Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Published
Factors associated with the adoption of technologies for avocado production systems
Factores asociados a la adopción tecnológica en sistemas de producción de aguacate
DOI:
https://doi.org/10.15446/agron.colomb.v41n3.110579Keywords:
technological changes, multivariate analysis, Persea americana, technological level (en)cambios tecnológicos, análisis multivariado, Persea americana, nivel tecnológico (es)
Downloads
The growth of avocado crops has led to an increase in technological needs and research to satisfy the demands of the value chain. There is a wide range of technologies applicable for this fruit crop, and there are challenges for transferring and adopting these processes. The objective of this work was to explore the determining factors in the adoption of technologies for avocado production systems and the perception of producers about these factors. For this, we carried out a socioeconomic characterization of avocado producers in Colombia including the recognition of the perception of producers regarding technological adoption variables and an exploratory factorial analysis to evaluate the adoption factors based on the perception and technological level (TL). We found that some socioeconomic variables are related to the TL of the production systems. Meanwhile, perceptions regarding the adoption variables varied depending on the TL of the producers. Low TL presented a greater number of determinant variables in adoption decision-making. In contrast, for the medium and high levels of TL, adoption of technology was based on economic analysis. This research provides evidence for the effect of socioeconomic factors on the adoption of technologies in avocado production systems and shows how the perception of producers regarding these adoptions involves determinants associated with TL.
El crecimiento de los cultivos de aguacate ha provocado un aumento de las necesidades tecnológicas y de investigación para satisfacer las demandas de la cadena de valor. Existe una amplia gama de tecnologías aplicables a este frutal, y existen desafíos para transferir y adoptar estos procesos. El objetivo de este trabajo fue explorar los factores determinantes en la adopción de tecnologías para los sistemas de producción de aguacate y la percepción de los productores sobre estos factores. Para esto, realizamos una caracterización socioeconómica de los productores de aguacate en Colombia incluyendo el reconocimiento de la percepción sobre las variables de adopción tecnológica y un análisis factorial exploratorio para evaluar los factores de adopción en función de la percepción y el nivel tecnológico (NT). Encontramos que algunas variables socioeconómicas están relacionadas con el NT de los sistemas de producción. Mientras tanto, las percepciones sobre las variables de adopción variaron dependiendo del NT de los productores. El NT bajo presentó mayor número de variables determinantes en la toma de decisiones de adopción. En contraste, para los niveles medio y alto de NT, la adopción de tecnología se basó en el análisis económico. Esta investigación proporciona evidencia del efecto de los factores socioeconómicos en la adopción de tecnologías en los sistemas de producción de aguacate y muestra cómo la percepción de los productores respecto a estas adopciones involucra determinantes asociados al NT.
References
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.), Action control. From cognition to behavior (pp. 11–39). SSSP Springer Series in Social Psychology. Springer. https://doi.org/10.1007/978-3-642-69746-3_2 DOI: https://doi.org/10.1007/978-3-642-69746-3_2
Ayisi, D. N., Kozári, J., & Krisztina, T. (2022). Do smallholder farmers belong to the same adopter category? An assessment of smallholder farmers innovation adopter categories in Ghana. Heliyon, 8(8), Article e10421. https://doi.org/10.1016/j.heliyon.2022.e10421 DOI: https://doi.org/10.1016/j.heliyon.2022.e10421
Barrios, D., Restrepo-Escobar, F. J., & Cerón-Muñoz, M. (2020). Factors associated with the technology adoption in dairy agribusiness. Revista Facultad Nacional de Agronomía Medellín, 73(2), 9221–9226. https://doi.org/10.15446/rfnam.v73n2.82169 DOI: https://doi.org/10.15446/rfnam.v73n2.82169
Cáceres-Zambrano, J., Jiménez-Hernández, C. N., & Barrios, D. (2022). Tendencias en investigación y desarrollo tecnológico en la cadena productiva de aguacate (Persea americana L.). Revista EIA, 19(38), Article 3826. https://doi.org/10.24050/reia.v19i38.1573 DOI: https://doi.org/10.24050/reia.v19i38.1573
Cáceres-Zambrano, J., Ramírez-Gil, J. G., & Barrios, D. (2022). Validating technologies and evaluating the technological level in avocado production systems: A value chain approach. Agronomy, 12(12), Article 3130. https://doi.org/10.3390/agronomy12123130 DOI: https://doi.org/10.3390/agronomy12123130
Castrillón Correa, Y. T. (2020). Intención de los productores de aguacate Persea americana Mill, variedad Hass para acogerse a la normatividad internacional de comercialización GLOBAL GAP [Undregraduate thesis, Universidad de Antioquia]. https://bit.ly/3QLc2xP
Cavanaugh, J. E., & Neath, A. A. (2019). The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. WIREs Computational Statistics, 11(3), Article e1460. https://doi.org/10.1002/wics.1460 DOI: https://doi.org/10.1002/wics.1460
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555 DOI: https://doi.org/10.1007/BF02310555
Curry, G. N., Nake, S., Koczberski, G., Oswald, M., Rafflegeau, S., Lummani, J., Peter, E., & Nailina, R. (2021). Disruptive innovation in agriculture: Socio-cultural factors in technology adoption in the developing world. Journal of Rural Studies, 88, 422–431. https://doi.org/10.1016/j.jrurstud.2021.07.022 DOI: https://doi.org/10.1016/j.jrurstud.2021.07.022
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results [Doctoral dissertation, Massachusetts Institute of Technology]. https://dspace.mit.edu/handle/1721.1/15192
Doss, C. R. (2003). Understanding farm-level technology adoption: Lessons learned from CIMMYT’s micro surveys in Eastern Africa. CIMMYT Economics Working Paper 03-07. http://hdl.handle.net/10883/1039
Foster, A. D., & Rosenzweig, M. R. (2010). Microeconomics of technology adoption. Annual Review of Economics, 2(1), 395–424. https://doi.org/10.1146/annurev.economics.102308.124433 DOI: https://doi.org/10.1146/annurev.economics.102308.124433
Gutiérrez García, G. A., Gutiérrez-Montes, I., Hernández Núñez, H. E., Suárez Salazar, J. C., & Casanoves, F. (2020). Relevance of local knowledge in decision-making and rural innovation: A methodological proposal for leveraging participation of Colombian cocoa producers. Journal of Rural Studies, 75, 119–124. https://doi.org/10.1016/j.jrurstud.2020.01.012 DOI: https://doi.org/10.1016/j.jrurstud.2020.01.012
Huyer, S. (2016). Closing the gender gap in agriculture. Gender, Technology and Development, 20(2), 105–116. https://doi.org/10.1177/0971852416643872 DOI: https://doi.org/10.1177/0971852416643872
Instituto Colombiano Agropecuario (ICA). (2016). Resolución No. 448 del 20 de enero de 2016. Por medio de la cual se establecen los requisitos para el registro ante el ICA de los predios de producción de vegetales para exportación en fresco, el registro de los exportadores y el registro de las plantas empacadoras de vegetales para la exportación en fresco. https://bit.ly/40r0frT
International Trade Centre (ITC). (2023). Trade Map. Trade statistics for international business development. https://www.trademap.org/
Juma, C. (2015). Agricultural innovation systems. In C. Juma (Ed.), The new harvest agricultural innovation in Africa (2nd ed., pp. 83–116). Oxford Academic. https://doi.org/10.1093/acprof:oso/9780190237233.003.0004 DOI: https://doi.org/10.1093/acprof:oso/9780190237233.003.0004
Kassambara, A., & Mundt, F. (2020). Factoextra: extract and visualize the results of multivariate data analyses. R Package Version 1.0.7. https://rpkgs.datanovia.com/factoextra/
Kassie, M., Jaleta, M., Shiferaw, B., Mmbando, F., & Mekuria, M. (2013). Adoption of interrelated sustainable agricultural practices in smallholder systems: Evidence from rural Tanzania. Technological Forecasting and Social Change, 80(3), 525–540. https://doi.org/10.1016/J.TECHFORE.2012.08.007 DOI: https://doi.org/10.1016/j.techfore.2012.08.007
Krishnan, R., Yen, P., Agarwal, R., Arshinder, K., & Bajada, C. (2021). Collaborative innovation and sustainability in the food supply chain- evidence from farmer producer organisations. Resources, Conservation and Recycling, 168, Article 105253. https://doi.org/10.1016/j.resconrec.2020.105253 DOI: https://doi.org/10.1016/j.resconrec.2020.105253
Lê, S., Josse, J., & Husson, F. (2008). FactoMineR: An R package for multivariate analysis. Journal of Statistical Software, 25(1), 1–18. https://doi.org/10.18637/jss.v025.i01 DOI: https://doi.org/10.18637/jss.v025.i01
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22, 5–55.
Lippe, R. S., & Grote, U. (2017). Determinants affecting adoption of GLOBALG.A.P. standards: A choice experiment in Thai horticulture. Agribusiness, 33(2), 242–256. https://doi.org/10.1002/agr.21471 DOI: https://doi.org/10.1002/agr.21471
Liu, T., Bruins, R. J. F., & Heberling, M. T. (2018). Factors influencing farmers’ adoption of best management practices: A review and synthesis. Sustainability, 10(2), Article 432. https://doi.org/10.3390/su10020432 DOI: https://doi.org/10.3390/su10020432
Lloret-Segura, S., Ferreres-Traver, A., Hernández-Baeza, A., & Tomás-Marco, I. (2014). El análisis factorial exploratorio de los ítems: una guía práctica, revisada y actualizada. Anales de Psicologia, 30(3), 1151–1169. https://doi.org/10.6018/analesps.30.3.199361 DOI: https://doi.org/10.6018/analesps.30.3.199361
Maertens, A., & Barrett, C. B. (2013). Measuring social networks’ effects on agricultural technology adoption. American Journal of Agricultural Economics, 95(2), 353–359. https://doi.org/10.1093/ajae/aas049 DOI: https://doi.org/10.1093/ajae/aas049
Makowski, D. (2018). The psycho package: An efficient and publishing-oriented workflow for psychological science. The Journal of Open Source Software, 3(22), Article 470. https://doi.org/10.21105/joss.00470 DOI: https://doi.org/10.21105/joss.00470
Morris, W., Henley, A., & Dowell, D. (2017). Farm diversification, entrepreneurship and technology adoption: Analysis of upland farmers in Wales. Journal of Rural Studies, 53, 132–143. https://doi.org/10.1016/j.jrurstud.2017.05.014 DOI: https://doi.org/10.1016/j.jrurstud.2017.05.014
Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO). (2023). FAOSTAT – Crops and livestock products. https://www.fao.org/faostat/en/#data/TCL
Oyetunde-Usman, Z., Olagunju, K. O., & Ogunpaimo, O. R. (2021). Determinants of adoption of multiple sustainable agricultural practices among smallholder farmers in Nigeria. International Soil and Water Conservation Research, 9(2), 241–248. https://doi.org/10.1016/j.iswcr.2020.10.007 DOI: https://doi.org/10.1016/j.iswcr.2020.10.007
Parra, C. A., & Knobloch, N. (2022). Exploring the barriers, opportunities, and motivation for agricultural entrepreneurship of rural Colombian students who participated in dual-credit programs. Journal of International Agricultural and Extension Education, 29(4), 93–108. https://doi.org/10.4148/2831-5960.1039 DOI: https://doi.org/10.4148/2831-5960.1039
Porteous, O. (2020). Trade and agricultural technology adoption: Evidence from Africa. Journal of Development Economics, 144, Article 102440. https://doi.org/10.1016/j.jdeveco.2020.102440 DOI: https://doi.org/10.1016/j.jdeveco.2020.102440
Quintero Ramírez, S., Marín Sánchez, B., Cubillos Jiménez, S., Ruiz Castañeda, W., & Giraldo Ramírez, D. (2019). Avocado and coffee supply chains specialization in Colombia. Procedia Computer Science, 158, 573–581. https://doi.org/10.1016/j.procs.2019.09.091 DOI: https://doi.org/10.1016/j.procs.2019.09.091
R Core Team. (2022). R: A language and environment for statistical computing. R Foundation. https://www.r-project.org/
Raiche, G., & Magis, D. (2020). nFactors: Parallel analysis and other non graphical solutions to the Cattell Scree test. R package version 2.4.1. https://cran.r-project.org/package=nFactors
Ramírez-Gil, J. G., Franco, G., & Henao-Rojas, J. C. (2019). Review of the concept of quality in Hass avocado and the pre-harvest and harvest factors that determine it under tropical conditions. Revista Colombiana de Ciencias Hortícolas, 13(3), 359–370. https://doi.org/10.17584/rcch.2019v13i3.10503 DOI: https://doi.org/10.17584/rcch.2019v13i3.10503
Revelle, W. (2021). psych: Procedures for psychological, psychometric, and personality research. https://cran.r-project.org/package=psych
Rogers, E. (1962). Diffusion of innovations (3rd ed.). Macmillan Publishing Co. Inc.
Ruzzante, S., & Bilton, A. (2021). Adoption of agricultural technologies in the developing world: A meta-analysis dataset of the empirical literature. Data in Brief, 38, Article 107384. https://doi.org/10.1016/j.dib.2021.107384 DOI: https://doi.org/10.1016/j.dib.2021.107384
Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC. https://doi.org/10.1201/9780429447273 DOI: https://doi.org/10.1201/9780429447273
Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625–649. https://doi.org/10.3102/0034654308325896 DOI: https://doi.org/10.3102/0034654308325896
Šūmane, S., Kunda, I., Knickel, K., Strauss, A., Tisenkopfs, T., des los Rios, I., Rivera, M., Chebach, T., & Ashkenazy, A. (2018). Local and farmers’ knowledge matters! How integrating informal and formal knowledge enhances sustainable and resilient agriculture. Journal of Rural Studies, 59, 232–241. https://doi.org/10.1016/j.jrurstud.2017.01.020 DOI: https://doi.org/10.1016/j.jrurstud.2017.01.020
Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960–967. https://doi.org/10.1016/j.promfg.2018.03.137 DOI: https://doi.org/10.1016/j.promfg.2018.03.137
Tami-Barrera, L. (2021). Socioeconomic and technological factors influencing technology adoption in cacao farms of two post-conflict regions in Colombia [Master thesis, The Pennsylvania State University]. https://bit.ly/474GHfd
The Consultative Group on International Agricultural Research (CGIAR) & Leveraging Evidence for Access and Development (LEAD). (2021). Gender in agriculture and food systems: An evidence gap map. https://bit.ly/49tGMLe
The World Bank. (2007). World Development Report 2008. Agriculture for development. https://doi.org/10.1596/978-0-8213-6807-7 DOI: https://doi.org/10.1596/978-0-8213-6807-7
Tiruneh, S., Yigezu, Y. A., & Bishaw, Z. (2015). Measuring the effectiveness of extension innovations for out-scaling agricultural technologies. African Journal of Agricultural Science and Technology, 3(7), 316–326.
Varshney, D., Joshi, P. K., Kumar, A., Mishra, A. K., & Kumar Dubey, S. (2022). Examining the transfer of knowledge and training to smallholders in India: Direct and spillover effects of agricultural advisory services in an emerging economy. World Development, 160, Article 106067. https://doi.org/10.1016/j.worlddev.2022.106067 DOI: https://doi.org/10.1016/j.worlddev.2022.106067
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 DOI: https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., Morris, M. G., Davis, G. B., & David, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). The Guilford Press. https://bit.ly/3QOUP6H
Xie, H., & Huang, Y. (2021). Influencing factors of farmers’ adoption of pro-environmental agricultural technologies in China: Meta-analysis. Land Use Policy, 109, Article 105622. https://doi.org/10.1016/j.landusepol.2021.105622 DOI: https://doi.org/10.1016/j.landusepol.2021.105622
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
CrossRef Cited-by
1. Diego Romero-Sánchez, Juan Carlos Barrientos Fuentes, Dursun Barrios. (2024). Factores asociados al uso de la banca digital en pequeños agronegocios de la región central de Colombia. Innovar, 34(94), p.e116820. https://doi.org/10.15446/innovar.v34n94.116820.
2. Juan Pablo Taramuel-Taramuel, Iván Alonso Montoya-Restrepo, Dursun Barrios. (2024). Challenges in the avocado production chain in Latin America: A descriptive analysis. Agronomía Colombiana, 42(2), p.e113982. https://doi.org/10.15446/agron.colomb.v42n2.113982.
3. Laura Alejandra Valbuena-Gaona, Hector Julio Villamil-Martha, Luz Mary Pardo-Ramírez, Joaquín Guillermo Ramírez-Gil. (2025). Citizen science and digital data for trend analysis and impact assessment of Prodiplosis as an emerging pest in foliage crops. Agronomía Colombiana, 43(1), p.e118919. https://doi.org/10.15446/agron.colomb.v43n1.118919.
Dimensions
PlumX
Article abstract page views
Downloads
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
© Centro Editorial de la Facultad de Ciencias Agrarias, Universidad Nacional de Colombia
Reproduction and quotation of material appearing in the journal is authorized provided the following are explicitly indicated: journal name, author(s) name, year, volume, issue and pages of the source. The ideas and observations recorded by the authors are their own and do not necessarily represent the views and policies of the Universidad Nacional de Colombia. Mention of products or commercial firms in the journal does not constitute a recommendation or endorsement on the part of the Universidad Nacional de Colombia; furthermore, the use of such products should comply with the product label recommendations.
The Creative Commons license used by Agronomia Colombiana journal is: Attribution - NonCommercial - ShareAlike (by-nc-sa)
Agronomia Colombiana by Centro Editorial of Facultad de Ciencias Agrarias, Universidad Nacional de Colombia is licensed under a Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional License.
Creado a partir de la obra en http://revistas.unal.edu.co/index.php/agrocol/.
