Using Multi - Criteria, AHP and Influence Approaches for Agriculture Development: A Case Study of Upper Mula Basin in Maharashtra (India)

Ravindra D. Gaikwad^1, and Vijay S. Bhagat²

¹S.N. Arts, D.J. Malpani Commerce and B.N. Sarda Science College Sangamner, Department of Geograpy, Affiliated to SavitribaiPhule Pune University, Pune, India 422605

²Post-graduate Teaching and Research Center in Geography, Agasti Arts, Commerce and Dadasaheb Rupwate Science College, Affiliated to SavitribaiPhule Pune University, Pune, India.422601

Cite this paper:
Ravindra D. Gaikwad and Vijay S. Bhagat. Using Multi - Criteria, AHP and Influence Approaches for Agriculture Development: A Case Study of Upper Mula Basin in Maharashtra (India). Applied Ecology and Environmental Sciences. 2021; 9(3):368-379. doi: 10.12691/aees-9-3-7

Abstract

Agricultural development is unique sign for development of agricultural base country. Multi-criteria, AHP and influence based analysis is suitable for agricultural development. Six criterions (Crops) sugarcane, vegetables, pulses, fruits, rice and grains were selected for development indicators of Upper Mula basin in Ahmednagar district, Maharashtra (India). Expert opinions for ranking the criterion selected for influence. Sugarcane, vegetables and fruits show higher influences on development of watershed arrangement in the study area. Further, crops grains and pluses were show significant influence in kharip season. Using AHP techniques for influences were calculated based on weights estimated. Normalized and distribution of specific crops using the values of influences within the sub-watersheds. Agriculture developments influence are classified into very low (< Mean-1STD), low (Mean-1STD to Mean), moderate (Mean to Mean + 1STD), high (Mean + 1STD to Mean + 2STD), and very high (Mean + 2STD<) and agricultural development are classified into high, moderate and low categories. The methodology is the effective tool for agricultural development of sub-watersheds.

Keywords:
AHP ranking multi-criteria influence weights

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