Future Cropland Research Articles

Cropland abandonment in mountainous China: Patterns and determinants at multiple scales and policy implications

Understanding the formation and causes of cropland abandonment is crucial for developing effective strategies to mitigate this process. However, studies on cropland abandonment often look at one spatial scale, although the relationship between abandonment and evaluated factors may change depending on the specific scale, which limits our understanding of the process of cropland abandonment. Taking mountainous Southern Sichuan of China as an example, we utilized satellite-derived cropland abandonment maps to assess the evolving patterns of cropland abandonment from 2003 to 2018 across various scales. We then carried out a multi-scale (parcel, local and regional) assessment of the relationships between selected 17 variables representing natural, location and socioeconomic conditions and cropland abandonment. Results showed, the cropland abandonment rate increased from 2 % in 2003 to 15 % by 2018, and cropland abandonment always showed a higher concentration in the central part of the study area. At different scales, a combination of factors, which determined cropland abandonment, differed. We also found the distance to the nearest county center was scale-dependent factor and statistically significant factor of cropland abandonment only at the local scale. Among the factors with large contributions, topography mattered at all three scales; cropland per capita, aging and agricultural labor per unit of cropland mattered at the parcel and regional scales. Cropland fragmentation, air temperature and distance to the nearest settlement mattered at the local scale; the distance to the nearest water system, the distance to nearest county center and agricultural mechanization mattered at the regional scale. This multi-scale assessment framework embodied with multi-variable and multi-temporal could provide a new perspective to reveal the complex nonlinear relationship between the determinants and cropland abandonment, and further help to predict future cropland abandonment hotspots and identify relevant land-use policies.

Climate Change Will Aggravate South Asian Cropland Exposure to Drought by the Middle of 21st Century

AbstractDrought has a paramount impact on global agriculture and food security. However, the study on future cropland areas that can incur drought is inadequate. This paper uses input parameters from 7 CMIP6 models for 7 future scenarios (SSP1‐1.9, SSP1‐2.6, SSP4‐3.4, SSP2‐4.5, SSP4‐6.0, SSP3‐7.0, and SSP5‐8.5) to measure South Asian cropland exposure to drought and its underlying factors. Some defined epochs such as 2021–2040 (near‐term), 2041–2060 (mid‐term), 2081–2100 (long‐term), and 1995–2014 (reference period) are designed to explore diverse outlooks of the change. The Standardized Precipitation Evapotranspiration Index and the Run theory methods are applied to detect drought. Results indicate an intensified cropland (under SSP4‐3.4, SSP3‐7.0, and SSP5‐8.5) in the Indo‐Gangetic Plain region of South Asia, where mostly the variation occurs among scenarios and periods. Notably, the future cropland exposed to drought will increase in the 2021–2040, and 2041–2060 periods, but it intends to decline during the 2081–2100. Relatively, the exposed cropland will upturn highest by 49.2% (SSP3‐7.0) in the mid‐term period and decrease by −8.2% (SSP5‐8.5) in the end future. Spatially, distributed cropland in the central, south‐west, and portion of the northeast of South Asia are subjective to be exposed largely, but it can drop greatly across the eastern part by the end future. Importantly, the climate change effect plays a grounding role in future exposure change over the region during the near to mid‐term periods, while the cropland change effect is predominant in the long‐term perspectives. However, these findings signify the urgency of policymaking focusing on drought mitigation to ensure food security.

Decoding the inconsistency of six cropland maps in China

Accurate cropland information is critical for agricultural planning and production, especially in food-stressed countries like China. Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades, considerable discrepancies exist among these products both in total area and in spatial distribution of croplands, impeding further applications of these datasets. The factors influencing their inconsistency are also unknown. In this study, we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020, including three state-of-the-art 10-m products (i.e., Google Dynamic World, ESRI Land Cover, and ESA WorldCover) and three 30-m ones (i.e., GLC_FCS30, GlobeLand 30, and CLCD). We also investigated the effects of landscape fragmentation, climate, and agricultural management. Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy (92.3%). These maps collectively overestimated Chinese cropland area by up to 56%. Up to 37% of the land showed spatial inconsistency among the maps, concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps, cropland fragmentation and management practices such as irrigation. Our work shed light on the promotion of future cropland mapping efforts, especially in highly inconsistent regions.

Impacts, carbon effects, and forecasts for cropland expansion in the Northern Tianshan Mountain Economic Zone.

Increasing population growth and cropland occupation means that new cropland is needed to achieve the goal of food security. In order to achieve the goal of "carbon neutrality," the carbon effect and ecological effect brought about by the cropland area increase urgently need to be studied. Methods such as land use transfer matrix, land use information map, InVEST model, and the ecological risk index were used to analyze the impact of cropland change in the Northern Tianshan Mountain Economic Zone and calculated the carbon effect caused by its increasing cropland area. The Ca-Markov model was used to predict and analyze the impacts of future cropland distribution and area. The results showed that, between 2000 and 2020, the area of cropland in the study area increased 3926 km2, and the area of cropland occupation by built-up was 857 km2. The new increased cropland was predominantly derived from low-cover grassland and unused land. The quality and the carbon stock of the cropland increased. The ecological risk index of cropland was 0.2613, 0.2624, and 0.1187 in 2000, 2010, and 2020, respectively. The proportion of low-risk index area was 54.43%, 55.49%, and 87.22% in 2000, 2010, and 2020, respectively. According to the current development pattern of cropland, from 2000 to 2030, the area of occupation cropland and the area of cropland may increase, the carbon stock may increase by 941,650 Mg due to the change in land use types associated with cropland, the ecological risk index may be 0.1113, and the proportion of low-risk index cropland may be 80.08%. As a carbon sink, cropland is important for local carbon neutralization alongside the sustainable adjustment of planting structure and farming methods. The results could be helpful for land resource management policy, under the conditions of following natural laws; it is feasible to appropriately expand the cropland area in order to support the gradually increasing population.

Estimating cropland requirements for global food system scenario modeling

IntroductionThe production of plant crops is foundational to the global food system. With the need for this system to become more sustainable while feeding an increasing global population, tools to investigate future food system scenarios can be useful to aid decision making, but are often limited to a calorie- or protein-centric view of human nutrition.MethodsHere, a mathematical model for forecasting the future cropland requirement to produce a given quantity of crop mass is presented in conjunction with the DELTA Model®: an existing food system scenario model calculating global availability of 29 nutrients against human requirements. The model uses national crop yield data to assign yield metrics for 137 crops.ResultsThe crops with the greatest variation between high and low yielding production were specific nuts, fruits, and vegetables of minor significance to global nutrient availability. The nut crop group showed the greatest overall yield variation between countries, and thus the greatest uncertainty when forecasting the cropland requirement for future increases in production. Sugar crops showed the least overall yield variation. The greatest potential for increasing global food production by improving poor yielding production was found for the most widely grown crops: maize, wheat, and rice, which were also demonstrated to be of high nutritional significance.DiscussionThe combined cropland and nutrient availability model allowed the contribution of plant production to global nutrition to be quantified, and the cropland requirement of future food production scenarios to be estimated. The unified cropland estimation and nutrient availability model presented here is an intuitive and broadly applicable tool for use in global food system scenario modeling. It should benefit future research and policy making by demonstrating the implications for human nutrition of changes to crop production, and conversely the implications for cropland requirement of food production scenarios aimed at improving nutrition.

Prediction of potential distribution of soybean in the frigid region in China with MaxEnt modeling

Soybean (Glycine max (L.) Merr.) is one of the most important grains and oil-producing plants grown in China. Understanding the potential suitable characteristics of areas where soybean is grown and predicting its potential habitat under different climate scenarios are a significant part of ensuring food security. This study compiled 65 occurrence locations of soybean and 32 environmental variables obtained from the WorldClim database. Nine environmental variables were selected for model training. We identified potential suitable distribution areas for soybean in the frigid region and predicted changes in its geographical distribution under four shared socioeconomic pathways, SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5, for the periods from 2021 to 2040, 2041 to 2060, 2061 to 2080, and 2081 to 2100 using the MaxEnt model. The results showed that annual mean temperature, elevation, and April solar radiation were the dominant factors affecting the distribution of soybean, contributing 48.8%, 17.9%, and 15.7% of the variability in the data, respectively. Highly suitable habitats (defined as having a suitability variable P of 0.66–1.0) for the current conditions included the Songnen and Sanjiang plains, covering about 2.36 × 105 km2. The total areas of highly (as defined above) and moderately suitable (0.33–0.66) habitats would be reduced under the four climate scenarios. However, the centroids of the highly suitable habitat had a small mobile range under different scenarios. These results along with previous research on the potential distribution of soybean offer useful information; ecological modeling approaches need to be considered in future crop planting management and land use.

Cropland Expansion Mitigates the Supply and Demand Deficit for Carbon Sequestration Service under Different Scenarios in the Future—The Case of Xinjiang

China’s double carbon initiative faces huge challenges, and understanding the carbon sequestration service of terrestrial ecosystems under future interannual regional land use change is important to respond to China’s carbon policy effectively. Previous studies have recognized the important impact of land use/land cover (LULC) planning on carbon sequestration in terrestrial ecosystem services (ESs). However, exploring trends in carbon sequestration under sustainable development scenarios that combine economic and ecological development, particularly the mechanisms that balance the supply and demand of carbon sequestration, still requires in-depth exploration in different geographical contexts. In this study, we present the LULC simulation framework from 2000 to 2030 for four different development scenarios in the Xinjiang region, located in an important Belt and Road region, including business as usual (BAU), rapid economic development (RED), ecological land protection (ELP), and sustainable development with both economic and ecological development (SD). Our results suggest that both the supply and demand of carbon stock in Xinjiang will increase in 2025 and 2030, with the demand exceeding the supply. However, our scenario planning mitigates the supply and demand deficit situation for carbon sequestration in the context of future cropland expansion in different scenarios. In summary, our study’s findings will enrich the study of carbon sequestration under future scenarios in the Belt and Road region. Xinjiang should pay more attention to the dynamic changes in landscape type structure and its carbon storage supply and demand caused by cultivated land expansion. Among the four scenarios, the spatial difference between carbon storage supply and demand based on the SD scenario is the smallest, which is more in line with the high-quality development of regional ecological security in Xinjiang.

Evaluation of development potential of cropland in Central Asia

With population growth and rapid urbanization, agricultural land has become a relatively scarce resource. Given the vast land area in Central Asia, exploring the potential of agricultural land development in Central Asia is a realistic guide to the rational use of land and the alleviation of strained human-land relations and food shortage problems. In this study, Multi-criteria Decision Analysis (MCDA) and Analytic Hierarchy Process (AHP) were used to construct a cropland development potential evaluation system in Central Asia. The results showed that: (1) The development of current cropland basically adapts to local natural conditions in Central Asia. Suitable irrigated cropland is mainly located in areas with low elevation and close to water sources such as rivers and canals. Suitable rain-fed cropland is mainly located in the areas with flat terrain and sufficient rainfall. (2) The land resources of Central Asia still have a large potential for development: 1,008,000 km2 for the irrigated cropland and 217,124 km2 for the rain-fed cropland, mainly concentrated in Kazakhstan and Turkmenistan. The final evaluation results on a province-level scale presented in this study provide a direct and effective reference for future cropland development in Central Asian countries.

Future land-use competition constrains natural climate solutions

Natural climate solutions (NCS) are an essential complement to climate mitigation and have been increasingly incorporated into international mitigation strategies. Yet, with the ongoing population growth, allocating natural areas for NCS may compete with other socioeconomic priorities, especially urban development and food security. Here, we projected the impacts of land-use competition incurred by cropland and urban expansion on the climate mitigation potential of NCS. We mapped the areas available for implementing 9 key NCS strategies and estimated their climate change mitigation potential. Then, we overlaid these areas with future cropland and urban expansion maps projected under three Shared Socioeconomic Pathway (SSP) scenarios (2020-2100) and calculated the resulting mitigation potential loss of each selected NCS strategy. Our results estimate a substantial reduction, 0.3–2.8 GtCO2 yr−1 or 4–39 %, in NCS mitigation potential, of which cropland expansion for fulfilling future food demand is the primary cause. This impact is particularly severe in the tropics where NCS hold the most abundant mitigation potential. Our findings highlight immediate actions prioritized to tropical areas are important to best realize NCS and are key to developing realistic and sustainable climate policies.

Probabilistic forecasting of remotely sensed cropland vegetation health and its relevance for food security

In a world where climate change, population growth, and global diseases threaten economic access to food, policies and contingency plans can strongly benefit from reliable forecasts of agricultural vegetation health. To inform decisions, it is also crucial to quantify the forecasting uncertainty and prove its relevance for food security. Yet, in previous studies both these aspects have been largely overlooked. This paper develops a methodology to anticipate the agricultural Vegetation Health Index (VHI) while making the underlying prediction uncertainty explicit. To achieve this aim, a probabilistic machine learning framework modelling weather and climate determinants is introduced and implemented through Quantile Random Forests. In a second step, a statistical link between VHI forecasts and monthly food price variations is established. As a pilot implementation, the framework is applied to nine countries of South-East Asia (SEA) with consideration of national monthly rice prices. Model benchmarks show satisfactory accuracy metrics, suggesting that the probabilistic VHI predictions can provide decision-makers with reliable information about future cropland health and its impact on food price variation weeks or even months ahead, albeit with increasing uncertainty as the forecasting horizon grows. These results - ultimately allowing to anticipate the impact of weather shocks on household food expenditure - contribute to advancing the multidisciplinary literature linking vegetation health, probabilistic forecasting models, and food security policy.

Linking food production and environmental outcomes: An application of a modified relative risk model to prioritize land-management practices

CONTEXTSustainable land-use systems require clear decision-making protocols across institutions and departments. However, food production and ecological conservation are often regarded as separate or even incompatible policy goals, resulting in sustainability challenges and unclear land-management practices. More recently, with the introduction of Sustainable Development Goals (SDGs), there has been growing interests in minimizing the possible trade-offs between the two policy goals through appropriate governance. OBJECTIVEThe objective of this study was to develop a systematic framework for assessing current land-management practices and specifying future priorities. METHODSThe systematic framework included three modules and considered agroecosystems covering each administrative unit as the research objects. In the risk assessment module, we modified traditional relative risk model (RRM) to assess the risk that current management practices may affect food production and non-provisioning ecosystem services in existing agroecosystems, respectively. The framework also included a module to identify potentially available cropland (PAC) based on a combination of agricultural suitability and environmental costs for future reclamation. Finally, priorities for future land-management practices were specified based on the trade-offs between cropland expansion, ecological restoration and productive restoration. Jiangsu Province in eastern China was taken as a case study to verify the applicability of our framework. RESULTS AND CONCLUSIONSUsing Jiangsu Province as a case study, we have verified the applicability of our framework in prioritizing future land-management practices at a county level. The framework can offer a prioritization mechanism for formulating targeted strategies under alternative scenarios to correspond with varying management goals. The RRM can separate adverse effects from multiple stressors on food production and environmental outcomes rather than confounding the two interactive functions, while the risk scores and ranks generated for each sub-region, stressor and endpoint can pave the way for detailed and targeted governances. In addition, the identification of PAC can highlight areas for future cropland expansion. SIGNIFICANCEThe proposed framework can provide important insights and tools for avoiding institutional conflicts regarding land-management governance. Due to the simplicity and comparability, it can not only be applicable to our particular site, but also be used for different locations or downscaled to a landscape level.

Representing responses to climate change in spatial land system models.

Modelling future change to land use and land cover is done as part of many local and global scenario environmental assessments. Nevertheless, there are still considerable challenges related to simulating land‐use responses to climate change. Mostly, climate change is considered by changing the temperature and precipitation, affecting the spatial distribution and productivity of future land use and land cover as result of differential changes in growing conditions. Other climate change effects, such as changes in the water resources needed to support future cropland expansion and intensification, are often neglected. In this study, we demonstrate how including different types of responses to climate change influences the simulation of future changes to land use and land cover, and land management. We study the influence of including different climate change effects in land system modeling step by step. The results show that land system models need to include numerous simultaneous climate change effects, particularly when looking at adaptation options such as implementing irrigation. Otherwise, there is a risk of biased impact estimates leading either to under‐ or overestimation of the consequences of land use change, including land degradation. Spatial land system models therefore need to be developed accounting for a multitude of climate change impacts, uncertainties related to climate data, and an assessment of the sensitivity of the outcomes toward the decisions of modellers on representing climate change impacts.

Cropland redistribution to marginal lands undermines environmental sustainability.

ABSTRACTCropland redistribution to marginal land has been reported worldwide; however, the resulting impacts on environmental sustainability have not been investigated sufficiently. Here we investigated the environmental impacts of cropland redistribution in China. As a result of urbanization-induced loss of high-quality croplands in south China (∼8.5 t ha–1), croplands expanded to marginal lands in northeast (∼4.5 t ha–1) and northwest China (∼2.9 t ha–1) during 1990–2015 to pursue food security. However, the reclamation in these low-yield and ecologically vulnerable zones considerably undermined local environmental sustainability, for example increasing wind erosion (+3.47%), irrigation water consumption (+34.42%), fertilizer use (+20.02%) and decreasing natural habitats (−3.11%). Forecasts show that further reclamation in marginal lands per current policies would exacerbate environmental costs by 2050. The future cropland security risk will be remarkably intensified because of the conflict between food production and environmental sustainability. Our research suggests that globally emerging reclamation of marginal lands should be restricted and crop yield boost should be encouraged for both food security and environmental benefits.

Coordinated intensification to reconcile the ‘zero hunger’ and ‘life on land’ Sustainable Development Goals

The Sustainable Development Goals (SDGs) encourage nations to substantially increase food production to achieve zero hunger (SDG 2) while preserving life on land (SDG 15). A key question is how to reconcile these potentially competing goals spatially. We use integer linear programming to develop an ‘integrated land use planning framework’ that identifies the optimal allocation of 17 crops under different hypothetical conservation targets while meeting agricultural demands by 2030. Intensifying existing cropland to maximum yield before allocating new cropland would reduce land requirement by 43% versus cropland expansion without intensification. Even with yield gap closure, tropical and sub-tropical crops still require expansion, primarily allocated to Venezuela, eastern Brazil, Congo Basin, Myanmar and Indonesia. Enforcement of protected areas, via avoiding conversion in 75% of Key Biodiversity Areas and 65% of intact areas, is vital to attain biodiversity targets but bears large opportunity costs, with agricultural rents dropping from $4.1 to $2.8 trillion. Although nationally constrained forest conservation efforts would earn 9% less agricultural rents compared to globally coordinated conservation solutions, they were also able to reduce intact habitat and forest loss (43% and 35% reduction). Our results demonstrate that careful choice of the allocation of future cropland expansion, could dramatically reduce—but not eliminate—the tradeoffs between the SDGs for food production and land biodiversity conservation.

Simulating Land-Use Changes and Predicting Maize Potential Yields in Northeast China for 2050.

Crop potential yields in cropland are the essential reflection of the utilization of cropland resources. The changes of the quantity, quality, and spatial distribution of cropland will directly affect the crop potential yields, so it is very crucial to simulate future cropland distribution and predict crop potential yields to ensure the future food security. In the present study, the Cellular Automata (CA)-Markov model was employed to simulate land-use changes in Northeast China during 2015–2050. Then, the Global Agro-ecological Zones (GAEZ) model was used to predict maize potential yields in Northeast China in 2050, and the spatio-temporal changes of maize potential yields during 2015–2050 were explored. The results were the following. (1) The woodland and grassland decreased by 5.13 million ha and 1.74 million ha respectively in Northeast China from 2015 to 2050, which were mainly converted into unused land. Most of the dryland was converted to paddy field and built-up land. (2) In 2050, the total maize potential production and average potential yield in Northeast China were 218.09 million tonnes and 6880.59 kg/ha. Thirteen prefecture-level cities had maize potential production of more than 7 million tonnes, and 11 cities had maize potential yields of more than 8000 kg/ha. (3) During 2015–2050, the total maize potential production and average yield decreased by around 23 million tonnes and 700 kg/ha in Northeast China, respectively. (4) The maize potential production increased in 15 cities located in the plain areas over the 35 years. The potential yields increased in only nine cities, which were mainly located in the Sanjiang Plain and the southeastern regions. The results highlight the importance of coping with the future land-use changes actively, maintaining the balance of farmland occupation and compensation, improving the cropland quality, and ensuring food security in Northeast China.

Global N2O Emissions From Cropland Driven by Nitrogen Addition and Environmental Factors: Comparison and Uncertainty Analysis

AbstractHuman activities have caused considerable perturbations of the nitrogen (N) cycle, leading to a ~20% increase in the concentration of atmospheric nitrous oxide (N2O) since the preindustrial era. While substantial efforts have been made to quantify global and regional N2O emissions from cropland, there is large uncertainty regarding how climate change and variability have altered net N2O fluxes at annual and decadal time scales. Herein, we applied a process‐based dynamic land ecosystem model (DLEM) to estimate global N2O emissions from cropland driven by synthetic N fertilizer application and multiple environmental factors (i.e., elevated CO2, atmospheric N deposition, and climate change). We estimate that global cropland N2O emissions increased by 180% (from 1.1 ± 0.2 to 3.3 ± 0.1 Tg N year−1; mean ±1 standard deviation) during 1961–2014. Synthetic N fertilizer applications accounted for ~70% of total emissions during 2000–2014. At the regional scale, Europe and North America were two leading regions for N2O emissions in the 1960s. However, East Asia became the largest emitter after the 1990s. Compared with estimates based on linear and nonlinear emission factors, our results were 150% and 186% larger, respectively, at the global scale during 2000–2014. Our higher estimates of N2O emissions could be attributable to the legacy effect from previous N addition to cropland as well as the interactive effect of N addition and climate change. To reduce future cropland N2O emissions, effective mitigation strategies should be implemented in regions that have received high levels of N fertilizer and regions that would be more vulnerable to future climate change.

Cropland expansion in the United States produces marginal yields at high costs to wildlife

Recent expansion of croplands in the United States has caused widespread conversion of grasslands and other ecosystems with largely unknown consequences for agricultural production and the environment. Here we assess annual land use change 2008–16 and its impacts on crop yields and wildlife habitat. We find that croplands have expanded at a rate of over one million acres per year, and that 69.5% of new cropland areas produced yields below the national average, with a mean yield deficit of 6.5%. Observed conversion infringed upon high-quality habitat that, relative to unconverted land, had provided over three times higher milkweed stem densities in the Monarch butterfly Midwest summer breeding range and 37% more nesting opportunities per acre for waterfowl in the Prairie Pothole Region of the Northern Great Plains. Our findings demonstrate a pervasive pattern of encroachment into areas that are increasingly marginal for production, but highly significant for wildlife, and suggest that such tradeoffs may be further amplified by future cropland expansion.

Multiple factors influence the consistency of cropland datasets in Africa

Accurate geo-information of cropland is critical for food security strategy development and grain production management, especially in Africa continent where most countries are food-insecure. Over the past decades, a series of African cropland maps have been derived from remotely-sensed data, existing comparison studies have shown that inconsistencies with statistics and discrepancies among these products are considerable. Yet, there is a knowledge gap about the factors that influence their consistency. The aim of this study is thus to estimate the consistency of five widely-used cropland datasets (MODIS Collection 5, GlobCover 2009, GlobeLand30, CCI-LC 2010, and Unified Cropland Layer) in Africa, and to explore the effects of several limiting factors (landscape fragmentation, climate and agricultural management) on spatial consistency. The results show that total cropland area for Africa derived from GlobeLand30 has the best fitness with FAO statistics, followed by MODIS Collection 5. GlobCover 2009, CCI-LC 2010, and Unified Cropland Layer have poor performances as indicated by larger deviations from statistics. In terms of spatial consistency, disagreement is about 37.9 % at continental scale, and the disparate proportion even exceeds 50 % in approximately 1/3 of the countries at national scale. We further found that there is a strong and significant correlation between spatial agreement and cropland fragmentation, suggesting that regions with higher landscape fragmentation generally have larger disparities. It is also noticed that places with better consistency are mainly distributed in regions with favorable natural environments and sufficient agricultural management such as well-developed irrigated technology. Proportions of complete agreement are thus located in favorable climate zones including Hot-summer Mediterranean climate (Csa), Subtropical highland climate (Cwb), and Temperate Mediterranean climate (Csb). The level of complete agreement keeps rising as the proportion of irrigated cropland increases. Spatial agreement among these datasets has the most significant relationship with cropland fragmentation, and a relatively small association with irrigation area, followed by climate conditions. These results can provide some insights into understanding how different factors influence the consistency of cropland datasets, and making an appropriate selection when using these datasets in different regions. We suggest that future cropland mapping activities should put more effort in those regions with significant disagreement in Sub-Saharan Africa.

Comparing the impact of future cropland expansion on global biodiversity and carbon storage across models and scenarios.

Land-use change is a direct driver of biodiversity and carbon storage loss. Projections of future land use often include notable expansion of cropland areas in response to changes in climate and food demand, although there are large uncertainties in results between models and scenarios. This study examines these uncertainties by comparing three different socio-economic scenarios (SSP1–3) across three models (IMAGE, GLOBIOM and PLUMv2). It assesses the impacts on biodiversity metrics and direct carbon loss from biomass and soil as a direct consequence of cropland expansion. Results show substantial variation between models and scenarios, with little overlap across all nine projections. Although SSP1 projects the least impact, there are still significant impacts projected. IMAGE and GLOBIOM project the greatest impact across carbon storage and biodiversity metrics due to both extent and location of cropland expansion. Furthermore, for all the biodiversity and carbon metrics used, there is a greater proportion of variance explained by the model used. This demonstrates the importance of improving the accuracy of land-based models. Incorporating effects of land-use change in biodiversity impact assessments would also help better prioritize future protection of biodiverse and carbon-rich areas.This article is part of the theme issue ‘Climate change and ecosystems: threats, opportunities and solutions’.

A database for global soil health assessment

Field studies have been performed for decades to analyze effects of different management practices on agricultural soils and crop yields, but these data have never been combined together in a way that can inform current and future cropland management. Here, we collected, extracted, and integrated a database of soil health measurements conducted in the field from sites across the globe. The database, named SoilHealthDB, currently focuses on four main conservation management methods: cover crops, no-tillage, agro-forestry systems, and organic farming. These studies represent 354 geographic sites (i.e., locations with unique latitudes and longitudes) in 42 countries around the world. The SoilHealthDB includes 42 soil health indicators and 46 background indicators that describe factors such as climate, elevation, and soil type. A primary goal of this effort is to enable the research community to perform comprehensive analyses, e.g., meta-analyses, of soil health changes related to cropland conservation management. The database also provides a common framework for sharing soil health, and the scientific research community is encouraged to contribute their own measurements.