Beyond synthetic fertilizers: Recycled organic residuals deliver balanced nutrition and sustained corn yield
Bibliographic record
Abstract
Context Recycling organic wastes into fertilizing residual materials (FRMs) offers a powerful strategy to replace synthetic fertilizers, reduce environmental impacts, and promote circular agriculture. Yet, their agronomic value in cool-climate regions such as Eastern Canada remains underexplored, particularly regarding nutrient balance. Objective The main objective of this study is to evaluate the agronomic performance of eight recycled FRMs derived from composts, digestates, sludges, and ashes applied in combination with mineral fertilizers on silage and grain corn across two contrasting soil textures over two seasons. Method A study was conducted on two experimental sites where FRMs substituted either one-third of the nitrogen or the total phosphorus requirements. Corn yield was measured on the central rows of each plot. The nutritional status of grain corn was assessed with the Compositional nutrient diagnosis (CND) at the silking stage. At the end of the experiment, soil samples (0–10 cm) were taken and analyzed for chemical properties. Results Results showed that all FRM treatments matched the yield of full mineral fertilization (10.55 Mg ha⁻¹ and 11.18 Mg ha⁻¹ on clay and loam soils, respectively). On the well-drained loam site, every FRM supported balanced crop nutrition, with composts and deinking paper sludge outperforming the mineral-only treatment. On the poorly drained clay soil, almost all treatments, even the mineral treatment, produced unbalanced grain corn. Only the digestates provided balanced nutrition, showcasing their superior mineralization potential. Conclusion These findings demonstrate that FRMs can effectively replace synthetic fertilizers without compromising yield or crop health. By transforming organic waste into high-value agronomic inputs, this approach enhances sustainability, cuts production costs, and strengthens the resilience of farming systems.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".