Minimum temperature mapping augments Australian grain farmers’ knowledge of frost

Abstract

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 (Tmin) 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 Tmin at farm scale (30 × 30 m grid). We use terrain derived variables to generate nightly Tmin 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 Tmin 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.