CATTLE MANURE IMPACTS ON ORGANIC WHEAT PRODUCTION <i></i>IN THE NORTHERN GREAT PLAINS
Bibliographic record
Abstract
Supplying adequate N when growing wheat (Triticum spp.) organically can be challenging. The impact of a one-time application of beef cattle manure (manure) at 0, 12, 25, and 50 Mg ha-1 on grain yield and quality in wheat/summerfallow (SF) and wheat/legume green manure (LGM) systems was determined from 2021 through 2024. Treatments were arranged in a RCB as a split plot with manure rates comprising main plots and crop phases (wheat after SF, wheat after LGM, SF, and LGM) comprising subplots. An additional main plot consisted of a urea fertilizer check. Wheat yields were greater following a manure application of 50 Mg ha-1 compared to low rates (≤ 12 Mg ha-1) and the urea treatment in 2023 and 2024 (P< 0.05). Persistent drought confounded wheat yield response in 2021 and 2022. Grain yield was lower when wheat followed LGM than SF in 2022 and 2023. Grain protein concentrations were comparable or higher at heavier (≥ 25 Mg ha-1) versus lighter (≤ 12 Mg ha-1) manure rates, and in later years when urea was applied versus the heavy manure rate due to yield-induced protein dilution. However, at manure rates ≥ 25 Mg ha-1, grain protein concentration generally remained above the 121 g kg-1 threshold of N deficiency. Impacts of preceding wheat with LGM compared to SF on grain protein concentration were inconsistent, as were manure and urea fertilizer effects on wheat grain test weight. A positive legacy effect can result when manure is applied at 50 Mg ha-1.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".