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Predicting optimum crop designs using crop models and seasonal climate forecasts

Citation

Rodriguez, D and de Voil, P and Hudson, D and Brown, JN and Hayman, P and Marrou, H and Meinke, H, Predicting optimum crop designs using crop models and seasonal climate forecasts, Scientific Reports, 8, (1) Article 2231. ISSN 2045-2322 (2018) [Refereed Article]


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Copyright Statement

Copyright 2018 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1038/s41598-018-20628-2

Abstract

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.

Item Details

Item Type:Refereed Article
Keywords:crop design, seasonal climate forecasting, crop modelling, GCM, POAMA
Research Division:Agricultural and Veterinary Sciences
Research Group:Agriculture, Land and Farm Management
Research Field:Agricultural Production Systems Simulation
Objective Division:Plant Production and Plant Primary Products
Objective Group:Other Plant Production and Plant Primary Products
Objective Field:Forest Product Traceability and Quality Assurance
Author:Meinke, H (Professor Holger Meinke)
ID Code:124560
Year Published:2018
Deposited By:Office of the Tasmanian Institute of Agriculture
Deposited On:2018-02-26
Last Modified:2018-03-27
Downloads:4 View Download Statistics

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