School of Agriculture and Food, Davis College of Agriculture, Natural Resources and Design, West Virginia University, Morgantown, WV 26506, USA.
West Virginia Agricultural and Forestry Experiment Station, Morgantown, WV 26506, USA.
J Anim Sci. 2023 Jan 3;101. doi: 10.1093/jas/skad269.
Technology that facilitates estimations of individual animal dry matter intake (DMI) rates in group-housed settings will improve production and management efficiencies. Estimating DMI in pasture settings or facilities where feed intake cannot be monitored may benefit from predictive algorithms that use other variables as proxies. This study examined the relationships between DMI, animal performance, and environmental variables. Here we determined whether a machine learning approach can predict DMI from measured water intake variables, age, sex, full body weight, and average daily gain (ADG). Two hundred and five animals were studied in a drylot setting (152 bulls for 88 d and 53 steers for 50 d). Collected data included daily DMI, water intake, daily predicted full body weights, and ADG using In-Pen-Weighing Positions and Feed Intake Nodes. After exclusion of 26 bulls of low-frequency breeds and one severe (>3 standard deviations) outlier, the final number of animals used for modeling was 178 (125 bulls, 53 steers). Climate data were recorded at 30-min intervals throughout the study period. Random Forest Regression (RFR) and Repeated Measures Random Forest (RMRF) were used as machine learning approaches to develop a predictive algorithm. Repeated Measures ANOVA (RMANOVA) was used as the traditional approach. Using the RMRF method, an algorithm was constructed that predicts an animal's DMI within 0.75 kg. Evaluation and refining of algorithms used to predict DMI in drylot by adding more representative data will allow for future extrapolation to controlled small plot grazing and, ultimately, more extensive group field settings.
在群体饲养环境中，有助于估算个体动物干物质采食量（DMI）率的技术将提高生产和管理效率。在牧场环境或无法监测饲料摄入量的设施中估算 DMI 时，可以受益于使用其他变量作为替代物的预测算法。本研究检查了 DMI、动物性能和环境变量之间的关系。在这里，我们确定了机器学习方法是否可以根据测量的水摄入量变量、年龄、性别、全身重量和平均日增重（ADG）来预测 DMI。205 头动物在干圈环境中进行了研究（152 头公牛 88 天，53 头阉牛 50 天）。收集的数据包括每日 DMI、水摄入量、每日预测的全身重量和使用 In-Pen-Weighing Positions 和 Feed Intake Nodes 的 ADG。排除了 26 头低频率品种的公牛和一个严重的（>3 个标准差）异常值后，用于建模的最终动物数量为 178 头（125 头公牛，53 头阉牛）。在整个研究期间，每隔 30 分钟记录一次气候数据。随机森林回归（RFR）和重复测量随机森林（RMRF）被用作机器学习方法来开发预测算法。重复测量方差分析（RMANOVA）被用作传统方法。使用 RMRF 方法，构建了一个可以在 0.75 公斤内预测动物 DMI 的算法。通过添加更具代表性的数据来评估和改进用于预测干圈中 DMI 的算法，将允许未来外推到受控的小面积放牧，并最终扩展到更广泛的群体野外环境。
