Department of Agricultural Sciences, University of Sassari, Viale Italia 39, 07100 Sassari, Italy; System Dynamics Italian Chapter, Italy.
Department of Agricultural Sciences, University of Sassari, Viale Italia 39, 07100 Sassari, Italy; System Dynamics Italian Chapter, Italy.
Animal. 2023 Dec;17 Suppl 5:100905. doi: 10.1016/j.animal.2023.100905. Epub 2023 Jul 5.
Systems perspectives and system dynamics have been widely used in decision-making for agricultural problems. However, their use in dairy farm management remains limited. This work demonstrates the use of systems approaches and feedback thinking in modelling for dairy farm management. The application of feedback thinking was illustrated with causal loop and stock-and-flow diagrams to disentangle the complexity of the relationship among farm elements. The study aimed to identify the dynamic processes of an intensive dairy farm by mapping the animal stocks (e.g., heifers, lactating cows, dry cows) with the final objective of anticipating the expected milk deliveries over a long time period. The project was conducted for a reference dairy farm that was intensively managed with a herd size of >2 500 cattle heads, which provided monthly farm records from Jan 2016 to Dec 2019. Model development steps included: (i) problem articulation with farm interviews and data analysis; (ii) the development of a dynamic hypothesis and a causal loop diagram; (iii) the development of a stock-and-flow cattle model describing ageing chains of heifers and cows and subsequent calibration of the model parameters; (iv) the evaluation of the model based on lactating cows and milk deliveries against farm historical records; and (v) the analysis of the model results. The model characterized the farm dynamics using three main feedback loops: one balancing loop of culling and two reinforcing loops of heifers' replacement and cows' pregnancy, pushing milk delivery. The model reproduced the historical oscillation patterns of lactating cows and milk deliveries with high accuracy (root mean square percentage error of 2.8 and 5.2% for the number of lactating cows and milk deliveries, respectively). The model was shown to be valid for its purpose, and applications of this model in dairy farm management can support decision-making practices for herd composition and milk delivery targets.
系统观点和系统动力学已广泛应用于农业问题的决策中。然而，它们在奶牛场管理中的应用仍然有限。本工作展示了系统方法和反馈思维在奶牛场管理建模中的应用。应用反馈思维通过因果回路和存量流量图来阐明农场要素之间关系的复杂性。本研究旨在通过绘制动物存量（例如，小母牛、泌乳牛、干奶牛）来识别集约化奶牛场的动态过程，最终目的是预测长时间段内的预期牛奶产量。该项目针对一个集约化管理的参考奶牛场进行，该奶牛场的畜群规模超过 2500 头牛，提供了 2016 年 1 月至 2019 年 12 月的每月农场记录。模型开发步骤包括：（i）通过农场访谈和数据分析阐明问题；（ii）开发动态假设和因果回路图；（iii）开发描述小母牛和奶牛老化链以及随后校准模型参数的存量流量奶牛模型；（iv）根据农场历史记录评估模型对泌乳牛和牛奶产量的预测；以及（v）分析模型结果。该模型使用三个主要反馈回路来描述农场动态：一个淘汰的平衡回路和两个小母牛替代和奶牛怀孕的增强回路，推动牛奶产量。该模型高度准确地再现了泌乳牛和牛奶产量的历史波动模式（泌乳牛数量和牛奶产量的均方根百分比误差分别为 2.8%和 5.2%）。该模型被证明是有效的，并且该模型在奶牛场管理中的应用可以支持畜群组成和牛奶产量目标的决策实践。
