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Intensified human activities can augment soil organic carbon (SOC) losses from the world’s croplands, making SOC a highly dynamic parameter both in space and time. Sentinel-2 spectral imagery is well placed to capture the spatiotemporal variability of SOC, but its capability has only been demonstrated for agricultural regions mostly located in Europe. Furthermore, most studies so far only used single-date images that resulted in spatially non-continuous SOC maps, hampering their ability to aid multiscale SOC assessments. Here, we aim to achieve spatially continuous mapping of SOC in croplands, by creating multitemporal bare soil composites that maximize cropland coverage in two regions of varying agroecosystems and landscape structure in the Northeast China Chernozem region and the Belgian Loam Belt. Bare soil pixels were extracted via spectral index thresholding that excluded contaminated pixels from external perturbance. Multitemporal soil composites were then obtained by averaging over multiple single-date bare soil images that were selected within pre-determined optimal time-windows, corresponding to the region-specific crop sowing periods when best possible surface conditions were expected. Results show that the optimal time-window filter ensured selective inclusion of single-date images that themselves yielded stable and robust SOC predictions across multiple years. Spectral-based models developed from multitemporal composites consistently produced better or similar prediction accuracies than single-date images for both study regions (R2: 0.52–0.62; RMSE: 0.17–0.21 g 100 g−1), while also achieved maximum cropland coverage (>82 %). Bootstrap modelling demonstrated that SOC mapping via multitemporal Sentinel-2 data was associated with small uncertainties. Investigations into the significant spectral bands that contributed to the prediction of SOC suggested that, regardless of the study regions, the physical relationship between spectral bands and SOC that predominantly exists for laboratory spectra is largely translated into Sentinel-2 platforms. This study highlights the widespread applicability of multitemporal Sentinel-2 remote sensing for effective and high-resolution SOC mapping, in order to detect localized soil degradation as well as to inform regional cropland management in diverse agroecosystems.

An adequate supply of micronutrients is essential for balanced plant growth. In Africa, micronutrient constraints are known to restrict crop productivity and impair human welfare and health. We analyzed a total of 816 topsoil and 796 subsoil samples from Sub-Saharan Africa, to provide baseline information on their readily available Al, B, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, V, and Zn concentrations. This work builds on previous global micronutrient surveys. The studied soils were low in hot water-extractable B, acid ammonium acetate-EDTA-extractable micronutrients, and potentially harmful metals. In comparison with tentative critical level values, B, Cu, Zn, Mo, and Fe in a considerable number of samples seemed to be at a low level for crop production. Apart from Al, Mo, V, Pb, and Cr, readily available element concentrations were higher in topsoil than subsoil. Concentrations of harmful metals (Cd, Ni, Pb) and of Mn and Mo were enriched in cultivated soils in comparison with semi-natural sites. Among the 11 Reference Soil Groups represented in the study, Arenosols were discernible as poor in Cr, Cu, and Fe. Other significant differences in microelement concentrations between soil groups were found only for Ni and V.

Although soil degradation is a major threat to food security and carbon sequestration, our knowledge of the spatial extent of the problem and its drivers is very limited in Southern Africa. Therefore, this study aimed to quantify the risk of soil structural degradation and determine the variation in soil stoichiometry and nutrient limitations with land use categories (LUCs) and climatic zones. Using data on soil clay, silt, organic carbon (SOC), total nitrogen (N), available phosphorus (P), and sulfur (S) concentrations collected from 4,468 plots on 29 sites across Angola, Botswana, Malawi, Mozambique, Zambia and Zimbabwe, this study presents novel insights into the variations in soil structural degradation and nutrient limitations. The analysis revealed strikingly consistent stoichiometric coupling of total N, P, and S concentrations with SOC across LUCs. The only exception was on crop land where available P was decoupled from SOC. Across sample plots, the probability (φ) of severe soil structural degradation was 0.52. The probability of SOC concentrations falling below the critical value of 1.5% was 0.49. The probabilities of soil total N, available P, and S concentrations falling below their critical values were 0.95, 0.70, and 0.83, respectively. N limitation occurred with greater probability in woodland (φ = .99) and forestland (φ = .97) than in cropland (φ = .92) and grassland (φ = .90) soils. It is concluded that soil structural degradation, low SOC concentrations, and N and S limitations are widespread across Southern Africa. Therefore, significant changes in policies and practices in land management are needed to reverse the rate of soil structural degradation and increase soil carbon storage.

Low soil fertility is challenging the sustainable production of yam and other staple crops in the yam belt of West Africa. Quantitative soil measures are needed to assess soil fertility decline and to improve crop nutrient supply in the region. We developed and tested a mid-infrared (mid-IR) soil spectral library to enable timely and cost-efficient assessments of soil properties. Our collection included 80 soil samples from four landscapes (10 km × 10 km) and 20 fields per landscape across a gradient from humid forest to savannah and 14 additional samples from one landscape that had been sampled within the Land Health Degradation Framework. We derived partial least squares regression models to spectrally estimate soil properties. The models produced accurate cross-validated estimates of total carbon, total nitrogen, total sulfur, total iron, total aluminum, total potassium, total calcium, exchangeable calcium, effective cation exchange capacity, and diethylenetriaminepentaacetic acid (DTPA)-extractable iron and clay content (R2>0.75). The estimates of total zinc, pH, exchangeable magnesium, bioavailable copper, and manganese were less predictable (R2>0.50). Our results confirm that mid-IR spectroscopy is a reliable and quick method to assess the regional-level variation of most soil properties, especially the ones closely associated with soil organic matter. Although the relatively small mid-IR library shows satisfactory performance, we expect that frequent but small model updates will be needed to adapt the library to the variation of soil quality within individual fields in the regions and their temporal fluctuations., Soil, 7 (2), ISSN:2199-3971, ISSN:2199-398X

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 Discussions

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mappedat all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGOfunded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. Inthis paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensivecompilation of soil samples (N ≈ 150, 000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and totalnitrogen (N), total carbon, Cation Exchange Capacity (eCEC), extractable — phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg),sulfur (S), sodium (Na), iron (Fe), zinc (Zn) — silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariatelayers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives)images. Our 5–fold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC=0.900) tomore poorly predictable extractable phosphorus (CCC=0.654) and sulphur (CCC=0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11,B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 mresolution covariates. Climatic data images — SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature — however, remainedas the overall most important variables for predicting soil chemical variables at continental scale. The publicly available 30–m soil maps aresuitable for numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmentalprograms, or targeting of nutrition interventions.

Mid-infrared (MIR) spectroscopy is emerging as one of the most promising technologies, as it is a rapid and cost-effective alternative to routine laboratory analysis for many soil properties. This study was conducted to evaluate the potential of mid-infrared spectroscopy for the rapid and nondestructive measurement of some important soil properties of Alfisols. A total of 336 georeferenced soil samples fromthe 0–15 cm soil layer of Alfisols that were collected from the eastern Indian states of Odisha and Jharkhand were used. The partial least-squares regression (PLSR), random forest, and support vector machine regression techniques were compared for the calibration of the spectral data with the wet chemistry soil data. The PLSR-based predictive models performed better than the other two regression techniques for all the soil properties, except for the electrical conductivity (EC). Good predictions with independent validation datasets were obtained for the clay and sand percentages and for the soil organic carbon (SOC) content, while satisfactory predictions were achieved for the silt percentage and the pH value. However, the performance of the predictive models was poor in the case of the EC and the extractable nutrients, such as the available phosphorus and potassium contents of the soil. Specific regions of the MIR spectra that contributed to the prediction of the soil SOC, the pH, and the clay and sand percentages were identified. The study demonstrates the potential of the MIR spectroscopic technique in the simultaneous estimation of the SOC content, the sand, clay, and silt percentages, and the pH of Alfisols from eastern India.

Improving fertilizer recommendations for farmers is essential to increase food security in smallholder landscapes. Currently, blanket recommendations are provided across agro-ecological zones, although fertilizer response and nutrient use efficiency by maize crop are spatially variable. We aimed to identify factors that could help to refine fertilizer recommendation by analyzing the variability in fertilizer response (FR) and the agronomic nitrogen use efficiency (N-AE). A literature search for on-farm studies across Kenya and Sub-Sahara Africa (SSA), excluding Kenya, yielded 71 publications. The variability in FR was studied using a meta-analysis whereas key factors that influence FR and N-AE were studied with linear regression models. On average, the FR was 2, but it varied considerably from 1 to 28.5 (excluding outliers). In SSA, 18% of the plots were non-responsive plots with an FR

To optimize sampling effort, knowledge of soil spatial variability is essential, but usually requires large and spatially detailed data sets. In this study, soils under secondary forests of four ages (1, 8, 15, 22 years) were compared within an agricultural landscape in the eastern Amazon (Igarape Acu, eastern Para state, Brazil). At three nested scales within each stand, topsoil samples were taken at two depths (0–10, 10–20 cm), scanned by VNIR, and analyzed chemometrically in the laboratory. Boosted regression trees (BRT) were then used to calibrate VNIR spectra to soil properties for a representative subsample. These models were moderately successful on cross-validated data (R2 ≥ 0.75) for total C, total N (R2 ≥ 0.74) and pH (R2 ≥ 0.62), as were linear mixed models regressing measured vs. predicted (via spectra) values on secondary forest age and soil depth. Differences in measured values were greatest between soil depths and 1-yr vs. older stands. Differences by stand age in pH, but not C and N were captured by regression models of predicted data. However, predicted values reflected with moderate accuracy the scale (samples within subplots, subplots within plots, plots within treatments) of measured variability in soil C, N, and pH. While there was little measured variability among samples within subplots, variability was highest at the plot scale for the 0–10 cm depth, and similar at the subplot and plot scale for the 10–20 cm depth. These results indicate that soil reflectance measures can be a useful tool for developing efficient soil sampling designs.

In this study, the utility of regression-kriging was investigated in building prediction models for soil properties using mid-infrared (7498 to 600 cm− 1) spectral data for soil samples collected from Nyando, Nzoia and Yala catchment areas in Kenya, sampled at 0–20 cm and 20–50 cm depths. Using a systematic technique, 158 samples were selected for analysis of a number of soil properties of interest using wet chemistry methods. We randomly divided the dataset into two groups: 118 samples in the calibration and 40 samples in the holdout validation set. The calibration set was first used to develop partial least squares regression (PLS) models for all the soil properties. Residuals from these models were used to generate semivariograms, which revealed a strong spatial dependence as determined by the ratio of nugget to sill for nitrogen, 9%; Al, 12%; and B, 36%, but with weak spatial dependence for exchangeable Ca (ExCa), 100%; and carbon, 76%. The fitted theoretical semivariograms were used to fit regression-kriging models. Lastly, both the PLS and regression-kriging models were assessed with the validation set and their prediction performance evaluated by R2 and root mean square error (RMSE). The results showed that regression kriging method gave lower RMSE values for all the evaluated soil properties except for ExCa, B and exchangeable acidity, with the best predictions, compared with the PLS model, obtained for ExMg (R2, 0.93 vs 0.88; RMSE, 6.1 vs 8.4 cmolc kg− 1) and total nitrogen (R2 = 0.92 vs R2 = 0.74; RMSE, 0.11%, RMSE = 0.2%). In this study, regression-kriging, which takes into account spatial variation normally ignored by other methods, improved use of infrared spectroscopy for predicting soil properties.

Soil organic carbon (SOC) is a key component of terrestrial ecosystems. Experimental studies have shown that soil texture and geochemistry have a strong effect on carbon stocks. However, those findings primarily rely on data from temperate regions or use model approaches that are often based on limited data from tropical and sub-tropical regions. Here, we evaluate the controls on soil carbon stocks in Africa, using a dataset of 1,580 samples. These were collected across Sub-Saharan Africa (SSA) within the framework of the Africa Soil Information Service (AfSIS) project, which was built on the well-established Land Degradation Surveillance Framework (LDSF). Samples were taken from two depths (0–20 cm and 20–50 cm) at 46 LDSF sites that were stratified according to Koeppen-Geiger climate zones. The different pH-values, clay content, exchangeable cations and extractable elements across various soils of the different climatic zones (i.e. from arid to humid (sub)tropical) allow us to identify different soil and climate parameters that best explain SOC variance across SSA. We tested if these SOC predictors differed across climatological conditions, using the ratio of potential evapotranspiration (PET) to mean annual precipitation (MAP) as indicator. For water-limited regions (PET/MAP > 1), the best predictors were climatic variables, likely because of their effect on the quantity of carbon inputs. Geochemistry dominated SOC storage in energy-limited systems (PET/MAP < 1), reflecting its effect on carbon protection. On a continental scale, climate (e.g. PET) is key to predicting SOC content in topsoil, whereas geochemistry, particularly iron-oxyhydroxides and aluminum-oxides, is more important in subsoil. Clay content had little influence on SOC at both depths. These findings contribute to an improved understanding of the controls on SOC stocks in tropical and sub-tropical regions., EGUsphere

Determination of the concentration of nutrients in the plant is key information for evaluating crop nutrient removal, nutrient use efficiency, fertilizer recommendations guidelines, and in turn for improving food security and reducing environmental footprints of crop production. Diffuse infrared (IR) reflectance spectroscopy is a powerful, rapid, cheap, and less pollutant analytical tool that could be substituted for traditional laboratory methods for the determination of the concentration of nutrients in plants. However, its accuracy for predicting the concentration of nutrients in rice plants is poorly known. This study aimed i) to determine macro- and micronutrients concentration that can be accurately predicted by near-infrared (NIR, 7498–4000 cm−1), mid-infrared (MIR, 4000–600 cm−1), or their combination (NIR-MIR, 7498–600 cm−1) spectra, ii) to identify the most suitable spectral range with the best prediction potential for the simultaneous analysis of nutrients concentrations in rice plants (straw and paddy) and iii) to assess the influence of agro-ecological zone and production system on nutrients concentrations in straw and paddy (unhulled grains) samples. Second-derivative spectra were fitted against plant laboratory reference data using partial least-squares regression (PLSR) to estimate six macronutrients (N, P, K, Ca, Mg, and S) and seven micronutrients (Na, Fe, Mn, B, Cu, Mo, and Zn) concentration in paddy and rice straw samples collected at harvest from 1628 farmers’ fields in 20 sub-Saharan African (SSA) countries. The modeling prediction potential was assessed by coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and ratio of performance to interquartile distance (RPIQ). Good prediction models (0.75

Soil aggregation is critical for assessing soil health; however, conventional aggregation measurement is laborious and expensive. The performance of near infrared diffuse reflectance spectroscopy (NIR) and basic soil properties for estimation of wet aggregation indices was investigated. Two samples sets representing different soils from across Lake Victoria Basin in Kenya were used for the study. A model calibration set ( n = 136) was obtained following a conditioned Latin hypercube sampling, and validation set ( n = 120) using a spatially stratified random sampling strategy. Spectral measurements were obtained for air-dried ( RPD (ratio of prediction deviation) of 1.4–2.0. Independent testing of NIR PLS gave RPD = 1.4 for macro and RPD = 1.2–1.0 for unstable and soil predictors. NIR could estimate macro and unstable fractions with moderate reliability, and; NIR was superior over soil properties for stability pedotransfer purposes. Further efforts should widely test performance for a wider range of soil types and calibration strategies for improved geographic transferability of models.

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky–Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries., Highlights • Global model performed better than local models. • MSC preprocessed spectra gave poor models, 1st and 2nd derivative spectra gave stable models. • Mehlich-3 calcium gave the highest coefficient of variation. • Total carbon global and subspace models using first derivative preprocessed spectra are similar.

Mango fruits contain substantial vitamins and dietary fibre. Vitamins vary among and within fruits depending on cultivar type and ripening stage. Conventional techniques of vitamins analysis are based on High Pressure Liquid Chromatography, which are costly and laborious. This study evaluated the potential of Fourier transform infrared-diffuse reflectance spectroscopy (FTIR-DRIFTS) technique in predicting β-carotene, α-tocopherol and l-ascorbic acid in pulps of four mango cultivar types (‘Apple’, ‘Kent’, ‘Ngowe’, and ‘Tommy Atkins’). Combination of ran dom forest (RF) and first derivative spectra developed the predictive models. Factorial ANOVA examined the interaction effect of cultivar type, site (‘Thika’, ‘Embu’ and ‘Machakos), and fruit canopy position (sun exposed/within crown) on β-carotene, α-tocopherol and l-ascorbic acid contents. RF Models gave R2 = 0.97, RMSE = 2.27, RPD = 0.72 for β-carotene; R2 = 0.98, RMSE = 0.26, RPD = 0.30 for α-tocopherol and R2 = 0.96, RMSE = 0.51, RPD = 1.96 for l-ascorbic acid. Generally cultivar type affected vitamin C, F (3, 282) = 7.812, p

Methods for rapid and accurate soil tests are needed for stability-related soil properties, which are valuable for non-agricultural land use. The study tested the applicability of mid-infrared (MIR) spectroscopy for rapid estimation of selected stability-related soil properties. Two sample sets, representing different soils from across the Lake Victoria basin of Kenya, were used for the study. A model calibration set (n = 135) was obtained following a conditioned Latin hypercube sampling, and a validation set (n = 120) was obtained from independent sites using a spatially stratified random sampling strategy. Air-dried and ground (

Methods for rapid and accurate soil tests are needed for the index properties of material attributes commonly applied in civil engineering. We tested the application of mid-infrared (MIR) spectroscopy for the rapid characterization of selected key stability-related soil properties. Two sample sets, representing different soils from across Lake Victoria basin in Kenya, were used for the study: A model calibration set (n=135) was obtained following a conditioned Latin hypercube sampling, and a validation set (n=120) was obtained from independent sites using a spatially stratified random sampling strategy. Air-dried ground (55%, 4.2; PI>30%, 2.7; LS>12%, 2.4; exchangeable sodium (eNa)>2cmol (+) kg−1, 2.3; exchangeable sodium percent (ESP)>10%, 1.8; W>8.3%, 1.6, and Activity number (A)>1.25units, 1.5. MIR can provide the rapid assessment of several soil properties that yield stability indices in material testing for engineering land use. Further studies should test the ability of MIR PLS for establishing broader calibrations across more diverse soil types and the direct correlation of MIR to material functional attributes.

Diffuse reflectance Fourier transformed mid-infrared (DRIFT-MIR) spectroscopy can predict many soil properties but extractable nutrients are often predicted poorly. This paper tested the combined DRIFT-MIR and total X-ray fluorescence (TXRF) spectroscopy analysis for prediction of soil properties related to soil fertility. A total of 700 soil samples from 44 stratified randomly located 100-km² sentinel sites distributed across Sub-Saharan Africa (SSA) were analyzed for physicochemical composition using conventional reference methods, and compared with MIR and TXRF spectra using Random Forests (RF) regression algorithm and an internal out-of-bag (OOB) validation. Mid-infrared spectra resulted in good prediction models (R² > 0.80) for organic C and total N, Mehlich-3 Ca and Al, and pH. Moderately predicted (R² > 0.60) were extractable Mg, P sorption index, sand, silt, and clay. Models were less satisfactory (R² < 0.60) for Mehlich-3 extractable K, Mn, Fe, Cu, B, Zn, P, S, and Na, exchangeable acidity and electrical conductivity (Ecd). Inclusion of total element concentration data from TXRF analysis in the MIR RF models significantly reduced root mean square error of prediction by 70% for Ecd, 66% for Mehlich-3 Na, 61% for Mehlich-3 S, and 50% for Mehlich-3 B due to detection by TXRF of some saline soils that were not well predicted by MIR. Overall, both methods predicted soil properties that relate to nutrient buffering capacity, including some exchangeable bases, pH, P sorption capacity, clay content, and organic matter content, and fingerprint basic soil mineralogy. Thus, further research should test whether MIR and TXRF fingerprinting could better predict soil nutrient supply capacity, as determined by crop nutrient uptake potential, than conventional soil P, K and micronutrient tests.

A thorough understanding of the variation in total soil element concentrations is important especially in the Sub-Saharan Africa (SSA) soil contexts for agricultural and environmental management at large scale. Fingerprinting of soil elemental composition may form a useful basis for evaluating soils in a way that relates to soil-forming factors and inherent soil functional properties. The objectives of this paper are to quantify the proportion of variability in total elemental composition by total X-ray fluorescence (TXRF) method of 1074 soil samples from the Africa Soil Information Service (AfSIS) Project baseline and to determine the relationships with soil forming factors. The samples were from 34 sentinel sites measuring 10×10km, randomized within major climate zones in SSA. Within each sentinel site there were sixteen spatially stratified 1km2 clusters, within which there were ten 100m2 plots. The within and between site patterns of variation in total element composition of 17 elements; Al, P, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, Sr, Y, Ta, and Pb, were explored. Total element concentration values were within the range reported globally for soil Cr, Mn, Zn, Ni, V, Sr, and Y and higher than reported range for Al, Cu, Ta, Pb, and Ga. There were significant variations (P70% of variation in soil element composition patterns can be predicted using information in existing databases or readily observable features. Successful use of TXRF technique would open up possibilities for using total soil elemental composition fingerprints as a useful basis for characterizing soils in a way that relates to soil-forming factors and inherent soil functional properties.

Soil properties are useful for assessing the potential of landscapes to provide terrestrial ecosystem services, but they are affected by anthropogenic activities and environmental factors including landscape attributes. This study assessed how soil properties are influenced by landscape attributes and their interactions in the highlands of Cameroon using the Land Degradation Surveillance Framework as a data collection tool. Soil properties (soil organic carbon [SOC], clay content, exchangeable bases [ExBas], electric conductivity [EC], boron [B], manganese [Mn], phosphorus [P], pH) were quantified within classes of landscape attributes. Soil samples were collected on 160 (1,000 m2) plots randomly located in a sentinel site of 100 km2 and were analyzed using a combination of conventional laboratory methods and midinfrared spectroscopy. Soil properties were highly affected by soil depths, land use types, slope gradients, and topographic positions, but less by their interactions. Significant interactions existed between land use types and topographic positions for SOC, EC, ExBas, and pH, and between slope gradients and topographic positions for pH, whereas Mn was influenced by the interaction between land use types and slope gradients. Most soil properties were higher in low altitude plots and those with higher vegetation cover but decreased in the upslope direction. The pH and clay contents were less affected by slope gradient confirming the inherent nature of the properties. These results are useful for site‐specific implementation of ecological intensification in areas with complex topography such as the highlands of Cameroon, offering a reference for future ecological policies and landscape restoration.

Assessment and monitoring of soil resources is needed for ensuring environmental sustainability of rice production in sub-Saharan Africa (SSA). Diffuse reflectance spectroscopy has the potential to offer means to evaluate and monitor soils at large scale in an efficient, affordable and timely manner. However, its accuracy for predicting chemical and physical properties of soils under rice cultivation is little known. The objectives of this study were to i) determine physical and chemical soil fertility properties which can be accurately analyzed using near infrared (NIR) (700–2500 nm), mid-infrared (MIR) (2500–16,670 nm), or a combination of both (NIR-MIR) (700–16,670 nm) spectral libraries, ii) compare their prediction potential for identifying the most suitable spectral range for simultaneous analysis of soil fertility properties, and iii) assess degrees of variation of 29 soil fertility properties for 2845 soil samples collected from 42 study sites in 20 SSA countries. Diffuse reflectance spectroscopy data were obtained using a Fourier-transform infrared spectrometer (FT-IR) Tensor 27 with a high-throughput screening (HTS-XT) extension unit. About 10% of the whole sample set was chosen for conventional wet chemistry soil analysis, and was used for developing best fitted models with partial least squares regression (PLSR) that can estimate soil fertility properties using infrared (IR) spectra. The prediction performances were evaluated by R2, RMSE and RPIQ. Good prediction models (0.75

Understanding spatial variability of soil properties is essential to support land management decisions. However, despite the growing worldwide emphasis on integrated landscape management, soil variations resulting from land use changes have rarely been documented. The study used the land health surveillance concept in combination with simple geostatistical approaches to describe key soil properties among land use types and characterize their spatial variability in the highlands of Cameroon. A total of 320 soil samples were collected in two sites with contrasting land uses (agricultural and pasture) and were analyzed for granulometric fraction, soil organic carbon (SOC), nitrogen (N), soil reaction (pH), phosphorous (P), calcium (Ca), potassium (K), magnesium (Mg), aluminum (Al) and zinc (Zn). The spatial correlations between the soil properties revealed the factors responsible for the observed differences and showed that wide ranges were obtained in agricultural site as opposed to pasture. SOC and N decreased in the order of forest > grassland > fallow > croplands > pasture due to inherent soil properties, anthropogenic activities, land cover/land use and topographic factors. Kriged maps provided detailed visualization of soil properties at landscape scale, and helped to identify critical areas for targeted land management interventions to improve land quality. The spatial distribution of selected soil properties showed a well-defined pattern of higher concentrations in the lowlands and valleys and areas with permanent vegetation cover in both sites. These results are useful for improving the efficient use of inputs such fertilizers. Context-specific land management based on spatial variability of soil properties is highly recommended and more research is required to generalize our knowledge about spatial variability of soil health indicators and the casual factors in the highlands of Cameroon. © 2017 Elsevier B.V.

In the West African drylands, SOC sequestration is seen as one of the prominent strategies to both enhance the resilience of agro-ecosystems and mitigate global greenhouse effects. However, there is a dearth of baseline data that impede the design of site-appropriate recommended management practices (RMPs) to improve and sustain SOC accrual. In this study, the Land Degradation Surveillance Framework (LDSF), a nested hierarchical sampling design was used to assess SOC and its spatial variability across the semi-arid zones of Ghana (Lambussie), Burkina Faso (Bondigui) and Mali (Finkolo). Soil samples were collected from three sites of 100 km2 stratified into 16 clusters and 160 plots and thereafter soil parameters were then analyzed using MIR spectroscopy. Regardless of soil strata, SOC storage with 95 % confidence level in semi-arid landscapes potentially ranged between 112 200 &pm; 14 000 and 253 000 &pm; 34 000 Mg C corresponding to 411 400 &pm; 51 333 Mg CO2-eq and 927 666.7 &pm; 124 666.7 Mg CO2-eq in the entire study area. On the other hand, investigation on the potential of climate change mitigation through SOC revealed contrasted figures as accumulation rates in cultivated lands ranged from 0.04 to 0.18 MgC ha−1 yr−1 and are balanced by higher depletion rates of −0.004 to −0.73 Mg ha−1 yr−1. This indicates the potential of semi-arid soils to store carbon through improved land management practices. Landscape study structured in cluster-level analysis revealed heterogeneity in the distribution of SOC stocks, a mandatory finer level analysis prior to effective decision-making about RMPs.

Agricultural intensification has been recognized as one of the solutions to increase food production to feed the ever-increasing population in sub-Saharan Africa. This can partly be achieved if quantitative and up-to-date information on soil health indicators are not available. This study used the land health surveillance framework, which combines ground-sampling schemes based on sentinel site and infrared spectroscopy to select a minimum dataset of soil health indicators to identify key land constraints for maize production and target potential interventions. We found high variability in soil properties in the study area which was mainly due to inherent soil conditions and land management practices. The most variable soil properties (CV > 0.38) were nitrogen (N), electric conductivity (ECd), exchangeable bases (ExBas), boron (B), calcium (Ca), potassium (K), magnesium (Mg), manganese (Mn) and phosphorus (P). Moderate variability (0.2 < CV < 0.38) was observed for carbon (C), silt and sand, while properties with least variability (CV < 0.2) were pH and aluminium (Al). The effects of land-use and soil depth were significant (p < 0.05) for most of the soil properties. Principal component analysis (PCA) identified soil nutrient availability, metal concentration and texture as the three main factors that explain most of the variability observed. Significant interactions were observed between soil properties confirming the need for a minimum dataset of indicators. ExBas, B, pH, Mn, ECd, P and clay content formed the minimum dataset of soil health indicators for the study area. The results also showed that the soils of the study site are marginally suitable for the production of maize (Zea mays L.). Low limitations with respect to exchangeable bases (Ca, Mg, K and Na) and severe limitations with respect to B (63%) were detected. However, potential for improvement exists through integrated soil management practices that include the use of organic and inorganic fertilizers, minimum soil tillage, and inclusion of legumes in crop rotations that could improve soil physical and chemical properties. © 2015 Elsevier B.V.

80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data.

Objectives: To evaluated the potential of combinations of MIR, TXRF and XRD techniques for prediction of soil properties. Methods: Using Random Forests (RF) algorithm, MIR prediction of soil properties for a set of georeferenced soil samples associated with the Africa Soil Information Service (AfSIS) project, from 44 sentinel sites, randomized across sub-Sahara Africa, were done . RF regression models were used to model the residuals of the predictions against TXRF spectra combined with MIR first derivative spectra and also against dominant mineralogy groupings measured using XRD. Results: MIR resulted in good prediction models (R>0.80) using RF out-of-bag validation for organic and total C and N, extractable Ca and Al, and pH. Acceptable models (R 2 >0.60) were obtained for exchangeable bases, but models were poor (R