The main exception to this general trend is the near term of A2, which showed an unexpected lower frequency of no allocation years . Under the climate only scenarios, where land use is held constant at 2008 crop proportions, future irrigation demand is projected to increase in the District . In the near and medium term, average demand is expected to increase by 80 to 90 thousand acre feet, with no notable differences between the B1 and A2 projections . The increase in demand is expected to continue in the latter part of the century, were the warmer and drier A2 climate sequence ultimately prompts higher irrigation demand than B1 . Relative to the historical period, this is an increase in irrigation demand of approximately 26 to 32 percent due to climate alone. Increased demand and greater impact of the GFDL A2 scenario observed in this study are consistent with previous projections for the Sacramento Valley as a whole . Table 3.5 and Figure 3.5 compare the difference in irrigation demand among the three adaptation scenarios relative to the historic period and climate only scenarios. Under Adaptation 1, demand varies to a small extent above and below the zero lines . This suggests two things. First, it indicates that A2 and B1 cropping patterns predicted by the econometric model, which are based on historic weather and market drivers, have less impact on irrigation demand than climate change alone. For example, increases in demand from climate alone are on the order of tens of thousands of acre feet, while the relative impact of Adaptation 1 is only a few thousand of acre‐feet . Second, since demand in the B1scenario shows a slight increase with Adaptation 1,grow bag for blueberry plants the cropping trend projected by econometric model may be less water efficient than the current cropping pattern.
In short, the econometric model predicts a cropping pattern that is likely to be the most economical or profitable in the short‐term rather than what might be the most water efficient. Differences between the A2 and B1 climate sequences highlight this possibility. Since the econometric model predicted similar cropping patterns for B1 and A2 prior to 2036, irrigation demand was also similar. However beginning in 2036, the acreage of alfalfa expands significantly under the B1 climate . Since alfalfa has high water requirements, its expanded acreage leads to a corresponding increase in total irrigation demand for B1 relative to A2 and the historic period . Adaptation 2 also shows increased demand compared to the historical baseline across all periods and emissions scenarios . However, the model indicates that the increase in demand can be minimized to some extent by shifting to a more diverse and water efficient cropping pattern. That said, the marginal savings towards the end of the century are still less than half of the increase in demand due to climate change alone . Adaptation 3 also shows a near‐term demand slightly greater than the historical period. However, as the diversified cropping pattern and improvements in irrigation technology are gradually implemented, far‐term demand declines to approximately 12 percent less than the historical mean for both the B1 and A2 climate sequences . This illustrates that “game‐changing” water savings—savings of the same order of magnitude of climate‐ induced increases—can occur through a combination of progressive irrigation technology improvement, and cropping patterns which are more water efficient and diversified.Because of an overall increase in irrigation demand, groundwater pumping also tends to increase in the far term under both the B1 and A2 climate . Under A2, the groundwater proportion of the District’s supply rises from a historical mean of around 49 percent in the near term to as high as 61 percent in the far term .
It should be mentioned that this historic estimate includes years prior to the operation of Indian Valley reservoir, thus the present fraction is somewhat lower than 49 percent. Overall, this corresponds to a volume of 118 thousand acre feet above the historical mean . Relative to the climate only scenarios, the marginal benefits of Adaptation 1 and Adaptation 2 are somewhat limited in the near and mid term . In short, by integrating cropping pattern changes and improvements in irrigation technology, groundwater pumping was maintained at levels close to the baseline in the near term and yielded reductions of 30 to 50 TAF in the far term. The survey of growers indicates that these are types of practices that growers foresee as potential adaptation measures in the future . Groundwater pumping, and building more pumps and wells, are adaptation practices that farmers seem likely to adopt in the future, and these are discussed further in Section 5.With the passage of the Global Warming Solutions Act of 2006 ,12 California has shown, in the absence of cohesive federal leadership, that local governments are able to adopt a bottom‐up approach to greenhouse gas mitigation . Specific targets set by AB 32 aim to reduce California’s GHG emissions to 1990 levels by 2020 and a further 80 percent by 2050. Recognizing the key role that land‐ use planning will play in achieving these goals, legislators also passed Senate Bill 375 13 in 2008, which requires regional administrative bodies to develop sustainable land‐use plans that are aligned with AB 32 . Agriculture currently occupies 25.4 percent of California’s total land area and generates approximately 6 percent of the state’s total GHG emissions . By contrast, urban areas in California makeup only 4.9 percent of the land area but are the primary source of the state’s transportation and electricity emissions, estimated at 39 percent and 25 percent, respectively .
Moreover, rapid urbanization in California has contributed to the loss of nearly 3.4 million acres of farmland over the last decade and has increased the emissions associated with urban sprawl . At present, AB 32 does not require agricultural producers to report their emissions or to implement mandatory mitigation measures as it does for California’s industrial sector . The state is, however, encouraging farmers to institute voluntary mitigation strategies through various public and private incentive programs . For example, voluntary mitigation projects within California’s agriculture and forestry sectors may be permitted to sell offset credits in a carbon market that has been proposed in the scoping plan laid out by the California Air Resources Board . While CARB and other state agencies have taken the lead in defining these policies, much of the responsibility for climate change planning and policy implementation has been delegated to local governments. For instance, AB 32 and SB 375 now require local governments to either address greenhouse gas mitigation in the environmental impact report that accompanies any update to their general plan or to carry out a specific “climate action plan” filed separately . Consequently, conducting an inventory of GHG emissions is now among the first steps taken by local governments as they plan for future development. To help local governments improve the quality and consistency of their emissions inventories, CARB has collaborated with several organizations to develop tools to standardize inventory methods. For example, the International Council on Local Environmental Initiatives has developed a software package known as the Clean Air Climate Protection Model to better align local methods with national and international standards . Such inventory tools are suitable for appraising emissions from government or municipal operations,blueberry grow bag but are less useful for “community‐wide” assessments. In particular, the emissions from agriculture are often missing from existing inventory tools geared to local planners due to problems of complexity, data availability, boundary effects, and consistency with methods designed for larger spatial scales . Methods to estimate emissions from agriculture within a local inventory framework would be a valuable asset for those developing mitigation and adaptation strategies in rural communities. In this paper, a local inventory of agricultural GHG emissions in 1990 and 2008 is presented for Yolo County, California. Recent mitigation and adaptation initiatives in Yolo County thus provide the policy context for this analysis .
The main objectives of this inventory of agricultural emissions are to: prioritize voluntary mitigation strategies; examine the benefits and trade‐offs of local policies and on‐ farm practices to reduce agricultural emissions; and discuss how involving agricultural stakeholders in the planning process can strengthen mitigation efforts and lay the groundwork for future adaptation.In this study, an inventory of Yolo County’s agricultural GHG emissions was conducted for both the AB 32 base year and the present period . To address the wide range in data availability and analytical capacity that exists across different national or regional scales, the Intergovernmental Panel on Climate Change advocates a three‐tiered approach for identifying the appropriate inventory methods used for the agriculture sector . This tiered system refers to the complexity and geographic specificity of the inventory method in question; with the Tier 1 methods using a simplified default approach and relatively coarse activity data, while the Tier 3 methods involve more sophisticated models and higher resolution activity data . The Tier 1 methods used here have been adapted for local activity data from three main sources: the CARB Technical Support Document for the 1990–2004 California GHG Emissions Inventory ; 2) the U.S. EPA Emissions Inventory Improvement Program Guidelines ; and 3) the 2006 IPCC Guidelines for National GHG Inventories . Supplementary materials , provide detailed equations, activity data, and emissions factors for each emissions category . While strategies to adapt inventory methods to local data were exchanged with the Yolo County Planning Division during the preparation of their recent climate action plan, the present study is an independent assessment of agricultural GHG emissions.Direct N2O emissions were calculated using a Tier 1 approach that estimated nitrogen inputs from the following sources: synthetic N fertilizers, crop residues, urine deposited in pasture, and animal manure . In Yolo County, 16 crop categories accounted for approximately 90 percent of irrigated cropland. The harvested area of each crop was taken from the county crop reports for 1990 and 2008 . To calculate the total amount of synthetic N applied in Yolo County, the recommended N rate for each crop was multiplied by its cropping area and then summed across all crop categories. For a given inventory year, the recommended N rate for each crop was obtained from archived cost and return studies published by the University of California Cooperative Extension . Nitrogen inputs from crop residues for alfalfa, corn, rice, wheat, and miscellaneous grains were calculated using crop production data taken from the county crop reports . Nitrogen excreted by livestock in the form of urine or manure was calculated for the six main livestock groups assuming year‐round production. Emissions from poultry were not calculated, since no large‐scale poultry operations exist in the county . Dairy cattle numbers for both inventory years were taken from the National Agricultural Statistics Service database , while all other livestock numbers were obtained from the county records . Dairy cattle and swine manure were assumed to be stored temporarily in anaerobic lagoons and then spread on fields. All other livestock categories were assumed to deposit their urine in pastures. Indirect N2O emissions were estimated based on the total amounts of N added as synthetic N fertilizer, urine, and manure; and calculated using standard values for the volatilization and leaching rates, and default emission factors .A Tier 1 approach was developed to calculate fuel consumption from mobile farm equipment. Each crop’s annual harvested area was multiplied by its average diesel fuel use per hectare from archived cost and return studies and then summed across all crop categories to determine the total amount of diesel fuel used each year . The amount of CO2, N2O, and methane emitted was determined by multiplying the total amount of diesel fuel consumed by mobile farm equipment by emission factors for each gas . The Tier 1 estimate of emissions from mobile farm equipment was then compared with results generated by the Yolo County Planning Division who used Tier 3 OFFROAD emissions model . The OFFROAD model estimates end‐use fuel consumption based on detailed information collected on equipment population, activity patterns, and emissions factors . A detailed summary of the OFFROAD model framework and activity data specifications is available from CARB .