Digital Transformation in Brazilian Agribusiness Value Chains: Driving Productivity, ESG Integration, and Regional Economic Resilience

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Authors: Okonkwo Faith Odinakachi, Ebunu Ikugbe Eldred, Kingsley Chibuzo Ozike, Chinenye Sandra Obi, Mohamed Saidu Kamara, Mary Olubukola Adebanjo, Chidume Frederick Toochi
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Abstract

This study examined digital transformation in Brazilian agribusiness value chains with an emphasis on productivity, ESG integration, and regional economic resilience. Specifically, the research investigated how data acquisition and connectivity, as dimensions of digital transformation, influence agribusiness outcomes. A descriptive survey research design was adopted, and data were collected from 161 employees across five selected Brazilian agribusiness firms using a structured questionnaire. The entire population was studied through a census approach, while Pearson product–moment correlation was employed for hypothesis testing at the 0.05 level of significance. Findings revealed that data acquisition has a significant positive relationship with productivity [p < 0.001], ESG integration (r = 0.355, [p < 0.001]), and economic resilience (r = 0.270, p = 0.001). Similarly, connectivity demonstrated significant positive relationships with productivity (r = 0.518, [p < 0.001]), ESG integration (r = 0.301, [p < 0.001]), and economic resilience (r = 0.192, p = 0.012). These results indicate that digital capabilities enhance operational efficiency, sustainability practices, and adaptive capacity within agribusiness value chains. The study concludes that digital transformation is a critical driver of agribusiness performance and resilience in Brazil. The study recommends increased investment in data analytics infrastructure, improved digital connectivity, workforce digital skills development, and supportive policy frameworks to strengthen agribusiness competitiveness and sustainability.

Fields of Study & Subjects

Digitalization and Economic Development in Agriculture Agriculture and Agroindustry Studies Smart Agriculture and AI

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
26 February 2026
Language
EN