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Türkiye’de zeytin (Olea europaea L.) üretimine uygun alanların coğrafi bilgi sistemleri (CBS) tabanlı çoklu kriter analizi ile belirlenmesi

Year 2021, Volume: 58 Issue: 1, 97 - 113, 31.03.2021
https://doi.org/10.20289/zfdergi.678474

Abstract

Amaç: Bu çalışmada, Türkiye’de iklim, toprak ve topoğrafik faktörlere göre zeytin yetiştiriciliği için optimum yetişme koşulları ve potansiyel arazi uygunluk sınıfları belirlenmiştir.
Materyal ve Yöntem: Zeytin yetiştiriciliği için arazi uygunluğunun belirlenmesinde, Coğrafi Bilgi Sistemleri Tabanlı Çok Kriterli Karar Verme yöntemi uygulanmıştır. Bu kapsamda; yıllık ortalama sıcaklık, Ocak ayı mutlak minimum sıcaklık, Ocak ayı ortalama sıcaklık, Mayıs ayı ortalama sıcaklık, yaz dönemi ortalama maksimum sıcaklık, nem, yağış, arazi kullanım kabiliyeti sınıfları, toprak derinliği, kısıtlayıcı toprak özellikleri, erozyon, yükseklik ve eğim değerlendirme kriterleri olarak alınmış ve uygunluk sınır değerleri belirlenmiştir. Analitik Hiyerarşi Süreci yaklaşımı ile kriterlerin birbirlerine göre göreceli üstünlükleri belirlenerek kriter ağırlıklıları atanmış ve ürün uygunluk sınıfları oluşturulmuştur.
Araştırma Bulguları: Zeytin üretimi için Türkiye’nin % 6.7’si (5 254 937 ha) yetiştirmeye uygun alanları oluştururken, orta derecede uygun alanlar % 6.3 (4 940 932 ha). oranında yer almıştır. Zeytin üretimi yapılan uygun alanlarda; yıllık ortalama sıcaklık 16.8°C, mutlak minimum sıcaklık -2.8°C, Ocak ayı ortalama sıcaklık 7.0°C, Mayıs ayı ortalama sıcaklık 19.6°C ve yıllık toplam yağış 668 mm olarak belirlenmiştir.
Sonuç: Zeytin yetiştiriciliğinde bölgesel farklılıkların oluşmasında sıcaklık en önemli ve ağırlıklı faktördür. CBS tabanlı çok kriterli değerlendirme yöntemi, ürün uygunluk sınıflamasında etkin olarak kullanılmıştır.

References

  • Anonim, 2006. Zeytin Yetiştiriciliği Kitabı TAGEM Yayın No: 61 İzmir.
  • Aguilera, F., Ruiz-Valenzuela, L., 2009. Study of the floral phenology of Olea europaea L. in Jaen province (SE Spain) and its relation with pollen emission. Aerobiologia 25, 217–225.
  • Ayaz, M. ve Varol, N. 2015. İklim Parametrelerindeki Değişimlerin (Sıcaklık, Yağış, Kar, Nispi Nem, Sis, Dolu ve Rüzgar) Zeytin Yetiştiriciliği Üzerine Etkileri. Zeytin Bilimi 5 (1), 33-40.
  • Ayehu, G. T. and Besufekad, S. A. 2015. Land Suitability Analysis for Rice Production: A GIS Based Multi-Criteria Decision Approach. American Journal of Geographic Information System 2015, 4(3): 95-104 DOI: 10.5923/j.ajgis.20150403.02
  • Beaufoy, G. 1998. “The reform of the CAP olive-oil regime: What are the implications for environment?” Hampshire:European Forum on Nature Conservation and Pastoralisme. Occasional publication No 14.
  • Bonofiglio, T., Orlandi, F., Sgromo, C., Romano, M. 2008. Influence of temperature and rainfall on timing of olive (Olea europaea) flowering in sothern Italy. New Zealand Journal of Crop and Horticultural Science, 2008, Vol. 36: 59—69.
  • Brunelli, M. 2014. Introduction to the analytic hierarchy process. pp. 82, New York, NY: USA, Springer Briefs in Operations Research.
  • Brito, C., Dinis, L-T., Moutinho-Pereira, J., M. Correia, C. 2019. Drought Stress Effects and Olive Tree Acclimation under a Changing Climate. Plants, 8, 232.
  • Ceballos-silva, A. and Lopez-Blanco, J. 2003. Delineation of Suitable Areas for Crops Using a Multi-Criteria Evaluation Approach and Land Use/Cover Mapping: A Case Study in Central Mexico, Agricultural Systems. 77, 117-136.
  • Drobne, S. and Lisec, A. 2009. Multi‐attribute decision analysis in GIS: weighted linear combination and ordered weighted averaging. Informatica: An International Journal of Computing and Informatics, 33(4), 459–474.
  • Eastman, J. R., Jin, W., Kyem, P. A. K., Toledano, J. 1995. Raster procedures for multicriteria/multiobjective decisions. Photogrammetry and Remote Sensing. 61(5); 539-547.
  • Efe, R., Soykan, A., Cürebal, İ., Sönmez, S. 2013. Dünyada, Türkiye’de, Edremit Körfezi Çevresinde Zeytin ve Zeytinyağı. Edremit Belediyesi Kültür yayınları No:7, ISBN: 978-605-62253-0-7.
  • Efe, R., Soykan, A., Sönmez, S., Cürebal, İ. 2009. Sıcaklık şartlarının Türkiye'de Zeytinin (Olea europaea L. subsp. europaea) Yetişmesine, Fenolojik ve Pomolojik Özelliklerine Etkisi. Ekoloji, 18, 70, 17-26.
  • ESRI, 2011. ArcGIS Desktop: Release 10. Redlands, California: Environmental Systems Research Institute.
  • FAO, 2019. FAOSTAT, http://www.fao.org/faostat/en/#data/QC
  • FAO, 1976. A Framework for Land Evaluation. Soils Bulletin No. 32, Food and Agricultural Organization of the United Nations, Rome, Italy.
  • FAO, 1985. Guidelines: land evaluation for irrigated agriculture. FAO Soils Bulletin 55.
  • Galán, C., García Mozo, H., Vázquez, L., Ruíz-Valenzuela, L., Díaz de la Guardia, C.,Domínguez-Vilches, E., 2008. Modeling olive crop yield in Andalusia-Spain. Agronomy Journal 100 (1), 98–104.
  • Gucci, R. and Fereres, E. 2012. “Fruit trees and vines. Olive,” in Crop Yield Response to Water. FAO Irrigation and drainage paper 66 (Rome: Food and Agriculture Organization of the United Nations), 300–313.
  • Guo, X., Yan, D., Fan, J., Zhu, W., Li, M. 2010. Using GIS and Fuzzy Sets to Evaluate the Olive Tree’s Ecological Suitability in Sichuan Province. Computing in Science and Engineering. Volume: 12 , Issue: 1, 20-27, 10.1109/MCSE.2010.17, IEEE.
  • Guzman Alvarez, J. R. 1999. Olive cultivation and ecology: The situation in Spain. Olivae 78: 41–49.
  • Guzman Alvarez, J. R. and Navarro Cerrillo, R. M. 2008. Modelling potential abandonment and natural restoration of marginal olive groves in Andalusia (south of Spain). Journal of Land Use Science, Vol. 3, No. 2–3,113-129.
  • Gümüşay, B. ve Topuz, H. 2006. Zeytinde Zararlı Böcekler, T.C. Tarım ve Köyişleri Bakanlığı Tarımsal Araştırmalar Genel Müdürlüğü Zeytincilik Araştırma Enstitüsü Müdürlüğü. Emre Basımevi, İzmir.
  • Günden, C. ve Miran, B. 2008. Bulanık Analitik Hiyerarşi Süreci Kullanılarak Çiftçi Kararlarının Analizi. Ege Üniverstesi, Ziraat Fak. Dergisi, 2008, 45 (3): 195-204.
  • Hossain, M. S. and Das, N. G. 2010. GIS-based multi-criteria evaluation to land suitability modelling for giant prawn (Macrobrachium rosenbergii) farming in Companigonj Upazila of Noakhali,Bangladesh. Computers and Electronics in Agriculture 70, 172–186.
  • Hutchinson, M.F. 1995. Interpolating Mean Rainfall Using Thin Plate Smoothing Splines. Int. J. Geogr. Info. Systems, 9, 385-403.
  • Hutchinson, M.F. 2000. ANUSPLIN Version 4.1. User Guide, Center for Resource and Environmental Studies, Australian National University, Canberra.
  • Jankowski, P. 1995. Integrating geographical information systems and multiple criteria decision‐making methods. International Journal of Geographical Information Science 9: 251‐273.
  • Liu, F., Peng, Y., Zhang, W., Pedrycz, W. 2017. On consistency in AHP and Fuzzy AHP. Journal of Systems Science and Information, 5(2), 128–147.
  • Kazemi, H. and Akinci, H. 2018. A land use suitability model for rainfed farming by Multi-criteria Decision making Analysis (MCDA) and Geographic Information System (GIS). Ecological Engineering 116, 1–6.
  • Koca, N. 2004. Çanakkale'de Zeytin Yetiştiriciliğinin Coğrafi Esasları. Marmara Coğrafya Dergisi Sayı:9, İstanbul.
  • Malczewski, J. 1999. GIS and Multicriteria Decision Analysis. New York: Wiley.
  • Malczewski, J. 2004. GIS-Based Land-Use Suitability Analysis: A Critical Overview, Prog. Plann, 62, 3–65 2004.
  • MGM, 2019. İnternet sitesi, https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=parametrelerinTurkiyeAnalizi (Erişim tarihi: 04.01.2019)
  • Montazar A., Behbahani , S. M. 2007. Development of an optimized irrigation system selection model using analytical hierarchy process. Biosystems Eng 98:155–165.
  • Nzeyimana, I., Hartemink, A. E., Geissen, V. 2014. GIS-Based Multi-Criteria Analysis for Arabica Coffee Expansion in Rwanda. PLOS ONE, 9(10), pp 1-9.
  • Orlandi, F., Vazquez M. N., Ruga, L., Bonofiglio, T., Fornaciari, M., Garcia-Mozo, H., Dominguez, E., Romano, B., Carmen, G. 2005. Bioclimatic Requirements for Olive Flowering İn Two Mediterranean Regions Located at The Same Latitude (Andalucia, Spain And Sicily, İtaly). Ann Agric Environ med, 12, 47-52.
  • Orlandi, F., Sgromo, C., Bonofiglio, T., Ruga, L., Romano, B., Fornaciari, M. 2010. Yield modelling in a Mediterranean species utilizing cause-effect relationships between temperature forcing and biological processes. Scientia Horticulturae123, 412–417.
  • Oteros, J., García-Mozo, H., Hervás-Martínez, C., Galán, C. 2013. Year clustering analysis for modelling olive flowering phenology. International Journal of Biometeorology 57 (4), 545–555.
  • Pertziger, F. and De Pauw. E. 2002. CLIMAP, An Excel-Based Software for Climate Surface Mapping. ICARDA, Aleppo, Syria.
  • Rossiter, D.G. 1996. A theoratical framework for land evaluation. Geoderma, 72:165-202.
  • Saaty, T. L. 1980. The Analytical Hierarchy Process, McGraw Hill, New York.
  • Saaty, T.L. and Vargas, L.G. 2001. Models, Methods, Concepts & Applications of Analytical Hierarchy Process, International Series in Operations Research and Management Sciences, New York.
  • Shalaby, A., Ouma, Y. O., Tateishi, R. 2006. Land suitability assessment for perennial crops using remote sensing and Geographic Information Systems: A case study in northwestern Egypt. Archives of Agronomy and Soil Science 52 (3), 243-261.
  • Sofo, A., Manfreda, S., Fiorentino, M., Dichio, B., Xiloyannis, C. 2008. The olive tree: a paradigm for drought tolerance in Mediterranean climates. Hydrology and Earth System Sciences.12:293-01.
  • SYS, C., Van Ranst, E., Debaveye, J., Beernaert, F. 1993. Crop Requirements, Part III. Agricultural publications No:7, General Administration for development Cooperation, Brussels, pp: 247.
  • Usta, A. Aybar, M. Bayram, S. Akçay, S. 2014. Akdeniz Bölgesinin Bir Maki Elemanı olan Zeytinin Trabzonda’ki Yerel Yayılımının Ekolojik Açıdan İncelenmesi. II Ulusal Akdeniz Orman ve Çevre Sempozyumu.
  • Temuçin, E. 1993. Türkiye’de Zeytin Yetişen Alanların Sıcaklık değişkenine Göre İncelenmesi, Ege Coğrafya Dergisi 7, 117-131, İzmir-Türkiye.
  • Therios, I. 2009. Olives: Crop Production Science in Horticulture 18. CABI Publishing; Wallingford, UK.
  • TUİK, 2016. İnternet sitesi, https://biruni.tuik.gov.tr/medas/?kn=92&locale=tr. (Erişim tarihi: 01.09.2019)
  • Tunalıoğlu, R. ve Gökçe, O. 2002. Ege Bölgesi’nde Optimal Zeytin Yayılış Alanlarının Tespitine Yönelik Bir Araştırma. Tarım ve Köyİşleri Bakanlığı, Tarımsal Ekonomi Araştırma Enstitüsü Yayınları. Ankara.
  • Va´zquez, L. M., Gala´n, C., Domı´nguez-Vilches, E. 2003. Influence of meteorological parameters on olea pollen concentrations in Co´rdoba (South-western Spain). International Journal Biometeorology, 48, 83–90.

GIS-based multi-criteria land suitability analysis for determining olive (Olea Europaea L.) cultivation areas in Turkey

Year 2021, Volume: 58 Issue: 1, 97 - 113, 31.03.2021
https://doi.org/10.20289/zfdergi.678474

Abstract

Objective: In this study, optimum growing conditions and potential land suitability classes for olive cultivation were determined according to climatic, soil and topographic factors in Turkey.
Material and Methods: In determining land suitability for olive production, Geographical Information Systems Based Multiple Criteria Decision Making method was applied. In this context; The annual average temperature, January absolute minimum temperature, January average temperature, May average temperature, average summer maximum temperature, humidity, rainfall, land use capability sub-class, soil depth, restrictive soil properties, erosion degree, altitude and slope were evaluated. Each criterion layer is produced in raster data format. With the AHP approach, the degree of influence of the criteria was determined and criterion weights were assigned. Land suitability classes were created by combining criterion maps with linear combination method.
Results: The potential suitability map for olive production was obtained in four classes as very suitable, medium suitable, less suitable and unsuitable. For olive production, 6.7 % (5 254 937 ha) of Turkey accounted for areas suitable for cultivation, while moderately suitable areas accounted for 6.3 % (4 940 932 ha). In suitable areas where olive production is made; The average annual temperature is 16.8°C, the absolute minimum temperature is -2.8°C, the average temperature for January is 7.0°C, the average temperature for May is 19.6°C and the total annual precipitation is 668 mm.
Conclusion: Temperature is the most important and weighted factor in the formation of regional differences in olive cultivation. GIS-based multi-criteria evaluation method has been used effectively in crop suitability classification.

References

  • Anonim, 2006. Zeytin Yetiştiriciliği Kitabı TAGEM Yayın No: 61 İzmir.
  • Aguilera, F., Ruiz-Valenzuela, L., 2009. Study of the floral phenology of Olea europaea L. in Jaen province (SE Spain) and its relation with pollen emission. Aerobiologia 25, 217–225.
  • Ayaz, M. ve Varol, N. 2015. İklim Parametrelerindeki Değişimlerin (Sıcaklık, Yağış, Kar, Nispi Nem, Sis, Dolu ve Rüzgar) Zeytin Yetiştiriciliği Üzerine Etkileri. Zeytin Bilimi 5 (1), 33-40.
  • Ayehu, G. T. and Besufekad, S. A. 2015. Land Suitability Analysis for Rice Production: A GIS Based Multi-Criteria Decision Approach. American Journal of Geographic Information System 2015, 4(3): 95-104 DOI: 10.5923/j.ajgis.20150403.02
  • Beaufoy, G. 1998. “The reform of the CAP olive-oil regime: What are the implications for environment?” Hampshire:European Forum on Nature Conservation and Pastoralisme. Occasional publication No 14.
  • Bonofiglio, T., Orlandi, F., Sgromo, C., Romano, M. 2008. Influence of temperature and rainfall on timing of olive (Olea europaea) flowering in sothern Italy. New Zealand Journal of Crop and Horticultural Science, 2008, Vol. 36: 59—69.
  • Brunelli, M. 2014. Introduction to the analytic hierarchy process. pp. 82, New York, NY: USA, Springer Briefs in Operations Research.
  • Brito, C., Dinis, L-T., Moutinho-Pereira, J., M. Correia, C. 2019. Drought Stress Effects and Olive Tree Acclimation under a Changing Climate. Plants, 8, 232.
  • Ceballos-silva, A. and Lopez-Blanco, J. 2003. Delineation of Suitable Areas for Crops Using a Multi-Criteria Evaluation Approach and Land Use/Cover Mapping: A Case Study in Central Mexico, Agricultural Systems. 77, 117-136.
  • Drobne, S. and Lisec, A. 2009. Multi‐attribute decision analysis in GIS: weighted linear combination and ordered weighted averaging. Informatica: An International Journal of Computing and Informatics, 33(4), 459–474.
  • Eastman, J. R., Jin, W., Kyem, P. A. K., Toledano, J. 1995. Raster procedures for multicriteria/multiobjective decisions. Photogrammetry and Remote Sensing. 61(5); 539-547.
  • Efe, R., Soykan, A., Cürebal, İ., Sönmez, S. 2013. Dünyada, Türkiye’de, Edremit Körfezi Çevresinde Zeytin ve Zeytinyağı. Edremit Belediyesi Kültür yayınları No:7, ISBN: 978-605-62253-0-7.
  • Efe, R., Soykan, A., Sönmez, S., Cürebal, İ. 2009. Sıcaklık şartlarının Türkiye'de Zeytinin (Olea europaea L. subsp. europaea) Yetişmesine, Fenolojik ve Pomolojik Özelliklerine Etkisi. Ekoloji, 18, 70, 17-26.
  • ESRI, 2011. ArcGIS Desktop: Release 10. Redlands, California: Environmental Systems Research Institute.
  • FAO, 2019. FAOSTAT, http://www.fao.org/faostat/en/#data/QC
  • FAO, 1976. A Framework for Land Evaluation. Soils Bulletin No. 32, Food and Agricultural Organization of the United Nations, Rome, Italy.
  • FAO, 1985. Guidelines: land evaluation for irrigated agriculture. FAO Soils Bulletin 55.
  • Galán, C., García Mozo, H., Vázquez, L., Ruíz-Valenzuela, L., Díaz de la Guardia, C.,Domínguez-Vilches, E., 2008. Modeling olive crop yield in Andalusia-Spain. Agronomy Journal 100 (1), 98–104.
  • Gucci, R. and Fereres, E. 2012. “Fruit trees and vines. Olive,” in Crop Yield Response to Water. FAO Irrigation and drainage paper 66 (Rome: Food and Agriculture Organization of the United Nations), 300–313.
  • Guo, X., Yan, D., Fan, J., Zhu, W., Li, M. 2010. Using GIS and Fuzzy Sets to Evaluate the Olive Tree’s Ecological Suitability in Sichuan Province. Computing in Science and Engineering. Volume: 12 , Issue: 1, 20-27, 10.1109/MCSE.2010.17, IEEE.
  • Guzman Alvarez, J. R. 1999. Olive cultivation and ecology: The situation in Spain. Olivae 78: 41–49.
  • Guzman Alvarez, J. R. and Navarro Cerrillo, R. M. 2008. Modelling potential abandonment and natural restoration of marginal olive groves in Andalusia (south of Spain). Journal of Land Use Science, Vol. 3, No. 2–3,113-129.
  • Gümüşay, B. ve Topuz, H. 2006. Zeytinde Zararlı Böcekler, T.C. Tarım ve Köyişleri Bakanlığı Tarımsal Araştırmalar Genel Müdürlüğü Zeytincilik Araştırma Enstitüsü Müdürlüğü. Emre Basımevi, İzmir.
  • Günden, C. ve Miran, B. 2008. Bulanık Analitik Hiyerarşi Süreci Kullanılarak Çiftçi Kararlarının Analizi. Ege Üniverstesi, Ziraat Fak. Dergisi, 2008, 45 (3): 195-204.
  • Hossain, M. S. and Das, N. G. 2010. GIS-based multi-criteria evaluation to land suitability modelling for giant prawn (Macrobrachium rosenbergii) farming in Companigonj Upazila of Noakhali,Bangladesh. Computers and Electronics in Agriculture 70, 172–186.
  • Hutchinson, M.F. 1995. Interpolating Mean Rainfall Using Thin Plate Smoothing Splines. Int. J. Geogr. Info. Systems, 9, 385-403.
  • Hutchinson, M.F. 2000. ANUSPLIN Version 4.1. User Guide, Center for Resource and Environmental Studies, Australian National University, Canberra.
  • Jankowski, P. 1995. Integrating geographical information systems and multiple criteria decision‐making methods. International Journal of Geographical Information Science 9: 251‐273.
  • Liu, F., Peng, Y., Zhang, W., Pedrycz, W. 2017. On consistency in AHP and Fuzzy AHP. Journal of Systems Science and Information, 5(2), 128–147.
  • Kazemi, H. and Akinci, H. 2018. A land use suitability model for rainfed farming by Multi-criteria Decision making Analysis (MCDA) and Geographic Information System (GIS). Ecological Engineering 116, 1–6.
  • Koca, N. 2004. Çanakkale'de Zeytin Yetiştiriciliğinin Coğrafi Esasları. Marmara Coğrafya Dergisi Sayı:9, İstanbul.
  • Malczewski, J. 1999. GIS and Multicriteria Decision Analysis. New York: Wiley.
  • Malczewski, J. 2004. GIS-Based Land-Use Suitability Analysis: A Critical Overview, Prog. Plann, 62, 3–65 2004.
  • MGM, 2019. İnternet sitesi, https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=parametrelerinTurkiyeAnalizi (Erişim tarihi: 04.01.2019)
  • Montazar A., Behbahani , S. M. 2007. Development of an optimized irrigation system selection model using analytical hierarchy process. Biosystems Eng 98:155–165.
  • Nzeyimana, I., Hartemink, A. E., Geissen, V. 2014. GIS-Based Multi-Criteria Analysis for Arabica Coffee Expansion in Rwanda. PLOS ONE, 9(10), pp 1-9.
  • Orlandi, F., Vazquez M. N., Ruga, L., Bonofiglio, T., Fornaciari, M., Garcia-Mozo, H., Dominguez, E., Romano, B., Carmen, G. 2005. Bioclimatic Requirements for Olive Flowering İn Two Mediterranean Regions Located at The Same Latitude (Andalucia, Spain And Sicily, İtaly). Ann Agric Environ med, 12, 47-52.
  • Orlandi, F., Sgromo, C., Bonofiglio, T., Ruga, L., Romano, B., Fornaciari, M. 2010. Yield modelling in a Mediterranean species utilizing cause-effect relationships between temperature forcing and biological processes. Scientia Horticulturae123, 412–417.
  • Oteros, J., García-Mozo, H., Hervás-Martínez, C., Galán, C. 2013. Year clustering analysis for modelling olive flowering phenology. International Journal of Biometeorology 57 (4), 545–555.
  • Pertziger, F. and De Pauw. E. 2002. CLIMAP, An Excel-Based Software for Climate Surface Mapping. ICARDA, Aleppo, Syria.
  • Rossiter, D.G. 1996. A theoratical framework for land evaluation. Geoderma, 72:165-202.
  • Saaty, T. L. 1980. The Analytical Hierarchy Process, McGraw Hill, New York.
  • Saaty, T.L. and Vargas, L.G. 2001. Models, Methods, Concepts & Applications of Analytical Hierarchy Process, International Series in Operations Research and Management Sciences, New York.
  • Shalaby, A., Ouma, Y. O., Tateishi, R. 2006. Land suitability assessment for perennial crops using remote sensing and Geographic Information Systems: A case study in northwestern Egypt. Archives of Agronomy and Soil Science 52 (3), 243-261.
  • Sofo, A., Manfreda, S., Fiorentino, M., Dichio, B., Xiloyannis, C. 2008. The olive tree: a paradigm for drought tolerance in Mediterranean climates. Hydrology and Earth System Sciences.12:293-01.
  • SYS, C., Van Ranst, E., Debaveye, J., Beernaert, F. 1993. Crop Requirements, Part III. Agricultural publications No:7, General Administration for development Cooperation, Brussels, pp: 247.
  • Usta, A. Aybar, M. Bayram, S. Akçay, S. 2014. Akdeniz Bölgesinin Bir Maki Elemanı olan Zeytinin Trabzonda’ki Yerel Yayılımının Ekolojik Açıdan İncelenmesi. II Ulusal Akdeniz Orman ve Çevre Sempozyumu.
  • Temuçin, E. 1993. Türkiye’de Zeytin Yetişen Alanların Sıcaklık değişkenine Göre İncelenmesi, Ege Coğrafya Dergisi 7, 117-131, İzmir-Türkiye.
  • Therios, I. 2009. Olives: Crop Production Science in Horticulture 18. CABI Publishing; Wallingford, UK.
  • TUİK, 2016. İnternet sitesi, https://biruni.tuik.gov.tr/medas/?kn=92&locale=tr. (Erişim tarihi: 01.09.2019)
  • Tunalıoğlu, R. ve Gökçe, O. 2002. Ege Bölgesi’nde Optimal Zeytin Yayılış Alanlarının Tespitine Yönelik Bir Araştırma. Tarım ve Köyİşleri Bakanlığı, Tarımsal Ekonomi Araştırma Enstitüsü Yayınları. Ankara.
  • Va´zquez, L. M., Gala´n, C., Domı´nguez-Vilches, E. 2003. Influence of meteorological parameters on olea pollen concentrations in Co´rdoba (South-western Spain). International Journal Biometeorology, 48, 83–90.
There are 52 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Murat Güven Tuğaç 0000-0001-5941-5487

Filiz Sefer 0000-0001-5553-4171

Publication Date March 31, 2021
Submission Date January 27, 2020
Acceptance Date June 3, 2020
Published in Issue Year 2021 Volume: 58 Issue: 1

Cite

APA Tuğaç, M. G., & Sefer, F. (2021). Türkiye’de zeytin (Olea europaea L.) üretimine uygun alanların coğrafi bilgi sistemleri (CBS) tabanlı çoklu kriter analizi ile belirlenmesi. Journal of Agriculture Faculty of Ege University, 58(1), 97-113. https://doi.org/10.20289/zfdergi.678474

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