Intensification of Field Pea Production: Impact on Agronomic Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Including grain‐legumes in cropping systems contributes to a reduction in greenhouse gas emissions and enhances agronomic and economic performance of cropping systems. The objective was to examine the potential for increasing the frequency of field pea ( Pisum sativum L.) (FP) in a spring wheat ( Triticum aestivum L.) (W)‐based cropping system. Three crop rotations, continuous pea (C‐Pea), W‐FP, and W‐W‐FP, were evaluated over a 10‐yr period (1998–2007) at Indian Head, SK. During the FPphase of C‐Pea and W‐FP, three starter N rates (5, 20, 40 kg N ha −1 ) were applied. One rate of N (80 kg N ha −1 ) was used in W. Rotation and N had similar effects on plant densities in either crop. Field pea grain yields were 25% lower with C‐Pea than W‐FP or W‐W‐FP but similar between W‐FP and W‐W‐FP. Starter N had some effect on FP grain yields at the higher N rate in W‐FP but not C‐Pea. Spring wheat grain yields were 3% greater on FP than W stubble. Grain protein in FP was 3.1% higher on C‐Pea than W‐P or W‐W‐FP while grain protein in W was 1 g kg −1 higher on FP than W stubble. Crop water use efficiency in FP and W was not affected by crop rotation. Based on the results of this study, we conclude that the frequency of FP in cropping systems in the subhumid and semiarid areas can be increased intermittently with only a 1‐yr cereal break between FP crops when combined with proper integrated crop management practices.
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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) | 0.001 | 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 it