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.
Decision Support System for Agrotechnology Transfer (DSSAT) is software application program that comprises dynamic crop growth simulation models for over 42 crops. DSSAT is supported by a range of utilities and apps for weather, soil, genetic, crop management, and observational experimental data, and includes example data sets for all crop models. The crop simulation models simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics. DSSAT has been applied to address many real-world problems and issues ranging from genetic modeling to on-farm and precision management to regional assessments of the impact of climate variability and climate change. DSSAT has been used for more than 30 years by researchers, educators, consultants, extension agents, growers, private industry, policy and decision makers, and many others in over 174 countries worldwide. Learn more…
The DSSAT Foundation will be sponsoring the 25th Annual Open Forum on Crop Modeling and Decision Support Systems during this year’s ASA-CSSA-SSSA 2022 International Annual Meeting. The forum is scheduled for Monday night, November 7, 2022, from 7:30 – 9:00 pm in Room 312 of the Baltimore Convention Center in Baltimore, Maryland. The forum is an informal discussion about crop modeling […]
Participants of the DSSAT 2022 Training Course held from August 15-19, 2022, at S. Seifullin Kazakh Agro Technical University (KATU), Nur Sultan, Kazakhstan The project entitled “Development of a decision-making system for the production of the main types of agricultural crops based on the adaptation of the DSSAT model for the growth and development of […]
Question When working with XBuild, I am getting a message: “One of the tools in DSSAT might not work correctly as you have a new version of dbgrid32.ocx in Windows\SysWOW64 or Windows\System32 folder.” How can I resolve this? Answer This error occurs when you have ArcGIS or ERDAS installed on your computer. To resolve this […]
Question Can someone tell me how to generate daily weather data from an historical daily meteorological data set using WeatherMan in DSSAT)? Answer In order to generate daily weather data you require climate data as input. WeatherMan has the capability to create the climate data if you import at least 5 to 10 years of […]
The steps to add a new cultivar line to DSSAT can be a confusing process if you are new to DSSAT or have never had DSSAT training. We will guide you through this process in the steps below. Step #1: Open the DSSAT Shell as shown in Figure 1. Figure 1 Step #2: Follow the […]
“Sod Couch” at the University of California Davis Virtual DSSAT Sprint participants around the world During the week of July 25-29, 2022, the 17th DSSAT Development Sprint was held in a “Hybrid” format based on the success of the prior DSSAT Development “Hybrid” Sprints.. The DSSAT Development Sprint was hosted by the Department of Land, […]
On June 23, 2022, Gerrit Hoogenboom conducted an on-line workshop and seminar entitled “Crop Modeling as a Tool for Understanding the Genotype x Environment Interaction.” The overall goal of the workshop was to introduce the participants of the workshop to the application of dynamic crop simulation models with emphasis on using crop models for understanding […]
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.