Macronutrient in soils and wheat from long-term agroexperiments reflects variations in residue and fertilizer inputs
Bibliographic record
Abstract
Abstract Previous studies in the long-term experiments at Pendleton, OR (USA), were focused on organic matter cycling, but the consequences of land management for nutrient status over time have received little attention. Soil and wheat (Triticum aestivum L.) tissue samples were analyzed to determine the macronutrient dynamics associated with residue management methods and fertilizer rate under a dryland winter wheat-fallow rotation. The treatments included: no burn residue incorporation with farmyard manure (FYM) or pea vines, no burn or spring burn with application of N fertilizer (0, 45, and 90 kg ha−1), and fall burn wheat residue incorporation. The results revealed no differences on the effect of residue burning on macronutrient concentration over time. After receiving the same treatments for 84 years, the concentrations of soil organic C, total N and S, and extractable Mg, K, P in the 0–10 cm depth significantly increased in FYM plots compared to the rest of the plots. The N fertilization rate of 90 kg ha−1 reduced the accumulations of P, K, and Ca in grain compared to the 0 and 45 kg N ha−1 applications. The results indicate that residue incorporation with FYM can play vital role in reducing the macronutrient decline over time.
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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 | 0.000 |
| 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 | 0.001 |
| 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".