Putting green clipping yield, canopy reflectance, and vegetative indices by time from colorant and spray oil combination product application
Schlossberg, Maxim (2022), Putting green clipping yield, canopy reflectance, and vegetative indices by time from colorant and spray oil combination product application, Dryad, Dataset, https://doi.org/10.5061/dryad.6hdr7sr4j
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.
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. Specifically, the daily clipping yield data, dCY (2nd worksheet/tab), that 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.
Mowing turfgrass at a 3.3-mm height of cut is fraught with sampling error risk. Sometimes the mower picks up fine sand particles that imbed in the clipping sample unnoticed. Alternatively, seams of the collection sacks develop gaps where clippings are irretrievably lost. These occurrences result in erroneously high or low mass measurements respectively. We do our best to prevent these issues, but of the 150 observations collected over 2 years, two dCY observations fell >3 standardized residuals from the predicted. More importantly, retention of these observations precluded abidance of the constant variance and normal distribution assumptions (of ANOVA).
Employ of garden variety dCY transformations were unsuccessful. Thus, we co-authors elected to omit the two outlying observations as missing data. We took the degrees of freedom penalty, which is clearly indicated in Table 2 of the ‘Related Works,’ where a 98-df error term is used to test the DAI and TRTxDAI effects (rather than 100). The reduced dataset (n=148) resulted in a better model fit, as indicated by its lesser -2 log likelihood fit statistic, relative to analysis of the full dataset (n=150). Model diagnostics on the reduced dataset met all required assumptions.
Again, the model diagnostics issue and resolution are squarely depicted in the two attached SAS outputs, and/or by reanalysis of dCY using the 2 Nov. (full) and 8 Nov. (reduced) dCY data sets freely available to you in ‘Data Files.’
Data files voluntarily uploaded to Dryad, at a cost of $120 US, may not be deleted. It is a strict Dryad policy. Thus, the authors were compelled to append the two regrettably-conflicting datasets with the above explanation, today, 30 Nov. 2022. We hope you have found this explanation helpful and encourage you to forward your questions or comments to Max Schlossberg at firstname.lastname@example.org
USDA-NIFA, Award: 1023224