Long‐term effects of fertilizer and manure applications on soil quality and yields in a sub‐humid tropical rice‐rice system
Bibliographic record
Abstract
Abstract Widespread yield stagnation and productivity declines in the rice–rice cropping system have been reported and many of the associated issues are related to soil quality. A long‐term experimental study was initiated in 1969 to assess the impact of continuous cultivation of rice as a single crop grown in wet as well as dry seasons using varying levels of chemical fertilizer and manure applications on soil quality indicators (physical, chemical and biological), a sustainable yield index ( SYI ) and a soil quality index ( SQI ). The treatments comprised chemical fertilizers and farmyard manure ( FYM ) either alone or in combination viz . control, N , NP , NK , NPK , FYM , N + FYM , NP + FYM , NK + FYM and NPK + FYM , laid out in a randomized complete block design with three replications. Soil samples were collected after the wet season rice harvest in 2010 and were analysed for physical, chemical and biological indicators of soil quality. A SYI based on long‐term yield data and SQI using principal component analysis ( PCA ) and nonlinear scoring functions were calculated. Application of NPK fertilizers in combination with FYM significantly increased the average grain yield of rice in both wet and dry seasons and enhanced the sustainability of the system compared to the control and plots in receipt of fertilizers. The SYI for the control was higher in the wet season than in the dry one, whereas the reverse was true for NPK + FYM treatment. The value of the dimensionless SQI varied from 1.46 in the control plot to 3.78 in the NPK + FYM one. A greater SYI and SQI in the NPK + FYM treatment demonstrated the importance of using a chemical fertilizer in combination with FYM . For the six soil quality indicators selected as a minimum data set ( MDS ), the contribution of DTPA ‐ Z n, available‐ N and soil organic carbon to the SQI was substantial ranging from 59.4 to 85.7 per cent in NPK + FYM and control plots, respectively. Thus, these soil parameters could be used to monitor soil quality in a subhumid tropical rice–rice system.
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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".