Effects of nitrogen fertilization and a commercial arbuscular mycorrhizal fungal inoculant on root rot and agronomic production of pea and lentil crops
Bibliographic record
Abstract
In the Canadian prairies, pulse crops such as field pea (Pisum sativum L.) and lentil (Lens culinaris L.) are economically important and widely grown. However, in recent years, root rot, caused by a variety of fungal and oomycete pathogens, including Aphanomyces euteiches, has become a limiting factor on yield. In this study, we examined the impacts of nitrogen (N) fertilization and a commercial arbuscular mycorrhizal fungal (AMF) inoculant on pea and lentil plant health and agronomic production at three locations in Saskatchewan: Swift Current, Indian Head and Melfort. The AMF inoculation had no impact on root rot severity, and therefore is not considered a reliable method to manage root rot in pea and lentil. In contrast, N fertilization led to reductions in root rot in Swift Current, but not the other two sites. However, N fertilization did reduce nodulation. When both pea and lentil are considered, the abundance of A. euteiches in soil increased from pre-seeding to mid-bloom. A negative correlation between soil pH and disease severity was also observed. The high between-site variability highlights the importance of testing root rot mitigation strategies under multiple soil conditions to develop site-specific recommendations. Use of N fertilizer as a root rot management strategy merits further exploration, including investigation into its interactions with other management strategies, soil properties, and costs and benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".