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Integrating effects of leaf nitrogen, age, rank, and growth temperature into the photosynthesis-stomatal conductance model LEAFC3-N parameterised for barley (Hordeum vulgare L.)

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  • Braune, Henning
  • Müller, Johannes
  • Diepenbrock, Wulf

Abstract

A crucial challenge for including biophysical photosynthesis–transpiration models into complex crop growth models is to integrate the plasticity of photosynthetic processes that is related to factors like nitrogen (N) content, age, and rank of leaves, or to the adaptation of plants to growth temperature (Tg). Here we present a new version of the combined photosynthesis-stomatal conductance model LEAFC3-N [Müller, J., Wernecke, P., Diepenbrock, W., 2005. LEAFC3-N: a nitrogen sensitive extension of the CO2 and H2O gas exchange model LEAFC3 parameterised and tested for winter wheat (Triticum aestivum L.). Ecological Modelling 183, 183–210.] that was revised, extended and completely re-parameterised for barley (Hordeum vulgare L.) with special regard for these factors to facilitate the use of the model in ecophysiological studies and in crop modelling. The analysis is based on novel comprehensive data on photosynthetic CO2 and light response curves measured at two oxygen concentrations and different temperatures on leaves of barley (H. vulgare L.) differing in leaf N and chlorophyll content. Plants were grown in climatic chambers or in the field at different N and Tg.

Suggested Citation

  • Braune, Henning & Müller, Johannes & Diepenbrock, Wulf, 2009. "Integrating effects of leaf nitrogen, age, rank, and growth temperature into the photosynthesis-stomatal conductance model LEAFC3-N parameterised for barley (Hordeum vulgare L.)," Ecological Modelling, Elsevier, vol. 220(13), pages 1599-1612.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:13:p:1599-1612
    DOI: 10.1016/j.ecolmodel.2009.03.027
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    References listed on IDEAS

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    1. Langensiepen, M. & Hanus, H. & Schoop, P. & Gräsle, W., 2008. "Validating CERES-wheat under North-German environmental conditions," Agricultural Systems, Elsevier, vol. 97(1-2), pages 34-47, April.
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    Cited by:

    1. Cao, XiaoLei & Zhou, ZuHao & Chen, XiangDong & Shao, WeiWei & Wang, ZiRu, 2015. "Improving leaf area index simulation of IBIS model and its effect on water carbon and energy—A case study in Changbai Mountain broadleaved forest of China," Ecological Modelling, Elsevier, vol. 303(C), pages 97-104.
    2. Honglei Ren & Xueyang Wang & Fengyi Zhang & Kezhen Zhao & Xiulin Liu & Rongqiang Yuan & Changjun Zhou & Jidong Yu & Jidao Du & Bixian Zhang & Jiajun Wang, 2023. "Salicylic Acid and Pyraclostrobin Can Mitigate Salinity Stress and Improve Anti-Oxidative Enzyme Activities, Photosynthesis, and Soybean Production under Saline–Alkali Regions," Land, MDPI, vol. 12(7), pages 1-15, June.
    3. Müller, Johannes & Eschenröder, André & Christen, Olaf, 2014. "LEAFC3-N photosynthesis, stomatal conductance, transpiration and energy balance model: Finite mesophyll conductance, drought stress, stomata ratio, optimized solution algorithms, and code," Ecological Modelling, Elsevier, vol. 290(C), pages 134-145.
    4. Zhang, Yanqun & Wang, Jiandong & Gong, Shihong & Xu, Di & Mo, Yan, 2019. "Straw mulching enhanced the photosynthetic capacity of field maize by increasing the leaf N use efficiency," Agricultural Water Management, Elsevier, vol. 218(C), pages 60-67.

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