Linking research disciplines for a more. Production challenges

Transcription

1 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 Sept 2016

2 Semi-Arid Tropic map - ICRISAT High evaporative demand (VPD) Drought is a major problem

3 Yield in an interaction G YIELD M E Turning complexity into opportunity!!!

4 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

5 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

6 S eed DW u nd er salin ity (No) Relationship between [Na+] and grain yield in chickpea S h o o t Na No relationship between Shoot [Na+] and yield

7 Relationship between seed set or seed size and grain yield in chickpea Yield Residual R 2 = Ratio seed number Ratio = seed number salinity seed number control High yield under salinity relates to better seed set (target reproductive biology)

8 Heat stress? 3 main ways of action:

9 Seed set Percentage Effect on reproductive biology Tolerant: ICCV92944 Sensitive: ICC Temperature Lower seed set at higher temperature From Devasirvatham et al., FPB

10 Effect on crop duration Climate scenario Time to maturity (d) Crop yield (kg/ha) % reduction from Current Current Current + 1 O C Current + 2 O C Current + 3 O C Current + 4 O C Current + 5 O C Shorter cycle lower yield From John Dimes - ICRISAT

11 Effect on plant water relations Water vapor Wet / cool air = low evaporative demand Under climate change: hotter air = higher evaporative demand

12 What about drought? Is this a problem? Where is it a problem? How much is it a problem?

13 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.)

14 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.

15 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

16 N orm alized transpiration Transpiration Transpiration response of plant to water deficit S tage I S tage II S tage III Soil FTSW water No stress until >60-70% soil water is depleted How plant manage water before stress is critical

17 Maximum VPD (kpa) Vapor pressure deficit (VPD) in the SAT Sahelian Center (Niger) 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

18 So, our work on drought /climate change: Looks at plant water use before stress occurs Takes close attention to atmospheric drought (high VPD)

19 LysiField: Lysimetric facility at ICRISAT Advantages: Water uptake (total, timing) Transpiration efficiency (TE) Long term (3 Wks-maturity) High capacity (5000 PVCs)

20 Water extraction in tolerant / sensitive chickpea Sensitive Tolerant No major water extraction differences in chickpea

21 Pod yield (g plant -1 ) Pod yield (g plant -1 ) Cowpea Pod yield and 7.0 water extraction Rainy season Bean Rainy season Water extracted (g plant -1 ) Water extracted (g plant -1 ) Post-rainy season Peanut Peanut Rainy season Water extracted (g plant -1 ) Water extracted (g plant -1 ) Total water extracted unrelated to grain yield

22 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 Tolerant 1 Sensitive Days after sowing Tolerant: Less water use at vegetative stage, more for grain filling

23 Water used (kg pl -1 ) Relationship between grain yield and water use 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

24 Why different patterns of water use even if no stress?

25 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 Vapor Pressure Deficit (VPD; kpa) Phenotyping focused on the Building block of plant Water Use (WU)

26 Leaf area (cm2) Canopy development differences in chickpea y = e x R² = y = e 0.31x R² = Node number on main stem Node number on main stem Coefficients used in crop modelling to test their potential effects on yield

27 Transpiration restriction under high VPD in chickpea See also Zaman-Allah et al FPB

28 Transpiration response to VPD in cowpea Tolerant Sensitive Mouride B UC-CB46 D If VPD < 2.09, TR = (VPD) If VPD 2.09, TR = (VPD) R² = 0.97 TR = (VPD) R² = 0.97 Tolerant lines have a breakpoint (water saving) Belko et al 2012 (Plant Biology)

29 Residual transpiration Total transpiration (g plant -1 ) What drives transpiration in that population?? Leaf area (69%) R² = 0.69 Conductance at high VPD (64% of residual) R² = 0.64 Get QTL for both these traits Leaf area (cm 2 plant -1 ) Transpiration rate under high VPD PhD training of Nouhoun Belko Burkina Faso

30 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_ CB46 SLA _1139-1_ IT93K LA _0834-1_ CB46 Leaf DW _0834-1_ CB46 Plant transp Total 6h _0834-1_ CB46 Conductance High VPD _0806-1_ IT93K Conductance Low VPD _0806-1_ IT93K Conductance Low VPD _0806-1_ IT93K Conductance Low VPD _0279-1_ IT93K SLA _0051-1_ CB46 Conductance high VPD _0425-1_ IT93K Select RILs having different dosage of these QTLs and test them across contrasting drought scenarios From Phil Roberts/Tim Close and team

31 So: Managing water use before stress is critical It involves either LA development (speed, size), or Tr restriction at high VPD What else?

32 Daily transpiration profile and Vapor pressure deficit (VPD) Tr Restriction VPD Transpiration Time of the day Low Tr at high VPD confer higher TE Sinclair et al 2005 FPB

33 Transpiration (g pl -1 cm -2 ) What possibly explains the large TE differences? Low TE High TE VPD (kpa) High TE lines are VPD-sensitive (TE = k / VPD)

34 Residual Seed weight (g plant-1) Grain yield (g plant-1) y = x R² = Maize R² = 0.65 Peanut Transpiration Efficiency (TE) Transpiration Efficiency (TE) Chickpea R² = Transpiration Efficiency High TE lines have higher yields

35 Pod yield (g plant -1 ) R² = 0.65 But. TE-Yield 16 relationship across VPD regimes High VPD Season (VPD>2kPa) 250% range R² = 0.03 Low VPD season (VPD<2kPa) 60% range TE variation and link to yield depend on VPD

36 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

37 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 JXB

38 Suite of environmental sensors a d e c b f g

39 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

40 QTLs for canopy traits on LG04 of chickpea QTL for vigor traits (LA, plant height, growth rate)

41 Leaf canopy conductance Load Cells 3D-LA Scanning + plant transpiration = live water budget 1500 scales early 2016

42 TR profile under natural VPD conditions Capacity to pin key phenotypic differences at specific times Vadez et al JXB

43 Transpiration rate (mg cm-2 min-1) TR profile under natural VPD conditions Groundnut kpa Water saving JL24 ICGV kpa 2.34 kpa Thermal time (degree-days) Capacity to pin key phenotypic differences at specific times Vadez et al JXB

44 Capacity: 150 kg Precision: 0.02% Data collected every ½ second Data integrated each ¼ hour (0.004g) 1488 load cells installed

45 Pots replaced by trays (90 kg soil) Trays = 0.25 m2 Real row sowing

46 plants with desired phenotype % of lines with desired phenotype No. of lines phenotyped Populations (1000s lines) LeasyScan Lysimetry Field

47 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

48 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

49 Testing the probability of yield increase Tr sensitivity to VPD in soybean 75% (wet) Median 25% (dry) Sinclair et al., 2010 Agron. J. 102:

50 Testing the probability of yield increase Fast root growth 75% (wet) Median 25% (dry) Sinclair et al., 2010 Agron. J. 102:

51 Testing traits by Crop simulation modeling Guide breeder decision (stochastic information on trait value) Breeder Decision VPD Sensitivity Fast root growth

52 Defining an optimum sowing window for lentils Ghanem, Marrou et al., Agric. Systems, 2015

53 Combining both longer phenology and optimal sowing date in lentils Ghanem, Marrou et al., Agric. Systems, 2015 Major yield benefits

54 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

55 Yield change from 20 to 40 plant m -2 Groundnut Major yield benefits Yield increase (g m -2 ) Vadez et al. in prep.

56 Setting priorities Adapt genotypes G genotype Yield M production E management Adjust management environment Characterize & forecast

57 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

58 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?

59 Relationship pod yield versus haulm N% in groundnut From Blummel et al., FCR High pod yield + Haulm quality is possible Grain + Feed + N residue

60 G ra in y ie ld (k g /h a ) Relationship pod yield versus haulm N% in cowpea r= ; P < 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 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

61 Relationship haulm yield versus haulm N% in peanut From Blummel et al., FCR Are we tapping enough Legumes N2 fixation potential?

62 Weight gain in sheep fed with groundnut haulm Live weight gains in sheep fed exclusively on groundnut haulms Groundnut cultivars Gain (g/d) ICGV ICGV TMV ICGS ICGS DRG ICGS ICGV ICGV ICGV Prob > F 0.02 Prasad et al There is an un-tapped potential for feed quality (in particular N) in legumes

63 $ per ton Price evolution of main fertilizer sources Anhydrous NH 3 Super Phosphate Year We ll need to return to legumes P nutrition will become a main issue

64 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) Cowpea Peanut WW-HN WW-LN WS-HN WS-LN Bean HN-WW LN-WW HN-WS LN-WS Chickpea 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

65 Genetic variation in the yield response to low soil N in chickpea 7.0 Seed yield (g plant-1) High N Seed yield (g plant-1) - Low N Genetic variation is there to be used

66 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

67 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

68 InterDrought-V Hyderabad International Convention Center (HICC) Hyderabad, India 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: v Agronomic management for water-limited environments