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Herbicide use is widespread in agricultural production to control weeds prior to and after planting and to “burndown” weeds in the spring for conservation tillage. Whether conservation tillage adoption leads to higher herbicide usage has been a question of policy relevance for decades in the United States. Older U.S. studies using standard statistical and economic techniques have not consistently demonstrated higher herbicide usage levels among producers practicing conservation tillage, but these studies did not fully account for other practices, economic, or agronomic factors. To provide a more timely and comprehensive understanding of the importance of herbicides to conservation tillage, this study achieves two objectives with the most recent, nationally representative data from the US Department of Agriculture. First, it describes trends and compares conservation tillage and herbicide usage among field corn and soybean producers, similar to previous studies using standard economic techniques. Second, a Classification and Regression Tree (CART) model is employed—a novel methodology relative to previous studies that offers distinct advantages over traditional regression modeling—to evaluate the importance of herbicide use for conservation tillage adoption while accounting for other factors. Pairwise mean comparisons for field corn and soybeans indicated that herbicide usage pre-emergence was significantly higher with conservation tillage, but there was no consistent, significant differences in herbicide usage post-emergence. The CART analysis (with prediction accuracy ranging from 68–72%) also showed that pre-emergent use of glyphosate was the strongest predictor (with predicted probabilities from 0.83–0.86) of conservation tillage for field corn in 2016 and soybeans in 2018. Other factors such as the use of crop rotations, highly erodible land, region, and farm size were also strong predictors of conservation tillage. These findings highlight the importance and complexity of herbicide use in the adoption of conservation tillage for U.S. field corn and soybeans.

Concentration in animal-based protein industries in the United States continues to garner the interest of policymakers, researchers, and consumers alike. We assess the impacts of industry concentration on animal productivity and downstream prices in the US broiler chicken industry between 1991 and 2019. We compile a dataset that matches annual, plant-level information on ownership and sales for all poultry processing facilities in the United States with market-level wholesale composite prices and bird yields. Consolidation over the last three decades has greatly contributed to industry concentration, leading to higher wholesale composite broiler prices (16.3%) and gains in animal productivity (2.4%).

Significant plant- and industry-wide disruptions have occurred in the U.S. meatpacking industry during the past several years. The result has been a reinvigorated interest in the possibility that industry concentration has facilitated anticompetitive behavior and a torrent of public policy proposals to improve resiliency. In this paper, we provide a contemporary synopsis of meat processing concentration statistics with the use of annual plant-level food safety and inspection service (FSIS) data that cover all federally inspected livestock processing facilities in the U.S. for the past 30 years. Beyond considering traditional concentration measures (e.g., CR4 and HHI), we exploit the plant-level nature of the data and consider trends in processing facility consolidation, ownership changes, and how regional procurement markets have changed over time.

Background: Climate change is altering the growing conditions under which agricultural crops are produced, putting at risk our ability to grow enough high quality food now and for future generations. At the same time, the production, processing, transportation, distribution, and sale of food causes greenhouse gas emissions (GHGEs) that contribute to climate change. Consumers may be able to alter their food choices to mitigate GHGEs caused by the food system. However, little is known about the carbon footprint of food choices in the U.S. or consumer interest in mitigating GHGs caused by the food system. Therefore, the objective of this research is to determine if there are GHGE mitigation opportunities through changes in U.S. consumer food choices and to assess consumer responses to climate driven quality changes in foods and beverages. Methods: This project used a variety of methods, including economic input-output life cycle assessment, stated preference valuation, ordinary least squares linear regression, multinomial logistic regression, and hedonic sensory evaluation using both primary and secondary data sources. Objective 1 was to estimate the life cycle greenhouse gas emissions (GHGEs) caused by U.S. household food expenditures using data collected from the National Household Food Acquisition and Purchase Survey and to assess association between GHGEs and household sociodemographics. Objective 2 used data generated in Objective 1 to determine if lower spending on meat by U.S. households is associated with reduced GHGEs, improved nutritional quality, greater accordance with the 2010 Dietary Guidelines for Americans, and lower food costs. Objective 3 assessed consumer responses to climate driven quality changes in brewed green tea and quantified consumer willingness to pay for climate mitigation in tea production and other product attributes such as environmental sustainability in production, nutrition, and ethical production attributes. Results: U.S. household food expenditures accounted for 6.8% of total U.S. GHGEs in 2013. The majority of GHGEs come from the purchase of meat and animal products and are generated at the agricultural stage of the food supply chain. More highly educated and white households generated more emissions from food expenditures compared to non-white households and those with lower educational attainment. Households purchasing less meat as a share of total food spending generated lower GHGEs, spent less on food, and purchased significantly lower amounts of unhealthy macronutrients and of some essential micronutrients per average U.S. adult. Among a sample of U.S. tea consumers there is significant interest for protecting the quality of consumable food and beverage products from the adverse impacts of climate change and for lowering GHGEs in agricultural production. Implications: U.S. food expenditures generate a significant share of total U.S. emissions. Reduced purchase of meats in the U.S. can help to mitigate GHGEs from the food supply chain while simultaneously helping U.S. consumers potentially improve the nutritional quality of their diets and lower food costs. However, U.S. consumers may need nutritional guidance when switching away from meat to ensure adequate intakes of select micronutrients. The results here suggest there exists a significant opportunity to market low emissions agricultural practices to U.S. consumers as a means of mitigating GHGEs.

The objective of this research is to estimate the economic impact of climate change on tea production in China. We use historical weather and production data from 1980 to 2011 to construct a yield response model from panel data that estimates the partial effect of specific weather factors on tea yields in China, with a specific focus on monsoon dynamics. We base our analysis upon methodologies employed in previous studies, many of which use a yield response model to estimate the impact climate change on crop yields or other economic indicators of agricultural productivity. Previous studies have tended to focus mostly on staple crops like maize as well as other food crops like fruits and vegetables, but tea is an important export crop for many developing countries. Instead of estimating the monsoon based on historical dates of onset, duration and retrieval we construct the actual approximated onset and retrieval dates based on the year-by-province cumulative precipitation function. To date our research is the first to estimate the monsoon period using this method. Results indicate that a later monsoon onset date, as well as a later monsoon retrieval date have a small but significant negative impact on yields over the last 30 years. A delay in either the monsoon onset or retrieval of only 3-4 days reduced yields by .1% each. However, we did not find a statistical association between the length of the monsoon. Total rainfall during the monsoon period was found to have a small and positive impact on yields, even when controlling for extreme temperatures and low precipitation. We also found a strong positive effect of increased maximum daily temperatures during the monsoon period on tea yields. Spring maximum daily temperatures did not have a significant impact on tea yields. Our results have important implications for tea producer in China in the face of future global climate changes.