The great importance of long-term field experiments (LTEs) in detailed estimates of C stocks under site-specific climate conditions and management systems made them a valuable source of data to verify the models of soil organic matter (SOM) dynamics. However, separate evaluation of the effects of management and climate factors on yields, as well as on C stocks, is needed to predict the global climate and land management impacts on soil sustainability in the future. We attempted to minimize this problem via integrating information from LTEs with the Climate-Soil-Yield model (CSY) outputs. The CSY is a system of dynamic simulation models of agroecosystem productivity and a stochastic weather generator for an annual series of daily meteoelements. The highest possible yields calculated with the CSY in the specified climatic year under optimal soil N regime were compared with the real ones achieved in the LTEs for different treatments. The objects were LTEs on soddy-podzolic soils of different textures: loamy sand (Experimental Farm of the All-Russia Institute of Organic Fertilizers and Peat, Vladimir Region, since 1968), sandy loam (Smolensk Experimental Station, Smolensk Region, since 1978) and clay loam (Dolgoprudny Experimental Farm-DAOS, Moscow Region, since 1931). The ratio of the observed to predicted yields for grain crops has a smaller inter-annual variability comparing with the yield dynamics. In fact, this ratio shows the effect of soil fertility on the temporal dynamics of crop yields; it has a tendency to achieve near-equilibrium state at different levels for control, mineral, and organic treatments. The changes in the observed trends for different fields with the same treatments are related to the initial level of soil fertility and different phases of the rotation presented in each field, i.e., to different crop-climatic year combinations. For winter wheat after 25 years of experimentation on the sandy loam soil, the exponential decrease in the ratio is seen; the values obtained for the organic fertilization treatment are higher than those the control. The proposed method shows the possibility for joining experimental information from different fields of LTEs, as well as for choosing the most appropriate periods for model runs under equilibrium soil fertility effect for a particular crop. This approach was used for yield predictions in estimation of the local effects of different management practices on soil organic C stock under future climate. The Rothamsted Carbon Model was used for simulations. Monthly temperature and precipitation from 2000 to 2050 for each LTE were determined from the nearby 0.5 degree resolution of future climate data from the University of East Anglia, Climate Research Unit, with preliminary comparison with the local meteorological data on 1961-1990. Monthly values were provided using outputs from the Hadley Centre climate model (HadCM3) forced by four IPCC CO2 emissions scenarios. Potential evapotranspiration for each polygon was calculated according to Ivanov. Crop model simulations suggest that the expected climate changes will result in the increased input item of the soil carbon budget and the decreased SOM decomposition rate owing to climate aridization. For sandy soils, the potential for C sequestration under the impact of climate change could be only 1t C/ha for control treatment, which demonstrates steady SOM loss. For organic fertilization treatment, the potential for carbon sequestration is estimated at 3 t C/ha with the most favorable climate conditions for C sink in 2030-2040. The following management practices that can lead to C sequestration were tested: alteration of crop rotation system, organic fertilization, and more extensive use of perennial crops. Calculations show that a 25% increase in the portion of perennial grass in the rotation can lead to a 20% increase in the SOM stock in the next 40 years, while the use of cereal-row crop rotation without grasses will decrease the SOM stock by 15% in the next 25 years. Our approach allows implementing LTE data to develop local adaptation strategies and choose the set of best management options for sustainable development at the least cost.