Horticulture

If our use of natural resources precludes future generations from using these resources, then it may not be a just policy when viewed from the eyes of future generations. In particular, using Rawlsian notion of justice, when generations make decision using veil of ignorance about which generation they belong to, if they are unlikely to decide to make reservoirs, then such reservoirs are made unjustly. Sidgwick put this statement differently by demanding anonymity in such utility ranking:i.e. the outcome of a preference ordering among different welfare paths between generations shouldn’t differ based on which generation is making the decision . Unfortunately, the concept of intergenerational equity has yet to be incorporated satisfactorily in economics: the major instrument in NPV calculation is a positive discount rate that leads to the finite NPV of infinite stream of incomes. Some sort of contradictions plague almost all proposed models such as paternalistic consumption models, paternalistic utility model, Chichilinsky’s model and Alvarez-cuadrado et al is model that improves on Chichilinsky’s model by guaranteeing optimal path of renewable resource extraction.

The major problem lies in the inability of comparing these utility streams: undiscounted utility often has NPV equal to infinity and one can’t Pareto rank them , Svensson. Discounting on the other hand has always been very controversial,blueberry grow pot in particular in environmentally sensitive projects. Our model uses discounting as a tool not only to ensure the finiteness of present value of the dam, but also in incorporating the stochasticity of future developments. Other studies of reservoirs have skipped the discussion of discounting altogether. Often,sustainability frontier of a dam is defined in terms of two ratios: Kw and Kt ; where Kw is defined as above and Kt is the ratio of storage to annual sediment arrival. Basson and Palmieri et al both provide a lengthy discussion on sustainability frontier in terms of Kw; and Kt : If a non-sustainable outcome is economically desirable to a sustainable outcome, then the reservoir can’t be sustained. Such economic desirability is expressed in terms of net present value of the reservoir under the two conditions. If NPV of the reservoir under sustainable outcome is higher than the NPV under unsustainable outcome, then the reservoir is sustainable. Often, higher Kt is more likely to yield sustainable outcome than lower Kt for a given Kw:We provide a new approach to model sedimentation management problem in large reservoir. This paper contributes to existing scant literature in what is being realized as an important topic in natural resources economics. Our model allows uncertainty in sedimentation accumulation, which is useful in understanding the impact of global warming or fluctuating weather if they contribute to the change in variance of sediment arrival rate in the reservoir.

This paper also contributes by providing a new algorithm to solve a particular type of boundary value problem arising due to the quadratic nature of the cost function. Quadratic cost function leads to nonlinear second order value function. Though there are several existing algorithms to tentatively solve these equations, all of them have some deficiencies. Some , such as nonlinear shooting methods, could be very slow, while others such as finite difference method are computationally cumbersome. We provide a new nested method that is a slight modification of the projection method provided by Judd. We calibrate our model by using the data from Tarbela dam in Pakistan. Tarbela is one of the most vulnerable dams in the world right now, because of its apparently high rate of erosion. We find that the dam could be sustainably operated for a particular linear cost function, and also for the quadratic cost function. However, we note that there are other issues that could make these assertions weaker. Removing sediments, for example, would also require finding a proper place to dump those sediments. Our model is simple and yet useful in understanding the issues surrounding the reservoir management. We provided many comparative statics results such as impact of increased sediments, impact of change in discount rate and impact of increased uncertainties on both value function and control functions. In both our basic model and our definition of sustainability, we focus on the major role played by water storage level on the value of the reservoir or on the sustainability criterion. Getting useful data on large dams is still very difficult.

Dams also differ by their location, their political significance and their strategic and even psychological meaning in the host country. Each dam is also likely to have its own specific cost function of removing sediments from the reservoirs. Reservoir operators are just recently beginning to think about sustainability of the reservoirs. Tarbela’s planners had originally planned the dam to operate for fifty years, a target they don’t like to stick to anymore. While the planners are now beginning to weigh different options for sediment removal, our results show that they are not too late in implementing those strategies. Future research in sediment management should look at the risk averseness of the planner. We use a risk neutral planner in our paper. Furthermore, since privately operated reservoirs are often licensed to run for a limited period , one would want to introduce a time dependent model to study the situation of private ownership. In this situation, optimization decision will yield a partial differential equation with time as one of the arguments. The welfare impact of allowing privately held reservoirs is also important next step in this field. However, the most important of all is better understanding of cost function. Right now, the understanding of cost function in sediment removal problems is very limited and it hinders effective management of reservoirs. Also, a major weakness of the model is its assumption that V0 and VK are known. The calibration assumes that VK is the cost of construction. It is an ad hoc assumption and probably is an underestimation of the value. A better understanding of such values could be derived only by, or at least in conjuction with, other economic techniques such as non-market valuation methods. Finally, in a lot of cases, a mixed model, in which different sedimentation strategies are used together are used. Modeling such a situation is more complex,hydroponic bucket but could be one of the topics of future research.Following the concept used by Kuznet to describe income-inequality relationship , an Environmental Kuznet Curve was developed to describe relation between environmental quality and income. Generally speaking, this relationship is considered to be of a quadratic shape. This means pollution goes up to a certain point as income increases, eventually declining above a certain level of income commonly known as a turning point. This type of relationship exists because countries generally pass through an agricultural phase into an industrial phase and then finally specialize in the service sector. In the agricultural phase, countries have little pollution.

As a country transforms to an industrial phase, pollution increases-originating from both point and non-point sources. Agricultural production becomes more intensive as little emphasis is placed on improving environmental practices and more emphasis is placed on the amount of food produced. Therefore, pollution continually increases.As the country transforms its economy to the service sector, pollution declines because the country imports pollution-intensive products from abroad. Therefore, one would observe a downward trend in total pollution. Income also increases in this phase of growth. Another reason why one would observe this EKC type of behavior is due to people’s preferences. It is generally thought that environmental quality is a luxury good; therefore, as per capita income rises, emphasis is placed on increasing environmental quality. This traditional inverted U-shape of the EKC has been challenged because many researchers claim that the relationship may not be depicted in a quadratic framework. For some pollutants, one would observe a cubic pattern whereas for other pollutants for which assimilation rates are low, the pattern may be monotonically increasing. Pearson as well as Cole, Ryaner, and Bates are dissatisfied with the econometric progress on functional form specifications in the studies of the EKC. To address these concerns about the shape and econometric estimation of the income- environmental quality relationship, other functional forms of income have been proposed and the relationship between income and pollution has been modeled in a non-parametric form. Semiparametric methods have also been used; where in addition to income and its different functional forms, additional variables have been also added to the regression model . A few authors have even considered adding variables such as governance in EKC models . Yet other rauthors have been frustrated with the sensitivity of the results to the slight changes in the data used . Therefore, the EKC concepts introduced by Grossman and Krueger and popularized by the World Bank have been getting increased attention. The objective of this paper is to look at how CO2, a stock pollutant, can be related to per capita income in Latin American countries. This study explores this relationship using both parametric and semiparametric panel data models. This study also shows that a parametric quadratic relationship is rejected in favor of a semiparametric estimate. Furthermore, we used hitherto unused data on forestry acreage in our study. We reviewed literature that examines the relationship between CO2 and per capita income discussing the results found within the literature pertaining to CO2 in terms of the model used and turning points. Several papers have revealed an inverted U-Shaped EKC relationship between CO2 and income using data from various countries utilizing various econometric methods.For example, Schmalensee, Stoker and Judson studied CO2 emissions data from 141 countries for the period 1950-1991, and use a spline functional form in a two-way fixed effects model. Sengupta used a fixed effects quadratic model in addition to data from 16 developed and developing countries. Carson , Jeon, and McCubbin utilized data from U.S. states.All three of these papers found an inverted relationship between CO2 and income. Bengochea-Morancho, Higon-Tamarit and Martinez-zarzoso analyzed 16 years of data from the European Union using a polynomial quadratic along with cubic specification in parametric fixed and random effects panel models; their study discovered an inverted, Ushaped EKC when examining a selected subset of countries.Panayotou, Peterson and Sachs used feasible generalized square method to establish the presence of an inverted U-shaped EKC in a subset of the 17 developed countries in their study. Other studies supporting an inverted U-shaped EKC are Moomaw and Unruh, Friedl and Getzner , and Millimet, List and Stengos. Contrarily, there are other papers that reject the inverted, U-shaped relationship between existing between CO2 and income. For example, Shafik and Bandopadhyay claimed that one might see a monotonously-increasing relationship between CO2 and income. To reach this conclusion, their study utilized 26 years of CO2 data from 118 to153 countries as well as polynomial specification in both fixed and random effects models. Holtz-Eakin and Seden used a two way fixed effects model with a quadratic functional form to analyze data from 108 countries, and unveiled that the turning point could be as high as $8 million dollar per capita. Agras and Chapman indicated that there may not be any turning point for CO2 based on their study of 34 countries using a fixed effects autoregressive distributed lag model. Moomaw and Unruh and Dijkgraff and Vollebergh used data from OECD countries from 1950-1992 and from 1960-1997, respectively; both rejecting the presence of a quadratic relationship between CO2 and income. Nguyen, in a study using a non-parametric method, indicates that there is a convergence in CO2 release among OECD countries. This view is also supported by Strazicich and List in their analysis of 21 industrial countries for the period ranging from 1960 to 1997. Other studies have also rejected an inverted U-shaped EKC .We have observed that different authors have used CO2 data from various sources to study the EKC relationship-with data originating from the World Bank, Oak Ridge National Laboratory, World Development Indicators, World Bank, OECD environmental data sources and the International Energy Agency. The postulating of the functional form was done utilizing linear, quadratic, cubic and spline functional forms. Estimation techniques used include parametric panels, fixed and random effects models, time series method, non-parametric methods, semiparametric methods, and pooled mean group estimations.

A defined urban boundary that reduces the area of interaction between urban and agricultural landscapes, however, could reduce any potential heat island and runoff effects on agriculture. By leaving larger tracts of agricultural landscapes intact, the interface problems are, to a degree, mitigated.Population expansion in Yolo County, the Sacramento Region, and northern California in general can be expected to expand customer base for agriculture . These potential increases are quite significant, on the order of two million additional residents in the Sacramento region and many more in northern California, and so might lead to expanded opportunities for locally‐ or regionally‐oriented agriculture. Expanded urban populations might also provide a financial base with which to pay rural communities for ecosystem services , and a prime market for ecotourism within agricultural areas.Strengthening the urban community’s interest and support of farmland preservation is a key challenge for mitigation of GHG emissions, and the long‐term viability of agriculture in Yolo County. During the past several decades California communities have come to accept increasingly higher densities within their borders,nft hydroponic system and there is no reason not to expect this trend to continue in the future.

Awareness of the value of local food production and other associated ecosystem services of sustainable agriculture is one of the motivations that will likely move people’s attitudes toward support of land use policies for infill and compact growth in the B1 and AB32+ scenarios. One basic assumption of time series analysis is that of stationarity. That is, the mean and variance of a time series are constant over time. When they are constant over time, the series is stationary, and when they change over time, the series is non-stationary. In most time series data and models, this stationary assumption is unlikely met, and violation of this assumption complicates statistical analysis of time series. The major consequence of non-stationarity for regression analysis is spurious correlation that leads to incorrect model specification. To avoid spurious regressions, it is important to test for non-stationarity of the time series. A quick glance at the data indicates that the key variables in our analysis are not stationary . The first step is to conduct a formal test whether the data are “trend stationary” or not. In particular we test if the series have a unit root . If a unit root is found, the data are nonstationary. However, if the data are trend stationary, then analysis can proceed by regressing levels on levels with some function of a trend included in the regression to detrend the data.

The presence of a unit root is often tested using the augmented Dickey‐Fuller method. The test is carried out for each variable by regressing the first‐difference on a constant, a time trend, once‐lagged level , and p lagged differences, where p is chosen by the analyst . For the unit root test, the t‐statistic on the lagged level is the relevant test statistic. The usual t critical values, however, are not applicable. Appropriate critical values are given in Enders . Results from the augmented Dickey‐Fuller tests are reported in Table 4.1. Among the climate related variables, the presence of a unit root can only be rejected for the precipitation variable, meaning that all climate variables but the precipitation variable are non-stationary. For acreage variables, none rejected the presence of a unit root, indicating all acreage variables are non-stationary. Test on most price variables cannot reject the presence of a unit root. A few price variables, mostly the prices of orchard crops, narrowly reject a unit root. But, overall, most price variables are non-stationary. Given the failure to reject unit roots by most variables, the next step is to test for cointegration.If the left‐hand side and right‐hand side variables are cointegrated, then analysis can proceed with an error correction model even though data are non-stationary. However, if there is not strong evidence of cointegration then regressing levels on levels will lead to spurious regression results and reliable estimates are only obtained by regressing first‐differences on first‐ differences. The variables are tested for cointegration by two separate methods.

The Engle and Granger method regresses levels on levels and tests the residuals for a unit root. If the residuals do not have a unit root, then they are cointegrated. Thus, the null hypothesis of the test is that the variables are not cointegrated. The Dickey‐Fuller critical values are not applicable in this case, but appropriate critical values are found in Enders . As a robustness check, cointegration is also tested using the Johansen test with critical values found in Enders . Engle‐Granger cointegration test results are given in Table 4.2. There was very little sensitivity of the results to the method used, so the Johansen test results are not reported. For each crop, the cointegration test was performed by regressing acres on its own lagged price and the relevant climate variable for that crop, and then testing the residual for a unit root with the augmented Dickey‐Fuller test. Precipitation was not included in the regression, since a unit root is rejected for precipitation. The relevant climate variable for all field and vegetable crops, except wheat, is summer growing degree days; wheat acres were regressed on winter growing degree days instead. The relevant variable for tree crops is chill hours. There is only evidence of cointegration for 4 out of the 13, and for 2 of these crops the null hypothesis is very narrowly rejected. Given the results from the unit root and cointegration tests, estimates from regressing levels on levels could simply be spurious correlations. While it may be acceptable to specify an error correction model for a few crops , we prefer that all acreage equations be estimated with the same methodology. Given the strong evidence in the data of unit roots with no cointegration between the variables, we regress differences on differences for every crop equation. Current agricultural practices rely on relatively large inputs of nitrogen to support crop growth. The majority of N in California is applied in the form of synthetic fertilizers. Organic sources of N from crop residues, animal urine, and animal manure also contribute a significant amount of N to agricultural soils. Emissions of N2O are a natural by‐product of the nitrification and denitrification processes carried out by soil microbes. Nitrous oxide emissions are only a small fraction of the total N applied and essentially represent inefficiencies in the microbial nitrification and denitrification processes. Direct N2O emissions are defined as those arising directly from farm fields following N application,hydroponic nft system while indirect emissions are the result of volatilization, leaching, and runoff which carry N off the farm and into the surrounding environment. The equations used to calculate direct N2O emissions from synthetic N fertilizers, crop residues, urine deposited in pasture, and animal manure are listed below. Indirect N2O emissions arise from applied N that is lost from farm fields either as gaseous ammonia and aqueous nitrate in runoff or leachate. The first pathway is due to the volatilization of NH3 from synthetic N fertilizers, urine deposited in pasture, and manure. Volatilized N is returned to the soil through atmospheric deposition, where it is subject to loss as N2O during nitrification and denitrification. Nitrate in runoff and leachate collects in streams and water bodies where it is undergoes denitrification. Indirect emissions are estimated based on the amount of N added as synthetic N fertilizer, urine and manure, default values for the volatilization and leaching, and emission factors established by the IPCC .

The combustion of fossil fuels to run agricultural machinery produces CO2 and smaller amounts of nitrous oxide and methane . To calculate emissions from mobile farm equipment for a given crop, data on diesel fuel use per unit area was obtained from the University of California Cooperative Extension’s cost and return studies . The fuel use data was then multiplied by each crop’s annual cultivated acreage and then summed across all crops to estimate Yolo County’s aggregate fuel consumption. The amount of CO2, N2O, and CH4 produced from the combustion of diesel fuel was determined using emission factors for each gas published by the U.S. EPA and EIA . The advantage of this method is that it captures changes in fuel consumption related to annual trends in acreagenfor specific crops. However, since the approach assumes a fixed set of management practices for a given crop it, will not reflect the adoption of alternative practices such as reduced tillage or the use of alternative fuel sources . Irrigation pump engines can run on diesel, natural gas, liquefied petroleum gas, butane, gasoline, or electricity from the grid or solar PV. Diesel‐fueled irrigation pumps are known to be a significant source of both gaseous emissions and particulate matter, thus they are monitored periodically by the California Air Resources Board. Since detailed data at the county/air district level are available only on diesel pumps, we have only included this pump type in our inventory. Emissions from other fossil fuel‐powered pumps are not included because adequate local data were not available. As of 2003, an estimated 643 diesel‐powered irrigation pumps were operated in Yolo County . Statewide, the number of diesel irrigation pumps was projected to increase by 3.5 percent between 1990 and 2010 . We estimated the 1990 and 2008 pump populations based on an assumption that the statewide trend was proportional to the increase in the number of pumps in the county. Input values for engine activity and average engine horsepower were taken from statewide survey data . Diesel fuel emission factors for CO2, N2O, and CH4 were taken from the U.S. EPA and the EIA .The main mechanism of CH4 production is enteric fermentation, which involves microbial breakdown of carbohydrates in the digestive system of ruminant livestock . Several non‐ruminant livestock also depend on enteric fermentation to help break down poor quality plant material in their caecum and large intestine, but produce less methane than ruminants. A secondary source of CH4 from livestock is the manure they produce, and more important, how it is stored. Manure deposited in the field orpaddock decomposes under aerobic conditions and thus produces little or no CH4. However, when manure is stored in lagoons, as is common in dairy and swine operations, large amounts of CH4 can be produced via anaerobic decomposition. Nitrogen in livestock urine and manure is also subject to loss as N2O during nitrification and denitrification. In this inventory, we assume that all N excreted by livestock is applied to soils either as urine or manure, and thus the emissions are included in the direct and indirect N2O emissions categories. This approach is justified given that the vast majority of livestock in Yolo County are grazed on pasture or rangelands and the manure management methods used by the small number of local dairy and swine operations are well‐known. Methane emissions from enteric fermentation and manure management were calculated for each livestock category using a Tier 1 approach and records of livestock numbers reported by the National Agricultural Statistics Service database or the Yolo County Agricultural Commissioner’s reports . The equations and tables below summarize the method used to estimate CH4 emissions from livestock.The major pathway by which climate change will affect the California economy is through its impact on the California water system. Therefore, a major component of the climate change research being conducted at the University of California, Berkeley, is an economic analysis of the California water system to assess the economic costs associated with changes in the reliability of supply for water users in various parts of the state. Compared to previous research, the approach that the research team has adopted for measuring the economic impacts of climate change has two distinctive features. First, the primary spatial unit of analysis is the service areas of individual retail water supply agencies—irrigation districts and urban water agencies—as opposed to broader geographic aggregates of districts such as depletion analysis areas. To the maximum extent possible, this analysis will be disaggregated to the level of the individual water district. It is important to avoid any further aggregation, because there is tremendous heterogeneity among different water districts even within the same county in California with respect to their water source, the nature and age of their water rights, their operational costs, their finances, the price they charge their retail customers, and other aspects of their terms of service.

Fuel use for irrigation pumping was calculated as the product of the number of diesel‐ powered irrigation pumps in the county, the estimated annual activity of each pump , the average brake horsepower of pump engines in the Yolo/Solano Air Quality Management District, and the brake specific fuel consumption per hour . As of 2003, an estimated 643 diesel‐powered irrigation pumps were operated in Yolo County . Statewide, the number of diesel irrigation pumps was projected to increase by 3.5 percent between 1990 and 2010 . In this inventory we estimated the 1990 and 2008 pump populations based on an assumption that the statewide trend was proportional to the increase in the number of pumps in the county. Input values for engine activity and average engine horsepower were taken from recent government reports which inventory statewide emissions from diesel irrigation pumps . The amount of CO2, N2O, and CH4 emitted was the product of the total amount of diesel fuel consumed by pumps operated in the county and the emission factor for each gas . A GIS database of rice field locations, area‐weighted SSURGO14 soil data and daily weather data from CIMIS15 was compiled for 66 rice fields in Yolo County. The DNDC model was validated against field results from two California‐based field experiments which assessed the affects of residue and water management on CH4 emissions .

Results from the model runs allowed us to generate an emissions factor specific to each management scenario by averaging the simulated CH4 emissions rates across all 66 fields. To account for changes in practice over time,blueberry grow bag size we assumed that 100 percent of the harvested rice area in 1990 was managed according to Scenario A, while in 2008 rice area was divided equally between Scenarios B and C. The percentage of the county’s rice area attributed to each scenario is roughly consistent with statewide estimates for residue burning, residue incorporation, and winter flooding . Finally, the management‐specific emissions factors were multiplied by the area under each management scenario and then summed across each management category to give the total CH4 emissions from rice cultivation for a given year. Further details on residue inputs, fertilizer rates, water management, and model calibration can be found in Sumner et al. and Holst and Buttner . A California‐specific method developed by the CARB was used to estimate emissions from residue burning. This approach is based on studies conducted by the University of California at Davis, which established emissions factors for CO2, N2O, and CH4 for the most commonly burned residues in California . For each gas, the harvested area was multiplied by the fraction of area burned, the crop mass burned per unit area, one minus the residue moisture content, and the corresponding emissions factor for each crop .

In the case of rice, the fraction burned was assumed to have declined from 99 to 11 percent between 1990 and 2008, which is consistent with statewide trends . Carbon dioxide emissions from the addition of limestone and urea were determined by multiplying the amount of each material applied in Yolo County by its default emission factor . The amount of each material was based on county sales records . In Yolo County, total agricultural emissions declined by 10.4 percent between 1990 and 2008 . The primary reason for this generalized decline was a notable reduction in both direct and indirect N2O emissions . Direct N2O emissions were the largest source of emissions during both inventory years, but decreased by 23.1 percent over the study period due to a countywide reduction in the amount of synthetic N fertilizer applied . This reduction in fertilizer use was driven by two important land use trends: a 6 percent reduction in the county’s irrigated cropland ; and a general shift away from crops that have high N rates coupled with an expansion in alfalfa and grape area which require less fertilizer . The large expansion of alfalfa acreage resulted in a moderate increase in the direct N2O emissions from crop residues , but this increase was not enough to offset the overall savings achieved by the displacement of corn and tomatoes. The direct N2O emissions from urine in pasture and manure application ranged between 5 percent and 15 percent of the total direct emissions and showed a small rise over the study period due to a proportional increase in livestock population. Estimates of nitrate lost through leaching and runoff accounted for approximately two‐ thirds of the indirect N2O emissions countywide, with ammonia volatilization responsible for the remaining one‐third .

More than 90 percent of indirect emissions originated from synthetic N fertilizers, while urine and manure from livestock were relatively minor sources. Consequently, the notable decline in indirect N2O emissions was also due to a decrease in the amount of synthetic N applied countywide. In both years, emissions of CO2, N2O, and CH4 from diesel‐powered mobile farm equipment were responsible for 20.0 to 23.0 percent of total agricultural emissions in Yolo County . This category showed little change in emissions over time in 1990 and 69.0 kt CO2e in 2008. This was because an increase in fuel consumption per unit area for several important crops offset the small decline in irrigated cropland . Total emissions from mobile farm equipment were 4 percent lower using the Tier 1 method as compared to estimates generated using the OFFROAD model . However, since the OFFROAD model uses equipment population and hourly usage data to estimate emissions, results from this Tier 3 method could not be used to disaggregate emissions by specific crop category. In both years, emissions of CO2, N2O, and CH4 from diesel‐powered mobile farm equipment were responsible for 20.0 to 23.0 percent of total agricultural emissions in Yolo County . While a reduction in county’s irrigated cropland may have been expected to save fuel and reduce associated emissions, this category showed little change in emissions over time . This was because an increase in fuel consumption per unit area for several important crops offset the small decline in irrigated cropland . Total emissions from mobile farm equipment were 4 percent lower using the Tier 1 method as compared to estimates generated using the OFFROAD model . However, since the OFFROAD model uses equipment population and hourly usage data to estimate emissions,blueberry box results from this Tier 3 method could not be used to disaggregate emissions by specific crop category. Diesel‐powered irrigation pumps emitted approximately 39.6 kt of CO2e in 1990 and 41.0 kt of CO2e in 2008 . This was equal to 11.7 to 13.5 percent of the total agricultural emissions. While irrigated cropland in the county has decreased overall, the amount of land with access to groundwater has continued to expand as new wells are drilled. The small increase in the number of wells operating in the county, therefore accounts for the proportional rise in emissions from irrigation pumping.In Yolo County, CH4 emissions from livestock contributed between 7.8 and 10.5 percent of the total agricultural emissions depending on the inventory year . This is lower than the proportion attributed to livestock statewide, which was more than 50 percent of all agricultural emissions in 2008 . The lower figure for the county essentially reflects the small number of dairy farms operated locally. By contrast, enteric fermentation from pasture‐raised beef cattle was the largest source of CH4 emissions from livestock in both inventory years . Since beef cattle and sheep populations have changed little since 1990, emissions from these livestock types were also relatively stable. While dairy cattle represented only 5 to 12 percent of the county’s cattle in any given year, an increase in the number of dairy cattle from approximately 800 to 2300 animals over the study period resulted in a 20.0 percent increase in total CH4 emissions from livestock . 

Using the Tier 1 method prescribed by CARB, emissions of CH4 from rice cultivation were estimated to increase from 25.9 to 31.2 kt CO2e between 1990 and 2008 . This increase was entirely due to a 20.3 percent expansion in the area under rice cultivation . Estimates generated using the DNDC model showed a larger increase in emissions over the study period ; this Tier 3 method accounted for changes in residue and water management in addition to the increase in cultivated area . Emissions of N2O and CH4 from residue burning contributed 2.0 percent to the total agricultural emissions in 1990 and declined to less than 1.0 percent in 2008, due to the phasing out of rice straw burning in accordance with State regulations . Emissions of N2O and CH4 were relatively small compared to the amount of CO2 emitted during combustion . Most inventory guidelines consider CO2 from residue burning to be a “bio-genic” emission, arguing that it is theoretically equivalent to the CO2 generated during the decomposition of the same crop residue in the soil over the course of the year . Consequently, CO2 from residue burning has been excluded from our inventory total. Emissions of CO2 from lime and urea application each contributed approximately 1 percent to the overall agricultural emissions, and both declined over the study period .One of the main findings of this study is that emissions from agriculture in Yolo County were already on the decline long before the implementation of recent mitigation policies. This trend is largely market‐driven, arising from broad economic factors that are prompting local farmers to shift more of their land to crops which happen to require less N fertilizer and diesel fuel. For instance, many local farmers point to the strong markets for wine grapes and alfalfa, which require fewer inputs as the main factor behind their recent local expansion . These Tier 1 methods do not fully capture the extent to which some growers are reducing fertilizer and fuel use in response to the rising cost and market volatility of inputs, rather than mitigation per se . It should be noted that interviews with Yolo County growers have documented numerous strategies to decrease energy use in cost‐effective ways, but they are often not yet integrated into the cost and return studies for Yolo County crop production .Another important factor contributing to the overall reduction in agricultural emissions was the 8,000 hectare decline in irrigated cropland. This loss of irrigated cropland raises two important questions. First, what type of land use is the cropland being displaced by? And second, how does the carbon footprint of other land uses compare to that of agriculture? Four countywide land‐use trends may explain the decline. Cropland could either be: left fallow, converted to non‐irrigated rangeland, restored to natural habitat, or developed for urban and industrial use. Shifting land use from irrigated cropland to fallow, rangeland, or natural habitat will generally reduce anthropogenic GHG emissions. The same cannot be said for cropland that is developed for urban uses. Urbanization accounted for the loss of about 6,500 acres of agricultural land between 1992 and 2008 . In 1990, emissions sources associated with Yolo County’s urban areas accounted for approximately 86 percent of the total GHG emissions countywide, while unincorporated areas supporting agriculture were responsible for the remaining 14 percent . If calculated on an area‐wide basis the county’s urban areas emitted approximately 152.0 t CO2e ha‐1 yr‐1 . By contrast, our inventory results indicate that in 1990 Yolo County’s irrigated cropland averaged 2.16 t CO2e ha‐1 yr‐1 and that livestock in rangelands emitted only 0.70 t CO2e ha‐1 yr‐1 . This 70‐fold difference in the annual rate of emissions between urbanized land and irrigated cropland suggests that land‐use policies, which protect existing farmland from urban development, are likely to help stabilize and or reduce future emissions, particularly if they are coupled with “smart growth” policies that prioritize urban infill over expansion .While avoided conversion of farmland will help curb emissions from urban sprawl, keeping farmland intact also affords numerous opportunities to mitigate emissions through changes in agricultural practice or by sequestering carbon in soils, perennial crops, or woody vegetation. In considering mitigation options, strategies should not hinder adaptation to climate change, as this could lead to loss of agricultural viability and potential urbanization, a much greater source of GHG emissions per acre.

The lower metal-humus complexes of Andisols in the present study as compared to Andisols from Réunion Island could be associated with the higher annual rainfall and temperature under tropical conditions that accelerated organic matter decomposition. In our study the negative correlation between soil pH and Fep and Alp was observed indicating favorable conditions for organo-metal complex formation under acidic conditions . Tonneijck et al. suggested SOM stabilization in volcanic ash soils in natural Andean ecosystems of Ecuador is through organo-metallic complex formation, low soil pH and toxic levels of Al, and physical protection of SOM in a very large micro-porosity.A place‐based approach for studying agricultural responses to climate change explores a broad set of biophysical and socioeconomic issues related to both greenhouse gas emissions and to adaptation to an uncertain climate. Few such studies exist. Instead, the scientific research on agriculture and climate change has focused on agricultural management practices to reduce the GHG emissions of carbon dioxide , nitrous oxide , and methane , or on the vulnerabilities of different crops to changes in seasonal weather, water supply, pests and diseases,pe grow bag and biophysical factors affecting agricultural production . These are only a few of the aspects necessary for planning for climate change in agricultural regions.

As many jurisdictions in the Western United States are now addressing regional impacts of climate change, there is a need for science‐based exploration tools for scientists, farmers, policymakers, and the general public to better understand the complexity of vulnerabilities and adaptation options for increasing agricultural sustainability in rural landscapes. California’s Climate Change Scenarios Project has focused on determining impacts from plausible climate change scenarios . Use of Global Circulation Models for future climate projections have used two scenarios from the International Panel on Climate Change that are based on story lines for high and low GHG emissions . For agriculture in California, climate change will have impacts on water availability, crop physiology, production , and pest and disease problems , especially for the A2 scenario by the end of this century. Addressing agricultural vulnerabilities and adaptive capacity is part of California’s new statewide climate adaptation strategy. A place‐based vulnerability approach deals with climate change as one of many other long‐range issues such as changes in commodity production, stewardship of natural resources, land use, population growth, and urbanization in a regional system. The capacity of a rural population to adapt with climate change and other uncertainties depends largely on its collective ability to assemble and process information and respond in site‐specific and context‐relevant ways .

Adaptive strategies will require input from many disciplines, including agronomy, ecology, economics, land use planning, and political science. And the involvement of multiple types of stakeholders must inform the assessment and planning process, so that adaptive management can proceed in response to a knowledge base that is continuously developing . The strong science‐policy interface for climate change in California has generated a great deal of agricultural interest in the implementation of the law to reduce statewide GHG emissions, California Assembly Bill 32 , known as the Global Warming Solutions Act of 2006.1 Under AB 32, the state’s GHG emissions are to be reduced to 1990 levels by 2020 through mandatory reporting, emission limits, and reduction measures, as implemented by the California Air Resource Board. It also establishes a goal of 80 percent reduction by 2050 and proposes a cap‐and‐trade policy for GHG emissions. Agricultural GHG emissions will not be included in the cap, but there may be potential for trading carbon offsets from agricultural practices. Senate Bill 375 connects land use planning with implementation of AB 32. It requires a Climate Action Plan for mitigation of GHG emissions in the unincorporated areas of each county in California. This process is engaging farmers and other agricultural stakeholders in detailed accounting of GHG emissions from production and processing practices, and thereby beginning to create greater awareness of vulnerabilities and adaptation options as well.

Yolo County is in the Sacramento Valley of Northern California. It extends westward from the Sacramento River to the Coast Range Mountains . The most important crops are tomatoes, alfalfa hay, wine grapes, and almonds. Upland summer‐dry grasslands and savannas are grazed by cattle. The few small towns and cities have experienced a changing mixture of urban, suburban, and farming‐based livelihoods through the past few decades. In Yolo County, there are approximately 500 farms with an average size of about 500 acres . Many farms produce sales ≥$100,000 per year. Yolo County is ranked 23 by value of sales of California’s 58 counties . Roughly 2 percent of the county’s production is consumed within the Sacramento region . The 653,452 acres of Yolo County are largely agricultural . Important farmland is 57 percent, and livestock grazing land is 24 percent, while urban and built‐up land is only 4.6 percent of the county’s acreage . During the past few decades, there has been a trajectory toward less crop diversification of county acreage, larger farm sizes, but fairly stable markets for commodities . Most commodities are managed with high intensification of agricultural inputs . The number of organic farms, however, is growing. A recent survey showed that many riparian corridors have low scores for soil quality and riparian health , and there is concern about transport of pesticides to the San Francisco Bay delta . Environmental quality is now receiving more attention, with active participation in programs from several agencies. Preservation of agricultural land has been a strong priority in Yolo County, and planning is focused on regional land use guidelines that maintain land in agricultural production and concentrate new development into urban areas . Regions within Yolo County are distinguished by their land forms , proximity to the Sacramento River and Delta , water availability , and the influence of small towns and cities. The regions differ in crop commodities. There is greater prevalence of wine grapes along the river, processing tomatoes in the alluvial plains, and organic fruits and vegetables in an isolated, narrow valley to the north. The regions also have different trends and targets for urban growth, rural housing, and wildlife habitat creation. Flooding along the Sacramento River poses the most significant regional hazard from climate change; water flows will increase by at least 25 percent by 2050 due to a decrease in snow pack in the Sierra Nevada .Climate simulations by Global Climate Models show that mean annual temperature will rise by 1°C to 3°C by 2050, the time frame of this case study . Heat wave days will increase two‐ to three‐fold by 2050. Precipitation is likely to decrease toward the end of the century, depending on the assumptions of each GCM. Hydrological changes suggest, however,growing bags that drought is already increasing and will become more severe and variable with time . Water supply has been considered the most uncertain aspect of climate change for farmers in Yolo County, who rely on groundwater for approximately 30 percent of their supply in a normal water year . It should be emphasized that GCM models are not “predictions,” but rather, are plausible scenarios of climate sequences over a long‐term period. The previous phase of this case study examined possible impacts of increased temperature and decreased precipitation on Yolo County crops . Horticultural crops will likely experience more problems from heat than field crops, due to greater temperature sensitivity of their reproductive biology, water content, visual appearance, and flavor quality . A warmer temperature regime is likely to shift more “hot‐season” horticultural crops, such as melon and sweet potato, into Yolo County’s horticultural “warm‐season” crop mix .

Warmer winter temperatures may allow “cool‐season” crops such as lettuce and broccoli, whose short growth seasons could permit two crops per year, unlike winter grains at present. Expansion of citrus production , and of heat and drought‐tolerant trees, such as olive , are likely options especially because reduction in winter chill hours will reduce flowering in stone fruits, nuts, and grapes . During the past 25 years, crop diversity has decreased in Yolo County . Diversity may increase if farmers find that resilience, especially to extreme events such as heat waves, is enhanced by a species mix that varies in stress tolerance . Forage production for livestock in upland grasslands and savannas may increase with warmer winter temperatures during the winter rainy season, but field experiments with elevated CO2 do not corroborate this expectation . More nitrogen limitation will likely occur under eCO2 . If N‐fixing legumes become more abundant in response to warmer winter temperatures, however, the N supply will increase. Thus, it is unclear if livestock production on these rangelands will actually increase due to climate change, especially in dry years, which require lower stocking rates, earlier animal removal dates, and transport to irrigated, permanent pasture.Pests and diseases are another major uncertainty: warmer temperatures can increase ranges and population sizes, and change the trophic interactions that currently provide biological control of invasive species . At present, no comprehensive compilations from California Department of Food and Agriculture or the National Plant Diagnostic Network exist to show new invasive species to target for a warmer climate . Some literature suggests that it is more efficient to focus on the spread of already naturalized species rather than from new potential invasive species at the importation stage . Yet, the Yolo County Agricultural Commissioner, John Young , notes that several recently arrived pests are becoming severe problems, such as the European grapevine moth in vineyards, spotted wing drosophila on cherries, and Japanese dodder on a wide range of cultivated and wild land plant species. Quarantines are especially difficult for Yolo County because so little of the crop production is consumed within its boundaries, and thus economic hardship occurs unexpectedly for all growers of a particular commodity. Discussions with the Yolo County University of California Cooperative Extension farm advisors indicated special concern for stripe rust on wheat , insect pests on nuts, medfly, corn ear worm on tomato, tomato spotted wilt virus, and earlier activity of perennial weeds such as bindweed . Very recently, alfalfa stem nematode has become a serious pest in the Sacramento Valley, possibly because winter minimum temperatures have reached the lower limit of reproduction for the species . On the other hand, some pests may become less serious; high summer temperatures are likely to reduce the fecundity and survival of the olive fly in this area, which will cause olive yields to increase . Decisions on strategies for adapting to these types of climate change vulnerabilities are not only made by growers. Public institutions, researchers, and non‐governmental organizations become involved in decision‐making by gathering information, stimulating awareness, and generating collective action. At present, California’s strong emphasis on reducing GHG emissions suggests that mitigation and adaptation should be dual components of climate change decision‐making. Some authors have made the case that most categories of adaptation measures have positive impacts on mitigation of GHG emissions . This may be too optimistic. First, agricultural soils may emit more potent GHG in a future CO2‐enriched atmosphere . Second, detailed analysis of crop management may show trade offs between mitigation and adaptation goals. An analysis of benefits of different management options for mitigation and adaptation benefits in Yolo County showed that synergies are often complex . Changes in crop diversity, irrigation methods, fertilizer management, and tillage practices often are more beneficial for either mitigation or adaptation. Rather than change a single practice, major changes in cropping systems will be needed to meet production and mitigation goals. For example, a conventional tomato system with furrow irrigation and knife injection of fertilizer emitted 3.4 times more N2O and had lower yields than an integrated tomato system with drip irrigation, reduced tillage and fertigation on the same soil type . But drip irrigation, unlike furrow irrigation, does not recharge groundwater,leaving farmers more vulnerable to long‐term drought. More comprehensive analysis of these complex relationships is needed.Analyzing changes in past crop acreages in relation to local climate history can provide a set of projections of potential climate‐induced changes in cropping patterns based on how farmers have responded to past climate change.

Participants mentioned that specific technologies or technical messages were often provided to farmers through interaction with the SANE representatives tackle agricultural and climate adaptation challenges with a focus on nutrition-sensitive agriculture. SANE representatives are also members of the NACDC. CGIARs and NACDC have in-degree scores of 4 and 3, respectively. Several participants noted that CGIARs work closely with Malawi’s technical agriculture departments to develop technologies that can be scaled up. A government representative from DAES noted that in some cases CGIARs will bring a new technology to Malawi that has yet to be tested locally. In those cases, the technology will first be analyzed by the appropriate MoAIWD department before it is disseminated throughout the extension system. Several participants noted that new technologies developed by international institutions like the CGIARs will be presented to extension stakeholders during a meeting of the NACDC. A Malawi NGO participant noted that it could take up to three years for the NACDC to analyze and disseminate a new technology because of the thorough process undertaken by stakeholders on the NACDC.

Another participant noted that the NACDC is spearheaded by representatives of MoAIWD and allows extension providers in the public sector, NGOs, farmer organizations,black flower bucket and the private sector to harmonize the messages that are disseminated to farmers. A Malawi NGO participant commented, “we always want to make sure that that the thematic areas that have been developed by the National Agriculture Content Development Committee are things that can be put in place at district level and demystified to fit the conditions that are prevailing on the ground for each district.” It is also worth noting that several participants had never heard of the NACDC and two others had only heard of the committee by name but did not know its function or membership. DCP was also common content developer referenced by participants and has an in-degree score of 3. Participants explained that DCP develops new crop technologies for farmers to use stating, “the crops department along with the agriculture researchers are responsible for developing technologies which we disseminate to farmers for their interventions.” In order to increase the production of crops like maize, DCP will partner with research institutions to develop new seeds and methods from increasing yields. Similarly, several participants mentioned that agricultural technologies are developed by MoAIWD’s technical departments and then disseminated to farmers through the public extension system.

Stakeholder Engagement .This section provides an analysis of the relationships within Malawi’s extension network that were described by participants in order to answer the research question, “how do extension providers engage and share information with other organizations to address climate change?” The network analyzed through this research contains 85 organizations and 170 unique relationships or ties between those organizations. The average degree of collaboration between organizations within the network is 6.2 or 7.3% of total organizations in the network. Therefore, most extension providers operating in Malawi are only connected to a few organizations in the network. The network density is .037 and reveals that organizations within the network are not as closely connected as they could be. The relational ties and node connectivity within Malawi’s extension network ties as described by participants through a sociogram are shown in Figure 7.Both one direction and two direction relationships that were described by participants with the size of node indicating the level of betweenness with other organizations in the network are illustrated in Figure 7. Thirty-eight out of the 85 organizations in the network, or 45%, are only connected to one other organization and are therefore operate within the periphery of the network. These 38 organizations are also seen on the edges of the network and therefore less connected to the greater extension community. It is also clear that certain organizations are central to the transfer of information within the network. Centrality measures for the 10 core organizations with the highest degrees of collaboration and information sharing within the extension network are shown in Table 5 . The average information in-degree is 3.1 and average information out-degree is 3.1.ADDs also have the highest information betweenness of 1936 which shows organizations that hold the network together and collaboration degree of 42 which shows the total number of ties of the organization.

The top three organizations identified are government and include ADDs, DAES, and MoAIWD. Other top organizations include those from Malawi NGOs, international NGOs, and farmer organizations. NACDC, Catholic Relief Services, and NASFAM also have high collaboration degrees indicating their importance in sharing information and connecting organizations within the network. Catholic Relief Services has the second highest betweenness score of 1350 indicating that this organization is situated between a large number of organizations and provides a connection between a high number of organizations that otherwise might not be able to share information as easily. Farmers have one of the highest out-degree collaboration degree , and betweenness score . This shows that farmers are critical links of information sharing between organizations in extension network. The third research question posed in this study and addressed in this section was, “what advisory methods do extension providers use to educate maize farmers about climate smart agricultural practices?” The advisory methods used by extension providers to communicate messages to farmers about climate smart agricultural practices are categorized into three distinct themes: ICTs, trainings, and written materials. The percentage of participants by organization type using each advisory method is shown in Table 6. Of the ICTs used by participants, radio was the most commonly referenced communication channel. Almost 58% of participants reported using radio platforms to disseminate messages to farmers including community radio and national radio stations often utilizing radio listening clubs comprising small groups of farmers with radio access within a village. According to a participant from the Malawi government, “when programs are aired on the radio, we encourage our farmers to go to their radio listening groups. So, if one farmer has a radio in a village, then that radio is used by a group of farmers to listen to the messages that are on the radio.” Organizations also utilize radio platforms to share weather forecasts and provide other timely messages to farmers. According to a participant from an international NGO, “we’re disseminating messages on climate change and weather forecasts through radio. They learn about the things that they need to know before the disaster comes.” The use of mobile phones was the second most common ICT referenced by participants. Short Message Service messages are sent directly to farmers by extension providers and are used by 53% of participants.

Participants noted that SMS allows farmers to receive both text messages and Interactive Voice Response messages. A representative of the Malawi government noted, “We send messages using phones and we reach about 24,000 Lead Farmers across all the districts.” Once Lead Farmers receive text messages, they will then share the information with fellow farmers in their locality. Participants also commented that SMS platforms often complement one another and increase access to information. Internet platforms are used by 32% of participants and included social media, WhatsApp groups, and videos. A participant from the Malawi government commented, “Some content is now being accessed online. We partner with Access Agriculture. Access Agriculture has a lot of videos that have been translated into Chichewa, the local language.” Increasingly,square black flower bucket organizations are leveraging internet content to disseminate information to farmers and increase knowledge about new agricultural technologies. According to a representative from an international NGO, “we have WhatsApp groups where farmers are able to share videos of the technologies that they’re learning. I’m also able to post some videos on nutrition or soil fertility management. These technologies are being promoted in order for them to increase production.” The use of these internet platforms broadens the source of content and allows for the rapid transfer of knowledge with those who have access. Almost 16% of participants mentioned using television programs to communicate messages to farmers. Participants commonly noted that farmers receive information about weather conditions from television weather forecasts. In other cases, documentaries are produced and then aired on television to disseminate information about agricultural technologies and innovations. One government representative noted that his department is responsible for the development and dissemination of content through electronic media including television. Call Centers and Mobile Vans are used by 11% of participants. Call Centers allow farmers to ask questions and receive quick feedback from extension experts. A participant from a Malawi NGO commented, “with the Call Centers, farmers can call for free to ask any question relating to their farming activities. That provides near real-time feedback to farmers for whatever inquiries they have.” Several participants from the Malawi government also noted using Mobile Audio Vans to disseminate various messages to farmers in the field. One stated, “if we want to reach out to the masses with some awareness messages that are very simple and not technical, we could use Mobile Vans. A Mobile Van is effective just to communicate simple issues on awareness.” Yet a Mobile Van might be less effective at communicating highly technical messages that require training and real-time feedback from farmers.

About 90% of participants mentioned using some form of trainings to communicate information to farmers and this was the most common advisory method used among extension providers. Common training methods utilized by participants included Lead Farmers, Farmer Field Schools, site visits, demonstrations, and the Model Village Approach. Trainings facilitated by Lead Farmers are common among NGOs, the government, farmer organizations, and private sector participants. Participants mentioned that Lead Farmers train up to fifty farmers at one time depending on their own skills, experience, and the community’s needs. One private sector participant noted that Lead Farmers are regarded as “knowledgeable individuals who can lead their fellow farmers.” Other participants vocalized additional benefits of Lead Farmers including high rates of technology adoption and trust among community members. Lead Farmers within this structure have different responsibilities to their villages, EPAs, and Districts. Lead Farmers involved in the Executive Committees of the Association may also hold substantial decision-making power and influence. Another participant noted that Farmer Field Schools are often used, “when we have specific issues that we want to deal with or when you have a particular problem that farmers face and it requires a lot of time to study that problem, look at the causes, and look at various ways of overcoming that issue.” The length of farmer trainings mentioned by participants varied from a single, one-hour to several, week-long trainings and the number of training participants ranged from ten to thirty. One participant from a farmer’s organization described the detail and intentionality of planning and facilitating a Farmer Field School Training. This participant explained the steps their organization takes including 1) determining the focal audience and their needs; 2) identifying the training’s objectives; 3) creating a workshop outline including proposed activities; 4) developing training support materials; 5) facilitating the training; and 6) implementing monitoring and evaluation components for future training improvement. The use of site visits and demonstrations were also mentioned by some of the participants as a way to train farmers. Participants explained that farm or site visits allow individual or small groups of farmers to receive feedback or learn about a new technology to adopt on their farms. Demonstrations allow farmers to observe the results of a new technology that they can choose to implement on their own farms.Several government participants also mentioned using the Model Village Approach to promote new agricultural technologies and innovations within a certain village. One government representative noted, “we conduct training on participatory rural appraisal or PRA with farmers in the Model Village. We also mount demonstration in the village depending on the technologies we are promoting in that particular village.” After observing a demonstration from another Model Village, Lead Farmers are often instructed to establish a model village back in their own villages. Participants noted that this process can help to facilitate widespread adoption of a particular technology among members of a village who are closely connected. Written materials are also common and were mentioned by 63% of participants. Written materials used to communicate messages to farmers included leaflets, extension manuals, and newspapers. A private sector participant noted, “it is the extension workers who use the government extension manual for promotion of modern agriculture production technologies including the right farm inputs.

There has been an overall agreement that past policies have been inefficient and that income-support policies “overshot” in assuring and providing food supply and have become too costly and barely affordable. This suggests the need for “decoupling”-setting entitlements criteria that do not affect production while attaining other objectives. Government policies have resulted in substantial excesses of the productive capacities of agriculture in the United States, Western Europe, and other developed countries. A policy refornl should consist of two elements. First, it will induce gradual down scaling of agricultural sectors of these countries so that, within a transitional period of, say, 10 years, agricultural markets will attain a sustainable set of equilibria which improves welfare and the performance of the agricultural and food production sectors. Once agricultural markets approach these equilibria,plastic flower bucket government policies will operate to attain sustainable growth in the future and make the agricultural sector more flexible and progressive. The design of a policy reform requires the identification of effective policy instruments and the development of procedures for setting their levels as well as for their enforcement, monitoring, and adjustment over time.

The assessment and selection of policies should be done within a decision-making framework that operates to increase economic efficiency while recognizing and incorporating political economic constraints. The next section introduces such a framework and spells out its implications regarding information and assessment criteria for the policy instruments’ selection. The paper, “Building Sustainable Coali tions for Welfare Improving Policies,” argues that efficiency and equity considerations have to be balanced when designing a policy reform that is both welfare improving and politically feasible. Thus, a framework for the determination and analysis of pol icy reform should include functional relationships measuring the economic welfare effects and political feasibility of policy instruments. Welfare economics provides justification for the use of appropriate summation of economic surpluses as a measure of economic welfare and efficiency. The recent literature on political economy provides alternative frameworks for modeling the politically feasible set of agricultural policies. The model here applies assumptions and fonnulations that are similar to the ones used in recent empirical political economic models of agricultural policy choices . Specifically, it assumes that a policymaker presents a propos:ll for a vote by a legislative body or even a referendum by the general population. The choices of the voters are assumed to be affected by the impacts of the policy on the welfare of different groups they represent, belong to, or support. It is also assumed that the policymakers cannot predict voters’ responses to policy proposals and that proposals are shaped so that the voting uncertainty is controlled.

Surplus measures can be utilized to obtain the distributional effects of policies within markets. For example, analysis of the market effects of a pesticide control policy can apply surplus measures to estimate the impacts of the policy on consumers, producers in different regions , pesticides, other input manufacturers, etc. . While applications of this type of analysis seem straightforward, note that appropriate incorporation of international trade considerations, monopolistic and oligopolistic behavior, and imperfect information and uncertainty consideration within this framework requires much ingenuity and effort. Still, these distribution effects can be presented in clearly defined monetary terms. Quantification of nonmonetary effects of policies might be very difficult, and expression of these effects in monetary terms is sometimes impossible. For example, a pesticide control policy may affect the health and well-being of growers, pesticide applicators, farm workers, and consumers; and the estimates of such effects are Subject to very high degrees of uncertainty . There is much controversy regarding the appropriate procedure for monetary assessment of days lost due to diseases and lives lost due to accidents . Therefore, for analysis, it may be useful to present estimates of the real impacts of policies on health and lives with a given degree of statistical significance and not mone tary estimates. The distributional effects of policies are represented by the function Win the model.

The model suggests that assessment of political support for policies also requires knowledge of H, the weight that different impacts have in shaping voting be havior. Several studies have applied econometric techniques such as logit and Tobit to congressional voting data to estimate impacts of certain factors on voting behavior. These studies clearly show that voting pallerns of individual representatives are consistent with the economic interest of the regions represented by these representatives. Further applications of these kinds of models, using appropriate measures of distributional effects as explanatory variables, are needed to allow better prediction of voting patterns in response to policy changes. etermination of a policy that values the decision problem in requires knowledge of the efficiency and distributional impacts of alternative policy instruments. The assessment of these policy impacts should not be restricted to analyses of impacts of individual policy instruments but should include assessments of the impacts of policy instl’uments’ mixtures. Policy instruments may have complementarity of substitution relationships. They operate jointly and in correlated manners to affect distributions of key variables such as farmers’ earnings and food prices and quantities. These correlations and dependency relationships suggest that prices of a policy mix are likely to be different sums of outcomes and impacts of the individual components. Just, Lichtenberg, and Zilberman have demonstrated that independent management of grains and water inventories by uncoordinated activities of government agencies may lead to substantial welfare losses. Lichtenberg and Zilberman have shown that the market efficiency impacts of environmental regulations in agriculture are likely to be substantially overestimated when the existence and effects of commodity programs are ignored.

Therefore, policy analyses and policy design should develop mechanisms for coordinated management of policy instruments and also assess impacts of policy mi xtures. he construction of a policy reform is an intensive and iterative process involving several rounds of negotiations at different levels. There are many participants in this process including government officials whomay initiate the process, representatives of interest groups , and the voters. These voters are not passive participants in the process but, rather, active participants in negotiations and modifications of proposals. There are many who view these process as a game with many participants and who analyze its outcome accordingly . The model presented above may provide the agencies which initiated the policy reform with a good .initial proposal and help to guide them in negotiations and when updating reform parameters. Figure 2 illustrates how the approach presented here can be incorporated in the iterative process of shaping the policy reform. First, a data collection and modeling effort will be taken to estimate parameters needed to predict impacts of alternative policies and the political importance of different power groups. Parallel to these data collection and estimation efforts, different interest groups and their political representative organizations should be identified. These available information will be used in the establishment of an initial proposal that will be negotiated with representatives of interest groups and other political power brokers. The negotiation process may lead to either passing of a proposal or to a reassessment of political realities,flower buckets wholesale redesign of policy, and a new round of negotiations. These negotiations will continue until an acceptable formula is reached. As both political and physical realities are changing over time, key elements of the policy reform will have to be modified and updated. Thus, the legislative process described in Figure 2 wi 11 have to be conducted once every 5 or 10 years. The modeling approach presented here will provide a consistent and systematic framework for assessment of realities and introduction of policy changes. Some of the classic works on the economics of public policy view policy design as the choice of instruments to allain social objectives. One of the implications of the model presented here is that policies are evaluated by their contribution to efficiency and the weighted welfare of different groups in the economy, where the weights reflect the political power of the different groups. Since surplus measures are used to evaluate the groups’ welfare, interest groups are designated in correspondence to goods analyzed in the model. Thus, producers of corn in different states, consumers of milk, and users of water which was contaminated by wheat production are examples of the types of interest groups treated by the model. Measures of welfare of these types of groups are likely to be related and even expressed in terms of objectives such as producers’ income, affordable food, and water quality. Hence, the model implies that policy instruments are selected in a way that maximizes social welfare as a multidimensional function of policy objectives. Thus, the outcome of the “political economic” framework presented here C:1Il be expressed in terms of the Tinbergen framework.

In the next section, the impacts of some of the instruments proposed as part of the policy reforms are analyzed qualitatively. Impacts of policy instruments will be analyzed in terms of policy objectives and in terms of welfare of certain groups. This is a “safety net” program that assures farmers a certain income floor. Entitlement for the program will depend on income, wealth, and a socioeconomic factors . This program may have two goals. One is to assist the very poor and raise their income and living standards constantly. The other is to assure rural citizens adequate income at low points of the business cycles. The two goals may require separate mechanisms and are likely to be relevant for separate populations. The first goal is a standard antipoverty goal and is appropriate for the very poor and disadvantaged in the rural sector. It has very little to do with agriculture since the very poor gain very little income at all limes. The second goal is to protect farmers and other members of the rural community from “hard time” periods when prices are low, yields are low, etc. Farmers may not be in the greatest need for such a program since they are protected by crop insurance, may use futures markets, etc. Individuals who provide services to farmers-small dealers, farm workers, etc.-are at least as vulnerable economically at the downside of agricultural business cycles as farmers; and such programs may protect them. One way to prevent misuse of this anticyciical income assurance program is to make it a subsidized insurance program. Eligible individuals-farmers, farm workers, and agro-businessmen-may need to buy the rights for this income-support program by paying a fee or a premium. The goal of assuring minimum income to all members of most sectors may be obtained best within the framework of general assistance programs that exist in many countries. Such programs may have stipulations that may discriminate against the rural sector . As agricultural support programs are being down scaled and eliminated, the range of entitlements of farmers and rural people for the benefits of general welfare programs should increase. The minimum income assurance program mentioned above is, in essence, a welfare program applicable only to part of the farm sector’s population. However, low farm product prices affect all members of the farming community and reduce farm incomes across the board; revenue assurance programs address this issue. In the program considered here, each entitled farmer is assigned a target revenue and an output base and receives the difference between the target revenue and output base times actual price when this difference is positive . This program is essentially a deficiency payment program where each farmer is assigned a revenue base instead of a yield and acreage base. The key element of this program is that it will be set for a long time, and the revenue basis will not be modified according to past behavior. Disallowing modification of the base according to performance will serve to reduce the impact of the program on supply . It is difficult not to modify entitlement bases as time passes since there are dynamic changes in land allocation, and base assignments need to resemble reality. Therefore, one cannot maintain a decoupled revenue support program forever. Such a program will be used for a transition period when agriculture is down scaled and the range of support provided by the program will gradually decline.

Micrographs and panoramic images were taken with an FEI transmission electron microscope at an acceleration voltage of 80 kV with a TVIPS TemCamF416 digital camera using the software EM-MENU . Panoramic images were aligned with the software IMOD.Sharp drops in employment have characterized the economic transition occurring in Central and Eastern Europe. Their persistence has been a surprise to those who anticipated rapid economic recovery . The situation in Bulgaria echoes that in other countries and is of political concern because of its impact on people and the country’s well being. The symptoms are well known . Overall employment dropped by 28 percent between 1989 and 1994, and agricultural employment declined by 13 percent. The principal reasons for these reductions are the abrupt decline in production and the restructuring of Bulgaria’s economy. But there are offsetting factors, such as technological changes, that have slowed the employment loss. An understanding of the dynamics of unemployment is needed if remedial strategies are to be developed. The research reported here adds a component to the extensive review of Bulgaria’s agricultural transition by Schmitz et al. Its focus is on rural employment and its relation to agriculture.

Non-agricultural employment fell more rapidly than agricultural employment but the impact of this on rural areas is not well understood. Agricultural employment declined less rapidly than did production,plastic pot manufacturers suggesting a shift in technology and the emergence of under-employment. There is inadequate information about which groups have been most affected or what has happened to the people. National data provides only a partial picture of this situation and therefore is an inadequate base for public policy choices. Further, there is the question raised by Bartholdy about the ability of data systems to retain accuracy during a period of fundamental change to a country’s economic system. The problem is how to obtain sufficient information about rural employment to permit better policy choices to be made. Two approaches have been followed to improve the data base for policy decisions. The first approach involves comparing agricultural employment with scientifically determined labor requirements for agricultural production. This permits measurement at the national level of how the surplus of agricultural labor changed between 1989 and 1994. The result helps in isolating the impact of output changes on employment from technology and other changes, and helps identify the size and character of the agricultural labor pool. It also permits an estimation of the impact of future changes in productivity and production patterns.

The second approach uses local data, obtained through a survey of village households and cooperatives, to provide information about rural employment that is not available from existing national data. It helps determine how rural households, enterprises, and communities have been affected by production and employment changes and how they might be helped. The village survey, conducted in the Summer of 1995, provided unique data that are being reported here for the first time.The problems and policy implications of unemployment in Central and Eastern Europe have been studied extensively, primarily at the aggregate rather than sectoral level . Some studies focus principally on policy or statistical issues . Important attention has been given to the dynamics of unemployment, including its persistence, and the flow between vacancies, unemployment and jobs. These studies examine policy implications of changes in productivity, real wages, employment and labor force participation rates . Other studies have emphasized the link between unemployment, income, and poverty . Relatively few studies have focused on Bulgaria and they tend not to emphasize agricultural and rural employment . The European Union study briefly analyzed the agricultural labor situation, using national statistics to comment on labor inefficiency. Mihailova computed national agricultural labor requirements using norms developed through studies of agricultural production processes. Sotsiologicheski Pregled, in a special issue, provided an overview of poverty, unemployment, and social policy as it affected rural and urban areas in Bulgaria.The practical problem of evaluating the employment situation in Bulgaria is tremendously difficult.

Rock lists 8 external and 5 internal factors affecting employment in Bulgaria, and concludes that transition has been hindered by an enormous number of external and internal constraints. Boeri commented that “conventional wisdom does not seem to offer many clues to the factors lying behind the dynamics of unemployment in CEEC.” Just the same, theory offers a framework for classifying the variables influencing labor demand and can be helpful in guiding the way through the complex network of cause and effect. The demand for agricultural labor is a function of output , wages, other input costs, and the structure of agriculture. Supply is a function of wages, opportunity costs , population, and the institutional structure within which decisions are made. Employment changes in Bulgaria, as in the rest of central and eastern Europe, have been caused by reductions in labor demand rather than reductions in its supply. First, output of agricultural produce has fallen because both domestic and foreign demand has fallen. Second, labor has become cheaper relative to other inputs and stimulates the use of more labor intensive technology. The third factor is the restructuring of agriculture through the privatization of land and the liquidation of collective farms that affects both technology and output. Both domestic and foreign agricultural demand have contracted, causing the agricultural demand curve to shift to the left. Given the generally inelastic nature of agricultural demand, prices would have had to drop precipitously to maintain the same volume of demand. Other factors shifted the aggregate agricultural supply curve, shifting it upwards and to the left. These included the increase in input costs relative to output prices and the disruptive effects of farm restructuring. The net effect of these shifts was that output declined, the real value of output dropped, and agriculture’s contribution to GDP fell. Since the focus here is on labor demand, the shifts in agricultural demand and supply are taken as exogenous. The effects of the change in wages relative to other input costs can be analyzed in the standard neoclassical two-factor model in which the demand for labor and capital depends on their relative values, w Ir, and on the level of output, Q. The pre-reform level of labor and capital usage depended on these factors,black plastic plant pots wholesale which in turn were influenced by technology and by various policy interventions. Post-reform there are at least 3 shocks to the system. The first is the drop in output caused by demand and supply shifts and agricultural restructuring. This shifts the “Q” isoquant in the model inwards . Because of technology changes, the new isoquant may not be based on the same production function as in the pre-reform period. Second, the stock of capital is diminished because of deferred maintenance and the resulting accelerated depreciation and non-renewal of obsolete assets. The third change is the fall in the relative cost of labor. The first factor can be neutral, although in Bulgaria it is evident that restructuring has caused a change in technology usage. The second and third factors favor the substitution of labor for capital. To the extent that there is a rational economic response to the new wir value and the output level, Q, then more labor will be used relative to capital than was the pre-reform case. This phenomena is investigated by comparing changes in agricultural labor requirements with shifts in agricultural employment. Restructuring refers to a complex mix of changes in farm land ownership and organizational relationships used in operating farms.

The outcome of restructuring by the end of 1994 was a mixture of smaller cooperatives, large scale farming companies, private partnerships, and family farms. The essence of restructuring is that it creates enterprises with different mixes of management skills, technology,resource mix, and objectives. These, in tum, lead to different outcomes in the factor ratios employed and the level of productivity. The balance between labor shedding by the more commercially operated farms versus the labor absorption of the more numerous, labor-intensive family farms, will depend on the relative numbers in each of these categories. This report makes no attempt to isolate the impact of restructuring on labor demand, but rather concentrates on measuring the impacts of output and technology changes. Since the explicit nature of technology in 1994 could only be inferred in the absence of updated studies on production labor requirements, the effects of output and technology were estimated according to the following model. The ratio of agricultural employment to agricultural labor requirements in 1989 is considered the measure of pre-reform agricultural technology. This measure is multiplied by labor requirements calculated for 1994 and provides an estimate of what employment would have been in 1994 if technology had not changed between 1994 and 1989. The difference between this number and employment in 1989 is the loss in employment caused by the decline in output. The difference between this number and actual employment in 1994 is a measure of employment change created by technology and other changes.The national employment data used here reports all persons carrying out certain activities in public and private enterprises and receiving payments or income. The amount of work performed is not specified. The data exclude work performed by students, army, or others in agricultural brigades prior to reform. 2 This was predominately for harvest. Consequently, the employment figure for 1989 understates the number of people actually performing agricultural work while the data for 1994 are more nearly correct with respect to harvesting. The data also exclude labor performed on private plots, even if some of the resulting products were sold on the market. Agricultural production in Bulgaria dropped by an estimated 29 percent between 1989 and 1994, more than double the rate of decline in agricultural employment . Agricultural labor requirements, as discussed in the following section, declined at an even faster rate of 38 percent. The differential between these 3 rates indicates clearly that more labor was used per unit of output in 1994 than was the case in 1989. How can this be explained? First the production structure in Bulgarian agriculture has changed resulting in different scales of operation and probably more labor intensive technology. Although some case studies indicate that new specialized farm organizations can obtain greater yields and use less labor the average of Bulgarian agriculture in 1994 was more labor intensive than before. Secondly, the non-availability of student and military help after 1989 had to be offset by employed labor in 1994. Consequently, employment could not shrink as rapidly as output.Normatives are labor inputs measured in number of workers or time needed to complete component parts of some agricultural process. Norms are the summation of labor inputs needed to complete a determined volume of work or to produce a defined quantity of product under specific conditions. The calculations of normatives and norms are based on careful observation and analysis of labor-using activities. Those used here were developed or compiled by the Research Institute for Agricultural Economics from observations made throughout the country. With knowledge of the technology applied and the output achieved, one can use norms to estimate how much labor would be required in producing that output under perfectly efficient conditions. Normatives and norms evaluate the important factors in plant and animal breeding. In plant breeding, these include technical, organizational, agricultural, biological, phYSiological, hygienic, and natural factors. In animal breeding the factors include the kind, purpose, and productivity of animals, the quantity and quality of fodder, and the type and characteristics of equipment, machinery, and buildings.We calculated the average number of days worked per year by employed persons by multiplying 240 days per year by the ratio of labor required to persons employed. The number of days worked per year dropped from 191 in 1989, after adjusting for work provided by brigades, to 148 days in 1994. The relative drop would have been greater if the employee work had not been displaced by brigades in 1989. These data show clearly that the surplus of labor in agriculture increased and that the existing work force is, on average, a part-time work force. The difference in agricultural labor requirements calculated for 1989 and 1994 reflect three important changes: a significant drop in agricultural production; an important shift toward more labor intensive practices; and a decline in the average number of days per year worked by those employed.

Since Mn dust loading is likely to be on the casual pathway for some of our predictors of exposure, such as farm worker shoes in the home and proximity to agricultural use of Mn fungicides, we used a structural equation model to evaluate casual pathways of exposure in the model that included participants with Mn dust measurements. We constructed a structural equation model to simultaneously estimate Mn tooth levels and Mn dust loading as outcome variables, with Mn dust loading also included as a predictor variable in the Mn tooth model. Maternal smoking during pregnancy was associated with a 34% and 40% decrease in MnPN levels in models with and without Mn house dust loading, respectively. We excluded from the multivariable models those variables that were not significant predictors of MnPN , including traffic density, Mn outdoor air concentration, acres of lettuce near the home, estimated total dietary Mn and iron intake, tap water consumption, estimated prenatal Mn tap water concentration and Mn tap water intake, maternal country of birth, maternal education, household income, housekeeping practices,raspberry grow in pots and maternal hematocrit to hemoglobin ratio during pregnancy. The coefficient of determination was 22% for the model with Mn tooth measurements and 29% for the model including both Mn tooth and house dust measurements.

There was no spatial auto correlation between the residuals for either model . Table 3 also provides the proportion of the variance explained for the predictor variables from multi-variable models of MnPN for all children with tooth measurements and those with Mn measured in both teeth and dust . The number of farm workers storing shoes in the home , maternal smoking during pregnancy , prenatal residence on Antioch Loam soil and agricultural use of Mn fungicides within 3 km of residence explained the greatest amount of variability of MnPN in the model without Mn house dust loading. Maternal smoking , prenatal residence on Antioch Loam soil , the number of farm workers storing shoes in the home , Mn house dust loading and maternal farm work during pregnancy explained the largest proportion of variability of MnPN in the model for children that also had Mn measured in prenatal house dust. Using structural equation models, the same predictor variables were significant and no new significant predictors of Mn levels in teeth were identified. The percentage change and significance level was nearly identical for maternal smoking, maternal farm work and residence on Antioch Loam soil which were predictors of Mn levels in teeth. However, agricultural use of Mn fungicides near the home and the number of farm worker shoes stored in the home were significant predictors of Mn dust loading. As a result, the percentage change associated with an increase in Mn dust loading corresponding to the interquartile range was 17.4% in the structural equation model compared to 3.4% in the ordinary regression model because Mn dust loading now included the effects of agricultural Mn fungicide use near the home and farm worker shoes stored in the home.

We report that Mn levels measured in prenatal dentin using laser ablation inductively coupled plasma mass spectroscopy were associated with estimates of prenatal environmental Mn exposure. Our findings suggest that deciduous teeth provide a biomarker of prenatal Mn exposure that is available retrospectively for the study of Mn related health effects, which would be especially useful in case-control studies. We observed that agricultural applications of widely used Mn-containing fungicides, maneb, and mancozeb, contribute to higher Mn tooth levels in this population of children living in an agricultural community. This is the first study to evaluate Mn measurements in deciduous teeth as an age-specific indicator of exposure from agricultural or industrial use of Mn. The only previous study that assessed Mn exposure from fungicides found that pregnant women who reported pesticide spraying less than a kilometer from their house had significantly higher blood Mn concentrations in a community where apple orchards were sprayed with mancozeb.An evaluation of Mn concentrations in house dust found higher levels in residences located within 500 m of agricultural fields than residences located farther from fields.Ethylenethiourea measured in urine has been used as an indicator of occupational exposure to maneb and mancozeb.In the CHAMACOS cohort, ethylenethiourea was detected in 24% of maternal urine samples collected near the beginning of the second trimester suggesting maternal exposure to maneb occurred during pregnancy in this cohort.38 Our results add to the existing evidence that household proximity to farmland and parental occupational take-home increases children’s exposure to other classes of pesticides.

Importantly, agricultural-related variables such as farm work by the mother, storage of farm worker’s shoes indoors and agricultural use of Mn containing fungicides within 3 km of the residence were significantly associated with increased tooth Mn levels and along with maternal smoking explained the largest proportion of the variance in this cohort. Including Mn house dust loading in the ordinary regression model reduced the amount of variability explained by the number of farm workers storing shoes in the home and agricultural use of Mn fungicides, and based on a structural equation model this was a result of Mn dust loading being on the casual exposure pathway for Mn from these sources. Nevertheless, the predictors we identified explained only 22–29% of the variability in Mn levels in prenatal dentin suggesting that other unknown factors contributed to Mn body burden. Iron status and iron metabolizing genes such as hemochromatosis and transferrin may play an important role in Mn biomarker levels. Mn levels in blood were 12% lower among women carrying any variant allele of HFE than women with no variant alleles and these results were replicated in a knockout mice model, suggesting that HFE contributes to variability in Mn exposure biomarkers.Mn levels in hair and estimated ambient Mn air concentrations near a ferromanganese refinery in Ohio were significantly correlated only when HFE or TF genotypes were included in the models.Women with low serum ferritin levels had higher blood Mn levels than the normal group in Korea.A limitation of the present study is that we did not have information on iron-metabolizing genes HFE and TF or serum ferritin levels. We did not observe a relationship between maternal hematocrit to hemoglobin ratio or estimated dietary iron intake during pregnancy and MnPN. Although we had few mothers that smoked in our population , we observed significantly lower MnPN levels in children whose mother smoked during pregnancy in multi-variable models. One previous study also observed a negative relationship between smoking and Mn blood levels in the second trimester but not at delivery,30 planter pot while a national study in Korea also found lower Mn blood concentrations among current and former smokers.Similar findings have previously been reported in relation to placental transfer of zinc; umbilical cord blood zinc levels were lower in mothers who smoked during pregnancy compared to nonsmokers.Mn is an essential nutrient that protects against oxidative stress.As a result, Mn levels in blood may be lower in smokers and less available for fetal transfer in pregnant smokers. Further studies are needed to evaluate the relationship between smoking and biomarkers of Mn and to identify the mechanisms by which smoking reduces Mn transfer to the fetus. We also found higher MnPN levels in children whose prenatal residence was located on Antioch Loam, soil which can be high in manganese content.A previous study found an association between Mn levels in soil outside the residence and Mn concentrations in house dust, showing that Mn levels in the home can be influenced by Mn soil concentrations.Previous exposure studies found that Mn levels in children’s hair decreased with residential distance from a ferromanganese alloy plant, and residential duration and proximity to the plant explained 37% of the variance.A recent study using a new method for cleaning hair prior to analysis found significantly higher Mn levels in children living in the vicinity of active, but not historic, ferroalloy plant emissions.

Mn measured in personal air for 38 children living near a ferromanganese refinery were associated with distance to the refinery but Mn in blood and hair were not.Higher nitrogen dioxide concentrations, a proxy for motor vehicle emissions, have been associated with higher Mn levels in cord blood.We did not observe an association between MnPN and traffic density, which is relatively low in our study area, but we did see a borderline significant increase in MnPN with estimated outdoor Mn air concentrations in the model that included Mn house dust loading. We observed higher Mn levels in teeth during the second trimester than the third trimester while previous studies have found higher maternal blood Mn concentrations later in pregnancy.While maternal blood Mn levels fluctuate during pregnancy, they do not necessarily reflect variations in fetal exposure. The use of dentin Mn allows us to measure fetal Mn exposure directly and we observed higher Mn levels in dentin formed during the second trimester in comparison to dentin formed later in gestation. There are no known variations in tooth mineralization over this period that would affect Mn uptake in dentin, and it is possible that the higher Mn levels in dentin formed during the second trimester reflect increased fetal uptake. Future studies should assess the within person variability in MnPN using multiple teeth per child and evaluate Mn levels in different types of teeth that develop at slightly different times. This study had a number of other limitations. We did not have information on time activity patterns for the mothers and using only residential locations to assess proximity to Mn fungicide use and other Mn sources could result in misclassification of exposure. We did not collect personal environmental or duplicate diet samples to measure Mn exposure. Drinking water quality data was collected for regulatory purposes not to determine exposure levels and sampling occurred irregularly over time. Most of our study population drank less than one glass per day of tap water and Mn was not detected frequently in public water supplies in our study area. Future studies should collect tap water samples for Mn analysis to better characterize potential exposure from drinking water. We used data from the Total Diet Study to estimate Mn intake via food items but this study may not be representative of Mn levels in food consumed by our population, however, the primary source of dietary intake in our population was from prenatal vitamin supplements. Strengths of this study include extensive prenatal questionnaire data and prenatal house dust samples with measured Mn concentrations and loadings for a subset of participants. We measured Mn levels in dentin for specific prenatal time points using knowledge of tooth mineralization instead of digesting the entire tooth and combining prenatal and postnatal exposures. Previous studies have used measurements in tooth enamel to estimate Mn exposure; however measurements in enamel cannot be readily linked to developmental timing of exposure because, unlike dentin, initial deposits of enamel matrix are not completely mineralized immediately but rather more slowly and diffusely during maturation. An additional strength of our study is the availability of prenatal latitude and longitude coordinates which allowed the use of GIS methods and publically available data on agricultural pesticide use, drinking water, hazardous air pollutants and traffic density resulting in limited exposure information bias. We were also able to evaluate a comprehensive set of exposure predictors including occupational information, household and demographic characteristics, dietary intake, drinking water consumption, outdoor air concentrations and house dust levels. In future analyses, we will evaluate measurements of MnPN for children newly enrolled in the CHAMACOS study at 9-years of age, utilizing multilevel Bayesian measurement error models to improve exposure estimates.We will also evaluate the relationship between Mn levels in teeth and neurodevelopment in the CHAMACOS cohort. In conclusion, we found that exposure variables related to Mn containing fungicides are related to higher levels of Mn body burden in children. Further, deciduous teeth are relatively easy to obtain and store and measurements in dentin provide a unique opportunity to retrospectively assess prenatal exposure. Do farm workers’ work histories affect their current wages? Based on search theory, we argue that workers with history of unemployment cannot afford to search as long as other workers and, as a result, obtain lower wages. Thus, an unemployed worker suffers from reduced income at the time of unemployment and lower wages in the future.

Research staff reviewed the 24-hr sampling record with the parents to ensure accuracy and completeness. Urine samples were stored in the sample refrigerator until daily collection by research staff. Trained, bilingual study staff administered daily questionnaires that assessed the child’s exposure to pesticides, including questions regarding dietary intake of fruits, vegetables, and juices; time spent indoors/outdoors; parental occupational exposures; and residential pesticide use over the previous 24-hr period. Study staff processed the samples at the study field office, recording the weight and volume . On 24-hr sampling days, staff were instructed to select the first FMV sample plus one to three randomly selected additional spot samples for individual analysis. The total volume of the 24-hr composite sample was based on the volume of the individually analyzed samples plus the volume of all samples that were included in the pooled sample. The DAP concentrations were based on volume-weighted averages of concentrations in the individually analyzed samples plus the pooled sample. Samples were stored at −80 °C until shipment on dry ice to the Centers for Disease Control and Prevention for analysis in August and September 2004. Laboratory methods and quality control procedures have previously been described in detail and are available in the Supplementary Materials.

Total dimethyl , total DE,best indoor plant pots and total DAP concentrations were calculated within each sample by summing molar concentrations. We computed metabolite levels in 24-hr samples using the volume-weighted average of concentrations in all samples collected in that 24-hr sampling period . In California, all agricultural pesticide use, including crop, active ingredient, date, pounds applied, and location of use within one square mile sections defined by the Public Lands Survey System are recorded in pesticide use reports by the California Department of Pesticide Regulation . We used the latitude and longitude of the participant’s home, geocoded from their street address, to map pesticide applications. We considered pesticide use within three kilometers of the home in the six months prior to each of the two 24-hr urine sampling days for each study participant, as these are within the range of distances and time periods that have been mostly strongly associated with OP concentrations in samples from this region . We included 11 OPs that devolve into DAPs that are used in the Salinas Valley, which is representative of the most commonly used OPs nationally in the same time period . These 11 OPs include eight DM and three DE pesticides. All estimates were adjusted for the proportion of time the residence was downwind of each pesticide application .

At each study visit, study staff asked parents to report whether their child had consumed fresh fruits or vegetables from a 21-item list since the previous visit. Parents were also asked to report their child’s consumption of any fruits or vegetables that were not on the list; canned, jarred, or frozen fruits and vegetables; and orange, apple, or other 100% fruit juice . Each year since 1991, the United States Department of Agriculture Pesticide Data Program has tested food commodities, including fruits and vegetables, for approximately 450 pesticides and their breakdown products . Using a food consumption-chemical residue approach described previously , we used these publicly available data to calculate the mean concentration of the 11 OPs of interest for each of the food items reported in our study.To estimate dietary OP exposure, we multiplied the estimated concentration of the 11 OPs in each food item by the estimated intake of that food item. Per the US EPA Cumulative Organophosphorus Risk Assessment guidelines, we also included omethoate, the dimethoate oxon, in our dietary assessment, however it was not detected on any of the food commodities of interest in 2004. We made the assumption that each reported consumption of a particular fruit or vegetable was equal to one serving and used data for children ages 3–6 years from the 2003–2004 National Health and Nutrition Examination Survey “What we Eat in America” study linked to Food Commodity Intake Database  codes to estimate the weight of each food item. We estimated total exposure for each OP by summing estimated intake across all food items. We included reported food consumption that we were certain had preceded the urine void. For 24-hr samples, we considered the average exposure from all produce reported on the current day and previous day .

For spot samples, we considered all produce reported on the day prior to sampling in order to ensure the produce was consumed before the sample was collected. We used USDA pesticide residue data from 2004 , when available. For commodities not analyzed in 2004, we used data from the most proximate year . PDP samples with values <LOD were set to 0.We used generalized estimating equation models using DAP, DM, and DE dose estimates from each 24-hr composite as the outcome variable and dose estimates from same-day spot as the predictor variable. We also used the combination of each same-day FMV and non-FMV spot sample as a predictor variable by computing the arithmetic average of the dose estimate from the individual samples. Missing voids from 24-hr samples were excluded from the analysis, as both the volume of the sample and DAP concentrations were unknown. Analyses were conducted for volume- and creatinine-adjusted dose estimates. All dose estimates were log10-transformed. We assessed the performance of the models for each predictor variable using the predictive power of the model defined as the coefficient of determination ; the root mean squared error , which is a measure of both precision and accuracy of the model; and the intraclass correlation , which measures agreement between the dose estimates. In this study of 25 children living in an agricultural region, we found that volume- and creatinine-adjusted non-FMV spot urine samples had relatively weak ability to predict 24-hr cumulative OP dose. Moreover, our results indicate that reliance on non-FMV spot samples may underestimate daily cumulative OP dose and the percentage of samples exceeding regulatory guidelines, regardless of the method used to account for expected daily urinary excretion. Models including the average of an FMV and non-FMV spot had the greatest ability to predict 24-hr dose, however models containing just an FMV sample were often similarly predictive of daily dose. Our findings are consistent with previous analyses in this population in which we found that spot urine samples had relatively weak ability to predict cumulative exposure over one week and that reliance on spot samples to reflect chronic OP pesticide exposure may result in exposure misclassification that could bias effect estimates towards null findings . Because 24-hr sampling, considered the “gold standard”, or the collection of multiple daily spot samples is infeasible in most epidemiologic studies, we recommend that future studies prioritize the collection of FMV samples to most accurately characterize OP dose. To our knowledge, only two other studies have examined the ability of same-day spot urine samples to predict 24-hr OP pesticide exposure or dose . In a study of 13 2–5 year old children, Kissel et al., analyzed OP metabolite concentrations from urine samples collected during each of two 24-hr sampling cycles in two different seasons and found that FMV samples were the best predictor of weighted average daily metabolite concentration in both creatinine-adjusted and unadjusted analyses . They also observed high intra-child variability in metabolite levels from urine samples collected on the same day . Their findings indicate that full 24-hr sampling may reduce measurement error due to within-person variability, however if spot sampling is to be conducted,blueberry container size collection of FMV samples are preferable for analytes with short half-lives . In another analysis of 20 farmers and their children, Scher et al., analyzed agreement between two OP parent compounds/metabolites and 3,5,6-trichloro-2-pyridinol in morning void samples with 24-hr composite exposure and dose estimates from urine collected between 24 h before through 96 h after pesticide application .

Compared to estimates based on 24-hr samples, investigators found that single morning void urine samples tended to overestimate daily exposure and dose estimates of 2,4-D and chlorpyrifos . More specifically, four children had chlorpyrifos dose estimates above the acute population adjusted dose regulatory level of 0.5 μg/kg/day based on morning void samples, whereas no 24-hr dose estimates exceeded EPA safety thresholds . Taken together with our results, these findings suggest that reliance solely on non-FMV spot samples may underestimate OP dose, whereas analysis of FMV samples alone may overestimate dose. Previous epidemiologic analyses among children living in the Salinas Valley have found DMs to drive associations between urinary DAPs and adverse child neurodevelopment . We observed that DMs had a substantial influence on OP dose estimates and ability of spot samples to predict 24-hr dose. There are a few possible explanations for this. First, of the 11 OPs examined in this analysis, 8 are DMs and only 3 are DEs. These eight DMs had a much higher total molar mass than the three DEs . Second, oxydemeton methyl, a highly toxic DM with a large RPF , increased in use in the Salinas Valley shortly after our study started ) and may be influencing the associations observed in our study and previous epidemiologic analyses from this region. Pesticide use trends have shifted drastically since we conducted this study and some of the most toxic OPs have largely been phased out of agricultural use in the Salinas Valley and across the United States. Additional investigations are needed to examine cumulative OP dose estimates and potential contributions from DEs and DMs with the current mixture of OPs being applied. In addition to the potential influence of specific OPs, it’s possible that DEs are chemically less stable and have higher intrinsic variability than DMs . We found that estimates adjusted for expected 24-hr creatinine had similar ability to predict daily OP dose as estimates adjusted for observed 24-hr creatinine excretion or urine volume. Conversely, in a study of 109 children living in an agricultural area in Washington State, investigators found that creatinine-adjusted doses tended to be lower than those calculated with daily urine volume . Previous studies have found that creatinine concentrations may be highly variable due to factors such as age, sex, BMI, diet, and fluid intake and that correcting for specific gravity may introduce less variability and may be a more robust method in studies focusing on children . Additional research may be needed to evaluate the validity of creatinine correction in children. Furthermore, we recommend that future studies collect urine specific gravity information, particularly given the ease of measuring this metric . This study has multiple strengths and implications for future risk assessments and epidemiologic studies. We extended previous examinations that estimated cumulative OP dose from diet and nearby agricultural pesticide use  separately by considering these exposures in conjunction. Additionally, this is one of only a few studies to examine cumulative OP pesticide dose among children living in an agricultural area and to examine the ability of spot samples to predict 24-hr dose. These results have important implications for risk assessments and could be applied to other non-persistent environmental chemicals. This study also has limitations. We did not have specific gravity measurements and could not compare adjustment for urinary dilution using specific gravity. Notably, while DAPs represent exposure to approximately 80% of the OPs used in the Salinas Valley , children may have been exposed to other OPs that do not devolve into DAPs. While California’s unique and comprehensive PUR database allowed us to estimate the mix of pesticides participants may have been exposed to from nearby agricultural pesticide use, relying solely on these data to estimate all non-dietary exposures may not adequately account for all sources and pathways of exposure. We examined agricultural pesticide applications near participants’ residences in the six months prior to each 24-hr sampling in order to tiy to account for exposures from multiple sources, including agricultural drift and accumulation of pesticides in the home , however participants may have also been exposed to pesticides via the take-home exposure pathway, particularly if they lived with farm workers . However, because the dose calculations incorporate the proportion of potential exposure to each pesticide in relation to total DEs and DMs applied, rather than a sum of each pesticide, and because we anticipate that children living with farm workers were likely exposed to a similar mixture of OPs from para-occupational exposures, we do not believe that this impacted our results substantially.

This is truer than ever now, with pressing fiscal issues preventing the expansion of most federal programs. How, then, can we influence the dietary quality of food stamp recipients, especially given the fact that increased benefits are unlikely to cause recipients to purchase healthier foods? I argue that the answer lies in creating marketplace incentives targeted to certain products , rather than the current FNS approach of developing nutrition-education and social-marketing messages . Congress and the USDA could create such an incentive program for food stamp participants by redirecting part or all of the funding currently distributed through the commodity support program. Any cuts or changes to the commodity support program would probably have to be designed to minimize impacts to existing food assistance programs, depending on commodity distribution. For example, some commodities that currently qualify for direct payments — which eventually make their way to entities such as food banks and schools through FNS food distribution programs — could be negatively affected by a reduction in commodity availability and price. A FSP incentive program could reduce the retail price of healthful food items by providing retailers,square plant pot wholesaler distributors and growers with reimbursements and direct subsidies to cover costs and lost revenues.

Lower costs would lead to increased demand, which, coupled with targeted subsidies and reimbursements, would act to stimulate production and increase retail access. The enactment of country-of origin labeling laws would provide a mechanism to ensure that only products of U.S. growers would qualify. Such an incentive program might work as follows. Food stamp recipients would receive a significant discount — 50%, for example — when they use benefits to purchase qualified products that meet certain nutritional guidelines at FNS-authorized retail stores. FNS would then direct reimbursements to retailers, wholesaler distributors and growers to make up for decreased revenues at the retail level. Because roughly 30% of the retail price of fruits and vegetables represents gross retail profits, reducing retail prices by 50% would allow for retail profit margins to remain constant with decreased revenues coming out of product costs, which would be paid by USDA directly to wholesaler-distributors. A similar transfer would occur at the wholesale level, with the USDA paying up to 100% of the amount normally paid to growers — roughly 20% of the retail price. The USDA would ensure that everyone’s gross profit remains constant. To do so, it would actually not need to reimburse the retailer for lost revenues at all . The retailer would continue to purchase produce from wholesaler-distributors, but a portion of that payment would in fact be made by the USDA, effectively discounting the price for retailers. This would allow retailers to charge customers a lower retail price while paying for costs and generating the same gross profits off larger gross margins, due to decreased product costs.

Instead of dedicating 70% of the retail price to pay for product costs, the retailer would now dedicate only 40%, thereby generating the same gross profits off a larger gross margin . The USDA would make payments at the farm gate and at the wholesale level. It would pay the wholesaler-distributor three-fifths of the discount, ensuring that the gross profit at the wholesale level remains equal to what it was before the price was discounted to the retailer. The remaining two-fifths would be paid to the grower, ensuring that their payments remain unchanged as well . Needless to say, the exact manner in which the USDA would pay reimbursements would need to be carefully designed and implemented to avoid market distortions and fraudulent activities. Similarly, the method for determining which foods do and do not qualify for discounts would need to be developed by an entity not influenced by the food industry or particular crop associations — perhaps the Institute of Medicine, which was recently charged with reformulating the Women, Infants, and Children food package.So far, I have discussed targeting incentives to purchases made only at traditional, FNS-authorized retail outlets such as supermarkets. Such a program would no doubt provide indirect incentives for the expansion of fruit and vegetable production nationwide . But because the vast majority of produce supplied to the conventional retail grocery industry is grown on the largest, most profitable farms, the bulk of payments would still be directed to those farms, as is the case currently with the commodity support program. However, the USDA could use this opportunity to ensure that smaller-scale and regionally based growers engaged in direct marketing benefit as well, by expanding the Farmers’ Market Nutrition Program, another FNS program that distributes coupons to WIC recipients and qualified seniors once yearly on an annual federal budget of only around $20 million .

Food stamp recipients, and perhaps WIC recipients, might also receive a 50% discount when benefits were used to purchase qualifying products at certified farmers’ markets, with reimbursements going to growers and market operators instead of wholesaler-distributors. Dedicating other funding, perhaps through the Risk Management Agency or Agriculture Marketing Service, toward a farmers’ market incentive program could increase the amount of discount offered, and provide farmers’ market operators and participating growers with a level of reimbursements necessary to subsidize the development and operation of farmers’ markets in currently under served low-income neighborhoods.Costs. When crunching the numbers, one finds that a redirection of all 2003 farm commodity payments to a marketplace based incentive program would represent $104 per month per food stamp household, or a 56% increase in the average monthly household benefit. Redirecting the 87% of farm commodity payments paid to the top 20% of farms would provide each food stamp household with an additional $90 in purchasing power each month. Remember that these dollars are not being paid directly to food stamp participants as benefits, but rather to retailers, wholesaler distributors and growers to create retail price reductions that apply to purchases made by participants. Furthermore, it is unlikely that these incentives would simply result in product substitution, because food stamp recipients — like the majority of Americans — do not currently purchase significant quantities of fruits, vegetables and whole-grain products. Benefits. Many low-income Americans find healthful foods expensive and hard to find, and they need and deserve targeted assistance to help purchase them. A typical food stamp household, with one female adult and two children ages 3 and 7, might receive roughly $250 in benefits each month. The Thrifty Food Plan is an economic model developed by the USDA Center for Nutrition Policy and Promotion to create a “market basket” of items that meet U.S. Dietary Guidelines for nutrient intakes while constraining costs; the TFP is used as the basis for food stamp allotments and assumes that all food is purchased at stores and prepared at home. According to the USDA,plastic potting pots the monthly cost of the TFP for this family in July 2003 — containing 25.2 pounds of vegetables other than potato products and 46.48 pounds of fruit — was $301.20 , of which perhaps $100 is allocated to purchase fruits and vegetables. However, it is highly unlikely that our typical food stamp family is following the TFP and purchasing anything close to 70 pounds of fruits and vegetables each month. This is because over half of all food purchases today are consumed outside the home, and because fruits and vegetables are often much more expensive and less available in the inexpensive restaurants, small neighborhood markets, and food-service settings likely to be frequented by low income Americans. What would in effect be half-off sales would provide a significant incentive for food stamp recipients to purchase more nutritious foods. Although these “sales” certainly would not guarantee that all food stamp recipients meet the recommendations in the 2005 Dietary Guidelines for Americans overnight, such incentives would no doubt cause a great many recipients to start purchasing and eating more health-promoting foods such as fruits, vegetables and whole grains .

In fact, these incentives might go a long way toward eliminating two of the main barriers that consumers cite as keeping them from eating a better diet: cost and access. What’s more, by linking incentives directly to products that have known health benefits, there is a high likelihood that these redirected subsidies would result in additional future cost savings, in the form of improved health, increased productivity, and other economic and social benefits. With such significant potential impacts, one must ask why the USDA isn’t more willing to consider making targeted cuts in the commodity support program in order to improve the FSP. Does it really make sense to support the production of products such as high-fructose corn syrup by giving corn growers direct subsidy payments, and to support the purchase of products like Coca-Cola by giving food stamp recipients benefits but no incentives to spend extra for nutrients instead of maximizing calories? Why not instead invest in the health and good dietary habits of low-income Americans, while providing marketplace support for the producers of health-promoting food products? The USDA and members of Congress would do well to ask themselves these questions, perhaps while they’re debating the 2007 Farm Bill .For example, cereal crops decreased in harvested area by 3.6% between 1985 and 2005, yet their total production increased by 29%, reflecting a 34% increase in yields per hectare. Oil crops, on the other hand, showed large increases in both harvested area and yield , resulting in a 125% increase in total production18. While most crops increased production between 1985 and 2005, fodder crops did not: on average, they saw an 18% production drop as a 26% loss in harvested area overrode an 11% increase in yields. Using geospatial data, we can examine how yield patterns have changed for key commodities . These geographic patterns show us where productivity gains have been successful, where they have not, and where further opportunities for improvement lie.The allocation of crops to nonfood uses, including animal feed, seed, bio-energy and other industrial products, affects the amount of food available to the world. Globally, only 62% of crop production is allocated to human food, versus 35% to animal feed and 3% for bio-energy, seed and other industrial products. A striking disparity exists between regions that primarily grow crops for direct human consumption and those that produce crops for other uses . North America and Europe devote only about 40% of their croplands to direct food production, whereas Africa and Asia allocate typically over 80% of their cropland to food crops. Extremes range from the Upper Midwestern USA to South Asia . As we face the twin challenges of feeding a growing world while charting a more environmentally sustainable path, the amount of land devoted to animal-based agriculture merits critical evaluation. For example, adding croplands devoted to animal feed to pasture and grazing lands , we find the land devoted to raising animals totals 3.73 billion hectares—an astonishing ,75% of the world’s agricultural land. We further note that meat and dairy production can either add to or subtract from the world’s food supply. Grazing systems, especially on pastures unsuitable for other food production, and mixed crop–livestock systems can add calories and protein to the world and improve economic conditions and food security in many regions. However, using highly productive croplands to produce animal feed, no matter how efficiently, represents a net drain on the world’s potential food supply.The environmental impacts of agriculture include those caused by expansion and those caused by intensification . Below, we use new data and models to examine both. Agricultural expansion has had tremendous impacts on habitats, biodiversity, carbon storage and soil conditions. In fact, worldwide agriculture has already cleared or converted 70% of the grassland, 50% of the savanna, 45% of the temperate deciduous forest, and 27% of the tropical forest biome. Today, agriculture is mainly expanding in the tropics, where it is estimated that about 80% of new croplands are replacing forests. This expansion is worrisome, given that tropical forests are rich reservoirs of biodiversity and key ecosystem services. Clearing tropical forests is also a major source of greenhouse gas emissions and is estimated to release around 1.1 3 1015 grams of carbon per year, or about 12% of total anthropogenic CO2 emissions. Slowing or halting expansion of agriculture in the tropics—which accounts for 98% of total CO2 emissions from land clearing—will reduce carbon emissions as well as losses of biodiversity and ecosystem services.