Abstract
Two experiments (field and pot) were conducted to evaluate the ability of partial least square regression (PLSR) using physiological and root traits to predict grain yield of maize. The genetic materials used for the experiments were six maize genotypes. Data was recorded on some growth, physiological and root traits. Data was analyzed using PLSR model of XLSTAT. There was a good prediction of grain yield of maize using phenological traits (R2 = 0.99 and RMSE = 17.73). The model gave a good fit in predicting grain yield with Sammaz 14 having the best prediction. Prediction model of grain yield using root and seedling traits also gave a good fit (r2 = 0.96). Sammaz 14 and TZE-COMP 5 had better fits. Prediction of grain yield of maize using some physiological traits of maize also produced a good fit (R2 = 0.86 and RMSE = 90.94). Prediction accuracy for Sammaz 14 was higher than the other genotypes. The good fits observed for all the predictions indicates the ability and usefulness of PLSR in predicting grain yield of maize and this can reduce the time of breeding programs in developing maize varieties that are tolerant to drought. Key words: Partial least square, maize, drought, root, and physiological traits.
Highlights
Maize is the third most important food grain for humankind after rice and wheat
There was a good prediction of grain yield of maize using phenological traits (R2 = 0.99 and root mean square error (RMSE) = 17.73)
Prediction of grain yield of maize using some physiological traits of maize produced a good fit (R2 = 0.86 and RMSE = 90.94)
Summary
R. Two experiments (field and pot) were conducted to evaluate the ability of partial least square regression (PLSR) using physiological and root traits to predict grain yield of maize. There was a good prediction of grain yield of maize using phenological traits (R2 = 0.99 and RMSE = 17.73). The model gave a good fit in predicting grain yield with Sammaz 14 having the best prediction. Prediction model of grain yield using root and seedling traits gave a good fit (r2 = 0.96). Prediction of grain yield of maize using some physiological traits of maize produced a good fit (R2 = 0.86 and RMSE = 90.94). The good fits observed for all the predictions indicates the ability and usefulness of PLSR in predicting grain yield of maize and this can reduce the time of breeding programs in developing maize varieties that are tolerant to drought
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