Context or Problem: High commodity prices reflecting increased global demand have encouraged the development of high-input management systems for soybean production in the US. Such systems are promoted as high-yield low-risk that can secure food production and enhance farmers' income.Objective or Research Question: The objective of this work was to assess the performance and downside yield risk of high-and low-input soybean management systems across the US.Methods: The high-input cropping system included fungicide, insecticide and biological seed treatments, soil and foliar fertilizer and foliar fungicide and insecticide applications. None of these inputs were applied in the low input system. Data were analyzed using a moment-based approach by evaluating the mean, variance, skewness, and kurtosis of soybean yield conditional on state (average of all locations in a state) and cropping system.Results: We found that the field-level yield effect of high-input systems was inconsistent (-4.9 to 12.7% of average yield) and state-specific. Although high-input management may increase mean soybean yield across the US, it likely increases the variance (risk) of soybean yields as well. Our analysis shows that the average cost of yield risk decreased minimally (<3% of average yield) in each state when switching from a low-input system to a high-input system. Conclusions: We conclude that high-input systems do not consistently and significantly protect soybean yield from downside yield risk or risk of extreme yields at the field level and should not necessarily be considered a broad-scale profitable and sustainable food-securing practice.Implications or Significance: These results further support the use of integrated pest management (IPM) for making input decisions instead of relying on prophylactic input applications as insurance against yield-limiting factors. We argue that future studies of food security and crop production should be region-specific and focus on identifying management practices with the greatest yield potential based on IPM practices rather than recom-mending broad-scale intensive management systems as insurance practice.

CONTEXT: Agricultural sustainability has three main pillars: a healthy environment, economic profitability, and social equity. Most programs, however, focus on environmental benefits when establishing protocols for farms, de-emphasizing or ignoring the economic and social aspects. This focus on the environmental aspects of agri-cultural sustainability misses the importance of economic factors that are commonly found to be key de-terminants of farmer adoption of best management practices. More research on the economic effects of sustainability practices on farm income and risk would improve understanding and facilitate communication with farmers about the tradeoffs and risks when using the protocols. OBJECTIVES: Our study has two primary objectives. First, we quantify the effects of sustainable practice adoption on the mean, variance, and skewness of yield for U.S. corn farmers. Second, based on the estimated yield risk model, we quantify the effects of sustainable practice adoption on farmer returns and the cost of risk to better understand the impacts of sustainability on farmer welfare. METHODS: Using the 2010 Agricultural Resource Management Survey, we construct a composite indicator proxying the intensity of sustainable practice adoption for 73 practices mostly intended to reduce soil erosion and improve nutrient and pest management. We use this indicator in a flexible moment-based approach to analyze field-level corn yield data. Estimation results are used to quantify expected farmer returns and the cost of risk (the risk premium). The cost of risk is decomposed into costs from variance (symmetric variation around the mean) and from skewness (downside risk from unusually low yields).RESULTS AND CONCLUSIONS: Results indicate that substantial opportunities exist for US corn growers to increase sustainable practice adoption. Increased sustainable practice adoption significantly increased mean yield and decreased yield variance and skewness. In addition, increased adoption of sustainable practices increased expected farmer returns and, in most cases, also increased the cost of risk mostly from increased costs from lower skewness (greater downside risk). The positive effect of increased expected income always dominated the negative effect of increased costs of risk, so that on average farmer welfare increased with higher levels of sustainable practice adoption.SIGNIFICANCE: Our study helps fill a literature gap by evaluating the economic aspects of sustainability, specifically farm income and the cost of risk. The results provide critical information for policy making and program establishing.

This chapter discusses aspects of human behavior that affect the evolution of insect resistance to management and how a better understanding of this behavior can be used to improve insect resistance management (IRM). While IRM can be thought of in terms of individual farmers, Clark and Carlson (1990) find that individual farmers treat insect resistance as a common property problem, which means they do not have the incentive to manage it appropriately from a societal perspective. Therefore, this chapter focuses on the problem from a public policy perspective. From this perspective, government regulators like the US Environmental Protection Agency (EPA) or stakeholder groups like the Arizona Cotton Growers Association are interested in formulating and implementing IRM policies in order to promote pest management practices that provide a greater benefit to society or association members. Since pest management decisions are ultimately made by farmers, the regulator or stakeholder group can only influence IRM indirectly. This creates what is referred to as a principal-agent problem. The principal would like the agent to use prescribed management strategies that may not be wholly in the interest of the agent. Therefore, the agent's response to the principal's prescription plays an important role in the principal's ability to achieve his/her objectives. This principal agent problem can be further complicated by the fact that farmer decisions are influenced by the decisions of seed, chemical, and other farm input suppliers through which regulators may choose to act.

We investigate the effects of sustainable practice adoption on the mean, variance, and skewness of corn yield and farmer net returns. Using the 2010 Agricultural Resource Management Survey for corn, a composite indicator proxying the level of sustainable practice adoption for 73 practices is combined with a flexible moment-based approach to analyze field-level corn yield data. Combined with cost data, the results are used to evaluate farmer welfare and the costs of risk based on certainty equivalent returns and the risk premium. The risk premium is then decomposed into costs from yield variability and from downside risk (unusually low yields). Results show increased use of more sustainable production practices among U.S. corn farmers increased mean yield and expected farmer returns, even though it also increased the cost of risk to farmers.

Soybean [Glycine max (L.) Merr.] is the most important legume crop in the United States, being rich in essential amino acids, essential fatty acids, and oil. However, soybean protein content has been declining for decades, and a comprehensive ecosystem-based approach to address that decline does not exist. Furthermore, feed production comprises about 90 percent of greenhouse gas emissions from pig and poultry production, so improving soybean meal protein has significant farm revenue and emissions implications. Our goal was to develop a system model that characterizes and quantifies the link between improved soybean protein, improved corn demand, and reduced emissions. Our research objectives were to (i) quantify and predict the feed value of improving soybean protein, and volume required, (ii) quantify the effect of improved soybean meal protein on increasing feed corn demand, and (iii) quantify the impact of increased soybean meal protein on emissions of swine and poultry operations. Our results show that when soybean meal protein increases from 44 percent to 50 percent, corn demand increases up to 13.8 percent, lifecycle emissions decrease by up to 4.6 percent in pig diets and decrease by up to 4.5 percent in poultry diets; while improving implied soybean value by enough to offset the volume lost by improving protein. Our findings also indicate that as soybean protein content declined, crop farmers have lost billions of dollars in corn and soybean revenue since about 2000 to synthetic amino acids and corn distillers-dried grain with solubles (DDGS), and GHG emissions in feed has been gradually increasing. These findings are significant to AOCS membership because they illustrate how GHG emissions can be reduced by improved soybean protein, thereby delivering on farmer goals of increasing feed value, and supporting food company goals of improving environmental sustainability.

Despite the promise of precision agriculture for increasing the productivity by implementing site-specific management, farmers remain skeptical and its utilization rate is lower than expected. A major cause is a lack of concrete approaches to higher profitability. When involving many variables in both controlled management and monitored environment, optimal site-specific management for such high-dimensional cropping systems is considerably more complex than the traditional low-dimensional cases widely studied in the existing literature, calling for a paradigm shift in optimization of site-specific management. We develop a machine learning algorithm that enables farmers to efficiently learn their own site-specific management through on-farm experiments. We test its performance in two simulated scenarios-one of medium complexity with 150 management variables and one of high complexity with 864 management variables. Results show that, relative to uniform management, site-specific management learned from 5-year experiments generates $43/ha higher profits with 25 kg/ha less nitrogen fertilizer in the first scenario and $40/ha higher profits with 55 kg/ha less nitrogen fertilizer in the second scenario. Thus, complex site-specific management can be learned efficiently and be more profitable and environmentally sustainable than uniform management.

Previous literature primarily focused on consumers’ preference for specific sustainable attributes, such as a product being organic, eco-friendly, locally grown, and fair trade. Little is known about consumers’ preference for sustainable program features. We conduct two online choice experiments with U.S. consumers and find that consumers consistently care about farmers’ engagements in sustainable programs, and they are willing to pay a price premium for products from such programs. Consumers also value promoting science in sustainability, establishing concrete measurements of sustainability, and communicating sustainable practices with consumers and downstream industries. We apply the latent class logit model to investigate the potential segmentation of consumers. Three consumer segments are identified based on participants’ heterogeneity in preferences. Our research provides useful information for designing new sustainability programs.

BACKGROUND Farmers around the world have used Bt maize for more than two decades, delaying resistance using a high-dose/refuge strategy. Nevertheless, field-evolved resistance toBacillus thuringiensis(Bt) toxins has been documented. This paper describes a spatially explicit population genetics model of resistance to Bt toxins by the insectOstrinia nubilalisand an agent-based model of farmer adoption of Bt maize incorporating social networks. The model was used to evaluate multiple resistance mitigation policies, including combinations of increased refuges for all farms, localized bans on Bt maize where resistance develops, area-wide sprays of insecticides on fields with resistance and taxes on Bt maize seed for all farms. Evaluation metrics included resistance allele frequency, pest population density, farmer adoption of Bt maize and economic surplus. RESULTS The most effective mitigation policies for maintaining a low resistance allele frequency were 50% refuge and localized bans. Area-wide sprays were the most effective for maintaining low pest populations. Based on economic surplus, refuge requirements were the recommended policy for mitigating resistance to high-dose Bt maize. Social networks further enhanced the benefits of refuges relative to other mitigation policies but accelerated the emergence of resistance. CONCLUSION These results support using refuges as the foundation of resistance mitigation for high-dose Bt maize, just as for resistance management. Other mitigation policies examined were more effective but more costly. Social factors had substantial effects on the recommended management and mitigation of insect resistance, suggesting that agent-based models can make useful contributions for policy analysis.

BACKGROUND:Most US maize, soybean and cotton farmers use Bt crops, insecticidal seed treatments, soil-applied insecticides, and foliar sprays to manage insect pests. Given the global economic importance of these crops, we examine farmer benefits of this insecticide use. Using a telephone survey, we document pest management practices and concerns, estimate adoption and farmer perceived values for these practices, and determine factors besides yield and cost that impact adoption and perceived value. RESULTS:Seed-based technologies (Bt seed, seed treatments) dominated insecticide use. Almost 80% of respondents' planted hectares used Bt crops and more than half used seed treatments, while about one-sixth used soil insecticides and one-sixth to one-third used foliar insecticides. Perceived farmer values per treated hectare were greatest for Bt cotton and foliar insecticides in cotton, especially after first bloom. Values for maize and other cotton insecticide uses were greater than for soybean. Aggregating over treated areas, the largest total values for each crop were for seed-based technologies. In addition to yield and cost, farmers showed significant concern for economic risk and human and environmental safety when making pest management decisions. These non-monetary concerns significantly affected the likelihood farmers used these practices and their perceived value. CONCLUSION:For these crops, seed-based insecticides dominate farmer insecticide use and the value they derive from insecticides. Because seed purchase is months before planting, farmers rely on risk-based integrated pest management to make pest management decisions, weighing both monetary and non-monetary factors when deciding whether the risks are sufficient to justify the use of insecticides. © 2020 Society of Chemical Industry.

With gene drives for agricultural pest control on the horizon, a survey suggests the public is receptive but concerned about risk.

Managing and mitigating agricultural pest resistance to control technologies is a complex system in which biological and social factors spatially and dynamically interact. We build a spatially explicit population genetics model for the evolution of pest resistance to Bt toxins by the insect Ostrinia nubilalis and an agent-based model of Bt maize adoption, emphasizing the importance of social factors. The farmer adoption model for Bt maize weighed both individual profitability and adoption decisions of neighboring farmers to mimic the effects of economic incentives and social networks. The model was calibrated using aggregate adoption data for Wisconsin. Simulation experiments with the model provide insights into mitigation policies for a high-dose Bt maize technology once resistance emerges in a pest population. Mitigation policies evaluated include increased refuge requirements for all farms, localized bans on Bt maize where resistance develops, areawide applications of insecticidal sprays on resistant populations, and taxes on Bt maize seed for all farms. Evaluation metrics include resistance allele frequency, pest population density, farmer adoption of Bt maize and economic surplus generated by Bt maize. Based on economic surplus, the results suggest that refuge requirements should remain the foundation of resistance management and mitigation for high-dose Bt maize technologies. For shorter planning horizons (< 16 years), resistance mitigation strategies did not improve economic surplus from Bt maize. Social networks accelerated the emergence of resistance, making the optimal policy intervention for longer planning horizons rely more on increased refuge requirements and less on insecticidal sprays targeting resistant pest populations. Overall, the importance social factors play in these results implies more social science research, including agent-based models, would contribute to developing better policies to address the evolution of pest resistance. Author Summary Bt maize has been a valuable technology used by farmers for more than two decades to control pest damage to crops. Using Bt maize, however, leads to pest populations evolving resistance to Bt toxins so that benefits decrease. As a result, managing and mitigating resistance has been a serious concern for policymakers balancing the current and future benefits for many stakeholders. While the evolution of insect resistance is a biological phenomenon, human activities also play key roles in agricultural landscapes with active pest management, yet social science research on resistance management and mitigation policies has generally lagged biological research. Hence, to evaluate policy options for resistance mitigation for this complex biological and social system, we build an agent-based model that integrates key social factors into insect ecology in a spatially and dynamically explicit way. We demonstrate the significance of social factors, particularly social networks. Based on an economic surplus criterion, our results suggest that refuge requirements should remain the foundation of resistance mitigation policies for high-dose Bt technologies, rather than localized bans, areawide insecticide sprays, or taxes on Bt maize seed.

As complete host resistance in soybean has not been achieved, Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum continues to be of major economic concern for farmers. Thus, chemical control remains a prevalent disease management strategy. Pesticide evaluations were conducted in Illinois, Iowa, Michigan, Minnesota, New Jersey, and Wisconsin from 2009 to 2016, for a total of 25 site-years (n = 2,057 plot-level data points). These studies were used in network metaanalyses to evaluate the impact of 10 popular pesticide active ingredients, and seven common application timings on SSR control and yield benefit, compared with not treating with a pesticide. Boscalid and picoxystrobin frequently offered the best reductions in disease severity and best yield benefit (P < 0.0001). Pesticide applications (oneor two-spray programs) made during the bloom period provided significant reductions in disease severity index (DIX) (P < 0.0001) and led to significant yield benefits (P = 0.0009). Data from these studies were also used in nonlinear regression analyses to determine the effect of DIX on soybean yield. A three-parameter logistic model was found to best describe soybean yield loss (pseudo-R-2 = 0.309). In modern soybean cultivars, yield loss due to SSR does not occur until 20 to 25% DIX, and considerable yield loss (-697 kg ha(-1) or -10 bu acre(-1)) is observed at 68% DIX. Further analyses identified several pesticides and programs that resulted in greater than 60% probability for return on investment under high disease levels.

Groundwater management questions often require understanding how water levels respond to precipitation. However, direct observations of precipitation alone rarely explain variation in water levels. This study introduces a modified method for comparing cumulative precipitation anomalies to groundwater level variation. This method transformed gridded monthly precipitation data from 1895 to 2018 into monthly deviations from different moving mean lengths at 90 USGS groundwater monitoring locations in Wisconsin. The precipitation data was then compared to water level variation at each site and correlations were calculated. The average optimal a priori moving mean window length for all sites was identified as 60 months. Fifty-four percent of the monitoring wells were moderately to highly correlated the cumulative deviation from 60-month precipitation and fewer than 30% were uncorrelated. Well depth, aquifer classification and location were tested as potential factors influencing the strength of correlation with groundwater levels and optimal mean length. Aquifer classification had no effect on correlation or optimal moving mean length indicating aquifer response rates are similar in both bedrock and unconsolidated aquifers. Correlation strength was also independent of well location. Well depth and casing in Cambrian/Ordovician formations were the only variables with a weak but statistically significant effect on correlation between the precipitation deviation and water level. This work illustrates how this method can help diagnose factors affecting monitoring well response, identify inconsistencies in a monitoring record and generate hypotheses regarding aquifer response to precipitation. This method leverages easily accessible datasets to serve as a starting point that engineers, groundwater professionals and resources managers can use to generate and test hypotheses about sites without prior knowledge of the geology or aquifer properties, extraction rates, and land cover.

Consumer interest in locally grown produce continues to increase in the USA. Small, diversified vegetable farms, including those managed organically, have been important contributors to meet this growing demand for local product. To be profitable in these markets, farmers must be able to appropriately price their products to cover production costs and provide themselves and their employees a living wage. Questions remain, however, as to the most effective method of assessing the cost of production of specific crops on these farms, in part due to the variability in labor inputs associated with diversified farming strategies. This study used a participatory approach to investigate both methodologies for varied widely, with high coefficients of variation calculated for all values, indicating high farm-to-farm variability in labor required for seasonal activities. Farmers reported both challenges with data collection, as well as successes in using data analysis to guide management decisions. This ongoing work highlights the value of collecting farm-specific data for use in cost-of-production determinations.

The federal crop insurance program is a major risk management program for U.S. farmers to manage production risks, but federal spending for the premium subsidy has been much debated. This study empirically estimates county-level demand for the two primary crop insurance policies – revenue and yield insurance – and allows substitution between the policies. Results show average own price elasticities of around -0.2 to -0.3 for insured acres and -0.3 to -0.4 for liability per planted acre, with significant cross-price effects indicating the importance of accounting for substitution between yield and revenue insurance. Based on our estimation results, we simulate policy scenarios to project the impact that premium subsidy reductions have on crop insurance participation and total insured liability. Policy simulations suggest a 30% reduction in premium subsidies would result in the share of planted acres insured to decrease 0.04% for yield insurance and 4.6% for revenue insurance, and the average liability per planted acre to decrease $0.23 for yield insurance and $23.41 for revenue insurance. State-specific results are also presented. These results can help policy makers better understand the tradeoffs for crop insurance and overall government support for agriculture.

The last few decades have seen a rapid increase in corn production, making corn the most important cereal in the world. This evolution is due in large part to rapid productivity growth for corn. Both improved genetics and improved farm management have contributed to large increases in corn yield. The paper reviews how genetics, biotechnology and management have interacted to increase agricultural productivity and reduce farm risk exposure. It documents the stellar performance of corn in terms of productivity growth. It also discusses the recent evolution of corn markets and evaluates the prospects for the future.