Description

Abstract: Assessment of vegetation productivity on rangelands using conventional methods such as clipping can be very time consuming and expensive, and becomes very impractical to do on a near real time basis. Near real time information is critical for timely decision making in the face of drought and other disasters, especially in the Gobi Region of Mongolia. In this study we sought to assess the ability of a forage simulation model (PHYGROW) to accurately predict forage standing crop at 300 sites, and whether the output from the simulation model could be combined with satellite greenness data to produce landscape maps of forage production on a near real-time basis. The assessment required 3 main steps: 1) model calibration, 2) model validation, and 3) map cross-validation. For model calibration and validation, we found that the PHYGROW model Generally did a good job of predicting forage biomass at the monitoring sites with the variability in model predicted biomass to be less than the error associated with actual clipping for biomass production. An assessment of the cross-validation for the landscape maps found a good relationship between forage model biomass and the map interpolated biomass, thus indicating that the forage model outputs can be useful for creating near-real time maps of forage production. These technologies will provide timely information on forage conditions to increase lead time for making risk mitigation decisions by herder groups and policy makers in Mongolia.