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Title: Optimizing cotton productivity: A comprehensive analysis of categorizing factor levels
Authors: Abdul Hameed, Muhammad Islam, Abdul Qayyum, Rabia Siddiqui, Umar Draz
Journal: Journal of Pure and Applied Agriculture (JPAA)
Publisher: Allama Iqbal Open University, Islamabad (AIOU)
Country: Pakistan
Year: 2023
Volume: 8
Issue: 3
Language: English
Keywords: Categorizations of factors levelsCotton cropEnhancement techniquesProductivityStatistical analysis
Cotton crop termed as a white gold of Pakistan due to its immense importance for foreign exchange. Across the years, production of cotton is critically decreasing in Pakistan.  In this study, efforts are made to layout the variables of interest in sequential way to enhance the cotton productivity. The dataset of 12504 crop cut experiments is collected from Crop Reporting Service, Punjab comprise from 2018-2021. Yield gap analysis, probability share, and ANOVA are applied to measure the variables and its levels. The probability shares of the farmers who are getting the optimum productivity for best factors’ levels are 13.78% for sowing time up to 2nd fortnight of April, 25.78% for 10 water/irrigations, 0.12% for 3 weedicides spray, 3.53% for 13-17 pesticides spray, 3.18% for DAP, 19.65% for urea and 75.15% for cotton varieties. New theory is constructed for the categorization of variable in term of probability share (%), yield gap, and optimum productivity and it identify that sowing time, weedicide spray, pest spray and DAP falls under major loss, while water/irrigation and urea fall under medium loss and cotton varieties falls under minor loss. The productivity of cotton could be enhanced from major to minor loss factors, but in diminishing order. Firstly, there is need to address major loss factor, and then on medium and minor factors to get over the loss in cotton productivity. Mean differences for the group of all variables found statistically significant. This study is helpful for making strong recommendations to farmers liable to enhance the cotton productivity and could be viewed as an unprecedented effort for the sweet homeland, Pakistan. This study may also lead a basis to build the good regression model for cotton yield enhancement practices.
To statistically elaborate yield gap analysis for significant factors influencing cotton productivity in Pakistan and introduce a new theory for categorizing variable levels based on probability share, optimum yield, and yield gap analysis to enhance productivity.
Secondary cross-sectional dataset of 12504 crop cut experiments collected from Crop Reporting Service (CRS), Agriculture Department, Punjab, Pakistan, spanning the years 2018 to 2022. Statistical methods applied include Yield gap analysis (absolute and relative), probability share calculation, and Analysis of Variance (ANOVA). A new theory was constructed to categorize variables into major, medium, and minor loss factors based on the percentage share of farmers achieving optimum input levels.
graph TD;
A[Data Collection: 12504 Crop Cuts 2018-2022] --> B[Data Preprocessing & Normality Check];
B --> C[Identify Agronomical Constraints & Levels];
C --> D[Statistical Analysis: Yield Gap, Probability Share, ANOVA];
D --> E[Determine Optimum Input Levels for Each Factor];
E --> F[Construct New Categorization Theory: Major/Medium/Minor Loss];
F --> G[Prioritize Interventions Based on Loss Category];
G --> H[Recommendations for Farmers];
Cotton productivity is critically decreasing in Pakistan, impacting the economy. The study confirms that adopting optimal input levels significantly increases yield, but adoption rates for these optimal levels are low (e.g., only 0.12% for 3 weedicide sprays). The newly constructed categorization theory prioritizes intervention: efforts should first address major loss factors (like sowing time, where 86.22% of farmers are not at the optimum level) before moving to medium and minor loss factors, as productivity enhancement follows a diminishing order across these categories.
The mean differences for the groups of all variables were statistically significant (F-Statistic confirmed significance for all factors).
Optimal factor levels identified: Sowing time up to the 2nd fortnight of April; 10 water/irrigations; 3 weedicides spray operations; 13-17 pesticides spray operations; 100 Kg/Acre DAP; 150-175 Kg/Acre Urea; and BT-SS-32 cotton variety (though 75.15% of farmers used other varieties).
Categorization of loss factors: Sowing time, weedicide spray, pest spray, and DAP fall under major loss; water/irrigation and urea fall under medium loss; and cotton varieties fall under minor loss.
Optimal use of identified factor levels can boost cotton productivity in Pakistan. The study successfully quantified the impact of various factors and established a prioritization framework (major, medium, minor loss) to guide farmers in implementing enhancement techniques effectively, which is crucial for Pakistan's economy heavily reliant on cotton exports.
1. The dataset comprised 12504 crop cut experiments collected from 2018-2021. (The text states 2018-2022 in the Methods section, but 2018-2021 in the Abstract; the abstract figure is used here).
2. The probability share for farmers achieving optimum productivity for cotton varieties was 75.15%. (The abstract states 75.15% for varieties, but Table 9 shows 75.15% for farmers using other than optimum levels for varieties). Correction based on Table 9/Abstract comparison: 75.15% of farmers use non-optimum varieties.
3. Sowing time up to the 2nd fortnight of April had a farmer adoption share of 13.78% at the optimum level.
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