Modelling Nitrogen Distribution in Virtual Plants, as Exemplified by Wheat Culm During Grain Filling
2009
Bertheloot, Jessica | Andrieu, Bruno | Fournier, Christian | Martres, Pierre | Sciences Agronomiques Appliquées à l'Horticulture (SAGAH) ; Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST | Environnement et Grandes Cultures (EGC) ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech | Écophysiologie des Plantes sous Stress environnementaux (LEPSE) ; Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) | Génétique Diversité et Ecophysiologie des Céréales (GDEC) ; Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP) | Absent | Li Baoguo | Marc Jaeger | Yan Guo
Titre du symposium précédent: International Symposium on Plant Growth Modeling, Simulation, Visualization and their Applications.
Mostrar más [+] Menos [-]Inglés. Nitrogen is fundamental for plant growth. In cereals, growing grains represent a strong sink that triggers nitrogen remobilisation from vegetative organs and results in plant death. A better understanding of this mechanism would help in optimizing crop productivity while reducing fertilization. This work presents an experimental analysis and a process-based model of the spatiotemporal nitrogen distribution during grain filling in winter wheat culms. Nitrogen was distributed homogeneously within individual laminae and sheaths, but a strong gradient existed between organs at successive positions along the culm. During grain filling, the changes in nitrogen content of individual laminae and sheaths showed identical patterns, differing only by a scale factor. Modelling N content of each lamina as the result of the turnover of photosynthetic nitrogen and supposing that all organs share a single pool of mobile nitrogen allowed predicting the observed patterns with high accuracy. This offers new insight for modelling plant nitrogen economy
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