Knowledge of potential yield of wheat is imperative for site specific fertilizer management. Data collected from field trials
conducted on wheat in Khyber Pakhtunkhwa (KPK) Province, Pakistan was used to predict potential yield of wheat. Various
regression models were employed to get the predictions. A complete diagnostic analysis of the residuals of each model is
presented. Multiple regression models give us limited prediction power for our data. Models like Classification and
Regression Trees (CART) and Random Forests are also explored. The models created are compared on the basis of
predictive power and miss-classification error rates. Our results revealed that Random Forests give us very good results if
yield is divided into three categories.
Muhammad Naveed Aman, Aman Ullah Bhatti. (2015) Comparison Of Regression Models To Predict Potential Yield Of Wheat From Some Measured Soil Properties, , Volume-52, Issue-1.
