New Developments in DSSAT Crop Modeling: Testing and Adding Crops (and some history) K.J. Boote, University of Florida

Transcription

1 New Developments in DSSAT Crop Modeling: Testing and Adding Crops (and some history) K.J. Boote, University of Florida Presentation at STICS Workshop La Rochelle, France, October 2017

2 Outline of Talk Origin of DSSAT crop models IBSNAT Project ( ) Mission, DSSAT V2.1 Improved science in 1990s: Generic models, V3.5 Improvements in 2000s: CSM, sequence-rotation Improvements in 2010s: temperature & CO2, V4.6 New models added over the years. Future Plans. Perennial Forage Model (brachiaria, cynodon, annual ryegrass, alfalfa) now in V4.7 release Examples: testing model response to temperature (dry bean, peanut, soybean, sorghum, millet). Next: Improve temperature response of maize models (with J. Lizaso) Presentation at STICS Workshop La Rochelle, France, October 2017

3 The IBSNAT Project ( ) Start of DSSAT Initial models were CERES-Maize & CERES-Wheat ( ), SOYGRO (1983), & PNUTGRO (1985). USAID-funded: model development & software Minimum Data Set Concept (J. Jones) Initial models each had their own weather, soils, management, & output. Not cross-compatible. Went to common I/O: weather, soils, management files, common printed and graphical outputs (J. Jones, 1986) This led to creation of DSSAT V2.1, released in 1986 with maize, wheat, soybean, peanut. By 1993, added dry bean, rice, sorghum, aroid, cassava, and potato Presentation at STICS Workshop La Rochelle, France, October 2017

4 Some Guiding Principles, IBSNAT Focus on experiments, data and understanding performance Common, minimum data requirements Models and experiments are complementary Models must be evaluated and used judiciously Application of models to optimize management to achieve desired goals Data without models is chaos; but models without data is fantasy! Source unknown Presentation at STICS Workshop La Rochelle, France, October 2017

5 1990 s Improved Science Creation of generic template model, CROPGRO SOYGRO, PNUTGRO, BEANGRO were initially as separate coding. Programs were similar, but had some hard-wired species parameterization. Minor code fixes had to be repeated in each model (Hoogenboom et al., 1991, 1993) Solution: Use common generic FORTRAN code for all GRO models, where species parameterization was moved into external read-in files for each crop Facilitated development of new crop models (chickpea, tomato, cowpea, macuna, faba bean, cotton) Created coupling points for pest damage effects Batchelor et al. (1993); Boote et al. (1993), Pinnschmidt For CROPGRO types, CERES-Rice, CERES-Maize Presentation at STICS Workshop La Rochelle, France, October 2017

6 1990 s Improved Science & Parameterization, DSSAT V3.5 (1998) Leaf-level, hourly hedgerow canopy photosynthesis included in CROPGRO (Boote and Pickering, 1994; Pickering et al., 1995). Two options (L hourly version and C daily version. L version default since V4.5 Added hourly energy balance option in CROPGRO (Pickering et al., 1995) in V3.5. Coupling lost (~2002) CO2 response functions parameterized (lookup functions for CERES & CROPGRO C-versions). Emergent outcome of rubisco kinetics in L-version. CO2 effect on transpiration, via canopy Rs (C-3, C-4) Mechanistic N2-fixation in CROPGRO legumes Presentation at STICS Workshop La Rochelle, France, October 2017

7 Software Tool Development by 1998 released as V3.5 (very stable version) DSSAT V3.5 release & book (Tsuji et al Understanding Options for Agricultural Production) Documentation of software, models, data collection WEATHERMAN weather entry, create weather files SBUILD soil data entry, create soil profiles XCREATE enter management-setup ~ File X GBUILD graphical output of state variables XCREATE Replaced by XBUILD SEASONAL shell & software to analyze weather probability effects of treatments/choices + Econ SEQUENCE crop rotation/carry-over (improved later) GENCALC genetic coefficient calculator Presentation at STICS Workshop La Rochelle, France, October 2017

8 Models added to DSSAT V3.5 by 1998 BEANGRO, patterned after SOYGRO (Hoogenboom et al., 1990, 1994) CERES-RICE created by U. Singh, at IRRI (~ 1993) CERES-Sorghum (Alagarswamy & Ritchie, 1991) CERES-Millet* (Ritchie & Alagarswamy, 1989) Cassava (Matthews & Hunt, 1994) Aroids (Singh et al., 1992, 1995) Chickpea* (Singh & Vermani, 1994) Potato (Griffin, Johnson, & Ritchie, 1993) * Indicates major improvements in Tomato*, adapted CROPGRO (Scholberg et al., 1996) Presentation at STICS Workshop La Rochelle, France, October 2017

9 2000s Improved Science & Parameterization CSM (Cropping System Model): all DSSAT crops share same soil-land unit, where soil water, N, and SOC carryover to next crop if using sequence/rotation (DSSAT V4, Jones et al., 2003; Hoogenboom et al., 2004). Included daily version of CENTURY SOC model option (Gijsman et al., 2002) vs Godwin s SOC module. Modularity introduced (initiate, state, rate, integrate), works well for some modules (phenology, leaf Ps, canopy Ps, SPAM, ET, N-fixation, SOC). Others not. Added additional crops: faba bean (2002), macuna (2002), cotton (2005), CANEGRO (2008), CASUPRO, sweet corn (2007), green bean (2007) Presentation at STICS Workshop La Rochelle, France, October 2017

10 2010s Improved Science & Parameterization Re-evaluated parameterization for response to CO2 (Boote et al., 2010), temperature (several models) Phosphorus module & response for some crops (maize-2006, peanut-2014, soybean, rice, sorghum) IXIM-Maize (Lizaso s model) incorporated in DSSAT Additional models (pigeonpea, canola) Developed standalone CROPGRO-Perennial Forage model (brachiaria, cynodon, panicum, alfalfa). Now in V4.7 release planned for fall Optimizer (GLUE in V4.6) Presentation at STICS Workshop La Rochelle, France, October 2017

11 DSSAT Future Needs/Plans Rigorous testing against ET data/drought trials (AgMIP) Re-link hourly energy balance in CROPGRO Improve soil temperature simulation Mechanistic rooting function (replace fixed SRGF) Add module for K fertility calibrate for crops Next release (V4.7) planned for Nov 2017, will include NWHEAT, safflower, sunflower & CROPGRO-Perennial Forage model (brachiaria, cynodon, alfalfa) Working on new crops (strawberry, teff) Link to QTLs Gene-based modeling Presentation at STICS Workshop La Rochelle, France, October 2017

12 DSSAT Foundation Initiatives DSSAT Foundation (2012 Present) Web site ( Free DSSAT access, downloadable Registration still required Open-source policy: Source code available upon request (GitHub). DSSAT Sprints (new modelers help code) Workshops and Training Annually Standards, Protocols, Modularity Publications and Dissemination Distributed to ~10,000 users in 90 countries DSSAT Listserver (8,500+ members) Presentation at STICS Workshop La Rochelle, France, October 2017

13 DSSAT Development Sprint July 25-29, University of Florida Next Sprint: January 8-12, 2018 at University of Florida

14 Training Courses DSSAT University of Georgia Next course May 14-19, 2018 DSSAT Arusha, Tanzania

15 CROPGRO-Perennial Forage Model Status and New Code for Storage Organ, C and N Reserves, Re-growth, & Dormancy K. J. Boote, S. Rymph, P. Alderman assisted by B. Pedreira, M. Lara, D. Pequeno, C. Pedreira, W. Malik Now in DSSAT V4.7 ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

16 CROPGRO-Perennial Forage Model Derived from CROPGRO model ( ) Coded for perennial and storage organ dynamics by Stuart Rymph, UF, 2004, & adapted for Paspalum notatum. (Rymph Ph.D. dissertation, UF, 2004) Adapted for Cynodon dactylon by K. Boote and P. Alderman (M.S. thesis, UF, 2007) Adapted for Brachiaria brizantha (B. Pedreira, 2011 paper) Adapted for Panicum maximum by M. Lara (2012 Agron. J.) Contrasted traits for Marandu, Mullato, & Tifton-85 at a single site, by D. Pequeno ( ) New: alfalfa* (W. Malik) In process: annual ryegrass (L. Moreno) ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

17 How are Perennial Forages Different Often do not flower or set seed. Therefore typical GC for phenology, life cycle, and seed-setting are useless. Must be able to harvest part of the shoot mass and continue simulations. Must be able to re-grow after 100% defoliation or after total winter dormancy. Need a storage reserve & memory of poor management or poor weather conditions. Need re-growth cycle re-staging and also annual effects Need rules for partitioning to and re-filling storage tissue as f(daylength, LAI, Ps, etc.) Need rules for mobilization of C and N reserves from storage for re-growth as f(daylength, LAI, Ps, etc.) ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

18 Re-growth depends on residual LAI, storage reserves (mass, TNC & N status), time of year Residual LAI and Canopy Ps of Tifton 85 are low after defoliation. Initial re-growth depends on TNC and N reserves, but Ps recovers well by 14 days (Alderman et al., 2011) Data, 4 N levels ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

19 Re-growth depends on residual LAI, storage reserves (mass, TNC & N status), time of year Rhizome TNC (CH2O) reserves of Tifton 85: cyclic behavior to minimum at 7-14 days, recovery during re-growth after defoliation (Alderman et al., 2011). 5-15% TNC, and 5-6 mt rhizome Strong N effects ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

20 Re-growth depends on residual LAI, storage reserves (mass, TNC & N status), time of year Residual stem TNC reserves of Tifton 85: cyclic behavior to minimum at 7-14 days and recovery during re-growth after defoliation (Alderman et al., 2011). Modest residual stem mass & modest TNC 4-8%. ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

21 Re-growth depends on residual LAI, storage reserves (mass, TNC & N status), time of year Leaf N conc of Tifton 85: cyclic behavior during re-growth, with new leaf adding to existing old leaf, then leaves aging with N conc. decline after 7-14 days (Alderman et al., 2011) Strong N effects ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

22 Code Changes for Perennial Added: ability to re-grow based on reserves despite zero LAI. Gives memory of poor prior management (low reserves). Winter dormancy. Added new state variable (stolon-rhizomestorage tissue) with TNC and N concentration Rules for partitioning DM, N, and TNC to storage tissue as f(daylength, LAI, Ps, etc.) Rules for mobilization of C and N reserves from storage for re-growth as f(daylength, LAI, Ps, etc.) ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

23 Additional input needed: MOW file Dates of harvest events: MOW: mass of residual aboveground stubble (kg/ha) after each harvest event. If missing, interpolate between dates to set stubble mass RSPLF, % leaf of remaining living stubble. MVS: leaf # re-stage after each harvest ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

24 Additional Forage-Related Outputs In addition to typical LAI, leaf, stem, storage, root, and % N of these organs Herbage = (shoot stubble) Herbage N = (shoot N stubble N) Herbage N conc (~CP) = herbage N / herbage mass Herbage %leaf Output herbage, herbage N, CP, and herbage % leaf in PLANTGRO.OUT and in a FORAGE.OUT file Output abscised dead shoot, leaf, and stem since last harvest in PLANTGRO.OUT ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

25 Stubble & Herbage, Panicum maximum (Lara et al., 2012) Herbage Mow=stubble ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

26 Simulated Dynamics: Veg N % and Biomass during Re-growth, P maximum (Lara, 2012) 9-10 d lag to peak N. Why? Add new leaf to existing old leaf pool.

27 Simulated vs. observed biomass in 2 seasons at Piracicaba, Brazil. Brachiaria brizantha Xaraes (Pedreira et al., 2011) Winter Use time-series Bayesian optimizer to solve for Tb & Topt of leaf photosynthesis & other processes. Works because of sampling in winter ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

28 Simulated vs. observed LAI in 2 seasons at Piracicaba, Brazil. Brachiaria brizantha Xaraes (Pedreira et al., 2011) ADSA Discover Conference, Integrated Solutions to Fiber Challenges Sep 19-22,2017, Chicago, IL

29 Simulation of irrigated Marandu (Brachiaria) at Piracicaba, Brazil. (Pequeno et al., 2013) Harvest frequencies of 28 and 42 days, over 2 years 400 kg ha -1 yr -1 of N split-applied after each harvest

30 STUBBLE CHARACTERIZATION

31 Simulated leaf, stem, shoot of irrigated Marandu (Brachiaria) at Piracicaba, Brazil. (Pequeno et al., 2013) Winter Shoot Stem Leaf International Conference on Forages in Warm Climates

32 Simulated leaf mass, storage mass, and storage TNC for irrigated Marandu (Brachiaria) at Piracicaba, Brazil. (Pequeno et al., 2013) Storage & root started from seed. Stable dynamics yr 2 & 3 Storage Root Leaf TNC %, SR International Conference on Forages in Warm Climates

33 Simulated storage TNC for irrigated Marandu (Brachiaria) at Piracicaba, Brazil. (Pequeno et al., 2013) TNC of 28-d and 42-day cycle, shows mobilization and re-fill dynamics per regrowth cycle and lower TNC in winter with recovery in summer. Lower with more frequent harvest. International Conference on Forages in Warm Climates

34 Simulation of Marandu (Brachiaria) response to N and irrigation, Jaboticabal, Brazil. (Miqueias Gomes dos Santos, Ph.D. Sandwich visitor to UF, 2017) Harvested frequently, nearly monthly over 2 years Trts: 7.5, 15, or 30 kg N ha -1 per Mg herbage harvested High production, but here is example of predicted CP

35 ESALQ - Univ. of Sao Paulo (Brazil) and Univ. of Florida Simulating Annual Ryegrass (Lolium multiflorum Lam.) Growth under Defoliation Regimes using the CROPGRO PFM Model (L. Moreno, C. Pedreira, D. Pequeno, K. Boote) Works for seed-established annual that has repeated harvests

36 kg DM/ha kg DM/ha kg DM /ha + Results: CROPGRO-PFM Annual Ryegrass 28d. harvest interval Days a er plan ng 95% LI SIM OBS d. harvest interval Days a er plan ng OBS SIM Days a er plan ng SIM OBS Overall stats: D = RMSE = Avg OBS:SIM ratio = 1.048

37 Adapting CROPGRO-PFM for Alfalfa Location: Aragon Spain Wafa Malik, PhD sandwich student during visit to UF MOW input file DATES: Harvest dates, MOW = stubble mass: the amount of live forage mass remaining 1000 kg/ha, RSPLF: percentage leaf of the stubble 20% MVS: a re-staged leaf number 2 Collected data (6 farmer fields): Leaf area index (LICOR-LAI- 2000) weekly Herbage DM Crude protein (harvested herbage) Crop management (tillage, fertilization and irrigation management. Simulation methods used The Penman-Monteith FAO 56 The CENTURY SOC model Leaf photosynthesis mode Adaption Process Set lower Cardinal Temperatures: V & R dev, Ps, LAI expansion, nodule growth, N-fix rate Set tissue composition Set Critical N conc for Ps like soybean Set partitioning: seedling phase, establ phase

38 209-A case (1 of 6 fields) 4500 Herbage (kg ha -1 ) Tops (kg ha -1 ) LAI Tops kg/ha Obs Sim RMSE 642 d-stat. 0.8 LAI Obs Sim RMSE 2.13 d-stat Jul-15 Jan-16 Aug-16 Mar-17 Sep Herbage Crude Protein (%) Herb. kg/ha Obs Sim RMSE 594 d-stat Mean Herb. CP % Obs. 22 Sim. 18 RMSE 5 d-stat Sep-15 Apr-16 Oct-16 May-17 Nov-17 Date (m-yy)

39 Crop Response to Elevated Temperature Stress: Improving Crop Models Against Data K. J. Boote 1, Vara Prasad 2, L. H. Allen, Jr. 3, J. W. Jones 1, and P. Singh 4 1 Univ. of Florida, 2 Kansas State Univ., 3 USDA-ARS, Gainesville, FL, 4 ICRISAT Presentation at STICS Workshop La Rochelle, France, October 2017

40 Objectives To test various DSSAT crop models for accurate response to elevated temperature against data collected in sunlit, controlled-environment chambers or fields. To document accuracy, or suggest changes to temperature functions for vegetative, photosynthetic, and reproductive processes. DSSAT models, especially CROPGRO, have external read-in files describing temperature sensitivities of processes. Change these without changing source code. Presentation at STICS Workshop La Rochelle, France, October 2017

41 Testing Simulated Temperature Effects on Life Cycle, Seed Yield, Biomass, and Seed HI of Peanut, Dry Bean, Soybean, & Sorghum Season-long experiments in sunlit, controlledenvironment chambers, at 350 or 700 ppm CO 2, and at fixed diurnal temperature cycles under natural diurnal irradiance cycles. Short day length. Field soil profile. Peanut, Soybean & Sorghum: treatments at 32/22, 36/26, 40/30, and 44/34 o C (max/min diurnal cycles). Dry bean: treatments at 28/18, 31/21, 34/24, 37/27, and 40/30 o C. Presentation at STICS Workshop La Rochelle, France, October 2017

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44 CROPGRO (and chambers) compute hourly temperature (based on Tmax/Tmin), with sinusoidal shape from sunset to sunrise, plus a decay function at night. Based on Parton and Logan Setpoint temperature Measured 35 o C Air Temperature ( o C) o C 23 o C Time EST

45 Crop or Pod, kg / ha Crop or Pod, kg / ha Biomass at harvest of Dry Bean Montcalm vs. Temperature Upper: at 700 ppm Lower: at 350 ppm Several modifications. 1) Modified temp function for leaf Ps: Topt2, from 31 to 34C, Tfail, from 36 to 42C 3) Modified Topt2 & Tmax of time to anth. 3) Increased crop life cycle: SDPM Mod Sim Obs - Crop Default Sim Mean Temperature, C Mod Sim Obs - Crop Default Sim Mean Temperature, C Presentation at STICS Workshop La Rochelle, France, October 2017

46 Days to Maturity Days to Maturity Days to Anthesis Days to Anthesis Modified Phenology: Anthesis and Maturity for Dry Bean Montcalm vs. Temperature Pre-R5: Topt2 from 37 to 31 C, Tmax from 45 to 44 C, & longer SDPM Default Simulation: Modified Sim Obs Sim Obs Mean Temperature, C Mean Temperature, C Sim to Normal Maturity Observed Harvest (PM?) 20 Sim to Normal Maturity Observed Harvest (PM?) Mean Temperature, C Mean Temperature, C

47 Seed Yield, kg / ha Seed Yield, kg / ha Mod Sim Observed Default Sim Seed Yield of Dry Bean Montcalm vs. Temperature Upper: at 700 ppm Lower: at 350 ppm Changes caused by Ps function, longer cycle, phen. No change to temp on seed-set or seed growth rate Mean Temperature, C Mod Sim Observed Default Sim Mean Temperature, C

48 What Modifications Were Needed in Temperature Functions for Dry Bean? Life cycle (moderate importance) Linear lookup Vstg, no changes, no data Pre-Seedfill, Topt2 37 to 31 C, Tmax 45 to 44 C Seedfill phase, No changes, but SD-PM was longer Podset and Seed Growth Rate (no change) Photosynthesis (major importance) Topt2, from 31 to 34 C (point of 0.8 max) Tmax, from 36 to 42 C (point of 0.0 failure) Temperature response of default model was good for podset and seed growth, but was poor for leaf photosynthesis (L).

49 Testing Simulated Temperature Effects on Life Cycle, Seed Yield, Biomass, and Seed HI of Soybean & Peanut Experiments in sunlit, controlled-environment chambers, with controlled temperature and CO 2, under natural diurnal irradiance cycles. Short day length. Season-long temperature treatments at 700 vpm CO 2 : 28/18, 32/22, 36/26, 40/30, 44/34, 48/38 o C (max/min diurnal cycles). Field soil profile. Compare observed growth to simulations of CROPGRO-soybean and peanut models. Temperature response of default models was good enough. No Changes Made!

50 Seed Yield, kg/ha Tested CROPGRO-Soybean response to temperature. Close mimic of observed. Above 30C (mean), soybean seed yield was reduced, until total failure at 39C Obs vpm Obs vpm Sim vpm Model-modif Sim vpm Sim - Orig. Model-Orig Minor change to phenology Mean Temperature, C

51 Crop or Pod, kg / ha Default CROPGRO-Peanut model response to temperature. Crop grown at 350 ppm CO2. Model mimics observed pattern of biomass & pod mass vs. temperature with pod failure at 39C Possibly increase Tfailure point for podset and seed growth rate. Sim - Pod Obs - Pod Sim - Crop Obs - Crop Mean Temperature, C

52 Seed Harvest Index, fraction Default Simulation: Peanut grown at 350 or 700 ppm CO2, Seed Harvest Index vs. Temperature Sim Obs Sim Obs Need 4C increase in Tfailure point for podset and seed growth rate Mean Temperature, C

53 What Modifications May Be Needed in Temperature Functions for Peanut and Soybean? Life cycle (minor importance): Pre-R1, Increase Topt1 28 to 30 C, Topt2 30 to 34 C, Tmax to 60 C (Nearly same for peanut & soybean!!!) Post-R1: Decrease Topt1 to 18C Topt2 to 26C (Soybean only, very different from peanut) Podset & Seed Growth Rate (important) - Peanut Rate of Pod-set, Tmax from 40 to 44 C Rate of Seed Growth, Tmax from 41 to 45 C Podset & Seed Growth Rate - Soybean (make slightly less temp-sensitive. Similar change as peanut) Photosynthesis (No change at high end, for either crop). Temperature response of peanut and soybean were good enough. No Changes Made!

54 Seed yield (g plant -1 ) Sorghum Yield Response to CO 2 and Temperature mol CO 2 mol mol CO 2 mol /22 36/26 40/30 44/34 Air temperature ( C) (daytime maximum/nighttime minimum) CERES-Sorghum V4.6 model re-calibrated with these data Sorghum, temperature sensitivity like rice, fails at 35C mean. Contrast to soybean & peanut, fail at 40C Elevated temperatures decreased seed yield. Zero yield and HI at 35 C, like rice. Elevated temperature delays anthesis, reduces pollen viability, ISGR, and Grain-Fill Duration

55 Calibration of CERES-Sorghum against data from sunlit, controlled-environment chambers (Prasad et al., 2006) Current version lacks temperature effect on grain-set, but has temperature effect on relative grain filling rate (RGFIL). RGFIL Tb, Top1 Top2 Tfail New Old Change to V4.6 made by Singh et al Agr. & Forest Met. 185:37-48.

56 Re-calibration of temperature parameterization of the CERES- Millet model (Singh et al., 2017). Boote modified temperature functions & added RGSET (acts in 9-d period prior to grain-set). Function Tb, Top1 Top2 Tfail PRFTC RGFIL RGSET RGLAI Will create RGSET for CERES-Maize & CERES-Sorghum!!! Gupta et al Field Crops Res. 171: % Seed-set of 46 pearl millet lines vs. moving average mean temperature. Threshold 34C corresponds to Tmax of 42C.

57 Cardinal upper temperatures ( C) for seed-set and seed growth rate in DSSAT models. Shapes are parabolic for the legumes and linear lookups for the cereals. Function Peanut Soybean Drybean Chickpea Sorghum Millet & Maize Seed-set - Topt Seed-set - Tfail Seed-GR- Topt Seed-GR - Topt Version Set? V V V V V V Conclusion: Prior to simulating climate change effects or heat-tolerant virtual cultivars, one must set upper threshold temperatures for seed-set and seed growth rate from elevated temperature experiments.

58 Present & Future AgMIP Activities Crop Yield Loss Conference (Oct 16-18): AgMIP team, will couple simple simulators of pest damage with multiple wheat models to predict pest effects on the crop without need for input of pest damage. AgMIP at ASA, Tampa, Oct 22, 2-4pm, Sunday: AgMIP General Interest Session from 2-3pm, Crop- Water-ET (Maize) from 3-4pm, same room SAVE THE DATE (Next AgMIP Global): 7 th AgMIP Global Workshop April 24-26, 2018 San Jose, Costa Rica