The productivity of crop depends on climate soil type and managemental practices. The crop production technologies have enhanced yields to considerable extent. The productivity forecast are made on time series date to formulate policies. in a class of regression where the independent variable is used to predict the dependent variable. The dependent variable is dichotomous. When the dependent variable has two categories, then it is binary logistic regression. When the dependent variable has more than two categories, then it is multinomial logistic regression. When the dependent variable category is to be ranked, then it is ordinal logistic regression (OLS). To obtain the maximum likelihood estimation, transform the dependent variable in the logit function. Logit is basically a natural log of the dependent variable and tells whether or not the event will occur. Like OLS, ordinal logistic regression does not assume a linear relationship between the dependent and independent variable. It does not assume homoscedasticity. Wald statistics tests the significance of the individual independent variable. To study the productivity of selected crops in Vidarbha. To examine the trend in selected food crops using logistic model. The productivity performance of selected crops indicated that Bajra, Gram and Mung crops have low productivity. Wheat, Jowar and Tur productivities are relatively higher. Exponential trend was found to be best fit for most of the crops. Logistic function was suitable for Mung in Amravati Division and Tur in the region.