Search tips
Search criteria 


Logo of annbotAboutAuthor GuidelinesEditorial BoardAnnals of Botany
Ann Bot. 2009 May; 103(7): 1103–1117.
Published online 2009 February 27. doi:  10.1093/aob/mcp040
PMCID: PMC2707913

Using a model-based framework for analysing genetic diversity during germination and heterotrophic growth of Medicago truncatula


Background and Aims

The framework provided by an emergence model was used: (1) for phenotyping germination and heterotrophic growth of Medicago truncatula in relation to two major environmental factors, temperature and water potential; and (2) to evaluate the extent of genetic differences in emergence-model parameters.


Eight cultivars and natural accessions of M. trunculata were studied. Germination was recorded from 5 to 30 °C and from 0 to −0·75 MPa, and seedling growth from 10 to 20 °C.

Key Results

Thermal time to reach 50 % germination was very short (15 °Cd) and almost stable between genotypes, while base temperature (2–3 °C) and base water potential for germination (−0·7 to −1·3 MPa) varied between genotypes. Only 35 °Cd after germination were required to reach 30 mm hypocotyl length with significant differences among genotypes. Base temperature for elongation varied from 5·5 to 7·5 °C. Low temperatures induced a general shortening of the seedling, with some genotypes more responsive than others. No relationship with initial seed mass or seed reserve distribution was observed, which might have explained differences between genotypes and the effects of low temperatures.


The study provides a set of reference values for M. trunculata users. The use of the ecophysiological model allows comparison of these values between such non-crop species and other crops. It has enabled phenotypic variability in response to environmental conditions related to the emergence process to be identified. The model will allow simulation of emergence differences between genotypes in a range of environments using these parameter values. Genomic tools available for the model species M. trunculata will make it possible to analyse the genetic and molecular determinants of these differences.

Key words: Core collection, emergence, Medicago truncatula, modelling, seed, temperature, water potential


Germination and heterotrophic growth are crucial steps for stand establishment of crops. Emergence can be highly variable for most crops and depend considerably on environmental conditions (Awadhwal and Thierstein, 1985; Durrant et al., 1988; Benjamin, 1990; Klos and Brummer, 2000; Valenciano et al., 2004). A precise description of plant functioning during the early stages before emergence, in relation to environmental conditions, is necessary in order to dissect which seed characteristics, stages and growing conditions lead to differences in emergence. It is also important to assess the possible genetic component of variation in emergence results (Eagles and Hardacre, 1979; Bettey et al., 2000; Cui et al., 2002; Rebetzke et al., 2007). Over the last decade, model plants have been the subject of rapid advances in genomics. Exploiting genetic diversity requires increasing knowledge of the phenotypic variability and there is a huge need for phenotyping collections to take advantage of the genetic and genomic tools that have been developed on model plants (genetic and physical maps, large collections of genetic resources and mutants).

Ecophysiological models that gather knowledge at the plant level provide a framework for the analysis of plant behaviour. They are designed to separate stages and to identify the influence of environmental conditions on biological processes at the plant-part level (Gutschick and Simmoneau, 2002; Tardieu et al., 2005; Moreau et al., 2006, 2007). Model equations can be used to predict processes, which is not the case when measuring single traits on plants. The parameter values can vary according to species, genotype or seed lot and they do not depend on environmental factors. Model equations and parameter values allow genotype and seed lot behaviours to be simulated under different conditions. Thus, ecophysiological models can help in the choice of phenotypic traits to measure on a wide range of lines at the plant scale. This is a key step in the analysis of inter- and intraspecific genetic diversity and could help to advance the analysis of genetic determinism of agronomic traits (Yin et al., 2000, 2003; Reymond et al., 2003; Tardieu, 2003; Quilot et al., 2005a, b; Tardieu et al., 2005; Hammer et al., 2006; Laperche et al., 2006, 2007). Such models have begun to be used to analyse the genetic diversity of several aspects of plant, leaf or root growth. However, the very early stages, from germination to the end of heterotrophic growth, have rarely been examined in this way (Cui et al., 2002; Zhang et al., 2005). It is necessary to combine knowledge provided by analysis of the sources of variation in crop stands, gathered in emergence models in which seed and seedling functioning in relation to environmental conditions are described with sufficient detail, with information provided by the tools available for one model species used in genomic studies.

Emergence models developed for crops (Bouaziz and Bruckler, 1989; Mullins et al., 1996; Finch-Savage et al., 1998; Dürr et al., 2001) separate the two phases leading to plant emergence: germination, defined as radicle protrusion (Bewley and Black, 1994), and seedling growth until emergence. These models take into account the influence of the main environmental factors in a seedbed, including soil temperature and water content. Differences in their input parameter values concerning seeds and seedlings can account for differences among genotypes.

We have chosen to study Medicago truncatula, one of the model legumes (Cook, 1999; Tivoli et al., 2006). Initially used for investigating the regulation of nodule development (Cook, 1999; Thoquet et al., 2002), recent work has also provided information about its grain-filling and seed characteristics (Gallardo et al., 2003; Djemel et al., 2005). However, little is known about its early stages, germination and heterotrophic growth, in relation to environmental conditions (Buitink et al., 2003; Gallardo et al., 2007; Garcia et al., 2007) and even less about the possible genetic diversity during these stages. Our objectives were to measure the ecophysiological parameters concerning germination and emergence for this species and to test the existence of a genetic influence on these parameter values. A first set of genotypes was chosen according to three main criteria: (1) originating from different natural biotopes in order to observe potentially different responses to environmental conditions; (2) belonging to a nested core-collection in order to maximize genetic diversity; and (3) being used for the creation of segregating populations, and of associated genetic maps in order to further analyse the genetic determinism once differences between genotypes had been observed. Nested core collections of M. trunculata represent the available germplasm in a limited sub-sample of accessions (Ronfort et al., 2006), and hence they can provide a first relevant set of genotypes for possible phenotypic variation. Due to high synteny of M. trunculata with other major crop legumes such as pea (Pisum sativum) and soybean (Glycine max; Yan et al., 2003; Choi et al., 2004), the genomic regions of interest for M. trunculata are expected to match those of other related species.

Finally, this ecophysiological analysis of the early stages of Medicago truncatula should help M. trunculata users before setting up genetic and genomic analyses in identifying parameters and ranges of environmental conditions related to emergence processes for which genetic diversity exist and should be further studied.


Plant material and seed production conditions

A total of eight genotypes of Medicago truncatula were studied (Table 1). ‘Jemalong A17’ is the reference for genomic studies and the node of several crosses for the production of recombinant inbred line (RIL) populations. ‘Borung’ and ‘Paraggio’ are cultivars mainly grown in South Australia. The five other genotypes are derived from natural populations collected around the Mediterranean basin: ‘F83005·5’, ‘DZA315·16’, ‘DZA045·5’, ‘DZA012.J’ and ‘SA28064’. They belong to the first set of the nested core collections (eight accessions, INRA Medicago truncatula Biological Resource Center Montpellier website:; Ronfort et al., 2006). Very little information was available regarding their sensitivity to abiotic stresses: ‘DZA045·5’ is known to be frost sensitive, while ‘F83005·5’ is frost tolerant and salt-stress sensitive. As seed production conditions (maternal environment) can greatly influence germination and early seedling stages, these genotypes were characterized for three different seed production conditions (termed M-05, M-06 and A-06; see below), in order to test whether genotype differences were observed regardless of seed lot. The number of genotypes studied varied from five to eight according to seed lot.

Table 1.
Description and characteristics of the studied genotypes

M-05 and M-06 seeds were produced in INRA greenhouses during the spring in 2005 and 2006 near Montpellier (43·61°N, 3·87°E; south of France, Mediterranean climate). A-06 seeds were produced in a growth chamber at the National Seed Testing Station (SNES, Angers). A commercial seedlot of ‘Paraggio’, produced in field conditions in Australia in 2004 (Seedco Australia Co-Operative Ltd), was also studied as an external reference (Aust-04).

Seeds were stored at a low temperature and 50 % relative humidity for 8–18 months before the experiments in order to avoid post-harvest dormancy, commonly observed during a 3–6 month period after harvest in M. trunculata (Garcia et al., 2007).

Seed physical and biochemical characters

Seed dry mass (SDM) was measured on a sub-sample of 100 seeds for each genotype of each seed lot. The proportion of teguments (percentage of mass of teguments relative to SDM, %T) was measured on 25 seeds after 24 h of imbibition to facilitate the separation of teguments. Total carbon and nitrogen contents (%C and %N) were measured on two replicates of five ground seeds, using a mass spectrometer (VG SIRA 9) linked to an automatic combustion analyser (Carlo Erba NA 1500). Sucrose and oligosaccharides contents of the raffinose family (RFO) were measured as they could be indicators of the seed maturation state at harvest. Indeed, in legumes the RFO seed contents differ from other species (Kuo et al., 1988), and during seed maturation and desiccation stachyose content progressively decreases while verbascose content increases (Rosnoblet et al., 2007). Soluble sugars were extracted from pre-weighed powder (three replicates of 15–20 previously dried seeds) with 1 mL methanol/water (80:20 v/v), with 40 µL melizitose used as the internal sugar standard. After heating for 15 min at 76 °C, the liquid was evaporated under vacuum. The pellet was then dissolved in 1 mL distilled water and centrifuged for 3 min at 14 000 g. Sucrose (‰sucrose), stachyose (‰stachyose) and verbascose (‰verbascose) contents were quantified using HPLC (DIONEX ICS-3000).

Input variables of the model and experimental protocols for their measurement

The SIMPLE model (SIMulation of PLant Emergence; Dürr et al., 2001) predicts germination and emergence time courses and final rates in relation to environmental conditions during sowing. This model has previously been parameterized for a number of crop species such as wheat, sugar beet, flax, mustard, bean and oilseed rape (Carrera and Dürr, 2003; Dorsainvil et al., 2005; Moreau-Valancogne et al., 2008), making it possible to compare different species using the same set of parameters. The functioning of the SIMPLE model and the list of equations and input variables have been described in Dürr et al. (2001) and Dorsainvil et al. (2005). We focused here on the input variables involved in germination and growth, in relation to temperature and water potential. Only the equations for calculating these input variables are presented. Experiments were carried out to obtain base temperature (Tb,germ) and base water potential (ψb,germ) values for seed germination, and base temperature values (Tb,elong) for seedling elongation for all seed lots and genotypes. Base values are the values below which no germination or elongation can occur (see calculations below). For germination as well as elongation, data collected at different temperatures were then plotted together: germination and elongation time courses were fitted to thermal time (sum of temperatures over the base temperature), as in the model, allowing seed lots and genotypes to be compared (see calculations below).

Experiments on seed germination

Before each experiment, seeds were scarified to avoid physical seed dormancy. Three replicates of 50 seeds were sown for each genotype from a given seed lot at each of the tested temperatures or water potentials. Seeds were individually weighed. When testing temperatures, seeds were sown in plastic boxes (5·5 × 12 × 18 cm) in pleated filter paper (ref. 3236 Whatman), moistened with 80 mL deionised water in order to obtain non-limiting water conditions for M. trunculata germination. Boxes were kept in incubators at 5, 10, 15, 20, 25 and 30 °C. Temperatures were recorded hourly with sensors (Testo 177-T3). Observations were carried out 2–5 times a day depending on the temperature being tested. When testing water potentials, boxes were incubated at 15 °C, in order to limit differences in germination time between genotypes due to temperature. The water potentials tested were −0·25, −0·5 and −0·75 MPa. Seeds were laid onto flat filter paper (ref. 3645 Whatman) in plastic boxes (5·5 × 12 × 18 cm), watered with 14 mL of osmotic solutions of high molecular weight PEG (Polyethylene glycol 8000, ref. SIGMA 25322-68-3) used at varying concentrations to control water potential, after Michel (1983). Seeds were considered to have germinated when the radicle protruded from the seed coat (≥1 mm). Seeds that remained hard despite scarification were not included in the results.

Seedling growth

Experiments were performed in the dark to mimic pre-emergence growth. Pots (6·5 cm diameter, 10 cm high) were incubated in growth chambers at 10, 15 and 20 °C. They were filled with 500 g of sand (SIFRACO quality NE34: SiO2 >99·7 %, solid density 2·65 g cm−3, mean particle size 200 µm). The gravimetric water content of the sand was raised to 0·2 kg kg−1 before sowing using a nutrient solution prepared for young seedling growth (Saglio and Pradet, 1980), and it was maintained constant in order to avoid water stress by application of deionised water during the experiment. Five seeds per pot were individually weighed, scarified, and sown at 1·5 cm depth in a known position within the pots so as to be able to relate each seedling's mass to its initial seed mass. Three pots (15 seedlings) were observed at each of the six observation times until seedling length reached a plateau value (maximal elongation under heterotrophic growth conditions), which was reached at 11, 15 and 30 d at 20, 15 and 10 °C, respectively.

At each observation time, seedlings were harvested, and hypocotyl and radicle lengths were measured for the seed batches M-05, ‘Paraggio’ Aust-04 and A-06. For M-06, only the final lengths were measured. The masses of the seedling parts were measured at the same time as seedling elongation in order to analyse biomass distribution. The seedling parts were separated, dried at 80 °C for 48 h and weighed for the experiments performed at 20 °C on seed lots M-05 and ‘Paraggio’ Aust-04, and at 10 °C on A-06. An additional experiment was carried out to evaluate the effect of initial seed mass on the maximum lengths of seedling parts. Seeds from lots M-05 and A-06 belonging to the same mass range (3·5–4·5 mg) were weighed and then grown in the same conditions as for the experiments at 20 °C. The final lengths of the seedling parts were measured.


A Gompertz function was fitted to the germination rates obtained for each replicate batch of 50 seeds:

equation image

where G(t) is the cumulative germination rate at time t from sowing, a is the maximum cumulative germination rate, and b and c are shape parameters. Adjustments were made for each temperature or water potential tested and used to calculate the time to reach y % (20 up to 80 %) cumulative germination. Values of Tb,germ or ψb,germ are defined as the x-intercept of the linear regression between the studied factor (temperature or water potential) and germination rate (Gummerson, 1986; Dahal and Bradford, 1994). We determined the range of temperatures and water potentials for which a strong linear relationship (r2 > 0·95) existed between germination rates [1/(time to reach y % germination)] to calculate the x-intercepts. Base temperature and base water potential are thus defined as the fitted values at which no germination occurs.

Weibull functions were fitted to the observed radicle and hypocotyl lengths:

equation image

where L(t) is the length at time t, α is the final length, and β and γ are shape parameters. Time was calculated from the time needed for 50 % germination onwards in order to separate the two stages (i.e. germination and heterotrophic growth) and to eliminate germination rate differences between genotypes. Elongation rates [1/(time in h to reach z mm); z= 20 up to 70 mm) were then calculated from these fittings for the three temperatures tested: 10, 15 and 20 °C. Base temperature for elongation (Tb,elong) was the x-intercept of the linear regression between elongation rate and temperature, and thus the fitted temperature value at which no elongation occurred.

Hence, time was expressed in thermal time tT, using the following equation:

equation image

where tT,i is cumulative thermal time on day i, T is mean temperature of day d, and Tb is the base temperature calculated for each genotype and seedlot. This calculation of time was used in eqns (1) and (2) to compare germination and growth of the different genotypes and seed lots. In the case of germination, day 1 is the sowing date; in the case of elongation, day 1 is the day at which half the seeds have germinated, in order to consider elongation time separately from germination.

Statistical analyses

Statistical analyses were performed in order to estimate the contribution of genotype and seed lot effects on the variability of results. First, a global analysis of variance was performed with genotype (G), seed lot (S) and temperature (T) or water potential (ψ) considered as fixed factors and all interactions (G × S, G × T, G × ψ and G × S × T or G × S × ψ) considered as random factors. We used the PROC MIXED procedure of SAS (SAS 9·1·3, SAS Institute, Inc.), which calculates the significance test using the restricted maximum likelihood (REML) method. Second, genotype effects were compared within each seed lot. All the mean comparisons were made using a Tukey test or Bonferroni's multiple comparison procedure when samples sizes differed (P < 0·05) using the STATGRAPHICS Plus 3·1 software.


Characteristics of the seeds

Several seed characteristics, expected to differ according to seed production conditions and genotypes that can influence germination and growth results, were measured. Seed dry mass (SDM) and proportion of teguments (%T) varied from 3·3 to 5·2 mg and from 8·8 to 12·1 %, respectively; they were negatively correlated (Table 2). For the SDM, there were significant effects of genotype and seed lot, whereas no effect was observed for %T. When considering only the four genotypes common to the three seed lots, the average SDM and %T of A-06 (3·9 mg, 10·5 %) were slightly lower than those of M-05 and M-06 (4·15 mg, 10·8 %), which could be related to the different seed production environments. Whatever the seed lot, ‘F83005·5’, ‘DZA315·16’ and ‘DZA012.J’ had the lowest SDM values (≤3·9 mg) whereas ‘DZA045·5’ had the highest (≥4·5 mg). SDM variations were positively correlated with time between pollination and natural abscission (data not shown). For %T, when considering the genotypes within a seed lot, ‘F83005·5’ had high %T values while ‘Paraggio’ had among the lowest. Even if %T values remained within a small range, they were positively correlated with the number of hard seeds remaining after scarification (data not shown).

Table 2.
Description of the genotypes and of their seed characteristics

The seed carbon and nitrogen contents (%C and %N) were very similar between seed lots and genotypes, and were 49 % and 6·8% on average, respectively (Table 2). The average %N calculated using the four common genotypes of each seed lot showed that A-06 had slightly higher %N than the other two seed lots. When considering the genotypes within a seed lot, differences were observed for %N, with ‘F83005·5’ always having the lowest value.

Stachyose and verbascose contents were high, as expected for legume seeds, but large variations were observed among genotypes (Table 2). The seed lot effect was only significant for ‰sucrose, with higher values for A-06, while the genotype effect was significant for all the sugar contents. Whatever the seedlot, ‰verbascose of ‘DZA045·5’ and ‘DZA012.J’ were higher than for those of the other genotypes. Conversely, ‰stachyose of both genotypes was lower, suggesting more conversion to verbascose during seed development.

Finally, several seed characteristics differed for A-06, which could be related to seed production conditions. Genotypic variations were observed across and within seed lots for seed mass, teguments, nitrogen and sugar contents.


Effect of temperature

Seeds were individually weighed to test for any putative effect of initial seed mass on germination. No relationships between SDM and germination rate and final percentage were observed regardless of seed lot, genotype and temperature (data not shown).

Final percentages of germination remained above 80 % for all the temperatures tested regardless of the seed lots and genotypes, except at 30 °C. Examples of germination time courses are presented for two contrasting temperatures, 20 °C and 5 °C, for M-05 and ‘Paraggio’ Aust-04, and the external reference (Fig. 1A). At 20 °C germination was very fast, except for ‘DZA012.J’. At 5 °C, ‘F83005·5’, ‘Jemalong A17’ and ‘Paraggio’ germinated more slowly than the others and hardly reached about 80 % of germinated seeds. Figure 2A shows the inverse of the time to reach 50 % germination (1/tG50) for the whole range of temperatures tested, for all genotypes and for the three seed lots. The optimum germination temperatures differed among genotypes and seed lots and were rather low, ranging from 15 to 25 °C. The germination rates of seed lot M-06 were the fastest, while A-06 had the slowest and showed a sharp decrease in germination rate above 20 °C (Fig. 2A). When comparing the genotypes for a given seed lot, two genotypes differentiated above 15 °C: ‘DZA012.J’ in both M-05 and M-06 and ‘DZA315·16’ in A-06. Whatever the seed lot, ‘Jemalong A17’ and ‘DZA012.J’ were greatly affected at the highest tested temperature (30 °C), and 1/tG50 values of ‘F83005·5’ were always significantly lower than those of the other genotypes at the lowest temperature tested (5 °C).

Fig. 1.
Germination for the seed lot M-05, and ‘Paraggio’ Aust-04: (A) at 20 °C and 5 °C, and (B) at 0 MPa and −0·75 MPa (at 15 °C). Bars denote s.e., and the lines are fitted Gompertz functions.
Fig. 2.
Germination rates (1/tG50; see text) for the three seed lots M-05, M-06 and A-06, and ‘Paraggio’ Aust-04: (A) as a function of temperature, and (B) as a function of water potential. Bars denote s.e.

Base temperature for germination (Tb,germ) was calculated from the linear zone of the relationship between temperature and germination rate, i.e. 5–15 °C (Fig. 2A). There was a significant effect of seed lot, mainly due to lower Tb,germ values for A-06 (0·9–1·9 °C; Table 3) whose germination rates were much lower than for the two other seed lots. In addition, ‘DZA315·16’ stood out from the other genotypes in this seed lot with a very low value (0·9 °C). For the two fast-germinating seed lots (M-05 and M-06), Tb,germ values were similar, varying from 1·9 to 3 °C. ‘F83005·5’ had the highest Tb,germ for both seed lots, and its value was significantly higher in M-06.

Table 3.
Base temperatures and water potentials for the different genotypes

Germination was plotted against calculated thermal time, using the genotype- and seedlot-specific Tb,germ. Using this expression of time, the germination time courses at 5, 10 and 15 °C (the range of temperatures used for Tb,germ calculations) overlapped for a given genotype for each seedlot, except for the final percentages, which were lower at low temperatures for some genotypes. Two examples for contrasting genotypes from seed lot M-05 are shown in Fig. 3A, namely ‘Jemalong A17’, the reference in genomics studies, and ‘F83005·5’, the genotype with the highest Tb,germ value. When all the data obtained at the different temperatures were pooled, the germination time courses also overlapped for the different genotypes of a given seed lot, with no differences remaining between genotypes in M-06, and differences in final germination rates only for the other seed lots (for example, M-05, Fig. 3B). Thermal times for 70 % germination were very short, ranging from 13 to 16 °Cd for M-05 and M-06, respectively, but were significantly higher for seed lot A-06 (16–25 °Cd, data not shown).

Fig. 3.
Germination time courses expressed in thermal time (°Cd): (A) for two genotypes ‘Jemalong A17’ and ‘F83005·5’ in the M-05 seedlot at 5, 10, 15 °C; and (B) all the temperatures pooled for each genotype ...

Effect of water potential

Final percentages of germination (FP) mostly remained over 80 % at −0·25 and −0·5 MPa, whereas the lowest water potential tested, −0·75 MPa, strongly affected the pattern of germination and final percentages (for example, M-05, Fig. 1B). Large differences in FP were observed between seed lots at −0·75 MPa, ranging from 14 to 60 % in M-05, 32 to 99 % for M-06 and 64 to 96 % in A-06. Considering the genotypes within a seed lot, ‘DZA315·16’ and ‘DZA012.J’ had the lowest FP and ‘DZA045·5’ the highest. Germination rates (1/tG50) decreased sharply with decreasing water potentials for all the genotypes in all the seed lots (Fig. 2B). Germination rates of A-06 were significantly lower than those of M-05 and M-06. With regard to the germination rates of the genotypes common to the three seed lots, ‘DZA045·5’ always had the highest germination rate from −0·25 to −0·75 MPa, particularly in M-06 and A-06. Base water potential values (ψb,germ) were calculated for all seed lots from the germination rates at 0, −0·25 and −0·5 MPa. For this range, the relationship between water potential and germination rate was linear (except for ‘DZA315·16’ in A-06 and M-06) and final percentages of germination were not altered. The effect of seed lot was not significant, although the average ψb value for A-06 was slightly high (Table 3). There was a significant genotype effect: ‘DZA045·5’ had markedly lower ψb,germ values (−1·25 MPa on average) while ‘DZA315·16’ and, to a lesser extent, ‘DZA012.J’, had the highest (−0·65 MPa and −0·7 MPa on average, respectively; Table 3). Further analysis of ‘DZA045·5’ and ‘DZA315·16’ seed imbibition (data not shown) did not reveal any differences in imbibition rate or in seed water content reached before germination, despite their contrasting ψb,germ values.

Finally, based on all germination results, the effect of seed lot was mainly observed on germination rates, with A-06 having consistantly lower values irrespective of the environmental factor (water potential or temperature). This maternal effect was also observed on FP, but with a strong interaction with genotype: ‘DZA012.J’ always had low FP under extreme conditions. For calculated base temperature and water potential values, ψb,germ had a high value, with strong differences between genotypes; ‘DZA045·5’ being relatively less sensitive to water stress. Tb,germ was less variable, although ‘F83005·5’ had a higher value than the other genotypes.

Seedling growth in the dark after germination

Hypocotyl and radicle elongation of the M-05 seed lot are shown at 20 °C and 10 °C in Fig. 4. The average final lengths (FLs) were calculated from the two last measurements when the plateau was reached (and the measurements did not differ statistically). Decreasing temperature significantly affected FL: the lower the temperature, the shorter the final lengths of both seedling parts (Fig. 4; Table 4). No effect of seed lot was observed, even though there was a trend for shorter FL in A-06 regardless of temperature. Hypocotyl and radicle FL were significantly different between genotypes (Table 4): ‘DZA315·16’ and ‘Paraggio’ had the longest radicles and hypocotyls, whereas ‘DZA045·5’ and ‘DZA012.J’ had the shortest, regardless of temperature and seed lot. In addition, for the three seed lots, ‘F83005·5’ seedling parts were the most reduced in length at 10 °C (Fig. 4; Table 4). An additional experiment was carried out to test whether the differences in FL between genotypes were partly related to variations in initial seed mass. The radicle and hypocotyl FL were measured for all the genotypes of all seed lots for a given range of seed mass (3·5–4·5 mg), and this showed that the genotype rankings remained the same as for when the whole range of initial seed masses was used (data not shown).

Fig. 4.
Hypocotyl and radicle elongation at 20 and 10 °C for the M-05 seed lot genotypes and ‘Paraggio’ Aust-04. Bars denote s.e., and lines are fitted Weibull functions.
Table 4.
Hypocotyl and radicle final lengths at 20, 15 and 10 °C

Figure 5 shows the relationships between hypocotyl elongation rates (the inverse of the time needed to reach a hypocotyl length of 30 mm; 1/tL30) and temperature for M-05 and A-06 (not measured for M-06). For M-05, a linear relationship was obtained for the range of temperatures tested (10, 15 and 20 °C), and this range was used to calculate the base temperature for elongation (Tb,elong). For A-06, the linear relationship between 1/tL30 and temperature was broken above 15 °C for all genotypes. We only used 10 and 15 °C in order to estimate Tb,elong for this seed lot, assuming there would be linearity between these two temperatures; thus, Tb,elong values for this seed lot were estimated with relatively less precision. For all seed lots and regardless of genotype, calculated Tb,elong values were always higher than Tb,germ values (Table 3). There were no significant effects of seed lot or genotype. Values ranged from 5·4 °C (‘DZA045·5’) to 7·1 °C (‘Jemalong A17’), except for ‘DZA315·16’ in A-06, where the low value of Tb,germ has already been noted (see above).

Fig. 5.
Hypocotyl elongation rate (1/tL30; see text) in relation to temperature for the seed lots M-05 and A-06, and ‘Paraggio’ Aust-04. Bars denote s.e.

The results were also used to calculate elongation in terms of thermal time with specific genotype values of Tb,elong, and examples are shown for ‘Jemalong A17’ and ‘F83005·5’ in M-05 (Fig. 6A). Expressed in terms of thermal time, the curves showing the progress of elongation overlapped for each genotype irrespective of the temperature at which growth took place. Differences remained in FL of ‘F83005·5’ because they were shorter at low temperatures. When the data for the different temperatures were pooled for each genotype, the elongation curves for all the genotypes of a given seed lot also tended to be superimposed, although with differences remaining in final length (Fig. 6B). Differences also remained in elongation rates; for example, the thermal time to reach 30-mm hypocotyl length after germination for M-05 varied from 22 to 34 °Cd depending on genotype, with ‘F83005·5’ and ‘DZA045·5’ having the highest values. Values for A-06 were generally higher (19 to 48 °Cd, not shown). Finally, regarding elongation results, A-06 was also distinguished by having lower FL whatever the temperature and altered elongation rates above 15 °C. Low temperatures had a strong effect on final lengths and there was a genotype effect, with ‘F83005·5’ being more sensitive than the others. Tb,elong values were higher than those of Tb,germ.

Fig. 6.
Elongation time courses expressed in thermal time (°Cd) after germination: (A) at 10, 15, 20 °C for two genotypes ‘Jemalong A17’ and ‘F83005·5’ in the M-05 seed lot; and (B) all the temperatures ...

Biomass changes during growth in the dark

The redistribution of the mass from the seed reserves was analysed in relation to temperature and genotype in parallel with seedling elongation in darkness. Changes in seedling mass and distribution over time are shown in Fig. 7 for two genotypes with contrasting hypocotyl elongation at 20 °C and 10 °C (‘Jemalong A17’ and ‘F83005·5’). Masses of seedling parts are expressed as percentages relative to the corresponding initial seed dry mass (SDM), as they could be influenced by this factor, and are referred to here as Mc for the cotyledons, Mh for the hypocotyl, Mr for the radicle and Ms for the whole seedling. In addition, time is expressed as thermal time after germination with genotype-specific values of Tb to allow comparison of experiments on seedling growth performed at different temperatures and on different genotypes. Using this method of presentation, no significant differences in changes in biomass of each seedling part were observed between genotypes and temperatures. This contrasted with the differences observed for final length.

Fig. 7.
Changes in dry mass of seedling parts over time for ‘Jemalong A17’ and ‘F83005·5’ at 20 °C and 10 °C. Symbols are as follows: radicle = squares; hypocotyl = circles; cotyledons = triangles; and whole ...

The same general trends were observed for changes in biomass regardless of temperature and genotype. Ms decreased only slightly with time, to reach 85–90 % of initial SDM at about 150 °Cd at the end of elongation; these net losses could only be due to respiration or exudation. In contrast, significant changes were observed for the different seedling parts. After germination, Mc decreased sharply from >80 % to about 30–40 % of initial SDM, reached at 50 °Cd. This decrease corresponded to the use of the cotyledon seed reserves; thereafter, Mc remained almost constant. During the same period, Mh increased sharply up to 40 % of initial SDM, reaching a plateau at about 40 °Cd. Mr remained low, increasing only at the beginning of growth up to 10 % of SDM, which was reached at about 15 °Cd, and then it slowly decreased. Thus, the reserves stored in cotyledons were mainly transferred to the hypocotyl, with no differences between temperatures despite the differences observed in final lengths.


A framework for germination and heterotrophic growth analysis to help functional-genomics approaches

This study used an ecophysiological model in order to examine germination and heterotrophic growth of Medicago truncatula, a species frequently studied for genetic and molecular purposes (Gallardo et al., 2003; Young et al., 2003; Buitink et al., 2006). This model framework separates the two stages before emergence and describes the influence of the main environmental factors during emergence under agronomic conditions. This proved essential in the present study, as differences observed between genotypes depended upon both the phases of growth and the environmental factors considered. We took advantage of the existence of nested core collections (Ronfort et al., 2006), which enabled us to observe genetic diversity despite the relatively small number of genotypes we studied. The natural diversity that still remains within M. trunculata is an advantage compared with working on more highly bred species, for which genetic variability at the early stages have been reduced by human selection or have been influenced by selective breeding for other aspects of plant growth. An example is the decrease in coleoptile length observed in some cultivars when breeding for limiting plant height in wheat (Botwright et al., 2001; Ellis et al., 2004; Rebetzke et al., 2007).

Genetic and molecular determinism of germination have been studied on model species such as Arabidopsis thaliana and Medicago truncatula but rarely with the aim of gaining knowledge for improving variables related to crop establishment. Many studies haved focussed on seed dormancy (e.g. Chibani et al., 2006; Lefebvre et al., 2006) as it is an important physiological trait of seeds and these two model species are prone to this obstacle to germination. The environmental factors or metabolic pathways studied are often those influencing dormancy (e.g. nitrate or hormone levels); however, dormancy is more characteristic of wild or weed species than of cultivated varieties. Early growth after germination has received even less attention (Gendreau et al., 1997; Cui et al., 2002). Collecting agro-ecophysiological information on the early stages for a model species should help contribute to a better link between ecophysiological knowledge of plant emergence and studies on genetic determinism of underlying mechanisms.

Main characteristics of Medicago truncatula germination and heterotrophic growth

Strong effect of seed production conditions and dormancy on early stages

Species of the Medicago genus, like other legumes, are prone to having hard seeds (Martin and De La Cuadra, 2004; Taylor, 2005; Zeng et al., 2005) and they must be scarified to avoid this supplementary source of variation in the results when studying germination responses to environmental factors. Moreover, post-harvest embryonic dormancy lasts several months for M. trunculata and can affect germination studies. Physical seed characteristics (smaller seeds and higher proportion of teguments, higher sucrose and stachyose contents), consistant lower germination and elongation rates distinguished the seed lot produced in a growth chamber (A-06) under a lower light intensity during plant growth. This seed lot's characteristics suggested less-mature seeds with residual dormancy for some genotypes, even though the pods had been harvested after natural abscission and been left for the recommended time in order to avoid this problem. More generally, it was important to study several seed lots for each genotype in order to be able to distinguish genotypic effects from seed-production conditions. Maternal effects were observed for germination rates and final percentages, seedling elongation rates and final lengths, as could be expected from previous studies (e.g. Squire et al., 1997; Clapham et al., 2000; Luzuriaga et al., 2006).

Fast germination and elongation rates but over a narrow range of environmental conditions

Despite the above considerations it has been possible to draw general conclusions, and this study shows that agro-ecophysiological models can be used to analyse the functioning of non-crop species such as Medicago. The range of temperatures over which M. trunculata germinates is wide, but the range in which the relationship between temperature and germination rate is linear is narrow (5–15 °C), and the optimum temperature was found to be rather low. One aim of the emergence model was to provide a mathematical formalism for comparing genotypes and seed lots, as well as species. In particular, when using the calculation of thermal time, which is often used for crop-development modelling, it appeared that the time taken to achieve the germination process was very short (15 °Cd to reach 80 % germination) and did not vary between genotypes; it was the threshold values above and below which germination no longer occurred that differed. Tb,germ varied from 2 to 3 °C, a range observed for several other species (Gummerson, 1986; Marshall and Squire, 1996; Tamet et al., 1996; Dorsainvil et al., 2005), and a value also observed for leaf development in Arabidopsis thaliana (Granier et al., 2002), consistent with M. trunculata belonging to the Galegoïd cool season growing group of legumes as opposed to Phaseolids, which have tropical origins and higher temperature requirements (Covell et al., 1986; Moreau-Valencogne et al., 2007). The very fast germination of M. trunculata is close to that of bean (Moreau-Valancogne et al., 2007), white mustard (Dorsainvil et al., 2005) and oilseed rape (Marshall and Squire, 1996). With respect to water potential, M. trunculata appeared to be very sensitive to decreasing potentials, and few cultivated species have such high ψb,germ requirements. The narrow range of environmental conditions – in terms temperature and water potential requirements – within which germination is both possible and rapid, is evocative of plant behaviour in the case of wild or weed species, waiting for favourable germination conditions within the soil. With respect to seedling growth, elongation (as well as germination) was very fast (about 30 °Cd after germination to reach 30 mm hypocotyl length) compared with other studied species (Dürr and Boiffin, 1995; Dorsainvil et al., 2005), but similar to other legumes (French bean: Moreau-Valancogne et al., 2007; pea: M. P. Raveneau et al., ESA Angers, France, unpbl. res.).

The differences in environmental requirements between species should be kept in mind when comparing the effects of both temperature and water potential on germination and growth, as the underlying mechanisms may not be the same (Nishiyama, 1972; Sung et al., 2003). Thus, results from studies on reactions to contrasting environmental factors may only be applicable to similar species.

Low temperatures considerably shortened the length of the seedling parts with no relationship to the allocation of reserves to the different plant parts, as previously observed for crop species (Dürr and Boiffin, 1995; Tamet et al., 1996). Bouaziz and Hicks (1990) observed similar results with water stress. Some M. trunculata genotypes were more responsive than others: many aspects of plant metabolism can be involved in this effect of low temperatures (Sung et al., 2003; Chinnusamy et al., 2007; Penfield, 2008) and these need to be investigated in order to analyse differences between the genotypes. Cell elongation is sensitive to hormonal changes (Ellis et al., 2004; Schopfer, 2006; Rebetzke et al., 2007), and several changes in cell wall and membrane properties can be involved. For example, extensins are proteins involved in cell elongation changes at low temperatures (Weiser et al., 1990), and changes in lipid metabolism can also influence membrane fluidity (Vaultier et al., 2006).

The other measurements made on seeds of the different genotypes did not reveal significant correlations between seed mass and germination rate, final percentage germination or final length. Seed sucrose and raffinose family (RFO) contents were measured as they may be involved in mechanisms related to dessication (Bailly et al., 2001; Rosnoblet et al., 2007) and cold tolerance (Gilmour et al., 2000; Wang et al., 2006). ‘DZA045·5’ had verbascose and stachyose contents that contrasted with other genotypes and it germinated the fastest at low water potentials. A-06 had higher sucrose contents. These results suggest that sucrose and RFOs, accumulated during seed maturation, could be important in germination and heterotrophic growth, but this requires further study. One difficulty for gaining more in-depth knowledge of potential mechanisms underlying genotypic differences in germination and heterotrophic growth is that the reviews available on potential mechanisms implied in abiotic stress tolerance (e.g. Sung et al., 2003; Wahid et al., 2007; Penfield, 2008) rarely focus on germination and early growth processes.

Existence of genetic diversity on model parameters

Our results should help users of M. trunculata to choose segregating populations with contrasting parental lines for genetic analysis of the response to environmental factors. They should also help to focus on the parameters responsible for this diversity. An important result is that the genotypic differences varied according to the environmental conditions and stages considered. During germination, genotypes differed at extreme temperatures and water potentials: ‘F83005·5’ was the least tolerant to low temperatures, ‘DZA012.J’ and ‘Jemalong A17’ were less tolerant to high temperatures, and ‘DZA045·5’ better tolerated low water potentials. When time was expressed as thermal time, the remaining germination differences between genotypes were low. The main differences were thus in the values of Tb,germ and ψb,germ and in the final germination percentages. During elongation, ‘F83005·5’ was once again the most responsive to low temperatures. When time was expressed as thermal time, as for germination, differences between genotypes remained for final length at low temperatures and, to a lesser extent, for elongation rates. ‘Paraggio’, the cultivar cropped in Australia and chosen for comparison with the genotypes of the core collection, had no extreme behaviour during the stages studied (which are not considered in crop selection). A part of the genetic diversity found for the parameters studied was observed for environmental conditions not taken into account in the model as they do not influence emergence. For instance, for germination the largest differences between genotypes were observed at 20 °C and above. An increase in genetic variation is not unexpected under extreme environmental conditions, such as supra-optimal temperatures not generally encountered by the ecotypes in their natural biotopes (Swindell, 2006). These temperatures were not taken into account in the thermal time calculations as they fell outside the linear relationship with temperature, and thus were outside of the limits of the model's function. In the field, high temperatures are rare at crop sowing and thus they do not often limit model predictions.

Other abiotic and biotic conditions that were not studied here can influence emergence results. The effects of mechanical obstacles are taken into account in the emergence model we used, as they have a strong impact under many sowing conditions (Awadhwal and Thierstein, 1985). These effects will be further studied on the genotypes of M. trunculata considered here. The effects of salinity or pH variations are not included in the emergence model we used, and neither are pathogen effects, and this can limit model predictions when considering specific sowing conditions. In particular, modelling pathogen infestations at sowing would be an important further improvement, given the significant decrease that has occurred in the range of pesticides that can be used, and the need for better represention of the complex effects of cropping systems on these infestations.

In spite of these limits, the emergence model can be used to simulate the emergence of the different genotypes in different sowing conditions. This should make it possible to evaluate the extent of variations in emergence in different environments that are due to genetic variation within the model's parameters. The model can also be used to test virtual ideotypes and to assist breeding programmes.


This research was funded by the Region Pays de Loire and INRA. We thank M. Delalande and D. Tauzin for information about the genotypes studied; the technical staff at the SNES, F. Pantin and E. Thierry for their contribution to these results; J. Buitink from INRA for her help in measurements with HPLC; and G. Hunault for his help in the statistical analyses.


  • Awadhwal NK, Thierstein GE. Soil crust and its impact on crop establishment: a review. Soil and Tillage Research. 1985;5:289–302.
  • Bailly C, Audigier C, Ladonne F, et al. Changes in oligosaccharides content and antioxidant enzyme activities in developing bean seeds as related to acquisition of drying tolerance and seed quality. Journal of Experimental Botany. 2001;52:701–708. [PubMed]
  • Benjamin LR. Variation in time of seedling emergence within populations: a feature that determines individual growth and development. Advances in Agronomy. 1990;44:1–25.
  • Bettey M, Finch-Savage WE, King GJ, Lynn JR. Quantitative genetic analysis of seed vigour and pre-emergence seedling growth traits in Brassica oleracea. New Phytologist. 2000;148:277–286.
  • Bewley JD, Black M. Seeds physiology of development and germination. New York: Plenum Publishing Corporation; 1994.
  • Botwright T, Rebetzke G, Condon T, Richards R. The effect of rht genotype and temperature on coleoptile growth and dry matter partitioning in young wheat seedlings. Australian Journal of Plant Physiology. 2001;28:417–423.
  • Bouaziz A, Bruckler L. Modelling wheat seedling growth and emergence: II. Comparison with field experiments. Soil Science Society of America Journal. 1989;53:1838–1846.
  • Bouaziz A, Hicks DR. Consumption of wheat seed reserves during germination and early growth as affected by soil water potential. Plant and Soil. 1990;128:161–165.
  • Buitink J, Ly Vu B, Satour P, Leprince O. The re-establishment of desiccation tolerance in germinated radicles of Medicago truncatula Gaertn. Seeds. Seed Science Research. 2003;13:273–286.
  • Buitink J, Leger J, Guisle I, et al. Transcriptome profiling uncovers metabolic and regulatory processes occurring during the transition from dessiccation-sensitive to dessiccation-tolerant stages in Medicago truncatula seeds. The Plant Journal. 2006;47:735–750. [PubMed]
  • Carrera A, Dürr C. Analysis of the effects of sowing date and sowing conditions on sugar beet emergence using the SIMPLE (SIMulation of PLant Emergence) model. Advances in Sugar Beet Research. 2003;5:95–106.
  • Chibani K, Ali-Rachedi S, Job C, Job D, Jullien M, Grappin P. Proteomic analysis of seed dormancy in Arabidopsis. Plant Physiology. 2006;142:1493–1510. [PubMed]
  • Chinnusamy V, Zhu J, Zhu JK. Cold stress regulation of gene expression in plants. Trends in Plant Science. 2007;12:444–451. [PubMed]
  • Choi HK, Mun JH, Kim DJ, et al. Estimating genome conservation between crop and model legume species. Proceedings of the National Academy of Sciences of the USA. 2004;101:15289–15294. [PubMed]
  • Clapham WM, Willcott JB, Fedders JM. Effects of seed maturation temperatures on seed yield characteristics and subsequent generations of Lupin. Crop Science. 2000;40:1313–1317.
  • Cook DR. Medicago truncatula – a model in the making! Current Opinion in Plant Biology. 1999;2:301–304. [PubMed]
  • Covell S, Ellis RH, Roberts EH, Summerfield RJ. The influence of temperature on seed germination rate in grain legumes. Journal of Experimental Botany. 1986;37:707–715.
  • Cui KH, Peng SB, Xing YZ, Xu CG, Yu SB, Zhang Q. Molecular dissection of seedling vigor and associated physiological traits in rice. Theorical and Applied Genetics. 2002;105:745–753. [PubMed]
  • Dahal P, Bradford KJ. Hydrothermal time analysis of tomato seed germination at sub-optimal temperature and reduced water potential. Seed Science Research. 1994;4:71–80.
  • Djemel N, Guedon D, Lechevalier A, et al. Development and composition of the seeds of nine genotypes of the Medicago truncatula species complex. Plant Physiology and Biochemistry. 2005;43:557–566. [PubMed]
  • Dorsainvil F, Dürr C, Justes E, Carrera A. Characterisation and modelling of white mustard (Sinapis alba L.). Emergence under several sowing conditions. European Journal of Agronomy. 2005;23:146–158.
  • Durrant MJ, Dunning RA, Jaggard K, Bugg RB, Scott RK. A census of seedling establishment in sugar-beet crops. Annals of Applied Biology. 1988;113:327–345.
  • Dürr C, Boiffin J. Sugarbeet seedling growth from germination to first leaf stage. Journal of Agricultural Science, Cambridge. 1995;124:427–435.
  • Dürr C, Aubertot J-N, Richard G, Dubrulle P, Duval Y, Boiffin J. SIMPLE: a model for SIMulation of PLant Emergence predicting the effects of soil tillage and sowing operations. Soil Science Society of America Journal. 2001;65:414–442.
  • Eagles HA, Hardacre AK. Genetic variation in maize (Zea Mays L.) for germination and emergence at 10 °C. Euphytica. 1979;28:287–295.
  • Ellis MH, Rebetzke GJ, Chandler P, Bonnett D, Spielmeyer W, Richards RA. The effect of different height reducing genes on the early growth of wheat. Functional Plant Biology. 2004;31:583–589.
  • Finch-Savage WE, Steckel JRA, Phelps K. Germination and post-germination growth of carrot seedling emergence: predictive threshold models and sources of variation between sowing occasions. New Phytologist. 1998;139:505–516.
  • Gallardo K, Le Signor C, Vandekerckhove J, Thompson RD, Burstin J. Proteomics of Medicago truncatula seed development establishes the time frame of diverse metabolic processes related to reserve accumulation. Plant Physiology. 2003;133:664–682. [PubMed]
  • Gallardo K, Le Signor C, Darmency M, et al. Seed biology of Medicago truncatula. In: Mathesius U, Journet EP, Sumner LW, editors. Ardmore, OK: The Samuel Roberts Noble Foundation; 2007. The Medicago truncatula Handbook Published online at .
  • Garcia J, Barker DG, Journet EP. Seed storage and germination. In: Mathesius U, Journet EP, Sumner LW, editors. Ardmore, OK: The Samuel Roberts Noble Foundation; 2007. The Medicago truncatula Handbook Published online at .
  • Gendreau E, Traas J, Desnos T, Grandjean O, Caboche M, Hofte H. Cellular basis of hypocotyl growth in Arabidopsis thaliana. Plant Physiology. 1997;114:295–305. [PubMed]
  • Gilmour SJ, Sebolt AM, Salazar MP, Everard JD, Thomashow MF. Overexpression of the Arabidopsis CBF3 transcriptional activator mimics multiple biochemical changes associated with cold acclimation. Plant Physiology. 2000;124:1854–1865. [PubMed]
  • Granier C, Massonnet C, Turc O, Muller B, Chenu K, Tardieu F. Individual leaf development in Arabidopsis thaliana: a stable thermal-time-based programme. Annals of Botany. 2002;89:595–604. [PubMed]
  • Gummerson RJ. Effects of constant temperatures and osmotic potentials on the germination of sugar beet. Journal of Experimental Botany. 1986;37:729–741.
  • Gutschick VP, Simonneau T. Modelling stomatal conductance of field-grown sunflower under varying soil water content and leaf environment: comparison of three models of stomatal response to leaf environment and coupling with an abscisic acid-based model of stomatal response to soil drying. Plant, Cell and Environment. 2002;25:1423–1434.
  • Hammer G, Cooper M, Tardieu F, et al. Models for navigating biological complexity in breeding improved crop plants. Trends in Plant Science. 2006;11:587–593. [PubMed]
  • Klos K, Brummer E. Field response to selection in alfalfa for germination rate and seedling vigor at low temperatures. Crop Science. 2000;40:1227–1232.
  • Kuo TM, Van Middlesworth JF, Wolf WJ. Content of raffinose oligosaccharides and sucrose in various plant seeds. Agricultural Food Chemistry. 1988;36:32–36.
  • Laperche A, Devienne-Barret F, Maury O, Le Gouis J, Ney B. A simplified conceptual model of carbon/nitrogen functioning for QTL analysis of winter wheat adaptation to nitrogen. Theoretical and Applied Genetics. 2006;113:1131–1146. [PubMed]
  • Laperche A, Brancourt-Hulmel M, Heumez E, et al. Using genotype × nitrogen interaction variables to evaluate the QTL involved in wheat tolerance to nitrogen constraints. Theoretical and Applied Genetics. 2007;115:399–415. [PubMed]
  • Lefebvre V, North H, Frey A, et al. Functional analysis of Arabidopsis NCED6 and NCED9 genes indicates that ABA synthesized in the endosperm is involved in the induction of seed dormancy. The Plant Journal. 2006;45:309–319. [PubMed]
  • Luzuriaga AL, Escudero A, Perez-Garcia F. Environmental maternal effects on seed morphology and germination in Sinapis arvensis (Cruciferae) Weed Research. 2006;46:163–174.
  • Marshall B, Squire GR. Non-linearity in rate-temperature relations of germination in oilseed rape. Journal of Experimental Botany. 1996;47:1369–1375.
  • Martin I, De La Cuadra C. Evaluation of different scarification methods to remove hardseededness in Trifolium subterraneum and Medicago Polymorpha accessions of the Spanish base genebank. Seed Science and Technology. 2004;32:671–681.
  • Michel BE. Evaluation of the water potentials of solution of polyethylene glycol 8000 both in the absence and presence of other solutes. Plant Physiology. 1983;72:66–70. [PubMed]
  • Moreau D, Salon C, Munier-Jolain N. Using a standard framework for the phenotypic analysis of Medicago truncatula: an effective method for characterizing the plant material used for functional genomic approaches. Plant, Cell and Environment. 2006;29:1087–1098. [PubMed]
  • Moreau D, Salon C, Munier-Jolain N. A model-based framework for the phenotypic characterization of flowering of Medicago truncatula. Plant, Cell and Environment. 2007;30:213–224. [PubMed]
  • Moreau-Valancogne P, Costes F, Dürr C, Crozat Y. Effects of bean seed production conditions on germination and elongation responses to temperature and water potential. In: Adkins SW, Navie SC, Ashmore S, editors. Seeds: biology, development and ecology. Wallingford, UK: CABI Publishing; 2007. pp. 333–341.
  • Moreau-Valancogne P, Coste F, Crozat Y, Dürr C. Field emergence of bean seed lots: characterisation, modelling and simulation. European Journal of Agronomy. 2008;28:309–320.
  • Mullins CE, Townend J, Mtakwa PW, et al. Emerge user guide: a model to predict crop emergence in the semi-arid tropics. Aberdeen, UK: Department of Plant and Soil Science, University of Aberdeen; 1996. p. 96.
  • Nishiyama I. Physiological kinks around 15, 30, 45 °C in plants. Research Bulletin of Hokkaido National Agricultural Experiment Station. 1972;47:125–168.
  • Penfield S. Temperature perception and signal transduction in plants. New Phytologist. 2008;179:615–628. [PubMed]
  • Quilot B, Kervella J, Génard M, Lescourret F. Analysing the genetic control of peach fruit quality through an ecophysiological model combined with a QTL approach. Journal of Experimental Botany. 2005;a 422:3083–3092. [PubMed]
  • Quilot B, Génard M, Lescourret F Kervella J. Simulating genotypic variation of fruit quality in advanced peach × Prunus davidiana cross. Journal of Experimental Botany. 2005;b 56:3071–3081. [PubMed]
  • Rebetzke GJ, Richards RA, Fettell NA, et al. Genotypic increases in coleoptile length improve stand establishment, vigour and grain yield of deep-sown wheat. Field Crops Research. 2007;100:10–23.
  • Reymond M, Muller B, Leonardi A, Charcosset A, Tardieu F. Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiology. 2003;131:664–675. [PubMed]
  • Ronfort J, Bataillon T, Santoni S, Delalande M, David JL, Prosperi JM. Microsatellite diversity and broad scale geographic structure in a model legume: building a set of nested core collection for studying naturally occurring variation in Medicago truncatula. BMC Plant Biology. 2006;6:28. doi:10.1186/1471-2229-6-28. [PMC free article] [PubMed]
  • Rosnoblet C, Aubry C, Leprince O, Ly Vu B, Rogniaux H, Buitink J. The regulatory gamma subunit SNF4b of the sucrose non-fermenting-related kinase complex is involved in longevity and stachyose accumulation during maturation of Medicago truncatula seeds. The Plant Journal. 2007;51:47–59. [PubMed]
  • Saglio P, Pradet M. Soluble sugars, respiration and energy charge during aging of excised maize root tips. Plant Physiology. 1980;66:516–519. [PubMed]
  • Schopfer P. Biomechanics of plant growth. American Journal of Botany. 2006;93:1415–1425. [PubMed]
  • Squire GR, Marshall B, Dunlop G, Wright G. Genetic basis of rate-temperature characteristic for germination in oil seed rape. Journal of Experimental Botany. 1997;48:869–875.
  • Swindell WR. The association among gene expression responses to nine abiotic stress treatments in Arabidopsis thaliana. Genetics. 2006;174:1811–1824. [PubMed]
  • Sung D-Y, Kaplan F, Lee K-J, Guy CL. Acquired tolerance to temperature extremes. Trends in Plant Science. 2003;8:179–187. [PubMed]
  • Tamet V, Boiffin J, Dürr C, Souty N. Emergence and early growth of an epigeal seedling (Daucus carota L.): influence of soil temperature, sowing depth, soil crusting and seed weight. Soil and Tillage Research. 1996;40:25–38.
  • Tardieu F. Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends in plant Science. 2003;8:9–14. [PubMed]
  • Tardieu F, Reymond M, Muller B. Linking physiological and genetic analyses of the control of leaf growth under changing environmental conditions. Australian Journal of Agricultural Research. 2005;56:937–946.
  • Taylor GB. Hardseededness in Mediterranean annual pasture legumes in Australia: a review. Australian Journal of Agricultural Research. 2005;56:645–661.
  • Thoquet P, Ghérardi M, Journet E-T, et al. The molecular genetic linkage map of the model legume Medicago truncatula: an essential tool for comparative legume genomics and the isolation of agronomically important genes. BMC Plant Biology. 2002;2:1. doi:10.1186/1471-2229-2-1. [PMC free article] [PubMed]
  • Tivoli B, Barange A, Sivasithamparam K, Barbetti J. Annual Medicago: from a model crop challenged by a spectrum of necrotrophic pathogens to a model plant to explore the nature of disease resistance. Annals of Botany. 2006;98:1117–1128. [PMC free article] [PubMed]
  • Valenciano JB, Casquero PA, Boto JA. Influence of sowing techniques and pesticide application on the emergence and the establishment of bean plants (Phaseolus vulgaris L.) Agronomie. 2004;24:113–118.
  • Vaultier MN, Cantrel C, Vergnolle C, et al. Desaturase mutants reveal that membrane rigidification acts as a cold perception mechanism upstream of the diacylglycerol kinase pathway in Arabidopsis cells. FEBS Letters. 2006;580:4218–4223. [PubMed]
  • Wahid A, Gelani S, Ashraf M, Foolad MR. Heat tolerance in plant: an overview. Environmental and Experimental Botany. 2007;61:199–223.
  • Wang R, Bai Y, Low NH, Tanino K. Seed size variation in cold and freezing tolerance during seed germination of winterfat (Krascheninnikovia lanata) (Chenopodiaceae) Canadian Journal of Botany. 2006;84:49–59.
  • Weiser RL, Wallner SJ, Waddell JW. Cell wall and extension mRNA changes during cold acclimation of pea seedlings. Plant Physiology. 1990;93:1021–1026. [PubMed]
  • Yan HH, Mudge J, Kim DJ, et al. Estimates of conserved microsynteny among genomes of Glycine max, Medicago truncatula and Arabidospis thaliana. Theorical and Applied Genetics. 2003;106:1256–1265. [PubMed]
  • Yin X, Chasaloow SD, Dourleijn CJ, Stam P, Kropff MJ. Coupling estimated effects of QTLs for physiological traits to a crop growth model: predicting yield variation among recombinant inbred lines in barley. Heredity. 2000;85:539–549. [PubMed]
  • Yin X, Stam P, Kropff M, Schapendonk Ad HC. Crop modelling, QTL mapping and their complementary role in plant breeding. Agronomy Journal. 2003;95:90–98.
  • Young ND, Mudge J, Ellis THN. Legume genomes: more than peas in a pod. Current Opinion in Plant Biology. 2003;6:199–204. [PubMed]
  • Zeng LW, Cocks PS, Kailis SG, Kuo J. Structure of the seed coat and its relationship to seed softening in the Mediterranean annual Legume. Seed Science and Technology. 2005;33:351–362.
  • Zhang ZH, Yu SB, Yu T, Huang Z, Zhu YG. Mapping quantative trait loci (QTLs) for seedling-vigor using recombinant inbred lines of rice (Oryza sativa L.) Field Crop Research. 2005;91:161–170.

Articles from Annals of Botany are provided here courtesy of Oxford University Press