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CRAFT: A Multi-Scale Multi-Model Gridded Framework for Forecasting Crop Production

CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) released an integrated modeling framework, CRAFT, developed for running gridded crop modeling simulations at 5 and 30 arc-minutes resolutions. Incorporating seasonal climate forecasts, CRAFT allows within-season yield forecasting, risk analysis, and climate change impact studies.

Abstract

Regional crop production forecasting is growing in importance in both, the public and private sectors to ensure food security, optimize agricultural management practices and use of resources, and anticipate market fluctuations. Thus, a model and data driven, easy-to-use forecasting and a risk assessment system can be an essential tool for end-users at different levels. This paper provides an overview of the approaches, algorithms, design, and capabilities of the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) for gridded crop modeling and yield forecasting along with risk analysis and climate impact studies. CRAFT is a flexible and adaptable software platform designed with a user-friendly interface to produce multiple simulation scenarios, maps, and interactive visualizations using a crop engine that can run the pre-installed crop models DSSAT, APSIM, and SARRA-H, in concert with the Climate Predictability Tool (CPT) for seasonal climate forecasts. Its integrated and modular design allows for easy adaptation of the system to different regional and scientific domains. CRAFT requires gridded input data to run the crop simulations on spatial scales of 5 and 30 arc-minutes. Case studies for South Asia for two crops, including wheat and rice, shows its potential application for risk assessment and in-season yield forecasting.

Vakhtang, S., Hansen, J., Sharda, V., Porter, C., Aggarwal, P., Wilkerson, C.J. and Hoogenboom, G., 2019. A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies.

https://doi.org/10.1016/j.envsoft.2019.02.006