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Modelling Wheat Stomatal Resistance in Hourly Time Steps from Micrometeorological Variables and Soil Water Status
Author(s) -
Neukam D.,
Böttcher U.,
Kage H.
Publication year - 2016
Publication title -
journal of agronomy and crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.095
H-Index - 74
eISSN - 1439-037X
pISSN - 0931-2250
DOI - 10.1111/jac.12133
Subject(s) - vapour pressure deficit , transpiration , environmental science , atmosphere (unit) , atmospheric sciences , air temperature , photosynthesis , soil water , water stress , vegetation (pathology) , water balance , energy balance , soil science , evapotranspiration , agronomy , meteorology , chemistry , ecology , medicine , physics , geotechnical engineering , engineering , pathology , biology , geology , biochemistry
An accurate estimation of stomatal resistance ( r S ) also under drought stress conditions is of pivotal importance for any process‐based prediction of transpiration and the energy budget of real crop canopies and quantification of drought stress. A new model for r S was developed and parameterized for winter wheat using data from field experiments accounting for the influences of net radiation ( R Net ), air temperature ( T Air ) and vapour pressure deficit of the atmosphere ( VPD ) interacting with an average water potential in the rooted soil (ψ RootedSoil ). r S is simulated with a limiting factor approach as maximum of the metabolic (related to photosynthesis) and hydraulic (related to drought stress) acting influences assuming that, if drought stress occurs, it will dominate stomatal control: r S = max( r S ( T Air ), r S ( R Net ), r S ( VPD , ψ RootedSoil )). This transitional approach is suited to reproduce measured daily time courses of r S with a varying accuracy for the single measurement dates but performed satisfactorily for the whole data set (r 2 = 0.63, RMSE = 59 s m −1 , EF = 0.60). This new semi‐empiric approach calculates r S directly from external environmental conditions. Therefore, it can be easily implemented in existing model frameworks as link between operational crop growth models that use the concept of radiation use efficiency instead of mechanistic photosynthesis modelling and soil–vegetation–atmosphere transport models.