Failures count too: effect of the application of commercial inoculum of arbuscular fungi in a vineyard during its plantation
Bibliographic record
Abstract
Symbiosis with arbuscular mycorrhizal fungi (AMF) has long been recognized for its positive impact on plant health. Today, various companies market AMF-based commercial inoculants as biofertilizers or biostimulants for sustainable agriculture. However, their consistent efficacy in real-world field settings remains uncertain. This study investigated the influence of a commercial AMF inoculant on a newly planted vineyard featuring a local grape cultivar grafted onto a common rootstock (‘Ritcher 110’). Over two years, the physiological well-being, growth, and productivity of 20 inoculated vines compared to 20 control counterparts were monitored. The impact of inoculation on soil bacterial diversity and the infectivity of soil was assessed. Notably, AMF-inoculated plants exhibited consistently lower values in photosynthesis, growth, and grape production, although statistical significance was not always reached. Additionally, the total production remained unaffected, but there was a significant decrease in °Brix and pH values, suggesting delayed grape ripening in mycorrhizal plants, potentially promoting secondary metabolites accumulation. Regarding soil effects, the inoculation's impact was slight, with no substantial changes in soil mycorrhizal infectivity and only slight shifts in the microbial community's metabolic profile. Numerous studies highlight the context-dependent nature of AMF inoculation's effects, making it challenging to predict outcomes in field conditions. Failures found in trials like the present one provides valuable scientific information, contributing to determine the prerequisites for effective biofertilizer use in commercial viticulture. Ultimately, the effectiveness of AMF-based biofertilizers remains contingent on specific conditions, exposing the need for additional research to ensure their consistent and reliable application.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".