Tuesday, November 14, 2006 - 10:30 AM

Modelling Crop N Dynamics in the APSIM Generic Crop Template.

Erik J. Van Oosterom1, Graeme L. Hammer1, Scott C. Chapman2, and Greg McLean1. (1) Agricultural Production Systems Research Unit, The Univ of Queensland, Brisbane, Qld 4072, Australia, (2) CSIRO Plant Industry, 306 Carmody Road, St. Lucia, Qld 4067, Australia

Current modules for the simulation of crop nitrogen (N) demand lack the functionality to capture genotypic differences in crop N dynamics and the associated effects on grain quality. In many models, crop N demand is determined by N concentrations of individual organs which decline with phenological stage. Such over-parameterisation effectively forces the model to correctly simulate observed data. In this paper, we present a module in which N demand of individual organs is independent of the developmental stage of the crop. Leaf N demand is determined by a genotype-specific maximum Specific Leaf Nitrogen (SLN). Grain N demand during the initial stages of grain filling is a genotypic parameter that represents accumulation of structural N. During the second half of grain filling, grain N uptake is a function of dry matter accumulation, representing accumulation of storage N. Translocation of N from vegetative parts is a function of the N status of these parts and is representative of enzyme activity. Precision in simulating plant N dynamics is influenced by ability to predict N uptake from the soil. The module is based on detailed observations from sorghum crops grown in eastern Australia, and is implemented in an object oriented, generic crop template within the APSIM cropping systems simulation platform. Genotypic differences in the crop N balance during grain filling, and hence in the ability of a crop to retain green leaf area during grain filling, are simulated as a consequence of genotypic differences in pre-anthesis biomass partitioning. This framework is suitable for a range of cereal grain crops and simulation runs suggest that the module could be used to optimize N fertilizer management to match N demands of specific genotypes.