Perspectiva Bayesiana para Estimar el Efecto de las Condiciones Edafoclimáticas Sobre el Comportamiento Fisiológico de la Quinua

dc.contributor.advisorPineda Rios, Wilmer Dario
dc.contributor.advisorGarcia Parra, Miguel Angel
dc.contributor.authorMoreno Amaya, Lizeth Andrea
dc.contributor.corporatenameUniversidad Santo Tomásspa
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001454199spa
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001518896spa
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001705364spa
dc.contributor.googlescholarhttps://scholar.google.com/citations?hl=es&user=5KmOl5oAAAAJspa
dc.contributor.googlescholarhttps://scholar.google.com/citations?hl=es&user=-Fhy-mcAAAAJspa
dc.contributor.googlescholarhttps://scholar.google.com/citations?hl=es&user=_9i6HskAAAAJspa
dc.contributor.orcidhttps://orcid.org/0000-0001-7774-951Xspa
dc.contributor.orcidhttps://orcid.org/0000-0001-6913-7855spa
dc.coverage.campusCRAI-USTA Bogotáspa
dc.date.accessioned2024-01-23T16:24:02Z
dc.date.available2024-01-23T16:24:02Z
dc.date.issued2024-01-19
dc.descriptionLas condiciones edafoclimáticas son esenciales para el desarrollo óptimo de la quinua, ya que incide en su desempeño fisiológico. Por lo tanto, es necesario identificar cultivares que se adapten y sean estables ante la interacción entre los cultivares y el ambiente. Por esta razón, este trabajo buscó analizar el desempeño fisiológico de siete cultivares de quinua en tres municipios del departamento de Boyacá, Colombia usando la metodología frecuentista y Bayesiana del modelo de efectos aditivos e interacción multiplicativa (AMMI). Según los resultados, la estimación Bayesiana tuvo mejor capacidad predictiva, así como un mejor desempeño en el estudio de adaptabilidad y estabilidad. Al respecto, el cultivar Pasankalla se destacó en términos de efecto principal y estabilidad. También se observó la adaptabilidad de los cultivares a lugares específicos, permitiendo el uso del efecto positivo de la interacción, evidenciado en el modelo Bayesiano.spa
dc.description.abstractThe edaphoclimatic conditions are essential for the optimal development of quinoa, as they influence its physiological performance. Therefore, it is necessary to identify cultivars that adapt and remain stable in the interaction between cultivars and the environment. For this reason, this study aimed to analyze the physiological performance of seven quinoa cultivars in three municipalities of the Boyacá department, Colombia, using the frequentist and Bayesian methodology of the additive main effects and multiplicative interaction (AMMI) model. According to the results, Bayesian estimation showed better predictive capacity as well as better performance in the study of adaptability and stability. In this regard, the cultivar Pasankalla stood out in terms of main effect and stability. The adaptability of cultivars to specific locations was also observed, allowing for the utilization of the positive effect of interaction, as evidenced by the Bayesian model.spa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagister en Estadística Aplicadaspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationMoreno Amaya, L. A. (s.f.). Perspectiva Bayesiana para Estimar el Efecto de las Condiciones Edafoclimáticas Sobre el Comportamiento Fisiológico de la Quinua. [Trabajo de Maestría, Universidad Santo Tomás]. Repositorio Institucional.spa
dc.identifier.instnameinstname:Universidad Santo Tomásspa
dc.identifier.reponamereponame:Repositorio Institucional Universidad Santo Tomásspa
dc.identifier.repourlrepourl:https://repository.usta.edu.cospa
dc.identifier.urihttp://hdl.handle.net/11634/53666
dc.language.isospaspa
dc.publisherUniversidad Santo Tomásspa
dc.publisher.facultyFacultad de Estadísticaspa
dc.publisher.programMaestría Estadística Aplicadaspa
dc.relation.referencesAjay, B., Bera, S., Singh, A., Kumar, N., Gangadhar, K., and Kona, P. (2020). Evaluation of genotype× environment interaction and yield stability analysis in peanut under phosphorus stress condition using stability parameters of ammi model. Agricultural Research, 9(4):477–486.spa
dc.relation.referencesAlandia, G., Rodriguez, J., Jacobsen, S.-E., Bazile, D., and Condori, B. (2019). Un nuevo escenario para la producci´on de quinua: Desaf´ıos para la regi´on andina. In Libro de Res´umenes. VII Congreso Mundial de la Quinua y otros granos Andinos. Ministerio de Agricultura, INDAP, Pontificia Universidad Cat´olica de Chile, ODEPA (eds), Santiago de Chile, volume 168spa
dc.relation.referencesAli, M., Elsadek, A., and Salem, E. (2018). Stability parameters and ammi analysis of quinoa (chenopodium quinoa willd.). Egyptian Journal of Agronomy, 40(1):59–74.spa
dc.relation.referencesBayomi, K., El-Hashash, E. F., Ghura, N. S., and El-Absy, K. M. (2022). Genotype by environment interaction effects on the crop of sugar beet (beta vulgaris l.) using multivariate analysis. Asian Journal of Research in Crop Science, 7(4):135–149.spa
dc.relation.referencesBazile, D., Mart´ınez, E. A., and Fuentes, F. (2014). Diversity of quinoa in a biogeographical island: A review of constraints and potential from arid to temperate regions of chile. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 42(2):289–298.spa
dc.relation.referencesBernardo J´unior, L. A. Y., Von Pinho, R. G., da Silva, C. P., Vieira J´unior, I. C., de Oliveira, L. A., and Silva, E. V. V. (2021). Ammi-bayesian models and use of credible regions in the study of combining ability in maize. Euphytica, 217(8):1–19.spa
dc.relation.referencesChito Trujillo, D. M., Ortega Bonilla, R. A., Ahumada Mami´an, A. F., and Rosero L´opez, B. (2017). Quinoa (chenopodium quinoa willd.) versus soja (glycine max [l.] merr.) en la nutrici´on humana: revisi´on sobre las caracter´ısticas agroecol´ogicas, de composici´on y tecnol´ogicas. Revista Espa˜nola de Nutrici´on Humana y Diet´etica, 21(2):184–198.spa
dc.relation.referencesCoronado, A. C. M., Hern´andez, E. H. M., Coronado, Y. M., and Mendoza, L. A. G. (2021). Una mirada al cultivo de la quinua en el departamento de Boyacá volumen 195. Editorial de la Universidad Pedagógica y Tecnológica de Colombia-UPTC.spa
dc.relation.referencesCrossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J. M., Viele, K., Liu, G., and Cornelius, P. L. (2011). Bayesian estimation of the additive main effects and multiplicative interaction model. Crop Science, 51(4):1458–1469spa
dc.relation.referencesCuevas, J., Crossa, J., Montesinos-L´opez, O. A., Burgue˜no, J., P´erez-Rodr´ıguez, P., and de Los Campos, G. (2017). Bayesian genomic prediction with genotype× environment interaction kernel models. G3: Genes, Genomes, Genetics, 7(1):41–53spa
dc.relation.referencesda Silva, A. Q., de Oliveira, L. A., da Silva, C. P., Mendes, C. T. E., de Medeiros, E. S., and S´afadi, T. (2020). Aplica¸c˜ao do modelo ammi-bayesiano no estudo de estabilidade e adaptabilidade genot´ıpica em dados de mostarda. Research, Society and Development, 9(9):e166997023–e166997023.spa
dc.relation.referencesda Silva, C. P., de Oliveira, L. A., Nuvunga, J. J., Pamplona, A. K. A., and Balestre, M. (2015). A bayesian shrinkage approach for ammi models. Plos one, 10(7):e0131414.spa
dc.relation.referencesde Melo, G. G., de Oliveira, L. A., da Silva, C. P., da Silva, A. Q., Nascimento, M. R., de Sousa Gon¸calves, R. J., Dos Santos, P. R., da Costa, A. F., Queiroz, D. R., and da Silva, J. W. (2023). Ammi-bayesian perspective in the selection of pre-cultivars of carioca beans in agreste-sert˜ao of pernambuco, brazil. Scientific Reports, 13(1):4700.spa
dc.relation.referencesdos S. Dias, C. T. and Krzanowski, W. J. (2003). Model selection and cross validation in additive main effect and multiplicative interaction models. Crop Science, 43(3):865–873.spa
dc.relation.referencesDudhe, M., Jadhav, M., Sujatha, M., Meena, H., Rajguru, A., Gahukar, S., and Ghodke, M. (2023). Waasb-based stability analysis and validation of sources resistant to plasmopara halstedii race-100 from the sunflower working germplasm for the semiarid regions of india. Genetic Resources and Crop Evolution, pages 1–18spa
dc.relation.referencesFAO (2013). Or´ıgenes e historia- international year of quinoa 2013. Tomado de: http://www.fao.org/ quinoa-2013/what-is-quinoa/origin-and-history/es/?no_mobile=1.spa
dc.relation.referencesFischer, G., Ram´ırez, F., and Casierra-Posada, F. (2016). Ecophysiological aspects of fruit crops in the era of climate change. a review. Agronom´ıa Colombiana, 34(2):190–199.spa
dc.relation.referencesFreiria, G. H., Gon¸calves, L. S. A., Zeffa, D. M., Lima, W. F., Fonseca J´unior, N. d. S., Prete, C. E. C., and Fonseca, I. C. d. B. (2020). Bayesian ammi applied to food-type soybean multi-environment trials. Revista Ciˆencia Agronˆomica, 51.spa
dc.relation.referencesGabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3):453–467.spa
dc.relation.referencesGarc´ıa-Parra, M., Roa-Acosta, D., and Bravo-G´omez, J. E. (2022). Effect of the altitude gradient on the physiological performance of quinoa in the central region of colombia. Agronomy, 12(9):2112.spa
dc.relation.referencesGarc´ıa-Parra, M., Zurita-Silva, A., Stechauner-Rohringer, R., Roa-Acosta, D., and Jacobsen, S.-E. (2020). Quinoa (chenopodium quinoa willd.) and its relationship with agroclimatic characteristics: A colombian perspective. Chilean journal of agricultural research, 80(2):290–302.spa
dc.relation.referencesGauch, H. G. and Zobel, R. W. (1988). Predictive and postdictive success of statistical analyses of yield trials. Theoretical and Applied genetics, 76:1–10.spa
dc.relation.referencesGelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2014). Bayesian data analysis (vol. 2).spa
dc.relation.referencesGelman, A. and Rubin, D. B. (1992). A single series from the gibbs sampler provides a false sense of security. Bayesian statistics, 4(1):625–631spa
dc.relation.referencesGuerrero, M. M. C., Chamorro, B. Y., and Romero, J. V. (2021). Estabilidad fenot´ıpica de arveja (pisum sativum l.) en la zona productora de nari˜no, colombia. Agronom´ıa Mesoamericana, 32(3):842–853.spa
dc.relation.referencesHeidelberger, P. and Welch, P. D. (1983). Simulation run length control in the presence of an initial transient. Operations Research, 31(6):1109–1144.spa
dc.relation.referencesHern´andez-Sampieri, R. and Torres, C. P. M. (2018). Metodolog´ıa de la investigaci´on, volume 4. McGrawHill Interamericana M´exicoˆ eD. F DF.spa
dc.relation.referencesHoff, P. D. (2009). A first course in Bayesian statistical methods, volume 580. Springer.spa
dc.relation.referencesJackman, S. (2009). Bayesian analysis for the social sciences, volume 846. John Wiley & Sonsspa
dc.relation.referencesKarkaj˙I, F. A., Hervan, E. M., Rousta˙I ˙I, M., B˙IHamta, M., and MOHAMMAD˙I, S. Comprehensive stability analysis of wheat genotypes through multi-environmental trials.spa
dc.relation.referencesLigarreto, G. A., Castro, O. A., and Ch´aves, B. (2015). Estabilidad fenot´ıpica de una colecci´on de fr´ıjol andino (phaseolus vulgaris l.) tipo arbustivo. Revista UDCA Actualidad & Divulgaci´on Cient´ıfica, 18(1):109–118.spa
dc.relation.referencesMardia, K. and Bibby. (1981). Multivariate analysis, pp 522.£ 14· 60. 1979. isbn 0 12 471252 5 (academic press). The Mathematical Gazette, 65(431):75–76.spa
dc.relation.referencesMendes, C. T. E., de Oliveira, L. A., da Silva, A. Q., da Silva, C. P., dos Santos, P. M., and S´afadi, T. (2020). Comparing frequentist and bayesian approaches in ammi analysis in a simulated scenario.spa
dc.relation.referencesMorillo Coronado, A. C., Manjarrez Hern´aez, E. H., and Morillo Coronado, Y. (2020). Evaluaci´on morfoagron´omica de 19 materiales de chenopodium quinoa en el departamento de boyac´a. Biotecnolog´ıa en el Sector Agropecuario y Agroindustrial, 18(1):84–96.spa
dc.relation.referencesMwale, S., Shimelis, H., Nkhata, W., Sefasi, A., Fandika, I., and Mashilo, J. (2022). Genotype-byenvironment interaction in tepary bean (phaseolus acutifolius a. gray) for seed yield. agronomy 2022, 13, 12.spa
dc.relation.referencesNuvunga, J. J., da Silva, C. P., de Oliveira, L. A., de Lima, R. R., and Balestre, M. (2019). Bayesian factor analytic model: An approach in multiple environment trials. Plos one, 14(8):e0220290spa
dc.relation.referencesOikeh, S., Menkir, A., Maziya-Dixon, B., Welch, R., Glahn, R. P., and Gauch, G. (2004). Environmental stability of iron and zinc concentrations in grain of elite early-maturing tropical maize genotypes grown under field conditions. The Journal of Agricultural Science, 142(5):543–551.spa
dc.relation.referencesOlivoto, T., L´ucio, A. D., da Silva, J. A., Marchioro, V. S., de Souza, V. Q., and Jost, E. (2019). Mean performance and stability in multi-environment trials i: combining features of ammi and blup techniques. Agronomy Journal, 111(6):2949–2960.spa
dc.relation.referencesPour-Aboughadareh, A., Khalili, M., Poczai, P., and Olivoto, T. (2022). Stability indices to deciphering the genotype-by-environment interaction (gei) effect: An applicable review for use in plant breeding programs. Plants, 11(3):414.spa
dc.relation.referencesPr¨ager, A., Munz, S., Nkebiwe, P. M., Mast, B., and Graeff-H¨onninger, S. (2018). Yield and quality characteristics of different quinoa (chenopodium quinoa willd.) cultivars grown under field conditions in southwestern germany. Agronomy, 8(10):197.spa
dc.relation.referencesQuevedo Buitrago, J. E. (2018). Aplicaci´on del modelo estadístico ammi como método de selección en mejoramiento de plantas de cultivos anuales.spa
dc.relation.referencesRaftery, A. E. and Lewis, S. M. (1995). The number of iterations, convergence diagnostics and generic metropolis algorithms. Practical Markov Chain Monte Carlo, 7(98):763–773.spa
dc.relation.referencesRodr´ıguez-Gonz´alez, R. E., Ponce-Medina, J. F., Rueda-Puente, E. O., Avenda˜no-Reyes, L., Paz Hern´andez, J. J., Santillano-Cazares, J., and Cruz-Villegas, M. (2011). Interacci´on genotipoambiente para la estabilidad de rendimiento en trigo en la regi´on de mexicali, bc, m´exico. Tropical and subtropical agroecosystems, 14(2):543–558.spa
dc.relation.referencesSilva, C. P. d., Mendes, C. T. E., Silva, A. Q. d., Oliveira, L. A. d., Von Pinho, R. G., and Balestre, M. (2023). Use of the reversible jump markov chain monte carlo algorithm to select multiplicative terms in the ammi-bayesian model. Plos one, 18(1):e0279537.spa
dc.relation.referencesVargas Escobar, E. A., Vargas S´anchez, J. E., and Baena Garc´ıa, D. (2016). An´alisis de estabilidad y adaptabilidad de h´ıbridos de ma´ız de alta calidad proteica en diferentes zonas agroecol´ogicas de colombia. Acta Agron´omica, 65(1):72–79.spa
dc.relation.referencesViele, K. and Srinivasan, C. (2000). Parsimonious estimation of multiplicative interaction in analysis of variance using kullback–leibler information. Journal of statistical planning and inference, 84(1- 2):201–219.spa
dc.relation.referencesYang, R.-C., Crossa, J., Cornelius, P. L., and Burgue˜no, J. (2009). Biplot analysis of genotype× environment interaction: Proceed with caution. Crop Science, 49(5):1564–1576.spa
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.subject.keywordAMMIspa
dc.subject.keywordBAMMIspa
dc.subject.keywordcultivars-environment interactionspa
dc.subject.keywordquinoaspa
dc.subject.lembEstadística Aplicadaspa
dc.subject.lembCultivos de Quinuaspa
dc.subject.lembValor Nutricionalspa
dc.subject.proposalAMMIspa
dc.subject.proposalBAMMIspa
dc.subject.proposalinteracción cultivar-ambientespa
dc.subject.proposalquinuaspa
dc.titlePerspectiva Bayesiana para Estimar el Efecto de las Condiciones Edafoclimáticas Sobre el Comportamiento Fisiológico de la Quinuaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.driveinfo:eu-repo/semantics/masterThesis
dc.type.localTesis de maestríaspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersion

Archivos

Bloque original

Mostrando 1 - 3 de 3
Cargando...
Miniatura
Nombre:
Trabajo_Final_Liz.pdf
Tamaño:
2.05 MB
Formato:
Adobe Portable Document Format
Descripción:
Articulo Principal
Thumbnail USTA
Nombre:
Lizeth Moreno - CRAI.pdf
Tamaño:
92.59 KB
Formato:
Adobe Portable Document Format
Descripción:
Carta Facultad Crai
Thumbnail USTA
Nombre:
CamScanner 22-01-2024 16.32.pdf
Tamaño:
647.65 KB
Formato:
Adobe Portable Document Format
Descripción:
Carta autor

Bloque de licencias

Mostrando 1 - 1 de 1
Thumbnail USTA
Nombre:
license.txt
Tamaño:
807 B
Formato:
Item-specific license agreed upon to submission
Descripción: