The Impact of Better Management Practices (BMPs) Among Cotton Farmers in Punjab, Pakistan
Bibliographic record
Abstract
Better management practices (BMPs) as a sustainable approach made it attractive for growers to control the provision of pollutants from agricultural activities as well as enhance the financial return. The experiments of cotton production were conducted in four different regions of Punjab in cotton-growing years 2017-2019. The objective of the study was to evaluate the potential impact of BMPs among cotton farmers by rationalizing the use of input resources (viz., seed, fertilizers, pesticides and water). The data were collected from randomly selected adopters of BMPs (n = 400) and non-adopters of BMPs (n = 100) through a well-structured pretested questionnaire using a multistage sampling procedure from four different regions of Punjab province. Descriptive analysis was employing an independent two-sample t-test to evaluate the significant effect of BMPs on the utilization of input resources and profitability of cotton production between adopters and non-adopters of BMPs. The results indicated that adopters of BMPs were efficiently used input resources (at p ≤ 0.001 & p ≤ 0.01) and significantly enhanced the average cotton yield (855.09 kg acre-1) in Punjab, while non-adopters of BMPs had a significantly high cost of production by 11% (35,655 PKR acre-1) and output was lower by 15% (751.70 kg acre-1) under conventional farming for cotton cultivation. The economic analysis revealed that the average gross income gained by adopters of BMPs was significantly high by 11% (72,648 PKR acre-1 at p ≤ 0.001) with the maximum net return of 36% (40,785 PKR acre-1 at p ≤ 0.001) as well as a good B:C (1.28) as compared to non-adopters of BMPs. This study provides useful information about the potential impact of BMPs among cotton farmers even without the extra use of inputs. It is concluded that precision in inputs and management practices with lower input costs can significantly improve cotton productivity leading to uplift the farmers’ profit.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".