Singh Bed, Das Ajoy, Bhakat Champak, Mishra Babita, Elangbam Shrilla, Sinver Mahendra, Ambili K S, Tarafdar Ayon
Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243 122, India.
Division of Livestock Production and Management, ICAR-National Dairy Research Institute, Eastern Regional Station, Kalyani, West Bengal, 741 235, India.
Trop Anim Health Prod. 2025 Jan 30;57(2):42. doi: 10.1007/s11250-025-04295-w.
Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to wastage of feed and an increase in the cost of production. This investigation aimed to predict DMI in Black Bengal goats by using body weight (BW), body condition score (BCS), average daily gain (ADG), and metabolic body weight (MBW) by applying an artificial neural network (ANN) model. A total of 144 observations were collected from 18 goats over a 4-month period for each input (BW, ADG, MBW and BCS) and output (DMI) variable. These input variables were taken fortnightly and correlated with DMI. The presence of a significant positive correlation between DMI with BW (r = 0.968, p < 0.01), BCS (r = 0.687, p < 0.01), ADG (r = 0.608, p < 0.01), and MBW (r = 0.971, p < 0.01) indicated potential for ANN model development. ANN model with 10 hidden layer neurons trained using GDX and LOGSIG transfer function emergeds as the high-performing model for predicting DMI in Black Bengal goats, achieving the highest R (0.9693) and the lowest MSE (0.0013) among the configurations considered. Comparison between the three models revealed that the DMI was estimated more accurately by the ANN model than by linear and second-order non-linear models. ANN may therefore be used to predict DMI with high accuracy and reliability in place of other regression methods.
干物质摄入量（DMI）的测定对于肉用山羊的有效管理至关重要，特别是在优化饲料利用率和生产效率方面。不幸的是，养殖户在准确预测DMI方面常常面临挑战，这会导致饲料浪费和生产成本增加。本研究旨在通过应用人工神经网络（ANN）模型，利用体重（BW）、体况评分（BCS）、平均日增重（ADG）和代谢体重（MBW）来预测黑孟加拉山羊的DMI。在4个月的时间里，从18只山羊身上收集了总共144组观测数据，用于每个输入（BW、ADG、MBW和BCS）和输出（DMI）变量。这些输入变量每两周采集一次，并与DMI进行相关性分析。DMI与BW（r = 0.968，p < 0.01）、BCS（r = 0.687，p < 0.01）、ADG（r = 0.608，p < 0.01）和MBW（r = 0.971，p < 0.01）之间存在显著正相关，这表明开发ANN模型具有潜力。使用GDX和LOGSIG传递函数训练的具有10个隐藏层神经元的ANN模型成为预测黑孟加拉山羊DMI的高性能模型，在所考虑的配置中实现了最高的R（0.9693）和最低的MSE（0.0013）。三种模型之间的比较表明，ANN模型比线性和二阶非线性模型更准确地估计了DMI。因此，ANN可用于高精度和可靠地预测DMI，以替代其他回归方法。
