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Summer crops are exposed to heat and drought stresses at critical stages during and after flowering, and their intensity and frequency are likely to increase with climate change. Agronomic stress avoidance offers the opportunity to temporally separate critical crop stages from heat and drought events. However, it might require sowing cold-sensitive summer crops earlier into colder than recommended soil temperatures. There is a need to understand how cold is too cold to sow summer crops early in late winter as well as what are the yield benefits and risks. Here, we quantify the likely benefits and trade-offs of sowing sorghum, a summer cereal, earlier to adapt to the increased frequency and intensity of heat and water stresses during flowering and grain filling. Two years of multi-environment (n =32) genotype by management trials were conducted across the main sorghum growing regions of Australia. Environments (E) consisted of the combination of years, sites, three times of sowing (early, spring, and summer), and the use of supplementary irrigation. At each E a factorial combination of four plant populations (M) and eight commercial sorghum hybrids (G) were sown with three replications. Crop growth and yield components were measured, and the APSIM model was used to simulate all trials and treatments to quantify risks and derive insights into functional relationships between simulated and measured environmental covariates, and measured crop traits. The tested hybrids showed small differences in cold tolerance during crop establishment. Across the tested environments, the G×M combinations produced up to 60 % variation in treatment yields across environment yields, which varied between <0.5 to about 10 t ha−1; this translated into a ∼5.5-fold variation in water use efficiency. Significant G×E and M×E interactions were observed for grain yield components. No G×M or G×E×M interactions were observed on yield or yield components. Early sowing was associated with a reduced risk of heat stress and water use transfer from vegetative to reproductive stages. Early sowing in late winter or early spring resulted in no significant yield gain or loss when all sites and years were included in the analysis. However, early sowing yielded between 1 and 2 t ha−1 more when the hottest sites and years were considered separately. This resulted from both the avoidance of heat stresses and milder or no terminal drought stresses. Early sowing of sorghum can reduce the likelihood of heat stresses around flowering as well as the likelihood of terminal drought stresses. Advantages include reduced yield losses in the hottest years and a transfer of water use to grain filling stages, resulting in increased grain yield and improved grain quality parameters. Early sowing, an agronomic adaptation, offers the opportunity to quickly adapt to the increase in the frequency and intensity of extreme hot events during critical crop stages. However, for the practice to be de-risked, there is a need to increase cold and chilling tolerance in sorghum and/or identify interventions that enhance seed germination and seedling vigour when the crop is sown early into cold soils. • Early sowing of sorghum can reduce the likelihood of heat stresses around flowering as well as the likelihood of terminal drought stresses. • The benefits of early sowing include yield increases in the hottest sites and seasons. • Adapting to heat stress in sorghum requires breeding to produce varieties of improved cold and chilling tolerance early in the season. [ABSTRACT FROM AUTHOR]

• Identifying optimum crop design to match the environment remains a useful concept to maximise crop yields and farmers' profits. • There is opportunity for field agronomic multi-environment experimentation to embrace system research approaches. • The value of applying a GxExM framework to increase yields and manage risks relates to our capacity to predict E at sowing and to the genetic diversity of the germplasm. 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. [ABSTRACT FROM AUTHOR]