Month: September 2023

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Some manufacturers report a canopy temperature reduction of up to 6oC when using their products

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, 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,cultivo de arandanos 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.In the introduction chapter, I discuss the nature of temperature challenges posed by climate change. The rising average temperatures, according to the empirical literature, might not be the major source of potential loss. Rather, it’s the elongating and fattening temperature distribution tails that would be responsible for much of the damage. Could there be a way for farmers to target these tails directly? If so, such technologies could have potential uses for climate change adaptation. It so happens that farmers already deal with temperature extremes, and are capable of tweaking the tails of temperature distributions to avoid losses. The introduction already discussed “air disturbance technology”, basically large wind generators, used to deal with some types of frosts . Solutions for right side temperature tails exist as well.

Of course, shading plants using nets or fabric is an existing practice, but these technologies are costly and not very flexible. However, other products that reflect sunlight and lower plant exposure to excess heat are available on the market. Perhaps the most common ones are based on a fine kaolin clay powder, which is mixed with water and sprayed directly on plants to form a reflective coat, sometimes referred to as a “particle film”. These products have been commercially available since 1999, and are shown to effectively lower high temperature damages by literally keeping plants cooler .cultivo de arandanosSpraying of this mix requires special rigs and equipment, but the costs are reasonable, and far lower than setting up shading in the form of nets . This technology can be thought of as cheap, disposable shading. Surprisingly, even though kaolin clay has been used by farmers to deal with other problems, less related to climate change , I could find no economic literature discussing this technology. As with the case of air disturbance technology, these types of technologies have mostly been ignored by economists. One reason for this gap in the literature could be that economists have not yet realized that these individual products and practices share a common conceptual framework: they are tweaking temperature distribution tails, while leaving the main probability mass untouched. This is an approach I call “Micro-Climate Engineering” . These are relatively small interventions in temperature distributions, limited in space and time, which aim to avoid the nonlinear effects of the extremes. Farmers know the available technologies for MCE and use them regularly, but their potential applications for climate change have not really been explored. The concept of MCE could be very important for climate change adaptation in agriculture, especially when considering the role of extreme temperatures on predicted future losses. MCE solutions, where feasible and profitable, could assist in preserving current crop yields and delaying more costly adaptation strategies. This chapter sets to explore the concept of MCE in general, and assess the gains from MCE in California pistachios as a case study. Specifically, pistachios are threatened by warming winter days, which could threaten existing acreage within the next twenty years .

This challenge stands out in the existing literature in three ways: first, while much of the climate change literature focuses on annual crops, pistachios are perennial. This means that the opportunity cost of variety switching are higher. Second, the challenge does not occur in the “growing season”, but on the winter months when trees are dormant and seemingly inactive. This emphasizes the importance of climate change effects year round, rather than just in the spring and summer. Third, the challenge stems from a biological mechanism that is not heat stress. Heat stress is perhaps the most obvious process by which rising temperatures can have adverse effects on yields, and by far the most studied in the economic literature on climate change. However, other biological mechanism are affected by weather as well, and can cause substantial yield losses. This paper incorporates agronomic knowledge on bloom disruption due to increased winter temperatures, a mechanism that is relatively unexplored in the economic literature. Scientists at the University of California Cooperative Extension have been experimenting with kaolin clay applications on pistachios,macetas redondas de plastico and the results seem promising . This could mean a great deal to growers and consumers. This chapter analyzes the potential economic gains from this MCE application in California pistachios. 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 the US, 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 . Declining chill is therefore considered a threat to California pistachios.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, these losses are predicted to reach up to 50% in some regions in the future.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 by MCE 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.

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Past county yields are from crop reports published by the California Department of Food and Agriculture

The gains are much higher than the ones found in the 1996 report. This is partly due to increased economic activity in general, but probably has to do with more adoption of smart irrigation as well. The total yearly gains in agriculture range between $492 million, taking only the intensive margin effects, and up to about $1,982 million considering the extra acres that can be grown with the saved water. A surprisingly large sector using CIMIS is landscaping and golf courses, with yearly monetary savings of at least $201 million for our survey sample alone. Several other user types were included in the survey, indicating a substantial role of CIMIS in areas crucial for California’s economy. Respondents use CIMIS to plan drainage in agricultural and urban settings, taking advantage of CIMIS historic rainfall records. CIMIS is used for water budgeting and even pricing. Researchers in the public and private sector use CIMIS for diverse purposes, from basic research to calibration and verification of other weather related products. These are just a few of many additional uses of CIMIS we know about, but do not quantify here due to the complicated methodological framework required. The economic gains from CIMIS surely surpass the ongoing costs of a system with less than a dozen employees. However, could these gains be achieved by the private sector? The decreasing costs of weather sensors mean that growers and other users could potentially access precise data on their own. If we wanted a cheap weather station, costing about $1,000, for every 1,000 acres of drip irrigated land in California, the total cost would surpass$2.8 million, plus some ongoing costs for maintenance. This, however, would prevent many benefits from the centralized aggregation of data and the historical records that are crucial for research and planning, as one could not assure that aggregation of the data from all these separate private stations would occur. While several online aggregators of weather information exist,planting in pots ideas many rely on the public information provided by networks such as CIMIS and other government bodies such as airports and air quality monitoring systems. It is not obvious that private aggregators would be profitable if they had to purchase this information, or what their WTP would be.

Moreover, the ET measurements which many growers use are usually not captured by commercial stations, and there are concerns regarding the reliability of ET approximations by other variables. The development of satellite technology might change these conditions in the future.California pistachios are a high value crop, with grower revenues of $1.8 billion in 2016. The most common variety is “Kerman” , and almost all the California acreage is planted in five adjacent counties in the southern part of the San Joaquin valley. In recent years, rising winter daytime temperatures and decreasing fog incidence have lowered winter CP counts. Climatologists have concluded that winter chill counts will continue to dwindle , putting pistachios in danger at their current locations. To better predict the trajectory for this crop and make informed investment and policy decisions, the yield response function to chill must first be assessed. This task has proven quite challenging. The effects of chill thresholds on bloom can be explored in controlled environments, but for various reasons these relationships are not necessarily reflected in commercial yield data. For example, Pope et al. report that the threshold level of CP for successful bud breaking in California pistachios was experimentally assessed at 69, but could not identify a negative response of commercial yields to chill portions of the same level or even lower. They use a similar yield panel of California counties, but only have one “representative” CP measure per county-year. Using Bayesian methodologies, they fail to find a threshold CP level for pistachios, and reach the conclusion that “Without more data points at the low amounts of chill, it is difficult to estimate the minimum-chill accumulation necessary for average yield.” The statistical problem of low variation in treatment at the growing area, encountered by Pope et al., is very common in published articles on pistachios. Simply put, pistachios are not planted in areas with adverse climate. Too few “bad” years are therefore available for researchers to work with when trying to estimate commercial yield responses.

An ideal experiment would randomize a chill treatment over entire orchards, but that is not possible. Researchers resort either to small scale experimental settings, with limitations as mentioned above, or to yield panels, which usually are small in size , length , or both. Zhang and Taylor investigate the effect of chill portions on bloom and yields in two pistachio growing areas in Australia, growing the “Sirora” variety. Using data from “selected orchards” over five years, they note that on two years where where chill was below 59 portions in one of the locations, bloom was uneven. Yields were observed, and while no statistical inference was made on them, the authors noted that “factors other than biennial bearing influence yield”. Elloumi et al. Investigate responses to chill in Tunisia, where the “Mateur” variety is grown. They find highly non-linear effects of chill on yields, but this stems from one observation with a very low chill count. Standard errors are not provided, and the threshold and behavior around it are not really identified. Kallsen uses a panel of California orchards, with various temperature measures and other control variables to find a model which best fits the data. Unfortunately, only 3 orchards are included in this study, and the statistical approach mixes a prediction exercise with the estimation goal, potentially sacrificing the latter for the former. Besides the potential over-fitting using this technique, the dependent variables in the model are not chill portions but temperature hour counts with very few degree levels considered, and no confidence interval is presented. Finally, Benmoussa et al. use data collected at an experimental orchard in Tunisia with several pistachio varieties. They reach an estimate for the critical chill for bloom, and find a positive correlation between chill and tree yields, with zero yield following winters with very low chill counts. However, they also have many observation with zero or near-zero yields above their estimated threshold, and the external validity of findings from an experimental plot to commercial orchards is not obvious.Pistachio growing areas are identified using USDA satellite data with pixel size of roughly 30 meters. About 30% of pixels identified as pistachios are singular. As pistachios don’t grow in the wild in California, these are probably missidentified pixels. Aggregating to 1km pixels, I keep those pixels with at least 20 acres of pistachios in them. Looking at the yearly satellite data between 2008-2017, I keep those 1km pixels with at least six positive pistachio identifications.

These 2,165 pixels are the grid on which I do temperature interpolations and calculations. Observed temperatures for 1984-2017 come from the California Irrigation Management Information System , a network of weather stations located in many counties in California, operated by the California Department of Water Resources. A total of 27 stations are located within 50km of my pistachio pixels. Missing values at these stations are imputed as the temperature at the closest available station plus the average difference between the stations at the week-hour window. Future chill is calculated at the same interpolation points,growing blueberries in pots with data from a CCSM4 model CEDA . These predictions use an RCP8.5 scenario. This scenario assumes a global mean surface temperature increase of 2o C between 2046-2065 . The data are available with predictions starting in 2006, and include daily maximum and minimum on a 0.94 degree latitude by 1.25 degree longitude grid. Hourly temperature are calculated from the predicted daily extremes, using the latitude and date . I then calibrate these future predictions with quantile calibration procedure , using a week-hour window. Past observed and future predicted hourly temperatures in the dormancy season are interpolated at each of the 2,165 pixels, and chill portions are calculated from these temperatures. Erez and Fishman produced an Excel spreadsheet for chill calculations, which I obtain from the University of California division of Agriculture and Natural Resources, together with instructions for growers . For speed, I code them in an R function . The data above are used for estimation and later for prediction of future chill effects. For the estimation part, I have a yield panel with 165 county-year observations. For each year in the panel, I calculate the share of county pixels that had each CP level. For example: in 2016, Fresno county had 0.4% of its pistachio pixels experiencing 61 CP, 1.8% experiencing 62 CP, 12% experiencing 63 CP, and so on.Figure 3.1 presents chill counts and their estimated effects in percent yield change for two time periods: 2000-2018 and 2020-2040. The top left panel shows the chill counts in the 1/4 warmest years between 2000 and 2018 . The top right panel shows the chill counts in the 1/4 warmest years in climate predictions between 2020 and 2040. Chill at the pistachio growing areas is likely to drop substantially within the lifespan of existing trees.Results from the polynomial regression are presented in Table 3.2 . The first coefficient is for an intercept term, and it is a zero with very wide error margins. This makes sense, as centering around the means also gets rid of intercepts. The second coefficient is positive, as we would expect, and statistically significant. The third coefficient is negative, as we would also expect since the returns from chill should decrease at some point, but not statistically significant even at the 10% level. However, as dropping it would eliminate the decreasing returns feature, I keep it at the cost of having a wide confidence area. With the estimated coefficients, I build the polynomial curve that represents the effect of temperatures on yields. It is presented in Figure 3.2 with a bold dashed line. The 90% confidence area boundaries are the dotted lines bounding it above and below. Note that the upper bound of the confidence area does not curve down like the lower one. This is the manifestation of the third coefficient’s P-value being greater than 0.1. In both cases, the confidence area was calculated by bootstrapping. The data was resampled and estimated 500 times, producing 500 curves with the resulting parameters. At each CP level, I take the 5th and 95th percentiles of bootstrapped curve values as the bounds for the confidence area. This approach also deals with the potential spatial correlation in error terms. 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.

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Plants are fertilized with controlled-release fertilizer applied at plant-out

The management of the agricultural lands will be guided by an advisory committee, but the overall goal is to develop models, for the greater watershed, of ecologically and economically sustainable methods for crop production. An additional research site is located on the Elkhorn Slough National Estuarine Research Reserve. The site includes a small pond drained by sloping uplands. It is very similar to the three drainages on the Azevedo ranch, with the important exception that it has never been cultivated. Although the pond is larger than any of the Azevedo marshes and is subject to greater flushing, it provides the opportunity to obtain background data on soils, sediments, and biota in the absence of agricultural disturbances. During the first two years of the study we established critical measurements, protocols, and characterizations of these watersheds under standard cultivation practices. These data will serve as a baseline for comparison once the property is converted to low-input sustainable agricultural management and habitat restoration is completed in the wetland buffer. Conversion and restoration will occur in 2 to 4 years, once the land has been fully paid for. The project is guided by a Technical Advisory Committee which meets monthly. Although this report marks the end of Project Number UCAL-WRC-W-801, the project is ongoing. Our long term goal is the investigation of linkages between different farm management practices and health of the adjacent slough, as monitored by sedimentation, input of anthropogenic chemicals, water quality, and the response of wetlands flora and fauna. In the future, we will implement and test alternative farming practices that lessen or eliminate the dependence on synthetic chemical inputs. We will also be able to assess the influence of border zones at the land-water margin as buffers between agricultural uplands and estuarine receiving waters. The lead author recently submitted a proposal to the UC Water Resources Center entitled, “Evaluating Vegetated Buffer Zones Between Commercial Strawberry Fields and the Elkhorn Slough Estuary.”Soil water and nitrate movement through the surface soil were studied using porous cup lysimeters. In the first year,tomatoes grow bags twelve lysimeters were installed in the Central Field and six in the grassland control site at the Elkhorn Slough NERR.

Lysimeters were place in pairs at one foot and two foot depths to sample the root zone and below the principal root zone. In the crop field, three pairs were placed low on the slope, and three pairs higher up on the slope. In the grassland all three pairs were placed at a similar slope position. First year results showed a great deal of variation in nitrate-nitrogen levels in strawberry bed soil-water. It was not possible to determine the direction of movement or any strong response to seasonality. Furthermore, we found that surface runoff was extremely significant in nutrient loading into the pocket marshes. We also became interested in focusing on the vegetated borders between the pocket marshes and the cropland as a potential means to reduce the soil-water nutrient content before it entered the marshes. Therefore, in year two, the lysimeters were moved to the uplands/wetlands interface, at the border of the cropped area. Stations consisting of four lysimeters placed at the two foot depth, one meter apart from one another, were placed at four locations in the field. Two stations were placed at the bottom of roadways, where surface flow is concentrated, and two stations were placed where there were no roads and surface flow was minimized. During sampling times, vacuums were drawn using a hand pump and left overnight. Water samples were collected the next day and nitrate-N levels measured on the same day in the lab using a selective nitrate ion probe. Subsamples were frozen and later analyzed for nitrate-N using a spectrophotometer. . Samples were collected approximately monthly from January to June 1993, with a final sample in August. During 1994, samples were collected twice-monthly, from January to April. These 1994 samples were also analyzed for ammonium-nitrogen and phosphorous content. In 1993, grassland soils dried up by mid-June so no samples could be collected. Grassland samples were not collected in 1994.The pocket marshes on the Azevedo property are separated from the slough by a railway dike , and water exchanges between the slough and the pocket marshes through culverts under the dike.

We call the marshes South Marsh, Central Marsh, and North Marsh. The heights of the culverts varies and there is a gradient of flushing and size with the North Marsh being the largest and best flushed and the South Marsh being small and having little if any connection to the tidal waters. The Central Marsh is intermediate in size and flushing with some input from a perched freshwater pond at the upland end of the marsh. The pocket marshes were added as sample sites to a hydrological monitoring program that has been sampling surface water at 21 stations around the slough since 1988 . Once each month we monitored water temperature, salinity, oxygen, turbidity using a Nephelometer, pH, phosphate, nitrate, and ammonium. Water in the pocket marshes was sampled from the 0.5 m depth without disturbing sediments. Water chemistry analyses were conducted by the Monterey County Water Resources Agency . Hydrologists developed contour maps of the marshes which established limits of pickle weed and upper limits for the potential restoration of tidal action. Characterization of the vegetation entailed the use of line intersect, point intersect, and quadrat methods. The sampling was stratified based on vegetation patterns. Vegetation was sampled in the upper mudflat zone, midPickle Weed . During one hour observation periods, all waterbirds present were counted using binoculars and a lOx spotting scope. Birds were observed from the railroad berm at five to eight feet above marsh level and 10 to 100 meters distant. Detailed results are reported in a senior thesis by Neuman and Hickey.Although strawberries are a perennial plant, they are treated as an annual in most coastal California strawberry growing areas. Because of the marginal nature of Elk hom Slough soils for fanning, strawberry culture in the region is generally done on a two-year cycle in order to avoid the expense of replanting every year. Field preparation begins in September when irrigation systems are removed and fields are ripped and chiseled in preparation for whole-field fumigation with a mix of methyl-bromide and chloropicrin. Fumigation destroys soil pathogens, weed seeds, and most soil biota. After fumigation,grow bags garden beds and furrows are formed and planting in raised beds begins in mid-October or November. Varieties used were Selva and Seascape. Drip irrigation systems are installed and plants are irrigated as necessary. Harvest begins in March! April and continues until fall. Plants left in the ground for a second harvest season yield a berry that is smaller and softer, and these fruits are generally not fresh-marketed but used for processing. The Azevedo Ranch is divided into two agricultural leases. Mr. C. directly leases the South Field, and Mr. S. subleases the central and North Fields from a shipping firm which also functions as a lender. Both leases were planted in fall 1991, including fumigation, and again in fall 1993. Mr. C. leases 30 acres and has leased that plot for 7 years.

Mr. S. farms a total of 64 acres, with 10 acres planted with cv. Seascape, and the remainder in cv. Selva. Mr. S. farms a total of 357 acres of strawberries in the Elkhorn Slough watershed, and this was his first crop on the Azevedo property. Prior to and during the harvest season, applications of insecticides, miticides, and fungicides are made on a regular basis, in a cycle sometimes called pick and-spray. Harvest crews work from one end of the field to the other, and sometimes they are still finishing up picking when the spray crew starts its work at the opposite end of the field. Mr. C. utilizes a tractor to apply pesticides, which is more typical of practices in the watershed, while Mr. S. uses farm laborers to apply pesticides by hand using hoses connected to a tank truck. The analysis of fertilizer applied on the north and Central Fields at plant-out in1993 was 18% nitrogen , 8% available phosphorous, and 13% soluble potassium. Mr. S. applies this material by hand to the surface of the beds, down the center, while Mr. C. drills the material into a slot in the center of the beds so that it is buried. In January 1993, Mr. S. applied granular 6-20-20 by hand to the top of the strawberry beds. In January 1994 he applied ammonium sulfate in the same fashion, at the rate of about 80 pounds per acre . Both growers apply nitrogen through the drip irrigation system during the summer. The Elkhorn Slough area is extremely active and has been subject to large scale changes over geologic time. Hydrologically, the area may have been a flood plain for one of the largest rivers in Pleistocene California. More recently, the area has been subject to continued changes in land-use patterns and vegetation cover due to human influences. The Azevedo Ranch lies within the Salinian Composite Terrane, which is bound by the San Andreas Fault to the east and the Sur-Nacimiento Fault Zone to the west. It is unrelated to contiguous terranes, suggesting that it has migrated from its presumed origin 1500 km to the south . The basement rocks consist of quartz-di orite-ganodio rite rock which are Precambiam to late Mesozoic. In the early Pleistocene the lower reaches of Elkhorn Slough and Elkhorn Valley appear to have been part of a large riverine system draining the Santa Clara Valley and/or the California Central Valley . In the late-Pleistocene the watershed area for the Elkhorn Valley was tectonically truncated, substantially reducing the volume of water moving through to the ocean and limiting the flushing and scouring of Elkhorn Valley. During the most recent glaciation event 16,000-18,000 years B.P., a channel over 29 m below present day sea level was cut through the slough. As the earth’s temperatures rose and sea-level began to rise, this channel was flooded. The sediments in the slough are characterized by a finer texture size as one approaches the top of the sediment layers from below where non-marine gravels dominate. During the past 5,000 B.P. until 1946, salt marshes developed along landward margins of the slough. These marshes reduced the energy of the water, allowing further sedimentation and development of marsh vegetation toward the axis of the slough. This is the process by which the Azevedo Ranch pocket marshes were created. Until 1946 the slough was a shallow, quiet-water embayment with restricted tidal action. In 1947 Moss Landing Harbor was built, opening the Elkhorn Slough to direct tidal action. The effect was rapid and dramatic, and today erosion of wetland habitat in the slough continues to be a major concern .There are three main land formations from which the major soil types have been derived: aeolian or colluvian Aromas Red Sands, wave cut terraces, and alluvial sand, silts, and clays. The mapped soils and their classifications are listed in Table 1. Several soil pits made in the terrace and Central Field suggest much greater diversity in soil types and origins on the Azev-vlo Ranch. Many seeps have been observed, where ground water surfaces through soil discontinuities or is forced to the surface by impermeable boundaries. Furthermore, a thick clay layer devoid of sands was found on the slopes of the marshes, suggesting lake deposited clays in a time of slower moving water. The discontinuity of the marine terrace sandstone indicates that it has been eroded by water draining from the uplands. The Alviso series is alluvial consisting of fine texture sizes. The soils have a great deal of organic matter, and unless artificially drained can be almost completely anaerobic below the soil surface. These soils are dominated by wetland plants, Salicornia virginica and Distichlis spieata. This soil encompasses the pocket marshes and their margins, including the area presently farmed. These are considered to be relatively young soils. The soil survey map for the Azevedo Ranch shows Arbuckle gravely loam on the terrace between the north and Central Field.

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Subsequent studies have largely confirmed these initial estimates

Unlike industrial systems, agricultural systems are subject to the influence of weather patterns, soil type, geography, and management practices. Even the same agricultural product may have drastically different input structures, hence environmental impacts, in different regions. Therefore, average data with generic descriptions of material and energy fluxes are hardly adequate to capture the high degree of system variability of agricultural products. With the rising interests in bio-fuels as a means to combat climate change across the world, we strongly recommend future studies in this area to take into consideration the spatial variability of biomass growth. Just as technological and environmental variability exists across states, there is probably certain variability within a state, too, that may not be precisely captured by state average data. This does not mean, however, that state-level data should be dismissed for the research question at hand because they are still likely more reflective of local or farm-level practices than national averages. In addition, state average data are especially valuable and representative, more so than farm-level data, in situations in which massive land shift between crops takes place within a state. Nevertheless, we encourage finer-scale, more detailed studies into land shift between cotton and corn and associated environmental impacts, which could not have been conducted in our analysis due to the data limitation and resources constraints.Additional research is needed to paint a more complete picture on the impact of cropland conversion to corn: In 2005, 41 states grew corn and 17 states grew cotton, among which only 19 of the corn-growing states and 7 of the cotton-growing states had data on major inputs that can be used to generate LCIs . Among these states, only three overlap, namely, North Carolina, Georgia, and Texas. Therefore, this study does not quantify the environmental impact and their trade-offs in other cotton-growing states where conversion to corn might have happened. Nevertheless,plastic garden container environmental implications of cotton-to-corn land shift in these other states are probably worse than that indicated by Fig. 2.1 and closer to that indicated in Fig. 2.2 because cropland in southern states are generally less suitable for corn growth than the Corn Belt.

Future studies pursuing this line of research may make the effort to quantify the magnitude of land shift in each cotton-growing state when relevant data on agricultural inputs, environmental outputs, and acreage of conversion become available. Furthermore, it is worth noting that spatially detailed data are often unavailable or incomplete, although such data can improve the environmental relevance of an LCA study. In this case, one may rely on assumptions or spatially generic data to fill the gaps, and this may increase the uncertainty of the LCA results . In our study, data on agricultural inputs such as fertilizers and pesticides were available at the state level, but we often relied on spatially generic emission factors to estimate their emissions . Further, the LCA results for corn and cotton were found to be moderately sensitive to the emission factors which are likely to vary across regions . Future spatially explicit LCAs on agricultural systems may take this into account and direct efforts to estimate spatially differentiated emission factors.For the potential to mitigate climate change, reduce dependence on oil imports, and invigorate rural economic development, bio-fuel development in the USA has been supported by an array of policy measures . Among them is the federal Renewable Fuel Standard , a mandate that requires 140 billion liters bio-fuels to be produced annually from different sources by 2022. Corn ethanol is currently the primary bio-fuel and is likely to continue dominating US bio-fuels market as cellulosic and other advanced bio-fuels are far from mass production . Driven by the favorable policies and high oil prices, corn ethanol production has increased eight-fold since 2000, to the current level of about 50 billion liter per year. Early Life Cycle Assessment research on corn ethanol was largely in support of the policies aiming partly at reducing greenhouse gas emissions. As is typically done in LCA, these studies quantified GHG emissions generated at each stage of corn ethanol’s life cycle, summed them up, and then compared the results against that of gasoline. Corn ethanol was found to have 10–20 % lower life cycle GHG emissions than gasoline and, therefore, concluded to provide a modest carbon benefit in replacing gasoline . However, the conclusion was later called into question, when the land use change effects of corn ethanol expansion emerged in the literature . Converting natural vegetation or forestland to corn field for ethanol production releases a substantial amount of carbon from soil and plant biomass, creating a “carbon debt” that could not be repaid in dozens of years or even a century . Similarly, diversion of existing cropland for ethanol could generate indirect LUC effect through market-mediated mechanisms . In this scenario, corn ethanol expansion reduces food supply, which could lead to conversion of natural vegetation or forestland elsewhere in the world to compensate for the diverted grains.

While the concept of iLUC has become widely accepted in academic and policy arenas , quantification of iLUC emissions is known to be difficult and highly uncertain . Plevin et al. , for example, estimated the range from 10 to 340 CO2e MJ−1 y−1. This wide range is due in large part to a lack of quality data and detailed understanding as to how the global agricultural market would respond to bio-fuels expansion . In contrast, the direct land use change emissions can be relatively accurately quantified . Previous studies used the concept of carbon payback time to measure the magnitude of dLUC effect of corn ethanol. While the initial carbon debt due to land conversion may be large, it can be repaid over time by the annual carbon savings corn ethanol yields in displacing gasoline because corn ethanol has lower life cycle GHG emissions. The first dLUC study estimated that 48 years would be required for corn ethanol to pay back its carbon debt if the Conservation Reserve Program land is converted and that 93 years would be required if central grassland is converted .Gelfand et al. conducted a field experiment on CRP land conversion to measure its carbon loss. They found that approximately 40 years would be required for the use of corn ethanol to pay back this carbon loss with the converted land under no-till management. In another study, Piñeiro et al. arrived at a similar estimate of approximately 40 years for the payback time for CRP land conversion to corn ethanol. However, these studies were based on several oversimplifications that may substantially affect their results. First, these studies assumed that newly converted land has the same yield as existing cornfields, neglecting the potential yield differences of newly converted land. In particular, CRP land is generally less fertile than cornfields that have been in continuous use . Thus, corn ethanol from CRP land generates lower annual carbon savings, hence a longer payback time. Land with extremely low yield may even fail to provide any carbon savings, in which case the carbon loss due to land conversion is permanently lost. Second, the dLUC studies relied primarily on life cycle assessments based on early bio-fuel conversion processes that did not reflect the productivity improvements that have occurred in the past decade due to yield and energy efficiency increases at both the corn growing and ethanol conversion stages . Recent studies have shown that corn ethanol’s carbon benefit has increased to up to 50 % , compared with earlier estimates of 10–20 % . The productivity of the gasoline production system over the same period of time has been fairly steady . The productivity improvements in the corn ethanol system result in greater amounts of annual carbon savings that, if considered, would yield a shorter payback time than previously estimated. Finally, the dLUC studies used the global warming potential 100 to assess the global warming impact of corn ethanol, gasoline, and dLUC emissions. This approach assumes equal weights to GHGs emitted at different times. More recent literature explores the application of different weights to GHG emissions emitted in different times. First,plastic pot from a scientific point of view, increasing background GHG concentrations in the atmosphere result in a diminishing marginal radiative forcing for a unit GHG emission . The rate at which the relative radiative forcing effect of a unit GHG emission diminishes depends on future atmospheric GHG concentrations.

Reisinger et al. , for example, estimated that the 100-year Absolute Global Warming Potential of CO2 from 2000 to 2100 could decrease by 2 to 36 % under various GHG concentration scenarios. Second, a series of articles have attempted to synchronize the temporal system boundary under which life cycle emissions are taken into account and the time horizon under which characterization factors are derived. For example, if GWP100 is to be used, one can set the temporal system boundary to the next 100 years and account for the radiative forcing effects that occur within that time horizon . One of the rationales is that the efforts to reduce GHG emissions today is perhaps more valuable than those in the future because climate change may bring about irreversible damages to the planet . In this class of literature, simple climate-carbon cycle model like Bern model or simple first-order decay model is used to calculate atmospheric load of GHGs over time, and corresponding radiative forcing . Background concentrations of GHGs are, however, generally assumed to be constant in the literature. Third, some argues that future climate change impacts should be discounted at certain rates using the net present value approach . These approaches use different rationales and involve varying degrees of subjectivity in, e.g., the choice of emission scenarios and discount rates. For the sake of simplicity, however, these approaches are collectively referred to as dynamic characterization method in this paper. The objective of this study is to re-examine corn ethanol’s CPT, taking into account the potential yield differences of converted land and technological advances within the corn ethanol system. We also examine how dynamic characterization of GHG emissions changes the CPT using one particular approach as an example. We focus on conversion of CRP land primarily for ease of comparison with previous studies and also because there is evidence indicating that conversion of CRP land to cornfield has occurred with the expansion of corn production in the past decade . We start with estimating the amount of annual carbon savings that can be generated by corn ethanol from an average cornfield and how the amount changes over time. For this analysis, we use the Bio-fuel Analysis Meta-Model with several modifications . Specifically, because the base year of EBAMM is 2001 , we incorporate into the model historical data on the process inputs and outputs of corn growth and ethanol conversion for 2005 and 2010 to reflect the system’s productivity improvements in the past decade . We project further productivity improvements to 2020 using projections in the Greenhouse gases, Regulated Emissions and Energy use in Transportation model . We assume that technology advancement stabilizes after 2020 . Detailed information is provided in Appendix B. We then incorporate yield differences into the model to approximate the amount of annual carbon savings that the CRP-corn ethanol system provides. The CRP program, established by the Food Security Act of 1985, is intended to retire highly erodible and environmentally sensitive cropland from production . Because highly erodible land is less productive in general, the program enrolls land with lower productivity indirectly . Additionally, due in part to the early payment scheme— the maximum acceptable rental rates—farmers tended to offer their low-quality land for CRP consideration while retaining productive land for continuous cultivation . As a result, CRP land appears less productive than other types of cropland, including land that shifts into or out of the cultivated cropland from less, other intensive uses . Direct measurements of crop yield on CRP land are scarce, but measurements of crop yield on marginal land, including CRP and shifting land, can be used as indications of the relative yield differences between CRP land and average croplands .

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

Public health concerns about pesticide exposure to young children have received increased attention following the publication of “Pesticides in the diets of infants and children” in 1993. In 1996, the U.S. Food Quality Protection Act required the U.S. Environmental Protection Agency to set food tolerances that account for dietary and non-dietary exposure and protect sensitive populations. Biomonitoring studies have confirmed that children are widely exposed to pesticides, including organophosphorus , pyrethroid, fungicide, and organochlorine pesticides. Diet is an important source of pesticide exposure in children. For example, Lu et al. reported that the median urinary concentrations of the specific metabolites for malathion and chlorpyrifos decreased to undetectable levels after the introduction of organic diets in school-aged children. Several studies have confirmed that children may also be exposed to pesticide contamination in home and daycare environments. Children living in agricultural areas may also be exposed to pesticides through drift during applications or volatilization from nearby fields and parental take-home exposures. Lu et al. found that children who live in agricultural communities had five times higher OP metabolite levels in their urine compared to children who resided in non-agricultural communities. These researchers also found higher residential OP pesticide contamination and/or elevated urinary metabolite levels in children living near orchards. Higher exposure to children living in agricultural areas has raised environmental justice concerns and has resulted in proposals to define farm worker children as a vulnerable population that need additional protections by the U.S. EPA. Identifying pesticide exposure determinants is needed to identify sources and pathways of pesticide exposure in children and contribute to policies aiming to reduce exposure. To date, no longitudinal studies have investigated factors associated with pesticide exposure in very young children. We hypothesize that exposure factors will vary over time given the changes in diet, behavior, and family practices that occur as children age. In this study,square flower bucket we report levels of OP pesticide metabolites in 6, 12, and 24 month old children participating in the CHAMACOS birth cohort study in the Salinas Valley of California, an agricultural area.

We examined potential determinants of exposure associated with OP urinary metabolite levels at each age point, including sex, child behavior, diet, home pesticide use, season, parental work status, and proximity of homes to fields. We focused on OPs because they are commonly used in the Salinas Valley and were the first pesticide class re-examined under the FQPA.Mothers were interviewed when the children were 6, 12, and 24 months old. Interviews were conducted in Spanish or English by bilingual interviewers. Information collected included demographics, household enumeration, occupational status, whether work clothes were worn into the home, home pesticide use, presence of pets, daily servings of child fruit and vegetable consumption based on a modified food frequency questionnaire, time spent in child care, location of child care relative to fields, and frequency of hand washing and how often child fingers, hands, or toes are placed in the mouth. The interview also included a Child Behavior Checklist which uses a standardized format to assess parent-reported behavioral characteristics of children. Based on the CBCL, we selected child temperament indicators that we hypothesized could be associated with behaviors that affect pesticide exposure: “Can’t sit still, restless, or hyperactive”, “Gets into everything”, “Quickly shifts from one activity to another”, and “Underactive, slow moving, or lacks energy.” Shortly after each interview, study staff conducted a home inspection. Recorded information included distance between the home and agricultural fields, carpeting, housekeeping quality, and a detailed inventory of home pesticides. Home visits were completed for 87%, 84%, and 87% of the enrolled children at 6-, 12-, and 24-months, respectively. Urine samples were analyzed by the Centers for Disease Control and Prevention in Atlanta, Georgia. We measured six non-specific DAP metabolites of OP pesticides metabolites: dimethylphosphate ; dimethyl-dithiophosphate ; dimethylthiophosphate ; and three diethyl alkylphosphate metabolites: diethylphosphate ; diethyldithiophosphate ; and diethyl-thiophosphate by isotope dilution gas chromatography-tandem mass spectrometry. We measured DAPs, rather than pesticide-specific metabolites, because there are no laboratory methods to measure specific metabolites of several OP pesticides used in the study area, such as oxydemeton-methyl. Approximately 80% of the OP pesticides used in the Salinas Valley devolve to a DAP metabolite . Creatinine concentrations were determined in urine using a commercially available diagnostic enzyme method . Laboratory quality control included repeat analysis of three in-house urine pools enriched with known amounts of pesticide residues whose target values and confidence limits were previously determined. The validity of each analytical run was determined using the Westgard rules for quality control. The limits of detection ranged from 0.08 g/L for DMDTP to 1.1 g/L for DMTP.

Metabolite levels below the LOD were randomly imputed based on a log-normal probability distribution. Because individual OP pesticides can devolve to more than one DAP metabolite, we summed the DAPs on a molar basis to reflect total DMAP or DEAP metabolites. Frozen field blanks, prepared earlier by CDC, were defrosted, re-packaged in the field in a manner identical to collection procedures for actual samples, and then shipped blinded to CDC. The mean levels of individual DAPmetabolites in 57 blank field samples were <2 g/L. The median values of the DAP metabolites in the field blanks were all below the detection limit. All data analyses were performed with Stata Version 10 . We first computed descriptive statistics and percentiles for individual and total DMAP and DEAP metabolites at each sampling time point. We used Pearson correlations and ANOVA to assess bivariate associations between the metabolite levels and potential exposure determinants selected a priori, including sex, age, produce intake, breastfeeding, season, distance to agricultural fields, occupation of household members, wearing work clothes or shoes into the home, home pesticide use, presence of carpets, presence of pets, and housekeeping quality. We examined post facto additional determinants which may be related to drift of pesticides from fields, including daily rainfall, behaviors which may modify exposures , time spent in child care, and proximity of child care to agricultural fields. We then constructed generalized linear mixed models with log10-transformed DMAP or DEAP metabolite levels as the dependent variables and potential exposure determinants found to have significant bivariate relationships. The models included a random effects term to adjust for the lack of independence of repeated measures on the same subject. Because children’s development, diet, and behavior differ at different age points, we also examined whether age modified any associations, with 12-month olds and 24-month olds compared to 6-month olds as the reference. All interaction terms were included in the final DMAP and DEAP models. Based on the final models, we used linear combination equations to compute the percent differences in log DMAP and DEAP metabolites for the predictor variables to determine the effect of these predictors on metabolite levels among the 6-, 12- and 24-month old children. To assess bias due to loss to follow up, we ran the models with weights equal to the inverse probability of inclusion in the final sample at each time-point. We then performed the analyses without the weights for comparison. For statistical analyses, we present results that are not adjusted for creatinine.We also included urinary creatinine as an independent variable in the final multi-variable mixed DMAP and DEAP models for comparison with models without the urinary creatinine variable.We investigated the relationship between potential exposure determinants and urinary pesticide metabolite levels in ~400 children followed through infancy and toddlerhood living in an agricultural community. All children had detectable levels of OP metabolites in their urine. Consistent with previous studies,black flower bucket the DMAP metabolite levels were higher than the DEAP metabolite levels. We observed three-fold higher DMAP levels in 24-month olds and two-fold higher levels in 12-month olds relative to 6 month olds; however DEAPs declined between 12 and 24 months. Nearby agricultural use of dimethyl and diethyl OP pesticides was generally stable over the study period, however, most residential uses of chlorpyrifos and diazinon, two diethyl OP pesticides, were cancelled.

CHAMACOS children turned 12 months during the first year of the residential ban, which was phased in gradually. Thus, the decrease in DEAP metabolite levels among 24-month olds may be related to reduced indoor contamination of chlorpyrifos and diazinon , due to the residential use ban. This hypothesis is supported by our finding in a separate study that chlorpyrifos and diazinon house dust levels declined in Salinas Valley homes between 2000 and 2006. However, the ontogenetic increase in DMAP levels cannot be explained by changes in dimethyl pesticide use which did not change substantially during this time. The increase in DMAP levels may be due to increasing exposure-related behaviors and changes in diet as the children age in an environment where dimethyl OP pesticide use was relatively constant. Associations between the two classes of DAP metabolites and exposure determinants were not consistent at different age points. Possible reasons include differences in usage patterns, physical-chemical properties of the pesticides, field degradation, environmental transport, and metabolism of the dimethyl versus the diethyl OP pesticides. For example, malathion, which devolves to a DMAP metabolite, has a relatively high vapor pressure compared to other OP pesticides, and, thus, may result in greater exposures via inhalation. The spring/summer season, when malathion use is higher, was associated with higher DMAP levels in six-month olds, who are not yet crawling, suggesting an inhalation exposure pathway. We also found that recent rainfall was associated with lower DMAP levels in the younger children, a finding consistent with our previous study that showed rainfall was associated with lower OP levels in air. Together, these findings support the hypothesis that inhalation may be an important pesticide exposure route for very young children. Overall, our findings suggest that agriculture-related determinants of pesticide exposure may be associated with measured exposure at some ages, but we did not observe consistent associations across age points, or between DMAP and DEAP metabolites. The high variability in pesticide application frequency and the nature of transient, non-persistent exposures in young children may create too much variability to statistically model the association of these variables and child exposures. In contrast, intake of fruits and vegetables was consistently and positively associated with both classes of urinary metabolites in children at all ages, and was statistically significant for DMAP metabolites in 6- and 24-month old children, suggesting that diet is an important pesticide exposure pathway. This finding is consistent with recent studies that indicate diet is an important source of pesticide exposure to children.Few studies report levels of pesticide metabolites in children 6- to 24-months old. Median total DAP metabolite levels in the CHAMACOS children at 6, 12, and 24 months of age were lower than levels in 10 crawling infants and 10 toddlers sampled in the Salinas Valley in 2002. These twenty children were from farm worker homes and sampled in the summer, when levels might have been higher; direct comparisons, however, are limited by the small sample size. Median total DMAP and DEAP metabolite levels in the CHAMACOS 6- to 24-month olds were lower by ~30–70% than levels in children 24- to 72-months old living in Washington state agricultural or suburban areas; however, the Washington children were older than the CHAMACOS participants and the samples were collected between 1997 and 1999, before restrictions on residential use of chlorpyrifos and diazinon were implemented. Thus, these populations may not be directly comparable. Creatinine-adjusted levels were similar to adjusted concentrations reported in 41 5- to 73-month old farm worker children living on the US/Mexico border. Due to age differences, it was not possible to compare DAP levels in these CHAMACOS children with levels in older children studied by the National Health and Nutrition Examination Survey. Representative pesticide-exposure studies of national and state-wide populations are needed to compare to regional or local studies in impacted communities. Our study has several limitations. In a setting where multiple OP pesticides are used, measurement of the non-specific DAP metabolites does not provide information on exposure to the specific parent OP compound. As noted above, the many OP pesticides used in the Salinas Valley have widely varying usage, environmental persistence, and physical-chemical properties, adding variability to bio-monitoring measurements and possibly biasing statistical models toward null results.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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