Farmer, Learning and Teaching: a Cluster Analysis of Technology Adopters in Avocado Farming in Colombia

Abstract

<p><strong>Background</strong>. The current challenges of agrifood chains make it necessary for them to be efficient from the point of view of production. This demands greater attention to the farmer's technological adoption process and to some aspects that may affect it. <strong>Objective.</strong> In<strong> </strong>this study a multivariate cluster analysis method was used, with the aim of identifying the influence of a farmer’s profile and their teaching–learning environment on the adoption of technology in avocado farming. <strong>Methodology</strong>. The investigation was conducted with 94 farmers in two rural municipalities, located in the same rural region. The questionnaire that was used included profile variables, learning styles, farmer learning preferences, and extension agent teaching methods. <strong>Results. </strong>Three clusters of adopters were formed and the technology adoption index was analyzed in seven categories, including 37 technologies and technological practices. The case study showed that the high adoption cluster included profiles of older farmers with experience and membership to producer organizations; this cluster was also the only group comprising a combination of farmers’ learning styles and preferences. However, the disconnection between an extension agent's teaching methods and the farmers’ learning is evident in all clusters. <strong>Implications</strong>. Our results provide important evidence regarding the importance of linking the profile, style, and learning preference in contextualized teaching methods, allowing for better development of farmers’ capacities for the adoption of technologies and practices. <strong>Conclusions</strong>. The analysis of clusters of adopters allowed farmers to be classified into high, medium and low rates of adoption of technology and technological practices. Each cluster presented certain differences in terms of learning styles and preferences, as well as a disconnection in the teaching-learning relationship.</p>

Exploring adoption factors of innovations in arracacha crop: A case study in Cajamarca, Colombia

“…A multivariate data analysis (Cluster) was performed to classify arracacha farmers following the methodology of Aguilar-Gallegos et al (2015) and Ramírez-Gómez et al (2023). First, the squared Euclidean distance was calculated with the data of the 28 innovations coded as a dichotomous variable (1=adopted, 0=not adopted).…”

“…The INAI is usually used to establish adoption through a global indicator. Binary variables were used to calculate similarity and dissimilarity coefficients to propose a method for grouping producers based on the combination of practices validated in different studies (Cheetham & Hazel, 1969;Núñez-Colín & Escobedo-López, 2011;Aguilar-Gallegos et al, 2015;Sánchez-Cañizares et al, 2022;Ramírez-Gómez et al, 2023;Zeleke et al, 2023). The method used for grouping the basic characterization units was Ward's minimum variance method.…”

“…This first cluster included less innovative farmers, with an average value of the overall INAI of 37.9%, which represents a low value according to other studies, where they refer to low indicators with values below 50% (Aguilar-Gallegos et al, 2015;Ramírez-Gómez et al, 2023). Cluster 1 shows low yields compared to Cluster 2 with 18.775 ± 7.909 t ha -1 ; however, farmers have a higher total farm area (12.577 ± 12.271 ha), and men represent 96.43% of the total participants.…”

Effect of Lippia palmeriS. Watson on the quality and fat characteristics of creole goat's milk

“…El clima en el sitio de estudio es desértico-seco con una temperatura media mensual de 21,2 o C en verano y 9 o C en invierno. La estación lluviosa principal ocurre de julio a septiembre, aunque pueden presentarse lluvias ocasionales en invierno (Ramírez et al, 2011). La estación seca es de febrero a junio (Troyo et al, 2014).…”

La Multifuncionalidad De Las Organizaciones Asociativas Rurales

“…Una de las temáticas importantes y a su vez poco abordadas, donde las OAR actúan como intermediarios en la innovación, consiste en las transiciones sostenibles, tanto hacia modelos agroecológicos como de producción orgánica, ya que las OAR intermediarias facilitan el conocimiento compartido y el apoyo a los productores dada su legitimidad y arraigo en los territorios rurales (Groot- Kormelinck et al, 2022). Finalmente el rol de intermediario de las OAR también ha demostrado un efecto positivo en procesos de adopción tecnológica entre los productores que son miembros activos (Ramírez-Gómez et al, 2023), por diversas razones, entre ellas la confianza y el acceso a fuentes de conocimiento e información (Wossen et al, 2017). Por lo tanto, estas temáticas pueden ampliarse en futuras investigaciones.…”