Department of Agricultural Sciences, University of Sassari, Sassari, 07100, Italy; University School for Advanced Studies IUSS Pavia, Pavia, 27100, Italy; Department of Animal Science, Texas A&M University, College Station TX 77843-2471, USA.
Department of Agricultural Sciences, University of Sassari, Sassari, 07100, Italy.
Animal. 2023 Dec;17 Suppl 5:101042. doi: 10.1016/j.animal.2023.101042. Epub 2023 Nov 28.
Climate change is expected to increase the number of heat wave events, leading to prolonged exposures to severe heat stress (HS) and the corresponding adverse effects on dairy cattle productivity. Modelling dairy cattle productivity under HS conditions is complicated because it requires comprehending the complexity, non-linearity, dynamicity, and delays in animal response. In this paper, we applied the System Dynamics methodology to understand the dynamics of animal response and system delays of observed milk yield (MY) in dairy cows under HS. Data on MY and temperature-humidity index were collected from a dairy cattle farm. Model development involved: (i) articulation of the problem, identification of the feedback mechanisms, and development of the dynamic hypothesis through a causal loop diagram; (ii) formulation of the quantitative model through a stock-and-flow structure; (iii) calibration of the model parameters; and (iv) analysis of results for individual cows. The model was successively evaluated with 20 cows in the case study farm, and the relevant parameters of their HS response were quantified with calibration. According to the evaluation of the results, the proposed model structure was able to capture the effect of HS for 11 cows with high accuracy with mean absolute percent error <5%, concordance correlation coefficient >0.6, and R > 0.6, except for two cows (ID #13 and #20) with R less than 0.6, implying that the rest of the nine animals do not exhibit heat-sensitive behaviour for the defined parameter space. The presented HS model considered non-linear feedback mechanisms as an attempt to help farmers and decision makers quantify the animal response to HS, predict MY under HS conditions, and distinguish the heat-sensitive cows from heat-tolerant cows at the farm level.
预计气候变化将增加热浪事件的数量，导致奶牛长时间暴露在严重热应激（HS）下，从而对奶牛生产性能产生相应的不利影响。在 HS 条件下模拟奶牛生产性能比较复杂，因为它需要理解动物反应的复杂性、非线性、动态性和延迟。在本文中，我们应用系统动力学方法来了解 HS 下奶牛观察到的产奶量（MY）的动物反应和系统延迟的动态。从一个奶牛场收集了关于 MY 和温湿度指数的数据。模型开发涉及：（i）阐述问题，通过因果关系图识别反馈机制和发展动态假设；（ii）通过存量和流量结构制定定量模型；（iii）校准模型参数；（iv）分析个体奶牛的结果。该模型在案例研究农场的 20 头奶牛中进行了逐步评估，并对其 HS 反应的相关参数进行了校准。根据结果评估，所提出的模型结构能够以高精度捕捉 11 头奶牛的 HS 影响，平均绝对百分比误差<5%，一致性相关系数>0.6，R>0.6，除了两头奶牛（ID#13 和#20）的 R 小于 0.6，这意味着其余九头动物在定义的参数空间内没有表现出对 HS 的敏感行为。所提出的 HS 模型考虑了非线性反馈机制，试图帮助农民和决策者量化动物对 HS 的反应，预测 HS 条件下的 MY，并在农场层面区分耐热奶牛和热敏感奶牛。
