Yang Song

Interests

Research

1. The interactions between environmental change and terrestrial biosphere processes, including land- surface energy balance, carbon, nitrogen, and phosphorus cycles, and the hydrological cycle.2. Using machine learning approaches to identify metagenomics-informed soil enzyme functional groups and their climate response at the regional and global scale.3. Using metagenomic information to improve the representation of microbially-mediated soil carbon, nitrogen, and phosphorus biogeochemical cycles in the Earth system models.4. Incorporating mechanisms informed with multi-scale observations and understandings of terrestrial processes into Earth system models to better simulate biospheric responses and feedbacks to environmental change.5. The impacts of ecosystem-climate-human interactions on ecosystem goods and services, focusing on agricultural production, bioenergy feedstock, carbon sequestration, and water supply.6. Development of big data and decision support tools for agriculture and bioenergy industry.

Courses

Scholarly Contributions

Journals/Publications

• Lin, T., Song, Y., Lawrence, P., Kheshgi, H. S., & Jain, A. (2021). Worldwide Maize and Soybean Yield Response to Environmental and Management Factors Over the 20th and 21st Centuries. JGR Biogeosciences, 126. doi:https://doi.org/10.1029/2021JG006304
  More info

  A land process model, Integrated Science Assessment Model, is extended to simulate contemporary soybean and maize crop yields accurately and changes in yields over the period 1901–2100 driven by environmental factors (atmospheric CO2 level ([CO2]) and climate), and management factors (nitrogen input and irrigation). Over the twentieth century, each factor contributes to global yield increase; increasing nitrogen fertilization rates is the strongest driver for maize, and increasing [CO2] is the strongest for soybean. Over the 21st century, crop yields are projected under two future scenarios, RCP4.5-SSP2 and RCP8.5-SSP5; the warmer temperature drives yields lower, while rising [CO2] drives yields higher. The adverse warmer temperature effect of maize and soybean is offset by other drivers, particularly the increase in [CO2], and resultant changes in the phenological events due to climate change, particularly planting dates and harvesting times, by 2090s under both scenarios. Global yield for maize increases under RCP4.5-SSP2, which experiences continued growth in [CO2] and higher nitrogen input rates. For soybean, yield increases at a similar rate. However, in RCP8.5-SSP5, maize yield declines because of greater climate warming, extreme heat stress conditions, and weaker nitrogen fertilization than RCP4.5-SSP2, particularly in tropical and subtropical regions, suggesting that application of advanced technologies, and stronger management practices, in addition to climate change mitigation, may be needed to intensify crop production over this century. The model also projects spatial variations in yields; notably, the higher temperatures in tropical and subtropical regions limit photosynthesis rates and reduce light interception, resulting in lower yields, particularly for soybean under RCP8.5-SSP5.
• Lin, T., Song, Y., Jain, A., Lawrence, P., & Kheshgi, H. S. (2020). Effects of environmental and management factors on worldwide maize and soybean yields over the 20th and 21st centuries. Biogeosciences Discuss. doi:https://doi.org/10.5194/bg-2020-68

Presentations

• Hu, T., Biederman, J., Smith, W. K., Zeng, X., & Song, Y. (2021, Dec). The feedback of Arizona Grassland to Longer Seasonal Droughts and its Implication for Dryland Carbon Cycling: Insights from Model-Experiment Integration. AGU2021. Online.
• Song, Y. (2021, Fall). Environmental resilience of vegetation and microbial communities from scientific understanding to real-world application. Civil & Architectural Engineering & Mechanics Department, University of Arizona. Tucson, AZ.
• Song, Y. (2021, Spring). Identifying enzyme functional composition across diverse environments for parameterizing microbial function in land surface models. RUBISCO Soil Carbon Work Group Meeting. Online: DOE.
• Song, Y., & Neri, P. (2021, Aug). The Climate Feedback of Light Reaction: A Meta-Analysis Utilizing SIF. DOE ESS PI meeting 2021. Online: DOE.
• Song, Y., Fan, C., Gautam, S., & Mishra, U. (2021, Dec). Microbial functional composition across diverse environments: a dataset to parameterize and benchmark microbial-mediated soil organic matter decomposition models. AGU2021. Online.
• Song, Y., Hu, T., Neri, P., Biederman, J., & Smith, W. K. (2021, Fall). The feedback of Arizona Grassland to Longer Seasonal Droughts and its Implication for Dryland Carbon Cycling: Insights from Model-Experiment Integration. 2021 Symposium on Resilience Research for Global Development Challenges. Tucson, AZ.
• Song, Y. (2020, 08/2020). Modeling microbial functional diversity mitigates projected soil carbon loss in response to climate warming. ESA 2020. Online.

Poster Presentations

• Hu, T., Biederman, J., Smith, W. K., Zeng, X., & Song, Y. (2021, Nov). The effect of Biological and Physical Processes on Soil Water Dynamics and its Feedback to Arizona Grassland. The 17th Annual Research Insights in Semiarid Ecosystem Symposium. Tucson AZ: USDA.
• Neri, P. J., Gu, L., & Song, Y. (2021, Dec). The Climate Feedback of Potential Photosynthetic Efficiency: A Meta-Analysis Utilizing SIF. AGU 2021. Online.