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Europ. J. Agronomy 45 (2013) 39– 51

Contents lists available at SciVerse ScienceDirect

European Journal of Agronomy

jo u r n al hom epage: www.elsev ier .com/ locate /e ja

luorescence-based versus reflectance proximal sensing of nitrogen content inaspalum vaginatum and Zoysia matrella turfgrasses

iovanni Agati a,∗, Lara Foschib, Nicola Grossib, Lorenzo Guglielminetti c, Zoran G. Cerovicd,arco Volterranib

Istituto di Fisica Applicata “Nello Carrara” CNR, via Madonna del Piano 10, 50019 Sesto Fiorentino, Firenze, ItalyCentro Ricerche Tappeti Erbosi Sportivi, Dipartimento di Agronomia e Gestione dell’Agroecosistema, Università di Pisa, Via S. Michele degli Scalzi 2, 56124 Pisa, ItalyDipartimento di Biologia, Università di Pisa, Via Mariscoglio 34, 56124 Pisa, ItalyEquipe de Biospectroscopie Végétale, Laboratoire d’Ecologie Systématique et Evolution, CNRS, UMR 8079, Bât. 362, Université Paris-Sud, 91405 Orsay Cedex, France

r t i c l e i n f o

rticle history:eceived 5 June 2012eceived in revised form 22 October 2012ccepted 29 October 2012

eywords:hlorophyll fluorescenceertilizationappingitrogenon-destructive indiceseflectanceurfgrass

a b s t r a c t

Newly developed non-destructive fluorescence-based indices were used to evaluate nitrogen (N) fer-tilization rates and leaf nitrogen content in Paspalum vaginatum and Zoysia matrella turfgrasses. Theywere defined by combinations of the red (RF) and far-red (FRF) chlorophyll fluorescence signals excitedunder ultraviolet (UV), green (G) or red (R) radiation, as follow: Flavonol index, FLAV = log(FRFR/FRFUV);Chlorophyll indices, CHL = FRFR/RFR and CHL1 = FRFG/FRFR; Nitrogen Balance Indices, NBI = FRFUV/RFR andNBI1 = FRFG · FRFUV/FRF2

R. Measurements were performed in situ by using a portable optical sensorable to scan 1 m2 plots, with a 0.2 m resolution, under 6 different nitrogen rates, from 0 to 250 kg ha−1,with four replicates each. From the same plots, reflectance spectra were recorded and several reflectance-based indices calculated. Most of them, as well as the fluorescence-based indices of chlorophyll, CHLand CHL1, had a quadratic response to N rate with a flattening above 150 kg ha−1 and 100 kg ha−1 forP. vaginatum and Z. matrella, respectively. The fluorescence-based NBI1 index was the only one able todiscriminate all the 6 N levels applied to both P. vaginatum and Z. matrella plots. This result is due tothe character of NBI1 as a ratio between an index of chlorophyll and an index of flavonols that presentopposite responses to N rates. The spatial heterogeneity within and between plots treated with dif-ferent levels of N was well represented by the map of the NBI indices. When the NBI1 and NBI wereregressed against leaf N content linear fits were obtained with high regression coefficients in both P. vagi-natum (R2 = 0.85–0.87, RMSE = 0.23–0.24% N) and Z. matrella (R2 = 0.75–0.78, RMSE = 0.20–0.22% N). The

2

best relationships between leaf N content and reflectance-based indices, found for R730/R1000 (R = 0.71,RMSE = 0.43% N) and MCARI (R2 = 0.80, RMSE = 0.22% N) for P. vaginatum and Z. matrella, respectively,were curvilinear and, therefore, less effective than NBI indices in the estimation of N. Nevertheless, areflectance vegetation index suitable as proxy of leaf N common to both turf species was not found. Ourresults indicate the high potential of the fluorescence-based method and sensors for the in situ proximalsensing of N status in the management of N fertilization in turfgrass.

. Introduction

Remote sensing of leaf nitrogen (N) content in precision agri-ulture is highly interesting for both economic and environmentaleasons. It allows the monitoring of crop fertilization in order toptimize plant growth and limit N applications to real plant needs.n this way, the cost for fertilizer N lost due to un-optimized N use

fficiency and the N-related pollution can be reduced (Samborskit al., 2009).

∗ Corresponding author. Tel.: +39 055 5225306; fax: +39 055 5225305.E-mail address: g.agati@ifac.cnr.it (G. Agati).

161-0301/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.eja.2012.10.011

© 2012 Elsevier B.V. All rights reserved.

Nitrogen represents a fundamental nutrient in turfgrass tomaintain the green color, adequate density and to allow recoveryfrom stresses such as drought, diseases and wear. Different lev-els of nitrogen are required for sports field turfgrass according tothe use of the playgrounds. In golf courses, a compromise in the Nrate must be found in order to obtain reasonable ball-roll distances,which are inversely related to N content, and still maintaining agood shoot density needed to have a significant wear toleranceand recovery (Koeritz and Stier, 2009). Managing athletic fieldsturf requires proper fertilizer rates and timing for wear resistance

and quick turf recovery from traffic damage. Additionally, attentionmust be paid in supplying over-fertilization that may have a neg-ative effect by reducing resistance to fungal diseases (Walters andBingham, 2007; Dordas, 2008). Consequently, a rapid and frequent

4 J. Agro

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0 G. Agati et al. / Europ.

onitoring of N content is required for applying an optimized mod-lation of fertilizers.

Determination of leaf N content using destructive methods isime-consuming and usually cannot be sufficiently representativef the spatial variability of large crop areas. Optical non-destructiveethods are attractive since they are fast, they can be applied to

ecord time courses on the same plots and, using vehicle-mountedensors, they can cover large areas within relatively short times. Inhe precision management of turfgrass, optical sensing was indi-ated as a supplemental or alternative method to visual qualityvaluation (Trenholm et al., 1999; Bell et al., 2004, 2009). Vehicle-ounted optical sensors can easily control large areas to produceaps of turfgrass quality (Bell et al., 2002).It is well known that there is a direct relationship between soil

nd leaf N contents and the leaf chlorophyll (Chl) concentrationEvans, 1983, 1989; Penuelas et al., 1994; Langsdorf et al., 2000;chlemmer et al., 2005). Since the content of Chl can affect theeflectance properties of leaves, many optical methods based oneflectance spectral measurements have been suggested to detecthl concentration (Yoder and Pettigrew-Crosby, 1995; Richardsont al., 2002; Gitelson et al., 2003) and then indirectly the Neficiency. What is the best index for Chl determination is stillontroversial. It is surprising that the Normalized Difference Veg-tation Index (NDVI), calculated as (RNIR − Rred)/(RNIR + Rred), is stillidely used to estimate non-destructively Chl, since several other

eflectance-based indices showed better correlations with leaf Chlontent (Richardson et al., 2002; Gitelson et al., 2003). Poor linearegressions were found between different NDVI indices and ChlR2 = 0.24) or N (R2 = 0.55) concentrations in wheat crops (Hansennd Schjoerring, 2003). Mistele and Schmidhalter (2008) found thatotal N content in maize was generally not well represented byifferent reflectance spectral indices (Mistele and Schmidhalter,008). Correlation coefficients of about 0.7 were found in sun-ower leaves between N content and derivative reflectance indicesPenuelas et al., 1994). Several reflectance indices have been pro-osed as proxy of turfgrass quality and nutrition (Trenholm et al.,999; Volterrani et al., 2005; Jiang and Carrow, 2007).

Chlorophyll meters, based on the measurement of leaf trans-ittance at specific wavelengths, have been widely used for the

on-destructive determination of the leaf Chl content and related deficiencies (Samborski et al., 2009). However, different factors

ignificantly affecting the reading of these sensors, such as plantrowth stage, leaf thickness, water status and irradiances muste considered (Samborski et al., 2009; Naus et al., 2010; Cerovict al., 2012). The sensitivity limits of the SPAD (Soil-Plant Anal-sis Development, Minolta Camera Co., Osaka, Japan) meter inhe diagnosis of N deficiencies in corn production were evidencedZhang et al., 2008). In a comparative study on noninvasive leafhl determination methods, SPAD Chl meters appeared to pro-uce lower satisfying results, i.e. higher root mean square errorf predicted Chl values, with respect to reflectance-based indicesRichardson et al., 2002). Applications of the SPAD Chl meter tourfgrass were reported by Rodriguez and Miller (2000), indicat-ng a limited usefulness of the device for the management of St.ugustinegrass. The leaf-clip technique adopted by the SPAD, mea-uring on a 2 mm × 3 mm area, can represent a limitation for itsse on the narrow leaf blades of most turfgrasses. However, somelose relationships between SPAD readings and nitrogen leaf con-ent in various cool season turfgrasses were found (Gáborcík, 2003).he Spectrum chlorophyll meter used by Mangiafico and Guillard2005) on Kentucky bluegrass (Poa pratensis L.) and on creepingentgrass (Agrostis stolonifera L.) (Lopez-Bellido et al., 2012) pro-

ided reliable indications of chlorophyll concentration in turfgrasseaves. This device works on a similar principle to the SPAD meterut it is applicable to relatively much larger (102 cm2) turf canopyreas.

nomy 45 (2013) 39– 51

Digital imaging and color analysis with the definition of a darkgreen color index were proposed as new tool for assessing leaf Nstatus (Karcher and Richardson, 2003; Rorie et al., 2011b), even ifcare must be paid to correct the acquired data for lighting (Rorieet al., 2011a).

Beside Chl, leaf flavonoids (Flav) represent a second class of com-pounds related to the plant N content. This was predicted by thecompetition between primary and secondary plant metabolisms(Jones and Hartley, 1999), for which, under N deficiency, a largerproportion of newly assimilated carbon will be allocated topolyphenols devoid of N used for plant defense rather than togrowing-related proteins. Several experimental studies proved thepresence of an inverse relationship between Flav contents and Nlevels in the leaves of different species (Norbaek et al., 2003; Leaet al., 2007; Olsen et al., 2009).

Epidermal Flav, which are representative of total leaf Flav (Kolband Pfündel, 2005; Agati et al., 2008), can be detected in situ by theChl fluorescence screening method (Agati et al., 2011). The methodis based on the filtering effect of UV-absorbing phenolic compoundspresent in leaf epidermises and fruit skins that are screeningunder-laying Chl. It is also called the ABC (Agati-Bilger-Cerovic)fluorescence method, referring to the authors who proposed andvalidated it by laboratory spectroscopic studies (Bilger et al., 1997;Cerovic et al., 2002; Agati et al., 2007). (Note that the order in theacronym is neither historical nor hierarchical, but simply alpha-betical and meant to be catchy: “simple as ABC”). The method wasextended recently to field studies by using portable sensors (cf.Tremblay et al., 2011 and references therein).

The combined optical detection of Chl and epidermal Flav wastherefore suggested as a powerful indicator of leaf N in wheat(Cartelat et al., 2005), indicating that a Chl-to-Flav ratio index isa better indicator of N status than chlorophyll alone because it isindependent of leaf mass per area (LMA) and has a larger responsespan due to an opposite response of Chl and Flav to N.

Fluorescence spectroscopy can also be used to estimate Chl con-tent, as an optical measurement alternative to reflectance, usingthe red-to-far-red ratio of the Chl fluorescence bands (Gitelsonet al., 1998, 1999; Buschmann, 2007). This ratio was found tobe correlated to the N supply in winter-barley and winter-wheat(Heege et al., 2008), in sugar beet (Langsdorf et al., 2000) and inwinter oilseed rape (Thoren and Schmidhalter, 2009), but poorly(McMurtrey et al., 1994) or not at all (Campbell et al., 2007) in corn.

The recently developed Multiplex portable sensor (Ben Ghozlenet al., 2010), based on the Chl fluorescence screening method,allowed for the simultaneous detection of both Chl and epidermalFlav compounds and was lately applied for the N deficiency evalu-ation in wheat (Martinon et al., 2011) and turfgrass (Lejealle et al.,2010).

In the present study, we investigated the potential of the Mul-tiplex sensor as an in situ non-destructive indicator of leaf Ncontent in both the Paspalum vaginatum and Zoysia matrella warmseason turfgrass species. Simultaneously, classical reflectance spec-troradiometric measurements were performed on the same plots,in order to compare and evaluate the performances of thereflectance-based estimation of N content with that of the Multi-plex fluorescence-based method. This allows us to choose the mostsuitable non-destructive methods for rapid in field assessment ofturfgrass N status for fertilization management.

2. Materials and methods

The trial was carried out in Pisa at the CeRTES (Center forResearch on Turfgrass for Environment and Sports), Pisa University(43◦40′N; 10◦23′E; 6 m a.s.l.) in 2010 on two mature turfs: Pas-palum vaginatum Swartz cv “Salam” and Zoysia matrella L. Merr. cv

G. Agati et al. / Europ. J. Agronomy 45 (2013) 39– 51 41

Fig. 1. (A) Schematic representation of the experimental plots, with 4 replicates per N supply. The N rates (kg ha−1) for each plot are reported and identified by differentcolors from red (0 kg ha−1) to dark green (250 kg ha−1). The 25 × 25 dots per plot, with a spatial separation of about 20 cm, represent the grid used for collection of theM y thep from( rred to

“sapta

Itatf

ultiplex measurements. (B) Maps of the NBI, CHL and FLAV indices determined banels) turfgrass plots treated with N as reported in (A). NBI and CHL values increaseFor interpretation of the references to color in this figure legend, the reader is refe

Zeon”. The swards were established on a silt-loam soil (sand 28%,ilt 55%, clay 17%) with 17 �g g−1 of available P2O5 (Olsen method)nd 248 �g g−1 of exchangeable K2O (Dirks Scheffer method) andH 7.8. During the trial period a turf height of 20 mm was main-ained by regular mowing with a reel blade mower. Irrigation waspplied as necessary to maintain healthy turfgrass.

From green-up to trial start no fertilizer was applied to the turf.n order to maximize the variability of nitrogen available to the

urf, the following 6 rates of nitrogen (ammonium sulphate) werepplied: 0, 50, 100, 150, 200 and 250 kg ha−1. Nitrogen applica-ion was carried out on 28 June 2010 and light irrigation followedertilizer application to avoid the risk of burns. The experimental

Multiplex data on the Paspalum vaginatum (left panels) and Zoysia matrella (right red to dark-green colors, while FLAV values increase from dark-green to red colors.

the web version of the article.)

design was a randomized block for each species with four repli-cates, each of 1 m2 area (Fig. 1). The weather conditions of globalirradiance, total rainfall and air temperature from the beginning ofthe vegetative period to the end of the trial are reported in Table 1.

2.1. The fluorimetric sensor measurements

The Multiplex 2 (Mx) fluorescence sensor (FORCE-A, Orsay,

France), described in detail elsewhere (Ben Ghozlen et al., 2010),consisted of a portable battery-powered fluorimeter with light-emitting diode (LED) matrices as light sources in the UV-A (370 nm),blue (460 nm), green (515 nm) and red (637 nm) spectral regions.

42 G. Agati et al. / Europ. J. Agronomy 45 (2013) 39– 51

Table 1Global irradiance, total rainfall and air temperature from the beginning of the vegetative period to the end of the trial.

Period Daily global irradiance (10 daysaverage) (MJ m−2 day−1)

Total rainfall over 10days (mm)

Air temperature at 5 cm abovethe ground (10 days average) (◦C)

1st 10 days 16.1 21.8 12.9April 2nd 10 days 16.0 15.2 12.6

3rd 10 days 17.4 40.2 17.3

1st 10 days 12.8 104.6 16.8May 2nd 10 days 15.7 71.4 17.2

3rd 10 days 22.2 0.6 21.3

1st 10 days 22.5 3.2 24.2June 2nd 10 days 18.3 64.4 23.9

3rd 10 days 23.4 0.4 24.1

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he Chl fluorescence in the red (RF) band, at 680–690 nm, and inhe far-red (FRF) band, at 730–780 nm, was acquired sequentially atll the excitation wavelengths. Since the LED sources were pulsednd synchronized to the detection, the sensor was insensitive tombient light and could be used directly in the field. The detectionrea of the sensor was circular with a 8 cm diameter at a distancef 10 cm from the light sources. A single measurement consisted inhe sequence of 4 excitation flashes and detection of the respectiveuorescence signals, repeated 500 times and averaged, for a totalcquisition time of less than 1 s. Different combinations of the RFnd FRF fluorescence signals at the various excitation bands coulde used as indices of different compounds, such as Flav, antho-yanins and Chl.

For the present experiment, the Chl fluorescence signals RFRnd FRFR, excited with red (R) light, FRFUV, excited with ultravioletUV) radiation and FRFG, excited with green (G) light, were used toalculate the Flav index

LAV = log(

FRFR

FRFUV

), (1)

he Chl indices

HL = FRFR

RFR(2)

HL1 = FRFG

FRFR(3)

nd two Nitrogen Balance Indices

BI = CHL

10FLAV= FRFUV

RFR(4)

BI1 = CHL1

10FLAV= FRFG FRFUV

FRF2R

(5)

The choice of these equations is based on the optical proper-ies of Chl and Flav and on previous spectroscopic (Bilger et al.,997; Cerovic et al., 2002) and in field (Cartelat et al., 2005; Cerovict al., 2009) studies. The definition of the FLAV index given in Eq.1), that compares the Chl fluorescence intensity under UV and Rxcitation, represents a differential absorption measurement (inccordance with the Beer–Lambert’s law) that is proportional tohe Flav content (Ounis et al., 2001; Agati et al., 2011).

The CHL index (Eq. (2)) is based on the partial reabsorption of RFy Chl itself (Gitelson et al., 1999; Buschmann, 2007), that dependsn the pigment concentration, while the FRF band is not reabsorbed.onsequently, the CHL index increases with the increase in Chl con-

entration. The CHL1 index (Eq. (3)) derives from the differencen the extinction coefficient of Chl between the G and R spectralegions. The G/R ratio of the light fraction absorbed by Chl, whichs proportional to the G/R excitation ratio of FRF, increases with

0.2 28.70.6 29.1

102.6 26.2

increasing Chl concentration. Eq. (3) assumes that the transmit-tance of the leaf epidermis in the G and R regions is constant and,therefore, it is not applicable to leaves containing anthocyanins, forexample.

The NBI and NBI1 indices were defined to be proportional tothe Chl-to-Flav ratio. They are based on the balance betweenprimary and secondary metabolism of plants under nitrogen con-trol, for which the synthesis of chlorophyll and Flav decreasesand increases, respectively, under nitrogen deficiency (Cartelatet al., 2005). Originally used as a ratio index devoid of units(surface or mass), NBI can also be considered as a proxy ofmass-based N (Nm). Indeed, Chl is often used as an index ofsurface-based N (Na). Because of the common control of LMAand epidermal flavonols by light, there is a very good correla-tion between surface-based epidermal flavonols and leaf mass perarea (LMA) (Meyer et al., 2006). Therefore, epidermal flavonolscan be considered as a proxy for LMA (g DM cm−2) and the NBI asChl/Flav ratio would be equivalent to a mass-based Chl: Chl cm−2/Flav cm−2 = Chl cm−2/LMA = Chl cm−2/g DM cm−2 = Chl/g DM.

Plots were scanned by moving the Mx sensor on a 5 × 5 samplinggrid (25 points) with a spatial separation of about 20 cm. For eachpoint, signals were integrated on a 50 cm2 (8-cm diameter) areapositioning the sensor on the top of the turf canopy. A schematicrepresentation of the arrangement of the experimental plots for thedifferent N rates is reported in Fig. 1A. Measurements were takenon 12 July 2010 right after mowing, two-weeks after nitrogen appli-cation, between 11:00 a.m. and 2:00 p.m. Scanning the 24 plots foreach species in a 4 m × 6 m parcel required about 1 h.

2.2. Reflectance measurements

Spectra were acquired using a LICOR 1800 spectroradiome-ter (LI-COR Inc., Lincoln, NE, USA) with a fiber optic light-guideand LICOR 1800-06 telescope. The telescope was mounted on apurpose-built trolley at about 1.5 m from the ground with a visionangle of 15◦. The monitored surface at ground level was 452 cm2.Measurements were taken right after mowing on 12 July 2010,between 11.30 am and 1.30 pm (solar time), in complete absence ofclouds. Reflectance measures were carried out in the 390–1100 nmspectral region at 5 nm intervals. The solar radiation reflected by abarium sulphate white panel was acquired every 10 min and usedas reference. The ratio between the turf reflected radiance and thatfrom the reference panel was used to calculate the turf spectralreflectance.

The reflectance-based vegetation indices, derived from the liter-ature (Horler et al., 1983; Penuelas et al., 1995; Tarpley et al., 2000;Zarco-Tejada et al., 2005; Clevers and Kooistra, 2012), evaluated inthis study are reported in Table 2.

G. Agati et al. / Europ. J. Agronomy 45 (2013) 39– 51 43

Table 2Reflectance-based vegetation indices used in this study.

Index Equation

Simple Ratio (SR) Indexa SR = RNIR/Rred; NIR = 760–900, red = 630–690SR900 = R900/R675; SR775 = R775/R675

Normalized Difference Vegetation Index (NDVI)a NDVI = (RNIR − Rred)/(RNIR + Rred)NDVIa = (R800–1100 − R600–700)/(R800–1100 + R600–700)NDVIb = (R800–900 − R500–600)/(R800–900 + R500–600)NDVI900 = (R900 − R675)/(R900 + R675)NDVI775 = (R775 − R675)/(R775 + R675)

Normalized Pigment Chlorophyll Index (NPCI)a NPCI = (R680 − R430)/(R680 + R430)Physiological Reflectance Index (PRI)a PRI = (R550 − R530)/(R550 + R530)Photochemical Reflectance Index (PRI)b PRI2 = (R530 − R570)/(R530 + R570)Simple Ratio Pigment Index (SRPI)a SRPI = R430/R680

Red Edge positionc The wavelength of reflectance’s inflection point between 678 and 740 nm,determined by the peak value of the 1st derivative of the reflectancespectrum

Red Edge/NIR Reflectance ratiosd RRed Edge/RNIR; Red Edge (700–760 nm); NIR (780–1100 nm)Gitelson and Merzlyak (GM)a GM1 = R750/R550; GM2 = R750/R700

CIgreene CIgreen = (R780/R550) − 1

CIRed Edgee CIRed Edge = (R780/R710) − 1

Zarco and Miller (ZM) Indexa ZM = R750/R710

Vogelmann (VOG) Indexa VOG1 = R740/R720

Triangular Vegegation Index (TVI)a TVI = 0.5·[120·(R750 − R550) − 200·(R670 − R550)]

MTVI1a MTVI1 = 1.2·[1.2·(R800 − R550) − 2.5·(R670 − R550)]

TCARIa TCARI = 3·[(R700 − R670) − 0.2·(R700 − R550)·(R700/R670)]

MCARIa MCARI = [(R700 − R670) − 0.2·(R700 − R550)]·(R700/R670)

OSAVIa OSAVI = (1 + 0.16)·(R800 − R670)/(R800 + R670 + 0.16)

OSAVI[705,750]e OSAVI[705,750] = (1 + 0.16)·(R750 − R705)/(R750 + R705 + 0.16)

MCARI/OSAVIe MCARIOSAVI = [(R700−R670)−0.2(R700−R550)](R700−R670)

(1+0.16)(R800−R670)/(R800−R670+0.16)

TCARI/OSAVIe TCARIOSAVI = 3[(R700−R670)−0.2(R700−R550)](R700−R670)

(1+0.16)(R800−R670)/(R800−R670+0.16)

MCARI/OSAVI[705,750]e MCARI

OSAVI [705, 750] = [(R750−R705)−0.2(R750−R550)](R750−R705)(1+0.16)(R750−R705)/(R750−R705+0.16)

TCARI/OSAVI[705,750]e TCARI

OSAVI [705, 750] = 3[(R750−R705)−0.2(R750−R550)](R750−R705)(1+0.16)(R750−R705)/(R750−R705+0.16)

a For references see Zarco-Tejada et al. (2005).b Penuelas et al. (1995).

2

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3

3

3

a

c Horler et al. (1983).d Tarpley et al. (2000).e For references see Clevers and Kooistra (2012).

.3. Samplings and leaf N content determination

For each plot, the turf quality (from 1 = poor to 9 = excellent), andhe color intensity (from 1 = very light green to 9 = very dark green)ere visually assessed following guidelines from the National Tur-

grass Evaluation Program (NTEP) (Greene et al., 2008).Turfgrass samples of 50 cm2, previously measured by the Mul-

iplex sensor, were used to determine for each plot the canopyitrogen content by the Kjeldahl method.

.4. Statistical analysis

The overall effect of the applied N rate on the different plantarameters was evaluated by one-way ANOVA. Multiple compari-on between N treatments was performed by using the all-pairwiseisher’s Least Significant Difference (LSD) test at the 95% confidenceevel. All statistical analyses were conducted using CoStat ver. 6.400oftware (CoHort software, Monterey, CA, USA).

. Results

.1. Response to nitrogen treatments

.1.1. Plant parametersThe average values of the leaf N content and turf quality

nd color intensity scores as function of N rates are shown in

Figs. 2 and 3 for P. vaginatum and Z. matrella, respectively. The leafN percentage determined destructively on grass samplings fromeach plot increased linearly from 1% to about 2.7% and 2%, for P.vaginatum (Fig. 2A) and Z. matrella (Fig. 3A), respectively, as theN rate applied to the soil was augmented from 0 to 250 kg ha−1.Both turfgrass color intensity and quality were also increasing withincreasing N rate. However, the trend was curvilinear reachinga maximum at about 200 kg ha−1 of applied N, beyond which adecrease in the plants response was observed (Figs. 2B, C and 3B, C).In P. vaginatum, the LSD analysis showed that the mean qualityscores for N treatments from 100 to 250 kg ha−1 were not sta-tistically different from each other (Fig. 2B). Within the same Nrate range, color intensity at 200 kg ha−1 was significantly higherthan the others (Fig. 2C). In Z. matrella, the maximal quality scoreat 150 kg ha−1 was not different from the one at 200 kg ha−1, buthigher than all the others (Fig. 3B). The color value at 250 kg ha−1

was not significantly different than values at 50 and 100 kg ha−1.Color intensity at 200 kg ha−1 was similar to that at 150 kg ha−1,but higher than the others. The value at 250 kg ha−1 was not signif-icantly different than those at 100 and 150 kg ha−1 (Fig. 3C).

3.1.2. Fluorescence-based indices

The spatial distribution of the NBI, CHL and FLAV Mx indices is

reported in Fig. 1B. The minimal NBI values were obtained for the0 kg ha−1 N rate (red color). The maximal NBI values were observedwith the 250 kg ha−1 and both 200 and 250 kg ha−1 N treatments for

44 G. Agati et al. / Europ. J. Agronomy 45 (2013) 39– 51

N rate (kg ha-1 )

0 50 100 150 200 250

Col

or

0

2

4

6

8

10

y = -0.00010x2 + 0.045x + 4.7 R2 = 0.927

Qua

lity

2

4

6

8

10

y = -0.00010x2 + 0.053x + 3.9 R2 = 0.963

leaf

N c

onte

nt (

%)

1.0

1.5

2.0

2.5

3.0

y = 0.00640x + 1.05

R2 = 0.93 7

P. vaginatum

C

B

A

Fig. 2. Leaf N content (A), turfgrass quality (B) and color intensity (C) as function ofthe N rate applied to the soil in Paspalum vaginatum. Bars represent the Least Sig-nqc

P(tlTwFarfis

Ha

Ca

N rate (kg ha -1 )

0 50 100 150 200 250

Col

or

0

2

4

6

8

10

y = -0.00010x2 + 0.047x + 4.1 R2 = 0.987

Qua

lity

2

4

6

8

10

y = -0.00010x2 + 0.033x + 5.1 R2 = 0.953

leaf

N c

onte

nt (

%)

1.0

1.5

2.0

2.5

y = 0.0040x + 1.0 R2 = 0.962

C

B

A Z. matrella

Fig. 3. Leaf N content (A), turfgrass quality (B) and color intensity (C) as function ofthe N rate applied to the soil in Zoysia matrella. Bars represent the Least SignificantDifference (LSD) ( = 0.05) from one-way ANOVA of data. Linear (A) and quadratic(B, C) polynomial fitting curves and the corresponding equations and coefficient of

ificant Difference (LSD) ( = 0.05) from one-way ANOVA of data. Linear (A) anduadratic (B, C) polynomial fitting curves and the corresponding equations andoefficient of determination are shown.

. vaginatum and Z. matrella, respectively. Mapping of the CHL indexFig. 1B, central panels) showed that plots without any N adminis-ration were well identified by the lowest values of CHL, that is theowest level of Chl, in most of the plots in P. vaginatum (red color).his was less evident in Z. matrella where only one out of four plotsith 0 kg ha−1 N rate showed marked low CHL values (red color).

or increasing N rates the CHL index did not show high spatial vari-bility. In Z. matrella, CHL levels for N rates in the 100–200 kg ha−1

ange (green – dark green colors) were clearly higher than thoseor 250 kg ha−1 of N (pale green color). Maps of the NBI1 and CHL1ndices were similar to those of NBI and CHL, respectively (data nothown).

The FLAV index was evidently inversely related to the N rate.igher levels of FLAV were found in P. vaginatum than in Z. matrella

s evidenced by the lower panels of Fig. 1B.

For each species, the 100 Mx measurements of NBI, NBI1, CHL,HL1 and FLAV obtained on the 4 replicates per N treatment wereveraged in order to evaluate their dependence on N rates. The

determination are shown.

average values of the Multiplex non-destructive indices as functionof the rate of N fertilization for the P. vaginatum and Z. matrellaturfgrass species are reported in Fig. 4.

The NBI and NBI1 indices increased with increasing N fertiliza-tion linearly in P. vaginatum and exponentially in Z. matrella (Fig. 4Aand D). In the latter, NBI index was insensitive to N treatmentbetween 200 kg ha−1 and 250 kg ha−1 N rates, while NBI1 mean val-ues were statistically significant different (P ≤ 0.001) among all thetreatment groups.

The FLAV index decreased exponentially with increasing N ratein both species (Fig. 4B and E).

For both species, the CHL and CHL1 indices followed a sec-ond order polynomial trend, increasing to a maximum and thendecreasing at the 250 kg ha−1rate (Fig. 4C and F). Using an exponen-

tial fitting curve, it was showed that CHL1 rose with a larger rateconstant than CHL. Saturation of both CHL and CHL1 for P. vaginatum

G. Agati et al. / Europ. J. Agronomy 45 (2013) 39– 51 45

NB

I

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

NB

I 1

0.05

0.10

0.15

0.20

0.25

NBINBI1

FLA

V

0.8

1.0

1.2

1.4 FLAVexp , R2 = 1

N rate (kg ha-1)

0 50 10 0 15 0 20 0 25 0

CH

L

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

CH

L1

1.08

1.10

1.12

1.14

1.16

1.18

1.20CHLCHL1

exp , R2 = 0.99exp , R2 = 1

linea r, R2 = 0.99

linea r, R2 = 0.99

NB

I

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.05

0.10

0.15

0.20

0.25

NBINBI1

FLA

V

0.8

1.0

1.2

1.4 FLAVexp, R2 = 1

N rate (kg ha-1)

0 50 10 0 15 0 20 0 25 0

1.12

1.14

1.16

1.18

1.20

1.22

1.24

CH

L

5.0

5.5

6.0

6.5

7.0

7.5

linea r, R2 = 0.99

linea r, R2 = 1

exp, R2 = 0.90exp, R2 = 0.98

P. vag ina tum Z. matrell aA D

B

FC

E

CHLCHL1

Fig. 4. Non-destructive NBI and NBI1 (A, D), FLAV (B, E), CHL and CHL1 (C, F) Multiplex indices as function of the N rate applied to the soil in Paspalum vaginatum (A–C) andZ ributef ondinw

oa

3

sai

Fo

oysia matrella (D–F) turfgrass. Values are means of 100 measurements equally distrom one-way ANOVA of data. Fitting curves, linear or exponential, and the correspere slightly shifted to right for visual optimization.

ccurred above 150 kg ha−1. In Z. matrella, CHL and CHL1 saturatedbove 50 kg ha−1 and 100 kg ha−1, respectively.

.1.3. Spectral reflectance parameters

The spectral reflectance characteristics of both turfgrasses were

trongly influenced by the nitrogen rate but to a different extent,s shown in Fig. 5. In P. vaginatum, the increase in fertilizationnduced a general reduction of reflectance in the visible region

450 525 600 675 750 825 900 975 1050

Ref

lect

ance

(%

)

0

10

20

30

40

50

60

70

0 kg ha-1

50250

P. vaginatum

Wavele

A

appli ed N

ig. 5. Reflectance spectra of P. vaginatum (A) and Z. matrella (B) for non-fertilized (0 kg haf 4 replicates.

d on the 4 replicates. Bars represent the Least Significant Difference (LSD) ( = 0.05)g coefficient of determination are shown. In panel F, CHL1 symbols (open squares)

and a reflectance increase in the NIR (Fig. 5A). More precisely,P. vaginatum showed a decrease in the values of reflectancecorresponding at the entire visible spectral range, both at thepeak of chlorophyll absorbance in the red and at its peak of

reflectance in the green passing from the non-fertilized testplots to those fertilized with N. For P. vaginatum, the relation-ship between each reflectance index tested and the N rate waswell fitted by an exponential curve, decreasing or rising, with

ngth (nm)

450 525 600 675 750 825 900 975 1050

0 kg ha-1

50250

Z. matrell aB

appli ed N

−1) and for test plots treated with 50 and 250 kg ha−1 N rate. Spectra are the average

46 G. Agati et al. / Europ. J. Agronomy 45 (2013) 39– 51

Table 3Average vegetation indices of turfgrasses as function of N rate.

N rate (kg ha−1) Paspalum vaginatum

Red Edge (nm) R730/R810 NDVIa ZM GM1 GM2 OSAVI OSAVI [705,750] TCARI/OSAVI

0 721.2 d 0.69 a 0.76 d 2.3 d 3.6 d 3.74 d 0.91 d 0.56 d 19.07 a50 724.0 c 0.61 b 0.81 c 2.9 c 4.4 c 4.94 c 0.96 c 0.67 c 14.86 b

100 724.7 b 0.59 c 0.82 b 3.1 b 4.6 b 5.37 b 0.98 b 0.70 b 14.06 bc150 725.8 a 0.56 d 0.84 a 3.4 a 5.0 a 6.01 a 1.00 a 0.73 a 13.47 cd200 726.0 a 0.55 d 0.84 a 3.5 a 5.1 a 6.06 a 1.00 a 0.74 a 13.07 d250 726.1 a 0.55 d 0.84 a 3.5 a 5.2 a 6.07 a 1.00 a 0.74 a 12.84 d

N rate (kg ha−1) Zoysia matrella

Red Edge (nm) R735/R1000 NDVIb PRI MCARI TCARI MCARI/OSAVI TCARI/OSAVI

0 721.3 d 0.71 a 0.69 d 0.093 a 10.8 a 16.6 a 12.0 a 18.5 a50 723.8 c 0.65 b 0.75 c 0.086 b 7.6 b 12.5 b 8.1 b 13.3 b

100 725.5 b 0.61 c 0.77 b 0.077 c 5.8 c 10.0 c 6.1 c 10.5 c150 725.8 b 0.60 cd 0.78 ab 0.078 c 5.4 c 9.4 cd 5.7 cd 9.9 c200 726.7 a 0.58 d 0.79 a 0.077 c 5.1 cd 8.9 cd 5.2 cd 9.2 c250 725.4 b 0.59 d 0.77 b 0.075 c 4.6 d 8.4 d 5.1 d 9.2 c

M ntly d

cTtgoi2NvernM2

TP

A

eans (n = 4) with the same letter within each column per species are not significa

oefficient of determination, R2, between 0.939 and 0.994. Inable 3, the indices showing the highest R2 (≥0.99) and ableo discriminate at least four levels of N rate are reported. Ineneral, not significant differences in the index values werebserved above 150 kg ha−1 of N (Table 3). In Z. matrella, reflectancen the NIR region remained constant around 53% up to the00 kg ha−1 N and then decreased to 49.3% at the 250 kg ha−1

rate (Fig. 5B). Because of this anomalous behavior, only fewegetation indices, those showed in Table 3, presented a goodxponential fit (R2 ≥ 0.90) of its relationship with N rate. Itesulted that some of the indices (PRI and TCARI/OSAVI) did

ot discriminate N rate above 100 kg ha−1. MCARI, TCARI andCARI/OSAVI were not significantly different between 100 and

00 kg ha−1.

able 4recision of the estimate (RMSE) and coefficient of determination (R2) for the curve fittin

Paspalum vaginatum

RMSE (% N) R2

Exponential fittingR730/R1000 0.43 0.71

NPCI 0.48 0.71

SRPI 0.50 0.71

NVVIa 0.52 0.71

R730/R810 0.53 0.73

PRI2 0.58 0.80

R730/R755 0.62 0.76NDVIb 0.70 0.71ZM 0.76 0.74Red Edge 0.77 0.72GM2 0.77 0.72CHL 0.52 0.78

CHL1 0.32 0.76

FLAV 0.24 0.84

NBI 0.23 0.87

NBI1 0.24 0.79

Linear fitting

FLAV 0.30 0.80

NBI 0.23 0.87

NBI1 0.24 0.85

ll the curve fitting were significant at P < 0.0001.

ifferent by LSD0.05 comparisons.

3.2. Relationship between non-destructive indices and leafnitrogen content

The relationship between the optical indices and the leaf N con-tent was studied by fitting experimental data with an exponentialfunction. The indices with R2 of the fitting >0.7 are presented inTable 4, along with the Root Mean Square Error (RMSE) of esti-mates. For P. vaginatum, NBI, NBI1 and FLAV presented the lowestRMSE (0.23–0.24% of N). While for Z. matrella, the most preciseindices resulted to be MCARI, TCARI, NBI1 and FLAV (RMSE = 0.22%of N), followed by MCARI/OSAVI and NBI (RMSE = 0.24 and 0.26%

of N, respectively). NBI for P. vaginatum and FLAV for Z. matrellahad the highest R2, 0.87 and 0.83 respectively. The index-leafN relationships with the lowest RMSE were also fitted by using

g of the relationship between optical indices and leaf nitrogen content.

Zoysia matrella

RMSE (% N) R2

MCARI 0.22 0.80TCARI 0.22 0.78MCARI/OSAVI 0.24 0.79TCARI/OSAVI 0.26 0.77PRI 0.36 0.71R735/R1000 0.37 0.73

CHL 0.52 0.73CHL1 0.30 0.81FLAV 0.22 0.83NBI 0.26 0.78NBI1 0.22 0.79

MCARI 0.24 0.71TCARI 0.25 0.70MCARI/OSAVI 0.26 0.69TCARI/OSAVI 0.27 0.66FLAV 0.20 0.78NBI 0.22 0.75NBI1 0.20 0.78

G. Agati et al. / Europ. J. Agronomy 45 (2013) 39– 51 47

leaf N content (%)

1.0 1.5 2.0 2.5 3.0

NB

I

0.0

0.3

0.6

0.9

1.2

1.5

leaf N content (%)

1.0 1.5 2.0 2.5 3.0

R73

0/R

1000

0.50

0.52

0.54

0.56

0.58

0.60

0.62

0.64

CH

L1

1.05

1.10

1.15

1.20

1.25

1.30

FLA

V

0.7

0.9

1.1

1.3

1.5

R2 = 0.87; *** *RMSE = 0.23%

R2 = 0.84 ; *** *RMSE = 0.24%

R2 = 0.76 ; *** *RMSE = 0.32 %

A B

C

Paspalum vaginatum

0 kg ha-1

50100150200250

applied N

R2 = 0.71 ; *** *RMSE = 0.43 %

D

Fig. 6. Relationships between the non-destructive Multiplex indices of chlorophyll, CHL1 (A), flavonols, FLAV (B), nitrogen balance, NBI (D) and R730/R1000 (C) and the nitrogencontent (%) of Paspalum vaginatum turfgrass leaves. Fitting curves are rising, y = y0 + a[1 − exp(−x/b)], (A), decreasing, y = y0 + a[exp(−x/b)], (B, C) exponential and linear (D).D N rateP ader is

afatZNw

F(o*

ata are grouped by colors from red to dark-green corresponding to the different

< 0.0001. (For interpretation of the references to color in this figure legend, the re

linear model (Table 4). In this case, in P. vaginatum, no dif-erence was observed for NBI. NBI1 had the same precision but

higher R2 (0.85 vs 0.79) with respect to the exponential fit-ing, and FLAV became less precise (RMSE of 0.30 vs 0.24% N). In

. matrella, NBI1 and FLAV were the most precise (RMSE = 0.20%), NBI improved its precision (RMSE from 0.26 to 0.22% N),hile all the reflectance indices, MCATI, TCARI, MCARI/OSAVI and

0.5 leaf N content (%)

0.5 1.0 1.5 2.0

MC

AR

I

3.5

5.0

6.5

8.0

9.5

11.0

12.5

CH

L1

1.05

1.10

1.15

1.20

1.25

1.30

0 kg50100150200250

C

R2 = 0.80; ****RMSE = 0.22 %

Zoysia mat

appli ed N

A

R2 = 0.81 ; *** *RMSE = 0.30 %

ig. 7. Relationships between the non-destructive Multiplex indices of chlorophyll, CHL1

C) and the nitrogen content (%) of Zoysia matrella turfgrass leaves. Fitting curves are risinr linear (B, D). Data are grouped by colors from red to dark-green corresponding to the***Significant at P < 0.0001. (For interpretation of the references to color in this figure leg

s. Precision of the estimate for each index is expressed by RMSE. ****Significant at referred to the web version of the article.)

TCARI/OSAVI, performed worse than those for the exponential fit-ting.

The best relationships between the optical indices and the leaf Ncontent are plotted in Figs. 6 and 7, for P. vaginatum and Z. matrella

respectively. In these figures, the individual spot Mx measurementson the very same grass samplings recorded before collection wereused.

leaf N content (%)

1.0 1.5 2.0 2.5

NB

I 1

0.00

0.05

0.10

0.15

0.20

0.25

0.30

FLA

V

0.5

0.7

0.9

1.1

1.3

ha-1 N

R2 = 0.78; ****RMSE = 0.20%

R2 = 0.78; ****RMSE = 0.20%

B

D

rell a

(A), flavonols, FLAV (B), nitrogen balance, NBI1 (D) and the reflectance-based MCARIg, y = y0 + a[1 − exp(−x/b)], (A) and decreasing, y = y0 + a[exp(−x/b)], (C) exponential

different N rates. Precision of the estimate for each index is expressed by RMSE.end, the reader is referred to the web version of the article.)

48 G. Agati et al. / Europ. J. Agronomy 45 (2013) 39– 51

Table 5Exponential curve fitting parameters of the response of reflectance-based indices, quality and color scores and fluorescence-based indices to different N rates.

Index Paspalum vaginatum Zoysia matrella

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

Red Edge 70.9 11.8 0.99** 58.1 65.1 0.94*

R810/R730 88.5 16.9 0.99***

R1000/R735 84.7 61.4 0.98**

NDVIb 45.2 76.8 0.97*

NDVIa 61.3 18.3 0.99**

PRI−1 83.3 44.7 0.94**

ZM 76.9 18.0 0.99***

GM1 90.9 15.4 0.99**

GM2 83.3 23.3 0.99**

MCARI−1 142.8 13.6 0.99***

TCARI−1 123.4 11.5 0.99***

OSAVI 71.4 27.8 0.99***

OSAVI[705,750] 57.5 21.5 0.99***

OSAVI/MCARI 114.9 6.7 0.99****

OSAVI/TCARI 66.7 41.4 0.99*** 99.0 14.7 0.99***

quality 49.7 31.8 1.00**** 43.9 98.5 0.82*

color 38.8 76.2 0.97** 51.3 71.4 0.96**

CHL 60.2 112.6 0.99*** 30.3 106.2 0.90*

CHL1 60.6 29.0 1.00*** 48.3 77.6 0.97**

−FLAV 142.9 4.5 1.00**** 107.5 9.2 1.00***

NBI 454.5 8.4 0.99*** 131.6 13.8 0.99***

NBI1 588.2 6.9 1.00*** 185.2 4.4 1.00****

Bold figures indicate the most significant values for the reflectance and fluorescence indices.* Significant at P < 0.1.

4

4

timmnFtr

s(

(ioeiwb2

trwFoensdmi

** Significant at P < 0.01.*** Significant at P < 0.001.

**** Significant at P < 0.0001.

. Discussion

.1. Non-destructive proxy for nitrogen rates

The linear relationship observed between the leaf N content andhe fertilization rate (Figs. 2A and 3A) implies that a non-destructivendex able to predict the N rate can indirectly be used to esti-

ate the leaf N accumulation. However, the N accumulation in dryatter (% N) in response to fertilization was very different in P. vagi-

atum (slope = 0.006, Fig. 2A) compared to Z. matrella (slope = 0.004,ig. 3A). These differences can be mitigated for practical applica-ions by normalizing all data to the well fertilized plot, used as aeference (Samborski et al., 2009).

For both species, the visual rating was not much usefulince unable to distinguish N treatments above 100 kg ha−1

Figs. 2B, C and 3B, C).The reflectance spectral analysis applied to P. vaginatum data

Fig. 5A and Table 3) was in accordance with previous stud-es carried out on the same species (Trenholm et al., 2001) andn bermudagrass (Volterrani et al., 2005; Xiong et al., 2007). Asxpected, with increasing N supply reflectance decreased in the vis-ble wavelength regions because of a Chl concentration increase,

hile in the NIR reflectance increased due to an increase of cropiomass, leaf area and turgidity (Hinzman et al., 1986; Xue et al.,004; Lee et al., 2011).

Reflectance-based indices in Z. matrella had in general the samerends with N rate as those in P. vaginatum, but with earlier satu-ation. We noticed, however, that the NIR reflectance of Z. matrellaas in contrast with what observed in P. vaginatum (Table 3 and

ig. 5), in Agrustis palustris (Keskin et al., 2004) and in the majority ofther plants (Curran and Milton, 1983; Hinzman et al., 1986; Filellat al., 1995). Differences in leaf anatomical traits such as leaf thick-ess, size of the bulliform cells and leaf water content that affect

ignificantly NIR reflectance (Lee et al., 2011) may explain the aboveiscrepancy. The upper limit of discrimination of N treatments forost of the vegetation indices was of 150 kg ha−1 and 100 kg ha−1

n P. vaginatum and Z. matrella, respectively (Table 3).

The fluorescence-based method gave results (Fig. 4) consistentwith those found in its application to similar studies on wheat(Cartelat et al., 2005) and other turfgrass species (Lejealle et al.,2010). The new introduced Mx indices, NBI1 and CHL1, performedbetter than NBI and CHL, respectively. This was probably due toa slightly higher sensitivity to Chl concentration changes of theFRFG/FRFR ratio with respect to FRFR/RFR NBI1 was the only indexable to monitor, for both species, N fertilization on the wholeN range considered. NBI and FLAV discriminated all the N treat-ments in P. vaginatum and all but the two last (200 kg ha−1 and250 kg ha−1) in Z. matrella.

4.1.1. Comparison of indicesIn order to better compare the efficiency of the different indices

in the estimation of the N rate applied to the soil, we have to con-sider the precision of the estimate given by the RMSE of residuals,but also the level of linearity of the response. To do this, at firstwe inverted some of the reflectance-based indices, to have all ofthem positively correlated to the N doses. The inverted indicestaken into account were R810/R730, 1/PRI, R1000/R735, 1/MCARI,1/TCARI, OSAVI/MCARI and OSAVI/TCARI. For the Mx FLAV index,defined as logarithm, the –FLAV was considered. The scatter plotsof all the indices, reflectance-based, fluorescence-based and qual-ity and color scores, as function of N rate were then fitted withan exponential function rising to a maximum, with equationy = y0 + a[1 − exp(−x/b)]. Good indices were considered those forwhich the above exponential relationship was the closest to theideal linear one (Richardson et al., 2002), that is those with thelargest rise constant b values. In fact, the rise constant representsthe N rate value at which the index reached 63.2% of its total incre-ment. The larger this value, the higher the discrimination power ofthe index.

Results of this analysis are reported in Table 5 along with

the RMSE values and the coefficients of determination of thefit. It can be seen that most of the relationships consid-ered were well represented by the exponential function, withR2 > 0.97. Sorting the indices on the RMSE basis, −FLAV and NBI1

J. Agronomy 45 (2013) 39– 51 49

onrptqRlri(c

wM

tO

4

NsrTNpn0NtRC(ippmitioeiF

wgwoatSFFdtc

4

sd

NBI1, plot average

0.00 0.05 0.10 0.15 0.20 0.25 0.30

NB

I 1, s

ampl

ing

0.00

0.05

0.10

0.15

0.20

0.25

0.30

y = 1.1 x - 0.00050

R 2 = 0.93

Fig. 8. Relationship between the NBI1 Multiplex index measured on the spots ofturfgrass samplings and the average NBI1 on each plot. Data are from P. vaginatum

G. Agati et al. / Europ.

ut-performed all other indices in monitoring the N rate for P. vagi-atum (RMSE = 4.5 kg ha−1) and Z. matrella (RMSE = 4.4 kg ha−1),espectively. Considering the linearity of the response, NBI1 wasreferable for both species (larger rise constant). On the other hand,he precision of NBI1 for P. vaginatum, with RMSE of 6.9 kg ha−1 wasuite close to that of −FLAV. The CHL index was the worst due toMSE larger than 100 kg ha−1 and considerable divergence from

inearity. Finally, in previous studies NBI was found to be moreobust than FLAV (and CHL) to leaf age differences (heterogene-ty along the monocot leaf) (Cartelat et al., 2005) and year effectsLejealle et al., 2010). Therefore, taking into account the above threeriteria, NBI1 appeared to be the most appropriate index of N rate.

For reflectance detection, the most linear responsive indicesere GM1 in P. vaginatum (rise constant = 90.9 kg ha−1) andCARI−1 (rise constant of 142.8 kg ha−1) in Z. matrella.Considering the RMSE criterion, Red Edge position was superior

han GM1 (RMSE of 11.8 versus 15.4 kg ha−1) in P. vaginatum andSAVI/MCARI was better than MCARI−1 in Z. matrella.

.2. Nitrogen mapping by the fluorescence-based indices

For both turfgrass species, the visual comparison between theBI maps (Fig. 1B, top panels) or NBI1 (data not shown) and the

cheme of the N supply (Fig. 1A) indicates that the NBI indices rep-esent well the distribution of N rate among the different plots.he CHL and CHL1 indices seem to be less suitable than NBI andBI1 in discriminating plots with different N supply (Fig. 1B, centralanels), since the distribution of the indices appears quite homoge-eous, especially for Z. matrella, independently of the N rate above

kg ha−1. In addition, the CHL index, but neither CHL1 nor FLAV andBI1, can be affected by the variation of Chl fluorescence yield due

o light intensity and quenching changes during the day that affectsF more than FRF. On the other hand, real physiological change ofhl content (Hoel and Solhaug, 1998) or chloroplast movementsNaus et al., 2010) during the day may affect also CHL1 and NBI1ndices. These changes are small (<10%) but it is recommended toerform fluorescence measurements at approximately the sameeriod of the day. FLAV index also provided fair maps of N treat-ents (Fig. 1B, lower panels). The higher level of Flav concentration,

ndicated by the higher FLAV values, in P. vaginatum with respecto Z. matrella can be due to a species specific expression of genesnvolved in the Flav biosynthetic pathway. As alternative, becausef the high sensitivity of Flav accumulation to light intensity (Agatit al., 2011), a difference in the leaf and canopy structure able tontercept more or less solar radiation can explain the diversity inlav content between the two species.

Due to the significant heterogeneity in the Mx indices observedithin the same plot (Fig. 1B), it was useful to check if a sin-

le measurement could be representative of the whole plot. Thisas proved to be true by comparing the values of the Mx indices

btained on individual spots of turfgrass samplings with those aver-ged on the whole plot, as shown in Fig. 8 for the NBI1 index. Inhis comparison, data for both species were considered together.imilar good linear regression was found for NBI, CHL, CHL1 andLAV indices (data not shown). Still, the recorded dispersion inigs. 8 and 1 (mapping) should be kept in mind when assessingata for the relationship between reflectance indices and leaf N con-ent (Figs. 6C and 7C), since reflectance was acquired on 450 cm2

ompared to N-sampling spots of only 50 cm2.

.3. Non-destructive proxy for leaf nitrogen contents

The relationships between Mx indices and the leaf N percentagehowed that the chemical analysis of N content introduced largerispersion of points than the optical non-destructive determination

(closed circles) and Z. matrella (open circles). The solid line indicates the linear fit-ting (y = 1.1x − 0.00050, R2 = 0.93), and the dashed lines indicate the 95% confidencelimits.

(compare horizontal versus vertical distribution of points under thesame color, same N rate, in Figs. 6 and 7).

For both species, a saturation in the CHL1 index occurred for leafN content above 1.5–2%, consequently, it cannot represent a goodproxy of N content. Indeed, the RMSE for leaf N content estimationby CHL1 was high (0.30–0.32% N).

NBI1, NBI and FLAV presented similar precision in the estimateof leaf N, however, the formers were preferable because of theirgood linear response for both species (Table 4). Inversion of theNBI1 versus leaf N content relationship can be used to predict thecrop N status and perform the adequately required fertilization.

Considering the R730/R1000 and MCARI reflectance-based indicesas function of the leaf N content, reported in Figs. 6C and 7C, respec-tively, it was confirmed that the dispersion of data introduced bythe optical measurements is lower than that introduced by thechemical analysis, as observed for the relationship between Mxindices and leaf N %. Both indices presented a flatting of the curvesabove about 1.75% of N. A second-order quadratic relationshipbetween NDVI and tissue N content, with an average R2 of 0.76 and0.83 for bermudagrass and bentgrass respectively, was previouslyreported (Bell et al., 2004). However, the authors did not show thelevel of saturation of the indices for the N prediction.

5. Conclusion

Our results showed that optical non-destructive methods canrepresent suitable tools for the evaluation of the level of N fertil-ization and the content of leaf N in P. vaginatum and Z. matrella.However, marked differences in the efficiency of the various indicesstudied were seen, depending also on the turf species. The NBI1Mx index resulted to be the most efficient in discriminating thedifferent N rate of soil fertilization, discerning all the 6 N levelsapplied to P. vaginatum and Z. matrella plots (Fig. 4A and D). Thebest reflectance-based indices were able to discriminate the first4 and 3 out of 6 N levels in P. vaginatum and Z. matrella, respec-tively (Table 3). It is worth to note that the CHL and CHL1 Mx indexbased on the detection of Chl alone presented a curvilinear responseto N rate similar to that observed for the reflectance-based andquality/color visually assessed indices.

NBI indices were also well linearly correlated to the leaf N con-tent. They are more sensitive than CHL and FLAV indices alone

and can reduce the problem of compound concentration gradientspresent along the leaf of monocots (Cartelat et al., 2005). Robust-ness of NBI against physiological influences in turfgrass can be

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ound in the literature (Lejealle et al., 2010) and will be furthertudied in the future.

The NBI mapping of our experimental spatial structure (Fig. 1),sed to avoid soil dependence, provided a nice simulation of the ineld real N spatial distribution achieved by using optical sensors.ince the Mx sensor can be easily mounted on an utility vehicleLejealle et al., 2010; Martinon et al., 2011), it can be applied for

apping large-dimension fields to perform N fertilization precisionanagement.The active fluorescence-based method has the advantage with

espect to the passive reflectance-based one that it can be appliednder any light condition, even during cloudy days, without needf reference measurements. Furthermore, the fluorescence ratioignals employed were insensitive to the soil contribution, whichndeed can affect reflectance, making them suitable even for lessense areas (Heege et al., 2008). Using indices calculated from theame fluorescence signal (FRF) under different excitation wave-engths, such as the CHL1, FLAV and NBI1 will also decreaseotential effects of variable Chl fluorescence yield.

Possible limitations of the fluorescence-based method mayccur in the case of change of Flav due to causes different than Ntatus, such as the response to a pathogen attack or to other nutrienteficiency. On the other hand, the information about the Flav con-ent provided by the Multiplex represents by itself an added valuef turf quality related to the protective role of these compoundsgainst plant diseases. The fluorescence-based method and sen-or can, therefore, represent an important technical improvementn the non-destructive monitoring of turf N status and the relatedrecision fertilization management.

cknowledgment

The authors wish to thank Filippo Lulli for assistance in the paperreparation.

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