Abstract
Abstract. 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 (RMSEpred) 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 (RPIQpred) 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 RMSEpred to 3.19–7.32 g kg−1 for TC and 0.24–0.89 g kg−1 for TN. RPIQpred 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.
Highlights
Soil health is critical to crop nutrition, agricultural production, food security, erosion prevention, and climate change mitigation via carbon storage
These components indicate that the majority of CSSL samples lie within the spectral domains of the Africa Soil Information Service (AfSIS) soil spectral library (SSL) as their principal component analysis (PCA) scores overlap
This overlapping is, less evident for the spectra of the South Kivu region and, to a lesser extent, for the samples of the Iburengerazuba and Tshuapa regions, which suggests that the type of soils in these regions may not be well represented by the AfSIS SSL compared to the other regions
Summary
Soil health is critical to crop nutrition, agricultural production, food security, erosion prevention, and climate change mitigation via carbon storage. Despite the expected severity of these impacts, our understanding of the effects on soils in the humid tropics of Africa is limited by sparse data and uneven distribution of low-latitude research. Within the tropics, both the future impacts and data gaps are most severe in the Congo Basin, which contains the second largest tropical forest ecosystem on Earth, represents a considerable reservoir of soil carbon, and is critically endangered by fast deforestation (Hansen et al, 2013). The human population in Uganda, Rwanda, and the DRC is projected to more than double in the coming 80 years (Vollset et al, 2020) Such dramatic growth will likely contribute to further conversion of forest to agricultural land. Accessibility of such data is limited, and gaps are still large in central Africa (Van Ranst et al, 2010), in part due to the high cost of specialized equipment and chemicals for analyses, limited accessibility to sampling areas, and lack of infrastructure
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