It is hypothesized that Utah beef producers in certain locations could intensify private land use via improved forages and irrigation. Although intensification could increase ranch productivity and help compensate for any future restrictions in public grazing, is the approach profitable and sustainable in a dynamic environment? We investigated the efficacy of intensification using linear programming for three size-classes of model ranches. Model solutions maximize returns net of forage costs; outputs include brood-herd dynamics, optimal forage mixes, and net returns. The model is driven by 11-year risk scenarios combining high or low precipitation with high or low beef prices. We then consider current or no access to public grazing—a policy uncertainty. In general, results support the idea that intensification could be profitable, sustainable, and strategically useful under several sets of conditions. Modeled brood-herds expand and contract in response to precipitation. Optimal forage use is dominated by reliance on treated, improved, and irrigated forages. Critical irrigated forages include alfalfa hay and improved pasture. Profitability generally increases with operation size, but when public grazing is eliminated, herd sizes and profitability drop. Small and medium-sized operations respond to loss of public grazing by using more irrigated pasture and alfalfa hay, while larger operations use a wider variety of irrigated and nonirrigated forages. Sensitivity analysis indicates that optimal forage mixes for all operations remain stable even when input costs for fossil fuels double. Further increases in fuel costs, however, begin to reduce the contributions from irrigated pasture and alfalfa hay. Low precipitation (drought) has very large and negative effects on profitability in general. When drought combines with restricted access to public grazing, profitability of small and medium-sized operations drops further while profitability of large operations increases. Empirical research is needed to test model results and examine what the limiting assumptions reveal about real-world production constraints.
You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither BioOne nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the BioOne website.
Vol. 62 • No. 3