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Determinantes de la adopción de tecnologías para el manejo eficiente del agua por los cultivadores de palma de aceite en la zona Norte Colombiana
dc.rights.license | Reconocimiento 4.0 Internacional |
dc.contributor.advisor | Arias Arias, Nolver Atanacio |
dc.contributor.advisor | Barrios, Dursun |
dc.contributor.author | Martínez Arteaga, Diana |
dc.date.accessioned | 2022-08-22T13:37:08Z |
dc.date.available | 2022-08-22T13:37:08Z |
dc.date.issued | 2022 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/81983 |
dc.description | ilustraciones, graficas |
dc.description.abstract | La palma de aceite es uno de los sectores con más desarrollo en la agricultura colombiana. Los palmicultores, invierten cada año recursos muy importantes (cerca de USD 10 millones) en investigación y desarrollo de tecnologías para afrontar retos como la eficiencia productiva, competitiva y sostenible. Sin embargo, la adopción de tecnologías de riegos presurizados, nutrición balanceada y manejo fitosanitario ha sido tradicionalmente muy baja. No obstante, el cultivo de palma de aceite ha venido creciendo en los últimos años en diferentes zonas geográficas del país, observándose siembras en condiciones edafoclimáticas de baja aptitud para la producción agrícola, como es el caso de la zona Norte, que presenta limitaciones en la disponibilidad de agua y desbalance de bases en el suelo, y los agricultores de la región comúnmente utilizan sistemas de riego por superficie, los cuales presentan eficiencias que difícilmente llegan al 50%. Así, con este trabajo de investigación se busca determinar los factores que pueden influir en la adopción de tecnologías para el manejo eficiente del agua por los palmicultores ubicados alrededor de la cuenca del rio Sevilla en el departamento del Magdalena. Para lograr el objetivo planteado, se tipificaron los productores de palma de aceite en función de las características demográficas y socioeconómicas, se identificaron las características influyentes en la adopción de riegos presurizados por parte de los productores y se implementó una versión extendida del modelo de aceptación de tecnología para predecir la intención de usar riegos presurizados por parte de los agricultores de palma de aceite. Los resultados revelaron que menos del 15% de los productores adoptan riegos presurizados. Además, los productores de palma de aceite de la muestra eran heterogéneos con respecto a las características socioeconómicas y demográficas. Los factores que más influyen en la adopción de tecnologías son la edad, el tamaño de la plantación y el acceso a extensión. En cuanto, a la aceptación de riegos presurizados puede predecirse adecuadamente a partir de las intenciones de los agricultores. Finalmente, si bien, en esta investigación se quiso integrar los diferentes aspectos que influyen en la adopción de tecnologías, es importante en futuros trabajos considerar la racionalidad económica como impulsor en la adopción de tecnologías. (Texto tomado de la fuente) |
dc.description.abstract | Oil palm is one of the most developed agricultural sectors in Colombian agriculture. Palm growers invest significant resources each year (close to USD 10 million) in the research and development of technologies to meet agribusiness challenges such as productive, competitive and sustainable efficiency. However, the adoption of pressurized irrigation technologies, balanced nutrition and phytosanitary management has traditionally been very low. However, the adoption of these technologies has traditionally been very low. In recent years, palm oil plantations have expanded to diverse geographic areas across the country. As a result, many new plantings have been done under edaphoclimatic conditions less suitable for agricultural production. The Northern region of Colombia is a great example, with limited water availability and less fertile soils. Farmers in the region commonly use surface irrigation systems, which present efficiencies that hardly reach 50%. Thus, this project seeks the factors the influence the adoption of technologies for efficient water management by palm growers in the Cuenca Rio Sevilla in the department of Magdalena. To achieve the stated objective, oil palm producers were classified according to demographic and socioeconomic characteristics, several factors on the adoption of pressurized irrigation were identified, and an extended version of the technology acceptance model was implemented. The results revealed that less than 15% of the farmers studied adopt pressurized irrigation. In addition, oil palm producers in the sample were heterogeneous with respect to socioeconomic and demographic characteristics. The factors with the most influence are age, size of the plantation, and access to the extension. As for the acceptance of pressurized irrigation, it can be adequately predicted from the intentions of the farmers. Finally, although in this research we have wanted to integrate the different aspects that influence the adoption of technologies, it is important in future works to consider economic rationality as a driving force in the adoption of technologies. |
dc.description.sponsorship | Fondo de Fomento Palmero |
dc.format.extent | 83 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | spa |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ |
dc.subject.ddc | 300 - Ciencias sociales::304 - Factores que afectan el comportamiento social |
dc.title | Determinantes de la adopción de tecnologías para el manejo eficiente del agua por los cultivadores de palma de aceite en la zona Norte Colombiana |
dc.type | Trabajo de grado - Maestría |
dc.type.driver | info:eu-repo/semantics/masterThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Bogotá - Ciencias Agrarias - Maestría en Gestión y Desarrollo Rural |
dc.contributor.researchgroup | Biogénesis |
dc.description.degreelevel | Maestría |
dc.description.degreename | Magíster en Medio Ambiente y Desarrollo |
dc.description.researcharea | Desarrollo Empresarial Agropecuario |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.department | Escuela de posgrados |
dc.publisher.faculty | Facultad de Ciencias Agrarias |
dc.publisher.place | Bogotá, Colombia |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá |
dc.relation.indexed | RedCol |
dc.relation.indexed | LaReferencia |
dc.relation.references | Abdulai, A., Owusu, V., and Bakang, J. . E. A. (2011). Adoption of safer irrigation technologies and cropping patterns: Evidence from Southern Ghana. Ecological Economics, 70. |
dc.relation.references | Abdullah, F. and Ward, R. (2016). Developing a general extended technology acceptance model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56:238–256. |
dc.relation.references | Adnan, N., Nordin, S. M., and bin Abu Bakar, Z. (2017). Understanding and facilitating sustainable agricultural practice: A comprehensive analysis of adoption behaviour among Malaysian paddy farmers. Land Use Policy, 68(May):372–382. |
dc.relation.references | Aguilar, G. N., Mu˜noz, R. M., Santoyo, C. V. H., and Aguilar, ´A. J. (2013). Influencia del perfil de los productores en la adopci´on de innovaciones en tres cultivos tropicales. Teuken Bidikay, 4:207–228. |
dc.relation.references | Aguilar-Gallegos, N., Muñoz-Rodríguez, M., Santoyo-Cortés, H., Aguilar-Ávila, J., & Klerkx, L. (2015). Information networks that generate economic value: A study on clusters of adopters of new or improved technologies and practices among oil palm growers in Mexico. Agricultural Systems, 135, 122–132. https://doi.org/10.1016/j.agsy.2015.01.003 |
dc.relation.references | Aitken, D., Rivera, D., Godoy-Faúndez, A., Holzapfel, E., 2016. Water scarcity and the impact of the mining and agricultural sectors in Chile. Sustain. 8, 128. https://doi.org/10.3390/su8020128 |
dc.relation.references | Ajzen. (1991). The Theory of Planned Behavior Organizational Behavior and Human Decision Processes. Organizational Behavior and Human Decision Processes, 50(2), 179–211. |
dc.relation.references | Alam, K., 2015. Farmers' adaptation to water scarcity in drought-prone environments: A case study of Rajshahi District, Bangladesh. Agric. Water Manag. 148, 196–206. https://doi.org/10.1016/j.agwat.2014.10.011 |
dc.relation.references | Alambaigi, A., & Ahangari, I. (2016). Technology Acceptance Model (TAM) As a predictor model for explaining agricultural experts behavior in acceptance of ICT. International Journal of Agricultural Management and Development, 6(2), 235–247. |
dc.relation.references | Alberti, G. (2013). An R script to facilitate Correspondence Analysis: a guide to the use and the interpretation of results from an archaeological perspective. Archeologia e Calcolatori, 25–53. http://eprints.bice.rm.cnr.it/9311/1/02_Alberti.pdf |
dc.relation.references | Álvarez, O., Ruiz, E., Mosquera, M., & Humberto, J. (2018). Evaluación económica de sistemas de riego para plantaciones de palma aceitera en la Zona Norte de Colombia. Revistas Palmas, 39(1), 69–85. https://publicaciones.fedepalma.org/index.php/palmas/article/view/12401 |
dc.relation.references | Araujo, D.F., Costa, R.N., Mateos, L., 2019. Pros and cons of furrow irrigation on smallholdings in northeast Brazil. Agric. Water Manag. 221, 25–33. https://doi.org/10.1016/j.agwat.2019.04.029 |
dc.relation.references | Ayompe, L. M., Schaafsma, M., & Egoh, B. N. (2021). Towards sustainable palm oil production: The positive and negative impacts on ecosystem services and human wellbeing. Journal of Cleaner Production, 278, 123914. https://doi.org/10.1016/j.jclepro.2020.123914 |
dc.relation.references | Azhar, B., Saadun, N., Prideaux, M., & Lindenmayer, D. B. (2017). The global palm oil sector must change to save biodiversity and improve food security in the tropics. Journal of Environmental Management, 203, 457–466. https://doi.org/10.1016/j.jenvman.2017.08.021 |
dc.relation.references | Barcelos, E., De Almeida Rios, S., Cunha, R.N.V., Lopes, R., Motoike, S.Y., Babiychuk, E., Skirycz, A., Kushnir, S., 2015. Oil palm natural diversity and the potential for yield improvement. Front. Plant Sci. 6, 1–16. https://doi.org/10.3389/fpls.2015.00190 |
dc.relation.references | Bergeron, F., Rivard, S., & De Serre, L. (1990). Investigating the support role of the information center. MIS Quarterly: Management Information Systems, 14(3), 247–260. https://doi.org/10.2307/248887 |
dc.relation.references | Bernal-Hernández, P., Ramirez, M., & Mosquera-Montoya, M. (2021). of environmental and economic performance of combined.pdf. Journal of Rural Studies, 83(March 2020), 215–225. https://doi.org/10.1016/j.jrurstud.2020.11.006 |
dc.relation.references | Bjornlund, H., Nicol, L., Klein, K.K., 2009. The adoption of improved irrigation technology and management practices-A study of two irrigation districts in Alberta, Canada. Agric. Water Manag. 96, 121–131. https://doi.org/10.1016/j.agwat.2008.07.009 |
dc.relation.references | Bjornlund, V., Bjornlund, H., 2019. Understanding agricultural water management in a historical context using a socioeconomic and biophysical framework. Agric. Water Manag. 213, 454–467. https://doi.org/10.1016/j.agwat.2018.10.037 |
dc.relation.references | Boretti, A., Rosa, L., 2019. Reassessing the projections of the World Water Development Report. NPJ Clean Water. 2, 1–6. https://doi.org/10.1038/s41545-019-0039-9 |
dc.relation.references | Bouma, J., Bulte, E., Van Soest, D., 2008. Trust and cooperation: Social capital and community resource management. J. Environ. Econ. Manag. 56, 155–166. https://doi.org/10.1016/j.jeem.2008.03.004 |
dc.relation.references | Cascallar, E., Musso, M., Kyndt, E., Dochy, F., 2015. Modelling for understanding AND for prediction/classification - the power of neural networks in research. Front. Learn. Res. 2, 67–81. https://doi.org/10.14786/flr.v2i5.135 |
dc.relation.references | Castillo, G.M.L., Engler, A., Wollni, M., 2021. Planned behavior and social capital: Understanding farmers' behavior toward pressurized irrigation technologies. Agric. Water Manag. 243, 106524. https://doi.org/10.1016/j.agwat.2020.106524 |
dc.relation.references | Cenipalma. (2020). Lineamientos para la Asistencia Técnica del gremio palmero Con apoyo del Fondo de Fomento Palmero. 1–21. https://www.cenipalma.org/wp-content/uploads/2020/04/Lineamientos-para-la-consolidacio%CC%81n-de-la-asistencia-te%CC%81cnica-palmera-2020-Vrs-Final-1.pdf |
dc.relation.references | Chen, H., Wang, J., Huang, J., 2014. Policy support, social capital, and farmers' adaptation to drought in China. Glob. Environ. Chang. 24, 193–202. https://doi.org/10.1016/j.gloenvcha.2013.11.010 |
dc.relation.references | Chin, W. W. (1998). Commentary issues and opinion on Structural Equation Modeling Clear reporting. Modern Methods for Business Research Methodology for Business and Management, 22(1), vii–xvi. |
dc.relation.references | Corley, R.H.V., 2009. How much palm oil do we need? Environ. Sci. Policy 12, 134–139. https://doi.org/10.1016/j.envsci.2008.10.011 |
dc.relation.references | Cragg, P. B., & King, M. (1993). Small-Firm Computing: Motivators and inhibitors. MIS Quarterly, 17(1), 47. https://doi.org/10.2307/249509 |
dc.relation.references | Cremades, R., Wang, J., Morris, J., 2015. Policies, economic incentives and the adoption of modern irrigation technology in China. Earth Syst. Dyn. 6, 399–410. https://doi.org/10.5194/esd-6-399-2015 |
dc.relation.references | DANE. (2018). National Council for Economic and Social Policy. Glosario, 61, 4. https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/censo-nacional-de-poblacion-y-vivenda-2018 |
dc.relation.references | Daryanto, S., Wang, L., & Jacinthe, P. A. (2017). Global synthesis of drought effects on cereal, legume, tuber and root crops production: A review. Agricultural Water Management, 179, 18–33. https://doi.org/10.1016/j.agwat.2016.04.022 |
dc.relation.references | Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008 |
dc.relation.references | Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982 |
dc.relation.references | Davis, F. D., & Venkatesh, V. (2004). Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Transactions on Engineering Management, 51(1), 31–46. https://doi.org/10.1109/TEM.2003.822468 |
dc.relation.references | Engler, A., Jara-Rojas, R., Bopp, C., 2016. Efficient use of water resources in vineyards: A recursive joint estimation for the adoption of irrigation technology and scheduling. Water Resour. Manag. 30, 5369–5383. https://doi.org/10.1007/s11269-016-1493-5 |
dc.relation.references | Fader, M., Shi, S., Von Bloh, W., Bondeau, A., Cramer, W., 2016. Mediterranean irrigation under climate change: More efficient irrigation needed to compensate for increases in irrigation water requirements. Hydrol. Earth Syst. Sci. 20, 953–973. https://doi.org/10.5194/hess-20-953-2016 |
dc.relation.references | Fedepalma. (2020). Informe de Gestión. https://fedepalma.info/wp-content/uploads/2021/06/Informe-Gestion-Fedepalma-2020-DIGITAL-B.pdf |
dc.relation.references | Fishbein. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Journal of Business Venturing, 5(3), 177–189. https://doi.org/10.1016/0883-9026(90)90031-N |
dc.relation.references | Flett, R., Alpass, F., Humphries, S., Massey, C., Morriss, S., & Long, N. (2004). The technology acceptance model and use of technology in New Zealand dairy farming. Agricultural Systems, 80(2), 199–211. https://doi.org/10.1016/j.agsy.2003.08.002 |
dc.relation.references | Gaitan, S. B., & Ríos, M. D. (2020). Socio-economic and technological typology of avocado cv. Hass farms from Antioquia (Colombia). Ciencia Rural, 50(7), 1–17. https://doi.org/10.1590/0103-8478cr20190188 |
dc.relation.references | Galioto, F., Chatzinikolaou, P., Raggi, M., Viaggi, D., 2020. The value of information for the management of water resources in agriculture: Assessing the economic viability of new methods to schedule irrigation. Agric. Water Manag. 227, 105848. https://doi.org/10.1016/j.agwat.2019.105848 |
dc.relation.references | Gilg, A., & Barr, S. (2006). Behavioural attitudes towards water saving? Evidence from a study of environmental actions. Ecological Economics, 57(3), 400–414. https://doi.org/10.1016/j.ecolecon.2005.04.010 |
dc.relation.references | González, V., 2015. The use of the multilayer perceptron for the classification of patterns in addictive behaviors 55, original in spanish. |
dc.relation.references | Hair, J. F. J., Hult, G. T. ., Ringle, C. ., & Sarstedt, M. (2017). A Primer on partial least Squares Structural Equation Modeling (PLS-SEM). Sage Publications. European Journal of Tourism Research, 6(2), 211–213. |
dc.relation.references | Henseler, J. (2010). On the convergence of the partial least squares path modeling algorithm. Computational Statistics, 25(1), 107–120. https://doi.org/10.1007/s00180-009-0164-x |
dc.relation.references | Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 |
dc.relation.references | Hunecke, C., Engler, A., Jara-Rojas, R., Poortvliet, P.M., 2017. Understanding the role of social capital in adoption decisions: An application to irrigation technology. Agric. Syst. 153, 221–231. https://doi.org/10.1016/j.agsy.2017.02.002 |
dc.relation.references | Ibragimov, A., Sidique, S.F., Tey, Y.S., 2019. Productivity for sustainable growth in Malaysian oil palm production: A system dynamics modeling approach. J. Clean. Prod. 213, 1051–1062. https://doi.org/10.1016/j.jclepro.2018.12.113 |
dc.relation.references | Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly: Management Information Systems, 21(3), 279–301. https://doi.org/10.2307/249498 |
dc.relation.references | Iskandar, M.J., Baharum, A., Anuar, F.H., Othaman, R., 2018. Palm oil industry in South East Asia and the effluent treatment technology—A review. Environ. Technol. Innov. 9, 169–185. https://doi.org/10.1016/j.eti.2017.11.003 |
dc.relation.references | Jara-Rojas, R., Bravo-Ureta, B.E., Engler, A., Díaz, J., 2013. An analysis of the joint adoption of water conservation and soil conservation in Central Chile. Land use policy 32. https://doi.org/10.1016/j.landusepol.2012.11.001 |
dc.relation.references | Jelsma, I., Woittiez, L.S., Ollivier, J., Dharmawan, A.H., 2019. Do wealthy farmers implement better agricultural practices? An assessment of implementation of Good Agricultural Practices among different types of independent oil palm smallholders in Riau, Indonesia. Agric. Syst. 170, 63–76. https://doi.org/10.1016/j.agsy.2018.11.004 |
dc.relation.references | Jordán, C., Speelman, S., 2020. On-farm adoption of irrigation technologies in two irrigated valleys in Central Chile: The effect of relative abundance of water resources. Agric. Water Manag. 236, 106147. https://doi.org/10.1016/j.agwat.2020.106147 |
dc.relation.references | Kaliba, A.R., Mushi, R.J., Gongwe, A.G., Mazvimavi, K., 2020. A typology of adopters and nonadopters of improved sorghum seeds in Tanzania: A deep learning neural network approach. World Dev. 127, 104839. https://doi.org/10.1016/j.worlddev.2019.104839 |
dc.relation.references | King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003 |
dc.relation.references | Koech, R., Langat, P., 2018. Improving irrigation water use efficiency: A review of advances, challenges and opportunities in the Australian context. Water (Switzerland) 10. https://doi.org/10.3390/w10121771 |
dc.relation.references | Lai, P. (2017). the Literature review of Technology Adoption Models and Theories for the Novelty Technology. Journal of Information Systems and Technology Management, 14(1), 21–38. https://doi.org/10.4301/s1807-17752017000100002 |
dc.relation.references | Lathief, M. F., Soesanti, I., & Permanasari, A. E. (2020). Combination of Fuzzy C-Means, Xie-Beni Index, and Backpropagation Neural Network for Better Forecasting Result. Iccetim 2019, 72–77. https://doi.org/10.5220/0009858200720077 |
dc.relation.references | Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40(3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4 |
dc.relation.references | Li, M., Bi, X., Wang, L., & Han, X. (2021). A method of two-stage clustering learning based on improved DBSCAN and density peak algorithm. Computer Communications, 167(January), 75–84. https://doi.org/10.1016/j.comcom.2020.12.019 |
dc.relation.references | Liang, H., Xue, Y., & Byrd, T. A. (2003). PDA usage in healthcare professionals: testing an extended technology acceptance model. International Journal of Mobile Communications, 1(4), 372–389. https://doi.org/10.1504/IJMC.2003.003992 |
dc.relation.references | Liu, Y., Ruiz-Menjivar, J., Zhang, L., Zhang, J., Swisher, M.E., 2019. Technical training and rice farmers' adoption of low-carbon management practices: The case of soil testing and formulated fertilization technologies in Hubei, China. J. Clean. Prod. 226, 454–462. https://doi.org/10.1016/j.jclepro.2019.04.026 |
dc.relation.references | Loevinsohn, M., Sumberg, J., Diagne, A., Whitfield, S., 2013. Under what circumstances and conditions does adoption of technology result in increased agricultural productivity? A Systematic Review (Prepared for the Department for International Development). |
dc.relation.references | Maleksaeidi, H., & Keshavarz, M. (2019). What influences farmers’ intentions to conserve on-farm biodiversity? An application of the theory of planned behavior in fars province, Iran. Global Ecology and Conservation, 20, e00698. https://doi.org/10.1016/j.gecco.2019.e00698 |
dc.relation.references | Martínez Ávila, M., & Fierro Moreno, E. (2018). Application of the PLS-SEM technique in Knowledge Management: a practical technical approach. In RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo (Vol. 8, Issue 16). https://doi.org/10.23913/ride.v8i16.336 |
dc.relation.references | Meijaard, E., Futures, B., Garcia-ulloa, J., Sheil, D., & Wich, S. (2018). A situation analysis by the IUCN Oil Palm Task Force (Issue June). https://portals.iucn.org/library/sites/library/files/documents/2018-027-En.pdf |
dc.relation.references | Ministerio de ambiente Colombia. (2017). Acuerdo De Voluntades Para La Deforestación Cero En La Cadena De Aceite De Palma En Colombia. 1–21. https://archivo.minambiente.gov.co/images/BosquesBiodiversidadyServiciosEcosistemicos/pdf/Acuerdo_cero_deforestacion/ACUERDO_DE_VOLUNTADES_PARA_LA_DEFORESTACION_CERO_EN_LA_CADENA_DE_ACEITE_DE_PALMA_EN_COLOMBIA_Texto_Final.pdf |
dc.relation.references | Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information and Management, 38(4), 217–230. https://doi.org/10.1016/S0378-7206(00)00061-6 |
dc.relation.references | Moore, G. C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. In Information Systems Research (Vol. 2, pp. 192–222). |
dc.relation.references | Mpanga, I.K., Idowu, O.J., 2021. A decade of irrigation water uses trends in Southwest USA: The role of irrigation technology, best management practices, and outreach education programs. Agric. Water Manag. 243, 106438. https://doi.org/10.1016/j.agwat.2020.106438 |
dc.relation.references | Nakano, Y., Tsusaka, T.W., Aida, T., Pede, V.O., 2018. Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania. World Dev. 105, 336–351. https://doi.org/10.1016/j.worlddev.2017.12.013 |
dc.relation.references | Nikouei, A., Zibaei, M., Ward, F.A., 2012. Incentives to adopt irrigation water saving measures for wetlands preservation: An integrated basin scale analysis. J. Hydrol. 464–465, 216–232. https://doi.org/10.1016/j.jhydrol.2012.07.013 |
dc.relation.references | Nonvide, G.M.A., 2018. Irrigation adoption: A potential avenue for reducing food insecurity among rice farmers in Benin. Water Resour. Econ. 24, 40–52. https://doi.org/10.1016/j.wre.2018.05.002 |
dc.relation.references | Oyetunde-Usman, Z., Olagunju, K.O., Ogunpaimo, O.R., 2021. Determinants of adoption of multiple sustainable agricultural practices among smallholder farmers in Nigeria. Int. Soil Water Conserv. Res. 9, 241–248. https://doi.org/10.1016/j.iswcr.2020.10.007 |
dc.relation.references | Park, N., Rhoads, M., Hou, J., & Lee, K. M. (2014). Understanding the acceptance of teleconferencing systems among employees: An extension of the technology acceptance model. Computers in Human Behavior, 39, 118–127. https://doi.org/10.1016/j.chb.2014.05.048 |
dc.relation.references | Parra Olivares, J.E., 2011. Multiple correspondence analysis model. Rev. Ciencias Soc. 2. https://doi.org/10.31876/rcs.v2i2.24801, original in spanish. |
dc.relation.references | Pérez, I., Janssen, M. A., & Anderies, J. M. (2016). Food security in the face of climate change: Adaptive capacity of small-scale social-ecological systems to environmental variability. Global Environmental Change, 40, 82–91. https://doi.org/10.1016/j.gloenvcha.2016.07.005 |
dc.relation.references | Pirker, J., Mosnier, A., Kraxner, F., Havlík, P., & Obersteiner, M. (2016). What are the limits to oil palm expansion? Global Environmental Change, 40, 73–81. https://doi.org/10.1016/j.gloenvcha.2016.06.007 |
dc.relation.references | Piwowar, A., Dziku, Maciej, Dziku, Maria, 2021. Water management in Poland in terms of reducing the emissions from agricultural sources – current status and challenges 2. https://doi.org/10.1016/j.clet.2021.100082 |
dc.relation.references | Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007. https://doi.org/10.1016/j.jbusres.2006.06.003 |
dc.relation.references | Qaim, M., Sibhatu, K. T., Siregar, H., & Grass, I. (2020). Environmental, economic, and social consequences of the oil palm boom. Annual Review of Resource Economics, 12, 321–344. https://doi.org/10.1146/annurev-resource-110119-024922 |
dc.relation.references | R Core Team, 2019. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 2, 1–12. |
dc.relation.references | Ramirez, A., 2013. The influence of social networks on agricultural technology Adoption. Procedia - Soc. Behav. Sci. 79, 101–116. https://doi.org/10.1016/j.sbspro.2013.05.059 |
dc.relation.references | Rezaei, R., Safa, L., & Ganjkhanloo, M. M. (2020). Understanding farmers’ ecological conservation behavior regarding the use of integrated pest management- an application of the technology acceptance model. Global Ecology and Conservation, 22, e00941. https://doi.org/10.1016/j.gecco.2020.e00941 |
dc.relation.references | Rodríguez-Hernández, C.F., Musso, M., Kyndt, E., Cascallar, E., 2021. Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation. Comput. Educ. Artif. Intell. 2, 100018. https://doi.org/10.1016/j.caeai.2021.100018 |
dc.relation.references | Rogers Everett. (1962). Diffusion of Innovations. Third Ed. Free Press, New York, USA., 24(3), 355–368. https://doi.org/10.1007/s10460-007-9072-2 |
dc.relation.references | Romero, H. M., Ayala, I., & Ruiz, R. (2007). Ecofisiología de la palma de aceite. PALMAS Especial, 28, 176–184. |
dc.relation.references | Rossi, F.R., Filho, H.M. de S., Miranda, B.V., Carrer, M.J., 2020. The role of contracts in the adoption of irrigation by Brazilian orange growers. Agric. Water Manag. 233, 106078. https://doi.org/10.1016/j.agwat.2020.106078 |
dc.relation.references | Rspo (2022). Smallholders retrieved from:. https://www.rspo.org/smallholders/. Accessed: 2022-03-18. |
dc.relation.references | Santika, T., Wilson, K. A., Budiharta, S., Law, E. A., Poh, T. M., Ancrenaz, M., Struebig, M. J., and Meijaard, E. (2019). Does oil palm agriculture help alleviate poverty? A multidimensional counterfactual assessment of oil palm development in Indonesia. World Development, 120:105–117. |
dc.relation.references | Sayer, J., Ghazoul, J., Nelson, P., and Klintuni Boedhihartono, A. (2012). Oil palm expansion transforms tropical landscapes and livelihoods. Global Food Security, 1(2):114– 119. |
dc.relation.references | Sharifzadeh, M. S., Damalas, C. A., Abdollahzadeh, G., and Ahmadi- Gorgi, H. (2017). Predicting adoption of biological control among Iranian rice farmers: An application of the extended technology acceptance model (TAM2). Crop Protection, 96(December 2018):88–96. |
dc.relation.references | Siderska, J. (2017). Neural model for assessing the value of social capital. Procedia Engineering, 182:643–650. |
dc.relation.references | Sun, H. and Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human Computer Studies, 64(2):53–78. |
dc.relation.references | Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22:960–967. |
dc.relation.references | Tan, Q., Cai, Y., Chen, B., 2017. An enhanced radial interval programming approach for supporting agricultural production decisions under dual uncertainties and differential aspirations. J. Clean. Prod. 168, 189–204. https://doi.org/10.1016/j.jclepro.2017.08.180 |
dc.relation.references | Teoh, C. H. (2010). “Key Sustainability Issues in the Palm Oil Sector.”Discussion Paper , WBG Multi-Stakeholders Collaboration. World Bank Group. https://www.biofuelobservatory.org/Documentos/Otros/Palm-Oil-Discussion-Paper-FINAL.pdf |
dc.relation.references | Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly: Management Information Systems, 15(1), 125–142. https://doi.org/10.2307/249443 |
dc.relation.references | van Dijk, W. F. A., Lokhorst, A. M., Berendse, F., & de Snoo, G. R. (2016). Factors underlying farmers’ intentions to perform unsubsidised agri-environmental measures. Land Use Policy, 59(December), 207–216. https://doi.org/10.1016/j.landusepol.2016.09.003 |
dc.relation.references | Venkatesh, Morris, D. (2003). User acceptance of information technology: Toward a unified view. Choice Reviews Online, 45(12), 45-6743-45–6743. https://doi.org/10.5860/choice.45-6743 |
dc.relation.references | Venkatesh, V. (2000). Determinants of perceived ease of use: integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model. Information Systems Research, 11(1), 3–11. vvenkate@rhsmith.umd.edu |
dc.relation.references | Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x |
dc.relation.references | Venkatesh, V., & Davis, F. D. (1996). A Model of the Antecedents of Perceived Ease of Use: Development and Test. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb01822.x |
dc.relation.references | Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 |
dc.relation.references | Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. 27(3), 425–478. |
dc.relation.references | Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126(September 2016), 207–216. https://doi.org/10.1016/j.techfore.2017.08.013 |
dc.relation.references | Wahbeh, H., Sagher, M. C. R. A., Back, W., Pundhir, M. A. P., & Travis, F. (2018). Review a S Ystematic R Eview of T Ranscendent S Tates. Explore: The Journal of Science and Healing, 14(1), 19–35. https://doi.org/10.1016/j.explore.2017.07.007 |
dc.relation.references | Wandel, J., Smithers, J., 2000. Factors affecting the adoption of conservation tillage on clay soils in southwestern Ontario, Canada. Am. J. Altern. Agric. 15, 181–188. https://doi.org/10.1017/s0889189300008754 |
dc.relation.references | Wang, J., Klein, K.K., Bjornlund, H., Zhang, L., Zhang, W., 2015. Adoption of improved irrigation scheduling methods in Alberta: An empirical analysis. Can. Water Resour. J. 40, 47–61. https://doi.org/10.1080/07011784.2014.975748 |
dc.relation.references | Wang, L. Y. K., Lew, S. L., Lau, S. H., & Leow, M. C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6), e01788. https://doi.org/10.1016/j.heliyon.2019.e01788 |
dc.relation.references | Wesley, A. S., & Faminow, M. (2014). BAckgrounD PAPer : reseArch AnD DeveloPment. https://deliverypdf.ssrn.com/delivery.php?ID=286116065002000089064013071106022097023057071055063005125104113022119097005087097014107106052111061028034095090106077017101025102070004033004121009014080086090028105056029010116091112086020103025006096127126066102018124096005092067085111096083023076091&EXT=pdf&INDEX=TRUE |
dc.relation.references | Woittiez, L.S., Wijk, M.T. Van, Slingerland, M., Noordwijk, M. Van, Giller, K.E., 2017. Yield gaps in oil palm: A quantitative review of contributing factors. Eur. J. Agron. 83, 57–77. https://doi.org/10.1016/j.eja.2016.11.002 |
dc.relation.references | Wu, J. H., Wang, S. C., & Lin, L. M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International Journal of Medical Informatics, 76(1), 66–77. https://doi.org/10.1016/j.ijmedinf.2006.06.006 |
dc.relation.references | Xie X L, Beni G. (1991). A validity measure for fuzzy clustering. In IEEE Transactions on Pattern Analysis and Machine Intelligence (Vol. 13, Issue 5, pp. 841–847). https://doi.org/10.1109/TPAMI.2011.60 |
dc.relation.references | Yang, C.-M., 2012. Technologies to improve water management for rice cultivation to cope with climate change editor's view. Crop. Environ. Bioinforma. 8, 193–207. |
dc.relation.references | Yang, Q., Zhu, Y., Wang, J., 2020. Adoption of drip fertigation system and technical efficiency of cherry tomato farmers in Southern China. J. Clean. Prod. 275, 123980. https://doi.org/10.1016/j.jclepro.2020.123980 |
dc.relation.references | Yazdanpanah, M., Hayati, D., Hochrainer-Stigler, S., & Zamani, G. H. (2014). Understanding farmers’ intention and behavior regarding water conservation in the Middle-East and North Africa: A case study in Iran. Journal of Environmental Management, 135, 63–72. https://doi.org/10.1016/j.jenvman.2014.01.016 |
dc.relation.references | Yigezu, Y.A., Mugera, A., El-Shater, T., Aw-Hassan, A., Piggin, C., Haddad, A., Khalil, Y., Loss, S., 2018. Enhancing adoption of agricultural technologies requiring high initial investment among smallholders. Technol. Forecast. Soc. Change 134, 199–206. https://doi.org/10.1016/j.techfore.2018.06.006 |
dc.relation.references | Zhang, B., Fu, Z., Wang, J., Zhang, L., 2019. Farmers' adoption of water-saving irrigation technology alleviates water scarcity in metropolis suburbs: A case study of Beijing, China. Agric. Water Manag 212, 349–357. https://doi.org/10.1016/j.agwat.2018.09.021 |
dc.relation.references | Zhang, N., Guo, X., & Chen, G. (2008). IDT-TAM Integrated Model for IT Adoption. Tsinghua Science and Technology, 13(3), 306–311. https://doi.org/10.1016/S1007-0214(08)70049-X |
dc.relation.references | Zulkefli, F., & Syahlan, S. (2017). An Overview of Acceptance and Adoption of Agricultural Innovation and Technology for Sustainable Palm Oil Industry. International Journal of Academic Research in Business and Social Sciences, 7(11). https://doi.org/10.6007/ijarbss/v7-i11/3467 |
dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.agrovoc | Elaeis guineensis |
dc.subject.agrovoc | Agua de riego |
dc.subject.agrovoc | Irrigation water |
dc.subject.proposal | Extensión Agrícola |
dc.subject.proposal | Tecnologías de riego |
dc.subject.proposal | Modelo de aceptación tecnológica |
dc.subject.proposal | Tipología de productores |
dc.subject.proposal | Agricultural extension |
dc.subject.proposal | Irrigation technologies |
dc.subject.proposal | Technology acceptance model |
dc.subject.proposal | Farm typology |
dc.title.translated | Determinants of the adoption of technologies for efficient water management by oil palm growers in the Colombian North |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/TM |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
oaire.fundername | Centro de Investigación de Palma de Aceite - Cenipalma |
dcterms.audience.professionaldevelopment | Estudiantes |
dcterms.audience.professionaldevelopment | Investigadores |
dcterms.audience.professionaldevelopment | Maestros |
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