Evaluation of Areawide Forecasts of Wind-borne Crop Pests: Sugarcane Aphid (Hemiptera: Aphididae) Infestations of Sorghum in the Great Plains of North America

Airborne pests pose a major challenge in agriculture. Integrated pest management programs have been considered a viable response to this challenge, and pest forecasting can aid in strategic management decisions. Annually recurrent areawide sugarcane aphid [Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae)] infestations of sorghum [Sorghum bicolor (L.) Moench (Poales: Poaceae)] in the Great Plains of North America is one of such challenges. As part of the response, a spatially-explicit individual-based model was developed that simulates sugarcane aphid infestations over the southern-to-central part of the region. In this work, we evaluated model forecasts using 2015-2018 field data. The ranges of forecasted days of first infestation significantly overlapped with those observed in the field. The average days of first infestation observed in the field were approximated by the model with differences of less than 28 days in Texas and southern Oklahoma (2015-2018), and in northern Oklahoma (2016-2017). In half of these cases the difference was less than 14 days. In general, the modeled average day of first infestation was earlier than the observed one. As conceptual modeling decisions may impact model forecasts and as various socio-environmental factors may impact spatio-temporal patterns of field data collection, agreement between the forecasts and the observed estimates may vary between locations and seasons. Predictive modeling has the potential to occupy a central position within areawide integrated pest management programs. More detailed consideration of local agricultural practices and local environmental conditions could improve forecasting accuracy, as could broader participation of producers in field monitoring efforts.

Automated summary

Airborne pests pose a significant challenge in agriculture, and integrated pest management programs and pest forecasting can help address this challenge and aid in decision-making. This study developed an individual-based model that simulated and predicted infestations of the sugarcane aphid in the Great Plains region of North America. The model forecasts showed some agreement with field observations, but this agreement varied between locations and seasons. Therefore, predictive modeling has the potential to play a central role in areawide integrated pest management programs, but it is important to consider local agricultural practices and environmental conditions, as well as involve producers in field monitoring efforts.