ORGANIC MULCHING TO CONSERVE SOIL NUTRITIONAL QUALITY AND ENHANCE WHEAT YIELD
Bibliographic record
Abstract
Nutritional degradation of soil is an alarming issue of present agriculture due to extensive farming to overcome food security. A field experiment was conducted during rabi season 2021- 22 at the research area of Arid Zone Research Centre (AZRC), Dera Ismail Khan to assess the efficiency of two types of mulches (farm manure (FM) and wheat straw (WS) to conserve soil nutrient capacity with improving wheat yield. Wheat variety “AARI-2011” was sown @ 150 kg/ ha and fertilizers were applied @ 120-90-60 kg/ha of NPK using Randomized Complete Block Design. The soil was sampled from 0-60 cm depth after the wheat crop harvesting and was examined for soil NO3 -1 nitrogen, available P and extractable K. Wheat straw and grain yields were taken at maturity showing significant improvement with increasing mulch application. Plant grain samples were also analyzed for nutrient (N, P and K) concentrations. The data were statistically analyzed using the ANOVA technique and the means of the treatments were compared using HSD (Tuckey’s) test with 5% significance. An eloquent increase in nutritional components NO3 -1, P and K of the top 30 cm soil layer and crop grain was observed. It was concluded that the application of organic wastes as soil cover not only conserves soil but also enhances its productivity. Therefore, it is recommended to use mulching materials to conserve soil and enhance productivity.
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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 | 0.000 | 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".