Combined application of sheep manure and organic fertilizer improves soil quality and microbial community structure and function in alpine mining areas

Abstract

The harsh soil conditions in alpine mining areas severely restrict plant growth, necessitating the urgent development of an optimal fertilization strategy to facilitate soil restoration and ecosystem recovery. Six treatments were incorporated: control with no fertilization (CK), 100% sheep manure (S), 100% commercial organic fertilizer (F), 60% sheep manure + 40% commercial organic fertilizer (M1), 50% sheep manure + 50% commercial organic fertilizer (M2), and 40% sheep manure + 60% commercial organic fertilizer (M3). The treatments' impacts on soil properties, microbial composition, functionality, and their interactions were systematically analyzed. The combined application of sheep manure and commercial organic fertilizer significantly enhanced soil organic matter (SOM) and available nutrients. The findings revealed that the M1 treatment showed the greatest improvement, with total nitrogen (TN), total phosphorus (TP), SOM, available nitrogen (AN), and available phosphorus (AP) increasing by 211.07%, 136.27%, 388.18%, 564.97%, and 282.53%, respectively, in comparison to the CK treatment. Nutrient addition significantly altered the structure of soil microbial communities and the abundance of functional microorganisms. But it had no significant effect on the Shannon and Simpson indices of the soil fungal community. Key factors such as TP, SOM, TN, AP, and AN significantly influenced bacterial distribution, while TP, AN, and AP played a crucial role in fungal distribution. Bacterial diversity and fungal functionality were mainly regulated by TP, while bacterial functionality was primarily controlled by pH and SOM. In conclusion, the M1 treatment appears to be a viable strategy for promoting soil restoration in alpine mining areas.IMPORTANCEEcological restoration in mining areas is a global challenge. We systematically investigated the effects of sheep manure, commercial organic fertilizer, and their combined application on soil physicochemical properties, microbial community structure, and functions in the Muli mining area of the Qinghai-Tibetan Plateau. Results showed that the combined application significantly improved soil quality, with the optimal ratio being 60% sheep manure and 40% commercial organic fertilizer. Furthermore, the study revealed the mechanisms by which nutrient addition enhances soil quality by analyzing the relationships between soil properties and microbial communities under different treatments. These findings provide valuable insights for restoring ecosystem functions in alpine mining areas of the Qinghai-Tibetan Plateau and promoting sustainable grassland agriculture.

Keywords: Alpine mining area; commercial organic fertilizer; sheep manure; soil microorganisms; soil quality.

Fig 1

Alpha diversity of soil bacterial and fungal communities under different treatments. Note: figures (a) to (d) show the OTUs index, Chao’s index, Shannon’s index, and Simpson’s index of the soil bacterial community, respectively. Figures (e) to (h) showed the OTUs index, Chao’s index, Shannon’s index, and Simpson’s index of soil fungal communities, respectively. CK, no fertilization; S, 100% sheep manure; F, 100% commercial organic fertilizer; M1, 60% sheep manure + 40% commercial organic fertilizer; M2, 50% sheep manure + 50% commercial organic fertilizer; M3, 40% sheep manure + 60% commercial organic fertilizer.

Fig 2

PCoA analysis based on Bray-Curtis distance of soil bacterial communities (a) and fungal communities (b) under different treatments.

Fig 3

Relative abundance of soil microbial communities at the phylum and genus levels across various treatments. Note: (a) and (c) indicate the relative abundance of bacterial communities at the phylum and genus levels, while (b) and (d) represent the relative abundance of fungal communities at the phylum and genus levels. a1–a11 represent Proteobacteria, Actinobacteriota, Chloroflexi, Bacteroidota, Firmicutes, Acidobacteriota, Myxococcota, Gemmatimonadota, Patescibacteria, Verrucomicrobiota, and others, respectively; b1–b8 represent Ascomycota, Unclassified_Fungi, Basidiomycota, Chytridiomycota, Olpidiomycota, Mortierellomycota, Monoblepharomycota, and others, respectively; c1–c11 represent Sphingomonas, Norank_JG30-KF-CM45, Norank_A4b, Nocardioides, Pseudarthrobacter, Devosia, Unclassified_Microbacteriaceae, Norank_Vicinamibacterales, Altererythrobacter, OLB13 and others, respectively; d1–d11 represent Thelebolus, Unclassified__Lasiosphaeriaceae, Schizothecium, Preussia, Unclassified__Sordariales, Gibberella, Unclassified__Fungi, Kernia, Unclassified_f__Sporormiaceae, Cephalotrichum, and others, respectively.

Fig 4

LDA score histograms were calculated for taxa with different abundances in soil bacterial communities (a) and fungal communities (b) under different treatments. c, Class; o, Order; f, Family; g, Genus.

Fig 5

Prediction of soil bacterial function under different treatments.

Fig 6

Prediction of soil fungal function under different treatments.

Fig 7

Mantel’s correlations analysis between soil physicochemical properties and bacterial (a) and fungal (b) diversity, respectively. Solid lines indicate significant correlations and dashed lines indicate insignificant correlations. Red lines indicate positive correlations and blue lines indicate negative correlations. Thick and thin lines indicate higher or lower corresponding correlations, respectively.

Fig 8

Redundancy analysis of dominant bacterial genera (a) and dominant fungal genera (b) with soil physicochemical properties. In the figure, A1–A10 represent Sphingomonas, Norank_JG30-KF-CM45, Norank_A4b, Nocardioides, Pseudarthrobacter, Devosia, Unclassified_Microbacteriaceae, Norank_Vicinamibacterales, Altererythrobacter, and OLB13, respectively; B1–B10 represent Thelebolus, Unclassified__Lasiosphaeriaceae, Schizothecium, Preussia, Unclassified__Sordariales, Gibberella, Unclassified__Fungi, Kernia, Unclassified_f__Sporormiaceae, and Cephalotrichum, respectively.

Fig 9

Structural equation modeling analysis of nutrient additions regulating microbial diversity and function by altering soil physicochemical properties. Blue and red arrows signify significant positive and negative impacts, respectively. The numerical values attached to the arrows represent standardized path coefficients. Numbers adjacent to arrows are standardized path coefficients, and the width of the arrow is proportional to the strength of the path coefficient. R² is the proportion of variance explained by the model. Significance levels: * indicates P < 0.05, ** indicates P < 0.01, and *** indicates P < 0.001.