PRECONDITIONING BEEF CALVES: ARE EXPECTED PREMIUMS SUFFICIENT TO JUSTIFY THE PRACTICE?

The concept of preconditioning calves has been around for a long time, yet adoption of the practice has been slow. Current trends in the beef industry likely will increase interest in preconditioning programs. This research estimates premiums received for preconditioned calves and the expected returns from a preconditioning program. Preconditioned calves sold in the fall received a premium of approximately $4.50-$5.50/cwt relative to non-preconditioned calves. Premiums were lower for calves sold in the winter, heavier calves, and when cattle markets were strong. Based on a 45-day post-weaning preconditioning program, cow-calf producers can increase returns about $14/head compared to selling calves at weaning.Livestock Production/Industries,

FORMULA-DERIVED VERSUS OBSERVED MARKET PRICES: AN APPLICATION FOR SEGREGATED EARLY WEANED PIGS

A formula (the "K-State formula") for deriving the price of segregated early weaned (SEW) pigs using corn, soybean meal, and market hog prices was estimated based on equating return on investment for the different phases of swine production farrow, nursery, and finish. USDA reported SEW pig prices were compared with prices derived from the K-State formula and several other common formulas. Based on root mean squared error and mean absolute error accuracy measures, the K-State formula did a better job of predicting spot-market prices than the other formulas. In terms of the K-State formula accurately predicting spot market prices, producers appear to form price expectations based on futures plus expected basis more so than simply futures prices or current cash prices. However, the manner in which the formula is used (i.e., method of choosing price expectations) will depend on the risk attitudes of the buyer and seller as well as the nature of their business relationship. Developing pricing formulas based on the framework outlined here (equal returns on investment) has merit for establishing prices in the absence of publicly reported information, however, it is important that users of the formula understand the conceptual framework of how and why it was developed.Livestock Production/Industries, Marketing,

DETERMINANTS OF FEEDER CATTLE PRICE-WEIGHT SLIDES

Feeder cattle price-weight slides are analyzed using transactions data on 46,123 pens of feeder cattle over a 10-year period. Fed cattle futures prices and corn prices are important determinants of price-weight slides. Cattle producers can use this information when making sell timing decision, purchase decisions, and managing production.Livestock Production/Industries,

POST-HARVEST GRAIN STORING AND HEDGING WITH EFFICIENT FUTURES

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.Agribusiness,

Differences among high, medium, and low profit dairy operations: an analysis of 2004-2008 Kansas Farm Management Association Dairy Enterprises

Dairy Research, 2009 is known as Dairy Day, 2009The financial bottom line, or net income, is a key factor in determining how successful a dairy has been historically as well as an indicator of the financial ease or struggles the dairy might have in the future. What causes net income to vary from one operation to another is a key question for dairy farmers. For example, does milk price received, feed cost, total cost, or milk production have the greatest impact on net return variability? In this study, we evaluated Kansas Farm Management Dairy Enterprise data from the past 5 years to determine correlation of revenue, production, and cost factors among groups of high, medium, and low profit dairy operations. High-profit producers had larger operations, had slightly greater total costs ($62.63 per cow), and received slightly lower milk prices ($0.56/100 lb of milk) compared with low-profit producers. In contrast, the high profit group produced significantly more milk per cow. Milk price received and cost per cow did not affect profit nearly as much as total milk produced per cow. This study was conducted with data reported by small to midsize dairy herds. Further research should examine whether these results hold true for large herds

The Effect of USDA Cattle on Feed Reports on Feeder Cattle Futures Prices

Livestock Production/Industries, Marketing,

FACTORS AFFECTING LIVE CATTLE BASIS

Cattle producers and beef packers need to understand basis determinants as they develop price expectations and make pricing, hedging, and forward contracting decisions. This study empirically estimated factors explaining variability in monthly fed cattle basis. The five main results regarding live cattle basis are 1) corn price is an important determinant, 2) a change in the value of the Choice-to-Select spread positively affects basis, 3) changes in the levels of captive supplies have no significant statistical or economic impact on basis 4) the June 1995 live cattle futures contract did not impact basis, and 5) both market fundamentals and seasonal components are important basis determinants.basis, fed cattle, cattle prices, Livestock Production/Industries, Marketing,

Could Packers Manipulate Cash Markets by Linking Contract and Futures Prices?

Marketing,

LIVESTOCK BASIS FORECASTS: HOW BENEFICIAL IS THE INCLUSION OF CURRENT INFORMATION?

Successful risk management strategies for agribusiness firms are contingent on the ability to accurately forecast basis. There has been substantial research on the actual use of basis forecasts, yet little research has been conducted on actually forecasting basis. This study evaluates the effect incorporating current basis information into a historical-average-based-forecast has on forecasting accuracy when forecasting live cattle and feeder cattle basis. Furthermore, the optimal weight to place on this current information is evaluated in an out-of-sample framework. Root mean squared errors are generated for both commodities and evaluated to determine the significance of these issues. Results suggest that livestock basis forecasters should consider incorporating a proportion of the difference in current basis and the historical average of the current week when making their projections. The optimal amount of current information to include declines as the time interval between the week the forecast is being made and the week being forecasted increases.livestock prices, hedging, basis forecasts, current information, Livestock Production/Industries, Marketing,

INCORPORATING CURRENT INFORMATION INTO HISTORICAL-AVERAGE-BASED FORECASTS TO IMPROVE CROP PRICE BASIS FORECASTS

Being able to accurately predict basis is critical for making marketing and management decisions. Basis forecasts can be used along with futures prices to provide cash price projections. Additionally, basis forecasts are needed to evaluate hedging opportunities. Many studies have examined factors affecting basis but few have explicitly examined the ability to forecast basis. Studies have shown basis forecasts based on simple historical averages compare favorably with more complex forecasting models. However, these studies typically have considered only a 3-year historical average for forecasting basis. This research compares practical methods of forecasting basis for wheat, soybeans, corn, and milo (grain sorghum) in Kansas. Across most of the multiple-year forecast methods considered, absolute basis forecast errors were slightly higher for the harvest forecasts than the post-harvest forecasts. Using an historical 3-year average to forecast basis for wheat and soybeans was optimal as compared to other multiple-year forecasts. For corn and milo, a 2-year average was the optimal multiple-year forecast method. Incorporating current market information, such as current nearby basis deviation from an historical average, into a harvest basis forecast improves accuracy for only the 4 weeks ahead of harvest vantage point, but improves the accuracy of post-harvest basis forecasts (24 weeks after harvest) from nearly all vantage points considered.Marketing,