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| Full TGIF Record # 317390 Item 1 of 1 |
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| Web URL(s): | https://archive.lib.msu.edu/tic/ressum/2021/2021.pdf#page=89 Last checked: 04/12/2022 Requires: PDF Reader Notes: Item is within a single large file |
| Publication Type: | Report |
| Author(s): | Soldat, Doug; Zhou, Qiyu |
| Author Affiliation: | Soldat: Ph.D. and University of Wisconsin-Madison; Zhou: University of Wisconsin-Madison |
| Title: | Building a better growth model to optimize nitrogen applications to bentgrass putting greens |
| Section: | Integrated turfgrass management Other records with the "Integrated turfgrass management" Section Ecophysiology: Light and temperature Other records with the "Ecophysiology: Light and temperature" Section |
| Source: | Mike Davis Program for Advancing Golf Course Management: 2021 Progress Reports. 2021, p. 82-87. |
| Publishing Information: | [New York, New York]: The United States Golf Association Green Section |
| # of Pages: | 6 |
| Keywords: | TIC Keywords: Agrostis; Application efficiency; Computer simulation models; Evapotranspiration; Golf green maintenance; Growth analysis; Growth rate; Machine learning; Nitrogen fertilization; Root zone; Software evaluation; Soil properties; Weather data; Yield response |
| Language: | English |
| References: | 0 |
| See Also: | Other Reports from this USGA research project: 2019-10-680 |
| Note: | Tables Graphs |
| USGA Summary Points: | Temperature, relative humidity and evapotranspiration were the key weather factors for estimating bentgrass growth. Foot traffic, nitrogen rate and soil moisture were weakly correlated with bentgrass growth. However, model accuracy substantially increased when these variables were included. A data-driven statistical model using the machine learning random forest algorithm can accurately predict bentgrass yield. However, the model was only effective for the location where the model was built, suggesting that individual golf courses need to build customized growth prediction models to manage nitrogen adaptively. This can be accomplished by collecting and recording clipping volume for at least one year. The machine learning random forest algorithm appears to be very helpful for guiding N application decisions, and when it was used over a two-year period on two different root zones, it resulted in acceptable turfgrass performance with about 50% less N fertilizer usage than the method that recommended the most N fertilizer (PACE Turf method) and about 30% less fertilizer than the traditional way that golf course superintendents schedule N applications. |
| ASA/CSSA/SSSA Citation (Crop Science-like – may be incomplete): | Soldat, D., and Q. Zhou. 2021. Building a better growth model to optimize nitrogen applications to bentgrass putting greens. USGA Turfgrass Environ. Res. Summ. p. 82-87. |
| Fastlink to access this record outside TGIF: | http://tic.lib.msu.edu/tgif/flink/RECNO/317390 |
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| Web URL(s) : | https://archive.lib.msu.edu/tic/ressum/2021/2021.pdf#page=89 Last checked: 04/12/2022 Requires: PDF Reader Notes: Item is within a single large file |
| MSU catalog number: | b3609415 |
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