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
If you are attending the ASA-CSSA 2021 International Annual Meeting Salt Lake City, Utah, come and join us at the DSSAT Foundation Open Forum, where we will exchange what’s new on the crop modeling research, collaboration opportunities, and related data and tools development activities.
Day, Date: November 8, 2021
Start Time: 7:30pm
End Time: 9:00 pm
Location: Room 151 DEF of the Salt Palace Convention Center
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.
Researchers increasingly seek to integrate results from multiple experiments. The ICASA V2.0 standards allow flexible description of field experiments. Major categories of data are management, soil, weather and crop responses. The standards may be implemented in diverse digital formats. Planned improvements emphasize data quality and appropriate usage.
Original by Samantha Murray, October 21, 2021. Sam is a senior public relations specialist at UF/IFAS Communications. She loves learning about and communicating science. Supply chains for French fries and pasta sauce are surprisingly resilient to climate change, according to the predictions of a new study.The study’s authors made their predictions using an innovative modeling […]
This Special Issue of The Journal of Agricultural Science is dedicated to the iCROPM 2020 Symposium. It includes six papers that are based on both oral and poster presentations from the symposium, including the final keynote presentation. Read the Editorial:iCROPM 2020: Crop Modeling for the Futureby, Gerrit Hoogenboom, Eric Justes, Christophe Pradal, Marie Launay, Senthold […]
Texas A&M AgriLife study projects the genetic profile that cultivars will need to adapt Original by Kay Ledbetter, October 13, 2021. Kay Ledbetter is an associate editor/senior writer/media relations specialist for Texas A&M AgriLife. She is responsible for writing news releases and feature articles from science-based information generated by the agency across the state, as […]
Original by Brad Buck, October 7, 2021. A senior public relations specialist for UF/IFAS Communications, Brad is a huge Gator fan. He grew up in Gainesville, loves movies, sports and finding great stories to tell. Data sets are often easy to find but complicated to use because scientists store and use information differently, and they […]
The DSSAT Foundation will be sponsoring the 24th Annual Open Forum on Crop Modeling and Decision Support Systems during this year’s ASA-CSSA-SSSA 2021 International Annual Meeting. The forum is scheduled for Monday night, November 8, 2021, from 7:30 – 9:00 pm in Room 151 DEF of the Salt Palace Convention Center in Salt Lake City, […]
DSSAT 2021 European Training Course at the Technical University of Munich The first DSSAT European Training Course was held from August 30 through September 4, 2021 at the Technical University of Munich. The workshop was hosted by Professor Senthold Asseng, Chair Digital Agriculture and Director of the Hans Eisenmann Forum of the Technical University of […]
Original by Susan McCarthy, National Agricultural Library, Ming Chan, National Agricultural Library, and Cheryl Porter, University of Florida in Research and Science Sep 08, 2021. Crop researchers are hungry for data to feed their crop models. There is a wealth of historical data that’s inaccessible because today’s crop model software applications cannot easily interpret it. USDA’s […]
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.