Projected patterns of land uses in Africa under a warming climate

Land-use change is a direct driver of biodiversity loss, projection and future land use change often consider a topical issue in response to climate change. Yet few studies have projected land-use changes over Africa, owing to large uncertainties. We project changes in land-use and land-use transfer under future climate for three specified time periods: 2021–2040, 2041–2060, and 2081–2100, and compares the performance of various scenarios using observational land-use data for the year 2020 and projected land-use under seven Shared Socioeconomic Pathways Scenarios (SSP): SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5 from 2015 to 2100 in Africa. The observational land-use types for the year 2020 depict a change and show linear relationship between observational and simulated land-use with a strong correlation of 0.89 (P < 0.01) over Africa. Relative to the reference period (1995–2014), for (2021–2040), (2041–2060), (2081–2100), barren land and forest land are projected to decrease by an average of (6%, 11%, 16%), (9%, 19%, 38%) respectively, while, crop land, grassland and urban land area are projected to increase by (36%, 58%, and 105%), (4%, 7% and 11%), and (139%, 275% and 450%) respectively. Results show a substantial variations of land use transfer between scenarios with major from barren land to crop land, for the whole future period (2015–2100). Although SSP4-3.4 project the least transfer. Population and GDP show a relationship with cropland and barren land. The greatest conversion of barren land to crop land could endanger biodiversity and have negative effects on how well the African continent's ecosystem’s function.


Changes in historical land-use in Africa
In the year 2020, we identified that grassland was the dominant land use type in the region, accounted for 44.78% of the total area.It was primarily distributed in the southern, eastern, and western regions of the continent as well as in some areas of the northern and Saharan regions.The second was barren land, accounted for 21.19% of the total area, it was mostly found in the Northern and Sahara region.The third was forest land, made up 16.53% of the total area and was most prevalent in the Central region.The Urban land accounted for 15.12% mainly dominated within the Western region of Africa.The cropland had made of 1.26% mainly distributed across all the regions and dominated within the Western area.The water bodies which accounted for 1.12% mainly dominated in Eastern region.The crop land in the region were scarce, likewise, urban land and waterbodies were dominant in Western (WAF) and Eastern region (EAF) respectively (Figs. 1 and 2a).However, the land use type over Northern region (NAF) and Sahara (SAH) was dominated by barren land accounted for (65.25%) and (66.85%) respectively, the cropland in NAF and SAH accounted for 12.41% and 11.41% respectively.Forestland were marginal in both NAF and SAH regions accounted for 1.02% and 1.22% respectively.Grassland accounted for 12.22% and 12.72% over NAF and SAH respectively.The urban land accounted for 6.80% and 6.25% respectively over NAF and SAH.Waterbodies appears scarce in both the two regions accounted for 6.80% and 6.25% respectively (Figs. 1  and 2b,c).However, Western region (WAF) accounted for 20.02%, 14.32%, 32.90%, 15.80%, 15.33%, and 1.63% respectively, for barren land, cropland, forest land, grassland, urban land, and waterbodies respectively (Figs. 1  and 2d).Likewise, Central region (CAF), accounted for 3.77%, 11.57%, 73.51%, 5.12%, 5.02%, and 1.01% for barren land, cropland, forest land, grassland, urban land, and waterbodies respectively (Figs. 1 and 2e).

Projected changes in land-use in Africa
The historical period (1995-2014), the areas of barren land, crop land, forest land, grass land, and urban land in African continent were approximately 1490 × 10 4 km 2 , 244 × 10 4 km 2 , 344 × 10 4 km 2 , 884 × 10 4 km 2 , and Table 1.Person's correlation coefficient between observation land-use of 2020 and SSPs 2020 projection area mean of all (SSPs) in Africa and its Regions.*Significant at 0.01 level.4.32 × 10 4 km 2 , respectively (Fig. 3a).However, the changes were calculated relative to the historical period under three period and was found out that barren land is expected to decrease except for SSP1-1.9 and SSP1-2.6 shown a mild increase, the areas of urban land and crop land area is projected to increase in all SSPs, with rapid increase in urban land under SSP4-3.4and SSP4-6.0reaching 28 × 10 4 km 2 accounted for 600%, and cropland shown a rapid increase under SSP3-7.0 at the end of twenty-first century reaching 674 × 10 4 km 2 accounted for 176%, the forest land were projected to decrease in all SSPs, the grassland were shown with the decrease in SSP1-1.9 and SSP1-2.6 and depict an increase under SSP3-7.0,SSP4-3.4,SSP4-6.0 and SSP5-8.5.

Regions SSPs Observation
The near-term period (2021-2040), the barren land area is projected to be approximately 1388 × 10 4 km 2 in all SSPs accounted for reduction of 6% compared to historical period (1995-2014), the crop land area is approximately 334 × 10 4 km 2 in all SSPs and was projected to be accounted for an increase of 36% relative to historical period, the forestland area was anticipated to reduce by an area approximately 312 × 10 4 km 2 accounted for 9% relative to the historical period, the grass land area is approximately 922 × 10 4 km 2 in all SSPs accounted for an increase in 4% compared to the historical period (1995-2014).Likewise, the urban land area is approximately 9.7 × 10 4 km 2 was anticipated to increase by 139% relative to the historical period (Fig. 3a).
The mid-term (2041-2060), the barren land area is projected to be approximately 1324 × 10 4 km 2 in all SSPs accounted for reduction of 11% compared to historical period (1995-2014), the crop land area is approximately www.nature.com/scientificreports/396 × 10 4 km 2 in all SSPs and was projected to be accounted for an increase of 58% relative to historical period, the forestland area was anticipated to reduce by an area approximately 278 × 10 4 km 2 accounted for 19% relative to the historical period, the grass land area is approximately 955 × 10 4 km 2 in all SSPs accounted for an increase in 7.9% compared to the historical period (1995-2014).Likewise, the urban land area is approximately 15.5 × 10 4 km 2 was anticipated to increase by 275% relative to the historical period (Fig. 3a).

Dynamic transfer process in future land-use
According to the future analysis of land-use transfer, the data suggest a rather abrupt change of decreasing rates of barren land use to cropland use which is most evident in Africa (see Fig. 4).We hypothesize that the transition change from barren land to cropland is related to market developments in the context of the global economic and food crisis 2007-2009, followed by crop to barren, forest to crop, and other reciprocal transfers of grass to crop, are the main land-use transfers in Africa (Fig. 4).Urban land transfers were minimal.
Interestingly, the land-use transfer on the Africa continent during mid-term (2041-2060), were still consistent to that of near-term, as barren land to crop land will be approximately 1. SSPs, with highest transfer of approximately 10 × 10 4 km 2 in both (SSP1-1.9 and SSP1-2.6), and the smallest was under SSP4-3.4,1.3 × 10 4 km 2 , with an average of approximately 7.5 × 10 4 km 2 .The transfer area of crop land to forest land will be approximately 2.8 × 10 4 km 2 to 4.2 × 10 4 km 2 under all SSPs, with highest transfer of approximately 4.7 × 10 4 km 2 in (SSP1-1.9,SSP1-2.6,SSP2-4.5), and the smallest was under SSP3-7.0,2.8 × 10 4 km 2 , with an average of approximately 3.7 × 10 4 km 2 .The minimal transfer between barren land to grass land were seen with an average of approximately 1.5 × 10 4 km 2 .Thereby, grass land to crop land also exhibit a minimal transfer with an average of approximately 1.1 × 10 4 km 2 (Fig. 4).At the end of the twenty-first century, long-term (2081-2100), the transfer area of barren land to crop land will be approximately 0.0 × 10 4 km 2 to 9 × 10 4 km 2 under all SSPs, with highest transfer of approximately 9 × 10 4 km 2 in both (SSP1-2.6), with an average of approximately 5.3 × 10 4 km 2 .The transfer area of forest to crop land will be

Spatial changes in the future transfer of barren land to cropland in Africa
Future land-use patterns in Africa show a significant conversion of barren land to cropland, which will represent the largest transfer area in comparison to the historical period (1995-2014), which can be observed in the strong decline in the rate of land use change in most regions of Africa.Furthermore, land degradation, caused by both climatic variability and human activities, has often been associated with cropland abandonment, subsequent expansion of agricultural land and deforestation elsewhere, as widely observed in tropical regions 5 .Our analysis went further to examine the regional variations in the conversion of barren land to cropland across the region, and the results show that the Western (WAF), Eastern (EAF), and a portion of the Southern (SAF) regions will be the ones where this conversion will be most pronounced (Fig. 5).Approximately 201 × 10 4 km 2 , 199 × 10 4 km 2 , 167 × 10 4 km 2 , 140 × 10 4 km 2 , 12 × 10 4 km 2 , 137 × 10 4 km 2 and 162 × 10 4 km 2 have changed between 2015 to 2100 under SSP1-1.9,SSP1-2.6,SSP2-4.5, SSP3-7.0,SSP4-3.4,SSP4-6.0 and SSP5-8.5, respectively.However, SSP4-3.4. in (Fig. 5e) predicts the lowest conversion in the future.

Spatial changes in the future transfer of forestland to cropland in Africa
The spatial pattern of the future land-use changes in Africa exhibits large conversion from forest land to crop land, and the shift will be largest transfer area in the future relative to historical period (1995-2014).Notably, the expansion of cropland led to the most significant land use characteristic on the African continent.Our analysis went further to examine the regional variations in the conversion of forest to cropland across the region, and the results show that the Western (WAF), Central (CAF), and Eastern (EAF) regions will dominate the conversion of forest to crop land (Fig. 6).The changes from 2015 to 2100 under SSP1-1.9,SSP1-2.6,SSP2-4.5, SSP3-7.0,SSP4-3.4,SSP4-6.0, and SSP5-8.5 witness an increase over time and is approximately 68 × 10 4 km 2 , 68 × 10 4 km 2 , 70 × 10 4 km 2 , 56 × 10 4 km 2 , 14 × 10 4 km 2 , 75 × 10 4 km 2 and 71 × 10 4 km 2 respectively (Fig. 6).

Future changes in population and GDP
We also investigate the socioeconomic aspects of land usage in Africa considering predicted GDP and population pathways (Fig. 7).In all scenarios, the population under SSPs increased noticeably, with the lowest increases occurring under SSP1 and SSP5 and the highest increases occurring under SSP3 and SSP4 (Fig. 7a).The GDP grows under SSPs in all scenarios, with the lowest growth rates under SSP3 and SSP4 and the strongest growth rates under SSP1 and SSP5 (see Fig. 7b).The observation population period of (2020) was approximately 1360.68 million.But the projected population under the near-term (2021-2040) is projected to decrease by approximately 1249.38 million (8.1%) compared to the reference period; however, the projected population under the mid-term (2041-2060) increased by approximately 1698.30 million (24.8%); similarly, the long-term period (2081-2100) was projected to increase by approximately 2341.89 million (72.1%).
The correlation of future socioeconomic variables (population and GDP) with crop land and barren land areas (Table 2) showed that population and GDP were significantly correlated with crop land and barren land areas.The result depicts that between population and crop land under near-term (2021-2040), mid-term (2041-2060) and long-term (2081-2100) exhibits a positive correlation in all SSPs (SSP1, SSP2, SSP3, SSP4 and SSP5) scenarios.However, from our analysis in Table 2, showed that strong positive correlation exhibits between GDP and crop land over Africa as indicated under all periods and all SSPs, from near-term to the end of the twenty-first century.
Interestingly, the result depicts that between population and barren land under near-term (2021-2040), midterm (2041-2060) and long-term (2081-2100) exhibits an inverse negative correlation in all SSPs (SSP1, SSP2, SSP3, SSP4 and SSP5) except for SSP1 (0.99) in mid-term (2040-2060) scenarios.However, from our analysis in Table 2, showed that negative correlation exhibits between GDP and barren land over Africa as indicated under all periods and all SSPs, but showed with a positive correlation in SSP1 near-term and long-term (0.98) (0.67) respectively, and long-term SSP2 (0.96) and SSP5 (0.87) at the end of the twenty-first century.

Conclusions and discussion
This study use observation land use, historical and projections simulation land use under various SSPs to project changes and conversion of land-use patterns in Africa and its sub-regions, and their relationships with socioeconomic changes (population and GDP) in different time scales, utilizing the most recent socioeconomic pathways from LUH2 project, and potential future socioeconomic conditions described in the Shared Socio-economic pathways SSPs 49 , (SSP1-1.9,SSP1-2.6,SSP2-4.5, SSP3-7.0,SSP4-3.4,SSP4-6.0, and SSP5-8.5) to project future land-use changes 45 .It was discovered that LUH2 performed better and can simulate future land-use change after the study utilized a linear relationship to detect the performance of the Simulation datasets with observational land-use.The analysis made use of observational data for 2020, and it also applied this methodology to LUH2 data in the future (from 850 to 2100).To provide a baseline, the years 1995 to 2014 were chosen as the reference period.The study estimated projected land-use change over three distinct time periods to evaluate the region's response to future climate change.The main conclusions of the study are as follows:    However, majority of the human effects on the earth system, including influences and interaction, were responsible for land-use changes 1,2 , and has been altered by humans within the last millennium 9,10 .SSPs are intended to have different environmental implications, while global land-use models differ by design all aim to modelled same global systems capturing same systems dynamics.Our findings are similar with previous studies investigating uncertainties in land use projections.For example 17,50,51 , both founds large differences in land-cover projections between models, with the highest variability occurring in future cropland areas.
Our findings suggests that cropland, grassland, and urban land will increase over time, exceeding barren land, forest land in the region.This will lead to the biggest expansion in agriculture across Africa in future land use.Climate change is a result of the climatic and socioeconomic changes that could affect how agricultural land is used in the future 4 .Our result agrees with 52 , that LULC analysis in Sialkot Pakistan revealed 4.14% increase in the built-up area and 3.43% decrease in vegetation cover of the city during 1989 to 2020.Both land covers are expected to change in the future (year 2030) by + 1.31% (built-up) and − 1.1% (vegetation).The LUH2 datasets are instruments for the future land-use change scenarios 53 , but there are uncertainties associated with these models, especially in relation to the African Monsoon circulation and precipitation 54 , these variations have been attributed primarily to global sea surface temperature (SST) 54 , which coincides with the period of rapid population growth and associated changes in land use 55 .However, African region could be considered as priorities for biodiversity loss, ecosystem disruptions and carbon storage loss, due to projected scenarios show an upwards trend of cropland expansion, all SSPs depict high conversion to cropland except SSP4-3.4shows a minimal conversion.Furthermore, previous land-use model intercomparison have highlighted uncertainty arising from differences between initial land-use input data, bioenergy production assumptions and yield responses to climate change associated with underlying crop models 14,56,57 .For example 50 , found out that models often allocate land-use change based on land use in adjacent grid cell (e.g.cropland expansion at the edge of existing agricultural area), therefore, can have a large influence on the dynamic of cropland expansion in future time steps 50 .
However, future food demand will likely be met by other means such as crop land expansion or greater reliance on imports which further increase cropland 58 .Our finding was in consistent with the study by 17 who found out that cropland expansion in Africa is likely to continue in the future, which could have significant impact on biodiversity and carbon storage through loss of biomass and soil carbon 17,59 in their studies over Africa suggests that cropland is expected to increase by approximately 51% (154 million hectares) from 2020 to 2090 under future land-use climate change scenarios.A future Africa Green Revolution may result in increased agricultural use and CO 2 emissions; therefore, the expansion of cropland may also be due to growing global market integration 60 .The total food production in Africa will only suffice to feed 1.35 billion people, at a time when the continent's population is expected to reach 3.5 billion, leaving a food deficit for 2.15 billion people 58 .Africa holds just 9% of the world's surface water, while accounting for over 17% of the world's total population 61,62 .
Additionally, the primary driver of the largest conversion of barren land to crop land might also related to regional climate change 1,6 .Similarly, from 63,64 reported a projected increase in precipitation across Africa from CMIP6 projections indicate enhanced precipitation across many regions under various scenarios, this increase in www.nature.com/scientificreports/precipitation will significantly favour the conducive environment for agricultural productivity 65 .Climate change is anticipated to make agricultural development more difficult as cropland in the region increases 66 .Overall, these findings highlight the need for in-depth research into additional concerns, particularly those across continents, especially considering the expansion of cropland within Africa and the impact of climate change.
The region is identified as a climate change hotspot and is experiencing rapid population growth 42 , food insecurity accounted for 811 million globally, including 282 million in Africans (accounted for 21%) are faced by climate related shocks, changes in land tenure and agrarian system of production, high-income inequality and economic downturns worsened by the COVID-19 pandemic 67 .
Although cropland is being expanded to increase agricultural output, this threatens biodiversity and carbon storage, which has an impact on how well ecosystem's function [68][69][70] .Only about 19% of African GDP is made up of the agricultural sector 58 .Despite this, Africa is a key hotspot for food insecurity and climate change vulnerability since most of its nations are not currently and will not be able to sustain themselves in the face of a changing environment 38 .
Socioeconomic development shift and population growth, urbanization and economic changes serves as a major driver to land use changes in different regions 24,71 , thereby this study uses the latest population, economic and land use simulation data sets available under various SSPs to project future land use changes and their relationships with socioeconomic changes in Africa in different time scales, the study also performed a relationship between land use simulation and observational land use to identify the performance of the SSPs with observation and was found to be strong linear relationships exist among the two datasets.The SSPs under different development pathways might result in the possibility of future changes to storyline for both socioeconomic and land use variables under different IAMs models scenarios to uncertainties of the projections 72 .Despite the high resolution of LUH2 datasets with multiples crops and pasture types and related management practices still shows uncertainties at some points 7 .
However, our studies found a strong relationship between population and crop land and GDP and crop land in Africa, these also agrees with the previous results regarding changes in socioeconomic level (population and GDP) [73][74][75] .
Our studies depict that averagely under all SSPs Africa population might reaches 2341.89 million accounted for (72%) increase compared with 2020 and the highest was under SSP3 and GDP in our finding grows to 3031.06 billion USD accounted for increase to (30.31%).These findings corroborated with previously result that SSP2 sees an increase of global average income under a future of global progress where developing countries achieve significant economic growth" 25,76,77 .According to 76 , GDP per capita is projected to increase in all African countries.Meanwhile, croplands constitute 10% of the total land area on the African continent 78 .However, over half (58.4%) of African croplands are located on drylands, where crop production is becoming increasingly difficult due to 'water shortages, land degradation, climate change and persistent poverty' 78,79 .Under climate change, in wet tropical regions drylands are expected to become wetter, while in Northern and Southern Africa, subtropical drylands will expand, and semi-arid zones may shift to arid or hyper-arid zones 79,80 .
The possible mechanism behind land-use change over Africa could perhaps reflect history linked with economic development, population growth, technology, and environmental changes 81 .The transition from constant to rising rates of land use change in Africa has been discussed in the context of shifting global food regimes and coincides with a period when global food production changed from agro technological intensification (driven by the Green Revolution in the 1960s) to the production for globalized markets and increasing trade, especially during the 1990s 82,83 .For example, higher rates for the changes in land-use in developing countries might be because of demand from developed countries in terms of global economy and international trade being an important agent (driver) in land use change, as affirmed by growing global market integration, changing in the opportunity created by market and outside policy intervention 5,60 .The changes might be in the area or the intensity of use 81 , as affirmed by 71 future changes in the area will have significant influences on the global economic growth, industrialization, and allocation of resources.
The result of this study has an important implication for policy interventions under climate change as the region is faced with serious climate change issues and extreme events (see AR4 and 5 36,37 ).The shift in socioeconomic development indices might result to climate change 24,71 , thereby to develop specific measures to mitigate such will help a long way in the regions.It is important to note that, under a changing climate, identified by land use change and socio-economic development some countries in Africa may not have the resources to export food products and may experience deficits as well while others will have a surplus 38 .This poses an important question that should be further explored.First, affordability, will the region be able afford to import the additional food needed to meet their population's demand despite their growing GDP under climate change.As the region is faced with population growth such research would be crucial in developing efficient short-and long-term plans to combat and adjust to regional and local land-use changes in relation to changing climate and socioeconomic parameters which is the most critical components in preparing and mitigating their consequences towards sustainable land-use management practices.
Future land use change with LUH2 datasets can be incorporated into land policy in several ways over Africa.Here are some possible approaches: Informing land-use planning: LUH2 datasets can provide valuable information on how land use is likely to change in the future under different scenarios.This information can be used to inform land-use planning decisions, such as where to locate new developments, how to manage natural resources, and how to protect sensitive ecosystems.
Assessing the impact of land-use policies: Land-use policies can have a significant impact on future land use.LUH2 datasets can be used to assess the impact of different land-use policies on future land use patterns.For example, policymakers can use LUH2 datasets to evaluate the impact of zoning regulations, conservation programs, and other land-use policies on future land use.
Identifying areas of high risk: LUH2 datasets can be used to identify areas that are at high risk of future landuse change.This information can be used to prioritize conservation efforts and to target interventions aimed at reducing the impact of land-use change on sensitive ecosystems.
Developing land-use scenarios: LUH2 datasets can be used to develop land-use scenarios that explore different future land-use patterns under different scenarios.These scenarios can be used to inform land-use planning decisions and to evaluate the impact of different land-use policies on future land use.
Overall, incorporating LUH2 datasets into land policy can help policymakers make more informed decisions about how to manage land use in a way that is sustainable and equitable.Therefore, can be used as policy in Africa by providing information on how land use and land cover will change over time, related to land management, conservation, and development.It is recommended to take extensive further research on the cropland exposure on disaster risk management and prioritizing areas in the future.

Study area
We concentrate on Africa and its six sub-regions: Northern Africa (NAF), the Sahara (SAH), Western Africa (WAF), Central Africa (CAF), Eastern Africa (EAF), and Southern Africa (SAF).These regions are selected as recommended by 84 , database https:// www.un.org/ en, all situated in the tropics 85 , with elevations ranging from 0 to 5895 m, and are geographically located between 32° N and 35° S and 19° W and 52° E. The vast territory of Africa, which covers over 30.37 million km 2 , has a climatologically diverse landscape 86 , Mount Kilimanjaro in Tanzania, which rises 19,340 feet above sea level, is its highest point.At 515 feet below sea level, Lake Assal in Djibouti is where it is at its lowest point (Fig. 8).The second-largest continent in the world, Africa is rich in natural resources including copper, gold, and diamonds.It consists of 53 nations, some of which are landlocked and have no direct access to the sea.Its borders are the Mediterranean Sea to the north, the Atlantic Ocean to the west, the Red Sea to the northeast, and the Indian Ocean to the southeast.The Nile, which travels 4145 km from Burundi to Egypt, is the longest river in Africa (Fig. 8).Lake Victoria, which spans 26,724 square miles and is located between Tanzania, Kenya, and Uganda, is the largest lake.Other notable rivers in Africa include the Senegal River, Niger River, Zambezi River, Orange River, Kasai River, Lualaba River, and Limpopo River.The region is the most tropical of all the continents because it contains both the Tropic of Cancer and the Tropic of Capricorn (Fig. 8).
Africa's average annual precipitation is less than 700 mm year −1 and the average temperature is between 15 and 27 °C.According to 87 the intertropical convergence zone (ITCZ) oscillation modulates the precipitation distribution in space and time, which helps define Africa's two unique weather patterns.Complex interactions between the vegetation cover and weather and climate are seen 88,89 .Land use and land cover (LULC) changes have been occurring on the continent for many years 90 .According to 89,90 , the period 1980-2005 reflects and captures all forms of anthropogenic activities in the region, particularly in global warming 89,90 .

Datasets
Historical land-use data Historical land use information was gathered from the ESA World Cover project, gotten from https:// world cover 2020.esa.int which uses modified Copernicus Sentinel-1 and -2 data at a resolution of 10 m.This project was built and validated in almost real-time, while also maximising the impact and uptake for the end users.According to 91 , the globe cover product is given in an elliptical WGS 1984 grid with a regular latitude/longitude grid (EPSG:4326).To achieve the research goal, only 2020 datasets were chosen for the study, and regional land use types were reclassified for comparison with future land use data.
The main objectives of LUH2 project are to produce an integrated set of land use scenarios data from 850 to 2100 linking the historical land use reconstruction to future, projections of land use changes, key agricultural management information and land use shifts at a resolution 7 of 0.25° × 0.25°.Input from CMIP6 for the period 2015-2100 was based on SSP-RCP scenarios from Integrated Assessment Models (IAMs) 72 .In this study, landuse data from 7 SSPs of 5 Integrated Assessment Models (IAMs) were used to support decision-making by providing insights on global environmental change and sustainable development change that are important to policy.(http:// luh.umd.edu/).
In particular, the impacts on the agricultural economy, land use and trade, as well as energy demand and supply, are important to consider when assessing the socio-economic effects of climate change mitigation policies using the Integrated Model to Assess Greenhouse Effect (IMAGE) 93 .Energy, climate, environment, and sustainable development are all examined holistically and cross-cuttingly in the Model for Energy Supply Strategy Alternatives and their General Environment Impact (MESSAGE) 94 .Global climate change and its effects on the context of land and climatic elements are integrated into the Asia Pacific Integrated Model (AIM) 29 .The Global Change Assessment Model (GCAM), another integrated assessment model, integrates the economic, energy, agricultural, and land-use systems with the climate 95,96 .The Regional Model of Investment and Development (REMIND) and Model of Agricultural Production and it Impacts on the Environment (MAgPIE) compose of REMIND-MAgPIE, this integrate assessment model framework, with REMIND integrating a microeconomic growth model and an energy model and MAgPIE being a global multiregional economic land-use optimization model 14,29,97 .

Reclassification of land-use types
The land use in Africa comprises of six major categories: desert/barren land, built-ups, waterbodies, farmlands/ shrubs, grasslands, and forestlands (https:// world cover 2020.esa.int).In LUH2, there are 12 types of land use categories, and water is assumed to be constant over time, including the future; therefore, future changes in water are not projected under LUH2 7 .However, Table 3 shows the land use type under LUH2, and the current land use types in Africa and those in LUH2 are unified and reclassified in to five major categories for consistent such as: barren land, cropland, forested land, grassland, and urban land (Table 1) 7 .

Dynamics land-use transfer process
Dynamic land use transfer involves a process by which allocation of land for various purposes, such as agriculture, industry, and residual usage evolved over time in response to social, economic, and environmental factors 81,101 .These changes are driven by a complex interaction of variables, including population increase, economic development, technological advancement, and changes in land value 71,102 .This process is known as dynamic land use, and it refers to the transfer of land from one designated use to another 103 .

Figure 1 .
Figure 1.Spatial distribution of land use types in 2020 (a) and (b) land use Areal types in square kilometres (km 2 ) across Africa and its Regions.
https://doi.org/10.1038/s41598-024-61035-0www.nature.com/scientificreports/i.The study discovered a 0.89 (P < 0.01) correlation between the observation land-use data for 2020 and the simulation land-use, showing that LUH2 datasets can simulate land use types of temporal changes and spatial distribution characteristics with the greatest accuracy.The observational land-use type in

Figure 8 .
Figure 8. Map of Africa including six selected regions.

Table 2 .
Correlation of socioeconomic variables (population and GDP) with the cropland and barren land areas (Significant at 0.05 level).