Comparing <i>Brassica</i> oilseed crop productivity under contrasting N fertility regimes in the semiarid northern Great Plains
Bibliographic record
Abstract
We compared the growth and yield of five Brassica spp. genotypes [two B.juncea (L.) Coss., two B. napus L. and one B. rapa L.] in 11 environments spanning two degrees of latitude and longitude in southern Saskatchewan in 1996–1998. All entries were managed with three levels of N fertility at each site. Plant height for the canola-quality B. juncea breeding line J904316 was 21% greater than the mean of the other genotypes, but shoot biomass did not differ for the same comparison. Cutlass Oriental mustard consistently yielded greatest across all environments while the yield of J904316 was highly variable among environments. The mean seed yield of Cutlass was 12% greater than the highest-yielding B. napus cultivar and 32% greater than the B. rapa cultivar, and had a greater harvest index. Earlier genotypes yielded greater than late-flowering and maturing genotypes within B. juncea and B. napus, but the earliest genotype, Maverick (B. rapa), yielded lower than all other genotypes. All Brassica spp. genotypes were responsive to N fertilization, and high total available N (average = 109, range = 91 to 131 kg N ha-1) was generally required to maximize seed yield, but not shoot biomass, under the conditions of this study. Pod density was the primary yield component influenced by N level. This study illustrates the potential adaptive advantage of B. juncea on the semiarid northern Great Plains. Key words: Brassica, canola, genotype, mustard, N fertility, semiarid, northern Great Plains
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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".