Biophysical suitability, economic pressure and land-cover change: a global probabilistic approach and insights for REDD+

There has been a concerted effort by the international scientific community to understand the multiple causes and patterns of land-cover change to support sustainable land management. Here, we examined biophysical suitability, and a novel integrated index of “Economic Pressure on Land” (EPL) to explain land cover in the year 2000, and estimated the likelihood of future land-cover change through 2050, including protected area effectiveness. Biophysical suitability and EPL explained almost half of the global pattern of land cover (R 2 = 0.45), increasing to almost two-thirds in areas where a long-term equilibrium is likely to have been reached (e.g. R 2 = 0.64 in Europe). We identify a high likelihood of future land-cover change in vast areas with relatively lower current and past deforestation (e.g. the Congo Basin). Further, we simulated emissions arising from a “business as usual” and two reducing emissions from deforestation and forest degradation (REDD) scenarios by incorporating data on biomass carbon. As our model incorporates all biome types, it highlights a crucial aspect of the ongoing REDD + debate: if restricted to forests, “cross-biome leakage” would severely reduce REDD + effectiveness for climate change mitigation. If forests were protected from deforestation yet without measures to tackle the drivers of land-cover change, REDD + would only reduce 30 % of total emissions from land-cover change. Fifty-five percent of emissions reductions from forests would be compensated by increased emissions in other biomes. These results suggest that, although REDD + remains a very promising mitigation tool, implementation of complementary measures to reduce land demand is necessary to prevent this leakage

Familial hypercholesterolaemia in children and adolescents from 48 countries: a cross-sectional study

Background: Approximately 450 000 children are born with familial hypercholesterolaemia worldwide every year, yet only 21% of adults with familial hypercholesterolaemia were diagnosed before age 18 years via current diagnostic approaches, which are derived from observations in adults. We aimed to characterise children and adolescents with heterozygous familial hypercholesterolaemia (HeFH) and understand current approaches to the identification and management of familial hypercholesterolaemia to inform future public health strategies. Methods: For this cross-sectional study, we assessed children and adolescents younger than 18 years with a clinical or genetic diagnosis of HeFH at the time of entry into the Familial Hypercholesterolaemia Studies Collaboration (FHSC) registry between Oct 1, 2015, and Jan 31, 2021. Data in the registry were collected from 55 regional or national registries in 48 countries. Diagnoses relying on self-reported history of familial hypercholesterolaemia and suspected secondary hypercholesterolaemia were excluded from the registry; people with untreated LDL cholesterol (LDL-C) of at least 130 mmol/L were excluded from this study. Data were assessed overall and by WHO region, World Bank country income status, age, diagnostic criteria, and index-case status. The main outcome of this study was to assess current identification and management of children and adolescents with familial hypercholesterolaemia. Findings: Of 63 093 individuals in the FHSC registry, 11 848 (188%) were children or adolescents younger than 18 years with HeFH and were included in this study; 5756 (502%) of 11 476 included individuals were female and 5720 (498%) were male. Sex data were missing for 372 (31%) of 11 848 individuals. Median age at registry entry was 96 years (IQR 58-132). 10 099 (899%) of 11 235 included individuals had a final genetically confirmed diagnosis of familial hypercholesterolaemia and 1136 (101%) had a clinical diagnosis. Genetically confirmed diagnosis data or clinical diagnosis data were missing for 613 (52%) of 11 848 individuals. Genetic diagnosis was more common in children and adolescents from high-income countries (9427 [924%] of 10 202) than in children and adolescents from non-high-income countries (199 [480%] of 415). 3414 (316%) of 10 804 children or adolescents were index cases. Familial-hypercholesterolaemia-related physical signs, cardiovascular risk factors, and cardiovascular disease were uncommon, but were more common in non-high-income countries. 7557 (724%) of 10 428 included children or adolescents were not taking lipid-lowering medication (LLM) and had a median LDL-C of 500 mmol/L (IQR 405-608). Compared with genetic diagnosis, the use of unadapted clinical criteria intended for use in adults and reliant on more extreme phenotypes could result in 50-75% of children and adolescents with familial hypercholesterolaemia not being identified. Interpretation: Clinical characteristics observed in adults with familial hypercholesterolaemia are uncommon in children and adolescents with familial hypercholesterolaemia, hence detection in this age group relies on measurement of LDL-C and genetic confirmation.Where genetic testing is unavailable, increased availability and use of LDL-C measurements in the first few years of life could help reduce the current gap between prevalence and detection, enabling increased use of combination LLM to reach recommended LDL-C targets early in life

Agricultural intensification and policy interventions: Exploring plausible futures for smallholder farmers in Southern Mali

Assessing how livelihoods in rural sub-Saharan Africa might change given future trends in socio-economic and biophysical conditions helps to identify and direct effective efforts towards poverty reduction. Based on existing literature, hypothetical changes in farmer practices and policy interventions were described and used to build five contrasting scenarios towards the year 2027. A simulation framework was developed to assess food self-sufficiency and income per capita now and in the future for a representative village of 99 households in Southern Mali. In the current situation, 26% of the farms were food self-sufficient and above the 1.9 US$ day−1 poverty line. This percentage would fall to 13% in the “Business as usual” scenario. In the “Dairy development” scenario, with intensification of livestock production and support to the milk sector, 27% of farms would be food self-sufficient and non-poor. Additional policy interventions targeting family planning and job creation outside agriculture would be needed to improve both household food self-sufficiency and income per capita. In this optimistic scenario, 77% of the farms would be non-poor and food self-sufficient in 2027. Additional programs to promote Integrated Pest Management, small-scale mechanization and mineral fertilizer on traditional cereals could allow a drastic increase in productivity and would lift 94% of the farm population out of poverty. Considering the entire heterogeneous farm population was crucial to accurately assess pathways out of poverty. Our study stresses the need for a strategic and multi-sectoral combination of interventions to improve livelihoods

A dynamic simulation model of land-use changes in Sudano-sahelian countries of Africa (SALU)

This paper presents a simulation model to project land-cover changes at a national scale for Sudano-sahelian countries. The aim of this study is to better understand the driving forces of land-use change and to reconstruct past changes. The structure of our model is heavily determined by its spatially aggregated level. This model represents, in a dynamic way, a simplified version of our current understanding of the processes of land-use change in the Sudano-sahelian region of Africa. For any given year, the land demand is calculated under the assumption that there should be an equilibrium between the production and consumption of basic resources derived from different land-uses. The exogenous variables of the model are human population (rural and urban), livestock, rainfall and cereals imports. The output are the areas allocated to fuelwood extraction, crops, fallow and pasture for every year. Pressure indicators are also generated endogenously by the model (rate of overgrazing and land degradation, labour productivity, average household "budget"). The parameters of the model were derived on the basis of a comprehensive review of the literature, mostly of local scale case studies of land-use changes in the Sahel. In agreement with farming system research, the model simulates two processes of land-use change: agricultural expansion at the most extensive technological level, followed by agricultural intensification once some land threshold is reached. The model was first tested at a national scale using data from Burkina Faso. Results simulate land-use changes at two time frequencies: high frequency, as driven by climatic variability, and low frequency, as driven by demographic trends. The rates of cropland expansion predicted by the model are consistent with rates measured for several case studies, based on fine spatial resolution remote sensing data. (C) 2001 Elsevier Science B.V. All rights reserved

SUSTAINABLE AND SMART CITY PLANNING USING SPATIAL DATA IN WALLONIA

Simulating population distribution and land use changes in space and time offer opportunities for smart city planning. It provides a holistic and dynamic vision of fast changing urban environment to policy makers. Impacts, such as environmental and health risks or mobility issues, of policies can be assessed and adapted consequently. In this paper, we suppose that “Smart” city developments should be sustainable, dynamic and participative. This paper addresses these three smart objectives in the context of urban risk assessment in Wallonia, Belgium. The sustainable, dynamic and participative solution includes (i) land cover and land use mapping using remote sensing and GIS, (ii) population density mapping using dasymetric mapping, (iii) predictive modelling of land use changes and population dynamics and (iv) risk assessment. The comprehensive and long-term vision of the territory should help to draw sustainable spatial planning policies, to adapt remote sensing acquisition, to update GIS data and to refine risk assessment from regional to city scale