ABSTRACT

Both mechanism models and empirical models can be used to describe the course of cucumber dry matter accumulation (DMA) (Challa, et al, 1996; Heuvelink, et al, 1996). Mechanism models generally have many sub-models for each DMA process, which can describe crop's growing course and have strong explanation function (Li, et al, 1999) completely. But empirical models show directly the relationship between crop development, growth rate, output and environmental factors by functions of multivariate regression equation, index equation, hyperbola equation and "S" curve, etc. It describes the results of observation. Most present cucumber dry matter accumulation models are explanatory. The application limitations of these models focus on that: (1) they have so many parameters that it is very complicated to be calculated (Challa, et al, 1996); (2) they need to input many variables and data, but the output is wavy; (3) the prediction is not very accurate. Contrarily, empirical model parameters are more easy to estimate and the calculation process is short, which is practical in real time control for greenhouse. The accumulation course of dry matter mainly includes photosynthesis and respiration. Temperature and light are two most important factors of cucumber DMA, if nutrition and water are sufficient and don't have pests (Chamont, et al, 1993). A lot of models describe crop growth with temperature accumulation or effective temperature accumulation (ETA) (Katharine, 1996). Illumination is also important to influence crop growth, even more important than temperature (Wurr, et al, 1988). Hand et al (1992) measured and analysed the net photosynthesis rate with everyday integrals of solar radiation. A forecasting regression equation of canopy net photosynthesis rate had been gotten. Comparisons of this regression equation with mechanism model of Thornley (1992) showed that light respond curves of the two models were very close. The objective of this study is to develop an empirical cucumber dry matter production model with a minimum number of parameters. Logistic growth equation was selected to build the empirical model. Although it is an ideal model for accumulation equation of dry matter (Li, 1996; Liu, 1994), its form is too simple. Because only is time adopted as independent variable, it could not describe the change of crop growth with the change of environmental conditions such as illumination and temperature, etc. Therefore the Logistic growth equation needs to be improved.