Abstract 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.

中文翻译：

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在多环境农场试验中解开基因型 x 管理相互作用

摘要 确定基因型 (G) 和农艺管理 (M) 的最佳组合，即作物设计，以匹配环境 (E)，即地点和预期的季节性条件，是最大化作物产量和农民利润的有用概念。然而，实施该概念需要从业者了解不同 E 结果和 GxM 组合的可能性，从而在管理风险的同时最大限度地提高收益。在这里，我们提出并展示了一个分析框架，用于在播种旱地玉米作物时为作物设计 (GxM) 提供信息，该框架结合了多环境田间试验和作物模拟建模的数据集，并考虑了风险偏好。在新南威尔士州和澳大利亚昆士兰州进行的重复的 G by M 农场和研究站试验（n = 10）网络，收集了超过三个季节（2014-2016）。试验包括商业玉米杂交组合，以一系列植物密度和行配置播种，产生的场地平均产量（环境产量）在 1576 和 7914 kg ha-1 之间变化。实验数据用于测试 APSIM-Maize 7.10 模拟实验结果的能力，并在计算机上创建了一个大型合成数据集，其中包含作物设计的多 E（站点 x 季节）因子组合。数据挖掘技术应用于合成数据集，以推导出概率模型，以根据播种时已知的变量预测可能的环境产量和相关风险，并为区分高产和低产作物的农民推导出简单的“经验法则”预测环境产量的下、中和上三分位数的设计。描述了四种风险概况，“动态”（即农民每年将根据预测的环境产量三分位数和相应的“经验法则”采用作物设计）、“高回报寻求者”（即农民每年将采用采用环境产量较高的三分位数优化产量的作物设计），“中间人”（即农民每年都会采用环境产量的中间三分位数优化产量的作物设计）和“风险规避” ”（即，农民每年都会采用为环境产量较低的三分位数优化产量的作物设计）。最低和最高表现的作物设计之间的产量差异约为。50 % 转化为大约。水分利用效率的 2 倍变化，即从 8 到 15 公斤谷物 mm−1 降雨量。APSIM-Maize 解释了实验数据集中 88% 的变异性。经验证的模型用于通过在同一区域内添加更多站点并使用 1950-2018 年期间的历史气候记录来扩展 E 采样的数量。播种时的作物可用水是预测季节落在三个环境产量三分位数内的可能性的良好预测指标。递归分区树表明，在环境产量的上、中和下三分位数内，植物密度和杂种是区分作物性能的主要变量。根据平均收益率的变化、收益率稳定性指数和下行风险，即实现非经济收益率的可能性，测试了由替代风险管理策略产生的收益率概率分布函数。我们得出结论：（i）对于旱地玉米种植，播种时的作物可用水量可用于为最佳作物设计提供信息，提高产量和产量稳定性并降低下行风险；(ii) 提议的框架有助于解开田间试验中复杂的 GxExM 相互作用，这些相互作用提供了一个可转移的平台来制定简单的规则，以在季节早期确定最佳作物设计。