Without timely detection and intervention, the health of the pigs in a herd can be compromised, resulting in additional costs of medications, diagnostics, and therapy. Moreover, it is difficult to give proper attention to all the animals and to identify subtle traits indicative of poor well-being in a timely manner. Manual surveillance may miss out to detect early signs of sickness (e.g., fever), particularly at night when the disease symptoms can be elevated. However, human observations are subjective, as it is difficult to delineate the factors associated with the pig’s mental and physical state (e.g., in stress or pain). Farm animal caretakers can know the extent of their pigs’ well-being with respect to their mental state (i.e., being calm, satisfied, relaxed, curious, playful, scared, stressed, or grunting) and physical state (i.e., healthy, medicated, or injured). Today, manual surveillance is generally conducted few times during the day in pig farms to visually screen for apparent signs of illness in their pigs, such as lethargy, lameness, and coughing. Reduced well-being may negatively influence the pig’s health, growth, behavior, and emotional state. In general, poor well-being is manifested by behavioral changes (e.g., abnormal movement, reduced feeding or drinking, lethargy, or aggressive nature), physiological changes (e.g., increased heart rate or respiration rate), and pathological changes (e.g., lesions, stress-related biomarkers, and other clinical signs). ![]() Good well-being is reached when the animal is in harmony with itself and its environment, whereas poor well-being happens when the animal is exposed to infections and adverse conditions resulting from different management practices. This requires frequent assessment of their well-being status and disease symptoms. Therefore, all stakeholders in the pig industry want to ensure that pigs display normal behavior and physiological functioning with the absence of lesions, diseases, or malnutrition. ![]() All rights reserved.The economics of a pig farm is dependent on the health and welfare status of pigs. However, further research is necessary to test the system under various field conditions and to evaluate the benefit of incorporating rumination data into herd management decisions.Īccelerometer cow monitoring rumination.Ĭopyright © 2018 American Dairy Science Association. From a practical and clinical point of view, the detected differences were negligible. In summary, the agreement between the Smartbow system with video analyses was excellent. The average number ± standard deviation of chewing cycles and rumination bouts was overestimated by Smartbow by 59.8 ± 79.6 (i.e., 3.7%) and by 0.5 ± 0.9 (i.e., 1.6%), respectively, compared with the video analyses. Algorithm testing revealed in an underestimation of the average ± standard deviation rumination time per 1-h period by the Smartbow system of 17.0 ± 35.3 s (i.e., -1.2%), compared with visual observations. The rumination time, chewing cycles, as well as rumination bouts detected by Smartbow were highly associated (r > 0.99) with the analyses of video recordings. Inter- and intra-observer reliability as well as the comparison of direct against video observations revealed in high agreements for rumination time and chewing cycles with Pearson correlation coefficients >0.99. Based on these analyses, half of the data was used for development (based on data of 50-h video analyses) and testing (based on data of additional 50-h video analyses) of the Smartbow algorithms, respectively. Out of this, one hundred 1-h video sequences were randomly selected and visually and manually classified by a trained observer using professional video analyses software. After exclusion of unsuitable videos, 2,490 h of cow individual 1-h video sequences were eligible for further analyses. ![]() Additionally, cows were video recorded for 19 d, 24 h a day. During the study, cows' rumination and other activities were directly observed for 20 h by 2 trained observers. A total mixed ration was fed twice a day via a roughage intake control system. Ten Simmental dairy cows in early lactation were equipped with 10-Hz accelerometer ear tags and kept in a pen separated from herd mates. Additionally, we tested the intra- and inter-observer reliability as well as the agreement of direct cow observations and video recordings. For this, the parameters were determined by analyses of video recordings as reference and compared with the results of the accelerometer system. The objective of this study was to evaluate the ear-tag-based accelerometer system Smartbow (Smartbow GmbH, Weibern, Austria) for detecting rumination time, chewing cycles, and rumination bouts in indoor-housed dairy cows.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |