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Can the Responses of Photosynthesis and Stomatal Conductance to Water and Nitrogen Stress Combinations Be Modeled Using a Single Set of Parameters?

Accurately predicting photosynthesis in response to water and nitrogen stress is the first step toward predicting crop growth, yield and many quality traits under fluctuating environmental conditions. While mechanistic models are capable of predicting photosynthesis under fluctuating environmental c...

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Detalles Bibliográficos
Autores principales: Zhang, Ningyi, Li, Gang, Yu, Shanxiang, An, Dongsheng, Sun, Qian, Luo, Weihong, Yin, Xinyou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5368885/
https://www.ncbi.nlm.nih.gov/pubmed/28400773
http://dx.doi.org/10.3389/fpls.2017.00328
Descripción
Sumario:Accurately predicting photosynthesis in response to water and nitrogen stress is the first step toward predicting crop growth, yield and many quality traits under fluctuating environmental conditions. While mechanistic models are capable of predicting photosynthesis under fluctuating environmental conditions, simplifying the parameterization procedure is important toward a wide range of model applications. In this study, the biochemical photosynthesis model of Farquhar, von Caemmerer and Berry (the FvCB model) and the stomatal conductance model of Ball, Woodrow and Berry which was revised by Leuning and Yin (the BWB-Leuning-Yin model) were parameterized for Lilium (L. auratum × speciosum “Sorbonne”) grown under different water and nitrogen conditions. Linear relationships were found between biochemical parameters of the FvCB model and leaf nitrogen content per unit leaf area (N(a)), and between mesophyll conductance and N(a) under different water and nitrogen conditions. By incorporating these N(a)-dependent linear relationships, the FvCB model was able to predict the net photosynthetic rate (A(n)) in response to all water and nitrogen conditions. In contrast, stomatal conductance (g(s)) can be accurately predicted if parameters in the BWB-Leuning-Yin model were adjusted specifically to water conditions; otherwise g(s) was underestimated by 9% under well-watered conditions and was overestimated by 13% under water-deficit conditions. However, the 13% overestimation of g(s) under water-deficit conditions led to only 9% overestimation of A(n) by the coupled FvCB and BWB-Leuning-Yin model whereas the 9% underestimation of g(s) under well-watered conditions affected little the prediction of A(n). Our results indicate that to accurately predict A(n) and g(s) under different water and nitrogen conditions, only a few parameters in the BWB-Leuning-Yin model need to be adjusted according to water conditions whereas all other parameters are either conservative or can be adjusted according to their linear relationships with N(a). Our study exemplifies a simplified procedure of parameterizing the coupled FvCB and g(s) model that is widely used for various modeling purposes.