In this paper, we use the Rothamsted Carbon Model to estimate how cropland mineral soil carbon stocks are likely to change under future climate, and how agricultural management might influence these stocks in the future. The model was run for croplands occurring on mineral soils in European Russia and the Ukraine, representing 74 Mha of cropland in Russia and 31 Mha in the Ukraine. The model used climate data from the HadCM3 climate model, forced by four Intergovernmental Panel on Climate Change (IPCC) emission scenarios representing various degrees of globalization and emphasis on economic vs. environmental considerations. Three land use scenarios were examined, business as usual (BAU) management, optimal management (OPT) to maximize profit, and soil sustainability (SUS) in which profit was maximized within the constraint that soil carbon must either remain stable or increase. Our findings suggest that soil organic carbon (SOC) will be lost under all climate scenarios, but less is lost under the climate scenarios where environmental considerations are placed higher than purely economic considerations (IPCC B1 and B2 scenarios) compared with the climate associated with emissions resulting from the global free market scenario (IPCC A1FI scenario). More SOC is lost towards the end of the study period. Optimal management is able to reduce this loss of SOC, by up to 44% compared with business as usual management. The soil sustainability scenario could be run only for a limited area, but in that area was shown to increase SOC stocks under three climate scenarios, compared with a loss of SOC under business as usual management in the same area. Improved agricultural soil management will have a significant role to play in the adaptation to, and mitigation of, climate change in this region. Further, our results suggest that this adaptation could be realized without damaging profitability for the farmers, a key criteria affecting whether optimal management can be achieved in reality.

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Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

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