Attributes of Farm Income Operating on Conservation Agriculture: The Multivariate and ANN Analytics

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  • Cornea Saha
  • S K Acharya
  • Monirul Haque
  • Riti Chatterjee
  • Anwesha Mandal


Conservation agriculture, Artificial Neural Network, Farm Income, snowball sampling,


Conservation agriculture (CA) is the combination of environmental management, modern and scientific agriculture which employs farmers’ ability to utilize, innovate, and adapt to changing situations, as well as their holistic acceptance of knowledge along with ensuring sustainability. Farm-level adoption of CA is related to reduced labour and agricultural inputs, more consistent yields, and increased soil nutrient exchange capacity. A good quality land yields good results to everyone, confers good health on the entire family, and causes growth of money, cattle, and grain. The present study depicts hard evidence by identifying marker variables impacting income augmentation through conservation agriculture. A score of 50 farmers has been selected from two blocks of Cooch Behar district of West Bengal, by non-probability snowballing sampling techniques with a total of eighteen independent variables along with income from major crop is used as the dependent variable through a structured interview schedule. A basket of multivariate analytical techniques has been applied along with Artificial Neural Network (ANN) as well. The results depict that a blend of diversified farming and farming experiences in CA contributed immensely to scale up income from conservation agriculture approaches.






How to Cite

Attributes of Farm Income Operating on Conservation Agriculture: The Multivariate and ANN Analytics. (2021). Indian Journal of Extension Education, 58(1), 44-48.

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