Energy Requirement in Agricultural Sector
by
V.K. Gupta
IASRI, Library Avenue, New Delhi - 110 012, India
Coauthors: Rajender Parsad (IASRI, New Delhi - 110 012)

The data on various aspects of energy usage in agricultural production system is being collected from the farmers of the selected villages in different agro-climatic zones under the All India Co-ordinated Research Project on Energy Requirement in Agricultural Sector. The information is collected on uses of Human Labour, Animal labour, Diesel, Electricity, Seed Rate, Farmyard Manure (FYM), Fertilizer, Chemicals, Machinery, Canal, etc. These are then converted into Mega Joule / hectare (MJ/ha) using internationally accepted conversion factors. The energy uses are also available on agricultural operations like tillage, sowing, bund making, fertilizer application, etc. Adding the energy levels from different sources generates the total energy used for crop production that forms another factor in the study. The data available on yields are converted into per hectare basis. As of now, the data is available on yield (kg/ha or MJ/ha), energy used (MJ/ha) from various sources and total energy used (MJ/ha).

The main objectives of the project are: Ø To establish the relationship between yield and total energy; yield and other sources of energy like human labour, animal labour, diesel, electricity, FYM, fertilizers, chemical, machinery, irrigation, canal, etc.

Ø To find out the optimum values of the various energy sources for maximum productivity.

To meet these first objective, first order and second order response surfaces are fitted. A pertinent question that arises here is as to whether a single regression equation (or response surface) will adequately describe the relationship for all categories of farmers under consideration or will different regressions be required for each category of farmers? A complete description of the response (the best fit of data) would be obtained by allowing each category to have its own regression equation (or response surface). This would be inefficient, however, if the responses were similar over all categories; the researcher would be estimating more parameters than necessary. On the other hand, a single regression equation (or response surface) to represent the response for all categories will not adequately characterize any one group and could be very misleading if the relationships differed among categories. Therefore, separate response surfaces for each category of farms was fitted and homogeneity of regression equations (or response surfaces) was also tested. Wherever, the regression equations were homogeneous, a common regression equation was fitted to the entire data set. Otherwise the analysis was carried out separately for each category. Categorization of the farmers on the basis of irrigated or rainfed, electricity use or non-use, bullock or tractor use, based on productivity levels like low ( 2000 Kg/ha), medium (2000 - 3250 Kg/ha), high ( 3250 Kg/ha), etc. or based on the ratio of total energy to yield (energy-yield ratio) like high (< 3.50), medium (3.50 - 4.00), low (4.00 - 5.00), very low 5.00 was also suggested.

To obtain the optimum energy levels for different sources like human energy, animal energy, diesel energy, electrical energy, FYM energy, fertilizer energy, machinery, irrigation, etc. to maximize the yield, a second order response surface was fitted. The co-ordinates of the stationary point were obtained by equating the first derivative of the fitted second order response surface equal to zero. Canonical analysis was performed to find the nature of the stationary point (point of maxima, minima or a saddle point). For the cases, for which the stationary point is a saddle point and lies within the input range, the response surface in the vicinity of the stationary point was explored. This exploration gives various combinations of input variables for a desired output in the vicinity of the predicted response at the stationary point. One can choose the input combination based on the practical considerations. However, using several sets of data, it has been observed that most of the time the regression coefficients are not significantly different from zero, particularly the second-degree coefficients; and/or saddle point lies outside the input range. It seems that the energy usage has not yet reached the saturation stage or plateau. In other words, the relationship of yield with energy levels of various factors appears to be linear in nature. Therefore, to obtain the levels of various inputs that maximize the yield per hectare, recourse is to be made to the use of Linear Programming (LP). In LP problem, the objective function and the constraints are very important. Therefore, one has to be cautious in defining the objective function and constraints. In the initial stages, it was thought that one should fit a multiple linear regression, and use the fitted multiple linear regression equation as an objective function and availability of the energy from different sources like human, animal, diesel, electricity, machinery, etc. as constraints. However, a close scrutiny reveals that such an objective function may be error prone like it may have large standard error of the estimated response, the regression coefficients may also have large standard errors, and moreover, many of the regression coefficients may not be significantly different from zero. Therefore, the use of such an objective function is to be avoided. The second option of the objective function is that we consider the data of energy usage and productivity of each farmer as a separate activity and define the objective function and constraints.The approach uses the maximization of yield subject to the constraints on the availability of energy from different sources like Human Labour, Animal Labour, Diesel, Electricity, Seed Rate, Farmyard Manure (FYM), Fertilizer, Chemicals, Machinery, Total Energy, etc. The procedure has also been used for minimization of total energy for obtaining a given level of yield. The concept of energy use efficiency has also been introduced. This technique is being exploited by the All India Co-ordinated Research Project on Energy Requirement in Agricultural Sector, Central Institute of Agricultural Engineering, Bhopal.

Date received: December 9, 2002

Copyright © 2002 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cakd-58.