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Basis risk – the risk that payoffs of a hedging instrument do not correspond to the underlying exposures – is cited as a primary concern for implementing weather data, we investigate several dimensions of weather basis risk in the U.S. corn market. Results suggest that while geographic basis risk can be significant, it should not preclude the use of geographic cross‐hedging, particularly with temperature as opposed to precipitation derivatives. Risk reduction is appreciable and the degree to which geographic basis risk impedes effective hedging diminishes as spatial aggregation in the risk exposure and hedging instrument increases.

The purpose of this paper is to investigate changes proposed in 2012 to commodity programs for the new Farm Bill. Both the Senate and House Agriculture Committee versions of the new Farm Bill eliminate current commodity programs including direct payments, create new revenue‐based commodity program options designed to cover “shallow” revenue losses, and also introduce supplemental crop insurance coverage for shallow revenue losses.

This paper documents the payment functions for the new revenue programs proposed in both the Senate and House Ag Committee Farm Bills, and also estimates expected payments for each using a model based on historical county yield data, farmer‐level risk rates from RMA, and commodity price levels from the March 2012 CBO baseline projections.

The authors find significant variation in expected per acre payment across programs, crops, and regions. In general, the Senate's bill would be expected to be preferred over the House's bill for corn and soybean producers, particularly those in the Midwest. Also, the RLC program in the House's Bill typically would be projected to pay much less than the Senate's SCO or ARC programs for most producers in the Midwest.

This study develops an extensive nationwide model of county and farm yield and price risks for the five major US crops and employs the model to evaluate expected payment rates and the distribution of payments under the House and Senate Farm Bill proposals. These analyses are important for program evaluation and should be of great interest to producers and policymakers.

The purpose of this paper is to contribute to the empirical evidence about crop yield distributions that are often used in practical models evaluating crop yield risk and insurance. Additionally, a simulation approach is used to compare the performance of alternative specifications when the underlying form is not known, to identify implications for the choice of parameterization of yield distributions in modeling contexts.

Using a unique high-quality farm-level corn yield data set, commonly used parametric, semi-parametric, and non-parametric distributions are examined against widely used in-sample goodness-of-fit (GOF) measures. Then, a simulation framework is used to assess the out-of-sample characteristics by using known distributions to generate samples that are assessed in an insurance valuation context under alternative specifications of the yield distribution.

Bias and efficiency trade-offs are identified for both in- and out-of-sample contexts, including a simple insurance rating application. Use of GOF measures in small samples can lead to inappropriate selection of candidate distributions that perform poorly in straightforward economic applications. The β distribution consistently overstates rates even when fitted to data generated from a β distribution, while the Weibull consistently understates rates; though small sample features slightly favor Weibull. The TCMN and kernel density estimators are least biased in-sample, but can perform very badly out-of-sample due to overfitting issues. The TCMN performs reasonably well across sample sizes and initial conditions.

Economic applications should consider the consequence of bias vs efficiency in the selection of characterizations of yield risk. Parsimonious specifications often outperform more complex characterizations of yield distributions in small sample settings, and in cases where more demanding uses of extreme-event probabilities are required.

The study helps provide guidance on the selection of distributions used to characterize yield risk and provides an extensive empirical demonstration of yield risk measures across a high-quality set of actual farm experiences. The out-of-sample examination provides evidence of the impact of sample size, underlying variability, and region of the probability measure used on the performance of candidate distributions.

The purpose of this paper is to assess the feasibility of risk-contingent credit (RCC) by presenting an experimental and participatory game designed to explain the concept of RCC to Kenyan pastoralists and dairy farmers. The paper investigates the uptake potential of RCC through qualitative assessment of field experiments and focus groups.

The paper presents a method of community engagement through a participatory game played in a series of Focus Group Discussions (FGDs). The paper also presents theoretical justification of RCC in credit market structure.

The game effectively explains the concept and mechanism of RCC by reflecting local situation and production potential. Participatory exercises within focus group discussions indicate that there exists a strong interest and support for RCC.

The methodology described in this paper can be used in extension programs for promoting innovative rural microcredit in developing countries but should be modified according to the local production and associated weather and market risks.

Micro-insurance and credit program delivery can be improved by the innovative approach of community engagement for explaining financial products.

The purpose of this paper is to provide a general discussion of how techniques from financial engineering can be used to investigate the economic costs of farm programs and to aid in the design of new financial products to implement margin protection for dairy farmers. Specifically the paper investigates the Milk Income Loss Contract (MILC) and the Dairy Margin Protection (DMP) program. In addition the paper introduces the concept of the Milk to Corn Price ratio to protect margins.

The paper introduces and reviews the tools of financial engineering. These include the stochastic calculus and Itô's Lemma. The empirical tool is Monte Carlo simulations. The approach is part pedagogy and part practice.

In this paper the authors illustrate how financial engineering can be used to price complex price stabilization formula in the USA and to illustrate its use in the design of new products.

In this paper the authors illustrate how financial engineering can be used to price complex price stabilization formula in the USA and to illustrate its use in the design of new products.

Farm programs designed to protect dairy farmers margins are designed in a seemingly ad hoc fashion. Assessments of programs such as MILC or DMP are conducted on an ex-post basis using historical data. The financial engineering approach presented in this paper provides the means to add significant depth to the assessment of such programs which can be used in conjunction with Monte Carlo simulation to identify alternative model structures before they are written into law.

This paper builds upon an existing literature. Its originality is in the application of financial engineering techniques to farm dairy policy.

The purpose of this paper is to provide empirical information about the past loss experience in major US crop insurance programs, and documents the impacts of ratings changes through time on the premiums and exposure to participants. The losses are also examined within the structure of the current SRA to identify impacts on insurance companies and the government by fund designation.

- The study uses RMA Summary of Business data and methods consistent with the use of loss-cost ratemaking to analyze loss performance across years with different starting prices and volatilities. Additionally, the RMA premium quoting system was replicated across years with the ability to adjust only one feature at a time to isolate the impacts of changes in individual rating elements from changes in market conditions. Tabulations are provided in map and table form to present the loss ratios through time, in aggregate across time, and within each of the possible funds in which exposures are held. Additionally, the tools developed allow a direct tabulation of the farmer-level premium impacts of individual changes in the policy premium system, and of changing conditions over time.

Corn and soybeans represent dominant shares of aggregate policy premiums and liability, and also are the crops that underwent the greatest degree of revision in rates over the recent past both due to rate study implications, and to loss rate experience. Despite commonly made arguments that payments associated with the drought of 2012 “more than wiped out all historic gains,” it appears that insurance worked very much as intended and that the loss ratios through time are within reasonable ranges of targets. Fund designation, and the separation under the most recent SRA of Group 1 and Group 2 states substantially dampened the loss sharing and ability to capture gains by private companies, and leads to fairly low rates of return on a pure fund-loss sharing basis for insurance companies. Finally, despite the extreme losses of 2012, the aggregate performance of corn relative to the remainder of the program exhibits lower than average loss rates both in aggregate and on a scale-adjusted basis.

The study provides an important means to isolate and assess implications of rate changes, and to associate causes of losses with rate charges. Additionally, the structure of the SRA, and possible future versions of the SRA are informed by both the aggregate, and the normalized performance results provided. And, the relative performance of major row, crops even with recent extreme losses, appears appropriate or positive to insurance companies after considering the impacts of the SRA on company exposure. In total, the evidence points toward appropriate movement toward target overall loss ratios in the US crop insurance program.

This paper provides an extensive empirical evaluation of ratings for major crop insurance policies and provides a unique means to decompose sources of changes in premiums and rates across locations and through time. It also provides an evaluation of the performance of crop insurance post-SRA in a manner that allows both totals and scale-adjusted performance to be assessed.

The purpose of this paper is to provide a brief and necessarily partial overview of the design, motivation, and use of the Ag-Analytics platform (ag-analytics.org), focussing on integration and warehousing of publicly available research data for broad communities of researchers, including those in the area of agricultural finance.

The paper walks the reader through an overview of the layout and utilization of the Ag-Analytics platform, including a few example applications of some of the tools and web API’s.

Much of the data researchers routinely use in agricultural and environmental finance and related fields are often – strictly speaking – publicly available; however the form in which they are distributed leads to great inefficiencies in data sourcing and processing which can be greatly improved. The goal of the Ag-Analytics open data/open source platform is to help researchers centralize and share in such efforts. Development of systems for disseminating, documenting, and automating the processing of such data can lead to more transparency in research, better routes for validation, and a more robust research community.

Some of the tools and methods are discussed, as well as practical issues in data sourcing and automation for research. A few high level introductory examples and applications are illustrated.

Development and adoption of such systems and data resources remains seriously lacking in social science research, particularly in the economics, natural resource, environmental, and agricultural finance spheres. This brief provides an overview of one such system which should be of value to researchers in this field and many others.

The aim of this paper is to explore the value preference space associated with the optimization and generalization performance of GEFeWSML.

In this paper, the authors modified the evaluation function utilized by GEFeWSML such that the weights assigned to each objective (i.e. error reduction and feature reduction) were varied. For each set of weights, GEFeWSML was used to evolve FMs for the face, periocular, and face + periocular templates. The best performing FMs on the training set (FMtss) and the best performing FMs on the validation set (FM*s) were then applied to the test set in order to evaluate how well they generalized to the unseen subjects.

By varying the weights assigned to each of the objectives, the authors were able to suggest values that would result in the best optimization and generalization performances for facial, periocular, and face + periocular recognition. GEFeWSML using these suggested values outperformed the previously reported GEFeWSML results, using significantly fewer features while achieving the same recognition accuracies statistically.

In this paper, the authors investigate the relative weighting of each objective using a value preference structure and suggest the best weights to be used for each biometric modality tested.

The application of circular economy (CE) has received wide coverage in the built environment, including public-private partnership (PPP) infrastructure projects, in recent times. However, current studies and practical implementation of CE are largely associated with construction demolition, waste and recycling management. Few studies exist on circular models and success factors of public infrastructures developed within the PPP contracts. Thus, the main objective of this article is to identify the models and key success factors associated with CE implementation in PPP infrastructure projects.

A systematic review of the literature was undertaken in this study using forty-two (42) peer-reviewed journal articles from Scopus, Web of Science, Google Scholar and PubMed.

The results show that environmental factors, sustainable economic growth, effective stakeholder management, sufficient funding, utilization of low-carbon materials, effective supply chain and procurement strategies facilitate the implementation of CE in PPP infrastructure projects. Key CE business models are centered around the extension of project life cycle value, circular inputs and recycling and reuse of projects.

Although the study presents relevant findings and gaps for further investigations, it has a limited sample size of 42 papers, which is expected to increase as CE gain more prominence in PPP infrastructure management in future.

The findings are relevant for decision-making by PPP practitioners to attain the social, economic and environmental benefits of transitioning to circular infrastructure management.

This study contributes to articulating the key models and measures toward sustainable CE in public infrastructure development.