近期论文
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论文及著作
Xu , J., Gao, J., Holanda, H. V. D., Rodríguez, L. F., Caixeta-Filho, J. V., Zhong, R., Jiang, H., Li , H., Du, Z., Wang , X., Wang, S., Ting, K. C., Ying, Y., and Lin, T. (2021) Double cropping and cropland expansion boost grain production in Brazil. Nature Food, 2, 264–273. (4月封面文章)
Zhang, B.1, Xu, J.1, Lin, Z., Lin, T., & Faaij, A. (2021). Spatially explicit analyses of sustainable agricultural residue potential for bioenergy in China under various soil and land management scenarios. Renewable and Sustainable Energy Reviews, 137, 110614. (共同一作)
Xu, J., Gauder, M., Zikeli, S., Möhring, J., Gruber, S., & Claupein, W. (2018). Effects of 16-year woodchip mulching on weeds and yield in organic farming. Agronomy Journal, 110(1), 359-368.
Xu, J., Gauder, M., Gruber, S., & Claupein, W. (2017). Yields of annual and perennial energy crops in a 12-year field trial. Agronomy Journal, 109(3), 811-821.
Gruber, S., Xu, J., Zikeli, S., Belz, R. Potential of wood chips for weed control, In: Adujemo, T.Ol, Vögele, R.T. (eds.) Biopesticides-botanical and microorganisms for improving agriculture and human health. Chapter 6, Logos Verlag Berlin. (著书章节)
Lin, T., Xu, J., Shen, X., Jiang, H., Zhong, R., Wu, S., Du, Z., Rodríguez, L., & Ting, K. (2019). A spatiotemporal assessment of field residues of rice, maize, and wheat at provincial and county levels in China. GCB Bioenergy, 11(10), 1146-1158.
Lin, Z., Zhong, R., Xiong, X., Guo, C., Xu, J., Zhu, Y., Xu, J., Ying, Y., Ting, K. C., Huang, J., & Lin, T. (2022). Large-scale rice mapping using multi-task spatiotemporal deep learning and Sentinel-1 SAR time series. Remote Sensing, 14(3), 699.
Xu, J., Zhu, Y., Zhong, R., Lin, Z., Xu, J., Jiang, H., Huang, J., Li, H., & Lin, T. (2020). DeepCropMapping: A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping. Remote Sensing of Environment, 247, 111946.
Lin, T., Zhong, R., Wang, Y., Xu, J., Jiang, H., Xu, J., Ying, Y., Rodríguez, L., Ting, K., & Li, H. (2020). DeepCropNet: A deep spatial-temporal learning framework for county-level corn yield estimation. Environmental Research Letters, 15(3), 034016.
Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J., Huang, J., Wang, S., Ying, Y., & Lin, T. (2019). A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level. Global Change Biology, 26(3), 1754-1766.
工作论文
Xu, J., Gao, J., Zhong, R., Xiong, X., Ying, Y., Yan, Z., & Lin, T. Redistribution of the US silage corn and its driving forces from 1980 to 2018. (submitted)
Xu, J.1, Gao, J.1, Xiong, X., Ying, Y., Yan, Z., & Lin, T. Nationwide expansion and regional concentration of greenhouse horticulture associated with China’s rapid urbanization. (in preparation)
国际会议及特邀报告
Xu, J., de Holanda, H. V., and Lin, T. Driving Process for the Booming Grain Production in Brazil: A Transition from Extensification to Intensification. Presented at 2019 ASA-CSSA-SSSA International Annual Meeting. San Antonio, Texas, Nov. 10-13, 2019
徐佳路,2022年第一期莱茵-美因论坛,德国法兰克福大使馆教育组农学-食品-生物经济专业学术专题讲座,特邀报告