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The Value of ENSO Forecast Information to Dual-Purpose Winter Wheat Production in the U.S. Southern High Plains

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  • 1 Plant Stress and Water Conservation Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Lubbock, Texas
  • | 2 Grazinglands Research Laboratory, Agricultural Research Service, U.S. Department of Agriculture, El Reno, Oklahoma
  • | 3 Agricultural Systems Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Fort Collins, Colorado
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Abstract

The value of El Niño–Southern Oscillation (ENSO) forecast information to southern high plains winter wheat and cattle-grazing production systems was estimated here by simulation. Although previous work has calculated average forecast value, the approach here was to estimate probabilities of the value of single forecasts from value distributions associated with categorical ENSO forecast conditions. A simple ENSO-phase forecast system’s value was compared with that of an ideal forecast method that exactly predicted the tercile category of regional November–March precipitation. Simulations were conducted for four price scenarios with wheat prices that randomly varied about a historical ($3.22 per bushel) and elevated ($7.00 per bushel) mean and with returns on live weight gain that are consistent with the grain producer leasing pasturage or owning cattle. In the simulations at $3.22 per bushel, the best practices for specific forecast conditions varied with cattle-ownership conditions. However, the ENSO-phase system’s value distributions were comparable to that of the perfect forecast system; thus more-accurate regional precipitation forecasts may not lead to more forecast value at the farm level. In the simulations at $7.00 per bushel, even perfect categorical forecasts produced only minor profit effects, a result that is attributed here to an increased profit margin rather than to increased wheat value. Under both wheat-price conditions, however, the best no-forecast baseline practices are also shown to have value relative to an arbitrarily chosen management practice. Thus, following practices optimized to climatic conditions and current price and cost conditions might increase profits when no forecast information is available.

Corresponding author address: Steve Mauget, Plant Stress and Water Conservation Laboratory, Agricultural Research Service, U.S. Department of Agriculture, 3810 4th St., Lubbock, TX 79415. Email: steve.mauget@ars.usda.gov

Abstract

The value of El Niño–Southern Oscillation (ENSO) forecast information to southern high plains winter wheat and cattle-grazing production systems was estimated here by simulation. Although previous work has calculated average forecast value, the approach here was to estimate probabilities of the value of single forecasts from value distributions associated with categorical ENSO forecast conditions. A simple ENSO-phase forecast system’s value was compared with that of an ideal forecast method that exactly predicted the tercile category of regional November–March precipitation. Simulations were conducted for four price scenarios with wheat prices that randomly varied about a historical ($3.22 per bushel) and elevated ($7.00 per bushel) mean and with returns on live weight gain that are consistent with the grain producer leasing pasturage or owning cattle. In the simulations at $3.22 per bushel, the best practices for specific forecast conditions varied with cattle-ownership conditions. However, the ENSO-phase system’s value distributions were comparable to that of the perfect forecast system; thus more-accurate regional precipitation forecasts may not lead to more forecast value at the farm level. In the simulations at $7.00 per bushel, even perfect categorical forecasts produced only minor profit effects, a result that is attributed here to an increased profit margin rather than to increased wheat value. Under both wheat-price conditions, however, the best no-forecast baseline practices are also shown to have value relative to an arbitrarily chosen management practice. Thus, following practices optimized to climatic conditions and current price and cost conditions might increase profits when no forecast information is available.

Corresponding author address: Steve Mauget, Plant Stress and Water Conservation Laboratory, Agricultural Research Service, U.S. Department of Agriculture, 3810 4th St., Lubbock, TX 79415. Email: steve.mauget@ars.usda.gov

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