Souza V C, Liebe D M, Price T P, Ellett M D, Davis T C, Gleason C B, Daniels K M, White R R
Department of Dairy Science, Virginia Tech, Blacksburg 24061.
Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg 24061.
J Dairy Sci. 2022 May;105(5):4048-4063. doi: 10.3168/jds.2021-20841. Epub 2022 Mar 2.
Individualized, precision feeding of dairy cattle may contribute to profitable and sustainable dairy production. Feeding strategies targeted at optimizing efficiency of individual cows, rather than groups of animals with similar characteristics, is a logical goal of individualized precision feeding. However, algorithms designed to make feeding recommendations for specific animals are scarce. The objective of this study was to develop and test 2 algorithms designed to improve feed efficiency of individual cows by supplementing total mixed rations (TMR) with varying types and amounts of top-dressed feedstuffs. Twenty-four Holstein dairy cows were assigned to 1 of 3 treatment groups as follows: a control group fed a common TMR ad libitum, a group fed individually according to algorithm 1, and a group fed individually according to algorithm 2. Algorithm 1 used a mixed-model approach with feed efficiency as the response variable and automated measurements of production parameters and top-dress type as dependent variables. Cow was treated as a random effect, and cow by top-dress interactions were included if significant. Algorithm 2 grouped cows based on top-dress response efficiency structure using a principal components and k-means clustering. Both algorithms were trained over a 36-d experimental period immediately before testing, and were updated weekly during the 35-d testing period. Production performance responses for dry matter intake (DMI), milk yield, milk fat percentage and yield, milk protein percentage and yield, and feed efficiency were analyzed using a mixed-effects model with fixed effects for feeding algorithm, top dress, week, and the 2- and 3-way interactions among these variables. Milk protein percentage and feed efficiency were significantly affected by the 3-way interaction of top dress, algorithm, and week, and DMI tended to be affected by this 3-way interaction. Feeding algorithm did not affect milk yield, milk fat yield, or milk protein yield. However, feeding costs were reduced, and hence milk revenue increased on the algorithm-fed cows. The efficacy of feeding algorithms differed by top dress and time, and largely relied on DMI shifts to modulate feed efficiency. The net result, for the cumulative feeding groups, was that cows in the algorithm 1 and 2 groups earned over $0.45 and $0.70 more per head per day in comparison to cows on the TMR control, respectively. This study yielded 2 candidate approaches for efficiency-focused, individualized feeding recommendations. Refinement of algorithm selection, development, and training approaches are needed to maximize production parameters through individualized feeding.
对奶牛进行个性化精准饲喂有助于实现盈利且可持续的奶牛生产。针对优化个体奶牛而非具有相似特征的牛群的饲喂效率制定饲喂策略，是个性化精准饲喂的一个合理目标。然而，旨在为特定动物提供饲喂建议的算法却很稀缺。本研究的目的是开发并测试两种算法，这两种算法旨在通过用不同类型和数量的额外添加饲料补充全混合日粮（TMR）来提高个体奶牛的饲料效率。将24头荷斯坦奶牛分配到以下3个处理组中的1组：对照组自由采食普通TMR，一组根据算法1进行个体饲喂，另一组根据算法2进行个体饲喂。算法1采用混合模型方法，以饲料效率作为响应变量，以生产参数的自动测量值和额外添加饲料类型作为因变量。将奶牛视为随机效应，如果显著则纳入奶牛与额外添加饲料的交互作用。算法2使用主成分和k均值聚类基于额外添加饲料的响应效率结构对奶牛进行分组。两种算法在测试前的36天实验期内进行训练，并在35天的测试期内每周更新。使用混合效应模型分析干物质摄入量（DMI）、产奶量、乳脂率和产量、乳蛋白率和产量以及饲料效率的生产性能响应，该模型对饲喂算法、额外添加饲料、周以及这些变量之间的二因素和三因素交互作用设置固定效应。乳蛋白率和饲料效率受到额外添加饲料、算法和周的三因素交互作用的显著影响，DMI也倾向于受到这种三因素交互作用的影响。饲喂算法对产奶量、乳脂产量或乳蛋白产量没有影响。然而，算法饲喂的奶牛的饲养成本降低，因此牛奶收入增加。饲喂算法的效果因额外添加饲料和时间而异，并且很大程度上依赖于DMI的变化来调节饲料效率。对于累积饲喂组而言，最终结果是，与TMR对照组的奶牛相比，算法1组和算法2组的奶牛每天每头分别多赚超过0.45美元和0.70美元。本研究产生了两种以效率为重点的个性化饲喂建议的候选方法。需要改进算法选择、开发和训练方法，以通过个性化饲喂最大化生产参数。
