Determinants of agricultural land abandonment in post-soviet European Russia

Socio-economic and institutional changes may accelerate land-use and land-cover change. Our goal was to explore the determinants of agricultural land abandonment within one agro-climatic and economic region of post-Soviet European Russia during the first decade of transition from a state-command to market-driven economy (between 1990 and 2000). We integrated maps of abandoned agricultural land derived from 30 m resolution Landsat TM/ETM+ images, environmental and socioeconomic variables and estimated logistic regressions. Results showed that post-Soviet agricultural land abandonment was significantly associated with lower average grain yields in the late 1980s, higher distance from the populated places, areas with low population densities, for isolated agricultural areas within the forest matrix and near the forest edges. Hierarchical partitioning showed that average grain yields in the late 1980s contributed the most in explaining the variability of agricultural land abandonment, followed by location characteristics of the land. While the spatial patterns correspond to the classic micro-economic theories of von Thünen and Ricardo, it was largely the macro-scale driving forces that fostered agricultural abandonment. In the light of continuum depopulation process in the studied region of European Russia, we expect continuing agricultural abandonment after the year 2000. --agricultural land abandonment,institutional change, land use change,spatial analysis,logistic regression,remote sensing,Russia

Determinants of agricultural land abandonment in post-soviet European Russia

Socio-economic and institutional changes may accelerate land-use and land-cover change. Our goal was to explore the determinants of agricultural land abandonment within one agro-climatic and economic region of post-Soviet European Russia during the first decade of transition from a state-command to market-driven economy (between 1990 and 2000). We integrated maps of abandoned agricultural land derived from 30 m resolution Landsat TM/ETM+ images, environmental and socioeconomic variables and estimated logistic regressions. Results showed that post-Soviet agricultural land abandonment was significantly associated with lower average grain yields in the late 1980s, higher distance from the populated places, areas with low population densities, for isolated agricultural areas within the forest matrix and near the forest edges. Hierarchical partitioning showed that average grain yields in the late 1980s contributed the most in explaining the variability of agricultural land abandonment, followed by location characteristics of the land. While the spatial patterns correspond to the classic micro-economic theories of von Thünen and Ricardo, it was largely the macro-scale driving forces that fostered agricultural abandonment. In the light of continuum depopulation process in the studied region of European Russia, we expect continuing agricultural abandonment after the year 2000

Modeling the spatial distribution of grazing intensity in Kazakhstan

<div><p>With increasing affluence in many developing countries, the demand for livestock products is rising and the increasing feed requirement contributes to pressure on land resources for food and energy production. However, there is currently a knowledge gap in our ability to assess the extent and intensity of the utilization of land by livestock, which is the single largest land use in the world. We developed a spatial model that combines fine-scale livestock numbers with their associated energy requirements to distribute livestock grazing demand onto a map of energy supply, with the aim of estimating where and to what degree pasture is being utilized. We applied our model to Kazakhstan, which contains large grassland areas that historically have been used for extensive livestock production but for which the current extent, and thus the potential for increasing livestock production, is unknown. We measured the grazing demand of Kazakh livestock in 2015 at 286 Petajoules, which was 25% of the estimated maximum sustainable energy supply that is available to livestock for grazing. The model resulted in a grazed area of 1.22 million km², or 48% of the area theoretically available for grazing in Kazakhstan, with most utilized land grazed at low intensities (average off-take rate was 13% of total biomass energy production). Under a conservative scenario, our estimations showed a production potential of 0.13 million tons of beef additional to 2015 production (31% increase), and much more with utilization of distant pastures. This model is an important step forward in evaluating pasture use and available land resources, and can be adapted at any spatial scale for any region in the world.</p></div

Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution

Landsat imagery is an unparalleled freely available data source that allows reconstructing horizontal and vertical urban form. This paper addresses the challenge of using Landsat data, particularly its 30m spatial resolution, for monitoring three-dimensional urban densification. We compare temporal and spatial transferability of an adapted DeepLab model with a simple fully convolutional network (FCN) and a texture-based random forest (RF) model to map urban density in the two morphological dimensions: horizontal (compact, open, sparse) and vertical (high rise, low rise). We test whether a model trained on the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether we could use the model trained on the Danish data to accurately map other European cities. Our results show that an implementation of deep networks and the inclusion of multi-scale contextual information greatly improve the classification and the model's ability to generalize across space and time. DeepLab provides more accurate horizontal and vertical classifications than FCN when sufficient training data is available. By using DeepLab, the F1 score can be increased by 4 and 10 percentage points for detecting vertical urban growth compared to FCN and RF for Denmark. For mapping the other European cities with training data from Denmark, DeepLab also shows an advantage of 6 percentage points over RF for both the dimensions. The resulting maps across the years 1985 to 2018 reveal different patterns of urban growth between Copenhagen and Aarhus, the two largest cities in Denmark, illustrating that those cities have used various planning policies in addressing population growth and housing supply challenges. In summary, we propose a transferable deep learning approach for automated, long-term mapping of urban form from Landsat images.Comment: Accepted manuscript including appendix (supplementary file

Dynamics of soil organic carbon in the steppes of Russia and Kazakhstan under past and future climate and land use

Changes in land use and climate are the main drivers of change in soil organic matter contents. We investigated the impact of the largest policy-induced land conversion to arable land, the Virgin Lands Campaign (VLC), from 1954 to 1963, of the massive cropland abandonment after 1990 and of climate change on soil organic carbon (SOC) stocks in steppes of Russia and Kazakhstan. We simulated carbon budgets from the pre-VLC period (1900) until 2100 using a dynamic vegetation model to assess the impacts of observed land-use change as well as future climate and land-use change scenarios. The simulations suggest for the entire VLC region (266 million hectares) that the historic cropland expansion resulted in emissions of 1.6⋅ 1015 g (= 1.6 Pg) carbon between 1950 and 1965 compared to 0.6 Pg in a scenario without the expansion. From 1990 to 2100, climate change alone is projected to cause emissions of about 1.8 (± 1.1) Pg carbon. Hypothetical recultivation of the cropland that has been abandoned after the fall of the Soviet Union until 2050 may cause emissions of 3.5 (± 0.9) Pg carbon until 2100, whereas the abandonment of all cropland until 2050 would lead to sequestration of 1.8 (± 1.2) Pg carbon. For the climate scenarios based on SRES (Special Report on Emission Scenarios) emission pathways, SOC declined only moderately for constant land use but substantially with further cropland expansion. The variation of SOC in response to the climate scenarios was smaller than that in response to the land-use scenarios. This suggests that the effects of land-use change on SOC dynamics may become as relevant as those of future climate change in the Eurasian steppes

Cold War spy satellite images reveal long-term declines of a philopatric keystone species in response to cropland expansion

Agricultural expansion drives biodiversity loss globally, but impact assessments are biased towards recent time periods. This can lead to a gross underestimation of species declines in response to habitat loss, especially when species declines are gradual and occur over long time periods. Using Cold War spy satellite images (Corona), we show that a grassland keystone species, the bobak marmot (Marmota bobak), continues to respond to agricultural expansion that happened more than 50 years ago. Although burrow densities of the bobak marmot today are highest in croplands, densities declined most strongly in areas that were persistently used as croplands since the 1960s. This response to historical agricultural conversion spans roughly eight marmot generations and suggests the longest recorded response of a mammal species to agricultural expansion. We also found evidence for remarkable philopatry: nearly half of all burrows retained their exact location since the 1960s, and this was most pronounced in grasslands. Our results stress the need for farsighted decisions, because contemporary land management will affect biodiversity decades into the future. Finally, our work pioneers the use of Corona historical Cold War spy satellite imagery for ecology. This vastly underused global remote sensing resource provides a unique opportunity to expand the time horizon of broad-scale ecological studies

Large greenhouse gas savings due to changes in the post-Soviet food systems

As the global food system contributes significantly to global greenhouse gas (GHG) emissions, understanding the sources of GHG emissions embodied in different components of food systems is important. The collapse of the Soviet Union triggered a massive restructuring of the domestic food systems, namely declining consumption of animal products, cropland abandonment, and a major restructuring of agricultural trade. However, how these complex changes have affected global GHG emissions is uncertain. Here, we quantified the net GHG emissions associated with changes in the former Soviet Union's food systems. Changes in food production, consumption, and trade together resulted in a net emissions reduction of 7.61 Gt carbon dioxide equivalents from 1992 to 2011. For comparison, this corresponds to one quarter of the CO2 emissions from deforestation in Latin America from 1991 to 2011. The key drivers of the emissions reductions were the decreasing beef consumption in the 1990s, increasing beef imports after 2000, mainly from South America, and carbon sequestration in soils on abandoned cropland. Ongoing transformations of the food systems in the former Soviet Union, however, suggest emissions will likely rebound. The results highlight the importance of considering agricultural production, land-use change, trade, and consumption when assessing countries emissions portfolios. Moreover, we demonstrated how emissions reductions that originate from a reduction in the extent and intensity of agricultural production can be compromised by increasing emissions embodied in rising imports of agricultural commodities.Volkswagen Foundation (BALTRAK)the German Federal Ministry of Food and Agriculture (BMEL) (GERUKA)The Swedish Research Council FormasThe Russian Foundation for Basic ResearchRussian Government Program of Competitive Growth of Kazan Federal UniversityEuropean Research Council (ERC)Peer Reviewe

Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer&rsquo;s and user&rsquo;s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase&rsquo;s and reviving phase&rsquo;s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere

Advancing the study of driving forces of landscape change

Over the past 25 years, the study of driving forces of landscape change has developed into a central theme in land change science by contributing to theory development, promoting the analysis of causation of change and gaining insights into how landscape development could be steered into a societally more desirable direction. Based on this progress, we designate important research avenues, reviewing critical challenges forming the base for advancing the study of driving forces of landscape change and addressing the question on how the study of driving forces can contribute to system transformative research. For each of the research avenues, we describe the current dominant approach and provide some specific ways of advancing both the conceptualization and the research methods. Together, advancing on these research avenues will promote a more social-ecological systems perspective to the study of driving forces of landscape change.ISSN:1747-4248ISSN:1747-423