CERES-Wheat a dynamic model to simulate the effects of cultivar, planting density, weather, soil water and nitrogen on crop growth, development and yield It’s primary purpose is for predicting potential alternative management strategies and tactics that affect yield and intermediate steps in the yield formation process, as well as multiyear analysis for risk assessment at the farm-level.

History Initial Development: CERES-Wheat 2.0 (1977) Dept. of Plant, Soil and Microbial Sciences, University of Michigan USDA- Agricultural Research Service (USDA-ARS) http://nowlin.css.msu.edu/wheat_book Current version: CERES-Wheat (module of DSSAT v4.6) Decision Support System for Agrotechnology Transfer (DSSAT) Maintained by a collaboration of the USDA, ICASA, ICFSAD, and multiple universities in the USA, Canada and Spain http://DSSAT.net Family: CERES-Wheat 2.0 was the first of a family of crop models, including CERES- corn, CERES-maize, CERES- sorghum, CERES- barley, etc. The entire CERES-n family is featured in all versions of DSSAT and the latest versions CSM (Cropping System Model, Jones et al. 2003) Parent algorithm to TAMW (Texas A&M) and SIMTAG (Australia and UK) The wheat modeling effort was started in 1977 when the USDA- ag research service was asked to help improve the US government’s capability to predict foreign and domestic wheat yields. This was one of the three models initially developed by USDA-ARS under the direction of Dr. Joe Ritchie and his lab at the University of Michigan, who was known for developing a successful evapotransipiration model for plants and soils with incomplete cover. Current versions are maintained within the Decision Support System for Agrotechnology Transfer (DSSAT) software. DSSAT features a suite of 28 tools to model crops, yield and management techniques. It’s development is a collaboration of several universities both foreign and domestic. DSSAT houses the entire CERES-n family. Two other models, TAMW and SIMTAG were both based off of the CERES-Wheat model, but were calibrated to their specific area of interest before distributing.

Purpose[1]: Predict potential alternative management strategies and tactics that affect yield and intermediate steps in the yield formation process Farm level: multiyear risk analysis Regional level: yield forecasting and analysis of crop production policies and resource conservation Key Assumptions[1]: Field is either N limited or H2O limited*, LAI estimated via genetics and phasic development, PAR = .5(total insolation) Exclusions[1]: pests*, catastrophic weather, soil salinity*, P, K and other essential nutrients*, N volatilization Temporal Scale[1]: Daily from time of planting until harvest maturity is predicted evaluated seasonally, annually or multi-annually Spatial Scale[1]: Field/Regional

Input Drivers[1]: Genetic Inputs* Management Inputs* Weather Inputs cultivar, rooting depth, phase durations, etc. Management Inputs* irrigation, fertilization, planting date(s), weight of organic residue of previous crop, depth of surface residue incorporation, C:N ration of surface residue, dry weight of root residue, row spacing, sowing depth, etc. Weather Inputs weather station attributes (lat, elevation), total solar radiation, max temp ˚C, min temp ˚C, total precipitation in m Soil Inputs* classification, albedo, drainage constant, annual avg. ambient temp ˚C, amplitude in mean monthly temp ˚C, thickness of soil layer, saturated water content, organic carbon concentration, ammonium, nitrate, pH, profile, nitrogen levels Key Outputs[1]: Date of anthesis, maturity date, grain yield (kg/ha), number of grains/ m2, max LAI, above ground biomass, N content of grain, above ground N uptake, grain N uptake, straw N uptake, weight of straw N and H20 stresses Root and stem weight, grain weight, leaf weight, root depth, plant top fraction, root length density

Output and Validation (ABOVE) [3] (ABOVE) Comparison of average simulated and observed grain yields (t ha−1) of winter wheat (cv. Mercia), 1991 to 1995, from the estate of Imperial College at Wye. Bars indicate one standard error of the mean. There were non-significant differences between observed and simulated values, P=0.908 and 0.968, respectively, for intercept and slope; this evaluation of model performance showed good agreement.[2] (LEFT) Model output. Predicted winter wheat (cv. Mercia) predicted yields (kg ha−1) for all combinations of nitrogen rates, planting dates and seed densities from the estate of Imperial College at Wye (1967–96). Bars indicate one standard error of the mean. [2]

Works Cited [1] Ritchie, J.T. and Godwin, D. “CERES Wheat 2.0” Web Publication. http://nowlin.css.msu.edu/wheat_book/ [2] Ghaffari, A., H. F. Cook, and H. C. Lee. "Simulating Winter Wheat Yields Under Temperate Conditions: Exploring Different Management Scenarios." European Journal of Agronomy 15.4 (2001): 231-40. [3] Jamieson et. al. “A comparison of the models AFRCWHEAT2, CERES-Wheat, Sirius, SUCROS2 and SWHEAT with measurements from wheat grown under drought.” Field Crops Research 55 (1998): 23-44 [4] Alderman P.D., Quilligan E., Asseng S., Ewert F., and Reynolds, M.P. (2013). Proceedings of the Workshop on Modeling Wheat Response to High Temperature. CIMMYT, El Batán, Mexico, 19-21 June 2013. Mexico, D. F.: CIMMYT