Residue Decomposition and Soil Nitrogen are Affected by Mowing and Fertilization of Marigold
Bibliographic record
Abstract
To suppress root‐lesion nematodes (Pratylenchus penetrans Cobb), marigold (Tagetes sp.) is grown as a rotation crop; however, little is known about its decomposition. The timing of N release to soil affects both the nutrition of the subsequent crop and also the environment, which could possibly be altered by biocides produced by marigold. Decomposition was quantified in the field by monitoring residues of marigold and cereal rye (Secale cereale L.), a common rotation crop, over time in litter bags subjected to different conditions. Marigold decomposition proceeded normally and without toxic effects on decomposers. In the fall of rotation years, topsoil NO3 concentration was usually higher under marigold (1.1 mg kg−1) than under rye rotation (0.3 mg kg−1), but this depended on the method of marigold management. In marigold plots, fall NO3 levels were greatest where plants were mowed early (August) or fertilized with 90 kg N ha−1 and lowest where plants were left standing over winter. In plots where marigold was mowed in September or left standing, fall NO3 levels were sometimes no higher than in rye plots. Overwinter N release from bags of marigold shoots (stems and leaves) on the soil surface (39 kg ha−1) was less than that from buried bags (119 kg ha−1). Together, these results suggest that a marigold rotation may be a viable alternative to rye, but to minimize N loss, marigold crops should be left standing over winter and preplant fertilized with 45 kg N 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".