Next Article in Journal
Rice Regeneration in a Genebank: 21 Years of Data
Previous Article in Journal
Histological Variations in Cucumber Grafted Plants and Their Effect on Yield
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 14
Issue 7
10.3390/agronomy14071378
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Soil Biopores and Non-Biopores Responses to Different Tillage Treatments in Sugarcane Fields in Guangxi, China

by
Song Wang
1,2,
Lei Gan
1,2,*,
Shuo Zhang
1,3,
Jian Li
1,3,
Cheng Chang
1,3,
Yu Zhang
1,3,
Hongxia Zhang
1,3,* and
Benhui Wei
4
1
Collaborative Innovation Center for Water Pollution Controland Water Safety in Karst Area, Guilin University of Technology, Guilin 541006, China
2
Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control, Guilin University of Technology, Guilin 541006, China
3
College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541006, China
4
Institute of Economic Crops, Guangxi Academy of Agricultural Sciences, Nanning 530007, China
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(7), 1378; https://doi.org/10.3390/agronomy14071378
Submission received: 17 May 2024 / Revised: 25 June 2024 / Accepted: 25 June 2024 / Published: 26 June 2024
(This article belongs to the Section Innovative Cropping Systems)
Browse Figures
Versions Notes

Abstract

:
Different types of soil macropores respond differently to various tillage practices, owing to disparities in origin, scale, morphology, and function, consequently exerting distinct effects on soil structure. This study aimed to investigate the response mechanisms of three different soil pore types (total macropores, non-biopores, and biopores) to two distinct tillage practices: smash-ridging tillage (T) and no-tillage (NT) in sugarcane fields. The parameters characterizing soil pore treatments in two and three dimensions were obtained using X-ray computed tomography scanning technology. ImageJ and MATLAB software were employed to analyze the data and separate soil macropores into biopores and non-biopores categories. The results showed that non-biopores predominated in two-dimensional cross-sectional areas in NT treatment, whereas biopores were more dominant in T treatment. Biopores in T treatment had a higher proportion of two-dimensional pores compared to NT treatment. A three-dimensional analysis indicated that total macropores had larger mean diameters (MD) and macroporosity, with more continuous tubular pores in T treatment than that in NT treatment. However, NT treatment had more numerous non-biopores with broader spatial distribution and complex morphology. Additionally, biopores in T treatment had larger MD and branching length density (LD). These vertically developed biopores, along with high macropore connectivity and under smash-ridging tillage, could improve soil water and pore conductivity. Therefore, smash-ridging tillage was more beneficial for sugarcane growth compared to no-tillage in Guangxi of China.

1. Introduction

Soil pore space is an indispensable component of ecosystems, facilitating the transportation of water, nutrients, air, and heat within the soil, while also providing crucial room for plant root expansion and animal activities [1,2,3]. The characteristics of soil macropores, such as their size, distribution, connectivity, and curvature, profoundly influence the movement of soil water [4,5,6]. Indeed, soil harbors various types of macropores, each distinct in origin, scale, shape, and function [7]. Non-biopores predominantly result from non-biological activities like tillage, freeze-thaw cycles, wetting, and drying, manifesting as filler voids or irregular sheet fissures that occupy the majority of soil pore space [8,9]. Biopores, on the other hand, stem from biological processes such as earthworm burrowing or plant root penetration into the soil [10]. Typically cylindrical and consistently contiguous, biopores extend along the soil profile [11,12]. Plant root pores, notably prevalent in the tilled soils of medium to high-yield agricultural fields, constitute a significant portion of soil biopores [13]. These pores serve as preferential pathways for water and solutes and as sites for microbial decomposition and transformation of essential plant nutrients such as carbon, nitrogen, and phosphorus [14,15,16,17]. The presence of biopores thus plays a pivotal role in facilitating plant growth.
As the primary sugarcane-producing region in China, over 90% of sugarcane in Guangxi is cultivated on dry slope land. However, under traditional cultivation methods, sugarcane field’s drought resistance and water retention capacity are not sufficiently high [18]. Additionally, the scarcity of water resources on dry slope land limits the implementation of drip irrigation and other modern irrigation techniques. Addressing the issue of soil moisture crucial for sugarcane growth is thus an urgent priority [19]. Soil structure reacts diversely to various farming practices, prompting extensive research by scholars both domestically and internationally on this matter. Thotakuri et al. [20] demonstrated that the combination of long-term no-tillage and crop rotation can enhance soil organic carbon, physical properties, and pore characteristics compared to conventionally tilled soils with a monosystem. Qian et al. [21] found that conventional tillage had a significant impact on soil pore characteristics, with improvements observed in pore densities, specific surface areas, fractal dimensions, and global connectivity. Budhathoki et al. [22] showed that conventional tillage practices resulted in significantly higher soil pore surface area density, length density, interconnectivity, and network density compared to no-tillage practices. Singh et al. [23] revealed that minimum tillage and no-tillage led to a significant increase in soil-saturated hydraulic conductivity compared to conventional intensive tillage, attributed to the greater presence of biopores and an increased number of macropores in conservation tillage practices. However, the impact of soil tillage on soil structure varies widely depending on geological, hydrological, climatic, crop types, and other conditions, making it difficult to generalize research findings. Nonetheless, the initial impact can be explained by the pore conditions within the soil.
In the past, soil pores were mainly analyzed using two-dimensional images such as tomographic soil profiles and thin sections [24,25,26]. However, two-dimensional images cannot depict the entire three-dimensional object at that scale. The noninvasive detecting methods applicable to soil embrace, such as time domain reflectometry (TDR) [27], electrical resistivity tomography (ERT), and ground penetration radar (GPR) [28]. In recent years, computed tomography (CT) has become increasingly popular in soil research due to its noninvasive nature and convenience. CT can identify and quantify the characteristics of three-dimensional pores in soil, including the microstructure of soil aggregates, and determine porosity, length, size, and other topological and geometric characteristics of soil macropores [29,30,31,32,33,34]. Wang et al. [35] found that with the progression of the mine reclamation age, there is a positive correlation between the development of macropores and soil water infiltration in the reclaimed soil. Hu et al. [36] found that closed shrubs exhibited increased soil macroporosity, macroporous network density, node density, length density, total volume, and total area compared to free grazing in thickets. Wen et al. [37] found that wetting during dry and wet cycling helped restore lost pores, increasing pore space and porosity, the presence of connected macropores played a crucial role in determining the saturated hydraulic conductivity of granite residual soils. In general, soil total macropores can be clarified as biopores and non-biopores. Using image recognition, it is now possible to distinguish between the two types based on their different morphological features [38]. Some researchers have obtained three-dimensional earthworm channels in soil columns that have been remolded by removing macropore voxels to a certain threshold [39]. However, this method has not been widely applied to macropores that are consistent in volume but not in shape [40]. The correlation between different soil pore types and farming measures has not yet been demonstrated for non-subtropical regions or non-karst background areas [41,42].
In this study, CT scanning and image separation methods were used to obtain three types of soil pores, including macropores and their biopores and non-biopores, in sugarcane fields in southern Guangxi, China, under smash-ridging tillage and no-tillage treatments. The objectives of this research are twofold: (1) to explore the diverse characteristics of soil macropores specific to sugarcane cultivation in this locale and (2) to elucidate the distinct response mechanisms exhibited by these three pore types to the contrasting practices of smash-ridging tillage and no-tillage, utilizing a three-dimensional visualization model. This study is of high scientific importance, as it improves the understanding of soil pore types and functions. Additionally, it serves as a valuable practical reference for optimizing sugarcane agricultural cropping patterns in Guangxi, China.

2. Materials and Methods

2.1. Experiment Area

The experimental field is situated in the Lijian Scientific Research Base, which is part of the Guangxi Academy of Agricultural Sciences, in Nanning City, China (N 23°14′, E 108°02′). The study area is primarily hilly, with slightly acidic soil pH levels ranging from 5.0 to 6.5. It is located in a subtropical monsoon climate zone, with annual average climate indicators including 21.7 °C air temperature, 1100–1700 mm of rainfall, 79% relative humidity, and 1800 h of total annual sunshine. Sugarcane, cassava, and peanuts are the main crops in this research area, with sugarcane being a tropical-subtropical solid herb with a fibrous root system. Sugarcane was planted in the study area and cultivated using traditional farming methods before 2017. The experiment began in May 2017 and sampling started after three years. The experimental area was divided into two treatments: no-tillage (NT) and smash-ridging (T). Under each treatment, three plots were randomly selected, for a total of six plots, each with an area of 50 square meters. The sugarcane variety used was Guiliu05-136 (this variety of sugarcane is characterized by tall and compact plants with medium to large stems, as well as upright, uniform cane stems. The cane buds have medium, rounded bodies). The irrigation and fertilization conditions of each plot were kept consistent. The experimental plots were managed using a perennial cultivation approach for sugarcane. In the no-tillage area, the soil remained untilled for 5 years without any form of tillage intervention. The tillage area was tilled using a special rotary machine to create a mound of soil into a ridge at a depth of 30 cm once every three years. The sugarcane field smash-ridging tillage mode employed in this study differs from both no-tillage and traditional tillage methods.

2.2. Sample Collection and Determination of Soil Physical and Chemical Properties

After removing surface debris and considering the sugarcane growth and ridge formation, sampling points were randomly selected within each plot. Disturbed bulk soil samples (weighing approximately 1.5 kg) and soil ringknife samples (ringknife volume of 100 cm3) were collected from 0–40 cm of soil. In each 10cm layer, five replicated soil samples were collected to measure the basic physical and chemical properties as well as the hydraulic properties of the soil. Including drying the samples at 105 °C in an oven to determine the soil bulk density [43], examining the soil texture composition using a laser particle size analyzer [44], using the potassium bichromate-dilution heat colorimetric method to measure the soil organic matter content [45], and using the double-ring method to determine the saturated hydraulic conductivity of the soil [46]. The specific process is shown in Figure 1.
High-strength compression-resistant PVC pipes (cylinder) with an inner diameter of 10 cm, a thickness of 0.5 cm, and a length of 50 cm (with a knife edge at the soil sampling end) were used for in situ soil column collection via percussive sampling. To exclude water evaporation and the impact of transport turbulence on the soil column structure, the soil column was sealed and shockproofed with a foaming agent, fresh-keeping film, and foam board before being transferred to a laboratory for structural CT imaging. Six soil columns were collected, with three replicates for each of the two treatments.

2.3. CT Scanning and Separation of Soil Pores

The CT scanning equipment used for this study was a GE DiscoveryCTHD750 machine, manufactured by GE in Boston, MA, USA. The parameter settings for the scanning process were as follows: scanning tube voltage of 120 kV, current of 300 mA, and a scanning voxel size of 0.4688 × 0.4688 × 0.625 (mm). The CT scanning process produced 640 images in DICOM (Digital Imaging and Communications in Medicine) format. These images had an image matrix size of 512 × 512 (pixel) and were imported into ImageJ 1.53c software. In ImageJ 1.53c software, various image processing techniques were applied to enhance the quality of the images. Initially, the brightness and contrast of the images were improved by stretching and equalizing the grayscale histogram. Additionally, a 3D Gaussian blur filter was employed to minimize noise in the grayscale images. To focus on the core area of the soil column and exclude the impact of soil column boundaries, a region measuring 60 × 60 × 400 (mm) was selected as the target of the analysis. Subsequently, an image-adjust plugin in ImageJ 1.53c was utilized to convert the grayscale image into a binary image, containing only pore space and soil. Manual thresholding was performed to determine the segmentation threshold between the pore and background [47]. This threshold was adjusted by comparing the binarized pore image with the original pore image to maximize the accuracy and conformity of the segmentation. To refine the binary image further, an expansion and erosion process was applied to remove pores with a volume smaller than 0.001 mm3. This step aimed to eliminate noise and fine connections between pores, resulting in a final binarized pore image with a diameter larger than 400 μm (matching the CT scan resolution). This processed image served as the basis for the subsequent separation of biopores from non-biopores.
To separate biopores from soil pores, two key assumptions must be met [38]: (1) biopores and non-biopores are distinct from each other; (2) biopores are cylindrical and continuous along their length with a specific volume, distinguishing them from the morphological features of non-biopores. The binarized soil pore images were processed using MATLAB2019a software for the specific purpose of differentiating biopores from abiotic pores, as illustrated in Figure 2. Initially, pores smaller than 500 voxels (equivalent to 0.06 mm3) were eliminated from consideration (Figure 2A). Subsequently, pores exhibiting a pore length (L) to pore equivalent radius (r) ratio of less than 20 within the total macropores were filtered out (as depicted in Figure 2B). By manually removing these macropores (as shown in Figure 2E), the remaining pores were identified as biopores (illustrated in Figure 2C). Those retaining a ratio (L/r) of less than 20 were categorized as abiotic pores (depicted in Figure 2F). Through this process, the images representing total macropores, biopores, and non-biopores were effectively segregated, providing a foundation for subsequent feature quantification and 3D reconstruction of soil pores. Additionally, the ratio of pore length (L) to pore equivalent radius (r) was defined, with the pore length roughly corresponding to the diagonal length of the pore’s bounding box [38]. The equivalent radius of the pore is determined by:
r = V π L 1 2
where r represents the pore equivalent radius, V is the volume of the pore, and L represents pore length.

2.4. Quantification of Soil Pore Characteristics

The total macropores, biopores, and non-biopores images obtained were reimported into ImageJ 1.53c software to achieve feature quantification and three-dimensional reconstruction of different pore types. The percentage and number of cross-sectional areas of soil pores were calculated based on two-dimensional image statistics. The percentage of the soil pore cross-sectional area is the percentage of pore area to the total area, which means that the higher the value is, the greater the percentage of pores is, and the looser the soil is. Similarly, the number of soil pore cross-sectional areas is the quantity of cross-sectional areas of disconnected pores, which could be used to quantify the degree of dispersion of pores on a two-dimensional scale. The pore mean diameter directly indicates the scale characteristics of soil pores and has important implications for water and gas movement. Larger pores usually promote water infiltration and gas exchange, while smaller pores may lead to higher capillarity. The Euler number is a topological parameter used to characterize the connectivity and complexity of pores, which assists in identifying the degree of connectivity and morphological complexity of the pore network. Compactness reflects the number of pores and the degree of compactness of the soil. Branch length describes the length and bifurcation of paths in the pore system. This is important for understanding the migration pathways of water and nutrients through the soil. The selection of these parameters is primarily based on their effectiveness and comprehensiveness in characterizing soil pore structure and function.
Macroporosity is the volume fraction of macropores in the ROI (Region of Interest), obtained via Imagj-BoneJ [48]. Macroporosity was calculated using the following equation:
Macroporosity = ∑ Volume   of   macropores Volume   of   ROI
The mean diameter (MD) of macropores was determined using the local thickness algorithm in the Bone-J plugin of ImageJ. This algorithm defines thickness as the diameter of the largest sphere at a specific point within the three-dimensional pore body [49]. MD was calculated using the following equation:
M D = ∑ i = 0 n D i V i ∑ i = 0 n V i
where Di and Vi are the diameter (mm) and volume (mm3) of each macropore, respectively, and n represents the number of macropores.
The Euler number (χ) represents the connectivity of the local macropore system [50]. The calculation formula can be expressed as:
χ = N − L + O
where N corresponds to the number of isolated macropores, L to the length of macropore branches, and O to the number of cavities.
Compactness (CP) describes the shape of the macropore body. Higher compactness indicates a greater deviation from a regular body [42]. The mean compactness is the volumetric-weighted average of the compactness of each pore calculated via the following equation:
C P = A 1.5 V
where A and V represent the surface area (mm2) and volume (mm3) of the macropore body, respectively.
The length density (LD) of macropore branches is the ratio of the pore branch length (mm) to the space volume (mm3) in a certain space volume [48]. The calculation formula for LD is as follows:
L D = ∑ L i V
where Li represents the macropore length (mm), and V represents the macropore volume (mm3).

2.5. Statistical Analysis

Significant differences were tested and statistically analyzed using a one-way ANOVA test and independent t-test of IBM SPSS Statistics 23, with a significance of p < 0.05, and images were created using Origin2021, Avizo2020, and ImageJ 1.53c. Pearson correlation analysis is commonly used to examine the linear relationship between two sets of variables [51]. In this study, we investigated the linear associations of 12 soil parameters using Pearson correlation analysis.

3. Results

3.1. Soil Physical and Chemical Properties

After analyzing Table 1, it is evident that the soil texture composition of the two treatments is similar, with both being sandy loam. As a whole, the soil organic matter of the T treatment increased by 69.73% compared to that of the NT treatment, and this trend was observed in each layer, with a significant difference found in the 20–40 cm layer (p < 0.05). Additionally, the mean value of saturated hydraulic conductivity in the T treatment increased by 80.97% compared to that of the NT treatment (p < 0.05). Furthermore, the mean value of soil organic matter content in the T treatment was 69.71% higher than in the NT treatment (p < 0.05).

3.2. Two-Dimensional Cross-Sectional Area of Soil Pore Space

In the two-dimensional interface depicted in Figure 3, the total macropores cross-section integral number (represented by the outer envelope) of the NT treatment exhibited localized increases compared to other soil layers in both the near-surface and subsoil layers. Conversely, the T treatment total macropores cross-sectional integral number demonstrated a consistent pattern of decreasing peaks with increasing depth. In the 0–20 cm soil layer, the cross-sectional integral numbers of both total macropores and biopores were significantly larger in the T treatment compared to the NT treatment, while the cross-sectional area of non-biopores decreased. Within the 20–40 cm soil layer, the cross-sectional integral numbers of total macropores and non-biopores decreased in the T treatment, while the number of cross-sectional integrals of biopores proportionally increased. The NT treatment soils were primarily characterized by a larger cross-sectional area of non-biopores, while the T treatment soils exhibited a greater cross-sectional area of biopores.

3.3. Two-Dimensional Transverse Pore Number of Soil Pores

In the NT treatment depicted in Figure 4, the proportion of cross-sectional non-biopores was notably high, whereas the percentage of cross-sectional biopores remained quite low (2.12%). Conversely, cross-sectional biopores accounted for 18.39% of the total macropores throughout the soil layer in the T treatment. Concerning total macropores and non-biopores, the number of cross-sectional pores was higher in the NT treatment compared to the T treatment (p < 0.05). Regarding biopores specifically, the number of cross-sectional pores in the T treatment increased by 546.24% compared to the NT treatment in the 0–10 cm soil layer (p < 0.05). This trend persisted across the subsequent layers, with the T treatment consistently exhibiting a greater number of cross-sectional pores than the NT treatment.

3.4. Three-Dimensional Mean Diameter of Soil Pores

In Figure 5, the mean diameters of soil pores are depicted. Relative to the NT treatment, the T treatment exhibited higher mean diameters across all three pore types. Specifically, for total macropores, the mean value of mean diameter in the T treatment increased by 59.12% compared to that of the NT treatment (p < 0.05), across all layers consistent throughout. Concerning non-biopores, the T treatment means that the 0–20 cm soil layer diameter surpassed that of the NT treatment. Regarding biopores, in the T treatment, the mean value of mean diameter in the 0–10 cm soil layer was 152.52% higher than that of the NT treatment (p < 0.05). This trend persisted across the subsequent layers, with the T treatment consistently displaying larger mean diameters compared to the NT treatment.

3.5. Three-Dimensional Macropores and Macroporosity of Soil

Upon analyzing Figure 6, concerning total macropores, the mean value of macroporosity in the NT treatment decreased by 12.47% compared to that of the T treatment (p < 0.05). However, the mean value of macropores in the NT treatment increased by 115.96% compared to that of the T treatment (p < 0.05). Regarding non-biopores, in line with the total macropores trend, the NT treatment exhibited lower macroporosity than the T treatment, while the NT treatment macropores surpassed that of the T treatment. Concerning biopores, the mean value of macroporosity of the T treatment decreased by 41.60% compared to that of the NT treatment. However, the mean value of macropores in the T treatment increased by 400.00% (p < 0.05) compared to the NT treatment.

3.6. Three-Dimensional Parameters of Soil Porosity

Table 2 illustrates the three-dimensional pore parameters of the soil total macropores. The mean values of the Euler number, compactness, and branch length density in the T treatment decreased by 59.78%, 27.26%, and 35.18% compared to the NT treatment. This consistent trend was observed across all layers.
Table 3 presents the three-dimensional pore parameters of biopore and non-biopore soils. Concerning non-biopores, the mean values the of Euler number, compactness, and branch length density in the NT treatment increased by 114.49%, 54.00%, and 103.21% compared to the T treatment. This consistent trend was observed across all layers. Regarding biopores, the mean values of the Euler number, compactness, and branch length density in the T treatment increased by 216.67%, 6.52%, and 312.5% compared to the NT treatment.

3.7. Three–Dimensional Reconstruction of Soil Pores

Figure 7 presents a three-dimensional reconstruction of the total macropores, revealing that the total macropore distribution range gradually decreased with increasing soil depth for both treatments (T and NT). Moreover, the figure shows that the total macropores distribution density of the 0-20 cm soil layer for the two treatments was higher than that of the 20–40 cm soil layer. Although the NT treatment had a wider distribution range, a higher distribution density, and a larger number of pores, most of these pores were isolated or less connected. On the other hand, the T treatment exhibited a smaller distribution range of pores, lower distribution density, and quantity than the NT treatment, but the pores had a more continuous tubular shape, especially in the 10–40 cm soil layer.
The three-dimensional reconstructions of biopores and non-biopores (Figure 8 and Figure 9) clearly illustrate the difference in pore types between the two treatments. In both treatments, the non-biopores (Figure 8) were mostly isolated. The number and volume of non-biopores in the 0–20 cm soil layer were much larger than those in the 20–40 cm soil layer, and the shape was more complex. The distribution range, density, and quantity of non-biopores in both treatments gradually decreased with increasing soil depth, although the decrease in the T treatment was more evident. The distribution and number of non-biopores in the T treatment showed a decreasing trend compared to each soil layer in the NT treatment, which had a more complex morphology and a broader geographical and spatial distribution of non-biopores.
The distribution range and number of biopores in three-dimensional space in the NT treatment were significantly smaller than those in the T treatment (Figure 9). Moreover, the volume and diameter of a single biopore in the NT treatment were much smaller than those in the T treatment, especially in the 0–10 cm soil layer. Biopores in the NT treatment had a monomorphic shape, fewer branches, shorter lengths, more horizon development, isolated distribution, short spans, and poor connectivity, such as those in the 10–40 cm soil layer of NT-1 and NT-3 (in the surface and bottom layers). In contrast, the biopores in the T treatment had more branches, longer lengths, diversified features, and more vertical development, especially in the middle soil layer of T-2 and T-3. Additionally, the longitudinal penetration length of biopores in the T treatment was approximately 80% of the soil depth, with a large spanning zone, network-like shape, and better connectivity.

3.8. Correlation Analysis of Soil Pore Space Parameters

Figure 10a displays the Pearson correlation between the 12 parameters of soil total macropores. Notably, sand exhibited a significant negative correlation with silt and a significant positive correlation with compactness (p < 0.05). Silt displayed a significant negative correlation with compactness and branch length density, and a significant positive correlation with bulk density and compactness (p < 0.05). Mean diameter and macroporosity demonstrated a highly significant positive correlation (p < 0.001). Macroporosity and branch length density were significantly positively correlated (p < 0.05). Macropores were highly significantly positively correlated with Euler number (p < 0.001) and significantly positively correlated with branch length (p < 0.05). Density and branch length density were more significantly positively correlated (p < 0.01).
As can be seen in Figure 10b, sand exhibited significant positive correlations with both macroporosity and compactness (p < 0.05). Conversely, silt exhibited significant negative correlations with macroporosity, compactness, and branch length density (p < 0.05). Bulk density showed a significant positive correlation (p < 0.05) with macroporosity, compactness, and branch length density. Moreover, macroporosity exhibited a highly significant positive correlation with both compactness and branch length density. Macropores displayed a highly significant positive correlation with Euler number (p < 0.001) and a significant positive correlation with branch length density (p < 0.05). Euler number was significantly and positively correlated with branch length density (p < 0.05), while compactness showed a more significant correlation with branch length density (p < 0.01).
An analysis of Figure 10c reveals that sand exhibited a significant and positive correlation with mean diameter (p < 0.05). Silt demonstrated a significant negative correlation with bulk density and a significant positive correlation with Euler number (p < 0.05). Macroporosity displayed a highly significant positive correlation with both macropores and branch length density (p < 0.001). Additionally, macropores exhibited a highly significant positive correlation with branch length density (p < 0.001).

4. Discussion

Smash-ridging tillage causes soil particles to aggregate and form larger aggregate structures [52], thus reducing the number of macropores in the study. Additionally, the top layer of soil may become loose during smash-ridging tillage, which increases the larger voids and channels in the soil [53]. Consequently, the number of soil cross-sectional macropores decreased and the fraction of cross-sectional area increased. Kaur et al. [48] demonstrated that tillage practices exert a significant influence on soil pore structure, with strip tillage exhibiting notably higher values across various pore characteristics such as macroporosity, network density, length density, and interconnectivity. The mean diameter of total macropores was found to be greater in the T treatment compared to the NT treatment. This is possibly attributed to the improved soil permeability resulting from smash-ridging tillage, facilitating enhanced water and gas penetration and subsequently enlarging pore diameters [54]. This process may also reduce soil density, contributing to an increase in pore mean diameter [20]. The NT treatment demonstrated smaller macroporosity relative to the T treatment. Wang et al. [55] revealed that reduced macroporosity leads to heightened infiltration resistance and soil consolidation, which hinder crop root growth and development. This may adversely affect sugarcane’s ability to absorb water and nutrients from various soil layers. As evidenced by studies indicating lower sugarcane root populations under multi-year no-tillage compared to other tillage practices [56]. However, contrary to the findings of this study, Yang et al. [57] observed that long-term no-tillage increased pore numbers and porosity of dryland pores larger than 80 μm while reducing soil bulk density within a 55 cm depth. The specific alterations within the 80–400 μm pore interval remain unclear, yet differing years of no-tillage, local environmental conditions, crop types, and management practices can yield significant disparities [58]. This is because the effects of smash-ridging tillage extend beyond initial disturbance and reorganization processes to influence subsequent pore development, including pore distribution, morphology, and structure. In the T treatment, the 10–40 cm soil layer exhibited more continuous tubular pores (Figure 7), promoting the development of elongated and straight pore structures, thereby enhancing water transportation efficiency [59]. Moreover, these large and interconnected pores facilitate improved soil water conductivity and air permeability [60]. Consequently, considering total macropores, smash-ridging tillage was deemed more effective in enhancing soil water and air conductivity compared to no-tillage in this investigation.
The response of the pore space in the surface layer of soils to root growth, animal activity, and other biological activities was most pronounced [61]. Therefore, the increase in the volume of individual biopores in such space occurred faster than in the lower soil layers in this study. Scarpar et al. [62] observed that the loosened conditions of the surface soil layer facilitated a dispersed growth pattern of sugarcane roots, consequently enhancing the crop’s nutrient and water uptake, thereby boosting crop yield. Enhanced soil aeration due to smash-ridging tillage promotes microbial respiration and metabolism while fostering a favorable environment for microbial proliferation [63]. Furthermore, the gradual decomposition of mulched straw and other plant residues on the soil surface by microorganisms increases the soil organic matter content [64]. Organic matter not only adsorbs water but also supports microbial growth and reproduction, thus fostering the formation and expansion of biopores [65]. Consequently, the T treatment exhibited higher average diameter, macropores, and macroporosity of biopores compared to the NT treatment. Zhang et al. [42] investigated the association between different types of soil pores and saturated hydraulic conductivity and showed that the mean diameter and compactness of biopores were increased.
Combined with the analysis of 3D pore data, it is evident that soil biopores and non-biopores exhibit distinct differences in morphology, structure, and spatial distribution, and their responses to the T and NT treatments diverge. Previous research indicates that when soil particles are displaced or fragmented, the proportion of pore branches and irregularity of the pore space gradually increases, thereby altering the morphology of the pore space [62,66]. However, in this study, the branch length density and compactness of the total pore space decreased under the T treatment. At the initial stage of smash-ridging tillage, almost all pores within the soil can be categorized as non-biopores based on their morphology. In this study, the non-biopores of the T treatment experienced a significant reduction in terms of number, volume, branch length density, and compactness, while the biopores exhibited a significant increase in number and volume. This finding demonstrates the conversion of a considerable portion of initially small and dispersed non-biopores into larger and well-connected biopores through the direct and indirect transformation of channels formed by soil biological activities. Biopores play a crucial role in influencing soil function due to their morphological characteristics, such as higher connectivity and a greater tendency to develop vertically compared to non-biopores (Figure 8 and Figure 9). Previous studies have also established an intrinsic link between biopores and connectivity indexes, as well as a close relationship between connectivity indexes and hydraulic conductivity [11,29]. Although the connectivity indexes of biopores in the two treatments did not differ significantly (Figure 9), the biopores under the T treatment displayed a predominant vertical development trend. This observation is supported by the saturated hydraulic conductivity of the soil, which was higher for the T treatment than the NT treatment. Research has demonstrated that the development of biopores, as the primary pathway for water and air circulation in the soil, directly affects hydraulic conductivity and air permeability, thereby indirectly influencing the length and density of crop roots through water availability [8,34]. This finding aligns with the greater length density of biopore branches observed under the T treatment in this study. Consequently, the continuous presence of biopores under the T treatment can significantly enhance subsoil nutrient uptake and utilization by crops [67]. Conversely, the well-developed biopores in the near-subsoil layer facilitate biological activities and the transport of nutrients from the soil surface to the near-subsoil layer, creating conditions characterized by low infiltration resistance and favorable aeration [2,67]. This process also provides accessible pathways and space for crop roots to penetrate and grow downward in the denser near-subsoil layer, allowing them to absorb beneficial substances from the deeper layers [15,68]. Therefore, considering the perspective of biopores and non-biopores, smash-ridging tillage proves to be more beneficial for sugarcane growth compared to no-tillage.
In summary, this non-intensive smash-ridging tillage mode is conducted once every three years. By initially disturbing and restructuring the soil, this approach forms a solid foundation for the first round of sugarcane growth by establishing an improved soil structure. Consequently, the subsequent development of sugarcane roots and the formation of root-type pores through decomposition create favorable conditions for multiple rounds of growth and development of sugarcane. Therefore, in the context of sugarcane cultivation in Guangxi, adopting the once-every-three-years smash-ridging tillage method proves to be more beneficial for soil water permeability and air permeability compared to no-tillage. As a result, it promotes the multi-round growth and development of sugarcane.

5. Conclusions

Previous studies have lacked a comprehensive 2D and 3D characterization of differences in soil pore properties under smash-ridging tillage and no-tillage practices. In this study, X-ray CT scanning technology and image separation methods were employed to distinguish between biopores and non-biopores in the soil macropores of sugarcane fields under smash-ridging tillage and no-tillage practices. The comprehensive two- and three-dimensional characterization of soil pores revealed significant variations in pore properties induced by different tillage practices.
Specifically, in the total macropores, smash-ridging tillage exhibited a substantial increase in various pore parameters (e.g., two-dimensional cross-sectional area, number of cross-sectional pores, mean diameter (MD), and macroporosity) compared to no-tillage. In terms of non-biopores, flour plowing led to a significant increase in pore parameters such as MD and macroporosity. Within the biopores smash-ridging tillage significantly enhanced all pore parameters. The 3D reconstruction maps unveiled that no-tillage resulted in an augmented presence of non-biopores with a wider spatial distribution and more intricate pore morphology. In contrast, smash-ridging tillage fostered the development of more interconnected and continuous tubular biopores. Moreover, this trend was primarily observed in the vertical direction, favorably influencing soil moisture and air conductivity. Consequently, smash-ridging tillage proved to be more conducive to creating a favorable foundation for sugarcane growth compared to no-tillage.
Furthermore, our findings suggest that biopores exhibit greater continuity than non-biopores, implying their potential effectiveness in predicting seepage simulation, which warrants further validation in future studies. As an enhanced understanding of biopores, the substantial implications of this research could significantly advance soil management practices. This provides theoretical knowledge of planting patterns in sugarcane fields in Guangxi, China.

Author Contributions

Writing—review and editing, conceptualization, S.W.; project administration, data analysis, and review, L.G. and H.Z.; methodology and investigation, S.Z., J.L., C.C. and Y.Z.; Writing review and supervision, B.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Science and Technology Planning Project of Guangxi, China (grant number AA20302020-2), the National Natural Science Foundation of China (grant numbers 42067002 and 42267040).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We are grateful to Guangxi Engineering Research Center for Comprehensive Treatment of Agricultural Non-point Source Pollution and Modern Industry College of Ecology and Environmental Protection, Guilin University of Technology.

Conflicts of Interest

The authors declare no conflict of interest.

Nomenclature

BDBulk Density
BLBranch Length
CPCompactness
KsSaturated Hydraulic Conductivity
LDlength density
MDMean Diameter
NTNo-Tillage
rEquivalent Radius
ROIRegion of Interest
SOMSoil Organic Matter
TSmash-ridging Tillage
X-ray CTX-ray Computed Tomography
ΧEuler number

References

  1. Athmann, M.; Kautz, T.; Pude, R.; KöPke, U. Root Growth in Biopores—Evaluation with in Situ Endoscopy. Plant Soil 2013, 371, 179–190. [Google Scholar] [CrossRef]
  2. Katuwal, S.; Norgaard, T.; Moldrup, P.; Lamandé, M.; Wildenschild, D.; de Jonge, L.W. Linking Air and Water Transport in Intact Soils to Macropore Characteristics Inferred from X-ray Computed Tomography. Geoderma Int. J. Soil Sci. 2015, 237–238, 9–20. [Google Scholar] [CrossRef]
  3. Mathieu, L.; Labouriau, R.; Holmstrup, M.; Torp, S.B.; Greve, M.H.; Heckrath, G.; Iversen, B.V.; Jonge, L.W.D.; Moldrup, P.; Jacobsen, O.H. Density of Macropores as Related to Soil and Earthworm Community Parameters in Cultivated Grasslands. Geoderma 2011, 162, 319–326. [Google Scholar] [CrossRef]
  4. Müller, K.; Katuwal, S.; Young, I.; McLeod, M.; Moldrup, P.; de Jonge, L.W.; Clothier, B. Characterising and Linking X-ray CT Derived Macroporosity Parameters to Infiltration in Soils with Contrasting Structures. Geoderma 2018, 313, 82–91. [Google Scholar] [CrossRef]
  5. Clothier, B.E.; Green, S.R.; Deurer, M. Preferential Flow and Transport in Soil: Progress and Prognosis. Eur. J. Soil Sci. 2010, 59, 2–13. [Google Scholar] [CrossRef]
  6. Jarvis, N.J. A Review of Non-equilibrium Water Flow and Solute Transport in Soil Macropores: Principles, Controlling Factors and Consequences for Water Quality. Eur. J. Soil Sci. 2007, 58, 523–546. [Google Scholar] [CrossRef]
  7. Najm, M.A.; Jabro, J.; Iversen, W.; Evans, R.G. New Method for the Characterization of Three-Dimensional Preferential Flow Paths in the Field. Water Resour. Res. 2010, 46. [Google Scholar] [CrossRef]
  8. Ringrose-Voase, A.J. Measurement of Soil Macropore Geometry by Image Analysis of Sections through Impregnated Soil. Plant Soil 1996, 183, 27–47. [Google Scholar] [CrossRef]
  9. Zhang, Z.; Lin, L.; Wang, Y.; Peng, X. Temporal Change in Soil Macropores Measured Using Tension Infiltrometer under Different Land Uses and Slope Positions in Subtropical China. J. Soil Sediments 2016, 16, 854–863. [Google Scholar] [CrossRef]
  10. Kautz, T. Research on Subsoil Biopores and Their Functions in Organically Managed Soils: A Review. Renew. Agric. Food Syst. 2015, 30, 318–327. [Google Scholar] [CrossRef]
  11. Luo, L.; Lin, H.; Li, S. Quantification of 3-D Soil Macropore Networks in Different Soil Types and Land Use Using Computed Tomography. J. Hydrol. 2010, 393, 53–64. [Google Scholar] [CrossRef]
  12. Wuest, S.B. Soil Biopore Estimation: Effects of Tillage, Nitrogen, and Photographic Resolution. Soil Tillage Res. 2001, 62, 111–116. [Google Scholar] [CrossRef]
  13. Giuliani, L.M.; Hallett, P.D.; Loades, K.W. Effects of Soil Structure Complexity to Root Growth of Plants with Contrasting Root Architecture. Soil Tillage Res. 2024, 238, 106023. [Google Scholar] [CrossRef]
  14. Banfield, C.C.; Dippold, M.A.; Pausch, J.; Hoang, D.T.T.; Kuzyakov, Y. Biopore History Determines the Microbial Community Composition in Subsoil Hotspots. Biol. Fertil. Soils 2017, 53, 573–588. [Google Scholar] [CrossRef]
  15. Bauke, S.L.; von Sperber, C.; Siebers, N.; Tamburini, F.; Amelung, W. Biopore Effects on Phosphorus Biogeochemistry in Subsoils. Soil Biol. Biochem. 2017, 111, 157–165. [Google Scholar] [CrossRef]
  16. Beven, K.; Germann, P. Macropores and Water Flow in Soils. Water Resour. Res. 1982, 18, 1311–1325. [Google Scholar] [CrossRef]
  17. Han, E.; Kautz, T.; Perkons, U.; Uteau, D.; Peth, S.; Huang, N.; Horn, R.; Köpke, U. Root Growth Dynamics inside and Outside of Soil Biopores as Affected by Crop Sequence Determined with the Profile Wall Method. Biol. Fertil. Soils Coop. J. Int. Soc. Soil Sci. 2015, 51, 847–856. [Google Scholar] [CrossRef]
  18. Chen, D.; Huang, Y.; Shen, D.; Zhou, W.; Ao, J.; Jiang, Y.; Huang, Z.; Munir, H.; Fahd, R. Effects of Seaweed Extracts on Promoting Growth and Improving Stress Resistance in Sugarcane. Asian Agric. Res. 2019, 11, 69–76. [Google Scholar] [CrossRef]
  19. Dlamini, P.J. Drought Stress Tolerance Mechanisms and Breeding Effort in Sugarcane: A Review of Progress and Constraints in South Africa. Plant Stress 2021, 2, 100027. [Google Scholar] [CrossRef]
  20. Thotakuri, G.; Chakraborty, P.; Singh, J.; Xu, S.; Kovács, P.; Iqbal, J.; Kumar, S. X-ray Computed Tomography—Measured Pore Characteristics and Hydro-Physical Properties of Soil Profile as Influenced by Long-Term Tillage and Rotation Systems. Catena 2024, 237, 107801. [Google Scholar] [CrossRef]
  21. Qian, Y.; Yang, X.; Zhang, Z.; Li, X.; Zheng, J.; Peng, X. Estimating the Permeability of Soils under Different Tillage Practices and Cropping Systems: Roles of the Three Percolating Pore Radii Derived from X-ray CT. Soil Tillage Res. 2024, 235, 105903. [Google Scholar] [CrossRef]
  22. Budhathoki, S.; Lamba, J.; Srivastava, P.; Williams, C.; Arriaga, F.; Karthikeyan, K.G. Impact of Land Use and Tillage Practice on Soil Macropore Characteristics Inferred from X-ray Computed Tomography. Catena 2022, 210, 105886. [Google Scholar] [CrossRef]
  23. Singh, D.; Mishra, A.K.; Patra, S.; Mariappan, S.; Singh, N. Near-Saturated Soil Hydraulic Conductivity and Pore Characteristics as Influenced by Conventional and Conservation Tillage Practices in North-West Himalayan Region, India. Int. Soil Water Conserv. Res. 2021, 9, 249–259. [Google Scholar] [CrossRef]
  24. Crestana, S.; Cesareo, R.; Mascarenhas, S. Using a Computed Tomography Miniscanner in Soil Science. Soil Sci. 1986, 142, 56–61. [Google Scholar] [CrossRef]
  25. Vogel, H.J. Morphological Determination of Pore Connectivity as a Function of Pore Size Using Serial Sections. Eur. J. Soil Sci. 2008, 48, 365–377. [Google Scholar] [CrossRef]
  26. Li, D.; Velde, B.; Zhang, T. Observations of Pores and Aggregates during Aggregation in Some Clay-Rich Agricultural Soils as Seen in 2D Image Analysis. Geoderma 2004, 118, 191–207. [Google Scholar] [CrossRef]
  27. Yan, G.; Bore, T.; Schlaeger, S.; Scheuermann, A.; Li, L. Dynamic Effects in Soil Water Retention Curves: An Experimental Exploration by Full-Scale Soil Column Tests Using Spatial Time-Domain Reflectometry and Tensiometers. Acta Geotech. 2024, 1–27. [Google Scholar] [CrossRef]
  28. Farias, É.S.; Machado, S.L.; Giacheti, H.L.; Cerqueira, A.G. Integrated Use of Georadar, Electrical Resistivity, and SPT for Site Characterization and Water Content Estimative. Soils Rocks 2023, 46, e2023006422. [Google Scholar] [CrossRef]
  29. Bastardie, F.; Capowiez, Y.; Cluzeau, D. 3D Characterisation of Earthworm Burrow Systems in Natural Soil Cores Collected from a 12-Year-Old Pasture. Appl. Soil Ecol. 2005, 30, 34–46. [Google Scholar] [CrossRef]
  30. Gregory, P.J.; Hutchison, D.J.; Read, D.B.; Jenneson, P.M.; Gilboy, W.B.; Morton, E.J. Non-Invasive Imaging of Roots with High Resolution X-ray Micro-Tomography. Plant Soil 2003, 255, 351–359. [Google Scholar] [CrossRef]
  31. Peth, S.; Horn, R.; Beckmann, F.; Donath, T.; Fischer, J.; Smucker, A.J.M. Three-Dimensional Quantification of Intra-Aggregate Pore-Space Features Using Synchrotron-Radiation-Based Microtomography. Soil Sci. Soc. Am. J. 2008, 72, 897–907. [Google Scholar] [CrossRef]
  32. Pierret, A.; Capowiez, Y.; Belzunces, L.; Moran, C.J. 3D Reconstruction and Quantification of Macropores Using X-ray Computed Tomography and Image Analysis. Geoderma 2002, 106, 247–271. [Google Scholar] [CrossRef]
  33. Pierret, A.; Moran, C.J.; Pankhurst, C.E. Differentiation of Soil Properties Related to the Spatial Association of Wheat Roots and Soil Macropores. Plant Soil 1999, 211, 51–58. [Google Scholar] [CrossRef]
  34. Smet, S.; Beckers, E.; Plougonven, E.; Léonard, A.; Degré, A. Can The Pore Scale Geometry Explain Soil Sample Scale Hydrodynamic Properties? Front. Environ. Sci. 2018, 6, 20. [Google Scholar] [CrossRef]
  35. Wang, S.; Gan, L.; Zhang, Y.; Gao, Z.; Luo, Z.; Zhou, H.; Zhang, H.; Zhang, H.; Huang, T. Effects of Different Reclamation Years and Modes on Soil Moisture Transport Pathways and Permeability Characteristics in an Open-Pit Mining Area in Guangxi. Water 2024, 16, 1307. [Google Scholar] [CrossRef]
  36. Hu, X.; Li, X.-Y.; Wang, P.; Liu, Y.; Wu, X.-C.; Li, Z.-C.; Zhao, Y.-D.; Cheng, Y.-Q.; Guo, L.-L.; Lyu, Y.-L.; et al. Influence of Exclosure on CT-Measured Soil Macropores and Root Architecture in a Shrub-Encroached Grassland in Northern China. Soil Tillage Res. 2019, 187, 21–30. [Google Scholar] [CrossRef]
  37. Wen, T.; Chen, X.; Shao, L. Effect of Multiple Wetting and Drying Cycles on the Macropore Structure of Granite Residual Soil. J. Hydrol. 2022, 614, 128583. [Google Scholar] [CrossRef]
  38. Zhang, Z.; Liu, K.; Zhou, H.; Lin, H.; Li, D.; Peng, X. Three Dimensional Characteristics of Biopores and Non-Biopores in the Subsoil Respond Differently to Land Use and Fertilization. Plant Soil 2018, 428, 453–467. [Google Scholar] [CrossRef]
  39. Capowiez, Y.; Sammartino, S.; Michel, E. Using X-ray Tomography to Quantify Earthworm Bioturbation Non-Destructively in Repacked Soil Cores. Geoderma 2011, 162, 124–131. [Google Scholar] [CrossRef]
  40. Pagenkemper, S.K.; Peth, S.; Puschmann, D.U.; Horn, R. Effects of Root-Induced Biopores on Pore Space Architecture Investigated with Industrial X-ray Computed Tomography; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2013. [Google Scholar] [CrossRef]
  41. Zhou, H.; Fang, H.; Mooney, S.J.; Peng, X. Effects of Long-Term Inorganic and Organic Fertilizations on the Soil Micro and Macro Structures of Rice Paddies. Geoderma Int. J. Soil Sci. 2016, 266, 66–74. [Google Scholar] [CrossRef]
  42. Zhang, Z.; Liu, K.; Zhou, H.; Lin, H.; Li, D.; Peng, X. Linking Saturated Hydraulic Conductivity and Air Permeability to the Characteristics of Biopores Derived from X-ray Computed Tomography. J. Hydrol. 2019, 571, 1–10. [Google Scholar] [CrossRef]
  43. Pan, L.; Chen, Y.; Xu, Y.; Li, J.; Lu, H. A Model for Soil Moisture Content Prediction Based on the Change in Ultrasonic Velocity and Bulk Density of Tillage Soil under Alternating Drying and Wetting Conditions. Measurement 2022, 189, 110504. [Google Scholar] [CrossRef]
  44. Svensson, D.N.; Messing, I.; Barron, J. An Investigation in Laser Diffraction Soil Particle Size Distribution Analysis to Obtain Compatible Results with Sieve and Pipette Method. Soil Tillage Res. 2022, 223, 105450. [Google Scholar] [CrossRef]
  45. Shwetha, P.; Varija, K. Soil Water Retention Curve from Saturated Hydraulic Conductivity for Sandy Loam and Loamy Sand Textured Soils. Aquat. Procedia 2015, 4, 1142–1149. [Google Scholar] [CrossRef]
  46. Nelson, D.W. Total Carbon, Organic Carbon, and Organic Matter; American Society of Agronomy Inc.: Madison, WI, USA, 1996. [Google Scholar]
  47. Yan, G.; Cheng, Z.; Ma, Y.; Scheuermann, A.; Li, L. Applying Imaging Technique to Investigate Effects of Solute Concentrations and Gas Injection Rates on Gas Bubble Generation. Geofluids 2022, 2022, 2046267. [Google Scholar] [CrossRef]
  48. Kaur, P.; Lamba, J.; Way, T.R.; Balkcom, K.S.; Sanz-Saez, A.; Watts, D.B. Characterization of Soil Pores in Strip-Tilled and Conventionally-Tilled Soil Using X-ray Computed Tomography. Soil Tillage Res. 2024, 239, 106035. [Google Scholar] [CrossRef]
  49. Ju, X.; Gao, L.; She, D.; Jia, Y.; Pang, Z.; Wang, Y. Impacts of the Soil Pore Structure on Infiltration Characteristics at the Profile Scale in the Red Soil Region. Soil Tillage Res. 2024, 236, 105922. [Google Scholar] [CrossRef]
  50. Vogel, H.J.; Weller, U.; Schlueter, S. Quantification of Soil Structure Based on Minkowski Functions. Comput. Geosci. 2010, 36, 1236–1245. [Google Scholar] [CrossRef]
  51. Ren, Z.; Zhao, H.; Mu, X.; Xu, Q.; Shi, K.; Yang, G. Spatiotemporal Variations of Extreme Weather Events and Climate Drivers in the Three Gorges Reservoir Area and Its Surrounding Regions from 1960 to 2020. Atmos. Res. 2024, 304, 107379. [Google Scholar] [CrossRef]
  52. Zhang, J.; Li, F.; Liao, P.; Khan, A.; Hussain, I.; Iqbal, A.; Ali, I.; Wei, B.; Jiang, L. Smash Ridge Tillage Strongly Influence Soil Functionality, Physiology and Rice Yield. Saudi J. Biol. Sci. 2021, 28, 1297–1307. [Google Scholar] [CrossRef]
  53. Zhou, W.; Zhan, P.; Zeng, M.; Chen, T.; Zhang, X.; Yang, G.; Guo, Y. Effects of Ant Bioturbation and Foraging Activities on Soil Mechanical Properties and Stability. Glob. Ecol. Conserv. 2023, 46, e02575. [Google Scholar] [CrossRef]
  54. da Luz, F.B.; Castioni, G.A.F.; Tormena, C.A.; dos Santos Freitas, R.; Carvalho, J.L.N.; Cherubin, M.R. Soil Tillage and Machinery Traffic Influence Soil Water Availability and Air Fluxes in Sugarcane Fields. Soil Tillage Res. 2022, 223, 105459. [Google Scholar] [CrossRef]
  55. Wang, J.; Qin, Q.; Bai, Z. Characterizing the Effects of Opencast Coal-Mining and Land Reclamation on Soil Macropore Distribution Characteristics Using 3D CT Scanning. Catena 2018, 171, 212–221. [Google Scholar] [CrossRef]
  56. Pagliai, M.; Vignozzi, N.; Pellegrini, S. Soil Structure and the Effect of Management Practices. Soil Tillage Res. 2004, 79, 131–143. [Google Scholar] [CrossRef]
  57. Yang, Y.; Wu, J.; Mao, Y.; He, F.; Zhang, J.; Gao, C.; Pan, X.; Wang, Y. Effect of no-tillage on pore distribution characteristics of soil profile. Chin. J. Eco-Agric. 2018, 26, 1019–1028. (In Chinese) [Google Scholar] [CrossRef]
  58. Halvorson, A.D.; Jantalia, C.P. Nitrogen Fertilization Effects on Irrigated No-Till Corn Production and Soil Carbon and Nitrogen. Agron. J. 2011, 103, 1423–1431. [Google Scholar] [CrossRef]
  59. Wang, H.; Qiu, X.; Liang, X.; Wang, H.; Wang, J. Biogas Slurry Change the Transport and Distribution of Soil Water under Drip Irrigation. Agric. Water Manag. 2024, 294, 108719. [Google Scholar] [CrossRef]
  60. Gao, H.; Yan, C.; Liu, Q.; Li, Z.; Yang, X.; Qi, R. Exploring Optimal Soil Mulching to Enhance Yield and Water Use Efficiency in Maize Cropping in China: A Meta-Analysis. Agric. Water Manag. 2019, 225, 105741. [Google Scholar] [CrossRef]
  61. Yudina, A.; Kuzyakov, Y. Dual Nature of Soil Structure: The Unity of Aggregates and Pores. Geoderma 2023, 434, 116478. [Google Scholar] [CrossRef]
  62. Scarpare, F.V.; van Lier, Q.D.J.; de Camargo, L.; Pires, R.C.M.; Ruiz-Corrêa, S.T.; Bezerra, A.H.F.; Gava, G.J.C.; Dias, C.T.D.S. Tillage Effects on Soil Physical Condition and Root Growth Associated with Sugarcane Water Availability. Soil Tillage Res. 2019, 187, 110–118. [Google Scholar] [CrossRef]
  63. Ao, Y.; Hou, R.; Fu, Q.; Li, T.; Li, M.; Cui, S.; Liu, D. Mechanisms of Soil Respiration and Its Temperature Sensitivity in Black Soil Farmland. J. Clean. Prod. 2023, 427, 139066. [Google Scholar] [CrossRef]
  64. Iheshiulo, E.M.-A.; Larney, F.J.; Hernandez-Ramirez, G.; St. Luce, M.; Chau, H.W.; Liu, K. Soil Organic Matter and Aggregate Stability Dynamics under Major No-till Crop Rotations on the Canadian Prairies. Geoderma 2024, 442, 116777. [Google Scholar] [CrossRef]
  65. Qianjin, C.; Zhongsheng, Z.; Jian, W.J. Natural Marsh-Pasture/Sugarcane Field Transitions Greatly Reduced Lignin-Derived Pyrolysis Products of Soil Organic Matter. J. Environ. Manag. 2023, 329, 117019. [Google Scholar] [CrossRef]
  66. Naveed, M.; Moldrup, P.; Arthur, E.; Wildenschild, D.; Eden, M.; Lamandé, M.; Vogel, H.J.; De Jonge, L.W. Revealing Soil Structure and Functional Macroporosity along a Clay Gradient Using X-ray Computed Tomography. Soil Sci. Soc. Am. J. 2013, 77, 403–411. [Google Scholar] [CrossRef]
  67. Uksa, M.; Schloter, M.; Kautz, T.; Athmann, M.; Fischer, D. Spatial Variability of Hydrolytic and Oxidative Potential Enzyme Activities in Different Subsoil Compartments. Biol. Fertil. Soils 2015, 51, 517–521. [Google Scholar] [CrossRef]
  68. Valentine, T.A.; Hallett, P.D.; Kirsty, B.; Young, M.W.; Squire, G.R.; Cathy, H.; Glyn, B.A. Soil Strength and Macropore Volume Limit Root Elongation Rates in Many UK Agricultural Soils. Ann. Bot. 2012, 110, 259–270. [Google Scholar] [CrossRef]
Agronomy 14 01378 g001
Figure 1. Malvern laser particle sizer analysis and soil organic matter determination. (a) Malvern laser particle size analysis; (b) soil organic matter determination.
Figure 1. Malvern laser particle sizer analysis and soil organic matter determination. (a) Malvern laser particle size analysis; (b) soil organic matter determination.
Agronomy 14 01378 g001
Agronomy 14 01378 g002
Figure 2. Flowchart for separation of biopores from non-biopores. (A) represents macropores larger than 500 voxels; (B) represents macropores larger than 500 voxels with an L/r ratio greater than 20. 20; (C) is the biopores; (D) represents macropores larger than 500 voxels with an L/r ratio less than 20 (A,B); (E) refers to manually excluded macropores from the category (B,C) of macropores; (F) represents non-biopores (A–C). The numbers 1 to 3 in the boxes denote the number of holes. The numbers 1 to 3 in the boxes represent the three steps involved in separating biopores from non-biopores.
Figure 2. Flowchart for separation of biopores from non-biopores. (A) represents macropores larger than 500 voxels; (B) represents macropores larger than 500 voxels with an L/r ratio greater than 20. 20; (C) is the biopores; (D) represents macropores larger than 500 voxels with an L/r ratio less than 20 (A,B); (E) refers to manually excluded macropores from the category (B,C) of macropores; (F) represents non-biopores (A–C). The numbers 1 to 3 in the boxes denote the number of holes. The numbers 1 to 3 in the boxes represent the three steps involved in separating biopores from non-biopores.
Agronomy 14 01378 g002
Agronomy 14 01378 g003
Figure 3. Cross-sectional integral numbers of soil pores under different treatments. (a) and (b) denote the cross-sectional integrals of NT and T treatments, respectively.
Figure 3. Cross-sectional integral numbers of soil pores under different treatments. (a) and (b) denote the cross-sectional integrals of NT and T treatments, respectively.
Agronomy 14 01378 g003
Agronomy 14 01378 g004
Figure 4. Number of cross-sectional pores in soil pores under different treatments. (a,b) represent the number of cross-sectional pores of NT and T treatments, respectively. Note: Significant differences between treatments at the same depth in the same column are indicated by capital letters (e.g., A and B), while lowercase letters (e.g., a and b) represent significant differences between different depths within the same treatment (p < 0.05).
Figure 4. Number of cross-sectional pores in soil pores under different treatments. (a,b) represent the number of cross-sectional pores of NT and T treatments, respectively. Note: Significant differences between treatments at the same depth in the same column are indicated by capital letters (e.g., A and B), while lowercase letters (e.g., a and b) represent significant differences between different depths within the same treatment (p < 0.05).
Agronomy 14 01378 g004
Agronomy 14 01378 g005
Figure 5. The mean diameter of soil pores in different treatments. (a,b) represent the mean diameter of NT and T treatments, respectively. Note: Significant differences between treatments at the same depth in the same column are indicated by capital letters (e.g., A and B), while lowercase letters (e.g., a and b) represent significant differences between different depths within the same treatment (p < 0.05).
Figure 5. The mean diameter of soil pores in different treatments. (a,b) represent the mean diameter of NT and T treatments, respectively. Note: Significant differences between treatments at the same depth in the same column are indicated by capital letters (e.g., A and B), while lowercase letters (e.g., a and b) represent significant differences between different depths within the same treatment (p < 0.05).
Agronomy 14 01378 g005
Agronomy 14 01378 g006
Figure 6. Three-dimensional macropores and macroporosity of soil under different treatments. (a,b) represent NT and T treatment macroporosity, (c,d) represent NT and T treatment macropores, respectively. Note: Significant differences between treatments at the same depth in the same column are indicated by capital letters (e.g., A and B), while lowercase letters (e.g., a and b) represent significant differences between different depths within the same treatment (p < 0.05).
Figure 6. Three-dimensional macropores and macroporosity of soil under different treatments. (a,b) represent NT and T treatment macroporosity, (c,d) represent NT and T treatment macropores, respectively. Note: Significant differences between treatments at the same depth in the same column are indicated by capital letters (e.g., A and B), while lowercase letters (e.g., a and b) represent significant differences between different depths within the same treatment (p < 0.05).
Agronomy 14 01378 g006
Agronomy 14 01378 g007
Figure 7. Three-dimensional reconstruction of soil total macropores. NT-1, NT-2, NT-3 and T-1, T-2, and T-3 represent the three randomized replicate soil columns of no-tillage and smash-ridging tillage, respectively.
Figure 7. Three-dimensional reconstruction of soil total macropores. NT-1, NT-2, NT-3 and T-1, T-2, and T-3 represent the three randomized replicate soil columns of no-tillage and smash-ridging tillage, respectively.
Agronomy 14 01378 g007
Agronomy 14 01378 g008
Figure 8. Three-dimensional reconstruction of soil non-biopores. NT-1, NT-2, NT-3 and T-1, T-2, and T-3 represent the three randomized replicate soil columns of no-tillage and smash-ridging tillage, respectively.
Figure 8. Three-dimensional reconstruction of soil non-biopores. NT-1, NT-2, NT-3 and T-1, T-2, and T-3 represent the three randomized replicate soil columns of no-tillage and smash-ridging tillage, respectively.
Agronomy 14 01378 g008
Agronomy 14 01378 g009
Figure 9. Three-dimensional reconstruction of soil biopores. NT-1, NT-2, NT-3 and T-1, T-2, and T-3 represent the three randomized replicate soil columns of no-tillage and smash-ridging tillage, respectively.
Figure 9. Three-dimensional reconstruction of soil biopores. NT-1, NT-2, NT-3 and T-1, T-2, and T-3 represent the three randomized replicate soil columns of no-tillage and smash-ridging tillage, respectively.
Agronomy 14 01378 g009
Agronomy 14 01378 g010
Figure 10. Correlation analysis of various parameters of soil macropores. Note: “***”, “**”, and “*”, indicate p < 0.001, p < 0.01, and p < 0.05 respectively. (a–c) denote the correlation analysis between each parameter of total macropores, biopores, and non-biopores respectively. Note: BD, SOM, Ks, MD, χ, CP, and BL stand for bulk density, soil organic matter, saturated hydraulic conductivity, mean diameter, Euler number, compactness, and branch length, respectively.
Figure 10. Correlation analysis of various parameters of soil macropores. Note: “***”, “**”, and “*”, indicate p < 0.001, p < 0.01, and p < 0.05 respectively. (a–c) denote the correlation analysis between each parameter of total macropores, biopores, and non-biopores respectively. Note: BD, SOM, Ks, MD, χ, CP, and BL stand for bulk density, soil organic matter, saturated hydraulic conductivity, mean diameter, Euler number, compactness, and branch length, respectively.
Agronomy 14 01378 g010
Table 1. Physical and chemical properties of soil in different treatments.
Table 1. Physical and chemical properties of soil in different treatments.
TreatmentDepth
(cm)		BD
(g·cm−3)		SOM
(g·kg−1)		Ks
(mm·h−1)		Clay
(%)		Silt
(%)		Sand
(%)
NT0–101.568 ± 0.038 Aa14.679 ± 0.340 Ab94.071 ± 28.587 Aa16.369 ± 1.189 Aa30.728 ± 0.589 Aa52.903 ± 0.679 Aa
10–201.545 ± 0.078 Aa11.774 ± 0.993 Ab59.066 ± 14.201 Ab15.733 ± 0.940 Aa32.163 ± 0.990 Aa52.105 ± 0.042 Aa
20–301.514 ± 0.019 Aa7.780 ± 1.150 Ab61.406 ± 19.783 Ab15.444 ± 0.889 Aa33.368 ± 1.091 Aa51.188 ± 0.465 Aa
30–401.523 ± 0.033 Aa7.666 ± 0.673 Ab239.099 ± 131.19 Aa15.050 ± 0.477 Aa32.619 ± 0.321 Aa52.331 ± 0.257 Aa
T0–101.545 ± 0.075 Aa18.481 ± 0.314 Aa103.682 ± 75.403 Ba14.117 ± 0.484 Aa33.597 ± 1.072 Aa52.286 ± 1.549 Aa
10–201.533 ± 0.045 Aa17.296 ± 0.897 Aa220.633 ± 21.695 Ba13.701 ± 0.650 Aa32.909 ± 2.043 Aa53.340 ± 2.693 Aa
20–301.495 ± 0.031 Aa17.669 ± 2.567 Ba408.753 ± 59.724 Aa14.561 ± 0.322 Aa35.634 ± 0.963 Aa49.805 ± 0.704 Ba
30–401.542 ± 0.006 Aa17.659 ± 0.149 Ba87.889 ± 28.614 Ba15.176 ± 0.471 Aa33.904 ± 1.900 Aa50.920 ± 2.157 Ba
Note: Significant differences between treatments at the same depth in the same column are indicated by capital letters (e.g., A and B), while lowercase letters (e.g., a and b) represent significant differences between different depths within the same treatment (p < 0.05). NT and T stand for no-tillage and smash-ridging tillage, respectively. BD, SOM, and Ks stand for bulk density, soil organic matter, and saturated hydraulic conductivity, respectively.
Table 2. Total macropores 3D pore parameters.
Table 2. Total macropores 3D pore parameters.
TreatmentSoil Layer
(cm)		χCPLD
(mm·cm−3)
Total macropores–NT0–10646 ± 224.292 Aa226 ± 79.842 Aa14.5 ± 3.143 Aa
10–20820 ± 353.087 Aa142 ± 73.415 Aa12.2 ± 3.258 Aa
20–30918 ± 204.512 Aa65 ± 11.590 Aa7.7 ± 3.100 Aa
30–40403 ± 55.752 Ab121 ± 73.381 Aa5.4 ± 2.224 Ab
Total macropores–T0–10250 ± 79.635 Aa157 ± 2.603 Aa13.8 ± 2.251 Aa
10–20387 ± 64.970 Aa129 ± 50.063 Aa7.9 ± 3.437 Ba
20–30318 ± 130.080 Aa57 ± 8.737 Ba2.2 ± 2.887 Ba
30–40166 ± 53.426 Aa52 ± 2.963 Ba1.9 ± 0.722 Ba
Note: Significant differences between treatments at the same depth in the same column are indicated by capital letters (e.g., A and B), while lowercase letters (e.g., a and b) represent significant differences between different depths within the same treatment (p < 0.05). NT and T stand for No-tillage and Smash-ridging tillage, respectively. χ, CP and BL stand for Euler number, compactness, and branch length, respectively.
Table 3. Three-dimensional pore parameters of biopores and non-biopores.
Table 3. Three-dimensional pore parameters of biopores and non-biopores.
TreatmentSoil Layer
(cm)		χCPLD
(mm·cm−3)
Non-biopores–NT0–10684 ± 226.189 Aa220 ± 76.397 Aa13.9 ± 3.132 Aa
10–20913 ± 328.716 Aa142 ± 74.545 Aa11.8 ± 3.133 Aa
20–30931 ± 213.373 Aa62 ± 13.642 Aa7.3 ± 3.238 Aa
30–40432 ± 75.757b Aa115 ± 68.280 Aa5.0 ± 2.022 Aa
Non-biopores–T0–10500 ± 143.469 Aa165 ± 50.257Aa9.4 ± 2.215Aa
10–20401 ± 52.849 Aa100 ± 34.469 Ba6.4 ± 3.187 Ba
20–30312 ± 132.786 Aa38 ± 5.696 Ba1.4 ± 0.491 Ba
30–40167 ± 56.616 Aa47 ± 6.642 Ba1.5 ± 0.669 Ba
Biopores–NT0–102 ± 0.577 Aa59 ± 5.696 Aa0.3 ± 0.120 Ab
10–20−1 ± 2.963 Aa74 ± 4.177 Aa0.4 ± 0.153 Aa
20–303 ± 1.155 Aa67 ± 5.508 Aa0.4 ± 0.176 Aa
30–402 ± 0.000 Aa76 ± 5.000 Aa0.5 ± 0.150 Ab
Biopores–T0–104 ± 22.898 Aa89 ± 39.552 Aa3.9 ± 0.265 Aa
10–204 ± 2.848 Aa73 ± 7.211 Aa1.4 ± 0.384 Ba
20–306 ± 3.383 Aa80 ± 3.215 Aa0.7 ± 0.240 Ba
30–405 ± 0.000 Aa52 ± 5.000 Aa0.6 ± 0.000 Ba
Note: Significant differences between treatments at the same depth in the same column are indicated by capital letters (e.g., A and B), while lowercase letters (e.g., a and b) represent significant differences between different depths within the same treatment (p < 0.05). NT and T stand for No-tillage and Smash-ridging tillage, respectively. χ, CP and BL stand for Euler number, compactness, and branch length, respectively.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, S.; Gan, L.; Zhang, S.; Li, J.; Chang, C.; Zhang, Y.; Zhang, H.; Wei, B. Soil Biopores and Non-Biopores Responses to Different Tillage Treatments in Sugarcane Fields in Guangxi, China. Agronomy 2024, 14, 1378. https://doi.org/10.3390/agronomy14071378

AMA Style

Wang S, Gan L, Zhang S, Li J, Chang C, Zhang Y, Zhang H, Wei B. Soil Biopores and Non-Biopores Responses to Different Tillage Treatments in Sugarcane Fields in Guangxi, China. Agronomy. 2024; 14(7):1378. https://doi.org/10.3390/agronomy14071378

Chicago/Turabian Style

Wang, Song, Lei Gan, Shuo Zhang, Jian Li, Cheng Chang, Yu Zhang, Hongxia Zhang, and Benhui Wei. 2024. "Soil Biopores and Non-Biopores Responses to Different Tillage Treatments in Sugarcane Fields in Guangxi, China" Agronomy 14, no. 7: 1378. https://doi.org/10.3390/agronomy14071378

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop