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

SOIL ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 693-715
Author(s):  
Laura Summerauer ◽  
Philipp Baumann ◽  
Leonardo Ramirez-Lopez ◽  
Matti Barthel ◽  
Marijn Bauters ◽  
...  
Keyword(s):  
Soil Properties ◽  
Information Service ◽  
Spectral Feature ◽  
Central Africa ◽  
Congo Basin ◽  
Total Carbon ◽  
Great Success ◽  
Spectral Library ◽  
Address Data ◽  
African Soil

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.

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

2021 ◽  
Author(s):  
Laura Summerauer ◽  
Philipp Baumann ◽  
Leonardo Ramirez-Lopez ◽  
Matti Barthel ◽  
Marijn Bauters ◽  
...  
Keyword(s):  
Soil Properties ◽  
Spectral Feature ◽  
Central Africa ◽  
Feature Space ◽  
Cost Effective ◽  
Congo Basin ◽  
Great Success ◽  
Climatic Regions ◽  
Continental Scale ◽  
Address Data

Abstract. 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.

scholarly journals Developing the Swiss soil spectral library for local estimation and monitoring

2021 ◽  
Author(s):  
Philipp Baumann ◽  
Anatol Helfenstein ◽  
Andreas Gubler ◽  
Armin Keller ◽  
Reto Giulio Meuli ◽  
...  
Keyword(s):  
Soil Properties ◽  
Chemical Diversity ◽  
Total Carbon ◽  
Data Driven ◽  
Organic Soils ◽  
Spectral Library ◽  
Total N ◽  
Organic C ◽  
Measured Range

Abstract. Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purposes we developed a national Swiss soil spectral library (SSL; n = 4374) in the mid-infrared (mid-IR), calibrating 17 properties from legacy measurements on soils from the Swiss biodiversity monitoring program (n = 3778; 1094 sites) and the Swiss long-term monitoring network (n = 596; 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5, 10, 20, 50, 100} committees of rules and {2, 5, 7, 9} neighbors to localize predictions with repeated by location grouped ten-fold cross-validation. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot-level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven sample selection method rs-local. Eleven soil properties were estimated with discrimination capacity suitable for screening (R2 > 0.6), out of which total carbon (C), organic C (OC), total N, organic matter content, pH, and clay showed accuracy eligible for accurate diagnostics (R2 > 0.8). Cubist and the spectra estimated total C accurately with RMSE = 0.84 % while the measured range was 0.1–⁠58.3 %, and OC with RMSE = 1.20 % (measured range 0.0–⁠27.3 %). Compared to general estimates of properties from Cubist, local modeling on average reduced the root mean square error of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar in terms of both their spectral input space and the measured values. This suggests that data-driven selection with RS-LOCAL leverages chemical diversity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring, and digital soil mapping. This SSL can be updated continuously, for example with samples from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.

scholarly journals Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring

SOIL ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 525-546
Author(s):  
Philipp Baumann ◽  
Anatol Helfenstein ◽  
Andreas Gubler ◽  
Armin Keller ◽  
Reto Giulio Meuli ◽  
...  
Keyword(s):  
Soil Properties ◽  
Chemical Diversity ◽  
Total Carbon ◽  
Data Driven ◽  
Organic Soils ◽  
Spectral Library ◽  
Soil Monitoring ◽  
Mid Infrared ◽  
Measured Range

Abstract. Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purpose, we developed a national Swiss soil spectral library (SSL; n=4374) in the mid-infrared (mid-IR), calibrating 16 properties from legacy measurements on soils from the Swiss Biodiversity Monitoring program (BDM; n=3778; 1094 sites) and the Swiss long-term Soil Monitoring Network (NABO; n=596; 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5,10,20,50, and 100} ensembles of rules (committees) and {2, 5, 7, and 9} nearest neighbors used for local averaging with repeated 10-fold cross-validation grouped by location. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven sample selection method RS-LOCAL. In total, 10 soil properties were estimated with discrimination capacity suitable for screening (R2≥0.72; ratio of performance to interquartile distance (RPIQ) ≥ 2.0), out of which total carbon (C), organic C (OC), total nitrogen (N), pH and clay showed accuracy eligible for accurate diagnostics (R2>0.8; RPIQ ≥ 3.0). CUBIST and the spectra estimated total C accurately with the root mean square error (RMSE) = 8.4 g kg−1 and the RPIQ = 4.3, while the measured range was 1–583 g kg−1 and OC with RMSE = 9.3 g kg−1 and RPIQ = 3.4 (measured range 0–583 g kg−1). Compared to the general statistical learning approach, the local transfer approach – using two respective training samples – on average reduced the RMSE of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar compared to validation samples, in terms of both their spectral input space and the measured values. This suggests that data-driven selection with RS-LOCAL leverages chemical diversity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring and digital soil mapping. This SSL can be updated continuously, for example, with samples from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.

scholarly journals State and evolution of the African rainforests between 1990 and 2010

2013 ◽  
Vol 368 (1625) ◽  
pp. 20120300 ◽  
Author(s):  
Philippe Mayaux ◽  
Jean-François Pekel ◽  
Baudouin Desclée ◽  
François Donnay ◽  
Andrea Lupi ◽  
...  
Keyword(s):  
West Africa ◽  
Forest Cover ◽  
Central Africa ◽  
Congo Basin ◽  
Landsat Images ◽  
Deforestation Rate ◽  
Rural And Urban ◽  
Moderate Resolution ◽  
Underlying Causes ◽  
Moderate Resolution Imaging Spectroradiometer

This paper presents a map of Africa's rainforests for 2005. Derived from moderate resolution imaging spectroradiometer data at a spatial resolution of 250 m and with an overall accuracy of 84%, this map provides new levels of spatial and thematic detail. The map is accompanied by measurements of deforestation between 1990, 2000 and 2010 for West Africa, Central Africa and Madagascar derived from a systematic sample of Landsat images—imagery from equivalent platforms is used to fill gaps in the Landsat record. Net deforestation is estimated at 0.28% yr −1 for the period 1990–2000 and 0.14% yr −1 for the period 2000–2010. West Africa and Madagascar exhibit a much higher deforestation rate than the Congo Basin, for example, three times higher for West Africa and nine times higher for Madagascar. Analysis of variance over the Congo Basin is then used to show that expanding agriculture and increasing fuelwood demands are key drivers of deforestation in the region, whereas well-controlled timber exploitation programmes have little or no direct influence on forest-cover reduction at present. Rural and urban population concentrations and fluxes are also identified as strong underlying causes of deforestation in this study.

scholarly journals The effect of total carbon on microscopic soil properties and implications for crop production

2012 ◽  
Vol 4 (3) ◽  
pp. 251-259 ◽  
Author(s):  
Inma LEBRON ◽  
Milton Earl MCGIFFEN Jr ◽  
Donald Louis SUAREZ
Keyword(s):  
Soil Properties ◽  
Crop Production ◽  
Total Carbon

scholarly journals Predicting soil carbon by efficiently using variation in a mid-IR soil spectral library

2021 ◽  
Author(s):  
Anatol Helfenstein ◽  
Philipp Baumann ◽  
Raphael Viscarra Rossel ◽  
Andreas Gubler ◽  
Stefan Oechslin ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Scale Up ◽  
Monitoring Program ◽  
Partial Least Square ◽  
Least Square ◽  
Total Carbon ◽  
Local Algorithm ◽  
Organic Soils ◽  
Infrared Range ◽  
Spectral Library

Abstract. Traditional laboratory methods of acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modelling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-infrared range includes soil samples from the Biodiversity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally-resolved data from the Swiss Soil Monitoring Network (NABO). Given the relatively low representation of organic soils and their organo-mineral diversity in the SSL, we aimed to develop both an efficient calibration sampling scheme and accurate modelling strategy to estimate soil carbon (SC) contents of heterogeneous samples between 0 m to 2 m depth from 26 locations within two drained peatland regions (HAFL dataset; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of SSL instances and their target-feature representations. We used partial least square regressions (PLSR) together with a 5 times repeated, grouped by location, 10-fold cross validation (CV) to predict SC ranging from 1 % to 52 % in the local HAFL dataset. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL spiked with local samples (2) and 15 subsets of local and SSL samples using the RS-LOCAL algorithm (3). Using local and RS-LOCAL calibrations with at least 5 local samples, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.94–0.96, bias = −0.6–1.5, RMSE = 2.6 % to 3.5 % total carbon). However, calibrations of representative SSL and local samples using RS-LOCAL only required 5 local samples for very accurate models (RMSE = 2.9 % total carbon), while local calibrations required 50 samples for similarly accurate results (RMSE 

scholarly journals Soil properties after 36 years of N fertilization under continuous corn and corn-soybean management

2021 ◽  
Author(s):  
Nakian Kim ◽  
Gevan D. Behnke ◽  
María B. Villamil
Keyword(s):  
Soil Properties ◽  
Crop Rotation ◽  
Crop Yields ◽  
Inorganic Nitrogen ◽  
Block Design ◽  
N Fertilization ◽  
Total Carbon ◽  
Nitrous Oxide Emissions ◽  
Continuous Corn

Abstract. Modern agricultural systems rely on inorganic nitrogen (N) fertilization to enhance crop yields, but its overuse may negatively affect soil properties. Our objective was to investigate the effect of long-term N fertilization on key soil properties under continuous corn [Zea mays L.] (CCC) and both the corn (Cs) and soybean [Glycine max L. Merr.] (Sc) phases of a corn-soybean rotation. Research plots were established in 1981 with treatments arranged as a split-plot design in a randomized complete block design with three replications. The main plot was crop rotation (CCC, Cs, and Sc), and the subplots were N fertilizer rates of 0 kg N ha−1 (N0, controls), and 202 kg N ha−1, and 269 kg N ha−1 (N202, and N269, respectively). After 36 years and within the CCC, the yearly addition of N269 compared to unfertilized controls significantly increased cation exchange capacity (CEC, 65 % higher under N269) and acidified the top 15 cm of the soil (pH 4.8 vs. pH 6.5). Soil organic matter (SOM) and total carbon stocks (TCs) were not affected by treatments, yet water aggregate stability (WAS) decreased by 6.7 % within the soybean phase of the CS rotation compared to CCC. Soil bulk density (BD) decreased with increased fertilization by 5 % from N0 to N269. Although ammonium (NH4+) did not differ by treatments, nitrate (NO3−) increased eight-fold with N269 compared to N0, implying increased nitrification. Soils of unfertilized controls under CCC have over twice the available phosphorus level (P) and 40 % more potassium (K) than the soils of fertilized plots (N202 and N269). On average, corn yields increased 60 % with N fertilization compared to N0. Likewise, under N0, rotated corn yielded 45 % more than CCC; the addition of N (N202 and N269) decreased the crop rotation benefit to 17 %. Our results indicated that due to the increased level of corn residues returned to the soil in fertilized systems, long-term N fertilization improved WAS and BD, yet not SOM, at the cost of significant soil acidification and greater risk of N leaching and increased nitrous oxide emissions.

New Evidence of Human Activities During the Holocene in the Lowland Forests of the Northern Congo Basin

Radiocarbon ◽  
2014 ◽  
Vol 56 (1) ◽  
pp. 209-220 ◽  
Author(s):  
Julie Morin-Rivat ◽  
Adeline Fayolle ◽  
Jean-François Gillet ◽  
Nils Bourland ◽  
Sylvie Gourlet-Fleury ◽  
...  
Keyword(s):  
Iron Age ◽  
Human Activities ◽  
Intermediate Phase ◽  
Central Africa ◽  
Bimodal Distribution ◽  
Congo Basin ◽  
Radiocarbon Dates ◽  
Late Iron Age ◽  
Two Phases ◽  
Botanical Remains

In the last decade, the myth of the pristine tropical forest has been seriously challenged. In central Africa, there is a growing body of evidence for past human settlements along the Atlantic forests, but very little information is available about human activities further inland. Therefore, this study aimed at determining the temporal and spatial patterns of human activities in an archaeologically unexplored area of 110,000 km2 located in the northern Congo Basin and currently covered by dense forest. Fieldwork involving archaeology as well as archaeobotany was undertaken in 36 sites located in southeastern Cameroon and in the northern Republic of Congo. Evidence of past human activities through either artifacts or charred botanical remains was observed in all excavated test pits across the study area. The set of 43 radiocarbon dates extending from 15,000 BP to the present time showed a bimodal distribution in the Late Holocene, which was interpreted as two phases of human expansion with an intermediate phase of depopulation. The 2300–1300 BP phase is correlated with the migrations of supposed farming populations from northwestern Cameroon. Between 1300 and 670 BP, less material could be dated. This is in agreement with the population collapse already reported for central Africa. Following this, the 670–20 BP phase corresponds to a new period of human expansion known as the Late Iron Age. These results bring new and extensive evidence of human activities in the northern Congo Basin and support the established chronology for human history in central Africa.

scholarly journals Response of African humid tropical forests to recent rainfall anomalies

2013 ◽  
Vol 368 (1625) ◽  
pp. 20120306 ◽  
Author(s):  
Salvi Asefi-Najafabady ◽  
Sassan Saatchi
Keyword(s):  
West Africa ◽  
Tropical Forests ◽  
Large Scale ◽  
Forest Canopy ◽  
Central Africa ◽  
Extreme Rainfall ◽  
Research Unit ◽  
Congo Basin ◽  
West And Central Africa ◽  
Rainfall Anomalies

During the last decade, strong negative rainfall anomalies resulting from increased sea surface temperature in the tropical Atlantic have caused extensive droughts in rainforests of western Amazonia, exerting persistent effects on the forest canopy. In contrast, there have been no significant impacts on rainforests of West and Central Africa during the same period, despite large-scale droughts and rainfall anomalies during the same period. Using a combination of rainfall observations from meteorological stations from the Climate Research Unit (CRU; 1950–2009) and satellite observations of the Tropical Rainfall Measuring Mission (TRMM; 1998–2010), we show that West and Central Africa experienced strong negative water deficit (WD) anomalies over the last decade, particularly in 2005, 2006 and 2007. These anomalies were a continuation of an increasing drying trend in the region that started in the 1970s. We monitored the response of forests to extreme rainfall anomalies of the past decade by analysing the microwave scatterometer data from QuickSCAT (1999–2009) sensitive to variations in canopy water content and structure. Unlike in Amazonia, we found no significant impacts of extreme WD events on forests of Central Africa, suggesting potential adaptability of these forests to short-term severe droughts. Only forests near the savanna boundary in West Africa and in fragmented landscapes of the northern Congo Basin responded to extreme droughts with widespread canopy disturbance that lasted only during the period of WD. Time-series analyses of CRU and TRMM data show most regions in Central and West Africa experience seasonal or decadal extreme WDs (less than −600 mm). We hypothesize that the long-term historical extreme WDs with gradual drying trends in the 1970s have increased the adaptability of humid tropical forests in Africa to droughts.

Sign in / Sign up

Export Citation Format

Share Document