Legume Production and Irrigation Strategies in the <scp>A</scp>ral Sea Basin: Yield, Yield Components, Water Relations and Crop Development of Common Bean (<i><scp>P</scp>haseolus vulgaris </i><scp>L</scp>.) and Mungbean (<i><scp>V</scp>igna radiata</i> (<scp>L</scp>.) <scp>W</scp>ilczek)
Bibliographic record
Abstract
Abstract With world population expected to reach 9.2 billion people by 2050, improved irrigation methods will be needed to increase the productivity of agricultural land and improve food supply worldwide. The objective of this work was to examine the effect of regulated deficit irrigation (RDI) and alternate furrow irrigation (AFI) on the yield and yield components of two legume species (common bean and mungbean) produced as a second crop following winter wheat in Uzbekistan, Central Asia. Water relations and crop development were also examined. The research was conducted during two successive growing seasons in the Fergana valley. Production of mungbean using the severe stress RDI treatment in combination with AFI resulted in the highest yields with the lowest quantity of applied water in 2004. In addition, yields of common bean in the moderate stress treatment were not different from the recommended schedule, although irrigation events were decreased from 4 to 2. AFI did not reduce yields, and it did not interact with RDI to reduce yields further. In general, mungbean yields were higher than those of common bean. The combination of AFI and RDI can allow legume production with reduced water inputs.
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 | 0.000 | 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".