International Journal of Horticulture, 2025, Vol.15, No.6, 299-311 http://hortherbpublisher.com/index.php/ijh 301 2.7 Data analysis techniques 2.7.1 Analysis of socio- economic data from the survey The survey’s socio-economic data, including both qualitative and quantitative information, was analyzed using software like SPSS and Microsoft Excel. 2.7.2 Economic analysis The analysis of production costs, gross margins, gross income, benefits-cost ratios, etc from potato production in the study area are all included in this section. 2.7.3 Cost of production For the purpose of analysed the cost of production, only the variable cost components for potato and maize production were considered. Seeds, organic manure, chemical fertilizers, micronutrients, pesticides irrigation, manpower, and tractor power were among the variable cost elements. The total cost of production was determined by adding up all variable input costs. 2.7.4 Benefit-cost analysis It is the ratio between the gross return and total cost of any business. It provides information about the investment done on the resources will give a profitable return or not. Mathematically, B/C ratio= GR/TVC Where, B/C ratio= Benefit cost ratio, GR= Gross revenue, TVC= Total variable cost 2.8 Logistic regression In order to identify the main socioeconomic determinants affecting access to subsidies, logistic regression analysis was performed for data analysis and to determine the main factors affecting access to subsidies, such as gender, cooperative membership, farm size, and educational attainment. A binary logistic regression analysis (Tranmer and Elliot, 2016) was used to find out the odds of determinants of receiving subsidies. The following model was used to find the variables: P(Y) = β0 + β1X1 + β2X2 +,..., + βnXn Where; X1, X2, ..., Xn are explanatory variables; β0, β1, β2, βn are unknown factors; and P(Y) represents the chance that Y will occur. 3Results 3.1 Socio-demographic characteristics of study area (a) The survey was conducted among 90 farmers out of which 50 farmers were small producers and 40 farmers were larger producers. The results showed that the overall average age of participants was 49 years, with small producers averaging 44 years and large producers at 46 years (Table 1). The standard deviation for the overall age was 17.72, indicating considerable variability within the population which was not statistically significant (p=0.577), suggesting that age distribution did not differ markedly between small and large producers. Table 1 Socio-demographic characteristics of study area (a) Variable Overall (N=90) Small producer (N=50) Large producer (N=40) Mean difference t-value p-value Age 49.90 (17.72) 43.960 (17.824) 46.080 (17.74) 2.11 0.56 0.577 Total family member 7.88 (2.79) 7.78 (2.501) 8.02 (3.15) 0.24 0.41 0.682 Dependency ratio 0.45 (0.19) 0.46 (0.168) 0.43 (0.22) -0.03 -0.75 0.450 Total land 13.60 (7.57) 7.60 (3.458) 20.33 (4.84) 12.72*** 14.51 <0.001 Irrigated land 6.99 (6.63) 3.21 (3.105) 11.72 (6.86) 8.51*** 7.83 <0.001 Rainfed land 6.24 (6.32) 4.26 (3.827) 8.72 (7.84) 4.46*** 3.52 <0.001 Male employment 3.02 (1.64) 2.94 (1.316) 3.12 (2.00) 0.18 0.85 0.600 Female employment 2.17 (1.08) 2.22 (1.21) 2.100 (0.90) -0.12 -0.52 0.604 Note: *** indicate significant at 1% level
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