Agricultural land suitability assessment with GIS-based multi-criteria decision analysis and geostatistical approach in semi-arid regions

Öz

For sustainable land use planning, evaluating land characteristics and making suitable land use decisions is a priority and critical step. In order to make these evaluations safely, spatial analyzes of many criteria should be made. In this study, the suitability of the land for wheat production was evaluated by Geographical Information Systems (GIS) based Multiple Criteria Decision Analysis (MCDA) in semi-arid conditions. In obtaining the land suitability map; fuzzy set model, Analytical Hierarchy Process (AHP) and GIS are integrated. Ecological criteria weights for agricultural land suitability were determined by AHP. In the suitability analysis, a total of criteria including soil and topographic features were evaluated. Geostatistical analysis approach was applied to determine the spatial variability of soil properties (sand, clay, silt, pH, OM, CEC, ESP, CaCO3, EC). The lowest variation among soil properties was observed in pH (3.8%), while the largest variation was observed in ESP content (107.5%). The nugget/sill ratio is poor for EC and pH, while other soil properties are moderately spatially dependent. According to the results of the analysis, 25.7% (3.226 km2) of the area is highly suitable, while 27.6% (3.457 km2) is moderately suitable and 19.5% (2.440 km2) is marginally suitable for wheat cultivation. In addition, 27.2% (3.415 km2) of the area is not suitable for agricultural production. The use of geostatistical modeling, MCDA and GIS together is very beneficial in making agricultural land management decisions.

Anahtar Kelimeler

• Land suitability
• Multi-criteria decision
• GIS
• Geostatistics
• Fuzzy model
• AHP

Kaynakça

1. AbdelRahman, M. A. E., Zakarya, Y. M., Metwaly, M. M., & Koubouris, G. (2020). Deciphering soil spatial variability through geostatistics and interpolation techniques. Sustainability, 13, 194. https://doi.org/10.3390/su13010194.
2. Aggag, A. M., & Alharbi, A. (2022). Spatial analysis of soil properties and site-specific management zone delineation for the South Hail Region, Saudi Arabia. Sustainability, 14, 16209. https://doi.org/10.3390/su142316209.
3. Arab, S. T., & Ahamed, T. (2022). Land suitability analysis for potential vineyards extension in afghanistan at regional scale using remote sensing datasets. Remote Sensing, 14, 4450. https://doi.org/10.3390/rs14184450. Arora, N. K. (2019) Impact of climate change on agriculture production and its sustainable solutions. Environmental Sustainability, 2, 95–96. https:// doi. org/ 10. 1007/ s42398- 019-00078-w.
4. Azadi, A., & Baninemeh, J. (2022). Performance analysis of geostatistical approach and PCA techniques for mapping cation exchange capacity in the South West of Iran. Eurasian Soil Science, 55(12), 1749–1760. https://doi.org/10.1134/S1064229322601494.
5. Bagherzadeh, A., & Gholizadeh, A. (2018). Assessment of soil fertility for sugar beet production using fuzzy AHP approach and GIS in the Northeastern region of Iran. Agricultural Research, 7(1), 61-71. https://doi.org/10.1007/s40003-018-0295-9. Baroudy, A. A. E. (2016). Mapping and evaluating land suitability using a GIS-based model. Catena, 140, 96–104. http://dx.doi.org/10.1016/j.catena.2015.12.010.
6. Behera, S. K., Mathur, R. K., Shukla, A. K., Suresh, K., & Prakash, C. (2018). Spatial variability of soil properties and delineation of soil management zones of oil palm plantations grown in a hot and humid tropical region of southern India. Catena, 165, 251–259. https://doi.org/10.1016/j.catena.2018.02.008.
7. Bogunovic, I., Kisic, I., Mesic, M., Percin, A., Zgorelec, Z., Bilandžija, D., Jonjic A., & Pereira, P. (2017). Reducing sampling intensity in order to investigate spatial variability of soil pH, organic matter and available phosphorus using co-kriging techniques. A case study of acid soils in Eastern Croatia. Archives of Agronomy and Soil Science, 63, 1852-1863. https://doi.org/10.1080/03650340.2017.1311013.
8. Burrough, P. A. (1996). Natural objects with Indeterminate Boundaries. in P.A. Burrough and A.U. Frank (eds.), Geographic Objects with Indeterminate Boundaries, London: Taylor & Francis, pp. 3–28. Burrough, P. A., & Mc Donnell, R. A. (1998). Principles of geographical information systems. Oxford Univ. Press, Oxford. 333 pp.

9. Cambardella, C. A., & Karlen, D. L. (1999). Spatial analysis of soil fertility parameters. Precision Agriculture, 1, 5–14. https://doi.org/10.1023/A:1009925919134.
10. Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F., & Konopka, A. E. (1994). Fieldscale variability of soil properties in Central Iowa Soils. Soil Science Society of America Journal, 58, 1501-1511.
11. Dedeoğlu, M., & Dengiz, O. (2019). Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS. Computers and Electronics in Agriculture, 167, 105062. https://doi.org/10.1016/j.compag.2019.105062.
12. Dengiz, O., & Baskan, O. (2009). Land quality assessment and sustainable land use in Salt Lake (Tuz Gölü) specially protected area. Environmental Monitoring and Assessment, 148(1-4), 233–243. https://doi.org/10.1007/s10661-008-0154-4.
13. Dengiz, O. (2020). Soil quality index for paddy fields based on standard scoring functions and weight allocation method. Archives of Agronomy and Soil Science, 66(3), 301–315. https://doi.org/10.1080/03650340.2019.1610880. Di Virgilio, N., Monti, A., & Venturi, G. (2007). Spatial variability of switchgrass (Panicum virgatum L.) yield as related to soil parameters in a small field. Field Crops Research, 101, 232–239. https://doi.org/10.1016/j.fcr.2006.11.009.
14. Durdevic, B., Jug, I., Jug, D., Bogunovic, I., Vukadinovic, V., Stipeševic, B., & Brozovic, B. (2019). Spatial variability of soil organic matter content in Eastern Croatia assessed using different interpolation methods. International Agrophysics, 33(1), 31-39. https://doi.org/10.31545/intagr/104372.
15. Emadi, M., Baghernejad, M., Emadi, M., & Maftoun, M. (2008). Assessment of some soil properties by spatial variability in saline and sodic soils in Arsanjan plain, Southern Iran. Pakistan journal of biological sciences, 11(2), 238–243. https://doi.org/10.3923/pjbs.2008.238.243.
16. Everest, T., Sungur, A., & Özcan, H. (2021). Determination of agricultural land suitability with a multiple criteria decision making method in Northwestern Turkey. International Journal of Environmental Science and Technology, 18, 1073–1088. https://doi.org/10.1007/s13762-020-02869-9.
17. Goovaerts, P. (1998). Geostatistical tools for characterizing the spatial variability of microbiological and physio-chemical soil properties. Biology and Fertility of Soils, 27, 315–334. https://doi.org/10.1007/s003740050439.
18. FAO. (1976). A Framework for land evaluation. Soil Bulletin, vol. 32. FAO, Rome. ISBN 92-5- 100111-1.
19. FAO. (1993). Guidelines for land-use planning. FAO Development Series I. FAO, Rome.
20. FAO. (2017). The future of food and agriculture. Trends and challenges.
21. FAO-TOB. (2020). Ulusal Bozkır Koruma Strateji ve Eylem Planı. Doğa Koruma Merkezi, Türkiye’nin Bozkır Ekosistemlerinin Korunması ve Sürdürülebilir Yönetimi Projesi Yayını. 167 sayfa. Ankara. Birleşmiş Milletler Gıda ve Tarım Örgütü (FAO), Tarım ve Orman Bakanlığı (TOB).
22. Jiang, H. L., Liu, G. S., Liu, S. D., Li, E. H., Wang, R., Yang, Y. F., & Hu, H. C. (2012). Delineation of site-specific management zones based on soil properties for a hillside field in central China. Archives of Agronomy and Soil Science, 58(10), 1075–1090. https://doi.org/10.1080/03650340.2011.570337a.
23. Kariuki, S. K., Zhang, H., Schroder, J. L., Hanks, T., Payton, M., & Morris, T. (2009). Spatial variability and soil sampling in a grazed pasture. Communications in Soil Science and Plant Analysis, 40(9-10), 1674–1687. https://doi.org/10.1080/00103620902832089.
24. Khan, M. Z., Islam, M. A., Sadiqul Amin, M., & Bhuiyan, M. M. R. (2019). Spatial variability and geostatistical analysis of selected soil. Bangladesh Journal of Scientific and Industrial Research, 54(1), 55-66. https://doi.org/10.3329/bjsir.v54i1.40731.
25. Kılıç, M. O., Ersayın, K., Gunal, H., Khalofah, A., & Alsubeie, M. S. (2022). Combination of fuzzy-AHP and GIS techniques in land suitability assessment for wheat (Triticum aestivum) cultivation. Saudi Journal of Biological Sciences, 29(4), 2634–2644. https://doi.org/10.1016/j.sjbs.2021.12.050.
26. Liu, Z., Zhou, W., Shen, J., He, P., Lei, Q., & Liang, G. (2014). A simple assessment on spatial variability of rice yield and selected soil chemical properties of paddy fields in South China. Geoderma, 235, 39-47. http://dx.doi.org/10.1016/j.geoderma.2014.06.027.
27. Lopez-Fando, C., & Pardo, M. T. (2011). Soil carbon storage and stratification under different tillage systems in a semi-arid region, Soil & Tillage Research 111: 224–230. https://doi.org/10.1016/j.still.2010.10.011.
28. Malczewski, J. (2006). GIS‐based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703-726. https://doi.org/10.1080/13658810600661508.
29. Malczewski, J. (2011). Local weighted linear combination. Transactions in GIS, 15(4), 439–455. https://doi.org/10.1111/j.1467-9671.2011.01275.x.
30. McBratney, A. B., & Pringle, M. J. (1999). Estimating average and proportional variograms of soil properties and their potential use in precision agriculture. Precision Agriculture 1(2), 125. https://doi.org/10.1023/A:1009995404447.
31. Mishra, G, Sulieman, M. M., Kaya, F., Francaviglia, R., Keshavarzi, A., Bakhshandeh, E., Loum, M., Jangir, A., Ahmed, I., Elmobarak, A., Basher, A., & Rawat, D. (2022). Machine learning for cation exchange capacity prediction in different land uses. Catena, 216, 106404. https://doi.org/10.1016/j.catena.2022.106404.
32. Mousavi, S. R., Sarmadian, F., Dehghani, S., Sadikhani, M. R., & Taati, A. (2017). Evaluating inverse distance weighting and kriging methods in estimation of some physical and chemical properties of soil in Qazvin Plain. Eurasian Journal of Soil Science, 6(4), 327–336. https://doi.org/10.18393/ejss.311210.
33. Mousavifard, S. M., Hamireza Momtaza, H., Sepehr, E., Davatgar, N., & Sadaghiani, M. H. R. (2013). Determining and mapping some soil physico-chemical properties using geostatistical and GIS techniques in the Naqade region, Iran. Archives of Agronomy and Soil Science, 59(11), 1573–1589. http://dx.doi.org/10.1080/03650340.2012.740556.
34. Nguyen, T. T., Verdoodt, A., Tran, V. Y., Nele Delbecque, N. Tran, T. C., & Van Ranst, E. (2015). Design of a GIS and multi-criteria based land evaluation procedure for sustainable land-use planning at the regional level. Agriculture, Ecosystems and Environment, 200, 1–11. http://dx.doi.org/10.1016/j.agee.2014.10.015.
35. Obalum, S. E., Chibuike, G. U., Peth, S., & Ouyang, Y. (2017). Ouyang soil organic matter as sole indicator of soil degradation. Environmental Monitoring and Assessment, 89(4), 1–19. https://doi.org/10.1007/s10661-017-5881-y.
36. Öner, N., Erşahin, S., Ayan, S., & Özel, H. B. (2016). Rehabilitation of semi-arid areas in central anatolia. Anatolian Journal of Forest Research, 2 (1-2), 32-44.
37. Özkan, B., Dengiz, O., & Turan, İ. D. (2020). Site suitability analysis for potential agricultural land with spatial fuzzy multi‑criteria decision analysis in regional scale under semi‑arid terrestrial ecosystem. Scientific Reports, 10:22074. https://doi.org/10.1038/s41598-020-79105-4.
38. Piccini, C., Marchetti, A., & Francaviglia, R. (2014). Estimation of soil organic matter by geostatistical methods: use of auxiliary information in agriculture and environmental assessment. Ecological Indicators, 36, 301–314. https://doi.org/10.1016/j.ecolind.2013.08.009.
39. Pilevar, A. R., Matinfar, H. R., Sohrabi, A., & Sarmadian, F. (2020). Integrated fuzzy, AHP and GIS techniques for land suitability assessment in semi-arid regions for wheat and maize farming. Ecological Indicators, 110, 105887. https://doi.org/10.1016/j.ecolind.2019.105887.
40. Pham, T. G., Kappas, M., Van Huynh, C., & Nguyen, L. H. K. (2019). Application of ordinary kriging and regression kriging method for soil properties mapping in hilly region of central Vietnam. ISPRS International Journal of Geo-Information, 8(3), 147. https://doi.org/10.3390/ijgi8030147.
41. Prosekov, A. Y., & Ivanova, S. A. (2018). Food security: the challenge of the present. Geoforum 91, 73–77. https://doi.org/10.1016/j.geoforum.2018.02.030.
42. Raffa, W. D., Bogdanski, A., & Tittonell, P. (2015). How does crop residue removal affect soil organic carbon and yield? A hierarchical analysis of management and environmental factors. Biomass and Bioenergy, 81, 345-355. http://dx.doi.org/10.1016/j.biombioe.2015.07.022.
43. Ramamurthy, V., Reddy, G. P. O., & Kumar, N. (2020). Assessment of land suitability for maize (Zea mays L) in semi-arid ecosystem of southern India using integrated AHP and GIS approach. Computers and Electronics in Agriculture, 179, 105806. https://doi.org/10.1016/j.compag.2020.105806.
44. Reza, S. K., Nayak, D. C., Chattopadhyay, T., Mukhopadhyay, S., Singh, S. K., & Srinivasan, R. (2016). Spatial distribution of soil physical properties of alluvial soils: a geostatistical approach. Archives of Agronomy and Soil Science, 62, 972-981. https://doi.org/10.1080/03650340.2015.1107678.
45. Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation; McGraw-Hill International: New York, NY, USA.
46. Sharma, R., & Sood, K. (2020). Characterization of spatial variability of soil parameters in apple orchards of himalayan region using geostatistical analysis, Communications in Soil Science and Plant Analysis, 1-13. https://doi.org/10.1080/00103624.2020.1744637.
47. Saidian, M., Godinez, L. J., & Prasad, M. (2016). Effect of clay and organic matter on nitrogen adsorption specific surface area and cation exchange capacity in shales (mudrocks). Journal of Natural Gas Science and Engineering, 33, 1095–1106. https://doi.org/10.1016/j.jngse.2016.05.064.
48. Selmy, S., Abd El-Aziz, S., El-Desoky, A., & El-Sayed, M. (2022). Characterizing, predicting, and mapping of soil spatial variability in Gharb El-Mawhoub area of Dakhla Oasis using geostatistics and GIS approaches. Journal of the Saudi Society of Agricultural Sciences, 2, 383–396. https://doi.org/10.1016/j.jssas.2021.10.013.
49. Shaloo, Singh, R. P., Bisht, H., Jain, R., Suna, T., Bana, R. S., Godara, S., Shivay, Y. S., Singh, N., Bedi, J., Begam, S., Tamta, M., & Gautam, S. (2022). Crop-suitability analysis using the analytic hierarchy process and geospatial techniques for cereal production in North India. Sustainability, 14, 5246. https://doi.org/10.3390/su14095246.
50. Sys, C. (1985). Land Evaluation. State University of Ghent, Belgium.
51. Şenol, H., Alaboz, P., Demir, S., & Dengiz, O. (2020). Computational intelligence applied to soil quality index using GIS and geostatistical approaches in semiarid ecosystem. Arabian Journal of Geosciences, 13, 1-20. https://doi.org/10.1007/s12517-020-06214-9.
52. Tashayo, B., Honarbakhsh, A., Azma, A., & Akbari, M. (2020). Combined fuzzy AHP–GIS for agricultural land suitability modeling for a Watershed in Southern Iran. Environmental Management, 66, 364–376. https://doi.org/10.1007/s00267-020-01310-8.
53. Tesfahunegn, G. B., Tamene, L., & Vlek, P. L. G. (2011). Catchment-scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia. Soil Tillage Res. 117, 124–139. https://doi.org/10.1007/s00267-020-01310-8.
54. Tóth, G., Hermann, T., Ravina da Silva, M., & Montanarella, L. (2018). Monitoring soil for 712 sustainable development and land degradation neutrality. Environ.Monit.Assess. 190, 57. https://doi.org/10.1007/s10661-017-6415-3.
55. Trangmar, B. B., Yost, R. S., & Uehara, G. (1985). Application of geostatistics to spatial studies 715 of soil properties. Advances in Agronomy, 38, 45–93. https://doi.org/10.1016/s0065-2113(08)60673-2.
56. Tuğaç, M. G. (2021). GIS-based land suitability classification for wheat cultivation using fuzzy set model. Int J Agric Environ Food Sci 5 (4):524-536. https://doi.org/10.31015/jaefs.2021.4.12.
57. TUİK. (2021). https://biruni.tuik.gov.tr/medas/?locale=tr Erişim tarihi: 20.11.2022.
58. Usowicz, B., & Lipiec, J. (2021). Spatial variability of saturated hydraulic conductivity and its links with other soil properties at the regional scale. Nature, Scientific Reports, 11, 8293. https://doi.org/10.1038/s41598-021-86862-3.
59. Ünalan, G., Yüksel, V., Tekeli, T, Gönenç, O., Seyirt, Z., & Hüseyin, S. (1976). Haymana-Polatlı yöresinin (güneybatı Ankara) Üst Kretase-Alt Tersiyer stratigrafisi ve paleocoğrafik evrimi. Türkiye Jeoloji Kurumu Bülteni, 19, 159-176.
60. Webster, R. (1985). Quantitative spatial analysis of soil in the field. Advances in Soil Science, 1–70. https://doi.org/10.1007/978-1-4612-5090-6_1.
61. Webster, R., & Oliver, M. A. (2001). Geostatistics for Environmental Scientist. John Wiley and Sons.
62. Wilding, L. P. (1985). Spatial variability: its documentation, accommodation and implication to soil surveys, In Nielsen D.R. and Bouma J. (Eds.). Soil Spatial Variability: Pudoc, Wageningen, Netherlands, 166–194.
63. Yeneneh, N., Elias, E., & Feyisa, G. L. (2022) Assessment of the spatial variability of selectedsoil chemical properties using geostatistical analysis in the north-western highlands of Ethiopia. Acta Agriculturae Scandinavica, Section B — Soil & Plant Science, 72(1), 1009-1019, https://doi.org/10.1080/09064710.2022.2142658.
64. Zhan, J., He, Y., Zhao, G., Li, Z., Yuan, Q., & Liu, L. (2020). Quantitative Evaluation of the spatial variation of surface soil properties in a typical alluvial plain of the lower yellow river using classical statistics, geostatistics and single fractal and multifractal methods. Applied Sciences, 10(17), 5796; https://doi.org/10.3390/app10175796.
65. Zadeh, L. A. (1965). Fuzzy sets. Inf. Control 8, 338–353.
66. 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. http://dx.doi.org/10.1016/j.compag.2015.04.004.