The GV threshold represents a trade-off between accuracy and inclusivity

A higher GV threshold will decrease the risk of including fallow fields in the classification, but it will also increase the risk of excluding fields of crops that should be included. Table S1 details accuracy by class with a 25% threshold for comparison. Using a 50% GV threshold increases both user and producer’s accuracies for all of the crops over the 25% GV threshold, while excluding almost 1700 fields with an average green vegetation fraction between 25–50%. When the higher threshold is used, infrequent crop categories such as cotton and other truck crops increase substantially in producer’s accuracy, and prevalent crop categories, such as almond/pistachio and other deciduous crops, increase substantially in user’s accuracy. The random forest is most likely to classify ambiguous pixels as one of the most common classes, and therefore over classifies common classes and under classifies rarer classes when a lower field threshold is used. With a 50% GV threshold, alfalfa, corn, other deciduous, subtropical, and tomato crops all showed user and producer accuracies over 90%. Of all of the crop categories, cotton had the lowest user and producer accuracies. The low accuracy of cotton may be attributable to a few different reasons. First, the sample size was small , so training data was limited. Second, early June was likely not the best time of year for the classification of cotton, because cotton is planted in March and April, but does not reach maturity for 180–200 days . Later in the summer may be a better time of year for quantifying total cotton extent. However,plant pots with drainage while the user and producer accuracies of cotton were low, only 22 fields of 4110 total fields in our dataset were planted in cotton. Therefore, the overall classification accuracy is not impacted much by this error.

To better understand the success of the random forest classifier, the mean decrease accuracy was calculated for each wavelength . MDA can be interpreted as the number of pixels that would be misclassified if a given wavelength was removed from the random forest classifier. The 400–750 nm range, encompassing visible and red-edge portions of the spectrum, showed overall higher MDA scores than the near infrared or shortwave infrared regions. The red-edge region in particular had the highest MDA score, followed by the blue region in the 400-nm range. Additionally, wavelengths on the edge of bands that were removed due to the high influence of water vapor showed higher importance than bands that did not border a water vapor region. For example, the NIR band at 957 nm showed a higher MDA score than any other band in the NIR. Some possible explanations for these results will be explored further in the discussion. Although 704 nm had the highest MDA, only about 80 pixels would be misclassified out of 150,000 if it were removed. The small error is likely due to the high correlation between bands. Using average water inputs per area in the Tulare Lake Hydrologic Region for each of these crop categories, the estimated required water inputs are shown in Table 2.10. The overall water requirement of the study site was estimated to have declined over the course of the drought by around 0.02 km3 of water yearly, due to increases in fallowed land. Figure 2.8 shows an average water requirement per crop group by the change in planted area from 2013 to 2015 to better understand whether the water requirement of a crop was a key factor in planting decisions.

This analysis revealed no strong correlation between the change in crop area and average water inputs, showing that the crops that needed the largest water inputs did not experience larger declines in area than the crops that required less water.Crop market value, crop value per unit water, and shifts in value over the drought may have played a role in planting decisions. It is interesting to note that tomatoes and grape vines were the two crops that showed increases in area as the drought progressed, and they are also the crops that have a high value per area. In 2014, fresh tomatoes had an average value in California of $27,088 per hectare and grapes of $14,960 per hectare. By contrast, the value per hectare for alfalfa, corn, and cotton were $3121, $1915, and $5772, respectively . While a detailed economic analysis is outside of the scope of this paper, the increase in area of higher value crops over the course of the drought is noteworthy and warrants further study. Finally, crop lifespan was analyzed in conjunction with changes in cropping area. Figure 2.9 shows changes in cropping area from 2013 to 2015 segmented by the average number of years that a crop is in production once planted. The results showed the greatest declines occurred in annual crops. The mid-lifespan group had smaller percentage declines, and the long lifespan group showed a small increase in the percentage of crop area over the span of the drought. The results indicated that there is a correlation between lifespan and change in crop area that is likely to be of importance when studying the agricultural impacts of future drought events.

Mapping crops is an integral part of understanding the water budget and needs of agricultural areas. However, mapping managed vegetation with remote sensing presents a set of challenges that are unique from natural vegetation. For one, annual crops are regularly planted, grown, harvested, and fallowed within a few months’ time. Land cover can change rapidly, and fields include crops in various stages of growth that may lead to large variations in plant cover, soil fraction, and shade. Secondly, crops are actively managed. Two fields of the same crop, side by side, may receive different management strategies in regard to pesticides, planting geometry, soil additives, soil type, irrigation system and schedule, and the timing of planting. These differences may lead to significant spectral variations within crop types . Thirdly, because crops are commonly rotated and fallowed, validation information needs to be precise in time in order to accurately capture the agricultural landscape that coincides with the flight schedule. The goal of this study was to determine how useful remotely sensed crop maps could be, given these caveats, for agricultural management at a regional level. Our estimates of crop extent may have been affected by different sources of error. First, because this study used a cutoff of 50% green vegetation or more at the pixel and field level, the final map likely under mapped crop area. Of the 8272 total fields registered in the validation data layers as having a crop growing at the time of the flights, 4114 had between zero and 50% green vegetation as assessed by MESMA, and were therefore not included in the analysis. Fields with less than 50% GV fraction could be indicative of recent harvests, new plantings, young crops, or fallowed land, which do not all result in the under mapping of active crop area. Additionally, because of this threshold, yearly changes in crop extent,plastic plant pots particularly of tree crops, could indicate growth of a crop instead of a new planting. For example, a field of almond trees may have a 45% green vegetation fraction in 2013, 48% in 2014, and 51% in 2015. In this case, the almond field would only be mapped in 2015, and would imply a sudden increase in planted almond extent, although it had existed for multiple years. Underestimating area is not unique to our study; it is a challenge of hard classifications in general.

Areal estimation of classes that are changing tend to be underestimated, while the magnitude of that change is overestimated when classified pixels are multiplied by the pixel size to the estimated area . One possible solution is to apply a regression correction such as implemented by the National Agricultural Statistics Service’s Cropland Data Layer . The CDL correction uses a sample site with ground truth area data to formulate a regression between CDL estimates and truth that is then applied to the CDL data to adjust the estimates . Such a correction would be advantageous for future studies, but it does require additional on-the-ground validation data that was not available for this study. Second, some green fields in the study area contained a crop that was not included in one of the nine mapped crop groups. However, the crops that were not included in the study comprised less than 1% of the crop area, so the errors associated with their absence in the study are assumed to be low. Third, crop rotation may be one reason that the extent of annual crops fluctuated more than the perennial crops. Crop rotation was not accounted for in our crop extent estimates, as one image per year is not adequate for capturing such changes. However, if crops are being rotated, we would still expect the total area of annual crops to stay somewhat stable through time, even if specific annual crops showed large fluctuations. Our finding that the total area of annual crops declined with drought is therefore not likely to be attributable to crop rotation. Finally, given that the study only used one time point in June for each of the three years, changes in estimated area could be indicative of a change in the timing of planted crops in addition to actual fallowing or crop substitution. For example, if farmers planted their tomato fields three weeks later in 2014 than they planted them in 2013, the tomatoes may not have reached the 50% green vegetation threshold by early June in 2014, which would have led to underestimates of tomatoes in that year. Other studies have relied on time series analyses to confirm fields as fallow , which suggests that a satellite-based imager with a moderate spatial resolution such as Landsat OLI could be used in tandem with aerial imagery to enhance the validity of fallowing results. The accuracies obtained in this study from AVIRIS exceeded those from simulated Landsat OLI and Sentinel-2B imagery, although even the Landsat and Sentinel results had high accuracies at the field level of above 85% accuracy for eight of the nine crop categories. The increased accuracy of AVIRIS over multi-spectral sensors is in contrast with the results of Clark , who found no classification improvement of hyperspectral data over multispectral data when classifying land cover in California, but showed similar results to Platt and Goetz , who found modest advantages of AVIRIS over Landsat for classifications at the urban–rural fringe. Since true Landsat and Sentinel imagery will have coarser spatial resolutions and a worse signal-to-noise ratio, we hypothesize that the accuracies of this classification would decrease if actual imagery, and not simulated imagery, were used. However, given the high accuracies of the simulated data, it seems that the extra spectral bands of AVIRIS, while somewhat advantageous for crop classification, do not confer a large added benefit over the 12 Sentinel bands. The sensor comparison and the results of the band importance analysis imply that 172 spectral bands, such as those that were used in this study, are somewhat redundant and could likely be pared down without losing much accuracy. The band analysis highlighted the most important wavelengths and regions of the spectrum for the random forest classifier, and pointed to biochemicals and structure as drivers of crop discrimination. The 400–750 nm region was particularly significant, especially the blue and red-edge regions, showing that the shape of the green peak and the chlorophyll absorptions provide valuable information for crop discrimination. The red-edge region has been shown to vary by vegetation stress, species, and time of year, which can all change the slope and inflection point of the red edge [67–69]. The blue region is sensitive to chlorophyll-a absorption and has been linked to senescence, carotenoids, and browning . The finding that these two regions are important for crop discrimination is similar to Immitzer et al. , who found the red edge and the blue bands to be the two most important bands for crop classification using Sentinel-2 data. Sentinel-2 contains three bands in the highly valuable red-edge region that may partially account for its ability to classify crops at accuracies that rival AVIRIS. However, when comparing the blue region, this study found bands 414 nm and 424 nm to be of high value for crop discrimination, which are shorter than the Sentinel blue band at 490 nm.

The first does not adjust for any observable determinants of farmland values

There are three further issues about equation that bear noting. First, it is likely that the error terms are correlated among nearby geographical areas. For example, unobserved soil productivity is likely to be spatially correlated. In this case, the standard OLS formulas for inference are incorrect since the error variance is not spherical. In absence of knowledge on the sources and the extent of residual spatial dependence in land value data, we adjust the standard errors for spatial dependence of an unknown form following the approach of Conley . The basic idea is that the spatial dependence between two observations will decline as the distance between the two observations increases.Throughout the paper, we present standard errors calculated with the Eicker-White formula that allows for heteroskedasticity of an unspecified nature, in addition to those calculated with the Conley formula. Second, it may be appropriate to weight equation . Since the dependent variable is county level farmland values per acre, we think there are two complementary reasons to weight by the square root of acres of farmland. First,growing raspberries in pots the estimates of the value of farmland from counties with large agricultural operations will be more precise than the estimates from counties with small operations and the weighting corrects for the heteroskedasticity associated with the differences in precision. Second, the weighted mean of the dependent variable is equal to the national value of farmland normalized by total acres devoted to agriculture in the country.

MNS estimate models that use the square roots of the percent of the county in cropland and total revenue from crop sales as weights, respectively. We also present results based on these approaches, although the motivation for these weighting schemes is less transparent. For example, they both correct for particular forms of heteroskedasticity but it is difficult to justify the assumptions about the variance covariance matrix that would motivate these weights. Further, although these weights emphasize the counties that are most important to total agricultural production, they do so in an unconventional manner. Consequently, the weighted means of the dependent variable with these weights have a non-standard interpretation. Third to probe the robustness of the hedonic approach, we estimate it with data from each of the Census years. If this model is specified correctly, the estimates will be unaffected by the year in which the model is estimated. If the estimates differ across years, this may be interpreted as evidence that the hedonic model is misspecified. As the previous section highlighted, the hedonic approach relies on the assumption that the climate variables are orthogonal to unobserved determinants of land values. We begin by examining whether these variables are orthogonal to observable predictors of farm values. While this is not a formal test of the identifying assumption, there are at least two reasons that it may seem reasonable to presume that this approach will produce valid estimates of the effects of climate when the observables are balanced. First, consistent inference will not depend on functional form assumptions on the relations between the observable confounders and farm values. Second, the unobservables may be more likely to be balanced . Table 3A shows the association of the temperature variables with farm values and likely determinants of farm values and 3B does the same for the precipitation variables.

To understand the structure of the tables, consider the upper-left corner of Table 3A. The entries in the first four columns are the means of farmland values, soil characteristics, and socioeconomic and locational characteristics by quartile of the January temperature normal, where normal refers to the long run county average temperature. The means are calculated with data from the five Censuses but are adjusted for year effects. Throughout Tables 3A and 3B, quartile 1 refers to counties with a climate normal in the lowest quartile, so, for example, quartile 1 counties for January temperature are the coldest. The fifth column reports the F-statistic from a test that the means are equal across the quartiles. Since there are five observations per county, the test statistics allows for county-specific random effects. A value of 2.37 indicates that the null hypothesis can be rejected at the 5% level. If climate were randomly assigned across counties, there would be very few significant differences. It is immediately evident that the observable determinants of farmland values are not balanced across the quartiles of weather normals. In 120 of the 120 cases, the null hypothesis of equality of the sample means of the explanatory variables across quartiles can be rejected at the 5% level. In many cases the differences in the means are large, implying that rejection of the null is not simply due to the sample size. For example, the fraction of the land that is irrigated and the population density in the county are known to be important determinants of the agricultural land values and their means vary dramatically across quartiles of the climate variables. Overall, the entries suggest that the conventional cross-sectional hedonic approach may be biased due to incorrect specification of the functional form of observed variables and omitted variables. With these results in mind, Table 4 implements the hedonic approach. The entries are the predicted change in land values from the benchmark increases of 5 degrees in temperatures and 8% in precipitation from 72 different specifications.

Every specification allows for a quadratic in each of the 8 climate variables. Each county’s predicted change is calculated as the sum of the partial derivatives of farm values with respect to the relevant climate variable at the county’s value of the climate variable multiplied by the predicted change in climate . These county-specific predicted changes are then summed across the 2,860 counties in the sample and reported in billions of 1997 dollars. For the year-specific estimates, the heteroskedastic-consistent and spatial standard errors associated with each estimate are reported in parentheses. For the pooled estimates, the standard errors reported in parentheses allow for clustering at the county level. The 72 sets of entries are the result of 6 different data samples, 4 specifications,plant pot with drainage and 3 assumptions about the correct weights. The data samples are denoted in the row headings. There is a separate sample for each of the Census years and the sixth is the result of pooling data from the five Censuses. Each of the four sets of columns corresponds to a different specification.The second specification follows the previous literature and adjusts for the soil characteristics in Table 2, as well as per capita income and population density and its square. MNS suggest that latitude, longitude, and elevation may be important determinants of land values, so the third specification adds these variables to the regression equation.The fourth specification adds state fixed effects. The exact controls are noted in the rows at the bottom of the table. Within each set of columns, the column “[1]” entries are the result of weighting by the square root of farmland. Recall, this seems like the most sensible assumption about the weights. In the “[2]” and “[3]” columns, the weights are the square root of the percentage of each county in cropland and aggregate value of crop revenue in each county. We initially focus on the first five rows, where the samples are independent. The most striking feature of the entries is the tremendous variation in the estimated impact of climate change on agricultural land values. For example, the estimates range between positive $265 billion and minus $422 billion, which are 19% and -30% of the total value of land and structures in this period. An especially unsettling feature of these results is that even when the specification and weighting assumption are held constant, the estimated impact can vary greatly depending on the sample.

For example, the estimated impact is roughly $200 billion in 1978 but essentially $0 in 1997, with specification #2 and the square root of the acres of farmland as the weight. This finding is troubling because there is no ex-ante reason to believe that the estimate from an individual year is more reliable than those from other years.23 Finally, it is noteworthy that the standard errors are largest when the square root of the crop revenues is the weight, suggesting that this approach fits the data least well. Figure 1 graphically summarizes these 60 estimates of the effect of climate change. This figure plots each of the point estimates, along with their +/- 1 standard error range. The wide variability of the estimates is evident visually and underscores the sensitivity of this approach to alternative assumptions and data sources. An eyeball averaging technique suggests that together they indicate a modestly negative effect. Returning to Table 4, the last row reports the pooled results, which provide a more systematic method to summarize the estimates from each of the 12 combinations of specifications and weighting procedures.The estimated change in property values from the benchmark global warming scenario ranges from -$248 billion to $50 billion . The weighted average of the 12 estimates is -$35 billion, when the weights are the inverse of the standard errors of the estimates. This subsection has produced two important findings. First, the observable determinants of land prices are poorly balanced across quartiles of the climate normals. Second, the hedonic approach produces estimates of the effect of climate change that are sensitive to specification, weighting procedure, and sample and generally are statistically insignificant. Overall, the most plausible conclusions are that either the effect is zero or this method is unable to produce a credible estimate. In light of the importance of the question, it is worthwhile to consider alternative methods to value the economic impact of climate change. The remainder of the paper describes the results from our alternative approach. We now turn to our preferred approach that relies on annual fluctuations in weather about the monthly county normals of temperature and precipitation to estimate the impact of climate change on agricultural profits. Table 5 presents the results from the estimation of four versions of equation , where the dependent variable is county-level agriculture profits and the weather measures are the variables of interest. The weather variables are all modeled with a quadratic. The data for these and the subsequent tables are from the 1987, 1992, and 1997 Censuses, since the profit variable is not available earlier in earlier Censuses. The specification details are noted at the bottom of the table. Each specification includes a full set of county fixed effects as controls. In columns and , the specification includes unrestricted year effects and these are replaced with state by year effects in columns and .Additionally, the columns and specifications adjust for the full set of soil variables listed in Table 1, while the columns and estimating equations do not include these variables. The first panel of the table reports the marginal effects and the heteroskedastic-consistent standard errors of each of the weather measures. The marginal effects measure the effect of a 1-degree change in mean monthly temperatures at the climate means on total agricultural profits, holding constant the other weather variables. The second panel reports p-values from separate F-tests that the temperature variables, precipitation variables, soil variables, and county fixed effects are jointly equal to zero. The third panel of the table reports the estimated change in profits associated with the benchmark doubling of greenhouse gases and the Eicker-White and Conley standard errors of this estimate. Just as in the hedonic approach, we assume a uniform 5 degree Fahrenheit and 8% precipitation increases. This panel also reports the separate impacts of the changes in temperature and precipitation. When the point estimates are taken literally, it is apparent that the impact of a uniform increase in temperature and precipitation will have differential effects throughout the year. Consider the marginal effects from the column specification, which includes the richest set of controls. For example, a 5- degree increase in April temperatures is predicted to decrease mean county-level agricultural profits by $1.35 million, compared to annual mean county profits of approximately $12.1 million. The increase in January temperature would reduce agricultural profits by roughly $0.70 million, while together the increases in July and October temperature would increase mean profits by $1.45 million. The increase in precipitation in January and July is predicted to increase profits, while the October and April increase would decrease profits.

IPF is also strongly associated with cigarette smoking in epidemiologic studies

The fate of particles is not well established, and there is little information on the distribution and retention of particles under conditions of ambient exposure. One purpose of this study was to design and implement an approach that would allow the assessment of particle retention as well as histologic analysis of response in different lung compartments. Our findings in this regard have been previously published and show that the deposition of particles is primarily in the centriacinar portion of the lung lobule . Unfortunately, no reported epidemiologic studies have directly assessed environmental exposure to mineral dusts and interstitial lung and airway disease in farmers or farm workers, although respiratory symptoms have been associated with exposure to agricultural dusts with high mineral content . A few studies, however, have suggested that pneumoconiosis and restrictive lung disease are increased in some agricultural populations exposed to mineral dusts. A fundamental question remains whether occupational exposure to agricultural dusts can cause pulmonary fibrosis, persistent inflammation,aeroponic tower garden system and cell/tissue remodeling in specific regions of the lungs where these mineral dusts are present.

The objective of this study was to document and quantify pathologic lesions in lung tissues from consecutive Hispanic males autopsied by the coroner’s office in Fresno, California, and to determine their relationship to agricultural work and to mineral dust retained in the lungs. We focused on pneumoconiosis and lesions of the small airways.We thank S. Smiley-Jewell for assisting with manuThis research has been funded in part by National Institute for Occupational Safety and Health U07/ CCU906162, U.S. Environmental Protection Agency grants R826246 and RD832414, and the Alberta Lung Association. The authors declare they have no competing financial interests. Received 2 September 2008; accepted 25 February 2009.2000. We report that young male agricultural workers have a higher prevalence of pneumoconiosis and small airway disease associated with mineral dust exposure than do non-agricultural workers living in the same environment.Left lungs from 112 Hispanic male autopsies were collected at the Fresno County coroner’s office from June 1994 to June 1995. Demographic information including age, residence duration in Fresno County, and occupational histories were obtained from the medical examiner and the coroner’s files. Smoking histories were available for a minority of subjects; therefore, smoking status was classified by pathologic criteria. The study subjects ranged in age from 16 to 73 years and had died suddenly or unexpectedly. An autopsy was performed at the coroner’s office to determine the cause and manner of death, as dictated by state statute. The autopsies were performed within 12‒24 hr after death. The research team independently evaluated a sagittal slice of lung.

The project was thoroughly reviewed and approved by the Human Subjects Review Committee of the University of California, Davis. We did not have access to nor did we contact the next of kin.The left lung of each deceased individual was cannulated through the left main stem bronchus and inflation fixed with 2% glutaraldehyde at a hydrostatic pressure of 30 cm of water for 2 hr from a constant-pressure gravity apparatus. The lungs were cut in the sagittal plane to include the main stem bronchus, hilar structures, and the medial aspect of both the upper and lower lobes of the left lung. If the left lung was not suitable because of trauma, the right lung was processed in an equivalent manner. Initial fixation and cutting was done by the coroner’s staff. The lung section was stored in fixative and shipped to the University of California, Davis. On arrival, each lung was photographed from the cut sagittal surface as well as a medial view, and selected gross features were documented on a standard form; these included pleural pigmentation, fibrosis, and emphysema. Selected airways were micro-dissected and examined for mucous plugs or aspirated material within the lumen.Gross examination revealed varying amounts of black pigmentation in the pleura, around bronchovascular bundles, in the centriacinar zones of the parenchyma, and within hilar lymph nodes. Airway micro-dissection showed that dust accumulation was less proximally but became distinct around small airways. Grossly recognizable emphysema was rarely seen. Many lungs showed parenchymal hemorrhage consistent with a traumatic death. Smoking-related small airway disease and mineral dust‒associated small airways disease were seen in 54.5% and 28.6% of all cases, respectively . Pneumoconiosis was observed in 20.9% of subjects, lymph node fibrosis associated with mineral dust accumulation in 48.7%, pathologic changes consistent with chronic bronchitis in 56.3%, and microscopic emphysema in 23.6%.

Asthmalike inflammation and airway wall remodeling were seen in 26.8% of 112 subjects . The crude prevalence of mineral dust small airways disease, pneumoconiosis, and pathologic changes consistent with chronic bronchitis was significantly higher among farm workers than among non-agricultural workers and approached statistical significance for lymph node fibrosis and emphysema. In univariate models of the relationship between pathologic disease and mineral dust deposition as evaluated by polarized light microscopy on tissue sections, mineral dust deposition was strongly and significantly associated with interstitial fibrosis, mineral dust small airway disease, pneumoconiosis, pathologic changes consistent with chronic bronchitis, emphysema, and lymph node fibrosis . These associations remained significant after adjustment for age and smoking status. Cigarette smoking was associated with an OR of < 1 for mineral dust small airways disease, but this association was small compared with the very strong association with mineral dust exposure . Agricultural work was kept in the model for chronic bronchitis over mineral dust because it had a higher point estimate , although it did not achieve statistical significance at p < 0.05. Fibrosis of the walls of membranous and respiratory bronchioles was seen in most of the subjects. Examples of airway lesions in the groups are shown in Figure 2. The fibrosis was significantly greater in the upper lobes compared with the lower lobes. Forty-one percent of the nonsmoking, non-agricultural workers showed no fibrosis of their respiratory bronchioles . Very few non-agricultural workers exhibited a severe grade of airway fibrosis . Agricultural workers had more severe grades of fibrosis. The severity of the small airway disease increased in the following order: nonsmoking non-agricultural workers; nonsmoking agricultural workers; smoking non-agricultural workers; smoking agricultural workers. The effects of smoking and agricultural work on grade of small airway disease appeared additive . By bright field and polarized light microscopy,dutch buckets for sale opaque and birefringent dust in farmers’ respiratory bronchioles was highly correlated with small airways fibrosis . There was a highly significant relationship between agricultural work and a finding of pneumoconiosis : A total of 32.1% of agricultural workers had either macules or nodules in their lungs compared with only 8.3% of non-agricultural workers . This association persisted in a multivariate analysis controlling for age and cigarette smoking. Prevalence of pneumoconiosis increased to 41.5% and 18.6% in agricultural and non-agricultural workers, respectively, when interstitial fibrosis was included as a feature of pneumoconiosis . Pneumoconiosis was also significantly associated with an increased score for birefringent mineral particles in the walls of small airways . The correlation between pneumoconiosis and the mineral dust score was r = 0.57. In multivariate models that controlled for age and cigarette smoking, agricultural work was a significant independent predictor of pneumoconiosis .

CSi and AlSi were the most prevalent exogenous minerals found in the lungs for all groups . Milligrams of quartz per 100 grams of lung, measured by XRD analysis, were significantly increased in agricultural workers who smoked compared with nonsmoking non-agricultural workers and in non-agricultural smokers compared with non-agricultural nonsmokers . Logistic regression analysis, adjusted for age and smoking, showed a significant relationship between the amount of quartz determined gravimetrically in the lung and the presence of mineral dust small airway disease . SEM/XRS analysis showed greater numbers of total mineral particles, silica particles, and AlSi workers compared with non-agricultural workers and in smokers compared with nonsmokers . The sizes of the mineral dust particles by type of particle and subject group are also shown in Table 7. The average median circular equivalent diameters for silica and silicate particles were < 1 µm for all four groups. Silicate particles were significantly larger than silica particles overall, with the greatest difference seen in the nonsmoking agricultural workers . Mineral dust small airway disease was significantly associated with the number of silica, AlSi, and total mineral dust particles in the lung in univariate models and with total particle number in multivariate models.The lungs of deceased farm workers living in an agricultural region of California had significantly higher rates of pneumoconiosis and interstitial fibrosis than the lungs of deceased non-agricultural workers living in the same general environment. Furthermore, the farm workers showed higher prevalence of chronic obstructive pulmonary disease , including emphysema, pathologic changes consistent with chronic bronchitis, and small airways disease than the lungs of deceased non-agricultural workers living in the same general environment. Multivariate analyses showed that these pathologic lesions were strongly associated with mineral dust in the lungs, as assessed by grade of birefringent particles in the small airways, the amount of quartz, and number of silica and silicate mineral dust particles in digested lung samples. The study indicates that agricultural work in the central valley of California carries a significant risk for mineral dust small airway disease, pneumoconiosis, and COPD. The relationship between mineral dust exposure, small airway disease, and pneumoconiosis was confirmed by three separate approaches. First, and most important, by light microscopy we directly confirmed a relationship between airway fibrosis, pneumoconiosis, and birefringent mineral particles. Second, in a subgroup of the cases, we showed, in bulk samples of lung tissue, that CSi was significantly associated with small airway fibrosis. Third, we showed by SEM and XRS that the total number of mineral particles was greater in the lungs of agricultural workers than in non-agricultural workers and that their concentration was significantly associated with airway fibrosis. We thus demonstrate that mineral dusts play an important role in lung disease for agricultural workers and that mineral dusts need to take their place beside the well-established roles of organic dusts and smoking in causing lung disease in farm workers. Our data also indicate that mineral dusts contribute to both obstructive and restrictive lung disease processes. The findings reported here are remarkable in view of the young age of the study population. Mineral dust pneumoconioses are generally considered to have long latencies until clinically apparent, on the order of 10‒20 years . The latency period until early pathologic changes that are not radiologically or clinically evident is unknown. The cases examined in this study were young Hispanic males who had lived an average of 16 years in Fresno County. Approximately one-half of these subjects were farm workers, whereas the others were in other blue-collar occupations. None of these individuals died of respiratory disease, and most were in apparent good health before death. This is the first population-based sample that we are aware of that shows small airway and interstitial lung disease associated with agricultural work. Case–control studies have suggested an association of idiopathic pulmonary fibrosis with agricultural dust exposure, and specifically with animal dust/vegetable dust, but these studies have involved multiple associations without verification of exposure . A case–control study of inorganic particles in the pulmonary hilar lymph nodes of patients with IPF found an association of increased silicon and aluminum compared with control lymph nodes . A study using SEM and energy-dispersive X-ray analysis suggested that silica/silicate exposure might be a risk factor for IPF .Similar to our findings, Nasr et al. have recently shown that cigarette smokers with interstitial fibrosis have increased burdens of silica and silicates in their lungs. Taken together, these studies indicate that exposure to inorganic dusts, whether overt or occult, may be a more common cause of pulmonary fibrosis than currently recognized and that the term “idiopathic” may be an inaccurate description for some cases of IPF.The prevalence of histologic-determined cigarette smoking in this population was 54.5%, which is significantly higher than population-based data on smoking prevalence among Hispanic males . However, smokers have excess injury deaths , which are heavily weighted in coroner’s cases . Furthermore, young Hispanics living in the United States have higher rates of smoking than non-Hispanic youth .

Subsurface flow is also an important mechanism of phosphorus losses from the farm

In Corn Cob Canyon Creek, the median SRP concentration was 0.11 mg/L at Lewis Road, where the stream emerges from an underground culvert, and 2.2 mg/L at Hudson Landing, downstream of row crops. At the downstream locations in both Carneros and Corn Cob Canyon Creeks, SRP concentrations exceeded the 0.12 mg/L target level in 100% of biweekly samples. In Uvas and Llagas Creeks in the south Santa Clara Valley, SRP concentrations were generally below the target SRP concentration of 0.12 mg/L at all locations. However, water quality problems occurred more frequently downstream of agricultural land use , where a greater percentage of collected samples were over the target concentration . In the Pajaro River, elevated SRP concentrations occurred in the river’s upstream reaches at Chittenden Gap, due in large part to flow from San Juan Creek and associated ditches that drain irrigated fields in the San Juan Valley. In contrast to tributaries draining the south Santa Clara Valley, San Juan Creek had elevated SRP levels , and was a particularly significant source of nutrients to the Pajaro River during summer months, when flow from other creeks declined. In addition to San Juan Creek, several agricultural ditches in the south Santa Clara and San Juan Valley regions that flow intermittently may also contribute nutrients to the Pajaro River. We hope to address these issues in future research.In addition to agriculture,stackable planters natural processes and urban runoff may also contribute phosphorus to waterways.

Although no increase in phosphorus levels was detected at urban sampling locations on Llagas and Uvas Creeks , urban runoff from the city of Watsonville may contribute to elevated SRP levels in Watsonville Slough and the lower Pajaro River, particularly during the winter storm season. At Ohlone Road, our most upstream site in the slough, surface runoff from the city of Watsonville may also contribute to elevated SRP levels during winter storms. It is also worth noting that Watsonville Slough at Ohlone Road had a period of very high SRP concentrations in the late summer through fall of 2003; these may be associated with erosion from a development project that occurred adjacent to the sampling site.Algal growth is generally limited by available nutrients. In freshwater systems, an increase in either phosphorus and/or nitrogen can stimulate production . Agronomically small losses from the farm are sufficient to stimulate algae growth in lakes and streams. For example, 0.03–0.05 mg PO4 -P L-1, which are very low concentrations, stimulate high growth rates in algae. The excess growth of algae or aquatic plants, a process termed eutrophication, can threaten drinking water supplies by creating toxic conditions, fouling water intakes, and changing the availability of oxygen in the water. In addition to compromising drinking water quality, elevated nutrient levels may increase or decrease the abundance of specific species in a freshwater system. The change in species abundance can affect the taste and odor of water, making it unpalatable or even toxic to some organisms.

These potential ecosystem changes have not been investigated in Central Coast surface waters. With the increase in algae, levels of dissolved oxygen in the water column during the day can become very high . At night the activity of microbes that break down decaying organic matter in the sediments can reduce oxygen concentrations to the point that some aquatic species have difficulty surviving. Thus, very high, low, or fluctuating concentrations of dissolved oxygen can indicate eutrophic conditions. Although nutrient levels influence the growth of algae, the overriding factors that control algae growth in streams are disturbance , light availability , and consumption by animals. Thus, even if nutrient levels are elevated, excess algae growth may not occur. This fact severely complicates the development of an enforceable numeric standard for phosphorus along the Central Coast, as it is difficult to find a direct relationship between nutrient levels and algal growth.We detected seasonal changes in SRP concentrations in many waterways. One prominent seasonal pattern was an increase in SRP concentrations during the late summer in waterways that receive discharge from cultivated lands. This late summer increase occurred in San Juan Creek, in the Pajaro River at Chittenden Gap, and in Corn Cob Canyon Creek , and may be due to the combined effects of irrigation discharges and decreasing stream flows, which limit the capacity of waterways to dilute nutrient inputs. In contrast, Watsonville Slough had its highest SRP concentrations from fall through spring, with concentrations declining to an annual low point in mid summer .

High SRP concentrations in the winter rainy season may be associated with increased surface runoff from agricultural fields located along the slough. Tile drains may also facilitate subsurface loses of phosphorus . In Carneros Creek, which is dry from approximately May until December each year, a third seasonal pattern emerged . SRP concentrations were moderately elevated at both upstream and downstream sites following the first winter rains, which suggests that soil phosphorus accumulates over the summer months and is flushed into the creek with the first rains. At Dunbarton Road where there is little cultivation upstream, sources may include natural decomposition in grasslands, cattle grazing, and rural residential land use; at downstream sites sources also include agricultural land use. At San Miguel Canyon Road, the downstream location, SRP concentrations increased again in the late winter and spring of 2002 and 2003, reaching very high levels that frequently exceeded 1 mg/L. Nutrient concentrations were highly erratic in 2002 and 2003, and subsequently declined in 2004, suggesting that nutrients originated from a point-source that has ceased to discharge. No seasonal concentration trends were observed in the upstream tributaries of the Pajaro River . At these locations SRP concentrations remained low throughout the year. We calculated the SRP load discharged by each tributary , and found loads varied seasonally corresponding with discharge . The SRP load was greatest at Chittenden during January and February, when discharge was also greatest. San Juan Creek was not sampled during this period, but likely accounts for a significant portion of the unaccounted load because it has elevated SRP concentrations and yearround flow. In the Pajaro River, there is a strong seasonal trend in SRP concentrations . Concentrations decline after the rainy season ends. Because SRP concentrations remain relatively high in the winter, rainfall probably transports SRP to surface waters. Furthermore, the loss of SRP from Santa Clara/San Benito Counties is highest during these rainfall periods . Because concentrations and export of SRP in the Pajaro River are rainfall dependent, it is difficult to determine long-term trends independent of recent rainfall patterns.Elevated phosphorus concentrations can cause excessive algal growth in waterways, and preventing excessive growth is the primary reason phosphorus concentrations are regulated. Algal biomass in the water column can be determined from the concentration of chlorophyll a,stacking pots which indicates the degree of excessive algal growth. We monitored chlorophyll a concentrations at several sites in the Pajaro River watershed on a biweekly basis and compared these concentrations to phosphorus. We found no direct relationship between chlorophyll a and phosphorus levels at any of the locations . The lack of a direct correlation between chlorophyll a and phosphorus levels indicates that P availability is only one of the factors controlling algal growth. Canopy cover and turbidity , algae-eating organisms , substrates that allow different types of algae to attach, and algae sources also play a role in algal growth and chlorophyll a concentrations. Furthermore, additions of nitrogen can stimulate algal growth in streams and rivers, which challenges the commonly held belief that phosphorus is the nutrient that controls the growth of algae in freshwater ecosystems. Our research group from the Center for Agroecology and Sustainable Food Systems has begun efforts to assess the growth patterns of algae in order to determine how elevated phosphorus and nitrogen levels influence these patterns. Under state legislation known as the Agricultural Discharge Waiver that took effect in January 2005, farmers are required to develop farm water quality plans to protect surface waters along the Central Coast. One goal of our research is to inform growers of current water quality conditions in waterways adjacent to their land so that they can take steps to reduce their impacts on waterways while continuing to farm profitably.

Because phosphorus is transported to waterways in storm and irrigation runoff, reducing soil erosion and surface runoff is an important step in reducing phosphorus losses from the farm .Growers can address these losses by matching P demand in plants with fertility management, keeping P concentrations in soils at agronomically responsive levels , and managing irrigation to minimize or eliminate runoff. It is important to note that many growers on the Central Coast and throughout the state have already initiated practices to reduce the loss of phosphorus from their farms. The University of California has several research projects in progress to document the impacts of changes in farm management, and a number of government agencies and NGOs are working with growers to improve water quality .Nitrate contamination of freshwater resources from agricultural regions is an environmental and human health concern worldwide . In agriculturally intensive regions, it is imperative to understand how management practices can enhance or mitigate the effect of nitrogen loading to freshwater systems. In California, managed aquifer recharge on agricultural lands is a proposed management strategy to counterbalance unsustainable groundwater pumping practices. Agricultural managed aquifer recharge is an approach in which legally and hydrologically available surface water flows are captured and used to intentionally flood croplands with the purpose of recharging underlying aquifers . AgMAR represents a shift away from the normal hydrologic regime wherein high efficiency irrigation application occurs mainly during the growing season. In contrast, AgMAR involves applying large amounts of water over a short period during the winter months. This change in winter application rates has the potential to affect the redox status of the unsaturated zone of agricultural regions with implications for nitrogen fate and transport to freshwater resources. Most modeling studies targeting agricultural N contamination of groundwater are limited to the root zone; these studies assume that once NO3 – has leached below the root zone, it behaves as a conservative tracer until it reaches the underlying groundwater or, these studies employ first order decay coefficients to simplify N cycling reactions . However, recent laboratory and field-based investigations in agricultural systems with deep unsaturated zones have shown the potential for N cycling, in particular denitrification, well below the root zone . For example, Haijing et al. found denitrifying enzyme activity as deep as 12 metersin an agriculturally intensive region in China. Lind and Eiland reported N2O production in sediments taken from 20 meter deep cores. Other studies have reported the capability of deep vadose zone sediments to denitrify in anerobic incubations with or without the addition of organic carbon substrates . Moreover, in agricultural settings, especially in alluvial basins such as in California with a history of agriculture, large amounts of legacy NO3 – has built up over years from fertilizer use inefficiencies and exists within the deep subsurface . It is not yet clear how this legacy nitrogen may respond to changing hydrologic regimes and variations in AgMAR practices, and more importantly, if flooding agricultural sites is enhancing nitrate transport to the groundwater or attenuating it by supporting in situ denitrification. Denitrification rates in the subsurface have been reported to vary as a function of carbon and oxygen concentrations, as well as other environmental factors . While total soil organic carbon typically declines with depth , dissolved organic carbon can be readily transported by water lost from the root zone to deeper layers and can therefore be available to act as an electron donor for denitrification . Oxygen concentration in the vadose zone is maintained by advective and diffusive transport, while oxygen consumption occurs via microbial metabolic demand and/or abiotic chemical reactions . The effects of drying and wetting cycles on oxygen concentrations in the deep subsurface are not well documented. However, in 1 meter column experiments, there is some evidence that O2 consumption proceeds rapidly as saturation increases and rebounds quickly during dry periods . These variations in oxygen concentration can influence N cycling and thus, transport to groundwater.

These problems are compounded by a lack of sharing of information between agencies

We note that a program to provide information on the prevalence of infractions represents a departure from the original purpose of the TIPP program. Specifically, such a program would entail inspection of a random sample of operations, rather than focusing on those with complaints or at high risk for infractions. Whereas the original TIPP model garners support among employers because it lessens unfair competition from non-compliant operators, a random-inspection program intended to provide unbiased information on the prevalence of infractions may encounter difficulty in gaining support from employers. Whereas employers support the original TIPP model because it lessens unfair competition from non-compliant operators, employers may be less likely to support a random inspection program intended to provide unbiased information on the prevalence of infractions. This report reviews enforcement actions in agriculture undertaken by the Targeted Industries Partnership Program , a coordinated multi-agency education and enforcement initiative started November 1, 1992,nft channel and led for three years by former state Labor Commissioner Victoria Bradshaw and Dr. William C. Buhl , In 1996, Roberta Mendonca succeeded Commissioner Bradshaw and now jointly directs this effort with Dr. Buhl.

TIPP focuses exclusively on the agricultural and cut-and-sew garment industries, sectors chat are widely believed to have high levels of non-compliance with safety and labor laws. The effort is aimed at improving compliance through positive encouragement, by providing education and assistance customized to employer needs, as well as vigorous enforcement of federal and state laws governing conditions of employment. A unique feature of TIPP is that it seeks to coordinate the efforts of nearly all agencies with authority for enforcement of safety and labor laws. This authority is widely dispersed among a myriad of federal, state, and local agencies, leading to potential problems such as duplication of effort and inadequate oversight.In a climate of significantly reduced federal and state support for regulatory activities, a coordinated effort holds the promise of using shrinking resources more efficiently. California’s 1995 farm cash receipts of more than $22 billion were nearly twice the amount for second-ranked Iowa. The Golden State is also the top producer of a remarkably diverse number of agricultural commodities, accounting for more than half of the nation’s major fresh vegetables and two-fifths of its fruits and nuts. Last year, California surpassed Wisconsin to become the leader in fluid milk production as well. Less well understood is that California also leads the nation in the annual growth rate of farm production. Despite six years of drought, a severe recession, urbanization and major storms, California’s farm production increased sharply in the past 15 years, led by very much larger outputs of fruits, vegetables, and ornamental horticultural products. These increases reflect both changes in consumer demand, especially greater per capita consumption of fresh fruits and vegetables, as well as major increases in exports, particularly to Asian markets.

Overall, the annual tonnage of California vegetable production has doubled in this period, while fruit tonnage has increased by 40%. These increases have expanded labor requirements. After taking account of improvements in worker productivity, overall labor demand in California agriculture has increased by about 20% during the past 15 years.” At the same time, the farm worker population has greatly expanded, largely through immigration. The number of persons employed in a single year in California agriculture is not accurately known, but is estimated to be 700,000,~’ accounting for more than one-fourth of the nation’s estimated 2.5 million hired farm workers. The Immigration Reform and Control Act of 1986 stimulated a substantial influx of immigrants, both authorized and unauthoried.Today, nine of every ten California farm workers are foreign-born; most are from Mexico or Central America. This new immigration has both broadened and deepened among the peoples of Mexico and Central America. Among the new migrants working in the fields of California, an estimated 50,000 are from indigenous groups in their countries of origin.16 The new immigrants have low levels of educational attainment; an estimated 70% are functionally illiterate.” Difficulties are multiplied for persons from indigenous groups, who may speak neither English nor Spanish and communicate in an unwritten indigenous language. As the number of farmers and unpaid family members working in agriculture steadily decreases, and as farms have become increasingly dominated by large businesses, California’s agriculture has become more dependent on hired workers.

Today, at least 80% of all work on California farms is performed by hired labor.” The single most important development in farm employment in recent years is the rapidly growing reliance on farm-labor contractors, i.e., labor market intermediaries who match workers with farm jobs. Nationally, farmers report a 60% increase in real dollar expenditures for contract labor since 1974, while direct-hire labor expenses, again expressed in constant dollars, have declined In California, this trend is even more pronounced, with reported farm-labor contractor employment doubling since 1978.20 Today, one in three California farm workers is employed by a labor contractor during the year12′ and at peak season labor contractor employees are a plurality in nearly all regions of the state. In the past 15 years most farmer-provided housing and transportation services have been eliminated.As a result, hired farm workers are even more dependent on labor contractors for shelter and transportation to and from work. Increasingly, farm-labor contractors provide services needed by the workers they employ. For at least half of those working for labor contractors, services such as transportation or housing are provided by contractors or their agents for fees that are charged as a condition of employment,mimicking the company towns of the last century. This privatization of farm worker services has absolved many farm operators of the cost and responsibility for the workers they need and has added to the economic burden of individual workers. Traditionally, enforcement agencies have relied heavily on informants; their efforts have been largely complaint-driven. The TDPP program, in contrast, generally operates by conducting “sweeps” based on industry and geographic targeting rather than pursuing specific operations identified through complaints. Staff involved in the sweep represent the various cooperating agencies. The purpose of multi-agency sweeps is not only to identify violations, but also to generate leads. These leads can then be immediately pursued to find other violations. Not surprisingly, given California’s leading role in farm production and employment,hydroponic nft the state is also the nation’s leader in reported occupational injuries and fatalities in agriculture.Because California has required universal workers’ compensation insurance for all private-sector employees for more than 50 years, the state also has occupational injury data available from the records of workers’ compensation insurance carriers. An enumeration of all paid workers’ compensation insurance claims in the most recently reported five-year period shows that hired farm workers experienced a total of 185,558 occupational injuries or illnesses, of which 51,098 resulted in at least one day of lost work time or other indemnity payment. In addition, there were 202 occupational fa tali tie.Thus, in each of these five years, hired or contract farm workers in California experienced an average of 37,100 occupational injuries, of which 10,200 involved at least one day of lost work time or other indemnity, and 40 occupational fatalities. Other independent sources of occupational injury data are consistent with these workers’ compensation insurance reports regarding hired farm workers, but also provide data on occupational injuries to self-employed farm operators and family members. In 1992, there were a reported 705 occupational injuries to California farmers or unpaid family members that required medical attention or resulted in lost work days, and there were 15 occupational fa tali tie.

In 1993, a cross-sectional survey conducted for NOSH by the U.S. Department of Agriculture found 2,679 occupational injuries to California farmers or unpaid family members that resulted in at least one-half day of lost work time.These data sources also document that not only do California direct-hire farm workers experience the largest number of occupational injuries of any state, but they also account for at least one-sixth2 or as many as one-founh of the total for the entire Thus, California is arguably the nation’s most important setting in which to address occupational injury among hired and contract farm workers. In 1992, the state’s Labor Commissioner, Victoria Bradshaw, with Dr. William C. Buhl of the U.S. Department of Labor, Wage and Hour Division, initiated a collaborative effort by several federal and state agencies to vigorously enforce safety and labor laws on California farms through joint action. Commissioner Bradshaw and Dr. Buhl were also convinced that partnering with other agencies could help address severe cutbacks in staffing that resulted from the state government’s fiscal crisis triggered by the California recession of the early 1990s. Known as the Targeted Industries Partnership Program , this effort focuses resources on the agricultural and cut-and-sew garment industries. TIPP seeks to encourage compliance through programs of public education, outreach to employers and periodic surprise enforcement sweeps involving dozens of agents. The lead agencies are the state Department of Industrial Relations- Division of Labor Standards Enforcement and the U.S. Department of Labor-Wage and Hour Division. Cooperative relationships have also been established with the U.S. Internal Revenue Service, the California Department of Employment Development, and various local agencies. According to the California Governor’s Farm Workers Services Coordinating Council, “The objective of TIPP is to provide comprehensive enforcement of existing labor and employment laws that protect farm workers and to maximize the enforcement effort through joint participation in inspection, referrals, and the targeting of systematic and flagrant violators.”The basic concept is to increase the effectiveness of enforcement efforts through a more efficient use of agency resources. As many as twelve enforcement agencies may be involved in a particular sweep. Agencies most frequently involved in TPP efforts include the following: Occupational Safety and Health Administration, State of California . This agency has responsibility for enforcement of workplace safety laws. As a state agency it enforces California law. In addition, under delegated authority from the Occupational Safety and Health Administration of the U.S. Department of Labor, it also enforces federal safety laws. California safety laws are sometimes stricter than corresponding federal laws. For example, under state law all farm employers are subject to regulation, but federal OSHA standards apply only to farms with 11 or more employees. For the past several years, Cal-OSHA has made safety law enforcement in agriculture one of its highest priorities. Cal-OSHA participates in the federal OSHA safety and health program, which uses the IMIS database. Thus, safety and health inspection reports are entered into the federal database from the local state Cal-OSHA offices. The MIS database uses a standardized report that includes more descriptive data than are available in the TIPP database. In 1993, CalOSHA health-and-safety reports were entered into both the TIPP and MIS database. However, in 1994 health-and-safety reports were entered only in the IMB database. Wage and Hour Division, U.S. Department of Labor fUSDOL- WHD. This agency has responsibility for enforcement of two important federal laws: the Fair Labor Standards Act and the Migrant and Seasonal Agricultural Worker Protection Act. Consequently, it is responsible for the registration of farm-labor contractors and crew leaders and for ensuring compliance with federal regulations governing their employment practices, including child labor. Labor Commissioner, Department of Indus~rWf Relations, State of Colifornia , and certain other conditions. This is a more stringent set of requirements than those for registrants with USDOL, but applies only to labor contractors who enter into direct agreements to provide labor services for farm operators. State law does not require licensing of crew leaders, individuals who are hired by labor contractors to provide and supervise crews of workers. County Agricultural Commissioner, State of Colifornia. The 58 county agricultural commissioners have responsibility for enforcement of laws governing pesticide use for commercial purposes. This authority is delegated by the U.S. Environmental Protection Agency as well as the Department of Pesticide Regulation of Cal-EPA. Since most reported commercial pesticide use is in agriculture and is associated with routine production practices, it is thought that those who are most familiar with these practices are likely to be the most qualified to carry out enforcement. This authority includes making sure that pesticides are used only for specific purposes authorized by both federal and state law as well making sure they are used safely. For the latter purpose, farm-labor contractors are required to register with the agricultural commissioner in each county where they conduct business.

The pre-reform economy heavily taxed the farm sector

Improvements in labor productivity have been strongly correlated with the outflow of labor from agriculture. In China, government restrictions on rural mobility constrained the outflow of labor from agriculture until the mid-1980s . The development of township and village enterprises created non-agricultural employment in the rural areas which was crucial for labor productivity to increase in agriculture after the productivity enhancing effects of decollectivization . In the FSU nations and some of the less developed CEECs household food security concerns limited the labor outflow from agriculture, or in some cases induced an inflow during early transition . However, some of the most important labor movement constraints in Russia and Ukraine today take the form of the social services, housing and health care . Mobility costs for workers are high due in a large part to poorly developed housing markets. Moreover in Russia most companies are now paying wages in kind and through fringe benefits rather than cash. Because most of the goods and services provided cannot be converted into cash, in some cases workers cannot finance the costs associated with moving to other regions .As argued above, a key aspect of the success of the reforms in creating sustainable agricultural growth is the reform of the state itself,nft system and its role in the economy. This does not apply only to its role in the creation of market institutions, but also regarding public investments .

In pursuit of institutional reform of rights and markets, and in the wake of financial and fiscal restructuring that frequently cause severe budget shortfalls, transitional governments have often reduced investments in and maintenance of the infrastructure that agriculture needs to be productive. In China, for example, central government investment in water and agricultural research plummeted in the first decade after reform. Several studies show that total factor productivity has been negatively affected by reduced investments . Observations from almost all transitioning countries report this fall in investment . One needs to be careful, however, to equate a drop off of government investment as a net fall in total investment, since in some cases the private sector will pick up the slack. In other cases, such as in Vietnam, instead of investing in its own agricultural research and development, its new open door policy has allowed better access to the international agricultural research system’s technology.Despite the possibility in some instances of substituting private for public capital, in agricultural some of the most expensive, but most productivity-enhancing projects are public goods. In these cases, it will be the role of the state to make the investments. In fact, if one examines agriculture productivity in the long run, by far most of the rise in TFP of successful agriculture economies in both developed and developing economies ultimately depends on investments in water control and public agricultural research and extension. One of the most often debated types of question is why have some countries adopted one set of policies or a certain reform strategy and others have not. In its most fundamental form, this is a question of what determines a country’s choice of reform strategy.

While there are a number of idiosyncratic factors that might be identified as the main reason for adopting a policy, we believe that the political necessity, or the nature of a country’s political economy, and initial conditions frequently determine policy choice. In this section we examine how political ideology and initial conditions help determine the choice of reform strategy. Before we do, however, we first want to raise a cautionary warning about how when addressing questions like this we often oversimplify a nation’s reform strategy, trying to group countries into a well-defined category. Using the example of Big Bang versus Gradualism, we show how the choice of reform strategy is not usually clear cut, but in reality is often more subtle. After the fall of the Berlin Wall, the debate on optimal reform for transition countries focussed on policy sequencing. China was often referred to as an example of a successful reform strategy, which combined an initial reform of property rights with a gradual liberalization process and thus created growth without the negative effects of disruptions. Others argued that the initial conditions and the economic structure of China were so different from Russia and CEECs that little could be learned from China and that the best policy in those countries was to liberalize and reform everything at once: the so-called “Big-Bang” option. Looking back, the insights emerging from the experience of transition countries so far suggest that the road to successful transition is more subtle.

Given the extraordinary growth in China’s economy, the resulting increase in income, and the wholesale reduction of poverty, the experience in China between 1980 and 1995 has been referred to as possibly “the greatest increase in economic well-being within a 15 year period in all of history” . Even if such a statement is only a fraction correct, it is hard to dispute the conclusion that China’s so-called “gradualist approach” to reform has been successful. However, several studies comparing economic performance of transition strategies in the non-Asian economies seem to come to a different conclusion. If one takes into account differences in initial conditions and external factors, such as regional conflicts, those countries which have reformed earliest and most radically are now doing best . Such a finding, if true, would suggest the superiority of radical reform. Based on our analysis, we believe that, first, the successful transitions in Asia and Europe have elements of both gradualism and rapid, one-stroke reform, and second, that to reconcile these apparently conflicting conclusions one needs to look at both the political environment and the initial conditions at the time of reform.In China, the successful reforms were initiated by a Communist Party that remained and continues to remain very much in charge of the nation. Especially in the early stages of reform, there was relatively little political change that coincided with the economic initiatives. Initially, agricultural reforms were implemented by the Communist Party as an attempt to reduce pressure from the rural population following dramatic failures of the Cultural Revolution. Although there was a turnover in power from Mao to Deng that gave the opportunity to implement a new set of policies, the directives and implementation of the reform measures were designed, tested, and revised within the context of a continuing government-party hierarchy. Somewhat paradoxically, while the reforms in China are often referred to as “gradual,” the initial property rights reforms in some ways led to more radical reform of land use rights and more complete decollectivization than in either Russia or CEECs. Because of the sharp, decisive change, affecting the incomes and livelihood of more than 70 percent of the population, the agricultural reform had a tremendous impact on the whole economy. The rise in food production and increases of supplies in the countryside and city took the pressure off the government. In addition, the rise of incomes created an immediate surge in demand for non-food products. Ironically, these actions, which initially looked like moves away from Socialism,hydroponic gutter probably did more to consolidate the rule of the Communist Party than any other measures taken during this period. In contrast, pre-1989 attempts by Communist Parties in some European countries to give more autonomy to collective farms were much less radical and had little impact. Even if agricultural reforms had been successful, fewer people would have been affected since only a relatively small share of the population was still employed in agriculture, and they were unlikely to satiate the demand for significant changes in the rest of society.But while this is a general characterization of what happened in the non-Asian transition states, in fact, the speed and the extent of the reforms differed dramatically between CEECs and the former Soviet Union. Perhaps unsurprisingly, political and administrative feasibility played an important role. As Wyplosz concluded: “With hindsight, the old debate, Big Bang versus gradualism, is more a question of feasibility“. In terms of political feasibility, the one factor that certainly has played an important role in determining the speed and extent of reform is the political and social consensus to move towards a market economy and democratic political institutions. For a variety of political, geographical, and cultural factors, the consensus was much stronger in CEECs, including the Baltic countries. Simply put, after decades of Soviet domination these countries were strongly motivated to move towards “the West.”

The same push and lesser opportunities made such radical moves less compelling for Russia. In terms of administrative feasibility, optimal policy sequencing of a gradual reform strategy, especially in more developed economies in Europe , would have required extensive information on the transformation process and the economy. Most observers question the feasibility of plotting out a rational, systematically-executed reform path ex-ante. As McMillan puts it: “If it were possible to plan the transition it would have been possible to plan the economy.” Moreover, in some key agricultural reforms, institutional constraints and explicit political objectives affected the final choice. For example, one of the most hotly disputed policies in agriculture was land reform. In CEECs the proposal to restitute farm land to former owners, many of whom were no longer active in agriculture, was vehemently opposed by collective farm managers and workers, and by many economists and advisors who supported land-to-the-tiller policies. However, a combination of institutional constraints — as individuals had retained nominal title of their land in most CEECs any other land reform procedure would have required land expropriation by the market-minded governments — and political objectives — in the Baltics restitution ensured that Russian immigrants would be excluded from land ownership — made land restitution the most common process of land reform in Europe from the Baltics to the Balkans .Although other factors also played a role, the initial technology played a decisive role in creating the success of the break- up of China’s collective farms .Agricultural production in China was much more labor-intensive than in CEECs or Russia. With labor intensive technology, the cost of a break-up of the collective farms in terms of losses of scale economies was smaller, and the gains from improved labor incentives from the shift to family farms were larger. The length of time under Socialism also matters. Central planning and Communist rule was imposed for a much longer time in Russia and most other former Soviet states and has certainly affected both the emergence of new institutions and the response of individuals to new opportunities and incentives. The lack of experience with market institutions decreases the demand for institutional change. Furthermore, surveys confirm that responses from households and individuals in regions where a tradition of private farming existed clearly differs from where this was not the case. Similarly, pre-reform trade patterns had an important impact. Repkine and Walsh show that across sectors recovery in the CEECs is primarily driven by companies that had links with Western markets before the reforms. Investment demand shocks marginalized companies and sectors producing for CMEA markets. At the same time, a gradual expansion in products of companies traditionally producing for Western markets occurred. With the FSU countries depending much more on CMEA markets than the CEECs, countries like Russia and the Ukraine experienced a stronger disruption of their demand system, and consequently suffered greater output declines. Finally, the pre-reform differences in the relative price faced by producers relative to those that prevailed in international markets can account for a significant part of the initial differences in performance of transitional agricultural economies. In China, shifting prices more towards international levels meant a favorable rise in the output to input price ratio for many crops.When prices were moved up towards international price levels, producers responded favorably. In contrast, in CEECs and Russia, where agricultural producers were heavily subsidized under the old regime, the removal of subsidies meant a steep plunge in the real price of output relative to inputs. Hence, even without the collapse of procurement and supply channels, one would expect rational producers in Europe and Russia to cut back on input use and reduce output. So, does the importance of initial conditions reduce the responsibility of reform era leaders for the poor performance that some transitioning countries have suffered? In some sense, the answer has to be yes.

Direct contact methods are costlier than indirect methods and have a more limited reach

The expenditures made by UCCE are shown in panel of Fig. 1. There is evidence of impact of the 2009–2010 recession on investments in 2010, which went down from over $90 million to less than $85 million. From 2010 onwards, we observe a steady decline in annual UCCE expenditures, to about $76 million in 2013. In 2007, the county offices that recorded some of the largest overall expenditures include Fresno, Tulare, San Diego, Humboldt-Del Norte, San Joaquin, Ventura, and Kern, in declining order. In 2013 we notice that leading counties in terms of overall expenditures were San Diego, Tulare, Kern, Plumas-Sierra, San Francisco, and San Mateo. Data on salaries of advisors employed in each county office was collected from the UCCE database as well. Expenditures on infrastructure are the amount remaining in the budget after subtracting total expenditures on salaries for the counties. These expenditures capture non-salary related expenditures, including benefits and travel provisions for county advisors, along with various expenditures on research and outreach programs taken up by the county offices. Full-time equivalent employment data was obtained for advisors employed by each county office. We observed an overall fall in both advisor FTE and advisor salaries, as represented in panel , Fig. 1. After 2010, both FTE and expenditures on salaries showed consistent decline. We observe , Fig. 1 an overall declining trend in expenditures on infrastructure, with a fall of about $5 million between 2009 and 2010.

This could be the effect of the 2009–2010 recession,dutch bucket hydroponic which also led to a fall in overall expenditures during that period. Panel reflects the decline in total expenditures that include both salaries and non-salary infrastructure related expenditures. The outcome variable in our empirical analysis is created using data on a number of component variables. UC ANR records data on a variety of methods in which knowledge, produced through investments in research and infrastructure, is disseminated. We use knowledge produced and knowledge disseminated interchangeably, because all knowledge produced by UCCE is publicly available and is disseminated. Hence, the methods of dissemination capture knowledge produced. These methods are categorized into three main knowledge groups. The first group includes data on classes, workshops, demonstrations, individual consultations, meetings or group discussions, educational presentations at meetings, and all other kinds of direct extension activities. The variable is named direct contact knowledge, and it includes all counts of knowledge dissemination from direct contact with growers. The second group is named indirect contact knowledge, and it includes counts of knowledge disseminated through indirect contact with possible clients via newsletters published and websites managed by UC ANR, television, radio programs or public service announcements, social marketing methods, mass-media efforts of knowledge dissemination, and other indirect extension efforts, including those through collaboration with other agencies. The last category is named research publication and other creative activity related knowledge.

This category includes counts of basic, applied or development research projects, program evaluation research projects, needs assessment research projects, educational products created via video and other digital media, curricula, and manuals created for educational purposes. We also include publications in peer-reviewed journals in this category. The above data on knowledge was recorded as counts. We were unable to categorize input variables into issues related to agriculture only, so to avoid overestimation issues, we include knowledge produced for all programs undertaken by UCCE for the period of the study. Using the data on all knowledge categories, we generated an index of knowledge as a weighted average of all the categories.6 We assigned weights to each category, based on relative importance of each kind of knowledge variable in terms of effectiveness. For this, we sent an electronic survey to the directors of all UCCE county offices in California. In the survey, we indicated the three above mentioned broad categories of knowledge production, with a number of subcategories. Respondents provided percentage weights for each broad category so the sum would add up to 100%. Within each broad category, respondents indicated percentage weights for each subcategory so the sum of the weights also equaled 100%. We obtained 10 replies from county directors after two rounds of surveys and created weights from the survey results. The completed surveys indicated that the most important effect on agricultural productivity is direct contact with farmers , followed by indirect contact with farmers , and finally research and publications . From the data collected on knowledge production variables, we identified seven federal planned programs : Climate Change, Healthy Families and Communities, Sustainable Food Systems, Water Quality, Quantity, and Security, Sustainable Energy, Endemic and Invasive Pests and Diseases, and Sustainable Natural Ecosystems. Climate Change was dropped from the official FPP categories from fiscal year 2013. Knowledge produced through indirect methods of contact is the most popular means of knowledge production, due to the comparatively lower cost of dissemination and wider reach to potential clientele.

Research projects, peer-reviewed publications, and the knowledge produced through them are also available to the public, but perhaps cater to a smaller audience compared to the other two methods. However, they are certainly a significant component in the direct interactions with farmers by specialists and county advisors. Over the period 2007–2013, we observe that all knowledge production declined as is illustrated in Fig. 2. Total knowledge produced in direct contact, indirect contact, and publication and research project methods of production have declined over time. Total number of counts of knowledge produced through all direct contact methods rose by 43%, from 15,059 in 2007 to 21,479 in 2011, but thereafter it continued falling until it reached a total count of 8282 in 2013, which is a 61% decrease compared to 2011. Knowledge produced through different methods of indirect contact with growers starts at 259,065 in 2007, and peaks at 405,386 in 2009, before falling down to nearly 43,000 counts per year in 2010. In 2013, the recorded number is 100,919, which is equivalent to a 61% reduction from the original levels in 2007. Research projects and peer-reviewed journal publications went down from 3349 in 2007 to 506 in 2013,dutch buckets system which is a percentage decline of nearly 85% of the 2007 value. Among all the counties, San Diego recorded the highest average count of knowledge production from direct methods, at 17147 , and Madera the lowest, at 3 . San Joaquin had the highest average count of knowledge production from indirect contact method at 49,225 , and Madera the lowest, at 0. San Luis Obispo had the highest value of average knowledge production through publications and research projects, at 308 , and Mariposa the lowest, at 1 . We also observe an overall falling trend in both inputs of knowledge production, such as county-level FTE, expenditures on salaries per unit FTE, expenditures on infrastructure per unit FTE, as well as output . In the next section, we report the results of our econometric estimates of the knowledge production function.Summary statistics of the variables in our analysis are reported in Table 1. We observe high levels of dispersion in the distribution of some of the knowledge variables. At the county level, San Joaquin, one of the most important agricultural producers, presents the highest mean knowledge index over 2007–2013, while Madera had the lowest. Mean advisor FTE number in San Joaquin was 353% higher than that in Madera; with 36% lower expenditures on salaries per unit FTE, and a 1% lower expenditures on infrastructure per FTE, compared to Madera county. The knowledge index, the weighted average of counts of the component variables, had been declining for the period of our study, as seen in Fig. 3. The cross-sectional average value of log went down from about 3.9 to about 2.75 over the period of 2007–2013, which reflected a 68% decline in the knowledge index. With these observations, it is important to know how our inputs impacted the average knowledge produced, and how these declining trends in inputs may have impacted knowledge production. Similar trends in knowledge production in agriculture are reported also by Alston et al. and by Ball et al. for the USA as a whole. Table 2 reports the regression results of Eq. , including two different models. Column reports the results for the case in which we include county and year level dummy variables to control for any factors that remain fixed across counties or years, possibly impacting the dependent variable.

This is a noticeable contribution to the literature because recent works on agricultural knowledge production function estimates have been focused on state level or national level. However, decisions on allocation of funding for knowledge production in extension activities have been made at the county level. The second version of the model includes a time trend instead of time-fixed effects. The specification with time trends allows to treat time effects on knowledge production as a continuous rather than fixed effect variable, which potentially can be more useful for policy makers. In the case of our analysis, these two models produced very similar results as is discussed below. We obtained statistically significant coefficients for all the input variables in both versions of our model reported in columns Model and Model of Table 2. A percentage rise in FTE impacted knowledge production positively by nearly 1.1%. A 1% rise in expenditures on salaries per unit FTE increased knowledge production by 0.86%. The coefficient estimate for the linear term of expenditures on infrastructures per unit FTE is positive and the coefficient estimate for the quadratic term is negative, supporting the theory of diminishing marginal returns to expenditures in infrastructure per FTE employee. In Model , we controlled for county-level fixed effects by introducing county dummy variables. Here, we de-trended the dependent variable as well as the independent variables by including a time trend variable in the model. We reported robust standard errors in the parentheses.Coefficient estimates for both the models are comparable to each other. While it is difficult to compare our results in Table 2 for an agricultural research and extension system to results of work on industrial knowledge production function, still there are several similarities in terms of the relative importance and the sign of the coefficients of the estimated knowledge function to the work of Czarnitzki et al. . We computed the elasticities of production, based on results in Table 2, which are reported in Table 3 below. The elasticity of production of knowledge with respect to FTE varied from 1.07 and 1.10, across the two models we estimated. The elasticity of knowledge production with respect to salary level varied between 0.86 and 0.87 across the two estimated models. The elasticity of knowledge production with respect to infrastructure expenditures varied between −0.39 and −0.31 across the two estimated models. The interpretation of these estimates is as follows: A 1% increase in FTE led to a 1.1% increase in average knowledge produced. Similarly, a 1% increase in expenditures on salaries per unit FTE would bring about a 0.87% increment in average knowledge produced by UCCE. The elasticity for expenditures on infrastructures per FTE for both models were calculated at the sample mean of this variable , using Eq. , as reported in Table 3. This value is negative, both in Model , and Model . Due to diminishing marginal returns, the relationship between this input and knowledge produced is concave, and the elasticity therefore depends upon the value of expenditures at which it is calculated. We computed the value of expenditures on infrastructure per unit FTE that corresponds to the turning point of the production function from a positive to a negative slope; this value equals $312,320.Expenditures on infrastructure per FTE less than this amount will yield a positive output elasticity; higher values will yield negative output elasticity, as is the case when we use the mean value. We observed that FTE is the most effective input in the knowledge production process, with an elasticity > 1. The advisor FTE employed by the county offices are engaged in various kinds of research and outreach operations and are the most important factor in the process of knowledge production. Dinar found similar evidence of significant positive marginal product of senior researchers on production of knowledge for the public agricultural research system in Israel.

Disease and market changes are two important factors for these changes

A recent analysis by the World Meteorological Organization concluded that uptake of climate forecasts by agricultural communities has been low due to lack of a clear understanding of their needs and insufficient interaction and communications among all involved stakeholders.California’s past history suggests that agriculture has the capacity to effectively transition to new climate regimes with economic success, but it may be only after a tortuous journey. Since 1850, California’s agriculture has been in a perpetual state of growth, transition, and adjustment . Large changes have occurred within the last 150 years in terms of acreage for California’s commodities, beginning with early mission attempts to raise livestock, grow grains, and develop horticulture; followed by the era of ruminants and then extensive wheat and barley production; then, the beginnings of intensive fruit, nut, and vegetable agriculture and large-scale beef and dairy production; ending with the present management-intensive, technologically dependent agricultural industry . During the past 40 years, the total acreage of agricultural land, including grazed land, has decreased from 37,000 to 28,000 acres,hydroponic gutter reflecting urbanization and greater intensification of existing agricultural lands. The production of horticultural crops has increased, while field crops have remained stable in acreage since the 1960s .

Lettuce, tomatoes, rice, and almonds have increased in acreage by more than 50% in the last 30 years, while two major crops of past production eras, barley and sugar beets, have declined by almost 100% during this period. Major shifts in production areas have occurred; for example, almond production in California has moved northwards over the past several decades. Within California, as the climate warms, production patterns will shift latitudinally northward, to higher elevations, or out of the state. A warmer and drier climate and expanding growing seasons could benefit olive and citrus production by extending their cultivation range northward . If crops are to decline or disappear from the Californian landscape with climate change, it is most likely to be those that use large amounts of water to produce crops of limited economic value . Many commodities in California have experienced highs and lows during the last century.Wheat production, for example, declined steadily through the 20th century due to bunt and stem rust diseases, loss of foreign markets, and competition with irrigated crops, until the 1970s when new disease-resistant varieties were introduced . For grapes, prohibition in 1919 caused a nearly total demise of the wine grape industry, which had already experienced shifts in production due to outbreaks of the invertebrate pest, phylloxera, by that time. The industry has now obviously rebounded to the point of being one of the main drivers of agricultural land use change in California.

For apricots, statewide production has decreased steadily in the past 40 years, especially since shifts, spurred by urbanization in the Santa Clara Valley, occurred due to less advantageous weather conditions in the San Joaquin Valley, but competition with foreign markets also decreased the demand for dried fruit products. Potato production historically has moved extensively around the state, experiencing fluctuations in production due to tuber-borne diseases and changes in processed vs. fresh consumption patterns. These examples show that California agriculture has the capability and agility to maintain agriculture productivity despite obstacles related to urbanization, pest and market changes for individual crops. Yet, the concern is that a changing climate may accelerate the rate at which producers must cope with specific management problems that arise, especially heat waves, water scarcity, and pests . A sequence of unfavorable years may force these land users to switch from horticultural to lower-income field crops, or to sell land for urbanization or ranchettes with affiliated small-scale agricultural enterprises. If the supply of a given commodity decreases due to climate change, and the price of that commodity increases, producers with the capacity to maintain production due to their microclimate or to technological ability may increase their profits . But, less capable producers will suffer greater losses, especially for high-input crops with large costs of production. Economic analysis of the trade offs between different production ratios of field vs. horticultural crops suggest that a shift towards more acreage of crops with lower input costs, such as field crops, compared to higher-input horticultural crops, could be advantageous in the long-run, despite lower maximum profit per acre, due to greater reliability of yield and income each year.Land use changes are driven not only by environmental factors such as climate, topography, and soil characteristics, but also by synergetic combinations of the five fundamental land use drivers . First, resource scarcity, which can lead to an increase in the pressure of production on resources, has profound implications for land use change.

It has been suggested that climate change may have either a “fertilization” effect, leading to increased yields or a “land-area” effect on crop production that would reduce arable land area and, subsequently, production . Water resources will likely be the primary environmental variable determining shifts in crop distribution since California’s water reserves are largely allocated for cropland irrigation . The loss of prime agricultural land to urbanization may also move production areas to lower quality soils, and to areas without sufficient water supplies . Second, changing opportunities and constraints, which are created by local, as well as national markets and policies, can also impact new land uses. Agriculture in California has been historically “demand-driven,” with food production goals of exporting products to the rest of the U.S. and international markets bringing huge profits to California. Depending on the cost of production and supply, either consumers or producers could gain from climate change . Climate change-induced alterations in agricultural productivity in one region can affect productivity in another region , such as the recent loss of California garlic production to China , possibly leading to collapses in one set of product markets that might trigger collapses or changes in those production systems . Third, outside policy intervention, motivated by improving or worsening agricultural conditions in different areas affected by climate change, could lead to protectionist policies seeking to improve domestic production and increase subsidies for irrigation or other inputs . Such policies can have the long-term effect of slowing economic growth, encouraging unsustainable practices, and/or increasing food insecurity. Nevertheless, incentives can potentially give rise to experimentation with new crops and products . Fourth, loss of adaptive capacity associated with increases in climate variability can greatly determine shifts in land use. Adaptation is defined by the IPCC as ‘adjustments in practices, processes or structures in response to actual or expected climatic stimuli or their effects, with an effort to reduce a system’s vulnerability and to ease its adverse impacts’. Adaptive capacity refers to a system’s increased options and capacity to reorganize after change or disturbance,hydroponic nft channel which is conferred by resilience, and is enhanced by diversification within agricultural landscapes, as well technology and access to information that increase options for successful responses . In California, for example, vegetable growers tend to minimize risks by diversifying production , while 70% of orchard producers produce only one commodity and are much more likely to rely on crop insurance as a risk-management tool . Both finding ways to produce the same crop at a profit, and relocating employment outside of agriculture, may be considered adaptation . Lastly, changes in social organization and attitudes towards climate change consequences might play a large role in determining land use shifts.

One examples is the Standard Williamson Act and the newer Farmland Security Zone , which compensate landowners for 10-20 year commitments to agricultural land use by property tax reductions . Another example is the USDA Cost-Sharing and Reserve Programs which compensate farmers for practices that increase water and air quality, wildlife habitat, or grassland conservation. Another issue is that cultural values, and even just the belief that climate change is actually taking place, strongly motivate the social response to climate and land use change . Stakeholders need to decide which risks should be retained and managed adaptively versus which risks should be shared through risk sharing contracts. Social and economic impacts of climate change must be evaluated at larger scales than site-specific studies, i.e., landscape or regional scales, to provide useful information .Agricultural land in California has gradually shifted to urban or other non-agricultural uses, driven by population growth and non-agricultural force. From 1990-2000, approximately 500,000 acres were converted from agricultural to non-agricultural uses . In one view, this trend towards less agricultural land will have minor effects on the total productivity and economic value of California agriculture. Essentially, this view builds on the high degree of past success that California has had in developing production strategies and markets for a diverse array of different types of commodities, as exemplified in Figure 8.2 by the changing geographic distribution of the top 10 counties in terms of agricultural production since 1929. A recent analysis predicts that although there will be a 10 percent net loss of farmland and irrigation water resources by 2030, this will be offset by yield growth attributable to climate change, crops with high value per acre, and growth in production per acre due to technological improvement . Climate change is assumed to increase yields of California crops by approximately 15%, based on the predictions using the simple quadratic models that were described in Section 5 . The demand for California vegetables, fruit and nuts is expected to grow, and cotton, alfalfa, and irrigated pasture acreage in the state is likely to shift to these crops. As long as relative prices and policy adjustments favor these shifts, and technological advances increase, a gradual increase is predicted in the value of food production in California, and net food exports to the rest of the world is expected to expand rather than contract. Alternatively, such successful adaptation of California agriculture to climate change might require a more cautious approach. There may be surprises in terms of weather events, for example, short-term heat waves floods, or pest outbreaks. Recent modeling has shown that California will experience longer heat waves, and more summer heat waves based on fine-scale, regional processes . In fact, extreme events may dictate outcomes from climate change more definitively than the expectation that gradual increases in mean temperatures and CO2 fertilization effects will reliably boost crop productivity. Adaptive capacity and resilience may be enhanced by taking a cautious strategy that acknowledges the need for land use changes that will assure productivity during gradual changes in climate, but also when extreme weather events, or unexpected surprises, occur. Based on the ecological literature, diversification is a key element to resilience in response to change or disturbance. Biodiversity, for example, can provide “insurance” or a buffer against environmental fluctuations . Since different species respond differently to change, more species can lead to more predictable aggregate community or ecosystem properties. Although certain species may appear to be functionally redundant for an ecosystem process at a given time, they may no longer be redundant through time. Based on this analogy, and the recognition that diversity in crops and farming systems lend economic and ecological resilience at the landscape level , it seems reasonable to adopt a diversification strategy as one element in the necessary technological advances for agriculture to cope with climate change in California. But while crop diversification can act to reduce farm business risks, there are start-up costs and problems for achieving economies of scale. Other risk-reducing strategies, such as crop insurance or the securing of off-farm income, may be readily available and preferred by producers . Another issue is the loss of wetlands, riparian corridors, and the fragmentation of farmland that is predicted to occur in California’s agricultural landscapes during the next century due to urbanization, as well as to water projects that must build levees and storage reservoirs to cope with higher stream flows . Not only do impacts on species protected by the Endangered Species Act, but impacts on other ecosystem services provided by these habitats, for example, water filtration, soil retention, or erosion regulation, need to be considered in planning land use strategies. Thus, it will be necessary to address whether adaptations to climate change by growers and institutions, will be at the expense of sustainable land use practices and extant natural ecosystems .

Greater groundwater use will result in subsidence and in higher pumping costs

Atmospheric particulate matter within the San Joaquin Valley is correlated with annual precipitation patterns , possibly suggesting that this relationship led to increased CCN, less atmospheric scrubbing , or a feedback between the two. Agriculture is an important source of dust in California. However, the amounts of dust can be reduced by employing conservation or minimum tillage practices. In the San Joaquin Valley, conservation tillage decreased amounts of both total and respirable dust to approximately one third of that compared to adjacent standard tillage treatments . Inclusion of a cover crop reduced this difference to approximately two thirds and three quarters of total and respirable dust, respectively . Precipitation is the main agent removing aerosols from the atmosphere, particularly smaller aerosols, which are more effective at scattering light. Thus, as more aerosols enter the atmosphere, potentially decreasing precipitation events by the ”second indirect effect,“ aerosol concentrations could increase as the rain’s natural scrubbing ability is lessened. This may be exacerbated in summer, especially if precipitation decreases in California due climate change . However,hydroponic grow systems as predicted surface temperatures increase, water vapor entering the atmosphere through evaporation will also increase , which could potentially cause increases in precipitation as global warming takes effect.

The net effect upon precipitation is difficult to predict accurately, as it is a function of many factors, which are understood and predicted with differing degrees of confidence. Although there are large uncertainties associated with precipitation , changes in the absolute volume of precipitation, in the number and/or duration of storm events, or a lengthening of the rainy season, could affect air quality.The complex interactions between agriculture, climate change, and air quality are a product of the mechanisms involved with source, sink , and the feed backs between air quality constituents ; California Air Resources Board ; Krupa ; Mennon. For instance, black carbon and organic carbon aerosols are formed in similar combustive processes, yet BC aerosols are generally associated with global heating, whereas OC aerosols are associated with global cooling, due to their respective absorptive and reflective properties . Although vegetation generally reduces climate change by sequestering C , isoprene emissions from vegetation, including crop and grassland , are nearly three times as reactive in smog formation than a weighted average of VOCs emitted from vehicle exhaust . Isoprene production from oak stands growing along the western slope of the Sierra Nevada, mixed with a combination of anthropogenic VOCs and NOx emissions, blown up-wind from the Central Valley, produce high levels of O3 concentrations in the Sierra foothills . Furthermore, both biogenic isoprene emissions and O3 formation can be accelerated from increased sunlight and heat, and the contribution of both to future exacerbations in air quality, regardless of current anthropogenic source reductions, are important variables in California climate change scenarios.

Based upon data from the South Coast Air Quality Management District, a slight increase in tropospheric O3 concentrations over the Los Angeles Basin, as a result of increased surface temperatures, is projected . Conversely, increased temperatures shift the solid form of ammonium nitrate, a dominant wintertime PM constituent, to its gaseous precursors, thereby reducing atmospheric PM concentrations and altering the adsorptive quality of reactive atmospheric N in ecosystems. The processes outlined above not only contribute to climate change, but their rates and extents, and hence, their impacts upon California agriculture, may be altered . The amount of sunlight available for photosynthesis directly affects crop growth cited in Chameides et al., and can be increased or decreased through cloud-aerosol interactions . For example, the direct effect of aerosols in China may be reducing current yields of rice and winter wheat by between 5% and 30%, by reducing the amount of light available to drive photosynthesis . However, because aerosols both scatter and absorb light, depending on their relative optical and absorptive properties , they are also predicted to have positive effects on net primary productivity , with increased light scattering resulting in increased productivity on under story leaves, and on cloudy days . While the air over the Central Valley is likely cleaner than that over China, crops with sufficient sun exposure can be expected to show similar negative growth trends, as estimated by Chamiedes et al , as the rate of aerosol production increases in California. Aerosols also play a role in determining the nature and magnitude of changes in the precipitation cycle, and their indirect effects , and as such represent a significant source of the uncertainty in current precipitation models.

Changes in the hydrological cycle may result in unexpected challenges for the ability of California to maintain its current agricultural productivity, and could be heavily influenced by aerosol levels. Agriculture complicates the interaction of aerosols and cloud formation by impacting the latter mechanism directly. In southeastern Australia, Lyons found there to be many more clouds over native vegetation than agricultural fields, probably because native vegetation is typically darker than cropland, leading to convective cloud formation. Another factor is that native vegetation is more ”rough” than cultivated fields, slowing down wind more effectively. In California, however, summer drought may change these relationships, as grasslands are lighter colored and more even in stature than irrigated cropland—especially perennial fruit and nut crops. As with the effects of CCN and increased temperatures on precipitation patterns, feed backs between aerosols emitted over agricultural fields, particularly through tillage , may counteract any decreases in precipitation by promoting cloud formation. However, the net effects remain highly uncertain.California agriculture has the potential to improve air quality, and help mitigate climate based plant and animal stresses, by implementing best management practices such as reduced till farming ,hydroponic growing water conservation , and crop rotations. High-input intensive agriculture has the potential to adversely affect air quality and climate stability by increasing smog formation, aerosol production, and reactive N introduced to the biosphere. Efforts to reduce tillage, lower soil N concentrations, maximize N use efficiency and optimize manure management, are simple approaches that could be implemented Decisions need to be made, and policies developed and implemented, if we are to effectively mitigate climate change impacts upon air quality and California agriculture. Statewide, one of the most promising agricultural practices that could enhance air quality is to reduce tillage, a best management practice that minimizes the number times a field is cultivated. Reduced tillage has the potential to mitigate poor air quality by limiting soil disturbance and formation of atmospheric aerosols , reducing fuel combustion, and decreasing NOx formation. Baker et al. found conservation tillage in the San Joaquin Valley to decrease amounts of both total and respireable dust, compared to adjacent standard tillage treatments. Currently, only 16% of California’s total farm acreage employs conservation tillage practices , creating a large potential for air quality improvement through reduced tillage incentives. Regulations are also a critical means to achieve air quality mitigation, specifically regarding tropospheric O3 reductions. Ozone concentrations in the San Bernardino Mountains peaked in 1978, and have been decreasing ever since, despite large increases in both vehicle miles traveled per capita and population in Southern California. Improvements in vehicle efficiency , as mandated by the California legislature, account for this trend . Complete elimination of O3 precursors from motor vehicles are estimated to remove $2.9 billion in crop damage in California .

Ozone mitigation is a classic example of a strategy that requires careful consideration of aspects other than just lowering the concentration of its precursors . VOCs differ largely in their reactivity . While some VOCs are essentially unreactive , others are very potent O3 precursors . Therefore, policy makers should not solely consider total VOC concentration alone as an effective mitigation strategy, but rather concentrate on those precursors that are highly reactive. The production of O3 depends on the relative concentrations of VOCs to NOx . For this reason, anthropogenic production of VOCs is controlled by state law, along with NOx production. Under current regulatory standards in California, dairy cattle are assumed to produce 12.8 lbs/cow/yr of VOCs. This dairy emission factor means that dairies have surpassed all other sources as smog producers . However, this dairy emission factor is based on a 1938 study in which methane from cows and other ruminant animals were measured. Recent VOC research conducted indicates that this number is greatly inflated, and that the emission factor for dairies should be closer to 2-3 lbs cow-1 year-1 . They also found that most VOCs produced from dairies are compounds like acetone, acetic acid, and several alcohols that are known to be low in their ability to form O3. Be that as it may, cattle do play an important role in climate change, because both animals and their manure produce a large portion of agriculture’s 38% contribution to statewide methane emissions . Mitigation efforts to reduce global warming effects from livestock production should focus on methane, rather than VOC emissions.While the numerous predictions of climate change scenarios are in general agreement in forecasting a rise in the mean temperature of California , the predictions for changes in the future precipitation patterns of California vary widely . In comparing the three most recent precipitation predictions from the PCM, HadCM2, and HadCM3 models under a range of IS92 emissions scenarios, they range from mildly drier than present to a slightly more wet climate scenario and an extremely dry climate scenario . The large disparity in estimates reflects the difficulty of extrapolating predictions of GHGs and temperature to climatic patterns that generate precipitation. However, one trend is present in all scenarios: as temperatures rise, precipitation type changes increasingly from snow to rain . Higher temperatures will produce reductions in snow pack accumulation in the Sierra Nevada Mountains, with subsequent effects on water storage, stream flow, and supply . Water stored in the snow pack is a major natural reservoir for California. It is the presence of this large snow pack that provides the majority of the irrigation water for the dry Central Valley during the growing season. Additionally, the shift in precipitation type may increase the risk of winter flooding, especially in the Delta region, where a series of levees keep the subsided delta islands dry. The frequency of flooding and other “extreme” weather events, such as El Niño and heavier winter storms, has been projected to increase with rising temperatures , but this issue has not been adequately addressed by climate models. In this section, the issue of climate change and its potential effects on California agricultural water resources will be treated as an issue of future scarcity. Even in scenarios with higher precipitation levels , earlier snow melt and flood control allocation in reservoirs decreased surface water storage and the subsequent ability for water deliveries during the growing season . This section identifies the potential impacts, then discusses potential mitigation and adaptation strategies and data gaps in current analyses.The earlier snow melt and runoff from increased temperatures and decreased snow pack will likely create some challenges for state reservoir managers. Managers would be forced to operate storage space conservatively, losing more water downstream and leaving less water for deliveries during the summer growing season . Projecting future water use for California, Tanaka and colleagues predict that agricultural water allocation in California will continue to decline relative to urban and environmental uses. Apart from environmental flows, 70% of California’s water is currently directed towards agricultural production. The rise in demand for water from the urban and industrial sectors will result in decreased water allocation to agriculture. Escalating water demand will require shifts in water sources as the reservoir system reaches capacity . Conjunctive water use will increase with less surface water from decreased snow pack and snow melt capture.Desalination in coastal regions for urban use and for aquifer treatment is expected as technology improvements reduce energy costs for treating water, and as water costs increase . Taken together, these eventualities represent a significant challenge to water resource managers and California agriculture. Sea level rise may have a major impact on California water transfers through the Sacramento-San Joaquin Delta. Increased salinity intrusion into the San Francisco estuary and potential failure of levees protecting low-lying land may degrade the quality and reliability of fresh water transfer supplies pumped at the southern edge of the Delta, or may require more fresh water releases to repel ocean salinity .

Urban productivity gaps in real wage terms are further reduced in both countries

The effective tracking rate for each KLPS round is around 85 percent.Similar to the IFLS, the KLPS includes detailed information on educational attainment, labor market participation, and migration choices. Employment data was collected in a wage employment module and a self-employment module, which both are designed to include both formal and informal employment. Most individuals were quite young during data collection for KLPS round 1, and few had wage employment or self-employment to report. Full employment histories, including more detailed questions, were collected during rounds 2 and 3, and it is from these rounds that we draw the data on individual earnings, hours worked, and wages used in the present analysis. The Kenya agricultural productivity data deserves detailed discussion. Whenever total household annual agricultural sales were sufficiently large, exceeding 40,000 Kenyan Shillings , full agricultural production and profit information was collected in the self-employment module and included in the present analysis. Agricultural wage employment is also common, and these data are always included. Limited questions on subsistence agricultural production were collected in KLPS rounds 1 and 2,grow bags garden but these are insufficient to create an individual productivity measure. More detailed information on agricultural productivity is contained in round 3, and this is included in the present analysis.

To create a measure of individual productivity comparable with other sectors, we focus on agricultural activities in which the respondent provided all reported labor hours; we also restrict attention to activities in which the respondent reports being the main decision-maker, since it seems likely that they are most knowledgeable about such activities . The profit in an agricultural activity is the sum of all crop-specific production – valued either through actual sales or at the relevant crop price if consumed directly – minus all input costs and hired labor costs. The individual wage divides this net profit by the labor hours the respondent supplied to the activity. KLPS respondents reported industry for all wage and self-employment. Most individuals are engaged in relatively low-skilled work. The most common industry for wage employment is services, at 58% overall and 74% for females . In rural areas, the most common industries for wage employment are services and agriculture , while in urban areas they are services, and manufacturing and construction . The largest self-employment industries are retail and services .KLPS round 3 collected detailed consumption expenditure data for a subset of individuals. However, because it was only collected for this round, we are unable to utilize it in panel estimation. Instead, in the panel analysis we utilize a proxy for consumption, the number of meals eaten in the previous day, which is available in both KLPS rounds 2 and 3.

Reassuringly, meals eaten is strongly correlated with our primary measures of labor productivity as well as consumption expenditures per capita ; see Appendix Table A3. As with Indonesia, in the meal consumption analysis, we are able to expand the sample to also include individuals without current earnings data. KLPS respondents provide a history of residential locations since their last interview, and this data includes residential district, town, and village, allowing us to classify individuals who lived in towns and cities as urban residents. The KLPS includes information on all residential moves that lasted at least four months in duration, a slightly more permissive definition than in the IFLS, and we are able to construct a monthly residential panel from March 1998 to October 2014.18 Combined with the retrospective labor productivity data, the main analysis sample is a monthly panel with 128,439 individual-month observations for 4,537 individuals. Figure 2, Panel B presents a map of Kenya, with each dot representing a respondent residential location during 1998–2014. Most residences in western Kenya are located in Busia district , with substantial migration to neighboring areas as well as to cities. Appendix Table A4 presents the list of main towns and cities, and shows that 70 percent of urban residential moves are to Kenya’s five largest cities, namely, Nairobi, Mombasa, Kisumu, Nakuru, and Eldoret. According to survey reports, men are slightly more likely than women to migrate for employment reasons while women are more likely to migrate for family reasons, including marriage . A smaller share of moves are for education. Summary statistics on employment sector and urban residence for KLPS respondents are presented in Table 1, Panels B and C. Panel B presents data for the main analysis sample; as described above, this contains subsistence agricultural information where available .

The employment shares in agriculture is much higher in rural areas than urban , as expected, but the share in rural areas is somewhat lower than expected, likely because subsistence agricultural activities were not captured in earlier KLPS rounds. For a more complete portrait, Panel C focuses on data from the 12 months prior to the KLPS-3 survey, which contains detailed information on subsistence agriculture, and here the agricultural employment share in rural areas is much higher. Recall that the Kenya sample is all rural at baseline . Similar patterns emerge regarding positive selection into urban migration, with educational attainment and normalized Raven’s matrix scores both far higher among those who migrate to cities . In particular, there is a raw gap of nearly 0.3 standard deviation units in Raven’s matrix scores between urban migrants and those who remain rural. Overall migration rates in Kenya are similar for females and males. Tables 3 and 4 report these patterns in terms of regression estimates, for urban migration and employment in non-agricultural work, respectively. As with Indonesia, controlling for educational attainment and gender, the Raven’s score is strongly positively correlated with urban migration .GLW estimate raw and adjusted agricultural productivity gaps of 138 and 108 log points in Indonesia, respectively . The estimate of this raw gap from the IFLS is somewhat smaller at 62 log points . The most straightforward explanation for this discrepancy is an issue of measurement. GLW observe that, in an analysis of 10 countries, the average agricultural productivity gap was 17 log points smaller when estimated in Living Standards Measurement Study data that is similar to the IFLS,grow bag for tomato and which is more likely to capture earnings in informal employment.That said, the raw gap we estimate in the IFLS remains substantial. Inclusion of control variables similar to those used by GLW to adjust macro data gaps reduces the estimated agricultural productivity gap in the IFLS to 51 and 32 log points . Estimating on the sub-sample for which we have scores from Raven’s matrix tests, the gap is reduced slightly, although note the smaller sample size in this case. Limiting the analysis to those who have productivity measurements at some point in time in both agricultural and non-agricultural employment, the productivity gap drops to 16 log points , suggesting that the selection on unobservable characteristics alluded to in Section 2 may play a meaningful role. Inclusion of fixed effects reduces the gap further , and using our preferred labor productivity measure, the log wage , as the dependent variable nearly eliminates the gap altogether: the coefficient estimate falls to 0.047 in column 7, and further to 0.045 when considering the real log wage . We follow a similar approach for Kenya, where the raw agricultural productivity gap falls from 79 log points to 56 with the inclusion of GLW’s controls , and to 32.6 log points when including an individual fixed effect. Using the preferred hourly wage measure reduces the gap to 13.4 log points , it falls further when adjusted with an urban price deflator , and neither fixed effects wage estimate is significant at traditional levels of confidence.

Comparing column 1 to column 7 in Table 5, the agricultural productivity gap is reduced by 92 percent in Indonesia and by 83 percent in Kenya. The standard errors are somewhat larger for Kenya, so the upper end of the 95% confidence interval includes a sizable gap of 37 log points, consistent with some non-trivial productivity gains to non-agricultural employment. That said, even this value remains far lower than the 108 and 71 log point effects that GLW estimate for Indonesia and Kenya, respectively, once they condition on observable labor characteristics . As noted in the introduction, these results for Indonesia and Kenya are presented graphically in Figure 1, Panels A and B and compared to GLW’s estimated productivity gaps.Table 6 presents the closely related exercise of estimating the labor productivity gap between residents of urban and rural areas. While the existing empirical literature has sometimes conflated these two gaps, Table 1 shows that employment in rural areas is not exclusively characterized by agriculture. To the extent that residential migration is costlier than shifting jobs , and the urban and non-agricultural wage premia are related but distinct parameters, one might suspect that an urban wage premium might even be more pronounced than the non-agricultural wage premium. The micro-data estimates from Indonesia and Kenya appear to be consistent with this view, at least at first glance: the raw gap reported in column 1 of Table 6 are 63 and 85 log points for Indonesia and Kenya, respectively. Similar to the agricultural productivity gap, the urban-rural productivity gap falls when additional explanatory variables are added in columns 2, 3 and 4, but remains substantial and statistically significant. Focusing the analysis only on those who have earnings measures in both urban and rural areas leads to a further reduction. Finally, the urban-rural earnings gap falls to 1.8 log points with the inclusion of individual fixed effects in Indonesia, and -0.7 log point for the preferred log wage measure . The analogous urban productivity effect estimate for Kenya is slightly larger at 13.2 log points . Thus, the productivity gap in Indonesia falls by 100 percent in Indonesia , and the reduction for Kenya is 84 percent with the inclusion of individual fixed effects. Once again, these results are summarized in Figure 1 .The selection model predicts that estimated productivity gaps would be higher among rural-to-urban migrants than for urban-to-rural migrants, given plausible patterns of selection bias. Table 7 explores this hypothesis in Indonesia by separately conditioning on birth location; Panel A contains those born rural and Panel B those born in urban areas. The same pattern of declining productivity gaps in each sub-sample is observed for non-agriculture and urban as additional controls are included. In the preferred log wage specifications in columns 4 and 8, productivity gaps are indeed somewhat larger for those born in rural areas, as predicted by the sorting model. The difference between estimates for those born in rural versus urban areas is small, suggesting rather tight bounds. For instance, the estimated productivity gain to non-agricultural employment is 5.9 log points for those born in rural areas and -0.8 for those born urban . While suggestive, note that the difference between these estimates is not significant. The discussion above establishes at least an 80 percent reduction in estimated sectoral productivity gaps once individual fixed effects are included in the analysis . The wage measures presented thus far are closely related to the labor productivity parameters that are the focus of most existing macroeconomic empirical literature. However, productivity and “utility” may diverge for many reasons, including price differences across regions, amenities, unemployment, and other factors. For instance, there could be considerable individual heterogeneity in the taste for rural versus urban amenities, e.g., comforts of home, ethnic homogeneity, better informal insurance, etc., in rural areas versus cosmopolitan cities’ better public goods and more novelty . Although it is impossible to fully capture these factors and convincingly measure individual welfare, to get somewhat closer to differences in living standards, we draw on consumption data from the IFLS. As described in Section 3, four rounds of the IFLS included questions on the value of household consumption which can be converted to per capita consumption. In the main specification, we include all individuals who have such consumption data, even if they lack earnings measures. The initial consumption gap between non-agriculture and agriculture is large and similar the productivity gap at 54 log points .