Influence of Planting Methods and Density on Performance of Chia (Salvia hispanica) and its Suitability as an Oilseed Plant
Bibliographic record
Abstract
A 2-year field experiment was conducted in 2012 and 2013 at the research fields of CSIR-Crops Research Institute to determine the best agronomic practices for the field establishment and cultivation of Chia (Salvia hispanica) and determine insect pests and diseases associated with the plant. The study also determined the nutritional composition of the plant under Ghanaian environment. A split-plot field trial with 2 planting methods (direct planting with seed; planting from seedling) as main plot and 3 planting density (10,000 plants/ha; 20,000 plantst/ha; 40,000 plantst/ha) as subplot were used for the agronomic evaluation of the plant. A sweep net was used to sweep diagonally across the field under two separate regimes to collect insects on the crop for the entomological investigations. Major insects collected were coreid bugs, lagria sp., Zonocerus variegatus and Diopsis thoracica. The method used for the pathological investigations were the moist blotter test and culture of pathogens on Potato Dextrose Agar. The results of the study in both years show high biomass and seed yield of chia when the crop was planted directly in the field. Narrow-row spacing of 0.5m x 0.5m consistently produced the highest biomass and seed yield in both years of the study. The results indicated that interaction between planting method and planting density positively influenced most of the growth and yield parameters of Chia plants. Nutritional profile analysis from the Chia seeds shows medium to high proximate and mineral composition. Disease evaluation revealed evidence of Fusarium wilt infection on Salvia hispanica in the field. The study recommends the adoption of direct planting method and narrow-row spacing (0.5m x 0.5m) (SP3) as the best option for the production of Chia plants in Ghana.
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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 | 0.001 |
| 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".