OUP accepted manuscript

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.