Seed Yield and Yield Stability of Chickpea in Response to Cropping Systems and Soil Fertility in Northern Latitudes
Bibliographic record
Abstract
Improved cultural practices are required to enhance the adaptability of chickpea (Cicer arietinum L.) in northern latitudes. Field experiments were conducted to determine the effects of cropping systems, cultivar choices, and soil fertility on the stand establishment, seed yield, and yield stability of chickpea in northern latitudes. Four cultivars were tested in no‐till barley (Hordeum vulgare L.), no‐till wheat (Triticum aestivum L.), and tilled‐fallow systems at six environments in southern Saskatchewan, 2004–2006. Crop received N fertilizer at 0, 28, 64, 84, and 112 kg N ha−1 with or without Rhizobium inoculant (GR). The no‐till barley and no‐till wheat systems did not differ from the tilled‐fallow system in days to plant emergence and stand establishment, and the two no‐till systems averaged 2100 kg ha−1of seed yield which was 83% of the yield in the tilled‐fallow system. In the absence of GR, increasing N rates increased seed yield significantly in the two no‐till systems, no yield responses in the tilled‐fallow system, and decreased plant density in all the three systems. Compared to the non‐GR control, the use of GR increased seed yield by 37% in the no‐till systems and 8% in the tilled‐fallow system. Chickpea inoculated with GR produced a similar yield as was fertilized at 112 kg N ha−1. Chickpea receiving fertilizer N plus GR produced a similar yield as the crop received GR only for all cultivars. Use of optimal cropping systems, improved cultivars with high yield stability, and application of effective N‐fixing inoculants will enhance the adaptability of chickpea in northern latitudes.
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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) | 0.000 | 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".