Occurrence of vermiform plant-parasitic nematodes in North Dakota corn fields and impact of environmental and soil factors
Bibliographic record
Abstract
Plant-parasitic nematodes (PPN) can negatively affect corn production. In this study, incidence and abundance of PPN were assessed in 300 corn fields across 20 counties in North Dakota during 2015 and 2016. Seventy-two percent of the fields were positive for PPN. The major genera identified were Helicotylenchus (incidence: 52%; mean density: 1513 nematodes kg−1 of soil; greatest density: 16 910 nematodes kg−1 of soil), Tylenchorhynchus (37%; 687; 9500), Paratylenchus (31%; 1484; 7800), Pratylenchus (20%; 399; 2125), Heterodera (9%; 555; 4500), Xiphinema (8%; 330; 900), Hoplolaimus (3%; 294; 500) and Paratrichodorus (1%; 124; 200). Neighbouring counties had greater similarity in PPN diversity than counties that are further apart, with western, north-eastern and south-eastern counties forming clusters of similar nematode occurrence and diversity. Canonical correspondence analysis was conducted to determine the association between incidence and abundance of these PPN populations and various soil edaphic and climatic factors. The analysis revealed that Hoplolaimus, Paratrichodorus, Paratylenchus and Pratylenchus were positively correlated with soil temperature, rainfall and per cent sand, while Helicotylenchus, Tylenchorhynchus and Xiphinema were positively correlated with pH, percent clay and percent organic matter. This is the first report of an extensive investigation of PPN communities in North Dakota corn fields. Research findings will be useful in future field experiments to determine the impact of PPN on corn in the Northern Great Plains.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".