The article established the HDRICE model by modifying the structure of the ORYZA1 model and revising its parametersby field experiments. The HDRICE model consists of the modules of morphological development of rice, daily dry matteraccumulation and partitioning, daily CO2 assimilation of the canopy, leaf area, and tiller development. The model preferablysimulated the dynamic rice development because of the thorough integration of the effects of temperature and light on therates of rice development, photosynthesis, respiration, and other ecophysiological processes. In addition, this model hasattainable grain yield in the test experiment that showed the potential yield of cultivar Xieyou 46 ranged from 11 to 13 tonsha-1. Besides, the model was used to optimize the combinations of the transplanting date, seedling age and density forcultivar Xieyou 46 at Jinhua area, and the population quantitative indices to attain the potential yield such as maximumstems, effective panicles, filled grain number/leaf area, and so on. The result showed that the combination of transplantingdate on July 25, seedling age of 35 days and base seedling density of 1.33 × 106 ha-1 is the optimum combination for thesecond hybrid rice production in Jinhua County, China. And the maximum stems, the effective panicles, the filled grain perpanicle, the peak of optimum LAI, LAI in later filling stage, and the filled grain number/leaf were 6.03 × 106 ha, 3.99 × 106 ha,119.2, 8.59, 5-6, and 0.64, respectively.
This research considers the mathematical relationship between concentration of Chla and seven environmental factors, i.e. Lake water temperature (T), Secci-depth (SD), pH, DO, CODMn, Total Nitrogen (TN), Total Phosphorus (TP). Stepwise linear regression of 1997 to 1999 monitoring data at each sampling point of Qiandaohu Lake yielded the multivariate regression models presented in this paper. The concentration of Chla as simulation for the year 2000 by the regression model was similar to the observed value. The suggested mathematical relationship could be used to predict changes in the lakewater environment at any point in time. The results showed that SD, TP and pH were the most significant factors affecting Chla concentration.
