Soil amendments for sustainable agriculture: Microbial organic fertilizers
Bibliographic record
Abstract
Abstract There is an enormous body of the literature, proving that soil amendments enhance plant growth/yield and prevent the impact of pathogens/toxins. To the best of our knowledge, there is no comprehensive assessment focused on the synergistic effects of microorganisms and soil amendments with biochar or compost for upgrading the current fertilizers towards a more sustainable agriculture. The main objective of the present work is to discuss the most current information needed for developing advanced soil amendments using microorganisms along with safe, sustainable and low‐cost waste sourced materials. The first step in designing the biofertilizer is the selection of a microorganism from among a variety of bacterial/fungi strains that have been identified as plant growth promoting (PGP) in the literature. This study classifies the effective types of soil microorganisms with respect to their functionality to facilitate the choice of the best compatible microbial strain(s) in order to satisfy the host environment requirements. The second part is dedicated to various inorganic and organic carriers, such as perlite, peat, fly ash and compost, for delivering of microorganism into the soils. The role of carriers in the survival and the functional contribution of the microbes to soil–plant systems are investigated. Lastly, biochar is evaluated as a promising microbial carrier together with its influence on the soil biota including microorganisms and plants. The superior features of biochar, for example high surface area, porosity, customizable structure, high stability, carbon sequestration and synergy, with other fertilizers are also discussed.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".