Submitted to: Agricultural Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/14/2009
Publication Date: 2/1/2009
Publication URL: http://hdl.handle.net/10113/32445
Citation: Phillips, B.L., Beeri, O., Scholljegerdes, E.J., Bjergaard, D., Hendrickson, J.R. 2009. Integration of Geospatial and Cattle Nutrition Information to Estimate Paddock Grazing Capacity in Northern U.S. Prairie. Agric. Syst. 100:72-79. Interpretive Summary: The amount and quality of forage changes from year to year, and it is difficult to assess the capacity of multiple grassland to support livestock nutritional requirements with ocular surveys alone. Improved, real-time estimates of grazing capacity could assist managers with optimizing resource utilization, such as adjusting stocking rates to match forage availability. We describe a new satellite data-driven model for determining the number of days a herd might be supported by forage available in specific pastures. The model using livestock nutritional requirements and satellite estimates of forage quantity (phytomass) and quality (crude protein). Rotational grazing systems would benefit from this model, where forage nutrition data for multiple pastures are needed to determine the length of time a herd might graze before needing rotation. We tested model results with a 28-day cattle trial in 2007 using a historically native, North Dakota pasture. We found the model underestimated actual grazing capacity by 4 days. Results were similar using data from two satellite sensors—the Landsat and the ASTER. These initial results suggest this model conservatively estimates cattle grazing capacity, but additional cattle trials are needed.
Technical Abstract: Spatiotemporal variability in forage quantity and quality requires regular assessment of the capacity for grasslands to support livestock nutritional requirements. Current methods for estimating grazing capacity are typically production-based and lack the forage quality data necessary to match nutrients in forage with livestock requirements in real time. The primary purpose of this paper is to describe a new model for estimating current grazing capacity using spectral data in Geographic Information Systems (GIS) for historically native pastures in North Dakota. We define grazing capacity as the number of days a specific pasture might support the nutritional requirements of beef cattle. The model is driven by livestock nutritional requirements and by estimates of forage quantity (phytomass) and quality (crude protein) available from the Landsat or Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite sensors. Since cattle consume unpredictable proportions of photosynthetically active vegetation (PV) and non-photosynthetically active vegetation (NPV), we designed two versions of the model: one using PV only, and one using total phytomass (PV + NPV). Our secondary purpose was to determine how the model output for the two model versions compared with actual cattle usage in a short-term field trial. The model using PV only underestimated grazing capacity by 4 days. The model using total phytomass (both PV and NPV) overestimated grazing capacity by 7 days. Results were similar using both Landsat and ASTER data. These initial results suggest the PV-only model may conservatively estimate cattle grazing capacity and support grassland sustainable management goals, but additional cattle trials are needed.