胎次和产奶量对奶牛泌乳性能的影响

【目的】明确高产与低产奶牛胎次对产奶量、乳成分和泌乳持续力的影响,为优化奶牛场的饲养管理模式及提高奶牛使用年限提供理论依据。【方法】基于北京、河北等地17个奶牛养殖场共12406头荷斯坦奶牛2021年春季的生产性能测定(DHI)报告,按高产(48.69±6.88 kg/d)和低产(23.47±5.28 kg/d)进行分组,对原数据经预处理后获得5158头高产奶牛和3110头低产奶牛的DHI报告;采用单因素方差分析(One-way ANOVA)进行方差齐性检验,通过Pearson双侧检验进行多变量相关分析,并以LDS和Duncan's多重比较对组间差异进行分析,2个变量间的相关性则通过Spearson相关系数进行判断。【结果】不同胎次对高产与低产奶牛的产奶量、乳成分和泌乳持续力均有影响:高产奶牛3胎和4胎的日产奶量显著高于其他胎次(P<0.05,下同),4胎以上高产奶牛的牛乳体细胞数(SCC)显著高于1胎和2胎高产奶牛;低产奶牛1胎的日产奶量、乳糖率显著高于其他胎次,且SCC显著低于除2胎外的其他胎次。在奶牛泌乳持续力方面,1胎高产奶牛的泌乳持续力显著高于3胎、4胎和5胎,1胎低产奶牛的泌乳持续力显著高于其他胎次,其他胎次间的泌乳持续力均无显著差异(P>0.05,下同);高产与低产奶牛的泌乳持续力与SCC间无显著相关性,但与日产奶量显著相关。除干物质含量与日产奶量、乳脂率与乳糖率的相关性外,高产奶牛和低产奶牛的产奶量、乳成分及SCC间的相关性基本一致,其中,日产奶量与乳糖率呈极显著正相关(P<0.01,下同),与SCC呈极显著负相关,而SCC与乳脂率和乳蛋白率均呈极显著正相关。【结论】高产奶牛最佳使用胎次为3~4胎,低产奶牛最佳使用胎次为1~3胎;高产与低产奶牛的胎次均影响其产奶量、乳成分及泌乳持续力,但不同胎次下产奶量和乳成分的变化趋势存在一定差异。因此,在提高饲养管理水平的基础上,应进一步优化营养供给,及时调整饲粮配方,合理对奶牛进行分群和规划,以达到奶牛的最佳使用胎次,增加养殖经济效益。

A Solution on Pork Quality Traceability from Farm to Dinner Table in Tianjin City,China

In order to meet government supervision of pork production safety as well as consumer＇s right to know what they buy and protect the public safety of pork food, this study adopts animal identification, intelligent personal digital assistant （PDA） reading and writing, general packet radio service （GPRS）, and other information technologies, proposes a pork tracking and traceability inferstructure based on pork production substrace flow and data flow, designs the metadata structure and related datatbases for farming, slaughtering, and retailing sector based on intensive pig farming and smallhold pig farming, develops three different data-recording systems, and finally establishes a public network platform for the information inquiry in light of ＂the administrative rules on identification and rearing files for animal and poultry＂ in China. The farming process information system supplies early warning for the usage of drugs and feed additives based on data of every individual pig and timely uploading all events data to remote traceability database when pigs are sold; the PDA data collecting system can collect farming events data for pigs fed by farmers and submit to the center database by GPRS; the web-based Tianjin＇s pork traceability platform can integrate all identifications and related pork quality data from farming, slaughtering to marketing by online, and achieve pork tracking from product origin to consumption and tracing in the turnover direction. It is feasible to realize pork quality traceability by identification technologies developed and/or integrated, metadata specifications designed, three data-recording systems deyeloped, and web-inquiring platform established. Some individual technical bottlenecks will be resolved with the development of communication technologies. The full implementation in Tianjin, China, will supply technical support for guaranteeing the quality and safety of pork production and meeting consumer＇s demands.

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

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.

Effects of different molecular weights of chitosan on methane production and bacterial community structure in vitro

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 ferme ntation,methane production and bacterial comm unity structure are not yet clear.A basal diet without chitosa n served as the control(CTL),and the treatment diets were supplemented with chitosan with different molecular weights:1000(1K),3000(3K),5000(5K),50000(5W)and 200000(20W)dry matter(DM).Six fermentation units per treatment were established.Gas chromatography was used to measure the co nee ntrations of metha ne,H2 and volatile fatty acids(VFAs).The bacterial 16S rRNA genes were sequeneed with an Illumina MiSeq platform and analysed to reveal the relative abunda nces of bacterial community taxa.The results showed that the propi onate proporti on was sign ifica ntly in creased 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 mbundances of Rikenellaceae_RC9_gut_group and Prevotellaceae_UCG_003 were significa ntly in creased in the 3K chitosa n group compared with the CTL group,whereas the relative abunda nee 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 propionmte production(Pv0.05).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.