Developing regional soil micronutrient management strategies through ensemble learning based digital soil mapping
Bibliographic record
Abstract
Mapping of soil micronutrient variability is critical for improving agronomic biofortification. This study used 1778 surface soil samples collected from four agro-climatic regions of the Indo-Gangetic Plain of India to produce digital soil maps of available Zn, Cu, Fe, and Mn using 52 environmental covariates at a resolution of 150 m. The micronutrient prediction accuracy was compared for 14 machine learning approaches and their ensemble model. The hybrid ensemble model outperformed all 14 base learners and was subsequently used for producing micronutrient maps. All four micronutrients exhibited sufficient spatial variability. Both available Zn and Fe maps exhibited lower prediction uncertainties. Moreover, the inter-relationship between micronutrient concentration in soil and rice grain was explored to understand the Zn and Fe biofortification potential. The linear regression models revealed moderate agreement between soil available and grain micronutrient concentrations, with R2 values of 0.52–0.63 for Zn and Fe, respectively. The developed models were used to predict grain Zn and Fe content from their respective soil concentrations, indicating the potential of the tested approach to identify specific pockets where rice varieties with biofortification potential can be planted. In the future, the digital soil mapping approach tested herein can help policymakers with regional decision-making, encouraging nutrient-based subsidy and investment opportunities and sustainable micronutrient recommendations toward micronutrient-enriched food. Further research is needed to develop a digital soil intelligence platform using micronutrient DSM products in resource-poor countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".