Abstract
Development of yield prediction model of rice crop for hilly and plain terrains of Uttarakhand
Highlights
Linear Regression (MLR) and Principal Component Analysis (PCA) to study the crop weather relationship and concluded that the MLR technique performs better than PCA for pre harvest forecasting of rice crop yield. Diwan et al (2018) developed crop yield forecast model by employing stepwise linear regression technique and found that temperature and relative humidity were significant predictors in crop yield forecast
Similar studies were conducted by Kalubarme and Ahuja, (1996); Chauhan et al, (2009) to develop agrometeorological data based rice yield prediction model for Karnal, central Punjab and Bulsar district of Gujarat respectively
Linear regression analysis was conducted to assess whether the weather parameters (Tmax, Tmin, rainfall, relative humidity and solar radiation) significantly predicted the yield of the rice crop of Udham Singh Nagar and Nainital district
Summary
Multiple linear regression models involved more than one independent variable and one dependent variable. Β0 is constant, βi’s are coefficients of Xi’s, Xi’s are the independent variables known as predictors and Y is the dependent variable and ε is the error. In addition to the Per cent Error (P.E.), Root Mean Square Error (RMSE) was calculated to compare the developed models.
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