C Souza Vinícius, B Gleason Claire, P Price Tanner, R Dos Reis Bárbara, Sujani Sathya, C Davis Ty, M Liebe Douglas, M Daniels Kristy, R White Robin
School of Animal Sciences, Virginia Tech, Blacksburg, VA 24061.
Present address: Department of Animal Science, University of São Paulo, Pirassununga, SP 13635-900, Brazil.
J Anim Sci. 2025 Jan 4;103. doi: 10.1093/jas/skaf317.
The increasing automation of dairy systems enables precision feeding, where dietary supplements can be tailored to individual animals. However, data describing short-term production responses to such supplementation are lacking, limiting the development of individualized feeding algorithms. This study aimed to 1) examine dairy cow responses to short-term changes in top dress supplementation and 2) evaluate two simple precision feeding strategies compared to a conventional total mixed ration (TMR). Twenty-four lactating Holstein cows were assigned to one of four top-dress treatments: soybean meal (SBM), corn grain (CG), corn gluten feed (GLT), or no supplement in a replicated 4 × 4 Latin square during a 36-d training phase. Subsequently, cows were reassigned to three feeding groups: Control (conventional TMR), algorithm 1 (based on mean past performance), or algorithm 2 (based on the slope of past responses), and evaluated over four 7-d testing periods. During training, dry matter intake (DMI) and milk yield (MY) increased on all top dress treatments relative to the Control treatment (P < 0.001), but feed efficiency (FE; MY/DMI) declined (P < 0.001). SBM reduced milk fat percentage and yield (P < 0.05), while milk protein, body weight (BW), and energy-corrected milk (ECM) were unaffected (P > 0.27). During testing, DMI tended to increase in Algorithm 1 vs. Control (25.2 vs. 22.7 kg/d; P = 0.08), with a corresponding tendency for reduced FE (1.45 vs. 1.61; P = 0.07). No performance differences were detected between algorithm 2 and Control (P > 0.50). Milk composition, ECM, and BW were similar across feeding groups (P > 0.33). Feed cost, milk revenue, and income over feed cost (IOFC) did not differ between precision-fed and control groups (P > 0.33). These findings suggest that short-term historical production responses may be insufficient to inform future supplementation decisions and that more advanced approaches, including new data streams, may be necessary to improve individual cow performance using precision feeding strategies.
奶牛系统自动化程度的不断提高实现了精准饲喂，即可以根据个体动物的需求定制日粮补充剂。然而，目前缺乏描述此类补充剂短期生产反应的数据，这限制了个性化饲喂算法的发展。本研究旨在：1）研究奶牛对表层补充剂短期变化的反应；2）评估两种简单的精准饲喂策略，并与传统全混合日粮（TMR）进行比较。在为期36天的训练阶段，24头泌乳期荷斯坦奶牛被分配到四种表层处理之一：豆粕（SBM）、玉米谷物（CG）、玉米麸质饲料（GLT）或不补充，采用重复的4×4拉丁方设计。随后，奶牛被重新分配到三个饲喂组：对照组（传统TMR）、算法1（基于过去的平均生产性能）或算法2（基于过去反应的斜率），并在四个7天的测试期内进行评估。在训练期间，与对照组相比，所有表层处理的干物质摄入量（DMI）和产奶量（MY）均增加（P<0.001），但饲料效率（FE；MY/DMI）下降（P<0.001）。豆粕降低了乳脂率和产量（P<0.05），而乳蛋白、体重（BW）和能量校正乳（ECM）不受影响（P>0.27）。在测试期间，算法1组的DMI相对于对照组有增加的趋势（25.2对22.7 kg/d；P=0.08），相应地FE有降低的趋势（1.45对1.61；P=0.07）。算法2组与对照组之间未检测到生产性能差异（P>0.50）。各饲喂组之间的乳成分、ECM和BW相似（P>0.33）。精准饲喂组和对照组之间的饲料成本、牛奶收入和饲料成本收益（IOFC）没有差异（P>0.33）。这些发现表明，短期的历史生产反应可能不足以指导未来的补充决策，可能需要更先进的方法，包括新的数据流，以使用精准饲喂策略提高个体奶牛的生产性能。
