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Diaz-Vallejo et al. Carbon Footprints 2026, 5, 12 Page 17 of 23

management, and residue, factors not captured in our dataset but known to strongly influence carbon
accumulation . The dominance of climatic and soil factors in our models also suggests that intensive
[75]
management homogenizes surface conditions, thereby reducing the sensitivity of SOC to environmental
gradients.

In contrast, pasture lands were predominantly influenced by soil properties, particularly soil suborder, silt +
clay content, and pH, which together explained 45% of the SOC variance. Neither MAT nor MAP were
significant predictors, contrasting with the global meta-analysis by Dlamini et al. , which showed that
[76]
climate conditions influence the magnitude of SOC responses to grassland degradation. This decoupling of
SOC from climate likely reflects the moderating effect of permanent vegetation cover and grazing intensity
on organic matter turnover. Fine-textured soils are expected to promote greater SOC content due to the
stabilization of organic matter by clay and silt particles, protecting it from decomposition [72,76] . The remaining
unexplained variance may be attributed to differences in grazing pressure, compaction, and nutrient inputs,
factors that shape SOC cycling but were not captured in our dataset.

Our findings also highlight the importance of soil suborders as a predictor of SOC, especially in forests and
pastures. While soil order classification integrates broad pedogenic processes, it often masks critical
variations related to moisture and temperature regimes or landforms . Suborder-level differentiation
[41]
[33]
captures these finer environmental controls and therefore provides a more precise framework for
interpreting SOC dynamics in tropical landscapes. Whereas Vaughan et al. reported strong relationships
[7]
between SOC and soil order, our results suggest that suborder-level information, linked to local formation
environments, offers improved explanatory power when assessing SOC concentrations across a diversity of
tropical soils.

Benchmark models for predicting SOC values regionally
Benchmark models for predicting SOC at regional scales are essential for assessing soil health and guiding
agricultural and soil conservation decisions. In this study, we evaluated two models, the Soil Health Gap and
the Scores Benchmark, to test their applicability in tropical systems with diverse climates, soil environments,
and vegetation types. Both frameworks serve distinct purposes: the Gap Benchmark identifies depletion or
recovery relative to reference sites, while the Scores Benchmark quantifies SOC status within the broader
regional distribution. Our results show that each has value and also inherent limitations when applied across
the heterogeneous landscapes of the tropics.

The Scores Benchmark offers the advantage of translating observed variability into quantitative scores,
allowing users to position their SOC values within the expected range for specific environmental
conditions.Score-based soil assessment frameworks, including Soil Management Assessment Framework
(SMAF ;), Comprehensive Assessment of Soil Health (CASH ;), and the more recent Soil Health
[23]
[22]
Assessment Protocol and Evaluation (SHAPE ;) approach, demonstrate how benchmarking can provide
[24]
regionally relevant, quantitative metrics that account for edaphic and climatic context and respond to
management practices.

In our dataset, soil type, land use, USDA texture class, mean annual temperature, and pH collectively
explained about ~50%-60% of SOC variability. Despite excluding explicit management data, the Scores
Benchmark achieved moderate predictive accuracy, indicating that environmental variables alone capture
much of the SOC variability across a range of soils of the tropics. Future developments should integrate
information on tillage, crop type, grazing intensity, and residue management to refine accuracy and extend
applicability. Given its ability to represent multiple controlling factors simultaneously and function across
scales, the Scores Benchmark provides a more flexible and transferable approach for soil health assessment in