India had the least reduced GY under drought stress condition than shallow rooted IR64. Among the diverse lines evaluated in this study, Aus lines stood out as having various drought resistant responses. Dular and Pokkali showed greatest RLD at depth. Genotype Dular and N22 manifested higher HI under drought stress. These trends indicated that Aus lines likely present a valuable genetic resource for improving drought resistance in rice.

In all lysimetric trials, under prolonged drought stress Aswina, Azucena, Dular and N22 manifested significantly increased water uptake rate compared to IR64. A substantial genotypic difference for TWU was noticed among the entries in all trials. Dular, Azucena, Aswina and N22 maintained consistently higher total water during drought stress period in all the trials as compared to IR64, and Nipponbare. Being a core of the IRRI germplasm, each ‘rice type’ is represented by a few lines only. Among the rice types studied, aus and deep water accessions are having higher water uptake ability than other rice types such as temperate japonica, tropical japonica, indica and aromatic accessions. As will be explained in next section, the greater ability of aus and deep water rice types over other rice types to exploit more soil moisture under drought stress may be explained by the differences in root distribution. Even there is a considerable variation within tropical japonica and indica. Azucena and Moroberekan of tropical japonica and Pokkali of indica had higher water uptake than rest of the genotypes within group. Water uptake rate is a function of RLD and specific water uptake rate per root length and therefore, their dynamic changes under drought stress determine the water capturing capacity of the plant (Kondo et al 2000). RLD at depth is a major factor determining water uptake rate from soil layer. As explained in previous section, under drought stress, the roots were deeper in soil profile by reducing RLD at the soil surface and increasing at deep layers. These modification in root distribution in response to drought stress by growing roots deeper into the soil occurred in all rice genotypes. The total root length density (RLDtot) was calculated by adding RLD present at 0-30cm, 3045cm, 45-60cm and 60-100cm soil depth. RLDtot present in deep soil layer (45100cm) was greatly increased (~30%) by drought stress (data not shown). This character is thought to be adoptive, where roots continue to explore the soil for water while shoot growth is inhibited. Sharp et al (1988) reported shoot growth

was more inhibited than root growth when soil moisture was limited. Genotypic differences for RLD were even greater at 60-100cm soil layer. Higher MRL with the mean of more than 85 cm was observed among the 11 genotypes but only six genotypes namely Aswina, Dular, FR13A, Azucena, N22 and Rayada were able to produce significantly higher RLD at 60-100cm soil depth. Interestingly, all these six genotypes absorbed more water during drought stress than remaining genotypes. As stress prolonged, genotypes with greater RLD at depth had taken higher water. This gives an indication of more roots helps in up taking higher water. The ability of rice genotype to modify its root distribution to exploit water present at deeper soil layer looks like a promising approach for drought resistance. Under drought stress it would help in achieving more efficient use of rainwater in order to maintain better plant growth. Several authors also reported that under drought stress deep-rooted cultivars had tendency to penetrate deeper soil layer to extract more water for its survival and growth (Puckridge and O’Toole 1981, Yashidha and Hasegawa 1982). The advantage of a deep root system towards drought tolerance was also substantiated in other crops (Chickpea; Silim and Sexena et al., 1993). Based on deep root growth and water uptake characters, Dular and N22 were identified as the genotypes having better drought avoidance mechanism. The appearance of superior rooting ability of Dular and N22 genotypes under different environmental conditions confirms its suitability as a parent for genetically enhancing drought resistance. The genetic variability of deep root traits to drought stress observed in our experiment has been well documented in the rice literature (Mambani and Lal 1983, Shashidhar 1990 and Nemoto et al., 1998). Under drought stress, plants with ability to uptake more water are more capable of resisting the drought stress. Reports on real-time water uptake rate measurements in crop plants are limited. However, with a simple but precise lysimetric system it could be possible to record accurate real time water uptake

rates. On the other hand water uptake measurement in field especially under rainfed lowland condition is difficult because of large site heterogeneity and also due to complicating effect of deep drainage and lateral water movement (Pantuwan et al., 1997, Kamoshita et al., 2000). Our study demonstrated that simulated lowland condition in lysimeters would give better prediction of the genotypic variation for water uptake, root traits and biomass measurements. Screening of root traits on large scale is expensive and laborious (Blum 1988, Pantuwan et al 1997). Thus when screening large entries for drought resistance, we suggest the selection of genotypes exhibiting higher water uptake and biomass. Overall our results clearly confirm that genetic differences in root depth play a key role in explaining differences in plant water uptake and consequently, in maintenance of biomass and GY accumulation under drought stress. 5.1.2 Analysis of Variance Analysis of variance pointed out highly significant differences among the genotypes for all the characters indicating presence of sufficient amount of variability for all the characters among the genotypes studied. 5.1.3 Genetic Variability Parameters The range in mean values does not reflect the total variance in the material studied. Hence, actual variance has to be estimated for the characters to know the extent of existing variability. So, the co-efficient of variation (PCV and GCV) which is calculated by considering the respective means have been used for the comparisons. High values of these parameters indicate wider variability and vice versa. In the same context, a narrow difference between the PCV and GCV implies lesser influence of environment on these traits. The PCV and GCV were highest for PH, TN, SDW, SB, HI, GY, TR, LRS and all root traits under both drought stress and well-watered condition suggesting that, these characters are under the influence of genetic control Hence, these

characters can be relied upon and simple selection can be practiced for further improvement.

PR and LWP recorded high PCV and moderate GCV values.

While, SC and RWC recorded high PCV and low GCV; and moderate PCV and low GCV values respectively. On the whole, co-efficient of variation indicated considerable amount of variability for most of the traits except PR, LWP, SC and RWC. The close correspondence between the estimates of GCV and PCV for most of the traits indicated lesser environmental influence on the expression of these traits, which is also reflected by their high heritability values. 5.1.4 Heritability and Genetic Advance Broad sense heritability gives an idea about portion of observed variability attributable to genetic differences. According to Johnson et al (1955), h2 estimates along with genetic gain would be more useful than the former alone in predicting the effectiveness of selecting the best individual. Therefore, it is essential to consider the predicted GA along with h2 estimate as a tool in selection programme for better efficiency. In our study, high h2 coupled with high GA as per cent of mean were recorded for PH, TN, SDW, SB, HI, GY, TR, LRS and all root traits. Thakur et al (1999) have reported high h2 coupled with high GA for TN. Latha (1996) and Gireesha (1999) reported higher h2 for root traits. Mane (2001) also reported higher h2 for SDW, TN, RDW and MRL. But, Ekanayake et al (1992) obtained low h2 for MRL and moderate h2 for RV. High h2 coupled with moderate GA as per cent of mean were recorded for PR and LWP, SC and RWC. Higher h2 for RDW was reported by Shashidhar et al (1990) and Shahid et al (1994). Hemamalini (1997) obtained higher expected GA as per cent of mean for RV. Gireesha (1999) reported moderate GA as per cent of mean for RV and root length and lowest GA as per cent of mean for PH followed

by RN. High h2 with high GA for GY and yield attributes were also reported by Bidhan et al 2001 and Bhandarkar et al 2003. The present investigation revealed high h2 coupled with high GA as per cent of mean for most of the characters except PR, LWP, SC and RWC indicating the presence of considerable variation and additive gene effects. Hence, improvement of these characters could be effective through phenotypic selection. 5.1.6 Correlation Studies and Path Co-efficient Analysis 5.1.6.1 Correlation Coefficients Drought tolerance is considered a complex trait and low heritability of GY under stress are major reasons for slow progress in breeding (Ouk et al 2006). To know the extent and nature of association between yield and other traits, knowledge of interaction of characters among themselves is very essential. In present investigation, correlation analysis was carried out in both field and lysimetric experiments. The results obtained are discussed below. Under DS 2008 field trial, deep root related traits i.e., RLD at 30-45cm, RSA at 30-45cm, RV at 30-45cm and RDW at 30-45cm are positively correlated with GY and SB under drought stress condition. On contrary GY is positively correlated with top root related traits under well-watered condition but was not significant in both the cases. Highly significant positive correlation is recorded between SB and GY. In WS 2008 lysimetric trial, SDW had highly significant and positive association with deep root traits and, TWU under drought stress condition. It has been clearly indicated that water extraction has been associated with root traits present at deep soil layer. On the other hand SDW under well-watered condition is highly significantly correlated with both top and deep roots. Similarly in DS 2009 lysimetric trial, SDW had positive association with all root and shoot traits but was significant only under well-watered condition. Highly significant and positive

correlation was also noticed among all root traits in both field and lysimetric trials. Such positive correlation between shoot and root traits were reported by Ekanayake et al (1985a), Cruz et al (1986), Latha (1996), Yadav et al (1997), Hemamalini (1997), Price et al (1997), Gireesha (1999) and Thanh et al (1999). 5.1.6.2 Path Co-efficient Analysis The relationship between yield and yield components may be negative or positive but it is the net result of direct effect of that particular trait and indirect effects via other traits. Hence, it is necessary to determine the path co-efficients which partition the observed correlation in to direct and indirect effects and also reveals the cause and effect relationship between yield and their related traits. In lysimetric trial, the path analysis indicated that high positive direct effect of RLD 60-100cm, PH and TN towards SDW under drought stress condition. Whereas under well-watered condition TN and RLD at 30-100cm recorded high positive direct effect towards SDW. Among indirect effects, RLD at 30-45cm, RLD at 45-60cm, MRL and TWU under drought stress condition showed high positive indirect effect on SDW via RLD at 60-100cm. whereas under wellwatered condition RLD at 0-30cm showed high positive indirect effect on SDW through TN and MRL via RLD at 30-100cm. All these indirect effects resulted in high positive significant correlation of respective characters with SDW. SDW in turn related with GY measured in field condition. Selection for SDW based RLD present at depth and TN would be most effective, since these two have maximum positive direct effects as well as indirect effects of other characters via these two traits. 5.1.7 Genetic Diversity Studies The amount of diversity available in the crop decides the success of any crop improvement programme with manifold objectives. Improvement in GY is normally attained through involvement of the genetically diverse parents in

breeding programmes. For identifying such diverse parents for crossing, by means of Mahalanobis’s D2 statistic has been used in several crops. It is a powerful tool used to quantify the genetic divergence between the genotypes and to relate clustering pattern with the geographical origin. D2 statistic has been employed widely to resolve divergence at intervarietal, species and subspecies levels in classifying problems in crop plants (Murthy and Tiwari, 1967 and Siddiq and Swaminathan, 1971). In the present investigation, OryzaSNP panel rice accessions were considered for assessment of nature of genetic diversity by adopting Mahalanobis’s (1936) concept of generalized distance (D2) considering root and shoot characters. The inter-cluster distance was not consistent with the geographic distribution of varieties in both drought stress and well-watered condition. The varieties belonging to diverse ecological regions clustered together whereas, genotypes of the same region have entered widely into separate groups. The absence of relation between genetic diversity and geographical diversity suggest that, forces other than geographic origin such as exchange of breeding material, genetic drift, variations, natural and artificial selections are responsible for diversity. Crossing of genotypes belonging to the same cluster is not expected to yield superior hybrids or desirable segregants. However, theoretically a general notion exists that larger the divergence between genotypes, higher will be the heterosis (Falconer, 1981). Therefore, it would be desirable to attempt crosses between genotypes belonging to distant clusters for getting highly heteroitc crosses. In this context, inter-cluster distance were worked out considering the twelve characters under drought stress and nine characters in well-watered condition and the intercluster D2 suggested wide diversity existed between these clusters. The selection of diverse genotypes from wider clusters would produce a broad spectrum of

variability in their respective environment for root and shoot traits studied which may enable further selection and improvement. Contribution of each character towards genetic divergence has been estimated from the number of times that each character appeared in the first rank. It has been observed that under drought stress condition RLD at 0-30cm contributed the maximum towards the genetic divergence followed by SDW, RSR and PH indicating the major role of these characters in differentiating of intercluster levels. In well-watered condition, RSR was the single largest contributor towards divergence followed by TN and RLD at 30-100cm, The above results imply that, in order to select genetically diverse genotypes for hybridization, the material should be screened for the important traits like, RLD at 0-30, SDW, RSR and PH under drought stress condition and RSR, TN and RLD 30-100cm under well-watered condition.. Deep root traits i.e., RLD at 45-60cm, RLD at 60-100cm and MRL, PH, TWU and SDW were recorded maximum mean values in the cluster II, which also contained genotypes with maximum RN, next to cluster I. Minimum mean values for all root traits, PH and SDW were observed in the cluster I, which also contained genotypes with minimum TWU, next to cluster III. Hence, it is worthy to note that in calculating cluster means, the superiority of particular genotype in respect to a given character get diluted by other genotype that are related and grouped in the same cluster but which are inferior or intermediary for that character in question. Hence, apart from selecting genotypes from the clusters which have high inter-cluster distance for hybridization, one can also think of selecting genotypes based on extent of genetic divergence in respect to a particular character of interest. This is to mean that, if breeder’s intension is to improve root traits, he can select genotypes which are highly divergent with respect to these characters.

5.2 Screening of Breeding Lines and Donors of IRRI-India Drought Breeding Network, and Parents of Mapping Population under Drought and WellWatered Condition Drought is a very serious problem in Asia as it alone occupies 34 m ha of rainfed lowland and 8 m ha of upland rice (Huke and Huke 1997) and which was frequently subjected to drought stress. To address yield reductions resulting from drought, rice breeders, particularly in eastern India and Thailand, have been working since 1950 on developing rainfed lowland rice varieties for drought tolerance. Some examples of rainfed varieties with improved yield and farmer’s acceptance are N22 and IET1444 (Rasi) in India; OS 6 in West Africa; C22 in the Philippines; and IR 32 and Mashuri (considered as the first rainfed mega variety) which are widely grown in some Asian lowland regions (Mackill et al 1996). Despite these advances, further progress in rice production under drought is still needed to support global rice demands. Drought tolerance is considered a complex trait. Lack of effective selection criteria for traits related to drought tolerance and low h2 of GY under stress are cited as major reasons for slow progress in breeding (Ouk et al 2006). However, recent reports indicate that in well managed trials, the heritability of GY under drought stress is comparable to that under non stress conditions, and that direct selection for GY under stress is effective (Kumar et al 2008; Venuprasad et al 2007, 2008). International and national efforts are being made to develop drought tolerant cultivars to increase and stabilize yields of this crop. Recently IRRI, Philippines has started a network programme with eight research centers in India to develop and screen the promising lines developed at their centers. State-of-theart drought-breeding methods was used to transfer the drought tolerance of donors into elite lines through pedigree breeding and through back cross breeding for improving the drought tolerance of currently grown mega varieties. (Kumar et al., 2007; Steel et al., 2008; Verulkar et al., 2010)

Till today most of plant breeding programs rely greatly on large scale evaluation of parents and progeny populations. This approach has been proven successful in many of the cases. Understanding the key traits responsible for superior performance of breeding lines is necessary for improving breeding programs through physiological research. In our present investigations we have chosen real time water uptake as a major trait for study and we also examined the root traits. The breeding lines of IRRI- India drought breeding network with consistent superior performance over popular checks under severe and moderate stress and with no yield penalty under irrigated conditions, along with popular checks, were selected and used for this experiment. In this study some of the local landraces of rice which were performing well under network trials are also used to unveil their mechanism. Along with them, parents of mapping population used so far in root QTL mapping studies were also used. 5.2.1 Mean Performances Drought stress severely reduced the PH, TN and SB in both the field and lysimetric experiment. Kondo et al (2000) also reported the same in rice. In field experiment, most of the entries did not produce any GYs in stress condition because of high temperature during grain filling and also due to severe drought stress. Most of drought resistant lines identified previously in IRRI-India network have recorded higher SB under drought stress condition. Under well-watered condition some of popular checks like Swarna and MTU1010 out yielded most of the breeding lines and donors tested. Among the breeding lines, IR 74371-70-1-1 and IR69515-6-KKN-4-UBN-4-2-1-1-1 recorded higher GY compare to other breeding lines. On the other hand ARB 6 and IR 78937-B-4-B-B-B recorded highest HI compared to all breeding lines and popular checks In lysimetric trials, substantial genotypic difference for TWU was noticed among the breeding lines and donors. Breeding lines such as ARB3, ARB 4, DGI 307, IR 74371-54-1-1, IR 52561-UBN-1-1-2, IR 74908-80-B-3-3 have up taken

significantly higher total water during drought stress period as compared to both drought susceptible (IR64) and drought resistant (Apo) checks. As drought stress prolonged, ARB 4, DGI 307, Brown Gora, IR 74371-54-1-1, IR 789908-80-B-3B, ARB 3, CBT 306 recorded significantly increased water uptake rate compared to susceptible check IR64. Among the donors and parents, Azucena, Brown Gora, IRAT 109, Vandana and Budda recorded significantly higher TWU during drought stress period. The greater drought resistance of breeding lines and donors over others may be explained by the differences real time water uptake rates. Genotypic differences for root traits were also recorded between high and low water up taking breeding lines. This gives an indication of more roots helps in up taking higher water. Many traits may appear to be of potential benefit to yield under drought. However, selection for yield alone will not guarantee that good lines have the deepest roots, because drought tolerance may be conferred through genetic superiority of other mechanisms, such as osmotic adjustment, accumulation and remobilization of stem reserves, superior spike photosynthesis, heat tolerant metabolism, and good emergence and establishment under moisture stress. Overall our results would suggest that breeding lines with higher water uptake will have an advantage over others, assuming its deeper roots helps in up taking higher water from deeper soil profile. 5.2.2 Analysis of Variance Analysis of variance indicating presence of sufficient amount of variability for all the characters among the genotypes studied. 5.2.3 Genetic Variability Parameters The PCV and GCV were highest for TN, SDW and all root traits under both drought stress and well-watered condition suggesting that, these characters are under the influence of genetic control. Similarly SB, GY, HI under wellwatered condition in field recorded high GCV and PCV. Hence, these characters

can be relied upon and simple selection can be practiced for further improvement. TWU under drought stress condition recorded high PCV and moderate GCV values. While, PH and TN measured in field recorded moderate PCV and GCV values under both drought stress and well-watered conditions. On the whole, co-efficient of variation indicated considerable amount of variability for most of the traits. The close correspondence between the estimates of GCV and PCV for most of the traits indicated lesser environmental influence on the expression of these traits, which is also reflected by their high h2 values. 5.2.4 Heritability and Genetic Advance In the present study, high h2 coupled with high GA as per cent of mean were recorded under both drought stress and well-watered conditions for all root and shoot traits measured in lysimetric trial except PH under drought stress condition. Similarly SB, GY and HI also recorded high values of heritability and GA as per cent mean. Moderate h2 coupled with moderate GA as per cent of mean were recorded for TWU. The present investigation revealed high h2 coupled with high GA as per cent of mean for most of the characters except PH and TWU indicating the presence of considerable variation and additive gene effects. Hence, improvement of these characters could be effective through phenotypic selection. 5.2.5 Correlation Studies The correlation analysis helps in examining the possibility of improving yield through indirect selection of its component traits which are highly correlated. In present investigation, correlation analysis was carried out in lysimetric experiments. Positive and significant association of root traits with TWU and SDW clearly indicated that TWU could be used directly in selecting plants with high root biomass.

5.3 Physiological and Molecular Dissection of Drought Avoidance Root Mechanisms in Adeysel NILs. 5.3.1. Studies on Water Uptake, Root Distribution under Well-Watered and Drought Stress Condition NIL’s are very important tool for both genetic and physiological dissection of drought tolerance in rice lines. Repeated studies under different rice ecosystems indicated that the Adeysel -NILs differ significantly for GY under drought stress but show similar yield potential, phenology, yield-related traits under nonstress conditions (Venuprasad et al under preparation). In this study, two pairs of NILs were used for characterizing real time water uptake, roots, shoots and physiological traits under well-watered and stress conditions where water uptake rates were recorded at regular intervals. These two pairs were derived from the two sister families i.e., IR77298-5-6-B and IR77298-14-1-2-B which were genetically quite close- the two lines had coefficient of parentage (COP) of 0.9 and SSR analysis revealed that these two lines are about 94% similar (Venuprasad et al. 2007). Genetic polymorphism study with SSRs further confirmed that the contrasting ‘+’ & ‘-‘ NILs in both the families are highly homogenous - the two sublines derived from IR77298-5-6-B, 5+ NIL and 5- NIL, differed by less than 2% while the two sublines derived from IR77298-14-12-B, 14+ NIL and 14- NIL, differed by less than 3% of the genome (Venuprasad et al. unpublished). Real-time water uptake studies clearly indicated that tolerant lines were having higher water uptake rate than susceptible lines at different intervals during drought stress. In two pairs of NILs, decreasing trend of water uptake was noticed in subsequent measurements even though it was not significant. However among parents reverse trend was noticed. The tolerant NILs (IR77298-14-1-2-B-10 and IR77298-5-6-B-18) recorded significantly higher deep root biomass and deep root surface area than the respective susceptible NIL’s (IR77298-14-1-2-B-13 and IR77298-5-6-B-11) under drought stress. Even under well-watered condition the

tolerant NILs produced a higher root growth than their corresponding susceptible NILs. Both the drought resistant NILs i.e., NIL 18 and NIL10 produced significantly higher SDW than its drought susceptible counterparts i.e., NIL 11 and NIL 13 and also than parents. TWU was correlated positively and significantly with all root traits and also with shoot dry weight. Bernier et al (2007) had observed that qtl12.1, a large-effect QTL associated with GY under drought stress, influences water uptake by better root system. Thus the differences between the + and – NILs observed in for GY under drought conditions in field may be due to differences in root system. It is concluded that differences in rice root architecture is essential to bring about significant yield advantage in lowland stress environments. 5.3.2. Comparative Expression of Four LEA Genes in Different Zones of Shoot and Roots under Different Soil Water Levels To study expression pattern of four LEA genes a reverse transcription PCR was carried out in different zones of root and shoot tissues under well-watered (FTSW 1.0) and drought stress (FTSW 0.2) condition. Among all four genes, HVA 1 in top shoot; Lea 4d and HVA 1 in below shoot were up regulated more under drought stress condition. Whereas in root tissue, activity of all four genes were more seen in top roots than deep roots under drought stress condition. However, under well-watered condition there was no difference among top and deep roots. At top root tissue, only HVA 1 gene was expressed in all the genotypes under well-watered condition. On the other hand, under drought stress condition all four genes were expressed in all six genotypes except gene Lea 2h in Dular. Similar results were also recorded at deep root tissue. Results have clearly indicated that all the four LEA genes were up regulated by water stress in most of the genotypes studied.

Among them maximum up

regulation was found for HVA1 gene followed by LEA 4d.

Recently a few studies have been conducted in rice on tissue-specific gene expression patterns in different parts of the root system under drought stress condition. Yang et al. (2004) identified and cloned sixty six transcripts that were differentially responded in different types of roots tissue of Azucena under drought stress. In another study, Wang et al 2007 noticed that the majority of genes expressed in upland rice and lowland rice are almost identical. From our investigation it is clear that up-regulation of all four genes was noticed in both tolerant and susceptible genotypes under drought stress so up regulation per se are not related to drought tolerance. Future line of work 1. Best genotypes identified from the study viz., Dular and N22 (deep rooted with high water uptake) can be used for recombination breeding to get genotypes with desirable characters. 2. Extensive phenotypic data obtained from our study using OryzaSNP panel can be used to connect with SNP data for doing association genetic studies. 3. Semi-quantitative gene expression data obtained from RT-PCR is not sufficient to explain LEA genes expression level in drought resistant and susceptible NILs so quantification with Real-time PCR will be needed. 4. Breeding lines with lower to medium water uptake can be used in studies involving other physiological parameters to dissect their superior performance under drought stress.

VI. SUMMARY The present investigation was carried with the primary objective of assessing genetic variability for traits associated with drought tolerance and relating them to performance in the field hence, a series of field and lysimetric experiments were conducted using OryzaSNP rice panel, advanced breeding lines, donors, parents of mapping population and near-isogenic lines under drought stress and well-watered condition. The salient features of the investigation are summarized below. 6.1 Genetic Diversity and Assessment of OryzaSNP Panel Rice Accessions for Drought Tolerance Based on Water Uptake, Root Distribution, Shoot and Yield Parameters under Different Moisture Regimes. 6.1.1 Mean Performances We observed considerable variation in root distribution across different soil layers in both drought stress and well-watered condition. Under drought stress condition, the roots were deeper in soil profile by reallocating RLD from soil surface to deep layers. These modification in root distribution, in response to drought stress occurred in all rice genotypes. The total root length density (RLDtot) in deep soil layer was greatly increased (~30%) by drought stress. This character is adoptive, as roots continue to explore the soil for water while shoot growth is inhibited. However, drought stress severely reduced above-ground traits such as plant height, tiller number, shoot biomass and grain yield in both the field experiments. Among the diverse lines evaluated under rainfed lowland condition, aus lines manifested various drought resistant responses. Among them, Dular recorded greatest RLD at depth and highest harvest index whereas Pokkali and N22 showed greatest RLD at depth and higher harvest index respectively under drought stress

condition. These trends indicated that aus lines likely present a valuable genetic resource for improving drought resistance. Repeated studies using lysimeters indicated that Dular, Azucena, Aswina and N22 have ability to uptake more water during drought stress period. On the other hand entries such as IR 64, and Nipponbare recorded less water uptake during drought stress period. Among the rice types studied, aus and deep water accessions had higher water uptake ability compared to other rice types such as temperate japonica, tropical japonica, indica and aromatic accessions. The greater ability of aus and deep-water rice types over other rice types to exploit more soil moisture under drought stress was explained by the differences in root distribution. Azucena and Moroberekan (tropical japonica) and Pokkali (indica) had higher water uptake than rest of the genotypes within the group. To date reports on a real time water uptake rate in most of crop plants is limited. However, with a simple but precise lysimetric system it was possible to record accurate real time water uptake rates. On the other hand water uptake measurement in field especially under rainfed lowland condition is difficult because of large site heterogeneity and also due to complicating effect of deep drainage and lateral water movement. This study demonstrated that simulated lowland condition in lysimeters would give better prediction of the genotypic variation for water uptake, root traits and biomass measurements. Overall this results clearly confirm that genetic differences in root depth play a key role in explaining differences in plant water uptake and consequently, in maintenance of biomass and grain yield accumulation under drought stress. 6.1.2 Genetic Variability Parameters, Heritability and Genetic advance The high PCV, GCV coupled with high h2and high GA was recorded for all the traits except some of physiological traits such as SC and RWC under both drought stress and well-watered condition. High PCV and GCV suggest that, these characters are under the influence of genetic control. Hence, these characters can

be relied upon and simple selection can be practiced for further improvement. The close correspondence between the estimates of GCV and PCV for most of the traits indicated lesser environmental influence on the expression of these traits.. High h2 and high GA recorded for all traits indicated the presence of considerable variation and additive gene effects. Hence, improvement of these characters could be effective through phenotypic selection. 6.1.3 Correlation coefficients and Path analysis In field condition, deep root related traits were positively correlated with GY and SDW under drought stress condition. In contrast GY was positively correlated with top root related traits under well-watered condition but was not significant in both the cases. In lysimetric trials, highly significant and positive correlation was recorded between deep root related traits with SDW under drought stress condition. Interestingly deep root traits measured under drought stress condition in lysimetric trials were highly significantly and positively correlated with GY measured in field. The path analysis indicated that selection for SDW based RLD present at depth and TN would be most effective, since these two have maximum positive direct effects as well as indirect effects of other characters via these two traits. 6.1.4 Genetic Diversity Studies OryzaSNP panel rice accessions were grouped into five and eight divergent clusters in drought stress and well-watered conditions respectively. The varieties belonging to diverse ecological regions clustered together whereas, genotypes of the same region have entered widely into separate groups. The absence of relation between genetic diversity and geographical diversity suggest that, forces other than geographic origin such as exchange of breeding material, genetic drift, variations, natural and artificial selections are responsible for diversity. The intercluster D2 recorded wide diversity between clusters.

It has been observed that under drought stress condition contribution of RLD at 0-30cm and SDW under drought stress; RSR and TN under well-watered condition maximum towards genetic divergence. In order to select genetically diverse genotypes for hybridization genetic material should be screened for abovementioned traits. Deep-root related traits, TWU and SDW were recorded maximum mean values in the cluster II. Whereas minimum mean values for above mentioned traits were observed in the cluster I. For improving root and agronomic performances, selection of diverse genotypes from wider clusters would produce a broad spectrum of variability which may enable further selection and improvement. 6.2 Screening of Breeding Lines and Donors of IRRI-India Drought Breeding Network, and Parents of Mapping Population under Drought and WellWatered Condition 6.2.1 Mean Performances Drought stress severely reduced the PH, TN and SB in both the field and lysimetric experiment. Most of drought resistant lines identified previously in IRRI-India network have recorded higher shoot biomass under drought stress condition. Whereas under well-watered condition some of popular checks like Swarna, MTU1010 out yielded most of the breeding lines and donors tested. Among the breeding lines, IR 74371-70-1-1 and IR69515-6-KKN-4-UBN-4-2-11-1 recorded higher GY compare to other breeding lines. On the other hand ARB 6 and IR 78937-B-4-B-B-B recorded highest HI compared to all breeding lines and popular checks Some of the top performing breeding lines identified under network trials such as ARB 3, ARB 4, DGI 307, IR 74371-54-1-1, IR 52561-UBN-1-1-2, IR 74908-80-B-3-3 were recorded more water uptake during entire drought stress period in lysimetric trial. In contrast, both drought susceptible (IR64) and resistant (Apo) checks were recorded less water uptake. Among the donors and parents,

Azucena, Brown Gora, IRAT 109, Vandana and Budda recorded significantly higher water uptake during drought stress period. The greater drought resistance of breeding lines and donors over others may be explained by the differences real time water uptake rates. Genotypic differences for root traits were also recorded between lines classified as high and low water up taking. This gives an indication of more roots helps in up taking higher water. Overall, this study indicated that breeding lines with higher water uptake have an advantage over others, assuming its deeper roots helps in up taking higher water from deeper soil profile. 6.2.2 Genetic Parameters and Correlation Analysis Highest PCV, GCV, h2 and GA were recorded for all root and shoot traits under both drought stress and well-watered condition suggesting that, these characters are under the influence of genetic control. Similarly results were also obtained under field condition for yield and yield related traits. Hence, these characters can be relied upon and simple selection can be practiced for further improvement. Positive and significant association of root traits with TWU and SDW clearly indicated that TWU can be used directly for selecting plants with high root biomass. 6.3 Physiological and Molecular Dissection of Drought Avoidance Root Mechanisms in Adeysel NILs. 6.3.1. Studies on Water Uptake, Root Distribution under Well-Watered and Drought Stress Condition Real-time water uptake studies clearly indicated that tolerant NILs (NIL10 and NIL18) have higher water uptake rate than susceptible NIL’s (NIL13 and NIL11) at different intervals during drought stress. It was due to their ability in producing more root biomass and root surface area at depth than the respective susceptible NIL’s. Deep root and higher water uptake helped both the drought resistant NILs in producing significantly higher shoot biomass than its drought

susceptible counterparts and also than parents. Thus the differences between the resistant and susceptible NILs observed for grain yield under drought conditions in field may be due to differences in root system. It is concluded that differences in rice root architecture is essential to bring about significant yield advantage in drought stress environments. 6.3.2. Comparative Expression of Four LEA Genes in Different Zones of Shoot and Roots under Different Soil Water Levels To study expression pattern of four LEA genes a reverse transcription PCR was carried out in different zones of root and shoot tissues under well-watered (FTSW 1.0) and drought stress (FTSW 0.2) condition. Increased activity of four genes was seen in top roots than deep roots under drought stress condition. However, under well-watered condition there was no difference between top and deep roots. In the top root tissue, only HVA 1 gene was expressed in all the genotypes under well-watered condition. On the other hand, under drought stress condition all four genes were expressed in all six genotypes except LEA 2h in Dular. Similar results were also recorded in deep root tissue. Results have clearly indicated that all the four LEA genes were upregulated by water stress in most of the genotypes studied.

Maximum upregulation was found for HVA1 gene

followed by LEA 4d. Thus it is clear that up-regulation was noticed in both tolerant and susceptible genotypes under drought stress so upregulation per se is not related to drought tolerance.

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PANTUWAN, G., FUKAI, S., COOPER, M., O’TOOLE, J. C. AND SARKARUNG, S., 1997, Root traits to increase drought resistance in rainfed lowland rice. In: Fukai,S., Cooper, M., Salisbury, J. (Eds), Proceedings of the symposium on breeding stratageies for rainfed lowland rice in drought- prone environments, ACIAR, Canberra, 77:170-178. PANTUWAN, G., FUKAI, S., COOPER, M., RAJATASEREEKUL, S. AND O‘TOOLE, J. C., 2002, Yield response of rice (Oryza sativa L.) genotypes to different types of drought under rainfed lowlands. Part 3. Plant factors contributing to drought resistance. Field Crops Res, 73: 181-200. PANWAR, A., DHAKA, R. P. S., AND VINOD KUMAR, 2007, Path analysis of grain yield in rice. Adv. Plant Sci, 20: 27-28. PARK, S., LI, J., PITTMAN, J. K., BERKOWITZ, G. A., YANG, H., UNDURRAGA, S., MORRIS, J., HIRSCHI, K. D. AND GAXIOLA, R. A., 2005, Up-regulation of a H+-pyrophosphatase (H+-PPase) as a strategy to engineer drought-resistant crop plants. Proc. Natl. Acad. Sci, 102: 18830-18835. PATIL, K., 2009 Molecular characterization, inheritance and validation of markers linked to aroma in rice (Oryza sativa L.) under aerobic condition Ph.D Thesis, University of Agricultural Sciences, Bangalore. PRABUDDA, H. R., 2002, A novel method for identification of near isogenic lines from mapping population: validated for root and shoot morphological characters in rice (Oryza sativa L.), M.Sc (Agri) Thesis, University of Agricultural Sciences, Bangalore. PRABUDDHA, H. R., MANJUNATHA, K., VENUPRASAD, R., VINOD, M. S., JUREIFA. J. H. AND SHASHIDHAR. H. E. 2008. Identification of Isogenic Lines and Near-Isogenic Lines: An innovative approach, validated for root and shoot morphological characters in a mapping population of rice (Oryza sativa L.). Euphytica, 160: 357-368.

PRICE, A. H. AND THOMAS, A. D., 1997, Genetic dissection of root growth in rice (Oryza sativa L.). II. Mapping quantitative trait loci using molecular markers. Theor. Appl. Genet, 95: 143-152. PRICE, A. H., TOWNEND, J., JONES, M. P., AUDEBERT, A. AND COURTOIS, B., 2002, Mapping QTLs associated with drought avoidance in upland rice grown in the Philippines and West Africa. Plant Mol. Biol, 48: 683-695. PUCKRIDGE, D. W. AND O’TOOLE, J. C., 1981, Dry matter and grain production of rice, using a line source sprinkler in drought studies. Field Crops Res, 3: 303-319. RAMANJULU, S. AND BARTELS, D., 2002, Drought- and desiccation-induced modulation of gene expression in plants. Plant Cell Environ, 25:141-151. RAMANUJAM, S. AND TIRUMALACHAR, D. K., 1967, Genetic variability of certain characters in red pepper (C. annum L.). Mysore J. Agric. Sci, 1: 3236. RAO, C. R., 1952, Advanced statistical methods in biometrical research. Jhon wiley and Sons, Inc., New York: 357-363. ROBINSON, H. F., COMSTOCK, R. E. AND HARVEY, P., 1966, Quantitative genetics in relation to breeding on the centennial of Mendelism. Indian J. Genet, 26: 171-177. SALVI, S. AND TUBEROSA, R., 2005, To clone or not to clone plant QTLs: present and future challenges. Trends Plant Sci, 10: 297-304. SARKARUNG, S. AND PANTUWAN, G., 1999, Improving rice for droughtprone rainfed lowland environments. In: Ito O, O’Toole J.C, Hardy B, (Eds). Genetic improvement of rice for water-limited environments, International Rice Research Institute, Los Banos, Philippines, 57-70. SERGEEVA, L. I., KEURENTJES, J. J. B., BENTSINK, L., VONK, J., VAN DER PLAS, L. K. W., KOORNNEEF, M. AND VREUGDENHIL, D., 2006, Vacuolar invertase regulate elongation of Arabidopsis thaliana roots

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Plate 1: Overview of well- watered treatment during DS 2008 at IRRI, Philippines  

 

Plate 2: Overview of drought stress treatment during DS 2008 at IRRI, Philippines

        

                    

                      

   

      

                       

                          

Plate 3: Method of root sampling under field condition using monolith sampler during DS 2008 at IRRI, Philippines          

 

Plate 4: Overview of well- watered treatment during DS 2009 at ICRISAT, India  

 

Plate 5: Overview of well- watered treatment during DS 2009 at ICRISAT, India

 

Plate 6: Overview of lysimetric experiment during WS 2008 at IRRI, Philippines 

 

Plate 7: Lysimetric system used for real- time water uptake measurements during WS 2008 and WS 2009 at IRRI, Philippines

 

Plate 8: Overview of lysimetric experiment during WS 2008 at IRRI, Philippines 

 

Plate 9: Root washing method followed during DS 2009 at ICRISAT, India.   

       

Plate 10: Overview of pot experiment during WS 2009 at IRRI, Philippines               

 

FTSW 1.0 (Well- watered)  

1

2

3

4

5

FTSW 0.2 (Drought stress) 6

1

2

3

4

5

6

A           

1

2

3

4

5

6

 

1

2

3

4

5

6

B          

 

 

Plate 11: Expression pattern revealed by semi quantitative RT-PCR of LEA genes in top root tissues of drought resistant and susceptible checks A: IR 64 (Drought susceptible)

B: Dular (Drought resistant)

Expected fragment sizes: 1 (LEA1a) – 254bp

       

2 (LEA2h) – 240bp

3 (LEA4d) – 206bp

4 (HVA1) – 249bp

5 (18S rRNA) – 500bp

6 – Negative control

   

FTSW 1.0 (Well- watered)  

1

2

3

4

5

FTSW 0.2 (Drought stress) 6

1

2

3

4

5

6

A           

1

2

3

4

5

 

1

6

2

3

4

5

6

  

B             

Plate 12: Expression pattern revealed by semi quantitative RT-PCR of LEA genes in deep root tissues of drought resistant and susceptible checks A: IR 64 (Drought susceptible)

B: Dular (Drought resistant)

Expected fragment sizes: 1 (LEA1a) – 254bp

       

2 (LEA2h) – 240bp

3 (LEA4d) – 206bp

4 (HVA1) – 249bp

5 (18S rRNA) – 500bp

6 – Negative control

    

FTSW 1.0 (Well- watered)     

1

2

3

4

5

FTSW 0.2 (Drought stress) 1

6

2

3

4

5

6

A          

 

1

2

3

4

5

6

 

1

2

3

4

5

6

B    

       

 

Plate 13: Expression pattern revealed by semi quantitative RT-PCR of LEA genes in top root tissues of drought resistant and susceptible NILs. A: NIL13 (Drought susceptible)

Expected fragment sizes: 1 (LEA1a) – 254bp

       

B: NIL10 (Drought resistant)

2 (LEA2h) – 240bp

3 (LEA4d) – 206bp

4 (HVA1) – 249bp

5 (18S rRNA) – 500bp

6 – Negative control

       

FTSW 1.0 (Well- watered)     

1

2

3

4

5

FTSW 0.2 (Drought stress) 1

6

2

3

4

5

6

A          

1

2

3

4

5

6

 

   1

2

3

4

5

6

B       

Plate 14: Expression pattern revealed by semi quantitative RT-PCR of LEA genes in deep root tissues of drought resistant and susceptible NILs. A: NIL13 (Drought susceptible)

Expected fragment sizes: 1 (LEA1a) – 254bp

   

B: NIL10 (Drought resistant)

2 (LEA2h) – 240bp

3 (LEA4d) – 206bp

4 (HVA1) – 249bp

5 (18S rRNA) – 500bp

6 – Negative control

FTSW 1.0 (Well- watered)     

1

2

3

4

5

FTSW 0.2 (Drought stress) 1

6

2

3

4

5

6

A   

       

  1

2

3

4

5

 

1

6

2

3

4

5

6

B   

      

 

Plate 15: Expression pattern revealed by semi quantitative RT-PCR of LEA genes in top root tissues of drought resistant and susceptible NILs. A: NIL11 (Drought susceptible)

Expected fragment sizes: 1 (LEA1a) – 254bp

       

B: NIL18 (Drought resistant)

2 (LEA2h) – 240bp

3 (LEA4d) – 206bp

4 (HVA1) – 249bp

5 (18S rRNA) – 500bp

6 – Negative control

       

FTSW 1.0 (Well- watered)     

1

2

3

4

5

FTSW 0.2 (Drought stress) 1

6

2

3

4

5

6

A         

  1

2

3

4

5

6

 

1

2

3

4

5

6

B  Plate 16: Expression pattern revealed by semi quantitative RT-PCR of LEA genes in deep root tissues of drought resistant and susceptible NILs. A: NIL11 (Drought susceptible)

Expected fragment sizes: 1 (LEA1a) – 254bp

       

B: NIL18 (Drought resistant)

2 (LEA2h) – 240bp

3 (LEA4d) – 206bp

4 (HVA1) – 249bp

5 (18S rRNA) – 500bp

6 – Negative control

FTSW 1.0 (Well- watered)  

1

2

3

4

5

FTSW 0.2 (Drought stress) 6

1

2

3

4

5

6

A       

   

1

2

3

4

5

6

 

1

2

3

4

5

6

B      

           

 

 

Plate 11: Expression pattern revealed by semi quantitative RT-PCR of LEA genes in top root tissues of drought resistant and susceptible checks A: IR 64 (Drought susceptible)

B: Dular (Drought resistant)

Expected fragment sizes: 1 (LEA1a) – 254bp

         

2 (LEA2h) – 240bp

3 (LEA4d) – 206bp

4 (HVA1) – 249bp

5 (18S rRNA) – 500bp

6 – Negative control

 

FTSW 1.0 (Well- watered)  

1

FTSW 0.2 (Drought stress)

2

3

4

5

6

2

3

4

5

6

1

2

1

2

3

4

5

6

          



1

3

4

5

6

           



 

 

Plate 12: Expression pattern revealed by semi quantitative RT-PCR of LEA genes in deep root tissues of drought resistant and susceptible checks A: IR 64 (Drought susceptible)

B: Dular (Drought resistant)

Expected fragment sizes: 1 (LEA1a) – 254bp

       

2 (LEA2h) – 240bp

3 (LEA4d) – 206bp

4 (HVA1) – 249bp

5 (18S rRNA) – 500bp

6 – Negative control

             

Table 1. List of OryzaSNP panel rice genotypes used for real time water uptake rates, root and grain yield parameters under both field and lysimetric experiments. Sl. No.

 

Genotypes

Rice types

Origin

History

1

Aswina

Deep water

Bangladesh

Traditional

2

Azucena

Trop. Japonica

Philippines

Traditional

3

Cypress

Trop. Japonica

USA

Improved

4

Dom Sufid

Aromatic

Iran

Traditional

5

Dular

Aus

India

Traditional

6

FR13A

Aus

India

Traditional

7

IR 64

Indica

Philippines

Improved

8

LTH

Temp. Japonica

China

Traditional

9

M 202

Temp. Japonica

USA

Improved

10

Minghui 63

Indica

China

Improved

11

Moroberekan

Trop. Japonica

Guinea

Traditional

12

N22

Aus

India

Traditional

13

Nipponbare

Temp. Japonica

Japan

Improved

14

Pokkali

Indica

India

Traditional

15

Rayada

Deep water

Bangladesh

Traditional

16

Sadu Cho

Indica

Korea

Traditional

17

Shz 2

Indica

China

Improved

18

Swarna

Indica

India

Improved

19

Tainung 67

Temp. Japonica

Taiwan

Improved

20

Zhenshan 97B

Indica

China

Improved

Table 16. Analysis of variance for root and shoot characters of OryzaSNP rice panel accessions under drought stress and well-watered condition in field experiment during DS 2008 Sources of variation Df RLD at 0-10cm RLD at 10-20cm RLD at 20-30cm RLD at 30-45cm RSA at 0-10cm RSA at 10-20cm RSA at 20-30cm RSA at 30-45cm RV at 0-10cm RV at 10-20cm RV at 20-30cm RV at 30-45cm RDW at 0-10cm RDW at 10-20cm RDW at 20-30cm RDW at 30-45cm Root Number Root to shoot ratio Plant Height Tiller Number Shoot dry weight Straw Biomass Harvest index Grain Yield

Replications 2 0.18 0.037 0.03 0.0005 19049.71 3398.08 324.47 45.06 0.73 0.15 0.09 0.01 0.01 0.002 0.0003 0.0001 14159.5 0.0001 338.18 4.72 4.20 5201.11 0.0001 423.18

Drought stress Genotypes 17 0.32** 0.31** 0.28** 0.013** 84092.61** 47264.18** 27061.73** 3605.36** 14.94** 4.40** 1.39** 0.20** 0.27** 0.051** 0.021** 0.002** 6487.91** 0.006** 1695.33** 154.85** 80.94** 125665.80** 0.04** 12535.70**

Error 34 0.08 0.016 0.02 0.0002 8984.33 3932.59 815.75 143.63 1.94 0.14 0.07 0.009 0.009 0.002 0.0007 0.0001 2447.66 0.0001 47.13 2.24 2.28 2317.90 0.0006 138.75

Replications 2 0.092 0.014 0.001 0.00003 47683.1 2668.8 182.4 1.50 5.64 1.55 0.29 0.001 0.007 0.003 0.0004 0.00003 2640.01 0.002 22.16 2.58 0.78 331.20 0.005 263.00

Well-watered Genotypes 17 2.21** 0.28** 0.098** 0.0008** 483077.50** 57947.60** 21178.00** 708.28** 68.32** 11.38** 4.29** 0.14** 0.81** 0.07** 0.016** 0.0005** 24673.72** 0.016** 1070.12** 79.82** 188.03** 230155.48** 0.014** 56655.25**

Error 34 0.23 0.02 0.003 0.00006 45134.20 2350.80 1556.20 23.09 5.35 0.51 0.22 0.006 0.02 0.002 0.0003 0.00003 648.90 0.0006 23.33 1.88 2.26 4766.20 0.002 601.78

Table 17. Analysis of variance for physiological characters of OryzaSNP rice panel accessions under drought stress condition in field experiment during DS 2008

Df

Photosynthesis rate

Transpiration rate

Relative water content

Stomatal conductance

Leaf water potential

Leaf rolling score

Replications

2

4.87

0.15

271.97

56351.62

0.09

14.88

Genotypes

17

27.95**

0.65**

107.98ns

26338.72ns

0.85**

9.60**

Error

34

3.38

0.05

99.67

31024.84

0.28

1.32

Sources of variation

ns=non significant

Table 18. Analysis of variance for shoot and grain yield characters of OryzaSNP rice panel accessions under drought stress and well-watered condition in field experiment during DS 2009

Sources of variation

Df

Plant height

Tiller number

Straw biomass

Grain yield

Harvest index

Wellwatered

Drought Stress

Wellwatered

Drought Stress

Wellwatered

Drought Stress

Well watered

Well-watered

Replications

2

308.60

16.23

17.47

17.14

5327.61

1452.33

4401.64

0.002

Genotypes

12

296.55**

610.60**

25.75**

54.15**

33182.78**

6946.82**

32253.67**

0.339**

Error

24

349.61

41.92

8.86

6.44

4539.00

1414.63

2558.08

0.004

Table 19. Mean, range and genetic parameters for shoot and physiological characters of OryzaSNP panel rice accessions under drought stress and well watered condition in field experiment during DS 2008. Characters

Mean

Range Minimum Maximum

PCV (%)

Drought stress 32.03

GCV (%)

h2 bs (%)

GA as per cent of mean

30.71

91.98

60.68

Plant height

16.67

10.27

28.84

Tiller number

0.10

0.05

0.24

48.88

47.36

93.90

94.55

Shoot dry weight

83.65

39.17

129.78

29.20

28.02

92.10

55.39

Straw biomass

525.75

173.93

905.69

39.64

38.57

94.66

77.30

Harvest index

0.20

0.03

0.38

57.96

56.56

95.23

113.71

Grain yield

122.30

13.04

254.05

53.44

52.56

96.75

106.50

Photosynthesis

17.66

13.30

25.20

19.26

16.20

70.74

28.07

Transpiration

2.13

1.62

3.20

23.80

20.90

77.13

37.81

Relative watered content

72.45

60.83

83.21

13.97

2.30

2.70

0.78

Stomatal conductance

557.26

376.33

701.67

30.80

7.09

-5.30

-3.36

Leaf water potential

2.64

1.27

3.35

26.04

16.45

39.90

21.40

Leaf rolling score

3.33

1.00

6.33

60.62

49.86

67.66

84.49

20.3

93.73

40.48

Well watered 20.96

Plant height

92.03

62.78

123.61

Tiller number

15.74

7.00

26.67

33.54

32.38

93.23

64.41

Shoot dry weight

18.55

9.79

38.47

43.20

42.43

96.47

85.85

Straw biomass

576.77

224.69

1470.30

49.01

47.52

94.03

94.93

Harvest index

0.27

0.13

0.38

28.44

25.13

78.05

45.73

212.29

83.62

618.16

65.42

64.39

96.88

130.55

Grain yield

Table 20. Mean, range and genetic parameters for root characters of OryzaSNP panel rice accessions under both drought stress and well-watered in field experiment during DS 2008. Characters

Drought stress

Well -watered

Range

Range GCV (%)

h2 bs (%)

GA as per cent of mean

Mean

Min

Max

PCV (%)

GCV (%)

h2 bs (%)

GA as per cent of mean

Mean

Min

Max

PCV (%)

RLD at 0-10cm

1.16

0.49

1.89

34.74

24.25

48.74

34.88

2.43

1.21

3.95

38.86

33.45

74.10

59.33

RLD at 10-20cm

0.63

0.16

1.45

53.75

49.82

85.92

95.13

0.50

0.21

1.25

66.14

59.64

81.29

110.76

RLD at 20-30cm

0.45

0.11

1.34

73.53

65.64

79.69

120.71

0.22

0.06

0.87

84.89

81.16

91.40

159.84

RLD at 30-45cm

0.09

0.02

0.24

74.03

72.20

95.12

145.06

0.03

0.01

0.06

65.62

58.73

80.10

108.29

RSA at 0-10cm

686.36

377.03

1036.77

26.87

23.05

73.59

40.74

1389.05

599.39

2053.86

31.47

27.51

76.38

49.52

RSA at 10-20cm

296.15

114.66

587.52

45.77

40.58

78.60

74.12

317.89

152.02

658.87

45.46

42.82

88.74

83.11

RSA at 20-30cm

164.46

45.25

372.34

59.47

56.88

91.47

112.06

143.58

41.97

361.92

62.67

56.33

80.78

104.29

RSA at 30-45cm

49.75

10.45

123.73

72.41

68.28

88.93

132.65

23.77

7.08

57.55

66.72

63.58

90.82

124.82

RV at 0-10cm

8.50

4.90

14.16

29.46

24.47

69.03

41.89

16.84

5.90

25.00

30.48

27.21

79.67

50.03

RV at 10-20cm

2.93

1.20

4.89

42.67

40.70

90.97

79.97

4.44

2.03

9.83

45.83

42.88

87.51

82.63

RV at 20-30cm

1.29

0.39

2.33

55.86

51.64

85.48

98.36

2.06

0.58

4.65

60.98

56.56

86.04

108.08

RV at 30-45cm

0.38

0.11

0.86

70.87

66.08

86.95

126.94

0.29

0.09

0.83

77.10

72.32

87.98

139.75

RDW at 0-10cm

0.94

0.63

1.97

33.54

31.80

89.87

62.10

1.44

0.46

2.89

37.19

35.83

92.84

71.12

RDW at 10-20cm

0.30

0.13

0.56

45.35

42.65

88.47

82.65

0.31

0.12

0.66

50.94

48.54

90.82

95.30

RDW at 20-30cm

0.13

0.03

0.35

65.53

62.46

90.85

122.65

0.12

0.03

0.29

61.60

59.44

93.10

118.15

RDW at 30-45cm

0.04

0.01

0.11

76.89

71.69

86.92

137.68

0.02

0.00

0.06

80.10

72.59

82.12

135.50

RN

187.34

113.00

259.67

32.88

19.59

35.49

24.04

231.18

60.50

412.00

40.25

38.71

92.50

76.70

RSR

19.20

9.33

38.00

37.95

37.14

95.78

74.88

0.12

0.05

0.34

63.79

60.48

89.91

118.15

RLD=Root length density

RSA= Root surface area RV=Root volume RDW= Root dry weight RN=Root number RSR= Root to shoot ratio

Table 21. Mean, range and genetic parameters for shoot and grain yield characters of OryzaSNP panel rice accessions measured under both drought stress and well-watered condition in field experiment during DS 2009.

Characters

Mean

Range Minimum

PCV (%)

GCV (%)

h2 bs (%)

GA as per cent of mean

Maximum Drought stress

Plant height

69.46

44.33

89.44

21.90

19.82

81.89

36.94

Tiller number

16.39

8.22

21.44

28.83

24.32

71.17

42.27

Straw biomass

360.66

218.66

550.00

31.88

23.98

56.59

37.16

Well-watered Plant height

91.85

70.44

103.78

19.83

4.57

-5.33

-2.17

Tiller number

16.56

10.89

21.33

22.99

14.33

38.85

18.40

Straw biomass

776.15

480

1257.33

30.58

25.17

67.78

42.70

Grain yield

244.41

126.66

473.33

45.66

40.70

79.46

74.75

Harvest Index

0.24

0.12

0.36

31.34

25.84

68.02

43.91

Table 2. Climatic conditions at experimental sites during 1-112 days after transplanting. Season/Locatio n

Max Temp (oC)

Min Temp (oC)

Mean Temp (oC)

Solar Radiation (mj/ m2)

Bright Sunshine (Hrs)

DS 2008/IRRI, Philippines

29.94

23.60

26.77

15.42

5.85

Figure 3. Rainfall pattern during stress period at experimental site during DS 2008, IRRI, Philippines.

Table 22: Phenotypic correlation co-efficients among root, shoot and grain yield traits of OryzaSNP panel rice accessions under drought stress in field experiment during DS 2008. X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X1 X1 X2 X3 X4

1.00

X20

0.22

0.02

0.02

0.74**

0.17

0.01

-0.01

0.55*

0.06

0.05

0.05

-0.10

-0.05

0.00

0.12

0.48*

0.37

-0.24

-0.18

1.00

0.53*

0.39

0.27

0.87**

0.55*

0.43

0.27

0.65**

0.51*

0.43

0.05

0.73**

0.24

0.38

0.33

-0.02

0.40

-0.30

1.00

0.18

-0.04

0.46*

0.88**

0.22

-0.02

0.40

0.79**

0.15

0.07

0.40

0.36

0.19

0.10

-0.13

0.23

-0.02

1.00

0.13

0.42

0.31

0.92**

0.28

0.48*

0.40

0.83**

0.09

0.36

0.01

0.69**

0.26

0.35

0.39

0.15

1.00

0.22

-0.03

0.12

0.75**

0.11

0.03

0.25

-0.04

-0.08

0.05

0.26

0.44

0.54*

-0.10

-0.11

1.00

0.49*

0.43

0.20

0.83**

0.49*

0.41

0.10

0.82**

0.32

0.43

0.26

-0.08

0.37

-0.16

1.00

0.35

-0.05

0.46*

0.89**

0.27

0.10

0.46*

0.47*

0.35

0.16

-0.14

0.30

-0.08

1.00

0.32

0.48*

0.45

0.90**

0.11

0.39

0.09

0.79**

0.23

0.36

0.50*

0.11

1.00

0.15

0.10

0.46*

0.17

0.00

0.09

0.40

0.42

0.60**

0.18

-0.05

1.00

0.50*

0.44

0.19

0.83**

0.44

0.52*

0.16

-0.13

0.36

0.03

1.00

0.38

0.11

0.49*

0.50*

0.48*

0.27

-0.08

0.32

-0.13

1.00

0.24

0.37

0.09

0.86**

0.17

0.40

0.60**

0.09

1.00

0.29

0.13

0.20

-0.12

0.00

0.54*

0.10

1.00

0.35

0.42

0.11

-0.28

0.48*

-0.08

1.00

0.18

0.24

-0.11

0.08

0.01

1.00

0.10

0.27

0.60**

-0.07

1.00

0.34

-0.19

-0.21

1.00

0.00

0.09

1.00

-0.02

X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19

1.00

X20 X1-Root length density at 0-10cm X5-Root surface area at 0-10cm X9-Root volume at 0-10cm X13-Root dry weight at 0-10cm X17- Total root number

X2- Root length density at 10-20cm X6- Root surface area at 10-20cm X10- Root volume at 10-20cm X14- Root dry weight at 10-20cm X18- Shoot dry weight

X3- Root length density at 20-30cm X7- Root surface area at 20-30cm X11- Root volume at 20-30cm X15- Root dry weight at 20-30cm X19- Straw biomass

X4- Root length density at 30-45cm X8- Root surface area at 30-45cm X12- Root volume at 30-45cm X16- Root dry weight at 30-45cm X20- Grain yield

Table 23. Phenotypic correlation co-efficients among root distribution, shoot and grain yield traits of OryzaSNP panel rice accessions under well watered condition in field experiment during DS 2008. X2 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X1 X3 X4 X1 X2 X3 X4

1.00

0.21

0.02

0.03

0.77**

0.15

-0.02

-0.05

0.59**

0.05

-0.14

-0.05

0.59**

0.13

-0.08

-0.10

0.73**

0.36

0.37

0.39

1.00

0.65**

0.31

0.16

0.84**

0.50*

0.26

0.01

0.54*

0.18

0.28

0.12

0.76**

0.51*

0.30

0.23

-0.03

0.28

0.06

1.00

0.51*

-0.12

0.55*

0.84**

0.54*

-0.13

0.32

0.50*

0.60**

-0.10

0.41

0.81**

0.65**

0.10

-0.06

0.04

0.15

1.00

0.04

0.18

0.55*

0.92**

0.06

0.00

0.55*

0.89**

-0.10

0.14

0.62**

0.81**

0.25

-0.01

-0.13

-0.01

1.00

0.11

-0.07

-0.04

0.81**

0.13

-0.01

-0.06

0.72**

0.19

-0.06

-0.13

0.68**

0.28

0.41

0.32

1.00

0.47

0.19

0.05

0.83**

0.29

0.23

0.19

0.90**

0.53*

0.25

0.14

0.02

0.27

-0.04

1.00

0.59**

0.04

0.38

0.75**

0.59**

-0.15

0.32

0.93**

0.66**

-0.01

-0.10

0.03

0.12

1.00

0.04

0.07

0.65**

0.95**

-0.14

0.15

0.67**

0.80**

0.14

-0.03

-0.13

-0.02

1.00

0.20

0.18

0.00

0.70**

0.15

0.03

0.00

0.60**

0.17

0.33

0.23

1.00

0.37

0.09

0.14

0.77

0.47*

0.12

-0.04

0.07

0.12

-0.23

1.00

0.59**

-0.16

0.16

0.81**

0.54*

-0.07

-0.08

0.02

0.07

1.00

-0.13

0.16

0.68**

0.87**

0.11

-0.07

-0.13

-0.03

1.00

0.38

-0.15

-0.10

0.68**

0.07

0.42

0.23

1.00

0.36

0.19

0.24

0.02

0.28

-0.10

1.00

0.69**

-0.03

-0.12

0.02

0.07

1.00

0.08

-0.04

-0.11

-0.02

1.00

0.35

0.23

0.31

1.00

-0.12

0.11

1.00

0.79**

X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19

1.00

X20 X1-Root length density at 0-10cm X5-Root surface area at 0-10cm X9-Root volume at 0-10cm X13-Root dry weight at 0-10cm X17- Total root number

X2- Root length density at 10-20cm X6- Root surface area at 10-20cm X10- Root volume at 10-20cm X14- Root dry weight at 10-20cm X18- Shoot dry weight

X3- Root length density at 20-30cm X7- Root surface area at 20-30cm X11- Root volume at 20-30cm X15- Root dry weight at 20-30cm X19- Straw biomass

X4- Root length density at 30-45cm X8- Root surface area at 30-45cm X12- Root volume at 30-45cm X16- Root dry weight at 30-45cm X20- Grain yield

Table 24: Water uptake rates (g/plant) measured at different intervals during drought stress period using OryzaSNP panel accessions in lysimetric experiment during WS 2008.

Genotypes

Water uptake (g/plant)-Days after stress imposition 14 21 28

Aswina

1767.0

1566.0

1725.0

5058.0

Azucena

1580.0

1489.0

1720.0

4789.0

Cypress

1329.0

1094.0

1261.0

3684.0

Dom Sufid

1456.0

1119.0

1385.0

3960.0

Dular

1552.0

1491.0

1547.0

4590.0

FR13A

1311.0

1148.0

1085.0

3311.0

IR 64

794.0

840.0

872.0

2506.0

LTH

1086.0

991.0

732.0.0

2809.0

M 202

1299.0

1065.0

917.0

3281.0

Minghui 63

1340.0

1040.0

1067.0

3447.0

Moroberekan

1190.0

1175.0

1211.0

3576.0

N22

1868.0

1424.0

1472.0

4764.0

Nipponbare

1306.0

975.0

944.0

3225.0

Pokkali

1491.0

1207.0

1228.0

3926.0

Rayada

1432.0

1462.0

1282.0

4176.0

Sadu Cho

1148.0

1177.0

1183.0

3508.0

SHZ2

1136.0

923.0

897.0

2956.0

Swarna

1042.0

924.0

1237.0

3203.0

Tainung 67

1026.0

808.0

985.0

2819.0

Zhenshan 97B

1291.0

868.0

710.0

2869.0

Mean

1322.0

1139.0

1173.0

3623.0

7.5

8.4

10.8

14.8

CD (0.05)

126.3

120.2

159.8

686.7

CD (0.01)

168.1

159.4

212.0

911.0

CV

Total (g/plant)

Table 25: Means of maximum root length (MRL), root number (RN) and root length density across different soil depths of OryzaSNP panel rice accessions under drought stress and well-watered treatment during WS 2008.

Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Nipponbare Pokkali Rayada Sadu Cho SHZ 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

Drought stress RLD (cm/cm3)

MRL (cm)

RN

93.60 93.80 85.60 93.40 94.60 91.20 76.80 64.60 89.20 86.30 91.00 92.60 81.60 86.40 83.30 83.20 70.60 81.40 87.50 70.60 84.90 7.16 7.61 10.10

100.80 49.00 81.40 160.30 141.30 99.80 86.40 122.40 131.40 115.60 71.70 129.00 106.40 128.00 95.00 87.20 120.60 70.60 138.00 113.60 107.40 11.15 15.00 19.90

0-30cm

30-45cm

45-60cm

0.70 0.59 0.61 1.10 1.20 0.60 1.43 1.12 0.90 0.60 0.39 1.16 0.45 1.60 1.95 0.82 0.46 1.45 0.64 0.73 0.93 6.18 0.07 0.09

2.41 1.31 1.27 1.15 1.60 1.41 0.99 0.73 0.94 0.68 0.52 2.27 0.85 1.29 3.77 0.50 0.23 1.09 0.65 1.03 1.23 29.11 0.45 0.60

1.56 1.23 0.62 1.10 1.36 1.17 0.74 0.25 0.49 0.45 0.19 1.78 0.14 0.45 1.84 0.32 0.04 0.35 0.45 0.73 0.76 26.38 0.25 0.33

60-100 cm 0.42 0.30 0.11 0.16 0.36 0.40 0.03 0.02 0.03 0.07 0.04 0.32 0.01 0.03 0.37 0.05 0.00 0.07 0.10 0.11 0.15 31.74 0.05 0.08

Well-watered RLD (cm/cm3)

MRL (cm)

RN

56.00 43.00 36.00 55.00 56.70 47.00 35.50 35.50 48.00 47.50 43.00 53.50 27.50 43.50 47.70 48.00 48.00 44.50 42.00 50.00 44.90 14.67 4.24 5.65

286.30 114.80 164.70 328.30 252.30 400.00 197.80 311.30 296.00 314.00 192.00 289.80 192.50 349.70 457.30 373.70 303.00 274.00 418.30 347.30 293.10 8.34 34.60 46.05

0-30cm

30-45cm

45-60cm

1.91 2.07 2.54 2.77 2.50 4.51 2.23 2.09 2.84 1.54 2.40 5.01 1.12 3.62 12.87 4.90 2.48 3.44 3.42 2.85 3.35 9.42 0.44 0.59

0.73 1.51 0.04 0.88 1.61 1.58 0.07 0.12 0.38 0.63 0.29 2.03 0.00 1.81 1.02 0.50 0.43 0.86 0.41 0.62 0.78 20.21 0.22 0.29

0.05 0.77 0.00 0.20 0.15 0.22 0.00 0.00 0.01 0.06 0.00 0.16 0.00 0.00 0.00 0.07 0.06 0.01 0.00 0.04 0.09 14.22 0.01 0.02

60-100 cm 0.00 0.01 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 25.80 0.00 0.00

Table 26: Means of root surface area (RSA, cm2) across different soil depths of OryzaSNP panel rice accessions measured under both drought stress and well-watered treatment in lysimetric experiment during WS2008 RSA (cm2) Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Nipponbare Pokkali Rayada Sadu Cho Shz 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

0-30cm 762.70 656.20 601.60 1067.00 1113.10 653.30 1262.40 1199.60 887.50 600.10 505.80 1117.50 461.80 1415.80 1583.10 749.00 455.20 1243.80 658.80 822.10 890.80 7.42 82.75 109.77

Drought stress 30-45cm 45-60cm 697.50 506.20 453.40 412.30 375.30 199.30 377.10 356.30 595.10 434.90 443.70 376.50 303.60 195.10 242.40 87.80 311.10 146.00 206.50 136.20 223.20 74.40 699.80 579.20 273.30 38.10 392.90 137.10 1150.20 584.40 157.50 100.60 62.10 14.70 359.10 112.60 215.60 157.20 352.10 223.60 394.60 243.60 11.61 18.12 57.57 55.67 76.37 73.85

60-100cm 381.30 314.10 106.20 128.40 331.70 350.40 25.60 18.10 24.30 63.80 56.60 309.40 10.90 26.90 288.50 42.90 2.10 71.90 101.80 101.50 137.80 22.35 39.26 52.08

0-30cm 2545.10 2332.20 2679.20 2885.50 2981.60 4779.10 2451.20 1871.70 2485.50 1542.20 2504.30 4965.20 949.00 3670.00 11025.60 4506.80 2392.20 3334.40 3257.90 2833.70 3299.60 12.22 571.02 759.92

Well-watered 30-45cm 45-60cm 395.60 34.00 581.20 226.80 19.30 0.00 382.70 90.20 713.10 77.20 763.40 147.10 40.80 0.00 61.20 0.00 148.00 8.70 328.90 37.60 130.40 0.00 820.60 98.80 0.00 0.00 972.05 0.00 483.10 0.00 197.40 44.40 197.00 37.20 355.20 3.00 202.50 0.00 263.90 32.50 352.80 41.90 28.45 8.38 140.69 4.88 187.23 6.50

60-100cm 0.00 9.29 0.00 21.81 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.78 0.00 0.00 0.00 1.74 21.95 0.52 0.69

Table 27: Means of root volume (RV, cm3) across different soil depths of OryzaSNP panel rice accessions measured under both drought stress and well-watered treatment in lysimetric experiment during WS 2008 RV (cm3) Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Nipponbare Pokkali Rayada Sadu Cho Shz 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

0-30cm 7.96 6.94 5.73 9.94 10.27 6.67 10.64 12.20 8.34 5.72 6.30 10.70 4.51 11.88 12.68 6.58 4.44 10.13 6.52 8.71 8.34 9.97 1.04 1.39

Drought stress 30-45cm 45-60cm 3.81 3.07 2.95 2.61 2.08 1.21 2.32 2.17 4.34 2.61 2.64 2.29 1.75 0.98 1.54 0.58 1.96 0.81 1.19 1.03 1.87 0.54 4.07 3.55 1.65 0.19 2.26 0.79 6.58 3.48 0.95 0.60 0.31 0.10 2.23 0.68 1.35 1.03 2.26 1.30 2.41 1.48 14.23 16.57 0.43 0.30 0.57 0.40

60-100cm 2.47 2.35 0.70 0.71 2.14 2.19 0.16 0.10 0.17 0.40 0.54 2.12 0.09 0.17 1.60 0.30 0.02 0.54 0.77 0.63 0.91 18.35 0.20 0.27

0-30cm 33.37 25.82 26.87 28.30 35.43 48.17 25.78 16.29 20.65 14.64 24.82 46.89 7.61 35.39 89.78 39.71 22.02 31.10 29.45 26.79 31.45 9.62 4.28 5.69

Well-watered 30-45cm 45-60cm 4.19 0.40 4.29 1.27 0.18 0.00 3.20 0.77 6.02 0.77 6.97 1.87 0.46 0.00 0.57 0.00 1.10 0.15 3.24 0.44 1.12 0.00 6.25 1.21 0.00 0.00 9.84 0.00 4.29 0.00 1.46 0.56 1.74 0.50 2.74 0.02 1.89 0.00 2.10 0.45 3.08 0.42 6.72 32.31 0.28 0.19 0.37 0.25

60-100cm 0.00 0.11 0.00 0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.01 21.80 0.00 0.01

Table 28: Means of root dry weight (RDW, g) across different soil depths of OryzaSNP panel rice accessions measured under both drought stress and well-watered treatment in lysimetric experiment during WS 2008 RDW (g) Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Nipponbare Pokkali Rayada Sadu Cho SHZ 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

0-30cm 0.51 0.44 0.43 0.62 0.72 0.51 0.73 0.66 0.51 0.64 0.45 0.86 0.26 0.69 0.74 0.42 0.27 0.57 0.44 0.51 0.55 10.48 0.07 0.09

Drought stress 30-45cm 45-60cm 0.22 0.17 0.21 0.11 0.13 0.08 0.15 0.11 0.24 0.25 0.15 0.13 0.08 0.02 0.08 0.03 0.09 0.04 0.14 0.07 0.06 0.03 0.25 0.20 0.07 0.01 0.10 0.03 0.37 0.16 0.07 0.04 0.03 0.01 0.09 0.04 0.06 0.05 0.09 0.06 0.13 0.08 16.64 14.98 0.02 0.01 0.03 0.02

60-100cm 0.09 0.16 0.04 0.03 0.29 0.13 0.01 0.01 0.00 0.06 0.01 0.25 0.00 0.01 0.06 0.04 0.00 0.01 0.02 0.03 0.06 23.50 0.02 0.03

0-30cm 1.77 0.82 1.46 2.27 2.28 3.63 1.51 0.84 1.26 1.22 1.74 3.64 0.39 2.44 5.84 2.29 1.40 1.59 1.68 1.43 1.97 13.31 0.37 0.49

Well-watered 30-45cm 45-60cm 0.15 0.01 0.22 0.18 0.01 0.00 0.15 0.03 0.31 0.03 0.36 0.06 0.01 0.00 0.01 0.00 0.05 0.00 0.16 0.02 0.03 0.00 0.26 0.02 0.00 0.00 0.46 0.00 0.23 0.01 0.06 0.01 0.06 0.00 0.09 0.00 0.05 0.00 0.07 0.00 0.14 0.02 10.68 26.33 0.02 0.01 0.03 0.01

60-100cm 0.00 0.14 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 35.70 0.00 0.01

Table 29: Means of root and shoot traits of OryzaSNP panel accessions under both drought stress and well-watered conditions in lysimetric experiment during WS 2008.

Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Nipponbare Pokkali Rayada Sadu Cho SHZ 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

Plant height (cm) 140.40 144.50 115.80 133.80 130.60 105.00 89.30 117.20 97.80 89.60 116.00 124.40 76.80 127.20 93.80 111.20 90.90 78.40 90.20 89.20 108.10 5.10 6.95 9.22

NA = not available

Drought stress Stomatal Tiller conductance Number ( mol m-2s-1) 14.00 48.60 6.50 68.00 9.40 58.40 11.25 120.33 12.60 69.26 14.20 66.33 17.80 59.34 15.00 100.00 12.60 88.00 18.80 56.67 5.33 181.33 14.20 92.67 15.80 190.33 15.80 121.00 27.00 138.50 16.40 70.50 14.00 101.33 21.60 90.75 7.60 115.67 11.00 41.33 14.00 93.90 13.08 31.82 2.30 37.63 3.06 49.93

Shoot dry weight (g/plant) 24.76 22.49 20.81 21.89 28.47 25.04 10.70 17.06 19.35 15.66 17.19 25.07 14.12 23.30 19.83 15.87 13.83 16.50 13.73 13.84 19.00 6.86 1.64 2.18

Plant height (cm) 165.00 152.50 125.70 148.70 145.40 141.30 111.00 134.30 108.10 114.30 140.30 140.60 78.30 165.70 112.00 146.70 105.70 86.20 121.70 100.70 127.20 7.72 13.90 18.50

Well-watered Stomatal Tiller conductance Number ( mol m-2s-1) 24.67 387.33 13.00 233.25 13.67 436.67 19.33 488.33 18.67 432.50 28.00 470.00 29.00 577.50 21.67 353.00 16.67 458.75 30.00 523.33 8.33 385.67 NA 357.50 15.33 461.67 20.33 497.00 40.67 426.67 24.00 468.33 25.67 473.33 26.00 563.33 15.00 505.00 19.67 590.00 21.60 454.50 5.60 23.30 1.65 149.79 2.20 199.34

Shoot dry weight (g/plant) 50.04 46.98 24.85 25.89 52.26 48.92 38.74 24.48 21.79 46.49 23.69 46.84 13.70 55.61 55.30 44.07 44.77 28.30 26.62 36.50 37.80 5.71 3.05 4.06

Table 30: Water uptake rate (g) measured at different intervals using OryzaSNP panel accessions under drought stress condition in lysimetric experiment during DS 2009. Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH Minghui 63 Moroberekan N22 Nipponbare Pokkali Rayada Sadu Cho Shan Huang Zhan 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

Water uptake (g/plant)-Days after stress imposition 5

8

12

15

18

23

690.0 533.3 606.0 405.0 732.0 524.0 604.0 990.0 662.0 637.5 784.0 452.5 602.0 632.0 437.5 680.0 660.0 606.0 528.0 619.2 6.7 52.3 69.5

687.5 663.3 330.0 532.5 796.0 632.0 542.0 1053.3 622.0 447.5 644.0 452.5 586.0 692.0 635.0 730.0 716.0 652.0 564.0 630.4 6.0 47.6 63.2

662.5 650.0 452.0 435.0 702.0 522.0 430.0 686.6 562.0 495.0 806.0 360.0 500.0 550.0 487.5 608.0 552.0 572.0 494.0 554.0 14.8 102.7 136.4

737.5 873.3 506.0 552.5 902.0 718.0 544.0 703.3 656.0 615.0 814.0 537.5 600.0 672.0 667.5 722.0 718.0 640.0 546.0 669.7 8.1 69.1 91.7

572.5 640.0 412.0 365.0 622.0 480.0 370.0 416.6 478.0 492.5 594.0 370.0 462.0 502.0 412.5 478.0 448.0 458.0 382.0 471.3 10.7 64.1 85.0

655.0 793.3 418.0 450.0 844.0 624.0 424.0 453.3 558.0 537.5 710.0 482.5 580.0 566.0 547.5 578.0 608.0 504.0 434.0 566.6 9.7 69.6 92.5

Total (g/plant) 4005.0 4153.3 2724.0 2740.0 4598.0 3500.0 2914.0 4303.3 3538.0 3225.0 4352.0 2655.0 3330.0 3614.0 3187.5 3796.0 3702.0 3432.0 2948.0 3511.4 19.9 883.0 1172.0

Table 31: Means of root parameters of OryzaSNP panel rice accessions under both drought stress and well-watered conditions in lysimetric experiment during DS 2009.

Drought stress

Well-watered

Root length density (cm cm-3)

Root surface area (cm2)

Root volume (cm3)

Root dry weight (g)

Root length density (cm cm-3)

Root surface area (cm2)

Root volume (cm3)

Root dry weight (g)

Aswina

0.76

3567.69

38.97

1.59

1.25

6498.90

78.53

2.41

Azucena

0.65

3621.13

46.59

1.65

0.25

1469.36

19.74

0.83

Cypress

0.34

1735.85

20.27

0.80

0.25

1382.91

17.80

0.42

Dular

1.32

6697.40

78.79

2.91

1.34

7483.93

97.30

3.33

IR 64

0.79

3731.76

41.20

0.98

1.15

6707.94

91.99

2.67

Moroberekan

0.81

4650.10

62.45

1.52

0.46

2301.06

26.79

1.31

N22

0.60

2924.65

33.06

1.31

0.68

3845.76

50.36

1.70

Nipponbare

0.29

1362.74

14.68

0.71

0.36

1486.85

14.63

0.73

Swarna

0.95

4850.63

56.95

1.65

1.25

7146.52

98.64

2.51

Mean

0.72

3682.44

43.66

1.46

0.78

4258.14

55.09

1.77

CV

11.05

11.99

2.92

14.67

26.46

25.80

12.50

27.91

CD (0.05)

0.10

573.93

7.27

0.27

0.26

1415.46

8.87

0.63

CD (0.01)

0.14

771.59

9.77

0.37

0.35

1902.95

11.93

0.85

Genotypes

Table 32: Means of shoot parameters of OryzaSNP panel accessions under drought stress and well-watered environment in lysimetric experiment during DS 2009. Well-watered Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH Minghui 63 Moroberekan N22 Nipponbare Pokkali Rayada Sadu Cho Shz 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

Plant height (cm)

Tiller number

84.60 92.67 86.50 115.80 109.80 82.75 74.00 87.50 65.60 92.67 93.33 54.80 105.20 68.60 101.50 73.50 63.50 69.20 75.60 84.06 12.29 13.02 17.28

12.40 6.00 3.75 9.20 9.00 12.00 12.75 8.00 12.60 4.33 13.33 16.00 6.80 39.00 12.00 15.25 14.00 4.40 10.60 11.65 14.62 2.14 2.84

Drought stress Shoot dry weight (g/plant) 17.98 8.00 8.33 19.56 20.46 19.34 16.36 6.81 17.30 9.05 12.63 10.05 11.06 21.24 20.49 18.47 11.60 8.10 17.56 14.44 12.93 2.35 3.12

Plant height (cm)

Tiller number

71.60 66.75 55.75 67.00 86.20 64.60 48.80 79.67 54.80 74.20 79.60 39.60 93.00 63.40 72.00 59.60 46.40 58.60 54.20 65.04 15.61 12.80 16.99

8.80 3.50 4.75 4.40 9.60 8.20 9.60 17.00 11.40 2.40 10.60 9.60 6.20 17.20 8.25 11.80 18.40 11.20 6.40 9.44 12.25 1.45 1.92

Shoot dry weight (g/plant) 6.17 5.19 2.58 3.75 10.30 5.09 3.78 14.14 5.46 3.09 7.41 4.92 6.30 6.13 4.84 6.38 6.82 7.03 3.36 5.93 12.94 0.96 1.28

Table 33: Water uptake rate measured at different intervals using OryzaSNP panel rice accessions under drought stress treatment in lysimetric experiment during WS 2009.

Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Nipponbare Pokkali Rayada Sadu Cho Shan Huang Zhan 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

Water uptake (g/plant)-Days after stress imposition 7 14 21 25 28 33 910.0 1608.2 1047.2 769.8 636.2 980.8 865.5 1374.0 1194.5 879.7 471.5 986.0 865.8 1223.2 830.8 666.4 394.4 744.6 808.0 1045.0 894.8 606.8 464.8 660.0 1046.0 1598.2 1360.4 1043.2 636.6 1074.8 693.0 1529.4 876.4 682.0 471.4 696.4 786.0 1333.2 724.6 563.0 363.4 639.4 907.0 1060.8 718.5 443.3 269.4 331.2 885.4 1044.6 625.0 531.4 316.6 537.2 987.8 1589.0 989.6 665.6 349.6 687.6 905.0 1307.2 1030.4 638.2 357.4 830.6 1129.4 1442.4 1014.2 763.4 472.2 706.6 589.0 824.8 470.6 402.8 292.4 397.4 912.2 1409.8 1286.0 884.8 511.4 1137.0 1302.5 1674.5 1156.0 841.6 582.2 797.4 1191.3 1426.0 1101.0 778.2 557.6 779.6 953.6 1254.8 904.0 642.0 472.0 635.8 822.2 1111.8 692.2 555.4 359.8 744.0 802.6 943.6 812.0 508.0 407.6 746.4 985.6 1465.4 1043.2 650.2 456.6 669.0 917.4 1313.3 938.6 675.8 442.2 739.1 12.1 8.0 11.7 14.9 20.9 13.8 139.9 131.4 135.8 126.9 116.3 129.0 185.6 174.3 180.2 168.3 154.3 171.2

35 284.0 164.8 238.6 278.0 255.4 347.2 215.0 177.6 175.6 220.2 297.0 279.0 248.0 215.4 312.2 254.8 397.4 212.8 197.8 162.4 246.7 17.2 52.7 69.9

Total (g/plant) 6236.2 5936.0 4963.8 4757.4 7014.6 5157.2 4624.6 3908.0 4115.8 5489.4 5366.4 5807.2 3225.0 6356.6 6658.0 5995.3 5259.6 4498.2 4418.0 5432.4 5261.0 10.46 465.66 614.22

Table 34: Mean total water uptake (TWU, g/plant) during stress period under drought stress condition using OryzaSNP panel rice genotypes belonging to different rice types in three lysimetric experiments (WS2008 /IRRI; DS 2009 /ICRISAT; WS 2009/ IRRI).

Season/Location

WS 2008/IRRI, Philippines

DS 2009/ICRISAT, India

WS 2009/IRRI, Philippines

Stress duration

21 (7-28DAS)

23 (1-23DAS)

35 (1-35DAS)

No of genotypes

TWU (g/plant)

SE

No of genotypes

TWU (g/plant)

SE

No of genotypes

TWU (g/plant)

SE

Aus

3

4530.0

240.0

3

4100.0

300.0

3

6030.0

510.0

Deep water

2

4650.0

110.0

2

3800.0

190.0

2

6450.0

210.0

Temperate japonica

4

3390.0

90.0

3

3460.0

470.0

4

3910.0

250.0

Tropical japonica

3

3920.0

330.0

3

3360.0

420.0

3

5420.0

280.0

Indica

7

3070.0

270.0

7

3350.0

120.0

7

5390.0

260.0

Aromatic

1

3960.0

-

1

2740.0

-

1

4750.0

-

Types

DAS=Dayes after stress

Table 35: Analysis of variance for real time water uptake rates of OryzaSNP rice panel accessions under drought stress condition in three lysimetric experiments (WS 2008, DS 2009 and WS 2009) Sources of variation df Water uptake- 7 DAS Water uptake- 14 DAS Water uptake- 21 DAS Total water uptake df Water uptake- 5 DAS Water uptake- 8 DAS Water uptake- 12 DAS Water uptake- 15 DAS Water uptake- 18 DAS Water uptake- 23 DAS Total water uptake df Water uptake- 7 DAS Water uptake- 14 DAS Water uptake- 21 DAS Water uptake- 25 DAS Water uptake- 28 DAS Water uptake- 33 DAS Water uptake- 35 DAS Total water uptake

Replications WS 2008 4 2825.46 20215.10 29506.30 1761.49 DS 2009 4 2805.23 504.58 3589.05 696.37 2527.87 415.22 559375.00 WS 2009 4 20524.10 40845.90 17578.60 12175.70 4623.80 12989.10 2936.60 385988.00

Genotypes

Error

19 289060.67** 239145.84** 445833.77** 2379.56**

76 10122.60 9110.78 16101.90 2972.24

18 84827.64** 117658.43** 51704.52** 65950.49** 34597.89** 70869.81** 1707722.80**

72 1725.02 1427.72 6645.29 3007.62 2586.12 3056.08 490528.00

19 125648.10** 301172.60** 206369.20** 123197.90** 55286.30** 197245.80** 29681..50** 1456685.60**

76 12337.90 10886.40 11632.50 10149.20 8533.30 10502.10 1754.20 574049.00

Table 36: Analysis of variance for root and shoot characters of OryzaSNP rice panel accessions under drought stress and wellwatered condition in lysimetric experiment during WS 2008 Sources of variation

Drought stress Genotypes 19 9.333** 3.262** 1.573** 0.108** 529625.40** 297331.12** 163344.61** 94253.43** 32.82** 10.20** 5.69** 3.83** 0.13** 0.04** 0.02** 0.04** 373.26** 3835.41** 8478.97** 2165.60** 123.27** 113.33**

df RLD at 0-30cm RLD at 30-45cm RLD at 45-60cm RLD at 60-100cm RSA at 0-30cm RSA at 30-45cm RSA at 45-60cm RSA at 60-100cm RV at 0-30cm RV at 30-45cm RV at 45-60cm RV at 60-100cm RDW at 0-30cm RDW at 30-45cm RDW at 45-60cm RDW at 60-100cm Maximum root length Root Number Stomatal Conductance Plant height Tiller Number Shoot Dry weight

Replications 4 0.082 0.016 0.04 0.002 2548.91 734.34 747.69 238.55 0.6 0.28 0.09 0.017 0.003 0.0006 0.0001 0.0005 24.486 146.144 1974.13 35.003 5.388 1.131

RLD-Root length density

RSA-Root surface area

Error 76 0.325 0.129 0.04 0.002 4316.09 2088.94 1953.26 971.56 0.69 0.11 0.05 0.027 0.003 0.0004 0.0001 0.0002 36.589 141.672 892.827 30.451 3.361 1.707

RV-Root volume

Replications 3 0.001 0.051 0.0002 0.000003 317657.5 2215.42 4.83 0.103 4.08 0.089 0.065 0.000009 0.027 0.0001 0.00001 9E-06 1.45 1506.11 3783.75 110.89 3.36 4.72

Well-watered Genotypes 19 24.37** 1.567*8 0.115** 0.0001** 17289062.6** 326519.5** 13989.13** 98.762** 1139.93** 24.50** 1.132** 0.0069** 6.038** 0.072** 0.007** 0.005** 208.18** 31283.82** 30034.88** 2446.48** 251.53** 668.74**

RDW- Root dry weight

Error 57 0.099 0.024 0.0001 0.000002 162633.4 9872.9 11.9 0.137 9.13 0.04 0.018 0.00001 0.068 0.0002 0.00002 9E-06 8.99 597.2 11191.1 96.37 1.36 4.66

Table 37: Analysis of variance root and shoot characters of OryzaSNP rice panel accessions under drought stress and wellwatered condition in lysimetric experiment during DS 2009

Drought stress

Well-watered

Sources of variation Replications

Genotypes

Error

Replications

Genotypes

Error

4

8

32

4

8

32

RDW at 0-100cm

0.027

2.157**

0.045

0.291

5.137**

0.243

RLD at 0-100cm

0.008

0.484**

0.006

0.104

1.089**

0.042

RSA at 0-100cm

331401

12401893.20**

198468

2223429

35934671.35**

1207154

RV at 0-100cm

38.03

2062.43**

31.84

15.32

6681.37**

47.44

4

18

72

4

18

72

137.019

966.285**

103.134

159.124

1399.651**

106.661

Tiller Number

3.392

103.39**

1.32

0.537

290.610**

2.892

Shoot Dry Weight

0.337

36.188**

0.586

9.92

130.02**

3.48

df

df Plant Height

RLD-Root length density

RSA-Root surface area

RV-Root volume

RDW- Root dry weight

Table 38: Mean, range and genetic parameters for root characters of OryzaSNP panel rice accessions under drought stress and well-watered conditions in lysimetric trial during WS 2008. Range Characters

Mean

Min.

RLD at 0-30cm RLD at 30-45cm RLD at 45-60cm RLD at 60-100cm RSA at 0-30cm RSA at 30-45cm RSA at 45-60cm RSA at 60-100cm RV at 0-30cm RV at 30-45cm RV at 45-60cm RV at 60-100cm RDW at 0-30cm RDW at 30-45cm RDW at 45-60cm RDW at 60-100cm Maximum root length Root Number

0.93 1.23 0.76 0.15 890.82 394.59 243.62 137.82 8.34 2.41 1.48 0.91 0.55 0.13 0.08 0.06 84.52 106.97

0.39 0.23 0.04 0.00 455.19 62.11 14.69 2.14 4.44 0.31 0.10 0.02 0.26 0.03 0.01 0.00 64.60 49.00

RLD at 0-30cm RLD at 30-100cm RSA at 0-30cm RSA at 30-100cm RV at 0-30cm RV at 30-100cm RDW at 0-30cm RDW at 30-100cm Maximum root length Root Number

3.36 0.86 3299.63 394.77 31.45 3.51 1.97 0.16 44.95 293.14

1.12 0.00 948.99 0.00 7.62 0.00 0.39 0.00 27.5 114.75

RLD-Root length density RDW- Root dry weight

Max.

PCV (%)

Drought stress 1.95 47.13 3.77 70.42 1.84 77.59 0.42 103.15 1583.12 54.66 1150.21 62.82 584.37 75.85 381.29 100.45 12.68 50.43 6.58 60.72 3.55 74.48 2.47 97.96 0.86 46.28 0.37 65.41 0.25 86.17 0.29 135.79 94.6 12.06 160.25 27.79 Well-watered 5.01 74.03 2.29 85.18 4965.17 63.89 972.08 81.54 89.78 54.35 9.84 83.45 5.84 63.34 0.54 102.66 56.75 17.06 457.25 31.05

RSA-Root surface area

GCV (%)

h2 bs (%)

GAM

46.72 64.12 72.97 98.14 32.07 61.74 73.66 97.93 23.51 59.03 72.62 96.23 22.65 63.26 84.86 133.74 9.71 25.46

98.28 82.91 88.44 90.53 34.43 96.58 94.29 95.05 21.73 94.51 95.05 96.49 23.96 93.53 96.98 97.00 64.79 83.91

95.42 120.27 141.37 192.37 38.77 124.99 147.34 196.68 22.57 118.21 145.84 194.72 22.84 126.04 172.15 271.34 16.10 48.03

73.42 84.13 62.71 80.90 53.49 82.92 61.92 102.06 15.70 29.91

98.38 97.57 96.34 98.42 96.87 98.73 95.59 98.83 84.71 92.78

150.02 171.19 126.80 165.32 108.45 169.73 124.71 209.00 29.77 59.34

RV-Root volume

Table 39: Mean, range and genetic parameters for shoot characters of OryzaSNP panel rice accessions under drought stress and well- watered conditions in lysimetric experiment during WS 2008. Range Characters Mean

Minimum

Maximum

PCV (%)

GCV (%)

h2 bs (%)

GA as per cent of mean

Drought stress Stomatal conductance

93.92

41.33

190.33

52.27

41.47

62.95

67.79

Plant height

108.11

76.80

144.50

19.78

19.11

93.34

38.04

Tiller number

14.04

5.33

27.00

37.31

34.94

87.71

67.42

Shoot dry weight

19.09

10.70

28.47

25.72

24.79

92.89

49.22

3623.00

2506.00

5058.00

23.02

17.58

58.36

27.67

Total water uptake

Well-watered Stomatal conductance

453.98

233.25

590.00

27.78

15.12

29.63

16.95

Plant height

127.20

78.27

165.67

20.56

19.06

85.91

36.38

Tiller number

20.88

8.00

40.67

38.28

37.87

97.86

77.17

Shoot dry weight

37.79

13.70

55.61

34.57

34.09

97.27

69.27

Table 40: Mean, range and genetic parameters for root and shoot characters of OryzaSNP panel rice accessions under both drought stress and well-watered condition in lysimetric experiment during DS 2009. Range Characters

Mean

Minimum

Maximum

PCV (%)

GCV (%)

h2 bs (%)

GA as per cent of mean

Drought stress Plant height

65.04

39.60

93.00

25.53

20.20

62.60

32.93

Tiller number

9.44

2.40

18.40

49.57

48.03

93.89

95.88

Root dry weight at 0-100cm

1.46

0.71

2.91

46.95

44.61

90.25

87.30

Root length density at 0-1000cm

0.72

0.29

1.32

44.13

42.72

93.73

85.20

3682.44

1362.74

6697.40

43.72

42.02

92.48

83.29

Root volume at 0-100cm

43.66

14.68

78.79

47.93

46.15

92.73

91.55

Shoot dry weight

5.92

2.58

14.14

46.88

45.06

92.38

89.22

3511.43

2655.00

4598.00

24.39

14.05

33.17

16.66

Root surface area 0-100cm

Total water uptake

Well-watered Plant height

84.06

54.80

115.80

22.74

19.13

70.80

33.15

Tiller number

11.65

3.75

39.00

66.78

65.16

95.21

130.98

Root dry weight at 0-100cm

1.77

0.42

3.33

62.53

55.96

80.08

103.16

Root length density at 0-1000cm

0.78

0.25

1.34

64.55

58.88

83.20

110.65

4258.14

1382.91

7483.93

67.05

61.89

96.55

117.67

Root volume at 0-100cm

55.09

14.63

98.65

67.29

66.12

81.36

133.83

Shoot dry weight

14.44

6.81

21.24

37.15

34.83

87.89

67.27

Root surface area 0-100cm

Table 4: Mean climatic conditions at experimental site during 1-112 days after transplanting

DS 2009,

Max Temp (oC)

Min Temp (oC)

Mean Temp (oC)

Solar Radiation (mj/ m2)

Bright Sunshine (Hrs)

37.68

22.06

29.874

20.85

9.00

ICRISAT, India

Figure 5: Rainfall pattern during stress period at experimental site during DS 2009, ICRISAT, India.

Table 41: Phenotypic correlation co-efficients among water uptake, root distribution and shoot traits of OryzaSNP panel rice accessions under drought stress in lysimetric experiment during WS 2008. X2 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X1 X3 X4 1.00

X1 X2 X3 X4

X20

0.54*

0.37

0.15

0.69**

0.57**

0.35

0.09

0.57**

0.55*

0.33

0.04

0.48*

0.45*

0.25

-0.01

-0.09

0.01

0.04

0.16

1.00

0.78**

0.73**

0.47*

0.91**

0.78**

0.64**

0.42

0.87**

0.79**

0.62**

0.36

0.85**

0.65**

0.49*

0.24

0.24

0.46*

0.52*

1.00

0.87**

0.36

0.84**

0.96**

0.86**

0.33

0.83**

0.95**

0.82**

0.43

0.86**

0.86**

0.73**

0.39

0.44*

0.57**

0.65**

1.00

0.20

0.75**

0.89**

0.94**

0.23

0.74**

0.90**

0.92**

0.24

0.80**

0.86**

0.81**

0.48*

0.48*

0.59**

0.74**

1.00

0.40

0.30

0.11

0.92**

0.41

0.26

0.06

0.71**

0.34

0.22

0.06

-0.01

0.11

0.14

0.23

1.00

0.86**

0.71**

0.36

0.96**

0.85**

0.67**

0.36

0.91**

0.74**

0.54*

0.27

0.27

0.49*

0.56**

1.00

0.89**

0.30

0.84**

0.96**

0.85**

0.39

0.89**

0.89**

0.74**

0.42

0.49*

0.59**

0.69**

1.00

0.14

0.72**

0.89**

0.97**

0.22

0.78**

0.87**

0.85**

0.50*

0.52*

0.62**

0.74**

1.00

0.36

0.26

0.09

0.73**

0.30

0.23

0.11

0.00

0.19

0.19

0.27

1.00

0.84**

0.69**

0.37

0.90**

0.76**

0.55*

0.30

0.29

0.51*

0.58**

1.00

0.87**

0.35

0.89**

0.90**

0.76**

0.44*

0.49*

0.59**

0.70**

1.00

0.20

0.74**

0.85**

0.88**

0.52*

0.53*

0.63**

0.74**

1.00

0.40

0.35

0.23

0.02

0.18

0.20

0.28

1.00

0.83**

0.65**

0.33

0.39

0.59**

0.62**

1.00

0.87**

0.48*

0.53*

0.62**

0.79**

1.00

0.50*

0.59*

0.65**

0.75**

1.00

0.49*

0.52*

0.55*

1.00

0.65**

0.72**

1.00

0.69**

X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19

1.00

X20 X1-Root length density at 0-30cm X5-Root surface area at 0-30cm X9-Root volume at 0-30cm X13-Root dry weight at 0-30cm X17-Maximum root length

X2- Root length density at 30-45cm X6- Root surface area at 30-45cm X10- Root volume at 30-45cm X14- Root dry weight at 30-45cm X18- Plant height

X3- Root length density at 45-60cm X7- Root surface area at 45-60cm X11- Root volume at 45-60cm X15- Root dry weight at 45-60cm X19- Total water uptake

X4- Root length density at 60-100cm X8- Root surface area at 60-100cm X12- Root volume at 60-100cm X16- Root dry weight at 60-100cm X20- Shoot dry weight

Table 42: Phenotypic correlation co-efficients among water uptake, root distribution and shoot traits of OryzaSNP panel rice accessions under well watered condition in lysimetric experiment during WS 2008.

Characters RLD at 0-30cm RLD at 30-100cm RSA at 0-30cm RSA at 30-100cm RV at 0-30cm RV at 30-100cm RDW at 0-30cm RDW at 30-100cm MRL RN PH TN SDW

RLD at 0-30cm 1                        

RLD at 30-100cm 0.23 1                      

RSA at 0-30cm 0.98** 0.31 1                    

RSA at 30100cm 0.25 0.97** 0.33 1

RV at 030cm 0.93** 0.41 0.96** 0.45* 1

RV at 30100cm 0.25 0.90** 0.33 0.97** 0.45* 1

RDW at 0-30cm 0.88** 0.42 0.91** 0.49* 0.94** 0.51* 1

RDW at 30-100cm 0.21 0.93** 0.28 0.93** 0.38 0.90** 0.38 1

MRL

RN

PH

TN

SDW

0.59** 0.09 0.58** 0.2 0.54* 0.28 0.59** 0.06 1

0.22 0.49* 0.29 0.53* 0.38 0.52* 0.41 0.37 0.34 1

-0.02 0.50* 0.07 0.53* 0.21 0.56** 0.2 0.54* 0.04 0.39 1

0.50* -0.09 0.49* 0 0.46* 0.07 0.42 0.02 0.51* 0.11 -0.19 1

0.41 0.66** 0.48* 0.72** 0.58** 0.74** 0.55* 0.69** 0.31 0.58** 0.45* 0.45* 1

* and ** indicates significant at 5 %and 1 % level respectively

RLD=Root length density RSA=Root surface area TN=Tiller number SDW= Shoot dry weight

RV=Root volume

RDW= Root dry weight

MRL=Maximum root length

RN=Root number

PH=Plant height

Table 43: Phenotypic correlation co-efficients among root and shoot traits of OryzaSNP panel rice accessions under drought stress in lysimetric trial during DS 2009. Characters Total water uptake

Total water uptake 1.00

RDW at 0-100cm

RDW at 0-100cm 0.65

RLD at 0-100cm 0.54

RSA at 0-100cm 0.54

RV at 0-100cm 0.53

PH

TN

SDW

0.58

0.13

0.64

1.00

0.75*

0.84**

0.81**

0.64

0.09

0.72*

1.00

0.92**

0.89**

0.43

0.28

0.61

1.00

0.97**

0.49

0.20

0.61

1.00

0.51

0.09

0.50

1.00

-0.29

0.46

1.00

0.46

RLD at 0-100cm RSA at 0-100cm RV at 0-100cm PH TN

1.00

SDW

Table 44: Phenotypic correlation co-efficients among root and shoot traits of OryzaSNP panel rice accessions under well-watered condition in lysimetric experiment during DS 2009. Characters RDW at 0-100cm

RDW at 0-100cm 1.00

RLD at 0-100cm

RLD at 0-100cm 0.88**

RSA at 0-100cm 0.93**

RV at 0-100cm 0.90**

PH

TN

SDW

0.22

0.35

0.76*

1.00

0.95**

0.92**

0.08

0.40

0.75*

1.00

0.97**

0.09

0.40

0.74*

1.00

0.08

0.41

0.75*

1.00

-0.53

0.27

1.00

0.30

RSA at 0-100cm RV at 0-100cm PH TN

1.00

SDW

* and ** indicates significant at 5 %and 1 % level respectively RLD=Root length density RSA=Root surface area SDW= Shoot dry weight

RV=Root volume

RDW= Root dry weight PH=Plant height

TN=Tiller number

Table 45: Path analysis (phenotypic) indicating direct and indirect effects of component characters on shoot dry weight (SDW, g/plant) under drought stress using OryzaSNP panel rice accessions in lysimetric experiment during WS 2008

RLD at 3045cm

RLD at 4560cm

RLD at 60100cm

MRL

PH

TWU

Correlation with SDW (g/plant)

RLD at 30-45cm

0.085

-0.061

0.317

0.024

0.094

0.060

0.517

RLD at 45-60cm

0.066

-0.079

0.374

0.038

0.176

0.073

0.648

RLD at 60-100cm

0.062

-0.068

0.432

0.046

0.188

0.076

0.736

MRL

0.020

-0.031

0.295

0.097

0.192

0.067

0.554

PH

0.020

-0.035

0.205

0.047

0.396

0.084

0.717

TWU

0.039

-0.044

0.254

0.051

0.257

0.129

0.685

Table 46: Path analysis (phenotypic) indicating direct and indirect effects of component characters on shoot dry weight (g/plant) under well-watered condition using OryzaSNP panel rice accessions in lysimetric experiment during WS 2008.

RLD at 0-30cm

RLD 30-100cm

MRL

PH

TN

Correlation with SDW (g/plant)

RLD at 0-30cm

-0.005

0.114

0.040

-0.004

0.262

0.408

RLD 30-100cm

-0.001

0.506

0.090

0.113

-0.048

0.660

MRL

-0.001

0.250

0.182

0.089

0.059

0.578

PH

0.000

0.251

0.071

0.229

-0.098

0.452

TN

-0.002

-0.046

0.021

-0.043

0.521

0.450

RLD=Root length density MRL=Maximum root length PH=Plant height TWU=Total water uptake SDW= Shoot dry weight

TN=Tiller number

Table 47: Grouping of OryzaSNP panel rice accessions based on D2 analysis using root and shoot traits measured in drought stress treatment during WS2008

Cluster

No of genotypes

I

2

II

III

IV

V

7

Genotypes

Rice types

Origin

History

SHZ 2

Indica

China

Improved

Nipponbare

Temp Japonica

Japan

Improved

Dular

Aus

India

Traditional

N22

Aus

India

Traditional

FR13A

Aus

India

Traditional

Aswina

Deep water

Bangladesh

Traditional

Azucena

Trop Japonica

Philippines

Traditional

Cypress

Trop Japonica

USA

Improved

Dom Sufid

Aromatic

Iran

Traditional

Zhenshan 97B

Indica

China

Improved

IR 64

Indica

Philippines

Improved

Sadu Cho

Indica

Korea

Traditional

Minghui 63

Indica

China

Improved

M 202

Temp Japonica

USA

Improved

LTH

Temp Japonica

China

Traditional

Moroberekan

Trop Japonica

Guinea

Traditional

Tainung 67

Temp Japonica

Taiwan

Improved

Pokkali

Indica

India

Traditional

Swarna

Indica

India

Improved

Rayada

Deep water

Bangladesh

Traditional

6

2

3

Table 48: Grouping of OryzaSNP panel rice accessions based on D2 analysis using root and shoot traits measured in well watered treatment during WS 2008

Cluster

I

II

III

IV

V

No of genotypes

3

Genotypes

Rice types

Origin

History

Aswina

Deep water

Bangladesh

Traditional

SHZ 2

Indica

China

Improved

Zhenshan 97B

Indica

China

Improved

Cypress

Trop Japonica

USA

Improved

Moroberekan

Trop Japonica

Guinea

Traditional

Azucena

Trop Japonica

Philippines

Traditional

Dom Sufid

Aromatic

Iran

Traditional

Dular

Aus

India

Traditional

FR13A

Aus

India

Traditional

Pokkali

Indica

India

Traditional

IR 64

Indica

Philippines

Improved

LTH

Temp Japonica

China

Traditional

Minghui 63

Indica

China

Improved

M 202

Temp Japonica

USA

Improved

Tainung 67

Temp Japonica

Taiwan

Improved

Sadu Cho

Indica

Korea

Traditional

Swarna

Indica

India

Improved

2

5

5

2

VI

1

N22

Aus

India

Traditional

VII

1

Rayada

Deep water

Bangladesh

Traditional

VIII

1

Nipponbare

Temp Japonica

Japan

Improved

Table 49: Average intra and intercluster D2 values using root and shoot traits measured in drought stress treatment during WS 2008 Cluster

I

II

III

IV

V

I

34.20 (5.85)

306.01 (17.49) 121.43 (11.02)

181.86 (13.49) 218.91 (14.80) 121.26 (11.01)

103.71 (10.18) 206.37 (14.37) 213.35 (14.61) 108.90 (10.44)

651.77 (25.53) 452.76 (21.28) 317.92 (17.83) 669.21 (25.87) 229.90 (15.16)

II III IV V

Table 50: Average intra and intercluster D2 values using root and shoot traits measured in well-watered treatment during WS 2008 Cluster I II III IV V VI VII

I

II

III

IV

V

VI

VII

VIII

78.13

294.17

267.67

170.44

149.75

627.08

1509.12

524.37

(8.83)

(17.15)

(16.36)

(13.05)

(12.23)

(25.04)

(38.84)

(22.89)

42.52

515.05

250.62

331.92

677.52

1952.20

289.93

(6.52)

(22.69)

(15.83)

(18.21)

(26.02)

(44.18)

(17.02)

197.05

463.40

316.32

340.33

1383.05

963.74

(14.03)

(21.52)

(17.78)

(18.44)

(37.18)

(31.04)

192.27

212.52

923.17

1792.23

300.43

(13.86)

(14.57)

(30.38)

(42.33)

(17.33)

277.52

664.11

1223.72

548.76

(16.65)

(25.77)

(34.98)

(23.42)

0.00

1289.54

1556.39

(0.00)

(35.91)

(39.45)

0.00

2846.87

(0.00)

(53.35) 0.00

VIII

(0.00) * Values in the parenthesis indicate Average intra and intercluster D values

Table 51: The nearest and farthest clusters from each cluster based on D2 values using root and shoot traits measured in drought stress treatment during WS 2008

Cluster No.

Nearest cluster with D2 value

Farthest cluster with D2 value

I

IV

V

II

IV

V

III

IV

V

IV

III

V

V

III

IV

Table 52: The nearest and farthest clusters from each cluster based on D2 values using root and shoot traits measured in well watered treatment during WS 2008 . Cluster No.

Nearest cluster with D2 value

Farthest cluster with D2 value

I

V

VII

II

IV

VII

III

I

VII

IV

I

VII

V

I

VII

VI

III

VII

VII

V

VIII

VIII

III

VII

Table 53: Per cent contributions of twelve root and shoot characters towards diversity in drought stress and well-watered condition. Drought stress Sl. No.

Character

Well-watered Per cent contribution

Character

Per cent contribution

1

Total root number

1.05

Total root number

Trace

2

Root length density at 0-30cm

24.21

Root length density at 0-30cm

3.16

3

Root length density at 30-45cm

1.57

Root length density at 30-100cm

18.95

4

Root length density at 45-60cm

2.63

Maximum root length

1.58

5

Root length density at 60-100cm

3.68

Stomatal conductance

Trace

6

Maximum root length

0.52

Plant height

4.21

7

Stomatal conductance

5.78

Tiller number

20.00

8

Plant height

17.89

Shoot dry weight

9.47

9

Tiller number

4.73

Root shoot ratio

42.63

10

Total water uptake

1.05

11

Shoot dry weight

18.94

12

Root to shoot ratio

17.89

Total

100.00

Total

100.00

Table 54. The mean values of clusters for twelve root and shoot characters measured under drought stress treatment in lysimetric experiment during WS 2008 RLD at RLD at RLD at Maximum Total Shoot Root to Root RLD at Stomatal Plant Tiller Clusters 304560Root Water Dry Shoot Number 0-30cm Conductance Height Number 45cm 60cm 100cm Length Uptake Weight Ratio I

113.50

45.45

0.54

0.09

0.01

76.10

145.83

83.85

14.90

3.23

13.88

22.93

II

108.78

85.12

1.63

1.26

0.30

92.06

74.80

127.81

11.76

4.30

24.08

44.75

III

107.30

93.13

0.81

0.49

0.05

77.83

69.31

99.07

15.27

3.14

15.73

49.38

IV

104.83

51.35

0.59

0.32

0.07

89.40

148.50

103.10

6.47

3.45

15.46

37.04

V

97.87

166.00

2.05

0.88

0.16

82.67

116.75

99.80

21.20

3.69

19.88

48.08

Table 55.: The mean values of clusters for root and shoot characters using root and shoot traits measured in well-watered treatment during WS 2008 Root RLD at 0- RLD at 30Maximum Stomatal Plant Tiller Shoot Dry Root To Clusters Number 30cm 100cm Root length Conductance Height Number Weight Shoot Ratio 312.17 2.41 0.65 51.33 483.56 123.78 23.33 43.77 37.36 I II

178.00

2.47

0.16

39.50

411.17

133.00

11.00

24.27

66.73

III

289.00

3.09

1.76

47.25

424.22

150.71

19.87

45.93

61.91

IV

306.60

2.43

0.33

41.70

487.43

117.88

22.47

31.63

45.68

V

323.88

4.17

0.72

46.25

515.83

116.45

25.00

36.18

56.82

VI

289.75

5.01

2.19

53.50

328.33

140.60

8.00

46.84

84.11

VII

457.25

12.87

1.02

47.67

426.67

112.00

40.67

55.30

110.28

VIII

192.50

1.12

0.00

27.50

461.67

78.27

15.33

13.70

30.09

RLD- Root length density

Table 5. List of parents of mapping population, donors and advanced breeding lines, of IRRI-India drought breeding network used for analyzing real time water uptake rates, root distribution and grain yield under both field and lysimetric experiments. Entry

Origin

History

Anjali

India

Traditional

Apo

Philippines

Improved

ARB 3

Bengaluru

Improved

ARB 4

Bengaluru

Improved

ARB 6

Bengaluru

Improved

ARB 8

Bengaluru

Improved

Azucena

Philippines

Traditional

Bala

India

Traditional

Birsa gora

India

Traditional

Black gora

India

Traditional

Brown gora

India

Traditional

Budda

Karnataka

Traditional

CBT 3 06

Coimbatore

Improved

CO 39

India

Improved

CR 143-2-2

Orissa

Improved

CT9993–5-10–1-M

Colombia

Improved

DGI 307

Philippines

Improved

IAC165

Brazil

Improved

IR 64

Philippines

Improved

IR 72667-16-1-B-B-3

Philippines

Improved

IR 74371-3-1-1

Philippines

Improved

IR 74371-46-1-1

Philippines

Improved

IR 74371-54-1-1

Philippines

Improved

IR 76569-259-1-1-3

Philippines

Improved

IR 78937-B-4-B-B-B

Philippines

Improved

IR 80013-B-141-4-1

Philippines

Improved

Table 5. Contd.. Entry

Origin

History

IR1552

Philippines

Improved

IR52561-UBN-1-1-2

Philippines

Improved

IR62266

Philippines

Improved

IR69502-6-SRN-3-UBN-2-B-2-2-2

Philippines

Improved

IR69515-6-KKN-4-UBN-4-2-1-1-1

Philippines

Improved

IR70213-10-CPA-4-2-2-2

Philippines

Improved

IR74371-70-1-1

Philippines

Improved

IR78908-80-B-3-B

Philippines

Improved

IRAT 109

Africa

Improved

IRRI 123

Philippines

Improved

Kalinga III

India

Traditional

Kinandang patong

Japan

Traditional

Labelle

Japan

Traditional

Mahamaya

India

Traditional

Masasuri

India

Improved

Moroberekan

Guina

Traditional

MTU 1010

India

Improved

RR 345-2

Raipur

Improved

RR 433-2

Raipur

Improved

Samba masasuri

India

Improved

Swarna

India

Improved

Thara

India

Traditional

Vandana

India

Traditional

Table 56: Means for shoot and grain yield parameters of parents of mapping population, donors and advanced breeding lines of IRRI-India drought breeding network under drought stress and well-watered condition in field experiment during DS 2009 Drought stress Entry Anjali Apo ARB 3 ARB 4 ARB 6 ARB 8 Azucena Birsa gora Brown gora Budda CBT 3 06 CO 39 CR 143-2-2 CT9993 DGI 307 IAC165 IR 64 IR 72667-16-1-B-B-3 IR 74371-3-1-1 IR 74371-46-1-1 IR 74371-54-1-1 IR 76569-259-1-1-3 IR 78937-B-4-B-B-B IR 80013-B-141-4-1 IR1552

Plant Height (cm) 70.44 75.11 65.00 67.00 69.67 71.00 80.22 83.78 73.89 76.33 79.78 66.33 62.33 59.89 62.89 68.67 52.67 65.33 69.00 68.89 55.78 86.67 65.89 71.22 48.22

Tiller Number 14.89 18.78 15.89 16.56 17.22 18.56 8.67 17.00 18.33 19.00 19.89 19.89 18.67 18.89 18.00 15.00 20.22 16.33 17.44 15.78 20.00 14.33 15.00 16.44 16.22

Straw Biomass (g/m2) 244.67 380.00 330.00 346.67 333.33 352.00 450.67 195.33 306.67 370.00 387.33 290.00 353.33 323.33 333.33 227.00 292.00 348.67 390.00 308.67 340.00 326.67 330.67 251.33 325.33

Plant Height (cm) 83.22 96.22 86.22 83.78 88.56 82.56 98.78 99.33 81.89 87.44 89.67 82.44 81.56 76.11 73.33 88.11 86.33 91.33 86.11 86.00 92.56 98.22 92.67 102.11 91.33

Well-watered Straw Tiller Biomass Number (g/m2) 11.00 215.33 13.78 440.00 15.22 347.33 16.22 310.00 15.11 260.00 13.00 403.33 15.89 734.67 8.67 370.00 14.67 180.67 14.11 440.67 16.67 256.67 12.78 665.33 14.33 276.67 19.22 840.00 16.33 336.67 13.89 661.33 20.89 386.67 14.56 430.00 17.22 591.33 21.78 390.00 15.00 480.00 18.11 398.00 16.22 294.00 17.78 386.67 18.83 586.00

Grain Yield (g/m2) 273.33 378.67 307.33 315.33 333.33 351.33 180.00 158.00 189.33 236.00 236.67 190.67 261.33 106.67 340.00 160.67 294.00 335.33 321.33 390.00 374.00 356.67 337.33 359.33 185.00

Harvest Index 0.56 0.46 0.47 0.50 0.56 0.47 0.21 0.30 0.51 0.35 0.48 0.22 0.49 0.11 0.50 0.20 0.43 0.44 0.35 0.50 0.44 0.47 0.53 0.48 0.24

Table 56. Contd.. Drought stress Genotypes IR52561-UBN-1-1-2 IR62266 IR69502-6-SRN-3-UBN-2-B-2-2-2 IR69515-6-KKN-4-UBN-4-2-1-1-1 IR70213-10-CPA-4-2-2-2 IR74371-70-1-1 IR78908-80-B-3-B IRRI 123 Kalinga III Labelle Mahamaya Samba Masasuri Moroberekan MTU 1010 RR 345-2 RR 433-2 Masasuri Swarna Thara Vandana Mean CV CD (0.05) CD (0.01)

Plant Height (cm) 77.11 63.22 67.33 64.11 63.89 73.56 78.89 61.11 84.44 82.67 68.33 75.11 63.22 60.00 78.00 75.56 49.33 44.33 77.89 87.22 69.14 8.23 9.22 12.26

Tiller Number 16.11 16.56 17.89 19.89 19.56 16.00 19.44 16.89 21.11 11.67 16.56 19.56 8.22 19.22 20.00 16.89 16.00 16.67 20.78 17.67 17.19 18.84 5.25 6.98

Straw Biomass (g/m2) 325.67 345.33 327.33 330.67 406.67 356.67 353.33 383.33 316.67 352.00 373.33 321.33 332.67 353.33 350.00 276.67 367.33 386.00 375.00 320.00 335.34 16.07 87.42 116.16

Plant Height (cm) 106.56 70.67 97.56 94.00 94.67 80.89 108.22 87.78 102.89 90.78 87.00 98.89 98.11 79.78 94.22 89.33 97.00 79.78 92.00 81.78 89.73 14.31 20.83 27.68

Well-watered Straw Tiller Biomass Number (g/m2) 17.44 561.33 19.00 905.33 16.67 366.67 19.00 416.67 15.78 422.00 18.89 443.33 17.67 383.33 21.11 486.67 13.00 300.00 14.22 676.00 15.67 303.33 15.00 522.00 18.56 754.67 18.56 553.33 16.67 290.00 18.56 289.33 19.00 350.00 16.56 416.67 14.89 328.67 18.22 261.33 16.35 438.04 19.87 13.06 5.27 92.86 7.00 123.39

Grain Yield (g/m2) 203.33 254.00 366.67 399.33 387.33 436.67 283.33 315.33 320.00 150.67 276.67 307.33 260.67 412.67 208.00 251.33 150.00 473.33 250.00 164.67 285.40 15.01 69.51 92.37

Harvest Index 0.19 0.22 0.50 0.48 0.47 0.50 0.43 0.39 0.52 0.18 0.48 0.37 0.26 0.43 0.42 0.46 0.30 0.53 0.43 0.39 0.41 9.71 0.06 0.08

Table 57: Analysis of variance for shoot characters under well-watered and drought stress condition using parents of mapping population, donors and breeding lies of IRRI-India drought breeding network in field experiment during DS 2009

Plant height

Sources of variation

df

Replications

Tiller number

Straw biomass

Grain yield

Harvest Index

Wellwatered

Drought stress

Wellwatered

Drought stress

Wellwatered

Drought stress

Wellwatered

Wellwatered

2

1633.34

92.28

138.51

38.53

7791.02

4989.25

9499.08

0.010

Genotypes

44

213.97**

301.84**

20.90*

22.44**

83867.47**

6523.71**

23119.66**

0.041**

Error

88

164.86

32.34

10.55

10.49

3275.23

2902.97

1835.55

0.001

Table 58: Mean, range and genetic parameters for shoot and grain yield characters of contrasting breeding lines and parents of mapping population measured under both drought stress and well-watered condition in field experiment during DS 2009.

Range Characters

Mean

PCV (%) Minimum

GCV (%)

h2 bs (%)

GA as per cent of mean

Maximum Drought stress

Plant height

69.14

44.33

87.22

15.98

13.70

73.0

24.21

Tiller number

17.19

8.22

21.11

22.13

11.61

27.0

12.54

Straw biomass

335.34

195.33

450.66

19.17

10.35

29.0

11.56

Well-watered Plant height

89.72

70.66

108.22

15.00

4.50

9.0

2.79

Tiller number

16.34

8.66

21.77

22.89

11.36

24.0

11.61

Straw biomass

421.60

180.67

840.00

39.63

37.41

89.0

72.77

Grain yield

287.43

106.67

473.33

33.11

29.51

79.0

55.18

Harvest Index

0.40

0.11

0.56

30.11

28.50

89.0

55.59

Table 59. Water uptake rates (g/plant) of parents of mapping population, donors and advanced breeding lines of IRRI-India drought breeding network in lysimetric experiment during DS 2009. Water uptake (g/plant)-Days after stress imposition 5

8

12

15

18

23

Total (g/plant)

Anjali

514.0

602.0

524.0

605.0

415.0

584.0

3244.0

Apo

500.0

615.0

503.0

675.0

460.0

663.0

3415.0

ARB 3

664.0

718.0

630.0

798.0

522.0

684.0

4016.0

ARB 4

695.0

748.0

668.0

825.0

528.0

745.0

4208.0

ARB 6

575.0

775.0

593.0

780.0

485.0

698.0

3905.0

ARB 8

570.0

670.0

610.0

753.0

553.0

747.0

3903.0

Azucena

533.0

663.0

650.0

873.0

640.0

793.0

4153.0

Bala

520.0

675.0

598.0

710.0

488.0

593.0

3643.0

Birsa gora

515.0

623.0

465.0

698.0

518.0

665.0

3483.0

Black gora

533.0

545.0

518.0

633.0

500.0

540.0

3268.0

Brown gora

653.0

833.0

633.0

795.0

575.0

723.0

4210.0

Budda

700.0

678.0

635.0

693.0

530.0

588.0

3823.0

CBT 3 06

674.0

722.0

626.0

760.0

534.0

672.0

3988.0

CO 39

722.0

810.0

680.0

792.0

514.0

620.0

4138.0

CR 143-2-2

550.0

586.0

528.0

730.0

540.0

656.0

3590.0

CT9993

628.0

683.0

643.0

743.0

518.0

620.0

3833.0

DGI 307

778.0

750.0

695.0

790.0

605.0

683.0

4300.0

IAC165

686.0

562.0

658.0

738.0

610.0

674.0

3928.0

IR 64

604.0

542.0

430.0

544.0

370.0

424.0

2914.0

IR 72667-16-1-B-B-3

600.0

542.0

514.0

654.0

502.0

576.0

3388.0

IR 74371-3-1-1

574.0

682.0

576.0

778.0

524.0

664.0

3798.0

IR 74371-46-1-1

392.0

402.0

334.0

472.0

360.0

374.0

2334.0

IR 74371-54-1-1

646.0

772.0

674.0

848.0

560.0

728.0

4228.0

IR 76569-259-1-1-3

636.0

546.0

592.0

700.0

522.0

606.0

3602.0

IR 78937-B-4-B-B-B

684.0

750.0

588.0

722.0

474.0

574.0

3792.0

IR 80013-B-141-4-1

467.0

473.0

443.0

533.0

473.0

477.0

2867.0

Genotypes

Table 59. Condt..

Genotypes  

Water uptake (g/plant)-Days after stress imposition

Total (g/plant)

5

8

12

15

18

23

IR1552

654.0

622.0

560.0

622.0

452.0

512.0

3458.0

IR52561-UBN-1-1-2

663.0

740.0

657.0

750.0

547.0

660.0

4017.0

IR62266 IR69502-6-SRN-3-UBN-2B-2-2-2 IR69515-6-KKN-4-UBN-42-1-1-1 IR70213-10-CPA-4-2-2-2

608.0

504.0

530.0

578.0

428.0

442.0

2910.0

618.0

655.0

578.0

700.0

523.0

610.0

3683.0

396.0

346.0

358.0

500.0

424.0

444.0

2468.0

436.0

398.0

418.0

530.0

424.0

486.0

2692.0

IR74371-70-1-1

678.0

666.0

596.0

728.0

550.0

630.0

3848.0

IR78908-80-B-3-B

760.0

673.0

633.0

770.0

583.0

703.0

4123.0

IRAT 109

614.0

728.0

618.0

800.0

620.0

670.0

4050.0

IRRI 123

478.0

383.0

403.0

450.0

385.0

433.0

2530.0

Kalinga III

658.0

684.0

588.0

692.0

532.0

634.0

3788.0

Kinandang patong

384.0

443.0

390.0

500.0

406.0

424.0

2653.0

Labelle

465.0

502.0

440.0

572.0

390.0

520.0

2948.0

Mahamaya

600.0

620.0

572.0

696.0

520.0

606.0

3614.0

Masasuri

548.0

664.0

562.0

700.0

498.0

632.0

3604.0

Moroberekan

638.0

428.0

495.0

615.0

493.0

538.0

3225.0

MTU 1010

800.0

495.0

598.0

700.0

488.0

613.0

3693.0

RR 345-2

590.0

636.0

544.0

630.0

498.0

578.0

3476.0

RR 433-2

523.0

735.0

555.0

683.0

468.0

585.0

3548.0

Samba masasuri

500.0

588.0

514.0

666.0

464.0

638.0

3370.0

Swarna

660.0

716.0

552.0

718.0

448.0

608.0

3702.0

Thara

602.0

592.0

578.0

696.0

570.0

656.0

3694.0

Vandana

722.0

714.0

610.0

724.0

510.0

578.0

3858.0

Mean

596.0

622.0

558.0

687.0

501.0

603.0

3570.0

CV

12.1

19.6

9.3

7.7

11.1

9.9

10.5

CD (0.05)

89.5

153.3

65.0

66.0

69.0

74.6

465.7

CD (0.01)

118.0

202.2

85.8

87.0

91.0

98.4

614.2

Table 60. Means of root parameters of contrasting parents of mapping population, donors and advanced breeding lines of IRRI-India drought breeding network under drought stress and well-watered conditions in lysimetric experiment during DS 2009 Drought stress Well-watered Root length Root Root length Root Genotypes Root dry Root surface Root dry Root surface density volume volume density 2 2 weight (g) area (cm ) weight (g) area (cm ) (cm cm-3) (cm3) (cm cm-3) (cm3) 1.08 0.43 1982.52 20.31 2.77 1.22 6693.04 81.25 Anjali 1.69 0.84 4082.34 43.87 2.48 1.11 5772.9 66.49 ARB 3 1.97 0.92 4537.35 49.32 2.00 0.8 3381.31 41.71 ARB 4 1.69 0.78 3777.94 40.91 2.18 0.96 4735.59 52.15 ARB 6 2.42 0.83 4320.04 49.79 2.68 0.9 5035.45 62.63 Brown Gora 1.02 0.57 2710.69 28.81 1.63 0.79 4309.91 51.85 Budda 1.07 0.71 3471.62 37.74 2.18 0.96 5324.96 66.31 CBT 3 06 1.51 0.81 4365.99 52.59 1.65 0.81 4023.58 44.55 DGI 307 0.98 0.79 3731.76 41.2 2.55 1.15 6707.94 91.99 IR 64 0.49 0.17 755.84 7.40 1.26 0.63 3334.82 39.06 IR 74371-46-1-1 1.33 0.64 3039.63 31.92 2.06 0.76 3997.68 46.27 IR 74371-54-1-1 1.52 0.68 3340.65 36.48 1.82 0.95 5534.52 71.7 IR74371-70-1-1 1.17 0.68 3214.75 33.62 1.97 0.85 4643.39 56.35 IR78908-80-B-3-B 1.03 0.61 2892.85 30.27 2.71 1.07 5645.34 65.97 Kalinga III 0.91 0.53 2279.07 21.71 1.31 0.94 4664.09 51.49 RR 345-2 1.65 0.8 4230.92 49.23 2.51 1.20 7146.52 98.64 Swarna 2.29 0.77 3858.29 42.68 2.33 0.89 4714.54 55.54 CT9993 1.57 0.79 3754.72 39.65 1.22 0.57 3161.36 39.25 CO 39 0.65 0.38 1921 22.23 0.63 0.43 2265.62 26.76 Black gora 0.66 0.28 1664.4 21.74 2.66 1.57 8872.89 112.4 Kinandang Patong 1.65 0.65 3621.13 46.59 0.83 0.25 1469.36 19.74 Azucena 1.52 0.81 4650.1 62.45 1.31 0.46 2301.06 26.79 Moroberekan 1.36 0.66 3281.98 36.84 1.94 0.88 4715.27 57.68 Mean 13.01 15.78 14.3 12.43 25.03 28.09 26.6 27.65 CV 0.22 0.12 590.76 5.74 0.61 0.3 1577.63 20.06 CD (0.05) 0.29 0.17 783.01 7.6 0.81 0.41 2091.01 26.58 CD (0.01)

Table 61. Means of shoot parameters of parents of mapping population, donors and advanced breeding lines of IRRI-India drought breeding network under drought stress and well-watered condition in lysimetric experiment during DS 2009 Drought stress Well-watered Genotypes Plant height Tiller Shot dry weight Plant height Tiller Shot dry weight (cm) number (g/plant) (cm) number (g/plant) 69.00 6.00 4.48 83.00 10.00 15.64 Anjali 59.00 7.75 3.59 83.60 9.80 12.61 Apo 64.40 9.80 8.27 86.20 10.00 16.52 ARB 3 63.20 10.00 6.81 90.20 10.00 16.74 ARB 4 64.75 11.00 8.01 85.67 11.67 18.56 ARB 6 58.25 9.00 8.05 77.00 12.60 17.68 ARB 8 71.50 7.50 4.10 108.50 16.00 22.26 Birsa Gora 77.00 9.60 5.80 98.80 12.20 19.63 Brown Gora 63.80 7.00 5.06 98.25 9.00 14.38 Budda 79.20 8.00 6.10 103.00 9.67 18.73 CBT 3 06 58.00 6.80 4.99 80.00 10.75 14.24 CR 143-2-2 63.33 10.33 8.25 75.00 11.25 16.85 DGI 307 45.60 7.80 3.98 74.00 12.75 16.36 IR 64 62.40 6.80 5.05 77.67 6.33 10.86 IR 72667-16-1-B-B-3 64.00 9.40 6.60 85.20 9.80 16.96 IR 74371-3-1-1 55.40 6.40 2.96 87.75 11.50 12.61 IR 74371-46-1-1 68.80 8.60 6.71 87.00 10.00 15.88 IR 74371-54-1-1 64.60 7.60 4.73 93.25 15.75 19.69 IR 76569-259-1-1-3 63.00 9.20 6.57 87.60 9.80 15.94 IR 78937-B-4-B-B-B 57.00 8.25 4.64 94.00 10.00 15.70 IR 80013-B-141-4-1 50.20 12.60 5.22 70.00 13.00 12.96 IR69502-6-SRN-3-UBN-2-B-2-2-2 54.60 5.80 2.61 77.60 13.40 15.96 IR69515-6-KKN-4-UBN-4-2-1-1-1 56.40 7.20 4.22 75.75 9.00 13.54 IR70213-10-CPA-4-2-2-2 69.80 9.00 6.00 81.60 9.00 11.01 IR74371-70-1-1 73.25 6.25 5.71 110.60 11.40 19.76 IR78908-80-B-3-B 55.00 6.50 2.32 74.60 12.80 15.18 IRRI 123

Table 61. Contd.. Genotypes Kalinga III Mahamaya Masasuri MTU 1010 RR 345-2 RR 433-2 Samba masasuri Swarna Thara Bala CT9993 CO 39 IR62266 IRAT 109 IR1552 IR52561-UBN-1-1-2 Black Gora IAC165 Kinandang Patong Labelle Azucena Moroberekan Vandana Mean CV CD (0.05) CD (0.01)

Plant height (cm) 79.20 74.20 72.00 54.50 73.80 71.00 49.60 50.20 66.80 63.75 61.60 64.20 59.80 77.60 55.60 78.25 55.80 89.20 70.60 65.20 66.75 74.20 75.60 64.92 14.60 11.82 15.59

Drought stress Tiller Shot dry weight number (g/plant) 10.40 5.12 7.80 5.23 10.20 5.86 10.75 6.44 6.20 5.19 7.00 4.19 14.40 3.84 16.80 6.82 7.40 4.81 7.50 7.80 8.00 8.13 9.60 7.59 8.80 4.96 4.20 7.40 7.60 7.55 7.75 7.21 7.20 4.72 6.80 6.35 2.40 2.63 6.40 4.04 3.50 5.19 2.40 3.09 8.00 7.00 8.07 5.55 22.08 14.40 2.21 1.00 2.91 1.32

Plant height (cm) 103.50 80.75 85.60 71.20 94.40 103.00 65.20 63.50 92.33 56.00 82.00 70.00 74.00 79.40 63.00 92.60 72.80 107.67 100.00 87.40 92.67 92.67 107.80 85.37 12.02 12.80 16.88

Well-watered Tiller Shot dry weight number (g/plant) 15.25 19.80 10.25 17.47 15.00 16.67 11.00 13.52 10.60 15.12 10.40 19.04 12.80 12.17 14.00 11.60 10.67 12.22 6.50 10.51 10.00 15.72 11.00 11.11 12.50 18.54 8.60 13.40 10.20 321.32 11.20 21.70 10.20 12.89 9.00 15.40 10.50 19.03 4.20 11.39 6.00 8.00 4.33 9.05 10.20 20.13 10.65 21.67 14.87 19.07 1.96 3.68 2.59 4.85

Table 62. Analysis of variance for real time water uptake rates under drought stress condition using parents of mapping population, donors and breeding lies of IRRI-India drought breeding network in lysimetric experiment during DS2009 Water uptake-Days after stress

Sources of variation

Df

Replications

5

8

12

15

18

23

Total

4

1334.44

110474.69

2389.38

1425.57

9816.88

4770.69

36329.35

Genotypes

48

52081.19**

66247.90**

37839.50**

49013.77**

20231.33**

44899.72**

1278832.40**

Error

192

5146.85

15104.21

2719.09

2795.29

3058.61

3574.64

139344.11

Table 63. Analysis of variance for root and shoot characters under drought stress and well-watered condition using parents of mapping population, donors and breeding lies of IRRI-India drought breeding network in lysimetric experiment during DS2009 Sources of variation

Drought stress

Well-watered

Replications

Genotypes

Error

Replications

Genotypes

Error

4

21

84

4

21

84

Root dry weight at 0-100cm

0.04

1.28**

0.03

0.62

2.03**

0.23

Root length density at 0-100cm

0.006

0.187**

0.01

0.028

0.44**

0.06

167097

5363033.5**

220634

446308

15199729.8**

1573437

19.58

859.58**

20.83

148.55

2749.88**

254.38

4

48

192

4

48

192

202.96

424.66**

89.82

112.45

847.04**

105.35

Tiller number

9.6

33.72**

3.14

3.25

31.13**

2.49

Shoot dry weight

0.22

13.47**

0.64

25.06

55.98**

8.7

Df

Root surface area 0-100cm Root volume at 0-100cm Df Plant height

Table 64. Mean, range and genetic parameters for root and shoot characters of contrasting donors, parents of mapping population and breeding lines of IRRI –India measured under both drought stress and well-watered condition in lysimetric experiment during DS 2009. PCV (%)

GCV (%)

h bs (%)

GA as per cent of mean

Range Characters

Mean

Minimum

Maximum

2

Drought stress Plant Height

66.37

45.60

79.20

19.28

12.60

43.00

0.16

Tiller Number

8.33

2.40

16.80

37.90

30.80

66.00

51.58

Root dry weight at 0-100cm

1.43

0.49

2.42

39.21

36.99

88.00

71.88

Root length density at 0-100cm

0.69

0.17

0.92

32.84

28.80

76.00

52.03

3430.91

755.84

4650.10

34.03

30.88

82.00

57.72

Root volume at 0-100cm

38.33

7.40

62.45

37.38

35.25

88.00

68.50

Shoot Dry Weight

5.96

2.96

8.27

32.09

28.68

79.00

52.81

3766.06

2334.00

4300.00

23.41

11.48

24.00

11.60

Root surface area 0-100cm

Total Water Uptake

Well-watered Plant Height

87.99

63.50

110.60

18.65

14.26

58.00

22.46

Tiller Number

10.48

4.33

15.25

27.00

22.54

69.00

38.75

Root dry weight at 0-100cm

1.97

0.83

2.77

39.74

30.86

60.00

49.38

Root length density at 0-100cm

0.86

0.25

1.22

42.18

31.47

55.00

48.37

4629.87

1469.36

7146.52

43.97

35.01

63.00

57.42

Root volume at 0-100cm

56.49

19.74

98.65

47.59

38.73

66.00

64.93

Shoot Dry Weight

15.25

8.00

19.80

27.54

19.87

52.00

29.54

Root surface area 0-100cm

Table 65. Phenotypic correlation co-efficients among root and shoot traits measured under drought stress in lysimetric trial during DS 2009 using contrasting parents of mapping population, donors and breeding lies of IRRI-India drought breeding network. Total RDW at 0RSA 0RV at 0Plant Tiller Shoot dry water RLD at 0-100cm 100cm 100cm 100cm height number weight uptake 1.00 0.47* 0.48* 0.50* 0.37 0.29 0.30 0.58** Total water uptake 1.00 0.68** 0.72** 0.69** 0.09 0.33 0.60** RDW at 0-100cm 1.00 0.87** 0.82** -0.02 0.31 0.56** RLD at 0-100cm 1.00 0.89** 0.04 0.34 0.54** RSA at 0-100cm 1.00 0.04 0.21 0.41 RV at 0-100cm 1.00 -0.14 -0.01 Plant height 1.00 0.55** Tiller number 1.00 Shoot dry weight Table 66. Phenotypic correlation co-efficients among root and shoot traits measured under well watered during DS 2009 using contrasting parents of mapping population, donors and breeding lies of IRRI-India drought breeding network.

RDW at 0-100cm RLD at 0-100cm RSA at 0-100cm RV at 0-100cm Plant height Tiller number Shoot dry weight

RDW at 0-100cm

RLD at 0-100cm

RSA 0-100cm

1.00

0.68** 1.00

0.73** 0.96** 1.00

RLD=Root length density RDW=Root dry weight

RV at 0100cm 0.71** 0.88** 0.97** 1.00

RSA=Root surface area RV=Root volume

Plant height 0.11 0.10 0.05 -0.01 1.00

Tiller number 0.38 0.40 0.42* 0.42* -0.11 1.00

Shoot dry weight 0.50* 0.43* 0.40 0.35 0.34 0.46* 1.00

Table 6: List of NILs and parents used for both gene expression and lysimetric experiments.

Accession IR77298-14-1-2-10 (NIL10) IR77298-14-1-2-13

Pedigree

Salient features

Adeysel X IR 64

Drought tolerant

Adeysel X IR 64

Drought susceptible

Adeysel X IR 64

Drought susceptible

Adeysel X IR 64

Drought tolerant

(NIL13) IR77298-5-6-11 (NIL11) IR77298-5-6-18 (NIL18) Adaysel

IR 64      

Line from India Mega variety from IRRI, Philippines

Tungro disease tolerant and moderately drought tolerant Drought susceptible

Table 67. Water uptake rates at different time intervals using NILs and its parents in lysimetric experiment during WS 2008 Genotypes IR77298-14-1-2-10 IR77298-14-1-2-13 IR77298-5-6-11 IR77298-5-6-18 Adaysel IR 64 Mean CV CD (0.05) CD (0.01)

Water uptake (g/plant)-Days after stress 7 14 21 1526.25 1347.50 1260.00 1461.25 1305.00 1125.00 1245.00 1192.50 962.50 1713.75 1672.50 1111.00 970.00 1261.25 1613.75 794.00 840.00 872.00 1285.04 1269.79 1157.38 7.58 8.47 10.85 126.73 120.23 159.84 168.12 159.49 212.03

Total (g/plant) 4133.75 3891.25 3400.00 4497.25 3845.00 2506.00 3712.21 10.07 572.80 791.90

Table 68. Means of root traits across different soil depth of Adeysel NILs along with parents under drought stress and well-watered treatment in lysimetric experiment during WS 2008

RN

MRL (cm)

Entry name IR77298-14-1-2-10 IR77298-14-1-2-13 IR77298-5-6-11 IR77298-5-6-18 Adaysel IR 64 Mean CV CD (0.05) CD (0.01)

147.50 140.30 125.60 148.30 183.20 92.50 139.60 6.40 13.37 18.49

85.20 90.50 75.00 83.00 86.50 76.70 82.80 5.80 7.22 10.00

0-30 cm 1.44 2.34 2.58 2.24 1.11 1.43 1.86 15.02 0.41 0.58

Drought stress RLD (cm/cm3) 30-45 45-60 60-100 cm cm cm 1.19 0.39 0.04 1.78 0.49 0.04 1.99 0.33 0.03 2.28 1.58 0.16 0.63 0.58 0.01 0.99 0.74 0.03 1.48 0.69 0.05 16.71 15.81 9.57 0.37 0.16 0.01 0.51 0.22 0.01

RN

335.60 160.00 366.60 279.50 245.50 230.60 264.10 17.17 84.23 119.81

MRL (cm)

49.30 44.00 45.00 51.00 35.00 35.50 43.30 8.64 6.81 9.68

0-30 cm 4.20 2.45 8.91 3.29 4.42 2.79 4.25 9.71 0.76 1.09

Well-watered RLD (cm/cm3) 30-45 45-60 60-100 cm cm cm 1.16 0.10 0.00 0.24 0.01 0.00 0.61 0.00 0.00 1.18 0.12 0.00 0.17 0.00 0.00 0.08 0.00 0.00 0.57 0.04 0.00 16.20 20.99 84.85 0.17 0.01 0.00 0.24 0.02 0.01

Table 69. Means of root surface area (cm2) across different soil depth of Adeysel NILs along with parents under both drought stress and well watered treatment in lysimetric experiment during WS 2008 Drought stress

Well watered

Entry name

0-30cm

30-45cm

45-60cm

60-100cm

0-30cm

30-45cm

45-60cm

60-100cm

IR77298-14-1-2-10 IR77298-14-1-2-13 IR77298-5-6-11 IR77298-5-6-18 Adaysel IR 64 Mean CV CD (0.05) CD (0.01)

1096.60 1602.50 1782.60 1554.30 840.10 1262.40 1356.40 6.20 125.75 173.86

315.40 439.50 522.00 597.20 175.90 303.50 392.30 6.13 36.71 50.76

103.60 131.40 77.10 423.10 154.00 195.10 180.70 20.66 54.17 74.89

36.10 28.30 20.50 128.00 11.60 25.50 41.70 11.74 7.38 10.20

3882.60 1967.90 6479.40 3180.00 3562.90 3015.00 3587.30 7.39 495.01 704.13

459.80 100.60 287.60 511.00 82.30 46.70 247.00 18.87 81.18 115.47

43.96 3.86 0.00 55.06 0.00 0.00 17.15 24.60 7.92 11.27

0.00 1.92 0.00 0.00 0.00 0.00 0.32 10.74 0.06 0.08

Table 70. Means of root volume (cm3) across different soil depth of Adeysel NILs along with parents under both drought stress and well watered treatment in lysimetric experiment during WS 2008 Entry name

IR77298-14-1-2-10 IR77298-14-1-2-13 IR77298-5-6-11 IR77298-5-6-18 Adaysel IR 64 Mean CV CD (0.05) CD (0.01)

Drought stress 0-30cm 8.07 10.66 11.89 10.49 6.11 10.64 9.64 8.17 1.18 1.64

30-45cm 1.58 2.05 2.63 2.94 0.92 1.75 1.98 8.30 0.24 0.33

45-60cm 0.53 0.66 0.34 2.12 0.78 0.98 0.90 15.43 0.21 0.29

Well watered 60-100cm 0.21 0.16 0.11 0.72 0.08 0.16 0.24 20.60 0.08 0.11

0-30cm 34.58 14.89 45.01 29.12 27.29 31.02 29.44 11.31 6.23 8.87

30-45cm 3.45 0.79 2.57 4.28 0.75 0.54 2.05 13.65 0.51 0.72

45-60cm 0.38 0.02 0.00 0.48 0.00 0.00 0.15 28.34 0.07 0.10

60-100cm 0.00 0.02 0.00 0.00 0.00 0.00 0.00 64.52 0.00 0.00

Table 71. Means of root dry weight (mg) across different soil depth of Adeysel NILs along with parents under both drought stress and well watered treatment in lysimetric experiment during WS 2008 Entry name

IR77298-14-1-2-10 IR77298-14-1-2-13 IR77298-5-6-11 IR77298-5-6-18 Adaysel IR 64 Mean CV CD (0.05) CD (0.01)

Drought stress 0-30cm 710.00 825.00 990.00 927.00 440.00 727.00 770.00 7.42 88.75 122.70

30-45cm 95.00 122.50 143.30 185.00 57.50 80.00 113.80 13.52 22.07 30.51

45-60cm 34.10 33.00 27.50 116.40 30.90 19.30 43.50 19.45 13.19 18.24

Well watered 60-100cm 14.20 8.60 12.00 42.80 7.40 5.40 15.10 15.96 3.62 5.00

0-30cm 2510.00 970.00 3651.60 2350.00 1910.00 1786.60 2149.40 14.42 561.63 798.89

30-45cm 193.30 30.60 126.60 213.30 35.00 9.50 101.30 32.31 59.61 84.79

45-60cm 16.66 0.00 0.00 22.43 0.00 0.00 6.51 33.46 3.96 5.64

60-100cm 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.64 6.81 9.68

Table 72. Means of shoot parameters of NILs and parents under both drought stress and well watered treatment in lysimetric experiment during WS 2008

Entries Name IR77298-14-1-2-10 IR77298-14-1-2-13 IR77298-5-6-11 IR77298-5-6-18 Adaysel IR 64 Mean CV CD (0.05) CD (0.01)

Plant height (cm) 87.75 98.50 94.75 94.75 98.50 86.50 93.46 4.54 6.42 8.88

Drought stress Shoot dry Tiller weight number (g/plant) 19.75 24.04 22.25 19.35 20.75 21.50 25.00 34.65 15.75 14.55 11.50 10.70 19.17 20.80 14.44 5.24 4.31 1.33 5.90 1.83

Stomatal conductance (mol-2s-1) 75.25 68.10 46.85 60.63 72.25 50.93 62.33 11.43 10.69 14.78

Plant height (cm) 122.27 111.70 116.33 112.73 114.00 111.00 114.67 5.11 10.66 15.17

Well watered Shoot dry Tiller weight number (g/plant) 29.67 54.87 25.00 39.40 32.33 54.70 32.33 47.04 33.50 30.57 29.00 37.96 30.31 44.22 12.43 9.58 6.85 7.58 9.75 10.78

Stomatal conductance (mol-2s-1) 1145.00 585.00 726.67 462.50 522.50 683.33 669.86 7.13 89.23 126.92

 

Table 7. List of LEA primers used for gene expression study and their sequence

Gene Ontology

Lea1a LOC_Os01g06630

Lea2h LOC_Os11g26790

Lea4d LOC_Os08g01370

HVA1 LOC_Os05g46480

 

Primer sequences F_Lea1a: 5'- GAG CTG CAG GAC CCG GAG AT -3'

Primer size (bp) 20

% GC content 65.0

Tm

61.7

Secondary structure

Expected size (bp)

weak 254

R_Lea1a: 5'- CAC TTG CGG CCC AGG TTC TTT -3'

21

57.1

60.7

weak

F_ Lea 2h: 5'- GCT CAA GCT CGT CTG AGG ATG AT -3'

23

52.2

58.7

moderate 240

R_ Lea 2h: 5'- CCT TGA TCT TGT CCA TGA TGC CCT -3'

24

50.0

59.1

weak

F_ Lea 4d: 5'- GCT ACG GAC TGG CTT CAT CAA CG -3'

23

56.5

60.1

weak 206

R_Lea4d: 5'- CCA CCT TGT GCT GCT GCT GAT -3'

21

57.1

60.5

strong

F_Hva1: 5'- CCA AGG AGG CGA CGA AGG AGA A -3'

22

59.1

61.1

weak 249

R_Hva1: 5'- CGA TGG CAG AGT CCT TGG TGT ACT -3'

24

54.2

60.5

weak

Table 73. Mean gene expression pattern under well water (1.0 FTSW) and severe drought stress (0.2FTSW) conditions in different zones of shoot using IR64 and Dular. Top Shoot Entry

Control (10 FTSW)

Stress (02 FTSW)

Lea1a

Lea 2h

Lea 4d

HVA1

Lea1a

Lea 2h

Lea 4d

HVA1

IR64

-

-

-

-

-

-

++++

+++

Dular

-

-

-

+

-

-

+++

++++

Below shoot IR64

-

-

-

-

-

+

++++

+++

Dular

-

-

+

++

++

-

++++

+++

 

Table 74. Mean gene expression pattern under well water (1.0 FTSW) and severe drought stress (0.2FTSW) conditions using top zone of root in NILs of Adeysel X IR64 with IR64 and Dular. Well water (1.0 FTSW)

Entry

Severe drought stress(0.2 FTSW)

Lea1a

Lea 2h

Lea 4d

HVA1

Lea1a

Lea 2h

Lea 4d

HVA1

IR64

-

++

+

+++

++

++

++++, +

Dular

+

-

-

++, ++ ++

++

-

+++

++++,+

NIL 10

-

-

+

++

++,+

+

++

+++

NIL13

-

-

+

+

+++

+

++++

+++++, ++

NIL 18

-

-

-

+

++

++

++++

++++, ++

NIL 11

-

-

+

+

++

++

+++

++++

Table 75. Mean gene expression pattern under well water (1.0 FTSW) and severe drought stress (0.2FTSW) conditions using deep zone of root in NILs of Adeysel X IR64 with IR64 and Dular. Well water (1.0 FTSW) Entry

Severe drought stress (0.2 FTSW)

Lea1a

Lea 2h

Lea 4d

HVA1

Lea1a

Lea 2h

Lea 4d

HVA1

IR64

-

++

+

++

+

-

++

++

Dular

-

-

-

+

-

-

++++

+++

NIL 10

-

-

+

++

-

+

-

++++

NIL13

-

-

-

-

+

-

+++

+++

NIL 18

-

-

-

+

++

-

++++

++++

NIL 11

-

-

-

-

-

++

++++

++++

   

Table 8. Means of root number (RN), root to shoot ratio (RSR) and root length density (RLD, cm cm-3) across different soil depths of OryzaSNP panel rice accessions under both drought stress and well- watered treatment in field experiment during DS 2008

Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Pokkali Sadu Cho SHZ 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

Drought stress RLD (cm cm-3) RN

RSR

191.83 0.09 113.00 0.22 120.50 0.08 219.67 0.08 231.00 0.07 196.00 0.08 189.00 0.07 198.50 0.06 168.00 0.07 259.67 0.11 131.67 0.15 115.67 0.07 212.50 0.05 209.50 0.06 243.50 0.11 212.67 0.15 131.17 0.10 228.33 0.09 187.34 0.09 26.41 12.07 82.13 0.01 110.21 0.02

Well- watered RLD (cm cm-3)

0-10cm 10-20cm 20-30cm 30-45cm 1.51 0.49 0.87 1.73 1.00 1.11 1.89 1.25 1.00 1.22 0.91 0.94 1.23 1.19 1.11 1.28 0.85 1.27 1.16 24.87 0.47 0.64

0.72 0.48 0.42 0.99 0.58 0.82 0.40 0.44 0.29 0.75 0.90 0.16 0.60 0.57 1.02 1.45 0.29 0.49 0.63 20.17 0.21 0.28

0.56 0.67 0.11 0.65 0.52 0.35 0.18 0.17 0.87 0.36 0.33 0.18 0.38 0.46 0.54 1.21 0.17 0.15 0.44 33.13 0.24 0.33

0.11 0.09 0.04 0.17 0.24 0.11 0.02 0.04 0.03 0.05 0.12 0.04 0.16 0.04 0.20 0.06 0.07 0.03 0.09 16.35 0.02 0.03

RN

RSR

164.00 0.10 174.50 0.25 177.00 0.07 357.50 0.11 275.50 0.08 412.00 0.34 225.00 0.11 60.50 0.05 160.00 0.07 310.50 0.12 153.00 0.14 132.50 0.16 225.00 0.05 400.33 0.07 251.00 0.11 234.00 0.10 229.50 0.18 249.33 0.06 232.84 0.12 11.02 20.26 42.28 0.04 56.75 0.05

0-10cm 10-20cm 20-30cm 30-45cm 2.21 1.84 1.82 3.95 2.65 3.07 2.31 1.21 1.23 2.77 1.63 1.76 2.30 3.84 1.80 3.64 3.41 2.29 2.43 19.78 0.8 1.07

0.51 1.19 0.26 0.60 0.36 0.48 0.24 0.34 0.22 1.25 0.43 0.28 0.44 0.40 0.35 0.71 0.21 0.46 0.49 28.61 0.23 0.32

0.26 0.29 0.11 0.18 0.20 0.08 0.13 0.25 0.06 0.87 0.17 0.24 0.10 0.16 0.34 0.22 0.07 0.20 0.22 24.90 0.09 0.12

0.01 0.02 0.02 0.03 0.06 0.03 0.02 0.02 0.01 0.06 0.03 0.05 0.03 0.02 0.04 0.01 0.01 0.02 0.03 29.20 0.013 0.017

   

100% 90%

Total root length (%)

80% 70% 30-45cm

60%

20-30cm 50%

10-20cm

40%

0-10cm

30% 20%

Dular

Moroberekan

Pokkali

Tng 67

Azucena

Aswina

Dom Sufid

FR13A

LTH

Mh 63

Shz 2

Sadu Cho

Cypress

Swarna

Zhenshan 97B

N22

M 202

0%

IR 64

10%

Genotypes

Figure 6: Percent total root length distribution with depth for genotypes of Oryza SNP panel under drought stress treatment measured during DS 2008.

-300

-100

Mh 63

N22

Total Root Length (cm)

Drought Induced Root Growth Interms of

100

500

300

700

900

1100

1300

Dular

Pokkali

Moroberekan

Dom Sufid

Aswina

Tng 67

Azucena

Sadu Cho

LTH

Swarna

FR13A

Shz 2

Cypress

M 202

Zhenshan 97B

IR 64

   

Drought Induced Root Growth

Genotypes

Figure 7: Drought induced root growth at depth (30-45cm) in all OryzaSNP rice panel accesions

   

Table 9. Means of root surface area (RSA, cm2) across different soil depths of OryzaSNP panel rice accessions under both drought stress and well-watered treatment in field experiment during DS 2008 RSA (cm2) Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Pokkali Sadu Cho SHZ 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

0-10cm 835.27 377.03 555.67 923.21 673.97 851.43 1036.77 647.49 561.78 721.04 562.23 578.20 729.10 715.89 747.83 675.52 453.10 775.59 690.06 13.81 157.35 211.16

Drought stress 10-20cm 20-30cm 376.96 249.89 280.05 236.76 233.16 45.25 430.62 254.88 306.40 202.71 292.60 145.20 179.63 59.77 187.92 59.33 128.38 315.04 281.29 155.86 414.51 147.78 114.66 70.81 282.92 139.05 240.91 142.75 469.09 213.50 587.52 372.33 149.21 75.11 321.50 70.84 293.18 164.27 21.18 17.35 104.10 47.41 139.70 63.62

30-45cm 71.04 52.02 18.74 87.66 123.73 88.67 10.44 18.52 15.31 25.93 76.98 19.48 82.01 18.74 97.03 44.25 38.35 16.57 50.30 24.09 19.89 26.70

0-10cm 1556.61 1142.31 1012.98 2053.87 1488.66 1924.03 1304.50 599.39 859.49 1242.22 1065.46 1350.17 1292.71 1958.67 1273.39 1823.63 1806.48 1315.07 1392.76 15.29 352.69 473.30

Well-watered 10-20cm 20-30cm 497.05 204.31 658.87 177.51 152.02 67.85 383.20 139.71 277.37 166.68 349.30 53.54 184.23 95.18 235.50 143.68 160.55 41.97 545.84 361.92 336.35 123.78 250.13 213.66 311.21 83.54 280.57 101.74 241.69 290.50 392.13 131.44 154.23 54.50 345.01 132.89 319.74 143.58 15.25 27.50 80.50 65.49 108.01 87.88

30-45cm 13.05 17.58 15.27 30.02 57.55 20.40 16.08 18.24 10.27 54.96 23.11 46.69 29.34 10.68 32.40 9.43 7.08 15.68 23.77 20.22 7.98 10.70

   

Table 10. Means of root volume (RV, cm3) across different soil depths of OryzaSNP panel rice accessions under both drought stress and well watered treatment in field experiment during DS 2008 RV (cm3) Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Pokkali Sadu Cho SHZ 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

0-10cm 9.89 5.82 7.21 10.15 9.21 14.28 11.46 6.75 6.36 8.59 7.00 7.10 8.74 8.77 10.08 7.35 4.90 9.53 8.51 16.39 2.31 3.10

Drought stress 10-20cm 20-30cm 4.52 2.31 3.59 1.71 2.65 0.39 3.95 2.05 3.38 1.65 2.25 1.24 1.66 0.41 1.62 0.42 1.19 2.28 2.01 1.41 3.98 1.36 1.75 0.59 2.79 1.04 2.14 0.91 4.50 1.70 4.88 2.32 1.59 0.68 4.26 0.68 2.93 1.29 12.82 21.29 0.62 0.45 0.83 0.60

30-45cm 0.59 0.39 0.12 0.60 0.86 0.79 0.34 0.11 0.11 0.18 0.64 0.13 0.55 0.12 0.63 0.29 0.29 0.11 0.38 25.60 0.16 0.21

0-10cm 22.19 11.41 11.82 22.01 17.14 25.00 15.18 5.90 12.19 13.98 14.89 21.19 15.63 21.32 18.39 19.00 19.42 16.41 16.84 13.75 3.84 5.16

Well watered 10-20cm 20-30cm 9.83 3.26 7.94 2.37 2.04 0.92 5.12 2.12 4.31 3.80 3.91 0.69 2.84 1.43 3.27 1.65 2.32 0.58 5.29 2.93 5.33 1.84 4.48 3.91 4.45 1.37 3.98 1.29 3.38 4.98 4.39 1.58 2.24 0.82 5.13 1.87 4.46 2.08 16.20 22.79 1.19 0.78 1.60 1.04

30-45cm 0.17 0.21 0.21 0.36 0.71 0.22 0.19 0.19 0.14 0.83 0.29 0.62 0.34 0.10 0.38 0.10 0.09 0.15 0.29 26.73 0.13 0.17

   

Table 11. Means of root dry weight (RDW, g) across different soil depths of OryzaSNP panel rice accessions under both drought stress and well-watered treatment in field experiment during DS 2008 RDW (g) Genotype Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Pokkali Sadu Cho SHZ 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

0-10cm 0.86 1.97 0.75 0.89 0.85 1.28 1.25 0.63 0.66 0.98 1.01 0.72 0.87 0.91 0.86 0.84 0.73 0.90 0.94 10.67 0.16 0.22

Drought stress 10-20cm 20-30cm 0.41 0.22 0.42 0.18 0.27 0.04 0.39 0.16 0.33 0.15 0.22 0.17 0.17 0.04 0.20 0.03 0.13 0.24 0.30 0.14 0.56 0.15 0.13 0.05 0.25 0.09 0.23 0.09 0.39 0.07 0.56 0.20 0.17 0.08 0.37 0.05 0.30 0.12 15.40 19.82 0.07 0.04 0.10 0.05

30-45cm 0.11 0.04 0.01 0.06 0.07 0.06 0.04 0.01 0.01 0.02 0.06 0.01 0.05 0.01 0.06 0.02 0.03 0.01 0.04 27.81 0.01 0.02

0-10cm 1.61 1.53 1.16 1.80 1.19 2.88 1.38 0.46 0.82 1.42 1.48 1.36 1.27 2.19 1.21 1.67 1.79 1.01 1.46 9.95 0.23 0.32

Well-watered 10-20cm 20-30cm 0.50 0.19 0.66 0.17 0.13 0.06 0.36 0.13 0.26 0.17 0.46 0.04 0.17 0.08 0.18 0.13 0.12 0.03 0.48 0.29 0.41 0.11 0.22 0.20 0.38 0.07 0.27 0.08 0.20 0.24 0.33 0.10 0.15 0.04 0.27 0.09 0.31 0.12 15.43 16.18 0.08 0.03 0.10 0.04

30-45cm 0.02 0.01 0.01 0.02 0.03 0.01 0.01 0.01 0.01 0.05 0.02 0.03 0.02 0.01 0.03 0.01 0.00 0.01 0.02 33.87 0.009 0.01

   

Table 12: Means of physiological character of OryzaSNP panel rice accessions measured under drought stress treatment in field experiment during DS 2008.

Genotype Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Pokkali Sadu Cho SHZ 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01) *NA- not available

Photosynthesis rate

Transpiration rate

Relative water content

18.25 25.20 17.10 15.10 18.20 13.30 15.05 NA 21.05 19.65 21.95 17.30 15.55 16.15 14.90 17.05 15.95 20.95 17.81 10.42 3.05 4.10

2.20 3.20 1.71 2.24 2.59 1.71 1.71 NA 2.39 2.49 2.97 2.07 1.62 1.86 1.69 2.16 1.70 2.26 2.15 11.38 0.40 0.53

60.83 72.60 76.83 68.94 79.53 74.28 77.06 67.74 83.21 76.90 75.17 69.25 76.98 66.82 61.85 74.08 67.56 74.48 72.45 13.78 16.57 22.24

Stomatal conductance (mol-2 s-1) 383.33 606.67 576.67 588.67 558.33 701.67 595.00 603.33 640.00 471.67 673.33 651.67 376.33 501.33 498.33 610.00 546.67 447.67 557.26 31.61 292.40 392.40

Leaf water potential -2.90 -3.08 -2.81 -1.27 -3.35 -2.81 -2.17 -2.64 -2.84 -1.87 -3.27 -2.54 -2.28 -3.13 -2.18 -2.35 -3.00 -3.00 -2.64 20.19 -0.88 -1.18

Leaf rolling score 1.00 1.00 6.33 4.33 5.62 3.67 4.33 5.67 5.00 1.00 1.00 1.67 3.00 4.33 4.33 3.00 3.00 1.67 3.33 34.47 1.90 2.55

   

Table 13. Means of shoot parameters of OryzaSNP panel rice accessions under both drought stress and well-watered treatments in field experiment during DS 2008 Drought stress Genotype Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Pokkali Sadu Cho SHZ 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

Well-watered

Plant height (cm)

Tiller number

Shoot dry weight (g/plant)

Plant height(cm)

Tiller number

Shoot dry weight (g/plant)

112.94 129.78 77.61 113.89 107.44 82.00 67.61 83.39 64.33 50.57 81.67 84.78 105.17 98.28 71.06 39.17 73.89 62.17 83.65 8.21 11.39 15.29

15.50 9.00 13.00 12.50 11.00 21.33 15.50 14.00 14.50 16.00 7.00 24.50 14.50 20.67 26.33 21.50 11.50 17.00 15.85 13.04 2.30 3.06

18.53 11.43 14.98 18.40 21.25 24.79 20.77 15.82 14.60 13.69 11.53 13.05 29.51 22.06 15.64 10.35 10.27 14.75 16.75 9.07 2.51 3.36

118.94 123.61 78.56 117.78 108.83 94.78 77.22 89.50 62.78 75.00 107.61 90.11 101.17 108.06 82.44 77.72 77.83 64.61 92.03 5.25 8.01 10.76

22.67 9.33 10.00 15.33 16.33 24.00 17.67 22.50 15.67 29.00 9.33 17.00 18.00 21.33 24.50 38.00 15.33 18.67 19.15 8.73 2.28 3.06

22.39 9.79 19.61 21.27 20.97 10.08 16.96 12.19 13.62 17.82 14.65 11.52 34.54 38.47 14.99 20.28 11.37 23.97 18.58 8.12 2.49 3.35

   

Table 14. Means of grain yield (GY g/m2), straw biomass (SB, g/m2) and harvest index (HI) of OryzaSNP panel rice accessions under both well watered and drought stress treatments in field experiment during DS 2008 Genotypes Aswina Azucena Cypress Dom Sufid Dular FR13A IR 64 LTH M 202 Minghui 63 Moroberekan N22 Pokkali Sadu Cho SHZ 2 Swarna Tainung 67 Zhenshan 97B Mean CV CD (0.05) CD (0.01)

2

(GY, g/m ) 43.27 158.81 138.30 122.92 254.05 84.62 146.94 43.71 133.13 13.04 137.98 252.13 135.57 126.88 81.85 103.14 52.89 166.58 121.99 9.63 19.55 26.24

Drought stress (SB, g/m2) 677.06 852.44 334.57 461.14 514.67 905.69 435.83 173.93 289.40 405.42 871.21 489.12 465.63 540.55 649.84 630.00 558.88 255.79 528.39 9.16 79.92 107.25

HI 0.06 0.13 0.27 0.20 0.33 0.09 0.25 0.20 0.31 0.03 0.03 0.34 0.23 0.19 0.11 0.14 0.09 0.38 0.19 12.66 0.04 0.05

2

(GY, g/m ) 85.30 101.30 83.62 153.81 192.78 233.32 409.93 114.10 146.11 273.20 91.56 166.20 193.84 260.86 371.76 618.16 173.93 154.77 212.48 11.56 40.72 54.65

Well watered (SB, g/m2) 577.00 724.53 224.69 561.39 546.68 798.50 881.68 272.57 436.63 559.33 591.60 452.43 395.39 508.75 620.61 1470.30 521.79 304.60 580.47 11.97 114.60 153.80

HI 0.13 0.15 0.29 0.22 0.26 0.23 0.32 0.29 0.28 0.32 0.14 0.26 0.32 0.34 0.38 0.30 0.25 0.32 0.27 20.26 0.09 0.11

   

Table 15. Means of shoot and grain yield parameters of OryzaSNP panel rice accessions under both drought stress and well-watered in field experiment during DS 2009. Drought stress Genotypes

Aswina Azucena Dom Sufid Dular FR13A IR 64 Moroberekan Pokkali Rayada Sadu Cho SHZ 2 Swarna Zhenshan 97B Mean CV CD (0.05) CD (0.01)

Plant height (cm) 76.11 80.22 86.00 79.67 64.44 52.67 63.22 89.44 61.44 85.33 64.22 44.33 55.89 69.46 9.32 10.91 14.86

Tiller number 13.89 8.67 14.89 14.78 20.00 20.22 8.22 19.11 21.44 17.33 17.44 16.67 20.44 16.39 15.48 4.27 5.82

Straw Biomass (g/m2) 363.33 450.67 310.00 218.67 484.00 292.00 332.67 550.00 432.67 240.67 305.33 403.33 305.33 360.67 21.01 63.38 86.37

Well-watered Plant height (cm) 99.89 98.78 100.78 97.33 95.78 86.33 98.11 103.78 92.22 90.33 80.44 79.78 70.44 91.85 20.36 31.50 42.93

Tiller number 15.11 15.89 10.89 15.56 21.33 20.89 18.56 15.22 20.11 15.11 15.44 16.56 14.56 16.56 17.98 5.01 6.83

Straw Biomass (g/m2) 940.00 734.67 1050.67 480.00 1257.33 773.33 754.67 720.00 761.33 574.00 632.00 833.33 578.67 776.15 17.36 113.53 154.71

Grain Yield (g/m2) 168.67 180.00 140.67 208.00 357.33 294.00 260.67 178.00 148.67 314.00 126.67 473.33 327.33 244.41 20.69 85.23 116.14

Harvest Index 0.16 0.21 0.12 0.30 0.22 0.28 0.26 0.21 0.16 0.35 0.16 0.36 0.36 0.24 17.72 0.11 0.15

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Genetic parameters, physiological and molecular analysis of root and

GENETIC PARAMETERS, PHYSIOLOGICAL AND MOLECULAR ANALYSIS OF ROOT AND SHOOT TRAITS RELATED TO DROUGHT TOLERANCE IN RICE (Oryza sativa L.) VEERESH GOWD...

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