Agronomists and stakeholders in California pistachios recognize this as a threat to this valuable crop

Anecdotal yield losses due to low chill have happened on relatively small scale and passed undetected in the county-level statistics, especially when only one or two chill measures per county were considered. In this case, while the resulting curves are very similar, I find the structural approach more convincing. First, it has a smaller confidence area, and therefore seems more precise. Second, a polynomial of low order will not approximate the process described by agronomists very well. However, estimating higher order polynomials results in estimates that are not statistically significant. The implications of my estimates for pistachio yields are depicted in the lower half of Figure 3.1. The bottom left panel shows the effects on the 1/4 warmest years in 2000– 2018. They are mostly between 10-20% yield decline. These rates are easy to miss due to substantial yield fluctuations in pistachios. What do these estimates mean for the future of California pistachios? Prediction of yield effects for the years 2020–2040 are depicted in the bottom right panel, again for the 1/4 warmest years in the 2020-2040. They show substantial yield drops, which could amount to costs in the hundreds of millions of dollars. Chapter 4 in this dissertation explores the potential gains from a technology that could help deal with low chill in pistachios: applying kaolin clay mixtures on the dormant trees to block sunlight. Thee expected net present value of this technology is estimated at the billions of dollar in economic gains. Considering my results, there may be significant gains from using these technologies even in warmer years today. Concluding this chapter, I want to stress the fact that even in the era of “big data” in agriculture, data availability is still a challenge when estimating yield responses to temperature in some crops, especially perennials and local varieties.

Weather information required for assessing potential damages and new technologies might not always be available for a researcher. This chapter develops a methodology to recover this relationship,raspberry grow in pots using local weather data and techniques for dealing with aggregated observations. I use this setup to empirically assess the yield effects of insufficient chill in pistachios, recovering this relationship from commercial yields for the first time in the literature. I then look at the threat of climate change to pistachio production in southern California. As winters get warmer, lowering chill portion levels are predicted to damage pistachio yields and disrupt a multi-billion dollar industry within the next 20 years. These results were made possible by using precise local weather data, applying relevant statistical methods, and using agronomic knowledge in the modeling process. This approach for information recovery from a small yield panel, with limited useful variability at first sight, could be useful for other crops as well.In the introduction chapter, I discuss the nature of temperature challenges posed by climate change. The rising average temperatures, according to the empirical literature, might not be the major source of potential loss. Rather, it’s the elongating and fattening temperature distribution tails that would be responsible for much of the damage. Could there be a way for farmers to target these tails directly? If so, such technologies could have potential uses for climate change adaptation. It so happens that farmers already deal with temperature extremes, and are capable of tweaking the tails of temperature distributions to avoid losses. The introduction already discussed “air disturbance technology”, basically large wind generators, used to deal with some types of frosts . Solutions for right side temperature tails exist as well.

Of course, shading plants using nets or fabric is an existing practice, but these technologies are costly and not very flexible. However, other products that reflect sunlight and lower plant exposure to excess heat are available on the market. Perhaps the most common ones are based on a fine kaolin clay powder, which is mixed with water and sprayed directly on plants to form a reflective coat, sometimes referred to as a “particle film”. These products have been commercially available since 1999, and are shown to effectively lower high temperature damages by literally keeping plants cooler . Some manufacturers report a canopy temperature reduction of up to 6oC when using their products. Spraying of this mix requires special rigs and equipment, but the costs are reasonable, and far lower than setting up shading in the form of nets . This technology can be thought of as cheap, disposable shading. Surprisingly, even though kaolin clay has been used by farmers to deal with other problems, less related to climate change , I could find no economic literature discussing this technology. As with the case of air disturbance technology, these types of technologies have mostly been ignored by economists. One reason for this gap in the literature could be that economists have not yet realized that these individual products and practices share a common conceptual framework: they are tweaking temperature distribution tails, while leaving the main probability mass untouched. This is an approach I call “Micro-Climate Engineering” . These are relatively small interventions in temperature distributions, limited in space and time, which aim to avoid the nonlinear effects of the extremes. Farmers know the available technologies for MCE and use them regularly, but their potential applications for climate change have not really been explored. The concept of MCE could be very important for climate change adaptation in agriculture, especially when considering the role of extreme temperatures on predicted future losses. MCE solutions, where feasible and profitable, could assist in preserving current crop yields and delaying more costly adaptation strategies. This chapter sets to explore the concept of MCE in general, and assess the gains from MCE in California pistachios as a case study. Specifically, pistachios are threatened by warming winter days, which could threaten existing acreage within the next twenty years .

This challenge stands out in the existing literature in three ways: first, while much of the climate change literature focuses on annual crops, pistachios are perennial. This means that the opportunity cost of variety switching are higher. Second, the challenge does not occur in the “growing season”, but on the winter months when trees are dormant and seemingly inactive. This emphasizes the importance of climate change effects year round, rather than just in the spring and summer. Third, the challenge stems from a biological mechanism that is not heat stress. Heat stress is perhaps the most obvious process by which rising temperatures can have adverse effects on yields, and by far the most studied in the economic literature on climate change. However, other biological mechanism are affected by weather as well, and can cause substantial yield losses. This paper incorporates agronomic knowledge on bloom disruption due to increased winter temperatures, a mechanism that is relatively unexplored in the economic literature. Scientists at the University of California Cooperative Extension have been experimenting with kaolin clay applications on pistachios, and the results seem promising . This could mean a great deal to growers and consumers. This chapter analyzes the potential economic gains from this MCE application in California pistachios. Introduced to California more than 80 years ago, and grown commercially since the mid 1970’s, pistachio was the state’s 8th leading agricultural product in gross value in 2016, generating a total revenue of $1.82 billion dollars. According to the California Department of Food and Agriculture , California produces virtually all pistachio in theUS,square plastic pots and competes internationally with Iran and Turkey . In 2016, five California counties were responsible for a 97% of the state’s pistachio crop: Kern , Fresno , Tulare , Madera , and Kings . Since the year 2000, the total harvested acres in these counties have been increasing by roughly 10% yearly. Each increase represent a 6 – 7 year old investment decision, as trees need to mature before commercial harvest . The challenge for California pistachios has to do with their winter dormancy and the temperature signals required for spring bloom. I discuss the dormancy challenge and the Chill Portion metric in Chapter 3. It is worth noting that in fact, for the areas covered in this study, chill portions are strongly correlated with the 90th temperature percentile between November and February, the dormancy season for pistachios. The correlation is very strong, with a goodness of fit rating of about 0.91. In essence, insufficient chill is a right side temperature tail effect, comparable with similar effects in the climate change literature. Chapter 3 estimates the yield response of pistachios to CP. Substantial losses are predicted below 60 CP.

Compared to other popular fruit and nut crops in the state, this is a high threshold , putting pistachio on the verge of not attaining its chill requirements in some California counties. In fact, there is evidence of low chill already hurting yields . Declining chill is therefore considered a threat to California pistachios. Chill in most of California has been declining in the past decades, and is predicted to decline further in the future. Luedeling, Zhang, and Girvetz estimate the potential chill drop for the southern part of San Joaquin valley, where virtually all of California pistachio is currently grown. For the measure of first decile, i.e. the amount of CP attained in 90% of years, they predict a drop from an estimate of 64.3 chill portions in the year 2000 to estimates ranging between 50.6 and 54.5  in the years 2045-2060.Together with increasing air temperatures, a drastic drop in winter fog incidence in the Central Valley has also been observed. This increases tree bud exposure to direct solar radiation, raising their temperature even further . The estimates cited above virtually cover the entire pistachio growing region, and the first decile metric is less useful for a thorough analysis of pistachios. I therefore need to create and use a more detailed dataset, in fact the same one described in Cahpter 3. Figure 3.1 shows the geographic distribution of chill and potential damage in the 1/4 warmest years of observed climate and predicted climate . While not very substantial in the past, these losses are predicted to reach up to 50% in some regions in the future.This section develops a model to assess the gains from MCE. This is a single year, short run market model, solving for price and quantity under different winter chill realizations. Equilibrium price and quantity are used to calculate welfare outcomes such as grower profits, consumer surplus, and the total welfare. For each realization, the model is solved twice: once with an option to use MCE, and one without it. The differences in welfare outcomes under the same conditions, with and without MCE, are the welfare gains from MCE. Note that in both cases, agents act optimally. MCE gains are therefore to be interpreted as the difference in welfare measures between a world with MCE and a world without it. I abstract from a benchmark with increased storage, which could theoretically alleviate inter-year fluctuations. Pistachios are usually stored for up to one year . The potential loss rates in a bad weather year are significant. Coping by storage in a meaningful way would require multi-year, double digit storage rate, which seems technically unfeasible.MCE could help overcome a climate challenge for California pistachios. I model the market and assess the potential welfare gains from a reflective coating technology that lowers the effective temperatures in pistachio orchards. The expected NPV in 2019, for the gains from this technology between 2020 and 2040, is predicted to be around $2.7-3.5 billion. These come from consumer surplus gains, as the total gains for growers in the main specifications are negative. The latter result is not unheard of in agricultural settings, where a negative supply shock can actually increase grower profits. For example, Carter et al. show that the 1979 labor strikes in California actually increased revenues and profits for lettuce growers. The simulation results shows the flip side of the coin: solving a supply shock can lower grower profits. While less tangible than actual registered profits, consumer surplus gains are real economic gains enjoyed by the public. This point holds even when discussing a narrower welfare framework for California alone.

Social and demographic characteristics for exam takers are not available

Some ordinances also provide procedures for handling formal complaints by neighbors. Most California counties and a number of cities now have right-to-farm ordinances, a popularity seemingly driven by the belief on the part of local officials and others that this is an easy way to provide farmland protection that avoids hard political choices. Because they are not regulatory tools and rely primarily on the dissemination of information, however, the ordinances lack teeth and legal effect. It is uncertain to what extent they have reduced conflicts in edge areas. But the ordinances do serve a useful purpose, according to many agricultural leaders and county officials, in educating residents and asserting as a policy matter the value of agriculture in particular communities . More generally, conflicts between farmers and urban neighbors over farm activities can be addressed by a variety of techniques for dealing with community-level disputes. Practitioners in this field make a distinction between conflict resolution and conflict prevention. Resolution processes often involve a form of third party mediation, in which facilitators get both sides together, factual information on the source and elements of the dispute is developed, alternatives are deliberated,planting in pots ideas and an effort is made to reach an agreement among the parties as to actions to be taken such as changes in farm management . The state of New York has formalized such processes, with a Community Dispute Resolution Center in each county with resources for dealing with edge and other local conflicts .

Preventing edge conflicts typically involves less formal methods, with the emphasis on encouraging farm operators to maintain open lines of communication with their urban neighbors. The assumption is that friendly relations can head off serious disputes in the future over specific matters. One piece of advice to farmers in a New York state guidebook on reducing edge conflicts is to notify neighbors in advance of the timing and need for particular practices that may generate negative impacts. The guidebook goes further to suggest 15 strategies that farmers can use to foster good neighbor relations, including farm tours, providing gifts of farm produce, and setting aside an acre or two for wildlife .Given the substantial returns to higher education in this setting , this is a very high stakes exam. Every year, approximately 9 million students in China take the exam to compete for admission to approximately 2,300 colleges and universities. The NCEE has two primary tracks: the arts track and the science track.All students are tested on three compulsory subjects regardless of track: Chinese, mathematics, and English, with each worth 150 points. Students in the arts track take an additional combined test that includes history, politics, and geography worth 300 points, while students in the science track take an additional combined test that includes physics, chemistry, and biology worth 300 points. Thus, regardless of track, the maximum achievable score for each student is 750 points. In our focal provinces, the Chinese and math exams are scheduled for 9– 11:30am and 3–5pm on June 7th, and the English and track test are scheduled for 9– 11:30am and 3–5pm on June 8th.Since provinces have some discretion in the design of their tests, exam difficulty can vary by track, province, and year. Our core analysis deploys province-by-year-by-track fixed effects to account for this possibility. The NCEE tests are graded one to two weeks after the exams are completed by professionals in hotels in each of the respective provincial capitals. Since this grading occurs in locations that differ from test takers in terms of both space and time, we are confident that the effect we estimate on NCEE scores is not the result of any potential impacts on graders. The NCEE data were obtained from the China Institute for Educational Finance Research at Peking University. This dataset provides a unique identifier and the total test score for the universe of students enrolled in a Chinese institution of higher education during our study period.

The dataset also reports the subject specialization for each student, allowing us to explore heterogeneity across the science and art tracks.Importantly, the student ID contains a six-digit code for county of residence, which allows us to match students to the county administrative centers. Testing facilities are located in local schools which are universally very close to county administrative center. 7 Therefore, we use the county administrative center to approximate the testing facilities. The information on which testing facility a student is assigned is unavailable. Our core analytic sample includes observations from approximately 1.3 million students. We supplement this dataset with data on the cutoff scores that determine admission eligibility to the elite universities in order to separately examine the impacts at the upper-end of the performance distribution.Data on daily agricultural fires are collected from two satellites named TERRA and AQUA, which rely upon Moderate Resolution Imaging Spectroradiometer sensors to infer ground-level fire activity. The satellites overpass China four times a day , and report all fire points detected with 1-km resolution . The fires are detected based on thermal anomalies, surface reflectance, and land use . Since the size of a fire cannot reliably be inferred from satellite data , we treat fires in adjacent pixels as distinct fires. We exploit data on fire radiative power, a measure of fire intensity, to at least partially probe the importance of this assumption. A fire is linked to NCEE performance within a county if it occurs within a 50- km of the county administrative center during the two-day exam period in each year. Alternative distances are explored as part of our robustness analyses. Since proximity to a fire is likely correlated with the economic benefits as well as the environmental harms from fires, we eschew distance-weighting strategies on fires in our core analysis. These are, nonetheless, explored in our robustness checks. Meteorological data is important for two reasons. First, as detailed in the next section, we exploit detailed data on wind direction to contrast impacts of those upwind and downwind of a given fire. Second, weather may also confound the interpretation of our results since the incidence of agricultural fires may be correlated with meteorological conditions. Our weather data are obtained from the National Oceanic and Atmospheric Administration of the United States.

We collect daily average weather data on temperature, precipitation, dew point, wind speed, wind direction and atmospheric pressure from 44 local weather stations during our sample period. Daily average wind direction is reported based on the hourly wind direction and wind speed through vector decomposition .8 Given the sensitivity of wind direction to topography and other quite localized factors, we assign wind to test locations based on monitor data from the source closest to the county administrative center, and drop counties with no wind stations within 50 km.9 We extract other weather data during the exam time and then convert from station to county using the inverse-distance weighting method . The basic algorithm calculates weather for a given site based on a weighted average of all station observations within a 50-km radius of the county center, where the weights are the inverse distance between the weather station and the county administrative center. While the detrimental impacts of agricultural fires on air quality have been documented in the environmental science literature,growing blueberries in pots data availability does not allow us to make this link explicitly in our setting. Ground monitoring pollution data at the station-day level in China is not available prior to 2011, and there are infamous stories of data manipulation of the Air Pollution Index and PM10 in China apply to the period prior to 2013 .10 In addition, satellite data is not well suited for ground-level measurement at fine temporal and spatial scales required for our analyses, especially during burning seasons with smoke plumes . Nonetheless, we provide a first-stage estimation, of sorts, by estimating the relationship between air pollution and agricultural fires using data from a more recent period: 2013–2016. Since NCEE data is not available for this period, we view this analysis as one designed to shed light on the mechanisms through which agricultural fires might impact cognitive performance. Daily pollution data are obtained from the China National Environmental Monitoring Center , which is affiliated with the Ministry of Environmental Protection of China. Monitoring stations report data for the six major air pollutants – particulate matter less than 10 microns in diameter , particulate matter less than 2.5 microns in diameter , sulfur dioxide, nitrogen dioxide, ozone, and carbon monoxide – that are used to construct the daily Air Quality Index in China. For each pollutant, we construct a two-day average concentration level, corresponding to the length of the exam period. Fires that took place more than 50 km from a county center are excluded from this analysis. We select all pollution monitoring stations within 50 km from a county administrative center and calculate the pollution level at the center using the IDW method. Our analysis relies on data from 212 distinct pollution monitors, with an average distance of 24.5 km. In this section, we explore the heterogeneity of our core results along two dimensions, as shown in Table 3. The first column simply reproduces the results from our preferred specification for our primary results .

Columns and of Table 3 explore heterogeneity along another dimension: the subject track. It appears that the impacts are negative and highly statistically significant for those in the science track while only marginally significant for those in the arts track. This may reflect the differential sensitivity of the prefrontal cortex – the part of the brain responsible for more mathematical style reasoning, and is consistent with other evidence on the impacts of environmental stressors on cognitive performance . This pattern of results might also, at least partly, be driven by the gender composition of students across tracks. While we do not have individual level gender data, the male ratio is typically much higher in science track than arts track and other work has found the cognitive performance of males to be more sensitive to PM pollution than females . The next four columns of Table 3 examine how the impacts of agricultural fires vary across the student ability distribution by estimating Equation using a quantile regression approach. This regression is especially important for two reasons. First, since we only observe NCEE scores for students that were eventually admitted to an institution of higher learning, we might be worried about sample selection resulting from negative effects at the lower end of the ability distribution. Second, differences in impacts across the ability distribution could have profound long-run impacts on income inequality given the highly nonlinear returns to scores. Our results find no impacts among low ability students, thus minimizing concerns about selection bias. Moreover, the impacts appear to be concentrated near the very top of the performance distribution – above the 75th percentile. This can be seen most clearly in Figure 5, which further breaks down estimates by decile. Column offers another perspective on the higher end of the ability distribution by focusing on the impacts of agricultural fires on the likelihood of admission into an elite university in China based on the cutoff scores that govern that process. The cutoff score in each province is the lowest score of students admitted to the first-tier universities in China. It is determined by the admission quota of each university and the ranking of student scores in each province. Upwind fires continue to have a significant negative impact on test performance. A one percentage point increase in the difference between upwind and downwind fires, decreases the probability of admission to an elite university by 0.027 percent . Given the sizable impacts of an elite education in China on lifetime earnings , these impacts should be viewed as economically meaningful, even if they may be largely re-distributional by privileging the admission of students from less exposed counties over those from more exposed ones. In this section, we provide a number of robustness checks. We begin by exploring alternative ways to assign the exposure of test takers to agricultural fires. The first column of Table 4 reproduces our main results, which limit our focus to fires within 50 km of a testing center.