Cuytec (Version 1.0): Cell phone application for the management of productive, reproductive and genealogical records in guinea pigs

• Rufino Paucar-Chanca Universidad Nacional de Huancavelica, Laboratorio de Mejoramiento Genético. Huancavelica, Perú
• Luz Eliana Caso-Huamani Universidad Nacional de Huancavelica, Laboratorio de Mejoramiento Genético. Huancavelica, Perú

DOI: https://doi.org/10.52973/rcfcv-e33251

Keywords: Guinea pig, mobile app, productive records, reproductive records, genealogy

Abstract

In recent years, the use of objective methodologies to predict the genetic value of animals based on genetic evaluations using the BLUP (Best Linear Unbiased Predictor) methodology has intensified; however, its application requires the organization of databases with a complete and reliable information structure that includes data: productive, reproductive and genealogical. Currently, the management of productive, reproductive and genealogy records in guinea pig production units is carried out in a traditional way (manually), limiting the use of genetic evaluations. Therefore, the objective of this work was to develop the first version of a cell phone application (Cuytec V–1.0), which facilitates the capture and processing of information with applications to genetic improvement, for which mathematical algorithms and real data were used. of the Guinea Pig Genetic Improvement Program (PMGC) of the National University of Huancavelica, Peru (UNH). Cuytec V–1.0 allows collecting (General Database System), processing (programming language logic) and consolidating information about the production, reproduction and genealogy of guinea pigs. Facilitating decision making and data processing using genetic and statistical models within a breeding program.

References

Pascual M, Cruz DJ, Blasco A. Modeling production functions and economic weights in intensive meat production of guinea pigs. Trop. Anim. Health Prod. 2017;49(7):1361–1367.

Quijandria B, Zaldivar M, Robison OW. Selection in guinea pigs: II. Direct response for litter size and body weight. J. Anim. Sci. 1983;56(4):820–828.

Ramos–Espinoza Y, Aguilar–Jara LL, Paucar–Chanca R. Parámetros productivos y reproductivos de cuyes (Cavia porcellus) de la raza Perú. Rev. Cientif. Fac. Cien. Vet. 2023;33(1):1–6. doi: https://doi.org/kc96

Patiño–Burbano RE, Cardona–Iglesias JL, Carlosama–Ojeda LD, Portillo–Lopez PA, Moreno DC. Parámetros zootécnicos de Cavia porcellus en sistemas productivos de Nariño y Putumayo (Colombia). Rev. CES Med. Vet. Zoot. 2019;14(3):29–41. doi: https://doi.org/kc94

Chauca L. Realidad y perspectiva de la crianza de cuyes en los países andinos. Arch. Latin. Prod. Anim. 2007;15(1):223–228.

Silva MN. Uso de IoT e IoNT en animales: Estudio de herramientas de recolección de datos para el análisis y comparación de datos vinculados a la sanidad animal. [La Plata]: Universidad Nacional de La Plata;2019. 38 p.

Paucar–Chanca R. Utilidad de marcadores SNP en la mejora genética de poblaciones altoandinas de alpacas. [Pamplona]: Universidad Pública de Navarra;2011. 40 p.

Bourdon RM. Understanding animal breeding. 2da. Ed.. Vol. 2. Upper Saddle River, NJ: Prentice Hall;2000. 523 p.

Sherman GB, Kachman SD, Hungerford LL, Rupp GP, Fox CP, Brown MD, Feuz BM, Holm TR. Impact of candidate sire number and sire relatedness on DNA polymorphism–based measures of exclusion probability and probability of unambiguous parentage. Anim. Genet. 2004;35(3):220–226.

Tosh JJ, Wilton JW. Effects of Data Structure on Variance of Prediction Error and Accuracy of Genetic Evaluation. J. Anim. Sci. 1994;72(10):2568–2577. doi: https://doi.org/kc95

Mrode RA, Thompson R. Linear models for the prediction of animal breeding values. 3ra. ed. Boston: CABI. 2014. 337 p.

Cardoso FF, Tempelman RJ. Genetic evaluation of beef cattle accounting for uncertain paternity. Livest. Prod. Sci. 2004;89(2–3):109–120.

Martelo RJ, Ponce AL, Acuña F. Guía metodológica para el diseño de un plan estratégico informático en instituciones de educación superior. Formación Univers. 2016;9(1):91–98.

Sánchez II. Estado del arte de las metodologías y modelos de los Objetos Virtuales de Aprendizaje (OVAS) en Colombia. Entornos. 2014;28(1):93–107.

Vranken E, Berckmans D. Precision livestock farming for pigs. Anim. Front. 2017;7(1):32–37.

Ahumada RC, Cervantes L, Martelo RJ. Sistema de información para la gestión de inventario y actividades en un hato ganadero. Espacios. 2020;41(50):215–230.

Vite HA, Vargas ON. Ganadería de precisión en la provincia de El Oro Diagnostico situacional. Espirales. 2018;2(1):1–16.

Gómez–Prada UE, Orellana–Hernández ML, Salinas–Ibáñez JM. Apropiación de Sistemas de Tecnologías de la Información para toma de decisiones de productores agroindustriales basada en videojuegos serios. Una revisión. Inform. Tecnol. 2019;30(5):331–340.

Saravanan N, Mahendiran A, Subramanian NV, Sairam N. An implementation of RSA algorithm in google cloud using cloud SQL. Res. J. Appl. Sci. Engineer. Technol. 2012;4(19):3574–3579.

Ayoze A. Curso de programacion web JavaScript, Ajax y jQuery. 2da. ed. IT Campus Academy. 2017; 430 p.