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.

Specific Objectives of the Project:
The goal of this project was to investigate the implications of measurement error in remotely sensed land-use data for accurately pinning down parameters in economic analyses relevant to California agriculture. Data products that identify land use and crop categories from satellite imagery are commonly used in research and planning models, but they inevitably contain classification errors. To understand the implications of remote-sensing land use products for accurately pinning down economic parameters, I systematically compared different land use data products for California and evaluated the magnitude of the measurement error relative to detailed field surveys.

Summary of Results:
Measurement error in derived data products is known to pose challenges for inference. When the error is non-classical, which is often the case with satellite data, then it can introduce bias in parameters that are otherwise well-identified. Knowledge about the nature of the error becomes important for credible inference. To better understand the implications of remote-sensing land-use products for accurately pinning down economic parameters, this paper first assesses the accuracy of two different remotely sensed land-use data products with California coverage by comparing them against field surveys from an agricultural region on the California coast. I then systematically compare the two across the state, a geography that features a diversity of crops unlike the rest of the United States. I find that overall accuracy varies widely between the two datasets - 4.7% to 5.9% in one and 77.8% to 87.25% in the other, depending on the year. This validation exercise illustrates the nature and magnitude of the measurement error in California, with important implications for policy.

Specific Objectives of the Project:
(a) Construct a database on how the 298 agencies registered at present to manage basins under SGMA are (i) structured legally, (ii) governed, and (iii) planning to implement the SGMA mandates; (b) Study differences in (iii) as a function of (i) and (ii) and other characteristics of the underlying groundwater resources, availability of surface water, agricultural products being produced, demographics, and urban/rural interfaces; and (c) Provide guidance to groundwater agencies, California Department of Water Resources (DWR), and other policy makers regarding SGMA implementation based on results adduced from (a) and (b).

Project Report/Summary of Results:
Regions of California will be facing significant reductions in water use in the coming years. This project provides an update on the progress made thus far towards implementing the Sustainable Groundwater Management Act (SGMA). For all high- and medium-priority basins, we record and discuss the composition of newly formed groundwater agencies and their proposed management actions by coding agency board seats by entity type and groundwater management activities by strategy type. We find that the majority of board seats are held by quasi-public water entities like irrigation districts and local agencies, skewing representation towards existing agricultural interests. The 92 unique GSAs participating in California’s high- and medium-priority basins grouped to form 43 Groundwater Sustainability Plans (GSPs) containing management actions that can be categorized into either supply augmentation or demand management. Of the 27 GSPs imposing demand management through groundwater allocations, 19 plans are also considering creating a market to trade those allocations. Basins not allowing trade are concentrated on the west side of the Central Valley. These actions may have large implications for the economic costs of SGMA.

Specific Objectives of the Project:
The objectives of this project were to explore the empirical linkages among groundwater extraction, groundwater quality, and land use in a groundwater basin that suffers from seawater intrusion. We estimated the probability that a farmer switches crops in response to changes in groundwater salinity. With an estimate of how decisions are impacted by water quality, we can evaluate the economic impacts of changes in water quality on farmer welfare, which has implications for understand the costs of climate change and sea-level rise.

Project Report/Summary of Results:
Many coastal agricultural regions are at risk of sea-level rise and groundwater overdraft, which lead to saltwater intrusion of underlying aquifers. Increased salinity levels in irrigation water can lead to crop yield reductions and degraded land quality. This project considers the value of groundwater quality in agricultural production using micro-level data from California's central coast. We combine panel measurements of groundwater salinity and fine spatial land use data with property ownership boundaries to predict the likelihood that farmers shift crops in response to a change in groundwater salinity. This allows us to estimate marginal damages associated with changing salinity while incorporating the adaptive response of crop switching. Results inform our understanding of the social marginal cost of groundwater extraction and the potential impacts of sea-level rise to coastal agriculture.