Factors controlling spatial variation in soil aggregate stability in a semi-humid watershed

doi:10.1016/j.still.2021.105187

Soil and Tillage Research

Volume 214, October 2021, 105187

https://doi.org/10.1016/j.still.2021.105187 Get rights and content

Highlights

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Soil properties were the primary controls on SAS variation across the watershed.
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Topography can affect SAS by influencing SOC dynamics, allocation of LUT or NDVI.
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Grassland restoration was recommended for preventing soil erosion in the study area.
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SEM gave new insights into the complex network of SAS drivers and the potential paths.

Abstract

Soil aggregate stability (SAS) is a key soil property that affects soil erosion and soil ability to support ecosystem functions. The effects of different environmental factors on SAS are extensively documented. However, the relative importance of the factors that drive variation in SAS at watershed scale is not entirely clear. To investigate the effects of the interactions of environmental variables on spatial variation in SAS, 88 sampling sites were selected across an entire watershed (1.1 km²) on the Chinese Loess Plateau (CLP), from where undisturbed soil samples were collected at the 0–10 and 10–20 cm soil depths. Three indices were used to evaluate the SAS — water-stable aggregates greater than 0.25 mm (WSA_>0.25, %), mean weight diameter (MWD, mm) and mean geometric diameter (MGD, mm). The results showed that variation of SAS across the watershed was moderate, with coefficient of variation (CV) of 23.5–38.9 %. From combined Spearman’s correlation analysis (r), redundancy analysis (RDA) and structural equation modelling (SEM), it was found that soil intrinsic properties, mainly soil texture and organic carbon content (SOC), were the primary control on SAS variation. Topographic attributes, primarily wetness index (TWI) and altitude, were also important controls on SAS. These controls were either the direct or indirect effect through SOC dynamics, spatial distribution of land use (LUT) or vegetation cover (NDVI). The effect of LUT on SAS was mainly driven by SOC and TWI at the 0–10 cm depth but by NDVI and TWI at the 10–20 cm depth. SAS was positively correlated with sand content and SOC, but negatively correlated with silt content, altitude, TWI and NDVI. For LUT, SAS in the apple orchard was significantly lower than in shrubland and grassland, however, it was comparable with that in forest. Considering the effects of improving soil structure and the related economic cost, natural restoration of grassland was a good choice for preventing soil erosion in the study area. The results of this study could deepen our understanding of the controls on SAS variation and therefore become useful in soil management and vegetation restoration decisions on CLP and other regions with similar conditions.

Introduction

Soil aggregates are the basic units of soil structure formed naturally by soil particles (Li et al., 2020; Xu et al., 2020). The stability of soil aggregates can affect soil ability to support ecosystem functions, including food production, flood control, pollution prevention and climate change mitigation. This is achievable through the impact of soil aggregates on a wide range of soil processes such as compaction, aeration, germination and rooting, water flux, solute transport, storage and stabilization of organic carbon, nutrient adsorption, biological activity and crusting (Ayoubi et al., 2012; Besalatpour et al., 2014, 2013; Rivera and Bonilla, 2020; Wang et al., 2019; Wu et al., 2016; Yu et al., 2020). Soil aggregate stability (SAS) is also a key determinant of soil erodibility and is an input parameter in spatially-distributed soil erosion models (Ayoubi et al., 2018; Shi et al., 2020). However, SAS has a high degree of spatial heterogeneity at different scales (Annabi et al., 2017; Bird et al., 2007). A good understanding of the controls on spatial variation in SAS is critical for accurately predicting soil erosion across landscapes and development of effective management strategies for sustainable ecosystem functions (Annabi et al., 2017).

There are vast studies on the effects of environmental factors on SAS. Studies have shown that SAS is not only affected by soil intrinsic properties such as soil texture (Dimoyiannis, 2012; Lado et al., 2004; Zuo et al., 2019), water retention (He et al., 2018) and organic carbon content (Abiven et al., 2009; Six et al., 1998; Yu et al., 2020), but also by external factors such as topography (Le Bissonnais et al., 2002; Zádorová et al., 2011), vegetation (Ilay and Kavdir, 2018; Wang et al., 2019), land use and associated management practices (Ayoubi et al., 2012, 2020; Okolo et al., 2020; Wang et al., 2018; Zhao et al., 2017). For example, clay particles are efficient for aggregation due to their large specific surface area, high cation exchange capacity and therefore high physicochemical interaction activity (Amézketa, 1999). Soil organic matter (SOM), as the main binding agent in aggregate formation, can increase SAS by promoting the formation of organic-mineral assemblages and forming hydrophobic coatings around aggregates (Vogelmann et al., 2017; Yu et al., 2017). Vegetation can improve SAS through root network connectivity, root-soil adhesion and root biochemistry (Li et al., 2020). As determinants of flow velocity and surface runoff generation, topographic attributes such as slope and aspect are strongly correlated with SAS (Le Bissonnais et al., 2002). Management practices (e.g., tillage) associated with land use affect SAS through mechanical weakening and breaking up of aggregate structures (Wacha et al., 2018). Although strong correlations exist between SAS and these environmental factors, the relative importance of these factors in terms of driving the variation in SAS is not entirely clear. This is because most studies have focused on the effects of individual factors on SAS rather than their interactive effects.

In fact, some of these influencing factors are interlinked. In addition to direct effects, they also exert indirect effects on SAS. For example, vegetation can indirectly affect soil SAS through soil property dynamics such as the fixation and decomposition of SOM (Wang et al., 2019, 2016b; Zhao et al., 2017). Topography can affect a series of eco-hydrological processes involving the movement and fate of soil water and particles, spatial allocation of vegetation cover and land use; all of which in turn affect SAS (Le Bissonnais et al., 2002; Wang et al., 2013; Zádorová et al., 2011). Therefore, it is essential to consider the interaction effects of the factors in order to better understand the variation in SAS, based on exhaustive spatial surveys of SAS and comprehensive environmental characteristics. The need for such information is increasing in hydrological modeling at watershed scales as basic units on which management practices are built.

Chinese Loess Plateau (CLP) is one of the most severely eroded areas in the world as it is characterized by loose loess soil, steep topography, heavy summer rainstorm and inappropriate land management (Zhang et al., 2019). Since the 1950s, a variety of measures have been implemented by the Chinese government to control soil erosion and restore vegetation on the plateau (Xin et al., 2016). The “Grain for Green” (GFG) project launched in 1999 is one of the most important programs in recent time that converts steep-slope croplands and wastelands into grasslands, shrublands and forests (Deng et al., 2019). The highly complex landscape is suitable for systematic investigation of the effects of environmental factors on SAS. Studies have been conducted on spatial variation in SAS in relation to land use (Wang et al., 2018; Wei et al., 2012), vegetation cover (Wang et al., 2019; Zhu et al., 2017) and vegetation succession (Cheng et al., 2015; Yao et al., 2019) in the region. However, little work has been done on spatial variation in SAS and the driving factors (direct or indirect) at watershed scale. This is because of the high cost and time-consuming efforts associated with studying SAS. The specific objectives of this study were to: i) investigate the level and spatial variation in SAS across an entire watershed; ii) assess the importance of major factors relative to spatial variation in SAS; and iii) elucidate the structural cause–effect relationships of SAS and the major factors.

Section snippets

Study area

This study was conducted in ShuangChaGou watershed (1.1 km²), located in Luochuan County, Shaanxi province, China (Fig. 1a). The study area has a warm temperate and semi-humid continental monsoon climate with mean annual precipitation of 622 mm. The precipitation is concentrated over summer (July to September) and mostly occurs in the form of storm. The mean annual temperature is 9.2 °C, with accumulated temperature above 10 °C of 3040 °C. The mean annual sunshine hours is 2552 h, with mean

SAS indices statistics and distribution

The descriptive statistical parameters for the soil aggregate fractions and SAS indices are given in Table 1. The aggregates were dominated by small macroaggregates for both soil depths, with averages of 41.2 and 38.1 % at the 0–10 cm and 10–20 cm depths, respectively. Microaggregates (29.1 and 30.4 %) and <0.053 mm fractions (24.2 and 26.7 %) accounted for the second and third highest proportions in the bulk soil, while large macroaggregates (5.6 and 4.8 %) was the lowest proportion. For the

SAS in the watershed

The watershed is characterized by low SAS. The soil MWD ranges were 0.33–1.87 and 0.27–1.75 mm for the 0–10 and 10–20 cm soil depths, respectively (Table 1). This is consistent with the results of other studies (0.22–2.84 mm) on CLP (Wang et al., 2019, 2018; Wei et al., 2012; Zhu et al., 2017). According to the classification criteria developed by Le Bissonnais (1996) — MWD < 0.4 mm is classed as very unstable, 0.4–0.8 mm as unstable, 0.8–1.3 mm as medium, 1.3–2.0 mm as stable and > 2.0 mm as

Conclusions

At the watershed scale, SAS was affected by various factors, including soil, topography, vegetation and land use. This had a potential effect on the formation of soil aggregates and the processes of breakdown. Soil intrinsic properties, primarily soil texture and SOC, were more important in controlling the variation in SAS. Topographic attributes such as TWI and altitude, can also affect SAS either by direct impacts or by indirect impacts through influencing soil property dynamics such as SOC

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB40020203), the National Natural Science Foundation of China (No. 41971045), and the Youth Innovation Promotion Association CAS. We sincerely thank the anonymous reviewers for their valuable assistance in improving the manuscript.

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Citation Excerpt :
Hills and mountains are the dominant landforms in the southern part of our study area and account for 80% of Fujian Province (Zhu et al., 2021a), so the main land use was forest in the south of the study area (Fig. 1d). Stable macro-aggregates tend to form when more SOM serves as a binding agent and when anthropogenic activities in forests are absent (Fig. 3 and S1) (Zhang et al., 2021). The central part of our study area is also covered by hills and mountains (>70%), and farmland, including dry land and irrigated paddy fields, is sporadically distributed on plains.
Soil water-stable aggregates (WSA) are a key indicator of soil stability and are critical for maintaining soil quality and controlling erosion. WSA have spatial heterogeneity and their spatial variability is influenced by different environmental factors at different scales. Few studies, however, have focused on the scale-specific controls of the WSA content on a mountain-plain landform. The objective of this study was to investigate the spatial distribution and scale-specific controls of WSA content using methods of Gaussian geostatistical simulation (GGS) and multivariate empirical mode decomposition (MEMD). A total of 101 soil samples were collected along a sinuous 4200-km transect at a mean interval of 42 km in southeastern China. The WSA content and nine environmental factors, i.e., soil bulk density, clay, silt, sand and organic-matter (SOM) contents, pH, elevation, slope and vegetation coverage (VC) were measured at each sampling site. WSA content was strongly spatially dependent, with a nugget/sill ratio of 7.63%. E-type maps from the GGS with different realizations (10, 25, 50, 100, 200 and 500) had similar spatial patterns, with high WSA content mostly in the south and west of the study area and low WSA content concentrated in the north. The spatial probability map indicated that the locations around Taihu Lake in the north of the study area had a high probability of poor aggregate stability. A multi-scale analysis of influencing factors indicated that intrinsic mode function 1 (IMF1) (23.76%) and IMF2 (23.64%) explained most of the spatial variability of WSA content. The correlations between WSA content and the environmental factors varied with the scale. The dominant controls were soil particle composition at small scale, pH and SOM content at moderate scale and elevation and VC at large scale. The prediction of WSA content at the measurement scale based on the scale-specific controls analyzed with MEMD (R²_adj = 0.577) outperformed the prediction of traditional multiple regression (R²_adj = 0.356). These results will support accurate practices and management for preventing soil erosion and land degradation.

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Factors controlling spatial variation in soil aggregate stability in a semi-humid watershed

Highlights

Abstract

Introduction

Section snippets

Study area

SAS indices statistics and distribution

SAS in the watershed

Conclusions

Declaration of Competing Interest

Acknowledgements

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