Search

Small sample size often limits forecasting tasks such as the prediction of production, yield, and consumption of agricultural products. Machine learning offers an appealing alternative to traditional forecasting methods. In particular, support vector regression has superior forecasting performance in small sample applications. In this article, we introduce support vector regression via an application to China's hog market. Since 2014, China's hog inventory data has experienced an abnormal decline that contradicts price and consumption trends. We use support vector regression to predict the true inventory based on the price‐inventory relationship before 2014. We show that, in this application with a small sample size, support vector regression outperforms neural networks, random forest, and linear regression. Predicted hog inventory decreased by 3.9% from November 2013 to September 2017, instead of the 25.4% decrease in the reported data.

Land values and cash rents are slow to adjust, and therefore the returns from owning farmland may be time varying and serially correlated. This article investigates a farmland portfolio’s nominal and real returns from a forward-looking perspective, taking into account time-varying return and serial correlation. The results indicate that the attractive average return level observed historically can be attained only over a long investment period. The risk involved in the long investment period, however, is also substantial. As a result, in mixed-asset investment portfolios, the allocations to farmland are much lower than traditional mean-variance optimization implies.

Purpose The purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical realized variance and the corresponding risk-neutral expected variance. Design/methodology/approach The authors compute variance risk premiums using historical derivatives data. The authors use regression analysis and time series econometrics methods, including EGARCH and the Kalman filter, to analyze variance risk premiums. Findings There are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the risk-neutral expected variance. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log risk-neutral expected variance. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns in the underlying commodity. Practical implications Commodity variance (i.e. volatility) risk cannot be hedged using futures markets. The results have practical implications for US crop insurance programs because the implied volatilities from the relevant options markets are used to estimate the price volatility factors used to generate premia for revenue insurance products such as “Revenue Protection” and “Revenue Protection with Harvest Price Exclusion.” The variance risk premia found implies that revenue insurance premia are overpriced. Originality/value The empirical results suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15 percent. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.

The novel coronavirus SARS‐CoV2 (COVID‐19) severely disrupted the U.S. food supply chain. In its initial aftermath, and as we contemplate a potential reignition, the food supply chain industries, researchers, and policy makers search for evidence, causes, and consequences. This article uses publicly available data on the pork and egg industries and a survey of the turkey industry as a first step to document the impact of COVID‐19. Researchers can learn from the experiences in industries where disruptions evolve differently in the face of simultaneous supply‐ and demand‐side shocks and that stem from differences in structures of the supply chains. This early evidence is used to motivate future research needs and highlight opportunities for industry investments in resiliency strategies.

Maize (corn) is the dominant grain grown in the world. Total maize production in 2018 equaled 1.12 billion tons. Maize is used primarily as an animal feed in the production of eggs, dairy, pork and chicken. The US produces 32% of the world’s maize followed by China at 22% and Brazil at 9% (https://apps.fas.usda.gov/psdonline/app/index.html#/app/home). Accurate national-scale corn yield prediction critically impacts mercantile markets through providing essential information about expected production prior to harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. We build a deep learning model to predict corn yields, specifically focusing on county-level prediction across 10 states of the Corn-Belt in the United States, and pre-harvest prediction with monthly updates from August. The results show promising predictive power relative to existing survey-based methods and set the foundation for a publicly available county yield prediction effort that complements existing public forecasts.

The supply chain connecting biofuel processing firms and suppliers of biomass is evolving, and processors face a choice in the collection and pricing strategies they will employ to procure biomass. One option is to pay a single price for biomass collected field-side (processor collection). Another is to pay a single price for biomass at the plant gate (supplier delivery). The literature in this area is relatively young, but there is a sense that the evolution of contracting and pricing structures will dictate the industry's success, and ultimately the costs of producing biofuels from dedicated and non-dedicated energy crops. We examine the collection and pricing choices for a cost-minimizing cellulosic biofuel processor, who initially has monopsony power in feedstock procurement in their collection area. We derive optimal prices, total expenditures on feedstocks, and the collection areas required to meet a processor's fixed input needs. We show that while societal welfare is greatest under supplier delivery; however, the processor will be indifferent between supplier delivery and processor collection unless they are concerned about entry of a competing processor. When this is the case, the processor can use the processor-collection mechanism as an effective deterrent to entry. Numerical simulation based on corn stover for biomass is used to illustrate optimal pricing and the extent of biomass collection areas for different procurement and pricing strategies. We use these findings to calculate the rates at which collection costs increase for a monopsonistic stover processor constrained to a defined procurement area, as might emerge as the industry moves toward commercialization. The derived marginal cost curve for a monopsonistic processor of stover is compared with the marginal cost curve across alternative feedstocks.

We develop a theoretical model of optimal hedging that nests expected utility and expected target utility theories. We use this model to characterize optimal hedging with and without reference price dependence. The model's theoretical predictions are tested with a unique database consisting of every forward contract written with a major grain marketing firm by Iowa corn producers over a five‐year period. Our results suggest that a current December futures price higher than a reference price triggers hedging activity. A likely candidate for producers’ reference price is a rolling average of the current futures price. We then use trading activity implied by the producers to determine if they benefit from the way they hedge. The evidence is mixed. Finally, we compare the producer forward contract data to the only publicly available data on producer hedging: The Commodity Futures Trading Commission Disaggregated Commitment of Traders Report (DCOT) for Short Hedgers. A hedge ratio constructed from the open interest in new futures contracts of the DCOT report is highly correlated with the producer hedge series in the Iowa data, providing evidence that DCOT data represent farmers’ hedging behavior reasonably well. This work has important implications for future research that uses the DCOT data, and provides new evidence about producers’ hedging behavior that marketing specialists and extension agents can use to enhance their educational efforts related to risk management.

Despite ample evidence of pork quality variability, at present there are few signals that would incentivize growers to produce higher quality pork. Using split-sample, choice experiment data from a nationwide online survey of U.S. pork chop eaters, this research determines changes in pork chop demand in response to a potential change in U.S. government policy that would introduce a new pork quality grading system based on color scores. Our simulations include novel short-run projections in which the conditional and latent class logit models are inverted to yield inverse demand curves. The inverse demand curves are used to calculate equilibrium prices and pork revenue given a fixed supply of different pork qualities. We supplement these calculations with a more traditional “long run” analysis in which prices are fixed and quantities of different qualities adjust. Compared to the status quo (control) of no quality grades, we find that two grading systems based on alternative nomenclatures (Select, Choice, Prime vs. Good, Better, Best) both have the potential to increase pork chop sales and revenue to the pork industry; however, we also find that if only the highest quality is labeled, revenue could fall as the increase in demand for the higher quality is offset by the fall in demand for the lower qualities. Results also highlight important heterogeneity in consumer preferences, and although sensory studies strongly suggest redder pork chops are more highly preferred, there remains a non-trivial share of consumers who prefer whiter pork even after quality grade labeling. Overall, this study offers several insights that can help inform labeling and quality grading policies.

Veterinary diagnostic laboratories (VDLs) play a significant role in the prevention and mitigation of endemic animal diseases and serve an important role in surveillance of, and the response to, outbreaks of transboundary and emerging animal diseases. They also allow for business continuity in livestock operations and help improve human health. Despite these critical societal roles, there is no academic literature on the economic impact of VDLs. We present a case study on the economic impact of the Iowa State University Veterinary Diagnostic Laboratory (ISUVDL). We use economic contribution analysis coupled with a stakeholder survey to estimate the impact. Results suggest that the ISUVDL is responsible for $2,162.46 million in direct output, $2,832.45 million in total output, $1,158.19 million in total value added, and $31.79 million in state taxes in normal years. In an animal health emergency this increases to $8,446.21 million in direct output, $11,063.06 million in total output, $4,523.70 million in total value added, and $124.15 million in state taxes. The ISUVDL receives $4 million annually as a direct state government appropriation for operating purposes. The $31.79 million in state taxes in normal years and the $124.15 million in state taxes in an animal health emergency equates to a 795% and 3104% return on investment, respectively. Estimates of the economic impact of the ISUVDL provide information to scientists, administrators, and policymakers regarding the efficacy and return on investment of VDLs.

We show that the weather premium, an anecdotal phenomenon in the U.S. corn futures market, can arise from a convex demand function. We further show that the magnitude of the weather premium depends on the carryout and expected yield at harvest. We use data from 1968 to 2015 to evaluate the accuracy of the December futures price as a forecast of the harvest price. A predictable component in the forecast error is consistent with the existence of a time†varying weather premium. We demonstrate that a passive strategy of routinely shorting the corn December futures does not provide an attractive risk†adjusted return.

The U.S. cellulosic biofuel mandate has not been enforced in recent years. Uncertainty surrounding the enforcement of the mandate in addition to high production and harvest cost have contributed to a delay in the widespread planting of bioenergy crops such as switchgrass and miscanthus. Previous literature has shown that under uncertainty and sunk cost, an investment threshold is further increased due to the value associated from holding the investment option. In this paper, we extend the previous literature by applying a real option switching model to bioenergy crop production. First, we calculate the county-level break-even price which triggers a switching away from traditional field crops (corn, soybeans, and wheat) to bioenergy crops under various scenarios differing by commodity prices, production cost and biomass price expectations. We show that the resulting break-even prices at the county-level can be substantially higher than previously estimated due to the inclusion of the option value. In a second step, we identify counties that are most likely to grow switchgrass or miscanthus by simulating a stochastic biomass price over time. Our results highlight two issues: First, switchgrass or miscanthus are not grown in the Midwest under any scenario. Under low agricultural residue removal rates, biomass crops are mostly grown in the Southeast. Second, under the assumption of a high removal rates, bioenergy crops are not grown anywhere in the U.S. since the cellulosic biofuel mandate can be covered by agricultural residues.

Purpose Portfolio risk in crop insurance due to the systemic nature of crop yield losses has inhibited the development of private crop insurance markets. Government subsidy or reinsurance has therefore been used to support crop insurance programs. The purpose of this paper is to investigate the possibility of converting systemic crop yield risk into “poolable” risk. Specifically, this study examines whether it is possible to remove the co-movement as well as tail dependence of crop yield variables by enlarging the risk pool across different crops and countries. Design/methodology/approach Hierarchical Kendall copula (HKC) models are used to model potential non-linear correlations of the high-dimensional crop yield variables. A Bayesian estimation approach is applied to account for estimation risk in the copula parameters. A synthetic insurance portfolio is used to evaluate the systemic risk and diversification effect. Findings The results indicate that the systemic nature – both positive correlation and lower tail dependence – of crop yield risks can be eliminated by combining crop insurance policies across crops and countries. Originality/value The study applies the HKC in the context of agricultural risks. Compared to other advanced copulas, the HKC achieves both flexibility and parsimony. The flexibility of the HKC makes it appropriate to precisely represent various correlation structures of crop yield risks while the parsimony makes it computationally efficient in modeling high-dimensional correlation structure.

Public and private investments in plant breeding have a proven track record of increasing agricultural productivity, significantly contributing to economic well-being or social welfare. Substantial investments in research and development are required before a new plant variety can be developed and released, which the private sector can only recoup through commercial sales coupled with property rights. We previously published outcomes from economic modeling implementing different categories and hypothetical variants of intellectual property protection (IPP) in the field of plant breeding and biotechnology. Our goal here is to portray these outcomes using examples that will be more immediately familiar to the plant-breeding and policy-making communities. In so doing, we do not add to the analyses and arguments already presented. Our objective here is to make more accessible to a broader audience subject matter already presented in a more formal economic format by Lence et al. (2015). We found that plant variety protection (PVP) and utility patents played important and complementary roles in promoting and adopting innovation. Voluntary licensing under patents had a major contribution to social welfare. Periods of protection longer than the current life span of a utility patent did not contribute maximally to the stock of social welfare. We performed a reality check comparing different types of innovation and assessment of time and risk to commercialization. We hope that this information can contribute to more effective implementation of IPP to further promote genetic gain and thus enable commercially funded plant breeders to maximally contribute to the benefit of society on a global basis.

This paper investigates the lead-lag relationships among soybean prices in United States, Brazilian, and Chinese futures markets. We focus on both long-run price co-movements and on short-run price relationships. Various co-integration methodologies and causality tests are applied to examine the changes in price relationships over time. The empirical results indicate the following: (a) the soybean futures market in the U.S. is still the most important and influential market, and the U.S. price, in the long-term, leads price changes in Brazil and China; (b) in the short-term, the overnight return of U.S. soybean futures and the daytime return of Chinese No. 1 soybean futures contemporaneously affect each other, but there is no significant causality between U.S. overnight return and the daytime return of Chinese No. 2 soybean futures; and, (c) a weak temporal seasonal causality between U.S. and Brazilian soybean futures price exists and more often than not Brazilian futures lead U.S. futures during the Brazilian growing season. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark

A recent study in Carbon Management, by Bach et al., argues that biochar amendments’ positive impacts on crop yields and soil carbon sequestration have been overestimated, and biochar amendment to soil is hence unlikely to be an economically viable technique for cropping system management or C abatement. We question the data selection and analysis techniques that the authors used to assess the effect of biochar on crop yield, biochar stability in soil, and biochar production cost. Although the research article was not intended as a meta-analysis – and hence the data reported need not be analyzed with the full rigor of a systematic review – we assert that the employed data set, while containing a sufficient quantity of data, requires closer inspection and a more careful interpretation to avoid significant bias in the conclusions. Furthermore, we assert that proper implementation of biochar and inclusion of non-yield benefits in the analysis would render it more economically viable for both cropping...

Biochar has the potential to increase crop yields when applied to agricultural land. We integrate agronomic and economic simulation models to determine the expected yield increase from biochar applications in the United States. We calculate the location-specific willingness to pay of U.S. farmers to apply biochar to their cropland if biochar increases yields over 20 years. In addition to the potential benefit of higher revenue for farmers, biochar applications also have policy implications if biochar production is combined with bio-fuel production or used to reduce greenhouse gas emissions from indirect land-use change. Thus, the results are then combined with an agricultural outlook model to determine the effects on global land-use change and net carbon emissions. Our results indicate that biochar application is most profitable for croplands in the Southeast U.S. due to the combination of high yield increases and availability of biomass to produce biochar. An increase in U.S. yields above trend by 1% for corn, soybeans, and wheat would decrease net total global emissions by 25–87 Tg of CO2-equivalent.

Options on agricultural commodities with maturities exceeding one year seldom trade. One possible reason to explain this lack of trading is that we do not have an accurate option pricing model for products where mean reversion in spot-price levels can be expected. Standard option pricing models assume proportionality between price variance and time to maturity. This proportionality is not a valid assumption for commodities whose supply response brings prices back to production costs. The model proposed here incorporates mean reversion in spot-price levels and includes a correction for seasonality. Mean reversion and seasonality are both observed in the soybean market. The empirical analysis lends strong support to the model.

Farmers may choose to apply nitrogen fertilizer at a rate that exceeds the average ex post agronomically optimal rate when the yield response to nitrogen varies across growing seasons. Negative environmental consequences such as nitrous oxide (N2O) emissions and/or water pollution can result when all the applied nitrogen is not needed by the crop. Here we consider a nonlinear market instrument targeting farmers’ nitrogen use, and by solving for the optimal nitrogen reduction using a model of expected utility of farm profits, we evaluate the induced N2O emission reductions that are consistent with the instrument introduced. The market instrument is nonlinear because of the expected nonlinear relationship between N2O and nitrogen application rates. Our simulations show that, in cases where farmers apply N at rates which exceed recommendations and the N2O response is likely to be non-linear, payments will induce participation in the program and will have a significant impact on both expected and actual N2O emissions without significantly harming expected or actual yields. Failure to consider this nonlinearity would deviate the attention away from N2O pollution because it would require large N reductions (and crop yields) to achieve equivalent N2O abatement.

Welfare trade-offs between intellectual property (IP) protections provided by patents and by plant variety protection (PVP) are explored. PVP breeders’ exemption weakens IP protection, but may speed the transfer of research gains across firms. A model is developed assuming firms optimise research given existing IP protection. A baseline scenario supporting each system is used to perform welfare analysis, and study how the balance is altered between systems. Survey data suggest patents are more appropriate for longer-term, higher-risk research, whereas PVP is better suited for traditional breeding. A scenario where patents and licensing co-exist dominates PVP in all commercially relevant areas.

This article charts the progress of CenUSA Bioenergy, a USDA-NIFA-AFRI coordinated agricultural project focused on the North Central region of the US. CenUSA’s vision is to develop a regional system for producing fuels and other products from perennial grass crops grown on marginally productive land or land that is otherwise unsuitable for annual cropping. This article focuses on contributions CenUSA has made to nine primary systems needed to make this vision a reality: feedstock improvement; feedstock production on marginal land; feedstock logistics; modeling system performance; feedstock conversion into biofuels and other products; marketing; health and safety; education, and outreach. The final section, Future Perspectives, sets forth a roadmap of additional research, technology development and education required to realize commercialization.

Denmark was among the first countries to ban the use of antimicrobials for growth promotion (AGPs) in animal production through an on-going series of actions and regulations since 1995. In 2010 the Yellow Card scheme was adopted to decrease total antimicrobial consumption in pig production through additional restrictions on pig farmers. The withdrawal of AGPs and other restrictions have reduced total antimicrobial use, but at the same time therapeutic drug use has increased and resistance in key zoonotic bacteria has not decreased. Improved use of vaccines and management practices can help reduce losses especially for weaner pigs, but come with additional costs to producers.

A partial solution to problems associated with anthropogenic greenhouse gas (GHG) emissions could be the development and deployment of carbon-negative technologies, i.e., producing energy while reducing atmospheric carbon dioxide levels. Biofuels have been considered a possibility but have faced limitations due to competition with food production and GHG emissions through indirect land-use change (ILUC). In this article, we show how emissions from ILUC can potentially be reduced by producing food and bioenergy from biochar amended soils. The possibility of yield improvements from biochar would reduce the land requirement for crop production and thus, lead to a reduction in emissions from ILUC. In our application, biochar and bio-oil are produced via fast pyrolysis of corn stover. Bio-oil is subsequently upgraded into a fuel suitable for use in internal combustion engines. Applying the U.S. regulatory method used to determine biofuel life cycle emissions, our results show that a biochar-induced yield improvement in the U.S. Midwest ranging from 1% to 8% above trend can lead to an ILUC credit between 1.65 and 14.79 t CO 2 -equivalent ha −1 year −1 when future emissions are assessed over the next 30 years. The model is generalizable to other feedstocks and locations and illustrates the relationship between biochar and crop production.

Increased biofuel production has been associated with direct and indirect land-use change, changes in land management practices, and increased application of fertilizers and pesticides. This has resulted in negative environmental consequences in terms of increased carbon emissions, water quality, pollution, and sediment loads, which may offset the pursued environmental benefits of biofuels. This study analyzes two distinct policies aimed at mitigating the negative environmental impacts of increased agricultural production due to biofuel expansion. The first scenario is a fertilizer tax, which results in an increase in the US nitrogen fertilizer price, and the second is a policy-driven reversion of US cropland into forestland (afforestation). Results show that taxing fertilizer reduces US production of nitrogen-intensive crops, but this is partially offset by higher fertilizer use in other countries responding to higher crop prices. In the afforestation scenario, crop production shifts from high-yielding land in the United States to low-yielding land in the rest of the world. Important policy implications are that domestic policy changes implemented by a large producer like the United States can have fairly significant impacts on the aggregate world commodity markets. Also, the law of unintended consequences results in an inadvertent increase in global greenhouse gas emissions.

Agricultural biofuels require the use of scarce land, and this land has opportunity cost. We explore the objective function of a social planner who includes a land constraint in the optimization decision to minimize environmental cost. The inclusion of this land constraint in our optimization model motivates the measurement of emissions on a per-hectare basis. Switchgrass and corn are modeled as competing alternatives to show how the inclusion of a land constraint can influence life cycle rankings and alter policy conclusions. With land use unconstrained, ethanol produced from switchgrass is always an optimal feedstock relative to ethanol produced from corn. With land use constrained, however, our results show that it is unlikely that switchgrass would be optimal in the midwestern United States, but may be optimal in southern states if carbon is priced relatively high. Whether biofuel policy advocates for one feedstock over another should consider these contrasting results.

The objective of this study was to quantify the environmental and economic impact of withdrawing growth-enhancing technologies (GET) from the U.S. beef production system. A deterministic model based on the metabolism and nutrient requirements of the beef population was used to quantify resource inputs and waste outputs per 454 × 10(6) kg of beef. Two production systems were compared: one using GET (steroid implants, in-feed ionophores, in-feed hormones, and beta-adrenergic agonists) where approved by FDA at current adoption rates and the other without GET use. Both systems were modeled using characteristic management practices, population dynamics, and production data from U.S. beef systems. The economic impact and global trade and carbon implications of GET withdrawal were calculated based on feed savings. Withdrawing GET from U.S. beef production reduced productivity (growth rate and slaughter weight) and increased the population size required to produce 454 × 10(6) kg beef by 385 × 10(3) animals. Feedstuff and land use were increased by 2,830 × 10(3) t and 265 × 10(3) ha, respectively, by GET withdrawal, with 20,139 × 10(6) more liters of water being required to maintain beef production. Manure output increased by 1,799 × 10(3) t as a result of GET withdrawal, with an increase in carbon emissions of 714,515 t/454 × 10(6) kg beef. The projected increased costs of U.S. beef produced without GET resulted in the effective implementation of an 8.2% tax on beef production, leading to reduced global trade and competitiveness. To compensate for the increase in U.S. beef prices and maintain beef supply, it would be necessary to increase beef production in other global regions, with a projected increase in carbon emissions from deforestation, particularly in Brazil. Withdrawing GET from U.S. beef production would reduce both the economic and environmental sustainability of the industry.

Futures markets on agricultural commodities typically trade with maximum maturity dates of less than four years. If these markets did trade with maturities eight or ten years distant, futures prices would have value as price forecasts and as a way to structure long-term swaps and insurance contracts. Agricultural commodity markets generally exhibit mean reversion in spot prices and convenience yields. Spot markets also exhibit seasonality. This study develops and implements a procedure to generate long-term futures curves from existing futures prices. Data on lean hogs and soybeans are used to show that the method provides plausible results. Copyright 2012, Oxford University Press.

Conventional agricultural life cycle assessments (LCAs) measure greenhouse gas (GHG) emissions for biofuel pathways as the amount of carbon dioxide equivalent emitted per unit of energy provided by the pathway (i.e. gCO2e/MJ). This measure of GHG emissions, as computed by the Environmental Protection Agency (EPA) is then used to determine the extent to which the corresponding biofuel pathway complies with the GHG emission standards set forth by the Energy Independence and Security Act (EISA) of 2007. Under current legislation, ethanol produced from corn grain is prohibited from qualifying as an advanced biofuel, even if it were to meet the GHG emission standards. This paper proposes a measure of GHG emissions based on a unit of land rather than the energy provided by a biofuel pathway utilizing only one feedstock. A hectare of corn thus provides two feedstocks for the biofuel pathway considered here; corn grain is used for production of ethanol while corn stover is subjected to fast pyrolysis for production of biochar and bio-oil. The bio-oil is then subsequently upgraded to a fuel suitable for use as a drop in fuel in internal combustion engines. A LCA of this pathway is conducted and it is found that such a pathway generates a 52.1% reduction in GHG emissions. This is a reduction that is sufficient to qualify the combined output of a hectare of corn as an advanced biofuel if the current restriction in EISA were removed.

This paper examines the role of uncertain crude oil prices, uncertain crop yields, and competition for acreage on corn, soybean, and switchgrass prices under biofuel production mandates. We find enforcement of the cellulosic mandate is costly in that it raises equilibrium prices of all three crops through competition for acreage. However, crude oil prices largely determine industry profitability even in the face of high crop prices. Further we find that uncertainty in crude oil prices and crop yields causes higher corn, soybean, and switchgrass prices compared to a baseline with no uncertainty.

This study examines aggregate county income growth across the 48 contiguous states from 1990 to 2005. To control for endogeneity, we estimate a two-stage spatial error model and implement a number of spatial bootstrap routines to infer parameter significance. Among the results, we find that outdoor recreation and natural amenities favor positive growth in rural counties and property taxes correlate negatively with rural growth. Comparing bootstrap inference with other models, including the recent General Moment heteroskedastic-robust spatial error estimator, we find similar conclusions suggesting bootstrapping can be effective in spatial models where asymptotic results are not well established.

We empirically investigate the GHG emissions embodied in the bilateral trade between the United States and China and the United States and India and their countryspecific and global environmental impacts. In order to address the concerns of “levelplaying-field” and carbon leakage associated with domestic carbon pricing scheme, various border carbon adjustments have been proposed in recent U.S. climate change legislation. Employing GTAP-E model, this study examines how and to what extent the proposed carbon tariffs and export subsidies potentially affect bilateral trade flow, domestic production, and the GHG emissions. Results indicate that carbon tariff effectively alleviates the impact of domestic carbon tax on vulnerable domestic industries and slightly raises their output and GHG emissions. In addition, it is also evident that a combined or full border adjustment policy has bigger impacts than an individual policy.

We use Bayesian Markov Chain Monte Carlo methods to investigate the linkage between the volatility of ethanol security prices and the uncertainty surrounding the profitability of ethanol production and the price variations of non-ethanol energy securities. The joint evolution of return and volatility is modeled as a stochastic process that incorporates jumps in both return and volatility. While a strong and significant correlation is found between the volatility of ethanol securities and profit uncertainty from June 2005 to July 2008, the dynamic pattern of ethanol stock volatility is strikingly similar to that of the S&P 500 energy sector index in the more recent period. Our evidence lends support to the findings in the literature on rational learning from uncertainty in determining the equity price and volatility during the adoption and development of a technological innovation.

PurposeWhile the demand for weather‐based agricultural insurance in developed regions is limited, there exists significant potential for the use of weather indexes in developing areas. The purpose of this paper is to address the issue of historical data availability in designing actuarially sound weather‐based instruments.Design/methodology/approachA Bayesian rainfall model utilizing spatial kriging and Markov chain Monte Carlo techniques is proposed to estimate rainfall histories from observed historical data. An example drought insurance policy is presented where the fair rates are calculated using Monte Carlo methods and a historical analysis is carried out to assess potential policy performance.FindingsThe applicability of the estimation method is validated using a rich data set from Iowa. Results from the historical analysis indicate that the systemic nature of weather risk can vary greatly over time, even in the relatively homogenous region of Iowa.Originality/valueThe paper shows that while the kriging method may be more complex than competing models, it also provides a richer set of results. Furthermore, while the application is specific to forage production in Iowa, the rainfall model could be generalized to other regions by incorporating additional climatic factors.

The present article examines society's welfare when goods with identical physical attributes can be produced using two alternative technologies, one of them less ethically desirable but less expensive for at least some producers. For the scenario where identification costs must be borne by producers and consumers of the high-quality good, the outcome under unregulated markets is identical to the optimal solution of a central planner constrained to neither ban the undesirable technology nor segregate the low-quality good. However, under certain circumstances the unregulated market equilibrium may be improved upon by government intervention that shifts the burden of identification costs to the producers of the low-quality good, or which bans the production of the low-quality good. The optimal intervention needs to be determined case-by-case and depends on consumer preferences, relative production costs, and relative costs of identification and fraud prevention.

Based on a transparent analytical model of multiple markets including corn, ethanol, gasoline, and transportation fuel, this study estimates the welfare changes for consumers and producers resulting from ethanol production and related support polices in 2007. The welfare estimation takes into account the second-best gain from eliminating loan deficiency payments. The results suggest the total social cost is about $0.78 billion for given market parameters. We validate the model's underlying assumption and test for the results' sensitivity to assumed parameters.

This study quantifies the impact of increasing ethanol production on wholesale/retail gasoline prices employing pooled regional time-series data from January 1995 to March 2008. We find that the growth in ethanol production kept wholesale gasoline prices $0.14/gallon lower than would otherwise have been the case. The negative impact of ethanol on retail gasoline prices is found to vary considerably across regions. The Midwest region has the biggest impact at $0.28/gallon, while the Rocky Mountain region had the smallest impact at $0.07/gallon. The results also indicate that the ethanol-induced reduction in gasoline prices comes at the expense of refiners’ profits. We find a net welfare loss of $0.5 billion from the ethanol support policies in multiple markets.

We project U.S. ethanol production and its impact on planted acreage, crop prices, livestock production, trade, and retail food costs. The projections are made using a multicommodity, multicountry, partial equilibrium model. Results indicate that expanded U.S. ethanol production will cause long-run crop prices to increase. In response to higher feed costs, livestock farmgate prices increase enough to cover the feed cost increases. If crude oil prices increase, the U.S. ethanol sector expands. Results of a 1988-type drought scenario combined with a large mandate for ethanol consumption show higher crop prices, a drop in livestock production, and higher food prices. Copyright 2008, Oxford University Press.

The welfare implications of intellectual property protection (IPP) for private sector agricultural research are analyzed, focusing on the realistic cases where countries provide different IPP levels, technology spills over across countries, and the public sector is involved in research. A model is developed to determine who benefits from, and who should pay for, the associated research. The article contains some interesting results on the implications of a harmonization of IPP policies through multilateral agreements or via technology that allows research firms to prevent the copying of plants and animals that express traits that have emerged from their research. Copyright 2008, Oxford University Press.

This study utilizes an analysis technique commonly used in marketing, the conjoint analysis method, to examine the relative utilities of a set of beef steak characteristics considered by a national sample of 1,432 US consumers, as well as additional localized samples representing undergraduate students at a business college and in an animal science department. The analyses indicate that among all respondents, region of origin is by far the most important characteristic; this is followed by animal breed, traceability, animal feed, and beef quality. Alternatively, the cost of cut, farm ownership, the use (or nonuse) of growth promoters, and whether the product is guaranteed tender were the least important factors. Results for animal science undergraduates are similar to the aggregate results, except that these students emphasized beef quality at the expense of traceability and the nonuse of growth promoters. Business students also emphasized region of origin but then emphasized traceability and cost. The ideal steak for the national sample is from a locally produced, choice Angus fed a mixture of grain and grass that is traceable to the farm of origin. If the product was not produced locally, respondents indicated that their preferred production states are, in order from most to least preferred, Iowa, Texas, Nebraska, and Kansas.

In this paper, we examine more closely some of the forces that underlie economic growth at the county level. In an effort to describe a much more comprehensive regional economic growth model, we address a variety of different hypotheses by introducing a large number of growth-related variables. When formulating our hypotheses and specifying our growth model, we make liberal use of geographic mapping software to describe the data so as to “paint” a picture of where growth spots exist. Our empirical estimation indicates amenities, state and local tax burdens, population density, amount of primary agriculture activity, and demographics have important impacts on economic growth. Copyright 2007, Oxford University Press.

This paper examines the incentive of atomistic agricultural producers within a specific geographical region to differentiate and collectively market products. We develop a model that allows us to analyze the market and welfare effects of the main types of real-world producer organizations, using it to derive economic insights regarding the circumstances under which these organizations will evolve, and describing implications of the results obtained in the context of an ongoing debate between the European Union and United States. As the anticipated fixed costs of development and marketing increase and the anticipated size of the market falls, it becomes essential to increase the ability of the producer organization to control supply in order to ensure the coverage of fixed costs. Whenever a collective organization allows a market (with a new product) to exist that otherwise would not have existed there is an increase in societal welfare. Counterintuitively, stronger property right protection for producer organizations may be welfare enhancing even after a differentiated product has been developed. The reason for this somewhat paradoxical result is that legislation aimed at curtailing the market power of producer organizations may induce large technological distortions.

Differences in the way GM grains are treated in the US and the EU motivate the question of whether there is a single correct policy towards identification of such products. This issue is addressed here, based on a mathematical model that assumes that the cost of maintaining identity on non-GM markets is borne by the non-GM industry. The economic welfare impacts of introducing GM technology depend on the levels of consumer concern and production cost savings, and the cost of maintaining non-GM grain identity. The introduction of a new cost-reducing GM technology often increases total welfare as one might expect. However, society loses when GM production cost-savings are small and consumers are highly concerned about GM grain because introducing GM technology creates a need to preserve the identity of non-GM grain. Taken together, the results suggest that the US may have maximized welfare by not requiring labeling of GM corn and soybeans, and that the recently approved EU legislation enforcing labeling of GM crops also makes sense in the EU. The reason for this is that consumer concern in the EU appears to be greater than in the US, and fewer EU producers stand to benefit from cost savings in GM corn and soybean production.