Efficiency of Edible Agriculture in Canada and the U.S. Over the Past Three and Four Decades
Bibliographic record
Abstract
We examine technological progress in the US and Canada to answer the question: has the efficiency (e.g., the edible energy efficiency, or EEE) for producing agricultural products in the US and Canada increased in recent decades? Specifically, we determined the energy efficiency of agriculture at the farm gate in recent decades by dividing the outputs (the total annual crop and animal output in energy units minus the feed used for animal production and the grain used for ethanol production) by the energy inputs: all the energy used by the nation to produce food (the energy used to generate and apply the fertilizer, pesticides, seed and to operate machinery) minus the energy inputs to produce grain for ethanol. Our data comes primarily from national and international agricultural censuses. Our study found that the energy efficiency of US agriculture has more than doubled from 0.8:1 in 1970 to 2.2:1 by 2000, then increased more slowly to 2.3:1 by 2009. The energy efficiency of the agricultural sector in Canada has not changed appreciably since 1980, and has varied about a mean of 2:1 from 1981 to 2009. Our study found that EEE improvements in the US could be attributable in part to advancements in crop production per hectare, and lower direct fuel consumption, but also a greater proportion of less energy-intensive corn and changes to the diet of livestock (e.g., increased use of meals and other by-products which have increased the availability of grain). Thus increases due to technological progress alone for the last several decades appear small, less than one percent a year.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".