Factors Affecting Soil Organic Carbon on Farmland in Fujian Analyzed by Geodetector Model

CHEN Mengyao; LIU Xiaoge; HUANG Dacang; ZHANG Liming; XING Shihe

doi:10.19303/j.issn.1008-0384.2024.06.013

Volume 39 Issue 6

Jun. 2024

Turn off MathJax

Article Contents

Article Navigation > Fujian Journal of Agricultural Sciences > 2024 > 39(6): 738-751

CHEN M Y, LIU X G, HUANG D C, et al. Factors Affecting Soil Organic Carbon on Farmland in Fujian Analyzed by Geodetector Model [J]. Fujian Journal of Agricultural Sciences，2024，39（6）：738−751 doi: 10.19303/j.issn.1008-0384.2024.06.013

Citation:

CHEN M Y, LIU X G, HUANG D C, et al. Factors Affecting Soil Organic Carbon on Farmland in Fujian Analyzed by Geodetector Model [J]. Fujian Journal of Agricultural Sciences，2024，39（6）：738−751 doi: 10.19303/j.issn.1008-0384.2024.06.013

Citation:

PDF( 2231 KB)

Factors Affecting Soil Organic Carbon on Farmland in Fujian Analyzed by Geodetector Model

doi: 10.19303/j.issn.1008-0384.2024.06.013

CHEN Mengyao^{1, 2
,},
LIU Xiaoge^{2, 3},
HUANG Dacang^{2, 3
,
,},
ZHANG Liming^{2, 3},
XING Shihe^{2, 3}

1.
College of Computer and Information, Fujian Agriculture and Forestry University, Fuzhou, Fujian　350002, China
2.
Key Laboratory of Soil Ecosystem Health and Regulation of Fujian Universities, Fuzhou, Fujian　350002, China
3.
College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, Fujian　350002, China

Received Date: 2024-04-23
Accepted Date: 2024-07-03
Rev Recd Date: 2024-06-05

Available Online: 2024-07-10

Publish Date: 2024-06-28

Abstract

Abstract

Objective Explore the spatial distribution and influencing factors of soil organic carbon (SOC) in cultivated land in Fujian Province. Method Based on the data generated from over 30,000 survey sites on farmland in Fujian, Pearson correlation coefficient and random forest model were employed to derive key factors affecting the SOC. The geodetector model was used to analyze the spatial SOC distribution in the province. Result The data on SOC of the province in 2008 ranged between 0.12 and 67.28 g·kg⁻¹ with a spatial pattern of being low in the southeast coastal areas and high in the west and central regions. The geodetector model was shown to render the most comprehensive and objective analysis among the three models tested. It concluded the climate-related conditions to be the major factors affecting the spatial differentiation of SOC on the farmland with top 6 rankings of: annual precipitation (0.168 5)＞annual average temperature (0.167 7)＞altitude (0.144 9)＞climate type (0.135 9)＞soil type (0.082 4)＞landform type (0.073 1). The interactive detectors further revealed the interaction between the annual precipitation and annual average temperature to exert the greatest influence on the SOC spatial differentiation (0.194 1), while the annual precipitation and soil type (0.192 3) and the annual precipitation and cultivated land use type (0.1918) followed. Conclusion Multiple factors affected the SOC on the farmland in Fujian in the past. For improving the spatial utilization efficiency and bettering the agriculture production layout on the land, it seemed imperative that all various factors highlighted in this study be taken into serious considerations.
- soil organic carbon,
- geodetector,
- spatial distribution,
- influencing factors,
- interaction

FullText(HTML)

References(34)

References

[1]	王兴, 钟泽坤, 王佳懿, 等. 黄土高原撂荒草地土壤碳库对两年增温增雨的响应 [J]. 土壤学报, 2023, 60(2):523−534. WANG X, ZHONG Z K, WANG J Y, et al. Responses of soil carbon pool of abandoned grassland on the Loess Plateau to two-years warming and increased precipitation [J]. Acta Pedologica Sinica, 2023, 60(2): 523−534. (in Chinese)
[2]	李园园, 王蕾, 刘琪璟, 等. 新疆喀纳斯自然保护区森林碳储量及碳密度变化 [J]. 干旱区研究, 2019, 36(5):1136−1145. LI Y Y, WANG L, LIU Q J, et al. Changes of carbon storage and carbon density of forests in the kanas national nature reserve, Xinjiang [J]. Arid Zone Research, 2019, 36(5): 1136−1145. (in Chinese)
[3]	张维理, KOLBE H, 张认连. 土壤有机碳作用及转化机制研究进展 [J]. 中国农业科学, 2020, 53(2):317−331. doi: 10.3864/j.issn.0578-1752.2020.02.007 ZHANG W L, KOLBE H, ZHANG R L. Research progress of SOC functions and transformation mechanisms [J]. Scientia Agricultura Sinica, 2020, 53(2): 317−331. (in Chinese) doi: 10.3864/j.issn.0578-1752.2020.02.007
[4]	WANG S, ZHUANG Q L, JIA S H, et al. Spatial variations of soil organic carbon stocks in a coastal hilly area of China [J]. Geoderma, 2018, 314: 8−19. doi: 10.1016/j.geoderma.2017.10.052
[5]	LI Q Q, ZHANG H, JIANG X Y, et al. Spatially distributed modeling of soil organic carbon across China with improved accuracy [J]. Journal of Advances in Modeling Earth Systems, 2017, 9(2): 1167−1185. doi: 10.1002/2016MS000827
[6]	TENG M J, ZENG L X, XIAO W F, et al. Spatial variability of soil organic carbon in Three Gorges Reservoir area, China [J]. The Science of the Total Environment, 2017, 599/600: 1308−1316. doi: 10.1016/j.scitotenv.2017.05.085
[7]	MA H H, PENG M, YANG Z, et al. Spatial distribution and driving factors of soil organic carbon in the Northeast China Plain: Insights from latest monitoring data [J]. The Science of the Total Environment, 2024, 911: 168602. doi: 10.1016/j.scitotenv.2023.168602
[8]	李艾雯, 冉敏, 宋靓颖, 等. 四川盆地耕地表层土壤有机碳含量空间分布特征及其影响因素 [J]. 长江流域资源与环境, 2023, 32(5):1102−1112. LI A W, RAN M, SONG L Y, et al. Spatial distribution characteristics and influencing factors of cropland topsoil organic carbon content in the Sichuan Basin [J]. Resources and Environment in the Yangtze Basin, 2023, 32(5): 1102−1112. (in Chinese)
[9]	张勇, 史学正, 赵永存, 等. 滇黔桂地区土壤有机碳储量与影响因素研究 [J]. 环境科学, 2008, 29(8):2314−2319. ZHANG Y, SHI X Z, ZHAO Y C, et al. Estimates and affecting factors of soil organic carbon storages in Yunnan-Guizhou-guangxi region of China [J]. Environmental Science, 2008, 29(8): 2314−2319. (in Chinese)
[10]	SUN T, TONG W J, CHANG N J, et al. Estimation of soil organic carbon stock and its controlling factors in cropland of Yunnan Province, China [J]. Journal of Integrative Agriculture, 2022, 21(5): 1475−1487. doi: 10.1016/S2095-3119(21)63620-1
[11]	LUO Y L, WANG K, LI H X, et al. Application of a combinatorial approach for soil organic carbon mapping in hills [J]. Journal of Environmental Management, 2021, 300: 113718. doi: 10.1016/j.jenvman.2021.113718
[12]	BAI Y X, ZHOU Y C. The main factors controlling spatial variability of soil organic carbon in a small Karst watershed, Guizhou Province, China [J]. Geoderma, 2020, 357: 113938. doi: 10.1016/j.geoderma.2019.113938
[13]	程金, 黄文卿, 张世昌, 等. 福建省表层土壤有机碳密度空间分布及影响因素分析 [J]. 农业资源与环境学报, 2023, 40(4):805−816. CHENG J, HUANG W Q, ZHANG S C, et al. Spatial distribution and influencing factors of topsoil organic carbon density in Fujian Province, China [J]. Journal of Agricultural Resources and Environment, 2023, 40(4): 805−816. (in Chinese)
[14]	ZHAO B H, LI Z B, LI P, et al. Spatial distribution of soil organic carbon and its influencing factors under the condition of ecological construction in a hilly-gully watershed of the Loess Plateau, China [J]. Geoderma, 2017, 296: 10−17. doi: 10.1016/j.geoderma.2017.02.010
[15]	林景亮. 福建土壤[M]. 福州: 福建科学技术出版社, 1991.
[16]	国土资源部. 国土资源部关于印发试行《土地分类》的通知[L]. 2001-08-21.
[17]	陈中星, 张楠, 张黎明, 等. 福建省土壤有机碳储量估算的尺度效应研究 [J]. 土壤学报, 2018, 55(3):606−619. CHEN Z X, ZHANG N, ZHANG L M, et al. Scale effects of estimation of soil organic carbon storage in Fujian Province, China [J]. Acta Pedologica Sinica, 2018, 55(3): 606−619. (in Chinese)
[18]	姚彩燕, 刘绍贵, 乔婷, 等. 基于时空变异的旱地土壤有机碳高效采样策略研究 [J]. 土壤学报, 2021, 58(3):638−648. YAO C Y, LIU S G, QIAO T, et al. Strategy for efficient sampling of upland soil based on spatiotemporal variation of the soil [J]. Acta Pedologica Sinica, 2021, 58(3): 638−648. (in Chinese)
[19]	PENG S Z, DING Y X, LIU W Z, et al. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017 [J]. Earth System Science Data, 2019, 11(4): 1931−1946. doi: 10.5194/essd-11-1931-2019
[20]	金林, 李研. 几种相关系数辨析及其在R语言中的实现 [J]. 统计与信息论坛, 2019, 34(4):3−11. JIN L, LI Y. Discrimination of several correlation coefficients and their implementation in R software [J]. Statistics & Information Forum, 2019, 34(4): 3−11. (in Chinese)
[21]	BREIMAN L. Random forests [J]. Machine Learning, 2001, 45: 5−32. doi: 10.1023/A:1010933404324
[22]	LIAW A, WIENER M. Classification and regression by randomForest [J]. R news, 2002, 2(3): 18−22.
[23]	WANG J F, LI X H, CHRISTAKOS G, et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China [J]. International Journal of Geographical Information Science, 2010, 24(1): 107−127. doi: 10.1080/13658810802443457
[24]	王劲峰, 徐成东. 地理探测器: 原理与展望 [J]. 地理学报, 2017, 72(1):116−134. WANG J F, XU C D. Geodetector: Principle and prospective [J]. Acta Geographica Sinica, 2017, 72(1): 116−134. (in Chinese)
[25]	SONG Y Z, WANG J F, GE Y, et al. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data [J]. GIScience & Remote Sensing, 2020, 57(5): 593−610.
[26]	LI Q Q, YUE T X, WANG C Q, et al. Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach [J]. CATENA, 2013, 104: 210−218. doi: 10.1016/j.catena.2012.11.012
[27]	JIANG R, WU P, SONG Y Z, et al. Factors influencing the adoption of renewable energy in the U. S. residential sector: An optimal parameters-based geographical detector approach [J]. Renewable Energy, 2022, 201: 450−461. doi: 10.1016/j.renene.2022.09.084
[28]	JENKINSON D S, ADAMS D E, WILD A. Model estimates of CO₂ emissions from soil in response to global warming [J]. Nature, 1991, 351: 304−306. doi: 10.1038/351304a0
[29]	REYNA-BOWEN L, LASOTA J, VERA-MONTENEGRO L, et al. Distribution and factors influencing organic carbon stock in mountain soils in Babia góra National Park, Poland [J]. Applied Sciences, 2019, 9(15): 3070. doi: 10.3390/app9153070
[30]	DAVIDSON E A, JANSSENS I A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change [J]. Nature, 2006, 440: 165−173. doi: 10.1038/nature04514
[31]	丰思捷, 赵艳云, 李元恒, 等. 内蒙古典型草原表层土壤有机碳储量差异及影响因素 [J]. 中国草地学报, 2019, 41(2):116−120. FENG S J, ZHAO Y Y, LI Y H, et al. The differences and influencing factors of topsoil organic carbon storage in typical steppe of inner Mongolia [J]. Chinese Journal of Grassland, 2019, 41(2): 116−120. (in Chinese)
[32]	周晓宇, 张称意, 郭广芬. 气候变化对森林土壤有机碳贮藏影响的研究进展 [J]. 应用生态学报, 2010, 21(7):1867−1874. ZHOU X Y, ZHANG C Y, GUO G F. Effects of climate change on forest soil organic carbon storage: A review [J]. Chinese Journal of Applied Ecology, 2010, 21(7): 1867−1874. (in Chinese)
[33]	ZHANG X F, ADAMOWSKI J F, LIU C F, et al. Which slope aspect and gradient provides the best afforestation-driven soil carbon sequestration on the China’s Loess Plateau? [J]. Ecological Engineering, 2020, 147: 105782. doi: 10.1016/j.ecoleng.2020.105782
[34]	丁金梅, 王维珍, 米文宝, 等. 宁夏草地土壤有机碳空间特征及其影响因素 [J]. 生态学报, 2023, 43(5):1913−1922. DING J M, WANG W Z, MI W B, et al. Spatial characteristics of soil organic carbon in grassland of Ningxia and its influencing factors [J]. Acta Ecologica Sinica, 2023, 43(5): 1913−1922. (in Chinese)