Intercropping of Corn with Soybean, Lupin and Forages: Silage Yield and Quality
Bibliographic record
Abstract
Intercropping of corn with legumes is an alternative to corn monocropping and has a number of advantages, for example, lower levels of inputs, lower cost of production and better silage quality than monocrop systems. An experiment was carried out at two sites in 1993 and 1994 to investigate the effects of seeding soybean or lupin alone or in combination with one of three forages (annual ryegrass, Lolium multiflorum Lam.; perennial ryegrass, Lolium perenne L.; red clover, Trifolium pratense L.) on silage yield and quality. The intercrop plots received 90 kg ha−1 less nitrogen fertilizer than monocrop plots, which received 180 kg ha−1. Corn biomass yield had a variable response to the treatments, but showed no change at most site‐years. Soybean and lupin biomass yields were decreased by intercropping (80–98 % for soybean, and 94–100 % for lupin). However, when corn growth was limited due to poor establishment at one site in 1994, soybean was able to grow well and produce yields similar to those of monocropped soybean. The three underseeded forages did not grow well during the period examined (up to silage harvest) and had no effect on the yield of any crop. Total silage yields were similar to corn monocrop biomass yields even during 1994 at the site with low corn population densities because soybean was able to compensate for reduced corn growth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".