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LeasyScan: A novel concept combining 3D imaging and lysimetry for hi-throughput phenotyping of traits controlling plant water budget

  1. LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget Vincent Vadez – Jana Kholová et al. JXB 2015 ICRISAT QUT – 21th July 2015
  2. • 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 • To reduce poverty, hunger, malnutrition and environmental degradation in dryland tropics Our Mission
  3. Overview Envirotyping & Relevant phenotyping Technology validation Designing the HT platform • Logistics • Installation & troubleshooting • Bioinformatics Future projects
  4. 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
  5. 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” Biomass & yield Water&Nutrients
  6. Lysimetric facility at ICRISAT • Field-like • Gravimetric - manual (WU, TE) • Long term (3 Wks-maturity) • Medium throughput (5000 PVCs/wk) • Stress scenarios Relevant phenotype?
  7. 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 (l plant-1) constitutive WU defines grain-filling under terminal drought Pre-/post-anthesis water use Vadez et al. 2012
  8. 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
  9. • Outdoors – environment of growth matters • Rapid access to canopy/water use at key time • Early development defines crop success • Rapid evaluation of environmental effects (Soil moisture, VPD) What HT-phenotyping platform? LA scanner Leaf area development scales Water extraction dynamics WU dynamics =LA x LA conductivity [transpiration/LA]
  10. Plant Eye prototypeTechnology testing Pushing technology limits in outdoor environment • Light, wind, plant structure Development of hardware/software not trivial • Reciprocal learning process
  11. Legumes – cowpea, chickpea, groundnut, pigeonpea, lentil Cereals – maize, sorghum, millet Technology testing - validation peanut y = 42.162x + 2940.1 R² = 0.9381 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 0 100 200 300 400 500 600 700 800 900 1000 3D-LA(mm2) destructive LA (cm2) 1000 cm2 R² = 0.9273 y = 55.466x + 7842.7 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 0 200 400 600 800 1000 1200 1400 3D-LA(mm2) destructive LA (cm2) cowpea 1200 cm2 y = 52.994x + 3506.4 R² = 0.7045 0 5000 10000 15000 20000 25000 30000 35000 0 100 200 300 400 500 3D-LA(mm2) destructive LA (cm2) chickpea 400 cm2 y = 21.665x + 24155 R² = 0.8635 0 20000 40000 60000 80000 100000 120000 0 1000 2000 3000 4000 3D-LA(mm2) destructive LA (cm2) millet 3000 cm2
  12. 1450 6256251750 350 15000 400600 2Position2Position Rail Wheel track 1000 12 5000 3500 Installation Specifications: No. of sectors: 3200 - 4800 SSD Scan Velocity: 50 mm/s SSD Velocity: max. 250 mm/s PlantEye Systems: 8 Throughput: 2000 - 3000 sectors/h Platform length: 129,5 m Load cells Scanners Meteo-station Irrigation
  13. Bioinformatics Massive time series Data processing??? Data selecting??? Data filtering?? Which data express canopy properties the best???
  14. Selection of relevant data expressing canopy properties y = 26.23x + 13346 R² = 0.8198 0 20000 40000 60000 80000 100000 120000 0 1000 2000 3000 4000 3Dleafarea(mm2) Destructive LA (cm2) LA extracted 4 h in the morning peanut y = 24.832x + 9606.3 R² = 0.9126 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 0 1000 2000 3000 4000 3Dleafarea(mm2) Destructive LA (cm2) LA extracted for whole day Open canopy during the day Closed canopy during the night Which scans express whole canopy the best?
  15. y = 34.002x + 3224.5 R² = 0.9206 0 20000 40000 60000 80000 100000 120000 140000 160000 0 1000 2000 3000 4000 5000 3DLA(mm2) Destructive LA (cm2) LA extracted at night 863B H77/833-2 PRLT Selection of relevant data expressing canopy properties y = 30.876x + 6258.7 R² = 0.859 0 20000 40000 60000 80000 100000 120000 140000 160000 0 1000 2000 3000 4000 5000 3DLA(mm2) Destructive LA (cm2) LA extracted during morning 863B H77/833-2 PRLT Canopy & wind Canopy & no wind Which scans express whole canopy the best?
  16. Rapid evaluation of environmental effects; e.g. NaCl treatment Vadez et al. 2015
  17. Hypothesis validation! A1 A B Vadez et al. 2015 Testing the production zone adapted genotypes; A1-severe drought A-mild drought B-rare drought Genotypes vary for drought related phenotype
  18. 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 growth Future projections Phenotype variability  Acceleration of breeding  Breeding specific for target agro-ecologies Yield improvement
  19. Take-home message: Phenotyping for the relevant traits (assessing causes of drought adaptation rather than consequences!) Phenotyping outdoors/environmental variables (environment of development matters!) Validation of technology & tech. development (each crop/conditions are different – link to developers) Bioinformatics (learning of the best ways of working with massive datasets) Phenotyping is a continuous learning process HT-phenotyping - a way to precision agriculture!
  20. Development requires multidisciplinarity! • Technology developers & engineers • Bioinformatists • Physiologists • Breeders • Modelers Thank you Mission To reduce poverty, hunger, malnutrition and environmental degradation in the dryland tropics
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