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Effects of different molecular weights of chitosan on methane production and bacterial community structure in vitro
TONG Jin-jin, ZHANG Hua, WANG Jia, LIU Yun, MAO Sheng-yong, XIONG Ben-hai, JIANG Lin-shu
2020, 19 (6): 1644-1655.   DOI: 10.1016/S2095-3119(20)63174-4
Abstract89)      PDF in ScienceDirect      
As a new feed additive, chitosan has been shown in recent years to have a certain role in reducing methane emissions from the gastrointestinal tracts of ruminants.  However, the effects of chitosan with different molecular weights on rumen fermentation, methane production and bacterial community structure are not yet clear.  A basal diet without chitosan served as the control (CTL), and the treatment diets were supplemented with chitosan with different molecular weights: 1 000 (1K), 3 000 (3K), 5 000 (5K), 50 000 (5W) and 200 000 (20W) dry matter (DM).  Six fermentation units per treatment were established.  Gas chromatography was used to measure the concentrations of methane, H2 and volatile fatty acids (VFAs).  The bacterial 16S rRNA genes were sequenced with an Illumina MiSeq platform and analysed to reveal the relative abundances of bacterial community taxa.  The results showed that the propionate proportion was significantly increased by the addition of chitosan with different molecular weights (P<0.05), while methane production and the acetate proportion were significantly decreased (P<0.05).  The relative abundances of Rikenellaceae_RC9_gut_group and Prevotellaceae_UCG_003 were significantly increased in the 3K chitosan group compared with the CTL group, whereas the relative abundance of Ruminococcaceae_NK4A214_group was significantly decreased (P<0.05).  Correlation analyses of the relative abundances of the bacterial genera showed that Prevotella was positively related to propionate production (P<0.05).  In conclusion, 3K chitosan could reduce methane production by replacing fibrolytic bacteria (Firmicutes and Fibrobacteres) with amylolytic bacteria (Bacteroidetes and Proteobacteria) in the bacterial community structure.
 
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Accuracy comparison of dry matter intake prediction models evaluated by a feeding trial of lactating dairy cows fed two total mixed rations with different forage source
PAN Xiao-hua, YANG Liang, Yves Beckers, XIONG Ben-hai, JIANG Lin-shu
2017, 16 (04): 921-929.   DOI: 10.1016/S2095-3119(16)61483-1
Abstract772)      PDF in ScienceDirect      
Dry matter intake (DMI) prediction models of NRC (2001), Fox et al. (2004) and Fuentes-Pila et al. (2003) were targeted in the present study, and the objective was to evaluate their prediction accuracy with feeding trial data of 32 lactating Holstein cows fed two total mixed rations with different forage source.  Thirty-two cows were randomly assigned to one of two total mixed ration groups: a ration containing a mixed forage (MF) of 3.7% Chinese wildrye, 28.4% alfalfa hay and 26.5% corn silage diet and another ration containing 33.8% corn stover (CS) as unique forage source.  The actual DMI was greater in MF group than in CS group (P=0.064).  The NRC model to predict DMI resulted in the lowest root mean square prediction error for both MF and CS groups (1.09 kg d–1 vs. 1.28 kg d–1) and the highest accuracy and precision based on concordance correlation coefficient for both MF and CS diet (0.89 vs. 0.87).  Except the NRC model, the other two models presented mean and linear biases in both MF and CS diets when prediction residuals were plotted against predicted DMI values (P<0.001).  The DMI variation in MF was caused by week of lactation (55.6%), milk yield (13.9%), milk fat percentage (7.1%) and dietary neutral detergent fiber (13.3%), while the variation in CS was caused by week of lactation (50.9%), live body weight (28.2%), milk yield (8.4%), milk fat percentage (5.2%) and dietary neutral detergent fibre (3.8%).  In a brief, the NRC model to predict DMI is comparatively acceptable for lactating dairy cows fed two total mixed rations with different forage source.
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