September 2023 – Page 2 – Nature Ecrogrow

The result is pixel-level water vapor and liquid water estimates over the entire study scene

Future studies are needed to assess the accuracy of SBG for such a classification and further evaluate its suitability to detect crop stress independent of a crop species map. The SBG mission will allow for further exploration of crop temperatures as they change temporally and as they differ across different climates and landscapes. In addition to SBG, with the ability to track diurnal changes in plant water globally, ECOSTRESS is well positioned to lead scientific understanding of crop water stress as it changes spatially and temporally . This study provides rationale for accounting for species and soil type when analyzing thermal data and enhances the importance of pairing the thermal data with ground data or VSWIR imagery from Landsat or MODIS that can enhance understanding of surface characteristics. Further, with paired VSWIR imagery, it suggests the potential for extrapolating the methodology in this paper for global study of crop tree stress. Atmospheric water vapor is a critical element of climate, an indicator of land surface hydrologic processes, and a potent greenhouse gas . As such, analysis of vapor patterns at a fine spatial scale can inform climate and plant water use studies . Imaging spectrometers such as NASA’s Airborne Visible Infrared Imaging Spectrometer measure reflected radiance at fine spatial and spectral resolution, and in so doing provide measurements of column water vapor as well as a highly detailed reflected signal from the land surface below . These two spatially corresponding products allow us to uniquely observe surface processes and characteristics as they relate to the atmospheric patterns above them in ways not previously possible. As such,blackberries in containers this research proposes to leverage water vapor and reflectance imagery to observe and assess spatial patterns of water vapor in the Central Valley of California to evaluate the assets and limitations of this dataset for evaluation of agricultural water use.

Observation and evaluation of water vapor over agricultural fields in California’s Central Valley have substantial value for water resource management, irrigation assessments, and regional climate patterns. The Central Valley contains one of the world’s largest contiguous areas of high irrigation density with more than 3.6 million irrigated hectares of farmland that use over 80% of the state’s managed water supply . Worldwide, arid and semi-arid regions such as the Central Valley see upwards of ninety percent of precipitated water returned back to the atmosphere via evapotranspiration . In the Central Valley, where precipitation is low and managed water inputs are extreme, annual ET exceeds precipitation by about 60% . These extreme irrigation inputs, therefore, significantly modify the spatial and temporal distribution of hydrologic flows across the region by transforming liquid water resources into transpired atmospheric water vapor that can be transported and distributed as rainfall elsewhere . Further, as local atmospheric water vapor is intensified by ET, it is indicative of agricultural water inputs and crop functioning throughout the region. Imaging spectrometers such as AVIRIS quantify column water vapor using several water absorption features across the infrared portion of the electromagnetic spectrum . Atmospheric water vapor absorption features occur at 0.94, 1.14, 1.38 and 1.88 µm, and the relative depth of these features can be used to derive atmospheric water content . Hyperspectral imagery is uniquely suited to estimate water vapor because its high spectral resolution captures water absorption features that multi-spectral sensors, such as Landsat, are designed to avoid. In addition, the fine spatial resolution of the retrievals enable observation of water vapor patterns that are obscured in spatially coarser imagery, such as MODIS and GPS.

While water vapor imagery is produced as a byproduct of most visible to shortwave infrared reflectance retrievals, few analyses have been conducted with this rich dataset, leaving many questions as to the utility of this data unanswered. A notable exception is the work of Ogunjemiyo et al. who studied water vapor over poplar plantations in Washington State to assess the feasibility of using AVIRIS-retrieved column water vapor as a tool to study plant ET. That study proposed a conceptual model of water vapor and its relationship to the surface , hypothesizing that plants with higher rates of transpiration will produce more water vapor, which will advect downwind and accumulate to a level detectable in the imagery, and that crops with higher water use rates will have steeper water vapor slopes, modified by wind speeds. Ogunjemiyo et al. found that the patterns and magnitude of retrieved water vapor in their study areas were consistent with wind direction and reasonable transpiration rates for poplars with unlimited access to water, concluding that AVIRIS water vapor is sensitive to ET under certain boundary layer conditions. However, while transpiration is the dominant source of water vapor in the atmospheric boundary layer , directly relating water vapor to the agricultural field that produced it will depend on multiple factors including atmospheric turbulence, time of day, wind magnitude and direction, and atmospheric stability. Here, we build upon the work of Ogunjemiyo et al. to further evaluate if AVIRIS is sensitive to field scale ET by testing a series of specific hypotheses to investigate how AVIRIS estimates of water vapor vary with the surface properties and atmospheric conditions that might be expected to influence water vapor in a complex agricultural environment . Relating water vapor patterns to crop water use will be more challenging to isolate in the Central Valley of California, which includes a complex arrangement of agricultural fields that vary in crop types, land management, irrigation practices, field sizes, and vegetated cover fractions.

Relative to the large, single crop, well-watered field studied in Ogunjemiyo et al. , the heterogeneity in crop type and water availability, smaller field sizes, and the semi-arid climate in our study area add complexity to the interaction between the atmosphere and underlying physical and plant physiological characteristics. Table 4.2 summarizes additional mechanisms that might influence the interactions summarized in the simple conceptual model . This study will also conduct analyses of water vapor at three scales – pixel, field, and scene – using three AVIRIS-derived water vapor images collected in early June of three different years. Our hypothesis are designed to refine understanding of whether consistent relationships between imagery, atmospheric conditions, and surface properties are derivable from hyperspectral imagery in a complex agricultural landscape and at which scales. Results will identify opportunities and limitations of using water vapor imagery to study ET at the ground surface in this important agricultural region. The Surface Biology and Geology mission has been identified in the 2017 Decadal Survey as a designated program element prioritized for development as a means to enhance our ability to monitor ecosystems,blackberry container natural hazards, and land use over time . The proposed sensor will capture a large number of spectral bands in the visible and shortwave infrared at a 30 m resolution, as well as multiple bands in the thermal infrared at a 60 m resolution. In order to simulate the capabilities of SBG, the HyspIRI Airborne Campaign flew the AVIRIS and MODIS-ASTER Simulator instruments throughout California seasonally from 2013 to 2017 at an altitude of 20 km. AVIRIS is a 224 band imaging spectrometer that captures wavelengths from 350 nm – 2500 nm at approximately 10 nm increments . MASTER captures 8 bands in the TIR region from 4-12 μm . NASA’s Jet Propulsion Lab preprocessed the imagery and produced orthorectified, high spectral resolution radiance and atmospherically-corrected reflectance imagery from AVIRIS at an 18 m spatial resolution and a land surface temperature product from MASTER at a 36 m spatial resolution. AVIRIS imagery was resampled to 36 m by pixel aggregation for consistency with LST imagery. Three dates of imagery from HAC were analyzed: June 6, 2013, June 3, 2014 and June 2, 2015 collected at 18:25, 21:41, and 18:59 UTC respectively. Pixel-level water vapor estimates were calculated from the radiance imagery using Atmospheric COR rection Now 6.0 atmospheric correction software . ACORN 6 models atmospheric gas absorptions and scattering effects using nonlinear least-squares spectral fitting with look-up tables of water column densities generated from MODTRAN 4 radiative transfer runs .ACORN 6 was run in mode 1.5 for atmospheric correction of hyperspectral data.

Chosen parameters include the use of both 940 nm and 1140 nm to derive water vapor, a midlatitude summer model, an average surface elevation of 100 meters, automated estimate of visibility and artifact suppression. The studied agricultural landscape was characterized using both the reflectance imagery and a geographic information system layer of field data. Multiple Endmember Spectra Mixture Analysis was run on the AVIRIS A reflectance imagery to estimate pixel-level fractions of green vegetation , soil, and non-photosynthetic vegetation . MESMA is a spectral mixture technique that models the spectral signature of each pixel using a linear combination of spectra from the classes within the scene. The chosen endmembers for each class are allowed to vary on a per-pixel basis to capture the diversity of spectra contained within GV, NPV, and soil. The resultant product is a sub-pixel map of vegetation fraction, both senesced and green, and soil throughout the study area. MESMA was run on each of the three image dates using one spectral library of 40 image-selected endmembers that were collected from all three dates. In addition to cover fractions, we obtained field size and crop species data. This information was obtained from GIS crop data layers provided by Tulare, Kings, Kern, and Fresno Counties. Field boundaries and crop information is gathered as part of a California’s required registration and permitting of agricultural fields that use pesticides. Field sizes ranged from <100 m2 to 2.7 km2 . Using the MESMA results and the crop map, which delineates field boundaries, a mean estimate of GV, Soil, and NPV was calculated for each field. An expected driving factor of water vapor patterns is wind, both its directionality and its magnitude. Therefore, we interpolated a map of wind over the study area during the studytimes and dates in order to create an estimate of wind activity against which to compare water vapor patterns. To calculate wind speed and wind direction, we relied on weather station data from 18 meteorological stations in or within close proximity to the study area . Weather stations used in the analysis are managed by various sources including government agencies, private firms, and educational institutions. Meteorological data were downloaded from the MesoWest and California Irrigation Management Information System networks. For each station, the wind speed and direction that most closely matched the flight time for each of the three dates were recorded. All observations were within 30 minutes of the flight time. Using the 18 data points for wind magnitude and direction, data were spatially interpolated across the study area using an inverse weighted distance formula. IDW relies on the idea that each estimated data point will be influenced by the known data surrounding it, and this influence will diminish with distance. IDW has been widely used in climatic studies for interpolating data such as temperature, rainfall, and wind and is computationally efficient , but has been found to have low accuracy when data is sparse or unevenly distributed . As our study area is relatively flat and stations are somewhat evenly disbursed, we felt IDW would be an appropriate and efficient method for wind interpolation. The result is a pixel-level estimation of wind speed and wind direction across the study area for each of the three study dates. Because small-scale trends of water vapor are observed in the scene and crops are not uniformly distributed throughout the area, direct comparisons of water vapor concentrations between fields or crop types would not be suitable for ET evaluation. Therefore, most of the analyses in this study area are conducted using field-scale, normalized water vapor values. In order to normalize comparisons, intra-field slope, trajectory, and intercept were proposed. To quantify the concentrations and gradients of water vapor as they vary over field of crops, a 3D linear trend surface was fit to the water vapor data over every field in the crop map for each year . Using the fitted surface, an intercept, slope, and trajectory were calculated for each field to explore the magnitude, rate of change, and directionality of the water vapor above it.

Fields and their boundaries were defined from the crop polygon validation database

The nine crop categories that had a sufficient number of fields to be included in this study were alfalfa, almonds and pistachios, corn, cotton, other deciduous crops, other truck crops, subtropical, tomatoes, and vines. Other crops that are grown in the area but are not being studied included cucurbits, grains, pasture, safflower, and sugar beet. Since these “other” crops are not similar in structure or phenology, we did not attempt to group them into a combined category for classification. In other agricultural regions where less frequent crops show a higher degree of similarity, adding an “other crops” group to the classification could be an appropriate way to decrease error. However, the number of fields and total area for each crop show that all of the “other” crops accounted for less than 1% of the total validated area in each of the three years, leading us to the assumption that the error due to the omission of these crops in our classification and crop area calculations will be low.The random forest classifier is an ensemble classification and regression technique that creates a forest of classification trees by randomly selecting subsets of the training data with replacement for each tree, randomly selecting a variable to split at each node, and then creating a multitude of decision trees that vote for the most popular class. Random forest was chosen for this study due to its computational efficiency and proven high performance . Five hundred trees were computed using 150,000 cases of nine crop classes with 172 spectral variables. We randomly selected fields from each year to be used for either training or validation in order to minimize inflated accuracies due to spatial autocorrelation. Seventy percent of fields were assigned as training data,growing raspberries in containers while the other 30% were set aside for independent validation. From the training fields, 50,000 pixels from each year were randomly chosen from the pixels that contained ≥50% green vegetation, and then combined, creating a training set of 150,000 pixels across the three dates.

As suggested by Millard and Richardson , pixels were randomly sampled to create a training dataset that was representative of the true class proportions within the study area. A random forest was generated from these 150,000 pixels, to be used to classify each of the three images. A 50% GV threshold was used as the cutoff for selecting pixels for training and validation in the random forest in order to maximize accuracy while also maintaining validation data for the most infrequent categories, particularly the tomato, cotton, and other truck crop classes that had the fewest training fields. To determine the threshold, 10 trial random forests were run to estimate the classification accuracy for each crop class using threshold levels ranging from 10% GV to 100% GV at 10% increments. Each run used 10,000 randomly selected pixels and populated 500 trees. We found that while the majority of the crop classes increased in accuracy as the threshold increased, the tomato class began declining in accuracy after the 50% threshold due to a significantly reduced training sample size, and there were no training or validation data available in our study area when the GV threshold was ≥80% . Therefore, in order to include tomatoes in our classification, we chose a 50% GV threshold. Additionally, mixed pixels may not be spectrally similar to the pure pixels of the classes that they contain, so using only pure pixels to train a classifier can increase the error in areas with a high proportion of mixed pixels . To this point, we aimed to choose a threshold that could capture diversity within each crop category, and felt that a high GV threshold would be restrictive in that it may exclude younger crops or certain species of crops within each class . Therefore, 50% was chosen as a compromise between attaining high accuracy within all of the classes while also fairly representing the diversity within each crop class. Training a classifier on mixed pixels for agricultural applications has been shown to have similar accuracy as training on pure pixels . For each year, a pixel-level classification was generated using the multi-year random forest. From this image classification, independent validation was conducted using the 30% of fields that were not used for training.

From the validation fields 10,000 pixels containing at least 50% or more green vegetation were randomly chosen from each image for a total of 30,000 validation pixels over the three dates. Since multi-croppings or inter plantings were excluded from the training and validation, each field was assumed to be growing only one crop type. Therefore, to improve the results of the classifier for analysis of changes in crop area, a majority filter was applied to the random forest classification result to reclassify each pixel of a field as the crop category to which the plurality of pixels in that field were classified. For example, if 10% of the pixels in a field were classified as tomato, 30% as alfalfa, and 60% as corn, all of the pixels in that field were reclassified as corn.Only fields that contained a certain threshold of green vegetation were included in the field level reclassification in order to remove fallow fields from analysis. Two different field-level GV thresholds, 25% and 50%, were chosen to assess the impact of a field-level threshold on accuracy results. Final crop planting assessments were conducted using the 50% field-level threshold for an increased accuracy of analysis. 2.2.5. Accuracy Assessments Three different classification accuracies were computed and will be discussed. The first is the pixel-level out-of-bag error calculated by the random forest. The OOB error is an estimate of error that uses subsampling and bootstrapping to estimate the error of a sample, using only trees of the random forest that do not include the data point being validated . The second reported accuracy is pixel-level independent validation using classified pixels that were not included in training the random forest and were not in the same field as any pixel in the random forest training set. The third accuracy is a field-level accuracy using the majority reclassification of pixels in each field. To assess the benefit of a 224-band spectrometer such as AVIRIS over more commonly available multispectral sensors,large plastic pots for plants a random forest classifier was run with simulated Landsat Operational Land Imager and simulated Sentinel-2B data for accuracy comparison. AVIRIS images from all three of the dates were spectrally convolved to Landsat OLI bands 1–7 and 9 and to all of the bands of Sentinel-2B.

The spatial resolution was kept constant at 18 m. These simulated images were then run in random forest using 500 trees, the same nine crop categories, and the same 150,000 training points that were used for the AVIRIS classification. OOB accuracy at the pixel level and field level were both computed for analysis.Using random forest and the majority-filtered reclassification with a 50% GV threshold, predictive crop maps were generated for each year. After random forest was run on each AVIRIS image, a multi-year field polygon layer was used to identify individual fields for majority reclassification. Therefore, classified fields were not constrained to those fields that were included in the validation layer from a specific year, but could include any field in the study area that contained 50% or more green vegetation, whether it was registered as part of the validation layer or not. From these maps, crop area was assessed to analyze changes in cropping patterns within the study area over the course of the drought. We then used these maps to evaluate the hypothesis that higher-value, perennial crops were prioritized during the drought by analyzing factors including water use, economic value, and crop lifespan against the change in the planted area. Independent validation produced an overall accuracy of 89.6% when accounting for all of the years and all of the crops, which was lower than the OOB accuracy by around 4%. This decline in accuracy when validating independently is likely because the independent validation accounted for the potential of spatial auto correlation, which is likely to inflate OOB results. The OOB assessment used the same data for training the classifier as for validation, whereas the independent validation relied on 30,000 pixels randomly selected from polygons separate from those used in training. These 30,000 pixels made up only 1.6% of the 1.87 million potential validation pixels; those with GV greater than 50% that were not used in the random forest. The tree categories, almond/pistachio, other deciduous, and subtropical had the highest consistency between years with overall accuracies changed by less than 4% between the years . Cotton and truck crops were less consistent in accuracy from year to year than the other crop categories, and this inconsistency may be due, in part, to these two classes having two of the three fewest numbers of pixels used in the random forest.

Accuracy for other truck crops in 2015 was not applicable , because the user’s accuracy was NA for that year . Accuracy was NA for tomato fields in 2013 because no tomato pixels were identified in the validation layer for that year. Despite this omission, tomatoes were included in the study, as they are a major crop group in the area with a sufficient number of training and validation polygons from 2014 to 2015. Table 2.5 details the errors associated with each crop type. The classification of alfalfa resulted in high accuracies of near or over 90%. Almond and pistachio trees showed consistently high user and producer accuracies of 94.0% and 97.2%, respectively. The random forest was more likely to erroneously classify other crop classes as almond and pistachio than it was to misclassify almond and pistachio trees. This result was likely because the pixels of almond and pistachio trees were very prevalent in the study area, leading to a large amount of randomly sampled pixels for training, and leading the classifier to favor this class over less frequently occurring classes. Other deciduous crops and subtropical crops were most likely to be misclassified by or as each other or as almond and pistachio, illustrating that tree crops were likely to be misclassified as another tree crop. Importantly, the three tree crop categories showed a tendency toward being over mapped, while the other six classes of non-tree species were all under mapped by the classifier. Of those, cotton and other truck were the most likely to be under mapped, with producer’s accuracies of 49.8% and 56.7%, respectively. The results showed that the classes that were more prevalent and had more validation data were more likely to have higher accuracy than the infrequent classes. 2.3.1.3. Field-Level Validation after Majority Filter The final majority-filtered reclassification of pixels to create fields that contained only one crop type had the highest accuracy at 94.4%. The overall accuracy is computed as the percentage of total fields that were correctly classified using random forest and a majority filter. The higher field-level accuracy obtained by the reclassification confirmed assumptions that using a majority filter would smooth out the stray pixels that may lie between rows of crops or that capture weeds or other plant matter growing near the crop, which may lead to classification confusion. When assessing the majority-filtered reclassification results by year , seven of the nine classes had accuracies over 80% in all three years, with other truck crops and cotton being the exceptions. An important finding is that the field-level classification improved the pixel-level classification the majority of the time when a 50% GV threshold was used at the field level. When assessing pixel and field-level accuracies over all three years , the only accuracy that decreased from the pixel to the field level was the user’s accuracy of almond and pistachio orchards. As the most common crop category, assessing the accuracy at the field level increased the over mapping of this class. However, all of the other user and producer’s accuracies increased. When looking at the accuracies separated by year and by class , 22 of the 27 classes improved in accuracy from the pixel to the field level. The field-level accuracies in tables 3 and 6 were computed using a 50% GV threshold, meaning that only fields that had at least 50% green vegetation or more were included in the accuracy assessment.

It is evident that there is less year-to-year variation in the national mean of temperature than precipitation

By using a panel of county level data and including county and state by year fixed effects, we rely on across county variation in county-specific deviations in weather within states. This means that our estimates are identified from comparisons of counties that had positive weather shocks with ones that had negative weather shocks, within the same state. Put in another way, this approach non-parametrically adjusts for all factors that are common across counties within a state by year, such as crop price levels. If production in individual counties affects the overall price level, which would be the case if a few counties determine crop prices, or there are segmented local markets for agricultural outputs, then this identification strategy will not be able to hold prices constant. The assumption that our approach fully adjusts for price differences seems reasonable for most agricultural products for at least two reasons. First, production of the most important crops is spread out across the country and not concentrated in a small number of counties. For example, McLean County, Illinois and Whitman County, Washington are the largest producers of corn and wheat, respectively, but they only account for 0.58% and 1.39% of total production of these crops in the US. Second, our results are robust to adjusting for price changes in a number of different ways. In particular, the qualitative findings are similar whether we control for shocks with year or state by year fixed effects.6 Returning to equation , consider the second term, which is the change in profits due to the weather-induced change in quantities. We would like to obtain an estimate of this term based on long run variation in climate, since this is the essence of climate change. Instead, our approach exploits short run variation in weather. Since farmers have a more circumscribed set of available responses to weather shocks than to changes in climate, it seems reasonable to assume that Short Run > Long Run. 7 For example, farmers may be able to change a limited set of inputs in response to weather shocks. But in response to climate change,plastic pots for planting they can change their crop mix and even convert their land to non-agricultural uses .

Consequently, our method to measure the impact of climate change is likely to be downward biased relative to the preferred long run effect. In summary, the use of weather shocks to estimate the costs of climate change may provide an appealing alternative to the traditional production function and hedonic approaches. Its appeal is that it provides a means to control for time invariant confounders, while also allowing for farmers’ short run behavioral responses to climate change. Its weakness is that it is likely to produce downward biased estimates of the long run effect of climate change.Agricultural Production. The data on agricultural production come from the 1978, 1982, 1987, 1992, and 1997 Censuses of Agriculture. The Census has been conducted roughly every 5 years since 1925. The operators of all farms and ranches from which $1,000 or more of agricultural products are produced and sold, or normally would have been sold, during the census year, are required to respond to the census forms. For confidentiality reasons, counties are the finest geographic unit of observation in these data. In much of the subsequent regression analysis, county-level agricultural profits are the dependent variable. This is calculated as the sum of the Censuses’ “Net Cash Returns from Agricultural Sales for the Farm Unit” across all farms in a county. This variable is the difference between the market value of agricultural products sold and total production expenses. This variable was not collected in 1978 or 1982, so the 1987, 1992, and 1997 data are the basis for our analysis. The revenues component measures the gross market value before taxes of all agricultural products sold or removed from the farm, regardless of who received the payment. Importantly, it does not include income from participation in federal farm programs, labor earnings off the farm , or income from non-farm sources. Thus, it is a measure of the revenue produced with the land. Total production expenses are the measure of costs. It includes expenditures by landowners, contractors, and partners in the operation of the farm business.

Importantly, it covers all variable costs . It also includes measures of interest paid on debts and the amount spent on repair and maintenance of buildings, motor vehicles, and farm equipment used for farm business. The primary limitation of this measure of expenditures is that it does not account for the rental rate of the portion of the capital stock that is not secured by a loan so it is only a partial measure of farms’ cost of capital. Just as with the revenue variable, the measure of expenses is limited to those that are incurred in the operation of the farm so, for example, any expenses associated with contract work for other farms is excluded.Data on production expenses were not collected before 1987. The Census data also contain some other variables that are used for the subsequent analysis. In particular, there are variables for most of the sub-categories of expenditures . These variables are used to measure the extent of adaptation to annual changes in temperature and precipitation. The data also separately report the number of acres devoted to crops, pasture, and grazing. Finally, we utilize the variable on the value of land and buildings to replicate the hedonic approach. This variable is available in all five Censuses. Soil Quality Data. No study of agricultural land values would be complete without data on soil quality and we rely on the National Resource Inventory for our measures of these variables. The NRI is a massive survey of soil samples and land characteristics from roughly 800,000 sites that is conducted in Census years. We follow the convention in the literature and use the measures of susceptibility to floods, soil erosion , slope length, sand content, clay content, irrigation, and permeability as determinants of land prices and agricultural profits. We create county-level measures by taking weighted averages from the sites that are used for agriculture, where the weight is the amount of land the sample represents in the county. Since the composition of the land devoted to agriculture varies within counties across Censuses, we use these variables as covariates. Although these data provide a rich portrait of soil quality, we suspect that they are not comprehensive. It is this possibility of omitted measures of soil quality and other determinants of profits that motivate our approach.

Climate Data. The climate and weather data are derived from the Parameter-elevation Regressions on Independent Slopes Model .This model generates estimates of precipitation and temperature at 4 x 4 kilometers grid cells for the entire US. The data that are used to derive these estimates are from the more than 20,000 weather stations in the National Climatic Data Center’s Summary of the Month Cooperative Files. The PRISM model is used by NASA,drainage for plants in pots the Weather Channel, and almost all other professional weather services. It is regarded as one of the most reliable interpolation procedures for climatic data on a small scale. This model and data are used to develop month by year measures of precipitation and temperature for the agricultural land in each county for the 1970 – 1997 period. This was accomplished by overlaying a map of land uses on the PRISM predictions for each grid cell and then by taking the simple average across all agricultural land grid cells.To replicate the previous literature’s application of the hedonic approach, we calculated the climate normals as the simple average of each county’s annual monthly temperature and precipitation estimates between 1970 and two years before the relevant Census year. Furthermore, we follow the convention in the literature and include the January, April, July, and October estimates in our specifications so there is a single measure of weather from each season. Table 1 reports county-level summary statistics from the three data sources for 1978, 1982, 1987, 1992, and 1997. The sample is limited to the 2,860 counties in our primary sample.Over the period, the number of farms per county declined from approximately 765 to 625. The total number of acres devoted to farming declined by roughly 8%. At the same time, the acreage devoted to cropland was roughly constant implying that the decline was due to reduced land for livestock, dairy, and poultry farming. The mean average value of land and buildings per acre in the Census years ranged between $1,258 and $1,814 in this period, with the highest average occurring in 1978. The second panel details annual financial information about farms. We focus on 1987-97, since complete data is only available for these years. During this period the mean county-level sale of agricultural products increased from $60 to $67 million. The share of revenue from crop products increased from 43.5% to 50.2% in this period. Farm production expenses grew from $48 million to $51 million. Based on the “net cash returns from agricultural sales” variable, which is our measure of profits, the mean county profit from farming operations was $11.8 million, $11.5 million, and $14.6 million or $38, $38, and $50 per acre in 1987, 1992, and 1997, respectively. The third panel lists the means of the available measures of soil quality, which are key determinants of lands’ productivity in agriculture. These variables are essentially unchanged across years since soil and land types at a given site are generally time-invariant. The small time-series variation in these variables is due to changes in the composition of land that is used for farming.

Notably, the only measure of salinity is from 1982, so we use this measure for all years. The final panels report the mean of the 8 primary weather variables for each year across counties. The precipitation variables are measured in inches and the temperature variables are reported in Fahrenheit degrees. On average, July is the wettest month and October is the driest. The average precipitation in these months in the five census years is 3.9 inches and 2.0 inches, respectively.Table 2 explores the magnitude of the deviations between counties’ yearly weather realizations and their long run averages. We calculate the long run average variables as the simple average of all yearly county-level measurements from 1970 through two years before the examined year. Each row reports information on the deviation between the relevant year by month’s realization of temperature or precipitation and the corresponding long run average. The first column presents the yearly average deviation for the temperature and precipitation variables across the 2,860 counties in our balanced panel. The remaining columns report the proportion of counties with deviations at least as large as the one reported in the column heading. For example, consider the January 1987 row. The entries indicate that 73% of counties had a mean January 1987 temperature that was at least 1 degree above or below their long run average January temperature . Analogously in October 1997, precipitation was 10% above or below the long run average in 95% of all counties. Our baseline estimates of the effect of climate change follow the convention in the literature and assume a uniform five degree Fahrenheit increase in temperature and eight percent increase in precipitation associated with a doubling of atmospheric concentrations of greenhouse gases .It would be ideal if a meaningful fraction of the observations have deviations from long run averages as large as 5 degrees and 8% of mean precipitation. If this is the case, our predicted economic impacts will be identified from the data, rather than by extrapolation due to functional form assumptions. In both the temperature and precipitation panels, it is clear that deviations of the magnitudes predicted by the climate change models occur in the data. It is evident that for all four months there will be little difficulty identifying the 8% change in precipitation. However in the cases of temperature, deviations as large as +/- 5 degree occur less frequently, especially in July. Consequently, the effects of the predicted temperature changes in these months will be identified from a small number of observations and functional form assumptions will play a larger role than is ideal.

Crop water requirements either remain constant or increase for some crops

A table of sensor specifications can be found in Appendix 3. The primary source of ground truth observations for the CDL products is the USDA Farm Service Agency Command Land Unit program . The FSA CLU comprises digitized polygon boundaries of “semi-permanent ‘fields’” and is a confidential NASS-internal data set. Auxiliary input data sources include the USGS National Elevation Data set , the Multi-Resolution Land Characteristics Consortium National Land Cover Dataset . Prior to 2006, classification was performed using a maximum likelihood classifier in the NASS-internal Peditor program, an image processing software written in Pascal and FORTRAN . Beginning in 2006, Rulequest Research’s See5.0 software was used to create a decision tree classifier. This is applied to the remotely sensed imagery using the MRLC NLCD Mapping Tool and ERDAS Imagine. Accuracy reports are presented in state-level metadata files each annual CDL survey. For supervised classification, ground-truthed observations are defined as polygons, and are subsequently buffered inward by 30 meters. This was done in part to reconcile differences between the different spatial resolutions of the remotely sensed imagery . Prior to 2016, this method of inward-buffering was used for validation and the construction of accuracy reports. However, this excluded edge pixels from the accuracy reports. This resulted in a somewhat inflated accuracy assessments. Starting in 2017, only “unbuffered” accuracy assessments are reported. In 2016, the CDL metadata included both “buffered” and “unbuffered” accuracy reports . Overall accuracy for California FSA crops tend to range between 80 and 90 percent. In order to determine the proportion of daily crop water requirements that were met by direct rainfall,livestock fodder system this study used precipitation observations from the Parameter-elevation Regressions on Independent Slopes Model climate mapping system.

Specifically, 800-m daily precipitation rasters were upscaled to 30 meters, using bilinear interpolation . The PRISM Climate Group at the Northwest Alliance for Computational; Science and Engineering at Oregon State University maintains daily 800-m and 4-km raster datasets of precipitation across the 48 conterminous states, spanning back to 1981. The group also maintains raster datasets of temperature , dewpoint temperature, vapor pressure deficit , and 30-year annual “normals” . PRISM rasters are freely available on the PRISM climate group homepage, with the exception of 800-m monthly and daily data, which must be ordered. PRISM precipitation rasters were commissioned by USDA through the Natural Resources Conservation Service to serve as the official spatial climate data sets of the USDA . PRISM rasters are created at a 30-arcsecond spatial resolution and are also available at a 2.5-arcminute resolution , matching previous USDA-NRCS 1961-1990 climate data sets developed in the 1990s. At its core, PRISM is is an interpolation technique that reproduces the spatial climate patterns of the United States, with a particular emphasis on the effect of elevation and slope on precipitation . The method was originally developed by Dr. Christopher Daly of Oregon State University in an attempt to reproduce the process that climatologists used to construct climate maps of the United States . At its core, the model incorporates data from surface weather stations . PRISM utilizes a linear climate-elevation relationship, rather than a multiple regression model due to difficulties in predicting “complex relationships between multiple independent variables and climate”. Instead, weather station observations are weighted by distance, elevation, coastal proximity, topographic facet, vertical layer, topographic position, and effective terrain . Accuracy estimates using single-deletion jackknife cross validation, leave-one-out cross validation, and a 70% prediction interval have been performed on various revisions to the PRISM method . Regional mean absolute error between predicted and observed precipitation and temperature tend to be similar overall and higher in the physiographically complex western United States.

A 2008 evaluation of PRISM for the central California coast saw good agreement between PRISM, WorldClim, and Daymet temperature observations for the central valley of California .The water footprint incorporates the effect of yields on crop water use. Assuming negligible losses of water, the crop water requirement assumed to be equivalent to the actual crop water use. The resultant water footprints can be considered a “best-case scenario”, as inefficiencies in water distribution and application can only increase the actual crop water use, increasing the blue component of the water footprint. Water footprints are expressed in units of cubic meter of water per metric ton of harvested product. From a resource management perspective, the WF of applied water is most valuable for regions that are predominantly reliant on surface water resources. Total WF figures are presented in Appendix 5, sections G-H. From 2008 to 2015, the blue WF was always orders of magnitude higher than the green WF , further demonstrating the minor role of direct rainfall toward satisfying crop water requirements in California . Across large regional extents, the overall water footprint for most hydrologic regions does not vary much year to year, with the exception of isolated fluctuations driven by changes in reported yield . These fluctuations are also visible in the crop specific annual totals . For example, low mint yields in Shasta County in 2013 inflate the 2013 water footprint for mint . This fluctuation is also visible in the regional WF statistic—due to the low intensity of agriculture in the North Lahontan region , the low yield bias on the WF is visible at the regional scale.A 2018 study by the University of California, Davis compared the consumptive use of water by crops in the Sacramento-San Joaquin Delta of California using seven different crop evpotranspiration models. This “Delta ET” study included methods that were based on crop coefficients and methods which are reliant on remotely sensed satellite measurements.

Monthly crop ET values were published along with the region of interest, for the 2015 and 2016 water years. Overall crop ET observations from this study were compared to the monthly mean of the seven ET models from the Delta ET study. Overall, there was general agreement between this study and the methods detailed in the Delta ET study. This study tended to underestimate crop ET each month by no greater than 50% . The highest proportion of underestimation occurred during the winter months. However, due to the small magnitude of wintertime ET,hydroponic nft gully this only resulted in a 22% cumulative underestimation . The Delta ET models include some land cover classes that this study does not model. The SIMS ET model implemented by the Delta ET study does not model semi agricultural/right-of-way and wet herbaceous/sub-irrigated pasture. The results of this study closely match the monthly results from SIMS within 1%.The water footprint can be thought as a measure of the effectiveness of a unit application of water, given yields as the test for effectiveness. Regionally, it is expected that the highest proportion of crop water use would occur in the intensively-cultivated central valley region . Compared to regions less suitable for agriculture . these regions are exceptional in their overall water use. However, they are not exceptional in the water footprint of agricultural activities . For example, Monterey county contained the largest overall average water footprint of agricultural production, in spite of the possessing a small proportion of overall agricultural water requirement. Among agricultural commodities, average water footprints agree with other assessments in terms of rank order of water footprint and overall crop water requirement. For example, nuts and grasses both have a large water footprint and large crop water requirement, compared to other crops modeled in this study . The large proportional crop water requirement could be function of crop-specific ET characteristics, or it could be an artifact of a large overall cultivated area. However, when compared the proportion of harvested acres, lower WF crops make up a slightly larger portion of cultivated acres than fruits and nuts . Droughts can be used to study the effects of reductions the overall amount of water available in a distribution system. For the drought period starting in 2012, reductions in harvested acres were observed, especially with grasses and some specialty crops.The equivalence of crop water use and crop water requirement was a central assumption in this study. The response of the water footprint under deficit irrigation can be an important topic for future work, as reductions in water use may be less effective from a footprint perspective if yields are dramatically affected. This study explored the distribution of water footprints across the State of California regionally, across different commodities, and across a 7 year period, marked by wet and dry climatic extremes. A model of crop water use was coupled with surveyed observations of precipitation, harvest statistics, and a land cover model. Findings from this study revealed an overall insensitivity of the water footprint to climatic extremes and significant inter-annual variability in the metric .

As a highly derivative metric, the water footprint accumulates errors from all of the data sources used in its calculation. Unreliable yield reports can dramatically change the water footprint, due to the power-law relationship between the water footprint and crop yield. By quantifying the uncertainty of this metric, the water footprint could become even more useful as a decision support tool. However, even exploring the relative proportions of water footprints are useful in defining the conceptual extent of the water-use for a given territory or commodity. Future studies can conduct sensitivity analysis of the metric, to examine which input parameters have the greatest effect on water footprint variability. In the course of this study, a framework was created and implemented in R that allows this analysis to be replicated and run with different inputs. This framework can be utilized in future analyses to compare the footprint metric with the ever improving agricultural methodologies found in California, from modeling irrigation efficiencies, to using improved land use surveys and methods of modeling crop evapotranspiration. The framework can also be applied to different regions, provided that there are harvest and crop ET models which adequately characterize the region. An understanding of the water footprint of agricultural production can provide information to the grower who wishes to maximize the economic return of a given volume of water, the state planner who wishes to maximize utility per unit of water allocated, the national administrator who wishes to understand national risks and strengths, or the informed citizen who wishes to align their consumptive activities with a vision for the conditions conferred to the next generation. This information is a critical component of the continuous motivation to characterize relationships between society and natural resource systems, with the ultimate goal of creating sustainable and resilient social and natural systems.Approximately 5 million workers in the United States work in the agricultural industry with potential exposure to a wide variety of respiratory toxicants. Among the various respiratory hazards not well studied is obstructive and restrictive lung disease caused by inorganic minerals . California agricultural workers have increased respiratory symptoms, decreased respiratory function, and increased mortality rates from chronic pulmonary disease compared with the general population . The agricultural environment of the Central Valley of California places individuals at increased risk of exposure to inorganic particles. This region encompasses a rich farming area as well as extensive urban development. The predominantly dry farming techniques of the Central Valley result in high levels of airborne dust from operations such as field preparation and harvesting of row crops and tree fruits . Although soil consists of a mixture of organic and inorganic materials, potential health effects from the ever-present mineral dusts have been largely overlooked. In theory, soil should contain representative portions of all major mineral classes in the earth’s crust; in reality, most agricultural soils are composed largely of silicate materials and crystalline silica with varying amounts of other mineral classes, depending on the local geologic history. The evidence that mineral dust exposure poses a significant hazard to agricultural workers for interstitial lung disease is based on a handful of case reports, inferences from exposures to mineral dusts in other industries, studies of wild and farm animals exposed to environmental dusts, and toxicologic studies . The deposition and clearance of particles within the respiratory system occurs in an in homogeneous manner.

Most growers along California’s Central Coast use phosphorus fertilizer to maintain high crop production

Combinatorial biosynthesis has been successfully applied to generate a library of fungicidal antimycin analogs, which are cytochrome C reductase inhibitors; fenpicoxamid for instance has been developed by Dow AgroSciences to control the wheat pathogen Zymoseptoria tritici. Based on detailed understanding of the bio-synthetic pathway of antimycin, diversity-oriented biosynthesis of about 400 analogs was achieved by altering the chemical identities of priming, extending, and tailoring building blocks. Several of these analogs exhibited stronger biological activities than the original NPs, while a few introduced orthogonal reactive handles in the molecules that enabled further chemical derivatization.The application of insecticides, herbicides, and fungicides with potent bio-activities and good safety profiles has played an indispensable role in improving the yield and quality of agricultural products. However, their continuous and excessive use has led to the emergence of resistance among plants and plant pathogens. Resistance gene-directed NP discovery has been demonstrated to be an effective strategy to uncover novel NPs with desired modes of action as lead candidates for new insecticides, fungicides, or herbicides to address the problem of growing resistance. Metabolic and bio-synthetic engineering of NP synthetic pathways in yeast can further improve titers for microbial production and biological activities for commercial applications. The increasing sophistication of these tools means that we are entering a renaissance of NP discovery for both pharmaceutical and agricultural applications.The Pajaro River and Elkhorn Slough watersheds on California’s Central Coast include some of the state’s most productive and highly valued agricultural lands. The watersheds’ streams and rivers serve as key municipal and agricultural water sources, recreational areas, and wildlife habitat.

Both watersheds drain into Monterey Bay, a nationally protected marine sanctuary,blueberry grow pot and water from the Elkhorn Slough watershed passes through Elkhorn Slough, the largest tidal salt marsh along the Central Coast and a critical resource for resident and migratory birds, fisheries, and other wildlife. Agricultural and urban land uses in the Pajaro River and Elkhorn Slough watersheds have compromised the quality of their waterways. Two nutrients, nitrogen and phosphorus , are of particular concern. High levels of nitrate-N in drinking water pose a threat to human health, and both nitrogen and phosphorus are linked to excessive growth or “blooms” of algae and other plants that can decrease the amount of dissolved oxygen in waterways below the levels that aquatic organisms need to survive. As part of state and federal efforts to protect and restore water quality, regulatory agencies have been charged with establishing target concentrations for pollutants in waterways that will protect beneficial uses1 . The Central Coast Regional Water Quality Control Board has set a preliminary target of 0.12mg/L for soluble reactive phosphorus concentrations, based on the lowest concentrations they have observed in waterways of the Pajaro watershed with excessive plant or algae growth. This pollution is thought to come primarily from “non-point” sources, which are unregulated discharges from urban and agricultural land uses.Increasing evidence suggests that crops cannot take up all of the phosphorus fertilizer being applied ; as a result, excess phosphorus accumulates in the soil. High levels of soil phosphorus in turn lead to higher phosphorus levels in water draining from agricultural fields . In the Pajaro River and Elkhorn Slough watersheds, high concentrations of phosphorus have been identified in several waterways. The RWQCB Watershed Management Initiative implicates agriculture as the primary source of this and other nutrient pollution . However, little empirical data exists to demonstrate that agriculture is responsible for nutrient loading into these waterways. In this research brief we present data from water quality monitoring conducted between October 2000 and September 2004, to demonstrate the way that agricultural land use influences phosphorus concentrations in streams and rivers.

We discuss the nature of phosphorus pollution from agriculture along the Central Coast, examine the implications of these data for agricultural regulations, and offer suggestions for reducing phosphorus losses from farmlands.The Pajaro River watershed drains approximately 1,300 square miles of land, with 7.5% of the watershed in agriculture. Agricultural activities are concentrated in three productive areas: on the flood plain of the Pajaro River near the towns of Watsonville and Aromas ; in South Santa Clara Valley near Gilroy and San Martin ; and in the San Juan Valley near San Juan Bautista and Hollister . Production near the coast is dominated by cool-weather vegetables, berries, flowers, and apples. In the warmer inland areas—east of the Santa Cruz and Gabilan ranges—growers rotate crops of cool- and warm-weather vegetables, along with grapes, flowers, and stone fruits. Approximately 70 square miles in size, the Elkhorn Slough watershed drains northern Monterey County and a small portion of San Benito County. Approximately 24% of the watershed is in agriculture , with strawberries and cool-weather vegetables making up the majority of cultivated acreage .To assess the role of agricultural land use on phosphorus levels in waterways, we began sampling two creeks in October 2000 in the Elkhorn Slough watershed , and several waterways in the Pajaro River watershed, including Corralitos Creek, Watsonville Slough, the Pajaro River, and publicly accessible agricultural drainage ditches. In October 2002 we expanded the project to include all tributaries of the Pajaro River to determine the proportion of nutrients each water basin contributes to the river. We collected water samples every 2 weeks at approximately 60 sites throughout the watershed. Sites were selected to bracket agricultural activity and other land uses in order to compare concentrations upstream and downstream of potential nutrient sources. In addition, several locations were sampled more frequently to capture storm event variability and to measure water discharge for calculations of nutrient loads . For brevity we report here on several key sites that demonstrate spatial and temporal patterns we found to be characteristic of the entire watershed.Naturally occurring phosphorus is derived from apatite, a common mineral consisting of calcium fluoride phosphate or calcium chloride phosphate.

The availability of P to plants in any soil is limited by the rate at which apatite dissolves. Relative to other plant macro-nutrients, inorganic P is fairly insoluble and binds to soil particles. This means it is typically retained in the soil profile and doesn’t leach into groundwater. Phosphorus availability to plants is greatest when the soil’s pH is around 6.55–7.5. In acid soils, dissolved phosphate can precipitate with iron and aluminum oxides, making it unavailable to growing plants, whereas in alkaline soils, dissolved phosphorus can precipitate with calcium. Both inorganic and organic forms of P are found in soils. Since soils tend to “hold” P, it is most commonly lost from soils via erosion. However, if sufficient amounts of P are added to soils over time in the form of fertilizers or other inputs, all the attachment sites on soil particles can become filled, at which point the soluble form of P will be lost through runoff or by leaching. The amount of phosphorus lost from agricultural fields varies greatly,hydroponic bucket and is specific to both local environmental conditions and land management practices. Conditions that increase erosion, runoff, and subsurface water flow also increase soil P losses. Therefore, climate, soil type, and slope can all influence P losses. In addition, a number of nutrient and soil management practices impact soil P movement, including the amount of P applied in fertilizer, the solubility of applied P, the timing of fertilizer applications in relation to plant use and irrigation or rain events, the presence of artificial drainage systems, and cover cropping and tillage practices that affect erosion and water infiltration. In general, most soil P is lost via surface runoff and erosion, but the amounts lost and the timing of such losses are unique to the conditions and management practices used at each ranch or farm. For example, the use of tile drainage systems, which are common in parts of the Pajaro River and Elkhorn Slough watersheds, can greatly increase subsurface P losses. Drains can affect P movement and loss in different ways. As water moves through the soil profile toward the drain, the soil can bind dissolved P, thus removing it from the water; however, tile drains also reduce the amount of time P fertilizer is in contact with soil particles, so overall a smaller fraction of applied P may be retained in the soil profile . Tile drains have also been shown to transport significant amounts of particulate P from topsoil to surface waters during storm events .

Conversely, in soils with poor drainage, installation of tile drains can reduce total P losses during storms by improving infiltration and reducing P lost via surface runoff . Therefore, determining the role of tile drains in P transport under local soil and climate conditions is important for managing P levels in the Central Coast region. In addition to agriculture, natural processes and urban runoff may also contribute P to waterways. Small amounts of P are deposited from the atmosphere in rainfall and in dry airborne particulates. Urban sources of P include residential fertilizer use, automotive products, and septic tanks and leach fields. In the past, detergents were a significant source of urban P pollution, but most detergents are now phosphate-free. In aquatic environments, particulate P can convert to dissolved forms and increase the pool of reactive, dissolved P . These reactive forms, called orthophosphate or soluble reactive phosphorus , are readily taken up by algae, and in excess levels may lead to algae “blooms” and eutrophication .Geographical patterns of dissolved phosphorus concentrations suggest that levels are influenced by land features as well as land use practices. Soil characteristics such as a shallow water table are associated with elevated stream SRP levels, particularly in agricultural areas. In the south Santa Clara Valley, SRP concentrations were low in all waterways with the exception of San Juan Creek. The San Juan drainage has a shallow, perched water table, and receives discharge from artificial tile drain systems, used in agricultural fields to remove water from the rooting zone of crop plants. In contrast, Llagas and Uvas Creeks, which do not receive tile drainage, had low SRP concentrations at all sites. Median SRP concentrations increased slightly at sites downstream of agriculture , but exceeded the target level on fewer than 20% of visits . San Benito Creek and Miller’s Canal, which were both sampled near agricultural fields, also had low median SRP concentrations. The use of tile drainage systems may account for higher SRP levels in waterways with shallow water tables and agricultural land use, including Watsonville Slough and Corn Cob Canyon Creek. Tile drainage systems can increase phosphorus losses by increasing soil infiltration rate and reducing the amount of phosphorus that adheres to soil particles . During winter storms, tile drains may also act as conduits for particulate phosphorus, carrying eroded topsoil to waterways . Non-agricultural land uses, and occurrence of mineral types naturally high in SRP, may also contribute to elevated SRP concentrations in some areas. While nutrients were generally higher at locations downstream of agriculture, Corralitos Creek had elevated nutrients both upstream and downstream of agriculture. At the most upstream site , SRP concentrations often exceeded the target level of 0.12 mg/L while two other nutrients, nitrate and ammonium were very low. The elevated SRP levels are not likely from fertilizer or septic sources, which also tend to be high in nitrogen compounds, but may be due to the mineral composition of soils in this drainage and/or soil erosion. Comparisons of sites upstream and downstream of agriculture revealed higher downstream SRP concentrations in many waterways, providing evidence that agricultural land is a source of phosphorus in surface waters. In the Elkhorn Slough watershed, SRP progressively increased with the amount of cultivated acreage located upstream . The phosphorus content of the soils in the watershed may play a role in how phosphorus moves through this system, but this has not been looked at systematically. In Carneros Creek at Dunbarton Road, which is at the upstream edge of cultivated acreage, the median SRP concentration was 0.10 mg/L, and at San Miguel Canyon Road, downstream of several miles of farmland, the median concentration was 0.53 mg/L. However, in addition to row crops, land use along Carneros Creek is mixed with ranches and rural homes, and more intensive monitoring is necessary to partition nutrient inputs from these potential sources.

Employees have greater assurance of working in a safe work environment

Agriculture also needs to compete with other types of land uses with urbanization being an important driver of agricultural land loss. By converting arable lands to a barren desert, desertification is a growing global concern, particularly in the MENA region and Iran. The redistribution of croplands from the low-quality lands to more suitable ones has potentials to improve crop yields and the sustainability of agriculture in Iran. A recent global-scale study concluded that by reallocating croplands to suitable environmental conditions, the global biomass production could increase by 30% even without any land expansion. However, reallocation planning requires accurate mapping of croplands, which is not currently available for Iran. Inefficient agricultural practices in unsuitable lands need to be avoided as they produce little yields at the cost of exacerbating land degradation and water scarcity problem. Our estimations shows that rainfed wheat production from a small acreage of 1.0 million ha in the medium suitability class can equal that from 5.5 million ha of lands in unsuitable or very poor areas . Although this conclusion may not hold for other crops grown in Iran, the wheat crop could be a good candidate to make such a generalization as wheat is the most widely cultivated crop in the country and is considered as a very low demanding plant, which has adapted to a broad range of contrasting environments. Redistribution of croplands, however,nft hydroponic system will not be a trivial task for both the Iranian decision makers and stakeholders due to various socio-economic and logistic barriers. Lands found suitable for agriculture may not be easily accessible if scattered sparsely or occur in remote areas.

Given the land and water limitations, increasing the crop production in Iran needs to be achieved through sustainable intensification, which has been found a promising approach for ensuring food security in several global-scale studies. As such, it is of vital importance for Iran to properly use its limited agricultural lands, improve water use efficiency, optimize crop pattern distribution, and adopt modern cultivation techniques. Practicing certain industrial agriculture methods in the unsuitable lands might be a viable strategy to sustainably maintain these lands in the agricultural sector while avoiding the potential socio-economic and political costs associated with redistribution of agricultural lands and farming populations. For example, protected agriculture can be established at some of these locations to cope with both land suitability and water availability constraints. While water insufficiency is a major limiting factor for both field and protected farming, the latter will be affected to a lesser extent. Our suitability assessment is based on a general set of requirements known to affect the productivity of a large number of crops, but there would exist crops with exceptional adaptive traits that can grow under less favourable conditions. Although we used the most updated geospatial data at the finest available resolution, the result of our suitability analysis should be interpreted in commensuration with the reliability and quality of the original data. For example, whereas the GlobCover database reliably maps the distribution of forests and rangelands in Iran, our visual inspection of satellite images showed that sometimes their utilized method lacks the required precision to distinguish cultivated from uncultivated croplands. Although soil erosion was not directly incorporated into our analysis, the use of slope at the very high resolution implicitly accounts for this effect. The interaction between variables and the quality of subsoil are among other factors that can be considered in the future studies.

This study used precipitation as the only water availability factor. Including surface water and groundwater availability can further improve the adequacy of the land evaluation analysis. Given the good correlation between water availability and land suitability for agriculture, the general findings of this study are not expected to change significantly by the inclusion of water availability conditions. Nevertheless, due to the current water shortage constraints across the country, the potential agricultural capacity of the country is likely to decrease when water availability is added to the analysis. Although global projections suggest that the suitable lands may expand with climate changes, how these changes, particularly in precipitation pattern, would affect the suitability of Iran’s land for crop production in the future is subject to high degree of uncertainty and needs further work.Agriculture is one of the most important industries in California, enjoying over $22 billion in farm cash receipts annually. In addition to economic benefits, national and state data show that agriculture is one of the most dangerous industries with respect to occupational illnesses and injuries. Because Latino and Latina workers provide the majority of production labor in the industry, they are at uniquely increased risk for occupational injury and illness. Regulation of the agricultural workplace is under the purview of several federal, state, and local agencies, including the U.S. Department of Labor, Occupational Safety and Health Administration, State of California , Department of Industrial Relations, Department of Pesticide Regulation, U.S. Environmental Protection Agency, county health departments, county agricultural commissioners, and the California Highway Patrol . The fragmentation of regulatory activities causes inefficiency and confusion on the part of employers, employees, and regulators. In particular, lack of information sharing between agencies leads to ineffective enforcement and educational efforts. Consequently, a pilot program was begun in 1992 that partnered agencies to improve efficiency through sharing of resources and information. The program, intended to target industries with a history of regulatory problems, was named the Targeted Industries Partnership Program . Agriculture and garment manufacturing were chosen as targeted industries because of their importance for California and their history of regulatory problems.

TIPP is jointly administered by the California Labor Commissioner’s Office and the U.S. Department of Labor, Wage and Hour Division . Participating agencies include the Department of Industrial Relations, Division of Labor Standards Enforcement ; the Employment Development Department ; and Cal-OSHA. During any given TIPP activity, up 10 twelve agencies may be involved. This coordinated approach helps to weave together what would otherwise be a haphazard patchwork of regulatory activity. Specific violations addressed by the TD?P inspectors include health and safety, farm-labor contractor laws , workers’ compensation insurance, and regulations pertaining to wage and hour requirements and record keeping. In spite of the importance of these efforts in promoting workplace welfare,hydroponic nft system responsible agencies have inadequate resources for enforcement, education, and epidemiological analysis that could provide insight into the patterns of violations and help focus agency efforts. The main research objective of this project is to characterize agricultural operations that have received notices of violation of health, safety, and labor regulations during 1993 and 1994 through TIPP and to identify patterns and risk factors for violation. Using a database of California farm operations developed and maintained by the California Institute for Rural Studies , we compared operations that received notices of violations through TIPP during 1993 and 1994 with chose that did not. This allowed us to develop a profile of operations at high risk for labor-law violations, identify and characterize risk factors, and describe patterns of violation. In addition, TIPP files were matched against the Licensed Farm Labor Contractor file to identify which TIPP citations were made to licensed farm-labor contractors, Regulatory agencies can use information profiling high-risk operations to target educational and enforcement programs within the agricultural sector. The program brings major benefit for both employees and employers. Farm operators who are in compliance with the law also benefit, because more widespread compliance means they are less likely to be competing with persons reducing operating costs through noncompliance. The state as a whole stands to benefit in that improved compliance brings about safer working conditions, leading to increased productivity and reduced lost-work time, medical expenses, and other associated losses. The general goal of this study was to identify agricultural operations receiving notices of violation through TIPP and compare them with operations that had not received notices of violation.

We linked reports of violations from the TIPP database for 1993 and 1994 to specific agricultural producers contained in a large database of over 37,000 California farm operators developed and maintained by the California Institute for Rural Studies. Through this linkage, we identified those producers with violations and compared this group to producers without violations. The results were used to develop a comparative profile of high-risk producers for the purpose of focusing educational and enforcement resources. We also linked the TIPP files to the CDIR’s Licensed Farm Labor Contractor file to identify which citations had been issued to licensed farm labor contractors. In this manner we were able to identify operations chat had received notices of violation through TIPP in 1993 and 1994 and identify them as farms, licensed farm-labor contractors, or unlicensed farm-labor contractors. For farmers we were able to compare cited operations with those that had not received notices of violation. Using standard statistical techniques, we compared these two groups to develop a profile of operations receiving notices of violation. Reports from the OSHA IMIS database and labor expense data were obtained from the relevant governmental agencies as described in this report.Data collection forms can be designed to facilitate data collection and accurate entry into computer systems for analysis. Design should include appropriate categories of violations- This process should be guided by considerations of how the data will be used. In particular, whether categories are broad and inclusive vs. narrow and precise will depend on how die data will be used from a regulatory stand point. If it is important to distinguish different types of violations, then a greater number of narrower categories will be required. Data forms can be prepared on optically readable forms. Scannable forms have a major advantage in that the completed form can simply be fed into a device that automatically reads the data and enters it into a computer for analysis. This process can save significant time, reduce data errors, and facilitate analysis and report writing. Efficiency could also be improved by Immediate on-site entry into laptop computers; these could also hold useful databases for on-site field use. Th duty of reports could be greatly increased by including further descriptive information relevant for the participating agencies. In the IMIS system, for example, information on number of employees and union status are included. Inclusion of descriptive information deemed relevant by the participating agencies would aid in understanding patterns of violation. Comment: Improved utilization of computers would allow more timely review of data and facilitate planning of agency activities. For example, data we analyzed for this report showed an increased risk of violations among berry producers. However, more recent information, conveyed to us in personal communications by our reviewers during the preparation of this report, suggests that current compliance among strawberry producers is high. Improved use of computers with short turn-around time for data review would allow agencies to react to changes as they occur. An inspection program utilizing an unbiased sample of local operations employing farm workers directly or through contractors would allow agencies to determine how commonly or frequently specific infractions occur. This would provide a truer picture than currently available of infractions among operations and allow agencies to develop educational, preventive, and enforcement strategies based on a more realistic view of infractions within the industry. In contrast, when information is based on complaints or leads, the resulting data represent a group of agricultural operations at high risk for violations; such a group is a biased sample- i-e., it is not representative of all agricultural operations. Similarly, operations that have not been inspected and cited may still have infractions of health-and-safety laws that have simply not been reported. Information on the true prevalence of specific infractions would be invaluable for developing policy and focusing resources, and information on true prevalence can only be obtained from a valid sampling system. A valid, unbiased sampling system ideally would involve random sample selection from a complete list of area operations. Although various state agencies maintain lists of agricultural operations for their purposes , the state does not maintain a comprehensive listing of operations utilizing farm labor. Developing and maintaining such a list requires ongoing commitment of resources.

Poor credit has been blamed for some of the overall reduction in farm output

One of the main differences between Russia and Ukraine where productivity fell and some of the CEECs, such as Hungary and the Czech Republic, where productivity increased strongly, is not so much the scale of the farm operations, but rather the degree to which their management structure was restructured. In the CEECs, farm enterprise budgets were hardened and on-farm decision making became independent and primarily concerned with turning a profit. Restructuring induced sharp shifts in input use, effective management reforms, and efficiency increases . In contrast, farm restructuring in Russia and in several other FSU countries has been mostly superficial. For example, while the outside imposition of production plans is officially abolished, local authorities continue to influence farm management through informal relationships . The inability to restructure farms has caused a decline of farm efficiency . Differences in restructuring are linked to land reform. In many CEECs land was either restituted to former owners, distributed to farm workers in delineated boundaries, or leased to new farms. Although the land reforms in these countries were complex and difficult to implement, they ended up with stronger and better defined property rights for the new landowners than most of the FSU countries. In the FSU countries, in contrast, land was distributed as paper shares or certificates to workers of the collectives and state farms. Individuals can not identify the piece of land that belongs to any given share, causing weak land rights for individuals and undermining their ability to withdraw land from the large farms. As a result, family farming is emerging only slowly,led grow lights and large farms have little incentive to restructure .Hence, there have been significant differences in the approaches of countries to land reform and the creation of property rights in rural areas.

Good rights and the incentives they created certainly were behind positive performance . Poor ones undoubtedly contributed to the poor performance and will continue to adversely affect future performance. All transitional countries also experienced some form of price and subsidy policy reforms. In China, leaders administratively increased agricultural output prices following decollectivization. The rise in prices in the late 1970s and early 1980s partly explains the increase in farm profits during this period, as it did in Vietnam a few years later . Price reform was much bolder outside of Asia. In most CEECs, leaders dismantled planning system by decontrolling agricultural prices and dramatically reducing subsidies in the early 1990s . In Russia, reformers also liberalized prices in the early reforms, although substantial subsidies to agriculture have continued. Unlike in the case of Asia, however, reforms did not lead to a large price increase and stimulate production. Falling incomes put downward pressure on domestic prices. In many countries the combination of the fall in the real price of output and the rise in the real price of inputs led to a crisis for the agricultural sector . Changes to the trading regime occurred at the same time and were equally decisive in all but the Asian economies. The collapse of the CMEA trading system led to trade disruptions in many countries, especially in those where CMEA trade integration was strongest. Access to some inputs was disrupted. The shift to hard currency payments for imports and falls in income led to a reduction in the demand for foreign agro-food products. In contrast, the early trade reforms saw only limited changes in China . Vietnam also slowly liberalized trade, but rice, one of the nation’s most important post-reform export products received special treatment by leaders. Disruptions in the agri-food chain compounded the agricultural crises in the countries of the FSU and the CEECs .

Pre-transition systems were strongly vertically integrated. The central planner directed both sides of transactions and enforced contracts involving exchanges between the various agents in the chain. The reforms removed state control over planning and resource allocation . In conjunction with reform in the rest of the economy, the strategy in agriculture involved rapid privatization and restructuring of upand down-stream enterprises. However the removal of the central planning and control system, in the absence of new institutions to enforce contracts, distribute information, and finance intermediation caused serious disruptions and negatively affected output throughout the economy . In this aspect China’s reforms deviate the most from those in other parts of the world . In the initial phase of property rights reforms, Chinese leaders chose not to disrupt agriculture any more by reforming the up- and downstream sectors. In essence, the procurement and input supply systems remained fully under the control of the state during the early reforms. The same state-run input supply channels that provided inputs to the communes channeled inputs to farmers. Likewise, the same procurement system that purchased the output of the commune and transferred it to the cities remained virtually unchanged and banks continued to finance these transactions. Sector officials carried out transfers of food from producer to consumer regions according to nearly the same plan as before reform. Even though the maintenance of the system of planned procurement and supply in China caused substantial allocative irrationalities, the benefit of such a strategy was that it did provide farmers access to inputs and product outlets during the period of property rights reforms and farm restructuring . With improved farm productivity it actually allowed increasing the supply of food to urban consumers. The leadership’s emphasis on stability mandated the gradual strategy . The deregulation of the input and output marketing was only allowed to take place at a more gradual pace several years after the initial reforms . The gradual liberalization strategy, called the dual track pricing system, allowed enterprises to reap the informational benefits from price liberalization while avoiding the disruption associated with the breakdown of the planning system.

It also allowed space for traders to slowly develop networks and figure out ways to finance commodity trade . The importance of creating new institutions to facilitate the exchange of inputs for outputs and the trade of commodities can also be seen by observing the differences in the recent performance of the CEECs and Russia. While output in Russia continued to decline, the output fall in the CEECs halted as these economies had begun to find new ways to undertake transactions in the food economy . In other words, an important source of increased productivity in transition economies has been the emergence of new institutions for information, product exchange, and contract enforcement. Such institutions have come in a variety of forms. The most successful ones have frequently depended on private enforcement mechanisms within the framework of specially designed contracts or institutional arrangements . Contracts between private agents act as substitutes for missing or imperfect public enforcement institutions . Successful institutions have offered enough flexibility to allow producers, suppliers,nft hydroponic and buyers to adjust to the continuously changing environment during transition. For example, while land lease contracts initially often took the form of short, single-season informal contracts, gradually they have evolved into more formal and longer-term contracts, reflecting reduced uncertainty and improved understanding of the market environment by both the owner of the land and the tenant. Leasing of equipment is another example of an institutional innovation adapted to transition as it mitigates farms’ collateral problems in financing new equipment. Foreign direct investment has played an important role in the reemergence of the institutions of exchange in some CEECs . Beyond supply of capital, foreign firms have offered producers a number of arrangements to encourage greater production and marketing and to overcome constraints that have limited economic activity since the onset of transition. For example, food processors have negotiated contracts with banks and input suppliers to provide farms with inputs that enable them to deliver high quality products to their company. Similarly, input supply firms have been involved with assisting farms to find guaranteed outlets for their products in order to stimulate farms’ demand for the company’s products.While an analysis of general transition is beyond the scope of this paper, it is impossible to ignore entirely the economy outside of the agricultural sector because it has had important effects on agricultural transition. General reforms have affected agricultural transition in a variety of important ways. We focus here on some key aspects. First, macro-economic stabilization, including the reform of fiscal and monetary institutions, is an essential element for sustainable growth. A recent comparison of overall performance of the CEECs and FSU countries concludes that rapid overall liberalization and sustained macroeconomic stabilization have laid the basis for gradual institutional change in the more advanced transition countries . At the same time, the report states that the persistence of soft budget constraints in less advanced countries has jeopardized stabilization. But far from disappearing, the state has a major role to play in the formation of macroeconomic policy.

Transition from a socialist to a market economy does not imply a withering away of the state, but rather a fundamental redefinition of the role of the state in the economy . The state must play a strong and leading role in developing market institutions. Interestingly, both in China and in the most advanced CEECs, the state has been able to do this, although often in different ways. In Russia, however, the state has not redefined its role. Instead, in many aspects, the Russian state has withered away and has been unable to fulfill some key roles for the development of a market economy . The state has eroded in establishing the rule of law, as private enforcement has gained the upper hand in Russia . In many sectors, non-state entities have begun to collect taxes and establish the basic conditions for macro-economic stability. For example, estimates put the share of transactions which are carried out as barter or with money substitutes at 75 to 85 percent in Russia . Obviously these developments have strongly affected the climate in which the agricultural transition has taken place, since farm producers, like agents in any industry, need stability, clear legal codes, and can benefit from investments made by the state. The nature of a good macro-economic environment may also attract or deter outside interests from entering the local economy. For example, the flow of FDI and foreign technology and know-how into the agri-food chain has been important in the CEECs, but less so in Russia. One of the main reasons for the high level of outside interest in some CEECs has been narrowed to the progress of the general reforms, the macro-economic situation, and the prospect of EU accession . Macro-economic stabilization and reform progress have not only improved access to foreign capital, technology, and know-how, but also access to domestic credit and capital sources for the farms. Credit markets have developed notoriously slow in the CEECs and FSU countries. Disruptions by privatization and overall restructuring severely limited farm credit access for investment purposes and working capital .The success of the recovery in some CEECs is at least partially due to improvements in the capital situation for the farms. Enterprise privatization also has had important impacts on agriculture. While land reform and farm privatization procedures were specific to agriculture, the privatization of companies involved in supplying inputs and credit to farms as well as food processing and distribution companies followed the general privatization procedures, a process that has differed significantly among countries. In a review of the successes and failures of privatization, Kornai concludes that three aspects of privatization strategies contributed to successful privatization. First, it is necessary to create favorable conditions for bottom-up development of the private sector, including the creation of new companies. Second, selling of state companies to strategic investors has led to more satisfactorily results than strategies based on some form of give-away, e.g. through vouchers. Finally, de facto privatization occurred by hardening the budget constraints of companies . All of these strategies have had a major impact on the performance of the agri-food sector. For example, Hungary, a country that has sold most of its food processing companies to foreign investors, received the highest per capita inflow of FDI in the agri-food sector, and has consequently experienced some of the greatest growth in output and productivity . Finally, external economic conditions, as well as government policies, have affected the outflow of labor from agriculture during transition, a trend that has major implications for agricultural productivity and rural incomes.

The model includes a non-linear term for investments in infrastructure

The remainder of the paper is organized as follows: Section 2 reviews previous works and places our paper within that literature. Section 3 develops the econometric methodology, departing from the previous published work on agricultural knowledge that is reviewed in Section 2. Section 4 describes the data and variable creation. Section 4 reports the empirical results, and Section 5 presents the conclusion and policy implications.The knowledge production function has various applications at societal and sectoral levels. A recent published theoretical framework addressing the role of knowledge in society’s growth was developed by Dolgonosov . Distinguishing between technological knowledge and general total knowledge, the author demonstrated that knowledge is essential to allow sustainable population growth within the carrying capacity of the planet. The role of knowledge production is essential, especially with the increasing population and environmental load. This framework suggests that society could introduce policies to improve the efficiency of knowledge production in various sectors. The literature distinguishes also between knowledge of various qualities. Cammarano et al. introduced the notion of quality of innovation output, using patent data from bio-pharmaceutical and equipment-producing companies. The analysis suggests a more productive knowledge process in which innovative firms use knowledge and information produced by external sources. Working on a related industry, Lauto and Valentin estimated a knowledge production function for what was coined the new science development model for clinical medicine,hydroponic dutch buckets in which research can be conducted in a transnational effort, or locally.

This is a very interesting distinction that may indicate the efficiency of transnational simultaneous research benefitting from a variety of conditions and its superiority to knowledge spillover of research conducted separately. However, the authors find that by its nature, transnational research may have lower efficiency and impact because it includes diverse aspects in quantitative comparisons. Some surprising findings are offered by Roper and Hewitt-Dundas , who estimates the interaction between knowledge stocks and flows and their impact on the firm’s innovation. They found that negative rather than positive effects between knowledge stocks and innovation , and knowledge flows dominate the effects of knowledge stocks on the innovation of the firm. Several works address the issue of networking and proximity among the knowledge creation centers , and the effects of collaboration within and between regions on knowledge productivity . Both works were applied to Europe. Ramani et al. develop a model of knowledge production function that can be estimated at both the firm and the sector level and apply it to the bio-food industry. The production function in this work allows to distinguish between the absorptive capacity to exploit inter- and intrasectoral spillovers. Marrocu et al. found that technological proximity outperforms the geographic proximity, suggesting that networking has a limited role in enhancing knowledge creation. The most relevant finding of De Noni et al. to our work is that the impact on knowledge productivity is stronger in the case of collaboration between regions with diversified knowledge base. From a different perspective, Verspagen and De Loo addressed the spillover effect of knowledge, both across sectors and over time using a knowledge flow matrix. The methodology is very relevant for knowledge production investments, but it is heavily dependent on data that might not be readily available everywhere. Two examples of recent studies that address spillover effects in knowledge production are Wang et al. and Neves and Sequeira . Wang et al. estimated the spillover effects in the semiconductor industry to find that the strength of the networking ties between companies explain the level of spillover effect in the knowledge production process. Spillover effects are expected to be stronger in weaker network ties. Neves and Sequeira conducted a meta-analysis of data from 15 published works to find expected, but reassuring results.

They quantify level of spillover effects and discover that the spillover effect will be larger when they include in the estimation of the knowledge production foreign inputs, and it will be lower when only rich economics are included in the estimation. Finally, universities are considered a hub for knowledge production, based on research conducted in addition to their role as educational institutions. Gurmu et al. used patents issued to universities during 1985–1999 as a measure of knowledge. They explained variation in knowledge by field of knowledge, R&D expenditures , as well as detailed human capital variables, and several control variables. Their results indicated marginal contribution of each research variable to the production of knowledge. While the literature review is by no means inclusive, it represents the many efforts that have been made in the literature for understanding the determinants of knowledge production. We will rely on these works while developing our analytical framework.The literature suggests that agriculture-related R&D inputs result in the production of knowledge, which upon application leads to improvement in productivity in the agricultural sector. Alston et al. , Birkhaeuser et al. , and Evenson estimated the impact of R&D and extension-related expenditures on agricultural productivity. The underlying theory is that expenditures made towards R&D and outreach impact productivity, and that impact of research expenditures is differential; old expenditures have a lower impact on current productivity. Evenson and Birkhaeuser et al. reported positive impacts of both R&D and cooperative extensions on productivity for studies from around the world. While these studies provide strong evidence of a long-term impact of R&D-related expenditure as well as the impact of farmer-extension agent contacts on productivity, there is a gap in our understanding of how well these proxies for agricultural knowledge represent actual knowledge produced. This is understandable because measurement of knowledge produced from investments in R&D is conceptually and computationally complicated. Griliches discussed the issues of measurement of knowledge production between public and private sector investments in R&D. He claimed that patents are a good approximation of knowledge and innovation, especially because of the commercial value attached to it. An industry or a firm likes to file for patents to have sole right on its invention and is paid for its use by others. Pavitt mentioned that patents are good proxy measures of innovative activities. Other studies have used patents as proxies for knowledge production.

Data on patents are well documented in the United States and in the rest of the world and are easily obtainable without the hassle of conversion of units. In the industrial sector, knowledge produced through research is mostly owned as private property by the innovating firm because of the related commercial incentive of private property ownership. This makes patents the most appropriate proxy variable for knowledge production function analysis in the case of private sector research. However, publicly funded research and especially agricultural research creates knowledge, most of which is publicly available. Pardey and Dinar used publications as a proxy for knowledge production. Publications are more prevalent in public research agencies, where research results are typically published in journals. Dinar used peer-reviewed journal publications in different fields as the dependent variable for his study of the agricultural research system in Israel. According to Pardey , publications have been chosen over patented and non-patented output like mechanical innovation processes or new biological material, books, State Agricultural Experiment Station bulletins, and newsletters. Publications capture the knowledge output of a station completely because they establish intellectual property rights of the researchers over their work,bato bucket which in turn affect their salary scale, promotion rate, and tenure status. Link analyzed the determinants of inter-farm differences on the composition of R&D spending, namely basic and applied R&D. He regressed these R&D components on profits, diversification, ownership structure, and subsidies. Jaffe found a significant positive impact of university research on corporate patents for a number of technical areas, such as drugs and medical technology, and electronics, optics and nuclear technology in the United States. The literature on the topic leads us to two main observations: a dearth of papers that deal with the analysis of the knowledge production function and the study of the impact of production inputs on knowledge produced; and the choice of variables representing knowledge produced through investments in R&D only provides a partial picture of the true process. There is little attempt to compute a comprehensive knowledge production variable that captures knowledge produced through all avenues. UCCE follows an input-output framework for research, which involves utilization of research inputs such as manpower and infrastructure, for the production of knowledge to be disseminated to potential clientele from a variety of different sectors. This knowledge is produced through basic and applied research, and extension work, which are targeted to address the needs of the clients at the county level. Agricultural knowledge that is generated by UCCE is public in nature and is freely available to all.

Because of this, it seems appropriate to use various types of peer-reviewed publications by advisors as the representative variable for knowledge. But publications are only a part of the total knowledge produced; there are other modes by which knowledge is produced and disseminated by UCCE. These need to be incorporated into the analysis to capture a more complete representation of the generated knowledge. To achieve this, we collected data on eleven different modes by which UCCE produces knowledge, all of which are aggregated to the county level to create a knowledge index that captures all UCCE knowledge produced.ichotomous variables representing county fixed effects are introduced in the model to control for factors that are common to a county, and possibly impact productivity. Year fixed effects can control for random shocks, e.g., budget surplus leading to a recruitment of more skilled advisors in a particular year, which may have led to larger number of total knowledge produced across all counties in a single year.This is included to capture possible diminishing marginal returns to infrastructure. Expenditure on infrastructure can be beneficial to knowledge production, but after a certain degree of provision the marginal effect may diminish. It makes little sense to keep building laboratories and offices if there are no researchers or staff to fill them. We follow Roper and Hewitt-Dundas , who introduced the plant size as a quadratic Schumpeterian resource indicator, which has also been shown by Jordan and O’Leary to have an inverted-U shaped relationship with knowledge production. A similar specification by Charlot et al. lumps all R&D costs in a quadratic relationship due to economies and diseconomies of scale. The quadratic specification of infrastructure expenses means that over-investment in research infrastructure may turn to be counter-productive and to result in diminishing marginal productivity of knowledge production. To test this hypothesis, the square term for log of infrastructure expenses was included in our model. The choice of the log-log model for the empirical analysis is to facilitate the computation of output elasticity for each of the inputs of production.The University of California Cooperative Extension was established a century ago with the purpose of educating the citizens about agriculture, home economics, mechanical arts and other practical professions.2 Through the course of almost a century since the Smith Lever Act of 1914, the UC Cooperative Extension has grown into an elaborate system that has branched out from handling mainly farm related issues to many other aspects concerning the farm, as well as the overall society. Extension advisors communicate practical research-based knowledge to agricultural producers, small business owners, youth, and consumers, who then adopt and adapt it to improve productivity and income. Today the UCCE works in six major areas,including Agriculture, 4-H Youth Development, Natural Resources, Leadership Development, Family and Consumer Sciences, and Community and Economic Development. This paper focuses on UCCE activities in agriculture. The University of California Division of Agriculture and Natural Resources headquartered in Oakland, California, is the source of data for the analysis in this paper. We collected annual budget data from the database for all UCCE county offices for the period of 2007 to 2013.Our data set includes complete data for seven years for 47 county offices, which serve the 58 counties in California. There are six groups of two counties each, which are served by a single county office. And there is one office that serves four counties. Upon comparing older UCCE budget data with real expenditures, we found that they follow similar time trends for each county office and could be used as proxies for expenditures. This data was converted into constant 2013 US dollars, using GDP deflator data from the World Bank database and is presented as such hereafter.Henceforth, we will refer to the UCCE budget as expenditures, to avoid ambiguity.

One advantage of this process-based approach lies with its reliance on simple regressions

Several California crop disease climate models are in development and are available, including fire blight , scab , alternaria leaf blight , and brown rot . For the farmer, potential adaptation strategies for pests include choice of crop, growing season, manipulative cultural practices, fertilization, pest control, and irrigation, or a combination of these , many of which are currently used to control weeds in agriculture. Yet, there are often trade offs involved that can benefit pests as well . An effective adaptation plan depends on accurately casting predictions, but such predictions are difficult when the impact of undesirable organisms is based on a complex network of interacting factors. Maladaptation can result in negative effects that are as serious as the climate change-induced effects being avoided . Nonetheless, two endeavors stand out as productive methods of ultimately reducing the impact of invasive plants and weeds in California’s changing climate: an increase in our understanding of interactions in an ecosystem context and increased vigilance. Though many competition experiments have been conducted on the effect of rising CO2 on weed-crop competition , both our understanding of how such effects change in an ecosystem context and how such an effect interacts with other aspects of climate change is rudimentary and is insufficient to formulate respectable predictions in California’s future climate. This is further confounded by uncertainty associated with future precipitation patterns and those of El Niño events in California. As a second adaptation, increased vigilance will serve to identify new invaders early, thus dramatically increasing the potential for successful eradication . In terms of increased vigilance,nft hydroponic the “guilty until proven innocent” approach in which each threat is assumed to be dangerous, shows promise.

Where resources are limited, likely problem areas should be targeted, such as disturbed habitat, especially along roadsides and other dispersal corridors, and points of entry. The impact of climate change on pest and disease outbreaks is difficult to predict because it involves changes in both the vigor of the predator and the vulnerability of its prey. Plants do not experience climate change alone, but as part of a wider ecosystem incorporating their pests, pathogens, symbionts and competitors . Although arthropod pests and weeds do interact with each other, strategies aimed at managing one or other of these classes of threats, rarely consider such interactions . Furthermore the great diversity in commodities produced in California, coupled with the abundance of natural vegetation and weeds can provide an important refuge for pests and diseases causing microbes to survive in, at times when their primary crop host plant may be absent. Species with small geographic ranges are more vulnerable to climate change than widespread ones . This is also true of specialist versus generalist pest species. One possible adaptation is to modify planting dates or the selection of cultivars that are resistant to emerging pests and disease causing microbes. As with weeds this dictates the need for vigilance and accurate predictions of pest/disease outbreaks. Implementation of multifaceted pest and disease management strategies such as those applied in IPM will likely enhance the adaptive capacity of producers in a changing climate. Many of the strategies currently used to control disease and pest outbreaks will likely be successful in the climate of the future. Human responses to climate-induced pestilence need to be adaptive and inventive. Agricultural pest control is already a complex and expensive endeavor. For example, increased pesticides are an obvious adaptation; however, this approach has many drawbacks .

When combating Pierce’s disease, for example, in addition to conventional methods such as inspection, pesticides, and host removal, other technologies that are being employed to better control the disease in California, including biological control, sequencing the pathogen genome , and identification and breeding of disease resistant vines . In order to buffer against the unknown interacting effects of climate change, bet-hedging strategies should be used that reduce host pools such as maximizing spatial and temporal crop intra-specific genetic variation . The judicious use of genetic technologies may also prove important in stemming invasions and epidemics by adding to our range of available tools to deal with such challenges. Issues of precipitation are critical. A warmer drier California will likely have a very different pest, weed and disease landscape than a warmer wetter California. Furthermore, research is needed to understand the effects of climate change on the ecology and evolution of agricultural pests. The effects of climate variability on coevolution, virulence, and resistance to control methods are at best poorly understood. For example, does the efficacy of taxon-specific chemical control shift, if at all, in warmer and/or more variable environments? This question is important across all taxonomic levels, from vertebrate pests to microbial pathogens. Changes in competitive balance and trophic interactions are difficult to predict for future climates. Nevertheless, field experiments can be conducted across existing climate gradients representing current and future conditions. Such studies are lacking. Landscape surveys are also instructive in pointing out the value of non-crop habitat in pest control, and in determining spatial and temporal gradients that affect pest distribution . The effect of higher temperatures on overall abundance of herbivorous insects remains unknown in the absence of equivalent data of their natural enemies . Furthermore, efforts to link information specific to California weather to disease and pest outbreaks are limited in their number .

Concerted efforts are needed to monitor and compile data, including historical records. The development and validation of prescriptive control models depend on these data. Currently, climate disease models in California are developed on an as-needed basis with temporary funding often provided by private agricultural interests . Hence, no long-term efforts or programs exist. Increased development is necessary in the use of continuing programs such as the disease warning systems recommended by Wu et al. . Long-term sharing, coordination, and modeling of pest outbreak and environmental data among the diverse climate regions within California would greatly improve our understanding and ability to prepare for, adapt to, and mitigate against future pest risks and disease causing agents. Pests and pathogens that may become significant in California agriculture need to be identified and appropriate quarantine and inspection measures implemented to avoid introduction. Looking to other regions where the climate is similar to that predicted for California in the coming century will also likely be instructive.Land use refers to the management regime humans impose on the biophysical attributes of the earth’s surface. Temperature or rainfall patterns associated with climate change may alter land use and land-cover distributions ,nft system and consequently basic patterns of productivity, stability, and sustainability in agroecosystems . Conversely, the effects of human-induced greenhouse gas fluxes and C sequestration that is attributed to land use and management can, in turn, impact the rate and magnitude of climate change . For example, cultivation of forest and grassland soils accounts for approximately 25% of the net loss of C in the United States, while N fertilization, no-till farming, and grassland restoration have only slightly reduced these losses . Issues of agricultural land use change are particularly interesting in regions with Mediterranean-type climates; they have typically experienced high population growth, urban expansion, and decreasing self-sufficiency in terms of producing their own food, due also to the export value of the many specialty commodities they produce. In California, these issues raise questions related to the sustainability of agriculture, both economically and environmentally. Given the potential growth of California’s population to 9 million people by the end of the century, urbanization is probably the single largest factor driving land use change in California’s agricultural landscapes, farmland loss, and the increasing utilization of wetlands and riparian corridors that serve as wildlife corridors .

Urbanization could result in a loss of 35% of the prime agricultural land in San Joaquin Valley counties, and much of the remaining agricultural land in coastal counties, even when climate change is not considered in the projections . This section will 1) introduce the approaches commonly used to assess climate change effects on land use, 2) discuss the fundamental drivers of land use change, and 3) evaluate knowledge gaps in current mitigation and adaptation strategies for climate change-induced land use shifts in California.Climate change impact assessments commonly employ a hierarchy of models which, ideally, are integrated to simulate the most important processes, interactions, and feed backs in the systems. At the top of the hierarchy are Global Circulation Models , which simulate global climatic patterns on a grid with cells sized between 2 and 9° longitude and/or latitude and several vertical layers thick. Results from GCMs are then used as inputs to biophysical models, which also rank at the top tier of the hierarchy. Outputs from biophysical models are subsequently used as inputs to economic models at, for example, the farm level . Models at the regional scale are more suitable to estimating climate change effects on land use. While some GCM predict gains of 20-50% in potential agricultural land for North America , regional models provide projections at greater resolution and detail. Regional models have forecast that certain crops will be forced to shift out of their current geographical range due to increasing temperatures , but these losses in productivity may be partially offset by increased productivity from increased CO2 levels . Other crops especially C4 plants might suffer lower yields due to elevated atmospheric CO2 levels , though California produces few C4 commodity crops. As Section 6 points out, less is known about how temperature and CO2 concentrations affect key developmental phases of horticultural crops, and thus their vulnerability to climate change. Climate analogs can provide some insights into land use change. Using the hot, dry decade of the 1930s as an analog of the possible climate that might occur in the Missouri, Iowa, Nebraska, and Kansas region as a consequence of climate change, Easterling and Apps modeled crop responses. They found that farm management changes and slight increases in productivity of some crops, for example, irrigated wheat, could eliminate 80% of the negative impact of the analog climate, thus minimizing potential land use change. In California, an analogy of climate change, the drought of 1987-1991 demonstrated that farmers increased their reliance on ground water, adopted water-conserving technologies, reduced water use per acre, moved away from water intensive crops, and fallowed more land . The drought instigated the official approval of water trading and demonstrates how extreme events can trigger rapid changes in land use and social institutions that increase adaptation to climate change. Different approaches have been used to predict climate change impacts on the agricultural landscape, sometimes resulting in very different outcomes. The first approach is a process-based one that arbitrarily or synthetically forecasts a specific climatic change by varying temperature, precipitation, or another model parameter and is likened to a simple sensitivity analysis .Some weaknesses of this approach include 1) the utilization of significant amounts of primary data that are constrained in time and/or space; 2) requisite stable equilibrium conditions; 3) omission of changes in crop physiology and ecosystem productivity, adaptive human behavior, and land use; and 4) neglect of interactions with land use and responses to environmental change . The California SWAP/CALVIN model is similar to this approach, and it predicts relatively feasible changes in terms of crop management and land use change to maintain crop productivity . A second approach models the responses of crops and farmer behavior based on extrapolation of responses of varying climates observed at other sites to the system of interest, and does not necessarily consider unique adaptations that may increase success during transition to a new climate regime . This latter approach is more akin to the approach of Hayhoe et al. . In this case, predicted effects of climate change on wine grape production are more negative than what would be indicated by the SWAP/CALVIN model, suggesting more problems associated with adaptation, and greater changes in land use patterns. Thus, different potentials for land use change emerge from different modeling efforts. More work is needed to improve the accuracy of modeled forecasts of climate change, and to produce results that are accessible and will allow a wide range of user communities in agriculture to adapt to climate change.