Performance of Chia on Different Sowing Dates: Characteristics of Growth Rate, Leaf Area Index, Shoot Dry Matter Partitioning and Grain Yield
Bibliographic record
Abstract
The objective of this study was to comparatively determine the characteristics of growth rate, leaf area index, shoot dry mass partitioning and grain yield of chia plants (Salvia hispanica L.) on different sowing dates. A field experiment was conducted in the crop year of 2016/2017 in five sowing dates (09/22/16, 10/28/16, 01/03/17, 02/08/17 and 03/24/17) with a randomized complete block design and four replicates. Plant growth was determined through field samplings to determine the dry matter mass and leaf area performed every 15 days. The following physiological indexes were calculated: relative growth rate, absolute growth rate, net assimilation rate, leaf area ratio, specific leaf area and leaf mass ratio. To weekly evaluate plant height, ten plants per plot were marked after emergence, and the final height was considered when plants reached physiological maturity. A useful area of 2.10 m² per plot was collected for evaluating grain yield. The physiological indexes indicated that at earlier sowing dates there is a greater plant growth, either in shoot dry matter mass, height and leaf area index. The leaf area index of branches is progressively increased with plant development and contributes significantly to total leaf area index of chia plants in all studied sowing dates. The main stem represents between 60 and 70% of the shoot dry matter accumulated in the early sowing dates, and between 40 and 50% in late sowing. The best sowing date in terms of grain yield is 01/03/17.
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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) | 0.001 | 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".