Binomial sequential sampling model to facilitate monitoring of greenbug, Schizaphis graminum (Rondani) populations in broom corn farms

Subject Areas : Agroecology Journal

Habibollah Khodabandeh ¹ , Shahram Shahrokhi Khaneghah ²

1 - Plant Protection Department, Miyaneh Branch, Islamic Azad University, Miyaneh, Iran
2 - Iranian Research Institute of Plant Protection, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.

Received: 2021-02-11 Accepted : 2021-02-11 Published : 2020-01-21

Keywords:  population estimation,  monitoring,  corn leaf aphid,  integrated pest management, spatial distribution,

Abstract :

This study was conducted to develop a binomial (presence-non presence) sequential sampling model for saving time in determining population density of greenbug, Schizaphisgraminum (Rondani) in broom corn fields. For this purpose, 50 broom corn stems were sampled every three days for counting the aphid number. The mean and variance of population at each sampling date were used to estimate spatial dispersion parameters. Then, Taylor’s Power Law parameters were used to prepare binomial sequential sampling models at two precision levels of 0.10 and 0.25. The spatial distribution of S. graminum in the field was clumped and its mean population at different sampling dates ranged from 0.14 to 25.45 aphids per stem. Comparison of models showed that sample size required for estimating aphid population increased significantly by reducing the precision level from 0.25 to 010. Therefore, the binomial sequential sampling model at the precision level of 0.10 was very time consuming and was not suitable for estimating aphid population density. However, the model at 0.25 precision level reduced the required sample size compared to the fixed sample size method. Overall, using binomial sequential sampling model at 25% precision level, the proposed precision level for pest management programs can reduce sampling time in comparison to the fixed sample size method and is recommended for estimating the pest population in integrated pest management programs in broomcorn farms