Land Suitability Mapping for Sustainable Agriculture in Egypt’s Western Desert: A GIS–AHP Framework

Year 2026, Volume: 11 Issue: 2, 288 - 300

Hesham Ezz , A.m. Abdel-wahab

https://doi.org/10.26833/ijeg.1756908

Abstract

This study presents a new way to combine the FAO land suitability framework with the Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) tools to assess how suitable the land is for farming in Egypt’s Western Desert, an important area that hasn't been thoroughly examined for its farming potential. Egypt faces increasing food security challenges due to limited arable land and rapid population growth, prompting the need to evaluate the feasibility of expanding cultivation into arid zones. The aim of this study is to assess land suitability across the Western Desert using a multi-criterion, spatially integrated model. Key input parameters include soil type, slope, evapotranspiration (ETo), precipitation, and land use/land cover (LULC). Datasets are sourced from SoilGrids250m, WorldClim, and remote sensing imagery. The AHP is used to assign weights based on expert evaluation, and a GIS-based weighted overlay analysis is applied to generate a suitability map. Results indicate that 20.74% of the area is highly suitable (S1), 41.56% moderately suitable (S2), and 37.36% marginally suitable (S3), with only 0.33% considered currently not suitable (N1). Notably, no areas fall under the permanently not suitable (N2) category, indicating strong potential for land reclamation if supported by appropriate interventions. These findings suggest that the Western Desert holds considerable agricultural promise, provided that challenges related to water availability, infrastructure, and sustainability are addressed. The study provides a transferable methodology to support evidence-based agricultural planning and national policy efforts aimed at land reclamation and food security enhancement.

Keywords

Agricultural land suitability , GIS , Remote Sensing , AHP method , Spatial analyst , Sustainability

References

• CIA. (2021). The World Factbook (2021 Archive) - Egypt. Retrieved December 21, 2021, from https://www.cia.gov/the-world-factbook/about/archives/2021/countries/egypt
• CIA. (2024). The CIA World Factbook 2024-2025. Skyhorse Publishing. Retrieved from https://www.skyhorsepublishing.com/9781510771185/the-cia-world-factbook-2022-2023/
• Egypt State Information Service. (2022). The “Mustaqbal Misr” project for agricultural production in the Western Desert. Retrieved January 17, 2022, from https://www.sis.gov.eg/Story/162008/The-Mustaqbal-Misr-project-for-agricultural-production-in-the-Western-Desert?lang=en-us 2025
• Calicioglu, O., Flammini, A., Bracco, S., Bellù, L., & Sims, R. (2019). The future challenges of food and agriculture: An integrated analysis of trends and solutions. Sustainability, 11(1), 222.
• Gupta, J. (2019). Global Environment Outlook – GEO-6: Healthy Planet, Healthy People. (UN Environment, Ed.). Cambridge University Press. https://doi.org/10.1017/9781108627146
• Li, W., Shi, Y., Zhu, D., Wang, W., Liu, H., Li, J., … Fu, S. (2021). Fine root biomass and morphology in a temperate forest are influenced more by the nitrogen treatment approach than the rate. Ecological Indicators, 130, 108031.
• Attia, M. R., Shaker, I. F., Awad, A. A. D. I., & Abdel-Wahab, A. M. (2024). Classification and Detection of Changes in Land Use and Land Cover Using Satellite Images and Gis- Near Suez Canal Area. ARPN Journal of Engineering and Applied Sciences, 19(8), 479–492. https://doi.org/10.59018/042466
• Malczewski, J. (2010). Multiple criteria decision analysis and geographic information systems. Trends in multiple criteria decision analysis, 369–395.
• Mustafa, A. A., Singh, M., Sahoo, R. N., Ahmed, N., Khanna, M., Sarangi, A., & 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.
• Unel, F. B., Kusak, L., & Yakar, M. (2023). GeoValueIndex map of public property assets generating via Analytic Hierarchy Process and Geographic Information System for Mass Appraisal: GeoValueIndex. Aestimum, 82, 51-69..
• Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3), 234–281.
• Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill International, New York, NY, USA.
• Hasanzaeh, M., Kamran, K. V., Feizizadeh, B., & Mollabashi, S. H. (2024). GIS based spatial decision-making approach for solar energy site selection, Ardabil, Iran. International Journal of Engineering and Geosciences, 9(1), 115–130.
• Purnamasari, R. A., Noguchi, R., & Ahamed, T. (2022). Land suitability assessment for cassava production in Indonesia using GIS, remote sensing, and multi-criteria analysis. In Remote Sensing Application: Regional Perspectives in Agriculture and Forestry (pp. 99–132). Springer.
• Zabihi, H., 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.
• Zolekar, R. B., & Bhagat, V. S. (2015). Multi-criteria land suitability analysis for agriculture in hilly zone: Remote sensing and GIS approach. Computers and Electronics in Agriculture, 118, 300–321.
• El Baroudy, A. A. (2016). Mapping and evaluating land suitability using a GIS-based model. Catena, 140, 96–104. https://doi.org/10.1016/j.catena.2015.12.010
• Akinci, H., Özalp, A. Y., & Turgut, B. (2013). Agricultural land use suitability analysis using GIS and AHP technique. Computers and Electronics in Agriculture, 97, 71–82. https://doi.org/10.1016/j.compag.2013.07.006
• Aldababseh, A., Temimi, M., Maghelal, P., Branch, O., & Wulfmeyer, V. (2018). Multi-criteria evaluation of irrigated agriculture suitability to achieve food security in an arid environment. Sustainability (Switzerland), 10(3), 1–33. https://doi.org/10.3390/su10030803
• Çelik, M. Ö., Kuşak, L., & Yakar, M. (2024). Assessment of groundwater potential zones utilizing geographic information system-based analytical hierarchy process, Vlse Kriterijumska Optimizacija Kompromisno Resenje, and technique for order preference by similarity to ideal solution methods: a case study in Mersin, Türkiye. Sustainability, 16(5), 2202.
• Hussain, S., Nasim, W., Mubeen, M., Fahad, S., Tariq, A., Karuppannan, S., … Ghassan Abdo, H. (2024). Agricultural land suitability analysis of Southern Punjab, Pakistan using analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) techniques. Cogent Food and Agriculture, 10(1), 1–17. https://doi.org/10.1080/23311932.2023.2294540
• Sari, F., & Koyuncu, F. (2021). Multi criteria decision analysis to determine the suitability of agricultural crops for land consolidation areas. International Journal of Engineering and Geosciences, 6(2), 64–73.
• Rahim, M. S. D. A., Salleh, S. A., Naharudin, N., Isa, N. A., Pardi, F., Abdullah, M. F., & Anuar, N. (2025). Using Geospatial-Driven Territorial Planning and Land Suitability Analysis for Sustainable Coconut Agriculture in Johor, Malaysia. International Journal of Engineering and Geosciences, 10(3), 313–328.
• Sekhar, S., Dutta, S., Sahana, M., Areendran, G., & Raj, K. (2022). Assessing impact of climate variability on potential agricultural land suitability in nalanda district, bihar. In Climate Change, Disaster and Adaptations: Contextualising Human Responses to Ecological Change (pp. 131–149). Springer.
• Anusha, B. N., Babu, K. R., Kumar, B. P., Sree, P. P., Veeraswamy, G., Swarnapriya, C., & Rajasekhar, M. (2023). Integrated studies for land suitability analysis towards sustainable agricultural development in semi-arid regions of AP, India. Geosystems and Geoenvironment, 2(2), 100131.
• Arulbalaji, P., Padmalal, D., & Sreelash, K. (2019). GIS and AHP techniques based delineation of groundwater potential zones: a case study from southern Western Ghats, India. Scientific reports, 9(1), 2082.
• Abdelkader, M., & Delali, A. (2012). Support system based on GIS and weighted sum method for drawing up of land suitability map for agriculture. Application to durum wheat cultivation in the area of Mleta (Algeria). Spanish Journal of Agricultural Research, 10(1), 34–43.
• Akbari, M., Neamatollahi, E., & Neamatollahi, P. (2019). Evaluating land suitability for spatial planning in arid regions of eastern Iran using fuzzy logic and multi-criteria analysis. Ecological indicators, 98, 587–598.
• Bilgilioğlu, S. S. (2021). Land suitability assessment for Olive cultivation using GIS and multi-criteria decision-making in Mersin City, Turkey. Arabian Journal of Geosciences, 14(22), 2434.
• El-Aziz, A., Salah, H., Okashaa, E. G. M., Ibrahim, A. G. M., El-Azem, A., & Alaa, H. (2024). Using GIS Tools and Remote Sensing Data to Assessment of Land Capability and Suitability for Agriculture in New Aswan Area, Aswan Governorate, Egypt. Egyptian Journal of Soil Science, 64(2), 335–372.
• Cai, S., Fan, J., & Yang, W. (2021). Flooding risk assessment and analysis based on GIS and the TFN-AHP method: a case study of Chongqing, China. Atmosphere, 12(5), 623. https://doi.org/10.3390/atmos12050623
• Hassan, I., Javed, M. A., Asif, M., Luqman, M., Ahmad, S. R., Ahmad, A., … Hussain, B. (2020). Weighted overlay based land suitability analysis of agriculture land in Azad Jammu and Kashmir using GIS and AHP. Pakistan Journal of Agricultural Sciences, 57(6), 1509–1519.
• Ahmad, M. S., Khan, S., & others. (2023). Comparative analysis of analytical hierarchy process (AHP) and frequency ratio (FR) models for landslide susceptibility mapping in Reshun, NW Pakistan. Kuwait Journal of Science, 50(3), 387–398. https://doi.org/10.1016/j.kjs.2023.01.004
• Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
• Ezz, H., & Abdelwares, M. (2020). Spatial and temporal variation of eto for egypt using remote sensing. ARPN Journal of Engineering and Applied Sciences, 15(1), 104–112.
• Abdel-Wahab, A. M., Shahin, D., & Ezz, H. (2023). A sustainable solution for flood and rain hazard using remote sensing & GIS: New Cairo. The Egyptian Journal of Remote Sensing and Space Sciences, 26(4), 892–900. https://doi.org/10.1016/j.ejrs.2023.10.002
• Takaku, J., & Tadono, T. (2017). Quality updates of ‘AW3D’global DSM generated from ALOS PRISM. In 2017 IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 5666–5669).
• Shangguan, W., Dai, Y., Duan, Q., Liu, B., & Yuan, H. (2014). A global soil data set for earth system modeling. Journal of Advances in Modeling Earth Systems, 6(1), 249–263.
• Hengl, T., de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., … others. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), 1–40.
• Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., & Singh, G. (2022). Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sensing, 14(16), 4101. https://doi.org/10.3390/rs14164101
• Morsy, S., & Hadi, M. (2022). Impact of land use/land cover on land surface temperature and its relationship with spectral indices in Dakahlia Governorate, Egypt. International Journal of Engineering and Geosciences, 7(3), 272–282.
• Saaty, T. L., & Vargas, L. G. (2012). Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. Springer New York, NY. https://doi.org/10.1007/978-1-4614-3597-6
• FAO. (1976). A framework for land evaluation (Vol. FAO Soils). Bernan Press (PA). Retrieved from https://www.fao.org/4/x5310e/x5310e00.htm
• Zhu, L., Gong, H., Dai, Z., Xu, T., & Su, X. (2015). An integrated assessment of the impact of precipitation and groundwater on vegetation growth in arid and semiarid areas. Environmental earth sciences, 74(6), 5009–5021.
• Ferreira, M. I. (2017). Stress Coefficients for Soil Water Balance Combined with Water Stress Indicators for Irrigation Scheduling of Woody Crops. Horticulturae 2017, 3 (2), 38.
• Ewunetu, T., Selassie, Y. G., Molla, E., Admase, H., & Gezahegn, A. (2025). Soil properties under different land uses and slope gradients: Implications for sustainable land management in the Tach Karnuary watershed, Northwestern Ethiopia. Frontiers in Environmental Science, 13, 1518068.
• Sheng, T. C. (1990). Watershed management field manual. Rome.
• Hossen, B., Yabar, H., & Mizunoya, T. (2021). Land suitability assessment for pulse (green gram) production through remote sensing, GIS and multicriteria analysis in the coastal region of Bangladesh. Sustainability, 13(22), 12360.
• Saaty, T. L. (1994). Fundamentals of decision making and priority theory with the analytic hierarchy process. RWS publications.
• El-Agha, D. E., Molle, F., Metwally, M. I., Emara, S. R., Shalby, A., Armanuos, A. M., … Gado, T. A. (2024). Toward sustainable management of groundwater in the deserts of Egypt. Hydrogeology Journal, 32(3), 663–678.
• Abu-Bakr, H. A. el-A., & Abdelmoniem, M. (2024). Groundwater potentiality delineation in Moghra, Egypt. Ain Shams Engineering Journal, 15(1), 102337
• Şimşek, F. F. (2025). Comparison of Agricultural Crop Type Classifications with Different Machine Learning Algorithms (RF-SVM-ANN-XGBoost) by Generating Ground Truth Data from Farmer Declaration Parcels. International Journal of Engineering and Geosciences, 10(2), 207–220.
• Akar, Ö., Saralıoğlu, E., Güngör, O., & Bayata, H. F. (2024). Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection. International Journal of Engineering and Geosciences, 9(1), 12–24.
• Tomco, V., Grabocka, E., & Harizaj, M. (2025). Leveraging Deep Learning in Remote Sensing: A Novel Approach for Agricultural Greenhouse Detection and Innovation Management. International Journal of Engineering and Geosciences, 10(2), 221–230.