Statistical Analysis of Cotton Cultivated Area, Production and Price
Bibliographic record
Abstract
Cotton is one of the most important commercial crop of Pakistan and significantly contributes in the economy. Statistically analysis of cotton status of cultivated area, production and price at very basic level can help for better future policy and management by providing primary figures. Therefore, a survey research study was conducted during year 2014 at Taluka Sakrand district Shaheed Benazirabad, Sindh, Pakistan. The Primary (ground reality) and secondary (history) data were collected. The questionnaire comprised on cultivated area acres of cotton, production/acre (kg) and price 40/kg ($US). A total of 294 farmers from 07 different union councils (Pir Zakri P.Z, Sakrand SKD, Hamal Faqir H.F, Dalel Dero D.D, Marvi MRI, Karam Jamali K.J and Guhram Mari G.M) were selected. The secondary data were obtained for 14 years. The results revealed by primary data showed that max-avg 12.52 acres and min-avg 5.20 acres cultivated area were measured in K.J and G.M respectively. While, the greatest cultivated area 25 acres in MRI and reduced 01 acre in H.F and SKD were recorded. In case of production, high-avg 883.6 kg/acre and highest 1200.00 kg/acreobserved in H.F. The low-avg 606.4 kg/acre with lowest 200.00 kg/acre noticed in K.J UC. Furthermore, high-avg-price in D.D and low-avg-price in P.Z, $24.13-19.99 40/kg respectively was observed. However, highest price $29.70 40/kg in H.F was concluded compare to rest of UCs. A strong correlation (0.98) was observed between H.F and G.M for all observations. The Secondary data results were obtained by using one-way analysis and F_ test was conducted to analyze the significance level tested at 5% (0.05). The F-values 0.64 and 0.7 for cultivated area and production were calculated for seven UCs. In the study, the secondary data results with 0.82 and 0.8 values suggest that H0 must be accepted. Hence, the correlation coefficients, between area and the production had strong correlation 0.8 for the seven UCs. Also, strong correlation 0.522 was detected between price and production.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".