Método de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite

dc.contributorDe Farías, Claudio Miceli
dc.contributorTalavera Portocarrero, Jesús Martín
dc.contributorCabrera Cruz, José Daniel
dc.contributorBayona Rodríguez, Cristihian Jarri
dc.contributorhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000069035
dc.contributorhttps://scholar.google.es/citations?hl=es#user=hses_w0AAAAJ
dc.contributorhttps://orcid.org/0000-0002-1815-5057
dc.contributorhttps://www.researchgate.net/profile/Jose_Cabrera_Cruz
dc.contributorGrupo de Investigación Pensamiento Sistémico - GPS
dc.creatorCulman Forero, María Alejandra
dc.date.accessioned2020-06-26T21:35:50Z
dc.date.accessioned2022-09-28T19:08:38Z
dc.date.available2020-06-26T21:35:50Z
dc.date.available2022-09-28T19:08:38Z
dc.date.created2020-06-26T21:35:50Z
dc.date.issued2018-03
dc.identifierhttp://hdl.handle.net/20.500.12749/3549
dc.identifierinstname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifierreponame:Repositorio Institucional UNAB
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3713045
dc.description.abstractDado que la agricultura es la actividad humana más dependiente de las condiciones climáticas, es vital que los agricultores tomen decisiones bien informadas. Desafortunadamente en Colombia, los agricultores generalmente tienden a decidir sobre una base de conocimiento limitada y esto somete sus sistemas productivos a la incertidumbre generada por la variabilidad y el cambio climático. Las causas de este problema se pueden resumir en tres situaciones: los agricultores no tienen acceso a información agrometeorológica y a previsiones agroclimáticas a nivel local; los agricultores no tienen la competencia para tomar decisiones basadas en la información; y los agricultores no tienen el recurso económico para respaldar sus decisiones. Este Trabajo de investigación se centra en atender la segunda causa, respecto a llevar la información agrometeorológica a información accionable para apoyar la toma de decisiones en la gestión del cultivo de palma de aceite. Suponiendo un escenario agrícola donde está desplegada una Red Inalámbrica de Sensores para adquirir datos locales y representativos en el campo, se formuló un método de Fusión de Datos que apoya la gestión del riego al inferir el estado del cultivo y decidir sobre la necesidad de riego. El método compromete dos niveles, un primer nivel de decisión que combina datos de la humedad del suelo, la temperatura ambiente y la humedad relativa para decidir sí regar o no regar el lote de cultivo mediante la técnica de Inferencia Dempster–Shafer; y un segundo nivel de evaluación a la decisión que combina datos de la evapotranspiración de cultivo, la precipitación y la decisión de riego en el lote de cultivo para calificar el desempeño de la decisión en el contexto de la plantación mediante la técnica de Lógica Difusa. El impacto del método en la gestión del cultivo de palma de aceite fue establecido por medio de la simulación de dos escenarios: lote de cultivo con riego gestionado por el primer nivel del método, y lote de cultivo sin riego. Los resultados indican un impacto potencial de incrementar en un 27% el rendimiento del cultivo, gracias a las decisiones de riego tomadas por el método.
dc.languagespa
dc.publisherUniversidad Autónoma de Bucaramanga UNAB
dc.publisherFacultad Ingeniería
dc.publisherMaestría en Telemática
dc.relationCulman Forero, María Alejandra (2018). Método de fusión de datos aplicado a redes inalámbricas para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite. Bucaramanga (Colombia) : Universidad Autónoma de Bucaramanga UNAB
dc.relationAbdelgawad, A., & Bayoumi, M. (2012). Data Fusion in WSN. In Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks (Volume 118, pp. 17–35). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4614-1350-9_2
dc.relationAbouzar, P., Michelson, D. G., & Hamdi, M. (2016). RSSI-Based Distributed Self-Localization for Wireless Sensor Networks Used in Precision Agriculture. IEEE Transactions on Wireless Communications, 15(10), 6638–6650. https://doi.org/10.1109/TWC.2016.2586844
dc.relationAbu Bakar, R., Darus, S. Z., Kulaseharan, S., & Jamaluddin, N. (2011). Effects of ten year application of empty fruit bunches in an oil palm plantation on soil chemical properties. Nutrient Cycling in Agroecosystems, 89(3), 341–349. https://doi.org/10.1007/s10705-010-9398-9
dc.relationACM. (2012). Computing Classification System, 2012 Revision. Retrieved from https://www.acm.org/publications/class-2012
dc.relationAcosta, A., & Munévar, F. (2003). Bud Rot in Oil Palm Plantations: Link to Soil Physical Properties and Nutrient Status. Better Crops International, 17, 22–25.
dc.relationAGRONET. (2014). Antecedentes y Objetivos. Retrieved February 9, 2015, from http://www.agronet.gov.co/agronetweb1/QuienesSomos/AntecedentesyObjetivos.aspx
dc.relationAGRONET. (2015a). Agroclima/Reporte Climatológico. Retrieved February 9, 2015, from http://agronet.gov.co/agronetweb1/Agroclima/ReporteClimatológico.aspx
dc.relationAGRONET. (2015b). Clima y Medio Ambiente. Retrieved February 9, 2015, fromhttp://www.agronet.gov.co/agronetweb1/Agroclima.aspx
dc.relationAhmed, K., & Gregory, M. (2014). Wireless Sensor Network Simulations Using Castalia and a Data-Centric Storage Case Study. In Simulation Technologies in Networking and Communications (pp. 459–494). Boca Raton: CRC Press. https://doi.org/doi:10.1201/b17650-22
dc.relationAiello, G., Giovino, I., Vallone, M., Catania, P., & Argento, A. (2017). A decision support system based on multisensor data fusion for sustainable greenhouse management. Journal of Cleaner Production. https://doi.org/https://doi.org/10.1016/j.jclepro.2017.02.197
dc.relationAkyildiz, I. F., & Kasimoglu, I. H. (2004). Wireless sensor and actor networks: Research challenges. Ad Hoc Networks, 2(4), 351–367. https://doi.org/10.1016/j.adhoc.2004.04.003
dc.relationAkyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–1014. https://doi.org/10.1109/MCOM.2002.1024422
dc.relationAkyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422. https://doi.org/10.1016/S1389-1286(01)00302-4
dc.relationAkyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422. https://doi.org/10.1016/S1389-1286(01)00302-4
dc.relationAkyildiz, I. F., & Vuran, M. C. (2010). Wireless Sensor Networks. (I. F. Akyildiz, Ed.). John Wiley & Sons. https://doi.org/10.1002/9780470515181
dc.relationAldana de la Torre, R., & Aldana de la Torre, J. (2011). Guía para el reconocimiento y manejo de insectos defoliadores y asociados a la pestalotiopsis. Bogotá. Retrieved from http://www.cenipalma.org/buenas-practicas-de-manejo
dc.relationAllen, R. G., Pereira, L. S., Raes, D., & Smith, M. (2006). ESTUDIO FAO RIEGO Y DRENAJE 56: Evapotranspiración del cultivo. Guías para la determinación de los requerimientos de agua de los cultivos. Roma: Food and Agriculture Organization of the United Nations (FAO). Retrieved from ftp://ftp.fao.org/docrep/fao/009/x0490s/x0490s.pdf
dc.relationAlvarado, A., Chinchilla, C., Bulgarelli, J., & Sterling, F. (1996). Agronomic factors associated to common spear rot/crown disease in oil palm. ASD Oil Palm Papers, (15), 8–28.
dc.relationAnisi, M. H., Abdul-Salaam, G., & Abdullah, A. H. (2015). A survey of wireless sensor network approaches and their energy consumption for monitoring farm fields in precision agriculture. Precision Agriculture, 16(2), 216–238. https://doi.org/10.1007/s11119-014-9371-8
dc.relationAPSIM Initiative. (n.d.-a). APSIM: about us. Retrieved January 20, 2018, from http://www.apsim.info/AboutUs.aspx
dc.relationAPSIM Initiative. (n.d.-b). Creating an APSIM met file using Excel. Retrieved January 18, 2018, from https://www.apsim.info/Documentation/CommonTasksinAPSIM/CreatinganAPSIMmetfileusingExcel.aspx
dc.relationAPSIM Initiative. (n.d.-c). What is the Operations Schedule Module? Retrieved January 18, 2018, from https://www.apsim.info/Documentation/Model,CropandSoil/InfrastuctureandManagementDocumentation/OPERATIONS.aspx
dc.relationAquino, G., Pirmez, L., Farias, C. M. de, Delicato, F. C., & Pires, P. F. (2016). Hephaestus: A multisensor data fusion algorithm for multiple applications on wireless sensor networks. In 2016 19th International Conference on Information Fusion (FUSION) (pp. 59–66).
dc.relationArango, M., Ospina, C., Sierra, J., & Martínez, G. (2011). Myndus crudus : vector del agente causante de la marchitez letal en palma de aceite en Colombia. Palmas, 32(2), 13–25.
dc.relationArias, N. A., & Motta, D. (2006). Resultados de la Transferencia de Tecnología basada en el modelo de acompañamiento de Cenipalma. Palmas, 27(2), 11–21.
dc.relationASOHOFRUCOL. (2014). Frutisitio. Retrieved June 22, 2017, from http://www.frutisitio.com
dc.relationAtzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
dc.relationBabuška, R. (1998). Fuzzy Modeling. In Fuzzy Modeling for Control (pp. 9–48). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-011-4868-9_2
dc.relationBakoumé, C., Shahbudin, N., Shahrakbah, Y., Cheah, S. S., & Nazeeb, M. A. T. (2013). Improved Method for Estimating Soil Moisture Deficit in Oil Palm (Elaeis guineensis Jacq.) Areas With Limited Climatic Data. Journal of Agricultural Science, 5(8). https://doi.org/10.5539/jas.v5n8p57
dc.relationBarcelos, E., Rios, S. de A., Cunha, R. N. V, Lopes, R., Motoike, S. Y., Babiychuk, E., … Kushnir, S. (2015). Oil palm natural diversity and the potential for yield improvement. Frontiers in Plant Science, 6, 190. https://doi.org/10.3389/fpls.2015.00190
dc.relationBarrera, O., Zabala, A., Molina, A., Rincón, V., & Torres, J. (2016). Extensión de Monitoreo Agroclimático–XMAC. Medellín. Retrieved from http://web.fedepalma.org/bigdata/reunion2016/poster/25poster.pdf
dc.relationBayes, M., & Price, M. (1763). An Essay towards Solving a Problem in the Doctrine of Chances. By the Late Rev. Mr. Bayes, F. R. S. Communicated by Mr. Price, in a Letter to John Canton, A. M. F. R. S. Philosophical Transactions (1683-1775), 53, 370–418. Retrieved from http://www.jstor.org/stable/105741
dc.relationBayona-Rodríguez, C. J., & Romero, H. M. (2016). Estimation of transpiration in oil palm ( Elaeis guineensis Jacq.) with the heat ratio method. Agronomía Colombiana, 34(2), 172–178. https://doi.org/10.15446/agron.colomb.v34n2.55649
dc.relationBayona, C. J. (2016a). Estación Biomet 1.
dc.relationBayona, C. J. (2016b). Estación Biomet 2.
dc.relationBayona Rodríguez, C. J., & Romero, M. (2016). Impacts of the dry season on the gas exchange of oil palm ( Elaeis guineensis ) and interspecific hybrid ( Elaeis oleifera x Elaeis guineensis ) progenies under field conditions in eastern Colombia. Agronomía Colombiana, 34(3), 329–335. https://doi.org/10.15446/agron.colomb.v34n3.55565
dc.relationBeltrán, J., Pulver, E., Guerrero, J., & Mosquera, M. (2015). Cerrando brechas de productividad con la estrategia de transferencia de tecnología productor a productor. Palmas, 36(2), 39–53. Retrieved from http://publicaciones.fedepalma.org/index.php/palmas/article/viewFile/11076/pdf_27
dc.relationBenítez, É., & García, C. (2014). The history of research on oil palm bud rot (Elaeis guineensis Jacq.) in Colombia. Agronomía Colombiana; Vol. 32, Núm. 3 (2014)DO - 10.15446/agron.colomb.v32n3.46240. Retrieved from https://revistas.unal.edu.co/index.php/agrocol/article/view/46240 Bessou, C., Verwilghen, A., Beaudoin-Ollivier, L., Marichal, R., Ollivier, J
dc.relationBessou, C., Verwilghen, A., Beaudoin-Ollivier, L., Marichal, R., Ollivier, J., Baron, V., … Caliman, J.-P. (2017). Agroecological practices in oil palm plantations: examples from the field. OCL, 24(3), D305. https://doi.org/10.1051/ocl/2017024
dc.relationBhuyan, B. (2010). Quality of Service (QoS) Provisions in Wireless Sensor Networks and Related Challenges. Wireless Sensor Network, 2(11), 861–868. https://doi.org/10.4236/wsn.2010.211104
dc.relationBID, & CEPAL. (2012). Valoración de daños y pérdidas. Ola invernal en Colombia 2010-2011. Bogotá: Misión BID - Cepal. Retrieved from http://www.cepal.org/publicaciones/xml/0/47330/OlainvernalColombia2010-2011.pdf
dc.relationBijarbooneh, F. H., Du, W., Ngai, E. C. H., Fu, X., & Liu, J. (2016). Cloud-Assisted Data Fusion and Sensor Selection for Internet of Things. IEEE Internet of Things Journal, 3(3), 257–268. https://doi.org/10.1109/JIOT.2015.2502182
dc.relationBilskie, J. (2001). Soil Water Status: content and potential. Retrieved from https://s.campbellsci.com/documents/de/technical-papers/soilh20c.pdf
dc.relationBlaak, G. (1997). Crop forecasting in oil palm, Elaeis guineensis. In Proceedings of the seminar Villefranche-sur-Mer 1994 (pp. 243–246). Office for Official Publications of the European Communities.
dc.relationBlundo Canto, G., Giraldo, D., Gartner, C., Alvarez-Toro, P., & Perez, L. (2016). Mapeo de Actores y Necesidades de Información Agroclimática en los Cultivos de Maíz y Frijol en sitios piloto -Colombia. Documento de Trabajo CCAFS No. 88. Cali.
dc.relationBogena, H. R., Herbst, M., Huisman, J. A., Rosenbaum, U., Weuthen, A., & Vereecken, H. (2010). Potential of Wireless Sensor Networks for Measuring Soil Water Content Variability. Vadose Zone Journal, 9, 1002–1013. https://doi.org/10.2136/vzj2009.0173
dc.relationBogena, H. R., Huisman, J. A., Baatz, R., Hendricks Franssen, H.-J., & Vereecken, H. (2013). Accuracy of the cosmic-ray soil water content probe in humid forest ecosystems: The worst case scenario. Water Resources Research, 49(9), 5778–5791. https://doi.org/10.1002/wrcr.20463
dc.relationBogena, H. R., Huisman, J. A., Meier, H., Rosenbaum, U., & Weuthen, A. (2009). Hybrid Wireless Underground Sensor Networks: Quantification of Signal Attenuation in Soil. Vadose Zone Journal, 8, 755–761. https://doi.org/10.2136/vzj2008.0138
dc.relationBogena, H. R., Huisman, J. A., Oberdörster, C., & Vereecken, H. (2007). Evaluation of a low-cost soil water content sensor for wireless network applications. Journal of Hydrology, 344(1), 32–42. https://doi.org/http://dx.doi.org/10.1016/j.jhydrol.2007.06.032
dc.relationBolourchi, P., & Uysal, S. (2013). Forest Fire Detection in Wireless Sensor Network Using Fuzzy Logic. In 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks (pp. 83–87). IEEE. https://doi.org/10.1109/CICSYN.2013.32
dc.relationBorgia, E. (2014). The Internet of Things vision: Key features, applications and open issues. Computer Communications, 54, 1–31. https://doi.org/10.1016/j.comcom.2014.09.008
dc.relationBos, M. G., Kselik, R. A. L., Allen, R. G., & Molden, D. J. (2009). Evapotranspiration. In Water Requirements for Irrigation and the Environment (pp. 13–80). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-1-4020-8948-0_2 Boshell, J. F. (2012). GES
dc.relationBoshell, J. F. (2012). GESTIÓN DE INFORMACIÓN AGROCLIMÁTICA EN COLOMBIA. Bo. Retrieved from http://www.cambioclimaticoandes.info/
dc.relationBoström, H., Andler, S. F., Brohede, M., Johansson, R., Karlsson, E., Laere, J. Van, … Ziemke, T. (2007). On the definition of information fusion as a field of research.
dc.relationBoulis, A. (2011). Castalia: A simulator for Wireless Sensor Networks and Body Area Networks. Version 3.2 - User’s Manual.
dc.relationBoulis, A., Ganeriwal, S., & Srivastava, M. B. (2003). Aggregation in sensor networks: an energy-accuracy trade-off. In Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003. (pp. 128–138). https://doi.org/10.1109/SNPA.2003.1203363
dc.relationBouma, J. (1997). Precision agriculture: introduction to the spatial and temporal variability of environmental quality. Ciba Foundation Symposium, 210, 5–13. Retrieved from http://europepmc.org/abstract/MED/9573467
dc.relationBranca, G., McCarthy, N., Lipper, L., & Jolejole, C. (2011). Climate-smart agriculture: a synthesis of empirical evidence of food security and mitigation benefits from improved cropland management. Mitigation of Climate Change in Agriculture Series (FAO). Food and Agriculture Organization of the United Nations (FAO). Retrieved from http://www.fao.org/docrep/015/i2574e/i2574e00.pdf
dc.relationBrisco, B., Brown, R. J., Hirose, T., McNairn, H., & Staenz, K. (1998). Precision Agriculture and the Role of Remote Sensing: A Review. Canadian Journal of Remote Sensing, 24(3), 315–327. https://doi.org/10.1080/07038992.1998.10855254
dc.relationBrown, H. E., Huth, N. I., Holzworth, D. P., Teixeira, E. I., Zyskowski, R. F., Hargreaves, J. N. G., & Moot, D. J. (2014). Plant Modelling Framework: Software for building and running crop models on the APSIM platform. Environmental Modelling & Software, 62, 385–398. https://doi.org/https://doi.org/10.1016/j.envsoft.2014.09.005
dc.relationBustillo, A. E. (2014). Manejo de insectos-plaga de la palma de aceite con énfasis en el control biológico y su relación con el cambio climático. Palmas, 35(4), 66–77.
dc.relationBustillo, A. E., & Arango, C. M. (2016). Las mejores prácticas para detener el avance de la Marchitez letal (ML) en plantaciones de palma de aceite en Colombia. Palmas, 37(4), 75–90.
dc.relationCadena, M. C., Devis-Morales, A., Pabón, J. D., Málikov, I., Reyna-Moreno, J. A., & Ortiz, J. R. (2006). Relationship between the 1997/98 El Niño and 1999/2001 La Niña events and oil palm tree production in Tumaco, Southwestern Colombia. Advances in Geosciences, 6, 195–199. https://doi.org/10.5194/adgeo-6-195-2006
dc.relationCaliman, J. P., Budi, M., & Salétes, S. (2001). Dynamics of nutrient release from empty fruit bunches in field conditions and soil characteristics changes. In Proceedings of the 2001 PIPOC International Palm Oim Congress, MPOB (pp. 550–556). Bangi.
dc.relationCaliman, J. P., Dubos, B., Tailliez, B., Robin, P., Bonneau, X., & Barros, I. de. (2004). Manejo de nutrición mineral en palma de aceite: situación actual y perspectivas. Palmas, 25(Especial), 42–60.
dc.relationCalveche, H. (1995). Manejo integrado de plagas de palma de aceite. Palmas, 16(Especial), 255–264.Retrieved from https://s.campbellsci.com/documents/us/product-brochures/b_cnr4.pdf
dc.relationCampbell Scientifc Inc. (2017). Brochure: CNR4 Kipp & Zonen’s Net Radiometer.
dc.relationCano, C. G., Esguerra, M. del P., García, N., Rueda, J. L., & Velasco, A. M. (2014). Inclusión financiera en Colombia. Bogotá. Retrieved from http://www.banrep.gov.co/sites/default/files/eventos/archivos/sem_357.pdf
dc.relationCao, X., Chen, J., Zhang, Y., & Sun, Y. (2008). Development of an integrated wireless sensor network micro-environmental monitoring system. ISA Transactions, 47(3), 247–255. https://doi.org/10.1016/j.isatra.2008.02.001
dc.relationCarr, M. K. V. (2011). THE WATER RELATIONS AND IRRIGATION REQUIREMENTS OF OIL PALM (ELAEIS GUINEENSIS): A REVIEW. Experimental Agriculture, 47(4), 629–652. https://doi.org/10.1017/S0014479711000494
dc.relationCastanedo, F. (2013). A Review of Data Fusion Techniques. The Scientific World Journal, 2013, 19. https://doi.org/10.1155/2013/704504
dc.relationCEA-IoT. (2016a). Líneas de trabajo CEA-IoT. Retrieved May 18, 2017, from http://www.cea-iot.org/lineas-de-trabajo/ CEA-IoT. (2016b). Quiénes somos CEA-IoT. Retrieved
dc.relationCEA-IoT. (2016b). Quiénes somos CEA-IoT. Retrieved May 18, 2017, from http://www.cea-iot.org/que-es/
dc.relationCENIPALMA. (2010). ¿QUIÉNES SOMOS? Retrieved February 7, 2015, from http://www.cenipalma.org/quienes-somos-cenipalma
dc.relationCENIPALMA. (2011). Buenas Prácticas de Manejo. Retrieved October 28, 2017, from http://www.cenipalma.org/buenas-practicas-de-manejo
dc.relationCENIPALMA. (2012). Guía de usuario del SMAC-Palma. Bogotá: Centro de Investigación en Palma de Aceite (Cenipalma), Federación Nacional de Cultivadores de Palma de Aceite (Fedepalma).
dc.relationCENIPALMA. (2014). Catálogo de estaciones.
dc.relationCENIPALMA. (2016). GeoPalma Portal: quiénes somos. Retrieved November 1, 2017, from http://geoportal.cenipalma.org/Quienes-Somos
dc.relationCENIPALMA. (2017a). Geopalma > XMAC > Boletines Agroclimáticos. Retrieved June 7, 2017, from http://geoportal.cenipalma.org/boletinesxmac
dc.relationCENIPALMA. (2017b). Informe de Labores CENIPALMA 2016. Retrieved from http://www.cenipalma.org/informes-de-gestion-cenipalma
dc.relationChaczko, Z., Ahmad, F., & Mahadevarr, V. (2005). Wireless sensors in network based collaborative environments. In 2005 6th International Conference on Information Technology Based Higher Education and Training (p. F3A/7-F3A13). https://doi.org/10.1109/ITHET.2005.1560284
dc.relationChang, C.-L., Huang, Y.-M., & Hong, G.-F. (2015). Using a Novel Wireless-Networked Decentralized Control Scheme under Unpredictable Environmental Conditions. Sensors (Basel, Switzerland), 15(11), 28690–28716. https://doi.org/10.3390/s151128690
dc.relationChaparro, F., & Cock, J. H. (2015). Estrategias para fomentar la innovación en el sector agropecuario como locomotora del desarrollo rural en Colombia. In Misión de Ciencia, Educación y Desarrollo -- Balance 20 años después (pp. 121–131). Bogotá: Instituto de Estudios del Ministerio Público (IEMP); Asociación Colombiana para el Avance de la Ciencia (ACAC).
dc.relationChen, Y., Shu, J., Zhang, S., Liu, L., & Sun, L. (2009). Data Fusion in Wireless Sensor Networks. 2009 Second International Symposium on Electronic Commerce and Security, 2, 504–509. https://doi.org/10.1109/ISECS.2009.170
dc.relationChinchilla, C., Alvarado, A., Albertazzi, H., & Torres, R. (2007). Tolerancia y resistencia a las pudriciones del cogollo en fuentes de diferente origen de Elaeis guineensis. Palmas, 28(Especial), 273–284.
dc.relationChoo, Y. M., Muhamad, H., Hashim, Z., Subramaniam, V., Puah, C. W., & Tan, Y. (2011). Determination of GHG contributions by subsystems in the oil palm supply chain using the LCA approach. The International Journal of Life Cycle Assessment, 16(7), 669–681. https://doi.org/10.1007/s11367-011-0303-9
dc.relationCIAT. (2011). Hoja Informativa No. 11: Agricultura Específica por Sitio Compartiendo Experiencias. Retrieved from http://ciat-library.ciat.cgiar.org:8080/jspui/bitstream/123456789/5276/1/hoja_informativa11_aesce.pdf
dc.relationCIAT, CCAFS, & MADR. (2016). Boletín Nacional Agroclimático - Diciembre 2016. Retrieved from http://www.ideam.gov.co/documents/21021/552413/Boletín+Agroclimático+No.+24+-+Diciembre.pdf/76c44a60-18c2-4c4d-bbb1-2a25b496ef84?version=1.0
dc.relationCIAT, CCAFS, & MADR. (2017a). Boletín Nacional Agroclimático - Abril 2017. Retrieved from http://www.ideam.gov.co/documents/21021/4748000/Boletin+Agroclimatico+No.+28+-+Abril.pdf/30ba182d-252d-48ab-af62-480c87e72cb3?version=1.0
dc.relationCIAT, CCAFS, & MADR. (2017b). Boletín Nacional Agroclimático - Marzo 2017. Retrieved from http://www.ideam.gov.co/documents/21021/4748000/Boletín+Agroclimático+No.+27+-+Marzo.pdf/260eab9c-7e33-43bf-a5ea-c1ea695bb3a3?version=1.0
dc.relationCIAT, CCAFS, & MADR. (2017c). Boletín Nacional Agroclimático - Mayo 2017. Retrieved from http://www.ideam.gov.co/documents/21021/4748000/Boletin+Agroclimatico+No.29+-+Mayo.pdf/860a4d07-2cd2-491e-9266-0cd9b4b861c5?version=1.2
dc.relationCoates, R. W., Delwiche, M. J., Broad, A., & Holler, M. (2013). Wireless sensor network with irrigation valve control. Computers and Electronics in Agriculture, 96, 13–22. https://doi.org/10.1016/j.compag.2013.04.013
dc.relationCock, J., Kam, S. P., Cook, S., Donough, C., Lim, Y. L., Jines-Leon, A., … Oberhür, T. (2016). Learning from commercial crop performance: Oil palm yield response to management under well-defined growing conditions. Agricultural Systems, 149, 99–111. https://doi.org/10.1016/j.agsy.2016.09.002
dc.relationCock, J., Oberthür, T., Isaacs, C., Läderach, P. R., Palma, A., Carbonell, J., … Anderson, E. (2011). Crop management based on field observations: Case studies in sugarcane and coffee. Agricultural Systems, 104(9), 755–769. https://doi.org/10.1016/J.AGSY.2011.07.001
dc.relationColciencias. (2016). Tipología de Proyectos Calificados como de Carácter Científico, Tecnológico e Innovación. Versión 4.
dc.relationColciencias. (2017). Plataforma SCIENTI - Colombia: Servicios de consulta. Retrieved October 21, 2017, from http://scienti.colciencias.gov.co:8083/ciencia-war/jsp/enRecurso/IndexRecursoHumano.jsp
dc.relationColesanti, U., & Santini, S. (2012). ctp-castalia. Retrieved November 17, 2017, from https://code.google.com/archive/p/ctp-castalia/
dc.relationCombley, R. (2011). Cambridge Business English Dictionary. New York: Cambridge University Press.
dc.relationComte, I., Colin, F., Grünberger, O., Follain, S., Whalen, J. K., & Caliman, J.-P. (2013). Landscape-scale assessment of soil response to long-term organic and mineral fertilizer application in an industrial oil palm plantation, Indonesia. Agriculture, Ecosystems & Environment, 169(Supplement C), 58–68. https://doi.org/https://doi.org/10.1016/j.agee.2013.02.010
dc.relationComte, I., Colin, F., Whalen, J. K., Grünberger, O., & Caliman, J.-P. (2012). Chapter three - Agricultural Practices in Oil Palm Plantations and Their Impact on Hydrological Changes, Nutrient Fluxes and Water Quality in Indonesia: A Review. In D. L. Sparks (Ed.), Advances in Agronomy (Vol. 116, pp. 71–124). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-394277-7.00003-8
dc.relationCorley, R. H. V. (1998). Productividad de la palma de aceite: Aspectos fisiológicos. Palmas, 19(Especial), 162–168. Retrieved from http://publicaciones.fedepalma.org/index.php/palmas/article/view/660/660
dc.relationCorley, R. H. V., & Tinker, P. B. (2016). The Oil Palm (5th ed.). John Wiley & Sons. https://doi.org/10.1002/9781118953297
dc.relationCorley, R. H. V., & Tinker, P. B. H. (2003). The Oil Palm (4th ed.). Blackwell Science Ltd. https://doi.org/10.1002/9780470750971
dc.relationCorley, R., & Tinker, P. (2003). The Oil Palm.
dc.relationCORPOICA. (2013). Modelos de Adaptación y Prevención Agroclimática – MAPA. Retrieved June 22, 2017, from http://www.corpoica.org.co/site-mapa/
dc.relationCORPOICA. (2016). SE-MAPA: Sistema de apoyo a la toma de decisión agroclimáticamente inteligente. Retrieved June 22, 2017, from http://www.corpoica.org.co/site-mapa/sistexp/
dc.relationCSRD. (2016). Alianza sobre Servicios Climáticos para el Desarrollo Resiliente. Retrieved from http://www.cs4rd.org/assets/documents/CSRD Brochure_Spanish.pdf
dc.relationCuller, D. E., & Hong, W. (2004). Introduction to Wireless Sensor Networks. Commun. ACM, 47(6), 30–33. https://doi.org/10.1145/990680.990703
dc.relationCulman, M., Portocarrero, J. M. T., Guerrero, C. D., Bayona, C., Torres, J. L., & Farias, C. M. de. (2017). PalmNET: An open-source wireless sensor network for oil palm plantations. In 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) (pp. 783–788). Calabria, Italy: IEEE. https://doi.org/10.1109/ICNSC.2017.8000190
dc.relationCYTED. (2014). Detalles de la Red 514RT0486: APLICACIONES PARA COMUNICACIÓN Y CONTROL DE REDES DE RIESGO SOBRE REDES Y SISTEMAS DE COMUNICACIÓN INALÁMBRICOS: RED TEMÁTICA RIEGONETS PARA LA APROPIACIÓN Y USO DE TIC EN EL SECTOR AGRÍCOLA (RIEGONETS). Retrieved June 25, 2017, from http://www.cyted.org/?q=es/detalle_proyecto&un=884
dc.relationDANE. (2015a). 3er Censo Nacional Agropecuario 2014: Caracterización de los productores residentes en el área rural dispersa censada. Retrieved from http://www.dane.gov.co/files/CensoAgropecuario/entrega-definitiva/Boletin-2-Productores-residentes/2-Boletin.pdf
dc.relationDANE. (2015b). 3er Censo Nacional Agropecuario 2014: Inventario agropecuario en las Unidades de Producción Agropecuaria (UPA). Retrieved from http://www.dane.gov.co/files/CensoAgropecuario/entrega-definitiva/Boletin-9-cultivos/9-Boletin.pdf
dc.relationDANE. (2015c). 3er Censo Nacional Agropecuario 2014: Las Unidades de Producción Agropecuaria (UPA), infraestructura, asistencia técnica y financiamiento. Retrieved from https://www.dane.gov.co/files/CensoAgropecuario/entrega-definitiva/Boletin-6-Infraestructura/6-Boletin.pdf
dc.relationDANE. (2015d). 3er Censo Nacional Agropecuario 2014: Uso, cobertura y tenencia del suelo. Retrieved from http://www.dane.gov.co/files/CensoAgropecuario/entrega-definitiva/Boletin-1-Uso-del-suelo/1-Boletin.pdf
dc.relationDANE. (2015e). Principales variables cadena Oleaginosas, Aceites y Grasas (2002-2014). Retrieved from https://colaboracion.dnp.gov.co/CDT/Desarrollo Empresarial/Oleaginosas, aceites, grasas.zip
dc.relationDANE. (2016). Producto Interno Bruto por Ramas de Actividad Económica. A precios Constantes - Series Desestacionalizadas - IV Trimestre de 2015. Retrieved from https://www.dane.gov.co/files/investigaciones/boletines/pib/bol_PIB_IVtrim15_oferta_demanda.pdf
dc.relationDANE. (2017a). Anexos Estadisticos: Boletin Comercio Exterior Enero-Diciembre 2016. Retrieved from http://www.dian.gov.co/dian/14cifrasgestion.nsf/e7f1561e16ab32b105256f0e00741478/a02b47038628e5610525733e0059549a?OpenDocument
dc.relationDANE. (2017b). Boletin Comercio Exterior Enero-Diciembre 2016. Retrieved from http://www.dian.gov.co/descargas/cifrasyg/EEconomicos/BoletinesComex/2016/BOLETIN_DE_COMERCIO_EXTERIOR_Enero_Diciembre_2015_2016.pdf
dc.relationDasarathy, B. V. (1997). Sensor fusion potential exploitation-innovativearchitectures and illustrative applications. Proceedings of the IEEE, 85(1), 24–38. https://doi.org/10.1109/5.554206
dc.relationDBpedia. (n.d.). DBpedia: agricultura de precisión. Retrieved June 26, 2016, from http://dbpedia.org/page/Precision_agriculture
dc.relationDDRS, FINAGRO, & Misión para la Transformación del Campo. (2014). MISIÓN PARA LA TRANSFORMACIÓN DEL CAMPO. SISTEMA NACIONAL DE CRÉDITO AGROPECUARIO: Propuesta de reforma. Retrieved from https://colaboracion.dnp.gov.co/CDT/Agriculturapecuarioforestal y pesca/Sistema Crédito Agropecuario.pdf
dc.relationDelerce, S., Dorado, H., Grillon, A., Rebolledo, M. C., Prager, S. D., Patiño, V. H., … Jiménez, D. (2016). Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches. PLOS ONE, 11(8), 1–25. https://doi.org/10.1371/journal.pone.0161620
dc.relationDelerce, S., Dorado, H., Grillon, A., Rebolledo, M. C., Prager, S. D., Patiño, V. H., … Jiménez, D. (2016). Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches. PLOS ONE, 11(8), 1–25. https://doi.org/10.1371/journal.pone.0161620
dc.relationDempster, A. P. (2008). The Dempster–Shafer calculus for statisticians. International Journal of Approximate Reasoning, 48(2), 365–377. https://doi.org/http://dx.doi.org/10.1016/j.ijar.2007.03.004
dc.relationDempster, A. P., & Kong, A. (1988). Uncertain evidence and artificial analysis. Journal of Statistical Planning and Inference, 20(3), 355–368. https://doi.org/http://dx.doi.org/10.1016/0378-3758(88)90097-3
dc.relationDevadas, R., Jones, S. D., Fitzgerald, G. J., McCauley, I., Matthews, B. A., Perry, E. M., … Kouzani, A. Z. (2010). Wireless sensor networks for in-situ image validation for water and nutrient management. In ISPRS 2010: Proceedings of ISPRS Technical Commission VII Symposium (pp. 187–192). Institute of Photogrammetry and Remote Sensing, Vienna University of Technology.
dc.relationDitschar, B., Jaramillo, R., & Fairhurst, T. H. (2012). La Plama de Aceite en América Central y América del Sur. In T. H. Fairhurst & R. Härdter (Eds.), Plama de Aceite: manejo para Rendimientos Altos y Sostenibles (pp. 13–32). PPIC-PPI-IPI.
dc.relationDNP. (2004). Oleaginosas, aceites y grasas. In Cadenas Productivas: Estructura, comercio internacional y protección (pp. 59–79). Revista Virtual Pro, Diciembre 2010, Grasas y aceites comestibles vegetales. Retrieved from http://www.revistavirtualpro.com/biblioteca/perfil-sectorial-oleaginosas-aceites-y-grasas
dc.relationdo Amaral Teles, D. A., Braga, M. F., Antoniassi, R., Junqueira, N. T. V., Peixoto, J. R., & Malaquias, J. V. (2016). Yield Analysis of Oil Palm Cultivated Under Irrigation in the Brazilian Savanna. Journal of the American Oil Chemists’ Society, 93(2), 193–199. https://doi.org/10.1007/s11746-015-2765-6
dc.relationDong, J., Zhuang, D., Huang, Y., & Fu, J. (2009). Advances in Multi-Sensor Data Fusion: Algorithms and Applications. Sensors, 9(10). https://doi.org/10.3390/s91007771
dc.relationDonough, C. R., Witt, C., & Fairhurst, T. H. (2009). Yield intensification in oil palm plantations through best management practice. Better Crops with Plant Food, 93(1), 12–14.
dc.relationDoussan, C., Pierret, A., Garrigues, E., & Pagès, L. (2006). Water Uptake by Plant Roots: II -- Modelling of Water Transfer in the Soil Root-system with Explicit Account of Flow within the Root System -- Comparison with Experiments. Plant and Soil, 283(1), 99–117. https://doi.org/10.1007/s11104-004-7904-z
dc.relationDuff, A. D. S. (1962). Bud Rot Disease of the Oil Palm. Nature, 195(4844), 918–919. Retrieved from http://dx.doi.org/10.1038/195918b0
dc.relationDufrene, E., & Saugier, B. (1993). Gas Exchange of Oil Palm in Relation to Light, Vapour Pressure Deficit, Temperature and Leaf Age. Functional Ecology, 7(1), 97–104. https://doi.org/10.2307/2389872
dc.relationDurrant-Whyte, H., & Henderson, T. C. (2008). Multisensor Data Fusion. In B. Siciliano & O. Khatib (Eds.), Springer Handbook of Robotics (pp. 585–610). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_26
dc.relationDurrant-Whyte, H., & Henderson, T. C. (2016). Multisensor Data Fusion. In B. Siciliano & O. Khatib (Eds.), Springer Handbook of Robotics (pp. 867–896). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-32552-1_35
dc.relationElsevier Ltd. (2011). SCOPUS. Retrieved November 29, 2016, from http://www.americalatina.elsevier.com/corporate/es/scopus.php
dc.relationEstrin, D., Girod, L., Pottie, G., & Srivastava, M. (2001). Instrumenting the world with wireless sensor networks. Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP ’01). 2001 IEEE International Conference on. https://doi.org/10.1109/ICASSP.2001.940390
dc.relationEvans, R., Cassel, D., & Sneed, R. E. (1996). Soil, Water and Crop Characteristics Important to Irrigation Scheduling. Retrieved from https://content.ces.ncsu.edu/soil-water-and-crop-characteristics-important-to-irrigation-scheduling
dc.relationFairhurst, T. (2010). Algunas prácticas clave de manejo para máximo rendimiento en cultivos maduros de palma de aceite Some key management practices for maximum yield in mature oil palm plantations Introducción. Palmas,31(Especial, Tomo I), 44–72.
dc.relationFairhurst, T. H., & Griffiths, W. (2014). Oil Palm: Best Management Practices for Yield Intensification. The International Plant Nutrition Institute (IPNI).
dc.relationFAO. (2007). AGROCOV: agricultura de precisión. Retrieved June 26, 2016, from http://aims.fao.org/aos/agrovoc/c_92363
dc.relationFAO. (2009a). Food Security and Agricultural Mitigation in Developing Countries: Options for Capturing Synergies. Rome: Food and Agriculture Organization of the United Nations (FAO). Retrieved from http://www.fao.org/docrep/012/i1318e/i1318e00.pdf
dc.relationFAO. (2009b). Harvesting Agriculture’s Multiple Benefits: Mitigation, Adaptation, Development and Food Security. Rome. Retrieved from http://www.ddrn.dk/filer/forum/File/ak914e00(2).pdf
dc.relationFAO. (2010). “Climate-Smart” Agriculture. Policies, Practices and Financing for Food Security, Adaptation and Mitigation. Rome: Food and Agriculture Organization of the United Nations (FAO). Retrieved from http://www.fao.org/docrep/013/i1881e/i1881e00.pdf
dc.relationFAO. (2013). Climate-smart agriculture: Sourcebook. Food and Agriculture Organization of the United Nations (FAO). Retrieved from http://www.fao.org/docrep/018/i3325e/i3325e.pdf
dc.relationFAO. (2015a). Climate-Smart Agriculture: A call for action. Rome: Food and Agriculture Organization of the United Nations (FAO). Retrieved from http://www.fao.org/3/a-i4904e.pdf
dc.relationFAO. (2015b). The impact of disasters on agriculture and food security. Food and Agriculture Organization of the United Nations (FAO). Retrieved from http://www.fao.org/3/a-i5128e.pdf
dc.relationFarias, C. M. (2014). A framework for developing Smart Space Applications using Shared Sensor Networks. Rio de Janeiro.
dc.relationFarias, C., Pirmez, L., Delicato, F., Carmo, L., Li, W., Zomaya, A. Y., & Souza, J. N. de. (2014). Multisensor data fusion in Shared Sensor and Actuator Networks. In 17th International Conference on Information Fusion (FUSION) (pp. 1–8). IEEE.
dc.relationFarias, C. M. De, Li, W., Delicato, F. C., Pirmez, L., Zomaya, A. Y., Pires, P. F., & Souza, J. N. De. (2016). A Systematic Review of Shared Sensor Networks. ACM Computing Surveys, 48(4), 1–50. https://doi.org/10.1145/2851510
dc.relationFEDEPALMA. (n.d.). Quiénes Somos. Retrieved February 7, 2015, from http://web.fedepalma.org/quienes-somos-fedepalma
dc.relationFEDEPALMA. (2008). Editorial. Es urgente mejorar el desempeño productivo del sector. Palmas, 29(4), 5–8. Retrieved from http://publicaciones.fedepalma.org/index.php/palmas/article/view/1359
dc.relationFEDEPALMA. (2009). Anuario Estadístico 2009: La agroindustria de la palma de aceite en Colombia y en el mundo. Bogotá: FEDEPALMA
dc.relationFEDEPALMA. (2012a). Anuario Estadístico 2007-2011: La agroindustria de la palma de aceite en Colombia y en el mundo. Bogotá: FEDEPALMA.
dc.relationFEDEPALMA. (2012b). Censo Nacional de Palma de Aceite Colombia 2011: Área sembrada según tamaño del cultivo de palma.
dc.relationFEDEPALMA. (2012d). Censo Nacional de Palma de Aceite Colombia 2011: Características de los sistemas de riego en las fincas según tamaño del cultivo.
dc.relationFEDEPALMA. (2013a). Anuario Estadístico 2013: La agroindustria de la palma de aceite en Colombia y en el mundo. Bogotá: FEDEPALMA.
dc.relationFEDEPALMA. (2013b). Informe de avance del proyecto de Unidades de Auditoría y Asistencia Técnica Ambiental y Social, UAATAS. Bogotá.
dc.relationFEDEPALMA. (2015). Anuario Estadístico 2015: La agroindustria de la palma de aceite en Colombia y en el mundo. Bogotá.
dc.relationFEDEPALMA. (2017). Anuario Estadístico 2017: La agroindustria de la palma de aceite en Colombia y en el mundo. Bogotá.
dc.relationFernández, M. (2013). Efectos del cambio climático en el rendimiento de cultivos por sectores. Retrieved from http://www.ideam.gov.co/documents/21021/21138/Efectos+del+Cambio+Climatico+en+la+agricultura.pdf/3b209fae-f078-4823-afa0-1679224a5e85
dc.relationFertiberia, S. A. (2017). DAP: NP Fosfato diamónico 18-46. Retrieved January 20, 2018, from http://www.fertiberia.com/es/agricultura/productos/categorias/tradicionales/complejos/fosfatos-amonicos/fosfato-diamonico-np-18-46-dap/
dc.relationFINAGRO. (2014). Perspectiva del sector agropecuario Colombiano. Bogotá:FINAGRO. Retrieved from https://www.finagro.com.co/sites/default/files/Perspectivas Agropecuarias-v5.pdf
dc.relationFitter, A., & Hay, R. (2002). 4 - Water. In A. Fitter & R. Hay (Eds.), Environmental Physiology of Plants (Third Edit, pp. 131–190). London: Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-08-054981-1.50009-2
dc.relationFlorea, M. C., Jousselme, A.-L., & Bossé, E. (2007). Fusion of imperfect information in the unified framework of random sets theory: Application to target identification.
dc.relationFontanilla, C., Mosquera, M., Ruíz, E., Beltrán, J., & Guerrero, J. (2015). Beneficio económico de la implementación de buenas prácticas en cultivos de palma de aceite de productores de pequeña escala en Colombia. Palmas, 36(2), 27–38. Retrieved from http://publicaciones.fedepalma.org/index.php/palmas/article/view/11075
dc.relationForero, J., Suaréz, D., Gómez, R., Garay, L., Barberi, F., & Ramírez, C. (2013). La eficiencia económica de los grandes, medianos y pequeños productores agrícolas colombianos. Retrieved from http://www.worldagricultureswatch.org/sites/default/files/documents/Forero Alvarez et al_2013.pdf
dc.relationFoster, H. (2003). Assessment of Oil Palm Fertilizer Requirements. In T. H. Fairhurst & R. Härdter (Eds.), Oil Palm: Management for Large and Sustainable Yields (pp. 257–284). Singapore: PPIC-PPI-IPI.
dc.relationFoster, H. L., Tayeb Dolmat, M., & Zin, Z. Z. (1985). Oil palm yields in the absence of N and K fertilisers in different environments in Peninsular Malaysia. Palm Oil Res. Inst. Malays. Occ. Paper, 15, 1–17.
dc.relationFranco Bautista, P. N. (2010). Contexto y sostenibilidad de la agroindustria de la palma de aceite. Bogotá: FEDEPALMA.
dc.relationGartner. (2013). Gartner IT Glossary > Telematics. Retrieved June 18, 2015, from http://www.gartner.com/it-glossary/telematics
dc.relationGarzón, E. M., Fino, W. J., & Munévar, F. (2005). Diversidad de suelos en la región palmera de Puerto Wilches y San Vicente de Chucurí, departamento de Santander (Colombia). Palmas, 26(4), 11–23.
dc.relationGhosh, S., Bell, D. M., Clark, J. S., Gelfand, A. E., & Flikkema, P. G. (2014). Process modeling for soil moisture using sensor network data. Statistical Methodology, 17, 99–112. https://doi.org/http://dx.doi.org/10.1016/j.stamet.2013.08.002
dc.relationGill Instruments Ltd. (2016). Brochure: 3-Axis Anemometer WindMaster Pro. Retrieved from http://gillinstruments.com/products/anemometer/windmaster-pro.html
dc.relationGillbanks, R. A. (2003). Standard Agronomic Procedures and Practices. In T. H. Fairhurst & R. Härdter (Eds.), Oil Palm: Management for Large and Sustainable Yields (pp. 135–172). Singapore: PPIC-PPI-IPI.
dc.relationGnawali, O., Fonseca, R., Jamieson, K., Moss, D., & Levis, P. (2009). Collection Tree Protocol. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (pp. 1–14). New York, NY, USA: ACM. https://doi.org/10.1145/1644038.1644040
dc.relationGoh, K. J. (2000). Climatic requirements of oil palm for high yields. In K. J. Goh (Ed.), Seminar on Managing Oil Palm For High Yields: Agronomic Principles (pp. 1–17). Kuala Lumpur: Malaysian Society of Soil Science. Retrieved from http://library.wur.nl/isric/fulltext/isricu_i26922_001.pdf
dc.relationGoh, K. J., Härdter, R., & Fairhurst, T. H. (2003). Fertilizing for Maximum Return. In T. H. Fairhurst & R. Härdter (Eds.), Oil Palm: Management for Large and Sustainable Yields (pp. 307–336). Singapore: PPIC-PPI-IPI.
dc.relationGoh, K. J., Mahamooth, T. N., Patrick Ng, H. C., Teo, C. B., & Liew, Y. A. (2016). Managing soil environment and its major impact on oil palm nutrition and productivity in Malaysia (No. 11). Selangor.
dc.relationGómez, P., Ayala, L., & Munévar, F. (2000). Characteristics and management of bud rot, a disease of oil palm. In Procceedings of the International Planters Conference (pp. 545–553).
dc.relationGoodman, I. R., Mahler, R. P. S., & Nguyen, H. T. (1997). Introduction. In Mathematics of Data Fusion (pp. 1–14). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-015-8929-1_1
dc.relationGros, X. E. (1997a). Data Fusion - A Review. In NDT Data Fusion (pp. 5–42). Oxford: Butterworth-Heinemann. https://doi.org/http://dx.doi.org/10.1016/B978-034067648-6/50004-9
dc.relationGros, X. E. (1997b). Perspectives of NDT Data Fusion. In NDT Data Fusion (pp. 180–187). Oxford: Butterworth-Heinemann. https://doi.org/https://doi.org/10.1016/B978-034067648-6/50009-8
dc.relationGross, G. A., Date, K., Schlegel, D. R., Corso, J. J., Llinas, J., Nagi, R., & Shapiro, S. C. (2014). Systemic test and evaluation of a hard+soft information fusion framework: Challenges and current approaches. In 17th International Conference on Information Fusion (FUSION) (pp. 1–8).
dc.relationGuo, W., Cui, S., Torrion, J., & Rajan, N. (2015). Data-Driven Precision Agriculture Opportunities and Challenges. In Soil-Specific Farming (pp. 353–372). CRC Press. https://doi.org/doi:10.1201/b18759-15
dc.relationGutierrez, J., Villa-Medina, J. F., Nieto-Garibay, A., & Porta-Gandara, M. A. (2014). Automated irrigation system using a wireless sensor network and GPRS module. IEEE Transactions on Instrumentation and Measurement, 63(1), 166–176. https://doi.org/10.1109/TIM.2013.2276487
dc.relationGutierrez Jaguey, J., Villa-Medina, J. F., Lopez-Guzman, A., & Porta-Gandara, M. A. (2015). Smartphone Irrigation Sensor. IEEE Sensors Journal, 15(9), 5122–5127. https://doi.org/10.1109/JSEN.2015.2435516
dc.relationGutman, G. E., & Robert, V. (2013). ICTs and information management (IM) in commercial agriculture: contributions from an evolutionary approach. In Information and communication technologies for agricultural development in Latin America: trends, barriers and policies (pp. 157–204). Santiago de Chile: ECLAC - United Nations.
dc.relationHall, D. L., & McMullen, S. A. H. (2004). Mathematical Techniques in Multisensor Data Fusion. Artech House.
dc.relationHall, D., & Llinas, J. (1997). An introduction to multisensor data fusion. In Proceedings of the IEEE (Vol. 85, pp. 6–23). IEEE. https://doi.org/10.1109/5.554205
dc.relationHan, X., Jin, R., Li, X., & Wang, S. (2014). Soil Moisture Estimation Using Cosmic-Ray Soil Moisture Sensing at Heterogeneous Farmland. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2014.2314535
dc.relationHansen, J., & Coffey, K. (2011). Agro-climate tools for a new climate-smart agriculture. International Research Institute for Climate and Society (IRI) and CGIAR Research Program on Climate Change, Agriculture and Food Security(CCAFS).
dc.relationHärdter, R., & Fairhurst, T. (2003). Introduction. In T. Fairhurst & R. Härdter (Eds.), Oil Palm: Management for Large and Sustainable Yields (pp. 1–12). PPIC-PPI-IPI.
dc.relationHatch, D. (2015). Desempeño del mercado de los seguros agropecuarios en las Américas: periodo 2008-2013. (D. Hatch, M. Núñez, & F. Vila, Eds.). San José: C. R.: IICA. Retrieved from http://www.iica.int/sites/default/files/publications/files/2016/b3818e.pdf
dc.relationHenson, I. E. (1991). Limitations to gas exchange growth and yield of young oil palm by soil water supply and atmospheric humidity. Transactions of the Malaysian Society of Plant Physiology, 2, 39–45.
dc.relationHenson, I. E. (1995). Carbon assimilation, water-use and energy balance of an oil palm plantation assessed using micrometeorlogical techniques. In Proc. of the 1993 PORIM International Palm Oil Congress - Update and Vision (Agriculture) (pp. 137–158). Bangi.
dc.relationHenson, I. E. (2005). Modelling seasonal variation in oil palm bunch production using a spreadsheet programme. Journal of Oil Palm Research, 17(June), 27–40.
dc.relationHenson, I. E. (2006). Modelling the impact of climatic and climate-related factors on oil palm growth and productivity. Selangor: Malaysian Palm Oil Board.
dc.relationHenson, I. E., & Harun, M. H. (2005). The influence of climatic conditions on gas and energy exchanges above a young oil palm stand in North Kedah, Malaysia. Journal of Oil Palm Research, 17, 73–91.
dc.relationHernandez Sampieri, R., Fernandez Collado, C., & Baptista Lucio, M. del P. (2010). Metodología de la investigación. Metodología de la investigación. McGraw-Hill. https://doi.org/- ISBN 978-92-75-32913-9
dc.relationHoffmann, M. (2015). Understanding potential yield in the context of the climate and resource constraint to sustainably intensify cropping systems in tropical and temperate regions. Georg-August-University Göttingen. Retrieved from http://hdl.handle.net/11858/00-1735-0000-0022-5FC1-4
dc.relationHoffmann, M. P., Donough, C. R., Cook, S. E., Fisher, M. J., Lim, C. H., Lim, Y. L., … Oberthür, T. (2017). Yield gap analysis in oil palm: Framework development and application in commercial operations in Southeast Asia. Agricultural Systems, 151, 12–19. https://doi.org/10.1016/j.agsy.2016.11.005
dc.relationHolzworth, D. P., Huth, N. I., DeVoil, P. G., Zurcher, E. J., Herrmann, N. I., McLean, G., … Keating, B. A. (2014). APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software, 62, 327–350. https://doi.org/https://doi.org/10.1016/j.envsoft.2014.07.009
dc.relationHopkins, R., Rodrigues, M., & Rinaldi, M. (2013). Trends and potential uses of ICTs in Latin American and the Caribbean agriculture. In Information and communication technologies for agricultural development in Latin America: trends, barriers and policies (pp. 77–156). Santiago de Chile: ECLAC - United Nations.
dc.relationHowland, F., Muñoz, L. A., Staiger-Rivas, S., Cock, J., & Alvarez, S. (2015). Data sharing and use of ICTs in agriculture: working with small farmer groups in Colombia. Knowledge Management for Development Journal, 11(2), 44–63. Retrieved from http://journal.km4dev.org/
dc.relationHukseflux. (n.d.). Brochure: HFP01SC. Retrieved from http://www.hukseflux.com/product/hfp01sc
dc.relationHuth, N. I., Banabas, M., Nelson, P. N., & Webb, M. (2014). Development of an oil palm cropping systems model: Lessons learned and future directions. Environmental Modelling & Software, 62, 411–419. https://doi.org/https://doi.org/10.1016/j.envsoft.2014.06.021
dc.relationIbrahim, M. H., Jaafar, H. Z. E., Harun, M. H., & Yusop, M. R. (2010). Changes in growth and photosynthetic patterns of oil palm (Elaeis guineensis Jacq.) seedlings exposed to short-term CO2 enrichment in a closed top chamber. Acta Physiologiae Plantarum, 32(2), 305–313. https://doi.org/10.1007/s11738-009-0408-y
dc.relationIDEAM. (2015). Informes técnicos: Boletín Agrometeorológico. Retrieved February 10, 2015, from http://www.pronosticosyalertas.gov.co/web/tiempo-y-clima/boletin-semanal-de-seguimiento-y-pronostico
dc.relationIDEAM. (2018). Sistema de Recepcion Satelital de Datos del IDEAM Hydras3. Retrieved January 18, 2018, from http://hydras3.ideam.gov.co/LOGIN.HTM
dc.relationIEEE. (2014). 2014 IEEE Thesaurus. Retrieved from http://www.ieee.org/documents/ieee_thesaurus_2013.pdf
dc.relationITU. (2012a). ITU-T: Security requirements for wireless sensor network routing - X.1313. Geneva. Retrieved from https://www.itu.int/rec/T-REC-X.1313-201210-I
dc.relationITU. (2012b). ITU-T: Terms and definitions for the Internet of things - Y.2069. TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU. Retrieved from http://www.itu.int/rec/T-REC-Y.2069-201207-I/en
dc.relationJanssen, J. A. E. B., Krol, M. S., Schielen, R. M. J., Hoekstra, A. Y., & de Kok, J. L.(2010). Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models. Ecological Modelling, 221(9), 1245–1251. https://doi.org/10.1016/j.ecolmodel.2010.01.011
dc.relationJarvis, A., Cock, J., Jimenez, D., Muñoz, L. A., Delerce, S., Howland, F., … Montoya, T. (2013). Agricultura específica por sitio compartiendo experiencias (AESCE) aplicada a la producción de frutales en Colombia. Retrieved from http://www.asohofrucol.com.co/archivos/biblioteca/biblioteca_175_Agricultura específica por sitio compartiendo experiencias aplicada a la producción de frutales en Colombia.pdf
dc.relationJarvis, A., & Escobar, D. (2014). Convenio MADR-CIAT: La adaptación al cambio climático, una necesidad para el sector palmicultor. Palmas, 35(4), 56–65.
dc.relationJayashri, B. S., & Rao, G. R. (2015). Reviewing the research paradigm of techniques used in data fusion in WSN. Proceedings of the International Conference on Computing and Communications Technologies, ICCCT 2015, 83–88. https://doi.org/10.1109/ICCCT2.2015.7292724
dc.relationJiménez, D., Dorado, H., Cock, J., Prager, S. D., Delerce, S., Grillon, A., … Jarvis, A. (2016). From Observation to Information: Data-Driven Understanding of on Farm Yield Variation. PLOS ONE, 11(3), 1–20. https://doi.org/10.1371/journal.pone.0150015
dc.relationJin, R., Li, X., Yan, B., Li, X., Luo, W., Ma, M., … Zhao, S. (2014). A Nested Ecohydrological Wireless Sensor Network for Capturing the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin, China. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2014.2319085
dc.relationJohannsen, C. J., & Carter, P. G. (2005). SITE-SPECIFIC SOIL MANAGEMENT. In D. Hillel (Ed.), Encyclopedia of Soils in the Environment (pp. 497–503). Oxford: Elsevier. https://doi.org/https://doi.org/10.1016/B0-12-348530-4/00892-4
dc.relationJourdan, C., & Rey, H. (1997a). Architecture and development of the oil-palm (Elaeis guineensis Jacq.) root system. Plant and Soil, 189(1), 33–48. https://doi.org/10.1023/A:1004290024473
dc.relationJourdan, C., & Rey, H. (1997b). Modelling and simulation of the architecture and development of the oil-palm (Elaeis guineensis Jacq.) root system. Plant and Soil, 190(2), 235–246. https://doi.org/10.1023/A:1004270014678
dc.relationKang, J., Jin, R., & Li, X. (2015). Regression Kriging-Based Upscaling of Soil Moisture Measurements From a Wireless Sensor Network and Multiresource Remote Sensing Information Over Heterogeneous Cropland. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2014.2326775
dc.relationKeong, Y. K., & Keng, W. M. (2012). Statistical Modeling of Weather-based Yield Forecasting for Young Mature Oil Palm. APCBEE Procedia, 4, 58–65. https://doi.org/10.1016/j.apcbee.2012.11.011
dc.relationKersting, K., Bauckhage, C., Wahabzada, M., Mahlein, A.-K., Steiner, U., Oerke, E.-C., … Plümer, L. (2016). Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants. In J. Lässig, K. Kersting, & K. Morik (Eds.), Computational Sustainability (pp. 99–120). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-31858-5_6
dc.relationKhaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44. https://doi.org/10.1016/j.inffus.2011.08.001
dc.relationKim, Y., & Evans, R. G. (2009). Software design for wireless sensor-based site-specific irrigation. Computers and Electronics in Agriculture, 66(2), 159–165. https://doi.org/https://doi.org/10.1016/j.compag.2009.01.007
dc.relationKitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering.
dc.relationKulkarni, R. V, Forster, A., & Venayagamoorthy, G. K. (2011). Computational Intelligence in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 13(1), 68–96. https://doi.org/10.1109/SURV.2011.040310.00002
dc.relationKwong, K. H., Wu, T.-T., Goh, H. G., Sasloglou, K., Stephen, B., Glover, I., … Andonovic, I. (2012). Practical considerations for wireless sensor networks in cattle monitoring applications. Computers and Electronics in Agriculture, 81, 33–44. https://doi.org/10.1016/j.compag.2011.10.013
dc.relationLamade, E., Purba, A. R., & Setiyo, I. E. (1998). Gas exchange and carbon allocation of oil palm seedlings submitted to waterlogging in interaction with N fertiliser application. In International Oil Palm Conference. Commodity of the past, today, and the future (pp. 573–584). Bali: Medan IOPRI 1998.
dc.relationLamade, E., Setiyo, I. E., & Purba, A. R. (1998). Gas exchange and carbon allocation of oil palm seedlings submitted to waterlogging in interaction with N fertilizer application. In IOPRI international oil palm conference: Commodity of the past, today, and the future, Bali, 23-25 september (p. 18). Montpellier: CIRAD-CP.
dc.relationLascano, R. J. (1998). Bases tecnológicas para el riego en palma de aceite. Palmas, 19(Especial), 229–241. Retrieved from http://publicaciones.fedepalma.org/index.php/palmas/article/view/668/668
dc.relationLascano, R. J., & Munévar, F. (2000). Criterios técnicos para la selección de sistemas de riego: Aplicación al cultivo de palma de aceite en Colombia. Palmas, 21(Especial. Tomo II), 270–279. Retrieved from http://publicaciones.fedepalma.org/index.php/palmas/article/view/840/840
dc.relationLee, J. S. H., Ghazoul, J., Obidzinski, K., & Koh, L. P. (2014). Oil palm smallholder yields and incomes constrained by harvesting practices and type of smallholder management in Indonesia. Agronomy for Sustainable Development, 34(2), 501–513. https://doi.org/10.1007/s13593-013-0159-4
dc.relationLeekwijck, W. Van, & Kerre, E. E. (1999). Defuzzification: criteria and classification. Fuzzy Sets and Systems, 108(2), 159–178. https://doi.org/https://doi.org/10.1016/S0165-0114(97)00337-0
dc.relationLI-COR Inc. (2011). Eddy Covariance Systems. Retrieved from https://www.licor.com/env/products/eddy_covariance/
dc.relationLI-COR Inc. (2015). Brochure: LI-190R Quantum Sensor. Retrieved from https://www.licor.com/env/products/light/quantum.html
dc.relationLiao, M.-S., Chuang, C.-L., Lin, T.-S., Chen, C.-P., Zheng, X.-Y., Chen, P.-T., … Jiang, J.-A. (2012). Development of an autonomous early warning system for Bactrocera dorsalis (Hendel) outbreaks in remote fruit orchards. Computers and Electronics in Agriculture, 88, 1–12. https://doi.org/10.1016/j.compag.2012.06.008
dc.relationLipper, L., Thornton, P., Campbell, B. M., Baedeker, T., Braimoh, A., Bwalya, M., … Torquebiau, E. F. (2014). Climate-smart agriculture for food security. Nature Clim. Change, 4(12), 1068–1072. Retrieved from http://dx.doi.org/10.1038/nclimate2437
dc.relationLiu, Q., Zhang, Y. Y., Shen, J., Xiao, B., & Linge, N. (2015). A WSN-based prediction model of microclimate in a greenhouse using an extreme learning approach. In 2015 17th International Conference on Advanced Communication Technology (ICACT) (pp. 133–137). https://doi.org/10.1109/ICACT.2015.7224772
dc.relationLuo, R. C., & Kay, M. G. (1989). Multisensor integration and fusion in intelligent systems. IEEE Transactions on Systems, Man, and Cybernetics, 19(5), 901–931. https://doi.org/10.1109/21.44007
dc.relationLuo, R. C., Yih, C.-C., & Su, K. L. (2002). Multisensor fusion and integration: approaches, applications, and future research directions. IEEE Sensors Journal, 2(2), 107–119. https://doi.org/10.1109/JSEN.2002.1000251
dc.relationMa, J., Zhou, X., Li, S., & Li, Z. (2011). Connecting agriculture to the internet of things through sensor networks. In Proceedings - 2011 IEEE International Conferences on Internet of Things and Cyber, Physical and Social Computing, iThings/CPSCom 2011 (pp. 184–187). https://doi.org/10.1109/iThings/CPSCom.2011.32
dc.relationMADR. (2015a). Boletín Nacional Agroclimático - Noviembre 2015. Retrieved from http://www.ideam.gov.co/documents/21021/552445/Boletín+Agroclimático+No.+11+-+Noviembre.pdf/5f521158-3b00-47a4-b365-3e30d04d3fa3?version=1.0
dc.relationMADR. (2015b). Boletín Nacional Agroclimático - Octubre 2015. Retrieved from http://www.ideam.gov.co/documents/21021/552445/Boletín+Agroclimático+No.+10+-+Octubre.pdf/920e0c38-05fe-4a7c-96e0-f677c8c71937?version=1.0
dc.relationMADR. (2015c). Prevención y Mitigación: Eventos Climáticos. Dirección de Innovación, Desarrollo Tecnológico y Protección Sanitaria. Retrieved from https://www.minagricultura.gov.co/Cambio_Climatico/Documents/Boletin_No2_enero20.pdf
dc.relationMADR. (2016a). Boletín Nacional Agroclimático - Febrero 2016. Retrieved fromhttp://www.ideam.gov.co/documents/21021/552413/Boletín+Agroclimático+No.+14+-+Febrero.pdf/6f802e77-70b0-4f3a-aa99-d0aebc90de4a?version=1.0
dc.relationMADR. (2016b). Documentos Estratégico: Plan Colombia Siembra. Bogotá. Retrieved from https://www.minagricultura.gov.co/planeacion-control-gestion/Gestin/ESTRATEGIA COLOMBIA SIEMBRA V1.pdf
dc.relationMADR, & FEDEPALMA. (2013). Área sembrada a 2013 de Palma de Aceite.
dc.relationMafuta, M., Zennaro, M., Bagula, A., Ault, G., & Chadza, H. G. T. (2013). Successful Deployment of a Wireless Sensor Network for Precision Agriculture in Malawi. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2013/150703
dc.relationMariño, P., Fontan, F. P., Dominguez, M. Á., & Otero, S. (2010). An Experimental Ad-Hoc WSN for the Instrumentation of Biological Models. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2010.2045970
dc.relationMariño, P., Fontán, F. P., Domínguez, M. A., & Otero, S. (2008). Deployment and Implementation of an Agricultural Sensor Network. 2008 Second International Conference on Sensor Technologies and Applications (Sensorcomm 2008). https://doi.org/10.1109/SENSORCOMM.2008.133
dc.relationMariño, P., Machado, F., Fontan, F. P., & Otero, S. (2008). Hybrid Distributed Instrumentation Network for Integrating Meteorological Sensors Applied to Modeling RF Propagation Impairments. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2008.915451
dc.relationMartinez, G. (2010). Pudrición del cogollo, Marchitez sorpresiva, Anillo rojo y Marchitez letal en la palma de aceite en América. Palmas, 31(1), 43–53.
dc.relationMartínez, H. J., Salazar, M., Barrios, C. A., & Espinal, C. F. (2005). LA CADENA DE LAS OLEAGINOSAS EN COLOMBIA: UNA MIRADA GLOBAL DE SU ESTRUCTURA Y DINAMICA 1991-2005. Retrieved from http://www.agronet.gov.co/www/docs_agronet/2005112162648_caracterizacion_oleaginosas.pdf
dc.relationMarulanda, B., Paredes, M., & Fajury, L. (2010). Acceso a servicios financieros en Colombia: retos para el siguiente cuatrienio. Retrieved from https://www.caf.com/media/3786/Bancarización.pdf
dc.relationMascarenhas, M. (2017). CIAT Blog: Pronósticos agroclimáticos al rescate…. Retrieved June 22, 2017, from http://blog.ciat.cgiar.org/es/pronosticos-agroclimaticos-al-rescate/
dc.relationMcBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future Directions of Precision Agriculture. Precision Agriculture, 6(1), 7–23. https://doi.org/10.1007/s11119-005-0681-8
dc.relationMcCarthy, N., Lipper, L., & Branca, G. (2011). Climate-smart agriculture: smallholder adoption and implications for climate change adaptation and mitigation. Mitigation of Climate Change in Agriculture Series (FAO). Food and Agriculture Organization of the United Nations (FAO). Retrieved from http://www.fao.org/docrep/015/i2575e/i2575e00.pdf
dc.relationMcCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P., & Freebairn, D. M. (1996). APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems, 50(3), 255–271. https://doi.org/https://doi.org/10.1016/0308-521X(94)00055-V
dc.relationMejía, J. (2000). Consumo de agua por la palma de aceite y efectos del riego sobre la producción de racimos, una revisión de literatura. Palmas, 21(1), 51–58. Retrieved from http://publicaciones.fedepalma.org/index.php/palmas/article/view/726/726
dc.relationMendel, J. M. (1995). Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83(3), 345–377. https://doi.org/10.1109/5.364485
dc.relationMirhosseini, M., Barani, F., & Nezamabadi-pour, H. (2017). QQIGSA: A quadrivalent quantum-inspired GSA and its application in optimal adaptive design of wireless sensor networks. Journal of Network and Computer Applications, 78, 231–241. https://doi.org/10.1016/j.jnca.2016.11.001
dc.relationMitchell, H. B. (2012). Data fusion: Concepts and ideas. Data Fusion: Concepts and Ideas. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-27222-6
dc.relationMitralexis, G., & Goumopoulos, C. (2015). Web Based Monitoring and Irrigation System with Energy Autonomous Wireless Sensor Network for Precision Agriculture. In B. De Ruyter, A. Kameas, P. Chatzimisios, & I. Mavrommati (Eds.), Ambient Intelligence: 12th European Conference, AmI 2015, Athens, Greece, November 11-13, 2015, Proceedings (pp. 361–370). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-26005-1_27
dc.relationMoher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, T. P. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLOS Medicine, 6(7), 1–6. https://doi.org/10.1371/journal.pmed.1000097
dc.relationMoreno, H., Molina, A., & Rincón, V. (2012). Uso de información meteorológica para el manejo agronómico de la palma de aceite (Guía No 1.). Centro de Investigación en Palma de Aceite (Cenipalma), Federación Nacional de Cultivadores de Palma de Aceite (Fedepalma).
dc.relationMosquera, M., Valderrama, M., Fontanilla, C., Ruíz, E., Uñate, M., Rincón, F., & Arias, N. (2016). Costos de producción de la agroindustria de la palma de aceite en Colombia en 2014. Palmas, 37(2), 37–53.
dc.relationMosquera, M., Valderrama, M., Ruíz, E., López, D., Castro, L., Fontanilla, C., & González, M. A. (2017). Costos de producción para el fruto de palma de aceite y el aceite de palma en 2015: estimación en un grupo de productores colombianos. Palmas, 38(2), 10–26.
dc.relationMunévar, F. (2004). Criterios agroecológicos útiles en la selección de tierras para nuevas siembras de palma de aceite en Colombia. Palmas, 25(especial), 148–159.
dc.relationMunévar, F., Acosta, A., & León, P. (2001). Factores edáficos asociados con la pudrición de cogollo de la palma de aceite en Colombia. Palmas, 22(2), 9–19.
dc.relationMunévar, F., López, A., Bernabé, R., & Reyes, A. (2011). Impacto del manejo agronómico integral en la productividad de la palma de aceite en Palmas Montecarmelo. Palmas, 32(4), 42–51.
dc.relationNakamura, E. F., Loureiro, A. a. F., & Frery, A. C. (2007). Information fusion for wireless sensor networks. ACM Computing Surveys, 39(3), 1–55. https://doi.org/10.1145/1267070.1267073
dc.relationNavarro-Hellín, H., Martínez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F., & Torres-Sánchez, R. (2016). A decision support system for managing irrigation in agriculture. Computers and Electronics in Agriculture, 124(Supplement C), 121–131. https://doi.org/https://doi.org/10.1016/j.compag.2016.04.003
dc.relationNelson, P., Huth, M. I., Banabas, M., Webb, M. J., & Goodrick, I. (2016). Ciclos de carbono y nitrógeno en plantaciones de palma de aceite: claves para la productividad y la sostenibilidad. Palmas, 37(Especial, Tomo I), 214–224.
dc.relationNelson, P. N., Banabas, M., Huth, N. I., & Webb, M. J. (2015). Quantifying trends in soil fertility under oil palm: practical challenges and approaches. In M. J. Webb, P. N. Nelson, C. Bessou, J.-P. Caliman, & E. S. Sutarta (Eds.), Sustainable Management of Soil in Oil Palm Plantings. Proceedings of a workshop held in Medan, Indonesia, 7–8 November 2013. (Vol. 144, pp. 60–64). Australian Centre for International Agricultural Research (ACIAR).
dc.relationNeufeldt, H., Jahn, M., Campbell, B. M., Beddington, J. R., DeClerck, F., De Pinto, A., … Zougmoré, R. (2013). Beyond climate-smart agriculture: toward safe operating spaces for global food systems. Agriculture & Food Security, 2(1), 12. https://doi.org/10.1186/2048-7010-2-12
dc.relationNezamabadi-pour, H. (2015). A Quantum-inspired Gravitational Search Algorithm for Binary Encoded Optimization Problems. Eng. Appl. Artif. Intell., 40(C), 62–75. https://doi.org/10.1016/j.engappai.2015.01.002
dc.relationNg, S. K. (2002). Nutrition and nutrient management of oil palm-New thrust for the future perspective. In Potassium for sustainable crop production. International symposium on role of potassium in India New Delhi. International Potash Institute, Basel, Switzerland and Potash Research Institute of India, Guregaon, Haryana, India (Vol. 2002, pp. 415–429). Retrieved from http://www.ipipotash.org/udocs/Nutrition and Nutrient Management of the Oil Palm.pdf
dc.relationNieto, L. E., & Gómez, P. L. (1991). Estado actual de la investigación sobre el complejo pudrición de cogollo de la palma de aceite en Colombia. Palmas, 12(2).
dc.relationNoleppa, S., & Cartsburg, M. (2016). Auf der Ölspur – Berechnungen zu einer palmölfreieren Welt. (I. Petersen, Ed.). Berlin: WWF Deutschland.
dc.relationOberthür, T., Donough, C. R., Indrasuara, K., Dolong, T., & Abdurrohim, G. (2012). Successful Intensification of Oil Palm Plantations with Best Management Practices: Impacts on Fresh Fruit Bunch and Oil Yield. In Proc. Int. Planters’ Conf. 2012 (pp. 67–102). Kuala Lumpur: Incorporated Society of Planters.
dc.relationOboh, B. O., & Fakorede, M. A. B. (1999). Effects of weather on yield components of the oil palm in a forest location in Nigeria. Journal of Oil Palm Research, 11(1), 79–89.
dc.relationOkoro, S. U., Schickhoff, U., Boehner, J., Schneider, U. A., & Huth, N. I. (2017). Climate impacts on palm oil yields in the Nigerian Niger Delta. European Journal of Agronomy, 85, 38–50. https://doi.org/https://doi.org/10.1016/j.eja.2017.02.002
dc.relationOlivin, J. (1968). Etude pour la localisation d’un bloc industriel de palmiers à huile. Oleagineux, 23(8–9), 499–504.
dc.relationOlivin, J. (1986). Study for the siting of a commercial oil palm plantation. Oleagineux, 41(3), 113–118.
dc.relationOlson, K. (1998). Precision Agriculture: Current Economic and Environmental Issues. In Sixth Joint Conference on Food, Agriculture, and the Environment.
dc.relationOpenSim Ltd. (2014). Download details: OMNeT++ 4.4.1 (source + IDE, tgz). Retrieved November 17, 2017, from https://omnetpp.org/component/jdownloads/download/32-release-older-versions/2272-omnet-4-4-1-source-ide-tgz
dc.relationOrtegón, A. (2004). Metodología para la realización de estudios de drenaje a nivel predial. Palmas, 25(Especial), 126–136.
dc.relationPalat, T., Nakharin, C., Clendon, J. H., & Corley, R. H. V. (2008). A review of 15 years of oil palm irrigation research in Southern Thailand. Planter, 84(989), 537–546.
dc.relationPalat, T., Nakharin, C., Clendon, J. H., & Corley, R. H. V. (2009). A review of 15 years of oil palm irrigation research in Southern Thailand. International Journal of Oil Palm Research, 6, 146–154. Retrieved from https://netafim.com/Data/Uploads/143-5 Oil palm Clendon et al. PPT Irrigation Trials Summary.pdf
dc.relationPalat, T., Smith, B. G., & Corley, R. H. V. (2000). Irrigation of oil palm in Southern Thailand. In E. Pushparajah (Ed.), International Planters Conference Tree Crops in the New Millenium: The Way Ahead (Vol. 1, pp. 303–315). Kuala Lumpur: ISP.
dc.relationParamananthan, S. (2003). Land selection for oil palm. In T. H. Fairhurst & R. Härdter (Eds.), Oil Palm: Management for Large and Sustainable Yields (pp. 27–57). Singapore: PPIC-PPI-IPI.
dc.relationParamananthan, S., Chew, P. S., & Goh, K. J. (2000). Towards a practical framework for land cultivation for oil palm in the 21st century. In Proc. Int. Planters Conf. “Plantation tree crops in the new millennium: the way ahead” (pp. 869–885). Kuala Lumpur: Incorp. Soc. Planters.
dc.relationPardon, L., Bessou, C., Saint-Geours, N., Gabrielle, B., Khasanah, N., Caliman, J.-P., & Nelson, P. N. (2016). Quantifying nitrogen losses in oil palm plantations: models and challenges. Biogeosciences, 13(19), 5433–5452. https://doi.org/10.5194/bg-13-5433-2016
dc.relationPardon, L., Bessou, C., Saint-Geours, N., Gabrielle, B., Khasanah, N., Caliman, J.-P., & Nelson, P. N. (2016). Quantifying nitrogen losses in oil palm plantations: models and challenges. Biogeosciences, 13(19), 5433–5452. https://doi.org/10.5194/bg-13-5433-2016
dc.relationPaucar, L. G., Diaz, A. R., Viani, F., Robol, F., Polo, A., & Massa, A. (2015). Decision support for smart irrigation by means of wireless distributed sensors. In 2015 IEEE 15th Mediterranean Microwave Symposium (MMS) (pp. 1–4). IEEE. https://doi.org/10.1109/MMS.2015.7375469
dc.relationPediaditakis, D., Tselishchev, Y., & Boulis, A. (2010). Performance and Scalability Evaluation of the Castalia Wireless Sensor Network Simulator. In Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques (p. 53:1--53:6). ICST, Brussels, Belgium, Belgium: ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering). https://doi.org/10.4108/ICST.SIMUTOOLS2010.8727
dc.relationPham, H. N., Pediaditakis, D., & Boulis, A. (2007). From Simulation to Real Deployments in WSN and Back. In 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (pp. 1–6). https://doi.org/10.1109/WOWMOM.2007.4351800
dc.relationPierce, F. J., & Elliott, T. V. (2008). Regional and on-farm wireless sensor networks for agricultural systems in Eastern Washington. Computers and Electronics in Agriculture, 61(1), 32–43. https://doi.org/10.1016/j.compag.2007.05.007
dc.relationPlant, R. E. (2001). Site-specific management: the application of information technology to crop production. Computers and Electronics in Agriculture, 30(1–3), 9–29. https://doi.org/10.1016/S0168-1699(00)00152-6
dc.relationPoo, D., Kiong, D., & Ashok, S. (2008). Object, Class, Message and Method BT - Object-Oriented Programming and Java. In D. Poo, D. Kiong, & S. Ashok (Eds.) (pp. 7–15). London: Springer London. https://doi.org/10.1007/978-1-84628-963-7_2
dc.relationPravia, M. A., Babko-Malaya, O., Schneider, M. K., White, J. V, Chong, C. Y., & Willsky, A. S. (2009). Lessons learned in the creation of a data set for hard/soft information fusion. In 2009 12th International Conference on Information Fusion (pp. 2114–2121).
dc.relationPye-Smith, C. (2011). Farming’s climate smart future: placing agriculture at the heart of climate-change policy. Wageningen, Netherlands: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the Technical Centre for Agricultural and Rural Cooperation (CTA). Retrieved from https://ccafs.cgiar.org/publications/farmings-climate-smart-future-placing-agriculture-heart-climate-change-policy#.WVFFpmg1_IU
dc.relationRaes, D., Steduto, P., Hsiao, T. C., & Fereres, E. (2012). Chapter 3: Calculation procedures. In AquaCrop Version 4.0: reference manual. FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS (FAO). Retrieved from http://www.fao.org/nr/water/docs/aquacropv40chapter3.pdf
dc.relationRajagopalan, R., & Varshney, P. K. (2006). Data-aggregation techniques in sensor networks: A survey. IEEE Communications Surveys & Tutorials, 8(4), 48–63. https://doi.org/10.1109/COMST.2006.283821
dc.relationRankine, I., & Fairhurst, T. H. (1999). Field Handbook Oil Palm Series Volume 3: Mature. Singapore: PPI/PPIC and 4T Consultants.
dc.relationRey, H., Dubos, B., Dufrene, E., & Quencez, P. (1998). Oil palm water profiles and water supplies in Cote d’Ivoire. Plantations, Recherche, Développement, 5, 47–57.
dc.relationReyes, R., Bastidas, S., & Peña, E. (1998). Crecimiento del sistema radical de la palma de aceite (Elaeis guineensis Jacq.) en Tumaco, Colombia. Palmas, 19(3), 31–35.
dc.relationRincón, V. O. (2015). Lotes CEPV.
dc.relationRival, A., & Levang, P. (2014). Palms of controversies: Oil palm and development challenges. Bogor, Indonesia: CIFOR. Retrieved from http://www.cifor.org/publications/pdf_files/Books/BLevang1401.pdf
dc.relationRivera-Mendes, Y. D., Cuenca, J. C., & Romero, H. M. (2016). Physiological responses of oil palm (Elaeis guineensis Jacq .) seedlings under different water soil conditions. Agronomía Colombiana, 34(2), 163–171. https://doi.org/10.15446/agron.colomb.v34n2.55568
dc.relationRobert, M., Thomas, A., & Bergez, J.-E. (2016). Processes of adaptation in farm decision-making models . A review. Agronomy for Sustainable Development, 36(64). https://doi.org/10.1007/s13593-016-0402-x
dc.relationRobert, P. (1993). Characterization of soil conditions at the field level for soil specific management. Geoderma, 60(1), 57–72. https://doi.org/http://dx.doi.org/10.1016/0016-7061(93)90018-G
dc.relationRobert, P. C. (2002). Precision agriculture: A challenge for crop nutrition management. Plant and Soil, 247(1), 143–149. https://doi.org/10.1023/A:1021171514148
dc.relationRobledo de Eikenberg, C. (2015). Construcción de un Modelo de Agricultura Competitiva en Colombia: una mirada al sector agrícola Colombiano. Retrieved from http://www.andi.com.co/es/PC/Paginas/AlDia-08-2015-1.aspx
dc.relationRogova, G. L., & Nimier, V. (2004). Reliability in Information Fusion: Literature Survey. In Proceedings of the Seventh International Conference on Information Fusion (Vol. 2, pp. 1158–1165).
dc.relationRomero, H. M., Araque, L., & Forero, D. (2008). La Agricultura de precisión en el manejo del cultivo de la palma de aceite. Palmas, 29(1), 13–21. Retrieved from https://publicaciones.fedepalma.org/index.php/palmas/article/view/1330
dc.relationRomero, H. M., Ayala, I., & Ruiz, R. (2007). Ecofisiología de la palma de aceite. Palmas, 28(Especial, Tomo I), 176–184.
dc.relationRos, M. (1997). Redes telemáticas: educación a distancia y educación cooperativa. Pixel-Bit: Revista de Medios Y Educación, (8). Retrieved from http://www.sav.us.es/pixelbit/pixelbit/articulos/n8/n8art/art83.htm
dc.relationRosenbaum, U., Bogena, H. R., Herbst, M., Huisman, J. A., Peterson, T. J., Weuthen, A., … Vereecken, H. (2012). Seasonal and event dynamics of spatial soil moisture patterns at the small catchment scale. Water Resources Research, 48(10), n/a--n/a. https://doi.org/10.1029/2011WR011518
dc.relationRoss, T. J. (2010). Properties of Membership Functions, Fuzzification, and Defuzzification. In Fuzzy Logic with Engineering Applications (pp. 89–116). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119994374.ch4
dc.relationRuan, J., & Shi, Y. (2016). Monitoring and assessing fruit freshness in IOT-based e-commerce delivery using scenario analysis and interval number approaches. Information Sciences, 373, 557–570. https://doi.org/10.1016/j.ins.2016.07.014
dc.relationRubiano, Y. (2005). Conceptos básicos para utilizar los levantamientos de suelos en el manejo agronómico de la palma de aceite. Bogotá: Cenipalma.
dc.relationRuiz-Garcia, L., Barreiro, P., & Robla, J. I. (2008). Performance of ZigBee-Based wireless sensor nodes for real-time monitoring of fruit logistics. Journal of Food Engineering, 87(3), 405–415. https://doi.org/10.1016/j.jfoodeng.2007.12.033
dc.relationRuiz-Garcia, L., Lunadei, L., Barreiro, P., & Robla, J. I. (2009). A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends. Sensors (Switzerland), 9(6), 4728–4750. https://doi.org/10.3390/s90604728
dc.relationRuíz, R. (2005). Desarrollo del racimo y formación de aceite en diferentes épocas del año según las condiciones de la Zona Norte. Palmas, 26(4), 53–58.
dc.relationRuiz Romero, R., & Henson, I. E. (2002). Photosynthesis and stomatal conductance of oil palm in Colombia: some initial observations. Planter, 78(915), 301–308.
dc.relationSáenz, A. (2005). Aspectos generales e importancia del agente causal de anillo rojo. Palmas, 26(2), 59–70.
dc.relationSales, N., Remedios, O., & Arsenio, A. (2015). Wireless sensor and actuator system for smart irrigation on the cloud. In IEEE World Forum on Internet of Things, WF-IoT 2015 - Proceedings (pp. 693–698). https://doi.org/10.1109/WF-IoT.2015.7389138
dc.relationSambhoos, K., Llinas, J., & Little, E. (2008). Graphical methods for real-time fusion and estimation with soft message data. In 2008 11th International Conference on Information Fusion (pp. 1–8).
dc.relationSánchez-Díaz, M., & Aguirreolea, J. (2000). Movimientos estomáticos y transpiración. In J. Azcón-Bieto & M. Talón (Eds.), Fundamentos de Fisiología Vegetal (pp. 31–42). Madrid: McGraw-Hill.
dc.relationSarangi, S., & Pappula, S. (2016). Adaptive Data-Centric Clustering with Sensor Networks for Energy Efficient IoT Applications. In 2016 IEEE 41st Conference on Local Computer Networks (LCN) (pp. 398–405). https://doi.org/10.1109/LCN.2016.68
dc.relationSatizábal, H., Barreto-Sanz, M., Jiménez, D., Pérez-Uribe, A., & Cock, J. (2012). Enhancing Decision-Making Processes of Small Farmers in Tropical Crops by Means of Machine Learning Models. In J.-C. Bolay, M. Schmid, G. Tejada, & E. Hazboun (Eds.), Technologies and Innovations for Development: Scientific Cooperation for a Sustainable Future (pp. 265–277). Paris: Springer Paris. https://doi.org/10.1007/978-2-8178-0268-8_18
dc.relationSchuster, E. W., Kumar, S., Sarma, S. E., Willers, J. L., & Milliken, G. A. (2011). Infrastructure for data-driven agriculture: identifying management zones for cotton using statistical modeling and machine learning techniques. 2011 8th International Conference & Expo on Emerging Technologies for a Smarter World. https://doi.org/10.1109/CEWIT.2011.6163052
dc.relationSelvaraju, R., Gommes, R., & Bernardi, M. (2011). Climate science in support of sustainable agriculture and food security. Climate Research, 47(1–2), 95–110. Retrieved from http://www.int-res.com/abstracts/cr/v47/n1-2/p95-110/
dc.relationShafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press. Retrieved from https://books.google.com.co/books?id=5KwpAQAACAAJ
dc.relationShafer, G. (1992). Dempster-shafer theory. In Encyclopedia of artificial intelligence (pp. 330–331).
dc.relationShafer, G. (1996). Probabilistic expert systems. In CBMS-NSF Regional Conference Series in Applied Mathematics. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611970043.fm
dc.relationShih, C.-W., & Wang, C.-H. (2016). Integrating wireless sensor networks with statistical quality control to develop a cold chain system in food industries. Computer Standards & Interfaces, 45, 62–78. https://doi.org/10.1016/j.csi.2015.12.004
dc.relationSilva, Á., & Cerón, J. (2010). La agroindustria de la palma de aceite en América. Palmas, 31(Especial-Tomo II), 245–257.
dc.relationSISPA. (2015). Evolución histórica anual de los rendimientos de aceite de palma en Colombia. Retrieved from http://sispaweb.fedepalma.org/SitePages/Home.aspx
dc.relationSivakumar, M. V. K., Gommes, R., & Baier, W. (2000). Agrometeorology and sustainable agriculture. Agricultural and Forest Meteorology, 103(1–2), 11–26. https://doi.org/10.1016/S0168-1923(00)00115-5
dc.relationSivanandam, S. N., Sumathi, S., & Deepa, S. N. (2007). Introduction. In Introduction to Fuzzy Logic using MATLAB (pp. 1–9). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-35781-0_1
dc.relationSmith, B. G. (1989). The Effects of Soil Water and Atmospheric Vapour Pressure Deficit on Stomatal Behaviour and Photosynthesis in the Oil Palm. Journal of Experimental Botany, 40(215), 647–651. Retrieved from http://www.jstor.org/stable/23692132
dc.relationSpectrum Technologies. (2012). Product Manual: WatchDog 2000 Series Full Weather Stations. Retrieved from https://www.specmeters.com/assets/1/22/2000_All_Series_WS3.pdf
dc.relationSquire, G. R., & Corley, R. H. V. (1987). Oil palm. In M. R. Sethuraj & A. S. Raghavendra (Eds.), Tree crop physiology (pp. 141–167). Amsterdam: Elsevier.
dc.relationSrbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., & Borozan, V. (2014). Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of Cleaner Production, 88, 297–307. https://doi.org/10.1016/j.jclepro.2014.04.036
dc.relationSteenwerth, K. L., Hodson, A. K., Bloom, A. J., Carter, M. R., Cattaneo, A., Chartres, C. J., … Jackson, L. E. (2014). Climate-smart agriculture globalresearch agenda: scientific basis for action. Agriculture & Food Security, 3(1), 11. https://doi.org/10.1186/2048-7010-3-11
dc.relationStevens Water Monitoring Systems Inc. (n.d.). Brochure: HydraProbe. Retrieved from http://www.stevenswater.com/products/sensors/soil/hydraprobe/
dc.relationStevens Water Monitoring Systems Inc. (2006). The Parameters of the HydraProbe. Retrieved from http://www.btnode.ethz.ch/pub/uploads/Internal/hydraprobe.pdf
dc.relationSudevalayam, S., & Kulkarni, P. (2011). Energy Harvesting Sensor Nodes: Survey and Implications. IEEE Communications Surveys & Tutorials, 13(3), 443–461. https://doi.org/10.1109/SURV.2011.060710.00094
dc.relationTaiz, L., & Zeiger, E. (2002). Plant Physiology. Annals of Botany (3 edition). Sinauer Associates. https://doi.org/10.1104/pp.900074
dc.relationTan, C. C. (2011). Nursery practices for production of superior oil palm planting materials. In Agronomic principles and practices of oil palm cultivation (pp. 145–169). Selangor: Agricultural Crop Trust (ACT).
dc.relationTan, H. Ö., & Körpeoǧlu, I. (2003). Power Efficient Data Gathering and Aggregation in Wireless Sensor Networks. SIGMOD Rec., 32(4), 66–71. https://doi.org/10.1145/959060.959072
dc.relationTexas Electronics Inc. (n.d.). Brochure: TR-525M. Retrieved from http://texaselectronics.com/rain-gauge-tr-525m-metric.html
dc.relationThe MathWorks, I. (2017). Build Mamdani Systems Using Fuzzy Logic Designer. Retrieved January 5, 2018, from https://la.mathworks.com/help/fuzzy/building-systems-with-fuzzy-logic-toolbox-software.html
dc.relationTinker, P. B. (1976). Soil requirements of the oil palm. In R. H. V. Corley, J. J. Hardon, & B. J. Wood (Eds.), Oil palm research (Vol. 1, pp. 65–81). Amsterdam: Elsevier.
dc.relationToro, F. (2009a). Colección Fotográfica Fedepalma: estacion metereologica 01. Retrieved November 21, 2017, from http://repfedepalma.catalogokohaplus.com:8080/fedepalma/xmlui/handle/12345/10681
dc.relationToro, F. (2009b). Colección Fotográfica Fedepalma: estacion metereologica 03. Retrieved November 21, 2017, from http://repfedepalma.catalogokohaplus.com:8080/fedepalma/xmlui/handle/12345/10684
dc.relationTorres, G. A., Sarria, G. A., Martinez, G., Varon, F., Drenth, A., & Guest, D. I. (2016). Bud Rot Caused by Phytophthora palmivora: A Destructive Emerging Disease of Oil Palm. Phytopathology, 106(4), 320–329. https://doi.org/10.1094/PHYTO-09-15-0243-RVW
dc.relationTorres, J. (1995). Riegos. In C. CASSALETT, J. TORRES, & C. ISAACS (Eds.), El cultivo de la caña en la zona azucarera de Colombia (pp. 193–210). Centro de Investigación de la Caña de Azúcar de Colombia (CENICAÑA). Retrieved from http://www.cenicana.org/pdf_privado/documentos_no_seriados/libro_el_cultivo_cana/libro_p193-210.pdf
dc.relationTorres, J., Ruiz, M., & Barrera, O. (2016). Xmac Palma: la herramienta climática al servicio del palmicultor. Bogotá.
dc.relationTurner, P. D. (1977). The effects of drought on oil palm yields in south-east Asia and the south Pacific region. In D. A. Earp & W. Newall (Eds.), International Developments in Oil Palm, Proceedings of theMalaysian International Agricultural Oil Palm Conference (pp. 673–694). Kuala Lumpur: The Incorporated Society of Planters.
dc.relationTurner, P. D., & Gillbanks, R. A. (2003). Oil palm cultivation and management (Second). Kuala Lumpur: Incorporated Society of Planters.
dc.relationVaisala. (2012). Brochure: HMP155 Humidity and Temperature Probe. Retrieved from http://www.vaisala.com/en/products/humidity/Pages/HMP155.aspx
dc.relationVan Kraalingen, D. W. G., Breure, C. J., & Spitters, C. J. T. (1989). Simulation of oil palm growth and yield. Agricultural and Forest Meteorology, 46(3), 227–244. https://doi.org/10.1016/0168-1923(89)90066-X
dc.relationVarshney, P. K. (2000). Multisensor Data Fusion. In R. Logananthara, G. Palm, & M. Ali (Eds.), Intelligent Problem Solving. Methodologies and Approaches: 13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2000 New Orleans, Louisiana, USA, June 19--22, 2000 Proceedings (pp. 1–3). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-45049-1_1
dc.relationVasisht, D., Kapetanovic, Z., Won, J., Jin, X., Chandra, R., Sinha, S., … Stratman, S. (2017). FarmBeats: An IoT Platform for Data-Driven Agriculture. In 14th {USENIX} Symposium on Networked Systems Design and Implementation, {NSDI} 2017 (pp. 515–529). Boston. Retrieved from https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/vasisht
dc.relationVerdouw, C. N., Beulens, A. J. M., & van der Vorst, J. G. A. J. (2013). Virtualisation of floricultural supply chains: A review from an Internet of Things perspective. Computers and Electronics in Agriculture, 99, 160–175. https://doi.org/10.1016/j.compag.2013.09.006
dc.relationVerdouw, C. N., Wolfert, J., Beulens, A. J. M., & Rialland, A. (2015). Virtualization of food supply chains with the internet of things. Journal of Food Engineering, 176, 128–136. https://doi.org/10.1016/j.jfoodeng.2015.11.009
dc.relationVerhagen, A., Booltink, H. W. G., & Bouma, J. (1995). Site-specific management: Balancing production and environmental requirements at farm level. Agricultural Systems, 49(4), 369–384. https://doi.org/http://dx.doi.org/10.1016/0308-521X(95)00031-Y
dc.relationVermeulen, S. J., Campbell, B. M., & Ingram, J. S. I. (2012). Climate Change and Food Systems. Annual Review of Environment and Resources, 37(1), 195–222. https://doi.org/10.1146/annurev-environ-020411-130608
dc.relationViani, F. (2016). Experimental validation of a wireless system for the irrigation management in smart farming applications. Microwave and Optical Technology Letters, 58(9), 2186–2189. https://doi.org/10.1002/mop.30000
dc.relationWald, L. (1999). Some terms of reference in data fusion. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/36.763269
dc.relationWallace, A. (1994). High‐precision agriculture is an excellent tool for conservation of natural resources. Communications in Soil Science and Plant Analysis, 25(1–2), 45–49. https://doi.org/10.1080/00103629409369002
dc.relationWang, J., & Yue, H. (2017). Food safety pre-warning system based on data mining for a sustainable food supply chain. Food Control, 73, 223–229. https://doi.org/10.1016/j.foodcont.2016.09.048
dc.relationWang, N., Zhang, N., & Wang, M. (2006). Wireless sensors in agriculture and food industry—Recent development and future perspective. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2005.09.003
dc.relationWerro, N. (2015). Fuzzy Set Theory. In Fuzzy Classification of Online Customers (pp. 7–26). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-15970-6_2
dc.relationWhite, F. (1991). Data Fusion Lexicon. San Diego. Retrieved from http://www.dtic.mil/dtic/tr/fulltext/u2/a529661.pdf
dc.relationWMO. (2003). Manual on the Global Observing System WMO-No. 544. WMO.
dc.relationWMO. (2008). Guide of Meteorological Instruments and Methods of Observation WMO-No. 8. WMO.
dc.relationWMO. (2010). Guide to Agricultural Meteorological Practices WMO-No. 134. WMO.
dc.relationWoittiez, L. S., Haryono, S., Turhina, S., Dani, H., T.P., D., & Smit, H. (2016). Smallholder Oil Palm Handbook Module 5: Pests and Diseases (3rd ed.). The Hague: Wageningen University and SNV International Development Organisation.
dc.relationWoittiez, L. S., van Wijk, M. T., Slingerland, M., van Noordwijk, M., & Giller, K. E. (2017). Yield gaps in oil palm: A quantitative review of contributing factors. European Journal of Agronomy, 83, 57–77. https://doi.org/10.1016/j.eja.2016.11.002
dc.relationWolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/https://doi.org/10.1016/j.agsy.2017.01.023
dc.relationWood, B. J., & Corley, R. H. V. (1993). The energy balance of oil palm cultivation. In Proceedings of 1991 PORIM International Palm Oil Conference, Agriculture (pp. 130–143). Kuala Lumpur: Palm Oil Research Institute of Malaysia.
dc.relationWu, C., & Aghajan, H. (2007). Model-based human posture estimation for gesture analysis in an opportunistic fusion smart camera network. In 2007 IEEE Conference on Advanced Video and Signal Based Surveillance (pp. 453–458). https://doi.org/10.1109/AVSS.2007.4425353
dc.relationYadav, S. G. S., & Chitra, A. (2015). Reviewing the process of data fusion in wireless sensor network : a brief survey, 8(2), 130–140.
dc.relationYager, R. R. (2011). A measure based approach to the fusion of possibilistic and probabilistic uncertainty. Fuzzy Optimization and Decision Making, 10(2), 91–113. https://doi.org/10.1007/s10700-011-9098-1
dc.relationYager, R. R. (2016). Multi-source Information Fusion Using Measure Representations. In S. Saminger-Platz & R. Mesiar (Eds.), On Logical, Algebraic, and Probabilistic Aspects of Fuzzy Set Theory (pp. 199–214). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-28808-6_12
dc.relationYang, M.-T., Chen, C.-C., & Kuo, Y.-L. (2013). Implementation of intelligent air conditioner for fine agriculture. Energy and Buildings, 60, 364–371. https://doi.org/http://dx.doi.org/10.1016/j.enbuild.2013.01.034
dc.relationYara International ASA. (2017). NITRAX-S 28-4-0-6S. Retrieved January 20, 2018, from http://www.yara.com.co/crop-nutrition/products/other/13a3-nitrax-s-28-4-0-6s/
dc.relationYick, J., Mukherjeea, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 58(12), 2292–2330. https://doi.org/10.1016/j.comnet.2008.04.002
dc.relationYuan, W., Krishnamurthy, S. V, & Tripathi, S. K. (2003). Synchronization of multiple levels of data fusion in wireless sensor networks. In Global Telecommunications Conference, 2003. GLOBECOM ’03. IEEE (Vol. 1, p. 221–225 Vol.1). https://doi.org/10.1109/GLOCOM.2003.1258234
dc.relationYusoff, S. (2006). Renewable energy from palm oil – innovation on effective utilization of waste. Journal of Cleaner Production, 14(1), 87
dc.relationZadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/http://dx.doi.org/10.1016/S0019-9958(65)90241-X
dc.relationZadeh, L. A. (1973). Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/TSMC.1973.5408575
dc.relationZadeh, L. A. (1975a). The concept of a linguistic variable and its application to approximate reasoning-III. Information Sciences, 9(1), 43–80. https://doi.org/http://dx.doi.org/10.1016/0020-0255(75)90017-1
dc.relationZadeh, L. A. (1975b). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199–249. https://doi.org/http://dx.doi.org/10.1016/0020-0255(75)90036-5
dc.relationZadeh, L. A. (1975c). The concept of a linguistic variable and its application to approximate reasoning—II. Information Sciences, 8(4), 301–357. https://doi.org/http://dx.doi.org/10.1016/0020-0255(75)90046-8
dc.relationZia, H., Harris, N., Merrett, G., & Rivers, M. (2015). Predicting discharge using a low complexity machine learning model. Computers and Electronics in Agriculture, 118, 350–360. https://doi.org/10.1016/j.compag.2015.09.012
dc.relationZimmermann, H.-J. (2010). Fuzzy set theory. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 317–332. https://doi.org/10.1002/wics.82
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.rightsAbierto (Texto Completo)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia
dc.titleMétodo de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite


Este ítem pertenece a la siguiente institución