An improved technique for agricultural implement draught analysis

Agricultural implement draft requirements show considerable spatial variability due to variations in soil properties and fracturing of soil. A large sample size is necessary to obtain a representative mean draft value for a given soil type and condition because of this variability. Moreover, empirical polynomial/multi-linear regression models for implement draft are often subjected to multi-collinearity problems. Proper design of experiments can assist in the complete elimination of these multicollinearity problems. An implement test procedure has been developed which addresses the problems of soil variability and multi-collinearity, Proper choice of values for independent variables in the experimental design phase can assist in transforming these variables to an orthogonal domain which completely overcomes multi-collinearity problems. The orthogonal regression technique using transformed variables and the conventional polynomial/multi-linear regression techniques using real variables were used to analyze draft data for a moldboard plow in a Capay clay soil to illustrate advantages of the orthogonal technique.