动物营养学报 >

2020 , Vol. 32 >Issue 10: 4904 - 4913

DOI: https://doi.org/10.3969/j.issn.1006-267x.2020.10.041

反刍与草食动物营养与饲料 RUMINANT AND HERBIVORE NUTRITION AND FEED

基于CPM-Dairy软件评估饲粮有效营养价值对荷斯坦奶牛生产性能预测的有效性及瘤胃微生物区系和血清生化指标的影响

  • 武圣儒 ,
  • 乔雨 ,
  • 郑辰 ,
  • 赵聪聪 ,
  • 雷新建 ,
  • 曹阳春 ,
  • 姚军虎
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  • 1. 西北农林科技大学动物科技学院, 杨凌 712100;
    2. 杨凌职业技术学院, 杨凌 712100
武圣儒(1992-),男,四川攀枝花人,副教授,博士,主要从事畜禽肠道微生物及营养素互作、畜禽营养表观遗传调控相关研究。E-mail:wushengru2013@163.com

收稿日期: 2020-04-10

  网络出版日期: 2020-10-22

基金资助

宁夏回族自治区科学技术厅应用开发项目（2018BBF33007）；陕西省特色产业创新链项目（2017TSCXL-NY-04-01）

收起

Predicted Accuracy of Performance Based on CPM-Dairy Software Evaluated Feed Effective Nutritional Value and Effects of Evaluated Feed Effective Nutritional Value on Ruminal Microflora and Serum Biochemical Indexes in Dairy Cows

  • WU Shengru ,
  • QIAO Yu ,
  • ZHENG Chen ,
  • ZHAO Congcong ,
  • LEI Xinjian ,
  • CAO Yangchun ,
  • YAO Junhu
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  • 1. College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China;
    2. Yangling Vocational and Technical College, Yangling 712100, China

Received date: 2020-04-10

  Online published: 2020-10-22

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摘要

本试验旨在通过对奶牛血清生化指标、瘤胃发酵参数与微生物区系及生产性能的综合评判，研究基于CPM （Cornell-Penn-Miner）-Dairy软件评估饲粮有效营养价值进行荷斯坦奶牛生产性能预测的有效性，为CPM-Dairy软件在奶牛饲料生产中的应用提供理论依据。采用4×4拉丁方试验设计，选择4头体况接近的奶牛，分别饲喂4种不同玉米淀粉添加量（0、300、600及900 g/d），但泌乳净能、代谢能和代谢蛋白质一致的饲粮。试验分4期进行，每期35 d，其中过渡期15 d，正试期20 d （最后6 d为采样期）。采集试验牛的瘤胃液、血液及奶样，对其瘤胃发酵参数、血清生化指标、泌乳性能及瘤胃微生物区系进行测定。结果显示：4种CPM-Dairy软件预测的有效营养价值相近的饲粮对奶牛干物质采食量、瘤胃发酵参数、血清生化指标和泌乳性能无显著影响（P>0.05）。试验牛实际乳产量为22.30~23.60 kg/d，与根据代谢蛋白质预测的产奶量无显著差异（P>0.05），但显著低于根据代谢能预测的产奶量（P<0.05）。4种饲粮未显著影响瘤胃微生物的多样性和丰富度（P>0.05），但显著影响了普雷沃氏菌属7和未分类拟杆菌目RF16群的丰度（P<0.05）。由此可见，基于CPM-Dairy软件评估饲粮有效营养价值（泌乳净能、代谢能及代谢蛋白质含量）可有效进行荷斯坦奶牛生产性能预测；本试验条件下，饲粮代谢蛋白质供给不足是奶牛未能充分发挥生产性能的主要原因。

关键词： CPM-Dairy软件; 奶牛; 瘤胃微生物区系; 代谢能; 代谢蛋白质; 玉米淀粉

本文引用格式

武圣儒 , 乔雨 , 郑辰 , 赵聪聪 , 雷新建 , 曹阳春 , 姚军虎 . 基于CPM-Dairy软件评估饲粮有效营养价值对荷斯坦奶牛生产性能预测的有效性及瘤胃微生物区系和血清生化指标的影响[J]. 动物营养学报, 2020 , 32(10) : 4904 -4913 . DOI: 10.3969/j.issn.1006-267x.2020.10.041

Abstract

This experiment was conducted to investigate the predicted accuracy of performance based on the evaluated feed effective nutritional value using Cornell-Penn-Miner (CPM)-dairy software through comprehensively evaluated the serum biochemical indexes, rumen fermentation parameters and ruminal microflora of dairy cows, in order to provide new evidence to use the CPM-Dairy software in dairy cow feed production. Based on a 4×4 Latin square design, four healthy dairy cows with similar condition were selected, and fed with 4 different diets in 4 periods. By using the CPM software, 4 diets with different additive amounts (0, 300, 600, and 900 g/d) of corn starch but with the same levels of lactation net energy, metabolic energy and metabolic protein. The experiment was divided into 4 periods, and 35 days in each period. Each period consisted 15 d of pretrial period and 20 d of trial period (the last 6 d of trial period was sample period. Rumen fluid blood and milk samples were collected to measure rumen fermentation parameters, serum biochemical indexes, milk performance and ruminal microflora. The results showed that the dry matter intake, rumen fermentation parameters, serum biochemical indexes and milk performance were not significantly affected by 4 diets which had similar effective nutritional value evaluated by CPM-Dairy dairy software (P>0.05). The actual milk yield was 22.30 to 23.60 kg/d, which was not significantly different with the milk yield predicted by metabolic protein (MP)(P>0.05), but significantly different from the milk yield predicted by metabolic energy (ME) (P<0.05). Moreover, 4 diets were not significantly affected the microbial diversity and richness (P>0.05), but significantly affected the abundances of Prevotella 7 and norank_f_Bacteroidales_RF-16_group (P<0.05). In conclusion, the effective nutritional value (net energy of lactation, ME and MP) in diet predicted by the CPM-Dairy software can effectively predict the performance of dairy cows. The absence of metabolic protein is the main reason which limits the performance of dairy cows in the present study.

Key words： CPM-Dairy software; dairy cows; ruminal microflora; metabolic energy; metabolic protein; corn starch

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