Pankaj Kumar Meghwal, Rajkumar Josmee Singh and Ram Singh
Agriculture is the most direct route to improving the diet of a person ensuring year-round access to adequate, safe and diverse nutrient-rich food. However, a resilient agricultural production system is the sine-qua-non to sustain food security amidst extreme climate change consequences. The present study made an attempt to imply multinomial logistic regression model to identify the factors which determined the decision on technology adaptation for nutrition-sensitive climate change vulnerable agriculture. The study was conducted with proportionate randomly selected 60 farmers of Neemuch and Mandsaur districts representing respectively moderate and high climate change vulnerable Malwa Plateau Agro-Climatic Zone of Madhya Pradesh respectively. Multinomial logistic regression analysis revealed that the Cox & Snell R2 and the Nagelkerke R2 values of 0.389 and 0.485 respectively determined that between 38.9% and 48.5% of the variability in dependent variable namely ‘Decision on technology adaptation for nutrition-sensitive climate change vulnerable agriculture’ is explained by the set of independent variables viz., ‘Age’, ‘Level of Education’, ‘Operational Land Holding’, ‘Annual Income’, ‘Awareness on Consequences of Climate Change in Nutrition-sensitive Agriculture’, ‘Knowledge of Mitigation and Adaptation of Nutrition-sensitive Climate Change Practices in Agriculture’, ‘Perception on Climate Change in Nutrition-sensitive Agriculture’, ‘Fatalism’, ‘Risk Orientation’ and ‘Social Cohesiveness’ used in the model. The overall predictive accuracy for the present model was 71.7%, suggesting that the model was useful.
Keyword: Multinomial logistic regression model; Cox & Snell R2; Nagelkerke R2; Predictive accuracy;