Category Archives: Agriculture

Characterization factors for water use were based on the ReCiPe model

We note that corn displacing cotton was only part of the complex land use dynamics in the past “ethanol decade” that involved also land shift from, for example, soybeans and hay to corn, cotton to soybeans, and natural vegetations to corn . The reason we focus only on cotton to corn here is that environmental impacts of land shift between cotton to corn, both high-input crops, are less clear than that between relatively low-input crops and high-input crops . In a recent study, Wallander et al. stated that “When acreage shifts from one high-input crop to another , however, ethanol induced changes may be negligible or could even reduce environmental externalities.” In this study, we seek to test the validity of this statement, focusing on regional environmental issues along with a growing body of literature on the non-GHG consequences of bio-fuels expansion . A land shift from one crop to the other can alter both direct, or on-site, and indirect, or offsite, environmental effects. For example, increased use of nitrogen fertilizers as a result of the land shift not only can elevate N related emissions such as NOx and N runoff but also requires more energy and material inputs in the process of fertilizer production. The system boundary of the study, therefore, was drawn to cover both direct and indirect emissions. In particular, we paid a special attention to direct environmental emissions from crop production given their significance relative to indirect emissions . We calculated indirect emissions embodied in input materials that take place along supply chains, using the Ecoinvent database . In our data compilation, we placed an emphasis on the crop growth and agricultural input structures at the state level,round nursery pots as previous studies showed that national, average data may fall short in capturing the environmental impacts of crop production at a regional level .

This is because agricultural systems display high degrees of variability across regions in terms of input structure due primarily to differences in geography, weather patterns, soil type, and management practices . Also, data on major agricultural inputs such as fertilizers and pesticides collected by the US Department of Agriculture are only available at the state level . The reference year of this study is 2005 given that cotton area experienced a substantial decline between 2005 and 2009. Major inputs in crop growth include fertilizers, pesticides, energies, and irrigation water. We obtained relevant state-level data from several USDA surveys and censuses reflecting cotton and corn farming practices around 2005 and then compiled a set of state-specific inventories. Not all inputs data, however, are available for every state that grows cotton and corn. The USDA Farm and Ranch Irrigation survey, for example, includes more states than surveys of energy and agrichemical use. Nevertheless, the states for which all inputs data are available capture the majority of US cotton and corn production. Specifically, the inventories we compiled cover 19 corn growing states, which account for 95 % of domestic corn production in 2005, and 9 cotton growing states, which account for 88 % of domestic cotton production in 2005. After compiling emissions data for cotton and corn, we evaluated their environmental impacts using characterization factors from life cycle impact assessment . Reflecting the relative significance of an emission or resource, characterization factors are used to aggregate emission results, usually including a large number of different substances, into a dozen of impact category scores that enable better comparison between alternatives . In this study, we focused on regional environmental aspects of cotton and corn, and based on our previous study , we selected eight impact categories to which cotton and corn production potentially contribute. These impact categories are acidification, eutrophication, smog formation, freshwater ecotoxicity, and water use as well as human health cancer, non-cancer, and respiratory effects.

Characterization factors for all categories except water use are taken from the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts developed for the USA by the EPA .Note that TRACI 2.0, compared with its original version , has incorporated the recently developed USEtox model for the ecotoxicty, human health cancer, and non-cancer impact categories .For comparison between the two crops, results are organized on the basis of per hectare produced. Figure 2.1 shows the average environmental impacts, weighted by state area harvested, of corn relative to that of cotton in 2005 in the USA. For most impact categories,corn and cotton per hectare show roughly similar environmental impacts, with relative magnitude ranging from 1.4 for acidification and 0.9 for human health cancer. For freshwater ecotoxicity, however, corn shows about one third of impact by cotton per hectare, and corn’s water use is less than half that of cotton. Above all, most of the environmental impacts associated with cotton and corn production are due to on-site environmental emissions rather than that embodied in input materials like fertilizers and pesticides. Their acidification effect is due in large part to application of nitrogen fertilizers and diesel combustion . Although N intensity of corn is much larger than that of cotton , corn farming uses much less diesel . Overall, the acidification impact of corn per hectare is 1.4 times that of cotton. The same can be said about smog formation. Not surprisingly, the two crops’ eutrophication impact is caused mainly by use of N and phosphate fertilizers. Although corn has higher nutrient application intensities than cotton, its average N and P leaching and runoff rates are lower ; thus, the two crops have a comparable eutrophication impact. Water use by cotton and corn comes primarily from irrigation: about 400 m3 is applied per hectare corn produced as opposed to 940 m3 applied per hectare cotton produced. Freshwater ecotoxicity for both crops is due in large part to pesticide use, and cotton per hectare has a freshwater ecotoxicity about three times that of corn. This is partly because pesticide application intensity of cotton is approximately twice as much as that of corn . Also, many of the pesticides such as cyfluthrin, lambdacyhalothrin, and cypermethrin used in cotton growth generally show higher toxicity-related characterization factors than the major ones used in corn growth. The two crops’ potential human health respiratory impacts are comparable, although that of cotton is slightly higher.

The respiratory effect is mainly caused by diesel combustion, application of N fertilizers, and emissions embodied in P fertilizers. Human health cancer and non-cancer impacts of corn per hectare are slightly larger than that of cotton. Heavy metals contained in phosphate constitute the major contributor to both crops’ non-cancer effect, but use of acephate, an insecticide, is also another important source of non-cancer impact for cotton. This is why corn’s relative magnitude of non-cancer effect is not as large as that of phosphate application intensity . The two crops’ potential human health cancer impact is due to a number of factors including diesel combustion and heavy metals brought about by phosphate as well as the cancer impact embodied in fertilizers. The results above indicate that corn and cotton grown per hectare in the USA on average generate roughly comparable impacts for most of the impact categories except for water use and freshwater ecotoxicity, where cotton shows lower impacts. The results seem consistent with the view of a recent USDA study , “When acreage shifts from one high-input crop to another , however, ethanolinduced changes may be negligible or could even reduce environmental externalities.” We argue that, however,plastic flower pots the average results as shown in Fig. 2.1 are inadequate to capture the net environmental impacts associated with land cover change from cotton to corn that took place in the USA. First, Fig. 2.1 is largely a portrait of corn and cotton growth in different regions and, weighted by state crop area, mainly represents the major crop-growing states where respective crops are likely the most suitable to grow. But, when land shifts from cotton to corn growth, it happens in cotton-growing areas in the South. Lands in these areas can be by and large considered marginal lands for corn in both geographic and economic senses as they are generally less suitable for corn growth than the Corn Belt. Further confounding the issue is the existence of large spatial variability among corn and cotton-growing states . The range of spatial variation in cotton growth is two to threefold for acidification, smog formation, eutrophication, human health non-cancer, and respiratory effects and four to sixfold for freshwater ecotoxicity and human health cancer effect. The range of spatial variation in corn growth is about two to threefold for acidification, smog formation, human health cancer, non-cancer, and respiratory effects and fourfold for eutrophication. Water use can vary by orders of magnitude for both crops as some states use little irrigation water while some rely heavily on irrigation . In short, the results for average corn and cotton as reflected in Fig. 2.1 fall short of representing the environmental performance of marginal corn in cotton-growing states and, therefore, should not be used for evaluating environmental impacts of land use change from cotton to corn or vice versa. 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, 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 .

Declining chill is therefore considered a threat to California pistachios

Another minor issue requiring the bootstrap approach is that the implicit potential yield estimation should change the degrees of freedom in the non-linear regressions when estimating the standard errors. In the lower panel of Figure 3.2, a histogram of positive shares is presented. That is, for each chill portion, the count of panel observations where the share of that chill portion was positive. The actual shares of the very low and very high portions are usually quite low. This shows the relatively small number of observations with low chill counts. The two yield effects curves look very similar in the relevant chill range. By both estimates, the yield loss is very close to 0 at higher chill portions, and starts declining substantially somewhere in the upper 60’s, as the experimental literature would suggest. Interestingly, the polynomial curve does not exceed zero effect, although it is not mechanically bounded from above like the logistic curve. This probably reflects the fact that historically, the average growing conditions has not deviated much from the optimal range. The “within” transformation hence did not deviate the potential yield much from the optimum in this case. At currently low chill portion ranges of 55-60, the effect is around −25%, again consistent with the stipulation of Pope et al. that a significant effect threshold would be located there. Considering alternate bearing and other factors contributing to the background fluctuation in yields, it is easy to understand how such effects on relatively small areas within the pistachio growing counties have not been picked up by researchers so far. Anecdotal yield losses due to low chill have happened on relatively small scale and passed undetected in the county-level statistics, especially when only one or two chill measures per county were considered. In this case, while the resulting curves are very similar, I find the structural approach more convincing. First, it has a smaller confidence area, and therefore seems more precise. Second, a polynomial of low order will not approximate the process described by agronomists very well. However,cut flower bucket estimating higher order polynomials results in estimates that are not statistically significant. The implications of my estimates for pistachio yields are depicted in the lower half of Figure 3.1.

The bottom left panel shows the effects on the 1/4 warmest years in 2000– 2018. They are mostly between 10-20% yield decline. These rates are easy to miss due to substantial yield fluctuations in pistachios. What do these estimates mean for the future of California pistachios? Prediction of yield effects for the years 2020–2040 are depicted in the bottom right panel, again for the 1/4 warmest years in the 2020-2040. They show substantial yield drops, which could amount to costs in the hundreds of millions of dollars. Chapter 4 in this dissertation explores the potential gains from a technology that could help deal with low chill in pistachios: applying kaolin clay mixtures on the dormant trees to block sunlight. Thee expected net present value of this technology is estimated at the billions of dollar in economic gains. Considering my results, there may be significant gains from using these technologies even in warmer years today. Concluding this chapter, I want to stress the fact that even in the era of “big data” in agriculture, data availability is still a challenge when estimating yield responses to temperature in some crops, especially perennials and local varieties. Weather information required for assessing potential damages and new technologies might not always be available for a researcher. This chapter develops a methodology to recover this relationship, using local weather data and techniques for dealing with aggregated observations. I use this setup to empirically assess the yield effects of insufficient chill in pistachios, recovering this relationship from commercial yields for the first time in the literature. I then look at the threat of climate change to pistachio production in southern California. As winters get warmer, lowering chill portion levels are predicted to damage pistachio yields and disrupt a multi-billion dollar industry within the next 20 years. These results were made possible by using precise local weather data, applying relevant statistical methods, and using agronomic knowledge in the modeling process.

This approach for information recovery from a small yield panel, with limited useful variability at first sight, could be useful for other crops as well.Introduced to California more than 80 years ago, and grown commercially since the mid 1970’s, pistachio was the state’s 8th leading agricultural product in gross value in 2016, generating a total revenue of $1.82 billion dollars. According to the California Department of Food and Agriculture , California produces virtually all pistachio in theUS, and competes internationally with Iran and Turkey . In 2016, five California counties were responsible for a 97% of the state’s pistachio crop: Kern , Fresno , Tulare , Madera , and Kings . Since the year 2000, the total harvested acres in these counties have been increasing by roughly 10% yearly. Each increase represent a 6 – 7 year old investment decision, as trees need to mature before commercial harvest . The challenge for California pistachios has to do with their winter dormancy and the temperature signals required for spring bloom. I discuss the dormancy challenge and the Chill Portion metric in Chapter 3. It is worth noting that in fact, for the areas covered in this study, chill portions are strongly correlated with the 90th temperature percentile between November and February, the dormancy season for pistachios. The correlation is very strong, with a goodness of fit rating of about 0.91. In essence, insufficient chill is a right side temperature tail effect, comparable with similar effects in the climate change literature. Chapter 3 estimates the yield response of pistachios to CP. Substantial losses are predicted below 60 CP. Compared to other popular fruit and nut crops in the state, this is a high threshold , putting pistachio on the verge of not attaining its chill requirements in some California counties. In fact, there is evidence of low chill already hurting yields .Chill in most of California has been declining in the past decades, and is predicted to decline further in the future. Luedeling, Zhang, and Girvetz estimate the potential chill drop for the southern part of San Joaquin valley, where virtually all of California pistachio is currently grown.

For the measure of first decile, i.e. the amount of CP attained in 90% of years, they predict a drop from an estimate of 64.3 chill portions in the year 2000 to estimates ranging between 50.6 and 54.5 in the years 2045-2060. Agronomists and stakeholders in California pistachios recognize this as a threat to this valuable crop . Together with increasing air temperatures, a drastic drop in winter fog incidence in the Central Valley has also been observed. This increases tree bud exposure to direct solar radiation, raising their temperature even further . The estimates cited above virtually cover the entire pistachio growing region, and the first decile metric is less useful for a thorough analysis of pistachios. I therefore need to create and use a more detailed dataset, in fact the same one described in Cahpter 3. Figure 3.1 shows the geographic distribution of chill and potential damage in the 1/4 warmest years of observed climate and predicted climate . While not very substantial in the past,flower display buckets these losses are predicted to reach up to 50% in some regions in the future.Figure 4.1 sketches the short run market model. The linear supply curves take weather as given. On an ideal weather season, the supply curve is S0. On a year with warm winter, the supply curve is multiplied by a coefficient smaller than one, i.e. shifts left and rotates counter-clockwise, resulting in curve S1. Without MCE, the intersection of demand with S1 determines the market equilibrium. Once that is solved, the welfare outcomes-consumer surplus, grower sector profits, and total welfare-are calculated as the areas above or under the appropriate curves. When MCE technology is available, a modified supply curve starts with a section overlapping S1, and then “bends” right towards S0. If demand is high enough, market equilibrium is attained at this bend. Again, the welfare outcomes with MCE are calculated with the equilibrium price and quantity, together with the demand and SMCE curves. The gains from MCE are the differences between these market outcomes, i.e. the outcomes with MCE minus the outcomes without it. Note that the expansion of supply byMCE is guaranteed to result in positive gains from MCE in terms of total welfare and consumer surplus: the price is lower and quantity is higher. As for the grower sector, it does enjoy extra profits from being able to produce more, but the resulting lower price also decreases its profits from the output that would have been produced anyway without MCE. Therefore, one cannot tell a priori if grower profits increase or decrease when MCE is available. The sign and magnitude will need to be determined in the simulations, given the various parameters and functional forms. The climate prediction data produce a point estimate of chill portions for each year in 2020-2040. For a given set of model parameters and climate predictions for 2020-2040, the model is solved numerically twice for each year in this range. The consumer, grower, and welfare gains are calculated for each year using these two simulations. Using a discount rate of 5%, I can calculate the Net Present Value of the MCE gains in 2019. For each scenario, I run this procedure for 100 “independent draws” of 2020-2040 prediction paths. For each one, an entire simulation is run to produce an NPV of the gains.

I report the Expected NPV , the mean of this distribution, and standard errors around it. More details on the numerical solution of the model can be found in appendix A.3.Before I present the simulated welfare gains, there is one more piece in the puzzle. The calibrated model is set with 2016 acreage . Pistachio acreage through 2020- 2040 is likely to be different, and most likely higher than that. However, the model does not include endogenous growth of planted and harvested pistachio acres. To give some bounds on the expected gains, I run the simulations with four different acreage growth scenarios, each specifying a different pistachio acreage growth path until 2040. All scenarios assume some growth path until 2030, when acreage stabilizes and stays fixed through 2040. The first scenario is “No Growth”, meaning that 2020-2040 climate predictions are cast over the 2016 acreage. This should give a lower bound for gains, as acreage is predicted to grow and not shrink. The second scenario is “Low Growth”, which sets the yearly growth of harvested acres until the year 2022 at 9.6%, the average rate since 2000, and then sets zero growth . The growth until 2022 is attributed to currently planted but not yet bearing acres. This assumes that we are on the brink of a dynamic equilibrium in growth, and therefore no new acres will be planted in California. This scenario should give estimates that are higher than the “No Growth” scenario, but still rather conservative. The third scenario is “High Growth”. This one sets the growth rate until 2022 at 14.6%, the average rate since 2010, and then lets pistachio acreage follow the historic path of almonds in California . That is, the growth rate of almonds when they had the corresponding pistachio acreage. This very optimistic growth prediction makes the “High Growth” scenario the upper bound for the gains from MCE. One potential concern with acreage growth is that growers might switch new acreage to unaffected counties, or plant more heat tolerant varieties. For this, the “High North” scenario takes the high growth rate, but all new acreage harvested from 2023 is located in an imaginary “North” county, where chill damages are virtually zero. Note that planting in the unaffected north has the same effect on supply as planting a more heat tolerant variety near the existing locations . This last scenario is, in my opinion, the most plausible in terms of MCE gain magnitudes. A summary of the growth rates is depicted in Figure 4.2. In all scenarios, demand grows by the total rate of acreage growth.

Fumigation with MeBr + CP however severely affected the activities of β-glucosidase and acid phosphatase

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, 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,black plastic plant pots wholesale 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 twoyear study period.

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 ].

Different isoenzymes in the reference material and soil may also have contributed to variation in enzyme stability towards fumigation with different pesticides. In order to show whether there is a direct relationship between the activity of any enzyme and its protein concentration in soil enzyme protein concentrations were calculated for acid phosphatase, β-glucosidase and arylsulfatase in the nonfumigated and fumigated soils. Specific enzyme protein concentrations were suggested to serve as a suitable measure to quantify the effects of environmental changes related to soil management, fertilization or pesticide application on soil biological properties . These numbers are indented to give an indication of enzyme protein concentrations in soils, not a precise measurement. Generally, lower enzyme protein concentrations in recently fumigated soils compared to control soils suggest that fumigation with MeBr + CP and the alternative biocides denatured the accumulated fraction of this enzyme protein in soil or was lethal to that portion of microorganisms that is the major source of the specific enzymes studied. The response of enzyme protein concentrations, however,black plastic plant pots bulk varied within the enzyme and fumigant studied. Even though the arylsulfatase protein concentration was comparable high among the three soil enzymes, it showed the lowest activity values in soils. These results suggest that arylsulfatase has a lower catalytic activity than acid phosphatase or β- glucosidase or is associated with locations in soil different from those of the other two enzymes. Our results suggest that the activity rate of any enzyme does not necessarily correspond to the protein concentration of this enzyme in a soil. In conclusion this study has shown that microbial and enzymatic processes were not affected by soil fumigation with the alternative pesticides propargyl bromide, InLine, Midas and chloropicrin in the longer term. Fumigation with the standard methyl bromidechloropicrin combination significantly affected some enzymatic processes in soil. However, results were inconsistent over the two study sites. These findings imply that the application of alternative fumigants will not affect the longer-term productivity of agricultural soils because hydrolytic enzymes regulate the rate at which organic materials are degraded and become available for plants. Despite the importance of these findings for strawberry production systems with a history of soil fumigation as a pest control tool, results may not apply to soils previously not fumigated. Further studies should test whether soil fumigation with these alternatives is affecting microbial and enzymatic processes relative to soils without fumigation history and other functional properties and the structural diversity of microbial communities. Animal agriculture causes many unsustainable, destructive problems on individuals, the environment, and the economy. These problems stem from animal agriculture on a broad scale and on a small scale – globally and at the University of California, Merced. Globally, animal agriculture causes deforestation, species extinction, drought, disease, ocean dead zones, greenhouse gas emissions — more than all transportation combined — water and air pollution, and global warming . Because the University of California, Merced has pledged to consume zero net energy, produce zero waste, and zero net greenhouse gas emissions –– referred to as “triple zero” –– these issues should come to light when the University of California, Merced talks about their 2020 Project .

However, these problems have been neglected and thus, by supporting a plant based diet, the University can model a sustainable environment, healthy faculty and students –– free from high levels of stress, anxiety, and disease, caused by unhealthy food options –– and the ultimate “triple zero”. Not offering healthier food causes busy students and faculty to either choose unhealthy food, that affects them physically and mentally, or skip eating; thus, leaving them with distorted eating. Students that are healthy both mentally and physically can put their full effort in their studies, as the type of food that students eat directly relates to their ability to produce their highest quality of work. Previous studies demonstrate how plant-based diets can lower stress, anxiety, and depression levels . Unfortunately, with the type of food offered in the cafeterias at the University, many students find themselves trapped in a spiraling downfall – mentally and physically – that leads to the inability to stay focused, increased stress and anxiety, and may lead to life threatening diseases and disorders, such as eating disorders. According to many nutritionists, diets lacking a significant amount of fruits and vegetables cause short-term effects including a lack of energy and focus and long-term effects including increased risks of cardiovascular disease, osteoporosis, cancer, and many other ailments . If students were able to eat a more plant-based diet – a diet free from meat, dairy, eggs, and any other animal byproducts such as honey and gelatin – and had access to a surplus of fruits, vegetables, whole grains, and legumes, then many of these problems could become extinct . If a vegan diet can show physical and mental health improvement in individuals at the university level, then eating disorders, stress, and anxiety – along with many other ailments – could potentially be reduced. The amount of destruction that animal agriculture does to the planet, to environments and to species is devastating, as animal agriculture is the root problem for the worlds increasing temperatures, species extinction, deforestation, and water quality. As many previous studies have shown, animal agriculture drains the earth of major resources . Animal agriculture enables the destruction of rain forests, ocean dead-zones, drought, production of greenhouse gases, and the “murder” of over six million animals every hour . An abundance of research supports the idea that animal agriculture –– industrial and free-range –– is unsustainable. While free-range farming is considered “better” than industrial farming it still causes many environmental, personal, and economical destructions . Farmers have forgotten that the methods of production determine the final value of their products; as results show that industrial farming increases the amount of food and money wasted, deforestation, greenhouse gas emissions, air and water pollution, species extinction, disease and poor food quality . In the United States alone, animals raised for food excrete 7 million pounds of waste every minute. This waste gets dumped into rivers and toxins are released into the air, destroying water and air purity. The drought in California is greatly due to the amount of water used by animal agriculture, because the animal agriculture industry uses 34 trillion gallons of water and 660 gallons to produce a single hamburger .

Technological advances are crucial to the climate benefit of the CRP-corn ethanol system

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. Due to the dominant use of HR and Bt crops, pests and weeds have evolved to be increasingly resistant . As a result, farmers may need to resort to earlier pest control practices that rely more on conventional pesticides, hence increasing crops’ freshwater ecotoxicity impact. Nevertheless, the dynamics of pest management, and associated ecological impacts, further corroborates the importance of understanding the dynamics of agricultural systems. For many of the impact categories studied, the environmental impacts of US corn and cotton on average are roughly comparable on a per hectare basis, while cotton consumes more water and generates much higher freshwater ecotoxicity impact. However, the average results, mainly reflecting corn produced in the Midwest and cotton produced in the South, are inadequate to capture the likely environmental consequences of corn expansion into cotton, which has taken place in cotton-growing states in the South. The state-level results show that a land use shift from cotton to corn relieves freshwater ecotoxicity but may aggregate many other regional environmental impacts. Due to the limitation of data, a definitive conclusion may not be drawn for other Southern states where the cotton-to-corn land use change has also occurred. But our finding of tradeoffs based on the three states is probably generalizable for these other states considering that cropland there is generally less suitable for corn production than in the Midwest. Taking into account marginal yield and technological advances, the CPT for converting the CRP grassland for corn ethanol production in early 2000s, when the ethanol industry begun to grow, 30 planter pot ranges from 15 years for highly productive grassland with average corn yield to 56 years for infertile grassland with only 50% of average corn yield.

Considering the diminishing climate effect of later GHG emissions within a 100-year time frame, the CPT estimates would increase to 17 to 88 years. Understandably, the shorter the payback time, the less strongly it would be affected by the consideration of emission timing.In the no technological advances scenario, most of the grassland would not produce any climate benefit within a 100-year time frame. Even for the highly productive grassland, it would take up to 46 years before the system could start generate carbon savings. Last, because the technology and productivity of the corn ethanol system changes over time, the timing of land conversion also plays a part in the CPT estimation. If land conversion took place in 2010, the CPT estimates would be 13 to 65 years, as opposed to 17 to 88 years for land conversion occurring in early 2000s. The environmental impacts per hectare crop harvested for most of categories studied were relatively stable in the past decade. This is because these impact categories are dominated by the direct and indirect emissions of nutrient, particularly nitrogen fertilizers, and the amount of nutrient inputs did not change much in the past decade. In contrast, the freshwater ecotoxicity impact per ha corn harvested declined by around 50% from 2001 to 2010 and per ha cotton produced declined by 60% from 2000 to 2007. These downward trends are due in large part to the increasing adoption of genetically modified organisms , which have resulted in reduced use of insecticides and replacement of some conventional herbicides with more benign ones, particularly glyphosate and compounds. Soybean production in the USA has also adopted GMOs widely, and this should have also led to a decline in soybean’s freshwater ecotoxicity as with corn and cotton. But because of the invasion of soybean aphid, a native of Asia, which have resulted in a substantial increase in insecticides use, the freshwater ecotoxicity impact per hectare soybean harvested increased by a factor of 4 from 2002 to 2012. In the meantime, on-farm irrigation water use per ha soybean harvested increased by about 50%.

This increase is due probably to the expansion of soybean into marginal land where intensive irrigation is needed. Implications of the above findings have been extensively discussed in individual chapters. Discussed below is the implication of considering marginal yield in the case of direct land use change for studies of indirect land use change and consequential LCA modelling. Some of the points, such as the importance of additional corn and carbon, have been somewhat touched upon in , but the discussion here is more detailed and from a methodological point of view. Early LCA estimates differed with respect to whether corn ethanol offers carbon benefits in displacing gasoline . Notably, the findings of the Cornell Professor David Pimentel were all negative , leading him to strongly oppose the use of corn ethanol . But subsequent LCA studies, with updated data and ethanol coproducts correctly accounted for, seemed to converge on that corn ethanol has a moderately smaller carbon footprint than gasoline, thus contributes to climate goals . However, a core factor was neglected in all these LCA studies, that is, land use change . The reason land use change did not come into play in these LCA studies is that they were basically a portrayal of exiting corn ethanol with corn grown on long-standing cornfield. But with increasing ethanol demand driven by federal policies like the renewable fuel standard aimed partly at mitigating climate change , what mattered was not the carbon footprint of existing corn ethanol but of additional corn ethanol. The key issue then became the supply of additional corn. Yield increase through intensification could produce more corn in the long run, but was hardly enough, and too uncertain, to meet annual ethanol expansion. The pressure was on land resources . Higher corn prices between 2005 and 2008 were driving farmers to bring new cornfield into production by converting natural habitats or to reallocate existing cropland to growing more corn . Either way, however, has dire carbon consequences that run counter to the initial climate goal of the federal policies.

Direct conversion of forest or grassland to grow corn for ethanol production would release a substantial amount of carbon stored in soil and plant biomass, creating a “carbon debt” that may take dozens of years to be repaid by carbon savings from substituting corn ethanol for gasoline . Similarly, reallocation of existing cropland to growing more corn could generate similar nets effects through market mediated mechanisms . For example, if the extra corn came at the expense of reduced soybean production, this could drive up global soybean prices and led farmers across the world to produce more soybeans by converting forest and grassland, resulting in loss of large amounts of carbon as well. In hindsight, that the majority of LCA studies failed to take account of land use change has a lot to do with the methodology they took, namely, attributional LCA . In these studies, corn ethanol’s carbon footprint was quantified in the simple accounting manner. They first estimated carbon emissions at different life-cycle stages based on existing, average corn farming practices and ethanol conversion technologies, and then summed them up and compared the total against the carbon footprint of gasoline. If they found that corn ethanol has a lower carbon footprint,plastic growers pots they would conclude that corn ethanol offers carbon benefits in displacing gasoline. Underneath the conclusion was the implicit assumption that the finding based on existing, average technologies would hold true for any amounts of additional corn ethanol. As argued above, however, the assumption is invalid. Because of land constraints, carbon emissions associated with additional corn ethanol would be much different from that associated with existing corn ethanol based on corn from long-standing cornfield . And it is the additional corn ethanol and associated carbon emissions that ultimately matter from both a policy perspective and in terms of reducing greenhouse gas emissions. In a word, consequential LCA looking into changes and effects is more relevant and better suited for addressing policy questions with potentially large economic and environmental consequences . But it should be noted that which specific methods to use for consequential modelling needs further research . The core to consequential modelling is the consideration of marginal changes, or processes actually to be affected by decisions at hand . In the case of dLUC, marginal changes include land conversion, additional corn production on the converted land, and additional ethanol produced and used. Particularly, the additional corn grown on the converted land sequesters additional carbon from the atmosphere. Without the additional carbon uptake, corn ethanol’s carbon benefits would not be possible as rightly pointed out by Searchinger . In short, it is everything that takes place on the converted land, together with additional ethanol production and use, that should serve as the basis for calculating corn ethanol’s total life-cycle carbon emissions in the case of dLUC .

Although Fargione et al. rightly considered land conversion and associated carbon loss, they relied on prior LCA studies , which were based on corn from long-standing cornfield, to estimate everything else. In so doing, they failed to recognize that newly converted land is generally not as fertile as cornfield persisting in cultivation and that corn ethanol originating from low-fertility land would provide smaller carbon benefits than corn ethanol originating from long-standing cornfield. Accounting for the actual yield of the converted land , as demonstrated by Yang and Suh , could substantially increase the time it takes for the use of corn ethanol to repay the carbon debt created by the initial land conversion. Exiting iLUC studies calculate corn ethanol’s total carbon emissions in the same way as do previous dLUC studies by adding carbon loss from land conversion to the carbon footprint of corn ethanol. When exposed with the same consequential reasoning, however, the iLUC literature commits the same error as committed in previous dLUC studies. But for iLUC effect it is beyond the actual yield or fertility of the converted land; what and how new crops are produced following land conversion matters. To drive home, let us consider a simple, hypothetical example of iLUC. Suppose, in response to increasing ethanol demand, part of U.S. corn was diverted to ethanol production at the expense of reduced exports to China. Total U.S. corn production and areas thus remained unchanged. This drove up Chinese corn prices and subsequently led Chinese people in rural areas to eat more rice, which drove up rice prices there and led Chinese subsistence farmers to convert reforested land to rice cultivation. What are the carbon consequences of corn ethanol expansion in this example? However, because U.S. corn production did not change or was not affected in this example, it is irrelevant to corn ethanol’s carbon consequences, as is the corn from longstanding cornfield in the case of dLUC. There was no additional carbon uptake from corn growth, nor were there additional carbon emissions from the use of agricultural inputs in corn production. What matters, instead, is the additional rice cultivation in China – which took place to compensate for the U.S. corn diverted to ethanol production – and associated carbon uptake and emissions . Of course, this is an extremely simplified example. Real-world consequences of U.S. corn ethanol expansion could be much more complicated, involving conversion of assorted natural habitats and different croplands brought into production in different countries. In any case, carbon uptake and emissions associated with whatever cropland being brought into production worldwide – including, likely, additional corn – should count towards the carbon consequences of ethanol expansion. Simply adding carbon loss from indirect land conversion across the world to the carbon footprint of U.S. corn ethanol is not meaningful from both theoretical and empirical perspectives. In addition to estimation of carbon loss from indirect land conversion , future studies need also direct efforts to account for what and how crops would be grown following land conversion and associated carbon uptake and emissions. In the chapter on carbon payback time , we assumed a perfect 1:1 displacement ratio between corn ethanol and gasoline on an energy basis, an assumption also used in previous carbon payback time studies .

There is little evidence of increased reliance on comparative advantage in China’s agriculture

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.9 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 . Figure 3 shows histograms for agriculture, “other” primary products, and manufactures. Figure 3 classifies subgroups indicating how many years the subgroup was in surplus. This means, that a subgroup that was in surplus for each of the 18 years would be in the cell at the extreme right of the histogram. The histogram for agriculture displays the strongest evidence of bimodality,plastic planters bulk which indicates persistence in trade flows. These histograms are therefore consistent with the C-statistic results, suggesting more persistence in the composition of agricultural trade, compared to the other two groups. Finally, the results of an additional test for association are reported in Table 6.

We regressed NB1994-96 on NB1980-82. The regression coefficients for all three groups are all statistically significant, but the coefficient for other primary products is smaller than for agriculture or manufacturing. Similarly, the R2 is relatively small for other primary products compared to either manufacturing or agriculture . These results support the conclusion that the trade patterns appear to change the most for “other” primary products, and the least for agriculture. The major finding of this paper is that China’s agricultural trade structure has not changed dramatically since 1980. China’s agricultural trade may loosely correspond to the basic principles of comparative advantage, but that in itself is not such a big achievement. Even under central planning, the obviousness of comparative advantage was such that in general terms, China’s agricultural trade corresponded to basic principles of comparative advantage. What is more striking is the modest and limited way that agricultural trade has expanded along comparative advantage lines, despite an increase in foreign trade overall, and the implicit evidence this provides of foregone opportunities for benefit from agricultural trade.Belize is a country in Central America and the Caribbean that is best described as a melting pot of diversity and culture. The biodiversity and natural heritage of the country is safeguarded through a system of terrestrial and marine protected areas under the National Protected Areas System Act regulating protected areas in Belize. There are 13 categories of protected areas in Belize, each with its own set of policies and procedures regulating permissible socioeconomic activities. The Forest Department manages terrestrial protected areas, while the Fisheries Department manages marine protected areas. Given the very large number of protected areas—approximately 100 in the NPAS—the aforementioned government departments often enter into comanagement agreements with conservation nongovernmental organizations or community-based organizations to accomplish effective management. Belize’s economy has been based on the exportation of raw products to the European Union and the United States of America. Traditional crops such as sugarcane, banana, and citrus products have been the main foreign exports. As global prices for these products change, so does the focus locally.

For example, the number one foreign exchange earner presently is tourism. For this industry to continue, the protection of the environment has become a top priority. This is because one of the main reasons tourists visit the country is for its rich flora and fauna, much of which still thrives in the mosaic of protected areas all over the country. Consequently, Belize really is prioritized as a system of protected areas maintaining interconnectivity from north to south as a wildlife corridor. Small but growing, Belize’s economy is very susceptible to the changes in global economic trends and since most of its foreign exchange is agriculture-based, climate change exacerbates that reality. This requires that climate-smart agricultural practices be adopted to mitigate the effects of changing weather patterns. This is important for continued local and foreign exchange earnings, but more importantly, for food and water security for the Belize population, as many communities still practice and rely on subsistence farming. As a response to changing weather patterns and a need to protect natural resources for both tourism and food security, an agroforestry concession system in the Maya Mountain North Forest Reserve has served as a pilot forest governance model that can be replicated in other forest reserves. Such system allows for greater attention on local communities who rely on granted access in protected areas to enhance their livelihoods. Access to the forest reserve has also created additional opportunities for women farmers of the Trio community, such as incentivizing honey production, an alternative nontimber forest product, as a socioeconomic activity. Apiculture complements the income generated from the sale of cacao beans and other crops and allows women to take a leading role in income-generation for the family in a traditional Maya community.“Forest reserve” is a category of terrestrial protected areas that allows communities to access natural resources in the conserved area. The MMNFR ranks 12th out of 56 protected areas that were evaluated for the National Protected Area Prioritization exercise of 2012, and is recognized as a key biodiversity area , prioritized for increased management effectiveness, under the Global Environment Facility–World Bank “Key Biodiversity Areas” project from 2015 to 2020 . This significant status was a contributing factor in formulating a conservation agreement that allowed for the first agroforestry concession within MMNFR. As a critical wildlife corridor in the Maya Golden Landscape —a large area of protected areas, agricultural and private lands, and communities—access to lands in the form of a concession creates a management presence that requires effective communication and coordination.

Population increase further adds to the pressures on Belize’s natural resources, increasing the priority to develop innovative approaches to provide for Indigenous and local communities who depend on the forest for food, housing materials, medicine and other necessities for their sustenance. This requires a landscape approach to natural resources management that puts forest-dependent communities at the center of the decision making process to implement adaptive ecofriendly extractive measures to ensure forest and livelihood sustainability. The community forest concession model is one of the tools that has been used to ensure that these communities become stewards of their surrounding natural resources. The Trio Farmers Cacao Growers Association from the community of Trio in Belize’s Toledo District is pioneering this community forest governance initiative . This local, organized group of 31 Maya farmers is registered under the Belize’s Company Registry, under Chapter 250 of the Companies Act . Villagers who were seeking access to farmland to continue their traditional farming practices formed TFCGA. In 2015, they signed the first-ever community forest concession in Belize. This is an agreement between the Forest Department and Ya’axché Conservation Trust , on behalf of TFCGA, the associate. The establishment of the conservation agreement grants the group rights to access the MMNFR for cacao-based agroforestry, beekeeping,collection pot and cultivation of annual crops, putting into practice sustainable climate-smart measures. Maya communities have traditionally used slash-and burn as a method of land clearing for agriculture. With the concession agreement and their access to a forest reserve, the organized group has been encouraged to cease this practice by adapting and practicing sustainable farming methods. The cacao-based agroforestry farming practice enhances the production of cacao beans while protecting standing forests and their biodiversity, and maintaining a healthy vegetation cover. This farming system is a long-term investment, as cacao production does not generate immediate income for the farmer, taking up to 4–5 years for the cacao plots to start to generate economically viable yields . This initiative, as part of a Community Outreach and Livelihoods program, targets socioeconomic challenges faced by a local, Indigenous forest community. The main focus is on food security, water conservation, and agricultural good practices, ensuring that anthropogenic disturbances do not continue to encroach on the remaining natural forests of the wild landscapes sought to be conserved. As a result, an annual crops section was considered and integrated as part of the agroforestry concession model.

Crops such as corn, beans, pepper, pumpkin, plantain, and root crops—staples of the Maya culture—are produced and surpluses are marketed locally. The adaptation of the cacao-based agroforestry system helps to ensure that our forests are managed sustainably. Cacao is emerging as a new foreign exchange earner, as there is a shift in the consumer demand for sustainably produced products. Through local observations, Ya’axché noted that most of the cacao currently being produced in Belize is within an agroforestry system. This is important since such an approach to production has minimal impact on the environment. This approach to farming curbs deforestation, creating an opportunity to manage natural forests in such a manner that shade-loving crops, like cacao, can be cultivated. Cacao is culturally important as it can be traced back to the ancient Maya civilization, where it played an important role as a currency and drink of the royal class. Although TFCGA has limited experience in growing cacao and farming within an agroforestry system, progress has been made, especially in shifting from the predominantly slash-and-burn farming practices common in many Maya communities.Empowering forest communities in the MGL to effectively manage their own natural resources fosters an innovative culture to learn and adapt best practices from success stories around the globe. Nontimber forest products are an integral part of Indigenous communities’ dependence on the forest, where beekeeping is placed as a high priority in the MGL for honey extraction. MMNFR has been considered as a location where apiaries can be maintained to boost the production of chemical-free “organic” honey. Seven female farmers from Trio have participated in capacity-building workshops, receiving technical assistance and material support from Ya’axché to continue the expansion of their apiaries. There is an opportunity to diversify the number of products that can be harvested, which can include pollen, wax, and royal jelly, among others. This group of women is a great proponent for the conservation of natural forests within MMNFR and other protected areas. Apiculture is complementing forest communities’ income, while fostering the development of a heightened stewardship of natural forests through an integrated management approach. Cacao has been a traditional crop in Maya communities, being used as a local drink for cultural activities such as communal planting and feasts, and as an offering during ritual ceremonies. To retain this traditional livelihood based on the harvesting of cacao, market demands have prompted initiatives to venture into local investments to increase cacao production as a supplement to the incomes of forest communities who continue with this practice. Ever since Ya’axché started to promote cacao-based agroforestry in the MGL, this climate-smart agricultural measure has gained traction as a response to the deforestation that occurred during Hurricane Iris in 2001 and the fires that followed. The Cacao Conservation Agreement of January 2016 was established after a forest concession was granted by Forest Department in June 2014. This led to the drafting of the 2014–2019 Agroforestry Concession Management Plan for the MMNFR, a conservation tool to oversee the cacao-based agroforestry model . Policies and procedures have been outlined to guide the effective management of the concession in the forest reserve.

The change in farm sales of floral products was much less dramatic

Average hourly earnings rose sharply between 2011 and 2012, and the increase was even greater in the San Joaquin Valley, which has over half of the state’s farm workers . Housing emerged as a major issue. Farm employers wanted to provide housing or a housing allowance only to the W-3 workers who are tied to their farms, but S 744 requires farm employers to provide housing or a housing allowance to both W-3 and W-4 visa holders. U.S. workers employed alongside W-3 and W-4 visa holders would not have to be provided with housing or a housing allowance. The amount of the housing allowance depends on whether the farm employer is in a metro or non-metro county. In California, W-visa workers would receive $295 a month in metro counties and $225 a monthly in non-metro counties in 2013, or $1.84 an hour in metro counties for full-time workers and $1.40 in non-metro counties. Almost all of California’s labor-intensive agriculture is in metro counties. A new W-2 visa program would admit more low-skilled workers, with the number eventually determined by a Bureau of Immigration and Labor Market Research, located in U.S. Citizenship and Immigration Services. Its $20 million budget raised from fees on W-2 workers and their employers. The Bureau would be charged with determining the annual change to the W-visa cap, devising methods to help employers who use guest workers to recruit U.S. workers, creating a methodology to designate “shortage occupations,” and making recommendations on employment-based visa programs. In order to hire W-2 workers, U.S. employers in metro areas with an unemployment rate of less than 8.5% would register themselves and their jobs and request W-2 visas for specific foreigners. Foreigners’ families could also receive W-2 visas, which would be valid for three years. Up to 20,000 W-2 visas could be issued in the first year, 35,000 in the second year, 55,000 in the third year, and 75,000 in the fourth year, and the number could rise further if certain conditions are met. No more than one-third of W-2 visa holders could be employed in construction. Where will U.S. employers get low skilled W-visa workers? Mexico-U.S. migration has been declining,blueberry containers and more Mexicans returned to Mexico, often after being deported from the US, than were admitted in recent years .

A century ago, most of the state’s farm workers were Asians. A combination of longer periods of U.S. employment and the opportunity to bring family members may bring more Asians to the United States as guest workers.About three-fourths of the hired workers on U.S. crop farms were born abroad, and over half of all farm workers are not authorized to work in the United States. Although most unauthorized workers are employed in non-farm jobs, California has a higher-than average share of unauthorized workers than most other states . The state’s share of unauthorized farm workers is also higher than average, which explains why California farmers have been in the vanguard of those advocating for immigration reform. If S 744 is enacted with its current agricultural provisions, there are likely to be three major changes. First, the hired farm work force is likely to become mostly legal, comprised first of currently unauthorized workers who become legal blue card holders and later legal guest workers. Second, labor costs should be stable, since average hourly earnings in California are well above the minimum wage that must be paid to guest workers. Even if farm employers have to pay a housing allowance of up to $2 an hour, the $9.64 that must be paid to guest workers in 2016, plus a $2 an hour housing allowance, is less than the average hourly earnings of crop workers in California in 2012, which were $12.56 an hour. Third, S 744’s agricultural provisions should provide labor certainty for California farmers, and give them advantages over farmers in lower-wage areas of the United States. The capacity to hire legal guest workers for up to six years at $9.64 an hour, with wage increases limited to 2.5% a year, should make it easier to plan investments in labor-intensive agriculture and secure financing for them. California farmers should benefit by the switch from a national minimum wage for guest workers rather than state-by state wages. The current Adverse Effect Wage Rates that must be paid to legal guest workers in 2013 range from $9.50 an hour in some southern states to $12 in Oregon and Washington; the California AEWR is $10.74.

The agricultural provisions of S 744 benefit currently unauthorized farm workers at the expense of future guest workers. Currently unauthorized farm workers and their families can become legal immigrants and leave the farm work force within five years, while future guest workers will have lower wages and perhaps fewer protections than current guest workers. Farm worker advocates and farm employers negotiated the agricultural provisions of S 744, and both have said they will strongly resist efforts to change what they describe as a “delicately balanced compromise.” If enacted, they should provide California agriculture with a legal work force at current costs.California’s nursery and floral industry will feel the effects of the “housing bubble” and the economic recession following its 2007 “burst” for many years. These effects are evident throughout the industry, ranging from the production of plants and material to structural aspects of product distribution. While there are no readily available empirical studies of the demand for nursery and floral products, it is widely accepted that housing and consumer income are important determinants of their demand. Thus, the economic downturn beginning in 2007, characterized by increasing unemployment, reduced consumer incomes, decreasing home prices, shrinking equities and foreclosures, would be expected to adversely affect the demand for nursery products. This article uses industry data to outline industry changes and to speculate on some possible implications of these changes.The California floral and nursery sector’s ties to the real estate industry, and the unique nature of its crops, contributed to uninterrupted sales growth between 1993 and 2007. This growth continued despite the major challenges presented by shipping restrictions related to pests and diseases, increased competition from imported flowers, the impact of increased energy costs on production and transportation, limited and expensive water supplies, and less-than-ideal weather conditions. As a result of plunging house prices and recession, the combined sales of nursery and floral products dropped in 2008, 2009 and 2010 before recovering slightly in 2011.

Data from USDA’s annual publication, California Agricultural Statistics, indicate that nursery production and sales typically ranked third among all California crops , while floral crops usually ranked around tenth. When combined,best indoor plant pots nursery and floral production typically ranked second in value of production among all California crops. As shown in Figure 1, total sales of California nursery and floral crops increased steadily from $2.71 billion in 1995 to a record $3.97 billion in 2007. Sales then decreased to about $3.37 billion in 2010 before recovering to $3.69 billion in 2011. Nursery and floral products’ share of total California agricultural sales increased from 9.6% in 1995 to a high of 12.5% in 2002 and then, with the exception of 2006, decreased steadily to 7.8% in 2011. Combined sales of nursery and floral products dropped to fourth place among all California agricultural products in 2011, following dairy, grapes, and almonds. Nursery and floral products’ decreasing share of total California agricultural sales beginning in 2002 is due to two major factors. Most important, for most of the period from 2002 through 2007, the rate of growth for other agricultural products outpaced the growth for nursery and floral products. Then with the onset of recession, combined nursery and floral sales decreased while some other major California commodities enjoyed increasing sales. Annual nursery and floral product sales decreased 4.7% from 2007 to 2008, then decreased 9.0% from 2008 to 2009, and 2.2% from 2009 to 2010. Finally, combined farm level nursery and floral sales increased 9.5% from 2010 to 2011.Nursery and floral products take a variety of paths in moving from the California producer to final customers, depending on the product and the nature and location of the customer. Due to the bulky nature and perishability of the products, most of the channels tend to be relatively short. For example, some producers have established retail outlets adjacent to their growing operations, especially in urban areas. Nursery operations supplying inputs to other growers tend to deal directly, or sometimes through a sales intermediary. Even large multi-product retailers who deal through wholesalers and jobbers often receive shipments directly from the nursery producer. While farm level sales of nursery and floral products decreased in both absolute and relative terms, the most dramatic impacts of the recession and housing problems occurred at the retail level.

Increasing unemployment and reduced consumer incomes combined with increased competition from alternative outlets to make retail florists an “endangered species.” At the same time, a collapse in home building put substantial pressure on specialized farm and garden stores and retail nurseries. Data from taxable retail sales reports and the directory of firms licensed to sell nursery products help to outline the changes occurring. Retailers and Taxable Sales: The California State Board of Equalization reports sales by type of retail outlet and the number of outlets. There are two retail store types for which nursery and floral products are the major products sold: florists and lawn and garden equipment and supplies stores . An increasing share of nursery and floral products are sold in other store types such as supermarkets, big box retailers , and food and variety stores, but we have no measure of the breakdown of sales by product line for any retailers. Changes in store numbers and annual sales for California florists between 2000 and 2011 are dramatic . The number of California florists increased from 5161 in 2000 to a peak of 6427 in 2008 , with store numbers increasing in 2008 even as sales began to plunge. Annual florists’ sales decreased over 34% from 2007 to 2008, 41.9% from 2008 to 2009, and another 2.5% from 2009 to 2010. Total sales by California florists in 2010 were only 37.4% of their level just three years earlier in 2007. Large numbers of florists began closing in 2008, with total numbers decreasing 25.3% by 2011 . Sales for California lawn and garden stores increased from just over $2.06 billion in 2000 to a high of over $2.96 billion in 2007 and then decreased over 25.2% the next two years before increasing 2.4% in 2010 and 5.4% in 2011 . However, the number of lawn and garden stores increased each year from 2000 through 2011 even when total sales decreased. Note that average per store sales peaked for both florists and lawn and garden stores in 2006 , decreased and reached a low in 2010 and then recovered with increased sales per store of 6.6% for florists and 2.2% per store for lawn and garden stores. Firms Licensed to Sell Nursery Products: Firms must be licensed by the California Department of Food and Agriculture to sell nursery products in California and licensed firms are listed in the annual Directory of Nurserymen and Others Licensed to Sell Nursery Stock in California. The firms by category were tabulated for 2003 and 2011 in a previous report and data for 2013 were tabulated for this report. The data in Table 2 show a significant reduction in the number of retailers between 2003 and 2011 with a slight recovery in 2013. There were also less dramatic decreases in the total numbers of middlemen as well as landscapers and producers from 2011 to 2013. Changing sales and reductions in the number of firms producing and marketing California nursery and floral products point to some rather basic structural changes with implications for both producers and consumers. First is the sharp reduction in the number of California florists and their total sales associated with the recession. The number of florists in 2011 dropped 1629 from the peak of 6427 in 2008 while sales decreased $753.26 million from 2007 to 2010.California farm-level floral product sales reached a high of $1.036 billion in 2007. Sales then dropped to $1.015 billion in 2008 and further to $937.0 million in 2009 before recovering to $1.015 billion in 2010.

The approach of down scaling IPCC storylines to analyze local land use scenarios is still relatively new

We address this need by investigating the GHG emissions and land use impacts of dramatically different urbanization storylines1 for an agricultural county within one of California’s rapidly growing metropolitan regions. In contrast to traditional urban-growth modeling, which projects scenarios into the future based on current and past policy, we take a “back casting” approach that seeks to consider the implications of radically different alternative strategies at a date far in the future. Accordingly, we propose strongly different story lines for 2050, develop modeling parameters based on these alternative futures, model the spread of new urban development across the landscape between now and then, and then estimate annual emissions from transportation and residential energy use in 2050 as well as the effects of urban growth on agricultural land. In terms of urbanization, our story lines range from business as usual in the county, with 65% of new households in traditional suburban or exurban densities, to a very-compact development scenario with only 10% of new households in these categories. Also factored into the scenarios are differential levels of urban rural connectivity such as local food marketing and consumption, which help build interest and support in the climate-related co-benefits of agriculture. The results are necessarily broad-brush, given that population, economic conditions, and political attitudes cannot be estimated with any degree of precision over such a long period. Still, such an approach can be useful to illustrate dramatically different policy approaches,cut flower transport bucket and indeed can be seen as necessary in order to give policymakers and the public a sense of the level of change required to meet climate change planning goals . As a foundation for our work, we use the Special Report on Emissions Scenarios storylines that the Intergovernmental Panel on Climate Change established in 2000.

According to the IPCC working group, “Scenarios are alternative images of how the future might unfold and are an appropriate tool with which to analyze how driving forces may influence future emission outcomes and to assess the associated uncertainties” . The IPCC scenarios are based on very broad storylines for alternative global futures, specifying different trajectories for population, globalization, economic growth, and environmental protection. The working group intended them to assist in the modeling of future GHG emissions, and also to assist with understanding of global warming impacts, climate adaptation , and mitigation . We chose the A2 and B1 scenarios for higher and lower GHG emissions, respectively, which can be conceptually down scaled to explore how future local land use patterns will respond to climate change . Scenario A2 has higher economic and population growth and less emphasis on sustainability priorities than Scenario B1. Because IPCC storylines do not include specific action to mitigate GHG emissions, we have added a third alternative, called AB32-Plus, which assumes continued development of State of California climate change policy as set out by a 2006 law, Assembly Bill 32 , as well as other state legislation and policy. In particular, Senate Bill 375 of 2008 requires each metropolitan area to develop a Sustainable Communities Strategy aimed at coordinating land use and transportation planning so as to reduce GHG emissions from transportation. Although coordinated transportation–land use planning in California certainly predates these pieces of climate change legislation , the state’s metropolitan areas began developing the new Sustainable Communities Strategies in the early 2010s , potentially establishing a stronger trajectory of urban growth planning. We sought to design urbanization assumptions within the AB32-Plus storyline so as to meet the state’s GHG mitigation goals as well as to achieve other benefits such as farmland preservation, greater provision of ecosystem services at the rural–urban interface, biodiversity conservation, improved rural livelihood options, and business opportunities that build social capital .

Our overall process then, was to review relevant literature, assemble storylines and scenario assumptions, model urbanization for the county with geographic information system–based software, calculate likely transportation and building emissions from new residential development for each scenario,and assess land use change implications. The analysis concludes with a number of strategies, some already in progress, which could inform a growth-management framework to limit urban development and enhance preservation of agricultural lands.However, Solecki and Oliveri used this strategy to examine conversion of agricultural to urban land in the New York City area, employing the SLEUTH urban-growth model to investigate A2 and B2 trend scenarios for 2020 and 2050, with 1960–1990 growth as a base. Modeling parameters primarily concerned the ways urban grid cells propagated in relation to existing development, urban edges, and transportation infrastructure. Solecki and Oliveri’s B2 scenario was substantially weaker in managing urban growth than the alternatives we envisioned developing. Although these authors found less urban sprawl with their environmentally oriented alternative, the percentage of land urbanized still more than tripled from 1990 to 2050. Rounsevell et al. Down scaled four SRES storylines for Europe and modeled land use for 2020, 2050, and 2080 time frames, though at a much larger spatial scale than ours . The main drivers for their model were global resource, market, and policy assumptions rather than local land use policy. Not surprisingly, their relatively green B1 and B2 scenarios performed best at preserving agricultural lands. Barredo and Gómez tested a cellular automata–based model through analysis of urban growth on 10,000 square kilometers around Madrid under three SRES scenarios for the 2000–2040 period. Model parameters focused on land accessibility, suitability, zoning status, and neighborhood effects. Their method produced distinctly different spatial clustering and distribution of development for their different storylines. Van Eck and Koomen applied two scenarios based on SRES storylines to model urban concentration and land use diversity in the Netherlands, finding that the latter produced significantly more urban sprawl and less concentration of development. None of these researchers, though, sought to further link their models to GHG emissions from the predicted development patterns.

More general analysis of urban growth has long supported the supposition that low-density suburban sprawl increases motor vehicle use, leading to higher GHG emissions compared with non-urban uses on the same land or with similar new populations living in denser urban environments with greater land use diversity . In a 2009 review of the literature, the National Research Council concluded that doubling residential density across a metropolitan area, combined with improved land use mix and transit, might lower household vehicle miles traveled by 5% to 12%, and perhaps by as much as 25% . The relationship is complex, however . In an analysis of 80 growth-scenario planning exercises in 50 US regions, Bartholomew attributed the relatively modest decreases in VMT usually shown within compact-development scenarios to the traditional insensitivity of travel-demand models to land use patterns,procona flower transport containers as well as the omission of other variables such as land use diversity and pricing. Sheer population and job densities may not be as important as residents’ accessibility to destinations and street-network design . Other factors such as the availability of public transit, bicycle and pedestrian infrastructure, and economic incentives probably play important roles as well.Research on relationships between urban form and GHG emissions is still in the early stages, and is based primarily on modeled rather than observed data. Andrews developed an exploratory land use–GHG emissions analysis framework that considers emissions from buildings, transportation, waste management, landscape management, urban heat islands, and electricity transmission and distribution. Applying this framework to typical types of development found in New Jersey towns, he found per capita CO2 emissions varying by a factor of two, with transportation emissions much lower in dense urban locations than in suburban ones, building emissions somewhat lower, and single family detached homes producing 33% more GHG from energy use than units in multifamily structures. Carbon sequestration within forests substantially lowered per capita human emissions in exurban locations compared with suburban or urban settings around the periphery of these towns in this East Coast location. This is less likely to be important in arid or primarily agricultural areas of the country, where the amount of woody vegetation is much lower. Waste management, urban heat-island effects, and electric transportation and distribution losses all proved relatively small factors in Andrews’s analysis. Ewing and Rong investigated the relation between suburban sprawl and residential-building energy consumption, finding 54% higher energy consumption for space heating for single-family detached units when compared with similar households in multifamily structures. However, they also found that urban areas have somewhat increased energy consumption for cooling, due to urban heat-island effects.

In a study of relatively low-density vs. high-density neighborhoods in the Toronto area, Norman, MacLean, and Kennedy found GHG emissions from the former approximately 81% higher for building operations and 365% higher for transportation activities. In a study of 11 metropolitan regions in the Midwestern US, Stone et al. estimated that an aggressive smart-growth scenario over 50 years could reduce the growth in transportation emissions from business-as-usual development by 34%, and that over business as usual, and that this land use strategy, combined with use of hybrid-electric vehicles, could reduce the growth in emissions by 97%. The relation between transportation emissions and building-related emissions will vary according to climate and geographical region. Randolph believes that in general sprawl has far greater impacts on transportation emissions than on building emissions. However, Andrews points out that, in some locations at least, building emissions are greater in quantity. Although the idea of modeling urban growth with very-low-GHG scenarios has been rare in academia, public agencies are beginning to move toward such back casting approaches in an effort to meet emissions-reduction targets and related legislation. As mentioned previously, California’s 2008 SB 375 legislation begins the process of encouraging such scenarios throughout California. The Sacramento Area Council of Governments , within the preparatory work for its 2012 Sustainable Communities Strategy , developed two significantly different future scenarios for the region based on different assumed energy efficiencies . Apparently, neither spatially explicit modeling of urbanization nor the official Sustainable Community Strategy were included, but this nevertheless represents a relatively strong environmentally oriented urban-growth vision given the current politics around land use. In fact, the Sustainable Community Strategy is not highly conducive to a carbon-neutral future, given that more than 25% of the region’s new housing in 2035 would continue to be built in the form of large-lot single-family homes outside of existing urbanized areas , adding to the region’s large existing stock of such homes. Modeling of the agency’s land use scenario, together with revised transpor-tation priorities, reduces transportation-related GHG emissions by 20% by 2020 compared to 2008, but emissions-reduction progress stalls thereafter, producing only an additional 3% improvement by 2035, far short of the trajectory needed for the state’s 2050 emission-reduction goal . If land use is to contribute toward meeting long-term GHG-reduction targets, dramatically different scenarios appear necessary.Yolo County is generally representative of agricultural counties in California’s Central Valley in that it contains a mix of irrigated perennial and row crops on alluvial plains, upland grazed rangelands, and small towns and cities. These agricultural landscapes also contain riparian corridors and other types of wetlands that are important for natural resource and biodiversity conservation. The Central Valley is one of the most productive agricultural regions in the world, yet is facing some of the most rapid population growth in the state. Urbanization in Yolo County is somewhat slower than in many other areas of the valley, having fallen to approximately 1% annually during the economic slowdown starting in the late 2000s. Total population was 200,709 in 2009; predictions for 2050 range from 320,000 to 394,000 . Given the county’s geography, urban expansion will almost certainly occur at the expense of farmland and open space if growth is not restricted to infill development within existing urban boundaries. Yolo County includes 653,452 acres of land . The incorporated cities of Davis, West Sacramento, Woodland, and Winters account for about 4.6% of the land area . In 1998, Yolo County alone contained about 40% of the prime farmland in the Sacramento region and yielded the highest farm market values out of all the counties . Thus, the jurisdiction is an important reservoir of productive farmland within an urban region.

Productivity was reported in amount per area with most crops reporting tons per acre

As a pixel is made up of the sum of its fractional surface components, we assume that the temperature of a pixel can be modeled by a linear mixture of its thermal components, that is, the sum of the LST for each of those components multiplied by their fractional portion of the pixel. To capture thermal variability within surface covers, each of the three components is broken down into sub-classes that are expected to share similar thermal properties, referred to as thermal classes going forward. These thermal classes resulted in each of the three surface covers having more than one thermal endmember, one for each thermal class. The endmembers that were used to model the expected temperature of each pixel were determined by the classes that were contained in that given pixel. To further evaluate crop-specific patterns of LST, we tested two hypotheses: 1) Crops with higher LST residuals, on average, will show declining yields over the study period, as would be indicative of stress; and 2) Crops with higher ET rates will shed more energy through latent heat flux and therefore have lower average LST values than crops with lower ET rates. To test the first hypothesis, yield data were obtained at the county level from the four counties that were part of the study area using annual agricultural statistics reports .The overall productivity for each crop type was calculated using an average of the county statistics, weighed by the relative area of that crop in each county. Because yield data are not available at the field-scale, county-level statistics were the closest proxy of productivity in the study area that could be obtained. Therefore, while the yield data and crop LST residuals are not directly relatable since the residuals only refer to a spatial subset of what is reported by the yield data,growing blackberries in containers the yield data is expected to give a general sense of which crops were faring well and which were most stressed within the study area.

To test the second hypothesis, we evaluated the correlation between average crop LST and the daily ET rate of each crop. ET rates were calculated as the product of the daily reference ET, as reported by the Belridge CIMIS station for each of the three dates, and the crop coefficient for each of the studied species, as calculated for June in the Southern San Joaquin Valley of California in a dry year . An evaluation of mean crop temperatures of pure pixels of each species by year showed that the temperature of each species relative to one another did not deviate greatly from year to year . The almonds had one of the top two coolest mean temperatures in each of the three years. The three citrus species, orange, lemons and tangerines, consistently had the three highest temperatures in each year. Cherries always had the highest average temperature of any crop except citrus. Every crop showed its highest mean temperature in 2014, likely attributable to the later flight time. The consistency suggests that thermal patterns are indicative of core biophysical properties, physiological properties, or irrigation practices that stay constant and allow for detailed analysis between species across time.Crops with higher residuals showed warmer measured temperatures than would be expected while crops with low residuals showed cooler temperatures than expected. High residuals are assumed indicative of stress. On average, crop residuals increased from 2013 to 2015 with average residuals of 0.14, 0.97, and 1.1 °C respectively. This positive year-to year trend of residuals indicates an increase in relative stress from the 2013 scene to the 2015 scene. This trend may be indicative of larger environmental and political consequences of the progressing drought with increased stress due to reduced irrigation and increased water restrictions. Alternately, the increase in relative stress could be resultant from more local scene and date-specific factors such as irrigation timing, differences in radiation load, or vapor pressure deficit.Fig 3.11 illustrates that the species-level trends in crop productivity from 2013-2015, as measured by yield per unit area, were captured well by the LST residual data. The percentage change in yield per unit area from 2013 to 2015 was compared with the average residual for each crop over all three years. We expected crops with higher LST residuals to have greater declines in yields, as would be the result of stressed vegetation.

Cherries and pistachios both showed the highest residuals and the largest declines in yields, a result that supported our hypothesis that high temperature residuals indicate unhealthy crops. Crops with the lowest residuals were hypothesized to be the least-stressed and therefore expected to have a relatively stable yield or an increase in yield. The crops with the lowest residuals did not have the largest increases in yield, however there was general agreement between the two trends overall with an inverse relationship apparent. While between-crop residual and yield data from 2013-2015 showed agreement, within-crop changes in residuals from year to year did not correlate with within-crop changes in yields. For example, both the average residual and the average yield of pistachio trees declined from 2013 to 2014, changes in stress that are opposite in implication. This suggests that this method is more suitable for comparing relative stress between crops than comparing stress of one crop over time .We calculated an expected LST for each pixel as a function of its fractional cover of soil, NPV, and GV and the expected temperature for the thermal classes contained within it. Although deviations from this relationship were presumed to indicate relative levels of plant stress, there may have been other factors that contributed to the deviations from the expected GV/LST pattern. For full interpretation of the residual results, the effect of various factors on the modeled, expected LST will be discussed: a) non-linearity of GV fraction estimation, b) shade effects, c) plant stress, d) error in fractional cover estimates, e) timing of flights, f) spatial variability in environmental variables and g) choice of thermal groups. First, expected LST is estimated using pixel fractions derived from MESMA, a linear spectral mixture model. However, in actuality spectral mixing is nonlinear due to multiple scattering of photons . This effect is expected to be prominent in agricultural orchards due to the vertical structure of the canopy, density of trees, and transmittance of radiation through the leaves . As shown in Somers et al., , tree-soil mixtures within a citrus orchard canopy as modeled by a linear mixture analysis will lead to an underestimation of GV for < 50% GV cover and an overestimation of green vegetation when GV cover is >50%.

These errors will likely be smaller with dark soils than bright soils because there are fewer photons reflected by darker objects . Nonlinearity can result in RMSE values of between 4 and 10% in citrus orchards for cover fractions . This error in GV fraction will lead the LST model to overestimate temperatures when pixels contain less than 50% GV and underestimate temperatures when the GV fraction exceeds 50% . Subsequently, pixels with low GV fraction will overestimate temperature, reducing the residual, while pixels with a high GV fraction will underestimate temperature,square pot increasing the negative residual. However, the errors due to multiple scattering in this study are expected to be low because canopy endmembers were used in the linear unmixing and these endmember already capture multiple scattering. Second, just as the linear spectral mixture does not account for photon interactions when estimating fractional cover, the linear thermal model used to model LST is also subject to nonlinear effects. Shade will cause error in soil temperature estimation that can lead to an overestimation of soil temperatures in mixed pixels. Thermal soil endmembers for the model were calculated based on the average temperature for pure soil pixels. A pure soil pixel is unlikely to be influenced by shadows, and its temperature will be a function of full solar radiation. However, as vegetation cover increases in a pixel, a larger percentage of the present soil will be shaded, up until the vegetation fraction reaches 100% and the effect cancels out . Shaded soil would be expected to be cooler than non-shaded soil, therefore the soil endmembers that are being used to model the soil temperature will be warmer than the actual shaded soil in mixed pixels. This will lead the temperatures of mixed pixels to be modeled as too warm, and the corresponding residuals to be too low. Similarly, vegetation is subject to shading effects as well as differences in structure and orientation that influence LST. Jones et al. found that leaf temperatures vary by as much as 15°C between full sun and deep shade. Therefore, factors such as the orientation of the leaves, canopy structure, and row spacing are all important controls on plant temperatures as they influence the amount of vegetation in a field that is shaded. These factors also affect the surface aerodynamic roughness, which governs how readily vegetation can transfer heat and moisture to the atmosphere. The height and structure of a crop canopy determines its aerodynamic roughness, with rougher vegetation being more tightly coupled to the atmospheric moisture deficit, which increases plant ET and decreases canopy temperature . In an aerodynamically rougher crop canopy, heat is also more readily transferred to the atmosphere by sensible heat flux. For these reasons, the remotely sensed surface temperature depends not only on the fractional cover of a pixel, but also on the composition of vegetation within a pixel. Two pixels with the same fractional cover of vegetation can have different thermal behaviors due to differences in the distribution of that vegetation, its height, and structure . The model aims to account for these influences by using canopy-level image endmembers and creating multiple thermal classes for different groups of perennial crops, so the overall error attributable to canopy shading is assumed to be small. Third, plant stress will alter the GV/LST relationship in a way that, while not introducing error, will lead LST residuals to vary by GV fraction. If a plant is stressed, its actual temperature will be warmer than expected, leading to a positive residual. While the model is designed for such a result, the side effect is that pixels with larger fractions of stressed vegetation will have higher residuals than pixels that have small fractions of stressed vegetation, as indicated by the increasing LST residuals with GV fractions in Fig 3.13C. Therefore, if plants are stressed, we expect that GV fraction and LST residual will have a positive correlation. We examined the relationship between LST residual and GV fraction for each of the studied crops in Figure 3.13 and found a trend of increasing residuals with increasing fractional cover, a result that we believe is indicative of crop stress. The relationship between residuals and GV fraction is shown by the positive linear trend lines in Figure 3.14 and the growing shaded area with fractional cover between the modeled and observed lines in Figure 3.13C. Fourth, an under or over estimation of fractional cover will propagate into LST residual errors; however, we do not believe that the distribution of errors will change the robustness of the results. Given mean LST values of 306.3 K, 321.3 K and 326.6 K for GV, NPV and soil respectively over all years and within the fields studied, the largest LST residual errors would result from a fraction error between soil and GV. MESMA has proven high fractional estimation accuracy for green vegetation. When looking at spectral separability between turfgrass, tree, paved, roof, soil, and NPV, Wetherley et al. found that mixtures of tree/soil were the second most separable pair after turfgrass/soil. Using synthetic mixtures, this study observed that soil, when mixed with tree, had a fractional accuracy of 0.976 while tree, when mixed with soil, had a fractional accuracy of 0.896. Therefore, we believe that fraction errors between GV and soil will be less than 10%. Furthermore, partitioning the landscape into soil and green vegetation is a necessary step in estimating crop stress and water use, and is therefore included in comparable models such as the VHI and WDI.

Transient responses to bio-available fractions may have occurred prior to our first measurement

As attested by the reduced MBC0 but increased CminSoil observed at 42 DAI, the community at this point appeared to be slower growing but better able to metabolize organic matter in an acid environment . A high rate of respiration to growth is a well-documented characteristic of stress adapted microbial communities . Stoichiometrically, a lower community metabolic efficiency could also help explain the observed increase in Nmin-Soil . Significant shifts in community tolerance to acidity have been observed within 36 d , making it plausible that some shift in acid tolerance could be observable within the 42 d under strong acid stress. The tendency towards higher net N mineralization in the S+ soils than the S- soils was much more pronounced with the addition of legume residues. The fact that both C and N mineralization responded so much more strongly to residue additions in the S+ than S- soils despite the former’s higher levels of soluble organic matter suggests that the mineralization pulses were not due to relief of substrate limitation. Since legume residues can complex with Al and reduce its activity , as well as temporarily consume protons through decarboxylation and ammonification of soluble organic acid anions , it is possible that the residues stimulated activity by relieving acid cation toxicity which had been limiting metabolism . As decarboxylation and ammonification produce CO2 and NH4 +, respectively , such detoxification products could also have contributed to the observed mineralization pulses. Liming produced mixed effects on C and N cycling processes. The most obvious effect of liming was a very large CO2 pulse from the unamended soil,growing blueberries in containers far exceeding the DSOC0 pool. It is likely that at least part of this was abiotic, issuing from the decomposition of carbonic acid from the liming reaction to CO2 .

While it is not possible to separate biotically and abiotically generated CO2, the fact that additional respiration due to residues was remarkably similar before and after leaching suggests that liming did not increase the capacity for respiration when adequate substrate was present. Contrary to our hypothesis, MBC0 and potential BG0 activity showed no signs of recovering after alleviation. However, the tendency towards higher MBC-Res with equivalent CO2-Res suggests that the community that grew in response to residue additions after liming was more efficient than that which responded at 42 DAI. The high Nmin-Res during stress and decline after liming suggests that high net N mineralization in response to substrate addition was caused by an inefficient community whose growth was limited by the adaptations required to survive in a stressful environment. Our results are in line with several studies which found that net N mineralization was not inhibited by salinity and acidity to the same extent as C mineralization and nitrification . Indeed, both net and gross N mineralization have sometimes been observed to be highest in the most acid soils within an experimental gradient . Similarly, a 400% increase in net N mineralization from vetch residues was measured in response to Al additions, despite reductions in C mineralization and MBC . The most direct explanation for this effect is that immobilization is slower than mineralization at low pH ; however, this does not always seem to be the case . The fact that the increase in MBC-Res after liming was not proportional to the decline in Nmin-Res suggests that reduced immobilization did not entirely explain the mineralization pulse. Other hypothesized mechanisms include increased losses by denitrification or volatilization as pH increases . Contrary to our hypothesis, compost had no effect on stress response and did not affect most indicators, regardless of S treatment. Compost is generally a stable, microbially processed product, rich in condensed, high molecular weight compounds, phenols and lignin and depleted in energetic compounds such as sugars .

This may explain why it did not have a measurable effect on microbial growth or most activity within our experimental time frame.Conversely, compost strongly and consistently increased Nmin-Res across all three sampling dates. As an increased Nmin-Res was likely a stress response, compost appears to have exacerbated the effects of stress, rather than buffering it as hypothesized. This paradoxical result could be explained if the increased net N mineralization under stress was partly due to a community shift towards one with less need for N relative to C. Fungi tend to have a higher C:N ratio than bacteria and are thought to generally be more acid tolerant . Rapid fungal but not bacterial growth rates have been observed within days of a labile residue addition to acid soils , and high fungal: bacterial ratios have been observed in experimentally acidified grassland soils . Since fungi generally have a wider C:N ratio than bacteria, they immobilize less N per unit C fixed. A faster fungal than bacterial growth response to residue additions could help explain why Nmin-Res values in the S+ treatments were on average more than double those in the S- treatments. The presence of a carbon source such as higher SOM or crop inputs often improves community stress adaptation . Adding compost could have facilitated that stress-induced community shift, such that the organisms which responded to residue additions in the C + S+ soil needed less N than their counterparts in the C-S+ soil . Strong community shifts are not necessarily evident in respiration measurements due to functional redundancy. For example, at the Hoosfield acid strip, fungal growth rates increased 30- fold as pH declined from 8.3 to 4.5, while respiration changed by less than one third . A compost-facilitated shift towards a less N-retentive community would be in line with two recent studies which observed that soils which were fungally dominated due to acid stress tended to use substrate less efficiently . This work presents the first data on the ability of a compost to moderate the effects of acid stress on nutrient cycling.

Green waste compost was chosen for this experiment, as it is typical of the type of compost the production and use of which is predicted to rapidly expand in California . It is important to note that these results may not be typical of all compost types. For example, a strong liming effect has been observed when poultry manure from layer hens was applied to an acid soil, likely due to the calcium carbonate in the feed . However, the strong and unexplained effect on N cycling suggests that further investigation with additional soil and compost types should be pursued. In particular, compost effect on microbial community structure under chemical stress should be investigated further. Additionally, the use of isotopically labeled residues would allow for mechanistic exploration of mineralization and immobilization dynamics. California’s agricultural sector critically affects both the national food supply and regional water resources. California has the largest agricultural sector in the country, producing two thirds of the fruits and nuts in the United States and approximately one third of its vegetables . California’s crop supply is also significant to the United States in that many crops grown in the state, such as almonds, garlic, olives, raisin grapes, pistachios, and walnuts,square pots are exclusively produced there . However, while California’s more than 400 commodities are central to US food supplies, they also necessitate high water inputs. High crop production and a semi-arid climate result in agricultural needs using over eighty percent of the state’s managed water supply . This reliance on irrigated inputs means that yearly crop prices and food supplies in the United States are susceptible to changes in the available water supply of California and impacted by local water management decisions . As California’s water supply becomes increasingly unpredictable due to changes in climate, this interconnection of food and water supplies at local to national scales is ever more important to understand. California’s highly variable water supply is a factor of its natural climatology but is further exacerbated by larger climate trends shaped by manmade influences. California has, for centuries, experienced oscillations between wet and dry periods that result in California having the greatest variations in annual precipitation of any state in the country . However, over the past century, an increase in surface temperature by 0.6-0.7° C has led to changes in California that are attributable to human GHG emissions and further affect water availability: earlier spring snow melt , an increase in percent of precipitation as rain rather than snow , warmer winter and spring temperatures , and less snow accumulation over the last fifty years .

Climate change will continue to augment the patterns of precipitation in California and intensify effects on water resources and agriculture. By early in the 21st century, the Bureau of Reclamation predicts that the Central Valley will experience a 1-degree Celsius rise in annual average temperature and a 2-degree C increase by mid-century that will likely be accompanied by a north-to-south trend of decreasing precipitation . This shift in temperature is projected to increase the frequency, intensity, and duration of droughts over the next century that will make our current water system performance levels impossible to sustain in the Central Valley . One way to prepare for the anticipated increase in drought is to study past events as an indicator of future effects. From 2012 to 2016 California experienced its worst drought in history . Water allotments were cut across the board and farmers, as the users of the majority of the state’s water, were especially hard hit . With the State Water Project and the Central Valley Project allocations cut to zero in some areas, agricultural communities in the Central Valley faced surface water reductions of an estimated 8.1 billion cubic meters a year from 2013 to 2014, amounting to a 36% reduction in surface water availability for farms . The study found that a 62% increase in groundwater extraction partially compensated for the reduction in surface water but threatened the health of California’s aquifers and moreover, still left farmers with an overall deficit of 1.9 bcm/y . This extreme event and climatological anomaly presents an opportunity to better understand how managed crops are impacted by water limitation. As lack of water will be a major limiting factor for agricultural production within the next century , patterns of crop water use and their response to reduced water availability need to be carefully analyzed so impacts to long-term food and water security can be better understood as we move into a new climate regime. Remote sensing provides new opportunities to monitor agricultural change with drought and capture spatial variations and trends in plant water use that traditional on-the ground methods like county-level reporting, lysimeters and eddy flux towers are unable to do given their limited spatial scope and significant time and labor inputs . Current crop monitoring initiatives in the United States primarily rely on imagery from earth-observing satellites such as Landsat , Moderate Resolution Imaging Spectroradiometer and the Advanced Spaceborne Thermal Emission and Reflection Radiometer to map crops and assess health and water use information . However, a new satellite, the Surface Biology and Geology Mission, has been proposed as an improvement in both spatial and spectral performance for ecosystem study . The SBG Mission will combine two sensors, a hyperspectral sensor in the visible through shortwave infrared at a 30 m resolution and a thermal sensor at a spatial resolution of 60 m for global coverage and a 5-19 day revisit. This mission has the potential to improve ability to assist crop and water managers in dynamic and diverse environments, such as the Central Valley of California, with resource accounting and drought response by capturing refined spectral information at a spatial scale that is fine enough to resolve individual fields. With the impending launch of this satellite, it is important to determine its scientific capabilities for routine observation of crops in California at a level that is of use to water managers. To test the capabilities of the SBG sensor, the Hyperspectral Infrared Imager Airborne Campaign flew the Airborne Visible/Infrared Imaging Spectrometer and MODIS/ASTER Airborne Simulator sensors on NASA’s ER-2 plane throughout California from 2013 to 2017 to simulate expected datasets from SBG . AVIRIS is a 224 band imaging spectrometer that captures spectral information from 350 to 2500 nm at ~10 nm increments .

Hiring and wages in casual labor markets in India are generally determined on a daily basis

FarmOS have not only defined data structures for many agricultural entities, but made it trivial to expand them in order to develop custom data structure’s using their schema. Agricultural data privacy laws surely have a ways to go in order to protect farmers from untrustworthy institutions. This issue, however, paired with the problem of data being leaked in breeches has led to the extensive research and development of new technologies which put precedence on data security and allow for operations, especially in machine learning, to be performed without compromising security. These new technologies include trusted execution environments , cloud operated machine learning as a service and fully homomorphic encryption amongothers. In this paper I express a collection of architectures built on a combination of some of these technologies as well as others for trusted and secure machine learning model training. A key software utilized is FarmStack. Digital Green is developing FarmStack as a peer-to-peer network protocol which secures data in transit through periodic attestation, network policy enforcement and endpoint application enforcement. Providing agricultural data owners with the means to allow others to use their data securely is the primary goal of the proposed data sharing and model training network architectures.Soil salinity is a known constraint on agricultural production in the Central Valley, particularly in the western San Joaquin Valley , where soils are naturally high in salts due to the marine origin of their Coastal Range alluvium parent material . In such a large region, it is difficult to quantify and map the full extent of soil salinity and its impact on agricultural production and profits. Many geological, meteorological and management factors affect the salinity levels of irrigated soils,raspberry cultivation pot including irrigation water quality, irrigation management, drainage conditions, rainfall and evapotranspiration totals and cultural practices.

Across a region such as WSJV, most of those factors vary at multiple spatial and temporal scales, making it difficult to extrapolate local point measurements of soil salinity to regional scales. Although agricultural salinity is a generally well known issue, communicating the full extent and severity of the problem to policymakers, stakeholders and other nonspecialists is a challenge. Detailed regional maps present the problem visually and can help spur action on planning, management and conservation. Letey argued that long-term sustainable and profitable agriculture in California can be achieved only if regional-scale salt balances can be obtained. Regional-scale salinity maps provide irrigation district managers, water resource specialists and state and federal authorities with timely information that can guide decisions on water allocation needs and groundwater regulation 2015. We qualitatively evaluated the correspondence of remote-sensing high salinity predictions with the presence of salt crusts. To map salt crusts across the WSJV, we used imagery from the 2014 USDA’s National Aerial Imagery Program survey . A supervised classification was used to identify salt crusts. The classification identified NAIP pixels with reflectance properties similar to those observed at locations known to be affected by salt crusts. This analysis identified salt crusts over 0.5% of WSJV farmland. Figure 3A depicts a site near Bakersfield where salt crusts are clearly visible in the NAIP ortho-imagery over fallow land but not in the neighboring corn field. There is excellent correspondence between the high salinity sections of the site as estimated by the remote-sensing map and the location of the salt crusts . To properly compare the NAIP salt pixel classification with figure 1, we aggregated the NAIP classification at the 32.8 × 32.8 yard resolution. Only the 32.8 × 32.8 yard cells that included more than 50% of NAIP salt crusts at the original 1.09 × 1.09 yard resolution were retained for further analysis. A total of 162,829 “salt-crusted” cells were identified. About 94.3% of the salt-crusted pixels were predicted by equation 2 to be ECe > 4 dS/m. In total, the salt crusted pixels had average ECe of 13.6 dS/m, first quartile of 9.7 dS/m, median of 13.5 dS/m and third quartile of 18.2 dS/m, indicating good correspondence between visibly saline soils and predictions of high salinity by equation 2.

Scudiero et al. indicated that remote-sensing estimations at low salinity levels might be imprecise because plants may not be sufficiently osmotically stressed at low salinity to affect crop health. The spatial variability of other soil properties that influence crop yield within a single field could lead to salinity estimation errors at low salinity. Although sub-field variations in soil texture are typically minor in WSJV, some fields exhibit significant variability over short distances. In these cases, soil heterogeneity influences crop performance, introducing uncertainty into the remote-sensing estimations of soil salinity. As an example, consider the remote-sensing salinity predictions for a slightly to moderately saline where NIRWV2 , REDWV2 and BLUEWV2 are the WorldView-2 bands employed in the calculation. The EVI was selected to show that vegetation indices other than CRSI can be used to assess soil salinity, provided they reflect plant status at the target location. The multi-temporal maximum EVI map from the three WorldView-2 images is visually similar to the ground-truth salinity map . The two maps are negatively correlated, with a coefficient of determination of 0.45 . Both maps were resampled to coarser resolutions to study the changes in the strength of their relationship. As shown in figure 5D, the strength of the salinity-EVI relationship increases as block support decreases. In particular, the scaled explained variance and the strength of spatial correlation increase to a maximum at block support of 20 meters , then steadily decrease as the resolution becomes coarser. The strength of the salinity relationship with EVI at the Landsat block support was similar to that at 20 meters, indicating that it could properly represent the salinity spatial patterns at this site, despite being slightly coarser than ideal. Since the early 1950s, irrigation has played an important role in improving the quality of WSJV soils. As an example, the long-term change in soil salinity for western Fresno County is discussed by Schoups et al. . Schoups and colleagues found that long-term irrigation helped reduce soil salinity across western Fresno County throughout the second half of the 20th century. When irrigation stops, there is a risk that these trends will reverse and that salinity will rapidly increase in lands with shallow groundwater, as observed in the long-term study of Corwin . Reduced water allocations have caused farmers to use potentially higher salinity groundwater in place of lower salinity surface water and to fallow fields during the ongoing drought. According to the CropScape database, during the drought, fallow land in WSJV increased from an average of 11.8% during the years 2007 to 2010 to 19.2%, 21.0%, 21.6%, 25.9% and 33.7% through the years 2011 to 2015. Land fallowing could lead to increases in root zone salinity, thereby potentially negatively affecting future crop growth in the WSJV .

When reducing water allocations to farmland, the risks of quick land salinization should be considered. Updated regional-scale inventories of salinity will provide information for better water management decisions to support statewide agriculture and preserve soil productivity, especially in years of drought, when water resources are limited. With water shortages and droughts likely to become longer and more frequent in the future , threats from increasing soil salinity are also likely to become more severe and should, therefore, be given serious consideration by landowners,low round pots water district managers, and federal, state and local agencies. Individual soil salinity maps such as presented in this paper can help landowners and water district managers select land they wish to retire or convert to other uses . But a much greater benefit would be realized if a soil salinity remote-sensing program were established in which maps were created every 5 to 10 years for salinity-affected areas of statewide importance, including the Central and Imperial Valleys. Such a remote-sensing program would allow for the first-time monitoring of soil salinity at regional and state levels, would permit new understandings of drivers and trends in agricultural soil salinity and would aid in the development and assessment of mitigation strategies and management plans. Our primary sample is spread across 12 blocks within 4 districts of the Jhark hand state in eastern India. The blocks were identified as being suitable for a drought-tolerant rice seed variety that we were testing using a randomized controlled trial. We selected a random sample of villages amongst those with 30 to 550 households. Within each village, enumerators located a village leader and asked for names of 35 people from separate households: the 25 largest rice farmers, male individuals that work on other farmer’s fields, and 3 female individuals that also work as casual agricultural laborers. Enumerators carried out a baseline survey with the farmers and workers during the period from late April to early June 2014. Our sample of laborers consists of people that are landless or have small amounts of land. This population makes up a non-trivial share of the people dependent on agriculture in rural India. In contrast to large landowners, these workers generate most of their income from supplying labor to the casual labor market.

Yet, most studies rely on data that aggregates labor market outcomes over a longer time period. This potentially misses short-term movement between occupations. To better measure labor-market outcomes in our context, we collected daily data on wages and employment. We did this by conducting phone surveys that took place during the transplanting and harvesting periods across the 2014, 2015, and 2016 cultivation seasons. Rice is the dominant crop in our sample area and is planted in late July / early August and is harvested in late November. Our phone surveys took place during these times to coincide with the peak periods for agricultural labor demand. During the first year surveyors attempted to contact the 10 laborers in each of the 200 villages. During each call respondents were asked whether they worked on another person’s farm or their own farm, the wage they received, whether the work took place in their own village, and their activity if they did not work in agriculture. This information was collected for the seven days preceding the phone call. We repeated this same process in the 2015 and 2016 seasons with a few important differences. First, we expanded the sample to include 6 female laborers per village. The additional three laborers were selected from a census that had been conducted in all villages on households with casual laborers.Second, starting with the 2015 harvesting survey, we expanded the recall window to 14 days to more easily capture the entire planting or harvesting period for each village. The phone surveys produced a high response rate: an average of 86 percent of the workers in the baseline sample were reached.These data allow us to observe daily employment outcomes for planting and harvesting across three agricultural seasons. In addition, we collected non-agricultural wages in the 2015 planting and both 2016 surveys. These observations consist mostly of casual work for a daily wage — rather than self employment. We observe the daily wage for 82 percent of the non-agricultural work days in these three surveys. This information, along with the individual-level panel on agricultural outcomes, allows us to measure the agricultural wage gap while controlling for unobserved heterogeneity across individuals.Since the people switching sectors give identification, it is useful to compare them to the individuals that work in agriculture for the entire sample period. About 20 percent of the workers from the baseline survey switched sectors. Table 1 shows the differences between these two groups. Switchers are predominantly male and generally poorer in several dimensions. For example, they are less likely to have access to electricity, more likely to be in households using the government’s rural employment guarantee , have larger households, and more likely to belong to lower castes. They are also more likely to have household members that migrate temporarily , but are not more likely to engage in permanent migration. Yet, switchers have no less land. The average laborer household cultivates 0.57 acres during the rainy season and only about 16 percent of households cultivate no land at all.Overall, the people that switch between local agricultural and non-agricultural work are neither the wealthiest or most educated. If anything, the switchers tend to come from poorer households.

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