During drought, authorities may impose water restrictions for irrigation with little regard for damage to turfgrass. Our objectives were to evaluate the performance of Kentucky bluegrass (Poa pratensis L.) (KBG) cultivars during a prolonged dry down and subsequent recovery in the transition zone near Manhattan, KS, USA. Irrigation was withheld from 30 bluegrasses for 82 days in 2010 and 62 days in 2011. Rainfall was excluded from the study area with an automated rainout shelter. Digital images were collected every one to two weeks during dry down and recovery to obtain percent green turfgrass coverage. All cultivars were predominantly brown (0 to 3% green cover) by the end of the 82‐day dry down in 2010, and less brown (7 to 27% green cover) by the end of the 62‐day dry down in 2011, probably because all plots inadvertently received 1.54 inches of precipitation during the 2011 dry down. All 30 bluegrasses recovered after lengthy dry downs in both years, but recovery was slower in 2010 because of greater drought stress during the longer dry down. Differences in performance were observed among cultivars during the dry downs and recoveries, but were inconsistent between years. Exceptions during the dry down were Apollo and Bedazzled, which lost green cover more slowly than other cultivars; Apollo also recovered more quickly after drought than other cultivars. Bluegrasses have excellent recuperative ability even after severe drought, and are good choices for areas where extended bans on turfgrass irrigation are possible.
The green leaf area index (LAI) is an important indicator of the photosynthetic capacity of turfgrass canopies. The measurement of LAI is typically destructive and requires large plots to allow for multiple sampling dates. Hyperspectral radiometry may provide a rapid, non-destructive means for estimating LAI. Our objectives were to: (1) evaluate the utility of hyperspectral radiometry to predict the LAI of Kentucky bluegrass (Poa Pratensis L.); and (2) determine regions of the spectrum that provide the best LAI predictions. An empirical prediction model of spectral data for LAI was conducted with partial least squares regression (PLSR). The PLSR method created viable, first-iteration models for five of 11 sampling dates (the coefficient of determination (R 2 ) is 0.52-0.85). Each model had its own set of factors that were analysed to determine their 'weights', or specific regions of the spectrum by which they were most strongly influenced. Second iterations of each model were then created using only those regions most strongly influenced, centred on 600, 690, 761, 960, 1330, and 1420 nm (±10 nm). Four of the five second-iteration models had LAI estimation capabilities greater than or similar to the first-iteration models (R 2 = 0.72-0.86), indicating that the information contained in all other wavelengths was redundant or irrelevant in regard to predictions of LAI. The robustness of prediction models varied over the growing season, possibly related to changes in canopy properties with environmental conditions. Results suggest hyperspectral radiometry has a significant potential to predict LAI in turfgrass, although different models may be required throughout the growing season.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.