Integration of geospatial and cattle nutrition information to estimate paddock grazing capacity in Northern US prairie

Rebecca Phillips, Ofer Beeri, Eric Scholljegerdes, David Bjergaard and John Hendrickson

Agricultural Systems, 2009, vol. 100, issue 1-3, pages 72-79

Abstract: Spatiotemporal variability in forage quantity and quality requires that regular assessment is needed 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. This paper describes a method for estimating short-term grazing capacity for small (1-20 ha) paddocks using cattle nutrition and high spatial resolution forage data in Geographic Information Systems (GIS) for mixed-grass prairie. We define grazing capacity as the number of days a specific paddock will support the nutritional requirements of beef cattle. We integrate previously published methods for estimating cattle nutritional requirements, forage quality (crude protein) and forage quantity (phytomass) to estimate grazing capacity based on current standing-crop. The model utilizes high-resolution (<30-m) satellite imagery or field data to estimate short-term grazing capacity for small paddocks. Three versions of the model were evaluated on one paddock under cattle use in 2007. One version was parameterized using data collected on June 22 from the Landsat Thematic Mapper (TM), one version was parameterized using data collected June 23 from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and one version was parameterized using data collected June 20 from field clippings. TM and ASTER versions underestimated grazing capacity by four days while the field version overestimated grazing capacity by one day. Results suggest integration of cattle nutrition and forage data in GIS could assist with stocking rate adjustments, but additional trials are needed.

Keywords: Landsat; thematic; mapper; Advanced; spaceborne; thermal; emission; and; reflection; radiometer; ASTER; Livestock; Grassland; Remote; sensing; Grazing; capacity (search for similar items in EconPapers)
Date: 2009

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