Grass-Cast: A New, Experimental Grassland Productivity Forecast for the Northern Great Plains

grass-cast video screenshot with windmill
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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. Now an innovative new Grassland Productivity Forecast or “Grass-Cast” has published its first forecast to help producers in the Northern Great Plains reduce this economically important source of uncertainty.

By Sharon Durham, USDA ARS Office of Communications

May 22, 2018

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. Now an innovative new Grassland Productivity Forecast or “Grass-Cast” has published its first forecast to help producers in the Northern Great Plains reduce this economically important source of uncertainty.

This new experimental grassland forecast is the result of a collaboration between the Agricultural Research Service (ARS), a part of the U.S. Department of Agriculture (USDA); National Drought Mitigation Center (NDMC); Colorado State University and the University of Arizona.

Grass-Cast uses over 30 years of historical data about weather and vegetation growth—combined with seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce above-normal, near-normal, or below-normal amounts of vegetation for grazing.

For the 3 scenarios (3 maps) below: "If precipitation between now and July 31st is above/near/below normal, we estimate that grassland production in your county (in pounds per acre) will be ____ % more or less than its 34-year average."

Maps above, produced on May 21, 2018. As of May 17, 2018, the NOAA Climate Prediction Center indicated equal chances (33% each) of precipitation in May-June-July being above/near/below normal for our region, so each of the maps above are equally likely. For counties in white, no forecast is available due to insufficient data or weak statistical relationships.

Click here to read the full press-release.