Comparison of energy inputs for inorganic fertilizer and manure based corn production. Canadian agricultural engineering
Bibliographic record
Abstract
Comparison of energy inputs for inorganic fertilizer and manure based corn production. Can. Agric. Eng. 42:009-017. Energy inputs were calculated for grain corn production with data from field experiments utilizing inorganic fertilizer and liquid swine manure as sources of plant nutrients. The calculations utilized energy coefficients taken from the literature and actual application rates of the various input products including seed corn, starter fertilizer and regular inorganic fertilizer, herbicides, fuel for field operations, and grain drying. The results showed that grain corn could be produced successfully by substituting manure for inorganic fertilizer. The energy savings in the manured treatments resulted largely from eliminating the energy in fertilizer manufacture and ranged from 31 to 34 % of the energy input for inorganic fertilizer based grain corn production. Published agricultural statistics were used to extrapolate the results to the entire Mixedwood Plains Ecozone which covers the lower Great Lakes and St. Lawrence River Valley regions of Ontario and Quebec. An estimated upper bound of 4.6 PJ (1 petajoule = 1015 joules) of energy could be saved annually by substituting livestock manure for inorganic fertilizer in the production of the entire acreage of grain corn grown in the ecozone. This estimate is based on the assumptions of availability of sufficient manure, and no credits being given for manure presently being used. Spatial analysis identified South
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".