William Alejandro Orjuela-Garzon

William Alejandro Orjuela-Garzon


Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 October 2021 in Sustainability
Verified
Journal Article
Open access
PDF file
WOWilliam Alejandro Orjuela-Garzon
William Alejandro Orjuela-Garzon
SQSantiago Quintero
Santiago Quintero
DGDiana P. Giraldo
Diana P. Giraldo
LLLaura Lotero
Laura Lotero
César Nieto-LondoñoCésar Nieto-Londoño
César Nieto-Londoño
The technology transfer (TT) process has been studied from different approaches to improve productivity and competitiveness in agricultural chains. However, the process is not always presented successfully due to heterogeneity and inequality in the technological capacities (TC) of the agents that are part of the transfer process, in addition to the geographical context, the interaction networks and decision rules, which are key factors to understand the TT phenomenon. In this context and as a case study, the Colombian National Federation of rice growers promoted the development and adoption of technology that increased crop competitiveness and sustainability by implementing a technology transfer program known as the Massive Technology Adoption Program (AMTEC—Adopción Masiva de Tecnología) on rice. With the AMTEC program, average production costs were reduced by 26% (USD 119 per hectare), and it increased average yields by 23% (1.27 tonnes per hectare), which shows the importance of introducing technologies in productive chains in developing countries. This research provides a better understanding of the TT processes, based on the analysis of the interaction dynamics and behaviour patterns between the agents (i.e., generators, intermediaries, or users) in the TT processes. As an analysis tool, the agent-based modelling paradigm (ABM) was proposed to study the emergence at the macro-level of behaviour patterns of a system from the interactions of semi-intelligent agents at the micro-level, using experiments.