Every spring, ranchers face the same difficult challenge—trying to guess how much grass will be available for livestock to graze during the upcoming summer. This innovative forecast tool can help producers in the Great Plains reduce this economically important source of uncertainty.
Grass-Cast uses almost 40 years of historical data on weather and vegetation growth combined with seasonal precipitation forecasts to predict if rangelands in individual grid cells (10 km x 10km, or ~ 6 miles x 6 miles) are likely to produce above-normal, near-normal, or below-normal amounts of vegetation. The tool is updated every two weeks to incorporate newly observed weather data and emerging trends in the forecast. As with any forecast, Grass-Cast’s accuracy depends on how far into the future the user looks. Its accuracy improves with time as the growing season unfolds, so the tool is intended to be consulted more than once during the growing season. The tool also provides a view of rangeland productivity in the broader region, to assist in larger-scale decision making—such as where grazing resources might be more plentiful if a rancher’s own region is at risk of drought.
It should be noted that Grass-Cast does not differentiate between desirable and undesirable forage species. In using the tool, it is therefore important for producers to know what proportion of a pasture is occupied by weeds and how well those weeds respond to rain (or lack of rain) compared to the desirable species. Furthermore, Grass-Cast does not directly account for local management practices, such as grazing intensity in previous years. Producers should therefore adjust Grass-Cast’s grid-level productivity estimates accordingly.
Producers should not rely on Grass-Cast as a sole source for making management decisions. Similarly, public land managers should not use Grass-Cast as a sole source of information for setting stocking rates, determining turnout dates, or other aspects of lease agreements, allotments, or permits.
The tool is the result of a collaboration between Colorado State University, the U.S. Department of Agriculture, the National Drought Mitigation Center, and the University of Arizona. Funding for the project was provided by the USDA Natural Resources Conservation Service, the USDA Agricultural Research Service, and the National Drought Mitigation Center.
