Leaf nitrogen concentration and chlorophyll meter readings as predictors of tall fescue nitrogen nutrition status

Field Crops Research 129 (2012) 46–58

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Leaf nitrogen concentration and chlorophyll meter readings as predictors of tall fescue nitrogen nutrition status Pedro M. Errecart a,∗ , Mónica G. Agnusdei a , Fernando A. Lattanzi b , María A. Marino c a

Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Balcarce, Balcarce, Argentina Lehrstuhl für Grünlandlehre, Technische Universität München, Freising-Weihenstephan, Germany c Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Balcarce, Argentina b

a r t i c l e

i n f o

Article history: Received 30 July 2011 Received in revised form 13 January 2012 Accepted 14 January 2012 Keywords: N diagnosis NNI Leaf age Leaf size SPAD Lolium arundinaceum

a b s t r a c t Determining the nitrogen nutrition index (NNI) of a crop requires measuring the amount and nitrogen concentration of standing biomass. This limits its use at farm conditions where simplicity and agility are required. In this study, two proxies of NNI were assessed: the nitrogen concentration of leaves located in the sward upper 5–7 cm layer (NUSL , g N kg−1 dry matter), and its greenness, as measured by a SPAD 502 handheld chlorophyll meter (SPADUSL , SPAD units). Seven field experiments carried out at Balcarce (Argentina), on two soil types, were conducted from 2008 to 2010, during autumn, late winter/early spring, spring and summer regrowths of tall fescue swards (Lolium arundinaceum (Schreb.) Darbysh.). Different nitrogen application rates were imposed to generate contrasting conditions of nitrogen availability. NNI, NUSL and SPADUSL were simultaneously measured four to seven times during sward regrowths. NUSL was closely associated with NNI. However, the regression parameters changed with elapsed time since sward initial cut. Once developmental effects were accounted for by fitting lineal regressions separately to thermal time intervals related to leaf appearance rate, equation parameters did not differ among years, seasons and sites: NNI = 0.024 (NUSL − 3.27), R2 = 0.89, N = 78; NNI = 0.026 (NUSL − 2.37), R2 = 0.81, N = 108; and NNI = 0.030 (NUSL − 3.26), R2 = 0.84, N = 102 for swards accumulating, respectively, <260, 260–440, and >440 growing degree days (GDD, base temperature 4 ◦ C) since the initial cut. Obtained equation parameters compared well to those reported for other C3 grasses, suggesting that the NNI–NUSL relationship is approximately constant among members of this functional subgroup. Likewise, SPADUSL was highly associated with NUSL and, therefore, with NNI. Developmental effects were also evident, but only two equations arised: NNI = −0.81LN (−0.82LN (SPADUSL /68.42)), R2 = 0.69, N = 72; and NNI = 0.035 (SPADUSL –18.50), R2 = 0.75, N = 169, for swards accumulating, respectively, <260 or >260 GDD since initial cut. The effect of the higher intrinsic variability of SPADUSL on its NNI predictive precision can be compensated increasing its sampling intensity, except at early stages of sward development when higher leaf N concentrations derive in the saturation of the chlorophyll meter. Still, SPADUSL emerges as an adequate method for performing an instantaneous, approximated, ‘in the field’ estimation of swards N status, thus allowing a prompt correction of N deficiencies through fertilization. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The nitrogen nutrition index (NNI) is the reference plant-based indicator of the nitrogen (N) nutrition status of crops and pastures. This index is defined as the ratio between actual shoot biomass N concentration and a critical N concentration (NCR ),

Abbreviations: GDD, growing degree days; NCR , critical N concentration; NNI, nitrogen nutrition index; NUSL , uppermost sward leaves nitrogen concentration (g N kg−1 dry matter); SPADUSL , uppermost sward leaves SPAD readings (SPAD units). ∗ Corresponding author at: Ruta 226 km 73.5, 7620, Balcarce, Argentina. Tel.: +54 2266 439100; fax: +54 2266 439112. E-mail address: [email protected] (P.M. Errecart). 0378-4290/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2012.01.008

defined as the minimal N concentration required for sustaining maximal growth rates (review by Lemaire and Gastal, 2009). NCR declines as shoot biomass increases during sward regrowth period, hence NNI determination becomes a laborious procedure requiring time-consuming quantifications of sward shoot biomass and N concentration, reducing its suitability for routine sward N nutrition diagnosis at farm level. Efforts are currently being made to generate surrogate methods allowing a quicker and more practical N nutrition status assessment methodology in perennial grasses (i.e., Duru, 2002; Farrugia et al., 2004), spring wheat (Ziadi et al., 2010) and corn (Ziadi et al., 2008, 2009). The development of more expedite diagnosing methods would allow farmers to routinely monitor the N status of crops, improve fertilization timing and rate adjustment, reduce N losses and hence enhance fertilizer use efficiency.

P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58

Lemaire et al. (1997) documented in maize, that the N concentration of the uppermost illuminated leaves of the canopy remained more or less constant under conditions of stable N nutrition status, even when the N concentration of shoot biomass declined as crop biomass increased. Then Gastal et al. (2001) established that for ryegrass stands, the N concentration of uppermost sward leaves (NUSL ) correlated well with its NNI. Thereafter, this association was further validated by Farrugia et al. (2004) on mixed stands of Agrostis spp., Holcus spp. and Lolium perenne, by Duru (2004) in cocksfoot (Dactylis glomerata L.) swards and recently, by Ziadi et al. (2010) in wheat. Hence, NUSL seems to be an unbiased estimate of sward NNI. However, the parameters of the NNI–NUSL relationship vary between studies, and in certain cases with sward development within studies. At present, it is not clear whether these variations are significant enough so as to consider this relationship as not stable among developmental stages, seasons and species. Taking advantage of the aforementioned relationship for the assessment of swards N status still requires, though, the assistance of a laboratory for leaf N concentration determination. The time required by such quantification limits the suitability of the NUSL approach for the rapid and easy N nutrition diagnosis required at farm conditions. Leaf N concentration has been shown to be strongly associated with readings of a handheld device – Minolta SPAD 502 – which measures leaf greenness (Chapman and Barreto, 1997; Di Salvo et al., 1999; Duru, 2002). The utilization of such a device giving instantaneous readings of leaf greenness could be an attractive option for estimating directly the NUSL and indirectly the NNI, given the high association between the latter variables. This SPADUSL approach showed promise as a tool for assessing the N nutrition status of orchardgrass swards in spring and summer growth seasons in France (Duru, 2002). Likewise, Ziadi et al. (2008, 2010) found value in this approach for maize and wheat crops. However, two important aspects remain unclear: first, does sward developmental stage affect the NNI–SPADUSL relationship? In wheat, for instance, the suitability of SPAD readings decreases because the association between NNI and the SPAD values of the uppermost collared leaf changed with developmental stage (Ziadi et al., 2010). Second, is such relationship conserved seasonally? These are crucial aspects for forage crops, as they undergo repeated cycles of defoliation/regrowth, and are used over extended periods of time spanning from several months to the entire year. Further, estimating NNI from SPADUSL , while quicker and cheaper, might involve a loss in precision of the N status prediction. Solving such a trade-off, that may well depend on what the diagnosis is needed for (e.g. research vs. farm monitoring), requires an analysis of the precision in sward NNI prediction by both proxies, NUSL and SPADUSL , as well as of the effect of sampling intensity on such precision. The aim of this work was to test the usefulness of NUSL and SPADUSL to diagnose the N status of tall fescue (Lolium arundinaceum (Schreb.) Darbysh.) swards. This species is the most widely used forage grass in Argentina, where it can sustain relatively high

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levels of animal production in marginal soils with poor mineral nutrition (Lattanzi et al., 2007). With this purpose, we analyzed data from a series of seven N-fertilization experiments carried out in two soil types, at Balcarce, Argentina, in successive years, during summer, spring, autumn, and late winter/early spring, in which we (i) evaluated the relationship between NNI and NUSL in tall fescue swards, (ii) compared it with those reported elsewhere for different C3 grasses, (iii) assessed the relationship between NNI and SPADUSL , and (iv) compared the precision of both proxies – NUSL and SPADUSL – for assessing tall fescue NNI. 2. Materials and methods 2.1. Site, soil and climate All experiments were carried out at the Estacion Experimental Agropecuaria Balcarce (INTA), in the southeast of Buenos Aires Province, Argentina (37◦ 45 S 58◦ 18 W, 130 m above sea level). The climate of the area is temperate subhumid–humid. During the last decade, mean annual temperature was 14.2 ◦ C, with monthly means ranging from 7.8 ◦ C in July to 21.4 ◦ C in January. Average annual rainfall and potential evapotranspiration were 990 mm and 950 mm, respectively. Tall fescue swards (L. arundinaceum) cv. El Palenque MAG INTA (temperate type) were established in 1999. In six out of seven experiments, the soil was a loamy textured Typic Natraquoll (Soil Survey Staff, 2006). The 20 cm depth topsoil had an organic matter content of 38 g kg−1 , pH 9 (soil:water 1:2.5), phosphorus content of 7 mg kg−1 (Bray I), an electric conductivity of 1.0 dS m−1 and 19% of exchangeable sodium (site A). Within site A, the six aforementioned experiments were carried out on independent areas. The remaining experiment (Spring 2009) was established on a loamy textured Argiaquoll, with topsoil organic matter content of 96 g kg−1 , pH 7.2, phosphorus content 8 mg kg−1 , electric conductivity 0.1 dS m−1 and exchangeable sodium of 11.3% (site B). 2.2. Initial management and meteorological records Swards were cut at 5 cm height at the beginning of each experiment. Subsequently, a phosphorus amendment was surface broadcasted as calcium triple superphosphate at a rate of 20 kg P ha−1 to provide non-limiting phosphorus availability. Thereupon, treatments (two to fives N rates according to the experiment, Table 1) were applied either as urea or calcium ammonium nitrate. The wide spectrum of N rates was assayed with the aim of generating an ample range of sward N nutrition status. All experiments were drip-irrigated when necessary, so that water applied plus received by rainfall was equal to or higher than reference evapotranspiration, calculated after Allen et al. (1998). Meteorological data was recorded at the experimental site by a weather monitoring station (iMETOS ag, Pessl Instruments GmbH, Weiz, Austria).

Table 1 Regrowth periods studied, N treatments applied and climatic data registered during the seven field experiments. Trial

Experimental period (accumulated growing degree days (GDD))c

N fertilization rates (kg N ha−1 ) and source

Mean incoming global radiation (MJ m−2 day−1 )

Mean air temperature (◦ C)

Early Spring 2008a Late Spring 2008a Early Spring 2009b Summer 2009a Summer 2010a Summer 2010ba Autumn 2009a

Aug 21–Nov 5/2008 (417) Oct 23/2008–Jan 5/2009 (903) Aug 19–Oct 14/2009 (404) Dec 30/2008–Feb 19/2009 (747) Dec 30/2009–Feb 16/2010 (812) Dec 30/2009–Feb 16/2010 (812) March 19–May 7/2009 (465)

0–75–150–225 Urea 0–75–150–225 Urea 0–75–150–350–500 CANd 40–200 CAN 0–75–150–350–500 CAN 40–200 CAN 0–75–150–225 Urea

14.4 22.2 13.1 23.5 21.5 21.5 12.1

9.4 15.1 9.3 20.5 20.7 20.7 13.3

a b c d

Site A. Site B. Base temperature 4 ◦ C. Calcium ammonium nitrate.

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P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58

Climatic conditions registered during each experiment are summarized in Table 1. 2.3. Experimental design and treatments Treatments were arranged in a split plot design, replicated in three (Summer 2009 and Summer 2010b) or two blocks (all other experiments). N fertilizer levels were randomly applied to the main plots. Main plots were further subdivided into subplots, which were randomly assigned to each sampling date. Four to seven forage harvests (according to the experiment) were carried out every 7–10 days on independent subplots (Table A1). Main plot size was 18 m2 , except for experiments Summer 2009 and Summer 2010b in which it was 37.5 m2 . 2.4. Sampling and measurements 2.4.1. Shoot biomass, N concentration and NNI In each subplot, a 0.1 m2 (0.2 by 0.5 m) quadrat was randomly selected. The forage biomass inside the quadrat was cut at ground level with battery-powered shears. Senescent material was discarded. Thereafter, samples were lyophilized (Rificor LA-B4, Rificor SH, Buenos Aires, Argentina) and then weighed in order to estimate accumulated shoot biomass (Mg ha−1 ). Samples were subsequently ground to pass a 40-mesh screen in a Thomas Wiley Mini-Mill (Thomas Scientific, Swedesboro, NJ, USA) and analyzed for total N concentration (g N kg−1 dry matter (DM)) according to Nelson and Sommers (1973, method A, without salicylic acid modification). Sward NNI was calculated as the ratio between measured shoot biomass N concentration and the NCR corresponding to each value of observed shoot biomass, obtained from the NCR curve developed for tall fescue by Lemaire and Salette (1984): NCR = 48 SB−0.32 (where SB stands for shoot biomass), and validated in our conditions by Agnusdei et al. (2010). 2.4.2. Uppermost sward leaves (USL) Sward uppermost layer of leaves was sampled as in Farrugia et al. (2004). Briefly, handful of leaves located in the uppermost strata of the canopy in the four outer boundaries of the quadrat sampled for NNI determination were cut with scissors at approximately 10 cm from the tip of the longest leaf. As regrowth advanced, this sampling procedure yielded not only fragments of – progressively larger – growing and youngest fully expanded leaves, but also increasing proportions of older leaf age categories occupying the uppermost layer of the canopy. At the lab, stems, seed heads and any senescent tissues present in the samples were eliminated. In five out of seven experiments, the uppermost sward leaves greenness was measured taking ten readings on the mid-point of ten representative (regarding leaf greenness) leaf segments with a SPAD 502 (Konica Minolta Sensing Inc., Osaka, Japan) handheld chlorophyll meter (SPADUSL , in SPAD units). Subsequently, samples were lyophilized, ground and analyzed for total N concentration (NUSL , g N kg−1 DM) as described previously. 2.5. Statistical analysis Ordinary least squares linear regression analysis was performed with the REG procedure of the SAS package (v 9.0, SAS Institute Inc. Cary, NC, USA). Slopes and intercepts of the linear functions were compared using dummy variables (Littell et al., 2002). A 5% significance level was used. Nonlinearity was tested by the addition of the quadratic component. If significant, appropriate nonlinear functions were fitted using SAS NLIN procedure. Regressions lines, and their corresponding 95% confidence bands, were estimated for NNI as a function of either NUSL or SPADUSL , and of SPADUSL as a function of NUSL . But assessing the

precision of the NNI prediction from either NUSL or SPADUSL should also take into account the inherent variability of the predictor variable. Hence, the error in the prediction of sward mean NNI from either NUSL or SPADUSL was estimated for the mean values of both predictor variables as the interval comprised between the lower and upper bounds of the confidence bands at predictor variables values equal to their mean minus and plus E/2, respectively, where E is the error in the estimation of NUSL or SPADUSL . E depends on the intrinsic variability of the predictor variable and sampling intensity: E = (zVAR)/(n)1/2 (Hoshmand, 1998), where z is the z score associated with a 95% level of confidence (1.96), VAR is the intrinsic variability of the predictor variable and n is sample size. VAR was estimated as the root of the ANOVA mean square of error (RMSE) generated by the MIXED procedure of SAS, treating as random either the effects of block (NUSL ) or of block and subsample (SPADUSL ). Assumptions of normality and homogeneity of variance were checked. Data from plots in which shoot biomass was lower than 1 Mg ha−1 were not considered, since in such conditions shading and light competition between plants are negligible and non significant changes in shoot biomass N concentration are expected. 2.6. Data digitization Published data (Di Salvo et al., 1999; Duru, 2002, 2004; Farrugia et al., 2004; Gastal et al., 2001; Ziadi et al., 2010) was digitized using the Engauge Digitizing software (http://digitizer.sourceforge.net). 3. Results 3.1. Climatic conditions Measured incoming global radiation differed significantly among experiments, spanning from 12 MJ m−2 day−1 to 24 MJ m−2 day−1 (Table 1). Likewise, mean air temperature during the regrowth periods ranged from 9 ◦ C to 21 ◦ C. Clearly, data were generated under a wide range of climatic conditions, although between-years variations – for a given season – were small. 3.2. Time-course of NNI, NUSL and SPADUSL The breadth of assayed N rates generated an ample range of sward NNI (Figs. 1 and 2). Control plots were severely deficient (NNI < 0.4), and N fertilization increased NNI up to values of 1.1. NNI generally decreased during each experiment, although less in those carried out in summer, probably because of higher soil organic-N mineralization rates and/or a slower shoot biomass N dilution due to a lower allocation of photosynthates towards structural tissues (absence of true stems) in the warm season. N fertilization also led to differences in both NUSL and SPADUSL (Table A1). As expected, all three variables – NNI, NUSL and SPADUSL – associated well to each other, as the decreasing trend showed by the NNI during the experiments was also experienced by NUSL and SPADUSL . A common feature of the initial experiments was the high level of N stress (e.g. NNI not exceeding 0.7, even after N applications of up to 225 kg N ha−1 in Early and Late Spring 2008, and Autumn 2009 experiments). This led us to assay broader ranges of N rates at Early Spring 2009 and Summer 2010 experiments, in order to reach nonlimiting N status: N application rates of 350–500 kg N ha−1 allowed to achieve NNI up to 1.1. However, it is remarkable that even under such heavy N application regimes, it was not possible to generate conditions of truly luxury consumption (NNI much higher than 1.0). The determinants of the low N availability are not clear, but a likely cause of N stress in ageing swards, like the evaluated in this study, could be a high level of N immobilization in the decomposing grass

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P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58

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Accumulated Shoot Biomass (Mg DM ha ) Fig. 1. Diagrams representing the effect of N rates (0 (triangles), 40 (squares), 75 (circles), 150 (inverted triangles), 200 (stars), 225 (rhombi), 350 (crosses) and 500 (circles with inner crosses) kg N ha−1 ) on the relationship between shoot N concentration and accumulated shoot biomass of tall fescue swards, in seven field experiments. Bars represent the S.E. of the mean. Dotted curves represent NNI isolines.

litter, as was previously reported for tall fescue (Loiseau et al., 1992) and green panic (Robbins et al., 1987, 1989). In addition, N losses through ammonia volatilization would have played a minor role, since in Early and Late Spring 2008 experiments – when urea was used as N source – up to 20% of applied N was lost through this process (data not shown). Soil–N losses through runoff and leaching are

an improbable cause of the low N uptake as experimental sites were located at depressed areas of the landscape and, thus, do not exhibit surface drainage (due to their insignificant slope). Their internal drainage is also limited by the presence of a Bt horizon that restricts subsoil permeability. Additionally, intense rainfall events were not registered during the experiments and daily drip irrigation was

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P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58

134 GDD 227 GDD 289 GDD 339 GDD 417 GDD

1.2

1.0

0.8

0.8

NNI

NNI

1.0

122 GDD 186 GDD 260 GDD 357 GDD 443 GDD 544 GDD 903 GDD

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238 GDD 349 GDD 452 GDD 691 GDD 812 GDD

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238 GDD 349 GDD 452 GDD 566 GDD 691 GDD 812 GDD

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214 GDD 323 GDD 444 GDD 747 GDD

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181 GDD 236 GDD 263 GDD 287 GDD 346 GDD 404 GDD

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N USL (g N kg-1 DM) Fig. 2. Linear regressions fitted between sward NNI and NUSL during the regrowth periods of tall fescue monocultures fertilized with different N rates, according to the thermal time (GDD) accumulated since sward initial cut, in seven field experiments. Coefficients of regressions fitted at each sampling date are presented in accompanying Table A2.

required to cover the negative balance between recorded rainfall and estimated reference evapotranspiration (data not shown). 3.3. Relationship between NNI and NUSL NNI correlated closely with NUSL . However, the relationship was not unique, since within each experiment, a given NNI value was associated with progressively lower NUSL values as time elapsed (Fig. 2 and Table A2). To evaluate the cause of this variation, for each experiment regressions were adjusted separately following two different criteria: accumulated shoot biomass or number of developed leaves per tiller since sward last cut. Hence, data were

subdivided in either three shoot biomass ranges – <2 Mg ha−1 , 2–3.5 Mg ha−1 and >3.5 Mg ha−1 – or three ranges of accumulated thermal time (base temperature 4 ◦ C) – <260 GDD, 263–417 GDD and >440 GDD. The latter corresponded to up to one, around two, or three or more newly developed leaves per tiller since sward initial cut, respectively (phyllochron of 175 GDD, after leaf appearance rate measurements made in the Summer 2009 experiment). In both cases, regressions of NNI as a function of NUSL fitted on all subdatasets were significant and explained a high proportion of observed variance (R2 > 0.80). However, while regression parameters differed among the seven field experiments when data were grouped according to shoot biomass, these differences disappeared

P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58 A

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Fig. 3. Linear regressions fitted between sward NNI and NUSL in tall fescue monocultures fertilized with different N rates and that have accumulated up to 260 GDD (diagram A), 263–417 GDD (diagram B) and more than 440 GDD (diagram C) since initial cut. Data obtained from seven field experiments: Early Spring 2008 (triangles), Late Spring 2008 (squares), Early Spring 2009 (stars), Summer 2009 (circles), Summer 2010 (rhombi), Summer 2010b (crosses) and Autumn 2009 (inverted triangles). Fitted equations were reformulated to explicitly show the x-axis intercept: NNI = slope (NUSL - x axis intercept). The y-axis intercept can be computed as the negative product of slope by x-axis intercept. Dotted lines are the 95% prediction bands.

when grouping by thermal time. Hence, unique NNI–NUSL relationships valid for all seasons, years and sites were fitted to each thermal time group (Fig. 3). Y-axis intercepts were close to zero and not significantly different among thermal groups (p > 0.68), while slopes varied significantly between groups (p < 0.01), becoming progressively steeper as accumulated thermal time increased. Hence, the maximal variation in the NNI prediction ascribable to developmental stage effects (∼0.22 NNI units) occurred under situations of high N availability. 3.4. Relationship between SPADUSL and NUSL and NNI SPADUSL and NUSL were closely associated. When regressing SPADUSL on NUSL , parameters of linear associations often differed significantly – after dummy variables comparison – among sampling dates. This effect was, however, explained by a saturation phenomenon experienced by the chlorophyll meter at high leaf N concentrations and the higher NUSL values at the earlier samplings

Fig. 4. Relationship between NUSL and SPADUSL in tall fescue monocultures fertilized with different N rates, in five field experiments: Early Spring 2008 (triangles), Late Spring 2008 (squares), Early Spring 2009 (stars), Summer 2010 (rhombi) and Autumn 2009 (inverted triangles). White, dark grey and black symbols are for samplings carried out on swards that had accumulated up to 260 GDD, 263–417 GDD, and more than 440 GDD since initial cut, respectively. Solid curve is the Gompertz function fitted to the data and dotted curves are the 95% prediction bands. Data obtained by Di Salvo et al. (1999) on tall fescue cultivars differing in their origin (mediterranean vs. template) was also included, as light grey rhombi in the background.

of each experiment, which consistently yielded lower slopes and higher y-intercepts at initial stages of sward regrowth (data not shown). Accordingly, parameters of linear regressions were not significantly different among sampling dates when comparisons were made excluding high N-data, e.g. NUSL values higher than 35 g N kg−1 . The nonlinearity of the SPADUSL –NUSL relationship was thus well described by a unique function of the Gompertz type, which was fitted to all data (Fig. 4). The NNI–SPADUSL relationship showed analogous developmental effects to those observed in the NNI–NUSL relationship (Fig. 2), with NNI values being associated with progressively lower SPADUSL values throughout regrowth (data not shown). Grouping data according to accumulated thermal time yielded highly significant NNI–SPADUSL relationships (Fig. 5). In this case, however, no difference was detected between swards that had accumulated either between 260 and 440 GDD or more than 440 GDD since initial cut. Nonlinearity – indicated by a significant increase in the amount of variance explained by the regression after the addition of the quadratic component – was only detected in swards that had accumulated up to 260 GDD since initial cut, and was adequately described by an inverse Gompertz function. 3.5. Precision of NUSL and SPADUSL as surrogate methods for estimating tall fescue NNI Both NUSL and SPADUSL correlated adequately with NNI (Figs. 3 and 5). However, considering that the relationship between SPADUSL and NNI is less direct than that between NUSL and NNI, lower precision is to be expected when predicting NNI from SPADUSL . Additionally, SPADUSL showed a higher intrinsic variability than NUSL (RMSE 4.75 vs. 3.83, respectively). The change in sward mean NNI predictive precision after variations in the sampling intensity of either NUSL or SPADUSL was calculated for the mean values of both predictor variables and is presented in Fig. 6. When employing a chlorophyll meter to assess the N status of swards that have accumulated more than 260 GDD since initial cut, the sampling effort must be doubled in order to achieve a precision comparable to that displayed by the NUSL method. Regarding swards at earlier stages of development – when NUSL values are higher – given the saturation shown by the device at high N concentrations (Fig. 4) and the consequent curve NNI–SPADUSL

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P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58

1.4

A

1.2

NNI

1.0 0.8

methods are based on the robust foundation of the NNI concept, and one of them further simplifies the N status assessment taking advantage of the agility of SPAD readings. However, for them to become functional, first is necessary to establish how general are those relationships, i.e., how stable are among developmental stages, seasons, years and species.

06 0.6

4.1. On the generality of the association between NNI and NUSL

0.4 NNI= -0.81 LN(-0.82 LN(SPAD

0.2

/68.42))

R =0.69, N= 72 0.0 25

30

35

40

45

50

SPAD USL B

1.4 1.2

NNI

1.0 0.8 0.6 0.4

NNI = 0.035 (SPADUSL - 18 18.50 .50)

02 0.2

R = 0.75, N= 169 0.0 25

30

35

40

45

50

SPAD USL

Predicted mean NNI estimation error (NNI units)

Fig. 5. Regressions between sward NNI and SPADUSL in tall fescue monocultures fertilized with different N rates, in five field experiments: Early Spring 2008 (triangles), Late Spring 2008 (squares), Early Spring 2009 (stars), Summer 2010 (rhombi) and Autumn 2009 (inverted triangles). White (diagram A), grey and black (diagram B) symbols are for samplings carried out on swards that had accumulated up to 260 GDD, 263–417 GDD, and more than 440 GDD since initial cut, respectively. Solid lines are fitted equations and dotted lines are the 95% prediction bands.

0.30

0.25

0.20

0.15

0.10

0.05

0.00 1

10

100

Number of samples (log-scale)

Fig. 6. Decrease in the error of the prediction of sward mean NNI with the increase in sampling intensity of either NUSL (solid, dashed and dotted black curves are for tall fescue swards that had accumulated up to 260 GDD, 263–417 GDD, and more than 440 GDD since initial cut, respectively) or SPADUSL (solid and dashed grey curves for tall fescue swards that had accumulated less or more than 260 GDD, respectively).

relationship (Fig. 5A), SPADUSL sampling intensity needs to be increased by even higher orders of magnitude to reach a precision comparable to NUSL , and the required SPADUSL sample number would turn out to be almost unfeasible if the desired predictive precision is very high (estimation error of 0.10 NNI units or less). Nevertheless, at initial stages of sward development, SPADUSL still can be used as a diagnosing tool for performing quick but approximated N status assessments. 4. Discussion The objective of this work was to evaluate two alternative methods for the diagnosis of the N nutrition of a C3 forage crop. Both

A close relationship was found between NNI and NUSL (R2 = 0.81–0.89, Fig. 3), in agreement with the pioneer work carried out by Gastal et al. (2001) in L. perenne monocultures, and also in concordance with the subsequent studies by Farrugia et al. (2004) on mixed pastures (Agrostis spp., Holcus spp. and L. perenne) and by Duru (2004) on D. glomerata stands. In our study, as well as in previous works (Duru, 2004; Ziadi et al., 2010), the parameters of the NNI–NUSL relationship changed along sward regrowth. Specifically, we found this variation to be more clearly related to the number of developed leaves per tiller than to accumulated shoot biomass, as parameters of the lineal regressions fitted separately to thermal time intervals related to leaf appearance rate did not differ among years, seasons and sites. Three mechanisms might have caused the change in the NNI–NUSL relationship with sward development: variations in (i) leaf size, (ii) leaf position within the canopy, and (iii) leaf age. The first two are directly related with the N dilution process. As analyzed extensively by Lemaire and Gastal (2009), the decline in NCR as shoot biomass increases originates partially from an ontogenic decrease in the leaf area/plant mass ratio as plants grow larger and compete for light. One aspect of the leaf area/plant mass ratio decline is an increase in specific leaf weight (SLW, leaf weight per unit of leaf area) as a consequence of the increment of both the sheath tube length and the lamina size of subsequent leaves (as shown by Lemaire and Gastal, 1997). This ontogenic increase in SLW is due to a higher proportion of supporting (low-N) vs. photosynthetic (high-N) tissues, generating a decrease in the N concentration of subsequent, larger leaves. The effect of leaf position within the canopy, on the other hand, is related to the non-uniform distribution of N resulting from the progressive remobilization of this nutrient from shaded leaves to support growth of young well illuminated leaves located at the top of the canopy. This is the other phenomenon leading to the decline in NCR as shoot biomass increases, and it has been documented in a wide range of species (review by Lemaire and Gastal, 2009). The applied sampling procedure consisting in cutting tips of well illuminated leaves would, however, only prevent for the N dilution process associated with the heterogeneous light profile within a dense canopy, but would not prevent for the N dilution inherent to the increase in SLW that is expected to occur with leaf enlargement. In consequence, taking into account the number of developed leaves per tiller after sward initial cut, unique NNI–NUSL relationships should emerge, as was reported in our work. Concerning the effect of leaf age of sampled canopy strata, the idea of an invariable N concentration in leaves located at the top of the canopy goes against the fact that, as sward develops, its uppermost layer includes an increasing proportion of older leaves. Age affects leaf N concentration because this nutrient can begin to be mobilized out of leaves soon after full expansion (review by Grindlay, 1997), and even before full expansion in N deficient plants (Robson and Deacon, 1978). Therefore, a given NNI would be expected to be associated with progressively lower NUSL values as time elapses and sward develops, as noted in the present work. Nevertheless, Ziadi et al. (2010) also reported variation within the growing season in the relationship between NNI and the N concentration in the uppermost leaf in spring wheat, even when in their study samplings only included well illuminated, recently expanded

P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58

leaves. In contrast to perennial grasses, where new leaves appear continuously, leaves stop to appear late in the growing season in annual crops. This phenomenon, which might explain the leaf N concentration decay observed in the referred study, represents a limitation for diagnosing the N status of annual crops at advanced stages of development. What about different species? We compared our results with those obtained in previous studies carried out in different species (Table 2). Both Gastal et al. (2001) and Farrugia et al. (2004) fitted unique lineal functions to their NNI–NUSL data. An equivalent analysis of our data yielded regression parameters that were not statistically different (p > 0.12) from that reported in those two studies. On the other hand, Duru (2004) obtained NNI–NUSL relationships whose parameters differed according to the thermal time elapsed since initial defoliation, but in contrast with the present study, fitted a unique function for orchardgrass swards that still had not reached their maximal number of living leaves per tiller (three new leaves per tiller and accumulated thermal time <500 GDD). In the present study, we show that the NNI–NUSL regression parameters vary progressively until tall fescue reaches its average ceiling leaf number (3.6 living leaves per tiller and accumulated thermal time ∼600 GDD, base temperature 4 ◦ C, Fig. 3). Still, the regression fitted by Duru (2004) on <500 GDD orchardgrass swards was not significantly different from our regression on tall fescue swards that had developed up to one new leaf per tiller (p > 0.17). Remarkably, all comparisons described above did not render any significant differences between fitted regression parameters despite a lack of standardization among studies in the sampling procedure for NNI determination: shoot biomass was either cut at 5 cm height (Gastal et al., 2001; Farrugia et al., 2004) or at ground level (Duru, 2004; present work). Since the NCR curve developed for tall fescue by Lemaire and Salette (1984) was based on shoot biomass harvests performed at around 5 cm height, the inclusion of the basal 4–5 cm of shoot biomass – where canopy N concentration is at its lowest (Lötscher et al., 2003) – in the NNI computation should lead to an underestimation of sward N status. However, the lack of significant variations among referred studies in the y-axis intercept of the fitted NNI–NUSL relationships would suggest that the dilution of total shoot biomass N concentration by the addition of the basal canopy layer is not as pronounced as expected. On the contrary, the regression reported by Ziadi et al. (2010) between wheat NNI and the N concentration in the uppermost collared lamina differed significantly from our regression for one new leaf tall fescue swards. However, this disagreement could be ascribed, at least partially, to the disparity in the uppermost leaves sampling method. Harvesting the entire leaf blade as done by Ziadi et al. (2010) instead of only its apical part would yield lower NUSL values, given the decreasing N concentration gradient that develops from tip to base in wheat leaf blades (Hu and Schmidhalter, 1998). The consistency shown by the NNI–NUSL relationship among different C3 grasses, seasons, years and sites, once developmental effects were taken into account, encourages us to consider it as stable for this functional subgroup. After sampling the whole uppermost strata of leaves yields herbage differing in mean leaf age, the method shows strong predicting capabilities only if the elapsed thermal time since sward initial cut is taken into account in the predictive equations. In view of this, NUSL critical levels of 45.5, 41.4 and 36.8 g N kg−1 DM are proposed for swards that have developed up to one, around two or three or more new leaves per tiller since initial cut. Alternatively, to overcome the constraint of taking into consideration sward accumulated thermal time at the moment of N status assessment, only the apical part of the youngest fully expanded leaf of each tiller could be harvested (Lemaire et al., 2008). In this situation, the predictive equation to apply is that fitted on swards that have developed up to one new leaf per tiller

53

since initial cut. However, this alternative approach entails the difficulty of sampling for a specific leaf category, particularly when canopies are short and dense. Under such situations, grabbing leaf fragments from sward uppermost strata would be more practical. 4.2. The value of SPAD readings as a predictor of sward N status Estimating sward N nutrition status by the quantification of N concentration of leaf fragments from the uppermost canopy strata facilitates N nutrition diagnosis, as the time consuming determination of shoot biomass is avoided. However, determining NUSL still demands collecting, drying and milling samples, and the assistance of a laboratory. While this may not be a major difficulty at a research station, it does become a significant obstacle for routinely monitoring crop N status in farms. Estimating NNI with a portable chlorophyll meter therefore further simplifies quantifying N deficiency, making real-time, ‘in the field’ diagnosis possible and allowing to promptly correct swards N deficiencies through fertilization. SPAD readings have repeatedly been suggested as a quick way to diagnose N nutrition in field crops (Fox et al., 2001; Olivier et al., 2006; Peng et al., 1993; Tremblay et al., 2010). However, the proposed relationships typically were specific to growth stage, season, species, year or site. This, we argue, is because they were not based on the general NNI concept. In the present study, SPAD readings were taken on leaves located in a constant position within the canopy and whose N concentration was highly correlated with sward NNI. Duru (2002) first demonstrated the potential aptitude of such approach for the assessment of cocksfoot N nutrition status, but recognized difficulties in its implementation associated with time-related effects on the composition of the sampled uppermost sward layer. In the present study we addressed the effect of time and leaf size/age on tall fescue uppermost leaf strata SPAD readings proposing two general relationships between NNI and SPADUSL : a linear one for swards with two or more newly developed leaves per tiller, and a nonlinear one for swards that developed up to one new leaf since initial cut (Fig. 5). Once developmental effects were accounted for, no effect of season, site or year was observed. SPADUSL emerged as less sensitive to the N dilution process than NUSL , explaining a lower percentage of observed NNI variation (69–75% vs. 81–89%). The nature of chlorophyll meter readings, based on the amount of light transmitted through the leaf, could largely explain the loss in NNI predictive precision of SPADUSL . In this sense, as light absorbance associates better with chlorophyll content per unit of leaf area than with its concentration per unit of leaf dry weight, SPAD readings usually correlate better with N content per unit of leaf area than with leaf N concentration on a dry weight basis (Blackmer et al., 1994; Peng et al., 1995). However, this was not observed in tall wheatgrass (Thinopyrum ponticum (Podp) Barkworth et Dewey) stands (Di Salvo, 2001). The lower sensitivity of SPADUSL to NNI variations could also originate in changes in the chlorophyll/N ratio within leaf tissues, which can be affected by properties of the light environment, e.g. photon flux density and red/far red ratio (Evans, 1996; Kull and Kruijt, 1998). Likewise, the nonlinearity of the NNI–SPADUSL relationship (Fig. 5) could derive from a chlorophyll–N concentration decoupling at high leaf N concentrations (Fig. 4) and the higher NUSL observed at earlier samplings. This pattern was already noted by Di Salvo et al. (1999) in both a temperate and a mediterranean cultivar of tall fescue. Indeed, a complete overlap is observed when the whole data set of both experiments is plotted together (Fig. 4), suggesting that a unique relationship may exist between N and SPAD in tall fescue monocultures, that can be adequately described by a Gompertz function with a maximum value of about 50 SPAD units. Due to the nonlinearity of the relationship, the critical SPADUSL – the reading associated with a NNI = 1.0 – is similar for swards of

54

P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58

Table 2 Summary of published studies carried out on the topic of N nutrition assessment of C3 species through the quantification of N concentration in leaves located at the uppermost strata of the canopy. C3 species (Reference)

Site

Season assayed

Sward uppermost leaves sampling method

Fitted equations

L. perenne (Gastal et al., 2001)

France

Spring

Mixed sward of Agrostis spp., Holcus spp. and L. perenne (Farrugia et al., 2004) D. glomerata (Duru, 2004)

North Wyke, UK

Spring, summer

NNI = 0.022 (NUSL − 3.19) NUSL = 35.56 NNI + 7.44 NNI = 0.027 (NUSL − 4.58) NUSL = 29.62 NNI + 10.34

Toulouse and Aveyron, France

Spring, summer

Handful of leaves cut from sward top 10 cm-layer Handful of leaves were erected and cut at 10 cm from the top of the canopy Sward top 7 cm-layer, lamina non erected

Triticum aestivum (Ziadi et al., 2010)

Québec, Canada

Starting at tillering stage (summer)

Complete blade of the uppermost collared leaf

NNI = 0.035 (NUCL –12.29) NUCL = 15.53 NNI + 24.18

L. arundinaceum (present study)

Balcarce, Argentina

Autumn, late winter/early spring, spring, summer

Handful of leaves were erected and cut at 10 cm from the top of the canopy

Up to one newly developed leaf per tiller: NNI = 0.024 (NUSL − 3.27) NUSL = 37.69 NNI + 6.04 Around two newly developed leaves: NNI = 0.026 (NUSL − 2.37) NUSL = 31.69 NNI + 6.62 Three or more newly developed leaves: NNI = 0.030 (NUSL − 3.26) NUSL = 28.08 NNI + 6.54 All data: NNI = 0.024 (NUSL − 0.71) NUSL = 32.57 NNI + 6.16

both developmental stages: 48.2 and 47.4 SPAD units for swards accumulating less or more than 260 GDD, respectively; whereas under N deficiency conditions the difference in NNI values predicted by both equations becomes higher. Nonetheless, the SPAD–N concentration relationship can differ among C3 grasses, and even among cultivars of a given species (Giunta et al., 2002). For instance, the SPADUSL –NUSL data from Duru (2002) on D. glomerata monocultures does not completely overlap with the tall fescue data of Fig. 4. Species – and cultivar – specificity in the SPADUSL –NUSL relationship would lead to species or cultivar-specific NNI–SPADUSL predictive equations. Di Salvo et al. (1999), however, reported no significant variation in SPAD values between two tall fescue cultivars. Such consistency, nevertheless, deserves to be corroborated on a broader cultivar spectrum, as well as in other species, with further experimentation. 5. Conclusions The N concentration in the upper illuminated leaves of the canopy was closely associated with tall fescue NNI, and the parameters of the fitted lineal regressions did not differ among years, seasons and sites, once developmental stage effects were accounted for. The parameters of the relationship neither differed significantly from those reported for other C3 grasses. This suggests uniformity in the NNI–NUSL relationship among members of this functional subgroup, and corroborates the aptitude of NUSL as an unbiased estimate of the N nutrition status of C3 forage crops. Nevertheless, the needed instantaneous, in-situ N nutrition assessment of forage and grain crops at the farm level requires the development of diagnosing procedures directly related with the NNI but that can be more easily adopted at the field. SPAD readings have been proposed as a quick way to diagnose N nutrition in field crops, but NNI–SPAD relationships obtained before usually

<500 GDD swards: NNI = 0.021 (NUSL − 1.39) NUSL = 43.92 NNI + 5.27 >500 GDD swards: NNI = 0.026 (NUSL − 4.33) NUSL = 34.65 NNI + 7.00

were specific to growth stage, season, species, year or site. However, in the present study, SPAD readings taken at the uppermost canopy leaves – whose N concentration was highly correlated with sward NNI – were strongly associated with tall fescue NNI, and the obtained predictive equations were not affected either by seasons, years or sites. Increasing SPADUSL sampling intensity would allow to achieve NNI predictive precisions on par with those displayed by the NUSL method. Still, defining a sampling protocol to apply at farm level requires additional work, as important issues like the effect of paddock spatial variability on sampling intensity were not addressed in the present work. At early developmental stages, however, the saturation shown by the chlorophyll meter in moderate to non-deficient swards somewhat limits SPADUSL diagnosing capabilities up to a tool able to perform quick but approximated N status assessments. Hence, in situations when precision of NNI prediction is of higher importance than celerity of estimation, such as N status assessment in experimentation field trials, NUSL quantification still emerges as a more appropriate diagnosing method.

Acknowledgments Authors wish to thank María Gabriela Cendoya and Adriana Cano for valuable assistance in the statistical analysis of data and German Berone, Guadalupe Continanza and two anonymous reviewers for critical reading of the manuscript and valuable suggestions. The study was financially and technically supported by the Instituto Nacional de Tecnología Agropecuaria (INTA).

Appendix A. See Tables A1 and A2.

P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58

55

Table A1 Effect of N rates on the time-course evolution of NUSL and SPADUSL in seven field experiments carried out on tall fescue monocultures. Field experiment (fertilization date)

Sampling dates (GDD)a

N rate (kg N ha−1 )

Mean NUSL , (g N kg−1 DM) and (S.E.)b

Mean SPADUSL and (S.E.)b

Early Spring 2008 (Aug. 21, 2008)

Sep. 26, 2008 (134)

0 75 150 225 0 75 150 225 0 75 150 225 0 75 150 225 0 75 150 225

19.2 (0.27) 25.3 (1.82) 26.9 (4.06) 31.7 (2.31) 17.8 (0.49) 20.4 (0.07) 21.7 (1.54) 25.1 (1.82) 16.2 (0.14) 18.3 (1.12) 22.2 (0.77) 23.5 (3.08) 15.5 (0.14) 17.8 (0.42) 20.3 (0.28) 22.4 (3.08) 15.3 (0.14) 17.8 (0.14) 19.6 (0.42) 21.3 (0.84)

34.3 (0.64) 39.0 (0.86) 41.6 (3.67) 45.7 (1.54) 30.9 (0.76) 34.2 (1.37) 36.7 (0.82) 38.3 (1.50) 28.8 (0.67) 31.4 (0.04) 36.5 (0.42) 37.9 (1.12) 26.0 (0.35) 31.0 (1.64) 34.2 (0.19) 33.5 (0.21) 28.0 (0.70) 28.6 (0.91) 29.2 (0.04) 32.0 (1.47)

0 75 150 225 0 75 150 225 0 75 150 225 0 75 150 225 0 75 150 225 0 75 150 225 0 75 150 225

20.8 (4.83) 28.8 (4.48) 31.4 (1.96) 37.4 (2.38) 14.0 (2.43) 24.1 (1.33) 34.2 (1.33) 34.9 (4.90) 17.5 (2.10) 21.6 (1.05) 24.5 (0.98) 29.4 (2.10) 18.5 (2.59) 22.0 (0.70) 22.3 (0.35) 29.0 (0.42) 16.4 (0.42) 17.8 (0.21) 22.8 (1.26) 25.2 (1.68) 15.8 (0.14) 16.8 (0.56) 19.5 (0.49) 23.0 (2.03) 15.7 (0.84) 16.1 (1.43) 16.9 (0.21) 18.3 (1.33)

37.2 (2.46) 37.7 (1.17) 40.9 (3.98) 40.2 (0.72) 28.3 (2.82) 35.8 (1.90) 42.5 (0.46) 40.3 (1.65) 29.6 (1.55) 34.2 (0.72) 39.9 (0.25) 40.7 (0.50) 32.0 (6.13) 32.6 (0.61) 32.8 (2.62) 37.4 (0.54) 31.2 (0.79) 33.1 (1.80) 37.6 (0.96) 37.8 (0.53) 29.0 (1.04) 31.0 (0.74) 35.4 (1.50) 36.6 (0.96) 28.1 (0.15) 29.8 (0.27) 31.7 (1.37) 31.9 (0.16)

0 75 150 350 500 0 75 150 350 500 0 75 150 350 500 0 75 150 350 500 0 75 150 350 500

n.a.c 37.2 (6.72) 40.7 (0.35) 45.4 (0.77) 44.3 (1.05) 23.0 (0.75) 31.6 (5.60) 34.7 (1.40) 43.8 (0.98) 45.2 (2.66) 20.7 (0.75) 29.3 (3.22) 30.8 (1.82) 39.2 (0.56) 39.3 (3.50) 18.5 (1.12) 23.6 (2.31) 25.3 (0.21) 32.6 (1.54) 36.6 (0.77) 19.0 (0.35) 22.5 (0.98) 23.7 (1.82) 32.6 (1.26) 35.5 (3.29)

n.a. 38.1 (0.55) 44.3 (0.42) 45.0 (0.93) 44.1 (1.31) 34.3 (0.86) 37.7 (1.55) 41.8 (0.63) 44.2 (0.79) 47.2 (0.92) 31.3 (0.86) 37.8 (1.04) 38.8 (0.26) 43.5 (0.81) 44.7 (0.56) 32.1 (1.40) 34.7 (2.78) 36.6 (1.28) 41.0 (0.02) 43.9 (1.22) 29.6 (0.14) 31.7 (0.02) 34.8 (0.08) 41.6 (1.30) 43.2 (1.59)

Oct. 14, 2008 (227)

Oct. 21, 2008 (289)

Oct. 28, 2008 (339)

Nov. 5, 2008 (417)

Late Spring 2008 (Oct. 23, 2008)

Nov. 5, 2008 (122)

Nov. 11, 2008 (186)

Nov. 18, 2008 (260)

Nov. 25, 2008 (357)

Dec. 2, 2008 (443)

Dec. 10, 2008 (544)

Jan. 5, 2009 (903)

Early Spring 2009 (Aug. 19, 2009)

Sep.10, 2009 (181)

Sep. 17, 2009 (236)

Sep. 23, 2009 (263)

Sep. 29, 2009 (287)

Oct. 6, 2009 (346)

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P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58

Table A1 (Continued ) Field experiment (fertilization date)

Sampling dates (GDD)a

N rate (kg N ha−1 )

Mean NUSL , (g N kg−1 DM) and (S.E.)b

Mean SPADUSL and (S.E.)b

Oct. 14, 2009 (404)

0

16.5 (0.56) 75 150 350 500

27.7 (0.47) 22.0 (0.49) 21.8 (0.98) 30.7 (3.78) 35.0 (2.23)

31.9 (0.28) 32.6 (0.42) 37.8 (0.89) 41.1 (1.20)

40 200 40 200 40 200 40 200

24.7 (2.13) 41.3 (2.69) 27.7 (5.49) 31.0 (4.93) 20.7 (0.26) 27.1 (1.51) 19.2 (0.97) 29.4 (2.87)

n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.

0 75 150 350 500 0 75 150 350 500 0 75 150 350 500 0 75 150 350 500 0 75 150 350 500 0 75 150 350 500

16.4 (1.31) 25.1 (2.38) 33.5 (0.49) 36.1 (3.08) 35.0 (2.10) 18.8 (0.28) 24.2 (2.38) 34.2 (0.95) 36.5 (6.02) 38.5 (0.98) 17.6 (1.40) 23.3 (3.01) 30.5 (1.40) 38.5 (0.42) 40.5 (0.14) 19.5 (2.10) 22.8 (1.26) 28.8 (0.84) 35.9 (0.49) 35.4 (0.42) 16.7 (1.47) 21.7 (0.98) 26.0 (1.40) 32.3 (0.98) 32.2 (0.95) 15.0 (0.14) 17.9 (0.98) 20.3 (0.70) 26.9 (2.24) 31.1 (1.05)

31.5 (0.94) 37.2 (0.86) 41.0 (1.43) 44.6 (2.12) 45.6 (1.43) 30.8 (0.12) 37.2 (3.36) 41.0 (0.29) 47.6 (1.23) 48.9 (0.83) 31.1 (0.59) 35.6 (0.13) 38.2 (0.48) 44.1 (0.89) 47.9 (1.01) 31.6 (1.35) 34.7 (0.39) 36.7 (1.84) 42.1 (1.20) 44.6 (0.88) 29.5 (0.53) 32.6 (2.01) 35.7 (3.30) 43.0 (0.07) 44.9 (0.38) 28.8 (1.40) 32.7 (0.61) 33.9 (1.46) 35.5 (0.49) 44.2 (1.44)

40 200 40 200 40 200 40 200 40 200

19.9 (1.12) 32.1 (0.92) 20.2 (0.58) 30.7 (1.63) 22.0 (1.08) 29.4 (1.06) 19.6 (0.31) 28.5 (0.38) 19.3 (0.54) 24.7 (1.32)

n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.

0 75 150 225 0 75 150 225 0 75 150 225 0 75 150 225

n.a. 26.0 (2.24) 31.4 (0.84) 30.9 (2.66) 18.4 (0.21) 21.1 (0.56) 22.5 (0.91) 25.3 (1.47) 16.8 (1.68) 17.8 (0.42) 19.2 (1.54) 22.7 (0.98) 16.8 (0.28) 18.1 (1.33) 18.6 (0.14) 19.7 (0.35)

n.a. 33.2 (2.76) 38.7 (0.09) 36.1 (0.31) 31.4 (1.19) 31.5 (0.07) 34.7 (0.21) 35.2 (1.20) 30.1 (0.52) 31.9 (0.22) 37.3 (0.38) 37.9 (0.49) 30.5 (0.79) 30.4 (0.66) 33.8 (0.82) 38.4 (0.72)

Summer 2009 (Dec. 30, 2008)

Jan. 13, 2009 (214) Jan. 21, 2009 (323) Jan. 29, 2009 (444) Feb. 19, 2009 (747)

Summer 2010 (Dec. 30, 2009)

Jan. 13, 2010 (238)

Jan. 20, 2010 (349)

Jan. 26, 2010 (452)

Feb. 1, 2010 (566)

Feb. 8, 2010 (691)

Feb. 16, 2010 (812)

Summer 2010b (Dec. 30, 2009)

Jan. 13, 2010 (238) Jan. 20, 2010 (349) Jan. 26, 2010 (452) Feb. 8, 2010 (691) Feb. 16, 2010 (812)

Autumn 2009 (March 19, 2009)

Apr. 8, 2009 (250)

Apr. 20, 2009 (349)

Apr. 28, 2009 (407)

May 7, 2009 (465)

a b c

Base temperature 4 ◦ C. Standard error of the mean. Data not available.

P.M. Errecart et al. / Field Crops Research 129 (2012) 46–58

57

Table A2 Regression coefficients and fraction of observed variance explained by linear models predicting NNI as a function of NUSL (g N kg−1 DM), fitted at each sampling date for seven field experiments. Field experiment (fertilization date)

Sampling dates (GDD)a

Slope

Early Spring 2008 (Aug. 21, 2008)

Sep. 26, 2008 (134) Oct. 14, 2008 (227) Oct. 21, 2008 (289) Oct. 28, 2008 (339) Nov. 5, 2008 (417)

0.027 0.030 0.032 0.028 0.029

Nov. 5, 2008 (122) Nov. 11, 2008 (186) Nov. 18, 2008 (260) Nov. 25, 2008 (357) Dec. 2, 2008 (443) Dec. 10, 2008 (544) Jan. 5, 2009 (903)

Late Spring 2008 (Oct. 23, 2008)

Early Spring 2009 (Aug. 19, 2009)

Summer 2009 (Dec. 30, 2008)

Summer 2010 (Dec. 30, 2009)

Summer 2010b (Dec. 30, 2009)

Autumn 2009 (March 19, 2009)

a b c * ** ***

x-Axis interceptb

R2

Statistical significancec

5.93 6.76 5.36 4.95 4.01

0.91 0.81 0.86 0.90 0.81

***

0.020 0.018 0.027 0.032 0.036 0.038 0.032

3.59 −0.74 5.16 6.20 5.91 5.14 2.66

0.94 0.89 0.83 0.92 0.87 0.62 0.55

***

Sep.10, 2009 (181) Sep. 17, 2009 (236) Sep. 23, 2009 (263) Sep. 29, 2009 (287) Oct. 6, 2009 (346) Oct. 14, 2009 (404)

0.028 0.031 0.031 0.038 0.023 0.028

10.11 11.36 10.04 9.83 −1.75 0.98

0.95 0.94 0.91 0.89 0.68 0.91

***

Jan. 13 2009 (214) Jan. 21, 2009 (323) Jan. 29, 2009 (444) Feb. 19, 2009 (747)

0.019 0.019 0.039 0.029

0.76 −1.13 7.27 3.20

0.90 0.72 0.92 0.98

**

Jan. 13, 2010 (238) Jan. 20, 2010 (349) Jan. 26, 2010 (452) Feb. 1, 2010 (566) Feb. 8, 2010 (691) Feb. 16, 2010 (812)

0.027 0.026 0.029 0.033 0.041 0.039

4.10 4.03 5.54 7.18 8.46 6.58

0.94 0.87 0.92 0.95 0.95 0.97

***

Jan. 13, 2010 (238) Jan. 20, 2010 (349) Jan. 26, 2010 (452) Feb. 8, 2010 (691) Feb. 16, 2010 (812)

0.030 0.026 0.033 0.041 0.041

5.57 5.43 7.24 8.96 8.46

0.99 0.72 0.65 0.88 0.95

***

Apr. 8, 2009 (250) Apr. 20, 2009 (349) Apr. 28, 2009 (407) May 7, 2009 (465)

0.026 0.025 0.031 0.065

5.42 2.36 5.04 12.16

0.79 0.71 0.84 0.65

**

** *** *** **

*** ** *** *** * *

*** *** *** ** ***

* ** ***

*** *** *** *** ***

*

n.s. ** **

** ** *

◦

Base temperature 4 C. The y-axis intercept is the negative product of slope by x-axis intercept. n.s.: non-significant. p < 0.05. p < 0.01. p < 0.001.

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