Table 1 reveals the total 120 farmers were selected for the King chilli cultivation; out of total 31.67 per cent was marginal farmers (up to 1 ha land) and 68.33 per cent was small farmers (1.01 to 2.00 ha land), respectively.
Table 2 reveals the ordinary least square (OLS) estimates of parameters of the Cobb-Douglas production function with respect to different size groups and all farm samples. The regression coefficient of land hiring charges was found to be positive and statistically significant at 1 per cent level on Group I (marginal), II (small) and Group III (overall) farm size groups, which was indicating the significant contribution towards the gross yield of the King chilli cultivation in all the selected farm size groups.Similar results are in the line with
Sharma and Kalita (2008) and
Sharma et al., (2018).The regression coefficient of seed cost was found to be positive and statistically significant at 5 per cent level in group I and II, indicating the significant contribution to the gross yield of the King chilli cultivation on both (marginal and small) farm size groups, respectively. Similar results were in the line with
Sharma (2012) and
Chishi and Sharma (2019).
Table 2, reveals that the regression coefficient of seed cost was found to be positive and statistically significant at 10 per cent in group III, indicating significant contribution to the gross yield of the King chilli cultivation in this group.The regression coefficient of transportation and Miscellaneous cost expenses and other expenses were found to be positive but non-significant in overall farm size groups.It was observed from the data that some regression coefficients were negative and non-significant indicating that although these inputs were important but played reverse role in the present level of production of King chilli cultivation, so it has negative contribution of these inputs to gross yield. Similar results were in the line with
Sharma (2006) and
Sharma (2012).
Table 2 reveals that the value of co-efficient of multiple determinations (R
2) ranged from 0.99.39 in group I to 0.9528 in group III, which explained variation in the dependent variable by the independent variables chosen in the function. The remaining variation of dependent variable might be due to those variables, which have not been captured in the function such as rainfall, temperature, humidity. The value of (R
2) in all farm samples was found to be 0.9939 indicating that 99.39 per cent of variation in the dependent variable was explained by the independent variables chosen in the function. The remaining 0.61 per cent of variation might be due to some other factor; which have not been captured in the function. Similar results were in the line with
Sharma (2013a) and
Yadav and Sharma (2019).
Table 3 reveals the return to scale was estimated and found to be greater than unity in group I, group II and group III, which was further indicating increasing return to scale, whereas it was less than unity means less than 1 on group III, indicating decreasing return to scale. The elasticities of production of each input revealed the estimated percentage change in gross return associated with one per cent change in the input, while other resources inputs are held constant. The estimated elasticity of production of each input, it was found to be less than unity in different size groups except for transportation in group III, indicating that use of one more unit of will keeping other inputs constant in group III would increase the gross yield by unit per cent, showing overall increasing return with respect to these particular variable, while negative and less than unity on the different farm size groups, indicating to reduce the investment immediately. Similar results were in the linewith
Sharma (2013b) and
Sachin et al., (2015).
To evaluate how efficiently the farmers of the study area were using their resources, the marginal value of product (MVP) of an input was compared with its respective costs in Table 3. The overall MVP of land hiring cost was worked out, which indicated that addition of one unit of unit would have increased the gross yield by the unit itself. Amongst the different size groups of growers has potential to all farm size groups. The overall MVP of inputs selected was worked out with negative signed, indicating that addition of one unit, would have decreased gross yield, amongst the different size groups of growers. Similar results were in the line with
Sharma (2013c);
Sharma (2015a).
While the MVP with positive signed was worked out at different level of test of significance, indicating that addition of one unit of would have increased gross yield by the unit selected and also having the future scope of investment to the input on group I, II and III. Finally, the overall situation, the ratio of MVP to its factor cost was found to be positive but less than unity for land hired cost and seed cost, transportation cost and miscellaneous expenses indicating a increasing as well as decreasing return of these inputs to the gross yield. Thus, there is no scope for increasing the expenditure of those inputs, while it was found to be negative but less than unity; indicating excess use of these inputs, which should be minimised to ensure significant contribution to the gross yield. So reshuffling of the resources from least potential to the fusible invest it required immediately to make the existing farming business more profitable towards the increase in the use of those inputs in order to secure higher returns within the available resources. Similar results were in the line with
Sharma et al., (2007) and
Sharma (2015b);
Yani and Sharma (2022).
An attempt was made to study the constraints faced by the king chilli growers in the study area. The results gathered from the research are expressed in descending order of their relative significant with the help of frequency, simple percentage and ranking. The ranking of constraints was found to be similar across various size groups of farmers. Therefore, problems faced by the king chilli growers are not according to different size groups. The above table represents the problems of sample farmers
(Borah and Sharma, 2021).
Table 4 revels the production problems of King chilli faced by the growers in the study area. Pest and diseases and lack of knowledge was most felt recorded as ranks I and II in the production problem. Lack of extension service, lack of marketing agency and lack of credit facility comes to III, IV and V, respectively isalso ranking for production problems. Therefore, proper training and demonstration to the farmers need to be imparted for improved package of practices for efficient resource use, unavailability of market, lack of capital and unavailability of labour is also a major problem faced by the king chilli growers which comes to rank VI, VII and VIII in ranking, so the farmers generally sell their produce directly to the consumer or to the retailer, it was also found that the Lack of transport facility and lack of storage facility act as one of major problem which rank to IX and X. Similar results were in the finding with
Sharma (2011);
Sharma (2014) and
Sharma (2016);
Yadav et al., (2021).
