In field non-invasive sensing of the nitrogen status in hybrid bermudagrass (Cynodon dactylon × C. transvaalensis Burtt Davy) by a fluorescence-based method

Europ. J. Agronomy 63 (2015) 89–96

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European Journal of Agronomy journal homepage: www.elsevier.com/locate/eja

In field non-invasive sensing of the nitrogen status in hybrid bermudagrass (Cynodon dactylon × C. transvaalensis Burtt Davy) by a fluorescence-based method Giovanni Agati a,∗ , Lara Foschi b , Nicola Grossi b , Marco Volterrani b a b

Istituto di Fisica Applicata “Nello Carrara” CNR, via Madonna del Piano 10, 50019 Sesto Fiorentino, Firenze, Italy Department of Agriculture, Food and Environment, University of Pisa, via del Borghetto n. 80, 56124 Pisa, Italy

a r t i c l e

i n f o

Article history: Received 28 July 2014 Received in revised form 18 November 2014 Accepted 21 November 2014 Available online 9 December 2014 Keywords: Chlorophyll fluorescence Fertilization Mapping Nitrogen Non-destructive indices Turfgrass

a b s t r a c t The level of N fertilization and the content of leaf N in Cynodon dactylon × C. transvaalensis Burtt Davy cv. ‘Tifway 419’ bermudagrass were evaluated non-destructively with a fluorescence-based method. It was applied directly into the field by using the Multiplex portable fluorimeter during two consecutive seasons (2010 and 2011). In the 2010 experiment, the nitrogen balance index (NBI1 ) provided by the sensor was able to discriminate (at P < 0.05) six different N levels applied, up to 250 kg ha−1 , with a precision (root mean square error, RMSE) in the rate estimate of 3.29 kg ha−1 . In 2011, the index was insensitive to the N treatment between 150 kg ha−1 and 250 kg ha−1 N rates, and its precision was 39.98 kg ha−1 . Calibration of the sensor by using the destructive analysis of turf samplings showed a good linear regression between NBI1 and the leaf N content for both 2010 (R2 = 0.81) and 2011 (R2 = 0.93) experiments. This allowed mapping of the leaf N spatial distribution acquired by the sensor in the field with a prediction error of 0.21%. Averaging the overall estimates of leaf N content per N treatment provided an upper limit of 200 kg ha−1 for the required fertilization, corresponding to a critical level of leaf N of about 2.3%. Our results confirm the usefulness of the new fluorescence-based method and sensor for a precise management of fertilization in turfgrass. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Fertilization of soils and plants represents a key factor to be properly controlled in satisfying the increasing demand for environmental sustainability (Tilman et al., 2002; Hirel et al., 2011). Indeed, unused nitrogen (N) released to the environment can have detrimental effects (Cameron et al., 2013). On the other hand, optimal amount and timing of supply of nutrients are required to improve plant growth and yield. The reduction of the fertilization rates to the minimum effectively needed by the plants to perform well would produce both economic and environmental benefits. To guarantee this, frequent and spatially extensive controls of the plant nutrient status all over the crop or turfgrass areas should be performed. Optical sensing systems are potential suitable tools to achieve this goal, rather than costly, time-consuming, and destructive laboratory analyses (Munoz-Huerta et al., 2013). A plethora of

∗ Corresponding author. Tel.: +39 055 5225306; fax: +39 055 5225305. E-mail address: [email protected] (G. Agati). http://dx.doi.org/10.1016/j.eja.2014.11.007 1161-0301/© 2014 Elsevier B.V. All rights reserved.

reflectance-based methods has been proposed for the non-invasive detection of the plant N content (Richardson et al., 2002; Gitelson et al., 2003; Samborski et al., 2009; Erdle et al., 2011; Caturegli et al., 2014), by using sensors in remote, proximal, or contact sensing. These techniques rely on the chlorophyll (Chl) absorption and scattering spectral properties of leaves and on the direct correlation existing between chlorophyll and leaf N concentration. However, the N–Chl correlation is not always maintained (Lee et al., 2011) and, mainly, saturation effects on the reflectance signals for samples with high chlorophyll concentrations may occur (Richardson et al., 2002). More recently, the chlorophyll fluorescence excitation screening method that combines indices of chlorophyll and of flavonols, with opposite N-dependence, has been employed as proxy of leaf N content in crops (Cartelat et al., 2005; Zhang et al., 2012; Longchamps and Khosla, 2014; Padilla et al., 2014) and turfgrasses (Lejealle et al., 2010; Agati et al., 2013). In Paspalum vaginatum and Zoysia matrella turfgrasses, it was proved that the fluorescencebased indices of N were superior than reflectance-based indices calculated by measurements on the same experimental plots. Active fluorescence-based methods are also more advantageous

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than passive reflectance-based ones since they can be applied under any light condition, even during cloudy days, without need of reference measurements. Yet fluorescence ratios are insensitive to the soil contribution, which indeed can affect reflectance, making them suitable even for less covered areas (Heege et al., 2008). Monitoring the N status in sports field turfgrasses is particularly important since their management requires frequent N applications from spring to fall in order to maintain good levels of color and quality (Trenholm et al., 1998) as well as adequate playability. Fine fertilization programs are also required to manage the stress due to close mowing regimes (Koeritz and Stier, 2009) and the occurrence of fungal diseases (Dordas, 2008). Bermudagrass (Cynodon spp.) are among the most commonly used warmseason turfgrass, diffused also in transition zones (Croce et al., 2004; Volterrani and Magni, 2004b) mainly because of their reduced demand of watering (Croce et al., 2001; Severmutlu et al., 2011). On the other hand, their widespread use is limited by a long dormancy periods during which they lose green color (Volterrani and Magni, 2004a). Both these constraints can be alleviated by specific N fertilization protocols (Pompeiano et al., 2013; Rimi et al., 2013). Optical tools are, therefore, particularly attractive for the management of this turfgrass species (Trenholm et al., 2000; Volterrani et al., 2005). In the present study, we evaluated the performance of a portable fluorescence sensor for the non-invasive mapping of the leaf nitrogen content in Cynodon dactylon × C. transvaalensis, on two consecutive seasons.

2. Materials and methods The trials were carried out at the University of Pisa (43◦ 40 N; 10◦ 23 E; 6 m a.s.l.) in July 2010 and May 2011 on a mature sward of C. dactylon × C. transvaalensis Burtt Davy cv. ‘Tifway 419’ bermudagrass. The bermudagrass was established on a silt–loam soil (sand 28%, silt 55%, clay 17%) with 17 ␮g g−1 of available P2 O5 (Olsen method) and 248 ␮g g−1 of exchangeable K2 O (Dirks Scheffer method) and pH 7.8. During the trial period, a turf height of 20 mm was maintained by regular mowing with a reel blade mower. Irrigation necessary to maintain healthy turfgrass was applied. From green-up to trial start no fertilizer was applied to the turf. In order to maximize the variability of nitrogen available to the turf, the following 6 rates of nitrogen (ammonium sulphate) were applied: 0, 50, 100, 150, 200, and 250 kg ha−1 . The N applications were carried out on 28 June 2010 and 27 April 2011. Light irrigation followed fertilizer application to avoid the risk of burns. The experimental design was a randomized block for each species with four replicates, each of 1 m2 area plot. The weather conditions of global irradiance, total rainfall and air temperature from the beginning of the vegetative period to the end of the trials are reported in Fig. 1 for both seasons. For each plot, the turf quality (from 1 = poor to 9 = excellent) and the color intensity (from 1 = very light green to 9 = very dark green) was visually assessed (Morris, 2000).

2.1. Fluorimetric sensor measurements The portable Multiplex 2 (Mx) fluorescence sensor (FORCE-A, Orsay, France), described in detail elsewhere (Ben Ghozlen et al., 2010; Agati et al., 2013), consisted of multiple (4) LED excitation sources and used two detection channels to record chlorophyll (Chl) fluorescence in the red (RF), at 680–690 nm, and in the far-red (FRF), at 730–780 nm, spectral bands. The Chl fluorescence signals RFR and FRFR , excited with red (R) light, FRFUV , excited with ultraviolet (UV) radiation and FRFG ,

Fig. 1. Global irradiance (A), total rainfall (B) and air temperature at 5 cm above the ground (C) from the beginning of the vegetative period to the end of the trial for the 2010 and 2011 experiments. Values of daily global irradiance and air temperature are the average over 10 days. Rainfall values are the total over 10 days.

excited with green (G) light, were used to calculate the flavonols (Flav) index: FLAV = log(

FRFR ), FRFUV

(1)

the Chl indices CHL =

FRFR . RFR

(2)

FRFG FRFR

(3)

CHL1 =

and two nitrogen balance indices NBI =

CHL 10FLAV

NBI1 =

=

CHL1 10FLAV

FRFUV . RFR

=

FRFG FRFUV FRF2R

(4) (5)

For details on the derivation and significance of the above expression, see Agati et al. (2013) and references therein. It has been shown for a long time that the CHL fluorescence ratio can be used as an accurate measure of the Chl content in plants (Gitelson et al., 1999; Buschmann, 2007). Furthermore, the Mx CHL index was already finely calibrated against Chl content in leaves (Tremblay

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et al., 2011) and fruits (Remorini et al., 2011; Betemps et al., 2012). On the other hand, the Mx FLAV index was proved to be linearly related to the flavonol content of fruit exocarp (Betemps et al., 2012; Pinelli et al., 2013). Twenty-five fluorescence measurements per plot were acquired by moving the Mx sensor on a 5 × 5 sampling grid with a spatial separation of about 20 cm. For each point, signals were integrated on a 50 cm2 (8-cm diameter) area positioning the sensor on the top of the turf canopy. Measurements were taken right after mowing on 12 July 2010 for the first experiment and on 11 May 2011 for the second experiment, 2-weeks after nitrogen application, between 11:00 a.m. and 2:00 p.m. 2.2. Calibration of the fluorimetric sensor After scanning the experimental plots by the Mx sensor, turfgrass samples of 50 cm2 located in the central-top part of each plot were measured again by the Mx and then collected for the destructive analysis of the canopy nitrogen content by the Kjeldahl method. The calibration curve for NBI1 was built by plotting the NBI1 value of each sample against the relative actual leaf N% determined by wet chemistry. A cross-validation procedure was used to evaluate the accuracy of the calibration curve. Three-fourth of the whole values, counting 2010 and 2011 samples, were randomly chosen to be used as calibration data set and the remaining one-fourth were used as validation data set. The procedure was repeated eight times, evaluating then the average values (±SD) of the coefficient of determination for the regression model of calibration and the root mean square error (RMSE) of prediction of validation tests. 2.3. Analysis of digital images The digital image analysis followed the method previously proposed for turfgrasses (Karcher and Richardson, 2003). All digital images of plots of the 2011 experiment were acquired by a RICOH R10 (Ricoh Italia, Vimodrone (MI), Italy) camera, with a size of 1280 × 960 pixels, and saved in the JPEG format. The average RGB levels of each image were extracted by using the Image-Pro Plus v.4.0 software (Media Cybernetics, Silver Spring, MD) and normalized to 255 to express them as percentage. The hue, saturation, and brightness (HSB) levels were then derived as: Hue = 60(2 +

Saturation =

(B − R) ) [max(R, G, B) − min(R, G, B)]

[max(R, G, B) − min(R, G, B)] max(R, G, B)

Brightness = max(R, G, B) The dark green color index (DGCI) was defined as DCGI =

[(hue − 60)/60 + (1 − saturation/100) + (1 − brightness/100)] 3

2.4. Statistical analysis Statistical analysis and curve fitting were carried out by using SigmaPlot for Windows Version 11.0 (Systat Software, Inc.). The results are given as mean ± standard deviations (SD).

Fig. 2. Non-destructive NBI1 (A), FLAV (B), and CHL1 (C) Multiplex indices as function of the N rate applied to the soil in Cynodon dactylon × C. transvaalensis turfgrass for the 2010 and 2011 experiments. Values are means of 100 measurements equally distributed on the 4 replicates. Bars represent the SD.

3. Results 3.1. Fluorescence-based indices versus N rate The average value of the NBI1 non-destructive index as function of the N rate is reported in Fig. 2A for the two 2010 and 2011 temporal experiments. It increased exponentially with increasing N fertilization, with different rise constants. The FLAV index decreased exponentially with increasing N rate for both experiments (Fig. 2B). On the other hand, the CHL1 index increased steeply passing from 0 to 100 kg ha−1 of N and then oscillated around a flat value (Fig. 2C). The NBI and CHL behavior versus N rate was similar to that of NBI1 and CHL1 , respectively (data not shown). The efficiency of the different fluorescence-based indices in the estimation of the N rate applied to the soil for the 2010 and 2011 experiments can be compared by using the parameters reported in Table 1. We considered the precision of the estimate given by the RMSE of residuals, but also the level of linearity of the response. Since the FLAV index showed a negative correlation with N rate, the −FLAV was considered. The scatter plots of all the indices as function of N rate were well fitted, with R2 > 0.95,

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Table 1 Exponential curve fitting parameters of the response of the fluorescence-based indices to different N rates. 2010

Rise constant (kg ha-1 ) RMSE (kg ha-1 ) R2

2011

NBI

NBI1

CHL

CHL1

−FLAV

NBI

NBI1

CHL

CHL1

−FLAV

172.41 5.22 0.998

178.57 3.29 0.999

40.82 71.26 0.983

35.71 82.53 0.98

107.53 2.77 0.999

100 46.92 0.958

90.91 39.98 0.968

62.5 72.97 0.968

41.67 76.93 0.96

71.43 39.6 0.982

by an exponential function rising to a maximum, with equation y = y0 + a[1 − exp(−x/b)]. The best indices were those with the most linear-like trend (Richardson et al., 2002), that is those with the largest rise constant b values. The larger this value, the higher the discrimination power of the index. We found a significant difference in linearity and precision between 2010 and 2011 data. According to the RMSE values, −FLAV out-performed all other indices in monitoring the N rate for both years (RMSE = 2.77 and 39.6 kg ha−1 for 2010 and 2011, respectively). However, considering the linearity of the response, NBI and NBI1 were preferable with respect to −FLAV (longer rise constants). On the other hand, the precision of NBI1 , with RMSE of 3.29 and 39.98 kg ha−1 for 2010 and 2011, respectively, was quite close to that of −FLAV. The CHL and CHL1 indices were the worst, with RMSE larger than 71 kg ha−1 and considerable divergence from linearity. 3.2. Visual assessment The visual assessment of plots determined the average values of color intensity and turf quality scores as function of N rates reported in Fig. 3A and B, respectively, for both seasonal experiments. These parameters were curvedly increasing with increasing

Fig. 3. Average values (n = 4) ± SD of color intensity (A) and turfgrass quality (B) as function of the N rate applied to the soil in Cynodon dactylon × C. transvaalensis for the 2010 and 2011 experiments.

N rate with a maximum at about 200 kg ha−1 of applied N. For both color and quality above 50 kg ha−1 of N, values recorded in 2010 were generally higher than those recorded in 2011. Fig. 4A and B shows the comparison between the color and quality parameters, respectively, and the percentage of leaf N determined destructively on turf samplings for both 2010 and 2011 experiments. Exponential fitting curves for the overall data set are also reported. 3.3. Estimate of leaf N content In Fig. 5, the NBI1 values of individual spots were plotted against the leaf N content determined destructively on the very same grass samplings collected after the optical measurements. A good linear regression was observed for both 2010 (R2 = 0.81) and 2011 (R2 = 0.93) experiments (Fig. 5A). This allowed to use a simple regression as calibration curve from which predicted values of leaf N could be estimated. Considering data of both seasons together, we obtained the calibration curve reported in Fig. 5B, with equation: NBI1 = −0.0654 + 0.187 × N%

(6)

and R2 = 0.86.

Fig. 4. Relationship between color intensity (A) and turfgrass quality (B), and the leaf N (%) content in Cynodon dactylon × C. transvaalensis turfgrass for the 2010 and 2011 experiments. Solid lines represent exponential (3 parameters) curve fitting of the overall data set.

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Fig. 5. (A) Relationship between the NBI1 index and the leaf N (%) content in Cynodon dactylon × C. transvaalensis turfgrass for two separate seasons, with equations of regression NBI1 = −0.0469 + 0.1829 × N% for 2010 and NBI1 = −0.1014 + 0.1994 × N% for 2011. (B) Calibration curve for NBI1 considering data from the two experiments together, with equation of regression NBI1 = −0.0654 + 0.187 × N%.

The prediction accuracy of this regression equation was evaluated by the cross-validation procedure described above (see Section 2). It resulted in a linear model for the NBI1 versus leaf N% relationship with R2 = 0.85 ± 0.02 (on n = 8 tests) and a prediction error (RMSE) of 0.21% ± 0.028% (on n = 8 tests). 3.3.1. Leaf N spatial distribution Inversion of Eq. (6) allowed for the assessment of leaf N from in field NBI1 measurements as N% =

(NBI1 + 0.0654) 0.187

(7)

The spatial distribution of the estimated bermudagrass leaf N% for the 2010 and 2011 experiments is reported in Fig. 6. Even within single 1-m2 plots a significant heterogeneity was observed. The 0 kg ha−1 N rate produced, as expected, minimal values of leaf N (red color). In 2010, the maximal N values were observed with the 250 kg ha−1 N treatment. While in 2011, maximal N values were distributed over the plots with treatments between 150 and 250 kg ha−1 N. 4. Discussion The effects of the N treatments on turf parameters and nondestructive indices appeared to be slightly different between the two temporal experiments. This is likely due to the different environmental conditions faced by the grass (Fig. 1). In the 2010 experiment, NBI1 was almost linearly related to N rate and was able to discriminate all the 6 N levels applied (at P < 0.05). In the second experiment, NBI1 was insensitive to N treatment between

Fig. 6. Maps of the leaf N content in Cynodon dactylon × C. transvaalensis turfgrass experimental plots estimated by the NBI1 index using the Eq. (7) for the 2010 (A) and 2011 (B) experiments.(For interpretation of the reference to color in the text, the reader is referred to the web version of this article.) Multiplex data were acquired on a 5 × 5 sampling grid (25 points), with a spatial separation of about 20 cm per plot, with 4 replicates per N supply and 6 different N rates from 0 to 250 kg ha−1 . (C) Pictures of the plots employed for the 2011 experiment. N rates (kg ha−1 ) are reported on each plots in (A) and (C). The color scale of N% for (A) is the same reported in (B).

150 kg ha−1 and 250 kg ha−1 N rates (Fig. 2A). The higher FLAV values observed in 2010 with respect to 2011 for all N rates (Fig. 2B) can be due to a higher solar irradiance received by the turf during the first experiment (compare values in Fig. 1A, considering that the first experiment measurements were collected at middle

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of July 2010 and the second one at middle of May 2011). In fact, it is well known that the flavonols content of plants is well correlated to solar light intensity (Agati et al., 2011). The CHL1 index indicated a larger content of chlorophyll in C. dactylon × C. transvaalensis leaves in 2010 with respect to 2011, for all the N rates (Fig. 2C). This difference agrees with that observed by visual estimation for turfgrass color and quality during the two experiments, generally higher in 2010 than 2011 (see Fig. 3). The NBI and NBI1 indices appeared to be the most efficient in estimating the N rates, considering, in addition, that they can be more robust than FLAV (and CHL) to leaf age differences (Cartelat et al., 2005) and seasonal changes (Lejealle et al., 2010). On the other hand, we must be aware that environmental conditions can affect the linearity of the response to N rate of both NBI and NBI1 , being this much higher in 2010 than in 2011 (Fig. 2A). The fluorescence-based indices as function of N rate (Fig. 2) gave results consistent with those from similar studies on wheat (Cartelat et al., 2005) and other turfgrass species (Lejealle et al., 2010; Agati et al., 2013), confirming that NBI and FLAV were suitable to monitor N fertilization much better than CHL. On the contrary, the subjective visual analysis of plots was not able to appreciate differences in the N treatments above 100 kg ha−1 N (Fig. 3). These parameters were also found inadequate to estimate leaf N contents above about 1.5% (Fig. 4). Calibration of NBI1 against the actual leaf N content was fairly linear and only slightly affected by the year-dependent environmental conditions (Fig. 5A). Therefore, the Mx sensor can be used to estimate leaf N% of bermudagrass over a wide range of values with a significant precision (0.21%) (Fig. 5). The obtained calibration curve was used to express the maps of the N-treated experimental plots directly in leaf N%, as reported in Fig. 6. Properly defining the relationship between the accumulation of N in leaf tissues of bermudagrass and the N rates is a complex task, depending also on the lapse of time between N application, turf cutting, and time of detection (Bowman et al., 2002; Lima de et al., 2010). This may partially explain the differences observed between the two seasons in the responses to N. However, averaging the overall data available (200 values per N treatment) of the estimated leaf N% per N treatment, it was possible to obtain the plot reported in Fig. 7A that models the leaf N content versus N rate correlation. Leaf N content increased continuously with increasing N rate up to 2.28 ± 0.26%, reached with 200 kg ha−1 N, and did not change significantly with the successive higher N application. The result we obtained was markedly different from that shown by plotting the average values of the leaf N% determined destructively on grass samplings as function of N rates (Fig. 7B). In this case, we observed an increase in leaf N from 1.16% to 1.8% as the N rate applied to the soil was augmented from 0 to 50 kg ha−1 . It remained constant with the 100 kg ha−1 N application and then increased to about 2.3% at 150 kg ha−1 . This level of leaf N was not significantly different from the ones at 200–250 kg ha−1 . The larger discrimination power provided by the optical method with respect to the destructive analysis is due to the much larger, more representative number of measurement points per treatment (200 versus 8). Digital image analysis, previously proposed to evaluate quality and N content in turfgrass (Karcher and Richardson, 2003), was tested on our bermudagrass plots of Fig. 6C. The results of this analysis are reported in Table 2. It can be seen that digital imaging discriminates the different N rates similarly than visual inspection and less than the fluorescence-based method. 4.1. Turfgrass species comparison The response of NBI1 to the fertilization rate found on C. dactylon × C. transvaalensis in the 2010 experiment was more curvilinear and similar with respect to those previously observed on the seashore

Fig. 7. Average values ± SD of leaf N content as function of the N rate applied to the soil in Cynodon dactylon × C. transvaalensis estimated by the NBI1 index (n = 200) (A) or determined by destructive analysis of turf samplings (n = 8) (B). Solid lines represent exponential (3 parameters) curve fitting of data. Means with the same letter are not significantly different according to the ANOVA test.

turfgrass paspalum and on zoysiagrass, respectively, by using the same fluorescence method during the same season (Agati et al., 2013). The NBI1 index sensitivity, evaluated over the whole range of N rates, appeared to be higher for hybrid bermudagrass than for the other species (10−3 kg−1 ha versus 0.72 × 10−3 kg−1 ha for P. vaginatum and 0.6 × 10−3 kg−1 ha for Z. matrella). Since values and N dependence of the CHL1 Chl index were similar for all species, the difference observed in the NBI1 response to N was mainly due to the variation with N of flavonols (see Eq. (5)). The lower level of Flav concentration, indicated by the lower FLAV values, in hybrid bermudagrass with respect to P. vaginatum and Z. matrella (Agati et al., 2013) can be due to a species-specific expression of genes involved in the Flav biosynthetic pathway. Otherwise, a difference in the leaf and canopy structure able to intercept more or less solar radiation can explain the diversity in Flav content between the species, in accordance with the high sensitivity of Flav accumulation to light intensity (Agati et al., 2011). Table 2 Digital image analysis of Cynodon dactylon × C. transvaalensis pictures reported in Fig. 6C (2011 experiment) fertilized with different N rates. N rate (kg ha-1 )

Hue (degrees)

Saturation (%)

Brightness (%)

DCGIa

0 50 100 150 200 250

72.1 c 81.6 b 83.8 ab 86.5 a 86.8 a 86 a

33 a 30.3 b 30.6 ab 28.7 b 28.4 b 28.7 b

61.8 ab 57.8 bc 53.5 c 56.9 bc 56 bc 54.6 c

0.418 c 0.493 b 0.518 a 0.528 a 0.534 a 0.533 a

Means (n = 4) with different letters within each column are statistically different according to the ANOVA test (P < 0.001). a Dark green color index, DCGI = [(hue − 60)/60 + (1 − saturation/100) + (1 − brightness/100)]/3.

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All the three species tested in 2010 presented a good linear relationship between NBI1 and the leaf N content (R2 between 0.78 and 0.85). Again, in this case, the sensitivity of the Mx index for C. dactylon × C. transvaalensis (slope of the regression line equal to 0.183) was higher than that of Z. matrella (regression line slope = 0.132) and P. vaginatum (regression line slope = 0.112). The seasonal effect on this relationship within the same species seems to be not significant, as observed comparing the 2010 and 2011 data for C. dactylon × C. transvaalensis (Fig. 5A). Accordingly, the correct evaluation of turf N contents would require calibration curves for the optical indices specific for each species. However, considering the acquired data for all species together, we still obtained a linear regression between NBI1 and leaf N%, with R2 = 0.63 (data not shown), that can represent an appropriate compromise to investigate turfgrass mixtures.

5. Conclusion Our results confirmed that optical non-destructive indices based on the Chl fluorescence excitation screening method are suitable to estimate the level of N fertilization and the content of leaf N in turfgrasses. The NBI1 Mx index resulted to be more efficient in discriminating the different N rate of soil fertilization with respect to both the CHL1 Mx index and the quality/color visually assessed indices or the digital image analysis indices. The linearity of the NBI1 response to N rate was seen to be affected by the seasonal conditions and further studies are required to deeply understand the causes of that. NBI1 was also well linearly correlated to the leaf N content independently of the season, but with a species-dependent sensitivity. Calibration of the NBI1 index permitted to visualize on a map the heterogeneity in leaf N content, simulated in our experimental plots, as detected by the Mx (Fig. 6). This approach can be easily extended to large-scale applications, by using the sensor mounted on an utility vehicle (Lejealle et al., 2010; Martinon et al., 2011), to correct N deficiencies in specific local areas of whole turf fields or crops. Applying this technique to our experimental sites, allowed to define for C. dactylon × C. transvaalensis an upper limit of 200 kg ha−1 of required fertilization, corresponding to the a critical level of leaf N content of about 2.3%. Although these values may depend on the particular protocols used in the present study and they should be confirmed under different experimental conditions, we believe that the reported fluorescence-based approach can represent a new useful tool for a precise management of fertilization in turfgrass, addressing both quality and environmental aspects.

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