Impact of Saline Water Irrigation (SWI) on Water Use Efficiency of Quinoa (Chenopodium quinoa Willd.) Under Tunisian Semi-arid Conditions
Bibliographic record
Abstract
This study was carried out in Cherfech Tunisia, at the experimental station of the National Research Institute of Rural Engineering, Water and Forests (INRGREF) during the growing season 2015. The main objectives are quantifying and valuing the Water consumption (WC) and Water Use Efficiency of quinoa (Chenopodium quinoa Willd.), under saline water irrigation at different concentrations (T0 = 1.2 dS m-1, T1 = 9.2 dS m-1 and T2 = 18 dS m-1). The TDM decreased from 6.7 to 13.4% due to the increase in the salt concentration of the irrigation water from 9.2 to 18 dS m-1. A reduction of 9.8 to 12.6% was marked for treatments T1 and T2. Also, the WUE PR-anthesis has registered a decrease of 8 and 12.5% respectively for T1 (WUEPR = 10.3 kg m-3) and T2 (WUEPR = 9.8 kg m-3) compared with the control T0 (WUEPR =11.2 kg m-3). However, irrigation water salinity showed no effect on the WUE post-anthesis T0 and T1 (WUEPS = 3 kg m-3). Nevertheless, a decrease about 15% was recorded in the T2 (WUEPS = 2.5 kg m-3). At harvest, the highest, WUETDM (5.43 kg m-3) was recorded under T0. However, the lowest WUETDM (5 kg m-3) was marked under T2; a decline of 7.9% was marked. Besides, the uppermost WUEGY (2.09 kg m-3) was recorded under T0. However, the smallest amount of WUEGY (1.1 kg m-3) was recorded under T2. A lessen of 47.4% was manifested on WUEGY due to the height reduction on yield in the T2.
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".