How different long‐term fertilization strategies influence crop yield and soil properties in a maize field in the North China Plain
Bibliographic record
Abstract
Abstract The impact of fertilization on maize ( Zea mays L.) yield and soil properties was investigated in a long‐term (> 18 y) experimental field in N China. A completely randomized block design with seven fertilizer treatments and four replications was used. The seven fertilizer treatments were (1) compost (COMP), (2) half compost plus half chemical fertilizer (COMP1/2), (3) balanced NPK fertilizer (NPK), (4–6) unbalanced chemical fertilizers without one of the major elements (NP, PK, and NK), and (7) an unamended control (CK). In addition to maize yield, soil chemical and biological properties were investigated. Compared to the balanced NPK treatment, maize yield from the COMP treatment was 7.9% higher, from the COMP1/2 was similar, but from the NP, PK, NK, and CK treatment were 12.4%, 59.9%, 78.6%, and 75.7% lower. Across the growing season, microbial biomass C and N contents, basal soil respiration, and fluorescein diacetate hydrolysis, dehydrogenase, urease, and invertase activities in the COMP and COMP1/2 treatments were 7%–203% higher than the NPK treatment. Values from all other treatments were up to 60% lower than the NPK treatment. Maize yield is closely related to the soil organic C (OC) and biological properties, and the OC is closely related to various biological properties, indicating that OC is a suitable indicator for soil quality. Our results suggest the most limiting nutrient for improving the yield or soil quality was P, followed by N and K, and balanced fertilization is important in maintaining high crop yield and soil quality. Additionally, increases in OC, N, and biological activities in COMP and COMP1/2 treatments imply that organic compost is superior to the chemical fertilizers tested.
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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 | 0.001 | 0.001 |
| 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".