A bibliometric analysis of computational and mathematical techniques in the cocoa sustainable food value chain

<h2>Abstract</h2> This study thoroughly examines the evolving research landscape of computational and mathematical techniques applied to cocoa farming from 2000 to 2020. Through a structured two-stage methodology, it conducts a bibliometric analysis of 1886 peer-reviewed documents, followed by a concept-centric review of 734 investigations explicitly focusing on cocoa and its derivatives. The findings highlight significant contributions spanning diverse scientific disciplines, including Chemistry, Biology, Social Sciences, Econometrics, Health, and Computer Science. The research introduces the Cocoa Sustainable Food Value Chain framework, showcasing its relevance in advancing genetic improvement, machinery optimization, health-focused food composition studies, and strategies for enhancing crop yields. Additionally, the study uncovers a growing interest in machine learning applications to address critical challenges in cocoa farming. These include innovations in post-production processes, assessing cocoa ripeness, pod counting, crop yield estimation, optimizing bean fermentation, organoleptic profiling, and the industrialization of cocoa-related machinery. Four key research gaps emerge concerning the integration of computational and mathematical techniques that can benefit smallholder cocoa farmers: (i) optimizing cocoa aggregation and distribution management, (ii) designing user-friendly high-tech solutions, (iii) facilitating agricultural technology adoption, and (iv) assessing the impact of agricultural policies. This research makes three pivotal contributions to the academic field. First, it broadens the conceptualization of agri-food supply chains by integrating sustainability and presenting a theoretical framework tailored to the extended cocoa value chain. Second, it highlights the lack of development in ICT and IoT solutions that support computational techniques for managing cocoa production. Third, it emphasizes the importance of creating and transferring high-tech tools to strengthen Good Agricultural Practices. This study underscores the urgent need to enhance the Cocoa Sustainable Food Value Chain and ensure its resilience and inclusivity by advocating for a digital transformation centered on smallholder farmers.

Authors

Institutions

Publication Details

Journal
Heliyon
Published
2025-02-26
DOI
https://doi.org/10.1016/j.heliyon.2025.e43015
Citations
3
Primary Topic
Food Chemistry and Fat Analysis
Type
article
Field-Weighted Citation Impact
3.49
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A bibliometric analysis of computational and mathematical techniques in the cocoa sustainable food value chain

Leonardo Hernán Talero Sarmiento, Henry Lamos-Díaz, Diana Teresa Parra Sánchez
3 citations
Heliyon
Food Chemistry and Fat Analysis
3.49
article

A bibliometric analysis of computational and mathematical techniques in the cocoa sustainable food value chain

Leonardo Hernán Talero Sarmiento, Henry Lamos-Díaz, Diana Teresa Parra Sánchez
article en
3 citations

Abstract

<h2>Abstract</h2> This study thoroughly examines the evolving research landscape of computational and mathematical techniques applied to cocoa farming from 2000 to 2020. Through a structured two-stage methodology, it conducts a bibliometric analysis of 1886 peer-reviewed documents, followed by a concept-centric review of 734 investigations explicitly focusing on cocoa and its derivatives. The findings highlight significant contributions spanning diverse scientific disciplines, including Chemistry, Biology, Social Sciences, Econometrics, Health, and Computer Science. The research introduces the Cocoa Sustainable Food Value Chain framework, showcasing its relevance in advancing genetic improvement, machinery optimization, health-focused food composition studies, and strategies for enhancing crop yields. Additionally, the study uncovers a growing interest in machine learning applications to address critical challenges in cocoa farming. These include innovations in post-production processes, assessing cocoa ripeness, pod counting, crop yield estimation, optimizing bean fermentation, organoleptic profiling, and the industrialization of cocoa-related machinery. Four key research gaps emerge concerning the integration of computational and mathematical techniques that can benefit smallholder cocoa farmers: (i) optimizing cocoa aggregation and distribution management, (ii) designing user-friendly high-tech solutions, (iii) facilitating agricultural technology adoption, and (iv) assessing the impact of agricultural policies. This research makes three pivotal contributions to the academic field. First, it broadens the conceptualization of agri-food supply chains by integrating sustainability and presenting a theoretical framework tailored to the extended cocoa value chain. Second, it highlights the lack of development in ICT and IoT solutions that support computational techniques for managing cocoa production. Third, it emphasizes the importance of creating and transferring high-tech tools to strengthen Good Agricultural Practices. This study underscores the urgent need to enhance the Cocoa Sustainable Food Value Chain and ensure its resilience and inclusivity by advocating for a digital transformation centered on smallholder farmers.

HeliyonVol. 11(6)
Universidad Industrial de Santander (CO), Universidad Autónoma de Bucaramanga (CO)
Zero hunger
Openalex Percentile: Top 9%
Food Chemistry and Fat Analysis
3.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.