Evaluation of different sigmoidal growth models and climate parameters for dry matter accumulation of oat
Author(s) -
Yalçın Çoşkun
Publication year - 2018
Publication title -
genetika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.24
H-Index - 15
eISSN - 1820-6069
pISSN - 0534-0012
DOI - 10.2298/gensr1803045c
Subject(s) - dry matter , gompertz function , sigmoid function , weibull distribution , environmental science , logistic function , mathematics , agronomy , statistics , biology , computer science , machine learning , artificial neural network
Coşkun Y. (2018): Evaluation of different sigmoidal growth models and climate parameters for dry matter accumulation of oat.Genetika, Vol 50, No.3, 1045-1054. The monitoring of the biological growth of field crops is important for planning and scheduling agricultural applications. In order to assess biological growth pattern and, dry matter accumulation of Yeniçeri oat variety were obtained in Çanakkale conditions in 2012-2013 and 2013-2014 growing seasons with continuous plant samplings from seedling emergence until seed maturation. Gompertz, Logistic, Logistic Power, Weibull, and Ratkowsky sigmoidal growth models are fitted to actual growth data and their predictions were compared. Results suggested that all sigmoidal growth models successfully explained oat dry matter accumulation a high R 2 values (over 99%) and low mean square errors, Weibull model fitted lower than others for first year with an R 2
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