Normalized difference vegetation index for rice management in El Espinal, Colombia

Aerial images and the Normalized Difference Vegetation Index (NDVI) of the stage after panicle initiation were evaluated as tools that help large-scale rice monitoring and decision-making that favors crop profitability. NDVI was used to identify problems in the development and growth of FEDEARROZ-20...

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Autores:: González Betancourt, Mauricio
Mayorga Ruíz, Zaira Liceth

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2018

Institución:: Tecnológico de Antioquia

Repositorio:: Repositorio Tdea

Idioma:: eng

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dc.title.none.fl_str_mv	Normalized difference vegetation index for rice management in El Espinal, Colombia
dc.title.translated.none.fl_str_mv	Índice de vegetación de diferencia normalizada para el manejo del arroz en El Espinal, Colombia
title	Normalized difference vegetation index for rice management in El Espinal, Colombia
spellingShingle	Normalized difference vegetation index for rice management in El Espinal, Colombia Normalized difference vegetation index Indice normalizado diferencial de la vegetación Indice différentiel normalisé de végétation Rice Arroz Riz Unmanned aerial vehicles Vehículos aéreos no tripulados Veículos aéreos não tripulados Véhicule aérien sans pilote NDVI
title_short	Normalized difference vegetation index for rice management in El Espinal, Colombia
title_full	Normalized difference vegetation index for rice management in El Espinal, Colombia
title_fullStr	Normalized difference vegetation index for rice management in El Espinal, Colombia
title_full_unstemmed	Normalized difference vegetation index for rice management in El Espinal, Colombia
title_sort	Normalized difference vegetation index for rice management in El Espinal, Colombia
dc.creator.fl_str_mv	González Betancourt, Mauricio Mayorga Ruíz, Zaira Liceth
dc.contributor.author.none.fl_str_mv	González Betancourt, Mauricio Mayorga Ruíz, Zaira Liceth
dc.subject.agrovoc.none.fl_str_mv	Normalized difference vegetation index Indice normalizado diferencial de la vegetación Indice différentiel normalisé de végétation Rice Arroz Riz Unmanned aerial vehicles Vehículos aéreos no tripulados Veículos aéreos não tripulados Véhicule aérien sans pilote
topic	Normalized difference vegetation index Indice normalizado diferencial de la vegetación Indice différentiel normalisé de végétation Rice Arroz Riz Unmanned aerial vehicles Vehículos aéreos no tripulados Veículos aéreos não tripulados Véhicule aérien sans pilote NDVI
dc.subject.proposal.none.fl_str_mv	NDVI
description	Aerial images and the Normalized Difference Vegetation Index (NDVI) of the stage after panicle initiation were evaluated as tools that help large-scale rice monitoring and decision-making that favors crop profitability. NDVI was used to identify problems in the development and growth of FEDEARROZ-2000. FEDEARROZ-2000 is a variety of rice, which is resistant to the “hoja blanca” virus and direct “sogata” damage that affect fields in tropical America. The temporal dynamics of the NDVI for FEDEARROZ-2000 were estimated. An NDVI lower than 0.8 in the Stage of Rice Panicle Development (SRPD) was related to areas with levelling problems, differences in the vegetative stage, water stress, and spacing between plants. The NDVI for the SRPD had a significant positive correlation with yield, 1,000 grain weight and the number of panicles (Pearson's R≥0.86; probability value P ≤0.04). NDVI mapping at milky stage helped to identify production environments and to schedule the harvest areas. Keywords: NDVI; rice; unmanned aerial vehicle
publishDate	2018
dc.date.issued.none.fl_str_mv	2018
dc.date.accessioned.none.fl_str_mv	2023-05-01T16:25:25Z
dc.date.available.none.fl_str_mv	2023-05-01T16:25:25Z
dc.type.spa.fl_str_mv	Artículo de revista
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dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.citationendpage.spa.fl_str_mv	47
dc.relation.citationissue.spa.fl_str_mv	205
dc.relation.citationvolume.spa.fl_str_mv	85
dc.relation.ispartofjournal.spa.fl_str_mv	DYNA
dc.relation.references.spa.fl_str_mv	Ray, D.K., Mueller, N.D., West, P.C. and Foley, J.A., Yield trends are insufficient to double global crop production by 2050. PloS one, 8(6), e66428, 2013. DOI: 10.1371/journal.pone.0066428 Ray, D.K., Gerber, J.S., MacDonald, G.K., and West, P.C., Climate variation explains a third of global crop yield variability. Nature communications, 6, pp. 1-9, 2015. DOI: 10.1038/ncomms6989. [ Links ] FEDEARROZ and CIAT (2014). Adopción masiva de tecnología (AMTEC). Validación de modelos y parametrización de la variedad FEDEARROZ. [en línea]. 2000. [Consultado: Julio 25 de 2017]. Disponible en: Disponible en: https://es.slideshare.net/cgiarclimate/fe-39197270 [ Links ] Mapplecroft. Índice de vulnerabilidad y adaptación al cambio climático en la región de América Latina y el Caribe. Corporación Andina de Fomento (CAF), 2014. [ Links ] Graterol, E. y Torres, E.A., Mejorando la competitividad del arroz en América Latina mediante el cierre de brechas de rendimiento. FLAR, CIAT, CGIAR, [en línea]. 2013. [Consultado: Nov. 21th of 2017]. Disponible en: Disponible en: http://flar.org/wp-content/uploads/2015/06/Taller-GRiSP-Cierre-de-brechas-ESP2.pdf [ Links ] Chica, J., Tirado, O. and Barreto, J.M., Indicadores de competitividad del cultivo del arroz en Colombia y Estados Unidos. Revista de Ciencias Agrícolas, 33(2), pp. 16-31, 2016. [ Links ] Sanint, L.R., Nuevos retos y grandes oportunidades tecnológicas para los sistemas arroceros: producción, seguridad alimentaria y disminución de la pobreza en América Latina y el Caribe, en: Degiovanni, B., Víctor, M., Martínez, R., César, P. and Motta, O., Producción eco-eficiente del arroz en América Latina. CIAT, 2010. [ Links ] World Water Assessment Programme -WWAP. Informe de las Naciones Unidas sobre el desarrollo de los recursos hídricos en el mundo 2016, París, UNESCO, [en líena]. 2016. [Consultado en: Julio 25 de 2017]. Disponible en: Disponible en: http://unesdoc.unesco.org/images/0024/002441/244103s.pdf [ Links ] Bouman, B.A.M., Humphreys, E., Tuong, T.P. and Barker, R. Rice and water. Advances in Agronomy 92, pp. 187-237, 2006. DOI: 10.1016/S0065-2113(04)92004-4 [ Links ] Degiovanni, B., Víctor, M., Martínez, R., César, P. and Motta, O., Producción eco-eficiente del arroz en América Latina . CIAT, 2010. [ Links ] González, M. y Alonso, A.M., Tecnologías para ahorrar agua en el cultivo de arroz. Nova, 14(26), pp. 67-82, 2016. DOI: 10.22490/24629448.1757 [ Links ] Preciado-Pérez L.G., Época oportuna de cosecha y calibración de cosechadoras para el cultivo del arroz. Memorias del curso internacional sobre el manejo del cultivo de arroz. Ibagué, [en líena]. 2014. [Consultado: enero 21 de 2017]. Disponible en: Disponible en: http://flar.org/ii-curso-internacional/memorias-curso-internacional/ [ Links ] Preciado-Pérez L.G., Pérdidas al cosechar en el momento no oportuno. Boletín de la Federación Nacional de Arroceros. [en línea]. 243, pp. 1, 2011. [Consultado: enero 21 de 2017]. Disponible en: Disponible en: http://www.fedearroz.com.co/revistanew/correo_243.pdf [ Links ] López-Pérez, A., Martínez-Menes, M.R. and Fernández-Reynoso, D.S., Priorización de áreas de intervención mediante análisis morfométrico e índice de vegetación. Tecnología y Ciencias del Agua, 6(1), 121-137, 2015. [ Links ] Chen, R.K. and Yang, C.M., Determining the optimal timing for using LAI and NDVI to predict rice yield. J. Photogramm. Remote Sens, 10(3), pp. 239-254, 2005. [ Links ] Li, L., Zhang, Q. and Huang, D., A review of imaging techniques for plant phenotyping. Sensors, 14(11), pp. 20078-20111, 2014. DOI: 10.3390/s141120078 [ Links ] Naito, H., Ogawa, S., Valencia, M.O., Mohri, H., Urano, Y., Hosoi, F. and Omasa, K., Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras. ISPRS Journal of Photogrammetry and Remote Sensing, 125, pp. 50-62, 2017. DOI: 10.1016/j.isprsjprs.2017.01.010 [ Links ] Barrero, O., Rojas, D., Gonzalez, C. and Perdomo, S., Weed detection in rice fields using aerial images and neural networks. In: Signal Processing, images and artificial vision, XXI Symposium on IEEE, pp.1-4, 2016. DOI: 10.1109/STSIVA.2016.7743317 [ Links ] Pulver, E., Manejo estratégico y producción competitiva del arroz con riego en América Latina. En: DeGiovanni, V., Martínez, C.P. y Motta, F., eds. Producción ecoeficiente del arroz en América Latina. CIAT, Colombia, 2010. [ Links ] Romero, L.E., Lozano, I., Garavito, A., Carabali, S.J., Triana, M., Villareal, N. and Lorieux, M., Major QTLs control resistance to rice hoja blanca virus and its vector Tagosodes orizicolus. G3: Genes, Genomes, Genetics, 4(1), pp. 133-142, 2014. DOI: 10.1534/g3.113.009373 [ Links ] Morales, F.J. and Jennings, P.R., Rice hoja blanca: a complex plant-virus-vector pathosystem. Plant Sciences Reviews, 5(043), pp 1-16, 2010. DOI: 10.1079/PAVSNNR20105043 [ Links ] Castilla L.y Florez E., SIFA: Sistema de fertilización en el cultivo de arroz a través de la web. Arroz, 62 (509), pp. 4-15, 2014. [ Links ] Garcés, G. y Castilla, L., Uso del clorofilometro (indice de verdor) como estrategia en la fertilización nitrogenada en el cultivo. Arroz, 63(517), pp. 34 - 43, 2015. [ Links ] Lin, W., Zhang, F.C., Jing, Y.S., Jiang, X.D., Yang, S.B. and Han, X.M., Multi-temporal detection of rice phenological stages using canopy spectrum. Rice Science, 21(2), pp. 108-115, 2014. DOI: 10.1016/S1672-6308(13)60170-5 [ Links ] Degiovanni, V.M., Gómez, J.A. y Sierra, J.M., Análisis de crecimiento y etapas de desarrollo de tres variedades de arroz (Oryza sativa L.) en Montería, Córdoba. Temas Agrarios, 9(1), 2004, pp 21-29. [ Links ] Dobermann, A. and Fairhurst, T., Rice: Nutrient disorders and nutrient management (Vol. 1). Int. Rice Res. Inst. 2000. [ Links ] Chaudhary R.C., Nanda J.S. y Tran. D.V. Guía para identificar las limitaciones de campo en la producción de arroz. FAO, Rome, 2003. [ Links ] Yoshida, H., Bhattacharjee, D. and Cabuslay, G.S., Relationship between plant type and root growth in rice. Soil Science and Plant Nutrition, 28, pp. 473-482, 1982. DOI: 10.1080/00380768.1982.10432387 [ Links ] Nuruzzaman, M., Yamamoto, Y., Nitta, Y., Yoshida, T and Miyazaki, A., Varietal differences in tillering ability of fourteen japonica and indica rice varieties. Soil Science and Plant Nutrition , 46, pp. 381-391, 2000. DOI: 10.1080/00380768.2000.10408792 [ Links ] García, J., Andres, S., Martínez, M. y Joel, L., Método para identificación de cultivos de arroz (Oryza sativa L.) con base en imágenes de satélite. Agronomía Colombiana, [en línea]. 28(2), pp. 281-290, 2010. Disponible en: http://www.redalyc.org/articulo.oa?id=180315602018 [ Links ] Castilla A., Morales F., Ramirez O. y Mayorga Z., Manejo agronómico por ambiente rumbo a una agricultura de precisión en el cultivo de arroz. Arroz, 63(519), pp. 38-43, 2015. [ Links ] Barrios, P.C., Giraldo, D., Llanos, L., Obando, D., Espinoza, J. and Gourdji, S., Agro-climatic risk management for better agricultural decision making in Latin America. Conference: ASABE 1st Climate Change Symposium, Illinois, 2015. DOI: 10.13031/cc.20152122545 [ Links ] Beget, M.E. and Di Bella, C.M., Flooding: the effect of water depth on the spectral response of grass canopies. J Hydrol, 335, pp. 285-294, 2007. DOI: 10.1016/j.jhydrol.2006.11.018 [ Links ] Cock, J.H. and Yoshida, S., Accumulation of 14 C-labelled carbohydrate before flowering and its subsequent redistribution and respiration in the rice plant. Japanese Journal of Crop Science, 41(2), pp. 226-234, 1972. DOI: 10.1626/jcs.41.226 [ Links ] Verma, R., Katara, J.L., Samantaray, S., Patra, B.C., Sahu, R.K., Patnaik, S. and Mohapatra, T., A practical guide for successful hybrid seed production in rice-A Profitable Venture, Research Gate, pp. 1-21, 2016. DOI: 10.13140/RG.2.1.2333.5443 [ Links ] Galiano, S.G., Assessment of vegetation indexes from remote sensing: Theoretical basis. Options Méditerranéennes, 67, pp. 65-75, 2012. [ Links ] Zheng, H., Cheng, T., Yao, X., Deng, X., Tian, Y., Cao, W. and Zhu, Y., Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Research, 198, pp. 131-139, 2016. DOI: 10.1016/j.fcr.2016.08.027 [ Links ] Wang, H., Lin, H., Munroe, D.K., Zhang, X. and Liu, P., Reconstructing rice phenology curves with frequency-based analysis and multi-temporal NDVI in double-cropping area in Jiangsu, China. Frontiers of Earth Science, 10(2), pp. 292-302, 2016. DOI: 10.1007/s11707-016-0552-9 [ Links ] Berrio, V.A.M., Téllez, J.M. y Velasquez, D.F.A., Uso de drones para el análisis de imágenes multiespectrales en agricultura de precisión. @limentech, Ciencia y Tecnología Alimentaria, 13(1), pp. 28-40, 2015. [ Links ] Ajith, K., Geethalakshmi, V., Ragunath, K.P., Pazhanivelan, S. and Dheebakaran, G., Rice yield prediction using MODIS-NDVI (MOD13Q1) and land based observations. Int. J. Curr. Microbiol. App. Sci, 6(12), pp. 2277-2293, 2017. DOI: 10.20546/ijcmas.2017.612.263 [ Links ] FEDEARROZ (2017, Nov. 10). Precio promedio mensual arroz paddy verde en Colombia Pesos / Tonelada 2009 - 2017. [en línea]. [Consultado: Nov 01, 2017]. Disponible en: Disponible en: http://www.fedearroz.com.co/new/precios.php [ Links ] Li, Z., Zhu, Q. and Gold, C., Digital terrain modeling: principles and methodology. CRC Press. Boca Raton, 2005. [ Links ]
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spelling	González Betancourt, Mauricioff407eb0-e361-450b-a3eb-9b6bb67b3551Mayorga Ruíz, Zaira Licetha024f255-3a93-4138-982e-3f645e8d10a7CentroaméricaColombia2023-05-01T16:25:25Z2023-05-01T16:25:25Z20180012-7353https://dspace.tdea.edu.co/handle/tdea/28392346-2183Aerial images and the Normalized Difference Vegetation Index (NDVI) of the stage after panicle initiation were evaluated as tools that help large-scale rice monitoring and decision-making that favors crop profitability. NDVI was used to identify problems in the development and growth of FEDEARROZ-2000. FEDEARROZ-2000 is a variety of rice, which is resistant to the “hoja blanca” virus and direct “sogata” damage that affect fields in tropical America. The temporal dynamics of the NDVI for FEDEARROZ-2000 were estimated. An NDVI lower than 0.8 in the Stage of Rice Panicle Development (SRPD) was related to areas with levelling problems, differences in the vegetative stage, water stress, and spacing between plants. The NDVI for the SRPD had a significant positive correlation with yield, 1,000 grain weight and the number of panicles (Pearson's R≥0.86; probability value P ≤0.04). NDVI mapping at milky stage helped to identify production environments and to schedule the harvest areas. Keywords: NDVI; rice; unmanned aerial vehicleSe evaluaron las imágenes aéreas y el NDVI como herramientas para la supervisión del arroz a gran escala. El índice de vegetación de diferencia normalizada (NDVI) se utilizó para identificar problemas en el desarrollo de la variedad de arroz FEDEARROZ-2000, la cual es resistente al virus de la hoja blanca y al daño directo de la "sogata". Se estimó la dinámica temporal del NDVI para FEDEARROZ-2000. En la Etapa de Desarrollo de la Panícula del Arroz (EDPA), el NDVI inferior a 0,8 se relacionó con áreas con problemas de nivelación, estrés hídrico y diferencias en el estado de las plantas. El NDVI de la EDPA tuvo una correlación positiva significativa con las panículas/m2, el peso de los 1000 granos, y con el rendimiento (Coeficiente de correlación de Pearson R≥0.86; Probabilidad≤0.04). El NDVI en la etapa lechosa ayudó a identificar ambientes de producción y a programar áreas para la cosecha. Palabras clave: NDVI; arroz; vehículo aéreo no tripulado10 páginasapplication/pdfengUniversidad Nacional de ColombiaColombiahttps://creativecommons.org/licenses/by-nc-nd/4.0/Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2http://www.scielo.org.co/scielo.php?pid=S0012-73532018000200047&script=sci_arttextNormalized difference vegetation index for rice management in El Espinal, ColombiaÍndice de vegetación de diferencia normalizada para el manejo del arroz en El Espinal, 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_970fb48d4fbd8a854720585DYNARay, D.K., Mueller, N.D., West, P.C. and Foley, J.A., Yield trends are insufficient to double global crop production by 2050. PloS one, 8(6), e66428, 2013. DOI: 10.1371/journal.pone.0066428Ray, D.K., Gerber, J.S., MacDonald, G.K., and West, P.C., Climate variation explains a third of global crop yield variability. Nature communications, 6, pp. 1-9, 2015. DOI: 10.1038/ncomms6989. [ Links ]FEDEARROZ and CIAT (2014). Adopción masiva de tecnología (AMTEC). Validación de modelos y parametrización de la variedad FEDEARROZ. [en línea]. 2000. [Consultado: Julio 25 de 2017]. Disponible en: Disponible en: https://es.slideshare.net/cgiarclimate/fe-39197270 [ Links ]Mapplecroft. Índice de vulnerabilidad y adaptación al cambio climático en la región de América Latina y el Caribe. Corporación Andina de Fomento (CAF), 2014. [ Links ]Graterol, E. y Torres, E.A., Mejorando la competitividad del arroz en América Latina mediante el cierre de brechas de rendimiento. FLAR, CIAT, CGIAR, [en línea]. 2013. [Consultado: Nov. 21th of 2017]. Disponible en: Disponible en: http://flar.org/wp-content/uploads/2015/06/Taller-GRiSP-Cierre-de-brechas-ESP2.pdf [ Links ]Chica, J., Tirado, O. and Barreto, J.M., Indicadores de competitividad del cultivo del arroz en Colombia y Estados Unidos. Revista de Ciencias Agrícolas, 33(2), pp. 16-31, 2016. [ Links ]Sanint, L.R., Nuevos retos y grandes oportunidades tecnológicas para los sistemas arroceros: producción, seguridad alimentaria y disminución de la pobreza en América Latina y el Caribe, en: Degiovanni, B., Víctor, M., Martínez, R., César, P. and Motta, O., Producción eco-eficiente del arroz en América Latina. CIAT, 2010. [ Links ]World Water Assessment Programme -WWAP. Informe de las Naciones Unidas sobre el desarrollo de los recursos hídricos en el mundo 2016, París, UNESCO, [en líena]. 2016. [Consultado en: Julio 25 de 2017]. Disponible en: Disponible en: http://unesdoc.unesco.org/images/0024/002441/244103s.pdf [ Links ]Bouman, B.A.M., Humphreys, E., Tuong, T.P. and Barker, R. Rice and water. Advances in Agronomy 92, pp. 187-237, 2006. DOI: 10.1016/S0065-2113(04)92004-4 [ Links ]Degiovanni, B., Víctor, M., Martínez, R., César, P. and Motta, O., Producción eco-eficiente del arroz en América Latina . CIAT, 2010. [ Links ]González, M. y Alonso, A.M., Tecnologías para ahorrar agua en el cultivo de arroz. Nova, 14(26), pp. 67-82, 2016. DOI: 10.22490/24629448.1757 [ Links ]Preciado-Pérez L.G., Época oportuna de cosecha y calibración de cosechadoras para el cultivo del arroz. Memorias del curso internacional sobre el manejo del cultivo de arroz. Ibagué, [en líena]. 2014. [Consultado: enero 21 de 2017]. Disponible en: Disponible en: http://flar.org/ii-curso-internacional/memorias-curso-internacional/ [ Links ]Preciado-Pérez L.G., Pérdidas al cosechar en el momento no oportuno. Boletín de la Federación Nacional de Arroceros. [en línea]. 243, pp. 1, 2011. [Consultado: enero 21 de 2017]. 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 incorporada 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.


Normalized difference vegetation index for rice management in El Espinal, Colombia

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