Energy use in open-field agriculture in the EU: A critical review recommending energy efficiency measures and renewable energy sources adoption
Bibliographic record
Abstract
This review combines results from a large number of studies investigating energy use in EU open-field agriculture, providing an overview of energy use and its concentrations. Such a review and its findings are important as it informs stakeholders and policymakers with evidence for supporting a green energy transition in open-field agriculture. Our review indicates that annual energy use in EU open-field agriculture is at least 1431 PJ, equivalent to around 3.7% of total EU annual energy consumption, with the majority of energy sourced from non-renewable energy sources. Our meta-analysis finds that the production of fertilizer is the largest energy consuming activity in EU agriculture, accounting for around 50% of all energy inputs. On-farm diesel use accounts for 31% of total energy inputs, while the production pesticides and seeds accounts for 5% of total energy inputs. Other energy uses, mainly irrigation, storage and drying, account for 8% of total energy inputs. This suggests that energy use in EU agriculture is significantly underreported and that around 55% of total energy inputs, associated with the production of fertilizers and pesticides, come from indirect sources which can be assigned to the agricultural sector but is used prior to reaching farms. The importance and potential of various fossil-energy-free technologies and strategies are discussed. In addition, this review highlights that in the medium and long term there is need for the development and application of detailed and standardized methodologies for energy use analysis of agricultural systems, as well as for meta-analyses investigating energy use in agriculture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".