Different characteristics of nitrogen utilization between lupin and soybean: can lupin utilize organic nitrogen in soils?
Why this work is in the frame
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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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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 it