Maryse Brancourt-Hulmel

Deviation from the grain protein concentration–grain yield negative relationship is highly correlated to post-anthesis N uptake in winter wheat

In plants, carbon and nitrogen (N) economies are intimately linked at the physiological and biochemical level. The strong genetic negative correlation between grain yield and grain protein concentration observed in various cereals is an illustration of this inter-relationship. Studies have shown that deviation from this negative relationship (grain protein deviation or GPD) has a genetic basis, but its physiological basis is still poorly understood. This study analysed data on 27 genotypes grown in multienvironment field trials, representing a wide range of agricultural practices and climatic conditions. The objective was to identify physiological processes related to the genetic variability in GPD. Under most environments, GPD was significantly related to post-anthesis N uptake independently of anthesis date and total N at anthesis. The underlying physiological trait might be related to genotypic differences in either access to soil N, regulation of N uptake by plant N status, or ability to maintain root activity during the grain-filling period. GPD is an interesting potential target in breeding as it appears to be relatively robust across different environments and would be valuable in increasing total N uptake by maturity.

Cell wall compositional modifications of Miscanthus ecotypes in response to cold acclimation.

Miscanthus, a potential energy crop grass, can be damaged by late frost when shoots emerge too early in the spring and during the first winter after planting. The effects of cold acclimation on cell wall composition were investigated in a frost-sensitive clone of Miscanthus x giganteus compared to frost-tolerant clone, Miscanthus sinensis August Feder, and an intermediate frost-tolerant clone, M. sinensis Goliath. Cellulose and lignin contents were higher in M. x giganteus than in the M. sinensis genotypes. In ambient temperature controls, each clone displayed different glucuronoarabinoxylan (GAX) contents and degree of arabinose substitution on the xylan backbone. During cold acclimation, an increase in (1→3),(1→4)-β-D-glucan content was observed in all genotypes. Uronic acid level increased in the frost sensitive genotype but decreased in the frost tolerant genotypes in response to cold. In all clones, major changes in cell wall composition were observed with modifications in phenylalanine ammonia-lyase (PAL) and cinnamyl alcohol dehydrogenase (CAD) activities in both non- and cold-acclimated experiments. A large increase in CAD activity under cold stress was displayed in each clone, but it was largest in the frost-tolerant clone, M. sinensis August Feder. The marked increase in PAL activity observed in the frost-tolerant clones under cold acclimation, suggests a reorientation of the products towards the phenylpropanoid pathway or aromatic synthesis. How changes in cell wall physical properties can impact frost tolerance is discussed.

A Review on Miscanthus Biomass Production and Composition for Bioenergy Use: Genotypic and Environmental Variability and Implications for Breeding

The lignocellulosic C4 perennial crop miscanthus and, more particularly, one of its species, Miscanthus × giganteus, are especially interesting for bioenergy production because they combine high biomass production with a low environmental impact. However, few varieties are available, which is risky due to disease susceptibility. Gathering worldwide references, this review shows a high genotypic and environmental variability for traits of interest related to miscanthus biomass production and composition, which may be useful in breeding programs for enhancing the availability of suitable clones for bioenergy production. The M. × giganteus species and certain clones in the Miscanthus sinensis species seem particularly interesting due to high biomass production per hectare. Although the industrial requirements for biomass composition have not been fully defined for the different bioenergy conversion processes, the M. × giganteus and Miscanthus sacchariflorus species, which show high lignin contents, appear more suitable for thermochemical conversion processes. In contrast, the M. sinensis species and certain M. × giganteus clones with low lignin contents were interesting for biochemical conversion processes. The M. sacchariflorus species is also interesting as a progenitor for breeding programs, due to its low ash content, which is suitable for the different bioenergy conversion processes. Moreover, mature miscanthus crops harvested in winter seem preferred by industry to enhance efficiency and reduce the expense of the processes. This investigation on miscanthus can be extrapolated to other monocotyledons and perennial crops, which may be proposed as feedstocks in addition to miscanthus.

Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression

Abstract Genotype-environment interaction has been analyzed in a winter-wheat breeding network using bi-additive factorial regression models. This family of models generalizes both factorial regression and biadditive (or AMMI) models; it fits especially well when abundant external information is available on genotypes and/or environments. Our approach, focused on environmental characterization, was performed with two kinds of covariates: (1) deviations of yield components measured on four probe genotypes and (2) usual indicators of yield-limiting factors. The first step was based on the analysis of a crop diagnosis on four probe genotypes. Difference of kernel number to a threshold number (DKN) and reduction of thousand-kernel weight from a potential value (RTKW) were used to characterize the grain-number formation and the grain-filling periods, respectively. Grain yield was analyzed according to a biadditive factorial regression model using eight environmental covariates (DKN and RTKW measured on each of four probe genotypes). In the second step, the usual indicators of yield-limiting factors were too numerous for the analysis of grain yield. Thus a selection of a subset of environmental covariates was performed on the analysis of DKN and RTKW for the four probe genotypes. Biadditive factorial regression models involved environmental covariates related to each deviation and included environmental main effect, sum of water deficits, an indicator of nitrogen stress, sum of daily radiation, high temperature, pressure of powdery mildew and lodging. The correlations of each environmental covariate to the synthetic variates helped to discard those poorly involved in interaction (with | correlation | <0.3). The grain yield of 12 genotypes was interpreted with the retained covariates using biadditive factorial regression. The models explained about 75% of the interaction sums of squares. In addition, the biadditive factorial regression biplot gave relevant information about the interaction of the genotypes (interaction pattern and sensitivities to environmental covariates) with respect to the environmental covariates and proved to be interesting for such an approach.

Crop diagnosis and probe genotypes for interpreting genotype environment interaction in winter wheat trials

Abstract Genotype*environment interaction has been analyzed with 12 genotypes and four probe genotypes in French wheat trials. An integrated approach was developed which combined crop diagnosis with the analysis of interaction by factorial regression. Crop diagnosis was helpful to characterize the environments and to select environmental variables. Such an approach succeeded in providing an agronomic explanation of genotype*environment interaction and in defining the responses or parameters for each genotype and each environment. Earliness at heading, susceptibility to powdery mildew and susceptibility to lodging were the three major genotypic covariates. Interaction could also be related to environment features, measured indirectly by the behavior of the four probe genotypes during the formation of yield, what we called the outputs of a simplified crop diagnosis, or described directly by indicators of yield-limiting factors. Two important crop diagnosis covariates were analyzed in order to characterize interaction during the formation of yield: the reduction in kernel number, which described the time-period until flowering, and the reduction in thousand kernel weight, which corresponded to the period after flowering. These variates were estimated for each probe genotype and allowed us to compare the behavior of the 12 genotypes to that of the probe genotypes. Both periods of the formation of yield contributed to the interaction, and ’Camp-Rémy’ was the probe of particular interest for the comparisons. When true environmental variates were used, factorial regression revealed that water deficits during the formation of grain number and level of nitrogen were predominant. Such an integrated approach could be exploited when varieties are tested in a network where numerous and diverse yield-limiting factors may occur.

Nitrogen enhanced photosynthesis of Miscanthus by increasing stomatal conductance and phosphoenolpyruvate carboxylase concentration

Miscanthus is one of the most promising bioenergy crops with high photosynthetic nitrogen-use efficiency (PNUE). It is unclear how nitrogen (N) influences the photosynthesis in Miscanthus. Among three Miscanthus genotypes, the net photosynthetic rate (PN) under the different light intensity and CO2 concentration was measured at three levels of N: 0, 100, and 200 kg ha−1. The concentrations of chlorophyll, soluble protein, phosphoenolpyruvate carboxylase (PEPC), ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) large subunit, leaf anatomy and carbon isotope discrimination (Δ) in the leaf were analyzed to probe the response of photosynthesis in Miscanthus genotypes to N levels. PN in all genotypes rose significantly as N application increased. The initial slope of response curves of PN to Ci was promoted by N application in all genotypes. Both stomatal conductance and Ci were increased with increased N supply, indicating that stomatal factors played an important role in increasing PN. At a given Ci, PN in all genotypes was enhanced by N, implying that nonstomatal factors might also play an important role in increasing PN. Miscanthus markedly regulated N investment into PEPC rather than the Rubisco large subunit under higher N conditions. Bundle sheath leakiness of CO2 was constant at about 0.35 for all N levels. Therefore, N enhanced the photosynthesis of Miscanthus mainly by increasing stomatal conductance and PEPC concentration.

Choosing probe genotypes for the analysis of genotype-environment interaction in winter wheat trials

Abstract Genotype-environment interaction was analyzed in French multi-environment wheat (Triticumaestivum L.) trials using probe genotypes and bi-additive factorial regression. Probe genotypes are specific genotypes in which the comparisons of yield components to reference values describe the most-important environmental factors that limited grain yield. The time-period until flowering was described by the deviation of kernel number from a threshold number while the grain-filling period was described by the reduction of thousand-kernel weight from a potential value. The aim of this paper was to determine the convenient number and the characteristics of probe genotypes to include in wheat breeding trials.Two sets of genotypes were used to model genotype-environment interaction: set 1 with 12 varieties tested in 18 environments and set 2 with ten lines tested in 14 environments. Set 2 was used for validation. Seven probe genotypes described the environments by providing environmental covariates, namely differences in yield components, for further analysis of interaction in set 1 and set 2. Interaction was modelled with bi-additive factorial regressions including differences in yield components. Several rounds of models were fitted to determine the optimal number of probe genotypes (i.e. environmental covariates) to introduce. From the seven probe genotypes, all the possible combinations including one to seven genotypes were studied. Significance of the combinations was tested with critical values obtained from simulations through 1,000 random permutations. Taking into account the information available on the probe genotypes, one would think that two, three or four probe genotypes would be sufficient, otherwise the number should reach four or five genotypes. In all cases, these numbers will provide models more-parsimonious than the classical AMMI model. The important information to be known on the probe genotypes prior their first multilocation experiment is: interaction pattern, earliness, and differences in yield component. Tested for the first time, a quadruplet is better than a triplet because the probability of choosing complementary genotypes increases with their number.

Miscanthus Clones Display Large Variation in Floral Biology and Different Environmental Sensitivities Useful for Breeding

A wider range of Miscanthus varieties is required to develop Miscanthus clones that are suitable for bioenergy production. For this reason, breeding programs need to be initiated using knowledge regarding the genetic influence on floral biological traits. The objective of the present study was to characterize the genotypic variation in flowering and panicle architecture traits in Miscanthus by studying (i) the clone effect on these traits and (ii) the clone sensitivity to environmental conditions. The flowering traits characterized were date of panicle emergence, date of flowering onset, and interval between these two traits. The panicle architecture traits characterized were total panicle length, longest panicle raceme size, raceme number per panicle, floral density, and total flower number per panicle. Eight clones were studied in a greenhouse under four environmental conditions including two day lengths (an 8-h short day length and a natural day length) and two temperature treatments (warm and cool). Miscanthus clones showed large differences in flowering and panicle architecture traits. Moreover, day length appeared to be the most important environmental factor creating differential clone sensitivities for the panicle emergence and the onset of flowering in contrast to temperature factor for the total flower number per panicle. In addition, the behavior of the clone Sacc was in contrast with that of the other clones for most of the traits studied. This knowledge will be useful to optimize the synchronization of flowering between Miscanthus clones for more successful breeding programs.

Influence of the radial stem composition on the thermal behaviour of miscanthus and sorghum genotypes

The hypothesis made is that thermal resistance of sorghum and miscanthus stem pieces taken at well-defined positions of the stem is simply related to their biochemical composition. For miscanthus, two different genotypes and two internode levels were selected. For each region, the stem was divided into three radial layers. For sorghum, two different genotypes were selected and the stem was divided into the same three radial layers. The results show that the thermal analysis is only sensitive to very large variations of compositions. But aside of such large composition differences, it is impossible to correlate thermal effects to biochemical composition even on very small size, well-identified pieces of plant materials. The interplay between sugar-based components, lignin and minerals is totally blurring the thermal response. Extreme care must be exercised when willing to explain why a given plant material has a thermal behaviour different of another plant material.

Identification of Factors Affecting Trait Stability via Factorial Regression and Environmental Variance: I. Methodology

Abstract Stability and genotype-by-environment interaction (GEI) are important for plant breeders. Our objective is to develop a method combining environmental variance and factorial regression to characterize genotype stability. To do this, we have examined kernel protein percentage of 16 winter wheat varieties grown in 13 environments. Genotype stability was assessed by environmental variance. Twenty-five covariates (24 environmental and 1 genotypic) were available. The method consisted of three steps. Firstly, the environmental variances were grouped according to their level defining six stability groups. Secondly, GEI was modelled with factorial regressions, using one or two environmental covariates. For each regression, the genotypes were characterized by their stability group and their slope sign(s) on the covariate(s). The third step identified the model that could best explain the interaction sum of squares and with slope signs of all the stable varieties opposite to those of the unstable varieties. Two environmental covariates, nitrogen accumulation rate per kernel and nitrogen mr2 at anthesis, clearly separated the most stable from the most unstable genotypes, giving new information for breeding stable varieties.