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Motivated by the long‐lasting debate on whether the United States Department of Agriculture's (USDA's) World Agricultural Supply and Demand Estimates (WASDE) forecasts are optimal, we employ an unknown loss method for ex post evaluation which assumes that the USDA forecasters' loss function is unknown. We conduct optimality tests of the WASDE forecasts for corn, soybeans, and wheat published during 1988–2019. Our results suggest that USDA forecasters generally realize optimality during the data‐generating process. Our findings are consistent with previous studies when narrowing down the more general unknown loss function to the symmetric or asymmetric loss function which assumes a specific shape for the loss function. This study provides implications based on the unknown loss function that the USDA forecasters can boost their information set as an alternative way to improve the WASDE forecasts. [EconLit Citations: D84, E37, Q13, Q14]. [ABSTRACT FROM AUTHOR]

Long‐term agricultural baseline projections are revised annually to incorporate new information. We evaluate the effectiveness of the US Department of Agriculture and Food and Agricultural Policy Research Institute crops and farm income baseline revisions to inform policymakers. The revisions are effective in improving the baseline projections in comparison to the initial projections, but they do not outperform the naïve projections. Upward revisions are found to be more effective than downward revisions. We find evidence of predictability and therefore a violation of weak efficiency for the revisions, which results from information rigidity for the crop variables and from information rigidity and strategic smoothing for the farm income variables. [ABSTRACT FROM AUTHOR]

We replicate and extend the analysis of Gale (1990) to provide insight into the role of economic conditions on farm distributions in the United States and abroad. Our underlying hypothesis is that the effects of the economic factors on the change in farm numbers remain the same (direction wise) for different times, farm sizes, and countries/regions. We investigate the role of economic conditions on the disappearing middle farms. We extend the analysis to include the time period 1960–2020 and more economic factors, estimate the models by farm size categories, using seemingly unrelated regression, and apply it to international settings (Brazil and the Eurozone). We find no evidence to support the disappearing middle farm hypothesis, despite a declining trend in the number of US midsize farms. The role of economic factors changes according to farm size and country. Economic factors (e.g., population, financial stress, and infrastructure) are important to explain the increase in small and midsize farm numbers in Brazil, and the decreasing small farm numbers in the Eurozone, showing opposite trends in farm numbers globally. [ABSTRACT FROM AUTHOR]

Agricultural baselines facilitate policy and investment decisions by governments and market participants by providing long‐term projections about the farm sector. Despite their importance in shaping agricultural policy, the agricultural baselines have not been rigorously evaluated. This study evaluates the accuracy and informativeness of two widely used baselines for the US farm sector published by the United States Department of Agriculture (USDA) and the Food and Agricultural Policy Research Institute (FAPRI) in three steps. First, we examine the average percent errors of the projections and perform tests of bias. Second, we use a testing framework based on the encompassing principle to test the predictive content of the projections for each horizon, determining the longest informative projection horizon. Third, we compare the USDA and FAPRI baseline projections using a multi‐horizon framework that considers all projection horizons jointly. We find that prediction error and bias increase with the horizon's length. The predictive content of the baselines projections for most variables diminishes after 4–5 years. The multi‐horizon comparison suggests that neither USDA nor FAPRI projections have uniform or average superior predictive ability over the other for most variables. Our findings are useful for the agencies producing these baselines and for the policymakers, agricultural businesses, and other stakeholders who use them. The study contributes to the recent literature on long‐term agricultural projections and establishes the groundwork for future research inquiries. [ABSTRACT FROM AUTHOR]

This study evaluates a system of USDA's Net Cash Income forecasts, released as part of the farm sector's income statement, which includes crop receipts, livestock receipts, government payments, farm‐related income, and expenses from 1986 to 2017. We examine these forecasts jointly for bias, accuracy, efficiency, and compositional consistency. Our findings demonstrate that underestimation in early Net Cash Income forecasts stems from underestimation in crop and livestock receipts as well as expenses forecasts. While most components except government payments contribute to the improvement in 12‐month‐ahead forecasts, improvements in 9‐month‐out forecasts are mostly due to crop receipts and expenses forecasts, and government payment forecasts were a main source of improvement in 6‐month‐ahead forecasts. Despite the observed biases and inefficiencies, these forecasts are compositionally consistent with the actual outcomes and represent realistic projections of the farm sector accounts. [ABSTRACT FROM AUTHOR]