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Evaluating Commodity Farm Program Selection and Economic Return Variability on Representative Farms in the Mississippi River Delta Region Using a Risk Return Framework

TL;DR: In this article, the authors evaluate the impact of the Agricultural Act of 2014 on agricultural producers in the Mississippi River delta region of the Mid-South and provide an estimate to the net present value of the cumulative net returns above variable costs to the producer for the five year life of the farm bill.
Abstract: The Agricultural Act of 2014, signed February 7, 2014, introduces a new era of federal support in the production of major agricultural commodities in the United States for the 2014 through 2018 crop years. The ultimate result of the Act was a 954-page piece of legislation that represented market-oriented policies such as the creation of an area-wide shallow loss revenue support program for covered commodities and a greater reliance on crop insurance products offered as a suite of risk management tools available to producers. The impact that this law has on agricultural producers in the Mississippi River delta region of the Mid-south is not yet fully known. Moving forward, the elimination of the direct payment program is likely to have an impact on farm income, as these payments were made annually and were decoupled from actual market prices. Various combinations of federal farm programs, chosen irrevocable, paired with multiple crop insurance products, that are purchased annually, will act to mitigate the risks of production. Simulation analysis provides a basis for evaluating the variability associated with production systems in the Mississippi River delta region. Three representative rice and soybean farms and six corn, cotton, and soybean farms were modeled as to determine the five year net returns resulting from price and yield risk as well as to evaluate alternative farm program and crop insurance selection. Financial performance of these farms is measured for varying levels of risk using a stochastic efficiency criteria. Results are presented for multiple combinations of the agriculture risk coverage and price loss coverage programs of the commodity title and revenue protection, supplemental coverage option endorsement, and the stacked income protection plan for producers of upland cotton contained in crop insurance title of the current farm law. For each farm at each location, an estimate to the net present value of the cumulative net returns above variable costs to the producer for the five year life of the farm bill is provided. Results from xi different farming operations suggest the preferred pairing of farm programs and crop insurance policies does vary across locale and crops.
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03 Jan 2007
TL;DR: In this paper, the authors focus on the conservation title of the 2008 Farm Bill, which has seven main conservation programs: the Conservation Reserve Program (CRP), the Wetlands Reserve Program, the Wildlife Habitat Incentives Program (WHIP), Environmental Quality Incentive Program (EQIP), the Conservation Stewardship Program (CSP), the Farmland Protection Program (FPP), and the Grassland Reserve Programs (GRP).
Abstract: There are fifteen titles in the Food, Conservation, and Energy Act of 2008 (known as the 2008 Farm Bill). Our focus is the conservation title, Title II, which has seven main conservation programs: the Conservation Reserve Program (CRP), the Wetlands Reserve Program (WRP), the Wildlife Habitat Incentives Program (WHIP), the Environmental Quality Incentives Program (EQIP), the Conservation Stewardship Program (CSP), the Farmland Protection Program (FPP), and the Grassland Reserve Program (GRP). All of these programs are continuations of programs from the 2002 Farm Bill or earlier. These conservation programs are of three basic policy types: land retirement programs that remove lands from agricultural production, working lands programs that encourage environmentally appropriate production practices, and agricultural preservation programs that focus on the nonagricultural socially desirable aspects of relatively low intensity agricultural land uses.

15Β citations

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TL;DR: The 2002 Farm Security and Rural Investment Act provides for three different types of payments to agricultural producers and landowners: direct, counter-cyclical and loan deficiency payments as mentioned in this paper. But these payments are not guaranteed.
Abstract: The 2002 Farm Security and Rural Investment Act provides for three different types of payments to agricultural producers and landowners: direct, counter-cyclical and loan deficiency payments. Eligiable recipients will have the opportunity to update their acreage bases and programs yields under a new set of rules.

14Β citations

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01 Jan 2015
TL;DR: In this article, the benefits of the new crop insurance offerings for cotton producers in the Texas High Plains were evaluated using simulation analysis and the results suggest that revenue protection combined with STAX is the optimal insurance selection for both risk neutral and risk averse producers.
Abstract: New crop insurance coverage offered by the 2014 Farm Bill will be available to cotton farmers beginning in 2015. Stacked Income Protection Plan (STAX) and Supplemental Coverage Option (SCO) are new crop insurance options, which are designed to protect farmers from shallow losses. STAX is only available for upland cotton producers, while SCO is available for all major farm program crops. The objective of this project is to assess the benefits of the new crop insurance offerings for cotton producers in the Texas High Plains. Representative dry land and mixed, irrigated and dry land farms were developed using consensus evaluations of panels of producers in two distinct areas of the High Plains. Our simulation analysis examined producer welfare benefits of alternative combinations of underlying yield or revenue insurance coverage and STAX or SCO. The results suggest that Revenue Protection combined with STAX is the optimal insurance selection for both risk neutral and risk averse producers.

5Β citations

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TL;DR: In this article, the authors provided an overall evaluation by commodity considering both liquidity and equity measures, and provided a discussion of the overall evaluation of crop farms by commodity, considering both liquidation probability and equity probability.
Abstract: Under the January 2007 Baseline, 20 of the 64 crop farms are considered in good liquidity condition (less than a 25 percent chance of negative ending cash in 2012). Five crop farms have between a 25 percent and a 50 percent likelihood of negative ending cash. The remaining 39 crop farms have greater than a 50 percent chance of negative ending cash. Additionally, 30 of the 64 crop farms are considered in good equity position (less than a 25 percent chance of decreasing real net worth during the study period). Nine crop farms have between a 25 percent and 50 percent likelihood of losing real net worth, and 25 crop farms have greater than a 50 percent probability of decreasing real net worth. The following discussion provides an overall evaluation by commodity considering both liquidity and equity measures.

2Β citations

References
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TL;DR: In this article, the authors present a procedure for decision analysis with preferences unknown, based on the revision of probabilities of the probability of a product's future performance and the utility of the utility.
Abstract: Contents : Introduction to Decision Analysis; Probablility; Revision of Probabilities; Utility; Procedures for Decision Analysis; Production under Risk; Whole-Farm Planning under Risk; Investment Appraisal; Decision Analysis with Preferences Unknown; Appendix; Author Index; Subject Index

1,016Β citations

Posted Contentβ€’
TL;DR: In this paper, a method of stochastic dominance analysis with respect to a function (SDRF) is described and illustrated, which can be applied for conforming utility functions with risk attitudes defined by corresponding ranges of absolute, relative or partial risk aversion coefficients.
Abstract: A method of stochastic dominance analysis with respect to a function (SDRF) is described and illustrated. The method, called stochastic efficiency with respect to a function (SERF), orders a set of risky alternatives in terms of certainty equivalents for a specified range of attitudes to risk. It can be applied for conforming utility functions with risk attitudes defined by corresponding ranges of absolute, relative or partial risk aversion coefficients. Unlike conventional SDRF, SERF involves comparing each alternative with all the other alternatives simultaneously, not pairwise, and hence can produce a smaller efficient set than that found by simple pairwise SDRF over the same range of risk attitudes. Moreover, the method can be implemented in a simple spreadsheet with no special software needed.

297Β citations

Journal Articleβ€’DOIβ€’
TL;DR: The Texas Risk Management Education Program (TRMEP) as mentioned in this paper was created by the Texas Agricultural Extension Service (TEES) to train risk management experts for assessing alternative farm management strategies.
Abstract: Simulation as an analytical tool continues to gain popularity in industry, government, and academics. For agricultural economists, the popularity is driven by an increased interest in risk management tools and decision aids on the part of farmers, agribusinesses, and policy makers. Much of the recent interest in risk analysis in agriculture comes from changes in the farm program that ushered in an era of increased uncertainty. With increased planting flexibility and an abundance of insurance and marketing alternatives farmers face the daunting task of sorting out many options in managing the increased risk they face. Like farmers, decision makers throughout the food and fiber industry are seeking ways to understand and manage the increasingly uncertain environment in which they operate. The unique abilities of simulation as a tool in evaluating and presenting risky alternatives together with an expected increase in commodity price risk, as projected by Ray, et al., will likely accelerate the interest in simulation for years to come. Increased interest in risk management tools for assessing alternative farm management strategies led to the creation of the Texas Risk Management Education Program (TRMEP) by the Texas Agricultural Extension Service. The risk management specialists with TRMEP help

272Β citations


"Evaluating Commodity Farm Program S..." refers background in this paper

  • ...Multivariate empirical distribution has the flexibility to impose the historical variability on any assumed mean value and accounts for the interrelationships occurring in the data (Richardson, Klose, and Gray, 2000; Flanders, 2008)....

    [...]

Journal Articleβ€’DOIβ€’
Jack Meyer1β€’

262Β citations


"Evaluating Commodity Farm Program S..." refers background in this paper

  • ...(32) π‘Ÿπ‘Ÿ π‘Žπ‘Ž(𝑀𝑀) = βˆ’π‘’π‘’β€²β€²(𝑀𝑀) 𝑒𝑒′(𝑀𝑀) Following Meyer, (1977) representation of a decision marker’s preferences, can be interpreted as a measure of that decision maker’s absolute aversion to risk....

    [...]

  • ...(32) rr aa(ww) = βˆ’uuβ€²β€²(ww) uuβ€²(ww) Following Meyer, (1977) representation of a decision marker’s preferences, can be interpreted as...

    [...]

  • ...(30) 𝐢𝐢 �𝑀𝑀𝑖𝑖𝑖𝑖� = 𝐺𝐺𝐺𝐺𝑁𝑁𝑁𝑁𝐺𝐺𝑖𝑖𝑖𝑖 + 𝐺𝐺𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 Following research by Hardaker, et al., (2004) this research will examine different levels of alternative farm program choices paired with and without crop insurance products that will compare uncertain outcomes, so values of w are stochastic....

    [...]

Journal Articleβ€’DOIβ€’
TL;DR: In this paper, a method of stochastic dominance analysis with respect to a function (SDRF) is described and illustrated, which can be applied for conforming utility functions with risk attitudes defined by corresponding ranges of absolute, relative or partial risk aversion coefficients.
Abstract: A method of stochastic dominance analysis with respect to a function (SDRF) is described and illustrated. The method, called stochastic efficiency with respect to a function (SERF), orders a set of risky alternatives in terms of certainty equivalents for a specified range of attitudes to risk. It can be applied for conforming utility functions with risk attitudes defined by corresponding ranges of absolute, relative or partial risk aversion coefficients. Unlike conventional SDRF, SERF involves comparing each alternative with all the other alternatives simultaneously, not pairwise, and hence can produce a smaller efficient set than that found by simple pairwise SDRF over the same range of risk attitudes. Moreover, the method can be implemented in a simple spreadsheet with no special software needed.

250Β citations