Specific Objectives of the Project:

1. Estimate the short- and long-term impacts of agritourism on farms’ revenue, use of farmland, agricultural production, and agriculturists’ welfare.
2. Assess the differential impacts between small and large farms

Project Report/Summary of Results:

This initiative aimed to evaluate the short- and long-term impacts of agritourism on farms' revenue, use of farmland, agricultural production, and agriculturists' welfare in California. We managed to collect daily data on Airbnb rural listings and transaction records in California between 2015 and 2022. We also successfully developed metrics to assess agritourism development, incorporating the number of farm stays openings and occupancies on Airbnb near primary cities such as Los Angeles, San Francisco, and San Diego. In our attempt to establish causality, we employed non-farm stays as an instrumental variable for agritourism operation. We then applied a 2SLS regression model to examine the effects of agritourism development on agricultural outcomes.

Our analysis successfully assessed the short- and long-term impacts of agritourism on California agriculture, identifying key trade-offs between short-term economic benefits and long-term agricultural productivity. The primary findings include:

1. Over the short run, agritourism was found to have positive effects on farms' economic performance and sustainability, with small farms benefiting more than large farms.
2. In the long-term, agritourism led to the diversion of farmland resources away from agricultural production, with more significant impacts on small farms. The results suggested a trade-off between short-term economic benefits and long-term agricultural productivity due to agritourism activities.
3. The spatial distribution of agritourism activities varied across California, with certain regions experiencing more significant impacts on agricultural production and land use.

Our findings have substantial policy implications, providing insights into the efficient allocation and use of farm resources, and aiding in the formulation of specific strategies for small farms. Policymakers are urged to consider the trade-offs between immediate economic benefits and long-term agricultural productivity when crafting policies to support agritourism. Furthermore, our findings can guide the development of targeted strategies for small farms to balance agritourism activities and agricultural production.

In conclusion, our research offers valuable insights for cost-benefit analysis, allowing policymakers to compare the net benefits of agritourism with alternative strategies such as agricultural diversification and off-farm works.

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:
(1) Estimate whether eucalyptus trees increase wildfire spread relative to other land use types, including other forest, cropland, or rangeland.
(2) Assess wildfire threat to California agriculturalists from eucalyptus trees

Project Report/Summary of Results: We exploit exogenous variation in wind direction to estimate whether eucalyptus trees increase fire spread. Contrary to popular belief, we find no evidence that eucalyptus trees increase total burn area relative to other forest types. Using the full sample of 188,926 fires in California between 1992 and 2015, we estimate that eucalyptus trees decrease total burn area by 8.8 percentage points. When we restrict the sample to the 7% of fires that burn more than 10 acres (0.04 km2), the effect of eucalyptus is indistinguishable from zero (0.8 percentage point decrease with a standard error of 0.6 percentage points). We conclude that California agriculturalists face little fire threat from eucalyptus trees. Removing eucalyptus trees does not appear to be an effective strategy for mitigating the threat of wildfires in California.

Specific Objectives of the Project
• Statistically describe the spatial and temporal distribution of pesticide use in Californian agriculture.
• Estimate the effect of pesticide applications on local air quality (PM2.5)
• Estimate human population exposure to pesticides through air.
Future work: Estimate the effect of pesticide use in Californian agriculture on health outcomes.

Project Report/Summary of Results
Many policymakers, public-health advocates, and citizen groups question whether current pesticide regulations properly equate the marginal social costs of pesticide applications to their marginal social benefits—with particular concern for negative health effects stemming from pesticide exposure. Additionally, recent research and policies in public health, epidemiology, and economics emphasize how fine particulate matter (PM2.5) concentrations harm humans through increased mortality, morbidity, mental health issues, and a host of socioeconomic outcomes. This paper presents the first empirical evidence that aerially applied pesticides increase local PM2.5 concentrations. To causally estimate this effect, I combine the universe of aerial pesticide applications in the five southern counties of California's San Joaquin Valley (1.8MM reports) with the U.S. EPA's PM2.5 monitoring network—exploiting (1) spatiotemporal variation in aerial pesticide applications and (2) variation in local wind patterns. I find significant evidence that (upwind) aerial pesticide applications within 1.5km increase local PM2.5 concentrations. The magnitudes of the point estimates suggest that the top decile of aerial applications may sufficiently increase local PM2.5 to warrant concern for human health.

Specific Objectives of the Project

Develop a framework for modelling different regulatory approaches to controlling methane emissions from California’s dairy sector.

Summary of Results

We have developed a simulation model that allows for different regulatory schemes controlling short lived climate pollutants from dairies. A tax, an emissions standard and a cap and trade. The model simulates the associated costs of control and provides scenarios for leakage to other states.

Specific Objectives of the Project
This project estimated the empirical effects of historical temperature on dairy production and quality, specifically measured by volume, fat content and protein content. This estimation will be used to both forecast the expected costs to the dairy sector of future climate change, and estimate the historical cost of recent climate change.

Project Report/Summary of Results
Dairy is a crucial part of the California and United States' food systems. Milk is California's largest agricultural commodity by value, producing approximately $8 billion per year for farmers. Dairy constitutes 19% of the protein content of the US diet, provides half of the total calcium and vitamin D, and provides a quarter of the total vitamin A and vitamin B12. Globally, dairy is also very important, constituting 10% of total protein and 26% of the total protein consumed from all animal sources. It is the largest animal product globally both in terms of contribution to protein supply and calorie supply.

This project finds that the production response of California dairies to temperature is increasing up to 15-20°C, and more steeply decreasing thereafter. The model estimates that taking an hour at 15-20°C and moving it to 35-40°C would result in a reduction in production of approximately 30% (for that hour), a statistically significant reduction. The quality measures (fat proportion and solids-not-fat proportion) exhibit a significant negative relationship with temperature, across the temperature spectrum, that is economically small. The model estimates that the same temperature change would result in reductions of these proportions of less than 0.2% (not percentage points). These results allow researchers to confidently work with data on raw quantities of dairy production, which is much more readily available than the nutritional quality data.

This project also finds that predominantly pasture-based dairy systems respond to hot temperatures to a much larger degree than concentrated feeding operations. We find that the production response for pasture-based counties is increasing up to 10-15°C and decreasing thereafter. The model estimates that taking an hour at 10-15°C and moving it to 20-25°C would result in a reduction in production of approximately 60%, a much larger impact than that for Central Valley counties. The difference can be explained by a combination of differences in the ease of protection of the cows (fans and evaporative cooling cannot be employed in pasture-based systems) and differences in the proportion of feed grown locally.

A next step in this project is a careful accounting of the impacts on upstream feed production for the Central Valley farms. A simple projection of future climate change would naïvely omi omit these important effects. A working paper will be available in August.

Specific Objectives of the Project

The objective of this minigrant project was to collect information on central pivot irrigation (CPI) in California through automated analysis of satellite imagery. We have successfully mapped the extent all detected CPI fields in California, along with a web service for vegetation analysis from NASA satellite imagery. The web service, called an Application Programming Interface (API), is useful for agricultural applications beyond the scope of this project.

Methods

The CPI fields were identified through kernel density differencing. The difference between a spatial convolution of circles and squares was calculated for all available NASA Landsat 8 annual composites (2013/2014).
The difference, in theory, will be greatest at the center of circles in the images. The convolution was run for plot sizes between 200m and 600m. The results served as a screening dataset, which was subsequently verified by hand using halfmeter, commercial satellite imagery supplied by DigitalGlobe. Ultimately, there were 121 CPI fields detected in the state of California. A sample is displayed in the first
figure, and a web map is available here (along with the generating code and raw data).

An additional webservice was built to calculate the vegetation density for any polygon and for any month between January 2000 and the present from NASA satellite imagery. The Enhanced Vegetation Index (EVI) is a measure of vegetation density, derived from monthly, cloudfree composites of NASA Landsat 7 scenes. The service is technically a Representational State Transfer (REST) API. The HTTPS GET request accepts a bounding box and a date range, and returns a series of EVI measurements useful for CPI analysis. Specifically, the service returns three measurements for both the inner circle and remaining corners of the CPI field: (1) the average EVI, (2) the standard deviation of the EVI, and (3) the area in meters squared. These three measures, when taken together, can calculate the test statistic to determine whether the vegetation was significantly different within the inner circle, relative to the rest of the plot. This test statistic is sufficient to identify whether CPI was used at any given month. Our initial analysis reveals that CPI fields are converted frequently to furrow or other irrigations systems there is a significant amount of switching.

Project Report/Summary of Results

The graph below shows the time series decomposition of the EVI series into its trend, seasonal, and idiosyncratic components. The returned series (which is graphed using code in the client library of the opensource repository) clearly shows an increase in vegetation intensity for this particular field in the past few years. This CPI field is 455,948m 2 in size with 171 observations between 20010101 and 20150509. There is a seasonal component that is bimodal. This type of analysis can be replicated for all 121 CPI plots in California and any arbitrary extent in the world.

Extensions and associated services

This online database with structured access points is highly useful for climate researchers. The service enables research into the behavior changes over time of agriculturalists in response to exogenous factors. One such factor is water level. A parallel endpoint (developed separately) tracks surface water for any polygon in the world over time. These services, when taken together, offer a powerful set of tools to show the behavioral response to water scarcity in California.

Specific Objectives of the Project

Use all available data California's major and specialty crops to estimate the impact of historical impact of climate change on California's Agricultural sector.

Summary of Results

We have put together a large dataset on county level crop yields for California by drawing on studies conducted under the auspices of the CEC's now defunct PIER project. We have extended these databases to current day using county level crop reports. We have matched these reported yields to the crop calendar for each crop and measures of weather from the data product provide d by Roberts and Schlenker. In order to come up with historical weather without climate change, we have obtained the IPCC's AR5 historical and future climate series with and without anthropogenic forcing. We have estimated yield response functions and are finishing the simulations this summer. This study was motivated by questions I received during an outreach activity with farming lobbyists organized by the Secretary of Agriculture in Modesto earlier this year.

Specific Objectives of the Project

Construct a forecasting model based on the most extensive retailer level consumption and price dataset ever collected for the State of California.

Summary of Results

We have built the code and data for the forecasting model and it works. We can generate results for Southern California and are working on completing the Northern California results. We hope to have a working paper targeted at Water Resources Research by the end of the summer. Progress was a bit slower than anticipated due to unforeseen complications in writing code that estimates 250,000+ models a few hundred times each.

Specific Objectives of the Project
Construct a family tree of academic agricultural and resource economists, which highlights the influence of members of the Giannini Foundation in current day agricultural economics research.

Project Report/Summary of Results
Agricultural and Resource Economics is one of the oldest sub fields in economic research. Its history is told in several review articles, yet no one has traced its origins in a systematic way. We have improved an existing academic family tree piece of software, which much like a regular family tree traces the history of the field by linking students and advisors.

The paper examines the effects of critical habitat (CH) designation on the value of vacant land. The regulation is designed to generate benefits by protecting habitat for endangered and threatened species under the ESA. Critical habitat is a controversial provision of the act, as property owners argue that it can result in large, negative economic impacts. In this paper we provide the first estimates of the impacts of CH designation on vacant land values, based on observed market transactions in two counties. We show that designation results in a statistically significant decrease in land value. Further, the market impact of critical habitat designation is shown to depend on local land use regulation. Parcels inside the urban growth boundary (UGB) designated as critical habitat lose in excess of 56% of their value. We also find that designated parcels outside the UGB experience a more moderate increase in value, perhaps because their value as mitigation land increases with designation as critical habitat.

The objective of the project was to identify the impact of ground level Ozone pollution of yields of California's main field and perennial crops.

We have completed an econometric model, which regresses county level yields by crop on ozone concentration and county and year fixed effects. We show a significant nonlinear impact of ozone concentration on yields of soybeans and corn. A fter instrumenting for Ozone concentrations using an indicator of the county level NOx control program, we show that the estimated effects are larger than in the OLS regression.