Responses to atmospheric CO₂ concentrations in crop simulation models: a review of current simple and semicomplex representations and options for model development

Abstract

Abstract

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

“…Improvement of crop models and parameterization is hindered by both the shortage of high‐quality experimental data, gaps in comprehensive experimental data sets as well as in full understanding of certain ecophysiological processes such as crop development, growth and grain formation and the various interactions between genotype, management and environment (Kersebaum et al., ). However, much more data exist than have actually been utilized to calibrate, and especially validate existing model structure and parameterization (Ainsworth & Long, ; Medlyn et al., ; Vanuytrecht & Thorburn, ). Up‐to‐date knowledge is often not exploited to improve model structure.…”

“…The reasons for the large uncertainty from crop model structure are complicated. For example these models have different modelling approaches for key crop development, growth, leaf area, photosynthesis and evapotranspiration, biomass accumulation and grain formation processes, as well as different temperature (Asseng et al., ; Wang et al., ) and CO 2 relationships (Duranda et al., ; Hasegawa et al., ; Kersebaum & Nendel, ; Vanuytrecht & Thorburn, ). It is essential to improve the model descriptions of temperature and CO 2 relationships and modelling approaches based on model comparison and evaluation and refinement utilizing suitable high‐quality experimental data (Hasegawa et al., ; Wang et al., ).…”

“…Therefore, model equations and model parameters should be further elaborated, for example by improving the temperature and CO 2 response functions in models using high‐quality experimental data under controlled environmental conditions (Asseng et al., ; Duranda et al., ; Gabaldón‐Leal et al., ; Maiorano et al., ; Rötter, Carter et al., ; Wang et al., ). The model description of CO 2 relationships is another key uncertainty source for future yield prediction needed to be reduced (Duranda et al., ; Hasegawa et al., ; Vanuytrecht & Thorburn, ). With the free‐air CO 2 enrichment (FACE) experimental data, a recent study showed that many maize models could not simulate the very low soil water content at anthesis and the increase in soil water and grain number brought about by elevated CO 2 concentration under dry conditions, model improvement with respect to simulating transpiration water use and its impact on water status during the kernel‐set phase was thereby suggested (Duranda et al., ).…”

“…Recently, uncertainty from model structure has been of key concern (Asseng et al., , ; Bassu et al., ; Castañeda‐Vera, Leffelaar, Álvaro‐Fuentes, Cantero‐Martínez, & Mínguez, ; Li et al., ; Martre et al., ; Palosuo et al., ; Pirttioja et al., ; Rötter, Carter et al., , ; Vanuytrecht & Thorburn, ; Wang et al., ). Major international efforts, such as the Modelling European Agriculture with Climate Change for Food Security (MACSUR) project (Ewert et al., ) and the Agricultural Model Inter‐comparison and Improvement Project (AgMIP) (Rosenzweig et al., ), have made great progress in quantifying and reducing uncertainty from model structure in simulating the response of crop yields to climate (e.g.…”

Increasing drought and diminishing benefits of elevated carbon dioxide for soybean yields across the US Midwest

“…(Jin et al., ). In APSIM, elevated [CO 2 ] stimulates C3 biomass production by directly increasing radiation use efficiency (RUE) (Vanuytrecht & Thorburn, ), which is modeled using a multiplier:in which [CO 2 ] e is the transient elevated [CO 2 ]; [CO 2 ] a is the ambient [CO 2 ] that equals 350 ppm by default; and TT is the CO 2 compensation point determined by daily mean temperature ( T mean ):…”