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Proportion of variance using varimax rotation and communality estimates for soil attributes and biomass or each of the retained factors for th 0-10 cm depth

Table 2 Proportion of variance using varimax rotation and communality estimates for soil attributes and biomass or each of the retained factors for th 0-10 cm depth

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Biomass (mg ha~'); is, infiltration rate at 5 min (cm min™!); i, infiltration rate after 3 h (cm min™'); J, cumulative infiltration (cm); FC, field capacity water content (cm? cm™ antecedent water content (cm? cm”); K,, saturated hydraulic conductivity (cm min!); fa, air-filled porosity (cm? cm); fe, effective porosity (cm? cm”); AWC, available capacity (cm); SOC, soil organic carbon (g kg~'); pp, soil bulk density; WSA, water stable aggregates (%); GMD, geometric mean weight diameter (mm); MWD, mean diameter (mm); EC, electrical conductivity (dS m_!); SN, total nitrogen (g kg”).
Biomass (mg ha~'); is, infiltration rate at 5 min (cm min™!); i, infiltration rate after 3 h (cm min™'); J, cumulative infiltration (cm); FC, field capacity water content (cm? cm™ antecedent water content (cm? cm”); K,, saturated hydraulic conductivity (cm min!); fa, air-filled porosity (cm? cm); fe, effective porosity (cm? cm”); AWC, available capacity (cm); SOC, soil organic carbon (g kg~'); pp, soil bulk density; WSA, water stable aggregates (%); GMD, geometric mean weight diameter (mm); MWD, mean diameter (mm); EC, electrical conductivity (dS m_!); SN, total nitrogen (g kg”).
Eigenvalue, proportion and cumulative variance explained by factor analysis using correlation matrix (standardized data) for the 0- 10 cm depth
Eigenvalue, proportion and cumulative variance explained by factor analysis using correlation matrix (standardized data) for the 0- 10 cm depth
Eigenvalue, proportion and cumulative variance explained by factor analysis using covariance matrix (raw data) for the 0-10 cm depth
Eigenvalue, proportion and cumulative variance explained by factor analysis using covariance matrix (raw data) for the 0-10 cm depth
Effect of management systems on factor scores for the upper slope of the 0-10 cm depth analysis of variance of individual soil attributes (Shukla et al., 2003b,c). Slope position was not significant for Factors 1 and 2, but was for Factors 3-5 (Table 6). The influence of slope position on Factor 3 (Soil Pore Connection) was due to biomass yield, which significantly differed between mid (5.32 Mg ha’ ') and lower (4.59 Mg ha’) slope positions. Factor 4 (Soil Texture) varied with slope position (Table 6). Factor 5 (Moisture Status) was higher at the upper slope position than that at the lower slope. Table 5
Effect of management systems on factor scores for the upper slope of the 0-10 cm depth analysis of variance of individual soil attributes (Shukla et al., 2003b,c). Slope position was not significant for Factors 1 and 2, but was for Factors 3-5 (Table 6). The influence of slope position on Factor 3 (Soil Pore Connection) was due to biomass yield, which significantly differed between mid (5.32 Mg ha’ ') and lower (4.59 Mg ha’) slope positions. Factor 4 (Soil Texture) varied with slope position (Table 6). Factor 5 (Moisture Status) was higher at the upper slope position than that at the lower slope. Table 5
Effect of slope position on factor scores for the 0-10 cm depth Table 6
Effect of slope position on factor scores for the 0-10 cm depth Table 6
Eigenvalue, proportion and cumulative variance explained by factor analysis using correlation matrix (standardized data) for the 10- 20 cm depth
Eigenvalue, proportion and cumulative variance explained by factor analysis using correlation matrix (standardized data) for the 10- 20 cm depth
Correlation among plant biomass and soil attributes for 10 to 20 cm depth across all slope position and management systems evaluated (n = 528) Table 7 each other (Table 7). Factor 7 was called ‘Soil Pore Connection 2’ because it had high positive loading from K, (0.74) and negative from AWC (—0.77). Factor 8 had high positive loadings from f, (0.81) and fe (0.90) and high negative loading from py (—0.72). Factor 8 was called ‘Soil Macropore’ because effective and air filled porosities were inversely related to py (Table 7). Factor 9 was called “Plant Production’ because it had high positive loading from biomass yield (0.78) and negative loading from soil pH (—0.77). Slope position influenced Factors 6 and 7 only, which was consistent with results from measured attributes (Shukla et al., 2003c).
Correlation among plant biomass and soil attributes for 10 to 20 cm depth across all slope position and management systems evaluated (n = 528) Table 7 each other (Table 7). Factor 7 was called ‘Soil Pore Connection 2’ because it had high positive loading from K, (0.74) and negative from AWC (—0.77). Factor 8 had high positive loadings from f, (0.81) and fe (0.90) and high negative loading from py (—0.72). Factor 8 was called ‘Soil Macropore’ because effective and air filled porosities were inversely related to py (Table 7). Factor 9 was called “Plant Production’ because it had high positive loading from biomass yield (0.78) and negative loading from soil pH (—0.77). Slope position influenced Factors 6 and 7 only, which was consistent with results from measured attributes (Shukla et al., 2003c).
Proportion of variance using rotated factor pattern using varimax rotation and communality estimate for soil attributes and biomass for each of the retained factors for the 10 to 20 cm depth
Proportion of variance using rotated factor pattern using varimax rotation and communality estimate for soil attributes and biomass for each of the retained factors for the 10 to 20 cm depth
Related topics:
Soil ScienceConventional tillageMean Weight DiameterSoil Bulk DensitySoil Aggregation
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