Effects of different long-term fertilization on the fractions of organic nitrogen and nitrogen mineralization in soils.
Bibliographic record
Abstract
【Objective】 A long-term field experiment located at the south edge of the Loess Plateau in Shaanxi was conducted to study the effects of different fertilizer treatments on the forms of organic nitrogen and the potential nitrogen mineralization (N0) in soils. 【Method】 Soil organic N was fractionated by acid hydrolysis-distillation method (Bremner method),and N mineralization was estimated by using 30-week aerobic incubation method of Stanford and Smith. 【Result】 The results showed that the range of organic nitrogen forms in soil was in the following order,amino acid Nnon-hydrolysable Nhydrolysable unidentified N ammonium Namino sugar N. Compared to the un-fertilization (CK) treatment,the addition of chemical fertilizers (NPK treatment) increased the different forms of organic N in soil,but the increasing rate was low. Compared with the CK treatment,the SNPK (NPK plus straw) and MNPK (NPK plus manure) treatments significantly increased the contents of different fractions of organic nitrogen in soil,especially the content of amino acid N,whereas they decreased the proportion of total hydrolysable N accounted for total nitrogen. Application of MNPK significantly increased the potentially mineralizable nitrogen (N0) and the rate of N mineralization. The significantly negative correlation was found between amino acid N and N0 (P0.05); and the negative correlationships between hydrolysable unidentified N and non-hydrolysable N and soil N0 were not statistically significant (P0.05). 【Conclusion】 Long-term application of NPK with manures or crop straw is an efficient way to enhance the nitrogen supplying capacity of soil,and amino acid N is the major contributor to the soil mineralized nitrogen.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".