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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.

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 purpose of this study is to examine how US farm sector performed in 2020, the first year of the pandemic. There were significant supply and demand shocks due to the pandemic. Furthermore, there was significant fluctuation in commodity prices and record high government payments in 2020. This study aims to examine the performance and position of US farm sector (financially) to system (and global economy) wide shocks.

The authors examine 2020 values for farm sector financial ratios before and after the onset of the Coronavirus (COVID-19) pandemic using the data from the United States Department of Agriculture to understand the financial position and performance of the US farm sector.

The authors find solvency ratios (which are indicators of the sector's ability to repay financial liabilities via the sale of assets) worsened in 2020 relative to pre-pandemic expectations. Efficiency ratios (which evaluate the conversion of assets into production and revenue) and liquidity ratios (which are indicators of the availability of cash to cover debt payments) showed mixed outcomes for the realized results in 2020 relative to the pre-pandemic forecasts. Four profitability ratios were stronger in 2020 relative to pre-pandemic expectations. All solvency, liquidity and profitability ratios plus 2 out of 5 efficiency ratios for 2020 were weaker than their respective average ratios obtained from 2000 to 2019 data.

This research is one of the first papers to use financial ratios to examine how the US farm sector performed in 2020 compared to expectations prior to the pandemic.

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 outbreak of the novel COVID-19 virus has spread throughout the world, causing unprecedented disruption to not only China's agricultural trade but also the world's agricultural trade at large. This paper attempts to provide a preliminary analysis of the impact of the COVID-19 pandemic on China's agricultural importing and exporting from both short- and long-term perspectives.

This study seeks to analyze how the outbreak of COVID-19 could potentially impact China's agricultural trade. With respect to exports, the authors have pinpointed major disruptive factors arising from the pandemic which have affected China's agricultural exports in both the short and long term; in doing so, we employ scenario analysis which simulates potential long-term effects. With regard to imports, possible impacts of the pandemic regarding the prospects of food availability in the world market are investigated. Using scenario analysis, the authors estimate the potential change in China's food market—especially meat import growth—in light of the implementation of the newly signed Sino-US Economic and Trade Agreement (SUETA).

The results show that China's agricultural exports have been negatively impacted in the short-term, mostly due to the disruption of the supply chain. In the long term, dampened external demand and potential imposition of non-tariff trade barriers (NTBs) will exert more profound and lasting negative effects on China's agricultural export trade. On the other hand, despite panic buying and embargoing policies from some exporting and importing countries, the world food availability and China's food import demand are still optimistic. The simulation results indicate that China's import of pork products, in light of COVID-19 and the implementation of SUETA, would most likely see a sizable climb in quantity, but a lesser climb in terms of value.

Agricultural trade in China has been a focal-point of attention in recent years, with new challenges slowing exports and increasing dependence on imports for food security. The outbreak of the COVID-19 pandemic adds significant uncertainty to agricultural trade, giving rise to serious concerns regarding its potential impact. By exploring the impact of the unprecedented pandemic on China's agricultural trade, this study should contribute to a better understanding of the still-evolving pandemic and shed light on pertinent policy implications.

The purpose of this paper is to use prediction markets to forecast an agricultural event: United States Department of Agriculture's number of cattle on feed (COF). Prediction markets are increasingly popular forecast tools due to their flexibility and proven accuracy to forecast a diverse array of events.

During spring 2008, a market was constructed comprised of student traders in which they bought and sold contracts whose value was contingent on the number of COF to be reported on April 18, 2008. During a nine‐week period, students were presented three types of contracts to forecast the number of COF. To estimate forecasts a uniform price sealed bid auction mechanism was used.

The results showed that prediction markets forecasted 11.5 million head on feed, which was about 1.6 percent lower than the actual number of COF (11.684 million). The prediction market also fared slightly worse than analysts' predictions, which on average suggested there would be about 11.795 million head (an over‐estimate of about 1 percent).

The contribution of this study was not to provide conclusive evidence on the efficacy of using prediction markets to forecast COF, but rather to present an empirical example that will spark interest among agricultural economists on the promises and pitfalls of a research method that has been relatively underutilized in the agricultural economics literature.

With the decline of US net farm income from $123.8 billion in 2013 to $71.5 billion forecasted for 2016, concern has developed regarding the future path of agricultural land values. The purpose of this paper is to examine the relationship between net farm income, cash rents and land values in the state of Kansas and provides insight regarding future land values.

This study estimates partial adjustment models for cash rent and land values and uses those results to infer long-run capitalization rates and earnings multipliers. These models are used to forecast Kansas land values through 2018 and also the long-run price of farmland given 2016 expectations.

Land adjusts to changes in Kansas net farm income slowly with a one-year elasticity of 6.7 percent. The long-run elasticity is 96.9 percent which is very close to the 100 percent suggested by the theoretical income capitalization model. The long-run multiplier for income in Kansas is 21.71 which implies a capitalization rate of 4.61 percent. The estimated results suggest that Kansas land values would peak in 2016 and begin to slowly decline. If market conditions were to remain the same, land values would ultimately decrease to $1,171 per acre, a 28 percent decline from current levels.

Declines of the magnitude in estimated land values could negatively affect the financial condition of the sector. Factors such as a change in the long-run capitalization rate or unexpected supply or demand shocks for agricultural commodities globally could certainly alter the long-term prospects. However, current expectations as of March 2016 suggest that farmers will face difficult conditions over the next few years.

The purpose of this paper is to explore the linkage between agricultural sector and macroeconomic factors with farm financial health. It considers whether agricultural lenders can more accurately anticipate changes in the credit quality of their portfolios by considering broad economic indicators outside the agriculture sector.

This paper examines firm, sector, and macroeconomic drivers of probability of default (PD) migrations from a sample of 153 grain farms of actual lender data from Farm Credit Mid-America’s portfolio. A series of ordered logit models are developed.

Farm-level and sector-level variables have the most significant impact on PD migrations. Equity to asset ratios, working capital to gross farm income ratios, and gross corn income per acre are found to be the most significant drivers of PD migrations. Macroeconomic variables are shown to unreliably forecast PD migrations, suggesting that agricultural lenders should emphasize firm and sector variables over macroeconomic factors in credit risk models.

This paper builds the literature on agricultural credit risk by testing a broader set of sector and macroeconomic variables than previous articles. Also, prior articles measured the direction but not magnitude of PD migrations; the ordered model in the analysis measures both.

The purpose of this paper is to examine the current farm economic downturn and credit restructuring by comparing it with the 1920s and 1980s farm crises from both economic and regulatory perspectives.

This paper closely compares critical economic and regulatory aspects of the current farm downturn with two previous farm crises in the 1920s and 1980s, and equally importantly, the golden eras that occurred before them. This study compares key aggregate statistics in land value, agricultural credit, lending regulations, and also evaluates the situations and impacts on individual farmer households by using three representative case studies.

The authors argue that there are at least three economic and regulatory reasons why the current farm downturn is unlikely to slide into a sudden collapse of the agricultural markets: strong, real income; growth in the 2000s, historically low interest rates; and more prudent agricultural lending practices. The current farm downturn is more likely a liquidity and working capital problem, as opposed to a solvency and balance sheet problem for the overall agricultural sector. The authors argue that the trajectory of the current farm downturn will likely be a gradual, drawn-out one like that of the 1920s farm crisis, as opposed to a sudden collapse as in the 1980s farm crisis.

The review provides empirical evidence for cautious optimism of the future trajectory of the current downturn, and argues that the current downturn is much more similar to the 1920s pattern than the 1980s crisis.