Precision farming at the nexus of agricultural production and the environment
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Precision farming enables agricultural management decisions to be tailored spatially and temporally. Site-specific sensing, sampling, and managing allow farmers to treat a field as a heterogeneous entity. Through targeted use of inputs, precision farming reduces waste, thereby cutting both private variable costs and the environmental costs such as those of agrichemical residuals. At present, large farms in developed countries are the main adopters of precision farming. But its potential environmental benefits can justify greater public and private sector incentives to encourage adoption, including in small-scale farming systems in developing countries. Technological developments and big data advances continue to make precision farming tools more connected, accurate, efficient, and widely applicable. Improvements in the technical infrastructure and the legal framework can expand access to precision farming and thereby its overall societal benefits.
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Such efficiency gains could happen through data-driven temporal and site-specific farm management, reduced waste, and the use of autonomous seeding or weed control.
“Such efficiency gains could happen through data-driven temporal and site-specific farm management, reduced waste, and the use of autonomous seeding or weed control, just to mention a few [104].”
From Artificial intelligence, systemic risks, and sustainability (2021) · cites this paper as [104]
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Self-citation
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Cited by
Sorted by citations
Artificial intelligence, systemic risks, and sustainability
Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia, D., et al. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101741 · Cited by 497 · see citing works
- Authors
- Finger, R., Swinton, S. M., El Benni, N., & Walter, A.
- Affiliations
- ETH Zurich (CH)
- Michigan State University (US)
- Research Division on Competitiveness and System Evaluation, Agroscope, 8356 Ettenhausen, Switzerland;
- Venue
- Annual Review of Resource Economics
- Publisher
- Annual Reviews
- Published
- 2019-10-05
- Volume
- 11
- Issue
- 1
- Pages
- 313-335
- ISSN
- 1941-1340 (ISSN-L)
- 1941-1359
- OpenAlex
- W2942230041
- Cited by
- 539
- References
- 121
- Citations by year
- 2026: 102 · 2025: 93 · 2024: 101 · 2023: 74 · 2022: 70 · 2021: 52 · 2020: 39 · 2019: 8
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Resource Economics (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (acceptedVersion, other-oa)
- ETH Zürich Research Collection
- Funders
- National Science Foundation (1027253)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Innovations and Practices
- Keywords
- precision agriculture, site-specific management, agricultural inputs, environmental costs of agriculture, smallholder farming, technology adoption incentives, big data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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