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Ying-Ping Wang

As seen in: Nature, Wiley Online Library, Science Magazine, Cell Reports, HAL
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Articles

Higher optimal temperature for vegetation transpiration than for photosynthesis

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By Fangyue Zhang, Philippe Ciais, Paul C. Stoy, Josep Peñuelas, Xu Lian, Ying-Ping Wang, David Makowski, Yiqi Luo, Shuli Niu, Guirui Yu, Jing Huang, Xiang Wang, Einara Zahn, Zheng Fu, Haoyu Xia
| Nature Verified
Abstract Plants assimilate carbon through photosynthesis (gross primary productivity, GPP) while losing water via transpiration (Trans), with both processes responding nonlinearly to temperature. Although the air temperature optimum of GPP (\({T}_{\mathrm{opt}}^{\mathrm{GPP}}\)) is well studied, the thermal response of Trans (\({T}_{\mathrm{opt}}^{\mathrm{Trans}}\)) remains unknown.
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Microbial growth rate is a stronger predictor of soil organic carbon than carbon use efficiency - Nature Ecology & Evolution

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By Junxi Hu, Rebecca M Varney, Stefano Manzoni, Philippe Ciais, Ying-Ping Wang, Yongxing Cui, Rose Z. Abramoff, Erik Schwarz, Haicheng Zhang
| Nature Verified
Abstract The extent to which microbial processes control soil organic carbon (SOC) dynamics remains uncertain. Carbon use efficiency (CUE), that is, the fraction of assimilated carbon allocated to growth, has been used as a key parameter but its relationship with SOC reflects carbon partitioning rather than the absolute magnitude of microbial fluxes.
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Using explainable AI to diagnose the representation of environmental drivers in process-based soil organic carbon models

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By Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, CSIRO Agriculture
| HAL
Article Dans Une Revue Biogeosciences Année : 2025 Using explainable AI to diagnose the representation of environmental drivers in process-based soil organic carbon models Domaines Origine Fichiers éditeurs autorisés sur une archive ouverte Licence hal-05468831 , version 1 (21-01-2026) Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, Philippe Ciais, et al.. Using explainable AI to diagnose the representation of environmental drivers in process-based soil organic carbon models.
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