DSSAT Cropping System Model
Gerrit Hoogenboom, Jim Jones, Cheryl Porter, Ken Boote, Vakhtang Shelia, Upendra Singh et al. (University of Florida in collaboration with the International Fertilizer Development Center, USDA-ARS, and many other organizations)
Overview
| Model category | CSM, gbCSM |
|---|---|
| Plant part | Whole_plant |
| Scale | Organs, Whole_plant, Field, Regional |
| Licence | open_source |
| Operating system | Windows, Linux, IOS |
| Programming language | Fortran |
| Format of model inputs and outputs | Text files |
| Species studied | Generic-crops |
| Execution environment | Stand-alone application |
| Modelling environment | DSSAT |
Scientific article
The DSSAT cropping system modelJ.W Jones,G Hoogenboom,C.H Porter,K.J Boote,W.D Batchelor,L.A Hunt,P.W Wilkens,U Singh,A.J Gijsman,J.T RitchieEuropean Journal of Agronomy, 2003 View paper
Model description
The Decision Support System for Agrotechnology Transfer (DSSAT) is a software application program that comprises crop simulation models for over 42 crops (as of Version 4.7) as well as tools to facilitate effective use of the models. The tools include database management programs for soil, weather, crop management and experimental data, utilities and application programs. The crop simulation models simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics.
DSSAT and its crop simulation models have been used for a wide range of applications at different spatial and temporal scales. This includes on-farm and precision management, regional assessments of the impact of climate variability and climate change, gene-based modeling and breeding selection, water use, greenhouse gas emissions, and long-term sustainability through the soil organic carbon and nitrogen balances. DSSAT has been in used by more than 14,000 researchers, educators, consultants, extension agents, growers, and policy and decision makers in over 150 countries worldwide.
Some case studies
Hoogenboom, G., J.W. White, and C.D. Messina. 2004. From genome to crop: Integration through simulation modeling. Field Crops Research 90(1):145-163.
Wallach, D., C. Hwang, M.J. Correll, J.W. Jones, K.J. Boote, G. Hoogenboom, S. Gezan, M. Bhaktae, and C.E. Vallejos. 2018. A dynamic model with QTL covariables for predicting flowering time of common bean (Phaseolus vulgaris) genotypes. European Journal of Agronomy 101(1):200-209.
Fang, H., S. Liang, G. Hoogenboom, J. Teasdale and M. Cavigelli. 2008. Corn yield estimation of remotely sensed data into the CSM-CERES-Maize model. International Journal of Remote Sensing 29(10):3011-3032.