Evaluación de la respuesta del genotipo al ambiente en rendimiento y calidad de papa amarilla diploide (Solanum tuberosum, Grupo Phureja)
ilustraciones, graficas
- Autores:
-
Silva Herrera, Harverth Hernan
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/83810
- Palabra clave:
- 630 - Agricultura y tecnologías relacionadas
Solanum tuberosum
environmental factors
Crop yield
Factores ambientales
Rendimiento de cultivos
Interacción genotipo ambiente
Covariables ambientales
Modelos lineales mixtos generalizados
Respuesta fenotípica
Epigenética
Genotype by environment interaction
Environmental covariates
Linear mixed model generalized
Phenotypic response
Epigenetics
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Evaluación de la respuesta del genotipo al ambiente en rendimiento y calidad de papa amarilla diploide (Solanum tuberosum, Grupo Phureja) |
| dc.title.translated.eng.fl_str_mv |
Evaluation of genotype response to environment on yield and quality of diploid yellow potato (Solanum tuberosum, Phureja Group) |
| title |
Evaluación de la respuesta del genotipo al ambiente en rendimiento y calidad de papa amarilla diploide (Solanum tuberosum, Grupo Phureja) |
| spellingShingle |
Evaluación de la respuesta del genotipo al ambiente en rendimiento y calidad de papa amarilla diploide (Solanum tuberosum, Grupo Phureja) 630 - Agricultura y tecnologías relacionadas Solanum tuberosum environmental factors Crop yield Factores ambientales Rendimiento de cultivos Interacción genotipo ambiente Covariables ambientales Modelos lineales mixtos generalizados Respuesta fenotípica Epigenética Genotype by environment interaction Environmental covariates Linear mixed model generalized Phenotypic response Epigenetics |
| title_short |
Evaluación de la respuesta del genotipo al ambiente en rendimiento y calidad de papa amarilla diploide (Solanum tuberosum, Grupo Phureja) |
| title_full |
Evaluación de la respuesta del genotipo al ambiente en rendimiento y calidad de papa amarilla diploide (Solanum tuberosum, Grupo Phureja) |
| title_fullStr |
Evaluación de la respuesta del genotipo al ambiente en rendimiento y calidad de papa amarilla diploide (Solanum tuberosum, Grupo Phureja) |
| title_full_unstemmed |
Evaluación de la respuesta del genotipo al ambiente en rendimiento y calidad de papa amarilla diploide (Solanum tuberosum, Grupo Phureja) |
| title_sort |
Evaluación de la respuesta del genotipo al ambiente en rendimiento y calidad de papa amarilla diploide (Solanum tuberosum, Grupo Phureja) |
| dc.creator.fl_str_mv |
Silva Herrera, Harverth Hernan |
| dc.contributor.advisor.none.fl_str_mv |
Cotes Torres, Jose Miguel Rodriguez Molano, Luis Ernesto |
| dc.contributor.author.none.fl_str_mv |
Silva Herrera, Harverth Hernan |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grupo de Investigación en Papa |
| dc.subject.ddc.spa.fl_str_mv |
630 - Agricultura y tecnologías relacionadas |
| topic |
630 - Agricultura y tecnologías relacionadas Solanum tuberosum environmental factors Crop yield Factores ambientales Rendimiento de cultivos Interacción genotipo ambiente Covariables ambientales Modelos lineales mixtos generalizados Respuesta fenotípica Epigenética Genotype by environment interaction Environmental covariates Linear mixed model generalized Phenotypic response Epigenetics |
| dc.subject.agrovoc.eng.fl_str_mv |
Solanum tuberosum environmental factors Crop yield |
| dc.subject.agrovoc.spa.fl_str_mv |
Factores ambientales Rendimiento de cultivos |
| dc.subject.proposal.spa.fl_str_mv |
Interacción genotipo ambiente Covariables ambientales Modelos lineales mixtos generalizados Respuesta fenotípica Epigenética |
| dc.subject.proposal.eng.fl_str_mv |
Genotype by environment interaction Environmental covariates Linear mixed model generalized Phenotypic response Epigenetics |
| description |
ilustraciones, graficas |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022-10-26 |
| dc.date.accessioned.none.fl_str_mv |
2023-05-17T16:01:39Z |
| dc.date.available.none.fl_str_mv |
2023-05-17T16:01:39Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/83810 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/83810 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
Aldabe, L. & Dogliotti, S. (2009). Bases fisiológicas del crecimiento y desarrollo del cultivo de papa (Solanum tuberosum L). Curso de Fisiología de Cultivos. Ciclo de Formación Central Agronómica. Universidad de la República. Uruguay. 16 p. Camargo, I., Intire, Q. & Gordón, R. (2011). Identifying megaenvironments to enhance the use of superior rice genotypes in Panama. Pesquisa Agropecuária Brasileira, 46(9), 1061- 1069. http://www.scielo.br/pab Crossa, J., Perez, S., Jarquin, D., Cotes, J., Viele, K., Liu, G. & Cornelius, P. (2011). Bayesian estimation of the additive main effects and multiplicative interaction model. Crop Science, 51(4), 1458-1469. http://dx.doi.org/10.2135/cropsci2010.06.0343 De Silva, C., Oliveira, L., Nuvunga, J., Pamplona, A., & Balestre, M. (2015). A Bayesian shrinkage approach for AMMI models. Plos one, 10(7), e0131414. https://doi.org/10.1371/journal.pone.0131414 Dias, K., Dos Santos, J., Krause, M., Piepho, H., Guimarães, L., Pastina, M. & Garcia, A. (2022). Leveraging probability concepts for cultivar recommendation in multi-environment trials. Theoretical and Applied Genetics, 1-15. https://doi.org/10.1007/s00122-022-04041-y Eberhart, S. & Russell, W. (1966). Stabililty parameters for comparing varieties. Crop Science, 6: 36-40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x Estrada, N. (1996). Los recursos genéticos en el mejoramiento de la papa en los países andinos. In: UNIPAPA-ICA-CORPOICA, Papas colombianas con el mejor entorno ambiental. Comunicaciones y Asociados Ltda (1st ed., pp. 1-14). http://hdl.handle.net/20.500.12324/32445 Finlay, K. & Wilkinson, F. (1963). The analysis of adaptation in a plant-breeding programme. Australian Journal Agricultural Resource, 14: 742-754. https://pdf.usaid.gov/pdf_docs/PNAAS139.pdf Gauch, H. & Zobel, W. (1988) Predictive and postdictive success of statistical analyses of yield trials. Theor Appl Genet 76: 1-10. https://doi.org/10.1007/BF00288824 Ibáñez, V., Berli, F., Masuelli, R., Bottini, R. & Marfil, C. (2017). Influence of altitude and enhanced ultraviolet-B radiation on tuber production, seed viability, leaf pigments and morphology in the wild potato species Solanum kurtzianum Bitter & Wittm collected from an elevational gradient. Plant Science, 261, 60-68. Doi: 10.1016/j.plantsci.2017.04.014 https://doi.org/10.1016/j.plantsci.2017.04.014 Josse, J., Van Eeuwijk, F., Piepho, H. & Denis, J. (2014). Another look at Bayesian analysis of AMMI models for genotype-environment data. Journal of Agricultural, Biological, and Environmental Statistics, 19(2), 240-257. http://www.jstor.org/stable/26452870 Lee, E., Deen, W., Hooyer, M., Chambers, A., Parkin, G., Gordon, R. & Singh, A. (2016). Involvement of year‐to‐year variation in thermal time, solar radiation and soil available moisture in genotype‐by‐environment effects in maize. Crop Science, 56(5), 2180-2192. https://doi.org/10.2135/cropsci2015.04.0231 Lozano, A., Zamora, V., Ibarra, L., Rodríguez, S., De la Cruz, E. & de la Rosa, M. (2009). Análisis de la interacción genotipo-ambiente mediante el modelo Ammi y potencial de producción de triticales forrajeros (X Triticosecale Wittm.). Universidad y ciencia, 25(1), 81-92. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0186- 29792009000100006&lng=es&nrm=iso Omer, S., Abdalla, A. W. H., Mohammed, M. H., & Singh, M. (2015). Bayesian estimation of genotype-by-environment interaction in sorghum variety trials. Communications in Biometry and Crop Science, 10, 82-95. https://www.researchgate.net/publication/282328349_Bayesian_estimation_of_genotype- by-environment_interaction_in_sorghum_variety_trials Omer, S. & Singh, M. (2017). Comparing Bayesian and Frequentist Approaches for GGE Bi-plot Analysis in Multi-Environment Trials in Sorghum. Eur Exp Biol, 7(6), 40. http://dx.doi.org/10.21767/2248-9215.100040 Pour, A., Khalili, M., Poczai, P. & 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. https://doi.org/10.3390/plants11030414 Shukla, G. (1972). Some statistical aspects of partitioning genotype- environment components of variability. Heredity 29:237-245. https://doi.org/10.1038/hdy.1972.87 Theobald, C. M., Talbot, M. & Nabugoomu, F. (2002). A Bayesian approach to regional and local-area prediction from crop variety trials. Journal of Agricultural, Biological, and Environmental Statistics, 7(3), 403-419. https://doi.org/10.1198/108571102230 Tinjacá, S. & Rodríguez, L. (2015). Catálogo de papas nativas de Nariño Colombia (1era ed.). Universidad Nacional de Colombia. http://www.seguridadalimentarianarino.unal.edu.co/sites/default/files/pdf- componentes/C.Mej-Gen_Catalogo%20papas%20Nativas.pdf Tolhurst, D., Gaynor, R., Gardunia, B., Hickey, J. & Gorjanc, G. (2021). Genomic Selection Using Environmental Covariates Within an Integrated Factor Analytic Linear Mixed Model. http://dx.doi.org/10.21203/rs.3.rs-995887/v1 Unidad de Planeación Rural Agropecuaria - UPRA. (2016). Cultivo Comercial de Papa - identificación de Zonas Aptas en Colombia, a escala 1:100.000. Imprenta Nacional. http://hdl.handle.net/11438/8630 Viele, K. & Srinivasan, C. (2000) Parsimonious estimation of multiplicative interaction in analysis of variance using Kullback-Leibler information. J. Stat. Plan. Infer. 84:201– 219. https://doi.org/10.1016/S0378-3758(99)00151-2 Yan, W., Kang, M., Ma, B., Woods, S. & Cornelius, P. (2007) GGE Biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:643-655. https://doi.org/10.2135/cropsci2006.06.0374. Bandera, E. & Pérez, L. (2018). Generalized Linear Mixed Models. Its application in plant breeding. Cultivos Tropicales, 39(1), 127-133. https://www.researchgate.net/publication/327751933_Review_Generalized_linear_mixed _models_Its_application_in_plant_breeding Bertan, I., Carvalho, F. & Oliveira, A. (2007). Parental selection strategies in plant breeding programs. Journal of crop science and biotechnology, 10(4), 211-222. https://www.researchgate.net/publication/254000574_Parental_Selection_Strategies_in_ Plant_Breeding_Programs Bradshaw, J. (2021). Potato breeding: theory and practice. Springer International Publishing. ISBN: 978-3-030-64414-7 Burgueño, J., Crossa, J., Cornelius, P. & Yang, R. (2008). Using factor analytic models for joining environments and genotypes without crossover genotype x environment interaction. Crop Sei. 48:1291-1305. https://doi.org/10.2135/cropsci2007.11.0632 Burgueño, J., Crossa, J., Cotes, J., Vicente, F. & Das, B. (2011). Prediction assessment of linear mixed models for multienvironment trials. Crop Science, 51(3), 944-954. https://doi.org/10.2135/cropsci2010.07.0403 Cabezas, M., (2002). Estimación de la interceptación de la radiación solar en papa criolla (Solanum phureja., Juz, et Buk.) en tres localidades colombianas. Trabajo de Grado Maestría. Facultad de Agronomía. Universidad Nacional de Colombia. 122 p. https://www.researchgate.net/publication/262446787_Estimating_yellow_potato_Solanum _phureja_Juz_et_Buk_solar_radiation_interception_in_three_Colombian_localities Camargo, I., Intire, Q. & Gordón, R. (2011). Identifying megaenvironments to enhance the use of superior rice genotypes in Panama. Pesquisa Agropecuária Brasileira, 46(9), 1061- 1069. https://www.researchgate.net/publication/289230956_Identifying_mega- environments_to_enhance_the_use_of_superior_rice_genotypes_in_Panama Crossa, J., Yang, R. & Cornelius, P. (2004). Studying crossover genotype x environment interaction using linear-bilinear models and mix ed models. J. Agrie. Biol. Environ. Stat. 9:362-380. https://www.jstor.org/stable/1400487 Delacy, I., Basford, K., Cooper, M. & Bull, J. (1996) Analysis of multienvironment trials an historical perspective. Plant Adaptation and Crop Improvement. Eds. M. Cooper and G. L. Hammer. CAB international, UK. https://www.cabdirect.org/cabdirect/abstract/19961610824 Edward, E., Gbur, E., Walter, W., Stroup, W., McCarter, K. & Durham, S. (2012). Analysis of generalized linear mixed models in the agricultural and natural resources sciences. Madison, Wis: American Society of Agronomy; 277 p. ISBN: 978-0-891-18182-8 Frutos, E. (2011). Interacción genotipo-ambiente: GGE Biplot y modelos AMMI. Departamento de Estadística. Universidad de Salamanca. (Disertación doctoral). http://hdl.handle.net/10366/121900 Gauch, H. (1982). Noise reduction by eigenvector ordinations. Ecology, 63(6), 1643-1649. https://doi.org/10.2307/1940105 Gauch, H. & Zobel, W. (1988) Predictive and postdictive success of statistical analyses of yield trials. Theor Appl Genet 76: 1-10. https://doi.org/10.1007/BF00288824 Heslot, N., Akdemir, D., Sorrells, M. & Jannink, J. (2014). Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theoretical and applied genetics, 127(2), 463-480. https://doi.org/10.1007/s00122-013-2231-5 Jarquín, D., Crossa, J., Lacaze, X., Du Cheyron, P., Daucourt, J., Lorgeou, J. & de los Campos, G. (2014). A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theoretical and applied genetics, 127(3), 595-607. https://doi.org/10.1007/s00122-013-2243-1 Laird, N. & Ware, J. (1982). Random-effects models for longitudinal data. Biometrics. 38(4):963-74. doi:10.2307/2529876. https://doi.org/10.2307/2529876 Marschner, H. 2011. Mineral Nutrition of Higher Plants, 3rd ed. New York ISBN: 9780080571874 Muthoni J. & Kabira J. (2016). Potato production under drought conditions: Identification of adaptive traits. International Journal of Horticulture, 6(12): 1-10. http://dx.doi.org/10.5376/ijh.2016.06.0012 Ñústez, C. (2011). Estudios fenotípicos y genéticos asociados a la calidad de fritura en Solanum phureja Juz et Buk. Tesis de doctorado en Ciencias Agropecuarias. Facultad de Agronomía. Universidad Nacional de Colombia. 204 p. https://repositorio.unal.edu.co/handle/unal/7859 Ñústez, C. (2018). Papas diploides: Un legado ancestral para la agricultura en Colombia. Universidad Nacional de Colombia. http://www.papaunc.com/blog/papas-diploides-un- legado-ancestral-para-la-agricultura-en-colombia Oliveira, I., Guilhen, J., Oliveira, P., Gezan, S., Schaffert, R., Simeone, M. & Pastina, M. (2020). Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates. Field Crops Research, 257, 107929. https://doi.org/10.1016/j.fcr.2020.107929 Paderewski, J., Gauch, H., Mądry, W. & Gacek, E. (2016). AMMI analysis of four‐way genotype× location× management× year data from a wheat trial in Poland. Crop Science, 56(5), 2157-2164. https://doi.org/10.2135/cropsci2015.03.0152 Piepho, H. 1997. Analyzing genotype-environment data by mixed models with multiplicative terms. Biometrics. 53:761—767. https://doi.org/10.2307/2533976 Piepho, H. 1998. Empirical best linear unbiased prediction in cultivar trials using factor analytics variance-covariance structures. Theor. Appl. Genet. 97:195-201. https://doi.org/10.1007/s001220050885 Piepho, H. & Möhring, J. (2006). Selection in cultivar trials- Is it ignorable? Crop Sci. 46: 192-201. https://doi.org/10.2135/cropsci2005.04-0038 Piepho, H., Möhring, J., Melchinger, A. & Büchse, A. (2008). BLUP for phenotypic selection in plant breeding and variety testing. Euphytica, 161(1), 209-228. https://doi.org/10.1007/s10681-007-9449-8 Rodríguez, L., Ñustez, C. & Estrada, N., (2009). Criolla Latina. Criolla Paisa y Criolla Colombia. nuevos cultivares de papa criolla para el departamento de Antioquia (Colombia). Agron. Colomb. 27(3). 289-303. Rueda, J. & Cotes, J. (2009). Evaluación de dos métodos de estabilidad fenotípica a través de validación cruzada. Revista Facultad Nacional de Agronomía-Medellín, 62(2), 5111-5123. https://repositorio.unal.edu.co/handle/unal/37088 Rykaczewska, K. (2015). The effect of high temperature occurring in subsequent stages of plant development on potato yield and tuber physiological defects. American Journal of Potato Research, 92(3), 339-349. https://doi.org/10.1007/s12230-015-9436-x Smith, A., Cullis, B. & Thompson, R. (2001). Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics. 57: 1138- 1147. https://doi.org/10.1111/j.0006-341X.2001.01138.x Smith, A., Cullis, B. & Thompson, R. (2005). The analysis of crop cultivar breeding and evaluation trials: An overview of current mixed model approaches. j. Agric. Sci. 143:1-14. https://doi.org/10.1017/S0021859605005587 Taiz, L. & Zeiger, E. (2006). Plant Physiology, 4th ed. ISBN: 9780878935079 Van Eeuwijk, F. (1995). Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models. Euphytica, 84(1), 1-7. Wang, T., He, P., Ahn, K., Wang, X., Ghosh, S. & Laud, P. (2015) A re-formulation of generalized linear mixed models to fit family data in genetic association studies. Frontiers in Genetics [Internet]. https://doi.org/10.3389/fgene.2015.00120 Al Soboh, G., Sully, R., & Andreata, S. (2002). Factors affecting specific gravity loss in crisping potato crops in Koo Wee Rup, Victoria. Department of Natural Resources and Environment. ISBN: 0 7341 0531 2 Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (2006). Evapotranspiración del cultivo: guías para la determinación de los requerimientos de agua de los cultivos. Roma: FAO, 298(0). ISBN: 92-5-304219-2 Bably, A. (2003). Estimation of evapotranspiration using statistical model. Camarda, D. (ed.), Grassin, I. L. (Ed.), Local resources and global trades: Environments and agriculture in the Mediterranean region. CIHEAM. 441-449. Beven, K. (1979). A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates. Journal of Hydrology, 44(3-4), 169-190. https://doi.org/10.1016/0022- 1694(79)90130-6 Bhattacharyya, B., Biswas, R., Sujatha, K. & Chiphang, D. (2021). Linear Regression Model to Study the Effects of Weather Variables on Crop Yield in Manipur State. Int. J. Agricult. Stat. Sci, 17(1), 317-320. ISSN: 0973-1903, e-ISSN: 0976-3392 Bornhofen, E., Todeschini, M., Stoco, M., Madureira, A., Marchioro, V., Storck, L. & Benin, G. (2018). Wheat yield improvements in Brazil: Roles of genetics and environment. Crop Science, 58(3), 1082-1093. Brancourt, M., Denis, J. & Lecomte, C. (2000). Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression. Theoretical and Applied Genetics, 100(2), 285-298. https://doi.org/10.1007/s001220050038 Betancur, L., Posada, S. & Solano, R. (2012). Application of the principal-component analysis in the evaluation of three grass varieties. Revista Colombiana de Ciencias Pecuarias, 25(2), 258-266. https://www.researchgate.net/publication/262505522_Application_of_the_principal- component_analysis_in_the_evaluation_of_three_grass_varieties Cambouris, A., St. Luce, M., Zebarth, B., Ziadi, N., Grant & C., Perron, I. (2016). Potato response to nitrogen sources and rates in an irrigated sandy soil. Agronomy Journal, 108(1), 391-401. http://dx.doi.org/10.2134/agronj2015.0351 Carlson, H. (1970). Production of potatoes for chipping. Uppsala, Sweden: The Agricultural College of Sweden. https://www.cabdirect.org/cabdirect/abstract/19711702322 Cecchi, H. (1999). Fundamentos teóricos e práticos em análise de alimentos (Theoretical and practical in food analysis) Campinas/SP. UNICAMP, Brazil. https://issuu.com/editoraunicamp/docs/20pp_fundamentos_teoricos_e_pratico Coulibali, Z., Cambouris, A. & Parent, S. (2020). Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada. PloS one, 15(8), https://doi.org/10.1371%2Fjournal.pone.0230888 Crossa, J., Burgueño, J., Cornelius, P., McLaren, G., Trethowan, R. & Krishnamachari, A. (2006). Modeling genotype× environment interaction using additive genetic covariances of relatives for predicting breeding values of wheat genotypes. Crop science, 46(4), 1722- 1733. https://doi.org/10.2135/cropsci2005.11-0427 Crossa, J. (2012). From genotype x environment interaction to gene x environment interaction. Curr. Genomics 13:225– 244. https://doi.org/10.2174/138920212800543066 Davies, H. (1990). Carboydrate metabolism during sprouting. Am J Potato 67:815–827 Feibert EBG, Shock CC, Saunders LD (1998) Nitrogen fertilizer requirements of potatoes using carefully scheduled sprinkler irrigation. HortScience 33:262–265. https://doi.org/10.1007/BF03044293 Duarte, D., Narváez, C., Restrepo, L., Kushalappa, A., & Mosquera, T. (2015). Development and validation of a liquid chromatographic method to quantify sucrose, glucose, and fructose in tubers of Solanum tuberosum Group Phureja. Journal of Chromatography B, 975, 18-23. https://doi.org/10.1016/j.jchromb.2014.10.039 Duarte, D., Ñústez, C., Narváez, C., Restrepo, L., Melo, S., Sarmiento, F. & Mosquera, T. (2016). Natural variation of sucrose, glucose and fructose contents in Colombian genotypes of Solanum tuberosum Group Phureja at harvest. Journal of the Science of Food and Agriculture, 96(12), 4288-4294. https://doi.org/10.1002/jsfa.7783 Escobedo, J., Gomes, E., Oliveira, A. & Soares, J. (2011). Ratios of UV, PAR and NIR components to global solar radiation measured at Botucatu site in Brazil. Renewable Energy, 36(1), 169-178. https://doi.org/10.1016/j.renene.2010.06.018 Fontes, P., Braun, H., Busato, C. & Cecon, P. (2010). Economic optimum nitrogen fertilization rates and nitrogen fertilization rate effects on tuber characteristics of potato cultivars. Potato Research, 53(3), 167-179. https://doi.org/10.1007/s11540-010-9160-3 Frisina, V. (2002). Modelagem das radiações global, difusa e fotossinteticamente ativa em ambiente protegido e suas relações com o crescimento e produtividade da cultura de pimentão (Capsicum annuum L.). https://repositorio.unesp.br/bitstream/handle/11449/101753/frisina_va_dr_botfca.pdf?seq uence=1&isAllowed=y Garrido, S. (1993). Interpretación de análisis de suelos. Hoja Divulgativa Nro, 5. https://www.mapa.gob.es/ministerio/pags/biblioteca/hojas/hd_1993_05.pdf Gil, F., Trasar, C., Leirós, M. & Seoane, S. (2005). Different approaches to evaluating soil quality using biochemical properties. Soil biology and biochemistry, 37(5), 877-887. https://doi.org/10.1016/j.soilbio.2004.10.003 Isikwue, B., Audu, M. & Eweh, E. (2015). Correlation of evapotranspiration with climatic parameters in some selected cities in Nigeria. Journal of earth sciences and Geotechnical Engineering, 5(4), 103-115. https://www.researchgate.net/publication/316857440_Correlation_of_Evapotranspiration_ with_Climatic_Parameters_in_some_selected_Cities_in_Nigeria Jhajharia, D., Kithan, S., & Fancon, A. (2006). Correlation between pan evaporation and meteorological parameters under the climatic conditions of Jorhat (Assam). Journal of Indian Water Resources Society, 26(1-2), 39-42. https://www.researchgate.net/publication/235004367_Correlation_between_pan_evaporat ion_and_meteorological_parameters_under_the_climatic_conditions_of_Jorhat_Assam Keller, S., Soolanayakanahally, R., Guy, R., Silim, S., Olson, M. & Tiffin, P., (2011). Climate-driven local adaptation of ecophysiology and phenology in balsam poplar, Populus balsamifera L. (Salicaceae). Am. J. Bot. 98, 99–108. https://doi.org/10.3732/ajb.1000317 Kumar, D., Ezekiel, R., & Khurana, S. P. (2003). Effect of location, season and cultivar on the processing quality of potatoes. J. Indian Potato Assoc, 30(3-4), 247-251. https://www.researchgate.net/publication/269108056_Effect_of_location_season_and_cul tivar_ob_the_processing_quality_of_potatoes Kumar, D., Singh, B. P., & Kumar, P. (2004). An overview of the factors affecting sugar content of potatoes. Annals of Applied Biology, 145(3), 247-256. https://doi.org/10.1111/j.1744-7348.2004.tb00380.x Landau, S., Mitchell, R., Barnett, V., Colls, J., Craigon, J. & Payne, R. (2000). A parsimonious, multiple-regression model of wheat yield response to environment. Agricultural and forest meteorology, 101(2-3), 151-166. https://doi.org/10.1016/S0168- 1923(99)00166-5 Liu, T., Dodds, E., Leong, S., Eyres, G., Burritt & D., Oey, I. (2017). Effect of pulsed electric fields on the structure and frying quality of “kumara” sweet potato tubers. Innovative food science & emerging technologies, 39, 197-208. https://doi.org/10.1016/j.ifset.2016.12.010 Lulai, E. & Orr, P. (1979). Influence of potato specific gravity on yield and oil content of chips. Am Potato J 56:379–390. https://doi.org/10.1007/BF02855348 Maier, N., Dahlenburg, A. & Williams, C. (1994). Effects of nitrogen, phosphorus, and potassium on yield, specific gravity, crisp colour, and tuber chemical composition of potato (Solanum tuberosum L.) cv. Kennebec. Australian Journal of Experimental Agriculture, 34(6), 813-824. ISSN: 0816-1089 Malosetti, M., Ribault, J. & van Eeuwijk, F. (2013). The statistical analysis of multi- environment data: Modeling genotype-by-environment interaction and its genetic basis. Front. Physiol. 4:1–17. https://doi.org/10.3389/fphys.2013.00044 Manly, B. & Alberto, J. (2016). Multivariate statistical methods: a primer. Chapman and Hall/CRC. ISBN: 9781498728966. Medrano, H., Escalona, J., Cifre, J., Bota, J. & Flexas, J. (2003). A ten-year study on the physiology of two Spanish grapevine cultivars under field conditions: effects of water availability from leaf photosynthesis to grape yield and quality. Funct. Plant Biol. 30, 607– 619. http://dx.doi.org/10.1071/FP02110 Nelson, D. C., & Shaw, R. (1976). Effect of planting and harvest dates, location in the hill and tuber size on sugar content of Kennebec potatoes. American Potato Journal, 53(1), 15-21. https://doi.org/10.1007/BF02854087 Oliveira, I., Guilhen, J., Oliveira, P., Gezan, S., Schaffert, R., Simeone, M. & Pastina, M. (2020). Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates. Field Crops Research, 257. https://doi.org/10.1016/j.fcr.2020.107929 Panishkan, K., Swangjang, K., Sanmanee, N. & Sungthong, D. (2012). Principal component analysis for the characterization in the application of some soil properties. International Journal of Environmental and Ecological Engineering, 6(5), 279-281. https://doi.org/10.5281/zenodo.1058231 Parisi, A. & Wong, J. (2000). An estimation of biological hazards due to solar radiation. Journal of Photochemistry and Photobiology, Biology. 54:126 – 30. https://doi.org/10.1016/S1011-1344(00)00006-3 Payne, R. (1993). Genstat 5 release 3 reference manuals. Oxford University Press. ISBN- 13: 978-0198523123 R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ Rahman, M., Tareq, T., Sarker, P., Rashid, E., Yasmeen, R., Ali, M. & Shimono, H. (2019). Genetic variation of phenotypic plasticity in Bangladesh rice germplasm. Field Crops Research, 243. https://doi.org/10.1016/j.fcr.2019.107618 Rodríguez, J., Sahagún, J., Villaseñor, H., Molina, J. & Martínez, Á. (2005). Interaction genotype x environment in the characterization of rainfed areas for wheat. Agrociencia. https://www.researchgate.net/publication/238706096_Interaction_genotypeXenvironment _in_the_characterization_of_rainfed_areas_for_wheat Sánchez, Á., Sadras, V. & Sánchez, D. (2021). Phenotypic plasticity in relation to inter- cultivar variation of garlic (Allium sativum L.) functional performance and yield-stability in response to water availability. Scientia Horticulturae, 285, 110128. https://doi.org/10.1016/j.scienta.2021.110128 Sadras, V., Reynolds, M., Vega, A., Petrie, P. & Robinson, R. (2009). Phenotypic plasticity of yield and phenology in wheat, sunflower and grapevine. F. Crop. Res. 110, 242–250. https://doi.org/10.1016/j.fcr.2008.09.004 Sadras, V., Lake, L., Leonforte, A., Mcmurray, L. & Paull, J. (2013). Screening field pea for adaptation to water and heat stress: associations between yield, crop growth rate and seed abortion. F. Crop. Res. 150, 63–73. https://doi.org/10.1016/j. fcr.2013.05.023 Satriani, A., Loperte, A. & Soldovieri, F. (2015). Integrated geophysical techniques for sustainable management of water resource. A case study of local dry bean versus commercial common bean cultivars. Agric. Water Manag. 162, 57–66. http://dx.doi.org/10.1016/j.agwat.2015.08.010 Schippers, P. (1976). The relationship between specific gravity and percentage dry matter in potato tubers. Am Potato J 53:111–122. https://doi.org/10.1007/BF02854115 Sellam, V. & Poovammal, E. (2016). Prediction of crop yield using regression analysis. Indian Journal of Science and Technology, 9(38), 1-5. https://dx.doi.org/10.17485/ijst/2016/v9i38/91714 Sinclair, T. & Horie, T. (1989). Leaf nitrogen, photosynthesis, and crop radiation use efficiency: a review. Crop. Sci. 29, 90-98. https://doi.org/10.2135/cropsci1989.0011183X002900010023x Crossa, J. & Cornelius, P. (1997). Sites regression and shifted multiplicative model clustering of cultivar trials site under heterogeneity of error variance. Crop sci. 37: 405 – 415. https://doi.org/10.2135/cropsci1997.0011183X003700020017 |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cotes Torres, Jose Miguel189f9409c75741ea87c92d88932d233bRodriguez Molano, Luis Ernesto14c1f7b6a8679e4b43e41165060ad109Silva Herrera, Harverth Hernanac2cdf9e93594cb81cb40c6419a69e0cGrupo de Investigación en Papa2023-05-17T16:01:39Z2023-05-17T16:01:39Z2022-10-26https://repositorio.unal.edu.co/handle/unal/83810Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficasLa IGA es un modelo teórico utilizado para explicar la respuesta diferencial de los genotipos al ambiente. Varios métodos estadísticos han sido desarrollados para descomponer la respuesta fenotípica basados principalmente en las medias generales, en el efecto del genotipo y su interacción con el ambiente, siendo tratada la varianza ambiental como factor de confusión. Sin embargo, la estimación del efecto ambiental a partir de los rasgos evaluados establecería una dependencia al genotipo, resultando en un menor ajuste y potencial predictivo del modelo. Se ha propuesto la integración de covariables ambientales a los modelos que asocien características edafoclimáticas con los rasgos de interés, con el propósito de aumentar el potencial predictivo y la varianza contenida por los modelos. El objetivo de este estudio fue evaluar la sensibilidad de los rasgos de interés en cultivos de papa amarilla diploide a covariables ambientales, seleccionando las covariables más relevantes cómo parámetros en modelos empíricos de regresión múltiple a partir de la varianza ambiental. Los resultados mostraron una alta variabilidad del rendimiento debida a covariables del componente hídrico, mientras que los rasgos de calidad se vieron principalmente afectados por rasgos de los componentes energéticos y fisicoquímicos del suelo. Los modelos ajustados explicaron la varianza debida intrínsecamente al ambiente, alcanzando ajustes superiores al 20%. Por lo tanto, se concluye que los rasgos presentan una alta sensibilidad fenotípica y la incorporación de covariables ambientales a los modelos de análisis de interacción genotipo por ambiente, podrían mejorar la comprensión de la estabilidad y adaptabilidad de los cultivares a partir de los datos obtenidos en pruebas multiambiente. (Texto tomado de la fuente)The IGA is a theoretical model used to explain the differential response of genotypes to the environment. Several statistical methods have been developed to decompose the phenotypic response based mainly on the general means, on the effect of the genotype and its interaction with the environment, treating the environmental variance as a confounding factor. However, the estimation of the environmental effect from the traits evaluated would establish a dependency on the genotype, resulting in a lower fit and predictive potential of the model. The integration of environmental covariates to the models that associate edaphoclimatic characteristics with the traits of interest has been proposed, with the purpose of increasing the predictive potential and the variance contained by the models. The aim of this study was to evaluate the sensitivity of the traits of interest in diploid yellow potato crops to environmental covariates, selecting the most relevant covariates as parameters in empirical multiple regression models based on environmental variance. The results showed a high yield variability due to covariates of the water component, while the quality traits were mainly affected by traits of the energetic and physicochemical components of the soil. The adjusted models explained the variance due intrinsically to the environment, reaching adjustments greater than 20%. Therefore, it is concluded that the traits have a high phenotypic sensitivity and the incorporation of environmental covariates to the genotype-by-environment interaction analysis models could improve the understanding of the stability and adaptability of cultivars from the data obtained in multi-environment trials.MaestríaMagíster en Ciencias AgrariasMejoramiento Genéticoxvii, 86 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en Ciencias AgrariasFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá630 - Agricultura y tecnologías relacionadasSolanum tuberosumenvironmental factorsCrop yieldFactores ambientalesRendimiento de cultivosInteracción genotipo ambienteCovariables ambientalesModelos lineales mixtos generalizadosRespuesta fenotípicaEpigenéticaGenotype by environment interactionEnvironmental covariatesLinear mixed model generalizedPhenotypic responseEpigeneticsEvaluación de la respuesta del genotipo al ambiente en rendimiento y calidad de papa amarilla diploide (Solanum tuberosum, Grupo Phureja)Evaluation of genotype response to environment on yield and quality of diploid yellow potato (Solanum tuberosum, Phureja Group)Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAldabe, L. & Dogliotti, S. (2009). Bases fisiológicas del crecimiento y desarrollo del cultivo de papa (Solanum tuberosum L). Curso de Fisiología de Cultivos. Ciclo de Formación Central Agronómica. Universidad de la República. Uruguay. 16 p.Camargo, I., Intire, Q. & Gordón, R. (2011). Identifying megaenvironments to enhance the use of superior rice genotypes in Panama. Pesquisa Agropecuária Brasileira, 46(9), 1061- 1069. http://www.scielo.br/pabCrossa, J., Perez, S., Jarquin, D., Cotes, J., Viele, K., Liu, G. & Cornelius, P. (2011). Bayesian estimation of the additive main effects and multiplicative interaction model. Crop Science, 51(4), 1458-1469. http://dx.doi.org/10.2135/cropsci2010.06.0343De Silva, C., Oliveira, L., Nuvunga, J., Pamplona, A., & Balestre, M. (2015). A Bayesian shrinkage approach for AMMI models. Plos one, 10(7), e0131414. https://doi.org/10.1371/journal.pone.0131414Dias, K., Dos Santos, J., Krause, M., Piepho, H., Guimarães, L., Pastina, M. & Garcia, A. (2022). Leveraging probability concepts for cultivar recommendation in multi-environment trials. Theoretical and Applied Genetics, 1-15. https://doi.org/10.1007/s00122-022-04041-yEberhart, S. & Russell, W. (1966). Stabililty parameters for comparing varieties. Crop Science, 6: 36-40. https://doi.org/10.2135/cropsci1966.0011183X000600010011xEstrada, N. (1996). Los recursos genéticos en el mejoramiento de la papa en los países andinos. In: UNIPAPA-ICA-CORPOICA, Papas colombianas con el mejor entorno ambiental. Comunicaciones y Asociados Ltda (1st ed., pp. 1-14). http://hdl.handle.net/20.500.12324/32445Finlay, K. & Wilkinson, F. (1963). The analysis of adaptation in a plant-breeding programme. Australian Journal Agricultural Resource, 14: 742-754. https://pdf.usaid.gov/pdf_docs/PNAAS139.pdfGauch, H. & Zobel, W. (1988) Predictive and postdictive success of statistical analyses of yield trials. Theor Appl Genet 76: 1-10. https://doi.org/10.1007/BF00288824Ibáñez, V., Berli, F., Masuelli, R., Bottini, R. & Marfil, C. (2017). Influence of altitude and enhanced ultraviolet-B radiation on tuber production, seed viability, leaf pigments and morphology in the wild potato species Solanum kurtzianum Bitter & Wittm collected from an elevational gradient. Plant Science, 261, 60-68. Doi: 10.1016/j.plantsci.2017.04.014 https://doi.org/10.1016/j.plantsci.2017.04.014Josse, J., Van Eeuwijk, F., Piepho, H. & Denis, J. (2014). Another look at Bayesian analysis of AMMI models for genotype-environment data. Journal of Agricultural, Biological, and Environmental Statistics, 19(2), 240-257. http://www.jstor.org/stable/26452870Lee, E., Deen, W., Hooyer, M., Chambers, A., Parkin, G., Gordon, R. & Singh, A. (2016). Involvement of year‐to‐year variation in thermal time, solar radiation and soil available moisture in genotype‐by‐environment effects in maize. Crop Science, 56(5), 2180-2192. https://doi.org/10.2135/cropsci2015.04.0231Lozano, A., Zamora, V., Ibarra, L., Rodríguez, S., De la Cruz, E. & de la Rosa, M. (2009). Análisis de la interacción genotipo-ambiente mediante el modelo Ammi y potencial de producción de triticales forrajeros (X Triticosecale Wittm.). Universidad y ciencia, 25(1), 81-92. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0186- 29792009000100006&lng=es&nrm=isoOmer, S., Abdalla, A. W. H., Mohammed, M. H., & Singh, M. (2015). Bayesian estimation of genotype-by-environment interaction in sorghum variety trials. Communications in Biometry and Crop Science, 10, 82-95. https://www.researchgate.net/publication/282328349_Bayesian_estimation_of_genotype- by-environment_interaction_in_sorghum_variety_trialsOmer, S. & Singh, M. (2017). Comparing Bayesian and Frequentist Approaches for GGE Bi-plot Analysis in Multi-Environment Trials in Sorghum. Eur Exp Biol, 7(6), 40. http://dx.doi.org/10.21767/2248-9215.100040Pour, A., Khalili, M., Poczai, P. & 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. https://doi.org/10.3390/plants11030414Shukla, G. (1972). Some statistical aspects of partitioning genotype- environment components of variability. Heredity 29:237-245. https://doi.org/10.1038/hdy.1972.87Theobald, C. M., Talbot, M. & Nabugoomu, F. (2002). A Bayesian approach to regional and local-area prediction from crop variety trials. Journal of Agricultural, Biological, and Environmental Statistics, 7(3), 403-419. https://doi.org/10.1198/108571102230Tinjacá, S. & Rodríguez, L. (2015). Catálogo de papas nativas de Nariño Colombia (1era ed.). Universidad Nacional de Colombia. http://www.seguridadalimentarianarino.unal.edu.co/sites/default/files/pdf- componentes/C.Mej-Gen_Catalogo%20papas%20Nativas.pdfTolhurst, D., Gaynor, R., Gardunia, B., Hickey, J. & Gorjanc, G. (2021). Genomic Selection Using Environmental Covariates Within an Integrated Factor Analytic Linear Mixed Model. http://dx.doi.org/10.21203/rs.3.rs-995887/v1Unidad de Planeación Rural Agropecuaria - UPRA. (2016). Cultivo Comercial de Papa - identificación de Zonas Aptas en Colombia, a escala 1:100.000. Imprenta Nacional. http://hdl.handle.net/11438/8630Viele, K. & Srinivasan, C. (2000) Parsimonious estimation of multiplicative interaction in analysis of variance using Kullback-Leibler information. J. Stat. Plan. Infer. 84:201– 219. https://doi.org/10.1016/S0378-3758(99)00151-2Yan, W., Kang, M., Ma, B., Woods, S. & Cornelius, P. (2007) GGE Biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:643-655. https://doi.org/10.2135/cropsci2006.06.0374.Bandera, E. & Pérez, L. (2018). Generalized Linear Mixed Models. Its application in plant breeding. Cultivos Tropicales, 39(1), 127-133. https://www.researchgate.net/publication/327751933_Review_Generalized_linear_mixed _models_Its_application_in_plant_breedingBertan, I., Carvalho, F. & Oliveira, A. (2007). Parental selection strategies in plant breeding programs. Journal of crop science and biotechnology, 10(4), 211-222. https://www.researchgate.net/publication/254000574_Parental_Selection_Strategies_in_ Plant_Breeding_ProgramsBradshaw, J. (2021). Potato breeding: theory and practice. Springer International Publishing. ISBN: 978-3-030-64414-7Burgueño, J., Crossa, J., Cornelius, P. & Yang, R. (2008). Using factor analytic models for joining environments and genotypes without crossover genotype x environment interaction. Crop Sei. 48:1291-1305. https://doi.org/10.2135/cropsci2007.11.0632Burgueño, J., Crossa, J., Cotes, J., Vicente, F. & Das, B. (2011). Prediction assessment of linear mixed models for multienvironment trials. Crop Science, 51(3), 944-954. https://doi.org/10.2135/cropsci2010.07.0403Cabezas, M., (2002). Estimación de la interceptación de la radiación solar en papa criolla (Solanum phureja., Juz, et Buk.) en tres localidades colombianas. Trabajo de Grado Maestría. Facultad de Agronomía. Universidad Nacional de Colombia. 122 p. https://www.researchgate.net/publication/262446787_Estimating_yellow_potato_Solanum _phureja_Juz_et_Buk_solar_radiation_interception_in_three_Colombian_localitiesCamargo, I., Intire, Q. & Gordón, R. (2011). Identifying megaenvironments to enhance the use of superior rice genotypes in Panama. Pesquisa Agropecuária Brasileira, 46(9), 1061- 1069. https://www.researchgate.net/publication/289230956_Identifying_mega- environments_to_enhance_the_use_of_superior_rice_genotypes_in_PanamaCrossa, J., Yang, R. & Cornelius, P. (2004). Studying crossover genotype x environment interaction using linear-bilinear models and mix ed models. J. Agrie. Biol. Environ. Stat. 9:362-380. https://www.jstor.org/stable/1400487Delacy, I., Basford, K., Cooper, M. & Bull, J. (1996) Analysis of multienvironment trials an historical perspective. Plant Adaptation and Crop Improvement. Eds. M. Cooper and G. L. Hammer. CAB international, UK. https://www.cabdirect.org/cabdirect/abstract/19961610824Edward, E., Gbur, E., Walter, W., Stroup, W., McCarter, K. & Durham, S. (2012). Analysis of generalized linear mixed models in the agricultural and natural resources sciences. Madison, Wis: American Society of Agronomy; 277 p. ISBN: 978-0-891-18182-8Frutos, E. (2011). Interacción genotipo-ambiente: GGE Biplot y modelos AMMI. Departamento de Estadística. Universidad de Salamanca. (Disertación doctoral). http://hdl.handle.net/10366/121900Gauch, H. (1982). Noise reduction by eigenvector ordinations. Ecology, 63(6), 1643-1649. https://doi.org/10.2307/1940105Gauch, H. & Zobel, W. (1988) Predictive and postdictive success of statistical analyses of yield trials. Theor Appl Genet 76: 1-10. https://doi.org/10.1007/BF00288824Heslot, N., Akdemir, D., Sorrells, M. & Jannink, J. (2014). Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theoretical and applied genetics, 127(2), 463-480. https://doi.org/10.1007/s00122-013-2231-5Jarquín, D., Crossa, J., Lacaze, X., Du Cheyron, P., Daucourt, J., Lorgeou, J. & de los Campos, G. (2014). A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theoretical and applied genetics, 127(3), 595-607. https://doi.org/10.1007/s00122-013-2243-1Laird, N. & Ware, J. (1982). Random-effects models for longitudinal data. Biometrics. 38(4):963-74. doi:10.2307/2529876. https://doi.org/10.2307/2529876Marschner, H. 2011. Mineral Nutrition of Higher Plants, 3rd ed. New York ISBN: 9780080571874Muthoni J. & Kabira J. (2016). Potato production under drought conditions: Identification of adaptive traits. International Journal of Horticulture, 6(12): 1-10. http://dx.doi.org/10.5376/ijh.2016.06.0012Ñústez, C. (2011). Estudios fenotípicos y genéticos asociados a la calidad de fritura en Solanum phureja Juz et Buk. Tesis de doctorado en Ciencias Agropecuarias. Facultad de Agronomía. Universidad Nacional de Colombia. 204 p. https://repositorio.unal.edu.co/handle/unal/7859Ñústez, C. (2018). Papas diploides: Un legado ancestral para la agricultura en Colombia. Universidad Nacional de Colombia. http://www.papaunc.com/blog/papas-diploides-un- legado-ancestral-para-la-agricultura-en-colombiaOliveira, I., Guilhen, J., Oliveira, P., Gezan, S., Schaffert, R., Simeone, M. & Pastina, M. (2020). Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates. Field Crops Research, 257, 107929. https://doi.org/10.1016/j.fcr.2020.107929Paderewski, J., Gauch, H., Mądry, W. & Gacek, E. (2016). AMMI analysis of four‐way genotype× location× management× year data from a wheat trial in Poland. Crop Science, 56(5), 2157-2164. https://doi.org/10.2135/cropsci2015.03.0152Piepho, H. 1997. Analyzing genotype-environment data by mixed models with multiplicative terms. Biometrics. 53:761—767. https://doi.org/10.2307/2533976Piepho, H. 1998. Empirical best linear unbiased prediction in cultivar trials using factor analytics variance-covariance structures. Theor. Appl. Genet. 97:195-201. https://doi.org/10.1007/s001220050885Piepho, H. & Möhring, J. (2006). Selection in cultivar trials- Is it ignorable? Crop Sci. 46: 192-201. https://doi.org/10.2135/cropsci2005.04-0038Piepho, H., Möhring, J., Melchinger, A. & Büchse, A. (2008). BLUP for phenotypic selection in plant breeding and variety testing. Euphytica, 161(1), 209-228. https://doi.org/10.1007/s10681-007-9449-8Rodríguez, L., Ñustez, C. & Estrada, N., (2009). Criolla Latina. Criolla Paisa y Criolla Colombia. nuevos cultivares de papa criolla para el departamento de Antioquia (Colombia). Agron. Colomb. 27(3). 289-303.Rueda, J. & Cotes, J. (2009). Evaluación de dos métodos de estabilidad fenotípica a través de validación cruzada. Revista Facultad Nacional de Agronomía-Medellín, 62(2), 5111-5123. https://repositorio.unal.edu.co/handle/unal/37088Rykaczewska, K. (2015). The effect of high temperature occurring in subsequent stages of plant development on potato yield and tuber physiological defects. American Journal of Potato Research, 92(3), 339-349. https://doi.org/10.1007/s12230-015-9436-xSmith, A., Cullis, B. & Thompson, R. (2001). Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics. 57: 1138- 1147. https://doi.org/10.1111/j.0006-341X.2001.01138.xSmith, A., Cullis, B. & Thompson, R. (2005). The analysis of crop cultivar breeding and evaluation trials: An overview of current mixed model approaches. j. Agric. Sci. 143:1-14. https://doi.org/10.1017/S0021859605005587Taiz, L. & Zeiger, E. (2006). Plant Physiology, 4th ed. ISBN: 9780878935079Van Eeuwijk, F. (1995). Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models. Euphytica, 84(1), 1-7.Wang, T., He, P., Ahn, K., Wang, X., Ghosh, S. & Laud, P. (2015) A re-formulation of generalized linear mixed models to fit family data in genetic association studies. Frontiers in Genetics [Internet]. https://doi.org/10.3389/fgene.2015.00120Al Soboh, G., Sully, R., & Andreata, S. (2002). Factors affecting specific gravity loss in crisping potato crops in Koo Wee Rup, Victoria. Department of Natural Resources and Environment. ISBN: 0 7341 0531 2Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (2006). Evapotranspiración del cultivo: guías para la determinación de los requerimientos de agua de los cultivos. Roma: FAO, 298(0). ISBN: 92-5-304219-2Bably, A. (2003). Estimation of evapotranspiration using statistical model. Camarda, D. (ed.), Grassin, I. L. (Ed.), Local resources and global trades: Environments and agriculture in the Mediterranean region. CIHEAM. 441-449.Beven, K. (1979). A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates. Journal of Hydrology, 44(3-4), 169-190. https://doi.org/10.1016/0022- 1694(79)90130-6Bhattacharyya, B., Biswas, R., Sujatha, K. & Chiphang, D. (2021). Linear Regression Model to Study the Effects of Weather Variables on Crop Yield in Manipur State. Int. J. Agricult. Stat. Sci, 17(1), 317-320. ISSN: 0973-1903, e-ISSN: 0976-3392Bornhofen, E., Todeschini, M., Stoco, M., Madureira, A., Marchioro, V., Storck, L. & Benin, G. (2018). Wheat yield improvements in Brazil: Roles of genetics and environment. Crop Science, 58(3), 1082-1093.Brancourt, M., Denis, J. & Lecomte, C. (2000). Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression. Theoretical and Applied Genetics, 100(2), 285-298. https://doi.org/10.1007/s001220050038Betancur, L., Posada, S. & Solano, R. (2012). Application of the principal-component analysis in the evaluation of three grass varieties. Revista Colombiana de Ciencias Pecuarias, 25(2), 258-266. https://www.researchgate.net/publication/262505522_Application_of_the_principal- component_analysis_in_the_evaluation_of_three_grass_varietiesCambouris, A., St. Luce, M., Zebarth, B., Ziadi, N., Grant & C., Perron, I. (2016). Potato response to nitrogen sources and rates in an irrigated sandy soil. Agronomy Journal, 108(1), 391-401. http://dx.doi.org/10.2134/agronj2015.0351Carlson, H. (1970). Production of potatoes for chipping. Uppsala, Sweden: The Agricultural College of Sweden. https://www.cabdirect.org/cabdirect/abstract/19711702322Cecchi, H. (1999). Fundamentos teóricos e práticos em análise de alimentos (Theoretical and practical in food analysis) Campinas/SP. UNICAMP, Brazil. https://issuu.com/editoraunicamp/docs/20pp_fundamentos_teoricos_e_praticoCoulibali, Z., Cambouris, A. & Parent, S. (2020). Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada. PloS one, 15(8), https://doi.org/10.1371%2Fjournal.pone.0230888Crossa, J., Burgueño, J., Cornelius, P., McLaren, G., Trethowan, R. & Krishnamachari, A. (2006). Modeling genotype× environment interaction using additive genetic covariances of relatives for predicting breeding values of wheat genotypes. Crop science, 46(4), 1722- 1733. https://doi.org/10.2135/cropsci2005.11-0427Crossa, J. (2012). From genotype x environment interaction to gene x environment interaction. Curr. Genomics 13:225– 244. https://doi.org/10.2174/138920212800543066Davies, H. (1990). Carboydrate metabolism during sprouting. Am J Potato 67:815–827 Feibert EBG, Shock CC, Saunders LD (1998) Nitrogen fertilizer requirements of potatoes using carefully scheduled sprinkler irrigation. HortScience 33:262–265. https://doi.org/10.1007/BF03044293Duarte, D., Narváez, C., Restrepo, L., Kushalappa, A., & Mosquera, T. (2015). Development and validation of a liquid chromatographic method to quantify sucrose, glucose, and fructose in tubers of Solanum tuberosum Group Phureja. Journal of Chromatography B, 975, 18-23. https://doi.org/10.1016/j.jchromb.2014.10.039Duarte, D., Ñústez, C., Narváez, C., Restrepo, L., Melo, S., Sarmiento, F. & Mosquera, T. (2016). Natural variation of sucrose, glucose and fructose contents in Colombian genotypes of Solanum tuberosum Group Phureja at harvest. Journal of the Science of Food and Agriculture, 96(12), 4288-4294. https://doi.org/10.1002/jsfa.7783Escobedo, J., Gomes, E., Oliveira, A. & Soares, J. (2011). Ratios of UV, PAR and NIR components to global solar radiation measured at Botucatu site in Brazil. Renewable Energy, 36(1), 169-178. https://doi.org/10.1016/j.renene.2010.06.018Fontes, P., Braun, H., Busato, C. & Cecon, P. (2010). Economic optimum nitrogen fertilization rates and nitrogen fertilization rate effects on tuber characteristics of potato cultivars. Potato Research, 53(3), 167-179. https://doi.org/10.1007/s11540-010-9160-3Frisina, V. (2002). Modelagem das radiações global, difusa e fotossinteticamente ativa em ambiente protegido e suas relações com o crescimento e produtividade da cultura de pimentão (Capsicum annuum L.). https://repositorio.unesp.br/bitstream/handle/11449/101753/frisina_va_dr_botfca.pdf?seq uence=1&isAllowed=yGarrido, S. (1993). Interpretación de análisis de suelos. Hoja Divulgativa Nro, 5. https://www.mapa.gob.es/ministerio/pags/biblioteca/hojas/hd_1993_05.pdfGil, F., Trasar, C., Leirós, M. & Seoane, S. (2005). Different approaches to evaluating soil quality using biochemical properties. Soil biology and biochemistry, 37(5), 877-887. https://doi.org/10.1016/j.soilbio.2004.10.003Isikwue, B., Audu, M. & Eweh, E. (2015). Correlation of evapotranspiration with climatic parameters in some selected cities in Nigeria. Journal of earth sciences and Geotechnical Engineering, 5(4), 103-115. https://www.researchgate.net/publication/316857440_Correlation_of_Evapotranspiration_ with_Climatic_Parameters_in_some_selected_Cities_in_NigeriaJhajharia, D., Kithan, S., & Fancon, A. (2006). Correlation between pan evaporation and meteorological parameters under the climatic conditions of Jorhat (Assam). Journal of Indian Water Resources Society, 26(1-2), 39-42. https://www.researchgate.net/publication/235004367_Correlation_between_pan_evaporat ion_and_meteorological_parameters_under_the_climatic_conditions_of_Jorhat_AssamKeller, S., Soolanayakanahally, R., Guy, R., Silim, S., Olson, M. & Tiffin, P., (2011). Climate-driven local adaptation of ecophysiology and phenology in balsam poplar, Populus balsamifera L. (Salicaceae). Am. J. Bot. 98, 99–108. https://doi.org/10.3732/ajb.1000317Kumar, D., Ezekiel, R., & Khurana, S. P. (2003). Effect of location, season and cultivar on the processing quality of potatoes. J. Indian Potato Assoc, 30(3-4), 247-251. https://www.researchgate.net/publication/269108056_Effect_of_location_season_and_cul tivar_ob_the_processing_quality_of_potatoesKumar, D., Singh, B. P., & Kumar, P. (2004). An overview of the factors affecting sugar content of potatoes. Annals of Applied Biology, 145(3), 247-256. https://doi.org/10.1111/j.1744-7348.2004.tb00380.xLandau, S., Mitchell, R., Barnett, V., Colls, J., Craigon, J. & Payne, R. (2000). A parsimonious, multiple-regression model of wheat yield response to environment. Agricultural and forest meteorology, 101(2-3), 151-166. https://doi.org/10.1016/S0168- 1923(99)00166-5Liu, T., Dodds, E., Leong, S., Eyres, G., Burritt & D., Oey, I. (2017). Effect of pulsed electric fields on the structure and frying quality of “kumara” sweet potato tubers. Innovative food science & emerging technologies, 39, 197-208. https://doi.org/10.1016/j.ifset.2016.12.010Lulai, E. & Orr, P. (1979). Influence of potato specific gravity on yield and oil content of chips. Am Potato J 56:379–390. https://doi.org/10.1007/BF02855348Maier, N., Dahlenburg, A. & Williams, C. (1994). Effects of nitrogen, phosphorus, and potassium on yield, specific gravity, crisp colour, and tuber chemical composition of potato (Solanum tuberosum L.) cv. Kennebec. Australian Journal of Experimental Agriculture, 34(6), 813-824. ISSN: 0816-1089Malosetti, M., Ribault, J. & van Eeuwijk, F. (2013). The statistical analysis of multi- environment data: Modeling genotype-by-environment interaction and its genetic basis. Front. Physiol. 4:1–17. https://doi.org/10.3389/fphys.2013.00044Manly, B. & Alberto, J. (2016). Multivariate statistical methods: a primer. Chapman and Hall/CRC. ISBN: 9781498728966.Medrano, H., Escalona, J., Cifre, J., Bota, J. & Flexas, J. (2003). A ten-year study on the physiology of two Spanish grapevine cultivars under field conditions: effects of water availability from leaf photosynthesis to grape yield and quality. Funct. Plant Biol. 30, 607– 619. http://dx.doi.org/10.1071/FP02110Nelson, D. C., & Shaw, R. (1976). Effect of planting and harvest dates, location in the hill and tuber size on sugar content of Kennebec potatoes. American Potato Journal, 53(1), 15-21. https://doi.org/10.1007/BF02854087Oliveira, I., Guilhen, J., Oliveira, P., Gezan, S., Schaffert, R., Simeone, M. & Pastina, M. (2020). Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates. Field Crops Research, 257. https://doi.org/10.1016/j.fcr.2020.107929Panishkan, K., Swangjang, K., Sanmanee, N. & Sungthong, D. (2012). Principal component analysis for the characterization in the application of some soil properties. International Journal of Environmental and Ecological Engineering, 6(5), 279-281. https://doi.org/10.5281/zenodo.1058231Parisi, A. & Wong, J. (2000). An estimation of biological hazards due to solar radiation. Journal of Photochemistry and Photobiology, Biology. 54:126 – 30. https://doi.org/10.1016/S1011-1344(00)00006-3Payne, R. (1993). Genstat 5 release 3 reference manuals. Oxford University Press. ISBN- 13: 978-0198523123R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/Rahman, M., Tareq, T., Sarker, P., Rashid, E., Yasmeen, R., Ali, M. & Shimono, H. (2019). Genetic variation of phenotypic plasticity in Bangladesh rice germplasm. Field Crops Research, 243. https://doi.org/10.1016/j.fcr.2019.107618Rodríguez, J., Sahagún, J., Villaseñor, H., Molina, J. & Martínez, Á. (2005). Interaction genotype x environment in the characterization of rainfed areas for wheat. Agrociencia. https://www.researchgate.net/publication/238706096_Interaction_genotypeXenvironment _in_the_characterization_of_rainfed_areas_for_wheatSánchez, Á., Sadras, V. & Sánchez, D. (2021). Phenotypic plasticity in relation to inter- cultivar variation of garlic (Allium sativum L.) functional performance and yield-stability in response to water availability. Scientia Horticulturae, 285, 110128. https://doi.org/10.1016/j.scienta.2021.110128Sadras, V., Reynolds, M., Vega, A., Petrie, P. & Robinson, R. (2009). Phenotypic plasticity of yield and phenology in wheat, sunflower and grapevine. F. Crop. Res. 110, 242–250. https://doi.org/10.1016/j.fcr.2008.09.004Sadras, V., Lake, L., Leonforte, A., Mcmurray, L. & Paull, J. (2013). Screening field pea for adaptation to water and heat stress: associations between yield, crop growth rate and seed abortion. F. Crop. Res. 150, 63–73. https://doi.org/10.1016/j. fcr.2013.05.023Satriani, A., Loperte, A. & Soldovieri, F. (2015). Integrated geophysical techniques for sustainable management of water resource. A case study of local dry bean versus commercial common bean cultivars. Agric. Water Manag. 162, 57–66. http://dx.doi.org/10.1016/j.agwat.2015.08.010Schippers, P. (1976). The relationship between specific gravity and percentage dry matter in potato tubers. Am Potato J 53:111–122. https://doi.org/10.1007/BF02854115Sellam, V. & Poovammal, E. (2016). Prediction of crop yield using regression analysis. Indian Journal of Science and Technology, 9(38), 1-5. https://dx.doi.org/10.17485/ijst/2016/v9i38/91714Sinclair, T. & Horie, T. (1989). Leaf nitrogen, photosynthesis, and crop radiation use efficiency: a review. Crop. Sci. 29, 90-98. https://doi.org/10.2135/cropsci1989.0011183X002900010023xCrossa, J. & Cornelius, P. (1997). Sites regression and shifted multiplicative model clustering of cultivar trials site under heterogeneity of error variance. Crop sci. 37: 405 – 415. https://doi.org/10.2135/cropsci1997.0011183X003700020017SAN-Nariño Project and More Nutritious Potatoes ProjectInternational Development Research Center (IDRC)FedepapaInvestigadoresORIGINAL1018476370.2023.pdf1018476370.2023.pdfTesis de maestría en Ciencias Agrarias - Correccionesapplication/pdf1294792https://repositorio.unal.edu.co/bitstream/unal/83810/2/1018476370.2023.pdf327105f35e702cfe678fada9ede890cbMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83810/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51THUMBNAIL1018476370.2023.pdf.jpg1018476370.2023.pdf.jpgGenerated Thumbnailimage/jpeg4689https://repositorio.unal.edu.co/bitstream/unal/83810/3/1018476370.2023.pdf.jpgf66eadaac7f1340b5fc5da7a1f9e5548MD53unal/83810oai:repositorio.unal.edu.co:unal/838102023-08-15 23:04:26.094Repositorio Institucional Universidad Nacional de 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