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

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

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 (

Suggested Citation

  • Phillips, Rebecca & Beeri, Ofer & Scholljegerdes, Eric & Bjergaard, David & Hendrickson, John, 2009. "Integration of geospatial and cattle nutrition information to estimate paddock grazing capacity in Northern US prairie," Agricultural Systems, Elsevier, vol. 100(1-3), pages 72-79, April.
    • Handle: RePEc:eee:agisys:v:100:y:2009:i:1-3:p:72-79
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    References listed on IDEAS

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    1. Grigera, Gonzalo & Oesterheld, Martin & Pacin, Fernando, 2007. "Monitoring forage production for farmers' decision making," Agricultural Systems, Elsevier, vol. 94(3), pages 637-648, June.
    2. Diaz-Solis, H. & Kothmann, M.M. & Grant, W.E. & De Luna-Villarreal, R., 2006. "Application of a simple ecological sustainability simulator (SESS) as a management tool in the semi-arid rangelands of northeastern Mexico," Agricultural Systems, Elsevier, vol. 88(2-3), pages 514-527, June.
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    1. Della Nave, Facundo N. & Ojeda, Jonathan J. & Irisarri, J. Gonzalo N. & Pembleton, Keith & Oyarzabal, Mariano & Oesterheld, Martín, 2022. "Calibrating APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions," Agricultural Systems, Elsevier, vol. 201(C).

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