Marja Simpson1, Deb Slinger2, Tania Moore3, Mehrshad Barary2, Hongtao Xing2, Allison Blake2
1 NSW Department of Primary Industries, Orange NSW, Australia, 2 NSW Department of Primary Industries, Wagga Wagga NSW, Australia, 3 NSW Department of Primary Industries, Griffith NSW, Australia
An understanding of the spatial and temporal distribution of abiotic factors, crop mega-environments, and their relationship to productivity can support focused research on genotype evaluation and breeding. The Northern Grains Region (NGR) is characterised by different climates ranging from tropical to subtropical in the north to temperate in the south, allowing production of a variety of grain crop genotypes. In this study, we examined a range of spatial climatic data to show decadal trends across the NGR. Additionally, we used multi-environment trial (MET) data analysis to examine crop yield responses to genotype (G) and environment (E) aiming to identify different mega-environments (ME) across the more optimal canola growing region across the NGR. The spatial data analysis on average winter growing season rainfall for the last three decades showed a reduction in rainfall across large parts of the NGR and an increase in maximum temperatures with a spread in area particularly in the northern part of the NGR. The MET analysis across five years (2013–2017) for canola genotypes identified a clustering of trial locations into two MEs based on yields and climatic parameters, with ME1 being characterised by cooler average temperatures and higher rainfall during the pre and post-flowering period compared to ME2. Averaged yields over the five years were higher in ME1 than ME2. However, annual variability of growing season rainfall resulted in good yields for some ME2 locations in some years. The findings from this study showed that the larger canola growing environment across the NGR can be grouped into two MEs that potentially suit different genotypes. Knowledge of the climatic characteristics of canola MEs and their geographic boundary can support targeted research in terms of crop adaptation.