This article considers the political, economic, and environmental factors associated with the allocation of federal LISA (Low Input/Sustainable Agriculture) funds among states. A tobit model is estimated with LISA allocations as the dependent variable. Results indicate that pressure groups are important. LISA funding depends positively on membership in environmental organizations, the number of farms, and the size of the rural-nonfarm population, while it depends negatively on the size of the urban population. States with host LISA institutions receive significantly more funding, as do states with Senators in leadership positions on key congressional agricultural committees.

Many states have crop insurance extension programs. This activity has been stimulated with the availability of funds to support extension from the Risk Management Agency (RMA) of the U.S. Department of Agriculture. Several new RMA programs were established under the Agricultural Risk Protection Act of 2000. The Targeted States Program is quite important for the Northeast. This program serves states that were traditionally underserved by crop insurance. It is available in all 12 northeastern states, plus Nevada, Utah, and Wyoming. The Targeted States Program funds one program in each state. A total of $4.5 million in funding is available each year. Amounts for state programs range from $157,000 in Rhode Island to $754,000 in Pennsylvania, which is a considerable amount of funding.

Social desirability (SD) represents the problem of subjects responding with social norms rather than individual values. This paper briefly surveys the SD literature and considers its relevance for contingent valuation (CV) studies. In an empirical study, undergraduate students were administered the Mariowe-Crowne Social Desirability Scale, as well as CV questions. High SD scores were hypothesized to imply a greater likelihood of offering a protest reason for a zero bid and to increase bids for socially desirable commodities. While all hypotheses were not supported, the empirical results suggest that SD can influence CV responses and should not be dismissed prematurely.

Various methods have been used to estimate risk indices with historical data. An industry perception of increasing milk price risk over time provides a standard for evaluating several techniques used to measure historical risk. Risk measures from a regression model and an ARIMA model were consistent with the perception of increasing risk.

A financial training program designed by Cooperative Extension specialists was provided to over 2,000 USDA/FSA borrowers from the Northeast during the period 1994–1999. Key to the success of the workshops was an in-depth, user-friendly curriculum that evolved over time, eventually replacing satellite-feed instruction with pre-taped videos. Cluster analysis classified nearly 70% of workshop participants as “Low Finance Priority” or “Low Finance Knowledge.” Farmers in these clusters received a relatively greater educational benefit from the program than those not in these clusters. Impact analysis indicated that perceived annual gain in farm net worth from application of workshop tools ranged from approximately $5,000 to $10,000. The training addressed the needs of producers typically isolated from Cooperative Extension because the workshop was the only extension program attended that year by nearly two-thirds of them.

Numerous studies have investigated how farmers should use forward pricing markets, but only limited research exists on how farmers actually use these markets. This study relies on data from a real-time forward pricing game employed by Maryland grain marketing clubs from 1994 through 1998. Hypotheses are tested regarding the consistency of farmer behavior with the research literature on hedging. Findings indicate that farmers do not achieve price enhancement, a result consistent with the efficient market hypothesis. However, pricing behavior does not conform to the implications of efficient market models in a number of respects, suggesting farmers may form different expectations than those conveyed by forward prices.

The importance of timeliness is investigated in the selection of machinery complements for double-crop wheat and soybean production in the southeastern coastal plain. An intertemporal stochastic simulation model was developed to generate probability distributions that were evaluated with stochastic dominance analysis. This research investigated the importance of intertemporal production linkages and inadequate soil moisture on machinery selection. Failure to include these dimensions can result in erroneous machinery choices.

Incorporation of futures markets into the theory of the firm under uncertainty has received considerable attention in risk management. A theoretical model of optimal firm decisions in cash and futures markets considering price, production, and financial risks is presented. Production and marketing strategies for corn and soybeans in Georgia and Illinois are analyzed to determine the optimal amount of futures contracting which may be a hedge or a speculative position. A partial hedge is optimal for most situations for risk averse producers when the amount hedged is variable. With fixed quantity transactions, speculative and cash positions, but not hedging, tend to be E-V efficient.

A model with omitted resource constraints is suggested as an alternative to a risk aversion model for explaining economic behavior. This paper uses two standard mathematical programming models to further explore this issue. One model is a standard profit maximization linear programming model and the other is a risk averse quadratic programming model with part of the constraints deleted. Theoretical investigation of these models demonstrates that risk aversion can substitute for omitted resource constraints. A small empirical model is then solved under both formulations. With resource constraints deleted, positive risk aversion is necessary to obtain a similar enterprise organization as under profit maximization with complete constraints. These two solutions are then interpreted with the theoretical optimality conditions.

Use of debt in financing agricultural firms is an issue of perennial interest. Much of this interest reflects farmers’ disastrous experience with debt during the Great Depression. The foreclosed mortgages and bankruptcies of that era reaffirmed an historical feeling that achieving a level of zero debt or financial leverage was a high priority goal. E. G. Johnson, who was Chief of the Economic and Credit Research Division of the Farm Credit Administration, articulated the position in the 1940 Yearbook of Agriculture that this goal is even more important than increasing profits: “It may be well to emphasize again that while credit properly used may help farmers to increase their income and raise their standard of living, the fact must not be overlooked that more credit will not cure all the ills of agriculture. The greatest need is to assist the farmers in getting out of debt, not deeper into it,” [6, p. 754]. As memories of the Great Depression faded, agricultural economists tended to emphasize the effect of debt on farm size and therefore net income.

In analyzing the impact of recent energy price increases on agriculture, agricultural economists have suggested the possibility of substitution of labor for farm machinery inputs [3, pp. 881-833] [17, pp. 195-196]. Since large energy input is embodied in farm machinery [14, p. 195], energy-price increases not only raised costs of machinery fuel, but also provided a cost-push effect on other fixed and variable machinery cost components. However, these potential price incentives have not been sufficient to reverse aggregate historical trends towards larger equipment in current machinery purchases [11, 15]. Understanding the nature of recent shifts in optimum machinery size on different farm sizes is important for consideration of future farm size and labor-capital structure of agriculture.

During the past six years, state-local government finance ranged from a situation of fiscal crisis in 1970, to a boom in 1972-73, and back to a crisis in 1974-75. This was far worse than any since the Great Depression. During the recession of 1970-71, rising state and local tax revenues and federal grants did not keep pace with increases in expenditures resulting primarily from inflation [21, p. 500]. More favorable conditions in 1972-73, though, saw researchers projecting large future surpluses for the remainder of the decade and calling for reductions in tax rates and increases in expenditures [11, pp. 364-366].

Rapid technological change has been a dominating factor in American agriculture in the last quarter of a century. Impact of this factor on the farm real estate market is well documented in agricultural economics literature. As Heady and Fuller and Van Vuuren have noted, technological change has been both land-substituting and land-embodying. This characteristic has resulted in positive pressure on land prices as farmers bid up prices of land for farm enlargement so they can more fully employ their fixed labor and capital resources. As adjustments in farm numbers and sizes have slowed in recent years, the dominating influence of farm consolidation is expected to be moderated. Fuller and Van Vuuren noted this possibility as: “… given the combination of numbers, ages, and alternatives, intensive efforts to salvage underemployment of operator and family labor by farm enlargement or by substantial employment on other farms have to be approaching an ultimate plateau”. The objective of this paper is to examine impact on farm real estate prices of changing economic conditions in rural areas. Particular attention is given to factors other than farm consolidation. The effect of these structural changes on the land market is empirically tested by reestimating Tweeten and Nelson's model with Georgia data for 1960-1974.

The adoption of multiphase electric power for electric irrigation has been limited in an area characterized by extremely rapid expansion of irrigated acreage despite production cost advantages. Theoretical and empirical evidence of failure in the existing market for multiphase power development are presented. Alternative development mechanisms are presented and discussed.

Achieving optimal use of wildlife resources is a classic problem in natural resource economics. Ciriacy-Wantrup argued that wildlife resources are a case of a fugitive resource for which private property rights cannot easily be defined and therefore government policy may be necessary to obtain optimal use [5, pp. 141–145]. Land use planning is one area of government policy in which limited attention has been given to management of wildlife resources. One reason for the neglect may be the lack of emphasis on estimating the value of wildlife recreation experiences associated with particular land uses. An exception is the work of Martin, Tinney, and Gum [16] who did not look at marginal land use changes but did consider the termination of all cattle ranching in Arizona and its effect on recreational and agricultural economic surpluses.

Agricultural economists have long recognized pest populations as common property resources, and, as such, pest control through chemical pesticide application involves a tradeoff between increased crop yields and reduced environmental quality (Carlson; Regev et al.). Integrated pest management (IPM) attempts to minimize this tradeoff by substituting pest information and management skills for chemical pesticides. In part, IPM involves monitoring pest populations in order to utilize beneficial biological interactions. Weather patterns, stage of crop growth, and natural biological enemies of pests are among the factors included in IPM. In addition, entomologists have extended the integrated control concept to include selective rather than nonselective pesticide application that is applied only when pest populations exceed the “economic threshold” level (Hall and Norgaard). In an earlier economic analysis of IPM, Hall concluded that the major advantages of IPM are: (1) a substantial reduction in overall pesticide use, (2) no significant reduction in profits, (3) no significant loss of yields, (4) an overall reduction in pest management costs, and (5) a reduction in risk for the producers.

Since the mid-1960s, agricultural economists have given much attention to research on farm-firm growth. One procedure used in this type of research has been multiperiod linear programming models, e.g. [1, 6, 9, 11, 14, 17]. Several researchers using the multiperiod linear programming framework have compared optimizing criteria. Generally, comparisons were made between maximizing some type of present value criterion and maximizing net worth at the end of the planning horizon. The different assumptions associated with the two maximizing criteria have resulted in different optimal growth patterns.

Economies of size for farm firms in the United States are a traditional interest of agricultural economists (Heady). Continued interest in this topic is related to the implication of economies of size for the size structure of farm firms. The structure issue has the potential to affect not only current farm firms but also agricultural marketing firms, rural communities, and consumers of agricultural commodities (Krause and Kyle). In the past, the relationship between economies of size and farm firm growth was the basis for research. More recently, the relationship of economies of size to public policy issues has gained attention (Bardnam, Hall and LeVeen, Seckler and Young).