GRM 2013: Drought phenotyping and modeling across crops -- V Vadez
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GRM 2013: Drought phenotyping and modeling across crops -- V Vadez
- 1. GCP-ARM – Lisbon 27-30 Sept 2013 Objective 5: Cross-crop issues Drought phenotyping and modeling across crops ICRISAT – CIAT – ISRA – Univ North Carolina
- 2. Water uptake / Root Water use / WUE Reproduction and partitioning Modeling Sub-Activity5:Training Trait value predicted Refined protocols More tools Better pheno- typing data Phenotyping of cell-based processes – toward gene discovery Purpose: Looking at similar traits across species
- 3. Lysimetric system: in CIAT and ICRISAT-Niger Total water extracted Kinetics of water extraction Root length density at different depth Relationships RLD vs Water extraction To measure: Lysimetric assessments
- 4. Root length density and water extraction Drought root length density (cm cm-3) 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 Droughtwaterextraction(kgplant-1) 5.5 6.0 6.5 7.0 7.5 8.0 8.5 BRB 191 PAN 127 SUG 131 VAX 1 BAT 477 DOR 364 CAL 143 VAX 3 RCW SEA 5 SEA 15 SER 16 SEQ 1003 SEQ 11CAL 96 SAB 259 RAA 21 ICA Quimbaya SER 8 Mean: 0.56 LSD0.05: 0.13 SEC 16 Mean: 6.84 LSD0.05: 1.53 r = 0.08 No relation between water extraction (WS) and root length / RLD Beans Chickpea
- 5. Post-rainy season Rainy season 0 2 4 6 8 10 12 14 16 0 1000 2000 3000 4000 5000 6000 7000 Podyield(gplant-1) Total water extracted (g plant-1) 0 1 2 3 4 5 6 7 8 9 10 0 1000 2000 3000 4000 5000 6000 7000 Podyield(gkg-1) Total water extracted (g plant-1) No relationship between total water extracted and grain yield 0 2 4 6 8 10 12 14 0 1000 2000 3000 4000 5000 6000 7000 Podyield(gplant-1) Total water extracted (g plant-1) Cowpea Peanut 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 0 1000 2000 3000 4000 5000 6000 7000 Podyield(gplant-1) Total water extracted (g plant-1) Bean Peanut Rainy seasonRainy season Pod yield and water extraction
- 6. Water extraction pattern (WS) Zaman-Allah, Jenkinson, Vadez 2011 JXB 0 1 2 3 4 5 6 7 8 9 10 21 28 35 42 49 56 63 70 77 84 91 98 CumulatedWaterUsed (kgpl-1) Days after sowing Flowering 8 Sensitive lines 12 Tolerant lines Tolerant: less WU at vegetative stage, more for reproduction & grain filling
- 7. Zaman-Allah, Jenkinson, Vadez 2011 JXB 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 Sensitive Tolerant Tolerant: EUW = 27 kg grain mm-1 Grain yield and post-anthesis water use Chickpea
- 8. Cowpea Similar results in cowpea and chickpea Grain yield and post-anthesis water use Water use PhD Thesis Omar Halilou
- 9. Seed yield relates to higher pre-flowering water use Nitrogen issue?? (Sinclair & Vadez 2013 Crop&Pasture Science) Pre-anthesis Beans Grain yield and pre- / post-anthesis water use
- 10. 0.0 2.0 4.0 6.0 8.0 WW-HN WW-LN WS-HN WS-LN Yield(g/plant)WS 0.0 2.0 4.0 6.0 8.0 WW-HN WW-LN WS-HN WS-LN Yield(g/plant)WS 0 2 4 6 8 10 12 14 16 HN-WW LN-WW HN-WS LN-WS Yield(gplant-1)WS Cowpea Bean Effect of high N (HN) or low N (LN) treatments under water stress (WS) and irrigation (WW) Peanut Among the three legumes, peanut is least sensitive to low N Low N is more a problem than drought for bean
- 11. Water use / WUE
- 12. 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 Canopy development dynamics Water use difference
- 13. Field trial 0 5 10 15 20 25 0 1000 2000 3000 4000 5000 6000 A = 2,91 Fleur 11 WW condition R² = 0,999 Nodes number Leafarea(cm²) Field trial 0 5 10 15 20 25 0 1000 2000 3000 4000 5000 6000 A = 2,63 ICG 1834 WW condition R²= 0.91 Nodes number Leafarea(cm²) PhD training of Oumaru Halilou - Niger Large variation available Peanut Coefficients relating leaf area to node number
- 14. y = 23.302e0.2562x R² = 0.9367 0 2000 4000 6000 8000 10000 12000 0 5 10 15 20 25 Leafareaoffiveplants (cm2) Node number on main stem y = 11.995e0.31x R² = 0.9607 0 2000 4000 6000 8000 10000 12000 0 5 10 15 20 25 Node number on main stem Coefficients relating leaf area to node number MSc training of Ruth Wangari - Kenya Chickpea
- 15. Rainy season (VPD<2kPa)R² = 0.03 0 1 2 3 4 5 6 7 8 9 10 0.0 1.0 2.0 3.0 R² = 0.65 0 4 8 12 16 0.0 1.0 2.0 3.0 Post Rainy Season (VPD>2kPa) TE variation and link to yield depends on season Transpiration efficiency – Peanut and relationship to yield Podyield(gplant-1) 250% range 60% range
- 17. 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 in cowpea Tolerant lines have a breakpoint (water saving) Tolerant Sensitive Belko et al – 2012 (Plant Biology)
- 18. Phenotypic variation in cowpea RIL CB46 x IT93K-503-1 (sensitive/Tolerant) 0 10 140 220120 180 5 100 20080 160 25 20 15 Plant transpiration (g plt-1 h-1) Total canopy conductivity (g cm-2 h-1) 0.0200 5 0.0300 0.03750.02750.0175 0 0.0325 25 0.02500.0225 0.0350 20 15 10 IT93K-503-1 CB46 IT93K-503-1 CB46 PhD training of Nouhoun Belko – Burkina Faso
- 19. R² = 0.64 -40 -30 -20 -10 0 10 20 30 40 50 0.000 0.010 0.020 0.030 0.040 0.050 0.060 Residualtranspiration Transpiration rate under high VPD What drives transpiration in that population?? Leaf area (69%) Conductance at high VPD (64% of residual) Get QTL for both these traits PhD training of Nouhoun Belko – Burkina Faso R² = 0.69 0 50 100 150 200 250 0 200 400 600 800 1000 1200 Totaltranspiration (gplant-1) Leaf area (cm2 plant-1)
- 20. QTLs from ICI Mapping – Drought tolerance traits VuLG1 VuLG2 VuLG3 VuLG4 VuLG5 VuLG6 VuLG7 VuLG8 VuLG9 VuLG10 VuLG11 Plant transp., leaf area, stem DW, leaf DW 12-18% phenotypic variance (High allele from CB46) Canopy conductance 12-16% phenotypic variance (High allele from IT93K-503-1) SLA, 20% phenotypic variance (High allele from CB46) SLA, 14% phenotypic variance (High allele from IT93K-503-1) From Phil Roberts/Tim Close and team
- 21. QTLs from ICI Mapping – Drought tolerance traits From Phil Roberts/Tim Close and team Select RILs having different “dosage” of these QTLs and test them across contrasting drought scenarios TraitName Chromo some Position (cM) Flanking markers LOD PVE(%) Additive effect Positive 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
- 22. 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
- 23. Modeling of critical traits
- 24. Marksim weather can be used to test trait effects Can we use data from weather generator??
- 25. -77 0 +9 Pod yield differences between rainfed and irrigated conditions • Drought affected countries for peanut: Senegal, Mali, Niger, Burkina + Few spots in Ivory Coast • Genotypes developed for WCA region can’t be the same for the entire region
- 26. -33 0 +1 15-30% yield decrease, especially at high latitudes % yield decrease for not having transpiration sensitive to high VPD:
- 27. -26 0 20% yield decrease almost everywhere % yield decrease for having shorter crop duration genotype
- 28. (a) (b) Yield increase with VPD response in soybean From Sinclair et al (in review) Probability of success
- 29. Training on drought phenotyping Long term training Few of the trainees: Ruth Wangari (Chickpea RIL) Abalo Hodo TOSSIM (Groundnut CSSL) Omar Halilou (Groundnut) – Crop modeling Nouhoun Belko (Cowpea) – Trait mapping – Crop modeling Jaumer Ricaurte (Bean) – Trait mapping – Crop modeling Training
- 30. In Summary / “products”: An approach to drought QTL for several water use traits in different crops Generation of scenarios / probability maps in the “production stage” for peanut, chickpea, soybean. Trainees (Oumaru, Belko, Ruth, Jaumer, …) on both eco- physiology of drought adaptation and modeling
Editor's Notes
- The overall structure of the project: Three aspects of plant adaptation to drought are looked at.Modelling is the “integrater” of these different components
- Experiments have continued in Year 3 at ICRISAT-Niamey, ICRISAT-Patancheru, and CIAT
- Last year we tested bean genotypes under a factorial of water regimes (WW and WS) and of nitrogen treatments (high N and low N), and found that bean suffered the effect of low N more than it suffered drought.This year, we tested the same thing in cowpea.Cowpea also suffers the effect of low N conditions, although less than beanThen cowpea suffers further the effect of water stress when grown in low N conditions
- In previous presentations we have shown that the leaf area vary between genotypes and could impact the overall plant water budget, leading to possible effect on plant adaptation to drought.Leaf area development can be predicted from exponential functions of the number of nodes on the main stemThe graph shows variations in the coefficients, indicating that leaf area develops larger/quicker in certain genotypes.We can then use these coeficients to test possible effects of changing such coefficient on yield
- We also measured these exponential function coefficients in different chickpea genotypes.Here also we have quite a large range of genetic variationThe graph gives an example of two contrasting genotypes
- Segregation in a RIL population of cowpea for plant transpiration and conductivity
- As expected, the leaf area explains about 2/3 of the variations in total transpiration. The residual transpiration variation (not explained by the leaf area), is then explained by the conductance under high VPDNext step is then to map QTL for these 2 traits
- QTL analysis was done by Phil Roberts and Tim Close’s team at UC Riverside6 very interesting QTL were identified for either leaf area or leaf conductanceVery exciting is the fact that these QTLs are contributed by different parents
- Alleles contributing to either higher leaf area come mostly from CB46Alleles contributing to either higher leaf conductancecome mostly from IT93K-503-1Since transpiration is contributed both by LA and conductance, it should be possible to select extreme “water use phenotypes” by choosing those either excluding, or including, all the alleles contributing to higher water use (eg LA alleles from CB46 and Leaf conductance alleles from IT93K-503-1).
- The graph indicates the percentage decrease in yield due to drought (ratio of rainfed yield/irrigated yield)The blue strip in the northern part (plus a spot in Ivory Coast) is really where efforts on drought need to be made.It means also that the genotypes that are developed for that region can’t be the same everywhere
- The top graph shows the effect on yield (%) in case the standard genotype does not have the capacity to restrict transpiration when the VPD is high.The bottom graph explore the validity of the statement that we need to breed short duration peanut. Here we modeled the effect of reducing the peanut crop cycle by about 10 days. Clearly, reducing the crop cycle by about 10 days leads to yield reduction of about 20%, even in the driest places.
- The top graph shows the effect on yield (%) in case the standard genotype does not have the capacity to restrict transpiration when the VPD is high.The bottom graph explore the validity of the statement that we need to breed short duration peanut. Here we modeled the effect of reducing the peanut crop cycle by about 10 days. Clearly, reducing the crop cycle by about 10 days leads to yield reduction of about 20%, even in the driest places.