Abstract. Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM). We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 × 5 km pixel resolution) were obtained from ISRIC -World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ∼ 1 and ∼ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ∼ 39 % of SOC variability from repeated 5-fold cross-validation. We report a combined SOC stock of 77.8 ± 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 ± 16.5 Pg) and croplands (13 ± 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 ± 42.2 and 76.8 ± 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates.

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Environmental context. Freshwater dissolved organic matter is a complex chemical mixture central to many environmental processes, including carbon and nitrogen cycling. Questions remain, however, as to its chemical characteristics, sources and transformation mechanisms. We studied the nature of dissolved organic matter in a lake system and found that it is influenced by anthropogenic activities. Human activities can therefore influence the huge amounts of carbon sequestered in lakes as dissolved organic matter.Abstract. Freshwater dissolved organic matter (DOM) is a complex mixture of chemical components that are central to many environmental processes, including carbon and nitrogen cycling. However, questions remain as to its chemical characteristics, sources and transformation mechanisms. Here, we employ 1-and 2-D nuclear magnetic resonance (NMR) spectroscopy to investigate the structural components of lacustrine DOM from Ireland, and how it varies within a lake system, as well as to assess potential sources. Major components found, such as carboxyl-rich alicyclic molecules (CRAM) are consistent with those recently identified in marine and freshwater DOM. Lignin-type markers and protein/peptides were identified and vary spatially. Phenylalanine was detected in lake areas influenced by agriculture, whereas it is not detectable where zebra mussels are prominent. The presence of peptidoglycan, lipoproteins, large polymeric carbohydrates and proteinaceous material supports the substantial contribution of material derived from microorganisms. Evidence is provided that peptidoglycan and silicate species may in part originate from soil microbes.

Harvesting wild food is an important coping strategy to deal with food insecurity in farming households across the Caribbean. The practice is tightly connected to the region's unique agrarian history, food heritage, traditional cuisine, and local knowledge of wild or semidomesticated plants. In Jamaica, small-scale farmers are the chief stewards of agrobiodiversity, and their food security and well-being are often dependent on wild food harvest. Yet, there is a paucity of empirical research on the relationship between wild food use, food security, and biodiversity conservation. In this paper, we use the knowledge and lived experience of rural farmers in a remote community (Millbank) at the edge of the Blue and John Crow Mountains National Park (BJMNP) to explore the relationship between wild food harvest and food insecurity within the context of protected area management. Specifically, we seek to (1) characterize different patterns of wild food harvest; (2) examine the relationship between food insecurity and wild food harvest, and (3) explore the implications of forest conservation measures for wild food harvest. Detailed interviews were conducted with 43 farmers to capture data on food insecurity, wild food collection, livelihood satisfaction, household characteristics, farming activities, livelihood strategies, and forest resource interaction. The Food Insecurity Experience Scale (FIES) was used to characterize food insecurity, while participatory techniques were used to develop indicators to assess the well-being of farmers. The results show strong evidence of a relationship between wild food harvest and food insecurity (p < 0.001). Overall, the findings support the importance of wild foods to the well-being of rural households and provide empirical evidence for its inclusion in food security, poverty, and biodiversity conservation policies.

Abundant wastes from the food and drink supply chain are valuable and infrequently used as anaerobic digestion (AD) substrates. This study quantifies their biomethane potential to contribute to solid waste reduction and energy production. 29 organic materials were evaluated: energy crops (6), pre-treated agricultural by-products (5), livestock slurries (3), agro-industrial wastes (7), fruit and vegetable wastes (4) and codigestion mixtures of chicken litter (CL) and fruit wastes (4). Results showed highest biogas yields for rendered fat washings (1379 ± 125 mL/g VS feedstock), fish waste (898 ± 107 mL/g VS feedstock) and potato waste (768 ± 27 mL/g VS feedstock). Synergistic benefits of co-digestion were evidenced. CL (20%) with avocado pulp (80%) led to 84% higher biogas than expected from contribution of single substrates.

Abstract. Country-specific soil organic carbon (SOC) maps are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM). We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included: support vector machines, random forest, kernel weighted nearest neighbors, partial least squares regression, and regression-Kriging based on stepwise multiple linear models. Country-specific training data and SOC predictors (5 × 5 km pixel resolution) were obtained from ISRIC-World-Soil-Information-System. In general, temperature, soil type, vegetation indices and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific data scenarios and were able to explain ~ 53 % of SOC variability (range

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