Interpretation of soil quality indicators for land suitability assessment – A multivariate approach for Central European arable soils

doi:10.1016/j.ecolind.2018.11.063

Ecological Indicators

Volume 99, April 2019, Pages 261-272

https://doi.org/10.1016/j.ecolind.2018.11.063 Get rights and content

Highlights

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Soil physical and chemical properties of 1045 plots are analysed.
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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.
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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.

Introduction

To prevent and mitigate soil degradation processes, spatial and temporal heterogeneity pedological data with readily measurable indicators, are essential for appropriate soil management strategies. Soil quality refers to the capacity of soils to function and sustain plant and animal life within natural and managed environments (Karlen et al., 1997). Soil quality cannot be directly obtained but rather inferred by measuring the appropriate soil physical, chemical and biological indicators (de Paul Obade and Lal, 2016).

Soil Quality Indices (SQIs) synthesize soil attributes into a format that enhances the understanding of soil processes and promotes appropriate management. The Soil Management Assessment Framework (SMAF) is an example of an SQI that operates in three steps (Andrews et al., 2004): (1) indicator selection; (2) interpretation of the selected indicators (scoring); and (3) aggregation of indicators in an index through weighted additive technique. Site-specific adaptations of these SQI are the most commonly used approaches today to evaluate impacts of agricultural practices, cropping systems (Armenise et al., 2013, Li et al., 2013, Ivezić et al., 2015, Raiesi and Kabiri, 2016, Biswas et al., 2017), land use change and land degradation (Masto et al., 2015, Raiesi, 2017). During a land suitability assessment (Kurtener and Badenko, 2000, Baja et al., 2007), the most important task is the evaluation of the productivity function of soils and the impact of soil properties on yield. However, this is complicated as soil properties, in various combination and to a different degree, influence crop yields and determine soil functions in a mixed manner.

Among the available soil quality indicators selection methods, Total Data Set (TDS) and Minimum Data Set (MDS) have been commonly used (Ghaemi et al., 2014, Rojas et al., 2016). In the MDS indicators are selected based on expert opinion or multivariate statistical analyses, most commonly through principal component analysis (PCA) (Andrews et al., 2004).

The second step is normalizing the MDS indicators by different numerical scales (usually between 0 and 1) using linear and non-linear scoring functions. The mathematical basis of this scheme is provided by the Fuzzy logic (Zhang et al., 2004, Busscher et al., 2007). This method is a clustering approach in which the true values of variables (membership) may be any real number between 0 and 1, where, in our case, 0 completely fails to fulfil, while 1 completely fulfils the demands of land use. Globally, the most commonly accepted linear and non-linear functions and integrating a method of scaled indicators with a weighted additive manner are provided by the SMAF (Andrews et al., 2004). In some cases, the selection, the linear interpretation, and determination of scoring thresholds of the indicators are based on the linear correlation between the indicators and yield (Thuithaisong et al., 2011, de Paul Obade and Lal, 2016, Biswas et al., 2017).

The need for the standardization of indices is a vital issue (de Paul Obade and Lal, 2016). We believe that the automation of the statistical selection of MDS is insufficient as the impact of selected soil parameters for the ecological functions is usually non-linear. Evidently, the functions of soils and soil quality are manifested under given conditions (climatic, hydrologic and topographic), and can only be interpreted according to land use type or the specific necessities of the plant grown in a specific soil. When selecting indicators soil quality indexes should be able to express changes in a variety of soil types even in relatively small areas (Juhos et al., 2015).

There is a limited number of Central European SQI references available (Ivezić et al., 2015, Teodor et al., 2018). In Hungary, soil quality indices based on simple indicators, are not in use for land evaluation (Makó et al., 2007, Debreczeniné et al., 2003, Tóth et al., 2007). The adaptation of soil quality indices to different environmental conditions is influenced by the employed soil analytical methods. In our opinion, the development of soil quality indices, especially for land suitability assessment, under the temperate climate of Central Europe requires a more complex multivariate approach.

Our objective, therefore, is the development of a novel soil quality assessment method based on a database representing some Central European cultivated soil types and Hungarian soil analytical methods. We intend to elaborate a multivariate soil evaluation method, which expresses the rate, quality and combination of the limiting factors on soil productivity. Our specific goals in this study included (1) the multivariate assessment of indicators determined according to the existing Hungarian standards (2) the determination of the direction and strength of their intercorrelation and (3) the comprehensive evaluation of the indicators by mathematical modelling and according to the scored indicators by soil types identification of limiting factors for plant growth. These goals were achieved by reviewing the results of Hungarian long-term experiments, the complex and mutual interpretation of the indicators by mathematical modelling as a function of agricultural land suitability.

Section snippets

Site description

The employed soil database, representative of Hungary’s farmlands, was compiled from the laboratory analyses of 1045 soil samples collected from a total land area of about 5000 ha. Each soil sample represents a homogeneous land parcel of a maximum of 5 ha. In all cases, samples were taken from a depth of 0 to 30 cm. The geographical location of the sampling sites is shown in Fig. 1. The soil types of the research sites and their qualifiers are shown in Table 1 according to the World Reference

Bivariate correlations between soil quality indicators

The descriptive statistics and the linear correlation matrix of the pedological indicators are shown in Table 4, Table 5, respectively. On the analysed database a strong correlation (r > 0.8) was found between pH and the CCE indicators, while the influence of base saturation was clearly observable on both parameters., a significant, but weak (r < 0.39) or moderate (r = 0.40–0.59) correlation exists among pH, Na and EC since salt accumulation and Na adsorption do not always occur together. In

Indicators used for soil quality indices

To estimate the impact of soil chemical properties on nutrient cycle as well as water and nutrient uptake, most authors studied pH-H₂O (occasionally pH-CaCl₂), electrical conductivity, cation exchange capacity (CEC) and exchangeable cations (Zhang et al., 2004, Qi et al., 2009, Masto et al., 2015). Under arid climates, exchangeable sodium percentage (ESP), sodium adsorption ration (SAR) and calcium carbonate equivalent (CCE) complete the list of analysed parameters. Nevertheless, due to the

Conclusions

Instead of the separate interpretation of soil indicators, their inter-correlations should be taken into account. Various soil physical and chemical properties must be incorporated as the nutrient availability of the soil is also affected by other soil properties. Soil moisture regime is also a more complex parameter and it is difficult to express using one simple indicator.

During the development of a soil quality index, the number of variables should be reduced relying on the outcomes of the

Acknowledgement

Supported by the ÚNKP-17-4-I and ÚNKP-18-4 New National Excellence Program of the Ministry of Human Capacities and Bolyai János Research Scholarship of the Hungarian Academy of Sciences (B. Madarász).

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Interpretation of soil quality indicators for land suitability assessment – A multivariate approach for Central European arable soils

Highlights

Abstract

Introduction

Section snippets

Site description

Bivariate correlations between soil quality indicators

Indicators used for soil quality indices

Conclusions

Acknowledgement

Soil Till. Res.

Geoderma

Sci. Total Environ.

Land Use Policy

Earth Sci. Front.

Soil Till. Res.

Appl. Soil Ecol.

J. Environ. Manage.

Iran. Ecol. Ind.

Soil Till. Res.

Geoderma

Iran. Ecol. Indic.

Ecol. Ind.

Ecol. Ind.

Soil Till. Res.

Soil Till. Res.

Geoderma Regional

Geoderma

Ecol. Ind.

Geoderma

The soil management assessment framework: a quantitative soil quality evaluation method

Soil Sci. Soc. Am. J.

Kukoricatermesztési adatok ökológiai csoportosítása faktor- és clusteranalízis segítségével. [Classification of maize yield data by factor and cluster analysis.]

Növénytermelés

Relationship of barely biomass and grain yields to soil properties within a field in the arid region: use of factor analysis

Acta. Agric. Scand. Section B-Soil Plant. Sci.

Spatial based compromise programming for multiple criteria decision making in land use planning

Environ. Model. Assess.

Comparison of soil amendments to decrease high strength in SE USA Coastal Plain soils using fuzzy decision-making analyses

Int. Agrophys.

Variability of selected soil properties and their relationships with soybean yield

Soil Sci. Soc. Am. J.

Összefüggés a talaj K-ellátottsága és a kukorica, őszi búza és lucerna K-hatások között a hazai szabadföldi kísérletekben, 1960–1990. [Potassium effects on yields of maize, winter wheat and alfalfa in long-term experiments in Hungary (1960–1990)]

Agrokémia és Talajtan

Összefüggés a talajsavanyúság mértéke és a mészhatások között, a hazai meszezési tartamkísérletek adatbázisán, 1950–2000. II. A kísérleti növények, a mészforma és a meszezés óta eltelt idő szerepe a mészhatások megjelenésében

Szemle. Agrokémia és Talajtan

Őszi búza N hatásokat befolyásoló tényezők vizsgálata az 1960 és 2000 között publikált hazai szabadföldi kísérletek adatbázisán. [Effects of soil organic matter on yields of winter wheat in long-term experiments in Hungary (1960–2000)]

Növénytermelés

Kukorica N hatásokat befolyásoló tényezők vizsgálata az 1960 és 2000 között publikált hazai szabadföldi kísérletek adatbázisán. [Effects of soil organic matter on yields of maize in long-term experiments in Hungary (1960–2000)]

Agrokémia és Talajtan

Lucerna N hatásokat befolyásoló tényezők vizsgálata az 1960 és 2000 között publikált hazai szabadföldi kísérletek adatbázisán. [Effects of soil organic matter on yields of alfalfa in long-term experiments in Hungary (1960–2000)]

Növénytermelés

Őszi búza P-hatásokat befolyásoló tényezők vizsgálata az 1960 és 2000 között publikált hazai szabadföldi kísérletek adatbázisán. [Phosphorus effects on yields of winter wheat in long-term experiments in Hungary (1960–2000)]

Növénytermelés

Kukorica P-hatásokat befolyásoló tényezők vizsgálata az 1960 és 2000 között publikált hazai szabadföldi kísérletek adatbázisán. [Phosphorus effects on yields of maize in long-term experiments in Hungary (1960–2000)]

Agrokémia és Talajtan

Lucerna P-hatásokat befolyásoló tényezők vizsgálata az 1960 és 2000 között publikált hazai szabadföldi kísérletek adatbázisán. [Phosphorus effects on yields of alfalfa in long-term experiments in Hungary (1960–2000)]

Növénytermelés

A D-e-Meter földminősítési viszonyszámok elméleti háttere és információtartalma. [The theoretical background and the information content of the D-e-Merter land quality values.] 23–37

Fruit quality of tomato affected by single and combined bioeffectors in organically system

Pakistan J. Agric. Sci.

Untersuchungen über die chemische Bodenanalyse als Grundlage für die Beurteilung des Nährstoffzustandes derBöden II

Kungliga Lantbrukshögskolans Annaler

Analysis of climate change in Hungary according to an extended Köppen classification system, 1971–2060

Q. J. Hung. Meteorol. Serv.

Modelling impacts of different climate change scenarios on soil water regime of a Mollisol

Cereal Res. Comm.