Agricultural Land Suitability Assessment using Fuzzy Logic and Geographic Information System Techniques

Authors

  • Atijosan A  COPINE, Obafemi Awolowo University Campus, Ile-Ife, Nigeria
  • Muibi K  COPINE, Obafemi Awolowo University Campus, Ile-Ife, Nigeria
  • Ogunyemi S  COPINE, Obafemi Awolowo University Campus, Ile-Ife, Nigeria
  • Adewoyin J  COPINE, Obafemi Awolowo University Campus, Ile-Ife, Nigeria
  • Badru R  COPINE, Obafemi Awolowo University Campus, Ile-Ife, Nigeria
  • Alaga A  COPINE, Obafemi Awolowo University Campus, Ile-Ife, Nigeria
  • Shaba A  NASRDA, Abuja, Nigeria

Keywords:

Fuzzy logic, G.I.S, S.I model, oil palm and land suitability map

Abstract

Fuzzy logic and Geographical Information System (GIS) based techniques were used for land suitability evaluation and production of land suitability maps for oil palm cultivation in Ife Central Local Government Area of Osun State, Southwestern Nigeria. Land and climatic characteristics data were obtained from a previous soil fertility evaluation of Ife Central Local Government Area. Ten land and climatic characteristics fitted to membership functions were used in computing land suitability index for each point observation based on the Sematic Import (SI) model. Average membership value for cation exchange capacity (CEC) is the lowest (0.216), whereas that of pH is the highest (0.809). Computed land suitability index for oil palm in the study area ranged from 0.32 to 0.52 with a mean of 0.44. CEC, annual rainfall and months of dry season with mean membership functions of 0.21, 0.22 and 0.24 respectively are the major constraints to oil palm cultivation. Land suitability map was produced using G.I.S based techniques. The use of fuzzy logic has proved valuable for identifying major constraints to oil palm cultivation. The production of suitability maps through the use of GIS techniques will further enhance decision making and strategies in overcoming these constraints.

References

  1. Ademola, K., Paul, L., and Alfred, S. 2004. Land evaluation for maize based on fuzzy set and interpolation. Environmental Management 33(2): 226-238. doi: 10.1007/s00267-003-0171-6.
  2. Adzemi, M. A. 2014. Comparison Methods of Estimation Potential Evapotranspiration for Oil Palm. Journal of Biology, Agriculture and Healthcare. ISSN 2224-3208 (Paper) ISSN 2225-093X (Online) 4(6), 45-47.
  3. Amit, J., and Babita, J. 2012. Fuzzy Modelling and Similarity based Short Term Load Forecasting using Swarm Intelligence-A step towards Smart Grid. J. C. Bansal et al. (eds.), Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), Advances in Intelligent Systems and Computing. doi: 10.1007/978-81-322-1041-2.
  4. Braimoh, A. K., and Vlek, P. L. G. 2004. Impacts of land-cover change on soil properties in Northern Ghana, Land degradation and Development, 15: 65–74. doi: 10.1002/ldr.590.
  5. Burrough, P. A., and McDonnell R.A. 2000. Principle of Geographical Information Systems. Oxford University Press, New York.
  6. Burrough, P.A. 1989. Fuzzy Mathematical Models for Soil Survey and Land Eavaluation.  Journal of Soil Science. 40, 477-492. doi: 10.1111/j.1365-2389.1989.tb01290.x
  7. Fang Q., Bryan C., Yuhong Z., Caiyun Z and Harini S. 2014. Modelling land suitability/capability using fuzzy evaluation. GeoJournal. 79:167–182. doi: 10.1007/s10708-013-9503-0
  8. FAO. 1976. A framework for land evaluation. Soils Bulletin 32. FAO, Rome. p 72.
  9. Md. P. A., Sadia W., Raman Bai V., and Ijaz-ul-Mohsin (2008). Development of New Water Quality Model Using Fuzzy Logic System for Malaysia. Open Environmental Sciences, 2, 101-106.  doi: 10.2174/1876325100802010101
  10. Ogunlade M., Aikpokpodion P and Braimoh A. 2012. Land suitability evaluation for cocoa production in Nigeria using fuzzy methodology, Int. J. Sustain. Crop Prod. 7(3): 13-20.
  11. Orewole M. O., Alaigba D.B., and Oviasu O.U. (2015). Riparian Corridors Encroachment and Flood Risk Assessment in Ile-Ife: A GIS Perspective. Open Transactions on Geosciences 2(1): 19-20. doi: 10.15764/GEOS.2015.01002 .
  12. Panel, A. P. (2014). Grain, fish, money: financing Africa’s Green and Blue Revolutions. Africa Progress Report 2014.
  13. Raman Bai. V., Reinier B and Mohan. S. (2009). Fuzzy logic water quality index and importance of water quality parameters.  Air, Soil and Water Research 2009:2, 51–59.
  14. Rahaman M. (1976). Review of the basement geology of Southwest Nigeria. Geology of            Nigeria, 41–58.
  15. Sivanandam, S. N., Sumathi S. and Deepa S. N. (2007) Introduction to Fuzzy Logic using MATLAB. Springer-Verlag Berlin Heidelberg. 2-4. doi: 10.1007/978-3-540-35781-0.
  16. Sys C. (1985). Land evaluation. State University of Ghent, Ghent; the Netherlands.
  17. Sarmadian, F., Keshavarzi, A., Rooien, A., Zahedi, G. , Javadikia, H. , & Iqbal, M. (2014). Support Vector Machines Based-Modeling of Land Suitability Analysis for Rainfed Agriculture. Journal of Geosciences and Geomatics, 2(4), 165-171. doi:10.12691/jgg-2-4-4.

Downloads

Published

2015-12-25

Issue

Section

Research Articles

How to Cite

[1]
Atijosan A, Muibi K, Ogunyemi S, Adewoyin J, Badru R, Alaga A, Shaba A, " Agricultural Land Suitability Assessment using Fuzzy Logic and Geographic Information System Techniques, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 1, Issue 5, pp.113-118, November-December-2015.