Determination of Organic Carbon Content of the Soils within the Greenhouses Built on Pyroclastic Deposits in Isparta Settlement Area

Year 2025, Volume: 8 Issue: 1, 15 - 28, 15.01.2025

Sinan Demir , Mehmet Emre Çağ

https://doi.org/10.47115/bsagriculture.1565025

Abstract

Soil organic carbon (SOC) is an important indication of soil health and helps to sustain soil fertility. As a result, determining its composition and the factors that influence it is critical for long-term soil nutrient management, especially in controlled conditions such as greenhouses. This study utilizes machine learning to classify SOC content in greenhouses built on pyroclastic deposits in the Isparta region. A dataset of 276 samples and eight variables—clay (%), silt (%), sand (%), soil electrical conductivity (EC), pH, elevation, slope, and aspect—were used to model SOC values. SOC content was classified into five classifications: very low (<0.6%), low (0.6-1.2%), medium (1.2-1.8%), good (1.8-2.3%), and high (>2.3%). In this study, five machine learning models—Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)—were evaluated using cross-validation to determine their classification accuracy, precision, recall, F-score, and ROC area. Random Forest (RF) and Decision Tree (DT) outperformed the other models, with RF achieving the highest overall accuracy (76.4%), precision (77.3%), and AUC (0.904), followed by DT at 75.4% and AUC of 0.874. This study shows the practicality of machine learning models in categorizing SOC content, highlighting their importance for long-term soil health and fertility control in greenhouse conditions. To improve model efficacy, future studies should include more auxiliary variables, such as soil physical and chemical qualities and lithological data, as well as a wider range of soil types.

Keywords

Soil organic carbon, Machine learning, Greenhouses, Topography, Soil properties, Volcanic materials

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

Supporting Institution

This work was supported by the Research Project Support Programme for Undergraduate Students (2209-A) of the Scientific and Technological Research Council of Türkiye (TUBITAK) under project number 1919B012209637. The funding body had no role in the design of the experiment, the collection, analysis, and interpretation of data, or in the writing of the manuscript. The relevant website for TUBITAK is https://tubitak.gov.tr/en/scholarships/lisans-onlisans/destek-programlari/2209-research-project-su

Project Number

1919B012209637

Thanks

This study is based on data derived from the project numbered 1919B012209637, supported by the Research Project Support Programme for Undergraduate Students (2209-A) of the Scientific and Technological Research Council of Türkiye (TUBITAK). I would like to express my sincere gratitude to TUBITAK for their generous funding and support, which enabled this research. I would also like to acknowledge the contributions of individuals who provided invaluable assistance throughout the project.

References

• Agaba S, Ferré C, Musetti M, Comolli R. 2024. Mapping soil organic carbon stock and uncertainties in an alpine valley (Northern Italy) using machine learning models. Land, 13(1): 62-78. https://doi.org/10.3390/land13010078
• Alaboz P, Dengiz O, Demir S. 2021. Barley yield estimation performed by ANN integrated with the soil quality index modified by biogas waste application. Zemdirbyste, 108(3): 217-226. https://doi.org/10.13080/z-a.2021.108.028
• Altay Y. 2022. Prediction of the live weight at breeding age from morphological measurements taken at weaning in indigenous Honamli kids using data mining algorithms. Trop Anim Health Prod, 54(3): 1-12. https://doi.org/10.1007/s11250-022-03174-y
• Bekana BT, Mohammed A. 2022. A review of soil organic carbon in Ethiopia's major land use types. Appl Res Sci Technol, 2(1): 25-35. https://doi.org/10.33292/areste.v2i1.21
• Bernardini LG, Rosinger C, Bodner G, Keiblinger KM, Izquierdo-Verdiguier E, Spiegel H, Retzlaff CO, Holzinger A. 2024. Learning vs. understanding: When does artificial intelligence outperform process based modeling in soil organic carbon prediction? N Biotechnol, 81: 20-31. https://doi.org/10.1016/j.nbt.2024.03.001
• Canpolat E, Turoğlu H. 2019. The interpretation of the geomorphological development of the volcanic field in the south and southwest of Isparta with lineament and circularity analysis. J Geomorphol Res, 2: 23-36.
• Corine. 2024. Ministry of Agriculture and Forestry of the Republic of Türkiye Corine Application. URL: https://corinecbs.tarimorman.gov.tr/ (accessed date: July 21, 2024).
• Demir S, Başayiğit L. 2021. The effect of physiographical change on profile development and soil properties. Turk J Agric Res, 8(3): 261-272. https://doi.org/10.19159/tutad.935710
• Demir S, Dedeoğlu M, Başayiğit L. 2024. Yield prediction models of organic oil rose farming with agricultural unmanned aerial vehicles (UAVs) images and machine learning algorithms. Remote Sens Appl, 33: 101-131. https://doi.org/10.1016/j.rsase.2023.101131
• Demir S. 2024a. Determination of suitable agricultural areas and current land use in Isparta Province, Türkiye, through a linear combination technique and geographic information systems. Environ Dev Sustain, 26: 13455-1349. https://doi.org/10.1007/s10668-023-04359-7
• Demir S. 2024b. Prediction of canopy cover for agricultural land classification in Land Parcel Identification System (LPIS) data using Planet-Scope multispectral images: A case of Gelendost District. BSJ Agri, 7(4): 407-417. https://doi.org/10.47115/bsagriculture.1490400
• Demir S. Başayiğit L. 2022. Classification of some biochemical properties with J48 classification tree algorithms in hyperspectral data. Veri Bil, 5(2): 20-28.
• Demiralay İ. 1993. Physical analysis of soil. The Publications Agricultural Faculty of Atatürk University. No.143. Erzurum, Türkiye, pp: 131.
• Derrien D, Barré P, Basile-Doelsch I, Cécillon L, Chabbi A, Crème A, Fontaine S, Henneron L, Janot N, Lashermes G, Quénéa K, Rees F, Dignac MF. 2023. Current controversies on mechanisms controlling soil carbon storage: Implications for interactions with practitioners and policy-makers. Agron Sustain Dev, 43(1): 1-28. https://doi.org/10.1007/s13593-023-00876-x
• Elitok Ö, Özgür N, Drüppel K, Dilek Y, Platevoet B, Guillou H. 2009. Origin and geodynamic evolution of late Cenozoic potassium-rich volcanism in the Isparta area, southwestern Turkey. Int Geol Rev, 51(5-6): 454-504. https://doi.org/10.1080/00206810902951411
• Eyduran SP, Akin M, Ercisli S, Eyduran E, Maghradze D. 2015. Sugars, organic acids, and phenolic compounds of ancient grape cultivars (Vitis vinifera L.) from Iğdır province of Eastern Türkiye. Biol Res, 48: 1-8. https://doi.org/10.1186/0717-6287-48-2
• Fathizad H, Taghizadeh-Mehrjardi R, Hakimzadeh Ardakani MA, Zeraatpisheh M, Heung B, Scholten T. 2022. Spatiotemporal assessment of soil organic carbon change using machine learning in arid regions. Agronomy, 12(3): 615-628. https://doi.org/10.3390/agronomy12030628
• Genç C. 2021. Türkiye's obligations under the Paris Climate Agreement and the impact of climate change on these obligations. MSc Thesis, İskenderun Technical University, Graduate Institute, Department of Aquaculture, Hatay, Türkiye, pp: 133.
• General Directorate of Combating Desertification and Erosion (ÇEM). 2018. Soil organic carbon project, technical summary. URL: https://kutuphane.tarimorman.gov.tr/vufind/Record/1178980 (accessed date: July 21, 2024).
• Grimm R, Behrens T, Märker M, Elsenbeer H 2008. Soil organic carbon concentrations and stocks on Barro Colorado Island—Digital soil mapping using Random Forests analysis. Geoderma, 146(1-2): 102-113. https://doi.org/10.1016/j.geoderma.2008.05.008
• Hiederer R, Köchy M. 2011. Global soil organic carbon estimates and the harmonized world soil database, EUR 25225 EN, Publications Office of the European Union, Brussels, Belgium, pp: 79.
• John K, Abraham Isong I, Michael Kebonye N, Okon Ayito E, Chapman Agyeman P, Marcus Afu S. 2020. Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil. Land, 9(12): 467-487. https://doi.org/10.3390/land9120487
• Kacar B. 2014. Soil analysis. Nobel Akademik Yayıncılık, Ankara, Türkiye, pp: 467.
• Koçak H. 2022. Performance comparison of classification algorithms in rainfall prediction. J Intell Syst: Theory Appl, 5(1): 16-26. https://doi.org/10.38016/jista.979285
• Koşkan Ö, Önder EG, Şen N. 2011. Use of canonical correlation for estimating relationship between variable sets. J Inst Sci Technol, 1(2): 117-123.
• Loria N, Lal R, Chandra R. 2024. Handheld in situ methods for soil organic carbon assessment. Sustain, 16(13): 55-92. https://doi.org/10.3390/su16135592
• Mayer M, Prescott CE, Abaker WE, Augusto L, Cécillon L, Ferreira GWD, James J, Jandl R, Katzensteiner K, Laclau JP, Laganière J, Nouvellon Y, Paré D, Stanturf J A, Vanguelova EI, Vesterdal L. 2020. Tamm review: Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis. For Ecol Manage, 466: 118100-118127. https://doi.org/10.1016/j.foreco.2020.118127
• MGM. 2024. Türkiye State Meteorological Service: Isparta climate data. Available from: https://www.mgm.gov.tr/Veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=ISPARTA (accessed date: 21 Jul 2024).
• Minasny B, McBratney AB, Mendonça-Santos MDL, Odeh IOA, Guyon B. 2006. Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Soil Res, 44(3): 233-244. https://doi.org/10.1071/SR05136
• Minasny B, Setiawan BI, Arif C, Saptomo SK, Chadirin Y. 2016. Digital mapping for cost-effective and accurate prediction of the depth and carbon stocks in Indonesian peatlands. Geoderma, 272: 20-31. https://doi.org/10.1016/j.geoderma.2016.02.026
• Minasny B, Setiawan BI, Saptomo SK, McBratney AB. 2018. Open digital mapping as a cost-effective method for mapping peat thickness and assessing the carbon stock of tropical peatlands. Geoderma, 313: 25-40. https://doi.org/10.1016/j.geoderma.2017.10.018
• Moharana PC, Yadav B, Malav LC, Kumar S, Meena RL, Nogiya M, Biswas H, Patil NG. 2024. Regional prediction of soil organic carbon dynamics for intensive farmland in the hot arid climate of India using the machine learning model. Environ Earth Sci, 83: 529. https://doi.org/10.1007/s12665-024-11834-5
• Odebiri O, Mutanga O, Odindi J, Naicker R. 2022. Modelling soil organic carbon stock distribution across different land-uses in South Africa: A remote sensing and deep learning approach. ISPRS J Photog Remote Sens, 188: 351-362. https://doi.org/10.1016/j.isprsjprs.2022.04.026
• Odebiri O, Odindi J, Mutanga O. 2021. Basic and deep learning models in remote sensing of soil organic carbon estimation: A brief review. Int J Appl Earth Obs Geoinf, 102: 102389. https://doi.org/10.1016/j.jag.2021.102389
• Padarian J, Stockmann U, Minasny B, McBratney AB. 2022. Monitoring changes in global soil organic carbon stocks from space. Remote Sens Environ, 281: 113260. https://doi.org/10.1016/j.jag.2021.102389
• Pouladi N, Gholizadeh A, Khosravi V, Borůvka L. 2023. Digital mapping of soil organic carbon using remote sensing data: A systematic review. Catena, 232: 107409. https://doi.org/10.1016/j.catena.2023.107409
• Saporetti CM, Fonseca DL, Oliveira LC, Pereira E, Goliatt L. 2022. Hybrid machine learning models for estimating total organic carbon from mineral constituents in core samples of shale gas fields. Mar Pet Geol, 143: 105783. https://doi.org/10.1016/j.marpetgeo.2022.105783
• Saputra DD, Sari RR, Hairiah K, Widianto Suprayogo D, van Noordwijk M. 2022. Recovery after volcanic ash deposition: Vegetation effects on soil organic carbon, soil structure, and infiltration rates. Plant Soil, 474: 163-179. https://doi.org/10.1007/s11104-021-05226-5
• Sönmez B, Özbahçe A, Akgül S, Keçeci M. 2018. Development of a geographic database for the fertility and organic carbon (SOC) content of Turkish soils. Final Project Report (TAGEM/TSKAD/11/A13/P03). Soil, Fertilizer, and Water Resources Central Research Institute. General Directorate of Agricultural Research and Policies, Ankara, Türkiye. URL: https://arastirma.tarimorman.gov.tr/toprakgubre/Belgeler/2018%20Y%C4%B1l%C4%B1%20Proje%20Raporlar%C4%B1/T%C3%BCrkiye%20Topraklar%C4%B1n%C4%B1n%20Baz%C4%B1%20Verimlilik%20ve%20Organik%20Karbon%20(TOK)%20%C4%B0%C3%A7eri%C4%9Finin%20Co%C4%9Frafi%20Veritaban%C4%B1n%C4%B1n%20Olu%C5%9Fturulmas%C4%B1.pdf (accessed date: July 21, 2024).
• Stockmann U, Adams MA, Crawford JW, Field DJ, Henakaarchchi N, Jenkins M, Zimmermann M. 2013. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric Ecosyst Environ, 164: 80-99. https://doi.org/10.1016/j.agee.2012.10.001
• Taghizadeh-Mehrjardi R, Schmidt K, Amirian-Chakan A, Rentschler T, Zeraatpisheh M, Sarmadian F, Valavi R, Davatgar N, Behrens T, Scholten T. 2020. Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by stacking machine learning models and rescanning covariate space. Remote Sens, 12(7): 1095. https://doi.org/10.3390/rs12071095
• TürkStat. 2023. Türkiye Statistical Institute: Isparta central crop production statistics. URL: https://biruni.tuik.gov.tr/medas/?locale=tr (accessed date: July 21, 2024).
• Wang B, Waters C, Orgill S, Cowie A, Clark A, Liu DL, Sides T. 2018. Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia. Ecol Indicat, 88: 425-438. https://doi.org/10.1016/j.ecolind.2018.01.049
• Wang Y, Chen S, Hong Y, Hu B, Peng J, Shi Z. 2023. A comparison of multiple deep learning methods for predicting soil organic carbon in Southern Xinjiang, China. Comput Electron Agric, 212: 108067. https://doi.org/10.1016/j.compag.2023.108067
• Xie B, Ding J, Ge X, Li X, Han L, Wang Z. 2022. Estimation of soil organic carbon content in the Ebinur Lake wetland, Xinjiang, China, based on multisource remote sensing data and ensemble learning algorithms. Sensors, 22(7): 2685. https://doi.org/10.1016/j.compag.2023.108067
• Yang RM, Zhang GL, Liu F, Lu YY, Yang F, Yang F, Li DC. 2016. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol Indicat, 60: 870-878. https://doi.org/10.1016/j.ecolind.2015.08.036
• Zeng P, Song X, Yang H, Wei N, Du L. 2022. Digital Soil Mapping of Soil Organic Matter with Deep Learning Algorithms. ISPRS Int J Geoinf, 11(5): 280-299. https://doi.org/10.3390/ijgi11050299
• Zhang L, Heuvelink GB, Mulder VL, Chen S, Deng X, Yang L. 2024. Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time. Sci Total Environ, 922: 170778. https://doi.org/10.1016/j.scitotenv.2024.170778