In a context where agricultural practices in Europe are likely to go toward extensive systems with lower inputs, it is important to determine the genetic improvement of winter wheat (Triticum aestivum L.) not only in high‐input agricultural systems but also in low‐input systems. This study assesses the improvement in agronomic traits of winter wheat cultivars cultivated in France during the second half of the 20th century at four agronomic treatments: two levels of fungicide were combined with two levels of nitrogen fertilizer. Fourteen cultivars introduced between 1946 and 1992 were grown for two years (1994 and 1995) at five locations. Selection played a major role in the increase in winter wheat yield after 1946. The contribution of selection to this increase depended on the agronomic treatment and varied from one third to one half. Reduction of height was the most important factor. New cultivars with shorter straw expressed higher harvest index values and more consistent higher yields since they were less susceptible to lodging. The ability to produce more kernels from a given total above‐ground biomass was the second factor. The number of kernels per unit area had increased over time without alteration of the weight of the kernels. The negative relationship between 1000‐kernel weight and kernel number/m2 was therefore shifted and new cultivars were thus able to fill more kernels than older entries. Modern cultivars used N more efficiently than their predecessors. The future challenge will be to obtain, in low‐input systems, the same genetic gains as in high‐input systems.

usually provides more directly comparable information, particularly about yield components. The main results In a context where agricultural practices in Europe are likely to of such studies are given in Table 1. Genetic gains for go toward extensive systems with lower inputs, it is important to determine the genetic improvement of winter wheat (Triticum aesti-grain yield varied from 5.8 kg ha Ϫ1 yr Ϫ1 to 59 kg ha Ϫ1 vum L.) not only in high-input agricultural systems but also in low-yr Ϫ1 . Theses gains represent 33 to 63% of the national input systems. This study assesses the improvement in agronomic M. Brancourt-Hulmel, INRA, Unité de Gé né tique et d'Amé lioration des Plantes, 80200 Estré es-Mons, France; G. Doussinault and M. Trotimproved NUE resulted from either an improved uptet, INRA, Unité de Gé né tique et d'Amé lioration des Plantes, 35650 take efficiency (plant N per unit of N taken up from Le Rheu, France; C. Lecomte, INRA, Station de Gé né tique et d'Amé lthe soil) or a greater N utilization efficiency (grain yield ioration des Plantes,

purposes (Lhuillier-Soundé lé et al., 1999). Nitrogen remobilization efficiency (NRE) is therefore estimated as In wheat (Triticum aestivum L.), nitrogen remobilization from the the fraction of stored nitrogen at flowering that is not vegetative organs of the crop to the grains has been shown to depend on environmental factors and genotype. We performed, for a set of 10 recovered in the vegetative parts at maturity (Cox et al., winter wheat genotypes, field experiments at six sites over a 2-yr period. 1985a, 1985b Van Sanford and Mackown, 1987). NRE By measuring nitrogen uptake at flowering (NUF from 32-284 kg depends on growing conditions during the grain filling ha Ϫ1 ), the amount of remobilized nitrogen (REMN from 24-228 kg period and genotype. ha Ϫ1 ) and nitrogen remobilization efficiency (NRE from 0.44-0.92) Palta et al. (1994) showed that NRE was high in the we were able to determine the effect of genotype and environment on Mediterranean-like conditions of western Australia, in the relationship between REMN and NUF. Environment and genowhich plants generally suffer from water stress during type had significant effects on nitrogen remobilization and nitrogen the grain-filling period. This increase in NRE may result remobilization efficiency, which mainly depended on treatment (nitroindirectly from limits on nitrogen uptake during the graingen and fungicide) and site. For environments without limiting factor filling period, forcing the plant to make greater use of during the grain-filling period, we found that REMN was not dependent on the genotype and could be estimated by a single two-parameter its stored nitrogen. Cox et al. (1986) showed that higher linear relationship (REMN ϭ 4.13 ϩ NUF ϫ 0.76, r 2 ϭ 0.97). We levels of nitrogen fertilization before flowering lead to analyzed the effect of drought stress before and after flowering, high a decrease in nitrogen remobilization efficiency as the temperature during these periods, nitrogen availability and disease resulting higher level of nitrogen availability just after pressure on REMN by comparing observed and estimated REMN. flowering renders nitrogen remobilization unnecessary. The effect of the environment on the relationship between nitrogen up-Similar results were obtained by Moll et al. (1982) and take at flowering and nitrogen remobilization depended on nitrogen Przulj and Momcilovic (2001) with barley (Hordeum vuluptake during grain-filling period and disease pressure and was also gare L.). Halloran (1981) reported an increase in NRE affected by genotype. Disease-resistant genotypes seemed to be able in conditions unfavorable for nitrogen uptake before to keep remobilization efficiency stable in conditions of strong disease anthesis, linked to drought or high temperature. Foliar pressure, whereas nitrogen remobilization efficiency decreased strongly in susceptible genotypes under the same conditions. diseases, such as brown and yellow rusts (cause by Puccinia spp.), Septoria blotch (caused by Septoria tritici Roberge in Desmaz), and powdery m...

The current knowledge of the winter frost hardiness and of the hardening and dehardening process of wheat (Triticum aestivum L. Thell.) makes it possible to elaborate a prediction model, including the genotype, the growth stage of the plant, the temperatures allowing hardening, and the speed and duration of hardening and dehardening. For each of these factors, several hypotheses were considered. Combination of these different hypotheses led to 24 potential models. Each of them was tested with data obtained under natural conditions, over 10 different winters. The prediction of the resistance level was good for the first frost wave, showing that a convenient predicting model can be based on relatively easy hypotheses. Conclusions can be made on certain hypotheses such as the susceptibility level at the coleoptile stage, the stage range where the maximal resistance increases, and the pattern of the hardening speed. For the subsequent frost waves, the divergence between the predicted and the observed resistance thresholds increased, irrespective of the leaf stage in the observed range. We show the role of the accumulated dehardening and the long time exposure to temperatures near the plant frost resistance threshold in the decrease of frost resistance. Nevertheless, other factors have to be considered in order to correctly predict the frost resistance thresholds for the second, third and subsequent frost periods. frost resistance / modeling / wheat / genotypic effect Résumé-Test d'un modèle de prévision de la résistance au gel du blé tendre d'hiver en conditions naturelles. Les connaissances acquises sur la résistance au gel hivernal et sur les processus d'endurcissement et de désendurcissement du blé (Triticum aestivum L. Thell.) permettent d'envisager l'élaboration d'un modèle de prévision qui prend en compte le génotype, le stade de la plante, les plages de températures qui permettent l'endurcissement, la vitesse et la durée de l'endurcissement et du désendurcissement. Pour chacun de ces facteurs, plusieurs hypothèses ont été envisagées. La combinaison de ces différentes hypothèses conduit à considérer 24 modèles potentiels. Chacun a été évalué à l'aide de données recueillies en conditions naturelles, au cours de 10 hivers. On obtient une bonne prévision des seuils de résistance au gel pour le premier coup de froid, et l'on peut conclure sur un certain nombre d'hypothèses comme le seuil de sensibilité au stade coléoptile, la gamme de stades au cours de laquelle la résistance maximale s'accroît, et la forme de la vitesse d'endurcissement. Pour des coups de froid ultérieurs, la divergence entre les seuils de résistance prévus et observés s'accroît avec le nombre de coups de froid, indépendamment du stade foliaire, dans la gamme de stades que nous avons explorée. Parmi les facteurs responsables de l'affaiblissement des plantes, nous mettons en évidence le rôle du désendurcissement cumulé et de l'exposition prolongée à des températures proches du seuil de résistance. Mais une prévision correcte de la résistance au gel...

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sured environmental variates (van Eeuwijk, 1995). Just as with AMMI, BIAREG provides axes or synthetic Genotype ϫ environment interaction is a commonly observed pheenvironmental variates to which genotypes differ maxinomenon in experiments in plant breeding and genetics. This interaction can be modeled by different statistical models which can include mally in sensitivity, but under the restriction of being covariates, such as in biadditive factorial regression, or in other ways, linear combinations of the initial environmental varisuch as in the classical additive main effect and multiplicative interacates. BIAREG can be considered as a generalization of tion (AMMI) model. The aim of this paper was to explain genotype ϫ both AMMI and factorial regression. environment interaction in multienvironment trials of winter wheat Biadditive factorial regression can be interpreted from (Triticum aestivum L.) by environmental variates to assess the genothe environmental viewpoint via the correlations betype sensitivities to the environmental conditions and to compare the tween the synthetic variates and the initial environmenresults from biadditive factorial regression (BIAREG) and AMMI. tal covariates. These correlations can be displayed in Car-Data consisted of 13 lines grown in France in 14 environments (combitesian diagrams as it is done in principal component nations of two years, four locations and two treatments). Grain yield analysis. The biplot can contain three types of vectors and heading date were measured and the environments were characterized by climatic data (water deficit, radiation, temperature above whose coordinates are determined by the genotype sen-25؇C) and by observations of powdery mildew infection (caused by sitivities, the environmental characterizations, and co-Erysiphe graminis DC F. sp. tritici), lodging, and nitrogen status. efficients for the environmental covariates within the AMMI model explained most of the genotype ϫ environment intersynthetic axes (van Eeuwijk, 1995). Environmental charaction (77.4%) but did not provide a direct biological explanation. acterizations come from the linear combinations of the BIAREG explained a slightly smaller part (74.0%) but with fewer initial environmental covariates. To clarify such a biplot, degrees of freedom. The contributions of the environmental variates these three types of vectors can be considered in two to the synthetic variates revealed two important subsets of initial separate plots (Brancourt-Hulmel et al., 2000): one incovariates-biotic variates and nitrogen status in contrast to climatic cluding the genotype sensitivities and the coefficients ones-associated with the interaction. The interactive pattern of the of the environmental covariates and the other congenotypes and the environments was similar for both models. BIA-REG is more powerful than AMMI because it provides a description taining the same genotype sensitivities and the environof the sensitivities of the genotypes in regard to the observed environ-mental characterizations. Th...