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Putting green clipping yield, canopy reflectance, and vegetative indices by time from colorant and spray oil combination product application

Cite this dataset

Schlossberg, Maxim (2022). Putting green clipping yield, canopy reflectance, and vegetative indices by time from colorant and spray oil combination product application [Dataset]. Dryad. https://doi.org/10.5061/dryad.6hdr7sr4j

Abstract

Multispectral radiometry resolutely quantifies canopy attributes of similarly managed monocultures over wide and varied temporal arrays. Likewise, liquid phthalocyanine-containing products are commonly applied to turfgrass as a spray pattern indicator, dormancy colorant, and/or product synergist. While perturbed multispectral radiometric characterization of putting greens within 24 h of treatment by synthetic phthalocyanine colorant has been reported, explicit guidance on subsequent use is absent from the literature. Our objective was to assess creeping bentgrass (Agrostis stolonifera L. ‘Penn G2’) putting green reflectance and growth one to 14 d following semi-monthly treatment by synthetic Cu II phthalocyanine colorant (Col) and petroleum-derived spray oil (PDSO) combination product at a 27 L ha–1 rate and/or 7.32 hg ha–1 soluble N treatment by one of two commercial liquid fertilizers. As observed in a bentgrass fairway companion study, mean daily shoot growth and canopy dark green color index (DGCI) increased with Col+PDSO complimented N treatment. Yet contrary to the fairway study results, deflated mean normalized differential red edge (NDRE) or vegetative index (NDVI) resulted from an associated Col+PDSO artifact that severely impeded near infrared (810-nm) putting green canopy reflectance. Regardless of time from Col+PDSO combination product treatment, the authors strongly discourage turfgrass scientists from employing vegetative indices that rely on 760- or 810-nm canopy reflectance when evaluating such putting green systems.

Methods

The requested information is described ad nauseum in the Materials & Methods section of the ‘Related Works.’

On 2. Nov., the author mistakenly uploaded a raw data file. Within, the first worksheet/tab titled MSR contained all 475 lines of MSR and vegetative index data. However, consideration for abidance of ANOVA assumptions precluded a small number of dependent variable observations, as employ of garden variety transformations were unsuccessful. Specifically, for percent reflectance of 510-, 560-, 610-, 660-, 760-, and 810-nm spectra; 2, 2, 2, 3, 3, and 4 observations were omitted as missing data, respectively. Likewise, since the dark green color index (DGCI) is calculated by 460, 560, and 660-nm reflectance, five (5) DGCI observations were conceded as missing data. Results described in the ‘Related Works’ report 510-, 560-, 610-, 660-, 760-, and 810-nm reflectance means and inference from 473-, 473-, 473-, 472-, 472-, and 471-observation datasets, respectively. No data were replaced and degree of freedom penalties were incurred in analysis reported in ‘Related Works.’

Likewise, the daily clipping yield data, dCY (2nd worksheet/tab) in the original 2 Nov. file upload, contained 150 observations. The statistical model and analysis of dCY data described in the ‘Related Works’ results report means and inference from a 148-observation dataset. The SAS output for each the reduced (n=148) and full (n=150) datasets are now included in data files.

Model diagnostics on the reduced datasets, uploaded 11 Dec., 2022 meet all required assumptions. For the dCY data, the model diagnostics issue and resolution are squarely depicted in the two attached SAS outputs. The same is true for the MSR data, but SAS outputs are not attached. Motivated parties are invited to reanalyze the above-noted dependent variables using the 2 Nov. (full) and 11 Dec. (reduced) data freely available to you in ‘Data Files.’

It is strict Dryad policy that voluntarily uploaded data files not be deleted. Thus, the authors were compelled to append the two regrettably-conflicting datasets with the above explanation, today, 11 Dec. 2022. We hope you have found this explanation helpful and encourage you to forward your questions or comments to Max Schlossberg at mjs38@psu.edu.

Funding

USDA-NIFA, Award: 1023224