Land Degradation Processes Research Articles

The global cost of international commitments on land restoration

AbstractLand restoration and sustainable land management projects have now been proposed for up to a billion hectares globally, responding to continuous land degradation processes. But, a lack of reliable cost estimates hinders implementation, which results in a high perceived risk of restoration investments. This study addresses this gap, using cost data from 243 restoration projects from multiple sources to estimate the cost ranges for different types of restoration more precisely. The analysis shows median restoration costs vary from 185 $US/ha to 3012 $US/ha depending on the restoration measure, implying current global land restoration commitment cost 0.04%–0.27% of annual global GDP to implement. Since the majority of these commitments are made by developing countries, international cost‐sharing mechanisms, through climate or private finance, are required to move from goal setting to implementation.

Plutonium isotopes can be used to model soil erosion in Kenya

Climate change poses an immediate threat to tropical soils with changes in rainfall patterns resulting in accelerated land degradation processes. To ensure the future sustainability of arable land, it is essential to improve our understanding of the factors that influence soil erosion processes. This work aimed to evaluate patterns of soil erosion using the activity of plutonium isotopes (Pu) at sites with different land use and clearance scale in the Winam Gulf catchment of Lake Victoria in Kenya. Erosion rates were modelled at potential erosive sites using the MODERN model to understand small-scale erosion processes and the effect of different management practices. The lowest soil redistribution rates for arable land were 0.10 Mg ha−1 yr−1 showing overall deposition, resulting from community-led bottom-up mitigation practices. In contrast erosion rates of 8.93 Mg ha−1 yr−1 were found in areas where steep terraces have been formed. This demonstrates the significance of community-led participation in effectively managing land degradation processes. Another key factor identified in the acceleration of soil erosion rates was the clearance of land with an increased rate of erosion over three years reported (0.45 to 0.82 Mg ha−1 yr−1) underlining the importance vegetation cover plays in limiting soil erosion processes. This novel application of fallout plutonium as a tracer, highlights its potential to inform the understanding of how soil erosion processes respond to land management, which will better support implementation of effective mitigation strategies.Graphical abstractModelling of soil erosion and deposition patterns using the MODERN model to calculate the depth of soil loss/gain.

Monitoring of agroecosystem desertification processes according to satellite data: experience and perspectives

Desertification is a major environmental threat that affects the lives of millions of people around the world. In order to effectively prevent this effect in agro-ecosystems at local and regional levels, it is necessary to have accurate information about its spatial distribution. Remote sensing plays a crucial role in this process due to its cost-effectiveness, ability to cover large areas, and use of a variety of sensors and data analysis methods. The combination of GIS, mathematical models and ground observations allows for more accurate data on land degradation. The purpose of this study is to summarize the experience of both Ukrainian and foreign researchers in the use of remote sensing for monitoring and assessment of desertification and land degradation processes at the global and national levels, as well as to identify areas for further improvement of remote sensing methods for integrated monitoring of desertification in agroecosystems and land degradation. This article reviews the experience of using satellite remote sensing data, mathematical models of desertification, and geographic information systems to identify areas prone to degradation in different parts of the world. Special attention is paid to the adaptation of these models to local conditions using additional parameters. The article also highlights the achievements of Ukrainian scientists in the field of satellite monitoring of agricultural landscapes, their classification and assessment based on remote sensing data. Prospects for further research are outlined. The use of an integrated approach combining geographic information systems, satellite data, analysis of environmental indicators, and mathematical models of desertification adapted to regional characteristics proves to be effective. The main directions remain the adaptation of models to regional conditions, the development of forecasting methods using remote sensing data, and the expansion of international data and experience exchange. It is important to continue to improve remote monitoring methods for the early detection of degradation processes and to strengthen research at the national level for the implementation of appropriate measures.

Distinct Assembly Patterns of Soil Antibiotic Resistome Revealed by Land-Use Changes over 30 Years.

Compared with the ever-growing information about the anthropogenic discharge of nutrients, metals, and antibiotics on the disturbance of antibiotic resistance genes (ARGs), less is known about how the potential natural stressors drive the evolutionary processes of antibiotic resistance. This study examined how soil resistomes evolved and differentiated over 30 years in various land use settings with spatiotemporal homogeneity and minimal human impact. We found that the contents of soil organic carbon, nitrogen, soil microbial biomass, and bioavailable heavy metals, as well as related changes in the antibiotic resistome prevalence including diversity and abundance, declined in the order of grassland > cropland > bareland. Sixty-nine remaining ARGs and 14 mobile genetic elements (MGEs) were shared among three land uses. Multiple factors (i.e., soil properties, heavy metals, bacterial community, and MGEs) contributed to the evolutionary changes of the antibiotic resistome, wherein the resistome profile was dominantly driven by MGEs from both direct and indirect pathways, supported by a partial least-squares path model analysis. Our results suggest that pathways to mitigate ARGs in soils can coincide with land degradation processes, posing a challenge to the common goal of managing our environment sustainably.

Rare microbial taxa as the major drivers of nutrient acquisition under moss biocrusts in karst area.

Karst rocky desertification refers to the process of land degradation caused by various factors such as climate change and human activities including deforestation and agriculture on a fragile karst substrate. Nutrient limitation is common in karst areas. Moss crust grows widely in karst areas. The microorganisms associated with bryophytes are vital to maintaining ecological functions, including climate regulation and nutrient circulation. The synergistic effect of moss crusts and microorganisms may hold great potential for restoring degraded karst ecosystems. However, our understanding of the responses of microbial communities, especially abundant and rare taxa, to nutrient limitations and acquisition in the presence of moss crusts is limited. Different moss habitats exhibit varying patterns of nutrient availability, which also affect microbial diversity and composition. Therefore, in this study, we investigated three habitats of mosses: autochthonal bryophytes under forest, lithophytic bryophytes under forest and on cliff rock. We measured soil physicochemical properties and enzymatic activities. We conducted high-throughput sequencing and analysis of soil microorganisms. Our finding revealed that autochthonal moss crusts under forest had higher nutrient availability and a higher proportion of copiotrophic microbial communities compared to lithophytic moss crusts under forest or on cliff rock. However, enzyme activities were lower in autochthonal moss crusts under forest. Additionally, rare taxa exhibited distinct structures in all three habitats. Analysis of co-occurrence network showed that rare taxa had a relatively high proportion in the main modules. Furthermore, we found that both abundant and rare taxa were primarily assembled by stochastic processes. Soil properties significantly affected the community assembly of the rare taxa, indirectly affecting microbial diversity and complexity and finally nutrient acquisition. These findings highlight the importance of rare taxa under moss crusts for nutrient acquisition. Addressing this knowledge gap is essential for guiding ongoing ecological restoration projects in karst rocky desertification regions.

Analysis of trends in productivity metrics in assessing land degradation: A case study in the Campania region of southern Italy

Land degradation is a critical issue at a global level and its progressive increasing greatly reduces soil ecosystem services. In this context, the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, defined the Sustainable Development Goals (SDGs) and indicated some targets of particular interest for a territory to be integrated into short- and medium-term national programs. Target 15.3, which aims to end desertification and restore degraded lands, is currently monitored by indicator 15.3.1, measured as the combination of three sub-indicators (trends in land cover change, land productivity and carbon stocks) as suggested by the United Nations Convention to Combat Desertification (UNCCD), the custodian agency for the SDG indicator. In our opinion, this assessment shows some weakness that are generally caused by a lack of information from direct field observations. The greatest limitation regards land productivity dynamics linked to the NDVI trajectory adopted by the UNCCD methodological approach. For this reason, the paper proposes an alternative approach that consists of using annual maximum NDVI value assessments instead of annual mean values for trajectory calculation. To come to these conclusions, the study addresses a reliability assessment by using remote sensing techniques via the Google Earth Engine (GEE) and analysing the NDVI evolution over time at 450 locations spread around the Campania region (southern Italy). To this end, a customised Graphical User Interface (GUI) was built on the GEE platform and a Google Earth time slider tool was applied to visualize land cover changes which occurred at each location over a period of 18 years (2001–2018). The survey was carried out on MODIS and Landsat 7 collections and showed that the new approach had a better performance than the UNCCD approach (90 % vs 62 % of successful reliability tests, up to 96 % considering results from Landsat images). The application of maximum NDVI values to assess productivity dynamics spatially shows, with regard to UNCCD data, more than double the percentages of degraded and stable lands and a drastic reduction in improved areas within the Campania region. Overall, this innovative approach appears to agree more closely with ground truth and the use of finer resolution data is more suitable for investigating land degradation processes within a regional context.

Impact of watershed management practices on vegetation, land use changes, and soil erosion in River Basins of the Atlas, Morocco

Soil erosion, a land degradation process triggered by natural and anthropogenic factors, seriously impacts landscapes and water resources. The influence of vegetation cover and land use changes on intensity of soil erosion of two catchments in mountainous regions of Morocco is evident, as it alters hydrologic response and sediment dynamics. This research aims to analyze the interactions among plants, soil, geology, meteorology, and orography, assessing soil erosion responses using the process-oriented IntErO model - Erosion Potential Method to determine erosion rates. The obtained results indicate that the Tiguert river basin experiences higher soil losses (Ggod = 5184.47 m³/god) and soil losses per square kilometre (Ggod/km² = 508.28 m³/km² god) compared to the Wanmroud catchment (Ggod = 2555.66 m³/god, Ggod/km² = 381.44 m³/km² god), confirming the theory that areas with denser and more effective vegetation cover experience lower soil erosion rates. Furthermore, the Wanmroud basin exhibits a more regular shape and lower watershed development coefficient, implying lower human impact. This study has shown the relationships between land use changes, vegetation cover, and soil erosion dynamics, offering valuable insights for sustainable land management practices in mountainous regions of Morocco.

Living with landslides: Land use on unstable hillslopes in a rural tropical mountainous environment in DR Congo

Landslides are processes that naturally occur on numerous hillslopes across the world. In inhabited regions, landslides are commonly seen as a threat and a land degradation process. Yet, in densely-populated rural mountainous regions in the tropics, local communities have often no choice but to live on steep terrains naturally impacted by landslides. Besides, landslides may also be a source of opportunities for these communities. However, little is known on the rationale underlying land use in landslides. The aim of this study was to assess the extent, modes of valorization and degree of satisfaction of famers exploiting landslides in a populated rural mountainous environment of DR Congo (territory of Kalehe). We interviewed 82 farmers living on 57 representative landslides, these mass movements having been selected according to their characteristics (size, type) and position along the hillslopes and taking into account accessibility or safety constraints. We show that almost all landslides are being exploited by farmers and that they adapt their land use to the type of landslide. Indeed, significant contrasts are observed between landslides and the surrounding hillslopes for subsistence crops, forests, eucalyptus plantations and pasture. Farmers also adapt land use according to local variations in slope or wetness within a given landslide. Nearly half of the farmers reported that their land was more valuable inside than outside landslides. Better soil fertility, higher soil moisture, lower sand or stone content, lower slopes are some of the main factors that increase the land value, offering more favorable conditions for cropping than on land outside landslides. Despite the perceived risk of landsliding, famers settlement on unstable slopes appears justified by the immediacy of the benefits that outweigh the potential dangers. Better understanding the reasons for the settlement of populations on unstable slopes may help devise better risk reduction strategies.

THE IMPACT OF HUMAN ACTIVITY AND CLIMATE CHANGE ON THE FARMING CULTURE

For the first time, the changes in the qualitative characteristics of the soil in the conditions of the lowering of the underground water level of the Ararat valley were investigated and compared as a result of the influence of human activities and natural phenomena. It was revealed that long-term use of land and the lowering of the underground water level led to the deterioration of the quality characteristics of the soil, in particular, to secondary salinization of soils and decreasing fertility. In order to prevent land degradation and desertification processes, it is recommended to limit the unaccountable use of underground water in the Ararat valley, to change the technologies and methods of soil cultivation, plant care and irrigation.It is necessary to enrich the soil with organic and mineral fertilizers and mulch it.

Soil Microbial Communities in Pseudotsuga sinensis Forests with Different Degrees of Rocky Desertification in the Karst Region, Southwest China

Rocky desertification (RD), a natural and human-induced process of land degradation in karst areas, has become the primary ecological disaster and one of the obstacles to sustainable ecological development in southwest China. Nevertheless, the variation of soil physical and chemical properties, bacterial and fungal communities, and their relationships in RD forests remains limited. Therefore, soil samples were collected from forests under four degrees of RD (NRD, non-RD; LRD, light RD; MRD, moderate RD; and SRD, severe RD) and subjected to high-throughput sequencing of 16S rRNA and ITS1 genes. The results showed a significant reduction in bacterial richness and diversity, while fungal richness and diversity decreased markedly and then showed a balanced trend with the increase in RD degree, indicating that bacteria and fungi did not present the same dynamics in response to the process of RD. The bacterial communities were dominated by Proteobacteria, Actinobacteria, Acidobacteria, and Chloroflexi, while the fungal communities were dominated by Basidiomycota, Ascomycota, and Mortierellomycota. The PCoA and NMDS demonstrated significant differences in microbial communities in study sites, among which the fungal communities in non-RD forest and LRD forest clustered together, suggesting that fungal communities were more stable than bacteria in RD forest. The db-RDA, Mantel test, and random forest model confirmed the important role of soil BD, pH, SOC, AN, and AP in driving microbial diversity and communities. The IndVal analysis suggested that Chloroflexi, Patescibacteria, Atheliales, and Cantharellales with high indicator values were identified as potential bio-indicators for RD forests. This study could not only improve our understanding of bacterial and fungal community dynamics across RD gradients, but also could provide useful information for the further use of microorganisms as indicators to reflect the environmental changes and ecosystem status during forest RD.

Remote sensing and spatial databases for investigating latent urban-rural dynamics in rural, inland districts of Southern Italy

It is well-known that rural-urban patterns help to capture socioeconomic interactions between different settlement forms. The sustainability challenge requires to consider the evolution of these patterns as a reliable indicator of the dynamics of land use change and potential land degradation processes occurred in a time frame. In this research, by using multisource data (Corine Land Cover, Keyhole KH-9 and Landsat satellite images), we trace the diachronic evolution (1990-2018) of the rural-urban pattern in the provinces of Avellino and Benevento (Campania region, Southern Italy) with a specific focus on the key municipality of Ariano Irpino (1975-2018). The analysis confirms the considerable urban growth occurred in the study area, mostly in the form of urban sprawl phenomena decoupled from population growth. This happens concurrently with a transformation of the agricultural sector projected toward a greater specialization favouring agritourism activities and valuable crops (e.g., vineyards). These findings can support policy makers in future planning activities by mixing conservation, mitigation, and restoration actions.

The LANDSUPPORT geospatial decision support system (S‐DSS) vision: Operational tools to implement sustainability policies in land planning and management

AbstractNowadays, there is contrasting evidence between the ongoing continuing and widespread environmental degradation and the many means to implement environmental sustainability actions starting from good policies (e.g. EU New Green Deal, CAP), powerful technologies (e.g. new satellites, drones, IoT sensors), large databases and large stakeholder engagement (e.g. EIP‐AGRI, living labs). Here, we argue that to tackle the above contrasting issues dealing with land degradation, it is very much required to develop and use friendly and freely available web‐based operational tools to support both the implementation of environmental and agriculture policies and enable to take positive environmental sustainability actions by all stakeholders. Our solution is the S‐DSS LANDSUPPORT platform, consisting of a free web‐based smart Geospatial CyberInfrastructure containing 15 macro‐tools (and more than 100 elementary tools), co‐designed with different types of stakeholders and their different needs, dealing with sustainability in agriculture, forestry and spatial planning. LANDSUPPORT condenses many features into one system, the main ones of which were (i) Web‐GIS facilities, connection with (ii) satellite data, (iii) Earth Critical Zone data and (iv) climate datasets including climate change and weather forecast data, (v) data cube technology enabling us to read/write when dealing with very large datasets (e.g. daily climatic data obtained in real time for any region in Europe), (vi) a large set of static and dynamic modelling engines (e.g. crop growth, water balance, rural integrity, etc.) allowing uncertainty analysis and what if modelling and (vii) HPC (both CPU and GPU) to run simulation modelling ‘on‐the‐fly’ in real time. Two case studies (a third case is reported in the Supplementary materials), with their results and stats, covering different regions and spatial extents and using three distinct operational tools all connected to lower land degradation processes (Crop growth, Machine Learning Forest Simulator and GeOC), are featured in this paper to highlight the platform's functioning. Landsupport is used by a large community of stakeholders and will remain operational, open and free long after the project ends. This position is rooted in the evidence showing that we need to leave these tools as open as possible and engage as much as possible with a large community of users to protect soils and land.

Assessing the Recent Trends of Land Degradation and Desertification in Romania Using Remote Sensing Indicators

Land degradation (LD) and desertification (DS) are a sensitive global issue including southern and south-eastern Europe, which is severely affected by climate change. In this study, a state-of-the-art approach for assessing the intensity of LD and DS processes using remote-sensing-derived indicators within a GIS environment was proposed. The analysis was carried out using the Principal Component Analysis based on integrating the significant trends of relevant biophysical parameters in Romania. The methodology was tested and validated at the national level in Romania. In total, 7.76% of the area was identified as LD and 60.8% of the total area tended to improve, and 31.44% was stable. Most of the regions with LD overlapped with the dryland areas, while improvement areas were identified outside of the drylands. In forested areas from high altitudes, a tendency to improve the condition of vegetation was observed, and most of the surfaces being protected were natural areas that have benefited from proper management. All these results can be used to adapt management practices to avoid, reduce, or restore the LD. The proposed model was based on globally available remote sensing datasets, with a high frequency of data acquisition and collection history that allows for the statistical analyses of changes on a global scale.

National Soil Strategy for Sustainable Agriculture (ENASAS): A new systemic approach in Mexico

AbstractThe National Soil Strategy for Sustainable Agriculture (ENASAS) arises in response to the increasing need to organize, coordinate and strengthen the implementation of actions oriented to the sustainable management of Mexico's agricultural soils. The measures recommended in this paper allow the conservation and maintenance of soils' essential functions for agriculture, food security and population well‐being. The health and quality of agricultural soils are under constant pressure from several factors, mainly anthropogenic. One of ENASAS's objectives is to identify and stop the causes involved in land degradation processes using a systemic approach. From this perspective, the participation of various actors in sustainable soil management allows the integration of different systems, practices, techniques and knowledge that promotes awareness among the entire population about the importance of soil resources. On the other hand, given current limitations on the availability of information necessary to support decision‐making and evaluate the result of the actions implemented, ENASAS promotes the generation of tools that provide reliable and updated data regarding Mexico's agricultural soils. In the same sense, this strategy encourages the development of scientific and technological research and the transfer of knowledge that meets the needs of farmers, particularly those who carry out their activities on a smaller scale. This perspective describes ENASAS as a multi‐institutional initiative that will support sustainable soil management practices across the country's agricultural systems of Mexico and contribute to the adaptation and mitigation of global environmental change.

A Study of Sandy Land Changes in the Chifeng Region from 1990 to 2020 Based on Dynamic Convolution

Desertification is the process of land degradation and the reduction or destruction of biological potential in arid, semi-arid, and semi-humid areas, and its impact on agricultural development and the ecological environment cannot be ignored. Therefore, many researchers have aimed to achieve the acquisition of large-scale sandy land areas using sandy land extraction algorithms based on remote sensing images. However, the sandy land extraction accuracy needs to be improved because of the variable contour features in the remote sensing images and the easy confusion with targets such as the Gobi and bare ground areas. In this study, we combine the dynamic convolution with a U-Net model and propose the DU-Net sandy land extraction model. The method is based on dynamic convolution, which can adaptively learn the complex features of the target and build a dynamic convolutional neural network to achieve high-accuracy extraction of complex targets. DU-Net achieved 86.32% in IoU, 93.22% in precision, 94.5% in recall, and 92.66% in F1-score in sandy land extraction accuracy, which are 4.68%, 2.33%, 3.09%, and 2.76% improvements, respectively, compared with the U-Net static neural network. Based on this, we obtained the spatial and temporal evolution trends of sandy land areas based on Landsat images in the Chifeng region in the Inner Mongolia Autonomous Region, China. Meanwhile, in order to investigate the mechanism of spatial and temporal changes in the sandy land area in the study region over the past 30 years, the direct and indirect effects of seven climatic and human socioeconomic activity factors on the changes in the sandy land area in the study region were evaluated based on a structural equation model. The results show that the sandy area in the Chifeng region tended to first increase and then decrease over the study period, with the sandy land area reaching its maximum around the year 2000. In addition, the main driving factor for the change in the sandy land area in the Chifeng region has been human socioeconomic activities, with climatic conditions as the secondary driving factor. The method proposed in this paper realizes the rapid extraction of sandy land areas with high accuracy at a large scale and with a long time series and provides a basis for assessing the effectiveness of ecosystem restoration projects.

The State of the World's Arable Land

For millennia, agriculture has been shaping landscapes on Earth. Technological change has increased agricultural productivity dramatically, especially in the past six decades, but also resulted in trade-offs such as land and soil degradation, emission of greenhouse gases (GHGs), and spreading of toxic substances. In this article we review the impacts of agriculture on the world's arable land. We start by synthesizing information on the extent of arable land and associated agricultural practices, followed by a review of the state of the art of soil health and soil carbon. We review processes of land degradation, emission of GHGs, and threats to biodiversity. To conclude, we review key social and economic aspects of arable land and identify some important concerns for the future. The article ends on a positive note describing a potential new pathway for agriculture—to gradually adopt polycultures of novel perennial grain crops.

Extraction of the bare rock rate in counties of typical karst areas based on unmanned aerial vehicle images and satellite data, China

AbstractRocky desertification is a land degradation process. The bare rock rate (BRR) is an important index. Satellite‐based methods for BRR estimation can generate BRR datasets, but the spatial resolution and accuracy are low. Unmanned aerial vehicle (UAV) data can be used for BRR extraction with much higher resolution and higher accuracy, but the data are laborious and costly to collect. This study proposed a stepwise upscaling method employing the UAV‐extracted BRR as the ground truth to establish a multiband regression model and calibrated the BRR derived from 30‐m resolution LANDSAT 8 data, which was then used to calibrate 500‐m resolution moderate resolution imaging spectroradiometer (MODIS) data. We compared the results with the satellite‐based BRR and the results of UAV‐MODIS upscaling methods. The results showed that (1) the UAV provides high‐resolution data, and exposed rocks can be efficiently extracted from UAV images. This method could replace ground surveys. (2) The accuracy of the stepwise upscaling method was higher than that of the nonstepwise upscaling method in large areas. R2 increased by 64.9%, and the root mean square error (RMSE) decreased by 82.9%. (3) Compared with that of the satellite‐based BRR, the accuracy of the multiband regression model‐extracted BRR was higher. R2 increased by 21.5%, and the RMSE decreased by 62.2%. This study demonstrated that the stepwise upscaling method and multiband regression can be combined to extract high‐precision and large‐scale BRR data, which can be used to serve ecological engineering monitoring and ecological governance.

Predicting gully erosion susceptibility in South Africa by integrating literature directives with regional spatial data

AbstractGully erosion has been identified as a severe land degradation process with environmental and socio‐economic consequences. Identifying areas susceptible to gully erosion will aid in developing strategies to inhibit future degradation. Various approaches have been implemented to predict and map gully erosion susceptibility but are mostly restricted to small geographical extents because of process limitations. Here, we introduce a novel method that predicts gully erosion susceptibility on a regional/national scale (1.22 million km2) by synthesising literature directives with a statistical approach. Findings from a literature review were used to extract physiographic properties associated with gully erosion that was conditioned to characterise susceptibility by using the Frequency Ratio model. The conditioned physiographic properties were aggregated by a weighted overlay procedure using an aggregation of controlling factors derived from the literature review as a weighting system. The gully susceptibility index (GSI) model was validated against a published gully inventory map (n = 163 019) and randomly generated 1‐km2 tessellation zones from which primary validation data were derived. Although uncertainties within the modelling procedure exist (e.g., gully site distribution, the spatial resolution of input data and determination of gully points), the validation shows that the GSI model is generally robust, identifying areas of contrasting susceptibilities. Furthermore, findings converge with other susceptibility metrics, which have been derived by different methodologies. Because empirical gully erosion research has been conducted worldwide, this model could be applied to regional‐scale gully susceptibility modelling assessments (as a solitary method or combined with primary data) in other parts of the world. Additionally, the GSI model can be adopted to model environmental change scenarios.

Assessing marginality of Camelina (C. sativa L. Crantz) in rotation with barley production in Southern Europe: A modelling approach

Camelina (C. sativa L. Crantz) is a promising biofuel crop with high potential for cultivation on marginal soils. In this work, seed yields have been modelled to assess suitable areas based on experimental field trials, meteorological data from the Monitoring Agricultural ResourceS (MARS) gridded agro-meteorological in Europe, soil properties from LUCAS, topography and land cover. Potential yields for Camelina-Barley rotation (CAMBAR) were modelled for the past 20 years using the mechanistic crop growth model ARMOSA that can estimate quantitatively several soil and water parameters and future forcing scenarios (RCP4.5 and RCP8.5) using the generation model HadGEM2-ES. The term marginal land is found in the literature to indicate unused agriculture land such as abandoned, underused, degraded and fallow. In this work, marginality is considered the economic feasibility of cultivation, which aims to identifying land where cost effective agricultural production is not possible under a given set of conditions. Marginal lands were identified when the average Camelina seed yield from the crop growth modelling was lower than average in European countries based on a comprehensive literature assessment. The analysis was targeted at regions with a predominantly Mediterranean climate. Simulation by the mechanistic crop model was carried out on a 25 km grid for soil texture, soil carbon average stock, slope and aspect found in the MARS agricultural area mask. Spatial data and subsequent editing and processing of each simulation were allocated to a 500 m spatial resolution via a Geographic Information Systems (GIS). The study area covers around 500,000 km2. The CAMBAR scenario obtained an average yield of 2468 kg ha-1 yr-1, with standard deviation (+- 641) due to fluctuations for extreme weather patterns. Regarding soil organic carbon (SOC), CAMBAR showed an increase of + 43 kg ha-1 yr-1, which aligns with other studies carried out in Mediterranean or continental climates under crop rotation with minimum tillage and straw retention. The results of the present scenarios showed a slight increase in SOC stock (0.1–0.15 % yr-1). In regions with sufficient precipitation throughout the crop cycle, the increase of SOC is lower than the entire study area average, and in few cases, the SOC stock was drastically decreased. However, the model shows that SOC stock can increase when Camelina is introduced in rotation with cereals in areas with high desertification risk. In Spain, in the regions of Castilla La Mancha, Castilla y León and Comunidad de Madrid accounting for 40% of the total area of investigation in Spain, equivalent to an area of 88,233 km2, the SOC increase was + 188, + 255 and + 236 kg ha-1 yr-1, respectively. The results of this work constitute a key contribution to policy development at the sub-national, national and EU level, through the investigation of low LUC/ILUC biofuel from marginal areas before these are lost due to land degradation processes and other anthropogenic impacts. Mapping the marginal areas is fundamental to showing potential for producing advanced biofuel crops.

Assessment of soil erosion hazard and its relation to land use land cover changes: Case study from alage watershed, central Rift Valley of Ethiopia

Soil erosion by water and wind is among the most crucial land degradation processes in Ethiopia. This is also the case for Alage watershed located in the cental Rift Valley system. This study aimed at assessment of soil erosion hazard and its relation to land use land cover change in the watershed during the period from 1984 to 2016 for a better land management. The study is based on application of Remote Sensing (RS) and Geographical Information System (GIS) to extract inputs factor values for the Revised Universal Soil Loss Equation (RUSLE). Time-series satellite imageries of Landsat TM 1984, ETM+ 2000 and OLI 2016 were used for land use land cover change detection and determination of cover management (C) factor of the RUSLE. Biophysical data such as rainfall, soil properties, land management practices including soil and water conservation measures within the watershed were collected using field survey and secondary data sources. Slope steepness and slope length factors were derived using Digital Elevaition Model (DEM). Long-term average annual soil loss rates were estimated by the RUSLE integrated with GIS for 1984, 2000 and 2016. Using satellite imageries, the land use land cover and changes within the watershed during the three periods were obtained through a supervised classification with maximum likelihood algorithim. The results of land use land cover change indicated that the proportion of rain-fed cropland, bare land and built up areas increased by 17.4%, 5.9% and 2.9% respectively over the three study period. In contrast the proportion of bush/shrub land, irrigated cropland, grass land, forested areas and waterbodies decresaed by 15.5%, 4.7%, 3.4%, 2.3% and 0.3% respectively during the same period. Estimated average annual soil loss rates showed an increasing trend from 24.3 ton ha−1 yr−1 in 1984 to 38 ton ha−1 yr−1 in 2016. Increasing trends of average annual soil loss rate is attributed to increased proportion of cropland, bare land and built up areas during those periods leading to decreased protective vegetation cover. Hotspot areas within the watershed require implementation of land management practices to prevent further degradation and expansion of gullies. This study is relevant to demonstrate environmental implication of land use land cover change for future land management practices and land use policy in the Rift Valley of central Ethiopia.