Specific Objectives of the Project:

1. Describe how nitrogen management practice adoption varies across farm size, crop types, and location. Analyze how grower behavior varies across fields and crop types.
2. Analyze whether there are diminishing marginal returns to nitrogen efficiency if multiple nutrient management practices are adopted.
3. Evaluate the effectiveness of practices designed to alter grower decision making on nitrogen application rates.
4. Determine a set of practices that growers find beneficial and have the strongest impact on nitrogen efficiency, to provide tangible recommendations to policymakers and technical advisers.

Summary of Research to Date:
Current results rely on data collected from the Irrigation and Nitrogen Management Plans that are required of agricultural producers in the Central Valley. The focus area is the East San Joaquin Valley (ESJV) in 2019, as this was the first region where data were required to be made publicly available. In the sample, there are 1713 growers applying nitrogen to 4810 fields.

We find in the ESJV, nitrogen application rates are highest for nut crops, with vegetable row crops not far behind. On average, vegetable row crops have the lowest nitrogen efficiency (calculated as N applied/N removed through biomass). The low nitrogen efficiency in vegetables is driven primarily by sweet potatoes. In total, split fertilizer applications, testing soil for residual nitrogen, and tissue testing are the most popular nutrient management practices adopted by growers. These results are similar to those found in surveys of growers in the Central Valley (Rudnick et al. 2021). The use of neutron probes, pressure bombs, and cover crops are the least likely to be adopted.

We regress nitrogen management practices adopted on nitrogen application rates, and nitrogen use efficiency, while accounting for irrigation system and crop and farmer fixed effects. We find that the adoption of cover crops reduces nitrogen application rates, but that several practices, including foliar nitrogen application, split fertilizer applications, tissue testing, soil testing, and pressure bombs, actually increase the nitrogen application rate. Tissue testing was significantly likely to improve nitrogen use efficiency, but use of soil testing and pressure bombs were associated with reduced efficiency.

Altogether, these results were a bit puzzling, and require a deeper exploration. We think there are two main challenges at play. The use of individual fixed effects, while they help combat omitted variable bias (farm and demographic characteristics), limit the variation in our current sample, especially since the majority of growers in the sample have a single field. We are in the process of securing more recent data to both expand the area of study to the full Central Valley, as well as the years of our sample, adding additional variation to the sample. Secondly, the number of practices in our data is likely leading to multicollinearity issues. We see significant evidence that farmers regularly adopt
“bundles” of practices in tandem, such as irrigation N tests and fertigation. To combat this issue, we use random forest techniques to isolate the variables that have the largest predictive power to explain nitrogen use, and reduce the number of practices in our model. The four main practices that have explanatory power include testing irrigation water for nitrogen, tissue testing, split nitrogen application, and foliar nitrogen application. We are also speaking with agronomists on alternate methods to group practices that are likely to be adopted together. We expect further insights and a concrete working paper to emerge with the application of these techniques to the expanded dataset.

Specific Objectives of the Project:

• Study the impact of extreme weather on an irrigated, specialty crop, adding to a literature largely focused on staple crop yields.
• Answer the following research questions: Has historical weather impacted the incomes of specialty crop producers through its effect on both yield and quality?Does the yield or quality effect dominate?

Summary of Results:

• To answer these questions, we use proprietary field-level data from a large tomato processor operating in California's processing tomato industry.
• In contrast to earlier work on irrigated crops, we find that extreme temperatures negatively affect both yield and quality, leading to reduced grower revenue.
• We find that yield responds negatively to exposure to hot temperatures and, to a lesser extent, cool temperatures.
• Further, quality declines with exposure to hot temperatures and growers receive a lower price per ton.
• Taken as a whole, we find that, relative to 24 hours of average temperatures, exposure to temperatures in excess of 30°C decreases revenue. Exposure to cool temperatures below 10°C causes a significant, but smaller, decrease in revenue.
• While the yield effect dominates, failing to account for quality significantly underestimates the true effect of temperature exposure on revenue by up to 20%.

Specific Objectives of the Project:

Assess the distributional impacts of wildfire on California agriculture, with a focus on the effects on yields, the damage on farmers’ assets and the health risk of smoke exposure to farmland workers.

Project Report/Summary of Results:

In the paper, we find striking results on the impacts of wildfire and related smoke on various agricultural outcomes:

1. Wildfire and smoke have significant persistent effects on cropland. Wildfire reduces the total planted crop area by 14.3% immediately, and continues to reduce the planted area by another 7.2% over the next 5 years. Meanwhile, wildfire increases the failed crop area by 6.3% contemporaneously and 26.2% over the next 5 years, and exposure to smoke increases the failed area by 38.7% of that year.
2. Wildfire has a composition impact on the crop and land-use. Wildfire causes losses to the planted area of most field crops, vegetables and fruits, while increases the planted area of nuts.
3. Yield impacts vary by crops. Exposure to wildfire and smoke reduces the yield of soybean by 5.5% and 7.9% respectively, reduces the yield of corn by 1.4% and 6.9% respectively, and have insignificant negative effects on the yield of rice and wheat.
4. Wildfire smoke significantly worsens the air quality, which leads to substantial impacts on the medical expenses and hospitality visits of farmland workers.
5. Wildfire and smoke have slight and imprecise negative impacts on livestock, investment of agricultural equipment, and crop land value.

Based on the estimates, we have plotted a map to show the spatial distribution of wildfire damages on agriculture. We have completed a first draft manuscript, which we will polish and submit this summer to a field top journal.

Specific Objectives of the Project:
The objective of this study is to quantify the role of American Viticultural Areas
(AVAs) in mediating the relationship between (1) an evolving climate (the long-run expected weather in a region), (2) weather variation around the regional norm (vintage effects), and (3) the variety-specific price premia and quality (expert rating scores) for varietal wines in different parts of California. The more specific objectives are (1) to compile data on prices and expert rating scores for California wines and match these to data on relevant measures of weather and climate, (2) to estimate statistical models of varietal wine prices (and ratings) as a function of these measures of weather and climate for each of the main varieties, and (3) to derive estimates of the location-specific relationship between prices (and ratings) and climate and draw inferences for the future matching of varieties to AVAs in light of climate projections.

Project Report/Summary of Results:
We made considerable progress on developing concepts, preparing and cleaning data, and consulting others on interpretation of weather and climate data from different sources. We have estimated preliminary models for parts of the work and are at advanced stages of preparation for the rest of it. We anticipate completing parts of the work in 2022, and some results may be finalized and published within this year, but the more complete analysis is expected to take at least another year—i.e., until mid-2023. Initial results are promising. We expect to complete at least two papers by mid-2023.