资源环境科技发展态势分析平台

Spatial modelling of agricultural production has been more frequently analysed to assist with regional and long-term planning. Typically, point-scale crop models are used to construct these analyses with researchers using various sources of input data, including observed and interpolated gridded climate data. Understanding the implication of data choice on production estimates is crucial to appreciate the consequences of selecting methodological approaches for agricultural model outputs. In this study, we compared site observed climate data and gridded data sets interpolated from site observations that are commonly used the Australian climate data set. We assessed the consequences of the differences in climate variables (biases) to the modelled outputs. Our results showed that the major differences between gridded and observed climate data sets were for rainfall variables. Interpolated gridded data tended to have larger rainfall frequency and smaller rainfall intensity, leading to lower surface runoff and higher soil evaporation that caused less plant water uses and less nitrogen uptake, and ultimately resulted in biases in simulated crop biomass and yield. In addition, the results indicated that agricultural models implemented with gridded data could produce similar overall model outputs as those driven by observed climate data, but can result in more uncertainties in simulated spatial outputs. This is particularly evident in regions with high rainfall. Our results showed that applying agricultural models with observed data and then interpolating these results spatially might be the optimal approach to minimize the biases in production modelling outputs and reduce computing time and storage.