dor_id: 4149877

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Veterinaria México OA", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Consejo Nacional de Ciencia y Tecnología (CONACyT); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Scientific Electronic Library Online (SciELO); Bibliografía Latinoamericana (Biblat); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Connecting research and researchers (ORCiD)

561.#.#.u: https://www.fmvz.unam.mx/

650.#.4.x: Biotecnología y Ciencias Agropecuarias

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://veterinariamexico.fmvz.unam.mx/index.php/vet/index

351.#.#.b: Veterinaria México OA

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://veterinariamexico.fmvz.unam.mx/index.php/vet/article/view/1150/942

100.1.#.a: Chay Canul, Alfonso J.; Tapia González, Jorge; Canul Solís, Jorge; Casanova Lugo, Fernando; Piñeiro Vázquez, Ángel T.; Portillo Salgado, Rodrigo; García Herrera, Ricardo; Vargas Bello Pérez, Einar

524.#.#.a: Chay Canul, Alfonso J., et al. (2023). Predictive biometrics of hair sheep through digital imaging. Veterinaria México OA; Vol. 10, 2023. Recuperado de https://repositorio.unam.mx/contenidos/4149877

245.1.0.a: Predictive biometrics of hair sheep through digital imaging

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Facultad de Medicina Veterinaria y Zootecnia, UNAM

264.#.0.c: 2023

264.#.1.c: 2023-09-06

653.#.#.a: body measurements; image analysis; linear regression equations; image-processing; tropical conditions

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY 4.0 Internacional, https://creativecommons.org/licenses/by/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico vetmexicooa@gmail.com

884.#.#.k: https://veterinariamexico.fmvz.unam.mx/index.php/vet/article/view/1150

001.#.#.#: 131.oai:ojs.pkp.sfu.ca:article/1150

041.#.7.h: eng

520.3.#.a: Direct collection of biometric measurements (bm) from sheep is an expensive and stressful procedure for animals; instead, indirect and novel methods have recently been used. The objective of this study was to use digital image analysis (dia) to predict biometric measurements of pelibuey sheep as a non-invasive approach under on-farm conditions. withers height (wh), body length (bl), body diagonal length (bdl) and rib depth (rd) were predicted in pelibuey ewes using dia. images were taken from the left flank from 65 non-pregnant and non-lactating pelibuey ewes using a digital camera and analyzed by dia. The bm determined from both in vivo and by dia presented a positive and moderate (p <0.05) correlation coefficients (r) of 0.43, 0.66, 0.73 and 0.75 for bl, bdl, wh and rd, respectively. regression equations from bm by dia had a determination coefficient (r2) of 0.19, 0.44, 0.54 and 0.56 for bl, bdl, wh and rd, respectively. The equations developed were from low to moderate precision (r2 = 0.18 to 55), moderate to high accuracy (cb > 0.69) and low to moderate reproducibility index (> 0.30). overall, the use of dia was able to predict the bm in pelibuey ewes with low to moderate precision and accuracy. factors affecting the accuracy and precision of this relationship should be further investigated.

773.1.#.t: Veterinaria México OA; Vol. 10 (2023)

773.1.#.o: https://veterinariamexico.fmvz.unam.mx/index.php/vet/index

022.#.#.a: ISSN electrónico: 2448-6760

310.#.#.a: Trimestral

264.#.1.b: Facultad de Medicina Veterinaria y Zootecnia, UNAM

doi: https://doi.org/10.22201/fmvz.24486760e.2023.1150

harvesting_date: 2023-11-08 13:10:00.0

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