论文部分内容阅读
根据生长在英国1年的、多点观察数据,模拟了Avalon品种冬小麦生长点的早期发育,用有关的天气资料优化了出苗至二棱期、二棱期至小穗分化末期,2个时期持续时间的参数值。对各种作物,通过作物发育指标累积值之间的剩余方差,又对不同模式进行了比较。以ARCWHEAT作物模式中的发育子模式为出发点,用日平均温度代替白天温度正弦曲线变化。将优化参数值与初始值加以比较,显示出前者对光周期的敏感较大,而对温度和春化敏感很小。研究发现,这些资料对温度和光周期的反应都是非线性关系,还没有一个表现出使这些数据与改进过的线性反应相一致。表示发育速率AR-CWHEAT的子模式,它作为一个对温度、光周期和春化分别作出对应的产物与不同的选择模式作比较。然而,就这些资料而论,没有一个模式比ARCWHEAT公式拟合得好,但也看不出是坏的。当用独立数据检验时,预测二棱期的优化参数比较精确,而预测小穗分化末期优化参数的精确与初始值相仿。
According to the data of 1-year observation and multi-point observation in England, the early development of winter wheat growth point of Avalon was simulated, and the emergence was from 2 to 3 and from 2 to the end of 2 years Time parameter value. For various crops, the residual variances between the cumulative values of crop development indicators were compared with those of different patterns. Taking ARCWHEAT crop model as a starting point, the daily average temperature is used to replace the daytime temperature sinusoid change. Comparing the optimized parameter values with the initial values shows that the former is more sensitive to photoperiod and less sensitive to temperature and vernalisation. The study found that none of these data have a nonlinear relationship with temperature and photoperiod response, and none of them has shown to be consistent with the improved linear response. Represents the sub-pattern of development rate AR-CWHEAT, which is compared with the different selection patterns as a product corresponding to temperature, photoperiod and vernalis, respectively. However, as far as these data are concerned, none of the models fits well with the ARCWHEAT formula, but neither does it seem to be bad. When using the independent data test, the prediction parameters of the two-edge period are more accurate, while the prediction parameters of the optimization parameters of the spikelet late stage are exactly the same as the initial values.