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CRAFT: A New Spatial Yield Forecasting Tool

The CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) is a software platform designed for yield forecasting at 5 or 30 arc-minutes spatial resolutions using an ensemble modeling approach. Twenty-three important food and feed crops have been implemented using the DSSAT Cropping System Model as the main crop model engine.

CRAFT is an initiative of CCAFS and was developed in partnership with the University of Florida. It has been successfully applied to forecast the in-season yield of wheat and rice cropping systems in Nepal and Bangladesh.  It has also been evaluated for yield forecasting and climate change applications in West Africa in collaboration with the WASCAL Project, Agrhymet, and the CASCAID Project of ICRISAT.

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

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.

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ICASA Data Standards Version 2.0

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.

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