A number of such interactions has been reported in the literature, and sources of yield variation, especially in rainfed systems, commonly arise primarily from the genotype × environment (G×E) interactions, rather than the genotype (G), i.e. The combination of these elements, which are either chosen (cultivar and management) or given (soil and climate) in any sown crop, generates greatly variable stress patterns and results in high genotype (G) × environment (E) × management (M) interactions. įrom a modeling point of view, crops are complex systems arising from interactions among genetic determinants, physiological processes, pedo-climatic factors and management practices. Suitably constructed process-based models provide a mean to reduce this gap in particular by dissecting the complexity of the genotype-environment interactions and by simulating expected impacts in various environmental conditions, including consideration of future climates. Progress in plant breeding is limited by the ability to predict plant phenotype based on its genotype, especially for complex traits such as yield. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: This study was partly funded by INRA-Environment and Agronomy Division, the Queensland Alliance for Agriculture and Food Innovation (QAAFI), the Grains Research & Development Corporation (GRDC) and the ARC Centre of Excellence for Translational Photosynthesis. Received: JAccepted: DecemPublished: January 22, 2016Ĭopyright: © 2016 Casadebaig et al. Pennsylvania State University, UNITED STATES Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.Ĭitation: Casadebaig P, Zheng B, Chapman S, Huth N, Faivre R, Chenu K (2016) Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO 2 (2 levels). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. A crop can be viewed as a complex system with outputs (e.g.
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