Advertisement

More Related Content

Similar to Precision agriculture for SAT; Near future or unrealistic effort?(20)

More from ICRISAT(20)

Advertisement

Precision agriculture for SAT; Near future or unrealistic effort?

  1. Precision agriculture for SAT; Near future or unrealistic effort? Jana Kholová and col. ICRISAT AuSoRGM- 22nd July - 2015
  2. Overview • Characterizing target environment • Relevant phenotype for SAT • Genetic determination of relevant phenotype • HT-phenotyping • Phenotype value • System complexity & link to socio-economy
  3. 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. Relevant phenotype??? 1. Target environments Kholová et al. 20133. Impact on production 7% 18% 18% 17% 40% 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
  4. 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 Which ”phenotype” is linked to yield improvement in target agro-ecology (SAT – terminal drought)? Yield is consequence of GxExM Research concepts – relevant phenotyping Focus on the “causal phenotype”
  5. Relevant phenotype for SAT? Grain Yield Grain Number Grain Size & N  Biomass RADN TE T RUE Rint vpd kl LAISLNRoots k  TN LNo A >A 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 WU 3 weeks after stress imposition (L plant-1) GrainYield(gplant-1) Grain yield and water use R² = 0.7436 0 2000 4000 6000 8000 10000 12000 0 2000 4000 6000 8000 10000 12000 14000 16000 Post-anthesiswateruse Pre-anthesis water use (g plant-1) constitutive WU defines grain-filling under terminal drought Pre-/post-anthesis water use Vadez et al. 2012
  6. Relevant phenotype for SAT? Constitutive WU : Grain Yield Grain Number Grain Size & N  Biomass RADN TE T RUE Rint vpd kl LAISLNRoots k  TN LNo A >A Vapor Pressure Deficit (VPD; kPa) Transpirationrate(gcm-2h- 1) 0 2 4 0 1 LA conductivity LA Thermal time & LA Basic research on WU componentsVadez et al. 2010-2015 Kholová et al. 2010-2014
  7. Grain Yield Grain Number Grain Size & N  Biomass RADN TE T RUE Rint vpd kl LAISLNRoots k  TN LNo A >A Example: WU components – genetic determination Effect of QTL depends on genetic background (stg 3A&B!) R16 (senescent parent)  + stg3A&3B QTL VPD response -> high TE S35 (senescent parent)  + stg3A&3B QTL small leaves Vadez et al. 2011 Stay-green ILs
  8. Grain Yield Grain Number Grain Size & N  Biomass RADN TE T RUE Rint vpd kl LAISLNRoot s k  TN LNo A >A “causal phenotype” (HT-phenotyping) “consequential phenotype” (High precision field trials) No.oflinesphenotyped %oflinesholdingdesired phenotype Phenotyping principle Platforms linkage! LeasyScan Lysimetry Field
  9. Value of phenotype? – in silico predictions Environment 0 500 1000 1500 2000 2500 200 300 400 500 600 700 800 LA(cm2) thermal time (degree days) S35 7001 6008 6026 6040 6016 Canopy size + = $ ? -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) Grain Pre-flowering Flowering Post-flowering Post-flowering relieved No stress Model
  10. Example: System complexity Crop value = f(quantity + quality; socio-economic context) Stay-green sorghum; grain quality 0 2 4 6 8 10 12 14 16 S35 7001 6008 6026 6040 6008 S35 7001 6026 6040 proteins(%) stay-green isolines ~ 20% QTL effect Control Drought ?Price per unit of protein?  Link to socioeconomics 4 5 6 7 8 9 10 11 12 protein(%) management Drought ~ 15% management effect Control
  11. RESEARCH APPLICATION Conclusions; Structure of research Value of traits (crop model & GxExM) Genetic determination of phenotype Environmental characterization & relevant traits ideotypes & management to regions (precision Ag for SAT) Breeding populations Socio-economics
  12. R4D requires multidisciplinarity! • Bioinformatists • Technology developers • Physiologists • Breeders • Modelers • Socioeconomists • Nutritionists…. Thank you Mission To reduce poverty, hunger, malnutrition and environmental degradation in the dryland tropics

Editor's Notes

  1. In phase I we characterized environment - in the main production region every 3-4 year the grain yield fails due to drought. We are trying to identify component traits which could bring yield advantage in the most frequent environment…
  2. Yield is the result of many plant mechanisms – in each environment mechanisms (building blocks) contributing to yield advantage are different – therefore it is better to focus on components of yield rather than yield itself. Phenotyping causes, rather than consequences!!!!
  3. Pre-anthesis WU linked to post-anthesis WU; post-anthesis WU linked to grain yiled
  4. WU in time is defined by LA and LA conductivity during the growth; there is substantial variability in populations
  5. To investigate adaptive traits variability we use stay-green NILs descended from senescent parents (R16&S35). The lines descended from R16 showed variability in TE and LA development; lines related to S35 showed variability in water extraction capacity, LA dynamics – these are various entry-point (components) which can lead to improvement of crop production. However, stg-QTL effect is not universal, stg B appears to work across backgrounds (despite effect is different in both backgrounds)
  6. What is the value of the variability in building blocks for breeding programmes?? Traditional multilocation trials can be approximated in silico with modell
Advertisement