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Annals of Botany logoLink to Annals of Botany
. 2006 Jan;97(1):85–96. doi: 10.1093/aob/mcj003

Co-ordinated Growth between Aerial and Root Systems in Young Apple Plants Issued from in vitro Culture

E COSTES 1,*, E GARCÍA-VILLANUEVA 1, C JOURDAN 2, J L REGNARD 1, Y GUÉDON 3
PMCID: PMC2000766  PMID: 16260441

Abstract

• Background and Aims In several species exhibiting a rhythmic aerial growth, the existence of an alternation between root and shoot growth has been demonstrated. The present study aims to investigate the respective involvement of the emergence of new organs and their elongation in relation to this phenomenon and its possible genotypic variation in young apple plants.

• Methods Two apple varieties, X6407 (recently named ‘Ariane’) and X3305 (‘Chantecler’ × ‘Baujade’), were compared. Five plants per variety, issued from in vitro culture, were observed in minirhizotrons over 4 months. For each plant, root emergence and growth were observed twice per week. Growth rates were calculated for all roots with more than two segments and the branching density was calculated on primary roots. On the aerial part, the number of leaves, leaf area and total shoot length were observed weekly.

• Key Results No significant difference was observed between varieties in any of the final characteristics of aerial growth. Increase in leaf area and shoot length exhibited a 3-week rhythm in X3305 while a weaker signal was observed in Ariane. The primary root growth rate was homogeneous between the plants and likewise between the varieties, while their branching density differed significantly. Secondary roots emerged rhythmically, with a 3-week and a 2-week rhythm, respectively, in X3305 and ‘Ariane’. Despite a high intra-variety variability, significant differences were observed between varieties in the secondary root life span and mean length. A synchronism between leaf emergence and primary root growth was highlighted in both varieties, while an opposition phase was observed between leaf area increments and secondary root emergence in X3305 only.

• Conclusion A biological model of dynamics that summarizes the interactions between processes and includes the assumption of a feedback effect of lateral root emergence on leaf emergence is proposed.

Keywords: Plant architecture, root–shoot equilibrium, rhythmic growth, synchronism, time series

INTRODUCTION

In perennial plants, organogenesis can occur throughout ontogeny due to the maintenance of cell division in meristems (Lyndon, 1998). The emergence of successive leaves is usually characterized by the time between two successive primordia, called the plastochrone. On a longer time scale, many plants also exhibit rhythmic growth with an alternation of organogenesis and rest periods (Hallé et al., 1978). At the whole-plant scale, many studies devoted to growth rhythmicity have demonstrated the existence of an alternation between root and shoot growth, for instance in Pinus taeda (Drew and Ledig, 1980), Theobroma cacao (Greathouse et al., 1971), Ligustrum sp. (Kuehny and Halbrooks, 1993) and Hevea brasiliensis (Thaler and Pagès, 1996). This has been interpreted as the result of competition for assimilates between root and aerial (shoot) growth, while root branching was promoted by leaf development (Aguirrezabal et al., 1993).

In temperate fruit trees which exhibit rhythmic growth, a similar alternating pattern has been demonstrated in adult trees, on an annual time scale, for instance in peach (Williamson and Coston, 1989) and apple (Psarras and Merwin, 2000). However, these studies did not distinguish between organogenesis and elongation processes as previously done for other non-fruiting species, probably because adult trees are too complex for this purpose. Because of the practice of grafting in the fruit industry, the interactions between root and aerial systems are of major interest. In the apple tree, which is commonly grown on dwarfing rootstocks, these interactions have been widely studied through the description of the effects of rootstock on the aerial part development (Schechter et al., 1991; Costes et al., 2001; Seleznyova et al., 2003), on fruiting (Avery, 1969; Larsen et al., 1992) or fruit characteristics (Drake, 1988; Daugaard and Callesen, 2002). The effects of rootstock on biomass allocation have also been reported (Zhu et al., 1999), and competition during the emergence and expansion of new organs, including cambial growth, has been shown to interact with fruit development (Costes et al., 2000; Berman and DeJong, 2003). However, less information is available regarding the patterns in aerial and root growth within a growth cycle. In addition, little information is available regarding the apple tree root system structure, i.e. the combination of root growth, branching and life span characteristics for several successive branching orders. This is probably due to the difficulty in observing in situ root systems (Atkinson, 1974, 1983). Because the root systems develop in an environment with less variation in temperature than the air but which is physically heterogeneous, roots were often viewed as opportunist in their development (Lynch, 1995). However, like the aerial part, roots have been shown to develop according to architectural rules, by the architectural comparison of both aerial and root systems of tropical trees (Atger and Edelin, 1994).

The main aims in the present study were to investigate: (a) whether the growth of the roots and shoots of the apple tree are co-ordinated and/or alternate, and when the emergence of new organs and elongation are involved in this phenomenon; and (b) the characteristics of roots belonging to at least two successive branching orders. In these two investigations, particular focus was given to the comparison between two varieties in order to highlight which process could depend on genetic factors. This study was carried out on young self-rooted trees issued from in vitro culture which were small enough to observe the variations in new organ emergence and elongation in both root and shoot growth.

MATERIALS AND METHODS

Two newly released apple varieties which both showed scab resistance and offered good fruit quality were studied: X6407, named ‘Ariane’ after selection by INRA Angers; and X3305 (‘Chantecler’ × ‘Baujade’) selected by INRA Bordeaux (Roche et al., 2003).

Both varieties were propagated vegetatively by in vitro culture on a culture medium adapted from Murashige (1962). Roots were induced with an indole 3-butyric acid application (5 g L−1) and the plants were placed in darkness at 18 °C for 5 d. The plants were then left without any hormone in the culture medium for 3 weeks.

After a total period of 3 months of in vitro culture, five plants per variety, as homogeneous as possible in their development, were selected to be transplanted in root observation boxes (minirhizotrons). The minirhizotrons were built as described in previous studies (Neufeld et al., 1989; Thaler and Pagès, 1996). The box dimensions were 0·50 × 0·22 × 0·03 m3. The back and sides were made of opaque PVC while the front, for observation purpose, was transparent. This side was covered by black plastic to keep the roots in the dark. The boxes were filled first with gravel to 3 cm and then with sterilized compost. The roots were kept separated from the substratum and sandwiched against the transparent side by nylon mesh (Neufeld et al., 1989).

Immediately after transplanting, the ten observed plants were placed in a climatic chamber at 25 °C with 100 % relative humidity and a light intensity of 55 µmol m−2 s−1 in PAR for 14 h per day. During the first 3 weeks after transplantation they were provided with the nutrient solution that was in the in vitro medium, but without growth regulators. During the experimentation follow-up, the plants were watered twice per week. Fungicide treatments against powdery mildew were applied only if necessary.

After transplantation, all the roots initiated during in vitro culture turned brown and some of them died. Within 14 d, new white roots developed from the survivor brown roots and grew straight down. In the following, these new roots are referred to as R1, their lateral roots as R2 and the laterals borne on R2, if any, are called R3. To avoid root growth constraints, the growth dynamics of both the aerial and root systems were observed until the longest R1 roots reached the bottom of the box. So the plants were observed for 111 d after transplantation. The root growth dynamics were recorded twice per week on a transparent plastic sheet by marking the roots with different coloured permanent markers (Neufeld et al., 1989).

The drawings were then digitized in 2D using Rhizodigit 1·2 software developed at CIRAD, France, by C. Jourdan and B. Mailhe. During digitizing, each new root was labelled with a specific number, and its type and initial 2D co-ordinates were recorded at its insertion point. On each segment corresponding to the root length developed between two observation dates, the top spatial co-ordinates and the observation date were recorded. After digitizing, different variables were computed with macro-functions developed in Excel software. For each plant, the number of newly developed roots was counted per unit of time and per type (R1, R2 and R3). The growth rate was calculated for all roots with more than two segments as the ratio between the length and the difference in the number of days between the first and the last observation dates. The branching density was calculated as the ratio between the number of lateral roots per unit length of the parent root.

On the aerial part, at each observation day, the number of nodes and leaves, and the total shoot length were noted. The length and width of each leaf were measured once per week. Then, leaf area was precisely measured on a sub-sample of leaves. A linear relationship was then calculated to correlate the length and width to the area (data not shown). A single relationship (y = 0·69x) was proved to be suitable to represent both cultivars with an acceptable r2 coefficient (0·88).

Because of the low number of plants per variety, intra- and inter-variety variations in the final characteristics were analysed by a Kruskal–Wallis non-parametric ANOVA using Statistica® version 6.0. At the end of the observation period, the aerial growth was characterized by four variables: the total shoot length, number of leaves, leaf area and the basal diameter. Primary roots, R1, were characterized by their total length, mean growth rate and mean branching density. Secondary roots, R2, roots were characterized by their length and life span, and by the percentage of R2 roots with more than one segment (poly-segmented R2 roots).

Growth dynamics were studied with time series analysis methods on three variables on the aerial system, i.e. weekly increase in the number of leaves, leaf area and shoot length using the statistical module of AMAPmod software (Godin et al., 1997). Five variables were considered on the root systems in order to compare their development and dynamics to the aerial part: mean total length and growth rates of both R1 and R2 roots, and weekly increase in number of R2 roots. To analyse the relationships between growth and branching in primary roots, the only R1 selected were those which emerged during the first month after transplantation, grew all through the observation time and reached at least 30 cm. One to seven roots per plant corresponded to these three criteria. On these roots, the number of R2 roots per week and the growth rate of the parent R1 during the corresponding week were analysed as time series.

For analysing the rhythmic variations of variables, trends, i.e. long-term changes in the mean level, were removed in order to obtain the stationary time series (Chatfield, 2003). Different filtering procedures were tested based on either first-order differencing or symmetric smoothing filters such as the binomial distributions B(n, p) with p = 0·5. Residual values, i.e. (original value − smoothed value) for each rank, were then extracted with a symmetric smoothing filter corresponding to the probability mass function of the binomial distribution B(4, 0·5). As proposed by Brockwell and Davis (2002), the first and the last terms of the series were repeated when applying this symmetric smoothing filter in order to overcome the end-effect problem. Sample autocorrelation and cross-correlation functions were then computed on residual values. Given τ observations in a sequence, x1,…, xτ, τ – k pairs of observations (x1, xk+1), (x2, xk+2),…, (x1, xτ−k, xτ) can be formed, considering the first observation in each pair as one variable, denoted by X(1), and the second observation as a second variable, denoted by X(k+1). The autocorrelation coefficient at lag k for a single sequence of length τ is thus given by (Brockwell and Davis, 2002; Chatfield, 2003):

graphic file with name M1.gif

where Inline graphic. When generalizing to a sample of sequences, the autocorrelation at lag k is computed from the Inline graphic pairs of observations extracted from all the sequences. The usual rule of thumb to indicate the uncertainty in an autocorrelation coefficient is that under the null condition of no correlation (purely random sequence), the autocorrelation coefficient at lag k has standard error that is roughly Inline graphic in the case of a sample of sequences (Diggle et al., 2002).

Correlation properties of multivariate sequences can be investigated by sample cross-correlation functions which are a direct generalization of sample autocorrelation functions in which the pair of variables is either X(1) and Y(k+1) or Y(1) and X(k+1) (Diggle, 1990; Chatfield, 2003). Hence, while the sample autocorrelation function is an even function of the lag in that r(k) = r(−k), the sample cross-correlation function is not an even function of the lag.

Since the application of linear filters for trend removal induces an autocorrelation structure (Diggle, 1990), the autocorrelation function induced by the selected filter on the reference model (deterministic trend + white noise) was chosen to show. A white noise series is a series of independent Gaussian random variables with zero mean and common variance parameter. The following convention was chosen to compute cross-correlation functions between variables 1 and 2: variable 1 was considered as reference and variable 2 shifted for increasing and decreasing lags. Consequently, the number of pairs of observations decreased with increasing absolute lag values and the maximum absolute lag value considered was fixed at 10.

RESULTS

Shoot growth

No significant difference was observed between the two varieties regarding the final mean values of the variables selected to characterize their aerial growth (Table 1). However, at the end of the observation period, ‘Ariane’ exhibited more variability in all variables than X3305.

Table 1.

Total number of leaves, leaf area, shoot length, basal diameter and number of primary roots per plant, of young ‘X3305’ and ‘Ariane’ apple plants 16 weeks after transfer from in vitro culture

Variety No. of leaves Leaf area (mm2) Shoot length (mm) Basal diameter (mm) No. of R1
X3305 16 ± 4·85 122·82 ± 86·42 56·70 ± 41·90 3·10 ± 0·41 19·2 ± 12·95
‘Ariane’ 19 ± 5·61 122·36 ± 102·99 49·16 ± 47·93 2·84 ± 0·47 18·6 ± 9·02
Varietal effect n.s. n.s. n.s. n.s. n.s.
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Mean values are given ± s.d.

Significant differences between varieties were tested by a Kruskal–Wallis non-parametric ANOVA (n.s., non-significant).

Sample autocorrelation functions showed significant coefficients at different lags depending on both the variable and variety (Fig. 1). In X3305, a rhythmic and particularly stable pattern appeared for both leaf area and shoot length (Fig. 1A). Significant and positive coefficients were observed at lags 3 and 6, while negative at lags 4 and 7. Thus, the variations in leaf area and shoot length were synchronized with a rhythm of 3 weeks, and were in opposition phase the following week. No significant autocorrelation coefficients were observed for the number of new leaves. In ‘Ariane’, the number of new leaves tended to alternate, i.e. exhibited a 2-week rhythm, but only the first autocorrelation coefficients were significant and the pattern died out rapidly (Fig. 1B). The variations in leaf area and shoot length were only negatively correlated at lag 2 and those in leaf area tended to be synchronized each 3 weeks. Finally, in X3305, the elongation variables had higher autocorrelation coefficients than the number of new leaves and exhibited a similar rhythm of 3 weeks. By contrast, in ‘Ariane’, leaf emergence had higher autocorrelation coefficients than leaf and shoot elongation, and tended to alternate.

Fig. 1.

Fig. 1.

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Sample autocorrelation functions of the variables characterizing the aerial growth: number of new leaves (nb N), variations in leaf area d(LA), and shoot length d(L) calculated per plant, for two apple varieties, X3305 (A) and ‘Ariane’ (B). Each unit on the x-axis corresponds to one lag of 7 d. Autocorrelation function induced by the selected filter on the reference model, i.e. deterministic trend + white noise (long-dashed lines) and randomness 95 % confidence limits (short-dashed lines) are indicated.

In X3305, the cross-correlation function between the variations in leaf area and shoot length appeared to be an even function of the lag since r(k) ≈ r(−k) (Fig. 2A). Indeed, the cross-correlation coefficient was highly significant at lag 0 while, at higher positive or negative lags, the coefficients were successively positive and negative with a symmetric rhythm of 3 weeks (negative at lags ±1, ±4 and ±7, positive at lags ±3 and ±6). This remarkable structure indicates that these two variables followed a similar rhythm. Their respective cross-correlation functions with the number of new leaves (Fig. 2B) were also quite similar with significant coefficients observed for positive lags while few or no significant coefficients were observed for negative lags. This dissymmetry of the cross-correlation function indicates that the elongation processes is correlated to the number of leaves that emerged in the weeks before, while the inverse is not true. The elongation process, in both leaves and shoots, was positively correlated with the number of leaves which emerged 1 week before (positive coefficients at lags 1), while in opposition phase with the number of leaves emerged 2 weeks before (negative coefficients at lags 2). This pattern tended to repeat with a 3-week rhythm. In ‘Ariane’, the variations in leaf area and shoot length were synchronized in the current week and 4 weeks before, with an opposition phase the week after the synchrony (Fig. 2C). The number of new leaves and the variations in both leaf area and shoot length were not significantly correlated regardless of the lag value and sign (Fig. 2D).

Fig. 2.

Fig. 2.

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Cross-correlation functions between variables of aerial growth calculated per plant and for two apple varieties: between leaf area d(LA) and shoot length d(L) for X3305 (A) and ‘Ariane’ (C); between leaf area d(LA) or shoot length d(L) and the number of new leaves (nb N) for X3305 (B) and ‘Ariane’ (D). Each unit on the x-axis corresponds to one lag of 7 d. Randomness 95 % confidence limits are indicated with short-dashed lines.

Root growth

Seven to forty roots per plant developed directly from the in vitro dead roots and were labelled R1. The total number of secondary roots per plant, labelled R2, also exhibited a large variation between plants (Table 2). A 6-fold coefficient was observed between the plants that had the lowest and largest number of secondary roots. Basically, the fewer the leaves, the fewer the roots each plant had. The converse was also true. Few tertiary roots developed for any plant (Table 3) and most had only one segment, which meant that they lived <1 week. Thus, these roots were not analysed in the following.

Table 2.

Mean length, growth rate, branching density, and number of secondary roots of a selection of primary roots (R1), which grew all over the observation period (16 weeks after transfer from in vitro culture), on young ‘X3305’ and ‘Ariane’ apple plants

No. of R2
No. of selected R1 Total length (cm; mean ± s.d.) Growth rate (cm d−1; mean ± s.d.) Branching density (no. R2 cm−1; mean ± s.d.) Total no. per plant Mean no. per R1 (±s.d.)
X3305 16 43·64 ± 8·55 0·51 ± 0·11 0·8 ± 0·4 747 34·00 ± 17·61
Plant effect n.s. n.s. n.s. n.s.
‘Ariane’ 19 40·42 ± 8·14 0·46 ± 0·10 0·4 ± 0·3 519 16·95 ± 12·95
Plant effect n.s. n.s. n.s. n.s.
Varietal effect n.s. n.s. ** **
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Significant differences between plants and varieties were tested by a Kruskal–Wallis non-parametric ANOVA (n.s., non-significant;

**

significant with P < 0·01).

Table 3.

Total number of secondary (R2) and tertiary roots (R3) per plant; total length (mm), median and centiles (25 % and 75 %) of the life span (days) for all R2 roots; mean growth rate (mm d−1) for R2 roots with more than one segment

Life span (days)
Total no. of R2 Total length (cm; mean ± s.d.) Median Centile No. of R2 with <1 segment Growth rate (cm d−1; mean ± s.d.) Total no. of R3
X3305 747 2·01 ± 1·63 3 0–10 410 0·24 ± 0·13 24
‘Ariane’ 519 2·34 ± 2·76 0 0–8 242 0·30 ± 0·16 20
Hierarchic ANOVA Variety ** **
Plant (variety) ** **
Kruskal–Wallis Plant effect (X3305) **
ANOVA Plant effect (Ariane) **
Varietal effect **
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Mean values are indicated ± s.d.

Significant difference between plants and varieties were tested by a Kruskal–Wallis non-parametric ANOVA for the life span and by a hierarchic ANOVA with plant effect within the variety effect, for both length and growth rate (n.s., non-significant;

*

significant with P < 0·05;

**

significant with P < 0·01).

Root system architecture

Among R1, a large variation was observed in the final length. However, the selected roots were homogeneous in their growth rate between plants and no difference was observed between varieties (Table 2). By contrast, their mean branching density was significantly different between X3305 and ‘Ariane’, with a higher density observed in X3305. As a consequence, the total number of R2 per plant as well as the mean number of R2 per selected R1 root was significantly lower in ‘Ariane’ than in X3305 (Table 2). About 50 % of R2 stopped growing during their first 3 d of growth, these roots developing a single segment. The number of R2 that were still growing from one observation date to the following decreased exponentially. Because of this exponential decrease, R2 life span was analysed through median and centile values. In both varieties, significant differences were observed between plants (Table 3). Despite this intra-variety heterogeneity, significant differences were also observed between varieties: the R2 life span was significantly shorter in ‘Ariane’ than in X3305. The mean length of R2 was also significantly different between both plants and varieties, with R2 slightly longer in ‘Ariane’ than in X3305. The mean R2 growth rate, life span and total length were calculated on the sub-sample of R2 that developed more than two segments (Table 3). In both varieties, these R2 grew nearly twice as slowly as R1 and their growth rate was significantly higher in ‘Ariane’ than in X3305. Their total length was also significantly different between varieties, while their life span was not different (data not shown). Finally, the differences in R2 between varieties can be summarized as follows: in X3305, the R2 were more numerous than in ‘Ariane’ and >50 % developed over at least two successive observations (i.e. >3 d), these roots growing relatively slowly and remaining quite short. By contrast, in ‘Ariane’, few R2 developed, <50 % of them remained alive for >3 d but these roots grew faster and became longer.

Root growth dynamics

The growth dynamics of primary and secondary roots were studied per plant through five variables which were calculated weekly: (a) the mean total length and the mean growth rate of both R1 and R2; and (b) the number of new R2. In both varieties, the sample autocorrelation functions of the variables related to R1 did not show a clear pattern and few or no coefficients were significant regardless of the lag (cross-correlation functions not shown). By contrast, sample autocorrelation functions of the variables related to R2 showed rhythmic patterns with significant coefficients (Fig. 3). In both varieties, R2 emerged rhythmically. Only one significant coefficient was observed at lag 3 in X3305 while an alternation, i.e. a 2-week rhythm with significant coefficients until lag 5, was observed in ‘Ariane’. The growth rates of R2 exhibited a quite similar pattern in both varieties with an opposition phase observed each 2 weeks (negative coefficients at lag 2). In X3305, a synchrony was also observed each 3 weeks for this variable. In both varieties, the mean length of R2 did not show significant autocorrelation coefficients.

Fig. 3.

Fig. 3.

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Sample autocorrelation functions of the variables characterizing secondary roots for two apple varieties X3305 (A) and ‘Ariane’ (B): number of new secondary roots (nbR2), their growth rate (GR R2) and mean total length (long R2). Each unit on the x-axis corresponds to one lag of 7 d. Autocorrelation function induced by the selected filter on the reference model, i.e. deterministic trend + white noise (long-dashed lines) and randomness 95 % confidence limits (short-dashed lines) are indicated.

In both varieties, the cross-correlation functions between the mean length and mean growth rate of R1 did not show significant coefficients, and these two variables were also poorly correlated with the equivalent variables in R2 (cross-correlation functions not shown). By contrast, the cross-correlation functions between the three variables considered on R2 showed significant coefficients (Fig. 4). In X3305, these three variables were synchronous in the current week (Fig. 4A). The cross-correlation function between R2 growth and both the number of R2 and their mean total length were dissymmetric, with higher coefficients for negative than positive lags. Thus, the variations in R2 growth rate were correlated to the two other variables observed in the weeks before, with a rhythm of 3 weeks (negative coefficients at lags −1, −4 and −7 and positive coefficients at lags −3, −5 and −8). The cross-correlation function between the number of R2 and their mean total length exhibited an even structure, with a rhythm of 3 weeks until lags ±3 or ±4, and which rapidly died out at higher lags. This particular structure was also observed on the cross-correlation function between the number of R2 and their mean total length in ‘Ariane’ (Fig. 4C), even though the coefficients were less significant and the rhythm was different (2-week).

Fig. 4.

Fig. 4.

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Cross-correlation functions between root variables: between the number of new secondary roots (nb R2), their growth rate (GR R2) and mean total length (long R2), calculated on selected primary roots for two apple varieties X3305 (A) and ‘Ariane’ (C); between the number of new secondary roots (nb R2) and the growth rate of primary roots (GR R1) and their mean total length (long R1) for X3305 (B) and ‘Ariane’ (D). Each unit on the x-axis corresponds to one lag of 7 d. Randomness 95 % confidence limits are indicated with short-dashed lines.

In X3305, the emergence of new R2  was correlated to both the R1 growth rate and mean length for negative lags only (Fig. 4B). A positive correlation was observed in the current week and 3 weeks before (lags 0 and −3), while an opposition phase was observed 1 week after (negative coefficients at lags −2 and 1). In ‘Ariane’, the number of R2 was significant correlated only with the R1 mean length and for negative lags (Fig. 4D). A positive coefficient was observed in the current week and with a 4-week rhythm towards negative lags. In both varieties, high correlation coefficients were observed for high negative lags, suggesting relatively long-term effects of the R1 growth rate in X3305 and the R1 total length in ‘Ariane’, on the number of new R2.

Comparison between aerial and root system dynamics

This comparison was performed at the whole-plant scale. The three variables studied in the aerial part were plotted against the five variables studied on root systems, and cross-correlation functions were considered for each pair of variables. Since the cross-correlation functions were very numerous, only the main results are presented.

In both varieties, the variables related to R1 were not correlated with the variations in leaf area and shoot length (cross-correlation functions not shown). By contrast, significant cross-correlation coefficients were observed with the number of new leaves (Fig. 5). In X3305, the number of leaves was positively correlated to the R1 mean length and growth rate that occurred 1 week after leaf emergence (lag −1) and this repeated with a 3-week rhythm. However, the pattern died quite rapidly with increasing lags (Fig. 5A and B). In ‘Ariane’ (Fig. 5C), the number of new leaves was alternatively positively and negatively correlated with the R1 mean length for negative lags, with a rhythm of 2 weeks beginning from a synchrony in the current week. Cross-correlation coefficients with the R1 growth rate were not significant (Fig. 5D). Finally, leaf emergence was synchronized with the R1 mean length in both varieties, and with the R1 growth rate only in X3305.

Fig. 5.

Fig. 5.

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Cross-correlation functions between the number of new leaves (nbN) and the mean total length of primary roots (long R1) for two apple varieties X3305 (A) and ‘Ariane’ (C); between the number of new leaves (nbN) and the growth rate of primary roots (GR R1) for X3305 (B) and ‘Ariane’ (D). Each unit on the x-axis corresponds to one lag of 7 d. Randomness 95 % confidence limits are indicated with short-dashed lines.

Regarding the variables related to R2, in both varieties, the cross-correlation functions between the number of new leaves and the number of new R2 exhibited a rhythmic pattern, with significant coefficients only for positive lags (Fig. 6A and C). Thus, leaf emergence was correlated with the number of new R2 that developed before them, with a rhythm of 3 weeks in X3305 and 2 weeks in ‘Ariane’. In X3305, no significant correlation was observed in the current week and the pattern died out after lag 5. In ‘Ariane’, leaf and R2 emergence were synchronous in the current week and each 2 weeks afterwards, with significant coefficients until high positive lags. The cross-correlation functions between the number of new leaves and R2 growth rate showed a rhythmic pattern only in X3305, with a 3-week rhythm (Fig. 6B). Similarly, higher correlation coefficients were observed between the R2 variables and the variations in both leaf area and shoot length in X3305 than in ‘Ariane’ (cross-correlation functions shown for leaf area only; Fig. 6). In X3305, the cross-correlation function between the leaf area and the number of R2 exhibited a rhythmic pattern for both positive and negative lags. These variables were negatively correlated in the current week and were positively correlated the week after, this pattern repeating each 3 weeks afterwards (negative coefficients at lags 0, ±3 and −6, positive at lags ±2 and ±5). Significant coefficients were observed between the shoot length and both the number of R2 and R2 growth rate, but only for negative lags and still with a 3-week rhythm (cross-correlation functions not shown).

Fig. 6.

Fig. 6.

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Cross-correlation functions between variables characterizing the aerial systems and secondary roots: between the number of new leaves (nbN), leaf area d(LA) and number of new secondary roots (nb R2) for two apple varieties X3305 (A) and ‘Ariane’ (C); between the same variables of aerial systems and the growth rate of secondary roots (GR R2) for X3305 (B) and ‘Ariane’ (D). Each unit on the x-axis corresponds to one lag of 7 d. Randomness 95 % confidence limits are indicated with short-dashed lines.

DISCUSSION

The present study highlighted the existence of rhythmic patterns on the studied plants, which involved different processes and periods depending on the variety. A 3-week rhythm was observed in X3305 and concerned mainly the elongation processes in the aerial part (leaf expansion and increases in shoot length; Fig. 1) while a 2-week rhythm was observed in ‘Ariane’, mainly in R2 emergence and to a lesser extent in leaf emergence (Figs 1 and 3). However, due to the low number of plants studied and to the development stage of the young plants that were just issuing from in vitro culture and still in the acclimation phase, the fluctuations in growth observed in the present study are not equivalent to previous observations in the field (for instance, Abbott, 1984; Costes and Lauri, 1995). In particular, the number of leaves that developed, as well as their rhythm of emergence, were very low. In ‘Ariane’, the fact that the successive leaves tended to emerge each 2 weeks while the total number of leaves per plant was not different between the varieties indicates that the number of new leaves that developed at once, and consequently the ‘signal’ recorded, was particularly low. On the other hand, leaves emerged more synchronously in ‘Ariane’ than in X3305. The differences in the leaf emergence rhythm between varieties are consistent with a stable leaf emergence rate for a given variety as demonstrated previously in peach (Kervella et al., 1995). These differences could also be due to different adaptations of the varieties to the experimental conditions. Indeed, environmental conditions such as temperature, light, water and nitrogen supply have been shown to influence growth components in many crops (Zhu et al., 1999; Médiène et al., 2002). The interaction between environmental conditions and variety behaviour could thus be further investigated since, in the present study, different environmental conditions were not compared.

The variations in leaf area and shoot length were rhythmic only in X3305, with a 3-week rhythm (Fig. 1). In ‘Ariane’, these processes were in opposition phase every 2 weeks. In both varieties, leaf and shoot elongation occurred synchronously (Fig. 3). This synchrony was symmetric and very clear in X3305 while it was only a tendency in ‘Ariane’. Increase in leaf area and shoot length occurred with a delay of 1 week after leaf emergence in X3305 while no clear synchronism between leaf emergence and elongation processes appeared in ‘Ariane’. This lack of synchronism in ‘Ariane’ may be due to the weakness of the leaf emergence signal. The existence of dependency between leaf emergence and shoot elongation was established many years ago through defoliation experiments that suppressed further shoot elongation (Abbott, 1984). Further investigations have shown the role of hormones in this dependency, in particular auxin and gibberellins (Steffens and Hedden, 1992; Zhao et al., 2002). The present study provides information on the relative timing of the processes and suggests that both this timing and the intensity of the signal could be variety dependent.

The growth rate observed in primary roots varied from 0·2 to 1 cm per day without any significant difference between varieties. These values are similar to those found in young citrus trees (Bevington, 1985), peach trees (Pagès et al., 1993) and oil palms (Jourdan and Rey, 1997) but lower than those observed in rubber tree taproots (Thaler and Pagès, 1996). In many other studies, root growth rates have been measured using different methods such as the selective placement of 32P (Atkinson, 1974) or the dried and fresh weight (Kuehny and Halbrooks, 1993; Zhu et al., 1999), which would not have allowed a direct comparison of the results to be made. The growth rate of secondary roots was about half that of primary roots. This decrease in growth rates with the successive branching orders is consistent with previous observations carried out in different species, for instance on oak tree (Riedacker et al., 1982), peach tree (Pagès et al., 1993) or oil palm (Jourdan and Rey, 1997).

In addition, the present results highlighted a significant difference in the growth rate of secondary roots between the two varieties studied even though a large heterogeneity between plants of the same variety was also observed. On average, secondary roots grew faster in ‘Ariane’ than in X3305. Similarly, the life span of the secondary roots was different between the two varieties. In ‘Ariane’ the secondary roots had a slightly lower longevity than those of X3305. In both varieties, the life span of secondary roots decreased exponentially over time and, consequently, most of the secondary roots died within the first 7 d of emerging. Despite different experimental conditions, the range of life spans of the secondary roots is consistent with previous studies and with about 20 % of secondary roots surviving >14 d, apple secondary roots appear to have a low to intermediate longevity, as previously stated for fine roots by Wells and Eissenstat (2001). For comparison, 40 % and 6 % of the roots survived >14 d in Prunus avium and Picea sitchensis, respectively (Black et al., 1998).

The branching density, calculated along selected primary roots which grew throughout the observation period, was also significantly different between the two varieties studied. The values obtained in the present study varied from 0·8 to 0·4 secondary roots per cm on average, according to the variety, and were in the same range of values as those for the peach tree, as mentioned by Pagès et al. (1993). The difference in root branching density between varieties showed that the root systems of ‘Ariane’ plants were half as branched as those of X3305 plants. This result combines with the difference in growth rate to show that each variety has specific rules that lead to a different hierarchy between the primary and secondary roots and thus to different root architecture. These structural differences may lead to different capabilities in terms of soil exploration and possibly in nutrient uptake since fine root physiology has been shown to depend upon root age and branching order (Wells and Eissenstat, 2003; Comas and Eissenstat, 2004). Moreover, recent studies have demonstrated that different internal hormone concentrations and transports, as well as external concentrations in ions and plant susceptibility could explain differences in root system architecture (Bhalerao et al., 2002; Lopez-Bucio et al., 2003). The present experimentation was designed to provide similar nutrient availability to all plants, ensuring a negligible effect of external concentrations of ions on root elongation and lateral root initiation. Thus, two main assumptions remain: that there are differences in variety susceptibility to external concentrations in ions, and differences in hormone concentration or transport. However, at this stage of the present investigation it is not possible to focus on one particular hormone among those possibly involved, i.e. auxin, cytokinines, abscisic acid and gibberellins (e.g. Casson and Lindsey, 2003).

In terms of the dynamics of root growth, fluctuations were observed in the number of R2 and in their growth rates, in both varieties, while R1 growth did not exhibit any evident rhythm (Fig. 3). Even though, as far as is known, no study on apple tree roots has focused on the root dynamics depending on branching order, similar fluctuations in root growth rates were observed in other perennial species (Drew and Ledig, 1980; Kuehny and Halbrooks, 1993; Thaler and Pagès, 1996). The relationships between the emergence of secondary roots and the fluctuations in the growth of primary roots were indicated mainly through positive cross-correlation coefficients between the number of new R2 and the R1 mean length during the current week (Fig. 4). Such relationships between growth and branching of primary roots are consistent with previous results obtained in the peach tree (Pagès et al., 1993).

The fluctuations in R1 length were synchronous with the emergence of leaves but with different rhythms and synchronisms between varieties (Fig. 5). The existence of synchronisms between these two processes in the current week in ‘Ariane’, or in the week before R1 elongation in X3305, suggests that leaf emergence may be involved in the timing of primary root elongation. Furthermore, R2 emergence also exhibited a rhythmic synchronism with leaf emergence, but their cross-correlation coefficients were higher for positive lags than for negative lags (Fig. 6). Thus, in contrast to primary roots, secondary roots were synchronous with the leaf emergence that occurred after them or, inversely, new leaves were correlated to the number of new R2 developed in the previous weeks. This suggests that new secondary roots enhanced the emergence of new leaves, possibly via a feedback effect that could result from an increase in water and nutrient uptake and supply towards the aerial part.

The emergence of R2 also showed significant cross-correlation coefficients with the variations in leaf area (Fig. 6). This relationship was more pronounced in X3305 than in ‘Ariane’, the lack of clear synchronism in ‘Ariane’ being interpreted previously as a consequence of the weakness of the aerial signals. In X3305, an opposition phase between the variations in leaf area and both the R2 emergence and growth rate was observed in the current week. This result is consistent with a previous study carried out in the rubber tree by Thaler and Pagès (1996) and with the assumption of a possible dependence between root branching and leaf development. Indeed, the initiation of secondary roots may be enhanced by growth factors synthesized in growing leaves such as auxin (Pagès et al., 1993), which has also been reported to be involved in shoot–root relationships in apple trees, especially via xylem differentiation (Soumelidou et al., 1994; Kamboj, 1997).

In conclusion, the synthesis of the results obtained in the present study led us to propose a conceptual model that represents the root–shoot relationship as a cyclic process involving three steps (Fig. 7). Indeed, leaf emergence was followed by variations in R1 length (or synchronous with them in Ariane), which was followed by new R2 emergence, while this latter event impacted on further leaf emergence. A parallel loop concerns the fluctuations in leaf area, which follow leaf emergence and may, as R1 growth, enhance R2 emergence. This integrated view of the plant is consistent with the existence of a functional equilibrium between shoots and roots at the whole-plant scale which is usually interpreted as a consequence of water status (Diaz-Perez et al., 1995), limitation in carbon acquisition (Drew, 1982) or carbon and nitrogen partitioning (Marcelis, 1996). This equilibrium has also been assumed in developing models of partitioning (e.g. Minchin and Thorpe, 1996; Thornley, 1998). Even though the proposed scheme needs further confirmation at the adult stage, on other crops and in other environmental conditions, it supports the assumption that the root–shoot equilibrium could involve the relative dynamics of both aerial and root parts and could have consequences on the structural organization of the root system.

Fig. 7.

Fig. 7.

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Schematic representation of cyclic succession of events between aerial and root systems with the corresponding time delays that were highlighted in time-series analysis (in italics).

Acknowledgments

E.G.-V. was financially supported by CONACYT-México. We gratefully thank G. Garcia and S. Feral for technical assistance in field observations and H. Boukcim for his help in building minirhizotrons. We acknowledge K. Renton for improving the English.

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