Machine learning models for net photosynthetic rate prediction using poplar leaf phenotype data.

Journal: PloS one
PMID: 32045452

Abstract

BACKGROUND: As an essential component in reducing anthropogenic CO2 emissions to the atmosphere, tree planting is the key to keeping carbon dioxide emissions under control. In 1992, the United Nations agreed to take action at the Earth Summit to stabilize and reduce net zero global anthropogenic CO2 emissions. Tree planting was identified as an effective method to offset CO2 emissions. A high net photosynthetic rate (Pn) with fast-growing trees could efficiently fulfill the goal of CO2 emission reduction. Net photosynthetic rate model can provide refernece for plant's stability of photosynthesis productivity.

Authors

  • Xiao-Yu Zhang
  • Ziyuan Huang
    Data Science, Harrisburg University of Science and Technology, Harrisburg, PA, United States of America.
  • Xuehui Su
    Jiaozuo Academy of Agriculture and Forestry Sciences, Jiaozuo, P. R. China.
  • Andrew Siu
    Amgen Inc., Thousand Oaks, CA, United States of America.
  • Yuepeng Song
    College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P. R. China.
  • Deqiang Zhang
    College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P. R. China.
  • Qing Fang
    Faculty of Science, Yamagata University, Yamagata, Japan.

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