MODIS Imagery Improves Pest Risk Assessment: A Case Study of Wheat Stem Sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA

Predicting the dispersal and invasion dynamics of ambrosia beetles through demographic reconstruction and process-explicit modeling

Evaluating potential routes of invasion of pathogens and vectors of sanitary importance is essential for planning and decision-making at multiple scales. An effective tool are process-explicit models that allow coupling environmental, demographic and dispersal information to evaluate population growth and range dynamics as a function of the abiotic conditions in a region. In this work we simulate multiple dispersal/invasion routes in Mexico that could be taken by ambrosia beetles and a specific symbiont, Harringtonia lauricola, responsible for a severe epiphytic of Lauraceae in North America. We used Xyleborus bispinatus Eichhoff 1868 as a study subject and estimated its demography in the laboratory in a temperature gradient (17, 20, 26, 29, 35 °C), which we then used to parameterize a process-based model to estimate its metapopulation dynamics. The maximum intrinsic growth rate of X. bispinatus is 0.13 with a thermal optimum of 26.2 °C. The models suggest important regions for the establishment and dispersal the states of Veracruz, Chiapas and Oaxaca (high host and secondary vectors diversity), the Isthmus of Tehuantepec (connectivity region), and Michoacán and Jalisco (important avocado plantations). The use of hybrid process-based models is a promising tool to refine the predictions applied to the study of biological invasions and species distributions.

Evaluation of Spatial Distribution of Three Major Leptocorisa (Hemiptera: Alydidae) Pests Using MaxEnt Model

We targeted three major Leptocorisa species (L. chinensis, L. acuta, and L. oratoria) and evaluated their potential distributions using MaxEnt. The results showed that most Asian countries and northern Australia would be suitable for at least one of these pest species, and climate change will expand their habitat northward. All of the developed models were evaluated to be excellent with AUC, TSS, and OR10%. Most of the recorded regions of the Leptocorisa species are consistent with the result of potential distributions predicted in this study. The results confirmed that the minimum temperature of the coldest month mainly influences the three Leptocorisa species distributions. The potential distributions of the three species cover major rice cultivation areas regardless of climate change, suggesting that it would be necessary to establish a sustainable control strategy for the pests.

Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges

Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India’s total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers’ reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS).

Effect of Precipitation and Temperature on Larval Survival of Cephus cinctus (Hymenoptera: Cephidae) in Barley Cultivars

Host plant traits strongly affect survivorship of insect herbivores, and host suitability is especially important for the wheat stem sawfly, Cephus cinctus Norton, which spends its entire egg, larval, and pupal periods in a single stem. Measuring larval survival inside stems from egg hatch through diapause-mediated dormancy is a potential measure of population size for the next year but is also useful in assessing effects of growing season precipitation and temperature. Larval growth is synchronized with host plant growth, and the larva cannot switch hosts. Thus, incorporating plant physiological time, as growing degree days (GDD), may yield a better prediction of larval survival. Therefore, we assessed wheat stem sawfly survival from early larval growth to the beginning of autumnal diapause in barley cultivars selected from across feed, forage, and two- or six-row malt groups. Field experiments were conducted in Gallatin and Chouteau counties, Montana, in 2016 and 2017. We used Kaplan-Meier estimation to assess larval survival among cultivars. We found that the survival of pre-diapause larvae was greatest in 'Hockett' (36.5%) and lowest in 'Celebration' (15.4%). Precipitation and temperature during the growing season affected temporal patterns for larval survival across study sites. Adjusting survivorship curves using site-specific GDD accumulation allowed cultivar-specific survivorship to be estimated more precisely for each site, despite differing environmental influences. Our findings suggest that measuring wheat stem sawfly survival across barley cultivars and standardizing by site-specific GDDs may provide better recommendations on barley cultivars that impede wheat stem sawfly population growth and reduce economic losses.

Drones: Innovative Technology for Use in Precision Pest Management

Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers.

Farming system and wheat cultivar affect infestation of, and parasitism on, Cephus cinctus in the Northern Great Plains

BACKGROUND

Cephus cinctus infestation causes $350 million in annual losses in the Northern Great Plains. We compared infestation and parasitism of C. cinctus in spring (including Kamut; Triticum turgidum ssp. turanicum) and winter wheat cultivars grown in organic and conventional fields in Montana, USA. In the greenhouse, we compared C. cinctus preference and survival in Kamut, Gunnison, and Reeder spring wheat cultivars.

RESULTS

Stems cut by C. cinctus varied by farming system and the seasonality of the wheat crop. No stems of Kamut in organic fields were cut by C. cinctus, but 1.5% [±0.35% standard error (SE)] of stems in conventional spring wheat, 5% (±0.70% SE) of stems in organic winter wheat, and 20% (±0.93% SE) of stems in conventional winter wheat fields were cut by C. cinctus. More larvae of C. cinctus were parasitized in organic (27 ± 0.03% SE) compared with conventional (5 ± 0.01% SE) winter wheat fields. Cephus cinctus oviposition, survival, and the number of stems cut were lowest in Kamut compared with Gunnison and Reeder.

CONCLUSION

Cephus cinctus infestation was more common in winter wheat than in spring wheat. Organic fields with fewer cut stems also supported more parasitoids. Kamut is a genetic resource for developing C. cinctus-resistant cultivars. © 2018 Society of Chemical Industry.