The county lost about 6500 acres of agricultural land to urbanization between 1992 and 2008 . Most of this was prime farmland and farmland of local importance. Compared with other jurisdictions in California, the county has been relatively successful at protecting agricultural land from urbanization through land-preservation programs, incentives for farmers, and land use policies which make it difficult to develop land zoned for agriculture. Urbanization presents both opportunities and challenges for agriculture. In some regions, it enhances awareness about how food is produced and generates markets for agricultural products such that farmers produce crops more intensively . But it is more typically accompanied by challenges: the loss of agricultural land due to subdivision and development; vandalism at the urban edge ; and conflicts with new suburban residents about the noise, odor, and potential spray-drift associated with farming operations. Where development takes place in a dispersed pattern that fragments agricultural land, farming may become difficult on some remaining agricultural parcels due to such conflicts as well as to difficulties in moving farm machinery from field to field on more congested roads, creating a ripple effect whereby more agricultural land is then converted to urban uses. Also, fragmentation and loss of farmland cause farmers to lose benefits associated with being part of a large farming community, such as sourcing inputs, accessing information, sharing equipment, and supporting processing and shipping operations .Within previous research, we and our colleagues developed a set of storylines reflecting different climate change and urbanization policies for Yolo County in 2050 . As in IPCC Scenario A2 , our A2 storyline assumes that population growth will remain high,container grown raspberries with an approximate doubling of the current county population, to 394,000 .
This storyline assumes that economic growth and technological innovation remain high, that drive-alone motor vehicles remain the main transportation mode, and that current land use policies remain in place. Although much urbanization will be on previously unbuilt land, there will be some focus on infill development, higher densities, and greater land use mix, as indeed is evident within current development and in county and city planning documents. In terms of suburban sprawl, therefore, the A2 storyline is by no means a worst-case scenario. Rather, it is a continuation of practices in the 1990–2010 period. If this storyline had been based on prevailing development patterns from 1950–1990, suburban densities would have been in the range of 4–6 units per acre instead of 8 , less development would have occurred in medium- and high density forms, and a higher percentage of larger ranchettes would have been created . Suburban sprawl would then have covered a much larger percentage of the county, taking far more agricultural land out of production. In the IPCC’s B1 storyline, societies become more conscious of environmental problems and resource limits, and adapt policy accordingly. Under our Yolo County B1 storyline, population growth slows, reaching a mid-range population size of 335,000 by 2050 . Economic development is moderate, with a shift from the production of goods to a more service-based economy that is connected to the larger global economy. Technological innovation remains high, with an emphasis on small-scale, green technologies. More compact urbanization occurs through higher densities, increased infill, and a focus on small, locally owned retail stores rather than big box commercial developments. Current transportation and emission policies become more stringent, and the use of high-efficiency vehicles and alternative transport modes increases. In terms of agricultural landscapes, strategies such as conservation easements and tax incentives expand to help maintain land in farming. Farmers also place more emphasis on increasing carbon sequestration, reducing GHG emissions from fossil fuels and fertilizers, and relying on ecologically based practices that reduce dependence on non-renewable inputs .
To the two IPCC-based storylines, we added a third with more explicit GHG-emissions regulation and sustainability policies. Under our AB32-Plus storyline, Yolo County experiences slower population growth, reaching only 235,000 in 2050 through policies or voluntary actions that affect family planning and migration . Moderate economic growth focuses on value-added agricultural production enhancing the economic viability of the rural sector, and closer alignment between the rural and urban sectors supports both farmland preservation and protection of ecosystem services . A less resource-intensive lifestyle gains acceptance. Urbanization remains at the current extent through strict land use planning policies and development emphasizing efficient use of land, mixed use, intensive infill, increased densities, and growth in urban and neighborhood centers. Public policy emphasizes alternative modes of transportation and far cleaner vehicles. In order to both mitigate and adapt to the changing climate, agricultural producers make major changes in management practices, focusing on ecological intensification and diversification of cropping systems rather than non-renewable inputs and monocultures. Markets for agricultural products become more locally based, and thanks to both more compact physical form of communities and changing economics, travel distances decrease.In order to understand the type, extent, and likely locations of urbanization in the county, we modeled these three urbanization storylines using UPlan geographic information system–based software, a rule-based land use allocation model developed by the Information Center for the Environment at the University of California, Davis . UPlan is an open-source, relatively simple model that can be run on a sub-county area, a county, or a group of counties. It is suitable for fast, broad-brush urbanization modeling of large land areas using multiple development scenarios, and more than 20 counties in California have used it for urban-growth projections, including a group of rural counties in the San Joaquin Valley which employed it to develop an urban-growth blueprint . It has also been employed to assess the impacts of urbanization policies and growth on natural resources , to understand the risk of wildfires in rural woodlands from urban growth , and to evaluate the effect of land use policies on natural land conversion . UPlan relies on a number of demographic inputs to create scenarios reflecting possible locations and forms of new urban development . The software divides households into four residential land use types based on density parameters, while assigning employees to nonresidential land use types , also by density. Researchers designate “attractors” and “discouragements” , and assign weights to each within each scenario. Accessibility and neighborhood attractor parameters can be added in this way, which also allows for detailed local knowledge of development history and policy to be incorporated. For example, within our A2 scenario we assigned relatively strong attraction values to freeway interchanges for commercial development and somewhat less strong attraction values for residential development, because without specific land-protection policies, highly accessible freeway locations tend to attract such development. Within our B1 and AB32-Plus scenarios we assigned increasingly strong attraction values to town and neighborhood centers, as well as to existing commercial strips and rail station areas, because over a 40-year period these are likely to be a focus of infill development policy.
Finally, UPlan uses “masks” to prohibit growth in certain locations because of logistical or ownership considerations. For example, we masked existing parks and wetlands in this way. However, we assigned discouragement values to floodplains, because despite environmental concerns, development continues to occur in Sacramento Valley floodplains. For the purposes of this project,blueberry plant pot we modified UPlan in several ways when compared to previous uses. Because our time frame is longer, and given that land use politics and regulation can change greatly over 40 years, we no longer required that the model place new urban development in areas conforming to the current county General Plan . We also modified UPlan to allow development within existing urban areas; the tool had been used previously mainly to consider growth on unbuilt county lands outside of existing cities and towns. To predict infill development more accurately, we added additional attractors such as existing commercial strips, shopping centers, freeway retail zones, neighborhood centers, and rail transit station areas, all of which can potentially be redeveloped with higher densities. Partly as a result of these changes, our urbanization assumptions for both the B1 and AB32-Plus scenarios are considerably stronger than used by other researchers. For example, even the strongest growth-management scenario considered by Neimeier, Bai, and Handy in a study of urbanization scenarios for the nearby San Joaquin Valley still allows substantial suburban sprawl. This approach may accurately reflect current political realities for that region , but it involves very different assumptions from our back casting approach, which aimed at investigating potentially very-low-GHG scenarios in 2050. Table 2 shows the primary modeling assumptions we used in UPlan. The software divides new development by land use type , and then, drawing upon these inputs, allocates it across the landscape into the geographic cells with the highest combined attraction weights and the user-defined land use order. The model uses 50 m 50 m cells, roughly half an acre. This is a fine-grained grid conducive to handling small increments of development such as often occur at infill and urban-edge locations. The final output is a map displaying the location by land use type of future urbanization, as well as associated tables. Throughout the scenario-development process, we sought to keep our assumptions relatively simple and transparent. In their study of the New York metropolitan area, Solecki and Oliveri added a layer of new roads for their A2 scenario. For Yolo County, there is no political demand for new roads under current conditions, and the location ofsuch routes if added would be highly conjectural. Likewise, the concatenation of new residential clusters outside of existing urban areas was not modeled, because access to roads and proximity to past rural development are probably strong enough to approximate this relatively weak clustering tendency. Lastly, we did not need to consider land slope in our modeling, because the vast majority of the county is quite flat, and the western hills are far removed from existing population centers.
Among the specific assumptions within our three scenarios, the most controversial is that our AB32-Plus scenario assumes that all new development takes place within existing urban areas. We did this to develop the strongest possible back casting scenario for reducing GHG emissions. New development in Yolo County’s largest city, Davis, is in fact currently almost entirely infill, because voters passed ballot measures in 2000 and 2010 preventing any development on open space or agricultural lands without a majority vote of city residents. Statewide, infill development has greatly increased in recent years due to scarcities of unbuilt land in places like the central Bay Area and the Los Angeles Basin and is likely to increase further due to overbuilding of lower-density housing and strong needs for denser forms of housing . Redevelopment of existing urban lands is a main goal of the Sacramento region’s 2004 Preferred Blueprint Scenario and Sustainable Communities Strategy , as well as state legislation such as SB 375. While many practical constraints pertain to infill , within a strongly environmental vision of the future there is no physical or procedural reason why 100% infill could not be achieved if the political obstacles could be overcome, for example through an escalation in the urgency of the climate challenge. For all scenarios, we established density levels that are fairly close to the density levels of recent development in the more urban portions of the state. Our categories were Very Low Density Residential, with an average lot size of one acre; Low Density Residential, with an average density of 8 units per acre ; Medium Density Residential, with an average density of 20 units per acre ; and High Density Residential, with an average of 50 units per acre . In terms of building types, the Medium Density category might consist of two-to-three-story apartment or condominium buildings with significant green space around them, while the High Density category might include three-to-five-story multifamily buildings in a more urban format as well as some townhouses. It is important to emphasize that none of these categories requires high-rise apartment living, although this development type is not forbidden, and might in fact be desirable for limited locations within the county during the study period. We apportioned development differently between these residential types for each scenario.