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Several farm safety net strategies are available to farmers as a source of financial protection against losses due to price instability, government policies, weather fluctuations and global market changes. Producers can employ these strategies combining crop insurance policies with countercyclical policies for several crops and production areas; however, less is known about the efficiency of these strategies in enhancing profit and reducing its variability. In this study, we examine the efficiency of these strategies at minimizing inter crop year farm profit variability.

We utilized relative mean of profit and coefficient of variation, to compare counterfactually calculated farm safety net strategies for a sample of 28,615 observations across 2,486 farms and four dryland crops (corn, soybean, sorghum and wheat) in Kansas spanning nine crop years (2014–2022). A no farm safety net strategy is used as the benchmark for every alternative strategy to ascertain whether a policy customization is statistically different from a no farm safety case.

The general pattern of the results suggests that program combination strategies that have a high-profit enhancement potential necessarily have low profit risk for dryland wheat and sorghum production. On the contrary, such a connection is absent for dryland corn and soybeans production. Low-cost farm safety net strategies that enhance corn and soybeans profits do not necessarily lower profit risks.

This paper is one of the first to use a large sample of actual farm-level observations to evaluate how combinations of safety net programs offered under the Title I (PLC, ARCCO and ARCIC) and XI (FCIP) of the U.S. Farm Bill rank in terms of profit level enhancement and profit risk reduction.

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.

Weaknesses in the grades and standards system in low-income countries across Sub-Saharan Africa undermine the transparency of agricultural markets. In the Democratic Republic of the Congo (DRC), Ghana and Mozambique rice is predominately sold in open bags and if rice price does not reflect its quality, then inefficiencies may lead to consumer welfare losses. Importantly, it is possible that impoverished communities are priced out of the market due to inflated and inefficient prices. The objective of this study is to examine determinates of rice price by estimating the impact of selected rice quality attributes on rice prices in Democratic Republic of the Congo, Ghana and Mozambique.

We collected 363 rice samples from open air markets in Bukavu (DRC), Nampula (Mozambique) and across Ghana in 2019. Each rice sample was analyzed in a food science lab for the quality attributes: percentage of chalk and brokens, chalk impact, length and length-to-width ratio. We used multiple regression analysis to estimate if and to what extent quality attributes were the drivers of price.

Findings suggest that there are irregularities in the Ghanaian market for broken rice and that regardless of quality, imported rice is priced higher than domestic rice. In the DRC and Mozambique, our results indicate price is driven by length and length-to-width ratio in the former and length-to-width ratio in the latter.

Rice samples were purchased from market vendors and thus consumer preferences for attributes were not revealed.

These results provide valuable insight to policymakers regarding the need for proper labeling and regulation of open bag rice sales in an effort to increase consumer welfare and improve food security.