Assessment of Non-Conventional Irrigation Water in Greenhouse Cucumber (Cucumis sativus) Production
Bibliographic record
Abstract
Climate change, urbanization and subsequent environmental changes are depleting freshwater resources around the globe. The reuse of domestic, industrial and agricultural wastewater is an alternative approach to freshwater that can be used for irrigation purposes. However, these wastewaters may contain hazardous and toxic elements, such as heavy metals that are hazardous for human health and the environment. Therefore, an experiment was conducted to evaluate the concentration of macro, micro and heavy metals in cucumber irrigated with different resources (tap water, greywater, dairy water and wastewater). The results showed that the use of different irrigation resources has increased the level of macro (sodium (Na), potassium (K), calcium (Ca), magnesium (Mg)), microelements (zinc (Zn), iron (Fe), manganese (Mn)), and heavy metals (copper (Cu), barium (Ba), lead (Pb) and cadmium (Cd)) in cucumber leaves and fruits. However, their levels were in the range that is safe for human health and the environment was as recommended by FAO maximum values of trace elements (Zn, 2.0; Fe 1.0; Mn, 0.2; Cu, 0.2; Pb, 5.0, and Cd, 0.01 mgL−1). Based on observations, it was also revealed that among different irrigation resources, the use of dairy water in cucumber improved its agronomic attributes and maximum plant yield (1191.02 g), while the different irrigation resources showed a non-significant impact on fruit diameter. However, total soluble solid contents (TSS) were more significant in cucumber fruits treated with wastewater (2.26 °brix) followed by dairy water (2.06 °brix), while the least TSS contents (1.57 °brix) were observed in cucumber plants treated with tap water. The significance of non-conventional irrigation water use in agriculture, particularly greenhouse cucumber (Cucumis sativus) production, is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".