Conservation Tracking

RCoE Action

Topic

Policy

Target

Title

This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 1x1 km size.

It derives from the GHS-POP - R2023A. Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 100x100 m size.

This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 1x1 km size.

Critical natural assets are defined as the natural and semi-natural terrestrial and aquatic ecosystems required to maintain 12 of nature’s ‘local’ contributions to people (local NCP) in the ocean (blue). 12 Local NCP for key benefits like security in food, water, hazards, material and culture. as follows: for food, pollinator habitat sufficiency ofr pollination dependent crop production, fodder production for livestock, wild riverine and marine fish catch ; for water, water quality regulation, via sediment retention and nutrient retention; for natural hazards, flood risk reduction and coastal risk reduction. For materials, timber production, fuelwood production and access to nature. For cultural benefits, coral reef tourism and access to nature for recreation or other uses. Criticality of the natural assets was defined on the basis of the highest value areas across all NCPs, the magnitude of benefits and the number of beneficiaries. Cropland, urban areas, bare areas and permanent snow and ice are excluded from the analysis.

The United Nations Educational, Scientific and Cultural Organization (UNESCO) seeks to encourage the identification, protection and preservation of natural heritage around the world considered to be of outstanding value to humanity. This is embodied in an international treaty called the Convention concerning the Protection of the World Cultural and Natural Heritage, adopted by UNESCO in 1972. What makes the concept of World Heritage exceptional is its universal application. World Heritage sites belong to all the peoples of the world, irrespective of the territory on which they are located.

The human imprint on the planet has a major impact on the functioning of the Earth system. Because the impact on the environment is closely intertwined with population dynamics, it is important to monitor and include these in the evaluation of land degradation. This layer displays the areas of concern for population change related issues derived from the convergence of global evidence of human-environment interactions that can lead to land degradation. It reflects the dynamics of increasing number of people in a certain area.

This layer is part of the World Altas of Desertification

According to UN estimates, the global population will increase by 2.4 billion between 2015 and 2050. Of this, an overwhelming 50 % will be concentrated in Africa (1.3 billion). There could be up to four times as many people in sub-Saharan Africa by the end of the century. The human imprint on the planet has a major impact on the functioning of the Earth system. Because the impact on the environment is closely intertwined with population dynamics, it is important to monitor and include these in the evaluation of land degradation. This layer displays the areas of concern for population density related issues derived from the convergence of global evidence of human-environment interactions that can lead to land degradation.

Critical natural assets are defined as the natural and semi-natural terrestrial and aquatic ecosystems required to maintain 12 of nature’s ‘local’ contributions to people (local NCP) on land. 12 Local NCP for key benefits like security in food, water, hazards, material and culture. as follows: for food, pollinator habitat sufficiency ofr pollination dependent crop production, fodder production for livestock, wild riverine and marine fish catch ; for water, water quality regulation, via sediment retention and nutrient retention; for natural hazards, flood risk reduction and coastal risk reduction. For materials, timber production, fuelwood production and access to nature. For cultural benefits, coral reef tourism and access to nature for recreation or other uses. Criticality of the natural assets was defined on the basis of the highest value areas across all NCPs, the magnitude of benefits and the number of beneficiaries. Cropland, urban areas, bare areas and permanent snow and ice are excluded from the analysis.

The dataset is the result of a crop simulation and machine learning exercise to evaluate the irrigation potentials in sub-Saharan Africa. Samples of points (representative for each Agro-ecological zone in Africa) have been selected from a cropland map (from Tubliello et al., 2023) and, by using the DSSAT crop model fed with soil information from the ISRIC project (https://isric.org/projects/soil-information-system-for-africa-soils4africa/) and 10 years of daily climate data (from NASAPOWER), simulations have been conducted in each location under rainfed conditions and optimal irrigation. This last has been achieved y letting the crop model add irrigation water each time a water stress was perceived. Other management practices have been set as realistic as possible, particularly the amount of provided fertilization has been kept low to represent current conditions. Yields have been finally averaged along the different years of simulations to generate expected yields. Finally, by using the XGboost algorithm fed with the same soil and climatic variables used for the crop model, yields under both rainfed and irrigation conditions have been imputed to all remaining cropland locations in Sub-Saharan Africa. What is presented here are the yield increases, as percentages over rainfed yields, obtainable in each location for the four simulated cereals: maize, millet, rice and sorghum.