Modeling vegetative development of berseem clover (Trifolium alexandrinum L.) as a function of growing degree days using linear regression and neural networks

Abstract

Accurate models of berseem clover (Trifolium alexandrinum L.) development in relation to growing degree-days (GDD) would be useful to both producers and researchers. Predictive ability of linear regression models of plant development may be limited by choice of threshold temperature and the non-linear nature of plant development. Neural networks provide a robust approach to dealing with non-linearity, and may therefore be useful for modeling plant development. In exp.1, a numerical scale of plant development was created and used to describe growth of four cultivars of berseem clover (Bigbee, Joe Burton, Saidi and Tabor) under controlled environmental conditions (constant temperature of 12, 18 or 24°C per 12-h photoperiod) for up to 18 wk of vegetative growth. Simple linear regression and neural networks were used to model plant development in relation to GDD using a range of threshold temperatures. Predictive ability of the models was compared with the results from a second controlled environment study (exp. 2). The r2 of the linear and neural models produced in exp. 1 were maximized at GDD threshold temperatures of 0 to 2°C. Results from exp. 2 indicated that the predictive ability of neural models matched or exceeded that of the linear models for all threshold temperatures evaluated. Results of the current study suggests that neural network models are relatively insensitive to base temperatures across the range tested and may therefore be preferable when a priori knowledge of temperature thresholds is not available.