The Effect of Beneficial Autochthonous Microorganisms (Bams) on the Chemical Quality of Agricultural Soils and Olive Production in the Wilaya of Tlemcen (Western of Algeria)
Bibliographic record
Abstract
Abstract Beneficial Autochthonous Microorganisms (BAMs) are a multi-purpose technology that has already been used in Latin America and Southeast Asia but is still unknown in Africa and particularly Algeria. The aim is to examine the effect of BAMs on agricultural soils and olive fields. The study was carried out in two degraded olive fields located in the Oued Tafna basin west of Tlemcen (The Semi-arid region in Western Algeria). The objective is to evaluate the effectiveness of BAMs on the chemical characteristics of the soils, particularly in terms of organic matter (MO), pH, electrical conductivity (EC), total limestone (CaCO3), moisture, organic carbon (C) and olive production and its weight. Statistical analysis (Independent Test) between all groups (treated and controls) showed a significant difference, in conductivity (EC) where we recorded “0.214±0.03μs /cm “ in treated soils vs. “0.198±0.029μs/cm “ in control soils a different statistical significance was recorded for the other parameters, however, it was observed that there is a relative increase in these parameters in the soils of the treated groups such as carbon (C): “2.85±1.06%” organic matter (MO): “4.91± 1.83%”, “pH=7.81±0.225”, “CaCO 3 27.76±4.99% and a decrease in humidity “15.11±3.77%” compared to control soils. The results of statistical analyses (Mann-Whitney Test and Suite Test) of olive production showed a clear alternation (every other year) in fruit production in the control group and a positive effect of BAMs on olive production in the treated group, with a slight increase of production (19 kg in 2019 vs. 30 kg in 2020), but without any significant difference regarding the weight of the fruit between the two sessions. However, a highly significant difference “p=0.00<0.001” in fruit weight was noted between the treated groups “3.908g” and “5.70g” and the control ones “4.40g “ and “5.00g” in 2019 and 2020 respectively. The use of MAB is an interesting technique to restore the chemical properties of degraded soils, and increase olive tree production. To achieve more reproducible results (in terms of quantity and quality), sufficient doses of these BAMs and periodicals must be added to provide a good nutritional supplement and reduce farmers’ use of pesticides and mineral fertilizers.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".