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resumen

Resumen
In dairy herds, cattle experience different health disorders, and their occurrence during lactation may cause important economic losses. The systematic analysis of productive and health records allows us to identify the risk factors of greatest negative incidence on profitability in commercial dairy farms. The aim of this work was to estimate the effects of clinical diseases on the productive and reproductive performance of dairy cattle. A total of 15,423 [ver mas...]
dc.contributor.authorMasia, F.
dc.contributor.authorMolina, G.
dc.contributor.authorVissio, Claudina
dc.contributor.authorBalzarini, Mónica Graciela
dc.contributor.authorDe la Sota, Rodolfo Luzbel
dc.contributor.authorPiccardi, Mónica Belén
dc.date.accessioned2022-10-11T11:43:43Z
dc.date.available2022-10-11T11:43:43Z
dc.date.issued2022-03-15
dc.identifier.issn1871-1413
dc.identifier.issn1878-0490 (online)
dc.identifier.otherhttps://doi.org/10.1016/j.livsci.2022.104894
dc.identifier.urihttp://hdl.handle.net/20.500.12123/13083
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1871141322000749
dc.description.abstractIn dairy herds, cattle experience different health disorders, and their occurrence during lactation may cause important economic losses. The systematic analysis of productive and health records allows us to identify the risk factors of greatest negative incidence on profitability in commercial dairy farms. The aim of this work was to estimate the effects of clinical diseases on the productive and reproductive performance of dairy cattle. A total of 15,423 lactations (≥3rd lactations) were analyzed; data involved records of three years obtained from 21 commercial dairy farms randomly selected from the main milk-producing provinces in Argentina. Lactations were classified as “with” (WD) or “without” (WOD) clinical diseases. The parameters production at peak (Ppeak) and 305-d cumulative milk production (305DY) were estimated and lactation curves were fitted using the MilkBot model. Statistical differences were found between lactations with at least one clinical disease (305DY 9,861.3 kg; Ppeak 39.5 kg/d) and those WOD (305DY 10,013.6 kg; Ppeak 40.2 kg/d). The reproductive indicators days open and days to culling were derived from survival analysis. Days open showed that 50% of cows with at least one clinical disease became pregnant 181 days (95% CI = 177; 185) after parturition, whereas in cows without clinical disease, pregnancy occurred on average 126 days (95% CI= 121; 132) after parturition. Furthermore, for days to culling, 50% of the diseased cows exhibited a culling time of 518 days (95%CI = 508; 527), whereas healthy cows remained in the herd for a longer period, on average 563 days (95% CI= 541; 586). The results suggest significant residual effects, i.e. the consequences of clinical disease are not limited to the period of disease progress, but extend to periods after the clinical resolution of the disease.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherElsevieres_AR
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.sourceLivestock Science 259 : 104894 (May 2022)es_AR
dc.subjectMilk Yieldeng
dc.subjectReproductioneng
dc.subjectCullingeng
dc.subjectDiseaseseng
dc.subjectArgentinaes_AR
dc.subjectRendimiento Lechero
dc.subjectReproducción
dc.subjectEliminación
dc.subjectEnfermedad
dc.subject.otherLactation Curveeng
dc.subject.otherNon-Pregnant Cowseng
dc.titleQuantifying the negative impact of clinical diseases on productive and reproductive performance of dairy cows in central Argentinaes_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.description.origenInstituto de Patología Vegetales_AR
dc.description.filFil: Masia, F. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentinaes_AR
dc.description.filFil: Masia, F. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentinaes_AR
dc.description.filFil: Molina, G. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentinaes_AR
dc.description.filFil: Molina, G. Universidad Nacional de Villa María. Instituto de Ciencias Básicas y Aplicadas; Argentinaes_AR
dc.description.filFil: Vissio, C. Universidad Nacional de Rio Cuarto. Facultad de Agronomía y Veterinaria; Argentinaes_AR
dc.description.filFil: Vissio, C. Universidad Nacional de Rio Cuarto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto para el Desarrollo Agroindustrial y de la Salud (IDAS); Argentinaes_AR
dc.description.filFil: Balzarini, Monica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentinaes_AR
dc.description.filFil: Balzarini, Monica. Instituto Nacional de Tecnología Agropecuaria (INTA). Unidad de Fitopatología y Modelización Agrícola; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentinaes_AR
dc.description.filFil: de la Sota, R.L. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentinaes_AR
dc.description.filFil: de la Sota, R.L. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Investigaciones en Reproducción Animal (INIRA); Argentinaes_AR
dc.description.filFil: Piccardi, M. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentinaes_AR
dc.description.filFil: Piccardi, M.Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentinaes_AR
dc.subtypecientifico


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