Site-specific variation in wheat grain protein concentration and wheat grain yield measured on an Australian farm using harvester-mounted on-the-go sensors

Accurately measuring and understanding the fine-scale relationship between wheat grain yield (GY) and the concomitant grain protein concentration (GPC) should provide valuable information to improve the management of nitrogen inputs. Here, GPC and GY were monitored on-harvester for three seasons across 27 paddocks on an Australian farming enterprise using two independent, on-the-go sensing systems. A Zeltex Accuharvest measured GPC (%) and a John Deere GreenStar system measured GY (t/ha). Local calibration in each season for Australian spring wheat significantly improved the prediction accuracy, precision, and bias of the Zeltex Accuharvest when compared with the initial factory calibration. Substantial variation in GPC and GY was recorded at the field scale, with the least variation recorded in both parameters in the wetter season. GY (CV = 38%) was twice as variable on average as GPC (CV = 19%) across the enterprise. At this enterprise scale, a negative correlation between GPC and GY was observed for a composite of the field data from all seasons (r = -0.48); however, at the within-field scale the relationship was shown to vary from positive (max. = +0.41) to negative (min. = -0.65). Spatial variation in GPC and GY at the within-field scale was described best in the majority of cases by an exponential semivariogram model. Within-field spatial variability in GPC is more strongly autocorrelated than GY but on average they share a similar autocorrelated range (a' = similar to 190 m). This spatial variability in GPC and GY gave rise to local spatial variation in the correlation between GPC and GY, with 85% of the fields registering regions of significant negative correlations (P < 0.01) and significant positive correlations observed in 70% of fields. The spatial pattern in these regions of significantly different correlations is shown to display spatial coherence from which inferences regarding the relative availability of soil nitrogen and moisture are suggested. The results point to the suitability of these on-the-go sensors for use in more sophisticated agronomic and environmentally targeted nitrogen-use analysis.