ZHANG Xiao-Yan, LIU Feng, WANG Feng-Yun, LIU Shu-Yun, FENG Wen-Jie, SHANG Ming-Hua, ZHU Jian-Hua. Simulation of dry matter partitioning and marketing date of greenhouseSimulation of dry matter partitioning and marketing date of greenhouse Phalaenopsis aphrodita Rchb. F. flower[J]. Chinese Journal of Eco-Agriculture, 2008, 16(6): 1453-1457. DOI: 10.3724/SP.J.1011.2008.01453
Citation: ZHANG Xiao-Yan, LIU Feng, WANG Feng-Yun, LIU Shu-Yun, FENG Wen-Jie, SHANG Ming-Hua, ZHU Jian-Hua. Simulation of dry matter partitioning and marketing date of greenhouseSimulation of dry matter partitioning and marketing date of greenhouse Phalaenopsis aphrodita Rchb. F. flower[J]. Chinese Journal of Eco-Agriculture, 2008, 16(6): 1453-1457. DOI: 10.3724/SP.J.1011.2008.01453

Simulation of dry matter partitioning and marketing date of greenhouseSimulation of dry matter partitioning and marketing date of greenhouse Phalaenopsis aphrodita Rchb. F. flower

  • To understand the basic quantification principles of greenhouse Phalaenopsis aphrodita at the procreative stage and to predict the dynamics of vegetation systems so as to rationally regulate the production of P. aphrodita, a simulation model of dry matter partitioning and flower marketing date was developed and validated for greenhouse P. aphrodita according to the correlation of its vegetative development,to temperature and radiation. The model was built on C++ Builder6.0 and is executable on Pentium(R) 4 CPU and 512MB memory computer on Windows XP platform. Results show that the model-simulated shoot, root, stem, leaf, stalk and flower dry weight excellently match with fieldmeasured values. Correlation coefficients between model-simulated and field-measured values are 0.99, 0.99, 0.94, 0.98, 0.97 and 0.99 (all significant at 0.01); with predicted relative errors of 1.19%, 1.79%, 5.66%, 1.22%, 2.90% and 1.53% respectively. Compared with existing vegetation models for greenhouse crops, our model not only has high prediction accuracy and robust functionality but also has easily acquirable parameters and great practicality. The model accurately predicts dry matter partitioning and flower marketing date of greenhouse P. aphrodita , which provides a decisionmaking support for production, management and optimization of environmental controls on greenhouse P. aphrodita.
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