Daniel Rodriguez and Peter de Voil
The University of Queensland, Centre for Crop Sciences, Queensland Alliance for Agriculture and Food Innovation, Gatton Campus, Gatton, Queensland, 4343, https://qaafi.uq.edu.au/, email@example.com
Over the last fifty years increases in grain yields have been the result of improvements from breeding, from agronomy and the cropping system, and from their interactions. There is also no doubt that the same drivers will be responsible for future yield gains. This calls for R&D efforts to be directed towards identifying and communicating optimum combinations of agronomic management (M) and cultivars (G), or crop designs (GxM), that make best use of available resources and expected seasonal conditions i.e. the environment (E). Our present understanding of crop stress physiology indicates that in hindsight, those optimum crop designs should be known, while the main problem is to predict relevant attributes of the environment (E), at the time of sowing, so that optimum GxM combinations could be informed. Here we tested our capacity to inform that “hindsight”, by linking a crop model (APSIM-Sorghum) with outputs from two seasonal climate forecasting systems to answer “What is the value of informing optimum crop designs?” This was achieved by using the APSIM-Sorghum model and outputs from two seasonal climate forecasting systems (i.e. POAMA-2 and ACCESS-S1), to inform farmers’ decisions at different time scales, ranging from weeks to a few months in sorghum cropping. Results showed that that by linking APSIM-Sorghum and POAMA-2 to inform optimum crop designs at sowing could increase average sorghum profits by up to 143 AU$ ha-1 year-1; and that that by linking APSIM-Sorghum and ACCESS-S1 could be used to inform the likelihood of favorable soil temperatures over the following few weeks crucial to achieve uniform crop stands in winter sown sorghum.