Effective microorganism combinations improve the quality of compost-bedded pack products in heifer barns: exploring pack bacteria-fungi interaction mechanisms
Growing global consumption of livestock products (especially dairy products) has led to an increased demand, resulting in the rapid development in livestock production. There were approximately 270 million dairy cows worldwide in 2020  and this number is increasing gradually to meet the rising demand of livestock products. Consequently, the substantial amount of animal waste contributes to global environmental degradation . Therefore, effective animal waste management and recycling have become critical challenges for sustainable livestock production systems nowadays. Proper utilization of animal waste includes composting , feed production , and biogas energy technology , while the implementation of an appropriate waste management system necessitates the consideration of political, economic, environmental, and other relevant factors. In recent decades, the biological fermentation bed system has yielded positive outcomes in swine and poultry production. This system effectively integrated emission control of livestock manure, in-situ treatment of livestock pollution and animal welfare. As such, it represents an eco-friendly, clean production technology capable of achieving “near zero emission” from livestock . However, the application of the fermentation bed system to dairy cows is relatively different from swine and poultry and is termed compost-bedded pack barns (CBP) or “freewalk housing” . Currently, the exploitation of CBP can improve animal health, growth performance, milk quality and welfare parameters in dairy cows . Furthermore, it can also mitigate environmental pollution caused by the disposal of manure and urine . The selection of bedding material is the crucial factor affecting the comfort of dairy cows in CBP, materials commonly used include sawdust, due to its good water absorption properties, as well as rice husk and straw, which offer good air permeability . The key factor in CBP is the aerobic fermentation of microorganisms within the pack, as the primary goal is to promote the growth and reproduction of effective microorganisms (EM). This leads to increased microbial activity, generating heat and maintaining a dry pack . Generally, EM mainly includes Lactobacillus , Bacillus , yeast, Actinomycetes and photosynthetic bacteria during the composting process which can produce cellulase, protease and several other enzymes that quickly decompose feces and urine, resulting in positive effects on composting and barn environment . Among them, Lactobacillus , Bacillus and yeast were widely used as microorganism supplements in livestock waste composting, benefiting the quality of compost products. For instance, Lactobacillus plantarum enhances the efficiency of sheep manure composting and the quality of final products . Inoculating Lactobacillus acidophilus , Lactobacillus fermentum , and Bacillus subtilis into bovine manure supported with molasses could improve their nutritional, economic, and energetic value . Co-inoculation with Trichoderma and Bacillus shortened the composting cycle and accelerated the degradation of lignocellulose . Moreover, these EM can also modulate the microbial community and metabolites during composting. For example, adding 5% Bacillus megaterium to the mixed compost of pig manure and wheat straw can improve the growth of ammonia-oxidizing bacteria during the high-temperature period, which regulate nitrification and denitrification processes, leading to reduced ammonia and nitrous oxide emissions . The addition of Bacillus coagulans increased the relative abundance of Firmicutes in the thermophilic and cooling stages and improved the total nitrogen content in cattle manure compost by modulating nitrification and denitrification activities . Furthermore, the adjunction of compound microbial inoculums ( Bacillus subtilis : Bacillus amyloliquefaciens : Bacillus licheniformis : Saccharomyces cerevisiae = 3:1:3:4) to pig and chicken manure compost increased the compost temperature, as well as the abundance of Actinobacteria and Bacteroidetes, while decreased the abundance of Firmicutes, resulting in enhanced fiber degradation and nitrogen retention . Till now, studies on EM in biological fermentation bed systems have primarily focused on swine and poultry, while mainstream commercial strains were mainly purchased from Japan, e.g. EM-Bokashi, which has been widely used in agriculture and livestock production . Recent research indicated the organic material generated from the compost barn confinement system could be used as an organic fertilizer and proved the positive effects on the plant growth and health . However, there is limited information regarding EM application in CBP, particularly concerning the impact of inoculating different functional EM combinations to the pack on composting, because the addition of EM combinations appeared to maximize the quality of the compost product. For example, co-supplementation of Bacillus subtilis and Saccharomyces cerevisiae into swine manure was more improved on compost maturity and gas emissions than single and non-supplementation . Therefore, we hypothesized that adding different EM combinations can improve the barn environment and the quality of compost-bedded pack products by modulating the microbial community within the pack. 

Experiment period, location and CBP facilities selection This experiment was conducted at the dairy farm of Edweigang Modern Animal Husbandry Co., Ltd. (Sihong, Jiangsu, China) from April to November 2019. The area is situated at 33°08 ∼ 33°44 north latitude and 117°56 ∼ 118°46 east longitude with 28.6 m average altitude having an average temperature of around 14.3 °C, 893.9 mm average annual precipitation and 2356.4 h average annual sunshine hours. Twelve CBP barns were selected from the dairy farm, with their specific location distribution shown in Fig. . Each barn featured a 580 m 2 pack area and a 174 m 2 feeding area (no bedding), having identical lighting and ventilation methods. Bedding material in the pack area consisted of 20% sawdust, 30% straw, 30% chaff and 20% dry cow dung with full cultivation at a depth of approximately 30 cm via a rototiller (WT-051575X, Shandong Weiken Technology Co., Ltd, China) during the daily morning feeding to promote the aerobic composting process. Pack microbial inoculation and bedding management Microbial inoculums were purchased in powder form from Haicheng Biotechnology Co., Ltd. (Yangzhou, China) that includes Compound Bacillus (B, 50% Bacillus subtilis , 30% Bacillus licheniformis and 20% Bacillus natto ), Lactobacillus plantarum (L) and Saccharomyces cerevisiae (S) having live spores of 50 billion cfu/g, 6 billion cfu/g and 6 billion cfu/g respectively. The inoculums also included calcium carbonate as a carrier and polyvinyl alcohol as the supporting agent. All CBP barns were subjected to the following four treatments (three barns each treatment) as described in Fig. : CON (no microbial inoculum added), LB (addition of L and B at 1:2 weight ratio), LS (addition of L and S at 1:2 weight ratio) and LBS (addition of L, B and S at 1:1:1 weight ratio). Microbial inoculums were mixed according to the specified ratios for each treatment, with 9 kg of mixed inoculums added per ton of bedding. Inoculums were configured using urea, salt, brown sugar and sterile water at a weight ratio of 10:1:1:100:1000, then fermented at a constant temperature of 37 ℃ in a fermenter for 3–5 days to activate the microbial inoculums. The activated inoculums were sprayed in different barns and turned the bedding evenly via a rototiller above-mentioned. The initial pack cultivation occurred in early April 2019, with fresh bedding and microbial inoculants added at regular intervals of half a month based on the fermentation status of the pack throughout the experiment period. By the end of November (before winter), all the packs were cleaned out and used for fertilizer evaluation. Management of heifers Holstein heifers (total 168 heads) with an initial age of 16 ± 1.8 months and a body weight of 244.64 ± 28.95 kg were housed in these barns, fourteen were raised in each barn, maintaining an average density of 13.8 m 2 per heifer. Heifers were given free access to the total mixed ration and clean drinking water. Total mixed ration composition included alfalfa, corn silage, corn, soybean meal, cottonseed meal and DDGS, having a 70:30 forage-to-concentrate ratio on the dry matter basis. Sampling procedures and measurement Barn environment assessment The sampling date was scheduled in the middle of April, July, and November. Environmental indicators of each barn were measured over three consecutive days, then three sampling sites were selected within each barn (Fig. ). At each site, three wet and dry bulb thermometers were placed evenly at a height of 1 m above the ground in each pen to record the temperature and relative humidity every 4 h daily (6 times a day), the average value of the above 3 sites and 6 time points were used for data analysis. The temperature-humidity index (THI) was calculated from the published formula . In addition, a microcomputer laser dust meter (PC-3A(S), Qingdao Juchuang Environmental Protection Group Co., Ltd., China) was used to determine PM 2.5 (particulate matter with a diameter ≤ 2.5 μm) and PM 10 (particulate matter with a diameter ≤ 10 μm) of total suspended solids in the atmosphere. Ammonia was collected using a 0.01 mol/L sulfuric acid absorption liquid via atmospheric sampling machine (SY-GS-IIIB, Beijing Zhonghui Tiancheng Technology Co., Ltd., China) and measured by the Nessler’ reagent-colorimetry method. Bedding sample collection and analysis Simultaneously during environmental parameters monitoring, pack surface temperature and deep temperature were measured with a mercury thermometer. Depth was detected at this site and soil samplers were used to collect bedding samples from the top, middle and bottom layers at each sampling site. Finally, samples from three sites in each barn were thoroughly mixed and prepared as test samples according to the quartering method. pH was immediately measured with a pH meter (pHS-3 C, Shanghai, China) after homogenization according to the bedding and distilled water at a 1:5 ratio (W/V). The bedding samples collected in November were used for nutrient detection, heavy metal and mycotoxins assessment and microbial sequencing. Moisture was determined by drying samples at 105 °C for 24 h. Total nitrogen (TN) was determined via a Kjeldahl analyzer (FoodALYT D4000, Bremen, Germany) and organic matter (OM) was calculated based on the ash content determined according to the protocol (method no. 923.03) . Total phosphorus (TP) content was determined by ammonium molybdate spectrophotometry, while the sodium hydroxide melting-flame photometric method was used to determine total kalium (TK) content. Heavy metal ion content, including hydrargyrum (Hg), plumbum (Pb), chromium (Cr), cadmium (Cd), arsenic (As) and cuprum (Cu), was determined by Inductively Coupled Plasma Mass Spectrometers (iCAP RQ ICP-MS, Thermo Scientific, US) followed by the published protocol . Furthermore, concentrations of mycotoxins (Aflatoxin B1, Deoxynivalenol, Ochratoxin and Zearalenone) were determined using the commercial manufacturer’s kits (CNW Technologies GmbH, Germany). Bedding microbial DNA extraction and 16 S/ITS rRNA sequencing Total genomic DNA of the bedding samples was extracted (HiPure Soil DNA Kit, D3142B, Magen, Guangzhou, China) and the quality and quantity of the extracted DNA were assessed (Nanodrop 2000, Thermo Fisher Scientific, Wilmington, DE, USA). Microbial populations in the bedding were characterized using bacterial 16 S rRNA and fungal ITS rRNA genes. The V3-V4 region of 16 S rRNA gene was amplified via GeneAmp ® 9700 PCR instrument (ABI, CA, US) using primers 341 F (5′CCTACGGGNGGCWGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) while ITS2 region of ITS rRNA genes was amplified with primers ITS3F (5′-GCATCGATGAAGAACGCAGC-3′) and ITS4R (5′-TCCTCCGCTTATTGATATGC-3′). Finally, these amplified fragments were sequenced on the Illumina Novaseq PE250 sequencing platform (Genepioneer Biotechnologies Co., Ltd., Nanjing, China) as specific protocols referred to our previous publication . Measurement of heifers’ blood indicators The last day before all the pack was cleaned in late November, 7 Holstein heifers were randomly selected from each pen and 10 mL blood samples were collected from the tail vein with a disposable syringe before morning feeding. Concentrations of white blood cells, red blood cells, hemoglobin and platelets were determined by an automatic blood cell analyzer (BC-2600, Mindray, China). Serum was collected by centrifuging the blood samples at 4 °C and 3000 rpm for 10 min. Concentrations of glutaminase, aspartate aminotransferase, alkaline phosphatase, glutamyl-transferase, total bilirubin, total protein, albumin, globulin, lactate dehydrogenase, creatine kinase, urea, creatinine, glucose, cholesterol, triglycerides, high-density lipoprotein and low-density lipoprotein were measured following the protocols of manufacturer’s kits (Nanjing Jiancheng Biotechnology Research Institute, Nanjing, China). Statistical analysis Data related to barn environment and pack physical index were analyzed using PROC MIXED of SAS software (version 9.3, SAS Institute Inc., Cary, NC, USA) with treatment, month, treatment × month interaction as fixed effects and repeated sample as the random effect. PROC GLM procedure was employed for heifers’ blood indicators, bedding nutrient indicators, heavy metal content and mycotoxins, Tukey’s test was used for multiple comparisons, with significance declared at P < 0.05 and tendency at 0.05 ≤ P < 0.1. Sequenced raw data were stitched and filtered followed by OTU clustering and species classification. Species richness and evenness were calculated in QIIME2. Principal coordinates analysis (PCoA) was performed based on Bray-Curtis dissimilarity matrix via “vegan” package and visualized via ggplot2 package in R program (v 3.6.1). Wilcoxon rank-sum test was utilized to analyze differences among treatments in phylum and genus levels. PICRUSt 2 package was performed to predict bacterial gene function according to the protocol detailed in our previous publication , while fungal functional annotation classification based on OTU abundance was carried out via FUNGuild software (Guilds_v1, https://github.com/UMNFuN/FUNGuild ) . Canonical correlation analysis (CCA) was applied to assess relationships between environmental factors, correlation networks were calculated and visualized by “Psych” R packages (with q -values < 0.05 or |r| >0.5 was considered a significant correlation) and Gephi software (version 0.9.2). 

This experiment was conducted at the dairy farm of Edweigang Modern Animal Husbandry Co., Ltd. (Sihong, Jiangsu, China) from April to November 2019. The area is situated at 33°08 ∼ 33°44 north latitude and 117°56 ∼ 118°46 east longitude with 28.6 m average altitude having an average temperature of around 14.3 °C, 893.9 mm average annual precipitation and 2356.4 h average annual sunshine hours. Twelve CBP barns were selected from the dairy farm, with their specific location distribution shown in Fig. . Each barn featured a 580 m 2 pack area and a 174 m 2 feeding area (no bedding), having identical lighting and ventilation methods. Bedding material in the pack area consisted of 20% sawdust, 30% straw, 30% chaff and 20% dry cow dung with full cultivation at a depth of approximately 30 cm via a rototiller (WT-051575X, Shandong Weiken Technology Co., Ltd, China) during the daily morning feeding to promote the aerobic composting process. 

Microbial inoculums were purchased in powder form from Haicheng Biotechnology Co., Ltd. (Yangzhou, China) that includes Compound Bacillus (B, 50% Bacillus subtilis , 30% Bacillus licheniformis and 20% Bacillus natto ), Lactobacillus plantarum (L) and Saccharomyces cerevisiae (S) having live spores of 50 billion cfu/g, 6 billion cfu/g and 6 billion cfu/g respectively. The inoculums also included calcium carbonate as a carrier and polyvinyl alcohol as the supporting agent. All CBP barns were subjected to the following four treatments (three barns each treatment) as described in Fig. : CON (no microbial inoculum added), LB (addition of L and B at 1:2 weight ratio), LS (addition of L and S at 1:2 weight ratio) and LBS (addition of L, B and S at 1:1:1 weight ratio). Microbial inoculums were mixed according to the specified ratios for each treatment, with 9 kg of mixed inoculums added per ton of bedding. Inoculums were configured using urea, salt, brown sugar and sterile water at a weight ratio of 10:1:1:100:1000, then fermented at a constant temperature of 37 ℃ in a fermenter for 3–5 days to activate the microbial inoculums. The activated inoculums were sprayed in different barns and turned the bedding evenly via a rototiller above-mentioned. The initial pack cultivation occurred in early April 2019, with fresh bedding and microbial inoculants added at regular intervals of half a month based on the fermentation status of the pack throughout the experiment period. By the end of November (before winter), all the packs were cleaned out and used for fertilizer evaluation. 

Holstein heifers (total 168 heads) with an initial age of 16 ± 1.8 months and a body weight of 244.64 ± 28.95 kg were housed in these barns, fourteen were raised in each barn, maintaining an average density of 13.8 m 2 per heifer. Heifers were given free access to the total mixed ration and clean drinking water. Total mixed ration composition included alfalfa, corn silage, corn, soybean meal, cottonseed meal and DDGS, having a 70:30 forage-to-concentrate ratio on the dry matter basis. 

Barn environment assessment The sampling date was scheduled in the middle of April, July, and November. Environmental indicators of each barn were measured over three consecutive days, then three sampling sites were selected within each barn (Fig. ). At each site, three wet and dry bulb thermometers were placed evenly at a height of 1 m above the ground in each pen to record the temperature and relative humidity every 4 h daily (6 times a day), the average value of the above 3 sites and 6 time points were used for data analysis. The temperature-humidity index (THI) was calculated from the published formula . In addition, a microcomputer laser dust meter (PC-3A(S), Qingdao Juchuang Environmental Protection Group Co., Ltd., China) was used to determine PM 2.5 (particulate matter with a diameter ≤ 2.5 μm) and PM 10 (particulate matter with a diameter ≤ 10 μm) of total suspended solids in the atmosphere. Ammonia was collected using a 0.01 mol/L sulfuric acid absorption liquid via atmospheric sampling machine (SY-GS-IIIB, Beijing Zhonghui Tiancheng Technology Co., Ltd., China) and measured by the Nessler’ reagent-colorimetry method. Bedding sample collection and analysis Simultaneously during environmental parameters monitoring, pack surface temperature and deep temperature were measured with a mercury thermometer. Depth was detected at this site and soil samplers were used to collect bedding samples from the top, middle and bottom layers at each sampling site. Finally, samples from three sites in each barn were thoroughly mixed and prepared as test samples according to the quartering method. pH was immediately measured with a pH meter (pHS-3 C, Shanghai, China) after homogenization according to the bedding and distilled water at a 1:5 ratio (W/V). The bedding samples collected in November were used for nutrient detection, heavy metal and mycotoxins assessment and microbial sequencing. Moisture was determined by drying samples at 105 °C for 24 h. Total nitrogen (TN) was determined via a Kjeldahl analyzer (FoodALYT D4000, Bremen, Germany) and organic matter (OM) was calculated based on the ash content determined according to the protocol (method no. 923.03) . Total phosphorus (TP) content was determined by ammonium molybdate spectrophotometry, while the sodium hydroxide melting-flame photometric method was used to determine total kalium (TK) content. Heavy metal ion content, including hydrargyrum (Hg), plumbum (Pb), chromium (Cr), cadmium (Cd), arsenic (As) and cuprum (Cu), was determined by Inductively Coupled Plasma Mass Spectrometers (iCAP RQ ICP-MS, Thermo Scientific, US) followed by the published protocol . Furthermore, concentrations of mycotoxins (Aflatoxin B1, Deoxynivalenol, Ochratoxin and Zearalenone) were determined using the commercial manufacturer’s kits (CNW Technologies GmbH, Germany). Bedding microbial DNA extraction and 16 S/ITS rRNA sequencing Total genomic DNA of the bedding samples was extracted (HiPure Soil DNA Kit, D3142B, Magen, Guangzhou, China) and the quality and quantity of the extracted DNA were assessed (Nanodrop 2000, Thermo Fisher Scientific, Wilmington, DE, USA). Microbial populations in the bedding were characterized using bacterial 16 S rRNA and fungal ITS rRNA genes. The V3-V4 region of 16 S rRNA gene was amplified via GeneAmp ® 9700 PCR instrument (ABI, CA, US) using primers 341 F (5′CCTACGGGNGGCWGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) while ITS2 region of ITS rRNA genes was amplified with primers ITS3F (5′-GCATCGATGAAGAACGCAGC-3′) and ITS4R (5′-TCCTCCGCTTATTGATATGC-3′). Finally, these amplified fragments were sequenced on the Illumina Novaseq PE250 sequencing platform (Genepioneer Biotechnologies Co., Ltd., Nanjing, China) as specific protocols referred to our previous publication . Measurement of heifers’ blood indicators The last day before all the pack was cleaned in late November, 7 Holstein heifers were randomly selected from each pen and 10 mL blood samples were collected from the tail vein with a disposable syringe before morning feeding. Concentrations of white blood cells, red blood cells, hemoglobin and platelets were determined by an automatic blood cell analyzer (BC-2600, Mindray, China). Serum was collected by centrifuging the blood samples at 4 °C and 3000 rpm for 10 min. Concentrations of glutaminase, aspartate aminotransferase, alkaline phosphatase, glutamyl-transferase, total bilirubin, total protein, albumin, globulin, lactate dehydrogenase, creatine kinase, urea, creatinine, glucose, cholesterol, triglycerides, high-density lipoprotein and low-density lipoprotein were measured following the protocols of manufacturer’s kits (Nanjing Jiancheng Biotechnology Research Institute, Nanjing, China). 

The sampling date was scheduled in the middle of April, July, and November. Environmental indicators of each barn were measured over three consecutive days, then three sampling sites were selected within each barn (Fig. ). At each site, three wet and dry bulb thermometers were placed evenly at a height of 1 m above the ground in each pen to record the temperature and relative humidity every 4 h daily (6 times a day), the average value of the above 3 sites and 6 time points were used for data analysis. The temperature-humidity index (THI) was calculated from the published formula . In addition, a microcomputer laser dust meter (PC-3A(S), Qingdao Juchuang Environmental Protection Group Co., Ltd., China) was used to determine PM 2.5 (particulate matter with a diameter ≤ 2.5 μm) and PM 10 (particulate matter with a diameter ≤ 10 μm) of total suspended solids in the atmosphere. Ammonia was collected using a 0.01 mol/L sulfuric acid absorption liquid via atmospheric sampling machine (SY-GS-IIIB, Beijing Zhonghui Tiancheng Technology Co., Ltd., China) and measured by the Nessler’ reagent-colorimetry method. 

Simultaneously during environmental parameters monitoring, pack surface temperature and deep temperature were measured with a mercury thermometer. Depth was detected at this site and soil samplers were used to collect bedding samples from the top, middle and bottom layers at each sampling site. Finally, samples from three sites in each barn were thoroughly mixed and prepared as test samples according to the quartering method. pH was immediately measured with a pH meter (pHS-3 C, Shanghai, China) after homogenization according to the bedding and distilled water at a 1:5 ratio (W/V). The bedding samples collected in November were used for nutrient detection, heavy metal and mycotoxins assessment and microbial sequencing. Moisture was determined by drying samples at 105 °C for 24 h. Total nitrogen (TN) was determined via a Kjeldahl analyzer (FoodALYT D4000, Bremen, Germany) and organic matter (OM) was calculated based on the ash content determined according to the protocol (method no. 923.03) . Total phosphorus (TP) content was determined by ammonium molybdate spectrophotometry, while the sodium hydroxide melting-flame photometric method was used to determine total kalium (TK) content. Heavy metal ion content, including hydrargyrum (Hg), plumbum (Pb), chromium (Cr), cadmium (Cd), arsenic (As) and cuprum (Cu), was determined by Inductively Coupled Plasma Mass Spectrometers (iCAP RQ ICP-MS, Thermo Scientific, US) followed by the published protocol . Furthermore, concentrations of mycotoxins (Aflatoxin B1, Deoxynivalenol, Ochratoxin and Zearalenone) were determined using the commercial manufacturer’s kits (CNW Technologies GmbH, Germany). 

Total genomic DNA of the bedding samples was extracted (HiPure Soil DNA Kit, D3142B, Magen, Guangzhou, China) and the quality and quantity of the extracted DNA were assessed (Nanodrop 2000, Thermo Fisher Scientific, Wilmington, DE, USA). Microbial populations in the bedding were characterized using bacterial 16 S rRNA and fungal ITS rRNA genes. The V3-V4 region of 16 S rRNA gene was amplified via GeneAmp ® 9700 PCR instrument (ABI, CA, US) using primers 341 F (5′CCTACGGGNGGCWGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) while ITS2 region of ITS rRNA genes was amplified with primers ITS3F (5′-GCATCGATGAAGAACGCAGC-3′) and ITS4R (5′-TCCTCCGCTTATTGATATGC-3′). Finally, these amplified fragments were sequenced on the Illumina Novaseq PE250 sequencing platform (Genepioneer Biotechnologies Co., Ltd., Nanjing, China) as specific protocols referred to our previous publication . 

The last day before all the pack was cleaned in late November, 7 Holstein heifers were randomly selected from each pen and 10 mL blood samples were collected from the tail vein with a disposable syringe before morning feeding. Concentrations of white blood cells, red blood cells, hemoglobin and platelets were determined by an automatic blood cell analyzer (BC-2600, Mindray, China). Serum was collected by centrifuging the blood samples at 4 °C and 3000 rpm for 10 min. Concentrations of glutaminase, aspartate aminotransferase, alkaline phosphatase, glutamyl-transferase, total bilirubin, total protein, albumin, globulin, lactate dehydrogenase, creatine kinase, urea, creatinine, glucose, cholesterol, triglycerides, high-density lipoprotein and low-density lipoprotein were measured following the protocols of manufacturer’s kits (Nanjing Jiancheng Biotechnology Research Institute, Nanjing, China). 

Data related to barn environment and pack physical index were analyzed using PROC MIXED of SAS software (version 9.3, SAS Institute Inc., Cary, NC, USA) with treatment, month, treatment × month interaction as fixed effects and repeated sample as the random effect. PROC GLM procedure was employed for heifers’ blood indicators, bedding nutrient indicators, heavy metal content and mycotoxins, Tukey’s test was used for multiple comparisons, with significance declared at P < 0.05 and tendency at 0.05 ≤ P < 0.1. Sequenced raw data were stitched and filtered followed by OTU clustering and species classification. Species richness and evenness were calculated in QIIME2. Principal coordinates analysis (PCoA) was performed based on Bray-Curtis dissimilarity matrix via “vegan” package and visualized via ggplot2 package in R program (v 3.6.1). Wilcoxon rank-sum test was utilized to analyze differences among treatments in phylum and genus levels. PICRUSt 2 package was performed to predict bacterial gene function according to the protocol detailed in our previous publication , while fungal functional annotation classification based on OTU abundance was carried out via FUNGuild software (Guilds_v1, https://github.com/UMNFuN/FUNGuild ) . Canonical correlation analysis (CCA) was applied to assess relationships between environmental factors, correlation networks were calculated and visualized by “Psych” R packages (with q -values < 0.05 or |r| >0.5 was considered a significant correlation) and Gephi software (version 0.9.2). 

Barn environment factors In this study, variations in temperature, relative humidity, and THI were observed across different sampling months. Temperature and THI values were highest in July, while relative humidity peaked in April (Fig. A-C). Although no significant differences were found among treatments ( P < 0.05), however, higher barn temperature led to an elevated THI (highest 84.3). Ammonia concentration also increased in July (month: P < 0.05; Fig. D). Furthermore, compared with CON, EM addition significantly reduced the content of barn environmental PM 10 and PM 2.5 (treatment: P < 0.05; Fig. E, F). Bedding general characteristics and nutrient contents Surface temperature (highest in July), deep temperature (highest in July) and depth (highest in November) of the bedding exhibited significant fluctuations with sampling month across the four treatments, though no significant differences were observed across treatments (Fig. A-C; month: P < 0.05; treatment: P > 0.05). The pH value of the bedding showed no significant variation across different months or treatments (Fig. D). According to Table , moisture content did not differ significantly among treatments ( P > 0.05), while OM content in LS and LBS was lower than CON ( P < 0.05). Conversely, TK content increased in these two treatments ( P < 0.05). Additionally, EM inoculation led to a significant increase in the TP content and a reduction in the C/N ratio ( P < 0.05). Bedding heavy metal and mycotoxin contents It can be seen from Table that LS and LBS increased total Cr concentration and decreased total Cu concentration compared with CON and LB ( P < 0.05), while there was no difference in concentrations of total Hg, Pb, Cd and As ( P > 0.05). Moreover, the above heavy metal content was within the scope of the national standard for organic fertilizer in China (NY525-2021). Table revealed EM inoculation reduced the content of aflatoxin B1 ( P < 0.05), while Ochratoxin A content in LS and LBS was lower than CON ( P < 0.05), and no significant difference was found in the concentrations of Deoxynivalenol and Zearalenone ( P > 0.05). Bedding bacterial community A total of 991,224 original readings of 16S rRNA genes were obtained from bedding samples, with an average of 82,602 valid tags following quality control. Finally, 2,790 OTUs were generated across all the samples, with an average of 1174 OTUs per sample. There were 243 shared OTUs and 120 / 192 / 245 / 147 unique OTUs in CON / LB / LS / LBS respectively (Fig. A). The PCoA results based on Bray-Curtis dissimilarity matrix (Fig. B) revealed significant differences in bacterial communities (PERMANOVA: P = 0.007), regarding bacterial alpha diversity (Table ), EM inoculation significantly reduced the Chao1 index ( P < 0.05) while Shannon and Simpson indexes had no differences among treatments ( P > 0.05). At the phylum level (Fig. C), Proteobacteria (28.27-47.15%), Bacteroidetes (30.53-38.54%) and Actinobacteria (6.19-12.15%) were top 3 dominant bacteria, while the relative abundance of Chloroflexi, Planctomycetes and Acidobacteria were lower in LB and LBS compare to CON ( P < 0.05, Fig. E). Conversely, the abundance of Actinobacteria in LS and LBS was higher compared with CON ( P < 0.05, Fig. E). in terms of genus level, Luteimonas , Chryseolinea and Galbibacter were predominant (Fig. D). EM inoculation significantly increased the relative abundance of Luteimonas and Galbibacter ( P < 0.05) (Fig. F). In addition, LS and LBS reduced the abundance of Chryseolinea but increased the abundance of Pseudomonas , Paracoccus and Aequorivita ( P < 0.05). Bedding fungal community Totally 640,128 raw tags were derived from ITS rRNA gene sequencing with an average of 53,344 effective tags obtained from all samples after quality control. Based on the effective tags of all samples, OTUs clustering with 97% identity resulted in 1067 OTUs (494 per sample on average). Shared OTUs were 114 and 96 / 58 / 46 / 54 were unique OTUs in CON / LB / LS / LBS (Fig. A). The rank abundance curve for relative abundance plateaued at an OTU rank of 400 and the species distribution was uniform which can be used for the comprehensive analysis of fungal components. The PCoA results (Fig. B) had shown significant differences in fungal communities among treatments (PERMANOVA: P = 0.045). Fungal alpha diversity results (Table ) indicated that EM inoculation significantly reduced Shannon and Simpson indexes ( P < 0.05), while no differences were found in Chao1 index ( P > 0.05). Ascomycota (65.99%), Basidiomycota (1.58%) and Mortierellomycota (0.91%) were 3 most abundant fungal communities at the phylum level (Fig. C), with no differences were found among treatments ( P > 0.05; Fig. E). At the genus level (Fig. D), Pestalotiopsis , Fusarium and Bipolaris were dominant, while EM inoculation increased the relative abundance of Pestalotiopsis ( P < 0.05). Notably, EM inoculation decreased the relative abundance of Scedosporium and Triadelphia ( P < 0.05; Fig. F). Moreover, LB and LBS increased the abundance of Cladorrhinum ( P < 0.05). Microbial functional enrichment Functional predictions based on KEGG pathways (Fig. A) revealed that bacterial pathways were predominantly associated with metabolism (69.12–71.19%), gene information processing (13.65–15.15%), environmental information processing (7.60-10.21%) and cellular processes (3.42–4.02%). EM inoculation significantly increased the abundance of relevant secondary pathway-related sequences in metabolism, including amino acid metabolism and carbohydrate metabolism (Fig. B, C). Key pathways impacted included arginine and proline metabolism, histidine metabolism, tyrosine metabolism, phenylalanine metabolism, tryptophan metabolism, pyruvate metabolism, starch and sucrose metabolism. Fungal functional annotation classification (Fig. D) revealed that the majority of functions were categorized as saprotroph (42.02%), pathotroph-saprotroph-symbiotroph (23.89%) and pathotroph (20.78%), which were further subdivided into undefined saprotroph, plant pathogen, animal pathogen, wood saprotroph and ectomycorrhizal. Notably, inoculating LBS in the bedding reduced the proportion of plant pathogen and animal pathogen categories while increasing the proportion of wood saprotroph and soil saprotroph categories (Fig. D). Heifers’ blood indicators Table indicated no differences were found in the whole blood cell indicators among treatments (P > 0.05), while EM addition significantly reduced the serum aspartate aminotransferase and total protein content in heifers ( P < 0.05). Notably, LB and LBS reduced the total cholesterol content compared with CON ( P < 0.05), LBS also led to an increase in high-density lipoprotein ( P < 0.05). Interaction of microbial community, barn environment factors and heifers’ blood indicators A significant correlation was observed between environmental factors and microbial community structure (Fig. ). Correlation scores with bacteria ranked as: Temperature > C/N ratio > TN > TK > pH value > OM > Moisture > TP (Fig. A). In contrast, the rankings for fungi were: TN > OM > temperature > TP > TK > C/N ratio > pH value > Moisture (Fig. B). Results showed both temperature and TN seem to have a high impact on the structure of bacterial and fungal communities, variance partitioning analysis (VPA) was performed to quantify the influence of these two environmental factors on community structure changes, these environmental factors contributed to explaining 59.93% of the variance in bacterial communities and 40.32% in fungal communities respectively, with highest explanatory ratio contributed by temperature (0.4391 for bacteria and 0.2330 for fungi). Spearman’s rank correlation network (Fig. ) demonstrated intricate and significant interactions among bedding microbiota (bacteria and fungi), barn environment factors and heifers’ blood indicators. Among all the interactions, linkages between bacteria and fungi communities were notably complex, and these microbial communities were also closely related to heifers’ blood indicators. In terms of barn environmental factors, PM 10 and PM 2.5 exhibited the most extensive connections with both bacteria/fungi communities and heifers’ blood indicators (7 connections in total), followed by ammonia (3 connections). 

In this study, variations in temperature, relative humidity, and THI were observed across different sampling months. Temperature and THI values were highest in July, while relative humidity peaked in April (Fig. A-C). Although no significant differences were found among treatments ( P < 0.05), however, higher barn temperature led to an elevated THI (highest 84.3). Ammonia concentration also increased in July (month: P < 0.05; Fig. D). Furthermore, compared with CON, EM addition significantly reduced the content of barn environmental PM 10 and PM 2.5 (treatment: P < 0.05; Fig. E, F). 

Surface temperature (highest in July), deep temperature (highest in July) and depth (highest in November) of the bedding exhibited significant fluctuations with sampling month across the four treatments, though no significant differences were observed across treatments (Fig. A-C; month: P < 0.05; treatment: P > 0.05). The pH value of the bedding showed no significant variation across different months or treatments (Fig. D). According to Table , moisture content did not differ significantly among treatments ( P > 0.05), while OM content in LS and LBS was lower than CON ( P < 0.05). Conversely, TK content increased in these two treatments ( P < 0.05). Additionally, EM inoculation led to a significant increase in the TP content and a reduction in the C/N ratio ( P < 0.05). 

It can be seen from Table that LS and LBS increased total Cr concentration and decreased total Cu concentration compared with CON and LB ( P < 0.05), while there was no difference in concentrations of total Hg, Pb, Cd and As ( P > 0.05). Moreover, the above heavy metal content was within the scope of the national standard for organic fertilizer in China (NY525-2021). Table revealed EM inoculation reduced the content of aflatoxin B1 ( P < 0.05), while Ochratoxin A content in LS and LBS was lower than CON ( P < 0.05), and no significant difference was found in the concentrations of Deoxynivalenol and Zearalenone ( P > 0.05). 

A total of 991,224 original readings of 16S rRNA genes were obtained from bedding samples, with an average of 82,602 valid tags following quality control. Finally, 2,790 OTUs were generated across all the samples, with an average of 1174 OTUs per sample. There were 243 shared OTUs and 120 / 192 / 245 / 147 unique OTUs in CON / LB / LS / LBS respectively (Fig. A). The PCoA results based on Bray-Curtis dissimilarity matrix (Fig. B) revealed significant differences in bacterial communities (PERMANOVA: P = 0.007), regarding bacterial alpha diversity (Table ), EM inoculation significantly reduced the Chao1 index ( P < 0.05) while Shannon and Simpson indexes had no differences among treatments ( P > 0.05). At the phylum level (Fig. C), Proteobacteria (28.27-47.15%), Bacteroidetes (30.53-38.54%) and Actinobacteria (6.19-12.15%) were top 3 dominant bacteria, while the relative abundance of Chloroflexi, Planctomycetes and Acidobacteria were lower in LB and LBS compare to CON ( P < 0.05, Fig. E). Conversely, the abundance of Actinobacteria in LS and LBS was higher compared with CON ( P < 0.05, Fig. E). in terms of genus level, Luteimonas , Chryseolinea and Galbibacter were predominant (Fig. D). EM inoculation significantly increased the relative abundance of Luteimonas and Galbibacter ( P < 0.05) (Fig. F). In addition, LS and LBS reduced the abundance of Chryseolinea but increased the abundance of Pseudomonas , Paracoccus and Aequorivita ( P < 0.05). 

Totally 640,128 raw tags were derived from ITS rRNA gene sequencing with an average of 53,344 effective tags obtained from all samples after quality control. Based on the effective tags of all samples, OTUs clustering with 97% identity resulted in 1067 OTUs (494 per sample on average). Shared OTUs were 114 and 96 / 58 / 46 / 54 were unique OTUs in CON / LB / LS / LBS (Fig. A). The rank abundance curve for relative abundance plateaued at an OTU rank of 400 and the species distribution was uniform which can be used for the comprehensive analysis of fungal components. The PCoA results (Fig. B) had shown significant differences in fungal communities among treatments (PERMANOVA: P = 0.045). Fungal alpha diversity results (Table ) indicated that EM inoculation significantly reduced Shannon and Simpson indexes ( P < 0.05), while no differences were found in Chao1 index ( P > 0.05). Ascomycota (65.99%), Basidiomycota (1.58%) and Mortierellomycota (0.91%) were 3 most abundant fungal communities at the phylum level (Fig. C), with no differences were found among treatments ( P > 0.05; Fig. E). At the genus level (Fig. D), Pestalotiopsis , Fusarium and Bipolaris were dominant, while EM inoculation increased the relative abundance of Pestalotiopsis ( P < 0.05). Notably, EM inoculation decreased the relative abundance of Scedosporium and Triadelphia ( P < 0.05; Fig. F). Moreover, LB and LBS increased the abundance of Cladorrhinum ( P < 0.05). 

Functional predictions based on KEGG pathways (Fig. A) revealed that bacterial pathways were predominantly associated with metabolism (69.12–71.19%), gene information processing (13.65–15.15%), environmental information processing (7.60-10.21%) and cellular processes (3.42–4.02%). EM inoculation significantly increased the abundance of relevant secondary pathway-related sequences in metabolism, including amino acid metabolism and carbohydrate metabolism (Fig. B, C). Key pathways impacted included arginine and proline metabolism, histidine metabolism, tyrosine metabolism, phenylalanine metabolism, tryptophan metabolism, pyruvate metabolism, starch and sucrose metabolism. Fungal functional annotation classification (Fig. D) revealed that the majority of functions were categorized as saprotroph (42.02%), pathotroph-saprotroph-symbiotroph (23.89%) and pathotroph (20.78%), which were further subdivided into undefined saprotroph, plant pathogen, animal pathogen, wood saprotroph and ectomycorrhizal. Notably, inoculating LBS in the bedding reduced the proportion of plant pathogen and animal pathogen categories while increasing the proportion of wood saprotroph and soil saprotroph categories (Fig. D). 

Table indicated no differences were found in the whole blood cell indicators among treatments (P > 0.05), while EM addition significantly reduced the serum aspartate aminotransferase and total protein content in heifers ( P < 0.05). Notably, LB and LBS reduced the total cholesterol content compared with CON ( P < 0.05), LBS also led to an increase in high-density lipoprotein ( P < 0.05). 

A significant correlation was observed between environmental factors and microbial community structure (Fig. ). Correlation scores with bacteria ranked as: Temperature > C/N ratio > TN > TK > pH value > OM > Moisture > TP (Fig. A). In contrast, the rankings for fungi were: TN > OM > temperature > TP > TK > C/N ratio > pH value > Moisture (Fig. B). Results showed both temperature and TN seem to have a high impact on the structure of bacterial and fungal communities, variance partitioning analysis (VPA) was performed to quantify the influence of these two environmental factors on community structure changes, these environmental factors contributed to explaining 59.93% of the variance in bacterial communities and 40.32% in fungal communities respectively, with highest explanatory ratio contributed by temperature (0.4391 for bacteria and 0.2330 for fungi). Spearman’s rank correlation network (Fig. ) demonstrated intricate and significant interactions among bedding microbiota (bacteria and fungi), barn environment factors and heifers’ blood indicators. Among all the interactions, linkages between bacteria and fungi communities were notably complex, and these microbial communities were also closely related to heifers’ blood indicators. In terms of barn environmental factors, PM 10 and PM 2.5 exhibited the most extensive connections with both bacteria/fungi communities and heifers’ blood indicators (7 connections in total), followed by ammonia (3 connections). 

Environmental monitoring of barns is crucial for optimizing dairy production, it was observed that THI in July ranged from 79 to 88, potentially leading to moderate heat stress of heifers . This highlights the importance of maintaining appropriate temperature and humidity in CBP during summer. Additionally, both pack surface and deep temperatures were elevated in July, further contributing to the heat stress risk. The increase in temperature was also associated with a rise in ammonia concentration within the barns, consistent with the previous publication . However, EM inoculation seemed to decrease PM 10 and PM 2.5 concentrations, high levels of respirable particulate matter are the primary factor leading to respiratory diseases in animals, it has been indicated that suspended solids in the air of livestock houses will be adsorbed on the surface of the pores or liquid of CBP, EM inoculation involved in the decomposition and digestion of malodorous substances in feces and urine during the adsorption process, thereby minimizing volatilization in the air . It has been reported that EM inoculants had a conspicuous beneficial effect on the fixation of TN and TP contents of compost . Additionally, the combination of EM inoculum ( Bacillus sp., Actinomycetes sp., Lactobacillus sp., Saccharomyces sp., and Trichoderma sp.) maximized the content of TP (48%) and TK (38%) , which aligned with the findings observed for LB and LBS in this experiment, suggesting the combination of Lactobacillus and Bacillus in improving the nutrient content of compost to some extent. Furthermore, C/N ratio of LBS was higher than that of LB and LS, indicating that the combination of three EM inoculums might be more beneficial to carbon sequestration during composting . High concentrations of metal elements and mycotoxins could lead to the spread chain of biological toxicity. It has been proposed that Lactobacillus and Bacillus were useful biosorbents for toxins and heavy metals to reduce their bioavailability . Candida utilis can reduce the mobility of composting heavy metals and mitigate the heavy metal residues in plants after subsequent fertilization . Moreover, inoculation of yeast strains was beneficial to the fixation of Cr in soil and the transfer of Cu and Zn , which might explain higher Cr and lower Cu contents in LS and LBS. Meanwhile, Ochratoxin A was decreased in LS and LBS, as yeast can effectively degrade and adsorb Ochratoxin A . Consistent with previous research that utilized cow manure and wood chips as composting materials, which identified Luteimonas , Pseudomonas and Steroidobacter as the predominant bacterial genera at the maturity stage , our study also observed these genera among the top ten. Furthermore, our findings corroborate previous observations that Ascomycetes and Pestalotiopsis were the dominant fungal phyla and genera during the decomposition stage when cow manure was used as the composting substrate . Moreover, inoculation of EM consisting of Lactobacillus spp., yeast species, Bacillus spp., photosynthetic bacteria and Actinomycetes spp., decreased the diversity of bacteria and fungi in swine feces as well as the bioavailability of Cu and Pb . Our study similarly found that EM inoculation reduced the diversity of bacteria and fungi in the bedding. Notably, EM inoculation increased the abundance of Actinobacteria , Pseudomonas and Pestalotiopsis , which are known to enhance the decomposition of OM, such as fiber, and contribute to compost maturity . In addition, the introduction of 32 cellulose-degrading bacterial species, including Paracoccus denitrificans , into a mixture of chicken manure and corn straw accelerated OM decomposition, extended the high-temperature period, and improved the maturity of the compost products . Aequorivita sp., known for its antibacterial and insect-repellent properties, has been employed to degrade organic pollutants . Our observations revealed an upregulation of Paracoccus and Aequorivita in LS and LBS. The co-digestion of yeast and chicken manure has been shown to increase the reaction rate of OM , suggesting that S inoculation may play a critical role in OM degradation in our study. Cladorrhinum (belonging to the Ascomycetes phylum), which increased in LB and LBS, shows promising applications in the biological control of plant pathogens and promotion of plant growth , which underscored the potential role of EM, particularly Bacillus , in pathogen inhibition during composting in our study. Previous research has demonstrated that an increase in the gene copy number associated with bacterial amino acid metabolism pathways during composting accelerates amino acid production and humus synthesis . Moreover, a recent study indicated that inoculation with Bacillus licheniformis enhances carbohydrate metabolism by enriching genes involved in starch and sucrose metabolism, glycolysis/gluconeogenesis, and pyruvate metabolism . Based on our study, EM combinations improved the metabolism of relevant amino acids and carbohydrates in the bedding, especially co-inoculation with L, B and S seemed to maximize the effect. It has been reported that pathogenic fungi gradually disappeared and woody saprophytes increased as composting time progressed . Saprophytic fungi also held great importance during the recycling phase in our experiment. Notably, the proportion of pathogens decreased and saprotrophs increased in LBS combination, it has been reported the co-addition of L, Bacillus subtilis and S reduced the relative abundance of pathogens in cabbage waste composting . Meanwhile, L, B and S all played crucial roles in inhibiting plant and soil pathogens , the synergistic combination of these three microorganisms appears to be a key factor in inhibiting fungal pathogens within the bedding and promoting decomposition in this experiment. Blood physiological and biochemical indexes reflect health, metabolism and immunity. EM inoculation reduced the blood aspartate aminotransferase and total protein content in heifers, the content of aspartate aminotransferase reflects the metabolic function of liver and shows a negative correlation with immune function , so EM inoculation might improve the barn environment (by reducing the content of respirable particulate matter), enhance body immunity, and reduce the burden on the liver. Studies have demonstrated that L, B and S have positive effects on animal lipid metabolism , it was worth noting that LBS reduced the total cholesterol content but increased the content of high-density lipoprotein, whereas the content of total cholesterol (negative correlation) and high-density lipoprotein (positive correlation) were all related to the ability of lipid metabolism. Therefore, the application of combinations of L, B and S at a 1:1:1 ratio on compost package might have a positive effect on lipid metabolism of heifers in this experiment. Recent studies indicated that composting temperature significantly affects bacterial microbiota , and the fungal community in compost products is positively correlated with TN, TP and TK . Similarly, in our study, temperature and TN were the primary factors affecting bacterial and fungal communities, respectively, which implied that EM gradually decomposes OM and harmful substances after composting, accelerating the formation of humus precursor substances. The complex network of bacteria and fungi in the bedding indirectly affects the barn environment and the heifers’ blood indicators, the interaction between these microbial communities can impact various environmental factors, such as air quality and nutrient cycling, which in turn can affect the health status of the heifers. Overall, we can report that the combination of L, B, and S appears to be the dominant combination in this study. 

In summary, inoculating EM into the bedding improved the barn environment by reducing PM 10 and PM 2.5 concentrations, it also increased high-density lipoprotein content and reduced cholesterol levels, ultimately enhancing the blood metabolism status of heifers. Moreover, EM inoculation elevated the relative abundance of cellulolytic bacteria and fungi in the bedding, such as Pseudomonas and Pestalotiopsis , thereby improving OM decomposition and accelerating composting. Additionally, EM inoculation served as a promoter by inhibiting pathogenic microbes and demonstrated good ecological recycling safety, evidenced by low mycotoxin content, along with high nutrient levels (TK & TP) (Fig. ). Overall, inoculation with L, B and S at 1:1:1 weight ratio had the most beneficial effect on compost product quality by modifying bacterial and fungal communities, with the improvement in barn environment and blood metabolism status of heifers. 

Below is the link to the electronic supplementary material. Supplementary Material 1 

