This study was conducted to evaluate the effect of collection durations on the energy values and nutrient digestibility of high-fiber diets in growing pigs with a time-based total fecal collection method. A total of 24 barrows (body weight (BW): 31.1 ± 1.5 kg) were allotted to a completely randomized design with three diets. Diets included a corn–soybean meal (CSM) basal diet and two additional diets containing 20% sugar beet pulp (SBP) or defatted rice bran (DFRB) by replacing corn, soybean meal, and soybean oil in the CSM diet, respectively. Each diet was fed to eight barrows for a 7-day adaptation period followed by a 7-day total feces and urine collection period. The 7-day collection duration was divided into three collection phases, namely, phase 1 (days 8 to 11), phase 2 (days 11 to 13), and phase 3 (days 13 to 15). Then, similar portions of feces and urine from the different collection phases were composited into three additional samples (days 8 to 11, days 8 to 13, and days 8 to 15, respectively). The results showed that the digestible energy (DE), metabolizable energy (ME), and apparent total tract digestibility (ATTD) of gross energy (GE) and nutrient in experimental diets decreased linearly as the collection durations increased from a 3-day to a 7-day collection (p < 0.05). However, there were no differences in the energy values, GE, and nutrient digestibility of diets and of high-fiber ingredients between the 5-day and 7-day collection durations. In conclusion, this study suggests that a 5-day collection duration is adequate to determine the energy values and nutrient digestibility of high-fiber diets containing SBP or DFRB in growing pigs by the time-based total fecal collection method.

Glycerol was generally added to the inoculum as a cryoprotectant. However, it was also a suitable substrate for microbial fermentation, which may produce more SCFAs, thereby decreased pH of the fermentation broth. This study investigated the effect of supplementing glycerol to inoculum on in vitro fermentation and whether an enhanced buffer capacity of medium could maintain the pH stability during in vitro batch fermentation, subsequently improving the accuracy of short chain fatty acids (SCFAs) determination, especially propionate. Two ileal digesta were fermented by pig fecal inoculum with or without glycerol (served as anti-frozen inoculum or frozen inoculum) in standard buffer or enhanced buffer solution (served as normal or modified medium). Along with the fermentation, adding glycerol decreased the pH of fermentation broth (p < 0.05). However, modified medium could alleviate the pH decrement compared with normal medium (p < 0.05). The concentration of total propionic acid production was much higher than that of other SCFAs in anti-frozen inoculum fermentation at 24 and 36 h, thereby increasing the variation (SD) of net production of propionate. The α-diversity analysis showed that adding glycerol decreased Chao1 and Shannon index under normal medium fermentation (p < 0.05) compared to modified medium (p < 0.05) along with fermentation. PCoA showed that all groups were clustered differently (p < 0.01). Adding glycerol improved the relative abundances of Firmicutes, Anaerovibrio, unclassified_f_Selenomonadaceae, and decreased the relative abundance of Proteobacteria (p < 0.05). The relative abundances of Firmicutes, such as Lactobacillus, Blautia and Eubacterium_Ruminantium_group in modified medium with frozen inoculum fermentation were higher than (p < 0.05) those in normal medium at 36 h of incubation. These results showed that adding glycerol in inoculum changed the fermentation patterns, regardless of substrate and medium, and suggested fermentation using frozen inoculum with modified medium could maintain stability of pH, improve the accuracy of SCFA determination, as well as maintain a balanced microbial community.

Two experiments were conducted to establish the prediction equations for AME and TME of corn based on chemical composition and enzymatic hydrolysate gross energy (EHGE) in roosters. In experiment 1, eighty 32-wk-old Hy-line Brown roosters with an average body weight of 2.55 ± 0.21 kg were randomly assigned to 10 diet treatments in a completely randomized design to determine AME and TME by the force-feeding method. Each treatment had 8 replicates with 1 bird per replicate. The 10 test diets used in the experiment were formulated with corn (including 96.10%) as the sole source of energy. In experiment 2, the EHGE of 14 corn samples was measured by the computer-controlled simulated digestion system (CCSDS) with 5 replicates of each sample. The average AME and TME values of corn were 14.58 and 16.46 MJ/kg DM, respectively. The EHGE of 14 corn samples ranged from 14.66 to 15.89 (the mean was 15.24) MJ/kg DM. The best-fit equations for corn based on chemical composition were AME (MJ/kg DM) = 14.5504 + 0.1166 × ether extract (EE) + 0.5058 × Ash - 0.0957 × neutral detergent fiber (NDF) (R² = 0.8194, residual standard deviation (RSD) = 0.0860, P < 0.01) and TME (MJ/kg DM) = 16.0625 + 0.1314 × EE + 0.4725 × Ash - 0.0872 × NDF (R² = 0.7867, RSD = 0.0860, P < 0.01). The best-fit equations for corn based on EHGE were AME (MJ/kg DM) = 7.8883 + 0.4568 × EHGE (R² = 0.8587, RSD = 0.0693, P < 0.01) and TME (MJ/kg DM) = 10.0099 + 0.4228 × EHGE (R² = 0.8720, RSD = 0.0608, P < 0.01). The differences between determined and predicted values from equations established based on EHGE were lower than those observed from chemical composition equations. These results indicated that EHGE measured with CCSDS could predict the AME and TME of corn for roosters with high accuracy.

As a precious indigenous goose resource in China, the Zhedong white goose occupies an essential position in the domestic goose industry. However, this breed spontaneously enters a prolonged non-laying period of over two months per year, which greatly limits egg production capacity and restricts the economic development of the goose industry. Herein, this study systematically compared serum physiological indices and serum and fecal metabolome, as well as fecal microbial communities, between laying and non-laying Zhedong white geese, aiming to reveal the key regulatory mechanisms underlying reproductive stage transition. Physiological analyses indicated that non-laying geese had higher serum levels of GnRH, PRL, APOA, and T-AOC, whereas the concentrations of LH, E₂, TNF-α, IL-1, and calcium were significantly reduced; FSH, PROG, and BA levels showed no significant differences between the two groups. Metabolomic analysis identified 277 upregulated and 403 downregulated DAMs in feces, and 386 DAMs in serum. The shared enriched pathways across serum and fecal samples encompassed arginine biosynthesis, histidine metabolism, and pantothenate and CoA biosynthesis, as well as steroid hormone biosynthesis. A total of 120 DAMs overlapped in two specimens, and the non-laying geese presented pronounced depletion of tryptophan-derived metabolites and steroid hormone-related metabolites. Metagenomic results showed no significant difference in gut microbial alpha diversity between groups, while their microbial community structures were clearly differentiated. A total of 774 upregulated and 854 downregulated microbial species were screened in non-laying geese, and these differential microbes were primarily enriched in pathways associated with reproductive hormone signaling, steroid biosynthesis and energy metabolism. Multi-omics correlation analysis verified close associations between differential microbes and reproductive-related metabolites. Certain probiotic strains, including Pediococcus pentosaceus and Lactococcus raffinolactis, were positively correlated with steroid hormones and tryptophan metabolites, and their abundances declined obviously in the non-laying stage. Collectively, this study elaborates the holistic changes in serum biochemistry, gut metabolome and microbiome in geese at different reproductive stages. The dysregulation of amino acid and steroid hormone metabolism, combined with the loss of beneficial intestinal microbes, jointly induces the non-laying phenotype. This study provides new perspectives for understanding the gut–reproductive axis and supplies promising biomarkers to improve the laying performance of geese.

The study’s objectives were three-fold. Firstly, the impacts of three advanced harvest times (10 days, 5 days, 0 days) on the nutrient composition of peanut vine (PV) were investigated to determine the optimal harvesting time for peanut vine; secondly, the chemical composition and fermentation characteristics of silage produced by combining peanut vine with either wheat bran (PV-WB silage) or corn meal (PV-CM silage) with adding different inoculants (named as TTX, DBN, and JNK) were evaluated; finally, the post-ensilage microbial community was analyzed to assess the effects of inoculants and silage substrates on the bacterial and fungal communities, as well as their interactions. The results indicated that harvesting peanut vine 5 days early significantly enhanced the retention of crude protein and reduced the content of acid detergent fiber and acid detergent lignin compared to harvesting at 0 days (p < 0.05). PV-WB silage exhibited higher crude protein content than PV-CM silage, irrespective of the inoculant used (p < 0.05). The application of the TTX inoculant notably reduced the acid detergent lignin content and enhanced the potential biodegradation, while also increasing the concentrations of acetic acid and lactic acid (p < 0.05). The bacterial community was predominantly composed of the genera Lactobacillus, Pediococcus, and Weissella; however, a greater abundance of Enterobacter and Enterococcus was obtained in the control (CON) treatment. The use of the JNK inoculant resulted in a Saccharomyces abundance exceeding 89%, which led to elevated levels of ammonia-N and higher pH values. In summary, the advanced 5 days to harvest peanut vines retained more crude protein content and decreased the acid detergent fiber and acid detergent lignin content. Fresh peanut vines co-silage with WB including TTX inoculant improved the peanut value nutrient preservation and fermentation parameters.