Modeling Emergence of Annual Bluegrass (Poa annua L.) in Hybrid Bermudagrass [C. dactylon (L.) Pers. x C. transvaalensis Burtt-Davy]

Annual bluegrass (Poa annua L.; ABG) is a troublesome weed of turfgrass systems. A model to predict ABG emergence patterns could aid in timing measures to control ABG. Field research was initiated in January 2019 at the East Tennessee AgResearch & Education Center (ETREC) (Knoxville, TN) to better understand environmental conditions associated with ABG emergence. Plots (1 m²) included both hybrid bermudagrass [C. dactylon (L.) Pers. x C. transvaalensis Burtt-Davy, cv. ‘Tifway’, at a 1.5 cm cutting height] and bare soil. Emerged ABG inside a 1000 cm² area in the center of each plot was counted weekly for 10 months; during June and July ABG was counted biweekly. Sensors in each plot captured air temperature data on 15-minute intervals. Air temperature data were expressed as cooling degree days accumulated after 21 June (i.e., the summer solstice) using a 21 C base temperature (CDD_21C). Python (v.3.8.7) was used (post-hoc) to fit non-linear functions to ABG emergence and CDD_21C data collected in 2019; models were then tested for validation in 2020. Fluctuations in CDD_21C accounted for ≥ 82% of the variance in yearly cumulative ABG emergence at ETREC over two seasons. Although ABG emergence was first noted at a similar CDD_21C benchmark each year (12 CDD_21C in 2019 and 8 CDD_21C in 2020), a yearly cumulative emergence model underpredicted 50 and 75% emergence in 2020. Peak ABG emergence occurred during a 4-week period in both 2019 and 2020; however, the timing of this 4-week period varied over years. Future research should be conducted using the 24-month dataset generated herein to develop new ABG emergence models using both CDD_21C and rainfall accumulation.