Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (9): 1791-1803.doi: 10.3864/j.issn.0578-1752.2025.09.009

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

Spatial Distribution Pattern and Transfer Function Construction of Soil Bulk Density in Nenjiang City, Heilongjiang Province

WANG BingJie1(), QIN ShiHan1, LI DeCheng2, HU WenYou2, JIANG Jun2, CHI FengQin3, ZHANG Chao4, ZHANG JiuMing3, XU YingDe1(), WANG JingKuan1   

  1. 1 College of Land and Environment, Shenyang Agricultural University, Shenyang 110866
    2 Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008
    3 Heilongjiang Academy of Black Soil Conservation and Utilization, Harbin 150086
    4 College of Land Science and Technology, China Agricultural University, Beijing 100083
  • Received:2024-07-22 Accepted:2024-09-19 Online:2025-05-01 Published:2025-05-08
  • Contact: XU YingDe

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Abstract:

【Objective】 Given the unclear degree of "hardening" of cultivated soil and its spatial distribution in the typical black soil region of Northeast China, this study took Nenjiang City, Heilongjiang Province for example, the spatial distribution pattern of soil bulk density were predicted and analyzed, and the soil bulk density transfer function based on factors such as organic matter content and compaction was established. 【Method】 Pearson correlation analysis and principal component analysis were used to evaluate the importance of various factors on soil bulk density, the random forest model was employed for predictive mapping, and multiple soil bulk density transfer functions were constructed and compared for accuracy. 【Result】 Soil bulk density was significantly negatively correlated with water content and organic matter content, and was affected by multiple factors such as clay, sand, pH, average annual temperature, and average annual precipitation, but the impact of topography was relatively small. Forecast mapping revealed that the soil bulk density showed a gradually increasing trend from the northeast (1.08-1.17 g·cm-3) to the southwest (1.30-1.40 g·cm-3) in Nenjiang City, and the soil bulk density in the subsoil was higher than that in the topsoil. In addition, soil bulk density and soil compaction increased with soil depth, but the soil bulk density changed slowly (1.20-1.44 g·cm-3), while the soil compaction increased rapidly mainly in the topsoil (0-20 cm) (the change amplitude was about 300 kPa). Besides, the binomial model and support vector machine had higher R2 values in the fitting of organic matter-bulk density and compaction-bulk density, respectively, and had better fitting effects. In the subsoil, the prediction accuracy of the soil bulk density transfer function constructed by compaction (R2=0.55, RMSE=0.1) was higher than that of the model constructed by soil organic matter (R2=0.43, RMSE=0.12). 【Conclusion】 The soil bulk density in Nenjiang City showed a distribution trend of being low in the northeast and high in the southwest, and was affected by multiple factors such as soil properties, climate and topography. In addition, the compaction transfer function could be used as a rapid diagnostic method for changes in soil bulk density and the degree of "hardening" of the black soil region in Northeast China.

Key words: Northeast black soil, soil bulk density, soil compaction, soil organic matter, spatial distribution, transfer function, random forest model

Fig. 1

Schematic diagram of the research area"

Table 1

Environmental variables and data sources for spatial prediction of soil bulk density"

变量类型
Variable category		 具体指标
Specific indicator		 数据来源
Data sources
土壤属性
Soil property		 pH、含水量、粉粒、砂粒、黏粒
pH, Water content, Silt, Sand, Clay		 SoilGrids250m 2.0
气候变量
Climate variable		 年均气温、年均降水量
Annual mean temperature (TEM), Annual mean precipitation (PRE)		 中国科学院资源环境科学数据中心（http://www.resdc.cn）
Resources and Environmental Science Data Center（http://www.resdc.cn）
地形变量
Terrain variable		 高程、坡度、坡向、剖面曲率、平面曲率、地形湿地指数、地形位置指数、
多尺度山谷平坦指数、多尺度脊顶平坦指数
DEM, Slope, Aspect, Profile curvature, Plan curvature, Topographic wetness index (TWI), Terrain position index (TPI), Multi-resolution valley bottom flatness (MrVBF), Multi-scale ridge top flatness (MrRTF)

Fig. 2

The correlation between soil bulk density and environmental factors a: pH; b: Soil organic matter; c: Soil water content; d: Clay; e: Sand; f: Silt; g: Annual mean temperature, TEM; h: Annual mean precipitation, PRE; i: Slope; j: Aspect; k: Plan curvature; l: Profile curvature; m: DEM; n: Topographic wetness index, TWI; o: Terrain position index, TPI; p: Multi-resolution valley bottom flatness, MrVBF; q: Multi-scale ridge top flatness, MrRTF. The same as below. Pearson correlation coefficient (r) was used to evaluate the correlation of variables, and P was used to determine significance (* P<0.05, ** P<0.01)"

Fig. 3

Principal component analysis results of soil bulk density (a) and (b) represent the principal component analysis results of soil bulk density in topsoil (0-20 cm) and subsoil (20-40 cm), respectively. Arrows of different colors represent different factors: the red arrow represents soil bulk density, the green arrow represents geographical factors, the blue arrow represents climatic factors, and the pink arrow represents soil properties. The length of the arrow and the angle with the sorting axis represent the impact and correlation strength of the environmental factor, respectively. TWI, TPI, MrRTF and MrVBF represent topographic wetland index, topographic position index, multi-scale ridge top flatness index and multi-scale valley flatness index, respectively"

Fig. 4

Relative importance of environmental variables in soil bulk density random forest model (A) and (B) represent the relative importance of environmental variables in topsoil (0-20 cm) and subsoil (20-40 cm), respectively"

Fig. 5

Prediction of soil bulk density using a random forest model"

Fig. 6

The variation characteristics of soil bulk density, compaction, organic matter content, and water content with depth"

Table 2

Comparison of soil bulk density transfer functions based on soil organic matter content"

方法
Method		 表层土壤 Topsoil 亚表层土壤Subsoil
R2 RMSE RSE R2 RMSE RSE
多元线性回归 Multiple linear regression 0.38 0.11 0.11 0.42 0.12 0.12
二项式 Binomial 0.43 0.11 0.11 0.43 0.12 0.12
广义线性 Generalized linear 0.45 0.10 0.12 0.41 0.11 0.14
支持向量机 Support vector machines 0.38 0.11 0.11 0.42 0.12 0.12
岭回归 Ridge regression 0.38 0.11 — 0.42 0.12 —
Lasso回归 Least absolute shrinkage and selection operator 0.38 0.11 — 0.42 0.12 —

Table 3

Comparison of soil bulk density transfer functions based on soil compaction"

方法
Method		 表层土壤 Topsoil 亚表层土壤 Subsoil
R2 RMSE RSE R2 RMSE RSE
最小二乘法 Ordinary least squares 0.16 0.13 0.13 0.55 0.10 0.10
广义线性 Generalized linear 0.16 0.13 0.15 0.07 0.10 0.13
支持向量机 Support vector machines 0.18 0.11 0.15 0.56 0.10 0.10
岭回归 Ridge regression 0.16 0.13 — — 0.10 —
Lasso回归 Least absolute shrinkage and selection operator 0.11 0.13 — — — —
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