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TURFGRASS SCIENCE

Bermudagrass Seasonal Responses to Nitrogen Fertilization and Irrigation Detected Using Optical Sensing

^a Dep. of Agronomy, Univ. of Florida, Gainesville, FL 32611
^b Dep. of Horticulture and Landscape Architecture
^c Dep. of Biosystems and Agricultural Engineering
^d Dep. of Plant and Soil Sciences, Oklahoma State Univ., Stillwater, OK 74078. Approved for publication by the Director of the Oklahoma Agricultural Experiment Station. Funding provided by the Oklahoma Turfgrass Research Foundation grant number AG-89-RS-140, The Oklahoma Agricultural Experiment Station project number OKLO 2392, and Toro Center for Advanced Turf Technology

^* Corresponding author (greg.bell{at}okstate.edu).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
The objective of this study was to evaluate seasonal differences in bermudagrass response to N fertilization and irrigation by using optical sensing. A second objective was to determine if optical sensing could measure N status when the turf response to N was confounded by differences in moisture status. Bermudagrasses (Cynodon dactylon L.) ‘Rivera’ and ‘Yukon’ were managed under three irrigation treatments and six N treatments during the growing seasons in 2003 and 2004. Turf quality, normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red light reflectance in relation to near infrared reflectance (R/NIR), and green light reflectance in relation to near infrared reflectance (G/NIR) were measured. Bermudagrass demonstrated a noticeable third-order polynomial seasonal trend in response to N and irrigation treatment, and this trend was characterized as early-, peak-, mid- and late-season responses. Normalized difference vegetation index and GNDVI demonstrated a better relationship with turf quality and N status than R/NIR and G/NIR. A comparison among the four indices showed NDVI to be more closely correlated with irrigation, N fertilization, and turf quality. Minimum acceptable and target NDVI were developed by seasonal period based on visual turf quality assessment. It was also found that NDVI response to N fertilization was not strongly affected by irrigation treatment and could be used as an indicator of N status and need regardless of irrigation treatment.

Abbreviations: GNDVI, green normalized difference vegetation index • G/NIR, green light reflectance in relation to near infrared reflectance • NDVI, normalized difference vegetation index • NIR, near-infrared • R/NIR, red light reflectance in relation to near infrared reflectance

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
OPTICAL SENSING MEASURES irradiance reflected from a plant canopy. Photosynthetically active radiation (400–700 nm), is strongly absorbed by plant pigments. Near-infrared (NIR) radiation (700–1300 nm) is highly reflected due to low absorption (Knipling, 1970; Asrar et al., 1984). Leaf physical characteristics, such as cell structure, water content, and pigment concentration affect plant canopy reflectance, transmittance, and absorption (Maas and Dunlap, 1989). Leaf chlorophyll content was negatively correlated to green light reflection (500–600 nm) and positively correlated to NIR reflection (Blackmer et al., 1994; Adcock et al., 1990). Red wavelength (600–700 nm) reflectance increased from N-deprived canopies while NIR reflectance decreased (Walburg et al., 1982).

Normalized difference vegetation index (NDVI), is a commonly used reflectance index. It is defined as (R_NIR – R_red)/(R_NIR + R_red), where R_NIR is the energy reflected in an NIR wave band and R_red is the energy reflected in a red wave band. Higher NDVI values are associated with greater turfgrass density and greenness and lower values indicate sparse or stressed turf. Normalized difference vegetation index has been used to effectively detect Zn stress in Bahiagrass (Paspalum notatum Flugge) (Schuerger et al., 2003), herbicide damage to bermudagrass (Cynodon dactylon L.) (Bell et al., 2000), and N content of creeping bentgrass (Agrostis palustris Huds., A. stolonifera L.) (Keskin et al., 2001). It has also been successfully correlated with turf quality visual rating on seashore paspalum (Paspalum vaginatum Swartz), hybrid bermudagrass (Cynodon dactylon L. x C. transvaalensis Burtt-Davy) (Trenholm et al., 1999), tall fescue (Festuca arundinacea Schreb), and creeping bentgrass (Bell et al., 2002).

Gitelson et al. (1996) proposed an alternative vegetation index using green wavelengths. The green normalized difference vegetation index (GNDVI) was calculated by replacing the red band in the NDVI equation with a green band. The green color reflected from plants and seen by human eyes has a peak wavelength of 550 nm. Greenness is generally recognized as an indication of N status for many agronomic crops (Blackmer et al., 1994). As early as the 1970s, Thomas and Oerther (1972) demonstrated that leaf N content could be quickly estimated by measuring leaf reflectance at 550 nm. Blackmer et al. (1994) showed that light reflectance near 550 nm was the best wavelength to separate N treatment differences in corn (Zea mays L.) leaves. Shanahan et al. (2001) compared NDVI and GNDVI as a means of assessing canopy variation and its impact on corn grain yield under five N rates and found that GNDVI was the most highly correlated with grain yield.

Since both NDVI and GNDVI have been related to plant N status, predicting N fertilizer by the use of optical sensing is possible. However, few experiments have compared the use of NDVI and GNDVI for turf (Bell et al., 2004), and season-long bermudagrass responses to N rate and irrigation measured by NDVI and GNDVI have not been documented. Although NDVI has been used to detect turf quality and N status, its ability to quantify water stress has not been examined. The objective of this study was to evaluate seasonal differences in bermudagrass response to N fertilization and irrigation during two growing seasons by using reflectance indices NDVI, GNDVI, R/NIR, a measurement of red light reflectance in relation to NIR reflectance, and G/NIR, a measurement of green light reflectance in relation to NIR reflectance. A second objective was to determine if optical sensing could measure N status when the turf response to N was confounded by differences in moisture status.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
This study was conducted at the Oklahoma State University Turfgrass Research Center, Stillwater, OK, from 3 June to 17 Oct. 2003 and from 15 May to 15 Oct. 2004. Two common bermudagrass cultivars, Yukon and Riviera, were established from seed in June 1999 on an Easpur silty clay loam (fine-loamy, mixed, thermic Fluventic Haplustoll) with pH 7.0. The two cultivars were each seeded to randomly selected plots that measured 14.6 by 7.3 m in three blocks. Mowing height was maintained at 38 mm similar to a typical home lawn in Oklahoma. This experiment was conducted as a split-split-split plot design. The main plots were two cultivars, Yukon and Riviera; with three irrigation rates, 2.54, 1.27, and 0.63 cm wk^–1, as subplots; six N rates, 0, 12, 24, 36, 48, and 60 kg ha^–1 mo^–1, as sub-subplots; and sample dates as the sub-sub-subplots.

Irrigation was installed in May 2003. Three 7.3- by 4.9-m irrigation plots were established within each cultivar plot using an in-ground sprinkler irrigation system. Two irrigation sprinklers were installed on opposing ends of each plot to ensure uniform water application. The sprinklers were I-10/I-20 Ultra Rotary Sprinklers (Hunter Industries, San Marcos, CA) with nozzles of 0.75 short radius (SR), 1.5SR, and 3.0SR depending on treatment. Plots were irrigated once per week, and all plots were irrigated simultaneously for the same length of time, receiving 0.63, 1.27, or 2.54 cm as determined by nozzle size. Natural rainfall was recorded weekly. When weekly rainfall exceeded 2.5 cm, the irrigation treatment was not applied the following week.

Within each irrigation plot, six 1.5- by 0.9-m plots were randomly identified to receive different N fertilization rates. Urea was applied every 2 wk as a liquid at rates of 0, 12, 24, 36, 48, and 60 kg ha^–1 mo^–1. Although home lawns are rarely fertilized frequently, 2-wk application intervals were used in this study for scientific purposes. This frequency of application helped to relieve the potential growth surges common to longer intervals between applications and higher application rates so that a more accurate seasonal response to N application could be determined. Home lawns in Oklahoma are commonly fertilized once per month at 48 kg ha^–1 N, combined with irrigation of approximately 2.5 cm wk^–1.

Optical sensing measurements were collected in 2003 and 2004 every 2 wk during the growing season immediately before application of fertilizer and irrigation treatments. Commercially available GreenSeeker Handheld sensors (NTech Industries, Ukiah, CA) were used to collect NDVI and R/NIR (red sensor) and GNDVI and G/NIR (green sensor) when the turf was dry and free from dew. The sensors provided illumination from sensor-integrated light emitting diodes (LEDs) and filtered all ambient radiation using a phase shift technique. Since the sensor only measured reflectance from integrated light sources, it was not affected by solar conditions and cloud cover and was capable of returning uniform measurements regardless of time of day. For this study, however, measurements were made at the same time each day so that the bermudagrasses would be in approximately the same state of environmental response each time measurements were made. The sensor is designed with a vertical focus range of 41 cm. The unit maintains accuracy when held between 81 and 122 cm above the turf surface. Care was taken to maintain sensor height within this vertical focus range when measurements were made. The red sensor produced and measured red and NIR radiance at wavelengths of 671 ± 6 nm and 780 ± 6 nm, respectively, and the green sensor produced and measured green and NIR radiance at wavelengths of 550 ± 6 nm and 780 ± 6 nm, respectively. Downward-facing detectors were used to measure the red or green and NIR reflectance from the bermudagrass canopy. The sensor scanned an area 0.6 m wide and 9.5 mm long in the direction of travel. It produced a pulse every 110 ms, resulting in 10 or more reflectance measurements in a 1.5-m-long plot at a normal walking speed. The NDVI and R/NIR or GNDVI and G/NIR for each measurement were recorded and stored on a PDA attached to the handheld unit and subsequently transferred to a desktop computer. The mean vegetation indices from each plot were calculated and used for statistical analyses.

Visual turf quality (1–9 scale; 9 = best quality, all plants were healthy and green; 1 = worst quality, all plants were dead or brown) was evaluated in 2004 immediately after optical sensing measurement. For the purpose of this study, a minimum and a target or satisfactory visual quality rating were determined. A visual quality rating of 6 is usually considered the minimum acceptable value for bermudagrass maintained under the conditions of this study. The target quality rating was determined by calculating the mean visual quality during June, July, and August, the optimum growing season for bermudagrass in Oklahoma, treated with 48 kg N ha^–1 mo^–1, a commonly recommended fertilizer rate, under 2.54 cm wk^–1 irrigation, the maximum irrigation rate in this study.

Classification into early, peak, mid-, and late seasons was based on the response curves of the optical indices, with each season having a unique response trend. The first sampling date in 2003 and the two first in 2004 comprised the early season, and there were three, four, and one sampling dates in peak, mid-, and late seasons, respectively, in both years. For convenience, the latest sampling date in a season was chosen as the break point between that season and the following one.

Years 2003 and 2004 were pooled together and analyzed by using ANOVA according to the general linear model procedure of SAS (SAS Institute, 1999). The significant means were separated by Fisher's protected LSD (P = 0.05). Regression analysis was performed when ANOVA indicated a significant seasonal effect. Relationships between optical sensing measurements and turf quality were determined by correlation and regression.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Seasonal Trend Identification and Response
An ANOVA for NDVI, GNDVI, R/NIR, and G/NIR indicated that a large source of variation in bermudagrass response to treatments occurred among sampling dates in both years of the study. This variation occurred in a pattern that was consistent in both growing seasons (Fig. 1 ). Bermudagrass seasonal response as characterized by NDVI and GNDVI increased to peak response followed by a gradual decline and then a slight increase at the end of the season. The response trend measured by R/NIR and G/NIR was similar but in the opposite direction because high R/NIR and G/NIR indicate poor turf performance and high NDVI or GNDVI indicates exceptional turf performance (Fig. 1). Seasonal reflectance variations from a bermudagrass canopy over a season have been documented before. Guertal and Shaw (2004) found that bermudagrass reflectance varied both in photosynthetically active wavelengths and in the NIR range from May to August. The authors suggested that the variation might be affected by multiple factors, including chlorophyll content changes over time. In our study, the temporal variations of the optical responses were regressed against days of the year (Fig. 1). The regressions generated from year 2004 were significant at P = 0.001, 0.01, 0.001, and 0.01 for NDVI, GNDVI, R/NIR, and G/NIR, respectively. However, none of the regressions in 2003 were significant at P = 0.05. The authors believe that the lack of fit experienced in 2003 was most likely due to fewer sampling dates in the early season. The first sampling date in 2003 was 20 June. In 2004 two earlier dates, 28 May and 11 June, were sampled. The early sampling dates appeared to be important for determining the origin of the third-order polynomial models clearly defined in 2004.

View larger version (22K): [in this window] [in a new window]   Figure 1. Seasonal trends in bermudagrass (Cynodon dactylon L.) growth characterized by normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red light reflectance in relation to near infrared reflectance (R/NIR), and green light reflectance in relation to near infrared reflectance (G/NIR) in 2003 and 2004.

A significant season x irrigation interaction was also observed in both bermudagrass NDVI and GNDVI responses (Table 1). Higher (1.27 and 2.54 cm wk^–1) irrigation rates resulted in both higher NDVI and GNDVI during the peak and midseason periods, but not during the early and late-season periods (Tables 2 and 3). Although irrigation frequency was adjusted for the occurrence of natural rainfall, seasonal rain might still be the reason that the irrigation rates did not cause a difference in bermudagrass NDVI and GNDVI during the early and late seasons.

Seasonal effects also influenced cultivar response (Table 5). In the peak and late seasons, there were no differences between cultivars indicated by NDVI and GNDVI. In the early season, however, Riviera had a significantly higher NDVI and GNDVI than Yukon. According to National Turfgrass Evaluation Program results, Riviera has significantly higher percentage living ground cover in the spring than Yukon (78.6 and 68.8, respectively; National Turfgrass Evaluation Program 2005, Table 13A). Our results also indicate that the two cultivars performed differently in the spring. This difference was detected by NDVI and GNDVI but not detected by R/NIR and G/NIR. Neither R/NIR nor G/NIR detected significant season x cultivar interaction, further indicating that these two indices are not as useful for detecting differences in turfgrass quality as are NDVI and GNDVI.

Bermudagrass Response to Irrigation Treatment
Irrigation also affected bermudagrass (Table 1). The NDVI indicated that bermudagrass quality averaged over both cultivars was significantly better when irrigated at 2.54 cm wk^–1 compared with 0.63 cm wk^–1, but the other vegetation indices did not identify significant differences (Table 7). Trend analysis (y = 0.0065x + 0.765; r² = 0.98) also indicated a significant (P < 0.05) linear relationship between NDVI and irrigation rate. However, the other indices tested, GNDVI, R/NIR, and G/NIR, did not indicate significant linear relationships with irrigation rate, suggesting that NDVI was the most effective vegetation index for measuring water status. In cotton (Gossypium hirsutum L.), Plant et al. (1999) found that NDVI decline coincided with the onset of measurable water stress. Trenholm et al. (1999) reported that NDVI could be used to identify water stress in turf. This study suggested that NDVI was capable of differentiating the low irrigation rate (0.63 cm wk^–1) from the high rate (2.54 cm wk^–1) and performed better for this purpose than GNDVI. However, the large differences (4x) between irrigation rates did not significantly affect the ability of NDVI and GNDVI to determine turf N status. No irrigation x N-rate interaction was detected by either NDVI or GNDVI (Table 1).

Practical Use of Optical Sensing for Nitrogen Fertilization
Regression analysis was performed to determine a corresponding minimum acceptable and target NDVI based on the minimum and target visual quality ratings. A visual quality rating of 6 was considered the minimum acceptable value in this study and in most turfgrass studies. The computed target visual quality rating for the study was 6.7 determined by the mean NDVI rating averaged over treatments receiving 48 kg ha^–1 N and irrigation at 2.54 cm wk^–1. In a practical situation, turf visual quality equal to or below the minimum acceptable value could be used to indicate that a fertilizer application was needed, and visual quality equal to or greater than the target visual quality would indicate that a fertilizer application was not needed. Since the mean NDVI differed among seasonal periods, it was necessary to adjust the minimum acceptable and target NDVI according to seasonal response. Linear regressions (P < 0.0001) were conducted in the early, peak, and midseasons. In each season, NDVI were averaged across irrigation and N combinations (n = 18). The resulting models were used to determine the minimum acceptable and target NDVI for each seasonal period. Regression was not conducted for the late season because only one sample set was included for that period (n = 108). Instead, the minimum acceptable and target NDVI in late season were estimated using the mean NDVI ± SE. The regression equation for early season was NDVI = 0.37 + 0.06 x visual rating (r² = 0.87). The regression equation for peak season was NDVI = 0.61 + 0.04 x visual rating (r² = 0.87); and for midseason was NDVI = 0.56 + 0.04 x visual rating (r² = 0.84). Minimum acceptable and target NDVI were estimated by seasonal period using the minimum acceptable and target visual quality. The minimum acceptable NDVI ranged from 0.75 to 0.87, and the target NDVI varied from 0.78 to 0.90 (Table 8).

View this table: [in this window] [in a new window]   Table 8. Minimum acceptable and target normalized difference vegetation index (NDVI) adjusted for early-, peak-, mid- and late-seasonal periods in 2004.

View larger version (22K): [in this window] [in a new window]   Figure 2. Frequency of bermudagrass (Cynodon dactylon L.) responses equal to or greater than minimum acceptable and target normalized difference vegetation index (NDVI) under irrigation and N treatment combinations in 2004.

	CONCLUSIONS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
In summary, bermudagrass response to N and irrigation treatments differed by seasonal period. This response trend could be characterized using optical sensing into early, peak-, mid-, and late-season periods that represent turf quality changes over the growing season. The rapid early-season increase to peak performance was followed by a gradual midseason decline and then by a slight recovery in turf quality during the late season. The vegetation indices examined in this study all identified the seasonal trend. A comparison of the four optical indices found that NDVI was the best indicator of season, N, and irrigation. Both NDVI and GNDVI were closely related to turf quality, but NDVI was more closely related. Based on the turf visual quality, minimum acceptable and target NDVI were developed and adjusted during each season and could serve as indicators of turf N status. Although irrigation rates differed by a factor of four, the difference among irrigation rates did not seriously deter the use of NDVI as N-status indicators. The study suggested that NDVI could serve as an N fertilizer indicator, and an N fertilizer program could be developed and adjusted according to seasonal changes in bermudagrass response to N fertilization described by NDVI.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Received for publication June 17, 2006.

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TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

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