The Benefits of Legume Crops on Corn and Wheat Yield, Nitrogen Nutrition, and Soil Properties Improvement
Why this work is in the frame
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Bibliographic record
Abstract
Legume crops leave N‐rich residues and improve soil properties that can boost the yield of subsequent crops. This study conducted at two sites in Québec, eastern Canada, identified the most appropriate preceding legume crops for subsequent corn ( Zea mays L.) and wheat ( Triticum aestivum L.) yield and N nutrition. Legumes were established in 2011, in monoculture or mixed with grain crops, for a total of 13 treatments: common bean ( Phaseolus vulgaris L.), soybean ( Glycine max L.), dry pea ( Pisum sativum L.), hairy vetch ( Vicia villosa Roth), alfalfa ( Medicago sativa L.), and crimson clover ( Trifolium incarnatum L.), hairy vetch/wheat, crimson clover/wheat, field pea/wheat, alfalfa/corn, hairy vetch/corn, crimson clover/corn) and a non‐N fixing crop (corn) as the control. In 2012, each plot was split and five N fertilizer rates applied to corn and wheat. Four legume systems (alfalfa, hairy vetch, crimson clover, and hairy vetch/wheat) significantly increased the soil structure stability, alkaline phosphatase and dehydrogenase activities at warmer St‐Mathieu‐de‐Beloeil location but not at the cooler St‐Lambert‐de‐Lauzon site. These legumes also significantly increased yields and N nutrition of corn and wheat at St Mathieu‐de‐Beloeil and of wheat only at St‐Lambert‐de‐Lauzon. Although legume N credit was found low (∼30 kg N ha −1 ), the N fertilizer replacement value was 51 to 77 kg N ha −1 for corn and up to 37 kg N ha −1 for wheat, depending on the preceding legume crop. This suggests that indirect effects related to improved soil properties impacted positively corn and wheat yield and N nutrition.
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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