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.     

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.     

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.     

Soil carbon sequestration due to post‐Soviet cropland abandonment: estimates from a large‐scale soil organic carbon field inventory

The break‐up of the Soviet Union in 1991 triggered cropland abandonment on a continental scale, which in turn led to carbon accumulation on abandoned land across Eurasia. Previous studies have estimated carbon accumulation rates across Russia based on large‐scale modelling. Studies that assess carbon sequestration on abandoned land based on robust field sampling are rare. We investigated soil organic carbon (SOC) stocks using a randomized sampling design along a climatic gradient from forest steppe to Sub‐Taiga in Western Siberia (Tyumen Province). In total, SOC contents were sampled on 470 plots across different soil and land‐use types. The effect of land use on changes in SOC stock was evaluated, and carbon sequestration rates were calculated for different age stages of abandoned cropland. While land‐use type had an effect on carbon accumulation in the topsoil (0–5 cm), no independent land‐use effects were found for deeper SOC stocks. Topsoil carbon stocks of grasslands and forests were significantly higher than those of soils managed for crops and under abandoned cropland. SOC increased significantly with time since abandonment. The average carbon sequestration rate for soils of abandoned cropland was 0.66 Mg C ha^?1 yr^?1 (1–20 years old, 0–5 cm soil depth), which is at the lower end of published estimates for Russia and Siberia. There was a tendency towards SOC saturation on abandoned land as sequestration rates were much higher for recently abandoned (1–10 years old, 1.04 Mg C ha^?1 yr^?1) compared to earlier abandoned crop fields (11–20 years old, 0.26 Mg C ha^?1 yr^?1). Our study confirms the global significance of abandoned cropland in Russia for carbon sequestration. Our findings also suggest that robust regional surveys based on a large number of samples advance model‐based continent‐wide SOC prediction.     

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.     

Land‐use conversion and changing soil carbon stocks in China's ‘Grain‐for‐Green’ Program: a synthesis

The establishment of either forest or grassland on degraded cropland has been proposed as an effective method for climate change mitigation because these land use types can increase soil carbon (C) stocks. This paper synthesized 135 recent publications (844 observations at 181 sites) focused on the conversion from cropland to grassland, shrubland or forest in China, better known as the ‘Grain‐for‐Green’ Program to determine which factors were driving changes to soil organic carbon (SOC). The results strongly indicate a positive impact of cropland conversion on soil C stocks. The temporal pattern for soil C stock changes in the 0–100 cm soil layer showed an initial decrease in soil C during the early stage (<5 years), and then an increase to net C gains (>5 years) coincident with vegetation restoration. The rates of soil C change were higher in the surface profile (0–20 cm) than in deeper soil (20–100 cm). Cropland converted to forest (arbor) had the additional benefit of a slower but more persistent C sequestration capacity than shrubland or grassland. Tree species played a significant role in determining the rate of change in soil C stocks (conifer < broadleaf, evergreen < deciduous forests). Restoration age was the main factor, not temperature and precipitation, affecting soil C stock change after cropland conversion with higher initial soil C stock sites having a negative effect on soil C accumulation. Soil C sequestration significantly increased with restoration age over the long‐term, and therefore, the large scale of land‐use change under the ‘Grain‐for‐Green’ Program will significantly increase China's C stocks.     

Downscaling land‐use data to provide global 30″ estimates of five land‐use classes

Land‐use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land‐use mapping. Assessments of land‐use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically downscaling mapped land‐use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land‐use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km²) estimates of five land‐use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio‐realms (unique combinations of biome and biogeographical realm) and downscaled using relationships with fine‐grained climate, land cover, landform, and anthropogenic influence layers. The downscaled land‐use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio‐realms produced global fine‐grained layers from the 2005 time step of the Land‐use Harmonization dataset. Coarse‐scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R²: 0.68 ± 0.19). Validation with the PREDICTS database showed the new downscaled land‐use layers improved discrimination of all five classes at PREDICTS sites (P < 0.0001 in all cases). Additional validation of the downscaled cropping layer with the geoWiki layer showed an R² improvement of 0.12 compared with the Land‐use Harmonization data. The downscaling method presented here produced the first global land‐use dataset at a spatial grain relevant to ecological processes that drive changes in biodiversity over space and time. Integrating these data with biodiversity measures will enable the reporting of land‐use impacts on biodiversity at a finer resolution than previously possible. Furthermore, the general method presented here could be useful to others wishing to downscale similarly constrained coarse‐resolution data for other environmental variables.     

Density influences accuracy of model‐based estimates for a forest songbird

Golden‐cheeked Warblers (Setophaga chrysoparia) are endangered songbirds that breed exclusively in the Ashe juniper (Juniperus ashei) and oak (Quercus spp.) woodlands of central Texas. Despite being the focus of numerous studies, we still know little about the size of the range‐wide breeding population and how density varies across the spectrum of juniper co‐dominated woodlands. Models that have been tested and shown to be accurate are needed to help develop management and conservation guidelines. We evaluated the accuracy and bias of density estimates from binomial mixture models, the dependent double‐observer method, and distance sampling by comparing them to actual densities determined by intensive territory monitoring on plots in the Balcones Canyonlands Preserve, Austin, Texas. We found that the binomial mixture models consistently overestimated density by 1.1–3.2 times (actual density = 0.07–0.46 males/ha), and the other two models overestimated by 1.1–29.8 times at low density and underestimated by 0.5–0.9 times at high density plots (actual density = 0.01–0.46 males/ha). The magnitude of error for all models was greatest at sites with few or no birds (<0.15 males/ha), with model performance improving as actual density increased. These non‐linear relationships indicate a lack of sensitivity with respect to true changes in density. Until systematic evaluation demonstrates that models such as those we tested provide accurate and unbiased density estimates for a given species over space and time, we recommend additional field tests to validate model‐based estimates. Continued model validation and refinement of point‐count methods are needed until accurate estimates are obtained across the density spectrum for Golden‐cheeked Warblers and other songbird species.     

The global distribution of cultivable lands: current patterns and sensitivity to possible climate change

Aim This study makes quantitative global estimates of land suitability for cultivation based on climate and soil constraints. It evaluates further the sensitivity of croplands to any possible changes in climate and atmospheric CO₂ concentrations. Location The location is global, geographically explicit. Methods The methods used are spatial data synthesis and analysis and numerical modelling. Results There is a cropland ‘reserve’ of 120%, mainly in tropical South America and Africa. Our climate sensitivity analysis indicates that the southern provinces of Canada, north‐western and north‐central states of the United States, northern Europe, southern Former Soviet Union and the Manchurian plains of China are most sensitive to changes in temperature. The Great Plains region of the United States and north‐eastern China are most sensitive to changes in precipitation. The regions that are sensitive to precipitation change are also sensitive to changes in CO₂, but the magnitude is small compared to the influence of direct climate change. We estimate that climate change, as simulated by global climate models, will expand cropland suitability by an additional 16%, mainly in the Northern Hemisphere high latitudes. However, the tropics (mainly Africa, northern South America, Mexico and Central America and Oceania) will experience a small decrease in suitability due to climate change. Main conclusions There is a large reserve of cultivable croplands, mainly in tropical South America and Africa. However, much of this land is under valuable forests or in protected areas. Furthermore, the tropical soils could potentially lose fertility very rapidly once the forest cover is removed. Regions that lie at the margins of temperature or precipitation limitation to cultivation are most sensitive to changes in climate and atmospheric CO₂ concentration. It is anticipated that climate change will result in an increase in cropland suitability in the Northern Hemisphere high latitudes (mainly in developed nations), while the tropics will lose suitability (mainly in developing nations).     

Microbial necromass in cropland soils: A global meta-analysis of management effects

Microbial necromass is a large and persistent component of soil organic carbon (SOC), especially under croplands. The effects of cropland management on microbial necromass accumulation and its contribution to SOC have been measured in individual studies but have not yet been summarized on the global scale. We conducted a meta-analysis of 481-paired measurements from cropland soils to examine the management effects on microbial necromass and identify the optimal conditions for its accumulation. Nitrogen fertilization increased total microbial necromass C by 12%, cover crops by 14%, no or reduced tillage (NT/RT) by 20%, manure by 21%, and straw amendment by 21%. Microbial necromass accumulation was independent of biochar addition. NT/RT and straw amendment increased fungal necromass and its contribution to SOC more than bacterial necromass. Manure increased bacterial necromass higher than fungal, leading to decreased ratio of fungal-to-bacterial necromass. Greater microbial necromass increases after straw amendments were common under semi-arid and in cool climates in soils with pH <8, and were proportional to the amount of straw input. In contrast, NT/RT increased microbial necromass mainly under warm and humid climates. Manure application increased microbial necromass irrespective of soil properties and climate. Management effects were especially strong when applied during medium (3–10 years) to long (10+ years) periods to soils with larger initial SOC contents, but were absent in sandy soils. Close positive links between microbial biomass, necromass and SOC indicate the important role of stabilized microbial products for C accrual. Microbial necromass contribution to SOC increment (accumulation efficiency) under NT/RT, cover crops, manure and straw amendment ranged from 45% to 52%, which was 9%–16% larger than under N fertilization. In summary, long-term cropland management increases SOC by enhancing microbial necromass accumulation, and optimizing microbial necromass accumulation and its contribution to SOC sequestration requires site-specific management.     

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.     

The influence of time,soil characteristics,and land‐use history on soil phosphorus legacies: a global meta‐analysis

Agriculturally driven changes in soil phosphorus (P) are known to have persistent effects on local ecosystem structure and function, but regional patterns of soil P recovery following cessation of agriculture are less well understood. We synthesized data from 94 published studies to assess evidence of these land‐use legacies throughout the world by comparing soil labile and total P content in abandoned agricultural areas to that of reference ecosystems or sites remaining in agriculture. Our meta‐analysis shows that soil P content was typically elevated after abandonment compared to reference levels, but reduced compared to soils that remained under agriculture. There were more pronounced differences in the legacies of past agriculture on soil P across regions than between the types of land use practiced prior to abandonment (cropland, pasture, or forage grassland). However, consistent patterns of soil P enrichment or depletion according to soil order and types of post‐agricultural vegetation suggest that these factors may mediate agricultural legacies on soil P. We also used mixed effects models to examine the role of multiple variables on soil P recovery following agriculture. Time since cessation of agriculture was highly influential on soil P legacies, with clear reductions in the degree of labile and total P enrichment relative to reference ecosystems over time. Soil characteristics (clay content and pH) were strongly related to changes in labile P compared to reference sites, but these were relatively unimportant for total P. The duration of past agricultural use and climate were weakly related to changes in total P only. Our finding of reductions in the degree of soil P alteration over time relative to reference conditions reveals the potential to mitigate these land‐use legacies in some soils. Better ability to predict dynamics of soil nutrient recovery after termination of agricultural use is essential to ecosystem management following land‐use change.     

A model‐based approach to characterize individual inbreeding at both global and local genomic scales

Inbreeding results from the mating of related individuals and may be associated with reduced fitness because it brings together deleterious variants in one individual. In general, inbreeding is estimated with respect to an arbitrary base population consisting of ancestors that are assumed unrelated. We herein propose a model‐based approach to estimate and characterize individual inbreeding at both global and local genomic scales by assuming the individual genome is a mosaic of homozygous‐by‐descent (HBD) and non‐HBD segments. The HBD segments may originate from ancestors tracing back to different periods in the past defining distinct age‐related classes. The lengths of the HBD segments are exponentially distributed with class‐specific parameters reflecting that inbreeding of older origin generates on average shorter stretches of observed homozygous markers. The model is implemented in a hidden Markov model framework that uses marker allele frequencies, genetic distances, genotyping error rates and the sequences of observed genotypes. Note that genotyping errors, low‐fold sequencing or genotype‐by‐sequencing data are easily accommodated under this framework. Based on simulations under the inference model, we show that the genomewide inbreeding coefficients and the parameters of the model are accurately estimated. In addition, when several inbreeding classes are simulated, the model captures them if their ages are sufficiently different. Complementary analyses, either on data sets simulated under more realistic models or on human, dog and sheep real data, illustrate the range of applications of the approach and how it can reveal recent demographic histories among populations (e.g., very recent bottlenecks or founder effects). The method also allows to clearly identify individuals resulting from extreme consanguineous matings.     

Effects of land use change and management on the European cropland carbon balance

We model the carbon balance of European croplands between 1901 and 2000 in response to land use and management changes. The process‐based ORCHIDEE‐STICS model is applied here in a spatially explicit framework. We reconstructed land cover changes, together with an idealized history of agro‐technology. These management parameters include the treatment of straw and stubble residues, application of mineral fertilizers, improvement of cultivar species and tillage. The model is integrated for wheat and maize during the period 1901–2000 forced by climate each 1/2‐hour, and by atmospheric CO₂, land cover change and agro‐technology each year. Several tests are performed to identify the most sensitive agro‐technological parameters that control the net biome productivity (NBP) in the 1990s, with NBP equaling for croplands the soil C balance. The current NBP is a small sink of 0.16 t C ha^?1 yr^?1. The value of NBP per unit area reflects past and current management, and to a minor extent the shrinking areas of arable land consecutive to abandonment during the 20th Century. The uncertainty associated with NBP is large, with a 1‐sigma error of 0.18 t C ha^?1 yr^?1 obtained from a qualitative, but comprehensive budget of various error terms. The NBP uncertainty is dominated by unknown historical agro‐technology changes (47%) and model structure (27%), with error in climate forcing playing a minor role. A major improvement to the framework would consist in using a larger number of representative crops. The uncertainty of historical land‐use change derived from three different reconstructions, has a surprisingly small effect on NBP (0.01 t C ha^?1 yr^?1) because cropland area remained stable during the past 20 years in all the tested land use forcing datasets. Regional cross‐validation of modeled NBP against soil C inventory measurements shows that our results are consistent with observations, within the uncertainties of both inventories and model. Our estimation of cropland NBP is however likely to be biased towards a sink, given that inventory data from different regions consistently indicate a small source whereas we model a small sink.     

Consequential Life Cycle Assessment of Pasture‐based Milk Production: A Case Study in the Waikato Region,New Zealand

Farm intensification options in pasture‐based dairy systems are generally associated with increased stocking rates coupled with the increased use of off‐farm inputs to support the additional feed demand of animals. However, as well as increasing milk production per hectare, intensification can also exacerbate adverse impacts on the environment. The objective of the present study was to investigate environmental trade‐offs associated with potential intensification methods for pasture‐based dairy farming systems in the Waikato region, New Zealand. The intensification scenarios selected were (1) increased pasture utilization efficiency (PUE scenario), (2) increased use of nitrogen (N) fertilizer to boost on‐farm pasture production (N fertilizer scenario), and (3) increased use of brought‐in feed as maize silage (MS) (MS scenario). Twelve impact categories were assessed. The PUE scenario was the environmentally preferred intensification method, and the preferred choice between the N fertilizer and MS scenarios depended upon trade‐offs between different environmental impacts. Sensitivity analysis was carried out to test the effects of choice associated with: (1) the approaches used to account for indirect land‐use change (ILUC) and (2) the competing product systems (conventional beef systems) used to handle the co‐product dairy meat for the climate change (CC) indicator. Results showed that the magnitude of the CC indicator results was influenced by the ILUC accounting approaches and the choice associated with a global marginal beef mix, but the relative CC indicator results for the three intensification scenarios remained unchanged.     

A socio‐ecological model for predicting impacts of land‐use and climate change on regional plant diversity in the Austrian Alps

Climate and land‐use change jointly affect the future of biodiversity. Yet, biodiversity scenarios have so far concentrated on climatic effects because forecasts of land use are rarely available at appropriate spatial and thematic scales. Agent‐based models (ABMs) represent a potentially powerful but little explored tool for establishing thematically and spatially fine‐grained land‐use scenarios. Here, we use an ABM parameterized for 1,329 agents, mostly farmers, in a Central European model region, and simulate the changes to land‐use patterns resulting from their response to three scenarios of changing socio‐economic conditions and three scenarios of climate change until the mid of the century. Subsequently, we use species distribution models to, first, analyse relationships between the realized niches of 832 plant species and climatic gradients or land‐use types, respectively, and, second, to project consequent changes in potential regional ranges of these species as triggered by changes in both the altered land‐use patterns and the changing climate. We find that both drivers determine the realized niches of the studied plants, with land use having a stronger effect than any single climatic variable in the model. Nevertheless, the plants' future distributions appear much more responsive to climate than to land‐use changes because alternative future socio‐economic backgrounds have only modest impact on land‐use decisions in the model region. However, relative effects of climate and land‐use changes on biodiversity may differ drastically in other regions, especially where landscapes are still dominated by natural or semi‐natural habitat. We conclude that agent‐based modelling of land use is able to provide scenarios at scales relevant to individual species distribution and suggest that coupling ABMs with models of species' range change should be intensified to provide more realistic biodiversity forecasts.