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.

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

Characterizing soil engineering properties and analyzing their spatial pattern has a key role in managing soils for dif- ferent land uses. A study was conducted to generate two soil engineering properties; shear strength (SS) and friction angle (FA) both related to slope stability from the database of soil agricultural indices. A total of 30 soils were analyzed in two batches of 15 for physicochemical and engineering properties. The first batch was subjected to correlation and regression analysis among properties, whilst the second was used to validate model predictions. Soil friction angle showed strong significant correlations with clay and sand percent. Further stepwise regression resulted in these two properties being the only predictors of peak and residual friction angle. None of the tested properties explained shear strength distribution among the soils. The validated model predicted friction angles for the larger database, which showed non-significant temporal differences from the present dataset used in this study. Spatially distribution of both peak and residual friction angles varied across Trinidad, higher friction angles being associated with higher slopes. Combination of this data with other spatial land attributes would greatly improve land management and slope stability prediction

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The Republic of Trinidad and Tobago is an archipelagic Small Island Developing State (SIDS), situated on the southern end of the chain of Caribbean islands. Several factors such as climate, topography, and hydrological characteristics increase its susceptibility and vulnerability to flooding which results in adverse socio-economic impacts. Many Caribbean islands, including Trinidad and Tobago lack a flood risk assessment tool which is essential for a proactive mitigation approach to floods, specifically in the Caribbean due to the incommensurate flooding events that occur because of the inherent characteristics of SIDS. This research focuses on the problem of flooding using susceptibility analysis, vulnerability analysis and risk assessment for the island of Trinidad, whilst also presenting a repeatable and appropriate methodology to assess these risks in regions that have similar characteristics to Trinidad. This is especially useful in Caribbean countries because of a lack of internal human capacity to support such efforts. Flood hazard indexes (FHI) and vulnerability indexes (VI) were generated for this study using subjective and objective weighting technique models to identify regions that are affected by flooding. These models were Analytical Hierarchy Process (AHP), Frequency Ratio (FR) and Shannon’s Entropy (SE). Comparative analyses of the three models were conducted to assess the efficacy and accuracy of each to determine which is most suitable. These were used to conduct a risk assessment to identify risks associated with each Regional Corporation of Trinidad. Results indicate that FR is the most accurate weighting technique model to assess flood susceptibility and risk assessment in Trinidad, with an Area Under the Curve (AUC) of 0.76 and 0.64 respectively. This study provides an understanding of the most appropriate weighting techniques that can be used in regions where there are challenges in accessing comprehensive data sets and limitations as it relates to access to advanced technology and technical expertise. The results also provide reasonably accurate outcomes that can assist in identifying priority areas where further quantitative assessments may be required and where mitigation and management efforts should be focused. This is critical for SIDS where vulnerability to flooding is high while access to financial and human resources is limited.

The COVID-19 pandemic impacts have arguable been more pronounced in the developing world, such as the Small Island States (SIDS) of the Caribbean, where a plethora of geophysical and socio-political factors have led to increased vulnerability, particularly in fragile sectors such as agriculture. The pandemic added another layer of complexity to the unstable agri-food systems of SIDS in the Caribbean. Measures to contain the unfolding crisis have tremendously disrupted food systems by threatening the production, distribution, and marketing of commodities which exposed the frailty of the region's food security. Caribbean SIDS are highly dependent on food imports and relies on international markets to secure food. Many are also dependent on agricultural exports and have a large portion of their population involved in agriculture making them particularly vulnerable to the rigors of the pandemic. Export restrictions on foodstuff and prohibitions due to lockdowns and border closures further exacerbated these challenges. Additionally, food and nutrition security in the region is also subjected to the effects of climate change and climate-related disasters. Dealing with the impacts of co-occurring disasters is, therefore, an ever-present threat. This study examines the impact of COVID-19 on the agri-food supply in the Caribbean. It also identified measures and initiatives adopted to cope with these disruptive consequences. The study involves the use of internet-based surveys and focus group discussions and internet-based surveys with stakeholders and online searches for related literature. A total of 96 farmers, 60 food distributors, 84 food service operators, and 237 consumers from the region participated in the online survey and 4 focus group discussions between January and November 2021. The results confirmed that the impacts of the COVID-19 pandemic were evident along the entire agri-food supply chain and numerous challenges and shocks were identified across all participating groups and countries. Some challenges and shocks such as loss of income and related challenges including lower sales and loss of markets affected all groups in the study but to varying degrees and based on socio-demographic factors. In general people of lower income status and smaller businesses were more susceptible to the negative impacts of the pandemic.

Caribbean countries share unique features such as small size, geographical location, limited natural resources, low economic status aligned with ambitious developmental agendas, all of which influences their vulnerability to natural disasters. Agriculture and tourism are the main economic drivers for Caribbean states. Notably, both these sectors are highly prone to natural disasters. Other sectors including forestry, biodiversity, coastal resources and inland water resources are also susceptible to climatic hazards. The eroding natural resource base aligned to these sectors demands appropriate management. Risk assessment is integral in planning and preparing for natural hazards. Several methods have been used in the Caribbean with varying success. Two successful examples are the Land Degradation Assessment (LADA) conducted in Grenada and the Landslide Mapping in Trinidad. The LADA project geospatially quantified the extent of land degradation and presented data in support of natural resource management. The Caribbean Disaster Emergency Management Agency (CDEMA) was a milestone establishment for regional disaster management. Introduction and implementation of the Comprehensive Disaster Management (CDM) strategy transformed disaster management from simply response and recovery, to include preparedness, prevention and mitigation. This approach included the appointment of national focal points in all participating countries, a feature that aimed to build and improve communication channels. Whilst mostly positive, the present approach has also showcased limitations to long term sustainability. Most islands lack effective governance structures with a dedicated budget to disaster management and where available, activities are centrally operated. Improving social resilience through community engagement is seen as critical to the success of CDM. Social media has also been shown to add real value to networking and communication in disaster management.

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