Tag Archives: blueberry plants in pots

The issue of limited information also has to do with the size of reporting units in the available data

California pistachios, on the other hand, are concentrated in the southern part of San Joaquin Valley. Moreover, they are planted in areas where the climatic conditions are mostly beneficial for them. Few events of adverse weather exist on record, which can be used for analysis. Therefore, the variance in CP in our range of interest is even more limited.The California Department of Food and Agriculture, as well as the US Department of Agriculture, usually report average yields on the county level. If the counties are large, compared to the growing area, few observations will be generated, and the averaging process will get rid of useful extreme observations on the sub-county level. The aggregated reporting problem, together with crop concentration, limits the possibilities of traditional econometric analysis on crop yields. I address this problem here for California pistachios, but the challenge might prove a barrier for research on other crops as well. Consider not only high value commercial crops concentrated in a few California counties, but also “orphan crops”: local crops which have received less attention from researchers and the private sector, yet generate substantial nutritional value for low income communities in developing countries. The African Orphan Crops Consortium, an initiative to promote research and use of these crops in Africa, list 101 crop of interest on its website, many of them perennial.2 Cullis and Kunert note that orphan crops “…are poorly documented as to their cultivation and use, and are adapted to specific agro-ecological niches and marginal land with weak or no formal seed supply systems”. Research on specific orphan varieties might therefore suffer from the same challenges of California pistachios: biological complexity, concentration of growing acreage,blueberry plants in pots and few data reporting units. In this chapter, I combine two approaches to estimate the yield response of California pistachios to winter CP count. The first approach is a “big data” one: I enhance a California yield panel of five counties with local temperatures at the pistachio growing areas. I use satellite data and temperature readings from local weather stations to create a large data set that can be connected with the yearly yields.

Substantially increasing the number of explanatory variables, this allows for more nuances observations. The second approach is an aggregate estimation methodology, previously used in agricultural productivity literature but –to my knowledge– not yet explored in climate literature. This approach notes that the observed outcome variable is a mix of unobserved sub-unit heterogeneity in the data generating process. Information about this heterogeneity is used to recover the relationship between temperatures and yields. The result of this exercise is the first successful recovery of the nonlinear yield response to winter chill in commercial pistachio production. I apply my findings to climate predictions in the current growing areas to show the potential impact of climate change on California pistachios in the next 20 years, and predict that a significant decline can be expected. 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,draining plant pots 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, 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. The support of CP through the panel is [36, 86]. Past county yields are from crop reports published by the California Department of Food and Agriculture. 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.

Water then replaced labor as the dominant issue in California agriculture

Expansion of agricultural production caused groundwater overdrafts to resume in the 1940s. However, construction on the CVP was suspended during the war years , delaying the availability of new surface-water supplies to production areas with over drafted groundwater supplies. In 1948, California permanently took over as the largest agricultural state in the Union in terms of value of production .California emerged from the first half of the 20th Century as the leading state in the U.S. military/industrial complex. Its agriculture had weathered the Depression, had regained health during WWII, and was poised to expand as the CVP came online. At mid-century, the future must have been seen as a time of great promise for the state. The second half of the century, at least until the 1990s, met that promise. California’s population grew in the next 50 years from 10 to 35 million people. California gross domestic product generally grew faster than that of the United States, meaning per-capita California GDP exceeded the U.S. GDP in most years. In fact, by the end of the century, California was being touted as either the fifth or sixth largest economy in the world, exceeding Canada in both population and GDP and Italy in GDP. The growth was fueled by rapid expansion, first in the aerospace industry and then in electronics and computers. California led the nation in both fields. Also, military expenditures remained high through the 1980s. For example, in the 1960s California received 20 percent of all U.S. defense contracts . Of course, when defense cutbacks came in the 1990s, California suffered a disproportionately high share of defense reduction. Immigration slowed substantially,drainage gutter a severe recession struck the state in the early 1990s, and the state continued to suffer through a prolonged and severe drought.

A rapid recovery in the second half of the 1990s, fueled in part by the “dot com” boom, quickly collapsed into a recession in the first years of the 21st Century, bringing with it severe financial difficulties for the state. We now proceed with the last two vignettes in our epochal history. It goes without saying that it becomes more difficult to describe California agriculture in simple or brief terms. Still, despite the increased complexity, the need for brevity persists. Therefore, what follows in Epoch 7 and Epoch 8 are at best highlights and more likely are selective illustrative anecdotes.The decades of the 1950s and 1960s were boom periods in California. The population nearly doubled from a little more than ten million in 1950 to almost 20 million in 1970. The 1950s were particularly explosive; population increased by 5.1 million—a more than 50 percent increase within one decade. Incomes grew quickly as the Cold War spurred rapid economic growth, particularly in the new aircraft and electronics industries as well as in older line industries such as agriculture and motion pictures. Massive investments in infrastructure continued in water projects, highways, airports, ports, higher education, and urban development. Virtually all of the increase in population was in burgeoning urban areas on the south coast, particularly in the Los Angeles basin and the San Francisco Bay Area to the north. With rapidly expanding housing growth, mostly in sprawling single-home subdivisions, urbanization accelerated the takeover of agricultural land. In just 20 years, Los Angeles County went from producing the highest value of agricultural production in the state—and in the nation—to being out of the “top ten” California counties in 1970. Vast stretches of Orange and San Diego Counties, longtime major producers of citrus and subtropical fruits and vegetables, were developed quickly, beginning in the 1960s with the Irvine Ranch and continuing through the 1970s and 1980s.

In the north rapid urbanization quickly consumed much of Santa Clara County’s agriculture, pushing fresh- and dried-fruit production into the Sacramento and northern San Joaquin Valleys. The rapid relocation of production was able to occur, in part, because the state’s stock of irrigated land increased from less than five million acres in 1945 to more than seven million acres in 1970, peaking at around 8.5 million acres in the 1980s. Virtually all of the expansion came from publicly funded large-scale projects. Water in the Delta-Mendota Canal in 1953 signaled completion of the CVP, which “brought over a million additional acres of San Joaquin Valley land into production by the mid 1950s” . The SWP was nearing completion at the end of the 1960s, bringing in excess of a half-million new acres into production in the southern San Joaquin Valley. The cumulative impacts of population and income growth, urbanization, and new production opportunities opened by water transfer led to rapid and significant changes in California agriculture. The changes involved expansion both in the suite of crops produced and in alterations in the location of production. We identify three examples. First, Southern California’s dairy industry moved from southern Los Angeles and northern Orange Counties to eastern Los Angeles County and then to western San Bernardino and Riverside Counties in the 1950s and 1960s. The dairy industry eventually migrated north into the southern San Joaquin Valley, where it is now concentrated in Tulare and Merced Counties. Second, the citrus industry experienced a similar migration, first east to Riverside and San Bernardino, then north. Today, more than 50 percent of the state’s production is in Tulare County, compared to nearly 45 percent of production in Los Angeles and Orange Counties in 1950. Third, rapid urban development in the south San Francisco Bay Area pushed deciduous fruit production out of the Santa Clara Valley into the Sacramento and Northern San Joaquin Valleys. Using prunes as an example , in 1950 nearly 80 percent of the 100,000 bearing acres of prunes were on the central coast. The ratio of non-bearing to bearing acreage was “0.09”.3 By 1960, the non-bearing to bearing ratio for the state had tripled to 0.34, but in the Sacramento Valley it was an astounding 0.82. In those two decades, prune acreage in the Sacramento Valley increased from 20,000 to 50,000 bearing acres.

By the end of the century, virtually all prunes would be grown in the upper Sacramento Valley. And with this massive relocation came substantial increases in yields because of new trees, better varieties, higher planting densities, and new cultural practices. Prune yield in 1950 was 1.46 tons per acre, in 1970 it was 2.08, and in 1987 it topped 3.0 tons. Crops also moved as new water became available. One significant example is almonds. In 1950 half of the state’s almonds were grown in the Sacramento Valley, 25 percent in the San Joaquin Valley, and the remainder in coastal counties. There were 90,000 bearing acres and about 18,000 non-bearing acres geographically distributed in the same ratio as production. Yields averaged 0.42 tons per acre. Statewide in 1970 there were 148,000 bearing acres and nearly 90,000 non-bearing acres . Of these, 74,000 bearing acres and 70,000 non-bearing acres were in the San Joaquin Valley. In 20 years, yields doubled to 0.84 tons per acre. By 2000, 80 percent of production was in the San Joaquin Valley, 20 percent in the Sacramento Valley, and virtually none on the coast. Yields now average well over a ton per acre. The expanded availability of both federal and state water,large square pots coupled with relatively high federal commodity price supports, also led to rapid expansions in cotton and rice production despite generally low and declining field-crop prices in the 1950s and 1960s. Along with an increase in production, a significant change in U.S. commodity policy in 1965 rapidly increased exports of basic commodities because these exports were now priced competitively in world markets. The bottom line is that the 1950s and 1960s saw the beginning of a second fundamental transformation of California crop agriculture in terms of expansion, changing composition, relocation, and greatly enhanced yields. The dominant driver of this transformation was productivity growth. Traditional field crops, as a share of production, declined steadily, to be replaced by higher-valued, income-sensitive crops. Higher incomes plus urbanization accounted for the rising importance of fresh vegetables and horticulture products in California agriculture. Rising incomes after WWII also fueled a rapid expansion in consumer demand for beef. U.S. consumption rose from somewhat more than 50 pounds per capita in 1950 to almost 95 pounds in the mid-1970s. California’s livestock sector responded to that demand expansion in a big way. One of the most phenomenal growth patterns observed was the practice of fattening slaughter beef in confined feedlots. Cattle numbers in California had been flat from 1900 to 1940, at approximately 1.4 million head. Numbers increased to 3.9 million head in 1969—a 250 percent increase . Again, California led the nation in new approaches to large-scale agricultural production. However, by the 1970s, large-scale feedlots were established in Arizona, Colorado, Texas, and the Midwest, areas generally more proximate to Great Plains and Midwestern feed supplies. Also, per-capita beef consumption steadily declined after the 1970s, stabilizing around 66 pounds per capita in the 1990s and early 2000s. California’s second beef boom was replaced by the significant expansion of the dairy industry. In 1950 there were 780,000 dairy cows in California—19,428 farms with an average of 40 cows per farm. Average production per cow was 7,700 pounds of milk per year. In 1970 there were slightly less than 5,000 farms, nearly a 400 percent reduction, but the average number of cows per farm had nearly quadrupled to 150. Each cow now produced an average of almost 13,000 pounds per year—yields nearly doubling in 20 years.

The dairy transformation had begun. It would play out dramatically over the next 30 years so that in 2001 there were but 2,157 dairy farms with an average of 721 cows each and yielding more than 21,000 pounds of milk per cow. Production increased even more rapidly because the number of cows also increased from 700,000 to 800,000 in the 1950s and 1960s to 1,555,000 in 2001. The dairy industry emerged as the dominant commodity in the agricultural portfolio of California. In 1993 California overtook Wisconsin as the number one milk producer in the nation and now accounts for 48 percent of the U.S. nonfat dry milk production , 28 percent of U.S. butter , and 18 percent of U.S. cheese production . There are many other stories that could be told about the boom period of the 1950s and 1960s, but the picture that emerges is clear: a dynamic, demand-driven agriculture responding to each instance of production relocation with substantially increased productivity. Aided and abetted by a constant supply of new technology, agriculture in the 1950s and 1960s grew rapidly. It existed in a state that was growing very rapidly and getting rich fast. Despite this record of rapid growth, the next three decades were going to be even more explosive but also more unstable. Whereas the 1950s and 1960s were characterized by relatively stable prices, increased price volatility in the next three decades would lead to substantial swings in the profitability and economic sustainability of firms in California agriculture.As California agriculture entered the last three decades of the 20th Century, and despite ongoing growth in specialty-crop production, it maintained a predominant basic-commodity orientation. Field crops together with livestock and livestock products accounted for 56 percent of the value of agricultural sales in 1970. Basic commodities were priced in national markets, and California producers responded to these national prices and transportation differentials. Government policy supported stable prices. By the end of the epoch, less government policy emphasis on domestic prices became the norm along with wider price swings induced by rapid changes in both consumer and export demand for California’s agricultural produce. Many vegetables, fruits, and nuts were exclusively produced in California. At the very least, if not exclusive to the entire U.S. production, they were definitely exclusive during certain production seasons. Specialty crops enjoyed multiple market options , but those options would become less easily accessible over time. European and Asian economies, which were growing markets throughout this period, gradually gained increased influence over agricultural prices, making the California producer more exposed to offshore economic conditions. While foreign economic conditions were not a significant factor at the start of this period, they emerged abruptly in the mid-1970s and added considerable turbulence to agricultural markets during the 1990s.