GIS-Based Land Suitability Classification for Wheat Cultivation Using Fuzzy Set Model

Year 2021, Volume: 5 Issue: 4, 524 - 536, 15.12.2021

Murat Güven Tuğaç

https://doi.org/10.31015/jaefs.2021.4.12

Cited By: 3

Abstract

In terms of food safety, it is important to use the lands correctly in agricultural production. In this study, potential crop suitability classes for wheat cultivation were created by using the fuzzy model and GIS together. Spatial and spectral factors considered as model inputs were separated four main groups, such as soil (drainage, depth, texture, CaCO3, stoniness, pH, organic matter, salinity, ESP), topography (slope), water availability (irrigation) and vegetation indices (NDVI). Criterion maps were standardized with the fuzzy membership model. Analytical Hierarchy Process was used to determine the weights of the factors. The vegetation change between years in the study area was determined by using NDVI values obtained from Landsat satellite images. In addition, the effect of temporal difference on land use and land suitability was evaluated. Land suitability index was created in GIS environment by weighted linear combination method and divided into four main suitability classes. The results with the Fuzzy method showed 9.7% (805 ha) of the study area as highly suitable for wheat, 46.5% (3868 ha) as medium suitable, 27.6% (2297 ha) as marginally suitable and 16.2% (1350 ha) as unsuitable. According to these classes, highly suitable and medium suitable classes are the areas that should be evaluated primarily in agricultural production. The Fuzzy model and GIS integration can be effectively used to identify priority areas for crop cultivation and sustainable land use management.

Keywords

Fuzzy set model , Analytical Hierarchy Process , Land suitability analysis , Geographic Information Systems , Wheat

References

• Al Taani, A., Al-husban Y., Farhan, I. (2021). Land suitability evaluation for agricultural use using GIS and remote sensing techniques: The case study of Ma’an Governorate, Jordan. The Egyptian Journal of Remote Sensing and Space Sciences 24. 109–117. Doi: https://doi.org/10.1016/j.ejrs.2020.01.001.
• Arcak, C. (1992). Bala Tarım İşletmesi Topraklarının Detaylı Toprak Etüd ve Haritalaması. TIGEM, sayı:18 (in Turkish).
• Baja, S., Dragovich, D. Chapman, D. (2007). Spatial Based Compromise Programming for Multiple Criteria Decision Making Modeling in Land Use Planning. Environmental Modelling and Assessment Vol. 12: 171-184. Doi: https://doi.org/10.1007/s10666-006-9059-1.
• Baja, S., Chapman., D. M., Dragovich, D. (2002). A conceptual model for defining and assessing land management units using a fuzzy modelling approach in GIS environment. Environmental Management, Vol. 29: 647-661. Doi: https://doi.org/10.1007/s00267-001-0053-8.
• Basso, B., Cammarano, D., Carfagna, E. (2013). “Review of crop yield forecasting methods and early warning systems”, In Proceedings of the First Meeting of the Scientific Advisory Committee of the Global Strategy to Improve Agricultural and Rural Statistics, FAO Headquarters, Rome, Italy, 18–19 July 2013.
• Bodaghabadi, M.B., Martínez-Casasnovas, J.A., Khakili, P., Masihabadi, M.H., Gandomkar, A. (2015). Assessment of the FAO traditional land evaluation methods, a case study: Iranian Land Classification method. Soil Use Manag. Doi: https://doi.org/10.1111/sum.12191.
• Braimoh, A.K., Vlek, P.L.G., Stein, A. (2004). Land evaluation for maize based on fuzzy set theory and interpolation. Environmental Management 33 (2), 226–238. Doi: https://doi.org/10.1007/s00267-003-0171-6.
• Burrough, P. A., McDonnell, R. A. (1998). Principles of Geographical Information Systems, Spatial Information System and Geostatistics, Oxford University Press, New York.
• Burrough, P.A., Frank, A.U (Eds). (1996). Geographic Objects with Indeterminate Boundaries, London: Taylor & Francis. Doi: https://doi.org/10.1201/9781003062660.
• Burrough, P. A. (1989). Fuzzy mathematical methods for soil survey and land evaluation. Journal of Soil Science, 40, 477-492. Doi: https://doi.org/10.1111/j.1365-2389.1989.tb01290.x
• Cengiz, T., Akbulak, C. (2009). Application of analytical hierarchy process and geographic information systems in land-use suitability evaluation: a case study of Dumrek village. Int. J. Sustain. Dev. World Ecol. 16, 286–294. Doi: https://doi.org/10.1080/13504500903106634.
• Chen, J. (2014). GIS-based multi-criteria analysis for land use suitability assessment in City of Regina. Environ. Syst. Res. 3, 1–10. Doi: https://doi.org/10.1186/2193-2697-3-13.
• Davidson, D.A., Theocharopoulos, S.P., Bloksma, R.J. (1994). A land evaluation project in Greece using GIS and based on Boolean and fuzzy set methodologies. International Journal of Geographic Information Systems, 8: 369-384. Doi: https://doi.org/10.1080/02693799408902007.
• Dedeoglu, M., Dengiz, O. (2019). Generating of land suitability index for wheat with hybrid system approach using AHP and GIS. Computers and Electronics in Agriculture, 167, 105062. Doi: https://doi.org/10.1016/j.compag.2019.105062.
• Dent, D., Young, A. (1981). Soil Survey and Land Evaluation. London: George Allen & Unwin Ltd.
• Eastman, J. R. (2012). IDRISI Selva tutorial. Idrisi Production, Clark Labs-Clark University, 45, 51–63.
• Elaalem, M., Comber, A., Fisher, P. (2011). A comparison of fuzzy AHP and ideal point methods for evaluating land suitability. Transactions in GIS 15 (3): 329–346. Doi: https://doi.org/10.1111/j.1467-9671.2011.01260.x.
• ESRI. (2015). ArcGIS ver 10.3. Environmental Systems Research Institute, Redlands, USA.
• Everest, T., Sungur, A., Özcan, H. (2020). Determination of agricultural land suitability with a multiple‑criteria decision‑making method in Northwestern Turkey. International Journal of Environmental Science and Technology. Doi: https://doi.org/10.1007/s13762-020-02869-9.
• Feizizadeh, B., Blaschke, T. (2013). Land suitability analysis for Tabriz County, Iran: a multi-criteria evaluation approach using GIS. Journal of Environmental Planning and Management, Vol. 56, No. 1, 1–23. Doi: http://dx.doi.org/10.1080/09640568.2011.646964.
• FAO. (1985). Guidelines: Land evaluation for irrigated agriculture. FAO Soils Bulletin 55, Rome. ISBN: 92-5-102243-7.
• FAO. (1976). A Framework for Land Evaluation. Soil Bulletin, vol. 32. FAO, Rome. ISBN 92-5-100111-1.
• Garofalo, P., Mastrorilli, M., Ventrella, D., Vonella, A. V., Pasquale Campi, P. (2020). Modelling the suitability of energy crops through a fuzzy-based system approach: The case of sugar beet in the bioethanol supply chain. Energy 196, 117160. Doi: http://doi.org/10.1016/j.energy.2020.117160.
• Halder, J.C. (2013). Land suitability assessment for crop cultivation by using remote sensing and GIS. J. Geogr. Geol. 5, 65–74. Doi: http://dx.doi.org/10.5539/jgg.v5n3p65.
• Herzberg, R., Pham, TG., Kappas, M., Wyss, D., Tran, CTM. (2019). Multi-Criteria Decision Analysis for the Land Evaluation of Potential Agricultural Land Use Types in a Hilly Area of Central Vietnam. Land, 8, 90; Doi: http://doi.org/10.3390/land8060090.
• IPCC. (2014). Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Porter, J.R., L. Xie, A.J. Challinor, K. Cochrane, S.M. Howden, M.M. Iqbal, D.B. Lobell, and M.I. Travasso, Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 485-533.
• Jiang, H., Eastman, J. R. (2000). Application of fuzzy measures in multicriteria evaluation in GIS. IJGIS 14(2):173–184. Doi: https://doi.org/10.1080/136588100240903.
• Keshavarzi, A., Sarmadian, F. (2009). Investigation of fuzzy set theory`s efficiency in land suitability assessment for irrigated wheat in Qazvin province using Analytic hierarchy process (AHP) and multi variate regression methods. Proc. ‘Pedometrics 2009’ Conf, August 26-28, Beijing, China.
• Labus, M., Nielsen, G., Lawrence, R., Engel, R., Long, D. (2002). Wheat yield estimates using multi-temporal NDVI satellite imagery. Int. J. Remote Sens. 23, 4169–4180. Doi: https://doi.org/10.1080/01431160110107653.
• Malczewski, J. (2006). GIS‐based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726. Doi: https://doi.org/10.1080/13658810600661508.
• Malczewski, J. (2004). GIS-based land-use suitability analysis: a critical overview. Progress in Planning 62, 3–65. Doi: https://doi.org/10.1016/j.progress.2003.09.002.
• McBratney, A. B., Odeh, I. O. A. (1997). "Application of Fuzzy sets in soil science: Fuzzy logic, Fuzzy measurements and Fuzzy decisions." Geoderma 77: 85-113. Doi: https://doi.org/10.1016/S0016-7061(97)00017-7.
• Maleki, P., Landi, A., Sayyad, Gh., Baninemeh, J., Zareian, Gh. (2010). Application of fuzzy logic to land suitability for irrigated wheat. 19th World Congress of Soil Science, Soil Solutions for a Changing World, 1 – 6 August, Brisbane, Australia.
• Mendel, J. M. (1995). Fuzzy Logic Systems for Engineering: A Tutorial, IEEE Proc., 83(3),pp. 345-377. Doi: https://doi.org/10.1109/5.364485.
• Mohammadrezae, N., Pazira, E., Sokoti, R., Ahmadi, A. (2014). Land Suitability Evaluation for Wheat Cultivation by Fuzzy-AHP, Fuzzy- Simul Theory Approach As Compared With Parametric Method in the Southern Plain Of Urmia. Bull. Env. Pharmacol. Life Sci., Vol 3, Spl Issue III,112-117.
• Munns, R., Gilliham, M. (2015). Salinity tolerance of crops – what is the cost? New Phytol. 208, 668–673. Doi: https://doi.org/10.1111/nph.13519.
• Nurmiaty, S., Baja, S. (2014). Using Fuzzy Set Approaches in a Raster GIS for Land Suitability Assessment at a Regional Scale: Case Study in Maros Region, Indonesia. Modern Applied Science; Vol. 8, No. 3; ISSN 1913-1844 E-ISSN 1913-1852. Published by Canadian Center of Science and Education. Doi: http://dx.doi.org/10.5539/mas.v8n3p115.
• Nwer, B. (2005). The application of land evaluation technique in the north-east of Libya, published PhD thesis, Cranfield University, Silsoe.
• Reshmidevi, T. V., Eldho, T. I., Jana, R. (2009). A GIS-integrated fuzzy rule-based inference system for land suitability evaluation in agricultural watersheds. Agricultural Systems 101, 101–109. Doi: http://dx.doi.org/10.1016/j.agsy.2009.04.001.
• Saaty, T.L. (1980). The Analytic Hierarchy Process. McGraw-Hill Publishing Company, New York, USA.
• Sicat, R. S,. Carranza, E. J. M., Nidumolu, U. B. (2005). Fuzzy modeling of farmers’ knowledge for land suitability classification. Agr Syst 83: 49-75. Doi: http://dx.doi.org/10.1016/j.agsy.2004.03.002.
• Sharififar, A., Ghorbani, H., Sarmadian, F. (2016). Soil suitability evaluation for crop selection using fuzzy sets methodology. Acta Agricul. Slovenica 107, 159–174. Doi: http://dx.doi.org/10.14720/aas.2016.107.1.16.
• Soil Survey Division Staff. (1987). Keys to Soil taxonomy. USDA Natural Resources Conservation Service, Washington DC.
• Sys, C., Van Ranst, E., Debaveye, J. (1993). Land Evaluation, part Ш : crop requirements. International Training Center for post graduate soil scientists. Ghent university, Ghent. 199 p.
• Tashayo, B., Honarbakhsh, A., Akbari, M., Eftekhari, M. (2020). Land suitability assessment for maize farming using a GIS-AHP method for a semi- arid region, Iran. Journal of the Saudi Society of Agricultural Sciences 19, 332–338. Doi: https://doi.org/10.1016/j.jssas.2020.03.003.
• Tang, H., Rast, E. V., Groenemans, R. (1997). Application of fuzzy set theory to land suitability assessment. Malaysian Journal of Soil Science 3: 39-58.
• Tang, H., Van Ranst, E., Sys, C. (1992). An approach to predict land production potential for irrigated and rainfed winter wheat in Pinan County, China. Soil Technology, 5, 213-224.
• Tercan, E., Dereli, M.A. (2020). Development of a land suitability model for citrus cultivation using GIS and multi-criteria assessment techniques in Antalya province of Turkey. Ecological Indicators 117, 106549. Doi: https://doi.org/10.1016/j.ecolind.2020.106549.
• Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ., 8, 127–150. Doi: https://doi.org/10.1016/0034-4257(79)90013-0.
• Tugac, M. G., Sefer, F. (2021). Türkiye’de zeytin (Olea europaea L.) üretimine uygun alanların coğrafi bilgi sistemleri (CBS) tabanlı çoklu kriter analizi ile belirlenmesi, Ege Univ. Ziraat Fak. Derg., 58 (1): 97-113 (in Turkish). Doi: https://doi.org/10.20289/zfdergi.678474.
• Van Diepen, C.A., Van Keulen, H., Wolf, J., Berkhout, J.A.A. (1991). Land evaluation: from intuition to quantification. In: B.A. Stewart (ed.), Advances in Soil Science. Springer, New York, 139-204 pp. Doi: https://doi.org/10.1007/978-1-4612-3030-4_4.
• Van Ranst, E., Tang, H. (1999). Fuzzy reasoning versus Boolean logic in land suitability assessment. Malaysian Journal of Soil Science 3:39-58.
• Voogd, J. H. (1982). Multicriteria evaluation for urban and regional planning. Delftsche Uitgevers Maatschappij. Doi: https://doi.org/10.6100/IR102252.
• Wang, F., Hall, G.B., Subaryono. (1990). Fuzzy information representation and processing in conventional GIS software: database design and applications. Int. Jl. Geographical Information Systems 4, 261-283. Doi: https://doi.org/10.1080/02693799008941546.
• Yalew, S. G., Griensven, A. V., Mul, M. L. Zaag, P. V. (2016). Land suitability analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP techniques. Model. Earth Syst. Environ., 2: 101. Doi: https://doi.org/10.1007/s40808-016-0167-x.
• Zabihi, H., Anuar Ahmad, A., Vogeler, I., Said, M, N; Golmohammadi, M., Golein, B., Nilashi, M. (2015). Land suitability procedure for sustainable citrus planning using the application of the analytical network process approach and GIS. Computers and Electronics in Agriculture 117, 114–126. Doi: https://doi.org/10.1016/j.compag.2015.07.014.
• Zadeh L.A. (1965). Fuzzy sets. Information and Control 8,338-353. Doi: http://dx.doi.org/10.1016/S0019-9958(65)90241-X.
• Zhang, J., Su, Y., Wu, J., Liang, H. (2015). GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China. Computers and Electronics in Agriculture 114, 202–211. Doi: https://doi.org/10.1016/j.compag.2015.04.004.