Ecozone dynamics of crop residue biomass, macronutrient removals, replacement costs, and bioenergy potential in corn–soybean–winter wheat cropping systems in southern Ontario
Bibliographic record
Abstract
Nkoa, R., Kendall, K. and Deen, W. 2014. Ecozone dynamics of crop residue biomass, macronutrient removals, replacement costs, and bioenergy potential in corn–soybean–winter wheat cropping systems in southern Ontario. Can. J. Plant Sci. 94: 981–993. In light of frequent price hikes of imported fuels and the everlasting growth in the world's crude oil consumption, producing energy from renewable, non-fossil sources, such as crop residues and dedicated energy crops, has gained an unprecedented attraction across developed countries. This study aimed at assessing the dynamics of crop residues biomass, NPK removals, bioenergy potentials, in southern Ontario. Two surveys were carried out across 20 counties, and data were analyzed using one-way classification random and three-way crossed classification mixed effects models, respectively. Corn stover was estimated at 7.5 t ha−1 yr−1, equivalent to 139.7×109 J ha−1 yr−1, and NPK-removal rates of 53, 4, and 62 kg ha−1, respectively. For wheat, the straw biomass was estimated at 4.3 t ha−1 yr−1, equivalent to 79.4×109 J ha−1 yr−1, and NPK removal rates of 28, 3, and 35 kg ha−1, respectively. Soybean straw biomass was estimated at 3.1 t ha−1 yr−1, equivalent to 24.76×109 J ha−1 yr−1, and NPK-removal rates of 33, 5, and 35 kg ha−1, respectively. Overall, cutting corn and winter wheat stalks at ground level rather than at 15 cm above ground yielded higher residue biomasses, which did not statistically translate into higher NPK removal rates due to lower NPK concentrations in the stubbles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".