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.

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:
The project includes four components:
1. Write analysis re-evaluating the economic logic that the Environmental Protection Agency used to justify the Navigable Waters Protection Rule.
2. Drawing on 2021 keynote I gave to Association of Environmental & Resource Economists, write summary article for Review of Environmental Economics and Policy
3. Use computer vision and artificial intelligence algorithms determining the scope of these water quality regulations
4. Study how drinking water policy affects human health

Summary of Results:
1. This was published at REEP (Keiser et al., listed at bottom), discussed with EPA staff, and the underlying report is cited extensively in the economic analysis of the Biden Administration’s new Clean Water Act rule (the revised definition of the ‘Waters of the United States’), cited below.
2. This was published as a separate article at REEP (citation listed at bottom of this report).
3. I have substantially advanced the research using computer vision and artificial intelligence algorithms. The EPA’s Economic Analysis includes text specifically calling for this analysis, which EPA staff told us they included to give more time for us to finish the analysis and so they could then use it in their new rulings. The status as of 8/29/22 is that this research has a convolutional neural network that uses near-infrared wavelengths from the National Agriculture Imagery Program (NAIP); rasterized data from the National Wetlands Inventory; and it is in the process of adding rasterized data from the National Hydrography Dataset, Soil Survey data (SSURGO), Sentinel satellite data, and others. The Supreme Court will hear hearings on Sackett v. EPA, a very related case, on October 3, 2022, and our goal is to publish a high-quality scholarly journal article before the Supreme Court’s decision is released in spring 2023, so a revised EPA rule could use our analysis as a potential input. While the analysis is moving forward rapidly every week, completing it carefully and fully with all these data takes a good amount of time in total.
4. This work has advanced substantially; the analysis has a working panel micro database of drinking water pollution measures for over 35 US states including many pesticides, linked to census blocks where people who drink the water live, health outcomes, and investments made through the Safe Drinking Water Act.

Specific Objectives of the Project:
Evaluate efficiency, efficacy and equity impacts of policies of aimed at mitigating greenhouse gas emissions (nitrous oxide) from agricultural soils.

Summary of Results:
This grant was written to support a larger research program that is expected to produce several papers over several years. The grant this year supported progress on several fronts.

First, we used the funds to purchase a data enclave that gave us access to ARMS data. We are using this to calibrate variance in soil conditions and management practices so that we can quantify the heterogeneity in the emissions per ton of fertilizer, and for change in management practices, using the DAYCENT biogeochemical model. This work has begun but is ongoing.

Second, after having a series of conversations with relevant experts, we decided to pivot the theoretical attention of our project to focus on the role of voluntary carbon offsets as the policy instrument of interest. This shifts our attention from tax/pricing methods and required a new theoretical structure. Using the grant we developed a framework for analyzing agricultural carbon offsets using an adapted mechanism design framework. We believe this was an important step because the policy momentum in reducing ag emissions seems to be largely in the direction of these offset programs (in particular, there is zero interest in the policy community in taxing fertilizer application), but structuring incentives as offsets creates unique incentive problems because offsets have to be based on a counterfactual, which is hard to estimate and leads to inaccuracy and gaming. We are now using this framework to explain the incentive implications of using offsets for nitrous oxide mitigation.

Third, using the progress on the empirical modeling and the theory, we submitted a multi-year, full scale grant to the Agriculture and Food Research Program at the USDA earlier this year. To complete the full version of the DAYCENT modeling, we need a soil expert to help us, and we need outside funds for that.

Proposed Objectives of the Project:
Develop a Colorado River Basin (CRB) wide hydro-economic model that provides stakeholders, policymakers, and constituent organizations with a lens to view the changes coming to the Colorado River Basin in the 21st Century through both economic policy and climate modifications.

Summary of Results:
The hydro-economic analysis is a valuable tool for addressing water management concerns, and the hydro-economic model of the Colorado River Basin (CRB) is a prime example of this. The model integrates physical, hydrological, and economic elements in a framework that captures the spatial and temporal relationships of the water system. Specifically, the hydro-economic model of the CRB includes agricultural production and water use, urban water use, water used for hydropower production, and environmental water use through environmental flow restrictions. Various levels of governance are represented in the model to ensure that the hydro-economic model of the CRB is responsive to the needs of the basin's decision-makers. This includes decision-makers at the country level (U.S. and Mexico), state level, Tribal Nation level, and smaller levels such as irrigation districts and urban centers. By including decision-makers at various levels, the model can provide insight into the economic and hydrological implications of water management decisions across the CRB.

The hydro-economic model results for the CRB provide a comprehensive view of the agricultural sector in the basin. The irrigated land in the model covers 2.6 million acres, which are distributed across 25 irrigation districts in the seven states. The model takes into account the irrigation of 39 different crops, categorized under three distinct irrigation systems: flooding, sprinkler, and drip. The agricultural sector in the CRB is a significant water user, diverting 8.9 million acre-feet of water that generates $1,773 million in net income. The states of Arizona, California, and Colorado account for 84% of water use and 90% of net income. On average, net income across the basin is $680 per acre, with the range per state varying from $200 to $1,200 per acre. The model highlights that 60% of cropland produces 90% of the net income and that 6% of the higher-value crops account for 40% of the total net income in the basin. Additionally, the model shows that 10% of water use in the basin generates 50% of the total net income, with a shadow price of $270 per acre-foot. These results suggest that there is potential for improving water use efficiency through interstate water exchange.

The hydro-economic model also includes the urban centers in and outside the basin that the Colorado River serves. The model encompasses a total of 379 cities with a population of 33.4 million inhabitants. The urban sector uses an estimated 525,000 acrefeet of water for domestic purposes, generating an economic surplus of $18,328 million. In addition, the non-domestic use of water in the urban centers is estimated to be 787,000 acre-feet. Therefore, the total urban use included in the model is 1.3 million acre-feet. The lower basin has the highest concentration of population, with California and Arizona representing 75% of the population and 86% of the economic surplus. These results highlight the significance of the urban centers in the basin's hydro-economic model and their role in determining the overall water use and economic impact. By including domestic and non-domestic water use in urban centers, the model provides a comprehensive view of the urban sector's water use in the CRB.

Finally, the hydro-economic model includes a hydropower production capacity of 4,223 MW, representing approximately 95% of the installed capacity in the basin. The nine largest hydropower plants in the basin produce 10,225 GWh annually, generating an annual benefit of $874 million. However, the three largest plants account for 84% of the total benefits. It's worth noting that hydropower production also helps to reduce greenhouse gas emissions. The hydropower production in the basin has avoided emissions of approximately 12,300 million lbs of CO2e. These results highlight the importance of hydropower production in the CRB's hydro-economic model, not only for its economic benefits but also for its contribution to mitigating climate change by avoiding the emission of greenhouse gases. The model provides a comprehensive view of the hydropower production in the basin, which can be helpful for decision-makers to optimize the use of this valuable resource.

As a next step, the project uses the hydro-economic model to evaluate potential policy changes to the current operation of the CRB, providing much-needed informationon the likely economic effects of different policy interventions may be on the CRB.

Specific Objectives of the Project
To obtain data on payments made to US farmers over the period from 2006–2021 under a variety of federal programs, including during the the Covid-19 pandemic. This data collection effort is meant to complement a project which uses ARMS data to construct a pseudopanel of farm-households, with longitudinal data on farm income and farm-household expenditures. The overall goal then is to relate various federal payments to the farm income and expenditures at a highly disaggregate level.

Summary of Results
We were able to obtain a complete record of all federal payments to U.S. producers under all of the programs administered by the USDA over the last two decades via the Freedom of Information Act. We were subsequently able to link these payments to producer characteristics, aggregated to the zip-code level, available in the Agricultural Resource Management Survey (ARMS) through 2019.

Specific Objectives of the Project:
This research develops a theoretical model of technology adoption at the farm level coupled with a consumer-facing advertising campaign as a means to establish environmental stewardship and meaningful product differentiation. Using increased market power and concentration in manufacturing and retailing and increased consumer demand for value-added and local foods as a starting point, we focused on the role marketing cooperatives can play in ensuring adoption of agricultural innovations even among small and medium size growers in California.
This research uses Blue Diamond, a marketing cooperative and leading and recognized consumer brand in almond production, as a case study to discuss and examine optimal advertising and cost-saving production technologies under imperfect competition.

Almonds are the second the most valuable commodity in California with a production value of $6.09 billion. California accounts for all U.S. production of almonds and 78% of worldwide production (CDFA, 2020). Blue Diamond is the largest producer in the state and as a marketing cooperative, allows roughly half of CA almond growers (about 3,500 growers) to sell their raw almonds under one label. Blue Diamond successfully markets a variety of value-added products to consumers, including roasted almonds, almond milk, nut crackers, and almond oil. Almond production is water intensive using about 40 acres-inches of water per acre annually. However, the almond industry has faced criticism for its water use over the last decade as California grapples with extreme drought. In 2015, almond production accounted for approximately 10% of California water use. In addition, irrigation is the greatest variable cost in almond production accounting for about 12-32% of annual operating costs depending on location. Regulated deficit irrigation
(RDI) is a technique that can lower water use in almond production. RDI decreases irrigation rates during the kernel-filling period inducing mild to moderate stress in the plant to limit vegetative growth without impacting fruit production. This technique can also decrease hull rot and improve hull split, can decrease irrigation and result in significant water savings at almost no difference in kernel weights.

We analyze a situation where Blue Diamond encourages its growers to adopt RDI. Adoption of this technology has a twofold effect. Adoption leads to a decrease in overall production costs, and can form the basis for meaningful product differentiation in a retail space characterized by increased concentration and product differentiation. Specifically, Blue Diamond can initiate an advertising campaign referencing the implementation of water conservation efforts and continue to position its brand as a leader in product innovation. Differentiating its products based on eco-friendly practices, especially to California consumers, can increase their margins, and allow them to incentivize adoption of the technology by growers.

Summary of Results:
We completed a draft theoretical model and reached out Blue Diamond for feedback, a discussion of a collaboration, data access and a potential empirical analysis. Focusing on a specific technology and evaluation of a targeted advertising or marketing campaign became infeasible due to data issues in general, and challenges in the implementation of RDI, however. We therefore adjusted the focus of this project to more broadly explore opportunities for product innovation and promotion of value-added products via marketing cooperatives in a proposed book chapter to be included in a forthcoming Handbook of Research on Cooperatives and Mutuals. It uses Blue Diamond's production research and marketing efforts as a case study.

In this chapter, we argue that marketing cooperatives (MCs) can have a competitive advantage over investor-owned firms (IOFs) when communicating complex product qualities where information asymmetries exist between consumers and producers. We review the existing literature and discuss opportunities for cooperatives in modern markets characterized by increased product differentiation. Marketing orders (regulated mandatory commodity research and promotion efforts organized by industry and geographic region) can support and complement cooperatives’ investments in product innovation and brand-specific advertising and their provided functions are compared and contrasted with functions provided by cooperatives. We argue that the cooperative business model can lend greater credibility and authenticity to health, sustainability, and local production claims and benefit more from the existence of marketing orders than IOFs. A discussion of the actions of the Almond Board of California and Blue Diamond illustrates how coordinated and collective producer and market research, product innovation, and complementary generic and brand-specific advertising can ensure MCs' long-term growth and success. The chapter concludes with a description of future data, and research needs to ensure that more MCs develop well-recognized consumer brands.

Specific Objectives of the Project:
The objective of this proposal is to isolate the effect of droughts and heat on access to drinking water and drinking water quality in rural agricultural communities in the San Joaquin Valley.

Summary of Results:
A first preliminary result indicates that farmers are responding to heat and surface water scarcity through the construction of groundwater wells. We estimate that for each acre foot (AF) of reduced surface water allocations for agriculture, the annual rate of agricultural well construction increases by 46%. Using an approximated cost of $75,000 to construct an agricultural well, this translates to a back-of-the-envelope $37 million dollars invested annually in extensive-margin adaptation behavior by California farmers. Our finding that extreme heat will increase groundwater extraction brings a new data point to our understanding of how climate change will influence water resources. Our results highlight that even if water supplies remain unchanged, warmer temperatures will increase demand for groundwater, with an additional harmful degree day increasing well construction by 1.2%.

A second set of preliminary results indicates that extreme heat and reductions in agricultural surface water supplies lower the depth to the groundwater table. A 1-AF of reduced agricultural surface water allocation to every California cropland acre, lowers local groundwater levels by an additional 4 feet. An additional harmful degree day reduces groundwater levels by 0.5 inches. Declining water tables suggest that the costs of climate change may be larger in the long-run if farmers cannot avail themselves on groundwater resources.

Extreme heat and surface water scarcity also lead to domestic well failures, with a 1 AF decrease in surface water supplies and an extra HDD increasing failures by 5 and 0.2 percentage points, respectively. These results are consistent with a theoretical framework and computational hydrology model in which increased groundwater consumption among agricultural users comes at the cost of drinking water supplies through the channel of a declining water table.

Specific Objectives of the Project:
• To utilize administrative data to identify whether pesticide applications made by alfalfa and safflower growers reduce pesticide applications made to nearby cotton fields in the San Joaquin Valley
• To construct a bio-economic model of lygus bug population development and migration to estimate the magnitude of any positive externality identified.

Summary of Results:
• Pesticide applications made by other growers to nearby fields of alfalfa, safflower and other host crops reduce pesticide applications to cotton fields.
• Pesticide applications made by a grower to nearby host crop fields of their own sometimes increase pesticide applications made by that grower to a cotton field.
• While the data do not enable us to determine the reasons for the latter, one possibility may be that some growers are more likely to apply pesticides.

The bio-economic model is still in development.

Specific Objectives of the Project:

• Document stylized facts regarding clustering of agricultural production and exporting in both the US and Mexico.
• Estimate the returns to scale in exporting by crop at the regional level.
• Develop a model which features agricultural value chains that can help explain the patterns in agricultural clustering that we observe.
• Estimate the model and quantify the importance of clustering and returns to scale for export patterns across crops and locations.
• Perform counterfactual analysis comparing the welfare benefits of infrastructure investment (e.g. roads) vs. more targeted investment in agricultural value chains.
• Make quantitative comparisons between US and Mexican agricultural exporting, illuminating some of the features that govern exporting in both countries.
• Publish the final article(s) in a top refereed journal, as well as a summary in the ARE update.

Summary of Results:
Aside from publishing the paper, all objectives have been attained. Most of the results are provided in James Sayre paper which will be made available here: http://jaysayre.com/.

Specific Objectives of the Project:
The Trump administration initiated a trade war in 2018 that triggered several countries, including Canada, China, and Mexico, to retaliate against the United States. These countries implemented waves of retaliatory tariffs that disproportionately impacted California agriculture. Canada and Mexico revoked their retaliatory tariffs in May 2019 as part of the USCMA negotiations. China then started to reduce retaliatory tariffs for selected agricultural products in response to the Phase One Deal signed in early 2020. While these tariff reversals reduced trade tensions between the United States and its trading partners, the effects of the trade war are still lingering. Using product-level trade data for the United States, China, Canada, and Mexico, our study aims to measure the degree of trade recovery after the retaliatory tariffs were fully or partially revoked in 2019 and 2020.

Summary of Results:
The Trump administration initiated several rounds of import tariffs in 2018, sparking a trade war that persisted into 2023. As a result, countries such as Canada and Mexico imposed retaliatory tariffs on U.S. goods. This study assesses the dynamic trade effects of the trade war tariffs, focusing on the impact of tariff enforcement and revocation among CUSMA countries. Our event study estimates indicate an immediate and substantial decline in trade for targeted varieties. In contrast, the trade recovery was more gradual and incomplete after tariff revocation. The average trade destruction effect was -37 percent during tariff enforcement, while we find limited evidence of significant trade diversion. Although the trade destruction caused by tariff enforcement was immediate, trade recovery was slower and took three to six months after tariff revocation. The average trade recovery effect for the post-tariff period was 31 percent. These results suggest that the trade war tariffs caused only limited long-term trade destruction. Additionally, we observe notable heterogeneity in the trade war effects across different good classifications and according to the tariff level. Our estimates indicate that e 2018 trade war led to trade destruction for targeted varieties among CUSMA countries of USD 17.7 billion in the first 18 months after tariff enforcement.

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.