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The potential of crop models in simulation of barley quality traits under changing climates: A review

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisReviewForskningpeer review



  • Ehsan Eyshi Rezaei, Leibniz Centre for Agricultural Landscape Research
  • ,
  • Luis Vargas Rojas, Purdue University
  • ,
  • Wanxue Zhu, Leibniz Centre for Agricultural Landscape Research, University of Göttingen
  • ,
  • Davide Cammarano

Most of the experimental and modeling studies that evaluate the impacts of climate change and variability on barley have been focused on grain yield. However, little is known on the effects of combined change in temperature, CO2 concentration, and extreme events on barley grain quality and how capable are the current process-based crop models capture the signal of climate change on quality traits. Here in this review, we initially explored the response of quality traits of barley to heat, drought, and CO2 concentration from experiential studies. Next, we reviewed the state of the art of some of the current modeling approaches to capture grain quality. Lastly, we suggested possible opportunities to improve current models for tracking the detailed quality traits of barley. Heat and drought stress increase the protein concentration which has a negative effect on malting quality. The rise of CO2 concentration significantly reduces the grain protein, again resulting in a decline of the malting and brewing quality since the nitrogen concentration of grains needs to be kept at a specific level. The current crop models that simulate barley grain quality are limited to simulation of grain nitrogen concentration, size, and number in response to climate extremes and CO2. Nevertheless, crop models fail to account for the complex interactions between the conflicting effects of rising temperatures and droughts as well as increasing CO2 concentrations on grain protein. They have mainly adapted wheat models that cannot capture barley's protein composition and whole grain malting quality. Implementation of experiments from gene to canopy scales which are explicitly designed to detect the interactions among environmental variables on detailed quality traits and couple the remote sensing plus data-driven approaches to crop models are possible opportunities to improve modeling of barley grain quality. The development of modeling routines can capture the detailed grain quality provide valuable tools for forming climate adaptive strategies. Equally important, they can guide breeding programs to develop climate-resilient but high-quality barley genotypes.

TidsskriftField Crops Research
Antal sider11
StatusUdgivet - okt. 2022

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