Investigating the Geostatistical Characteristics of the Aphid Population of Acyrthosiphon pisum Haris in Alfalfa Fields in Dezful and Borujerd Regions

Document Type : Research Article

Authors

• Iman Sabouri ¹
• Arash Rasekh ¹
• Amir Mohseni Amin ²

¹ Department of Plant Protection, College of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

² Iranian Research Institute of Plant Protection, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

10.22067/jpp.2024.87278.1180

Abstract

Introduction
Acyrthosiphon pisum Harris aphid is one of the most important aphid species in alfalfa fields. In the case of a large population, it directly and indirectly damages the host plants. Reports have shown their presence in several regions worldwide, including Iran, particularly on fodder plants and most plants belonging to the legume family. Geostatistical analysis provides an alternative approach for the characterization of spatially variable ecological data, particularly for insect population. Geostatistics includes two parts, variography and kriging. The foundation of geostatistics is semi-variogram, which is a variance diagram based on the distance between samples, which shows the structure of spatial relationship among the samples. Kriging is one of the geostatistical methods that are used to estimate the regional variable value in different areas of the farm. Regarding geostatistical estimation, the value of a quantity in areas with known coordinates is estimated by using the same quantity in other points with known coordinates. This method allows for precise estimation of the value of a local variable in areas where sampling has not been conducted. These maps can accurately predict population changes and the likelihood of reaching the economic threshold in various parts of the farm. As a result, they can be highly effective in pest management programs.
 
Materials and Methods
This research was carried out in the crop year of 2021-2022 in two three-year alfalfa fields located in two cities of Dezful (Khuzestan) of Baghdadi variety and Borujard (Lorestan) of Hamadani variety, with an area of ​​five and seven hectares, respectively. To record the population density of desired pest in different parts of the selected farms, the number and location of each sampling station were determined based on their length and width coordinates (in meters) were considered constant during the sampling period. To determine the location of sampling stations, first, four main directions were identified in the fields and from the southwest area of ​​the fields, and to eliminate the marginal effects of the fields, a point with coordinates (0,0) was selected two or three meters from the edge of fields. Each of desired fields was divided into regular grids of 25 x 25 meters. The farms in the Dezful and Borujerd areas were examined, and a total of 90 and 130 sampling sites were identified and labeled with plates. The parameters of each station were recorded on these plates. The sampling started in March 2022 and lasted until mid-November 2022. To sample the population of aphids, a 1 x 1m quadrat was used in such a way that two quadrats were randomly thrown in each grid and six stems were selected from each box, and a total of 12 stems were selected and shaken inside a pan, and counted the aphids. A total of 22 sampling stages were performed. Excel 2010, SPSS ver. 26 and GS+ version 5.1 software were used for data analysis. The data related to each station were checked for normality by helping Kolmogorov-Smirnov test, and if necessary, they were placed in the determined coordinates by helping one of the appropriate data conversion methods. Then, variogram curves, as well as kriging maps for each sampling date were presented in the data that had the highest R² and degree of spatial dependence.
 
Results and Discussion
The results of surveying the geostatistical characteristics of A.pisum aphid in an alfalfa field in Borujerd city showed that out of 22 sampling cases, 12 cases were fitted with spherical model, eight cases with exponential model, and two cases with linear model (without spatial structure), which in all cases which were fitted with two spherical and exponential models, spatial dependence was observed. In these two models, the ratio of the nugget to the sill was very low, which resulted in an increase in the percentage of spatial dependence. The degree of spatial dependence varied between 0.500 and 0.999. The effective range in the spherical model was between 121.9 and 221.2 meters. In each of the variograms, except for three datasets, in the remaining datasets, the R² was high and between 0.55 and 0.95. Its distribution pattern was aggregative. Regarding this aphid species, in alfalfa fields of Dezful city, out of 21 sampling cases, 12 cases were fitted with spherical model, eight cases with exponential model and one case with the linear model. The spatial dependency in the two models ranged from 0.500 to 0.986, indicating a correlation between the distributions of aphids. Furthermore, the population distribution of this aphid species was shown to be aggregated. The effective range for them in the spherical model was between 137.9 and 493.4 meters. The percentage of the degree of spatial dependence for both farms was estimated to be more than 0.5, which means that more than 50% of variability among the data can be described by the spatial dependence, as a result of which the high degree of spatial dependence increases the accuracy of statistical estimation. By kriging maps, the density points of aphids population were checked on different dates, which can be used for advance warning at the beginning of the damaging stages, and by spraying about one-fifth of the field, a high percentage of the pest population can be reduced and avoid unnecessary spraying.
 
Conclusions
Finally, better outcomes in terms of the environment and the control of the targeted pest may be achieved with less pesticide use by applying the science of geostatistics and creating kriging maps. To accomplish this significant objective, using targeted pest site control is a good option. Moreover, preparing spatial distribution patterns of insects and the preparation of distribution maps (kriging) make it easier to achieve this goal.

Keywords

• Kriging
• Pest management
• Population dynamics
• Semivariance
• Variography

Main Subjects

• pests

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©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

 

References

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