Page 45 - Azerbaijan State University of Economics

RISK REDUCTION THROUGH OPTIMAL GRAZING SCENARIOS FOR
LIVESTOCK/FORAGE SYSTEMS IN TEXAS PANHANDLE

the grass for grazing as projections and risk are comparable or favorable

compared to dryland grain production. Scenario 4 (RG) includes 640 acres of
st
native rangeland available for stocker cattle grazing from May 1 though
st
August 31 . A larger acreage is assumed for RG since it has never been
farmed or irrigated and is more likely to occur in larger units. In addition, the
larger acreage or NR provides productive potential similar to that of the other

scenarios.
Scenarios 5-8 are combinations of scenarios 1-4. Scenario 5 (SG)

includes bluestem and native range for summer grazing, Scenario 6 (SWC)
includes wheat for winter grazing and sorghum-sudan for summer grazing,

Scenario 7 (SCG) includes bluestem and sorghum-sudan for summer

grazing, and Scenario 8 (WCG) includes wheat and native range for winter
grazing.

Linear mathematical models are developed for each scenario. The

objective function for each optimizes the Net Returns to land, owner labor,
and management (NR) for the individual producer given the scenario

constraints. Returns are generated in each model from the sale of stocker
cattle or grain sales, and require estimation of livestock and grain prices

along with livestock weights and grain yields. Activities and constraints for
WH are shown in Table 1 to illustrate the model structure.

Cattle prices were obtained from the U.S. Department of Agriculture

(USDA) Agriculture Marketing Service office in Amarillo, Texas. Monthly
weighted average prices are for the period of 1992-2009, for medium and

large 1-2 feeder cattle weights ranging from 350-775 lbs. as reported for the
Amarillo livestock auction (USDA, 2009). The Amarillo auction is

representative of the region and accessible to small producers who may sell
relatively small numbers of cattle. In addition, direct cattle trade in the region

is based on the auction prices. Prices distributions, variance, coefficients of

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