Matthew M. Conley

Vegetative establishment of zoysiagrass ( Zoysia spp.) is often slow, which can limit its adoption on golf courses. Understanding how establishment practices interact with cultivar selection and seasonal conditions is critical to improving success. The objective of this study was to quantify the effects of planting time, sprigging rate, and sand topdressing on the establishment speed, turf quality, and shear strength of two zoysiagrass ‘Stadium’ ( Zoysia matrella ) and ‘LOBO’ ( Zoysia japonica × Zoysia matrella ). A field experiment was conducted in Palm Desert, CA, including four planting dates (March, April, May, and July), three sprigging rates (30, 45, and 60 m ³ ha ^{− 1} ), and two planting methods (with or without 1 cm sand topdressing) on a previously herbicide‐killed bermudagrass turf. Establishment was monitored using percent turf cover, visual quality, and shear strength. Establishment was analyzed using mixed‐effects models incorporating days after planting and cumulative growing degree days. Planting date affected establishment: March resulted in the slowest establishment, while April and May were much quicker. Stadium established faster and produced higher visual quality than LOBO. Increasing sprigging rate improved visual quality and establishment, while topdressing reduced cumulative cover in early plantings but resulted in quicker cover during warmer planting periods. Across treatments, topdressing reduced surface shear strength by approximately 37%. These findings demonstrate that successful zoysiagrass establishment depends on aligning cultivar choice and management practices with seasonal conditions, with mid‐spring planting and higher sprigging rates providing the most reliable balance of rapid establishment, turf quality, and surface stability.

Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (η_p² = 0.63). CIELUV v* explained the greatest model variation (R²m = 0.94) and responded most to fertilizer (η_p² = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems.

Water scarcity poses a substantial challenge for turfgrass irrigation in the drought- and heat-stressed Desert Southwest region of the United States. Bermudagrass (Cynodon spp.), renowned for its exceptional drought resistance, is the predominant warm-season turfgrass in the region. Selecting and using drought-resistant bermudagrass cultivars remains a primary strategy for sustaining the turfgrass industry in the region. This study evaluated 48 hybrid bermudagrasses (Cynodon dactylon × C. transvaalensis Burtt-Davy), including two commercial cultivars (‘TifTuf’ and ‘Tifway’, as controls), under 80% × ETo (0.8ET), 60% × ETo (0.6ET) and 40% × ETo (0.4ET) reference evapotranspiration (ETo) replacement irrigation systems at Maricopa, AZ. The experiment was laid out in a split-plot design with two replications, where the 3 irrigation treatments were assigned to main plots and 48 genotypes were in sub-plots. Analysis of data from two years (2022 and 2023) revealed significant differences among bermudagrass hybrids, irrigation treatments, and their interaction effects. The hybrids exhibited substantial variation for spring green-up, density, turf color, and quality. With the largest deficit irrigation treatment 40% × ETo (0.4ET), OSU2104, OSU2106, and OSU2105 showed greater mean greenness and aesthetic quality scores than recorded for ‘TifTuf’ (6.5), a popular drought-tolerant cultivar. The results highlight the prevalence of genetic variation in germplasm with potential for development of improved varieties for drought tolerance.

Fertilizer application is a critical component of turfgrass management as it influences growth, color, stress tolerance, and overall quality. However, limited information exists on how fertilizer application, particularly nitrogen (N), affects hybrid bermudagrass performance and actual plant evapotranspiration (ET_a) in both well-watered and deficit irrigation scenarios. A 7-week greenhouse experiment was conducted over two replicated runs to evaluate responses of ‘TifTuf’ hybrid bermudagrass (Cynodon dactylon × C. traansvalensis Burtt Davy) to three nitrogen rates (0, 2.4, and 4.8 g N m⁻² month⁻¹) and three irrigation levels (1.0, 0.65, and 0.30 × ET_a). Fertilized turfgrass exhibited 11–12% greater ET_a compared to unfertilized turfgrass, with no significant differences between the two fertilizer rates. Under well-watered conditions (1.0 × ET_a), the high nitrogen rate significantly improved visual quality (7.8) relative to the unfertilized control (7.1) and the low-rate treatment (7.4). High-rate fertilizer application significantly enhanced visual quality at both deficit levels (7.2 and 6.6, at 0.65 and 0.30 × ET_a, respectively) compared to the unfertilized control (6.2 and 5.9, at 0.65 and 0.30 × ET_a, respectively). At 0.30 × ET_a, low-rate fertilizer application also significantly improved visual quality (7.0) compared to the unfertilized control. Soil nitrate-N levels increased with higher nitrogen application (1.30 ppm, 0.48 ppm, and 0.37 ppm, respectively, for high-rate, low-rate, and unfertilized), and shoot tissue analysis revealed greater N concentration in fertilized turfgrass (1.51%, 1.24%, and 0.85%, respectively, for high-rate, low-rate, and unfertilized). Clipping production and water use efficiency (WUE) were also improved with fertilization, although root development was hindered at the 0.30 × ET_a irrigation level. These findings demonstrate that nitrogen fertilization improves visual quality, shoot growth, WUE, and drought response; however, tradeoffs such as elevated water use and nitrate-N leaching risk necessitate careful management to balance turfgrass performance with water conservation and ecosystem service preservation.

Bermudagrass is a warm-season turfgrass commonly grown in drought-prone areas. Harnessing natural genetic variation available in germplasm is a principal strategy to enhance its resilience to drought stress. This study was carried out to assess the comparative performance of bermudagrass hybrids under drought conditions and their subsequent recovery following the drought period. A total of 48 hybrids, including 2 commercial cultivars, ‘Tifway’ and ‘TifTuf’, were established under optimum growth conditions in the greenhouse and then subjected to drought stress by withholding irrigation for four weeks. The dry-down experiment was laid out in a randomized complete block design with four replications. Turf color, visual quality, and active spectral reflectance data were collected weekly and used to assess the health and vigor of the hybrids during progression of the drought stress for four weeks and through recovery after rewatering. Analysis of variance revealed significant differences among the hybrids for color, visual quality, and spectral vegetation indices. A multivariate analysis grouped the hybrids into drought-tolerant with full recovery after rewatering, moderately tolerant, and susceptible to extended drought stress without recovery. These results showed the prevalence of genetic variation for drought tolerance and proved instrumental in the development of bermudagrass cultivars resilient to drought stress and improved water use efficiency.

Bermudagrass is a warm season turfgrass commonly grown in drought prone areas. Harnessing natural genetic variation available in germplasm is a principal strategy to enhance its resilience to drought stress. This study was carried out to assess the comparative performance of bermudagrass hybrids under drought conditions and their subsequent recovery following the drought period. A total of 48 hybrids including two commercial cultivars, ‘Tifway’ and ‘TifTuf’, were established under optimum growth conditions in the greenhouse and then subjected to drought stress by withholding irrigation for four weeks. The dry down experiment was laid out in a randomized complete block design with four replications. Turf color, visual quality, and active spectral re-flectance data were collected weekly and used to assess the health and vigor of the hybrids during progression of the drought stress for four weeks and through recovery after re-watering. Analysis of variance revealed significant differences among the hybrids for color, visual quality, and spectral vegetation indices. A multivariate analysis grouped the hybrids into a drought tolerant with full recovery after re-watering, moderately tolerant, and susceptible to extended drought stress without recovery. These results showed the prevalence of genetic diversity for drought tolerance that prove instrumental in the development of bermudagrass cultivars resilient to drought stress and improved water use efficiency.

Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red–Green–Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = −0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = −0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = −0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = −0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations.

Global climatic pressures and increased human demands create a modern necessity for efficient and affordable plant phenotyping unencumbered by arduous technical requirements. The analysis and archival of imagery have become easier as modern camera technology and computers are leveraged. This facilitates the detection of vegetation status and changes over time. Using a custom lightbox, an inexpensive camera, and common software, turfgrass pots were photographed in a greenhouse environment over an 8-week experiment period. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results indicate that Red Green Blue-based (RGB) imagery with simple controls is sufficient to measure the effects of plant treatments. Notable correlations were observed for corrected imagery, including between a percent yellow color area classification segment (%Y) with human visual quality ratings (VQ) (R = -0.89), the dark green color index (DGCI) with clipping productivity in mg d-1 (mg) (R = 0.61), and an index combination term (COMB2) with water use in mm d-1 (mm) (R = -0.60). The calculation of green cover area (%G) correlated with Normalized Difference Vegetation Index (NDVI) (R = 0.91) and its RED reflectance spectra (R = -0.87). A CIELAB b*/a* chromatic ratio (BA) correlated with Normalized Difference Red-Edge index (NDRE) (R = 0.90), and its Red-Edge (RE) (R = -0.74) reflectance spectra, while a new calculation termed HSVi correlated strongest to the Near-Infrared (NIR) (R = 0.90) reflectance spectra. Additionally, COMB2 significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analysis of typical image file formats and corrections that included JPEG (JPG), TIFF (TIF), geometric lens correction (LC), and color correction (CC) were conducted. Results underscore the need for further research to support image corrections standardization and better connect image data to biological processes. This study demonstrates the potential of consumer-grade photography to capture plant phenotypic traits.

The development of management plans which lead to water efficient landscapes is a growing need in the turfgrass community. While deficit irrigation as a scheduling method can improve water conservation, more information is desired on how to best leverage other management practices, such as mowing height when deficit irrigation is imposed. The objectives of this study were to characterize actual evapotranspiration (ET_a), turfgrass visual quality, clipping production, and root development of ‘TifTuf’ bermudagrass (Cynodon dactylon × C. transvaalensis Burt Davy) when irrigated at full (1.0 × ET_a) and deficit levels (0.65 and 0.30 × ET_a), and cut at four separate mowing heights (2.5, 5.0, 7.5, and 10.0 cm) over two 8-week experimental runs. An elevated ET_a was observed at the 7.5 cm and 10.0 cm mowing heights compared to the 2.5 cm mowing height in both runs, and the 5.0 cm mowing height in one run. The visual quality decreased throughout both study periods and mostly for the deficit irrigation treatments, with visual quality falling below minimum acceptable levels at the lowest irrigation level (0.30 × ET_a) 5 weeks into run A, and 8 weeks into run B. Despite an elevated ET_a and a higher root dry weight at higher mowing heights (7.5 and 10.0 cm), clipping production and visual quality was generally higher at lower mowing heights (2.5 and 5.0 cm) for both full and deficit irrigation levels. These results demonstrate that mowing height can significantly influence bermudagrass water use, as well as responses to deficit irrigation. When maintaining ‘TifTuf’ bermudagrass at heights above 2.5 cm, the results from this study indicate a lower water use and improved response to deficit irrigation at mowing heights ≤ 5 cm.

Active radiometric reflectance is useful to determine plant characteristics in field conditions. However, the physics of silicone diode-based sensing are temperature sensitive, where a change in temperature affects photoconductive resistance. High-throughput plant phenotyping (HTPP) is a modern approach using sensors often mounted to proximal based platforms for spatiotemporal measurements of field grown plants. Yet HTPP systems and their sensors are subject to the temperature extremes where plants are grown, and this may affect overall performance and accuracy. The purpose of this study was to characterize the only customizable proximal active reflectance sensor available for HTPP research, including a 10 °C increase in temperature during sensor warmup and in field conditions, and to suggest an operational use approach for researchers. Sensor performance was measured at 1.2 m using large titanium-dioxide white painted field normalization reference panels and the expected detector unity values as well as sensor body temperatures were recorded. The white panel reference measurements illustrated that individual filtered sensor detectors subjected to the same thermal change can behave differently. Across 361 observations of all filtered detectors before and after field collections where temperature changed by more than one degree, values changed an average of 0.24% per 1 °C. Recommendations based on years of sensor control data and plant field phenotyping agricultural research are provided to support ACS-470 researchers by using white panel normalization and sensor temperature stabilization.