Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Environment-sensitivity functions for gross primary productivity in light use efficiency models
Agricultural and Forest Meteorology, Volume 312, Article 108708, Year 2022
Notification
URL copied to clipboard!
Description
The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products. © 2021
Authors & Co-Authors
Wutzler, Thomas
Germany, Jena
Max Planck Institute for Biogeochemistry
Koirala, Sujan
Germany, Jena
Max Planck Institute for Biogeochemistry
Cuntz, Matthias
France, Champenoux
Centre de Recherche Grand Est-nancy
Ibrom, Andreas
Denmark, Lyngby
Technical University of Denmark
Besnard, Simon
Germany, Jena
Max Planck Institute for Biogeochemistry
Netherlands, Wageningen
Wageningen University & Research
Šigut, Ladislav
Germany, Jena
Max Planck Institute for Biogeochemistry
Czech Republic, Prague
Academy of Sciences of the Czech Republic
Moreno-Martínez, Álvaro
Spain, Valencia
Universitat de València
Weber, Ulrich
Germany, Jena
Max Planck Institute for Biogeochemistry
Wohlfahrt, Georg
Austria, Innsbruck
Universität Innsbruck
Cleverly, Jamie R.
Australia, Sydney
University of Technology Sydney
Migliavacca, Mirco
Germany, Jena
Max Planck Institute for Biogeochemistry
Woodgate, William L.
Australia, Brisbane
The University of Queensland
Australia, Canberra
Commonwealth Scientific and Industrial Research Organisation
Merbold, Lutz
Switzerland, Zurich
Forschungsanstalt Agroscope Reckenholz-tanikon
Veenendaal, Elmar M.
Netherlands, Wageningen
Wageningen University & Research
Carvalhais, Nuno
Germany, Jena
Max Planck Institute for Biogeochemistry
Portugal, Lisbon
Universidade Nova de Lisboa
Statistics
Citations: 19
Authors: 15
Affiliations: 12
Identifiers
Doi:
10.1016/j.agrformet.2021.108708
ISSN:
01681923
Research Areas
Environmental
Study Approach
Qualitative