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An Agricultural Resources Model for San Diego County

Members: Lisa Ackerman Jeff Bannon Achira Leopaitrana Kaz Yamada. Advisors: Antonio Bento John Melack Client: San Diego County. An Agricultural Resources Model for San Diego County. Project Objectives. Design an agricultural resource threshold for CEQA that:

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An Agricultural Resources Model for San Diego County

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  1. Members: Lisa Ackerman Jeff Bannon Achira Leopaitrana Kaz Yamada Advisors: Antonio Bento John Melack Client: San Diego County An Agricultural Resources Model for San Diego County

  2. Project Objectives • Design an agricultural resource threshold for CEQA that: • Involves statistically significant and unbiased ranking criteria • Takes into account the unique forms of agriculture in San Diego County • Considers the needs of the county, developers, and the general public. • Helps the county prioritize land for conservation

  3. Agriculture in San Diego • Farms • Small yet numerous • 1-9 acres (65% < 9 acres) • Family owned • Products / Crops • High Valued, specialized crops • Economies of scope vs. economies of scale • SDC leads California in organic farms

  4. SDC Agricultural Products

  5. Paradox • Productivity factors: • Climate • Water • Soil • Paradox • Soil quality • High valued crops • Low valued crops • San Diego County Farm Bureau, Crop Report Highlights,, 2000

  6. Why Preserve Ag Land in SDC? • Economic benefits • Rural amenities/lifestyles • Recreational sites • Environmental habitat • Open space • Areas for ground water re-charge

  7. Contributing Factors to Ag in SDC • Climate • 4 climate zones • Access to markets • Topography • Water drainage • Water • Supports / pricing

  8. Limiting Factors to Ag in SDC • Soil • Quality • Water • High prices • Sources • Municipal • Well

  9. Threats to SDC Ag • Water shortages / increasing prices • Urban Sprawl • By 2020, 600,000 acres needed • Urban / rural interface issues • Nuisance laws • Pollution

  10. The LESA approach • Developed by Soil Conservation Service to meet public policy needs regarding issues of farmland conversion and protection. • Typically used as a project evaluation tool for citing decisions • LESA models developed by consensus, with public involvement • LE vs SA

  11. Description Factor Scores Factor Weight Weighted Factor Scores LE factors (50%) 1. Land Capability Classification Flexibility of the soil to different uses 60 X 0.25 = 15 2. Storie Index Suitability of soil to intensive agriculture (based on profile, slope, etc) 55 X 0.25 = 13.75 I.LE Subtotal 28.75 SA factors (50%) 3. Project Size Acreage by soil class 40 X 0.15 = 6 4. Water Resources Availability Water sources and restrictions (physical and economic) 10 X 0.15 = 1.5 5. Surrounding Agricultural Land Fraction of agricultural usage within 0.25 mile buffer around project site (the zone of influence or ZOI) 80 X 0.15 = 12 6. Surrounding Protected Resource Land Fraction of ZOI that has a Williamson contract, is parkland or has a natural resource easement. 0 X 0.5 = 0 II.SA Subtotal 19.5 Total (100%) 48.25 LESA Approach

  12. Drawbacks of LESA • Subjective • Ranks • Weights • Choice of variables • Autocorrelation • Potential for over weighting of factors • Leads to over complication of the model • Ambiguity • Sites with different characteristics can receive the same score

  13. Our approach: spatial hedonics • Land will be sold if the value in housing exceeds the net present value of agricultural income • NPV = fxn(LE factors, SA factors, other amenities) • Hedonics uses OLS regression to estimate the contributions of independent variables to the value of the dependent variable • Variables that don’t impact the value of land can be eliminated • Controls for the non-agricultural factors that affect land price but are unrelated to agriculture (e.g. ocean view)

  14. Potential Variables

  15. SANDAG • Major Statistical Areas: 1990 and 2000 Census tracts, Cities, Urbanized Area • Base Map Features: Coastline, Elevation contours, Freeways, Water Bodies, Roads • District Boundaries: government, education, sanitary, water • Land Cover and Activity: Land Use (1995,1999), Schools, Hospitals, Large Employers, Planned Land Use, Infill • Sensitive Lands/ Natural Resources: Agricultural Contract Lands and Preserves, Flood Plains, Climate Zones, Soils Series, Steep Slope Areas, 1995 Vegetation, Linkages, Watersheds

  16. Procedure • Derive model of the form: Sale price/acre =  + 1(size) + 2(irrigated) + 3(crop) + … n-2(year) + n-1(climate) + n(dist to school) +  • Assemble data and solve for coefficients with S-Plus • Isolate relative importance of various LE and SA factors based on the magnitude of their influence on the value of land • After the statistically significant factors are isolated and ranked, we can convert the results into a simple procedure (such as a spreadsheet application) that can be used to value land

  17. Benefits of this approach • Is objective: the results reflect people’s preferences that are teased out of actual transactions in the land market • Variables are evaluated for importance • Variables are more explicitly defined

  18. Concerns • Will it work? • Can we get the data? • Potential for omitted variables? • Can we produce a model that SDC can use? • Should the model be static or dynamic?

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