Abstract

Considering the important role of soil fertility and nutrient management in the modern agriculture seems to be a key step in appropriate site-specific fertilizers management for crop production.  The present study was conducted to prepare a soil fertility zonation map based on soil nutrient elements including total nitrogen, available potassium and phosphorus, magnesium, manganese and iron and soil chemical parameters comprising cation exchange capacity, organic carbon, salinity and pH by integrated Fuzzy and AHP approaches for potato production in Rokh plain, northeast of Iran. In this regard the most important soil chemical parameters and nutrient elements in 0-30 cm depth of the soil was analyzed and mapped.  The S-shaped fuzzy membership function was subsequently defined for each factor to fuzzify soil fertility parameters. The soil fertility map was prepared by weighing factor layers by the AHP approach and summation of factor layers by IDW interpolation function in GIS. The values of the soil fertility index in the scale of 0 to 1 ranged from 0.104 to 0.574, classified the study area in very low (922.90 km2), low (566.10 km2) and moderate fertility (14.86 km2) classes which comprises 61.37%, 37.64% and 0.99% of the surface area, respectively. A regression between soil fertility values and potato yield in the study area revealed a high correlation (R2 = 0.91) between the observed results which validate the zonation of the fertility classes in the region.

Keywords: Potato, fuzzy, AHP, fertility index, Rokh plain.

References

Bottero, M., Comino, E., Riggio, V., 2011. Application of the analytic hierarchy process and the analytic network process for the assessment of different wastewater treatment systems. Environmental Modelling and Software 26(10): 1211-1224.

Burrough, P.A., 1989. Fuzzy mathematical methods for soil survey and land evaluation. European Journal of Soil Science 40(3): 477-492.

Burrough, P.A., MacMillan, R.A., van Deursen, W., 1992. Fuzzy classification methods for determining land suitability from soil profile observations and topography. European Journal of Soil Science 43(2): 193-210.

Burrough, P.A., McDonnell, R.A., Lyoyd, C.D., 2015. Principles of geographical information systems.  3rd Edition, Oxford University Press, UK. 317p.

Cassel-Gintz, M.A., Lüdeke, M.K.B., Petschel-Held, G., Reusswig, F., Plöchl, M., Lammel, G., Schellnhuber, H.J., 1997. Fuzzy logic based global assessment of the marginality of agricultural land use. Climate Research 8(2):135-150.

Chan, F.T.S., Chan, M.H., Tang, N.K.H., 2000. Evaluation methodologies for technology selection. Journal of Materials Processing Technology 107(1-3): 330–337.

Chang. N.B., Parvathinathan. G., Jeff. B.B., 2007. Combining GIS with fuzzy multicriteria decision-making for landfill siting in a fast-growing urban region.  Journal of Environmental Management 87(1): 139-153.

Dey, P.K., Ramcharan, E.K., 2008.  Analytic hierarchy process helps select site for limestone quarry expansion in Barbados.  Journal of Environmental Management 88(4): 1384-1395.

Ewert, F., Rounsevell, M.D.A., Reginster, I., Metzger, M.J., Leemans, R., 2005. Future scenarios of European agricultural land use: I. Estimating changes in crop productivity. Agriculture, Ecosystems & Environment 107(2-3): 101-116.

Kremenová, O., 2004. Fuzzy modeling of soil maps. McS Thesis. Helsinki University of Technology,  Department of Surveying, Finland. p 81

Lagacherie, P., 2005. An algorithm for fuzzy pattern matching to  allocate  soil  individuals  to  pre-existing  soil  classes. Geoderma 128: 274–288.

Levary, R.R., Wan, K., 1998, A simulation approach for handling uncertainty in the analytic hierarchy process. European Journal of Operational Research 106 (1): 116-122.

Malczewski, J., 1999. GIS and multicriteria decision analysis. John Wiley & Sons Inc. 392p.

McBratney, A.B., Mendonca Santos, M.L., Minasny, B.,  2003. On digital soil mapping. Geoderma 117(1-2): 3–52.

McBratney, A.B., Odeh, I.O.A., 1997. Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma 77(2-4): 85–113.

Oberthür, T., Dobermann, A., Aylward, M., 2000. Using auxiliary information to adjust fuzzy membership functions for improved mapping of soil qualities. International Journal of Geographical Information Science 14(5): 431- 454.

Saaty, T. L. 1980. The Analytic Hierarchy Process McGraw Hill, Inc., New York, 54p.

Saaty, T., Vargas, L.G., 2001. Models, methods, concepts and applications of the analytic hierarchy process. Kluwer Academic Publishers. 333p.

Saaty, T.L., 1990. The analitic hierarchy process in conflict management. International Journal of Conflict Management 1(1): 47–68.

Saaty, T.L., 1994. Fundamentals of decision making and priority theory with the AHP. RWS publications, Pittsburg, Pennsylvania, USA.

Saaty, T.L., 2003. Decision-making with the AHP: Why is the principal eigenvector necessary? European Journal of Operational Research 145(1): 85-91.

Sanchez Moreno, J.F., 2007. Applicability of knowledge-based and fuzzy theory-oriented approaches to land suitability for upland rice and rubber, as compared to the farmers’ perception.  A case study of Lao PDR. McS Thesis. International Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands. 133 p.

Sys, C.E., Van Ranst, E., Debaveye, J., Beernaert, F., 1993. Land evaluation. Part III: Crop requirements. Agricultural Publications No.7. G.A.D.C., Brussels, Belgium, 191p. Available at [access date: 23.10.2017]: http://hdl.handle.net/1854/LU-233235

Vahidnia, M.H., Alesheikh, A.A., Alimohammadi, A., 2009. Hospital site selection using fuzzy AHP and its derivatives. Journal of Environmental Management 90 (10): 3048-3056.

Walkley, A., Black, I.A., 1934. An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science 37(1): 29-38.

Westermann, D.T., 2005. Nutritional requirements of potatoes. American Journal of Potato Research 82(4): 301-307.

Winter, M., 2009. Agricultural land use in the era of climate change: The challenge of finding ‘Fit for Purpose’data. Land Use Policy 26(1): S217-S221.

Yang, L., Zhu, A.X., Li, B.L., Qin, C.Z., Pei, T., Liu, B.Y., Li, R.K., Cai, Q.G., 2007.  Extraction  of  knowledge  about  soil environment  relationship  for  soil mapping  using  fuzzy  c means (FCM) clustering. Acta Pedologica Sinica 44: 16–23.

Zadeh, A.L., 1996. Fuzzy Sets. In:  Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems. Selected papers by Lotfi A.Zadeh. Advances in Fuzzy Systems – Application and Theory, Vol.6.  Klir, G.J., Yuan, B. (Eds.). World Scientific Publishing Co Pte Ltd. Singapoure. pp. 394-432.

Zhang,.  B.,  Zhang,  Y.,  Chen,  D.,  White,  R.E.,  Li,  Y.,  2004.  A quantitative evaluation system of soil productivity for intensive agriculture in China. Geoderma 123(3-4): 319-331.

Zhu, A.X., Hudson, B., Burt, J.E., Lubich, K., Simonson, D., 2001. Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of America Journal 65(5): 1463–1472.