Anhui Key Laboratory of Livestock and Poultry Product Safety Engineering, Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Science, Hefei, Anhui, 230031, China.
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China.
Poult Sci. 2024 Nov;103(11):104185. doi: 10.1016/j.psj.2024.104185. Epub 2024 Aug 20.
The production performance of laying hens is influenced by various environmental factors within the henhouse. The intricate interactions among these factors make the impact process highly complicated. The exact relationships between production performance and environmental variables are still not well understood. In this study, we measured the production performance of laying hens and various environmental variables across different parts of the henhouse, evaluated the weight of each environmental variable, and constructed a laying rate prediction model. Results displayed that body weight, laying rate, egg weight and eggshell thickness of hens decrease gradually from WCA to FA (P < 0.05). Serum levels of FSH and LH, as well as antibody level of H5 Re-13, gradually decrease from WCA to FA (P < 0.05). Moreover, the values for temperature (T), temperature-humidity index (THI), air velocity (AV), carbon dioxide (CO), and particulate matter (PM) gradually increase from WCA to FA (P < 0.05). Conversely, the relative humidity (RH) value gradually decreases from FA to WCA (P < 0.05). Additionally, the weights of the environmental variables, determined using a combination of the grey relational analysis (GRA) and analytic hierarchy process (AHP), were as follows in descending order: RH, THI, T, light intensity (LI), AV, PM, NH, and CO. When the number of decision trees in the laying rate prediction model was set to 2,500, the results displayed a high level of agreement between the model's predictions and the observed outcomes. The model's performance evaluation yielded an R value of 0.89995 for the test set, suggesting strong predictive effects. In conclusion, the current study revealed significant differences in both the production performance of laying hens and the environmental variables across different parts of the henhouse. Furthermore, the study demonstrated that different environmental factors have distinct impacts on laying rate, with humidity and temperature identified as the primary factors. Finally, a multi-variable prediction model was constructed, exhibiting high accuracy in predicting laying rate.
蛋鸡的生产性能受到鸡舍内各种环境因素的影响。这些因素之间错综复杂的相互作用使得影响过程非常复杂。生产性能与环境变量之间的确切关系还不太清楚。在这项研究中，我们测量了蛋鸡在鸡舍不同部位的生产性能和各种环境变量，评估了每个环境变量的权重，并构建了一个产蛋率预测模型。结果表明，从 WCA 到 FA，母鸡的体重、产蛋率、蛋重和蛋壳厚度逐渐降低（P<0.05）。从 WCA 到 FA，母鸡血清中 FSH 和 LH 的水平以及 H5 Re-13 的抗体水平逐渐降低（P<0.05）。此外，从 WCA 到 FA，温度（T）、温湿度指数（THI）、空气速度（AV）、二氧化碳（CO）和颗粒物（PM）的值逐渐升高（P<0.05）。相反，相对湿度（RH）值从 FA 到 WCA 逐渐降低（P<0.05）。此外，使用灰色关联分析（GRA）和层次分析法（AHP）相结合的方法确定环境变量的权重，按降序排列依次为：RH、THI、T、光照强度（LI）、AV、PM、NH 和 CO。当产蛋率预测模型中的决策树数量设置为 2,500 时，模型的预测结果与观察结果高度一致。模型的性能评估显示，测试集的 R 值为 0.89995，表明具有较强的预测效果。总之，本研究揭示了鸡舍不同部位蛋鸡生产性能和环境变量存在显著差异。此外，研究表明，不同的环境因素对产蛋率有不同的影响，湿度和温度是主要因素。最后，构建了一个多变量预测模型，在预测产蛋率方面具有较高的准确性。
