Estimation of cattle weight gain under the influence of meteorological and nutritional variables by applying a multiple linear regression model in Sabanalarga, Colombia
The present investigation arose from the current problem in the entire territory of the Department of Atlántico in the Republic of Colombia, in which the livestock sector currently lacks a reliable modernization that contributes to the planning and profitability of meat production, translated into w...
- Autores:
-
Rueda Galofre J.V.
Mora García Y.A.
Adie Villafañe J.
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13627
- Acceso en línea:
- https://hdl.handle.net/11323/13627
https://repositorio.cuc.edu.co/
- Palabra clave:
- Cattle
Linear regression
Livestock
Meteorological
Nutritional
Statistics
Variables
Weight gain
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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dc.title.eng.fl_str_mv |
Estimation of cattle weight gain under the influence of meteorological and nutritional variables by applying a multiple linear regression model in Sabanalarga, Colombia |
title |
Estimation of cattle weight gain under the influence of meteorological and nutritional variables by applying a multiple linear regression model in Sabanalarga, Colombia |
spellingShingle |
Estimation of cattle weight gain under the influence of meteorological and nutritional variables by applying a multiple linear regression model in Sabanalarga, Colombia Cattle Linear regression Livestock Meteorological Nutritional Statistics Variables Weight gain |
title_short |
Estimation of cattle weight gain under the influence of meteorological and nutritional variables by applying a multiple linear regression model in Sabanalarga, Colombia |
title_full |
Estimation of cattle weight gain under the influence of meteorological and nutritional variables by applying a multiple linear regression model in Sabanalarga, Colombia |
title_fullStr |
Estimation of cattle weight gain under the influence of meteorological and nutritional variables by applying a multiple linear regression model in Sabanalarga, Colombia |
title_full_unstemmed |
Estimation of cattle weight gain under the influence of meteorological and nutritional variables by applying a multiple linear regression model in Sabanalarga, Colombia |
title_sort |
Estimation of cattle weight gain under the influence of meteorological and nutritional variables by applying a multiple linear regression model in Sabanalarga, Colombia |
dc.creator.fl_str_mv |
Rueda Galofre J.V. Mora García Y.A. Adie Villafañe J. |
dc.contributor.author.none.fl_str_mv |
Rueda Galofre J.V. Mora García Y.A. Adie Villafañe J. |
dc.subject.proposal.eng.fl_str_mv |
Cattle Linear regression Livestock Meteorological Nutritional Statistics Variables Weight gain |
topic |
Cattle Linear regression Livestock Meteorological Nutritional Statistics Variables Weight gain |
description |
The present investigation arose from the current problem in the entire territory of the Department of Atlántico in the Republic of Colombia, in which the livestock sector currently lacks a reliable modernization that contributes to the planning and profitability of meat production, translated into weight gain. The main focus of the study gravitated around the ignorance of the real effect exerted by meteorological and nutritional factors on the weight gain of cattle. As a possible solution, it was proposed to carry out a statistical analysis by means of a multiple linear regression model where cattle weight gain was the dependent variable to study under the influence of the following independent variables: accumulated precipitation for two weeks (mm), average daily precipitation for two weeks (mm), average daily forage height consumed for two weeks (cm), percentage daily average of forage consumed during two weeks (%), average protein percentage of forage consumed during two weeks (%), the average maximum temperature recorded during two weeks (°C), the average minimum temperature recorded during two weeks (°C), average daily temperature variation recorded for two weeks (°C) and average relative humidity recorded for two weeks (%). All independent data values were collected in the field. Once the analysis was carried out, it was concluded that there was statistical evidence to affirm that only the independent variables "accumulated precipitation", "average precipitation", "average minimum temperature" and "relative humidity" significantly influenced the changes observed in profit of cattle weight, being formulated a multiple linear regression model that contained only the mentioned variables, the rest were discarded. On the other hand, for the constructed linear regression model, the coefficient of determination R2 = 89.3691% was obtained, that is, for the significance level α = 0.05 (95% confidence level), this determined that the model of Multiple linear regression (A) explained the behavior of the average monthly cattle weight gain by 89.3691%. It was concluded, therefore, that the present work gives veracity to the determination of previous investigations where it is also concluded that the meteorological variables directly affect the changes associated with the weight of cattle for meat production. |
publishDate |
2023 |
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2023 |
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2024-11-05T12:23:58Z |
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2024-11-05T12:23:58Z |
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Artículo de revista |
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10.18697/ajfand.124.23800 |
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dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofjournal.none.fl_str_mv |
African Journal of Food, Agriculture, Nutrition and Development |
dc.relation.references.none.fl_str_mv |
Instituto Geográfico Agustín Codazzi (IGAC). Ganadería ‘al rojo vivo’ solo debería imponerse en el 2,4% de Colombia: IGAC. 2017. Available by: https://igac.gov.co/es/noticias/ganaderia-al-rojo-vivo-solo-deberia-imponerse-en-el-24- de-colombia-igac Accessed February 2021. Munive JMJ and LGH Ladino Diagnóstico participativo en las zonas rurales del municipio de Sabanalarga, Atlántico (Colombia). Tlatemoani: revista académica de investigación. 2021; 12(37): 35-59. Aiyar A and P Pingali Pandemics and food systems-towards a proactive food safety approach to disease prevention and management. Food Security. 2020; 12(4): 749- 756. https://doi.org/10.1007/s12571-020-01074-3 Khoshnevisan B, Duan N, Tsapekos P, Awasthi MK, Liu Z, Mohammadi A and H Liu A critical review on livestock manure biorefinery technologies: Sustainability, challenges, and future perspectives. Renewable and Sustainable Energy Reviews. 2021; 135, 110033. https://doi.org/10.1016/j.rser.2020.110033 Newton JE, Nettle R and JE Pryce Farming smarter with big data: Insights from the case of Australia's national dairy herd milk recording scheme. Agricultural systems. 2020; 181, 102811. https://doi.org/10.1016/j.agsy.2020.102811 Carvajal Gamarra ME and OJ Monsalve Parra Sistema de gestión de aprendizaje (LMS) como apoyo a los procesos de enseñanza-aprendizaje basado en software libre para el sector ganadero en el departamento de Santander. Graduate Thesis, 2021. Repositorio Institucional, Universidad Cooperativa de Colombia. http://hdl.handle.net/20.500.12494/36261 Accessed August 2021. Marshall K, Salmon GR, Tebug S, Juga J, MacLeod M, Poole J and A Missohou Net benefits of smallholder dairy cattle farms in Senegal can be significantly increased through the use of better dairy cattle breeds and improved management practices. Journal of Dairy Science. 2020; 103(9): 8197-8217. https://doi.org/10.3168/jds.2019- 17334 Aquino J Ganancia de peso en bovinos de raza Nelore, Brahman y Gyr en un Sistema de Confinamiento Familiar en el municipio de Capinota. Final Project to obtain the Diploma Certificate in “SANIDAD Y PRODUCCION INTENSIVA EN BOVINOS”, 2022. http://hdl.handle.net/123456789/28332 Accessed August 2021. Jacome Icaza PJ La tecnificación de la agricultura como condición para lograr el desarrollo rural en la producción de arroz (Oryza sativa L.)” del cantón Babahoyo. Bachelor's thesis, 2019. Babahoyo: UTB. Mascaró ED, Genero GA, Gimenez M, Ferrán AM, Castaldo AO, Calvo, C, and JM Halac Evaluación de un producto comercial en base a polisacáridos bacterianos propuesto como promotores de crecimiento en bovinos para carne; 2022. https://repo.unlpam.edu.ar/handle/unlpam/7680 Accessed November 2020. Dominguez-Castaño P Factores que influencian el desempeño y la rentabilidad bruta en bovinos de carne en pastoreo bajo condiciones tropicales. Revista de Investigaciones Veterinarias del Perú. 2022; 33(4): e20534-e20534. https://revistasinvestigacion.unmsm.edu.pe/index.php/veterinaria/article/download/205 34/18637/83502 Accessed July 2022. Bender FE, Douglass LW, and A Kramer Statistical methods for food and agriculture. CRC Press; 2020. Available by: https://www.taylorfrancis.com/books/mono/10.1201/9781003073116/statisticalmethods-food-agriculture-filmore-bender-larry-douglass-amihud-kramer Accessed September 2021. Weber VADM, Weber FDL, Gomes RDC, Oliveira Junior ADS, Menezes GV, Abreu UGPD and H Pistori Prediction of Girolando cattle weight by means of body measurements extracted from images. Revista Brasileira de Zootecnia. 2020; 49. https://doi.org/10.37496/rbz4920190110 Shi Y, Li C, and M Zhao The effect, mechanism, and heterogeneity of grassland rental on herders' livestock production technical efficiency: evidence from pastoral areas in Northern China. Environment, Development and Sustainability. 2022; 1-29. https://link.springer.com/article/10.1007/s10668-022-02639-2 Amat J Correlación lineal y Regresión lineal simple; 2016. Available under a Attribution 4.0 International (CC BY 4.0) at: https://www.cienciadedatos.net/documentos/24_correlacion_y_regresion_lineal#Regr esi%C3%B3n_lineal_simple Accessed March 2020. Amat J Introducción a la Regresión Lineal Múltiple. 2016. Available under Attribution 4.0 International (CC BY 4.0) at: https://www.cienciadedatos.net/documentos/25_regresion_lineal_multiple Accessed March 2020. Vetter TR and P Schober Regression: the apple does not fall far from the tree. Anesthesia and Analgesia. 2018; 127(1): 277-283. https://doi.org/10.1213/ANE.0000000000003424 Ali MZ, Carlile G and M Giasuddin Impact of global climate change on livestock health: Bangladesh perspective. Open Veterinary Journal. 2020; 10(2): 178-188. https://doi.org/10.4314/ovj.v10i2.7 Matere J, Simpkin P, Angerer J, Olesambu, E, Ramasamy S and F Fasina Predictive Livestock Early Warning System (PLEWS), Monitoring forage condition and implications for animal production in Kenya. Weather and Climate Extremes. 2020; 27, 100209. https://doi.org/10.1016/j.wace.2019.100209 Huang W, Li T, Liu J, Xie P, Du S and F Teng An overview of air quality analysis by big data techniques: Monitoring, forecasting, and traceability. Information Fusion. 2021: 75, 28-40. https://doi.org/10.1016/j.inffus.2021.03.010 |
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18 páginas |
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Colombia |
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Sabanalarga |
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Kenya |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© (2023),https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rueda Galofre J.V.Mora García Y.A.Adie Villafañe J.ColombiaSabanalarga2024-11-05T12:23:58Z2024-11-05T12:23:58Z20231684-5358https://hdl.handle.net/11323/1362710.18697/ajfand.124.238001684-5374Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The present investigation arose from the current problem in the entire territory of the Department of Atlántico in the Republic of Colombia, in which the livestock sector currently lacks a reliable modernization that contributes to the planning and profitability of meat production, translated into weight gain. The main focus of the study gravitated around the ignorance of the real effect exerted by meteorological and nutritional factors on the weight gain of cattle. As a possible solution, it was proposed to carry out a statistical analysis by means of a multiple linear regression model where cattle weight gain was the dependent variable to study under the influence of the following independent variables: accumulated precipitation for two weeks (mm), average daily precipitation for two weeks (mm), average daily forage height consumed for two weeks (cm), percentage daily average of forage consumed during two weeks (%), average protein percentage of forage consumed during two weeks (%), the average maximum temperature recorded during two weeks (°C), the average minimum temperature recorded during two weeks (°C), average daily temperature variation recorded for two weeks (°C) and average relative humidity recorded for two weeks (%). All independent data values were collected in the field. Once the analysis was carried out, it was concluded that there was statistical evidence to affirm that only the independent variables "accumulated precipitation", "average precipitation", "average minimum temperature" and "relative humidity" significantly influenced the changes observed in profit of cattle weight, being formulated a multiple linear regression model that contained only the mentioned variables, the rest were discarded. On the other hand, for the constructed linear regression model, the coefficient of determination R2 = 89.3691% was obtained, that is, for the significance level α = 0.05 (95% confidence level), this determined that the model of Multiple linear regression (A) explained the behavior of the average monthly cattle weight gain by 89.3691%. It was concluded, therefore, that the present work gives veracity to the determination of previous investigations where it is also concluded that the meteorological variables directly affect the changes associated with the weight of cattle for meat production.18 páginasapplication/pdfengAfrican Scholarly Science Communications Trust (ASSCAT)Kenyahttps://www.ajfand.net/Volume23/No9/Rueda-Galofre23800.pdfEstimation of cattle weight gain under the influence of meteorological and nutritional variables by applying a multiple linear regression model in Sabanalarga, ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85African Journal of Food, Agriculture, Nutrition and DevelopmentInstituto Geográfico Agustín Codazzi (IGAC). Ganadería ‘al rojo vivo’ solo debería imponerse en el 2,4% de Colombia: IGAC. 2017. Available by: https://igac.gov.co/es/noticias/ganaderia-al-rojo-vivo-solo-deberia-imponerse-en-el-24- de-colombia-igac Accessed February 2021.Munive JMJ and LGH Ladino Diagnóstico participativo en las zonas rurales del municipio de Sabanalarga, Atlántico (Colombia). Tlatemoani: revista académica de investigación. 2021; 12(37): 35-59.Aiyar A and P Pingali Pandemics and food systems-towards a proactive food safety approach to disease prevention and management. Food Security. 2020; 12(4): 749- 756. https://doi.org/10.1007/s12571-020-01074-3Khoshnevisan B, Duan N, Tsapekos P, Awasthi MK, Liu Z, Mohammadi A and H Liu A critical review on livestock manure biorefinery technologies: Sustainability, challenges, and future perspectives. Renewable and Sustainable Energy Reviews. 2021; 135, 110033. https://doi.org/10.1016/j.rser.2020.110033Newton JE, Nettle R and JE Pryce Farming smarter with big data: Insights from the case of Australia's national dairy herd milk recording scheme. Agricultural systems. 2020; 181, 102811. https://doi.org/10.1016/j.agsy.2020.102811Carvajal Gamarra ME and OJ Monsalve Parra Sistema de gestión de aprendizaje (LMS) como apoyo a los procesos de enseñanza-aprendizaje basado en software libre para el sector ganadero en el departamento de Santander. Graduate Thesis, 2021. Repositorio Institucional, Universidad Cooperativa de Colombia. http://hdl.handle.net/20.500.12494/36261 Accessed August 2021.Marshall K, Salmon GR, Tebug S, Juga J, MacLeod M, Poole J and A Missohou Net benefits of smallholder dairy cattle farms in Senegal can be significantly increased through the use of better dairy cattle breeds and improved management practices. Journal of Dairy Science. 2020; 103(9): 8197-8217. https://doi.org/10.3168/jds.2019- 17334Aquino J Ganancia de peso en bovinos de raza Nelore, Brahman y Gyr en un Sistema de Confinamiento Familiar en el municipio de Capinota. Final Project to obtain the Diploma Certificate in “SANIDAD Y PRODUCCION INTENSIVA EN BOVINOS”, 2022. http://hdl.handle.net/123456789/28332 Accessed August 2021.Jacome Icaza PJ La tecnificación de la agricultura como condición para lograr el desarrollo rural en la producción de arroz (Oryza sativa L.)” del cantón Babahoyo. Bachelor's thesis, 2019. Babahoyo: UTB.Mascaró ED, Genero GA, Gimenez M, Ferrán AM, Castaldo AO, Calvo, C, and JM Halac Evaluación de un producto comercial en base a polisacáridos bacterianos propuesto como promotores de crecimiento en bovinos para carne; 2022. https://repo.unlpam.edu.ar/handle/unlpam/7680 Accessed November 2020.Dominguez-Castaño P Factores que influencian el desempeño y la rentabilidad bruta en bovinos de carne en pastoreo bajo condiciones tropicales. Revista de Investigaciones Veterinarias del Perú. 2022; 33(4): e20534-e20534. https://revistasinvestigacion.unmsm.edu.pe/index.php/veterinaria/article/download/205 34/18637/83502 Accessed July 2022.Bender FE, Douglass LW, and A Kramer Statistical methods for food and agriculture. CRC Press; 2020. Available by: https://www.taylorfrancis.com/books/mono/10.1201/9781003073116/statisticalmethods-food-agriculture-filmore-bender-larry-douglass-amihud-kramer Accessed September 2021.Weber VADM, Weber FDL, Gomes RDC, Oliveira Junior ADS, Menezes GV, Abreu UGPD and H Pistori Prediction of Girolando cattle weight by means of body measurements extracted from images. Revista Brasileira de Zootecnia. 2020; 49. https://doi.org/10.37496/rbz4920190110Shi Y, Li C, and M Zhao The effect, mechanism, and heterogeneity of grassland rental on herders' livestock production technical efficiency: evidence from pastoral areas in Northern China. Environment, Development and Sustainability. 2022; 1-29. https://link.springer.com/article/10.1007/s10668-022-02639-2Amat J Correlación lineal y Regresión lineal simple; 2016. Available under a Attribution 4.0 International (CC BY 4.0) at: https://www.cienciadedatos.net/documentos/24_correlacion_y_regresion_lineal#Regr esi%C3%B3n_lineal_simple Accessed March 2020.Amat J Introducción a la Regresión Lineal Múltiple. 2016. Available under Attribution 4.0 International (CC BY 4.0) at: https://www.cienciadedatos.net/documentos/25_regresion_lineal_multiple Accessed March 2020.Vetter TR and P Schober Regression: the apple does not fall far from the tree. Anesthesia and Analgesia. 2018; 127(1): 277-283. https://doi.org/10.1213/ANE.0000000000003424Ali MZ, Carlile G and M Giasuddin Impact of global climate change on livestock health: Bangladesh perspective. Open Veterinary Journal. 2020; 10(2): 178-188. https://doi.org/10.4314/ovj.v10i2.7Matere J, Simpkin P, Angerer J, Olesambu, E, Ramasamy S and F Fasina Predictive Livestock Early Warning System (PLEWS), Monitoring forage condition and implications for animal production in Kenya. Weather and Climate Extremes. 2020; 27, 100209. https://doi.org/10.1016/j.wace.2019.100209Huang W, Li T, Liu J, Xie P, Du S and F Teng An overview of air quality analysis by big data techniques: Monitoring, forecasting, and traceability. Information Fusion. 2021: 75, 28-40. https://doi.org/10.1016/j.inffus.2021.03.0102475824741923CattleLinear regressionLivestockMeteorologicalNutritionalStatisticsVariablesWeight gainPublicationORIGINALESTIMATION OF CATTLE WEIGHT GAIN UNDER THE INFLUENCE OF.pdfESTIMATION OF CATTLE WEIGHT GAIN UNDER THE INFLUENCE OF.pdfapplication/pdf597487https://repositorio.cuc.edu.co/bitstreams/35118f9f-9275-4878-bb70-9ad04393a4f6/download3732366aa1b0cbf5d13c1b36f87157adMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/38077c94-8aca-4c43-8914-d8d9cf72effa/download73a5432e0b76442b22b026844140d683MD52TEXTESTIMATION OF CATTLE WEIGHT GAIN UNDER THE INFLUENCE OF.pdf.txtESTIMATION OF CATTLE WEIGHT GAIN UNDER THE INFLUENCE OF.pdf.txtExtracted texttext/plain33030https://repositorio.cuc.edu.co/bitstreams/44e15fb3-dbf2-4f1b-9934-42b4b67005ec/download7fbcc72d531ce4c7b6a619ae23df7c3aMD53THUMBNAILESTIMATION OF CATTLE WEIGHT GAIN UNDER THE INFLUENCE 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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