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以云南省建水县稻区1986～1997年连续24年间黑光灯下监测诱集的水稻三化螟种群数量及越冬代虫口基数为依据,结合当地1986～1997年最高温度、最低温度、平均温度及相对湿度等主要气象资料,采用多元回归法分析了三化螟物候和多度与气象条件间的相互关系。结果表明,三化螟成虫种群数量在年际间变化明显,从1986～1997年连续24年间,灯下三化螟成虫的始见期与1～2月的平均温度显著相关,当年11月到次年2月,灯下未诱集到三化螟成虫,从7月到9月,灯下虫量迅速增加,于9月灯下虫量达高峰。灯下三化螟成虫种群与最高温度、最低温度和平均温度间具有显著相关性,随着温度的升高,灯下虫量逐渐增加,但随着相对湿度的增加而降低,降雨量对灯下虫量无明显影响。灯下虫量与气象因素间的多元回归分析及逐步回归分析后获得逐步回归方程Y=-546.67+10.52X2-0.52X4+6.25X5,相关系数R=0.38(F=12.95,P<0.01)。灯下虫量与气象因素间的总体多元回归方程为Y=-723.17-3.81X1+26.00X2-10.82X3-0.48X4+7.67X5(F=12.39,P<0.01),其中X1为最高温度;X2为平均温度;X3为最低温度;X4为降雨量;X5为相对湿度。越冬代幼虫的虫口密度与次年3月和4月灯下成虫数量具有显著的相关性,且越冬代幼虫的虫口密度是影响灯下虫量的关键因子。
Based on the population of rice stem borer (rice borer) and the number of overwintering insect population on the basis of monitoring and trapping in black light during 1986 to 1997 in the rice area of Jianshui County of Yunnan Province from 1986 to 1997, and combining with the local maximum temperature, minimum temperature, average temperature And relative humidity and other major meteorological data, using multivariate regression analysis of the relationship between phenological and multi-level and meteorological conditions. The results showed that the adult population of the rice borer had a significant change from year to year. From 1986 to 1997 for 24 consecutive years, the initial period of the adult rice borer was significantly correlated with the average temperature from January to February, The following year in February, the light was not trapped into the yellow rice borer adults, from July to September, the lamp quickly increase the amount of light, the lamp in September the highest volume of light. There was a significant correlation between adult population of the rice borer and the highest temperature, the lowest temperature and the average temperature. With the increase of temperature, the quantity of insects in the lamp increased gradually, but decreased with the increase of relative humidity. Under the amount of insects no significant effect. The multivariate regression analysis and stepwise regression analysis between the amount of insect and meteorological factors in the lamp obtained the stepwise regression equation Y = -546.67 + 10.52X2-0.52X4 + 6.25X5, and the correlation coefficient R = 0.38 (F = 12.95, P <0.01). The overall multivariate regression equation between lampworm and meteorological factors was Y = -723.17-3.81X1 + 26.00X2-10.82X3-0.48X4 + 7.67X5 (F = 12.39, P <0.01), where X1 was the highest temperature and X2 Is the average temperature; X3 is the lowest temperature; X4 is the rainfall; X5 is the relative humidity. The population density of overwintering larvae was significantly correlated with the number of adults under lamp in March and April of the following year, and the population density of overwintering larvae was the key factor affecting the quantity of larvae.