Dataset variability and carbonate concentration influence the performance of local visible-near infrared spectral models

The application of visual and near infrared soil spectroscopy (vis–NIR) is an easy and cost-efficient way to gain a wide variety of soil information to cover high spatial and temporal resolution in large-scale soil surveys and in local field-scale studies. However, unlike for conventional methods, the prediction accuracy of vis–NIR spectral models cannot yet be estimated before the data collection, which hampers its application at the local scale where often a high precision is required (e.g., field experiments). In this study we used soil data from six agricultural fields in Eastern Switzerland and calibrated i) field-specific (local) models and ii) general models (combining all fields) for organic carbon, total carbon, total nitrogen, permanganate oxidizable carbon and pH using partial least squares regression. 24 out of 30 local models showed an accurate or even excellent performance (ratio of performance to deviation (RPD) > 2) and the root mean square errors (RMSE) of prediction were, except for pH, maximum five times higher than the lab measurement error. The variability of a specific soil property and the mean carbonate concentration in the dataset were the two factors influencing the performance of the local models. We found a significant relationship between the coefficient of variation in the dataset and the metrics for model performance (R2, percental RMSE and RPD). Starting from a tolerable prediction error for the spectral measurements, the regressions can be used to develop a sampling design that matches the corresponding target variability. The five inaccurately performing local models with RPD < 2 were on the two fields with highest carbonate content raising the question if local vis–NIR models are suitable for soils with high carbonate concentration. General models combining the datasets from all six fields showed an accurate overall performance but the RMSE on the field level were higher compared to the local models

Seasonality, drivers, and isotopic composition of soil CO2 fluxes from tropical forests of the Congo Basin

Soil respiration is an important carbon flux and key process determining the net ecosystem production of terrestrial ecosystems. To address the lack of quantification and understanding of seasonality in soil respiration of tropical forests in the Congo Basin, soil CO2 fluxes and potential controlling factors were measured annually in two dominant forest types (lowland and montane) of the Congo Basin over 2 years at varying temporal resolution. Soil CO2 fluxes from the Congo Basin resulted in 3.45 +/- 1.14 and 3.13 +/- 1.22 mu mol CO2 m(-2) s(-1) for lowland and montane forests, respectively. Soil CO2 fluxes in montane forest soils showed a clear seasonality with decreasing flux rates during the dry season. Montane forest soil CO2 fluxes were positively correlated with soil moisture, while CO2 fluxes in the lowland forest were not. Smaller differences of delta C-1(3) values of leaf litter, soil organic carbon (SOC), and soil CO2 indicated that SOC in lowland forests is more decomposed than in montane forests, suggesting that respiration is controlled by C availability rather than environmental factors. In general, C in montane forests was more enriched in C-13 throughout the whole cascade of carbon intake via photosynthesis, litterfall, SOC, and soil CO2 compared to lowland forests, pointing to a more open system. Even though soil CO2 fluxes are similarly high in lowland and montane forests of the Congo Basin, the drivers of them seem to be different, i.e., soil moisture for montane forest and C availability for lowland forest

Organic matter cycling along geochemical, geomorphic and disturbance gradients in forests and cropland of the African Tropics – Project TropSOC Database Version 1.0

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 &ldquo;Tropical soil organic carbon dynamics along erosional disturbance gradients in relation to variability in soil geochemistry and land use&rdquo; (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&rsquo;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.</p

Best performances of visible–near-infrared models in soils with little carbonate – a field study in Switzerland

Conventional laboratory analysis of soil properties is often expensive and requires much time if various soil properties are to be measured. Visual and near-infrared (vis–NIR) spectroscopy offers a complementary and cost-efficient way to gain a wide variety of soil information at high spatial and temporal resolutions. Yet, applying vis–NIR spectroscopy requires confidence in the prediction accuracy of the infrared models. In this study, we used soil data from six agricultural fields in eastern Switzerland and calibrated (i) field-specific (local) models and (ii) general models (combining all fields) for soil organic carbon (SOC), permanganate oxidizable carbon (POXC), total nitrogen (N), total carbon (C) and pH using partial least-squares regression. The 30 local models showed a ratio of performance to deviation (RPD) between 1.14 and 5.27, and the root mean square errors (RMSE) were between 1.07 and 2.43 g kg−1 for SOC, between 0.03 and 0.07 g kg−1 for POXC, between 0.09 and 0.14 g kg−1 for total N, between 1.29 and 2.63 g kg−1 for total C, and between 0.04 and 0.19 for pH. Two fields with high carbonate content and poor correlation between the target properties were responsible for six local models with a low performance (RPD < 2). Analysis of variable importance in projection, as well as of correlations between spectral variables and target soil properties, confirmed that high carbonate content masked absorption features for SOC. Field sites with low carbonate content can be combined with general models with only a limited loss in prediction accuracy compared to the field-specific models. On the other hand, for fields with high carbonate contents, the prediction accuracy substantially decreased in general models. Whether the combination of soils with high carbonate contents in one prediction model leads to satisfying prediction accuracies needs further investigation

Best performances of visible-near-infrared models in soils with little carbonate - a field study in Switzerland

Conventional laboratory analysis of soil properties is often expensive and requires much time if various soil properties are to be measured. Visual and near-infrared (vis-NIR) spectroscopy offers a complementary and cost-efficient way to gain a wide variety of soil information at high spatial and temporal resolutions. Yet, applying vis-NIR spectroscopy requires confidence in the prediction accuracy of the infrared models. In this study, we used soil data from six agricultural fields in eastern Switzerland and calibrated (i) field-specific (local) models and (ii) general models (combining all fields) for soil organic carbon (SOC), permanganate oxidizable carbon (POXC), total nitrogen (N), total carbon (C) and pH using partial least-squares regression. The 30 local models showed a ratio of performance to deviation (RPD) between 1.14 and 5.27, and the root mean square errors (RMSE) were between 1.07 and 2.43 g kg( - 1) for SOC, between 0.03 and 0.07 g kg( - 1) for POXC, between 0.09 and 0.14 g kg( - 1) for total N, between 1.29 and 2.63 g kg (- 1) for total C, and between 0.04 and 0.19 for pH. Two fields with high carbonate content and poor correlation between the target properties were responsible for six local models with a low performance (RPD < 2). Analysis of variable importance in projection, as well as of correlations between spectral variables and target soil properties, confirmed that high carbonate content masked absorption features for SOC. Field sites with low carbonate content can be combined with general models with only a limited loss in prediction accuracy compared to the field-specific models. On the other hand, for fields with high carbonate contents, the prediction accuracy substantially decreased in general models. Whether the combination of soils with high carbonate contents in one prediction model leads to satisfying prediction accuracies needs further investigation.ISSN:2199-3971ISSN:2199-398

The central African soil spectral library: a new soil infrared repository and a geographical prediction analysis

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.ISSN:2199-3971ISSN:2199-398

Filling a key gap: a soil infrared library for central Africa

Information on soil properties is crucial for soil preservation, improving food security, and the provision of ecosystem services. Especially, 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 gained 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 1,800 soils from ten distinct geo-climatic regions throughout the Congo Basin and wider African Great Lakes region. We selected six hold-out core regions from our SSL, augmented them with the continental AfSIS SSL, which does not cover central African soils. We present three levels of geographical extrapolation, deploying Memory-based learning (MBL) to accurately predict carbon (TC) and nitrogen (TN) contents in the selected regions. The Root Mean Square Error of the predictions (RMSEpred) values were between 0.38–0.86 % and 0.04–0.17 % for TC and TN, respectively, when using the AfSIS SSL only to predict the six regions. Prediction accuracy could be improved for four out of six regions when adding central African soils to the AfSIS SSL. This reduction of extrapolation resulted in RMSEpred ranges of 0.41–0.89 % for TC and 0.03–0.12 % for TN. In general, MBL leveraged spectral similarity and thereby predicted the soils in each of the six regions accurately; the effect of avoiding geographical extrapolation and forcing regional samples in the local neighborhood (MBL-spiking) was small. We conclude that our CSSL adds valuable soil diversity that can improve predictions for the regions compared to using the continental scale 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

Soil geochemistry as a driver of soil organic matter composition: Insights from a soil chronosequence

A central question in carbon research is how stabilization mechanisms in soil change over time with soil development and how this is reflected in qualitative changes in soil organic matter (SOM). To address this matter, we assessed the influence of soil geochemistry on bulk SOM composition along a soil chronosequence in California, USA, spanning 3 million years. This was done by combining data on soil mineralogy and texture from previous studies with additional measurements on total carbon (C), stable isotope values (δ13C and δ15N), and spectral information derived from diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). To assess qualitative shifts in bulk SOM, we analysed the peak areas of simple plant-derived (S-POM), complex plant-derived (C-POM), and predominantly microbial-derived organic matter (OM; MOM) and their changes in abundance across soils with several millennia to millions of years of weathering and soil development. We observed that SOM became increasingly stabilized and microbial-derived (lower Cg:gN ratio, increasing δ13C and δ15N) as soil weathering progressed. Peak areas of S-POM (i.e. aliphatic root exudates) did not change over time, while peak areas of C-POM (lignin) and MOM (components of microbial cell walls (amides, quinones, and ketones)) increased over time and depth and were closely related to clay content and pedogenic iron oxides. Hence, our study suggests that with progressing soil development, SOM composition co-varied with changes in the mineral matrix. Our study indicates that structurally more complex OM compounds (C-POM, MOM) play an increasingly important role in soil carbon stabilization mechanisms as the mineral soil matrix becomes increasingly weathered.ISSN:1726-4170ISSN:1726-417

Soil geochemistry as a driver of soil organic matter composition: insights from a soil chronosequence

A central question in carbon research is how stabilization mechanisms in soil change over time with soil development and how this is reflected in qualitative changes in soil organic matter (SOM). To address this matter, we assessed the influence of soil geochemistry on bulk SOM composition along a soil chronosequence in California, USA, spanning 3 million years. This was done by combining data on soil mineralogy and texture from previous studies with additional measurements on total carbon (C), stable isotope values (δ13C and δ15N), and spectral information derived from diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). To assess qualitative shifts in bulk SOM, we analysed the peak areas of simple plant-derived (S-POM), complex plant-derived (C-POM), and predominantly microbial-derived organic matter (OM; MOM) and their changes in abundance across soils with several millennia to millions of years of weathering and soil development. We observed that SOM became increasingly stabilized and microbial-derived (lower C : N ratio, increasing δ13C and δ15N) as soil weathering progressed. Peak areas of S-POM (i.e. aliphatic root exudates) did not change over time, while peak areas of C-POM (lignin) and MOM (components of microbial cell walls (amides, quinones, and ketones)) increased over time and depth and were closely related to clay content and pedogenic iron oxides. Hence, our study suggests that with progressing soil development, SOM composition co-varied with changes in the mineral matrix. Our study indicates that structurally more complex OM compounds (C-POM, MOM) play an increasingly important role in soil carbon stabilization mechanisms as the mineral soil matrix becomes increasingly weathered.ISSN:1810-6277ISSN:1810-628

Soil nutrient depletion and tree functional composition shift following repeated clearing in secondary forests of the Congo Basin

The Congo Basin's rapidly growing population still largely depends on shifting cultivation for both energy and food security. This nexus of population growth and ecological impact will continue to exacerbate landscape degradation in the coming decades. To quantify the effects of land-use intensity on soil nutrient stocks and the functional composition of young regrowth forest in the Congo Basin, we used fallows of different ages that had been subjected to a varying number of clearing cycles. We show that repeated clearing substantially affected soil cation stocks, reducing total K, Mg and Ca in the upper 20 cm of soil by roughly 20% per clearing cycle. Additionally, we show that plant-available nitrogen (ammonium and nitrate) and phosphorus decline in the topsoil with increasing land-use intensity. Furthermore, the tree functional composition of young fallows changed after repeated clearing cycles: we observed a decrease in abundance of pioneer species and an increase in nitrogen fixing species early in succession. Variation in soil total nutrient stocks was decoupled from changes in vegetation, and soil plant-available nutrients only marginally explained tree functional composition changes. We conclude that land-use intensity substantially affects both soil total and plant-available nutrients in a shifting cultivation system, as well as the functional composition of the regenerating vegetation. However, compositional changes of the tree community are only partly driven by land-use intensity effects on soil plant-available nutrients