Organic Inputs and Chemical Fertilizer on Carbon Mineralization From Two Ultisols
Bibliographic record
Abstract
There are challenges that limit the use of organic inputs for soil fertility management. Amongst them is the limited knowledge of factors that affect rates of decomposition and nutrient release from different organic inputs. A study was conducted on surface soil samples of two Ultisols to determine factors affecting carbon (C) mineralization from selected organic inputs. A loamy sand (LS) from a Kandiustult and a sandy clay loam (SCL) from a Paleustult were used. Fine earth fractions of the soils mixed with organic inputs with and without chemical fertilizer were incubated for 13 weeks and the CO2 evolved was measured. Organic inputs used were biomasses of Cajanus cajan, Tephrosia vogelii, Crotalaria juncea, Mucuna pruriens, a mixture of native grasses and shrubs and composted cattle manure. The latter two inputs are traditionally used by farmers, while the leguminous plants were recommended by scientists. Treatments with chemical fertilizer only, representing the conventional farming practice, and a control with soil alone were included. Addition of organic inputs with or without fertilizer increased total CO2 emissions by 81 to 129% on the LS and by 18 to 34% on the SCL. Adding chemical fertilizer significantly (p < 0.05) increased C mineralization rate constant (k) by 116% on the LS and 48% on the SCL. The mean residence time of organic carbon from treatments grouped by input type followed the order: Control > Traditional > Legumes > Conventional on both soils. In general, the k on the LS was double that on the SCL. The type of organic input, soil texture and application of chemical fertilizer significantly affected C mineralization rates from the soils.
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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".