Authors
Shahrear Roman, Hadi Hadi, Wyclife Agumba Oluoch, David Zilberman, Robert Finger, David Wuepper
Published in
Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 43. Pages e2530081123. Oct 27, 2026. Epub Oct 19, 2026.
Abstract
Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers' and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations' profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.
PMID:
42896053
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.
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