Effect of Partial Root-Zone Drying Irrigation Technique (PRD) on the Total Dry Matter, Yield and Water Use Efficiency of Potato under Tunisian Semi-Arid Conditions
Bibliographic record
Abstract
Three field experiments were conducted in the plot of Technical Centre for Potato and Artichoke during three cropping season 2009, 2010 and 2011.The objective of this research is to evaluate the effect of the partial root-zone drying irrigation technique (PRD) on the dry matter, yield, water consumption and water use efficiency of potato (Spunta Variety). Four treatments were applied from the initiation of tuberisation stage to potato harvesting: FI treatment received 100% of Etc; PRD80, PRD70, and PRD60 treatments received respectively: 80, 70 and 60% of FI at each irrigation event and it was shifted between the two sides of the plants every 2 to 10 days. The highest water consumption was observed in FI as 336.5, 376, and 341.8 mm in the 2009, 2010 and 2011 respectively, and the lowest was found in PRD60 as 280.4, 294.5, and 256.1 mm in the 2009, 2010 and 2011 respectively. The maximum tuber yield was obtained from the FI as 25.6, 42.9 and 39.1 tha-1, in the 2009, 2010 and 2011 respectively. There was no significant difference between FI and PRD60 with respect to tuber yields (LSD 5%).The highest water use efficiency (WUEY) was found in PRD60 as 13.4 kg m-3, in the second experiment (2010) and the lowest was found in PRD80 as 6.8 kg m-3 in the first experiment (2009). In the other hand, a linear relation between the accumulated total dry matter and the accumulated water consumption was able to be revealed.
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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".