Path coefficient analysis, a different approach to identify soil quality indicators

Abstract

Soil quality indicators related to water erosion reduction can assist with correct soil management. The objective of this research was to identify some variables that could be used as soil quality indicators with the aid of path coefficient analysis in order to reduce water erosion. The research was carried out in the field between May 2011 and April 2013 in southern Brazil on an Inceptisol. The following treatments were studied under simulated rainfall conditions: 1) no-tilled, cultivated and covered by cultural residue of ryegrass (Lolium multiflorum) (HCR); 2) no-tilled, cultivated and covered by crop residue of vetch (Vicia sativa) (HCV); 3) cultivated and scarified soil containing ryegrass roots (HRR); 4) cultivated and scarified soil containing vetch roots (HRV); and 5) bare and chiselled soil (BHR). Eight simulated rainfalls were applied in each treatment. Flow velocity, soil and water losses as well as variables or soil attributes influenced by management were quantified. Path coefficient analysis verified that the coverage, surface roughness, water infiltration rate and total organic carbon have the greatest direct or indirect relationships with soil and water losses or runoff velocity. These variables were indicative of soil quality, particularly its resistance to water erosion. In a rough soil, the total organic carbon, root mass and macroporosity of the soil are more important as indicators for soil resistance to water erosion.

Leaf Photosynthetic Capacity of Sunlit and Shaded Mature Leaves in a Deciduous Forest

“…Repeated measure analysis of variance (ANOVA) was performed to determine the variations among the months and years (p < 0.05). The relationships of photosynthetic capacity with biochemical and leaf structural parameters were analyzed by Pearson's correlation, with the possible direct and indirect effects of each parameter on the photosynthetic capacity being further identified by path analysis [57][58][59]. Data analysis was conducted using the packages "plantecophys" and "agricolae" in R 3.5.3.…”