2019
DOI: 10.1016/j.ecolind.2018.11.063
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Interpretation of soil quality indicators for land suitability assessment – A multivariate approach for Central European arable soils

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Cited by 88 publications
(50 citation statements)
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“…In addition to their important hydraulic properties, Table 3 illustrates that the macro-pores create the soil air capacity (AC) through their easy draining under normal conditions [56,57]. Macro-pores and a high AC are very beneficial in lowland areas, where they counteract the development of anoxic soil conditions during rainy periods [58,59].…”
Section: Discussionmentioning
confidence: 99%
“…In addition to their important hydraulic properties, Table 3 illustrates that the macro-pores create the soil air capacity (AC) through their easy draining under normal conditions [56,57]. Macro-pores and a high AC are very beneficial in lowland areas, where they counteract the development of anoxic soil conditions during rainy periods [58,59].…”
Section: Discussionmentioning
confidence: 99%
“…where w is the weight to be assigned to each parameter (subscript v is vegetation, gw is groundwater, s is soil, sl is slope and wb stands for surface water bodies), and R is the parameter scores given for retention in vegetation (R v ), groundwater bodies (R gw ), soil (R s ) and surface water bodies (R wb ), and for slope (R sl ) and soil sealing (R ss ). The values of the component factors of the equation were estimated from findings of other research projects in Hungary (Kertész, Á. et al 2010;Juhos, K. et al 2019). An indispensable precondition for the experiments carried out in the study areas was a Digital Elevation Model (DEM) of proper resolution.…”
Section: Generation Of the Twi Lu Mapsmentioning
confidence: 99%
“…Many studies use normalisation of indicator scores on the basis of linear or non-linear scoring functions (D'Hose et al 2014;Sharma et al 2005) and integrate the normalised indicators into SQIs using additive or multiplicative techniques (D'Hose et al 2014;De Laurentiis et al 2019;Lima et al 2013;Masto et al 2008;Sharma et al 2005). The statistical tests for confirming MDS include, amongst others, PCA and varimax rotation followed by simple linear regression (Juhos et al 2016), cluster analysis (Dilly et al 2018) and correlation, PCA and discriminant function analysis (DFA) (Juhos et al 2019). For SQI evaluation, some previous work has used either ANOVA or MANOVA to test associations between SQIs and soil management groups (de Andrade Barbosa et al 2019; de Paul Obade and Lal 2016; Kiani et al 2017;Molaeinasab et al 2018;Nakajima et al 2015).…”
Section: Statistical Testsmentioning
confidence: 99%
“…Since soil physical, chemical and biological properties or processes vary spatio-temporally, the corresponding indicators included in SQIs inevitably varies among environmental settings and agricultural systems (Bai et al 2018;Doran 2002;Spiegel et al 2015). SQIs therefore need to be designed and interpreted as setting-specific (Biswas et al 2017;Juhos et al 2019;Raiesi and Kabiri 2016). But even in a given setting or agricultural system, it is important to characterise the heterogeneity of the relationships between physical, chemical and biological parameters (Dilly et al 2018).…”
Section: Sqi Evaluationmentioning
confidence: 99%