Category Archives: Agriculture

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 .

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

Turning to manufacturing, in Table 6, the subgroups that were in surplus in 1980-82 accounted for 31.82% of the value of normalized agricultural trade in 1994-96. Of these goods, 4.93% of trade moved to balance by 1994-96 and 0.81% moved to deficit. Adding up the diagonal elements in Table 6, we find that 65.5% of the trade in manufacturing was persistent, from 1980 to 1996. These results suggest almost as much persistence in manufacturing trade compared to agricultural trade. As a statistical measure of trade persistence, we can use a transformation of the standard chisquared test, Cramer’s C-statistic, suggested by Carolan et. al.. The C-statistic lies between zero and one, with one representing complete association between the beginning and the ending trade balance. From Tables 7, 8 and 9 we find the C-statistic is 0.66 for agriculture, 0.39 for other primary products, and 0.54 for manufactures. These results suggest there was the least change in the trade balances over the 1980-1996 time period for agriculture, because the C-statistic is relatively high. For manufacturing and other primary products the results suggest there was relatively more change in the trade balances over the time period studied, because the C-statistics are lower.9 Rather than just comparing the beginning and ending time periods, we can construct histograms for agriculture and manufacturing, based on the number of years each subgroup runs a surplus . Figure 3 shows histograms for agriculture, “other” primary products, and manufactures. Figure 3 classifies subgroups indicating how many years the subgroup was in surplus. This means, that a subgroup that was in surplus for each of the 18 years would be in the cell at the extreme right of the histogram. The histogram for agriculture displays the strongest evidence of bimodality,plastic planters bulk which indicates persistence in trade flows. These histograms are therefore consistent with the C-statistic results, suggesting more persistence in the composition of agricultural trade, compared to the other two groups. Finally, the results of an additional test for association are reported in Table 6.

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

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

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

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

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

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

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

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

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

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

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