The aim of the present study was to develop a model to identify posture and behavior from data collected by a triaxial accelerometer located on the left flank of dairy cows and evaluate its accuracy and precision. Twelve Italian Red-and-White lactating cows were equipped with an accelerometer and observed on average for 136 ± 29 min per cow by two trained operators as a reference. The acceleration data were grouped in time windows of 8 s overlapping by 33.0%, for a total of 35133 rows. For each row, 32 different features were extracted and used by machine learning algorithms for the classification of posture and behavior. To build up a predictive model, the dataset was split in training and testing datasets, characterized by 75.0 and 25.0% of the observations, respectively. Four algorithms were tested: Random Forest, K Nearest Neighbors, Extreme Boosting Algorithm (XGB), and Support Vector Machine. The XGB model showed the best accuracy (0.99) and Cohen’s kappa (0.99) in predicting posture, whereas the Random Forest model had the highest overall accuracy in predicting behaviors (0.76), showing a balanced accuracy from 0.96 for resting to 0.77 for moving. Overall, very accurate detection of the posture and resting behavior were achieved.
Simple SummaryLivestock production emerges as one of the main contributors of ammonia emissions; in fact, as the literature reports, the excess of nitrogen fed in form of feed protein is excreted in manure and converted into ammonia. Defining beef cattle protein requirements, specifically for each breed and farming system, is fundamental to ensuring an adequate supply of protein, while avoiding N losses due to an over-estimation of needs. In this study, we compared two different levels of protein in beef cattle diets to better understand the exact amount that would meet the animals’ requirements, while avoiding waste. Results showed that, on one hand, the decrease of protein in the diet could actually compromise animals’ daily weight gains, but on the other hand, it did not reduce the income of the farmer, because the diet was cheaper, and improved the efficiency in the use of the digestible protein for growth.AbstractThis study aimed to evaluate the effect of decreasing dietary crude protein (CP) on the performance of finishing Charolais bulls in the Italian rearing system. Animals were fed two diets, differing only in the CP level (low protein (LP), 13.5% CP versus control (CON), 15.0% CP). Dry matter (DM) intake (DMI) and animals’ weights were recorded to obtain average daily gain (ADG) and feed conversion rate (FCR). Feed and fecal samples were collected to evaluate digestibility of diet components. Daily cost of the ration (DRC), feed cost per kg of daily weight gain (CDG) and daily gross margin (DGM) were calculated to analyze the possible benefits of decreasing the protein level. Meat quality analyses were also conducted. Higher DMI (10.6 versus 10 kg/d; p < 0.05) and ADG (1.47 versus 1.36 kg/d; p < 0.05) were observed for CON. No differences in FCR or digestibility were found. Even if the DRC was lower (p < 0.05) for the LP diet (2.26 versus 1.97 €; CON versus LP), no difference was reported for CDG and DGM. Meat lightness and redness were significantly lower and higher in the LP, respectively. To conclude, the CP requirement in these rearing conditions appeared to be higher than 13.5%.
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