Soil moisture gradients shape microbial communities and influence cranberry yield: a case study on subirrigation
Bibliographic record
Abstract
Water management is vital in cranberry farming, balancing plant needs and supporting flood-based harvesting. This study examines subirrigation, a technique that reduces water use and enhances yields, by utilizing natural soil moisture gradients in two fields to assess its effects on yield and soil bacteria. Considering 166,551 observation points collected over four years, we confirmed significant differences in soil moisture between the eastern and western sides of the fields, with lower water table depths on the subirrigated sides. Using 16 S rRNA sequencing, we examined soil bacterial communities, focusing on nitrogen cycling. Subirrigated areas, with lower moisture levels, showed higher cranberry yields (up to 45.67 t/ha) and a greater abundance of beneficial bacteria such as Burkholderia and Arthrobacter. The results also suggest an increase in predicted bacterial genes linked to nitrogen mineralization, denitrification, and nitrate assimilation as soil moisture levels rise, which, notably, correlates negatively with cranberry yield. Conversely, DNRA (nirD) and ANRA (NasA and NasB) genes appear to be indirectly favored in environments with lower soil moisture. Our findings not only shed light on the intricate relationships between bacterial genera, nitrogen metabolism, and environmental factors but also underscore the potential of sustainable agricultural practices in enhancing soil health.
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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) | 0.000 | 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 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".