Author: naturehydro

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

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,square flower bucket 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, study staff conducted a home inspection. Recorded information included distance between the home and agricultural fields, carpeting, housekeeping quality, and a detailed inventory of home pesticides. Home visits were completed for 87%, 84%, and 87% of the enrolled children at 6-, 12-, and 24-months, respectively. Urine samples were analyzed by the Centers for Disease Control and Prevention in Atlanta, Georgia. We measured six non-specific DAP metabolites of OP pesticides metabolites: dimethylphosphate ; dimethyl-dithiophosphate ; dimethylthiophosphate ; and three diethyl alkylphosphate metabolites: diethylphosphate ; diethyldithiophosphate ; and diethyl-thiophosphate by isotope dilution gas chromatography-tandem mass spectrometry. We measured DAPs, rather than pesticide-specific metabolites, because there are no laboratory methods to measure specific metabolites of several OP pesticides used in the study area, such as oxydemeton-methyl. Approximately 80% of the OP pesticides used in the Salinas Valley devolve to a DAP metabolite . Creatinine concentrations were determined in urine using a commercially available diagnostic enzyme method . Laboratory quality control included repeat analysis of three in-house urine pools enriched with known amounts of pesticide residues whose target values and confidence limits were previously determined. The validity of each analytical run was determined using the Westgard rules for quality control. The limits of detection ranged from 0.08 g/L for DMDTP to 1.1 g/L for DMTP.

Metabolite levels below the LOD were randomly imputed based on a log-normal probability distribution. Because individual OP pesticides can devolve to more than one DAP metabolite, we summed the DAPs on a molar basis to reflect total DMAP or DEAP metabolites. Frozen field blanks, prepared earlier by CDC, were defrosted, re-packaged in the field in a manner identical to collection procedures for actual samples, and then shipped blinded to CDC. The mean levels of individual DAPmetabolites in 57 blank field samples were <2 g/L. The median values of the DAP metabolites in the field blanks were all below the detection limit. 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,black flower bucket 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.Few studies report levels of pesticide metabolites in children 6- to 24-months old. Median total DAP metabolite levels in the CHAMACOS children at 6, 12, and 24 months of age were lower than levels in 10 crawling infants and 10 toddlers sampled in the Salinas Valley in 2002. These twenty children were from farm worker homes and sampled in the summer, when levels might have been higher; direct comparisons, however, are limited by the small sample size. Median total DMAP and DEAP metabolite levels in the CHAMACOS 6- to 24-month olds were lower by ~30–70% than levels in children 24- to 72-months old living in Washington state agricultural or suburban areas; however, the Washington children were older than the CHAMACOS participants and the samples were collected between 1997 and 1999, before restrictions on residential use of chlorpyrifos and diazinon were implemented. Thus, these populations may not be directly comparable. Creatinine-adjusted levels were similar to adjusted concentrations reported in 41 5- to 73-month old farm worker children living on the US/Mexico border. Due to age differences, it was not possible to compare DAP levels in these CHAMACOS children with levels in older children studied by the National Health and Nutrition Examination Survey. Representative pesticide-exposure studies of national and state-wide populations are needed to compare to regional or local studies in impacted communities. Our study has several limitations. In a setting where multiple OP pesticides are used, measurement of the non-specific DAP metabolites does not provide information on exposure to the specific parent OP compound. As noted above, the many OP pesticides used in the Salinas Valley have widely varying usage, environmental persistence, and physical-chemical properties, adding variability to bio-monitoring measurements and possibly biasing statistical models toward null results.

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Similar findings have been replicated in various crops and locations around the world

The amounts of greenhouse gas emissions produced, by feces, pesticides, and fertilizers, is more than all means of transportation, combined . As more space is needed for the farming of these animals, rainforests are cut down, and species habitats are destroyed; thus, killing off the populations . Because of the “efficient” way cattle, pigs, and chickens are slaughtered, food quality has declined, and even though 70 billion animals are slaughtered for food a year, 340.6 million never make it the the shelves of the grocery store. . If slaughter houses had glass walls, everyone would be vegetarian.Previous research shows that omnivorous diets cause individuals to be increasingly susceptible to a variety of chronic diseases . College students with unbalanced diets, lacking a sufficient amount of fruits and vegetables, face short-term and long-term effects. Short-term effects include decreased energy and focus, while long-term effects include risk of cardiovascular disease, osteoporosis and cancer. Students that are unable to plan meals, snack frequently, and lack time and money, find themselves eating an abundance of processed foods . A healthy diet enables students to have the energy and focus to study more efficiently, as the right diet is crucial in being successful and healthy in college. A study, conducted by Beezhold et al, shows the effects of a plant-based diet, a vegetarian diet, and an omnivorous diet, on an individual’s mental health. Individuals from the ages 25-60 years old were asked questions, via an online survey, regarding their diet, health, lifestyle, wellness, and questions that measured their stress, anxiety, and depression levels. The results showed that age and gender related directly to the levels of stress and anxiety. Overall, individuals with a pant-based diet, showed a decrease in stress and anxiety levels. This study shows how plant-based diets have a positive effect on an individual’s mental health; in addition to their physical health – which has already been previously proven . Alessandra Seiter, in her article “From My Eating Disorder to My Life’s Purpose” explains how she saved her mind, body, and spirit from being overtaken by her eating disorder,procona valencia by adapting to a plant-based diet. After a year and a half of strict meal times and cardio routines, Seiter was introduced to veganism.

At first, Seiter used veganism as another excuse to reject high calorie foods, and she used veganism to mask her eating disorder. After listening to Colleen Patrick-Goudreau’s Vegetarian Food for Thought podcast, Seiter realized the devastating injustices inflicted onto the beautiful beings that reside with humans. The pain and sorrow that Seiter felt towards the individuals inflicting this pain on innocent creatures, was enough to drive her out of her eating disorder. This article specifically describes how veganism can be the driving force into recovery and the power of healing that a plant-based diet has . Further investigation will show how it is sustainable for the University to adapt a plant-based menu — or at the very least, plant-based options — and how those options can provide pathways of health, prosperity, and sustainability of the planet and its inhabitants. In the Central Valley of California, eating a plant-based diet is extremely uncommon, as it is the prime area of animal agriculture, as well as part of the culture. Therefore, college students at the University of California, Merced, that have adapted the plant-based lifestyle are likely to have difficulty finding healthy foods that are in line with their eating habits. While dining at the University provides vegetarian options, they have yet to offer options that are strictly vegan. Supplying plant-based food options is a simple and easy solution when compared to the severe destructions of animal agriculture. If the University continues neglecting to provide healthy food options, individuals and the environment will greatly suffer. In addition to supplying more food options, the University should advocate for vegan clubs and demonstrate the unsustainability of animal agriculture. Workshops and courses should be offered to teach students the importance of being knowledgeable of their food choices. These clubs and workshops would show how animal agriculture is unsustainable –– farmers are unable to keep up with the growing population and the growing demand for meat, which leads to the destruction of rainforests, species extinction, ocean dead zones, water and air quality, climate change, and disease. The perfect opportunity to discuss and teach students about animal agriculture is in the required “CORE 001” class. The class addresses water conservation and the triple zero affect; however, it fails to mention the root cause of such deprivations and overuses.

The University of California, Merced will not reach their goals of sustainability without addressing animal agriculture. Thus, the University should add to their statement of sustainability that sustainability means the ability to provide enough food to nourish the world. Plant-based diets rely heavily on fruits, vegetables, whole grains, beans, legumes, nuts and seeds; therefore, individuals eating plant-based obtain a higher number of vitamins, minerals, phytochemicals, and fiber. Purchasing these foods saves an individual $750 a year. Health benefits for these individuals include a reduced risk of many conditions such as: type 2 diabetes, cardiovascular disease, heart disease, hypertension, stroke, obesity, and some cancers, because the cholesterol is not consumed, and plant-based foods are free of added hormones and antibiotics. In a well planned plant-based diet, little risks of deficiency exist, and the diet is fit for all individuals including children, pregnant women, and elderly . The students and faculty on campus would experience an increase in energy and focus, that is much needed to fit the high demands of student and teacher lifestyles. Thus, students struggling with stress, anxiety and depression, will find themselves overcoming such struggles. As the University shifts to offering more plant-based foods, the overall health, well being, and productivity of students and faculty will increase. As previously described, breeding, raising, and feeding animals for food is an extreme waste of the worlds natural resources. Agricultural land and the resources used in animal agriculture, takes up 40% of the earths land, while a plant based diet requires a significantly less amount of land, water, and fossil fuels. Ten billion individuals could be well nourished with the food that is grown to feed cattle, whereas only 82% of the starving children see food fed to animals that are sent to be eaten in westernized countries. Thus, as less of the worlds land is used for animal agriculture, more space is available for the growing population and the food used to feed the animals can be used to nourish the entire world. If the University can encourage and support individuals that live a plant-based lifestyle, other students and faculty will be influenced to follow suit, along with their families. Since the University is advocating that they are the most sustainable campus, they should be addressing the many destructions that animal agriculture has on the planet. Once they do, the University will be “the most sustainable” campus. As stated earlier, as the University begins to offer more plant-based food, the overall health, well being, and productivity of students and faculty will increase. Weather is a key input for agricultural production.

A vast economic literature is dedicated to the role of weather information in grower decision making, market outcomes, and commodity futures. On one hand, better information about the weather can help growers optimize their use of other inputs, increasing efficiency in production and avoiding costs related with uncertainty. On the other hand, some economic models can show—under some assumptions—that more precise weather information might not be welfare increasing, as ex-ante uncertainty about the weather can lead to extra investment in other inputs. That is, when growers have better forecast of adverse weather, output would be further reduced from its level under uncertainty . There is also some concern about weather forecasts acting as signals for collusion among growers,flower bucket but simple price mechanisms can technically reduce output and welfare with better weather prediction even in a competitive market . Notwithstanding these warnings by economists, the economic gains from weather information are usually deemed positive, even if their magnitude is sometimes contested . Much of the seminal economic literature on the value of weather information was written between the 1960’s and the 1990’s, when significant improvements in forecasting was achieved with the advance of computing power and complex meteorology models . This literature is based on the agricultural practices and available data of that time. While literature about the value of weather information seems to have plateaued in the 2000’s, perhaps as forecasting technologies matured and stabilized, the surge of precision agriculture could re-ignite interest in this topic. Heterogeneity within fields and precise growing strategies, based on exact measurement of weather variables , is increasingly the subject of research and technological application . Uncertainty regarding real-time weather on micro scales poses conceptually similar questions to those dealt with by the weather forecast literature in the past. At the same time, new discussions on the value of weather information and the government’s role in providing it have been revived with advances in remote sensing and satellite technology . The technical and scientific capabilities required to gather and analyze weather data, as well as the non-rival nature of weather information as a product, meant that much of the development of weather services has been done by governments. Johnson and Holt point out that this led to a significant economic literature, assessing the potential gains from better weather information given the public expenditures.

Their survey of the relevant literature mostly includes econometric studies, where the output gains from improved forecasting are estimated and the economic gains from providing them are then calculated per hectare. Other methodologies include survey based valuation, paired with economic data and modeling. Anaman and Lellyett assess the gains from a weather information system for cotton growers in Australia, finding the benefit-cost ratio of the system at 12.6 . Klockow, McPherson, and Sutter conduct a survey based study of the value of the Mesonet network in Oklahoma. Less than 4% of Oklahoma’s cropland is irrigated, and the modest value they find for Mesonet information mostly comes from risk management. Interestingly, there are few such examples of an economic study about a specific weather information system in the published literature, as opposed to numerous studies on the value of information for growers. Johnson and Holt do mention, for example, that weather forecast services in Sweden and New Zealand have gone through “extensive privatization”, but do not cite any articles analyzing these decisions. The first part of this dissertation is an analysis of economic gains from the California Irrigation Management Information System , a network of weather stations and data center run by the California Department of Water Resources. For over 30 years, this system has been used by growers, consultants, and other users in California agriculture. This chapter presents the preliminary findings from a thorough report on the value of CIMIS, showing substantial gains not only in agriculture but also in landscape management, regulation, research, and industry.Climate change poses a major challenge for agriculture, as predicted shifts in temperature and precipitation patterns around the world affect agricultural productivity . Early studies on climate change in agriculture first focused on the impacts of changing mean temperatures, and more recent empirical literature emphasizes the importance of temperature variance and extreme heat on yields, especially during the growing season . For example, Schlenker and Roberts show sharp drops in the yields of corn, soybean, and cotton, when exposed to degree days above 28–300C.Climate scientists affirm that heat waves will increase in frequency and duration as the process of climate change advances . Researching yield responses to high temperatures, especially when the relationship seems non-linear orthreshold like, is therefore essential for prediction of climate change effects on agriculture. This can only be done with adequate weather information. Chapter 3 presents an analysis of the yield response of pistachios to hot winters. This is also a temperature distribution tail problem, at least when looking at temperatures between November and March. Daytime temperatures in California winters have been rising in the past 20 years, and are predicted to rise further in the future. This can have detrimental implications for pistachios, a major California crop, but estimating the yield response function has been a challenge so far. I use CIMIS data and innovative techniques to recover this relationship and predict the potential threat of climate change to California pistachios.

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Displacement ratio is an important factor in the calculation of carbon payback time

That is to say, 1 additional MJ of corn ethanol is assumed to take the place of 1 MJ of gasoline. For example, suppose gasoline production is 500 MJ this year and is predicted to reach 600 MJ next year to keep up with rising demand under business-as-usual , and then comes 100 MJ of corn ethanol in the second year. If gasoline production remains 500 MJ in the second year, with the other 100 MJ of demand met by corn ethanol, this is considered a perfect 1:1 displacement ratio. Due to the complexity of economic systems and human behaviour, however, it is more likely less than one unit of gasoline will be displaced by corn ethanol . The introduction of corn ethanol into the market will put downward pressure on gasoline prices, leading to a higher demand for the fuel. To continue with our example, because of the higher demand, suppose 550 MJ of gasoline and 100 MJ of corn ethanol are produced and consumed in the second year, all else being equal. Thus the net result is that 50 MJ of gasoline is displaced by 100 MJ of corn ethanol .A 10% decrease from the perfect displacement ratio would increase the CPT by 63% for unproductive land yield to 27% for highly productive land . If only 0.6 MJ of gasoline is displaced, most of the marginal land would fail to provide any carbon benefits within the 100-year time horizon studied. If only 0.5 MJ of gasoline is displaced, even the most productive land would fail to yield any carbon benefits within the time horizon studied. These results suggest that whether corn ethanol provides carbon benefits depends importantly on the extent to which gasoline can be displaced by additional corn ethanol production. In future research,blueberry in pot effort may be directed to estimate a more realistic displacement ratio that takes into account such market mechanisms as supply-demand price changes than the perfect ratio assumed in this and previous CPT studies. Models such as the partial equilibrium analyses can be used to derive such market-mediated displacement ratios .

Concern has been raised over the eco-toxicity impact of emerging pesticides and the lack of characterization models to evaluate them. This is a general question of data gap. In fact, in addition to emerging pesticides, there are also pesticides whose usage data are withheld by the USDA . However, the ecotoxicity impact of these ‘undocumented’ pesticides is likely small as a large majority of the pesticides applied to the crops studied are covered by both usage and characterization data. Specifically, such data are available for 50 to 90 different types of pesticides; they generally account for 90% to 95% of the total amount of all pesticides applied; and they include the key pesticides that contribute the largest toxicity impacts identified by recent research . It is worth noting that in terms of the number of pesticides covered, our analyses in chapters 2 and 4 are by far the most comprehensive in comparison to similar studies, which evaluated at most a dozen of pesticides . Nevertheless, our analyses may benefit from evaluating the possible ecotoxicity impact of the “uncovered” pesticides. For emerging pesticides, their characterization factors may be derived from models such as the USEtox based on their physicochemical properties and ecotoxicity effect data if available. For pesticides without usage data, their total usage is in fact aggregated in the total amount of pesticides applied and can be derived by subtracting the pesticides with usage data. Next, sensitivity analysis can be carried out to compute the possible range of their total ecotoxicity impact by assuming different amounts for individual pesticides subject to the total usage derived. Following the approach developed in previous studies , we assumed a generic factor for the fraction of pesticides in aquatic systems through leaching and runoff. However, this factor is likely to vary by pesticide – due to differences in their intrinsic physio-chemical properties – and by location – due to differences in local topographic, climatic, and soil conditions. To better estimate pesticide emissions after application, future studies may conduct field experiments – at least for the key pesticides identified – or rely on more sophisticated models than used in this dissertation, such as the PestLCI, that take into consideration pesticides’ properties, environmental factors, and application methods . Soil microbial communities are shaped by diverse, interacting forces.

In agroecosystems, management practices such as crop rotation, fertilization, and tillage alter soil physicochemical parameters, influencing the diversity and composition of bulk soil bacterial and fungal communities. Plant roots create additional complexity, establishing resource-rich hotspots with distinct properties from the bulk soil and selectively recruiting microbial communities in the rhizosphere. Root uptake of ions and water coupled with exudation of carbon-rich compounds results in a rhizosphere soil compartment where microbial cycling of nitrogen, phosphorous, and other nutrients is rapid, dynamic, and competitive in comparison to the bulk soil. Although impacts of agricultural management and the rhizosphere environment on microbiomes and their ecological outcomes have frequently been analyzed separately, understanding interactions has important implications for assembly, ecology, and functioning of rhizosphere microbial communities which are critical to plant health and productivity. Agricultural management establishes soil physicochemical properties that influence microbial community composition, structure, and nutrient-cycling functions. Organic fertilizer increases bulk soil microbial diversity and heterogeneity, and organically managed systems differ from conventional systems in bacterial and fungal community composition. Co-occurrence network analysis has shown that these taxonomic shifts can shape patterns of ecological interactions regulating structure, function, and potential resilience of soil microbial communities. In fact, nutrient management strategies are strong drivers of co-occurrence network structural properties, although outcomes across regions and agroecosystems are inconsistent and also a function of other environmental and management factors. Plant roots are similarly powerful drivers of microbial community assembly, creating rhizosphere communities that are taxonomically and functionally distinct from bulk soil. The strength of plant selection, or rhizosphere effect, is evident in observations of core microbiomes across different field environments. As for management, plant effects on microbial communities also extend beyond taxonomy to network structure. Rhizosphere networks have frequently been found to be smaller, less densely connected, and less complex than bulk soil networks, although counterexamples exist.

Whether plasticity in rhizosphere recruitment can occur across management gradients and how such plasticity could impact plant adaptation to varying resource availabilities in agroecosystems remains unclear. The potential for adaptive plant-microbe feed backs is especially relevant for acquisition of nitrogen , an essential nutrient whose availability in agroecosystems is controlled by interactions between fertility management practices and microbial metabolic processes. Microbial communities supply plant-available N through biological N fixation and mineralization of organic forms, and limit N losses by immobilizing it in soil organic matter. Conventional and organic agroecosystems establish unique contexts in which these transformations occur, shaping microbial communities through system-specific differences in soil N availability and dominant N forms as well as quantity and quality of soil organic matter. Organic fertility inputs such as compost and cover crop residues alter the abundance, diversity,plastic planters wholesale and activity of various nitrogen-cycling microorganisms, while synthetic fertilizers mainly increase the abundance of Acidobacteria and can decrease the abundance of ammonia-oxidizing archaea. Synthetic fertilizers may affect microbial community structure via changes in pH, increasing the abundance of acid-tolerant taxa indirectly through soil acidification, or may alter the relative abundance of specific taxa even when pH is relatively constant. Changes in microbial community structure and activity in bulk soil affect not just the rates but also the outcomes of agriculturally and environmentally relevant Ncycling processes such as denitrification. Roots are also key regulators of N transformations, leading to higher rates of N cycling that are more closely coupled to plant demand in the rhizosphere than in bulk soil compartments. The maize rhizosphere harbors a distinct denitrifier community and is enriched in functional genes related to nitrogen fixation , ammonification , nitrification , and denitrification relative to soil beyond the influence of roots. Understanding regulation of tight coupling of rhizosphere N cycling processes to plant demand could provide new avenues for more efficient and sustainable N management, particularly in an era of global change. However, it is necessary to go beyond exploration of individual effects of plant selection and agricultural management on rhizosphere microbial communities and consider how these factors interact. This knowledge can contribute to managing rhizosphere interactions that promote both plant productivity and agroecosystem sustainability. While management-induced shifts in bulk soil microbiomes affect environmental outcomes, plant-regulated rhizosphere communities are more directly relevant to yield outcomes. Improved understanding of how plant selection changes across management systems is thus an essential component of sustainable intensification strategies that decouple agroecosystem productivity from environmental footprints, particularly in organic systems where yields are formed through transformation of natural resources rather than transformation of external synthetic inputs.

When management and plant rhizosphere effects shape rhizosphere microbial communities, a number of scenarios are possible: one could be greater than the other , their effects could be additive , or they could interact . Typically, these effects are considered additive , where management shapes bulk soil communities and plant effects act consistently, such that rhizosphere communities are distinct from bulk soil and differ from one another to the same degree as their respective bulk soil communities. However, variation in rhizosphere microbiomes and co-occurrence networks between management systems and the unique responses of bulk soil and rhizosphere bacteria to cropping systems point toward M × R interactions shaping microbial community composition. Nonetheless, the functional significance of these interactive effects on critical functions such as N cycling is complex and remains difficult to predict. For example, biological N fixation is driven in large part by plant demand, but high inputs of synthetic fertilizer reduce rates of biological N fixation, diminishing the role of soil microbial communities in supplying plant nutrients and increasing the potential for reactive N losses. Understanding how the M × R interaction affects ecological functions is thus a knowledge gap of critical agricultural and environmental relevance. Adaptive plant-microbe feed backs in the rhizosphere have been described for natural ecosystems, but whether this can occur in intensively managed agricultural systems where resources are more abundant is less clear. We asked whether adaptation to contrasting management systems shifts the magnitude or direction of the rhizosphere effect on rhizosphere community composition and/or N-cycling functions across systems. For instance, can the same genotype selectively enrich adaptive functions that increase N mineralization from cover crops and compost when planted in an organic system and also reduce denitrification loss pathways from inorganic fertilizer when planted in a conventional system? We hypothesized that an M × R interaction would result in differences in the magnitude or direction of the rhizosphere effect on microbial community structure and functions and that differences between rhizosphere communities, cooccurrence network structure, or N-cycling processes would reflect adaptive management-system-specific shifts. To test these hypotheses, we investigated microbial community composition and co-occurrence patterns in bulk and rhizosphere samples from a single maize genotype grown in a long-term conventional-organic field trial. We further quantified the abundance of six microbial N-cycling genes as case study for M × R impacts on rhizosphere processes of agricultural relevance. Our approach integrated ordination, differential abundance and indicator species analyses, construction of co-occurrence networks, and quantitative PCR of N-cycling genes to gain a deeper understanding of the factors that shape rhizosphere community and ecological interactions.A greater number of ASVs showed a significant response to plant selection in conventional than organic soil . Five bacterial and five fungal ASVs were differentially abundant between the conventional bulk and rhizosphere soils , as compared to one bacterial and two fungal ASVs in the organic bulk and rhizosphere soils . The number of differentially abundant taxa between the rhizosphere communities of the two systems was at least as great as the number responding to within-system rhizosphere effects . More fungal than bacterial ASVs were differentially abundant between these rhizosphere communities: 24 fungal ASVs but only six bacterial ASVs were significantly different in abundance between CR and OR, indicating strong M × R interactions.

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Thirty-five concession plots were designated and allocated to farmers of the TFCGA

Compliance, monitoring, and surveillance are prioritized to minimize the environmentally degrading threats to the forests within the MGL, thus contributing to the Reducing Emissions from Deforestation and Forest Degradation initiatives of Belize. It is expected that the 31 farmers who gained rights to access to individual plots for cacao farming to be accomplished by the fifth year of planting. This has required an investment in materials, supplies, and capacity building for shade management and cacao pruning to enhance the health of trees to gain high-quality yields in a chemical-free environment, using natural agroecological measures. Cacao and other shade-loving fruit trees are planted in a setting mimicking that of a natural forest. This system addresses food security, as there is a high number of crops being cultivated within the land space where the concession has been granted. Biodiversity conservation is also enhanced since no hunting is allowed and the presence of fauna is being monitored to better understand how the integrity of the forest is maintained in a forest reserve with a management presence and intervention. The implementation of this agroforestry model aims at reducing the need to cut or clear more forested areas to plant crops, thus decreasing the expansion of the agriculture frontier.An effective internal governance structure is a key component of successful organized groups. This is perhaps one of the biggest hurdles to be overcome by TFCGA. Through the COL Program, Ya’axché has been able to provide ongoing sessions in decision-making, conflict management,10 plastic plant pots and strategic planning for the eventual autonomy of the forest community group. Great emphasis is being placed on developing the leadership and governance capacity by adapting best-practices measures. There is hope that in the near future TFCGA will become autonomous with a developed model that is easy to replicate in other forest reserves locally, regionally, and/or nationally.

Adapting alternative techniques can become challenging, as it requires breaking away from traditional practices—a behavioral change that must occur. In the 20 years of its existence, Ya’axché has built a strong relationship with eight communities in the MGL, based on respect, trust, and mutual understanding. The COL program at Ya’axché serves as the bridge between organized communities. This highlights the time extension officers invest in working closely with farmers to deliver technical support and materials in cacao-based agroforestry, beekeeping, and Inga alley cropping. Model farms using each of these climate-smart agricultural practices have been established and training sessions are delivered to other community members and groups, like TFCGA, using a farmer field school methodology approach. These model farms within the communities of the MGL are accessible for others to visit, increasing the probability of such models to be replicated. The strengthening of Indigenous communities equips them with the skills and tools to seek long-term investments. This facilitates opportunities in diversification to: access financial support to invest in climate-resilient practices; serve as model for the development of policies that will regulate cacao-based agroforestry; and gain recognition as a system that mitigates climate change impacts on communities and forests.Both protected areas and local communities are impacted by climate change and as such, there is always a need to be creative in overcoming this reality in communities where the impact is felt first-hand due to crop failure, flooding events, and drought. Creating alternative farming practices such as Inga alley cropping, a slash-and-mulch method implemented in the community agroforestry concessions, and apiculture will lead to climate-resilient communities that view protected areas as a source for livelihood improvements. A cacao-based agroforestry concession is now seen as a tool connecting forest communities to protected areas and including them in their sustainable use. Coordination and communication are the elements that have been prioritized at the grassroots level to influence a model of forest governance that is recognized by the regulating body, the Forest Department. TFCGA is governed by an executive committee composed of eight members with leadership roles and responsibilities. Having signed an articles and memorandum of association to be a legal community-based business group, capacity-building programs are elemental to strengthen TFGCA’s leadership and governance capacities to become a self-sustainable forest community group.

The group does not practice slashand-burn anymore and has embraced the guidance that Ya’axché continues to provide, in order to improve subsistence farming through guided measures that take into account the health of forested lands. Members of other communities pose a threat to the agroforestry concession since outside of the concession and forest reserve area there is no regulation of the use of pesticides. This can compromise crop production and its value-added status as being from a chemical-free area where agroecological practices are now prevailing. Inclusive dialogue has been strengthened as a response in conflict resolution to establish a buffer zone that will serve as a barrier between adjacent farmlands and the cacao-based agroforestry plots. The buffer zone is crucial to protect and conserve the integrity of the forest reserve as part of an integral block in the system of protected areas.Humanity has made giant strides toward eliminating hunger and malnutrition. Although continuous effort is needed to fight extreme poverty and hunger in some areas , today we produce more than enough food to feed the world adequately. In 2014, global cereal production reached a new record of 2.5 billion metric tons . Agricultural productivity growth has made substantial contributions to these successes. Since the start of the green revolution in the 1960s, agricultural productivity has experienced a consistent and rapid growth worldwide. For example, global land productivity, measured as an output of 185 crop and livestock commodities per harvested and pastured area, grew by a factor of 2.5 from 1961 to 2005, while labor productivity, the output per farmer, grew by a factor of 1.7 during the period . Global yield for maize, wheat, rice and soybean in 2007 was 2 to 3 times as large as it was in 1961 . These remarkable trends in productivity growth have taken place as a result of rapid adoption of, together with sustained improvements in, genetic technologies and agronomic management practices . Among them are plant breeding that results in improved hybrids and varieties, application of synthetic fertilizers and pesticides, and investments in irrigation infrastructure . Along with the successes of agriculture, however, came what Jonathan Foley terms the other inconvenient truth: “that we now face a global crisis in land use and agriculture that could undermine the health, security, and sustainability of our civilization” .

Indeed, agriculture has been identified as one of the major drivers of global environmental change, and is pushing the earth system beyond its safe operating boundaries . Through the intensive use of synthetic fertilizers and planation of leguminous crops, agriculture has critically disturbed the global nitrogen and phosphorus cycle, resulting in a wide range of environmental issues including eutrophication of lakes and coastal areas . Agriculture constitutes the single largest use of land, about 60 times as large as the area of all cities and suburbs combined , and poses the greatest threat to ecosystems . Irrigation accounts for 70% of water withdraws,plastic pot large contributing to water shortage and scarcity in many areas of the world . Further, agriculture is also the largest emitter of greenhouse gases through intensification and land conversion such as deforestation . Last but not least, agriculture dominates pesticide use, which, among others, contaminates surface and ground water and leads to aquatic biodiversity loss . Despite the severity of existing environmental impacts of agriculture, more challenges lie ahead. Global food demand is likely to double in 2050 relative to the 2005 level , driven by population growth and the continuous spread of economic prosperity in developing countries. If the current trend of agricultural practices were to continue, by 2015 about 1 billion hectare of land would be cleared globally, 250 Mt y -1 of nitrogen fertilizers would be used, and 3 Gt y -1 of greenhouse gases would be released . And yet the entrance of agriculture into the energy industry across the world brings more pressure to bear on land, water, and energy that are essential for the production of food for human consumption . In the U.S., for example, corn was primarily used for food and feed before the expansion of the ethanol industry, which now consumes >40% of the total production . As a result, corn area harvested has also expanded substantially , resulting in massive displacement of grassland as well as cropland like cotton . Rapid bio-fuels expansion worldwide, but primarily in the U.S. and EU, has contributed substantially to global food price hikes in the past few years . The increases in food prices have generated dire economic and social consequences worldwide especially for the poor in developing countries. It is against this background that this dissertation investigates three topics related to U.S. agricultural systems. The first chapter explores the environmental implications of land use change from cotton to corn driven partly by ethanol expansion. Previous studies in this area have centered on corn ethanol’s life-cycle GHG emissions , particularly with respect to direct and indirect conversion of natural habitats such as grassland and forest . Insufficient attention has been paid to land use change between crops and associated impacts on the local environment. In the past “ethanol decade,” however, substantial increases in corn prices, due in part to ethanol expansion, not only resulted in considerable conversion of grassland to corn production, but also greatly escalated the dynamics of land use change between crops . There were, for example, land use shifts from soybean, hay, and cotton to corn and from cotton to soybean.

The reason to target cotton to corn, rather than other changes in land use, is as follows. Input requirements for both corn and cotton production are high, thus the environmental implications of land use shift from one to the other are much less clear than from high-input crops to low-input crops or vice versa. The second chapter of the dissertation re-evaluates the calculation of carbon payback time in the case of converting grassland for corn ethanol production. Previous research on the CPT of corn ethanol neglected two important elements that may substantially affect their results, namely, the actual corn yield of the newly converted land and technological advances of the corn ethanol system. The analysis also tests the effect of considering emission timing on the estimates of CPT using dynamic characterization factors as proposed recently in a growing body of literature . The third chapter explores potential changes in the environmental impacts of major crops in the past decade. LCA has been increasingly applied to agricultural systems, as reflected in the number of agricultural LCA databases built in the past few years . As with LCA studies in general, agricultural LCAs often rely on static and single-year inventory data with commonly 5 to 10 years of data age. Literature suggests, however, that agricultural systems may be highly dynamic due to the increasingly changing climate and technological advances such as improved energy efficiency and deployment of genetically modified crops . These factors may bring about substantial changes in the use of input materials and the yield of crops, hence changes in their environmental impacts. Concerns about the negative environmental impacts of fossil fuels, particularly those on climate change and energy security, have driven the recent interest in bio-fuels in the USA . Several federal policies have been put in place to foster bio-fuels development, among which is the ethanol production mandate in the renewable fuel standard . As a result of the favorable policies and gasoline prices, production of corn ethanol in the USA has expanded substantially since 2005, with an annual increase of over six billion liters . Previous research, however, has shown that bio-fuels policies may have caused unintended consequences that not only undermine the goal of the federal policies to reduce greenhouse gas emissions but also degrade local environmental quality . Increasing ethanol demand has contributed to high corn prices, incentivizing farmers to convert grassland into corn growth in the Corn Belt . This direct land use change threatens wildlife habitats and creates a carbon debt that may take up to >100 years to be paid off by replacing gasoline with corn ethanol . Also, due to intensive use of agrochemicals and irrigation water, growing corn on grassland puts further pressure on local water quality and scarcity .

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Short-term finance is typically only available at abnormally high rates of interest

The large decrease in sales by florists with only a small change in farm level sales is due to a significant change in retail market shares for floral products. Specifically, other outlets such as supermarkets gained market share for floral products at the expense of individual florists. The situation for lawn and garden equipment and supplies stores is much different than florists or other retailers of nursery products. While total sales decreased after the peak occurring in 2007, the number of retail licenses continued to increase. This is not the case for other retailers handling nursery products. As shown in Table 2, there are fewer producers as well as incidental and specialized nursery retailers. The number of retailers licensed to sell nursery stock decreased from a total of 6,471 in 2003 to 3,022 in 2013, a 3,449 reduction in number of outlets. Given much smaller reductions in wholesale nursery sales, the surviving retailers are larger on average and probably have smaller operating margins than was typical for florists. This very significant reduction in the number of California retailers handling nursery and floral products has implications for both producers and consumers. Some producers undoubtedly lost their major retail customers while many lost important retail outlets. The impact of the loss of outlets was not uniform but it was widespread. This consolidation of outlets may offer some economies in distribution but the short-run impact on floral and nursery product sales will be negative. Products are not as available at the consumer level as previously, which tends to reduce consumer choice and negatively impact impulse buying. A change from specialized to multi-product retailers tends to reduce customer service and may reduce product assortments. And, finally, the changes noted may be associated with more market power in the hands of surviving retailers. With varying degrees of enthusiasm, the governments of the central and eastern European Countries all aspire to join the European Union . These aspirations were given strong encouragement at the EU’s 1993 Copenhagen Summit,blueberry container size at which time associated CEECs were told they would eventually gain membership.

Along the path to accession, however, lie difficult policy choices and delicate negotiations concerning the pace and terms of economic integration. Of these, among the most challenging are those affecting the fate of agriculture in the emerging market economies. Accession to the EU has historically implied the integration of the new member into the community’s Common Agricultural Policy , a complicated system of interventions whose most prominent and expensive features are designed to support prices of program commoditiesl through intervention purchases, and to shield markets from external competition through tariff barriers. As in previous accession negotiations, EU negotiators will be concerned about the impact of accession agreements on the EU treasury, while CEEC governments will be attentive to their implications for national budgets. Furthermore, many producer groups in the West will be nervous about granting market access to Eastern competitors; the political clout of these interests will constrain the negotiations. As with the accession of southern members Greece, Portugal, and Spain, the new members would be substantially poorer and less technically developed than those currently in the Union, raising the possibility of the need for substantial technical assistance. In the case of the CEECs, other issues arise that have no clear precedent. First, there is the unusual size and importance of agriculture in these countries. Depending on the chosen measure, these nations would increase the size of the Union’s agricultural economy by roughly one third. 1 In each nation, agriculture accounts for a larger share of employment and GOP than is typical in the current Union. Second, these countries share with their western neighbors a similar continental, temperate climate, and similar growing conditions. In the long run, after a period of restructuring, their agricultural sectors could display patterns of comparative advantage similar to those in the current EU member states, a prospect that makes concerns about competition even more pronounced than in past expansions . Third, these countries are presently going through a profound process of economic transformation that hopes to shed the legacy of the socialist period in favor of a market-based system of production. Eastern governments will have to consider how an accession agreement will affect the ongoing process of market development and enterprise restructuring currently unfolding in these emerging economies.

Finally, the requirements of the Uruguay Round of the GAIT -will be an important new factor regulating agricultural trade, imposing new constraints on allowable treaty terms. The overall success of the accession accords may be determined primarily by factors outside agriculture. Nonetheless, the treatment of agriculture promises to playa central, and delicate, role in the accession negotiations. Nearly a decade after the region embraced market economics, their agricultural sectors continue to struggle with the transition from a socialist production system. While it is problematic to make generalizations across the entire region, we can identify a few of the key characteristics of today’s CEEC agriculture that are likely to have first-order impacts on the prospects for long term performance . Farm enterprises in these countries can be broadly grouped, by size, into two types: large enterprises that are primarily the successors to state and collective farms organized during the socialist period; and smaller, usually privately-owned, operations. These latter farms, sometimes covering less than one hectare, have often been established by former members of the collective farms who have taken their land out of collective enterprises in an attempt to “make it on their own.” Both types of farms are typically under capitalized, or have a mix of capital goods inappropriate to the kind of production in which they are engaged. In the face of woefully imperfect capital markets, farms are typically unable to undertake investments to improve their efficiency, even in cases in which such investment would be profitable , depending, of course, on the cost of debt.Credit constraints are a particularly severe problem for the smaller farms, which tend to lack either demonstrable collateral or social clout. Persistent problems with land titling, and generally with the development of a market for land, impede the ability to offer land as collateral, further exacerbating problems in the market for long-term credit. Capital market imperfections are, therefore, one of the key barriers preventing an improvement in the technical efficiency of East European farms, which consistently lags that in the EU.

These problems are aggravated by the poorly developed state of public goods in rural areas, including transport and storage infrastructure and market information . In the socialist period, much of this rural infrastructure was provided from within the large enterprises. A system of infrastructure supporting independent farms has not yet emerged. These features the split between large and small farms, the low level of technical development on most farms, imperfections in market for agricultural fmance, poor provision of public goods, and a history of government-controlled prices-define the landscape of agriculture in Central and Eastern Europe. These are the initial concerns that government policymakers in the region have to consider as they chart their agricultural strategies over the coming years. Official statements from CEEC policymakers have expressed multiple goals for agriculture during the transition. To the Czech Ministry of Agriculture, for example, an ideal scenario would include the transformation of agriculture along free-market lines; preparation for eventual integration to the EU’s CAP program, and maintenance of a “domestic equilibrium” that would keep farm incomes and output from collapsing during an excessively violent transition . A central motivation for the present paper is the observation, under-appreciated in policy circles, that these goals may be Inconsistent, and that there are points of tension between the goal of creating agricultural economies that respond rationally to market signals,growing raspberries in container and the desire to bring agriculture into alignment with the heavily-regulated CAP programs of the EU. In particular, a single-minded focus on convergence to EU norms can inappropriately distract policymakers from steps that create incentives to improve productive efficiency. Policies that encourage the restructuring of agricultural enterprise during the interim period prior to joining CAP allow factors to flow toward efficient uses. The terms of agriculture under the treaties of accession will have important implications for CEEC decision makers choosing pre-accession agricultural support policies. If CAP is maintained substantially unchanged from its current form , then producers in the new environment will enjoy higher prices, supported through commodity subsidy programs and trade barriers. If a version of CAP covered Central and Eastern Europe, the current owners of land would reap windfall profits, as these benefits became capitalized into land values . . CEEC governments have a number of instruments that they can deploy in order to encourage such transformation. They can adopt policies to encourage the reorganization of agricultural enterprises, to move from a system dominated by huge state and cooperative agricultural enterprises into one more responsive to market signals, including a mix of large and small farms. CEEC governments can also control spen~ing on relevant public goods such as public information and rural infrastructure. They can vary the degree of the economy’s openness to foreign trade, through the erection of tariff and import quotas, export subsidies, and other trade management activities. Commodity price supports and other market manipulation schemes will also continue to offer their rent-seeking temptations.

Indeed, price supports and tariff barriers can have desirable effects, from the theory of the second-best: in the presence of a distortion in one input market-that for credit-a government imposed distortion in the output market can have beneficial effects, by transferring resources to producers that are able to use it efficiently. At the same time, however, distortive policies can create price instability. In this context, free trade can substitute for price supports as a market stabilizing mechanism, operating more effectively and at lower cost. Both distortive and laissezfaire approaches may, however, compare unfavorably with policies that address market imperfections directly. Of course, use of any instruments has associated costs, both directly taxing the government treasury and indirectly imposing adjustment burdens on society. Thus, in bargaining over the treatment of agriculture in accession, and in selecting appropriate pre-accession policies, CEEC policymakers must therefore be prepared to juggle a complicated set of interactions and trade offs. The nature of these trade offs can be clarified through a heuristic version of a comparative statics exercise. Suppose that a government knew with certainty the date and terms under which it would join the CAP, and was cbntemplating a restructuring program that would appropriately position the agricultural sector for successful entry. For a given date of entry, a relatively aggressive restructuring program would create multiple effects, including an increase in the efficiency and flexibility of the agricultural sector; an increase in producer profits and aggregate national wealth in the long term following CAP integration; a short-term decrease in output, as established patterns of production are disrupted; an ambiguous effect on output in the long term; and an increase in the short-term costs of adjustment, including social costs such as unemployment. The government’s fundamental decision problem is how to balance these trade offs, i.e., how to deploy judiciously the policy instruments at its disposal in order to position the agricultural sector for a successful entry into CAP while keeping it robust during the interim period and, perhaps, subsequent to a major reform in the CAP. To be sure, a number of questions concerning the interaction between the terms of accession to the EU and pre-accession policies naturally arise. Let us assume that the CAP will not be altered in the near term and, therefore, that the program’s current form represents a credible policy commitment by the EU, both to its own farmers and to prospective member states of Eastern Europe. 2 How will alternative accession scenarios impact the budgets of the EU and the CEEC national governments, respectively? Under what forms of the accession contract, if any, should the CEECs use the pre-accession period to mimic the EU by adopting CAP-like policies? Do price supports encourage or inhibit efficiency-enhancing restructuring of farm enterprises? Should the restructuring process receive public subsidy? In other words, how should the burdens of the restructuring process be divided between the public and private sectors?

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Impacts on agriculture from urbanization will then be disproportionate to the land area covered

The county lost about 6500 acres of agricultural land to urbanization between 1992 and 2008 . Most of this was prime farmland and farmland of local importance. Compared with other jurisdictions in California, the county has been relatively successful at protecting agricultural land from urbanization through land-preservation programs, incentives for farmers, and land use policies which make it difficult to develop land zoned for agriculture. Urbanization presents both opportunities and challenges for agriculture. In some regions, it enhances awareness about how food is produced and generates markets for agricultural products such that farmers produce crops more intensively . But it is more typically accompanied by challenges: the loss of agricultural land due to subdivision and development; vandalism at the urban edge ; and conflicts with new suburban residents about the noise, odor, and potential spray-drift associated with farming operations. Where development takes place in a dispersed pattern that fragments agricultural land, farming may become difficult on some remaining agricultural parcels due to such conflicts as well as to difficulties in moving farm machinery from field to field on more congested roads, creating a ripple effect whereby more agricultural land is then converted to urban uses. Also, fragmentation and loss of farmland cause farmers to lose benefits associated with being part of a large farming community, such as sourcing inputs, accessing information, sharing equipment, and supporting processing and shipping operations .Within previous research, we and our colleagues developed a set of storylines reflecting different climate change and urbanization policies for Yolo County in 2050 . As in IPCC Scenario A2 , our A2 storyline assumes that population growth will remain high,container grown raspberries with an approximate doubling of the current county population, to 394,000 .

This storyline assumes that economic growth and technological innovation remain high, that drive-alone motor vehicles remain the main transportation mode, and that current land use policies remain in place. Although much urbanization will be on previously unbuilt land, there will be some focus on infill development, higher densities, and greater land use mix, as indeed is evident within current development and in county and city planning documents. In terms of suburban sprawl, therefore, the A2 storyline is by no means a worst-case scenario. Rather, it is a continuation of practices in the 1990–2010 period. If this storyline had been based on prevailing development patterns from 1950–1990, suburban densities would have been in the range of 4–6 units per acre instead of 8 , less development would have occurred in medium- and high density forms, and a higher percentage of larger ranchettes would have been created . Suburban sprawl would then have covered a much larger percentage of the county, taking far more agricultural land out of production. In the IPCC’s B1 storyline, societies become more conscious of environmental problems and resource limits, and adapt policy accordingly. Under our Yolo County B1 storyline, population growth slows, reaching a mid-range population size of 335,000 by 2050 . Economic development is moderate, with a shift from the production of goods to a more service-based economy that is connected to the larger global economy. Technological innovation remains high, with an emphasis on small-scale, green technologies. More compact urbanization occurs through higher densities, increased infill, and a focus on small, locally owned retail stores rather than big box commercial developments. Current transportation and emission policies become more stringent, and the use of high-efficiency vehicles and alternative transport modes increases. In terms of agricultural landscapes, strategies such as conservation easements and tax incentives expand to help maintain land in farming. Farmers also place more emphasis on increasing carbon sequestration, reducing GHG emissions from fossil fuels and fertilizers, and relying on ecologically based practices that reduce dependence on non-renewable inputs .

To the two IPCC-based storylines, we added a third with more explicit GHG-emissions regulation and sustainability policies. Under our AB32-Plus storyline, Yolo County experiences slower population growth, reaching only 235,000 in 2050 through policies or voluntary actions that affect family planning and migration . Moderate economic growth focuses on value-added agricultural production enhancing the economic viability of the rural sector, and closer alignment between the rural and urban sectors supports both farmland preservation and protection of ecosystem services . A less resource-intensive lifestyle gains acceptance. Urbanization remains at the current extent through strict land use planning policies and development emphasizing efficient use of land, mixed use, intensive infill, increased densities, and growth in urban and neighborhood centers. Public policy emphasizes alternative modes of transportation and far cleaner vehicles. In order to both mitigate and adapt to the changing climate, agricultural producers make major changes in management practices, focusing on ecological intensification and diversification of cropping systems rather than non-renewable inputs and monocultures. Markets for agricultural products become more locally based, and thanks to both more compact physical form of communities and changing economics, travel distances decrease.In order to understand the type, extent, and likely locations of urbanization in the county, we modeled these three urbanization storylines using UPlan geographic information system–based software, a rule-based land use allocation model developed by the Information Center for the Environment at the University of California, Davis . UPlan is an open-source, relatively simple model that can be run on a sub-county area, a county, or a group of counties. It is suitable for fast, broad-brush urbanization modeling of large land areas using multiple development scenarios, and more than 20 counties in California have used it for urban-growth projections, including a group of rural counties in the San Joaquin Valley which employed it to develop an urban-growth blueprint . It has also been employed to assess the impacts of urbanization policies and growth on natural resources , to understand the risk of wildfires in rural woodlands from urban growth , and to evaluate the effect of land use policies on natural land conversion . UPlan relies on a number of demographic inputs to create scenarios reflecting possible locations and forms of new urban development . The software divides households into four residential land use types based on density parameters, while assigning employees to nonresidential land use types , also by density. Researchers designate “attractors” and “discouragements” , and assign weights to each within each scenario. Accessibility and neighborhood attractor parameters can be added in this way, which also allows for detailed local knowledge of development history and policy to be incorporated. For example, within our A2 scenario we assigned relatively strong attraction values to freeway interchanges for commercial development and somewhat less strong attraction values for residential development, because without specific land-protection policies, highly accessible freeway locations tend to attract such development. Within our B1 and AB32-Plus scenarios we assigned increasingly strong attraction values to town and neighborhood centers, as well as to existing commercial strips and rail station areas, because over a 40-year period these are likely to be a focus of infill development policy.

Finally, UPlan uses “masks” to prohibit growth in certain locations because of logistical or ownership considerations. For example, we masked existing parks and wetlands in this way. However, we assigned discouragement values to floodplains, because despite environmental concerns, development continues to occur in Sacramento Valley floodplains. For the purposes of this project,blueberry plant pot we modified UPlan in several ways when compared to previous uses. Because our time frame is longer, and given that land use politics and regulation can change greatly over 40 years, we no longer required that the model place new urban development in areas conforming to the current county General Plan . We also modified UPlan to allow development within existing urban areas; the tool had been used previously mainly to consider growth on unbuilt county lands outside of existing cities and towns. To predict infill development more accurately, we added additional attractors such as existing commercial strips, shopping centers, freeway retail zones, neighborhood centers, and rail transit station areas, all of which can potentially be redeveloped with higher densities. Partly as a result of these changes, our urbanization assumptions for both the B1 and AB32-Plus scenarios are considerably stronger than used by other researchers. For example, even the strongest growth-management scenario considered by Neimeier, Bai, and Handy in a study of urbanization scenarios for the nearby San Joaquin Valley still allows substantial suburban sprawl. This approach may accurately reflect current political realities for that region , but it involves very different assumptions from our back casting approach, which aimed at investigating potentially very-low-GHG scenarios in 2050. Table 2 shows the primary modeling assumptions we used in UPlan. The software divides new development by land use type , and then, drawing upon these inputs, allocates it across the landscape into the geographic cells with the highest combined attraction weights and the user-defined land use order. The model uses 50 m 50 m cells, roughly half an acre. This is a fine-grained grid conducive to handling small increments of development such as often occur at infill and urban-edge locations. The final output is a map displaying the location by land use type of future urbanization, as well as associated tables. Throughout the scenario-development process, we sought to keep our assumptions relatively simple and transparent. In their study of the New York metropolitan area, Solecki and Oliveri added a layer of new roads for their A2 scenario. For Yolo County, there is no political demand for new roads under current conditions, and the location ofsuch routes if added would be highly conjectural. Likewise, the concatenation of new residential clusters outside of existing urban areas was not modeled, because access to roads and proximity to past rural development are probably strong enough to approximate this relatively weak clustering tendency. Lastly, we did not need to consider land slope in our modeling, because the vast majority of the county is quite flat, and the western hills are far removed from existing population centers.

Among the specific assumptions within our three scenarios, the most controversial is that our AB32-Plus scenario assumes that all new development takes place within existing urban areas. We did this to develop the strongest possible back casting scenario for reducing GHG emissions. New development in Yolo County’s largest city, Davis, is in fact currently almost entirely infill, because voters passed ballot measures in 2000 and 2010 preventing any development on open space or agricultural lands without a majority vote of city residents. Statewide, infill development has greatly increased in recent years due to scarcities of unbuilt land in places like the central Bay Area and the Los Angeles Basin and is likely to increase further due to overbuilding of lower-density housing and strong needs for denser forms of housing . Redevelopment of existing urban lands is a main goal of the Sacramento region’s 2004 Preferred Blueprint Scenario and Sustainable Communities Strategy , as well as state legislation such as SB 375. While many practical constraints pertain to infill , within a strongly environmental vision of the future there is no physical or procedural reason why 100% infill could not be achieved if the political obstacles could be overcome, for example through an escalation in the urgency of the climate challenge. For all scenarios, we established density levels that are fairly close to the density levels of recent development in the more urban portions of the state. Our categories were Very Low Density Residential, with an average lot size of one acre; Low Density Residential, with an average density of 8 units per acre ; Medium Density Residential, with an average density of 20 units per acre ; and High Density Residential, with an average of 50 units per acre . In terms of building types, the Medium Density category might consist of two-to-three-story apartment or condominium buildings with significant green space around them, while the High Density category might include three-to-five-story multifamily buildings in a more urban format as well as some townhouses. It is important to emphasize that none of these categories requires high-rise apartment living, although this development type is not forbidden, and might in fact be desirable for limited locations within the county during the study period. We apportioned development differently between these residential types for each scenario.

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Previous studies have also observed correlation between thermal stress and agricultural yields

While any fractional cover estimate will be subject to error, we believe MESMA is an improvement over these other models for reasons that will be discussed in Section 4.2. Finally, we do not believe that the errors in temperature estimation that result from errors in fractional estimation will preferentially affect any specific crop species. Therefore, while there will be pixel-level error due to fractional cover estimations, these errors should balance out and allow for relative stress comparisons when analysis of crop temperatures is aggregated to the field and/or species levels. Fifth, differences in flight timing will contribute to noise in the interpretation of results. Crop stress fluctuates with time of day, and plant transpiration has been found to plateau midday and then decrease in the afternoon as soil water content and soil water potential decrease . Therefore, temperature residuals are not only a function of the overall health of the crop during the year and season the imagery was collected but also the water availability on the day and time it was captured. While flight timing should be considered when interpreting findings, we do not believe that the effect is significant enough to overwhelm the yearly signal of increasing stress that is likely due to drought. Temperature residuals were shown to increase from 2013 to 2015. If this trend were due to the timing that the data were collected, we would expect the timing to have a similar trend as the residuals. However, the flight in 2014 was flown latest in the day yet did not have the highest residuals. Additionally, 2013 and 2015, which were flown at similar times of day, had the greatest differences in average LST residual, which is the opposite result as would be expected if flight timing were the main driver of residuals. Therefore, flight timing is an important factor to consider when interpreting LST patterns, but its effect on this study is assumed to be small. If designing a study to compare water stress between months or years,blueberries in containers consistency in timing of acquisition would enhance interpretability of results. Sixth, this study assumed that environmental variables such as air temperature, radiation, and wind did not deviate significantly across the study scene.

In our study area, which has little variability in elevation and has similar vegetation types throughout, we assume the climate is relatively stable. However, some variability will exist across space that will cause error. If this model were applied to a study area that encompassed multiple climate zones, thermal endmembers would need to be calculated separately for each zone to account for known differences in environmental conditions. For this reason, we suggest that this method be implemented over relatively homogeneous areas, with respect to topography and climate. Finally, this model requires a priori information about the landscape and informed knowledge to select appropriate thermal groups. In this study, a crop map that was compiled on-the-ground at the county level was used to inform thermal classes. In lieu of a similar map, crop information could also be gathered from a classification of VSWIR imagery or from a national crop map such as the Cropland Data Layer . With that information, crops could be separated into groups that are similar physiologically and bio-physically such that they are expected to have similar thermal behavior. One of the limitations of the model is its sensitivity to choice of thermal groups. While we expect this method to be transferable to other regions, types of landscapes, and thermal groupings, further work would be necessary to test this hypothesis. This study found a significant inverse relationship between green vegetation and LST. Other studies have observed similar trends between temperature and vegetation . However, unlike other models that use vegetation indices to account for this relationship, such as the VHI or WDI, our model uses a mixing model. The mixing model is an improvement upon the vegetation indices as it leads to a more accurate estimate of fractional cover in a diverse landscape such as our study area. NDVI is sufficient for estimating fractional cover in simple landscapes with little spectral variability, but a mixture model is better suited for estimating fractions in spectrally and spatially diverse landscapes . NDVI is also sensitive to variability in soil background reflectance, which is accounted for with a mixing model. MESMA, in particular, has the added benefit of using multiple endmembers per surface cover component, which allows these components to be grouped by spectral similarity. We found that there is significant thermal variability within the broad surface covers of GV, soil, and NPV.

These findings imply that a detailed knowledge of the landscape, beyond basic surface cover fractions, should be considered for interpretation of LST in agricultural areas. We explored thermal patterns in agricultural orchards and found that core thermal differences exist between crop groups of citrus, perennial fruits, and nuts that may not be attributable to stress. These findings indicate that physiological differences between crops result in different thermal behaviors that will impact interpretability of stress. Results are also in agreement with the observation of Roberts et al. , who found that LST and GV cluster by dominant plant species in LST/GV space. Moreover, we found that there is thermal variability in soil that is correlated to soil albedo and in NPV that is hypothesized to vary by structure. This result is consistent with studies of soil moisture, which have observed that moisture will lower the albedo and temperature of a dry soil . The method detailed in this paper acknowledges the variability within soil, NPV, and GV and uses a crop map and MESMA endmembers to account for some of this thermal variability. Current remote sensing methods that estimate agricultural stress either require field specific inputs that limit the scale of applicability or are wide-reaching but too simplistic in their assumptions such that all GV, soil, and NPV are treated similarly regardless of structural complexity, albedo, or functional group. Field-level models such as the WDI and CWSI account for differences in species by requiring crop-specific data, but these intensive inputs limit broad spatial analysis. Other models that have been developed for analysis across large areas, such as satellite-based ESI and VHI, do not account for the degree of thermal variability that was found in this study to be present in an agricultural landscape. While ESI and VHI do use NDVI and, in the case of ESI, also LAI to account for thermal differences within green vegetation, these parameters will not account for thermal variability from soil type or moisture, NPV, or even species-level thermal variability. An advantage of this method is that it segments GV, soil and NPV into groups that should have similar thermal behaviors while requiring no site-specific inputs other than a crop map. Its internalized calibration makes this method scalable across time and space.

In lieu of a crop map, MESMA endmember groups, such as were used for NPV and soil, may be a suitable substitute for grouping GV into thermal classes. However, further study would be necessary to test this hypothesis. Monitoring crop stress is important in anticipating the future of the agricultural landscape and can provide insight into plant water status and plant stress that could help to identify unhealthy crops to mitigate impacts that could lead to decreased yields and economic losses. We found that thermal imagery collected at only one date per year over three years of drought was able to identify the species that were facing the highest degrees of stress, in agreement with county-level yield data. Thermal remote sensing has been observed to correlate with fruit quality in orchards with open canopies and has been used as an indicator of regional agricultural drought as measured by crop yields . Subsequent analysis of our model would be necessary to determine if the results are robust at the field level,planting blueberries in pots but even the regional correlation with measured crop yields has important implications for farmers, policymakers, and scientists analyzing food and water resources. Moreover, temperature patterns were correlated with expected ET rates for crops in California in a dry year, further bolstering the hypothesis that LST patterns can be used to infer information about crop water use and stress. We hypothesize that deviations in the expected linear relationship between ET and LST are the result of irrigation management decisions such as irrigation method, timing, or applied amount that will affect the health and productivity of the crops. ET rates were calculated under the assumption that crops were watered with surface irrigation systems, suggesting that expected ET rates would increase by 3-6% if drip or micro irrigation were applied . As the frequency of drip irrigation for orchards has increased dramatically in the past couple decades, this change in method may be a factor in deviations from the ET/LST relationship. We also hypothesize that deviations in the ET/LST relationship are a factor of drought management techniques such as reduced watering. In line with this assumption, walnuts consistently had the lowest temperatures and residuals of the three nut crops in the study. This finding is consistent with suggested drought irrigation management techniques that recommend against deficit irrigation for walnuts as they are highly susceptible to damage if faced with water stress and are not as tolerant to these practices as either almonds or pistachios . The lower walnut temperature may, therefore, be the result of continued, consistent irrigation in comparison to almonds and pistachios, which receive deficit irrigation.

The temperature residuals capture the difference between expected and measured temperature and therefore act to as an important source of information about ET rates, the irrigation management practice, and stress. These thermal analyses are important for prioritizing water resources, especially in times of drought when water is limited. Additionally, maps of thermal stress could be valuable to assess the representativeness of in situ measurements of carbon dioxide, water vapor, sensible heat, or other fluxes over a heterogeneous landscape . This method of quantifying stress could also be complementary to surface energy balance models such as Disaggregated ALEXI and Mapping Evapotranspiration with Internalized Calibration . As our approach provides a relative measure of stress and DisALEXI and METRIC estimate actual ET, the surface energy balance models could be used to test the sensitivity of our approach to changes in ET. Alternately, the segmentation of soil and NPV into thermal classes in our method may be of use for refining the evaporation component of DisALEXI or METRIC for increased accuracy in crop ET estimation. Our approach has the added benefit of requiring only VSWIR imagery, thermal imagery, and a crop map that provides a level of detail of, at minimum, plant functional groups, and these minimal inputs allow for ease of implementation. Given these inputs, the approach suggested in this paper is probably best suited for agricultural applications at a spatial scale where environmental variables do not vary highly, such as the study scene in this paper. Within a relatively homogenous area, this method could be applied routinely using consistent thermal groups in order to identify which fields are most stressed and/or to gain information about irrigation management practices, particularly during drought as studied in this paper. In order to improve the ability of this method to capture within crop change in drought stress through time, further analysis would need to be conducted with a dataset that collects at approximately the same time for every capture. With our dataset that contained large differences in time of capture, these timing differences made such analyses beyond the scope of this paper. This study showed that thermal signatures of agricultural crops are correlated with crop species and fractional cover. Therefore, LST data on its own without information about surface structure and composition is challenging to interpret in the context of crop stress. The SBG mission would provide spatially and temporally paired thermal and VSWIR imagery globally which would allow for detailed analysis of LST patterns that take into account fractional cover and surface type. The ability of SBG to monitor crop stress would be enhanced by a crop classification that could group crop fields into relevant thermal classes, removing the need for accurate GIS data layers.

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The AVIRIS results are analyzed for portability and band importance

MASTER is a thermal sensor that captures 8 bands of emissivity between 4-12 μm, used to represent the proposed SBG thermal bands . The AVIRIS data was resampled to a resolution of 18 m while the MASTER data was resampled to a 36 m resolution. This paired dataset was flown over a portion of the Southern Central Valley seasonally while the state experienced severe drought effects. This unique dataset allows for study of remote sensing capabilities while also providing valuable information as to the response of crops in California to drought. The goal of this dissertation is to use data from the HyspIRI Airborne Campaign to evaluate how hyperspectral and thermal imagery can be used to improve upon current initiatives to account for and manage food and water resources in the face of a changing climate. This research will study patterns of agriculture and crop water use in the Central Valley as they shift throughout the course of an intense drought period from 2013-2015. These patterns will be investigated using imaging spectrometry from AVIRIS and thermal imaging from MASTER by mapping crops into relevant water use groups and then analyzing three indirect measures of crop water use from the imagery: choice of crop plantings, land surface temperatures, and water vapor patterns. Moreover,large plastic pots this dissertation will serve as a proof of concept for actively monitoring and measuring agriculture from space when the proposed SBG satellite is launched. In Chapter 2, I use three hyperspectral images acquired from AVIRIS over the course of the 2013-2015 drought in the Central Valley of California to both evaluate the performance of hyperspectral imagery for crop classification and to study farmer decision making with drought. A random forest classifier is run on the AVIRIS imagery to classify crops into groups of similar water use. Results are then compared to equivalent classifications using Landsat Operational Land Imager and Sentinel-2 imagery.

The results of this classification are then used to study the prevalence of crops as they change with increasing drought. Analysis highlights the economic and environmental drivers of planting decisions, and what this means for the future of California agriculture. In Chapter 3, I use spatially coincident AVIRIS and MASTER imagery from 2013, 2014 and 2015 to study the health of perennial crops over drought. First, I use a mixing model on AVIRIS imagery to decompose the scene into its fractional makeup of green vegetation , non-photosynthetic vegetation , and soil. Next, I model the expected temperature of each pixel as the fractional linear sum of its thermal components. I then calculate a thermal residual for each pixel as the difference between its measured temperature from MASTER and the modeled temperature. This method strips away thermal variability due to air temperature, time of day, fractional cover, structure, and moisture to allow for direct thermal comparisons between pixels and crop species. Thermal variability within agricultural fields is quantified and crop health is assessed. In Chapter 4, I evaluate spatiotemporal patterns of water vapor as they occur over agricultural fields in the Central Valley to evaluate the potential of this imagery to assist with agricultural applications. I use pixel-level column water vapor estimates derived from AVIRIS radiance imagery, surface characteristics obtained from AVIRIS reflectance imagery, and interpolated maps of wind to investigate relationships between the atmosphere and the surface. I propose and test a set of hypotheses for how water vapor will interact with the landscape in a diverse and complex agricultural scene at the pixel, field and scene scales. Results and analysis further knowledge of opportunities and limitations for using water vapor imagery to better understand crop water use. Although California faces substantial variability in inter annual precipitation and is accustomed to multi-year dry periods, the 2012 to 2016 drought was exceptional in its severity, and may be emblematic of greater shifts in California’s climate associated with anthropogenic warming .

Climate projections for California indicate that mean and extreme temperatures are likely to increase over the next century, which will increase the risk of experiencing future droughts of the severity of the 2012–2016 event . Future droughts will undoubtedly continue to put strain on water supplies, but the magnitude and extent to which these events impact water resources will depend not only on the characteristics of the drought, but also on the adaptive responses of people . In California, where the agriculture sector uses roughly 80% of the state’s managed water , agriculture simultaneously shows high vulnerability to a warming climate while also offering the greatest opportunity to mitigate the intensity of future drought impacts through adaptation strategies . Consequently, it is critical to study how we can monitor crop management response in real-time in order to assist with policy making during drought and analyze the ways in which the long-term sustainability of food and water security can be improved. This research used annual hyperspectral remote sensing imagery to assess the accuracy at which imaging spectroscopy can be used to map crops into categories of similar water demand and analyze changes in cropping patterns in a portion of the Central Valley. The study takes advantage of data collected over three years of a multi-year drought as a unique opportunity to measure agricultural response and adaptation in times of drought. Climate change is likely to significantly affect regional agricultural patterns and crop yields , in part due to management decisions such as fallowing fields or switching crop varieties or species . Therefore, monitoring how crop patterns change during droughts is a direct measure of adaptive response. Cropping decisions impact society in multiple ways by altering regional water requirements , food yields , economic production , and pesticide exposure . Consequently, accurate and timely crop maps are necessary to support long-term adaptation planning for a broad range of sectors, and are of use to farmers, managers, policymakers, and scientists.

Remote sensing has the potential to map crops and monitor changes in crop area more efficiently and frequently than time and labor-intensive on-the-ground crop accounting. Hyperspectral imagery, which samples hundreds of spectrally contiguous wavelengths, has the potential to identify crops at a single time point with a single sensor at higher accuracies than a broadband sensor . This ability is critical to enabling managers and scientists to stay abreast of rapidly changing planting choices and assess current risks, which is a need that current mapping initiatives with remote sensing are unable to fulfill. Most remote sensing mapping initiatives in the United States rely on satellites such as Landsat and the Moderate Resolution Imaging Spectrometer because of their large spatial and temporal coverage, ease of accessibility, and free availability . The National Agricultural Statistics Service ’s Cropland Data Layer is the most comprehensive current agricultural mapping initiative for the United States with an easily accessible crop map published at yearly intervals at a 30-m resolution . It relies on data from Deimos-1,raspberry container the United Kingdom’s Disaster Monitoring Constellation 2 , and Landsat 8 Operational Land Imager and produced an overall accuracy of 81.1% in California in 2016, with accuracies of crop groups ranging from a low of 32.8% for berries to 77.6% for forage crops. Although widely used and highly useful, the CDL has limitations concerning reproducibility and timeliness. First, by using three sensors, not all of which produce publicly available data, reproducing this map or using this methodology on a different study area or at a different time would not be possible. Furthermore, with maps published at the end of each year, the CDL does not offer near real-time or mid-growing season assessments of crop area. Another method of crop mapping uses multi-temporal MODIS imagery to classify crops using annual crop phenology for identification . These studies illustrate the ability of time series datasets to produce detailed and accurate crop classification maps at the end of an agricultural year in a single study area, but this methodology also faces challenges that hinder its practical and scientific usefulness in California. First, the spatial resolution of MODIS is not fine enough to individually classify many fields. For example, the average size of a field in the area of this study is approximately 0.2 km2 . Therefore, even at its finest resolution of 250 m, most MODIS pixels will result in mixtures of different fields or crop types, and are therefore best suited for croplands at larger scales . Second, multitemporal crop mapping is limited in its spatial scope due to a spatial variation in phenology that would decrease the accuracy if it was applied over a large spatial area . Third, the co-registration of multiples images and the need for cloud-free images create challenges for time-series analysis that single-data hyperspectral analyses do not face .

Finally, the need for multiple images throughout time obviates the ability to conduct real-time crop assessments. Hyperspectral imagery can act as a complement to these current crop-mapping initiatives, as it has the potential to identify crops at a single time point with greater accuracy than broadband sensors, and therefore can provide mid-season assessments of crop area without a yearly time-series . Discriminating crop types is challenging due to differing biophysical traits, development stages, variable management practices, regional weather and topography, and the timing of plantings . Despite these complications, various studies have successfully shown the ability to use hyperspectral imagery to classify crops and cultivars . By discriminating crop types with a single image from one time point, hyperspectral imagery can serve as a time-critical agricultural management tool, providing scientists, farm managers, and policymakers with improved information regarding the agricultural landscape and on-the-ground food and water needs. This study uses airborne hyperspectral imagery over a portion of the Central Valley to assess the accuracy of imaging spectroscopy for agricultural classifications and conducts a case study to display the utility of these classifications for analyzing changes in farming decisions. The results of this study, while limited in their spatial scope due to the use of airborne imagery, are salient in light of recently available Sentinel-2 data and the proposed HyspIRI mission, which would provide repeat, global hyperspectral imagery. In order to separate soil or fallow pixels from those of agricultural plant matter, a spectral mixture analysis was run on each of the three images to obtain fractional green vegetation cover. Multiple End member Spectra Mixture Analysis uses a linear mixture model to unmix pixels into fraction images while allowing the number and types of end members to vary on a per-pixel basis, thus better accounting for end member variability. Pixels were modeled as a mixture of green vegetation , soil, nonphotosynthetic vegetation , and shade. Image end members were chosen from each of the three images from 2013, 2014, and 2015 by selecting pixels with high overall reflectance from each of the three end member categories that were well-distributed spatially throughout the image in order to capture the variability from north to south along the flight line. A combined library of all of the chosen end members, consisting of eight NPV, 10 Soil, and 21 GV endmembers, was used for analysis in order to obtain consistent results throughout the years. MESMA was partially constrained by requiring shade fractions to vary between 0–0.8, and setting a maximum allowable root mean squared error of 0.025. The spectral mixture result was then shade normalized by dividing each non-shade component, GV, NPV, and soil by the sum total of all of the non-shade components in that pixel to obtain physically realistic fraction estimates . Only those pixels that contained 50% or more shade-normalized GV were chosen for training and validation, as this was decided as the threshold for classifying a pixel as a crop.Due to the high diversity of crop species in the Central Valley, we focused on a smaller set of crop classes that would be of the most practical use to stakeholders such as water managers, farmers, and scientists. Crops were classified into categories defined and used by the California Department of Water Resources to estimate water use . The crops within each category have similar rates of development, rooting depths, and soil characteristics, and are therefore presumed to have similar water requirements. Categories were included in the classification if they were prominent in the area, defined as ≥20 fields of that category, each of which contained ≥50% green vegetation, in the validation layers .

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The hedonic approach attempts to measure directly the effect of climate on land values

There is a growing consensus that emissions of greenhouse gases due to human activity will lead to higher temperatures and increased precipitation. It is thought that these changes in climate will impact economic well being. Since temperature and precipitation are direct inputs in agricultural production, many believe that the largest effects will be in this sector. Previous research on the benchmark doubling of atmospheric concentrations of greenhouse gases is inconclusive about the sign and magnitude of its effect on the value of US agricultural land . Most previous research employs either the production function or hedonic approach to estimate the effect of climate change.Due to its experimental design, the production function approach provides estimates of the effect of weather on the yields of specific crops that are purged of bias due to determinants of agricultural output that are beyond farmers’ control . Its disadvantage is that these experimental estimates do not account for the full range of compensatory responses to changes in weather made by profit maximizing farmers. For example in response to a change in climate, farmers may alter their use of fertilizers, change their mix of crops, or even decide to use their farmland for another activity . Since farmer adaptations are completely constrained in the production function approach, it is likely to produce estimates of climate change that are biased downwards. Its clear advantage is that if land markets are operating properly, prices will reflect the present discounted value of land rents into the infinite future. In principle, this approach accounts for the full range of farmer adaptations. The limitation is that the validity of this approach requires consistent estimation of the effect of climate on land values.

Since at least the classic Hoch and Mundlak papers,growing blueberries it has been recognized that unmeasured characteristics are an important determinant of output and land values in agricultural settings.2 Consequently, the hedonic approach may confound climate with other factors and the sign and magnitude of the resulting omitted variables bias is unknown. In light of the importance of the question, this paper proposes a new strategy to estimate the effects of climate change on the agricultural sector. We use a county-level panel data file constructed from the Censuses of Agriculture to estimate the effect of weather on agricultural profits, conditional on county and state by year fixed effects. Thus, the weather parameters are identified from the county specific deviations in weather about the county averages after adjustment for shocks common to all counties in a state. This variation is presumed to be orthogonal to unobserved determinants of agricultural profits, so it offers a possible solution to the omitted variables bias problems that appear to plague the hedonic approach. Its limitation is that farmers cannot implement the full range of adaptations in response to a single year’s weather realization, so its estimates of the impact of climate change are biased downwards. Our analysis begins with a reexamination of the evidence from the hedonic method. There are two important findings. First, the observable determinants of land prices are poorly balanced across quartiles of the long run temperature and precipitation averages. This means that functional form assumptions are important in this approach. Further, it may suggest that unobserved variables are likely to covary with climate. Second, we replicate the previous literature’s implementation of the hedonic approach and demonstrate that it produces estimates of the effect of climate change that are very sensitive to decisions about the appropriate control variables, sample and weighting.

We find that estimates of the effect of the benchmark doubling of greenhouse gasses on the value of agricultural land range from -$420 billion to $265 billion , which is an even wider range than has been noted in the previous literature. Despite its theoretical appeal, the wide variability of these estimates suggests that the hedonic method may be unreliable in this setting.The results from our preferred approach suggest that the benchmark change in climate would reduce annual agricultural profits by $2 to $4 billion, but the null effect of zero cannot be rejected. When this reduction in profits is assumed permanent and a discount rate of 5% is applied, the estimates suggest that the value of agricultural land is reduced by $40 to $80 billion, or –3% to –6%. Notably, we find modest evidence that farmers are able to undertake a limited set of adaptations in response to weather shocks. In the longer run, they can engage in a wider variety of adaptations, so our estimates are downwards biased relative to the preferred long run effect. Together the point estimates and sign of the likely bias contradict the popular view that climate change will have substantial negative effects on the US agricultural sector. In contrast to the hedonic approach, these estimates of the economic impact of global warming are robust. For example, the overall effect is virtually unchanged by adjustment for the rich set of available controls, which supports the assumption that weather fluctuations are orthogonal to other determinants of output. Further, the qualitative findings are similar whether we adjust for year fixed effects or state by year fixed effects . This finding suggests that the estimates are due to output differences, not price changes. Finally, we find substantial heterogeneity in the effect of climate change across the United States. The largest negative impacts tend to be concentrated in areas of the country where farming requires access to irrigation and fruits and vegetables are the predominant crops .

The analysis is conducted with the most detailed and comprehensive data available on agricultural production, soil quality, climate, and weather. The agricultural production data is derived from the 1978, 1982, 1987, 1992, and 1997 Censuses of Agriculture and the soil quality data comes from the National Resource Inventory data files from the same years. The climate and weather data are derived from the Parameter-elevation Regressions on Independent Slopes Model . This model generates estimates of precipitation and temperature at small geographic scales, based on observations from the more than 20,000 weather stations in the National Climatic Data Center’s Summary of the Month Cooperative Files during the 1970-1997 period. The PRISM data are used by NASA, the Weather Channel, and almost all other professional weather services. The paper proceeds as follows. Section I motivates our approach and discusses why it may be an appealing alternative to the hedonic and production function approaches. Section II describes the data sources and provides some summary statistics. Section III presents the econometric approach and Section IV describes the results. Section V assesses the magnitude of our estimates of the effect of climate change and discusses a number of important caveats to the analysis. Section VI concludes the paper. The production function approach relies on experimental evidence of the effect of temperature and precipitation on agricultural yields. The appealing feature of the experimental design is that it provides estimates of the effect of weather on the yields of specific crops that are purged of bias due to determinants of agricultural output that are beyond farmers’ control . Consequently, it is straightforward to use the results of these experiments to estimate the impacts of a given change in temperature or precipitation. Its disadvantage is that the experimental estimates are obtained in a laboratory setting and do not account for profit maximizing farmers’ compensatory responses to changes in climate. As an illustration, consider a permanent and unexpected decline in precipitation. In the short run,square plant pots farmers may respond by increasing the flow of irrigated water or altering fertilizer usage to mitigate the expected reduction in profits due to the decreased precipitation. In the medium run, farmers can choose to plant different crops that require less precipitation. And in the long run, farmers can convert their land into housing developments, golf courses, or some other purpose. Since even short run farmer adaptations are not allowed in the production function approach, it produces estimates of climate change that are downward biased. For this reason, it is sometimes referred to as the “dumb-farmer scenario.”

In an influential paper, Mendelsohn, Nordhaus, and Shaw proposed the hedonic approach as a solution to the production function’s shortcomings . The hedonic method aims to measure the effect of climate change by directly estimating the effect of temperature and precipitation on the value of agricultural land. Its appeal is that if land markets are operating properly, prices will reflect the present discounted value of land rents into the infinite future. To successfully implement the hedonic approach, it is necessary to obtain consistent estimates of the independent influence of climate on land values and this requires that all unobserved determinants of land values are orthogonal to climate.4 We demonstrate below that temperature and precipitation normals covary with soil characteristics, population density, per capita income, latitude, and elevation. This means that functional form assumptions are important in the hedonic approach and may imply that unobserved variables are likely to covary with climate. Further, recent research has found that cross sectional hedonic equations appear to be plagued by omitted variables bias in a variety of settings .5 Overall, it may be reasonable to assume that the cross-sectional hedonic approach confounds the effect of climate with other factors . This discussion highlights that for different reasons the production function and hedonic approaches are likely to produce biased estimates of the economic impact of climate change. It is impossible to know the magnitude of the biases associated with either approach and in the hedonic case even the sign is unknown. In this paper we propose an alternative strategy to estimate the effects of climate change. We use a county-level panel data file constructed from the Censuses of Agriculture to estimate the effect of weather on agricultural profits, conditional on county and state by year fixed effects. Thus, the weather parameters are identified from the county-specific deviations in weather about the county averages after adjustment for shocks common to all counties in a state. This variation is presumed to be orthogonal to unobserved determinants of agricultural profits, so it offers a possible solution to the omitted variables bias problems that appear to plague the hedonic approach. This approach differs from the hedonic one in a few key ways. First, under an additive separability assumption, its estimated parameters are purged of the influence of all unobserved time invariant factors. Second, it is not feasible to use land values as the dependent variable once the county fixed effects are included. This is because land values reflect long run averages of weather, not annual deviations from these averages, and there is no time variation in such variables. Third, although the dependent variable is not land values, our approach can be used to approximate the effect of climate change on agricultural land values. Specifically, we use the estimates of the effect of weather on profits and the benchmark estimates of a uniform 5 degree Fahrenheit increase in temperature and 8% increase in precipitation to calculate the expected change in annual profits . Since the value of land is equal to the present discounted stream of rental rates, it is straightforward to calculate the change in land values when we assume the predicted change in profits is permanent and make an assumption about the discount rate. Since climate change is a permanent phenomenon, we would like to isolate the long run change in profits. Consider the difference between the first term in equation in the short and long run in the context of a change in weather that reduces output. In the short run, supply is likely to be inelastic , which means that Short Run > 0. This increase in prices will help to mitigate farmers’ losses due to the lower production. However, the supply of agricultural goods is more elastic in the long run, so it is sensible to assume that Long Run is smaller in magnitude and perhaps even equal to zero. Consequently, the first term may be positive in the short run but small, or zero in the long run. Although our empirical approach relies on short run variation in weather, it may be feasible to abstract from the change in profits due to price changes . Recall, the price level is a function of the total quantity produced in the relevant market in a given year.

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Type-1 crops describe field and row crops that have a period of senescence and defoliation

Recent efforts aimed at establishing, standards for data quality indicators and other scoring criteria are driven in part by a desire to properly account for sources of uncertainty in life-cycle assessments . Similar desires have been expressed towards water footprint assessments. As described by Hoekstra : “The field has to mature still in terms of calibrating model results against field data, adding uncertainties to estimates and inter-model comparisons as done in the field of climate studies”. Additionally, researchers now rely on computational methods to synthesize the large quantity of environmental data and observations that are characteristic of studies conducted at large temporal or regional scales. It is still uncommon for data and computational methods to be published along with the completed studies, which obstructs the reproducibility of many hydrologic studies . These later reasons motivated the form of this study—an elementary water footprint analysis decomposed into a reproducible framework. As a case study in resource sustainability, the State of California presents a unique combination of agricultural and economic activities, resource constraints, and environmental monitoring efforts. Among the United States, California has the greatest population, greatest total farm sales , and if considered separately, would rank as the fifth largest economy in the world, by gross domestic product . Nine out of California’s one hundred million acres contain irrigated agriculture; which requires 30 million acre feet of irrigation in an average year, accounting for 80% of the state’s water use . This freshwater requirement is met in part from a vast network of water storage and conveyance infrastructure, which transfer water from the northern third of California,barley fodder system where 2/3 of the precipitation and runoff occurs, to the southern two-thirds, where 3/4 of the anthropogenic water demands are located .

Management of California’s freshwater resources are constrained by dynamic availability on one side and strong, persistent demands on the other. Seasonal variations in precipitation affect the availability of freshwater resources in California The state has recently endured a 5-year drought from 2011-2016, marked by a period from 2012 to 2014 that had the worst drought severity in the past millennium. On the other side, California’s water resources underpin its standing as one of the most productive agricultural exporters in the world and as an important component of the nation’s food security. In 2015, California produced more than 99 percent of the United States’ almonds, pistachios, walnuts, grapes, peaches, and pomegranates . In the same year, international exports accounted for approximately 26 percent of the state’s agricultural production by volume, adding up to 44 percent of the total agricultural sales by value. California is the sole national exporter of many valuable commodities, including almonds, walnuts, and pistachios, which all lie in the top five of the state’s agricultural exports by value . Unpredictable seasonal availability and uncertain international appetite makes it difficult to predict the nature of future constraints and pressures on California’s water resources. There is no guarantee that future climatic, economic, or resource environments will accommodate all of the things that societies value: healthy produce, delicious animal foods, verdant natural vistas, thriving native wildlife, and the autonomy that comes from regional food security. The current attention placed in life-cycle sustainability indicators demonstrates an awareness of the desire to maintain environmental, social, and economic systems without limiting the ability of future generations to meet their needs .

When coupled with scenario analysis, these indicators can support strategic decisions to ensure the security of natural resource supplies. Water footprint assessments have been used to quantify the impact of lifestyles on California’s water resources and have been proposed as policy support tools . Additionally, these assessments have been used to describe the effect of California water resource challenges on international trade networks . While water footprint assessments align with the resource sustainability challenges of California, water scarcity is a problem shared by many nations globally . Therefore, reproducible sustainability assessments are useful in their ability to be applied and compared between different environmental and economic systems.This study used the California Irrigation Management Information System to obtain daily reference evapotranspiration observations across the state. Specifically, the Spatial CIMIS data product was used to obtain raster representations of daily ET0 at a 4 km spatial resolution. This data was upscaled to 30 meters, using bilinear interpolation . The original data is housed and maintained by the California Department of Water Resources , and can be accessed through the CIMIS web interface. CIMIS comprises a network of over 100 automated weather stations that measure the different meteorological parameters at urban and rural sites throughout California. The system was originally established as a project of DWR and the University of California, Davis in 1982 . Each station is sited away from buildings and trees, on a bed of healthy grass that is: “well maintained, properly irrigated and fertilized and mowed or grazed frequently to maintain a height between 10 to 15 centimeters ” . Hourly weather observations are transmitted nightly to Sacramento, where the data are used to compute an average daily evapotranspiration of the reference grass surface underneath each station, using a modified version of the 1977 FAO Penman-Monteith ET0 equation .

The CIMIS Equation differs in its use of a wind function and a method of calculating net radiation from mean hourly solar radiation .The ET0 observations are made publicly available with the primary purpose of aiding agricultural growers develop irrigation schedules. While the CIMIS network provides station-specific ET0 calculations, the Spatial CIMIS data product produces a continuous daily ET0 calculation across the entire state. This is accomplished by using raster observations from the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellite system as inputs to the ASCE-Penman-Monteith ET equation . Spatial CIMIS also interpolates temperature and wind measurements from CIMIS stations, to serve as inputs to the ASCE-PM equation . Radiative inputs to the ASCE-PM equation are derived from a clear sky factor that is directly related to cloud cover, as observed by GOES satellite data. Specifically, Spatial CIMIS uses GOES visible imagery to derive a clearness parameter that is directly related to cloud cover in a given grid cell. This is combined with a clear sky solar radiation model developed for the Heliosat-II model . Heliosat-II is a software commissioned by the Solar Radiation Data project, with the purpose of converting images acquired by geostationary meteorological satellites into maps of global solar irradiation, received at ground level . The model incorporates a seasonal turbidity factor, which describes atmospheric attenuation of light due to aerosols and gases. Additional description of inputs to the Spatial CIMIS implementation of the ASCE-PM equation can be found in Appendix. Spatial CIMIS has a weakness in estimating solar radiation in scenarios where changes in the surface albedo can be mistaken for cloud cover. This typically occurs in regions that have snowfall and persistent fog,hydroponic barley fodder system both common winter conditions for some regions in California. Grid cells that contain snow cover and/or fog that persist for greater than 14 days lead to an underestimation of cloud cover and an over-prediction of net radiation during cloudy days Hart et al., 2009. Depending on the location in California, some studies have found good agreement between Spatial CIMIS ET0 and other methods, while others have used Spatial CIMIS after applying correction factors . This study used crop coefficients from Basic Irrigation Scheduling to scale Spatial CIMIS ET0 into crop-specific estimations of evapotranspiration ETc. Kc values for 45 unique crops were selected from the BIS software. These values were supplemented with Kc values from the Consumptive Use Program Plus for garlic and oranges and values from the University of California Division of Agriculture and Natural Resources for some orchard crops. Kc values for peppermint and unspecified caneberries were selected from the AgriMet crop coefficients, which were assembled by the United States Bureau of Reclamation , Pacific Northwest region. Kc values for unstressed Pomegranites were obtained from a study conducted at the Ben-Gurion University of the Negev, Israel. BIS is an application implemented in Microsoft Excel that is used for the planning of irrigation schedules for crops in California .

The software was developed as a collaboration between the University of California, Davis, the California Department of Water Resources, and the University of California Cooperative Extension. The program is currently hosted by the UC Davis Biometerology Group and can be accessed at the BIS home page. Among other uses, BIS is used to determine irrigation schedules, irrigation timings, and maximum allowable soil water depletion for 66 unique crop types. It accomplishes this by estimating crop evapotranspiration given mean climate data for a particular region. BIS partitions evapotranspiration into the component of water evaporated from spoil and plant surfaces and the component transpired by leaves . As the crop matures, the ratio of T to ET increases, until the transpiration component dominates crop ET. To account for the variable ETc , BIS defines: Kc values at different stages in a crop’s life cycle, typical planting and harvest days, and the proportion of the growing period dedicated to each growth stage. These coefficients are defined according to the FAO-56 “single crop coefficient” method, which assigns values according to 4 growth stages of a typical crop: initial growth, crop development, mid-season, and late-season . These growth stages characterize a crop’s daily Kc function, a curve that describes how the values vary as a function of the time in the crop’s growing period. BIS distinguishes between four main crop types.They are characterized by crop coefficients with three inflection points, at 10% ground shading, 75% ground shading, and the onset of senescence. Some type-1 crops such as peas and lettuce, are harvested before their period of senescence. They are characterized by two inflection points, at 10% ground shading and 75% ground shading. Type-2 crops have Kc values that are essentially fixed for most of the season. These include alfalfa, pasture, and most types of turfgrass. Shading of soil by dormant grass may cause an over prediction of soil evaporation and total ETc, however the error may be slight due to the lower overall ETo during the cold winter season Richard L. Snyder, 2014. Type-3 crops do not have a water requirement prior to shoot and leaf growth in the spring and can be characterized by a Kc curve with two inflection points. Type-4 crops represent orchard crops that have fixed Kc values throughout their growing season—similar to type-2 crops. Type-4 crops include subtropical orchards . This study assigned Kc values to individual grid cells according to the crop cover, as observed in the Cropland Data Layer . The United States Department of Agriculture National Agricultural Statistics Service has produced land cover raster image products for major agricultural regions since 1970, and for the 48 conterminous states since 2009 . Annual CDL images can be viewed through CropScape, a web GIS application maintained by USDA-NASS and the Center for Spatial Information Science and Systems at George Mason University. CDL rasters can be downloaded from the CropScape web service, or at the National Resources Conservation Service Geospatial Data Gateway. The CDL was first created by the USDA NASS Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section . It was based on an image processing and acreage estimation software named Peditor, written in the 1970s and maintained through 2006 . The stated goal of the NASS CDL program is to provide commodity acreage estimates to the Agricultural Statistics Board and other agricultural stakeholders. CDL rasters use standard land cover categories, with an emphasis on agricultural land covers. Records for the State of California begin in the 2007 calendar year; CDL products have a 56-meter spatial resolution from 2007-2009, and a 30-meter spatial resolution from 2009-present. Currently, the CDL is primary constructed from the supervised classification of remotely sensed satellite imagery, from the Advanced Wide Field Sensor onboard the Indian Remote Sensing satellite, RESOURCESAT-1 . This is supplemented with imagery from land imaging sensors onboard the United States Geological Survey Landsat satellites and 16-day Normalized Difference Vegetation Index composites, from the National Aeronautics and Space Administration moderate-resolution imaging spectroradiometer .