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Weak-form rationality of fixed-event forecasts implies that forecast revisions should not be correlated. However, significant positive correlations between consecutive forecast revisions were found in most USDA forecasts for U.S. corn, soybeans, wheat, and cotton. This study developed a statistical procedure for correction of this inefficiency which takes into account the issue of outliers, the impact of forecast size and direction, and the stability of revision inefficiency. Findings suggest that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts.

The increased availability and adoption of precision agriculture technologies has left researchers to grapple with how to best utilize the associated high-frequency large-volume of data. Since the wealth of information from precision equipment can easily be aggregated in real-time, this poses an interesting question of how aggregates of high-frequency data may complement, or substitute for, publicly released periodic reports from government agencies.

This study utilized advances in event study and yield projection methodologies to test whether simulated weekly harvest-time yields potentially drive futures price that are significantly different from the status quo. The study employs a two-step methodology to ascertain how corn futures price reactions and price levels would have evolved if market participants had access to weekly forecasted yields. The marginal effects of new information on futures price returns are first established by exploiting the variation between news in publicly available information and price returns. Given this relationship, the study then estimates the counterfactual evolution of corn futures price attributable to new information associated with simulated weekly forecasted yields.

The results show that the market for corn exhibits only semi-strong form efficiency, as the “news” provided by the monthly Crop Production and World Agricultural Supply and Demand Estimates reports is incorporated into prices in at most two days after the release. As expected, an increase in corn yields relative to what was publicly known elicits a futures price decrease. The counterfactual analysis suggests that if weekly harvest-time yields were available to market participants, the daily corn futures price will potentially be relatively volatile during the harvest period, but the final price at the end of the harvest season will be lower.

The study uses simulation to show the potential evolution of corn futures price if market participants had access to weekly harvest-time yields. In doing so, the study provides insights centered around the ongoing debate regarding the economic value of USDA reports in the presence of growing information availability within the private sector.

This study aims to investigate the role of international price volatility and inventories on domestic market price dynamics in the case of agricultural commodities.

A structural model is employed to uncover relationships among commodity price, price volatility, inventories and convenience yield. Monthly producer price data along with annual data on trade, consumption, inventories and tariffs for 71 countries and 13 commodities covering 2010–2019 are assembled to estimate the model. With a first-stage Least Absolute Shrinkage and Selection Operator (LASSO) estimator to identify the best instrument set, a nonlinear approach is used to estimate the model.

Results show that international market information plays a critical role in domestic market price dynamics. International price volatility has a stronger effect on domestic prices than that of international inventories.

Current upheaval in commodity markets requires an understanding of how prices move together and inventories affect that movement. A country's internal price is not independent of the effects of global market events.

Although hypotheses exist that global market information (volatility and inventories) helps countries manage domestic commodity prices, there have been limited studies on this relationship, especially with a structured model and cross-country data.

The purpose of this paper is to test for bubbles in the US hard red spring (HRS) wheat market from 2004 to 2014, with particular focus on 2007-2008 when the market experienced record-high price volatility.

The authors apply a recently developed bubble testing procedure to cash, rolling nearby futures contract, and individual futures contract prices of HRS wheat sampled at daily, weekly, and monthly frequencies. Two critical value (CV) sequences are derived to date-stamp bubbles, one from Monte Carlo simulations, and the other from recursive wild bootstrap procedure.

The authors find that regardless of the price series adopted, sampling frequency chosen, or CVs used, bubbles account for only a small fraction of the HRS wheat price behavior during 2004-2014. However, much sharper differences are detected regarding the key policy question of bubble behavior during 2007-2008. Individual futures contract prices during this period suggest only a minimal number of bubble days, while rolling nearby futures and cash prices indicate bubbles lasting much longer. Since theory suggests that prices for individual futures contracts are more likely to provide a clearer test of bubble components, the authors conclude there is little evidence that the spike in spring wheat prices to $25 per bushel in 2007-2008 was a bubble.

This paper is the first in the literature to examine the sensitivity of bubble testing to different types of data, sampling frequencies, and inference procedures.

The purpose of this paper is to examine the market impacts of US biofuels and biofuel policies.

Two methods of analysis are employed. The first method looks back in time and estimates what US crop prices would have been during the 2005 to 2009 marketing years under two scenarios. The second method of analysis is forward looking and examines the market impacts of the blender tax credit and mandate on the distribution of prices in the 2011 calendar and marketing year.

The results developed in the previous two sections show that US ethanol policies modestly increased maize prices from 2006 to 2009 and that market impacts of the policies will be larger under tighter market conditions.

More flexible US biofuel policy including removing the blenders tax credit, which does not help US biofuel industry as long as the mandates are in place, and relaxing blending mandates when feedstock supplies are low.

This report makes three contributions to understanding the extent to which US biofuel policies contribute to higher agricultural and food prices. First, estimates of the impact of US ethanol policies on crop and food prices reveal that the impacts of the subsidies were quite modest. The second contribution is to provide estimates of the impact on agricultural commodity prices and food prices from market‐driven expansion of ethanol. The final contribution of this report is improved insight into how current US biofuel policies are expected to affect crop prices in the near future.

Greater attention to and anxiety about farm animal welfare emerged at the end of the 20th century, as worries over food safety and food quality (connected to the BSE, FMD, avian influenza and other epidemics) pushed farm animal welfare into public discourse and political debate. This chapter looks at one of the ways in which consumers’ concerns and anxieties about animal welfare are addressed by the Soil Association (the United Kingdom), whose standard is based on a scheme of production that endorses animals’ natural life in the case of certification of organic eggs in the United Kingdom. Drawing on STS approaches it addresses the processes of producing ‘naturalness’ as food ‘attribute’ (to borrow from economics) and how ‘the natural life of hens’ is achieved in the context of eggs’ production.

The purpose of this study is to analyze structural changes that took place in the cotton industry and develop a statistical model that reflects the current drivers of U.S. upland cotton prices. This study concludes that a structural break in the U.S. cotton industry occurred in 1999, and that world cotton supply has become an important determinant of U.S. cotton prices. The model developed here forecasts changes in U.S. cotton price based on changes in U.S. cotton supply, changes in U.S. stocks-to-use ratio (S/U), changes in China's net imports as a share of world consumption, the proportion of U.S. cotton engaged in the loan program, and changes in world supply of cotton.

The United States (US) sugar program protects domestic sugar farmers from unrestricted imports of heavily-subsidized global sugar. Sugar-using firms (SUFs) criticize that program for causing US sugar prices to be higher than world sugar prices. This study examines the financial performance of publicly traded SUFs to determine if they are performing at an economic disadvantage in terms of accounting profitability, risk and economic profitability compared to other industries.

Firm-level financial accounting and market data from 2010 to 2019 were utilized to construct financial metrics for publicly traded SUFs, agribusinesses and general US firms. These financial metrics were analyzed to determine how SUFs compare to their agribusiness peer group and general US companies. The comprehensive financial analysis in this study covers: (1) accounting profit rates, (2) drivers of profitability, (3) economic profit rates, (4) trend analysis and (5) peer comparisons. Quantile regression analysis and Wilcoxon–Mann–Whitney statistics are employed for statistical comparisons.

Regarding various profitability and risk measures, SUFs outperform their agribusiness peers and the general benchmark of all US firms in terms of accounting profit rates, risk levels and economic profit rates. Furthermore, compared to other US industries using the 17 French and Fama classifications, SUFs have the highest return on investment and economic profit rate―measured by the Economic Value Added® margin―and the second-lowest opportunity cost of capital, measured by the weighted average cost of capital.

This study finds nothing to suggest that the US sugar program hinders the financial success of SUFs, contrary to recent claims by sugar-using firms. Notably in this analysis is the evaluation of economic profit rates and a series of robustness techniques.

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.