Introducing a new parallel computing system for calibrating genetic parameters within the crop modules of the Cropping System Model (CSM) of DSSAT The Generalized Likelihood Uncertainty Estimation Parallelized (GLUEP)
February 19-23, 2024, the DSSAT Team from the University of Florida (UF) participated in the 14th AMEI Development Sprint hosted by the Leibniz Centre for Agricultural Landscape Research (ZALF) located in Müncheberg, Germany.
DSSAT Open Forum Session during the 2024 Annual Meeting of the American Society of Agronomy-Crop Science Society of America-Soil Science Society of America
Ankara University, in collaboration with the University of Florida, developed a project entitled “Capacity Building on the Dissemination of the Use of Agro-technological Decision Support Systems in Agriculture.” The project has been funded by the US Embassy in Türkiye under the auspices of a US-Türkiye bilateral Program.
Hybrid Meeting, University of Florida, Gainesville | January 17-20, 2023
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.
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.
Improving Soil Fertility Recommendations in Africa
The new book gives a detailed description of the application of DSSAT in simulating crop and soil processes within various Agro-ecological zones in Africa. The book provides examples of the application of DSSAT models to simulate nitrogen applications, soil and water conservation practices including effects of zai technology, phosphorus and maize productivity, generation of genetic coefficients, long-term soil fertility management technologies in the drylands, microdosing, optimization of nitrogen x germplasms x water, spatial analysis of water and nutrient use efficiencies and, tradeoff analysis.
Dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process. Dynamic crop simulation models are tools that predict the phenotype (i.e. observable characteristics) of plants grown in specific environments. In these models, genotypic differences among cultivars are represented by empirical […]
Budding agricultural researchers are being trained in crop modeling to take on pressing and ever-present challenges of land, climate and food security. In a recent training program at ICRISAT-Mali, eight students learned to model agrosystems with DSSAT software, taking into account smallholder farming constraints. Crop modeling is a process that describes different stages of crop […]
Incorporating genotype-specific parameters and realistic trait physiology will advance crop growth models. Plant breeders face an urgent mission: of adapting crops to climate change and feeding an increased world population. Crop models can help breeders select cultivars and cultivar traits for different target environments. For instance, models can be used to evaluate traits (e.g., life […]
The 14th DSSAT Development Sprint was held from January 11-15, 2021 as a Virtual Meeting due to continuing Covid-19 Pandemic. The DSSAT Development Sprint was hosted by the Institute for Sustainable Food Systems and the Department of Agricultural & Biological Engineering of the University of Florida. One of the main goals of the DSSAT Development […]
A new system automatically transforms existing process-based crop models into different languages and simulation platforms. This new approach, described in in silico Plants, will improve the reproducibility, exchange and reuse of process-based crop models (PBM). PBM are increasingly implemented as autonomous components describing each biophysical process. According to lead author, Dr. Cyrille Ahmed Midingoyi, researcher […]
This is one of a series of blogs written by our Youth in Data delegates who participated in the 2020 CGIAR Convention on Big Data in Agriculture. The global event was held virtually 19-23 October 2020. Feature photo: Alfonso Cortés / CIMMYT. During my experience as a Youth in Data 2020 delegate at the 2020 CGIAR […]
The 13th DSSAT Development Sprint was held from July 20-24, 2020 as a Virtual Meeting due to continuing Covid-19 Pandemic. The DSSAT Development Sprint was hosted by the Institute for Sustainable Food Systems and the Department of Agricultural & Biological Engineering of the University of Florida. One of the main goals of the DSSAT Development […]