Challenges and Solutions: Analysis on Adoption of Production Practices for Sugarcane Growers
Bibliographic record
Abstract
The present research focuses on identifying the challenges faced by sugarcane growers in adopting recommended production practices in the Naushahro Feroze district. Five villages were selected from the Talukas of Moro and Naushahro Feroze through a multistage sampling technique, and a random sample of 75 farmers was drawn from the study area. Data were collected using a multistage sampling plan. Results revealed that a majority of respondents (60%) were aware that proper land ploughing contributes to yield stability. Regarding fertilizer application timing, most respondents had only partial knowledge, while 70% demonstrated complete knowledge about irrigating fields every 10–15 days. Findings also indicated that 40% of respondents adopted appropriate land preparation practices. The major constraints reported by farmers included the high cost of fertilizers, lack of access to credit facilities, and the unavailability of fertilizers in the required quantities. In light of these constraints, respondents suggested providing credit at lower interest rates and in a timely manner, reducing the cost of complex fertilizers, and conducting demonstration trials on sustainable cultivation practices in sugarcane to validate their effectiveness. They also recommended that sugar factories adhere to proper varietal harvesting schedules, organize training programs on sustainable cultivation practices, and ensure the availability of pest- and disease-resistant varieties through sugar factories and research stations. Additional suggestions included organizing group discussions, arranging exposure visits to educate growers on sustainable practices, and establishing sugarcane grower clubs to facilitate regular meetings with scientists and progressive farmers.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".