Effect of Applying Organic Amendments and Chickpea Integration on Soil Chemical Properties in Different Cropping Systems in Central Kenya
Bibliographic record
Abstract
A study was carried out to determine the effect of applying farm yard manure (FYM) and Minjingu rock phosphate (MRP) on soil available nitrogen, phosphorus and organic carbon. The study involved field experiments under varying precipitation pattern, soil fertility levels and cropping systems over four growth seasons. The experimental design was a randomized complete block (RCBD) with four replications in a split plot arrangement where the main plots were the three cropping systems; monocropping, intercropping and crop rotation and the split plots were FYM and MRP and sampling done at crop physiological maturity. Soil pH, N, P K and C increased in the different treatments in the following order control < MRP < FYM in the three cropping systems across the four growing seasons at both sites. In maize under rotation with chickpea control had; 0.281% N, 2.82% C and 10.68 ppm P. FYM; 0.554% N, 4.41% C and18.24 ppm P. MRP; 0.45% N, 3.6% C and 41.08 ppm P. Maize chickpea intercrop control; 0.389% N, 3.192% C and 13.4 ppm P. FYM; 0.531% N, 4.98% C and 41.02 ppm P. MRP; 0.49% N, 4.08% C and 50.9 ppm P. Soil under maize monocrop exhibited; control; 0.2% N, 2.59% C and11.26 ppm P. FYM; 0.416% N, 3.83% C and 18.01 ppm P. MRP; 0.28% N, 3.13% C and 26.1ppm P. Almost a similar trend was observed in maize and tomato plots at both sites in all the growing seasons. Thus it can be deduced that, FYM and MRP application and legume integration in cropping systems improves soil fertility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".