Our approach included calibration and testing of several APSIM models (maize Zea mays L. APSIM can simulate more than 20 crops and forests (e.g., alfalfa, eucalyptus, cowpea, pigeonpea, peanuts, cotton, lupin, maize, wheat, barley, sunflower, sugarcane, chickpea, tomato). The relevance to plant breeding of this capability in complex trait dissection and simulation is discussed. We explored whether APSIM performs well in the Midwest, so that the associated model capabilities would be available for application in this region.
#Apsim model used for what crops full
These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Introducing these genetic effects associated with plant height into the model generated emergent simulated phenotypic differences in green leaf area retention during grain filling via effects associated with nitrogen dynamics. The paper outlines APSIM's structure and provides details of the concepts behind the different plant, soil and management modules. Genotypes differing in height were found to differ in biomass partitioning among organs and a tall hybrid had significantly increased radiation use efficiency: a novel finding in sorghum. Experiments on diverse genotypes of sorghum that underpin the development and testing of the adapted crop model are detailed.
#Apsim model used for what crops software
The model builds on existing approaches within the APSIM software platform. The approach quantifies capture and use of radiation, water, and nitrogen within a framework that predicts the realized growth of major organs based on their potential and whether the supply of carbohydrate and nitrogen can satisfy that potential. It is designed to exhibit reliable predictive skill at the crop level while also introducing sufficient physiological rigour for complex phenotypic responses to become emergent properties of the model dynamics. A generic cereal crop growth and development model is outlined here. The APSIM crop model was used to simulate the productivity of four annual crops (barley, forage maize, oats, and spring wheat) over five sites in Sweden ranging between 55 and 64N. Suitably constructed crop growth and development models have the potential to bridge this predictability gap. This new framework provides a library of plant organ and process submodels that can be coupled, at runtime, to construct a model in much the same way that models can be coupled to construct a. Progress in molecular plant breeding is limited by the ability to predict plant phenotype based on its genotype, especially for complex adaptive traits. The APSIM potato model has been described in part by Brown et al., 2011 and developed using the Plant Modelling Framework (PMF) of Brown et al., 2014.
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