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Validity of accessible critical nitrogen dilution curves in perennial ryegrass for seed production
Field Crops Research 111 (2009) 152–156
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Validity of accessible critical nitrogen dilution curves in perennial ryegrass for seed production Rene´ Gislum *, Birte Boelt University of Aarhus, Faculty of Agricultural Sciences, Department of Genetics and Biotechnology, Research Centre Flakkebjerg, DK-4200 Slagelse, Denmark
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 May 2007 Received in revised form 17 November 2008 Accepted 17 November 2008
The objectives were to test if accessible critical nitrogen dilution curves (NDCs) in rapeseed, pea, alfalfa, tall fescue, wheat, annual ryegrass and linseed could be used in grass species for seed production and to develop a critical NDC especially in grass species for seed production. The critical NDC is based on critical plant nitrogen concentration (%Nc) which is defined as the minimum plant nitrogen (N) concentration in plants needed for maximum growth rate of the crop. Critical NDC is a sequence of %Nc values starting at approximately 1 t ha1. Field experiments in perennial ryegrass (Lolium perenne L.) and red fescue (Festuca rubra L.) for seed production were carried out at different locations in Denmark from 1996 to 2005. At least three different N application rates were applied in each experiment. Shoot biomass (t ha1) and N concentration expressed in percentage of dry matter were measured for calculation of %Nc and critical NDC. The %N decreases during the growing season from a maximum of 4.6 to a minimum of 0.76 while shoot biomass increases from 2.3 to 13.8 t ha1. Therefore it was not possible to calculate a %Nc at a biomass lower than 2.3 t ha1 and the NDC then has to be extrapolated to estimate a. Based on this we suggest that the NDC developed in grass species for seed production should be further improved. The conclusion from the use of accessible NDCs was that NDCs in linseed, wheat, and annual ryegrass were better to describe %Nc in grass species for seed production than the NDCs in tall fescue, alfalfa, pea and rapeseed. These findings should be used to continue the interesting and necessary work on developing a NDC in grass species for seed production. ß 2008 Elsevier B.V. All rights reserved.
Keywords: Critical N dilution curve Grass seed production Perennial ryegrass Nitrogen
1. Introduction The critical plant nitrogen concentration (%Nc) is defined as the minimum nitrogen (N) concentration in plants needed for maximum growth rate of the crop (Ulrich, 1952). If the concept is further extended to a sequence of %Nc values at different shoot biomasses it is defined as a critical N dilution curve (NDC) (Lemaire and Gastal, 1997). The discrepancy between actual N concentration in a crop and the corresponding %Nc at the same shoot biomass has been defined as the N nutrition index (NNI) (Lemaire and Gastal, 1997) and indicates the intensity of the N deficiency (NNI < 1) or the N excess (NNI > 1) of the crop. The use of NNI to increase the utilisation of applied N is very appealing in agriculture where the former sole goal – high productivity – has been replaced by a new goal, to develop novel production systems with a low environmental impact. Application of an NDC in grass seed production in practice is based on the assumption that an N deficient crop will reduce the
* Corresponding author. Tel.: +45 89993500; fax: +45 89993501. E-mail address: [email protected] (R. Gislum). 0378-4290/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2008.11.009
final seed yield. For example, if a crop is determined to be in N deficiency according to the NDC it will give a low seed yield, unless interventions are made. In grass species for seed production different split N application strategies have been tested, and in some cases an application strategy based on an early and late N application had a higher seed yield than an application of the total amount of N at the initiation of the spring growing season (Nordestgaard, 1992). In other cases the later N application in the split N application strategy initiated vegetative growth of the grasses, early and severe lodging and consequently a negative effect on the seed yield. The problem is to define under which circumstances an additional N application will be advantageous. Accordingly, implementation of NDC and NNI could be the solution. The equation for the NDC is: %N ¼ aðWÞb
(1)
where %N is the N concentration in shoots expressed in g 100 g1 of shoot biomass and W is the shoot biomass in t ha1. The a coefficient is the plant %N at 1 t shoot biomass ha1 and b is the pattern of decrease of %N during increased shoot biomass.
R. Gislum, B. Boelt / Field Crops Research 111 (2009) 152–156
Using this equation different a and b coefficients have been estimated for a range of crops e.g. linseed (Fle´net et al., 2006), annual ryegrass (Marino et al., 2004), rapeseed (Colnenne et al., 1998), pea (Ney et al., 1997), wheat (Justes et al., 1994), tall fescue (Lemaire and Denoix, 1987; Lemaire and Salette, 1984) and alfalfa (Lemaire et al., 1985). Greenwood et al. (1990) used a number of different crops to show a clear distinction in the a coefficient between C3 and C4 species. They suggested a general a coefficient of 5.7 for C3 plants and 4.1 for C4 plants, with a smaller variation in a within the C3 species. In cases with only vegetative growth a general value for the b coefficient of 0.34 for both C3 and C4 crops was found (Greenwood et al., 1990). Hardwick (1987) showed that in the vegetative growth the N in the plant is strongly linked to its metabolic activity and the shoot biomass. In contrast to vegetative growth is the reproductive growth where a part of N is in the storage tissues and therefore not available for biomass production. In this case the conclusion from Hardwick will probably not be valid (Greenwood et al., 1990). This was supported by Lemaire and Gastal (1997) who, based on the difference in the storage of N in vegetative and reproductive
153
growth of the plants, suggested to determine empirically the a and b coefficients for each kind of crop in order to obtain a reference curve for the variation of %Nc to calculate the NNI. No NDC exists in grass species for seed production and before an NDC is developed it should be tested if accessible NDCs could be used. The aims of this study were (a) to test the validity of accessible NDCs in grass species for seed production and (b) to develop an NDC in grass species for seed production. 2. Materials and methods 2.1. Field experiments Nine field experiments with perennial ryegrass (Lolium perenne L.) and red fescue (Festuca rubra L.) for seed production were established together with a cover crop at different locations in Denmark. All of them were harvested in the period from 1996 to 2005 (Table 1). The net-plot size was 8.5 m 2.5 m in experiments I, II, VII–X, whereas the net-plot size was 24 m 2 m in experiments III–VI. The purpose of all experiments was to define
Table 1 Characteristics of the field experiments. Experiment
Development I I I II
Dataset
of critical 1 2 3 4–6
Universal Transversal Mercator Grid, zone 32 (easting/northing) N dilution model 651600/6133380 651600/6133380 651600/6133380 651600/6133380
Soil type
Fine Fine Fine Fine
sandy sandy sandy sandy
Species
loam loam loam loam
Perennial Perennial Perennial Perennial
Cultivar
Year
N application rate (kg ha1)
N application date
Sampling time
ryegrass ryegrass ryegrass ryegrass
Borvi Borvi Borvi Borvi
2001 2002 2003 1997
80, 115, 150 80, 115, 150 80, 115, 150 0, 50, 100, 120, 140 0, 50, 100, 120, 140 0, 50, 100, 120, 140 0, 40, 80, 120, 160, 200
28 02 02 20
31 March
14 21 17 04 24 04
31 March
18 May, 19 June
25 March
06 May
0, 40, 80, 120, 160, 200 0, 40, 80, 120, 160, 200 0, 40, 80, 120, 160, 200 0, 40, 80, 120, 160, 200 0, 40, 80, 120, 160, 200 0, 40, 80, 120, 160, 200 (Autumn 60 or 120) 60, 90, 120 (Autumn 60) 60, 90, 120 (Autumn 60, 90 or 120) 60, 90, 120 80, 115, 150 80, 115, 150 80, 115, 150 40, 80, 120, 160, 200 40, 80, 120, 160, 200 40, 80, 120, 160, 200 40, 80, 120, 160, 200 0, 50, 100, 120, 140 0, 50, 100, 120, 140
9 April
12 May
14 April
12 May
27 march
18 May
24 March
18 May
25 March
07 May
09 April
04 May
17 March
22 May
05 March
24 May
20 March
12 May
28 March
02 April
22 24 21 10 12 21
02 April
10 June, 26 June
31 March
25 May, 30 June
25 April
24 May, 25 June, 18 Jul 25 June, 18 Jul
II
7–8
651600/6133380
Fine sandy loam
Perennial ryegrass
Tivoli
1998
II
9–10
651600/6133380
Fine sandy loam
Perennial ryegrass
Borvi
1998
III
11
691000/6137000
Coarse loamy sand
Perennial ryegrass
Score
1999
Fine loamy sand
Perennial ryegrass
Score
1999
Test of critical N dilution model IV 12 572000/6338000 IV
13
596000/6142000
Sandy clay loam
Perennial ryegrass
Taya
1999
IV
14
574000/6191000
Fine sandy loam
Perennial ryegrass
Taya
2000
IV
15
588000/6132000
Sandy clay loam
Perennial ryegrass
Pimpernel
2000
V
16
575000/6191000
Fine sandy loam
Perennial ryegrass
Merci
2001
VI
17
597000/6136000
Sandy clay loam
Perennial ryegrass
Pimpernel
2001
VII
18–19
651600/6133380
Fine sandy loam
Red fescue
Pernille
2003
VII
20
651600/6133380
Fine sandy loam
Red fescue
Pernille
2004
VII
21–23
651600/6133380
Fine sandy loam
Red fescue
Pernille
2005
VIII VIII VIII IX
24–25 26 27 28–30
651600/6133380 651600/6133380 651600/6133380 651600/6133380
Fine Fine Fine Fine
Perennial Perennial Perennial Perennial
ryegrass ryegrass ryegrass ryegrass
Borvi Borvi Borvi Prana
2001 2002 2003 2001
IX
31–32
651600/6133380
Fine sandy loam
Perennial ryegrass
Prana
2002
IX
33–34
651600/6133380
Fine sandy loam
Perennial ryegrass
Prana
2003
IX
35–36
651600/6133380
Fine sandy loam
Perennial ryegrass
Prana
2004
II
37–39
651600/6133380
Fine sandy loam
Perennial ryegrass
Borvi
1996
II
40–41
651600/6133380
Fine sandy loam
Perennial ryegrass
Nui
1996
sandy sandy sandy sandy
loam loam loam loam
March April April March
02 April 04 April
25 April
May May June June, 03 July, July May, 19June
May, 11 June April May May, 22 May, June May, 25 June
154
R. Gislum, B. Boelt / Field Crops Research 111 (2009) 152–156
the optimum N application rate or the optimum N application strategy to achieve the greatest seed yield. In all of the experiments there was a satisfactory plant stand after harvest of the cover crop. Calcium ammonium nitrate was used as N source in all experiments and was applied at initiation of the spring growth. Weeds were controlled according to normal practice using herbicides. The N treatments were arranged in randomised block designs with three or four replications.
the criterion described above. The experiments used in the development of NDC were carried out on research stations (datasets 1–10) and at a farmers field (dataset 11). The range in shoot biomasses was from 2.3 to 13.8 t ha1. A curve based on a scaled and displaced error function was fitted to the %Nc:
2.2. Sampling and samples analysis
where %Nci is the critical N concentration at the ith sampling, Wi is the shoot biomass at the ith sampling. a and b are unknown parameters to be estimated. The curve is now referred to as the reference NDC.
In experiment I three different sowing rates (2, 5 or 8 kg ha1) were used and a separate dataset was created for each sowing rate. In experiment II an autumn N application of 30 kg ha1 was applied when spring N application was 120 kg ha1 and 60 kg N ha1 was applied when spring N application rate was 140 kg ha1. In experiment VII the species was red fescue and three autumn N application rates were tested in combination with three spring application rates. Experiment VII was therefore divided into six dataset, one dataset for each autumn N application rate and year. Each dataset only consisted of one sampling time. In all experiments plant samples were cut at ground level with three cuts per plot. Each cut was 0.0625 m2. Fresh weight was measured in all samples before they were oven-dried at 80 8C for at least 24 h and pooled to one sample per plot. Shoot biomass (t ha1) was calculated. The samples were ground and an aliquot of each sample was analysed for total N using the Kjeldahl method (wet digestion in H2SO4–H2O2) or the Dumas method (flash combustion with automatic N analyser). The sampling time varied (Table 1) but the main sampling time was at stem elongation. Before shedding the grass seed crop was harvested directly with a trial combiner, and seeds were dried to 12% moisture. Seed samples were cleaned using an air-screen seed cleaner by passing them through 0.5 cm sieves. After seed drying and cleaning, one sample from each N application rate in each dataset was analysed for purity using international standardised methodology (ISTA, 1996) and seed yield expressed as 100% clean seed was calculated. Only seed yields from first year seed harvest were used. 2.3. Determination of critical N concentration The number of sampling times in each experiment varied from 1 to 5. Each sampling time in each experiment was considered as an independent dataset. In this way a total of 41 datasets were available. In each dataset the shoot biomasses (t ha1) were compared by analysis of variance where N application rate and replicates were fixed effects. Homogeneous groups were determined at a 10% level of significance. Only datasets where it was possible to divide the data into the following two groups were used, (a) the N limiting group – an increase in N application rate has a significant effect on the shoot biomass and (b) the N nonlimiting group – an increase in N application rate has no significant effect in the shoot biomass but has a positive effect on N concentration. A linear regression model was developed on shoot biomass and %N for the N limiting group. The average shoot biomass was calculated from the N non-limiting group. When only three N rates were applied, the dataset used to develop the critical N dilution model was the dataset where shoot biomass of the lowest N application rate was significantly lower than shoot biomass measured in the two highest N application rates, which showed no mutual significant difference. The critical N concentration (%Nc) is the intersection between an oblique regression line corresponding to an increase of N concentration without any increase in shoot biomass and a vertical line corresponding to the increase of N concentration without any increase in shoot biomass. Individual %Nc values were calculated for the datasets that fulfilled
%Nci ¼ aðW i Þb
(2)
2.4. Validity of accessible NDC The %Nc was plotted against accessible NDC to test if these curves could describe the calculated %Nc better than the reference NDC. Furthermore, in 30 datasets (datasets 12–41) the shoot biomasses (t ha1) were compared by analysis of variance where N application rate and replicates were fixed effects. Homogeneous groups were determined at a 10% level of significance. Each dataset was divided into two groups, (a) the N limiting group – an increase in N application rate has a significant effect on the shoot biomass and (b) the N non-limiting group – an increase in N application rate has no significant effect in the shoot biomass. The two groups from each dataset were plotted together with the NDC with the purpose to test if data from group a were placed above the NDC and data from group b was placed below the NDC. Yield data from all 41 datasets available were used to test if selected NDC and the reference NDC could be used to show if N limited the seed yield. In each dataset the seed yields (kg ha1) were compared by analysis of variance where N application rate and replicates were fixed effects. If the main effect of N was significant the seed yields were divided into two groups, (a) the significant highest seed yield and (b) lower seed yields. Homogeneous groups were determined at a 5% level of significance. Plant N concentration and shoot biomass for the two groups were plotted together with selected NDC and the reference NDC with the purpose to show if data from group a, was placed above the NDC and/or the reference NDC and data from group b was placed below the NDC and/or the reference NDC. All analyses were performed using the procedures PROC GLM module within the Statistical Analysis System version 8.2, software package (SAS, 1999). 3. Results The statistical criterion for calculation of %Nc was fulfilled in 11 of the 41 datasets. In Fig. 1 the %N and shoot biomass are shown for the 11 datasets together with %Nc, which is the intercept between the oblique and the vertical line in each dataset. As shoot biomass increases there is a distinct decrease in %N. The %N decreases from a maximum of 4.6 to a minimum of 0.76 while shoot biomass increases from 2.3 to 13.8 t ha1. Plot of reference NDC and selected accessible NDC shows that NDC in rapeseed, pea, alfalfa and tall fescue were considerably above the 11 calculated %Nc (Fig. 2). The NDC in wheat, annual ryegrass, linseed and the reference NDC were more in line with calculated %Nc, however, no one seems to describe %Nc well. A further test of the selected NDC in linseed, wheat, annual ryegrass and the reference NDC reveals that NDC in linseed and annual ryegrass had the lowest number of misclassified data points which were not used to calculate %Nc (datasets 12–41) (Fig. 3). The use of NDC in linseed, wheat, annual ryegrass and the reference NDC as a method to define if N limits the seed yield during
R. Gislum, B. Boelt / Field Crops Research 111 (2009) 152–156
Fig. 1. Shoot nitrogen concentration (%N) vs. shoot biomass for datasets 1–11 (+). Critical nitrogen concentration defined as the intersection between the vertical and oblique dotted lines for each dataset is indicated with a circle (*).
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Fig. 4. Examination of reliability of critical nitrogen dilution curves corresponding to different species plotted with shoot N concentration vs. shoot biomass in perennial ryegrass from datasets 12 to 17 and 24 to 41. Closed circles (*) are results from nitrogen application rates that were not limiting for seed yield. Open circles indicate nitrogen application rates that were significantly different.
4. Discussion 4.1. Calculation of %Nc
Fig. 2. Critical nitrogen concentration vs. shoot biomass plotted with critical nitrogen dilution curves of different species. a and b values are shown in Table 2.
the spring growing season is shown in Fig. 4. There is no clear difference in the number of misclassified data points using the NDC in linseed, annual ryegrass or the reference NDC, whereas the number of misclassified data points using the wheat NDC is higher.
Fig. 3. Examination of reliability of critical nitrogen dilution curves of different species plotted with shoot nitrogen concentration vs. shoot biomass for datasets not used to develop critical nitrogen concentrations. Perennial ryegrass (* and *) are from datasets 12 to 17 and red fescue (& and &) are from datasets 18 to 23. Closed marks (* and &) indicate N application rates that were not limiting for biomass accumulation. Open marks indicate nitrogen application rates that were significantly different.
The calculation of %Nc is the first step in developing a NDC. However, in the present experiment it was no possible to calculate a %Nc at a biomass lower than 2.3 t ha1. The a coefficient has then to be estimated by extrapolating the NDC to a shoot biomass of 1 t ha1, which is the dry weight where a is defined. The reason for the lack of %Nc at very low biomass productions (1–2 t ha1) is most probably due to the fact that all plant samples were taken during the spring growing season before the seed harvest. At the time of the first sampling the grass seed plants are approximately 1-year-old due to the fact that most perennial ryegrass is undersown in spring barley and has reached a shoot biomass of more than 1 t ha1 irrespective of the amount of N applied. If the purpose is to develop an NDC it is probably necessary to include samples taken in the year before the seed harvest. In this way, samples obtained before and after the winter period could be used. However, this last could be problematic due to the effect of winter on the plant growth. The higher b coefficient in the reference NDC compared to NDC in other crops (Table 2) is a result of the low %Nc during the reproductive growth of the grass, defined as samples taken after the date of heading. If data points taken during the reproductive growth are excluded the maximum shoot biomass is 6 t ha1. If the reference NDC is developed on this reduced dataset the b coefficient would be 0.6. Hence, the effect of including the reproductive growth period is a higher b coefficient. The b coefficient in the reduced dataset is then comparable with the one calculated in linseed (Fle´net et al., 2006), but still higher than b calculated in e.g. wheat (Justes et al., 1994) or annual ryegrass (Marino et al., 2004). Similar results have been shown in linseed where the main difference in %Nc between linseed and other C3 species was observed after the emission of flower buds (Fle´net et al., 2006). The reason for this difference when reproductive growth is included is suggested to be connected with a change in the distribution of N between vegetative and storage tissues (Greenwood et al., 1990). When an increasing proportion of N is transferred from senescing plant parts to storage tissue, predominately as NO3, it is not available for vegetative growth and the relative growth rate will
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R. Gislum, B. Boelt / Field Crops Research 111 (2009) 152–156
Table 2 b Values of a and b coefficients found in the literature in the regression %N = a(W) , where %N is the N concentration (g N (100 g shoot biomass)1) and W is the shoot biomass (t ha1). Species
a
b
References
Linseed (Linum usitatissimum L.) Annual ryegrass (Lolium multiflorum Lam.) Tall fescue (Festuca arundinacea Schreb.) Alfalfa (Medicago sativa L.) Pea (Pisum sativum L.) Wheat (Triticum aestivum L.) Rapeseed (Brassica napus L.) Perennial ryegrass (Lolium perenne L.)
4.69 4.1 4.8 4.6–5.5 5.1 5.3 4.48 6.36
0.53 0.38 0.32 0.29–0.36 0.32 0.44 0.25 0.71
Fle´net et al. (2006) Marino et al. (2004) Lemaire and Salette (1984), Lemaire and Denoix (1987) Lemaire et al. (1985) Ney et al. (1997) Justes et al. (1994) Colnenne et al. (1998) This paper
decrease. The only possibility for the plant to obtain a maximum relative growth rate is through increased N uptake through the roots. As the %Nc is becoming stable it is concluded that N in the soil is completely depleted when shoot biomass goes towards maximum. The result is a low %Nc during the reproductive growth of the plants. 4.2. Implications for practical grass seed production It is very appealing to implement NDC as a diagnostic tool in grass seed production. The NDC should be used to decide if an additional N application is necessary to increase the shoot %N above the NDC at the current biomass. The concomitant increase in shoot %N by an additional N application should secure that shoot %N is not the limiting factor to achieve a high seed yield. Another and more obvious strategy would of course be to apply excess N rates and avoid shoot %N to be below NDC. However, several results have shown the negative effect of excess N rates in grass species for seed production (Gislum et al., 2005; Gislum, 2003). Even though the range in biomass in an NDC is from approximately 1 to several t ha1 the period where an additional N application will have a positive effect on the final seed yield is limited. During the reproductive growth period the N uptake from the soil is low and the effect of N application on the seed yield is therefore limited. The purpose is therefore to secure that the plants have accumulated sufficient amounts of N before the reproductive growth to develop the seeds. Focus should therefore be on using the NDC in the period of growth where an application of N has a positive effect on the relative growth rate and the amount of N accumulated in the plant. In perennial ryegrass most field experiments with the aim to test a split N application have focused on N application at stem elongation and/or anthesis (Gislum and Boelt, 1998; Nordestgaard, 1992; Nordestgaard, 1979; Hebblethwaite and Ivins, 1978). At these crop development stages the grasses are able to utilise the applied N even though the degree of utilisation to increase the seed yield will vary. The growth stage of the plants should therefore be taken into consideration when plant samples are taken and it is not recommended to use the NDC as a diagnostic tool after anthesis. We conclude that accessible NDCs in linseed wheat, and annual ryegrass were better to describe %Nc in grass for seed production than the NDCs in tall fescue, alfalfa, pea and rapeseed. The reference NDC should be further developed and the focus should be to estimate the a coefficient. These findings should be used to
continue the interesting and necessary work on developing a NDC in grass species for seed production. References Colnenne, C., Meynard, J.M., Reau, R., Justes, E., Merrien, A., 1998. Determination of a critical N dilution curve for winter oilseed rape. Ann. Bot. 81, 311–317. Fle´net, F., Gue´rif, M., Boiffin, J., Dorvillez, D., Champolivier, L., 2006. The critical N dilution curve for linseed (Linum usitatissimum L.) is different from other C3 species. Eur. J. Agron. 24, 367–373. Gislum, R., Boelt, B., 1998. Timing of spring nitrogen application in amenity-types of Lolium perenne L. grown for seed. J. Appl. Seed Prod. 16, 67–70. Gislum, R., 2003. Dynamics of nitrogen use efficiency in grass seed crops. Ph.D. thesis. University of Aarhus, Faculty of Agricultural Sciences, Department of Genetics and Biotechnology, Denmark. Gislum, R., Boelt, B., Jensen, E.S., Wollenweber, B., Kristensen, K., 2005. Temporal variation in nitrogen concentration of above ground perennial ryegrass applied different nitrogen fertiliser rates. Field Crops Res. 91, 83–90. Greenwood, D.J., Lemaire, G., Gosse, G., Cruz, P., Draycott, A., Neeteson, J.J., 1990. Decline in percentage N of C3 and C4 crops with increasing plant mass. Ann. Bot. 66, 425–436. Hardwick, R.C., 1987. The nitrogen content of plants and the selfthinning rule of plant ecology: a test of the core-skin hypothesis. Ann. Bot. 60, 439–446. Hebblethwaite, P.D., Ivins, J.D., 1978. Nitrogen studies in Lolium perenne grown for seed. II. Timing of nitrogen application. J. Br. Grassl. Soc. 33, 159–166. ISTA, 1996. International rules for seed testing. Seed Sci. Technol. 24 . Justes, E., Mary, B., Meynard, J.M., Machet, J.M., Thelier-Huche, L., 1994. Determination of a critical N dilution curve for winter wheat crops. Ann. Bot. 74, 397–407. Lemaire, G., Salette, J., 1984. Relation entre dynamique de croissance et dynamique de pre`le´vement d’azote pour un peuplement de gramine´es fourrage`res. I. Etude de l’effet du milieu. Agronomie 4, 423–430. Lemaire, G., Cruz, P., Gosse, G., Chatier, M., 1985. Etude des relations entre la dynamique de pre´evement d’azote et la dynamique de croissance en matie´re se´che d’un peuplement de luzerne (Medicago sativa L.). Agronomie 5, 685–692. Lemaire, G., Denoix, A., 1987. Croissance estivale en matie`re se`che de peuplements de fe´tuque e´leve´e (Festuca arundinacea Schreb.) et de dactyle (Dactylis glomerata L.) dans l’Ouest de la France. I. Etude en conditions de nutrition azote´e et d’alimentation hydrique non-limitantes. Agronomie 7, 373–380. Lemaire, G., Gastal, F., 1997. N uptake and distribution in plant canopies. In: Lemaire, G. (Ed.), Diagnosis of the Nitrogen Status in Crops. Springer-Verlag Publishers, Heideberg, ISBN 3-540-62223-3, pp. 3–43. Marino, M.A., Mazzanti, A., Assuero, S.G., Gastal, F., Echeverria, H.E., Andrade, F., 2004. Nitrogen dilution curves and nitrogen use efficiency during winter-spring growth of annual ryegrass. Agron. J. 96, 601–607. Ney, B., Dore´, T., Sagan, M., 1997. The N requirement of major agricultural crops: grain legumes. In: Lemaire, G. (Ed.), Diagnosis of the Nitrogen Status in Crops. Springer-Verlag Publishers, Heideberg, ISBN 3-540-62223-3, pp. 107–118. Nordestgaard, A., 1979. Various time of application of nitrogen in spring by seed production of perennial rye grass (Lolium perenne L.). Tidsskrift for Planteavl 83, 523–536. Nordestgaard, A., 1992. Split nitrogen application in perennial rye grass (Lolium perenne L.) for seed production. SP beretning 2217, 163–168 (in Danish, with English abstract). SAS, 1999. Statistical Analysis Software, Version 8.02. SAS Institute, Cary, NC, USA. Ulrich, A., 1952. Physiological bases for assessing the nutritional requirements of plants. Annu. Rev. Plant Physiol. 3, 207–228.
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Validity of accessible critical nitrogen dilution curves in perennial ryegrass for seed production Rene´ Gislum *, Birte Boelt University of Aarhus, Faculty of Agricultural Sciences, Department of Genetics and Biotechnology, Research Centre Flakkebjerg, DK-4200 Slagelse, Denmark
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 May 2007 Received in revised form 17 November 2008 Accepted 17 November 2008
The objectives were to test if accessible critical nitrogen dilution curves (NDCs) in rapeseed, pea, alfalfa, tall fescue, wheat, annual ryegrass and linseed could be used in grass species for seed production and to develop a critical NDC especially in grass species for seed production. The critical NDC is based on critical plant nitrogen concentration (%Nc) which is defined as the minimum plant nitrogen (N) concentration in plants needed for maximum growth rate of the crop. Critical NDC is a sequence of %Nc values starting at approximately 1 t ha1. Field experiments in perennial ryegrass (Lolium perenne L.) and red fescue (Festuca rubra L.) for seed production were carried out at different locations in Denmark from 1996 to 2005. At least three different N application rates were applied in each experiment. Shoot biomass (t ha1) and N concentration expressed in percentage of dry matter were measured for calculation of %Nc and critical NDC. The %N decreases during the growing season from a maximum of 4.6 to a minimum of 0.76 while shoot biomass increases from 2.3 to 13.8 t ha1. Therefore it was not possible to calculate a %Nc at a biomass lower than 2.3 t ha1 and the NDC then has to be extrapolated to estimate a. Based on this we suggest that the NDC developed in grass species for seed production should be further improved. The conclusion from the use of accessible NDCs was that NDCs in linseed, wheat, and annual ryegrass were better to describe %Nc in grass species for seed production than the NDCs in tall fescue, alfalfa, pea and rapeseed. These findings should be used to continue the interesting and necessary work on developing a NDC in grass species for seed production. ß 2008 Elsevier B.V. All rights reserved.
Keywords: Critical N dilution curve Grass seed production Perennial ryegrass Nitrogen
1. Introduction The critical plant nitrogen concentration (%Nc) is defined as the minimum nitrogen (N) concentration in plants needed for maximum growth rate of the crop (Ulrich, 1952). If the concept is further extended to a sequence of %Nc values at different shoot biomasses it is defined as a critical N dilution curve (NDC) (Lemaire and Gastal, 1997). The discrepancy between actual N concentration in a crop and the corresponding %Nc at the same shoot biomass has been defined as the N nutrition index (NNI) (Lemaire and Gastal, 1997) and indicates the intensity of the N deficiency (NNI < 1) or the N excess (NNI > 1) of the crop. The use of NNI to increase the utilisation of applied N is very appealing in agriculture where the former sole goal – high productivity – has been replaced by a new goal, to develop novel production systems with a low environmental impact. Application of an NDC in grass seed production in practice is based on the assumption that an N deficient crop will reduce the
* Corresponding author. Tel.: +45 89993500; fax: +45 89993501. E-mail address: [email protected] (R. Gislum). 0378-4290/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2008.11.009
final seed yield. For example, if a crop is determined to be in N deficiency according to the NDC it will give a low seed yield, unless interventions are made. In grass species for seed production different split N application strategies have been tested, and in some cases an application strategy based on an early and late N application had a higher seed yield than an application of the total amount of N at the initiation of the spring growing season (Nordestgaard, 1992). In other cases the later N application in the split N application strategy initiated vegetative growth of the grasses, early and severe lodging and consequently a negative effect on the seed yield. The problem is to define under which circumstances an additional N application will be advantageous. Accordingly, implementation of NDC and NNI could be the solution. The equation for the NDC is: %N ¼ aðWÞb
(1)
where %N is the N concentration in shoots expressed in g 100 g1 of shoot biomass and W is the shoot biomass in t ha1. The a coefficient is the plant %N at 1 t shoot biomass ha1 and b is the pattern of decrease of %N during increased shoot biomass.
R. Gislum, B. Boelt / Field Crops Research 111 (2009) 152–156
Using this equation different a and b coefficients have been estimated for a range of crops e.g. linseed (Fle´net et al., 2006), annual ryegrass (Marino et al., 2004), rapeseed (Colnenne et al., 1998), pea (Ney et al., 1997), wheat (Justes et al., 1994), tall fescue (Lemaire and Denoix, 1987; Lemaire and Salette, 1984) and alfalfa (Lemaire et al., 1985). Greenwood et al. (1990) used a number of different crops to show a clear distinction in the a coefficient between C3 and C4 species. They suggested a general a coefficient of 5.7 for C3 plants and 4.1 for C4 plants, with a smaller variation in a within the C3 species. In cases with only vegetative growth a general value for the b coefficient of 0.34 for both C3 and C4 crops was found (Greenwood et al., 1990). Hardwick (1987) showed that in the vegetative growth the N in the plant is strongly linked to its metabolic activity and the shoot biomass. In contrast to vegetative growth is the reproductive growth where a part of N is in the storage tissues and therefore not available for biomass production. In this case the conclusion from Hardwick will probably not be valid (Greenwood et al., 1990). This was supported by Lemaire and Gastal (1997) who, based on the difference in the storage of N in vegetative and reproductive
153
growth of the plants, suggested to determine empirically the a and b coefficients for each kind of crop in order to obtain a reference curve for the variation of %Nc to calculate the NNI. No NDC exists in grass species for seed production and before an NDC is developed it should be tested if accessible NDCs could be used. The aims of this study were (a) to test the validity of accessible NDCs in grass species for seed production and (b) to develop an NDC in grass species for seed production. 2. Materials and methods 2.1. Field experiments Nine field experiments with perennial ryegrass (Lolium perenne L.) and red fescue (Festuca rubra L.) for seed production were established together with a cover crop at different locations in Denmark. All of them were harvested in the period from 1996 to 2005 (Table 1). The net-plot size was 8.5 m 2.5 m in experiments I, II, VII–X, whereas the net-plot size was 24 m 2 m in experiments III–VI. The purpose of all experiments was to define
Table 1 Characteristics of the field experiments. Experiment
Development I I I II
Dataset
of critical 1 2 3 4–6
Universal Transversal Mercator Grid, zone 32 (easting/northing) N dilution model 651600/6133380 651600/6133380 651600/6133380 651600/6133380
Soil type
Fine Fine Fine Fine
sandy sandy sandy sandy
Species
loam loam loam loam
Perennial Perennial Perennial Perennial
Cultivar
Year
N application rate (kg ha1)
N application date
Sampling time
ryegrass ryegrass ryegrass ryegrass
Borvi Borvi Borvi Borvi
2001 2002 2003 1997
80, 115, 150 80, 115, 150 80, 115, 150 0, 50, 100, 120, 140 0, 50, 100, 120, 140 0, 50, 100, 120, 140 0, 40, 80, 120, 160, 200
28 02 02 20
31 March
14 21 17 04 24 04
31 March
18 May, 19 June
25 March
06 May
0, 40, 80, 120, 160, 200 0, 40, 80, 120, 160, 200 0, 40, 80, 120, 160, 200 0, 40, 80, 120, 160, 200 0, 40, 80, 120, 160, 200 0, 40, 80, 120, 160, 200 (Autumn 60 or 120) 60, 90, 120 (Autumn 60) 60, 90, 120 (Autumn 60, 90 or 120) 60, 90, 120 80, 115, 150 80, 115, 150 80, 115, 150 40, 80, 120, 160, 200 40, 80, 120, 160, 200 40, 80, 120, 160, 200 40, 80, 120, 160, 200 0, 50, 100, 120, 140 0, 50, 100, 120, 140
9 April
12 May
14 April
12 May
27 march
18 May
24 March
18 May
25 March
07 May
09 April
04 May
17 March
22 May
05 March
24 May
20 March
12 May
28 March
02 April
22 24 21 10 12 21
02 April
10 June, 26 June
31 March
25 May, 30 June
25 April
24 May, 25 June, 18 Jul 25 June, 18 Jul
II
7–8
651600/6133380
Fine sandy loam
Perennial ryegrass
Tivoli
1998
II
9–10
651600/6133380
Fine sandy loam
Perennial ryegrass
Borvi
1998
III
11
691000/6137000
Coarse loamy sand
Perennial ryegrass
Score
1999
Fine loamy sand
Perennial ryegrass
Score
1999
Test of critical N dilution model IV 12 572000/6338000 IV
13
596000/6142000
Sandy clay loam
Perennial ryegrass
Taya
1999
IV
14
574000/6191000
Fine sandy loam
Perennial ryegrass
Taya
2000
IV
15
588000/6132000
Sandy clay loam
Perennial ryegrass
Pimpernel
2000
V
16
575000/6191000
Fine sandy loam
Perennial ryegrass
Merci
2001
VI
17
597000/6136000
Sandy clay loam
Perennial ryegrass
Pimpernel
2001
VII
18–19
651600/6133380
Fine sandy loam
Red fescue
Pernille
2003
VII
20
651600/6133380
Fine sandy loam
Red fescue
Pernille
2004
VII
21–23
651600/6133380
Fine sandy loam
Red fescue
Pernille
2005
VIII VIII VIII IX
24–25 26 27 28–30
651600/6133380 651600/6133380 651600/6133380 651600/6133380
Fine Fine Fine Fine
Perennial Perennial Perennial Perennial
ryegrass ryegrass ryegrass ryegrass
Borvi Borvi Borvi Prana
2001 2002 2003 2001
IX
31–32
651600/6133380
Fine sandy loam
Perennial ryegrass
Prana
2002
IX
33–34
651600/6133380
Fine sandy loam
Perennial ryegrass
Prana
2003
IX
35–36
651600/6133380
Fine sandy loam
Perennial ryegrass
Prana
2004
II
37–39
651600/6133380
Fine sandy loam
Perennial ryegrass
Borvi
1996
II
40–41
651600/6133380
Fine sandy loam
Perennial ryegrass
Nui
1996
sandy sandy sandy sandy
loam loam loam loam
March April April March
02 April 04 April
25 April
May May June June, 03 July, July May, 19June
May, 11 June April May May, 22 May, June May, 25 June
154
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the optimum N application rate or the optimum N application strategy to achieve the greatest seed yield. In all of the experiments there was a satisfactory plant stand after harvest of the cover crop. Calcium ammonium nitrate was used as N source in all experiments and was applied at initiation of the spring growth. Weeds were controlled according to normal practice using herbicides. The N treatments were arranged in randomised block designs with three or four replications.
the criterion described above. The experiments used in the development of NDC were carried out on research stations (datasets 1–10) and at a farmers field (dataset 11). The range in shoot biomasses was from 2.3 to 13.8 t ha1. A curve based on a scaled and displaced error function was fitted to the %Nc:
2.2. Sampling and samples analysis
where %Nci is the critical N concentration at the ith sampling, Wi is the shoot biomass at the ith sampling. a and b are unknown parameters to be estimated. The curve is now referred to as the reference NDC.
In experiment I three different sowing rates (2, 5 or 8 kg ha1) were used and a separate dataset was created for each sowing rate. In experiment II an autumn N application of 30 kg ha1 was applied when spring N application was 120 kg ha1 and 60 kg N ha1 was applied when spring N application rate was 140 kg ha1. In experiment VII the species was red fescue and three autumn N application rates were tested in combination with three spring application rates. Experiment VII was therefore divided into six dataset, one dataset for each autumn N application rate and year. Each dataset only consisted of one sampling time. In all experiments plant samples were cut at ground level with three cuts per plot. Each cut was 0.0625 m2. Fresh weight was measured in all samples before they were oven-dried at 80 8C for at least 24 h and pooled to one sample per plot. Shoot biomass (t ha1) was calculated. The samples were ground and an aliquot of each sample was analysed for total N using the Kjeldahl method (wet digestion in H2SO4–H2O2) or the Dumas method (flash combustion with automatic N analyser). The sampling time varied (Table 1) but the main sampling time was at stem elongation. Before shedding the grass seed crop was harvested directly with a trial combiner, and seeds were dried to 12% moisture. Seed samples were cleaned using an air-screen seed cleaner by passing them through 0.5 cm sieves. After seed drying and cleaning, one sample from each N application rate in each dataset was analysed for purity using international standardised methodology (ISTA, 1996) and seed yield expressed as 100% clean seed was calculated. Only seed yields from first year seed harvest were used. 2.3. Determination of critical N concentration The number of sampling times in each experiment varied from 1 to 5. Each sampling time in each experiment was considered as an independent dataset. In this way a total of 41 datasets were available. In each dataset the shoot biomasses (t ha1) were compared by analysis of variance where N application rate and replicates were fixed effects. Homogeneous groups were determined at a 10% level of significance. Only datasets where it was possible to divide the data into the following two groups were used, (a) the N limiting group – an increase in N application rate has a significant effect on the shoot biomass and (b) the N nonlimiting group – an increase in N application rate has no significant effect in the shoot biomass but has a positive effect on N concentration. A linear regression model was developed on shoot biomass and %N for the N limiting group. The average shoot biomass was calculated from the N non-limiting group. When only three N rates were applied, the dataset used to develop the critical N dilution model was the dataset where shoot biomass of the lowest N application rate was significantly lower than shoot biomass measured in the two highest N application rates, which showed no mutual significant difference. The critical N concentration (%Nc) is the intersection between an oblique regression line corresponding to an increase of N concentration without any increase in shoot biomass and a vertical line corresponding to the increase of N concentration without any increase in shoot biomass. Individual %Nc values were calculated for the datasets that fulfilled
%Nci ¼ aðW i Þb
(2)
2.4. Validity of accessible NDC The %Nc was plotted against accessible NDC to test if these curves could describe the calculated %Nc better than the reference NDC. Furthermore, in 30 datasets (datasets 12–41) the shoot biomasses (t ha1) were compared by analysis of variance where N application rate and replicates were fixed effects. Homogeneous groups were determined at a 10% level of significance. Each dataset was divided into two groups, (a) the N limiting group – an increase in N application rate has a significant effect on the shoot biomass and (b) the N non-limiting group – an increase in N application rate has no significant effect in the shoot biomass. The two groups from each dataset were plotted together with the NDC with the purpose to test if data from group a were placed above the NDC and data from group b was placed below the NDC. Yield data from all 41 datasets available were used to test if selected NDC and the reference NDC could be used to show if N limited the seed yield. In each dataset the seed yields (kg ha1) were compared by analysis of variance where N application rate and replicates were fixed effects. If the main effect of N was significant the seed yields were divided into two groups, (a) the significant highest seed yield and (b) lower seed yields. Homogeneous groups were determined at a 5% level of significance. Plant N concentration and shoot biomass for the two groups were plotted together with selected NDC and the reference NDC with the purpose to show if data from group a, was placed above the NDC and/or the reference NDC and data from group b was placed below the NDC and/or the reference NDC. All analyses were performed using the procedures PROC GLM module within the Statistical Analysis System version 8.2, software package (SAS, 1999). 3. Results The statistical criterion for calculation of %Nc was fulfilled in 11 of the 41 datasets. In Fig. 1 the %N and shoot biomass are shown for the 11 datasets together with %Nc, which is the intercept between the oblique and the vertical line in each dataset. As shoot biomass increases there is a distinct decrease in %N. The %N decreases from a maximum of 4.6 to a minimum of 0.76 while shoot biomass increases from 2.3 to 13.8 t ha1. Plot of reference NDC and selected accessible NDC shows that NDC in rapeseed, pea, alfalfa and tall fescue were considerably above the 11 calculated %Nc (Fig. 2). The NDC in wheat, annual ryegrass, linseed and the reference NDC were more in line with calculated %Nc, however, no one seems to describe %Nc well. A further test of the selected NDC in linseed, wheat, annual ryegrass and the reference NDC reveals that NDC in linseed and annual ryegrass had the lowest number of misclassified data points which were not used to calculate %Nc (datasets 12–41) (Fig. 3). The use of NDC in linseed, wheat, annual ryegrass and the reference NDC as a method to define if N limits the seed yield during
R. Gislum, B. Boelt / Field Crops Research 111 (2009) 152–156
Fig. 1. Shoot nitrogen concentration (%N) vs. shoot biomass for datasets 1–11 (+). Critical nitrogen concentration defined as the intersection between the vertical and oblique dotted lines for each dataset is indicated with a circle (*).
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Fig. 4. Examination of reliability of critical nitrogen dilution curves corresponding to different species plotted with shoot N concentration vs. shoot biomass in perennial ryegrass from datasets 12 to 17 and 24 to 41. Closed circles (*) are results from nitrogen application rates that were not limiting for seed yield. Open circles indicate nitrogen application rates that were significantly different.
4. Discussion 4.1. Calculation of %Nc
Fig. 2. Critical nitrogen concentration vs. shoot biomass plotted with critical nitrogen dilution curves of different species. a and b values are shown in Table 2.
the spring growing season is shown in Fig. 4. There is no clear difference in the number of misclassified data points using the NDC in linseed, annual ryegrass or the reference NDC, whereas the number of misclassified data points using the wheat NDC is higher.
Fig. 3. Examination of reliability of critical nitrogen dilution curves of different species plotted with shoot nitrogen concentration vs. shoot biomass for datasets not used to develop critical nitrogen concentrations. Perennial ryegrass (* and *) are from datasets 12 to 17 and red fescue (& and &) are from datasets 18 to 23. Closed marks (* and &) indicate N application rates that were not limiting for biomass accumulation. Open marks indicate nitrogen application rates that were significantly different.
The calculation of %Nc is the first step in developing a NDC. However, in the present experiment it was no possible to calculate a %Nc at a biomass lower than 2.3 t ha1. The a coefficient has then to be estimated by extrapolating the NDC to a shoot biomass of 1 t ha1, which is the dry weight where a is defined. The reason for the lack of %Nc at very low biomass productions (1–2 t ha1) is most probably due to the fact that all plant samples were taken during the spring growing season before the seed harvest. At the time of the first sampling the grass seed plants are approximately 1-year-old due to the fact that most perennial ryegrass is undersown in spring barley and has reached a shoot biomass of more than 1 t ha1 irrespective of the amount of N applied. If the purpose is to develop an NDC it is probably necessary to include samples taken in the year before the seed harvest. In this way, samples obtained before and after the winter period could be used. However, this last could be problematic due to the effect of winter on the plant growth. The higher b coefficient in the reference NDC compared to NDC in other crops (Table 2) is a result of the low %Nc during the reproductive growth of the grass, defined as samples taken after the date of heading. If data points taken during the reproductive growth are excluded the maximum shoot biomass is 6 t ha1. If the reference NDC is developed on this reduced dataset the b coefficient would be 0.6. Hence, the effect of including the reproductive growth period is a higher b coefficient. The b coefficient in the reduced dataset is then comparable with the one calculated in linseed (Fle´net et al., 2006), but still higher than b calculated in e.g. wheat (Justes et al., 1994) or annual ryegrass (Marino et al., 2004). Similar results have been shown in linseed where the main difference in %Nc between linseed and other C3 species was observed after the emission of flower buds (Fle´net et al., 2006). The reason for this difference when reproductive growth is included is suggested to be connected with a change in the distribution of N between vegetative and storage tissues (Greenwood et al., 1990). When an increasing proportion of N is transferred from senescing plant parts to storage tissue, predominately as NO3, it is not available for vegetative growth and the relative growth rate will
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R. Gislum, B. Boelt / Field Crops Research 111 (2009) 152–156
Table 2 b Values of a and b coefficients found in the literature in the regression %N = a(W) , where %N is the N concentration (g N (100 g shoot biomass)1) and W is the shoot biomass (t ha1). Species
a
b
References
Linseed (Linum usitatissimum L.) Annual ryegrass (Lolium multiflorum Lam.) Tall fescue (Festuca arundinacea Schreb.) Alfalfa (Medicago sativa L.) Pea (Pisum sativum L.) Wheat (Triticum aestivum L.) Rapeseed (Brassica napus L.) Perennial ryegrass (Lolium perenne L.)
4.69 4.1 4.8 4.6–5.5 5.1 5.3 4.48 6.36
0.53 0.38 0.32 0.29–0.36 0.32 0.44 0.25 0.71
Fle´net et al. (2006) Marino et al. (2004) Lemaire and Salette (1984), Lemaire and Denoix (1987) Lemaire et al. (1985) Ney et al. (1997) Justes et al. (1994) Colnenne et al. (1998) This paper
decrease. The only possibility for the plant to obtain a maximum relative growth rate is through increased N uptake through the roots. As the %Nc is becoming stable it is concluded that N in the soil is completely depleted when shoot biomass goes towards maximum. The result is a low %Nc during the reproductive growth of the plants. 4.2. Implications for practical grass seed production It is very appealing to implement NDC as a diagnostic tool in grass seed production. The NDC should be used to decide if an additional N application is necessary to increase the shoot %N above the NDC at the current biomass. The concomitant increase in shoot %N by an additional N application should secure that shoot %N is not the limiting factor to achieve a high seed yield. Another and more obvious strategy would of course be to apply excess N rates and avoid shoot %N to be below NDC. However, several results have shown the negative effect of excess N rates in grass species for seed production (Gislum et al., 2005; Gislum, 2003). Even though the range in biomass in an NDC is from approximately 1 to several t ha1 the period where an additional N application will have a positive effect on the final seed yield is limited. During the reproductive growth period the N uptake from the soil is low and the effect of N application on the seed yield is therefore limited. The purpose is therefore to secure that the plants have accumulated sufficient amounts of N before the reproductive growth to develop the seeds. Focus should therefore be on using the NDC in the period of growth where an application of N has a positive effect on the relative growth rate and the amount of N accumulated in the plant. In perennial ryegrass most field experiments with the aim to test a split N application have focused on N application at stem elongation and/or anthesis (Gislum and Boelt, 1998; Nordestgaard, 1992; Nordestgaard, 1979; Hebblethwaite and Ivins, 1978). At these crop development stages the grasses are able to utilise the applied N even though the degree of utilisation to increase the seed yield will vary. The growth stage of the plants should therefore be taken into consideration when plant samples are taken and it is not recommended to use the NDC as a diagnostic tool after anthesis. We conclude that accessible NDCs in linseed wheat, and annual ryegrass were better to describe %Nc in grass for seed production than the NDCs in tall fescue, alfalfa, pea and rapeseed. The reference NDC should be further developed and the focus should be to estimate the a coefficient. These findings should be used to
continue the interesting and necessary work on developing a NDC in grass species for seed production. References Colnenne, C., Meynard, J.M., Reau, R., Justes, E., Merrien, A., 1998. Determination of a critical N dilution curve for winter oilseed rape. Ann. Bot. 81, 311–317. Fle´net, F., Gue´rif, M., Boiffin, J., Dorvillez, D., Champolivier, L., 2006. The critical N dilution curve for linseed (Linum usitatissimum L.) is different from other C3 species. Eur. J. Agron. 24, 367–373. Gislum, R., Boelt, B., 1998. Timing of spring nitrogen application in amenity-types of Lolium perenne L. grown for seed. J. Appl. Seed Prod. 16, 67–70. Gislum, R., 2003. Dynamics of nitrogen use efficiency in grass seed crops. Ph.D. thesis. University of Aarhus, Faculty of Agricultural Sciences, Department of Genetics and Biotechnology, Denmark. Gislum, R., Boelt, B., Jensen, E.S., Wollenweber, B., Kristensen, K., 2005. Temporal variation in nitrogen concentration of above ground perennial ryegrass applied different nitrogen fertiliser rates. Field Crops Res. 91, 83–90. Greenwood, D.J., Lemaire, G., Gosse, G., Cruz, P., Draycott, A., Neeteson, J.J., 1990. Decline in percentage N of C3 and C4 crops with increasing plant mass. Ann. Bot. 66, 425–436. Hardwick, R.C., 1987. The nitrogen content of plants and the selfthinning rule of plant ecology: a test of the core-skin hypothesis. Ann. Bot. 60, 439–446. Hebblethwaite, P.D., Ivins, J.D., 1978. Nitrogen studies in Lolium perenne grown for seed. II. Timing of nitrogen application. J. Br. Grassl. Soc. 33, 159–166. ISTA, 1996. International rules for seed testing. Seed Sci. Technol. 24 . Justes, E., Mary, B., Meynard, J.M., Machet, J.M., Thelier-Huche, L., 1994. Determination of a critical N dilution curve for winter wheat crops. Ann. Bot. 74, 397–407. Lemaire, G., Salette, J., 1984. Relation entre dynamique de croissance et dynamique de pre`le´vement d’azote pour un peuplement de gramine´es fourrage`res. I. Etude de l’effet du milieu. Agronomie 4, 423–430. Lemaire, G., Cruz, P., Gosse, G., Chatier, M., 1985. Etude des relations entre la dynamique de pre´evement d’azote et la dynamique de croissance en matie´re se´che d’un peuplement de luzerne (Medicago sativa L.). Agronomie 5, 685–692. Lemaire, G., Denoix, A., 1987. Croissance estivale en matie`re se`che de peuplements de fe´tuque e´leve´e (Festuca arundinacea Schreb.) et de dactyle (Dactylis glomerata L.) dans l’Ouest de la France. I. Etude en conditions de nutrition azote´e et d’alimentation hydrique non-limitantes. Agronomie 7, 373–380. Lemaire, G., Gastal, F., 1997. N uptake and distribution in plant canopies. In: Lemaire, G. (Ed.), Diagnosis of the Nitrogen Status in Crops. Springer-Verlag Publishers, Heideberg, ISBN 3-540-62223-3, pp. 3–43. Marino, M.A., Mazzanti, A., Assuero, S.G., Gastal, F., Echeverria, H.E., Andrade, F., 2004. Nitrogen dilution curves and nitrogen use efficiency during winter-spring growth of annual ryegrass. Agron. J. 96, 601–607. Ney, B., Dore´, T., Sagan, M., 1997. The N requirement of major agricultural crops: grain legumes. In: Lemaire, G. (Ed.), Diagnosis of the Nitrogen Status in Crops. Springer-Verlag Publishers, Heideberg, ISBN 3-540-62223-3, pp. 107–118. Nordestgaard, A., 1979. Various time of application of nitrogen in spring by seed production of perennial rye grass (Lolium perenne L.). Tidsskrift for Planteavl 83, 523–536. Nordestgaard, A., 1992. Split nitrogen application in perennial rye grass (Lolium perenne L.) for seed production. SP beretning 2217, 163–168 (in Danish, with English abstract). SAS, 1999. Statistical Analysis Software, Version 8.02. SAS Institute, Cary, NC, USA. Ulrich, A., 1952. Physiological bases for assessing the nutritional requirements of plants. Annu. Rev. Plant Physiol. 3, 207–228.