Land Suitability Evaluation for Cassava Production Using Integral Value Ranked Fuzzy AHP and GIS Techniques
AUTHORS
Atijosan Abimbola,COPINE (Advanced Space Application Laboratory),Obafemi Awolowo University Campus, Ile-Ife, Nigeria
Essien Ewang,Centre for Satellite Technology Development,Obasanjo Space Centre, Abuja, Nigeria
Badru Rahmon,COPINE (Advanced Space Application Laboratory),Obafemi Awolowo University Campus, Ile-Ife, Nigeria
Alaga Taofeek,COPINE (Advanced Space Application Laboratory),Obafemi Awolowo University Campus, Ile-Ife, Nigeria
ABSTRACT
This study presents an improved integral value ranked Fuzzy Analytic Hierarchy Process (FAHP) and Geographic Information System (GIS) based Multi-Criteria Decision Making (MCDM) technique to help decision makers/farmers evaluate and map suitable lands for optimum cassava production. Selected input/ suitability factors chosen from literature and experts’ opinion were: pH, organic carbon, cation exchange capacity, slope, aspect, elevation, temperature, relative humidity, rain, distance from river and road. The improved integral value ranked FAHP method was used in prioritizing and assigning weights to each causative factor in the MCDM process due to its effectiveness, consistency and ease of implementation. Land suitability maps were created using GIS techniques based on the aggregation of the various input factors and their derived weights. The final outcome of the aggregation was reclassified into four classes using standard deviation classification method (this method shows how much a feature deviate from the mean). Results obtained showed that 40% of the total area was highly suitable (S1), 36% was moderate suitability (S2), 20% was marginally suitable (S3) and 4% was not suitable (N). Results also showed that pH and organic content of the soil were the major determinant of soil suitability for cassava cultivation in the study area. This study showed the effectiveness of the proposed approach in assessing and mapping suitable areas for optimum cassava production within the study area.
KEYWORDS
Land suitability, Cassava, PSO, GIS, Multi-criteria decision
REFERENCES
[1] Agus, F., Sholeh, R., Hatta, H. R., Munawwarah, and T., “Fuzzy analytical hierarchy process for land suitability analysis compared to analytical hierarchy process,” In 1st International Conference on Science and Technology for Sustainability, Oct., (2014)
[2] Rossiter and D. G., “A theoretical framework for land evaluation,” Geoderma, vol.72, no.3, pp.165-190, (1996)
[3] Baja, S., Arif, S., Neswati, and R., “Developing a user-friendly decision tool for agricultural land use allocation at a regional scale,” Mod. Appl. Sci., (2017)
[4] Nurmiaty, N., Baja, and S., “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, pp.115-125, (2014)
[5] Habibie, M. I., Noguchi, R., Shusuke, M., Ahamed, and T., “Land suitability analysis for maize production in Indonesia using satellite remote sensing and GIS-based multicriteria decision support system,” GeoJournal, pp.1-31, (2019)
[6] Biratu, G. K., Elias, E., and Ntawuruhunga, P., “Soil fertility status of cassava fields treated by integrated application of manure and NPK fertilizer in Zambia,” Environmental Systems Research, vol.8, no.1, p.3, (2019)
[7] Alamu, E. O., Ntawuruhunga, P., Chibwe, T., Mukuka, I., and Chiona, M. “Evaluation of cassava processing and utilization at household level in Zambia,” Food Security, vol.11, no.1, pp.141-150, (2019)
[8] Guo, Y., “Closing yield gap of cassava for food security in West Africa,” Nature Food, vol.1, no.6, pp.325-325, (2020)
[9] Jiang, D., Wang, Q., Ding, F., Fu, J., and Hao, M., “Potential marginal land resources of cassava worldwide: A data-driven analysis,” Renewable and Sustainable Energy Reviews, pp.167-173, (2019)
[10] Nguyen, T. H., Williams, S., and Paustian, K., “Impact of ecosystem carbon stock change on greenhouse gas emissions and carbon payback periods of cassava-based ethanol in Vietnam. Biomass and bioenergy,” 100, pp.126-137, (2017)
[11] Heumann, B., Walsh, J., and McDaniel P, “Assessing the application of a geographic presence-only model for land suitability mapping,” Ecological Inform, vol.6, no.5, pp.257–269, (2011)
[12] Laskar, A., “Integrating GIS and multicriteria decision making techniques for land resource planning,” Netherlands: ITC, Dec., (2003)
[13] Din, G. Y., and Yunusova, A. B., “Using AHP for evaluation of criteria for agro-industrial projects,” International Journal of Horticulture and Agriculture, vol.1, no.1, (2016)
[14] Qureshi, M. R. N., Singh, R. K., and Hasan, M. A., “Decision support model to select crop pattern for sustainable agricultural practices using fuzzy MCDM. Environment,” Development and Sustainability, vol.20, no.2, pp.641-659, (2018)
[15] Kahsay, A., Haile, M., Gebresamuel, G., and Mohammed, M., “Land suitability analysis for sorghum crop production in northern semi-arid Ethiopia: Application of GIS-based fuzzy AHP approach,” Cogent Food & Agriculture, vol.4, no.1, pp.1507184, (2018)
[16] Pamučar, D., Gigović, L., Bajić, Z., and Janošević, M., “Location selection for wind farms using GIS multi-criteria hybrid model: An approach based on fuzzy and rough numbers,” Sustainability, vol.9, no.8, pp.1315, (2017)
[17] Tahri, M., Maanan, M., Maanan, M., Bouksim, H., and Hakdaoui, M., “Using fuzzy analytic hierarchy process multi-criteria and automatic computation to analyze coastal vulnerability,” Progress in Physical Geography, vol.41, no.3, pp.268-285, (2017)
[18] Akbari, M., Neamatollahi, E., & Neamatollahi, P., “Evaluating land suitability for spatial planning in arid regions of eastern Iran using fuzzy logic and multi-criteria analysis,” Ecological indicators, pp.98, pp.587-598, (2019)
[19] Purnamasari, R. A., Ahamed, T., and Noguchi, R., “Land suitability assessment for cassava production in Indonesia using GIS, remote sensing and multi-criteria analysis,” Asia-Pacific Journal of Regional Science, vol.2, no.1, pp.1-3, (2019)
[20] Ahmed, F., and Kilic, K., “Modification to fuzzy extent analysis method and its performance analysis. In industrial engineering and systems management (IESM),” International Conference on IEEE, pp.435-438, Oct., (2015)
[21] Chang, D.Y. Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res., pp.95, pp.649-655, (1996)
[22] Mikhailov, L., “Deriving priorities from fuzzy pairwise comparison judgments,” Fuzzy Sets and Systems, pp.134, pp.365-385, (2003)
[23] Liou, T. S., and Wang, M. J. J., “Ranking fuzzy numbers with integral value. Fuzzy sets and systems,” vol.50, no.3, pp.247-255, (1992)
[24] Vincent, F. Y., and Dat, L. Q., “An improved ranking method for fuzzy numbers with integral values. Applied Soft Computing,” pp.14, pp.603-608, (2014).
[25] Ruan, J., Shi, P., Lim, C., and Wang, X., “Relief supplies allocation and optimization by interval and fuzzy number approaches,” Information Sciences, pp.303, pp.15-32, (2015)
[26] Kabir, G. and Sumi, R. S., “Integrating fuzzy analytic hierarchy process with PROMETHEE method for total quality management consultant selection,” Production & Manufacturing Research, vol.2, no.1, pp.380-399, (2014)
[27] Wang, Q., Wang, H., and Qi, Z., “An application of nonlinear fuzzy analytic hierarchy process in safety evaluation of coal mine,” Safety Science, pp.86, pp.78-87, (2016)
[28] Diallo, M. D., Wood, S. A., Diallo, A., Mahatma-Saleh, M., Ndiaye, O., Tine, A. K., and Guisse, A., “Soil suitability for the production of rice, groundnut, and cassava in the peri-urban Niayes zone,” Senegal. Soil and Tillage Research, pp.155, pp. 412-420, (2016)
[29] Heng, T., de Jesus, J. M., Heuvelink, G. B., Gonzalez, M. R., Kilibarda, M., Blagotić, A., and Guevara, M., “A SoilGrids250m: Global gridded soil information based on machine learning,” PLoS one, vol.12, no.2, (2017)
[30] Ahukaemere, C. M., & Obasi, N. S., “Potentials of soils derived from Asu river group and Asata Nkporo shale for arable crop production in Ebonyi State,” Nigeria. Bulgarian Journal of Soil Science, vol.3, no.1, pp.48-62, (2018)
[31] Gbadegesin, A. S., Abua, M. A., & Atu, J. E., “Variation in soil properties on cassava production in the coastal area of Southern cross river State,” Nigeria. Journal of Geography and Geology, vol.3, no.1, pp.94, (2011)
[32] Purnamasari, R. A., Noguchi, R., and Ahamed, T., “Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing,” Computers and Electronics in Agriculture, pp.166, (2019)
[33] Wezel, A., Steinmüller, N., and Friederichsen, J. R., “Slope position effects on soil fertility and crop productivity and implications for soil conservation in upland northwest Vietnam. Agriculture,” Ecosystems & Environment, vol.91, no.1-3, pp.113-126, (2002)
[34] Schneider, D. P., Deser, C., Fasullo, J., & Trenberth, K. E., “Climate data guide spurs discovery and understanding,” Eos, Transactions American Geophysical Union, vol.94, no.13, pp.121-122, (2013)
[35] Odjugo P., “The impact of tillage systems on soil microclimate, growth and yield of cassava (Manihot utilisima) in Midwestern Nigeria,” African Journal of Agricultural Research, vol.3, no.3, pp.225-233, (2008)
[36] Zadeh, L. A., “Fuzzy sets,” Information and Control, vol.8, no.3, pp.338-353, (1965)
[37] Javanbarg, M., Scawthorn, C., Kiyono, J., and Shahbodaghkhan, B., “Fuzzy AHP-based multicriteria decision making systems using particle swarm optimization,” Expert systems with application, vol.39, pp.960-966, (2012)
[38] Tsiko, R., and Haile, T.S., “Integrating geographical information systems, fuzzy logic and analytical hierarchy process in modelling optimum sites for locating water reservoirs,” A Case Study of the Debub District in Eritrea. Water, vol.3, pp.254-290, (2011) DOI: 10.3390/w3010254.(CrossRef)(Google Scholar)
[39] Xinyi D, “Dam site selection using an integrated method of AHP and GIS for decision making support in Bortala,” Northwest China. Unpublished MSc Thesis, Lund University, Lund, Sweden, Sept., (2016)
[40] Modarres, M., Sadi-Nezhad, S., and Arabi, F., “Fuzzy analytical hierarchy process using preference ratio: A case study for selecting management short course in a business school,” International Journal of Industrial Engineering Computations, vol.1, no.2, pp.173-184, (2010)