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United States Department of Agriculture

Agricultural Research Service

Research Project: BIOLOGICALLY BASED MANAGEMENT OF CEREAL APHIDS

Location: Wheat, Peanut and Other Field Crops Research

Title: Spectral vegetation indices selected for quantifying Russian wheat aphid (Diuraphis noxia) feeding damage in wheat

Authors
item Mirik, Mustafa -
item Ansley, R -
item Michels, JR., Gerald -
item Elliott, Norman

Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 1, 2011
Publication Date: March 25, 2012
Citation: Mirik, M., Ansley, R.J., Michels, Jr., G.J., Elliott, N.C. 2012. Spectral vegetation indices selected for quantifying Russian wheat aphid (Diuraphis noxia) feeding damage in wheat. Precision Agriculture. 13(4):501-516.

Interpretive Summary: In terms of both the increased cost of pest control and reduced final yield, the effects of insect infestation in agricultural crops are of major economic interest. One of the economically important insect pests of wheat is the Russian wheat aphid, which can reduce grain yield and vegetative biomass up to 83% and 76%, respectively. Russian wheat aphid feeding in wheat induces a distinctive visible symptom, which occur as clusters in fields and are referred to as "hot spots." These hot spots can be traced, indentified, and isolated from the non-infested areas for site specific Russian wheat aphid control by measuring the reflected light from both areas. Measurements of reflected light by an imaging or non-imaging sensor have been viewed to be superior to traditional visual damage severity estimates because a sampling unit can be repeatedly, objectively, and nondestructively examined in a fast, accurate, robust, and inexpensive way. This is particularly important for large areas where Russian wheat aphid control decisions have to be made. We showed that there were robust relationships between Russian wheat aphid feeding damage and spectral vegetation indices. Non-infested wheat had significantly higher and lower reflectance in the NIR and visible spectrums, respectively, than Russian wheat aphid-infested wheat. These results indicate that remotely sensed data have high potential to identify and separate "hot spots" from non-infested areas within a wheat field or among the wheat fields. The result is significant from the standpoint of Russian wheat aphid monitoring for purposes of area-wide management programs and for Russian wheat aphid site specific management within wheat fields.

Technical Abstract: In terms of both the increased cost of pest control and reduced final yield, the effects of insect infestation in agricultural crops are of major economic interest. One of the economically important insect pests of wheat (Triticum aestivum L.) is the Russian wheat aphid (RWA: Diuraphis noxia Mordvilko) because this aphid can reduce grain yield and vegetative biomass up to 82.9% and 76.5%, respectively. The RWA feeding (RWAF) in wheat induces a distinctive visible symptoms, which take place as clusters rather than uniform referred to "hot spots." These hot spots can be traced, indentified, and isolated from the non-infested areas for site specific RWA control by measuring the reflected light from both areas. Measurements of reflected light by an imaging or non-imaging sensor have been viewed to be superior to traditional visual damage severity estimates because a sampling unit can be repeatedly, objectively, and nondestructively examined in a fast, accurate, robust, and inexpensive way. This is particularly and practically important for large areas where RWA control decision has to be made. Hence, this research was designed to: 1) quantify the relationship between selected spectral vegetation indices and RWAF damage (RWAFD) to wheat growing under irrigated, dryland, and greenhouse conditions; 2) to examine spectral vegetation indices calculated for both wheat canopies with and without RWA infestation; and 3) investigate the potential use of percentage reflectance to discern and identify differences in spectral characteristics of infested and non-infested wheat. Simple linear regression analyses showed that there were robust relationships between RWAFD and spectral vegetation indices, with coefficients of determination (r2) ranging from 0.80 to 0.86 for greenhouse experiment, from 0.62 to 0.90 for irrigated wheat, and from 0.50 to 0.87 for dryland wheat. Values of selected indices were significantly reduced due to RWAF compared to non-infested wheat. Non-infested wheat had significantly higher and lower reflectance in the NIR and visible spectrums, respectively, than RWA-infested wheat. These results indicate that remotely sensed data have high potential to identify and separate "hot spots" from non-infested areas within a wheat field or among fields for site specific RWA control.

Last Modified: 4/19/2014
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