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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.

Unlike peripheral lower extremity vascular disease, upper extremity vascular disease is relatively uncommon. While atherosclerosis and embolic disease are the most common causes of upper extremity ischemia, a wide variety of systemic diseases and anatomic abnormalities can affect the upper extremity. Upper extremity ischemia poses a significant diagnostic and therapeutic challenge for both clinicians and radiologists. Although history and physical examination remain the mainstays of diagnosis, imaging can be vital in confirming suspected disease and guiding treatment planning. Digital subtraction angiography is often the preferred method for detection of upper extremity vascular disease, particularly for characterization of complex arteriovenous anatomy such as in vascular malformations and for evaluation of dialysis fistulas and grafts. However, this modality is invasive, requires iodinated contrast agents and radiation, and may fail to demonstrate significant extraluminal disease. More recently, magnetic resonance (MR) angiography techniques have made important advances, permitting higher temporal and spatial resolution that is preferable for diagnosing upper extremity vascular disorders. In this review, the authors present an overview of upper extremity MR angiography techniques and protocols, revisit the often variable vascular anatomy of the arm and hand, and offer examples of various pathologic entities diagnosed with MR angiography. Finally, several imaging pitfalls that one must be aware of for accurate diagnosis are illustrated and reviewed.

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