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.