CERES
Ritchie, J.T. et al. (Michigan State University)
Overview
| Model category | CSM, gbCSM |
|---|---|
| Plant part | Shoot |
| Scale | Organs, Whole_plant, Field |
| Licence | open_source |
| Operating system | Windows, Linux, IOS |
| Programming language | Fortran |
| Format of model inputs and outputs | Text files |
| Species studied | Maize, Wheat, Soybean, Peanut, Rice, Potato, Tomato, Drybean, Sorghum, Millet, Pasture, Chickpea, Cowpea, Velvetbean, Brachiaria-grass, Fababean |
| Execution environment | Stand-alone application |
| Modelling environment | DSSAT |
Scientific article
A Comprehensive Review of the CERES-Wheat, -Maize and -Rice Models’ PerformancesBruno Basso,Lin Liu,Joe T. RitchieAdvances in Agronomy, 2016 View paper
Model description
The Crop Environment Resource Synthesis (CERES) models have been developed and utilized for the last 30 years to simulate crop growth in response to climate, soil, genotypes and management across locations throughout the world. Over 30 simulated variables of the CERES models have been tested in 43 different countries under various experimental treatments. Across all testing conditions, the CERES models simulated grain yield with a root mean square error (RMSE) of less than 1400 kg/ha (∼10% relative error, RE), 1200 kg/ha (∼20% RE) and 800 kg/ha (∼10% RE) for maize, wheat, and rice, respectively. Phenological development was simulated with less than 7 days difference from the observations in most studies. The CERES models simulated aboveground biomass, harvest index, evapo-transpiration, and soil water reasonably well too. The simulations of grain number, grain weight , intercepted photosynthetically active radiation, leaf area index, soil temperature, and nitrogen dynamics were less accurate. In fact the average error of CERES model simulations tends to be higher under marginal crop growing conditions such as extreme heat or cold, water and nutrient deficit conditions.
Some case studies
Some studies using CERES:
Adam, M., Dzotsi, K., Hoogenboom, G., Traoré, P., Porter, C., Rattunde, H., Nebie, B., Leiser, W.L., Weltzien, E., Jones, J.W., 2018. Modelling varietal differences in response to phosphorus in West African sorghum. European Journal of Agronomy (in press).
Akinseye, F.M., Adam, M., Agele, S.O., Hoffmann, M.P., Traore, P.C.S., Whitbread, A.M., 2017. Assessing crop model improvements through comparison of sorghum (sorghum bicolor L. moench) simulation models: A case study of West African varieties. Field Crops Research 201, 19-31.
Asseng, S., Ewert, F., Rosenzweig, C., Jones, J.W., Hatfield, J.L., Ruane, A.C., Boote, K.J., Thorburn, P.J., Rotter, R.P., Cammarano, D., Brisson, N., Basso, B., Martre, P., Aggarwal, P.K., Angulo, C., Bertuzzi, P., Biernath, C., Challinor, A.J., Doltra, J., Gayler, S., Goldberg, R., Grant, R., Heng, L., Hooker, J., Hunt, L.A., Ingwersen, J., Izaurralde, R.C., Kersebaum, K.C., Muller, C., Naresh Kumar, S., Nendel, C., O/’Leary, G., Olesen, J.E., Osborne, T.M., Palosuo, T., Priesack, E., Ripoche, D., Semenov, M.A., Shcherbak, I., Steduto, P., Stockle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Travasso, M., Waha, K., Wallach, D., White, J.W., Williams, J.R., Wolf, J., 2013. Uncertainty in simulating wheat yields under climate change. Nature Clim. Change 3, 827-832.
Araya, A., Kisekka, I., Gowda, P.H., Prasad, P.V.V., 2017. Evaluation of water-limited cropping systems in a semi-arid climate using DSSAT-CSM. Agricultural Systems 150, 86-98.
Ottman, M.J., Anthony Hunt, L., White, J.W., 2013. Photoperiod and vernalization effect on anthesis date in winter-sown spring wheat regions. Agron. J. 105, 1017-1025.
White, J.W., Herndl, M., Hunt, L.A., Payne, T.S., Hoogenboom, G., 2008. Simulation-based analysis of effects of Ppd and Vrn loci on flowering in wheat. Crop Sci. 48, 678-687.