Eight years of crop rotation and tillage effects on crop production and N fertilizer use
Bibliographic record
Abstract
Although tillage systems and crop rotations can affect crop production and uptake of nutrients, their long-term effects, particularly their interactions, are not well-documented. Therefore, we measured the N, P, and K contents and yields of crops through two rotation cycles, especially wheat (Triticum aestivum L.), of four crop rotations managed under conventional tillage (CT) and no-tillage (NT) systems. The study was conducted 1993 through 2000 on a sandy loam soil in northwestern Alberta, Canada. The four-course crop rotations were: (i) field pea (Pisum sativum L.)-wheat-canola (Brassica rapa L.)-wheat; (ii) red clover (Trifolium pratense L.) green manure-wheat-canola-wheat; (iii) fallow-wheat-canola-wheat, and (iv) continuous wheat (CW). The crops were fertilized using regional recommendations based on soil test results. Previous crop effect on wheat yield was in the order: field pea = red clover green manure > fallow > canola > wheat (CW); it had little influence on N, P or K content in wheat grain or straw. There was no interaction of tillage with crop rotation on wheat production or nutrient content. Tillage treatments affected neither production of other rotation crops nor their nutrient concentrations. During the second rotation cycle, N fertilizer requirement decreased, and wheat yield was 22% higher, under NT as compared to CT. This study showed that (i) field pea is an attractive replacement for red clover green manure; and (ii) recommendations for N from soil test results should factor in the type of tillage system used. Key words: Canola, field pea, red clover, nitrogen, tillage, wheat
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".