A REGRESSION MODEL OF DRY MATTER CUCUMBER

The objective of this study is to develop a regression cucumber dry matter production model with a minimum number of parameters. Cucumber The substrate was peat mixed with vermiculite. Five experiments were fulfilled totally in 3 different places in Beijing of China from 2004 to 2005. Cucumber growth data (dry matter weight of leaf, stem, fruit and petiole) were measured and environmental data (temperature, light intensity and day length) were collected. Data collected from 1 experiment in solar greenhouse was used to build the model, which was further validated with the data collected from other 4 experiments in solar greenhouse. A regression model for cucumber dry matter production was established. Based on Logistic curve, the time state variable was expressed as a logistic function about effective temperature accumulation (ETA) and effective light intensity accumulation (ELIA). ETA was defined as the sum of the temperature that was higher than physiological zero point in certain period, and ELIA was defined as the sum of the light intensity that was higher than light compensation point multiplied with time in certain period. Temperature, light intensity and day length were synthetically considered. The model had less state variables, and provided the relationships between the cucumber dry matter accumulation (DMA) per plant and environmental data (temperature, radiation and day length). The result of simulation was satisfied, because RMSE value was less than 6, and the R 2 value of the results was 0.99. It indicated that the regression model for cucumber dry matter production was reasonable and feasible.