PUBLICATIONS & PRODUCTS

Publications

  • Neri, P., Gu, L., and Song, Y*.: The effect of temperature on photosystem II efficiency across plant functional types and climate, Biogeosciences, 21, 2731–2758, https://doi.org/10.5194/bg-21-2731-2024, 2024.
  • Gu, L., Grodzinski, B., Han, J., Marie, T., Zhang, Y., Song, Y., Sun, Y., 2023. An exploratory steady‐state redox model of photosynthetic linear electron transport for use in complete modelling of photosynthesis for broad applications. Plant, Cell & Environment.. https://doi.org/10.1111/pce.14563
  • Lin, T., Kheshgi, H.S, Song, Y., Vorosmarty C. J., and Jain A., K. (2023), Which crop has the highest bioethanol yield in the United States? Frontiers in Energy Research. https://doi.org/10.3389/fenrg.2023.1070186
  • Gu, L., Grodzinski, B., Han, J., Marie, T., Zhang, Y., Song, Y., Sun, Y. (2022), Granal thylakoid structure and function: explaining an enduring mystery of higher plants. New Phytologist. DOI: https://doi.org/10.1111/nph.18371.
  • Lin, T., Song, Y., Lawrence, P., Kheshgi, H.S., Jain, A.K. (2021). Worldwide Maize and Soybean Yield Response to Environmental and Management Factors Over the 20th and 21st Centuries. Journal of Geophysical Research: Biogeosciences 126.. doi:10.1029/2021jg006304.
  • Song Y, Yao Q, Yang X et al. Metagenomics-informed soil biogeochemical models projected less carbon loss in tropical soils in response to climate warming, 20 September 2021, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-918612/v1].
  • Lin, T-S, Song, Y., Jain, A K, Lawrence, P, and Kheshgi, H. S. (2020): Effects of environmental and management factors on worldwide maize and soybean yields over the 20th and 21st centuries, Biogeosciences Discuss., https://doi.org/10.5194/bg-2020-68. 
  • Yao Q, Li Z, Song Y, Wright J S, Guo X, Biswas A, Tringe S G, Tfazly M M , Pasa-Tolic L, Hazen C T, Turner L B, Mayes M and Pan C (2019) :  Community proteogenomics reveals the systemic impact of Phosphorus availability on microbial functions in tropical soil, Nature Ecology & Evolution, 2: 499-509.
  • Johnston, R E, Kim M, Hatt K J, Phillips R J, Yao Q, Song Y, Hazen C T, Mayes A M and Konstantinidis T K (2019) : Phosphate addition increases tropical soil respiration primarily by deconstraining microbial population growth, Soil Biology and Biochemistry, 130: 43-54. [Link]
  • Song Y, Jain A K, Landuyt W, Kheshgi H S (2016): The interplay between bioenergy grass production and water resources in the United States of America, Environmental Science and Technology, 50(6) : 3010-3019. [PDF]
  • Fu Y H, Piao S, Ciais P, Huang M, Menzel A, Peaucelle M, Peng S, Song Y, Vitasse Y, Zeng Z, Zhao H, Zhou G; Peñuelas J; Janssens I A (2016): Long-term linear trends mask phenological shifts, International Journal of Biometeorology, 60(11) : 1611-1613.
  • Fu Y H, Zhao H, Piao S, Peaucelle M, Peng S, Zhou G, Ciais P, Huang M, Menzel A, Penuelas J, Song Y, Vitasse Y, Zeng Z, and Janssens I A (2016): Declining global warming effects on the phenology of spring leaf unfolding. Nature, 526(7571), 104–107.
  • Song Y, Jain A K, Landuyt W, Kheshgi H S, and Khanna M (2014): Estimates of Biomass Yield for Perennial Bioenergy Grasses in the United States. Bioenergy Research, 8(2), 688-715. [PDF] 
  • Niyogi D, Liu X, Andresen J, Song Y, Jain A K, Kellner O, Takle E S, Doering O C (2014): Crop Models can capture the impacts of  climate variability on corn yield, Geophysical Research Letters, 42(9): 3356-3363.
  • Housh M, Cai X, Ng T, McIsaac G, Ouyang Y, Khanna M, Jain A, Eckhoff S, Gasteyer S, AI-Qadi I, Bai Y, Yaeger M, Ma S, and Song Y (2014): System of systems model for analysis of biofuel development. Journal of Infrastructure Systems, doi : 10.1061/(ASCE)IS.1943-555X.0000238
  • Song Y, Jain A K, and Mclsaac G F (2013): Implementation of dynamic crop growth processes into a land surface model : evaluation of energy, water and carbon fluxes under corn and soybean rotation. Biogeosciences, 10: 8039-8066. [PDF]
  • El-Masri B, Barman R, Meiyappan P, Song Y, Liang, M, and Jain, A K (2013): Carbon dynamics in the Amazonian Basin : Integration of eddy covariance and ecophysiological data with a land surface model. Agricultural and Forest Meteorology, 182 : 156-157.
  • Jain A K, Meiyappan P, Song Y, and House J I (2013) : CO2 emissions from land use change affected more by nitrogen cycle, than by the choice of land cover data. Global Change Biology, 19: 2893-2906.
  • Wang Z P, Song Y, Han X G, Yu Q, and Gulledge J (2009): China’s grazed temperate grassland are a net source of atmospheric methane. Atmospheric Environment, 13(43): 2148-2153.
  • Wang Z P, Han X G, Wang G G, Song Y, and Gulledge J (2008): Aerobic methane emission from plants in the Inner Mongolia steppe. Environmental Science and Technology, 42(1): 62-68.

Products

  • The Machine Learning model for predicting the omics-informed spatial distribution of soil enzyme functional classes
  • Continuum Microbial Enzyme Decomposition Model (CoMEND)
  • Integrated Science Assessment Model (ISAM)