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Crop design for specific adaptation in variable dryland production environments

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Hammer, G. L., McLean, G., Chapman, S., Zheng, B., Doherty, A., Harrison, M. T., van Oosterom, E. and Jordan, D. (2014) Crop design for specific adaptation in variable dryland production environments. Crop and Pasture Science, 65 (7). pp. 614-626.

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Article Link: http://dx.doi.org/10.1071/CP14088

Publisher URL: http://www.publish.csiro.au/paper/CP14088


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.

Item Type:Article
Business groups:Crop and Food Science
Keywords:crop improvement, crop modelling, G × E, genotype by environment interaction, plant breeding, trait simulation.
Subjects:Science > Botany > Genetics
Plant culture > Field crops
Live Archive:21 Jan 2015 04:12
Last Modified:03 Sep 2021 16:50

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