Nitrogen inputs and irrigation frequency influence population dynamics of <i>Mesocriconema xenoplax</i> under grapevines
Bibliographic record
Abstract
Abstract Nitrogen (N) fertilization and irrigation are critical for tree fruit and grape production in semi-arid regions of Western North America. Growers are increasingly considering more conservative fertilization and irrigation practices in order to optimize fruit quality while minimizing environmental impacts. The implications for pest populations of such shifts in production practices are not well known and warrant consideration. The objective of this research was to determine the effects of drip irrigation frequency (daily vs approximately every third day) and N fertilizer rate (ranging from 0 to 64 kg N/ha/year) on population densities of the ring nematode, Mesocriconema xenoplax, in a vineyard. The experiment was a split-plot randomized complete block design with irrigation frequency applied as whole-plot treatments and N input applied as subplot treatments. Nematode populations in root zone soils were assessed in spring, summer and fall of 2010 and 2011. There was a significant irrigation frequency × N input interaction, with M. xenoplax population densities increasing with N input under daily irrigation but not under low frequency irrigation. The data suggest that reductions in fertilizer N input and irrigation frequency, that have minimal impacts on fruit quality and yield, can also minimize M. xenoplax population buildup.
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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".