Abstract
A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. 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. 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). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. 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 CO2 (2 levels). 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). 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. Main (i.e. linear) and interaction (i.e. 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. 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.
Highlights
Progress in plant breeding is limited by the ability to predict plant phenotype based on its genotype, especially for complex traits such as yield
A number of such interactions has been reported in the literature [8, 9], 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. G×E > G as observed for field pea in Canada [10], sunflower in Argentina [11], sorghum in Australia [12], wheat in north-east Australia [13] and globally [14] and maize in Midwestern US states [15, 16] Modeling approaches have been developed to better understand genotype × environment × management (G×E×M) interactions and attempt to take advantage of genetic and environmental resources more efficiently
While the results from the sensitivity analysis strongly depend on the ranges of variation for the input traits, such ranges are scarcely available for all the considered traits despite numerous studies and reviews giving informative indications of partial genetic ranges for some traits [47,48,49,50]
Summary
Progress in plant breeding is limited by the ability to predict plant phenotype based on its genotype, especially for complex traits such as yield. Constructed process-based models provide a mean to reduce this gap in particular by dissecting the complexity of the genotypeenvironment interactions and by simulating expected impacts in various environmental conditions [1,2,3], including consideration of future climates [4, 5]. From a modeling point of view, crops are complex systems arising from interactions among genetic determinants, physiological processes, pedo-climatic factors and management practices. 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 [6, 7] and results in high genotype (G) × environment (E) × management (M) interactions. Hammer et al [17] show that the multi-year risk of crop failure for farms within a given sorghum region can be reduced by the adoption of better combinations of GxM (“local G” and “local M”) compared to use of the combination of “global G” and “global M” that would be adopted if using the entire sorghum production area
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