Projects Funded for Shuo Yu


Examining Incentives for U.S. Farmers to Increase Carbon Sequestration by Changing Agricultural Practices

Ellen M. Bruno and Shuo Yu


Proposed Objectives of the Project:
The role of agriculture in the sharp increase in atmospheric carbon dioxide is well documented. Amelung et al. (2020) highlight that cropland soils have great potential for carbon sequestration, especially those with large yield gaps or large historic soil organic carbon losses. A recent body of evidence suggests that certain agricultural practices (e.g., limited tillage, residue retention, modified fertilizer and manure choices, cover crops, and biochar) can increase soil carbon sequestration and reduce greenhouse gas (GHG) emissions (e.g., Koyama et al., 2016; Cha-un et al., 2017; Runkle et al., 2018; Tang et al., 2019; Babu et al., 2020). Our study primarily assessed the impact of cover crops on water quality in the United States.

Summary of Results:
Cover cropping involves planting crops during ‘off’ seasons to protect the soil. The USDA identifies their primary benefits as erosion control, soil health improvement, and water quality enhancement. These crops minimize soil erosion and runoff, reduce nutrient and pollutant transport, and can even fix atmospheric nitrogen, decreasing fertilizer use.

Prior studies, such as those by Plastina et al. (2020) and Delgado et al. (2021), primarily utilize experimental data or simulation models. Chen et al. (2022) examined the relationship between cover crops and soil erosion, identifying reduced erosion in areas with more cover crops. Similarly, Aglasan et al. (2021) linked higher cover crop usage to decreased insurance losses from environmental factors.

We sourced cover crop acreage from the 2012 and 2017 agricultural censuses and top 10 crops planting acreage from USDA NASS. Climate data, including max/min temperatures and precipitation, was obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF). Using these data, we derived county-level cover crop adoption rates and climate variables including growing degree days, extreme degree days, and mean precipitation. The data was then merged with harmonized water quality data from 1980-2022 for the Mississippi/Atchafalaya river basin retrieved based on Krasovich et al. (2022).

We then executed a panel data regression of the logarithm of nitrogen compound reading (mg/L) on cover crop adoption rate controlling for no tillage adoption rate, conservation adoption rate and climatic factors. Our initial regression indicated that a 1% increase in cover crop adoption reduced nitrogen compounds in the water by 0.58%. However, after incorporating county and year fixed effects, this relationship reversed and became statistically insignificant. This might be attributed to omitted variable problems such as self-selection by farmers into cover cropping based on perceived water quality issues, moral hazard problems where farmers may increase chemical usage due to increased capital from subsidies for cover crops, or the effect is lagged since it takes time for the farmers to find the most efficient and effective way to do cover cropping.

Future efforts will focus on constructing county-level cover crop adoption rates using remote sensing data from Landsat and MODIS (Seifert et al.,2019; Zhou et al., 2022) and building a Python pipeline to retrieve soil quality data (Chaney et al., 2019) and National Hydrography Dataset (US Geological Survey, 2021). This data will help in conducting more robust analyses to investigate the effect of winter cover crop planting on water quality improvement and to address any omitted variable bias. Furthermore, we will consider the farmers' annual fertilizer expenses to determine any moral hazard issues.

Cover crops hold promise in enhancing water quality. While our preliminary findings hint at their benefits, further detailed analysis is necessary to provide a comprehensive understanding.


Solar Farms Land Supply: A Dynamic Discrete Choice Model

Jeffrey Perloff, Shuo Yu, and Sara Johns


Specific Objectives of the Project:
The Biden administration’s goal is to eliminate fossil fuel electricity generation by 2035. Further, the Department of Energy’s Solar Futures Study projects that 40% of U.S. electricity generation could come from solar by 2035, which would require installing 30 GW/year until 2025 and 60 GW/year between 2025 and 2030 (DOE, 2021). The administration’s renewable energy targets are estimated to require an area larger than the Netherlands for solar energy (Rystad Energy, 2021).

These targets raise concerns about renewable energy development’s impact on land use and agricultural productivity. Many developers prefer to build solar farms on flat, clear agricultural land with low construction costs. Solar developers lease the land from the farmers. This lease price is typically much higher than what farmers would receive from leasing their land for agriculture. This project addresses the factors that influence whether farmers lease their land for solar and any unexplained cost farmers face by leasing their land for solar.

Summary of Results:
Illinois passed a law in 2016 and then a follow-on law in 2021 to allocate funding toward renewable energy development and addressing climate change. One program created by these laws was the Adjustable Block Program, which guaranteed prices of renewable energy credits (RECs) for community solar (≤ 2 MW) projects. The program received significantly higher demand than the allocated funding could support, so a lottery was held in 2019 to choose 112 projects out of 919 applications. The Agency published the lottery results, including the winning and losing projects’ sizes, locations, and developers. An application required that the developer had site control (lease agreement/option) of the area listed. Thus, we observe many farmers who agreed to lease their land for solar and many sites that developers thought were suitable for solar development.

We supplemented this field-level dataset with the published lottery results, estimates of lease value provided by a company specializing in new energy development on farmlands, and multiple spatial layers that include climate data, approximations of productivity, and proximity to the nearest infrastructure. We restricted our sample to fulfill the requirements established through conversations with multiple developers, agricultural land information platforms, and an officer at Illinois Power Agency. We then used linear fixed effects models and instrument-based estimation methods to analyze the data.

According to our analysis, a 10% increase in leasing prices is associated with a 4% increase in the likelihood that farmers are willing to lease their land for solar purposes. Not surprisingly, lower-productivity fields are more likely to be leased for solar projects. A 10% increase in field productivity results in a 28% decrease in the probability a farmer agrees to have a solar project installed.

Additionally, farmers who experience higher levels of climate risk are more likely to contract to lease. For instance, if a field experiences an average of 10% more extreme degree days or excessive rainfall during the planting season over the previous three years, the probability of leasing increases by 11% and 15%, respectively.

In summary, this study finds that lease prices, field productivity, and climate risk strongly affect farmers’ decisions to lease land for solar energy projects. These findings have important implications for policymakers, farmers, and other stakeholders in the renewable energy sector.


Short-Term Impact of the Trade War on U.S. Soybean Futures Prices and Spreads

Jeffrey Perloff and Shuo Yu


Specific Objectives of the Project
We quantified the short-term impact of tariffs and the corresponding relief payments on soybean futures prices.

Summary of Results
Due to the 2018–2019 trade wars, U.S. agricultural and food products have suffered eight waves of retaliatory tariffs from Canada, China, Mexico, the EU, and Turkey. The COVID-19 pandemic further isolated the economies in 2020. We studied the futures market effects of these tariffs on various U.S. crops, particularly soybeans.

We estimated reduced-form regressions of real futures prices or spreads on retaliatory tariffs and a set of event indicators to quantify the short-term impact of retaliatory tariffs and the corresponding relief payments on soybean prices. We controlled for the COVID-19 epidemic, related U.S. government direct payments, weather shocks, and information from USDA reports. Our analysis used price data from the Barchart website, tariff data from official documents published by the tax bureaus of each country and the World Trade Organization (WTO) Tariff Download Facility (TDF) database, USDA World Agriculture Supply and Demand Estimation Reports, and weather data from the Google earth engine from 2004 to 2020.

We found that a 25% increase in a retaliatory tariff, holding projections and weather variables constant, decreased the real futures price by 14.25% while the tariff was in place. The effect on the futures price spread grew with the length of the spread. It reached its peak at a one-year spread. Thus, the price pass-through of the tariff increase was large, and farmers suffered from the retaliatory tariff in the short run.