Influence of Diverse Cropping Sequences on Durum Wheat Yield and Protein in the Semiarid Northern Great Plains
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it