Modeling and analysis of greenhouse environmental factors in north China based on path analysis and stepwise regression

Abstract

To explore the relationship between environmental factors in a greenhouse on sunny/cloudy days, an environmental factor model was developed using path analysis and stepwise regression analysis. The environmental factors studied include greenhouse air temperature (GAT), greenhouse air humidity (GAH), soil temperature (ST), soil humidity (SH), greenhouse radiation (GR), and carbon dioxide concentration (CDC). The results showed that on a sunny day, the models can describe the GAT and GAH well (R2=0.957, 0.936), and the model’s tested determination coefficient was above 0.87. However, due to the delay and other main control factors of ST and SH, the models’ determination coefficient was poor (R2=0.587, 0.625). However, there was a fifth-order polynomial fitting relationship between ST and SH (R2=0.817). On a cloudy day, the coupling effect between dependent variables and environmental factors was well described (R2 ? 0.97). The model test results for GAT and ST were better (R2=0.997, 0.981), and the GAH and SH model test results were also good (R2=0.789,0.882). In summary, the established coupling model of greenhouse environmental factors was suitable for simple greenhouse environment prediction, allowing greenhouse managers to easily predict greenhouse environmental change trends and reduce the cost of testing, laying a foundation for the subsequent establishment of a simpler, more accurate greenhouse factor model.