A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies
Autor: | Pramod K. Aggarwal, James Hansen, Cheryl Porter, Vaishali Sharda, Carol Jo Wilkerson, Gerrit Hoogenboom, Vakhtang Shelia |
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Rok vydání: | 2019 |
Předmět: |
Risk analysis
Decision support system Environmental Engineering Food security 010504 meteorology & atmospheric sciences Computer science Ecological Modeling Simulation modeling Climate change 04 agricultural and veterinary sciences 01 natural sciences Toolbox Risk analysis (engineering) 040103 agronomy & agriculture 0401 agriculture forestry and fisheries DSSAT Predictability Software 0105 earth and related environmental sciences |
Zdroj: | Environmental Modelling & Software. 115:144-154 |
ISSN: | 1364-8152 |
DOI: | 10.1016/j.envsoft.2019.02.006 |
Popis: | 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. |
Databáze: | OpenAIRE |
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