Yield and nutrient uptake of barley and camelina amended with manure from cattle fed barley, triticale dried distillers grains with solubles, and flaxseed diets
Bibliographic record
Abstract
Since animal diet affects manure properties, it is expected that manures from different diets may elicit differences in crop response. The objective of the study was to assess how application of manure from cattle fed with dried distillers grains with solubles (DDGS) and flaxseed (Linum usitatissimum L.) affected N and P availability, dry matter, and nutrient uptake in barley (Hordeum vulgare L.) and camelina [Camelina sativa (L.) Crantz] under controlled environment conditions. Treatments included manure from diets with barley grain (BARL), or barley grain replaced with triticale (× Triticosecale Wittmack) DDGS, flaxseed (FLAX), or both DDGS and flaxseed (DDGS/FLAX). Crops were grown for four consecutive phases (barley–camelina–barley–camelina). The DDGS/FLAX treatment had significantly higher barley dry matter weight (DMW) in phase 1 and camelina DMW in phase 2 than the other three manures (17 vs. 5–13 g kg−1 soil in phase 1 and 10 vs. 1–7 g kg−1 soil in phase 2). Barley N uptake in phase 1 and camelina N uptake in phase 2 were significantly higher for DDGS/FLAX than the other manures (222 vs. 82–191 mg kg−1 soil, phase 1; 146 vs. 20–102 mg kg−1 soil, phase 2). Our results indicate that cattle diet modifications have the capacity to tailor manure properties for optimum crop production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".