于强，男，1962年10月生，西北农林科技大学教授，博士生导师，国家海外高层次人才引进人才，陕西省“三秦学者”智慧农业创新团队负责人。1994年获得南京大学气候学专业博士学位。先后在荷兰瓦赫宁根大学、美国农业部、澳大利亚科学与工业研究组织、加拿大阿尔伯塔大学做访问研究，2009-2019担任悉尼科技大学教授。曾任中国农学会农业气象分会副理事长、中澳水资源联合研究中心秘书长、中科院禹城综合试验站副站长、栾城生态试验站站长及生态系统网络观测与模拟重点实验室副主任等职。主要从事农田生态系统生产力与水分和养分循环机理的实验与模型研究，主持和参加完成国家863课题、国家973项目、国家自然科学基金等研究项目20余项。近年来，主要开展智慧农业模型算法研究。已在Science、Nature Food、GCB、RSE等国际知名学术期刊上发表SCI论文300余篇，已培养博士研究生30多名。

1980.09-1984.07 南京气象学院，农业气象学学士
1984.09-1987.06 中国农业大学，农业气象学硕士
1991.09-1994.11 南京大学，气候学专业博士
1994.11-1996.12 中国科学院上海植物生理生态研究所博士后
1997.01-1998.04 中国科学院大气物理研究所副研究员
1998.04-2009.02 中国科学院地理科学与资源研究所研究员（期间分别任禹城综合试验站副站长、栾城生态农业试验室站长、生态网络观测与模拟院重点实验室副主任等职）
1999.12-2000.06 荷兰瓦赫宁根大学访问学者
2003.01-2003.07 美国农业部，访问学者
2005.01-2006.01 澳大利亚联邦科工组织McMaster学者
2006.03-2006.08 加拿大阿尔伯塔大学访问学者
2009.03-2019.12 澳大利亚悉尼科技大学教授
2016.06-今 西北农林科技大学 研究员

智慧农业、气候变化对农业的影响、生态过程与模拟

中国科学院“百人计划”项目：作物生产力机理与模型（1998-2000），主持
国家自然科学基金面上项目“地下水对地面过程的作用及对农业生态的影响”（40071008,2001-2003），主持
中国科学院重大项目“华北平原典型农田生态系统碳通量观测研究”（KZCX-SW-01-01B-12，2002-2005），主持
科技部“973计划”项目“碳循环中国陆地生态系统碳循环及其驱动机制研究”课题：“典型陆地生态系统碳通量/储量的对比研究”，子课题：‘华北平原农田生态系统碳通量/储量观测研究’（2002CB412501，2003-2007），主持
中国科学院国际合作团队计划：“人类活动与生态系统变化”，主持第二课题‘区域生态系统格局、过程和服务功能变化及其驱动机制’。 （CXTD-Z2005-1，2005-2008）
科技部“973计划”项目“我国农田生态系统重要生态过程与调控对策研究”，课题‘主要作物产量形成的生理生态学机制与水肥供需协调原理’专题负责人。（2005CB121106，2005-2010）
科技部“863计划”探索导向类项目：“华北平原作物水分过程与产量形成的协同模型”。（2006AA10Z223，2007-2010），主持
科技部“863计划”探索导向类项目：“华北平原粮食作物生产力和水资源利用分析系统（GIS-ChinaAgrosys）的研发”。（2008AA10Z215，2008-2010），主持
自然科学基金面上项目：“华北平原农业生产对气候变化的响应机制及其模拟研究”。（41171086，2012-2015），主持
自然科学基金面上项目：“作物生产系统中水分传输阻力的实验解析与水分利用效率的模拟研究”。（41371119，2014-2017），主持
中国科学院国际合作伙伴计划“气候变化对中国黄河中游和塞尔维亚萨瓦河流域农业水土环境影响评估及适应对策”课题四“农业水土环境适应气候变化的管理策略”。（161461KYSB20170013，2018-2020），主持
自然科学基金重点项目：“陆地水循环过程的综合集成与模拟”，主持子课题“作物需水和生产力形成对气候变化的响应模拟及其参数化”。（41730645，2018-2022）
自然科学基金国际（地区）合作与交流项目：“全球变化背景下“粮食-能源-水”耦合系统的多尺度模拟与整体优化：黄河与密西西比河流域可持续性比较研究”。（41961124006，2020-2023），主持

1 Jiang, T.C., He, L., Feng, H., He, J.Q.*, Yu, Q.*, 2025. Understanding the impacts of extreme temperature and humidity compounds on winter wheat traits in China. Agricultural and Forest Meteorology. 362: 110354.
2 Li, L.C., Wang, B.*, Feng, P.Y., Lu, C.Q., Jagermeyr, J., Asseng, S., Luo, J.J., Harrison, M.T., He, Q.S., Liu, K., Liu, D.L., Li, Y., Feng, H., Yang, G.J.*, Zhao, C.J., Siddique, K.H.M., Tian, H.Q., Yu, Q.*, 2025. Global warming increases the risk of crop yield failures driven by climate oscillations. One Earth, 8(6):
3 Kang, X.F., Wu, D.R.*, Tan, J.J., Wang, P.J., Ma, Y.P., Yang, J.Y., Wang, C.Y., Huo, Z.G., Tian, Q., Yu, Q.*, 2024. Performance of nine maize phenology models in China under historical climate change conditions. Agricultural and Forest Meteorology. 358: 110234.
4 Li, C.C., Zhang, X.Z., Guo, J.P., Yu, Q.*, Zhang, Y.Q.*, 2024. Decadal variation and trend of boundary layer height and possible contribution factors in China. Agricultural and Forest Meteorology, 347: 109910.
5 Wang, B.*, Jagermeyr, J., OLeary, G., Wallach, D., Ruane, A.C., Feng, P.Y., Li, L.C., Liu, D.L., Waters, C., Yu, Q., Asseng, S.*, Rosenzweig, C., 2024. Pathways to indentify and reduce uncertainties in agricultural climate impact assessments. Nature Food, 5(7): 550-556.
6 Yang, J.J., Lu, X.L., Liu, Z.Q., Tang, X.H., Yu, Q.*, Wang, Y.F.*, 2024. Atmospheric drought dominates changes in global water use efficiency. Science of the Total Environment, 934: 173084.
7 Jiang, T.C., Wang, B., Duan, X.N., Liu, D.L., He, J.Q., He, L., Jin, N., Feng, H., Yu, Q.*, 2023. Prioritizing agronomic practices and uncertainty assessment under climate change for winter wheat in the Loess Plateau, China. Agricultural Systems 212: 103770.
8 Li, L.C., Wang, B.*, Feng, P.Y., Jagermeyr, J., Asseng, S., Muller, C., Macadam, I., Liu, D.L.*, Waters, C., Zhang, Y.J., He, Q.S., Shi, Y., Chen, S., Guo, X.W., Li, Y., He, J.Q., Feng, H., Yang, G.J., Tian, H.Q., Yu, Q.*, 2023. The optimizing of model ensemble composition and size can enhance the robustness of crop yield projections. Communications Earth & Environment. 4:362.
9 Zhang, Y.Q.*#, Li, C.C.#, Chiew F.H.S., Post D.A., Zhang, X.Z., Ma, N., Tian, J., Kong, D.D., Leung, L.R., Yu, Q., Shi, J.C., Liu, C.M., 2023. Southern Hemisphere dominates recent decline in global water availability. Science, 382: 579-584.
10 He, Q.S., Wang, B.*, Li, L.C., Cowie, A., Simmons, A., Zhou, H.X., Tian, Q., Li, S.E., Li, Y., Liu, K., Yan, H.L., Harrison, M.T., Waters, C., de Voil, P., Yu, Q.*, Liu, D.L.*, 2022. Identifying effective management practices for climate change adaptation and mitigation: A win-win strategy in southeast Australia. Agricultural Systems, 202: 103527.
11 Li, S.Y., Wang, B.*, Feng, P.Y., Liu, D.L.*, Li, L.C., Shi, L.J., Yu, Q.*, 2022. Assessing climate vulnerability of historical wheat yield in south-eastern Australia’s wheat belt. Agricultural Systems, 196: 103340.
12 Shi, Y., Zhang, Y.J., Wu, B.Y., Wang, B., Li, L.C., Shi, H., Jin, N., Liu, D.L., Miao, R.Q., Lu, X.L., Geng, Q.L., Lu, C.Q., He, L., Fang, N.F., Yue, C., He, J.Q., Feng, H., Pan, S.F., Tian, H.Q.*, Yu, Q.*, 2022. Building social resilience in North Korea can mitigate the impacts of climate change on food security. Nature Food, 3(7): 499-511
13 Cheng, G., Harmel, R.D.*, Ma, L.W., Derner, J.D., Augustine, D.J., Bartling, P.N.S., Fang, Q.X., Williams, J.R., Zilverberg, C.J., Boone, R.B., Hoover, D., Yu, Q.*, 2021. Evaluation of APEX modifications to simulate forage production for grazing management decision-support in the Western US Great Plains. Agricultural Systems, 191: 103139.
14 Li, L.C., Wang, B., Feng, P.Y., Wang, H.H., He, Q.S., Wang, Y.K., Liu, D.L., Li, Y., He, J.Q., Feng, H., Yang, G.J.*, Yu, Q.*, 2021. Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China. Agricultural and Forest Meteorology, 309-309: 108558.
15 Feng, P.Y., Wang, B.*, Liu, D.L.*, Waters, C., Xiao, D.P., Shi, L.J., Yu, Q.*, 2020. Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique. Agricultural and Forest Meteorology, 285-286: 107922.
16 Shi, Y., Jin, N.*, Ma, X.L., Wu, B.Y, He, Q.S., Yue, C., Yu, Q.*, 2020. Attribution of climate and human activities to vegetation change in China using machine learning techniques. Agricultural and Forest Meteorology, 294: 108146.
17 Wang, B., Feng, P.Y., Water, C., Cleverly, J., Liu, D.L., Yu, Q.*, 2020. Quantifying the impacts of pre-occurred ENSO signals on wheat yield variation using machine learning in Australia. Agricultural and Forest Meteorology, 291: 108043.
18 Wang, B., Feng, P.Y., Liu, D.L.*, O’leary G.J., Macadam, I., Waters, C., Asseng, S., Cowie, A.L., Jiang, T.C., Xiao, D.P., Ruan, H.Y., He, J.Q., Yu, Q.*, 2020. Sources of uncertainty for wheat yield projections under future climate are site-specific. Nature Food, 1(11): 720-728.
19 Wu, D.R., Wang, P.J., Jiang, Z.Y., Huo, Z.G., Shi, K.Q., Yang, Y., Yu, Q.*, 2020. Use of a plastic temperature response function reduces simulation error of crop maturity date by half. Agricultural and Forest Meteorology, 280: 107770.
20 Zhuang, W., Shi, H., Ma, X.L., Cleverly, J., Beringer, J., Zhang, Y., He, J., Eamus, D., Yu, Q.*, 2020. Improving Estimation of Seasonal Evapotranspiration in Australian Tropical Savannas using a Flexible Drought Index. Agricultural and Forest Meteorology, 295: 108203.
21 Feng, P.Y., Wang, B., Liu, D.L., Waters, C., Yu, Q.*, 2019. Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia. Agricultural and Forest Meteorology, 275: 100-113.
22 Feng, P.Y., Wang, B., Liu, D.L., Yu, Q.*, 2019. Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in south-eastern Australia. Agricultural Systems, 173: 303-316.
23 Feng, P.Y.*, Wang, B.*, Macadam, I., Taschetto, A.S., Abram, N.J., Luo, J.J., King, A.D., Chen, Y., Li, Y., Liu, D.L., Yu, Q., Hu, K.M.*, 2022. Increasing dominance of Indian ocean variability impacts Australian wheat yields. Nature Food, 3(10):862-870.
24 Zu, Q.*, Mi, C.R., Liu, D.L., He, L., Kuang, Z.M., Fang, Q.X., Ramp, D., Li, L., Wang, B., Chen, Y.L., Li, J., Jin. N., Yu, Q.*, 2018. Spatio-temporal distribution of sugarcane potential yields and yield gaps in Southern China. European Journal of Agronomy, 92: 72-83.
25 Yu, Q.*, Li, L., Luo, Q.Y., Derek, E., Xu, S.H., Chen, C., Wang, E.L., Liu, J.D., Nielsen, D.C., 2014. Year patterns of climate impact on wheat yields. International Journal of Climatology, 34: 518-528.
26 Lu, P.L., Yu, Q.*, Wang, E., Liu, J.D., Xu, S.H., 2008. Effects of climatic variation and warming on rice development across South China. Climate Research, 36(1): 79-88.
27 Yu, Q.*, Wang, E.L., Smith, C.J., 2008. A modelling investigation into the economic and environmental values of 'perfect' climate forecasts for wheat production under contrasting rainfall conditions. International Journal of Climatology, 28(2): 255-266.
28 Wang, J., Yu, Q.* and Lee, X.H., 2007. Simulation of crop growth and energy and carbon dioxide fluxes at different time steps from hourly to daily. Hydrological Processes, 21(18): 2474-2492.
29 Yu, Q.*, Xu, S.H., Wang, J., Lee, X.H., 2007. Influence of leaf water potential on diurnal changes in CO2 and water vapour fluxes. Boundary-Layer Meteorology, 124(2): 161-181.
30 Fang, Q.X., Yu, Q.*, Wang, E.L., Chen, Y.H., Zhang, G.L., Wang, J., Li, L.H., 2006. Soil nitrate accumulation, leaching and crop nitrogen use as influenced by fertilization and irrigation in an intensive wheat-maize double cropping system in the North China Plain. Plant and Soil, 284(1-2): 335-350.
31 Li, L.H., Yu, Q.*, Zheng, Y.F., Wang, J., Fang, Q.X., 2006. Simulating the response of photosynthate partitioning during vegetative growth in winter wheat to environmental factors. Field Crops Research, 96(1): 133-141.
32 Lu, P.L., Yu, Q.*, Liu, J.D., Lee, X.H., 2006. Advance of tree-flowering dates in response to urban climate change. Agricultural and Forest Meteorology, 138(1-4): 120-131.
33 Wang, J., Yu, Q.*, Li, J., Li, L.H., Li, X.G., Yu, G.R., Sun, X.M., 2006. Simulation of diurnal variations of CO2, water and heat fluxes over winter wheat with a model coupled photosynthesis and transpiration. Agricultural and Forest Meteorology, 137(3-4): 194-219.
34 Wu, D.R., Yu, Q.*, Lu, C.H., Hengsdijk, H., 2006. Quantifying production potentials of winter wheat in the North China Plain. European Journal of Agronomy, 24(3): 226-235
35 Yu, Q.*, Saseendran, S.A., Ma, L., Flerchinger, G.N., Green, T.R., Ahuja, L.R., 2006. Modeling a wheat-maize double cropping system in China using two plant growth modules in RZWQM. Agricultural Systems, 89(2-3): 457-477.
36 Yu, Q.*, Zhang, Y.G., Liu, Y.F., Shi, P.L., 2004. Simulation of the stomatal conductance of winter wheat in response to light, temperature and CO2 changes. Annals of Botany, 93(4): 435-441.
37 Yu, Q.*, Liu, J.D., Zhang, Y.Q., Li, J., 2002. Simulation of rice biomass accumulation by an extended logistic model including influence of meteorological factors. International Journal of Biometeorology, 46(4): 185-191.
38 Yu, Q.*, Liu, Y.F., Liu, J.D., Wang, T.D., 2002. Simulation of leaf photosynthesis of winter wheat on Tibetan Plateau and in North China Plain. Ecological Modelling, 155(2-3): 205-216.
39 Yu, Q.*, Goudriaan, J., Wang, T.D., 2001. Modelling diurnal courses of photosynthesis and transpiration of leaves on the basis of stomatal and non-stomatal responses, including photoinhibition. Photosynthetica, 39(1): 43-51.
40 Yu, Q.*, Hengsdijk, H., Liu, J.D., 2001. Application of a progressive-difference method to identify climatic factors causing variation in the rice yield in the Yangtze Delta, China. International Journal of Biometeorology, 45(2): 53-58.
41 Yu, Q.*, Liu, J.D., Luo, Y., 2000. Applicability of some stomatal models to natural conditions. Journal of Integrative Plant Biology, 42(2): 203-206.
42 Yu, Q.*, Wang, T.D., 1998. Simulation of the physiological responses of C3 plant leaves to environmental factors by a model which combines stomatal conductance, photosynthesis and transpiration. Journal of Integrative Plant Biology, 40(8): 740-754

中国科学院百人计划
国家海外高层次引进人才