Rodriguez et al Nature Scientific Reports 2018.pdf (2.34 MB)
Predicting optimum crop designs using crop models and seasonal climate forecasts
journal contribution
posted on 2023-05-19, 16:22 authored by Rodriguez, D, de Voil, P, Hudson, D, Brown, JN, Hayman, P, Marrou, H, Holger MeinkeHolger MeinkeExpected 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.
History
Publication title
Scientific ReportsVolume
8Article number
2231Number
2231Pagination
1-13ISSN
2045-2322Department/School
Tasmanian Institute of Agriculture (TIA)Publisher
Nature Publishing GroupPlace of publication
United KingdomRights statement
Copyright 2018 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/Repository Status
- Open