Humalite enhances the growth, grain yield, and protein content of wheat by improving soil nitrogen availability and nutrient uptake
Bibliographic record
Abstract
Abstract Background The application of synthetic chemical inputs in current agricultural practices has significantly increased crop production, but their use has caused severe negative consequences on the environment. Humalite is an organic soil amendment that is rich in humic acid and found in large deposits in southern Alberta, Canada. Humic products can enhance nutrient uptake and assimilation in plants by reducing nutrient losses and enhancing bioavailability in the soil. Aim Here, we evaluated the effects of different humalite rates in the presence of nitrogen, phosphorus, potassium (NPK) at recommended rates on soil nitrogen availability, wheat growth, grain yield, seed nutritional quality, and nitrogen use efficiency (NUE) under controlled environmental conditions. Methods A series of studies were conducted by applying five different rates of humalite (0, 200, 400, 800, and 1600 kg ha−1) with NPK at recommended rates. Soil nitrogen availability and shoot and root growth parameters were recorded at flowering stage. NUE was calculated based on the grain yield at maturity stage. Results Plants grown in the presence of humalite augmented root morphological parameters (root length, volume, and surface area), plant biomass (shoot and root), and nutrient uptake (N, P, K, and S) compared to the plants supplied with recommended fertilizer alone. Furthermore, humalite application significantly increased grain yield (14%–19%), seed protein content (23%–30%), and NUE (14%–60%) compared to the fertilizer application alone. Conclusion These findings suggest that humalite can be used as an organic soil amendment to reduce synthetic fertilizer application and improve plant growth and yield while enhancing fertilizer use efficiency.
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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) | 0.001 | 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".