Categorisation of soils based on potassium reserves and production systems: implications in K management
Why this work is in the frame
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Bibliographic record
Abstract
Crop fertilisation with potassium in rainfed agriculture in India is not practised, merely on the assumption that Indian soils are rich in potassium and crops do not need external K supply. However, under continuous cropping in rainfed regions, huge crop K removals are reported, up to 150–200 kg/ha annually, depending upon amount and distribution of rainfall and biomass production. Thus, most of the crops essentially deplete soil K reserves. The present study evaluates the soil K reserves under diverse rainfed production systems and categorises rainfed soils based on different soil K fractions. Depth-wise sampling was done from 21 locations across different soil types under 8 production systems, and various fractions of soil K were determined. Total K was highest in Inceptisols (1.60–2.28%), followed by Aridisols (1.45–1.84%), Vertisols and Vertic sub-groups (0.24–1.72%), and Alfisols and Oxisols (0.30–1.86%), showing a wide variation within each group. Nonexchangeable K reserves were found in a proportionate manner to total K in most of the soil profile. Unlike nonexchangeable K reserves, Vertisols had higher exchangeable K than Inceptisols and Alfisols/Oxisols. Nonexchageable K showed significant positive correlation with total K in Inceptisols and Vertisols, whereas it was non-significant in Alfisols/Oxisosls. However, significant positive correlations were recorded with exchangeable K and nonexchangeable K in all soil types, indicating the dynamic equilibrium between 2 soil K fractions. Nonexchangeable K reserves were included along with exchangeable K in categorising soils into 9 groups for evolving better strategies to manage soil K fertility in rainfed agriculture in India. Finger millet and groundnut crops at Bangalore and Anantapur regions (category I) need immediate attention on K nutrition, as these soils are low in both exchangeable and nonexchangeable K. Similarly, crops grown on soils of S.K. Nagar, Ballowal-Saunkri, and Rakh-Dhiansar, with low exchangeable K and medium nonexchangeable K, would need K fertilisation as these crops (maize and pearlmillet) are K-exhaustive (category II). Pearl millet and upland rice in category III and cotton in category IV need K additions at critical stages. Upland rice in category V needs a maintenance dose of K. In category VI, cereal crops may not need K additions immediately as they have medium exchangeable K and high nonexchangeable K. Long-term sorghum cropping may need K supply after few years (category VII). Soils in category VIII are adequate in nonexchangeable K and medium exchangeable K and the crops, groundnut, cotton, sorghum, and soybean, may not need external K immediately. For soils in category IX, K fertilisation is not required to the crops (sorghum and soybean) as these soils have high exchangeable and nonexchangeable K.
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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 | 0.004 | 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 it