Modelling the nitrogen dynamics of maize crops – Enhancing the APSIM maize model
| Autor: | E. Munaro, Graeme Hammer, Greg McLean, Mark E. Cooper, S. Soufizadeh, E.J. van Oosterom, Scott Chapman, Angelo Massignam, Charlie Messina |
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| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
Biomass (ecology) biology Simulation modeling Soil Science 04 agricultural and veterinary sciences Plant Science Agricultural engineering Sorghum biology.organism_classification 01 natural sciences Crop Agronomy Robustness (computer science) 040103 agronomy & agriculture Range (statistics) 0401 agriculture forestry and fisheries Plant breeding Scale (map) Agronomy and Crop Science 010606 plant biology & botany Mathematics |
| Zdroj: | European Journal of Agronomy. 100:118-131 |
| ISSN: | 1161-0301 |
| DOI: | 10.1016/j.eja.2017.12.007 |
| Popis: | Crop growth simulation models require robust ecophysiological functionality to support credible simulation of diverse genotype × management × environment (G × M × E) combinations. Most efforts on modeling the nitrogen (N) dynamics of crops use a minimum, critical, and maximum N concentration per unit biomass based empirically on experimental observations. Here we present a physiologically more robust approach, originally implemented in sorghum, which uses the N content per unit leaf area as a key driver of N demand. The objective was to implement the conceptual framework of the APSIM sorghum nitrogen dynamics model in APSIM maize and to validate the robustness of the model across a range of G × M × E combinations. The N modelling framework is described and its parameterisation for maize is developed based on three previously reported detailed field experiments, conducted at Gatton (27°34′S, 152°20′), Queensland, Australia, supplemented by literature data. There was considerable correspondence with parameterisation results found for sorghum, suggesting potential for generality of this framework for modelling crop N dynamics in cereals. Comprehensive model testing indicated accurate predictions at organ and crop scale across a diverse range of experiments and demonstrated that observed responses to a range of management factors were reproduced credibly. This supports the use of the model to extrapolate and predict performance and adaptation under new G × M × E combinations. Capturing this advance with reduced complexity compared to the N concentration approach provides a firm basis to progress the role of modelling in exploring the genetic underpinning of complex traits and in plant breeding and crop improvement generally. |
| Databáze: | OpenAIRE |
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