Influence of 24 annual applications of fertilisers and/or manure to alfalfa on forage yield and some soil properties under dryland conditions in northern China
Bibliographic record
Abstract
A field experiment was established in 1984 at Changwu, Shaanxi, China, to determine the long-term effects of three annual fertiliser and/or manure treatments [no fertilisation (CK), application of phosphorus (P) fertiliser alone at 26 kg P ha–1, and application of P fertiliser at 26 kg P ha–1 + nitrogen (N) fertiliser at 120 kg N ha–1 + animal manure at 75 Mg ha–1 (PNM)] to alfalfa (Medicago sativa L.) on forage dry matter yield (DMY) from 1985 to 2008 (24 growing seasons), and some soil properties (moisture content, and concentration of organic matter, total N, total P and available P in soil) in 2001, 2004 and 2006. Compared with the unfertilised CK, application of fertiliser and/or manure resulted in a significant increase of forage DMY in 19 of 24 years, with a maximum DMY usually in the PNM treatment. Cumulative DMY over 24 years (from 1985 to 2008) increased linearly in all three treatments, and it was higher by 22.72 Mg ha–1 with PNM and only by 7.78 Mg ha–1 with P compared with the CK treatment. Soil moisture contents in deep soil profiles did not differ among treatments in most cases. Soil organic matter, total N, total P and available P in soil increased with fertiliser and/or manure treatments, especially when PNM was applied over a long period. In conclusion, the findings suggest that combined applications of inorganic fertilisers and organic manure to alfalfa can provide substantial benefits in terms of both forage yield and stand longevity, while also improving soil quality on the Loess Plateau of northern China.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".