A new predictive model for Plants Photosynthesis Influenced by Major Climatic Conditions

2019 3rd International Conference on Energy and Environmental Science

能源学;生态环境科学

Liu, Z.S.^1 ; Yang, W.Y.^2 ; Yu, X.L.^3

College of Agriculture, Shandong Agricultural University, Taian, Shandong, China^1

College of Automation, Shenyang Aerospace University, Shenyang, Liaoning, China^2

School of Science, Beijing University of Posts and Telecommunications, Beijing, China^3

关键词: Casual relationships;    Climatic conditions;    Data and information;    Non-linear relationships;    Photosynthesis rate;    Predictive modeling;    Short-wave radiation;    Vegetation biomass;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/291/1/012016/pdf
DOI  :  10.1088/1755-1315/291/1/012016

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Climate change, particularly global warming, is significantly affected by atmospheric CO2 dynamics. Plant photosynthesis is capable of fixing a large amount of airborne CO2 and converts it into vegetation biomass and thus alleviates the greenhouse effect from atmospheric CO2. However, how climate change and climate condition impact the dynamics of plant photosynthesis is still highly uncertain. Here we combined high frequency land surface measurements of photosynthetic CO2 fixation data and information theory to understand the casual relationship from climate drivers on the photosynthesis rate. We found that temperature and shortwave radiation dominated photosynthesis more at forest site, while precipitation dominated photosynthesis more at grass land site. More importantly, linear regression based analysis failed to identify such relationships, which confirmed the important role of information theory in identifying nonlinear relationship within a complex system.