Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach
Blackberry production in Colombia contributes to the nation´s gross domestic profit, employment and farmers’ social well-being. It is considered of great economic importance as blackberry fruits are used as raw material for the agroindustry. In this manner, production instability affects farmers’ ec...
- Autores:
-
Cancino, Susan
Cancino Escalante, Giovanni Orlando
Cancino, Daniel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9780
- Acceso en línea:
- https://hdl.handle.net/11323/9780
https://repositorio.cuc.edu.co/
- Palabra clave:
- Predictive capacity
Univariate analysis
Production
Data modeling
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- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach |
| dc.title.translated.none.fl_str_mv |
Un modelo Box Jenkins ARIMA para modelar y pronosticar la producción de mora de castilla en Colombia |
| title |
Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach |
| spellingShingle |
Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach Predictive capacity Univariate analysis Production Data modeling |
| title_short |
Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach |
| title_full |
Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach |
| title_fullStr |
Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach |
| title_full_unstemmed |
Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach |
| title_sort |
Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach |
| dc.creator.fl_str_mv |
Cancino, Susan Cancino Escalante, Giovanni Orlando Cancino, Daniel |
| dc.contributor.author.none.fl_str_mv |
Cancino, Susan Cancino Escalante, Giovanni Orlando Cancino, Daniel |
| dc.subject.proposal.eng.fl_str_mv |
Predictive capacity Univariate analysis Production |
| topic |
Predictive capacity Univariate analysis Production Data modeling |
| dc.subject.proposal.none.fl_str_mv |
Data modeling |
| description |
Blackberry production in Colombia contributes to the nation´s gross domestic profit, employment and farmers’ social well-being. It is considered of great economic importance as blackberry fruits are used as raw material for the agroindustry. In this manner, production instability affects farmers’ economic profitability; therefore, forecasting plays an important role in monitoring production as well as in farmers´ planting decision and resource allocation. Hence, the purpose of the study was to model and forecast blackberry production in Colombia using a Box-Jenkins ARIMA approach for the period 1992-2023. A quantitative, nonexperimental, correlational and descriptive research design was selected. The appropriateness of the model and its predictive capacity was assessed by verifying the different goodness-of-fit criteria. Results showed that the ARIMA (1,1,0) was the most suitable model as it captured the behavior of the actual time series. Based on the forecasted values it is expected a 5.47% increase in blackberry production for the period 2021-2023 which will consequently improve farmers´ income and thus contribute to the reduction in poverty |
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Cancino, S. E., Cancino-Escalante, G. E. & CancinoRicketts, D. F. (2023). Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach. Económicas CUC, 44(1), 69–82. DOI: https://doi.org/10.17981/ econcuc.44.1.2023.Econ.4 |
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10.17981/econcuc.44.1.2023.Econ.4 |
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2382-3860 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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Cancino, S. E., Cancino-Escalante, G. E. & CancinoRicketts, D. F. (2023). Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach. Económicas CUC, 44(1), 69–82. DOI: https://doi.org/10.17981/ econcuc.44.1.2023.Econ.4 0120-3932 10.17981/econcuc.44.1.2023.Econ.4 2382-3860 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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Afzal, M., Rehman, H. U. & Butt, A. R. (2002). Forecasting: a dilemma of modules (A Comparison of Theory Based and Theory Free Approaches). Pakistan Economic and Social Review, 40(1), 1–18. Available: https://www.jstor.org/stable/25825233 Agronet. (s.f.). Blackberry statistical data [Database]. Available: http://www.agronet.gov.co/estadistica/Paginas/home.aspx Akın, M., Eyduran, S., Çelik, Ş., Aliyev, P., Ayko, S. & Eyduran, E. (2021). Modeling and forecasting cherry production in Turkey. The Journal of Animal and Plant Science, 31(3), 773–781. https://doi.org/10.36899/JAPS.2021.3.0267 Arguello, R. & Valderrama-González, D. (2015). Sectoral and poverty impacts of agricultural policy adjustments in Colombia. Agricultural Economics, 46(2), 259–280. https://doi.org/10.1111/agec.12155 Burhan, A. & Khalid, M. (2006). Forecasting Kinnow production in Pakistan: An econometric analysis. International Journal of Agriculture & Biology, 8(4), 455– 458. Available from https://www.fspublishers.org/published_papers/6916_..pdf Brandt, J. & Bessler, D. (1984). Forecasting with Vector Autoregressions versus a Univariate ARIMA Process: An empirical example with U.S. Hog Prices. North Central Journal of Agricultural Economics, 6(2), 29–36. https://doi. org/10.2307/1349248 Brooks, C. (2019). Introductory econometrics for finance. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108524872 Cancino, S., Cancino-Escalante, G. y Cancino-Ricketts, D. (2020). Modelo econométrico para el cultivo de mora de castilla (Rubus glaucus) en los municipios de Pamplona y Chitagá, Norte de Santander, Colombia. Aibi Revista de Investigación, Administración e Ingeniería, 8(1), 37–43. https://doi. org/10.15649/2346030X.752 Cancino, S., Cancino, G. y Cancino, D. (2021). Análisis de regresión de los factores que afectan la rentabilidad económica de la producción de curuba. Dictamen Libre, (29), 1–13. https://doi.org/10.18041/2619-4244/dl.29.7861 Cárdenas M, Echavarría J, Hernández G, Maiguashca A, Meisel A, Ocampo, J. y Zárate, J. (2018). Coyuntura del sector agropecuario colombiano. [Informe de la Junta Directiva al Congreso de la República]. Bogotá, D.C.: Banco de la República. Disponible en https://www.banrep.gov.co/es/recuadro-2-coyuntura-delsector-agropecuario-colombiano Chen, C.-K. (2008). An integrated enrollment forecast model. AIR IR Applications, 15, 1–18. Available: https://eric.ed.gov/?id=ED504328 Dickey, D. & Fuller, W. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348 E-views (version 9.0). Econometric Views. [Software]. London: IHS. Available: www. eviews.com Franco G. y Giraldo, M. (1998). El cultivo de la mora. Mosquera: Corporación Colombiana de Investigación Agropecuaria-CORPOICA. Disponible en http://bibliotecadigital.agronet.gov.co/handle/11348/4039 FAO. (s.f.). FAOSTAT. Crops and livestock products. [Database]. Available: https:// www.fao.org/faostat/en/#data/QCL Gajbihe, S., Wankhade, R. & Mahalle, S. (2010). Forecasting chickpea production in India using ARIMA model. International Journal of Commerce and Business Management, 3(2), 212–215. Available from http://researchjournal.co.in/upload/ assignments/3_212-215.pdf Gujarati, D. y Porter, D. (2010). Econometría. México D.F.: McGraw Hill. Hamjah, M. A. (2014). Forecasting Major Fruit Crops Productions in Bangladesh using Box-Jenkins ARIMA Model. Journal of Economics and Sustainable Development, 5(7), 96–107. Available from https://core.ac.uk/download/ pdf/234646336.pdf Henley D. (2012). The agrarian roots of industrial growth: rural development in South-East Asia and sub-Saharan Africa. Development Policy Review, 30(51), 25–47. https://doi.org/10.1111/j.1467-7679.2012.00564.x Iqbal, M. A. (2020). Application of regression techniques with their advantages and disadvantages. Elektor Magazine, 4(1), 11–17. Available: https://www.elektormagazine.com/magazine-archive/2020 Judge, G., Hill, R., Griffiths W., Lutkepohl, H. & Lee, T. (1991). An introduction to the theory and practice of econometrics. [2 ed.]. New York: Wiley Khan, S. & Khan, U. (2020). ARIMA Models Using Economic Variables of Bangladesh. Asian Journal of Probability and Statistics, 10(2), 33–47. https://doi.org/10.9734/ajpas/2020/ v10i230243 Khan, D., Ullah, A., Bibi, Z., Ullah, I., Zulfiqar, M. & Khan, Z. (2020). Forecasting area and production of guava in Pakistan: An econometric analysis. Sarhad Journal of Agriculture, 36(1), 272–281. http://dx.doi.org/10.17582/journal. sja/2020/36.1.272.281 Majid, R. & Mir, S. (2018). Advances in Statistical Forecasting Methods: An Overview. Economic Affairs, 63(4), 815–831. https://doi.org/10.30954/0424-2513.4.2018.5 Mehmood, S. & Ahmad, Z. (2013). Time series model to forecast area of mangoes from Pakistan: An application of univariate ARIMA model. Academy of Contemporary Research, 2(1), 10–15. Meyler, A., Kenny, G. & Quinn, T. (1998). Forecasting Irish inflation using ARIMA models [Technical Paper 3/RT/98]. Dublín: Central Bank of Ireland. Available: https://www.centralbank.ie/docs/default-source/publications/research-technical-papers/3rt98---forecasting-irish-inflation-using-arima-models-(kenny-meyler-and-quinn).pdf Phillips, P. & Perron, P. (1988). Testing for a unit root in time series regression. Biometricka, 75(2), 335–346. https://doi.org/10.2307/2336182 República de Colombia. MinAgricultura. (2021). Cadena productiva de la mora Dirección de Cadenas Agrícolas y Forestales. Marzo de 2021. Bogotá, D.C.: Minagricultura. Recuperado de https://sioc.minagricultura.gov.co/Mora/Documentos/2021-03-31%20Cifras%20Sectoriales.pdf Restrepo, M., Luna-Ramirez, J. & Castaño-Quintero, V. (2019). Evaluation of the shelf-life of blackberry pulp fortified with physiologically active compounds. Respuestas, 24(2), 16–26. Available: https://revistas.ufps.edu.co/index.php/respuestas/article/download/1827/2203?inline=1 Sánchez-López, E., Barreras-Serrano, A., Pérez-Linares, C., Figueroa-Saavedra, F. & Olivas-Valdez, J. (2013). Using an ARIMA model to forecast bovine milk production in Baja California, México. Tropical and Subtropical Agroecosystems, 16(3), 315–324. Available: https://www.revista.ccba.uady.mx/ojs/index. php/TSA/article/view/1290 Ullah, A., Khan, D. & Zheng, S. (2018). Forecasting of peach area and production wise econometric analysis. The Journal of Animal & Plant Sciences, 28(4), 1121–1127. Available: http://www.thejaps.org.pk/docs/v-28-04/23.pdf |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)Derechos de autor 2023 Susan Elsa Cancino, Giovanni Orlando Cancino Escalante, Daniel Francisco Cancino Rickettshttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cancino, SusanCancino Escalante, Giovanni OrlandoCancino, Daniel 2023-01-18T17:00:00Z2023-01-18T17:00:00Z2022-08-22Cancino, S. E., Cancino-Escalante, G. E. & CancinoRicketts, D. F. (2023). Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approach. Económicas CUC, 44(1), 69–82. DOI: https://doi.org/10.17981/ econcuc.44.1.2023.Econ.40120-3932https://hdl.handle.net/11323/978010.17981/econcuc.44.1.2023.Econ.42382-3860Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Blackberry production in Colombia contributes to the nation´s gross domestic profit, employment and farmers’ social well-being. It is considered of great economic importance as blackberry fruits are used as raw material for the agroindustry. In this manner, production instability affects farmers’ economic profitability; therefore, forecasting plays an important role in monitoring production as well as in farmers´ planting decision and resource allocation. Hence, the purpose of the study was to model and forecast blackberry production in Colombia using a Box-Jenkins ARIMA approach for the period 1992-2023. A quantitative, nonexperimental, correlational and descriptive research design was selected. The appropriateness of the model and its predictive capacity was assessed by verifying the different goodness-of-fit criteria. Results showed that the ARIMA (1,1,0) was the most suitable model as it captured the behavior of the actual time series. Based on the forecasted values it is expected a 5.47% increase in blackberry production for the period 2021-2023 which will consequently improve farmers´ income and thus contribute to the reduction in povertyLa producción de mora de castilla en Colombia contribuye al producto interno bruto, al empleo y al bienestar social de los agricultores del país. Es considerado de gran importancia económica una vez que los frutos de la mora son utilizados como materia prima para la agroindustria. De esta manera, la inestabilidad de la producción afecta la rentabilidad económica de los agricultores; por lo tanto, el pronóstico de la producción de mora posee un importante papel en la asignación de recursos y la toma de decisiones de los agricultores. Por lo tanto, el propósito del estudio fue modelar y pronosticar la producción de mora en Colombia utilizando un enfoque ARIMA de Box-Jenkins para el período 1992-2023. Se seleccionó una investigación tipo cuantitativa, no experimental, correlacional y descriptiva. Se evaluó la adecuación del modelo y su capacidad predictiva mediante la verificación de los diferentes criterios de bondad de ajuste. Los resultados mostraron que ARIMA (1,1,0) fue el modelo más adecuado una vez que capturó el comportamiento de la serie temporal actual. Con base en los valores pronosticados se espera un aumento de 5,47% en la producción de mora para el período 2021-2023 lo que mejorará los ingresos de los agricultores y contribuirá, así a la reducción de la pobreza en el campo.14 páginasapplication/pdfengCorporación Universidad de la CostaColombiahttps://revistascientificas.cuc.edu.co/economicascuc/article/view/4203Modeling and forecasting blackberry production in Colombia using a Box Jenkins ARIMA approachUn modelo Box Jenkins ARIMA para modelar y pronosticar la producción de mora de castilla en ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://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_970fb48d4fbd8a85ColombiaEconómicas CUCAfzal, M., Rehman, H. U. & Butt, A. R. (2002). Forecasting: a dilemma of modules (A Comparison of Theory Based and Theory Free Approaches). Pakistan Economic and Social Review, 40(1), 1–18. Available: https://www.jstor.org/stable/25825233Agronet. (s.f.). Blackberry statistical data [Database]. Available: http://www.agronet.gov.co/estadistica/Paginas/home.aspxAkın, M., Eyduran, S., Çelik, Ş., Aliyev, P., Ayko, S. & Eyduran, E. (2021). Modeling and forecasting cherry production in Turkey. The Journal of Animal and Plant Science, 31(3), 773–781. https://doi.org/10.36899/JAPS.2021.3.0267Arguello, R. & Valderrama-González, D. (2015). Sectoral and poverty impacts of agricultural policy adjustments in Colombia. Agricultural Economics, 46(2), 259–280. https://doi.org/10.1111/agec.12155Burhan, A. & Khalid, M. (2006). Forecasting Kinnow production in Pakistan: An econometric analysis. International Journal of Agriculture & Biology, 8(4), 455– 458. Available from https://www.fspublishers.org/published_papers/6916_..pdfBrandt, J. & Bessler, D. (1984). Forecasting with Vector Autoregressions versus a Univariate ARIMA Process: An empirical example with U.S. Hog Prices. North Central Journal of Agricultural Economics, 6(2), 29–36. https://doi. org/10.2307/1349248Brooks, C. (2019). Introductory econometrics for finance. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108524872Cancino, S., Cancino-Escalante, G. y Cancino-Ricketts, D. (2020). Modelo econométrico para el cultivo de mora de castilla (Rubus glaucus) en los municipios de Pamplona y Chitagá, Norte de Santander, Colombia. Aibi Revista de Investigación, Administración e Ingeniería, 8(1), 37–43. https://doi. org/10.15649/2346030X.752Cancino, S., Cancino, G. y Cancino, D. (2021). Análisis de regresión de los factores que afectan la rentabilidad económica de la producción de curuba. Dictamen Libre, (29), 1–13. https://doi.org/10.18041/2619-4244/dl.29.7861Cárdenas M, Echavarría J, Hernández G, Maiguashca A, Meisel A, Ocampo, J. y Zárate, J. (2018). Coyuntura del sector agropecuario colombiano. [Informe de la Junta Directiva al Congreso de la República]. Bogotá, D.C.: Banco de la República. Disponible en https://www.banrep.gov.co/es/recuadro-2-coyuntura-delsector-agropecuario-colombianoChen, C.-K. (2008). An integrated enrollment forecast model. AIR IR Applications, 15, 1–18. Available: https://eric.ed.gov/?id=ED504328Dickey, D. & Fuller, W. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348E-views (version 9.0). Econometric Views. [Software]. London: IHS. Available: www. eviews.comFranco G. y Giraldo, M. (1998). El cultivo de la mora. Mosquera: Corporación Colombiana de Investigación Agropecuaria-CORPOICA. Disponible en http://bibliotecadigital.agronet.gov.co/handle/11348/4039FAO. (s.f.). FAOSTAT. Crops and livestock products. [Database]. Available: https:// www.fao.org/faostat/en/#data/QCLGajbihe, S., Wankhade, R. & Mahalle, S. (2010). Forecasting chickpea production in India using ARIMA model. International Journal of Commerce and Business Management, 3(2), 212–215. Available from http://researchjournal.co.in/upload/ assignments/3_212-215.pdfGujarati, D. y Porter, D. (2010). Econometría. México D.F.: McGraw Hill.Hamjah, M. A. (2014). Forecasting Major Fruit Crops Productions in Bangladesh using Box-Jenkins ARIMA Model. Journal of Economics and Sustainable Development, 5(7), 96–107. Available from https://core.ac.uk/download/ pdf/234646336.pdfHenley D. (2012). The agrarian roots of industrial growth: rural development in South-East Asia and sub-Saharan Africa. Development Policy Review, 30(51), 25–47. https://doi.org/10.1111/j.1467-7679.2012.00564.xIqbal, M. A. (2020). Application of regression techniques with their advantages and disadvantages. Elektor Magazine, 4(1), 11–17. Available: https://www.elektormagazine.com/magazine-archive/2020Judge, G., Hill, R., Griffiths W., Lutkepohl, H. & Lee, T. (1991). An introduction to the theory and practice of econometrics. [2 ed.]. New York: WileyKhan, S. & Khan, U. (2020). ARIMA Models Using Economic Variables of Bangladesh. Asian Journal of Probability and Statistics, 10(2), 33–47. https://doi.org/10.9734/ajpas/2020/ v10i230243Khan, D., Ullah, A., Bibi, Z., Ullah, I., Zulfiqar, M. & Khan, Z. (2020). Forecasting area and production of guava in Pakistan: An econometric analysis. Sarhad Journal of Agriculture, 36(1), 272–281. http://dx.doi.org/10.17582/journal. sja/2020/36.1.272.281Majid, R. & Mir, S. (2018). Advances in Statistical Forecasting Methods: An Overview. Economic Affairs, 63(4), 815–831. https://doi.org/10.30954/0424-2513.4.2018.5Mehmood, S. & Ahmad, Z. (2013). Time series model to forecast area of mangoes from Pakistan: An application of univariate ARIMA model. 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Available: http://www.thejaps.org.pk/docs/v-28-04/23.pdf8269144Predictive capacityUnivariate analysisProductionData modelingPublicationORIGINALUn modelo Box Jenkins ARIMA para modelar y pronosticar la producción de mora de castilla en Colombia.pdfUn modelo Box Jenkins ARIMA para modelar y pronosticar la producción de mora de castilla en Colombia.pdfArtículoapplication/pdf730948https://repositorio.cuc.edu.co/bitstreams/7548c340-95e0-4d9e-a9e8-2a504d9ef31b/downloadfd2bcaec7c2d2a1ca7cbac3ce5ac63f8MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/a928dcf4-d817-42e9-92ad-d5c52d154cd4/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTUn modelo Box Jenkins ARIMA para modelar y pronosticar la producción de mora de castilla en Colombia.pdf.txtUn modelo Box Jenkins ARIMA para modelar y pronosticar la producción de mora de castilla en Colombia.pdf.txtExtracted texttext/plain34614https://repositorio.cuc.edu.co/bitstreams/fa1c8992-7070-4c3f-ab9f-f3ba614c1362/download061f860f8e147743937e78a39b0e4b50MD53THUMBNAILUn modelo Box Jenkins ARIMA para modelar y pronosticar la producción de mora de castilla en Colombia.pdf.jpgUn modelo Box Jenkins ARIMA para modelar y pronosticar la producción de mora de castilla en Colombia.pdf.jpgGenerated Thumbnailimage/jpeg14670https://repositorio.cuc.edu.co/bitstreams/b81048f9-a64a-4eb3-bf51-52059247ea2a/download39a427e889996c6aeb9ca03050ae5140MD5411323/9780oai:repositorio.cuc.edu.co:11323/97802024-09-17 12:44:27.809https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos de autor 2023 Susan Elsa Cancino, Giovanni Orlando Cancino Escalante, Daniel Francisco Cancino Rickettsopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ada en las Obras Colectivas.

b.	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.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
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).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	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).

b.	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.

c.	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.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	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.

ii.	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.

e.	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.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
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.

6. Limitación de responsabilidad.
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.

7. Término.

a.	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.

b.	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.

8. Varios.

a.	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.

b.	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.

c.	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.

d.	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.
 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