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Yongfa You

Assistant Professor
Virginia Tech

yongfayou@vt.edu



Short Bio

I am a tenure-track Assistant Professor in the School of Plant and Environmental Sciences at Virginia Tech. My research program focuses on understanding, quantifying, and predicting the complex dynamics of agroecosystems in response to environmental challenges such as climate change, land use change, rising CO2 concentrations, nitrogen deposition, and disturbances like droughts and heatwaves. I also investigate how management practices, such as nitrogen fertilization, tillage, irrigation, and cover cropping, influence these systems. By combining process-based agricultural modeling, remote sensing, field observations, and emerging AI techniques, my goal is to develop actionable solutions and decision-support tools that advance sustainable agriculture while addressing critical environmental consequences, including soil degradation, water pollution, yield variability, and greenhouse gas emissions. Through my extension work, I aim to equip farmers, land managers, and other stakeholders with the knowledge and tools to participate in carbon markets and adopt practices that protect natural resources and improve the resilience and profitability of agricultural systems.

Research Interest

Education

Appointments

Grants and Selected Awards

Professional Affiliations

Research

  1. Agricultural Systems Modeling
    Developing and applying scalable agroecosystem models to understand, quantify, and predict the dynamics of agricultural systems under various environmental and management scenarios, bridging plot- to landscape- and regional-level insights.

  2. Agricultural Sustainable Assessment
    Developing and applying systematic frameworks to evaluate the environmental, economic, and social dimensions of agricultural systems, addressing critical challenges such as food security, climate change mitigation, water and air pollution, and resource optimization, to provide actionable insights that enhance agricultural resilience, improve resource efficiency, and ensure long-term sustainability.

  3. AI-Driven Predictions of Agricultural System Dynamics
    Leveraging AI techniques, such as machine learning and deep learning, integrated with multi-source data, to predict short-term agricultural system dynamics such as food production, greenhouse gas emissions, and soil organic carbon sequestration, facilitating proactive interventions to mitigate risks, boost productivity, and promote sustainable agricultural management.

  4. Science-Informed Solutions for Climate-Smart Agriculture
    Developing science-informed solutions and data-driven decision-support systems to promote climate-smart agriculture, facilitate carbon market participation, foster collaboration among farmers, policymakers, and industry leaders, and enhance extension programs for effectively translating research into actionable strategies.

Publications

Global nitrous oxide budget (1980–2020)
Tian, H., Pan, N., Thompson, R. L., Canadell, J. G., Suntharalingam, P., Regnier, P., Davidson, E. A., Prather, M., Ciais, P., Muntean, M., Pan, S., Winiwarter, W., Zaehle, S., Zhou, F., Jackson, R. B., Bange, H. W., Berthet, S., Bian, Z., Bianchi, D., Bouwman, A. F., Buitenhuis, E. T., Dutton, G., Hu, M., Ito, A., Jain, A. K., Jeltsch-Thömmes, A., Joos, F., Kou-Giesbrecht, S., Krummel, P. B., Lan, X., Landolfi, A., Lauerwald, R., Li, Y., Lu, C., Maavara, T., Manizza, M., Millet, D. B., Mühle, J., Patra, P. K., Peters, G. P., Qin, X., Raymond, P., Resplandy, L., Rosentreter, J. A., Shi, H., Sun, Q., Tonina, D., Tubiello, F. N., van der Werf, G. R., Vuichard, N., Wang, J., Wells, K. C., Western, L. M., Wilson, C., Yang, J., Yao, Y., You, Y., Zhu, Q. (2024) Earth System Science Data, 16(6), 2543–2604.
Balancing non‐CO2 GHG emissions and soil carbon change in U.S. rice paddies: A retrospective meta‐analysis and agricultural modeling study
Zhang, J., Tian, H., You, Y., Liang, X.Z., Ouyang, Z., Pan, N., Pan, S. (2024). AGU Advances, 5(1), p.e2023AV001052.
Exploring the impact of urban regeneration programs on wildlife and human well-being: A case study in Nanning, China
Chang, S., Su, K., Jiang, X., You, Y., Li, C., Wang, L. (2024). Ecological Indicators, 159, p.111640.
Net greenhouse gas balance in U.S. croplands: How can soils be part of the climate solution?
You, Y., Tian, H., Pan, S., Shi, H., Lu, C., Batchelor, W.D., Cheng, B., Hui, D., Kicklighter, D., Liang, X.Z., Li, X., Melillo, J., Pan, N., Prior, S. A., Reilly, J. (2023) Global Change Biology, 30(1), p.e17109.
Dynamics of crop production and greenhouse gas balance in a changing environment: Data-driven systems approach for sustainable agriculture in the United States
You, Y. (2023) Auburn University (PhD Dissertation)
Soil legacy nutrients contribute to the decreasing stoichiometric ratio of N and P loading from the Mississippi River Basin
Bian, Z., Tian, H., Pan, S., Shi, H., Lu, C., Anderson, C., Cai, W.-J., Hopkinson, C. S., Justic, D., Kalin, L., Lohrenz, S., McNulty, S., Pan, N., Sun, Ge, Wang, Z., Yao, Y., You, Y. (2023). Global Change Biology, 29(24), pp.7145-7158.
Uncertainty in land use obscures global soil organic carbon stock estimates
Gang, C., Shi, H., Tian, H., Pan, S., Pan, N., Xu, R., Wang, Z., Bian, Z., You, Y., Yao, Y. (2023). Agricultural and Forest Meteorology, 339, p.109585.
Dynamics of global dryland vegetation were more sensitive to soil moisture: Evidence from multiple vegetation indices
Liu, H., Liu, Y., Chen, Y., Fan, M., Chen, Y., Gang, C., You, Y., Wang, Z. (2023). Agricultural and Forest Meteorology, 331, p.109327.
Impacts and predictions of urban expansion on habitat connectivity networks: A multi-scenario simulation approach
Chang, S., Su, K., Jiang, X., You, Y., Li, C., Wang, L. (2023). Forests, 14(11), p.2187.
Construction and analysis of multi-species ecological network, a case study of the Southeast Qinghai–Tibetan Plateau
Zeng, J., Su, K., Li, C., Lu, J., Jiang, X., You, Y. (2023). Forests, 14(11), p.2149.
Identification of priority areas to provide insights for ecological protection planning: A case study in Hechi, China
Li, C., Su, K., Liang, X., Jiang, X., Wang, J., You, Y., Wang, L., Chang, S., Wei, C., Zhang, Y., Liao, Z. (2023). Ecological Indicators, 154, p.110738.
Assessing the impact of climate and human activities on ecosystem services in the Loess Plateau ecological screen, China
Wei, C., Zeng, J., Wang, J., Jiang, X., You, Y., Wang, L., Zhang, Y., Liao, Z., Su, K. (2023). Remote Sensing, 15(19), p.4717.
Spatiotemporal variation and coupling of grazing intensity and ecosystem based on four quadrant model on the Inner Mongolia
Liao, Z., Su, K., Jiang, X., Wang, J., You, Y., Wang, L., Chang, S., Wei, C., Zhang, Y., Li, C. (2023). Ecological Indicators, 152, p.110379.
How to optimize high-value GEP areas to identify key areas for protection and restoration: The integration of ecology and complex networks
Wang, L., Wang, S., Liang, X., Jiang, X., Wang, J., Li, C., Chang, S., You, Y., Su, K. (2023). Remote Sensing, 15(13), p.3420.
Response of ecosystem services to impervious surface changes and their scaling effects in Loess Plateau ecological screen, China
Zhang, Y., Su, K., Jiang, X., You, Y., Zhou, X., Yu, Z., Chen, Z., Wang, L., Wei, C., Liao, Z. (2023). Ecological Indicators, 147, p.109997.
Increase in precipitation and fractional vegetation cover promote synergy of ecosystem services in China’s arid regions—Northern sand-stabilization belt
Wei, C., Su, K., Jiang, X., You, Y., Zhou, X., Yu, Z., Chen, Z., Liao, Z., Zhang, Y., Wang, L. (2023). Frontiers in Ecology and Evolution, 11, p.1116484.
Incorporating dynamic crop growth processes and management practices into a terrestrial biosphere model for simulating crop production in the United States: Toward a unified modeling framework
You, Y., Tian, H., Pan, S., Shi, H., Bian, Z., Gurgel, A., Huang, Y., Kicklighter, D., Liang, X.Z., Lu, C., Melillo, J., Miao, R., Pan, N., Reilly, J., Ren, W., Xu, R., Yang, J., Yu, Q., Zhang, J. (2022). Agricultural and Forest Meteorology, 325, p.109144.
Using a remote-sensing-based piecewise retrieval algorithm to map chlorophyll-a concentration in a highland river system
Ma, Y., Sun, D., Liu, W., You, Y., Wang, S., Sun, Z., Wang, S. (2022). Remote Sensing, 14(23), p.6119.
Divergent responses of terrestrial carbon use efficiency to climate variation from 2000 to 2018
Gang, C., Wang, Z., You, Y., Liu, Y., Xu, R., Bian, Z., Pan, N., Gao, X., Chen, M., Zhang, M. (2022). Global and Planetary Change, 208, p.103709.
Growth stage-dependent responses of carbon fixation process of alpine grasslands to climate change over the Tibetan Plateau, China
You, Y., Wang, S., Pan, N., Ma, Y., Liu, W. (2020). Agricultural and Forest Meteorology, 291, p.108085.
Urban vegetation slows down the spread of coronavirus disease (COVID‐19) in the United States
You, Y., Pan, S. (2020). Geophysical Research Letters, 47(18), p.e2020GL089286.
A remote sensing method for retrieving chlorophyll-a concentration from river water body
Liu, W., Wang, S., Ma, Y., Shen, M., You, Y. (2020). Journal of Geo-information Science, 22(10), 2062-2077.
Improved modeling of gross primary productivity of alpine grasslands on the Tibetan Plateau using the Biome-BGC model
You, Y., Wang, S., Ma, Y., Wang, X., Liu, W. (2019). Remote Sensing, 11(11), p.1287.
Study on hierarchical building extraction from high-resolution remote sensing imagery
You, Y., Wang, S., Wang, B., Ma, Y., Shen, M., Liu, W., Xiao, L. (2019). Journal of Remote Sensing, 23(1), pp.125-136.
Remote sensing retrieval of turbidity in alpine rivers based on high spatial resolution satellites
Liu, W., Wang, S., Yang, R., Ma, Y., Shen, M., You, Y., Hai, K., Baqa, M.F. (2019). Remote Sensing, 11(24), p.3010.
Application of remote sensing to identify and monitor seasonal and interannual changes of water turbidity in Yellow River Estuary, China
Wang, S., Shen, M., Ma, Y., Chen, G., You, Y., Liu, W. (2019). Journal of Geophysical Research: Oceans, 124(7), pp.4904-4917.
Building detection from VHR remote sensing imagery based on the morphological building index
You, Y., Wang, S., Ma, Y., Chen, G., Wang, B., Shen, M., Liu, W. (2018). Remote Sensing, 10(8), p.1287.
Turbidity patterns identification based on self-organizing maps at Yellow River Estuary
Shen, M., Wang, S., Ma, Y., Su, L., You, Y. (2018). Journal of Geo-information Science, 20(8), 1190-1200.
Intelligent road and bridge disease detection method based on UAV images
Peng, Y., Wang, S., Fu, X., Shen, M., You, Y. (2017). Bulletin of Surveying and Mapping, (8), p.67.

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Ph.D. Position in Climate-Smart Agriculture at Virginia Tech

I am seeking a highly motivated Ph.D. Student to join my research group in the School of Plant and Environmental Sciences at Virginia Tech. Our research group addresses some of the most pressing challenges facing global agriculture and the environment. We are dedicated to monitoring, assessing, and predicting agroecosystem dynamics (e.g., crop yield, greenhouse gas emissions, and soil organic carbon) under various influencing factors, such as climate change, agricultural management, rising CO2 levels, land use changes, and disturbances like droughts and heatwaves. Our mission is to develop science-informed, actionable solutions that enhance food security, mitigate climate change, and promote sustainable and resilient agricultural systems. We employ data-driven systems approaches that integrate domain knowledge with process-based agricultural modeling, artificial intelligence, geospatial data science, and big data analytics.

For more information, please refer to this link .