Assessing the energy trap of industrial agriculture in North America and Europe: 82 balances from 1830 to 2012
Bibliographic record
Abstract
Early energy analyses of agriculture revealed that behind higher labor and land productivity of industrial farming, there wasa decrease in energy returns on the energy invested (EROI), in comparison to more traditional organic agricultural systems.Studies on recent trends show that efficiency gains in production and use of inputs have again somewhat improved energyreturns. However, most of these agricultural energy studies have focused only on external inputs at the crop level, concealingthe important role of internal biomass flows that livestock and forestry recirculate within agroecosystems. Here, we synthe-size the results of 82 farm systems in North America and Europe from 1830 to 2012 that for the first time show the changingenergy profiles of agroecosystems, including livestock and forestry, with a multi-EROI approach that accounts for the energyreturns on external inputs, on internal biomass reuses, and on all inputs invested. With this historical circular bioeconomicapproach, we found a general trend towards much lower external returns, little or no increases in internal returns, and almostno improvement in total returns. This “energy trap” was driven by shifts towards a growing dependence of crop productionon fossil-fueled external inputs, much more intensive livestock production based on feed grains, less forestry, and a struc-tural disintegration of agroecosystem components by increasingly linear industrial farm managements. We conclude thatovercoming the energy trap requires nature-based solutions to reduce current dependence on fossil-fueled external industrialinputs and increase the circularity and complexity of agroecosystems to provide healthier diets with less animal products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".