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与奶牛场未断奶犊牛每日体重增加相关的因素。

Factors associated with daily weight gain in preweaned calves on dairy farms.

机构信息

School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire, LE12 5RD, United Kingdom.

School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire, LE12 5RD, United Kingdom.

出版信息

Prev Vet Med. 2021 May;190:105320. doi: 10.1016/j.prevetmed.2021.105320. Epub 2021 Mar 6.

Abstract

The preweaning period is vital in the development of calves on dairy farms and improving daily liveweight gain (DLWG) is important to both financial and carbon efficiency; minimising rearing costs and improving first lactation milk yields. In order to improve DLWG, veterinary advisors should provide advice that has both a large effect size as well as being consistently important on the majority of farms. Whilst a variety of factors have previously been identified as influencing the DLWG of preweaned calves, it can be challenging to determine their relative importance, which is essential for optimal on-farm management decisions. Regularised regression methods such as ridge or lasso regression provide a solution by penalising variable coefficients unless there is a proportional improvement in model performance. Elastic net regression incorporates both lasso and ridge penalties and was used in this research to provide a sparse model to accommodate strongly correlated predictors and provide robust coefficient estimates. Sixty randomly selected British dairy farms were enrolled to collect weigh tape data from preweaned calves at birth and weaning, resulting in data being available for 1014 calves from 30 farms after filtering to remove poor quality data, with a mean DLWG of 0.79 kg/d (range 0.49-1.06 kg/d, SD 0.13). Farm management practices (e.g. colostrum, feeding, hygiene protocols), building dimensions, temperature/humidity and colostrum quality/bacteriology data were collected, resulting in 293 potential variables affecting farm level DLWG. Bootstrapped elastic net regression models identified 17 variables as having both a large effect size and high stability. Increasing the maximum preweaned age within the first housing group (0.001 kg/d per 1d increase, 90 % bootstrap confidence interval (BCI): 0.000-0.002), increased mean environmental temperature within the first month of life (0.012 kg/d per 1 °C increase, 90 % BCI: 0.002-0.037) and increased mean volume of milk feeding (0.012 kg/d per 1 L increase, 90 % BCI: 0.001-0.024) were associated with increased DLWG. An increase in the number of days between the cleaning out of calving pen (-0.001 kg/d per 1d increase, 90 % BCI: -0.001-0.000) and group housing pens (-0.001 kg/d per 1d increase, 90 % BCI: -0.002-0.000) were both associated with decreased DLWG. Through bootstrapped elastic net regression, a small number of stable variables have been identified as most likely to have the largest effect size on DLWG in preweaned calves. Many of these variables represent practical aspects of management with a focus around stocking demographics, milk/colostrum feeding, environmental hygiene and environmental temperature; these variables should now be tested in a randomised controlled trial to elucidate causality.

摘要

在奶牛场,哺乳期是小牛发育的关键时期,提高日增重(DLWG)对财务和碳效率都很重要;可以降低饲养成本并提高初乳产量。为了提高 DLWG,兽医顾问应该提供既有较大影响又在大多数农场都具有重要意义的建议。尽管先前已经确定了许多因素会影响预断奶小牛的 DLWG,但确定它们的相对重要性具有挑战性,这对最佳农场管理决策至关重要。正则化回归方法(例如岭回归或套索回归)通过惩罚变量系数来提供解决方案,除非模型性能有明显改善。弹性网络回归结合了套索和岭回归惩罚,并在本研究中使用,以提供稀疏模型来适应强相关预测因子,并提供稳健的系数估计。从英国随机选择了 60 个奶牛场,在小牛出生和断奶时收集称重带数据,在对数据进行过滤以去除质量较差的数据后,来自 30 个农场的 1014 头小牛的数据可用,其平均 DLWG 为 0.79 kg/d(范围为 0.49-1.06 kg/d,SD 为 0.13)。收集了农场管理实践(例如初乳、饲养、卫生协议)、建筑尺寸、温度/湿度和初乳质量/细菌学数据,结果确定了 293 个潜在变量影响农场水平的 DLWG。引导弹性网络回归模型确定了 17 个变量,它们具有较大的影响大小和较高的稳定性。在第一个畜栏组内增加最大预断奶年龄(每增加 1 天增加 0.001 kg/d,90% 引导置信区间(BCI):0.000-0.002)、在生命的第一个月内增加平均环境温度(每增加 1°C 增加 0.012 kg/d,90%BCI:0.002-0.037)和增加平均牛奶喂养量(每增加 1 L 增加 0.012 kg/d,90%BCI:0.001-0.024)均与 DLWG 增加相关。牛舍清理之间的天数增加(每增加 1 天减少 0.001 kg/d,90%BCI:-0.001-0.000)和畜栏组之间的天数增加(每增加 1 天减少 0.001 kg/d,90%BCI:-0.002-0.000)均与 DLWG 减少相关。通过引导弹性网络回归,确定了少数稳定变量,这些变量最有可能对预断奶小牛的 DLWG 产生最大影响。其中许多变量代表管理的实际方面,重点是存栏人口统计学、牛奶/初乳喂养、环境卫生和环境温度;这些变量现在应在随机对照试验中进行测试,以阐明因果关系。

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