There is considerable interest and value in identifying the gap between crop yields that have actually been achieved, and yields that could have potentially been achieved. A suite of methods currently exist to estimate the yield potential of a crop, but there are no approaches that predict the site- and season-specific yield potential using datasets that are readily available and easily accessible for farmers. The aim of this study was to fill this need and develop a novel approach to identify crop yield gaps through site- and season-specific models of crop yield potential. The study focused on cotton lint yield, with data from 14 different seasons and 68 different fields from a collection of large, irrigated cotton farms in eastern Australia. This abundance of yield data was then joined with other spatial and temporal datasets that describe yield, such as rainfall, temperature, soil, and management. A quantile random forest machine learning model was then used to model yield at 30 m resolution, where the 95th percentile predictions were treated as the yield potential. The yield gaps at a 30 m resolution were then estimated for all seasons and sites. The results were compared to a more traditional ‘historical maximum yield’ approach, where no data modelling and only empirical yield data was used to estimate the yield potential. This revealed that there was a general agreement between the two approaches, although the quantile machine learning approach is both site- and season-specific, not just site-specific. Overall, there is a great need for alternative approaches to estimate yield potential and yield gaps, as the approaches currently available possess many limitations. The approach developed in this study has the potential for wide-spread adoption in broadacre cropping systems, and if the causes of yield gaps are identified, could lead to the implementation of management strategies to close them.

确定实际实现的作物产量与可能实现的产量之间的差距具有相当大的兴趣和价值。目前存在一套估计作物产量潜力的方法，但没有任何方法可以使用农民容易获得且易于获取的数据集来预测特定地点和季节的产量潜力。本研究的目的是满足这一需求并开发一种新方法，通过作物产量潜力的特定地点和季节模型来确定作物产量差距。该研究侧重于棉绒产量，数据来自澳大利亚东部大型灌溉棉花农场的 14 个不同季节和 68 个不同田地。然后将这种丰富的产量数据与描述产量的其他空间和时间数据集结合起来，如降雨、温度、土壤和管理。然后使用分位数随机森林机器学习模型对 30 m 分辨率的产量进行建模，其中 95% 的预测被视为产量潜力。然后估计所有季节和地点在 30 m 分辨率下的产量差距。结果与更传统的“历史最大产量”方法进行了比较，其中没有数据建模，仅使用经验产量数据来估计产量潜力。这表明两种方法之间存在普遍共识，尽管分位数机器学习方法既针对特定地点又针对特定季节，而不仅仅是针对特定地点。总体而言，非常需要替代方法来估计产量潜力和产量差距，因为目前可用的方法具有许多局限性。