Effect of 3-year rotation sequences and pearl millet on population densities of<i>Pratylenchus</i><i>penetrans</i>and subsequent potato yield
Bibliographic record
Abstract
Various crops were evaluated in 3-year rotation sequences for their ability to reduce population densities of the potato root-lesion nematode, Pratylenchus penetrans, and improve subsequent potato (Solanum tuberosum) yield in field microplots. Forage and grain pearl millets (Pennisetum glaucum) were evaluated against oats (Avena sativa), Indian mustard (Brassica juncea), soybean (Glycine max), rye (Secale cereale), potato, and corn (Zea mays). Population densities of P. penetrans were high under rye, potato, oats, and Indian mustard and low under both forage and grain pearl millets. A single year of grain pearl millet after two consecutive years of potato or rye significantly reduced P. penetrans populations compared with 3 years of potato or rye and two consecutive years of corn, Indian mustard, oats, or soybean. One year of forage or grain pearl millet prior to potato decreased P. penetrans populations below the damage threshold of 1000 kg-1 of soil. The subsequent potato crop produced the highest marketable and total yields following either three consecutive crops of forage or grain pearl millet or a single crop of forage or grain pearl millet preceded by rye or soybean. Total yields of potato 'Superior' were significantly negatively correlated with soil densities of P. penetrans in both fall 2002 (P < 0.001, r =−0.539) and spring 2003 (P < 0.001, r =−0.423). Both forage pearl millet 'CFPM 101' and the grain pearl millet hybrid ‘CGPM H-1’ have potential for the magagement of P. penetrans in Quebec's potato-production system.
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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) | 0.000 | 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".