Pitfall Traps and Grain Samples as Indicators of Insects in Farm-Stored Wheat

Reed, Carl; Wright, V. F.; Mize, Terry; Pedersen, John; Evans, B.

doi:10.1093/jee/84.4.1381

Cited by 32 publications

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“…Dowdy and McGaughey (1994) conducted a detailed survey in Kansas and found several beetle species to be abundant in farm-stored wheat and commercial facilities during the Þrst 2 mo of storage after the wheat was harvested and binned. Reed et al (1991) also found lesser grain borer in farm-stored wheat in Kansas. Insect pests have been detected during the summer in wheat stored in Oklahoma (Cuperus et al 1986).…”

mentioning

confidence: 77%

“…Several studies recommend waiting until September for initial cooling at 12Ð15ЊC (Storey et al 1979, Noyes et al 1987, Cuperus et al 1990, partly because of the belief that high harvest temperatures will inhibit insect population growth (Noyes et al 1987). However, several Þeld-sampling studies have shown that insect pests can be found in stored wheat during the summer (Cuperus et al 1986, Reed et al 1991, Dowdy and McGaughey 1994. Modeling research by Flinn et al (1997) has shown that insect populations in South Dakota, Kansas, and Oklahoma can be suppressed below economic levels with automatic aeration controllers starting at harvest.…”

Section: Discussionmentioning

confidence: 99%

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Aeration Management for Stored Hard Red Winter Wheat: Simulated Impact on Rusty Grain Beetle (Coleoptera: Cucujidae) Populations

Arthur

Flinn

2000

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Simulation studies were conducted to determine temperature accumulations below defined thresholds and to show the impact of controlled aeration on populations of the rusty grain beetle, Cryptolestes ferrigineus (Stephens), a major secondary pest of stored wheat, Triticum aestivum (L.). Recorded data from weather stations in Texas, Oklahoma, Kansas, eastern New Mexico, and eastern Colorado (356 total) were used to determine hours of temperature accumulation below 23.9 degrees C in June and July, 15.6 degrees C in September and October, and 7.2 degrees C in December. At an airflow rate of 0.0013 m3/s/m3 (0.1 cubic ft3/min/bu), which requires 120 h of temperatures below the specified threshold to complete an aeration cycle, summer cooling at 23.9 degrees C in bulk-stored wheat could be completed throughout the hard red winter wheat zone except for extreme southern Texas. An early-autumn cooling cycle at 15.6 degrees C could not be completed throughout most of Texas and Oklahoma before the end of September. The late-autumn cooling cycle could be completed in all states except Texas by the end of November. Five geographic regions were delineated and the times required for completion of the summer, early-autumn, and late-autumn cooling cycles within each region were estimated. Population growth of the rusty grain beetle was modeled for San Antonio, TX; Abilene, TX; Tulsa, OK; Topeka KS; and Goodland, KS, by predicting the numbers of adults in the top, outer middle, outer periphery, and the center of the bin during a 1-yr storage season. Populations of C. ferrugineus in San Antonio and Austin were predicted to exceed the Federal Grain Inspection Service (FGIS) threshold of two beetles per kilogram of wheat in all four levels of the bin during late autumn, decline during the winter, and increase the following spring. In Midland, TX, and Oklahoma City, OK, populations were predicted to exceed the threshold only in the top and outer middle of the bin, whereas populations in the Kansas locations were not predicted to exceed the threshold at any time.

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confidence: 77%

Section: Discussionmentioning

confidence: 99%

Aeration Management for Stored Hard Red Winter Wheat: Simulated Impact on Rusty Grain Beetle (Coleoptera: Cucujidae) Populations

Arthur

Flinn

2000

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“…The effort to correlate trap catches with population density is a very complex procedure and demands further experimentation (Lippert and Hagstrum 1987, Reed et al 1991, Vela-Coiffieret al 1997, Hagstrum et al 1998. Using bino mial methods (adults present or not) the popula tion density can be estimated with a simple in spection of the traps, without counting or identi fying the adults caught.…”

Section: Discussionmentioning

confidence: 99%

“…To obtain the desirable accuracy level, taking in account the whole quantity of the stored grain, is a target which can't be practically achieved by taking a large number of samples . Even then, sample size could not be statistically representa-tive to insure safe conclusions (White and Loschiavo 1986, Reed et al 1991, Wilkin and Van Natto 1997.…”

Section: Received For Publication August 11999mentioning

confidence: 99%

Prediction of Infestation by Beetles in Stored Wheat Using Two Sampling Methods

Athanassiou

Buchelos

2017

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Studies were conducted in order to assess the use of binomial sampling for prediction of infestation level in stored wheat. In each of three steel silos with 1500 metric tones of wheat each, located in central Greece, 14 probe traps were placed on 15 June 1997. The traps were checked for adult coleoptera every 15 days, from 30 June until 30 January 1998. On the same dates, 14 wheat samples were taken adjacent to the trap locations, using a grain trier. Most abundant species were found to be Cryptolestes ferrugineux and Tribolium castaneum in the traps, while Sitophilus oryzae in the samples. Regarding all species detected, traps were proved to be more effective as compared to the samples. Taylor's Power Law was used, in order to estimate y-intercept and slope values for each species. The comparison of these parameters indicated that a single (weighted) equation can describe equally well the relation between the mean and the variance, according to Taylor's Power Law, for all adults found, regardless of species. The parameters of this relation were utilized to connect the ratio of sampling units containing one or more adults and the mean number of adults per sampling unit (x), using Wilson and Room's model. Regarding trap catches, the same model can be used to predict an infestation, with a sufficient precision level, mainly when x<5; on the contrary, the results were not satisfactory in the case of adult numbers in the samples.

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“…Stored-grain pitfall traps are typically used for monitoring several species of stored-grain beetles (Coleoptera) in silos, warehouses and processing plants [31]. They are placed inside the bulk grain near the surface (Figure 3).…”

Section: Trap Type #2: the Stored-grain Pitfall Trapmentioning

confidence: 99%

Automated Remote Insect Surveillance at a Global Scale and the Internet of Things

2017

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Abstract:The concept of remote insect surveillance at large spatial scales for many serious insect pests of agricultural and medical importance has been introduced in a series of our papers. We augment typical, low-cost plastic traps for many insect pests with the necessary optoelectronic sensors to guard the entrance of the trap to detect, time-stamp, GPS tag, and-in relevant cases-identify the species of the incoming insect from their wingbeat. For every important crop pest, there are monitoring protocols to be followed to decide when to initiate a treatment procedure before a serious infestation occurs. Monitoring protocols are mainly based on specifically designed insect traps. Traditional insect monitoring suffers in that the scope of such monitoring: is curtailed by its cost, requires intensive labor, is time consuming, and an expert is often needed for sufficient accuracy which can sometimes raise safety issues for humans. These disadvantages reduce the extent to which manual insect monitoring is applied and therefore its accuracy, which finally results in significant crop loss due to damage caused by pests. With the term 'surveillance' we intend to push the monitoring idea to unprecedented levels of information extraction regarding the presence, time-stamping detection events, species identification, and population density of targeted insect pests. Insect counts, as well as environmental parameters that correlate with insects' population development, are wirelessly transmitted to the central monitoring agency in real time and are visualized and streamed to statistical methods to assist enforcement of security control to insect pests. In this work, we emphasize how the traps can be self-organized in networks that collectively report data at local, regional, country, continental, and global scales using the emerging technology of the Internet of Things (IoT). This research is necessarily interdisciplinary and falls at the intersection of entomology, optoelectronic engineering, data-science, and crop science and encompasses the design and implementation of low-cost, low-power technology to help reduce the extent of quantitative and qualitative crop losses by many of the most significant agricultural pests. We argue that smart traps communicating through IoT to report in real-time the level of the pest population from the field straight to a human controlled agency can, in the very near future, have a profound impact on the decision-making process in crop protection and will be disruptive of existing manual practices. In the present study, three cases are investigated: monitoring Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) using (a) Picusan and (b) Lindgren trap; and (c) monitoring various stored grain beetle pests using the stored-grain pitfall trap. Our approach is very accurate, reaching 98-99% accuracy on automatic counts compared with real detected numbers of insects in each type of trap.

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Pitfall Traps and Grain Samples as Indicators of Insects in Farm-Stored Wheat

Cited by 32 publications

References 0 publications

Aeration Management for Stored Hard Red Winter Wheat: Simulated Impact on Rusty Grain Beetle (Coleoptera: Cucujidae) Populations

Aeration Management for Stored Hard Red Winter Wheat: Simulated Impact on Rusty Grain Beetle (Coleoptera: Cucujidae) Populations

Prediction of Infestation by Beetles in Stored Wheat Using Two Sampling Methods

Automated Remote Insect Surveillance at a Global Scale and the Internet of Things

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