Fertility evaluation of land for maize cultivation using GIS, fuzzy logic and ANP (Case study: four basins of Golestan province)

Document Type : Research Paper

Authors

1 Department of Agricultural Sciences, Agronomy Engineering, University of Payam-e- Noor, Iran

2 Prof., Gorgan Uinivesiy of Agricultural and Natural Resources Sciences, Gorgan, Iran.

3 Assoeiated Prof., Agricultural Research and Natural Resources Research Centre of Golestan province, Iran

Abstract

Abstract
Background and objective: One of the most important factors in the production of high yield of plants is the presence of soil nutrient elements. Therefore, the accurate study of the amount of available elements such as macronutrients and micronutrients in soil is essential for the production of important products such as maize. For this purpose, preparing the soil fertility map of four basins agricultural in Golestan province was examined by Geographical Information System (GIS), Fuzzy logic and Analytic Network Process (ANP) for maize cultivation.
Materials and methods: This research was carried out in four basins of Qaresoo, Qarnabad, Mohammadabad and Zaringol in Golestan province. The information of 858 sampled points were used for the preparation of soil elements layers including K and P available, total N, Ca, Mg, Fe, Zn, Cu and Mn available of the soil. First, the raster layer of each element was prepared in ArcGIS10.4 media by ordinary Kriging method in the agricultural lands of the study area. Then, with the help of fuzzy functions, each fuzzy standardization element map was prepared, the maps for each element were weighed using ANP and the soil fertility map was prepared by the layers' overlay the soil fertility map was prepared. In order to adapt the layers produced with the nutritional needs of maize, nutritional requirements of this plant were determined using the scientific resources. Finally, the final layer was divided into five categories of very high fertility, high, moderate, low and very low.
Results: The results showed, the fuzzy value of the agricultural land in the study area was between 0.30 and 0.78. 119771.47 ha, equivalent to 59.87% of the studied area, had a moderate fertility that was more in the middle part of the study area and 80212.65 ha, equivalent to 40.9% had low fertility that was more in the north and south. 84.24 ha, equivalent to 0.04 percent had high fertility and two categories of very low fertility and very high fertility had no share of the study area. Among the basins, Zaringol basin had the highest fertility and the Mohammadabad basin had the lowest fertility. The mean fuzzy value showed, the elements of Mn and K had the highest fuzzy value and the elements of Ca and Zn had the lowest fuzzy value, respectively. The elements of K, P, N, Ca, Mg, Fe, Zn, Cu and Mn reduced the fertility value of the studied lands' area 69.38, 76.02, 96.98, 94.68, 47.81, 49.10, 96.98, 69.57 and 30.28%, respectively.
Conclusion: The results of the fertility zoning of the four studied basins with the help of fuzzy logic and ANP for maize cultivation showed, almost the entire study area (with the exception of 84.24 ha) faced with problems in terms of fertility. Zaringol basin was the most fertile basin for corn cultivation and Qaresoo, Qarnabad and Mohammadabad basins were in the next rank of fertility, respectively.

Keywords

References

  1.  Aama Azghad, A., Khorassani, R., Mokarram, M., and Moezi, A. 2010. Soil fertility evaluation based on soil K, P and organic matter factors for wheat by using fuzzy logic-AHP and GIS techniques. J. Water Soil. 24: 5. 973-984. (In Persian)

    1. Ayoubi, M., Sokouti, R., and Malakouti, M.J. 2016. Study and prediction of the spatial variation of soil organic matter, phosphorus and potassium, case study: north part of Urmia plain. J. Sci. Technol. Agric. Natur. Resour. Water Soil Sci. 20: 76. 177-187. (In Persian)
    2. Bagherzadeh Chahojoyi, A., and Gholizadeh Noghabi, A. 2015. The zoning of soil fertility status based on the content of absorbable nutrients (macros and micro-nutrients) for sugar beet cultivation in Freiman plain using model FAPH and GIS. The First National Conference on Novel Finding in Biosciences and Agriculture. Zabol University, Tehran, 21 May. (In Persian)
    3. Bijanzadeh, E., and Mokarram, M. 2017. Assessment the soil fertility classes for common bean (Phaseolus Vulgaris L.) production using fuzzy-analytic hierarchy process (AHP) method. Austr. J. Crop Sci. 11: 4. 464-473.
    4. Braimoh, A.K., Vlek, P.G., and Stein, A. 2004. Land evaluation for maize based on fuzzy set and interpolation. Environ. Manag. 33: 226-238.
    5. Dobermann, A., and Oberthur, T. 1997. Fuzzy mapping of soil fertility- a case study on irrigated riceland in the Philippines. Geoderma. 77: 317-339.
    6. Elaalem, M. 2012. Land suitability evaluation for sorghum based on Boolean and fuzzy-multi-criteria decision analysis methods. Int. J. Environ. Sci. Develop. 3: 4. 357-361.
    7. Garcia-Melon, M., Onate J.F., Aznar-Bellver, J., Aragones-Beltran, P., and Poveda-Bautista, R. 2008. Farmland appraisal based on the analytic network Process. J. Glob. Optim. 42: 143-155.
    8. Ghibi, M.N., Asadi, F., and Tehrani, M.M. 2014. Guidance integrated management soil fertility and maize nutrient. Soil and Water Research Institute, Iran. 55 p. (In Persian)
    9. Hailu, H., Mamo, T., Keskinen, R., Erik Karltun, E., Gebrekidan, H., and Bekele, T. 2015. Soil fertility status and wheat nutrient content in vertisol cropping systems of central highlands of Ethiopia. Agric. Food Secur. 4: 19. 1-10.
    10. Hamzeh, S., Mokarram, M., and Alavipanah, S.K. 2014. Combination of fuzzy and AHP methods to assess land suitability for barley: case study of semi-arid lands in the southwest of Iran. Desert. 19: 2. 173-181.
    11. Information and Communication Technology Center. 2016. Agricultural statistics, crop year 2014-2015, volume 1: crops. assistance of planning and economic. Ministry of Agriculture- Jahad, Iran. 174 p. (In Persian)
    12. Kamkar, B., Safahani Langroudi, A.R., and Mohammadi, R. 2011. The use of nutrients in crop plants. Jihad Daneshgahi of Mashhad. 500 p. (Translated in Persian)
    13. Kazemi Poshtmasari, H., Tahmasebi Sarvestani, Z., Kamkar, B., Shataei, Sh., and Sadeghi, S. 2012. Agroecological zoning of agricultural lands in Golestan province for canola cultivation by Geographic Information System (GIS) and Analytical Hierarchy Process (AHP). J. Crop Prod. 5: 1. 123-139. (In Persian)
    14. Kazemi Poshtmasari, H., Tahmasebi, Z., Kamkar, B., Shataei, Sh., and Sadeghi, S. 2012. Evaluation of geostatistical methods for estimating and zoning of macronutrients in agricultural lands of Golestan province. Water Soil Sci. 22: 1. 201-220. (In Persian)
    15. Keshavarzi, A. 2010. Land suitability evaluation using fuzzy continuous classification (a case study: Ziaran region). Mod. Appl. Sci. 4: 10. 72-81.
    16. Khajehpour, M.R. 2013. Cereal crops. Jihad Daneshgahi of Isfahan Industrial university Press, Isfahan, Iran. 763 p. (In Persian)
    17. Kihara, J., Huising, J., Nziguheba, G., Waswa, B.S., Njoroge, S., Kabambe, V., Iwuafor, E., Kibunja, C., Esilaba, O.A., and Coulibaly, A. 2016. Maize response to macronutrients and potential for profitability in sub-Saharan Africa. Nutr. Cycl. Agroecosyst. 105: 3. 171-181.
    18. Mohammed, S., and Ahmed, M.M. 2016. Soil suitability assessment for Pearl Millet cropping in Musawa area, Katsina State, Nigeria. J. Plant Agric. Res. 2: 1. 37-43.
    19. Mokarram, M., and Hojati, M. 2017. Using ordered weight averaging (OWA) aggregation for multi-criteria soil fertility evaluation by GIS (case study: southeast Iran). Comput. Electron. Agric. 132: 1-13.
    20. Mokarram, M., and Bardideh, M. 2012. Soil fertility evaluation for wheat cultivation by fuzzy theory approache and compared with boolean method and soil test method in GIS area. Agron. J. (Pajouhesh and Sazandegi). 96: 111-123. (In Persian)
    21. Mustafa, A.A., Singh, M., Sahoo, R.N., Ahmed, N., Khanna, M., Sarangi, A., and Mishra, A.K. 2011. Land suitability analysis for different crops: a multi criteria decision making approach using remote sensing and GIS. Researcher. 3: 12. 61-84.
    22. Nasrollahi, N., Kazemi, H., and Kamkar, B. 2017. Feasibility of ley-farming system performance in a semi-arid region using spatial analysis. Ecol. Indic. 72: 239-248.
    23. Njoroge, R., Otinga, A.N., Okalebo, J.R., Pepela, M., and Merckx, R. 2017. Occurrence of poorly responsive soils in western Kenya and associated nutrient imbalances in maize (Zea mays L.). Field Crops Res. 210: 162-174.
    24. Nosratpour, S., Ardalan, M., Farajnia, A., and Esmali, A. 2010. Investigation of spatial distribution of soil fertility factors in Maraghe farms by means of geographic information systems. Watershed Manag. Res. J. (Pajouhesh & Sazandegi). 87: 2-7. (In Persian)
    25. Rodríguez, E., Peche, R., Garbisu, C., Gorostiza, I., Epelde, L., Artetxe, U., Irizar, A., Soto, M., Becerril, J.M., and Etxebarria, J. 2016. Dynamic quality index for agricultural soils based on fuzzy logic. Ecol. Indic. 60: 678-692.
    26. Romano, G., Dal Sasso, P., Trisorio Liuzzia, G., and Gentile, F. 2015. Multi-criteria decision analysis for land suitability mapping in a rural area of southern Italy. Land Use Policy. 48: 131-143.
    27. Roshani, G., and Gharanjiki, A. 2015. Digital mapping of soil fertility for agricultural service centers of Golestan province using Kriging method. J. Agri. Engine. 37: 2. 87-99. (In Persian)
    28. Roy, R.N., Finck, A., Blair, G.J., and Tandon, H.L.S. 2006. Plant nutrition for food security. A guide for integrated nutrient management. FAO fertilizer and plant nutrition bulletin. 16: 368 p.
    29. Saaty, T.L. 1999. Fundamentals of the Analytic Network Process. Proceedings of ISAHP, Kobe, Japan, August. 12-14.
    30. Sadeghi, S., Kazemi, H., Tahmasebi Sarvestani, Z., Kamkar, B., and Shataei, Sh. 2014. Evaluation of different interpolation methods for determination of spatial variability of micronutrients in agricultural lands of Golestan province some. J. Soil Manag. Sustain. Prod. 4: 3. 323-334. (In Persian)
    31. Sarmadian, F., and Keshavarzi, A. 2014. The use of a hybrid fuzzy-AHP system on the evaluation and mapping of soil fertility. J. Water Soil Resur. Conser. 3: 2. 45-56. (In Persian)
    32. Sehhat, S., and Parizadi, E. 2010. Applying analytical network process in SWOT (case study in Iran insurance company). J. Ind Manang. 1: 2. 105-120. (In Persian)
    33. Seyedmohammadi, J., Esmaeelnejad, L., and Ramezanpour, H. 2016. Increasing efficiency of soil fertility map for rice cultivation using fuzzy logic, AHP and GIS. J. Water Soil. 30: 4. 1114-1129. (In Persian)
    34. Shad, R., Ebadi, H., Mesgari, M., and Vafaeinezhad, A. 2009. Design and implementation of an applied GIS for industrial estates site selection using fuzzy, weight of evidence and genetic methods. J. College Engin. 43: 4. 417-429. (In Persian)
    35. Shahbazi, K., and Besharati, H. 2013. Overview of agricultural soil fertility status of Iran. Land Manag. 1: 1. 1-15. (In Persian)
    36. Sharififar, A., Ghorbani, H., and Sarmadian, F. 2016. Soil suitability evaluation for crop selection using fuzzy sets methodology. Acta Agric. Slov. 107: 1. 159-174.
    37. Sikora, F.J., and Moore, K.P. 2014. Soil test methods from the southeastern United States. Southern cooperative series bulletin. 419: 219 p. (Available on-line at http://www.clemson.edu/sera6/.)
    38. Tang, H.J., and Van Ranst, E. 1992. Testing of fuzzy set theory in land suitability assessment for rainfed grain maize production. Pedologie. 42: 129-147.
    39. Tena, W. and Beyene. 2011. Identification of growth limiting nutrient(s) in Alfisols: soil physio- chemial properties, nutrient concentrations and biomass yield maize. Am. J. Plant Nutr. Fert. Technol. 1: 1. 23-35.
    40. Tuan, N.T., Jian-Jun, Q., Verdoodt, A., Hu, L., and Van Ranst, E. 2011. Temperature and precipitation suitability evaluation for the winter wheat and summer maize cropping system in the Huang-Hai plain of China. Agric. Sci. in China. 10: 2. 275-288.
    41. Zhang, B., Zhang, Y., Chen, D., White, R.E., and Li, Y. 2004. A quantitative evaluation system of soil productivity for intensive agriculture in China. Geoderma. 123: 319-331.
    42. Zhang, J., Su, Y., Wu, J., and Liang, H. 2015. GIS based land suitability assessment for tobacco production and fuzzy set in Shandong province of China. Comput. Electron. Agric. 114: 202-211.
Journal of Crop Production
Volume 13, Issue 3
December 2021
Pages 1-22

Files

Share

How to cite

Statistics