1. SISTEM RATING LAHAN PERTANIAN Earl Yamamoto, State Department of Agriculture February 5, 2000

2. Deskripsi: – Statewide USDA & UH soil surveys Soil data used by all systems – Agricultural suitability as limited by soil & climatic conditions System favors mainland field crop & mechanization – 8 Classes I-VIII, best to worse Effective cutoff=LCC Class IV – Productivity estimated only for limited crops Sugar, pine, pasture, woodland – Soils mapped statewide Land Capability Classification - USDA 1972

3. Acreage in Agricultural District – LCC I, II & III statewide: 381,609 acres (estimate) – Percent LCC I, II & III: 20.6% of ag district

4. Overall Productivity Ratings, Detailed Land Classification LSB, UH 1965-1972 Description – Developed concurrent with USDA soil survey – Soils grouped into land types based on soil & productive capabilities – Two sets of productivity ratings: Overall Productivity Rating- “A”, very good to “E”, not suitable Crop Productivity ratings for Pine, sugar, vegetables, forage, grazing, orchard, timber – Soil types drawn over aerial photos (variable scales)

5. Description – Part of national effort (USDA) to inventory important farmlands – National criteria applied, adapted by USDA, CTAHR & DOA – Adopted by State Board of Agriculture, 1977 – Broad range of factors considered S oils, climate, moisture supply, input use, etc., Production-related factors generalized Advance slide  ALISH : DOA/USDA, UH/CTAHR 1977-78

6. Description – 3 classes of important agricultural lands Prime – Soils with best physical, chemical, & climatic properties for mechanized field crops – Excludes built-up land/urban, water bodies Unique – Land other than prime for unique high-value crops--coffee, taro, watercress, etc. Other important agricultural lands – State or local important lands for production, not prime or unique; needing irrigation or requiring commercial production management Advance slide  ALISH : DOA/USDA, UH/CTAHR 1977-78

7. Acreage in Agricultural District  ALISH statewide: 846,363 acres (estimate)  Percent ALISH: 45.8% of ag district 6.Strengths & weaknesses of ALISH Strengths Criteria defined, can be reapplied National standard: being used by USDA & other states, basis for agricultural programs, ag grants & loans, & agricultural policy nationwide Prime lands data is GIS-ready: surveyed, digitized, maintained by USDA, shared with State GIS Takes into account local, unique crops: coffee, taro, watercress Weaknesses Unique not as well-defined, no clear cut criteria Maps need updating to reflect urbanization & current crop conditions & potential, e.g., papaya in Kapoho

8. Description – 1983 State Land Evaluation & Site Assessment Commission (Act 273, Session Laws, 1983) Standards & criteria for identifying important agricultural lands Inventory of important agricultural land – LESA system Numeric scoring system USDA system to determine impact of federal activity on farmland Used to identify lands or evaluate individual sites LESA: LESA Commission 1983-86 D.LESA Description State of Hawaii Land Evaluation & Site Assessment Commission established by Act 273 of 1983 legislative session, to develop standards & criteria for identifying important agricultural lands, inventory of important agricultural lands LESA system Background Numerical land rating system Adapted from USDA system, initially developed to determine impact of federal activity on farmland System can be used to identify lands or evaluate individual sites

9. Description – Three components Agricultural production goals Land evaluation (LE) – Soils, topography, climate Site assessment (SA) – Non-physical properties (location, land use) LESA: LESA Commission 1983-86 3.Three components Agricultural production goals Land evaluation, primarily physical properties (soils, topography, climate) Site assessment, relative quality of site or area based on non-physical properties like location, land use, to reflect agricultural viability

10. Description – Ag production goals for crop acreage requirements Amount of land required to attain ag production objectives Estimates based on current & expected levels of production, population & per capita consumption Typical crops profiled: – Sugar, pine, mac nuts, coffee, local dairy, eggs/poultry Crop acreage used to set cutoff score for LESA IAL lands Advance slide  LESA: LESA Commission 1983-86

11. Description – Land Evaluation (LE) Combines 5 soil ratings into single score for land capability – LCC – ALISH – LSB – Modified Storie Index – Soil Potential Index LE score is weighted average Advance slide  LESA: LESA Commission 1983-86

12. Description – Site Assessment (SA) Based on USDA LESA manual, selected locational, environmental, operational factors 10 site factors; categories of factors: – Farm productivity/profitability – Land use potential/conflicting uses – Conformance with government programs/policies Soils rated for each criterion, weighted, summed – Final LESA rating= (LE rating+SA score) divided by 2 – Threshold score for LESA IAL based on projected acreage – Mapping & GIS coverage limited Advance slide  LESA: LESA Commission 1983-86

13. Acreage in Agricultural District  LESA IAL statewide: 759,534 acres (estimate)  Percent LESA IAL: 41.1% of ag district Strengths & weaknesses of LESA Strengths Takes into account other land use policy considerations Attempts at comprehensiveness with use of all indices for LE portion Most current in terms of existing conditions Weaknesses Most complicated of systems i.Lots of factors, variables ii.Score & methodology not easy to understand iii.Can result in multiple scores in large sites Some of LE indices used are outdated, need to be reconstructed for current/future crops Problems with SA criteria i.Some factors vague, difficult to define ii.Subjectivity in assigning values & weight to factors: no two people would necessarily interpret same way; open to manipulation iii.Source data for mapping is of poor quality or not available; has yet to be mapped as required iv.Tends to bias toward conversion of ag land Agricultural production goals: i.Limited to crop regime at one point in time; poor predictor of future opportunities, too many uncertainties (technological change, change in markets) ii.Link to land requirements means that when ag land is converted to non-ag use, new land must be found to meet ag production goals Not GIS-ready: Needs to be redigitized to reflect scores

14. Common features – Soils-based with factors for topography, climate Vary in consideration of other attributes like crop yield – Limitations to agricultural productivity considered in some form Mostly physical and climatic limitations – All are available on State GIS in some form Pembandingan Sistem-sistem Common features (For most part) Soils- or agronomy-based, soils data (soils, topography, climate), vary in degree to which other attributes like crop yield are considered All incorporate limitations to agricultural productivity in some form, but mostly physical and climatic limitations All are resident in some form on State GIS

15. Perbedaan yang utama: – Soils-based systems exclude other factors related to ag profitability – Determination of ag land requirements LESA system unique in its use of agricultural production goals Other systems do not predetermine land requirements – Incorporation of land use policy considerations LESA includes policy criteria Land use policy dealt with in other systems only by the exclusion of urbanized, built-up, subdivided land Pembandingan Sistem-sistem B.Major differences LE-only systems omit other factors related to ag profitability, like distance to markets, farm size, etc. Determination of ag land requirements LESA system unique in its use of agricultural production goals to determine land requirements Other systems do not predetermine land requirements; acreage limited only by lack of suitability for crop use Incorporation of land use policy considerations Major component of LESA is factoring in policy criteria Land use factored in other systems only by the exclusion of urbanized, built-up, subdivided land

16. Amount of land rated suitable for agriculture LEAST LCC21% of ag district LSB24% LESA41% ALISH46% MOST Pembandingan Sistem-sistem

17. Evaluation criteria (based on CTAHR, 1990) – Ease of use Low cost, clear explanations, factors well-defined – Objectivity Measurable factors with quantifiable data – Consistency Scores would be same across individuals, clear definitions, interpretations consistent, no incentive for score manipulation – Adaptability Can be readily updated to reflect change – GIS-readiness Advance slide  Pembandingan Sistem-sistem

18. Ease of Use – Easiest LCC Straightforward use of soils data ALISH LSB Crop indices & inputs would need to be reassessed; more cost to State – Difficult LESA Most complex, scoring system is opaque, mapping problems; most costly to define & use Advance slide  Pembandingan Sistem-sistem

19. Objectivity Most objective LCC LSB Criteria clear/quantifiable for both Less objective ALISH No standardized way to define “unique” Least LESA Factors not clear, difficult to quantify & map Pembandingan Sistem-sistem Objectivity 1.Most objective: LCC & LSB criteria clear/quantifiable 2.Less objective: ALISH because criteria for “unique” not clear 3.Least objective: LESA, factors not clear, difficult to quantify or map

20. Consistency  Most consistent LCC LSB Properties, criteria clear  Less so ALISH Both “unique” & “other” introduce variability  Least LESA Variability in interpreting, assigning values/weights to factors Pembandingan Sistem-sistem 3. Consistency Most: LCC, LSB Less consistent: ALISH Least: LESA, variability in interpreting, assigning values to factors

21. Adaptability  Most adaptable ALISH Criteria can be reapplied, accommodates unique crops  Less so LCC Criteria constant, least sensitive to local crop potential LSB Dated, system indexed to sugar & pine & farm practices at time  Least LESA Components outdated; indexed to sugar & pine; productivity goals rigid; most difficult to update Pembandingan Sistem-sistem 4.Adaptability Most: ALISH, criteria relatively constant, easy to reapply, allows for consideration of crops unique to Hawaii & diversified ag on less productive lands Less: LCC, does not account for unique local conditions, crops, improvements in ag management/inputs, otherwise, criteria fairly constant, can be reapplied LSB, needs considerable reworking to update indicator crops for productivity Least: LESA, lots of factors requiring update, remapping; some LE factors old, need to be reconstructed; productivity goals not flexible; keeping system current potentially involves reevaluating all factor scores for all soil mapping units statewide (time- & labor- intensive)

22. GIS-readiness  Most GIS-ready LCC USDA NRCS maintains GIS soils data, source of State GIS data ALISH On State GIS, USDA soils data for update available  Less so LSB On State GIS, data old  Least GIS-ready LESA Data on State GIS of questionable value/need to redigitize; problems encountered in mapping factors Pembandingan Sistem-sistem Advance slide 

23. ... good ag lands WITH irrigation... without irrigation Example of how one factor--irrigation--changes ratings

24. LSB “C” “D” ALISH “Unique” Two views of Lanai pineapple under different rating systems-- LSB “D” vs. ALISH “Unique” Advance slide 

25. LSB Two views of Hanalei Valley taro under different rating systems-- LSB “E” vs. ALISH “Unique” ALISH “unique” Advance slide 

26. 3. All need to be updated to reflect present conditions--some more than others 4. In general, system is more robust if: Emphasis is on resource suitability System criteria are well-defined Summary 1. Each of the systems has limitations in application-- none ideal 2. Ratings change with change in conditions or opportunities

27. In considering a system... – Purpose of ratings: identify resource, system will be soils-based – Factors of land use policy more appropriate for public decision making process, creates problems if built into rating system – Must weigh value of additional time/money spent on development & maintenance of system

28. Credits Department of Agriculture James Nakatani, Director Earl Yamamoto State Office of Planning, DBEDT David Blane, Director Ruby Edwards Chris Chung Dennis Kim, State GIS Program