Dongsheng Yu) 23 and zhaoquanying@gmail.com (Quanying Zhao) 24 ABSTRACT 25 Understanding the impacts of climate change and agricultural management practices on soil 26 organic carbon (SOC) dynamics is critical for implementing optimal farming practices and 27 maintaining agricultural productivity. This study examines the influence of climatic variables 28 and agricultural management on carbon sequestration potentials in Tai-Lake Paddy soils of 29 China using the DeNitrification-DeComposition (DNDC, version 9.1) model, with a 30 high-resolution soil database (1:50,000). Model simulations considered the effects of 31 no-tillage, the application rates of manure, N fertilization, and crop residue, water 32 management, and changes in temperature and precipitation. We found that the carbon 33 sequestration potential in the top soils (0-30 cm) for the 2.32 Mha paddy soils of the Tai-Lake 34 region varied from 4.71 to 44.31 Tg C under the feasible management practices during the 35 period of 2001-2019. The sequestration potential significantly increased with increasing 36 application of N-fertilizer, manure, conservation tillage, and crop residues, with an annual 37 average SOC changes ranged from 107 to 121 kg C ha -1 yr -1 , 159 to 326 kg C ha -1 yr -1 , 78 to 38 128 kg C ha -1 yr -1 , and 489 to 1005 kg C ha -1 yr -1 , respectively. Toward mitigating greenhouse 39 emissions and N losses, no-tillage and increase of crop residue return to soils as well as 40 manure application are recommended for agricultural practice in this region. Our analysis of 41 climate impacts on SOC sequestration suggests that the rice paddies in this region will 42 continue to be a carbon sink under future warming conditions. Specifically, with rising air 43 temperature of 2.0 ℃ and 4 ℃, the average annual SOC changes were 52 and 21 kg C ha -1 44 yr -1 , respectively. 45 46

Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error 237(RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture.

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