Predicting fiber digestibility in Holstein dairy cows fed dry-hay based rations through machine learning

Calculating the requirements and predicting the feed digestibility are essential to building robust dairy cattle rationing programs. In the field, a huge number of in vivo observations are needed to develop accurate equations and reliable predictions. The aim of this study was to develop an equation to estimate total-tract potentially digestible NDF digestibility (TTpdNDFD) for lactating cows fed hay-based rations. Individual data from 11 studies, 69 cows, 35 different treatments, and 1,614 observations were included in this study. To develop the prediction equation, the following traits, descriptors of the total mixed ration, were used: ash, starch, crude protein, NDF, acid detergent fiber, acid detergent lignin, undegradable NDF and potential degradable NDF. Before building the equation with bidirectional stepwise selection in the JMP software, outliers were removed and multicollinearity was checked for all the predictors of fiber digestibility. The model was trained with 10-folds cross-validation. Results showed an R2 of 0.91 and 0.90, and a RMSE of 2.99 and 3.26 in the model for training and validation respectively. The promising performance of the model suggested that, the fiber digestibility in lactating dairy cows fed dry-hay-based rations can be accurately predicted in advance just by using the diet characteristics. From the obtained equation we predicted the weight and slope of the included covariates, and outcomes confirm that in general the TTpdNDFD is reduced as dietary starch and fast fermentable fiber increase. This study found that the equation extracted from a neural network, when combined with precision farming techniques, can improve management of lactating cows and optimize feed planning, monitoring, and cost. It can be used in areas where silages are not used in rations. This provides evidence that accurate equations can be developed from historical data for precision feeding implementation. Further research is needed to expand the dataset and develop equations that can be applied on a large scale. Improving accuracy would involve incorporating representative data from other areas with similar diets into the training set.