Category Archives: Agriculture

A record of agricultural burn events was provided by the Air Pollution Control District

Our study found that the incidence of drift-related pesticide poisoning was higher among female and younger agricultural workers and in western states. These groups were previously found to have a higher incidence of pesticide poisoning . It is not known why the incidence is higher among female and younger agricultural workers, but hypotheses include that these groups are at greater risk of exposure, that they are more susceptible to pesticide toxicity, or that they are more likely to report exposure and illness or seek medical attention. However, we did not observe consistent patterns among workers in other occupations. This finding requires further research to identify the explanation. The higher incidence in the western states may suggest that workers in this region are at higher risk of drift exposure; however, it may also have resulted from better case identification in California and Washington states through their higher staffed surveillance programs, extensive use of workers’ compensation reports in these states, and use of active surveillance for some large drift events in California. Nonoccupational exposure. This study found that more than half of drift-related pesticide poisoning cases resulted from nonoccupational exposures and that 61% of these nonoccupational cases were exposed to fumigants. California data suggest that residents in agriculture-intensive regions have a 69 times higher risk of pesticide poisoning from drift exposure compared with other regions. This may reflect California’s use of active surveillance for some large drift events. Children had the greatest risk among nonoccupational cases. The reasons for this are not known but may be because children have higher pesticide exposures,macetas por mayor greater susceptibility to pesticide toxicity, or because concerned parents are more likely to seek medical attention. Recently several organizations submitted a petition to the U.S. EPA asking the agency to evaluate children’s exposure to pesticide drift and adopt interim prohibitions on the use of drift-prone pesticides near homes, schools, and parks . Contributing factors. Soil fumigation was a major cause of large drift events, accounting for the largest proportion of cases.

Because of the high volatility of fumigants, specific measures are required to prevent emissions after completion of the application. Given the unique drift risks posed by fumigants, U.S. EPA regulates the drift of fumigants separately from non-fumigant pesticides. The U.S. EPA recently adopted new safety requirements for soil fumigants, which took effect in early 2011 and include comprehensive measures designed to reduce the potential for direct fumigant exposures; reduce fumigant emissions; improve planning, training, and communications; and promote early detection and appropriate responses to possible future incidents . Requirements for buffer zones are also strengthened. For example, fumigants that generally require a > 300 foot buffer zone are prohibited within 0.25 miles of “difficult to-evacuate” sites . We found that, of the 738 fumigant-related cases with information on distance, 606 occurred > 0.25 miles from the application site, which suggests that the new buffer zone requirements, independent of other measures to increase safety, may not be sufficient to prevent drift exposure. This study also shows the need to reinforce compliance with weather-related requirements and drift monitoring activities. Moreover, applicators should be alert and careful, especially when close to non-target areas such as adjacent fields, houses, and roads. Applicator carelessness contributed to 79 events , of which 56 events involved aerial applicators. Aerial application was the most frequent application method found in drift events, accounting for 249 events . Drift hazards from aerial applications have been well documented . Applicators should use all available drift management measures and equipment to reduce drift exposure, including new validated drift reduction technologies as they become available. Limitations. This study requires cautious interpretation especially for variables with missing data on many cases . This study also has several limitations. First, our findings likely underestimate the actual magnitude of drift events and cases because case identification principally relies on passive surveillance systems. Such under reporting might have allowed the totals to be appreciably influenced by a handful of California episodes in which active case finding located relatively large numbers of affected people. Pesticide-related illnesses are under reported because of individuals not seeking medical attention , misdiagnosis, and health care provider failure to report cases to public health authorities . Data from the National Agricultural Workers Survey suggests that the pesticide poisoning rates for agricultural workers may be an order of magnitude higher than those identified by the SENSOR-Pesticides and PISP programs . Second, the incidence of drift cases from agricultural applications may have been underestimated by using crude denominators of total population and employment estimates, which may also include those who are not at risk. On the other hand, the incidence for agricultural workers may have been overestimated if the denominator data under counted undocumented workers. Third, the data may include false-positive cases because clinical findings of pesticide poisoning are nonspecific and diagnostic tests are not available or rarely performed. Fourth, when we combined data from SENSOR-Pesticides and PISP, some duplication of cases and mis-classification of variables may have occurred, although we took steps to identify and resolve discrepancies.

Also, SENSOR-Pesticides and PISP may differ in case detection sensitivity because the two programs use slightly different case definitions. Lastly, contributing factor information was not available for 48% of cases, either because an in-depth investigation did not occur or insufficient details were entered into the database. We often based the retrospective coding of contributing factors on limited data, which may have produced some misclassification.Burning fields to remove crop stubble, weeds and pests occurs worldwide, and California’s estimated emissions from the burning of crop residue ranks fifth nationally . These emissions potentially contribute to particulate matter levels in the San Joaquin Valley, which often exceed standards for ambient air each season of the year . Studies have documented thousands of chemicals in smoke; they can exist in gas, liquid and solid form. During burning, plant matter breaks apart and gases condense on particles or form particles. Most particulate matter in smoke is smaller than 2.5 micrometers in diameter and can be transported over long distances . The California Air Resources Board estimates annual tons of particulate matter and gases emitted from field, orchard and weed burning for California counties ; their estimates are derived from burns of crop residue in a laboratory . Studies have documented emissions of 14 semivolatile polycyclic aromatic hydrocarbons , the most abundant of which is naphthalene . A respiratory carcinogen , naphthalene is predominantly found in the gas phase of air sampling, with the remainder measured in the particulate phase . Few ambient air monitoring studies have been conducted in the United States during agricultural burns, either adjacent to burns or in towns and communities . Educational efforts for the general public have mostly focused on smoke from wildfires and have included public health recommendations for those exposed to elevated particulate matter and visibility guidelines for those air levels . CARB has also distributed a lengthy educational pamphlet for farmers . However, it was unknown whether health educational outreach efforts specifically targeting agricultural burning were needed. Particulate matter emissions from field burning in Imperial County — a rural desert county in California’s southeast corner — rank among the highest for any county in the state . The agricultural area of Imperial County is anirrigated desert valley, where a variety of crops including vegetables, hay and grain are grown . Fields of bermudagrass, which is grown both for hay and seed, are burned primarily in the winter, while wheat stubble is burned during the summer. Less than 3% of homes in Imperial County use wood as a house heating fuel . During the winter when night temperatures drop, inversions commonly occur; cooler ground level air, including pollutants, are trapped near the Earth’s surface by an upper layer of warmer air. For fields to be burned,macetas por mayor plastico the Air Pollution Control District requires that the estimated inversion layer must be at 3,000 feet or higher, and the burn must be initiated between 10 a.m. and 3 p.m. Farmers who have applied for burn permits are usually notified by the district the day before the targeted burn date that their fields may be burned. Thus, our air monitoring studies required methods that could be rapidly deployed. Our methods and results are described in greater detail in a report to the funding agency .We selected three schools and one church based on their proximity to burns in previous years and installed portable Environmental Beta Attenuation Monitors . We measured hourly average concentrations of PM2.5 and meteorological variables for 69 days starting on Jan. 14, 2009. E-BAM PM2.5 measurements are not recognized as a Federal Equivalent Method or a Federal Reference Method , one of which is required to determine if levels legally exceed air standards. However, E-BAM measurements have proven comparable to FRM measurements in field tests .

During the E-BAM monitoring period, 15,686 acres were burned on 35 allowable burn days; the acreage burned daily ranged from 0 to 1,400 acres. Average 24-hour PM2.5 concentrations were highest — 12 micrograms per cubic meter — at the northern station and lowest at the western station . The lower levels in Seeley may have been because the predominant wind direction was from the west, and sources of pollution, including burned fields, were predominantly to the east of the Seeley station. All daily PM2.5 levels were below the federal standard for unhealthy air, 35 µg per cubic meter. However, at the Calipatria station the 95th percentile of 24-hour concentrations was above 16 µg per cubic meter, which corresponds to moderate air quality where “aggravation of heart or lung disease in people with cardiopulmonary disease and older adults” is possible . We also compared 8-hour average PM2.5 concentrations at the four locations. There was little difference during the day , with levels slightly lower on field-burn days compared to no field-burn days . In contrast, from the early evening to the morning of the next day , average PM2.5 concentrations on field-burn days were 23% higher than on no-field-burn days. Additionally, on days when there was an agricultural burn within 2 miles of the Calipatria station , during the evening-to-morning period the average 8-hour concentrations were 19.5 to 20.7 µg per cubic meter, 170% higher than on days when there were no burns within 2 miles . Following the burns near the Calipatria station, on the subsequent 2 days when there were no additional burns , the evening-to-morning levels remained slightly above levels on days with no burns . Higher particulate matter levels from evening-to-morning hours associated with agricultural burning in Imperial County are consistent with air pollution dynamics. Air pollutants may rise during the day as the Earth’s surfaces are heated and then be brought down to ground level by the descent of an evening inversion layer. The night and next-day accumulation of smoke is described in a CARB pamphlet for farmers .We monitored five specific burns of 65 to 150 acres of bermudagrass stubble during the E-BAM monitoring period. For four burns, ground-level winds were low at 2 to 3 miles per hour , and the plume from the burn rose up to the apparent height of the inversion layer where it was observed to spread out, sometimes in the opposite direction of the ground wind direction. The ground-level plumes dispersed within about an hour, but the upper plumes remained visible, apparently limited by the inversion layer, until sunset. At one of the five burns, the Dunham burn, the wind speed was higher , and the ground-level smoke plume engulfed a house on the same property as the burned field and drifted onto an adjacent field. We deployed portable particulate matter monitors — active-flow and passive personal DataRAM nephelometers — which continuously measured PM2.5 and PM10 , respectively. This monitoring was done at three locations surrounding each of the five burns for 24 to 72 hours. Two locations were near the burns and were places of public access, homes or telephone poles; the other was at the nearest E-BAM, which was farther away . At the 15 locations, field difficulties including power outages, supply delivery problems and apparent equipment or software malfunctions limited monitoring to 11 and 13 locations for the PM2.5 and PM10 nephelometers, respectively.

The coconut palm also gave back to the communities who tended to the trees

GS1 transcripts and glutamine synthetase enzyme activity also increased with increasing NH4 + and NO3 – availability in sorghum roots, suggesting this response may be widespread among plant species. Interestingly, inclusion of soil GWC in multiple linear regression models increased the proportion of GS1 expression variability explained to nearly 30% ; soil water content increases microbial activity as well as the mass flow and diffusion of inorganic N to roots. Further research will undoubtedly show how other factors like crop physiological N demand relative to C fixation and P availability increase the interpretability of N uptake and assimilation gene expression in roots.The N cycling scenarios identified on this set of organic fields corresponded at least in part with landscape clusters based on landscape and soil characteristics . Fields that balanced high yields with low potential for N loss and high internal N cycling capacity were part of PAM cluster 1, which had the highest productive capacity rating . Landscape clusters encompassing more marginal soils included both low-yielding fields exhibiting N deficiency or high-yielding fields that used inputs of highly available N like seabird guano to alleviate N deficiency . But these inputs led to the highest soil NO3 – levels and thus came at the cost of higher potential for N loss. Long-term efforts to increase internal soil N cycling capacity would help alleviate both N deficiency and the need for such large inputs of labile N. Whether farmers are willing to invest in management to increase soil N cycling capacity depends in part on how likely they perceive the benefits to be, especially on marginal soils. The discussions that we had with each farmer in this study indicated genuine interest in adaptive management to further tighten plant-soil N cycling, but this may not always be the case. Indeed,macetas redondas grandes the proportion of management vs. inherent soil characteristics responsible for driving differences in N cycling is challenging to untangle. Farmers may allocate more resources to more productive land and likewise fewer resources to more marginal land, or may selectively transition more marginal land to organic management.

Documenting the multiple services provided by increases in soil quality and facilitating information exchange among organic growers such as through the landscape approach used here may help build momentum for efforts to improve soil quality and plant-soil-microbe N cycling.The health food movement’s latest trend in its ongoing rejection of carbohydrates in favor of fats alarmed cardiologists and public health experts. Studies in the journal of the American Heart Association noted a possible link between coconut oil’s high levels of LDL cholesterol, colloquially known as the “bad cholesterol” which carries a higher risk for coronary disease.In a now infamous 2018 talk at the University of Freiburg, Harvard epidemiologist Karin Michels called coconut oil “pure poison.”Delivered in German, the talk captured headlines in countries that are net importers of coconut products and also commanded the attention of exporting nations and industry trade groups. India’s horticultural minister demanded that Michels retract her statement while the International Coconut Community , a twenty-nation member organization headquartered in Jakarta, issued multiple defenses of coconut oil’s superfood status.Setting aside the merits of competing health claims, the ICC’s response to Michels was a rare albeit brief instance in which the global political economy of coconut oil became visible to North American consumers. The North American demand for coconut products tethers small- and large-scale coconut planters and wage pickers in the South and Southeast to a multitude of producer associations, cooperatives, national governments, and multinational marketing companies who deliver the product to health-conscious consumers. Coconuts—an enduring symbol of tropical ease—are big business. The Philippines, which the Calboms held as evidence of the oil’s benefits, produces an estimated 1.9 million tons of coconut products each year and account for forty-nine percent of the world’s exports. Coconut farms are found in most of the country’s eighty-one provinces, covering 3.3 million hectares or thirty percent of farmlands.

This high output persists despite high poverty rates among coconut farmers, maturing trees with waning production, recurring infestations of coconut scale insects requiring tree felling, and an intensification of destructive typhoons precipitated by the climate crisis.Production depends on forest clearing for new planting, in turn exacerbating the climate crisis behind the industry’s woes. How does a commodity produced by an ailing industry attain and sustain the allure of a natural superfood? Adrienne Bitar’s Diet and the Disease of Civilizationoffers an answer from the perspective of consumption. Coconuts, she writes, play a leading role in a larger North American “food story” in which eating against the grain can recapture “an original, innocent world and mourn the descent of the human race into modern disease.”Diet jeremiads decrying the “fall of man” include the Paleo diet, in which men and women are urged to eat like evolutionary ancestors and the Detox diet, which calls for abstention from refined and processed foods. Coconuts also feature in Pacific Islander efforts to decolonize the everyday dietary. Citing alarmingly high rates of diabetes and obesity, Dr. Terry Shintani’s The HawaiiDiet positions the replacement of fried and refined foods with “foods eaten in Hawai’i before the onset of Western influence” as part of a larger personal, cultural, and ecological healing from the ravages of colonialism.But in making this case, fall of man diets “eternalize a timeless past,” homogenize diversity among Pacific Islanders, and sharpen alleged innate and biological differences between Pacific Islanders. The diets, Bitar writes, exemplify what Renato Rosaldo calls “imperialist nostalgia”—a romanticization of that which has been lost to colonial violence in the name of progress of development.This nostalgia for the coconut echoes outside of diet culture as well. Recall, for example, LinManuel Miranda’s invitation to “consider the coconut” as the Motunui villagers of Disney’s Moanapraise the tree, its husk, fibers, water, and meat as “all we need”. Hsu and Vázquez’s “molecular intimacies of empire” can move us toward an account of the coconut’s superfood status that incorporates production. Indeed, the seemingly paradoxical relationship between “superfood” and “ailing agriculture” illuminates the processes by which US empire and capital accumulation extend across geographic space and render biological materials into component parts such as oil and synthetic materials while relegating the risks of those processes to producers and laborers at the supply end of the commodity chain. This essay’s focus is therefore on the American agricultural entrepreneurs, tropical research stations, and penal farms that built a coconut plantation economy in the southern Philippines after 1898.

These Southern Philippine plantations were just one site in what others identify as a transimperial “coconut zone” extending west from the equatorial Pacific Islands to southern India and were also akin to Dole’s pineapple empire in Hawai’i and United Fruit’s banana empire in Central America.Coconuts, pineapples, and bananas constituted an American equatorial fruit empire that fed upon and nurtured discourses of tropicality—the late nineteenth century division of the globe into tropical and temperate worlds. Tropicality held that planning for temperate winters instilled EuroAmericans with traits conducive to industry while the heat and humidity of tropical climes produced a fecund nature and indolent natives who lived off, rather than mastered,maceta 25l the land.The exaggerated fecundity of the tropics was simultaneously a threat to white bodies and a justification for Indigenous dispossession that imagined precontact idylls in which fruits sprang forth from nature rather than human cultivation. The agricultural entrepreneurs of the fruit empire cast coercive labor regimes as necessary improvements on primitive agricultural methods. They neutralized fears of tropical landscapes by stressing their singular ability to “tame” jungles and domesticated foreign foods by emphasizing health. The promotional materials of United Fruit anointed the banana a “superfood” as early as the 1920s.A robust scholarship on tropical commodities has since reconnected the American appetite for bananas and pineapples to colonial plantation.The colonial plantations of the southern Philippines, however, were severed from the this larger history of fruit empires largely because the sites produced copra, the dried kernels from which the oil is expelled, and coconut oil was initially valued for its industrial applications. Coconut plantations preceded the embrace of coconut as a food by decades. The following essay offers an episodic accounting of the American coconut empire in the southern Philippines. It begins with the union of Euro-American industrialization and economic botany and colonial state power in the making of coconut plantations and a Philippine copra export industry in the early twentieth century. Coconut oil oozed unseen into soap, candles, and dynamite before making a more visible debut during the first world war as an ingredient in the butter substitute oleomargarine. Because Euro-American consumers already saw oleomargarine as unnatural, advertisers emphasized coconut oil’s whiteness as a sign of purity, healthfulness, and closeness to nature. But in the interwar period, North American dairy and cottonseed farmers cast Filipinos and their copra as impure in their effort to restrict its import. Their campaign blurred what were already fuzzy boundaries between the natural and the primitive, and between individual bodily risk and risk to the body politic. Yet the nearly four million Filipinos linked to the coconut commodity chain ultimately bore a collective risk that scholars call the “body burdens” of toxic exposure.Imprisoned laborers risked malaria by clearing forests for plantations while planters and pickers later faced exposure to the pesticides and herbicides used to manage the ecological risks of monocropping.

The US racialization of Philippine copra as impure placed what one Philippine official called a “black mark” on the country’s copra in global markets.Independence and the looming loss of US markets in 1946 led Philippine planters to encourage Filipinos to bear the risks of monocropping by eating more of the coconuts they grew and to forge new alliances with other Southeast Asian producers. Such alliances paved the way for the International Coconut Committee. The marketing machinery of the ICC coupled with the interwar association of coconut oil as “unrefined,” and a second world war literature on the coconut as a survival food primed the coconut for its reinvention as a superfood. Far from a traditional food of the tropical Pacific, the coconut’s place in the Philippine economy and dietary is an exemplar of the edible and unequal intimacies of empire.Botanists have long debated the origins and migration of the coconut palm tree across the equatorial Pacific and Indian Ocean regions. Because the husks containing the kernel, water, and meat can root after exposure to seawater, nineteenth-century plant geographers speculated that maritime currents, rather than mariners, carried the husks from a singular origin point in either the Americas or East Asia. The thesis, much like tropicality, minimized the human role in plant propagation and has since yielded to a new consensus that allows for a multisited provenance and a guiding human hand.Asian–mainland travellers likely introduced the tree to the Philippine archipelago between the fifteenth and sixteenth centuries, where it coevolved with the coastal ecology. Coconut palms thrive in sandy soils with circulating ground water. It gives back to the coast by blunting the impact of typhoons and absorbing “wash-over” into its dense root systems.Coconut fronds became shingles that roofed nipa homes; its husked fibers caulked ships; shells and husks could be used as household tools and burnt for fuel. Food vendors sweetened rice cakes with coconut sugar and fermented the tree’s sap into vinegar and tuba, a potent alcohol. Baked in open air under the hot sun, the kernels of the coconut formed copra, from which oil for cooking, washing, lubricating, and medicine was pressed. These myriad uses may have protected small cultivators from debt tenancy as financial capital encouraged the planting of sugar and hemp. The coconut was so ubiquitous that landlords in southeastern Luzon’s hemp exporting Kabikolregion allowed fallen nuts to compost in the soil.This would change within two decades of US rule at which point copra constituted thirty percent of Philippine exports—third behind the far more established trade in sugar and hemp.The rapid rise in copra exports points to the centrality of economic botany and scientific agriculture in making the American colonial state in the Philippines. The US declared war on Spain in 1898, the same year that the United States Department of Agriculture opened an Office of Foreign Seed and Plant Introduction .

Soil pH usually affects the activity of nitrifier and denitrifier microorganisms

Increased C and N substrates can supply more essential substrates for N cycling microorganisms. For instance, Song et al. demonstrated the importance of substrate availability to fast growth of temperature-sensitive N2O producing microorganisms. The microbiome shift was closely associated with fast N mineralization at warm temperatures, resulting in increased N2O emissions. The increased microbial mineralization can produce more CO2, leading to O2 depletion , and eventually accelerated denitrification . The tropical zone with the highest annual temperature/rainfall and microbial activities had lower N2O emission compared to the warm temperate zone . We attribute this to accelerated completion of denitrification , C substrate loss and less accumulation of inorganic N. High rainfall may create wet and O2 limited conditions , which can accelerate completion of the denitrification process by converting N2O to N2 . Heavy rains may also transport C/N substrates and N2O formation deeper into the soil profile, where relatively more N2O can be consumed before it escapes to the atmosphere. Further, N cycling in tropical systems is generally very efficient between the soil and vegetation, which limits the accumulation of NH4 + and NO3 − in the soil thereby attenuating nitrification and denitrification processes. Hence, lower N2O emission was observed in tropical compared to warm temperate climates . With respect to crop type, our analysis showed that manure application increased N2O emission in soils of all upland crops, except for beans . A lack of enhanced N2O emissions from paddy rice cultivation following manure application was also noted and attributed to: the dominantly anaerobic conditions associated with paddy rice cultivation that limits nitrification and promotes conversion of N2O to N2 ; and low sample size in rice systems may affect the statistical robustness. In general,macetas cuadradas grandes cultivation of leguminous beans uptakes large amounts of base cations from soils and release H+ , leading to lower soil pH and based-cation fertility.

This may inhibit N2O production, as nitrifiers and denitrifiers prefer relatively neutral or mildly alkaline environments . Additionally, leguminous beans are N2-fixers and tend to receive lower manure applications resulting in lower production of N2O compared to other crops. The WFPS had a significant effect on N2O emission, with soils having a moderate WFPS experiencing the highest N2O emission . Soils with WFPS at 50–90% appear to provide the optimum conditions for denitrifier activity and N2O production. At these intermediate WFPS conditions, there is likely some O2 available to allow nitrification to proceed and the generation of NO3 − provides substrate for denitrification to occur in adjacent anaerobic microsites. In contrast, the major processin soils with WFPS b50% is nitrification with denitrification inhibited by the presence of O2 . When the WFPS is N90%, soil porosity is water-saturated, leading to greater conversion of N2O to N2 under strongly anaerobic conditions . It was notable that short-term application of manure produced higher N2O emission than long-term application . While the exact mechanisms remain unknown, one possible reason is that manure application enhances microbial growth and proliferation and stimulates soil N cycling by providing more available substrates and generating more anaerobic microsites . Once the N cycling microorganisms adapt to regular manure application, they may become less responsive to further manure applications over time. In addition, regular application of manure may lead to higher microbial biomass and therefore a higher capacity of soil microbial community to retain N, resulting in more uptake of N by the microbial community and less N2O emission. Another possibility is improved soil abiotic properties resulting from long-term manure application. As manures are applied annually, several soil properties would be progressively altered to a new steady-state compared to initial soil conditions.Zhou et al. showed no differences in N2O emissions from different manure sources , consistent with the findings of our meta-analysis. Raw manure resulted in higher N2O emission than pre-treated manure, consistent with the results of Nkoa . In general, raw manure has higher inorganic N and a lower C: N ratio than pre-treated manure . Higher inorganic N contents induce higher N2O emission, as NO3 − and NH4 + are essential substrates for denitrification and nitrification, respectively. Manures with a high C:N ratio would enhance microbial N assimilation , resulting in uptake of inorganic N from indigenous soil sources.

The lack of available N substrates would thereby decrease soil N2O emission. However, Zhu et al. demonstrated that manure pre-treatment did not reduce N2O emissions and Chantigny et al. showed no difference in N2O emissions between pre-treated and raw manures. We attribute these contradictory results to factors such as the high heterogeneity of manure, contrasting manure sources and pretreatment methods. In this meta-analysis, we did not specify manure forms or pretreatments for manure . Instead, we focused on the in-situ response of N2O emission to manure application from the perspective of agricultural soil rather than manure source management. As showed in Fig. 4, pre-treated manure showed lower effect size compared to raw manure. Manure treatment, for example, compost and digest, will change the physical, chemical and biological properties of the manure radically, resulting in the difference for N and C content in raw/pre-treated manure and soil N2O emission after manure application. Thus, a detailed quantitative index of manure characteristics may be more suitable for explaining the mechanisms mediating N2O emission from soil than qualitative categorical descriptions such as manure preparation and manure type. The overall increase in soil N2O emission resulting from manure application was consistently greater than zero , and the responses of N2O emission differed with manure characteristics. Different microbial activity and growth induced by different manure characteristics likely account for differences in N2O emission. In this analysis, manures with the highest N content had the highest soil N2O emissions compared with manures with medium and low N contents . This is in accordance with the consensus that higher inorganic N availability directly enhances nitrification-denitrification processes, resulting in higher N2O emission. Our analysis also found that manures with medium C content or C:N ratio had significantly lower N2O emission compared to those with lower or higher C contents and C:N ratios. Normally, when manures have a C:N ratio b 5 or low C content, they provide ample N for microbial growth and proliferation, resulting in net N mineralization .

Excessive inorganic N produced from mineralization can stimulate soil nitrification and denitrification processes, contributing to increased soil N2O emission. When the C:N ratio increases, the N content in manure cannot meet the N requirement for microbial growth and proliferation, and the microorganisms will utilize indigenous N from the soil resulting in microbial N immobilization . This process competes with heterotrophic denitrification and autotrophic nitrification to utilize the NO3 − and NH4 + substrates, respectively. Further, high manure C:N ratios or C content may initially enhance microbial activity, leading to consumption of O2 and development of anaerobic conditions . As a result, denitrification may persist for longer time periods, leading to increasing N2O emission .Soil texture did not significantly affect N2O emission following manure application . This is contradictory with several previous laboratory studies that found higher N2O emissions from fine-texture soils than coarse-texture soils . In general, soil texture strongly affects soil pore distribution, and thereby regulates water and O2 availability . Soils with coarse textures would favor nitrification as the dominant process . In contrast, denitrification preferentially occur in soils with fine textures ,frambuesas cultivo where O2 availability is often low . However, our analysis showed no difference in N2O emission between soils with coarse and fine textures from field trials . This was probably due to the long-term effects of manure application to fields, as continuous and intensive application can greatly change initial soil properties .In general, nitrifiers prefer neutral to moderately alkaline conditions , and heterotrophic denitrifiers are more active in neutral rather than acidic environments . Thus, N2O emission may be expected to be higher in neutral or alkaline soils compared to acidic soils. In contrast, our analysis revealed that the initial soil pH had no significant effect on N2O emission, contradictory with some previous laboratory studies . A potential reason for this discrepancy may result from manure being an effective acidic soil amelioration amendment that can increase soil pH . After manure application, the final soil pH may be increased to a neutral or alkaline value attenuating possible effects from the initially acidic soil conditions. Given this potential pH buffering and/or soil acidity amelioration effect, the activity of nitrifier and denitrifier communities between initially different pH soils may not be as pronounced as expected based on the initial soil pH values. Our analysis further found that initial soil organic C content significantly affected N2O emission and soils with moderate SOC content had the largest N2O emission. We attributed this to differential C-use efficiency among microorganisms. Soils with low SOC often have low microbial activity , which may lead to low N2O emission. Soils with high SOC may have their C persevered by chemical/physical protection mechanisms or SOC may have a high C/N ratio resulting in N-limitation for microbes. Additional research is warranted to better understand the role of soil carbon dynamics in N2O emission.

Overall, initial soil properties were not highly predictive of N2O emission response to manure application in field trials. As our analysis utilized a global dataset, several interacting factors that regulate N2O emission within a given site are obscured by combing with data from other regions. Additionally, intensive manure application may substantially alter the initial soil properties, making them non-representative of post-manure application conditions. In addition, the lack of significant effects of soil properties may be related to many confounding factors in the field trials, which may obscured the individual effect that can be observed in laboratory experiments on N2O emission with manure application. Compared to field trials, laboratory experiments are typically short-term incubations and receive less cumulative manure application . Therefore, WFPS, which can be controlled and measured during field experiments, is often a better predictor of N2O emission than initial soil properties, such as soil texture, pH and organic matter. Using real world data generated from field trials for our meta-analysis was an important distinction of our analysis since laboratory experiments are not able to capture all the complexities and interaction associated with field trials.California’s electricity system is undergoing unprecedented change. California’s current goals call for meeting 50% of the state’s retail electricity sales with renewable energy by 2030 and reducing greenhouse gas emissions to 40% below 1990 levels by 2030 . A 50% renewable electricity system in California will have a high penetration of variable solar and wind generation. Fluctuations and uncertainty of variable generation will make the operation of an already complex electricity system even more complicated. One way to offset the unpredictability of renewable resources is through DR programs, by which end users are induced to change their electric demand to match the supply. Historically, DR resources have been used to reduce the system level peaks . As California moves closer to its target of 50% renewables, traditional DR can provide local reliability, but more importantly faster time scale DR services will be more important for facilitating the intermittency of renewable generation. With higher penetration of intermittent renewable sources, the grid needs to deal with generation variability. Intra-hour variability and short-duration ramps are one of the immediate challenges faced by a 50% renewable grid. However, other challenges arise as the California grid decarbonizes over time. Historically peak hours were defined as the hours between 12pm-6pm . Proliferation of solar generation in California is forcing those peak hours to shift to later hours in the day 1 . This is most commonly referred to as the “Duck Curve” , where the increased solar generation is significantly dropping the net electricity demand during the day, which in turn results in significant ramps in the later hours . Agricultural irrigation pumping is a significant component of California’s electric demand and a resource that can provide DR services to the grid and contribute to its stability. In addition, distribution feeders that serve agricultural customers often have low diversity in their types of customer loads, and exercise of a large number of irrigation pumps on a single feeder can cause over-voltage issues .

The issue of limited information also has to do with the size of reporting units in the available data

California pistachios, on the other hand, are concentrated in the southern part of San Joaquin Valley. Moreover, they are planted in areas where the climatic conditions are mostly beneficial for them. Few events of adverse weather exist on record, which can be used for analysis. Therefore, the variance in CP in our range of interest is even more limited.The California Department of Food and Agriculture, as well as the US Department of Agriculture, usually report average yields on the county level. If the counties are large, compared to the growing area, few observations will be generated, and the averaging process will get rid of useful extreme observations on the sub-county level. The aggregated reporting problem, together with crop concentration, limits the possibilities of traditional econometric analysis on crop yields. I address this problem here for California pistachios, but the challenge might prove a barrier for research on other crops as well. Consider not only high value commercial crops concentrated in a few California counties, but also “orphan crops”: local crops which have received less attention from researchers and the private sector, yet generate substantial nutritional value for low income communities in developing countries. The African Orphan Crops Consortium, an initiative to promote research and use of these crops in Africa, list 101 crop of interest on its website, many of them perennial.2 Cullis and Kunert note that orphan crops “…are poorly documented as to their cultivation and use, and are adapted to specific agro-ecological niches and marginal land with weak or no formal seed supply systems”. Research on specific orphan varieties might therefore suffer from the same challenges of California pistachios: biological complexity, concentration of growing acreage,blueberry plants in pots and few data reporting units. In this chapter, I combine two approaches to estimate the yield response of California pistachios to winter CP count. The first approach is a “big data” one: I enhance a California yield panel of five counties with local temperatures at the pistachio growing areas. I use satellite data and temperature readings from local weather stations to create a large data set that can be connected with the yearly yields.

Substantially increasing the number of explanatory variables, this allows for more nuances observations. The second approach is an aggregate estimation methodology, previously used in agricultural productivity literature but –to my knowledge– not yet explored in climate literature. This approach notes that the observed outcome variable is a mix of unobserved sub-unit heterogeneity in the data generating process. Information about this heterogeneity is used to recover the relationship between temperatures and yields. The result of this exercise is the first successful recovery of the nonlinear yield response to winter chill in commercial pistachio production. I apply my findings to climate predictions in the current growing areas to show the potential impact of climate change on California pistachios in the next 20 years, and predict that a significant decline can be expected. 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,draining plant pots 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, 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. The support of CP through the panel is [36, 86]. Past county yields are from crop reports published by the California Department of Food and Agriculture. 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.

The monetary cost of water saved can be viewed as savings on the intensive margin

Geo-engineering proposals involve global scale interventions in the atmosphere and hydrosphere that would revert some of the changes in the total temperature distribution worldwide . In contrast, MCE is a small scale concept, aiming to tweak the temperature tail distributions where necessary rather than shifting the entire distribution year round. Many MCE technologies already exist and are used by growers, making sense both on the technical and economic dimensions. I believe many more examples are out there to be found, and many more will evolve as growers adapt to climate change.Respondents seem satisfied with CIMIS services. About 72% of respondents reported using CIMIS at least occasionally. The user types reporting “often” using CIMIS the most were Agriculture, followed by Golf Course Management and Water Districts. These user types are indeed likely to use CIMIS on a day to day basis, at least for some part of the year. In research and planning, on the other hand, one might use CIMIS to draw data only at an initial stage of a given task. In general terms, of the respondents who report using CIMIS to some extent, 77% say it is at least “moderately important” for their operations, with 22% reporting CIMIS as “extremely important”. The frequency of use and importance scores are positively correlated: frequent users also report high importance of CIMIS to their operations, which makes sense. The correlations between frequency and satisfaction, and between importance and satisfaction, seem less pronounced. There might be users who use CIMIS infrequently, perhaps because only a smaller part of their tasks involve the weather or climate information provided. Nevertheless, they seem satisfied with CIMIS services, as the satisfaction scores are relatively high. We also asked respondents to rank factors which hinder further use of CIMIS. Various answers were provided, given the results of initial surveys,blueberry packaging container and there was also room to specify other answers. Two main concerns exist, especially for users in agriculture: how reliable is the data and how to integrate it into existing systems and practices.

Many growers and consultants in agriculture complement CIMIS with other data sources, such as soil moisture sensors, irrigation logs, and flow meters. Integrating information from multiple sources into decision making is a challenge faced by virtually all growers.599 respondents, about a quarter of our survey, reported agriculture to be their primary business. Out of these, about half work on one farm, and the rest are consultants of sorts . 89% of respondents in agriculture report using CIMIS to some extent. Growers and consultants were asked to report their total acreage, selecting into pre-determined ranges. Summing these, we have 318,156 acres covered by growers, and almost 3 million acres covered by consultants. Many of the questions for growers and consultants were similar. One notable exception is regarding water use. The team decided not to ask growers how much water they use, fearing that growers would not want to share this information and would not finish the survey. However, consultants were asked how much water their clients use on average. This question was presented in the online survey as a slider bar, with a default at the lower bar , and an option to check a “Not applicable” box. This box was not checked very often. Instead, it seems like many consultants who did not want to answer this questions left the slider bar at the default value of 0.5 AF/acre. This is a very low value for irrigated crops, and we assume that all these responses are basically non-answers. Ignoring them, the average reported water use is 2.96 AF/acre per year . This seems like a very reasonable distribution for water use in irrigated crops. Indeed, the USDA’s most recent Farm and Ranch Irrigation Survey reports a total of 7,543,928 irrigated acres in California, with a total of 23,488,939 AF of water applied, and a resulting average water use of 3.11 AF/acre, only a minor deviation of the reported average. Given the responses from agricultural consultants, we seem to have captured a very large portion of the drip irrigated acres in California. As a baseline for valuation, we will use the total 2013 drip irrigated acreage from the USDA survey, 2.8 million acres. While some growers might use CIMIS with gravitational or sprinkler systems as well, our understanding of the qualitative and quantitative responses is that CIMIS is mostly important for drip.

We exclude the potential of CIMIS values on non-drip acreage, noting that our estimates would therefore be conservative in that sense.One can also consider gains on an extensive margin. The water saved by use of CIMIS is likely to be used in agriculture as well. This means more acres can be grown with the same initial amount of water. The “full” economic value of the water saved by CIMIS in agriculture is the value of agricultural output that can be produced with it on acres not irrigated before. This following analysis includes the economic value of growing alone, without the added values of post-harvest and economic multiplier effects, and probably a safe lower bound. We do not, however, include a counter-factual productivity of non-irrigated land. In California, this is probably range land or acreage that is too sloped for traditional irrigation methods, and therefore of very low economic productivity. With 1.92 million AF of water saved by CIMIS, and an average use of 2.5 AF/acre by growers , the savings from CIMIS can water an extra 768,000 acres in California. To put this in context, this is about double the total walnut acreage in 2016. Because of economic and technical constraints of water transport, it is hard to determine which crops would be planted in these extra acres. A conservative approximation assumes that the water saved by CIMIS serves to replicate the existing distribution of crops , taking the average value of productivity of an acre as the benchmark. The weighted average of grower revenue per acre in 2016 was $3,757 per acre1 . Multiplying by 768,000 acres, a conservative approximation for the contribution from CIMIS to California’s GDP via agriculture is about $2.89 billion. CIMIS allows for more precise irrigation, which means not only saving water but also increasing yields: water application can be adjusted to the plant requirements, which might depend on the weather and growing phase. We ask growers and consultants how does CIMIS contribute in increasing yields, ranking from 1 to 5 . How should we quantify these ranked contributions? Taylor, Parker, and Zilberman mention average yield effects of drip irrigation, ranging between 5% and 25% increase in output. This extra yield effect is explained by allowing for more consistent soil humidity and the precision of the irrigation.

This aspect of drip depends on weather and ET information, such as the one provided by CIMIS, to assess the water intake by plants and the appropriate amount of water required. We calculate an average yield effect of CIMIS by reconciling the respondent rankings with a portion of the yield effects from drip irrigation. For a lower estimate, rankings between 1 and 3 are attributed 0% yield effect,blueberry packaging boxes and the rankings of 4 and 5 get 5%. For a higher estimate, ranking of 1 gets 0% yield increase, ranking of 2 and 3 get 5% yield increase, and the rankings of 4 and 5 get a 10% yield increase. These percent yield effects are then averaged among the respondents. We aggregate growers and consultants with equal weights. 41% of respondents rank the importance of CIMIS for yield effects at 4-5. The low estimate for yield contribution of CIMIS results in 2% output increase, and the higher estimate at 5.9% increase. At a conservative estimate of per-acre income of $3,757 for growers, this represents an extra yearly income of $76 – $222 per acre. For the 2.8 million acres using drip irrigation, this would account for $213 – $622 million yearly from the contribution of CIMIS to yields. Assuming again the demand is elastic with a coefficient of -2, these estimates would halve to $107 – $311 million. These are gains from water saving in parks, golf courses, and gardens. They were assessed as a small portion of the total gains from CIMIS in the 1996 report by Parker et al., totaling about $2.3 million . Our current estimate for these gains is much higher. The discrepancy from the 1996 report is due to several factors. First, we believe to have reached out to more respondents in this sector. Second, water prices in California have gone up substantially. Third, there might be more use of CIMIS and smart irrigation planning in the sector compared to 20 years ago. We focus on responses from landscape managers and golf course managers. They report their operating acreage , the average water use, and the estimated saving rate by using CIMIS. We have 28 respondents in golf courses with 6,750 acres in total, and 137 respondents in landscape management with 179,000 acres. The total sum is about 21 times the acreage of the equivalent category in the 1996 report. Based on the initial interviews, we grouped them into a single user category, but still asked them to select into landscape or golf later in the survey. Table 2.2 proved us wrong. Surprisingly, it turned out that the users in landscape management reported much higher water saving rates with CIMIS. This could potentially be explained by technology: big turf areas are still likely to be irrigated with sprinklers, which allow lower savings rates even if CIMIS is used for optimal water calculations. On the other hand, a lot of non-turf landscaping might be irrigated with drip. The total amount of water, saved yearly with CIMIS according to our respondents, is 220,707 AF. Water prices for these types of users are much higher than in agriculture. We can use the municipal water rates to get an estimate of the monetary savings. The EBMUD rates, effective 2018, are $5.29 per 100 cubic feet or $4.12 for non-potable water. The Los Angeles Department of Water and Power charges commercial, industrial and governmental users by tiers.

For January 2019, the tier 1 rates are $5.264 per 100CF, and tier 2 rates are $8.667. The specific tier 1 allotment is set for each user. However, some non-profit users might get rates as low as $2.095 for tier 1 and $3.595 for tier 2. For comparison with agriculture, note that the lowest rate cited above for municipal water is more than four times higher than the “high” rate for agriculture in Taylor, Parker, and Zilberman . The spread of prices, even within municipalities, suggests that they might not reflect the marginal cost of providing water to consumers. However, water utilities have regulated rates and usually work on a “cost plus” basis, such that the water rates should reflect their real average cost. These rates can therefore be used to assess the economic gains from water savings. The different municipal rates serve to construct bounds for our estimates. This first order approximation does not take into account the potential elasticity in water demand, or the potential effect of CIMIS in lowering residential water pricing by curbing down demand. However, we think they are good benchmarks and could definitely serve as an estimate for order of magnitude. The lower rate is the LADWP non-profit rate, which might not apply for many CIMIS users. Assuming nobody exceeds their tier 1 allocation, the value of water savings amounts to $201 million per year. For a higher EBMUD rate of $5.29, the savings amount to $509 million per year. For a reasonable upper bound, assuming we are in Los Angeles and 90% of the water consumption is in tier 1 , the sum is $539 million. Unlike the case of agriculture, we do not believe the survey responses in this category have captured all the relevant acreage. Neither do we have a good sense of the total relevant acreage in California, which could indicate by what factor these estimated gains could be extrapolated. However, the sums are substantial as they are. We take them as our total estimates for gains from CIMIS, noting that they are an under-estimate in this sense. This chapter analyzes the gains from CIMIS, focusing on agriculture and some urban uses.

The marshes are separated from the main channel of the slough by a railroad berm

Sampling in the present study occurred during the silking period of maize, when crop N uptake reaches a maximum. The rhizosphere may be N-depleted in comparison to bulk soil, and microbial N limitation may account for the decreased abundance of these N-cycling genes. Differences in soil organic matter or shifts in root exudation during development  leading to altered rhizosphere carbon availability may also account for the change in direction of the rhizosphere effect in the present study as compared to the literature. Increased sampling frequency over the course of the growing season paired with metabolomic analysis of root exudates would provide insight into the mechanisms linking root C release and N uptake dynamics to microbial N-cycling gene abundances. We hypothesized that differences in N-cycling gene abundance between conventional and organic systems would reflect adaptive shifts, increasing the abundance of gene pathways linking system-specific N inputs to plant-available species, but this hypothesis was not supported. Only two of six genes were affected by soil management history. The abundance of the nosZ and bacterial amoA genes, the only genes affected by the M × R interaction, was higher in the organic system . The increase in abundance of the nosZ gene could potentially indicate greater conversion of N2O to N2 and decreased greenhouse gas production, while increased abundance of the amoA gene may reflect increased conversion of ammonium to nitrite and subsequent nitrification products. Higher soil carbon as a result of long-term organic matter applications at this site may contribute to higher abundances of the nosZ gene in bulk and rhizosphere soil in this system. Putz et al. found that higher soil organic carbon under a ley rotation increased expression of the nrfA and nosZ genes relative to the nirK gene as compared to a conventional cereal rotation,grow bags for gardening favoring higher rates of dissimilatory nitrate reduction to ammonium and lower rates of denitrification. However, previous work in the treatments examined in the present study found that abundances of the amoA and nosZ genes were not correlated with gross rates of N transformation processes.

Prediction of cropping system impacts on microbial N cycling therefore requires a nuanced integration of gene abundances with parameters such as carbon availability, moisture content, and temperature within soil aggregate microenvironments over time. That few differences were observed late in the growing season between N-cycling genes in systems receiving organic or inorganic N inputs is consistent with the results of a meta-analysis by Geisseler and Scow, which found that N fertilizer impacts on microbial communities tend to fade over time. Sampling occurred at silking in the present study, long after the preplant fertilizer and compost applications that likely maximize differentiation between systems. Potential N limitation in the rhizosphere in both systems may also have outweighed management effects. Co-occurrence networks, which provide insight into ecological interactions among microbial taxa, were influenced by M, R, and M × R effects. Bulk and rhizosphere bacterial networks from the conventional system had the same number of nodes but were more densely connected than networks from the corresponding soil compartment in the organic system . Other bulk soil comparisons of organic and conventional agroecosystems using networks constructed from OTU-level data have found conventional networks to have more nodes or, alternatively, fewer nodes and edges than organic networks. Clearly, predicting cooccurrence patterns of incredibly diverse microbial communities based on a conventional-versus-organic classification is too simplistic. Agricultural management is likely better represented as a continuum than discrete categories, and causal relationships between specific practices and network topological properties have yet to be determined. An M × R interaction was also observed for network properties in which size, density, and centralization were lower in the rhizosphere network from the conventional system than from the organic system . These network properties follow the same pattern as alpha diversity of bacterial communities, suggesting a shared yet perplexing cause: while the mechanism remains unclear, rhizosphere communities appear to be converging from very distinct bulk soils towards similar diversity and structural metrics. Conventional agriculture is hypothesized to disrupt the connections between bulk soil and rhizosphere networks, as tillage and mineral fertilization are proposed to disturb fungi and soil fauna that serve as a bridge between bulk soil and rhizosphere environments.

While tillage does not differ between the systems we measured, fertilization effects are likely partly responsible for the observed interaction. Regardless of the mechanisms involved, the system specific direction of the rhizosphere effect on cooccurrence network properties suggests that management and plant influence interactively determine not only which taxa are present, but how they interact, with potential implications for agriculturally relevant functions and ecological resilience. Hub ASVs were identified in each network based on high values for normalized betweenness centrality, a metric often used to describe keystone taxa. Organic networks had lower normalized betweenness centrality values than conventional networks . Lower betweenness centrality values for hub taxa may indicate that network structure depends less on individual species, potentially increasing resilience to environmental stresses that could destabilize networks overly dependent on hub taxa sensitive to those specific stresses. Different hub ASVs were identified in each rhizosphere environment, but information on the ecology of these taxa is generally absent from the literature. Although it would be misleading to state that these taxa are keystone species in their respective habitats without experimental validation, the fact that many of these taxa were also identified through indicator species analysis suggests that they play important ecological roles. Future work could explore the genomes of these ASVs to discern why they are important in their respective agricultural systems and test the hypothesis that they serve as keystone species using synthetic communities. Concluding whether adaptive plant-microbe feed backs result in an M × R interaction leading to shifts in other rhizosphere processes is complicated by the importance of poorly understood fungal communities and methodological limitations of this study. Numerous fungal taxa respond to the M × R interaction according to our differential abundance analysis , yet knowledge of these taxa remains limited due in part to the constraints of culture-dependent methods prevalent in the past. Nonetheless, fungi influence inter-kingdom interactions and agriculturally relevant processes in the rhizosphere, and novel molecular biology tools could be used to improve our understanding of key fungal regulators identified in these analyses.

Metagenomics and -transcriptomics would facilitate a much more comprehensive analysis of potential functional shifts. A highly useful starting point would be to delve into dynamic variation in microbial genes involved in carbon metabolism and nitrogen cycling in the rhizosphere, in combination with root exudate metabolomics and measurements of root N uptake. Stable isotope labeling and in situ visualization methods could further complement our understanding of how management, plant roots, and their interactive effects shape rhizosphere processes. The scope of this study was intentionally restricted to a single genotype of one crop in two management systems to limit the main sources of variation to the management and rhizosphere effects that were of primary interest, but the limits to inference of this small-scale study must be considered. Other studies in maize have found that strong legacy effects of soil managementhistory are generally acted upon in a similar manner by two maize cultivars and that rhizosphere bacterial community composition varies only slightly among hybrids from different decades of release. Testing whether these limited effects of plant selection hold true for additional contrasting genotypes and genetic groups of maize would further complement this work. Furthermore, variation in root system architecture across crop genotypes might interact with tillage and soil properties responsive to management effects. Management practices such as the inclusion of forage or cover crops planted in stands rather than rows might affect the differentiation of bulk and rhizosphere soil uniquely from systems based on perennial crops, successive plantings of row crops in the same locations,garden grow bags and/or minimal tillage. Study designs incorporating more genotypes, management systems, and cultivation environments would therefore be highly useful to test how results of this study may be extrapolated to other settings. Future studies should also identify functional genes that are upregulated or downregulated in the rhizosphere under specific agricultural management practices. Whether such functional shifts are adaptive will provide insight into the relationship between agroecology and ecology. Positive eco-evolutionary feedbacks resulting in adaptive microbial communities have been described in unmanaged ecosystems, for example, habitat-adapted symbiosis in saline or arid environments. If similar adaptive recruitment can occur with annual crops in the context of agroecosystems, maximizing this process should be added to the list of rhizosphere engineering strategies and targets for G × E breeding screens. Finally, while our results provide evidence that management and plant influence interact to shape microbial communities at one sampling point, we highlight the need to reframe the M × R interaction as a dynamic process. Rhizosphere communities may be more different from one another than bulk soil communities because roots develop right after tillage and fertilization, when management systems are most distinct. Plants are not static entities, but active participants in the ongoing process of rhizosphere recruitment. As an alternative to the “rhizosphere snapshot,” we propose a “rhizosphere symphony” model that acknowledges the active role of root exudates in orchestrating the composition and function of microbial communities.

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

Characterization factors for water use were based on the ReCiPe model

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

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

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

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

In doing so, we demonstrate that average data reflecting national situations are inadequate to capture the likely environmental impacts of corn expansion into cotton on marginal land at regional level. Our results for three states North Carolina, Georgia, and Texas show that corn expansion into cotton in the South relieves freshwater ecotoxicity but may aggregate many other regional environmental impacts. Overall, our study confirms the earlier studies that demonstrated the importance of understanding “marginal” impacts in LCA : environmental consequences of the policies that encourage converting cotton to corn cultivation in the regions where corn is generally less suitable to grow cannot be understood by comparing average environmental profiles of cotton and corn. Our results also favor “consequential thinking,” as an analytical paradigm, in bio-fuel LCA, while our study is not intended to demonstrate how to perform a “consequential LCA,” as an operational model . Corn ethanol, supported by several federal policies as a means of reducing GHG emissions by displacing gasoline , has been a point of heavy dispute in the last decade . However, it has become increasingly clear that although corn ethanol may have the potential to combat climate change , its large-scale expansion is reported to generate adverse environmental consequences including, notably, direct, and indirect land use changes . These adverse consequences, first, undermine the climate objectives of the public policies. Second, for intensive use of agrochemicals and irrigation water, corn expansion adds to the pressure on local water quality and scarcity issues . Our study focused on yet another consequence related to ethanol expansion, namely, land cover change from cotton to corn, and analyzed the potential implications of such change for local environments. Contrary to the previous view that land shift between cotton and corn, both high-input crops, may cause negligible net environmental impacts , our study revealed a more complex picture. Although land switch from cotton to corn relieves ecotoxicity, it likely aggravates other various environmental problems depending on where the crops are grown. Note that our study only covers part of the effects bio-fuels policies have generated on crop conversions. To understand the overall environmental impacts of bio-fuel policies through crop conversions, further research is needed to estimate the environmental aspects of other crops affected, particularly soybean , and the magnitude of land shifts between the crops. Our results highlight the importance of potential, unintended consequences that cannot be adequately captured when average data are employed. Understanding the actual mechanisms under which certain policy induces marginal changes at a regional and local level is crucial for evaluating its net impact. Our results also show the importance of recognizing potential trade-offs between environmental objectives in policy making. Climate policies focusing narrowly on carbon, for instance, could shift burden to regional issues like water scarcity and eutrophication . Therefore, environmental policy making should attend to not only unintended effects within its targeted problems like the indirect LUC effect , but also those across impact categories to avoid or minimize burden shifting across impact categories. Also, our study reinforces previous research with respect to spatial variability in agricultural systems .

Declining chill is therefore considered a threat to California pistachios

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

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

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

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

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

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

Pesticide effects on soil microorganisms are difficult to evaluate because of the heterogeneous physical-chemical nature of soil, resulting in uncertainties about their distribution and fate within soil microsites. Previous studies on the effects of potential MeBr alternatives on the size, composition and activity of soil microorganisms are limited to one or a few fumigants, a relative short time period, and/or the laboratory . Recovery of microbial processes in the laboratory compared to the field may be reduced due to the absence of re-colonization by nonfumigated soils . Furthermore, the effect of alternative fumigants on soil microbial processes was studied on soils with a 10-year history of fumigation with MeBr + CP combinations followed by a 2 to 3 year break prior to the initiation of these field experiments at Watsonville and Oxnard, respectively. Consequently, results obtained from these soils with a long-term fumigation history may not apply to soils previously not fumigated . The results presented in this work are part of a longer study to evaluate application methods and efficacy of chemical MeBr alternatives to control weeds and pathogens in strawberry production systems in California, USA. The response of microbial performance to soil fumigation with InLine, CP, PrBr and Midas relative to the standard MeBr + CP application and a control soil was determined at 1, 4, and 30 weeks after fumigation in 2000, the first year of the study. Fumigation initially inhibited microbial respiration, nitrification potential, and activities of dehydrogenase, acid phosphatase and arylsulfatase . After 30 weeks,black plastic plant pots wholesale microbial activities in fumigated and control soils were similar at both sites, with exception of acid phosphatase and arylsulfatase activities in selected treatments that remained lower in the fumigated soils.

Soil microbial biomass C and β-glucosidase activity were not affected by fumigation with MeBr + CP and alternatives throughout the whole study period in the first year . This paper focused on the effects of repeated soil fumigation with MeBr + CP, PrBr, InLine, Midas, and CP on the size and activity of soil microorganisms and hydrolytic enzymes, which control the degradation of organic substances and the rate at which nutrient elements become available for plants . Microbial respiration was significantly decreased in Oxnard soils fumigated with MeBr + CP, but not affected by the four selected alternative fumigants at both sites. In this study, microbial respiration showed a low sensitivity to detect changes in soil microbial activity due to repeated application of the standard MeBr + CP combination and alternative fumigants. This finding is in contrast with the high sensitivity of respiration measurements to treatment of soils with heavy metals and pesticides . Significant lower respiration rates in Oxnard soils fumigated with MeBr+ CP compared to recently not fumigated control soils however, may indicate a decreased biological activity. Soil fumigation had no significant effect on microbial biomass C, and the results for microbial biomass N were inconsistent over the two experimental locations. Therefore, the effects of soil fumigation on total microbial biomass content provided little information on possible changes in the size of microbial populations. The overall low response of microbial biomass and respiration to repeated soil fumigation may be related to selected effect on sensitive microbial populations and the growth of resistant species. The latter may feed on cell debris, leading to restructuring of soil microbial populations as indicated elsewhere . Selected specialized bacteria may also use the fumigants as a source of carbon and energy, as documented for agricultural soils repeatedly subjected to MeBr fumigation . The effect of soil fumigation on the activities of dehydrogenase, β-glucosidase, acid phosphatase and arylsulfatase varied among the soil enzymes and within the two study sites. At the Watsonville site, soil fumigation with alternative fumigants generally had no significant effect on the activities of the four soil enzymes studied over the twoyear study period.

These results suggest that biochemical reactions involved in organic matter degradation and P mineralization were affected by fumigation to a greater extent than were those reactions reflecting the general oxidative capabilities of microbial communities or involved in S mineralization in soils. In contrast, at the Oxnard site, β- glucosidase and acid phosphatase activities were relatively stable towards repeated soil fumigation, but dehydrogenase activity was significantly decreased by MeBr + CP. The reasons for these site-related variations in the response of soil enzyme activities to soil fumigants remain unclear. The two study sites showed very similar soil physical and chemical properties, such as clay and organic C contents. Variations may have occurred in the actual soil moisture content and temperature at the time of fumigation, which were proved to be crucial for the efficacy of pesticide applications . The results also suggest that the four alternative fumigants had no longer-term impact on enzyme reactions involved in organic matter turnover and nutrient cycling in soil. The inhibitory and/or activation effects of any compound in a soil matrix on enzyme activity are largely controlled by the reactivity of clay and humic colloids . The finding that MeBr + CP and the alternative fumigants led to a greater inhibition of the activities of the reference enzymes than that of soils suggests that free enzymes are more sensitive to soil fumigation than enzymes that are associated with the microbial biomass or enzymes adsorbed to clay or humic colloids. Ladd and Butler hypothesized that some enzymes are stabilized in the soil environment by complexes of organic and mineral colloids and therefore are partially protected from denaturation by fumigation. Similar results were observed for acid phosphatase, β-glucosidase and arylsulfatase in chloroform fumigated soils . Furthermore, reference enzymes were purified from one source for each protein, whereas soil enzymes derive from various sources leading to a set of isoenzymes [i.e., enzymes that catalyze the same reaction but may differ in origin, kinetic properties or amino acid sequencing ].

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

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

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

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

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

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

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

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