Impact of deficit irrigation and addition of biochar and polymer on soil salinity and tomato productivity
Bibliographic record
Abstract
The aim of this study is to investigate impact of soil amendments (4% biochar, 0.4% polymer, and a combination of them) on soil moisture and salinity distribution, tomato yield, and water-use efficiency (WUE). Open-field experiments were conducted during two successive growing seasons in 2017 and 2018. The experiment consisted of three levels of irrigation treatments: 100%, 80%, and 60% of crop evapotranspiration (ETc); and two different water qualities: fresh 0.9 dS m−1 and saline electrical conductivity 3.6 dS m−1. Results revealed that at 100% of ETc, soil water distribution increased by 12.94%, 37.87%, and 42.21% at depths 0–15, 15–30, and 30–45 cm, with the addition of biochar, respectively, compared with control at same depths under freshwater, but the addition of polymer was increased by 6.35%, 16.56%, and 16.37%, respectively. While combination treatments increased by 15.70%, 24.80%, and 41.26%, at the depths aforementioned. Salt concentration was increased by 59.10% with biochar, whereas decreasing by 7.19% and 57.63% with polymer and mixture treatments, respectively. The results also showed that biochar and mixture treatments improved yield compared with the polymer and control, whereas saline water decreased the yield compared with freshwater. With deficit irrigation, WUE was increased by 28.54%, 40.98%, and 68.93% at 100%, 80%, and 60% of ETc, respectively, indicating it could be used as an irrigation management strategy under arid and semiarid field conditions.
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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".