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With the help of information technologies, which are developing day by day, it has become easier to perform agricultural analyzes. Positional analyses can be performed with the help of Geographical Information Systems by gathering Climate, Soil, Topography and İrrigation data related to Agriculture. These analyses enables to generate analyses for agricultural investment maps, areas of agricultural conformity, plant pattern determination, etc. The purpose of this study is to prepare "Product Based Agricultural Risk Analysis Maps". Climate, Soil, Topography and Irrigation data, which are important in the growing of agricultural products are collected, severity and prospects for risk analysis are determined separately and risk values are established for each risk factor. The total risk value was calculated by prioritizing risk factors using the Analytical Hierarchy Process (AHP), one of the multi-criteria decision-making methods. Thanks to AHP, a methodology for calculating scenario-based risk values has been developed taking into account different probabilities. With the developed model, risk maps were created for climate, soil, topography and water constraints. The total risk map was obtained by combining the risk maps created with AHP. In this study, a model was established by taking the Peach and Fig product of Manisa and as a result of the model, Total Risk values were divided into classes such as "High Risk Areas", "Medium Risk Areas", "Low Risk Areas" and "Strictly Not Recommended Areas" according to the scores they received positional.
Ahmed, H. R., & Terribile, F. (2013). İntroducing a new parametric concept for land suitability assessment. İnternational. Journal of Environmental Science and Development, 4(1), 15-19. https://doi.org/10.7763/ıjesd.2013.v4.295
Altay, Y., & Keskin, İ. (2018). Determination of factors affecting wheat production in altınekin district by risk analysis. Selcuk Journal of Agriculture and Food Sciences, 32(3), 496-501. https://doi.org/10.15316/SJAFS.2018.128
Chuong, H. V. (2008). Multicriteria land suitability evaluation for crops using gis at the community level in central vietnam. International Symposium On Geoinformatics For Spatial Infrastructure Development In Earth And Allied Sciences, Hanoi, Vietnam.
Çınar, Y. (2004). Multi attribute decision making and an implementation on evaluation of financial performances of banks Publication No. 141376) [Master’s Thesis, Ankara University]. YÖK National Thesis Center.
El-Sheikh, R. F. A., Ahmad, N., N. Shariff, A. R. M., Balasundram, S. K., & Yahaya, S. (2010). An agricultural investment map based on geographic information system and multi-criteria method. Journal of Applied Sciences, 10(15), 1596-1602. https://doi.org/10.3923/jas.2010.1596.1602
Eroğlu, Ö. Z., & Mısırlı, A. (2012). Şeftali ıslahı ve gelişimi. Bahçe, 41(2), 37-46.
Gitz, V., A. Meybeck, L. Lipper, C. D. Young, & Braatz, S. (2016). Climate change and food security: risks and responses. Food And Agriculture Organization Of The United Nations (FAO), 110, 2-4.
Gür, İ., & Pırlak, L. (2011). Determination of phenological and pomological characters of some peach cultivars grown in Egirdir ecological conditions. Batı Akdeniz Tarımsal Araştırma Enstitüsü Derim Dergisi (in Turkish), 28(2), 27-41.
Islam, M. M., Ahamed, T., & Noguchi, R. (2018). Land suitability and ınsurance premiums: a GIS-based multi-criteria analysis approach for sustainable rice production. Sustainability, 10(6), 1759. https://doi.org/10.3390/su10061759
Jafarian, E., & Rezvani, M. A. (2012). Application of fuzzy fault tree analysis for evaluation of railway safety risks: an evaluation of root causes for passenger train derailment. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 226(1), 14-25. https://doi.org/10.1177/0954409711403678
Mokarram, M., S. A., Marnani, A. A., Moezi, & Hamzeh, S. (2011). Land suitability evaluation using ordered weight averaging with fuzzy quantifier in Shavoor plain, Iran. Research on Crops, 12(2), 593-599.
Nyeko, M. (2012). GIS and multi-criteria decision analysis for land use resource planning. Journal of Geographic Information System, 4(04), 341-348. https://doi.org/10.4236/jgis.2012.44039
OKP. (2013). Türkiye Cumhuriyeti Kalkınma Bakanlığı Onuncu Kalkınma Planı (2014-2018) in Turkish. Retrieved January 29, 2023, from https://www.sbb.gov.tr/wp-content/uploads/2022/08/Onuncu_Kalkinma_Plani-2014-2018.pdf
Öztekin, T., Susam, T., & Gerçekçioğlu, R. (2008). Determination the appropriateness of Tokat Kazova lands to peach cultivation using geographic information system. Journal of Tekirdag Agricultural Faculty, 5(2), 215-225.
Pablo J., Hubbard, N., & Loudjani, P. (2014). Precision Agriculture: An Opportunıty For Eu Farmers - Potential Support With The Cap 2014-2020. Directorate-General For Internal Policies, Agriculture And Rural Development, European Parlement. Retrieved January 29, 2023, from https://Www.Europarl.Europa.Eu/Regdata/Etudes/Note/Join/2014/529049/İpol-Agri_Nt(2014)529049_En.Pdf
Sharma, R., Kamble, S. S., & Gunasekaran, A. (2018). Big GIS analytics framework for agriculture supply chains: A literature review identifying the current trends and future perspectives. Computers and Electronics In Agriculture, 155, 103-120. https://doi.org/10.1016/j.compag.2018.10.001
TUIK. (2021a). Adrese Dayalı Nüfus Kayıt Sistemi Sonuçları, 2022 (in Turkish). Retrieved April 24, 2023, from https://data.tuik.gov.tr
TUIK. (2021b). Türkiye tarım alanı istatistikleri (in Turkish). Retrieved April 24, 2023, from https://data.tuik.gov.tr
UN. (2013). United Nations, Department of Economic and Social Affairs, Population Division, Population Estimates and Projections Section. World population prospects: the 2012 revision. Retrieved April 05, 2023, from http://esa.un.org/unpd/wpp/Excel-Data/population.htm.
WHO. 2011. FAO/WHO guide for application of risk analysis principles and procedures during food safety emergencies. Retrieved April 05, 2023, from https://apps.who.int/iris/handle/10665/44739
Yeniay, E., & Şık, A. (2022). Risk analysis using geographic information systems by determining the factors affecting yield in plant production: a case study from Ankara, Turkey. Journal Of Agricultural Sciences, 44-44. https://doi.org/10.15832/ankutbd.900997