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Forest Ecosystems 2023, 10(2): 100101 https://doi.org/10.1016/j.fecs.2023.100101
Research Article | Open Access | Issue | Published: 24 February 2023

Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Show Author's Information Shuai Wanga,b,c,  Bol Rolandc,d,  Kabindra Adhikarie,  Qianlai Zhuangf,  Xinxin Jina( ),  Chunlan Hana( ),  Fengkui Qiana
College of Land and Environment, Shenyang Agricultural University, Shenyang, 110866, China
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
Institute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
School of Natural Sciences, Environment Centre Wales, Bangor University, Bangor, LL57 2UW, UK
USDA-ARS, Grassland, Soil and Water Research Laboratory, Temple, TX, 76502, USA
Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, 47907, USA
Keywords:
Forest ecosystem, Spatial-temporal variation, Soil organic carbon stocks, Carbon sink, Digital soil mapping
Cite this article:
Wang S, Roland B, Adhikari K, et al. Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China. Forest Ecosystems, 2023, 10(2): 100101. https://doi.org/10.1016/j.fecs.2023.100101
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Abstract Full text About this article

Forest soil carbon is a major carbon pool of terrestrial ecosystems, and accurate estimation of soil organic carbon (SOC) stocks in forest ecosystems is rather challenging. This study compared the prediction performance of three empirical model approaches namely, regression kriging (RK), multiple stepwise regression (MSR), random forest (RF), and boosted regression trees (BRT) to predict SOC stocks in Northeast China for 1990 and 2015. Furthermore, the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified. A total of 82 (in 1990) and 157 (in 2015) topsoil (0–20 ​cm) samples with 12 environmental factors (soil property, climate, topography and biology) were selected for model construction. Randomly selected 80% of the soil sample data were used to train the models and the other 20% data for model verification using mean absolute error, root mean square error, coefficient of determination and Lin's consistency correlation coefficient indices. We found BRT model as the best prediction model and it could explain 67% and 60% spatial variation of SOC stocks, in 1990, and 2015, respectively. Predicted maps of all models in both periods showed similar spatial distribution characteristics, with the lower SOC in northeast and higher SOC in southwest. Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods. SOC stocks were mainly stored under Cambosols, Gleyosols and Isohumosols, accounting for 95.6% (1990) and 95.9% (2015). Overall, SOC stocks increased by 471 ​Tg ​C during the past 25 years. Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories. The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks. Overall, our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.


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About this article

Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Show Author's information Shuai Wanga,b,c, Bol Rolandc,d, Kabindra Adhikarie, Qianlai Zhuangf, Xinxin Jina( ), Chunlan Hana( ), Fengkui Qiana
College of Land and Environment, Shenyang Agricultural University, Shenyang, 110866, China
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
Institute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
School of Natural Sciences, Environment Centre Wales, Bangor University, Bangor, LL57 2UW, UK
USDA-ARS, Grassland, Soil and Water Research Laboratory, Temple, TX, 76502, USA
Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, 47907, USA

Abstract

Forest soil carbon is a major carbon pool of terrestrial ecosystems, and accurate estimation of soil organic carbon (SOC) stocks in forest ecosystems is rather challenging. This study compared the prediction performance of three empirical model approaches namely, regression kriging (RK), multiple stepwise regression (MSR), random forest (RF), and boosted regression trees (BRT) to predict SOC stocks in Northeast China for 1990 and 2015. Furthermore, the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified. A total of 82 (in 1990) and 157 (in 2015) topsoil (0–20 ​cm) samples with 12 environmental factors (soil property, climate, topography and biology) were selected for model construction. Randomly selected 80% of the soil sample data were used to train the models and the other 20% data for model verification using mean absolute error, root mean square error, coefficient of determination and Lin's consistency correlation coefficient indices. We found BRT model as the best prediction model and it could explain 67% and 60% spatial variation of SOC stocks, in 1990, and 2015, respectively. Predicted maps of all models in both periods showed similar spatial distribution characteristics, with the lower SOC in northeast and higher SOC in southwest. Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods. SOC stocks were mainly stored under Cambosols, Gleyosols and Isohumosols, accounting for 95.6% (1990) and 95.9% (2015). Overall, SOC stocks increased by 471 ​Tg ​C during the past 25 years. Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories. The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks. Overall, our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Keywords: Forest ecosystem, Spatial-temporal variation, Soil organic carbon stocks, Carbon sink, Digital soil mapping

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Received: 03 September 2022
Revised: 10 February 2023
Accepted: 10 February 2023
Published: 24 February 2023
Issue date: April 2023

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