Th2_Integrating Physiology, Crop Modeling and Genetics

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3rd Africa Rice Congress
Theme 2: Intensification and diversification
Mini symposium: determinants of agricultural productivity in Africa’s rice-based systems
Author: Dingkuhn et al.

Published in: Technology, Travel
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  • Graph 1:Sowing date in the Sahel strongly affects crop duration even in modern varieties (e.g., WS vs. Hot-dry season)Graph 2:Sowing in September-November caused near-total sterility (cold). Sterility sometimes also high for sowing in Feb-Mar (heat)Graph 3:Much of the sterility could be explained with minimum T(water) at booting stage (ca. 2 wk before flowering)RIDEV:A 1st model simulating this was developed in 1995 and extensively used by NARS and ARC for risk analyses and crop calendar planning
  • Weakness of old RIDEV:No consideration of TOA and T(panicle)Need for new study focusing on micro climate and heat
  • Important:This is hours after sunrise- T-dependent shift in TOA by up to 4h (e.g., from 9 am (warm) to 1 pm (cool))
  • Manual scene takingManual image analysisLongitudinal and transversal gradient analyses
  • Observations at different times of dayRH or VPD is main determinant of Panicle-air T differenceRs, wind, solar angle etc. also have effectsThese effects were combined in a multiple regression model and used in RIDEV to predict panicle T for any given time of day.That model was compared with a mechanistic energy balance model (IM2PACT, Japan) which gave the same results
  • Highest spikelet sterility in Senegal cool seasonLowest in Senegal hot season (!)We wanted to disaggregate this sterility into 3 different fractions (causes)
  • Panicle exertion – relative position of the panicle neck node to the top of the enclosing leaf sheath (broken line); relative position of lowest spikelets on the panicle (solid line)Short-strawed high yielding rice – increased risk of incomplete panicle exertion (short peduncles)Sahel 108 – selected by breeders in Senegal aiming at avoiding bird damage and improving light use through panicles hidden deep in the canopy
  • Graph 1:At minimum water temperature below 18-20 oC, sterility increases, except the tolerantChomrongCold tolerance: involves anti-oxidative enzymes protecting the tissues, production of more pollen to increase probability of successful pollinationGraph 2:If cases of cold-sterility are taken out of the analysis, the remaining cases show a good correlation of sterility with heat at anthesis. But only if PANICLE T (not air!) and TOA (not Tmin or Tmax or Tmean) are used as reference.
  • Th2_Integrating Physiology, Crop Modeling and Genetics

    1. 1. GRiSP Integrating Physiology, Crop Modeling and Genetics to Tackle Thermal Stresses in Rice: The RIDEV Approach Michael Dingkuhn (IRRI/CIRAD), Julie Mae Pasuquin (IRRI), Cecile Julia (CIRAD), Richard Pasco (IRRI), Jean-Christophe Soulie (CIRAD) funded by GIZ, AfricaRice, CCAFS and CIRAD Context of GRiSP Global Rice Phenotyping Network
    2. 2. GRiSP Rationale  Thermal adaptation is fundamental for agro-ecological fit  Temperature governs rice phenology and spikelet fertility  Climate change is changing thermal environments  Accuracy of crop models is still poor re: thermal effects  We need…  Better predictive tools to map climate change impact  Better understanding of adaptive traits: Physiology & Genetics
    3. 3. GRiSP History: The 1990s research at WARDA Thermal constraints to irrigated rice in Senegal Effect of sowing date on crop duration and sterility Days to flowering • Thermal and photoperiod effects on phenology • Chilling causes spikelet sterility % sterility Sowing date Sowing date % sterility Tw(min) at booting 1995 development of RIDEV predicting phenology and thermal sterility as risk analysis and decision aide for cropping calendars
    4. 4. New study on rice phenology and sterility GRiSP responses to T (Thesis of Cecile Julia & ongoing CIRAD/IRRI/CCAFS project)  Emphasis on microclimate   NEW  NEW  Meristem T => phenology Floodwater T => chilling stress at microspore stage Panicle T => heat stress at anthesis Time of day of anthesis (TOA)  RIDEV v.2 to characterize genetic diversity
    5. 5. Philippines - Hot and dry season 2009 36 32 28 24 20 16 12 8 01/03 32 28 24 20 16 12 11/03 21/03 31/03 10/04 20/04 8 10/05 30/04 20/05 Senegal - Cold and dry season 2010 44 40 36 36 Temperature (°C) Phenology TOA Panicle transp. cooling 19/06 France - Temperate Summer 2009 40 Temperature (°C) Traits observed 5 4 3 2 1 0 44 09/06 VPD (KPa) 4 environments 30/05 Date Date DS Philippines HDS Senegal CDS Senegal Temp. summer France VPD (KPa) 36 5 4 3 2 1 0 40 Temperature (°C) Temperature (°C) IR64 IR72 Sahel108 Chomrong (N22 failed) 44 40 4 genotypes 44 5 4 3 2 1 0 VPD (KPa) Scope of study: Senegal - Hot and dry season 2010 VPD (KPa) GRiSP 5 4 3 2 1 0 32 28 24 20 16 32 28 24 20 16 12 12 8 15/01 25/01 04/02 14/02 24/02 06/03 16/03 26/03 Date 8 01/08 Tair Max Tair Min Twater max Twater min 11/08 21/08 31/08 Date 10/09 20/09 30/09
    6. 6. GRiSP Results Time of day of anthesis (TOA) shows adaptive plasticity Warm nights advance TOA => Escape from midday heat Humid days advance TOA => Escape from heat caused by absence of transpiration cooling Mean air temp (min) during last 7d before anthesis (oC)
    7. 7. GRiSP Panicle temperature: IR imagery in the field Pan2 Pan1 Flagleaf4 Flagleaf1 Flagleaf2 Flagleaf3 Leaf5 Pan3 Pan4 ca. 4900 IR observations on in-situ panicle T Microclimate recording % sterility observed at maturity
    8. 8. Relative humidity or vapor pressure deficit is the main determinant of Ta-Tp difference GRiSP 14 Senegal cool-dry season Senegal hot-dry season France summer 12 14 10 TD (observed) [°C] c Example: Senegal cool-dry season 12 TD=Ta-Tp (°C) 10 y = 1.45x - 0.99 R² = 0.79 1:1 8 6 4 2 0 -2 8 -4 -4 6 -2 0 2 4 6 8 TD (predicted) [°C] 10 12 14 Model prediction (sim:obs) 4 Panicle cooler than air 2 0 Panicle warmer than air -2 -4 0 1 Humid 2 3 4 VPD (kPa) 5 6 Arid 7
    9. 9. GRiSP The panicle is warmest not in the hottest, but in the most humid environment (b) Air and Panicle Temperature at TOA (calculated) 32 Temperature (°C) 30 28 26 24 22 PHIL_DS Phils SEN_HS Sen.-hot Site SEN_CS Sen.-cool FR_HS France
    10. 10. Temperature induced spikelet sterility Chomrong 100 IR64 S108 IR72 (c) 90 80 Sterility (%) GRiSP 70 Disaggregate observed sterility into its components  Incomplete panicle exertion  Chilling at microspore stage  Heat at anthesis (at TOA) 60 50 40 30 20 10 0 Phils PHIL_DS Sen.-hot SEN_HS Sen.-cool SEN_CS Site France FR_HS
    11. 11. Incomplete panicle exertion GRiSP  occurred in cold-night environments  explained some of observed sterility Chomrong Panicle exsertion (%) 160 IR64 S108 IR72 Last grain Neck node (b) 140 120 100 Sterile fraction of panicle caused by non-exertion 80 60 40 PHIL_DS Phils SEN_HS SEN_CS Sen.-hot Sen.-cool Site FR_HS France
    12. 12. GRiSP 2. Chilling effect at microspore stage on sterility (Tmeristem = Twater) 100 Phil-ds Sen-cs 90 Sen-hs Fr-hs Sterility (%) 80 70 60 50 40 Chomron 30 20 10 0 12 14 16 18 20 22 24 26 28 T water (min) at microspore stage (°C) 3. Heat effect at flowering stage on sterility (Tp at TOA)
    13. 13. GRiSP Conclusions from experimental study Rice has highly effective adaptations to thermal stresses:  Avoidance  Transpiration cooling of panicle  Good panicle exertion (long peduncle)  Escape  Time of day of anthesis (TOA) and its adaptive plasticity  Tolerance  To cold, as shown for cv. Chomrong  Heat tolerant check cv. N22 failed (seed problems) Heat stress more likely in warm-humid than hot-dry climates!
    14. 14. GRiSP A new modeling tool RIDEV V.2  Simulator of…  Phenology incl. microclimate & photoperiod effects  G and E effects on TOA  Sterility caused by…  Chilling effects on microsporogenesis (water Tmin)  Chilling effects on panicle exertion (air Tmin)  Heat effects on pollination (Tpanicle at TOA)  Prediction (forward mode)  Climate change impact mapping, plant type optimization  Agronomy (crop calendar; optimization)  Heuristic parameterization of genotypes (reverse mode)  Phenomics (extraction of genotypic parameter values from experimental data)
    15. 15. GRiSP Outlook Use of RIDEV for Phenomics/GWAS  Indica GWAS panel (>200 acc., ORYTAGE project)  Field-phenotyped for phenology and sterility in 12 environments:  6 sowing dates in Senegal  3 altitudes x 2 years in Madagascar  Extraction of genotypic response parameters across environments (Heuristics):  Cardinal temperatures Tb and To  Thermal duration of phenological phases  Photoperiod-sensitivity  Chilling sensitivity of microsporogenesis  Chilling sensitivity of panicle exertion  Heat sensitivity of anthesis  Association study using GBS and 700K Oryza SNP chip
    16. 16. GRiSP Thank you Merci Salamat po

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