Effects of Short-term Tillage of a Long-term No-Till Land on Crop Yield and Nutrient Uptake in Two Contrasting Soil Types
Bibliographic record
Abstract
<p>Pre-seeding tillage of long-term no-till (NT) land may alter crop production by changing the availability of some nutrients in soil. Effects of short-term (4 years) tillage (hereafter called reverse tillage [RT]) of land previously under long-term (29 or 30 years) NT, with straw management (straw removed [SRem] and straw retained [SRet]) and N fertilizer rate (0, 50 and 100 kg N ha<sup>-1</sup> in SRet, and 0 kg N ha<sup>-1</sup> in SRem plots), were determined on plant yield (seed + straw, or harvested as forage/silage at soft dough stage), and N and P uptake in growing seasons from 2010 to 2013 at Breton (Gray Luvisol [Typic Cryoboralf] loam) and from 2009 to 2012 at Ellerslie (Black Chernozem [Albic Argicryoll] loam), Alberta, Canada. Plant yield, N uptake and P uptake tended to be greater with RT compared to NT in most cases at both sites, although significant in a few cases only at Ellerslie. On average over both sites, RT produced greater plant yield by 560 kg ha<sup>-1</sup> yr<sup>-1</sup>, N uptake by 5.8 kg N ha<sup>-1</sup> yr<sup>-1</sup>, and P uptake by 1.8 kg P ha<sup>-1</sup> yr<sup>-1</sup> than NT. There was no consistent beneficial effect of straw retention on plant yield, N uptake and P uptake in different years. Plant yield, N uptake and P uptake increased with N fertilization at both sites, with up to the maximum rate of applied N at 100 kg N ha<sup>-1</sup> in 3 of 4 years at Breton and in 2 of 4 years at Ellerslie. In conclusion, our findings suggested some beneficial impact of occasional tillage of long-term NT soil on crop yield and nutrient uptake.</p>
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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 | 0.001 |
| 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 | 0.000 |
| 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".