Selected Chemical Properties, Microbial Activity and Biomass of Soils Amended with Aqueous Neem Leaf Extract
Bibliographic record
Abstract
With declining fertility levels of soils and the high cost of agricultural inputs, such as commercial fertilizers and pesticides, the use of organic inputs has increased in Zambia. While neem products have been shown to improve soil fertility status, several negative effects on soil organisms have also been cited. The negative effects have been attributed to several secondary metabolites produced by the neem plant. In Zambia, neem leaf extract is applied by small scale farmers to enhance soil fertility and promote crop productivity. This study reports the suitability of aqueous neem leaf extract as a soil amendment and its effect on soil microbial biomass and activity in local soils. Neem leaves were characterized before being used to prepare aqueous neem extract in the concentrations 2, 5, 10, 15, and 20 % in water. The extract was characterized for selected mineral components and then applied to 5 kg of soil on a weekly basis for five weeks. Each week, for ten weeks, the effect of the extract on microbial biomass and activity were determined using the Chloroform Fumigation and Incubation (CFI) and soil respiration methods, respectively. Selected soil chemical characteristics were determined at the start and end of the experiment. Results indicated that the chemical composition of the neem leaves was comparable to that observed by others and was similar to that of other tree leaves used for preparing leaf extracts. Amending soils with neem did not significantly improve selected chemical properties but only marginally increased soil calcium levels. Neem leaf extract enhanced soil microbial activity up to 10 %, but showed inhibitory effects at 15 and 20 % concentrations. Microbial biomass was also depressed by neem leaf extract at 20 %. The reduction in both microbial activity and biomass was possibly due to the negative effects of the neem secondary metabolites in the leaf extract at these higher concentrations. Although the application of neem leaf extract at 10 % percent or higher can inhibit both microbial biomass and activity, some mineralizable components in the extracts can support growth and activity of some microorganisms in the soil. Based on these results, the application of neem leaf extract at 10 % percent or higher can inhibit both microbial biomass and activity and marginally improve soil Ca levels. The use of neem leaf extract can therefore be of benefit to soils with critically low levels of Ca.
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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) | 0.000 | 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".