Interpretation of soil quality indicators for land suitability assessment – A multivariate approach for Central European arable soils. (April 2019)

• Record Type: Journal Article
• Authors: Juhos, Katalin; Czigány, Szabolcs; Madarász, Balázs; Ladányi, Márta
• Is Part Of: Ecological indicators. Volume 99(2019)
• Page Start: 261
• Page End: 272
• Publication Date: 2019-04
• Subjects: Indicator scoring functions -- Principal component analysis -- Soil quality index -- Available nutrients -- Soil moisture regime; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2018.11.063
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Soil physical and chemical properties of 1045 plots are analysed. The direction and strength of the intercorrelation of indicators are determined. Indicators are interpreted nonlinearly as a function of agricultural suitability. The variance of interpreted indicators are analysed. A minimum data set for soil quality assessment are selected. Abstract: Soils and their functions are critical to ensuring the provision of various ecosystem services. Many authors nevertheless argue that there is a lack of satisfactory operational methods for quantifying the contributions of soils to the supply of ecosystem services. Therefore, it is difficult to automate and standardize the mathematical and statistical methods for the selection of indicators and their scoring. Our objective is the development of a novel soil quality and ecological indicator selection and scoring method based on a database representing the most common Hungarian soils typical for arable lands of Central Europe (Chernozems, Phaeozems, Luvisols, Cambisols, Gleysols, Solonetz, Arenosols). For evaluation purposes, soil texture, depth to groundwater table, soil organic matter (SOM), pH, calcium carbonate equivalent (CCE), electrical conductivity (EC), Na, available N, P, K, Mg, S, Cu, Zn and Mn of 1045 plots representing a total land area of about 5000 ha at 0–30 cm layer were analyzed. We classified the samples into 25 soil types. Using correlation, principal component analysis and discriminant analysis the direction and strength of the intercorrelation of indicators and their combinations were determined. Indicators were classified into the following categories: (1) indicators that characterize nutrient retention and cation exchange capacity: texture, SOM, EC and Na; (2) available nutrients, relatively independent from management practices: K, Mg, Cu; (3) indicators that determine base saturation: pH, CCE, available Mn; (4) highly variable available nutrients: N, S, P, Zn. By reviewing the results of Hungarian long-term experiments, we interpreted the soil indicators as a function of agricultural suitability. Following the parameterized and non-linear interpretation of the indicators, we analysed the variance of soils, in terms of their agricultural land suitability. According to the intercorrelation of input indicators and variance of scored indicators the minimum data set for soil quality assessment includes texture, depth of groundwater table, SOM, pH, Na, available K, P and Zn. In order to further advance our soil quality assessment model, our following goals target the determination the hierarchical ranking and grouping of soil parameters in a combined manner.