Ilgar Dağı periglasyal şekilleri üzerinde oluşmuş toprakların erozyon duyarlılıklarının belirlenmesi ve yapay sinir ağı (YSA) ile tahmin edilmesi

Year 2022, Issue 47, 258 - 279, 30.09.2022

Volkan DEDE Orhan DENGİZ İnci DEMİRAĞ TURAN Kuttusi ZORLU Sena PACCİ Soner SERİN

https://doi.org/10.32003/igge.1097942

Cited By: 1

Abstract

Periglasyal şekiller, geçmiş dönem iklim koşullarına bağlı olarak gelişen ve günümüz iklim koşulları altındada devinim gösteren oluşumlardır. Bu şekiller, Dünya’nın yüksek enlemlerinin yanı sıra alçak enlemlerinin yüksek dağlık alanlarında da yayılış göstermektedir. Kuzeydoğu Anadolu’da, Küçük Kafkaslar (4090 m) üzerinde yer alan Ilgar Dağı (2918 m) da söz konusu periglasyal şekillerin dağılış gösterdiği önemli noktalar arasındadır. Tipik bir volkan konisi görünümünde olan Ilgar Dağı’nın jeolojisini,temelde Üst Miosen ve Alt Pliosen yaşlı bazalt, tüf ve aglomera oluştururken, zirveler bölümünü ise Pleistosen yaşlı andezitler meydana getirmektedir. Ilgar Dağı’nın Öküzkoku ve Mısıkanadlı parazit konilerinin yamaçlarında girland, çember ve taş kümelerinden oluşan periglasyal şekiller görülmektedir. Bu çalışmada, (1) Ilgar Dağı periglasyal şekilleri üzerinde gelişen toprakların fiziko-kimyasal özelliklerinin belirlenmesi ve (2) bazı erozyon duyarlılık parametrelerinin (Strüktür stabilite indeksi-SSI, dispersiyon oranı-DO ve kabuk oluşumu-CF) tahmin edilmesi amaçlanmıştır. Bu amaçla sahadan alınan 25 adet örneklem verisi analiz edilerek toprakların fiziko-kimyasal özellikleri saptanmıştır. Söz konusu toprak özellikleri girdi olarak kullanılarak, erozyon duyarlılık parametreleri (CF, DO, SSI) yapay sinir ağı (YSA) ile tahmin edilmiştir. Bulgular, toprakların organik madde içeriklerinin yüksek olması, topraklarda kabuk oluşumuna dolayısıyla da fiziksel bozunumun oldukça düşük düzeylerde kalmasına neden olurken; kum oranının yüksek olmasının ise SSI ve DO değerinin de yüksek olmasına neden olduğu görülmüştür. Ayrıca YSA ile tahmin edilen yüksek erodobilite faktörü % 82 ile CF olmuştur.

Keywords

Periglasyal şekiller, erodobilite, yapay sinir ağı (YSA), Ilgar Dağı, Küçük Kafkaslar, Türkiye

Determination of erosion susceptibilities of soils formed on the periglacial landforms of mount Ilgar and its estimation using artificial neural network (ANN)

Abstract

Periglacial landforms are formations that develop depending on the climatic conditions of the past period and show alteration under today's climatic conditions. These landforms are distributed in the high mountain areas of the low latitudes as well as the high latitudes of the Earth. Mount Ilgar (2918 m a.s.l.), located on the Lesser Caucasus (4090 m a.s.l.) in Northeastern Anatolia, is among the important points where the landforms are distributed. The geology of Mount Ilgar, which has the appearance of a typical volcanic cone, is composed of Upper Miocene and Lower Pliocene aged basalt, tuff and agglomerate, while Pleistocene aged andesites form the summits. Periglacial landforms consisting of non sorted steps, mud circles and stony earth circles are observed on the slopes of parasite cones called Öküzkoku and Mısıkan of Mount Ilgar. In this study, it was aimed to (1) determine the physico-chemical properties of soils developed on the periglacial landforms of Mount Ilgar and (2) estimate various erosion susceptibility parameters (Structural stability index-SSI, dispersion ratio-DR and crust formation-CF). For this purpose, the physico-chemical properties of the soils were determined by analyzing 25 sample data, collected from the field. Erosion susceptibility parameters (SSI, DR, CF) were estimated by artificial neural network (ANN) by using the soil properties as input. The results show that the high organic matter content of the soils causes the crust formation in the soils, thus keeping the physical degradation at very low levels; it was observed that the high sand content caused the SSI and DO values to be high. In addition, the highest erodibility factor estimated by ANN was CF with 82%.

Keywords

Periglacial landforms, erodibility, artificial neural network (ANN), Mount Ilgar, Lesser Caucasus, Turkey

References

• Abbot, J., & Marohasy, J. (2012). Application of artificial neural Networks to rainfall forecasting in Queensland, Australia. Adv.Atmos. Sci., 29, 717-730. https://doi.org/10.1007/s00376-012-1259-9
• Acheampong, A. O., & Boateng, E. B. (2019). Modelling carbon emissions intensity: Application of artificial neural network. J. Clean. Prod., 225, 833-856. https://doi.org/10.1016/j.jclepro.2019.03.352
• Alaboz, P., Dengiz, O., Demir, S., & Şenol, H. (2021). Digital mapping of soil erodibility factors based on decision tree using geostatistical approaches in terrestrial ecosystem. Catena, 207-105634. https://doi.org/10.1016/j.catena.2021.105634
• Alexakis, D. D., Tapoglou, E., & Vozinaki, A.E.K. (2019). Integrated use of satellite remote sensing, artificial neural networks, fileds pectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosion. Remote Sens., 11(9), 1106. https://doi.org/ 10.3390/rs11091106
• Almeida, C. M., Gleriani, J. M., & Castejon, E. F. (2008). Using neural networks and cellular automata for modelling intra-urban land use dynamics. Int. J. Geogr. Inf. Sci., 22(9), 943–963. https://doi.org/10.1080/13658810701731168
• Aşkın, T., Türkmen F., & Tarakçıoğlu, C. (2016). Ordu ili merkez ilçe topraklarında erozyon riskinin jeoistatistiksel tekniklerle değerlendirilmesi. Toprak Bilimi ve Bitki Besleme Dergisi, 4(2) 69-75.
• Bajracharya, R. M., Elliot, W. J., & Lal, R. (1992). Interrill erodibility of some Ohio soils based on field rainfall simulation. Soil Science Society of America Journal, 56, 267-272.
• Barthes, B., & Roose, E. (2002). Aggregate stability as an indicator of soil susceptibility to runoff and erosion; validation at several levels. Catena, 47(2), 133-149. https://doi.org/10.1016/S0341-8162(01)00180-1
• Bennett, H. H. (1955). Elements of soil conservation. (2.ed.) New York, McGraw-Hill, 358p.
• Bilgili, M. (2011). The use of artificial neural networks for forecasting the monthly mean soil temperatures in Adana. Turkey. Turkish J. Agric. For., 35, 83-93. https://doi.org/10.3906/tar-1001-593
• Bissonnais, Y., Bruand, A., & Jamagne, M. (2007). Laboratory experimental study of soil crusting: Relations between aggregate breakdown mechanisms and crust structure. Catena, 16, 377- 392.
• Blake, G. R., & Hartge, K. H. (1986). Bulk density. In A. Klute (Eds.), Methods of soil analysis: Part 1 Physical and mineralogical methods, (pp. 363-375). SSSA Book Series.
• Bose, B. K. (1994). Expert-system, fuzzy-logic, and neural-network applications in power electronics and motion control. Proceeding of the IEEE, 82(8), 1303-1323. https://doi.org/10.1109/5. 301690
• Bouyoucos, G. J. (1962). Hydrometer method improved for making particle size analyses of soils. Agronomy Journal, 54(5), 464-465. https://doi.org/10.2134/agronj1962.00021962005400050028x
• Bu, C. F., Wu, S. F., & Yang, K.B. (2014). Effects of physical soil crusts on infiltration and splash erosion in three typical Chinese soils. International Journal of Sediment Research, 29(4), 491-501. https://doi.org/10.1016/S1001- 6279(14)60062-7
• Canpolat, M., & Demiralay, İ. (1995). Organik materyal ilave edilmiş toprakların agregat stabilitesi, briket hacim ağırlığı ve kırılma değeri arasındaki ilişkiler. Türkiye Toprak İlmi Derneği Toprak ve Çevre Sempozyumu. Cilt II. Yayın No: 7, ss: A-116 A-124, Ankara.
• Cebeci, D. (2013). Kurumsal kredi değerlendirmede bulanık AHP-yapay sinir ağları temelli bir yaklaşım ve bir uygulama çalışması, (Yüksek Lisans Tezi, YTÜ Fen Bilimleri Enstitüsü, İstanbul).
• Celilov, C., & Dengiz, O. (2019). Erozyon duyarlılık parametrelerinin farklı enterpolasyon yöntemleriyle konumsal dağılımlarının belirlenmesi: Türkiye, Ilgaz Milli Park toprakları. Türkiye Tarımsal Araştırmalar Dergisi, 6(3), 242- 256. https://doi.org/10.19159/tutad.502457
• Chaudhri, K. G., Brown, K.W., & Holder, C.B. (1976). Reduction of crust impedence to simulated seedling emergence by the addition of manure. Soil Science, 122, 216–222.
• Colucci, R. R., Boccali, C., Zebre, M., & Guglielmin, M. (2016). Rock glaciers, protalus ramparts and pronival ramparts in the South-eastern Alps. Geomorphology, 269, 112-121. https://doi.org/10.1016/j.geomorph.2016.06.039
• Coppola, E., Poulton, M., Charles, E., Dustman, J., & Szidarovszky, F. (2003). Application of artificial neural Networks to complex ground water management problems. Natural Resources Research, 12, 303–320 (2003). https://doi.org/10.1023/B:NARR.0000007808.11860.7e
• Çelik, P., & Dengiz, O. (2018). Akselendi ovası tarım topraklarının temel toprak özellikleri ve bitki besin elementi durumlarının belirlenmesi ve dağılım haritalarının oluşturulması. Türkiye Tarımsal Araştırmalar Dergisi, 5(1), 9- 18. https://doi.org/10.19159/tutad.322336
• Çiçek, İ., Gürgen, G., Tunçel, H., & Doğu, A.F. (2004). Glacial morphology of Eastern Black Sea Mountains, Turkey. Caucasian Geographical Review, 4, 46-51.
• Dede, V., Dengiz, O., Demirağ Turan, İ., Türkeş, M., Gökçe, C., & Serin, S. (2020). Ilgaz Dağları periglasyal şekillerinde oluşmuş toprakların fizikokimyasal özellikleri ile bazı erozyon duyarlılık parametreleri arasındaki ilişkilerin belirlenmesi. Ankara Üniversitesi, Coğrafi Bilimler Dergisi, 18, 99-123. https://doi.org/10.33688/ aucbd.689755
• Dengiz, O., & Başkan, O. (2010). Characterization of soil profile developmet on different ladscape in semi-arid Region of Turkey a case study; Ankara-Soğulca catchmet. Anadolu Tarım Bilimleri Dergisi, 25(2),106-112.
• Drewes, J., Moreiras, S., & Korup, O. (2018). Permafrost activity and atmospheric warming in the Argentinian Andes. Geomorphology, 323, 13-24. https://doi.org/10.1016/j.geomorph.2018.09.005
• Ebrahimi, M., Sarikhani, M. R., Sinegani, A. A. S., Ahmadi, A., & Keesstra, S. (2019). Estimating the soil respiration under different landuses using artificial neural network and linear regression models. Catena, 174, 371–382. https://doi.org/10.1016/j.catena.2018.11.035
• Fadare, D. A. (2010). The application of artificial neural Networks to mapping of wind speed profile for energy application in Nigeria. Appl. Energy, 87(3), 934–42. https://doi.org/10.1016/j.apenergy.2009.09.005
• Farhat, A., & Cheok, K.C. (2017). Improving Adaptive Network Fuzzy Inference System with Levenberg Marquardt Algorithm. 2017 Annual IEEE International Systems Conference (SysCon). Montreal, QC, Canada.
• Feng, L., & Zhang, J. (2014). Application of artificial neural networks in tendency forecasting of economic growth. Econ. Model., 40, 76-80. https://doi.org/10.1016/j.econmod.2014.03.024
• Foth, H. D. (1990). Fundamentals of soil science. (8.ed.) New York, John Wiley & Sons, 1990. 368p.
• Gholami, V., Booij, M. J., Tehrani, E. N., & Hadian, M.A. (2018). Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data. Catena. 163, 210-218. https://doi.org/10.1016/j.catena.2017.12.027
• Gholami, V., Sahour, H., & Hadian Amri, M. A. (2021). Soil erosion modeling using erosion pinsand artificial neural networks. Catena, 196, 104902. https://doi.org/10.1016/j. catena.2020.104902
• Giardino, J., & Vick, S. (1987). Geologic engineering aspects of rock glaciers, In: Giardino, J., Shroder, J., Vitek, J., (Eds.), Rock Glaciers, Allen and Unwin, London, 265-287.
• Hamilton, S., & Whalley, W. (1995). Rock glacier nomenclature: a reassesment. Geomorphology, 14, 73-80. https://doi.org/10.1016/0169- 555X(95)00036-5
• Hosseinpour, S., Aghbashlo, M., Tabatabaei, M., & Khalife, E. (2016). Exact estimation of biodiesel cetane number (CN) from its fatty acid methyl esters (FAMEs) profile using partial least square (PLS) adapted by artificial neural network (ANN). Energy Conversion and Management 124:389-98. https://doi.org/10.1016/j.enconman.2016.07.027
• Huang, W., & Foo, S. (2002). Neural network modeling of salinity variation in Apalachicola River. Water Research, 36(1), 356-362. https://doi.org/10.1016/s0043-1354(01)00195-6
• Humlum, O. (1998). The climatic significance of rock glaciers. Permafrost and Periglacial Processes, 9, 375-395. https://doi.org/10.1002/(SICI)1099-1530(199810/12)9:4<375::AID-PPP301>3.0.CO;2-0
• İç, S., & Gülser, C. (2008). Tütün atığının farklı bünyeli toprakların bazı kimyasal ve fiziksel özelliklerine etkisi. Anadolu Tarım Bilimleri Dergisi, 23(2), 104-109.
• İnce, A. (2018). Yapay sinir ağları ve rastgele orman yöntemleri ile Landsat 8 görüntülerinden otomatik kıyı çizgisi çıkartılması, (Yüksek Lisans Tezi, Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul).
• Jackson, M. L. (1958). Organic matter determination for soils. Soil chemical analysis.
• Kalogirou, S. A. (2000). Applications of artificial neural-networks for energy systems. Applied Energy, 67(1-2),17- 35. https://doi.org/10.1016/S0306-2619(00)00005-2
• Kanar, E., & Dengiz, O. (2015). Madendere havzası topraklarında arazi kullanım/arazi örtüsü ile bazı erozyon duyarlılık indeksleri arasındaki ilişkinin belirlenmesi. Türkiye Tarımsal Araştırmalar Dergisi, 2(1), 15-27.
• Karagöktaş, D., & Yakupoğlu, T. (2014). Erozyon araştırma sahasına dönüştürülmesi planlanan bir alanda aşınabilirlik ve toprak özellikleri arasındaki ilişkiler. Toprak Bilimi ve Bitki Besleme Dergisi, 2(1), 6-12.
• Kemper, W. D., & Rosenau, R. C. (1986). Aggregate stability and size distribution. In A. Klute (Eds.), Methods of soil analysis: Part 1 Physical and mineralogical methods, (pp. 363-375). SSSA Book Series.
• Keskin, İ. (2013). 1/ 100.000 Ölçekli Türkiye Jeoloji Haritaları, Ardahan E-49 ve F-49 Paftaları. MTA Genel Müdürlüğü, Jeoloji Etütleri Dairesi, No: 181.
• Kim, M., & Gilley, J. E. (2008). Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas. Comput. Electron. Agric. 64, 268-275. https://doi.org/10.1016/j.compag.2008.05.021
• Knight, J., Harrison, S., & Jones, D.B. (2019). Rock glaciers and the geomorphological evolution of deglacierizing mountains. Geomorphology, 324, 14-24. https://doi.org/10.1016/j.geomorph.2018.09.020
• Kurter, A. (1991). Glaciers of Middle East and Africa Glaciers of Turkey, Satellite Image Atlas of the World, (Ed. R. S. Williams ve J. G. Ferrigno) USGS Professional Paper, 1386-G-1, 1-30.
• Kurter, A., & Sungur, K. (1980). Present Glaciation in Turkey, World Glacier Inventory, Proceedings of the workshop at Riederalp, Switzerland, 17-22 September 1978. International Association of Hydrologial Sciences, 126,155-160.
• Lal, R. (1998). Soil quality and agricultural sustainability. CRC press.
• Leo, M. W. (1963). A rapid method for estimating structural stability of soils. Soil Science, 96(5), 342-346.
• Li, Q., Yue, T., Wang, C., Zhang, W., Yu, Y., Li, B., Yang, J., & Bai, G. (2013). Spatially distributed modeling of soil organic matter across China: an application of artificial neural network approach. Catena, 104, 210-218. https://doi.org/10.1016/j.catena.2012.11.012
• Licznar, P., & Nearing, M. A. (2003). Artificial neural networks of soil erosion and runoff prediction at the plot scale. Catena, 51, 89-114. https://doi.org/10.1016/S0341-8162(02)00147-9
• Lozinski, von W. (1909). Über die Mechanische Vermitterung der Sandsteine im Gemassigten Klima. Bulletin International de I’Academiedes Sciences de Cracovie class des Sciences Mathematique et Naturalles, 1, 1-25.
• Luk, K. C., Ball, J. E., & Sharma, A. (2001). An application of artificial neural Networks for rainfall forecasting. Math Comput Model, 33, 683–93. https://doi.org/10.1016/S0895-7177(00)00272-7
• Lutz, J. H., & Chandler, F. R. (1947). Forest Soils. John Wiley and Sons, Inc. New York.
• Mallants, D., Mohanty, B. P., Jacques, D., & Feyen, J. (1996). Spatial variability of hydraulic properties in a multi- layered soil profile. Soil Science, 161(3), 167-181.
• Miller, W. P., & Baharrudin, M.K. (1987). Interrill erodibility of highly weathered soils. Communication in Soil Science and Plant Analysis, 18, 933-945.
• Moghadam, H., Tayyebi, A., & Helbich, M. (2017). Transition index maps for urban growth simulation: application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation. Environ Monit Assess, 189, 300. https://doi.org/10.1007/s10661-017-5986-3
• Mohammadi, B., Mehdizadeh, S., Ahmadi, F., Lien, N. T. T., Linh, N. T. T., & Pham, Q. B. (2021). Developing hybrid time series and artificial intelligence models for estimating air temperatures. Stoch Env Res Risk Assess, 35, 1189– 1204. https://doi.org/10.1007/ s00477-020-01898-7
• Najah, A., El-Shafie, A., Karim, O. A., & El-Shafie, A. H. (2013). Application of artificial neural Networks for water quality prediction. Neural Comput & Applic 22, 187-201. https://doi.org/10.1007/s00521-012-0940-3
• Ngatunga, E.L.N., Lal, I., & Singer, M. J. (1984). Effect of surface management on runoff and soil eroison from some plot at Milangano, Tanzania. Geoderma, 33, 1-12.
• Odabaş, M. S., Kayhan, G., Ergun, E., & Şenyer, N. (2016). Using artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of Saint John’s Wort Leaves. Commun Soil Sci Plant Anal, 47(2), 237-245.
• Odabaş, M.S., Leelaruban, N., Şimsek, H., & Padmanabhan, G. (2014). Quantify ingimpact of droughts on barley yield in North Dakota, usa using multiple linear regression and artificial neural network. Neural Network World, 24(4), 343–355, 1.
• Oliva, M., Sarıkaya, M. A. & Hughes, P., (2020). Holocene and earlier glaciations in the Mediterranean Mountains. Mediterranean Geoscience Reviews, 2, 1-4. https://doi.org/10.1007/s42990-020-00025-6
• Oliva, M., Serrano, E., Gomez-Ortiz, A., Gonzalez-Amuchastequi, M.J., Nieuwendan, A., Palacios, D., Perez-Alberti, A., Pellitero-Ondicol, R., Ruiz-Fernandez, J., Valcarcel, M., Vieira, G., & Antoniades, D. (2016). Spatial and temporal variability of periglaciation of the Iberian Peninsula. Quaternary Science Reviews, 137, 176-199.
• Oliva, M., Zebre, M., Guglielmin, M., Hughes, P., Çiner, A., Vieira, G., Bodin, X., Andres, N., Colucci, R.R., Garcia- Hernandez, C., Mora, C., Nofre, J., Palacios, D., Perez-Alberti, A., Ribolini, A., Ruiz-Fernandez, J., Sarıkaya, M. A., Serrano, E., Urdea, P., Valcarcel, M., Woodward, J. C., & Yıldırım, C. (2018). Permafrost conditions in the Mediterranean region since the Last Glaciation. Earth-Science Reviews, 185, 397-436.
• Özdemir, N. (2013). Toprak ve su koruma. Ondokuz Mayıs Üniversitesi Ziraat Fakültesi Yayınları, No:22, Üçüncü Baskı, 232s, Samsun.
• Öztürk, E. (2013). Organik düzenleyicilerin toprak kaybı ve toprak kalitesi üzerindeki etkilerinin laboratuvar koşullarında belirlenmesi. (Doktora Tezi, Ondokuz Mayıs Üniversitesi Fen Bilimleri Enstitüsü, 151s, Samsun).
• Öztürk, E., & Özdemir, N. (2006). Topraklarda kabuk tabakası oluşumu, çeşitleri ve önlenmesi. Anadolu Tarım Bilimleri Dergisi, 21(2), 275-282.
• Pacci, S., Kaya, N. S., Demirağ Turan, İ., Odabaş, M. S., & Dengiz, O. (2022). Comparative approach for soil quality index based on spatial multi-criteria analysis and artificial neural network. Arabian Journal of Geosciences, 15(1), 1-15. https://doi.org/10.1007/s12517-021-09343-x
• Parlak, M., Yiğini, Y., & Ekinci, H. (2014). Çanakkale Umurbey ovası topraklarının erozyona duyarlılığının mevsimsel değişimi. ÇOMÜ Ziraat Fakültesi Dergisi, 2(1), 123-131.
• Pieri, C. (1989). Fertilité des terres de savane. Bilan de trente annéesderecherche et de développement agricole ausuddu Sahara. IRAT, Paris, 444 pp.
• Saygın, F., Dengiz, O., İç, S., & İmamoğlu, A. (2019). Bazı fiziko-kimyasal toprak özellikleri ile bazı erodibilite parametreleri arasındaki ilişkilerin mikro havza ölçeğinde değerlendirilmesi. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi, 20(1), 82-91. https://doi.org/10.17474/artvinofd.481642
• Suo, X. M., Jiang, Y.T., Yang, M., Li, S. K., & Wang, C.T. (2010). Artificial neural network to predict leaf population chlorophyll content from cotton plant images. Agric. Science in China, 9(1), 38-45. https://doi.org/10.1016/S1671- 2927(09)60065-1
• Temizel, K.E., Odabaş, M. S. Şenyer, N. Kayhan, G. Bajwa, S. Çalışkan, O., & Ergun, E. (2014). Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions. Central European Journal of Biology, 9 (12): 1226-1234. https://doi.org/10.2478/s11535-014-0356-4
• Uxa, T., Mida, P., & Krizek, M. (2017). Effect of climate on morphology and development of sorted circles and polygons. Permafrost and Periglacial Processes, 28, 663-674. https://doi.org/10.1002/ppp.1949
• Velichko, A. A., & Nechaev, V. P. (1992). Cryogenic regions during the Last Glacial Maximum (permafrost). In Frenzel, B., Pecsi, M., and Velichko, A. A., Gustav Fischer Verlag (Eds.) Atlas of Paleoclimates and Paleoenvironments of the Northern Hemisphere, Stuttgart 108-109.
• Wahrhaftig, C., & Cox, A. (1959). Rock glaciers in the Alaska Range. Geological Society of America Bulletin, 70(4), 383-436.
• Whalley, W. B., & Martin, H.E. (1992). Rock glaciers: II models and mechanism. Progress in Physical Geography, 16(2), 127-186. https://doi.org/10.1177/030913339201600201
• Wilding, L. P. (1985). Spatial variability: it's documentation, accommodation and implication to soil surveys. In Nielsen, D. R. and J. Bouma (Eds.). Soil Spatial Variability. Pudoc, Wageningen, The Netherlands, p. 166-194.
• Yıldız, N., Akbulut, Ö., & Bircan, H. (1998). İstatistiğe giriş. Şafak Yayınevi. Erzurum.
• Yılmaz, E., Alagöz, Z. V. & Öktüren, F. (2005). Toprakta agregat oluşumu ve stabilitesi. S.Ü. Ziraat Fakültesi Dergisi, 19(36), 78-86.
• Yılmaz, K., Çelik, İ., Kapur, S., & Ryan, J. (2005). Clay minerals, Ca/Mg ratioand Fe-Al-oxides in relation to structural stability, hydraulic conductivity and soil erosion in southeastern Turkey. Turkish journal of agriculture and forestry, 29(1), 29-37.