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Exploring adoption factors of innovations in arracacha crop: A case study in Cajamarca, Colombia
Explorando los factores de adopción de innovaciones en el cultivo de arracacha: un estudio de caso en Cajamarca, Colombia
DOI:
https://doi.org/10.15446/agron.colomb.v41n3.111345Keywords:
Arracacia xanthorrhiza, multivariate analysis, rural development strategies, technology adoption, farmers (en)Arracacia xanthorrhiza, análisis multivariado, estrategias de desarrollo rural, adopción de tecnología, agricultores (es)
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Currently, the demand for new arracacha (Arracacia xanthorrhiza Bancr.) varieties has increased in Colombia; however, yields are still below their potential. This is because farmers in the country still find it challenging to adopt innovations that allow them to improve both productivity and their income. Furthermore, there is limited information and scientific documentation on arracacha cultivation. Therefore, the aim of this study was to analyze the factors influencing the adoption of innovations for cultivating arracacha in the municipality of Cajamarca, Colombia, through a multivariate analysis. The research was carried out with 104 farmers, and surveys containing variables such as the productive activity dynamics and the profile of the farmer were applied. Two conglomerates (clusters) of adopters were created, and the innovation adoption index (INAI) was analyzed in eight categories, including 28 technologies. The factors that were most differentiated and significant were those related to the farm, such as management, organization and health indexes, as well as factors related to farmer characteristics, such as level of schooling.
Actualmente, la demanda por nuevas variedades de arracacha (Arracacia xanthorrhiza Bancr.) se ha incrementado en Colombia; no obstante, los rendimientos aún están por debajo de su potencial. Esto porque a los agricultores del país todavía se les dificulta adoptar innovaciones que permitan mejorar tanto la productividad como sus ingresos. Además, existe limitada información y documentación científica sobre este tema para el cultivo de la arracacha. Por lo tanto, el objetivo de este estudio fue analizar los factores que influyen en la adopción de innovaciones para el cultivo de la arracacha en el municipio de Cajamarca, Colombia, a través de un análisis multivariado. La investigación se llevó a cabo con 104 agricultores y se aplicaron encuestas que contenían variables de la dinámica de la actividad productiva y el perfil del agricultor. Se crearon dos conglomerados (clústeres) de adoptantes y se analizó el índice de adopción de innovaciones (INAI) en ocho categorías, incluyendo 28 tecnologías. Los factores que más se diferenciaron y tuvieron significancia fueron los grupos de aspectos relacionados con la finca, como los índices de administración, organización y sanidad, además de factores relacionados con rasgos del agricultor como el grado de escolaridad.
References
Abiola, A., Zandjanakou-Tachin, M., Aoudji, K. N. A., Avocevou-Ayisso, C., & Kumar, P. L. (2020). Adoption of roguing to contain banana bunchy top disease in south-east Bénin: Role of farmers’ knowledge and perception. International Journal of Fruit Science, 20(4), 720–736. https://doi.org/10.1080/15538362.2019.1673277 DOI: https://doi.org/10.1080/15538362.2019.1673277
AGROSAVIA.(2023). Corporación colombiana de investigación agropecuaria. Balance social 2022. http://hdl.handle.net/20.500.12324/38187
Aguilar-Ávila, J., Martínez González, E. G., Aguilar Gallegos, N., & Altamirano Cárdenas, J. R. (2020). Análisis de procesos de innovación en el sector agroalimentario y rural. Universidad Autónoma de Chapingo. https://repositorio.chapingo.edu.mx/items/3ae2c974-05e0-4bae-840e-8640b4d1f626
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 DOI: https://doi.org/10.1016/j.agsy.2015.01.003
Alcaldía Municipal de Cajamarca. (2020). Plan de desarrollo municipal 2020 – 2023 Cajamarca y Anaime, está en nuestras manos.
Ball, G. H., & Hall, D. J. (1965). ISODATA, a novel method of data analysis and pattern classification. Stanford Research Inst Menlo Park, CA.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for social network analysis. Analytic Technologies, Harvard, MA.
Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1–27. https://doi.org/10.1080/03610927408827101 DOI: https://doi.org/10.1080/03610917408548446
Cheetham, A. H., & Hazel, J. E. (1969). Binary (presence-absence) similarity coefficients. Journal of Paleontology, 43(5), 1130–1136. https://www.jstor.org/stable/1302424
Chelang’a, N. C., Kariuki, I. M., Obare, G. A., & Otieno, D. O. (2023). Determinants of adoption of GLOBAL G.A.P. standards: Evidence from smallholder French beans farmers in Murang’a County, Kenya. Cogent Food & Agriculture, 9(1), Article 2176949. https://doi.org/10.1080/23311932.2023.2176949 DOI: https://doi.org/10.1080/23311932.2023.2176949
Chhom, V., Tsusaka, T. W., Datta, A., & Ahmad, M. M. (2023). Factors influencing paddy producers’ profitability and sale to markets: evidence from Battambang Province, Cambodia. Cogent Food & Agriculture, 9(1), Article 2193311. https://doi.org/10.1080/23311932.2023.2193311 DOI: https://doi.org/10.1080/23311932.2023.2193311
Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227. https://doi.org/10.1109/TPAMI.1979.4766909 DOI: https://doi.org/10.1109/TPAMI.1979.4766909
Despotović, J., Rodić, V., & Caracciolo, F. (2019). Factors affecting farmers’ adoption of integrated pest management in Serbia: An application of the theory of planned behavior. Journal of Cleaner Production, 228, 1196–1205. https://doi.org/10.1016/j.jclepro.2019.04.149 DOI: https://doi.org/10.1016/j.jclepro.2019.04.149
Dhakal, A., & Kumar Rai, R. (2020). Who adopts agroforestry in a subsistence economy?—Lessons from the Terai of Nepal. Forests, 11(5), Article 565. https://doi.org/10.3390/f11050565 DOI: https://doi.org/10.3390/f11050565
Foguesatto, C. R., & Machado, J. A. D. (2022). Adoption of sustainable agricultural practices in Brazil: understanding the influence of socio-economic and psychological factors. Journal of Agribusiness in Developing and Emerging Economies, 12(2), 204–222. https://doi.org/10.1108/JADEE-11-2020-0256 DOI: https://doi.org/10.1108/JADEE-11-2020-0256
Garnica Montaña, J. P., Villamil Carvajal, J. E., Vargas Berdugo, Á. M., Rodríguez Rodríguez, O. J., & Atencio Solano, L. M. (2021). Modelo productivo de arracacha (Arracacia xanthorrhiza Bancr.): Agrosavia la 22 para la región andina de Colombia. https://doi.org/10.21930/agrosavia.nbook.7404500 DOI: https://doi.org/10.21930/agrosavia.nbook.7404500
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Applied Statistics, 28(1), 100–108. https://doi.org/10.2307/2346830 DOI: https://doi.org/10.2307/2346830
Hubert, L. J., & Levin, J. R. (1976). A general statistical framework for assessing categorical clustering in free recall. Psychological Bulletin, 83(6), 1072–1080. https://doi.org/10.1037/0033-2909.83.6.1072 DOI: https://doi.org/10.1037//0033-2909.83.6.1072
Ifie, B. E., Kwapong, N. A., Anato-Dumelo, M., Konadu, B. A., Tongoona, P. B., & Danquah, E. Y. (2022). Assessment of farmers readiness to adopt maize hybrid varieties for high productivity in Ghana. Acta Agriculturae Scandinavica, Section B — Soil & Plant Science, 72(1), 506–515. https://doi.org/10.1080/09064710.2021.2018032 DOI: https://doi.org/10.1080/09064710.2021.2018032
Khumairoh, U., Lantinga, E. A., Suprayogo, D., Schulte, R. P. O., & Groot, J. C. J. (2019). Modifying the farmer field school method to support on-farm adaptation of complex rice systems. The Journal of Agricultural Education and Extension, 25(3), 227–243. https://doi.org/10.1080/1389224X.2019.1604391 DOI: https://doi.org/10.1080/1389224X.2019.1604391
Köhler, H.-D., & González Begega, S. (2014). Elementos para un concepto sociológico de innovación. Empiria. Revista de Metodología de Ciencias Sociales, (29), 67–88. https://doi.org/10.5944/empiria.29.2014.12942 DOI: https://doi.org/10.5944/empiria.29.2014.12942
Kuivanen, K. S., Michalscheck, M., Descheemaeker, K., Adjei-Nsiah, S., Mellon-Bedi, S., Groot, J. C. J., & Alvarez, S. (2016). A comparison of statistical and participatory clustering of smallholder farming systems – A case study in Northern Ghana. Journal of Rural Studies, 45, 184–198. https://doi.org/10.1016/j.jrurstud.2016.03.015 DOI: https://doi.org/10.1016/j.jrurstud.2016.03.015
Li, W., Ruiz-Menjivar, J., Zhang, L., & Zhang, J. (2021). Climate change perceptions and the adoption of low-carbon agricultural technologies: Evidence from rice production systems in the Yangtze River Basin. Science of the Total Environment, 759, Article 143554. https://doi.org/10.1016/j.scitotenv.2020.143554 DOI: https://doi.org/10.1016/j.scitotenv.2020.143554
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
Martínez, J. M., & Pachón, E. M. (2021). Multivariate analysis of the adoption of cacao productive technologies: Evidence from a case study in Colombia. Economía Agraria y Recursos Naturales, 21(1), 79–102. https://doi.org/10.7201/EARN.2021.01.04 DOI: https://doi.org/10.7201/earn.2021.01.04
Mathios Flores, M., Alegre Orihuela, J. C., & Aguilar Vásquez, J. (2019). Caracterización de hatos ganaderos en la cuenca baja del río Shanusi Alto Amazonas – Loreto – Perú. Aporte Santiaguino, 11(2), Article 225. https://doi.org/10.32911/as.2018.v11.n2.577 DOI: https://doi.org/10.32911/as.2018.v11.n2.577
Mercado Escamilla, F., Ayala Garay, A. V., Flores Trejo, A., Oble Vergara, E., & Almaguer Vargas, G. (2019). Factores que influyen en la adopción de innovaciones en productores de naranja en Álamo, Veracruz. Agricultura Sociedad y Desarrollo, 16(2), 183–198. https://www.scielo.org.mx/scielo.php?pid=S1870-54722019000200183&script=sci_abstract DOI: https://doi.org/10.22231/asyd.v16i2.1006
Muñoz, M., Aguilar, J., Rendón, R., & Altamirano, J. R. (2007). Análisis de la dinámica de innovación en cadenas agroalimentarias. Universidad Autónoma Chapingo – CIESTAAM/PIIAI, Chapingo. https://repositorio.chapingo.edu.mx/items/0d384900-2235-4668-987b-10f50537f570
Navarro-Niño, D. A., Villamil Carvajal, J. E., Garnica Montaña, J. P., & Jaramillo-Barrios, C. I. (2022). Evaluación de un enfoque modificado de escuelas de campo en la apropiación de conocimientos en agricultores de arracacha. Ciencia y Agricultura, 19(3), 1–16. https://doi.org/10.19053/01228420.v19.n3.2022.14721 DOI: https://doi.org/10.19053/01228420.v19.n3.2022.14721
Nikitha, P., Sudha Rani, V., Samuel, G., & Madhavilata, A. (2018). Relationship between profile characteristics of farmers and their perception about suitability of extension teaching methods in Telangana State. International Journal of Current Microbiology and Applied Sciences, 7(9), 2298–2302. https://doi.org/10.20546/ijcmas.2018.709.284 DOI: https://doi.org/10.20546/ijcmas.2018.709.284
Núñez-Colín, C. A., & Escobedo-López, D. (2011). Uso correcto del análisis clúster en la caracterización de germoplasma vegetal. Agronomía Mesoamericana, 22(2), 415–427. https://doi.org/10.15517/am.v22i2.8746 DOI: https://doi.org/10.15517/am.v22i2.8746
Pérez, C. (2004). Técnicas de análisis multivariante de datos. Pearson Prentice Hall.
Piñeiro, V., Arias, J., Dürr, J., Elverdin, P., Ibáñez, A. M., Kinengyere, A., Opazo, C. M., Owoo, N., Page, J. R., Prager, S. D., & Torero, M. (2020). A scoping review on incentives for adoption of sustainable agricultural practices and their outcomes. Nature Sustainability, 3(10), 809–820. https://doi.org/10.1038/s41893-020-00617-y DOI: https://doi.org/10.1038/s41893-020-00617-y
R Core Team. (2022). R: A Language and environment for statistical computing. R Foundation for Statistical Computing.
Ramírez-Gómez, C. J., Robledo Velasquez, J., & Aguilar-Avila, J. (2020). Trust networks and innovation dynamics of small farmers in Colombia: An approach from territorial system of agricultural innovation. Revista de la Facultad de Ciencias Agrarias, 52(2), 253–266. https://revistas.uncu.edu.a /ojs3/index.php/RFCA/article/view/3887
Ramírez-Gómez, C. J., & Rodríguez-Espinosa, H. (2022). Sistemas territoriales de innovación en la agricultura: un enfoque para agrocadenas. Fondo Editorial Biogénesis. https://revistas.udea.edu.co/index.php/biogenesis/article/view/345730
Ramírez-Gómez, C. J., Rodríguez Espinosa, H., & Restrepo-Betancur, F. (2023). Farmer, learning and teaching: A cluster analysis of technology adopters in avocado farming in Colombia. Tropical and Subtropical Agroecosystems, 26(1), Article 006. https://doi.org/10.56369/tsaes.4434 DOI: https://doi.org/10.56369/tsaes.4434
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019). Diffusion of innovations 1. In D. W. Stacks, M. B. Salwen, & K. C. Eichhorn (Eds.), An integrated approach to communication theory and research (3rd ed.). Routledge. https://doi.org/10.4324/9780203710753-35 DOI: https://doi.org/10.4324/9780203710753-35
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7 DOI: https://doi.org/10.1016/0377-0427(87)90125-7
Sánchez- Sánchez, A., Santoyo-Cortés, V. H., De La Vega-Mena, M., Muñoz-Rodríguez, M., & Martínez González, E. G. (2020). Adopción de innovaciones y factores asociados en empresas familiares agropecuarias y agroindustriales de México. Estudios Gerenciales, 36(154), 43–55. https://doi.org/10.18046/j.estger.2020.154.3424 DOI: https://doi.org/10.18046/j.estger.2020.154.3424
Sánchez-Cañizares, S. M., Guerrero-Baena, D. M., & Gómez-Limón, J. A. (2022). Factors influencing the simultaneous adoption of risk management instruments in Mediterranean irrigated agriculture. Journal of Environmental Planning and Management, 65(6), 1088–1109. https://doi.org/10.1080/09640568.2021.1922364 DOI: https://doi.org/10.1080/09640568.2021.1922364
Sarle, W. S. (1983). Cubic clustering criterion. SAS® technical report A-108. https://www.google.com/url?esrc=s&q=&rct=j&sa=U&url=https://support.sas.com/documentation/onlinedoc/v82/techreport_a108.pdf&ved=2ahUKEwjb6ZLo8tWBAxVUmbAFHR7WBmMQFnoECAkQAg&usg=AOvVaw28SzE-dlfaED6XXoNoiQdi
Sennuga, S. O., Baines, R. N., Conway, J., & Angba, C. W. (2020). Awareness and adoption of good agricultural practices among smallholder farmers in relation to the adopted villages programme: The case study of Northern Nigeria. Journal of Biology, Agriculture and Healthcare, 10(6), 34–49. https://doi.org/10.7176/JBAH/10-6-06 DOI: https://doi.org/10.7176/JBAH/10-6-06
Serote, B., Mokgehle, S., Du Plooy, C., Mpandeli, S., Nhamo, L., & Senyolo, G. (2021). Factors influencing the adoption of climate-smart irrigation technologies for sustainable crop productivity by smallholder farmers in arid areas of South Africa. Agriculture, 11(12), Article 1222. https://doi.org/10.3390/agriculture11121222 DOI: https://doi.org/10.3390/agriculture11121222
Sileshi, M., Kadigi, R., Mutabazi, K., & Sieber, S. (2019). Determinants for adoption of physical soil and water conservation measures by smallholder farmers in Ethiopia. International Soil and Water Conservation Research, 7(4), 354–361. https://doi.org/10.1016/J.ISWCR.2019.08.002 DOI: https://doi.org/10.1016/j.iswcr.2019.08.002
Sotomayor, O., Rodríguez, A., Rodriguez, M., & Rodrigues, M. S. (2011). Competitividad, sostenibilidad e inclusión social en la agricultura: Nuevas direcciones en el diseño de políticas en América Latina y el Caribe. CEPAL.
Tenjo Galarza, J., & Jaimes, C. A. (2018). Ingresos y educación en el sector rural colombiano. Revista de Economía Institucional, 20(38), 209–233. https://doi.org/10.18601/01245996.v20n38.09 DOI: https://doi.org/10.18601/01245996.v20n38.09
Torres-Avila, A., Aguilar-Ávila, J., Santoyo-Cortés, V. H., Martínez-González, E. G., & Aguilar-Gallegos, N. (2022). Innovation in the pineapple value chain in Mexico: Explaining the global adoption process of the MD-2 hybrid. Agricultural Systems, 198, Article 103386. https://doi.org/10.1016/j.agsy.2022.103386 DOI: https://doi.org/10.1016/j.agsy.2022.103386
Tu, V. H., Can, N. D., Takahashi, Y., Kopp, S. W., & Yabe, M. (2018). Modelling the factors affecting the adoption of eco-friendly rice production in the Vietnamese Mekong Delta. Cogent Food & Agriculture, 4(1), Article 1432538. https://doi.org/10.1080/23311932.2018.1432538 DOI: https://doi.org/10.1080/23311932.2018.1432538
Tudela, J. W., Málaga-Luna, M., & Aliaga-Melo, G. (2021). Análisis de la adopción de innovaciones agrícolas en productores de quinua de Cabana, Puno Perú. Revista de Investigaciones Altoandinas Journal of High Andean Research, 23(2), 93–102. https://doi.org/10.18271/ria.2021.242 DOI: https://doi.org/10.18271/ria.2021.242
Ullah, A., Mishra, A. K., & Bavorova, M. (2023). Agroforestry adoption decision in green growth initiative programs: key lessons from the Billion Trees Afforestation Project (BTAP). Environmental Management, 71, 950–964. https://doi.org/10.1007/s00267-023-01797-x DOI: https://doi.org/10.1007/s00267-023-01797-x
Van Oorschot, J. A. W. H., Hofman, E., & Halman, J. I. M. (2018). A bibliometric review of the innovation adoption literature. Technological Forecasting and Social Change, 134, 1–21. https://doi.org/10.1016/j.techfore.2018.04.032 DOI: https://doi.org/10.1016/j.techfore.2018.04.032
Vargas-De la Mora, A. L., Castillo-Santiago, M. A., Randhir, T. O., Hernández-Moreno, M. C., Cach-Pérez, M. J., & Camacho-Valdéz, V. (2021). Conocer para mejorar: factores que influyen en la transición hacia sistemas silvopastoriles en la costa de Chiapas. Tropical and Subtropical Agroecosystems, 24(3), Article 108. https://doi.org/10.56369/tsaes.3689 DOI: https://doi.org/10.56369/tsaes.3689
Vásquez Pérez, H. V., Barrantes Bravo, C. A., Vigo Mestanza, C. N., & Maicelo Quintana, J. L. (2022). Factores socioeconómicos que influyen en la adopción de tecnologías para mejoramiento genético de ganado vacuno en Perú. Agricultura, Sociedad y Desarrollo, 19(3), 312–330. https://doi.org/10.22231/asyd.v19i3.1358 DOI: https://doi.org/10.22231/asyd.v19i3.1358
Yue, M., Li, W., Jin, S., Chen, J., Chang, Q., Glyn, J., Cao, Y., Yang, G., Li, Z., & Frewer, L. J. (2023). Farmers’ precision pesticide technology adoption and its influencing factors: Evidence from apple production areas in China. Journal of Integrative Agriculture, 22(1), 292–305. https://doi.org/10.1016/j.jia.2022.11.002 DOI: https://doi.org/10.1016/j.jia.2022.11.002
Zeleke, B. D., Geleto, A. K., Asefa, S., & Komicha, H. H. (2023). The role of social capital in addressing seed access constraints and adoption intensity: Evidence from Arsi Highland, Oromia Region, Ethiopia. Heliyon, 9(2), Article e13553. https://doi.org/10.1016/j.heliyon.2023.e13553 DOI: https://doi.org/10.1016/j.heliyon.2023.e13553
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