Growing Degree Day Models Predict Class B PGR Performance on Bentgrass Greens.

A growing degree day (GDD) model can effectively estimate performance of the plant growth regulator (PGR) trinexapac-ethyl (TE). This model increase application precision to sustain season long growth suppression on cool-season golf putting greens. Paclobutrazol (PC) is another commonly used PGR. Unlike TE, PC is root absorbed which may confound GDD model development. The objective of this research was to develop a GDD model to predict the performance of PC when applied to creeping bentgrass putting green turf. This experiment occurred at the OJ Noer Research Turf Facility in Madison, WI during 2009 and JSA Turf Center in Mead, NE during 2014. The sand-based putting greens were seeded with ‘L-93’ creeping bentgrass (Agrostis stolonifera) and mowed at 3 mm with typical putting green management. The experiment was a RCBD with three replicates. Plots measured 0.9 x 1.8 m in WI and 1.5 x 1.5 m in NE. Treatments included a non-treated control, PC applied at full rate every 400 GDD or 1000 GDD, and half or full rates of PC + TE applied every 1000 GDD. Full rates for PC and TE were 0.20 and 0.05 kg a.i. ha^-1, respectively. GDDs were calculated in Celsius with base 0°C. Clippings were collected three d wk^-1 with sand debris removed. Sinewave regression was used to fit relative clipping yield to GDD post PGR re-application. Regression models accurately predicted performance of PC and PC+TE when the PGRs were re-applied every 1000 GDD. Model r² values ranged from 0.56 to 0.91. Max suppression rebound occurred at 245 and 667 GDD (55% and 145% of control), respectively at the full PC rate. The ideal PC interval is approximately 350 GDD at full rate. The 400 GDD interval was too infrequent to sustain growth suppression. Combination of TE and PC was additive with a quicker time to maximum growth suppression. The half and full rate models suggest application rate is important for PC. Research is ongoing for other PGRs over a range of application rates. GDD models continue to be an effective way to estimate PGR performance and help aid re-application scheduling.