Full TGIF Record # 313733
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Web URL(s):https://archive.lib.msu.edu/tic/ressum/2020/2020.pdf#page=151
    Last checked: 08/05/2021
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Author(s):Kreuser, William C.
Author Affiliation:University of Nebraska-Lincoln
Title:Growing degree day models to guide PGR application rates
Section:Integrated turfgrass management
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Ecophysiology: Light and temperature
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Source:Turfgrass and Environmental Research Program: 2020 Research Summaries. 2020, p. 143-147.
Publishing Information:[New York, New York]: The United States Golf Association Green Section
# of Pages:5
See Also:Other Reports from this USGA research project: 2018-09-659
Note:Pictures, color
USGA Summary Points:Developed PGR GDD models for three cultivars of ultradwarf bermudagrass putting greens in NC, MS and TN with our collaborators. Peak suppression for prohexadione-calcium ranged from 50-45% with ideal re-application intervals of 120-160 GDD (base 10C). Peak suppression ranged from 49-62% with re-application intervals ranging from 216-300 GDD (base 10C) for trinexapac-ethyl. Warm-season results were published in Crop Science. Results have been added to GreenKeeper App. Developed PGR GDD models for paclobutrazol applications on creeping bentgrass putting greens. Clipping yield suppression ranged from 29-62% of the non-treated control depending on application rate. The ideal re-application interval ranged from 269-302 (base 0C) and model R2 values ranged from 0.41 to 0.86. Results have been added to GreenKeeper App. Results were published in Crop Science during 2018. Flipped PGR models were tested to estimate the amount of PGR remaining in the plant when the PGRs were applied prior to their ideal re-application interval. A half-life approach model was used to schedule PGR application rate. The models tested resulted in an intensification of clipping yield suppression and increase phytotoxicity overtime. This indicates the models were too aggressive. Created a new PGR GDD model to account for the clipping yield suppression of multiple applications of PGR and DMI fungicides. This new model incorporates segmented regression with a linear clipping suppression response until a break point at 23.1% suppression and then switches to logarithmic decay regression as measured peak clipping yield suppression increased. The model was developed from a combination of various datasets and had an R2 value of 0.763. The new PGR model was used to evaluate putting green performance when PGR ingredients were mixed in 2020. Mixing trinexapac-ethyl with paclobutrazol increased green speed by 8.4 inches compare to the non-treated control. This mixture in combination with higher levels of nitrogen fertilizer sustained high putting green stand density and acceptable color. This study will be replicated in 2021.
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
2020. Growing degree day models to guide PGR application rates. USGA Turfgrass Environ. Res. Summ. p. 143-147.
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    Last checked: 08/05/2021
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MSU catalog number: b3609415
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