The spatially explicit water requirement satisfaction index (WRSI*) is an indicator of crop performance based on the availability of water to the crop during a growing season. FAO studies have shown that WRSI can be related to crop productivity using a linear yield-reduction function specific to a crop (FAO, 1977; FAO, 1979; FAO, 1986). Later, Verdin and Klaver (2002) and Senay and Verdin (2003) demonstrated a regional implementation of WRSI in a grid cell based modeling environment. WRSI for a season is based on the water supply and demand a crop experiences during a growing season. It is calculated as the ratio of seasonal actual evapotranspiration (AET) to the seasonal crop water requirement (WR). AET WRSI = --------------- * 100 WR Read more here http://earlywarning.usgs.gov/fews/product/126 * Originally developed by FAO, the WRSI has been adapted and extended by USGS in a geospatial application to support FEWS NET monitoring requirements. References FAO, 1977. Crop water requirements. FAO Irrigation and Drainage Paper No. 24, by Doorenbos J and W.O. Pruitt. FAO, Rome, Italy. FAO, 1979. Agrometeorological crop monitoring and forecasting. FAO Plant Production and Protection paper No. 17, by M. Frère and G.F. Popov. FAO, Rome, Italy. FAO, 1986. Early Agrometeorological crop yield forecasting. FAO Plant Production and Protection paper No. 73, by M. Frère and G.F. Popov. FAO, Rome, Italy. Senay, G.B. and J. Verdin, 2003. Characterization of Yield Reduction in Ethiopia Using a GIS-Based Crop Water Balance Model. Canadian Journal of Remote Sensing, vol. 29, no. 6, pp. 687-692. Verdin, J. and R. Klaver, 2002. Grid cell based crop water accounting for the famine early warning system. Hydrological Processes, 16:1617-1630.

The spatially explicit water requirement satisfaction index (WRSI*) is an indicator of crop performance based on the availability of water to the crop during a growing season. FAO studies have shown that WRSI can be related to crop productivity using a linear yield-reduction function specific to a crop (FAO, 1977; FAO, 1979; FAO, 1992). Later, Verdin and Klaver (2002) and Senay and Verdin (2003) demonstrated a regional implementation of WRSI in a grid cell based modeling environment. WRSI for a season is based on the water supply and demand a crop experiences during a growing season. It is calculated as the ratio of seasonal actual evapotranspiration (AET) to the seasonal crop water requirement (WR). AET WRSI = --------------- * 100 WR Read more here http://earlywarning.usgs.gov/fews/product/126 * Originally developed by FAO, the WRSI has been adapted and extended by USGS in a geospatial application to support FEWS NET monitoring requirements. References FAO, 1977. Crop water requirements. FAO Irrigation and Drainage Paper No. 24, by Doorenbos J and W.O. Pruitt. FAO, Rome, Italy. FAO, 1979. Agrometeorological crop monitoring and forecasting. FAO Plant Production and Protection paper No. 17, by M. Frère and G.F. Popov. FAO, Rome, Italy. FAO, 1992. Early Agrometeorological crop yield forecasting. FAO Plant Production and Protection paper No. 73, by M. Frère and G.F. Popov. FAO, Rome, Italy. Senay, G.B. and J. Verdin, 2003. Characterization of Yield Reduction in Ethiopia Using a GIS-Based Crop Water Balance Model. Canadian Journal of Remote Sensing, vol. 29, no. 6, pp. 687-692. Verdin, J. and R. Klaver, 2002. Grid cell based crop water accounting for the famine early warning system. Hydrological Processes, 16:1617-1630.

The spatially explicit water requirement satisfaction index (WRSI*) is an indicator of crop performance based on the availability of water to the crop during a growing season. FAO studies have shown that WRSI can be related to crop productivity using a linear yield-reduction function specific to a crop (FAO, 1977; FAO, 1979; FAO, 1999). Later, Verdin and Klaver (2002) and Senay and Verdin (2003) demonstrated a regional implementation of WRSI in a grid cell based modeling environment. WRSI for a season is based on the water supply and demand a crop experiences during a growing season. It is calculated as the ratio of seasonal actual evapotranspiration (AET) to the seasonal crop water requirement (WR). AET WRSI = --------------- * 100 WR Read more here http://earlywarning.usgs.gov/fews/product/126 * Originally developed by FAO, the WRSI has been adapted and extended by USGS in a geospatial application to support FEWS NET monitoring requirements. References FAO, 1977. Crop water requirements. FAO Irrigation and Drainage Paper No. 24, by Doorenbos J and W.O. Pruitt. FAO, Rome, Italy. FAO, 1979. Agrometeorological crop monitoring and forecasting. FAO Plant Production and Protection paper No. 17, by M. Frère and G.F. Popov. FAO, Rome, Italy. FAO, 1999. Early Agrometeorological crop yield forecasting. FAO Plant Production and Protection paper No. 73, by M. Frère and G.F. Popov. FAO, Rome, Italy. Senay, G.B. and J. Verdin, 2003. Characterization of Yield Reduction in Ethiopia Using a GIS-Based Crop Water Balance Model. Canadian Journal of Remote Sensing, vol. 29, no. 6, pp. 687-692. Verdin, J. and R. Klaver, 2002. Grid cell based crop water accounting for the famine early warning system. Hydrological Processes, 16:1617-1630.

The spatially explicit water requirement satisfaction index (WRSI*) is an indicator of crop performance based on the availability of water to the crop during a growing season. FAO studies have shown that WRSI can be related to crop productivity using a linear yield-reduction function specific to a crop (FAO, 1977; FAO, 1979; FAO, 1999). Later, Verdin and Klaver (2002) and Senay and Verdin (2003) demonstrated a regional implementation of WRSI in a grid cell based modeling environment. WRSI for a season is based on the water supply and demand a crop experiences during a growing season. It is calculated as the ratio of seasonal actual evapotranspiration (AET) to the seasonal crop water requirement (WR). AET WRSI = --------------- * 100 WR Read more here http://earlywarning.usgs.gov/fews/product/126 * Originally developed by FAO, the WRSI has been adapted and extended by USGS in a geospatial application to support FEWS NET monitoring requirements. References FAO, 1977. Crop water requirements. FAO Irrigation and Drainage Paper No. 24, by Doorenbos J and W.O. Pruitt. FAO, Rome, Italy. FAO, 1979. Agrometeorological crop monitoring and forecasting. FAO Plant Production and Protection paper No. 17, by M. Frère and G.F. Popov. FAO, Rome, Italy. FAO, 1999. Early Agrometeorological crop yield forecasting. FAO Plant Production and Protection paper No. 73, by M. Frère and G.F. Popov. FAO, Rome, Italy. Senay, G.B. and J. Verdin, 2003. Characterization of Yield Reduction in Ethiopia Using a GIS-Based Crop Water Balance Model. Canadian Journal of Remote Sensing, vol. 29, no. 6, pp. 687-692. Verdin, J. and R. Klaver, 2002. Grid cell based crop water accounting for the famine early warning system. Hydrological Processes, 16:1617-1630.

This rubber suitability raster dataset, with a pixel resolution of 1 x 1 km, covers the Lower Mekong Basin. The suitability of pixels in the region was assessed for six crop species: Cassava, Robusta Coffee, Maize, Rainfed Rice, Rubber, and Soya based on a comparison of land and hydrological conditions to the requirements of the different crops. This was achieved via the application of a modified version of the Land Use Suitability Evaluation Tool (LUSET) (developed by the International Rice Research Institute (IRRI)) and run for both current conditions and for predicted 2050 conditions. USAID Mekong ARCC worked with IRRI to modify the LUSET tool and apply it to produce these datasets.

This robusta coffee suitability raster dataset, with a pixel resolution of 1 x 1 km, covers the Lower Mekong Basin. The suitability of pixels in the region was assessed for six crop species: Cassava, Robusta Coffee, Maize, Rainfed Rice, Rubber, and Soya based on a comparison of land and hydrological conditions to the requirements of the different crops. This was achieved via the application of a modified version of the Land Use Suitability Evaluation Tool (LUSET) (developed by the International Rice Research Institute (IRRI)) and run for both current conditions and for predicted 2050 conditions. USAID Mekong ARCC worked with IRRI to modify the LUSET tool and apply it to produce these datasets.

This cassava suitability raster dataset, with a pixel resolution of 1 x 1 km, covers the Lower Mekong Basin. The suitability of pixels in the region was assessed for six crop species: Cassava, Robusta Coffee, Maize, Rainfed Rice, Rubber, and Soya based on a comparison of land and hydrological conditions to the requirements of the different crops. This was achieved via the application of a modified version of the Land Use Suitability Evaluation Tool (LUSET) (developed by the International Rice Research Institute (IRRI)) and run for both current conditions and for predicted 2050 conditions. USAID Mekong ARCC worked with IRRI to modify the LUSET tool and apply it to produce these datasets.

This cassava suitability raster dataset, with a pixel resolution of 1 x 1 km, covers the Lower Mekong Basin. The suitability of pixels in the region was assessed for six crop species: Cassava, Robusta Coffee, Maize, Rainfed Rice, Rubber, and Soya based on a comparison of land and hydrological conditions to the requirements of the different crops. This was achieved via the application of a modified version of the Land Use Suitability Evaluation Tool (LUSET) (developed by the International Rice Research Institute (IRRI)) and run for both current conditions and for predicted 2050 conditions. USAID Mekong ARCC worked with IRRI to modify the LUSET tool and apply it to produce these datasets.

This rainfed rice suitability raster dataset, with a pixel resolution of 1 x 1 km, covers the Lower Mekong Basin. The suitability of pixels in the region was assessed for six crop species: Cassava, Robusta Coffee, Maize, Rainfed Rice, Rubber, and Soya based on a comparison of land and hydrological conditions to the requirements of the different crops. This was achieved via the application of a modified version of the Land Use Suitability Evaluation Tool (LUSET) (developed by the International Rice Research Institute (IRRI)) and run for both current conditions and for predicted 2050 conditions. USAID Mekong ARCC worked with IRRI to modify the LUSET tool and apply it to produce these datasets.

This soya suitability raster dataset, with a pixel resolution of 1 x 1 km, covers the Lower Mekong Basin. The suitability of pixels in the region was assessed for six crop species: Cassava, Robusta Coffee, Maize, Rainfed Rice, Rubber, and Soya based on a comparison of land and hydrological conditions to the requirements of the different crops. This was achieved via the application of a modified version of the Land Use Suitability Evaluation Tool (LUSET) (developed by the International Rice Research Institute (IRRI)) and run for both current conditions and for predicted 2050 conditions. USAID Mekong ARCC worked with IRRI to modify the LUSET tool and apply it to produce these datasets.