Migratory herbivorous waterfowl track multiple resource waves during spring migration.

East Asian herbivorous waterfowl intensively use farmland in spring, next to their natural habitat. Accordingly, they might have expanded their migration strategy from merely tracking the green wave of newly emerging vegetation to also incorporating the availability of post-harvest agricultural seeds (here dubbed the seed wave). However, if and how waterfowl use multiple food resources to time their seasonal migration is still unknown. We test this migration strategy using 167 spring migration tracks of five East Asian herbivorous waterfowl species and mixed-effect resource selection function models. We found that all study species arrived at their core stopover sites in the Northeast China Plain after agricultural seeds became available, extended their stay after spring vegetation emerged and arrived at their breeding sites around the emergence of vegetation. At the core stopover sites, all study species used snowmelt as a cue to track seed availability, although smaller-bodied species tended to arrive later. At the breeding sites, swans tracked the onset of vegetation emergence and geese tracked the mid- or end phases of snowmelt. Our findings suggest that waterfowl track multiple resource waves to fine-tune their migration, highlighting new opportunities for conservation.

Recognizing Agricultural Headwaters as Critical Ecosystems.

Agricultural headwaters are positioned at the interface between terrestrial and aquatic ecosystems and, therefore, at the margins of scientific disciplines. They are deemed devoid of biodiversity and too polluted by ecologists, overlooked by hydrologists, and are perceived as a nuisance by landowners and water authorities. While agricultural streams are widespread and represent a major habitat in terms of stream length, they remain understudied and thereby undervalued. Agricultural headwater streams are significantly modified and polluted but at the same time are the critical linkages among land, air, and water ecosystems. They exhibit the largest variation in streamflow, water quality, and greenhouse gas emission with cascading effects on the entire stream networks, yet they are underrepresented in monitoring, remediation, and restoration. Therefore, we call for more intense efforts to characterize and understand the inherent variability and sensitivity of these ecosystems to global change drivers through scientific and regulatory monitoring and to improve their ecosystem conditions and functions through purposeful and evidence-based remediation.

The power of attention: Government climate-risk attention and agricultural-land carbon emissions.

Climate change is a common challenge faced by all humanity. Promoting emission and carbon reduction in agricultural land is the most important priority for addressing climate change and realizing sustainable development. Based on data from 296 prefecture-level cities in China from 2011 to 2021, this study utilizes machine-learning and text-analysis methods to construct an indicator of government climate-risk attention (GCRA). It combines a two-way fixed-effects model to investigate how GCRA affects agricultural-land carbon emissions (ALCE) and carbon intensity (ALCI) and the mechanism of the impact. The results indicate that (1) GCRA substantially reduces ALCE and ALCI, and the conclusions are robust to a battery of tests. Furthermore, (2) mechanism analysis reveals that GCRA primarily uses three mechanisms-strengthening environmental regulation, promoting agricultural green-technology innovation, and upgrading agricultural-land mechanization-to reduce ALCE and lower ALCI. Additionally, (3) heterogeneity analysis suggests that the carbon-emission reduction effect of GCRA is more significant in the east, in arid and humid climate zones, and in non-grain-producing regions. Finally, (4) spatial-spillover effect analysis and quantile regression results demonstrate that GCRA also significantly inhibits carbon emissions and the carbon intensity of nearby agricultural land, with the inhibition effect becoming more pronounced at higher levels of government attention. This study's discoveries are helpful in promoting the emission reduction and carbon sequestration of agricultural land and provide references for developing countries to cope with climate change.

Balancing river water pollution and agricultural development: A tradeoff threshold approach.

Balancing environmental protection and social-economic development in agricultural land use management is a dilemma for decision-makers. Based on the modelling of the impacts of land use changes on river water pollution by SWAT model, the tradeoff between tea plantation expansion and river water quality was detected. SWAT model performs well in simulating the non-point source (NPS) pollution in agricultural watershed. The results showed that the tea plantation area expanded dramatically from 44 km2 in 2000 to 169 km2 in 2020 at the high cost of forest land. Consequently, the mean contents of NO3--N and TN have significantly increased by 100% and 91% respectively in the past 20 years. And the NO3--N in river water accounted for over 80% of TN in the tea plantation area. The NO3--N and TN concentrations were positively related with the proportions of tea plantation area (Tea%) at different periods. The high pollution levels of NO3--N and TN are priority control targets for river water quality management. The results indicated that the proportion of tea plantation thresholds lead to abrupt changes in river water quality. When the Tea% exceeded 3.0% in 2000, the probability of N pollution increased sharply. Whereas in 2020, it is suggested that the Tea% should not exceeds 18% to avoid sudden deterioration of water quality. The critical interval value of the Tea% for sudden change in N pollution showed an obvious increase tendency. The accelerating of nutrient pollution in rivers reduced the sensitivity of water quality to tea plantation expansion. Our results can provide new insights and empirical evidence for balancing the tradeoff between agricultural development and river water quality protection by demonstrating the carrying capacity threshold of river water environment for the expansion tea plantation.

Modeling spatial trends and exchange fluxes of contaminants in agricultural soil under pollution prevention measures.

Modeling the long-term trends of contaminants in topsoil under controlled measures is critical for sustainable agricultural environmental management. Traditional mass balance equations cannot predict spatial variation and exchange flux of regional soil contaminants for it lacks a method of assigning input-output parameters to each simulated cell. To overcome this limitation, we allocate the estimated source contribution flux to the spatial grid cell in the regional chemical mass balance by integrated positive matrix factorization (P-RCMB) with historical trends quantification. Focusing on Cd and As, which are elements with elevated risks of food intake and volatilization/infiltration, the model is applied to 30 ha of agricultural land near the enterprise. Predictions indicate an additional 13.5% of the soil is contaminated, and approximately 2.57 ha may accrue after 100 years at the site, with an uncertainty range of 0.98-5.3 ha. Clean water irrigation (CWI) reduces contamination expansion by approximately 42%, including approximately 4813 g ha-1 yr-1 net As infiltration, playing a dominant role in preventing the formation of severely contaminated soil. Stop straw return, green fertilizers use, and reduced atmospheric deposition control the exchange flux of Cd (114.9 g ha-1 yr-1) in moderate/slight contamination areas. For the different contaminants' cumulative trends in dryland and paddy fields, achieving a net cumulative flux close to zero in marginally contaminated areas presents a viable approach to optimize current emission standards. if trade-off straw removal and additional fertilizer inputs, a straw return rate of approximately 40% in Cd-contaminated soil will yield overall benefits. This model contributes valuable insights and tools for policymaking in contaminated land sustainable utilization and emission standard optimization.

Phytoremediation capacity of hybrid aspen at sites affected by industry and agriculture.

Fast-growing Populus spp. are well-acknowledged to restore contaminated soils from heavy metals in industrial areas. Thus far, there is no knowledge about the phytoremediation capacity of Populus spp. plantations in hemiboreal Estonia conditions to restore industrially polluted areas. The objective of this study was to assess the soil contamination rate of heavy metals (As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb and Zn) and their uptake by mature hybrid aspen (Populus tremula × Populus tremuloides Michx.) in plantations in different industrial pollution areas (e.g. cement factory, oil shale mining). For the reference, industrially polluted plantations were compared with the low pollution area hybrid aspen plantation on former agricultural soil, which was influenced by fertilization and liming before afforestation. Twenty-one years after afforestation, soil samples were collected from the 0-10 cm topsoil layer. Aboveground biomass sampling was performed for bark and stem wood by ingrowth cores to separate wood formed during early (1-10 years) and late (11-21 years) stand development. Two decades after the afforestation of industrially polluted areas, the heavy metal concentrations in the soil were higher than the reference plantation and the standard reference for unpolluted soils in most cases. The highest concentrations of heavy metals in woody biomass were in the oil shale quarry spoil; because of poor growth, the accumulated pools in aboveground biomass were low. Cd differed from other metals and accumulated less in wood and more in bark. The concentration of heavy metals (Cd, Cr, Cu, Fe, Mn, Ni and Zn) was higher in the first decade of stand formation (1-10 years) than in the last 10 years (11-21 years). High pools of heavy metals were accumulated in aboveground biomass in the reference plantation, indicating the considerable removal of heavy metal residues from the previous fertilization and liming source with harvest. Two decades of afforestation with hybrid aspen is too short for complete ecosystem restoration from heavy metals in industrially polluted areas.

Past and future land use change dynamics: assessing the impact of urban development on agricultural land in the Pantura Jabar region, Indonesia.

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017-2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87-88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29-86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual.

Soil Bacterial Assemblage Across a Production Landscape: Agriculture Increases Diversity While Revegetation Recovers Community Composition.

Aboveground ecological impacts associated with agricultural land use change are evident as natural plant communities are replaced with managed production systems. These impacts have been extensively studied, unlike those belowground, which remain poorly understood. Soil bacteria are good candidates to monitor belowground ecological dynamics due to their prevalence within the soil system and ability to survive under harsh and changing conditions. Here, we use soil physicochemical assessment and 16S rRNA gene sequencing to investigate the soil physical and bacterial assemblage changes across a mixed-use agricultural landscape. We assess soil from remnant vegetation (Eucalyptus mallee), new and old vineyards, old pasture, and recently revegetated areas. Elevated concentrations of nitrogen (NO3-) and plant-available (Colwell) phosphorus were identified in the managed vineyard systems, highlighting the impact of agricultural inputs on soil nutrition. Alpha diversity comparison revealed a significant difference between the remnant mallee vegetation and the vineyard systems, with vineyards supporting highest bacterial diversity. Bacterial community composition of recently revegetated areas was similar to remnant vegetation systems, suggesting that bacterial communities can respond quickly to aboveground changes, and that actions taken to restore native plant communities may also act to recover natural microbial communities, with implications for soil and plant health. Findings here suggest that agriculture may disrupt the correlation between above- and belowground diversities by altering the natural processes that otherwise govern this relationship (e.g. disturbance, plant production, diversity of inputs), leading to the promotion of belowground microbial diversity in agricultural systems.

What drives bat activity at field boundaries?

Field boundaries are important habitat for bats in agricultural landscapes, serving as commuting and foraging areas for many species. The goal of our study was to better understand the drivers of bat activity in agricultural landscapes to inform conservation policy and make specific recommendations for habitat management. We placed sixty-four full spectrum bat detectors at random recording locations, weekly, along field boundaries in North Somerset between July and October 2020. We used an automated classifier to analyse recordings and performed error rate modelling to account for and remove the majority of error in automated classifications. We used generalised additive models to explore bat response to recording location metrics, controlling for spatial-autocorrelation and temporal differences in sampling. We validated our models with k-fold cross-validation and explored the utility of our models for predicting bat activity at new sites. We found that field boundary characteristics better describe bat activity than adjacent field characteristics or measures of local landscape for the majority of species studied. Bat activity was higher along tall, wide, vegetated field boundaries containing trees; there was lower activity at arable recording locations. Still, bat activity was highly variable and predictive error was high. We found a large spatial effect driving activity patterns, meaning models are not able to predict activity beyond the extent of the study area. We recommend management strategies that give incentives to farmers for replacing fences with hedgerows, planting hedgerow trees, and maintaining tall and outgrown field boundaries.

Land footprint and GHG emissions from global food loss.

BACKGROUND: Efficient land use represents a global challenge in the context of high levels of food loss (FL) and waste (FLW) and increasing greenhouse gas (GHG) emissions from global agricultural activities. This study aimed to estimate the land footprint (LF) associated with FL worldwide. It also estimated the GHG emissions from crop residues and their burning, and their relationship with food loss for the main crops worldwide. The study analyzed data from the Food and Agriculture Organization of the United Nations (FAO) regarding land use, FL, and global GHG emissions from crop residues. RESULTS: The findings suggest that the average LF associated with FL worldwide is about 69 million ha per year, and the main food items responsible for most of the LF associated with FL are maize, wheat, and rice. The annual average emissions derived from burning crop residues of FL are 48.8 kilotons year-1 of CH4 and 1.26 kilotons year-1 of N2 O, and the emission of N2 O derived from crop residues of FL is about 24.1 kilotons year-1 , considering the three crops. CONCLUSION: Food loss implies high levels of LF and GHG emissions, reinforcing the need for proper public and private initiatives worldwide to reduce FL and waste (FLW). Organizations such as the FAO and the Organisation for Economic Co-operation and Development (OECD) should incorporate indicators regarding FLW reduction to evaluate and monitor countries' performance. An international agreement also needs to be addressed to engage the world's nations in the reduction of FLW levels. © 2023 Society of Chemical Industry.

Determination of Soil Quality Index of agricultural land adjacent to industrial sites of Jamuria C.D.Block, Paschim Bardhaman District, West Bengal.

A healthy soil is crucial for food security, but human activities, particularly industrialization, are degrading the soil's quality. This study aims to assess and compare the Soil Quality Index (SQI) at three industrial sites: the iron and steel industry, the open cast coal mining industry, and the brick kiln industry, along with a control field. To measure the SQI, the weighted additive method was applied to the nine selected physico-chemical properties of soil: soil temperature, soil moisture, Bulk Density, pH, N, P, K, OC, and EC. Principal Component Analysis coupled with multiple correlation methods was used to determine the Minimum Data Set. The most dominant factors near the iron and steel industrial site are soil temperature, N, K, and EC, while N, OC, soil moisture, P, K, and EC are the most significant factors near the open cast coal mine. In the case of the brick kiln industrial site, soil moisture, OC, K, soil temperature, and P have the highest loadings. The calculated SQI indicates that the quality of soil is in better condition in the control field (0.6475), while the soil adjacent to the coal mining industrial site (0.1426) is in the worst state, followed by the iron and steel industrial site (0.1611) and the brick kiln industrial site (0.289). To attain sustainable agricultural practices in industrial landscapes, efficient management of nutrient contents and phytoremediation can be helpful.

Towards diverse agricultural land uses: socio-ecological implications of European agricultural pathways for a Swiss orchard region.

Diverse agricultural land uses are a typical feature of multifunctional landscapes. The uncertain change in the drivers of global land use, such as climate, market and policy technology and demography, challenges the long-term management of agricultural diversification. As these global drivers also affect smaller scales, it is important to capture the traits of regionally specific farm activities to facilitate adaptation to change. By downscaling European shared socioeconomic pathways (SSPs) for agricultural and food systems, combined with representative concentration pathways (RCP) to regionally specific, alternative socioeconomic and climate scenarios, the present study explores the major impacts of the drivers of global land use on regional agriculture by simulating farm-level decisions and identifies the socio-ecological implications for promoting diverse agricultural landscapes in 2050. A hilly orchard region in northern Switzerland was chosen as a case study to represent the multifunctional nature of Swiss agriculture. Results show that the different regionalised pathways lead to contrasting impacts on orchard meadows, production levels and biodiversity. Increased financial support for ecological measures, adequate farm labour supplies for more labour-intensive farming and consumer preferences that favour local farm produce can offset the negative impacts of climate change and commodity prices and contribute to agricultural diversification and farmland biodiversity. However, these conditions also caused a significant decline in farm production levels. This study suggests that considering a broader set of land use drivers beyond direct payments, while acknowledging potential trade-offs and diverse impacts across different farm types, is required to effectively manage and sustain diversified agricultural landscapes in the long run. Supplementary information: The online version contains supplementary material available at 10.1007/s10113-023-02092-5.

Life Cycle Environmental Impacts of Wastewater-Derived Phosphorus Products: An Agricultural End-User Perspective.

Recovering phosphorus from wastewater in more concentrated forms has potential to sustainably recirculate phosphorus from cities to agriculture. The environmental sustainability of wastewater-based phosphorus recovery processes or wastewater-derived phosphorus products can be evaluated using life cycle assessment (LCA). Many LCA studies used a process perspective to account for the impacts of integrating phosphorus recovery processes at wastewater treatment plants, while some used a product perspective to assess the impacts of producing wastewater-derived phosphorus products. We demonstrated the application of an end-user perspective by assessing life cycle environmental impacts of substituting half of the conventional phosphorus rock-based fertilizers used in three crop production systems with wastewater-derived phosphorus products from six recovery pathways (RPs). The consequential LCA results show that the substitution reduces global warming potential, eutrophication potential, ecotoxicity potential, and acidification potential of the assessed crop production systems in most RPs and scenarios. The end-user perspective introduced in this study can (i) complement with the process perspective and the product perspective to give a more holistic picture of environmental impacts along the "circular economy value chains" of wastewater-based resource recovery, (ii) enable systemwide assessment of wide uptake of wastewater-derived products, and (iii) draw attention to understanding the long-term environmental impacts of using wastewater-derived products.

Suitability evaluation of potential arable land in the Mediterranean region.

The existing cultivated land in the Mediterranean region faces great pressure from various sources. A suitability evaluation of potential arable land is urgent for helping adaptation measures to mitigate the impacts of climate change and human pressure on agricultural production in the Mediterranean region. We integrated 15 biophysical and socio-economic factors from GIS and remote sensing data to perform a suitability evaluation of potential arable land in the Mediterranean region using analytical hierarchy process and radial basis function artificial neural network methods. Moreover, we analyzed the gap between potential arable land and existing cultivated land and compared the evaluation results between the analytical hierarchy process and artificial neural network methods. The results show that the suitability index of potential arable land based on artificial neural network with 6 neurons has the best correlation with average yield and average harvested area. The land area with a suitability grade over medium level accounts for 62.95% of the potential arable land area, of which 45.71% is uncultivated land. Cyprus, France, Greece, Italy, Lebanon, Portugal, Spain and Turkey have great opportunities for agricultural development. Radial basis function artificial neural network outperforms analytical hierarchy process, has better verification results, and requires less input. This study provides an initial insight into the agricultural land suitability of 16 countries around the Mediterranean Sea and introduces a research idea for agricultural land suitability evaluation.

Distinguishing the impacts of land use change in intensity and type on ecosystem services trade-offs.

Changes in land use intensity and types can affect the structure and function of ecosystems, and thus ecosystem services (ESs) as well as their interactions. However, the impacts of changes in land use intensity on ESs remain poorly understood. Through four different land use scenarios, we distinguished the independent contribution of changes in agricultural land use intensity and types to grain production (GP), water purification (WP), and their trade-offs in the Dongting Lake Basin. The results showed that from 1990 to 2015, GP increased across 58.07% of the total area, but WP decreased across 64.81% of the study area. The two ESs simultaneously increased or decreased across 41.93% of the total area. Watersheds covering 48.72% of the study area where GP increased and WP decreased were mainly distributed in areas with increased land use intensity. The other regions where GP decreased and WP increased were mainly distributed in areas with decreased land use intensity. The scenario analysis of GP, WP, and their trade-offs showed that the areas where agricultural land use intensity was the dominant factor were as large as 1.95 times, 2.38 times, and 2.43 times those dominated by land use type respectively, under the same climate conditions. This study highlighted the importance of changes in agricultural land use intensity on ES, which provided further supporting to ES-based land use management.

Baseline assessment of the hydrological network and land use in riparian buffers of Pampean streams of Uruguay.

The integrated assessment of stream networks and terrestrial land use contributes a critical foundation for understanding and mitigating potential impacts on stream ecology. Riparian zone delineation and management is a key component for regulating water quality, particularly in agricultural watersheds. We present a national assessment of riparian zone land uses according to stream order for the entire hydrological network in the Uruguayan landscape in Southeastern South America. We classified over 82,500 km of streams and rivers in Uruguay into seven Strahler order classes and delineated riparian buffers of 100 and 500 m, depending on stream order, covering a total of 13% of the terrestrial land area in Uruguay. Natural vegetation cover in riparian zones averaged 77% among basins, whereby natural grassland dominated first and second order stream buffers at 58% and 49%, respectively. This highlighted the importance of grasslands in headwater regions of the country. Riparian forests formed corridors along larger streams, representing a mere 9% of buffers in first order streams but reaching 46% of buffers of 6th order streams. Among the six major basins of Uruguay, we found differences in the relative importance of riparian forests and crop cover in headwater stream riparian zones, as well as differences in relative crop cover within riparian zones. Results show that streams in subtropical grassland landscapes originate in open grassland environments, which has major implications for thermal regimes, carbon inputs, and stream biodiversity. Riparian buffer management should consider geographic differences among different basins and ecoregions within Uruguay.

Spatial variability of soil nutrients in apple orchards and agricultural areas in Kinnaur region of cold desert, Trans-Himalaya, India.

The mountain ecosystem is highly vulnerable to climate changes fraught with a multitude of problems related to environment, food, and nutritional security. Quantification of the soil fertility status can provide an efficient way to devise strategies for sustainable crop production. The lack of information on the soil fertility status prompted us to delineate the spatial variability of the soil attributes, viz., pH, electrical conductivity (EC), soil organic carbon (OC), and the macronutrients (nitrogen (N), phosphorus (P), and potassium (K)). The extensive soil sampling was carried out from the apple orchards (AO) and potential areas under agricultural land (AL) in Kinnaur region of cold desert, Trans-Himalaya, India. Descriptive statistics was employed for the exploratory analysis of data representing a wide variation (coefficient of variation, CV = 5.70-58.62%). The available N and P, categorized as low (< 280 kg ha-1) to medium (280-560 kg ha-1) and low (4-10 kg ha-1) to high (> 25 kg ha-1), respectively, were the main limiting factors in crop production. The availability of the K was categorized as medium (118-280 kg ha-1) to high (> 280 kg ha-1). The geostatistical analysis was carried out to check the spatial dependency in the dataset. The principal component analysis (PCA) was carried out and the dominant PCs were used in fuzzy c-means clustering for the delineation of management zones (MZs). The management zones highlight the need for area-specific interventions for ameliorating soil degradation and increasing apple productivity. The soil nutrient maps in spatial scale would help to provide precise fertilizer recommendations for sustainable production and environmental conservation.

A new index on agricultural land greenhouse gas emissions in Africa.

Africa emits the lowest amounts of greenhouse gases (GHGs) into the global GHG budget. However, the continent remains the most vulnerable continent to the effects of climate change. The agricultural sector in Africa is among the most vulnerable sectors to climate change. Also, as a dominant agricultural sector, African agriculture is increasingly contributing to climate change through GHG emissions. Research has so far focused on the effects of GHG emissions on the agricultural and other sectors with very little emphasis on monitoring and quantifying the spatial distribution of GHG emissions from agricultural land in Africa. This study develops a new index: African Agricultural Land Greenhouse Gas Index (AALGGI) that uses scores and specific scale ranges for carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) to map the spatial variations in regional GHG emissions across Africa. The data for the three main GHGs (CO2, CH4, and N20) were downloaded from FAOSTAT. The data were analyzed through the newly developed African Agricultural Land Greenhouse Gas Index (AALGGI). This is an empirical index with scores ranging from 0 to 10, with higher scores indicating higher levels of emissions. The results show that Southern and North African regions have the lowest amounts of agricultural land GHG emissions, with AALGGIs of 3.5 and 4.5, respectively. East Africa records the highest levels of GHG emissions, with an AALGGI of 8 followed by West Africa with an AALGGI of 7.5. With the continental mean or baseline AALGGI being 5.8, East and Middle Africa are above the mean AALGGI. These results underscore the fact that though Africa, in general, is not a heavy emitter of GHGs, African agricultural lands are increasingly emitting more GHGs into the global GHG budget. The low AALGGIs in the more developed parts of Africa such as Southern and North Africa are explained by their domination in other GHG emitting sectors such as industrialization and energy. The high rates of emissions in East Africa and Middle Africa are mainly linked to intensive traditional farming practices/processes and deforestation. These findings underscore the need to further leverage climate change mitigation actions and policy in Africa and most importantly the co-benefits of mitigation and adaptations in the most vulnerable regions.

Heavy metal pollution and health risk assessment of agricultural land in the Southern Margin of Tarim Basin in Xinjiang, China.

The study aimed to analyze the level of heavy metal pollution in the agricultural lands in the southern margin of Tarim Basin, Xinjiang, and its risks to human health. A total of 1765 soil samples were collected from the Cele-Ruoqiang area. The contents of soil As, Cd, Cr, Cu, Hg, Ni, Pb and Zn were analyzed using the pollution load index method. The results showed that soil was slightly contaminated with Hg and Cd; the multivariate statistical analysis indicated that Cr, Ni, As, Cu, Zn, and Cd were formed mostly by geochemical genesis, whereas Hg and Pb contents by the geological origin and human activities. The health risk assessment showed that there is no unacceptable non-carcinogenic health risk. As levels posed carcinogenic risk, however, they were within human tolerance. Thus, this study provides a scientific basis for local agricultural production safety and prevention and control of soil heavy metal pollution.

Modelling agricultural land abandonment in a fine spatial resolution multi-level land-use model: An application for the EU.

In the majority of EU Member States, agricultural land is expected to decrease not only due to land-use changes in favour of urban expansion and afforestation but also to land abandonment processes. The knowledge on location and extent of agricultural land abandonment is relevant for estimating local external effects and adapting policy interventions. Currently, multi-level land-use models are able to capture determined processes of demand-driven redevelopment. However, land abandonment is much more difficult to capture because of its more ambiguous definition and the lack of data on its spatial distribution. This paper presents a method to explicitly model agricultural abandonment as a choice of disinvestment, which in turn is embedded in a utility-based land-use modelling framework that projects land-use changes for the EU and the UK. Validation exercises using observed spatial distribution of abandoned farmland show that the proposed method allows to model abandonment with acceptable accuracy.