The study consists of an economic analysis of the behaviour of the Chicago wheat basis during the two decades from 1960 to 1980. First, there is a descriptive analysis of changes to the basis between the 1960's and 1970's. Second, an econometric model which can be used to explain observed and possible future movements in the basis is estimated.

The econometric model provides for the direct estimation of both intra- and inter-seasonal movements of the basis. In developing this model a single-equation “quasi reduced-form” model incorporating the theoretically important factors determining the supply and demand for storage and the resulting price from month to month is specified.

Model estimation involves the incorporation of a lagged dependent variable and a set of seasonal dummy variables to account for the cyclical behaviour of the basis. The Generalized Least Squares regression results reveal that the basis lagged one period and the level of Chicago wheat stocks are the main factors explaining the current basis. A negative coefficient on national commercial stocks may be an indication that the variable is a proxy for hard wheat stocks. Changes in these stocks result in opposite movements in the demand for soft wheat (Chicago) stocks and hence the Chicago (soft wheat) basis.

While the estimated model “explains” a relatively high proportion of overall basis variation, it is not entirely satisfactory in predicting the direction of change in the basis. It also exhibits some downward bias when used to make forward predictions of the magnitude of the basis.

Mary of the modelling problems encountered during the analysis probably stem from incorporating intra- and inter-seasonal basis movements into the one model. A more productive approach to an analysis of this type would be to concentrate on estimating and predicting only one type of basis movement rather than both.

A five year linear programming farm planning model was constructed to include the use of sewage sludge as a primary crop nutrient source. The five year planning period permitted the inclusion of nitrogen decay rates reflecting the residual amounts of nitrogen from sludge and manure which are mineralized over time. Net farm incomes were maximized and compared for a medium sized dairy farm representative of those found in Pennsylvania. Twenty-two scenarios depicting various operating conditions were examined. Some of the variations included the purchase of sludge at different prices, the use of free nine percent nitrogen or 4.2 percent nitrogen sludge, spring and fall application of sludge and manure, the use of free sludge when heifer corral manure was sold, and sludge use under various nitrogen application restrictions. Variables of interest in the optimal programs were than compared and discussed.

Under the conditions assumed for the analyses, net revenues were higher when sludge was used than when it was not. However, only a small percentage difference between the highest and the lowest net revenues resulted over the five year period, given a variety of operating circumstances. The combination of nine percent nitrogen sludge, manure, and fertilizer yielded the highest net revenue when sludge was free and applied either annually in the spring or semiannually in the spring and fall. Further, when sludge was purchased at $10.00/dry ton and $20.00/dry ton for spring and fall application, the combination of nine percent nitrogen sludge and manure again resulted in the highest net revenue.

The nitrogen application restrictions were at their upper limits when sludge use was included in the optimal solution, but were not binding when sludge was not used. More sludge was applied when the nitrogen application restrictions were relaxed, resulting in higher net income. The reduced costs associated with the non-negativity constraint on sludge indicated that it would often be more costly to use more than the optimal amount of sludge, than less than the optimal amount, because less manure would be used and more fertilizer would have to be purchased to maintain nitrogen application limits and meet crop nutrient requirements.

The calculation of net present values for the two sludge contents considered provided value estimates which were in accord with the linear programming solutions. Although the programming results are situation-specific, the model's structure and coefficients may be modified to accommodate different farm activities and restrictions.

Three measures of agricultural research spillovers considered entail estimates based on (1) level of agricultural research expenditures, (2) marginal products of agricultural research and extension expenditures, and (3) marginal products of agricultural research and extension expenditures weighted by the level of agricultural research expenditures.

The methods produce diverse spillovers but results indicate that spillovers generally exceed regional benefits attributable to a region's investment in agricultural research and extension. Also, ratios of spillovers to regional benefits exceed ratios of federal to state expenditures on agricultural research suggesting potential social benefits from additional financial support of agricultural research by the federal government.