Water shortages have become more chronic as periodic droughts prolong and water demand for urban and agricultural use increases. Plant drought responses involve coordinated mechanisms in both above‐ and below‐ground systems, yet most studies lack comparisons of root and canopy responses under water scarcity and recovery. This is particularly true of research focused on warm‐season turfgrasses in sandy soils with extremely low water holding capacity. To address the lack of examination of coordinated stress and recovery responses, this study compared the above‐ and below‐ground plant responses during a dry‐down period of 21 days and recovery among four warm‐season turfgrass species in the field. Canopy drought responses and recovery were quantified using digital image analysis. In situ root images were captured using a minirhizotron camera system. Common bermudagrass [Cynodon dactylon (L.) Pers.] endured the entire drought period without losing 50% green cover while other species lost 50% green cover in 11–34 days predicted from the regression. The interspecific differences in drought resistance were mainly due to root characteristics. Other drought mechanisms appear to be responsible for differences identified in drought resistance between “Zeon” and “Taccoa Green” manilagrass [Zoysia matrella (L.) Merr.]. Recovery was delayed for up to 2 weeks in the second year, warranting further evaluation for turfgrass persistence under long‐term drought. Three‐week drought posed no threat to the survival of zoysiagrass. Species and genotypic variations were found in achieving full post‐recovery, which can be used to develop water conservation strategies and to adjust consumer expectations.
Water-displacement and WinRHIZO root-scanning methods were compared for efficacy of root damage assessment. Results from both methods were similar and a highly significant relationship was found between the two methods in trial one (r2 = 0.9968, P < 0.0001) and trial two (r2 = 0.9988, P < 0.0001). Both protocols provide consistent root volume measurements; however, water displacement is preferred as an economical method if a quick evaluation of a large amount of roots is essential. For a more detailed root morphological and architectural analysis, WinRHIZO root scanning provides additional information about several root parameters that cannot be measured by simple water displacement.
Development of a remote sensing system that can reliably identify nutrient deficiencies may reduce time spent sampling turfgrass areas and allow for site‐specific applications of fertilizers. The objectives of this research were to evaluate the use of a ground‐based remote sensing system and partial least‐squares (PLS) regression to predict the N concentration, biomass production, chlorophyll content, and visual quality of creeping bentgrass (Agrostis stolonifera L. ‘Penncross’) growing under varying N rates, and to compare PLS regression to other vegetative indices. The study consisted of three N treatments (0.0, 12.2, and 24.4 kg ha−1 15 d−1) arranged in a randomized complete block design. Spectral radiance measurements were obtained from plots using a fiber‐optic spectrometer to calculate vegetative indices. The PLS regression analysis yielded a strong relationship between actual and predicted N concentration of creeping bentgrass plant tissue during 2002 and 2003 (r2 = 0.95 and 0.71 respectively). However, PLS regression failed to produce a prediction for the chlorophyll concentration. Regressing the normalized vegetation index (NDVI), Stress1 (R706/R760), and Stress2 (R706/R813) ratios against N concentration yielded better results in 2003 when there were distinct differences in N concentration between the N rates. These results indicate that the traditional vegetation indices like NDVI might be better suited for determining the relative N status of turfgrass plants when compared against a well‐fertilized control. More research will be required to determine if the PLS regression analysis produces prediction models that are able to specifically identify a particular nutrient deficiency or plant stress, and how the results will vary between grass species.
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.