Browsing Rangeland Ecology & Management, Volume 66, Number 4 (July 2013) by Authors
Temperature and Precipitation Affect Steer Weight Gains Differentially by Stocking Rate in Northern Mixed-Grass PrairieReeves, Justin L.; Derner, Justin D.; Sanderson, Matt A.; Petersen, Mark K.; Vermeire, Lance T.; Hendrickson, John R.; Kronberg, Scott L. (Society for Range Management, 2013-07-01)Cattle weight gain responses to seasonal weather variability are difficult to predict for rangelands because few long-term (>20 yr) studies have been conducted. However, an increased understanding of temperature and precipitation influences on cattle weight gains is needed to optimize stocking rates and reduce enterprise risk associated with climatic variability. Yearling steer weight gain data collected at the USDA-ARS High Plains Grasslands Research Station at light, moderate, and heavy stocking rates for 30 years (1982-2011) were used to examine the effects of spring (April-June) and summer (July-September) temperature and precipitation, as well as prior-growing-season (prior April-September) and fall/winter (October-March) precipitation, on beef production (kg ha-1). At heavier stocking rates, steer production was more sensitive to seasonal weather variations. A novel finding was that temperature (relatively cool springs and warm summers) played a large predictive role on beef production. At heavier stocking rates, beef production was highest during years with cool, wet springs and warm, wet summers, corresponding to optimum growth conditions for this mixed C3-C4 plant community. The novelty and utility of these findings may increase the efficacy of stocking rate decision support tools. The parsimonious model structure presented here includes three-month seasonal clusters that are forecasted and freely available from the US National Oceanic and Atmospheric Administration up to a year in advance. These seasonal weather forecasts can provide ranchers with an increased predictive capacity to adjust stocking rates (in advance of the grazing season) according to predicted seasonal weather conditions, thereby reducing enterprise risk.