We examine how forest taxation should be designed when tax revenues are used to finance expenditures on wildfire risk mitigation and when forest carbon storage has value. A model is solved sequentially in two stages by a forest tax planner and a representative private landowner. The planner considers two forest tax instruments currently used by state agencies in Oregon: 1) a per acre land tax, and 2) a unit tax on timber harvest. A numerical representation of the model shows that the optimal tax rates depend on whether private landowners are compensated for stored carbon. Our results show that neither the acre-based fee or a harvest tax is able to incentivize the same levels of sequestration as a carbon price. However, a neutral tax like the acre-based fee is preferred when the external benefits of carbon sequestration are captured by the private landowner.

Using county-level data from 1997 to 2018, we examine the effects of unconventional oil and gas (UOG) industry growth on agricultural acreage in the U.S. We find that on average, each active UOG well reduces crop acreage by 3.3 acres in counties with UOG production. However, the impacts vary by region. The relationship is positive in Southwest, U-shaped in Great Plains, and negative in Appalachia. Variations of impacts across regions result from differences in geology and historic developments of energy and agricultural sectors.

This paper decomposes the changes of ChinaÕs agricultural non-point source pollution (ANSP) into five factors through the logarithmic mean Divisia index method: emission intensity, production scale, labor intensification, urbanization, and population scale factors. Moreover, we further explore the contribution of each factor at different agricultural policy stages and the impact of subsidies on emissions. Our main findings show that the emission intensity is the main restraining factor of pollution while production scale plays the greatest effect on aggravating loads. Besides, the incentive effect of agricultural subsidies reduces the emissions but the expansion of government fiscal expenditures will lead to an increase.

The U.S. Department of Agriculture (USDA) publishes monthly Ending Stocks projections, providing an estimate of the end-of-marketing-year inventory of a particular commodity, which effectively summarizes its supply and demand outlook. By comparing USDAÕs projections of balance sheet variables against their realized values from marketing years 1992/3 to 2019/20, we decompose ending stocks forecast errors into errors of the other supply and demand components. We apply a decision-tree-based ensemble Machine Learning (ML) algorithm, the Extreme Gradient Boost Tree (EGBT), that uses a gradient boosting framework and is robust to multicollinearity. Our results indicate that export and production misses are the key contributors to ending stocks projection errors. Because foreign imports of U.S. products are likely tied to foreign production deficits, we likewise investigate how U.S. export errors are linked to USDAÕs foreign production and export forecast misses, country-by-country, and show that misses on production and export levels in China, Mexico, Brazil, and European Union cost USDA the most. Overall, our results make a strong case that better information about production expectations, both domestically and worldwide, will contribute to more efficient agricultural balance sheet forecasts.

This study tests the assumption of weak separability between dairy and nondairy milk productsÕ demand by using food scanner data from 2012 to 2017 and estimating linear-approximate EASI demand systems. Our results show that the weak separability structures can be rejected. First, this finding shows that nondairy milk products compete with dairy milk for consumersÕ budget allocated to milk. Second, although milk demand studies often do not include nondairy milk, or assume weak separability, the exclusion of these products, or the separability assumptions may lead to biased estimates.

The proliferation of personal, household and workplace sensors and devices has created individual environments rich with purposeful and incidental feedback capable of altering behavior. We formulate an empirical learning model suitable for understanding individual behavioral responses in such environments. We estimate this model using data collected about the joint personal decisions of food selection, intake, and waste during a study in which users photographed their meal selections and plate waste over the course of a week with a cell phone. Despite neutral recruitment language and no expectation that participants would alter food intake in response to the assessment procedures, we found a substantial learning-by-doing effect in plate waste reduction as those who document greater plate waste in their captured photographs waste less on subsequent days. Further we identified that participants reduced plate waste by learning to eat more rather than by learning to reduce the amount of food selected.

The cost of providing environmental goods and services by private landholders is often highly uncertain. However, standard bidding models for conservation tenders often ignore this uncertainty. As a result, they fail to suggest suitable mechanisms to reduce the negative impact of cost uncertainty. We contribute to this knowledge gap by developing an optimal bidding model for a risky and budget-constrained tender in the presence of an emph{embedded insurance} mechanism, offering income protection. Results from our analysis show that, relative to uninsured landholders, landholders paying an actuarial fair premium tendered lower bids, potentially improving the cost-effectiveness of allocating conservation contracts.

This paper uses farm survey data from the western margin of the Corn Belt to estimate the causal effects of adopting different conservation practices-- conservation tillage (CT), cover crops (CC), and diversified crop rotation (DCR)-- on the perceived change in yield, production cost and profit by farmers in South Dakota. We use propensity score kernel matching to correct the sample selection bias induced by non-random adoption of different conservation practices. We find that farmers who adopt CT and DCR are more likely to perceive an increase in profit and yield and a decrease in production cost for CC adopters.

In December of 2017, the Craft Beverage and Modernization Tax Reform Act (CBMTRA) lowered Federal beer excise taxes for a period of two years, and the Taxpayer Certainty and Disaster Tax Act of 2020 made the reduction permanent. We evaluate the ramifications of the CBMTRA on producers, consumers, and tax receipts, as well as quantify potential differential effects among the micro, regional, and macro brewing sectors. Although the excise tax reduction was supposed to primarily support the micro brewing sector, we find that the CBMTRA provided a larger combined benefit to the regional and macro brewing sectors.

Understanding the role of risk in farmland leasing contract choices is important to assess the welfare consequences of farm policies or environmental changes that affect production risk. We use a unique dataset of landowners and tenants in Kansas to examine the role of risk in their farmland leasing contract choices. We find that greater production risk and more risk-averse landowners encourage fixed cash rent contracts. As there can be potentially many variables that affect contract choices, we use a penalized regression to show that the inclusion of relationship variables leads to little change in the main results.

Previous hedonic assessments have largely relied on the assumption that bull buyers have homogeneous demands for bull attributes. However, quality differentiations and heterogeneous demands support the existence of submarkets. This analysis investigates market segments using a finite mixture model and 13 years of bull auction data. Results indicate that valuations of bull attributes vary across implicit buyer segments. Differences in demand may be influenced by a variety of factors, including, but not limited to, farm goals, labor availability, and end-use marketing arrangements for calves. Results have important implications for signaling quality cues throughout the industryÕs breeding sectors.

The COVID-19 pandemic induced numerous supply chain shocks in U.S. agricultural markets, creating a desire to disentangle commodity price impacts caused by unique changes in food and nonfood agricultural product demand. Using a data-modified version of the relative-price-of-a-substitute method, we distinguish the consequences of the sharp decline in U.S. automotive fuel demand from the consequences of nonethanol demand changes in the U.S. corn market. Our results suggest thatÑdue to the renewable fuel standard and ethanol-gas price linkagesÑCOVID-19 affected corn markets more so than other agricultural commodities. The onset of the COVID-19 pandemic reduced Illinois cash prices for corn by approximately 18%. The majority of this impact (approximately 16%) was driven by pandemic-induced reductions in ethanol demand. Ethanol-driven and total impacts were greater in locations farther from terminal markets.

In addition to the unprecedented size of the program, the Coronavirus Food Assistance Program (CFAP) was unique in that it was the first US farm support program that allowed producers to enroll through an online portal, rather than in-person through a local Farm Service Agency (FSA) office. This research investigates the extent to which broadband connectivity affected access to US government farm support under CFAP. We construct a dataset that matches county-level information on per-capita CFAP payment receipts with county-level per capita measures of broadband availability and broadband usage in 2020 from the US Federal Communications Commission (FCC) and the Microsoft Corporation. We find that a 1-percentage-point increase in county-level broadband availability is associated with a $2.13 increase in county payments per capita under CFAP. This relationship doubles in magnitude when we account for actual broadband usage levels. However, this relationship is inherently non-linear along the rural-urban divide. A marginal change in connectivity increases government farm support by as much as $20 per capita in the most rural areas.

Genomic enhanced expected progeny differences (GE-EPDs) combine expected progeny differences (EPDs) with DNA information to improve EPD accuracy values. In 2020, Tennessee cattle producers completed a between-subjects choice experiment for bulls marketed with either EPDs or GE-EPDs. Panel Tobit regression results indicate that, on average across all considered EPDs, producers were not willing to pay significantly more for GE-EPDs than EPDs. However, producers were willing to pay more for the calving ease direct EPD if it was genomic enhanced. This is the first known study to evaluate producersÕ value of improved accuracy scores associated with GE-EPDs.

This paper investigates the association between Mobile Internet Use (MIU) and climate adaptation of Vietnamese coffee farmers. We find that farmers with access to mobile internet are more likely to take adaptation measures and obtain higher coffee yields using both simple regression and instrumental variable (IV) models. Our data suggests that the adaptation results are driven by changes in water and crop management practices as well as mediated by farmersÕ access to weather forecasts and farm price information. Policy support for MIU may enhance climate resilience of farmers in developing countries.

We propose a method that incorporates specific business needs and community goals to identify community assets that most constrain local economic development. Access to a managerial workforce was the most common highly ranked constraint, but the set of most constraining assets varies across communities. Thus, a Òone size fits allÓ development policy is not appropriate. We also find that constraint rankings are highly correlated among communities that share tourism potential, that share energy resources, or that rely upon production agriculture. Development practitioners may craft a suite of development policies, each tailored to communities of a given typology.

The impacts of wildfire are widely felt across the US and expected to increase in coming years. However, little is known about the long-term impacts of wildfire on recreation. We evaluate the impact of wildfire on reservations to United States Forest Service (USFS) campgrounds and find that wildfires decrease camping reservations up to six years after a fire occurs. The impacts vary across USFS regions, and our analysis reveals the important role of forest cover in determining the magnitude and duration of impacts. Our results imply that wildfires reduce benefits to campers which can translate into less spending in nearby communities.

Our study examines selectivity bias in the U.S. cattle procurement market. We hypothesize that feedlots optimize profits by selecting specific cattle to sell either in the cash market or through alternative marketing agreements. High-quality cattle are more likely to be sold in the alternative market as prices are not fully calculated until after a lot is harvested, allowing for premiums to be awarded for carcass quality. Consequently, it is assumed low-quality cattle are sold in the cash market to avoid potential carcass discounts. Depending on a feedlotÕs size, relationship with packers, and marketing costs, these selection assumptions may not be accurate and may bias prices. Using cattle transaction data, both the Heckman two-step model and the generalized Roy model find selection bias is present in the cash market. Selection bias is statistically insignificant in the alternative market as information asymmetries are alleviated by detailed carcass data at the time of pricing.

Using panel data for the years 2013, 2015, and 2017 collected through field surveys in eight counties in the North China Plain, we examine the relationship between smallholdersÕ land rental behavior and their (agricultural) incomes with a particular focus on the heterogeneous specialization among smallholders. We find that farming-specialized households experience a significant higher increase in agricultural income and a larger decrease in poverty incidence by land renting in than nonspecialized households. Off-farm specialized households had a decreased likelihood of being poor by renting out land, whereas nonspecialized households had no decrease in poverty incidence after land renting out.

Despite substantial effort to conceptualize wealth to support positive community economic development, little research tests the relationship between development outcomes and community wealth. This research assesses the relationship between the value-added food and agriculture business (VAFAB) sector and the stocks of community wealth by leveraging a new dataset of the stocks of community wealth and National Establishment Time Series data. We find significant relationships between the growth of VAFAB establishments and employment, and the stocks of community wealth. These results have implications for economic developers and policy makers in prioritizing investments should they want to grow the local VAFAB sector.

Government rebates provide monetary incentives to encourage consumersÕ adoption of eco-friendly technologies. Understanding how consumers perceive the value of rebate is crucial to policy-makers. We use the smart irrigation system as an example and design choice experiments that present rebates in two formats, i.e., the total device cost and the cost consumers needed to pay versus the total device cost and the rebate value. We found that consumers discount the value of the rebate more when presented with rebate value. Additionally, the framing of incentives has a spillover effect on the perceived value of a seemingly unrelated attribute, i.e., water saving.

Increasing milling potential could provide more food for human consumption at current yields and input uses. We estimate the impact of increasing rice milling yields in Arkansas from 2004-2020 using actual yields by variety. The results suggest that a marginal one-percent increase in head rice yield could increase the number of rice rations between 0.89 and 1.05 million annually, or up to 2.94 and 3.5 million annually if the genetics of all Arkansas rice was at least at the standard of a popular purebred variety. Improving rice milling yields can have significant food security implications.

This study estimates change in productivity and profitability and their respective components for two major Indian pulse crops, pigeon peas and chickpeas. Results show that average profitability declined during the period under consideration (2009-2014) for both pulse crops. Lower profits are driven by increases in input prices and decreases in total factor productivity, output growth, and output (constant) prices. The reduction in total factor productivity is primarily due to a slow increase in output. Finally, the technical efficiency estimates are lower than cereal crops like rice and wheat, 72% for chickpeas and 71% for pigeon peas.

The social license between the US public and food producers has grown increasingly complex, especially related to the treatment of farm animals and workers. In this study, we employ two best-worst-scaling experiments to elicit the publicÕs preferences regarding animal and worker welfare farm practices within the dairy and poultry industries. Using a latent class modeling approach, we find that US consumers generally value animal welfare practices more than worker welfare practices. Yet, population sub-groups with strong preferences for worker welfare practices exist, especially among older, highly educated, Democrats primarily from the Northeastern US