Effect of cultivar selection on symbiotic nitrogen fixation and yield traits of pea cultivars in intercropping with wheat
Bibliographic record
Abstract
In recent decades, agricultural practices have shifted from diverse cropping systems to monocropping, leading to soil degradation, nutrient depletion, and reduced biodiversity, which threaten long-term productivity and ecosystem sustainability. This study aimed to explore how legume cultivar selection influences pea (Pisum sativum L.)-wheat (Triticum aestivum L.) intercropping, focusing on symbiotic nitrogen (N) fixation, yield, seed N, and land productivity. A greenhouse experiment was conducted using various pea cultivars that were released in different decades [Century (1960), Trapper (1970), CDC Golden (2002), CDC Amarillo (2012), and CDC Spectrum (2016)] under monocropping and intercropping with wheat to evaluate the yield parameters and symbiotic N fixation capabilities of pea. The old, long-vined pea cultivars (Century and Trapper) had higher seed dry weight (62.9–66.3%), number of pods (82.7–100%) and number of seeds (126.9–163.5%) than the newer, moderate vine length cultivars (CDC Golden, CDC Amarillo and CDC Spectrum) under intercropping. On the other hand, the companion wheat crop had a greater yield (29.8–69.9%) and seed N (31.1–65.5%) when intercropped with the newer pea cultivars. Intercropping enhanced N fixation (0.7–7.5%) in peas across cultivars; however, the older cultivars contributed more to the overall system’s N fixation and N carry-over compared to the newer cultivars. While the harvest index, land equivalent ratio (LER), and N-based LER (LERN) of intercropped wheat were not significantly higher than mono-cropped wheat, the increased partial LER and LERN for wheat highlight intercropping benefits. Overall, newer pea cultivars enhanced pea-wheat intercropping by improving productivity and resource efficiency, highlighting the importance of legume cultivar selection in intercropping.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".