The World Resources Institute (WRI) is a global research non-profit organization established in 1982 with funding from the MacArthur Foundation under the leadership of James Gustave Speth. WRI's activities are focused on seven areas: food, forests, water, energy, cities, climate and ocean.
Forest and landscape restoration (FLR) represents a critical nexus of climate change mitigation, biodiversity conservation, and sustainable development. Despite substantial federal investments and commitments, empirical subnational research quantifying the relationships between governance structures, funding mechanisms, and restoration outcomes remains scarce, and integrated implementation frameworks bridging institutional, technical, and socio-economic dimensions are largely absent from the literature. This study presents a mixed-methods analysis of FLR implementation gaps across Maryland, Virginia, and West Virginia. Three Mid-Atlantic Appalachian states selected for their contrasting ecological conditions, governance structures, and restoration trajectories that collectively represent the heterogeneity of subnational restoration challenges. We examined 147 restoration projects (2019-2024), conducted 25 stakeholder interviews, and analyzed federal funding allocations ($428 million) through spatial and temporal frameworks. Our findings reveal five critical implementation barriers: (1) policy incoherence across federal-state-local jurisdictions creating 34% project delays; (2) chronic underfunding with 63% of projects receiving less than 60% of planned budgets; (3) technical capacity deficits affecting 71% of rural communities; (4) inadequate stakeholder engagement mechanisms reducing project sustainability by 45%; and (5) insufficient monitoring frameworks limiting adaptive management. We introduce an Integrated Restoration Implementation Framework (IRIF) that uniquely integrates policy coordination, sustainable financing, technical capacity building, and community engagement within a unified adaptive management cycle, operationalized through empirically derived thresholds, to guide evidence-based interventions. Quantitative analyses demonstrate that multi-stakeholder governance models increase restoration success rates by 2.3-fold (p < 0.001), while integrated funding mechanisms improve long-term sustainability by 67%. Theoretically, this study advances socio-ecological systems scholarship by providing empirical evidence that multi-scalar governance configurations and integrated stakeholder engagement mechanisms are principal determinants of restoration success, advancing the evidence base for adaptive governance approaches in complex federal systems. Our findings provide actionable intelligence for policymakers and practitioners, while underscoring that sustainable FLR in complex federal systems depends on coherent multi-level governance architectures coordinating institutional mandates, financial resources, technical capacity, and community agency across jurisdictional scales.
Oil palm smallholders in Indonesia use low amounts of fertilizer, leading to nutrient deficiencies and low yields and profits. This study examines the reasons for limited fertilizer use to identify opportunities for improving fertilizer management. This is vital to support smallholder livelihoods and to reduce the need to clear new land for oil palm cultivation. We explored agronomic and socio-economic factors influencing fertilizer use in oil palm smallholder fields in Indonesia. Methods included farmer diaries (n = 958 fields), surveys, and in-depth interviews (n = 44), conducted across six oil palm regions in Indonesia over three years (Jan 2020–Dec 2022). We used these data to calculate a nutrient score relative to attainable yield, and to identify enabling and constraining factors for implementing good fertilizer management. We found that half of the smallholders did not apply fertilizer, while the rest applied low doses and achieved low nutrient scores. Factors explaining low fertilizer use included financial constraints, limited fertilizer availability, and logistical issues. We show that, despite potential negative consequences for future yields and income, smallholders postponed fertilizer application in response to high fertilizer prices. Conversely, this study indicates that farmers who had access to credit, via oil palm collectors and/or farmer groups, used more fertilizer, and achieved higher nutrient scores. Enhancing fertilizer use among oil palm smallholders to prevent yield decline and income loss requires policy interventions to increase knowledge of sustainable fertilizer management among smallholders and fertilizer suppliers, as well as measures to facilitate access to credit, and ensure the timely availability of good-quality fertilizer.
Natural ecosystems are increasingly threatened by global agricultural supply chains, and a narrow policy focus on forests has fueled agricultural expansion into ecologically significant but severely overlooked non-forest ecosystems, including grasslands and open wetlands. While a few emerging policies attempt to protect non-forest ecosystems, a globally consistent assessment of their conversion extent and drivers, especially related to livestock production and commodity-specific supply chain demand, remains lacking. Here, we conducted a spatially explicit analysis to identify pasture and cropland expansion into non-forest ecosystems between 2005 and 2020, as well as conversion-linked primary agricultural commodities and their underlying demand drivers (end uses and final market destinations). We found that the conversion rate of natural non-forest ecosystems was nearly four times that of lands with tree cover exceeding 5 m (a common forest height threshold), with Brazil contributing 13% of the global total and Russia, India, China, and the United States each contributing about 6%. While drivers varied greatly across regions, globally 50% of the conversion was linked to pasture, and 27, 17, and 6% to cropland for food, feed, and other uses (mainly bioenergy), respectively. Among conversion-linked commodities, most livestock-associated products served domestic demand, while 32% of feed crops and 20% of all crops were exported, with export shares reaching 70 to 80% in Brazil and Argentina. These findings reveal important areas for non-forest ecosystem conservation and highlight the need for integrated policies to prevent leakage across different ecosystems and different sustainable development goals while also aligning local actions with global supply chain governance.
There is increasing interest in global dynamic soil information with changes in soil properties mapped over time and at high spatial resolution. Thanks to long-term, multi-temporal, and fine- and medium-resolution satellite missions such as Landsat, MODIS, Copernicus Sentinel and similar, it is possible to produce globally consistent predictions of key soil variables that match other 10–30 m spatial resolution global data sets. This paper describes data preparation, modeling, and production of OpenLandMap-soildb: global dynamic predictions of soil organic carbon content, soil organic carbon density, bulk density, soil pH in H2O, soil texture fractions (clay, sand and silt) and USDA subgroup soil types (USDA soil taxonomy subgroups) at 30 m spatial resolution based on spatiotemporal Machine Learning (Quantile Regression Random Forest with output predictions showing the mean plus the 68 % probability lower and upper prediction intervals). To train the models, a large compilation of soil samples imported from legacy soil projects was used: 216 000 soil samples with soil carbon density (kg m−3), 408 000 soil samples with soil carbon content (g kg−1), 272 000 soil samples with soil pH in H2O, 363 000 soil samples with clay, silt and sand content (%) and 134 000 samples with bulk density oven dry (t m−3). Soil carbon and soil pH were mapped with 5-year time-intervals; soil texture fractions, bulk density, and soil types were mapped for recent years only. The cross-validation results indicate Root Mean Square Error (RMSE) of 17.7 (kg m−3; 0.486 in log-scale) and Concordance Correlation Coefficient (CCC) of 0.88 for SOC density, RMSE of 51.3 (g kg−1; 0.574 in log-scale) and CCC of 0.87 for SOC content, RMSE of 0.15 (t m−3) and CCC of 0.92 for bulk density of fine-earth, RMSE of 0.51 and CCC of 0.91 for soil pH, RMSE of 8.4 % and CCC of 0.87 for soil clay content, and RMSE of 12.6 % and CCC of 0.84 for soil sand content respectively. The most important variables for predicting soil organic carbon density (kg m−3) were: soil depth, Landsat-based uncalibrated Gross Primary Productivity (GPP), Normalized Difference Vegetation Index (NDVI) and CHELSA bioclimatic indices. The global distribution of soil pH can be primarily explained by the CHELSA Aridity Index (long-term), annual precipitation, and salinity grade. The global stocks for 2020–2022+ period for 0–30 cm depth interval are estimated at 461 Pg (Peta grams); the results further indicate that, in the last 25 years, the world has lost at least 11 Pg of SOC in the top soil. Suggestions are made on how to set up global permanent monitoring stations to accurately track land degradation and enable land restoration projects. The training data set is available at https://doi.org/10.5281/zenodo.4748499 (Hengl and Gupta, 2025), while the resulting data products can be accessed at https://doi.org/10.5281/zenodo.15470431 (Consoli et al., 2025) and https://world.soils.app (OpenGeoHub Foundation, 2026). Both datasets are released under a CC-BY license.
Accurate canopy height information is essential for quantifying forest carbon, monitoring restoration and degradation, and assessing habitat structure, yet high-fidelity measurements from airborne laser scanning (ALS) remain unevenly available globally. Here we present Canopy Height Map version 2 (CHMv2), a global, meter-resolution canopy height map derived from high-resolution optical satellite imagery using a depth estimation model built on DINOv3 and trained against ALS canopy height models. Compared to existing products, CHMv2 substantially improves accuracy, reduces bias in tall forests, and better preserves fine-scale structure such as canopy edges and gaps. These gains are enabled by a large expansion of geographically diverse training data, automated data curation and registration, and a loss formulation and data sampling strategy tailored to canopy height distributions. We validate CHMv2 against independent ALS test sets and against tens of millions of GEDI and ICESat-2 observations, demonstrating consistent performance across major forest biomes.
