Corn-Soybean Intercropping Improved the Nutritional Quality of Forage Cultivated on Podzols in Boreal Climate
Bibliographic record
Abstract
Intercropping systems could be a potential source of nutrient-rich forage production in cool climates on podzolic soils common in boreal ecosystems. In this study, we evaluated the effects of corn–soybean intercropping (IC) on the nutritional quality of forage. Two silage corn varieties were cultivated as monocropping (MC) or were intercropped with three forage soybean varieties using a randomized complete block design. IC significantly increased the crude protein (22%) and decreased the acid detergent (14%) and neutral detergent (6%) fibers. Forage net energy, total digestible nutrients, ash, dry matter intake, digestible dry matter and relative feed value were also significantly increased (p ≤ 0.05) in the IC treatments compared to corn MC. The macro and micro nutrients were higher in IC than corn MC. Intercropping increased the omega 3 fatty acid (FA) contents (67%) compared to corn MC. IC also increased the active microbial community in the plant root zone, which may contribute to the improvement in forage nutritional quality because the active soil microbial community composition showed significant correlations with soluble sugars, soluble proteins and potassium contents of the forage. These results demonstrate that corn–soybean IC could be a suitable cropping system to increase the nutritional quality of forage cultivated on podzols in boreal climates. The resultant forage has the potential to be a source of high-value animal feed for livestock production in cool climate regions of the world.
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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".