Inoculating plant growth-promoting bacteria - effects on soil hydraulic properties and tomato root development under water stress conditions
Bibliographic record
Abstract
The plant growth promoting bacteria (PGPB) harbored in the rhizosphere develop specialized mechanisms that may have a key role to ameliorate soil properties and plant growth under prolonged dry conditions. Accordingly, this study aimed to assess the effects of bacterial growth on the soil hydraulic properties and the root response under water stress conditions induced by drip irrigated tomato. At pot scale, a silty soil was inoculated with two PGPB strains (Micrococcus yunnanensis M1 and Pseudomonas stutzeri SR7-77) to cultivate tomato plants under three different water regimes: full irrigation (100% of Pot Capacity- PC), moderate and severe water stress levels (75 and 50% of PC, respectively). Bacterized soil altered the pore size distribution of the rhizosphere compared to no-bacterized soil, increasing root zone plant-available water holding capacity. On the contrary, PGPB occupying the pores reduced the saturated hydraulic conductivity near-saturated soil conditions compared to the uninoculated trial. PGPB shown root surface density (RSD) equal to 0.540 % and 0.355 % to inoculated SR7-77 and M1 tests, respectively and under 50% PC, compared to 0.097% to the uninoculated soil test. Soil water potential values, retrieved through soil water retention parameters, were more negative to M1 and SR7-77 strains tests, corresponding to water hold in the pores with smaller radii conferring resistance to the plant following contrasting stresses. The results demonstrated that PGPB elongated continuous transmission pores and bridged with air-filled spaces in stressed periods.
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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 | 0.001 |
| 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".