Assessment of Soil Quality under Different Soil Management Strategies: Combined Use of Statistical Approaches to Select the Most Informative Soil Physico-Chemical Indicators

Abstract

Assessment of soil quality under different management practices is crucial for sustainable agricultural production and natural resource use. In this study, different statistical methods (principal component analysis, PCA; stepwise discriminant analysis, SDA; partial least squares regression with VIP statistics, PLSR) were applied to identify the variables that most discriminated soil status under minimum tillage and no-tillage. Data collected in 2015 from a long-term field experiment on durum wheat (Triticum durum Desf.) were used and twenty soil indicators (chemical, physical and biological) were quantified for the upper soil layer (0–0.20 m). The long-term iteration of different management strategies affected soil quality, showing greater bulk density, relative field capacity (RFC), organic and extractable carbon contents (TOC and TEC) and exchangeable potassium under no-tillage. PCA and SDA confirmed these results and underlined also the role of available phosphorous and organic carbon fractions as variables that most discriminated the treatments investigated. PLSR, including information on plant response (grain yield and protein content), selected, as the most important variables, plant nutrients, soil physical quality indicators, pH and exchangeable cations. The research showed the effectiveness of combining variable selection methods to summarize information deriving from multivariate datasets and improving the understanding of the system investigated. The statistical approaches compared provided different results in terms of variables selected and the ranking of the selected variables. The combined use of the three methods allowed the selection of a smaller number of variables (TOC, TEC, Olsen P, water extractable nitrogen, RFC, macroporosity, air capacity), which were able to provide a clear discrimination between the treatments compared, as shown by the PCA carried out on the reduced dataset. The presence of a response variable in PLSR considerably drove the feature selection process.

Seasonal and Soil Use Dependent Variability of Physical and Hydraulic Properties: An Assessment under Minimum Tillage and No-Tillage in a Long-Term Experiment in Southern Italy

“…In general, relationships between BEST-derived variables and other measured variables met expectations because, for example, P MAC (or AC) increased as BD decreased [36]; in the same way, similar relationships were detected when relative field capacity was considered, as relatively higher RFC values highlight a reduced availability of soil air (and vice versa) [37]. However, regarding the accuracy of capacity-based soil indicators obtained by BEST, a further evaluation was carried out by comparing the summer data from this investigation (BD, P MAC , AC, RFC) with the corresponding measurements (i.e., from measured soil water retention curves) carried out in the same plots in spring 2015 (see data reported in Table 2 by Stellacci et al [37]). Starting from very similar BD values (differences within a factor 1.2 or 1.3 under NT or MT), the three remaining indicators showed, on average, relatively higher discrepancies under NT (a factor of 2.3) than MT (1.7), with the highest difference (overestimation by a factor of 4) for the air capacity under no-tillage.…”

“…Many soil properties exhibit a two-stage response to soil management [17,40] and thus a complete understanding of the processes investigated requires that almost stable conditions are obtained. In this respect, the key role of long-term field experiments is well known and recognized [23,37,41]. In this study, as previously reported, the time elapsed from the last main soil tillage allowed for the study of relatively comparable soil conditions.…”

Short- and Medium-Term Effects of On-Farm Compost Addition on the Physical and Hydraulic Properties of a Clay Soil

“…According to the procedure indicated by Reynolds et al [13], the pore volume distribution functions of the considered treatments were calculated. These waterretention-curve-derived indicators are widely used and suggested in the literature [30,31], also to verify plausible correlations with respect to independent variables (for example, K s ) and check the consistency of the results [32,33].…”

The Differentiation in Cultivated Land Quality between Modern Agricultural Areas and Traditional Agricultural Areas: Evidence from Northeast China

“…Practically speaking, cultivated land fertility quality refers to the potential productivity determined by soil physicochemical properties and soil nutrient elements [63]. Cultivated land project quality can adjust the suitability of cultivated land through changing infrastructure and ancillary facilities.…”