A short lasting fall or spring frost lasts a few hours and can cause substantial damages

Part of the modeled gains in consumer surplus are enjoyed elsewhere, as the majority of pistachio output is currently exported. However, export demand is usually considered more elastic than domestic demand, making the share of local consumer surplus gains disproportionate to the share of local consumption. At a share of 1/3 of total consumption, let us assume that Californians still enjoy half of the consumer surplus gains from MCE . Adjusting Table 4.1, the total welfare gains in California are strictly negative when the demand is unrealistically inelastic, εD = −0.5, and strictly positive for more realistic demand assumptions . The scope of consumer surplus gains brings us to the potential gains from public investment in R&D for MCE solutions. With social returns from investments largely exceeding private ones, this type of research is a good candidate for prioritizing in public research fund allocation . The case for public research is made stronger by the fact that there seems to be little private incentive to invest in MCE, at least in this case. I see MCE technologies mostly as an adaptation of existing ones to solve a climate problem. Therefore, innovations in the field would be hard to make proprietary by the innovator. Moreover,growing pot innovators are likely to come from the industry: a large growing firm would have the resources and access to enough pistachio acreage to run experiments and develop new MCE solutions. But if this firm sees that a world with MCE is worse, why invest in innovation?

Adding market power to the equation makes an even stronger potential case for public R&D: the total welfare gains are higher, and the incentives for innovation could be even lower. What might be the implications of MCE technologies in a broader sense? One could imagine, with further agronomic research, other MCE technologies applied to other fruit and nut crops, and even for annuals such as corn or soybeans. Of course, these are less profitable than pistachios, but they face similar challenges, and MCE solutions are not necessarily very expensive. Other implications could be with the distribution of climate change damage incidence. Technologies might only be available to growers in countries better off financially, further exacerbating international income disparities. An interesting potential for MCE technologies could be in accelerating the transition of agricultural practices closer to the poles, sometimes referred to as the “crop migration” . For example, MCE solutions for frost could accelerate the expansion of viticulture to higher latitudes. The simulation based valuation methodology in this chapter has its caveats. Modeling supply and demand as linear is obviously a simplification. The assumptions on growth and distribution of acreage are based on past growth patterns, and might not reflect unexpected future changes in market conditions. The future chill predictions are in line with other predictions by climatologists, yet might fail to materialize. Nevertheless, by choosing various scenarios, basing the parameter ranges in the literature, and choosing conservatively when possible, I believe to have gotten a reasonable range for the potential gains from MCE in California pistachios. They are in the low billions for a crop of secondary importance to California agriculture. I believe this shows a great potential of MCE technologies for climate change adaptation in general.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, 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,square pot 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. Similar findings have been replicated in various crops and locations around the world. 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. It turns out that Pistachios, a billion dollar crop in California, could be threatened by warming winter within the next 20 years. While the scope and magnitude of our current climate crisis might be unprecedented in human history, this is not the first time that humans are facing climatic challenges in agriculture. Olmstead and Rhode show how, through the 19th and 20th centuries in North America, wheat growers managed “…to push wheat cultivation repeatedly into environments once thought too arid, too variable, and too harsh to farm”. The transition was made possible mostly by the development of new varieties. Plant breeding toward that end required information on the climate both in the progenitor native areas and the areas where the eventual new varieties would be planted . Adaptation to climate can be on the physical dimension as well. Specific interventions can be designed to change the physical environment surrounding plants. The most obvious intervention is building irrigation systems, to compensate for lack of adequate rainfall and soil moisture. But examples of adaptation to temperature by physical means exist as well. This type of intervention is common for a left tail effect: frost. To avoid it, only a slight increase in temperature is required, and growers know how to do that. Some examples for dealing with frost are hundreds of years old. The Tiwanaku civilization formed a system of raised fields on the shores of lake Titikaka in the 7–12 centuries C.E. Fields in select locations were raised with extra soil, up to a few feet above the ground level. Water from nearby springs was diverted and run through canals dug in these raised fields. This provided not only moisture for the plants, but also converted the top soil level into a large heat storage unit. On frost nights, which are common in this high area, the heat stored in the soil kept the near-surface temperatures on raised fields higher than the normal air temperatures, preventing plants from freezing . Without modern weather instruments, the Tiwanaku realized that slight differences in ambient temperatures can have crucial consequences, and planned their fields according to their understanding of the climate. This system yielded far better than regular dry farming practiced before in this area, and supported a larger population than the one residing on the lake shores in the 1990’s. Eventually, as climate became drier, the water level of lake Titikaka dropped and the springs dried up, resulting in the collapse of the Tiwanaku culture . Despite its eventual failure, this technology was successful in abating frost damage for centuries, maintaining a population of hundreds of thousands and showing the power of human intervention on the field level to tackle a temperature distribution tail challenge. In Europe, traditional methods of dealing with frosts in vineyards include lighting small fires or “frost candles”. A more modern approach uses big fans, circulating the cold air in the inverted layer with the warmer air on top of it. Farmers have been using “air disturbance technology” in the US since the 1950’s . Wind generators are used around the world to protect wine grapes, fruits, and even tea from spring frosts. In some cases, a similar effect can be achieved with sprinklers . Interestingly, little economic literature has focused on air disturbance technologies. Stewart, Katz, and Murphy assess the value of weather information in the Yakima Valley of central Washington, in the context of frost prediction and air disturbance technologies. This descriptive study was published in the Bulletin of the American Meteorological Society.

Soil at both sites was fumigated once a year in summer over a two-year period

Enzyme activities can be used as an index of microbial functional diversity, although accumulated enzymes may contribute considerably to the overall enzyme activity of a soil. A semi-quantitative method to determine enzyme protein contents in soil based on the specific activities of reference enzymes and enzyme activity values of soils was reported by Klose and Tabatabai in order to prove whether there is a direct correlation between the activity of any enzyme and its protein concentration in soil. This approach is based on the assumption that the compositions of the reference enzymes are similar to those in soils. Protein concentrations were suggested to serve as a suitable measure to quantify the effects of environmental changes, for example after application of pesticides, on soil biological properties . The understanding of the impacts of pesticide fumigants on key biochemical reactions involved in organic matter degradation and soil nutrient dynamics is important in order to evaluate the ecological significance of fumigation on the soil system. The toxicity of fumigants is related to their interference with respiratory enzymes, including pyruvate dehydrogenase, their ability to chelate metal cations such as Cu, the inhibition by the unchelated ion,blueberry grow bag and toxic degradation products such as methyl isothiocyanate . MeBr can be degraded in soils by the following three pathways : a) chemical hydrolysis to form methanol and bromide, b) methylation to soil organic matter and release of bromide ion, and c) microbial oxidation to form formaldehyde and bromide ion.

Biological hydrolysis and other microbial processes involving enzymatic processes are also likely to contribute to the degradation of MeBr in soil . Microbial respiration, nitrification potential, and dehydrogenase and arylsulfatase activities were inhibited by MeBr + CP and the alternatives PrBr, InLine, Midas and CPEC one week after soil fumigation . After 30 weeks, there was no difference in microbial biomass and activities between the treatments studied, with the exception of lower acid phosphatase and arylsulfatase activities in fumigated soils. These results indicate that there are short- and long-term differences in the response of various microbial and enzymatic processes to MeBr + CP and alternative fumigants and thus, of the various functions of the soil biota in ecosystems. A limitation of this study is that it was conducted for a maximum of 37 weeks; it remains unknown if MeBr + CP and alternative fumigants have longer-term impacts on soil biochemical processes under field conditions after multiple applications. The objective of this study was to evaluate the effect of repeated soil fumigation with MeBr + CP and two registered and two non-registered alternative fumigants on microbial biomass and respiration, the activities of dehydrogenase, acid phosphatase, β-glucosidase and arylsulfatase, and enzyme protein concentrations in soils. Furthermore, the effect of these fumigants was evaluated on dry proteins containing β-glucosidase, acid phosphatase and arylsulfatase in the absence of immobilizing or protecting constituents of soil .

The selected alternative fumigants represent the actual formulations that likely will be used by growers for strawberry production. Dehydrogenase activities were selected because they reflect the total oxidative activities of soil microorganisms and are important in oxidizing soil organic matter. Acid phosphatase catalyzes the hydrolysis of a variety of organic phosphomonoesters and is therefore important in soil organic P mineralization and plant nutrition. The enzyme β-glucosidase catalyzes the hydrolysis of cellobiose, and thus plays a major role in the initial phases of the decomposition of organic C compounds. Arylsulfatase is believed to be partly responsible for S cycling in soils as it participates in the process whereby organic sulfate esters are mineralized and made available for plants. The first aim of the present study was to test whether soil fumigation with these four potential pesticides will alter important soil functions that, in turn, will affect the long-term productivity of agricultural soils. The second aim of this study was to evaluate the effects of soil fumigation on the activities of enzyme proteins, which may be present in the soil as free enzymes and not protected by clay-humus complexes. Free enzymes are likely to be more sensitive to environmental factors as intracellular or adsorbed enzymes, which are protected by the cell envelope or by clay-humic complexes. Field studies were conducted in California, USA, in the central region in Watsonville and in the southern region in Oxnard in 2000 and 2001. Both sites are located in intensive strawberry production areas of California. Soil at both locations had not been fumigated for the past 2 and 3 years prior to this experiment for Watsonville and Oxnard site, respectively. However, before that soil at both sites had been fumigated routinely with MeBr + CP for the past 10 years. The soil in Watsonville is classified as an Elder sandy loam . The soil in Oxnard is classified as a Hueneme sandy loam .

The past 50-year average annual precipitation is 582 mm and 385 mm at Watsonville and Oxnard, respectively. The average annual maximum and minimum temperature at Watsonville is 19.5ºC and 10.7ºC, respectively. Corresponding values for Oxnard are 21.2ºC and 10.7ºC. Commercial agricultural practices for the area were followed . The soil was tilled and beds were formed in Watsonville at 132 cm center-to-center and in Oxnard at 173 cm center-to-center . Slow release fertilizer was applied to the beds at the rate of 400 kg ha-1 y -1. A drip irrigation system was used consisting of two drip tapes , with emitters spaced 30 cm apart and an emitter flow rate of 0.87 l min-1 at 70 kPa, placed 10 cm and 30 cm from the bed center at a soil depth ranging from 2 to 5 cm. Fumigation treatments were randomized in a complete block design with four replicates per treatment at each site. Fumigants used, fumigant rates and application methods are summarized in Table 1. Each replicate consisted of three neighboring 15-m long beds.Soil in Watsonville was fumigated on August 10, 2000 and September 27, 2001, the soil in Oxnard was fumigated on September 1, 2000 and August 24, 2001. At the time of fumigation, the average daily soil temperature within the raised bed ranged between 16 to 20ºC, and the average soil water content was less than 85% of field capacity . About 4 weeks after fumigation bareroot strawberry [Fragaria X ananassa Duchesne, variety “Diamante” and “Camarosa” ] was transplanted in 2000 and 2001. Pesticide effects on soil microorganisms are difficult to evaluate because of the heterogeneous physical-chemical nature of soil, resulting in uncertainties about their distribution and fate within soil microsites. Previous studies on the effects of potential MeBr alternatives on the size,blueberry grow bag size composition and activity of soil microorganisms are limited to one or a few fumigants, a relative short time period, and/or the laboratory . Recovery of microbial processes in the laboratory compared to the field may be reduced due to the absence of re-colonization by nonfumigated soils . Furthermore, the effect of alternative fumigants on soil microbial processes was studied on soils with a 10-year history of fumigation with MeBr + CP combinations followed by a 2 to 3 year break prior to the initiation of these field experiments at Watsonville and Oxnard, respectively. Consequently, results obtained from these soils with a long-term fumigation history may not apply to soils previously not fumigated . The results presented in this work are part of a longer study to evaluate application methods and efficacy of chemical MeBr alternatives to control weeds and pathogens in strawberry production systems in California, USA. The response of microbial performance to soil fumigation with InLine, CP, PrBr and Midas relative to the standard MeBr + CP application and a control soil was determined at 1, 4, and 30 weeks after fumigation in 2000, the first year of the study. Fumigation initially inhibited microbial respiration, nitrification potential, and activities of dehydrogenase, acid phosphatase and arylsulfatase . After 30 weeks, microbial activities in fumigated and control soils were similar at both sites, with exception of acid phosphatase and arylsulfatase activities in selected treatments that remained lower in the fumigated soils. Soil microbial biomass C and β-glucosidase activity were not affected by fumigation with MeBr + CP and alternatives throughout the whole study period in the first year .

This paper focused on the effects of repeated soil fumigation with MeBr + CP, PrBr, InLine, Midas, and CP on the size and activity of soil microorganisms and hydrolytic enzymes, which control the degradation of organic substances and the rate at which nutrient elements become available for plants . Microbial respiration was significantly decreased in Oxnard soils fumigated with MeBr + CP, but not affected by the four selected alternative fumigants at both sites. In this study, microbial respiration showed a low sensitivity to detect changes in soil microbial activity due to repeated application of the standard MeBr + CP combination and alternative fumigants. This finding is in contrast with the high sensitivity of respiration measurements to treatment of soils with heavy metals and pesticides . Significant lower respiration rates in Oxnard soils fumigated with MeBr + CP compared to recently not fumigated control soils however, may indicate a decreased biological activity. Soil fumigation had no significant effect on microbial biomass C, and the results for microbial biomass N were inconsistent over the two experimental locations. Therefore, the effects of soil fumigation on total microbial biomass content provided little information on possible changes in the size of microbial populations. The overall low response of microbial biomass and respiration to repeated soil fumigation may be related to selected effect on sensitive microbial populations and the growth of resistant species. The latter may feed on cell debris, leading to restructuring of soil microbial populations as indicated elsewhere . Selected specialized bacteria may also use the fumigants as a source of carbon and energy, as documented for agricultural soils repeatedly subjected to MeBr fumigation . The effect of soil fumigation on the activities of dehydrogenase, β-glucosidase, acid phosphatase and arylsulfatase varied among the soil enzymes and within the two study sites. At the Watsonville site, soil fumigation with alternative fumigants generally had no significant effect on the activities of the four soil enzymes studied over the two year study period. Fumigation with MeBr + CP however severely affected the activities of β-glucosidase and acid phosphatase . These results suggest that biochemical reactions involved in organic matter degradation and P mineralization were affected by fumigation to a greater extent than were those reactions reflecting the general oxidative capabilities of microbial communities or involved in S mineralization in soils. In contrast, at the Oxnard site, β- glucosidase and acid phosphatase activities were relatively stable towards repeated soil fumigation, but dehydrogenase activity was significantly decreased by MeBr + CP. The reasons for these site-related variations in the response of soil enzyme activities to soil fumigants remain unclear. The two study sites showed very similar soil physical and chemical properties, such as clay and organic C contents. Variations may have occurred in the actual soil moisture content and temperature at the time of fumigation, which were proved to be crucial for the efficacy of pesticide applications . The results also suggest that the four alternative fumigants had no longer-term impact on enzyme reactions involved in organic matter turnover and nutrient cycling in soil. The inhibitory and/or activation effects of any compound in a soil matrix on enzyme activity are largely controlled by the reactivity of clay and humic colloids . The finding that MeBr + CP and the alternative fumigants led to a greater inhibition of the activities of the reference enzymes than that of soils suggests that free enzymes are more sensitive to soil fumigation than enzymes that are associated with the microbial biomass or enzymes adsorbed to clay or humic colloids. Ladd and Butler hypothesized that some enzymes are stabilized in the soil environment by complexes of organic and mineral colloids and therefore are partially protected from denaturation by fumigation. Similar results were observed for acid phosphatase, β-glucosidase and arylsulfatase in chloroform fumigated soils . Furthermore, reference enzymes were purified from one source for each protein, whereas soil enzymes derive from various sources leading to a set of isoenzymes [i.e., enzymes that catalyze the same reaction but may differ in origin, kinetic properties or amino acid sequencing ].

We estimated nitrogen input from biological fixation for soybean

To reflect the trend of farm energy efficiency gains, we adopted the estimates from the widely used GREET model , which shows an efficiency increase of about 30% for corn and soybean growth over the last decade. Few studies exist on cotton and wheat on-farm energy change, thus we assumed a similar 30% efficiency gain for them over the timescale investigated. Note that we did not consider nitrogen from manure considering that it is small relative to other nitrogen sources .Building on our previous studies , we estimated a large number of emissions from all the agricultural inputs applied based on emission factors from various models and references . Most of the emissions are pesticides and speciated Volatile Organic Compounds . Estimation of pesticide emissions was slightly more complicated than that of other emissions, thus a detailed explanation is in order. Several approaches to pesticides emissions have been applied in literature and LCA databases. For example, the Ecoinvent database assumes that all pesticides remain in soil after application . The PestLCI model, on the other hand,plastic grow bag treats agricultural soil as part of the technosphere and excludes the impacts of pesticides on ecosystems in the soil .

And yet there is another approach that estimates pesticide emissions to different compartments . We adopted the third approach here. Following Berthoud et al. , we used a pesticide’s vapor pressure to approximate its air emissions, assumed a generic factor of 0.5% of the total applied for pesticides lost to water systems through runoff and leaching, and assumed the remaining fraction, capped at 85% of the total applied, for pesticides emitted to soil.Last, the data we compiled are at the state level, but given our emphasis on the change of environmental impacts of U.S. agriculture on average we aggregated the state-level results to present totals. We also aggregated the three different types of wheat into one “wheat” by adding up their annual agricultural inputs and outputs. In deriving the impacts per ton of crop produced, we followed previous studies and used 3-year average yield data to reduce annual variation caused by possible extreme weathers such as droughts and floods. For example, 2001 impact per ton for corn was calculated by dividing 2001 impact per ha by the average corn yield of 2000, 2001, and 2002. As Fig. 4.2 reflects, changes in the average irrigation water use from 2002 to 2012 were also moderate for corn, cotton, and wheat, with variations <20% between 2002 and 2007 or between 2002 and 2012. In contrast, a noticeable upward trend can be observed for soybean. Average irrigation water use per ha soybean produced increased by around 50%, from 180 m3 in 2002 to 270 m3 in 2012. On a per ton basis, the percentage increase is 30%, from 4300 to 5600 m3 , due to yield increase over the period. Behind this upward trend are several factors, including the slightly increasing irrigation intensity for irrigated area, but the major contributor is the growth in area irrigated and its share in the total area harvested .

What led to the growth in soybean area irrigated is unclear, however, and further research is needed. Here, we offer a possible explanation. In the past “ethanol decade,” soybean and corn areas substantially expanded, into other cropland and also grassland . Because such marginal land as grassland is on average not as fertile as existing corn or soybean land , irrigation might have been applied to boost or maintain yield. Consequently, as total soybean and corn areas expanded, so also did the area irrigated. In the case of corn, however, although area irrigated grew from 4.0 to 5.4 million ha between 2002 and 2012, its share in the total area harvested only slightly increased . Additionally, irrigation intensity for area irrigated decreased from 1480 to 1234 m3 ha-1 . As a result, average irrigation use per ha or per ton corn produced barely changed from 2002 to 2012. Major contributors include reduced use of herbicides atrazine and acetochlor, and of insecticides terbfos, dimethenamid, and, especially, chlorpyrifos . The downward trend is likely due to the continuous expansion of herbicide resistant and insect-resistant corn, particularly glyphosate tolerant and Btcorn. Since its introduction in 1996, HR corn has now expanded to over 70%of cornfield , resulting in increasing use of glyphosate compounds in place of conventional herbicides like atrazine and acetocholor. In fact, glyphosate and related compounds had gradually surpassed atrazine and other herbicides over the past decade to become the most commonly applied pesticide . As glyphosate compounds are relatively less toxic to ecosystems compared with the replaced herbicides like atrazine and acetochlor , the overall ecotoxicity impact of corn attributable to herbicides decreased moderately between 2001 and 2010. Meanwhile, Bt corn has also dominated U.S. cornfield now , offering both economic and environmental benefits by protecting yield and reducing handling and use of insecticides .

This likely further contributed to the downward trend of corn’s freshwater ecotoxicity impact. Similar to corn, the freshwater ecotoxicity impact of cotton decreased by 60% from 2000 to 2007, due to the reduced use of herbicides chlorpyrifos, lambda–cyhalothrin, and particularly cyfluthrin . Application of cyfluthrin reduced from 11 g ha-1 in 2000 to 4 g ha-1 in 2007. Similar to corn, the downward trend in cotton’s freshwater ecotoxicity impact was attributable to the expansion of HR and Bt varieties, which are now planted 95% and 75% of U.S. cotton field respectively . Our result on decreasing freshwater ecotoxicity impact of corn and cotton due to changes in pesticide use and patterns reinforces previous findings . Unlike corn and cotton, soybean’s freshwater ecotoxicity impact quintupled between 2002 and 2012. HR soybean has also expanded dramatically in the US, now planted on 95% of soybean field . Along with the expansion, application of glyphosatecompounds per ha has increased by over 60% between 2002 and 2012, and now they account for 80% of total pesticides applied in soybean growth. However, the benefits of HR soybean seem to have been entirely offset by the increasing use of insecticides lambdacyhalothrin, cyfluthrin, and chlorpyrifos . This is due to the invasion of soybean aphid, a species native to eastern Asia and first detected in North America in 2000, and application of insecticides has been the primary means of pest management . Since its first detection, soybean aphid had rapidly spread to 30 states in the U.S. by 2009 and become a major source of economic loss in soybean production . As a result, the total quantity of insecticides applied to soybean quadrupled between 2002 and 2012, resulting in a 3-fold increase in soybean’s freshwater ecotoxicity impact. The freshwater ecotoxicity impact of wheat increased by about 40% from 2000 to 2009, attributable partly to increased use of several insecticides including chlorpyrifos, cyfluthrin, beta–cyfluthrin, and lambda–cyhalothrin. Also, pesticide application rate in general increased from 0.45 kg ha-1 in 2000 to 0.88 kg ha-1 in 2009. Unlike the other major crops, however,PE grow bag there is not a clear explanation for the upward trend. One possible reason may be the growing resistance of pests as a result of increasing pesticide use. Further research is needed in this area. We conducted sensitivity analysis to test the robustness of the changes in freshwater ecotoxicity impact, considering that it is our major finding and that large uncertain is involved in the estimation of pesticide emissions and assessment of their ecotoxicity impact . First, the proportion in which pesticides are emitted to water systems was identified as the major contributor to crops’ freshwater ecotoxicity. Literature also shows it may vary greatly, from 5% to 0.1% or even less . We thus built 3 scenarios to test the sensitivity of the ecotoxicity result to different leaching and runoff rates. Additionally, we also tested the sensitivity of the trends to other analytical approaches to pesticide emissions , with one assuming all pesticides to remain in soils and the other excluding the impact of pesticides on agricultural soils. All 5 scenarios are presented in Fig. 4.4, which reinforces the trends identified of freshwater ecotoxicity impact regardless of different runoff and leaching rates and analytical approaches to pesticide emissions. Second, impact assessment of freshwater ecotoxicity is also highly uncertain, with the uncertainty range for TRACI 2.0 being likely 1-2 orders of magnitude . However, detailed information on the distribution of each characterization factor is not available yet, thus a full uncertainty analysis is not feasible at this stage. To further test the robustness of the ecotoxicity results, we applied two other characterization models  to evaluate the aquatic ecotoxicity impact of pesticide emissions.

For corn, cotton, and soybean, the other two models confirm the directionality of the changes but generally show a lower magnitude of change . This is due in part to differences in the number of pesticides covered by the three models and in part to differences in the relative ecotoxicity potential they assign to each pesticide. Generally, IMPACT 2002+ and CML 2001 cover a smaller number of pesticides than TRACI 2.0, thus they may not capture all the changes in pesticide use and patterns that are captured by TRACI 2.0. For wheat, however, the three characterization models seem to disagree on the directionality as well as the magnitude of changes. A detailed comparison, together with contribution analysis, is provided in the Appendix C. In this study, we evaluated several non-global environmental impacts of U.S. corn, cotton, soybean, and wheat, and analyzed how they changed in the past decade. Due likely to the increasing adoption of genetically modified varieties, freshwater ecotoxicity impact per ha corn produced declined by around 50% from 2001 to 2010 and per ha cotton produced declined by 60% from 2000 to 2007. Due to the invasion of alien species and increasing use of insecticides, freshwater ecotoxicity impact per ha soybean produced increased by 3-fold from 2002 to 2012. In the meantime, on-farm irrigation water use per ha soybean harvested increased by about 50%. In comparison, other non-global impacts were relatively stable. The major implication of our study is that identifying the underlying drivers of the dynamical mechanisms in agricultural systems would be essential for making informed agricultural decisions and policies, prioritizing LCA data update needs, and interpreting LCA results. By evaluating the relative ecotoxicity potential of a large number of pesticides, we found that the use of GM crops have contributed to substantial declines in corn and cotton’s freshwater ecotoxicity impact. This finding provides an opportunity for better assessing the trade offs between the potential impacts of GM and conventional crops, as opposed to comparisons based mainly on the total quantity of pesticides applied . Additionally, our results suggest that updates on agricultural inventory data can be done selectively, with regular updates needed for impact categories that are highly dynamic, such as pesticide related ecotoxicity. Studies relying on single-year and outdated data may inaccurately portray a crop’s ecotoxicity impact; even just a few years of data age may under or overestimate the ecotoxicity impact. This also implies that we should exercise caution when interpreting an LCA study in which ecotoxicity impact of agricultural processes plays an important role in the overall conclusion. Broadly, our study highlights the importance of understanding the dynamics in the input and output structure of a process or a technology in LCA . The focus of our study was to evaluate how environmental impacts of agriculture might have changed in the past decade. Our results that show decreasing freshwater ecotoxicity impacts for corn and cotton are not intended to prove that GM crops are overall more ecologically friendly than conventional crops. Other impacts of GM crops that could not have been evaluated due to the limitations of the current LCIA methods should also be taken into consideration in such comparisons. Current LCIA methods, for example, are not able to properly evaluate potential adverse effects of Bt toxin on populations of non-target species and elevated risk of species invasiveness through genetic modifications . In addition, it should be noted that the trend of decreasing ecotoxicity impact is unlikely to continue for cotton and corn.

Agricultural imports grew at an average rate of 5.9 percent over the same time period

However, the real value of China’s agricultural trade grew at only 2 percent per year, on average, from 1980 to 1996. The overall composition of China’s agricultural trade is presented in Tables 1 through 3. Tables 1 and 2 report exports and imports, respectively, over the five year 1992-96 time period. For the purposes of summarizing these extensive trade data, we have broken the agricultural trade figures in Tables 1 and 2 into four categories: grains, horticultural products, animal products, and other.China’s total agricultural exports were valued at $10.6 billion in 1996. Exports of grains were valued at $1.4 billion in 1996 and edible oil seeds and oils accounted for most of these grain exports. Maize exports were near zero in 1995 and 1996 . Earlier, in 1992 and 1993, maize exports were much more important and maize alone accounted for 13 percent of total agricultural exports in each of those two years. Prior to the export blockade, grains accounted for over 27 percent of total agricultural exports. In 1996, China’s horticultural exports totaled $5.1 billion, up from $3.5 billion in 1992. As a share of total agricultural exports, horticultural products increased from 39 percent in 1992 to 48 percent in 1996. Fruit and vegetable products are by far the most important component of horticultural exports, followed by “other crops” and vegetables . Exports of animal products also grew over this 1992 to 1996 time period,plastic square flower bucket from $2.0 to $3.4 billion, and from 22 to 33 percent of total agricultural exports. Unlike, grains and horticultural products, no one commodity has dominated animal product exports.

Processed poultry, processed swine, and raw wool were the most valuable exports in 1996 but in total these three commodities accounted for less than 15 percent of animal product exports.Turning to Table 2, we find that China’s agricultural imports grew from $4.9 billion in 1992 to $9.9 billion in 1996. Grains typically make up over one-half the value of imports, with wheat and vegetable oils and fats the major imports. In 1992, wheat plus vegetable oils and fats made up over 40 percent of total agricultural exports, with wheat at 30 percent and vegetable oils/fats at 10 percent. In 1996, these two commodity groups still had a 40 percent share of imports, but wheat’s share fell to 19 percent and vegetable oils/fats increased to 21 percent. From 1992 to 1996, the value of horticultural imports increased from $0.8 to $1.4 billion, but horticultural’s share in total agricultural imports fell from 18 percent to 14 percent over this period. Fruits tend to be the most important horticultural import, but imports are diversified across this product grouping. The share of animal products in total imports also fell over this period from 22 percent to 17 percent . Raw wool is by far the most important item in this group, accounting for over one-half of animal product imports. The data in Table 2 are the official imports and for some commodities they significantly under report the value of trade due to smuggling from Hong Kong. This is especially true for horticultural and animal products and this issue is discussed below.

We can utilize Table 3 to comment on Wang’s finding that China’s pattern of agricultural trade is consistent with its resource endowment, importing land intensive bulk commodities and exporting labor intensive horticultural and consumer ready products. For this purpose, we have aggregated China’s agricultural trade into the same categories defined by Wang : bulk commodities, consumer ready products, horticultural and other food products, and processed intermediary products. The make-up of these four categories is explained in the notes to Table 3.Our database for Table 3 covers the 1992 to 1996 time period, whereas Wang’s analysis was based on 1995 and 1996 data alone. Because of the export blockade, we believe the 1992 to 1994 time period gives a clearer picture of the economic forces within China that are influencing trade patterns, but of course it is still a very short time period. With the information revealed by these additional years, the conclusions by Wang are found to be questionable. Consider the top panel of Table 3. This panel shows that from 1992 to 1994, bulk commodities were indeed an important component of exports, accounting for anywhere from 25% to 29% of China’s exports. As expected, there was a sharp decline in the share of bulk commodity exports in 1995 during the blockade. In 1995 and 1996, China became a net exporter of rice and maize, shifting away from a net exporter position in the 1992-1994 time period. From 1992 to 1996 there was little change in the percentage of exports explained by two of the categories; horticultural and other food products, and processed intermediary products. The pattern of imports over the 1992 to 1996 time period is shown in the bottom panel of Table 3. The most striking result associated with these data is the lack of any trend, measured by the relative import percentages shown in the bottom one-half of the panel. Either bulk commodities or processed intermediary products account for around 90% of China’s official imports of agricultural products. The bulk imports are heavily concentrated in grains, vegetable oils, and cotton. In 1996, these commodities accounted for over 50% of the value of imports. In recent years, China has been the world’s largest importer of cotton, with annual imports average about 800,000 mt.

However, in 1998/99 China will revert to becoming a net cotton exporter. China also has excessive stockpiles of grain and cotton. For instance, for 1998/99, China’s cotton stockpile is estimated to be 3.3 mmt, or 40 percent of the world’s stocks.It is somewhat puzzling that the share of land-intensive agricultural exports such as grain and cotton has not declined, because China does not have a comparative advantage in land-intensive commodities. One possible explanation to this puzzle could lie with the domestic “two-tier” pricing system. The “two-tier” price system may cause trade patterns to diverge from what might be expected from domestic resource endowments and this possibility has not been adequately examined in the literature. The potential trade distortions caused by the domestic pricing policy also has important implications for future trade policy reform. More than 95% of China’s marketed cotton and 50% of the marketed grain6 is procured by the government. Under the “two-tier” pricing system, COFCO in the case of grain, and China’s National Textiles Import and Export Corporation in the case of cotton,plastic plant pot could earn profits from exporting even when the domestic free market price is lower than the world price. COFCO and CHINATEX will have an incentive of export grain and cotton whenever the world price PW > PP + marketing costs + taxes. This is the case even if the domestic free market price PF > PW . This was true in 1993-94 when the domestic free market prices rose significantly as the slowdown in domestic production created excess domestic demand, and at the same time, China’s grain exports reached historical records. In 1993-94, domestic grain prices increased dramatically and the State Grain Bureau could have sold grain into the domestic market to stabilize prices, but instead they increased exports and reduced imports. Partly to override these perverse incentives, the central government eventually imposed a grain export embargo in 1995. More recently,COFCO has exported corn to world markets, even though world prices were below domestic free market prices.7 Figure 1 displays China’s net agricultural exports for four aggregate groups: grains, animal products, horticultural products and “other.” Although there are erratic swings in the value of exports, China continues to be a net exporter of grains . The data in Figure 1 show exports of horticultural products have grown, and these are products where China probably does have a comparative advantage. However, China’s official trade data do not account for smuggling. If net exports of horticultural products were adjusted for imports smuggled into China, the rise in horticultural exports on the part of China would not be so strong. To help illustrate this point with regard to smuggling, Figure 2 shows net exports of fruits and vegetables from China, Hong Kong, and the two combined. We see from Figure 2 that both Hong Kong’s imports and China’s exports of fruits and vegetables have risen significantly in the past ten years. However, China’s exports did drop off in 1996 and 1997.

If we combine the two, we find that the 1997 value of net exports into the region was not much different from that of the late 1980s. China tends to directly import bulk agricultural commodities, whereas a large share of the processed food and consumer ready products are first imported into Hong Kong and then re-exported to mainland China. For example, almost all of the U.S. meat, fruit, and vegetable exports to China are routed through Hong Kong. Despite a relatively small population of only 6 million, Hong Kong imported over $14.2 billion in agricultural products in 1996 more than official imports into mainland China. Hong Kong ranks as one of the top Asian markets for farm products, and it is the second largest Asian market for U.S. horticultural products. As an additional measure of its importance, Hong Kong imports 20 percent of U.S. fruit and vegetable exports and it has been a growing market. However, these imports are not all for domestic consumption purposes and, in fact, Hong Kong officially re-exports about 55 percent of its agricultural imports. There is a large two way trade in agricultural products between Hong Kong and China. Hong Kong’s imports from China include poultry, fruits, vegetables, rice, and nuts. At the same time, Hong Kong exports substantial amounts of poultry, fruits, vegetables, nuts, oil seeds, and cotton to China. In addition to the legal shipments from Hong Kong to China, there is a large illegal trade . Undocumented shipments of fresh fruit may account for up to 70 percent of Hong Kong’s imports . For example, the value of chicken parts smuggled into China alone could amount to over $300 million per year . It is difficult to estimate the total dollar value of undocumented agricultural exports from Hong Kong to China, but it could exceed $1 billion per year. For “other” primary products, the subgroups that were in surplus in 1980-82 accounted for 40.34% of the value of normalized agricultural trade in 1994-96. Of these goods, 15.74% of trade moved to balance by 1994-96 and 10.34% moved to a deficit. Adding up the diagonal elements in Table 5, we find that 44.4% of the trade in manufacturing was persistent, from 1980 to 1996. These results suggest less persistence in “other” primary products trade compared to agricultural trade. Turning to manufacturing, in Table 6, the subgroups that were in surplus in 1980-82 accounted for 31.82% of the value of normalized agricultural trade in 1994-96. Of these goods, 4.93% of trade moved to balance by 1994-96 and 0.81% moved to deficit. Adding up the diagonal elements in Table 6, we find that 65.5% of the trade in manufacturing was persistent, from 1980 to 1996. These results suggest almost as much persistence in manufacturing trade compared to agricultural trade. As a statistical measure of trade persistence, we can use a transformation of the standard chisquared test, Cramer’s C-statistic, suggested by Carolan et. al.. The C-statistic lies between zero and one, with one representing complete association between the beginning and the ending trade balance. From Tables 7, 8 and 9 we find the C-statistic is 0.66 for agriculture, 0.39 for other primary products, and 0.54 for manufactures. These results suggest there was the least change in the trade balances over the 1980-1996 time period for agriculture, because the C-statistic is relatively high. For manufacturing and other primary products the results suggest there was relatively more change in the trade balances over the time period studied, because the C-statistics are lower.Rather than just comparing the beginning and ending time periods, we can construct histograms for agriculture and manufacturing, based on the number of years each subgroup runs a surplus .

We focus on responses from landscape managers and golf course managers

About 60% of respondents are aged 45 and above, and only about 17% are aged 25-35. While this might be the result of the age distribution in the major fields of occupation which are potential CIMIS users, it could also be that the current interface of CIMIS caters less to younger potential users who might seek the data elsewhere. About a quarter of respondents are women, and their share decreases at higher age groups. This probably reflects the changing labor force characteristics in CIMIS related professions over the past few decades. In terms of geographic location, most respondents report only one area of activity, with the San Joaquin Valley leading the count. Figure 2.1 below shows the shares of respondents in each region. Note that we allowed more than one response for location. We ask all respondents to rank each type of data, offered by CIMIS, according to the frequency they search for it. Figure 2.2 shows the breakdown of answers for each of the frequency choices. ET and precipitation are large shares of the “often” column. These shares decrease when moving in the “never” direction. On the other hand, one can observe an opposite trend for insolation , soil temperature, and relative humidity, which seem to be of less interest for respondents. Interestingly, air temperature seems less correlated with the frequency response, with response rate for “often” lower than “sometimes”. This could stem from the use of air temperature data: while irrigation requires using ET data often,flower harvest buckets air temperature data applications might require less frequent data pulls. Respondents seem satisfied with CIMIS services. About 72% of respondents reported using CIMIS at least occasionally.

The user types reporting “often” using CIMIS the most were Agriculture, followed by Golf Course Management and Water Districts. These user types are indeed likely to use CIMIS on a day to day basis, at least for some part of the year. In research and planning, on the other hand, one might use CIMIS to draw data only at an initial stage of a given task. In general terms, of the respondents who report using CIMIS to some extent, 77% say it is at least “moderately important” for their operations, with 22% reporting CIMIS as “extremely important”. The frequency of use and importance scores are positively correlated: frequent users also report high importance of CIMIS to their operations, which makes sense. The correlations between frequency and satisfaction, and between importance and satisfaction, seem less pronounced. There might be users who use CIMIS infrequently, perhaps because only a smaller part of their tasks involve the weather or climate information provided. Nevertheless, they seem satisfied with CIMIS services, as the satisfaction scores are relatively high. We also asked respondents to rank factors which hinder further use of CIMIS. Various answers were provided, given the results of initial surveys, and there was also room to specify other answers. Two main concerns exist, especially for users in agriculture: how reliable is the data and how to integrate it into existing systems and practices. Many growers and consultants in agriculture complement CIMIS with other data sources, such as soil moisture sensors, irrigation logs, and flow meters. Integrating information from multiple sources into decision making is a challenge faced by virtually all growers. 599 respondents, about a quarter of our survey, reported agriculture to be their primary business. Out of these, about half work on one farm, and the rest are consultants of sorts . 89% of respondents in agriculture report using CIMIS to some extent. Growers and consultants were asked to report their total acreage, selecting into pre-determined ranges. Summing these, we have 318,156 acres covered by growers, and almost 3 million acres covered by consultants. Many of the questions for growers and consultants were similar. One notable exception is regarding water use. The team decided not to ask growers how much water they use, fearing that growers would not want to share this information and would not finish the survey. However, consultants were asked how much water their clients use on average. This question was presented in the online survey as a slider bar, with a default at the lower bar , and an option to check a “Not applicable” box.

This box was not checked very often. Instead, it seems like many consultants who did not want to answer this questions left the slider bar at the default value of 0.5 AF/acre. This is a very low value for irrigated crops, and we assume that all these responses are basically non-answers. Ignoring them, the average reported water use is 2.96 AF/acre per year . This seems like a very reasonable distribution for water use in irrigated crops. Indeed, the USDA’s most recent Farm and Ranch Irrigation Survey reports a total of 7,543,928 irrigated acres in California, with a total of 23,488,939 AF of water applied, and a resulting average water use of 3.11 AF/acre, only a minor deviation of the reported average. Given the responses from agricultural consultants, we seem to have captured a very large portion of the drip irrigated acres in California. As a baseline for valuation, we will use the total 2013 drip irrigated acreage from the USDA survey, 2.8 million acres. While some growers might use CIMIS with gravitational or sprinkler systems as well, our understanding of the qualitative and quantitative responses is that CIMIS is mostly important for drip. We exclude the potential of CIMIS values on non-drip acreage, noting that our estimates would therefore be conservative in that sense.Growers in our survey reported an average CIMIS water saving effect of 24.2%. The reported saving rates seem to be distributed evenly among crops and grower acreage. The average water saving rates reported for consultants is 21.5%, a slightly lower rate than the growers, but this difference is not meaningful in an economic or statistical way. Figure 2.3 plots the distributions of reported savings by growers and consultants, with very similar means and medians. Regressing the reported savings rate on all user types, one cannot reject the null hypothesis that the mean water saving effect is equal between growers and consultants with 95% confidence . Since each group deals with different acreages, we interpret this result as lack of substantial economies of scale in water saving by CIMIS. The monetary cost of water saved can be viewed as savings on the intensive margin. One can also consider gains on an extensive margin. The water saved by use of CIMIS is likely to be used in agriculture as well. This means more acres can be grown with the same initial amount of water. The “full” economic value of the water saved by CIMIS in agriculture is the value of agricultural output that can be produced with it on acres not irrigated before. This following analysis includes the economic value of growing alone, without the added values of post-harvest and economic multiplier effects, and probably a safe lower bound. We do not, however, include a counter-factual productivity of non-irrigated land. In California,round flower buckets this is probably range land or acreage that is too sloped for traditional irrigation methods, and therefore of very low economic productivity.

With 1.92 million AF of water saved by CIMIS, and an average use of 2.5 AF/acre by growers , the savings from CIMIS can water an extra 768,000 acres in California. To put this in context, this is about double the total walnut acreage in 2016. Because of economic and technical constraints of water transport, it is hard to determine which crops would be planted in these extra acres. A conservative approximation assumes that the water saved by CIMIS serves to replicate the existing distribution of crops , taking the average value of productivity of an acre as the benchmark. The weighted average of grower revenue per acre in 2016 was $3,757 per acre1 . Multiplying by 768,000 acres, a conservative approximation for the contribution from CIMIS to California’s GDP via agriculture is about $2.89 billion. This number may appear very high, yet this calculation took various conservative assumptions:in the calculation of the water saved, in assuming the value of extra acreage, and in not including post-harvest added value and multiplier effects. To be even more conservative, let us assume that the elasticity of demand for the products grown on these extra acres is -2. That is, an increase of 1% in quantity would drop the price by about 0.5%. This is a reasonable estimate for elasticities of high value crops . The resulting extra income for growers is then about $1.44 billion dollars. CIMIS allows for more precise irrigation, which means not only saving water but also increasing yields: water application can be adjusted to the plant requirements, which might depend on the weather and growing phase. We ask growers and consultants how does CIMIS contribute in increasing yields, ranking from 1 to 5 . How should we quantify these ranked contributions? Taylor, Parker, and Zilberman mention average yield effects of drip irrigation, ranging between 5% and 25% increase in output. This extra yield effect is explained by allowing for more consistent soil humidity and the precision of the irrigation. This aspect of drip depends on weather and ET information, such as the one provided by CIMIS, to assess the water intake by plants and the appropriate amount of water required. We calculate an average yield effect of CIMIS by reconciling the respondent rankings with a portion of the yield effects from drip irrigation. For a lower estimate, rankings between 1 and 3 are attributed 0% yield effect, and the rankings of 4 and 5 get 5%. For a higher estimate, ranking of 1 gets 0% yield increase, ranking of 2 and 3 get 5% yield increase, and the rankings of 4 and 5 get a 10% yield increase. These percent yield effects are then averaged among the respondents. We aggregate growers and consultants with equal weights. 41% of respondents rank the importance of CIMIS for yield effects at 4-5. The low estimate for yield contribution of CIMIS results in 2% output increase, and the higher estimate at 5.9% increase. At a conservative estimate of per-acre income of $3,757 for growers, this represents an extra yearly income of $76 – $222 per acre. For the 2.8 million acres using drip irrigation, this would account for $213 – $622 million yearly from the contribution of CIMIS to yields. Assuming again the demand is elastic with a coefficient of -2, these estimates would halve to $107 – $311 million. Weather data can have quality effects on crops. For example, using ET data and drip irrigation, the quality of tomatoes can be increased by controlled irrigation deficit in proper timing. For tomatoes grown under a contract, reaching threshold quality levels raises the price received by the grower . Another potential use of weather data is in pest control, avoiding not only yield loss but quality degradation as well. These two examples reflect a relationship between quality and price that has long been established in the literature . To assess the contribution of CIMIS to quality, we also asked respondents to rank it from 1 to 5 . We assume that a score of 4-5 represents a quality index resulting in a price increase of 5%. About 45% of all respondents report a score of 4-5. The average price increase due to quality is therefore 2.2%, or $83 per acre. For 2.8 million acres, this results in a total increased revenue of $231 million. Note that this price increase is due to quality improvement, and thus not accompanied by a quantity reduction in our analysis. These are gains from water saving in parks, golf courses, and gardens. They were assessed as a small portion of the total gains from CIMIS in the 1996 report by Parker et al., totaling about $2.3 million . Our current estimate for these gains is much higher. The discrepancy from the 1996 report is due to several factors. First, we believe to have reached out to more respondents in this sector. Second, water prices in California have gone up substantially. Third, there might be more use of CIMIS and smart irrigation planning in the sector compared to 20 years ago.

Farmers need to know that they won’t suffer economically to implement these measures

Future work could explore the genomes of these ASVs to discern why they are important in their respective agricultural systems and test the hypothesis that they serve as keystone species using synthetic communities. Concluding whether adaptive plant-microbe feed backs result in an M × R interaction leading to shifts in other rhizosphere processes is complicated by the importance of poorly understood fungal communities and methodological limitations of this study. Numerous fungal taxa respond to the M × R interaction according to our differential abundance analysis , yet knowledge of these taxa remains limited due in part to the constraints of culture-dependent methods prevalent in the past. Nonetheless, fungi influence inter-kingdom interactions and agriculturally relevant processes in the rhizosphere, and novel molecular biology tools could be used to improve our understanding of key fungal regulators identified in these analyses. Metagenomics and -transcriptomics would facilitate a much more comprehensive analysis of potential functional shifts. A highly useful starting point would be to delve into dynamic variation in microbial genes involved in carbon metabolism and nitrogen cycling in the rhizosphere,30 litre plant pots in combination with root exudate metabolomics and measurements of root N uptake.

Stable isotope labeling and in situ visualization methods could further complement our understanding of how management, plant roots, and their interactive effects shape rhizosphere processes. The scope of this study was intentionally restricted to a single genotype of one crop in two management systems to limit the main sources of variation to the management and rhizosphere effects that were of primary interest, but the limits to inference of this small-scale study must be considered. Other studies in maize have found that strong legacy effects of soil managementhistory are generally acted upon in a similar manner by two maize cultivars and that rhizosphere bacterial community composition varies only slightly among hybrids from different decades of release.Testing whether these limited effects of plant selection hold true for additional contrasting genotypes and genetic groups of maize would further complement this work. Furthermore, variation in root system architecture across crop genotypes might interact with tillage and soil properties responsive to management effects. Management practices such as the inclusion of forage or cover crops planted in stands rather than rows might affect the differentiation of bulk and rhizosphere soil uniquely from systems based on perennial crops, successive plantings of row crops in the same locations, and/or minimal tillage. Study designs incorporating more genotypes, management systems, and cultivation environments would therefore be highly useful to test how results of this study may be extrapolated to other settings. Future studies should also identify functional genes that are upregulated or downregulated in the rhizosphere under specific agricultural management practices.

Whether such functional shifts are adaptive will provide insight into the relationship between agroecology and ecology. Positive eco-evolutionary feed backs resulting in adaptive microbial communities have been described in unmanaged ecosystems, for example, habitat-adapted symbiosis in saline or arid environments. If similar adaptive recruitment can occur with annual crops in the context of agroecosystems, maximizing this process should be added to the list of rhizosphere engineering strategies and targets for G × E breeding screens. Finally, while our results provide evidence that management and plant influence interact to shape microbial communities at one sampling point, we highlight the need to reframe the M × R interaction as a dynamic process. Rhizosphere communities may be more different from one another than bulk soil communities because roots develop right after tillage and fertilization, when management systems are most distinct . Plants are not static entities, but active participants in the ongoing process of rhizosphere recruitment. As an alternative to the “rhizosphere snapshot,” we propose a “rhizosphere symphony” model that acknowledges the active role of root exudates in orchestrating the composition and function of microbial communities. Altered root exudation during development and in response to water and nutrient limitation can upregulate or downregulate microbial taxa and functions, as a conductor brings together different sections of instruments in turn during a symphony.

Although it is unknown whether this plasticity in exudate composition occurs in response to agricultural management, observations of changed exudate quantity and quality in response to soil type and long-term N fertilization suggest that it is possible. Differences in the timing of nutrient availability between management systems, such as delayed N release from cover crop mineralization compared to mineral fertilizer, could thus result in management-system-specific exudate dynamics and rhizosphere microbial communities, i.e., an M × R interaction. If true, this mechanism suggests that we may be able to manipulate the sound of the symphony by talking to the conductor: plant-driven strategies may be instrumental in maximizing beneficial rhizosphere interactions throughout the season.The Elkhorn Slough is located in the Central Monterey Bay area and feeds into the head of the Monterey Submarine Canyon in the newly designated Monterey Bay National Marine Sanctuary. The slough is described by the Department of Fish and Game as “one of the most ecologically important estuarine systems in California” . Elkhorn Slough was designated as an environmentally sensitive habitat in the 1976 California Coastal Plan and over 1400 acres of the slough are in the National Estuarine Research Reserve System. Water quality in the Elkhorn Slough is heavily influenced by both past and present human activities on the land surrounding the slough. This is especially true of agriculture. Non-point source pollutants from farm use of chemical fertilizers and pesticides have been identified as a primary cause of water quality degradation in the Elkhorn Slough. Agriculture is one of the main land uses in the slough watershed with about 26% of the local watershed in agricultural production. Of this land, strawberry production accounts for the greatest area under production . Field testing and monitoring of alternative farming practices that decrease dependence on synthetic chemical inputs has been extremely limited. What is needed is the development of farming systems that are economically as well as environmentally sustainable. The Azevedo Ranch site encompasses 137 acres, approximately 120 of which are currently in strawberry cultivation. The land is jointly owned by The Nature Conservancy and the Monterey County Agricultural and Historical Land Conservancy, whose stated goal is to keep this property in open space in perpetuity. The property will be divided into a wetlands buffer zone surrounding three “pocket marshes,” and an upland agricultural zone. The marshes are separated from the main channel of the slough by a railroad berm. They are connected to tidal water by culverts through the berm,25 liter pot plastic making each independent. The buffer zone, which is currently in cultivation, will be restored with native vegetative cover including native bunch grasses, Coast Live Oaks, and maritime chaparral. The upper agricultural zone will encompass 83 acres and will eventually be converted to low-input sustainable agriculture. The management of the agricultural lands will be guided by an advisory committee, but the overall goal is to develop models, for the greater watershed, of ecologically and economically sustainable methods for crop production.

An additional research site is located on the Elkhorn Slough National Estuarine Research Reserve. The site includes a small pond drained by sloping uplands. It is very similar to the three drainages on the Azevedo ranch, with the important exception that it has never been cultivated. Although the pond is larger than any of the Azevedo marshes and is subject to greater flushing, it provides the opportunity to obtain background data on soils, sediments, and biota in the absence of agricultural disturbances. During the first two years of the study we established critical measurements, protocols, and characterizations of these watersheds under standard cultivation practices. These data will serve as a baseline for comparison once the property is converted to low-input sustainable agricultural management and habitat restoration is completed in the wetland buffer. Conversion and restoration will occur in 2 to 4 years, once the land has been fully paid for. The project is guided by a Technical Advisory Committee which meets monthly. Although this report marks the end of Project Number UCAL-WRC-W-801, the project is ongoing. Our long term goal is the investigation oflinkages between different farm management practices and health of the adjacent slough, as monitored by sedimentation, input of anthropogenic chemicals, water quality, and the response of wetlands flora and fauna. In the future, we will implement and test alternative farming practices that lessen or eliminate the dependence on synthetic chemical inputs. We will also be able to assess the influence of border zones at the land-water margin as buffers between agricultural uplands and estuarine receiving waters. The lead author recently submitted a proposal to the UC Water Resources Center entitled, “Evaluating Vegetated Buffer Zones Between Commercial Strawberry Fields and the Elkhorn Slough Estuary.” Erosion of soils from strawberry fields is a major mechanism of transport of agricultural chemical residues into slough surface waters. About 75% of the anthropogenic erosion in the Eu.horn Slough watershed is attributed to strawberry production . While a background rate of erosion for most soils is about 1 ton/acre/year, erosion from these strawberry lands ranges from 8 to 145 tons/acre/year, with the highest rates occurring during heavy rains. Costs of erosion and sediment damages are estimated at over $3 million/year, or $7911acre of strawberry land . These estimates do not include any factor for environmental damage to the estuary. The Soil Conservation Service has recommended a variety of management practices designed specifically to address the problem of erosion from strawberry fields in the Elkhorn Slough watershed . Unfortunately, many local growers have not yet implemented these practices. A demonstration project can test and report on these and other practices to convince reluctant growers that these techniques work. A recent report on farming practices in the Elkhorn Slough watershed showed that growing practices are strongly correlated with grower ethnicity, and that outreach programs must be targeted for specific under served groups to be effective . Growers apply synthetically and naturally compounded forms of nitrogen, potassium, phosphorous, and other plant nutrients to soils. Some portion of these minerals is taken up by the crop, some is retained in the soil, and some is subject to export from the system through downward leaching, surface runoff, or erosion. Nitrogen, in the form of nitrate, is especially prone to leaching and is a significant problem in groundwater in the Elkhorn Slough watershed. A significant percentage of wells in the Elkhorn Slough watershed are contaminated with unacceptable levels of nitrate . High levels of nitrate in groundwater are associated with agricultural activities, especially strawberry production around the slough. Other nutrients, such as phosphorous, tend to associate closely with soil colloids, and are prone to transport on eroded sediments. There are little or no data to address the potential of fertilizer nutrients being transported into the slough. Research by Broenkow and Smith suggests that tidal water may be the major source of nitrogen in the slough, as local nitrogen concentrations seem to be controlled mainly by the tide. Strong pulses of nitrogen enter the slough after winter rains, but they are soon flushed by the tide. Past measurements have shown low nitrate and phosphate levels in slough water, though no new measurements of slough channel surface water have been made since 1980. Soil water and nitrate movement through the surface soil were studied using porous cup lysimeters. In the first year, twelve lysimeters were installed in the Central Field and six in the grassland control site at the Elkhorn Slough NERR. Lysimeters were place in pairs at one foot and two foot depths to sample the root zone and below the principal root zone. In the crop field, three pairs were placed low on the slope, and three pairs higher up on the slope. In the grassland all three pairs were placed at a similar slope position. First year results showed a great deal of variation in nitrate-nitrogen levels in strawberry bed soil-water. It was not possible to determine the direction of movement or any strong response to seasonality. Furthermore, we found that surface runoff was extremely significant in nutrient loading into the pocket marshes.

Subsequent studies have largely confirmed these initial estimates

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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