Bending the curve of terrestrial biodiversity needs an integrated strategy.

Increased efforts are required to prevent further losses to terrestrial biodiversity and the ecosystem services that it  provides1,2. Ambitious targets have been proposed, such as reversing the declining trends in biodiversity3; however, just feeding the growing human population will make this a challenge4. Here we use an ensemble of land-use and biodiversity models to assess whether-and how-humanity can reverse the declines in terrestrial biodiversity caused by habitat conversion, which is a major threat to biodiversity5. We show that immediate efforts, consistent with the broader sustainability agenda but of unprecedented ambition and coordination, could enable the provision of food for the growing human population while reversing the global terrestrial biodiversity trends caused by habitat conversion. If we decide to increase the extent of land under conservation management, restore degraded land and generalize landscape-level conservation planning, biodiversity trends from habitat conversion could become positive by the mid-twenty-first century on average across models (confidence interval, 2042-2061), but this was not the case for all models. Food prices could increase and, on average across models, almost half (confidence interval, 34-50%) of the future biodiversity losses could not be avoided. However, additionally tackling the drivers of land-use change could avoid conflict with affordable food provision and reduces the environmental effects of the food-provision system. Through further sustainable intensification and trade, reduced food waste and more plant-based human diets, more than two thirds of future biodiversity losses are avoided and the biodiversity trends from habitat conversion are reversed by 2050 for almost all of the models. Although limiting further loss will remain challenging in several biodiversity-rich regions, and other threats-such as climate change-must be addressed to truly reverse the declines in biodiversity, our results show that ambitious conservation efforts and food system transformation are central to an effective post-2020 biodiversity strategy.

Mapping the effects of drought on child stunting.

As climate change continues, it is expected to have increasingly adverse impacts on child nutrition outcomes, and these impacts will be moderated by a variety of governmental, economic, infrastructural, and environmental factors. To date, attempts to map the vulnerability of food systems to climate change and drought have focused on mapping these factors but have not incorporated observations of historic climate shocks and nutrition outcomes. We significantly improve on these approaches by using over 580,000 observations of children from 53 countries to examine how precipitation extremes since 1990 have affected nutrition outcomes. We show that precipitation extremes and drought in particular are associated with worse child nutrition. We further show that the effects of drought on child undernutrition are mitigated or amplified by a variety of factors that affect both the adaptive capacity and sensitivity of local food systems with respect to shocks. Finally, we estimate a model drawing on historical observations of drought, geographic conditions, and nutrition outcomes to make a global map of where child stunting would be expected to increase under drought based on current conditions. As climate change makes drought more commonplace and more severe, these results will aid policymakers by highlighting which areas are most vulnerable as well as which factors contribute the most to creating resilient food systems.

Feasibility of a large multi-center translational research project for newly diagnosed breast and ovarian cancer patients with affiliated biobank: the BRandO biology and outcome (BiO)-project.

INTRODUCTION: Large translational research projects may contribute to further progress in cancer treatment by exploring molecular biology, immunologic approaches and identification of new prognostic and predictive factors. Therefore, the BRandOBio-project combines a clinical registry for collection of patient and tumor characteristics with a biobank comprising tumor and liquid biopsies. In addition, sociodemographic, environmental and lifestyle factors of included patients with primary newly diagnosed breast or ovarian cancer, other rare malignant ovarian tumors or gestational trophoblastic disease are prospectively collected. METHODS: The target population includes the German "Alb-Allgäu-Bodensee Region" which constitutes the outreach area of the University Hospital Ulm with affiliated academic centers and private practices. Clinical data combined with primary tumor tissue samples and longitudinal repeatedly collected blood samples [before, 6 (in high-risk situations), 12, 36 and 60 months after treatment and at relapse] will be acquired from more than 4000 patients within the next years. Standardized questionnaires are given to patients of the University Hospital Ulm and eight selected external sites for assessing life style and cancer risk factors. Concomitantly, storage of paraffin-embedded tumor samples as well as liquid biopsy samples will allow translational research projects, for example in terms of investigating circulating DNA and germ line DNA from cell pellets. RESULTS: Starting in January 2016 at the University Hospital Ulm, 19 additional external sites started recruiting patients in March 2017. As of September 15th 2019, 2151 patients with newly diagnosed cancers could be recruited (2044 breast cancer; 107 ovarian cancer). Nearly all patients provided biological samples (tumor and liquid biopsy) and about 80% returned the standardized questionnaire. After 1 year follow-up, blood samples were available from more than 80% of the participating patients. CONCLUSIONS: The BRandO BIO study is a large prospective cohort study with integrated comprehensive biobank and evaluation of sociodemographic and life style factors of gynecological cancer patients in a well-defined geographical area in the South West of Germany. Continuous high patient recruitment and stable rates over 80% for returned questionnaires as well as for repeated blood sampling show high acceptance of the BRandO study program and confirms feasibility of the project.

Addressing the need for improved land cover map products for policy support.

The continued increase of anthropogenic pressure on the Earth's ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products - and there are many available - are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users' specific needs would fundamentally change the relationship between users and land cover products - requiring more government support to make these systems a reality.

Estimating the global distribution of field size using crowdsourcing.

There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.

Agricultural diversification as an important strategy for achieving food security in Africa.

Farmers in Africa have long adapted to climatic and other risks by diversifying their farming activities. Using a multi-scale approach, we explore the relationship between farming diversity and food security and the diversification potential of African agriculture and its limits on the household and continental scale. On the household scale, we use agricultural surveys from more than 28,000 households located in 18 African countries. In a next step, we use the relationship between rainfall, rainfall variability, and farming diversity to determine the available diversification options for farmers on the continental scale. On the household scale, we show that households with greater farming diversity are more successful in meeting their consumption needs, but only up to a certain level of diversity per ha cropland and more often if food can be purchased from off-farm income or income from farm sales. More diverse farming systems can contribute to household food security; however, the relationship is influenced by other factors, for example, the market orientation of a household, livestock ownership, nonagricultural employment opportunities, and available land resources. On the continental scale, the greatest opportunities for diversification of food crops, cash crops, and livestock are located in areas with 500-1,000 mm annual rainfall and 17%-22% rainfall variability. Forty-three percent of the African cropland lacks these opportunities at present which may hamper the ability of agricultural systems to respond to climate change. While sustainable intensification practices that increase yields have received most attention to date, our study suggests that a shift in the research and policy paradigm toward agricultural diversification options may be necessary.

Land management: data availability and process understanding for global change studies.

In the light of daunting global sustainability challenges such as climate change, biodiversity loss and food security, improving our understanding of the complex dynamics of the Earth system is crucial. However, large knowledge gaps related to the effects of land management persist, in particular those human-induced changes in terrestrial ecosystems that do not result in land-cover conversions. Here, we review the current state of knowledge of ten common land management activities for their biogeochemical and biophysical impacts, the level of process understanding and data availability. Our review shows that ca. one-tenth of the ice-free land surface is under intense human management, half under medium and one-fifth under extensive management. Based on our review, we cluster these ten management activities into three groups: (i) management activities for which data sets are available, and for which a good knowledge base exists (cropland harvest and irrigation); (ii) management activities for which sufficient knowledge on biogeochemical and biophysical effects exists but robust global data sets are lacking (forest harvest, tree species selection, grazing and mowing harvest, N fertilization); and (iii) land management practices with severe data gaps concomitant with an unsatisfactory level of process understanding (crop species selection, artificial wetland drainage, tillage and fire management and crop residue management, an element of crop harvest). Although we identify multiple impediments to progress, we conclude that the current status of process understanding and data availability is sufficient to advance with incorporating management in, for example, Earth system or dynamic vegetation models in order to provide a systematic assessment of their role in the Earth system. This review contributes to a strategic prioritization of research efforts across multiple disciplines, including land system research, ecological research and Earth system modelling.

Climate change mitigation through livestock system transitions.

Livestock are responsible for 12% of anthropogenic greenhouse gas emissions. Sustainable intensification of livestock production systems might become a key climate mitigation technology. However, livestock production systems vary substantially, making the implementation of climate mitigation policies a formidable challenge. Here, we provide results from an economic model using a detailed and high-resolution representation of livestock production systems. We project that by 2030 autonomous transitions toward more efficient systems would decrease emissions by 736 million metric tons of carbon dioxide equivalent per year (MtCO2e⋅y(-1)), mainly through avoided emissions from the conversion of 162 Mha of natural land. A moderate mitigation policy targeting emissions from both the agricultural and land-use change sectors with a carbon price of US$10 per tCO2e could lead to an abatement of 3,223 MtCO2e⋅y(-1). Livestock system transitions would contribute 21% of the total abatement, intra- and interregional relocation of livestock production another 40%, and all other mechanisms would add 39%. A comparable abatement of 3,068 MtCO2e⋅y(-1) could be achieved also with a policy targeting only emissions from land-use change. Stringent climate policies might lead to reductions in food availability of up to 200 kcal per capita per day globally. We find that mitigation policies targeting emissions from land-use change are 5 to 10 times more efficient--measured in "total abatement calorie cost"--than policies targeting emissions from livestock only. Thus, fostering transitions toward more productive livestock production systems in combination with climate policies targeting the land-use change appears to be the most efficient lever to deliver desirable climate and food availability outcomes.

Mapping global cropland and field size.

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.

African crop yield reductions due to increasingly unbalanced Nitrogen and Phosphorus consumption.

The impact of soil nutrient depletion on crop production has been known for decades, but robust assessments of the impact of increasingly unbalanced nitrogen (N) and phosphorus (P) application rates on crop production are lacking. Here, we use crop response functions based on 741 FAO maize crop trials and EPIC crop modeling across Africa to examine maize yield deficits resulting from unbalanced N : P applications under low, medium, and high input scenarios, for past (1975), current, and future N : P mass ratios of respectively, 1 : 0.29, 1 : 0.15, and 1 : 0.05. At low N inputs (10 kg ha(-1)), current yield deficits amount to 10% but will increase up to 27% under the assumed future N : P ratio, while at medium N inputs (50 kg N ha(-1)), future yield losses could amount to over 40%. The EPIC crop model was then used to simulate maize yields across Africa. The model results showed relative median future yield reductions at low N inputs of 40%, and 50% at medium and high inputs, albeit with large spatial variability. Dominant low-quality soils such as Ferralsols, which are strongly adsorbing P, and Arenosols with a low nutrient retention capacity, are associated with a strong yield decline, although Arenosols show very variable crop yield losses at low inputs. Optimal N : P ratios, i.e. those where the lowest amount of applied P produces the highest yield (given N input) where calculated with EPIC to be as low as 1 : 0.5. Finally, we estimated the additional P required given current N inputs, and given N inputs that would allow Africa to close yield gaps (ca. 70%). At current N inputs, P consumption would have to increase 2.3-fold to be optimal, and to increase 11.7-fold to close yield gaps. The P demand to overcome these yield deficits would provide a significant additional pressure on current global extraction of P resources.

Downgrading recent estimates of land available for biofuel production.

Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 million ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development.

Building a community-based open harmonised reference data repository for global crop mapping.

Reference data is key to produce reliable crop type and cropland maps. Although research projects, national and international programs as well as local initiatives constantly gather crop related reference data, finding, collecting, and harmonizing data from different sources is a challenging task. Furthermore, ethical, legal, and consent-related restrictions associated with data sharing represent a common dilemma faced by international research projects. We address these dilemmas by building a community-based, open, harmonised reference data repository at global extent, ready for model training or product validation. Our repository contains data from different sources such as the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) Joint Experiment for Crop Assessment and Monitoring (JECAM) sites, the Radiant MLHub, the Future Harvest (CGIAR) centers, the National Aeronautics and Space Administration Food Security and Agriculture Program (NASA Harvest), the International Institute for Applied Systems Analysis (IIASA) citizen science platforms (LACO-Wiki and Geo-Wiki), as well as from individual project contributions. Data of 2016 onwards were collected, harmonised, and annotated. The data sets spatial, temporal, and thematic quality were assessed applying rules developed in this research. Currently, the repository holds around 75 million harmonised observations with standardized metadata of which a large share is available to the public. The repository, funded by ESA through the WorldCereal project, can be used for either the calibration of image classification deep learning algorithms or the validation of Earth Observation generated products, such as global cropland extent and maize and wheat maps. We recommend continuing and institutionalizing this reference data initiative e.g. through GEOGLAM, and encouraging the community to publish land cover and crop type data following the open science and open data principles.

How many people need to classify the same image? A method for optimizing volunteer contributions in binary geographical classifications.

Involving members of the public in image classification tasks that can be tricky to automate is increasingly recognized as a way to complete large amounts of these tasks and promote citizen involvement in science. While this labor is usually provided for free, it is still limited, making it important for researchers to use volunteer contributions as efficiently as possible. Using volunteer labor efficiently becomes complicated when individual tasks are assigned to multiple volunteers to increase confidence that the correct classification has been reached. In this paper, we develop a system to decide when enough information has been accumulated to confidently declare an image to be classified and remove it from circulation. We use a Bayesian approach to estimate the posterior distribution of the mean rating in a binary image classification task. Tasks are removed from circulation when user-defined certainty thresholds are reached. We demonstrate this process using a set of over 4.5 million unique classifications by 2783 volunteers of over 190,000 images assessed for the presence/absence of cropland. If the system outlined here had been implemented in the original data collection campaign, it would have eliminated the need for 59.4% of volunteer ratings. Had this effort been applied to new tasks, it would have allowed an estimated 2.46 times as many images to have been classified with the same amount of labor, demonstrating the power of this method to make more efficient use of limited volunteer contributions. To simplify implementation of this method by other investigators, we provide cutoff value combinations for one set of confidence levels.

Estimating global economic well-being with unlit settlements.

It is well established that nighttime radiance, measured from satellites, correlates with economic prosperity across the globe. In developing countries, areas with low levels of detected radiance generally indicate limited development - with unlit areas typically being disregarded. Here we combine satellite nighttime lights and the world settlement footprint for the year 2015 to show that 19% of the total settlement footprint of the planet had no detectable artificial radiance associated with it. The majority of unlit settlement footprints are found in Africa (39%), rising to 65% if we consider only rural settlement areas, along with numerous countries in the Middle East and Asia. Significant areas of unlit settlements are also located in some developed countries. For 49 countries spread across Africa, Asia and the Americas we are able to predict and map the wealth class obtained from ~2,400,000 geo-located households based upon the percent of unlit settlements, with an overall accuracy of 87%.

A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia.

Here we present a geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference dataset collected by visual interpretation of very high spatial resolution imagery, in a national-scale crowdsourcing campaign (targeting seven generic LC classes) and a series of expert workshops (targeting seventeen detailed LC classes) in Indonesia. The interpreters were citizen scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the quality assessment indicators. We envisage that the dataset will be relevant for: (1) the LC mapping community (researchers and practitioners), i.e., as reference data for training machine learning algorithms and map accuracy assessment (with appropriate quality-filters applied), and (2) the citizen science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of contributions from the crowd/non-experts, demonstrated for LC mapping in Indonesia for the first time to our knowledge, within the context of complementing traditional data collection by expert interpreters.

A crowdsourced global data set for validating built-up surface layers.

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki ( https://www.geo-wiki.org/ ) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas.