Modelling nature? The digital twinning and untwinning of urban farms

Abstract

Data-driven solutions and innovations have been extended to various industries, including agriculture, to increase efficiency. With the emergence of smart farming and agriculture 4.0, advanced technologies such as IoT, big data, machine learning, and cloud computing are increasingly integrated into farming. Among these, technologies such as digital models, digital twins and the metaverse are now used to control various farming activities remotely based on real-time data. However agricultural ecosystems, involving uncertain nature-based factors and complex human-nature interactions cannot always be accurately represented or predicted by digital models. Digital models aim to reduce natural environments into controllable game-like situations and undermine the multifaceted human and natural intelligences at play in agricultural ecosystems. This paper critically examines this reductionist view of the digital modelling of nature within the context of smart farming. We approach digital twinning not only as a technical tool but also as a conceptual lens that reflects a broader epistemological shift; one that assumes complex agro-ecological systems can be fully rendered calculable. By engaging with the idea of untwinning, we argue that this approach fails to account for the context-sensitive, dynamic dimensions of farming, particularly those rooted in traditional farming knowledge. Using the case of smart farming initiatives in Chiang Mai, Thailand, we show how digital modelling technologies, while offering certain efficiencies, face challenges as some factors in agro-ecological systems cannot be fully captured or controlled by technological solutions.