Computational Agriculture and Economic Complexity: Agriculture as a cyber-physical capability system for innovation, resilience, and development

Abstract

Agriculture is entering a phase in which biological production is increasingly organized through digital observation, predictive modeling, and algorithmic coordination. What was historically a craft of seasonal intelligence is becoming a dense cyber-physical system in which ecological processes, field operations, sensor networks, machine learning, robotic actuation, and market infrastructures continuously interact. This paper develops the argument that computational agriculture should be understood not only as a productivity upgrade, but as a structural transformation in economic complexity. When farms become information-rich decision environments, they generate capabilities that spill across biotechnology, agricultural machinery, environmental services, logistics, finance, and rural innovation. The farm shifts from a traditional production site to an experimental and operational node within a broader capability network. Understanding this transformation requires an interdisciplinary synthesis joining agronomy, systems thinking, artificial intelligence, infrastructure studies, and innovation economics.

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