Quinoa productivity and quality under irrigated conditions through crop establishment methods in Pakistan
Bibliographic record
Abstract
Abstract Quinoa (Chenopodium quinoa Willd.) is gaining global recognition for its nutritional benefits and adaptability to various environments. However, there is limited data on how different planting methods affect the productivity and quality of quinoa genotypes with varying panicle shapes, especially under irrigated conditions. We hypothesized that different crop establishment techniques influence the performance of quinoa genotypes in terms of phenology, weeds, growth, and yield aiming to enhance both yield and grain quality. To test this, a 2‐year field experiment (2021–2022 and 2022–2023) was conducted at Ayub Agricultural Research Institute, Faisalabad, Pakistan. The experiment used split‐plot design with three replications incorporating three sowing methods in main plots: ridge sowing (RS), line sowing (LS), and broadcasting (BC) and three quinoa genotypes with different panicle architectures: UAF‐Q7 (intermediate), Line‐105 (glomerulate), and Line‐22 (amarantiform) in subplots. Results indicated that LS method improved morphological and yield attributes like stem thickness, terminal panicle length and weight, grain yield, 1000‐grain weight, and biological yield compared to RS and BC. Among genotypes, Line‐105 showed superior performance in morphology, biomass, and yield, while UAF‐Q7 had higher grain protein content. Panicle number per plant showed positive correlation with terminal panicle weight, grain yield, 1000‐grain weight, and harvest index. LS also resulted in highest crop growth rate and grain quality. The study suggests that LS is a better planting technique and Line‐105 is a suitable genotype for achieving high grain yield under irrigated 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 | 0.001 |
| 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".