An artificial intelligence (AI)-powered agricultural advisory system, termed NDEMRI (Nurturing Digital Extension via Mobile and Responsive Intelligence), has been developed to provide evidence-based farming guidance to rural communities across sub-Saharan Africa through short message service (SMS). Designed for compatibility with basic GSM-enabled mobile phones and independent of internet access for end-users, the system integrates large language models (LLMs) via the ChatGPT API to generate contextually relevant, linguistically localized responses to a wide array of agricultural queries. A quasi-experimental evaluation was conducted in the northern regions of Cameroon over a four-month period, employing a matched control group methodology involving 831 treatment farmers and 400 controls. Statistically significant improvements were observed among participants using NDEMRI, with mean crop yields increasing by 16.6% and agricultural incomes rising by 23%, relative to the control group. Adoption of improved agronomic practices was notably higher among users of the system. A total of 2,487 unique messages were exchanged, covering themes such as pest management, planting schedules, soil health, and post-harvest storage, with 78% of users reporting that system responses were context-sensitive and adapted to local climatic and cultural conditions. The technical architecture is characterized by modular natural language understanding pipelines, embedded guardrails to minimize model hallucinations, and a reproducible framework for contextualization based on regional agricultural datasets. A detailed economic analysis demonstrated the financial sustainability of the intervention, with favorable cost-benefit ratios and scalability potential. These findings offer robust empirical evidence that the integration of accessible communication technologies with state-of-the-art AI can overcome infrastructural limitations, enhance decision-making in low-resource farming environments, and serve as a viable model for transforming agricultural extension services across the African continent.

Green supplier selection (GSS) as a critical strategic element is placed in the limelight in contemporary supply chain management (SCM), owing to the growing emphasis on environmental responsibility and sustainability. This study presents a fuzzy multi-criteria decision-making (FMCDM) framework, employing Fuzzy Logarithmic Percentage Change-Driven Objective Weighting (FLOPCOW) method to determine the relative importance of sustainability criteria under uncertainty. A panel of five academic and industry experts was selected to identify 21 criteria, which were categorized into three main dimensions including environmental performance (C1), resource efficiency (C2), and corporate sustainability policies (C3). Triangular fuzzy numbers (TFNs) were adopted to model linguistic ambiguities in expert judgments whereas fuzzy normalization was applied to ascertain the weights of criteria. Key findings indicated that corporate sustainability policies (C3) were prioritized as the most influential dimension, followed by environmental performance (C1) and resource efficiency (C2). This suggested the centrality of institutional governance in advancing long-term sustainability objectives. Sub-criteria analysis further revealed ecological training programs, air emissions control, and sustainability reporting as the most critical indicators in the interplay of operational practices and transparent governance. FLOPCOW has effectively processed expert opinions with the use of fuzzy normalization, hence advocating a clear and repeatable approach for the evaluation of green suppliers. Furthermore, it highlighted the importance of policy-based criteria in supplier assessment and organizations could then align their purchasing decisions with sustainability goals by considering more on governance-related factors like compliance and stakeholder engagement.