Influence of Diverse Cropping Sequences on Durum Wheat Yield and Protein in the Semiarid Northern Great Plains
Bibliographic record
Abstract
Crops grown in previous years impact the amounts of residual soil water and nutrients available for subsequent plant growth. Appropriate sequences allow efficient use of the available soil resources by the crop to increase yields at a system's level. This study was conducted to determine whether the grain yield and grain crude protein concentration (GCPC) of durum wheat ( Triticum turgidum L.) were related to crops grown in the previous 2 yr. Durum was grown following pulses [chickpea ( Cicer arietinum L.), lentil ( Lens culinaris Medik.), and dry pea ( Pisum sativum L.)], oilseed [mustard ( Brassica juncea L.) or canola ( B. napus L.)], and spring wheat ( Triticum aestivum L.) in southwest Saskatchewan from 1996 to 2000. Durum increased grain yields by 7% and GCPC by 11% when grown after pulse crops rather than after spring wheat. Durum after oilseeds increased grain yield by 5% and GCPC by 6%. Pulse and oilseed crops grown for the previous 2 yr increased durum grain yield 15% and GCPC 18% compared with continuous wheat systems. Fall residual soil NO 3 –N and available soil water accounted for 3 to 28% of the increased durum yield in two of five site‐years, whereas those two factors accounted for 12 to 24% of the increased GCPC in three of five site‐years. Durum grain yield was negatively related to GCPC. The relationship was stronger when durum was preceded by oilseeds compared with pulses. Broadleaf crops in no‐till cropping systems provide significant rotational benefits to durum wheat in the 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) | 0.001 | 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".