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Record W4362557126 · doi:10.3390/stresses3020030

Engineering Approach for Production of Arbuscular Mycorrhizal Inoculum Adapted to Saline Soil Management

2023· article· en· W4362557126 on OpenAlexaff
Karima Bencherif, Frédéric Laruelle, Benoît Tisserant, Yolande Dalpé, Anissa Lounès‐Hadj Sahraoui

Bibliographic record

VenueStresses · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsAgriculture and Agri-Food Canada
FundersEuropean Commission
KeywordsSoil salinityAgronomySalinityBiofertilizerBiologyBiomass (ecology)Environmental scienceEcology

Abstract

fetched live from OpenAlex

(1) Background: Soil salinity impacts plant growth and productivity in steppic regions. Thus, the current study aims at producing, at low cost, an inoculum composed of native arbuscular mycorrhizal fungi (AMF) strains adapted to saline soil management. (2) Methods: The propagation of the native AMF strains was carried out in three natural saline soils presenting increasing salinity levels (4.5, 8.5 and 9.3 dS·m−1 in Boughzoul (BG), Salt Rocket (SR) and Zaafrane (ZA) sites, respectively). Three host plant species (alfalfa, clover and leek) were tested as trap cultures. AMF spore richness and diversity, the quantification of soil microbial biomass and the determination of the most probable number (MPN) were carried out before and after 24 months culture. Moreover, the mycorrhizal rates of the host plant species were assessed. (3) Results: The moderate saline soil (SR) planted with alfalfa was found to be the most suitable for AMF inoculum production with a maximum of 650 spores, 10 g−1 of soil, a mycorrhizal rate of 86%, 70 propagules·g−1 of soil and the highest microbial biomass content. (4) Conclusion: This study points out the best combination for the production of a more adapted AMF biofertilizer for saline soil management and proposes several indicators for biofertilizer quality evaluation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.211
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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