Crop design for specific adaptation in variable dryland production environments
Graeme L. Hammer A F , Greg McLean B , Scott Chapman C , Bangyou Zheng C , Al Doherty B , Matthew T. Harrison D , Erik van Oosterom A and David Jordan E
+ Author Affiliations
- Author Affiliations
A The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, Qld 4072, Australia.
B Department of Agriculture, Forestry, and Fisheries, PO Box 102, Toowoomba, Qld 4350, Australia.
C CSIRO Plant Industry, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, Qld 4067, Australia.
D Tasmanian Institute of Agriculture, PO Box 3523, Burnie, Tas. 7320, Australia.
E The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Hermitage Research Station, 604 Yangan Road, Warwick, Qld 4370, Australia.
F Corresponding author. Email: g.hammer@uq.edu.au
Crop and Pasture Science 65(7) 614-626 https://doi.org/10.1071/CP14088
Submitted: 21 March 2014 Accepted: 7 July 2014 Published: 7 August 2014
Abstract
Climatic variability in dryland production environments (E) generates variable yield and crop production risks. Optimal combinations of genotype (G) and management (M) depend strongly on E and thus vary among sites and seasons. Traditional crop improvement seeks broadly adapted genotypes to give best average performance under a standard management regime across the entire production region, with some subsequent manipulation of management regionally in response to average local environmental conditions. This process does not search the full spectrum of potential G × M × E combinations forming the adaptation landscape. Here we examine the potential value (relative to the conventional, broad adaptation approach) of exploiting specific adaptation arising from G × M × E. We present an in-silico analysis for sorghum production in Australia using the APSIM sorghum model. Crop design (G × M) is optimised for subsets of locations within the production region (specific adaptation) and is compared with the optimum G across all environments with locally modified M (broad adaptation). We find that geographic subregions that have frequencies of major environment types substantially different from that for the entire production region show greatest advantage for specific adaptation. Although the specific adaptation approach confers yield and production risk advantages at industry scale, even greater benefits should be achievable with better predictors of environment-type likelihood than that conferred by location alone.
Additional keywords: crop improvement, crop modelling, G × E, genotype by environment interaction, plant breeding, trait simulation.
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