PIMA COTTON (GOSSYPIUM BARBADENSE L.) LINES ASSESSMENT FOR DROUGHT TOLERANCE IN UZBEKISTAN
Bibliographic record
Abstract
Globally, increasing water and energy demand is expected to reach 6.9 trillion cubic meters by 2030, exceeding 40% of the available water supplies. Climate change and rising temperatures caused water shortages due to lesser and irregular rainfalls, leading to lower production of crops. The research to assess drought tolerance of Pima cotton (Gossypium barbadense. L) lines in Uzbekistan revealed the line, T-450 as the most promising for drought environments. The research, in a randomized complete block design (RCBD) in three replications with a factorial arrangement and two irrigation regimes (non-stress and water stress at the seedling stage), was conducted at the experimental field of the Institute of Genetics and Plant Experimental Biology, District Zangi-Ota, Tashkent Region, Uzbekistan. Nine Pima cotton lines, i.e., Сурхон-14 (control cultivar), Т-1, Т-5440, Т-2006, Т-10, Т-167, Т-5445, Т-450, and Т-663 with diverse agronomic characters, were selected for their potential yield during 2019, 2020, and 2021 cropping seasons under two different environments (optimal and water deficit condition). Drought indices revealed significant differences among lines, except the golden mean (GM). Results in the ranking method indicate that among the drought tolerance indices, mean productivity (MP), geometric mean productivity (GMP), stress tolerance index (STI), mean relative performance (MRP), relative efficiency index (REI), and relative drought index (RDY), show the most suitable indicators because of their high correlation with seed cotton yield. Cluster analysis and three-dimensional plots showed the cotton inbred lines with the highest tolerance to drought under both irrigation conditions. The first three principal components (PCs) explained 67.54% of total variation and the PC1 can be nominated as a potentially stable yield. The biplot diagram based on PCs and drought tolerance indices showed that MP, GMP, STI, MRP, REI, and YI were the best indices for screening the tolerant cotton inbred lines, such as, T-450.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".