Mostrar el registro sencillo del ítem

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.contributor.advisorDe Farías, Claudio Micelispa
dc.contributor.advisorTalavera Portocarrero, Jesús Martínspa
dc.contributor.advisorCabrera Cruz, José Danielspa
dc.contributor.advisorBayona Rodríguez, Cristihian Jarrispa
dc.contributor.authorCulman Forero, María Alejandraspa
dc.date.accessioned2020-06-26T21:35:50Z
dc.date.available2020-06-26T21:35:50Z
dc.date.issued2018-03
dc.identifier.urihttp://hdl.handle.net/20.500.12749/3549
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.spa
dc.description.tableofcontentsINTRODUCCIÓN 24 1. DESCRIPCIÓN DEL TRABAJO 28 1.1 PROBLEMA 28 1.2 PREGUNTA DE INVESTIGACIÓN 31 1.3 MOTIVACIÓN 31 1.4 HIPÓTESIS 32 1.5 JUSTIFICACIÓN 33 2. OBJETIVOS 36 2.1 GENERAL 36 2.2 ESPECÍFICOS 36 3. MARCO REFERENCIAL 37 3.1 MARCO CONCEPTUAL 37 3.1.1 Fusión de Datos 37 3.1.2 Método a partir de la Fusión de Datos basado en la Inferencia 39 3.1.3 Redes Inalámbricas de Sensores 40 3.1.4 Telemática 40 3.1.5 Agrometeorología 40 3.1.6 Naturaleza de los datos agrometeorológicos 41 3.1.7 Gestión del cultivo de palma de aceite 42 3.2 MARCO TEÓRICO 47 3.2.1 Fusión de datos aplicada a sensores 47 3.2.2 Técnicas de Fusión de Datos basadas en la Inferencia 49 3.2.3 Agrometeorología y Redes Inalámbricas de Sensores 54 3.2.4 Agricultura ante los cambios tecnológicos y climáticos 56 3.3 ESTADO DEL ARTE 59 3.3.1 Soluciones que integran Redes Inalámbricas de Sensores y Fusión de Datos para apoyar la toma de decisiones en la agricultura 60 3.3.2 Soluciones que integran Redes Inalámbricas de Sensores y otras áreas para apoyar la toma de decisiones en la agricultura 64 3.3.3 Síntesis sobre las soluciones reportadas y la toma de decisiones en la agricultura 68 3.4 MARCO CONTEXTUAL Y ANTECEDENTES 70 3.4.1 La palma de aceite en Colombia 70 3.4.2 Corporación Centro de Investigación en Palma de Aceite – CENIPALMA 71 3.4.3 Iniciativas en el territorio colombiano para una agricultura climáticamente inteligente 72 3.4.4 Red Temática CYTED RiegoNets 74 3.4.5 Centro de Excelencia y Apropiación en Internet de las Cosas – CEA–IoT 75 3.5 MARCO LEGAL Y NORMATIVO 77 4. ASPECTOS METODOLÓGICOS 78 4.1 ENFOQUE Y TIPO DE INVESTIGACIÓN 78 4.2 UNIVERSO Y MUESTRA 79 4.2.1 Universo y muestra para recolección de información 79 4.2.2 Universo con potencial de ser impacto por contribuciones 80 4.3 TÉCNICAS E INSTRUMENTOS DE RECOLECCIÓN DE DATOS 80 4.4 ACTIVIDADES REALIZADAS 80 4.4.1 Fase 1: análisis de técnicas de Fusión de Datos y oportunidades de investigación 81 4.4.2 Fase 2: propuesta de un método, utilizando una técnica de Fusión de Datos basada en la Inferencia 83 4.4.3 Fase 3: comparación del comportamiento agronómico de parcelas de palma de aceite 85 5. FUSIÓN DE DATOS APLICADA A REDES INALÁMBRICAS DE SENSORES PARA APOYAR LA TOMA DE DECISIONES EN AGRICULTURA: TÉCNICAS Y OPORTUNIDADES DE INVESTIGACIÓN 87 5.1 REVISIÓN DE LA LITERATURA 87 5.1.1 Preguntas de investigación 88 5.1.2 Proceso de búsqueda 89 5.1.3 Criterios de exclusión, inclusión y calidad 91 5.1.4 Extracción de datos 93 5.1.5 Resultados de la búsqueda 94 5.2 TÉCNICAS DE FUSIÓN DE DATOS APLICADAS A INFORMACIÓN RECOLECTADA POR REDES INALÁMBRICAS DE SENSORES PARA APOYAR LA TOMA DE DECISIONES EN AGRICULTURA 97 5.3 OPORTUNIDADES DE INVESTIGACIÓN EN REDES INALÁMBRICAS DE SENSORES Y FUSIÓN DE DATOS PARA APOYAR LA TOMA DE DECISIONES EN AGRICULTURA 112 5.3.1 Problemas abiertos en Redes Inalámbricas de Sensores y Fusión de Datos 112 5.3.2 Oportunidades de investigación en Redes Inalámbricas de Sensores y Fusión de Datos 114 6. 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 118 6.1 SOBRE LA PALMA DE ACEITE 118 6.1.1 Relación entre el suelo, el cultivo y el clima en la productividad de la palma de aceite 119 6.1.2 Información agrometeorológica disponible en palma de aceite en Colombia 125 6.1.3 Decisiones para apoyar la gestión del cultivo de palma de aceite 130 6.2 MÉTODO DE FUSIÓN DE DATOS AGROMETEOROLÓGICOS 148 6.2.1 Fusión de datos a nivel de lote 149 6.2.2 Fusión de datos a nivel de plantación 164 6.3 VALIDACIÓN DEL MÉTODO DE FUSIÓN DE DATOS AGROMETEOROLÓGICOS 180 6.3.1 Simulación de la fusión de datos a nivel de lote 180 6.3.2 Simulación de la fusión de datos a nivel de plantación 193 7. COMPORTAMIENTO AGRONÓMICO DE PARCELAS PARA MEDIR EL IMPACTO DEL MÉTODO DE FUSIÓN DE DATOS EN LA GESTIÓN DEL RIEGO EN CULTIVOS DE PALMA DE ACEITE 203 7.1 SIMULACIÓN DEL COMPORTAMIENTO AGRONÓMICO DEL LOTE DE ESTUDIO 203 7.2 COMPORTAMIENTO AGRONÓMICO DEL LOTE DE ESTUDIO BAJO DOS ESCENARIOS: CON RIEGO Y SIN RIEGO 212 7.2.1 Resultados de la simulación del comportamiento agronómico del lote de estudio 212 7.2.2 Impacto del método de inferencia en la productividad e ingresos del cultivo de palma de aceite 219 8. CONCLUSIONES 222 8.1 CONCLUSIONES 222 8.2 CONTRIBUCIONES 225 8.3 TRABAJO FUTURO 228 REFERENCIAS 231 ANEXOS 287spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
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 aceitespa
dc.title.translatedData fusion method applied to wireless sensor networks to support decision-making in the management of oil palm cropseng
dc.degree.nameMagíster en Telemáticaspa
dc.coverageBucaramanga (Colombia)spa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
dc.rights.localAbierto (Texto Completo)spa
dc.publisher.facultyFacultad Ingenieríaspa
dc.publisher.programMaestría en Telemáticaspa
dc.description.degreelevelMaestríaspa
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.localTesisspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.subject.keywordsSystems Engineeringeng
dc.subject.keywordsTelematicseng
dc.subject.keywordsWireless communication systemseng
dc.subject.keywordsWireless technologyeng
dc.subject.keywordsElectronic data processingeng
dc.subject.keywordsInvestigationseng
dc.subject.keywordsAnalysiseng
dc.subject.keywordsDecision supporteng
dc.subject.keywordsAgricultureeng
dc.subject.keywordsData fusioneng
dc.subject.keywordsOil palmeng
dc.subject.keywordsCrop managementeng
dc.subject.keywordsWireless sensor networkseng
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga - UNABspa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
dc.relation.referencesCulman 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 UNABspa
dc.relation.referencesAbdelgawad, 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_2spa
dc.relation.referencesAbouzar, 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.2016.2586844spa
dc.relation.referencesAbu 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/s10705-010-9398-9spa
dc.relation.referencesACM. (2012). Computing Classification System, 2012 Revision. Retrieved from https://www.acm.org/publications/class-2012spa
dc.relation.referencesAcosta, A., & Munévar, F. (2003). Bud Rot in Oil Palm Plantations: Link to Soil Physical Properties and Nutrient Status. Better Crops International, 17, 22–25.spa
dc.relation.referencesAGRONET. (2014). Antecedentes y Objetivos. Retrieved February 9, 2015, from http://www.agronet.gov.co/agronetweb1/QuienesSomos/AntecedentesyObjetivos.aspxspa
dc.relation.referencesAGRONET. (2015a). Agroclima/Reporte Climatológico. Retrieved February 9, 2015, from http://agronet.gov.co/agronetweb1/Agroclima/ReporteClimatológico.aspxspa
dc.relation.referencesAGRONET. (2015b). Clima y Medio Ambiente. Retrieved February 9, 2015, fromhttp://www.agronet.gov.co/agronetweb1/Agroclima.aspxspa
dc.relation.referencesAhmed, 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/b17650-22spa
dc.relation.referencesAiello, 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.2017.02.197spa
dc.relation.referencesAkyildiz, 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.2004.04.003spa
dc.relation.referencesAkyildiz, 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.2002.1024422spa
dc.relation.referencesAkyildiz, 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)00302-4spa
dc.relation.referencesAkyildiz, 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)00302-4spa
dc.relation.referencesAkyildiz, I. F., & Vuran, M. C. (2010). Wireless Sensor Networks. (I. F. Akyildiz, Ed.). John Wiley & Sons. https://doi.org/10.1002/9780470515181spa
dc.relation.referencesAldana 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-manejospa
dc.relation.referencesAllen, 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.pdfspa
dc.relation.referencesAlvarado, 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.spa
dc.relation.referencesAnisi, 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/s11119-014-9371-8spa
dc.relation.referencesAPSIM Initiative. (n.d.-a). APSIM: about us. Retrieved January 20, 2018, from http://www.apsim.info/AboutUs.aspxspa
dc.relation.referencesAPSIM Initiative. (n.d.-b). Creating an APSIM met file using Excel. Retrieved January 18, 2018, from https://www.apsim.info/Documentation/CommonTasksinAPSIM/CreatinganAPSIMmetfileusingExcel.aspxspa
dc.relation.referencesAPSIM Initiative. (n.d.-c). What is the Operations Schedule Module? Retrieved January 18, 2018, from https://www.apsim.info/Documentation/Model,CropandSoil/InfrastuctureandManagementDocumentation/OPERATIONS.aspxspa
dc.relation.referencesAquino, 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).spa
dc.relation.referencesArango, 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.spa
dc.relation.referencesArias, N. A., & Motta, D. (2006). Resultados de la Transferencia de Tecnología basada en el modelo de acompañamiento de Cenipalma. Palmas, 27(2), 11–21.spa
dc.relation.referencesASOHOFRUCOL. (2014). Frutisitio. Retrieved June 22, 2017, from http://www.frutisitio.comspa
dc.relation.referencesAtzori, 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.2010.05.010spa
dc.relation.referencesBabuška, 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_2spa
dc.relation.referencesBakoumé, 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.v5n8p57spa
dc.relation.referencesBarcelos, 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.2015.00190spa
dc.relation.referencesBarrera, 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.pdfspa
dc.relation.referencesBayes, 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/105741spa
dc.relation.referencesBayona-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.v34n2.55649spa
dc.relation.referencesBayona, C. J. (2016a). Estación Biomet 1.spa
dc.relation.referencesBayona, C. J. (2016b). Estación Biomet 2.spa
dc.relation.referencesBayona 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.v34n3.55565spa
dc.relation.referencesBeltrán, 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_27spa
dc.relation.referencesBenítez, 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, Jspa
dc.relation.referencesBessou, 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/2017024spa
dc.relation.referencesBhuyan, 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.2010.211104spa
dc.relation.referencesBID, & 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.pdfspa
dc.relation.referencesBijarbooneh, 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.2015.2502182spa
dc.relation.referencesBilskie, J. (2001). Soil Water Status: content and potential. Retrieved from https://s.campbellsci.com/documents/de/technical-papers/soilh20c.pdfspa
dc.relation.referencesBlaak, 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.spa
dc.relation.referencesBlundo 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.spa
dc.relation.referencesBogena, 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/vzj2009.0173spa
dc.relation.referencesBogena, 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.20463spa
dc.relation.referencesBogena, 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/vzj2008.0138spa
dc.relation.referencesBogena, 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.2007.06.032spa
dc.relation.referencesBolourchi, 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.2013.32spa
dc.relation.referencesBorgia, 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.2014.09.008spa
dc.relation.referencesBos, 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). GESspa
dc.relation.referencesBoshell, J. F. (2012). GESTIÓN DE INFORMACIÓN AGROCLIMÁTICA EN COLOMBIA. Bo. Retrieved from http://www.cambioclimaticoandes.info/spa
dc.relation.referencesBoström, 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.spa
dc.relation.referencesBoulis, A. (2011). Castalia: A simulator for Wireless Sensor Networks and Body Area Networks. Version 3.2 - User’s Manual.spa
dc.relation.referencesBoulis, 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.2003.1203363spa
dc.relation.referencesBouma, 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/9573467spa
dc.relation.referencesBranca, 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.pdfspa
dc.relation.referencesBrisco, 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/10.1080/07038992.1998.10855254spa
dc.relation.referencesBrown, 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.2014.09.005spa
dc.relation.referencesBustillo, 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.spa
dc.relation.referencesBustillo, 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.spa
dc.relation.referencesCadena, 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-6-195-2006spa
dc.relation.referencesCaliman, 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.spa
dc.relation.referencesCaliman, 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.spa
dc.relation.referencesCalveche, 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.pdfspa
dc.relation.referencesCampbell Scientifc Inc. (2017). Brochure: CNR4 Kipp & Zonen’s Net Radiometer.spa
dc.relation.referencesCano, 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.pdfspa
dc.relation.referencesCao, 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.2008.02.001spa
dc.relation.referencesCarr, 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/S0014479711000494spa
dc.relation.referencesCastanedo, F. (2013). A Review of Data Fusion Techniques. The Scientific World Journal, 2013, 19. https://doi.org/10.1155/2013/704504spa
dc.relation.referencesCEA-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. Retrievedspa
dc.relation.referencesCEA-IoT. (2016b). Quiénes somos CEA-IoT. Retrieved May 18, 2017, from http://www.cea-iot.org/que-es/spa
dc.relation.referencesCENIPALMA. (2010). ¿QUIÉNES SOMOS? Retrieved February 7, 2015, from http://www.cenipalma.org/quienes-somos-cenipalmaspa
dc.relation.referencesCENIPALMA. (2011). Buenas Prácticas de Manejo. Retrieved October 28, 2017, from http://www.cenipalma.org/buenas-practicas-de-manejospa
dc.relation.referencesCENIPALMA. (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).spa
dc.relation.referencesCENIPALMA. (2014). Catálogo de estaciones.spa
dc.relation.referencesCENIPALMA. (2016). GeoPalma Portal: quiénes somos. Retrieved November 1, 2017, from http://geoportal.cenipalma.org/Quienes-Somosspa
dc.relation.referencesCENIPALMA. (2017a). Geopalma > XMAC > Boletines Agroclimáticos. Retrieved June 7, 2017, from http://geoportal.cenipalma.org/boletinesxmacspa
dc.relation.referencesCENIPALMA. (2017b). Informe de Labores CENIPALMA 2016. Retrieved from http://www.cenipalma.org/informes-de-gestion-cenipalmaspa
dc.relation.referencesChaczko, 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.2005.1560284spa
dc.relation.referencesChang, 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/s151128690spa
dc.relation.referencesChaparro, 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).spa
dc.relation.referencesChen, 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.2009.170spa
dc.relation.referencesChinchilla, 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.spa
dc.relation.referencesChoo, 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/s11367-011-0303-9spa
dc.relation.referencesCIAT. (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.pdfspa
dc.relation.referencesCIAT, 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=1.0spa
dc.relation.referencesCIAT, 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=1.0spa
dc.relation.referencesCIAT, 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=1.0spa
dc.relation.referencesCIAT, 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=1.2spa
dc.relation.referencesCoates, 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.2013.04.013spa
dc.relation.referencesCock, 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.2016.09.002spa
dc.relation.referencesCock, 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.2011.07.001spa
dc.relation.referencesColciencias. (2016). Tipología de Proyectos Calificados como de Carácter Científico, Tecnológico e Innovación. Versión 4.spa
dc.relation.referencesColciencias. (2017). Plataforma SCIENTI - Colombia: Servicios de consulta. Retrieved October 21, 2017, from http://scienti.colciencias.gov.co:8083/ciencia-war/jsp/enRecurso/IndexRecursoHumano.jspspa
dc.relation.referencesColesanti, U., & Santini, S. (2012). ctp-castalia. Retrieved November 17, 2017, from https://code.google.com/archive/p/ctp-castalia/spa
dc.relation.referencesCombley, R. (2011). Cambridge Business English Dictionary. New York: Cambridge University Press.spa
dc.relation.referencesComte, 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.2013.02.010spa
dc.relation.referencesComte, 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/B978-0-12-394277-7.00003-8spa
dc.relation.referencesCorley, 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/660/660spa
dc.relation.referencesCorley, R. H. V., & Tinker, P. B. (2016). The Oil Palm (5th ed.). John Wiley & Sons. https://doi.org/10.1002/9781118953297spa
dc.relation.referencesCorley, R. H. V., & Tinker, P. B. H. (2003). The Oil Palm (4th ed.). Blackwell Science Ltd. https://doi.org/10.1002/9780470750971spa
dc.relation.referencesCorley, R., & Tinker, P. (2003). The Oil Palm.spa
dc.relation.referencesCORPOICA. (2013). Modelos de Adaptación y Prevención Agroclimática – MAPA. Retrieved June 22, 2017, from http://www.corpoica.org.co/site-mapa/spa
dc.relation.referencesCORPOICA. (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/spa
dc.relation.referencesCSRD. (2016). Alianza sobre Servicios Climáticos para el Desarrollo Resiliente. Retrieved from http://www.cs4rd.org/assets/documents/CSRD Brochure_Spanish.pdfspa
dc.relation.referencesCuller, D. E., & Hong, W. (2004). Introduction to Wireless Sensor Networks. Commun. ACM, 47(6), 30–33. https://doi.org/10.1145/990680.990703spa
dc.relation.referencesCulman, 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.2017.8000190spa
dc.relation.referencesCYTED. (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=884spa
dc.relation.referencesDANE. (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.pdfspa
dc.relation.referencesDANE. (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.pdfspa
dc.relation.referencesDANE. (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.pdfspa
dc.relation.referencesDANE. (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.pdfspa
dc.relation.referencesDANE. (2015e). Principales variables cadena Oleaginosas, Aceites y Grasas (2002-2014). Retrieved from https://colaboracion.dnp.gov.co/CDT/Desarrollo Empresarial/Oleaginosas, aceites, grasas.zipspa
dc.relation.referencesDANE. (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.pdfspa
dc.relation.referencesDANE. (2017a). Anexos Estadisticos: Boletin Comercio Exterior Enero-Diciembre 2016. Retrieved from http://www.dian.gov.co/dian/14cifrasgestion.nsf/e7f1561e16ab32b105256f0e00741478/a02b47038628e5610525733e0059549a?OpenDocumentspa
dc.relation.referencesDANE. (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.pdfspa
dc.relation.referencesDasarathy, B. V. (1997). Sensor fusion potential exploitation-innovativearchitectures and illustrative applications. Proceedings of the IEEE, 85(1), 24–38. https://doi.org/10.1109/5.554206spa
dc.relation.referencesDBpedia. (n.d.). DBpedia: agricultura de precisión. Retrieved June 26, 2016, from http://dbpedia.org/page/Precision_agriculturespa
dc.relation.referencesDDRS, 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.pdfspa
dc.relation.referencesDelerce, 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.0161620spa
dc.relation.referencesDelerce, 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.0161620spa
dc.relation.referencesDempster, 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.2007.03.004spa
dc.relation.referencesDempster, 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)90097-3spa
dc.relation.referencesDevadas, 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.spa
dc.relation.referencesDitschar, 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.spa
dc.relation.referencesDNP. (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-grasasspa
dc.relation.referencesdo 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/s11746-015-2765-6spa
dc.relation.referencesDong, 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/s91007771spa
dc.relation.referencesDonough, 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.spa
dc.relation.referencesDoussan, 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-zspa
dc.relation.referencesDuff, A. D. S. (1962). Bud Rot Disease of the Oil Palm. Nature, 195(4844), 918–919. Retrieved from http://dx.doi.org/10.1038/195918b0spa
dc.relation.referencesDufrene, 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/10.2307/2389872spa
dc.relation.referencesDurrant-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_26spa
dc.relation.referencesDurrant-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_35spa
dc.relation.referencesElsevier Ltd. (2011). SCOPUS. Retrieved November 29, 2016, from http://www.americalatina.elsevier.com/corporate/es/scopus.phpspa
dc.relation.referencesEstrin, 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.2001.940390spa
dc.relation.referencesEvans, 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-schedulingspa
dc.relation.referencesFairhurst, 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.spa
dc.relation.referencesFairhurst, T. H., & Griffiths, W. (2014). Oil Palm: Best Management Practices for Yield Intensification. The International Plant Nutrition Institute (IPNI).spa
dc.relation.referencesFAO. (2007). AGROCOV: agricultura de precisión. Retrieved June 26, 2016, from http://aims.fao.org/aos/agrovoc/c_92363spa
dc.relation.referencesFAO. (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.pdfspa
dc.relation.referencesFAO. (2009b). Harvesting Agriculture’s Multiple Benefits: Mitigation, Adaptation, Development and Food Security. Rome. Retrieved from http://www.ddrn.dk/filer/forum/File/ak914e00(2).pdfspa
dc.relation.referencesFAO. (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.pdfspa
dc.relation.referencesFAO. (2013). Climate-smart agriculture: Sourcebook. Food and Agriculture Organization of the United Nations (FAO). Retrieved from http://www.fao.org/docrep/018/i3325e/i3325e.pdfspa
dc.relation.referencesFAO. (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.pdfspa
dc.relation.referencesFAO. (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.pdfspa
dc.relation.referencesFarias, C. M. (2014). A framework for developing Smart Space Applications using Shared Sensor Networks. Rio de Janeiro.spa
dc.relation.referencesFarias, 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.spa
dc.relation.referencesFarias, 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/10.1145/2851510spa
dc.relation.referencesFEDEPALMA. (n.d.). Quiénes Somos. Retrieved February 7, 2015, from http://web.fedepalma.org/quienes-somos-fedepalmaspa
dc.relation.referencesFEDEPALMA. (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/1359spa
dc.relation.referencesFEDEPALMA. (2009). Anuario Estadístico 2009: La agroindustria de la palma de aceite en Colombia y en el mundo. Bogotá: FEDEPALMAspa
dc.relation.referencesFEDEPALMA. (2012a). Anuario Estadístico 2007-2011: La agroindustria de la palma de aceite en Colombia y en el mundo. Bogotá: FEDEPALMA.spa
dc.relation.referencesFEDEPALMA. (2012b). Censo Nacional de Palma de Aceite Colombia 2011: Área sembrada según tamaño del cultivo de palma.spa
dc.relation.referencesFEDEPALMA. (2012d). Censo Nacional de Palma de Aceite Colombia 2011: Características de los sistemas de riego en las fincas según tamaño del cultivo.spa
dc.relation.referencesFEDEPALMA. (2013a). Anuario Estadístico 2013: La agroindustria de la palma de aceite en Colombia y en el mundo. Bogotá: FEDEPALMA.spa
dc.relation.referencesFEDEPALMA. (2013b). Informe de avance del proyecto de Unidades de Auditoría y Asistencia Técnica Ambiental y Social, UAATAS. Bogotá.spa
dc.relation.referencesFEDEPALMA. (2015). Anuario Estadístico 2015: La agroindustria de la palma de aceite en Colombia y en el mundo. Bogotá.spa
dc.relation.referencesFEDEPALMA. (2017). Anuario Estadístico 2017: La agroindustria de la palma de aceite en Colombia y en el mundo. Bogotá.spa
dc.relation.referencesFernández, 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-afa0-1679224a5e85spa
dc.relation.referencesFertiberia, 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/spa
dc.relation.referencesFINAGRO. (2014). Perspectiva del sector agropecuario Colombiano. Bogotá:FINAGRO. Retrieved from https://www.finagro.com.co/sites/default/files/Perspectivas Agropecuarias-v5.pdfspa
dc.relation.referencesFitter, 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/B978-0-08-054981-1.50009-2spa
dc.relation.referencesFlorea, M. C., Jousselme, A.-L., & Bossé, E. (2007). Fusion of imperfect information in the unified framework of random sets theory: Application to target identification.spa
dc.relation.referencesFontanilla, 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/11075spa
dc.relation.referencesForero, 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.pdfspa
dc.relation.referencesFoster, 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.spa
dc.relation.referencesFoster, 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.spa
dc.relation.referencesFranco Bautista, P. N. (2010). Contexto y sostenibilidad de la agroindustria de la palma de aceite. Bogotá: FEDEPALMA.spa
dc.relation.referencesGartner. (2013). Gartner IT Glossary > Telematics. Retrieved June 18, 2015, from http://www.gartner.com/it-glossary/telematicsspa
dc.relation.referencesGarzón, 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.spa
dc.relation.referencesGhosh, 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.2013.08.002spa
dc.relation.referencesGill Instruments Ltd. (2016). Brochure: 3-Axis Anemometer WindMaster Pro. Retrieved from http://gillinstruments.com/products/anemometer/windmaster-pro.htmlspa
dc.relation.referencesGillbanks, 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.spa
dc.relation.referencesGnawali, 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/10.1145/1644038.1644040spa
dc.relation.referencesGoh, 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.pdfspa
dc.relation.referencesGoh, 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.spa
dc.relation.referencesGoh, 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.spa
dc.relation.referencesGómez, 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).spa
dc.relation.referencesGoodman, 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_1spa
dc.relation.referencesGros, 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/B978-034067648-6/50004-9spa
dc.relation.referencesGros, 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/B978-034067648-6/50009-8spa
dc.relation.referencesGross, 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).spa
dc.relation.referencesGuo, 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/b18759-15spa
dc.relation.referencesGutierrez, 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.2013.2276487spa
dc.relation.referencesGutierrez 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.2015.2435516spa
dc.relation.referencesGutman, 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.spa
dc.relation.referencesHall, D. L., & McMullen, S. A. H. (2004). Mathematical Techniques in Multisensor Data Fusion. Artech House.spa
dc.relation.referencesHall, D., & Llinas, J. (1997). An introduction to multisensor data fusion. In Proceedings of the IEEE (Vol. 85, pp. 6–23). IEEE. https://doi.org/10.1109/5.554205spa
dc.relation.referencesHan, 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.2014.2314535spa
dc.relation.referencesHansen, 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).spa
dc.relation.referencesHärdter, 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.spa
dc.relation.referencesHatch, 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.pdfspa
dc.relation.referencesHenson, 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.spa
dc.relation.referencesHenson, 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.spa
dc.relation.referencesHenson, I. E. (2005). Modelling seasonal variation in oil palm bunch production using a spreadsheet programme. Journal of Oil Palm Research, 17(June), 27–40.spa
dc.relation.referencesHenson, I. E. (2006). Modelling the impact of climatic and climate-related factors on oil palm growth and productivity. Selangor: Malaysian Palm Oil Board.spa
dc.relation.referencesHenson, 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.spa
dc.relation.referencesHernandez 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 978-92-75-32913-9spa
dc.relation.referencesHoffmann, 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/11858/00-1735-0000-0022-5FC1-4spa
dc.relation.referencesHoffmann, 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.2016.11.005spa
dc.relation.referencesHolzworth, 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.2014.07.009spa
dc.relation.referencesHopkins, 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.spa
dc.relation.referencesHowland, 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/spa
dc.relation.referencesHukseflux. (n.d.). Brochure: HFP01SC. Retrieved from http://www.hukseflux.com/product/hfp01scspa
dc.relation.referencesHuth, 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.2014.06.021spa
dc.relation.referencesIbrahim, 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-yspa
dc.relation.referencesIDEAM. (2015). Informes técnicos: Boletín Agrometeorológico. Retrieved February 10, 2015, from http://www.pronosticosyalertas.gov.co/web/tiempo-y-clima/boletin-semanal-de-seguimiento-y-pronosticospa
dc.relation.referencesIDEAM. (2018). Sistema de Recepcion Satelital de Datos del IDEAM Hydras3. Retrieved January 18, 2018, from http://hydras3.ideam.gov.co/LOGIN.HTMspa
dc.relation.referencesIEEE. (2014). 2014 IEEE Thesaurus. Retrieved from http://www.ieee.org/documents/ieee_thesaurus_2013.pdfspa
dc.relation.referencesITU. (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-Ispa
dc.relation.referencesITU. (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/enspa
dc.relation.referencesJanssen, 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.2010.01.011spa
dc.relation.referencesJarvis, 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.pdfspa
dc.relation.referencesJarvis, A., & Escobar, D. (2014). Convenio MADR-CIAT: La adaptación al cambio climático, una necesidad para el sector palmicultor. Palmas, 35(4), 56–65.spa
dc.relation.referencesJayashri, 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/ICCCT2.2015.7292724spa
dc.relation.referencesJiménez, 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.0150015spa
dc.relation.referencesJin, 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.2014.2319085spa
dc.relation.referencesJohannsen, 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/B0-12-348530-4/00892-4spa
dc.relation.referencesJourdan, 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:1004290024473spa
dc.relation.referencesJourdan, 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:1004270014678spa
dc.relation.referencesKang, 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.2014.2326775spa
dc.relation.referencesKeong, 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.2012.11.011spa
dc.relation.referencesKersting, 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_6spa
dc.relation.referencesKhaleghi, 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.2011.08.001spa
dc.relation.referencesKim, 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.2009.01.007spa
dc.relation.referencesKitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering.spa
dc.relation.referencesKulkarni, 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.2011.040310.00002spa
dc.relation.referencesKwong, 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.2011.10.013spa
dc.relation.referencesLamade, 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.spa
dc.relation.referencesLamade, 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.spa
dc.relation.referencesLascano, 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/668/668spa
dc.relation.referencesLascano, 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/840/840spa
dc.relation.referencesLee, 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/s13593-013-0159-4spa
dc.relation.referencesLeekwijck, 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)00337-0spa
dc.relation.referencesLI-COR Inc. (2011). Eddy Covariance Systems. Retrieved from https://www.licor.com/env/products/eddy_covariance/spa
dc.relation.referencesLI-COR Inc. (2015). Brochure: LI-190R Quantum Sensor. Retrieved from https://www.licor.com/env/products/light/quantum.htmlspa
dc.relation.referencesLiao, 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.2012.06.008spa
dc.relation.referencesLipper, 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/nclimate2437spa
dc.relation.referencesLiu, 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.2015.7224772spa
dc.relation.referencesLuo, 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/10.1109/21.44007spa
dc.relation.referencesLuo, 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.2002.1000251spa
dc.relation.referencesMa, 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.2011.32spa
dc.relation.referencesMADR. (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=1.0spa
dc.relation.referencesMADR. (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=1.0spa
dc.relation.referencesMADR. (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.pdfspa
dc.relation.referencesMADR. (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=1.0spa
dc.relation.referencesMADR. (2016b). Documentos Estratégico: Plan Colombia Siembra. Bogotá. Retrieved from https://www.minagricultura.gov.co/planeacion-control-gestion/Gestin/ESTRATEGIA COLOMBIA SIEMBRA V1.pdfspa
dc.relation.referencesMADR, & FEDEPALMA. (2013). Área sembrada a 2013 de Palma de Aceite.spa
dc.relation.referencesMafuta, 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/10.1155/2013/150703spa
dc.relation.referencesMariño, 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.2010.2045970spa
dc.relation.referencesMariño, 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.2008.133spa
dc.relation.referencesMariño, 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.2008.915451spa
dc.relation.referencesMartinez, G. (2010). Pudrición del cogollo, Marchitez sorpresiva, Anillo rojo y Marchitez letal en la palma de aceite en América. Palmas, 31(1), 43–53.spa
dc.relation.referencesMartínez, 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.pdfspa
dc.relation.referencesMarulanda, 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.pdfspa
dc.relation.referencesMascarenhas, M. (2017). CIAT Blog: Pronósticos agroclimáticos al rescate…. Retrieved June 22, 2017, from http://blog.ciat.cgiar.org/es/pronosticos-agroclimaticos-al-rescate/spa
dc.relation.referencesMcBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future Directions of Precision Agriculture. Precision Agriculture, 6(1), 7–23. https://doi.org/10.1007/s11119-005-0681-8spa
dc.relation.referencesMcCarthy, 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.pdfspa
dc.relation.referencesMcCown, 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-Vspa
dc.relation.referencesMejía, 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/726/726spa
dc.relation.referencesMendel, J. M. (1995). Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83(3), 345–377. https://doi.org/10.1109/5.364485spa
dc.relation.referencesMirhosseini, 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.2016.11.001spa
dc.relation.referencesMitchell, H. B. (2012). Data fusion: Concepts and ideas. Data Fusion: Concepts and Ideas. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-27222-6spa
dc.relation.referencesMitralexis, 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_27spa
dc.relation.referencesMoher, 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.1000097spa
dc.relation.referencesMoreno, 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).spa
dc.relation.referencesMosquera, 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.spa
dc.relation.referencesMosquera, 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.spa
dc.relation.referencesMunévar, F. (2004). Criterios agroecológicos útiles en la selección de tierras para nuevas siembras de palma de aceite en Colombia. Palmas, 25(especial), 148–159.spa
dc.relation.referencesMunévar, 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.spa
dc.relation.referencesMunévar, 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.spa
dc.relation.referencesNakamura, 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/10.1145/1267070.1267073spa
dc.relation.referencesNavarro-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.2016.04.003spa
dc.relation.referencesNelson, 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.spa
dc.relation.referencesNelson, 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).spa
dc.relation.referencesNeufeldt, 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/10.1186/2048-7010-2-12spa
dc.relation.referencesNezamabadi-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.2015.01.002spa
dc.relation.referencesNg, 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.pdfspa
dc.relation.referencesNieto, 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).spa
dc.relation.referencesNoleppa, S., & Cartsburg, M. (2016). Auf der Ölspur – Berechnungen zu einer palmölfreieren Welt. (I. Petersen, Ed.). Berlin: WWF Deutschland.spa
dc.relation.referencesOberthür, 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.spa
dc.relation.referencesOboh, 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.spa
dc.relation.referencesOkoro, 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.2017.02.002spa
dc.relation.referencesOlivin, J. (1968). Etude pour la localisation d’un bloc industriel de palmiers à huile. Oleagineux, 23(8–9), 499–504.spa
dc.relation.referencesOlivin, J. (1986). Study for the siting of a commercial oil palm plantation. Oleagineux, 41(3), 113–118.spa
dc.relation.referencesOlson, K. (1998). Precision Agriculture: Current Economic and Environmental Issues. In Sixth Joint Conference on Food, Agriculture, and the Environment.spa
dc.relation.referencesOpenSim 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-tgzspa
dc.relation.referencesOrtegón, A. (2004). Metodología para la realización de estudios de drenaje a nivel predial. Palmas, 25(Especial), 126–136.spa
dc.relation.referencesPalat, 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.spa
dc.relation.referencesPalat, 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.pdfspa
dc.relation.referencesPalat, 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.spa
dc.relation.referencesParamananthan, 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.spa
dc.relation.referencesParamananthan, 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.spa
dc.relation.referencesPardon, 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-13-5433-2016spa
dc.relation.referencesPardon, 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-13-5433-2016spa
dc.relation.referencesPaucar, 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.2015.7375469spa
dc.relation.referencesPediaditakis, 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.SIMUTOOLS2010.8727spa
dc.relation.referencesPham, 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.2007.4351800spa
dc.relation.referencesPierce, 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.2007.05.007spa
dc.relation.referencesPlant, 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)00152-6spa
dc.relation.referencesPoo, 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_2spa
dc.relation.referencesPravia, 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).spa
dc.relation.referencesPye-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_IUspa
dc.relation.referencesRaes, 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.pdfspa
dc.relation.referencesRajagopalan, 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.2006.283821spa
dc.relation.referencesRankine, I., & Fairhurst, T. H. (1999). Field Handbook Oil Palm Series Volume 3: Mature. Singapore: PPI/PPIC and 4T Consultants.spa
dc.relation.referencesRey, 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.spa
dc.relation.referencesReyes, 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.spa
dc.relation.referencesRincón, V. O. (2015). Lotes CEPV.spa
dc.relation.referencesRival, 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.pdfspa
dc.relation.referencesRivera-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.v34n2.55568spa
dc.relation.referencesRobert, 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-xspa
dc.relation.referencesRobert, 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-Gspa
dc.relation.referencesRobert, P. C. (2002). Precision agriculture: A challenge for crop nutrition management. Plant and Soil, 247(1), 143–149. https://doi.org/10.1023/A:1021171514148spa
dc.relation.referencesRobledo 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.aspxspa
dc.relation.referencesRogova, 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).spa
dc.relation.referencesRomero, 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/1330spa
dc.relation.referencesRomero, H. M., Ayala, I., & Ruiz, R. (2007). Ecofisiología de la palma de aceite. Palmas, 28(Especial, Tomo I), 176–184.spa
dc.relation.referencesRos, 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.htmspa
dc.relation.referencesRosenbaum, 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/10.1029/2011WR011518spa
dc.relation.referencesRoss, 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.ch4spa
dc.relation.referencesRuan, 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.2016.07.014spa
dc.relation.referencesRubiano, Y. (2005). Conceptos básicos para utilizar los levantamientos de suelos en el manejo agronómico de la palma de aceite. Bogotá: Cenipalma.spa
dc.relation.referencesRuiz-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.2007.12.033spa
dc.relation.referencesRuiz-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/s90604728spa
dc.relation.referencesRuíz, 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.spa
dc.relation.referencesRuiz Romero, R., & Henson, I. E. (2002). Photosynthesis and stomatal conductance of oil palm in Colombia: some initial observations. Planter, 78(915), 301–308.spa
dc.relation.referencesSáenz, A. (2005). Aspectos generales e importancia del agente causal de anillo rojo. Palmas, 26(2), 59–70.spa
dc.relation.referencesSales, 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.2015.7389138spa
dc.relation.referencesSambhoos, 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).spa
dc.relation.referencesSánchez-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.spa
dc.relation.referencesSarangi, 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.2016.68spa
dc.relation.referencesSatizábal, 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_18spa
dc.relation.referencesSchuster, 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.2011.6163052spa
dc.relation.referencesSelvaraju, 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/spa
dc.relation.referencesShafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press. Retrieved from https://books.google.com.co/books?id=5KwpAQAACAAJspa
dc.relation.referencesShafer, G. (1992). Dempster-shafer theory. In Encyclopedia of artificial intelligence (pp. 330–331).spa
dc.relation.referencesShafer, 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.fmspa
dc.relation.referencesShih, 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.2015.12.004spa
dc.relation.referencesSilva, Á., & Cerón, J. (2010). La agroindustria de la palma de aceite en América. Palmas, 31(Especial-Tomo II), 245–257.spa
dc.relation.referencesSISPA. (2015). Evolución histórica anual de los rendimientos de aceite de palma en Colombia. Retrieved from http://sispaweb.fedepalma.org/SitePages/Home.aspxspa
dc.relation.referencesSivakumar, 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)00115-5spa
dc.relation.referencesSivanandam, 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_1spa
dc.relation.referencesSmith, 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/23692132spa
dc.relation.referencesSpectrum Technologies. (2012). Product Manual: WatchDog 2000 Series Full Weather Stations. Retrieved from https://www.specmeters.com/assets/1/22/2000_All_Series_WS3.pdfspa
dc.relation.referencesSquire, 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.spa
dc.relation.referencesSrbinovska, 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.2014.04.036spa
dc.relation.referencesSteenwerth, 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/10.1186/2048-7010-3-11spa
dc.relation.referencesStevens Water Monitoring Systems Inc. (n.d.). Brochure: HydraProbe. Retrieved from http://www.stevenswater.com/products/sensors/soil/hydraprobe/spa
dc.relation.referencesStevens Water Monitoring Systems Inc. (2006). The Parameters of the HydraProbe. Retrieved from http://www.btnode.ethz.ch/pub/uploads/Internal/hydraprobe.pdfspa
dc.relation.referencesSudevalayam, 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.2011.060710.00094spa
dc.relation.referencesTaiz, L., & Zeiger, E. (2002). Plant Physiology. Annals of Botany (3 edition). Sinauer Associates. https://doi.org/10.1104/pp.900074spa
dc.relation.referencesTan, 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).spa
dc.relation.referencesTan, H. Ö., & Körpeoǧlu, I. (2003). Power Efficient Data Gathering and Aggregation in Wireless Sensor Networks. SIGMOD Rec., 32(4), 66–71. https://doi.org/10.1145/959060.959072spa
dc.relation.referencesTexas Electronics Inc. (n.d.). Brochure: TR-525M. Retrieved from http://texaselectronics.com/rain-gauge-tr-525m-metric.htmlspa
dc.relation.referencesThe 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.htmlspa
dc.relation.referencesTinker, 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.spa
dc.relation.referencesToro, F. (2009a). Colección Fotográfica Fedepalma: estacion metereologica 01. Retrieved November 21, 2017, from http://repfedepalma.catalogokohaplus.com:8080/fedepalma/xmlui/handle/12345/10681spa
dc.relation.referencesToro, F. (2009b). Colección Fotográfica Fedepalma: estacion metereologica 03. Retrieved November 21, 2017, from http://repfedepalma.catalogokohaplus.com:8080/fedepalma/xmlui/handle/12345/10684spa
dc.relation.referencesTorres, 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-RVWspa
dc.relation.referencesTorres, 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.pdfspa
dc.relation.referencesTorres, J., Ruiz, M., & Barrera, O. (2016). Xmac Palma: la herramienta climática al servicio del palmicultor. Bogotá.spa
dc.relation.referencesTurner, 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.spa
dc.relation.referencesTurner, P. D., & Gillbanks, R. A. (2003). Oil palm cultivation and management (Second). Kuala Lumpur: Incorporated Society of Planters.spa
dc.relation.referencesVaisala. (2012). Brochure: HMP155 Humidity and Temperature Probe. Retrieved from http://www.vaisala.com/en/products/humidity/Pages/HMP155.aspxspa
dc.relation.referencesVan 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-Xspa
dc.relation.referencesVarshney, 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_1spa
dc.relation.referencesVasisht, 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/vasishtspa
dc.relation.referencesVerdouw, 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.2013.09.006spa
dc.relation.referencesVerdouw, 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.2015.11.009spa
dc.relation.referencesVerhagen, 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-Yspa
dc.relation.referencesVermeulen, 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-020411-130608spa
dc.relation.referencesViani, 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.30000spa
dc.relation.referencesWald, L. (1999). Some terms of reference in data fusion. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/36.763269spa
dc.relation.referencesWallace, 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/10.1080/00103629409369002spa
dc.relation.referencesWang, 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.2016.09.048spa
dc.relation.referencesWang, 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.2005.09.003spa
dc.relation.referencesWerro, 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_2spa
dc.relation.referencesWhite, F. (1991). Data Fusion Lexicon. San Diego. Retrieved from http://www.dtic.mil/dtic/tr/fulltext/u2/a529661.pdfspa
dc.relation.referencesWMO. (2003). Manual on the Global Observing System WMO-No. 544. WMO.spa
dc.relation.referencesWMO. (2008). Guide of Meteorological Instruments and Methods of Observation WMO-No. 8. WMO.spa
dc.relation.referencesWMO. (2010). Guide to Agricultural Meteorological Practices WMO-No. 134. WMO.spa
dc.relation.referencesWoittiez, 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.spa
dc.relation.referencesWoittiez, 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.2016.11.002spa
dc.relation.referencesWolfert, 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.2017.01.023spa
dc.relation.referencesWood, 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.spa
dc.relation.referencesWu, 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.2007.4425353spa
dc.relation.referencesYadav, S. G. S., & Chitra, A. (2015). Reviewing the process of data fusion in wireless sensor network : a brief survey, 8(2), 130–140.spa
dc.relation.referencesYager, 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/s10700-011-9098-1spa
dc.relation.referencesYager, 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_12spa
dc.relation.referencesYang, 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.2013.01.034spa
dc.relation.referencesYara 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/spa
dc.relation.referencesYick, J., Mukherjeea, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 58(12), 2292–2330. https://doi.org/10.1016/j.comnet.2008.04.002spa
dc.relation.referencesYuan, 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.2003.1258234spa
dc.relation.referencesYusoff, S. (2006). Renewable energy from palm oil – innovation on effective utilization of waste. Journal of Cleaner Production, 14(1), 87spa
dc.relation.referencesZadeh, 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-Xspa
dc.relation.referencesZadeh, 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.1973.5408575spa
dc.relation.referencesZadeh, 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)90017-1spa
dc.relation.referencesZadeh, 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)90036-5spa
dc.relation.referencesZadeh, 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)90046-8spa
dc.relation.referencesZia, 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.2015.09.012spa
dc.relation.referencesZimmermann, H.-J. (2010). Fuzzy set theory. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 317–332. https://doi.org/10.1002/wics.82spa
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000069035*
dc.contributor.cvlacCabrera Cruz, José Daniel [0000069035]
dc.contributor.googlescholarhttps://scholar.google.es/citations?hl=es#user=hses_w0AAAAJ*
dc.contributor.googlescholarCabrera Cruz, José Daniel [0000069035]
dc.contributor.orcidhttps://orcid.org/0000-0002-1815-5057*
dc.contributor.orcidCabrera Cruz, José Daniel [0000-0002-1815-5057]
dc.contributor.researchgatehttps://www.researchgate.net/profile/Jose_Cabrera_Cruz*
dc.contributor.researchgateCabrera Cruz, José Daniel [Jose_Cabrera_Cruz]
dc.subject.lembIngeniería de sistemasspa
dc.subject.lembTelemáticaspa
dc.subject.lembSistemas de comunicación inalámbricaspa
dc.subject.lembTecnología inalámbricaspa
dc.subject.lembProcesamiento electrónico de datosspa
dc.subject.lembInvestigacionesspa
dc.subject.lembAnálisisspa
dc.description.abstractenglishSince agriculture is the human activity most dependent on climatic conditions, it is vital that farmers make informed decisions. Unfortunately, in Colombia, farmers tend to decide on a limited knowledge base, and this subjects their production systems to the uncertainty generated by climate variability and change. The causes of this problem can be summarized in three situations: farmers do not have access to agrometeorological information and agroclimatic forecasts at the local level, farmers do not have the competence to make decisions based on the information, and farmers do not have the economic resource to back their decisions. This research work focuses on addressing the second cause, about bringing the agrometeorological information to actionable information to support decision making in the management of oil palm cultivation. Assuming an agricultural scenario where a Wireless Sensor Network is deployed to acquire local and representative data in the field, a Data Fusion method was formulated that supports irrigation management by inferring the state of the crop and deciding on the need for irrigation. The method involves two levels, the first level of decision that combines data on soil moisture, ambient temperature, and relative humidity to decide whether to water or not irrigate the crop plot using the Dempster-Shafer Inference technique. And the second level of evaluation to the decision, which combines data of the crop evapotranspiration, precipitation and the decision of irrigation in the crop plot to qualify the performance of the decision in the context of the plantation using the Fuzzy Logic technique. The impact of the method in the management of oil palm cultivation was established through the simulation of two scenarios: crop plot with irrigation managed by the first level of the method, and crop plot without irrigation. The results indicate a potential impact of increasing crop yield by 27%, thanks to the irrigation decisions made by the method.eng
dc.subject.proposalSoporte a la decisiónspa
dc.subject.proposalFusión de datosspa
dc.subject.proposalAgrometeorologíaspa
dc.subject.proposalPalma de aceitespa
dc.subject.proposalGestión del cultivospa
dc.subject.proposalRedes Inalámbricas de sensoresspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.contributor.researchgroupGrupo de Investigación Pensamiento Sistémico - GPSspa
dc.contributor.researchgroupGrupo de Investigaciones Clínicasspa
dc.contributor.apolounab
dc.coverage.campusUNAB Campus Bucaramangaspa
dc.description.learningmodalityModalidad Presencialspa
dc.contributor.linkedinCabrera Cruz, José Daniel [josé-daniel-cabrera-cruz-23900b10]


Ficheros en el ítem

Thumbnail
Nombre:
2018_Tesis_Culman_Forero_Maria ...
Tamaño:
3.137Mb
Formato:
PDF
Descripción:
Tesis
Ver/
Thumbnail
Nombre:
2018_Articulo_Culman_Forero_Ma ...
Tamaño:
632.5Kb
Formato:
PDF
Descripción:
Artículo
Ver/
Thumbnail
Nombre:
2018_Licencia_Culman_Forero_Ma ...
Tamaño:
169.5Kb
Formato:
PDF
Descripción:
Licencia
Ver/

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Atribución-NoComercial-SinDerivadas 2.5 Colombia
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución-NoComercial-SinDerivadas 2.5 Colombia