Estimating the impact of manure nitrogen losses on total nitrogen application on agricultural land in Canada
Bibliographic record
Abstract
Yang, J. Y., Huffman, E. C., Drury, C. F., Yang, X. M. and De Jong, R. 2011. Estimating the impact of manure nitrogen losses on total nitrogen application on agricultural land in Canada. Can. J. Soil Sci. 91: 107-122. About 1 million tonnes (1 Tg=1012 g) of livestock manure N are applied to farmland in Canada each year. Comprehensive information on manure N production and losses from manure during on-farm storage, handling and field application is scarce, especially at a regional scale. However, manure N losses during storage and land application are of considerable concern with respect to nitrogen use efficiency and environmental pollution of air, soil and water. In this paper, manure N production, manure N losses during storage and land application and manure N mineralization from organic manure and the resultant manure N available for annual crops were estimated using the Census of Agriculture database, Farm Environmental Management Survey data and manure N loss factors obtained from the literature. A database of fertilizer N application rates for field crops was developed at the regional scale based on recommendations provided in agronomic extension bulletins and fertilizer N sales. Fertilizer N and available manure N (i.e., total manure N produced minus N losses plus N mineralized from manure applied in previous years) were allocated to each of 24 crops at the regional scale from 1981 to 2006. The amount of manure N produced in Canada increased by 18.7% from 0.928 Tg in 1981 to 1.102 Tg in 2006. We estimated that 35.6% of the manure N produced was immediately available to crops, 25.6% was lost during storage and land application and 38.8% was carried over to the next year as organic N. The amount of fertilizer N applied to crops increased dramatically from 0.928 Tg in 1981 to a peak level of 1.68 Tg in 2000. There were significant changes in manure N production and application to farmland both on a regional and a temporal basis.
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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 | 0.000 |
| 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".