Linking research disciplines for a more targeted Understanding, efficient Connecting, breeding Scaling: of legume cultivars Using for harsh biology farming to solve environments food Production challenges Vincent Vadez ICRISAT Australian Pulse Conference Congress Tamworth 12-15 Sept 2016
Semi-Arid Tropic map - ICRISAT High evaporative demand (VPD) Drought is a major problem
Yield in an interaction G YIELD M E Turning complexity into opportunity!!!
Framing the right target(s) CC / drought : What have we learnt? From knowing to breeding: HT Phenotyping Linking & scaling with crop simulation Legumes in the scope of farming systems
Framing the right target(s) CC / drought : What have we learnt? From knowing to breeding: HT Phenotyping Linking & scaling with crop simulation Legumes in the scope of farming systems
S eed DW u nd er salin ity (No) Relationship between [Na+] and grain yield in chickpea 15 10 5 0 0 0.2 0.4 0.6 0.8 S h o o t Na No relationship between Shoot [Na+] and yield
Relationship between seed set or seed size and grain yield in chickpea Yield Residual 6.0 4.0 2.0 0.0-2.0-4.0-6.0-8.0 R 2 = 0.65 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Ratio seed number Ratio = seed number salinity seed number control High yield under salinity relates to better seed set (target reproductive biology)
Heat stress? 3 main ways of action:
Seed set Percentage Effect on reproductive biology 100 90 80 70 60 50 40 30 20 10 Tolerant: ICCV92944 Sensitive: ICC5912 0 25 27 29 31 33 35 37 39 Temperature Lower seed set at higher temperature From Devasirvatham et al., 2012 - FPB
Effect on crop duration Climate scenario Time to maturity (d) Crop yield (kg/ha) % reduction from Current Current 133 1736 - Current + 1 O C 124 1612 7.1 Current + 2 O C 117 1503 13.4 Current + 3 O C 111 1406 19.0 Current + 4 O C 108 1322 23.8 Current + 5 O C 105 1238 28.7 Shorter cycle lower yield From John Dimes - ICRISAT
Effect on plant water relations Water vapor Wet / cool air = low evaporative demand Under climate change: hotter air = higher evaporative demand
What about drought? Is this a problem? Where is it a problem? How much is it a problem?
Yield gap analysis in chickpea Potential yield Water-limited Potential yield Target zones for: Intensification Drought adapted lines Yield loss A Hajjarpour et al (in prep.)
Yield losses due to drought (g m -2 ) (Groundnut) Major yield losses No loss to drought Yield loss (g m -2 ) Vadez et al., in prep.
Framing the right target(s) CC / drought : What have we learnt? From knowing to breeding: HT Phenotyping Linking & scaling with crop simulation Legumes in the scope of farming systems
N orm alized transpiration Transpiration Transpiration response of plant to water deficit 1.2 1.0 0.8 0.6 0.4 0.2 S tage I S tage II S tage III 0.0 1.0 0.8 0.6 0.4 0.2 0.0 Soil FTSW water No stress until >60-70% soil water is depleted How plant manage water before stress is critical
Maximum VPD (kpa) Vapor pressure deficit (VPD) in the SAT 8 7 6 Sahelian Center (Niger) 5 4 3 2 1 0 Patancheru (India) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec High VPD Effect on plant water balance Water Use Efficency = k/vpd
So, our work on drought /climate change: Looks at plant water use before stress occurs Takes close attention to atmospheric drought (high VPD)
LysiField: Lysimetric facility at ICRISAT Advantages: Water uptake (total, timing) Transpiration efficiency (TE) Long term (3 Wks-maturity) High capacity (5000 PVCs)
Water extraction in tolerant / sensitive chickpea Sensitive Tolerant No major water extraction differences in chickpea
Pod yield (g plant -1 ) Pod yield (g plant -1 ) 14 12 10 8 6 4 2 0 Cowpea Pod yield and 7.0 water extraction Rainy season 6.0 5.0 4.0 3.0 Bean Rainy season 2.0 1.0 0.0 0 1000 2000 3000 4000 5000 6000 7000 0 1000 2000 3000 4000 5000 6000 7000 Water extracted (g plant -1 ) Water extracted (g plant -1 ) 15 10 Post-rainy season Peanut 10 8 6 Peanut Rainy season 5 4 2 0 0 0 1000 2000 3000 4000 5000 6000 7000 0 1000 2000 3000 4000 5000 6000 7000 Water extracted (g plant -1 ) Water extracted (g plant -1 ) Total water extracted unrelated to grain yield
Water used (kg pl -1 ) Zaman-Allah et al 2011 Borrell et al 2014 Vadez et al 2013 Water extraction time profile (chickpea) 10 9 Vegetative Reprod/ Grain fill 8 7 6 5 4 3 2 Tolerant 1 Sensitive 0 21 28 35 42 49 56 63 70 77 84 91 98 Days after sowing Tolerant: Less water use at vegetative stage, more for grain filling
Water used (kg pl -1 ) Relationship between grain yield and water use 10 9 8 7 6 5 4 3 2 1 0 21 28 35 42 49 56 63 70 77 84 91 98 Days after sowing Tolerant Sensitive Tolerant: less WU at vegetative stage, more for reproduction & grain filling EUW = 27 kg mm -1 Zaman-Allah, Jenkinson, Vadez 2011 JXB
Why different patterns of water use even if no stress?
LA Transpiration rate (g cm -2 h -1 ) Why different patterns of water use even if no stress? Leaf Canopy development Canopy conductance TE Water use 1 Water use 0 Thermal time 0 2 4 Vapor Pressure Deficit (VPD; kpa) Phenotyping focused on the Building block of plant Water Use (WU)
Leaf area (cm2) Canopy development differences in chickpea 12000 12000 10000 y = 23.302e 0.2562x 10000 8000 R² = 0.9367 8000 y = 11.995e 0.31x 6000 6000 R² = 0.9607 4000 4000 2000 2000 0 0 5 10 15 20 25 0 0 5 10 15 20 25 Node number on main stem Node number on main stem Coefficients used in crop modelling to test their potential effects on yield
Transpiration restriction under high VPD in chickpea See also Zaman-Allah et al 2011 - FPB
Transpiration response to VPD in cowpea Tolerant Sensitive Mouride B UC-CB46 D If VPD < 2.09, TR = 0.0083 (VPD) 0.002 If VPD 2.09, TR = 0.0013 (VPD) + 0.015 R² = 0.97 TR = 0.0119 (VPD) - 0.0016 R² = 0.97 Tolerant lines have a breakpoint (water saving) Belko et al 2012 (Plant Biology)
Residual transpiration Total transpiration (g plant -1 ) What drives transpiration in that population?? Leaf area (69%) 250 200 150 100 50 R² = 0.69 Conductance at high VPD (64% of residual) 50 40 30 20 10-20 -30-40 R² = 0.64 Get QTL for both these traits 0 0 200 400 600 800 1000 1200 Leaf area (cm 2 plant -1 ) 0 0.000-10 0.010 0.020 0.030 0.040 0.050 0.060 Transpiration rate under high VPD PhD training of Nouhoun Belko Burkina Faso
QTLs from ICI Mapping Drought tolerance traits Chromo Position Flanking Additive Positive TraitName some (cm) markers LOD PVE(%) effect allele Plt DW 2 4 1_0113-1_0021 3.1 15.5 0.3 CB46 SLA 2 31 1_1139-1_1061 3.6 14.4-11.5 IT93K-503-1 LA 2 85 1_0834-1_0297 4.0 18.5 57.0 CB46 Leaf DW 2 85 1_0834-1_0297 2.8 13.4 0.2 CB46 Plant transp Total 6h 2 85 1_0834-1_0297 2.9 13.1 8.9 CB46 Conductance High VPD 5 19 1_0806-1_0557 3.2 16.3 0.0 IT93K-503-1 Conductance Low VPD 5 20 1_0806-1_0557 2.8 13.3 0.0 IT93K-503-1 Conductance Low VPD 5 23 1_0806-1_0557 3.3 14.0 0.0 IT93K-503-1 Conductance Low VPD 7 13 1_0279-1_1482 3.6 15.0 0.0 IT93K-503-1 SLA 9 25 1_0051-1_0048 4.9 19.7 13.5 CB46 Conductance high VPD 9 52 1_0425-1_1337 2.6 11.5 0.0 IT93K-503-1 Select RILs having different dosage of these QTLs and test them across contrasting drought scenarios From Phil Roberts/Tim Close and team
So: Managing water use before stress is critical It involves either LA development (speed, size), or Tr restriction at high VPD What else?
Daily transpiration profile and Vapor pressure deficit (VPD) Tr Restriction VPD Transpiration 6 9 12 15 18 Time of the day Low Tr at high VPD confer higher TE Sinclair et al 2005 FPB
Transpiration (g pl -1 cm -2 ) What possibly explains the large TE differences? 0.014 0.012 0.010 Low TE 0.008 0.006 0.004 High TE 0.002 0.000 0.62 1.05 1.58 2.01 2.43 3.05 3.45 VPD (kpa) High TE lines are VPD-sensitive (TE = k / VPD)
Residual Seed weight (g plant-1) Grain yield (g plant-1) 10.0 8.0 6.0 y = 7.8686x - 37.2 R² = 0.6224 Maize 16 12 R² = 0.65 Peanut 4.0 8 2.0 4 0.0 4.00 4.50 5.00 5.50 6.00 Transpiration Efficiency (TE) 0 0.0 1.0 2.0 3.0 Transpiration Efficiency (TE) 2.5 2.0 1.5 1.0-1.0-1.5-2.0-2.5 Chickpea R² = 0.3746 0.5 0.0-0.50.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 Transpiration Efficiency High TE lines have higher yields
Pod yield (g plant -1 ) 12 8 4 R² = 0.65 But. TE-Yield 16 relationship across VPD regimes High VPD Season (VPD>2kPa) 250% range 0 10 8 6 0.0 1.0 2.0 3.0 R² = 0.03 Low VPD season (VPD<2kPa) 60% range 4 2 0 0.0 1.0 2.0 3.0 TE variation and link to yield depend on VPD
Framing the right target(s) CC / drought : What have we learnt? From knowing to breeding: HT Phenotyping Linking & scaling with crop simulation Legumes in the scope of farming systems
LeasyScan at ICRISAT Leaf canopy area Capacity: 4,800 plots Throughput: 2,400 plots/hour 12 scans / day Traits: LA, Height, Leaf angle, Vadez et al 2015 - JXB
Suite of environmental sensors a d e c b f g
A1 A B Agro-systems A1-severe drought A-mild drought B-mild drought Eco-types vary for water stress adaptation Vadez et al. 2015
QTLs for canopy traits on LG04 of chickpea QTL for vigor traits (LA, plant height, growth rate)
Leaf canopy conductance Load Cells 3D-LA Scanning + plant transpiration = live water budget 1500 scales early 2016
TR profile under natural VPD conditions Capacity to pin key phenotypic differences at specific times Vadez et al 2015 - JXB
Transpiration rate (mg cm-2 min-1) TR profile under natural VPD conditions Groundnut 20.0 18.0 16.0 2.55 kpa Water saving JL24 ICGV91114 14.0 3.12 kpa 2.34 kpa 12.0 10.0 8.0 6.0 4.0 2.0 0.0 180 191 203 Thermal time (degree-days) Capacity to pin key phenotypic differences at specific times Vadez et al 2015 - JXB
Capacity: 150 kg Precision: 0.02% Data collected every ½ second Data integrated each ¼ hour (0.004g) 1488 load cells installed
Pots replaced by trays (90 kg soil) Trays = 0.25 m2 Real row sowing
plants with desired phenotype % of lines with desired phenotype No. of lines phenotyped Populations (1000s lines) LeasyScan Lysimetry Field
Framing the right target(s) CC / drought : What have we learnt? From knowing to breeding: HT Phenotyping Linking & scaling with crop simulation Legumes in the scope of farming systems
Transparency and accessibility Climate (4) of the model algorithm Crop T o min T o max Rainfall Radiation Crop (48) The SSM crop model Dynamic of accumulation and allocation of dry matter Dynamic of N uptake 48 MEASURABLE parameters No optimisation Soil (11) Depth Hydrolic properties Initiation of water content Management (2) Sowing date Sowing density Process driven algorithm Soil Dynamic of soil water content Soltani and Sinclair 2012 Marrou et al, 2014 IFLRC VI & ICLGGV II
Testing the probability of yield increase Tr sensitivity to VPD in soybean 75% (wet) Median 25% (dry) Sinclair et al., 2010 Agron. J. 102: 475-482
Testing the probability of yield increase Fast root growth 75% (wet) Median 25% (dry) Sinclair et al., 2010 Agron. J. 102: 475-482
Testing traits by Crop simulation modeling Guide breeder decision (stochastic information on trait value) Breeder Decision VPD Sensitivity Fast root growth
Defining an optimum sowing window for lentils Ghanem, Marrou et al., Agric. Systems, 2015
Combining both longer phenology and optimal sowing date in lentils Ghanem, Marrou et al., Agric. Systems, 2015 Major yield benefits
Comparing genetic and agronomic interventions Faster root growth Deeper water extraction 30mm Irrigation at R5 M G Models are a tool for making decisions Vadez et al. FCR - 2012
Yield change from 20 to 40 plant m -2 Groundnut Major yield benefits Yield increase (g m -2 ) Vadez et al. in prep.
Setting priorities Adapt genotypes G genotype Yield M production E management Adjust management environment Characterize & forecast
Framing the right target(s) CC / drought : What have we learnt? From knowing to breeding: HT Phenotyping Linking & scaling with crop simulation Legumes in the scope of farming systems
Distribution of CGIAR GL program resources across goals Reduced Poverty Improved food and nutrition security and health Improved natural resources systems and ecosystems services. Are we tapping enough Legumes for their system role?
Relationship pod yield versus haulm N% in groundnut From Blummel et al., 2012 - FCR High pod yield + Haulm quality is possible Grain + Feed + N residue
G ra in y ie ld (k g /h a ) Relationship pod yield versus haulm N% in cowpea 1 5 0 0 r= -0.4 1 ; P < 0.0 0 0 1 1 0 0 0 5 0 0 0 1.0 1.5 2.0 2.5 H a u lm n itro g e n c o n te n t (% ) From Boukar et al., IITA F ig u re 2 : R e la tio n s h ip s b e tw e e n h a u lm n itro g e n c o n te n ts a n d g ra in y ie ld s in 1 0 1 IIT A a c c e s s io n s o f c o w p e a a c ro s s tw o y e a rs High pod yield + Haulm quality is possible Grain + Feed + N residue
Relationship haulm yield versus haulm N% in peanut From Blummel et al., 2012 - FCR Are we tapping enough Legumes N2 fixation potential?
Weight gain in sheep fed with groundnut haulm Live weight gains in sheep fed exclusively on groundnut haulms Groundnut cultivars Gain (g/d) ICGV 89104 137 ICGV 9114 123 TMV 2 111 ICGS 76 76 ICGS 11 76 DRG 12 66 ICGS 44 65 ICGV 86325 83 ICGV 92020 95 ICGV 92093 109 Prob > F 0.02 Prasad et al. 2010 There is an un-tapped potential for feed quality (in particular N) in legumes
$ per ton Price evolution of main fertilizer sources 800 600 400 Anhydrous NH 3 Super Phosphate 200 0 1960 1970 1980 1990 2000 2010 Year We ll need to return to legumes P nutrition will become a main issue
Yield (g/plant) WS Yield (g/plant) Yield (g/plant) WS Yield (g plant -1 ) WS Effect of high N (HN) or low N (LN) under water stress (WS) and irrigation (WW) 8.0 6.0 Cowpea Peanut 4.0 2.0 0.0 8.0 6.0 WW-HN WW-LN WS-HN WS-LN Bean 8.0 6.0 16 14 12 10 8 6 4 2 0 HN-WW LN-WW HN-WS LN-WS Chickpea 4.0 4.0 2.0 2.0 0.0 WW-HN WW-LN WS-HN WS-LN 0.0 WW-HN WW-LN WS-HN WS-LN Peanut is least sensitive to low N Low N more a problem than drought in bean
Genetic variation in the yield response to low soil N in chickpea 7.0 Seed yield (g plant-1) High N 6.0 5.0 4.0 3.0 2.0 1.0 0.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 Seed yield (g plant-1) - Low N Genetic variation is there to be used
Key messages Importance of water at critical times Scaling from traits to yield on the ground Breeding as a crucible for different disciplines Crop Simulation to guide breeding/agronomic targets Providing options with stochastic values Need to advocate the multiple benefits of legumes
Donors: B&MG Foundation USAID GCP CRPs Collaborators: TR Sinclair (NCSU) B Sine / N Belko / Ndiaga Cisse (CERAAS) M Ghanem (ICARDA) H Marrou (Supagro Montpellier) T Close / P Roberts (UC Riverside) T Colmer / K Siddique / NC Turner (UWA) M Blummel (ILRI) S Beebe / IN Rao (CIAT) Colleagues: Jana Kholova KK Sharma / F Hamidou HD Upadhyaya / PM Gaur / RK Varshney / M Thudi Students: M Tharanya S Sakthi S Medina R Pushpavalli Technicians / Data analysts: Srikanth Malayee Rekha Badham Dharani Suresh M Anjaiah N Pentaiah Thank you
InterDrought-V Hyderabad International Convention Center (HICC) Hyderabad, India 21-25 February, 2017 Conference Topics: v Setting the biophysical context v Maximising dryland crop production v Plant productivity under drought Effective capture of water Transpiration efficiency Vegetative Growth Reproductive development, yield, yield quality v Breeding for water-limited environments InterDrought Chair: Francois Tardieu, INRA, France InterDrought Vice-Chair: J S Sandhu, ICAR, India Conference Organization Chair: Rajeev Varshney, ICRISAT, India Contact: r.k.varshney@cgiar.org, id5.icrisat@gmail.com Website: www.ceg.icrisat.org/idv v Agronomic management for water-limited environments