Accuracy of Equations Predicting the Phyllochron of Wheat

Authors

Gregory S. McMaster, USDA-ARSFollow
Wallace Wilhelm, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

November 1995

Comments

Published in Crop Sci. 35:30-36 (1995).

Abstract

Predicting the rate of leaf appearance, or phyllochron, aids in understanding and modeling grass development and growth. Nine equations predicting the phyllochron of wheat (Triticum aestivum L.) were evaluated using field data from a variety of locations, cultivars, and management practices. Each equation is referred to by the last name of the first author; if there is more than one equation by the first author, additional descriptors were included. The BAKER and KIRBY equations predict the phyllochron based on changes in daylength following seedling emergence; CAO-TEMP and CAO-DAY use a curvilinear relationship with temperature and daylength, respectively; CAO-T&D uses the ratio of temperature to daylength; VOLK mathematically refines CAO-T&D; MIGLIETTA uses an ontogenetic decline in the rate of leaf appearance; and MIGLIETTA-DAY adds photoperiod effects to MIGLIETTA. No equation adequately predicted the phyllochron. The r² values between predicted and measured phyllochron for winter wheat and spring wheat cultivars, respectively, were BAKER (0.001,0.486), KIRBY (0.002,0.487), CAO-DAY (0.000, 0.174), MIGLIETTA-DAY (0.013, 0.008), MIGLIETTA (0.002, 0.405), CAO-TEMP (0.100,0.190), CAO-FIELD (0.078,0.036), CAOT& D (0.066,0.030), and VOLK (0.119,0.043). All equations predicted the phyllochron for spring wheat cultivars better than winter wheat cultivars. BAKER and MIGLIETTA showed no bias towards either over or underestimating the phyllochron; KIRBY tended to overestimate the phyllochron; and the remaining equations were biased towards underestimating the phyllochron. Equations developed from field data had the greatest range of predicted phyllochrons. Based on multiple criteria, the BAKER equation best predicted the phyllochron for the experimental data set. Other factors must be added to the equations to improve predictions. Much opportunity exists to improve our ability to predict the phyllochron.