Treated wastewater reuse and its impact on soil properties and potato and corn growth
Bibliographic record
Abstract
Water scarcity is a growing challenge in semi-arid regions. Many farmers have resorted to treated wastewater (TWW) as an available and low-cost water source. This study investigated the impact of irrigating potato ( Solanum Tuberosum ) and corn ( Zea mays ) with tertiary-treated (TW) and secondary-treated (SW) wastewater compared to freshwater, over two years. We studied the impact of TWW reuse on soil properties, soil microbes, crop yield, and potato tuber health. Irrigation of both corn and potato with TW significantly increased organic matter (OM) content; on average across both years and crops OM increased by about 35 % under SW and 42 % under TW. TWW irrigation also increased cation exchange capacity (CEC) by the second year under SW and TW in potato (average 67 %), and by the second year under TW in corn (average 13 %). TWW also enhanced soil fertility with no heavy metals contamination. However, potato field irrigated with SW showed high levels of total and thermotolerant coliforms in soil, exceeding predefined thresholds, in the second season. No microbial contamination was recorded in TW-irrigated fields, however, it raised salinity concerns compared to control with 935 mg Na /kg in TW soil compared to 465 mg/kg in control soil during the first season in potato soil. Significant increases in potato tillers, number of tubers (average 6 tubers/plant in TW vs 3 tubers/plant in the control), and tuber weight were recorded in season two under TW irrigation. Both SW and TW increased corn biomass during both seasons. In conclusion, TW is a sustainable alternative water source that enhances crop yields and improves soil quality. This study highlighted the critical role of TWW management and monitoring to address challenges such as salinity and microbial contamination. Further research is required to optimize TWW long-term reuse sustainable agriculture, balancing crop benefits while safeguarding human health. • Irrigation with treated wastewater increased organic matter, enhanced soil fertility, and increased potato and corn yield. • Heavy metals concentrations in TWW-irrigated soils remained within safe limits. • Potato field with secondary-treated wastewater showed some contamination with total coliform. • Sodium level in soils irrigated with tertiary-treated wastewater require monitoring and management. • With monitoring, tertiary-treated wastewater could be better option than secondary-treated wastewater for irrigation.
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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 | 0.001 | 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".