Relationship between Soil Characteristics and Stand Structure of Robinia pseudoacacia L. and Pinus tabulaeformis Carr. Mixed Plantations in the Caijiachuan Watershed: An Application of Structural Equation Modeling

Abstract

Abstract:In order to study the multi-factor coupling relationships between typical Robinia pseudoacacia L. and Pinus tabulaeformis Carr. mixed plantations in the Caijiachuan basin of the Loess Plateau of Shanxi Province, West China, 136 sample plots were selected for building a structural equation model (SEM) of three potential variables: terrain, stand structure, and soil characteristics. Additionally, the indicators (also known as observed variables) were studied in this paper, including slope, altitude, diameter at breast height (DBH), tree height (TH), tree crown area, canopy density, stand density, leaf area index (LAI), soil moisture content, soil maximum water holding capacity (WHC), soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), ammonia-nitrogen (NH 3 -N), nitrate-nitrogen (NO 3 -N), and available phosphorus (AP). The results showed that terrain was the most important factor influencing soil moisture and nutrients, with a total impact coefficient of 1.303 and a direct path coefficient of 0.03, which represented mainly positive impacts; while correspondingly stand structure had a smaller negative impact on soil characteristics, with a total impact coefficient of −0.585 and a direct path coefficient of −0.01. The terrain also had a positive impact on the stand structure, with a total impact coefficient of 0.487 and a direct path coefficient of 0.63, indicating that the topography factors were more suitable for site conditions and both the stand structure and the soil moisture and nutrient conditions were relatively superior. By affecting the stand structure, terrain could restrict some soil, water, and nutrient functions of soil and water conservation. The influence coefficients of the four observed variables of DBH, stand density, soil water content, and organic matter, and potential variable topography reached 0.686, −0.119, 1.117, and 0.732, respectively; and the influence coefficients of soil moisture, organic matter and stand structure were −0.502 and −0.329, respectively. Therefore, besides observing the corresponding latent variables, the observed variables had a considerable indirect influence on other related latent variables. These relationships showed that the measures, such as changing micro-topography and adjusting stand density, should effectively maintain or enhance soil moisture and nutrient content so as to achieve improved soil and water conservation benefits in the ecologically important Loess Area.

Multifactor relationships between stand structure and soil and water conservation functions of Robinia pseudoacacia L. in the Loess Region

“…The slopes ranged from 15° to 39° and from 900 to 1300 m above sea level (Table 1). Additional details of the Caijiachuan watershed, taking shrubs and herbs as examples, can be found in the published results of Wei Xi et al (2018) [17].…”

“…All of these studies utilized SEM as an instrument to evaluate these relationships. However, SEM has rarely been applied in the field of stand structure and soil and water conservation functions except to study the relationships between soil characteristics and stand structure in mixed plantations [17].…”

Effects of Grass and Forests and the Infiltration Amount on Preferential Flow in Karst Regions of China

“…Compared with traditional statistical methods, SEM introduces latent variables that can simultaneously consider endogenous variables, which can allow errors of measurement in parameter estimation whilst analyzing the direct and indirect effects between variables [47]. The SEM method consists of structural equations and measurement models that are generally represented by three matrix equations, as follows [48].…”

“…Many latent variables cannot be directly observed during the infiltration process because of the imperceptibility of soil, and these latent variables cause errors. SEM has been shown to be verifiable [48]. To accurately study preferential flow, we need to consider not only the correlation between variables but also the errors.…”

“…Hence, we designed a relationship model between latent variables and estimated the fit of the initial model with the measured data. This study used SEM to construct and evaluate the index system, which can reflect the interrelationship between latent variables (exogenous and endogenous latent variables) [48]. In this study, we used the software Amos 7.0 (Analysis of Moment Structures, developed by James L. Arbuckle.…”