Temporal dynamics of major insect pests in early season rice and their key meteorological driving factors in Zengcheng, Guangdong

doi:10.16178/j.issn.0528-9017.20250677

Abstract

Abstract:

This study aimed to investigate the driving mechanisms of meteorological factors on the population dynamics of major early rice pests (Chilo suppressalis, rice planthoppers, and Cnaphalocrocis medinalis) in the Zengcheng area, to provide a theoretical basis for regional precision pest control. During the early rice growing season (March-July) of 2024-2025, smart pest forecasters and microclimate monitoring stations were deployed in Zhucun Sub-district, Zengcheng District, Guangzhou City, to systematically monitor pest population numbers and ten meteorological factors, enabling an in-depth analysis of their relationships. The results revealed significant interannual divergence in the population dynamics of all three pests. Chilo suppressalis exhibited a substantially higher outbreak intensity in 2025, with its first peak occurring 7 days earlier, a 2.79-fold increase in abundance, and the highest peak surging 11.06-fold with a duration prolonged by 5 days compared with 2024. Rice planthoppers displayed a “peak inversion” pattern: the early peak abundance in 2025 decreased by 70.63%, the outbreak was delayed until the wax-ripening stage, and the peak value increased by 17.35 times compared with 2024. Cnaphalocrocis medinalis showed an overall population decrease of 69.83% in 2025, and its first population peak shifted 12 days earlier to the late tillering stage. The outbreaks of all three pests were regulated by specific meteorological cascade effects. Specifically, the “low-baseline, high-outbreak” interannual pattern of Chilo suppressalis was primarily driven by the synergistic effects of extreme temperature events and precipitation variability; the “annual peak inversion” of rice planthoppers was controlled by the synergistic interaction between precipitation patterns and wind speed thresholds; and the “marked interannual differences” in Cnaphalocrocis medinalis populations were mainly driven by extreme temperature thresholds and synergistic interactions among high temperature, rainfall, and wind. Based on these findings, we recommend that precision pest control practices for early rice in Zengcheng prioritize monitoring the trends of meteorological factors highly correlated with specific pests and conduct comprehensive analyses of the dynamic evolution of key synergistic meteorological factors, thereby optimizing the timing strategies for precision pest control in the Zengcheng rice area.

Key words: Oryza sativa, Chilo suppressalis, rice planthopper, Cnaphalocrocis medinalis, population dynamics, meteorological factors, Zengcheng

CLC Number:

S435.112

CUI Can, QIAN Linghan, GUAN Yun, WU Yanzhou, ZHANG Hualu, ZHONG Yingmei, LAI Weizhao, LI Shenlei. Temporal dynamics of major insect pests in early season rice and their key meteorological driving factors in Zengcheng, Guangdong[J]. Journal of Zhejiang Agricultural Sciences, 2026, 67(1): 125-135.

Figures/Tables 6

Fig.1 Interannual population dynamics of Chilo suppressalis during the early rice seasons of 2024 and 2025

Fig.2 Interannual population dynamics of rice planthoppers during the early rice seasons of 2024 and 2025

Fig.3 Interannual population dynamics of Cnaphalocrocis medinalis during the early rice seasons of 2024 and 2025

Table 1 Correlation between populations of three major rice pests and meteorological factors in early-season rice

气象因子	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	Y₁	Y₂	Y₃
X₁	0.725^**	0.916^**	0.396^*	0.354^*	0.327^*	0.362^*	-0.167	-0.320^*	0.010	0.327^*	0.595^**	-0.034
										(0.040)	(<0.001)	(0.834)
X₂		0.629^**	0.267	0.353^*	0.259	0.348^*	-0.347^*	-0.252	-0.018	0.615^**	0.562^**	-0.109
										(<0.001)	(<0.001)	(0.501)
X₃			0.317^*	0.293	0.252	0.291	-0.184	-0.349^*	0.017	0.180	0.416^**	-0.025
										(0.267)	(0.008)	(0.881)
X₄				0.738^**	0.904^**	0.771^**	0.447^**	0.003	-0.043	0.447^**	0.223	0.297
										(0.004)	(0.168)	(0.063)
X₅					0.871^**	0.974^**	0.194	0.065	-0.183	0.353^*	0.306	0.513^**
										(0.025)	(0.055)	(0.001)
X₆						0.907^**	0.498^**	0.160	-0.069	0.439^**	0.199	0.403^*
										(0.005)	(0.218)	(0.010)
X₇							0.208	0.048	-0.128	0.382^*	0.285	0.491^**
										(0.015)	(0.075)	(0.001)
X₈								0.521^**	0.085	0.116	-0.186	0.270
										(0.475)	(0.250)	(0.092)
X₉									-0.041	0.017	-0.049	0.097
										(0.916)	(0.765)	(0.553)
$X 1 0$										-0.240	-0.320^*	-0.143
										(0.136)	(0.044)	(0.378)

Table 1 Correlation between populations of three major rice pests and meteorological factors in early-season rice

气象因子	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	Y₁	Y₂	Y₃
X₁	0.725^**	0.916^**	0.396^*	0.354^*	0.327^*	0.362^*	-0.167	-0.320^*	0.010	0.327^*	0.595^**	-0.034
										(0.040)	(<0.001)	(0.834)
X₂		0.629^**	0.267	0.353^*	0.259	0.348^*	-0.347^*	-0.252	-0.018	0.615^**	0.562^**	-0.109
										(<0.001)	(<0.001)	(0.501)
X₃			0.317^*	0.293	0.252	0.291	-0.184	-0.349^*	0.017	0.180	0.416^**	-0.025
										(0.267)	(0.008)	(0.881)
X₄				0.738^**	0.904^**	0.771^**	0.447^**	0.003	-0.043	0.447^**	0.223	0.297
										(0.004)	(0.168)	(0.063)
X₅					0.871^**	0.974^**	0.194	0.065	-0.183	0.353^*	0.306	0.513^**
										(0.025)	(0.055)	(0.001)
X₆						0.907^**	0.498^**	0.160	-0.069	0.439^**	0.199	0.403^*
										(0.005)	(0.218)	(0.010)
X₇							0.208	0.048	-0.128	0.382^*	0.285	0.491^**
										(0.015)	(0.075)	(0.001)
X₈								0.521^**	0.085	0.116	-0.186	0.270
										(0.475)	(0.250)	(0.092)
X₉									-0.041	0.017	-0.049	0.097
										(0.916)	(0.765)	(0.553)
$X 1 0$										-0.240	-0.320^*	-0.143
										(0.136)	(0.044)	(0.378)

Table 2 Stepwise regression models for abundance of three major pests in early-season rice

害虫种类	气象因子	线性回归方程	F值	p值	调整R²
二化螟	X₁、X₂、X₄	Y₁=-18 554.202-12.905X₁+1 241.607X₂+555.958X₄	14.117	<0.001	0.502
稻飞虱	X₁、X₃、X₁₀	Y₂=7 762.875+53.881X₁-57.996X₃-738.285X₁₀	15.300	<0.001	0.524
稻纵卷叶螟	X₅	Y₃=-1 409.818+104.573X₅	13.546	0.001	0.243

Table 3 Partial correlation analysis of Chilo suppressalis and rice planthoppers population densities with meteorological factors

害虫	气象因子	偏相关系数(r)	T检验值	p值
二化螟	X₁	-0.377	-2.443	0.020
	X₂	0.637	4.958	<0.001
	X₄	0.473	3.222	0.003
稻飞虱	X₁	0.624	4.791	<0.001
	X₃	-0.430	-2.858	0.007
	X₁₀	-0.434	-2.891	0.006

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