Energy and greenhouse gas intensity of corn (Zea mays L.) production in Ontario: A regional assessment
Bibliographic record
Abstract
Abstract. Jayasundara, S., Wagner-Riddle, C., Dias, G. and Kariyapperuma, K. A. 2014. Energy and greenhouse gas intensity of corn (Zea mays L.) production in Ontario: A regional assessment. Can. J. Soil Sci. 94: 77-95. Analysis of the environmental impact of corn (Zea mays L.) uses, such as biofuels and bioproducts, requires cradle to farm-gate life-cycle analysis of energy use and net greenhouse gas (GHG) emissions associated with corn production. Previous analyses have been based on case studies. Here we present an analysis based on census data for Ontario at the county level that was performed for three scenarios: (1) corn cultivated only for grain; (2) corn cultivated for grain and 30% stover harvest; and (3) corn cultivated for grain and cob harvest. Energy intensity of corn grain at the county level varied from 1.75 to 2.17 GJ Mg-1 grain, with the largest proportions of energy being consumed for grain drying (33%), production and supply of nitrogen (N) fertilizer (30%) and diesel use for field work (17%). Overall GHG emission intensity of grain corn varied from 243 to 353 kg CO2eq Mg-1 grain, of which 72% were associated with N inputs [34% soil nitrous oxide (N2O) from synthetic fertilizer N (SFN), 13% from SFN production and 10% from applied manure N]. Energy intensity of corn stover and cobs was 0.96 and 0.36 GJ Mg-1 dry matter, respectively, with the largest proportion of energy associated with production and supply of replacement nutrients. Intensity of GHG emission was 79 and 31 kg CO2 eq Mg-1 dry matter for stover and for cobs, respectively. Counties with higher corn yields at lower N application rates and reduced tillage tended to produce corn with lower energy and GHG intensity per Mg grain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".