Blog entries filtered by: Agroecology, Models
Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) and HarvestChoice are now working together to apply innovative, “bio-economic” approaches to improve the food security of poor people in sub-Saharan Africa and South Asia. Together, our organizations will merge biological, environmental and economic tools to track global food pests and better target strategic investments to improve global food security.
SPAM2000 (any version) is based on a large set of data which centers as much as possible on the year 2000: land cover / land use (Boston University’s MODIS-derived land cover 2000 and JRC’s GLC2000), crop suitability (Global AEZ Zones 2000 by FAO and IIASA), irrigated areas (FAO and CESR of University of Kassel), population density (CIESIN), and, most importantly, national and sub-national crops statistics for 2000.
How to use the weather generator in the DSSAT Crop Systems Model for climate change studies, and what should I know?
Good question.
For crop modeling researchers who are in need of finding soil profiles at regional-scale in Sub-Saharan Africa (SSA), this post gives a spatial dataset that delineates SSA into 588 units and corresponding soil profiles, based on the WISE v1.1 and HC27 soil profile databases.
In August, we posted a new collection of more than 3,400 soil profiles that are converted/formatted for crop model applications, based on the WISE 1.1 Soil Profile Database. Utilizing this new soil profiles, as described in the post, we anticipate crop modeling studies to expand their coverage areas even to the locations where no soil measurement data was previously available. For the questions of exactly how, here is a quick example application that can help you find the one(s).
Many of HarvestChoice spatial datasets are organized and released on 10-km grids. To make spatial analyses easier for researchers (even without having access to GIS platform), we put data layers from multiple themes together in one denormalized big table. This post describes the methodology and presents a prototype.
As a quick demonstration to estimate crop yield levels at regional-scale with various management assumptions, this post describes how crop systems models can be used to assess yield gap of rainfed maize due to the limited supply of soil nitrogen. This methodology can help researchers to find what is the most critical factor that limits crop yield productivity in a given environment condition and how to address the constraint.
By modeling the decomposition of soil organic matter dynamics, crop systems models can simulate the effects of soil nutrient depletion under low-input extractive field management practices, as well as soil carbon sequestration under regenerative management practices.
Through the collaboration with HarvestChoice, a team of scientists at the Universities of Georgia and Florida converted 3,404 soil profiles from the World Soil Information (ISRIC) WISE Global Soil Profile Database v1.1 to the DSSAT-compatible format.
Farming entails a great deal of choices and uncertainties. From season to season, weather varies, price fluctuates, soil degrades, pest damages, and climate changes. Farmers everywhere must cope with these uncertainties. Throughout the history of agriculture, many options have been developed to help manage these risks, increase yields, increase efficiency, and, more recently, promote the sustainability of the overall system.