Adaptation of chickpea to high latitude areas with short growing seasons: biomass and seed yield responses
Bibliographic record
Abstract
This study was conducted to determine plant establishment, biomass and seed yield of chickpea under diverse environmental and crop management conditions. Four cultivars were grown on three types of seedbed using N fertilizer rates of 0, 28, 64, 84, and 112 kg N ha-1 with and without Rhizobium inoculant (GR), at six sites in Saskatchewan, Canada. On average, chickpea grown on fallow seedbed produced the highest straw biomass, 5.8 t ha-1, or 28% greater than chickpea grown on barley stubble and 13% greater than being grown on wheat stubble. Similarly, chickpea grown on fallow produced seed yield of 2.5 t ha-1, 22 and 14% greater than chickpea grown on barley and wheat stubble, respectively. The cultivar CDC-Frontier produced biomass of 7.6 t ha-1, 13% greater than CDC-Xena and 7% greater than Amit and CDC-Anna. Increasing N rates from 0 to 112 kg ha-1 without GR increased biomass production and seed yield in a linear relationship with the slopes being 0.556, 0.475, and 0.089 (t ha-1 per kg of N fertilizer) for biomass produced on barley-, wheat-, and fallow-seedbeds, respectively, and the slopes for seed yield being 0.231, 0.226, and 0.055, respectively. CDC-Frontier produced the greatest biomass and seed yield and was the most stable cultivar across the diverse growing environments, whereas CDC-Xena had the lowest productivity with highest variability. This study showed that there was large variability in primary production of chickpea biomass and seed yield in these high latitude areas, but the variability can be minimized by adopting best management practices such as optimizing seedbed conditions, selecting cultivars with high yield potentials, and use of effective N-fixing inoculants.
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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".