In an increasingly turbulent world, designing resilient farming systems is critical. Recent socio-ecological research has hypothesized that the general resilience of farming systems to disturbances is related to the interplay among four key resilience attributes—Agencies, Buffers, Connectivity, and Diversity (ABCD). However, the relative importance of these attributes in coping with multiple concurrent disturbances remains unclear. This study draws on longitudinal socio-ecological data, including biotic, abiotic, and socio-political shocks and community responses, to explore how ABCD attributes mediate farming systems' resilience. We combined satellite-derived soil moisture content, green soil cover, and aboveground biomass data, with focus group discussions in twelve communities, to analyze the land restoration outcomes under multiple disturbances. Our results demonstrate that land restoration “bright spots” exhibited sustained resilience despite exposure to simultaneous shocks. We further found that attributes, agency and buffers were essential for coping with multiple disturbances, while the contributions of connectivity and diversity were more context dependent, varying with the type of disturbance.

Globally, soils are subjected to various management practices and stressors which can lead to degradation. This makes their protection essential for sustaining many functions and services as well as maintaining the overall life support system of Earth. National monitoring programmes are increasingly implemented to evaluate the state and trend of soils, a move which has been advocated by the Mission Soil in Europe. In soil science, frameworks have been established to interpret and communicate soil monitoring results, concentrating on the concept of quality, a term which can be interpreted in many ways. This paper explores the multifaceted meaning of soil quality, addressing its implications for future soil health assessments. It achieves this by focusing on the context of the Mission Soil. Soil health is a holistic concept embracing emergence, complexity and highlighting long-term vitality and resilience. In contrast, soil quality is often viewed through the lens of its capacity to meet specific human needs and functions, typically in a shorter timeframe. The concept of quality is assessed through indicators where the choice of framework significantly influences selection and interpretation. However, selecting appropriate soil indicators across Europe is challenging due to diverse climate, topography, geology and soil types, resulting in varied soil processes. Therefore, establishing clear principles and criteria for soil indicator selection is essential. Our paper identifies four distinct frameworks for soil quality assessment: ‘Fitness for Purpose’, ‘Free from Degradation’, ‘External Benchmarking’ and ‘Value Assessment’, with each possessing a unique role and application. Notably, the ‘Free from Degradation’ framework is emphasised for its alignment with soil protection efforts and its relevance to soil threats. This makes it particularly suitable for pan-European assessments conducted by the European Union Soil Observatory (EUSO).

Pastures and grasslands are the largest land cover of Earth's surface, comprising fundamental landscapes for water and nutrient cycling, food production, biodiversity conservation and land management in the planet. Monitoring the conditions and productivity aspects of these lands can lead to major contributions for land degradation mitigating in line with sustainable development goals defined by the United Nations (UN) 2030 agenda. Nevertheless, an operational approach able to monitor productivity of pastures and grasslands at global scale and high spatial resolution (e.g. 30-m) is a challenging research problem. Aiming to contribute with this topic, the current work present a methodology to derive 30-m bi-monthly time-series of Gross Primary Productivity (GPP) for pastures and grasslands of the world based on GLAD Landsat ARD (collection-2) and a customized Light Use Efficiency Model (LUE). The Landsat imagery were aggregated by every two months and gapfilled by an temporal interpolation based on Fast Fourier Transform (FFT). The complete, consistent and gapfilled Landsat time-series was used to estimate the Fraction of Photosynthetically Active Radiation (FPAR) and Land Surface Water Index (LSWI), which combined with 1-km MODIS temperature (MOD11A2) and 1° CERES Photosynthetically Active Radiation (SYN1deg v4.1 - PAR) images resulted in 30-m global GPP time-series product from 2000 onwards. Our preliminary validation approach, based on FLUXNET2015 data, indicated a R2 of 0.67 and RMSE of 2.06 for in-situ stations located Europe. We are working to release the first version of the product as open data (CC-BY license) in the context of the Global Pasture Watch project and the World Resources Institute's Land & Carbon Lab, establishing partnerships with local organization and research institute to collect feedback and additional validation data to improve further versions of the product.

AbstractThe paper describes the production and evaluation of global grassland extent mapped annually for 2000–2022 at 30 m spatial resolution. The dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using GLAD Landsat ARD-2 image archive, accompanied by climatic, landform and proximity covariates, spatiotemporal machine learning (per-class Random Forest) and over 2.3 M reference samples (visually interpreted in Very High Resolution imagery). Custom probability thresholds (based on five-fold spatial cross-validation) were used to derive dominant class maps with balanced user’s and producer’s accuracy, resulting in f1 score of 0.64 and 0.75 for cultivated and natural/semi-natural grassland, respectively. The produced maps (about 4 TB in size) are available under an open data license as Cloud-Optimized GeoTIFFs and as Google Earth Engine assets. The suggested uses of data include (1) integration with other compatible land cover products and (2) tracking the intensity and drivers of conversion of land to cultivated grasslands and from natural / semi-natural grasslands into other land use systems.

The paper describes the production and evaluation of global grassland dynamics mapped annually for 2000-2022 at 30~m spatial resolution. The dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using GLAD Landsat ARD-2 image archive, accompanied by climatic, landform and proximity covariates, spatiotemporal machine learning (per-class Random Forest) and over 2.3M reference samples (visually interpreted in Very High Resolution imagery). Custom probability thresholds (based on five-fold spatial cross-validation) were used to derive dominant class maps with balanced precision and recall values, 0.64 and 0.75 for cultivated and natural/semi-natural grassland, respectively. The produced maps (about 4~TB in size) are available under an open data license as Cloud-Optimized GeoTIFFs and as Google Earth Engine assets. The suggested uses of data include (1) integration with other compatible land cover products and (2) tracking the intensity and drivers of conversion of land to cultivated grasslands and from natural / semi-natural grasslands into other land use systems.

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.

Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk. In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Land degradation is a systematic loss of function in terrestrial ecosystems: a serious drop in primary productivity, biomass and biodiversity. This means the land produces less than what it is able to at its natural capacity and even eventually deteriorates completely with little to no production at all. No plants. No animals. No water. Where this occurs extensively enough, it also changes the climate. Since the start of the industrial revolution 150 years ago, land degradation has been continuously accelerating at a frightening pace. But all is not lost! We now understand how land degradation can be reversed by systematic land conservation practices, scaling up of agroecological systems, landscape-scale rehydration and massive planting of grasses, shrubs and forests, by leaning into nature and designing and building resilient ecosystems. But where is the potential for (land) restoration the highest? And how can you get involved and help re-green, re-store and expand resilient terrestrial ecosystems?

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mappedat all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGOfunded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. Inthis paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensivecompilation of soil samples (N ≈ 150, 000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and totalnitrogen (N), total carbon, Cation Exchange Capacity (eCEC), extractable — phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg),sulfur (S), sodium (Na), iron (Fe), zinc (Zn) — silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariatelayers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives)images. Our 5–fold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC=0.900) tomore poorly predictable extractable phosphorus (CCC=0.654) and sulphur (CCC=0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11,B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 mresolution covariates. Climatic data images — SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature — however, remainedas the overall most important variables for predicting soil chemical variables at continental scale. The publicly available 30–m soil maps aresuitable for numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmentalprograms, or targeting of nutrition interventions.

We introduce a software tool for optimal sampling design in the context of farm-scale soil carbon auditing, where the amount of sequestered soil carbon will be estimated from a random sample. Existing tools do not use available ancillary information, or do not have the functionality needed for farm-scale soil carbon auditing. Using a grid of predicted carbon content with associated uncertainty, the software optimises a stratified random sampling design, such that the profit is maximised on the basis of sequestered carbon price, sampling costs, and a trading parameter that balances farmer's and buyer's risks due to uncertainty of the estimated amount of sequestered carbon. As the algorithm is computationally intensive, the package is written in Julia for speed. From a case study we conclude that our software is an effective tool for farm-scale soil carbon auditing, and that it outperforms the existing tools in terms of efficiency and functionality.

Using the term "Open data" has become a bit of a fashion, but using it without clear specifications is misleading i.e. it can be considered just an empty phrase. Probably even worse is the term "Open Science" — can science be NOT open at all? Are we reinventing something that should be obvious from start? This guide tries to clarify some key aspects of Open Data, Open Source Software and Crowdsourcing using examples of projects and business. It aims at helping you understand and appreciate complexity of Open Data, Open Source software and Open Access publications. It was specifically written for producers and users of environmental data, however, the guide will likely be useful to any data producers and user.

Potential natural vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. This paper presents results of assessing machine learning algorithms-neural networks (nnet package), random forest (ranger), gradient boosting (gbm), K-nearest neighborhood (class) and Cubist-for operational mapping of PNV. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8,057 modern pollen-based site reconstructions), (2) distribution of forest tree taxa in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly fraction of absorbed photosynthetically active radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief, and lithologic variables, were used as explanatory variables. The overall results indicate that random forest gives the overall best performance. The highest accuracy for predicting BIOME 6000 classes (20) was estimated to be between 33% (with spatial cross-validation) and 68% (simple random sub-setting), with the most important predictors being total annual precipitation, monthly temperatures, and bioclimatic layers. Predicting forest tree species (73) resulted in mapping accuracy of 25%, with the most important predictors being monthly cloud fraction, mean annual and monthly temperatures, and elevation. Regression models for FAPAR (monthly images) gave an R-square of 90% with the most important predictors being total annual precipitation, monthly cloud fraction, CHELSA bioclimatic layers, and month of the year, respectively. Further developments of PNV mapping could include using all GBIF records to map the global distribution of plant species at different taxonomic levels. This methodology could also be extended to dynamic modeling of PNV, so that future climate scenarios can be incorporated. Global maps of biomes, FAPAR and tree species at one km spatial resolution are available for download via http://dx.doi.org/10.7910/DVN/QQHCIK.

We introduce Julia package ospats+ for optimal sampling design in the context of farm-scale soil carbon auditing. The main difference with package ospats is that ospats+ maximises the expected profit for the farmer, rather than the statistical criterion of estimation precision. The package is written in Julia for speed of computation. Our methodology has been discussed in general terms by de Gruijter et al. (2016), here we go into the computational aspects. Using a grid of predicted carbon content with associated uncertainty, we optimise a stratified random sampling design: number of strata, stratification of the grid, total sample size and sample sizes within strata. The expected profit is maximised on the basis of sequestered carbon price, sampling costs, and a trading parameter that balances farmer's and buyer's risks due to uncertainty of the estimated amount of sequestered carbon. The core of the methodology is optimisation of the stratification by the Ospats method (de Gruijter et al., 2015), an iterative procedure that re-allocates grid points to strata on the basis of pairwise generalised distances between grid points. The distances are a function of the locations, the predictions and the covariances of the prediction errors. We illustrate the use of ospats+ with an application to an Australian farm.

Soil organic carbon content in × 5 g / kg (to convert to % divide by 2) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. The maps are provided using Byte type to significantly reduce file size. Predicted from a global compilation of soil points. Also available for download: soil organic stock maps in in kg / m2 (https://doi.org/10.5281/zenodo.1475453) and bulk density maps in kg / m3 (https://doi.org/10.5281/zenodo.1475970). Processing steps are described in detail here. Antarctica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, organic.carbon = variable: soil organic carbon content in x 5 g / kg, usda.6a1c = determination method: laboratory method code, m = mean value, 250m = spatial resolution / block support: 250 m, b10..10cm = vertical reference: 10 cm depth below surface, 1950..2017 = time reference: period 1950–2017, v0.2 = version number: 0.2,

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0-30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable-phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms- random forest and gradient boosting, as implemented in R packages ranger and xgboost-and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40-85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatiotemporal statistical modeling framework.

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

A novel method for soil carbon auditing at farm scale based on data value is presented. Using a map of carbon content with associated uncertainty, it optimizes stratified random sampling: number of strata, stratum boundaries, total sample size and sample sizes within strata. The optimization maximizes the expected profit for the farmer on the basis of sequestered carbon price, sampling costs, and a trading parameter that balances farmer's and buyer's risks due to uncertainty of the estimated amount of sequestered carbon. The stratification is optimized by a novel method (Ospats), an iterative procedure that re-allocates grid points to strata on the basis of pairwise differences between predictions and covariances of prediction errors. Optimal sample sizes are calculated from variance predictions by Ospats. An application on an Australian farm has shown that soil carbon changes across farms and regions can be audited effectively using the proposed method. It is concluded that sample bulking and returning to the same sites in subsequent sampling rounds are not recommendable.