TY - JOUR
T1 - A bibliometric analysis of computational and mathematical techniques in the cocoa sustainable food value chain
AU - Talero-Sarmiento, Leonardo H.
AU - Parra-Sanchez, Diana T.
AU - Lamos-Diaz, Henry
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/3/20
Y1 - 2025/3/20
N2 - 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.
AB - 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.
KW - Cocoa
KW - Literature review
KW - Machine learning
KW - Mathematical programming
KW - Simulation
KW - Statistics
UR - http://www.scopus.com/inward/record.url?scp=85219364887&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2025.e43015
DO - 10.1016/j.heliyon.2025.e43015
M3 - Articulo en revista no especializada
AN - SCOPUS:85219364887
SN - 2405-8440
VL - 11
JO - Heliyon
JF - Heliyon
IS - 6
M1 - e43015
ER -