THESIS | Spatial projections of agricultural land use
This tool creates a time series of downscaled (gridded) crop and pasture land use outcomes, given aggregated cropland and pasture demands for world regions under a particular climate and socioeconomic scenario. The spatial cropland and pasture projections can then be used as input to CLM or to the THESIS crop yield tool to compute future climate effects on crop yields.
The first component of the tool (MATLAB, not currently available for download) estimates downscaling model parameters based on:
- Historical spatial distribution of cropland and pasture data
- Historical climate data (observations or model output), and
- Historical biophysical and socioeconomic factors, including soil conditions, GDP, and urban and rural population.
The second component (Fortran) generates a future time series of downscaled crop and pasture data based on:
- The historical downscaling parameter estimates computed with the first component
- Future regional total crop and pasture time series
- Baseline current day downscaled crops and pasture data
- Future projections of climate outcomes and other biophysical and socioeconomic factors.
Meiyappan, P., Dalton, M., O'Neill, B.C., Jain, A.K., 2014. Spatial modeling of agricultural land use change at global scale. Ecological Modelling, 291, 152-174, DOI: 10.1016/j.ecolmodel.2014.07.027.
Ren, X., Weitzel, M., O’Neill, B.C., Lawrence, P., Meiyappan, P., Levis, S., Balistreri, E.J., Dalton, M., 2016. Avoided economic impacts of climate change on agriculture: Integrating a land surface model (CLM) with a global economic model (iPETS). Climatic Change, 1-15. DOI: 10.1007/s10584-016-1791-1.
Citation for Model Code
Meiyappan P., Kauffman, B. (2016, July 22) “Spatial Projections of Agricultural Land Use Tool (Version 1)” NCAR THESIS Tools Library. Retrieved from https://svn-iam-thesis-release.cgd.ucar.edu/agriculture_spatial/. DOI: 10.5065/D6GT5KK3.
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