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[目的]进一步提高对西兰花根肿病预测预报与持续控制能力。[方法]应用最小二乘法、频次分布、聚集度指标、m~*-m回归分析和Taylor幂法则等对病株的空间分布型进行了分析。[结果]西兰花根肿病病株田间分布趋向于聚集分布。m~*-m回归分析表明病株空间分布的基本成分是个体群,病株个体间相互吸引;病害在大田中存在明显的发病中心,个体群在田间呈均匀分布格局,即分布的基本成分个体群之间趋于均匀分布。Taylor幂法则分析表明,西兰花根肿病病株个体的空间格局随着病株密度的提高越趋均匀分布。在此基础上,提出了Iwao最适理论抽样模型N=273.954 1/m-59.698 5,并建立序贯抽样模型T_0(N)=0.368 4N±1.926 8N(1/2),即:调查株数N时,若累计发病率超过上界可定为防治对象田,若累计发病率未达到下界时,可定为不防治田,若累计发病率在上下界之间,则应继续调查,直到最大样本数m_0=0.368 4时,也即发病率15%,所需抽样数684株。[结论]该研究结果对于病害防治具有十分重要的指导意义。
[Objective] To further improve the prediction and control of broccoli clubroot. [Method] The spatial distribution patterns of diseased plants were analyzed by least square method, frequency distribution, degree of aggregation index, m ~ * -m regression analysis and Taylor power law. [Result] The distribution of broccoli clubroot tends to aggregate in the field. m ~ * -m regression analysis showed that the basic components of the diseased plants were individual groups and the diseased plants were mutually attracted. There was a clear disease center in the field, and the individual groups were evenly distributed in the field, that is, the distribution of the basic components Individual groups tend to be evenly distributed. Taylor’s power law analysis showed that the spatial pattern of individuals with clubroot’s disease became more and more uniform along with the increase of disease density. On this basis, Iwao the most appropriate theoretical sampling model N = 273.954 1 / m-59.698 5, and establish sequential sampling model T_0 (N) = 0.368 4N ± 1.926 8N (1/2), that is: the number of survey N , If the cumulative incidence exceeds the upper bound can be set as control field, if the cumulative incidence did not reach the lower bound, can be set as non-control field, if the cumulative incidence in the upper and lower bounds, should continue to investigate until the largest sample The number of m_0 = 0.368 4, the incidence rate of 15%, the required number of samples 684 strains. [Conclusion] The results of this study have very important guiding significance for disease prevention and control.