Data-Driven Agropreneurship (DDA): Empowering Farmers through Predictive Analytics

Data-Driven Agropreneurship (DDA) represents a transformative approach in modern agriculture, utilizing predictive analytics to empower farmers with data-driven decision-making capabilities. This paper explores the significant benefits of adopting DDA, including improved crop yields, optimized resource management, and enhanced market opportunities. Despite these advantages, several challenges impede its widespread adoption, such as data access, technological literacy, and inadequate infrastructure. This paper also discusses potential solutions to overcome these barriers, emphasizing the importance of government support, public-private partnerships, and capacity-building initiatives. By addressing these challenges, stakeholders can foster an environment conducive to the successful implementation of DDA, ultimately leading to a more sustainable, productive, and profitable agricultural sector.

Authors

Publication Details

Journal
Magna Scientia Advanced Research and Reviews
Published
2024-11-05
DOI
https://doi.org/10.30574/msarr.2024.12.2.0177
Citations
3
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
1.80
Controls
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article

Data-Driven Agropreneurship (DDA): Empowering Farmers through Predictive Analytics

Adekunle Stephen Toromade, Oluwatosin Omotola Ajayi, Ayeni Olagoke
3 citations
Magna Scientia Advanced Research and Reviews
Smart Agriculture and AI
1.80
article

Data-Driven Agropreneurship (DDA): Empowering Farmers through Predictive Analytics

Adekunle Stephen Toromade, Oluwatosin Omotola Ajayi, Ayeni Olagoke
article en
3 citations

Abstract

Data-Driven Agropreneurship (DDA) represents a transformative approach in modern agriculture, utilizing predictive analytics to empower farmers with data-driven decision-making capabilities. This paper explores the significant benefits of adopting DDA, including improved crop yields, optimized resource management, and enhanced market opportunities. Despite these advantages, several challenges impede its widespread adoption, such as data access, technological literacy, and inadequate infrastructure. This paper also discusses potential solutions to overcome these barriers, emphasizing the importance of government support, public-private partnerships, and capacity-building initiatives. By addressing these challenges, stakeholders can foster an environment conducive to the successful implementation of DDA, ultimately leading to a more sustainable, productive, and profitable agricultural sector.

Magna Scientia Advanced Research and ReviewsVol. 12(2)
Openalex Percentile: Top 13%
Smart Agriculture and AI
1.80
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