Data di Pubblicazione:
2015
Citazione:
INTERNET OF THINGS (IOT) AND DAIRY FARM AUTOMATION / S. Leonardi ; tutor: F. M. Tangorra ; coordinator: G. Savoini. DIPARTIMENTO DI SCIENZE VETERINARIE PER LA SALUTE, LA PRODUZIONE ANIMALE E LA SICUREZZA ALIMENTARE, 2015 Feb 12. 27. ciclo, Anno Accademico 2014. [10.13130/leonardi-stefania_phd2015-02-12].
Abstract:
The objectives of the thesis were: (i) to evaluate the use of automatic systems and the related sensor-based technologies (Precision Dairy Farming – PDF - systems) in three important areas of a dairy farming; (ii) to assess different methods of estimating liner compression (LC) by using a new test device and a novel artificial teat sensor, both specifically designed and built. Four studies were carried out to achieve these goals.
In the first study “Use of a proactive herd management system in a dairy farm of northern Italy: technical and economic results” the reproductive and economical performances of an AMS farm that adopted a proactive herd management system (Herd Navigator™) were analyzed. Reproductive and economic data were recorded before and one year after the installation of Herd Navigator™. Number of days open reduced from 166 to 103 days, number of days between the first and second insemination decreased from 45 to 28 days, and days for identifying an abortion were 80 % less, from 31 to 6 days. The preliminary results highlighted the usefulness of the proactive herd management system implemented for the reproduction management. A basic economic model was proposed to evaluate the potential economic benefits coming from the introduction of this technology. The model considered the benefits deriving from the reduction of reproduction problems and, consequently, of days open. Considering the effects related to the above mentioned aspects in a case study involving 60 dairy cows, a return on investment over 5 years was calculated.
In the second study “Evaluation of an electronic system for automatic calving detection on a dairy farm”, a GSM-based remote alarm system for automatic calving detection was evaluated - in terms of sensitivity and PPV- as useful and reliable tool to detect the exact moment of calving in the field. Up to date, various monitoring technologies and protocols have been proposed to predict the exact moment of the calving but none of them have been adopted widely by producers due to high costs, difficulties of execution or lack of quality staff. Visual observation of the cow’s behavior is still the most frequent. The system object of the study, showed very high sensitivity and PPV, respectively 100% and 95 %, allowing the farm staff to be present at the moment of calving in 100 % of cases when cow were monitored using this system. Cows not monitored by this system, were assisted only in 17% of cases (P<0.001). The farm staff, if present during this crucial and important moment, could assist the animal preventing possible problems for the cow and the calf. This possibility would be of great interest particularly with heifers and with problematic cows.
In the third study “Evaluation of the performance of the first automatic milking system for buffaloes”, the response of buffaloes to automatic milking and the related performance of the system were investigated. Automatic milking systems (AMS) are a revolutionary innovation in dairy cow farming and can now be considered a well-established technology. In 2008, automatic milking of dairy buffaloes was introduced for the first time in a commercial farm in southern Italy. The aim of this study was to evaluate the response of buffaloes to automatic milking, examining the relationships between milking interval, milk production, and milking time for this species. A total of 7,550 milking records from an average of 40 buffaloes milked by an AMS were analyzed during a 3-mo experimental period at a commercial farm with Italian Mediterranean buffaloes in southern Italy. Date and time of animal identification, milk yield, milking duration, milking interval, and average milk flow rate were determined for each milking. The results were also used to pr
Tipologia IRIS:
Tesi di dottorato
Keywords:
precision dairy farming systems; automatic milking systems; methods of estimating liner compression
Elenco autori:
S. Leonardi
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