Eddy L. LaDue

Dairy Farm Management and Long-Term Farm Financial Performance

The financial performance and relationships between several management factors and financial performance are examined in a panel of 107 New York dairy farms. A panel regression model with fixed effects is estimated in an effort to identify management factors that influence profitability. The model is estimated with two-stage least squares to account for endogenous farm size and debt use variables. Production management factors such as farm size, rate of milk production, and milking system had a positive impact on farm profitability. Financial management variables for the type of accounting system used and the debt use were also significantly related to profitability. Unlike the findings of many other studies, measures of human capital did not have a statistically significant impact on profitability.

Financial management practices and farm profitability

The adoption of several basic financial management practices is examined for a group of New York dairy farms. The study provides estimates of the extent to which various business analysis and control, investment analysis and decision making, and capital acquisition practices have been adopted. Many practices, such as net present value analysis, are not widely adopted by farmers. The relationship between the adoption of financial management practices and farm profitability is also examined. Results suggest that the adoption of financial management practices, such as using investment analysis techniques, significantly impacts farm financial performance.

Credit risk migration and downgrades experienced by agricultural lenders

Agricultural credit risk migration is examined using loan records gathered from four agricultural lenders. Results indicate that lender risk ratings are much more stable than ratings based on credit scores estimated from financial statements, highlighting the importance played by nonfinancial factors such as management capacity, character, and collateral in assessing credit risk. Additionally, the borrower’s risk tier, personal characteristics, and the stage of the business life cycle provide useful information in predicting credit quality downgrades, while the primary agricultural enterprise does not impact the likelihood of a downgrade.

A stochastic simulation model for evaluating forage machinery performance

Abstract The selection of machinery on a dairy farm is the focus of the study. A simulation model is constructed that evaluates alternative forage machinery complements on New York State dairy farms. Attention is focussed on the machinery use, forage crop production and concentrate purchase. The key measure of performance is the total cost of acquiring feed for the dairy herd. The total cost includes the machinery investment and operating costs and the cost of feeds required to supplement that produced on the farm. The simulation model, written in Fortran, utilises a daily timestep. Each day the feasibility of machinery operation is determined, machinery operations are scheduled and the end of day status is determined.

The Costs and Returns of Agricultural Credit Delivery

Borrower-level data from 963 agricultural lending relationships are used to examine how several factors influence the costs and returns of extending agricultural credit. The results provide estimates of the costs and returns of agricultural lending and the extent to which these costs and returns are influenced by factors such as loan volume, lender/borrower relationship factors, and contract terms. The findings indicate that economies of size exist in agricultural credit delivery and that lenders pass most of these benefits on to borrowers through lower interest rates. In addition, the impacts of lender/borrower relationship factors were relatively small.

Application of Recursive Partitioning to Agricultural Credit Scoring

Recursive Partitioning Algorithm (RPA) is introduced as a technique for credit scoring analysis, which allows direct incorporation of misclassification costs. This study corroborates nonagricultural credit studies, which indicate that RPA outperforms logistic regression based on within-sample observations. However, validation based on more appropriate out-of-sample observations indicates that logistic regression is superior under some conditions. Incorporation of misclassification costs can influence the creditworthiness decision.

Floating versus Fixed-Rate Loans in Agriculture: Effects on Borrowers, Lenders, and the Agriculture Sector

Deregulation, changes in monetary policy, and rapidly fluctuating inflation rates have significantly altered the financial market environment in which agricultural lenders and borrowers must function. Market interest rates have become highly variable. Rural lenders are no longer insulated from these market forces. The unexpected fluctuation in interest rates, particularly the large and rapid rise in rates that occurred during 1980-81, inflicted losses on fixed rate lenders, encouraging them to look for ways to modify their interest rate exposure. The method chosen by many lenders was a shift to floating or variable interest rates.

Accounting for loan amount and credit rating when calculating lifetime value of agricultural lending relationships

Using empirical default probabilities and profitability distributions, a simulation model is developed to identify the long‐term value of relationships among differing credit rating and loan amount groups. According to the results generated from a set of lending relationships, agricultural lenders are pricing low and moderate credit rating customers such that similar long‐term values are found among the groups. Also, large loan amount relationships generate more dollars of lifetime value. The large relationships, however, earn fewer dollars of lifetime value per dollar of loan amount among risk peers. Implications are also drawn for the retention rates of existing customers.

Impact of Alternative Remote Access Computer Systems on Extension Programs

A question of concern to extension workers is whether use of computer programs via a remote access delivery system significantly improves extension education programs; and, if so, whether the benefits to extension programs are worth their cost. Relative costs are particularly important in light of the findings of Candler, Boehlje, and Saathoff that extension software must meet higher clarity, speed, and reliability standards than corresponding research software. They contend that while the researcher is normally able and willing to work with the computer and computer programs until useful results are generated, the extension worker wants to use the computer without unrealistic solutions, software malfunction, or hardware breakdown. This note presents the results of a study designed to assess the cost and value of remote access computer systems in an extension farm management program. Three different delivery technologiesmail-in, touch-tone phone, printing terminal-are compared, and the impact of remote access use on certain characteristics of extension programs is assessed.

The Role of Credit Evaluation in Agricultural Finance: Dicussion

Although Chhikara pointed out some of the severe limitations of the linear probability model, he was very kind to discriminant analysis models. The linear discriminant model is appropriate only if the explanatory variables are normally distributed. A high proportion of the financial ratios and other variables used in credit evaluation are not normally distributed. For example, most solvency ratios, such as percent equity, have a high proportion of the observations near one end of the range, a long tail in one direction, and a very short tail or no tail in the other direction. Given the non-normality of the distributions of many of the explanatory variables, discriminant analysis is clearly not an appropriate choice for most analyses. Continued use of discriminant analysis was justifiable when there were no good alternatives. But the development of a wide variety of logit and probit models, and the increase in computing power that makes maximum likelihood estimation of these models technically and financially feasible, have provided good alternatives. Future use of discriminant analysis in credit evaluation should be limited to situations where the authors clearly demonstrate that the explanatory variables have normal or near normal distributions. Further, tests of the performance of newly developed models should not use discriminant analysis results as the basis of comparison. Another important characteristic of the current state of the art is the pathetic level of understanding of misclassification costs. All of the models reviewed are explicitly or implicitly based on a minimization of misclassification costs. Clearly, the misclassification costs are not the same for all types of decisions. The costs of making a loan to a borrower who ultimately defaults are much different in both character and magnitude than the costs of not making a loan to a farmer who would be a good borrower. However, most of the models conducted to date, particularly those dealing with agricultural loans, have either ignored or assumed away the differences in misclassification costs. While this is