In this study, semivariance was used to quantitatively measure the spatial heterogeneity for the egg population of cotton bollworm during a growing season. The typical characteristic parameters of theoretical semivari...In this study, semivariance was used to quantitatively measure the spatial heterogeneity for the egg population of cotton bollworm during a growing season. The typical characteristic parameters of theoretical semivariance models against lag distances were applied to measure components of spatial heterogeneity： trend, range, spatial dependence, and the strength of spatial dependence. Then, kriging interpolation was used to evaluate the population risk of cotton bollworm exceeding economic thresholds. From early June through early September, the population densities were sampled 10 times in the study field. Results showed that the spatial patterns were related to population density. For its low-density population, the spatially heterogeneous trends were usually of spherical shapes; but for highdensity ones, the trends shifted to Gaussian shapes. The spatial dependence appeared at varied distances ranging from 52 meters to 936 meters, and the spatial dependence was in the range of 0.39-288.60, which changed with population densities. While having high heterogeneity, the strength of spatial dependence became much stronger. Results of population risk analysis showed that there was a high risk during its early stages, especially in mid-June. In August, population risk was so low that it did not need to be controlled.展开更多
文摘In this study, semivariance was used to quantitatively measure the spatial heterogeneity for the egg population of cotton bollworm during a growing season. The typical characteristic parameters of theoretical semivariance models against lag distances were applied to measure components of spatial heterogeneity： trend, range, spatial dependence, and the strength of spatial dependence. Then, kriging interpolation was used to evaluate the population risk of cotton bollworm exceeding economic thresholds. From early June through early September, the population densities were sampled 10 times in the study field. Results showed that the spatial patterns were related to population density. For its low-density population, the spatially heterogeneous trends were usually of spherical shapes; but for highdensity ones, the trends shifted to Gaussian shapes. The spatial dependence appeared at varied distances ranging from 52 meters to 936 meters, and the spatial dependence was in the range of 0.39-288.60, which changed with population densities. While having high heterogeneity, the strength of spatial dependence became much stronger. Results of population risk analysis showed that there was a high risk during its early stages, especially in mid-June. In August, population risk was so low that it did not need to be controlled.
