Assessing population maturity of three perennial grass species: Influence of phenology and tiller...

Assessing population maturity of three perennialgrass species: Influence of phenology and tillerdemography along latitudinal and altitudinalgradients

N. Rossignol*,†, D. Andueza*,†, P. Carr�ere‡, P. Cruz§, M. Duru§, J.-L. Fiorelli¶,

A. Michaud*,†, S. Plantureux**,††, E. Pottier‡‡ and R. Baumont*,†

*INRA, UMR1213 Herbivores, Saint-Gen�es-Champanelle, France, †Clermont Universit�e, VetAgro Sup, UMR

Herbivores, Clermont-Ferrand, France, ‡INRA, UR874 Ecosyst�eme Prairial, Crou€el, Clermont-Ferrand, France,

§INRA, UMR 1248 AGIR, Castanet-Tolosan, France, ¶INRA, UR 0055 ASTER, Mirecourt, France,**Universit�e de Lorraine, LAE, UMR1121 Agronomie et Environnement, Vandoeuvre, France, ††INRA, LAE,

UMR1121 Agronomie et Environnement, Vandoeuvre, France, ‡‡Institut de l’Elevage/CIIRPO, Site

exp�erimental du Mourier, Saint-Priest-Ligoure, France

Abstract

To assess the whole-sward maturity, which is a pri-

mary concern for grassland managers, we studied

three forage grass species with contrasting phenology

over a range of climatic conditions among sites. We

considered two main issues: (i) How is grass popula-

tion maturity related to population phenology, and is

this relation affected by environmental factors? and

(ii) Is the sum of temperatures a good index to

describe phenological development under contrasting

climates? To explore the role of temperature accumu-

lation in species population development, we

described the phenological development of popula-

tions of three grass species along latitudinal and altitu-

dinal gradients at eight locations across France. We

used a numerical index of tiller development to quan-

tify sward morphological structure and discriminate

phenological peak from average maturity. We report

that phenological development rates were similar

among sites for each species, but required fewer grow-

ing degree days to start at higher latitudes and alti-

tudes. However, we found that population maturity

and phenological peak differed significantly due to

among-site variability in vegetative tiller percentage in

whole-population biomass. Our results underlined the

importance of considering tiller distribution among

phenological stages, especially tiller development syn-

chrony, together with phenology to assess sward

maturity in semi-natural permanent grasslands.

Keywords: phenology, perennial grasses, growing

degree days, tiller development synchrony, sward

maturity, sward morphological structure

Introduction

The species richness of semi-natural grasslands pro-

vides many benefits for ecosystem function and agri-

cultural production (Hector et al., 1999). Ecosystem

services vary over the year due to vegetation phenol-

ogy. Taking into account grassland vegetation phenol-

ogy appears critical to assess appropriate management

practices for balancing ecological and agronomic goals

such as grassland sustainability or forage productivity

and quality (Pontes et al., 2007; Gaujour et al., 2012).

However, the prediction of vegetation phenology in

permanent grassland is complex due to plant diversity

(Mart�ınkov�a et al., 2002; Huyghe et al., 2008) and

phenological variability with climate and weather pat-

tern variability (Bloor et al., 2010).

Plant phenology is the seasonal timing of life cycle

events such as flowering or seed maturity. The pheno-

logical pattern of a population can be defined quanti-

tatively as a statistical distribution characterized by

mean, mode, i.e. peak (time of occurrence), variance

(synchrony among individuals) and skewness (Rath-

cke and Lacey, 1985; Forrest and Miller-Rushing,

2010). Plant phenology can vary widely between

species and is influenced by environment (reviewed

Correspondence to: R. Baumont, INRA, UMR1213 Herbi-

vores, Theix, F–63122 Saint-Gen�es-Champanelle, France.

E-mail: [email protected]

Received 15 October 2012; revised 8 February 2013

doi: 10.1111/gfs.12067 © 2013 John Wiley & Sons Ltd. Grass and Forage Science 1

Grass and Forage Science The Journal of the British Grassland Society The Official Journal of the European Grassland Federation

by Rathcke and Lacey, 1985; Fenner, 1998). Hence,

average timing of phenological stages at community

scale depends mainly on floristic composition in tem-

perate grasslands (Mart�ınkov�a et al., 2002; Ansquer

et al., 2009). Air temperature is shown to be a major

environmental factor for plant phenology in temper-

ate systems (Sparks et al., 2000; Fitter and Fitter,

2002; Cleland et al., 2006). Relations between plant

development rates and temperature led to the use of

growing degree days (GDD) as a tool to assess the tim-

ing of phenological events among locations on a tem-

perature basis (Bonhomme, 2000) and to study year-

on-year variability in response to climate change (Cle-

land et al., 2007). For example, Jenkins et al. (2002)

found that onset of vegetation activity in forests can

be predicted by mean GDD sum across a large climate

range in the USA. The GDD method accurately pre-

dicted the start of pollination period of oak popula-

tions across different regions in Spain (Garc�ıa-Mozo

et al., 2002). As vegetative material is economically

important for livestock production (Moore et al.,

1991), studies that relate grassland vegetation devel-

opment to temperature are often restricted to the veg-

etative stage (White et al., 1997; Bartholomew and

Williams, 2005). The phenological development of

dominant perennial grass species is notoriously under-

documented in semi-natural European temperate

grasslands. Recently, Ansquer et al. (2009) reported

that the GDD method can be used to satisfactorily

predict the flowering time of different grassland spe-

cies in France.

It is well established that the quantity and quality

of forage grass production are affected by variations in

plant morphology during growth and phenological

development (Moore and Moser, 1995; Buxton,

1996). Flowering coincides with the peak of biomass

production (Robson et al., 1988), whereas many forage

quality traits such as leaf/stem ratio (Duru et al.,

2000), digestibility (Valente et al., 2000), crude protein

and fibre contents (Mitchell et al., 2001) all change

unfavourably for animal feeding as the vegetation

increases in maturity. Furthermore, the morphological

development of a plant influences both accessibility

and palatability for herbivores (Briske, 1991). This

relation between forage quality and plant develop-

ment has led to the use of population-scale sward

phenology to guide management practices (Huyghe

et al., 2008). However, as developmental morphology

can vary strongly within a tiller population of forage

grasses, the occurrence of main phenological peaks is

not necessarily a pertinent descriptor of sward matu-

rity when defined as the average level of development

of the tiller population. Accordingly, individual tillers

do not respond similarly to environmental cues, which

can result in a skewed distribution of tillers among

phenological stages – notably in the presence of a pro-

portion of non-flowering tillers depending on species

strategies and requirements (Laterra et al., 1997; Mat-

thew et al., 2000). Bahmani et al. (2000, 2002)

reported that in temperate areas, there appear to be

effects of latitude, with development of higher propor-

tions of flowering tillers at higher latitudes.

Research on plant population phenology has lar-

gely focused on the average timing of a target pheno-

logical stage of interest, such as flowering. The role of

individual-level phenology on variance and skewness

of overall phenological distribution at population scale

has received little attention (Forrest and Miller-Rush-

ing, 2010) despite its ecological implications (Elzinga

et al., 2007). Moore et al. (1991) proposed a numerical

index of individual tiller development that makes it

possible to quantitatively describe tiller distribution

among phenological stages and to express sward matu-

rity as the average phenological stage in the tiller pop-

ulation. Here, we applied this method to assess the

impact of development synchrony among tillers (vari-

ance) on the relation between phenology (distribution

mode or peak) and maturity (distribution mean) of

perennial grass populations along latitude and altitude

gradients. It also remains unclear whether phenologi-

cal stages follow each other at constant time intervals

for a given species or whether they are controlled

independently by environmental factors (Rathcke and

Lacey, 1985). Hence, while it has been shown that in

temperate systems flowering time is related to temper-

atures (Sparks et al., 2000; Fitter and Fitter, 2002),

there is also evidence (Ansquer et al., 2009) for corre-

lations in the timings of successive phenological stages

of different grass species.

In this study, we considered two main issues: (i)

how is grass population maturity related to population

phenology, and is this relation affected by environ-

mental factors? and (ii) is the sum of temperatures a

good index to describe phenological development

under contrasting climates? We described the pheno-

logical development of populations of three grass

species along latitude and altitude gradients to obtain

contrasting climates. For each species studied, the

same cultivars were grown for 2 years in pure stands

at eight locations across France. We used a numerical

index of phenological stages to quantify phenological

distributions and to assess whether site location

affected development synchrony among tillers and

subsequently the relation between phenological peak

and average maturity of populations. This study was

an attempt to test whether relations between GDD

and population development differed among locations

for each species, focusing specifically on whether this

variation can be explained by the latitude or altitude

of the population sites.

© 2013 John Wiley & Sons Ltd. Grass and Forage Science

2 N. Rossignol et al.

Material and methods

Study sites

The experimental design was established at eight loca-

tions in France (Figure 1). Six sites formed a latitude

gradient: S1 (Le Pin, 48°44′N; 00°08′E; altitude 204 m

a.s.l; mean 2010 temperature 9.7°C; mean 2010 rainfall:

626 mm), S2 (Mirecourt, 48°18′N; 06°08′E; 289 m a.s.l;

temp. 9.0°C; rainfall 808 mm), S3 (Jalogny, 46°25′N;04°38′E; 250 m a.s.l; temp. 10.2°C; rainfall 921 mm),

S4 (Crouel, 45°46′N; 03°09′E; 350 m a.s.l; temp.

10.7°C; rainfall 728 mm), S5 (Le Mourier, 45°40′N;01°18′E; 280 m a.s.l; temp. 10.5°C; rainfall 877 mm)

and S6 (Toulouse, 43°36′N; 01°26′E; 150 m a.s.l; temp.

12.9°C; rainfall 695 mm). Three sites in close proximity

formed an altitude gradient: S4 (Crouel, 350 m a.s.l.),

S7 (Theix, 45°43′N; 03°01′E; 850 m a.s.l.; temp. 7.22°C;rainfall 885 mm) and S8 (Laqueuille, 45°39′N; 02°44′E;1100 m a.s.l.; temp. 5.8°C; rainfall 1142 mm). Climate

is temperate for all sites. Climatic data were collected

from the INRA database Climatik.

Experimental design: monospecific grassstands

At each site, three perennial grass species with con-

trasting phenology were sown in pure stands in

autumn 2008, with three 6 m 9 1.5 m plots per

species: meadow foxtail (Alopecurus pratensis L., cv.

Levocska), an early flowering species; cocksfoot (Dacty-

lis glomerata L., cv. Starly), an intermediate species,

and timothy (Phleum pratense L., cv. Rasant), a late-

flowering species. To avoid any confounding effect of

site and fertility, a non-limiting nutrient status was

obtained by applying phosphorous (100 kg ha�1

year�1) and potassium (150 kg ha�1 year�1) in 2009

and nitrogen (120 kg ha�1 year�1) in 2009 and 2010

at 150° GDD.During the experiment, plots were kept pure by

hand-weeding, and spaces between plots were kept

mown at ground level. Experimental plots were cut in

October 2009. In 2010, samples of grass biomass were

collected at each site using a thermic calendar (tem-

perature accumulation) around 400°, 500°, 600°,800°, 1000°, 1200° and 1400° GDD. Temperature

accumulation was calculated as the sum of mean daily

temperatures (°C) since 1 February, with temperatures

bounded to a minimum of 0°C and a maximum of

18°C. At each site, plant biomass was sampled when

the GDD thresholds were reached (Figure 2). Biomass

sampling was carried out by cutting the whole above-

ground biomass at 5 cm above ground level in a

10 cm 9 150 cm area within each experimental plot.

Plant biomass was then stored at �18°C until analysis.

At site S3, sampling was performed on only four dates.

At each site, 63 biomass samples were harvested (3

species 9 3 replicates 9 7 dates), except for S3 where

36 samples were harvested, resulting in a total of 477

biomass samples.

Numerical index of tiller development

Tiller development was described using an index

adapted from Moore et al. (1991). The numerical

index (Table 1) used in the present study consisted of

twelve successive phenological events (stages) ranging

from vegetative (1.5) to mature seed (4.8). For each

sample, 40–50 tillers were randomly chosen and

sorted by phenological stage and characterized by phe-

nological index value (Table 1). Sampled tillers were

then dried (at 60°C, 72 h) and weighed.

Data analysis

As each individual stage has an associated numerical

index (Table 1), the growth stages can be easily quan-

tified at the population scale by averaging the index

value attributed to each individual tiller. Mean Stage

by Count (MSC) and the associated standard deviation

were calculated as the mean and standard deviation of

all numerical indices of the tillers within a population

formed by the three replicates for a given site 9 spe-

cies 9 date combination. MSC was used as an index

S1 S2

S3

S4-7-8S5

S6

100 km

Figure 1 Location of experimental sites. Six sites formed a

latitude gradient: S1 (Le Pin, 48°44′N), S2 (Mirecourt, 48°18′N), S3 (Jalogny, 46°25′N), S4 (Crouel, 45°46′N), S5 (Le Mou-

rier, 45°40′N) and S6 (Toulouse, 43°36′N). Three close-by

sites formed an altitude gradient: S4 (Crouel, 350 m a.s.l.), S7

(Theix, 850 m a.s.l.) and S8 (Laqueuille, 1100 m a.s.l.). Other

site information is given in the Methods section.


Maturity of grass species along latitudinal and altitudinal gradients 3

of average population maturity, and the associated

standard deviation was used to assess level of develop-

ment synchrony among tillers within the population.

Mean stage by weight (MSW) of the populations was

calculated the same way as for MSC but on a weight

basis:

MSW ¼X4:8i¼1:5

SiWi

Wð1Þ

where Si and Wi are the growth stage and the weight

of tiller i, respectively, and W is total weight of the til-

ler population. As MSW accounts for the contribution

of each stage to whole-population grass biomass, it

gives more importance to mature tillers in the calcula-

tion of whole-sward maturity.

Three distinct classes of tillers, i.e. vegetative,

reproductive and elongating vegetative tillers, can

co-occur in a population (Virkaj€arvi et al., 2012),

potentially resulting in a bimodal distribution of tillers

among development stages. To gain descriptive insight

into the morphological structure of the population and

discriminate for phenological peak of reproductive til-

lers, we tested whether bimodality can be evidenced

in tiller biomass distribution. Each distribution was

studied over the three replicates for a given site 9 spe-

cies 9 date combination. For each tiller biomass distri-

bution, we estimated (1) a single Gaussian distribution

(with a mean m and standard deviation s) with density

function f(x):

f ðxÞ ¼ 1

sffiffiffi2

ppexp � 1

2

x �m

s

� �2� �

¼ Nðx;m; sÞ ð2Þ

and (2) a double Gaussian distribution, i.e. mixture of

two Gaussian distributions (with means m1 and m2,

400

600

800

1000

1200

1400

Gro

win

g de

gree

s da

ys

Le Pin (S1)Mirecourt (S2)Jalogny (S3)Crouel (S4)Le Mourier (S5)

Toulouse (S6)Theix (S7)

Laqueuille (S8)

Figure 2 Temperature accumulation over time according to study site.

Table 1 Numerical indices of 12 phenological stages recorded on individual tillers (Adapted from Moore et al., 1991)

Phase

Numerical index

Stage Index Description

Vegetative (Leaf development) V 1.5 Presence of leaves

Elongation (Stem elongation) E1 2.0 First node palpable

E2 2.5 Two or more nodes palpable

E3 2.9 Flag leaf emergence

Reproductive (Floral development) R1 3.1 Inflorescence emergence/Spikelets enclosed or partially emerged

R2 3.3 Spikelets fully emerged, peduncle not emerged

R3 3.5 Spikelets fully emerged, peduncle fully elongated

R4 3.8 Anther emergence/anthesis

Seed ripening (Seed development) S1 4.0 Caryopsis visible

S2 4.1 Milk

S3 4.4 Soft dough

S4 4.8 Physiological maturity and seed ripe



associated standard deviations s1 and s2 and p the rela-

tive proportion of the two distributions) with density

function f(x):

f ðxÞ ¼ p:Nðx;m1; s1Þ þ ð1� pÞ:Nðx;m2; s2Þ ð3Þ

Estimations of the theoretical distribution parame-

ters were performed using the maximized log-likeli-

hood method. To assess which of the two distributions

best fit the empirical data, we calculated a contrast

value K, defined as:

K ¼X

Xi � Yið Þ2 ð4Þ

where Xi is the tiller biomass observed at a stage i,

and Yi the tiller biomass expected under the estimated

distribution in stage i. K gave a more accurate mea-

sure of the goodness-of-fit to our discontinuous data

(data can only take the value of one of the twelve

stage classes) than more classical indices like Akaike’s

criterion that are supposedly better adapted to contin-

uous data. We defined the MSWr index (r for ‘repro-

ductive’) either as the mean m of the single Gaussian

distribution or as the mean m2 of the double Gaussian

distribution, i.e. the right-side peak of the distribution,

depending on whether the single or double Gaussian

distribution best fits empirical distribution respectively.

Mean stage by weight was used to assess whole-

sward maturity on a weight basis, and MSWr was

used to assess the actual phenological stage reached by

reproductive tillers. Parameter p of the double Gauss-

ian distribution was used to estimate the relative

percentage of tiller biomass in vegetative and repro-

ductive tiller groups. To assess how whole-sward

maturity (MSW index) and the phenological develop-

ment of reproductive tillers (MSWr index) of each

species were related to temperature accumulation

according to site, we separately assessed relations for

the latitude gradient (site S1–S6) and altitude gradient

(sites S4, S7 and S8). We used ANCOVA analyses with

GDD as covariate and sites as categorical variable. Lin-

ear regressions of MSW or MSWr with GDD were

then calculated. Statistical analyses and distribution

estimations were performed using GNU-project R soft-

ware (R 2.13.2, R Foundation for Statistical Comput-

ing, http://www.R-project.org).

Results

MSC of tiller populations

At the beginning of the season (400° GDD), the MSC of

populations of D. glomerata and P. pratense was close to

1.5 (Figure 3c,e), indicating that tillers were almost all

at vegetative stage, whereas populations of A. pratensis

showed higher values ranging from 1.62 � 0.378 (site

S6) to 1.98 � 0.697 (site S5; Figure 3a), indicating a

development that ranged from vegetative stage

(MSC = 1.5) to first stage of elongation (MSC = 2.1).

As expected, MSC values increased for all three spe-

cies over time. Most of the A. pratensis populations

reached peak MSC values at 1200° GDD, except for S7populations which peaked at 1400° GDD. Depending

on sites, the peak MSC values (�SD) for A. pratensis

populations ranged from 2.30 � 0.917 (S4) to

3.48 � 1.11 (S6). Depending on sites, the MSC of

D. glomerata populations increased from 500°, 600° or

800° GDD and peaked at 1200° or 1400° GDD, rangingfrom 1.72 � 0.593 (S5) to 4.22 � 0.809 (S3). The MSC

of P. pratense populations increased from 500°, 600° or

800° GDD and peaked at 1400° GDD, ranging from

2.89 � 0.352 (S4) to 3.59 � 0.569 (S1). Overall, the

range of variation for the MSC recorded between sites

for each grass species studied was large, whereas the

increase in MSC over time appeared rather weak (Fig-

ure 3): only two of the 24 populations studied showed

an MSC value higher than 3.5, which corresponds to

the end of inflorescence development just before the

anthesis stage.

Variance in the MSC values (Figure 3b,d,f) reveals

the variability in maturity that exists within a popula-

tion of tillers, i.e. the synchrony of development among

tillers. At the beginning of the season, MSC variance

was low for D. glomerata and P. pratense because the

populations consisted mainly of vegetative-stage tillers.

A. pratensis populations showed variance from the

beginning of the season. Overall, the eight P. pratense

populations displayed relatively low levels of variance,

ranging from 0% to 7% (coefficient of variation) at

400° and from 9% to 25% at 1400°, revealing high syn-

chrony in tiller development within each population. In

contrast, A. pratensis and D. glomerata populations dis-

played high levels of variance, revealing that tiller devel-

opment was not synchronous within these populations.

Distribution of tiller biomass amongdevelopment stages: bimodality

Analysis of tiller biomass distribution among develop-

ment stages revealed that part of the asynchronous

development among tillers was due to the occurrence

of two groups with distinct average development stages

within populations. To illustrate this, at 1065 °GDD on

site S1 (Figure 4), for A. pratensis and D. glomerata pop-

ulations, about 50% of the tiller biomass was found in

the reproductive development stages, with the remain-

der in the vegetative stage. Consequently, the MSW

calculated over the whole population did not reflect

the actual stage of development of most tillers. In



A. pratensism

ean

MS

C

S1S2S3S4S5S6S7S8

400 600 800 1000 1200 1400

01

23

45

0·0

0·2

0·4

0·6

0·8

1·0

A. pratensis

MS

C c

oeffi

cien

t of v

aria

tion

S1S2S3S4S5S6S7S8

D. glomerata

mea

n M

SC

S1S2S3S4S5S6S7S8

D. glomerata

MS

C c

oeffi

cien

t of v

aria

tion

S1S2S3S4S5S6S7S8

01

23

45

01

23

45

P. pratense

GDD

mea

n M

SC

S1S2S3S4S5S6S7S8

P. pratense

GDD

MS

C c

oeffi

cien

t of v

aria

tion

S1S2S3S4S5S6S7S8

400 600 800 1000 1200 1400

400 600 800 1000 1200 1400 400 600 800 1000 1200 1400

400 600 800 1000 1200 1400

400 600 800 1000 1200 1400

0·0

0·2

0·4

0·6

0·8

1·0

0·0

0·2

0·4

0·6

0·8

1·0

(a) (b)

(c) (d)

(e) (f)

Figure 3 Mean stage by count (MSC, left) and the associated coefficient of variation (right) calculated over the whole popula-

tion among sites (S1: Le Pin; S2: Mirecourt; S3: Jalogny; S4: Crouel; S5: Le Mourier; S6: Toulouse; S7: Theix; S8: Laqueuille) and

for the three species studied.



P. pratense populations, however, reproductive tillers

accounted for 95% of the total tiller biomass.

The occurrence of two groups having different aver-

age stages of maturity was detectable from the start of

the experiment for all eight populations of A. pratensis

studied, and it was detected consistently over the course

of the experiment (Appendix 1). On average, the less

mature group represented half of the whole tiller popu-

lation, with variations ranging from 21% (S1) to 69%

(S2) at the end of experiment (data not shown). For

D. glomerata populations, two groups of tillers with dis-

tinct average maturity levels were identifiable later than

for A. pratensis depending on site (from 600°GDD to

1200°GDD), but once identified, the presence of two

groups was then consistently detected until the end of

the experiment (Appendix 1). On average, vegetative

populations accounted for about 30% of the tiller bio-

mass, but with significant variations among sites, rang-

ing from just 5% (S3) to 82% (S5) (data not shown).

For P. pratense populations, the point at which two

groups were detected varied from 600°GDD to

1200°GDD depending on site studied, but unlike

D. glomerata populations, it was not always evident over

the course of the experiment (Appendix 1). Proportions

of the vegetative-stage tiller group were less than 20%

on average (data not shown). Overall, tiller biomass

distribution among development stages contrasted

between A. pratensis and D. glomerata populations com-

pared with P. pratense populations. For P. pratense, the

four tiller development phases (vegetative, elongation,

floral and seed development) occurred successively

and, at the end of the experiment, floral and seed devel-

opment phases were the major groups, whereas the

vegetative phase never co-occurred (Figure 5, example

of site S1). In contrast, for A. pratensis and D. glomerata,

vegetative phase growth was still present within the til-

ler population until the end of the experiment and co-

occurred with the final development phase, i.e. seed

development (Figure 5).

Relationship between mean tillers populationdevelopment and growing degree days

Overall, we found that mean stage by weight (MSW)

was significantly related to GDD for all three species

(Table 2). The MSW of A. pratensis populations

increased very slightly over time, whereas the MSW

of P. pratense increased via a much steeper slope

compared with the two other species (Figure 6). Con-

sequently, the three linear regressions crossed each

other during the season, thus showing a complete

inversion of the species ranking based on whole-popu-

lation maturity (Figure 6a,b). The same trend was also

found along the latitude and altitude gradients. How-

ever, linear regressions between MSW and GDD

explained the global variance of the data sets for

A. pratensis and D. glomerata populations rather weakly

(Figure 6a,b). Hence, MSW increased with tempera-

ture accumulation but the rate of whole-population

maturation differed significantly among sites for

A. pratensis and D. glomerata along the latitude gradient

(i.e. differences in slopes of regressions among sites;

Table 2). Maturation rate was higher at site S6 than

S5 for A. pratensis and D. glomerata. Maturation rates

were higher (steeper slopes) for P. pratense populations

than for the other two species and did not differ

among sites (no slope differences among sites;

Table 2).

MSWr = 4·53 (0·53)

A. pratensis

Development stages

Till

ers

biom

ass

(g)

1·5 2·1 2·5 2·9 3·3 3·75 4·2 4·75

020

4060

80 MSC = 2·92 (SD 1·239)MSW = 2·77

MSWr =3·90 (0·59)

D. glomerata

MSC = 2·16 (SD 1·068)MSW = 2·57

Development stages

Till

ers

biom

ass

(g)

1·5 2·1 2·5 2·9 3·3 3·75 4·2 4·75

020

4060

80

MSWr =3·21 (0·95)

P. pratense

Development stages

Till

ers

biom

ass

(g)

MSC = 3·07 (SD 0·301)MSW = 3·17

1·5 2·1 2·5 2·9 3·3 3·75 4·2 4·75

020

4060

80

Figure 4 Tiller biomass distributions among development stages for Alopecurus pratensis, Dactylis glomerata and Phleum pratense

populations: example of site S1 (Le Pin) at 1065 growing degree days (14 June). Mean stage by count (MSC) and mean stage by

weight (MSW) calculated for the whole population are shown on top of figure. Mean stage by weight of the reproductive frac-

tion of the population (MSWr) is indicated near each of the corresponding peak, with its proportion of whole biomass given in

brackets. Bar shading indicates the stage development phase: vegetative (white), elongation (light grey), floral development (dark

grey) and seed development (black).



For each species studied, MSWr values were line-

arly related to GDD with a significant effect of sites on

intercepts but not on slopes of linear regressions

(Table 2), indicating that the development rates of

reproductive tillers were similar among sites for each

species. The increase in MSWr with GDD was stronger

than the increase in MSW with GDD, especially for

A. pratensis and D. glomerata (Figure 5c,d), and the spe-

cies ranking observed at the beginning of the experi-

ment was maintained until the end of the experiment

(Figure 5c,d). At the end of the experiment, MSWr

values were highest for A. pratensis (range: 4.47–4.77),intermediate for D. glomerata (range: 3.80–4.51) and

lowest for P. pratense (range: 3.06–3.95; Figure 5). As

the MSWr index was calculated over the more devel-

oped fraction of the population, the MSWr value is

more likely to reflect the actual phenological stage

reached by populations. On average, at 1400° GDD,

A. pratensis was close to seed maturity, D. glomerata

was approaching mid-seed development phase and

P. pratense was approaching mid-floral development

phase. The range of variation between MSWr values

predicted with GDD by the linear regressions and the

observed values was mostly explained by an influence

of the sites where the populations were grown. Hence,

along the latitude gradient, slopes of linear regressions

were unaffected by site but the intercept values varied

depending on site, being significantly higher in north-

ern sites than southern sites (Figure 7). Along the alti-

tude gradient, we found a site effect on linear

regressions for D. glomerata and P. pratense only. For

these species, the intercept values of the linear regres-

sions were significantly lower for site S4 than sites S7

and S8 (Figure 7). Reproductive tiller development

therefore occurred at the same rate with temperature

accumulation among sites, but it begins at a lower

GDD in elevated or northern sites than other sites.

Discussion

Evaluation of grass stand maturity using meanstage of development

This study used the average morphological develop-

ment among tillers to assess maturity of grass popula-

tions, as suggested by Moore et al. (1991), and

compared its time-course patterns over a season. We

studied three species of contrasting phenology and

classified into distinct functional types (Cruz et al.,

2010) over a range of climatic conditions among sites.

We found that maturation rates of grass populations

were linearly related to temperature accumulation,

thus confirming previous studies conducted on forage

grasses (Mitchell et al., 1997). Although these relations

differed depending on sites, consistent trends were

observed among plant species. Hence, the early flow-

ering species (A. pratensis) matured at a slower rate

than the late-flowering species (P. pratense), which led

A. pratensis

D. glomerata

P. pratense

400 600 800 1000 1200 1400

0·0

0·2

0·4

0·6

0·8

1·0

0·0

0·2

0·4

0·6

0·8

1·0

0·0

0·2

0·4

0·6

0·8

1·0

400 600 800 1000 1200 1400

400 600 800 1000 1200 1400

Growing degree days

Growing degree days

Growing degree days

Bio

mas

s pr

opor

tions

Bio

mas

s pr

opor

tions

Bio

mas

s pr

opor

tions

Figure 5 Tiller biomass distribution among the four devel-

opment phases during the course of the experiment for grass

populations of Alopecurus pratensis, Dactylis glomerata and

Phleum pratense: example of site S1 (Le Pin). Development

phases: vegetative (white), elongation (light grey), floral devel-

opment (dark grey) and seed development (black).



to an inversion of species ranking for average maturity

over time. These population-scale differences in matu-

ration dynamics were consistent with a study by

Michaud et al. (2011) on French permanent grasslands

showing that differences in dominant plant functional

types drive the rate of decline in herbage quality

over time. The slow maturation rate of A. pratensis

populations compared with P. pratense populations was

not due to slow tiller development rate but rather to

high heterogeneity in morphological sward structure.

For example, we report that at 1065°GDD on site S1

(Figure 4), A. pratensis and P. pratense populations both

showed a maturity equivalent to a homogeneous pop-

ulation with all tillers at the flower emergence stage

(stage 3). Phleum pratense populations, however, were

characterized by a high development synchrony

among tillers, with most tillers effectively nearing the

flower emergence stage, whereas A. pratensis popula-

tions showed weak development synchrony among til-

lers, a percentage of which were approaching the seed

maturity stage while the other percentage was still in

vegetative stage. In this case, the use of all-population

phenological peak (seed maturity) as a descriptor of

whole-sward maturity led to an overestimation com-

pared to the MSW index, thus underlining the impor-

tance of taking into account both phenology and tiller

demography parameters such as percentage of flower-

ing tillers (Jonsdottir, 1991; Brock et al., 1996) for

understanding how grass population maturity evolves

over time (Duru, 2008). As forage quality is related to

morphological development, the MSW index could be

used to assess average nutritive value of a species pop-

ulation. It should be noted, however, that low devel-

opment synchrony among tillers can indicate the

presence of mature plant parts, which were shown to

limit herbivore selection for a feeding patch (Flores

et al., 1993), mixed with palatable vegetative tillers.

Tiller population demography and persistencestrategies

Permanent grasslands are characterized by a dominant

vegetation of perennial grasses (Louault et al., 2005;

Benot et al., 2011) that can persist for several years in

the sward by vegetative multiplication (Briske, 1991;

Murphy and Briske, 1992; Zobel et al., 2010) with a

potentially very low annual percentage of seedling

establishment (Benson and Hartnett, 2006). Seasonal

patterns of tiller replacement reveal that different grass

species employ different persistence strategies. Mat-

thew et al. (1993) proposed a categorization into two

classes. In the reproductive pathway, most of the

replacement tillers are formed from decapitated flow-

ering tillers in late summer, and tiller demography is

characterized by a high percentage of flowering tillers

and a low percentage of spring tillers remaining as

vegetative tillers in late summer. Here, the seasonal

patterns of tiller distribution among development

Table 2 Results of ANCOVA testing for the effects of growing degree days (GDD) and site on Mean Stage by Weight index for

the whole population (MSW) and for reproductive tillers (MSWr) along latitude and altitude gradients

Alopecurus pratensis Dactylis glomerata Phleum pratense

Latitude

MSW

GDD F1,27 = 22.01*** F1,27 = 35.08*** F1,27 = 53.51***

Site F5,27 = 0.47 F5,27 = 7.62*** F5,27 = 9.10***

GDD 9 site F5,27 = 3.70* F5,27 = 5.11** F5,27 = 2.49

MSWr

GDD F1,27 = 253.84*** F1,26 = 632.94*** F1,27 = 870.61***

Site F5,27 = 2.74* F5,26 = 5.86*** F5,27 = 8.94***

GDD 9 site F5,26 = 0.54 F5,26 = 1.82 F5,27 = 1.99

Altitude

MSW

GDD F1,15 = 11.08** F1,15 = 62.05*** F1,15 = 12.52***

Site F2,15 = 4.16* F2,15 = 3.65 F2,15 = 18.57***

GDD 9 site F2,15 = 1.45 F2,15 = 0.01 F2,15 = 0.80

MSWr

GDD F1,15 = 176.48*** F1,15 = 372.38*** F1,15 = 240.76***

Site F2,15 = 1.77 F2,15 = 12.87*** F2,15 = 16.63***

GDD 9 site F2,15 = 1.90 F2,15 = 2.59 F2,15 = 1.28

*, P < 0.05; **, P < 0.01; ***P, < 0.001.



stages found for P. pratense populations were consis-

tent with the reproductive persistence strategy as

reported by Matthew et al. (2000). Unlike P. pratense,

populations of A. pratensis and D. glomerata showed

lower percentages of flowering tillers and higher per-

centages of spring tillers remaining vegetative, which

is consistent with the vegetative persistence strategy.

Matthew et al. (2000) reported that in the vegetative

pathway, the high rate of spring tiller recruitment

may be counterbalanced by a high rate of tiller

mortality, which could explain why, in our study,

non-flowering tillers of A. pratensis and D. glomerata

populations remained in the early development stages.

Persistence strategies impacted on whole-sward

maturity, as synchronous tiller development led to an

increase in sward maturity that was closely tied to

phenology for P. pratense but that was buffered by the

percentage of young vegetative tillers for the two

other species. Percentage of non-flowering tillers thus

emerges as a major parameter of sward maturity

(Duru et al., 2000) and was found here to vary among

sites. We did not find any clear pattern in flowering

tiller percentages related to latitude for the three spe-

cies studied, but previous studies on Lolium perenne

suggest that in temperate areas there are a higher

percentages of flowering tillers at higher latitudes

400 600 800 1000 1200 1400

A. pratensisD. glomerataP. pratense

MSW dynamics - Latitude gradientwhole population

Growing degree days

Mea

n st

age

by w

eigh

t


MSW dynamics - Altitude gradient

whole population

Growing degree days

Mea

n st

age

by w

eigh

t


MSWr dynamics - Latitude gradientreproductive tillers

Growing degree days

Mea

n st

age

by w

eigh

t

12

34

5

12

34

5

12

34

5

12

34

5


MSWr dynamics - Altitude gradientreproductive tillers

Growing degree days

Mea

n st

age

by w

eigh

t

A.p : MSW = 1·94+2·03 10–3 x GDD ; R2 = 0·850D.g : MSW = 0·47+2·53 10–3 x GDD ; R2 = 0·783P.p : MSW = 0·61+2·05 10–3 x GDD ; R2 = 0·900

A.p : MSW = 1·88+2·09 10–3 x GDD ; R2 = 0·882D.g : MSW = 0·66+2·57 10–3 x GDD ; R2 = 0·823P.p : MSW = 0·73+2·17 10–3 x GDD ; R2 = 0·817

MSW = 2·18+3·92 10–4 x GDD ; R2 = 0·269MSW = 1·00+1·30 10–3 x GDD ; R2 = 0·588MSW = 0·65+1·96 10–3 x GDD ; R2 = 0·907

MSW = 2·13+4·57 10–4 x GDD ; R2 = 0·260MSW = 1·18+1·23 10–3 x GDD ; R2 = 0·721MSW = 0·82+1·93 10–3 x GDD ; R2 = 0·847

400 600 800 1000 1200 1400

400 600 800 1000 1200 1400400 600 800 1000 1200 1400

(a) (b)

(c) (d)

Figure 6 Relations between mean stage by weight indexes for whole-sward maturity (MSW) or phenological development of

reproductive tillers (MSWr) and GDD among all sites along a latitude gradient (left) and an altitude gradient (right) in the three

species studied. Parameters for linear regressions are given in the figures.



(Bahmani et al., 2000, 2002). Management practices

(e.g. cutting date, residue removal) and climatic events

in late summer and autumn affecting vegetative tiller

population are also reported to affect percentage of

flowering tillers the following year (Young et al., 1998;

Loeppky and Coulman, 2001; Hendrickson et al.,

2005), which may make this percentage difficult to

predict. The three grass species studied are character-

ized by resource acquisition strategies with abilities for

rapid exploitation of nutrients in fertile grasslands

(Grime et al., 1997; Cruz et al., 2010). The high per-

centage of vegetative tillers associated with the high

variability among sites found for A. pratensis and

D. glomerata may be linked to their ability to rapidly

exploit the soil resource in heterogeneous environ-

ments (Laterra et al., 1997). Moreover, early flowering

is often associated with quick resource accumulation

(Rathcke and Lacey, 1985). The late phenology and

low variability in tiller demography observed for

P. pratense would tend to suggest competitive ability in

low-disturbance environments. These results confirm

that P. pratense is better suited for utilization as a hay

crop than for grazing.

Clonal traits contribute to syndromes of plant strat-

egies related to nutrient acquisition–retention (Rusch

et al., 2011), and competition for light and nutrients

can affect rate of tillering and also percentage of flow-

ering tillers (Laterra et al., 1997), which raises the

question of how percentages of flowering tillers in

pure stands could be modified by interspecific compe-

tition.

Relations between grass population phenologyand growing degree days

Average phenological development of reproductive til-

lers over time gave estimated timings of successive phe-

nological events (heading, flowering, etc.) across the

population. Phenological development of reproductive

tillers was linearly related to temperature accumulation.

Unlike the observed results for whole-population matu-

ration rates, the slopes of these relations did not differ

significantly among sites for each species studied, which

showed that the interval in growing degree days

required between two successive phenological events

was unaffected by site location. These results confirm

that the GDD method can predict the occurrence of

phenological events in plant populations as shown in

previous studies (Sparks et al., 2000; Garc�ıa-Mozo et al.,

2002; Jenkins et al., 2002). They suggest, however, that

phenological stages are not controlled independently by

environmental factors but follow each other at constant

GDD intervals for each species (Rathcke and Lacey,

1985). Ansquer et al. (2009) reported correlations

between dates of elongation start, flowering and seed

ripening for several permanent grassland species in

France and suggested that the impact of growing condi-

tions on the start of stem elongation may explain the

timing of flowering. Our results support the view that

A. pratensis

D. glomerata

P. pratense

S1 S2 S3 S4 S5 S6 S4 S7 S8

Latitude gradient Altitude gradient

S1 S2 S3 S4 S5 S6 S4 S7 S8

S1 S2 S3 S4 S5 S6 S4 S7 S8

2·5

2·0

1·5

1·0

0·5

0·0

2·5

2·0

1·5

1·0

0·5

0·0

2·5

2·0

1·5

1·0

0·5

0·0

b

ab ab

a

ab

a

abca

a

aba

b ab ab ab ab a N.S.

b b b b

c bc b b

a

MS

W -

Inte

rcep

t val

ueM

SW

- In

terc

ept v

alue

MS

W -

Inte

rcep

t val

ue



Figure 7 Intercept values for MSWr index according to site

(S1: Le Pin; S2: Mirecourt; S3: Jalogny; S4: Crouel; S5: Le

Mourier; S6: Toulouse; S7: Theix; S8: Laqueuille). Models for

latitude (left side of the figure) and altitude (right side of the

figure) data sets. Identical letters indicate absence of signifi-

cant among-site differences for latitude or altitude gradients

(multiple comparisons: Tukey’s HSD post hoc test.) Left side,

from S1 to S6: North–South latitude gradient; Right side,

from S4 to S8: increasing altitude gradient.



phenological development rate of reproductive tillers

was constant among sites for the three grass species

studied, but with significant delay among sites in terms

of the timing of phenological stages. Indeed, a given

phenological stage required less accumulated tempera-

ture at higher latitude (e.g. S1, 48°N) than at lower lati-

tude (e.g. S6, 43°N) for all three species and at higher

altitude (e.g. S8, 1100 m a.s.l.) than lower altitude (e.g.

S4, 350 m a.s.l.) for D. glomerata and P. pratense. These

results contrast with the findings of Ansquer et al.

(2009) who found little evidence for an altitude effect

on flowering time at species level. Grass species phenol-

ogy is known to be influenced by abiotic factors other

than temperature, such as day-length photoperiod, pre-

cipitation or resource availability (Hovenden et al.,

2008; Martiniello and Teixeira da Silva, 2011). Indeed,

the fact that grass species are characterized by day-

length requirements for flowering initiation may

explain the latitudinal and altitudinal effects observed

here (Heide et al., 1985; Wu et al., 2004; Martiniello

and Teixeira da Silva, 2011). Integrating photoperiod

should, therefore, improve the accuracy of grass

phenology predictions based on temperature (Bloor

et al., 2010).

Conclusions

This study found that phenological development

required less GDD to be triggered in higher latitudes

and altitudes among sites, but that the rates of devel-

opment were similar for each species among sites, i.e.

GDD intervals between stages were unaffected by site

location. We thus concluded that the GDD method

can satisfactorily describe species phenological devel-

opment in different locations in France and may prove

useful for a generic model of seasonal herbage quality

(Duru et al., 2000; Jouven et al., 2006; Duru, 2008).

This study also clearly demonstrated, however, that

the phenological stage (phenological peak) of the pop-

ulation cannot be used as a reliable indicator of sward

maturity (average development) due to the presence

of a percentage of vegetative tillers in the population.

The percentage of vegetative tillers was found to be

species dependent but was also highly variable among

sites. The accuracy of taking into account the biomass

investment in vegetative and reproductive tillers at

population scale may thus remain a challenge for

modelling.

This work conducted in pure stands could also pro-

vide baseline data for further research on the adapta-

tions of native grass populations to latitudinal and

altitudinal gradients and on the impact of species mix-

tures of contrasting phenology on the evolution of for-

age quality at plant community scale in permanent

grasslands.

Acknowledgments

This experiment was conducted as part of the ‘CASDAR

Prairies permanentes’ project funded by the French

Ministry for Agriculture. N.R. received a postdoctoral

grant from the Conseil R�egional d’Auvergne. The

authors wish to thank A. Torrent (Theix), E. Lecloux

(Toulouse), P. Note (Laqueuille), G. Leau, D. Leconte

(Le Pin), J. Renon (Jalogny) and D. Ragon (Le Mourier)

for their skilful assistance with the experiment.

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APPENDIX

1

Goodness-of-fitvaluesofthesingle

(SG)anddouble

(DG)Gaussiandistributionsestim

atedondata

sets

oftillerbiomass

amongphenologicalstagesforeach

speciesstudied(A

lopecuruspratensis,Dactylisglom

erata

andPhleum

pratense),

each

site

(S1:LePin;S2:Mirecourt;S3:Jalogny;S4:Crouel;S5:LeMourier;

S6:

Toulouse;S7:Theix;S8:Laqueuille)anddate

(GDD).

Valuesin

bold

indicate

thebest-fitdistribution.Best

fitofdouble

Gaussiandistributionindicatesthe

occurrence

oftw

opeaksin

biomass

distributionamongphenologicalstages,

whichrevealsthepresence

oftw

ogroupsoftillers

withcontrastedphenological

developmentwithin

thepopulation

GDD

S1

S2

S3

S4

S5

S6

S7

S8

SG

DG

SG

DG

SG

DG

SG

DG

SG

DG

SG

DG

SG

DG

SG

DG

Alopecuruspratensis

400

1.90

0.34

1.56

0.33

1.77

0.71

0.50

0.14

1.30

0.42

0.36

0.21

0.43

0.09

0.67

0.14

500

2.39

0.86

2.16

0.71

––

0.83

0.38

2.75

0.53

0.37

0.34

0.64

0.33

1.10

0.60

600

4.06

2.07

2.16

1.22

3.65

1.04

1.73

0.41

3.07

1.32

2.49

0.91

1.74

0.73

2.22

1.05

800

3.84

1.86

4.00

1.87

––

2.15

0.25

4.93

2.07

3.50

2.21

3.93

1.01

3.14

0.75

1000

5.80

2.04

5.99

1.56

––

3.47

0.73

5.05

1.28

5.29

2.02

4.12

0.89

6.06

1.87

1200

5.20

1.84

2.71

0.73

5.10

1.58

1.86

0.54

2.81

1.22

11.19

1.45

3.98

1.64

4.60

2.08

1400

3.55

2.51

4.27

2.32

4.34

1.31

2.71

1.03

1.50

1.30

4.31

1.34

3.67

1.32

1.84

0.87

Dactylisglom

erata

400

1.10�8

0.15

1.10�8

3.10�5

2.10�8

0.01

5.10�9

7.10�6

7.10�9

4.10e�6

7.10�9

5.10�4

0.01

0.06

0.04

0.06

500

2.10�8

1.10�3

2.10�8

3.10�8

––

9.10�9

1.10�3

2.10�8

7.10�7

1.10�8

4.10�4

0.25

0.01

0.27

0.41

600

3.35

1.25

0.32

0.43

0.44

1.02

0.079

0.077

1.10�8

2.10�4

2.10�8

6.10�4

1.53

0.55

2.66

1.49

800

7.00

1.92

7.62

2.83

––

1.34

0.52

0.19

0.12

0.88

0.45

5.56

0.97

3.97

1.03

1000

8.85

2.85

11.20

3.21

––

5.15

0.67

5.26

0.93

5.61

1.84

10.67

2.34

6.31

2.48

1200

9.62

1.00

7.33

1.57

5.27

3.12

4.29

2.14

4.30

0.44

17.45

3.78

11.14

1.81

6.59

2.91

1400

14.14

3.92

11.26

1.09

16.09

2.38

6.66

2.10

0.30

0.17

15.91

4.47

13.97

2.65

7.50

1.43

Phleum

pratense

400

2.10�8

0.02

2.10�8

4.10�6

2.10�8

0.18

6.10�9

0.04

1.10�8

4.10�4

1.10�8

3.10�4

0.12

0.14

0.07

0.08

500

4.10�8

1.10�3

0.43

0.17

––

1.10�8

5.10�3

2.10�8

8.10�6

2.10�8

0.03

0.20

0.24

0.65

0.81

600

0.95

1.77

1.24

1.31

0.58

1.17

2.10�8

3.10�5

3.10�8

1.10�5

0.17

0.34

1.57

0.28

0.24

0.21

800

2.95

0.91

0.41

0.17

––

0.84

1.34

0.80

1.37

0.94

1.39

1.91

0.86

1.53

2.25

1000

5.11

2.79

3.41

0.86

––

0.47

0.38

2.23

1.48

0.29

0.26

4.22

1.07

3.33

0.92

1200

4.60

4.81

4.43

1.03

3.26

1.26

2.56

0.84

3.18

1.21

2.24

1.68

5.58

3.82

7.34

2.64

1400

12.90

4.82

7.19

2.22

5.55

4.60

3.55

0.75

5.61

4.18

4.00

0.94

10.48

2.45

8.37

4.75



Assessing population maturity of three perennial grass species: Influence of phenology and tiller...

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