Dairy Science Group, School of Life and Environmental Sciences, Faculty of Science, University of Sydney, Camden 2570, Australia.
Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Melbourne 4003, Australia.
J Dairy Sci. 2019 Mar;102(3):2551-2559. doi: 10.3168/jds.2018-14801. Epub 2019 Jan 11.
In pasture-based automatic milking systems (AMS), a decrease in robot utilization (RU) often occurs in the early morning hours. Novel feeding strategies that encourage voluntary cow traffic throughout 24 h could help mitigate this problem. We determined the effect of 3 distinct pasture allocation methods on RU patterns throughout a 24-h period. The experiment was conducted at the University of Melbourne's Dookie research farm in northern Victoria, Australia. Three Lely Astronaut A3 robotic milking units (Lely, Maassluis, the Netherlands) milked 133 cows, grazing pasture, with concentrate offered at milking in the robots. The farm operated a system of 3-way grazing, with active access to each pasture allocation: 2030-0400 h (allocation A), 0400-1330 h (allocation B), and 1330-2030 h (allocation C). Treatments varied in the quantity of feed offered per hour of active access to each of the 3 pasture allocations. The control treatment offered the same proportion of feed (corrected for active access time) in all 3 pasture allocations (allocation A = 31.3%, B = 39.6%, and C = 29.2%). The day treatment offered the largest proportion of feed during the day (allocation A = 20%, B = 40%, and C = 40%), following the cows' diurnal pattern of feeding activity. The night treatment offered the largest proportion of feed at night (allocation A = 42%, B = 40%, and C = 18%). Due to the nature of pasture-based AMS, treatments could not be applied simultaneously. Therefore, treatments were applied to the entire herd and repeated twice over 42 d, lasting 7 d/treatment, with the first 3 d for habituation, followed by 4 d of data collection. Robot utilization (milkings/h) varied throughout 24 h between treatments, with the night treatment recording greater RU at 0800, 1800, and 1900 h and lower RU between 2100 to 0100 h, compared with the day treatment. The proportion of the herd milking between 0000 and 0600 h was greater for the control (43.3%) and day (45.3%) treatments compared with the night treatment (25.8%). Herd-average daily pasture intake was similar (10.5 kg of dry matter) for all treatments. This experiment is the first to demonstrate the manipulation of RU by varying the quantity of pasture offered. However, the use of variable allocation alone did not eliminate the decrease in RU between 0000 and 0600 h, with the timing of allocation also likely to play a role. We recommend a further research focus on combining both timing and quantity of pasture allocated to improve RU in pasture-based AMS.
在基于牧场的自动挤奶系统 (AMS) 中，机器人利用率 (RU) 经常在清晨下降。鼓励奶牛在 24 小时内自由通行的新型饲养策略可能有助于缓解这一问题。我们确定了 3 种不同的牧场分配方法对 24 小时内 RU 模式的影响。该实验在澳大利亚维多利亚州北部的墨尔本大学迪基研究农场进行。3 个利拉伐 Astronaut A3 机器人挤奶单元（利拉伐，马塞卢斯，荷兰）为 133 头奶牛挤奶，这些奶牛在牧场上放牧，在机器人中挤奶时提供精饲料。该农场采用 3 路放牧系统，可自由进入每个牧场分配区：0400-1330 h（分配 A）、1330-2030 h（分配 B）和 2030-0400 h（分配 C）。治疗方法在每小时对 3 个牧场分配区的主动访问中提供的饲料量上有所不同。对照处理在所有 3 个牧场分配区（分配 A = 31.3%、B = 39.6%和 C = 29.2%）中提供相同比例的饲料（根据主动访问时间校正）。日处理在白天提供最大比例的饲料（分配 A = 20%、B = 40%和 C = 40%），遵循奶牛的日摄食活动模式。夜间处理在夜间提供最大比例的饲料（分配 A = 42%、B = 40%和 C = 18%）。由于基于牧场的 AMS 的性质，不能同时应用处理方法。因此，将处理方法应用于整个牛群，并在 42 天内重复 2 次，每次处理持续 7 天，前 3 天适应，然后收集 4 天数据。RU（挤奶/小时）在 24 小时内因处理方法而异，与日处理相比，夜间处理在 0800、1800 和 1900 小时记录的 RU 更高，在 2100 至 0100 小时记录的 RU 更低。与夜间处理（25.8%）相比，控制（43.3%）和日处理（45.3%）的奶牛在 0000 至 0600 小时之间挤奶的比例更大。所有处理的平均日牧场摄入量相似（10.5 公斤干物质）。这项实验是第一个通过改变提供的牧场数量来控制 RU 的实验。然而，仅使用可变分配并不能消除 0000 至 0600 小时之间 RU 的下降，分配的时间也可能起作用。我们建议进一步研究将牧场分配的时间和数量结合起来，以提高基于牧场的 AMS 中的 RU。
