Re-inventing model-based decision support with Australian dryland farmers. 3. Relevance of APSIM to commercial crops.

By Peter S. Carberry, Zvi Hochman, James R. Hunt, Neal P. Dalgliesh, R. Mccown, J. Whish, M. Robertson, M. Foale, P. Poulton, H. V. Rees. 2009

106 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Ecology, Evolution, Behavior and Systematics, Plant Science

View Paper: https://www.semanticscholar.org/paper/9ed180231f1ed77d2395086ceb314db3d1648402

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Citations in the corpus

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt (2020, background): “Successful yield forecasting approaches using crop models in the USA (Morell et al., 2016; Togliatti et al., 2017) and Australia (Carberry et al., 2009), have shown potential to couple explanatory with predictive power to evaluate adaptive management strategies (Jones et al., 2017).”
Water and temperature stress define the optimal flowering period for wheat in south-eastern Australia (2017, background): “Simulation of wheat growth, development and yield in APSIM has been extensively validated in numerous studies across southern Australia (Asseng et al., 2001; Carberry et al., 2009; Hochman et al., 2009; Lilley et al., 2003; Lilley and Kirkegaard, 2007), and no further validation was undertaken here.”
Yield improvement and adaptation of wheat to water-limited environments in Australia—a case study (2014, methodology): “Kirkegaard and Hunt (2010) presented data from a simulation study using APSIM, a well-validated crop model in Australia (Carberry et al. 2009), over a 48-year period (1962–2009) for a commercial farm in north-western Victoria with an average growing-season rainfall (April–October) of ~250mm.”
Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate. (2009, unknown): “In addition, these data informed the YP team about important decisions regarding the costs and benefits associated with the effort required to achieve adequate precision in input data (see Carberry et al. 2009, this issue, for awider treatment of this issue).”
Optimising grain yield and grazing potential of crops across Australia’s high-rainfall zone: a simulation analysis. 1. Wheat (2015, background): “The APSIMWheat model’s predictions of grain yield and crop biomass have been widely validated across a range of environments and further validation was not undertaken here (Asseng et al. 1998; Paydar et al. 2005; Bell et al. 2009; Carberry et al. 2009; Oliver et al. 2009).”
Re-inventing model-based decision support with Australian dryland farmers. 1. Changing intervention concepts during 17 years of action research (2009, methodology): “The necessary ‘truth test’ could be either impressive approximations of a farmer’s historical yields or of a local FARMSCAPE field trial or monitored crop (Carberry et al. 2009, this issue).”