Plant Abiotic Stress

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Challenges and Prospective of

Plant Abiotic Stress Volume 1

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Pasala Ratnakumar Kiran Bhagat Yogeshwar Singh

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List of Contributors A. Anna Durai, Sugarcane Breeding Institute, Division of Crop Improvement, Coimbatore, Tamil Nadu, India.

C. Appunu, Sugarcane Breeding Institute, Division of Crop Improvement, Coimbatore, Tamil Nadu, India.

Ajay Arora, Division of Plant Physiology, Indian Agricultural Research Institute, New Delhi, India.

Chandra Obul Reddy Puli, Department of Plant Sciences, School of Life Sciences, Yogi Vemana University, Vemanapuram, Kadapa, Andhra Pradesh, India.

Ajay V. Narwade, Department of Genetics and Plant Breeding, N.M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India. Amit K. Singh, National Research Centre on Plant Biotechnology, IARI, New Delhi, India. Amolkumar U. Solanke, National Research Centre on Plant Biotechnology, IARI, New Delhi, India. Anjali Kumari, National Institute of Abiotic Stress Management (NIASM), Malegaon, Baramati, Distt. Pune, Maharashtra, India. Ashish Jondhale, Department of Agricultural Botany, College of Agriculture, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, Distt. Ratnagiri, Maharashtra, India. B. Lal, Central Rice Research Institute, Cuttack, Odisha, India. B. S. Satapathy, Regional Rainfed Lowland Rice Research Station, Kamrup, Assam, India. Ban Yogesh G, Department of Genetics and Plant Breeding, N.M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India.

Chandra Sekhar Akila, Department of Biotechnology, School of Life Sciences, Yogi Vemana University, Vemanapuram, Kadapa, Andhra Pradesh, India. Chandrakant Singh, Department of Genetics and Plant Breeding, N.M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India. D. Balasimha, Central Plantation Crops Research Institute (CPCRI), Regional Station, Vittal, Karnataka, India. D. Pattanayak, National Research Centre on Plant Biotechnology, IARI, New Delhi, India. D. V. Patil, National Institute of Abiotic Stress Management (NIASM), Malegaon, Baramati, Distt. Pune, Maharashtra, India. G. Suresha, Sugarcane Breeding Institute, Division of Crop Production, Coimbatore, Tamil Nadu, India. G. V. Nagamallaiah, Department of Botany, Yogi Vemana University, Vemanapuram, Kadapa, Andhra Pradesh, India.

Haritha Bollinedi, Division of Genetics, Indian Agricultural Research Institute, New Delhi, India. J. F. Hausman, Department of Environment and Agrotechnologies (EVA), Centre de Recherche PublicGabriel Lipmann, Rue de Brill, 41, L4422, Belvaux, GD Luxembourg. Jayanna Naik Banavath, Department of Plant Sciences, School of Life Sciences, Yogi Vemana University, Vemanapuram, Kadapa, Andhra Pradesh, India. J. L. Katara, Central Rice Research Institute, Cuttack, Odisha, India. K. Mohanraj, Sugarcane Breeding Institute, Division of Crop Improvement, Coimbatore, Tamil Nadu, India. K. B. Pun, Regional Rainfed Lowland Rice Research Station, Kamrup, Assam, India. K. Sergent, Depart ment of Environment and Agrotechnologies (EVA), Centre de Recherche PublicGabriel Lipmann, Rue de Brill, 41, L4422, Belvaux, GD Luxembourg Kanika, National Research Centre on Plant Biotechnology, IARI, New Delhi, India. Kiran Bhagat, National Institute of Abiotic Stress Management (NIASM), Malegaon, Baramati, Distt. Pune, Maharashtra, India. Krishna Kumar Guduru, Department of Plant Sciences, School of Life Sciences, Yogi Vemana University, Vemanapuram, Kadapa, Andhra Pradesh, India.

L. K. Chugh, Bajra Section, CCS HAU, Hisar, Haryana, India. Lekshmy S., Division of Plant Physiology, Indian Agricultural Research Institute, New Delhi, India. Mathithumilan B., Department of Crop Physiology, UAS, GKVK, Bangalore, Karnataka, India. M. J. Sadawarti, Central Potato Research Station, Gwalior, Madhya Pradesh, India. Mohanraju B., Department of Crop Physiology, UAS, GKVK, Bangalore, Karnataka, India. Mukesh Kumar, Central Plantation Crops Research Institute (CPCRI), Kasaragod, Kerala, India. P. Suresh Kumar, National Institute of Abiotic Stress Management (NIASM), Malegaon, Baramati, Distt. Pune, Maharashtra, India. P. B. Taware, National Institute of Abiotic Stress Management (NIASM), Malegaon, Baramati, Distt. Pune, Maharashtra, India. P. S. Sha Valli Khan, Department of Botany, Yogi Vemana University, Vemanapuram, Kadapa, Andhra Pradesh, India. Pallavi P. Deokate, National Institute of Abiotic Stress Management (NIASM), Malegaon, Baramati, Distt. Pune, Maharashtra, India. Poonam Kashyap, Project Directorate for Farming Systems Research, Modipuram (Meerut), Uttar Pradesh, India.

Pranita Jondhale, Department of Soil Science, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, Maharashtra, India.

S. Elain Apshara, Central Plantation Crops Research Institute (CPCRI), Regional Station, Vittal, Karnataka, India.

Prathiba M. D., Department of Crop Physiology, UAS, GKVK, Bangalore, Kranataka, India.

Shashank P. R., Central Rice Research Institute, Cuttack, Odisha, India.

Preeti Goyal, Department of Biochemistry, CCS HAU, Hisar, Haryana, India. Priyanka Gautam, Central Rice Research Institute, Cuttack, Odisha, India. Pruthvi V, Department of Crop Physiology, University of Agricultural Sciences, Bangalore, India. R. Arun Kumar, Vivekananda Parvatiya Krishi Anusnadhan Sansthan, Division of Crop Production, Almora, Uttarakhand, India. Raghavendrarao S., National Research Centre on Plant Biotechnology, IARI, New Delhi, India. Rahul S Lathe, Department of Crop Physiology, University of Agricultural Sciences, Bangalore, India.

Sheshshayee M.S., Department of Crop Physiology, UAS, GKVK, Bangalore, Kranataka, India. Shrawan Singh, Division of Horticulture & Forestry, Central Agricultural Research Institute (CARI), Port Blair, Andaman & Nicobar Islands, India. Sravani Konduru, Department of Plant Sciences, School of Life Sciences, Yogi Vemana University, Vemanapuram, Kadapa, Andhra Pradesh, India. Sudhakar Podha, Department of Biotechnology, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, Andhra Pradesh, India. Sudhir Kumar, Project Directorate for Farming Systems Research, Modipuram (Meerut), Uttar Pradesh, India.

Raju B. R., Department of Crop Physiology, UAS, GKVK, Bangalore, Kranataka, India.

T. Manjunatha, Sugarcane Breeding Institute, Division of Crop Improvement, Coimbatore, Tamil Nadu, India.

Ratnakumar P., National Institute of Abiotic Stress Management (NIASM), Malegaon, Baramati, Distt. Pune, Maharashtra, India.

Teekam Singh, Regional Rainfed Lowland Rice Research Station, Kamrup, Assam, India.

Reena Devi, Depart ment of Biochemistry, CCS HAU, Hisar, Haryana, India. S Lenka, Regional Rainfed Lowland Rice Research Station, Kamrup, Assam, India.

Thakare Harish S., Department of Genetics and Plant Breeding, N.M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India.

Tripti Dogra, National Research Centre on Plant Biotechnology, IARI, New Delhi, India. Upendra Kumar, Central Rice Research Institute, Cuttack, Odisha, India. V. K. Choudhary, National Institute of Biotic Stress Management, Raipur, Chattisgarh, India. Vanita Jain, KAB II, Indian Council of Agriculture Research, New Delhi, India.

Varakumar Pandit, Department of Plant Sciences, School of Life Sciences, Yogi Vemana University, Vemanapuram, Kadapa, Andhra Pradesh, India. Yogeshwar Singh, National Institute of Abiotic Stress Management (NIASM), Malegaon, Baramati, Distt. Pune, Maharashtra, India.

CONTENTS VOLUME - I Page No. 1

1.1 1.2 1.2.1 1.2.2 1.2.3 1.2.4 1.2.5 1.2.6 1.3 1.3.1 1.4 1.5 2

2.1 2.2 2.3 2.4 2.5 3

3.1

Plant tolerance to abiotic stress: A physiological approach Kiran Bhagat, Ratnakumar P., Yogeshwar Singh, P. Suresh Kumar, Pranita Jondhale, Ashish Jondhale, Teekam Singh, Ajay V. Narwade, Poonam Kashyap, P.B. Taware , Pallavi P. Deokate and Anjali Kumari Introduction Plant Stress, Acclimation and Adaptation Elevated CO2 Concentration Light Intensity High Temperature Low Temperature Water Logging Conditions Water-Deficit Conditions Accumulation of Secondary Metabolites Accumulation of Phenolics as a Stress Response Strategies for Regulation and Management of Abiotic Stresses in Crop Production Conclusion References Abiotic stress signal perception: Receptors and its role Pruthvi V and Rahul S Lathe Introduction Receptor-like Kinases (RLKs) Abscisic Acid Receptors G-protein Coupled Receptors (GPCRs) Future Prospective References Role of transcription factors in abiotic stress management Mukesh Kumar, Kiran Bhagat, Preeti Goyal, Reena Devi and L. K. Chugh Introduction

3.2 3.3 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5 3.4 3.5 3.6 4

4.1 4.2 4.3 4.4 4.5 4.5.1 4.5.2 4.5.3 4.5.4 4.5.5 4.5.6 4.5.7 4.5.8 4.5.9 4.5.10 4.5.11 4.5.12 4.6

Stress Signalling Pathways: An Overview Transcription Factors Abiotic Stress-Inducible Genes Transcriptional Factors Involved in Abiotic Stress Response Transcriptional Factor Involved in Response to Drought Stress Transcriptional Factor Involved in Response to Flooding Stress Transcriptional Factor Involved in Response to Salinity Stress Chilling and Cold Stress: Gene Regulation and Transcriptional Factor Post Translational Regulation of Transcription Factor Conclusions References Proteomic approaches for the analysis of salt tolerance in plant P.S. Sha Valli Khan, G.V. Nagamallaiah, K. Sergeant and J.F. Hausman Introduction Effect of Salinity on Plants Responses of Plants to Salinity Proteomic Approach Proteomics in Understanding Plant Responses to Salinity Salt Stress Signal Transduction Transcription and Protein Metabolism Osmotic Homeostasis Ion Homeostasis ROS Homeostasis Photosynthesis Carbohydrate and Energy Metabolism Cytoskeleton and Cell Wall Components Ammonium Assimilation and Amino Acid Metabolism Polyamines Metabolism Purine Metabolism Fatty Acid Metabolism Concluding Remarks and Perspectives References

5

5.1 5.2 5.3 5.4 5.4.1 5.4.2 5.5 5.6 6

6.1 6.2 6.2.1 6.2.2 6.2.3 6.2.4 6.3 6.3.1 6.3.2 6.4

7

7.1 7.2 7.3 7.4

Signaling and uptake mechanisms of nitrogen and potassium under nutrient stress in plants Lekshmy S and Vanita Jain Introduction Uptake and Assimilation of Nitrogen Potassium Uptake and Utilization CBL-CIPK Network Mediated Signaling for Nitrogen and Potassium Deficiency Nitrate Transporter Chl1 (Atnrt1.1) Potassium Channel AKT1 Interaction between Nitrogen and Potassium Nutrition Conclusion and Future Perspectives References Heat stress responses in plants: Insight into physiological and molecular mechanisms Amolkumar U. Solanke, Raghavendrarao S., Kanika and D. Pattanayak Introduction Plant Signalling to Heat Stress Plasma membrane signalling ROS generation and signalling Unfolded protein response Heat Shock Factors and Heat Shock Proteins Genetic Improvement of Plants against Heat Stress Breeding approaches Genetic engineering for high temperature tolerance Conclusions References Bio-engineering of osmolytes production in plants: A novel approach to counter abiotic stress C. Appunu, T. Manjunatha, A. Anna Durai, K. Mohanraj, G. Suresha and R. Arun Kumar Introduction Major Function of Osmolytes Biosynthesis Pathway: Metabolic Engineering for Accumulation of Osmolytes in Crop Plants Metabolic Engineering of Osmolytes in Crop Plants in India

7.5 7.6

8

8.1 8.2 8.2.1 8.2.2 8.2.3 8.3 8.4 8.5 8.6 8.7 8.8 8.9 9

9.1 9.2 9.2.1 9.2.2 9.2.3 9.2.4 9.3 9.4 9.4.1 9.4.2

International and National Status on Osmoprotectants Biosynthesis in Sugarcane Conclusions References Genetic engineering of stay green traits for development of drought tolerant crops Ajay Arora Introduction Significance of plant hormones vis-à-vis senescence Cytokinins: anti-senescent and drought tolerant phytohormone Plant stress and senescence hormone: ABA Ethylene Association between Plant Productivity and Senescence Genetic Manipulation of Senescence for Productivity Enhancement Modification of Leaf Senescence by using other Targets through Transgenic Transgenic Crop Plants with Auto-regulated Expression of the IPT Gene during Leaf Senescence Promoters Impact on Auto-regulated IPT Gene Expression Senescence-responsive Promoter Elements Conclusions and Future Perspectives References Role of microbes in mitigating effect of abiotic stress in agriculture Kanika, Amit K. Singh, Amolkumar U. Solanke and Tripti Dogra Introduction Ethylene and Stress Effect of Ethylene on Plants Genes Involved in Stress Signalling and Response Ethylene Biosynthetic Pathway Transgenic Plants with Altered Ethylene Biosynthetic Pathway Mechanism Beneficial Effect of ACC Deaminase in Overcoming Deleterious Effect of Various Stresses Abiotic Stresses Biotic Stresses

9.5 9.6

10

10.1 10.2 10.3 10.3.1 10.3.2 10.3.3 10.3.4 10.3.5 10.4 10.4.1 10.4.2 10.4.3 10.4.4 10.5 10.5.1 10.5.2 10.5.3 10.6 10.7 10.7.1 10.7.2 10.8

Plant Gene Expression Modified by Bacteria with ACC Deaminase Conclusion and Future Prospective References Abiotic Stress in Rice: Mechanism of Adaptation Teekam Singh, K B Pun, Kiran Bhagat, B Lal, B S Satapathy, MJ Sadawarti, JL Katara, S Lenka, Priyanka Gautam Introduction Abiotic Stresses in Rice Stress Physiology and Adaptation Mechanism High Temperature and Humidity Heat Stress at Different Ontogenetic Stages High Night Temperature Temperature and CO2 Interaction Genetic Improvement for Heat Tolerance Drought and Adaptation Mechanisms Basis of Grain Formation Failure under Drought Breeding Approaches for Drought Stress Resistant Agronomic Approaches to Cope with Drought Integrated Holistic Approaches to Combat Drought Adaptation Mechanism of Rice to Flooded or Submergence Physiology and Molecular Basis of Submergence Tolerance Submergence Stress during Early Seedling or Germination Stage Submergence Stress during Vegetative Stage Medium Deep and Deep Water Stress Salinity Stress and Adaptation Mechanism of Salinity Stress Adaptive Mechanisms for Salinity Stress Nutritional Deficiency References

VOLUME II Page No. 11

Drought adaptive traits in rice: Need for comprehensive approach Raju, B R, Mathithumilan, B, Pratibha, M D, Sheshshayee, M S, Mohanraju, B, Haritha Bollinedi, Shashank, P R and Upendra Kumar 11.1 Introduction 11.2 The Challenges and Target 11.3 Drought and Drought Mitigation 11.4 Conceptual Framework to Improving Rice Adaptation in Rainfed Condition 11.4.1 Traits that have Relevance in Improving Rice Crop Adaptation 11.4.1.1 Root Traits 11.4.1.2 WUE is other Major Trait Relevant to Enhance Drought Adaptation 11.4.2 Comprehensive Strategies to QTLs Discovery and Pyramiding for Drought Adaptive Traits 11.5 QTL Mining to Identify Candidate Genes 11.6 Conclusive Remarks References 12

12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 12.10 12.11

Abiotic stresses and Vegetable production in Tropical Islands Shrawan Singh Introduction Andaman and Nicobar Islands Soil and Climate of Islands Vegetable Scenario Abiotic Stress and Management Water Stress Heavy Rains Salinity and Vegetable Production Temperature Extremes Wind Stress Light Stress

12.12 12.13 12.14 12.15 12.15.1 12.15.2 12.15.3 12.15.4 12.15.5 12.16 12.17 12.18 12.19 12.20

Agro-Chemical Stress High Relative Humidity Imbalance in Plant Nutrition Management Efforts Indigenous Vegetables Suitable Varieties of Vegetable Crops Protected Cultivation Weed Management Nutrient Management Vegetables in Cropping System Integrated Farming System Integrated Management of Diseases and Pests Land Resource Management Techniques Climate Resilient Vegetable Cultivation References

13

Genetic engineering of groundnut for abiotic stress challenge and prospects Jayanna Naik Banavath , Varakumar Pandit , Sravani Konduru, Krishna Kumar Guduru, Sudhakar Podha, Chandra Sekhar Akila and Chandra Obul Reddy Puli Introduction Area, Production and Productivity Abiotic Stress Effect on Groundnut Physiological and Biochemical Responses Molecular Responses Gene Expression Induced by Abiotic Stresses Proteomic Approach to Identify Functional Gene Products Induced by Abiotic Stress Genetic Engineering Approach to Improve Abiotic Stress Tolerance Development of Reliable Regeneration Protocols Development of Transformation Methodologies Agrobacterium-mediated Transformation Micro-projectile Bombardment In planta Method Markers Used for Groundnut Transformation Evaluation Methods for Transgenic Groundnut Plants

13.1 13.1.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.8.1 13.8.2 13.8.3 13.8.4 13.8.5

13.9 13.10 13.11

14

14.1 14.2 14.2.1 14.2.2 14.2.3 14.2.3.1 14.2.4 14.2.4.1 14.2.4.2 14.2.4.3 14.2.4.4 14.2.5 14.2.5.1 14.2.5.2 14.2.5.3 14.2.5.4 14.3 14.3.1 14.3.1.1 14.3.1.2 14.3.1.3 14.3.1.4 14.4 14.4.1 14.4.2 14.4.3

Transgenic Plants for Groundnut Abiotic Stress Tolerance Transgenic Plants-Regulatory Measures, Risks and Concerns Challenges and Prospects References Abiotic stress responses in sugarcane Ajay V. Narwade, Kiran Bhagat, D.V. Patil, Anjali Kumari, Ban Yogesh G, Thakare Harish S and Chandrakant Singh Introduction Impact of Drought on Sugarcane Growth and Yield Biophysical and Biochemical Parameters Water Relations in Sugarcane Quantifying Water Use by Sugarcane Water Stress and Growth and Development Soil Water Potential and Germination Water Potential and Leaf Initiation Leaf And Stalk Elongation Response of Leaf Area to Water Stress Photosynthesis, Partitioning and Water Stress Radiation Interception Water Stress and Photosynthesis Photosynthesis, Enzyme Activity and Metabolite Levels Dry Matter Partitioning Implications of Water Stress Physiology and Priorities for Future Research Irrigation Management Early Canopy Development Phase (Lai < 2) Late Canopy Development (LAI > 2) and Stalk Elongation Phase Maturing Phase (Drying-off) Optimum use of Limited Water Breeding Strategies for Abiotic Stress Management Commercially Important Traits in Sugarcane Sugarcane Genotypes Selection Early Arrowing

14.4.4 14.4.5 14.5

Drought Associated Genes High Brix Conclusions References

15

Cocoa (Theobroma cacao l.) towards climate resilient horticulture S. Elain Apshara and D. Balasimha Introduction Effect of Agro- Climatic Conditions Effect of Shade Effect of Irrigation Effect of Photosynthesis Breeding for Drought Tolerance in Cocoa Visual Estimates of Vigour and Physiological Traits in Cocoa Estimation of Vigour of Cocoa Trees Flowering, Fruiting and Flushing Rates Branching Pattern, Canopy Size and Shape Canopy Density and Light Interception Pruning Intensity and Canopy Architecture Drought Tolerance Studies at CPCRI Water Logging Carbon Sequestration Climate Change Conclusion References

15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.7.1 15.7.2 15.7.3 15.7.4 15.7.5 15.8 15.8.1 15.8.2 15.8.3 15.9

16

16.1 16.2 16.2.1 16.2.2 16.2.3 16.2.4 16.3 16.3.1 16.3.2

Abiotic Stresses –management and mitigation in fruit crops Poonam Kashyap and Sudhir Kumar Introduction Pre-Harvest Stresses Drought Temperature Stress Water Logging Salinity Post-Harvest Stresses Temperature Extremes Quality of Air

16.3.3 16.3.4 16.4 16.4.1 16.4.2 16.4.3 16.4.4 16.5

Mechanical Injury Desiccation Approaches to Mitigate Abiotic Stresses Treatments to Enhance Stress Resistance Germplasm Selection Marker - Assisted Breeding Molecular Engineering Conclusions References

17

Physiological and breeding approaches for abiotic stress in cotton Ajay V. Narwade, Kiran Bhagat, D.V. Patil, Anjali Kumari, Thakare Harish S, Chandrakant Singh and Ban Yogesh G Introduction Water Deficit Stress Effects of Water Deficit Stress on Morphological Characteristics Effects of Water Deficit Stress on Physiological Characteristics Carbohydrate Production and Water Stress Plant Mechanism Underlying Resilience to Water Deficit Effect of Water Deficit on Yield and Fiber Quality Effect of High Temperature Stress Effect of Salinity Stress Causes of Salinity Stress Effect of Salinity on Growth and Yield Mechanisms of Salinity Tolerance Management against Salinity Conditions Effect of Water-logging Breeding Approaches for Abiotic Stress Management Sources of Drought Resistance and Breeding Methods Drought Escape and Early Maturity Recent Approaches to Study Abiotic Stress in Cotton Functional Genomics

17.1 17.2 17.2.1 17.2.2 17.2.3 17.2.4 17.2.5 17.3 17.4 17.4.1 17.4.2 17.4.3 17.4.4 17.5 17.6 17.6.1 17.6.2 17.7 17.7.1

17.7.2 17.8

Structural Genomics Future Prospective References

18

Soil and water conservation measures in resource scarce dryland areas: a way to mitigate abiotic stresses in climate change scenario P. Suresh Kumar, V.K. Choudhary, Yogeshwar Singh, D.D. Nangare, P. Ratnakumar, A. Sangeetha, Kiran Bhagat and P.B. Taware Introduction Characteristics of Dryland/ Rainfed Agriculture Some of the Major Constraints in Dryland Agriculture Water and Soil Conservation Measures Water Harvesting Water Harvesting within Field Catchment Water Harvesting by External Catchment Integrated Watershed Management (IWM) Approach A Work Layout for Watershed Development Components of Watershed Benefits of Watershed Management Agronomical Measures in Soil and Water Conservation Horticulture and Agroforestry: A Sustainable Management Option Contribution of Trees towards Improvement of Soil Water Regime Future Thrust Policy Interventions Conclusion References

18.1 18.2 18.2.1 18.3 18.4 18.4.1 18.4.2 18.5 18.5.1 18.5.2 18.5.3 18.6 18.7 18.7.1 18.8 18.9 18.10

CHAPTER 11 DROUGHT ADAPTIVE TRAITS IN RICE: NEED FOR COMPREHENSIVE APPROACH Raju, B.R.1*, Mathithumilan, B.1, Pratibha, M.D.1, Sheshshayee, M.S.1, Mohanraju, B.1, Haritha Bollinedi2, Shashank, P.R.3 and Upendra Kumar3

Abstract To feed the growing population, improvement in adaptability of crop plants to water limited condition has become an urgent task under the background of global crisis of water resource. Though, plants exhibit diverse adoptive mechanisms that differ across developmental, morphological, biochemical and physiological levels. From the ecological perspective, any mechanism or trait has relevance if it determines the survival of a species under drought. From the crop physiologist’s view, to improve crop yield requires the optimization of water relation traits, effective water mining by root traits and efficient use of water for biomass production are the most relevant traits to achieve a comprehensive improvement in drought tolerance as well as productivity. These are being complex polygenic traits, discovery of QTL by association mapping and subsequent validation in trait specific mapping provides a powerful option for dissection of complex traits. Introgression of consistent root and WUE QTLs on to single elite background helps in development of superior pre-breeding as well as mega varieties.

11.1 Introduction Rice, the most prominent millenary crop caters to over 50% of the dietary requirement of the world’s population. It has played an important role in ensuring food security since ages (Luo and Zhang 2001). Cereal grain production should achieve an annual growth rate of 1.92% by 2030 against the current rate of 0.62% for India to remain food secure (Economic 1

Department of Crop Physiology, UAS, GKVK, Bangalore 65

2

Division of Genetics, Indian Agricultural Research Institute, New Delhi 110012

3

Central Rice Research Institute, Cuttack 753006, Odisha

*

Author for correspondence: [email protected]

312 survey of India 2012). Considering the current annual production growth rate (0.6%) increasing yield is certainly a dreadful challenge. Agriculture is one of the vital industries which would be critically affected by climate change, population pressure and water scarcity. Thus, enhancing food production under the looming water crisis poses the one of the greatest challenges to modern agricultural research. Ironically, the most populated continent viz., Asia and Africa are also the places where around 90% of the dietary carbohydrate come from rice. These continents are currently sealing under sever water deficit conditions, and hence sustaining rice production with reduced water availability is yet another challenge. It can therefore be visualized yield potentials are the two important tasks towards attaining food security. Rice cultivation is a water intensive practice accounting 56% of the world’s irrigated area of all crops and in Asia, it accounts about 46% of all the fresh water used in agriculture practices (Dawe 2005). However, rice is cultivated in diverse ecosystems with extreme variation with extreme variation in water availability of the total 156 million hectare (mha) of land contributing to rice cultivation (IRRI 2006), globally, 79 mha comes under puddled cultivation which contributes to little over 65% of global rice production. A significant portion of rice production (35%) comes from rainfed low land where water availability is generally high during the monsoon seasons. Though in these ecosystems the yield is quite high, the average yield in Indian condition is around 2.3 tons per hectare (t. ha-1). The low average yield arises due to a larger area under rainfed and upland conditions. Though only, 7 per cent of the world’s rice area is devoted to upland rice culture more than 100 million people depend this condition for their daily staple food requirement (http:// www.fao.org). The upland rainfed conditions are characterized by low water availability. Garrity et al. (1986) estimated that rice production decreased by 13 to 35 % depending on rainfall. These areas are designated as “drought prone” in the classification of rainfed rice environments i.e. upland and rainfed lowland. Further, water scarcity coincides with prevalence of problematic soils with poor physical and chemical properties. To enhance the yield potential of rice further, several breeding programs were initiated in the mid 1990s. Though a large area of rice cultivation comes from rainfed ecosystems, over half all fresh water used in agriculture is used for cultivating rice (Dawe 2005). With an increase in demand for water from industry and domestic sectors, it is predicated that

313 increase in rice production must come with reduced amount of water. Therefore, increasing the yield potentials and saving water rice fields are major challenges (Bouman et al. 2002). 11.2 The Challenges and Target Breeding for semi dwarf and use of heterosis concepts during green revolution (1960-1970) led to increase in rice production by 2 fold to overcome famine. During this period there was sudden leap in production of rice yields across the world and the average yield increased from 1.7 t ha-1 (1960) to 3.6 t ha-1 (1990). Further, improvement in total rice production can be achieved either by increasing the area under irrigation. With the enhanced demand for water from other sectors coupled with erratic and often insufficient rainfall increasing the irrigated area is hard to achieve. On the other hand, despite the advancement in methodology for genetic enhancement, yield levels in the last few years have stagnated. Thus, the challenges are very clear. Save water and increase productivity under limited water scenario. This challenge necessitates the development of drought tolerant rice cultivars with sustained yield levels, if not more (Luo and Zhang 2001). Several water saving strategies like saturated soil culture (Borrell et al. 1997 ; Tabbal et al. 2002), alternate wetting and drying (Cabangon et al. 2001), system of rice intensification (Stoop et al. 2002) and aerobic (Bouman et al. 2002) methods have been developed as water saving strategies. These technologies are effective in addressing the water limited conditions by increasing water productivity (rainfall/irrigation), reducing unproductive losses (seepage, percolation and evaporation), minimizing emission of green house gases and management costs (Bouman et al. 2001; Bouman et al. 2002; Subramanian et al. 2006). Among these, an aerobic practice is most relevant as an important water saving strategy. However a significant yield reduction up to 40% is normally enhanced (Bouman et al. 2002). Therefore, sustaining productivity through development of improved cultivars more resilient water limited condition is need for an hour (Semenov and Halford 2009; Reynolds et al. 2011; Chapman et. al. 2012). Thus, development of strategy to save water while sustaining yield potentials is the target. Improving yield levels under water limited conditions warrants pyramiding several diverse traits into single elite genetic background. Considering the complexity of drought tolerance, pyramiding relevant traits is the challenge.

314 11.3 Drought and Drought Mitigation Plants exhibit diverse adoptive mechanisms that differ across developmental, phenological, morphological, biochemical and physiological levels. Though drought accosting mechanisms viz., Drought escape (DE) Drought avoidance (DA) and Drought tolerance (DT) possess various connotations (Table 1), they are usually involved together with the plant functions (Turner et al. 2001). However, little efforts have been made in dissecting the genetic determinants of drought resistance, because of its complex nature. In this context, a better knowledge on the genetic makeup and molecular basis of traits (primary and secondary) that underlie the adaptive response of crops across a broad range of soil moisture is an essential prerequisite toward more effective and targeted breeding activities (Araus et al. 2002; Salekdeh et al. 2009). Drought-mitigation traits have been classified as primary traits, secondary traits and integrative traits (Table 2). These drought responses traits have been reviewed extensively and their significance is substantiated (Paterson 1995; Sheshshsyee et al. 2003; Lafitte et al. 2004; Passioura 2007; Kamoshita et al. 2008; Serraj et al. 2009; Songsri et al. 2008; Passioura 2010; Passioura 2012; Tuberosa 2012; rest of the references are therein). However, encouraging heritability of physiological or secondary traits that are highly correlated with yield gives a good opportunity for plant breeding in drought-prone regions (Stiller et al. 2005). Hence, the major focus has been to device novel approaches (analytical, trait based breeding) for achieving the task of trait pyramiding to improve drought stress tolerance of rice crop to sustain productivity under water limited conditions. 11.4 Conceptual Framework to Improving Rice Adaptation in Rainfed Condition Realization has dawned that the richness of the genomic information that has been accumulating would not be well mobilized without connecting it to accurate phenotypic information. Though, large numbers of segregating populations were developed with different genetic backgrounds, there has not been much progress in high throughput phenotyping of traits (Fischer et al. 2012). The question that arises must address which traits would be most relevant to the breeder. Breeding efforts to improve productivity under water limited conditions is dependent on the issues ·

Traits that have relevance in improving rice crop adaptation

·

Comprehensive strategies to discover QTLs/gene and their pyramiding

315 Several simple criteria for selecting for improving drought tolerance were proposed by Arus et al. (2008). Any trait that has relevance only when it has a ‘causal’ relationship with drought tolerance. The trait must not lead to reduction in yield when stress is taken off. The trait must not have high G x E and hence must exhibit stability. From the crop physiologist’s perspective, plant water relations play a very crucial role in determining the level of drought adaptation in plants. Furthermore, any trait would have relevance only when it sustains productivity by maintaining better water relation with superior crop growth rates (Richard et al. 2002; Udayakumar and Prasad 1994). The past decade witnessed a significant increase in efforts towards analyzing the relevance of drought avoidance traits. Drought avoidance is the major factor in drought-resistant performance in plants while drought tolerances (dehydration tolerance) act as the second line of defence subsequent to dehydration avoidance (Blum, 2005). Among a large number of traits enumerated for drought avoidance (Table 1), water mining and water conservation traits have relevance in maintaining water relation and metabolism under water limited conditions (Table 2, 3) (O’Toole and Bland 1987; Lynch, 2007). From this context, Passioura crop yield (CY) model (1977) components water use (WU), water use efficiency; (WUE) and Harvest index (HI) became major target traits for crop improvement under water limited conditions. Thus maximizing yield instead of survival under water limited can be achieved only by the understanding and dissection of CY functions i.e. WU (function of evaporative demand and supply, root traits), WUE (dry matter per unit of water used/ transpiration) and HI (economic yield to biological yield). Reports mentioned that none of these CY functions are independent, this suggests that improvement in these traits can be possible by adopting analytical/prebreeding approaches while selecting for secondary traits (Araus et al. 2002; Richards et al. 2002; Condon et al. 2004;Tambussi et al. 2007). Therefore understanding of physiological, genetics and molecular regulation of CY components alone could help in rice improvement (Fukai and Cooper 1995; Serraj et al. 2009) under drought/rainfed environment.

316 Table 1: List of important mechanism/ traits associated with drought adaptation in rice for which variability was reported in literature Traits

Reference

Drought escape (DE)

 

Rapid Phenology/ plasticity

Chaves et al. 2003; Barrnabs et al. 2008

Photoperiod sensitivity

Kumar et al. 2007

Dehydration avoidance (DA)

 

Minimizing water loss/ Pessimists Stomatal conductance

Ludlow and Muchow 1990

Leaf rolling

Courtois et al. 2000

Stay greenness

Ishimaru et al. 2001

Canopy temperature

Garrity and O’Toole 1995

Water Use efficiency

Dingkuhnu et al. 1991

Epicuticular wax

Baenziger et al, 1983

Maximizing water uptake/Optimists Root traits

O’Toole and Bland 1987; Ludlow 1989

Hydraulic conductivity

Henry et al. 2011

Dehydration tolerance (DT)

 

Osmotic adjustment

Lilley and Ludlow, 1996

Cell-membrane stability

Tripathy et al. 2000

Antioxidant capacity and chaperons

Moran et al. 1994, Mittler 2002

Desiccation tolerance

Lilley and Ludlow 1996

Cellular level tolerance

Raju et al. 2012

Relative water content

 Courtois  et al. 2000

Table 2: List of putative drought adaptive traits in rice for which variability and QTLs has been reported Traits

Reference

Morphological traits

 

Biomass (IT)

Lian et al. 2005

Flowering time (ST)

Brondani et al. 2002

Leaf area (IT)

Yan et al. 2003

317

Leaf & shoot dry weight (IT)

Yan et al. 2003; Lian et al. 2005

Leaf emergence rate (ST)

Dong et al. 2004

Leaf rolling (ST)

Venuprasad et al. 2007b

Panicle exsertion (ST)

Hittalmani et al. 2003

Plant height (PT)

Yan et al. 1998b

Root traits (PT)

Nguyen et al. 1997; Kamoshita et al. 2008

Tiller number (IT)

Yan et al. 1998a

Panicle associated traits (IT)

Hittalmani et al. 2003

Yield (IT)

Hittalmani et al, 2003; Bernier et al. 2007

Physiological traits

 

Abscisic acid content (ST)

Quarrie et al. 1997

Canopy temperature (ST)

Garrity and O’Toole 1995

Cell viability (PT)

Miura, et al. 2002

Gas exchange parameters (PT)

Centritto, et al. 2009

Leaf senescence / Stay green (ST)

Ishimaru, et al. 2001

Membrane stability (PT)

Tripathy, et al. 2000

Osmotic adjustment (PT)

Lilley and Ludlow 1996

SCMR (ST)

Yue et al. 2006; Mohankumar 2012

Stomata conductance (PT)

Dingkuhn et al. 1991; Price et al. 1997, Sinclair et al. 2011

Leaf water status/relations (ST)

Courtois et al. 2000

13

Water Use Efficiency by C (PT)

Dingkuhn et al. 1991; Condon et al. 2004; Impa et al. 2005

Other traits

 

Protein content (PT)

Ye et al. 2010

Harvest index (IT)

Hittalmani et al. 2003

Drought index score (IT)

Bernier et al. 2009

Spikelet sterility (ST)

Hittalmani et al. 2003

Note: Letters in parenthesis refers to, PT: Primary traits, ST: Secondary traits, IT: Integrated traits. Now they need to be tested for their relationship with performance under drought stress, and suitable high-throughput screening strategies must be developed.

318 11.4.1. Traits that have Relevance in Improving Rice Crop Adaptation 11.4.1.1 Root Traits Rice plants have adopted several drought resistance mechanisms to overcome drought effects, which ranges from cellular level to whole plant level. Rice plants maintain growth and productivity by maintaining tissue water relations and positive carbon by mining water from deeper soil profiles (Fukai and Copper et al. 1995). Root system architecture (RSA) describes the spatial organization of root systems, which is critical for root function in challenging environments, and it as potential to boost or stabilize yields in drought, and reduce the need for unsustainable fertilizers (Christopher et al. 2013). Roots exhibit an surprising level of morphological plasticity in response to soil physical conditions (Kato et al. 2006; Lynch 2007; Siopongco et al. 2009), a peculiarity that allows plants to adapt better to the chemical and physical properties of the soil, particularly under drought conditions (Bacon et al. 2000; Yu et al. 2007). Genetic variation in rice root morphological traits (root length, root diameter, root depth root pulling force, deep root to shoot ratio, root number, root growth plasticity, root penetration ability and root length density) has been exploited greatly than anatomy traits under different moisture regimes with using phenotyping methods in diverse genetic resources in rice (Table 3 and 4). Root traits have been claimed to be critical in improving water relation which in turn increases yield, apart from nutrient absorption under moisture stress (Serraj et al. 2004; Li et al. 2005; Lynch 2007; Araus et al. 2008; Songsri et al. 2008). Despite a few contradicting evidences, there is equivocal consensus on the importance of root traits for drought adoption (Pantuwan et al. 2002). Nevertheless, progressing in screening for root traits and capturing genetic variability in this trait has been extremely slow. The main drawback is being difficulty in phenotyping and their use as selection criteria in field-grown plants. A number of techniques are available for the estimation of root traits in the soil profile (Table 4). As an alternative to root phenotyping in field experiments, a number of studies have measured roots in plants grown under controlled conditions. This allows more rapid and accurate analysis of root features. A reasonable compromise to avoid both the unnatural conditions present in hydroponics and/or aeroponics and the difficulty of studying roots in the field is circumvented by growing plants in pots, columns (PVC pipes), root structures, monolith, minirhizotrons, and/or observation chambers filled with soil (Smit et al. 2000; Azhiri-Sigari et al. 2000; Wade et al. 2000; Sheshshayee et al. 2011a, b). The most ideally

319 adopted methods for root studies are outlined in the Table 4. Table 3: List of root traits in rice for which variability and QTLs were reported. (Synthesised from Gowda, et. al. (2011)) Root Characters

Proposed function

Reference

Root length

Potential for absorption of soil moisture and nutrients in deeper soil layer

Nicou et al. 1970; Kato et.al. 2006

Root branching

Power of soil exploration (the major contribution to total root length)

Fitter 1991; Ingram et al. 1994

Root diameter

Potential for penetration ability, Armenta-Soto et al. branching, hydraulic 1983 conductivity

Root dry weight

To explore a greater soil volume Yadav et al. 1997

Root length density

Rate of water and nutrient uptake

Mohankumar et al. 2010

Root number

Physical strength, potential for root system architecture

Armenta-Soto et al. 1983

Root pulling force

For root penetration into deeper soil layers

O’Toole and Bland 1987

Root to shoot ratio

Assimilate allocation

Asch et al. 2004

Root volume

The ability to permeate large volume of soil

Mohankumar et al. 2012

Hardpan penetration ability

Ability to penetrate subsurface hardpans

Babu et al. 2001; Clark et al. 2000, 2008

Deep root to shoot ratio

Potential for absorption of soil Yoshida and Hasegawa, moisture and nutrient in deeper 1982 soil layers

Hydraulic conductivity

Rate of water uptake

 Henry et al.  2011

Table 4: List of phenotyping methods for root traits reported in the literature for rice. Methodology for Phenotyping

Reference

2D root system platform

Clark et al. 2012

Basket method

Uga et al. 2009, 2011

Hydroponic system

Henry et al. 2011

PVC pipes

Hemamalini et al. 2000.

320

PVC tubes (Diameter of 0.2 m and a height of 0.6 m).

Asch et al. 2005

Root boxes (25cm in length, 2 cm in width and 40cm in depth)

Wang et al. 2009

Root structure (60 feet length, 8 feet width., 5 feet height)

Sheshshayee et al. 2011a,b

Soil-filled glass rhizotrons for visualizing roots

Price et al. 2002

Three-Dimensional Root Phenotyping

Clark et al. 2011

Wax layers

Acuna et al. 2007

Wax-petrolatum layer system

Yu et al. 1995

Semiautomated 3D Root Imaging

Christopher et al. 2013

Apart from these methods for root phenotyping, recently several new methods have been developed which could divulge the two and three dimensional root architecture such as gel- or soil-filled chambers, soil sacs, pouches, paper rolls, X-ray microtomo graphy and magnetic resonance imaging (MRI) have also been used to investigate bi- and tri-dimensional root architecture, reviewed by Sheshshayee et al. (2011a, b) and Tuberose (2012). Root traits are under the control of multiple genes and display a quantitative inheritance pattern and hence show a significant hence genotype x environment (G×E) interaction (McCouch and Jung 2013). Further, root traits also displays tremendous plasticity that depends on the soil conditions and stress intensity (Kano et al. 2011). This plasticity of root traits has tremendous relevance in developing adaptations to varied stress effect (Kato et al. 2006). Thus, traits plasticity emphasise the importance of understanding and capturing genetic variability in root traits in target environment. Most of the QTL studies have measured root traits in containers under controlled conditions, although it has yet to be proven whether these results reflect true genetic differences (Steele et al. 2007). Identification of QTLs associated with drought is has relevance in selection of superior varieties (Bernier et al. 2008). Accordingly, QTLs related to several root architecture associated traits have been identified in rice (Kamoshita et al. 2008; McCouch and Jung 2013). Most of the QTL mapping studies for rice root traits conferring drought tolerance have been conducted using progenies (F2, back cross inbred lines, doubled haploid lines and mostly recombinant inbred lines) derived from different subspecies groups (japonica x indica) rather than same species (Fischer et al. 2012; McCouch and Jung 2013). Often, these parental lines exhibit slight morphological differences, but their progeny

321 exhibit considerable genetic variability for many root traits (transgressive segregation). QTL discovery has been achieved exploiting these transgressive segregants. The number of such lines used ranged from 56 (Hemamalini et al. 2000) to 220 (Kamoshita et al. 2002). Variation in root architecture associated traits among the progenies led to identification of QTLs that ranged from 1 to 19 and the phenotypic variability explained by any one QTL ranged from about 4% to as much as 66.6% (Gowda et al. 2011). More recently, variation in traits among naturally varying germplasm accessions is being analysed to identify genomic region that governs complex traits (McCouch and Jung 2013). Mohankumar et al (2012) phenotyped a panel of diverse germplasm accessions of rice for variation in root traits and identified SSR markers by LD based association mapping. With the significant progress made in genotyping genomic resources coupled with superior computational facilities, identifying QTL has become quicker, and cheaper. However, trait identification, and phenotyping appears to be the region major bottle neck, especially for improving drought tolerance. Using these populations, a number of QTLs mapped for root traits, but their application in MAS has still remained. Major reason seems is the lack of precise and high through phenotyping strategies (Sheshshayee et al. 2011a, b). Our conceptual frame work emphasis that maintenance of leaf water relations through effective water mining by root traits and efficient use of water for biomass production, most relevant to achieve a comprehensive improvement in drought tolerance as well as productivity (Sheshshayee et al. 2011a, b). 11.4.1.2 WUE is other Major Trait Relevant to Enhance Drought Adaptation WUE is defined in different ways. At the single leaf-level, instantaneous WUE (WUE is) also referred to as transpiration efficiency (TE), is generally measured as the net amount of carbon assimilated (A) per mole of water transpired (transpiration rate, T) (Farquhar and Richards 1984; Farquhar et al. 1989; Condon et al. 2002; Bacon 2004). A similar parameter, intrinsic WUE (WUE intrinsic or WUEic) is defined as the ratio between photosynthesis (A) and stomatal conductance (gs) (Choi et al. 2007), which is thought to be more closely associated with independent physiological responses of gas exchange traits to specific environmental conditions. For agronomists and plant breeders, WUE is typically calculated as the accumulated dry matter divided by the amount of water consumed by the crop during the whole growth cycle (WUE integrative or WUEie)

322 (Condon et al. 2004; Tuberosa, et al. 2007). Accurate water budget and crop biomass measurements required for WUE estimation is labour intensive, time-consuming and expensive, and therefore unattractive to plant breeders, especially under field conditions (Table 5). Further, effort was made to understand the genetic diversity of TE in different crop species by identifying surrogates that are closely associated with traits. Reports suggested the existence positive relationship of TE with chlorophyll content, SCMR and leaf nitrogen (Rao et al. 2001; Bindumadhava et al. 2003). Studies from in-house and across the world, showed the presence of genotypic variation in rice for gas exchange parameters and WUEic (A/gs). Because of instantaneous nature of measurement at leaf level it was low throughput and is the major limitation. During 1980s innovation of promising Carbon Isotope Discrimination (CID,??13C) Technique was came to field has a time averaged surrogate for measuring WUE at whole plant level (O’Leary 1981; Farquhar et.al. 1982; Udayakumar and Prasad 1994; Sheshshayee et. al. 2003) and it accelerated the breeding activities. Carbon isotope discrimination (CID) measures the ratio of stable carbon isotopes (13C/12C) in the plant dry matter compared to that of the CO2 in atmosphere (O’Leary 1981; Condon et al. 1990). The linking ?13C with the observed variations in WUE is well developed (Farquhar, et.al., 1982) and widely adopted (Sheshshayee et al. 2003 and reference therein). Commonly, but not always (Turner et al. 2007), ?13C negatively associated with WUE over the period of biomass accumulation (Condon et al. 1990, 2004; Araus et. al., 2002; Rebetzke et al. 2002; Chen et al. 2011). Under drought stress also ?13C is also a good predictor of stomatal conductance (Condon et al. 2002) and WUE in crops (Tambussi et al. 2007). Further, ?13C being used as a surrogate for WUE in different crop species including rice (Dingkuhn et al. 1991; Peng et al. 1998; Impa et al. 2005; Mohankumar et al. 2012).These approaches were used to study WUE in tropical japonica with those of indica cultivars based on leaf gas exchange rates (A/T) under irrigated conditions, Peng et al. (1998) found that indica cultivars had generally higher T than tropical japonica lines, and the A/T ratio was 25– 30% higher for the tropical japonica than for indica. Moreover, lower ?13C values in the tropical japonica compared to indica, confirmed the observed differences in A/T. Several reports in rice showed the correlation between WUEic and whole plant WUE using gas exchange and stable isotope studies respectively (Sheshshayee et al. 2003). Further, Impa et al. 2005 confirmed the relationship between gravimetrically determined WUE and ?13C among

323 the rice germplasm and showed the utilization of genetic variability of WUE as a trait in breeding program. Table 5: Several common definitions of water-use efficiency (WUE) for which phenotyping methods are reported in the literature. Level/Time scale

Equation/ Formula

Methods

Reference

Leaf (Minutes or hours)

WUEInstantaneous = Photosynthesis (A, µ mol m-2 s-1)/ Transpiration (T, m mol water m-2 s-1)

Infar red gas analyser (IRGA)

Peng et al. 1998 Impa et al. 2005

WUEIntrinsic = Infar red gas Photosynthesis (A, analyser (IRGA) µ mol m-2 s-1)/ Stomatal conductance (gs, mol water m-2 s-1)

Hall et al. 1992 Choi et al. 2007

WUE= SCMR vs. SLA SPAD chlorophyll Rao et al. 2001 (negative relation) meter reading Sheshshayee et al. (SCMR), Specific 2006 leaf area (SLA) Crop (Weeks to months)

WUEintegrative = Aboveground biomass/ Gravimetric Seasonal method evapotranspiration WUEeconomic = Grain yield/Seasonal evapotranspiration

Seedling/ whole plant 13C = (13Ca-13Cp)/ (Time averaged) (1+13Cp/1000)

Impa et al. 2005 Tuberosa et al. 2007

Gravimetric method

Reviewed by Chen et al. 2011

Isotope Ratio Mass Spectrometer (Carbon isotope discrimination)

Farquhar et al. 1982, 1989 Udayakumar and Prasad 1994 Sheshshayee et al. 2003

Despite the determination of significant genetic variability using a robust, high throughput assessment approach, breeders were not enthusiastic in exploiting the variability in WUE. The inconsistency of the relationship between WUE and biomass which showed, positive, negative and even natural correlation (Richards et al. 2002; Sheshshayee et al. 2003; Condon et al. 2004), was the primary concern in large scale breeding programs (Sheshshayee et al. 2012). Udayakumar et al. (1998) critically explained the hidden secrets of gs dependent (conductance/ stomata mediated) and gm dependent (capacity/ mesophyll mediated) WUE. More

324 recently, Sheshshayee et al. (2012) analysed the sub components of WUE and discussed how WUE can still be a potential trait that can be considered for crop improvement. They demonstrated with experimental data that increasing WUE has tremendous significance after optimizing water use (through root) and /or light interception characters through canopy cover. Improved WUE could potentially be achieved through two possible approaches (Flexas et al. 2010) by: 

Increasing CO2 diffusion to the carboxylation sites by maintaining gs, which could be attained by increasing mesophyll conductance to CO2 (gm)



Improving the Rubisco carboxylation efficiency, which could be realized by introducing carboxylase enzyme from other species?

Rice genotypes with inherently higher gm were capable of maintaining higher A under-water-deficit conditions (Centritto et al. 2009). This understandings opens up an option for studying genetic variation, selection and utilization of high WUE in crop improvement using molecular breeding and transgenic approach. Continuous genetic variation of these traits generally under considerable environmental influence is governed by QTL. Understanding the genetic basis of WUE is important for crop improvement under waterlimited environments. The first QTL identified for ?13C was reported in tomato (Lycopersicon esculentum and L. pennellii) by Martin and Nienhuis (1989) and subsequently QTL for ?13C have been reported in other species including rice (Impa et al. 2005; Nadarajan et al. 2005; Laza et al. 2006; Takai et al. 2006; Xu et al. 2009; This et al. 2010). The first report on identifying a gene from QTL was made by Masle et al. (2005). They cloned a gene called ERECTA, a Leucine rich receptor (LRR- RLK) that was responsible for the leaf surface pattering. Takai et al. (2009) found that a QTL controlling leaf ?13C on the long arm of chromosome 3 in rice was associated with gs. Diab et al. (2008) reported that QTL for ?13C and transpiration were on the same locus (gwm389). However, no single QTL for ?13C with large effect has been identified in cereals including in rice, and most QTL identified for ?13C have small effects. Two years of dryseason field testing at IRRI have recently confirmed the association between grain yield and CID among rice lines segregating for a major QTL of yield under drought (Source: IRRI website). A few groups discovered QTLs and candidate genes for WUE, which has been extensively reviewed by Chen et al. (2011).

325

11.4.2 Comprehensive Strategies to QTLs Discovery and Pyramiding for Drought Adaptive Traits Ever since the first rice genome mapping, and sequence information of diverse rice varieties are piling up in the genome databases. Availability of high throughput genotyping also assisted in the increased number of linkage maps and genetic stocks in rice (McCouch et al. 2002; Fischer et al. 2012). Drought research in rice is receiving increasing attention due to these information surges that open up the vistas in identifying QTLs and genes for drought tolerance. Two approaches are being widely used for the identification of QTLs and genes related to drought (Plate1). The map based QTLs/ gene identification is one such approach which uses a mapping population developed from contrasting parents, they are characterized phenotypically in target environments and genotyped with molecular markers. The linkage analysis results in the identification of drought-related genes (QTL) and subsequently, the fine mapping and map-based cloning approaches are applied to obtain the candidate genes and linked marker for MAS. The second approach involves the creation and screening of drought-resistant (or drought sensitive) mutants. Based on their drought tolerance performance and sequence changes at regulatory genes, the candidate genes are identified and their functions are determined. Though a large number of published data are available on drought related candidate genes and QTLs (http://www.plantstress.com/), validation of such linked markers and genes are still in the preliminary stage and are debatable (Pennisi 2008). In spite of such great effort, no QTL cloning or drought tolerant variety has been achieved purely by genomics research to date in rice for root and WUE traits. Problem associated with QTL discovered from previous studies could be ·

QTLs introgressed were not fine-mapped with appropriate selectable markers, so that the desired gene might have been lost in the selection process

·

The QTLs identified had a small effect on the phenotype

In QTL mapping, the main factor limiting the precision of QTL localization is the number of progenies used in the study. Even after identifying QTLs, estimated effects of the identified QTLs are often inconsistent in different genetic backgrounds (Table 6).

326 An over estimation of the QTL effects in limited progenies leads to wrong predictions, a phenomenon called the ‘Beavis effect’ (Bernardo 2008; Xu 2003). Thus, the challenge for molecular breeders is to discover heritably stable major QTLs that function independently of genetic background, and to develop an effective breeding method for the application of such QTLs under drought condition. In order to provide a stable and reliable prediction of QTL effects, reasonable population size, replicated field trials from multi-sites and across seasons are usually required. Using more reliable molecular markers and better statistical techniques are also important to address the problem (Kearsey and Farquhar 1998). Complex traits have been the target of slow but consistent breeding gain over the past century. Association mapping (AM) / genome wide association studies (GWAS) has emerged as a promising method for complex trait dissection (McCouch and Jung 2013) and it focuses on association within populations of unrelated individuals, which examines a collection of diverse accessions viz., varieties, landraces and breeding lines without generating mapping populations (Fig.1 and Table 6). These accessions represent historical recombination events thus most of the alleles whole represents either strong linkage or linkage disequilibrium (LD). Further, such populations also reveal significant allele diversity and hence, the population genetics based LD mapping would increase the resolution of QTL (Plate1 and Table 6) (Flint-Garcia et al. 2003; McCouch et al. 2004; Yu and Buckler 2006; Abdurakhmonov and Abdukarimov 2008; Rafalski 2010). Major factors that affect association mapping include the level of linkage disequilibrium, population structure and stratification, familial relatedness and complexity of target traits (Abdurakhmonov and Abdukarimov 2008; Rafalski 2010). Beyond the requirement for prior molecular knowledge, the other principal disadvantage of association mapping is the spurious marker trait associations that can arise from population structure (Krill et al. 2010; Famoso et al. 2011). Many of these false positive results can be avoided via a mixed linear model (MLM) approach, which takes population structure and varietal relatedness into account (Yu et al. 2006). This is because if individuals that share a trait are also more closely related to each other than to those that do not share the trait, then a significant number of unlinked markers will co-segregate with the trait due to common ancestry rather than to physical linkage. Other probable problem could be epistatic and population specific effects (i.e. where the same allele can have different phenotypic effects in different populations because of variation in genetic backgrounds). Finally, it is

327 necessary to properly control for genome-wide false positive rates due to multiple hypothesis testing when doing association as well as GWAS, since in a large collection of marker (especially SNPs), some associations will exist simply by chance (http://www.ricediversity.org/).

Figure 1: Overview of QTL discovery from Bi-parental mapping and Association mapping Table 6: Comparison between Bi-parental mapping and Association mapping. Bi parental Mapping

Association Mapping

Structured population

Unstructured population

Low resolution

High resolution

Analysis of two alleles

Many alleles

Moderate marker density

High marker density

Mapping single trait

Multiple trait mapping

Markers may not work across population

Markers will work across population

Table 7: Factors that control LD in a population are reported in the literature. Factor

Effect

Recombination rate

Higher recombination lowers LD

Mating systems

Selfing species High LD

Mating systems

Out-crossing species Low LD

Genetic isolation between lineages

Increases LD

328

Population subdivision

Increases LD

Population admixture

Increases LD

Natural and artificial selection locally

Increases LD

Population size

Small populations have more LD

Balancing selection

Increases LD

Mutation rate

High mutation rate decreases overall LD

Epistatic interactions

Increase LD

Although diverse groups of unrelated individuals are studied in association mapping, a subtle kinship between individuals is inevitable. This relatedness results is false discovery of marker- traits association. A greater worry is the possibility of omitting these QTLs. These potentially dangerous errors are often referred to as type I and type II errors, respectively (Rafalski 2010). Edward Buckler and his co-workers evolved a novel statistical approach to circumvent these errors. They demonstrated that assessing the kinship among individuals can significantly overcome false positive association between markers and traits. They referred this strategy as association in structured population. Germplasm collections of diverse genetic backgrounds and with different selection histories, likely differ in their alleles (Bernardo 2008) and the same QTL would be expected to be present in different populations, assuming that the particular QTL is stable or consistent. By utilizing all the historic recombination events from germplasm development, association mapping can provide a high resolution genetic map (fine mapping) and provide more precise locations of individual QTL (Oraguzie, et. al., 2007; Maccaferri, et. al., 2011), or a step towards positional cloning (Rafalski 2010), which is more challenging but more rewarding improving quantitative traits (Sorkheh et al. 2008). Association analysis in species with low rates of observable recombination (inbreeding species and those with low levels of DNA level diversity) can be carried out with a moderate number of markers evenly distributed across the genome via genome scanning. Self pollinated species (rice) generally have less levels resolution and experience low levels of recombination and thus a high linkage disequilibrium (late breakdown of linked genomic regions) (Table 7). As a valuable complementary tool in detecting marker–trait associations, LD mapping has been extensively utilized in cereals, especially in maize (Yu and Buckler 2006) and rice (Garris et al. 2003; Yan et al. 2009; Agrama et al. 2007; Mather et al. 2007; McCouch et al.

329 2004), and enables researchers to use modern genetic technologies to exploit natural diversity and locate valuable genes in the genome (Zhu et al. 2008). Genetic diversity scans and association studies have been carried out in rice for yield (Agrama et al. 2007) and quality traits (Samuel et al. 2010), but the resolution and sensitivity of these studies has been limited by the small number of markers (Table 8). There are many reports of successful association between DNA polymorphisms and qualitative traits in plants, but fewer reports for complex traits (Table 8). The significant improvement in genetics and genomics resources coupled with the progress in computational statistics has clearly enhanced the possibility of higher success in Marker-Trait Association of complex traits and direction and utilization in crop improvement programs (Zhu et al. 2008; Yu et al. 2006; Pritchard et al. 2000 a,b). Association mapping (SSR) and GWAS (SNP) scans of population genetic parameters in rice crops have been used to identify loci under selection (Garris et al. 2003; McCouch et al. 2004; McNally et al. 2009; Yan et al. 2009; Huang et al. 2010) and dissect quantitative traits (Zhao et al. 2011) (Table 8). By using this genomic information, genome-wide association studies (GWAS) have been used to elucidate the genetic basis of agronomic traits and complex traits in rice (Huang et al. 2010; Zhao et al. 2011). Advantage of this approach in rice is the homozygous nature of most rice varieties, which makes it possible to characterize them genomically once and phenotype many times (Table 6 and 7). Quantitative traits like roots and WUE that have been difficult to improve in rice because they are controlled by many genes, each with a small effect and are highly influenced by the environment. In such situations, the lines that do well in one year or in one background may fails in another field or background. These constraints make the prospect of marker-assisted selection (MAS) for these traits very attractive, though challenging. Therefore employing combination of association mapping and linkage mapping can provide both the power and resolution for detecting stable QTL for traits of interest (Krill et al. 2010; Manenti et al. 2009). Ultimately the effectiveness of any MAS procedure will depend on the designing of a comprehensive approach for developing superior rice varieties with enhanced drought tolerance (Fig. 2).

330 Table 8: Progress in association mapping studies reported in rice using co-dominant marker systems Investigation

Marker system Sample Markers size size

Reference

Population structure analysis

SSRs

216

21

Gao et al. 2002

Population structure analysis

SSRs and SNPs

-

-

Garris et al. 2003

Population Structure analysis

Sequencing

82

500 Kb * Mather et al. 2007

Population Structure analysis

SSRs

150

274

Zhang et al. 2011

Glutinous phenotype analysis

Sequencing

105

2.7 Kb *

Olsen and Purugganan 2002

Starch quality

SSRs and SNPs

577

577

Bao et al. 2006

Yield and its components

SSRS

103

123

Agrama et al. 2007

Heading date, plant height and panicle length

SSRs

170

132

Wen et al. 2009

Stigma and spikelet characteristics

SSRs

90

108

Yan et al. 2009

Grain quality and flowering time

SSRs

192

97

Samuel et al. 2010

Multiple agronomic traits

SNPs

517

WGS

Huang et al. 2010

Genomic diversity and introgression studies

SNPs

395

1536

Zhao et al. 2010

Aluminium Tolerance

SSRs and SNPs

383

44K

Famoso et al. 2011

Complex traits

SNPs

413

44K

Zhao et al.2011

Genotyping for breeding applications

SNP

Sets

384

Thomson et al. 2011

Note: SNPs: Single Nucleotide Polymorphisms; SSRs: Simple Sequence Repeats, *: specific region in genome (targeted re-sequencing) and WGS: Whole genome sequence using next generation platform.

331 11.5 QTL Mining to Identify Candidate Genes In recent years, more than 8000 genomic region on different chromosome in rice have been discovered by QTL mapping. Although GRAMENE is a large resource for major crop genomes including rice, it does not provide an easy way for mining biological data underlying QTL. Selection of candidate genes relies on a wealth of information gained through traditional genetics and molecular approaches and this biological information on rice are stored on the public domain databases (Table 9). Scientists targeted these databases to apply bioinformatics approaches and data integration systems to find the most promising candidate genes and to elucidate functions of rice genes. The application of omics tools to the study of agricultural- related questions is having a great impact on the understanding of the mechanisms underlying rice plant tolerance to stress which involves a complex process with both transcriptional and post-transcriptional modulation (Mazzucotelli et al. 2008; Tuberosa and Salvi 2006). Indeed, the release of the highquality rice genome sequence performed by the IRGSP, accelerated cloning of QTL for both biotic and abiotic stresses. To discover important rice genes, several public resources have been developed for gene mining in QTL and to under the molecular regulations (Table 9). It provides biologically supported evidences that are essential for targeting groups or networks of genes involved in controlling traits underlying QTL (Thongjuea et al. 2009; Nagamura et al. 2011). Towards this direction, it has been designed and developed several rice databases to integrate catalogs from the public domain databases on rice that involve genetic information, genome annotation, expressed sequence tags (ESTs), protein information such as protein domains, gene ontology (GO), metabolic pathway information, prediction of protein– protein interaction and stress-responsive genes. List of rice databases reported in literatures is tabulated in Table 9. Rice Gene Thresher is one of the system integrated these diverse data sources and provides powerful web-based applications, and flexible tools for delivering customized set of biological data on rice (Thongjuea et al. 2009). This system supports whole-genome gene mining for QTL by querying marker intervals or genomic loci and providence biologically supported evidences that are essential for targeting groups or networks of genes involved in controlling traits underlying QTL.

332 Table 9: List of rice databases reported in literature for crop improvement studies. Source: Swamy et al. 2013. Database name

Database Link

Rice genome sequence data

www.msu.edu, www.bgi.org

Full-length cDNA clones

http://cdna01.dna.affrc.go.jp/cDNA/

Rice biological and molecular data

www.gramene.org, www.graingenes.org http://www.ncgr.ac.cn/ricd http://cdna01.dna.affrc.go.jp/PIPE

Rice annotation

http://rice.plantbiology.msu.edu/ http://rapdb.dna.affrc.go.jp/ http://ricegaas.dna.affrc.go.jp/ http://www.shigen.nig.ac.jp/rice/oryzabase/top/ top.jsp

Transcription factor

http://drtf.cbi.pku.edu.cn/ http://ricetfdb.bio.uni-potsdam.de/

Rice expression

http://cdna02.dna.affrc.go.jp/RED/ http://mpss.udel.edu/ http://www.ricearray.org/index.shtml http://ricexpro.dna.affrc.go.jp/

Rice co-expression

http://ricefrend.dna.affrc.go.jp/

Comparative genomics

http://greenphyl.cirad.fr/

Mining genes from QTLs

http://rice.kps.ku.ac.th:8080/RiceGeneThresher http://agri-trait.dna.affrc.go.jp/

Rice mutants

http://rmd.ncpgr.cn/ http://www.ricefgchina.org/mutant

Small RNA

http://sundarlab.ucdavis.edu/smrnas/ http://retroryza.fr/

Rice proteome

http://gene64.dna.affrc.go.jp/RPD/main_en.html

BAC/EST resources

www.genome.arizona.edu

Phenotype of the mutants

http://tos.nias.affrc.go.jp/

SNP resources

http://www.plantgenome.uga.edu/snp http://www.ricesnp.org/ http://shenghuan.shnu.edu.cn/ricemarker

Rice diversity

http://www.ricediversity.org/

333

Figure 2: A comprehensive approach for developing the superior rice varieties with enhanced drought tolerance

By using available database resource we can reach from QTL to candidate gene, then from candidate gene to SNP discovery (Table 9). In silico analysis showed importance of genomic information for the identification and isolation of novel and superior alleles of agronomically important genes from crop gene pools to suitably deploy for the development of improved cultivars. Allele mining is a promising approach to dissect naturally occurring allelic variation at candidate genes controlling key agronomic traits which has potential applications in crop improvement programs. It helps in tracing the evolution of alleles, identification of new haplotypes and development of allele-specific markers for use in markerassisted selection. Therefore, combination of association and linkage mapping leads to identification of markers from genic region and/or candidate genes would result in the identification of the most effective allelic contribution of markers. Introgressing superior allelic variation (haplotype) of QTL would significantly enhance the success of MAS in introgressing drought tolerance traits. 11.6 Conclusive Remarks Development of rice varieties exhibiting drought stress tolerance

334 is of prime importance to cope with the water limitation conditions and ever increasing need for food cereals. Despite of the availability rice genome sequence information, genome-wide molecular markers and present knowledge on genetics and molecular basis of drought tolerance, further this information should be mobilized to understand and dissect the complex traits for their effective utilization. Recent advances in rice “-omics” are facilitating the characterization of novel genes and providing the basis for superior allele as well as candidate gene identification and allele mining. Trait based genomic breeding with high through put phenotyping, genotyping, bioinformatics and critical statistical analysis is highly essential to exploit their full potentials to develop drought adaptive cultivars. References: Abdurakhmonov I and Abdukarimov A. 2008. Application of association mapping to understanding the genetic diversity of plant germplasm resources. International Journal of Plant Genomics doi:10.1155/2008/574927. Acuna T I B , Pasuquin E and Wade L J. 2007. Genotypic differences in root penetration ability of wheat through thin wax layers in contrasting water regimes and in the field. Plant and Soil 301:135-149. Agrama H A, Eizenga G C and Yan W. 2007. Association mapping of yield and its components in rice cultivars. Molecular Breeding 19:341-356. Araus J L, Slafer, G A, Reynolds M P and Royo C. 2002. Plant breeding and drought in C3 cereals: what should we breed for? Annals of Botany 89: 925–940. Araus J L, Slafer, G A, Royo C and Serret M D. 2008. Breeding for yield potential and stress adaptation in cereals. Critical Review of Plant Science 27: 377–412. Armenta-Soto J, Chang T T, Loresto G C and o’Toole J. 1983. Genetic analysis of root characteristics in rice. SABRAO Journal of Breeding and Genetics 15:103–118. Asch F, Dingkuhn M, Sow A and Audebert A. 2005. Drought-induced changes in rooting patterns and assimilate partitioning between root and shoot in upland rice. Field Crops Research 93: 223–236. Azhiri-Sigari T A, Yamauchi A, Kamoshita A and Wade L J. 2000. Genotypic variation in response of rainfed lowland rice to drought and re-watering. 2. Root growth. Plant Production Science 3:180–188. Babu, R C, Shashidhar H E, Lille J M, Ray J D, Sadasivam S, Sarkarung S and Et al. 2001. Variation in root penetration ability, osmotic adjustment and dehydration tolerance among accessions of rice adapted to rainfed lowland and upland ecosystems. Plant Breeding 120: 233–238. Bacon M A. 2000. Water use efficiency in plant biology. Water Use Efficiency in Plant Biology, pp 1–26. Bacon MA (Eds.). Blackwell Publishing, Oxford, UK. Baenziger P S, Wesenberg D M and Sicher R C. 1983. The effects of genes controlling barley leaf and sheath waxes on agronomic performance in irrigated and dry land

335 environments. Crop Science 23: 116–20. Bao J S, Corke H and Sun M. 2006. Microsatellites, single nucleotide polymorphisms and a sequence tagged site in starch-synthesizing genes in relation to starch physicochemical properties in nonwaxy rice (Oryza sativa L.). Theoretical and Applied Genetics 113:1185–1196. Barnaba B, Jager K and Feher A. 2008. The effect of drought and heat stress on reproductive processes in cereals. Plant, Cell and Environment 31:11–38. Beavis W D. 1998. QTL analyses: power, precision, and accuracy. Molecular Dissection of Complex Traits, pp 145-162. Paterson AH (Eds). CRC Press, New York. Bernardo R. 2008. Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Science 48:1649–1664. Bernier J, Kumar A, Venuprasad R, Spaner D and Atlin G. 2007. A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice. Crop Science 47:507–518. Bernier J, Serraj R, Kumar A, Venuprasad R, Impa S, Gowda V, Owane R, Spaner D and Aatlin G. 2009. Increased water uptake explains the effect of QTL12.1, a largeeffect drought-resistance QTL in upland rice. Field Crop Research 110: 139–146. Bindumadhava H, Sheshshayee M S, Shankar A G, Prasad T G and Udayakumar M. 2003. Use of SPAD chlorophyll meter to assess transpiration efficiency of peanut. Breeding of drought resistant peanut. Proceedings of a Collaborative Review Meeting, pp 3-9. 25–27 Feb 2002, Hyderabad, India. (Cruickshank AW, Rachaputi NC, Wright GC and Nigam SN, eds.). ACIAR Proceedings No. 112. Canberra, Australia. Blum A and Ebercon A. 1981. Cell membrane stability as a measure of drought and heat tolerance in wheat. Crop Science 21:43–47. Blum A. 1988. Plant breeding for stress environments. Boca Raton, FL: CRC Press. Blum A. 2005. Drought resistance, water-use efficiency, and yield potential–are they compatible, dissonant, or mutually exclusive? Australia Journal of Agricultural Research 56:1159–1168. Borrell A K, Garside A L and Fukai S. 1997. Improving efficiency of water use for irrigated rice in a semi-arid tropical environment. Field Crops Research 52: 231–248. Bouman B A M and Tuong T P. 2001. Field water management to save water and increase its productivity in irrigated rice. Agricultural Water Management 49(1): 11-30. Bouman, B A M, Xiaoguang W, Huaqui W, Zhiming Z, Junfang W, Changgui and Bin. 2002. Aerobic rice (Han Dao): A new way of growing rice in water short areas. Proceedings of the 12th International Soil Conservation Organization Conference, pp. 175-181. May 26-31. Tsinghua University Press, Beijing, China. Bouman, B A M, Peng S, Castaneda A R and Visperas R M. 2005. Yield and water use of irrigated tropical aerobic rice systems. Agricultural Water management 74: 87 105. Brondani C, Rangel N, Brondani V and Ferreira E. 2002. QTL mapping and introgression of yield-related traits from Oryza glumaepatula to cultivated rice (Oryza sativa)

336 using microsatellite markers. Theoretical and applied genetics 104: 1192-1203. Cabangon R J, Castillo E J, Bao L X, Lu J, Wang G H, Cui Y L, Toung T P, Bouman B A M, Li, Y H, Chen C D and Wang J Z. 2001. Impact of alternate wetting and drying irrigation on rice growth and resource-use efficiency. Water saving irrigation for rice (Barker R, Loeve R, Li,Y H and Tuong T P Eds.). Proceedings of an International Workshop held in Wuhan, China, International Water Management Institute Colombo, Sri Lanka. Centritto M, Lauteri M, Monteverdi M C and Serraj R. 2009. Leaf gas exchange, carbon isotope discrimination, and grain yield in contrasting rice genotypes subjected to water deficits during the reproductive stage. Journal of Experimental Botany 60: 2325– 2339. Chapman S C, Chakraborty S, Dreccer M F and Howden S M. 2012. Plant adaptation to climate change-opportunities and priorities in breeding. Crop Pasture Science 63: 251–268. Chaves M M, Maroco J P and Pereira J S. 2003. Understanding plant responses to drought – from genes to the whole plant. Functional Plant Biology 30: 239–264. Chen J, Scott X C and Anyia O A. 2011. Gene discovery in cereals through quantitative trait loci and expression analysis in water-use efficiency measured by carbon isotope discrimination. Plant, Cell and Environment 34: 2009–2023. Choi W J, Chang S X and Bhatti J S. 2007. Drainage affects tree growth and C and N dynamics in a minero-trophic peatland. Ecology 88: 443–453. Christopher N, Anjali T S, Iyer P, Jill T, Paul A R and Et al. 2013. 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. PNAS 1695–1700. Clark L J, Aphale S L and Barraclough P B. 2000. Screening the ability of rice roots to overcome the mechanical impedance of wax layers: importance of test conditions and measurement criteria. Plant and Soil 219:187–196. Clark L J, Price A H, Steele K A, Whalley W R. 2008. Evidence from near-isogenic lines that root penetration increases with root diameter and bending stiffness in rice. Functional Plant Biology 35:1163–1171. Clark R T, Famoso A N, Zhao K, Shaff J E, Craft J E, Bustamante C D, Mccouch S R, Aneshansley D J and Kochian L V. 2012. High-throughput 2D root system phenotyping platform facilitates genetic analysis of root growth and development. Plant Cell and Environment doi:  10.1111/j.1365-3040.2012.02587.x. Clark R, Maccurdy R, Jung J, Shaff J, Mccouch S R, Aneshansley D and Kochian L. 2011. Three-dimensional  root  phenotypingwith  a  novel  imaging  and  software platform. Plant Physiology 156 (2): 455-465. Condon A G, Farquhar G D and Richards R A. 1990. Genotypic variation in carbon isotope discrimination and transpiration efficiency in wheat. Leaf gas exchange and whole plant studies. Australian Journal of Plant Physiology 17: 9–22. Condon A G, Richards R A, Rebetzke J and Farquhar G D. 2004. Breeding for high water-use efficiency. Journal of Experimental Botany 55:2447–2460.

337 Condon A G, Richards R A, Rebetzke G J and Farquhar G D. 2002. Improving intrinsic water-use efficiency and crop yield. Crop Science 42:122–131. Courtois B, Mclaren G, Sinha P K, Prasad K, Yadav R and Shen L. 2000. Mapping QTLs associated with drought avoidance in upland rice. Molecular Breeding 6:55–66. Dawe. 2005. Increasing water productivity in rice-based systems in Asia: past trends, current problems, and future prospects. Plant Production Science 8(3): 221-230. Diab A A, Kantety R V, Ozturk N Z, Benscherd, Nachit M M and Sorrells M E. 2008. Drought-inducible genes and differentially expressed sequence tags associated with components of drought tolerance in durum wheat. Scientific Research and Essays 3: 9–26. Dingkuhn M, Farquhar G D, de Datta S K and o’Toole J C. 1991. Discrimination of 13C among upland rices having different water use efficiencies. Australian Journal of Agricultural Research 42: 1123–1131. Dong Y, Kkamiunte H, Ogawa T, Tsuzuki E, Tera H, Lin D and Matsuo M. 2004. Mapping of QTLs for leaf developmental behaviour in rice (Oryza sativa L.). Euphytica 138: 169–175. Economic survey of India. 2012. Ministry of Finance, Government of India, pp 181. Famoso A N, Zhao K, Clark R T, Tung C W, Wright M H and Et al. 2011. Genetic architecture of aluminium tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS Genetics 7(8):e1002221. doi:10.1371/journal.pgen.1002221. Farquhar G D, o’Leary M H and Berry J A. 1982. On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves. Australian Journal of Plant Physiology 9:121–137. Farquhar G D and Richards R A. 1984. Isotopic composition of plant carbon correlates with water-use efficiency of wheat genotypes. Australian Journal of Plant Physiology 11: 539– 552. Farquhar G D, Ehleringer J R and Hubick K T. 1989. Carbon isotope discrimination and photosynthesis. Annual Review of Plant Physiology and Plant Molecular Biology 40: 503–537. Fischer K S, Fukai S, Kumar U, Leung H and Jongdee B. 2012. Field phenotyping strategies and breeding for adaptation of rice to drought. Frontiers in Physiology 3(282): 121. Fitter A H. 1991. The ecological significance of root system architecture: an economic approach. Plant Root Growth: An Ecological Perspective. Blackwell Scientific Publishers, London. Flexas J, Galmes J, Galle A, Gulias J, Pou A, Ribas-Carbo M, Tomas M and Medrano H. 2010. Improving water use efficiency in grapevines: potential physiological targets for biotechnological improvement. Australian Journal of Grape and Wine Research 16:106–121. Flint-Garcia S A, Jampatong C, Darrah L L and McMullen M D. 2003. Quantitative trait

338 locus analysis of stalk strength in four maize populations. Crop Science 43:13–22. Fukai S and Cooper M. 1995. Development of drought-resistant cultivars using physiomorphological traits in rice. Field Crops Research 40: 67–86. Gao L Z, Schaal B A, Zhang C H and Jia J Z. 2002. Assessment of population genetic structure in common wild rice Oryza rufipogon Griff. using microsatellite and allozyme markers. Theoretical and Applied Genetetics 106:173–180. Garris A, McCouch and Kresovich S. 2003. Population structure and its effects on haplotype diversity and linkage disequilibrium surrounding the xa5 locus of rice (Oryza sativa L.). Genetics 165:759–769. Garrity D P, Oldeman L R and Morris R A. 1986. Rainfed lowland rice ecosystems: Characterization and distribution. A Progress in Rainfed Lowland Rice, pp 3–23 International Rice Research Institute, Los Banos, Philippines. Garrity D P and o’Toole J C. 1995. Selection for reproductive stage drought avoidance in rice by infrared thermometry. Agronomy Journal 87:773-779. Gowda V R P, Henry A, Akira Y, Shashidhar H E and Serraj R. 2011. Root biology and genetic improvement for drought avoidance in rice. Field Crops Research doi:10.1016/j.fcr.2011.03.001. Hall A E, Mutters R G and Farquhar G D. 1992. Genotypic and drought-induced differences in carbon isotope discrimination and gas exchange of cowpea. Crop Science 32:1– 6. Hemamalini G S, Shashidhar H E and Hittalmani S. 2000. Molecular marker assisted tagging of morphological and physiological traits under two contrasting moisture regimes at peak vegetative stage in rice (Oryza sativa L.). Euphytica 112: 69–76. Henry A, Gowda V R P, Torres R O, McNally K and Serraj R. 2011. Genetic variation in root architecture and drought response in Oryza sativa: rainfed lowland field studies of the Oryza SNP panel. Field Crops Research 120:205–214. Hittalmani S, Huang N, Courtois B, Venuprasad R, Shashidhar HE, Zhuang JYand Et al. 2003. Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia. Theorerical and Applied Genetics 107:679–690 Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z and Et al. 2010. Genome-wide association studies of 14 agronomic traits in rice landraces. Nature Genetics 42: 961-967. Impa S M, Nadarajan S, Boominathan P, Shashidhar G, Bindhumadhava H and Sheshshayee MS. 2005. Carbon Isotope Discrimination Accurately reflects variability in WUE Measured at a whole plant level in Rice. Crop Science 45: 2517 - 2522. Ingram K T, Bueno F D, Namuco O S, Yambao E B and Beyrouty C A. 1994. Rice root traits for drought resistance and their genetic variation. Rice Roots: Nutrient and Water Use. Kirk GJD (Ed.).International Rice Research Institute, Manila, Philippines. IRRI. 2006. Bringing Hope, Improving Lives: Strategic Plan 2007–2015. Los Banos: IRRI.

339 Ishimaru K, Yano M, Aoki N, Ono K, Hirose T and Et al. 2001. Toward the mapping of physiological and agronomic characters on rice function map: QTL analysis and comparison between QTLs and expressed sequence tags. Theoretical and Applied Genetics 102: 793–800. Kamoshita A, Babu R C, Boopathi N M and Fukai S. 2008. Phenotypic and genotypic analysis of drought-resistance traits for development of rice cultivars adapted to rainfed environments. Field Crops Research 109:1–23. Kano-Nakata M, Inukai Y, Wade L J, Siopongco J D L C and Yamauchi A. 2011. Root development and water uptake, and shoot dry matter production under water deficit conditions in two CSSLs of rice: functional roles of root plasticity. Plant Production Science 14:329-339. Kato Y, Abe J, Kamoshita A and Yamagishi J. 2006. Genotypic variation in root growth angle in rice (Oryza sativa L.) and its association with deep root development in upland fields with different water regimes. Plant and Soil 287:117–129. Kearsey M J and Farquhar A G L. 1998. QTL analysis in plants. Where are we now? Heredity 80:137–42. Krill A M, Kirst M, Kochian L V, Buckler E S and Hoekenga O A. 2010. Association and linkage analysis of aluminium tolerance genes in maize. PLoS One 5(4): 19921998. Kumar R, Venuprasad R and Atlin G. 2007. Genetic analysis of rain- fed lowland rice drought tolerance under naturally occur ring stress in eastern India: heritability and QT L effects. Field Crops Research 103:42–52. Lafitte R, Blum A and Atlin G. 2004. Using secondary traits to help identify droughttolerant genotypes. Breeding Rice for Drought-prone Environments, pp 37–48. Fischer KS, Lafitte R, Fukai S, Atlin G and Hardy B. (Eds.). IRRI, Los Baños, the Philippines. Laza M R, Kondo M, Ideta O, Barlaan E and Imbe T. 2006. Identification of quantitative trait loci for 13C and productivity in irrigated lowland rice. Crop Science 46:763– 773. Li Z C, Mu P, Li C P, Zhang HL, Li Z K, Gao Y M and Wang X K. 2005. QTL mapping of root traits in a doubled haploid population from a cross between upland and lowland japonica rice in three environments. Theoretical and Applied Genetics 110:1244– 1252. Lian X, Xing Y, Yan H, Xu C, Li X and Zhang Q. 2005. QTLs for low nitrogen tolerance at seedling stage identified using a recombinant inbred line population derived from an elite rice hybrid. Theoretical and Applied Genetics 112:85-96 Lilley J M, Ludlow M M, McCouch S R and o’Toole J C. 1996. Locating QTL for osmotic adjustment and dehydration tolerance in rice. Journal of Experimental Botany 47:1427–1436. Ludlow M M and Muchow R C. 1990. A critical evaluation of traits for improving crop yields in water-limited environments. Advances in Agronomy 43: 107–153. Luo L Z and Zhang Q F. 2001. The status and strategies on studying drought resistance of

340 rice. Chinese Journal of Rice Science 15: 209–214. Lynch J P. 2007. Roots of the second green revolution. Australian Journal of Botany 55:493–512. Maccaferri M, Sanguineti M C, Demontis A and Et al. 2011. Association mapping in durum wheat grown across a broad range of water regimes. Journal of Experimental Botany 62:409–438. Manenti G, Galvan A, Pettinicchio A, Trincucci G, Spada E and Et al. 2009. Mouse genomeWide Association Mapping Needs Linkage Analysis to Avoid False-Positive Loci. PLoS Genetics 5(1): e1000331. Martin B and Nienhuis J 1989. Restriction fragment length polymorphisms associated with water use efficiency in tomato. Science 243:1725–1728. Masle J, Gilmore S R and Farquhar G D. 2005. The ERECTA gene regulates plant transpiration efficiency in Arabidopsis. Nature 436: 866-870. Mather K A, Caicedo A L, Polato N R, Olsen K M, McCouch S and Purugganan M D. 2007. The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics 177:2223-2232. Mazzucotelli E, Mastrangelo A M, Crosatti C, Guerra D, Stanca A M and Cattivelli L. 2008. Abiotic stress response in plants: when posttranscriptional and posttranslational regulations control transcription. Plant Science 174: 420-431. McCouch S R and Jung J K. 2013. Getting to the roots of it: Genetic and hormonal control of root architecture. Frontiers in Plant Science 4:186. McCouch S R, Zhao K, Wright M, Tung CW, Ebana K, Thomson M, Reynolds A, Wang D, Declerck G, Ali ML, McClung A, Eizenga G and Bustamante C. 2010. Development of genome-wide SNP assays for rice. Breeding Science 60:524–535. McCouch S, Garris A, Semon M, Lu H, Coburn J, Redus M, Rutger N, Edwards J, Kresovich S, Nielsen R, Jones M and Tai T. 2004. Genetic diversity and population structure in rice. Plant and Animal Genomes XII Conference, 10-14 January, Town & Country Convention Centre, San Diego, CA, W6. McCouch S, Teytelman L, Xu Y, Lobos K B, Clare K, Walton M, Fu B, Maghirang R, Li Z, Xing Y, Zhang Q, Kono I, Yano M, Fjellstrom R, Declerck G, Schneider D, Carinhour S, Ware D and stein L. 2002. Development and mapping of 2240 new SSR markers for rice (Oryza sativa L.). DNA Research 9:199–207 McNally K L, Childs K L, Bohnert R, Davidson R M, Zhao K and Et al. 2009. Genome wide SNP variation reveals relationships among landraces and modern varieties of rice. PNAS 106: 12273 – 12278. Mittler R. 2002. Oxidative stress, antioxidants and stress tolerance. Trends in Plant Science 7:405–410. Miura K, Lin S Y, Yano M and Nagamine T. 2002. Mapping quantitative trait loci controlling seed longevity in rice (Oryza sativa L.). Theoretical and Applied Genetics 104:981– 986.

341 Mohankumar M V, Madhura J N, Rathanakar M S, Annes S, Rajanna M P, Mallikarjuna N M, Raju B R, Sumanthkumar K, Sheshshayee M S, Prasad T G and Udayakumar M. 2012. Linkage disequilibrium mapping for root traits and WUE in rice germplasm accessions. Proceedings of the International symposium on 100 years of rice science and looking beyond. Vol I: pp 74. 9-12 January, TNAU, Coimbatore. Mohankumar M V. 2010. Association Mapping for Drought Tolerance Traits Like Root Traits and Water use Efficiency in Rice Germplasm Accessions. Paper presented at the National symposium on genomics and crop improvement: relevance and reservations. February 25–27. ANGRAU, Hyderabad, India, Moran J F, Becana M, Iturbe-ormaetxe I, Frechilla S, Klucas R V and Aparicio-tejo P. 1994. Drought induces oxidative stress in pea plants. Planta 194:346–352 Nadaradjan S, Impa S M, Sheshshayee M S and Prasad T G. 2005. Overlapping markers for WUE and Carbon isotope discrimination in Doubled haploid population of Rice. Journal of Plant Biology 32(2): 117-122. Nagamura Y, Antonio BA, Sato Y, Miyao A, Namiki N, Yonemaru J, Minami H, Kamatsuki K, Shimura K, Shimizu Y and Hirochika H. 2011. Rice TOGO Browser: A Platform to Retrieve Integrated Information on Rice Functional and Applied Genomics. Plant Cell Physiology 52(2):230-237. Nageswararao R C, Talwar H S and Wright G C. 2001. Rapid assessment of specific leaf area and leaf N in peanut (Arachis hypogaea L.) using chlorophyll meter. Journal of Agronomy and Crop Science 189:175–182. Nicou R, Seguy L and Haddad G. 1970. Comparison of rooting in four upland rice varieties with and without soil tillage. Agronomy in Tropical 25:639–659. o’Leary M H. 1981 Carbon isotope fractionation in plants. Phytochemistry 20:553–567. o’Toole J C and Bland W L. 1987. Genotypic variation in crop plant root systems. Advances in Agronomy 41:91–145. Olsen K M and Purugganan M D. 2002. Molecular evidence on the origin and evolution of glutinous rice. Genetics 162:941–950. Oraguzie N, Rikkerink E, Gardiner S and Desilva H. 2007. Association Mapping in Plants. Springer, New York, NY, USA. Pantuwan G, Fukai S, Cooper M, Rajatasereekul S and o’Toole J C. 2002a. Yield response of rice (Oryza sativa L) genotypes to different types of drought under rain fed lowlands. I. Grain yield and yield components. Field Crops Research 73:153– 168. Passioura J B. 1977. Grain yield, harvest index and water use of wheat. Journal of the Australian Institute of Agricultural Science 43:117–120. Passioura J and Angus J F. 2010. Improving productivity of crops in water limited environments. Advances in Agronomy 106:37–75. Passioura J B. 1982. The role of root system characteristics in the drought resistance of crop plants. Drought resistance in crops with emphasis on rice, pp 71-82. IRRI, Los Banos, Philippines.

342 Passioura J. 2007. The drought environment: physical, biological and agricultural perspectives. Journal of Experimental Botany 58:113–117. Passioura J. 2012. Phenotyping for drought tolerance in grain crops: when is it useful to breeders? Functional Plant Biology 39:851–859. Paterson A H. 1995. Molecular dissection of quantitative traits: progress and prospects. Genome Research 5:321–333. Peng S, Laza R C, Khush G S, Sanico A L, Visperas R M and Garcia F V. 1998. Transpiration efficiencies of indica and improved tropical japonica rice grown under irrigated conditions. Euphytica 103:103–108. Pennisi E. 2008. The blue revolution, drop by drop, gene by gene. Science 320:171–173. Pradeepa N, Shanmugapriya P, Prince K, Kavitha S, Poornima R, Prabhakar M and Chandra babu R. 2012. In Silico analysis of a consensus QTL for drought resistance in rice (Oryza Sativa L.). Online Journal of Bioinformatics 13(1):1-13. Price A H, Steele K A, Moore B J, Jones R G W. 2002. Upland rice grown in soil-filled chambers exposed to contrasting water-deficit regimes. II. Mapping quantitative trait loci for root morphology and distribution. Field Crops Research 76:25–43. Price A H, Tomos A D. 1997. Genetic dissection of root growth in rice (Oryza sativa L.). II. Mapping quantitative trait loci using molecular markers. Theorerical and Applied Genetics 95:143–152. Pritchard J K and Przeworski M. 2001. Linkage disequilibrium in humans models data. American Journal of Human Genetics 69:1–14. Pritchard J K and Rosenberg N A. 1999. Use of unlinked genetic markers to detect population stratification in association studies. American Journal of Human Genetics 65:220–228. Pritchard J K, Stephens M and Donnelly P. 2000a. Inference of population structure using multilocus genotype data. Genetics 155: 945–959. Pritchard J K, Stephens M, Rosenberg N A and Donnelly P. 2000b. Association mapping in structured populations. American Journal of Human Genetics 67: 170–181. Quarrie S A, Laurie D A, Zhu J, Lebreton C, Semikhodskii A and Et al. 1997. QTL analysis to study the association between leaf size and abscisic acid accumulation in droughted rice leaves and comparisons across cereals. Plant Molecular Biology 35: 155–165. Rafalski J A. 2010. Association genetics in crop improvement. Current Opinion in Plant Biology 13:174–180. Raju B R, Narayanaswamy B R, Mohankumar M V, Mohanraju B, Sheshshayee M S, Rajanna M P, Prasad T G and Udayakumar M. 2012. Better roots and superior cellular level tolerance are essential to maintain spikelet fertility under water limited conditions. Proceedings of the International symposium on 100 years of rice science and looking beyond. Vol II: pp 411. 9-12 January. TNAU, Coimbatore. Rebetzke G J, Condon A G, Richards R A and Farquhar G D. 2002. Selection for reduced carbon isotope discrimination increases aerial biomass and grain yield of rainfed

343 bread wheat. Crop Science 42:739–745. Reynolds M, Manes Y, Izanloo A and Langridge P. 2009. Phenotyping approaches for physiological breeding and gene discovery in wheat. Annals of Applied Biology 155: 309–320. Reynolds M, Manes Y, Izanloo A and Langridge P. 2011. Raising yield potential of wheat: Overview of a consortium approach and breeding strategies. Journal of Experimental Botany 62:439–452. Richards R A, Rebetzke G J, Condon A G and Van Herwaarden A F. 2002. Breeding opportunities for increasing the efficiency of water use and crop yield in temperate cereals. Crop Science 42:111–121. Salekdeh G H, Reynolds M P, Bennett J and Boyer J. 2009. Conceptual framework for drought phenotyping during molecular breeding. Trends in Plant Science 14:488– 496. Samuel A, Ordonez J, Silva J and Oard J H. 2010. Association mapping of grain quality and flowering time in elite japonica rice germplasm. Journal of Cereal Science 51:337-343 Semenov M A and Halford N G. 2009. Identifying target traits and molecular mechanisms for wheat breeding under a changing climate. Journal of Experimental Botany 60:2791–2804. Serraj R, Krishnamurthy L, Kashiwagi J W, Kumar J, Chandra S and Crouch J H. 2004. Variation in root traits of chickpea (Cicer arietinum L.) grown under terminal drought. Field Crops Research 88:115–127. Serraj R, McNally K L, Slamet–Loedin I, Kohli A, Haefele S M, Atlin G and Kumar A. 2009. Drought resistance improvement in rice: an integrated genetic and resource management strategy. Plant Production Science 14:1–14. Sheshshayee M S, Bindumadhava H, Rao N R, Prasad T G, Udayakumar M, Wright G C and Nigam S N. 2006. Leaf cholorophyll concentration relates to transpiration efficiency in Peanut. Annals of Applied Biology 148: 7-12. Sheshshayee M S, Ehab A K, Rohini S, Namita S, Mohanraju B, Nataraja K N, Prasad T G and Udayakumar M. 2011a. Phenotyping for root traits and their improvement through biotechnological approaches to sustaining crop productivity. Root Genomics, pp 205 – 232. Vashney RK (Eds). Springer. Sheshshayee M S, Mohankumar M V, Raju B R, Pratibha M D, Rajanna M P, Mohanraju B and Udayakumar M. 2012. Enhancing water use efficiency besides effective use of water is a potential strategy in developing rice cultivars suitable for semi-irrigated aerobic cultivation. International Dialogue on perception and Prospects of Designer Rice, pp 261-272. Muralidharan K and Siddiq EA (Eds.). Society for Advancement of Rice Research, Directorate of Rice Research, Hyderabad 500030, India. Sheshshayee M S, Shashidhar G P, Madhura J N, Beena R, Prasad T G, Udayakumar M. 2011b. Phenotyping Groundnuts for Adaptation to Drought. Mannevaux, pp 371391 Phenotyping document II.2 Legumes (Eds). CIMMYT, Maxicco. Sheshshayee M S, Bindumadhava H, Shankar A G, Prasad T G and Udayakumar M. 2003.

344 Breeding strategies to exploit water use efficiency for crop improvement. Journal of Plant Biology 30 (2): 253-268. Sinclair T R. 2011. Challenges in breeding for yield increase for drought. Trends in Plant Science 16:289–293. Siopongco J D L C, Sekiya K, Yamauchi A, Egdane J, Ismail A M and Wade L J. 2009. Stomatal responses in rainfed lowland rice to partial soil drying: comparison of two lines. Plant Production Science 12:17–28. Smit A L, Bengough A G, Engels C, Van Noordwijk M, Pellerin S and Van de Geijn S C. 2000. Root Methods: A Handbook. Springer-Verlag, Berlin. Songsri P, Jogloy S, Holbrook C C, Kesmala T, Vorasoot N, Akkasaeng C and Patanothai A. 2008. Association of root, specific leaf area and SPAD chlorophyll meter reading to water use efficiency of peanut under different available soil water. Agriculture and Water Management 96:7 90 – 7 98. Sorkheh K, Malysheva-Otto L V, Wirthensohn M G, Tarkesh- Esfahani S and MartinezGomez P. 2008. Linkage disequilibrium, genetic association mapping and gene localization in crop plants. Genetics and Molecular Biology 31:805–814. Steele K A, Virk D S, Kumar R, Prasad S C and Witcombe J R. 2007. Field evaluation of upland rice lines selected for QTLs controlling root traits. Field Crops Research 101: 180–186. Stiller W N, Read J J, Constable G A and Reid P E. 2005. Selection for water use efficiency traits in a cotton breeding program: cultivar differences. Crop Science 45: 1107– 1113. Stoop W A, Uphoff N and Kassam A. 2002. A review of agricultural research issues raised by the system of rice intensification (SRI) from Madagascar: Opportunities for improving farming systems for resource-poor farmers. Agricultural Systems 71: 249–274. Subramanian E and Martin G 2006. Effect of chemical, cultural and mechanical methods of weed control on wet seeded rice. Indian Journal of Weed Sciences 38(3&4): 218-220. Swamy B P M and kumar A. 2013. Genomics-based precision breeding approaches to improve drought tolerance in rice. Biotechnology Advances doi.org/10.1016/j. Tabbal D F, Bouman B A M, Bhuiyan S I, Sibayan E B and Sattar M A. 2002. On-farm strategies for reducing water input in irrigated rice; case studies in the Philippines. Agricultural Water Management 56:93–112. Takai T, Ohsumi A, San-Oh Y, Laza M R C, Kondo M, Yamamoto T and Yano M. 2006. Detection of a quantitative trait locus controlling carbon isotope discrimination and its contribution to stomatal conductance in Japonica rice. Theoretical and Applied Genetics 118: 1401–1410. Tambussi E A, Bort J and Araus J L. 2007. Water use efficiency in C 3 cereals under Mediterranean conditions: a review of physiological aspects. Annals of Applied Biology 150:307–321.

345 This D, Comstock J, Courtois B, Xu Y, Ahmadi N, Vonhof W M, Fleet C, Setter T and McCouch S. 2010. Genetic analysis of water use efficiency in rice (Oryza sativa L.) at the leaf level. Rice 3:72–86. Thomson M J, Zhao K, Wright M, Kenneth L, Mcnally, Jessica R and Et al. 2011, Highthroughput single nucleotide polymorphism genotyping for breeding applications in rice using the Bead Xpress platform. Molecular Breeding DOI 10.1007/s11032011-9663-x Thongjuea S, Ruanjaichon V, Bruskiewich R and Vanavichit A. 2009. Rice GeneThresher: a web-based application for mining genes underlying QTL in rice genome. Nucleic Acids Research 37:1996-1999. Tripathy J N, Zhang J X, Robin S, Nguyen TT and Nguyen H T. 2000. Mapping quantitative trait loci for cell membrane stability in rice. Theoretical and Applied Genetics 100:1197–1202. Tuberosa R and Salvi S. 2006. Genomics-based approaches to improve drought tolerance of crops. Trends in Plant Science 11: 405-12. Tuberose R. 2012. Phenotyping for drought tolerance of crops in the genomics era. Frontiers in physiology 3: 1-26. Tuberosa R, Giuliani S, Parry M A J and Araus J L. 2007. Improving water use efficiency in Mediterranean agriculture: what limits the adoption of new technologies? Annals of Applied Biology 150:157–162. Turner N C, Wright GC and Siddique K H M. 2001. Adaptation of grain legumes (pulses) to water limited environments. Advances in Agronomy 71: 193–123. Turner N C, Palta J A, Shrestha R, Ludwig C, Siddique K H M and Turner D W. 2007. Carbon isotope discrimination is not correlated with transpiration efficiency in three cool-season grain legumes (Pulses). Journal of Integrative Plant Biology 49: 1478– 1483. Udayakumar M and Prasad T G. 1994. 13C isotope discrimination in plants- A potential technique to determine WUE. Selection for WUE in grain legumes, pp 42-45. Rao RCN and Wright GC. (Eds). ICRISAT, Andhra Pradesh, India. Udayakumar M, Sheshshayee M S, Nataraj K N, Bindumadhava H, Devendra R, Aftab Hussain I S and Prasad T G. 1998. Why breeding for water use efficiency has not been successful. An analysis and alternate approach to exploit this trait for crop improvement. Current Science 74:994-1000. Uga Y, Ebana K, Abe J, Morita S, Okuno K and Yano M. 2009. Variation in root morphology and anatomy among accessions of cultivated rice (Oryza sativa L.) with different genetic backgrounds. Breeding Science 59:87–93. Uga Y, Okuno K and Yano M. 2011. Dro1, a major QTL involved in deep rooting of rice under upland field conditions. Journal of Experimental Botany doi:10.1093 / jxb/ erq429. Venuprasad R, Zenna N, Choi R I, Amante M, Virk P S, Kumar K and Atlin G N. 2007b. Identification of marker loci associated with tungro tolerance and drought tolerance in near-isogenic rice lines derived from IR64/Aday Sel. International Rice Research

346 Notes 31:27–29. Wade L J, Fukai S, Samson B K, Ali A, Mazid M A. 2000. Rainfed lowland rice: physical environment and cultivar requirements. Field Crops Research 64:3–12. Wang H, Inukai Y and Yamauchi A. 2009, Root development and nutrient uptake. Critical Review of Plant Science 25:279–301. Wen W, Mei H, Feng F, Yu S, Huang Z, Wu J, Chen L, Xu X and Luo L. 2009. Population structure and association mapping on chromosome 7 using a diverse panel of Chinese germplasm of rice (Oryza sativa L.). Theoretical and Applied Genetics 119 (3): 459-470. Xu Y. 2003.Theoretical Basis of the Beavis Effect. Genetics 165: 2259–2268. Xu Y, This D, Pausch R C, Vonhof W M, Coburn J R, Comstock J P and Mccouch S R. 2009. Leaf-level water use efficiency determined by carbon isotope discrimination in rice seedlings: genetic variation associated with population structure and QTL mapping. Theoretical and Applied Genetics 118:1065–1081. Yadav R, Courtois B, Huang N and Mclaren G. 1997. Mapping genes controlling root morphology and root distribution in a double-haploid population of rice. Theoretical and Applied Genetics 94:619–632. Yan C J, Liang G H, Chen F, Li X, Tang S Z, Yi C D, Tian S, Lu J F and Gu M H. 2003. Mapping quantitative trait loci associated with rice grain shape based on an indica/ japonica backcross population. Acta genetica Sinica 30:711-716 Yan J Q, Zhu J, He C X, Benmousa M and Wu P. 1998a, Quantitative trait loci analysis for developmental behavior of tiller number in rice (Oryza sativa L.). Theoretical and Applied Genetics 97: 267–274. Yan J Q, Zhu J, He C X, Benmousa M and Wu P. 1998b. Molecular marker assisted dissection of developmental behavior of plant height in rice (Oryza sativa L.). Genetics 150: 1257–1265. Yan W G, Yong Li, Hesham A, Agrama, Dagang L, Fangyuan G, Xianjun L and Guangjun. 2009. Association mapping of stigma and spikelet characteristics in rice (Oryza sativa L.). Molecular Breeding 24(3): 277–292. Ye G, Liang S and Wan J. 2010. QTL mapping of protein content in rice using single chromosome segment substitution lines. Theoretical and Applied Genetics 121:741– 750. Yoshida S and Hasegawa S. 1982. The rice root system: its development and function. In: Drought Resistance in Crops, with Emphasis on Rice. International Rice Research Institute, Manila, Philippines. Yu J and Buckler E. 2006. Genetic association mapping and genome organization of maize. Current Opinion in Biotechnology 17:155–160. Yu J, Pressoir G, Briggs W, Vroh B I, Yamasaki M, Doebley J F and Et al. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics 38:203–208.

347 Yu L X, Ray J D, O’Toole J C and Nguyen H T. 1995. Use of wax-petrolatum layers for screening rice root penetration. Crop Science. 35:684-687. Yue B, Xue W Y, Luo L J and Xing Y Z. 2006. QTL analysis for flag leaf characteristics and their relationships with yield and yield traits in rice. Acta genetica Sinica 33: 824-832. Zhang P, Li J, Li X, Liu X, Zhao X, Lu Y. 2011. Population structure and genetic diversity in a rice core collection (Oryza sativa l.) investigated with SSR markers. PLoS One 6(12): e27565. Zhao K T, Eizenga G C, Wright M H, Ali M L, Price A H, Norton G J, Islam M R, Reynolds A, Mezey J, Mcclung A M, Bustamante C D and Mccouch S R. 2011. Genome-wide association mapping reveals rich genetic architecture of complex traits in Oryza sativa. Nature Communications 2 (467):1-10. Zhao K, Wright M, Kimball J, Eizenga g, Mcclung A, Kovach M, Tyagi W, Ali M L, Tung C W, Reynolds A, Bustamante C D and Mccouch S R. 2010. Genomic diversity and introgression in O. sativa reveal the impact of domestication and breeding on the rice genome. PLoS One 5(5): doi: 10.1371/journal.pone.0010780. Zhu C, Gore M, Buckler E S and Yu J. 2008. Status and prospects of association mapping in plants. Plant Genome 1: 5–20.

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