Improving Soil Fertility and Maize Growth in Suboptimal Land Through Application of Humic Acid
Bibliographic record
Abstract
Humic acid (HA) has been reported to increase plant growth and crop yields, as well as improve soil fertility. However, the potential utilization of HA extracted from various organic waste composts as organic amendment in suboptimal soils has not been studied in depth. The experiment used a two-factor Completely Randomized Design (CRD) with three replications. Four types of HA were used, namely bagasse HA (BHA), water hyacinth HA (WHA), market waste HA (MHA), and commercial HA (CHA). It also comprised of four doses HA i.e., 0.05, 0.10, 0.15 and 0.20% (of soil on w/w base). The results revealed that fluctuations in soil pH and nutrient release with the HA application had a variable quadratic response pattern. Organic carbon increased by 17%, while total N and available P decreased by 5% and 38.6% during the last weeks of incubation. The HA application could improve the growth response and nutrient uptake of maize significantly. CHA0.20% was the best interaction treatment which had the highest average value on dry weight and NPK uptake, which were 98.0 g pot-1, 178.8 mg plant-1, 27.4 mg plant-1 and 216.9 mg plant-1, respectively. The scanning electron microscopic (SEM) showed that HA could increase in the length and density of maize root hairs. Furthermore, the HA application significantly increased pH, CEC, C-organic content and availability of soil nutrients.
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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 | 0.000 |
| 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 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".