Mycorrhizal Fungi Inoculation Improves Capparis spinosa’s Yield, Nutrient Uptake and Photosynthetic Efficiency under Water Deficit
Bibliographic record
Abstract
Agricultural yields are under constant jeopardy as climate change and abiotic pressures spread worldwide. Using rhizospheric microbes as biostimulants/biofertilizers is one of the best ways to improve agro-agriculture in the face of these things. The purpose of this experiment was to investigate whether a native arbuscular mycorrhizal fungi inoculum (AMF-complex) might improve caper (Capparis spinosa) seedlings’ nutritional status, their morphological/growth performance and photosynthetic efficiency under water-deficit stress (WDS). Thus, caper plantlets inoculated with or without an AMF complex (+AMF and −AMF, respectively) were grown under three gradually increasing WDS regimes, i.e., 75, 50 and 25% of field capacity (FC). Overall, measurements of morphological traits, biomass production and nutrient uptake (particularly P, K+, Mg2+, Fe2+ and Zn2+) showed that mycorrhizal fungi inoculation increased these variables significantly, notably in moderate and severe WDS conditions. The increased WDS levels reduced the photochemical efficiency indices (Fv/Fm and Fv/Fo) in −AMF plants, while AMF-complex application significantly augmented these parameters. Furthermore, the photosynthetic pigments content was substantially higher in +AMF seedlings than −AMF controls at all the WDS levels. Favorably, at 25% FC, AMF-colonized plants produce approximately twice as many carotenoids as non-colonized ones. In conclusion, AMF inoculation seems to be a powerful eco-engineering strategy for improving the caper seedling growth rate and drought tolerance in harsh environments.
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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) | 0.001 | 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".