Remote and wearable sensors can be combined with smart algorithms to continuously monitor a wide range of animal responses linked with stress, health status and welfare. The idea of real time monitoring assumes a simple way to measure variable that can give an early warning for the farmer providing clear and suitable alerts to help them in their routine. The prompt reaction to any change in health, welfare and productive status is the key for the reduction in drugs usage and for the improvement of animal wellbeing. In intensive poultry farms, enteric disorders represent a major health issue; these pathologies could be multifactorial and are a major cause of performances reduction. Monitoring poultry health status takes a key role for management to reduce chemicals/drugs and their costs. Nowadays, the preventive use of antibiotics in intensive farming system is common and this practice could lead to the spreading of drugs in the environment, contributing to the phenomenon of antibiotic resistance. Due to the high priority of this issue, it is of great importance the early detection of any health problem in intensive farming. Precision Livestock Farming, through the combination of cheap technologies and specific algorithms, can provide valuable information for farmers starting from the huge amount of data collected in real time at farm level. This study was aimed to the application of a PLF diagnostic tool, sensible to the variation of volatile organic compounds, to promptly recognize enteric problems in intensive farming, supporting veterinarians and enabling specific treatments in case of disease.

Application of an early warning to detect enteropathies in intensive broiler farming / E. Tullo, F. Borgonovo, G. Grilli, A. Micheletti, G. Aletti, S. Lolli, V. Ferrante, M. Guarino - In: International Livestock Environment Symposium[s.l] : ASABE, 2018. - pp. 1-6 (( Intervento presentato al 10. convegno International Livestock Environment Symposium tenutosi a Omaha nel 2018 [10.13031/iles.18-073].

Application of an early warning to detect enteropathies in intensive broiler farming

E. Tullo;F. Borgonovo;G. Grilli;A. Micheletti;G. Aletti;S. Lolli;V. Ferrante;M. Guarino
2018

Abstract

Remote and wearable sensors can be combined with smart algorithms to continuously monitor a wide range of animal responses linked with stress, health status and welfare. The idea of real time monitoring assumes a simple way to measure variable that can give an early warning for the farmer providing clear and suitable alerts to help them in their routine. The prompt reaction to any change in health, welfare and productive status is the key for the reduction in drugs usage and for the improvement of animal wellbeing. In intensive poultry farms, enteric disorders represent a major health issue; these pathologies could be multifactorial and are a major cause of performances reduction. Monitoring poultry health status takes a key role for management to reduce chemicals/drugs and their costs. Nowadays, the preventive use of antibiotics in intensive farming system is common and this practice could lead to the spreading of drugs in the environment, contributing to the phenomenon of antibiotic resistance. Due to the high priority of this issue, it is of great importance the early detection of any health problem in intensive farming. Precision Livestock Farming, through the combination of cheap technologies and specific algorithms, can provide valuable information for farmers starting from the huge amount of data collected in real time at farm level. This study was aimed to the application of a PLF diagnostic tool, sensible to the variation of volatile organic compounds, to promptly recognize enteric problems in intensive farming, supporting veterinarians and enabling specific treatments in case of disease.
early warning system; health problem; intensive farming; PLF; poultry farming; volatile organic compounds
Settore AGR/10 - Costruzioni Rurali e Territorio Agroforestale
Settore AGR/20 - Zoocolture
Settore VET/05 - Malattie Infettive degli Animali Domestici
Settore SECS-S/02 - Statistica per La Ricerca Sperimentale e Tecnologica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/687429
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