Categorisation of soils based on potassium reserves and production systems: implications in K management
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".