An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO₂ concentration [CO₂] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO₂]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO₂] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO₂] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO₂] and temperature is needed to reduce the uncertainty from the individual models.

在气候变化情景下准确估计作物产量对于量化我们养活不断增长的人口和发展农艺适应性以满足未来粮食需求的能力至关重要。大豆是最广泛种植的谷物豆科植物，也是我们食物链中蛋白质的主要来源，目前仍缺乏对基于过程的生态生理模型的产量模拟进行协调评估，以评估气候变化影响。在第一个大豆多模型研究中，我们使用了十个能够模拟不同温度和大气 CO ₂浓度 [CO ₂] 以量化大豆产量模拟中响应这些因素的不确定性。首先使用来自五个对比环境的高质量测量数据对模型进行参数化。_{我们发现，模型对温度升高和 [CO 2} ]的模拟产量响应存在相当大的差异。例如，在阿根廷最凉爽的地方，温度上升 + 3 °C，一些模型模拟产量将减少多达 24%，而另一些模型模拟产量增加高达 29%。在我们巴西最温暖的地方，模型模拟了产量下降，从 + 3 °C 温度上升时下降 38% 到对产量没有影响。同样，当增加 [CO ₂] 从 360 到 540 ppm，模型模拟了从 6% 到 31% 的产量增加。模型校准并没有减少模型间的可变性，但对修改某些模型的产量对温度的响应产生了意想不到的影响。模型响应的高度不确定性表明单个模型对气候变化食品预测的适用性有限。然而，跨模型模拟的整体平均值是减少与单个模型及其参数化相关的大豆产量模拟的高不确定性的有效工具。集合，集合平均产量对温度和 [CO _2的响应] 与文献报道的相似。我们的研究首次证明了使用一组谷物豆类模型进行气候变化食物预测所带来的好处，并强调需要在升高的 [CO ₂ ] 和温度下进行进一步的大豆模型开发，以减少个体的不确定性楷模。