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Agroecological-oriented areawide pest management leverages the innate ability of agroecosystem to suppress pests, and thus to utilize ecosystem services, a key component of sustainable agriculture. A growing body of knowledge on interactions between pests and their natural enemies allows us to recognize the complexity of these interactions that often depend on environmental circumstances. Sorghum aphid, Melanaphis sorghi (Theobald) (Hemiptera: Aphididae), is a recent but established pest of sorghum in the Great Plains of North America. Both predators and parasitoids prey on sorghum aphid but their activity and impact change throughout the area and throughout the year. Both landscape and weather factors have been shown to affect the abundance and numerical responses of these insects, consistent with observations in other aphid species. In this study we used data on counts of sorghum aphids, lady beetles (Coleoptera: Coccinellidae), and parasitoid wasps Aphelinus nigritus Howard (Hymenoptera: Aphelinidae) and Lysiphlebus testaceipes (Cresson) (Hymenoptera: Braconidae) collected in Kansas, Oklahoma and Texas states of the United States. We analyzed insect dynamics in the context of landscape and weather factors. We built multiple regression models using data from the years 2017–2019 for metrics such as maximum number of insects per leaf, response time of natural enemies to pest presence, and speed of increase in insect abundance. Our results indicate that various aspects of landscape composition, landscape configuration, and weather affect various insect groups and various aspects of insect dynamics in the field. Moreover, characteristics of specific landscape categories seemed to be more informative than overall measure of landscape diversity. Our study provides insights on interactions along both spatial and temporal scales, with the latter considered understudied. [ABSTRACT FROM AUTHOR] more...

The idea that noncrop habitat enhances pest control and represents a win-win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win-win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies. more...

Introduction: This study considers concepts and tools of landscape ecology and geographic information systems (GIS) to prioritize insect monitoring in large-scale crops, using the cotton agroecosystem of the Texas Gulf Coast and two plant bug species (Creontiades signatus Distant and Pseudatomoscelis seriatus (Reuter) [Hemiptera: Miridae]) as a case study. The two species differed in host plants and time span as cotton pests. Methods: C. signatus and P. seriatus abundance in early growth of cotton were regressed on landscape metrics. Comparisons of three approaches to select landscape variables in stepwise multiple regressions were made across spatial scales and two weeks of insect data extracted from monitoring of 21 cotton fields, years 2010 through 2013. Results and discussion: The spatial variation of plant bug abundance and the landscape features were substantial, aiding the regression approach. For full stepwise regression models using 18 landscape variables, regression model fit using C. signatus data was modestly better in week one of sampling when C. signatus adults and young nymphs were detected (R 2 range of 0.56 to 0.82), as compared with model fit at week two (R 2 range of 0.49 to 0.77). The smallest scale (2.5 km radius) models had the greatest number of variables selected and highest R 2, while two broader scales (5 and 10 km) and truncating the models to three variables produced a narrower range of R 2s (0.49 to 0.62) and more consistent entry of variables. Wetland composition had a consistent positive association with C. signatus abundance, supporting its association with seepweeds which are common in coastal wetlands. When selected, the composition of cotton and grassland/shrubland/pasture also had a positive association with C. signatus abundance. Aggregation metrics were also relevant, but composition metrics in the models were arguably more easily utilized in prioritizing insect monitoring. In contrast, there were few significant regressions using P. seriatus data, possibly due to the widespread distribution of its weedy host plants and lower abundance. Overall, selected landscape features served as indicators of C. signatus infestation potential in cotton particularly grown near coastal wetlands, but landscape features were not useful for P. seriatus infestation potential in cotton. [ABSTRACT FROM AUTHOR] more...

Simple Summary: The sorghum aphid is an invasive pest of grain sorghum grown in North America that was first observed in 2013 along the Gulf Coastal Plains ecoregion of Texas, Louisiana (USA), and Mexico. In the decade since its invasion, results point to increasing suppression of the sorghum aphid as most likely attributable to a native complex of predators and parasitoids that have shifted spatially and temporally. Indicators of increased suppression observed across six years and five locations from south to north Texas were as follows: (1) aphid abundances trending downwards across the years, (2) overall natural enemy abundances trending upwards during the same time period, and (3) a key parasitoid and coccinellid species increasing in dominance. In light of these findings, the importance of monitoring and stewarding natural enemies of invasive insect pests is discussed as part of a comprehensive strategy to measure and reduce the impact of a pest invasion in large-scale agroecosystems. Melanaphis sorghi (Theobald) (Hemiptera: Aphididae), commonly called the sorghum aphid, is an invasive pest of sorghum (Sorghum bicolor) (L.) in North America. It was first observed in 2013 along the Gulf Coastal Plains ecoregion of Texas, Louisiana (USA), and Mexico, where it quickly established itself as an economically important pest within a few years. This ecoregion contains an established complex of aphid natural enemies, including both predators and parasitoids. In the decade since its invasion, indicators of increased suppression observed across six years and five locations from south to north Texas were as follows: (1) aphid abundances trending downwards across the years, (2) overall natural enemy abundances trending upwards during the same time period, and (3) a key parasitoid and coccinellid species increasing in dominance. Two key taxa, Aphelinus nigritus (Howard) (Hymenoptera: Aphelinidae) and six species of coccinellids (Coleoptera: Coccinellidae), were likely responsible for the majority of the pest's suppression. In light of these findings, the importance of monitoring and stewarding natural enemies of invasive insect pests is discussed as part of a comprehensive strategy to measure and reduce the impact of a pest invasion in large-scale agroecosystems. [ABSTRACT FROM AUTHOR] more...

This study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolor L.) crop. IDC is a nutritional disorder that stunts a plants' growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs. Core Ideas: A small unmanned aircraft system was utilized to collect multispectral imagery.Soil background noise was assessed and removed via image thresholding and maximum likelihood classification.A total of 25 vegetation indices were evaluated with and without soil.A two‐class distance measure assessed which vegetation index best detected iron deficiency chlorosis.Normalized green, normalized difference red‐edge, and MERIS terrestrial chlorophyll indices performed best. [ABSTRACT FROM AUTHOR] more...