Submitted to: Meeting Abstract
Publication Type: Abstract only
Publication Acceptance Date: 3/1/2009
Publication Date: 3/27/2009
Citation: Mauget, S.A., Zhang, X.J., Ko, J. 2009. The value of ENSO forecast information to dual purpose winter wheat production in the US Southern High Plains[abstract]. Climate Prediction Science Application Workshop. March 24-27, 2009. Norman, Oklahoma. p. 37. Interpretive Summary:
Technical 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, our approach 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 bu-1) and elevated ($7.00 bu-1) mean, and with returns on live weight gain consistent with the grain producer leasing pasturage or owning cattle. In the $3.22 bu-1simulations 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 $7.00 bu-1 simulations even perfect categorical forecasts produced only minor profit effects, which is attributed here to increased profit margins rather than increased wheat value. But under both wheat price conditions the best no-forecast baseline practices are also shown to have value relative to an arbitrarily chosen management practice. Thus following practices optimized to climatology and current price and cost conditions might increase profits when no forecast information is available.