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This study estimates the price determinants of show quality quarter horses sold at auction. Several characteristics including genetic and physical traits, quality of pedigree, and performance record of the horse, as well as the horse's offspring, were found to significantly impact selling price. Sale order positively affected price and appears to be driven by buyers rather than intentional ordering of the horses. A common practice at horse auctions is for the seller to reject the final bid offered and buy back the horse. Model-predicted prices for these buy-back horses indicate they are not undervalued by the final bids, based on their characteristics.

This research compares practical methods of forecasting basis, using current market information for wheat, soybeans, corn, and milo (grain sorghum) in Kansas. Though generally not statistically superior, an historical one-year average was optimal for corn, milo, and soybean harvest and post-harvest basis forecasts. A one-year average was also best for wheat post-harvest basis forecasts, whereas a five-year average was the best method for forecasting wheat harvest basis. Incorporating current market information, defined as basis deviation from historical average, improved the accuracy of post-harvest basis forecasts. A naive forecast incorporating current information was often the most accurate for post-harvest basis forecasts.

Can remotely sensed imagery improve hedonic land price models? A remotely sensed variable was added to a hedonic farmland value model as a proxy for land productivity. Land cover data were used to obtain urban and recreational effects as well. The urban and recreational effects were statistically significant but economically small. The remotely sensed productivity variable was statistically significant and economically large, indicating that knowing the "greenness" of the land increased the explanatory power of the hedonic price model. Thus, depending upon the cost of this information, including remotely sensed imagery in traditional hedonic land price models is economically beneficial.

This study simulates whether Kansas wheat, soybean, corn, and milo producers could have profitably used deferred futures plus historical basis cash price expectations for post-harvest unhedged and hedged grain storage decisions from 1985-97. The signaled storage decision is compared to a representative Kansas producer whose crop sales mimic average Kansas marketings each year. Using 23 grain price locations, the simulations resulted in an 11 cents per bushel annual increase in grain storage profits for wheat, 27 cents for soybeans, -17 cents for corn, and -20 cents for milo; however, storage profit differences varied substantially across locations. Hedging tended to decrease risk, but not impact profitability.

This study evaluates agricultural forecasting accuracy in an analysis of responses to the Annual Outlook Survey conducted by the American Agricultural Economics Association from 1983 through 1995. Representative extension and composite, production, and price forecasts for several commodities are constructed from the survey data. These forecasts are compared to each other and to U.S. Department of Agriculture (USDA) and futures-based forecasts. Relationships between forecast features and accuracy are examined. Generally, extension forecasts are more accurate than USDA forecasts for livestock series, but not more accurate for crops. Composite forecasts are often more accurate than either extension or USDA forecasts.

The forecasting accuracy of five competing na•ve and futures-based localized cash price forecasts is determined. The third-week's price each month from 1987-96 is forecasted from several vantage points. Commodities examine include those relevant to Midwest producers: the major grains, slaughter steers, slaughter hogs, several classes of feeder cattle, cull cows, and sows. Relative forecasting accuracy across forecast method is compared using regression models of forecast error. The traditional forecast method deferred futures plus historical basis has the greatest accuracy- even for cull cows. Adding complexity to forecasts, such as including regression models to capture nonlinear bases or biases in futures markets, does not improve accuracy.

Extension marketing economists commit substantial resources to outlook and market analysis. Producers demand this information and use it to make production and marketing decisions. This study analyzes responses to a marketing survey of producers and extension marketing economists to discern similarities and differences in their perceptions regarding market timing, futures market efficiency, and risk management. Producer and extension perceptions are consistent with regard to several marketing issues, although they are not always consistent with published research results. Both producers and extension economists disagree that producers will receive a lower average price by forwarding contracting, and many do not believe hedging reduces risk and lowers expected return. Extension marketing economists rate risk reduction as a less important goal of marketing strategies than do producers.

Three procedures are used to test Fama semistrong from efficiency of harvesttime price of Kansas City July wheat futures from 1947 through 1995. The three methods are (a) testing for jointly significant parameter estimates on nonfutures explanatory variables in econometric forecasting models, (b) testing the relative accuracy between model-based forecasts and using deferred futures prices as forecasts, and (c) testing for abnormal profits associated with simulated futures trading signaled by the forecasts. Kansas City July wheat futures are generally efficient. Furthermore, relative to the efficiency associated with forecasts constructed one to two months before harvest, the efficiency associated with the five- to six-month period before harvest has increased, especially since the early 1980s.

Cattle feeders appear irrational when they place cattle on feed when projected profit is negative. Long futures positions appear to offer superior returns to cattle feeding investment. Cattle feeder behavior suggests that they believe a downward bias in live cattle futures persists and that cattle feeders use different expectations than the live cattle futures market price when making placement decisions. This study examines feeder cattle placement determinants, comparing performance of expected hedgeable profit with past actual profit in explaining feeder cattle placements. Past actual profit is a more important placement determinant than expected profit based upon the live cattle futures market, even though hedgeable profit provides a superior forecast of future profit. In addition, potential deterrents to cattle feeders' use of futures as a substitute for cattle ownership are discussed.