Nitrogen dynamics of chickpea: Effects of cultivar choice, N fertilization, <i>Rhizobium</i> inoculation, and cropping systems
Bibliographic record
Abstract
Understanding N dynamics in relation to cultural practices may help optimize N management in annual legume crops. This study was conducted at six environsites (location × year combinations) in southern Saskatchewan, 2004-2006, to quantify N uptake, N2 fixation, and N balance in chickpea (Cicer arietinum L.) in relation to cultivar choice, cropping systems, rhizobial inoculation, and soil N fertility. The cultivars Amit, CDC Anna, CDC Frontier, and CDC Xena were grown at N fertilizer rates of 0, 28, 56, 84, and 112 kg N ha-1 with no Rhizobium and at 0, 28, and 84 kg N ha-1 combined with Rhizobium inoculation, evaluated in both conventional tilled-fallow and continuously cropped no-till systems. Flax was used as a non-N-fixing reference crop. The cultivar CDC Xena had the lowest yield (1.57 Mg ha-1) and seed N uptake (54.4 kg N ha-1), with N use efficiency (NUE, 13.2 kg seed N kg-1) being 17% less than the average of the other cultivars. Consequently, N balance (N input via fertilizer and N-fixation minus N exported) was -32.4 kg N ha-1 for CDC Xena and less negative than the average of the other cultivars (-39.8 kg N ha-1). Inoculated chickpea took up 10 kg ha-1 more N into the seed and 5 kg ha-1 more N into the straw than chickpea that was not inoculated. The amount of N fixed as a percentage of total N uptake was 15% for non-inoculated chickpea and 29% for inoculated chickpea, resulting in negative N balance regardless of cropping system. Increasing N fertilizer rates decreased NUE, with the rate of decrease being greater for non-inoculated chickpea compared with inoculated chickpea. We conclude that optimum productivity of chickpea can be achieved with application of effective Rhizobium inoculants, and that best N management practices must be adopted in the succeeding crops due to a large negative N balance after a chickpea crop.Key words: Chickpea, Cicer arietinum, N fertilizer, N2 fixation, Rhizobium inoculants, N balance, nitrogen use efficiency, N uptake
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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".