# Resource Use Efficiency of Major Field Crops in Reasi District of Jammu Region of Jammu and Kashmir State

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3 Resource Use Efficiency of Major Field Crops in Reasi District of Jammu Region of Jammu and Kashmir State the efficiency parameters and the production elasticties of the respective input variables for the given population at a particular period, t. The fitted Cobb-Douglas production may be written for the present case with six input variables as: y= ax x x x b1 b2 b3 b On log transformation, the above function can be transformed to a linear form as: Or, Log y = log a 0 + b 1 log x 1 + b 2 log x 2 + b 3 log x b 6 log x 6 Where y = Gross return (`/ha X 1 = Human Labour X 2 = seed (`/ha) X 3 = FYM (`/ha) X 4 = Urea (`/ha) X 5 = DAP (`/ha) X 6 = Plant protection chemical (`/ha) b 1..., b 6 are the parameters to be estimated Marginal value productivities (MVP) of resource used was worked out with the help of regression coefficient obtained from these crops. MVP of a particular resource represents the expected addition to the gross returns caused by the additional one unit of the resource input while the other input are held constant. To examine the productivity of different inputs used in production of studied crops, marginal value productivities of inputs were estimated at geometric mean levels of inputs. To calculate Marginal Value Productivity (MVP) of resource x i, the following formula had been used: ( ) ( ) GM Y MVP = bˆi GM Xi Where MVP (x i ) is the Marginal Value Productivity of i th resource. bˆi is the regression coefficient (estimated) GM (Y) is the Geometric Mean of Output. GM (X i ) is the Geometric Mean of Inputs. 17 RESULTS AND DISCUSSION Regression function result and marginal value productivity of wheat Yield of wheat was regressed on various factors of production viz., seed, FYM, urea, DAP and plant protection chemical. These variables were taken as the explanatory variables. The regression result pertaining to this is given in Table 1. Table 1: Estimated regression coefficient of various factors, their standard error and MVP of wheat production Variable Regression coefficient Standard error Constant MVP Human Labour 0.859* Seed FYM Urea 0.067* DAP Pesticide Coefficient of Determination (R 2 ) 0.74** F-Value * Significant at 1% level of probability ** Significant at 5% level of probability The crop production function for wheat with R 2 value at 0.74 was statistically significant meaning thereby that 74% of wheat production of this area was being explained by the above-mentioned variables. Further, it is clear from the table that for wheat, urea and human labour were found to be significant at 1% level of probability. The value of regression coefficient for these variable were and 0.859, respectively. The results further explained that regression coefficient of human labour, farmyard manure, urea, DAP and plant protection chemical with positive sign indicated that one percent increase in use of these inputs after keeping the use of all other inputs constant, the return of crop could increase by 0.859%, 0.002%, 0.067%, 0.009% and 0.004%, respectively. The negative sign of the regression coefficient of seed in the function showed that one percent additional expenditure on the use of seed would reduce the return of wheat by 0.321%. Similar finding were observed by Kumar and Kumar

4 Bharti et al. (2004). This showed that this input was being excessively used at present and thus the additional use of this input should be checked immediately so as to avoid any further fall in the return of wheat. Table 2: Estimated regression coefficient of various factors, their standard error and MVP of maize production Variable Regression coefficient Standard error Constant MVP Human Labour Seed * FYM 0.095* Urea 0.046* DAP 0.016* Pesticide Coefficient of Determination (R 2 ) 0.87 F- Value * Significant at 1% level of probability ** Significant at 5% level of probability Further, it could be seen that the positive marginal value productivity of human labour (1.231), FYM (0.312), urea (0.021), DAP (0.525) and plant protection chemical (0.314) of wheat crop indicated that an additional rupee one spent on area. Thus there was a scope to invest more on human labour, farmyard manure, urea, DAP and plant protection chemical. But, in maize production, use of farmyard manure depends on rainfall. Thus there seemed to very little scope to increase more farmyard manure and fertilizer application in maize production. Similar finding were observed by Kumar and Kumar (2004). The negative marginal value productivity estimated for seed at ` showed that use of seed was more than optimum leading to fall in returns with each additional unit of this input. Regression function result and marginal value productivity of maize The yield of maize was regressed on various factors of production viz human labour, seed, FYM, urea, DAP and plant protection chemical taken as the explanatory variables. The perusal of data depicted in table 2 depicted R 2 value 0.87 as statistically significant meaning thereby that 87% of variation in maize production was due to above mentioned variables. Table further indicated that the regression coefficient of seed, FYM, urea, DAP of maize was found to be significant at 5% level of probability. Human labour and plant protection chemical were found to be non-significant and their values were 0.522, and , respectively. Further human labour, farmyard manure, urea, DAP were with positive regression coefficient whereas plant protection chemical has negative. The regression coefficient of the labour was, however, positive (0.522) but non-significant, where for plant protection chemical variable it was negatively non significant. The negative sign of the regression coefficient of seed in the function showed that one percent additional expenditure on the use of seed would reduce the return of maize by 0.046%. This showed that seed was being excessively used at present and thus the additional use of this input should be checked immediately so as to avoid any further fall in the yield maize. Similar findings were observed by Singh (2011) and Kumar and Kumar (2004). The marginal value productivity of human labour, farmyard manure, urea, DAP and plant protection chemical was positive with its value at 1.156, 0.032, 0.025, and 0.212, respectively. This indication that there still existed a scope to invest more on these inputs. The negative marginal value productivity estimated for seed ` showed that use of seed and more than optimal leading to fall in return with each additional unit of this input by the marginal value productivity of the respective input. Similar finding were observed by Kumar and Kumar (2004). CONCLUSION It is concluded that the sample holding under dryland ecosystem comprise maize wheat cropping system, respectively. By using Cobb-Douglas production function, it was found that the resources used in maize and wheat production like seed, FYM, urea and DAP are underutilized indicating that there is further scope to increase their quantity which in turn will boost the returns. REFERENCES Digest of Statistics Directorate of Economics and Statistics, Government of Jammu and Kashmir. Economic Survey Directorate of Economics & Statistics, Planning and Development, Government of Jammu & Kashmir. 18

5 Resource Use Efficiency of Major Field Crops in Reasi District of Jammu Region of Jammu and Kashmir State Kumar, S. and Kumar, S Resource Use Efficiency and Returns from Selected Food grains Crops of Himachal Pradesh: A Study of Low Hill Zone. Indian Journal of Economics, 23(2): Singh, Sukhwinder, Park, J. and Litten, B. Jennie The economic sustainability of cropping systems in Indian Punjab: A farmers perspective. Paper presented at the EAAE congress on Change and Uncertainty Challenges for Agriculture, Food and Natural Resources. Zurich Switzerland. State of Indian Agriculture Government of India Ministry of Agriculture & Farmers Welfare Department of Agriculture, Cooperation & Farmers Welfare Directorate of Economics and Statistics New Delhi. 19

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