Asian Journal of Dairy and Food Research
Chief EditorHarjinder Singh
Print ISSN 0971-4456
Online ISSN 0976-0563
NAAS Rating 5.44
SJR 0.151
Chief EditorHarjinder Singh
Print ISSN 0971-4456
Online ISSN 0976-0563
NAAS Rating 5.44
SJR 0.151
Remunerative Potential of Small Ruminants (Sheep and Goat) under Rainfed Conditions in the Jammu Region of South Asia
Submitted16-09-2022|
Accepted22-06-2023|
First Online 14-07-2023|
Data were collected with the help of scientifically prepared schedules from the respondents of three main areas of Jammu region i.e., Kathua, Udhampur and Rajouri, respectively (Fig 3 and 4). These regions were selected purposively for the study as there were large presence of rainfed areas in the said regions, respectively.
Where,
‘Y’and Xi (i=1,2,3,…..n) represents the levels of output and levels of inputs. In addition, the respective constants. bo and bi’s (i = 1,2,3….n) reflects the efficiency parameters as well as the production elasticities of the particular input variables for the given population at a particular period ‘t’. The term ‘u’ represents the error term.
The fitted Cobb-Douglas production may be represented in the mathematical form for the present case with six input variables (in case of crops) as follows:
In the above functional model,
‘Y’ = Depicts the dependent variable.
‘Xi’ = Depicts the independent variable.
‘a’ = Constant representing the intercept or the production function and finally.
‘bi’ = Reflects the regression coefficients of the respective resource variables.
Subsequently, the above function has been modified into logarithmic transformation to suit the specific needs of the study:
The fitted Cobb-Douglas production for the present case with six variables has been represented as follows:
Where,
Y = Gross returns of the small ruminant’s enterprise in rupees per small ruminants per day as a dependent variable.
X1 = Total cost on dry fodder in rupees per small ruminants per day.
X2 = Total cost on green fodder in rupees per small ruminants per day.
X3 = Total cost on concentrates in rupees per small ruminants per day.
X4 = Total cost on labour in rupees per small ruminants per day.
X5 = Total cost on medicine and veterinary care in rupees per small ruminants per day.
X6 = Total cost on miscellaneous items in rupees per small ruminants per day.
MVP was worked out so as to reflect the addition of gross value of farm production per unit increase in the ‘ith’ resource, with all the resources kept fixed at their geometric mean levels. The MVP for the small ruminant’s enterprise in the study area was calculated by utilizing the following formula (Heady and Dillion, 2002):
Where,
b = Regression coefficient of particular independent variable.
Y = Geometric mean of dependent variable.
X = Geometric mean of independent variable.
Py = Price of dependent variable.
Statistical significance was measured by t-statistic i.e.,
It is a technique for the optimization of a linear objective function subject to linear equality and linear inequality constraints. The method of linear programming is also known as the method of linear optimization. Further, this method was employed to achieve the best outcome, for example, the maximum profit or the lowest cost, in a mathematical model whose requirements are expressed in the form of linear relationships. This method was employed to maximize the profit function of the farming enterprises in the proposed research.
Linear programming technique was chosen because this is one of the most powerful and efficient tools of analysis among the various analytical tools available for allocation of available limited farm resources among alternative enterprises. The model is set up to maximize sum of net value of production (net returns) of small ruminants’ enterprise, subject to number of constraints on various food and non-food variables. Mathematically, the problem is stated as follows:
Maximize:
Subjected to constraints
Where,
Z = Net returns from all small ruminants’ activities in the model.
Cj = Per small ruminants per day net returns from the jth activity.
Xj = The level of jth activity providing cj returns per small ruminants per day.
aij = The per small ruminants per day amount of the ith resource required in jth activity, also known as the technical or input-output coefficients.
bi = The amount of ith resource available to the farmer for the activity xj. where, i = 1,2,….., m.
j = 1, 2,… n, number of variables.
i = 1, 2,… m, number of constraints.
One of the most important components of the linear programming model is the identification of resource limitations. In this context, the following two types of constraint was designed for the same.
Family labour was calculated on the basis of the number of the family members that were actually in the process of the working. This restrictions with regard to the family labour were imposed for all the different types of small ruminants.
For the availability of capital/input in small ruminants’ enterprise, the constraint was set up in such a way that the cost in a particular year is to be fulfilled by the net returns from the previous year.
Lindo software was used for running the linear programming (simplex) model.
Small ruminants found out to be remunerative for the farmers in the rainfed regions of Jammu division. If we consider Kathua district, total cost worked out to be Rs. 43.02 per ruminant per day, whereby, labour forms the major component. Gross returns were found out to be Rs. 51.93 per ruminant per day (Table 1). In case of Udhampur district, total cost per ruminant per day was revealed out to be Rs. 44.11. Labour is still the dominant cost component here. Shifting our focus now to district Rajouri, we can observe that the gross returns in this district found a place in the midway i.e., higher than Udhampur and lower than that of Kathua, respectively and labour is still the dominant cost component in this district. Thus, labour came out to be the dominant component in the cost structure in rearing of small enterprise. On the returns side, there was not a single dominant factor. Rather, the position of dominance was found oscillating between sale of mutton and sale of small ruminants, respectively. In case of Kathua district, sale of mutton was revealed out to be the major component (about 49 percent). Similarly, sale of mutton was also found out to be the dominant revenue generating source for the farmers in Udhampur district, where it forms about 42 per cent in the composition of gross returns, respectively. The high share of ‘sale of mutton’ in Kathua district is attributed to the better marketing facilities prevalent in this district. Moreover, blocks like Dinga Amb were found not to be very far of from the main city, thereby, opening new vistas for farmers in mutton business. If we consider the case of Rajouri now, ‘sale of small ruminants’ comprised up of the major share in the total returns for the farmers. It comprised up of about 41 per cent of the total returns. It is to be asserted here the fact, that, small ruminants were observed to be used as the source for meeting the immediate case needs of the farmers in the districts as small ruminants had found to have high liquidity in comparison to other assets that farmers were in possession of. Small ruminants were found to be used as the source of financial security (Oluwatayo et al., 2012, Wodajo et al., 2020, Alhaji et al., 2013). Net returns were found to be high in case of Rajouri district. Therefore, it becomes imperative that use of small ruminants as a highly liquid assets should be propagated so that the positive outcome out of it could be generated.
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