Voluntary horizontal sharing of confidential food safety data among companies for joint analysis can improve food safety, efficiency, and decision-making, especially for rare events. Despite its potential benefits, horizontal data sharing in the food industry has lagged, with limited research exploring the reasons for hesitation. To address this gap, we conducted in-depth semi-structured interviews with 27 food industry leaders. Four themes emerged: (1) benefits of data sharing, (2) technical barriers, (3) trust as a determinant of data sharing decisions, and (4) data governance as a solution. Across these themes, we found that companies face trade-offs when deciding to share food safety data, weighing risks against benefits. Data sharing decisions were strongly influenced by trust in stakeholders (e.g., industry peers, regulatory bodies, customers) and in data protection measures. Data governance emerged as a solution to concerns stemming from trust, such as loss of control once data is shared. Taken together, the findings reveal underlying tensions between individual firm incentives and collective benefits, including uneven cost-benefit distributions, opportunism concerns, and participation cost asymmetries. These findings offer timely insights to guide data sharing initiatives and prioritize areas for future research.

The introduction of cover crops, owing to their positive effects on soil organic carbon (SOC) sequestration, is a potential management practice that can mitigate agricultural greenhouse gas emissions. In this study, we leveraged a 42-year-old continuous cotton (Gossypium hirsutum L.) experiment under no tillage, to evaluate the effect of hairy vetch (Vicia villosa; HV) and no cover crop (NC) under N rates of 0 (no fertilizer [NF]) and 67 kg N ha-1 (fertilized [F]), on net global warming potential (GWP) and greenhouse gas intensity (GHGI). The annual SOC sequestration rate was not significantly different in the F (115.1 kg ha-1 year-1) and HV (107.4 kg ha-1 year-1) treatments compared to the NF (103.2 kg ha-1 year-1) and NC (110.8 kg ha-1 year-1) treatments. Soil under HV and F treatments behaved as a net source of GHGs in 2022, with a GWP of 243 and 294 kg CO2-eq ha-1 year-1, respectively. By contrast, in 2023, these treatments were net sinks of GHGs. Despite the increase in cotton lint yield under legume cover cropping and N fertilization, the GHGI followed the same trend as the net GWP, being net source of GHG in 2022 and a net sink in 2023. Nearly all estimated C gains were offset by N2O emissions under these treatments in 2022-2023. Our results indicate that GHG mitigation through the adoption of legume cover cropping within cotton systems in humid subtropical climates is constrained by low soil C sequestration potential and elevated N₂O emissions.

This analysis assessed the partial net returns of a triazole, at-plant fungicide (i.e., Xyway LFR@FMC) including no fungicide (control) and 1.11 L ha-1 rate under three water scenarios in west Tennessee. In 2022, all water regimes and fungicide treatments had positive average partial net returns compared to rainfed (RF) with no fungicide treatment. However, in 2023 due to beneficial rainfall and low disease pressure during the growing season, low yield differences between treatments resulted in negative partial net returns for all treatments compared to RF with no fungicide treatment. The annualized capital recovery cost of the irrigation equipment was one of the reasons for the negative partial net returns across treatments and particularly water regimes for 2023. An additional factor that influenced the partial net returns analysis was the decline in corn prices between the 2022/23 and the 2023/24 marketing year. Although the Xyway LFR@FMC fungicide application can be profitable for corn production, different environmental factors will determine yield and net return differences each year. Clearly, the investment in irrigation systems has a multiyear return on investment and the application of fungicide is completed before weather and disease pressure is known. As such, long-term weather variability will play an important role in the net returns of Xyway LFR@FMC fungicide application.

Biofuels are a fixture of modern US fuels markets due largely to the Renewable Fuel Standard, which was passed 20 years ago. We provide a broad overview of modern federal biofuel policies, highlighting key industry trends and policy controversies. We divide our focus into past versus present issues. Past issues include concerns over food price impacts and land use changes due to the policies, as well as the limited ability of mandates to spur investment and bring down the cost of low-carbon cellulosic ethanol production. Present issues include the increasing importance of state policies and the rise of the renewable diesel industry. We conclude by discussing emerging markets for sustainable aviation fuel and the return of concerns around food price effects, land use change impacts, and the scarcity of low-cost, low-carbon biofuels.

Climate-smart agriculture promises to mitigate climate change by sequestering carbon in soils on working lands. However, this promise faces substantial policy challenges due to ecological variation, costly measurement, and uncertainty. We summarize the latest scientific literature on carbon sequestration in agricultural soils, and we describe the current policy environment. With that background, we present an economic framework for policy analysis. We conclude by emphasizing (a) the need for better measurement and for policy that is robust to poor measurement, and (b) the importance of improving agricultural productivity to avoid future carbon losses from expanded agricultural land use.

Time-of-use (TOU) electricity prices are increasingly being adopted to reduce consumption during the higher marginal cost afternoon hours. There is ample evidence that TOU rates reduce average consumption during the peak price hours of the day, but it is unknown how these energy savings are distributed across days. Using a unique dataset from households with smart thermostats, we find that adopting TOU rates causes large decreases in peak period AC usage, resulting in energy savings that are concentrated on the hottest, highest demand days when the benefits of conservation are the greatest.

While wildlife damages are a relatively small part of crop insurance claims in the United States, reported damages from wildlife are growing and producers are concerned this issue is getting worse. Wildlife agencies advocate for hunting as a control to wildlife damages to crops but to our knowledge, no study has econometrically explored if hunting could reduce crop damage. Therefore, we analyze if county-level white-tailed deer (Odocoileus virginianus) harvest and other factors like habitat and field fragmentation are associated with corn and soybean acres being indemnified due to wildlife damages in Tennessee. We estimate a fractional probit model using crop insurance data to measure crop damages and a county-level white-tailed deer harvest dataset. We find a higher coverage level of a policy increases the likelihood of both corn and soybean acres being indemnified due to wildlife damage. We also find white-tailed deer harvest during the period post-harvest in the previous crop year (November through January) slightly decreases the likelihood of a claim being made due to wildlife damage for corn but was not found to mitigate losses to soybeans. The whitetail hunting season appears to align better with corn production to reduce crop losses than with soybeans. This study will be useful for wildlife and crop agencies in state and federal governments to develop policies to more effectively utilize hunting to reduce crop insurance claims from wildlife damages.

Prevented planting payments reimburse crop producers for losses from not being able to plant. These payments provide critical protection to producers; however, these payments, which are determined using a nationwide, crop-specific coverage factor, have been questioned to induce moral hazard. Depending on the region and crop insurance coverage, payments from this provision exceed producers’ losses. This paper estimates the prevented planting coverage factor by coverage level and region that would equitably reimburse corn and soybean producers for their losses. We find the prevented planting coverage factor has significant variation across coverage levels and location within our study region. The prevented planting coverage factor was found to decline as the policy coverage level increases. The further north in the study region the higher the coverage factor, likely due to increased land rent expenses. The results provide a unique perspective of how these coverage factors would vary to equitably compensate producers for losses, which addresses the moral hazard concerns with prevented planting.

This article presents findings from interviews that were conducted with agriculture and food system researchers to understand their views about what it means to conduct ‘responsible’ or ‘trustworthy’ artificial intelligence (AI) research. Findings are organized into four themes: (1) data access and related ethical problems; (2) regulations and their impact on AI food system technology research; (3) barriers to the development and adoption of AI-based food system technologies; and (4) bridges of trust that researchers feel are important in overcoming the barriers they identified. All four themes reveal gray areas and contradictions that make it challenging for academic researchers to earn the trust of farmers and food producers. At the same time, this trust is foundational to research that would contribute to the development of high-quality AI technologies. Factors such as increasing regulations and worsening environmental conditions are stressing agricultural systems and are opening windows of opportunity for technological solutions. However, the dysfunctional process of technology development and adoption revealed in these interviews threatens to close these windows prematurely. Insights from these interviews can support governments and institutions in developing policies that will keep the windows open by helping to bridge divides between interests and supporting the development of technologies that deserve to be called “responsible” or “trustworthy” AI.

Our food system is complex, multifaceted, and in need of an upgrade. Population growth, climate change, and socioeconomic disparities are some of the challenges that create a systemic threat to its sustainability and capacity to address the needs of an evolving planet. The mission of the AI Institute of Next Generation Food Systems (AIFS) is to leverage the latest advances in AI to help create a more sustainable, efficient, nutritious, safe, and resilient food system. Instead of using AI in isolation, AIFS views it as the connective tissue that can bring together interconnected solutions from farm to fork. From guiding molecular breeding and building autonomous robots for precision agriculture, to predicting pathogen outbreaks and recommending personalized diets, AIFS projects aspire to pave the way for infrastructure and systems that empower practitioners to build the food system of the next generation. Workforce education, outreach, and ethical considerations related to the emergence of AI solutions in this sector are an integral part of AIFS with several collaborative activities aiming to foster an open dialogue and bringing closer students, trainees, teachers, producers, farmers, workers, policy makers, and other professionals.

Incentives in agriculture are highly distorted. It has long been argued that these distortions were a key explanation for differences in supply and productivity across countries, but the empirical evidence is limited. We revisit this issue using data on policy distortions across 63 countries for the period 1961–2011. We estimate the effects of differential changes in agricultural distortions across countries on supply and productivity. We highlight concerns in our analysis and previous work about endogeneity that biases the estimated effect downward—countries that lose comparative advantage are likely to increase support for agriculture. We address these concerns by including country and region-time fixed effects, along with a rich set of controls. Overall, we find evidence that enhanced incentives through policy changes can increase the rate of production growth, with about half of the increase due to productivity increases. This result is strongest in Sub-Saharan Africa where anti-agricultural policies on exports were reduced and in Europe where pro-agricultural policies on imports were reduced, driven largely by external pressure. Endogeneity appears to be strongest in Asia where countries have followed the typical pattern of raising support for agriculture during industrialization due to a rising farm-urban income gap.

Incentives in agriculture are highly distorted. It has long been argued that these distortions were a key explanation for differences in supply and productivity across countries, but the empirical evidence is limited. We revisit this issue using data on policy distortions across 63 countries for the period 1961-2011. We estimate the effects of differential changes in agricultural distortions across countries on supply and productivity. We highlight concerns in our analysis and previous work about endogeneity that biases the estimated effect downward-countries that lose comparative advantage are likely to increase support for agriculture. We address these concerns by including country and region-time fixed effects, along with a rich set of controls. Overall, we find evidence that enhanced incentives through policy changes can increase the rate of production growth, with about half of the increase due to productivity increases. This result is strongest in Sub-Saharan Africa where anti-agricultural policies on exports were reduced and in Europe where pro-agricultural policies on imports were reduced, driven largely by external pressure. Endogeneity appears to be strongest in Asia where countries have followed the typical pattern of raising support for agriculture during industrialization due to a rising farm-urban income gap.

Governments, researchers, and developers emphasize creating “trustworthy AI,” defined as AI that prevents bias, ensures data privacy, and generates reliable results that perform as expected. However, in some cases problems arise not when AI is not trustworthy, technologically, but when it is. This article focuses on such problems in the food system. AI technologies facilitate the generation of masses of data that may illuminate existing food-safety and employee-safety risks. These systems may collect incidental data that could be used, or may be designed specifically, to assess and manage risks. The predictions and knowledge generated by these data and technologies may increase company liability and expense, and discourage adoption of these predictive technologies. Such problems may extend beyond the food system to other industries. Based on interviews and literature, this article discusses vulnerabilities to liability and obstacles to technology adoption that arise, arguing that “trustworthy AI” cannot be achieved through technology alone, but requires social, cultural, political, as well as technical cooperation. Implications for law and further research are also discussed.

Abstract Irrigated cropland continues to increase in Tennessee to sustain yield when seasonal temperatures are high and water deficit occurs. Corn (Zea mays L.) production is directly related to water availability. Inefficient irrigation scheduling can result in excessive or inadequate water applications. The objective of this study was to optimize profitable corn yield using soil water sensors to improve irrigation scheduling. Three irrigation regimes plus a rainfed/check were evaluated in a field study at the University of Tennessee Milan Research and Education Center in 2020 and 2021. Soil water level was monitored with Meter Inc. Teros 21 matric potential sensors and ZL6 soil‐water loggers. Irrigation‐scheduling decisions were based on rainfall, plant growth stages, and soil water sensors. An optimal irrigation schedule was determined within this study that increased yield by 13.0% and 3.0%, respectively, in 2020 and 2021 while also having the lowest cost among irrigation treatments. Results indicate that irrigation scheduling can improve yield, conserve water, and increase economic returns.

The Renewable Fuel Standard (RFS) specifies the use of biofuels in the United States and thereby guides nearly half of all global biofuel production, yet outcomes of this keystone climate and environmental regulation remain unclear. Here we combine econometric analyses, land use observations, and biophysical models to estimate the realized effects of the RFS in aggregate and down to the scale of individual agricultural fields across the United States. We find that the RFS increased corn prices by 30% and the prices of other crops by 20%, which, in turn, expanded US corn cultivation by 2.8 Mha (8.7%) and total cropland by 2.1 Mha (2.4%) in the years following policy enactment (2008 to 2016). These changes increased annual nationwide fertilizer use by 3 to 8%, increased water quality degradants by 3 to 5%, and caused enough domestic land use change emissions such that the carbon intensity of corn ethanol produced under the RFS is no less than gasoline and likely at least 24% higher. These tradeoffs must be weighed alongside the benefits of biofuels as decision-makers consider the future of renewable energy policies and the potential for fuels like corn ethanol to meet climate mitigation goals.

Purpose The purpose of this study is to estimate the amount of cash flow deficit, if any, needed to maintain the operating costs and service debt of a startup cow–calf enterprise. The study compares long-term profitability and risk between starting small and building a herd to full carrying capacity or by starting at desired herd capacity. Design/methodology/approach A dynamic cattle growth model was developed to capture expanding and maintaining the desired herd size. Discounted cash flow (DCF) models over a 15-year period were calculated to estimate net present value (NPV), modified internal rate of return (MIRR) and cash flow deficit to keep the business operating and service debt. Simulation analyses were conducted considering price and production risk. Findings Starting at the desired herd size was preferred, according to NPV/MIRR and cash flow deficit, but the differences were not substantial. Assuming the operation is liquidated at book values, there was a 36.3% probability of this enterprise having a zero or positive NPV. If the conservative terminal value assumption is relaxed up to feasible market values, the cow–calf enterprise is economically attractive at an estimated 2.4% opportunity cost of capital. However, the producer would experience a cash flow deficit during the first seven years, which was simulated to be $14,892 and $15,985 annual for both strategies. Originality/value Innovative methods used in this study include varying the annual opportunity cost of capital as a function of financing decisions, stochastic prices by cattle type and stochastic weaning weights that are a function of a dynamic cattle model.

PurposeThe authors examined the impact of the Market Facilitation Program (MFP) and Coronavirus Food Assistance Program (CFAP) payments to United States agricultural producers on non-real estate agricultural loans.Design/methodology/approachThe authors used quarterly, state-level commercial bank data from 2016–2020 to estimate dynamic panel models.FindingsThe authors found MFP and CFAP payments not associated with the percentage of non-real estate agricultural loans with payments over 90 days late. However, these payments associated with the percentage of non-real estate agricultural loans with payments between 30 and 89 days late. The available data utilized cannot consider when producers received the actual payment and what they specifically did with those funds.Originality/valueThe contribution of this study is for US policymakers and agricultural lenders. The findings could be helpful in designing and implementing future ad hoc payment programs and provide an understanding of potential shortcomings of the current safety net for agricultural producers in the Farm Bill. Additionally, findings can assist agricultural lenders in predicting the impact of ad hoc payments on their distressed loan portfolios.

Farmers and politicians in North Dakota and nearby states claim that dramatic increases in shipments of crude oil by rail in 2013–14 caused service delays and higher costs. We investigate these claims, accounting for other potential sources of rail congestion. We show that grain price spreads between the market hub and regional elevators expanded significantly when crude oil shipments increased. However, the incidence of those effects was borne mostly by buyers paying higher prices at the hub rather than farmers receiving lower prices. The effects differ by the type of grain being transported. Wheat markets were affected much more than corn and soybeans, most likely because shipping delays were more costly for wheat than corn and soybeans. When rail capacity is scarce, railroads use railcar auctions to price discriminate over the time sensitivity of a shipment.