The value of El Niñ o-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 ENSOphase 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.

Remotely sensed crop reflectance data can be used to simulate crop growth using within-season calibration. A model based on GRAMI, previously modified to simulate cotton (Gossypium hirsutum L.) growth, was revised and tested to simulate leaf area development and to estimate lint yield of water-stressed cotton. To verify the model, cotton field data, such as leaf area index (LAI), lint yield, and remotely sensed vegetation indices (VI), were obtained from an experimental field treated with various irrigation levels at the Plant Stress and Water Conservation Laboratory at Lubbock, Texas from 2002 to 2004. The model was validated using field data obtained separately from verification data at the same location in 2005. A hand-held multispectral radiometer with 16 spectral bands was used to measure reflectance. Five VI designs of interest were evaluated and used as input values for within-season calibration of the model. Simulated VI and LAI were in agreement with the measured VI and LAI, with r 2 values from 0.96 to 0.97 and RMSE values from 0.02 to 0.24 in validation. Simulated lint yields were in agreement with measured lint yields, with r 2 values from 0.63 to 0.67 and RMSE values from 28.3 to 100.0. The model was not very sensitive to the higher irrigation treatments in reproducing lint yield. We believe that validation with more data sets can deal with this matter. The VI worked equally well in reproducing measured cotton growth when they were used for within-season calibration. The results of this calibration scheme suggest that remote sensing data could be used to adjust modeled cotton growth for various water-stressed conditions.

A time series analysis method based on the calculation of Mann-Whitney U statistics is described. This method samples data rankings over running time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-Carlo generated null parameters. Based on the Z statistics' magnitudes this algorithm can identify time windows containing significant incidences of low or high data rankings, where the window length is determined by the sample size. By repeating this process with sampling windows of varying duration ranking regimes of arbitrary onset and duration can be objectively identified in a time series. The simplicity of the procedure's output -a time series' most significant nonoverlapping ranking sequences -makes it possible to graphically identify common temporal breakpoints and patterns of variability in the analyses of multiple time series. This approach is demonstrated using United States annual temperature data during 1896-2008.

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