Soil Bacterial Community Associated With High Potato Production and Minimal Water Use
Bibliographic record
Abstract
In agriculture, water consumption for crop production represents 69% of all water use. Precision irrigation systems based on available soil water have been developed to improve crop production, reducing water use. Besides the improvements in irrigation management, a better resolution of the optimal water level is required, and revealing the impact of soil matric potential on the soil microbial community may help. Here, the effect of four soil matric potential treatments (-15 kPa, -25 kPa, -30 kPa, and -45 kPa) was evaluated on the soil microbial community across three potato cultivars and two soil types (silt and sand). The results confirmed the soil matric potential with -25 kPa as the optimal irrigation level, which promoted high potato production using the minimal water use. The irrigation levels affected the composition, predicted functionality, and the ecological network of soil bacterial community. Water excess (-15 kPa) and deficit (-30 kPa, and -45 kPa) promoted an increase in microbial interaction and alpha-diversity. The results suggested higher Positive/Negative ratio for phyla Proteobacteria, Actinobacteria, and Acidobacteria in optimal irrigation level (-25 kPa) than other irrigation levels. Also, correlation analyses revealed an interesting association between the irrigation levels, potato production, and bacterial functionality, especially in the carbon cycle (fixation and emission). Therefore, this study proposed important associations between the soil bacterial community and water management, focusing on high potato production and minimal water use. The advancement of this knowledge may lead to a more comprehensive assessment of the optimal irrigation levels of others crop production systems. Furthermore, the inclusion of biological mechanisms, especially microbial interactions, in agriculture studies has the potential to contribute to the development of water management practices conducive to both increasing crop yield and maintaining a sustainable soil environment.
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 | 0.000 | 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".