黑龙江省嫩江市土壤容重空间分布格局及传递函数构建

doi:10.3864/j.issn.0578-1752.2025.09.009

中国农业科学 ›› 2025, Vol. 58 ›› Issue (9): 1791-1803.doi: 10.3864/j.issn.0578-1752.2025.09.009

• 土壤肥料·节水灌溉·农业生态环境 • 上一篇下一篇

黑龙江省嫩江市土壤容重空间分布格局及传递函数构建

王冰洁¹(), 秦诗涵¹, 李德成², 胡文友², 姜军², 迟凤琴³, 张超⁴, 张久明³, 徐英德¹(), 汪景宽¹

¹ 沈阳农业大学土地与环境学院，沈阳 110866
² 中国科学院南京土壤研究所，南京 210008
³ 黑龙江省黑土保护利用研究院，哈尔滨 150086
⁴ 中国农业大学土地科学与技术学院，北京 100083

收稿日期:2024-07-22 接受日期:2024-09-19 出版日期:2025-05-01 发布日期:2025-05-08
通信作者:
徐英德，E-mail：yingdexu@126.com
联系方式: 王冰洁，E-mail：wxue925@163.com。
基金资助:
国家重点研发计划(2021YFD1500202); 国家自然科学基金(42207383)

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 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 Published:2025-05-01 Online:2025-05-08

摘要/Abstract

摘要：

【目的】针对东北典型黑土区耕地土壤“变硬”程度及其空间分布不明确问题，以黑龙江省嫩江市为例，对土壤容重空间分布格局进行预测分析，并建立基于有机质含量、紧实度等因素的土壤容重传递函数。【方法】通过Pearson相关性分析和主成分分析评估土壤属性、气候变量以及地形变量对土壤容重影响的重要程度，运用随机森林模型进行土壤容重预测制图。同时，基于多元线性回归和支持向量机等多种模型构建土壤容重传递函数，并比较其精度。【结果】土壤容重与土壤含水量、有机质含量呈显著负相关，同时受黏粒、砂粒、pH及年均气温和年均降水量等土壤和气候因素的影响，地形因素影响相对较小。进一步预测制图发现，嫩江市土壤容重均呈现由东北（1.08—1.17 g·cm^-3）向西南（1.30—1.40 g·cm^-3）逐渐递增，且呈现亚表层土壤容重高于表层的趋势。此外，土壤容重和紧实度随土壤深度增加而增加，但土壤容重变化较缓（1.20—1.44 g·cm^-3），而紧实度主要在表层（0—20 cm）迅速增加（变化幅度约为300 kPa）；土壤有机质含量随深度增加而降低。通过土壤容重与有机质含量、紧实度等参数进行函数拟合，发现二项式模型和支持向量机分别在有机质—容重、紧实度—容重的拟合中具有较高的R²值，表现出较好的拟合效果和较强的解释力。在亚表层土壤中，通过紧实度构建的土壤容重传递函数预测精度（R²=0.55，RMSE=0.1）高于通过土壤有机质构建的模型（R²=0.43，RMSE=0.12）。【结论】嫩江市土壤容重呈东北低西南高的分布趋势，且受土壤属性、气候和地形等多种因素的共同影响。基于紧实度的传递函数可作为东北黑土容重变化及“变硬”程度的快速诊断方法。

关键词: 东北黑土, 土壤容重, 土壤紧实度, 土壤有机质, 空间分布, 传递函数, 随机森林模型

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

王冰洁, 秦诗涵, 李德成, 胡文友, 姜军, 迟凤琴, 张超, 张久明, 徐英德, 汪景宽. 黑龙江省嫩江市土壤容重空间分布格局及传递函数构建[J]. 中国农业科学, 2025, 58(9): 1791-1803.

WANG BingJie, QIN ShiHan, LI DeCheng, HU WenYou, JIANG Jun, CHI FengQin, ZHANG Chao, ZHANG JiuMing, XU YingDe, WANG JingKuan. Spatial Distribution Pattern and Transfer Function Construction of Soil Bulk Density in Nenjiang City, Heilongjiang Province[J]. Scientia Agricultura Sinica, 2025, 58(9): 1791-1803.

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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)

黑龙江省嫩江市土壤容重空间分布格局及传递函数构建

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

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方法 Method	表层土壤 Topsoil			亚表层土壤Subsoil
方法 Method	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	—

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