The clearest distinction in this figure appeared to be whether the growing sites are for-profit or nonprofit

For instance, 75 percent of the articles returned in our Web of Science search were published after 2009 and 18 percent were published in 2017-18. A more recent search of these terms in April of 2018 returns 1622 records revealing a continued growth in literature on urban agriculture. Of these records, journal articles dominate . Other records include book reviews, article reviews, proceedings papers, and meeting abstracts. The main contributing journals included Land Use Policy , Landscape & Urban Planning , Agriculture & Human Values , Sustainability , and Local Environment ; however, the sources were quite diverse. Each record represents a single document and together they form the corpus used to build the reference topic model. Prior to processing, we removed any stop words, punctuation, and URLs. We performed LDA topic modelling using the MALLET program . We produced various topic models using three granularities , and used the models with the greatest log likelihood . We then examined their topic composition and removed topics dominated by non-meaning-bearing terms including time and location indicators and general publication information . These topics were identified using the alpha hyperparameter, where relatively high values indicated that the topic was common throughout the corpus and therefore not meaningful for examining differences within our sample. After these adjustments, we determined that the 25-topic model was ideal for analysis using personal expert knowledge on urban agriculture literature. The reference topic model was created in order to perform inference on content produced by urban agriculture growing sites and regional organizations in San Diego County – in other words, to interpret the content produced by the key actors identified above . We created a corpus including all textual content from the websites of agencies in our sample,plant pot with drainage with content from each of the 48 observations contained in a single document in the corpus.

Textual content included any written descriptions on the website including history, mission and vision statements, program descriptions, excluding locations, contact, and event info. For growing sites associated with larger organizations or institutions, we also collected basic descriptive content from the parent website. By applying the reference model to all the documents, each document is characterized in terms of topic composition, allowing comparisons among documents . The output of the inferencing process is a document-topic distribution matrix, from which we computed a matrix of cosine similarities among documents. In order to visualize these similarities, we used a dimensionality reduction technique known as multidimensional scaling . In the resulting output, each document is described as a 2-D point in Cartesian coordinates, where proximity relates to similarity. The resulting discursive map displayed the inferenced website corpus, with each point representing a single growing site or organization. The location of each point relates to its particular topic composition. The distance between points is indicative of their discursive similarity – the closer two points are in the discursive map, the more similar their topic composition; the farther apart, the more dissimilar. We investigated this map, but also created a series of variations, altering the symbology of the discursive map to reflect particular features of the sites. This allowed us to examine the connections between characteristics like growing methods and topic composition. We were also interested in discovering clusters among the data points, and so we utilized k-means clustering to identify meaningful groups in our data . K-means is a heuristic algorithm that attempts to partition aninput dataset into k groups, allowing researchers to explore clusters within a dataset. Our data seemed to occupy primarily three quadrants in the discursive map, and so we chose to identify three classes. This algorithm was run for 1,000 iterations and the results with the lowest sum of squared errors – a metric that explains the difference between each observation and its corresponding k-means centroid – were chosen as representative. This analysis complemented our visual analysis of symbology patterns. The growing methods symbology illustrating the practices used by growing sites revealed a distinct, but blurry pattern between motivation and practice.

When analyzing the map using this symbology, a general pattern emerged in which technologically-advanced sites tended to group in the top-left quadrant of the map with two outliers: Go Green Agriculture and Archi’s Acres. The absence of innovation in these outliers’ top-three loadings suggested that other topics precede technology in how these growing sites describe themselves despite their use of advanced technologies. Generally, soil-based sites occupied the right side of the discursive map; however, soil-based farms such as Suzie’s Farm, Good Taste Farm, and Point Loma Farms were grouped in with the soilless sites. Growing site and organization descriptions of their processes did drive their location on the discursive map. For instance, the soilless sites often described the inventive and underrepresented practices they use to grow produce in the urban environment. However, the content did not end there. Other topics like social movements, climate change, and food access were also present among these sites. We saw a similar trend with sites using a community gardening model. When we explored the entire topic loadings of growing sites and organizations, ignoring practice-based topics like innovation and community gardening topics, we saw that the clusters have far more similarities than differences. Interestingly, these soilless sites are typically affiliated with businesses as opposed to nonprofits which dominate the right side of the map, where most soil-based sites are located . Indeed, we expected that business and nonprofit website content would vary and these results provide evidence to that effect. San Diego Food System Alliance, the leading regional nonprofit organization, is located in the center of the map. This location is not surprising in the context of neoliberal governance in which cities and regional organizations are more focused on building consensus and supporting apolitical agendas, rather than taking on political causes .The affiliation symbology illustrating the relationship between institutional affiliation and content was less coherent than the other symbologies displayed in previous figures, but still offered important insights. Growing sites were affiliated with a variety of institutions including schools, churches, organizations hosting training and educational programs, and for-profit businesses.

Education sites were located throughout the map suggesting that training and skill-building are not major dividing factors in discourse. In other words, many different types of organizations claim to focus on education. However, church, community, and school gardens tended to concentrate in the top-right section of the map, which is typically associated with soil-based community gardens. However, it cannot be assumed that the for-profit sites lack social mission. For example, Archi’s Acres, a for-profit hydroponic farm in Escondido, includes a social enterprise function focusing on training veterans in hydroponic farming. Sundial Farms, a veteran- and immigrant-owned, hydroponic farm in the Innovation cluster, is a direct result of this program. This social function features prominently in its website content: “At Archi’s, we believe a key aspect of successful business is how it meets its responsibility to the community in which it operates and the customers which make up its marketplace. We do this by integrating into our business model an opportunity to support others including our military service members and veterans.” This broader social mission may explain its topic loadings and the absence of innovation as a primary topic. The overall uniqueness of this growing site may explain its peripheral location in the discursive map. Solutions Farms, an aquaponic operation associated with Solutions for Change,growing blueberries in pots was the only nonprofit located in the for-profit dominated section of the map. The organization aims to alleviate family homelessness in the county through skill development, including training in aquaponic farming. However, innovation is the primary topic in their content, influencing their location among other sites whose discourse is focused on innovation.Multivariate clustering was performed on the discursive map to identify clusters in the sites and group them accordingly. Figure 8 contains the k-means results including three classes . Transitional sites were identified by creating a 4-class result . The topic compositions of sites in each cluster were examined and the clusters were given descriptive names reflecting their dominant topics : Innovation, Community, and Access. The transitional sites – those that broke off into their own group in the 4-class result – were signified using an overlaid line pattern. These sites were close to or straddled the center axes of the map.The Innovation cluster was distinct from the other clusters. The predominant topic amongst this group was innovation, which includes words and phrases like rooftop farming, zero-acreage farming, soilless, aquaponics, buildings, hydroponic, vertical, greenhouses, indoor, and technology as well as production, yield, growth, and quality. Unsurprisingly, all of the technologically-advanced sites resided in this cluster with the exception of Valley View Farms, which experiments with hydroponics, but focuses primarily on animal farming. Among the topic loadings in this group were community gardening, food access, social movements, climate change, water management, food production, and food security. This cluster also consisted primarily of for-profit growing sites with the exception of Roger’s Community Garden located on the University of California, San Diego campus. An interesting outlier is Go Green Agriculture, a hydroponic farm, which is located on the border of the Community cluster. This location is likely driven by its top topics, which include community gardening, location, and climate change, which are well-represented in both the Innovation and Community cluster. The Community cluster emphasized connections with local residents, primarily promoting home and community gardening – community gardening was the most prevalent topic in this cluster.

Although, this cluster overlaped considerably with the Access cluster, there was a clear emphasis on environmental topics including ecosystem conservation, water management, location, water contamination, innovation, and climate change. The social movement topic was also prevalent throughout this cluster with many of its sites expressing a dedication to alternative forms of organization. It is also worth noting that the socio-economic characteristics of the two neighborhoods are also quite different. Southeastern San Diego, specifically zip-code 92102 where Mt. Hope Community Garden is located, is a primarily Hispanic community, followed by White , African American , and Asian . The median income is at $42,464 with only 24 percent of the population exceeding $75,000 annually . The sites and organizations in this cluster also placed considerably less emphasis on environmental topics in favor of more social topics including public health, food production, and urban greening. Still, topics like ecology and climate change were present suggesting that environmental and social concerns were not mutually exclusive. The sites in the Access cluster were also predominantly affiliated with educational and training programs. Two particularly interesting examples are UrbanLife Farms and Second Chance Youth Garden. Both growing sites are wings of social justice organizations that offer job training and skills development for youth living in City Heights and Southeastern San Diego – communities that have seen considerable disinvestment and suffer from high unemployment . Other growing sites like Rolling Hills Grammar School and Literacy Garden and Olivewood Gardens and Learning Center also focus on youth programming. Not all the growing sites in this cluster work with youth. New Roots Farm concentrates on providing resettled refugees with land for farming, small-business training, and nutrition education to help them adjust to a new life away from their home country. This mission guided its topic loading of food security, social movements, and food access. The five urban agriculture supporting organizations we surveyed spanned the Community and Access clusters. Slow Food San Diego, Slow Food Urban San Diego, San Diego Roots Sustainable Food Project , and San Diego Community Garden Network are located in the Community cluster. San Diego Food System Alliance was located at the border between the Community and Access clusters suggesting that food access was a more prominent topic for the organization. Further, its central position illustrated the consensus focus of the organization, which caters to a diverse group of actors including politicians, businesses, and nonprofit organizations. Overall, the placement of the organizations made sense as they are nonprofit facilitators for other sites aimed at broader social goals like increasing food access and building community.

Past county yields are from crop reports published by the California Department of Food and Agriculture

The gains are much higher than the ones found in the 1996 report. This is partly due to increased economic activity in general, but probably has to do with more adoption of smart irrigation as well. The total yearly gains in agriculture range between $492 million, taking only the intensive margin effects, and up to about $1,982 million considering the extra acres that can be grown with the saved water. A surprisingly large sector using CIMIS is landscaping and golf courses, with yearly monetary savings of at least $201 million for our survey sample alone. Several other user types were included in the survey, indicating a substantial role of CIMIS in areas crucial for California’s economy. Respondents use CIMIS to plan drainage in agricultural and urban settings, taking advantage of CIMIS historic rainfall records. CIMIS is used for water budgeting and even pricing. Researchers in the public and private sector use CIMIS for diverse purposes, from basic research to calibration and verification of other weather related products. These are just a few of many additional uses of CIMIS we know about, but do not quantify here due to the complicated methodological framework required. The economic gains from CIMIS surely surpass the ongoing costs of a system with less than a dozen employees. However, could these gains be achieved by the private sector? The decreasing costs of weather sensors mean that growers and other users could potentially access precise data on their own. If we wanted a cheap weather station, costing about $1,000, for every 1,000 acres of drip irrigated land in California, the total cost would surpass$2.8 million, plus some ongoing costs for maintenance. This, however, would prevent many benefits from the centralized aggregation of data and the historical records that are crucial for research and planning, as one could not assure that aggregation of the data from all these separate private stations would occur. While several online aggregators of weather information exist,planting in pots ideas many rely on the public information provided by networks such as CIMIS and other government bodies such as airports and air quality monitoring systems. It is not obvious that private aggregators would be profitable if they had to purchase this information, or what their WTP would be.

Moreover, the ET measurements which many growers use are usually not captured by commercial stations, and there are concerns regarding the reliability of ET approximations by other variables. The development of satellite technology might change these conditions in the future.California pistachios are a high value crop, with grower revenues of $1.8 billion in 2016. The most common variety is “Kerman” , and almost all the California acreage is planted in five adjacent counties in the southern part of the San Joaquin valley. In recent years, rising winter daytime temperatures and decreasing fog incidence have lowered winter CP counts. Climatologists have concluded that winter chill counts will continue to dwindle , putting pistachios in danger at their current locations. To better predict the trajectory for this crop and make informed investment and policy decisions, the yield response function to chill must first be assessed. This task has proven quite challenging. The effects of chill thresholds on bloom can be explored in controlled environments, but for various reasons these relationships are not necessarily reflected in commercial yield data. For example, Pope et al. report that the threshold level of CP for successful bud breaking in California pistachios was experimentally assessed at 69, but could not identify a negative response of commercial yields to chill portions of the same level or even lower. They use a similar yield panel of California counties, but only have one “representative” CP measure per county-year. Using Bayesian methodologies, they fail to find a threshold CP level for pistachios, and reach the conclusion that “Without more data points at the low amounts of chill, it is difficult to estimate the minimum-chill accumulation necessary for average yield.” The statistical problem of low variation in treatment at the growing area, encountered by Pope et al., is very common in published articles on pistachios. Simply put, pistachios are not planted in areas with adverse climate. Too few “bad” years are therefore available for researchers to work with when trying to estimate commercial yield responses.

An ideal experiment would randomize a chill treatment over entire orchards, but that is not possible. Researchers resort either to small scale experimental settings, with limitations as mentioned above, or to yield panels, which usually are small in size , length , or both. Zhang and Taylor investigate the effect of chill portions on bloom and yields in two pistachio growing areas in Australia, growing the “Sirora” variety. Using data from “selected orchards” over five years, they note that on two years where where chill was below 59 portions in one of the locations, bloom was uneven. Yields were observed, and while no statistical inference was made on them, the authors noted that “factors other than biennial bearing influence yield”. Elloumi et al. Investigate responses to chill in Tunisia, where the “Mateur” variety is grown. They find highly non-linear effects of chill on yields, but this stems from one observation with a very low chill count. Standard errors are not provided, and the threshold and behavior around it are not really identified. Kallsen uses a panel of California orchards, with various temperature measures and other control variables to find a model which best fits the data. Unfortunately, only 3 orchards are included in this study, and the statistical approach mixes a prediction exercise with the estimation goal, potentially sacrificing the latter for the former. Besides the potential over-fitting using this technique, the dependent variables in the model are not chill portions but temperature hour counts with very few degree levels considered, and no confidence interval is presented. Finally, Benmoussa et al. use data collected at an experimental orchard in Tunisia with several pistachio varieties. They reach an estimate for the critical chill for bloom, and find a positive correlation between chill and tree yields, with zero yield following winters with very low chill counts. However, they also have many observation with zero or near-zero yields above their estimated threshold, and the external validity of findings from an experimental plot to commercial orchards is not obvious.Pistachio growing areas are identified using USDA satellite data with pixel size of roughly 30 meters. About 30% of pixels identified as pistachios are singular. As pistachios don’t grow in the wild in California, these are probably missidentified pixels. Aggregating to 1km pixels, I keep those pixels with at least 20 acres of pistachios in them. Looking at the yearly satellite data between 2008-2017, I keep those 1km pixels with at least six positive pistachio identifications.

These 2,165 pixels are the grid on which I do temperature interpolations and calculations. Observed temperatures for 1984-2017 come from the California Irrigation Management Information System , a network of weather stations located in many counties in California, operated by the California Department of Water Resources. A total of 27 stations are located within 50km of my pistachio pixels. Missing values at these stations are imputed as the temperature at the closest available station plus the average difference between the stations at the week-hour window. Future chill is calculated at the same interpolation points,growing blueberries in pots with data from a CCSM4 model CEDA . These predictions use an RCP8.5 scenario. This scenario assumes a global mean surface temperature increase of 2o C between 2046-2065 . The data are available with predictions starting in 2006, and include daily maximum and minimum on a 0.94 degree latitude by 1.25 degree longitude grid. Hourly temperature are calculated from the predicted daily extremes, using the latitude and date . I then calibrate these future predictions with quantile calibration procedure , using a week-hour window. Past observed and future predicted hourly temperatures in the dormancy season are interpolated at each of the 2,165 pixels, and chill portions are calculated from these temperatures. Erez and Fishman produced an Excel spreadsheet for chill calculations, which I obtain from the University of California division of Agriculture and Natural Resources, together with instructions for growers . For speed, I code them in an R function . The data above are used for estimation and later for prediction of future chill effects. For the estimation part, I have a yield panel with 165 county-year observations. For each year in the panel, I calculate the share of county pixels that had each CP level. For example: in 2016, Fresno county had 0.4% of its pistachio pixels experiencing 61 CP, 1.8% experiencing 62 CP, 12% experiencing 63 CP, and so on.Figure 3.1 presents chill counts and their estimated effects in percent yield change for two time periods: 2000-2018 and 2020-2040. The top left panel shows the chill counts in the 1/4 warmest years between 2000 and 2018 . The top right panel shows the chill counts in the 1/4 warmest years in climate predictions between 2020 and 2040. Chill at the pistachio growing areas is likely to drop substantially within the lifespan of existing trees.Results from the polynomial regression are presented in Table 3.2 . The first coefficient is for an intercept term, and it is a zero with very wide error margins. This makes sense, as centering around the means also gets rid of intercepts. The second coefficient is positive, as we would expect, and statistically significant. The third coefficient is negative, as we would also expect since the returns from chill should decrease at some point, but not statistically significant even at the 10% level. However, as dropping it would eliminate the decreasing returns feature, I keep it at the cost of having a wide confidence area. With the estimated coefficients, I build the polynomial curve that represents the effect of temperatures on yields. It is presented in Figure 3.2 with a bold dashed line. The 90% confidence area boundaries are the dotted lines bounding it above and below. Note that the upper bound of the confidence area does not curve down like the lower one. This is the manifestation of the third coefficient’s P-value being greater than 0.1. In both cases, the confidence area was calculated by bootstrapping. The data was resampled and estimated 500 times, producing 500 curves with the resulting parameters. At each CP level, I take the 5th and 95th percentiles of bootstrapped curve values as the bounds for the confidence area. This approach also deals with the potential spatial correlation in error terms. Another minor issue requiring the bootstrap approach is that the implicit potential yield estimation should change the degrees of freedom in the non-linear regressions when estimating the standard errors. In the lower panel of Figure 3.2, a histogram of positive shares is presented. That is, for each chill portion, the count of panel observations where the share of that chill portion was positive. The actual shares of the very low and very high portions are usually quite low. This shows the relatively small number of observations with low chill counts. The two yield effects curves look very similar in the relevant chill range. By both estimates, the yield loss is very close to 0 at higher chill portions, and starts declining substantially somewhere in the upper 60’s, as the experimental literature would suggest. Interestingly, the polynomial curve does not exceed zero effect, although it is not mechanically bounded from above like the logistic curve. This probably reflects the fact that historically, the average growing conditions has not deviated much from the optimal range. The “within” transformation hence did not deviate the potential yield much from the optimum in this case. At currently low chill portion ranges of 55-60, the effect is around 25%, again consistent with the stipulation of Pope et al. that a significant effect threshold would be located there. Considering alternate bearing and other factors contributing to the background fluctuation in yields, it is easy to understand how such effects on relatively small areas within the pistachio growing counties have not been picked up by researchers so far.