Scientia Agricultura Sinica ›› 2007, Vol. 40 ›› Issue (9): 2028-2035 .

• HORTICULTURE • Previous Articles     Next Articles

Simulation of Development and Growth of Greenhouse Single Flower Cut Chrysanthemum II. Modelling of Dry Matter Production and Partitioning

  

  1. 南京农业大学农学院
  • Received:2006-03-22 Revised:2006-11-20 Online:2007-09-10 Published:2007-09-10

Abstract: 【OBJECTIVE】Dry matter production and partitioning is the base of external quality formation of cut chrysanthemum. A simulation model was developed to predict the biomass production, dry weight of organs and shoot fresh and dry weight of single flower cut chrysanthemum for the optimization of crop and climate management for greenhouse single flower cut chrysanthemum production.【METHOD】A model of dry matter production and partitioning of greenhouse single flower cut chrysanthemum was developed based on the effects of temperature and radiation on chrysanthemum growth. Experiments with different varieties and planting dates were carried out in greenhouses, to collect data to develop and validate the model. 【RESULTS】The results show that the simulated value agree well with the observed ones. The determination coefficient (R2) and root mean squared error (RMSE) between the simulated and the measured value based on the 1:1 line for biomass production, dry weight of leaf, stem and flower, and fresh weight of shoot per plant were, respectively, 0.97, 0.97, 0.97, 0.83, 0.99; 67.56g·m-2, 0.35, 0.44, 0.24 and 3.73 g·plant-1. For the prediction of biomass production, the prediction accuracy of the PTEP based model is significantly higher than that of the photosynthesis process based crop growth model ( R2 and RMSE were 0.82和274.68 g·m-2 , respectively ). 【CONCLUSION】 Based on the results obtained in this study, it can be concluded that the model developed in this study can give satisfactory prediction of greenhouse single flower cut chrysanthemum biomass production and partitioning. Compared with the photosynthesis process based crop growth model, the model developed in this study is more applicable for the optimization of climate and crop management for greenhouse cut chrysanthemum production.

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