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奶牛乳成分与奶牛健康、饲养和代谢数据的关系。

Relationship between milk constituents from milk testing and health, feeding, and metabolic data of dairy cows.

机构信息

Institute of Livestock Farming, Mecklenburg-Vorpommern Research Centre for Agriculture and Fisheries, 18196 Dummerstorf, Germany; Department of Animal Health and Welfare, Faculty of Agricultural and Environmental Sciences, University of Rostock, 18059 Rostock, Germany.

Institute of Livestock Farming, Mecklenburg-Vorpommern Research Centre for Agriculture and Fisheries, 18196 Dummerstorf, Germany.

出版信息

J Dairy Sci. 2020 Nov;103(11):10175-10194. doi: 10.3168/jds.2019-17981. Epub 2020 Aug 26.

Abstract

A new evaluation scheme to assess the nutritional status of dairy cows on the basis of milk constituents was derived from 7.37 million German records of milk testing. The aim of this work was to validate this new scheme. Two data sets with fertility and health information (data set A) and with measured energy and nutrient intake and metabolic characteristics (data set B) were analyzed. Data set A included 32 commercial dairy farms in northeast Germany, with 72,982 records of 43,863 German Holstein cows; data set B included 12 German experimental farms, with 49,275 records of 1,650 German Holstein, Simmental, and Brown Swiss cows. Milk traits were linked to health disorders and metabolic and feeding characteristics. Frequently used limits of milk constituents were compared with ranges of the new "Dummerstorf feeding evaluation." To distinguish an optimal from a deficient energy supply, a milk protein content ≥3.20% (previously used) and a milk fat:protein ratio (FPR) ≤1.4 (new scheme) were chosen and compared with feed energy intake in relation to demand. Energy status was more often correctly assigned by FPR than by milk protein content (80.7 and 68.7%, respectively). Over all data, the new optimum range of milk urea between 150 and 250 mg/L was better suited to dietary crude protein intake in relation to demand than the previously used range of 150 to 300 mg/L (42.4 and 38.0%, respectively). Ketosis or blood values associated with ketosis such as β-hydroxybutyrate >1.2 mmol/L or nonesterified fatty acids >1,000 µmol/L, as well as strong mobilization of body weight ≥1.5 kg/d, loss of back fat thickness ≥10 mm, and loss of body condition score ≥1 unit in first 60 days in milk were compared with different milk trait thresholds. For the updated scheme FPR >1.4 was used in combination with either milk protein content below the individual statistical lower limit of milk protein content, or milk fat content greater than the individual statistical upper limit of milk fat content; FPR >1.5 was taken as a frequently used threshold. For these ketosis indicators, the new scheme had higher sensitivities. Energy oversupply or the risk of overconditioning could not be identified by milk constituents alone. Urinary acid-base content was not related to milk content. Similarly, milk testing data did not allow a clear distinction to be made between the diagnoses of acidosis and, for example, ketosis. Essential requirements for good herd management are the continuous observation of milk testing data in combination with other established instruments of feeding and animal monitoring.

摘要

基于奶成分的奶牛营养状况评估新方案源自 737 万份德国奶样检测记录。本研究旨在验证该新方案。使用了包含生育和健康信息的数据集 A(data set A)和包含能量与营养摄入及代谢特征的测量数据的数据集 B(data set B)进行分析。数据集 A 包括德国东北部的 32 个商业奶牛场,涉及 72982 头德国荷斯坦奶牛的 43863 个记录;数据集 B 包括德国 12 个实验奶牛场,涉及 1650 头德国荷斯坦牛、西门塔尔牛和瑞士褐牛的 49275 个记录。奶样特征与健康障碍和代谢及饲养特征相关联。常使用的奶成分界限与新的“Dummerstorf 饲养评估”范围进行了比较。为了区分最佳能量供应和能量不足,选择了牛奶蛋白含量≥3.20%(之前使用)和牛奶脂肪:蛋白比(FPR)≤1.4(新方案),并将其与需求相关的饲料能量摄入进行了比较。与牛奶蛋白含量相比,FPR 更能正确分配能量状态(分别为 80.7%和 68.7%)。在所有数据中,150 至 250mg/L 之间的新的最佳范围的牛奶尿素更适合于与需求相关的日粮粗蛋白摄入,而之前使用的范围是 150 至 300mg/L(分别为 42.4%和 38.0%)。酮病或与酮病相关的血液值,如β-羟丁酸>1.2mmol/L 或非酯化脂肪酸>1000µmol/L,以及体重减少≥1.5kg/d、背脂厚度损失≥10mm 和体况评分损失≥1 个单位在产奶的前 60 天,与不同的奶成分阈值进行了比较。对于更新的方案,FPR >1.4 与牛奶蛋白含量低于个体统计牛奶蛋白含量下限或牛奶脂肪含量高于个体统计牛奶脂肪含量上限结合使用;FPR >1.5 被视为常用的阈值。对于这些酮病指标,新方案的敏感性更高。仅通过奶成分无法识别能量供应过剩或过育肥的风险。尿液酸碱含量与奶含量无关。同样,奶样检测数据也无法清楚地区分酸中毒与例如酮病的诊断。良好牛群管理的基本要求是结合其他饲养和动物监测手段,对奶样检测数据进行持续观察。

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