The objective of this study was to assess precision dairy monitoring technology (PDMT) measured variable effects on 3 postpartum diseases: metritis (MET; n = 112), hyperketonemia (KET; n=46), and hypocalcemia (CAL; n=90). The PDMT variables included lying time, step count, lying bouts, rumination time, eating time, time around the feedbunk, reticulorumen temperature, milk yield, milk conductivity, milk fat percentage, milk protein percentage, milk fat:protein ratio, milk lactose percentage, and body weight. A uterine discharge examination was conducted to determine MET at 3, 5, 7, 11, 14, 17, 19, and 21 DIM using a MetriCheck (Simcro Tech Ltd, Hamilton, New Zealand) device. A scale of 1 to 3 was used, and cows with scores ≥ 2 were classified as MET. A cow was classified with KET when any of the blood samples that were collected at 3, 7, 14, and 21 days in milk (DIM) were over the threshold (β-hydroxybutyrate concentration ≥ 1.2 mmol/L of blood). A cow was classified with CAL when any of the samples were below the calcium threshold (calcium concentration ≤ 8.6 mg/dL of blood serum). The relationships between each PDMT variable and disease status were analyzed individually using the GLM procedure of SAS 9.3. The 21 DIM means of variables were compared with disease status (Yes or No) for MET, KET, or CAL. Cows with CAL spent less time ruminating (HR tag, 466 ± 95 min/d vs. 507 ± 92 min/d; respectively, P = 0.04), experienced longer lying time (IceQube, 9.4 ± 1.6 h/d vs. 8.2 ± 1.7 h/d, respectively, P < 0.01) and displayed lesser neck activity than cows without CAL (HR tag, 373 ± 101 units of movement/d vs. 444 ± 123 units of movement/d, respectively, P = 0.01). Cows with KET spent less time ruminating (HR tag, 447 ± 107 min/d vs. 494 ± 85 min/d, P = 0.01; SmartBow, 510 ± 89 min/d vs. 547 ± 69 min/d, P = 0.03; CowManager SensOor, 544 ± 140 min/d vs. 595 ± 96 min/d, respectively, P = 0.05), fewer steps (AfiAct Pedometer Plus, 3,355 ± 766 steps/d vs. 3,723 ± 877 steps/d; respectively, P = 0.01, respectively), lesser neck activity (HR tag, 351 ± 105 units of movement/d vs. 417 ± 110 units of movement/d, respectively, P < 0.01), greater lying times (IceQube, 9.4 ± 1.7 h/d vs. 8.7 ± 1.6 min/d, respectively, P = 0.02) than cows without KET. Multiparous cows with KET had heavier body weights than multiparous cows without KET (AfiWeigh, 753 ± 70 kg vs. 715 ± 78 kg, respectively, P = 0.02), and yielded less milk (AfiMilk MPC milk meter 31 ± 10 kg/d vs. 37 ± 8 kg/d, respectively, P = 0.03). Cows with MET took fewer steps (IceQube, 1104 ± 343 steps/d vs. 1335 ± 375 steps/d, respectively, P < 0.01) and had greater fat:protein ratio than cows without MET(1.35 ± 0.05 vs. 1.18 ± 0.09, respectively, P = 0.02).

这项研究的目的是评估精密乳制品监测技术（PDMT）对三种产后疾病的可变影响：子宫炎（MET; n = 112），高酮血症（KET; n = 46）和低钙血症（CAL; n = 90））。PDMT变量包括卧床时间，步数，卧床次数，反刍时间，进食时间，料槽周围的时间，网状温度，产奶量，牛奶电导率，牛奶脂肪百分比，牛奶蛋白质百分比，牛奶脂肪：蛋白质比率，牛奶乳糖百分比和体重。使用MetriCheck（新西兰汉密尔顿的Simcro Tech Ltd）设备进行子宫排泄检查，以确定3、5、7、11、14、17、19和21 DIM的MET。使用1到3的量表，将得分≥2的母牛分类为MET。当在牛奶中第3、7、14和21天收集的任何血液样本中的一头（DIM，）超过阈值（血液中的β-羟基丁酸酯浓度≥1.2 mmol / L）。当任何样品的钙含量低于钙阈值（血钙浓度≤8.6 mg / dL）时，用CAL对一头母牛进行分类。使用SAS 9.3的GLM程序分别分析每个PDMT变量与疾病状态之间的关系。将21种DIM变量的平均值与MET，KET或CAL的疾病状态（是或否）进行比较。拥有CAL的母牛反省的时间更少（HR标签，466±95 min / d vs. 507±92 min / d; P  = 0.04），躺卧时间更长（IceQube，9.4±1.6 h / d vs. 8.2± 1.7 H / d分别P<0.01），且颈活动度低于没有CAL的母牛（HR标签，分别为373±101单位运动/ d与444±123单位运动/ d，P  = 0.01）。患有KET的母牛反省的时间更少（HR标签，447±107 min / d vs. 494±85 min / d，P  = 0.01; SmartBow，510±89 min / d vs. 547±69 min / d，P  = 0.03 ; CowManager SensOor，分别为544±140 min / d和595±96 min / d，P = 0.05），步数更少（AfiAct计步器Plus，3,355±766步/ d对3,723±877步/ d; P 分别为0.01）和较小的颈部活动（HR标签，分别为351±105单位运动/ d与417±110单位运动/ d，P <0.01）， 比不使用KET的母牛的卧床时间更长（IceQube，9.4±1.7 h / d与8.7±1.6 min / d，P = 0.02）。与无KET的多头母牛相比，有KET的多头母牛体重更重（AfiWeigh分别为753±70 kg和715±78 kg，P  = 0.02），并且产奶量更少（AfiMilk MPC牛奶表为31±10 kg / dvs。 37±8 kg / d，P  = 0.03）。含MET的奶牛步数减少（IceQube，分别为1104±343步/ d与1335±375步/ d，P <0.01），脂肪/蛋白质比比不含MET的奶牛高（1.35±0.05 vs.1.18±0.09 ，分别为P  = 0.02）。