Use of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradients

Autores:: García-Parra, Miguel Angel
Moreno-Amaya, Lizeth Andrea
Moreno-López, Nidia Milena

Tipo de recurso:: Article of journal

Fecha de publicación:: 2025

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:: eng

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dc.title.spa.fl_str_mv	Use of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradients
title	Use of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradients
spellingShingle	Use of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradients No-lineal model Landrace Logistic model Gompertz model Weibull model Quinoa Modelo no lineal Variedades locales Modelo Logístico Modelo Gompertz Modelo Weibull Quinua
title_short	Use of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradients
title_full	Use of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradients
title_fullStr	Use of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradients
title_full_unstemmed	Use of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradients
title_sort	Use of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradients
dc.creator.fl_str_mv	García-Parra, Miguel Angel Moreno-Amaya, Lizeth Andrea Moreno-López, Nidia Milena
dc.contributor.author.eng.fl_str_mv	García-Parra, Miguel Angel Moreno-Amaya, Lizeth Andrea Moreno-López, Nidia Milena
dc.subject.none.fl_str_mv	No-lineal model Landrace Logistic model Gompertz model Weibull model Quinoa Modelo no lineal Variedades locales Modelo Logístico Modelo Gompertz Modelo Weibull Quinua
topic	No-lineal model Landrace Logistic model Gompertz model Weibull model Quinoa Modelo no lineal Variedades locales Modelo Logístico Modelo Gompertz Modelo Weibull Quinua
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-01-01 00:00:00
dc.date.available.none.fl_str_mv	2025-01-01 00:00:00
dc.date.issued.none.fl_str_mv	2025-01-01
dc.type.spa.fl_str_mv	Artículo de revista
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dc.type.local.eng.fl_str_mv	Journal article
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dc.type.content.eng.fl_str_mv	Text
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dc.identifier.issn.none.fl_str_mv	2011-2173
dc.identifier.doi.none.fl_str_mv	10.19053/uptc.24223719.18739
dc.identifier.eissn.none.fl_str_mv	2422-3719
dc.identifier.url.none.fl_str_mv	https://doi.org/10.19053/uptc.24223719.18739
identifier_str_mv	2011-2173 10.19053/uptc.24223719.18739 2422-3719
url	https://doi.org/10.19053/uptc.24223719.18739
dc.language.iso.eng.fl_str_mv	eng
language	eng
dc.relation.references.eng.fl_str_mv	Abd El-Moneim, D., E.I.S. ELsarag, S. Aloufi, A.M. El-Azraq, S.M. ALshamrani, F.A. Safhi, and A.A. Ibrahim. 2021. Quinoa (Chenopodium quinoa Willd.): genetic diversity according to ISSR and SCoT markers, relative gene expression, and morpho-physiological variation under salinity stress. Plants 10(12), 2802. Doi: https://doi.org/10.3390/plants10122802 Adamczewska-Sowińska, K., J. Sowiński, and A. Jama-Rodzeńska. 2021. The effect of sowing date and harvest time on leafy greens of quinoa (Chenopodium quinoa Willd.) yield and selected nutritional parameters. Agriculture 11(5), 405. Doi: https://doi.org/10.3390/agriculture11050405 Anchico-Jojoa, W., J. Peixoto, and A. Oliveira. 2023. Agronomic characterization and interaction of genotype by environment of quinoa under conditions of Brazil and Colombia. Hortic. Bras. 41, e2629. Doi: https://doi.org/10.1590/s0102-0536-2023-e2629 Archontoulis, A. and F. Miguez, 2015. Nonlinear regression models and applications in agricultural research. Agron. J. 107(2), 786-798. Doi: https://doi.org/10.2134/agronj2012.0506 Asif, A., S. Khan, B. Asif, D. Ibrar, Z. Hasnain, M.S. Ismail, M. Rizwan, S. Ullah, S. Bashir, A. Rais, S. Irshad, T. Abbas, N. Farooq, J. Alkahtani, and M.S. Elshikh. 2022. Preliminary study on growth and yield potential of ten elite lines of quinoa (Chenopodium quinoa) cultivated under varying sowing dates. Plants 11(16), 2116. Doi: https://doi.org/10.3390/plants11162116 Bazile, D., S-E. Jacobsen, and A. Verniau. 2016. The global expansion of quinoa: trends and limits. Front. Plants Sci. 7, 622. Doi: https://doi.org/10.3389/fpls.2016.00622 Bertero, H., R. King, and A. Hall. 1999. Modelling photoperiod and temperature responses of flowering in quinoa (Chenopodium quinoa Willd.). Field Crops Res. 63(1), 19-34. Doi: https://doi.org/10.1016/s0378-4290(99)00024-6 Carranza, C., O. Lanchero, D. Miranda, and B. Chaves. 2009. Growth analysis of ‘Batavia’ lettuce (Lactuca sativa L.) cultivated in a saline soil of the Bogota Plateau. Agron. Colomb. 27(1), 41-48. EL-Harty, E.H., A. Ghazy, T.K. Alateeq, S.A. Al-Faifi, M.A. Khan, M. Afzal, S.S. Alghamdi, and H.M. Migdadi. 2021. Morphological and molecular characterization of quinoa genotypes. Agriculture 11(4), 286. Doi: https://doi.org/10.3390/agriculture11040286 Fang, S.L., Y.H. Kuo, L. Kang, C.C. Chen, C.Y. Hsieh, M.H. Yao, and B.J. Kuo. 2022. Using sigmoid growth models to simulate greenhouse tomato growth and development. Horticulturae 8(11). Doi: https://doi.org/10.3390/horticulturae8111021 García-Parra, M., J. García-Molano, and Y. Deaquíz-Oyola. 2019. Physiological performance of quinoa (Chenopodium quinoa Willd.) under agricultural climatic conditions in Boyaca, Colombia. Agron. Colomb. 37(2), 144-152. Doi: https://doi.org/10.15446/agron.colomb.v37n2.76219 García-Parra, M., D. Roa-Acosta, and J.E. Bravo-Gómez. 2022a. Effect of the altitude gradient on the physiological performance of quinoa in the central region of Colombia. Agronomy 12(9), 2112. Doi: https://doi.org/10.3390/agronomy12092112 García-Parra M., D. Roa-Acosta, A. Colmenares-Cruz, R. Stechauner-Rohringer, J.A. Ramirez, L. Cuellar-Rodríguez, and D. Soler-Fonseca. 2022c. Analysis of the physiological and yield performance of six quinoa cultivars in colombia under controlled conditions. Rev. Cienc. Tecnol. Agropecu. 23(3), e2512. Doi: https://doi.org/10.21930/rcta.vol23_num3_art:2512 García-Parra, M., R. Stechauner-Rhoringer, D. Roa-Acosta, D. Ortíz-Gonzalez, J. Ramirez-Correa, N. Plazas-Leguizamón, and A. Colmenares-Cruz. 2022b. Chlorophyll fluorescence and its relationship with physiological stress in Chenopodium quinoa Willd. Not. Bot. Horti Agrobot. Cluj-Napoca 48(4), 1742-1755. Doi: https://doi.org/10.15835/nbha48412059 García-Parra, M., A. Zurita-Silva, R. Stechauner-Rohringer, D. Roa-Acosta, and S.-E. Jacobsen, 2020. Quinoa (Chenopodium quinoa Willd.) and its relationship with agroclimatic characteristics: A Colombian perspective. Chil. J. Agric. Res. 80(2), 290-302. Doi: https://doi.org/10.4067/S0718-58392020000200290 Habib, Z., S. Ijaz, I.U. Haq, A. Hashem, G.D. Avila-Quezada, E.F. Abd-Allah, and N.A. Khan. 2024. Empirical phenotyping and genome-wide association study reveal the association of panicle architecture with yield in Chenopodium quinoa. Front. Microbiol. 15, 1349239. Doi: https://doi.org/10.3389/fmicb.2024.1349239 Hinojosa, L., J.A. González, F.H. Barrios-Masias, F. Fuentes, and K.M. Murphy. 2018. Quinoa abiotic stress responses: a review. Plants 7(4), 106. Doi: https://doi.org/10.3390/plants7040106 Hunt, R. 1990. Basic growth analysis. Springer, Dordrecht. Doi: https://doi.org/10.1007/978-94-010-9117-6 Hussain, M.I., A. Muscolo, M. Ahmed, M.A. Asghar, and A.J. Al-Dakheel. 2020. Agro-morphological, yield and quality traits and interrelationship with yield stability in quinoa (chenopodium quinoa willd.) genotypes under saline marginal environment. Plants 9(12), 1763. Doi: https://doi.org/10.3390/plants9121763 Jayme-Oliveira, A., W.Q.R. Júnior, M.L.G. Ramos, A.C. Ziviani, and A. Jakelaitis. 2017. Crescimento e desenvolvimento de amaranto, quinoa e milheto sob diferentes regimes hídricos no Cerrado. Pesq. Agropec. Bras. 52(8), 561-571. Doi: https://doi.org/10.1590/S0100-204X2017000800001 Manjarres-Hernández, E.H., D.M. Arias-Moreno, A.C. Morillo-Coronado, Z.Z. Ojeda-Pérez, and A. Cárdenas-Chaparro. 2021. Phenotypic characterization of quinoa (Chenopodium quinoa Willd.) for the selection of promising materials for breeding programs. Plants 10(7), 1339. Doi: https://doi.org/10.3390/plants10071339 Moreno-Medina, B., F. Casierra-Posada, and M. Blanke. 2016. Índices de crecimiento en plantas de mora (Rubus alpinus Macfad) bajo diferentes sistemas de poda. Rev. Colomb. Cienc. Hortic. 10(1), 28-39. Doi: https://doi.org/10.17584/rcch.2016v10i1.4457 Oustani, M., S. Mehda, M.T. Halilat, and H. Chechouni. 2023. Yield, growth development and grain characteristics of seven Quinoa (Chenopodium quinoa Willd.) genotypes grown in open-field production systems under hot-arid climatic conditions. Sci. Rep. 13, 1991. Doi: https://doi.org/10.1038/s41598-023-29039-4 Paine, C.E.T., T.R. Marthews, D.R. Vogt, D. Purves, M. Rees, A. Hector, and L.A. Turnbull. 2012. How to fit nonlinear plant growth models and calculate growth rates: an update for ecologists. Methods Ecol. Evol. 3(2), 245-256. Doi: https://doi.org/10.1111/j.2041-210x.2011.00155.x Pinzón-Sandoval, E., W. Pineda-Rios, and P. Serrano-Cely. 2023. Mathematical models for describing growth in peach (Prunus persica [L.] Batsch.) fruit cv. Dorado. Rev. Colomb. Cienc. Hortic. 15(3). Doi: https://doi.org/10.17584/rcch.2021v15i3.13259 Prusinkiewicz, P. 2003. Modeling plant growth and development. Curr. Opin. Plant Biol. 7(1), 79-83. Doi: https://doi.org/10.1016/j.pbi.2003.11.007 Ragaveena, S., A.S. Edward, and U. Surendran. 2021. Smart controlled environment agriculture methods: a holistic review. Rev. Environ. Sci. Biotechnol. 20(4), 887-913. Doi: https://doi.org/10.1007/s11157-021-09591-z Riccardi, M., G. Mele, C. Pulvento, A. Lavini, R. d’Andria, and S.-E. Jacobsen. 2014. Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components. Photosynth. Res. 120, 263-272. Doi: https://doi.org/10.1007/s11120-014-9970-2 Rouabah, A., H. Meiss, J. Villerd, F. Lasserre-Joulin, V. Tosser, A. Chabert, and O. Therond. 2022. Predicting the abundances of aphids and their natural enemies in cereal crops: Machine-learning versus linear models. Biol. Control 169, 104866. Doi: https://doi.org/10.1016/j.biocontrol.2022.104866 Shah, S.S., L. Shi, Z. Li, G. Ren, B. Zhou, and P. Qin. 2020. Yield, agronomic and forage quality traits of different quinoa (Chenopodium quinoa Willd.) genotypes in Northeast China. Agronomy 10(12), 1908. Doi: https://doi.org/10.3390/agronomy10121908 Stanschewski, C.S., E. Rey, G. Fiene, E.B. Craine, G. Wellman, V.J. Melino, D.S.R. Patiranage, K. Johansen, S.M. Schmöckel, D. Bertero, H. Oakey, C. Colque-Little, I. Afzal, S. Raubach, N. Miller, J. Streich, D.B. Amby, N. Emrani, M. Warmington, M.A.A. Mousa, D. Wu, D. Jacobson, C. Andreasen, C. Jung, K. Murphy, D. Bazile, M. Tester, and Quinoa Phenotyping Consortium 2021. Quinoa phenotyping methodologies: An international consensus. Plants 10(9), 1-52. Doi: https://doi.org/10.3390/plants10091759 Sulaiman, M., M. Umar, K. Nonlaopon, and F.S. Alshammari. 2022. The quantitative features analysis of the nonlinear model of crop production by hybrid soft computing paradigm. Agronomy 12(4), 799. Doi: https://doi.org/10.3390/agronomy12040799 Taaime, N., K. El Mejahed, M. Moussafir, R. Bouabid, A. Oukarroum, R. Choukr-Allah, and M. El Gharous. 2022. Early sowing of quinoa cultivars, benefits from rainy season and enhances quinoa development, growth, and yield under arid condition in Morocco. Sustainability 14(7), 4010. Doi: https://doi.org/10.3390/su14074010 Tang, P., A. Ren, Z. Jiang, R. Wang, K. Cui, X. Wu, M. Sun, Z. Gao, and S. Anwar. 2024. Evaluation of quinoa varieties for adaptability and yield potential in low altitudes and correlation with agronomic traits. Agronomy 14(4), 852. Doi: https://doi.org/10.3390/agronomy14040852 Thiam, E., A. Allaoui, and O. Benlhabib. 2021. Quinoa productivity and stability evaluation through varietal and environmental interaction. Plants 10(4), 714. Doi. https://doi.org/10.3390/plants10040714 Wang, N., F. Wang, C.C. Shock, C. Meng, Z. Huang, L. Gao, and J. Zhao. 2021. Evaluating quinoa stem lodging susceptibility by a mathematical model and the finite element method under different agronomic practices. Field Crops Res. 271, 108241. Doi: https://doi.org/10.1016/j.fcr.2021.108241
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spelling	García-Parra, Miguel AngelMoreno-Amaya, Lizeth AndreaMoreno-López, Nidia Milena2025-01-01 00:00:002025-01-01 00:00:002025-01-012011-217310.19053/uptc.24223719.187392422-3719https://doi.org/10.19053/uptc.24223719.18739application/pdfengSociedad Colombiana de Ciencias Hortícolas-SCCH and Universidad Pedagógica y Tecnológica de Colombia-UPTCRevista Colombiana de Ciencias Hortícolas - 2024http://creativecommons.org/licenses/by-nc-sa/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ciencias_horticolas/article/view/18739No-lineal modelLandraceLogistic modelGompertz modelWeibull modelQuinoaModelo no linealVariedades localesModelo LogísticoModelo GompertzModelo WeibullQuinuaUse of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradientsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85Abd El-Moneim, D., E.I.S. ELsarag, S. Aloufi, A.M. El-Azraq, S.M. ALshamrani, F.A. Safhi, and A.A. Ibrahim. 2021. Quinoa (Chenopodium quinoa Willd.): genetic diversity according to ISSR and SCoT markers, relative gene expression, and morpho-physiological variation under salinity stress. Plants 10(12), 2802. Doi: https://doi.org/10.3390/plants10122802Adamczewska-Sowińska, K., J. Sowiński, and A. Jama-Rodzeńska. 2021. The effect of sowing date and harvest time on leafy greens of quinoa (Chenopodium quinoa Willd.) yield and selected nutritional parameters. Agriculture 11(5), 405. Doi: https://doi.org/10.3390/agriculture11050405Anchico-Jojoa, W., J. Peixoto, and A. Oliveira. 2023. Agronomic characterization and interaction of genotype by environment of quinoa under conditions of Brazil and Colombia. Hortic. Bras. 41, e2629. Doi: https://doi.org/10.1590/s0102-0536-2023-e2629Archontoulis, A. and F. Miguez, 2015. Nonlinear regression models and applications in agricultural research. Agron. J. 107(2), 786-798. Doi: https://doi.org/10.2134/agronj2012.0506Asif, A., S. Khan, B. Asif, D. Ibrar, Z. Hasnain, M.S. Ismail, M. Rizwan, S. Ullah, S. Bashir, A. Rais, S. Irshad, T. Abbas, N. Farooq, J. Alkahtani, and M.S. Elshikh. 2022. Preliminary study on growth and yield potential of ten elite lines of quinoa (Chenopodium quinoa) cultivated under varying sowing dates. Plants 11(16), 2116. Doi: https://doi.org/10.3390/plants11162116Bazile, D., S-E. Jacobsen, and A. Verniau. 2016. The global expansion of quinoa: trends and limits. Front. Plants Sci. 7, 622. Doi: https://doi.org/10.3389/fpls.2016.00622Bertero, H., R. King, and A. Hall. 1999. Modelling photoperiod and temperature responses of flowering in quinoa (Chenopodium quinoa Willd.). Field Crops Res. 63(1), 19-34. Doi: https://doi.org/10.1016/s0378-4290(99)00024-6Carranza, C., O. Lanchero, D. Miranda, and B. Chaves. 2009. Growth analysis of ‘Batavia’ lettuce (Lactuca sativa L.) cultivated in a saline soil of the Bogota Plateau. Agron. Colomb. 27(1), 41-48.EL-Harty, E.H., A. Ghazy, T.K. Alateeq, S.A. Al-Faifi, M.A. Khan, M. Afzal, S.S. Alghamdi, and H.M. Migdadi. 2021. Morphological and molecular characterization of quinoa genotypes. Agriculture 11(4), 286. Doi: https://doi.org/10.3390/agriculture11040286Fang, S.L., Y.H. Kuo, L. Kang, C.C. Chen, C.Y. Hsieh, M.H. Yao, and B.J. Kuo. 2022. Using sigmoid growth models to simulate greenhouse tomato growth and development. Horticulturae 8(11). Doi: https://doi.org/10.3390/horticulturae8111021García-Parra, M., J. García-Molano, and Y. Deaquíz-Oyola. 2019. Physiological performance of quinoa (Chenopodium quinoa Willd.) under agricultural climatic conditions in Boyaca, Colombia. Agron. Colomb. 37(2), 144-152. Doi: https://doi.org/10.15446/agron.colomb.v37n2.76219García-Parra, M., D. Roa-Acosta, and J.E. Bravo-Gómez. 2022a. Effect of the altitude gradient on the physiological performance of quinoa in the central region of Colombia. Agronomy 12(9), 2112. Doi: https://doi.org/10.3390/agronomy12092112García-Parra M., D. Roa-Acosta, A. Colmenares-Cruz, R. Stechauner-Rohringer, J.A. Ramirez, L. Cuellar-Rodríguez, and D. Soler-Fonseca. 2022c. Analysis of the physiological and yield performance of six quinoa cultivars in colombia under controlled conditions. Rev. Cienc. Tecnol. Agropecu. 23(3), e2512. Doi: https://doi.org/10.21930/rcta.vol23_num3_art:2512García-Parra, M., R. Stechauner-Rhoringer, D. Roa-Acosta, D. Ortíz-Gonzalez, J. Ramirez-Correa, N. Plazas-Leguizamón, and A. Colmenares-Cruz. 2022b. Chlorophyll fluorescence and its relationship with physiological stress in Chenopodium quinoa Willd. Not. Bot. Horti Agrobot. Cluj-Napoca 48(4), 1742-1755. Doi: https://doi.org/10.15835/nbha48412059García-Parra, M., A. Zurita-Silva, R. Stechauner-Rohringer, D. Roa-Acosta, and S.-E. Jacobsen, 2020. Quinoa (Chenopodium quinoa Willd.) and its relationship with agroclimatic characteristics: A Colombian perspective. Chil. J. Agric. Res. 80(2), 290-302. Doi: https://doi.org/10.4067/S0718-58392020000200290Habib, Z., S. Ijaz, I.U. Haq, A. Hashem, G.D. Avila-Quezada, E.F. Abd-Allah, and N.A. Khan. 2024. Empirical phenotyping and genome-wide association study reveal the association of panicle architecture with yield in Chenopodium quinoa. Front. Microbiol. 15, 1349239. Doi: https://doi.org/10.3389/fmicb.2024.1349239Hinojosa, L., J.A. González, F.H. Barrios-Masias, F. Fuentes, and K.M. Murphy. 2018. Quinoa abiotic stress responses: a review. Plants 7(4), 106. Doi: https://doi.org/10.3390/plants7040106Hunt, R. 1990. Basic growth analysis. Springer, Dordrecht. Doi: https://doi.org/10.1007/978-94-010-9117-6Hussain, M.I., A. Muscolo, M. Ahmed, M.A. Asghar, and A.J. Al-Dakheel. 2020. Agro-morphological, yield and quality traits and interrelationship with yield stability in quinoa (chenopodium quinoa willd.) genotypes under saline marginal environment. Plants 9(12), 1763. 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Doi: https://doi.org/10.1016/j.fcr.2021.108241151119Revista Colombiana de Ciencias Hortícolashttps://revistas.uptc.edu.co/index.php/ciencias_horticolas/article/download/18739/16110PublicationOREORE.xmltext/xml2880https://repositorio.uptc.edu.co/bitstreams/466c2021-7b0e-452e-b5ee-ec65caee9fd5/download13bb3354458585c2781a17216d583846MD51falseAnonymousREAD001/21470oai:repositorio.uptc.edu.co:001/214702026-09-29 21:35:29.129http://creativecommons.org/licenses/by-nc-sa/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Use of mathematical models to simulate the growth of quinoa crops (Chenopodium quinoa Willd.) under three different altitudinal gradients

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