Abstract
Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40°C with 5°C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25°C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15°C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy.
Highlights
Seed germination rate is often used to evaluate the suitability of an environment for the cultivation of different plant species [1,2,3]
Considering optimum germination is usually defined as a germination percentage of not lower than the maximum germination minus one-half of its confidence interval (P = 0.05), optimum germination was found to be reached when the tested seeds were grown under a temperature regime with a cool-temperature between 10~25°C combined with a warm-temperature between 25~35°C
The germination percentages of all five cultivars were lower than 50% under constant temperatures within the thermal range from 10 to 30°C, while no germination was registered at constant 35°C
Summary
Seed germination rate is often used to evaluate the suitability of an environment for the cultivation of different plant species [1,2,3]. Several mathematic models have been developed to simulate the germination response to temperature based on the experimental data [4,5,6,7,8,9] These previous models were mainly used to predict: I. the time, under a constant temperature condition (cumulative temperature), required for the expected germination of a specific variety [4,5,6], and II. The core functions of the published temperature-germination response models [11,12] consist on the estimation of populations’ thermal response parameters [14,15,16,17,18] or on optimization of polynomial equations using iterative curve fitting [6,10] These functions constructed by various scientists are usually different from each other because their parameters are selected for fitting the germination data of a particular batch of seeds. The back propagation (BP) algorithm is recruited to optimize the GA based temperature-germination model [10]
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