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A Generalized Vernalization Response Function for Winter Wheat
Author(s) -
Streck Nereu Augusto,
Weiss Albert,
Baenziger P. Stephen
Publication year - 2003
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2003.1550a
Subject(s) - vernalization , cultivar , winter wheat , function (biology) , agronomy , biology , mathematics , photoperiodism , horticulture , evolutionary biology
Vernalization is a process required for certain plant species to enter the reproductive stage through an exposure to low, nonfreezing temperatures. These plant species include some fall‐planted cereals, among them winter wheat ( Triticum aestivum L.). A three‐stage linear function is currently used in wheat simulation models to describe the developmental response to the duration of the vernalization treatment, expressed as effective vernalization days (VD). This function lacks generality because the value of its coefficients varies with genotype. The objective of this study was to develop a generalized nonlinear vernalization response function for winter wheat. The nonlinear vernalization function developed in this study has coefficients with biological meaning. Data of final leaf number at different VD treatments in 12 winter wheat cultivars from 19 trials, which are from published research and from a growth chamber experiment conducted as part of this study, were used as independent data for evaluating the nonlinear vernalization function. These data sets represent a wide range of winter wheat cultivars developed in different parts of the world. The generalized nonlinear vernalization function described the developmental response to VD better (RMSE = 0.032) than the three‐stage linear functions (RMSE = 0.060 for cultivar Karl 92 and RMSE = 0.129 for cultivar Arapahoe). It is concluded that the vernalization response of winter wheat can be described by a general vernalization function. This conclusion implies that a reduction in the input data requirements is possible for winter wheat simulation models.