Subsequent studies have largely confirmed these initial estimates

It is worth pointing out that that while using nutrients much more intensively, corn growth in TX does not generate a substantially larger eutrophication impact than cotton. This is because nutrient runoff and leaching rates of corn in TX are generally smaller than that of cotton . For all states, as with the average situation in Fig. 2.1, land shift from cotton to corn would relieve freshwater water ecotoxicity impact. In summary, our study calls for an attention to policy-induced land cover change from cotton to corn and associated environmental issues. In doing so, we demonstrate that average data reflecting national situations are inadequate to capture the likely environmental impacts of corn expansion into cotton on marginal land at regional level. Our results for three states North Carolina, Georgia, and Texas show that corn expansion into cotton in the South relieves freshwater ecotoxicity but may aggregate many other regional environmental impacts. Overall, our study confirms the earlier studies that demonstrated the importance of understanding “marginal” impacts in LCA : environmental consequences of the policies that encourage converting cotton to corn cultivation in the regions where corn is generally less suitable to grow cannot be understood by comparing average environmental profiles of cotton and corn. Our results also favor “consequential thinking,” as an analytical paradigm, in bio-fuel LCA,30 plant pot while our study is not intended to demonstrate how to perform a “consequential LCA,” as an operational model .

Corn ethanol, supported by several federal policies as a means of reducing GHG emissions by displacing gasoline , has been a point of heavy dispute in the last decade . However, it has become increasingly clear that although corn ethanol may have the potential to combat climate change , its large-scale expansion is reported to generate adverse environmental consequences including, notably, direct, and indirect land use changes .These adverse consequences, first, undermine the climate objectives of the public policies. Second, for intensive use of agrochemicals and irrigation water, corn expansion adds to the pressure on local water quality and scarcity issues . Our study focused on yet another consequence related to ethanol expansion, namely, land cover change from cotton to corn, and analyzed the potential implications of such change for local environments. Contrary to the previous view that land shift between cotton and corn, both high-input crops, may cause negligible net environmental impacts , our study revealed a more complex picture. Although land switch from cotton to corn relieves ecotoxicity, it likely aggravates other various environmental problems depending on where the crops are grown. Note that our study only covers part of the effects bio-fuels policies have generated on crop conversions. To understand the overall environmental impacts of bio-fuel policies through crop conversions, further research is needed to estimate the environmental aspects of other crops affected, particularly soybean , and the magnitude of land shifts between the crops. Our results highlight the importance of potential, unintended consequences that cannot be adequately captured when average data are employed. Understanding the actual mechanisms under which certain policy induces marginal changes at a regional and local level is crucial for evaluating its net impact. Our results also show the importance of recognizing potential trade-offs between environmental objectives in policy making.

Climate policies focusing narrowly on carbon, for instance, could shift burden to regional issues like water scarcity and eutrophication . Therefore, environmental policy making should attend to not only unintended effects within its targeted problems like the indirect LUC effect , but also those across impact categories to avoid or minimize burden shifting across impact categories. Also, our study reinforces previous research with respect to spatial variability in agricultural systems . Unlike industrial systems, agricultural systems are subject to the influence of weather patterns, soil type, geography, and management practices. Even the same agricultural product may have drastically different input structures, hence environmental impacts, in different regions. Therefore, average data with generic descriptions of material and energy fluxes are hardly adequate to capture the high degree of system variability of agricultural products. With the rising interests in bio-fuels as a means to combat climate change across the world, we strongly recommend future studies in this area to take into consideration the spatial variability of biomass growth. Just as technological and environmental variability exists across states, there is probably certain variability within a state, too, that may not be precisely captured by state average data. This does not mean, however, that state-level data should be dismissed for the research question at hand because they are still likely more reflective of local or farm-level practices than national averages. In addition, state average data are especially valuable and representative, more so than farm-level data, in situations in which massive land shift between crops takes place within a state. Nevertheless, we encourage finer-scale, more detailed studies into land shift between cotton and corn and associated environmental impacts, which could not have been conducted in our analysis due to the data limitation and resources constraints.

Additional research is needed to paint a more complete picture on the impact of cropland conversion to corn: In 2005, 41 states grew corn and 17 states grew cotton, among which only 19 of the corn-growing states and 7 of the cotton-growing states had data on major inputs that can be used to generate LCIs . Among these states, only three overlap, namely, North Carolina, Georgia, and Texas. Therefore, this study does not quantify the environmental impact and their trade-offs in other cotton-growing states where conversion to corn might have happened. Nevertheless, environmental implications of cotton-to-corn land shift in these other states are probably worse than that indicated by Fig. 2.1 and closer to that indicated in Fig. 2.2 because cropland in southern states are generally less suitable for corn growth than the Corn Belt. Future studies pursuing this line of research may make the effort to quantify the magnitude of land shift in each cotton-growing state when relevant data on agricultural inputs, environmental outputs, and acreage of conversion become available. Furthermore, it is worth noting that spatially detailed data are often unavailable or incomplete, although such data can improve the environmental relevance of an LCA study. In this case, one may rely on assumptions or spatially generic data to fill the gaps,grow raspberries in a pot and this may increase the uncertainty of the LCA results . In our study, data on agricultural inputs such as fertilizers and pesticides were available at the state level, but we often relied on spatially generic emission factors to estimate their emissions . Further, the LCA results for corn and cotton were found to be moderately sensitive to the emission factors which are likely to vary across regions . Future spatially explicit LCAs on agricultural systems may take this into account and direct efforts to estimate spatially differentiated emission factors.For the potential to mitigate climate change, reduce dependence on oil imports, and invigorate rural economic development, bio-fuel development in the USA has been supported by an array of policy measures . Among them is the federal Renewable Fuel Standard , a mandate that requires 140 billion liters bio-fuels to be produced annually from different sources by 2022. Corn ethanol is currently the primary bio-fuel and is likely to continue dominating US bio-fuels market as cellulosic and other advanced bio-fuels are far from mass production . Driven by the favorable policies and high oil prices, corn ethanol production has increased eight-fold since 2000, to the current level of about 50 billion liter per year.

Early Life Cycle Assessment research on corn ethanol was largely in support of the policies aiming partly at reducing greenhouse gas emissions. As is typically done in LCA, these studies quantified GHG emissions generated at each stage of corn ethanol’s life cycle, summed them up, and then compared the results against that of gasoline. Corn ethanol was found to have 10–20 % lower life cycle GHG emissions than gasoline and, therefore, concluded to provide a modest carbon benefit in replacing gasoline . However, the conclusion was later called into question, when the land use change effects of corn ethanol expansion emerged in the literature . Converting natural vegetation or forestland to corn field for ethanol production releases a substantial amount of carbon from soil and plant biomass, creating a “carbon debt” that could not be repaid in dozens of years or even a century . Similarly, diversion of existing cropland for ethanol could generate indirect LUC effect through market-mediated mechanisms . In this scenario, corn ethanol expansion reduces food supply, which could lead to conversion of natural vegetation or forestland elsewhere in the world to compensate for the diverted grains. While the concept of iLUC has become widely accepted in academic and policy arenas , quantification of iLUC emissions is known to be difficult and highly uncertain . Plevin et al. , for example, estimated the range from 10 to 340 CO2e MJ−1 y−1. This wide range is due in large part to a lack of quality data and detailed understanding as to how the global agricultural market would respond to bio-fuels expansion . In contrast, the direct land use change emissions can be relatively accurately quantified . Previous studies used the concept of carbon payback time to measure the magnitude of dLUC effect of corn ethanol. While the initial carbon debt due to land conversion may be large, it can be repaid over time by the annual carbon savings corn ethanol yields in displacing gasoline because corn ethanol has lower life cycle GHG emissions. The first dLUC study estimated that 48 years would be required for corn ethanol to pay back its carbon debt if the Conservation Reserve Program land is converted and that 93 years would be required if central grassland is converted .Gelfand et al. conducted a field experiment on CRP land conversion to measure its carbon loss. They found that approximately 40 years would be required for the use of corn ethanol to pay back this carbon loss with the converted land under no-till management. In another study, Piñeiro et al. arrived at a similar estimate of approximately 40 years for the payback time for CRP land conversion to corn ethanol. However, these studies were based on several oversimplifications that may substantially affect their results. First, these studies assumed that newly converted land has the same yield as existing cornfields, neglecting the potential yield differences of newly converted land. In particular, CRP land is generally less fertile than cornfields that have been in continuous use . Thus, corn ethanol from CRP land generates lower annual carbon savings, hence a longer payback time. Land with extremely low yield may even fail to provide any carbon savings, in which case the carbon loss due to land conversion is permanently lost. Second, the dLUC studies relied primarily on life cycle assessments based on early bio-fuel conversion processes that did not reflect the productivity improvements that have occurred in the past decade due to yield and energy efficiency increases at both the corn growing and ethanol conversion stages . Recent studies have shown that corn ethanol’s carbon benefit has increased to up to 50 % , compared with earlier estimates of 10–20 % . The productivity of the gasoline production system over the same period of time has been fairly steady . The productivity improvements in the corn ethanol system result in greater amounts of annual carbon savings that, if considered, would yield a shorter payback time than previously estimated. Finally, the dLUC studies used the global warming potential 100 to assess the global warming impact of corn ethanol, gasoline, and dLUC emissions. This approach assumes equal weights to GHGs emitted at different times. More recent literature explores the application of different weights to GHG emissions emitted in different times. First, from a scientific point of view, increasing background GHG concentrations in the atmosphere result in a diminishing marginal radiative forcing for a unit GHG emission . The rate at which the relative radiative forcing effect of a unit GHG emission diminishes depends on future atmospheric GHG concentrations.

Analyses were repeated with creatinine-adjusted values to confirm our bivariate results

We then used this model as the foundation for simulation experiments which compare the effects of alternative scenarios regarding agricultural trade and support policies, both before and after accession to the European Union. The purpose of these experiments was to analyze the interactions between the accession “contract,” transition policies, sectoral perfonnance, and the pace of enterprise restructuring. A robust conclusion of the model is that the long-term health of the agricultural sector in these nations is likely to depend more on the choice of transition policies than on the terms of accession to the EU. The defining feature of successful transition programs is that they provide some form of subsidy to long-term investment, some mechanism by which landowners can overcome credit constraints and enhance the productivity of their enterprises. Mechanisms involving price supports and tariff barriers do have this desired effect. This result follows from the theory of the second-best, due to the presence of the distorted credit market. At the same time, however, and somewhat counter-intuitively, these distortive policies create price instability. Free trade can substitute for price support as a market-stabilizing mechanism,container raspberries operating more effectively and at lower cost. Both distortive and laissez-faire approaches are dominated by policies that address the credit constraining directly by subsidizing credit.

Such targeted approaches provide superior outcomes at lower cost. Our results also have a methodological implication, viz., that static analyses, or analyses that assume near-equilibrium market behavior, can fail to pick up or properly to address the importance of the transition dynamics associated with enterprise restructuring. A robust conclusion of the model is that land will tend to shift toward large, efficient holdings. This outcome reflects the lower effective interest rates available to these units. Thus, not only the availability of long-term credit, but the price of short-term credit, are central determinants of the model dynamics. The shift in land towards large farms also reflect to some degree the model’s inability to capture the advantage of smaller units in production of commodities such as vegetables. On the policy front, our analysis suggests that a focus on achieving “convergence” with EU norms may constitute an unwise distraction from the real business at hand: to create the conditions for enterprise restructuring that will improve the productivity of land and other factors. The central problem with such thinking is that it confuses the behavior of developed nations with behavior that will make a nation develop. It is no more intelligent for the CEEes to undertake the burdens of lavish agricultural price supports than it is for the poor to spend their scarce resources on champagne and caviar in the hope of thereby becoming rich.

A desire for structural alignment with the ED in no way implies the advisability of policy alignment during the transition period. At the same time, we find a basis for rejecting the laissez-faire approaches advocated by “Big Bang” theorists. Indeed, in a situation in which market institutions are badly underdeveloped, price support can provide a mechanism-albeit a very inefficient one-to counter the deleterious effects of these imperfections. Governments can play their most constructive role, however, by fostering the creation of functional market institutions that allow for productivity increases. Identifying the factors that impede such improvements, and designing the mechanisms to correct them, should be the goal for future research on agricultural policy in transition economies. The first task is to take a careful, elaborated look at enterprise restructuring, and of the factors that determine farmers’ investment behavior.Public health concerns about pesticide exposure to young children have received increased attention following the publication of “Pesticides in the diets of infants and children” in 1993. In 1996, the U.S. Food Quality Protection Act required the U.S. Environmental Protection Agency to set food tolerances that account for dietary and non-dietary exposure and protect sensitive populations. Biomonitoring studies have confirmed that children are widely exposed to pesticides, including organophosphorus , pyrethroid, fungicide, and organochlorine pesticides. Diet is an important source of pesticide exposure in children. For example, Lu et al.reported that the median urinary concentrations of the specific metabolites for malathion and chlorpyrifos decreased to undetectable levels after the introduction of organic diets in school-aged children. Several studies have confirmed that children may also be exposed to pesticide contamination in home and daycare environments. Children living in agricultural areas may also be exposed to pesticides through drift during applications or volatilization from nearby fields and parental take-home exposures. Lu et al. found that children who live in agricultural communities had five times higher OP metabolite levels in their urine compared to children who resided in non-agricultural communities.

These researchers also found higher residential OP pesticide contamination and/or elevated urinary metabolite levels in children living near orchards. Higher exposure to children living in agricultural areas has raised environmental justice concerns and has resulted in proposals to define farm worker children as a vulnerable population that need additional protections by the U.S. EPA. Identifying pesticide exposure determinants is needed to identify sources and pathways of pesticide exposure in children and contribute to policies aiming to reduce exposure. To date, no longitudinal studies have investigated factors associated with pesticide exposure in very young children. We hypothesize that exposure factors will vary over time given the changes in diet, behavior, and family practices that occur as children age. In this study, we report levels of OP pesticide metabolites in 6, 12, and 24 month old children participating in the CHAMACOS birth cohort study in the Salinas Valley of California, an agricultural area. We examined potential determinants of exposure associated with OP urinary metabolite levels at each age point, including sex, child behavior, diet, home pesticide use, season, parental work status, and proximity of homes to fields. We focused on OPs because they are commonly used in the Salinas Valley and were the first pesticide class re-examined under the FQPA. Mothers were interviewed when the children were 6, 12, and 24 months old. Interviews were conducted in Spanish or English by bilingual interviewers. Information collected included demographics, household enumeration, occupational status, whether work clothes were worn into the home, home pesticide use, presence of pets, daily servings of child fruit and vegetable consumption based on a modified food frequency questionnaire, time spent in child care, location of child care relative to fields, and frequency of hand washing and how often child fingers, hands, or toes are placed in the mouth. The interview also included a Child Behavior Checklist which uses a standardized format to assess parent-reported behavioral characteristics of children. Based on the CBCL, we selected child temperament indicators that we hypothesized could be associated with behaviors that affect pesticide exposure: “Can’t sit still, restless, or hyperactive”, “Gets into everything”, “Quickly shifts from one activity to another”, and “Underactive, slow moving, or lacks energy.” Shortly after each interview,draining pots study staff conducted a home inspection. Recorded information included distance between the home and agricultural fields, carpeting, housekeeping quality, and adetailed inventory of home pesticides. Home visits were completed for 87%, 84%, and 87% of the enrolled children at 6-, 12-, and 24-months, respectively. All data analyses were performed with Stata Version 10 . We first computed descriptive statistics and percentiles for individual and total DMAP and DEAP metabolites at each sampling time point. We used Pearson correlations and ANOVA to assess bivariate associations between the metabolite levels and potential exposure determinants selected a priori, including sex, age, produce intake, breastfeeding, season, distance to agricultural fields, occupation of household members, wearing work clothes or shoes into the home, home pesticide use, presence of carpets, presence of pets, and housekeeping quality. We examined post facto additional determinants which may be related to drift of pesticides from fields, including daily rainfall, behaviors which may modify exposures , time spent in child care, and proximity of child care to agricultural fields. We then constructed generalized linear mixed models with log10-transformed DMAP or DEAP metabolite levels as the dependent variables and potential exposure determinants found to have significant bivariate relationships. The models included a random effects term to adjust for the lack of independence of repeated measures on the same subject. Because children’s development, diet, and behavior differ at different age points, we also examined whether age modified any associations, with 12-month olds and 24-month olds compared to 6-month olds as the reference. All interaction terms were included in the final DMAP and DEAP models. Based on the final models, we used linear combination equations to compute the percent differences in log DMAP and DEAP metabolites for the predictor variables to determine the effect of these predictors on metabolite levels among the 6-, 12- and 24-month old children. To assess bias due to loss to follow up, we ran the models with weights equal to the inverse probability of inclusion in the final sample at each time-point. We then performed the analyses without the weights for comparison. For statistical analyses, we present results that are not adjusted for creatinine.

We also included urinary creatinine as an independent variable in the final multi-variable mixed DMAP and DEAP models for comparison with models without the urinary creatinine variable. We investigated the relationship between potential exposure determinants and urinary pesticide metabolite levels in ~400 children followed through infancy and toddlerhood living in an agricultural community. All children had detectable levels of OP metabolites in their urine. Consistent with previous studies, the DMAP metabolite levels were higher than the DEAP metabolite levels. We observed three-fold higher DMAP levels in 24-month olds and two-fold higher levels in 12-month olds relative to 6 month olds; however DEAPs declined between 12 and 24 months. Nearby agricultural use of dimethyl and diethyl OP pesticides was generally stable over the study period, however, most residential uses of chlorpyrifos and diazinon, two diethyl OP pesticides, were cancelled. CHAMACOS children turned 12 months during the first year of the residential ban, which was phased in gradually. Thus, the decrease in DEAP metabolite levels among 24-month olds may be related to reduced indoor contamination of chlorpyrifos and diazinon , due to the residential use ban. This hypothesis is supported by our finding in a separate study that chlorpyrifos and diazinon house dust levels declined in Salinas Valley homes between 2000 and 2006. However, the ontogenetic increase in DMAP levels cannot be explained by changes in dimethyl pesticide use which did not change substantially during this time. The increase in DMAP levels may be due to increasing exposure-related behaviors and changes in diet as the children age in an environment where dimethyl OP pesticide use was relatively constant. Associations between the two classes of DAP metabolites and exposure determinants were not consistent at different age points. Possible reasons include differences in usage patterns, physical-chemical properties of the pesticides, field degradation, environmental transport, and metabolism of the dimethyl versus the diethyl OP pesticides. For example, malathion, which devolves to a DMAP metabolite, has a relatively high vapor pressure compared to other OP pesticides, and, thus, may result in greater exposures via inhalation. The spring/summer season, when malathion use is higher, was associated with higher DMAP levels in six-month olds, who are not yet crawling, suggesting an inhalation exposure pathway. We also found that recent rainfall was associated with lower DMAP levels in the younger children, a finding consistent with our previous study that showed rainfall was associated with lower OP levels in air. Together, these findings support the hypothesis that inhalation may be an important pesticide exposure route for very young children. Overall, our findings suggest that agriculture-related determinants of pesticide exposure may be associated with measured exposure at some ages, but we did not observe consistent associations across age points, or between DMAP and DEAP metabolites. The high variability in pesticide application frequency and the nature of transient, non-persistent exposures in young children may create too much variability to statistically model the association of these variables and child exposures. In contrast, intake of fruits and vegetables was consistently and positively associated with both classes of urinary metabolites in children at all ages, and was statistically significant for DMAP metabolites in 6- and 24-month old children, suggesting that diet is an important pesticide exposure pathway. This finding is consistent with recent studies that indicate diet is an important source of pesticide exposure to children.

Each grid box shows the correlation for the quarters with the highest mean rainfall

Most of the production increases supporting these surpluses may occur in Eastern and Southern Asia and Northern America, where our modeling suggests 47% and 28% of new production will occur as a consequence of 25 to 35% increases in yields. Our projected yields in Eastern Asia and Northern America reach 7,500 kg ha−1 . Yields of this magnitude assume further innovation and increasing petrochemical inputs, and may not be technically feasible . However, regionally, a continuation of recent trends that include vast disparities of access to food will probably expose hundreds of millions more people to chronic food insecurity, even if increasing cereal demands as a result of biofuels and increased consumption are ignored. With a 2030 population of about 2.1 billion, Southern Asia will face substantial food availability challenges. Our 2030 projections suggest a per capita cereal production of 193 kg per person per year . This value is slightly greater than our arbitrary subsistence threshold of 190 but substantially less than the 2007 value of 231. Hence, while our theoretical ‘food balance’ suggests sufficiency, real conditions will probably result in chronic food shortages for large segments of this diverse region who have negligible purchasing power. By 2050, our theoretical food balance suggests that regional cereal production might be adequate for only 90% of the population, leaving a shortfall equivalent to the amount required by 373 million people. Substantial water scarcity intensified by anthropogenic increases in air temperature and evaporation will further hamper agricultural expansion. Central Asia appears likely to face challenges similar to those of Southern Asia. Eastern and Western Africa,blueberry plant size where cereal crops provide the majority of calories, will face substantial and increasing food security challenges.

Per capita cereal production in Eastern Africa may decrease from a low 131 kg per person per year in 2007 to a very low 84 kg per person per year in 2030. This decline almost triples the theoretical food imbalance from -96 million in 2007 to -277 million people in 2030. This corresponds to 32% of the total population in 2007 and 56% of the population in 2030. Western Africa faces a similar, albeit more modest, decline in per capita production . Our theoretical food balance suggests that this could expose about 61 million people, or 14% of the population to chronic food shortages. This analysis suggests that Africa and Asia will experience continuing decreases in food availability and security. Rapidly growing populations and increasing temperature will place further demands on scarce water supplies. Biofuels and rising demand by the global middle class will probably compete for global production, raising prices and reducing food access for rural and urban poor. Eighty-eight percent of the 2007–2030 population growth will occur in African and Asian countries which will be strongly influenced climatically by the rapidly warming Indian and Pacific tropical Oceans .What do global climate change models tell us about 21st century rainfall? The models , on average, suggest increases in tropical rainfall over the Indian Ocean and tropical Pacific Ocean . In these regions with very warm surface waters, there is a clear relationship between SSTs and tropical atmospheric dynamics. Future warming of the oceans appears likely to increase rainfall over the tropical Indian and Pacific basins. This increased oceanic rainfall will release large amounts of energy into the atmosphere, impacting global and regional circulations.

These impacts may be quantified using the 21st century climate change simulations to calculate the PC1 and IO climate indicators . In general, the areas with increasing precipitation correspond to the geographic footprint of both PC1 and IO, and the models examined suggest that both PC1 and IO will increase by 2050 . The global response, which corresponds strongly to warming in the central Pacific, appears to increase in all quarters. The IO warming appears much greater during March-April-May and December-January-February than JuneJuly-August or September-October-November . However, there is an inherent uncertainty in all these projections due to differences in model formulations, natural 10-year variations in the climate and the imperfect simulations of key processes, such as El Niño. To quantify this uncertainty the differences between the simulations can be examined and the 68% confidence intervals obtained from these differences evaluated . In summary, the models appear to agree on substantial increases in the PC1 and IO indicators, implying associated changes in the Indian and Pacific Oceans circulations, but there is still a high level of uncertainty as to the size of the changes. When using climate change simulations, it is important to realize how poorly the models used in the IPCC assessments represent rainfall over land. The average seasonal correlation between 1980–2000 observed and modeled rainfall was examined .Multi-model ensemble estimates were made for each model, the correlations estimated, and then averaged across the models. In general, areas over the tropical oceans fit well with the climate models and have good correlations. Brown boxes denote areas where the IPCC models tend to perform very poorly, with correlations of less than 0.3. Dark green areas are reasonably skillful . In the Indian and Pacific Oceans, these areas also tend to be areas with substantial increases in rainfall predicted . However, over almost all land areas these evaluations suggest very small correlation coefficients.

This low level of skill makes analysis of simulations of ‘raw’ climate change rainfall problematic. Since the IPCC models tend to perform poorly over land and reasonably well over the oceans, this study adopted an alternative approach, based on hybrid-dynamicstatistical reformulations.Hybrid dynamic-statistical reformulations provide one potential way to overcome the limitations of global climate models. Instead of using the climate model precipitation directly, this analysis uses regression to relate changes at some location to large scale climate indicators . This is especially useful when there is good evidence linking changes in tropical oceanic rainfall and SSTs to terrestrial rainfall . Precipitation reformulations , based on the 1st and 2nd principal components of global precipitation suggest that substantial rainfall declines may occur over Central America, northern South America, Africa, and parts of Southern Asia, and Australia. For more detailed spatial analysis, regressions between African rainfall and PC1 and IO time-series may be used to downscale anticipated 21st century shifts in these climate forcings . The season with the highest mean rainfall was selected . Regression equations linking PC1 and IO to the local rainfall were then estimated. For most areas, these models explained 40–70% of the variance. For parts of sub-tropical Eastern Africa and Southern Africa near the Indian Ocean, increasing IO and PC1 values are associated with increasing aridity, warm anomalies in the south-central Indian Ocean and moderate-to-strong El Nino Southern Oscillation ,plant raspberry in container typically associated with below normal MAM or DJF rainfall . These historical relationships, combined with projected increases in the IO and PC1 indicators , suggest continued declines in rainfall across southern Ethiopia, Somalia, Kenya, northern Tanzania, southern Mozambique and southern Zimbabwe. While considerable uncertainty remains, it appears plausible, and even likely, that portions of Zimbabwe, Mozambique, Tanzania, Kenya, Somalia and southern Ethiopia may experience greenhouse gas induced rainfall reductions over the next 40 years. Therefore, if warming of the Pacific and Indian Ocean continues, as suggested by climate change models , anthropogenic drought appears likely to impact one of the most food insecure regions of the world. Our conclusions are generally in agreement with the most recent 4th IPCC finding that semiarid Africa may experience large-scale water stress and yield reductions by 2020 . Our work, however, avoids the direct use of terrestrial precipitation simulations due to their low accuracy . Focusing on downscalings of climate forcing diagnostics , however, suggests further drying, especially for Eastern Africa, where the IPCC report suggests that precipitation will increase.

Future expansion of this work into Asia could help confirm the potential decline in the Asian monsoon suggested by our global reformulations .In Africa, the trends determining food security are complex. Selected agricultural, food aid and population statistics for 18 semiarid food insecure countries in Western Africa, Eastern Africa and the eastern part of Southern Africa include combined data for Ethiopia and Eritrea as they were united before 1993. Geographic variations between these three regions play a strong role in their level of agricultural self-sufficiency. In 2005, the Western African countries had, on average, three times as much harvested area as Eastern Africa . Per capita harvested areas for southeastern portions of Southern Africa are only slightly higher than those for Eastern Africa . There are also considerable differences in per capita harvested area between the countries in each of these regions. For these countries, harvested area largely determines national cereal production totals.6 Over the period 2001– 2005, the relatively food secure Sahelian countries 7 have percapita agricultural capacity values above 190 kg person−1 year−1 . Over the same period, the southeastern Africa and Greater Horn countries had agricultural capacity values of 122 and 99 kg person−1 year−1 , respectively. Seed and fertilizer inputs were limited. In 2005, fertilizer inputs were typically below 20 kg ha−1 in these low productivity zones. Low yield growth combined with declining per capita harvested area has led to decreases in per capita agricultural capacity . Because of increases in population, these food insecure countries in Eastern, Southern and Western Africa have experienced, respectively, 18, 22, and 28% reductions in per capita harvested area between 1979 and 2005. Between 1979 and 2005, fertilizer increased in the Sahel and Greater Horn and declined in eastern Southern Africa. Of the four main users of fertilizer in 2005, Kenya had increased its fertilizer use from 21 to 67 kg ha−1 . Zambia, Zimbabwe, and Swaziland saw substantial reductions from the early 1980s. In these semiarid countries, a strong dependence on rainfed smallholder farming practices results in quasi-linear relationships between seasonal rainfall, grain yields, and food deficits. Hence, the agricultural capacity multiplied by rainfall is strongly related to per capita production. The inverse of this measure is related to food aid. For each country, the food imbalance measure was regressed against 1979–2005 WFP humanitarian assistance. This gives a pragmatic means of translating changes in rainfall, cropped area, seed use and fertilizer use into an index of potential food aid requirements, supported empirically by historical aid figures. Due to the low per capita production, the resulting model performed well at a regional scale for Eastern and Southern Africa but was less accurate for the Western African countries . Agricultural sufficiency may also be expressed as a theoretical food balance, based on an assumed annual cereals requirement of 190 kg per capita. Changes in the theoretical food balance agree strongly with changes in WFP food aid,8 explaining 70% and 85% of their variance at national and regional scales. Combining observed 1994–2003 agricultural capacity trends with our projected rainfall tendencies, this model can be used to project 2000 to 2030 food aid requirements . We show historical WFP aid figures, historical model aid figures, and results from four sets of aid projection scenarios. The first scenario assumes that recent trends in population, rainfall, crop area, seed use, and fertilizer use continue for the next 30 years. The second scenario is the same, but with the change in rainfall inferred from our 1950–2005 Indian Ocean regressions and 21st century climate simulations. The third scenario assumes that precipitation levels will remain similar to those observed today. The fourth is an ‘agricultural growth’ scenario, in which observed rainfall trends continue, but per capita food availability is assumed to increase by 2 kg per person per year. These results suggest that the interaction between drought and declining agricultural capacity may be explosive, dangerous and costly, with annual aid totals increasing by 83% by 2030. The ‘observed’ versus ‘projected’ trends differ primarily for the Sahel . The impact of climate change on the Sahel is keenly debated, and our analysis explicitly ignores influences from the Atlantic Ocean. Current agricultural capacity and rainfall trends will probably produce a 60% increase in food aid expenditures in the next two decades, and will probably lead to a 43% increase in food insecurity in Africa. These figures are significant because food aid is an indicator of many related problems including child malnutrition, as well as declines in health, productivity and economic growth .

The intercept is an important measure of water vapor at both the field and scene levels

The slope acts as a measure of moisture advection as a factor of wind at the field-level.At the scale of an individual field, the intercept quantifies the build-up of moisture over a field, while at the scale of the entire study site, the spatial pattern of intercepts highlight advection of moisture across the scene. The trajectory is equivalent to the azimuth of the water vapor trend at the field-level. To assess the strength of the modeled, fitted surface, r-squared and p-values were also calculated. Only fields that had statistically significant linear trends were analyzed. The water vapor occurring above an example field and its corresponding fitted plane are shown in Figure 4.4. Water vapor concentrations were explored as their distributions vary by day and within the scene. Within each scene 1,000 random pixels were selected and a Pearson’s R was calculated to analyze the correlation between GV fraction and LST with water vapor concentrations in order to test Hypothesis A. GV fraction was obtained from MESMA and LST from the corresponding MASTER imagery. If expected correlations are found,planting blueberries in a pot these correlations would be indicative of water vapor relating to the surface beneath it.

Green vegetation transpires and produces water vapor, which will lead pixels with more vegetation to have higher water vapor. These surfaces should also have lower temperatures as evapotranspiring plants shed energy through latent heat. We tested Hypothesis B by examining patterns of water vapor intercepts against prevailing wind direction. Over the study area, we expected the water vapor concentration, as quantified through the intercept of the fitted water vapor plane, to increase downwind due to moisture advection. For example, if the wind is blowing from the North, we would expect fields in the southern part of the study area to show higher intercepts than fields in the northern part of the study area. We evaluated this hypothesis in each of the three years by mapping out intercepts in the study area and qualitatively assessing their relationship to the calculated wind direction. At the field level, we analyzed gradients of water vapor as they vary over agricultural fields in line with expectations of vapor as conceptualized in Figure 4.1 and as explained through Hypotheses C through K in Section 1. As such, we tested Hypotheses C, D, E, and F by evaluating the relationship between wind speed and direction with the slope of water vapor. Even if pixel or scene-level trends were not identified in an image, we included all dates of imagery in the field-level analysis as we hypothesize that trends may be happening at variable scales so null results at one level does not preclude significant results at another. The trends of water vapor above fields will be a factor of both wind speed and direction.

We expected to find that, within fields predominately covered in green vegetation , the relationship between water vapor slope and wind would show a quadratic relationship with relatively high or low winds creating water vapor gradients less steep than winds that are of an “intermediate” magnitude. Higher winds will move water vapor at a faster rate, which will lead to shallower gradients. However, this concept should only hold once the winds reach a certain threshold magnitude and a stable directionality as light and/or inconsistent winds will not produce any gradients. To test this hypothesis we plotted wind magnitude against water vapor slope in each of the three years. We also expected to find water vapor surfaces that aligned in directionality with the wind. We calculated the difference between the estimated wind direction and the trajectory of the water vapor above each field as the directional difference. For those fields that had directional differences of less than 30° and a statistically significant slope of vapor, we analyzed their characteristics such as crop type and GV fraction to understand what types of fields our set of hypotheses holds for. Second, we tested the impact of field size on water vapor slope in fields of >50% GV to examine Hypothesis G. We plotted field size against water vapor gradient while hypothesizing that we would find a positive relationship. Steeper gradients would be expected above large fields as they have a larger surface area over which the vapor can advect. Third, we observed the relationship between GV fraction and water vapor slope in order to test hypotheses H and I.

We separated fields into groups of similar field size to control for the impact of this factor and then studied the correlation between green vegetation cover and water vapor slope and intercept within each of those groups. We hypothesized that fields with lower vegetation cover would show a poor relationship between GV fraction and water vapor slope and/or intercept while fields containing a majority GV fraction would have a positive correlation with water vapor slope and/or intercept. We used a 50% GV threshold as was set in Shivers et al. . Field-level correlations between GV and intercept would be expected in situations with low winds and higher build-up of water vapor whereas strong correlations between GV and slope would be expected if consistent, moderate winds created advection of moisture across fields. Positive correlations would indicate that fields with more transpiring vegetation are adding more moisture to the air than less vegetated fields. A higher concentration of water vapor would be confirmed though a positive correlation with water vapor slope if winds are consistent and moderate, or an increase in intercept if winds are faint and/or variable. Fourth, this study evaluated Hypotheses J and K by evaluating the slopes and intercepts of the fitted water vapor surfaces over fields of different irrigated crop species. These intercepts indicate the magnitude of water vapor above a field while the slope is indicative of the trend of vapor over a field. A one-way ANOVA was performed to assess differences in slopes between the crop species, and results were evaluated with expected ET rates. ET rates were approximated using the expected crop ET coefficient for irrigated crops for June in the Southern San Joaquin Valley of California in a dry year . We expect crops that transpire more to have significantly higher slopes than crops with lower ET rates. To further examine expected patterns of water vapor as it relates to ET while controlling for some level of complexity within the scene,raspberries in pots we chose three crops that are prevalent in the study area and looked at their LST as it related to water vapor slope. We explored water vapor over fields of alfalfa, almonds, and cherries. We included all fields which had a fractional green cover of 50% or more. We aimed to investigate the hypothesis that fields with lower temperatures would have steeper water slopes. Fields with lower LST are assumed to be healthier and less stressed than those with higher LST because plants that have adequate water will transpire and cool themselves . The three dates of imagery showed different spatial trends of water vapor. In 2013, water vapor showed a clear increasing trend from southwest to northeast, which is noticeable but not as defined in 2015 . The 2014 and 2015 scenes showed decreasing water vapor values in the northernmost portion of the scene as the Central Valley transitions into the mountains and the elevation increases. Besides the decreasing water vapor in the northernmost part of the scene, the remainder of the 2014 image is not indicative of any other trends. When observing the imagery at a larger scale, the water vapor from 2013 and 2015 shows strong coupling with the ground surface below with agricultural field boundaries clearly defined. This result may be indicative of surface-atmosphere interactions or simply an artifact of the reflectance retrieval. In contrast, the 2014 imagery shows patterns of vapor that are more resonant of vapor or clouds that do not relate directly to the surface structure below it. We hypothesize that the difference may be attributable to the moisture level of the atmosphere, the differences in the timing of image acquisition, or the height of the water vapor in the scene. The 2014 imagery had both the driest atmosphere at 10.6 mm and also was the image that was acquired latest in the day.

Given the appearance of the water vapor imagery, we hypothesize that the water vapor in 2014 was located well above the terrain while the water vapor in the 2013 and 2015 images were lower in the atmosphere, closer to the terrain. If our study site had larger elevation gradients, we could test this hypothesis with the method laid out in Roberts et al. . However, the flatness of our study area precludes such an analysis. Computation of water vapor intercepts and interpolation of wind directionality allowed for comparison between water vapor abundance and patterns of wind as laid out in Hypothesis B. Figure 4.7 shows the directionality of the wind and the water vapor intercept maps side-by-side for comparison. Of the three dates, the 2013 imagery shows the most clear pattern of advected moisture that generally agrees with the wind map, especially in the northern portion of the study area. The intercept map shows water vapor concentration increasing from south to north while the wind direction map shows a south to north trend of wind in the northern part of the study area. As crops transpire and water vapor advects, theintercepts above fields show increasing moisture. The southern portion of the study area shows less agreement with winds, indicating winds coming from the northeast but a water vapor gradient increasing from west to east. We hypothesize that this may be due to differences in temporal scales or wind interpolation error, as noted in the discussion. The 2014 and 2015 images show water vapor that are not as clear in their trends. The 2014 wind map shows winds primarily from the north and west. The northern winds do generally agree with water vapor intercepts that seem to increase from north to south. The 2015 water vapor intercepts show patterns that are somewhat similar to 2013 with a general south to north increase in moisture, except for in the most northern portion of the flight line. Variability in winds makes evaluation between intercepts and trends challenging. Moreover, while the wind map is a snapshot at the time of flight, the intercept map likely represents a trend of water vapor over a time period of many hours, which further complicates analyses. However, results show some approximate agreement between winds and advected moisture, especially in 2013. Hypotheses C-F proposed expected relationships between the directionality of water vapor and its slope with both wind magnitude and wind direction. When looking at fields that were predominately covered in green vegetation , we found patterns that were somewhat consistent with our hypotheses that a moderate wind speed would show higher slopes than very low wind speeds or high wind speeds. Although r-squared values were low, each year showed a significant quadratic relationship between water vapor slope and wind magnitude . The 2014 image also had a significant linear trend, but the quadratic relationship showed a higher r-squared. Because wind speeds were lower in 2014, on average, than the other two years, we hypothesize that 2014 would have shown a more definitive quadratic trend if the 2014 scene had more higher wind speed values. These quadratic trends, although accompanied by considerable spread, are in line with our hypotheses.Analyzing these directionally aligned fields by GV cover and crop type in each year, we found no significant characteristics related to GV when these sub-selected fields were compared to all fields in the study. Examining histograms of GV fraction within the fields that showed directional agreement, no discernable pattern was found. High GV fields were as likely to align in trajectory with wind direction as the low GV fields. In fact, the mean GV of the selected fields were 0.45, 0.46 and 0.43 for the three years, in comparison to 0.47 for the average of all fields in the study. However, segmentation by crop type did show some interesting results. Looking at nine of the most prevalent crops, large differences are seen in the percentage of these crops that showed directional agreement with the wind .

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

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

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

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

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

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

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

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

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

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

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

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

IPF is also strongly associated with cigarette smoking in epidemiologic studies

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The pre-reform economy heavily taxed the farm sector

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

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

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

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

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