AgWise: Modular decision support tools integrating data, models, and AI for site-specific agronomy and soil health management recommendations - Abera, Wuletawu; Mkuhlani, Siyabusa; Tibebe, Degefie; Assefa, Feben; Leroux, Louise; Seid, Jemal; Srivastava, Amit; Aguilar Ariza, Andres; Devkota Wasti, Mina; Ibrahim, Ali; Kouadio, Louis; Liben, Feyera; et al. (2025)
Smallholder agriculture faces a persistent paradox: while agronomic science and data availability have advanced rapidly, farm-level decisions are still guided largely by blanket recommendations and fragmented advice. Many digital tools now claim to offer site-specific guidance, yet most remain focused on prediction rather than decision making - estimating yields, risks, or responses without translating them into coherent, feasible choices for real farms. The result is a widening gap between scientific knowledge, digital intelligence, and the complex realities of smallholder farming systems shaped by soil constraints, climate variability, resource limitations, and household priorities. AgWise responds to this challenge by rethinking how agronomic intelligence is structured and deployed. Rather than treating artificial intelligence as a stand-alone solution, AgWise is designed as an embedded decision intelligence system that integrates agronomic science, data, models, expert knowledge, and AI within a single, coherent framework. Its core premise is that agronomic recommendations must be biophysically grounded, constraint-aware, and context-sensitive to be actionable at scale. AgWise therefore combines process-based crop and soil models, empirical evidence from field experiments, expert-driven rules, and AI-enabled orchestration to generate recommendations that respect both environmental realities and farmer objectives. Crucially, AgWise is modular, not monolithic. Different analytical engines serve distinct roles - models ensure physical consistency; data-driven methods capture observed responses, rules enforce feasibility and safety, and AI supports integration, explanation, and learning. This modularity allows AgWise to operate across diverse crops, agroecologies, and data environments, while remaining transparent, adaptable, and scientifically auditable. AgWise is not a chatbot, a black-box AI recommender, a single agronomic model, or a mobile application. AgWise is a modular decision-support architecture that converts inputs (data + models + rules + constraints + objectives) into a set of feasible advisory packages. By moving beyond isolated predictions toward integrated decision support, AgWise enables farmers, advisory services, and institutions to navigate real trade-offs—between productivity and risk, short-term gains and soil restoration, and profitability and resilience. In doing so, it offers a scalable pathway for translating decades of agronomic research into actionable, context-aware intelligence, supporting the transformation of smallholder agriculture toward greater sustainability, resilience, and impact. This document is a conceptual paper that articulates the design philosophy, decision logic, and system-level thinking underpinning AgWise. Its primary goal is to clarify what AgWise is, and to explain how agronomic advisories are structured within the system. The document is not intended as a technical manual or implementation guide. Detailed algorithms, parameterizations, and workflows will be described in complementary technical documents and scientific publications.