Forests are among the most species rich habitats and the way they are managed influences their capacity to protect biodiversity. To fulfill increasing wood demands in the future, planted and ...non-planted wood production will need to expand. While biodiversity assessments usually focus on the impacts of deforestation, the effects of wood harvest are mostly not considered, especially not in a spatially explicit manner. We present here a global approach to refine the representation of forest management through allocating future wood production to planted and non-planted forests. Wood production, following wood consumption projections of three Shared Socioeconomic Pathways, was allocated using likelihood maps for planted and production forests. On a global scale, plantations for wood production were projected to increase by 45–65% and harvested area in non-planted forests by 1–17%. The biodiversity impacts of changes in wood production patterns were estimated by applying two commonly used indicators: (1) changes in species richness and (2) changes in habitat-suitable ranges of single species. The impact was analyzed using forest cover changes as reference. Our results show that, although forest cover changes have the largest impact on biodiversity, changes in wood production also have a significant effect. The magnitude of impacts caused by changes of wood production substantially differs by region and taxa. Given the importance of forest production changes in net negative emission pathways, more focus should be put on assessing the effects of future changes in wood production patterns as part of overall land use change impacts.
There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated ...field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.
This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available.
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•Numerous studies on land-use decision-making, are limited to local scale case-studies.•We study the contextual conditions of local land-use change decision-making globally.•The ...spatial distribution of decision-making can be explained by local spatial contexts.•We present global maps of different decision-making modes.•Our results can improve global land-use models by accounting for variation in decision-making.
Humans have changed most of the terrestrial surface by changing land-use and land-cover. The spatial distribution and extent of land-cover changes have been studied and mapped widely, using remote sensing and geospatial technologies. Although there are numerous studies on the human decisions underlying such changes, they are limited to local case-studies. How such local-scale patterns of decision-making can be used to explain land-use change globally is unknown. Using a collection of local studies from a literature review, we studied the contextual conditions of different modes of land-use change decision-making and present global maps of the potential distribution of decision-making in land-use change. We find that decision-making in land-use can be explained, to a large extent, by the socio-economic, climatic and soil conditions of a location, captured by global data proxies of these conditions. Survival and livelihood objectives are positively associated to the spatial variation in childhood malnutrition and distance to roads, and negatively to total economic output of an area. Economic objectives on the other hand, are positively associated to total economic output, but also to the annual precipitation at the location. Similar trends are observed when looking at more detailed decision-making types: survivalist, subsistence-oriented and market-oriented smallholder decision-making types are more likely found in areas with higher poverty levels and overall lower levels of socio-economic development. The spatial distribution can be used to understand the occurrence of land-use intensification trajectories and to account for variation in decision-making in global land-use models. Finally, we provide a representation of the spread of case-studies and which contexts are poorly represented by case-studies.
•We present a typology and map of the distribution of Mediterranean land systems.•Spatial data on land cover, land management and livestock are integrated using GIS.•A significant part of the ...Mediterranean is covered by multifunctional landscapes.•Compared to existing classifications, we achieved higher thematic detail.
In the Mediterranean region, land systems have been shaped gradually through centuries. They provide services to a large and growing population in a region that is among the most vulnerable to future global change. The spatial extent and distribution of Mediterranean land systems is, however, unknown. In this paper, we present a new, expert-based classification of Mediterranean land systems, representing landscapes as integrated social-ecological systems. We combined data on land cover, management intensity and livestock available on the European and global scale in a geographic information system based approach. We put special emphasis on agro-silvo-pastoral mosaic systems: multifunctional Mediterranean landscapes hosting different human activities that are not represented in common land cover maps. By analyzing location conditions of the identified land systems, we demonstrated the significance of both bio-physical (precipitation, soil) and socio-economic (population density, market influence) factors driving the occurrence of these systems. Agro-silvo-pastoral mosaic systems were estimated to cover 23.3% of the Mediterranean ecoregion and exhibited to a certain extent similar characteristics as forest and cropland systems. A reanalysis using data that are available with global coverage indicated that the choice of datasets leads to significant uncertainties in the extent and spatial pattern of these systems. The resulting land systems typology can be used to prioritize and protect landscapes of high cultural and environmental significance.
Abstract
The Amazon biome is being pushed by unsustainable economic drivers towards an ecological tipping point where restoration to its previous state may no longer be possible. This degradation is ...the result of self-reinforcing interactions between deforestation, climate change and fire. We assess the economic, natural capital and ecosystem services impacts and trade-offs of scenarios representing movement towards an Amazon tipping point and strategies to avert one using the Integrated Economic-Environmental Modeling (IEEM) Platform linked with spatial land use-land cover change and ecosystem services modeling (IEEM + ESM). Our approach provides the first approximation of the economic, natural capital and ecosystem services impacts of a tipping point, and evidence to build the economic case for strategies to avert it. For the five Amazon focal countries, namely, Brazil, Peru, Colombia, Bolivia and Ecuador, we find that a tipping point would create economic losses of US$256.6 billion in cumulative gross domestic product by 2050. Policies that would contribute to averting a tipping point, including strongly reducing deforestation, investing in intensifying agriculture in cleared lands, climate-adapted agriculture and improving fire management, would generate approximately US$339.3 billion in additional wealth and a return on investment of US$29.5 billion. Quantifying the costs, benefits and trade-offs of policies to avert a tipping point in a transparent and replicable manner can support the design of regional development strategies for the Amazon biome, build the business case for action and catalyze global cooperation and financing to enable policy implementation.
•Likelihood determinants for forest categories differ in different world regions.•Forest conditions, accessibility and environmental properties are important drivers.•Our final maps represent global ...patterns of forest classes and forest uses.•The maps visualize the current issues with available data.
Forests provide numerous ecosystem services, such as timber yields, biodiversity protection and climate change mitigation. The type of management has an effect on the provision of these services. Often the demands for these services can lead to conflict – wood harvest can negatively impact biodiversity and climate change mitigation capacity. Although forest management differences are important, spatially explicit data is lacking, in particular on a global scale. We present here a first systematic approach which integrates existing data to map forest management globally through downscaling national and subnational forest data. In our forest management classification, we distinguished between two levels of forest management, with three categories each. Level 1 comprised primary, naturally regrown and planted forests. Level 2 distinguished between different forest uses. We gathered documented locations, where these forest categories were observed, from the literature and a database on ecological diversity. We then performed multinomial logit regression and estimated the effect of 21 socio-economic and bio-physical predictor variables on the occurrence of a forest category. Model results on significance and effect direction of predictor variables were in line with findings of previous studies. Soil and environmental properties, forest conditions and accessibility are important determinants of the occurrence of forest management types. Based on the model results, likelihood maps were calculated and used to spatially allocate national extents of level 1 and level 2 forest categories. When compared to previous studies, our maps showed higher agreement than random samples. Deviations between observed and predicted plantation locations were mostly below 10 km. Our map provides an estimation of global forest management patterns, enhancing previous methodologies and making the best use of data available. Next to having multiple applications, for example within global conservation planning or climate change mitigation analyses, it visualizes the currently available data on forest management on a global level.
In order to meet future food demand while sustainably managing available land and water resources, irrigated agriculture in semi-arid regions needs to adapt as a response to climate and ...socio-economic change. In this study, we focus on the Mediterranean region, a dynamic region, which is highly dependent on irrigated agriculture. We provide insight on adaptation strategies implemented on farm level, by doing a systematic review of studies in the region. Our analysis reports 286 implemented adaptations, on 124 different locations throughout the Mediterranean. Additionally, 142 drivers and 324 effects of adaptations were noted. We identified 31 adaptation strategies in 5 main categories: (1) water management, (2) sustainable resource management, (3) technological developments, (4) farm production practices, and (5) farm management. Strategies in the categories water management and farm production practices are most often implemented by farmers in the region. The main driver in the area is water scarcity and adaptations often affected water use and resources in addition to farm practices. Subsequently, we studied the spatial context of adaptations by analyzing the location factors of the five main strategies, using Geographic Information Systems and maximum entropy modeling. Our results show that farmers are more likely to adapt in less rural areas with lower poverty values and better market access, and in areas with higher temperatures and less rainfall. This demonstrates that both biophysical and socio-economic factors determine the context in which adaptations are implemented and that considerable spatial variability in the area exists.
Salinization poses a major challenge for modern agriculture with considerable areas being salt-affected worldwide. However, these lands can be cultivated by applying saline agriculture, involving ...soil, water and salt-tolerant crop management methods. The agricultural use of saline soils helps in addressing food security in times of population growth and climate change. Therefore, there is a need to map saline soils and examine conditions under which saline agriculture can be implemented.
The aim of this study is to identify locations and surface area of saline soils. The potential areas for saline agricultural production are mapped using the QGIS software with a focus on the most promising lands for saline agriculture. To identify these most promising areas, we apply criteria of soil salinity, soil fertility, soil pH, water availability, presence of irrigation equipment, as well as depleted water basins.
Our results show that the total area of salt-affected soils equals 17 million km2, but the largest potential for saline agriculture lays in saline soils above 4 dS/m ECe in non-depleted water basins totalling to 2 million km2. We conclude that further socio-economic analysis is needed to fully determine countries which should be prioritized in exploring the future potential for sustainable food production.
•The total area of salt-affected soils worldwide equals 17 million km2.•The most promising saline soils for saline agriculture total to 2 million km2.•A most restrictive factor limiting the potential area for saline agriculture is water availability.•Future mapping should focus on high populated areas with favourable socio-economic conditions.
Meeting the growing demand for food in the future will require adaptation of water and land management to future conditions. We studied the extent of different adaptation options to future global ...change in the Mediterranean region, under scenarios of water use and availability. We focused on the most significant adaptation options for semiarid regions: implementing irrigation, changes to cropland intensity, and diversification of cropland activities. We used Conversion of Land Use on Mondial Scale (CLUMondo), a global land system model, to simulate future change to land use and land cover, and land management. To take into account future global change, we followed global outlooks for future population and climate change, and crop and livestock demand. The results indicate that the level of irrigation efficiency improvement is an important determinant of potential changes in the intensity of rain-fed land systems. No or low irrigation efficiency improvements lead to a reduction in irrigated areas, accompanied with intensification and expansion of rain-fed cropping systems. When reducing water withdrawal, total crop production in intensive rain-fed systems would need to increase significantly: by 130% without improving the irrigation efficiency in irrigated systems and by 53% under conditions of the highest possible efficiency improvement. In all scenarios, traditional Mediterranean multifunctional land systems continue to play a significant role in food production, especially in hosting livestock. Our results indicate that significant improvements to irrigation efficiency with simultaneous increase in cropland productivity are needed to satisfy future demands for food in the region. The approach can be transferred to other similar regions with strong resource limitations in terms of land and water.
•The extent of land degradation varies from 15 to 34 % depending on the data.•Region is undergoing land degradation on a large scale, affecting high quality soils.•Global datasets indicate ...improvement, while local shows an opposite trend.•Soil characteristics should be included to the counterbalancing assessment.
Central Asia hosts one of the largest continuous grassland areas on our planet, that is of vital importance for food security, biodiversity and carbon sequestration. However, this region is also subjected to some of the most intense land degradation processes, related to large scale land use change and climate change.
To combat land degradation and pursue sustainable land management the concept of land degradation neutrality (LDN) has been proposed. LDN assessments are recommended to be based on global data in regions that lack sufficient regional data such as Central Asia. However, it remains unclear how the selection of datasets influences the estimated extent of land degradation. We followed the LDN framework and first calculated changes to three LDN indicators: land productivity, land cover, and Soil Organic Carbon (SOC). We then evaluated the impact of using different regional and global land cover data on the extent of land degradation in the region. Finally, we calculated a regionally specific integrated indicator on soil quality that we used to assess the “like-for-like” principle which enables counterbalancing losses and gains.
Our results indicate that particularly the selection of land cover data has a significant impact on the overall assessment of degradation state. The extent of land between 2000 and 2019 varies considerably depending on the data and ranges between 15 and 34 % of the whole Central Asian region. Our findings reveal that the area of degraded land on high quality soils is twice as high as the area of high quality soils where the land condition has improved (12 % versus 6 %). In areas with low soil quality, 13 % was subject to degradation, and 10 % improved.
Whilst the degradation extent varies according to the selected land cover datasets, our results demonstrates that Central Asia is undergoing land degradation affecting high quality soils. Sensitivity analysis of multiple datasets can reduce the risk of misjudgement in degradation extent assessments, especially in regions with mosaic ecosystems, such as grasslands.
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