Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate.

By Zvi Hochman, H. V. Rees, Peter S. Carberry, James R. Hunt, R. Mccown, A. Gartmann, D. Holzworth, S. Rees, Neal P. Dalgliesh, W. Long and 3 more. 2009

183 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Environmental Chemistry, Forestry

View Paper: https://www.semanticscholar.org/paper/4bbc6c80837b4e2e9acc4bb36c7a395cf9817d94

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In Ecology, Evolution, Behavior and Systematics

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Citations in the corpus

Re-inventing model-based decision support with Australian dryland farmers. 1. Changing intervention concepts during 17 years of action research (2009, background): “A succession of attempts to achieve this second objective is reported by Hochman et al. (2007), and the story of the current and most successful, Yield Prophet, is reported later in the series (Hochman et al. 2009, this issue, Paper 4).”
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).”
APSIM - Evolution towards a new generation of agricultural systems simulation (2014, methodology): “Agricultural simulation models have an important role in informing farmer practice (Hochman et al., 2009b), breeding strategies (Cooper et al., 2009) and government policy (Bezlepkina et al., 2010) that aim at addressing challenges such as food security and climate mitigation and adaptation.”
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. 3. Relevance of APSIM to commercial crops. (2009, methodology): “Its many applications to Australian wheat crops largely confirm the view that most commercial crops fall below their water-use potential inAustralia (Cornish and Murray 1989; Beeston et al. 2005; Sadras and Angus 2006; Hochman et al. 2009a).”
Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt (2020, background): “Compared to Australia’s wheat yield predictions (Carberry et al., 2009) using the APSIM model, our approach was similar, but had two key differences: 1) we started the model approximately 4-months prior to planting (1 January) and not at planting time, and 2) we included a weather forecast component”