Evaluation of Agricultural Land Suitability: Application of Fuzzy Indicators

  • Dmitry Kurtener
  • H. Allen Torbert
  • Elena Krueger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5072)

Abstract

The problem of evaluation of agricultural land suitability is considered as a fuzzy modeling task. For assessment of land suitability, it is proposed to use fuzzy indicators. Application of individual fuzzy indicators gives opportunity for assessment of suitability of lands as degree or grade of performance when the lands are used for agricultural purposes. Using composite fuzzy indicator it is possible to obtain weighted average estimation of land suitability. This theoretical technique is illustrated with a simple example.

Keywords

land suitability evaluation fuzzy set theory fuzzy indicator 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baja, S., Chapman, D.M., Dragovich, D.: A conceptual model for defining and assessing land management units using a fuzzy modeling approach in GIS environment. Environmental Management 29, 647–661 (2002)CrossRefGoogle Scholar
  2. 2.
    Baja, S., Chapman, D.M., Dragovich, D.: Using GIS-based continuous methods for assessing agricultural land use potential in sloping areas. Environment and Planning B: Planning and Design 29, 3–20 (2002)CrossRefGoogle Scholar
  3. 3.
    Baja, S., Chapman, D.M., Dragovich, D.: Spatial based compromise programming for multiple criteria decision making in land use planning. Environmental Modeling and Assessment 12, 171–184 (2007)CrossRefGoogle Scholar
  4. 4.
    Banai, R.: Fuzziness in geographical information systems: contributions from the analytical hierarchy process. International Journal of Geographical Information Systems 7, 315–329 (1983)Google Scholar
  5. 5.
    Bouaziz, R., Chakhar, S., Mousseau, V., Ram, S., Telmoudi, A.: Database design and querying within the fuzzy semantic model. Information Sciences 177, 4598–4620 (2007)MATHCrossRefGoogle Scholar
  6. 6.
    Bogardi, I., Bardossy, A., Mays, M.D., Duckstein, L.: Risk assessment and fuzzy logic as related to environmental science, SSSA Special publ. 47 (1996)Google Scholar
  7. 7.
    Busscher, W., Krueger, E., Novak, J., Kurtener, D.: Comparison of soil amendments to decrease high strength in SE USA Coastal Plain soils using fuzzy decision-making analyses. International Agrophysics 21, 225–231 (2007)Google Scholar
  8. 8.
    Burrough, P.A.: Principles of Geographical Information Systems for Land Resource Assessment. Oxford University Press, New York (1986)Google Scholar
  9. 9.
    Burrough, P.A., McDonnell, R.A.: Principles of Geographical Information Systems. Oxford University Press, New York (1998)Google Scholar
  10. 10.
    Burrough, P.A.: Fuzzy mathematical methods for soil survey and land evaluation. Journal of Soil Science 40, 477–492 (1989)CrossRefGoogle Scholar
  11. 11.
    Burrough, P.A., MacMillan, R.A., van Deursen, W.: Fuzzy classification methods for determining land suitability from soil profile observations and topography. Journal of Soil Science 43, 193–210 (1992)CrossRefGoogle Scholar
  12. 12.
    Carver, S.: Integrating multi-criteria evaluation with geographical information systems. International Journal of Geographical Information Systems 5, 321–339 (1991)CrossRefGoogle Scholar
  13. 13.
    Chakhar, S., Mousseau, V.: Spatial multicriteria decision making. In: Shehkar, S., Xiong, H. (eds.) Encyclopedia of Geographic Information Science, pp. 747–753. Springer, New York (2008)Google Scholar
  14. 14.
    Chakhar, S., Mousseau, V.: Multicriteria spatial decision support systems. In: Shehkar, S., Xiong, H. (eds.) Encyclopedia of Geographic Information Science, pp. 753–758. Springer, New York (2008)Google Scholar
  15. 15.
    De la Rosa, D., van Diepen, C.A.: Qualitative and Quantitative Land Evaluations. Encyclopedia of Life Support System. EOLSS-UNESCO (2002)Google Scholar
  16. 16.
    Dent, D., Young, A.: Soil Survey and Land Evaluation. George Allen & Unwin, Boston (1981)Google Scholar
  17. 17.
    Eastman, J.R., Jiang, H.: Fuzzy measures in multi-criteria evaluation. In: Second International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Studies, Fort Collins, Colorado, pp. 527–534 (1995)Google Scholar
  18. 18.
    Jager, R.: Fuzzy Logic in Control. Delft TU Publisher, Delft (1995)Google Scholar
  19. 19.
    Jankowski, P.: Integrating geographical information systems and multiple criteria decision making methods. International Journal of Geographical Information Systems 9, 251–273 (1995)CrossRefGoogle Scholar
  20. 20.
    Jankowski, P., Nyerger, T.L., Smith, A., Moore, T.J., Horvath, E.: Spatial group choice: a CDSS tool for collaborative spatial decision making. International Journal of Geographical Information Systems 11, 566–602 (1997)Google Scholar
  21. 21.
    Jiang, H., Eastman, J.R.: Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Science 14, 173–184 (2000)CrossRefGoogle Scholar
  22. 22.
    Joerin, F., Theriault, M., Musy, A.: Using GIS and outranking multicriteria analysis for land-use suitability assessment. International Journal of Geographical Information Science 15, 153–174 (2001)CrossRefGoogle Scholar
  23. 23.
    Kaufmann, A., Gupta, M.M.: Fuzzy Mathematical Models in Engineering and Management Science. Elsevier Science Publ., Amsterdam (1988)MATHGoogle Scholar
  24. 24.
    Karlen, D.L., Mausbach, M.J., Doran, J.W., Cline, R.G., Harris, R.F., Schuman, G.E.: Soil quality: a concept, definition, and framework for evaluation. Soil Science Society of America Journal 61, 4–10 (1997)Google Scholar
  25. 25.
    Kaiumov, M.K.: Handbook on yield programming (in Russian). Moscow (1977)Google Scholar
  26. 26.
    Katorgin, I.U.: Analysis and estimation of agrolandscapes of Stavropol region using GIS technologies (in Russian). Stavropol State University, Stavropol (2004)Google Scholar
  27. 27.
    Kurtener, D., Badenko, V.: Questions of integration of some ecological models into geoinformation system. In: UDMS 1999 on Information Technology in the Service of Local Government Planning and Management. UDMS Press, Venice (1999)Google Scholar
  28. 28.
    Kurtener, D., Yakushev, V., Badenko, V., Pourabbas, E.: Development of methodology of multiple assessment of landscape parcels on the base fuzzy models integrated into GIS environment. Special publ. No 1. SPBISTRO, St. Petersburg (1999)Google Scholar
  29. 29.
    Kurtener, D., Badenko, V., Cudlip, W.: Development of the methodology of multiple assessment of burned areas in forest regions for the planning of restoration actions. In: Kurtener, D.A., Yakushev, V.P. (eds.) Agrophysical and Ecological Problems of Agriculture in the 21st Century, vol. 2, pp. 54–62. SPBISTRO, St. Petersburg (2000)Google Scholar
  30. 30.
    Kurtener, D., Badenko, V.: Precision agriculture experimentation on the base of fuzzy models synthesized with GIS. Aspects of Applied Biology 61, 139–143 (2000)Google Scholar
  31. 31.
    Kurtener, D., Badenko, V.: Development of the methodology of assessment of site-specific residue management actions on the basis of fuzzy models integrated into a GIS environment. In: 15th ISTRO conference on agroecological and ecological aspects of soil tillage. ISTRO Press, Fort Worth, Texas (2000)Google Scholar
  32. 32.
    Kurtener, D., Badenko, V.: Applications of GIS knowledge management for decision making in the field of land market and land consolidation. In: UDMS 2000. UDMS Press, Delft (2000)Google Scholar
  33. 33.
    Kurtener, D., Badenko, V.: Methodological framework based on fuzzy set theory for land use management. J. Braz. Comp. Soc. 6, 26–32 (2000)CrossRefGoogle Scholar
  34. 34.
    Kurtener, D., Rossi, L., Badenko, V.: Development of fuzzy direction of GIS knowledge management with the use of Eurimage products. In: Kurtener, D.A., Yakushev, V.P. (eds.) Agrophysical and Ecological Problems of Agriculture in the 21st Century, vol. 2, pp. 14–26. SPBISTRO, St. Petersburg (2000)Google Scholar
  35. 35.
    Kurtener, D., Badenko, V.: Applications of GIS knowledge management for spatial planning of water resources for sustainable development of European agriculture. In: 19th European Regional Conference on Sustainable Use of Land and Water, Brno (2001)Google Scholar
  36. 36.
    Kurtener, D., Badenko, V.: Fuzzy Algorithms to Support Spatial Planning. In: Geertman, S., Stillwell, J. (eds.) Planning Support Systems in Practice. Springer, Berlin (2002)Google Scholar
  37. 37.
    Kurtener, D., Krueger-Shvetsova, E., Dubitskaia, I.: Quality estimation of data collection. In: UDMS 2004, pp. 9.101–9.109. UDMS Press, Giorggia-Venice (2004)Google Scholar
  38. 38.
    Kurtener, D., Krueger-Shvetsova, E., Dubitskaia, I.: Field agriculture experimentation: assessment of geo referenced data quality. In: IAMFE/RUSSIA, pp. 120–127. IAMFE Press, St. Petersburg (2004)Google Scholar
  39. 39.
    Krueger-Shvetsova, E., Kurtener, D.: A management fuzzy indicators for precision agriculture. In: Kurtener, D.A., Yakushev, V.P. (eds.) Agrophysical and Ecological Problems of Agriculture in the 21st Century., vol. 4, pp. 31–43. SPBISTRO Press, St. Petersburg (2003)Google Scholar
  40. 40.
    Krueger-Shvetsova, E., Kurtener, D.: Evaluation of cultivation practices using fuzzy multi-attributive decision-making approach. In: Kurtener, D.A., Yakushev, V.P. (eds.) Agrophysical and Ecological Problems of Agriculture in the 21st Century, vol. 4/2, pp. 44–53. SPBISTRO Press, St. Petersburg (2003)Google Scholar
  41. 41.
    Mays, M.D., Bogardi, I., Bardossy, A.: Fuzzy logic and risk-based soil interpretations. Geoderma 77, 299–315 (1997)CrossRefGoogle Scholar
  42. 42.
    Malczewiski, J.: GIS and multicriteria decision analysis. Wiley&Sons, New York (1999)Google Scholar
  43. 43.
    Malczewski, J.: Fuzzy Screening for Land Suitability Analysis. Geographical and Environmental Modelling 6, 27–39 (2002)CrossRefGoogle Scholar
  44. 44.
  45. 45.
    McBratney, A.B., Whelan, B.M., Taylor, J.A., Pringle, M.J.: A management opportunity index for precision agriculture. In: 5th International Conference on Precision Agriculture and Other Resource Management, Bloomington, Minnesota (2000)Google Scholar
  46. 46.
    Pereka, J.M.C., Duckstein, L.: A multiple criteria decision making approach to GIS-based land suitability evaluation. International Journal of Geographical Information Systems 7, 407–424 (1993)CrossRefGoogle Scholar
  47. 47.
    Pedrycz, W., Gomide, F.: An introduction to fuzzy sets. MIT Press, Cambridge (1998)MATHGoogle Scholar
  48. 48.
    Ross, T.J.: Fuzzy Logic with Engineering Applications. McGraw-Hill, New York (1995)MATHGoogle Scholar
  49. 49.
    Rigby, D., Howlett, D., Woodhouse, P.: Management sustainability indicators for natural resource management & policy: A review of indicators of agricultural and rural livelihood sustainability. Working paper 1, Research project No. R7076CA (2000)Google Scholar
  50. 50.
    Senes, G., Toccolini, A.: Sustainable land-use planning in protected rural areas in Italy. Landscape and Urban Planning 42, 107–117 (1998)CrossRefGoogle Scholar
  51. 51.
    Sicat, R.S., Carranza, E.M., Nidumolu, U.B.: Fuzzy modeling of farmers’ knowledge for land suitability classification. Agricultural Systems 83, 49–75 (2005)CrossRefGoogle Scholar
  52. 52.
    Smith, P.N.: Fuzzy evaluation of land-use and transportation options. Environment and Planning B 19, 525–544 (1992)CrossRefGoogle Scholar
  53. 53.
    Torbert, H.A., Searcy, S.W., Kenimer, A.L., Roades, J.: Precision farming effects on corn productivity and water quality. In: Second international conference on geospatial information in agriculture and forestry, Lake Buena Vista, Florida (2000)Google Scholar
  54. 54.
    Voogd, H.: Multicriteria Evaluation for Urban and Regional Planning. Pion, London (1983)Google Scholar
  55. 55.
    Walker, J.: Environmental indicators and sustainable agriculture. In: McVicar, T.R., Rui, L., Walker, J., Fitzpatrick, R.W., Changming, L. (eds.) Regional Water and Soil Assessment for Managing Sustainable Agriculture in China and Australia, ACIAR Monograph No. 84, pp. 323–332 (2002)Google Scholar
  56. 56.
    Xiang, W.N., Gross, M., Fabos, J.G., Macdougall, E.B.: A fuzzy group multi-criteria decision making model and its application to land-use planning. Environment and Planning B 19, 61–84 (1992)CrossRefGoogle Scholar
  57. 57.
    Yakushev, V.P., Kurtener, D.A., Badenko, V.L., Kudashev, E.V., Cudlip, W.: Methodology of multiple assessment of landscape parcels on the base of fuzzy set theory models integrated into geographic information systems (GIS). Russian Agricultural Science 4, 42–43 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dmitry Kurtener
    • 1
  • H. Allen Torbert
    • 2
  • Elena Krueger
    • 3
  1. 1.Agrophysical Research InstituteSt. PetersburgRussia
  2. 2.USDA-ARS National Soil Dynamics LaboratoryAlabamaUSA
  3. 3.Independent researcherWestminsterUSA

Personalised recommendations