Estimating changes of residual soil nitrogen in Chernozemic soils in Canada
Bibliographic record
Abstract
Yang, J. Y., Huffman, T., Drury, C. F., Yang, X. M., De Jong, R. and Campbell, C. A. 2012. Estimating changes of residual soil nitrogen in Chernozemic soils in Canada. Can. J. Soil Sci. 92: 481–491. Chernozemic soils (Mollisols) account for approximately 68% of total farmland in the prairies and 54% of farmland in Canada. Although many field studies have focused on the importance of N in Chernozemic soils, few modelling studies have been conducted to examine the risk of N contamination to the environment The objective of this research was to estimate temporal and spatial changes in residual soil nitrogen (RSN) on Chernozemic soils at the 1:1 million regional scale. An annual N budget was developed for the study area for the period 1981 to 2006, using the Canadian Agricultural Nitrogen Budget (CANB v3.0) model. The difference between N inputs and outputs is considered RSN, which is defined as the inorganic N left in the soil after harvest. Average RSN levels in the sub-humid Black and Dark Gray Chernozemic soils increased from 7–9 kg N ha−1in 1981 to 20–23 kg N ha−1in 2006. Changes in RSN were much less pronounced in the arid and semi-arid Brown and Dark Brown soil zones, where average values increased from approximately 1 kg N ha−1to 4–7 kg N ha−1over 25 yr. Commercial fertilizer, manure and biological N2fixation were the three main sources of the increased N inputs. Drought conditions also contributed to the surplus of N in some years by reducing crop growth and thus the amount of N removed in grain and forage crops. In Chernozemic soils, more careful use of chemical fertilizer N, improved manure N management and greater use of legume-cereal rotations are recommended as methods to maintain soil fertility and reduce nitrogen loss to the environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".