Effect of organic amendments on soil structure, microbial community and water transport in the Qinghai Lake watershed, North-Eastern Qinghai–Tibet Plateau

ABSTRACT

The effect of organic amendments on soil structure, microbial community and water transport in alpine ecosystems remains uncertain. Six fertilization regimes from a 2-year field experiment were selected to study this mechanism, i.e. plant litter (PL), organic fertilizer (OF), low concentration of glucose (LCG), medium concentration of glucose (MCG), high concentration of glucose (HCG) and the control (CT). Our results indicated that the soil macroporosity, branch density, length density and node density of soil macropores treated with OF, PL, HCG, MCG and LCG were larger than those of soils treated with CT. The phospholipid fatty acids (PLFAs), bacteria, fungi, actinomycetes, gram-positive bacteria (G⁺) and gram-negative bacteria (G⁻) of soils treated with OF, PL, HCG and MCG were larger than those of soils treated with LCG and CT. In contrast, the dyed area ratio and the maximum wetting depth of soils treated with OF, PL, LCG, HCG and MCG were smaller than those treated with CT. There were significant positive correlations between soil organic matter (SOM) and connectivity of soil macropores. Therefore, SOM accumulation significantly improved the soil pore structure, induced the development of the soil microbial community, increased the soil water retention ability and decreased the generation of preferential flow.

KEYWORDS:

• Soil macropore
• soil microbial community
• organic amendments
• SOM
• soil water

Introduction

Organic amendments typically enhance soil fertility by supplying organically complexed C, N and/or phosphorus, improving aggregate stability and stimulating soil organic carbon (SOC) mineralization and soil aggregation simultaneously (Bipfubusa et al. 2008; Abiven et al. 2009; Chivenge et al. 2011; Diacono and Montemurro 2011; Pronk et al. 2012; Waring et al. 2022). Studies have mainly focused on the effects of organic amendments on soil physical and chemical characteristics (Rumpel and Kögel-Knabner 2011; Krause et al. 2018; Xiao et al. 2019; Bierer et al. 2020, 2021; Majrashi et al. 2022). Some studies have shown that organic amendments alleviate soil degradation by improving the soil microbial community and enzymes (Ling et al. 2014; Ye et al. 2021). Wang et al. (2021) indicated that organic amendments significantly modified the C dynamics, bacterial and fungal community compositions and network topological patterns in all aggregate sizes. Shu et al. (2022) indicated that organic amendments significantly increased microbial diversity components and shifted microbial community structure compared to mineral-only fertilization. To date, researches on the effect of organic amendments on soil structure and water transport of alpine meadow soil on the Qinghai–Tibet Plateau are lacking.

Soil structure is the three-dimensional arrangement of solid soil constituents and voids across different scales (Rabot et al. 2018), and it results from interactions of biotic and abiotic factors, including climate, mineral composition, organic matter, roots, fungal hyphae, soil fauna and tillage. Studies have mainly focused on the effects of organic amendments on soil structure (Zaher et al. 2005; Hafida et al. 2007; Papadopoulos et al. 2009; Yilmaz and Sonmez 2017; Wan et al. 2020). The organic carbon from organic amendments acts as a binder, binding silt and clay particles together to form microaggregates (Six et al. 2004; Zhou et al. 2016). Previous studies have shown that soil organic matter (SOM) can also enhance aggregate stability by modifying aggregate pore space (Zaher et al. 2005; Hafida et al. 2007; Papadopoulos et al. 2009). However, the effect of organic amendment application on soil pore structure in alpine meadows is still largely neglected, especially in alpine soils. Soil pores affect the storage and transport of air, water and nutrients required for the survival of soil microbes, determining the distribution and composition of the soil microhabitat (Mummey and Stahl 2004; Bach et al. 2018), as well as the diffusion of substrates into microbial cells (Nunan et al. 2006). However, the effects of soil pore structure caused by SOM on soil microbial community and water transport in alpine ecosystems remain uncertain.

Organic amendments could change SOM by stimulating SOC mineralization (Pronk et al. 2012; Lal 2015). SOM is an important factor influencing soil pore structure and soil water transport (Dal Ferro et al. 2012, 2013; Zhou et al. 2013, 2022; Bell et al. 2023). Some studies have shown that SOM can promote the development of soil pore structure (Grosbellet et al. 2011; Gao et al. 2021). Grosbellet et al. (2011) indicated that SOM leads to an increase in the soil macroporosity. Zhou et al. (2013) used synchrotron X-ray microtomography and digital image analysis techniques to determine the microstructure of soils with different SOM contents and found that SOM improved the soil pore structure and increased soil pore connectivity. Dal Ferro et al. (2013) found that SOM significantly increased the connectivity of soil pores and changed the distribution of soil pores from small to large pores, as evidenced by an increase in pores in the range of >560 μm and a decrease in pores in the range of 80–320 μm. Some studies have shown that SOM couldn’t change the soil pore structure (Deurer et al. 2009; Williams et al. 2017). Deurer et al. (2009) found that the increase in SOM did not increase the proportion of large pores larger than 2 mm in diameter. Jarvis et al. (2007) also found that soil organic carbon reduced preferential solute transport in cultivated topsoil. Gao et al. (2021) indicated that the increase in SOM contributed to the improvement in soil water retention. Therefore, the effect of SOM on soil pore structure and soil water transport is still controversial and needs to be further investigated.

Alpine ecosystems are very sensitive to climate change and play an important role in Asia and even in global climate change (Qiu 2008). As one of the most important types of alpine grasslands, alpine meadows cover an area of 0.58 million km². The mattic epipedon in alpine ecosystem plays a crucial role in soil water retention owing to its densely intertwined grass roots and high topsoil organic matter content (;; Zhi et al., 2017), in which soil organic matter content is up to about 10% (Kaiser, et al., 2008; Yang et al., 2017). The mattic epipedon layer plays an important role in maintaining the water-holding function of Tibetan plateau and alpine soils (Yang et al., 2017). Some studies have showed that SOM plays a dominant role in controlling soil water retention in this alpine meadow environment (Yang et al. 2014). Yang et al. (2014) found that SOM affected soil water retention mainly through altering soil structural parameters and soil bulk density at high matric potentials, and the water adsorbing capacity of soil organic matter directly affected water retention at low matric potentials. However, whether the effect of SOM accumulation on soil pore structure and water distribution in alpine meadows is positive or negative, which needs to be studied thoroughly through experimental observations.

To fill the above knowledge gaps, we hypothesized that (1) organic amendments significantly improve the pore structure of alpine meadow soil, increasing the soil porosity, number of pores and connectivity; (2) SOM induced by organic amendments improves the soil water retention and reduced the preferential flow; and (3) soil macropore parameters induced by organic amendments are the main parameters influencing the soil microbial community.

Materials and methods

Study site and experimental design

This study was conducted in the watershed of the Shaliu River, the second largest river in the Qinghai Lake Watershed, situated in the northeastern of the Qinghai–Tibet Plateau, which lies in the semiarid, cold and high-altitude climate zone. Mean annual temperature and precipitation between 1957 and 2015 were 0.1°C and 400 mm in the watershed, respectively. Approximately 70%–80% of the annual precipitation occurs in summer and autumn. Winters are clear, dry and cold, and the summer is warm and wet. The soil type was classified as subalpine meadow soil, equivalent to Matti-Gelic Cambsols according to the FAO UNESCO system (USS Working Group WRB 2015).

In June 2019, the study area was divided into 18 plots, which were grouped randomly and supplemented with different organic amendments. There were three replicate groups for each treatment, and the plot size was 1.5 m × 1.5 m; the groups were as follows: 1) the control (CT); 2) organic fertilizer (OF, sheep dung, 1 kg m⁻²); 3) plant litter (PL, dead Kobresia pygmaea, 1 kg m⁻²); 4) low concentration of glucose (LCG, 0.5 kg m⁻²); 5) medium concentration of glucose (MCG, 1 kg m⁻²); and 6) high concentration of glucose (HCG, 2 kg m⁻²) (Fig. S1). All organic amendments were spread evenly on the soil surface layer based on the local fertilization practices. After all the plots were laid out, they were covered with a dust-proof net and enclosed with fences to prevent trampling by cattle and sheep (Fig. S2). The enclosed time was 2 years, and after application, the soil samples were collected in June 2021.

Soil sampling and analysis

The undisturbed soil samples using PVC pipes were collected from 18 plots after material application in June 2021. PVC pipe with a length of 30 cm and a diameter of 7.5 cm was selected for soil column sample collection, and the soil samples were completely filled into the PVC pipe during sampling. Core extraction followed the procedure of Sammartino et al. (2012). The core was extracted by pushing, slowly and vertically, into the soil, the penetration of the cylinder progressing centimetre by centimetre, carefully removing the soil around the tube bottom at each step, to minimise mechanical stresses during extraction. After sampling, a PVC grid was glued to the base of the cylinder, and the cylinder was then glued to an annular sample holder to protect the bottom of the core from mechanical disturbance. To protect the samples during transport from field to laboratory, the cores were placed in sealed plastic bags and were wrapped with a sponge and packed with wheat straw. The soil samples for microbial determination were collected in the 0–10 cm and 10–20 cm layers, stored in a refrigerator. The samples were sieved (2 mm), and any visible roots and stones were manually removed. Then, the soil physical and chemical properties and phospholipid fatty acids (PLFAs) were analysed.

Disturbed soil samples for soil physical and chemical characteristics were collected at 0–10 cm, 10–20 cm and 20–30 cm (Wang and Hu 2023) with three replicates. The SOC content was measured by a carbon and nitrogen analyser (Nelson and Sommers 1996), and the obtained organic carbon content was multiplied by 1.724 to calculate the SOM content. Soil particle size analysis (soil texture) was performed by the pipet method (Gee and Bauder 1986). pH was determined following Thomas (1996). The dry sieving method and the aggregate analyser (DIK–2012) were selected to measure the percentage of mechanically stable soil aggregates (Elliott 1986) and water – stable aggregates in the fractions of >2 mm, 1–2 mm, 0.5–1 mm, 0.25–0.5 mm, 0.1–0.25 mm and <0.1 mm, and the MWD and geometric mean diameter (GMD) of mechanically stable aggregates were calculated as follows (Tagar et al. 2020):

(1) $\mathrm{MWD}=\sum _{\mathit{i}=1}^{\mathit{n}}\overline{X_i}{W}_i$(1)

(2) $\mathrm{GMD}=\exp [(\sum _{\mathit{i}=1}^n{W}_{i}ln\overline{X_l})/(\sum _{\mathit{i}=1}^n{W}_i)]$(2)

where Xi is the mean diameter of the aggregates sieved within the diameter range of i, and Wi is the percentage of the aggregate content sieved within the diameter range of i. The soil characteristics of study area are listed in Table 1.

Table 1. Physical and chemical properties of soil in the study area.

Soil depth	Bulk density (g cm^−3)	SOC (g kg^−1)	pH	Clay (%)	Silt (%)	Sand (%)
0–10 cm	0.95	84.25	5.80	5.40	47.82	46.78
10–20 cm	1.07	42.24	5.98	5.05	48.91	46.04
20–30 cm	1.33	6.44	6.38	4.81	46.11	49.08

X-ray computed tomography scanning and image analysis

The scanning tool was the dual-ray source X-ray computed tomography system of NanoVoxel–5000 series produced by Tianjin Sanying Precision Instrument Limited Company. The undisturbed soil columns were scanned by X-ray computed tomography. The scanning parameters were as follows: the scanning voltage was 150 kV, the current was 250 μA, the exposure time was 0.1 s, and the scanning resolution was 68 μm.

The interpretation of soil macropores followed the procedure of Hu et al. (2022) in Avizo 9.0 (FEI, 2016). A cylindrical cropping tool was applied to obtain a region of interest (ROI) to remove the cracks that formed on the margin of soil columns during sampling disturbances. The threshold value of soil macropores was selected according to the grey scale histogram (Figure 1). The images were visually inspected to confirm the reasonability of the selected threshold value. The selected threshold value was then applied to the all slices of ROI to get binarization data. The soil macropore networks were visualized by volume rendering. Soil macropore parameters such as macroporosity, pore number, pore surface area, pore volume and pore equivalent diameter were calculated using the ‘Interactive Thresholding’ and ‘Label Analysis’ modules. The ‘Auto Skeleton’ module was selected to skeletonize and calculate the soil length density, node density, branch density, hydraulic radius and connectivity of the macropores.

Figure 1. Grey scale intensity of soil columns scanned using X-ray computed tomography and blue denotes soil macropores.

PLFA analysis

A modified Bligh–Dyer technique was employed for soil PLFA extraction (Brant et al., 2006). Total lipids were extracted in a single-phase methanol–chloroform–phosphate buffer (2:1:0.8) system and separated into neutral lipids, glycolipids and polar lipids. Then, the polar lipids were trans-esterified, and the fatty acid methyl esters were separated, quantified and identified by capillary gas chromatography. Among them, methyl nondecanoate (19:0) was used as the internal standard. The calculations of PLFA, gram-positive bacteria (G⁺), gram-negative bacteria (G⁻), fungi, bacteria and actinomycetes followed the procedures of Sun et al. (2011) and Bossio et al. (2006).

Dyeing experiment

Dyeing experiments were carried out before and 1 year after the application of materials. In each plot, a 0.5 m × 0.5 m area was selected for the brilliant blue staining tracer experiment. An iron frame with a size of 0.5 m × 0.5 m × 0.3 m was placed approximately 15 cm into the soil. The staining tracer was a brilliant blue solution with a concentration of 3 g L⁻¹. The 37.5 L of brilliant blue solution was mixed evenly and poured into the iron frame for infiltration. After 24 hours of infiltration, the soil profile was excavated in the plot to expose the staining area on the profile. The staining depth was measured with an iron ruler, and the area was photographed. Photoshop and MATLAB software were used to binarize the images and calculate the maximum dyeing depth and dyeing area ratio after processing.

Statistical analyses

All the statistical analyses were conducted using SPSS 20 (SPSS Inc., U.S.A.). Homogeneity of variances and normal distribution of the data were tested before applying the parametric methods. Differences between the macropore characteristics of different treatments were analysed by one-way analysis of variance (ANOVA), and differences were considered significant at the level of p < 0.05. Pearson correlations were conducted to evaluate the linkages among moss coverage, soil macropores and soil water retention.

Results

The effects of organic amendments on soil organic matter

Table 2 shows the changes in the SOM content in response to OF, PL, LCG, MCG, HCG and CT in alpine meadows. At the soil depth of 0–10 cm, the contents of SOM were significantly greater in the soils treated with OF and HCG than those in the other treatments (p < 0.05), with values of 37.2 g kg⁻¹ and 35.96 g kg⁻¹, respectively, followed by the treatments with MCG (30.44 g kg⁻¹), PL (30.08 g/kg) and LCG (29.14 g/kg). The SOM contents treated with CT were 27.13 g/kg, which were significantly lower than those of soils treated with OF, PL, LCG, MCG and HCG (p < 0.05).

Table 2. Soil organic matter (g/kg) under different treatments after 2 years.

Treatments	0–10 cm	10–20 cm	20–30 cm	0–30 cm
CT	27.13 ± 1.35bc	21.14 ± 0.19c	19.55 ± 6.16ab	22.61 ± 2.38 cd
OF	37.2 ± 2.39a	28.43 ± 1.01a	24.34 ± 3.23ab	29.99 ± 0.95a
PL	30.08 ± 3.33b	27.01 ± 4.96ab	23.6 ± 5.51ab	26.89 ± 3.18abc
LCG	29.14 ± 4.56b	23.96 ± 2.15abc	19.4 ± 6.65ab	24.17 ± 4.22 cd
MCG	30.44 ± 4.94b	22.72 ± 4.45bc	22.55 ± 3.37ab	25.24 ± 4.25bcd
HCG	35.96 ± 0.92a	27.32 ± 3.38ab	25.73 ± 2.8a	29.67 ± 1.87ab

CT: the control; OF: organic fertilizer; PL: plant litter; LCG: low concentration of glucose; MCG: middle concentration of glucose; HCG: high concentration of glucose.

At the soil depth of 10–20 cm, the SOM contents of the OF, HCG and PL treatments were significantly greater than those in the other treatments (p < 0.05), with values of 28.43 g kg⁻¹, 27.32 g kg⁻¹ and 27.01 g kg⁻¹, respectively, followed by that in the LCG (23.96 g kg⁻¹) and MCG (22.72 g kg⁻¹) treatments. The lowest SOM content was found in the CT, with values of 21.14 g kg⁻¹.

Overall, in the soil depth range of 0–30 cm, the SOM contents were significantly greater in the OF (29.99 g kg⁻¹), HCG (29.67 g kg⁻¹), PL (26.89 g kg⁻¹), MCG (25.24 g kg⁻¹) and LCG treatment (24.17 g kg⁻¹) treatments than those in the treatment of CT (22.61 g kg⁻¹) (p < 0.05).

The effects of organic amendments on soil structure

Figure 2 shows the 3D visualization of soil macropores treated with OF, PL, LCG, MCG, LCG and CT in alpine meadows. Soils of OF with the highest SOM contents had the high macropore number, the dense distribution and the best connectivity. Soils of CT had the low macropore number, the sparse distribution of pores and poor connectivity. The number and connectivity of pores in the treatment of HCG were significantly higher than those in the treatment of LCG (p < 0.05).

Figure 2. Three-dimensional visualization of soil macropores under different treatments (blue denotes soil macropores).

Table 3 shows the characteristics of soil pore parameters after the application of OF, PL, LCG, MCG, HCG and CT. There were significant differences in the pore surface area density, pore number density, macroporosity, pore branching density, pore length density and pore node density among the different treatments (p < 0.05). The macropore surface area density of soils applied with the treatments of OF, PL, MCG and HCG (0.1241 mm² mm⁻³, 0.0794 mm² mm⁻³, 0.0653 mm² mm⁻³ and 0.1039 mm² mm⁻³) with the highest SOM contents was significantly greater than soils treated with CT and LCG (0.0503 mm² mm⁻³ and 0.0573 mm² mm⁻³) with lower SOM contents. The macroporosity, pore number density, branch density, length density, and node density applied with the treatment of OF, PL, LCG, MCG and HCG were significantly greater than that treated with CT (p < 0.05). The macroporosity (0.0092 mm³ mm⁻³) applied with the OF with the highest SOM contents was significantly greater than those treated with CT and LCG (0.0037 mm³ mm⁻³ and 0.0046 mm³ mm⁻³). The pore node density applied with the OF (1186.0533 no. mm⁻³) was significantly greater than that treated with CT and LCG (593.4897 no. mm⁻³ and 660.8383 no. mm⁻³) (p < 0.05).

Table 3. Soil macropore characteristics of all soil columns as determined using the X-ray computed tomography under different treatments.

Treatments	Mean macropore size (mm)	Mean Volume (mm^3)	Mean angle (°)	Surface area density (mm^2 mm^−3)	Macropore number density (no mm^−3)	Macroporosity (mm^3 mm^−3)	Branch density (no mm^−3)	Length density (mm mm^−3)	Node density (no mm^−3)	Hydraulic radius (mm)	Connectivity	Tortuosity
CT	0.2249 ± 0.0264	0.0496 ± 0.0328	59.1274 ± 1.8443	0.0503 ± 0.0203c	1077.4604 ± 808.2376b	0.0037 ± 0.001c	553.1759 ± 269.034b	40.3899 ± 14.4718b	593.4897 ± 307.2544b	0.1721 ± 0.0101	0.0032 ± 0.0023	0.8712 ± 0.2468
OF	0.2208 ± 0.0102	0.0423 ± 0.0099	59.2037 ± 3.1644	0.1241 ± 0.0408a	2159.7985 ± 542.48a	0.0092 ± 0.0033a	1274.5265 ± 306.115a	81.198 ± 16.2706a	1186.0533 ± 382.4581a	0.1668 ± 0.0032	0.0066 ± 0.0021	0.9256 ± 0.0971
PL	0.2143 ± 0.0077	0.0333 ± 0.0045	57.3615 ± 5.5066	0.0794 ± 0.0184bc	1661.1081 ± 238.329ab	0.0056 ± 0.0016bc	911.7636 ± 204.6398ab	64.2462 ± 14.3972ab	942.4471 ± 192.6662ab	0.166 ± 0.0074	0.0027 ± 0.0027	0.7964 ± 0.0627
LCG	0.2166 ± 0.0175	0.04 ± 0.0127	59.4959 ± 0.5669	0.0573 ± 0.017c	1174.7296 ± 285.9208b	0.0046 ± 0.0013bc	608.1306 ± 174.5142b	45.8297 ± 12.8669b	660.8383 ± 185.666b	0.1779 ± 0.0124	0.0102 ± 0.0065	0.7869 ± 0.0589
MCG	0.2202 ± 0.0145	0.043 ± 0.0131	58.1956 ± 4.0693	0.0653 ± 0.0089bc	1266.1563 ± 440.5827b	0.0052 ± 0.0013bc	721.951 ± 132.3521b	52.3402 ± 3.3251b	751.1259 ± 131.8166b	0.176 ± 0.0195	0.0099 ± 0.011	0.8471 ± 0.0917
HCG	0.2172 ± 0.0019	0.0364 ± 0.0037	60.5133 ± 2.3642	0.1039 ± 0.0308ab	1347.6608 ± 188.073ab	0.0071 ± 0.0015ab	898.0382 ± 97.8814ab	63.6459 ± 11.9083ab	909.4413 ± 115.5011ab	0.1704 ± 0.0055	0.0048 ± 0.0009	0.815 ± 0.0642

CT:the control; OF: organic fertilizer; PL: plant litter; LCG: low concentration of glucose; MCG: middle concentration of glucose; HCG: high concentration of glucose.

The effects of organic amendments on the soil microbial community

The total PLFAs of soils applied with OF, PL, MCG and HCG were significantly greater than those of CT and LCG (Table 4, p < 0.05). There were significant differences in the total PLFAs between the CT and OF, MCG and HCG. There were no significant differences in the total PLFAs among the OF, PL, MCG and HCG. The total PLFAs were mostly distributed in the top 10 cm of soils under the OF and PL treatments (Figure 3).

Figure 3. Total PLFAs, total bacteria, G⁺ bacteria, G⁻ bacteria, fungi and actinomycetes under different treatments.

Table 4. Total phospholipid fatty acids (PLFAs), total bacterial, fungal, actinomycete, gram-positive bacterial and gram-negative bacterial contents under different treatments.

Treatments	Total PLFAs (nmol/gDW)	Bacteria (nmol/gDW)	Fungi (nmol/gDW)	Actinomycetes (nmol/gDW)	F/B	G^+ (nmol/gDW)	G^− (nmol/gDW)	G^+/G^−
CT	18.58 ± 1.03bc	17.5 ± 0.9bc	1.08 ± 0.14b	0.61 ± 0.13bc	0.06 ± 0.01b	7.74 ± 0.47bc	8.17 ± 0.59 cd	0.96 ± 0.09
OF	38.18 ± 4.01a	35.83 ± 3.72a	2.34 ± 0.31a	0.98 ± 0.13a	0.07 ± 0ab	14.86 ± 1.41a	18.22 ± 2.66a	0.83 ± 0.13
PL	31.37 ± 8.38a	29.28 ± 7.9a	2.09 ± 0.51a	0.95 ± 0.3ab	0.07 ± 0.01a	13.12 ± 3.32a	13.67 ± 3.82ab	0.97 ± 0.03
LCG	15.76 ± 1.46c	14.83 ± 1.36c	0.93 ± 0.1b	0.59 ± 0.15c	0.06 ± 0b	6.57 ± 0.69c	6.97 ± 0.52d	0.96 ± 0.06
MCG	32.22 ± 6.26a	30.14 ± 5.84a	2.08 ± 0.42a	0.85 ± 0.2abc	0.07 ± 0ab	12.72 ± 1.77a	14.77 ± 3.73ab	0.88 ± 0.11
HCG	34.57 ± 10.67a	32.38 ± 10.1a	2.19 ± 0.57a	0.9 ± 0.26abc	0.07 ± 0ab	13.66 ± 4.16a	16.15 ± 5.15ab	0.85 ± 0.08

CT:the control; OF: organic fertilizer; PL: plant litter; LCG: low concentration of glucose; MCG: middle concentration of glucose; HCG: high concentration of glucose.

The bacteria, fungi, actinomycetes, G⁺ and G⁻ of soils treated with OF, PL, MCG and HCG were larger than those of soils treated with CT and LCG (p < 0.05). The contents of total bacteria, fungi, actinomycetes, protozoa, G⁺ bacteria and G⁻ bacteria were distributed in the deep soil layer among all treatments (Figure 3). Significant differences in soil total PLFAs, bacteria, G⁺ and G⁻ were observed between soils under CT and OF, PL, MCG and LCG (Table 4). Significant differences in soil total PLFAs, bacteria, fungi, actinomycetes, G⁺ and G⁻ were observed between soils in the OF, PL and LCG.

The effects of organic amendments on soil water distribution

Table 5 shows the dyed area and maximum dyed depth of alpine meadow soils treated with different organic amendments. In the range of 0–10 cm of soils, the dyed area of soils treated with OF, PL, LCG, MCG and LCG was significantly lower than soils treated with CT. At the depth of 10–20 cm, the dyed areas of soils treated with CT were significantly larger than soils treated with OF, PL, LCG, MCG and HCG. At the soil depth of 20–30 cm, soils treated with CT had the largest dyed area of 35% (p < 0.05). Overall, soils treated with CT had the largest dyed area of 65% in the 0–30 cm soil depth, which was significantly larger than soils treated with OF, PL and HCG.

Table 5. The dyed area ratio and the maximum wetting depth under different treatments.

Treatments	The dyed area ratio（×100%）				Maximum wetting depth (cm)	Soil water (%, 0–30 cm)
	0–10 cm	10–20 cm	20–30 cm	0–30 cm
CT	0.91 ± 0.05ab	0.69 ± 0.22a	0.35 ± 0.23a	0.65 ± 0.15a	26.57 ± 1.38ab	1.55 ± 0.32c
OF	0.76 ± 0.17bc	0.20 ± 0.16 cd	0.15 ± 0.26abc	0.37 ± 0.12bc	17.82 ± 9.06b	2.06 ± 0.08a
PL	0.51 ± 0.03d	0.27 ± 0.17bcd	0.21 ± 0.19abc	0.33 ± 0.09c	24.26 ± 5.41ab	1.87 ± 0.12ab
LCG	0.93 ± 0.02ab	0.42 ± 0.25abcd	0.06 ± 0.08bc	0.47 ± 0.11abc	19.59 ± 5.41ab	1.74 ± 0.07bc
MCG	0.87 ± 0.10abc	0.52 ± 0.25abc	0.07 ± 0.07bc	0.49 ± 0.14abc	22.71 ± 3.23ab	1.74 ± 0.07bc
HCG	0.71 ± 0.20c	0.16 ± 0.11d	0.10 ± 0.13abc	0.32 ± 0.15c	19.03 ± 9.17b	2.00 ± 0.08ab

CT:the control; OF: organic fertilizer; PL: plant litter; LCG: low concentration of glucose; MCG: middle concentration of glucose; HCG: high concentration of glucose.

Relationships between organic matter and soil structure, microbial community, and water transport

Strong correlations between SOM and surface area density, number density, macroporosity, branch density, length density, node density and connectivity of soil macropores were observed, as shown in Table 6. There were significant positive correlations between the SOM and surface area density, number density, macroporosity, branch density, length density, node density and connectivity of soil macropores. SOM was significantly and positively correlated with the MWD of dry aggregates, >0.25 mm dry aggregates, MWD of water-stable aggregates and >0.25 mm water-stable aggregates. The saturated water content was significantly and positively correlated with the content of SOM in soil depth of 0–30 cm (Table 6).

Table 6. Correlations between soil organic matter and soil structure (0–30 cm).

	Surface area density	Number density	Macroporosity	Branch density	Length density	Node density	Connectivity	Mean weight diameter of dry aggregate	>0.25 mm dry aggregate	Mean weight diameter of water-stable aggregate	>0.25 mm water-stable aggregate	Saturated water content
SOM	0.562**	0.425*	0.558**	0.523**	0.491*	0.488*	0.433*	0.614**	0.616**	0.439*	0.424*	0.671**

*Correlation is significant at the 0.05 level; **Correlation is significant at the 0.01 level.

Table 7 shows the relationships between the soil structure and the soil microbial community. The soil microbial community was significantly and positively correlated with the surface area density, number density, macroporosity, branch density, length density and node density of soil macropores. The ratio of G⁺/G⁻ was significantly and negatively correlated with the surface area density, number density, macroporosity, branch density, length density and node density of soil macropores. Soil bulk density was significantly and negatively correlated with the contents of total PLFAs, bacteria and fungi.

Table 7. Correlations between the soil microbial community and soil pore structure.

	Equivalent diameter	Mean volume	Mean angle	Surface density area	Number density	Macroporosity	Branch density	Length density	Node density	Hydraulic radius	Connectivity	Bending
Total PLFAs	−0.107	−0.161	0.257	.589**	.551**	.536*	.637**	.510*	.588**	−0.208	−0.02	0.149
Bacterial	−0.106	−0.16	0.259	.590**	.553**	.538*	.639**	.512*	.590**	−0.208	−0.019	0.152
Fungi	−0.12	−0.176	0.215	.558**	.508*	.497*	.591**	.473*	.550**	−0.198	−0.035	0.116
Actinomycetes	−0.346	−0.399	0.371	.478*	.588**	0.337	.558**	0.399	.512*	−0.39	−0.179	−0.099
F/B	−0.228	−0.227	−0.173	0.003	0.006	−0.047	0.006	−0.011	0.006	−0.118	−0.036	−0.186
G^+	−0.119	−0.183	0.203	.553**	.525*	.473*	.596**	.477*	.539*	−0.248	−0.054	0.119
G^−	−0.096	−0.142	0.301	.616**	.569**	.587**	.668**	.537*	.624**	−0.179	−0.003	0.176
G^+/G^−	−0.031	−0.015	−0.332	−.472*	−0.354	−.611**	−.505*	−.466*	−.515*	−0.177	−0.138	−0.174

*Correlation is significant at the 0.05 level; **Correlation is significant at the 0.01 level.

Discussion

The results showed that the increase in the organic matter content of soils treated with organic amendments significantly improved the pore structure of alpine meadow soil, reduced the soil bulk density and increased the porosity, pore number and connectivity of the soil. These results are consistent with previous studies that showed a positive correlation between SOM content and soil pore space (Garcia-Orenes et al. 2005, 2013; Tejada and Gonzalez 2008). Garcia-Orenes et al. (2005) indicated the application of biosolids increased significantly the organic carbon, carbohydrates and aggregate stability percentage, resulting in a decrease of the bulk density of soils. An increase in SOM content will improve soil structural stability, thus increasing soil porosity and aeration (Tejada and Gonzalez 2008). SOM will mix with denser mineral components in the soil, and soil bulk will decrease and porosity will increase due to the dilution effect of adding lower density organic matter to the denser mineral components (Chen et al., 2013). Moreover, microorganisms in the soil will improve soil structure by decomposing fresh organic matter, causing soil particles to aggregate and ultimately increasing soil pore connectivity (Zhou et al. 2016). This is consistent with our results, which showed that the total PLFAs, bacteria, fungi, actinomycetes, G⁺ and G⁻ of soils treated with OF, PL, LCG, MCG and HCG were larger than soils treated with CT. Rabbi et al. (2020) indicated that adding glucose to the soil increased the SOM content and found a significant increase in porosity and pore connectivity in glucose-amended soils. Our results also showed that SOM content was highly significantly and positively correlated with macroporosity, pore surface area density and pore branch density and significantly and positively correlated with pore number density, pore length density, pore node density and connectivity. Therefore, organic amendments significantly improved the pore structure of alpine meadow soil, increasing soil macroporosity, branch density, length density and node density.

The results showed that the macroporosity, branch density, length density and node density of soils treated with OF, PL, LCG, MCG and HCG were larger than those of soils treated with CT. Jarvis (2007) observed that vertically convoluted and connected soil macropores facilitated the predominance of water transport through macropore infiltration. The connectivity of macropores enables them to conduct water, thus resulting in soil nutrient runoff (Pachepsky et al. 2000;). It is thus expected that water would travel more readily through the macropores in the soils treated with OF, PL, LCG, MCG and HCG than through those treated with CT. Therefore, it is expected that the soil water retention in the soils treated with OF, PL, LCG, MCG and HCG was smaller than that in soils treated with CT. This is consistent with our studies showing that the dyed area ratio and the maximum wetting depth of soils treated with OF, PL, LCG, MCG and HCG were smaller than those of soils treated with a CT. The increase in SOM content reduced the dyed area ratio and the maximum wetting depth, so that SOM significantly contributed to the water-holding capacity of the soil and weakened the generation of dominant soil flow. SOM possesses certain hydrophilic properties that can improve the ability to adsorb water (Yang et al. 2014). The results also showed that the saturated water content was significantly and positively correlated with the content of SOM (Table 6). Yang et al. (2014) found that the extensive accumulation of SOM plays a dominant role in controlling soil water retention in this alpine environment by controlling water retention in alpine soils. Ebel (2012) indicated that SOM was the main factor influencing the differences in soil water retention. Gao et al. (2021) indicated that the increase in SOM due to shrub encroachment contributed to the improvement in soil water retention. Therefore, organic amendments significantly increased the soil water retention ability and reduced the generation of preferential soil flow in alpine meadows.

The results showed that the soil microbial community was significantly and positively correlated with the surface area density, number density, macroporosity, branch density, length density and node density of soil macropores. Moreover, the surface area density, number density, macroporosity, branch density, length density and node density of soil macropores were the main connectivity parameters (Hu et al. 2019). This is consistent with previous studies showing that the spatial distribution of soil microbes was affected by soil porosity and pore size (Kravchenko et al. 2014; Jien et al. 2021). Soil pores affect the storage and transport of air, water and nutrients required for the survival of soil microbes, determining the distribution and composition of the soil microhabitat (Mummey and Stahl 2004; Bach et al. 2018), as well as the diffusion of substrates into microbial cells (Nunan et al. 2006). Yoo et al. (2006) pointed out that the pore size distribution could provide useful information for the mechanistic relationship between the soil structure and microbes. These results indicated that soil pore connectivity is a combination of many habitat attributes and can affect the structure and survival of the microbial community by changing the oxygen content (Kuikman et al. 1990; Young et al. 1998). Therefore, the soil macropore parameters (surface area density, number density, macroporosity, branch density, length density and node density) were the main parameters influencing the soil microbial community.

Conclusions

The findings of this study verified our hypothesis that organic amendments significantly improved the pore structure of alpine meadow soil, increasing soil porosity, number of pores and connectivity, and organic amendments improved the soil water retention and reduced the preferential flow. The soil macroporosity, branch density, length density and node density of soils treated with OF, PL, LCG, MCG and HCG were larger than those of soils treated with CT. The total PLFAs, bacteria, fungi, actinomycetes, G⁺ and G⁻ of soils treated with OF, PL, LCG, MCG and HCG were larger than those of soils treated with CT. In contrast, the dyed area ratio and the maximum wetting depth of soils treated with OF, PL, LCG, MCG and HCG were smaller than those treated with CT. There were significant positive correlations between the content of SOM and surface area density, number density, macroporosity, branch density, length density, node density and connectivity of soil macropores. The content of SOM was significantly and positively correlated with the MWD of dry aggregates, >0.25 mm dry aggregates, MWD of water-stable aggregates and >0.25 mm water-stable aggregates. Soil macropore parameters (surface area density, number density, macroporosity, branch density, length density and node density) were the main parameters influencing the soil microbial community. Therefore, SOM accumulation significantly improved the soil pore structure, increased the soil water retention ability and reduced the generation of preferential soil flow.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/03650340.2023.2289513

Additional information

Funding

This study was financially supported by the National Science Foundation of China (Grant number: 42371107), and Project Supported by State Key Laboratory of Earth Surface Processes and Resource Ecology (2022-TS-03).