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Peer-reviewedCC BY 4.0Proceedings of the National Academy of Sciences of the United States of America

Modeling the global potential of smart farming innovations

  • Shahrear Roman1
  • Hadi Hadi1
  • Wyclife Agumba Oluoch1
  • David Zilberman2
  • Robert Finger3
  • David Wuepper1
  1. 1Land Economics Group, Institute for Food and Resource Economics, University of Bonn, Bonn 53115, Germany
  2. 2Department of Agricultural and Resource Economics, University of California Berkeley, Berkeley, CA 94720
  3. 3Agricultural Economics and Policy Group, Department of Management, Technology, and Economics, ETH Zürich, Zürich 33 8092, Switzerland
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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.

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Cite

BibTeXRIS
BibTeX
@article{roman2026modeling,
  title = {{Modeling the global potential of smart farming innovations}},
  author = {Shahrear Roman and Hadi Hadi and Wyclife Agumba Oluoch and David Zilberman and Robert Finger and David Wuepper},
  journal = {Proceedings of the National Academy of Sciences of the United States of America},
  year = {2026},
  month = oct,
  doi = {10.1073/pnas.2530081123},
  url = {https://doi.org/10.1073/pnas.2530081123},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Modeling the global potential of smart farming innovations
AU  - Shahrear Roman
AU  - Hadi Hadi
AU  - Wyclife Agumba Oluoch
AU  - David Zilberman
AU  - Robert Finger
AU  - David Wuepper
PY  - 2026
DA  - 2026/10/19
JO  - Proceedings of the National Academy of Sciences of the United States of America
DO  - 10.1073/pnas.2530081123
UR  - https://doi.org/10.1073/pnas.2530081123
AB  - 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.
SN  - 0027-8424
SN  - 1091-6490
ER  -