UMR Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, 75005 Paris, France.
UMR Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, 75005 Paris, France.
J Dairy Sci. 2018 Feb;101(2):1648-1660. doi: 10.3168/jds.2016-12453. Epub 2017 Nov 23.
The aim of this study was to quantify the effects of progesterone profile features and other cow-level factors on insemination success to provide a real-time predictor equation of probability of insemination success. Progesterone profiles from 26 dairy herds were analyzed and the effects of profile features (progesterone slope, cycle length, and cycle height) and cow traits (milk yield, parity, insemination during the previous estrus) on likelihood of artificial insemination success were estimated. The equation was fitted on a training data set containing data from 16 herds (6,246 estrous cycles from 3,404 lactations). The equation was tested on a testing data set containing data from 10 herds (8,105 estrous cycles from 3,038 lactations). Predictors were selected to be implemented in the final equation if adding them to a base model correcting for timing of insemination and parity decreased the overall likelihood distance of the model. Selected variables (cycle length, milk yield, cycle height, and insemination during the previous estrus) were used to build the final model using a stepwise approach. Predictors were added 1 by 1 in different order, and the model that had the smallest likelihood distance was selected. The final equation included the variables timing of insemination, parity, milk yield, cycle length, cycle height, and insemination during the previous estrus, respectively. The final model was applied to the testing data set and area under the curve (AUC) was calculated. On the testing data set, the final model had an AUC of 58%. When the farm effect was taken into account, the AUC increased to 63%. This equation can be implemented on farms that monitor progesterone and can support the farmer in deciding when to inseminate a cow. This can be the first step in moving the focus away from the current paradigm associated with poorer estrus detection, where each detected estrus is automatically inseminated, to near perfect estrus detection, where the question is which estrous cycle is worth inseminating?
本研究旨在量化孕酮谱特征和其他牛水平因素对授精成功的影响，提供授精成功概率的实时预测方程。分析了 26 个奶牛场的孕酮谱，评估了谱特征（孕酮斜率、周期长度和周期高度）和奶牛特征（产奶量、胎次、前发情期授精）对人工授精成功可能性的影响。该方程基于包含 16 个牛场（3404 个泌乳期的 6246 个发情周期）数据的训练数据集进行拟合。该方程在包含 10 个牛场（3038 个泌乳期的 8105 个发情周期）数据的测试数据集上进行了测试。如果将预测因子添加到基础模型中，基础模型会校正授精时间和胎次，从而降低模型的整体似然距离，则选择预测因子纳入最终方程。选择的变量（周期长度、产奶量、周期高度和前发情期授精）用于使用逐步方法构建最终模型。以不同的顺序逐个添加预测因子，并选择具有最小似然距离的模型。最终方程包含授精时间、胎次、产奶量、周期长度、周期高度和前发情期授精等变量。最终模型应用于测试数据集，并计算曲线下面积（AUC）。在测试数据集上，最终模型的 AUC 为 58%。当考虑农场效应时，AUC 增加到 63%。该方程可应用于监测孕酮的农场，并支持农民决定何时给奶牛授精。这可以作为从当前与较差发情检测相关的范式转变的第一步，在当前范式中，每个检测到的发情期都会自动授精，转变为近乎完美的发情检测，在当前范式中，问题是哪个发情周期值得授精？
