LUSMLeiden University School of Management (The Netherlands)
LUSMLove You So Much
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A-horizon depth was directly measured in pits opened to a 60 cm depth within each LUSMS.
Three randomly collected soil samples (25 x 25 x 20 cm) from a 1 [m.sup.2] area were passed through a 10 mm sieve to separate and then count the average number of earthworms in each LUSMS sampling unit.
After interpolated by polynomial Lagrangian interpolation method in the CurveExpert version 1.3, the corresponding transformed value was analyzed for each untransformed indicator value in each LUSMS. The X-axis for the functions represented a site-specific expected range of values of the soil properties.
Both methods were applied to data from each LUSMS. The SQI values were then compared with those from natural forest land systems to assess the degree of soil degradation or improvement.
A standard PCA was conducted using all SQ indicators that showed significantly differences among the LUSMS. Under each principal component (PC), only the variables having high factor loadings and eigenvalues >1 that explained at least 5% of the data variations were retained for indexing.
One-way analysis of variance (ANOVA) was performed to determine the effects of LUSMS on SQI.
A moderate to strong correlation (r > 0.7) among many SQ indicators within the different LUSMS was observed, indicating a multicollinearity effect (data not shown).
Phosphorus is frequently a limiting factor for crop production in northern Ethiopia soils, so its inclusion in a SQ index is logical for assessing SQ degradation within the various LUSMS. Based on these two factors, PC2 is referred to as the "soil macro-nutrient factor."
These indicators also influenced the SQ in an opposite direction (Table 4); their inclusion in any SQ indexing is crucial to assess variability associated with the various LUSMS. Again, based on the critical indicators, PC3 is referred to as the "soil physical property factor." The fourth PC is referred to as the "soil micro-nutrient factor," because Fe is the highly loaded variable Table 4).
Based on the four PCs, a composite PCA-SQI consisting of soil OC, TN, CEC, TP, silt, DBD, and Fe was chosen to assess SQ variability among the LUSMS. Weighting factors were developed based on the percent variation explained by the first four PCs (Table 4), resulting in a final normalized PCA based SQI equation:
An unscreened-SQI using the PCA and EO selected minimum datasets showed the highest SQI values for the [II.sup.L] (homothetic transformation method) and [I.sup.N] (CurveExpert method of NLSF) models used to compare the eight LUSMS (Tables 5 and 6).
Among LUSMS, LS1 had a significantly higher (P [less than or equal to] 0.05) unscreened-SQI value whereas a lower value in LS8 using either the PCA or EO selected datasets.