Impact of Safe Rock® Minerals, Mineral Fertilizers, and Manure on the Quantity and Quality of the Wheat Yield in the Rice–Wheat Cropping System
Bibliographic record
Abstract
Rice–wheat (RW) rotation is the largest agriculture production system in South Asia with a multifaceted role in maintaining the livelihood of people. The customary practices and indiscriminate use of synthetic fertilizers have culminated in the decline of its productivity and profitability during the past two decades, thus affecting the sustainability of wheat. Safe Rock® Minerals (SRM) is a multi-nutrient rich natural rock mineral with great potential to manage soil degradation, reducing the input of fertilizers, improving soil fertility, and plant health. Thus, a field trial was conducted at the research farm of ICAR—Indian Agricultural Research Institute, New Delhi from 2016 to 2018 to evaluate the impact of Safe Rock® Minerals (SRM) on biometric parameters, productivity, quality, and nutrient uptake by conventional wheat and System of Wheat Intensification (SWI) in the wheat–rice cropping system. The results indicate that SWI performed better in terms of growth, yield, and quality parameters than conventional wheat. Among nutrient management practices; the highest growth, yield, and yield attributes of wheat were achieved with the use of SRM application 250 kg ha−1 + 100% Recommended Dose of Fertilizer (RDF). SRM application also increased grain protein content significantly. In conclusion, the integrated use of SRM with organic manures can serve as an eco-friendly approach for sustainable wheat production.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".