We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSE_CV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models’ performance fairly. We found RMSE_CIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models’ performance with those achieved by the National Research Council’s equation.

中文翻译：

---

改进奶牛干物质摄入量测试日预测的多国方法

我们使用胎次，泌乳周，产奶量，牛奶中红外（MIR）光谱以及基于MIR的体重，脂肪，蛋白质，乳糖和脂肪酸含量的预测来预测奶牛的干物质摄入量。该数据集包含534个奶牛的10,711个样本，这些奶牛具有地理多样性（澳大利亚，加拿大，丹麦和爱尔兰）。我们使用不同的结构和使用最高贡献变量的单层人工神经网络（ANN）来建立偏最小二乘（PLS）回归。在人工神经网络中，我们用对25个第一个PLS因子的投影替换了光谱，解释了99％的光谱变异性，从而降低了模型的复杂性。牛无关的10×10倍交叉验证（CV）以均方根误差（RMSE _CV）表现最佳）的PLS回归值为3.27±0.08 kg，而ANN的值为3.25±0.13 kg。尽管可用数据差异很大，但我们还进行了国家独立验证（CIV），以公平地衡量模型的性能。我们发现，对于PLS，RMSE _CIV从3.73到6.03千克不等；对于ANN，RMSE _CIV从3.69到5.08千克不等。最终，基于与国家/地区无关的验证，我们与国家研究委员会的等式讨论了开发模型的性能。