Network analysis was effective in establishing the soil quality index and differentiated among changes in land-use type

IF 6.1 1区 农林科学 Q1 SOIL SCIENCE Soil & Tillage Research Pub Date : 2024-11-05 DOI:10.1016/j.still.2024.106352
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Abstract

Understanding the implications of land-use type on soil quality and function is critical to the adoption of suitable agricultural management practices in a specific region. Principal component analysis (PCA) is a widespread technique for calculating soil quality index (SQI), but it cannot correctly evaluate soil quality in some cases. Network analysis (NA) is a novel and effective technique for calculating SQI for determining susceptibility in different land uses but it is still limited. Moreover, few studies have compared NA and PCA to quantify soil quality. This study aimed to develop valid and accurate SQIs through NA and PCA to estimate the impacts of land-use types (cropland, forest, and grassland) on SQIs in Tongliao and Qiqihar, which are the two regions subject to frequent wind erosion in northeast China. A total of 27 soil physical, chemical, and biological properties were measured for the selection of the minimum data set (MDS), and eight SQI values were determined for each study site using additive or weighted methods and linear or nonlinear scoring functions. Results indicated that most soil attributes and SQIs varied markedly among three land-use types and were greater in grasslands or forests than in croplands. The amount of MDS generated using NA was considerably low, but soil physicochemical and biological properties were comprehensively covered. SOC in Tongliao and SHC in Qiqihar were selected by both methods and were considered the most sensitive soil quality indicators for detecting the effects of land-use types. The soil sensitivity index of the SQI calculated by NA (1.34–2.02) was higher than that of the SQI calculated by PCA (1.30–1.80). Thus, NA was more effective than PCA in computing the SQI and differentiated among changes in land use better as a simple and stable tool. The SQI developed through NA using the weighted method and nonlinear scoring function is a suitable and practical quantitative tool for SQI assessment, which is proposed to be used for soil quality assessment for various land-use types in northeast China.
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网络分析可有效确定土壤质量指数,并区分土地利用类型的变化
了解土地利用类型对土壤质量和功能的影响对于在特定地区采用合适的农业管理方法至关重要。主成分分析法(PCA)是计算土壤质量指数(SQI)的一种常用技术,但在某些情况下并不能正确评估土壤质量。网络分析(NA)是计算 SQI 的一种新颖而有效的技术,可用于确定不同土地用途的易感性,但其应用范围仍然有限。此外,很少有研究对 NA 和 PCA 在量化土壤质量方面进行比较。本研究旨在通过 NA 和 PCA 建立有效、准确的 SQIs,以估算东北地区风蚀频发的通辽和齐齐哈尔两地的土地利用类型(耕地、森林和草地)对 SQIs 的影响。为选择最小数据集(MDS),共测量了 27 种土壤物理、化学和生物属性,并使用加法或加权法以及线性或非线性评分函数确定了每个研究地点的 8 个 SQI 值。结果表明,大多数土壤属性和 SQIs 在三种土地利用类型之间存在明显差异,草地或森林中的差异大于耕地中的差异。使用 NA 生成的 MDS 数量较少,但全面涵盖了土壤理化和生物属性。两种方法都选择了通辽的 SOC 和齐齐哈尔的 SHC,它们被认为是检测土地利用类型影响的最灵敏的土壤质量指标。NA 计算的 SQI 土壤敏感性指数(1.34-2.02)高于 PCA 计算的 SQI 土壤敏感性指数(1.30-1.80)。因此,作为一种简单稳定的工具,NA 在计算 SQI 方面比 PCA 更有效,也更能区分土地利用的变化。利用加权法和非线性评分函数,通过 NA 建立的 SQI 是一种适用、实用的 SQI 定量评价工具,建议用于东北地区不同土地利用类型的土壤质量评价。
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来源期刊
Soil & Tillage Research
Soil & Tillage Research 农林科学-土壤科学
CiteScore
13.00
自引率
6.20%
发文量
266
审稿时长
5 months
期刊介绍: Soil & Tillage Research examines the physical, chemical and biological changes in the soil caused by tillage and field traffic. Manuscripts will be considered on aspects of soil science, physics, technology, mechanization and applied engineering for a sustainable balance among productivity, environmental quality and profitability. The following are examples of suitable topics within the scope of the journal of Soil and Tillage Research: The agricultural and biosystems engineering associated with tillage (including no-tillage, reduced-tillage and direct drilling), irrigation and drainage, crops and crop rotations, fertilization, rehabilitation of mine spoils and processes used to modify soils. Soil change effects on establishment and yield of crops, growth of plants and roots, structure and erosion of soil, cycling of carbon and nutrients, greenhouse gas emissions, leaching, runoff and other processes that affect environmental quality. Characterization or modeling of tillage and field traffic responses, soil, climate, or topographic effects, soil deformation processes, tillage tools, traction devices, energy requirements, economics, surface and subsurface water quality effects, tillage effects on weed, pest and disease control, and their interactions.
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