玉米田桃蛀螟幼虫的空间分布型与抽样技术

doi:10.3864/j.issn.0578-1752.2022.10.007

Abstract

Abstract:

【Objective】The yellow peach moth, Conogethes punctiferalis, as an agricultural insect pest, its damage to maize ears has become more and more serious in Huang-Huai-Hai maize-producing areas of China in recent years, threatening the safe production of maize and the food safety. The spatial distribution pattern is an important ecological attribute of insect population, the objective of this study is to research the spatial distribution pattern of C. punctiferalis larvae in maize fields, clarify the spatial distribution characteristics of the pest, and to provide scientific bases for formulating field sampling program of C. punctiferalis larvae in maize fields, forecasting and effective management of the insect pest on maize.【Method】The spatial distribution pattern of the population of C. punctiferalis larvae in maize fields was studied by traditional statistical method (aggregation indexes, Taylor’s power law and Iwao’s regression model) and geostatistical method. Based on the Iwao’s regression model, the theoretical sampling number of C. punctiferalis larvae in fields was determined, and the maximum theoretical sampling number with different admissible errors (D=0.1, 0.2, 0.3) and the putative economic thresholds (m₀=0.5, 1, 1.5, 2 larvae per plant) was also determined by the sequential sampling.【Result】The results of the two kinds of statistical methods showed that the spatial distribution pattern of C. punctiferalis larvae belonged to aggregation distribution. The analysis of some aggregation indexes showed that spatial distribution pattern of C. punctiferalis larvae belonged to aggregation distribution. The results of Taylor’s power law showed that the spatial distribution pattern of C. punctiferalis larvae belonged to aggregation distribution, and the aggregation intensity increased with the population density. The Iwao’s regression model proved that the spatial distribution pattern of C. punctiferalis larvae belonged to negative binomial distribution in aggregation distributions. The parameters of semivariogram models indicated that the optimal fitting models of C. punctiferalis larvae were the spherical, exponential and linear models. The three-dimensional and two-dimensional maps from Kriging interpolations showed that the aggregation centers of C. punctiferalis larvae were located at the edges of the fields. Based on sampling technique from the Iwao’s regression model, the theoretical sampling number of C. punctiferalis larvae in maize fields was determined when the confidence probability t=2 and different mean densities m=0.5, 1, 2, 3, 4, 5, 10 and 15. The maximum theoretical sampling number was also determined by the sequential sampling. Assuming t=2, D=0.1, 0.2, 0.3, when m₀=0.5 larva per plant, the maximum theoretical sampling numbers were 3 417, 854 and 380, respectively; when m₀=1 larva per plant, the maximum theoretical sampling numbers were 1 717, 429 and 191, respectively; when m₀=1.5 larvae per plant, the maximum theoretical sampling numbers were 1 150, 287 and 128, respectively; when m₀=2 larvae per plant, the maximum theoretical sampling numbers were 867, 217 and 96, respectively.【Conclusion】The spatial distribution pattern of C. punctiferalis larvae belongs to the negative binomial distribution in aggregation distributions, and the aggregation centers were located at the edges of the fields. The maximum theoretical sampling number based on the sequential sampling in maize fields can be used for monitoring and management of C. punctiferalis larvae.

Key words: Conogethes punctiferalis, aggregation indices, geostatistics, spatial distribution pattern, sampling technique

LI ShaoHua,WANG YunPeng,WANG RongCheng,YIN Ping,LI XiangDong,ZHENG FangQiang. Spatial Distribution Pattern and Sampling Technique of Conogethes punctiferalis Larvae in Maize Fields[J].Scientia Agricultura Sinica, 2022, 55(10): 1961-1970.

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References 30

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田块 Field	样方数 Number of quadrat	均值m Mean	方差S² Variance	平均拥挤度m* Mean crowding	丛生指数I Clumping index	聚块指数m/m Patch index*	久野指数C_A Cassie index	扩散系数C Dispersion coefficient	分布型 Distribution pattern
1	52	54.40	377.62	60.34	5.94	1.11	0.11	6.94	聚集型 Aggregation
2	92	81.90	778.18	90.40	8.50	1.10	0.10	9.50	聚集型 Aggregation
3	60	30.10	104.40	32.57	2.47	1.08	0.08	3.47	聚集型 Aggregation
4	60	25.50	231.68	33.59	8.09	1.32	0.32	9.09	聚集型 Aggregation
5	60	72.13	293.10	75.20	3.06	1.04	0.04	4.06	聚集型 Aggregation
6	60	33.88	242.75	40.05	6.16	1.18	0.18	7.16	聚集型 Aggregation
7	60	16.47	127.41	23.20	6.74	1.41	0.41	7.74	聚集型 Aggregation
8	72	11.99	47.84	14.98	2.99	1.25	0.25	3.99	聚集型 Aggregation
9	105	21.68	54.07	23.17	1.49	1.07	0.07	2.49	聚集型 Aggregation
10	36	10.39	28.82	12.16	1.77	1.17	0.17	2.77	聚集型 Aggregation

田块 Field	模型 Model	块金值C₀ Nugget	基台值C₀+C Sill	变程a Range (m)	空间结构比率C₀/(C₀+C) Proportion of spatial structure (%)	决定系数R² Coefficient of determination	空间格局 Spatial pattern
1	指数型 Exponential	71.00	434.30	11.13	16.35	0.66	聚集型 Aggregation
2	球型 Spherical	129.00	774.70	2.23	16.65	0	聚集型 Aggregation
3	指数型 Exponential	24.70	103.20	1.44	23.93	0.01	聚集型 Aggregation
4	指数型 Exponential	0.10	241.40	4.75	0.04	0.82	聚集型 Aggregation
5	球型 Spherical	43.30	251.40	4.32	17.22	0.63	聚集型 Aggregation
6	球型 Spherical	0.10	267.00	6.21	0.04	0.76	聚集型 Aggregation
7	线型 Linear	0.30	0.43	17.56	69.77	0.29	聚集型 Aggregation
8	球型 Spherical	11.60	48.78	1.50	23.78	0	聚集型 Aggregation
9	指数型 Exponential	19.90	61.38	3.06	32.42	0.26	聚集型 Aggregation
10	球型 Spherical	4.09	29.09	2.05	14.06	0	聚集型 Aggregation

Spatial Distribution Pattern and Sampling Technique of Conogethes punctiferalis Larvae in Maize Fields

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允许误差D Admissible error	平均密度Mean density
允许误差D Admissible error	0.5	1	2	3	4	5	10	15
D=0.1	3417	1717	867	583	441	356	186	130
D=0.2	854	429	216	146	110	89	47	32
D=0.3	380	191	96	65	49	40	21	14

经济阈值（头/株） Economic threshold (larvae/plant)		调查株数 Number of plants
经济阈值（头/株） Economic threshold (larvae/plant)		40	60	80	100	120	140	160	180	200
m₀=0.5	上限 Upper limit	38	53	66	79	92	105	117	129	141
m₀=0.5	下限 Lower limit	2	7	14	21	28	35	43	51	59
m₀=1	上限 Upper limit	66	92	117	141	165	189	212	236	259
m₀=1	下限 Lower limit	14	28	43	59	75	91	108	124	141
m₀=1.5	上限 Upper limit	92	129	165	201	236	270	304	338	372
m₀=1.5	下限 Lower limit	28	51	75	99	124	150	176	202	228
m₀=2	上限 Upper limit	117	166	213	259	304	350	394	439	483
m₀=2	下限 Lower limit	43	74	107	141	176	210	246	281	317

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