1,721,006 research outputs found
Effetto del sistema di ventilazione sull'ambiente, lo stato di salute e le performance in vitelloni in fase di ristallo
No full textAn easy decision-making graphic tool to improve herd level milk yield in a local scale dairy farming system
Full text linkSeveral features prevent dairy farms from reaching their full potential milk yield levels. A plurality of methods are available to analyse a farm's yield gap, but in practice, farmers rarely use them to understand their main constraints to production. We propose a simple and graphical approach to tune the limiting (feed-related) or reducing (management-related) factors to evaluate the likelihood of being a high-yielding farm. We gathered data from 32 farms within a local-scale dairy system in Northern Italy. Data regarded milk yield (MY), dry matter intake (DMI), feeding ration's homogeneity index (Hi), feed sorting (Si) index, ration's geometric mean particle length (GMPL), ration digestibility, income over feed cost (IOFC) and MY summer-winter ratio (SWR). Farms were classified according to their MY levels into high (H) and low or medium (L + M), with a 36.7 kg x cow(-1) day(-1) threshold. At an ANOVA model for MY class, H farms resulted in higher IOFC (p < 0.001), GMPL (p = 0.046), DMI (p = 0.006), digestible DM (DDM, p = 0.013), digestible crude protein (DCP, p = 0.011), digestible starch (Dstarch, p = 0.001), and feed efficiency (FE, p = 0.003). At a logistic AIC stepwise regression, the GMPL (odds = 6.528, 95% CI = 1.11-64.2) and DMI (odds = 3.889, 95% CI = 1.43-16.5) favoured farms being classified in the H production class. The nomogram was used to calculate a confusion matrix, achieving an overall accuracy of 0.70, demonstrating its ability to transform predictive models into a graphical, realisable tool
Conservazione del Lolium multiflorum Lam., 4. Impiego del fieno e dell'insilato di loiessa nell'alimentazione di vacche allattanti allevate in confinamento per la produzione del vitello
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Attitudine alla produzione della carne di vitelloni di sei razze da latte e a duplice attitudine
No full textAssessment of the effectiveness of a portable NIRS instrument in controlling the mixer wagon tuning and ration management
Full text linkThe adoption of the mixer wagon and total mixed ration aimed to decrease dysmetabolic diseases and improve feed efficiency in dairy cows. Differences between theoretical and eaten diets are imputable to errors in diet preparation or cow feed sorting. We proposed a method to measure the chemical composition and particle size distribution of the ration and determined its peNDF content through a portable Near Infra-Red spectrophotometer that allowed the calculation of two indexes: the homogeneity and the sorting indexes. In a cohort of 19 Italian Holstein breeding farms, we studied the correlation of these indexes with the mixer wagon settings. Determination coefficients in the validation (Rv2) for dry matter, crude protein, aNDF, and starch were 0.91, 0.54, 0.86, and 0.67, respectively. The ration fractions (%, w/w of wet weight) retained by the 3.8 and 1.8 mm sieves, and the bottom showed Rv2 of 0.46, 0.49, and 0.53, respectively. The homogeneity index regressed negatively with the mixer wagon load fullness (R2 = 0.374). The homogeneity-binary classification showed an odds ratio of 1.72 for dry matter and 0.39 for aNDF (p < 0.05). The sortingbinary classification showed an odds ratio of 2.54 for aNDF (p < 0.05). The studied farms showed low peNDF values (median = 17.9%)
Machine learning to detect posture and behavior in dairy cows: Information from an accelerometer on the animal’s left flank
Full text linkThe 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
Short communication: The relationship between dietary particle size and undegraded neutral detergent fibre in lactating dairy cows: A prospective cohort observational study
Full text linkPhysically effective NDF (peNDF) and undegraded aNDF at 240 h (uNDF.240) are important parameters for characterizing NDF in fibre evaluation and are associated with dietary physical form and fibre digestibility characteristics. A new concept that combines peNDF and uNDF.240, physically effective uNDF.240 (peuNDF.240 = pef × uNDF.240), was recently established. The peuNDF.240 value allows determination of dry matter intake (DMI), and the productive response of cows even in the absence of variation in DMI or when cows are fed rations with low uNDF.240 and high peNDF or rations with high uNDF.240 and more finely chopped fibre. The aim of this study was to improve our understanding of the relationships between dietary uNDF.240 content to other fibre fractions, average cow DMI, gross feed efficiency, and milk yield at the farm level. Furthermore, the relation between peuNDF.240 and the productive response of cows was also investigated at the farm level. In the Po’ Valley, which is a representative area for dairy production in Italy, a cohort of 22 Holstein dairy farms was monitored over two years (2019–2020). Information regarding average cow DMI, milk yield, and ration composition was obtained through interviews with farmers, and feed samples were collected and chemically analysed. Farms were classified according to their dietary uNDF.240 (% of DM) content: low (uL) ≤ 8.29 or high (uH) > 8.29. Farms with low dietary uNDF.240 used less alfalfa forage as a fibre source compared with farms with high dietary uNDF.240 (6.27 vs. 15.5 % of DM) and showed higher average milk yield (35.9 vs. 33.6 kg/cow/day, respectively) and similar DMI (23.9 vs. 24.3 kg/cow/day, respectively). Dietary peuNDF.240 was negatively related to milk yield (milk yield = 47.4 – 1.87 peuNDF.240, R2 = 0.62, adjusted R2 = 0.60, residual standard error (RSE) = 1.87, P = 0.001) and gross feed efficiency (gross feed efficiency = 1.96 – 0.08 peuNDF.240, R2 = 0.65, adjusted R2 = 0.64, RSE = 0.07, P = 0.001). The results of this study have practical significance for farmers, as they suggest that the inclusion of low digestible forages in the ration (i.e., late-harvested alfalfa characterized by high uNDF.240) may require more fine shredding to reduce the overall value of peuNDF.240 and increase cow production
L'utilizzazione dell'energia negli ovini in accrescimento: sezionatura delle carcasse e composizione dei tagli commerciali.
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