The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysis

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This study examines the increasing role of artificial intelligence (AI) in Costa Rica’s agricultural sector, emphasizing its potential to enhance sustainability, resource management, and market competitiveness. Using a mixed-methods approach, the research integrates structural equation modeling (SEM...

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Detalles Bibliográficos
Autores: Okot, Tom, Pérez, Edward
Formato: artículo
Fecha de Publicación:2025
Institución:Universidad Nacional de Trujillo
Repositorio:Revistas - Universidad Nacional de Trujillo
Lenguaje:español
inglés
OAI Identifier:oai:ojs.revistas.unitru.edu.pe:article/6559
Enlace del recurso:https://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/6559
Nivel de acceso:acceso abierto
Materia:Artificial Intelligence (AI)
competitiveness
productivity
resource optimization
sustainable agriculture
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spelling The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysisOkot, Tom Pérez, Edward Artificial Intelligence (AI)competitivenessproductivityresource optimizationsustainable agricultureThis study examines the increasing role of artificial intelligence (AI) in Costa Rica’s agricultural sector, emphasizing its potential to enhance sustainability, resource management, and market competitiveness. Using a mixed-methods approach, the research integrates structural equation modeling (SEM), multivariate regression analysis, text mining, and scenario analysis to provide a comprehensive evaluation of AI adoption. AI-driven solutions optimize key agricultural processes, including climate pattern prediction, soil condition monitoring, crop disease detection, and pest management. Quantitative findings indicate a strong correlation between AI adoption and improved productivity, economic benefits, and environmental conservation, particularly through optimized fertilizer and pesticide use and enhanced water management. However, challenges such as high implementation costs, limited digital infrastructure, and farmer resistance remain significant barriers. Text mining analysis reveals widespread concerns over data privacy, technical complexity, and financial investment, highlighting the importance of targeted training programs. Scenario analysis further suggests that government support and technological advancements could significantly accelerate AI adoption over the next decade. The study underscores the need for strategic partnerships among government agencies, educational institutions, and technology providers to bridge the digital divide and encourage AI adoption. These findings not only inform Costa Rican agricultural policy and innovation strategies but also provide a replicable model for other emerging economies aiming to integrate AI sustainably into agricultural systems.Universidad Nacional de Trujillo2025-07-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículo evaluado por parestext/htmlapplication/pdfhttps://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/6559Scientia Agropecuaria; Vol. 16 Núm. 3 (2025): julio-septiembre; 469-480Scientia Agropecuaria; Vol. 16 No. 3 (2025): julio-septiembre; 469-4802306-67412077-9917reponame:Revistas - Universidad Nacional de Trujilloinstname:Universidad Nacional de Trujilloinstacron:UNITRUspaenghttps://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/6559/6917https://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/6559/6836Derechos de autor 2025 Scientia Agropecuariahttps://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessoai:ojs.revistas.unitru.edu.pe:article/65592025-07-07T10:41:57Z
dc.title.none.fl_str_mv The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysis
title The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysis
spellingShingle The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysis
Okot, Tom
Artificial Intelligence (AI)
competitiveness
productivity
resource optimization
sustainable agriculture
title_short The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysis
title_full The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysis
title_fullStr The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysis
title_full_unstemmed The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysis
title_sort The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysis
dc.creator.none.fl_str_mv Okot, Tom
Pérez, Edward
author Okot, Tom
author_facet Okot, Tom
Pérez, Edward
author_role author
author2 Pérez, Edward
author2_role author
dc.subject.none.fl_str_mv Artificial Intelligence (AI)
competitiveness
productivity
resource optimization
sustainable agriculture
topic Artificial Intelligence (AI)
competitiveness
productivity
resource optimization
sustainable agriculture
description This study examines the increasing role of artificial intelligence (AI) in Costa Rica’s agricultural sector, emphasizing its potential to enhance sustainability, resource management, and market competitiveness. Using a mixed-methods approach, the research integrates structural equation modeling (SEM), multivariate regression analysis, text mining, and scenario analysis to provide a comprehensive evaluation of AI adoption. AI-driven solutions optimize key agricultural processes, including climate pattern prediction, soil condition monitoring, crop disease detection, and pest management. Quantitative findings indicate a strong correlation between AI adoption and improved productivity, economic benefits, and environmental conservation, particularly through optimized fertilizer and pesticide use and enhanced water management. However, challenges such as high implementation costs, limited digital infrastructure, and farmer resistance remain significant barriers. Text mining analysis reveals widespread concerns over data privacy, technical complexity, and financial investment, highlighting the importance of targeted training programs. Scenario analysis further suggests that government support and technological advancements could significantly accelerate AI adoption over the next decade. The study underscores the need for strategic partnerships among government agencies, educational institutions, and technology providers to bridge the digital divide and encourage AI adoption. These findings not only inform Costa Rican agricultural policy and innovation strategies but also provide a replicable model for other emerging economies aiming to integrate AI sustainably into agricultural systems.
publishDate 2025
dc.date.none.fl_str_mv 2025-07-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Artículo evaluado por pares
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/6559
url https://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/6559
dc.language.none.fl_str_mv spa
eng
language spa
eng
dc.relation.none.fl_str_mv https://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/6559/6917
https://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/6559/6836
dc.rights.none.fl_str_mv Derechos de autor 2025 Scientia Agropecuaria
https://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Derechos de autor 2025 Scientia Agropecuaria
https://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional de Trujillo
publisher.none.fl_str_mv Universidad Nacional de Trujillo
dc.source.none.fl_str_mv Scientia Agropecuaria; Vol. 16 Núm. 3 (2025): julio-septiembre; 469-480
Scientia Agropecuaria; Vol. 16 No. 3 (2025): julio-septiembre; 469-480
2306-6741
2077-9917
reponame:Revistas - Universidad Nacional de Trujillo
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instname_str Universidad Nacional de Trujillo
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reponame_str Revistas - Universidad Nacional de Trujillo
collection Revistas - Universidad Nacional de Trujillo
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