Recycling Olive Mill Wastewater to Calcareous Soil: Effect of Preplanning Application Period on Phytotoxicity, Corn Growth, and Nutrient Uptake
Bibliographic record
Abstract
This study investigated the effects of applying olive mill wastewater (OMWW) at different periods prior to corn (Zea mays) sowing on germination rate (GR), growth, and soil nutrient availability in calcareous soil. The OMWW was applied at rates of 0, 20, 40, and 60 m3 ha−1 and was allowed to remain in soil for zero, one, two, three, or four months before sowing corn seeds. Immediate planting after OMWW application significantly reduced the GR, with rates of 83%, 75%, and 63% at 20, 40, and 60 m3 ha−1, respectively. Germination improved when corn was sown one month after OMWW application, with a GR of 92% at both 20 and 40 m3 ha−1 and 79% at 60 m3 ha−1. The GR increased to 96% for the 40 and 60 m3 ha−1 rates when OMWW was applied two months before planting. The adverse impact on GR disappeared when OMWW was in the soil for three months before sowing, providing a GR similar to the unamended control. Corn dry matter yield also improved when OMWW was applied two to three months before planting. The phytotoxic effects of OMWW, due to its high polyphenol content, diminished over time due to rapid degradation in calcareous soils. Soil available N and P were highest, and plant N, P, and K content increased, when OMWW was applied two months prior to planting. Soil pH decreased from 7.8 to 7.2 at 60 m3 ha−1 of OMWW at planting time. Results suggest that OMWW can enhance soil quality and corn growth if applied one to two months before planting to avoid possible negative impact on germination. This work bridges the gap between waste management and sustainable agriculture, offering practical guidelines for OMWW utilization.
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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".