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dc.contributor.author
Galli, Julio Ricardo  
dc.contributor.author
Cangiano, Carlos Alberto  
dc.contributor.author
Pece, M. A.  
dc.contributor.author
Larripa, M. J.  
dc.contributor.author
Milone, Diego Humberto  
dc.contributor.author
Utsumi, S. A.  
dc.contributor.author
Laca, E. A.  
dc.date.available
2018-06-07T21:04:33Z  
dc.date.issued
2017-10  
dc.identifier.citation
Galli, Julio Ricardo; Cangiano, Carlos Alberto; Pece, M. A.; Larripa, M. J.; Milone, Diego Humberto; et al.; Monitoring and assessment of ingestive chewing sounds for prediction of herbage intake rate in grazing cattle; Cambridge University Press; Animal; 12; 5; 10-2017; 973-982  
dc.identifier.issn
1751-7311  
dc.identifier.uri
http://hdl.handle.net/11336/47802  
dc.description.abstract
Accurate measurement of herbage intake rate is critical to advance knowledge of the ecology of grazing ruminants. This experiment tested the integration of behavioral and acoustic measurements of chewing and biting to estimate herbage dry matter intake (DMI) in dairy cows offered micro-swards of contrasting plant structure. Micro-swards constructed with plastic pots were offered to three lactating Holstein cows (608±24.9 kg of BW) in individual grazing sessions (n=48). Treatments were a factorial combination of two forage species (alfalfa and fescue) and two plant heights (tall=25±3.8 cm and short=12±1.9 cm) and were offered on a gradient of increasing herbage mass (10 to 30 pots) and number of bites (~10 to 40 bites). During each grazing session, sounds of biting and chewing were recorded with a wireless microphone placed on the cows? foreheads and a digital video camera to allow synchronized audio and video recordings. Dry matter intake rate was higher in tall alfalfa than in the other three treatments (32±1.6 v. 19±1.2 g/min). A high proportion of jaw movements in every grazing session (23 to 36%) were compound jaw movements (chew-bites) that appeared to be a key component of chewing and biting efficiency and of the ability of cows to regulate intake rate. Dry matter intake was accurately predicted based on easily observable behavioral and acoustic variables. Chewing sound energy measured as energy flux density (EFD) was linearly related to DMI, with 74% of EFD variation explained by DMI. Total chewing EFD, number of chew-bites and plant height (tall v. short) were the most important predictors of DMI. The best model explained 91% of the variation in DMI with a coefficient of variation of 17%. Ingestive sounds integrate valuable information to remotely monitor feeding behavior and predict DMI in grazing cows.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Cambridge University Press  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/  
dc.subject
Acoustic Analysis  
dc.subject
Chew-Bite  
dc.subject
Chewing  
dc.subject
Ingestive Behavior  
dc.subject
Ruminants  
dc.subject.classification
Ciencias de la Computación  
dc.subject.classification
Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Monitoring and assessment of ingestive chewing sounds for prediction of herbage intake rate in grazing cattle  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2018-05-31T18:19:13Z  
dc.journal.volume
12  
dc.journal.number
5  
dc.journal.pagination
973-982  
dc.journal.pais
Reino Unido  
dc.journal.ciudad
Cambridge  
dc.description.fil
Fil: Galli, Julio Ricardo. Universidad Nacional de Rosario; Argentina  
dc.description.fil
Fil: Cangiano, Carlos Alberto. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina  
dc.description.fil
Fil: Pece, M. A.. Universidad Nacional de Rosario; Argentina  
dc.description.fil
Fil: Larripa, M. J.. Universidad Nacional de Rosario; Argentina  
dc.description.fil
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina  
dc.description.fil
Fil: Utsumi, S. A.. Michigan State University; Estados Unidos  
dc.description.fil
Fil: Laca, E. A.. University of California at Davis; Estados Unidos  
dc.journal.title
Animal  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1017/S1751731117002415