On farm experimentation (OFE) has been a long-standing method for farmers to assess alternative management at scales relevant to their farming practices. Through the use of spatially distributed designs, whether simple strips or other ‘whole-of-block’ trials, OFE can provide information such as which treatment should be recommended at specific locations, and make important contributions to precision agriculture. However, when treatment response data sets become large, such as with tens of thousands of field observations that are readily collected using on-the-go sensors, existing geostatistical systems for analysing such experiments become computationally intensive, if not impossible. To enable farmers, or their consultants, to generate high-resolution treatment response and recommendation maps on their own computers within a reasonable time, we present a fast and adaptive local cokriging tool for non-colocated and non-stationary OFE data. It uses a spatially-varying neighbourhood radius. It has a graphical user interface accessible via QGIS, a free and open source software. The adaptive local cokriging is demonstrated on three OFE examples. It performs indistinguishably from global cokriging on a small data set, but for large data sets, for which global cokriging is impractical, it predicts significantly more accurately than spatial splines or sampling-based cokriging. It outperforms cokriging base on a fixed number of nearest neighbours when this fixed number is not carefully chosen.

农场试验（OFE）是农民评估与他们的耕作方式相关的替代管理方法的一种长期方法。通过使用空间分布的设计，无论是简单地带试验还是其他“整块”试验，OFE都可以提供信息，例如应在特定位置推荐哪种处理方法，并为精确农业做出重要贡献。但是，当处理响应数据集变大时（例如，使用移动传感器可以轻松收集成千上万的现场观察结果），用于分析此类实验的现有地统计系统将变得计算量大，甚至是不可能的。为了使农民或其顾问能够在合理的时间内在他们自己的计算机上生成高分辨率的治疗响应和推荐图，我们提出了一种快速且自适应的局部cokriging工具，用于非定位和非平稳的OFE数据。它使用空间变化的邻域半径。它具有可通过QGIS（免费和开源软件）访问的图形用户界面。在三个OFE实例上演示了自适应局部cokriging。它在较小的数据集上与全局cokriging的区别不明显，但是对于大型数据集（对于全局cokriging而言是不切实际的），它的预测比空间样条或基于采样的cokriging准确得多。如果没有仔细选择此固定数量的邻居，则其性能要优于基于固定数量的最近邻。一个免费的开源软件。在三个OFE实例上演示了自适应局部cokriging。它在较小的数据集上与全局cokriging的区别不明显，但是对于大型数据集（对于全局cokriging而言是不切实际的），它的预测比空间样条或基于采样的cokriging准确得多。如果没有仔细选择此固定数量的邻居，则其性能要优于基于固定数量的最近邻。一个免费的开源软件。在三个OFE实例上演示了自适应局部cokriging。它在较小的数据集上与全局cokriging的区别不明显，但是对于大型数据集（对于全局cokriging而言是不切实际的），它的预测比空间样条或基于采样的cokriging准确得多。如果没有仔细选择此固定数量的邻居，则其性能要优于基于固定数量的最近邻。