Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: a global assessment

The potential for climate change mitigation by bioenergy crops and terrestrial carbon sinks has been the object of intensive research in the past decade. There has been much debate about whether energy crops used to offset fossil fuel use, or carbon sequestration in forests, would provide the best climate mitigation benefit. Most current food cropland is unlikely to be used for bioenergy, but in many regions of the world, a proportion of cropland is being abandoned, particularly marginal croplands, and some of this land is now being used for bioenergy. In this study, we assess the consequences of land‐use change on cropland. We first identify areas where cropland is so productive that it may never be converted and assess the potential of the remaining cropland to mitigate climate change by identifying which alternative land use provides the best climate benefit: C₄ grass bioenergy crops, coppiced woody energy crops or allowing forest regrowth to create a carbon sink. We do not present this as a scenario of land‐use change – we simply assess the best option in any given global location should a land‐use change occur. To do this, we use global biomass potential studies based on food crop productivity, forest inventory data and dynamic global vegetation models to provide, for the first time, a global comparison of the climate change implications of either deploying bioenergy crops or allowing forest regeneration on current crop land, over a period of 20 years starting in the nominal year of 2000 ad . Globally, the extent of cropland on which conversion to energy crops or forest would result in a net carbon loss, and therefore likely always to remain as cropland, was estimated to be about 420.1 Mha, or 35.6% of the total cropland in Africa, 40.3% in Asia and Russia Federation, 30.8% in Europe‐25, 48.4% in North America, 13.7% in South America and 58.5% in Oceania. Fast growing C₄ grasses such as Miscanthus and switch‐grass cultivars are the bioenergy feedstock with the highest climate mitigation potential. Fast growing C₄ grasses such as Miscanthus and switch‐grass cultivars provide the best climate mitigation option on ≈485 Mha of cropland worldwide with ~42% of this land characterized by a terrain slope equal or above 20%. If that land‐use change did occur, it would displace ≈58.1 Pg fossil fuel C equivalent (C_eq oil). Woody energy crops such as poplar, willow and Eucalyptus species would be the best option on only 2.4% (≈26.3 Mha) of current cropland, and if this land‐use change occurred, it would displace ≈0.9 Pg C_eq oil. Allowing cropland to revert to forest would be the best climate mitigation option on ≈17% of current cropland (≈184.5 Mha), and if this land‐use change occurred, it would sequester ≈5.8 Pg C in biomass in the 20‐year‐old forest and ≈2.7 Pg C in soil. This study is spatially explicit, so also serves to identify the regional differences in the efficacy of different climate mitigation options, informing policymakers developing regionally or nationally appropriate mitigation actions.     

Pasture intensification is insufficient to relieve pressure on conservation priority areas in open agricultural markets

Agricultural expansion is a leading driver of biodiversity loss across the world, but little is known on how future land‐use change may encroach on remaining natural vegetation. This uncertainty is, in part, due to unknown levels of future agricultural intensification and international trade. Using an economic land‐use model, we assessed potential future losses of natural vegetation with a focus on how these may threaten biodiversity hotspots and intact forest landscapes. We analysed agricultural expansion under proactive and reactive biodiversity protection scenarios, and for different rates of pasture intensification. We found growing food demand to lead to a significant expansion of cropland at the expense of pastures and natural vegetation. In our reference scenario, global cropland area increased by more than 400 Mha between 2015 and 2050, mostly in Africa and Latin America. Grazing intensification was a main determinant of future land‐use change. In Africa, higher rates of pasture intensification resulted in smaller losses of natural vegetation, and reduced pressure on biodiversity hotspots and intact forest landscapes. Investments into raising pasture productivity in conjunction with proactive land‐use planning appear essential in Africa to reduce further losses of areas with high conservation value. In Latin America, in contrast, higher pasture productivity resulted in increased livestock exports, highlighting that unchecked trade can reduce the land savings of pasture intensification. Reactive protection of sensitive areas significantly reduced the conversion of natural ecosystems in Latin America. We conclude that protection strategies need to adapt to region‐specific trade positions. In regions with a high involvement in international trade, area‐based conservation measures should be preferred over strategies aimed at increasing pasture productivity, which by themselves might not be sufficient to protect biodiversity effectively.     

Land cover change or land‐use intensification: simulating land system change with a global‐scale land change model

Land‐use change is both a cause and consequence of many biophysical and socioeconomic changes. The CLUMondo model provides an innovative approach for global land‐use change modeling to support integrated assessments. Demands for goods and services are, in the model, supplied by a variety of land systems that are characterized by their land cover mosaic, the agricultural management intensity, and livestock. Land system changes are simulated by the model, driven by regional demand for goods and influenced by local factors that either constrain or promote land system conversion. A characteristic of the new model is the endogenous simulation of intensification of agricultural management versus expansion of arable land, and urban versus rural settlements expansion based on land availability in the neighborhood of the location. Model results for the OECD Environmental Outlook scenario show that allocation of increased agricultural production by either management intensification or area expansion varies both among and within world regions, providing useful insight into the land sparing versus land sharing debate. The land system approach allows the inclusion of different types of demand for goods and services from the land system as a driving factor of land system change. Simulation results are compared to observed changes over the 1970–2000 period and projections of other global and regional land change models.     

Spatiotemporal land use modelling to assess land availability for energy crops – illustrated for Mozambique

A method and tool have been developed to assess future developments in land availability for bioenergy crops in a spatially explicit way, while taking into account both the developments in other land use functions, such as land for food, livestock and material production, and the uncertainties in the key determinant factors of land use change (LUC). This spatiotemporal LUC model is demonstrated with a case study on the developments in the land availability for bioenergy crops in Mozambique in the timeframe 2005–2030. The developments in the main drivers for agricultural land use, demand for food, animal products and materials were assessed, based on the projected developments in population, diet, GDP and self‐sufficiency ratio. Two scenarios were developed: a business‐as‐usual (BAU) scenario and a progressive scenario. Land allocation was based on land use class‐specific sets of suitability factors. The LUC dynamics were mapped on a 1 km² grid level for each individual year up to 2030. In the BAU scenario, 7.7 Mha and in the progressive scenario 16.4 Mha could become available for bioenergy crop production in 2030. Based on the Monte Carlo analysis, a 95% confidence interval of the amount of land available and the spatially explicit probability of available land was found. The bottom‐up approach, the number of dynamic land uses, the diverse portfolio of LUC drivers and suitability factors, and the possibility to model uncertainty mean that this model is a step forward in modelling land availability for bioenergy potentials.     

A spatially explicit representation of conservation agriculture for application in global change studies

Conservation agriculture (CA) is widely promoted as a sustainable agricultural management strategy with the potential to alleviate some of the adverse effects of modern, industrial agriculture such as large‐scale soil erosion, nutrient leaching and overexploitation of water resources. Moreover, agricultural land managed under CA is proposed to contribute to climate change mitigation and adaptation through reduced emission of greenhouse gases, increased solar radiation reflection, and the sustainable use of soil and water resources. Due to the lack of official reporting schemes, the amount of agricultural land managed under CA systems is uncertain and spatially explicit information about the distribution of CA required for various modeling studies is missing. Here, we present an approach to downscale present‐day national‐level estimates of CA to a 5 arcminute regular grid, based on multicriteria analysis. We provide a best estimate of CA distribution and an uncertainty range in the form of a low and high estimate of CA distribution, reflecting the inconsistency in CA definitions. We also design two scenarios of the potential future development of CA combining present‐day data and an assessment of the potential for implementation using biophysical and socioeconomic factors. By our estimates, 122–215 Mha or 9%–15% of global arable land is currently managed under CA systems. The lower end of the range represents CA as an integrated system of permanent no‐tillage, crop residue management and crop rotations, while the high estimate includes a wider range of areas primarily devoted to temporary no‐tillage or reduced tillage operations. Our scenario analysis suggests a future potential of CA in the range of 533–1130 Mha (38%–81% of global arable land). Our estimates can be used in various ecosystem modeling applications and are expected to help identifying more realistic climate mitigation and adaptation potentials of agricultural practices.     

Hotspots of uncertainty in land‐use and land‐cover change projections: a global‐scale model comparison

Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.     

Gross changes in reconstructions of historic land cover/use for Europe between 1900 and 2010

Historic land‐cover/use change is important for studies on climate change, soil carbon, and biodiversity assessments. Available reconstructions focus on the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). This leads to a serious underestimation of land‐cover/use dynamics with impacts on the biogeochemical and environmental assessments based on these reconstructions. In this study, we quantified to what extent land‐cover/use reconstructions underestimate land‐cover/use changes in Europe for the 1900–2010 period by accounting for net changes only. We empirically analyzed available historic land‐change data, quantified their uncertainty, corrected for spatial‐temporal effects and identified underlying processes causing differences between gross and net changes. Gross changes varied for different land classes (largest for forest and grassland) and led to two to four times the amount of net changes. We applied the empirical results of gross change quantities in a spatially explicit reconstruction of historic land change to reconstruct gross changes for the EU27 plus Switzerland at 1 km spatial resolution between 1950 and 2010. In addition, the reconstruction was extended back to 1900 to explore the effects of accounting for gross changes on longer time scales. We created a land‐change reconstruction that only accounted for net changes for comparison. Our two model outputs were compared with five commonly used global reconstructions for the same period and area. In our reconstruction, gross changes led in total to a 56% area change (ca. 0.5% yr^?1) between 1900 and 2010 and cover twice the area of net changes. All global reconstructions used for comparison estimated fewer changes than our gross change reconstruction. Main land‐change processes were cropland/grassland dynamics and afforestation, and also deforestation and urbanization.     

Doubling protected land area may be inefficient at preserving the extent of undeveloped land and could cause substantial regional shifts in land use

Projection of land use and land-cover change is highly uncertain yet drives critical estimates of carbon emissions, climate change, and food and bioenergy production. We use new, spatially explicit land availability data in conjunction with a model sensitivity analysis to estimate the effects of additional land protection on land use and land cover. The land availability data include protected land and agricultural suitability and is incorporated into the Moirai land data system for initializing the Global Change Analysis Model. Overall, decreasing land availability is relatively inefficient at preserving undeveloped land while having considerable regional land-use impacts. Current amounts of protected area have little effect on land and crop production estimates, but including the spatial distribution of unsuitable (i.e., unavailable) land dramatically shifts bioenergy production from high northern latitudes to the rest of the world, compared with uniform availability. This highlights the importance of spatial heterogeneity in understanding and managing land change. Approximately doubling the current protected area to emulate a 30% protected area target may avoid land conversion by 2050 of less than half the newly protected extent while reducing bioenergy feedstock land by 10.4% and cropland and grazed pasture by over 3%. Regional bioenergy land may be reduced (increased) by up to 46% (36%), cropland reduced by up to 61%, pasture reduced by up to 100%, and harvested forest reduced by up to 35%. Only a few regions show notable gains in some undeveloped land types of up to 36%. Half of the regions can reach the target using only unsuitable land, which would minimize impacts on agriculture but may not meet conservation goals. Rather than focusing on an area target, a more robust approach may be to carefully select newly protected land to meet well-defined conservation goals while minimizing impacts to agriculture.     

Yield potential of Miscanthus energy crops in the Loess Plateau of China

Growing second‐generation energy crops on marginal land is conceptualized as one of the primary means of future bioenergy development. However, the extent to which marginal land can support energy crop production remains unclear. The Loess Plateau of China, one of the most seriously eroded regions of the world, is particularly rich in marginal land. On the basis of the previous field experiment of planting Miscanthus species in Qingyang of the Gansu Province, herein, we estimated the yield potential of Miscanthus lutarioriparius, the species with the highest biomass, across the Loess Plateau. On the basis of the radiation model previously developed from Miscanthus field trials, annual precipitation was introduced as an additional variable for yield estimate in the semiarid and semihumid regions of the Loess Plateau. Of 62 million hectares (Mha) of the Loess Plateau, our model estimated that 48.7 Mha can potentially support Miscanthus growth, with the average yield of 17.8 t ha^?1 yr^?1. After excluding high‐quality cropland and pasture and land suitable for afforestation, a total of 33.3 Mha of presumably marginal land were left available for producing the energy crop at the average yield of 16.8 t ha^?1 yr^?1 and the total annual yield of 0.56 billion tons. The analysis of environmental factors indicated that erosion, aridity, and field steepness were the primary contributors to the poor quality of the marginal land. The change of land uses from traditional agriculture to energy crop production may prevent further erosion and land degradation and consequently establish a sustainable economy for the region.     

Mitigation of unwanted direct and indirect land‐use change – an integrated approach illustrated for palm oil,pulpwood, rubber and rice production in North and East Kalimantan,Indonesia

The widespread production of cash crops can result in the decline of forests, peatlands, rice fields and local community land. Such unwanted land‐use and land‐cover (LULC) change can lead to decreased carbon stocks, diminished biodiversity, displaced communities and reduced local food production. In this study, we analysed to what extent four main commodities, namely, palm oil, pulpwood, rice and rubber, can be produced in North and East Kalimantan in Indonesia without such unwanted LULC change. We investigated the technical potential of four measures to mitigate unwanted LULC change between 2008 and 2020 under low, medium and high scenarios, referring to the intensities of the mitigation measures compared with those implemented in 2008. These measures are related to land sparing through (i) the improvements of yields, (ii) chain efficiencies, (iii) chain integration and (iv) the steering of any expansion of these commodities to suitable and available underutilised (potentially degraded) lands. Our analyses resulted in a land‐sparing potential of 0.4–1.2 Mha (i.e. 24–62% of the total land demand of the commodities) between 2008 and 2020, depending on the land‐use projection of the four commodities and the scenario for implementing the mitigation measures. Additional expansion on underutilised land is the most important mitigation measure (45–62% of the total potential), followed by yield improvements as the second most important mitigation measure (32–46% of the total potential). Our study shows that reconciling the production of palm oil, pulpwood, rice and rubber with the maintenance of existing agricultural lands, forests and peatlands is technically possible only (i) under a scenario of limited agricultural expansion, (ii) if responsible land zoning is applied and enforced and (iii) if the yields and chain efficiencies are strongly improved.     

A Land System representation for global assessments and land‐use modeling

Current global scale land‐change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land‐use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human‐environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi‐)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land‐use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land‐change models that include the human drivers of land change are best parameterized at sub‐global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions.     

Bioenergy production potential of global biomass plantations under environmental and agricultural constraints

We estimate the global bioenergy potential from dedicated biomass plantations in the 21st century under a range of sustainability requirements to safeguard food production, biodiversity and terrestrial carbon storage. We use a process‐based model of the land biosphere to simulate rainfed and irrigated biomass yields driven by data from different climate models and combine these simulations with a scenario‐based assessment of future land availability for energy crops. The resulting spatial patterns of large‐scale lignocellulosic energy crop cultivation are then investigated with regard to their impacts on land and water resources. Calculated bioenergy potentials are in the lower range of previous assessments but the combination of all biomass sources may still provide between 130 and 270 EJ yr^?1 in 2050, equivalent to 15–25% of the World's future energy demand. Energy crops account for 20–60% of the total potential depending on land availability and share of irrigated area. However, a full exploitation of these potentials will further increase the pressure on natural ecosystems with a doubling of current land use change and irrigation water demand. Despite the consideration of sustainability constraints on future agricultural expansion the large‐scale cultivation of energy crops is a threat to many areas that have already been fragmented and degraded, are rich in biodiversity and provide habitat for many endangered and endemic species.     

Influence of spatially dependent,modeled soil carbon emission factors on life‐cycle greenhouse gas emissions of corn and cellulosic ethanol

Converting land to biofuel feedstock production incurs changes in soil organic carbon (SOC) that can influence biofuel life‐cycle greenhouse gas (GHG) emissions. Estimates of these land use change (LUC) and life‐cycle GHG emissions affect biofuels' attractiveness and eligibility under a number of renewable fuel policies in the USA and abroad. Modeling was used to refine the spatial resolution and depth extent of domestic estimates of SOC change for land (cropland, cropland pasture, grassland, and forest) conversion scenarios to biofuel crops (corn, corn stover, switchgrass, Miscanthus, poplar, and willow) at the county level in the USA. Results show that in most regions, conversions from cropland and cropland pasture to biofuel crops led to neutral or small levels of SOC sequestration, while conversion of grassland and forest generally caused net SOC loss. SOC change results were incorporated into the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model to assess their influence on life‐cycle GHG emissions of corn and cellulosic ethanol. Total LUC GHG emissions (g CO₂eq MJ^?1) were 2.1–9.3 for corn‐, ?0.7 for corn stover‐, ?3.4 to 12.9 for switchgrass‐, and ?20.1 to ?6.2 for Miscanthus ethanol; these varied with SOC modeling assumptions applied. Extending the soil depth from 30 to 100 cm affected spatially explicit SOC change and overall LUC GHG emissions; however, the influence on LUC GHG emission estimates was less significant in corn and corn stover than cellulosic feedstocks. Total life‐cycle GHG emissions (g CO₂eq MJ^?1, 100 cm) were estimated to be 59–66 for corn ethanol, 14 for stover ethanol, 18–26 for switchgrass ethanol, and ?7 to ?0.6 for Miscanthus ethanol. The LUC GHG emissions associated with poplar‐ and willow‐derived ethanol may be higher than that for switchgrass ethanol due to lower biomass yield.     

The HYDE 3.1 spatially explicit database of human‐induced global land‐use change over the past 12,000 years

Aim This paper presents a tool for long‐term global change studies; it is an update of the History Database of the Global Environment (HYDE) with estimates of some of the underlying demographic and agricultural driving factors. Methods Historical population, cropland and pasture statistics are combined with satellite information and specific allocation algorithms (which change over time) to create spatially explicit maps, which are fully consistent on a 5′ longitude/latitude grid resolution, and cover the period 10,000 bc to ad 2000. Results Cropland occupied roughly less than 1% of the global ice‐free land area for a long time until ad 1000, similar to the area used for pasture. In the centuries that followed, the share of global cropland increased to 2% in ad 1700 (c. 3 million km²) and 11% in ad 2000 (15 million km²), while the share of pasture area grew from 2% in ad 1700 to 24% in ad 2000 (34 million km²) These profound land‐use changes have had, and will continue to have, quite considerable consequences for global biogeochemical cycles, and subsequently global climate change. Main conclusions Some researchers suggest that humans have shifted from living in the Holocene (emergence of agriculture) into the Anthropocene (humans capable of changing the Earth's atmosphere) since the start of the Industrial Revolution. But in the light of the sheer size and magnitude of some historical land‐use changes (e.g. as result of the depopulation of Europe due to the Black Death in the 14th century and the aftermath of the colonization of the Americas in the 16th century) we believe that this point might have occurred earlier in time. While there are still many uncertainties and gaps in our knowledge about the importance of land use (change) in the global biogeochemical cycle, we hope that this database can help global (climate) change modellers to close parts of this gap.     

Continental estimates of forest cover and forest cover changes in the dry ecosystems of Africa between 1990 and 2000

Aim

This study provides regional estimates of forest cover in dry African ecoregions and the changes in forest cover that occurred there between 1990 and 2000, using a systematic sample of medium‐resolution satellite imagery which was processed consistently across the continent.

Location

The study area corresponds to the dry forests and woodlands of Africa between the humid forests and the semi‐arid regions. This area covers the Sudanian and Zambezian ecoregions.

Methods

A systematic sample of 1600 Landsat satellite imagery subsets, each 20 km × 20 km in size, were analysed for two reference years: 1990 and 2000. At each sample site and for both years, dense tree cover, open tree cover, other wooded land and other vegetation cover were identified from the analysis of satellite imagery, which comprised multidate segmentation and automatic classification steps followed by visual control by national forestry experts.

Results

Land cover and land‐cover changes were estimated at continental and ecoregion scales and compared with existing pan‐continental, regional and local studies. The overall accuracy of our land‐cover maps was estimated at 87%. Between 1990 and 2000, 3.3 million hectares (Mha) of dense tree cover, 5.8 Mha of open tree cover and 8.9 Mha of other wooded land were lost, with a further 3.9 Mha degraded from dense to open tree cover. These results are substantially lower than the 34 Mha of forest loss reported in the FAO's 2010 Global Forest Resources Assessment for the same period and area.

Main conclusions

Our method generates the first consistent and robust estimates of forest cover and change in dry Africa with known statistical precision at continental and ecoregion scales. These results reduce the uncertainty regarding vegetation cover and its dynamics in these previously poorly studied ecosystems and provide crucial information for both science and environmental policies.     

A large‐area,spatially continuous assessment of land cover map error and its impact on downstream analyses

Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high‐resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel‐wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative “downstream” (map‐based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps’ spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ～45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National‐scale maps derived from higher‐resolution imagery were most accurate, followed by multi‐map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland‐adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%–500% greater than in input cropland maps, but ～40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users.     

Assessing uncertainties in land cover projections

Understanding uncertainties in land cover projections is critical to investigating land‐based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro‐economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.     

Agricultural intensification in Brazil and its effects on land‐use patterns: an analysis of the 1975–2006 period

Does agricultural intensification reduce the area used for agricultural production in Brazil? Census and other data for time periods 1975–1996 and 1996–2006 were processed and analyzed using Geographic Information System and statistical tools to investigate whether and if so, how, changes in yield and stocking rate coincide with changes in cropland and pasture area. Complementary medium‐resolution data on total farmland area changes were used in a spatially explicit assessment of the land‐use transitions that occurred in Brazil during 1960–2006. The analyses show that in agriculturally consolidated areas (mainly southern and southeastern Brazil), land‐use intensification (both on cropland and pastures) coincided with either contraction of both cropland and pasture areas, or cropland expansion at the expense of pastures, both cases resulting in farmland stability or contraction. In contrast, in agricultural frontier areas (i.e., the deforestation zones in central and northern Brazil), land‐use intensification coincided with expansion of agricultural lands. These observations provide support for the thesis that (i) technological improvements create incentives for expansion in agricultural frontier areas; and (ii) farmers are likely to reduce their managed acreage only if land becomes a scarce resource. The spatially explicit examination of land‐use transitions since 1960 reveals an expansion and gradual movement of the agricultural frontier toward the interior (center‐western Cerrado) of Brazil. It also indicates a possible initiation of a reversed trend in line with the forest transition theory, i.e., agricultural contraction and recurring forests in marginally suitable areas in southeastern Brazil, mainly within the Atlantic Forest biome. The significant reduction in deforestation that has taken place in recent years, despite rising food commodity prices, indicates that policies put in place to curb conversion of native vegetation to agriculture land might be effective. This can improve the prospects for protecting native vegetation by investing in agricultural intensification.     

Modeled spatial assessment of biomass productivity and technical potential of Miscanthus × giganteus,Panicum virgatum L., and Jatropha on marginal land in China

This article identifies marginal land technically available for the production of energy crops in China, compares three models of yield prediction for Miscanthus × giganteus, Panicum virgatum L. (switchgrass), and Jatropha, and estimates their spatially specific yields and technical potential for 2017. Geographic Information System (GIS) analysis of land use maps estimated that 185 Mha of marginal land was technically available for energy crops in China without using areas currently used for food production. Modeled yields were projected for Miscanthus × giganteus, a GIS‐based Environmental Policy Integrated Climate model for switchgrass and Global Agro‐Ecological Zone model for Jatropha. GIS analysis and MiscanFor estimated more than 120 Mha marginal land was technically available for Miscanthus with a total potential of 1,761 dry weight metric million tonne (DW Mt)/year. A total of 284 DW Mt/year of switchgrass could be obtained from 30 Mha marginal land, with an average yield of 9.5 DW t ha^?1 year^?1. More than 35 Mha marginal land was technically available for Jatropha, delivering 9.7 Mt/year of Jatropha seed. The total technical potential from available marginal land was calculated as 31.7 EJ/year for Miscanthus, 5.1 EJ/year for switchgrass, and 0.13 EJ/year for Jatropha. A total technical bioenergy potential of 34.4 EJ/year was calculated by identifying best suited crop for each 1 km² grid cell based on the highest energy value among the three crops. The results indicate that the technical potential per hectare of Jatropha is unable to compete with that of the other two crops in each grid cell. This modeling study provides planners with spatial overviews that demonstrate the potential of these crops and where biomass production could be potentially distributed in China which needs field trials to test model assumptions and build experience necessary to translate into practicality.