LAUSR

Abstract 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.