Foresight to 2035 in cold climate fruit trees: scenario alignment, Delphi method, and complement with Python libraries
Authors: Zartha Sossa, Jhon Wilder,; Gutiérrez Posada, Nolberto; Zuluaga Monsalve, Adriana María; Valencia Grisales, Liliana; Rodríguez Torres, Jhoan Sebastián; Orozco Mendoza, Gina Lía; Escobar Mora, Nelson Javier; Palacio Piedrahíta, Juan Carlos; Moreno Sarta, John Fredy; González Candia, Julio
"Cold-climate fruit trees, such as avocado, blackberry, curuba, lulo, and goldenberry, are economically and socially significant in Andean countries. This study employs foresight methodologies to support strategic planning, prioritize innovations, and reduce uncertainty in decision-making within this agro-industrial chain. Scenario analysis and the Delphi method were applied, analyzing 34 variables using MICMAC, identifying nine as key. Three future objectives were formulated and assessed with key stakeholders through MACTOR software. Cross-impact matrices and five hypotheses guided the construction of a preferred scenario, evaluated using SMICProbExpert software. In the Delphi method, experts reviewed 77 topics across five thematic categories, prioritizing 31 critical technologies and innovations. Artificial intelligence and Python-based natural language processing validated the results, ensuring analytical rigor. The findings provide a strategic framework for aligning efforts and resources with key variables, future objectives, and projected scenarios. This supports the development of targeted projects, policies, and innovation strategies, fostering an environment conducive to open innovation. By promoting collaboration and knowledge exchange, this approach enhances technological advancement and sustainable growth in the sector, with a long-term vision toward 2035."
