Cropping sequence affects wheat productivity on the semiarid northern Great Plains
Bibliographic record
Abstract
Extension of the commonly used spring wheat (Triticum aestivum L.)-fallow rotation to include broadleaf crops requires information on their effects on a following wheat crop. We grew a spring wheat test crop on the stubbles of wheat and seven broadleaf crops: desi chickpea (Cicer arietinum L.), dry bean (Phaseolus vulgaris L.), dry pea (Pisum sativum L.), lentil (Lens culinaris L.), mustard (Brassica juncea L.), safflower (Carthamus tinctorius L.), and sunflower (Helianthus annuus L.). This study was conducted near Swift Current, SK, from 1993 to 1997, and Congress, SK, from 1995 to 1997. After harvest, soil water differed among crop stubbles and by sampling depth. To the 60-cm depth, only soil under dry bean stubble held more water (8 mm), while soil under lentil, desi chickpea, sunflower and safflower stubbles held less water (6, 8, 9 and 17 mm, respectively) than wheat stubble (P < 0.05). From 60 to 120 cm, soil under dry pea and dry bean held more water (7 and 10 mm, respectively), and under sunflower and safflower stubbles less (7 and 14 mm, respectively), than under wheat stubble (P < 0.05). Lentil, dry bean and dry pea stubbles averaged 5, 6 and 9 kg ha -1 greater soil N in the 0- to 120-cm soil depth than wheat stubble (P < 0.05). The average yield of wheat grown on the four pulse crop stubbles was 21% greater than yields on wheat stubble, but did not differ from the oilseed stubbles (P < 0.01). Compared to wheat stubble, wheat grown on broadleaf crop stubbles had higher grain protein concentrations, increasing by 8 and 5%, for pulses and oilseeds, respectively (P < 0.01). Nitrogen removal in the wheat test crop grain yield averaged 15 kg ha -1 for pulse stubbles compared with wheat stubble. Soil N contribution by pulse stubbles was an important factor contributing to wheat growth under a dryland cropping system on the northern Great Plains. Key words: Crop sequence, spring wheat, pulse crops, N cycling, water use
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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".