Information on soil properties is crucial for soil preservation, the improvement of food security, and the provision of ecosystem services. In particular, for the African continent, spatially explicit information on soils and their ability to sustain these services is still scarce. To address data gaps, infrared spectroscopy has achieved great success as a cost-effective solution to quantify soil properties in recent decades. Here, we present a mid-infrared soil spectral library (SSL) for central Africa (CSSL) that can predict key soil properties, allowing for future soil estimates with a minimal need for expensive and time-consuming wet chemistry. Currently, our CSSL contains over 1800 soil samples from 10 distinct geoclimatic regions throughout the Congo Basin and along the Albertine Rift. For the analysis, we selected six regions from the CSSL, for which we built predictive models for total carbon (TC) and total nitrogen (TN) using an existing continental SSL (African Soil Information Service, AfSIS SSL; n=1902) that does not include central African soils. Using memory-based learning (MBL), we explored three different strategies at decreasing degrees of geographic extrapolation, using models built with (1) the AfSIS SSL only, (2) AfSIS SSL combined with the five remaining central African regions, and (3) a combination of AfSIS SSL, the remaining five regions, and selected samples from the target region (spiking). For this last strategy we introduce a method for spiking MBL models. We found that when using the AfSIS SSL only to predict the six central African regions, the root mean square error of the predictions (RMSE_pred) was between 3.85–8.74 and 0.40–1.66 g kg⁻¹ for TC and TN, respectively. The ratio of performance to the interquartile distance (RPIQ_pred) ranged between 0.96–3.95 for TC and 0.59–2.86 for TN. While the effect of the second strategy compared to the first strategy was mixed, the third strategy, spiking with samples from the target regions, could clearly reduce the RMSE_pred to 3.19–7.32 g kg⁻¹ for TC and 0.24–0.89 g kg⁻¹ for TN. RPIQ_pred values were increased to ranges of 1.43–5.48 and 1.62–4.45 for TC and TN, respectively. In general, predicted TC and TN for soils of each of the six regions were accurate; the effect of spiking and avoiding geographical extrapolation was noticeably large. We conclude that our CSSL adds valuable soil diversity that can improve predictions for the Congo Basin region compared to using the continental AfSIS SSL alone; thus, analyses of other soils in central Africa will be able to profit from a more diverse spectral feature space. Given these promising results, the library comprises an important tool to facilitate economical soil analyses and predict soil properties in an understudied yet critical region of Africa. Our SSL is openly available for application and for enlargement with more spectral and reference data to further improve soil diagnostic accuracy and cost-effectiveness.

Summary: The lack of field‐based data in the tropics limits our mechanistic understanding of the drivers of net primary productivity (NPP) and allocation. Specifically, the role of local edaphic factors – such as soil parent material and topography controlling soil fertility as well as water and nutrient fluxes – remains unclear and introduces substantial uncertainty in understanding net ecosystem productivity and carbon (C) stocks. Using a combination of vegetation growth monitoring and soil geochemical properties, we found that soil fertility parameters reflecting the local parent material are the main drivers of NPP and C allocation patterns in tropical montane forests, resulting in significant differences in below‐ to aboveground biomass components across geochemical (soil) regions. Topography did not constrain the variability in C allocation and NPP. Soil organic C stocks showed no relation to C input in tropical forests. Instead, plant C input seemingly exceeded the maximum potential of these soils to stabilize C. We conclude that, even after many millennia of weathering and the presence of deeply developed soils, above‐ and belowground C allocation in tropical forests, as well as soil C stocks, vary substantially due to the geochemical properties that soils inherit from parent material.

The African Tropics are hotspots of modern-day land use change and are, at the same time, of great relevance for the cycling of carbon (C) and nutrients between plants, soils, and the atmosphere. However, the consequences of land conversion on biogeochemical cycles are still largely unknown as they are not studied in a landscape context that defines the geomorphic, geochemical, and pedological framework in which biological processes take place. Thus, the response of tropical soils to disturbance by erosion and land conversion is one of the great uncertainties in assessing the carrying capacity of tropical landscapes to grow food for future generations and in predicting greenhouse gas fluxes from soils to the atmosphere and, hence, future earth system dynamics. Here we describe version 1.0 of an open-access database created as part of the project “Tropical soil organic carbon dynamics along erosional disturbance gradients in relation to variability in soil geochemistry and land use” (TropSOC). TropSOC v1.0 (Doetterl et al., 2021, https://doi.org/10.5880/fidgeo.2021.009) contains spatially and temporally explicit data on soil, vegetation, environmental properties, and land management collected from 136 pristine tropical forest and cropland plots between 2017 and 2020 as part of monitoring and sampling campaigns in the eastern Congo Basin and the East African Rift Valley system. The results of several laboratory experiments focusing on soil microbial activity, C cycling, and C stabilization in soils complement the dataset to deliver one of the first landscape-scale datasets to study the linkages and feedbacks between geology, geomorphology, and pedogenesis as controls on biogeochemical cycles in a variety of natural and managed systems in the African Tropics. The hierarchical and interdisciplinary structure of the TropSOC database allows linking of a wide range of parameters and observations on soil and vegetation dynamics along with other supporting information that may also be measured at one or more levels of the hierarchy. TropSOC's data mark a significant contribution to improve our understanding of the fate of biogeochemical cycles in dynamic and diverse tropical African (agro-)ecosystems. TropSOC v1.0 can be accessed through the Supplement provided as part of this paper or as a separate download via the websites of the Congo Biogeochemistry Observatory and GFZ Data Services where version updates to the database will be provided as the project develops.

Heterotrophic soil respiration is an important component of the global terrestrial carbon (C) cycle, driven by environmental factors acting from local to continental scales. For tropical Africa, these factors and their interactions remain largely unknown. Here, using samples collected along topographic and geochemical gradients in the East African Rift Valley, we study how soil chemistry and fertility drive soil respiration of soils developed from different parent materials even after many millennia of weathering. To address the drivers of soil respiration, we incubated soils from three regions with contrasting geochemistry (mafic, felsic and mixed sediment) sampled along slope gradients. For three soil depths, we measured the potential maximum heterotrophic respiration under stable environmental conditions and the radiocarbon content (Δ ¹⁴C) of the bulk soil and respired CO₂. Our study shows that soil fertility conditions are the main determinant of C stability in tropical forest soils. We found that soil microorganisms were able to mineralize soil C from a variety of sources and with variable C quality under laboratory conditions representative of tropical topsoil. However, in the presence of organic carbon sources of poor quality or the presence of strong mineral-related C stabilization, microorganisms tend to discriminate against these energy sources in favour of more accessible forms of soil organic matter, resulting in a slower rate of C cycling. Furthermore, despite similarities in climate and vegetation, soil respiration showed distinct patterns with soil depth and parent material geochemistry. The topographic origin of our samples was not a main determinant of the observed respiration rates and Δ ¹⁴C. In situ, however, soil hydrological conditions likely influence soil C stability by inhibiting decomposition in valley subsoils. Our results demonstrate that, even in deeply weathered tropical soils, parent material has a long-lasting effect on soil chemistry that can influence and control microbial activity, the size of subsoil C stocks and the turnover of C in soil. Soil parent material and its control on soil chemistry need to be taken into account to understand and predict C stabilization and rates of C cycling in tropical forest soils.

Waterlogging in agriculture poses severe threats to soil properties, crop yields, and farm profitability. Remote sensing data coupled with drainage systems offer solutions to monitor and manage waterlogging in agricultural systems. However, implementing agricultural projects such as drainage is associated with high uncertainty and risk, with substantial negative impacts on farm profitability if not well planned. Cost–benefit analyses can help allocate resources more effectively; however, data scarcity, high uncertainty, and risks in the agricultural sector make it difficult to use traditional approaches. Here, we combined a wide range of field and remote sensing data, unsupervised machine learning, and Bayesian probabilistic models to: (1) identify potential sites susceptible to waterlogging at the farm scale, and (2) test whether the installation of drainage systems would yield a positive benefit for the farmer. Using the K-means clustering algorithm on water and vegetation indices derived from Sentinel-2 multispectral imagery, we were able to detect potential waterlogging sites in the investigated field (elbow point = 2, silhouette coefficient = 0.46). Using a combination of the Bayesian statistical model and the A/B test, we show that the installation of a drainage system can increase farm profitability by 1.7 times per year compared to the existing farm management. The posterior effect size associated with yield, cropping area, and time (year) was 0.5, 1.5, and 1.9, respectively. Altogether, our results emphasize the importance of data-driven decision-making for agriculture project planning and resource management in the wake of smart agriculture for food security and adaptation to climate change.

The African Tropics are hotspots of modern-day land-use change and are, at the same time, of great relevance for the cycling of carbon (C) and nutrients between plants, soils and the atmosphere. However, the consequences of land conversion on biogeochemical cycles are still largely unknown as they are not studied in a landscape context that defines the geomorphic, geochemically and pedological framework in which biological processes take place. Thus, the response of tropical soils to disturbance by erosion and land conversion is one of the great uncertainties in assessing the carrying capacity of tropical landscapes to grow food for future generations and in predicting greenhouse gas fluxes (GHG) from soils to the atmosphere and, hence, future earth system dynamics. Here, we describe version 1.0 of an open access database created as part of the project “Tropical soil organic carbon dynamics along erosional disturbance gradients in relation to variability in soil geochemistry and land use” (TropSOC). TropSOC v1.0 contains spatial and temporal explicit data on soil, vegetation, environmental properties and land management collected from 136 pristine tropical forest and cropland plots between 2017 and 2020 as part of several monitoring and sampling campaigns in the Eastern Congo Basin and the East African Rift Valley System. The results of several laboratory experiments focusing on soil microbial activity, C cycling and C stabilization in soils complement the dataset to deliver one of the first landscape scale datasets to study the linkages and feedbacks between geology, geomorphology and pedogenesis as controls on biogeochemical cycles in a variety of natural and managed systems in the African Tropics. The hierarchical and interdisciplinary structure of the TropSOC database allows for linking a wide range of parameters and observations on soil and vegetation dynamics along with other supporting information that may also be measured at one or more levels of the hierarchy. TropSOC’s data marks a significant contribution to improve our understanding of the fate of biogeochemical cycles in dynamic and diverse tropical African (agro-)ecosystems. TropSOC v1.0 can be accessed through the supplementary material provided as part of this manuscript or as a separate download via the websites of the Congo Biogeochemistry observatory and the GFZ data repository where version updates to the database will be provided as the project develops.