Personal profile
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 2 Zero Hunger -
SDG 4 Quality Education -
SDG 8 Decent Work and Economic Growth -
SDG 9 Industry, Innovation, and Infrastructure -
SDG 11 Sustainable Cities and Communities -
SDG 12 Responsible Consumption and Production -
SDG 13 Climate Action -
SDG 17 Partnerships for the Goals
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Collaborations and top research areas from the last five years
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Estimation of global methane soil sink using synthesized datasets and knowledge-guided machine learning
UNIVERSITY OF COLORADO AT BOULDER, U.S. DEPARTMENT OF ENERGY (USDOE)
9/1/23 → 8/31/26
Project: Research project
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High-resolution integrated assessments of tillage practice impacts on crop production and agroecosystem sustainability in the US Midwest - combining meta-analysis, airborne-satellite sensing, and process-based modeling
Liu, L. (PI) & Jin, Z. (Prior Principal Investigator)
UNIV OF ILLINOIS AT URBANA-CHAMPAIGN, UNITED STATES DEPARTMENT OF AGRICULTURE
2/15/23 → 2/14/26
Project: Research project
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Improving the N rate calculator and MRTN for Illinois farmers by leveraging NREC historical N trials and new computing tools
Liu, L. (PI) & Jin, Z. (Prior Principal Investigator)
UNIVERSITY OF ILLINOIS, ILLINOIS NUTRIENT RESEARCH AND EDUCATION
10/1/21 → 11/30/25
Project: Research project
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SCC-IRG Track 1: Co-Producing Community - An integrated approach to building smart and connected nutrient management communities in the US Corn Belt
Liu, L. (PI), Jin, Z. (Prior Principal Investigator) & Jin, Z. (Other Role)
UNIV OF ILLINOIS AT URBANA-CHAMPAIGN, THE NATIONAL SCIENCE FOUNDATION
10/1/21 → 12/31/25
Project: Research project
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Advancing regenerative agriculture: An Airborne-Satellite-Artificial Intelligence integrative technique to monitor soil organic carbon in the U.S. Corn Belt
Liu, L. (PI) & Jin, Z. (Prior Principal Investigator)
UNIV OF ILLINOIS AT URBANA-CHAMPAIGN, FOUNDATION FOR FOOD AND AGRICULTURE RESE
1/2/21 → 12/31/24
Project: Research project
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Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics
Yang, J., Peng, B., Wang, Y., Ma, Z., Zhao, Q., Liu, L., Jia, X., Kumar, V., Pan, M., Jia, M., Li, X., Nieber, J., Jin, Z. & Guan, K., Jun 1 2026, In: Water Research. 297, 125613.Research output: Contribution to journal › Article › peer-review
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Knowledge-Guided Machine Learning for Global Change Ecology Research
Jin, Z., Liu, L., Yang, Q., Jia, X., Tao, S., Guo, Y., Ghosh, R., Wang, S., Zhu, Q., Jung, M., Guan, K., Kumar, V., Reichstein, M., Fang, J. & Luo, Y., Feb 2026, In: Global change biology. 32, 2, e70742.Research output: Contribution to journal › Review article › peer-review
Open Access -
Retraction notice to “Knowledge-guided machine learning captures key mechanistic pathways for better predicting spatio-temporal patterns of growing season N2O emissions in the U.S. Midwest” [Agricultural and Forest Meteorology 373 (2025) 110750] (Agricultural and Forest Meteorology (2025) 373, (S0168192325003697), (10.1016/j.agrformet.2025.110750))
Ye, L., Liu, L., Yang, Y., Li, Z., Zhou, W., Peng, B., Xu, S., Kumar, V., Yang, W. H., Tang, J., Jin, Z. & Guan, K., Mar 1 2026, In: Agricultural and Forest Meteorology. 378, 110998.Research output: Contribution to journal › Comment/debate › peer-review
Open Access -
Towards Fine-Tuning-Based Site Calibration for Knowledge-Guided Machine Learning
Zeng, R., Mulla, D. J., Shekhar, S., Sharma, A., An, S., Yang, M., Zhang, S. & Liu, L., Jan 2026.Research output: Contribution to conference › Paper › peer-review
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Knowledge-guided machine learning captures key mechanistic pathways for better predicting spatio-temporal patterns of growing season N2O emissions in the U.S. Midwest
Ye, L., Liu, L., Yang, Y., Li, Z., Zhou, W., Peng, B., Xu, S., Kumar, V., Yang, W. H., Tang, J., Jin, Z. & Guan, K., Oct 15 2025, In: Agricultural and Forest Meteorology. 373, 110750.Research output: Contribution to journal › Article › peer-review
Open Access1 Link opens in a new tab Scopus citations