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How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield?

Authors: orcid Claas Nendel;
Claas Nendel
ORCID
Harvested from ORCID Public Data File

Claas Nendel in OpenAIRE
orcid Eckart Priesack;
Eckart Priesack
ORCID
Harvested from ORCID Public Data File

Eckart Priesack in OpenAIRE
Enli Wang; Jon I. Lizaso; orcid Albert Olioso;
Albert Olioso
ORCID
Harvested from ORCID Public Data File

Albert Olioso in OpenAIRE
James W. Jones; orcid Kurt Christian Kersebaum;
Kurt Christian Kersebaum
ORCID
Harvested from ORCID Public Data File

Kurt Christian Kersebaum in OpenAIRE
+31 Authors

How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield?

Abstract

This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thunen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 models was less than 1 Mg ha−1. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.

Countries
Finland, France, France, Germany, France, France, France, France, United States
Keywords

[ SDV.BV ] Life Sciences [q-bio]/Vegetal Biology, 550, maíz de la india, évapotranspiration, 630, zea mays, modèle de culture, nutrition [F61 - Physiologie végétale], Atmospheric carbon dioxide concentration, Zea Mays ; Atmospheric Carbon Dioxide Concentration ; Multi-model Ensemble ; Stomata Conductance ; Grain Number ; Water Use, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, Photosynthèse, Multi-model ensemble, dióxido de carbono, climate change, Rendement des cultures, rendimiento, Grain number, ta1171, cambio climatico, approvisionnement eau, multi-model ensemble, Stomatal conductance, water use, Zea mays, 333, dioxyde de carbone, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, weather data, carbonic anhydride, ta415, U10 - Méthodes mathématiques et statistiques, ta1183, carbon dioxide, culture de mais, Modèle de simulation, yield, atmospheric carbon dioxide concentration, Évapotranspiration, donnée météorologique, uso del agua, stomatal conductance, estimation de rendement, grain number, concentration atmosphérique, Dioxyde de carbone, Water use, agrovoc: agrovoc:c_24242, agrovoc: agrovoc:c_8504, agrovoc: agrovoc:c_3245, agrovoc: agrovoc:c_1302, agrovoc: agrovoc:c_5812, agrovoc: agrovoc:c_2741, agrovoc: agrovoc:c_10176

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