Scientia Agricultura Sinica ›› 2024, Vol. 57 ›› Issue (12): 2378-2389.doi: 10.3864/j.issn.0578-1752.2024.12.009

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Study on Dominant Factors Affecting Spatial Variation of Soil Organic Carbon in Hulunbuir Grassland

XUE Wei1,2(), XU LiJun2(), NIE YingYing2, WU XinJia2, YAN YiDan2, YE LiMing3, LIU XinWei1,2()   

  1. 1 College of Resources and Environment, Qingdao Agricultural University, Qingdao 266109, Shandong, China
    2 State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China/National Observation and Research Station of Hulunber Grassland Ecosystem/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    3 Department of Geology, Ghent University, Ghent Belgium
  • Received:2023-08-08 Accepted:2023-11-30 Online:2024-06-16 Published:2024-06-25
  • Contact: XU LiJun, LIU XinWei

Abstract:

【Background】 Soil organic carbon (SOC) is a key component of the global carbon cycle in grassland ecosystems. In the context of climate change and grassland soil degradation, the study of the grassland carbon cycle has garnered extensive attention, particularly the in-depth analysis of the dynamics and driving factors of soil organic carbon in grasslands at different temporal and spatial scales. However, the estimation of long-term dynamic changes and the analysis of drivers for spatial variation in grassland SOC are primarily based on remote sensing modeling methods and simulation predictions, rather than direct measurements. 【Objective】 The aim of this study was to evaluate the relative importance of SOC spatial variation factors and their variation characteristics in different periods of Hulunbuir grassland. 【Method】 Based on the data of the second national soil survey in the 1980, the soil profiles of 31 sample sites in Hulunbuir grassland in 2022 were collected again, and the SOC changes and driving factors of Hulunbuir grassland were quantitatively analyzed in these two periods by establishing a multivariate linear model and a generalized additive model. 【Result】 From 1980 to 2022, the SOC of Hulunbuir grassland increased from 17.25 g·kg-1 to 17.62 g·kg-1 in 40 years, with an increase of 0.37 g·kg-1. The relative importance of climatic factors increased from 22.1% in the 1980 to 72.9% in 2022, compared with a decrease in the relative importance of the topography and utilization intensity factors, which decreased from 38.8% and 39.2% in the 1980 to 13.5% and 13.5% in 2022, respectively. 【Conclusion】 The climatic factors, topography and use intensity jointly dominated the spatial variation of soil organic carbon in Hulunbuir grassland. Over the past 40 years, the climate factors have evolved from a secondary contributor to grassland SOC change to a major controlling factor.

Key words: organic carbon, spatial variation, regression model, climate change, topography, driving factors, Hulunbuir grassland

Fig. 1

Hulunbuir grassland research area and sampling point locations"

Fig. 2

Comparison of SOC content in 1980 and 2022"

Fig. 3

SOC content under different utilization intensities in 1980 and 2022"

Table 1

1980 model parameter table"

变量 Variable 系数 Coefficient 标准误 Standard error t t-value P P-value
截距 Intercept -1.528e+03 5.152e+02 -2.965 <0.001 **
分类变量 Categorical variables
利用强度 Utilization intensity G1 6.971 2.615 2.666 0.017 *
G2 5.106 2.425 2.105 0.050 .
G0 8.366 2.528 3.310 0.004 **
连续变量 Continuous variables
地形景观 Landscape index Slope 7.029 1.410 4.980 <0.001 ***
气候因子 Climatic factor PET 3.856 2.992 2.872 0.009 **
PET2 -3.204e-03 1.066e-03 -3.005 0.008 **
PET3 8.807e-07 2.921e-07 3.015 0.008 **

Table 2

2022 model parameters table"

变量 Variable 系数 Coefficient 标准误差 Standard error t t-value P P-value
截距 Intercept -54.987 15.437 4.153 0.004 **
分类变量 Categorical variable
利用强度 Utilization intensity G1 19.095 4.597 2.666 0.001 **
G2 19.035 4.924 3.866 0.002 **
G3 27.668 4.831 5.727 0.002 **
连续变量 Continuous variable
地形因子 Landscape index Elevation 0.079 0.021 3.859 0.002 **
气候因子 Climatic factor 有效自由度 edf 参考自由度 Ref.df FF-value PP-value
s(MAT) 7.330 8.035 8.390 <0.001 ***
S(MAA) 6.852 7.680 2.966 0.029 *

Fig. 4

Relative contribution rate of variables in 1980 model and 2022 model"

Table 3

Comparison of the 1980 model and 2022 model soil organic carbon prediction accuracies between this study and other studies found in the recent literatures"

排序
Ranking
来源
Source
指标
Index
地区
Location
RMSE
(g·kg-1)
MAE
(g·kg-1)
MAPE
(%)
R2
1 本研究(2022年) This study is in 2022 SOC 中国 China 1.90 1.47 14.44 0.92
2 SMITH et al.[45] (2019) SOC 美国 Amican 0.95 0.62 61.51 0.57
3 WU et al.[46] (2022) SOC 中国 Chna 1.66 1.35 8.86 0.18
4 PETER et al.[47] (2015) SOC 英国 UK 1.59 1.17 28.88 0.35
5 LEE et al.[48] (2016) SOC 蒙古国 Mongolia 12.37 8.48 3.27 0.58
6 本研究(1980年) This study is in 1980 SOC 中国China 3.20 2.61 20.08 0.77
7 ZHANG et al.[49] (2018) SOC 中国China 3.09 2.34 30.51 0.11

Table 4

Comparison of the regression coefficients of climate factors in this study with recent SOC model"

MAT MAT2 MAP MAA PET PET2 PET3
本研究(2022) This study is in 2022 *** NA NA * NA NA NA
本研究(1980) This study is in 1980 NA NA NA NA 3.856** -0.003** 8.81e-07**
英国(2015) UK(2015)[47] -10.680*** 0.612*** NA NA NA NA NA
中国内蒙古(2022)
Inner Mongolia, China(2022)[46]
1.240* NA 0.0122 NA NA NA NA
美国(2018) America(2018)[45] -0.109 NA 0.0141*** NA NA NA NA
蒙古国(2016) Mongolia(2016)[48] -0.962 NA 0.006 0.725*** NA NA NA
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doi: 10.1007/s11707-021-0940-7
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