Tillage, crop residue and crop sequence effects on nitrogen availability in a legume-based cropping system
Bibliographic record
Abstract
A field study was conducted to determine the effects and interactions of crop sequence, tillage and residue management on labile N pools and their availability because such information is sparse. Experimental treatments were no-till (NT) vs. conventional tillage (CT), and removal vs. retention of straw, imposed on a barley (Hordeum vulgare L.)-canola (Brassica rapa L.)-field pea (Pisum sativum L.) rotation. 15 N-labelling was used to quantify N uptake from straw, below-ground N (BGN), and fertilizer N. Straw retention increased soil microbial biomass N (MBN) in 2 of 3 yr at the four-leaf growth stage of barley, consistent with observed decreases in extractable soil inorganic N at seeding. However, crop yield and N uptake at maturity were not different between straw treatments. No tillage increased soil MBN, crop yield and N uptake compared to CT, but had no effect on extractable soil inorganic N. The greater availability of N under NT was probably related to soil moisture conservation. Tillage effects on soil and plant N were mostly independent of straw treatment. Straw and tillage treatments did not influence the uptake of N from its various sources. However, barley following pea (legume/non-legume sequence) derived a greater proportion of its N from BGN (13 to 23% or 9 to 23 kg N ha -1 ) than canola following barley (nonlegumes) (6 to 16% or 3 to 9 kg N ha -1 ). Fertilizer N constituted 8 to 11% of barley N uptake and 23 to 32% of canola N uptake. Straw N contributed only 1 to 3% of plant N uptake. This study showed the dominant influence of tillage on N availability, and of the preceding crop or cropping sequence on N uptake partitioning among available N sources. Key words: Crop residue, crop sequence, labile nitrogen, nitrogen uptake, pea, tillage
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".