Risk Analysis Using Geographic Information Systems by Determining the Factors Affecting Yield in Plant Production: A case study from Ankara, Turkey

Yıl 2022, Cilt: 28 Sayı: 4, 677 - 690, 17.10.2022

Emre Yeniay , Aydın Şık

https://doi.org/10.15832/ankutbd.900997

Cited By: 1

Öz

Performing agricultural analysis is becoming much more effortless due to the rapid improvements in information technologies. Geographic Information Systems (GIS) provide more detailed data about climate, soil, topography, and irrigation values regarding agriculture; thus, allowing for performing detailed location analyses. These analyses cover agricultural investment maps, agricultural propriety areas, and plant pattern detections. The purpose of this study is to develop product-based agricultural risk analysis maps. Climate, soil, topography, and irrigation data are essential in the cultivation of agricultural products. With risk analysis, the risk values are determined for each risk factor. Applying the Analytical Hierarchy Process (AHP), which is one of the multi-criteria decision-making methods, the total risk value is calculated by prioritizing the risk factors. AHP is an efficient methodology developed to calculate scenario-based risk values by considering various possibilities. In this study, a model is generated by studying apricot, sour cherry, and almond farming in Ankara. As a result of the development of a GIS model for Ankara, the total risk values were mapped as "high-risk areas", "medium-risk areas", "low-risk areas" and "strongly not recommended areas" according to the points they received spatially. When the maps were examined in detail; it was determined that apricot crops in Ankara province are more sensitive to climate, soil, and topography conditions than other products. Since apricot is affected by late spring frosts, it is recommended that risk factors can be reduced by taking climatic measures in areas where soil structure is suitable. It has been determined that the sour cherry crop is less sensitive to climatic and topographic conditions and is more affected by the risk factors from the soil layers; while the almond crop is more affected by the climatic conditions, though it is more tolerant to soil conditions. According to these results, apricot can be grown in large areas with medium and high-risk levels, and in limited areas with low-risk levels. Almond with a very high-risk level can be grown in large areas compared to apricot, and sour cherry can be grown in similar-sized areas with apricot, but with a lower risk level than apricot.

Anahtar Kelimeler

Risk Maps, Multi-Criteria Decision Model (MCDM), Analytical Hierarchy Process (AHP), Fault Tree Analyses (FTA), Agricultural Product Risk Maps.

Kaynakça

• Ahmed H R & Terribile F (2013). Introducing a New Parametric Concept for Land Suitability Assessment. International Journal of Environmental Science and Development, 4(1): 15-19.
• Arzu G, Halil G.S, İncir Araştırma İstasyonu Müdürlüğü, Aydın, Türkiye Tarımsal Araştırmalar Dergisi - Turkish Journal of Agricultural Research 1(1): 98-108
• Chen Y, Yu J, Shahbaz K & Xevi E (2009). A GIS-Based sensitivity analysis of multi-criteria weights. In: 18. World IMACS / MODSIM Congress, 13-17 July 2009, Cairns, Australia.
• Chuong H V (2008). Multicriteria land suitability evaluation for crops using GIS at the community level in Central Vietnam. In: International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, 2008, Hanoi, Vietnam.
• Çınar Y (2004). Çok nitelikli karar verme ve ‘bankaların mali performanslarının değerlendirilmesi’ örneği. Ankara Üniversitesi Sosyal Bilimler Enstitüsü.
• Deniz T, Nuray D, Reliability Analysis Of A Dragline Using Fault Tree Analysis, Bilimsel Madencilik Dergisi Arşiv Cilt 56, S. 2
• Elsheikh R F A, Ahmad N, Shariff R, Balasundram S K & Yahaya S (2010). An agricultural ınvestment map based on geographic ınformation system and multi-criteria method. Journal of Applied Sciences, 10(15): 1596-1602.
• Ferit Ç, Hilmi K, Mesut Ö, Eşref T & Ramazan K, Türkiye Kuru İncir İhracatında İklim Faktörlerinin Etkisinin Belirlenmesine Yönelik Bir Değerlendirme, Erbeyli İncir Araştırma Enst., İncirliova, Aydın, OMÜ Zir. Fak. Dergisi, 2007,22(1):11-19
• Islam Md 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.
• İbrahim G, Lütfi P, Eğirdir Ekolojik Şartlarında Yetiştirilen Bazı Şeftali Çeşitlerinin Fenolojik Ve Pomolojik Özelliklerinin Tespiti, Batı Akdeniz Tarımsal Araştırma Enstitüsü Derim Dergisi, 2011, 28 (2):27-41
• Kılıç Ş (2011). Agroecological land use potential of Amik Plain. Turkish Journal of Agriculture and Forestry, 35(4): 433-442.
• Mokarram M & Aminzadeh F (2010). GIS-based multi-criteria land suitability evaluation using ordered weight averaging with fuzzy quantifier: A case study in shavur plain, Iran. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(2): 508-512.
• Monica P, Annalia B, Marcelo M, Geographıc Informatıon Systems And Rısk Assessment, JRC Scientific and Techical Reports, January 2008
• Nyeko M (2012). GIS and multi-criteria decision analysis for land use resource planning. Journal of Geographic Information System, 4(04): 341-348.
• Öztekin T, Susam T, Gerçekçioğlu R, Tokat Kazova Arazilerinin Şeftali Yetiştiriciliğine Uygunluklarının Coğrafi Bilgi Sistemi Yardımıyla Belirlenmesi, Tekirdağ Ziraat Fakültesi Dergisi, 2008 5 (2)
• Rathore, V.; Rao, M.J. The Performance of PMFBY and Other Crop Insurance Models in India. Int. J. Adv. Res. Dev. 2017, 2, 2455–4030.
• 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.
• Şenel B & Şenel M (2013). Risk Analizi: Türkiye’de gerçekleşen trafik kazaları üzerine hata ağacı analizi uygulaması, Anadolu Üniversitesi Sosyal Bilimler Dergisi, 13(3): 65-83.
• Yasemin K, Ustun S, Fatih Mehmet K, Mustafa O, Agro-Ecological Zones and Land Use Planning at the Kuzgun Dam Irrigation Area, Atatürk Univ., J. of the Agricultural Faculty, 48 (2): 99-105 , 2017
• Yasin A, İsmail K, Determination of Factors Affecting Wheat Production in Altınekin District by Risk Analysis, Selcuk Journal of Agriculture and Food Sciences, (2018) 32 (3), 496-501
• Zavadskas E K & Turskis Z (2011). Multiple criteria decision making (MCDM) methods in economics: An overview. Technological and Economic Development of Economy, 17(2): 397-427.
• Z. Özdemir Eroğlu, A. Mısırlı, Şeftali Islahı Ve Gelişimi, Bahçe 41 (2): 37 - 46 (2012)
• Climate change and food security: risks and responses, 2015 Food And Agrıculture Organızatıon Of The Unıted Natıons http://www.fao.org/3/i5188e/i5188e.pdf
• Coğrafi Bilgi Sistemleri, Uzaktan Algılama ve Araştırma Merkezi, SDÜ, 2020 https://uacbs.sdu.edu.tr/tr/cografi-bilgi-sistemleri/cografi-bilgi-sistemleri-11790s.html, Isparta
• Guide for application of risk analysis principles and procedures during food safety emergencies 2011 FAO/WHO http://www.fao.org/3/ba0092e/ba0092e00.pdf Rome, Italy
• Precision agriculture in Europe Legal, social and ethical considerations 2017 European Parliamentary Research Service https://www.europarl.europa.eu/RegData/etudes/STUD/2017/603207/EPRS_STU(2017)603207_EN.pdf Belgium
• Precısıon Agrıculture: An Opportunıty For Eu Farmers - Potentıal Support Wıth The Cap 2014-2020 European Parlement Dırectorate-General For Internal Polıcıes, Agrıculture And Rural Development https://www.europarl.europa.eu/RegData/etudes/note/join/2014/529049/IPOL-AGRI_NT%282014%29529049_EN.pdf
• Ankara Coğrafyası 2020 Ankara Valiliği http://www.ankara.gov.tr
• Türkiye Tarım Alanı İstatistikleri, Türkiye Nüfus İstatistikleri, https://data.tuik.gov.tr/