| Autor: |
Rodriguez D; Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, PO Box 102, Toowoomba, Queensland, 4350, Australia. d.rodriguez@uq.edu.au., de Voil P; Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, PO Box 102, Toowoomba, Queensland, 4350, Australia., Hudson D; Bureau of Meteorology, PO Box 1289, Melbourne, Victoria, 3008, Australia., Brown JN; CSIRO Agriculture and Food, 15 College Rd., Sandy Bay, Tasmania, 7005, Australia., Hayman P; South Australian Research and Development Institute, PO Box 397, Adelaide, South Australia, 5066, Australia., Marrou H; SupAgro, 2 place Viala, 34060, Montpellier, cedex 02, France., Meinke H; University of Tasmania, and Tasmanian Institute of Agriculture, Private Bag 98, Hobart, Tasmania, 7001, Australia. |
| Abstrakt: |
Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in dryland cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected growing environment (E). Our 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 E, at the time of sowing, so that optimum GxM combinations could be informed. Here we test our capacity to inform that "hindsight", by linking a tested crop model (APSIM) with a skillful seasonal climate forecasting system, to answer "What is the value of the skill in seasonal climate forecasting, to inform crop designs?" Results showed that the GCM POAMA-2 was reliable and skillful, and that when linked with APSIM, optimum crop designs could be informed. We conclude that reliable and skillful GCMs that are easily interfaced with crop simulation models, can be used to inform optimum crop designs, increase farmers profits and reduce risks. |
