Particulate organic matter and soil mineral nitrogen concentrations are good predictors of the soil nitrogen supply to canola following legume and non-legume crops in western Canada
Bibliographic record
Abstract
St. Luce, M., Ziadi, N., Zebarth, B. J., Whalen, J. K., Grant, C. A., Gregorich, E, G., Lafond, P., Blackshaw, R. E., Johnson, E. N., O'Donovan, J. T. and Harker, K. N. 2013. Particulate organic matter and soil mineral nitrogen concentrations are good predictors of the soil nitrogen supply to canola following legume and non-legume crops in western Canada. Can. J. Soil Sci. 93: 607-620. Accurate estimation of potential nitrogen (N) availability from preceding crops is essential to improve N fertilizer management in agricultural soils. Labile organic N fractions such as microbial biomass N (MBN), water-extractable organic N (WEON), particulate and light fraction organic matter N (POMN, LFOMN) are sensitive to management-induced changes and have the potential to predict N availability. This study assessed the impact of preceding legume [field pea (Pisum sativum L.), faba bean (Vicia faba L.), faba bean green manure] and non-legume crops [canola (Brassica napus L.) and wheat (Triticum aestivum L.)] on labile organic N fractions, mineral N (NH4-N+NO3-N), potentially mineralizable N (N 0 ) and soil N supply (canola grain yield and N uptake), and whether these soil parameters for the top 15 cm of soil could be used as indicators of soil N supply across no-till sites in western Canada. Labile organic N fractions and N 0 were similar regardless of preceding crop. Soil N supply was greatest following faba bean green manure at four of five sites. POMN was the best single predictor of soil N supply (R 2=0.56 and R 2=0.69 for yield and N uptake, respectively). Soil N supply was primarily related to the combined effects of POMN, mineral N and sand content, which explained 68 and 71% of the variation in grain yield and N uptake, respectively. This study demonstrated that POMN and mineral N are relatively good predictors of soil N supply to canola in western Canada. Accounting for these parameters as well as soil texture may help improve N fertilizer recommendations for canola.
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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".