Economic losses due to crop damage caused by frost in Australia are estimated to be many hundreds of millions of dollars. In broadacre cropping, pre-sowing management to alleviate frost risk, and timely post-frost decisions rely on locally relevant information about the extent and severity of frosts. Yet temperature information currently available to grain farmers often has limited local relevance due to distance from meteorological stations. The availability of minimum temperature (T_min) maps at an appropriate scale would facilitate improved farmer decision-making in response to frost risk and frost events.

This study deployed temperature loggers and utilised Multivariate Adaptive Regression Splines (MARS) modelling to develop maps of T_min at farm scale (30 × 30 m grid). We use terrain derived variables to generate nightly T_min maps across a whole farm, based on data from a single on-farm weather station. Based on cross-validation, only elevation and elevation standard deviation were found to be useful predictors. Validation of the model against different years and locations resulted in good predictive RMSE values in the range 0.72 to 1.61°C. Classification accuracy scores (F1) for prediction of temperatures being above or below a 2°C threshold ranged from 83% to 96%.

A priority of this work was to understand how the minimum temperature mapping complements farmers’ understanding of frost, and how it would contribute to improved management of frost in their cropping systems. Evaluation of the maps by farmers showed general agreement that the maps complemented local knowledge, with the main interest in the maps being as a guide to know where to start looking for frost damage.

We have developed a method to generate T_min maps based on a data from a single on-site temperature logger combined with terrain data and demonstrated that these maps are appropriately accurate and at a scale that is relevant to farmer management actions.

在澳大利亚，由于霜冻造成的农作物损失造成的经济损失估计达数亿美元。在大面积耕种中，为减轻霜冻风险而进行的播前管理，及时的霜后决策取决于有关霜冻程度和严重程度的本地相关信息。然而，由于距气象站的距离，当前可供谷物农民使用的温度信息通常在当地的适用性有限。适当比例的最低温度（T _min）图的可用性将有助于改善农民应对霜冻风险和霜冻事件的决策能力。

这项研究部署了温度记录仪，并利用多元自适应回归样条（MARS）建模来绘制农场规模（30×30 m网格）的T _min图。我们基于来自单个农场气象站的数据，使用地形得出的变量来生成整个农场的夜间T _min图。基于交叉验证，仅发现海拔和海拔标准偏差是有用的预测因子。针对不同的年份和位置对模型进行验证，得出了良好的预测RMSE值，范围为0.72至1.61°C。用于预测温度高于或低于2°C阈值的分类准确度分数（F1）为83％至96％。

这项工作的重点是了解最低温度映射如何补充农民对霜冻的理解，以及它将如何有助于改善耕作系统中霜冻的管理。农民对地图的评估表明，人们普遍同意，这些地图是对当地知识的补充，对地图的主要兴趣是作为指南，以了解从哪里开始寻找霜冻损害。

我们已经开发了一种方法，可以根据单个现场温度记录器的数据与地形数据相结合来生成T _min图，并证明这些图是适当准确的，并且其规模与农民的管理行为有关。