Capturing and communicating impact of citizen science for policy: A storytelling approach.

In response to the need for approaches to understand how citizen science is currently influencing environmental policy and associated decision making, we devised the Citizen Science Impact StoryTelling Approach (CSISTA). We iteratively designed instruments to be used as tools primarily for citizen science practitioners seeking to understand or communicate policy impacts. We then trialled the CSISTA and associated instruments on four exemplary citizen science initiatives, using different forms of inquiry and collaboration with respective initiative leaders. In this paper, we present CSISTA, with details of the steps for implementing inquiry and storytelling instruments. Additionally, we reflect on insights gained and challenges encountered implementing the approach. Overall, we found the versatility and structure of CSISTA as a process with multiple guiding instruments useful. We envision the approach being helpful, particularly with regards to: 1) gaining an understanding of a citizen science initiative's policy and decision-making impacts; 2) creating short policy impact stories to communicate such impacts to broader audiences; or 3) fulfilling both goals to understand and communicate policy impacts with a unified approach. We encourage others to explore, adapt, and improve the approach. Additionally, we hope that explorations of CSISTA will foster broader discussions on how to understand and strengthen interactions between citizen science practitioners, policy makers, and decision makers at large, whether at local, national, or international scales.

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.

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.

A harmonized data set of ruminant livestock presence and grazing data for the European Union and neighbouring countries.

Improving the sustainability of the European livestock sector requires recent data at high spatial resolutions. Only then can we analyse potential negative impacts of livestock related to ecosystem degradation, and positive ones such as preserving cultural landscapes through grazing. Data on livestock numbers in Europe are provided by the European statistical office but at a coarse spatial resolution aggregated to statistical regions like NUTS2 or even coarser. While data on a more detailed level, such as local administrative areas, are available from individual national statistical offices, a collection of harmonized data has not been available until now, limiting the use of livestock data in environmental and agricultural studies. We collected data from all European Union Member States and neighbouring countries, resulting in the most up to date and spatially explicit dataset on livestock numbers publicly available to all researchers. We provide data on livestock numbers for more than 70,000 administrative units in 43 countries and territories. In addition, we provide data on the share of cattle that are grazing.

Tracking unaccounted greenhouse gas emissions due to the war in Ukraine since 2022.

Accounting and reporting of greenhouse gas (GHG) emissions are mandatory for Parties under the Paris Agreement. Emissions reporting is important for understanding the global carbon cycle and for addressing global climate change. However, in a period of open conflict or war, military emissions increase significantly and the accounting system is not currently designed to account adequately for this source. In this paper we analyze how, during the first 18 months of the 2022/2023 full-scale war in Ukraine, GHG national inventory reporting to the UNFCCC was affected. We estimated the decrease of emissions due to a reduction in traditional human activities. We identified major, war-related, emission processes from the territory of Ukraine not covered by current GHG inventory guidelines and that are not likely to be included in national inventory reports. If these emissions are included, they will likely be incorporated in a way that is not transparent with potentially high uncertainty. We analyze publicly available data and use expert judgment to estimate such emissions from (1) the use of bombs, missiles, barrel artillery, and mines; (2) the consumption of oil products for military operations; (3) fires at petroleum storage depots and refineries; (4) fires in buildings and infrastructure facilities; (5) fires on forest and agricultural lands; and (6) the decomposition of war-related garbage/waste. Our estimate of these war-related emissions of carbon dioxide, methane, and nitrous oxide for the first 18 months of the war in Ukraine is 77 MtCO2-eq. with a relative uncertainty of +/-22 % (95 % confidence interval).

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.

Citizen science for monitoring the health and well-being related Sustainable Development Goals and the World Health Organization's Triple Billion Targets.

Achieving the health and well-being related Sustainable Development Goals (SDGs) and the World Health Organization's (WHO) Triple Billion Targets depends on informed decisions that are based on concerted data collection and monitoring efforts. Even though data availability has been increasing in recent years, significant gaps still remain for routine surveillance to guide policies and actions. The COVID-19 crisis has shown that more and better data and strengthened health information systems are needed to inform timely decisions that save lives. Traditional sources of data such as nationally representative surveys are not adequate for addressing this challenge alone. Additionally, the funding required to measure all health and well-being related SDG indicators and Triple Billion Targets using only traditional sources of data is a challenge to achieving efficient, timely and reliable monitoring systems. Citizen science, public participation in scientific research and knowledge production, can contribute to addressing some of these data gaps efficiently and sustainably when designed well, and ultimately, could contribute to the achievement of the health and well-being related SDGs and Triple Billion Targets. Through a systematic review of health and well-being related indicators, as well as citizen science initiatives, this paper aims to explore the potential of citizen science for monitoring health and well-being and for mobilizing action toward the achievement of health and well-being related targets as outlined in the SDG framework and Triple Billion Targets. The results demonstrate that out of 58 health and well-being related indicators of the SDGs and Triple Billion Targets covered in this study, citizen science could potentially contribute to monitoring 48 of these indicators and their targets, mostly at a local and community level, which can then be upscaled at a national level with the projection to reach global level monitoring and implementation. To integrate citizen science with official health and well-being statistics, the main recommendation is to build trusted partnerships with key stakeholders including National Statistical Offices, governments, academia and the custodian agencies, which is mostly the WHO for these health and well-being related targets and indicators.

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.

Simulating the spatial distribution of pollutant loads from pig farming using an agent-based modeling approach.

This research developed an agent-based model (ABM) for simulating pollutant loads from pig farming. The behavior of farmer agents was captured using concepts from the theory of planned behavior. The ABM has three basic components: the household or farmer agent, the land patches, and global parameters that capture the environmental context. The model was evaluated using a sensitivity analysis and then validated using data from a household survey, which showed that the predictive ability of the model was good. The ABM was then used in three scenarios: a baseline scenario, a positive scenario in which the number of pigs was assumed to remain stable but supporting policies for environmental management were increased, and a negative scenario, which assumed the number of pigs increases but management measures did not improve relative to the baseline. The positive scenario showed reductions in the discharged loads for many sub-basins of the study area while the negative scenario indicated that increased loads will be discharged to the environment. The scenario results suggest that to maintain the development of pig production while ensuring environmental protection for the district, financial, and technical support must be provided to the pig producers. The experience and education level of the farmers were significant factors influencing behaviors related to manure reuse and treatment, so awareness raising through environmental communication is needed in addition to technical measures.

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.