Different characteristics of nitrogen utilization between lupin and soybean: can lupin utilize organic nitrogen in soils?
Bibliographic record
Abstract
It is well known that lupin forms cluster roots, which help in dissolving insoluble P in soils. In nonleguminous species, cluster roots also appear to contribute to the utilization of organic N in soils. In white lupin ( Lupinus albus L.), however, the characteristics of its organic N utilization have not been studied. Therefore, we examined whether white lupin can utilize organic N in soils. Soybean ( Glycine max (L.) Merr.), which does not form cluster roots, was used as a control plant. Seedlings of lupin and soybean were cultivated in soils with different N sources (non-N, ammonium sulphate, ammonium sulphate plus cattle farmyard manure, or cattle farmyard manure). The rate of glycine uptake by excised roots was determined in a hydroponic experiment to investigate the ability of lupin and soybean to directly utilize amino acids. Nitrogen accumulation in soybean corresponded to the decrease in inorganic N in the soils. In contrast, N accumulation in lupin was higher than the decrease in inorganic N in the soil, especially with the cattle farmyard manure treatment, indicating that lupin derived more N from an organic N source. Wheat ( Triticum aestivum L.) cultivated with lupin in a pot accessed more available N than wheat with soybean or wheat in monoculture, suggesting that lupin roots themselves or the lupin rhizosphere microorganisms were able to decompose organic N in soils. Excised roots of lupin, especially cluster roots, exhibited higher rates of glycine uptake than roots of soybean. In conclusion, lupin decomposed organic N in the rhizosphere and was able to absorb amino acids from decomposition in addition to any inorganic N produced by further microbial decomposition.
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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".