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Improving Crop Models: Incorporating New Processes, New Approaches, and Better Datasets

Improving Crop Models: Incorporating New Processes, New Approaches, and Better Datasets. Jon I. Lizaso ( jon.lizaso@upm.es ) Technical University of Madrid. 13th ESA Congress 25-29 August 2014, Debrecen, Hungary. Overview. Crop models improved in response to:

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Improving Crop Models: Incorporating New Processes, New Approaches, and Better Datasets

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  1. Improving Crop Models: Incorporating New Processes, New Approaches, and Better Datasets Jon I. Lizaso (jon.lizaso@upm.es) Technical University of Madrid 13th ESA Congress 25-29 August 2014, Debrecen, Hungary

  2. Overview • Crop models improved in response to: • Better crop/environment understanding • New scientific questions • Need for better accuracy (especially under stress conditions) • Incorporating new processes • Anthesis-Silking Interval (ASI) in maize • Incorporating new approaches • Sink-limited kernel set in maize • The need for quality and diversity of datasets

  3. Early crop models • Early models described canopy light capture and photosynthesis • De Wit, 1965; Monteith, 1965; Duncan et al., 1967 • Personal computers not available • Apple II released in 1977 • IBM PC released in 1981 • Later models incorporated development, growth and partitioning, and yield • Hesketh, Baker & Duncan 1971, 1972; Baker, Hesketh & Duncan, 1972 • Almost 50 years of model improvement • Better understanding • New questions • Better accuracy (stresses) • Review: Boote et al., 2013. Plant, Cell & Environment

  4. Improving models: new processes • Crop simulation models are a deliberate simplification of a field grown crop • Modelers decide what process to include: Objectives • Models evolve: • Including new processes • Including new approaches (substitute/complement previous) • Re-parameterization or Re-calibration (quality datasets) • Example of incorporating a new process: • Anthesis-Silking Interval (ASI) in maize • Yield is sink-limited • Kernel set is source-limited (under most field conditions)

  5. Maize monoecious plant Staminate flowers shedding pollen Grain yield depends on the synchrony between Anthesis & Silking for adequate pollination and kernel set ASI Monoecious: Separate male & female flowers in the plant Pistillate flowers with stigmata

  6. Incorporating new processes: ASI • Strong relationship of maize grain yield with ASI • Especially under water stress • Modern hybrids, with enhanced stress-tolerance, show similar trend • Incorporated ASI simulation into CERES-Maize

  7. Incorporating new processes: ASI PAR SRAD CO2 • Incorporated into CERES-Maize v4.5 • Flowering event changed from silking to anthesis • The model calculates the average shoot growth rate (SGR) during a thermal time window around flowering • Thermal time window delimited by two user-specified parameters LAI Pop Dens k EarGrowth Row Spac RUE BAGDD (SPE) AAGDD (SPE) Barrenness Part Ear 1: AvgShootGrowthRate (SGR) ASI (MIN) TEMP WSTR SLPF NSTR PSTR KSTR

  8. Incorporating new processes: ASI • Model assumes no stress when: • SGR > 5 g/plant day • Two new cultivar parameters: • ASNS (ASI under no stress) • ASEN (sensitivity to stress) • Under no stress: ASI=ASNS • Under stress silk extrusion is delayed according to ASEN

  9. Incorporating new processes: ASI • The model estimates kernel number as a function of ASI, according to Bolaños & Edmeades (1993) • For negative ASI values (protogyny), it uses a function calculated from Lizaso et al. (2003, 2007) Kernel Number 2: ASI OnsetLinGrainFill SGR ASNS (CUL) ASEN (CUL)

  10. Incorporating new processes: ASI THRE (ECO) PLTPOP 4: Barrenness(EPP) Yield G2 (CUL) 3: KernelNumber (KN) ASI OnsetLinGrainFill • Model calculates barrenness as a function of SGR • Since kernels are set on ears, barren ears are checked with ASI SGR

  11. Incorporating new processes: ASI P5 (CUL) G3 (CUL) EPP 5: Yield KN • Finally, yield is calculated with: • kernel number (KN) • ears/plant (EPP) • onset of linear grain fill OnsetLinGrainFill ASI

  12. Incorporating new processes: ASI THRE (ECO) P5 (CUL) G3 (CUL) 4: Barrenness (EPP) 7: EarGrowth 5: Yield Part Ear 3: KernelNumber (KN) BAGDD (SPE) AAGDD (SPE) G2 (CUL) 1: AvgShootGrowthRate (SGR) 2: ASI 6: OnsetLinGrainFill ASNS (CUL) ASEN (CUL) DSGFT (ECO)

  13. Incorporating new processes: ASI • Some preliminary results indicate the new model is working reasonably well • Additional testing is required under various conditions and stresses

  14. Improving models: new approaches • Maize grain yield is sink-limited. The potential size of the sink, kernel set, is determined around flowering • However, maize kernel set is usually source-limited • Maize models simulate kernel numbers: • Light captured • Photosyntheticrate • Growthrate • Example of incorporating a new approach: • Sink-limited kernel set in maize Edmeades and Daynard, 1979

  15. Simulating kernel set in maize • If pollen becomes limited, as in hybrid seed production, or there is poor synchrony between anthesis and silking, kernel set may be sink-limited • Example of incorporating a new approach that complements current procedure: sink-limited kernel set • Pollen dynamics • Silk dynamics • Relationship linking pollen & silks J. Lizaso, 2005

  16. 8 53 212 Dynamics of pollen shed: measuring pollen rates Self-adhesive traps are located daily at silks level. Fluorescence microscopy produces images that are processed with image-analysis software. This result in pollen counts as pollen grains cm-2 d-1 (Fonseca et al., 2002) J. Lizaso, 2007

  17. Dynamics of pollen shed: measuring pollen rates Gauss functions adequately describe daily pollen rates for hybrids and inbreds J. Lizaso, 2007

  18. Dynamics of pollen shed: simulating pollen rates • To simulate ear-level pollen rates (grain cm-2 d-1) 2 pieces of information are required: • Progression of population reaching anthesis (%) • Daily pollen production from individual tassels (grain plant-1 d-1). These values can be calculated from: • Total pollen produced per tassel (million grains/tassel) • Duration of pollen shed per tassel (d) Total pollen produced per tassel can be field measured or estimated from tassel morphology (Fonseca et al., 2003) J. Lizaso, 2007

  19. 2.64 millions 8 days Dekalb 611 8 pl m-2 Dekalb 611 8 pl m-2 Dekalb 611 8 pl m-2 Dynamics of pollen shed: simulating pollen rates

  20. Dynamics of silk appearance:measuring silk extrusion • Silks are cut and ears are covered with glassine bags to prevent pollination • Each day 2 cm pieces are cut from the silk bouquet and are kept in alcohol at 4º C • Silks are counted and monomolecular functions are fit J. Lizaso, 2007

  21. Dynamics of silk appearance:simulating silk extrusion • Silk simulation requires field measurements of: • Progression of population reaching silking • Pattern of silk extrusion from individual ears: • Number of silks per ear • Duration of silk extrusion • Measurements of number of silks are facilitated by measuring the perimeter of the bouquet (Schneider, 2005) J. Lizaso, 2007

  22. Asgrow 740 8 pl m-2 Asgrow 740 8 pl m-2 Asgow 740 8 pl m-2 Dynamics of silk appearance:simulating silk extrusion

  23. Asgrow 740 8 pl m-2 Dekalb 611 8 pl m-2 Linking pollen & silks: kernel set relationship

  24. Evaluating a complementary approach Lizaso et al., 2007 Results from seed production fields show the processes are quite predictable and our procedures capture them Too many inputs from male and female inbreds Yet useful for seed industry When both, source- and sink-limited conditions were simulated the new model showed excellent accuracy J. Lizaso, 2005

  25. Towards the future: the quest for quality & diverse datasets • A number of current efforts to improve crop models: • AgMIPProgram: Pilot studies on wheat, maize, rice, and ongoing work on sugarcane, potato, sorghum-millet, peanut, soybean • MACSUR Project: Focusing on European agriculture, more interested in crop rotations, pastures, and livestock • Model packages: DSSAT, APSIM, CropSyst, STICS, EPIC, and others • Beyond the number of processes included, and the approach chosen, a permanent concern for model improvement/testing is the quality and diversity of datasets especially in areas and processes poorly represented J. Lizaso, 2005

  26. Towards the future: the quest for quality & diverse datasets Field data collection must continue especially in areas and processes poorly represented AgMIP maize team showed that an ensemble of 19 models was superior simulating maize yield than any single model So, how many models are enough? As the ensemble size increased, relative variation dropped differently for each site Bassu et al., 2014 J. Lizaso, 2005

  27. Thanks … köszönet …

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