1,721,118 research outputs found
Monitoraggio della lince euroasiatica in Friuli Venezia Giulia e iniziale valutazione dei suoi fabbisogni alimentari
No full text
Rapid methods of analysis of silages to improve feeding management in dairy farms
No full textIn modern dairy farms, optimization of feeding is critical to maintain animal health, lower impact and maximise profitability. However, large feed and forage variability reduce consistency of composition of diet delivered to dairy cows. A feeding management plan must implement a program of feed sampling and analysis (St-Pierre and Cobanov, 2007), that ensure small variation of nutrients in diets. Traditional reference wet chemistry methods are accurate, but expensive, they can be implemented at specialized labs with relative long time of response. As an alternative to the traditional methods, near infrared spectroscopy (NIR) has received an increasing attention, with a large use in many commercial and private labs. Proficiency programs (like the National Forage Testing Association) have proven that NIR can be accurate in respect to reference method in the quantification of organic feed nutrients. The paper will highlight the advantages of the use of NIR in feeding program in term of precision feeding and animal performance. It will also evaluate the challenges of on-site analysis for dairy farm as well as for biogas plants. The paper will also briefly cover the alternative methods for the determination of mineral nutrients with rapid and non-destructive methods, like x-ray fluorescence (XRF)
The left side flank as source of information for animal behaviour and welfare in dairy cow.
No full textThe development of devices and methods able to spot changes of behaviours and physiological parameters from normality in a timely manner, is one of the aims of Precision Livestock Farming. With this vision, the aim of the present study was to develop a model to identify animals’ posture and predefined behaviours (moving, feeding, resting, ruminating and standing still) from data collected by a single tri-axial accelerometer located on the left side flank of dairy cows and evaluate its accuracy and precision. This spot was chosen because in ruminants, beyond behaviour, it potentially enables also the monitoring of rumen and turaco-abdominal contractions associated with breathing and involved in both urination and defecation. Twelve Italian Red-and-White lactating dairy cows were equipped with a tri-axial accelerometer located on the left side paralumbar fossa and were observed on average for 136 ± 29 min per cow by two trained observers who continuously recorded animals’ posture and behaviour as a reference. Acceleration data were grouped in time windows of 8s overlapping by 33%, 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 behaviour. To build up a predictive model, the dataset was split in training and testing datasets, characterized by 75 and 25% of the observations, respectively. Four algorithms were tested: Random Forest, K Nearest Neighbors, Extreme Boosting Algorithm (XGB) and Support Vector Machine. As regards behaviour classification a Convolutional Neural Network model (CNN, a Deep Learning Model), made of 8 layers, was also tested. Among machine learning models XGB showed the best accuracy (0.99) and Cohen’s kappa (0.99) in predicting posture, whereas Random Forest had the highest overall accuracy in predicting behaviours (0.76), showing a balanced accuracy from 0.96 for resting to 0.77 for moving. The higher rate of misclassification was found between feeding, moving and standing still. The Deep Learning model showed an overall accuracy in predicting behaviour of 0.92. Overall, the application of a single tri-axial accelerometer at the left side paralumbar fossa of mid-lactating dairy cows gave very accurate results regarding the prediction of posture and resting behaviour using machine learning models, whereas precision and accuracy for ruminating and feeding behaviours were greatly improved by the use of CNN
METODO PER IL RILEVAMENTO DELLA MOTILITÀ RUMINALE IN ANIMALI DA ALLEVAMENTO
No full textMetodo per il rilevamento della motilità ruminale in animali da allevamento, il
quale comprende: una fase di applicazione 5 di un accelerometro (1) in
corrispondenza della fossa di un fianco di un animale ruminante; una fase di
rilevamento di una serie temporale di misure di accelerazione (Xa, Ya, Za),
effettuata mediante l’accelerometro (1); una fase di discriminazione per ottenere,
dalle suddette misure di accelerazione (Xa, Ya, Za), un gruppo di selezione di
10 tali misure di accelerazione (Xa, Ya, Za) rilevate in un primo intervallo
temporale (T1) e indicative di una prima condizione comportamentale
dell’animale, quale una condizione di decubito dell’animale; una fase di
elaborazione delle misure di accelerazione (Xa, Ya, Za) rilevate nel suddetto
primo intervallo temporale (T1), la quale calcola corrispondenti parametri di
15 motilità ruminale (PM) indicativi del rilevamento di contrazioni ruminali
dell’animale
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