Adriaens Ines, Huybrechts Tjebbe, Geerinckx Katleen, Daems Devin, Lammertyn Jeroen, De Ketelaere Bart, Saeys Wouter, Aernouts Ben
KU Leuven, Department of Biosystems, MeBioS, Kasteelpark Arenberg 30, Box 2456, 3001 Leuven, Belgium.
KU Leuven, Department of Biosystems, MeBioS, Kasteelpark Arenberg 30, Box 2456, 3001 Leuven, Belgium.
Theriogenology. 2017 Nov;103:44-51. doi: 10.1016/j.theriogenology.2017.07.040. Epub 2017 Jul 29.
Reproductive performance is an important factor affecting the profitability of dairy farms. Optimal fertility results are often confined by the time-consuming nature of classical heat detection, the fact that high-producing dairy cows show estrous symptoms shorter and less clearly, and the occurrence of ovarian problems. Today's commercially available solutions for automatic estrus detection include monitoring of activity, temperature and progesterone. The latter has the advantage that, besides estrus, it also allows to detect pregnancy and ovarian problems. Due to the large variation in progesterone profiles, even between cycles within the same cow, the use of general thresholds is suboptimal. To this end, an intelligent and individual interpretation of the progesterone measurements is required. Therefore, an alternative solution is proposed, which takes individual and complete cycle progesterone profiles into account for reproduction monitoring. In this way, profile characteristics can be translated into specific attentions for the farmers, based on individual rather than general guidelines. To enable the use of the profile and cycle characteristics, an appropriate model to describe the milk progesterone profile was developed. The proposed model describes the basal adrenal progesterone production and the growing and regressing cyclic corpus luteum. To identify the most appropriate way to describe the increasing and decreasing part of each cycle, three mathematical candidate functions were evaluated on the increasing and decreasing parts of the progesterone cycle separately: the Hill function, the logistic growth curve and the Gompertz growth curve. These functions differ in the way they describe the sigmoidal shape of each profile. The increasing and decreasing parts of the P4 cycles were described best by the model based on respectively the Hill and Gompertz function. Combining these two functions, a full mathematical model to characterize the progesterone cycle was obtained. It was shown that this approach retains the flexibility to deal with both varying baseline and luteal progesterone values, as well as prolonged or delayed cycles.
繁殖性能是影响奶牛场盈利能力的重要因素。最佳繁殖力结果常常受到传统发情检测耗时特性的限制，高产奶牛发情症状持续时间较短且不明显，以及卵巢问题的出现。如今市面上用于自动发情检测的解决方案包括活动监测、体温监测和孕酮监测。孕酮监测的优势在于，除了发情检测外，还能检测怀孕和卵巢问题。由于即使在同一头奶牛的不同周期之间，孕酮水平变化也很大，使用通用阈值并不理想。为此，需要对孕酮测量值进行智能且个性化的解读。因此，提出了一种替代解决方案，该方案在繁殖监测中考虑个体完整的周期孕酮水平。通过这种方式，可根据个体而非通用指南，将水平特征转化为对养殖户的具体提示。为了能够利用水平和周期特征，开发了一个合适的模型来描述牛奶中的孕酮水平。所提出的模型描述了肾上腺基础孕酮分泌以及周期性黄体的生长和消退。为了确定描述每个周期上升和下降部分的最合适方法，分别在孕酮周期的上升和下降部分对三个数学候选函数进行了评估：希尔函数、逻辑生长曲线和冈珀茨生长曲线。这些函数在描述每个水平的S形曲线的方式上有所不同。孕酮周期的上升和下降部分分别用基于希尔函数和冈珀茨函数的模型描述得最好。将这两个函数结合起来，得到了一个完整的数学模型来表征孕酮周期。结果表明，这种方法在处理不同的基线和黄体孕酮值以及延长或延迟的周期方面具有灵活性。
