Fuzzy linear programming for integrated farming systems in multi-objective environment
DOI:
https://doi.org/10.59797/ija.v58i1.4157Keywords:
Fuzzy linear programming, Integrated farming system, Multi-objective environmentAbstract
Crop production alone from predominantly small farms in India is quite inadequate to sustain the farm families. They have to depend on other land based enterprises, viz. crop, livestock, fishery, poultry, duckery, agroforestry etc. to meet the multifarious needs of farm family. Integrated Farming Systems (IFS) provides scope to integrate different enterprises with the objectives to generate additional farm income and employment, and improve the socio-economic condition of small and marginal farmers. IFS operate under different physical, biological, socio- economic and technological environments. Designing a suitable IFS becomes complex due to the presence of un- certainties such as productivity, market prices of farm produce, non-availability of capital and labour at appropri- ate time. In the present study, Fuzzy Linear Programming (FLP) is used for developing suitable compromise inte- grated farming system models for farmers in north Indian situations under multi-objective environment. Based on the socio-economic survey, three objectives, capital requirement (CR), labour employment (LE) and farm income (FI) are considered for development of model. All three objective functions are represented by linear membership functions in fuzzy multi-objective framework. It is observed from compromise solution obtained by FLP that capital requirement, labour employment, farm income are 493 071, 897 man-days, 604 860, respectively with degree of satisfaction (?)0.462. Analysis of compromise solution in multi-objective environment also indicated that capital requirement, labour employment and farm income have differed considerably as compared to individual optimal solutions obtained by solving linear programming individually for each objective function. Sensitivity analysis stud- ies indicated that effect of linear/nonlinear membership functions is having significant effect on degree of satisfac- tion.References
ARDB, 2010. Agricultural Research Data Book. Indian Agricultural Statistics Research Institute, New Delhi Behera, U.K, Yates, C.M., Kebreab, E. and France, J. 2008. Farm- ing systems methodology for efficient resource management at the farm level: an Indian perspective. Journal of Agricul- tural Sciences, Cambridge 146: 49305 FAO, 1995. The farming systems approach to development and ap- propriate technology generation. Food and Agriculture Orga- nization of the United Nations, Rome_CIT_Ganesh, M. 2007. Introduction to Fuzzy Sets and Fuzzy Logic_CIT_Prentice Hall of India Private Limited, New Delhi Gill, M.S., Samra, J.S. and Singh, G. 2005. Integrated farming sys- tem for realizing high productivity under shallow water table conditions. Research Bulletin of Punjab Agriculture Univer- sity, Ludhiana, India_CIT_Loader, R. and Amartya, L. 1999. Participatory Rural Appraisal : extending the research methods base. Agricultural Systems 62: 7385_CIT_Loucks, D.P., Stedinger, J.R. and Haith, D.A. 1981. Water resources systems planning and analysis, Prentice Hall, Englewood Cliffs, New Jersey_CIT_Sasikumar, K. and Mujumdar, P.P. 1998. Fuzzy optimization model for water quality management of a river system. Journal of Water Resources Planning and Management, ASCE 124: 7988_CIT_Sidhu, M.S., Joshi A.S. and Lavleen, K. 2007. Problem and pros- pects of Agriculture in Punjab. Productivity 48(1): 10112_CIT_Taha, H.A. 2005. Operations Research: An Introduction, Prentice- Hall of India Private Limited, New Delhi_CIT_Zimmermann, H.J. 1996. Fuzzy set theory and its applications, Al- lied Publishers, New Delhi.




