Trichoderma-based fertilizer enhances quality of Elymus breviaristatus silage via microbial and metabolic modulation

Wenqian Xu; Xuefeng Yuan; Jun Cao; Xiaorui Cui; Keyi Fu; Gangsheng Wang; Ning Ling; Yali Yin; Qian Shi; Zunji Shi

doi:10.1038/s42003-026-10028-y

. 2026 May 6;9:1092. doi: 10.1038/s42003-026-10028-y

Trichoderma-based fertilizer enhances quality of Elymus breviaristatus silage via microbial and metabolic modulation

Wenqian Xu ¹, Xuefeng Yuan ¹, Jun Cao ¹, Xiaorui Cui ¹, Keyi Fu ¹, Gangsheng Wang ^1,², Ning Ling ¹, Yali Yin ³, Qian Shi ⁴, Zunji Shi ^1,^✉

PMCID: PMC13469633 PMID: 42092115

Abstract

Elymus breviaristatus is an important alpine forage, there is limited information regarding its potential use as silage and how fertilizer treatments affect the ensiling process in this forage crop. Here, we investigated how organic fertilizer (M), nitrogen-phosphorus-potassium fertilizer (NPK), and Trichoderma harzianum fertilizer (B) affect Elymus breviaristatus silage quality (30 days and 60 days ensiling) via microbiome and metabolome analyses. Before ensiling, plant height and chlorophyll content increased by 29.93% and 39.72%, respectively, in the B group. After 60 days of ensiling, the M group had reduced crude protein and elevated butyric acid, the NPK group had higher ammonia nitrogen and butyric acid, and the B group had increased crude protein and lactic acid. These quality shifts correlated with microbial and metabolic changes. In the M group, alpha-linolenic acid metabolism was downregulated and Alternaria enriched, while the NPK group had enhanced flavone biosynthesis and a reduced level of Lactiplantibacillus. The B group had enhanced glycine, serine, and threonine metabolism, and displayed the most complex microbial networks along with increased levels of Lactiplantibacillus and Aspergillus. Overall, these results demonstrate that Trichoderma-based fertilization enhances silage quality by promoting accumulation of beneficial microbes and increasing flux through specific metabolic pathways, potentially offering a sustainable strategy for alpine forage improvement.

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Subject terms: Microbial communities, Plant physiology

Microbial and metabolic analysis revealed that Trichoderma-based fertilizers improve the silage quality of Elymus breviaristatus.

Introduction

Elymus breviaristatus cv. Tongde (E. breviaristatus) is a perennial grass native to the Qinghai-Tibetan Plateau, known for its excellent adaptability to harsh alpine environments, including drought, cold, salt-alkali resistance, and its high palatability to various livestock¹. However, in high-altitude and cold regions, a seasonal imbalance in feed supply is a pronounced issue^2,3. Therefore, enhancing the nutritional quality and preservation efficiency of E. breviaristatus is crucial to ensuring a stable forage supply for livestock⁴. In this context, the quality of silage—along with its associated microbial communities and fermentation metabolism—plays a pivotal role in determining the feed value, safety, and storage stability of this forage⁵. Understanding how different fertilization treatments affect the silage quality and microbiota of E. breviaristatus is essential for optimizing its use in sustainable alpine livestock systems.

Extensive research has shown that fertilization can improve grassland soil structure and elevate forage yield and nutritional quality^6,7. Nitrogen, phosphorus and potassium fertilizers (NPK), organic fertilizers can change the nutritional status of forage through different mechanisms⁸. Nitrogen, phosphorus and potassium are essential nutrients for plant growth and development, and the application of fertilizers containing these elements can significantly improve the yield and quality of forage⁹. Organic fertilizers are rich in various organic, inorganic substances and microorganisms, which can increase crop yield and quality by improving the physical, chemical, and biological properties of soil¹⁰.

Numerous studies have also shown that the application of plant growth promoting microorganisms can improve plant nutrient utilization and growth while reducing fertilizer input^11–13. In China, the beneficial effects of Trichoderma harzianum (T. harzianum) on plants have been extensively studied, and some strains have been commercially applied in the field¹⁴. T. harzianum can improve plant resistance to salt stress and inhibit plant pathogens^15,16, the hydrolase produced by it can also convert lignocellulose into fermentable sugars¹⁷. For example, T. harzianum significantly increased the plant height, the stem diameter, the stem yield and the total sugar content of sweet sorghum¹⁶. Fundamentally, T. harzianum mainly enhances plant growth and quality by increasing the abundance of beneficial bacteria¹⁴. Compared to NPK fertilizers and organic fertilizers, they not only significantly reduce the pollution of agro-ecosystems¹⁸, but also have a direct impact on plant epiphytic microorganisms. It should be noted that the epiphytic microorganisms play an important role in the silage quality of forage. Thus, T. harzianum fertilizer is expected to improve the nutritional quality of forage and its silage by promoting beneficial epiphytic microbes.

During the silage process, a complex community of plant epiphytic microorganisms, the initiator of silage fermentation, converts water-soluble carbohydrates into organic acids under anaerobic conditions¹⁹. The accumulation of organic acids decreases the pH, thereby suppressing undesirable microorganisms and preserving the nutritional quality of the silage²⁰. Compared with hay, silage is a major source of diet for ruminants in cold regions²¹. The epibiotic microbial community composition of forage has a great influence on the fermentation quality of silage²². Usually, lactic acid additives can be used to change the epibiotic microbial community composition to improve the quality of silage, but butyric acid fermentation may also occur²³. The application of T. harzianum fertilizer has the potential to enhance silage quality by altering the epiphytic microorganism community of forage. However, limited studies have been conducted on how T. harzianum fertilizers affect silage quality, microbial community, and metabolites of forage in alpine ecosystems.

In this study, we investigated the silage quality of E. breviaristatus treated with NPK fertilizer, organic fertilizer, and T. harzianum fertilizer. To clarify the mechanism by which fertilization influenced silage fermentation, we conducted microbiome and metabolome analyses during the ensiling process. We hypothesized that different fertilization strategies distinctly regulated microbial succession and metabolic pathways during ensiling, thereby resulting in variable fermentation characteristics and chemical profiles of E. breviaristatus. The findings were expected to offer a viable approach for enhancing silage quality in cold and arid regions.

Results

Growth characteristics, microbial population and chemical composition of E. breviaristatus and soil physicochemical properties

Soil physicochemical properties were shown in Fig. 1A–J (Supplementary data 1). The soil pH (Fig. 1A) and available P content (Fig. 1D) in the NPK group and the B group were significantly higher than that in the CK group (P < 0.05). However, there were no significant differences in soil NO₃⁻-N content (Fig. 1B), NH₄⁺-N content (Fig. 1C), available K content (Fig. 1E), SOC content (Fig. 1F), moisture content (Fig. 1G), temperature (Fig. 1H), salinity (Fig. 1I), and electric conductivity (Fig. 1J) among groups. Compared with the CK group, the plant height (Fig. 1K) and chlorophyll content (Fig. 1L) of the B group (treated with T. Harzianum) increased significantly by 29.93% and 39.72%, respectively (P < 0.05). However, no significant differences were observed among groups in fresh weight (Fig. 1M), dry weight (Fig. 1N), and fresh-dry ratio (Fig. 1O).

As shown in Table 1 (Supplementary data 2), in the E. breviaristatus raw samples, the number of aerobic bacteria in the B group was significantly lower than that in the CK group (P < 0.05). However, there were no significant differences among the treatment groups in the counts of lactic acid bacteria, yeast and Enterobacteriaceae. In the M and NPK group, the content of NDF increased significantly compared to the CK group (P < 0.05). However, the content of CP, EE and ADF of E. breviaristatus in different groups had no remarkable difference.

Table 1.

Effects of different fertilization treatments on the microbial population and chemical composition before silage of E. breviaristatus

Items	Treatments				SEM	P value
Items	CK	M	NPK	B	SEM	P value
Lactic acid bacteria (Log10 cfu g⁻¹ FW)	5.32 ± 0.60^a	5.56 ± 0.56^a	5.15 ± 0.46^a	5.52 ± 0.99^a	0.179	0.858
Aerobic bacteria (Log10 cfu g⁻¹ FW)	5.73 ± 0.17^a	5.35 ± 0.18^ab	5.24 ± 0.39^ab	5.15 ± 0.04^b	0.074	0.017
Yeast (Log10 cfu g⁻¹ FW)	5.34 ± 0.26^a	4.63 ± 0.61^a	5.56 ± 0.47^a	4.78 ± 0.60^a	0.175	0.290
Enterobacteriaceae (Log10 cfu g⁻¹ FW)	4.27 ± 1.09^a	4.61 ± 0.12^a	4.40 ± 0.95^a	4.36 ± 1.03^a	0.168	0.906
DM (g/Kg)	268.50 ± 2.25^a	278.22 ± 1.21^a	276.56 ± 0.81^a	265.86 ± 1.75^a	2.247	0.205
CP (g/Kg DM)	159.63 ± 5.40^a	152.59 ± 3.95^a	157.71 ± 2.32^a	158.60 ± 2.42^a	3.863	0.913
EE (g/Kg DM)	81.60 ± 2.05^a	84.17 ± 0.51^a	76.16 ± 0.87^a	78.93 ± 1.02^a	1.946	0.472
NDF (g/Kg DM)	516.81 ± 2.39^b	531.17 ± 3.43^a	537.17 ± 4.04^a	517.20 ± 2.19^b	3.533	0.002
ADF (g/Kg DM)	298.97 ± 4.35^a	303.39 ± 1.96^a	298.10 ± 6.91^a	300.63 ± 2.19^a	3.937	0.962

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Different superscript lowercase letters indicate significant differences between different fertilization treatments (P < 0.05).

CK no fertilization control, M organic fertilizer group, NPK nitrogen, phosphorus, and potassium fertilizer group, B T. harzianum fertilizer group, DM dry matter, CP crude protein, EE ether extract, NDF neutral detergent fiber, ADF acid detergent fiber, SEM standard error of the mean.

Fertilization effects on microbial and chemical profiles of E. breviaristatus silage

The microbial dynamics during silage fermentation played a crucial role in determining the final silage quality. Microbial population of E. breviaristatus after silage was shown in Table 2 (Supplementary data 3). At 30 days of silage, the number of Enterobacteriaceae in the B group was significantly lower than that in the CK group (P < 0.05), while no significant differences were observed in the counts of lactic acid bacteria, aerobic bacteria, and yeast among different groups. At 60 days of silage, the number of lactic acid bacteria was significantly reduced in the NPK group (P < 0.05), the number of aerobic bacteria and yeast was significantly increased in the M group compared to the CK group (P < 0.05), but there was no significant difference in the counts of Enterobacteriaceae among different groups. Overall, from 30 to 60 days of silage, all groups showed a decreasing trend in the populations of lactic acid bacteria, aerobic bacteria, yeasts, and Enterobacteriaceae, indicating microbial succession over time during the silage process.

Table 2.

Effects of different fertilization treatments on microbial population and chemical composition after silage of E. breviaristatus

Items	Ensiling days	Treatments				SEM	P value
Items	Ensiling days	CK	M	NPK	B	SEM	T	D	T × D
Lactic acid bacteria (Log10 cfu g⁻¹ FW)	30	7.07 ± 0.22^Aa	7.23 ± 0.68^Aa	7.00 ± 0.06^Aa	7.40 ± 0.49^Aa	0.093	0.016	0.003	0.244
Lactic acid bacteria (Log10 cfu g⁻¹ FW)	60	6.56 ± 0.41^Aa	6.86 ± 0.70^Aa	5.77 ± 0.39^Bb	7.09 ± 0.66^Aa	0.093	0.016	0.003	0.244
Aerobic bacteria (Log10 cfu g⁻¹ FW)	30	7.49 ± 1.25^Aa	7.46 ± 0.26^Aa	7.45 ± 0.13^Aa	7.24 ± 0.30^Aa	0.069	0.089	<0.001	0.125
Aerobic bacteria (Log10 cfu g⁻¹ FW)	60	5.56 ± 0.91^Bb	6.51 ± 0.50^Ba	5.91 ± 1.37^Bb	5.79 ± 0.14^Bb	0.069	0.089	<0.001	0.125
Yeast (Log10 cfu g⁻¹ FW)	30	6.63 ± 0.27^Aa	6.78 ± 0.41^Aa	6.84 ± 0.28^Aa	6.94 ± 0.08^Aa	0.123	0.290	<0.001	0.343
Yeast (Log10 cfu g⁻¹ FW)	60	5.46 ± 0.85^Bb	6.61 ± 0.59^Aa	5.39 ± 0.08^Bb	5.74 ± 1.57^Bb	0.123	0.290	<0.001	0.343
Enterobacteriaceae (Log10 cfu g⁻¹ FW)	30	7.17 ± 0.49^Aa	6.87 ± 0.66^Aa	7.38 ± 0.32^Aa	4.98 ± 1.31^Ab	0.102	0.003	<0.001	<0.001
Enterobacteriaceae (Log10 cfu g⁻¹ FW)	60	5.40 ± 0.49^Ba	5.20 ± 1.10^Ba	5.11 ± 1.08^Ba	5.51 ± 0.32^Aa	0.102	0.003	<0.001	<0.001
DM (g/Kg)	30	489.91 ± 14.70^Aa	429.77 ± 2.48^Ab	445.68 ± 3.90^Ab	486.19 ± 0.75^Aa	2.037	<0.001	<0.001	0.045
DM (g/Kg)	60	453.60 ± 7.50^Ba	409.45 ± 2.89^Bb	401.83 ± 3.86^Bb	457.80 ± 2.88^Ba	2.037	<0.001	<0.001	0.045
CP (g/Kg DM)	30	202.37 ± 2.79^Ab	203.71 ± 3.93^Ab	226.12 ± 2.71^Aa	229.81 ± 3.69^Aa	2.483	<0.001	0.007	0.314
CP (g/Kg DM)	60	232.89 ± 1.14^Bb	210.76 ± 1.91^Ac	234.07 ± 1.09^Ab	240.76 ± 2.74^Ba	2.483	<0.001	0.007	0.314
EE (g/Kg DM)	30	104.80 ± 1.71^Aa	106.17 ± 2.46^Aa	104.99 ± 1.17^Aa	104.49 ± 3.42^Aa	2.442	0.568	<0.001	0.615
EE (g/Kg DM)	60	90.01 ± 4.37^Ba	89.46 ± 4.06^Ba	92.10 ± 2.80^Ba	89.96 ± 4.18^Ba	2.442	0.568	<0.001	0.615
NDF (g/Kg DM)	30	469.34 ± 0.95^Ab	479.60 ± 3.37^Aa	420.89 ± 2.17^Ad	437.76 ± 2.41^Ac	0.852	<0.001	<0.001	<0.001
NDF (g/Kg DM)	60	447.17 ± 2.40^Bb	463.17 ± 2.31^Ba	435.08 ± 6.60^Bc	433.53 ± 7.68^Ac	0.852	<0.001	<0.001	<0.001
ADF (g/Kg DM)	30	251.31 ± 0.95^Bb	277.90 ± 8.91^Aa	257.01 ± 4.01^Bb	253.39 ± 0.76^Ab	1.755	<0.001	<0.001	0.261
ADF (g/Kg DM)	60	275.06 ± 2.42^Aa	283.43 ± 3.06^Aa	277.47 ± 9.51^Aa	268.41 ± 8.27^Aa	1.755	<0.001	<0.001	0.261
WSC (g/Kg DM)	30	23.52 ± 4.16^Aa	26.60 ± 3.42^Aa	23.67 ± 3.11^Aa	24.10 ± 2.35^Aa	1.098	0.025	<0.001	<0.001
WSC (g/Kg DM)	60	10.13B ± 8.67^Aa	5.53 ± 2.46^Ba	8.50 ± 4.41^Ba	10.41 ± 5.14^Ba	1.098	0.025	<0.001	<0.001

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Different superscript lowercase letters indicate significant differences between different fertilization treatments (P < 0.05). Different superscript uppercase letters indicate significant differences between ensiling days (P < 0.05).

CK no fertilization control, M organic fertilizer group, NPK nitrogen, phosphorus and potassium fertilizer group, B T. harzianum fertilizer group, DM dry matter, CP crude protein, EE ether extract, NDF neutral detergent fiber, ADF acid detergent fiber, WSC water-soluble carbohydrates, 30 30 days of silage of E. breviaristatus, 60 30 days of silage of E. breviaristatus, SEM standard error of the mean, T fertilization treatment, D ensiling day, T × D interaction between ensiling day and treatment.

Fertilization treatments significantly influenced the chemical composition of E. breviaristatus during the silage process (Table 2, Supplementary data 3). At 30 days of silage, the DM content was significantly lower in the M and NPK groups than in the CK group (P < 0.001), decreasing by 12.28% and 9.03%, respectively. The CP content was significantly higher (P < 0.001), while the NDF content was significantly lower, in the B and NPK groups than in the CK group (P < 0.001). In contrast, both NDF and ADF contents were significantly higher in the M group than in the CK group (P < 0.001). At 60 days of silage, the DM content in the M and NPK groups were significantly reduced than in the CK group (P < 0.001), decreasing by 9.73% and 11.41%, respectively. The CP content in the B group was significantly increased while the CP content in the M group was significantly reduced in contrast to the CK group (P < 0.001). Compared with the CK group, the NDF content of the B group was significantly reduced while the NDF content of the M group was significantly increased (P < 0.001), which was in agreement with that at 30 days of silage. The contents of EE and WSC had no significant difference under different treatments during the whole silage process. As for the changes of chemical composition during the silage process, the contents of DM, EE, WSC, and NDF showed a decreasing trend, while the contents of CP and ADF showed an increasing trend from 30 to 60 days. Notably, the CP content of the B group was always the highest in the whole silage process.

Fertilization effects on fermentation characteristics of E. breviaristatus silage

Fertilization treatments also influenced the fermentation characteristics of E. breviaristatus during the ensiling process (Table 3, Supplementary data 3). At 30 days of silage, the LA concentration was remarkably elevated in the M, NPK, and B groups compared to the CK group, the BA concentration was significantly increased in the M group (P < 0.001). However, the pH-value and AA concentration were not significantly different among treatment groups. At 60 days of silage, the AN concentration in the NPK group increased significantly (P < 0.001). In addition, the concentrations of LA and AA were significantly increased in the B group (P < 0.001). And the BA concentration was significantly increased in the M and NPK group in contrast to the CK group (P < 0.001). As the silage process progressed, the pH displayed a decreasing trend, while the concentrations of AN, LA, and AA showed an increasing trend from 30 to 60 days. At the end of silage, the AN concentration was highest in the NPK group, the BA concentration was highest in the M group, and the highest LA concentration was found in the B group.

Table 3.

Effects of different fertilization treatments on fermentation characteristics after silage of E. breviaristatus

Items	Ensiling days	Treatments				SEM	P value
Items	Ensiling days	CK	M	NPK	B	SEM	T	D	T × D
pH	30	4.81 ± 0.29^Aa	4.23 ± 0.42^Aa	4.41 ± 0.33^Aa	4.87 ± 0.24^Aa	0.052	0.112	<0.001	0.082
pH	60	3.47 ± 0.19^Ba	3.64 ± 0.25^Ba	3.36 ± 0.08^Ba	3.12 ± 0.29^Ba	0.052	0.112	<0.001	0.082
AN (g/Kg DM)	30	13.28 ± 1.11^Bab	11.32 ± 1.52^Bb	14.44 ± 1.24^Ba	12.77 ± 1.32^Bab	0.277	<0.001	<0.001	0.001
AN (g/Kg DM)	60	16.87 ± 0.66^Ab	16.86 ± 0.65^Ab	23.16 ± 2.45^Aa	16.06 ± 0.61^Ab	0.277	<0.001	<0.001	0.001
LA (g/Kg DM)	30	4.05 ± 0.73^Bb	5.46 ± 0.89^Ba	5.86 ± 0.19^Ba	6.04 ± 0.99^Ba	0.236	<0.001	<0.001	<0.001
LA (g/Kg DM)	60	16.19 ± 0.62^Ab	16.42 ± 0.62^Ab	16.63 ± 0.21^Ab	26.16 ± 2.88^Aa	0.236	<0.001	<0.001	<0.001
AA (g/Kg DM)	30	8.37 ± 0.27^Ba	9.96 ± 1.04^Ba	9.89 ± 2.01^Ba	10.17 ± 1.69^Ba	0.218	<0.001	<0.001	0.002
AA (g/Kg DM)	60	13.79 ± 1.03^Ab	14.73 ± 0.55^Ab	20.16 ± 0.85^Aa	19.95 ± 1.79^Aa	0.218	<0.001	<0.001	0.002
BA (g/Kg DM)	30	4.30 ± 0.57^Ab	7.35 ± 0.91^Aa	5.48 ± 0.29^Ab	4.41 ± 0.58^Ab	0.136	<0.001	0.596	0.001
BA (g/Kg DM)	60	4.64 ± 0.86^Ab	7.09 ± 0.71^Aa	6.61 ± 0.80^Aa	4.38 ± 1.03^Ab	0.136	<0.001	0.596	0.001

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CK no fertilization control, M organic fertilizer group, NPK nitrogen, phosphorus and potassium fertilizer group, B T. harzianum fertilizer group, AN ammonia nitrogen, LA lactic acid, AA acetic acid, BA butyric acid, 30 30 days of silage of E. breviaristatus, 60 60 days of silage of E. breviaristatus, SEM, standard error of the mean, T fertilization treatment, D ensiling day, T × D interaction between ensiling day and treatment.

Microbial diversity of E. breviaristatus before and after silage

The rarefaction curve approached a plateau (Fig. 2A, Supplementary data 4), indicating that the number of additional features no longer increased with additional sequencing data, and that the current sequencing depth sufficiently covered most bacterial species present in the samples. As the silage progressed, the ACE index decreased gradually in each group (Fig. 2B, Supplementary data 4). The CPCoA based on the Bray-Curtis matrix showed that the β-diversity of bacterial communities was significantly different between raw samples and silage samples (Fig. 2C, Supplementary data 4). For bacterial community β-diversity at different periods (Supplementary data 4), there was no significant difference between pairwise comparisons before silage (Fig. 2D, P = 0.57) and 30 days of silage (Fig. 2E, P = 0.25), but there was significant difference at 60 days of silage (Fig. 2F, P = 0.04). Then, in order to compare the differences of bacterial community between two groups, we conducted ANOSIM for 60 days silage, and the results showed that the P-values of CK vs. M, CK vs. NPK, CK vs. B, M vs. NPK, M vs. B, NPK vs. B were 0.018, 0.203, 0.049, 0.913, 0.09, and 0.086, respectively.

Similarly, we performed microbial diversity analysis of fungal communities for E. breviaristatus before and after ensiling (Supplementary Fig. 1). The rarefaction curve also tended to be flat (Supplementary Fig. 1A), indicating sufficient sequencing depth and comprehensive species coverage. The ACE index of the M group decreased significantly from 30 days to 60 days of silage (Supplementary Fig. 1B), and the other groups also showed a downward trend. In lined with the variation of bacterial community diversity, the β-diversity of fungal community diversity was significantly different between raw samples and silage samples (Supplementary Fig. 1C). Moreover, for β-diversity of fugal community at different periods, there was no significant difference before silage (Supplementary Fig. 1D, P = 0.38) or at 30 days of silage (Supplementary Fig. 1E, P = 0.38), the difference was significant only at 60 days of silage (Supplementary Fig. 1F, P = 0.01), which was also consistent with the changes in bacterial communities. Thus, the bacterial and fungal communities’ diversity jointly implied that the differences of silage microorganisms would become more obvious with the prolongation of silage time. The results of ANOSIM showed that the P-values for 60 days silage of CK vs. M, CK vs. NPK, CK vs. B, M vs. NPK, M vs. B, NPK vs. B at were 0.37, 0.539, 0.104, 0.089, 0.132, 0.007, respectively.

Microbial community composition of E. breviaristatus before and after silage

Epiphytic microorganisms of silage materials had important effects on silage quality during fermentation. Therefore, we analyzed the composition of epiphytic microorganisms in E. breviaristatus before and after silage. The bacterial phylum composition before and after silage were shown in Fig. 3A (Supplementary data 5), and the specific relative abundance value of bacterial phylum was shown in Supplementary Table 3. The predominant phyla in raw samples were Proteobacteria (CK 66.51%, M 42.05%, NPK 65.23%, B 58.00%), Firmicutes (CK 30.49%, M 52.12%, NPK 29.42%, B 34.38%) and Actinobacteria (CK 2.94%, M 3.42%, NPK 3.23%, B 4.80%). Notably, from pre-silage to 30 days of silage, Firmicutes markedly increased, while that of Proteobacteria markedly decreased. From 30 days to 60 days of silage, Actinobacteria markedly decreased. Then, we performed LEfSe analysis to explore the microbial markers with significant differences among different groups (Fig. 3B, Supplementary data 6). In the raw samples, p_Proteobacteria, o_Enterobacterales, c_Gammaproteobacteria, f_Erwiniaceae, and g_Pentoea were differentially abundant taxa in the CK group, while f_Bacillales_Incertae_Sedis_XII and g_Exiguobacterium in the NPK group, and f_Carnobacteriaceae and g_Carnobacterium in the B group. After 30 days silage, g_Enterococcus, f_Enterococcaceae, g_Leuconostoc, and f_Streptococcaceae were enriched in the M group, f_Bacillaceae_1 and g_Bacillus was enriched in the NPK group, and g_Lactococcus was enriched in the B group. When silage was for 60 days, o_Lactobacillales, c_Bacilli, p_Firmicutes, f_Lactobacillaceae, g_Aerococcus, and f_Aerococcaceae appeared as the differentially abundant taxa in the B group. We also used a cladogram to visualize microorganisms that differ significantly among groups (Fig. 3C). The cladogram was presented as a tree-like structure, with branches colored according to groups. Taxonomic levels from phylum to genus were represented from the outmost to the innermost rings.

Most studies on silage fungi had focused on aerobic metamorphism, while changes in fungal communities during fermentation had not been fully elucidated²⁴. The fungi community composition before and after E. breviaristatus silage in four groups were shown in Fig. 3D (Supplementary data 5), and the specific relative abundance value of fungal phylum was shown in Supplementary Table 4. The dominant fungal phyla before and after silage were Ascomycota (CK 87.98%, M 95.75%, NPK 91.28%, B 90.01%) and Basidiomycota (CK 6.59%, M 1.00%, NPK 5.48%, B 3.54%), consistent with existing research²⁵. With the extension of silage time, Ascomycota significantly decreased, while Basidiomycota significantly increased. The LDA score chart of LEfSe analysis for fungal community was showed in Fig. 3E (Supplementary data 6). Before silage, the differentially abundant taxa were c_Dothideomycetes and o_Pleosporales in the M group, and p_Ascomycota, g_Articulospora, f_Helotiaceae, and o_Helotiales in the NPK group. At 30 days silage, f_Phaeosphaeriaceae, f_Pleosporaceae, and g_Alternaria were enriched in the M group, and g_Neosetophoma was enriched in the NPK group. When silage was for 60 days, the differentially abundant taxa were g_Holocotylon in the M group, and c_Eurotiomycetes, f_Aspergillaceae, g_Aspergillus, o_Eurotiales, c_Agaricomycetes, f_Lycoperdaceae, p_Basidiomycota, o_Agaricales, and g_Lycoperdon in the B group. We used a cladogram to illustrate significantly different fungal taxa among groups (Fig. 3F).

Finally, we screened for microorganisms that were significantly different at the genus level after 60 days of E. breviaristatus silage (Fig. 3G–L, Supplementary data 7). The M group showed a significant increase in Staphylococcus and Alternaria (Fig. 3I, L), accompanied by a marked reduction in Lactiplantibacillus and Enterococcus (Fig. 3G). In the NPK group, a significant reduction also was observed in Lactiplantibacillus and Enterococcus (Fig. 3G). In the B group, the marked increases were observed in Lactiplantibacillus, Lycoperdon and Aspergillus (Fig. 3G, J, K).

Bacterial and fungal co-occurrence networks in E. breviaristatus before and after silage

The epiphytic microbial community served as a critical driver in the initial phase of silage fermentation, exhibiting intricate and multifaceted interactions throughout the process⁴. The co-occurrence network effectively demonstrated the complex interactions among epiphytic microorganisms, silage duration, and fertilizer treatments. Under different fertilization treatments and silage time, the bacterial (Fig. 4) and fungal community (Supplementary Fig. 2) networks of E. breviaristatus had different complexity and stability (Supplementary data 8). Topological parameters such as the numbers of nodes and edges, average degree, and average clustering coefficient can be used to measure the complexity and stability of the network. The number of nodes, edges, average degree and average clustering coefficient of the network for bacteria and fugal community were shown in Supplementary Fig. 3 and Supplementary Fig. 4. The network of bacterial communities showed that the B group was more stable and complex before and after 60 days of silage, evidenced by higher number of nodes, edges, average degree and average clustering coefficient (Fig. 4J, L). For fungal communities, the higher network complexity and stability were observed in both NPK and B groups before and after silage (Supplementary Fig. 2G–L).

Fig. 4 — Bacterial community co-occurrence networks of pre-silage in the (A) CK group, D M group, G NPK group and J B group. Bacterial community co-occurrence network of 30 days silage in the (B) CK group, E M group, H NPK group and K B group. Bacterial community co-occurrence network of 60 days silage in the (C) CK group, F M group, I NPK group and L B group.

Metabolic analysis of E. breviaristatus silage at 60 days

We performed pseudotargeted metabolomics detection to investigate the effects of fertilization treatments on the metabolites of E. breviaristatus silage after 60 days (Supplementary data 9). The OPLS-DA model was used to evaluate the results of CK vs. M (Fig. 5A, Q² = 0.821, R²X = 0.479), CK vs. NPK (Fig. 5B, Q² = 0.986, R²X = 0.4), CK vs. B (Fig. 5C, Q² = 0.944, R²X = 0.538), M vs. NPK (Supplementary Fig. 5A, Q² = 0.975, R²X = 0.486), M vs. B (Supplementary Fig. 5B, Q² = 1, R²X = 0.483) and NPK vs. B (Supplementary Fig. 5C, Q² = 1, R²X = 0.702). The OPLS-DA model was acceptable when values of Q² and R²X were greater than 0.4, and it was batter when Q² and R²X closer to 1²⁶. Our results showed significant differences in all pairwise comparisons, indicating that the three fertilization treatments had significant impact on the metabolites of silage after 60 days compared to the CK group. The permutation test of each model was conducted for further verification (Supplementary Fig. 6), which only proved that CK vs. NPK (Supplementary Fig. 6B) and CK vs. B (Supplementary Fig. 6C) models were valid. Next, we performed volcanic plots for differential metabolites of CK vs. M (Fig. 5D), CK vs. NPK (Fig. 5E), CK vs. B (Fig. 5F), M vs. NPK (Supplementary Fig. 5D), M vs. B (Supplementary Fig. 5E) and NPK vs. B (Supplementary Fig. 5F). Compared with the CK group, there were 56 up-regulated metabolites and 104 down-regulated metabolites in the M group, 31 up-regulated metabolites and 23 down-regulated metabolites in the NPK group, and 45 up-regulated metabolites and 18 down-regulated metabolites in the B group. The retention time (RT), declustering potential (DP), and collision energy (CE) of the differential metabolites can be found in the Supplementary data 10.

Fig. 5 — OPLS-DA score chart of (A) CK vs. M, B CK vs. NPK, C CK vs. B. Volcano plot of (D) CK vs. M, E CK vs. NPK, F CK vs. B.

KEGG enrichment pathway analysis of differential metabolites and the correlation analysis between key metabolites and differential microorganisms

To better understand the functional roles of differential metabolites during the silage process, we conducted KEGG enrichment pathway analysis for E. breviaristatus silage at 60 days (Fig. 6A–C, Supplementary data 10). In the figure, a -log10(p) value greater than 1.3 indicated statistical significance (Pvalue < 0.05), highlighting key enriched metabolic pathways. Compared with the CK group, phenylalanine, tyrosine, and tryptophan biosynthesis was key up-regulated metabolic pathway in the M group, while alpha-Linolenic acid metabolism was key down-regulated metabolic pathway (Fig. 6A). Flavone and flavonol biosynthesis were key up-regulated metabolic pathway in the NPK group (Fig. 6B). Glycine, serine and threonine metabolism was key up-regulated metabolic pathway in the B group (Fig. 6C).

Fig. 6 — KEGG enrichment pathway of (A) CK vs. M, B CK vs. NPK, C CK vs. B. Correlation heatmap of (D) CK vs. M, E CK vs. NPK, F CK vs. B.

We also performed correlation analysis between key metabolites from the enriched pathways of differential metabolites and differential microorganisms at genus level for 60 days silage of E. breviaristatus (Supplementary data 7, Supplementary data 10). In the M group, Lactiplantibacillus showed a significant negative correlation with L-Glutamyl-L-Tyrosine and L-Tyrosine (Fig. 6D). Alternaria was also significantly negatively correlated with 13(S)-Hydroperoxylinolenic acid and 7-iso-Jasmonic acid (Fig. 6D). In the NPK group, Lactiplantibacillus was negatively correlated with quercetin (Fig. 6E). In the B group, Staphylococcus was significantly negatively correlated with O-Phospho-L-serine, Aspergillus was significantly positively associated with O-Phospho-L-serine and betaine (Fig. 6F).

Discussion

Fertilizers effect on plant growth, microbial population of E. breviaristatus before ensiling and soil physicochemical properties

In our study, the soil pH and available P content increased significantly in the NPK group (Fig. 1A, D). In the NPK group, the increase in available phosphorus was attributed to the direct application of the fertilizer²⁷. While in the B group, the rise resulted from the activation of beneficial microorganisms by T. harzianum, which converted phosphorus from an unavailable form into an available form^28,29. The observed increase in soil pH aligned with the rise in available phosphorus, which was consistent with reports from other studies^30,31. The increased available P content in the NPK and B groups led to an increase in the plant height of E. breviaristatus (Fig. 1K), although the increase was not significant in the NPK group. It was plausible that, in addition to improving soil fertility, the T. harzianum biofertilizer positively influenced the root system, consequently leading to a more significant effect of plant height³². Soil moisture content, temperature, salinity, and electrical conductivity altered soil nutrient availability and enzyme activities, thereby further influenced plant growth^33,34. However, fertilization had no significant effect on these soil parameters (Fig. 1G–J). Plant height was usually positively correlated with the total above-ground biomass³⁵. Chlorophyll content reflected the physiological connection between photosynthetic capacity and plant growth, and could be used to infer the growth status and biomass production of plants³⁶. In this study, the B group also significantly increased the chlorophyll content (Fig. 1L) in E. breviaristatus, consistent with previous findings in cucumber and Coptis chinensis^37,38. Interestingly, the increase in plant height and chlorophyll content did not have an impact on the yield. This might be due to factors such as the planting density and photosynthetic capacity of E. breviaristatus. High planting density would reduce the stomatal density of leaves and net photosynthesis, leading to a decrease in dry matter accumulation and ultimately affecting the increase in yield^39,40.

Before silage, the number of aerobic bacteria in the B group was significantly lower than that in the CK group (Table 1). This might because T. harzianum grew rapidly and occupied most of the growth space, while the cell wall degrading enzymes were generated to decompose microbial cells and rapidly absorb required nutrients, thus inhibiting the growth of aerobic bacteria³⁸. It was well known that the growth of certain aerobic bacteria was often accompanied by the increase of temperature and pH, leading to the deterioration of silage⁴¹. Therefore, a lower abundance of aerobic bacteria was generally beneficial for improving silage quality. The NDF was the main cell wall component in forage, which directly affected the digestibility of ruminants. Lower NDF content in forage made it easier to be decomposed by ruminants⁴². Our results showed that the NDF content of E. breviaristatus in the M and NPK groups was significantly increased compared to the CK group (Table 1), which might due to the excessive concentrations of sheep manure and NPK fertilizer used⁴³.

Fertilizers effect on silage quality and microbial population of E. breviaristatus

Enterobacteriaceae were considered to be the main adverse bacteria during silage fermentation, as they competed with lactic acid bacteria for available substrates, degrading plant proteins, and may produce toxic compounds⁴⁴. In the B group, the counts of Enterobacteriaceae were decreased significantly compared to the CK group at 30 days of silage (Table 2). A lower number of Enterobacteriaceae in the B group would ensure better preservation for silage quality. Lactic acid bacteria was the key microorganisms of silage, which could ensure the production of high-quality silage by inhibiting other harmful microorganisms²⁴. Yeast in silage could cause decreased aerobic stability and increased the risk of mycotoxin production. When oxygen entered, it achieved growth by using sugar and lactic acid as a soluble substrate⁴⁵. Compared with the CK group, the number of lactic acid bacteria was significantly reduced in the NPK group, the number of aerobic bacteria and yeast was significantly increased in the M group at 60 days of silage (Table 2). NPK fertilizer, although effective in elevating the CP content of fresh grass, was found to decrease the WSC concentration and reduce the rate of pH decline^46,47, thereby inhibiting the dominant fermentation of lactic acid bacteria⁴⁸. A lower number of lactic acid bacteria in the NPK group and higher number of aerobic bacteria and yeast in the M group might enhanced the degradation of silage. Throughout the silage period, the DM content decreased and NDF increased markedly in the M group, while the CP content increased and NDF decreased significantly in the B group (Table 2). The lower DM and CP content in the M group were attributed to aerobic deterioration caused by the higher number of aerobic bacteria and yeasts at 60 days of silage⁴⁹. The silage fiber content was decreased with the increase of CP⁵⁰, which were found in the B group in this study. The CP content was a key indicator of silage quality, with higher levels generally reflecting better nutritional value⁵¹.

AN was used to reflect the degree of protein degradation in the evaluation of silage quality, and the higher the concentration of AN meant the more protein degradation⁵². At 60 days of silage, the AN concentration increased significantly in the NPK group (Table 3). Meanwhile, the CP content in the NPK group, exhibiting a significant increase at 30 days of silage, no longer showed a significant increase at 60 days. This change was closely related to the significant decrease in the number of lactic acid bacteria. The acidity of LA was the strongest among the silage organic acids and played a decisive role in the reduction of pH, while appropriate AA was beneficial to the aerobic stability of silage⁵³. Our results showed that the concentrations of LA and AA increased significantly in the B group (Table 3). In addition, the BA concentration increased observably in the M and NPK group compared to the CK group (Table 3). These may be due to the increase of butyric acid bacteria in the M group, which was accompanied by an increase in yeast and aerobic bacteria⁵⁴, and the significant decrease of lactic acid bacteria in the NPK group.

Microbial succession and fermentation

To further explore the relationship among bacterial community, fertilization treatments and ensiling duration, we conducted high-throughput sequencing of bacterial populations before and after ensiling. As silage progressed, the ACE index of bacteria community gradually decreased in all groups (Fig. 2B), which was due to the acidic environment created by lactic fermentation. The acidic environment inhibited the growth of other microorganisms, so the bacterial community became less diverse^55,56. The ACE index of fungal community also showed a downward trend (Supplementary Fig. 1B). This might due to lactic acid bacteria could reduce the diversity and richness of the fungal, and the anaerobic and acidic environment was not favorable to the diversity of fungal communities during silage progress^57,58. For bacterial and fungal community β-diversity at different periods, the difference was significant only at 60 days of silage (Fig. 2F, P = 0.04; Supplementary Fig. 1F, P = 0.01). The differences in the microbial communities of each group at 60 days of silage might explain the differences in chemical composition and fermentation characteristics of silage among the groups at the same period⁵⁹. From pre-silage to 30 days of silage, Firmicutes increased significantly, while that of Proteobacteria decreased significantly (Fig. 3A, Supplementary Table 3), as a result of Firmicutes’ ability to ferment sugars into organic acids and inhibit Proteobacteria under anaerobic silage conditions⁶⁰. Consistent with previous study, Enterobacterales, Erwiniaceae and Pentoea were enriched in the CK group of raw samples in our study (Fig. 3B), which could compete nutrients with lactic acid bacteria and cause silage degradation⁶¹. And in lactic acid-producing bacteria, Lactococcus, Enterococcus and Leuconostoc usually appeared in the early stage of silage⁵⁵, then Lactobacillus grew rapidly with the decrease of pH value and became the dominant strains in the late silage⁶², which were consistent with our results.

Furthermore, we screened out the differential microorganisms of E. breviaristatus ensiling for 60 days. The M group showed a significant increase in Staphylococcus and Alternaria (Fig. 3I, L), and a marked reduction in Lactiplantibacillus (Fig. 3G). The reproduction of Staphylococcus and Alternaria could increase dry matter loss, and mycotoxins produced by Alternaria could also inhibit protein synthesis⁶³. Hence, the proliferation of Staphylococcus and Alternaria was associated with a decline in both DM and CP content (Table 2). In silage, Lactiplantibacillus had a positive effect on silage fermentation characteristics, including lowering pH, accumulating lactic acid, inhibiting BA and AN production⁶⁴. Therefore, the reduction of Lactiplantibacillus in the M group contributed to an elevated concentration of BA at 60 days silage (Table 3). The B group showed a significant increase in Lactiplantibacillus, Lycoperdon and Aspergillus (Fig. 3G, J, K). Research had demonstrated that Lycoperdon pyriforme effectively mitigated the fermented odors in soy beverages without compromising protein, suggesting that Lycoperdon genus might enhance protein preservation during silage fermentation⁶⁵. According to most studies, Aspergillus could produce a variety of enzymes, including cellulase, hemicellulose and xylanase, which could promote the decomposition of plant cell wall components⁶⁶. Therefore, the B group demonstrated higher content of CP and LA coupled with the reduced levels of NDF at 60 days silage (Tables 2 and 3). The microbial community changes observed in this study were relevant and aligned with findings from similar silage studies^67,68. A corresponding co-occurrence network analysis was also undertaken to explore these correlations.

For bacterial co-occurrence networks, the higher network complexity and stability were observed in the B group (Fig. 4J, L). These findings indicated that the B group exhibited greater microbial diversity with more complex species composition and reduced inter-species competition, possibly driven by nutritional determinants including crude proteins, that might have mitigated competitive pressures within the bacterial community⁴. It was worth noting that as the silage progressed, the complexity of the bacterial community gradually decreased. The observed phenomenon likely resulted from the acidic conditions developed during silage fermentation, which inhibited the propagation of bacterial community, ultimately resulting in reduced diversity within the bacterial community⁶⁹. Phyllospheric microorganisms had vital functions in plant health, such as facilitating in nutrient acquisition, promoting growth, and defending against pathogen⁷⁰. Previous studies had mostly focused on the effects of T. harzianum fertilizer on rhizosphere and soil microorganisms^14,71. Moreover, in this study, T. harzianum fertilizer also had an impact on phyllospheric microorganisms. T. harzianum was known to produce various volatile organic compounds and secondary metabolites that could suppress plant pathogens and stimulate plant defense responses^72,73. These biochemical activities indirectly influenced the phyllosphere microbial community by modifying the leaf surface environment⁷⁴. This mechanism provided a possible explanation for the observed changes in the phyllospheric microorganisms under T. harzianum. In conclusion, the most intricate microbial network observed in the B group suggested that T. harzianum fertilizer increased the plant height, chlorophyll content and silage quality after 60 days of E. breviaristatus via modifying the microbial dynamics.

Metabolite shifts and KEGG enrichment pathway of E. breviaristatus after silage

The silage process was mainly achieved through microbial metabolic pathways, which mediated the transformation of metabolites and the degradation of substrates⁷⁵. In the M group, Lactiplantibacillus was also significantly negatively correlated with L-Glutamyl-L-Tyrosine and L-Tyrosine, both involved in the phenylalanine, tyrosine and tryptophan biosynthesis pathway (Fig. 6D). L-Glutamyl-L-Tyrosine, a glutamate-tyrosine dipeptide generated during protein degradation^76,77. The reduced Lactiplantibacillus abundance was correlated with increased proliferation of detrimental microorganisms⁷⁸, potentially leading to the enhanced protein degradation in silage and consequent accumulation of L-Glutamyl-L-Tyrosine. Alternaria showed a significant negative correlation with 13(S)-Hydroperoxylinolenic acid and 7-iso-Jasmonic acid (Fig. 6D), which were key metabolites in the downregulated alpha-Linolenic acid metabolism. Alpha-Linolenic acid could enhance plant resistance to pathogens⁷⁹. The markedly increased Alternaria in the M group (Fig. 3L), which was pathogenic in silage⁸⁰, was consistent with the downregulated alpha-Linolenic acid metabolism. Overall, the downregulation of alpha-Linolenic acid metabolism and beneficial bacteria such as Lactiplantibacillus jointly promoted a significant increase in harmful microbes like Alternaria. This ultimately led to the reduced CP and DM contents, along with the increased BA concentration, indicating a significant decline in the silage quality of the M group.

In the NPK group, the flavonoid and flavonol biosynthesis belonged to the metabolic pathway of phenylpropanoid, which produced a large number of polyphenols⁸¹. The production of polyphenols was a response of plant cells to biotic stresses suffered during the fermentation period and mold infection, which implied deterioration of the silage⁸². Correlation analysis results showed that Lactiplantibacillus was negatively correlated with quercetin in the NPK group (Fig. 6E). Quercetin was a key metabolite of flavone and flavonol biosynthesis, and had strong antioxidant properties⁸³. Lactiplantibacillus had a positive effect on reducing oxidative stress⁸⁴, and its reduced abundance might lead to an increase of oxidative stress and quercetin as antioxidant metabolite. In general, up-regulated flavonoid and flavonol biosynthesis and reduced Lactiplantibacillus increased the contents of harmful substances such as AN and BA in silage of the NPK group.

Glycine, serine, and threonine were the direct precursors for protein synthesis^85,86, facilitating protein synthesis in the B group. Correlation analysis results indicated that Staphylococcus was significantly negatively correlated with O-Phospho-L-serine (Fig. 6F), which was a key metabolite in the upregulated glycine, serine and threonine metabolism. Staphylococcus consumed amino acids during metabolism including L-serine, a precursor for O-Phospho-L-serine synthesis⁸⁷. Thus, the decline of Staphylococcus likely contributed to the increased O-Phospho-L-serine. O-Phospho-L-serine acted as a critical intermediate, either dephosphorylating to form L-serine or serving as a precursor for L-cysteine, both of which were essential for protein biosynthesis⁸⁸. Aspergillus was significantly positively associated with O-Phospho-L-serine and betaine in glycine, serine and threonine metabolism (Fig. 6F). Aspergillus could produce a variety of enzymes⁶⁶, breaking down cellulose and hemicellulose to release substrates that stimulated microbial amino acid metabolism, likely enhancing the production of O-Phospho-L-serine and betaine. Betaine was a osmotic protectant that improved microbial fermentation and promoted the production of LA⁸⁹. Consequently, the upregulation of glycine, serine and threonine metabolism, the decreased Staphylococcus and the increased Aspergillus, were associated with the elevated CP content and the LA accumulation, as well as a decline in NDF in the B group.

Our results revealed that the silage quality of E. breviaristatus was significantly influenced by fertilization strategies, with its variation driven by specific microbial and metabolic shifts. The quality deterioration in the M and NPK groups was correlated with the suppression of Lactiplantibacillus and alpha-linolenic acid metabolism, and the promotion of flavonoid biosynthesis. In contrast, the quality enhancement in the B group was correlated with the inhibition of Staphylococcus, the promotion of Aspergillus, and the activation of glycine, serine, and threonine metabolism. Thus, targeted fertilization proved to be a viable strategy for optimizing silage quality by reprogramming the forage microbiota and metabolites, thereby advancing sustainable feed production in the Qinghai-Tibetan Plateau.

Materials and methods

Experimental design

The experimental site was located in Tongde County (100°39′E, 35°09′N, and 3270–3500 m above sea level), Hainan Tibetan Autonomous Prefecture, Qinghai Province, China. The annual ambient temperature ranged from –0.77 to 1.69 °C, and the total annual precipitation was 699 mm⁹⁰. The region had a plateau continental climate without an absolute frost-free period. The experimental area was flat, with soil characterized as dark chestnut soil⁹¹.

E. breviaristatus seeds were acquired from Qinghai Sanjiang Group Co., Ltd (Xining, China). Four treatments were set up (CK, no fertilization control; M, organic fertilizer group; NPK, nitrogen, phosphorus, and potassium fertilizer group; B, T. harzianum fertilizer group), with six replicates set for each treatment. The area of the each replicate plot was 15 m² (3 m × 5 m), and the row spacing was 1 m. The sowing was carried out in late May 2023, with a sowing amount of 7.5 kg·hm⁻², and the soil was covered by 1–2 cm after sowing. The line spacing of strip sowing was 30-40 cm. All three fertilizers were carried out in June. The decomposed sheep manure (organic matter ≥45%, 330 kg·hm⁻²) was used for the M group. The nitrogen, phosphorus and potassium fertilizer (N: P: K = 15: 15: 15, 225 kg·hm⁻²) was used for the NPK group. The T. harzianum fertilizer (NJAU4742) (Jiangsu Sweeper Biological Technology Co., Ltd, Nanjing, China) (7.5 kg·hm⁻²) from Nanjing Agricultural University was used for the B group, which was diluted with water at a ratio of 1:100 before spraying. The strain (NCBI accession: LVVK00000000.1) was originally isolated from mature compost and preserved at the Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization^92,93. T. harzianum was cultured on potato dextrose agar (PDA) at 28 °C for 5 days. The spores were then harvested by washing the cultures with sterile water and filtering the suspension through four layers of sterile gauze, and the conidial concentration was finally 2 × 10⁹ CFU/g⁹².

Growth characteristics analysis and sample collection

Growth characteristics were measured in September 2023. Ten plants were randomly selected in each plot to measure the height and calculate the average value. Similarly, ten plants were randomly selected from each plot, and 3 leaves were selected from each plant to determine the chlorophyll content, and then the average value was obtained. Three 1 m² quadrats were selected from each plot, and the above-ground parts of all plants in the quadrat were weighed, fresh weight was recorded, and the average value was obtained. Next, 400 g was sampled in each plot, and after being brought back to the laboratory, the plants were deoxidized at 105 °C for 30 min, and then dried at 65 °C to constant weight for determining the dry weight. Finally, the fresh-dry ratio was calculated according to fresh weight and dry weight.

Furthermore, in September 2023, E. breviaristatus was collected and brought back to the laboratory for silage preparation, and the soil from each plot was collected for determination of physical and chemical properties.

Silage preparation

After bringing back to the laboratory, a portion of the E. breviaristatus was reserved for pre-silage quality and silage microbiota analysis. The remaining portion was cut short to 2–3 cm and ensiled immediately at local room temperature, with 400 g of each bag (polyethylene plastic bag, 25 cm × 35 cm), which was opened and sampled at 30 and 60 d of silage, respectively. A total of 48 silage bags (4 treatments × 6 replicates × 2 durations) were prepared, representing two durations for each of the plot. Chemical composition, fermentation characteristics, microbial population, and microbial community were analyzed before and after ensiling. In addition, the silage samples were taken at 60 d and filled with 5 mL centrifuge tubes for pseudotargeted metabolomics analysis.

Chemical composition, fermentation characteristics and microbial population analysis

For raw E. breviaristatus and silage samples, 20 g of the sample was taken and dried in an oven at 65 °C for 72 h to determine the dry matter content⁷⁷, and the hay was crushed and sieved through a 0.45 mm sieve for the determination of crude protein (CP), crude fat (EE), neutral detergent fiber (NDF), and acid detergent fiber (ADF). In order to calculate CP content using a Kjeldahl apparatus⁹⁴, the flow injection analyzer (FIAstar 5000 Analyzer) from Grassland Science and Agriculture College of Lanzhou University was used to measure the total nitrogen content. The contents of EE, NDF and ADF were determined by Fat Analyzer (ANKOM XT10i) and Automatic Fiber Analyzer (ANKOM DELTA), and then the NDF and ADF contents were calculated according to the normal cellulose method⁹⁵.

Before and after ensiling, 20 g of the sample was taken and added with 180 mL of distilled water for juice extraction, and the juice was filtered through four layers of gauze into two 10 mL centrifuge tubes. One tube was stored in the ultra-low temperature freezer (–80 °C) for the determination of ammonia nitrogen (NH₃-N, AN) and water-soluble carbohydrates (WSC)⁴. According to anthrone sulfuric acid colorimetric method and phenol sodium hypochlorite colorimetric method, the contents of WSC and AN were determined, and their concentrations were computed using the method described by Broderick and Thomas^96,97. The other tube was acidified with 50% H₂SO₄ (7.14 mol·L⁻¹) after the determination of pH using a pH meter (Hanna Instruments Italia Srl Co., Ltd, Padua, Italy)⁹⁸, which was also stored in the ultra-low temperature freezer (–80 °C) for the determination of lactic acid (LA), acetic acid (AA) and butyric acid (BA)⁹⁹. When to be tested, the samples were thawed and filtered with a 0.22 μm filter, then determined by high performance liquid chromatograph (Agilent HPLC 1260, Inc., Waldbronn, Germany). The chromatographic Column was selected as ShodexRspak KC-811S-DVB gel Column (300*8 mm) and 2.5 mmol·L⁻¹ sulfuric acid was used as mobile phase. The flow rate was 0.6 mL·min⁻¹, the sample size was 10 μL, the column temperature was 50 °C, and the detection wavelength was 210 nm.

10 g of the raw E. breviaristatus and silage samples were respectively added into a conical flask containing 90 mL 0.85% sterile saline, put into a shaker set at 180 rpm for 20 min and then filtered into a centrifuge tube with four layers of sterile gauze. The collected 2 mL of filtrate was used for counting of lactic acid bacteria, aerobic bacteria, yeasts and Enterobacteriaceae¹⁰⁰, the rest of the filtrate was centrifuged, and the precipitate obtained was used for microbial analysis²³. Lactic acid bacteria were cultured on De Man Rogosa Sharpe (MRS) agar and incubated under aerobic conditions at 37 °C for 72 h for count^4,101. Aerobic bacteria, yeast and Enterobacteriaceae were cultured on nutrient agar (NA), potato dextrose agar (PDA) and violet-red bile agar (VRBA) in a constant temperature incubator at 37 °C for 48 h, and then counted¹⁰².

Soil physicochemical properties analysis

Six grams of the dried soil sample was weighed into a 50 mL centrifuge tube, added 15 mL of distilled water, placed it on a shaker at 180 rpm for 1 h. One pH meter (Hanna Instruments Italia Srl Co., Ltd, Padua, Italy) was used to measure the pH of the soil sample.

The determination of nitrate nitrogen (NO₃⁻-N) and ammonium nitrogen (NH₄⁺-N) were performed on fresh soil samples. Five grams of soil was weighed into a 250 mL conical flask. Then, 50 mL of a 2 mol·L⁻¹ potassium chloride (KCl) solution was added to the flask¹⁰³. The mixture was shaken at 180 rpm for 30 min in an orbital shaker. After shaking, it was allowed to settle for 5 min. The supernatant was filtered through qualitative filter paper into a dry, clean plastic bottle. Finally, the concentrations of NO₃⁻-N and NH₄⁺-N were determined using a Smartchem 450 automatic discontinuous chemical analyzer (AMS-Alliance Co., Ltd, Rome, Italy).

Available phosphorus (P) was determined by sodium bicarbonate leaching-molybdenum-antimony colorimetric method¹⁰⁴. 2.5 g soil was weighed into a 200 mL plastic bottle, following extraction with 50 mL of 0.5 mol·L⁻¹ sodium bicarbonate (NaHCO₃) solution and one spoonful of activated carbon. The mixture was shaken for 30 min and then immediately filtered through a phosphorus-free filter paper. The available P content was measured using a Smartchem 450 discrete auto analyzer (AMS-Alliance Co., Ltd, Rome, Italy).

Soil available potassium (K) was determined by the ammonium acetate extraction-flame photometry method¹⁰⁵. 5 g of air-dried soil was weighed into a 200 mL plastic bottle and extracted with 50 mL of 1 mol·L⁻¹ neutral ammonium acetate (CH₃COONH₄) solution. The mixture was shaken at 180 rpm for 30 min and then filtered. The filtrate was analyzed by a flame photometry (Sherwood Scientific Co., Ltd, Cambridge, England).

Soil organic carbon (SOC) was determined by the sulfuric acid-potassium dichromate oxidation method¹⁰⁶. 0.1 to 1 g of air-dried soil was weighed into a dry, heat-resistant glass tube. Then, 5 mL of 0.8 mol·L⁻¹ potassium dichromate (K₂Cr₂O₇) was added and homogenized, after which 5 mL of concentrated sulfuric acid (H₂SO₄) was introduced and mixed thoroughly. The reaction mixture was heated in an oil bath at 180 °C for 5 min. Finally, the residual K₂Cr₂O₇ was titrated with ferrous sulfate (FeSO₄) solution, and the SOC content was calculated from the consumption of K₂Cr₂O₇.

Microbial analysis

The DNA of microbial sediments stored in the ultra-low temperature freezer (-80 °C) before and after silage was extracted using the DNA Kit (Biogoethe Biotechnology Co., Ltd, Wuhan, China). The PCR reaction system was 25 μL (1 μL of each forward and reverse primer, 1 μL of DNA template, 12.5 μL of 2x SanTaq PCR Mix, and 9.5 μL of ultrapure water). The cycling conditions were 95 °C for 10 min, followed by 32 cycles of 94 °C for 45 s, 52 °C for 45 s, and 72 °C for 45 s, with a final extension at 72 °C for 10 min¹⁰⁷. For bacteria, the 16S rRNA gene V3–V4 region was amplified using primers 341 F (5′-CCTAYGGGRBGCASCAG-3′) and 806 R (5′-GGACTACNNGGGTATCTAAT-3′). For fungi, the ITS region was amplified using primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′). The sequencing library was prepared according to the 16S rRNA and ITS metagenomic sequencing protocol (Illumina Co., Ltd, San Diego, CA, USA). Equal amounts of 16S rRNA and ITS amplicons were pooled, and paired-end sequencing (2 × 300 bp) was carried out on an Illumina MiSeq platform (Shanghai Majorbio Bio-pharm Technology Co., Ltd). After demultiplexing, sequences were merged with FLASH and quality filtered using fastp. Denoising was done using the Deblur plugin in Qiime2, generating amplicon sequence variants (ASVs). To reduce the impact of sequencing depth, sequences were rarefied to 24,000 reads per sample, resulting in an average Good’s coverage of 97.90%. A standardized abundance table was created by calculating each feature’s relative abundance in the total number of reads.

Sequencing data were processed and analyzed with vegan, reshape2, and ggplot2 packages using R software (version 4.2.1)^26,108. Alpha diversity was assessed using the ACE index, while constrained principal coordinate analysis (CPCoA) was used to evaluate beta diversity among treatment groups. Analysis of similarities (ANOSIM) was conducted to compare microbial communities before and after silage. Linear discriminant analysis of effect size (LEfSe) was performed to explore the composition of microbial communities and to screen for differential genera. In the LEfSe analysis, the significance level for both the Kruskal-Wallis and Wilcoxon rank tests was set at 0.05, and the threshold for the differential feature LDA score was set at 2. Finally, we conducted co-occurrence network analysis of bacterial and fungal communities at genus level to explore the relationship between fertilization treatments and microbial communities.

Pseudotargeted metabolomics analysis

The pseudotargeted metabolome integrated the extensive scope of non-targeted metabolome with the high accuracy of targeted metabolome. Metabolites were extracted from silage samples for 60 days (Metware Biotechnology Co., Ltd., Wuhan, China). The chemical identities of metabolites were confirmed using the self-built MWDB (Metware Database) and a widely targeted UPLC-MS/MS platform. Qualitative analysis was performed based on precursor ion pair, secondary spectrum, and retention time (RT). Quantitative analysis was conducted using multiple reaction monitoring (MRM).

After centrifugation (10,000 g, 10 min), metabolites were absorbed using a CNWBOND Carbon-GCB SPE Cartridge (250 mg, 3 mL; ANPEL Laboratory Technologies Co., Ltd, Shanghai, China) and filtered (SCAA-104, 0.22 μm pore size). Using an LC-ESI-MS/MS system (Shimadzu UFLC system, Kyoto, Japan; Applied Biosystems 4500 QTRAP MS, Foster City, California, USA) for detection of the extracts. The chromatographic conditions were as follows. Waters ACQUITY UPLC HSS T3 C18 column (2.1*100 mm), water/acetonitrile gradient, flow rate 0.4 ml/min, temperature 40 °C and injection volume 5 μL. The effluent was introduced into a triple quadrupole-linear ion trap mass spectrometer (QTRAP; Applied Biosystems, Foster City, CA, USA) equipped with an electrospray ionization (ESI) source for detection. All mass spectrometric analyses in triple quadrupole (QQQ) scan mode were performed using QTRAP system. MRM transitions were monitored for each metabolite based on their elution times, with further optimization of declustering potential (DP) and collision energy (CE).

We employed the false discovery rate (FDR) to correct the P-values for multiple testing in metabolomics analysis. Pseudotargeted metabolomics data results were integrated and analyzed using SIMCA14.1. Principal component analysis (PCA) was performed for all treatment groups, followed by the orthogonal partial least-squares discriminant analysis (OPLS-DA) model and permutation test for the pairwise comparison analysis, and then variable importance in the project (VIP) value of the pairwise comparison was outputted. Then, the fold change (FC) value and P value of metabolic data were calculated, and the metabolites, which were consistent with FC > 2 or < 0.5, Pvalue < 0.05 and VIP > 1, were selected and namely for the differential metabolites. Next, the differential metabolites were used to make volcanic maps using GraphPad Prism 8, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment was analyzed (http://www.kegg.jp/kegg/pathway.html). Finally, bacterial and fungal genera with significant differences were selected for correlation analysis with key differential metabolites.

Statistics and reproducibility

For all the microbial count data, we performed log transformation. Before all the analyses of variance, we conducted the Shapiro-Wilk test and Levene’s test (Supplementary Table 1 and 2). Chemical composition and microbial population of raw E. breviaristatus (4 treatments × 6 replicates) under different fertilization treatments were analyzed using the one-way analysis of variance (ANOVA) (IBM SPSS 29.0). The effects of different fertilization treatments and silage time on the chemical composition, microbial population, and fermentation characteristics of E. breviaristatus (4 treatments × 6 replicates × 2 durations) were analyzed by two-way ANOVA (IBM SPSS 29.0). The data were presented as mean ± standard deviation and Duncan’s multiple comparison test was used for the pairwise comparison, with a P value < 0.05 indicating a significant difference^4,58.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information^{(1.4MB, pdf)}

42003_2026_10028_MOESM2_ESM.pdf^{(43KB, pdf)}

Description of Additional Supplementary Files

Supplementary Data 1-10^{(969.3KB, xlsx)}

Reporting Summary^{(1.3MB, pdf)}

Acknowledgements

This study was financially supported by the Fundamental Research Funds for the Leading Scientist Project of Qinghai Province (2023-NK-147).

Author contributions

W.Q.X.: Investigation, methodology, Visualization, data curation, writing-original draft preparation, and writing-review and editing. Z.J.S., X.F.Y., J.C., X.R.C., K.Y.F., Y.L.Y. and Q.S.: Investigation. Z.J.S. and G.S.W.: Writing-review & editing. Z.J.S. and N.L.: Project administration and funding acquisition, methodology. All authors read and approved the final manuscript.

Peer review

Peer review information

Communications Biology thanks Pichad Khejornsart, Chika C. Anotaenwere and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Usman Irshad and David Favero.

Data availability

The 16S rRNA gene sequences for all samples are available in the NCBI SRA database with the accession code PRJNA1248902, while the ITS gene sequencing data is accessible under PRJNA1249239 in the NCBI SRA database. The metabolomics data in MetaboLights with the accession code MTBLS14092. Source data can be obtained from the Supplementary Data 1-9.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

The online version contains supplementary material available at 10.1038/s42003-026-10028-y.

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PERMALINK

Trichoderma-based fertilizer enhances quality of Elymus breviaristatus silage via microbial and metabolic modulation

Wenqian Xu

Xuefeng Yuan

Jun Cao

Xiaorui Cui

Keyi Fu

Gangsheng Wang

Ning Ling

Yali Yin

Qian Shi

Zunji Shi

Abstract

Introduction

Results

Growth characteristics, microbial population and chemical composition of E. breviaristatus and soil physicochemical properties

Fig. 1. Effects of different fertilization treatments on soil properties and growth characteristics of E. breviaristatus.

Table 1.

Fertilization effects on microbial and chemical profiles of E. breviaristatus silage

Table 2.

Fertilization effects on fermentation characteristics of E. breviaristatus silage

Table 3.

Microbial diversity of E. breviaristatus before and after silage

Fig. 2. Effects of different fertilization treatments on bacterial community diversity before and after E. breviaristatus silage.

Microbial community composition of E. breviaristatus before and after silage

Fig. 3. Effects of different fertilization treatments on bacterial and fungal community composition before and after E. breviaristatus silage.

Bacterial and fungal co-occurrence networks in E. breviaristatus before and after silage

Fig. 4. Co-occurrence networks of bacterial communities at genus level before and after E. breviaristatus silage.

Metabolic analysis of E. breviaristatus silage at 60 days

Fig. 5. Pseudotargeted metabolomics analysis of E. breviaristatus silage for 60 days.

KEGG enrichment pathway analysis of differential metabolites and the correlation analysis between key metabolites and differential microorganisms

Fig. 6. Analysis of KEGG enrichment pathway of differential metabolites in E. breviaristatus silage for 60 days, and correlation analysis between key metabolites and differential genera.

Discussion

Fertilizers effect on plant growth, microbial population of E. breviaristatus before ensiling and soil physicochemical properties

Fertilizers effect on silage quality and microbial population of E. breviaristatus

Microbial succession and fermentation

Metabolite shifts and KEGG enrichment pathway of E. breviaristatus after silage

Materials and methods

Experimental design

Growth characteristics analysis and sample collection

Silage preparation

Chemical composition, fermentation characteristics and microbial population analysis

Soil physicochemical properties analysis

Microbial analysis

Pseudotargeted metabolomics analysis

Statistics and reproducibility

Reporting summary

Supplementary information

Acknowledgements

Author contributions

Peer review

Peer review information

Data availability

Competing interests

Footnotes

Supplementary information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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RESOURCES

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