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Predicting Plant Height of Greenhouse Grown Crops with a Polynomial Growth Rate Model
Author(s) -
Snipen Lars Gustav
Publication year - 1998
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/(sici)1521-4036(199807)40:3<295::aid-bimj295>3.0.co;2-3
Subject(s) - greenhouse , growth rate , forcing (mathematics) , mathematics , polynomial , growth model , bayesian probability , statistics , agronomy , biology , mathematical analysis , geometry , mathematical economics
A prediction model for the growth of plant height is developed, using polynomials in time to describe the growth rate. The growth rate is affected by forcing factors through the polynomial coefficients. A random slope model is used to describe the difference in growth rate for plants grown under similar conditions. Maximum likelihood estimates of model parameters are obtained and a selection procedure is employed to estimate the model complexity using Schwarz' bayesian criterion as a measure of predictive power. The procedure is applied to data sets for greenhouse grown poinsettias. The use of polynomials to describe the time effects on the growth rate makes the strategy versatile, and it can be used to predict the growth of many different crops. Many forcing factors of different types can be incorporated simultaneously in the model. Confidence of predictions are also quantifies, which is important when the results are applied in a practical situation, e.g. in climate control of commercial greenhouses.