• Evaluation of GPFARM for Simulation of Forage Production and Cow-Calf Weights

      Andales, Allan A.; Derner, Justin D.; Bartling, Patricia N. S.; Ahuja, Lajpat R.; Dunn, Gale H.; Hart, Richard H.; Hanson, Jon D. (Society for Range Management, 2005-05-01)
      A modeling approach that assesses impacts of alternative management decisions prior to field implementation would reduce decision-making risk for rangeland and livestock production system managers. However, the accuracy and functionality of models should be verified before they are used as decision-making tools. The goal of this study was to evaluate the functionality of the Great Plains Framework for Agricultural Resource Management (GPFARM) model in simulating forage and cow-calf production in the central Great Plains. The forage production module was tested in shortgrass prairie using April-October monthly biomass values from 2000 through 2002 for warm-season grasses (WSG), cool-season grasses (CSG), shrubs, and forbs. The forage module displayed excellent (99% explained variance) agreement in the 2001 calibration year in tracking growth and senescence trends of WSG and CSG, which constitute the vast majority of the aboveground biomass. Less agreement (35%-39% explained variance) was observed for shrubs and forbs. The model-explained variances of biomass in 2000 and 2002 (verification years) were 80% for WSG, 67% for CSG, 78% for shrubs, and 82% for forbs. Further development is needed to improve predicted plant response to environmental stresses. The cow-calf production module was tested in northern mixed-grass prairie using June-November monthly average cow and calf weights from 1996 through 2001 for March-calving, moderately stocked Hereford pairs. Overall, GPFARM performed well and tracked cow (81% explained variance) and calf (94% explained variance) pre- and postweaning weights. The GPFARM model has functional utility for simulating forage and cow-calf production with satisfactory accuracy at semiarid-temperate sites, such as southeastern Wyoming and northeastern Colorado. Continued development will focus on improving plant response to environmental stresses and testing the model’s functionality as a decision support tool for strategic and tactical ranch management.