If you are attending the ASA-CSSA-SSSA 2022 International Annual Meeting in Baltimore, Maryland, come and join us at the DSSAT Open Forum where we will exchange what’s new on crop modeling research, collaboration opportunities, and development on data and tools.
Dr. Cynthia Rosenzweig of the United States will receive the 2022 World Food Prize for her seminal contributions to understanding and predicting the impacts of the interaction between climate and food systems. Through designing and leading rigorous, collaborative observational and modeling research, she provided the evidence used by thousands of decision-makers in more than 90 countries to both mitigate and adapt to climate change in local, national and global food systems.
Hybrid Meeting, University of Florida | January 18-21, 2022
Improvement and Application of Agroecosystem Models: The DSSAT experience
Presented by Gerrit Hoogenboom during the Symposium – “Improvement and Application of Crop Growth and Agroecosystem Models for Knowledge Advancement and Sustainable Development” as part of the 2021 ASA-CSSA-SSSA Annual Meeting held in Salt Lake City, Utah, USA
The DSSAT crop modeling ecosystem is one of the oldest and most widely used crop modeling platforms across the world. The success of DSSAT is based on the inclusiveness and participatory approach that has been used since the original development of the CERES and CROPGRO family of models and the emphasis on sharing data and model code. DSSAT is not just a software program, but an ecosystem of:
• Crop model users;
• Crop model trainers;
• Crop model developers;
• Models for the most important food, feed, fiber, and fuel crops;
• Tools and utilities for data preparation;
• Minimum data for model calibration and evaluation; and
• Application programs for assessing real-world problems.
Advances in crop modelling for a sustainable agriculture
This collection summarises key advances in crop modelling, with a focus on developing the next generation of crop and whole-farm models to improve decision making and support for farmers.
Chapters in Part 1 review advances in modelling individual components of agricultural systems, such as plant responses to environmental conditions, crop growth stage prediction, nutrient and water cycling as well as pest/disease dynamics. Building on topics previously discussed in Part 1, Part 2 addresses the challenges of combining modular sub-systems into whole farm system, landscape and regional models. Chapters cover topics such as integration of rotations and livestock, as well as landscape models such as agroecological zone (AEZ) models. Chapters also review the performance of specific models such as APSIM and DSSAT and the challenges of developing decision support systems (DSS) linked with such models. The final part of the book reviews wider issues in improving model reliability such as data sharing and the supply of real-time data, as well as crop model inter-comparison.
Cross-continental disease and crop modeling collaborations to beat back wheat blast
Cross-continental collaborations facilitated by the CGIAR Platform for Big Data in Agriculture thrive to beat back the threat of wheat blast in Brazil and Bangladesh.
Wheat blast disease is a major threat to smallholder farmers. The disease was first discovered in Brazil in 1985. Decades later it escaped from South America when it crept its way across the ocean and appeared in Bangladesh in 2016. Wheat blast outbreaks are linked to the right climate conditions. More accurate weather forecasts, coupled with disease models are key for farmers to adapt to the threat of the disease. Effective forecasting and warning systems can also help farmers avoid unnecessary fungicide use, thereby saving them money and reducing environmental risks.
Because the disease is new, knowledge of wheat blast epidemiology and modeling was limited in Bangladesh. That’s why scientists at the International Maize and Wheat Improvement Center (CIMMYT) reached out to Professor Jose Mauricio Fernandes, a Crop Pathologist, and Mr. Felipe de Vargas, a Computer Scientist, within the Universidade de Passo Fundo (UPF) in Brazil… Read more…
CRAFT: A New Spatial Yield Forecasting Tool
The CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) is a software platform designed for yield forecasting at spatial resolutions of either 5 or 30 arc-minutes using an ensemble modeling approach. Currently the DSSAT, APSIM, and SARRA-H crop simulation models have been implemented for nine important food and feed crops using the AgMIP IT tools. CRAFT was an initiative of CCAFS and was developed in partnership with the Asia Risk Center, Washington State University, and the University of Florida.
Dr. Arjan Gijsman passed away on June 13, 2021, after a very long battle with the merciless disease Multiple Scleroris. Arjan played an active role in the development and advancement of the Cropping System Model (CSM) of DSSAT. To recognize Arjan’s contributions to crop modeling and DSSAT, he is included as a co-author of the […]
DSSAT Foundation – The University of Georgia – Griffin Campus, Griffin, Georgia USA Scientists from across the world met from May 17-22, 2022 on the University of Georgia Griffin Campus to learn about the latest version of the Decision Support System for Agrotechnology Transfer (DSSAT) computer software program. The DSSAT crop modeling ecosystem helps researchers […]
Cynthia Rosenzweig UNITED STATES Dr. Cynthia Rosenzweig of the United States will receive the 2022 World Food Prize for her seminal contributions to understanding and predicting the impacts of the interaction between climate and food systems. Through designing and leading rigorous, collaborative observational and modeling research, she provided the evidence used by thousands of decision-makers […]
Since the release of DSSAT Version 4.7.5, the DSSAT model development team has been working diligently to improve the Cropping System Model (CSM) of DSSAT as well as the various tools, utilities, and application programs. Much progress was made during the DSSAT Development Sprints or Hackathons that are held bi-annually. We evaluated the first version […]
Unfortunately, for Linux/Mac users, there is no installation for these platforms yet. Most of the tools we use in DSSAT were developed for Windows platform. In this case, you need to install a Virtual Machine in order to run Windows platform. Step 1: To get started with the installation you need to install the VirtualBox. […]
Participants of the DSSAT 2022 Training Course held from March 14-18, 2022 at the ILRI Sholla Campus under the auspices of the Decision Support Modeling Tools for Ethiopia The Community of Practice of the Decision Support Modeling Tools for Ethiopia (DSMT-E) brings together users and developers of a wide range of simulation models. The DSSAT […]
During the week of January 18-21, 2022, the 16th DSSAT Development Sprint was held in a “Hybrid” format due to continuing Covid-19 Pandemic. The DSSAT Development Sprint was hosted by the Food Systems Institute and the Department of Agricultural & Biological Engineering of the University of Florida. In-person participants included six representatives from the University […]
If you are planning to use DSSAT in any reports or publications, please make sure to refer to the version number you used. The version and sub-version numbers can be found in the top section of your output files, e.g., 4.8.X (replace X with current version). In addition, please use the following two references for DSSAT and the Cropping System Model. Other related publications can be found in the Documentation section under DSSAT References and Model References.
Hoogenboom, G., C.H. Porter, K.J. Boote, V. Shelia, P.W. Wilkens, U. Singh, J.W. White, S. Asseng, J.I. Lizaso, L.P. Moreno, W. Pavan, R. Ogoshi, L.A. Hunt, G.Y. Tsuji, and J.W. Jones. 2019. The DSSAT crop modeling ecosystem. In: p.173-216 [K.J. Boote, editor] Advances in Crop Modeling for a Sustainable Agriculture. Burleigh Dodds Science Publishing, Cambridge, United Kingdom (http://dx.doi.org/10.19103/AS.2019.0061.10)
Hoogenboom, G., C.H. Porter, V. Shelia, K.J. Boote, U. Singh, J.W. White, W. Pavan, F.A.A. Oliveira, L.P. Moreno-Cadena, J.I. Lizaso, S. Asseng, D.N.L. Pequeno, B.A. Kimball, P.D. Alderman, K.R. Thorp, M.R. Jones, S.V. Cuadra, M.S. Vianna, F.J. Villalobos, T.B. Ferreira, W.D. Batchelor, J. Koo, L.A. Hunt, and J.W. Jones. 2021. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.8 (DSSAT.net). DSSAT Foundation, Gainesville, Florida, USA.
Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. DSSAT Cropping System Model. European Journal of Agronomy 18:235-265.