Yield projection using productive variables and various types of banana (Musa spp.) seed in Turbo-Colombia.

The productivity of banana cultivation is influenced by different physical, chemical and biological factors, which, in turn, vary between lots, farms and geographical areas; added to this the high climatic variability, high cost of inputs, decrease in labor and adjustment to the supply-demand of the...

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Autores:
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Pedagógica y Tecnológica de Colombia
Repositorio:
RiUPTC: Repositorio Institucional UPTC
Idioma:
spa
OAI Identifier:
oai:repositorio.uptc.edu.co:001/10695
Acceso en línea:
https://revistas.uptc.edu.co/index.php/ciencia_agricultura/article/view/14706
https://repositorio.uptc.edu.co/handle/001/10695
Palabra clave:
Musaceae
Propagación Asexual
Precocidad
Cormo
Productividad
Musaceae
Asexual Propagation
Precocity
Productivity
Corm
Rights
License
Copyright (c) 2022 Miguel Bernal Monterrosa
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oai_identifier_str oai:repositorio.uptc.edu.co:001/10695
network_acronym_str REPOUPTC2
network_name_str RiUPTC: Repositorio Institucional UPTC
repository_id_str
dc.title.en-US.fl_str_mv Yield projection using productive variables and various types of banana (Musa spp.) seed in Turbo-Colombia.
dc.title.es-ES.fl_str_mv Proyección de rendimiento usando variables productivas y diversos tipos de semilla de banano (Musa spp.) en Turbo-Colombia.
title Yield projection using productive variables and various types of banana (Musa spp.) seed in Turbo-Colombia.
spellingShingle Yield projection using productive variables and various types of banana (Musa spp.) seed in Turbo-Colombia.
Musaceae
Propagación Asexual
Precocidad
Cormo
Productividad
Musaceae
Asexual Propagation
Precocity
Productivity
Corm
title_short Yield projection using productive variables and various types of banana (Musa spp.) seed in Turbo-Colombia.
title_full Yield projection using productive variables and various types of banana (Musa spp.) seed in Turbo-Colombia.
title_fullStr Yield projection using productive variables and various types of banana (Musa spp.) seed in Turbo-Colombia.
title_full_unstemmed Yield projection using productive variables and various types of banana (Musa spp.) seed in Turbo-Colombia.
title_sort Yield projection using productive variables and various types of banana (Musa spp.) seed in Turbo-Colombia.
dc.subject.es-ES.fl_str_mv Musaceae
Propagación Asexual
Precocidad
Cormo
Productividad
topic Musaceae
Propagación Asexual
Precocidad
Cormo
Productividad
Musaceae
Asexual Propagation
Precocity
Productivity
Corm
dc.subject.en-US.fl_str_mv Musaceae
Asexual Propagation
Precocity
Productivity
Corm
description The productivity of banana cultivation is influenced by different physical, chemical and biological factors, which, in turn, vary between lots, farms and geographical areas; added to this the high climatic variability, high cost of inputs, decrease in labor and adjustment to the supply-demand of the finished product forces the producer to optimize resources and carry out interventions to schedule harvests. The objective of this research was to make a performance projection, considering production variables. The study was carried out during the second half of 2021 and first quarter of 2022 in the department of Antioquia, municipality of Turbo, for the projection was taken into account seed type, weeks to harvest, cluster weight, population, return, collection and loss. Possible scenarios are presented with their respective interactions and performance response. The experimental design was in completely random blocks with 3 repetitions, the data were analyzed with R Studio 2022.02 software, non-parametric tests (Kruskal-Wallis; Yuen) and comparison of means with a post-analysisKruskal-Wallis with a 95% confidence level. Significant differences were found (P<9e-5), where the treatment of corm + pseudostem was the one that presented the best indicators with a number of weeks accumulated at harvest of 28.40±0.35 and a cluster weight of 24.3±0.19 kg.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2024-07-05T18:11:42Z
dc.date.available.none.fl_str_mv 2024-07-05T18:11:42Z
dc.date.none.fl_str_mv 2022-12-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.es-ES.fl_str_mv texto
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.none.fl_str_mv https://revistas.uptc.edu.co/index.php/ciencia_agricultura/article/view/14706
10.19053/01228420.v19.n3.2022.14706
dc.identifier.uri.none.fl_str_mv https://repositorio.uptc.edu.co/handle/001/10695
url https://revistas.uptc.edu.co/index.php/ciencia_agricultura/article/view/14706
https://repositorio.uptc.edu.co/handle/001/10695
identifier_str_mv 10.19053/01228420.v19.n3.2022.14706
dc.language.none.fl_str_mv spa
dc.language.iso.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://revistas.uptc.edu.co/index.php/ciencia_agricultura/article/view/14706/12423
dc.rights.en-US.fl_str_mv Copyright (c) 2022 Miguel Bernal Monterrosa
http://creativecommons.org/licenses/by/4.0
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Copyright (c) 2022 Miguel Bernal Monterrosa
http://creativecommons.org/licenses/by/4.0
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
dc.publisher.en-US.fl_str_mv Universidad Pedagógica y Tecnológica de Colombia
dc.source.en-US.fl_str_mv Ciencia y Agricultura; Vol. 19 No. 3 (2022): Septiembre-Diciembre
dc.source.es-ES.fl_str_mv Ciencia y Agricultura; Vol. 19 Núm. 3 (2022): Septiembre-Diciembre
dc.source.none.fl_str_mv 2539-0899
institution Universidad Pedagógica y Tecnológica de Colombia
repository.name.fl_str_mv Repositorio Institucional UPTC
repository.mail.fl_str_mv repositorio.uptc@uptc.edu.co
_version_ 1839633869036322816
spelling 2022-12-122024-07-05T18:11:42Z2024-07-05T18:11:42Zhttps://revistas.uptc.edu.co/index.php/ciencia_agricultura/article/view/1470610.19053/01228420.v19.n3.2022.14706https://repositorio.uptc.edu.co/handle/001/10695The productivity of banana cultivation is influenced by different physical, chemical and biological factors, which, in turn, vary between lots, farms and geographical areas; added to this the high climatic variability, high cost of inputs, decrease in labor and adjustment to the supply-demand of the finished product forces the producer to optimize resources and carry out interventions to schedule harvests. The objective of this research was to make a performance projection, considering production variables. The study was carried out during the second half of 2021 and first quarter of 2022 in the department of Antioquia, municipality of Turbo, for the projection was taken into account seed type, weeks to harvest, cluster weight, population, return, collection and loss. Possible scenarios are presented with their respective interactions and performance response. The experimental design was in completely random blocks with 3 repetitions, the data were analyzed with R Studio 2022.02 software, non-parametric tests (Kruskal-Wallis; Yuen) and comparison of means with a post-analysisKruskal-Wallis with a 95% confidence level. Significant differences were found (P<9e-5), where the treatment of corm + pseudostem was the one that presented the best indicators with a number of weeks accumulated at harvest of 28.40±0.35 and a cluster weight of 24.3±0.19 kg.La productividad del cultivo de banano está influenciada por diferentes factores físicos, químicos, biológicos, los cuales, a su vez, varían entre lotes, fincas y zonas geográficas; sumado a esto la alta variabilidad climática, alto costo de insumos, disminución en mano de obra y ajuste a la oferta-demanda del producto terminado obliga al productor a optimizar recursos y realizar intervenciones para programar las cosechas. El objetivo de esta investigación fue realizar una proyección de rendimiento, considerando variables de producción. El estudio se realizó durante el segundo semestre de 2021 y primer trimestre de 2022 en el departamento de Antioquia, municipio de Turbo, para la proyección se tuvo en cuenta tipo de semilla, semanas a cosecha, peso de racimos, población, retorno, recobro y merma. Se plantean posibles escenarios con sus respectivas interacciones y su respuesta en rendimiento. El diseño experimental fue en bloques completamente al azar con 3 repeticiones, los datos se analizaron con el software R Studio 2022.02, se realizaron pruebas no paramétricas (Kruskal-Wallis; Yuen) y comparación de medias con un análisis post-hoc de Kruskal-Wallis con un nivel de confianza del 95%. Se encontraron diferencias significativas (P<9e-5), donde el tratamiento de cormo + pseudotallo fue el que presento los mejores indicadores con un número de semanas acumuladas a cosecha de 28.40±0.35 y un peso de racimo de 24.3±0.19 kg.application/pdfspaspaUniversidad Pedagógica y Tecnológica de Colombiahttps://revistas.uptc.edu.co/index.php/ciencia_agricultura/article/view/14706/12423Copyright (c) 2022 Miguel Bernal Monterrosahttp://creativecommons.org/licenses/by/4.0http://purl.org/coar/access_right/c_abf2Ciencia y Agricultura; Vol. 19 No. 3 (2022): Septiembre-DiciembreCiencia y Agricultura; Vol. 19 Núm. 3 (2022): Septiembre-Diciembre2539-0899MusaceaePropagación AsexualPrecocidadCormoProductividadMusaceaeAsexual PropagationPrecocityProductivityCormYield projection using productive variables and various types of banana (Musa spp.) seed in Turbo-Colombia.Proyección de rendimiento usando variables productivas y diversos tipos de semilla de banano (Musa spp.) en Turbo-Colombia.info:eu-repo/semantics/articletextohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Bernal Monterrosa, MiguelDelgado Bejarano, Laura001/10695oai:repositorio.uptc.edu.co:001/106952025-07-18 11:01:23.499metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCrepositorio.uptc@uptc.edu.co