Influence of Nitrogen Fertilizer on Ash, Organic Carbon, Phosphorus, Potassium, and Fiber of Forage Corn Intercropped by Three Cultivars of Berseem Clover as Cover Crops in Semi Arid Region of Iran
Bibliographic record
Abstract
Supplementation of animal feeds with high quality needs new agriculture management, like intercropping system. In order to determine qualitative characteristics of forage corn intercropped by berseem clover cultivars in different levels of nitrogen fertilizer, an experiment was conducted in 2010 at Research Farm, Faculty of Agriculture, Islamic Azad University, Khorasgan Branch, Esfahan. A factorial layout within randomized complete block design with 3 replications was used. Cultivars were Karaj, Sacromont and Multicut, and nitrogen levels were included 0, 40 and 60 kg/ha. The nitrogen fertilizer was provided from urea source (46% pure N). Cultivar had significant effect on ash percentage, P, organic carbon of soil and soil nitrogen percentage. Nitrogen had significant influence on ash, K, organic carbon of soil, soil nitrogen percentage, light transmission, solar radiation absorption and extinction coefficient. Organic carbon of soil and soil nitrogen percentage just significantly influenced by cultivar and nitrogen interaction. In this experiment the highest ash percentage, organic carbon of plant, ENDF was obtained in forage corn intercropped by Multicut. The maximum organic carbon of soil and soil nitrogen percentage also related to forage corn and Multicut intercropping. The highest ash percentage, organic carbon, ENDF, organic carbon of soil, soil nitrogen percentage and solar radiation absorption was obtained in forage corn and Multicut intercropping.
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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) | 0.000 | 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 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".