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Computational intelligence applied to soil quality index using GIS and geostatistical approaches in semiarid ecosystem

  • ICCESEN 2017
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Abstract

The importance of soil quality is increasing every passing day for sustainable agriculture. In recent years, the investigation of the classification of soil quality with some classification methods known as machine learning algorithms draws attention. The study carried out for this purpose was hold on the farmland of Isparta University of Applied Sciences. Soil quality index was determined with a linear combination technique approach and analytical hierarchical process (observed values) and estimated by decision trees (predicted values). Total and minimum data sets (27 and 15 indicators, respectively) were evaluated by both methods, and all four outputs were compared. Deterministic (Inverse Distance Weighted-1, 2, 3 powers and radial based functions—completely regularized spline, spline with tension, multiquadric) and scholastic (spherical, exponential, Gaussian belonging to ordinary kriging, simple kriging and universal kriging) models were used in the creation of the distribution maps of observed and predicted values. No statistically significant differences were found in the comparison of soil quality index obtained using both data sets (P > 0.05). In the decision tree where organic matter was determined as the root node, quality classes can be predicted at 91.1% by separating sand, wilting point, and EC properties into branches as an internal node. Area under the curve value in evaluating the estimation accuracy was found as 0.991, 0.960, and 0.943 for I, II, and III classes, respectively (P = 0.00). It was determined that estimation can be done with 91.7% sensitivity and 90.9% specificity at 0.38 cut-off value for class III soils. Consequently, the highest accuracy in distribution maps of predicted and observed soil quality index values were found with the Gaussian semivariogram model of the ordinary and simple kriging for both data sets.

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References

  • Alaboz P, Işıldar AA, Müjdeci M, Şenol H (2017) Effects of different vermicompost and soil moisture levels on pepper (capsicum annuum) grown and some soil properties. Yuzuncu Yıl Univ J Agric Sci 27(1):30–36

    Google Scholar 

  • Adeyolanu OD, Are KS, Oluwatosin GA, Ayoola OT, Adelana AO (2013) Evaluation of two methods of soil quality assessment as influenced by slash and burn in tropical rainforest ecology of Nigeria. Arch Agron Soil Sci 59(12):1725–1742

    Google Scholar 

  • Akgül M, Başyiğit L (2005) Detailed soil survey and mapping of Suleyman Demirel University farmıng land. Suleyman Demirel Univ J Inst Sci 9(3):1–10

    Google Scholar 

  • Alaboz P (2019) The development of prediction models to determine some soil moisture constants by penetration resistance measurements. Doctoral thesis. Süleyman Demirel University Institute of science, 142s, Isparta

    Google Scholar 

  • Albayrak AS, Yılmaz K (2009) Data mining: decision tree algorithms and an application on ise data. Suleyman Demirel Univ J Faculty Econ Admin Sci 14(1):31–52

    Google Scholar 

  • Andrews SS, Karlen DL, Mitchell JP (2002) A comparison of soil quality indexing methods for vegetable production systems in northern California. Agric Ecosyst Environ 90:25–45

    Google Scholar 

  • Arcak S, Kütük AC, Haktanır K, Çaycı G (1997) The effects of tea wastes on soil enzyme activity and nitrification. Pamukkale Univ J Eng Sci 3(1):261–266

    Google Scholar 

  • Arshad MA, Martin S (2002) Identifying critical limits for soil quality indicators in agro-ecosystems. Agric Ecosyst Environ 88(2):153–160

    Google Scholar 

  • Askari MS, Holden NM (2015) Indices for quantitative evaluation of soil quality under grassland management. Geoderma 230–231:131–142

    Google Scholar 

  • Aydın A, Dengiz O (2019) Determination of physico-chemical and nutrient element content of soils formed under semi-humid ecological environment. Acad J Agric 8(2):301–312

    Google Scholar 

  • Baldock JA (2007) Composition and cycling of organic carbon in soil. In: Nutrient cycling in terrestrial ecosystems. Springer, Berlin Heidelberg, pp 1–35

    Google Scholar 

  • Borůvka L, Vacek O, Jehlička J (2005) Principal component analysis as a tool to indicate the origin of potentially toxic elements in soils. Geoderma 128(3–4):289–300

    Google Scholar 

  • Bouyoucous GA (1951) Determination of particle size in soils. Agron J 42:438–443

    Google Scholar 

  • Bremner JM (1982) Total nitrogen, methods of soil analysis. Am Soc Agron Mongrn 10(2):594–624

    Google Scholar 

  • Cheng J, Ding C, Li X, Zhang T, Wang X (2016) Soil quality evaluation for navel orange production systems in central subtropical China. Soil Tillage Res 155:225–232

    Google Scholar 

  • Dedeoğlu M, Dengiz O (2018) Determination of land suitability classes by using integrated geographic information systems with multi-criteria decision making analysis. J Süleyman Demirel Univ Fac Agric 13(2):60–72

    Google Scholar 

  • Demirtok M, Kılıç Ş, Doğan K (2015) Mapping of microbial activities in the widespread soil series of Amik plain. Soil-water J 4(2):14–20

    Google Scholar 

  • Dengiz O (2002) Determination of land quality using parametric approach in Gölbaşı district of Ankara province. Selcuk J Agric Food Sci 16(30):59–60

    Google Scholar 

  • Dengiz O (2020) Soil quality index for paddy fields based on standard scoring functions and weight allocation method. Arch Agron Soil Sci 66(3):301–315

    Google Scholar 

  • Dengiz O, Sarıoğlu FE (2013) Parametric approach with linear combination technique in land evaluation studies. J Agric Sci 19:101–112

    Google Scholar 

  • Dengiz O (2013) Land suitability assessment for rice cultivation based on GIS modeling. Turk J Agric Forest 37(3):51

    Google Scholar 

  • Doran J, Tim K, Maria T (1997) Field and laboratory solvita soil test evaluation. USDA-ARS. Department of Agronomy. University of Nebraska, Lincoln

    Google Scholar 

  • Doran JW, Parkin TB (1994) Defining and assessing soil quality. In: Doran JW, Coleman DC, Bezdicek DF, Stewart BA (eds) Defining soil quality for a sustainable environment. SSSA Spec. Publ., 35, SSSA ASA, Madison, pp 1–21

    Google Scholar 

  • Doran JW (2002) Soil health and global sustainability: translating science into practice. Agric Ecosyst Environ 88(2):119–127

    Google Scholar 

  • FAO (1990) Micronutrient assessment at the country level p 1–208. An international study (Ed., M. Sillanpa). FAO Soil Bulletin 63. Published by FAO, Rome, İtaly

  • Görmüş M, Özkul M (1995) Stratigraphy of the area between Gonen-Atabey (Isparta) and Aglasun (Burdur). J Sci Ins Suleyman Demirel Univ 1:43–64

    Google Scholar 

  • Haghighi Fashi F, Gorji M, Sharifi F (2017) Least limiting water range for different soil management practices in dryland farming in Iran. Arch Agron Soil Sci 63(13):1814–1822. https://doi.org/10.1080/03650340.2017.1308688

    Article  Google Scholar 

  • Hateffard F, Dolati P, Heidari A, Zolfaghari AA (2019) Assessing the performance of decision tree and neural network models in mapping soil properties. J Mt Sci 16(8):1833–1847

    Google Scholar 

  • Hazelton P, Murphy B (2016) Interpreting soil test results: what do all the numbers mean? CSIRO publishing

  • Hofmann ED, Hoffmann GG (1966) Die bestimmung der biologischen tätigkeit in böden mit enzymmethoden. Adv Enzymol Relat Areas Mol Biol 28:365–390

    Google Scholar 

  • Isermayer H (1952) Eine einpache method zur bestimmung der pflanzenatmung under carbonate in boden. ZPflanzenernahr, Dung

    Google Scholar 

  • Jackson ML (1958) Soil chemical analysis prentice hall. Inc., Englewood Cliffs, NJ, vol 498, pp 183–204

    Google Scholar 

  • Jian S, Li J, Chen J, Wang G, Mayes MA, Dzantor K, Hui ED, Luo Y (2016) Soil extracellular enzyme activities, soil carbon and nitrogen storage under nitrogen fertilization: a meta-analysis. Soil Biol Biochem 101:32–43

    Google Scholar 

  • Kacar B (2016) Physical and chemical soil analysis. Nobel Press, Turkish

    Google Scholar 

  • Karagöz Y, Kösterelioğlu İ (2008) Developing evaluation scale of communication skills with factor analysis. Dumlupınar Univ J Social Sci 21

  • Karlen DL, Mausbach MJ, Doran JW, Cline RG, Harris RF, Schuman GE (1997) Soil quality: a concept, definition, and framework for evaluation. Soil Sci Soc Am J 61:4–10

    Google Scholar 

  • Karlen DL, Stott DE (1994) A framework for evaluating physical and chemical indicators of soil quality. Defin soil qual sustain environ 35:53–72

    Google Scholar 

  • Kavzoğlu T, Çölkesen İ (2010) Classification of satellite images using decision trees: Kocaeli case. Electron J Map Technol 2(1):36–45

    Google Scholar 

  • Kheir RB, Greve MH, Bøcher PK, Greve MB, Larsen R, McCloy K (2010) Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: the case study of Denmark. J Environ Manag 91(5):1150–1160

    Google Scholar 

  • Kılıç S (2013) ROC analysis in clinical decision making. Psych Behav Sci 3(3):135

    Google Scholar 

  • Koca YK, Acar M, Turgut YŞ (2019) Evaluation of quality of agricultural soils with geostatistical modeling. Harran J Agric Food Sci 23(4):489–499

    Google Scholar 

  • Lindsay WL, Norvell WA (1978) Development of a DTPA soil test for zinc, iron, manganese, and copper. Soil Sci Soc Am J 42(3):421–428

    Google Scholar 

  • Liu Z, Zhou W, Shen J, Li S, Ai C (2014) Soil quality assessment of yellow clayey paddy soils with different productivity. Biol Fertil Soils 50:537–548. https://doi.org/10.1007/s00374-013-0864-9

    Article  Google Scholar 

  • Malone BP, Minasny B, McBratney AB (2017) Using R for digital soil mapping. Springer International Publishing, Basel

    Google Scholar 

  • Masto RE, Chhonkar PK, Purakayastha TJ, Patra AK, Singh D (2008) Soil quality indices for evaluation of long-term land use and soil management practices in semi-arid sub-tropical India. Land Degrad Dev 19(5):516–529

    Google Scholar 

  • Mihalikova M, Dengiz O (2019) Towards more effective irrigation water usage by employing land suitability assessment for various irrigatıon techniques. Irrigation and drainage. https://doi.org/10.1002/ird.2349

  • Mukherjee A, Lal R, Zimmerman AR (2014) Effects of biochar and other amendments on the physical properties and greenhouse gas emissions of an artificially degraded soil. Sci Total Environ 487:26–36

    Google Scholar 

  • Mulla DJ, McBratney AB (2000) Soil spatial variability. In: Sumner ME (ed) Handbook of soil science. CRS press, Boca Raton, pp 321–352

    Google Scholar 

  • Navarro SA, Gil-Vázquez JM, Delgado-Iniesta MJ, Marín-Sanleandro P, Blanco-Bernardeau A, Ortiz-Silla R (2015) Establishing an index and identification of limiting parameters for characterizing soil quality in Mediterranean ecosystems. Catena 131:35–45

    Google Scholar 

  • NEN 5140 (1996) Nederlandse norm - Geotechniek. Bepaling van de conusweerstand en de plaatselijke wrijvingsweerstand van grond. Elektrische sondeermethode. Nederlands Normalisatie-instituut, Delft, p 6

  • Qi Y, Darilek JL, Huang B, Zhao Y, Sun W, Gu Z (2009) Evaluating soil quality indices in an agricultural region of Jiangsu Province, China. Geoderma 149(3–4):325–334

    Google Scholar 

  • Olsen SR (1954) Estimation of available phosphorus in soils by extraction with sodium bicarbonate (no. 939). US Department of Agriculture

  • Özyazıcı MA, Dengiz O, Aydoğan M, Bayraklı B, Kesim E, Urla Ö, Yıldız H, Ünal E (2016) Levels of basic fertility and the spatial distribution of agricultural soils in central and eastern Black Sea region. Anadolu J Agri Sci 31(1):136–148

    Google Scholar 

  • Pehlivan G (2006) CHAID analysis and an application. Yıldız Technical University, Institute of Science, Unpublished Master Thesis

    Google Scholar 

  • Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281

  • Saaty TL (1980) The analytic hierarchy process. New York: McGraw-Hill, (This book has been tranlated into Chinese by S. Xu et al.; information is available from them at the Inst. Of Systems Engineering, Tianjing Univ., Tianjin, China.), A Translation into russian by R. Vachnadze is currently underway

  • Saaty TL (2008) Decision making with the analytic hierarchy process, international journal services. Sciences 1(1):83–98

  • Schoeneberger PJ, Wysocki DA, Benham EC (2012) Soil survey staff field book for describing and sampling soils. In: Version, 3. National Soil Survey Center LincolnNE, Natural Resources Conservation Service

    Google Scholar 

  • Soil Survey Staff (1992) Procedures for collecting soil samples and methods of analysis for soil survey. Soil Survey Invest. Rep. I. U.S. Gov. Print. Office, Washington D.C. USA

  • Soil Survey Staff (1993) Soil survey manual. USDA Handbook. No: 18 Washington D.C.

  • Seker C, Özaytekin HH, Negiş H, Gümüş İ, Dedeoğlu M, Atmaca E, Karaca Ü (2017) Identification of regional soil quality factors and indicators: a case study on an alluvial plain (Central Turkey). Solid Earth 8(3):583–595

    Google Scholar 

  • Şenol H, Dengiz O, Alaboz P (2019) Determination of spatial variability of soil quality index based on multi criteria decision analysis. International Soil Congress 2019 17-19 June Ankara, Turkey

  • Tabatabai MA, Bremner JM (1972) Assay of urease activity ın soils. Soil Biol Biochem 4:479–487

    Google Scholar 

  • Tatlıdil H (2002) Applied multivariate statistical analysis. Academy Printing House, Ankara

    Google Scholar 

  • Tunçay T, Başkan O, Bayramin İ, Dengiz O, Kılıç Ş (2018) Geostatistical approach as a tool for estimation of field capacity and permanent wilting point in semiarid terrestrial ecosystem. Arch Agron Soil Sci 64(9):1240–1253. https://doi.org/10.1080/03650340.2017.1422081

    Article  Google Scholar 

  • Turan İD, Dengiz O (2017) Erosion risk prediction using multi-criteria assessment in Ankara Güvenç Basin. J Agric Sci 23(3):285–297

    Google Scholar 

  • Turner BL, Hopkins DW, Haygarth PM, Ostle N (2002) β–glucosidase activity in pasture soils. Appl Soil Ecol 20:157–162

    Google Scholar 

  • Tripathi DK, Singh VP, Chauhan DK, Prasad SM, Dubey NK (2014) Role of macronutrients in plant growth and acclimation: recent advances and future prospective. In: improvement of crops in the era of climatic changes. Springer, New York, pp 197–216

    Google Scholar 

  • U.S Salinity Laboratory Staff (1954) Diagnosis and improvement of salina and alkali soils. Agricultural Handbook 60 U.S.D.A.

  • Vågen TG, Winowiecki LA, Tondoh JE, Desta LT, Gumbricht T (2016) Mapping of soil properties and land degradation risk in Africa using MODIS reflectance. Geoderma 263:216–225

    Google Scholar 

  • Van Wambeke A R (2000) The Newhall simulation model for estimating soil moisture and temperature regimes. Department of Crop and Soil Sciences. Cornell University, Ithaca, NY. USA

  • Vasu D, Singh SK, Ray SK, Duraisami VP, Tiwary P, Chandran P, Anantwar SG (2016) Soil quality index (SQI) as a tool to evaluate crop productivity in semi-arid Deccan plateau, India. Geoderma 282:70–79

    Google Scholar 

  • Wilding LP, Bouma J, Goss DW (1994) Impact of spatial variability on ınterpretative modelling. ın: quantitative modelling of soil forming processes. R.B. Bryant ve Arnold R.W. (Ed.) SSSA special publication number 39. SSSA.Inc. Madison Wisconsin.USA

  • Witt SF, Witt CA (1992) Modeling and forecasting demand in tourism. Academic Press, Londra

    Google Scholar 

  • Wu C, Liu G, Huang C, Liu Q (2019) Soil quality assessment in Yellow River Delta: establishing a minimum data set and fuzzy logic model. Geoderma 334:82–89

    Google Scholar 

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Acknowledgment

We would like to thank the Presidency of Scientific Research Projects Management Unit of Suleyman Demirel University, which financially supported the part of this study with Project FYL-2018-6743.

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Authors and Affiliations

  1. Faculty of Agriculture, Department of Soil Science and Plant Nutrition, Isparta University of Applied Sciences, Isparta, Turkey

    Hüseyin Şenol, Pelin Alaboz & Sinan Demir

  2. Faculty of Agriculture, Department of Soil Science and Plant Nutrition, Ondokuz Mayıs University, Samsun, Turkey

    Orhan Dengiz

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  1. Hüseyin Şenol
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  2. Pelin Alaboz
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  3. Sinan Demir
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  4. Orhan Dengiz
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Correspondence to Pelin Alaboz.

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This article is part of the Topical Collection on Geo-Resources-Earth-Environmental Sciences

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Şenol, H., Alaboz, P., Demir, S. et al. Computational intelligence applied to soil quality index using GIS and geostatistical approaches in semiarid ecosystem. Arab J Geosci 13, 1235 (2020). https://doi.org/10.1007/s12517-020-06214-9

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  • DOI: https://doi.org/10.1007/s12517-020-06214-9

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