Towards a national, remote-sensing-based model for predicting field-scale crop yield

Existing agricultural grain yield models predict yield at the field scale, or at regional scales (like districts and countries), but not both with consistent accuracy. Here we describe a scalable, satellite-based yield model called C-Crop. It is calibrated locally and so has field-scale accuracy. Its input data can be inferred remotely (namely crop type, foliage cover and air temperature) and so it can be potentially applied at any regional scale. We calibrated C-Crop using harvester-derived yield data for canola (31 field-years) and wheat (160 field-years), across the Australian cropping zone. C-Crop explained 69 and 68% of the observed variability in field-scale canola and wheat yields, respectively, with errors in the order of 33% and 32% of total yield. Given its simplicity, C-Crop is an effective model for estimating field-scale crop yields and has the potential to be applied across large regions.