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Soil Respiration Variability: Contributions of Space and Time Estimated Using the Random Forest Algorithm

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Abstract

Soil respiration modeling (i.e., simulation of carbon dioxide emissions from the soil surface) makes it possible to analyze and forecast changes in the carbon cycle in terrestrial ecosystems. Along with classical regression models, researchers currently use machine learning methods based on neural networks or regression tree ensembles. However, models produced using these methods are often used as ‘black boxes’, which hinders the analysis of process mechanisms. This paper demonstrates capabilities of the Random Forest algorithm that can be successfully used to estimate effects exercised by various factors on soil respiration based on features importance measurements. Using variance separation, predictors have been classified either as spatial (biotope type, soil type, vegetation type, and soil moisture content) or temporal (soil and air temperature, NDVI, LAI, FPAR, and SPEI). Several models were produced based on 5670 respiration measurements performed during five growing seasons (2012–2016) on 30 sampling plots in south-taiga pine forests and meadows that feature different vegetation and soil types but are confined to the same small area. Different models include different sets of predictors (all predictors, temporal predictors only, spatial predictors only, and temperature and humidity only), and their accuracy reaches R2 = 0.88 (MSE = 0.47). It is established that soil respiration depends primarily on temporal factors whose importance ranges from 76 to 91%. In forests, the effect of spatial factors on respiration is stronger than in meadows.

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ACKNOWLEDGMENTS

The author is grateful to D.A. Bedin for consulting on machine learning, to E.L. Vorobeichik for useful comments and assistance in manuscript improvement, to S.Yu. Kaigorodov and T.Yu. Gabershtein for soil diagnostics and analysis, and to N.O. Sadykova for discussing the text and commenting on it.

Funding

The field studies were supported by the Ural Branch, Russian Academy of Sciences, project no. 12-P-4-1057; the data analysis and preparation of this article, by the Ministry of Science and Higher Education of the Russian Federation, projects no. 122021000076-9 and FEUZ-2021-0014, respectively.

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Smorkalov, I.A. Soil Respiration Variability: Contributions of Space and Time Estimated Using the Random Forest Algorithm. Russ J Ecol 53, 295–307 (2022). https://doi.org/10.1134/S1067413622040051

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