Microbial communities and natural fermentation of ...

Bioresource Technology 265 (2018) 282–290

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Bioresource Technology journal homepage: www.elsevier.com/locate/biortech

Microbial communities and natural fermentation of corn silages prepared with farm bunker-silo in Southwest China

T

Hao Guana,1, Yanhong Yana,1, Xiaoling Lia, Xiaomei Lia, Yang Shuaia, Guangyan Fenga, ⁎ ⁎ Qifan Rand, Yimin Caic, Ying Lib, ,2, Xinquan Zhanga, ,2 a

Animal Science and Technology College, Sichuan Agricultural University, Chengdu, China Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China c Japan International Research Center for Agricultural Science (JIRCAS), Tsukuba, Ibaraki 305-8686, Japan d Institute of Grass Science, ChongQing Academy of Animal Husbandry, ChongQing, China b

G R A P H I C A L A B S T R A C T

A R T I C LE I N FO

A B S T R A C T

Keywords: Corn Bunker-silo silage Climatic factor Fermentation quality Natural microbial community

This study analyzed the variation of microbial communities, their achieved fermentation quality, and the association between microbial diversity and environmental factors after ensiling of 96 samples prepared with bunker-silo in Southwest China. Most of natural corn silages achieved good fermentation, e.g., low pH value (< 4.2) and high levels of lactic acid (36.26–79.83 mg/g DM). Weissella species were the dominant epiphytic bacteria in raw material, while Lactobacillus and Acetobacter species were prevalent in silages. Natural Lactobacillus and Pediococcus species produced more lactic acid during ensiling, while the production of acetic acid was highly positively correlated with both Acetobacter and Bradyrhizobium species. Rainfall and humidity affected community of epiphytic bacteria on the corn material, and the temperature affected richness of bacterial species during ensiling. The results confirmed that microbial community of silages in hot and humid area is unique and climatic factors ultimately affect the fermentation quality through influencing microbial community.

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Corresponding authors. E-mail addresses: [email protected] (Y. Li), [email protected] (X. Zhang). These authors contributed equally to this article and are co-first authors. 2 These authors contributed equally to this article and are joint corresponding authors. 1

https://doi.org/10.1016/j.biortech.2018.06.018 Received 12 April 2018; Received in revised form 7 June 2018; Accepted 8 June 2018 Available online 08 June 2018 0960-8524/ © 2018 Elsevier Ltd. All rights reserved.


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collected from farm field in different regions of Southwest China. These samples originated from the five major ecological areas of Sichuan Province, Chongqing, and Guizhou Province. These five major ecological areas are: the Chengdu plain area (Chengdu.P) (103°39′E–104°26′E, 30°33′N–31°16′N), the Qinba mountain area (Qinba.M) (107°51′E–108°00′E, 31°21′N–31°36′N), the Hongya hilly mountain area (Hongya.M) (103°22′E–105°21′E, 29°53′N–30°6′N), the Yunyang mountain area (Yunyang.M) (108°45′E, 30°52′N), and the Guizhou mountain area (Guizhou.M) (104°53′E–107°33′E, 25°46′N–25°49′N). Samples were collected from August 2016 to November 2016 (the fermenting time of all samples lasted about two months). The silage (a total of 500 g each) was sampled at a depth of 30 cm from the face of the bunkers, with three repetitions per bunker. Samples were aseptically collected and transported in ice boxes and stored at −80 °C before use. All samples were subjected to microbial community, chemical composition, and fermentation quality analysis.

1. Introduction Silage preparation and storage is one of the most effective techniques to ensure animal feed supply. Lactic acid bacteria (LAB) mainly convert water soluble carbohydrates (WSC) and also a little lignocellulolytic material into organic acids under anaerobic conditions. Due to this acidification, the silage can be stored for a long time (Dunière et al., 2013). Corn and alfalfa are the most important forages crops used for ensiling (Weinberg and Ashbell, 2003) worldwide and the main raw material for silage in Southwestern China is corn as well (Zhu et al., 2010). However, the quality of corn silage in this area is neither stable nor controllable compared to that in northern China or other temperate regions. It is known that management practices (eg. packing and filling, silage additives, sealing, weighting the plastic cover, and feed-out rate) would affect the quality of large scale bunkersilo silage. In addition, more and more studies showed that climatic conditions affect all stages (field, ensiling, storage, and feed-out phases) of silage production and utilization, especially in the hot and humid areas (Bernardes et al., 2018), which including Southwest China. These climatic conditions favor the propagation of many bacterial and fungal pathogens that cause stalk rot, smut, leaf blight, and southern rust and moreover, are prone to mycotoxin-producing molds from the common genera Penicillium, Aspergillus, and Fusarium (Samapundo et al., 2005). These climatic factors not only affect forage crop growth and disease incidence, but also influence the silage fermentation and aerobic stability (Kim and Adesogan, 2006). In addition, rainfall during the harvest period can increase protein hydrolysis in the silo (Mcdonald et al., 1991) and effluent production (Fransen and Strubi, 1998), consequently reducing dry matter (DM) recovery. Ensiling at high temperatures reduces both lactic acid concentration and aerobic stability, while increasing pH and DM loss (Ashbell et al., 2002). Most of these previous results have been obtained on grass silages and the experiments examined the effect of either temperature or moisture on silage fermentation. However, it remains poorly understood how these climatic conditions affect the microbial community and ultimately influence the fermentation quality of silage. Silage production is a completely microbial-based fermentation process (Muck, 2013). Despite the fact that silage-associated microorganisms may significantly affect both silage quality and ruminant health, a comprehensive assessment of the microbial community composition and silage fermentation is missing for farm bunker silos. Some studies focused on changes in microbial communities in bench-scale model ensiling systems in the laboratory, which did not reflect the real process in large-scale silage production (Muck, 2013). These studies have either used culture-based isolation techniques or characterized denaturing gradient gel electrophoresis (DGGE) bands, which provide very limited microbial community characteristics. Recently, next-generation sequencing (NGS) has been applied to an increasing number of studies to investigate environmental microbial communities, including silage (Li et al., 2015; Ni et al., 2017; Zhang et al., 2018; Zhao et al., 2016). However, these studies only focused on the effects of the use of additives on microbial communities in silages but did not characterize silage ecology in different regions and in response to different climate conditions. Thus, the aim of this study was to apply NGS to determine the bacteria community of corn materials and mature silages from farm field and bunker silos in Southwest China. In addition, the influence of climatic factors including high temperature and humidity on the microbial community and fermentation quality of silage was also firstly studied.

2.2. Chemical and fermentation profile analyses DM contents of corn material samples were measured via drying the samples in a forced-air oven at 65 °C for 72 h and then grinding them in a Willey mill with a 1-mm sieve. The water-soluble carbohydrate (WSC) content was estimated via the thracenone-sulphuric acid method (AOAC, 1990). The water extracts of silage samples were prepared by homogenizing a 20 g sample in 180 mL of water in an industrial blender for 1 min. Portions of the water extract were filtered via filter paper and acidified with meta-phosphoric acid solution (20% wt/vol) and centrifuged at 12,000×g (5810R; Eppendorf, Hamburg, Germany) for 15 min at 4 °C to determine the concentrations of lactic, acetic, butyric, and propionic acids via high-performance liquid chromatography (HPLC, KC-811, Shimadzu Co. Ltd, Kyoto, Japan). The HPLC was coupled with a UV detector model SPD-10A VP that used 210 nm as detection wave length. To determine the NH3-N content, 1 mL (250g/L, wt/vol) trichloroacetic acid (TCA) aliquot was added to 4 mL of the filtrate and the solution was kept at 4° C overnight to precipitate protein. The solution was then centrifuged at 18,000×g for 15 min and the supernatant was analyzed for NH3-N (Weatherburn, 1967). 2.3. Microbial analysis The microbial population was quantified in fresh forage. Water extracts from samples were prepared via homogenizing a 20 g of sample in 180 mL of sterile saline (0.85% NaCl) in an industrial blender for 1 min. Subsequently, the pH values of the extracts were measured with a potentiometer (PHSJ-5; LEICI, Shanghai, China). Portions were subjected to serial dilutions ranging from 10−1 to 10−10. Sterile plates were prepared with De Man, Rogosa, and Sharpe agar (Difco, Land Bridge, Beijing, China) for LAB, Violet Red Bole Agar (Difco, Land Bridge, Beijing, China) for Enterobacter, and Potato Dextrose Agar (Difco, Land Bridge, Beijing, China) for yeasts and molds (Y&M). Plates of LAB were anaerobically incubated at 37 °C for 48 h, the Enterobacter plates were aerobically incubated at 37 °C for 24 h, and the Y&M plates were aerobically incubated at 25 °C for 4 d. 2.4. Bacterial community analysis 2.4.1. DNA extraction Each sample (50 g) was frozen (−80 °C) and passed through a 4 mm sieve after freeze-drying and smashing. Subsample (5 g) was ball milled for 1 min at room temperature (Duniere et al., 2017) and total DNA was extracted via the TIANamp Bacteria DNA isolation kit (DP302-02, Tiangen, Beijing , China). All samples were purified via purification and recovery of the DNA kit column (DP214-02, Tiangen, Beijing, China) and then eluted in nuclease free water. The quality and purity of the extracted DNA was checked via 1% agarose gel electrophoresis and

2. Materials and methods 2.1. Study sites and sample collection 48 corn material samples and 48 of their silage samples were 283


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is unclear, the high temperature and high humidity may influence their DM. As an important factor, the WSC content supports the growth of LAB that produce lactic acid to drop the pH (Moselhy et al., 2015). In present study, WSC contents in all samples exceeded 10% of DM, the samples taken from HY-SF had the highest (P < 0.05) WSC content of 17.88% of DM. The WSC content of freshly chopped corn was similar to that reported by a previous study (Kleinschmit et al., 2005). Differences were found between different areas, which was also caused by different corn varieties. CP contents were ranged from 4.85% to 10.86%, which is similar to previous studies (Kim and Adesogan, 2006; Queiroz et al., 2013). These different CP contents may be related to cultivation and fertilization (Miao et al., 2006). For different silage materials or ensiling processes, the microbe numbers and species composition differ (Khota et al., 2016). The total amount of microbes varies widely in fresh silage materials, ranging from 102 to 109 cfu/g of fresh matter (FM) (Zhang and Cai, 2014) , including the variety of bacteria (Lactobacilli, Bacilli, Clostridia, and acetic acid bacteria) and other microorganisms (molds and yeasts). Most microbes cause nutrient degradation in forage, with a proportion between beneficial microorganisms and undesirable microorganisms of about 1:10 (Pahlow et al., 2003). Among these, the number of LAB is limited (< 105 cfu/g of FM) (Pang et al., 2011), while the maximum content of LAB typically occurs either during the mature stage, or during the middle period of some seasons, such as second and third stubble for alfalfa and grasses or early maturity for corn (Lindgren et al., 2006). In this study, the numbers of LAB found in all samples were above 2 log10 cfu/g of FM, and the highest (P < 0.05) numbers of LAB were observed in XH-FJ and XH-HJ-SY samples, both exceeding 6 log10 cfu/g of FM. Yeasts, molds, and enterobacteria were detected in all samples. XH-FJ and GZ-PA exhibited the lowest (P < 0.05) number of molds, both had less than 2 log10 cfu/g of FM. Yeast and enterobacteria showed a high count, ranged from 4 to 5 log10 cfu/g of FM, except for those from GZ-PA. Generally, the numbers of LAB, yeasts, molds, and enterobacteria were lower compared to those of bunker-made corn raw material collected in northern China during August (Wang et al., 2014) and the forages of barley, oat, and triticale in Canada during June (Duniere et al., 2017). Therefore, we speculate that high temperature and humidity could inhibit the growth of these microorganisms.

spectrophotometry (optical density at 260/280 nm ratio). The qualified DNA samples were stored at −20 °C for future analysis. 2.4.2. Sequencing 16S rRNA genes of distinct regions (16S V4) were amplified used the specific primers 515F (5′-GTTTCGGT GCCAGCMGCCGCGGTAA-3′) and 806R (5′-GCCAA TGGACTACHVGGGTWTCTAAT-3′) with the barcode. Samples with a bright main strip between 400 and 450 bp were chosen for further experiments. PCR products were mixed in equal density ratios. Sequencing libraries were generated via the TruSeq®DNA PCRFree Sample Preparation Kit (Illumina, USA) following manufacturer's recommendations, index codes were added. Finally, the library was sequenced on an IlluminaHiSeq2500 platform and 250 bp paired-end reads were generated. 2.4.3. Sequences analyses Next-generation sequencing reads were assembled using FLASH (V1.2.7) (Magoč and Salzberg, 2011). Low quality reads were removed according to the QIIME quality control process (V1.7.0) (Caporaso et al., 2010). Chimeric sequences were removed by using both UCHIME denovo and UCHIME reference to obtained final effective tags (Edgar, 2013). Sequence analyses were performed via Uparse software (Uparse v7.0.1001) (Edgar, 2013). A 97% similarity cutoff was used to define operational taxonomic units (OTUs). The representative sequences of each OTU were aligned to the Greengene Database (Desantis et al., 2006) based on the RDP classifier algorithm (Version 2.2) (Wang et al., 2007) to annotate taxonomic information. Alpha diversity metrics (Observed-species, Chao1, Shannon, Simpson, ACE, and Good-coverage) and beta diversity metrics (weighted UniFrac and unweighted UniFrac) were calculated with QIIME software (Version 1.7.0). PCoA analysis was conducted with the WGCNA package, stat packages, and the ggplot2 package in R software (Version 2.15.3). The sequence data reported in this study have been deposited in the NCBI database (Accession No. SRP132757). 2.4.4. Statistical analyses Microbial populations were estimated as colony-forming units (cfu)/g of either forage or silage and were log transformed prior to statistical analysis. Then, the statistical analyses were performed using the GLM procedure of the Statistical Package for the Social Sciences (SPSS Version 19.0, SPSS Inc., Chicago, IL, USA) to examine the differences between samples. Tukey’s honest significant difference (HSD) test was employed for different sample means and significance was declared at P < 0.05.

3.2. Fermentative changes of pH, NH3-N/TN, and organic acid contents in silage The fermentative changes in pH, NH3-N/TN, lactic acid, acetic acid, propionic acid, and butyric acid of silage are shown in Table 2. The DM content strongly influences the silage fermentation during ensiling process, since LAB (which is responsible for the fermentation) requires moisture activity for growth and reproduction (Hu et al., 2009). However, differences in the DM content in the current study affected the decline in pH during the ensilage period little, which may be because their differences in DM content were not sufficient to affect the temporal behavior of the pH. Moreover, the pH values of silages from CQ.YY, CQ.TY, and GZ.PA were significantly higher (P < 0.05) than that of other samples. In contrast to the pH value, the silages of those three sites exhibited a higher (P < 0.05) percentage of NH3-N/TN compared to silages of other sites. Among them, samples from the Yunyang.M (YY.MA) showed the highest value, which was 14% higher (P < 0.05) than other samples. The poor fermentation quality in silages from CQ.YY and CQ.TY was likely attributable to an unsuccessfully fermentation process, since the microbial community in this silage became increasingly complex after fermentation (Fig. 1a and b). This is in contrast to the standard quality of a successful silage (Mcdonald et al., 1991). The content of lactic acid in all samples was within a range of 21.06–79.83 mg/g DM, which is in accordance with previous reports (Kim and Adesogan, 2006). However, compared to previous studies on

3. Results and discussion 3.1. The chemical composition and microbial population of plate culture before ensiling Chemical composition and microbial population of corn raw materials in different regions are shown in Table 1. All corn samples used in this study were harvested at the milk-ripe stage, since corn at different growth stage and harvest time influences chemical composition and fermentation quality of their silages (Johnson et al., 1999). In the present study, the DM contents of all corn raw materials were ranged from 21% to 38%, the highest DM content was found in SL (P < 0.05), and the lowest DM content was found in the CZ-GMY region (P < 0.05), respectively. The DM contents of CZ-GMY, CZ-GM, and HY-LP were similar with 21.38%, 21.22%, and 21.17%, respectively. This may be due to these corn were harvested at same growth stage of maturation (Cone et al., 2008; Phipps et al., 2000). In addition, the DM content in edible corn samples was significantly lower (P < 0.05) than that of silage corn, and significantly lower (P < 0.05) compared to samples from North America, Europe, and Northern China (Neylon and Kung, 2003; Phipps et al., 2000; Wang et al., 2014). This reason for DM 284


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Table 1 Chemical composition and microbial population of corn material prior to ensiling. Area

Sample

DM (%)

pH

WSC (% DM)

CP (% DM)

LAB (Log cfu/g FM)

Molds (Log cfu/g FM)

Yeasts (Log cfu/g FM)

Enterobacter (Log cfu/g FM)

Chengdu.P

SL QBJ DY CZ-GMY CZ-GM XH-HJ-SY XH-HJ-QZ XH-FJ HY-SF HY-LP HY-YP AY CQ-TY CQ-YY GZ-PA GZ-DS

37.31a 24.37efg 25.93de 21.38h 21.22h 30.07c 22.55fgh 25.37de 35.02b 21.17h 27.42d 26.16de 32.87b 24.63ef 24.21efg 22.03gh

5.97b 5.71c 5.21e 6.94a 6.93a 4.74f 5.32de 5.41de 5.43de 5.48d 5.70c 5.70c 5.75c 6.11b 5.99b 5.35de

15.73ab 10.37e 10.67de 13.52bcde 11.64cde 14.42abc 13.18bcde 14.17bc 17.88a 13.83bcd 16.61ab 11.88cde 15.52ab 14.23bc 14.53abc 13.17bcde

10.86a 7.14d 6.56d 7.93c 8.47c 6.90d 6.71d 6.87d 9.84b 8.16c 4.85f 8.46c 7.15d 7.04d 5.81e 7.10d

4.37g 4.11h 4.67ef 4.98d 5.30c 6.32a 5.55bc 6.40a 5.69b 5.40c 4.88de 4.41fg 4.83de 4.42fg 2.90i 3.95h

2.13de 3.47bc 3.88abc 3.44bc 3.24bcd 3.58bc 3.87abc 1.20e 2.72cd 2.69cd 2.93bcd 3.95abc 3.94abc 4.17ab 1.43e 4.87a

4.40e 4.75d 4.99abcd 4.93bcd 5.20abc 4.96bcd 4.84cd 5.28ab 5.09abcd 5.21abc 4.98abcd 5.34a 5.09abcd 5.09abcd 2.84g 4.46f

4.38d 4.39d 5.41ab 5.40ab 5.30ab 5.19b 5.45ab 5.53a 5.48ab 5.36ab 5.44ab 4.83c 5.47ab 5.20ab 2.77e 5.27ab

Qinba.M

Hongya.M

Yunyang.M Guizhou.M

a–h

Data are means of three samples, means in the same column followed by different letters differ (P < 0.05). FM, fresh material; DM, dry matter; WSC, water soluble carbohydrates; CP, crude protein; LAB, lactic acid bacteria; P, plain; M, mountain; SL, shuangliu; QBJ, qingbaijiang; DY, deyang; CZ-GMY, chongzhou-guiminyin; CZ-GM, chongzhou-guimu; XH-HJ-SY, xuanhan-huangjin-shiyong; XH-HJ-QZ, xuanhan-huangjin-qingzhu; XH-FJ, xuanhan-feijian; HY-SF, hongya-shifan; HY-LP, hongya-liuping; HY-YP, hongya-yangping; AY, anyue; CQ-TY, chongqing-tianyou; CQ-YY, chongqing-yunyang; GZ-PA, guizhou-pu'an; GZ-DS, guizhou-dushan.

The composition of the epiphytic community (as demonstrated by 16S ribosomal DNA sequencing), emphasized that the most dominant bacterial genera were Weissella, Pseudomonas, Lactobacillus, and Leuconostoc. Previous reports indicated Leuconostoc and Weissella species as obligatory heterofermentative LAB species that are associated with numerous forage crops and silages (Cai et al., 1998). After fermentation, lactobacilli was found to be the predominant microbial genus, with a relative abundance exceeding 50%. Lactobacilli was followed by acetobacter and weissella. Previous studies reported that lactobacilli can become a dominant genus in successful silages (Mcdonald et al., 1991; McEniry et al., 2008). However, the high abundance of acetobacter was not common in the reported laboratory small-scale silage, while it was regularly detected in bunker-made corn silage, particularly Acetobacter pasteurianus (Dolci et al., 2011; Wang et al., 2014). According to the reports, the prevalence of acetobacter seemed to be very controversial in the silage. The presence of acetic acid bacteria in silage is undesirable due to its ability to initiate aerobic deterioration and thus degrade lactic acid and acetic acid, while producing carbon dioxide and water (Spoelstra

corn silage, most of the samples in this experiment had a higher acetic acid content (Johnson et al., 2002). Among them, the highest (P < 0.05) acetic acid content was observed at the HY-YP and HY-SF sites, while the highest (P < 0.05) content of butyric acid was detected at CZ-GM. Propionic acid was not detected in any of the silages after ensiling. Perhaps this is related to the low fermentation ability of natural LAB, and the large numbers of acetic acid producing bacteria present in these materials.

3.3. Microbial diversity of silages before and after ensiling A total of 96 samples (48 of corn material and 48 of their silage) were analyzed, generating a total of 12,810,434 high-quality reads with an average 133,442 reads per sample. 6,501,082 of the reads with an average of 135,439 per sample originated from the 48 corn material samples; a total of 6,309,352 reads with an average of 131,445 reads per sample originated from 48 of their silage samples. Relative abundance of the silage bacterial microbiome both before and after fermentation collected in Southwest China is shown in Fig. 1. Table 2 Fermentative changes in pH, NH3-N/ TN, and organic acid content of corn silages. Area

Sample

pH

NH3-N/TN (%)

Lactic acid (mg/g DM)

Acetic acid (mg/g DM)

Propionic acid (mg/g DM)

Butyric acid (mg/g DM)

Chengdu.P

SL QBJ DY CZ-GMY CZ-GM XH-HJ-SY XH-HJ-QZ XH-FJ HY-SF HY-LP HY-YP AY CQ-TY CQ-YY GZ-PA GZ-DS

3.77de 3.72de 4.00c 4.11c 4.14c 4.06c 3.99c 3.71de 3.63e 3.71de 3.82d 3.79de 4.87a 4.98a 4.44b 3.74de

2.23cfg 2.46defg 3.53cdefg 4.70cde 3.94cdefg 4.40cdef 5.10cd 5.45c 1.73fg 3.20cdefg 4.32cdef 1.41g 14.61a 14.54a 10.16b 3.16cdefg

36.26fg 51.33bcde 39.66efg 38.70efg 38.96efg 54.06bcd 57.40bc 64.20b 79.83a 60.30bc 46.73cdef 58.46bc 21.06h 29.70gh 42.00defg 59.26bc

22.90b 2.56d 14.30bcd 1.50d 1.23d 16.33bc 25.30b 3.30cd 47.70a 24.46b 55.90a 25.96b 12.43bcd 8.73cd 6.10cd 1.20d

ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND

5.90b 6.43b 5.46b 6.73b 11.80a 6.23b 6.76b 0c 2.46c 1.46c 1.70c 6.56b 6.43b 5.56b 1.20c 1.30c

Qinba.M

Hongya.M

Yunyang.M Guizhou.M

a–h

Data are means of three samples, means in the same column followed by different letters differ (P < 0.05). DM, dry matter; TN, total nitrogen; ND, not detected; P, plain; M, mountain; SL, shuangliu; QBJ, qingbaijiang; DY, deyang; CZ-GMY, chongzhou-guiminyin; CZ-GM, chongzhou-guimu; XH-HJ-SY, xuanhan-huangjin-shiyong; XH-HJ-QZ, xuanhan-huangjin-qingzhu; XH-FJ, xuanhan-feijian; HY-SF, hongya-shifan; HY-LP, hongyaliuping; HY-YP, hongya-yangping; AY, anyue; CQ-TY, chongqing-tianyou; CQ-YY, chongqing-yunyang; GZ-PA, guizhou-pu'an; GZ-DS, guizhou-dushan. 285


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Fig. 1. Relative abundance of bacterial composition in corn bunker-silo silages before and after fermentation collected from five major area in Southwest China at genus level: (a): Average values for all samples before and after ensiling; (b): Average values for each area of the five major area before (CD.P.X; QB.MA.X; HY.MA.X; YY.MA.X; GZ.MY.X) and after (CD.P.F; QB.MA.F; HY.MA.F; YY.MA.F; GZ.MY.F) ensiling; (c) Relative abundance of bacterial microbiome in every sample before and after ensiling.

different regions before and after ensiling was also found to be different: Weissella was dominant in corn raw materials sampled from Qinba.M (QB.MA), while the abundances of both acetobacter and lactobacilli were similar in Hongya.M (HY.MA) after ensiling. The bacterial alpha diversities of the samples were evaluated via Chao1 and ACE indexes (Fig. 2). In general, after anaerobic fermentation, the complex microbial communities of the raw materials are gradually replaced by LAB, which is one of the criteria for the successful silage (Mcdonald et al., 1991). Therefore, the microbial diversity will sharply reduce after successful fermentation. In this study, Chao1 and ACE indexes decreased in all samples after ensiling except for silages taken from Yunyang.M (YY.MA) (Fig. 2a and b). The bacterial community structures of samples from these areas differed from those of other places (Fig. 2c and d). Considering the quality of the fermentation, the quality of the samples taken from the YY.MA site was found to also be poorer compared to those taken from other areas. However, Fig. 1b shows that LAB was still dominant in YY.MA samples, but the low abundance of other bacteria still remained higher than those of other samples. This suggested that the epiphytic LAB found on the raw materials differed from high acid-producing LAB (such as Lactobacillus plantarum). Consequently, although they have successfully dominated the fermentation, they did not produce sufficient LA to reduce the pH, and thus inhibiting the growth of other undesirable microorganisms. In addition, the samples from pre-ensiled corn and silages were gathered simultaneously (Fig. 2c and d). Higher numbers of bacterial core microbiome OTUs were identified in fresh corn forage (Fig. 2e) compared to after ensiling (Fig. 2f). The bacterial core microbiome of fresh forage composed of 605 shared OTUs. The unique OTUs in different area ranged from 59 for YY.MA to 316 for Chengdu.P (CD.P) (Fig. 2e). The

et al., 1988). However, acetobacter also oxidizes ethanol to acetic acid under aerobic conditions (Nanda et al., 2001). Consequently, acetic acid has been suggested to inhibit the propagation of fungi and to prolong the aerobic stability of silage (Kristensen et al., 2010). Li and Nishino (2011) reported that despite the high content of acetic acid in silages, none of the farmers acknowledged problems with the silage they sampled. A similar situation was found in our study, since farmers (also farmers in U.S.) occasionally obtained high acetic acid silages, which reduces intake. Interestingly, high acetic acid silages, other than those inoculated with Lactobacillus buchneri, are consumed at an expected based level with standard nutritive characteristics. The difference between these silages is mainly caused by the microbial species that produces the acetic acid. The main factor affecting its intake is probably not the existence of acetic acid but some other product that was not measured in our study. Therefore, when we will better identify the species that causes such problems, we will be able to advice on improved management strategies or additives to prevent such problems. In view of the high content of acetobacter and acetic acid in this area, attention should be paid to further research. Generally, the composition of microorganisms before and after ensiling has undergone great changes. Lactobacillus, Acetobacter, Brevibacillus, Leuconostoc, and Bacillus species significantly (P < 0.01) increased after ensiling, while Weissella, Pseudomonas, Acinetobacter, Burkholderia, Sphingobacterium, Methylobacterium, and Pantoea species significantly (P < 0.01) decreased. These results indicate that after anaerobic fermentation, epiphytic pathogenic bacteria on corn raw material sharply decreased, while the bacteria producing acid started to dominate. Due to this decrease of the pH, the silage can be stored for a longer period (Mcdonald et al., 1991). The bacterial composition of 286


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Fig. 2. Differences in bacterial community diversity, richness, and structure of corn bunker-silo silages collected from Southwest China: (a,b) Community diversity and richness of the corn bunker-silo silages; (c,d) Principal Coordinate Analysis (PCoA) of the bacterial community of the corn bunker-silo silages before and after ensiling; (e,f) Venn diagram depicting unique or shared bacterial OTUs in silages taken from five major ecological zones before and after ensiling. The number of bacterial core microbiome OTUs is highlighted with a bold black frame.

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Fig. 3. Association analysis between bacterial diversity and environmental factors: (a,b) Association analysis between bacterial diversity and temperature, extremely high temperature, humidity, precipitation, and water soluble carbohydrates before and after ensiling at genus level; (c,d) Association analysis between alpha diversity and temperature, extremely high temperature, humidity, precipitation, water soluble carbohydrates before and after ensiling. The environmental factor information is displayed vertically, while the bacterial diversity (a,b) and alpha diversity (c,d) index information are displayed horizontally, respectively. The corresponding value of the middle heat map is the Spearman correlation coefficient r, which ranges between −1 and 1, r < 0 indicates a negative correlation (blue), r > 0 indicates a positive correlation (red), and ‘*’ and ‘**’ represent P < 0.05 and P < 0.01, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

though plastic sheets had been used to cover the raw materials. This is also reflected in the number of molds that were attached to raw materials (> 4 Log cfu/g FM, Table 1). Therefore, we suggest this to be the reason for the poor quality of silage prepared in this area. In addition, we found negative correlations between the average temperature or extreme temperature with the relative abundance of genus Methylobacterium (−0.704), Sphingomonas (−0.578), Aureimonas (−0.545), and Devosia (−0.444) (Fig. 3a); however, humidity had a highly positive correlation with the abundance of these bacteria. This suggested that undesirable bacteria are suitable for growth in a cool and humid environment. In addition, humidity correlated with the observed species at P < 0.01 (0.443) and Simpson index at P < 0.05 (0.378) (Fig. 3c). The average precipitation correlated with Simpson index at P < 0.01 (0.469) and with the Shannon index at P < 0.05 (0.350). This indicates that humidity and average precipitation are the main factors to affect the microbial diversity in raw materials. If the filling time is excessively long, the result will be affected more strongly by these unfavorable climatic factors, thus leading to the increased abundance of undesired microorganism. These in turn damage the quality of the silage, especially for silage without additive addition. Consequently, studies have shown that chemical additives exert good effects on preventing and controlling the corruption of high moisture forage silage, especially of acid-based additives (Pys et al., 2010; Takano et al., 1975). This can also be used as a reference for making silage during unfavorable weather conditions. Therefore, we suggest that shortening the time of filling and application of acid-based additives could help to reduce the loss caused by adverse climate factors. After ensiling, highly positive correlations (P < 0.01) of average temperature and extreme temperature were found with the abundance of genes Microbacterium (0.462) and Acidibacter (0.584) (Fig. 3b). Furthermore, highly positive correlations (P < 0.01) were found for humidity with genes Sporolactobacillus (0.487), Yersinia (0.487), Pantoea (0.578), Lactococcus (0.399), Aeriscardovia (0.442), Acidibacter (−0.512), and Gluconobacter (0.412). The average precipitation is also highly correlated (P < 0.01) with genes Brevibacillus (0.675), Bacillus (0.374), Pseudomonas (−0.388), Sporosarcina (−0.698), Pediococcus

diversity of the bacterial core microbiome (composed of 145 shared OTUs in terminal silage) declined after ensiling (Fig. 2f). The number of unique OTUs in all areas decreased after ensiling, except for YY.MA, where the unique OTU numbers increased sharply from 59 to 2673 after ensiling. These results suggest similar microbial communities in successful silage that not only share core bacteria. Unique bacteria may also decrease in successful silages, which is in accordance with the results of the microbiome association found in small grain silages. 3.4. Association analysis between bacterial communities and environmental factors There are many factors that affect the microbial community in silage, such as, chemical composition including moisture and WSC (McEniry et al., 2010), and regional factors (Villa et al., 2010). Usually, regional factors include local environmental factors, such as temperature, humidity, and precipitation. These are also known as the main factors that affect the microbial diversity of silage, especially in hot and humid areas (Muck, 2013). Here, we composed an association analysis between bacterial abundance and environmental factors on fresh raw materials and silages (Fig. 3). In general, corn bunker-silo silage is produced during summer to earlier autumn (from July to October), which is a time of frequent rainfall in Southwest China. This is also the most undesirable condition for farmers, as rainfall reduces the quality of silage. In this study, significant positive correlations (P < 0.01) were found between the average precipitation and the relative abundance of genus Lactobacillus (ρ = 0.720, Spearman), Acetobacter (0.544), Lactococcus (0.680), and Leuconostoc (0.726) on fresh raw materials (Fig. 3a). This indicates that the average precipitation was the main factor to positively affect the epiphytic bacteria of the silage material. One possible reason is that excessive rainfall prolonged the time required for silage production, which enabled the continuous propagation of epiphytic microorganisms. This also explains the poor quality of YY.MA silage samples. We re-investigated this silage and found that the silage filling time took longer than other samples. During this period, this area experienced a continuous rainfall for a week, even 288


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Fig. 4. Correlation analysis of the bacterial community with organic acid and NH3-N/TN. Organic acid and NH3-N/TN information are displayed vertically, respectively, and the bacterial community information is displayed horizontally. The corresponding value of the middle heat map is the Spearman correlation coefficient r, which ranges between −1 and 1, r < 0 indicates a negative correlation (blue), r > 0 indicates a positive correlation (red), and ‘*’ and ‘**’ represent P < 0.05 and P < 0.01, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

(0.548), and Lachnoclostridium (0.660). These results show that although climatic factors influence some specific microorganisms in silage, they do not affect lactobacillus, acetobacter, and Leuconostoc, which are the main bacteria in silages. This indicates that when silage enters a completely anaerobic environment, climate factors exert a minimal impact on the main microorganisms. The growth of Bacillus spp. occurs during the later stages of aerobic spoilage of silage (Giffel et al., 2002). The positive correlation between rainfall and these bacteria demonstrates that rainfall may negatively influenced the bacteria needed for optimal growth, and thus resulting in silage spoilage. The WSC content was found to be negative correlated with lactobacilli at P < 0.05 (−0.345), while it was highly positively correlated with acetobacter (0.600). The WSC content of corn samples was generally sufficiently high; therefore, the WSC content had no effect on lactobacilli. However, due to unknown reasons, acetobacter was found to be sensitive to the WSC content. As shown in Fig. 3d, both average temperature and extreme temperature correlated with the observed species (0.440), ACE (0.401) at P < 0.01. A highly positive correlation (P < 0.01) of average precipitation with observed species (−0.460), Shannon index (−0.779), Simpson index (−0.566), and goods coverage (0.370) was found as well. This indicates that although climate factors did not affect the relative abundance of main bacteria in silage (eg. lactobacillus, acetobacter, and Leuconostoc), temperature and extreme high temperature significantly affected bacterial species richness during silage. In summary, rainfall and humidity affected community of epiphytic bacteria on the corn material, and the temperature affected richness of bacterial species during ensiling. We speculate that with the continuous optimization of the ensiling process, climate factors will have less and less impact on microbial communities, thereby reducing its influence on silage quality.

the composition of the microorganism population after ensiling with the contents of ammonia nitrogen (NH3-N), lactic acid, acetic acid, and butyric acid (Fig. 4). The content of lactic acid was highly positive correlated (P < 0.01) with lactobacilli (0.950) and pediococci (0.548), while the content of acetic acid was highly positive correlated (P < 0.01) with both genus Acetobacter (0.942) and Bradyrhizobium (0.564). The content of butyric acid was highly positive correlated (P < 0.01) with genus Yersinia (0.485), Leuconostoc (0.385), Pantoea (0.397), Aeriscardovia (0.401), and Sphingobacterium (0.399). These results were consistent with previous reports (Mcdonald et al., 1991), showing that there is a distinct relationship between acetic acid bacteria and the production of acetic acid in silage.

3.5. Correlation analysis of the bacterial community with fermentation products

Appendix A. Supplementary data

4. Conclusions This is the first study to define the bacterial communities of natural corn silage in hot and humid areas via next-generation sequencing. As expected, lactobacilli were dominant species after ensiling, followed by acetobacter, which was directly related to acetic acid produced in silages. Lactobacilli and pediococci grew well in low pH condition and produced more lactic acid during ensiling, and they effectively influenced fermentation quality. Rainfall and humidity were found to be the main factors to affect the community of epiphytic bacteria in corn material, and the temperature was the main factor influence silage fermentation and microbial community during ensiling. Acknowledgements This study was funded by the earmarked fund for Modern Agroindustry Technology Research System of China (grant numbers CARS34).

Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.biortech.2018.06.018.

Microorganisms affect the silage quality via a series of metabolites, e.g., lactobacilli mainly affects the lactic acid production (Hu et al., 2009). Enterobacteria can ferment lactic acid to acetic acid and other products (Ostling and Lindgren, 2010), and the presence of serraia was typically associated with the production of 2,3-butanediol (Mcdonald et al., 1991). In our study, we also performed a correlation analysis of

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