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Water stress and climate change adaptation: From trait dissection to yield

  1. Water stress and climate change adaptation: From trait dissection to yield Vincent Vadez – Jana Kholova Aparna Kakkera, K Siva Sakhti, M Tharanya, Susan Medina, Srikanth Malayee, Sudhakarreddy Palakolanu, Sunita Choudhary, Rekha Baddam, Suresh Dharani, Santosh Deshpande, Rakesh Srivastava, Tom Hash ICRISAT NGGIBCI meeting – India 18-20 Feb 2015
  2. Today’s presentation Basic considerations on CC / Drought Transpiration response to VPD Possible mechanisms and role of aquaporin Breeding application Linking the pieces with crop simulation
  3. Grain Yield Grain Number Grain Size & N  Biomass RADN TE T RUE Rint vpd kl LAISLNRoots k  TN LNo A >A APSIM Generic Crop Template, from Graeme Hammer Yield and its determinants Yield is not a trait Phenotyping to focus on the building blocks
  4. FTSW 0.00.20.40.60.81.0 Normalizedtranspiration 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Stage I Stage II Stage III Plants don’t suffer stress until >60% soil water is depl How plant manage water when there is water is critical Basic response of plant expose to water deficit Control of leaf water losses
  5. What is a “drought tolerant” plant? A plant with: • enough water to fill up grains • no more water after grain filling Hypotheses: • Tap water? • Save/manage water? Focus on traits affecting plant water budget
  6. Maximum temperature in the SAT Hypothetic Temperature threshold 0 5 10 15 20 25 30 35 40 45 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec MaximumT°C 1983-HQ 1992-HQ 2001-HQ 2012-HQ 1983-ISC 1990-ISC 1998-ISC Headquarter Sahelian Center T°C rarely crosses critical limits for SAT crops
  7. 0 1 2 3 4 5 6 7 8 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec MaximumVPD Sahelian Center Headquarter Vapor pressure deficit (VPD) in the SAT Prevalent high VPD Effect on plant water balance VPD threshold
  8. Region Season Temp. Response (°C) Rainfall Response (%) Africa Min 25 50 75 Max Min 25 50 75 Max. West Africa Annual 1.8 2.7 3.3 3.6 4.7 -9 -2 2 7 13 East Africa Annual 1.8 2.5 3.2 3.4 4.3 -3 2 7 11 25 Southern Africa Annual 1.9 2.9 3.4 3.7 4.8 -12 -9 -4 2 6 Asia Min 25 50 75 Max Min 25 50 75 Max. East Asia Annual 2.3 2.8 3.3 4.1 4.9 2 4 9 14 20 Southern Asia Annual 2.0 2.7 3.3 3.6 4.7 -15 4 11 15 20 S.E. Asia Annual 1.5 2.2 2.5 3.0 3.7 -2 3 7 8 15 Introduction IPCC report 2007
  9. Introduction A changing climate: What are we sure about? •A steady increase in temperature (1.5-2°C to 4-5 °C) •CO2 increase What are we less sure about? •Rainfall quantity and variability •Extreme temperature events
  10. 0 200 400 600 800 1000 1200 Time (days) Degredays Flowering with CC (+ 2°C) Flowering with Current climate About 8 days differences Crop cycle dynamics vs water use A loss in light capture Degre-day accumulation in chickpea (base = 8°C)
  11. Climate scenario Mean seasonal temperature (OC) Time to maturity (d) % reduction Crop yield (kg/ha) % reduction from Current Current 19.6 133 - 1736 - Current + 1OC 20.6 124 6.5 1612 7.1 Current + 2OC 21.6 117 12.0 1503 13.4 Current + 3OC 22.6 111 15.9 1406 19.0 Current + 4OC 23.6 108 18.7 1322 23.8 Current + 5OC 24.6 105 20.5 1238 28.7 From John Dimes - ICRISAT Effect on yield in pigeonpea Crop cycle dynamics vs water use Shorter cycle lower yield
  12. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0 1 2 3 4 WU(kgplant-1week-1) Weeks after panicle emergence ICMH01029 ICMH01040 ICMH01046 PRLT2/89-33 Vadez et al 2013 – Plant Soil H77/833-2 ICMH02042 Terminal drought sensitive Terminal drought tolerant Tolerant: less WU at vegetative stage, more for reproduction & grain filling Water extraction pattern (WS) in pearl millet Flowering
  13. R² = 0.7108 0 4 8 12 16 20 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 GrainYield(gplant-1) R² = 0.552 0 4 8 12 16 20 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 GrainYield(gplant-1) Late stress Early stress Water uptake in week 3 after booting Higher yield from higher post-anthesis water use
  14. 0 1 2 3 4 5 6 7 8 9 10 21 28 35 42 49 56 63 70 77 84 91 98 Waterused(kgpl-1) Days after sowing Water extraction at key times Less water extraction at vegetative stage, more for grain filling Zaman-Allah et al 2011 See Borrell et al 2014 See Vadez et al 2013 Sensitive Tolerant Trait dissection Vegetative Reprod/ Grain fill Conductance Canopy area
  15. Lysimetric facility at ICRISAT Morphology Functionality Shift in how we look at roots Kinetics of water uptake 2800 “small” PVC / 1600 “large” PVC Limitations / Challenges: • Capacity/automation (load cells) • 3-D in-situ Strengths: • Water use efficiency • Water extraction at key times
  16. Variation for water use efficiency • Huge genetic variation • Variants used in breeding FunctionalitySorghum Pearl millet
  17. Today’s presentation Basic considerations on CC / Drought Transpiration response to VPD Possible mechanisms and role of aquaporin Breeding application Linking the pieces with crop simulation
  18. Terminal drought sensitive Terminal drought tolerant 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.50 1.00 1.50 2.00 2.50 3.00 3.50 VPD (kPa) H77/2 833-2 PRLT-2/89-33 Transpiration(gcm-2h-1) From Kholova et al 2010b 2 mechanisms of water saving: •Low Tr at low VPD •Further restriction of Tr at high VPD Transpiration response to high VPD – Pearl millet
  19. Transpiration response to high VPD - Peanut
  20. Mouride IfVPD<2.09,TR=0.0083(VPD)–0.002 IfVPD≥ 2.09,TR=0.0013(VPD)+0.015 R²=0.97 B UC-CB46 TR=0.0119(VPD)-0.0016 R²=0.97 D Transpiration response to VPD - cowpea Tolerant lines have a breakpoint (water saving) Tolerant Sensitive Belko et al – 2012 (Plant Biology)
  21. Staygreen ILs (Stg3 – Stg B) are VPD-sensitive 0.0000 0.0020 0.0040 0.0060 0.0080 0.0100 0.0120 9 11 13 15 17 Transpiration(gcm-2h-1) Time of the day (h) stg1 stg3 stg4 stgB R16 B35 Recurrent R16 Stg3 StgB Transpiration response to VPD in Sorghum 1 - Introgression lines
  22. S35 background Transpiration response to high VPD In staygreen introgression lines ILs do not differ from recurrent S35 for the Tr sensitivity to VPD 0.000 0.002 0.004 0.006 0.008 0.010 0.012 10.00 11.30 13.00 14.30 Transpirationrate(gcm-2h-1) Time of the day stg1 stg3 stg4 stgB stgB S35 B35 Recurrent S35 Stg3 StgB
  23. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 500 1000 1500 2000 2500 3000 3500 Staygreenscore Water uptake at three weeks after panicle emergence Unfilled Profile R2 = 0.76** Filled Profile R2 = 0.79**
  24. Vapor Pressure Deficit (VPD, in kPa) Transpirationrate(gcm-2h-1) 0.0 2.0 4.0 0.0 1.0 A – Insensitive to VPD – High rate at low VPD B – Sensitive to VPD – High rate at low VPD C – Sensitive to VPD – Low rate at low VPD D – Insensitive to VPD – Low rate at low/high VPD Main types of Tr response to VPD Water use difference Leaf conductance differences = water Vadez et al 2013 – FPB in press
  25. 0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.62 1.05 1.58 2.01 2.43 3.05 3.45 Transpiration(gpl-1cm-2) VPD (kPa) VPD-insensitive VPD-sensitive Transpiration response to VPD in Sorghum 2 - Germplasm
  26. 2.0 3.0 4.0 5.0 6.0 7.0 152 Germplasm tested TE 10 lowest TE are all VPD-Insensitive 10 highest TE are all VPD-sensitive High TE lines limit transpiration at high VPD Why are VPD-sensitive sorghum so interesting?
  27. 4 replications RH & T hourly recording Weighing: 7-11am = low VPD 11am-15pm = high VPD 8” pots re-saturated every day soil evaporation minimized with plastic beads How to phenotype at large scale?
  28. Capacity: 4,800 plots Throughput: 2,400 plots/hour Traits: LA, Height, Leaf angle, … LeasyScan at ICRISAT Leaf canopy area and conductance
  29. Canopy Scanning + plant transpiration = live water budget Leaf canopy conductance Load Cells
  30. Capacity: 4,800 plots Throughput: 2,400 plots/hour Traits: LA, Height, Leaf angle, … LeasyScan at ICRISAT Leaf canopy area and conductance
  31. Leaf area See Chapuis et al 2012 From Welcker et al 2014 Leafarea Water use Leaf canopy area Trait dissection Possible Field applications Wind + Light TºC + RH % From Deery et al 2014 Lidar scanning Leaf area response to environmental conditions Leafelongationrate Atmospheric drought Soil drought
  32. Canopy Scanning + plant transpiration = live water budget Leaf canopy conductance Load Cells Limitations / challenges: • Load cells capacity • Data management / analysis Strengths: • Throughput • Meta-data
  33. Today’s presentation Basic considerations on CC / Drought Transpiration response to VPD Possible mechanisms and role of aquaporin Breeding application Linking the pieces with crop simulation
  34. Possible mechanisms?? ??? Hydraulic Possibly located in the roots
  35. Apoplastic Pathway (Structural) Symplastic Pathway (AQP) Water pathways in the root cylinder Two pathways have different hydraulic conductance Hypothesis: Aquaporin control plant water loss ? ????
  36. Apoplastic path inhibition: H-Ferrocyanide +CuSO4 Symplast path inhibition: AgNO3,
  37. Follow-up of transpiration before/after inhibition
  38. 0 0.2 0.4 0.6 0.8 1 1.2 Normalizedtranspiration Time Apoplast & symplast inhibition at low VPD Apoplastic & Symplastic inhibition Symplastic inhibition Apoplastic inhibition Apoplastic transport predominant Low VPD small differences/effects VPD-sensitive VPD - insensitive
  39. VPD - insensitive 0 0.2 0.4 0.6 0.8 1 1.2 Normalizedtranspiration Time(mins) Apoplast & symplast inhibition at high VPD Symplastic inhibition Apoplastic inhibition Apoplastic transport less predominant High VPD larger differences/effects VPD-sensitive
  40. VPD-insensitive VPD-sensitive Any difference in aquaporin expression In sorghum contrasting for VPD response?? 0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016 0.018 0.62 1.05 1.58 2.01 2.43 3.05 3.45 Transpiration(gpl-1cm-2) VPD (kPa)
  41. 0 2 4 6 8 10 12 14 16 18 Low TE High TE HighVPD/LowVPD PIP1;1 PIP1;2 PIP1;3 PIP1;4 PIP2;1 PIP2;2 PIP2;4 PIP2;5 PIP2;6 PIP2;7 PIP2;8 PIP2;9 PIP2;10 PIP relative expression (High VPD/Low VPD) VPD – insensitive line increases expression of PIP2 PIP2;6 PIP2;9 PIP2;7 VPD-Insensitive VPD-Sensitive
  42. Today’s presentation Basic considerations on CC / Drought Transpiration response to VPD Possible mechanisms and role of aquaporin Breeding application Linking the pieces with crop simulation
  43. Crop at ICRISAT - H Abiotic constraints
  44. SOL BIJ HYD TAN BAD Maharashtra Karnat aka Andhra Pradesh Network of location for post-rainy sor
  45. Field variability at the ICRISAT-Niger s
  46. Effects of human settlement activities on millet growth in the Sahel (micro-variability) Aerial photograph showing residual effects of changes in soil productivity due to farmers' settlement activities. Numbers indicate the years during which the settlement of the farmers remained at a particular site. The picture was taken 75 days after sowing from an altitude of about 300 m above ground. Hardpans (indicated by lacking plant growth) within the boundaries of former settlement areas are the result of clay applications to the foundations of the five houses belonging to the one extended family. Note that the increases in millet growth in former settlement areas lasted four to five years. Buerkert et al. 1996. Plant and Soil 180, 29-38.
  47. 0 1 2 3 4 5 6 7 8 9 10 21 28 35 42 49 56 63 70 77 84 91 98 Waterused(kgpl-1) Days after sowing Water extraction at key times Less water extraction at vegetative stage, more for grain filling Zaman-Allah et al 2011 See Borrell et al 2014 See Vadez et al 2013 From Deery et al 2014 See Prashar et al 2013 Sensitive Tolerant Trait dissection Possible Field applications Early vigor (RGB / NDVI) Infra Red imaging Canopy T°C Staygreen Vegetative Reprod/ Grain fill Conductance Canopy area
  48. Terminal drought sensitive Terminal drought tolerant 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.50 1.00 1.50 2.00 2.50 3.00 3.50 Evaporative demand (VPD) H77/2 833-2 PRLT-2/89-33 Canopyconductance Modulate conductance Decrease TR at high VPD Leaf canopy response to VPD Water saving Canopy TºC Link to root anatomical differences Trait dissection Possible Field applications Infra Red imaging From Araus and Cairns 2014 From Burton et al 2012 Root anatomy
  49. Leafarea Thermal time A – Fast early LA B – Slow early LA C – Fast early LA / small max LA D – Slow early LA / small max LA Traits: Leaf area development dynamics Speed of development / size of canopy = water So far no in-vivo way to measure
  50. Today’s presentation Basic considerations on CC / Drought Transpiration response to VPD Possible mechanisms and role of aquaporin Breeding application Linking the pieces with crop simulation
  51. average yield 0 200 400 600 800 1000 1200 vegetative pre-flowering post-flowering post-flowering relieved mild stress weighedyield(kg/ha) vegetative pre-flowering post-flowering post-flowering relieved mild stress 4. Adaptive traits enhancing crop production & resilience in given environmental characterization 1. Well-defined area of interest Kholová et al. 2013 3. Impact on the crop production 7% 18% 18%17%40% PHASE I major stress patterns 0 0.2 0.4 0.6 0.8 1 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 thermal time ( o Day) S/D vegetative pre-flowering post-flowering post-flowering relieved mild 2. Environmental patterns
  52. Adaptive traits??? Grain Yield Grain Number Grain Size & N  Biomass RADN TE T RUE Rint vpd kl LAISLNRoot s k  TN LNo A >A Crop production Component traits contributing to drought adaptation Kholová et al. 2014 (FPB Traits related to water utilization
  53. “EFFICIENT WATER MANAGEMENT” • enough water to fill up grains • no more water after grain filling • water is turned to biomass with max. efficiency •Save water •Tap water • Increase WUE Crop production Traits related to water utilization MODELLING: predicting traits’ value in given agr
  54. S35 (senescent background)  7001- stgB - small leaves, H2O extr.  6008 – stgA - gr. dynamics, tillering  6026 – stg2 - large leaves Material: senescent parental lines &stay-green ILs Grain Yield Grain Number Grain Size & N  Biomass RADN TE T RU E Rint vpd kl LA I SL N Ro o t s k  TN LNo A >A R16 (senescent background)  K359w -stgB&3 – high TE, gr. dyn.  K648 - stg4 – short phyllochron ct of QTL depends on genetic backgroun (stg B!) Vadez et al. 2011
  55. 0 500 1000 1500 2000 2500 200 300 400 500 600 700 800 LA(cm2) thermal time (degree days) S35 7001 6008 6026 0 5 10 15 20 25 0 200 400 600 800 TPLA TTemerg_to_flag TPLA varying TPLAmax 16 18 20 22 24 0 0.2 0.4 0.6 0.8 1 1.2 100 200 300 400 500 600 700 800 900 100011001200130014001500 S/D thermal time intervals High TPLAmax Low TPLAmax -1000 -800 -600 -400 -200 0 200 400 600 800 1000 0 500 1000 1500 2000 2500 3000 Grainyieldgain(kgha-1) original grain yield (kg ha-1) Smaller canopy (low TPLAmax) -1000 -800 -600 -400 -200 0 200 400 600 800 1000 0 2000 4000 6000 8000 Stoveryieldgain(kgha-1) Original stover yield (kg ha-1) Smaller canopy (low TPLAmax) Pre-flowering Flowering Post-flowering Post-flowering relieved No stress 0 500 1000 1500 2000 2500 0 200 400 600 800 1000 1200 1400 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 stover(kgha-1);grain(kgha-1) thermal time (degree day) LAI(m2m-2);S/D High TPLAmax Low TPLAmax EXAMPL E: TPLA variabilit y Kholová et al. 2014 (FPB % of parameter change & physiological meanining stress scenario grain (kg ha-1 ) stover (kg ha-1 ) zone grain (kg ha-1 ) stover (kg ha-1 ) value estimate (Rs ha- 1 ) 0.05 pre-flowering -86 (-70;0) 160 (39;254) Central -71 (-142;28) 259 (130;387) 230 larger canopy flowering -190 (-366;0) 328 (120;490) FarSouth -25 (-203;180) 418 (266;576) 1715 post-flowering -127 (-278;0) 410 (294;541) North -97 (-212;21) 338 (184;499) 235 post-flowering-relieved -143 (-214;-78) 373 (257;452) South -67 (-189;8) 385 (240;481) 920 no stress 56 (-46;143) 348 (197;449) -0.05 pre-flowering 37 (0;51) -75 (-127;-15) Central 34 (-10;51) -128 (-160;-61) -130 smaller canopy flowering 126 (43;159) -189 (-223;- 129) FarSouth -3 (-116;97) -184 (-254;-113) -965 post-flowering 61 (-5;119) -207 (-286;- 129) North 56 (-14;140) -184 (-248;-102) -80 post-flowering-relieved 44 (10;80) -145 (-180;- 101) South 34 (0;81) -146 (-194;-84) -220 no stress -32 (-77;16) -140 (-203;-79)
  56. average yield 0 200 400 600 800 1000 1200 vegetative pre-flowering post-flowering post-flowering relieved mild stress weighedyield(kg/ha) vegetative pre-flowering post-flowering post-flowering relieved mild stress 4. Which traits confer advantage in the most frequent environment? Way forward1. Well-defined area of interest 2. Environmental patterns 3. Effect of environment on production Kholova et al 2013 major stress patterns 0 0.2 0.4 0.6 0.8 1 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 thermal time ( o Day) S/D vegetative pre-flowering post-flowering post-flowering relieved mild
  57. Figure 1 North Central South Far South Maharasthra Karnataka Andhra Pradesh Southern Northern Central Far South Sorghum growing area in India
  58. Characterizing drought dynamics - based on S/D ratio simulation and clustering Type 3 intermittent stress Type 2 pre-flowering stress Type 1 flowering stress Type 4 post-flowering stress 2 How to characterize
  59. major stress patterns 0 0.2 0.4 0.6 0.8 1 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 thermal time (o D) S/D vegetative pre-flowering post-flowering post-flowering relieved mild 3. Which traits confer advantage in the most frequent environment? 2. Environmental pattern Sorghum growing area
  60. grain yield gain (low TR) -300 -200 -100 0 100 200 300 400 0 500 1000 1500 2000 2500 3000 3500 original yield (kg/ha) yieldgain(kg/ha) 1 postflowering 2 flowering 3 postflowering-relieved 4 no stress 5 preflowering Original yield (kg ha-1) 0 Yield increase (kg/ha) with transpiration sensitivity to high VPD: Rabi sorghum Yieldincrease
  61. -1 0 +33 Crop modelling used to predict trait effects 15-30% yield increase at high latitudes % yield increase with transpiration sensitivity to high VPD: Peanut
  62. The VPD response lead to higher TE It is itself related to differences in AQP gene expression Major yield increase possible across crops Breeding (donors identified) Harness genetics – Phenotyping (new platform) In Summary…
  63. Thank you Collaborators: F. Chaumont (Univ. Louvain) G. Hammer / A. Borrell / G McLean / E van Oosterom (Univ. Queensland) B Sine / N Belko / Ndiaga Cisse (CERAAS) C Messina (Pioneer) Donors: B&MG Foundation GCP ACIAR DFID ICRISAT Technicians / Data analyst: Srikanth Malayee Rekha Badham M Anjaiah N Pentaiah Students: M Tharanya S Sakthi T Rajini Colleagues: KK Sharma / T Shah / F Hamidou HD Upadhyaya / R Srivastava / Bhasker Raj SP Deshpande / PM Gaur
  64. More, Better, Faster, Cheaper: practical needs for improving the rate of genetic gain Advances in below and above-ground phenotyping Vincent Vadez & Team ICRISAT Global Goods – Bill & Melinda Gates Foundation 29 Oct 2014
  65. Crop simulation of trait effect on yield See Sinclair et al 2010 See Cooper et al 2014 Grain yield (g m-2) Traits targeted to specific zones Chose test locations
  66. 0 10 20 30 393 108 Fold-increase Genotypes Aquaporin gene expression PIP2;6 PIP2;7 PIP2;9 PIP1;2 PIP1;3 PIP1;4 Trait variability Genomics (Genetics) See Cooper et al 2014 Multi-location testing Crop Simulation (Validation) Linking-up the pieces Trait dissection Field phenotyping See Lynch et al 2014 See Granier et al 2014 See Cobb et al 2013 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.50 1.00 1.50 2.00 2.50 3.00 3.50 Evaporative demand (VPD) Canopyconductance Thank you
  67. RESEARCH APPLICATION Development of BCNAM pop (best stay-green donors X best rabi ge Predicted value of adaptive traits TRAIT = $ per ha HT- PHENOTYPING Environmental characterization & trait identification Ideotypes suiting the targe with viable management opt
  68. Lysimetric evaluation Transpiration in pots 0.000 0.004 0.008 0.012 0.016 0.020 0.62 1.05 1.58 2.01 2.43 3.05 3.45 Transpiration (gcm-2h-1) VPD Low TE High TE 0 1 2 3 4 5 6 7 Low TE High TE TE grain yield gain (low TR) -300 -200 -100 0 100 200 300 400 0 500 1000 1500 2000 2500 3000 3500 original yield (kg/ha) yieldgain(kg/ha) 1 postflowering 2 flowering 3 postflowering-relieved 4 no stress 5 preflowering Original yield (kg ha-1) 0 AQP gene expression Modeling of Tr restriction effect on yield
  69. Expansion of modelling (Best generated grid-data&Future climatic projections&whole India modelling (with various crops)) Expansion of the concept to WCA (Madina) Sorghum physiology researc @ICRISAT(AusSoRGM 2014) Vincent Vade Jana Kholová & TEAM
  70. • ICRISAT is a non-profit, non-political, International Agricultural Research Institute • Established in 1972, operating with an annual budget of US$ 83 million (2013) • Member of the Consultative Group on International Agricultural Research (CGIAR) • Our mandate crops: Sorghum, Pearl millet, Pigeon pea, Chick pea & Groundnut • A prosperous, food-secure and resilient dryland tropics Our Vision • To reduce poverty, hunger, malnutrition and environmental degradation in dryland tropics Our Mission
  71. V. Vadez – C.Tom Hash – Rattan Yadav – T. Nepolean – J. Kholová – HS Talwar-G. Hammer – E. vanOosterom - A. Borrell – G. McLean – A. Doherty - T.R. Sinclair – I.M. Rao – S. Beebe – J. Ehlers – Mainassara Zaman A. – F. Hamidou – P.M. Gaur – E. Monyo – B. Ntare – J. Devi-Mura – S. Choudhary …… Our approach brings together physiologists, breeders & modelers Thank you Mission To reduce poverty, hunger, malnutrition and environmental degradation in the dryland tropics
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