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Identifying optimum combinations of genotype (G) and agronomic management (M) i.e. crop design, to match the environment (E) i.e. site and expected seasonal conditions, is a useful concept to maximise crop yields and farmers’ profits. However, operationalising the concept requires practitioners to understand the likelihood of different E outcomes and GxM combinations that would maximise yields while managing risks. 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. A 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. The trials consisted of combinations of commercial maize hybrids, sown at a range of plant densities and row configurations producing site average yields (Environment-yield) that varied between 1576 and 7914 kg ha−1. Experimental data were used to test the capacity of APSIM-Maize 7.10 to simulate the experimental results, and to in-silico create a large synthetic data set of multi-E (sites x seasons) factorial combination of crop designs. Data mining techniques were applied on the synthetic data set, to derive a probabilistic model to predict the likely Environment-yield and associated risk from variables known at sowing, and to derive simple “rules of thumb” for farmers that discriminate high and low yielding crop designs across the lower, middle and upper tercile of the predicted Environment-yields. Four risk profiles are described, a “Dynamic” (i.e. each year the farmer would adopt a crop design based on the predicted Environment-yield tercile and corresponding “rules of thumb”), “High rewards seeker” (i.e. each year the farmer would adopt the crop design that optimises yield for the higher tercile of Environment-yields), “Middle’er” (i.e. each year the farmer would adopt the crop design that optimises yield for the middle tercile of Environment-yields), and “Risk averse” (i.e. each year the farmer would adopt the crop design that optimises yield for the lower tercile of Environment-yields). The difference in yield between the lowest and highest performing crop design was ca. 50 % which translates into a ca. 2-fold change in water use efficiency, i.e. from 8 to 15 kg grain mm−1 rainfall. APSIM-Maize explained 88 % of the variability in the experimental data set. The validated model was used to extend the number of E sampled by adding additional sites within the same region and using historical climate records for the period 1950–2018. Crop available water at the time of sowing was a good predictor for the likelihood of the season falling within each of the three Environment-yield terciles. Recursive partitioning trees showed that plant density and hybrid were the main variables discriminating crop performance within the upper, middle and lower terciles of Environment-yields. The probability distribution functions for yield resulting from the alternative risk management strategies were tested in terms of changes in the mean yield, an index of yield stability, and down-side risk i.e. the likelihood of achieving a non-economic yield. We conclude that (i) for dryland maize cropping, the crop water availability at the time of sowing can be used to inform optimum crop designs, increase yields and yield stability and reduce down-side risks; and (ii) the proposed framework is useful to untangle complex GxExM interactions in field experimentation that provide a transferable platform to develop simple rules to identify optimum crop designs early in the season.