3/30/2024 0 Comments Run factorial apsimA network of replicated, G by M on-farm and on-research station trials (n = 10), conducted across New South Wales and Queensland, Australia, over three seasons (2014–2016) was collected. Here we propose and demonstrate an analysis framework to inform crop designs (GxM) at the time of sowing of a dryland maize crop, that combines data sets from multi-environment field experimentation and crop simulation modelling, and that accounts for risk preference. However, operationalising the concept requires practitioners to understand the likelihood of different E outcomes and GxM combinations that would maximise yields while managing risks. site and expected seasonal conditions, is a useful concept to maximise crop yields and farmers’ profits. crop design, to match the environment (E) i.e. Identifying optimum combinations of genotype (G) and agronomic management (M) i.e.
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