To develop a body weight (BW) prediction model using milk production traits and present a useful indicator for energy balance (EB) evaluation in dairy cows. Data were collected from 30 Holstein cows using an automatic milking system. BW prediction models were developed using multiple linear regression (MLR), local regression (LOESS), and deep neural networks (DNN). Milk production traits readily available on commercial dairy farms, such as energy-corrected milk (ECM), fat-to-protein ratio, days in milk (DIM), and parity, were used as input variables for BW prediction. The EB was evaluated as the difference between energy intake and energy demand. The DNN model showed the greatest predictive accuracy for BW compared with the LOESS and MLR models. The BW predicted using the DNN model was used to calculate the energy demand. Our results revealed that the day on which the EB status transitioned from negative to positive differed among cows. The cows were assigned to one of the three EB index groups. EB index 1 indicated that the day of EB transition was within DIM ≤ 70. The EB indexes 2 and 3 were 70 < DIM ≤ 140 and 140 < DIM ≤ 305, respectively. EB index 3 had the lowest EB, which is the slowest to transition from a negative to a positive energy balance compared with EB indexes 1 and 2. The highest ECM and feed efficiency were observed for EB index 3. The calving interval was the shortest for EB index 1. EB of individual cows during lactation can be estimated and monitored with moderately high accuracy using EB indexes.

利用产奶特性开发体重 (BW) 预测模型，并为奶牛能量平衡 (EB) 评估提供有用的指标。使用自动挤奶系统从 30 头荷斯坦奶牛收集数据。BW 预测模型是使用多元线性回归 (MLR)、局部回归 (LOESS) 和深度神经网络 (DNN) 开发的。商业奶牛场容易获得的牛奶生产特性，例如能量校正牛奶 (ECM)、脂肪与蛋白质比率、产奶天数 (DIM) 和胎次，被用作体重预测的输入变量。EB 被评估为能量摄入和能量需求之间的差异。与 LOESS 和 MLR 模型相比，DNN 模型对 BW 的预测精度最高。使用DNN模型预测的BW来计算能源需求。我们的结果显示，奶牛 EB 状态从阴性转变为阳性的日期各不相同。这些奶牛被分配到三个 EB 指数组之一。EB指数1表示EB过渡日在DIM≤70内。EB指数2和3分别为70<DIM≤140和140<DIM≤305。EB 指数 3 的 EB 最低，与 EB 指数 1 和 2 相比，从负能量平衡向正能量平衡转变最慢。观察到 EB 指数 3 的 ECM 和饲料效率最高。产犊间隔最短。 EB 指数 1. 使用 EB 指数可以以中等精度估算和监测泌乳期间个体奶牛的 EB。