Our study area covered the climatic and soil biophysical conditions representative of the West African yam belt. We selected four landscapes, two in Côte d’Ivoire and two in Burkina Faso. Each landscape (approximately 10 km × 10 km) represents a diverse geographic ecoregion. The landscapes cover a gradient between humid forest and the northern Guinean savannah. Specifically, the landscape Liliyo in Côte d’Ivoire is at 5.88∘ N and in the humid forest zone. The predominant soil type is Ferralsol . The landscape Tiéningboué in Côte d’Ivoire is at 8.14∘ N and belongs to the forest savannah transitional zone. The soils are dominated by Nitisols and Lixisols . The landscape Midebdo is at 9.97∘ N and in the sub-humid savannah of Burkina Faso. Its dominant soil types include Lixisols, Gleysols, and Leptosols . The landscape Léo is at 11.07∘ N and in the northern Guinean savannah of Burkina Faso and has Lixisols and Vertisols as the dominant soil types . The mean annual rainfall was approximately 1300 mm in Liliyo, and 900 mm in Tiéningboué, Midebdo, and Léo.

During July and August 2016, we sampled the soil from a total of 80 fields under yam cultivation across the four landscapes, i.e., 20 yam fields in each landscape. The fields were selected in advance by taking into account visual variation in soil color and texture across the landscape. The yam fields selected contained the maximum soil variability based on soil color and cropping history, taking into account both local farmers' knowledge on soil fertility and agronomic extension expertise. Yam is typically planted on soil mounds, ranging from 5000 to 10 000 mounds ha-1 with a single yam plant per mound. Within each field, we sampled the soil at four adjacent mounds in square arrangement, which were spaced between 0.5 and 2 m. At each mound, six to eight auger cores (25 mm in diameter) to the 0.3 m depth were taken at a radius between 0.15 and 0.3 m away from the center of a mound, depending on the size of the mounds. Then the soils from the four mounds were combined into one composite sample per field (around 500 to 1000 g of soil).

An additional set of 14 composite soil samples was collected by the International Center for Research in Agroforestry (ICRAF) at Liliyo from one sentinel site called “Petit-Bouaké” . Sampling took place between 25 and 29 August 2015 at positions that were previously selected for the Land Degradation Surveillance Framework (LDSF) in a spatially stratified manner . The soil samples received from ICRAF were within the same landscape as the sampled soils in Liliyo within YAMSYS but sampled from different positions. All soil samples were air-dried and stored in plastic bags until further analysis.

The air-dried soil samples were crushed and sieved at 2 mm. About 60 to 70 g of the sieved soil was oven-dried at 60∘C for 24 h, of which 20 g was ball-milled. All chemical analyses except soil pH were conducted both on the soils sampled in yam fields (n=80) and the LDSF soils obtained from ICRAF (n=14).

The milled soils were analyzed for total C and macronutrient (N and S) concentrations using an elemental analyzer (vario PYRO cube, Elementar Analysensysteme GmbH, Germany). For each of the four landscapes, two soils were selected and analyzed based on three analytical replicates for quantifying within-sample variance of the elemental analysis. For the remaining samples, the analysis was not repeated. Sulfanilamide was used as a calibration standard for the dry combustion. For pH determination, 10 g of air-dried soil per sample was placed in a 50 mL Falcon tube, and 20 mL of de-ionized water was added. The samples were shaken in a horizontal shaker for 1.5 h and measured for pH using a pH electrode (Benchtop pH/ISE meter model 720A, Orion Research Inc., USA).

Bioavailable micronutrient (Fe, Mn, Zn, and Cu) concentrations in soils were determined with the diethylenetriaminepentaacetic acid (DTPA) extraction method, as described in . The extracting solution consisted of 0.0005 M DTPA, 0.01 M CaCl2, and 0.1 M triethanolamine. Briefly, 10 g of the sieved (< 2 mm) soils was extracted with 20 mL of DTPA solution. Micronutrient concentrations in the filtrates were measured by inductively coupled plasma optical emission spectroscopy (ICP-OES; using a Shimadzu ICPE-9820 plasma atomic emission spectrometer). Final DTPA-extractable concentrations of Fe, Mn, Zn, and Cu were calculated back to per kilogram of dry soil. For each landscape, two soils were selected and analyzed in triplicate to assess analytical errors. For the remaining soils the analysis was not repeated.

For each sample, the concentrations of total elements (Fe, Si, Al, K, Ca, P, Zn, Cu, and Mn) in the soil were assessed by energy dispersive X-ray fluorescence spectrometry (ED-XRF) measurements on 4 g of the milled soil with a SPECTRO XEPOS instrument (SPECTRO Analytical Instruments GmbH, Germany). The soil was mixed with an equal amount of wax using a ball mill and pressed into pellets. Exchangeable cations (Ca2+, Mg2+, K+, Na+, and Al3+) were determined with the BaCl2 method . About 2 g of the air-dried soil (< 2 mm) was extracted by shaking for 2 h with 30 mL of 0.1 M BaCl2 on a horizontal shaker (120 cycles min-1). The suspension was filtered through no. 40 filter paper (Whatman, Brentford, UK). For each landscape, two soils were analyzed in analytical triplicate. The concentrations of exchangeable cations in the BaCl2 extract were measured by ICP-OES (using a Shimadzu ICPE-9820 plasma atomic emission spectrometer). Different BaCl2 extract dilutions were used in order to obtain an optimal signal intensity for the quantification of specific elements across all samples. Concentration of H+ per kilogram of dry soil was calculated based on the pH measured in the BaCl2 extractant. The BaCl2 extraction does only slightly modify pH and is therefore an appropriate method to calculate effective CEC (CECeff) at native soil pH. Using the concentrations of the BaCl2-extractable cations (i.e., Ca2+, Mg2+, K+, Na+, Al3+, and H+), CECeff was calculated as the sum of exchangeable cations in centimoles (cmol) of cation charge per kilogram of dry soil. Exchangeable acidity was defined by the sum of exchangeable Al3+ and H+. Base saturation in percent was calculated as a ratio of the sum of basic cations (Ca2+, Mg2+, K+, and Na+) in cmol(+) per kilogram of soil to the CECeff multiplied by 100.

Particle size analysis was conducted by the International Institute of Tropical Agriculture (IITA) in Cameroon, as described in . Briefly, 50 g of dried 2 mm sieved soil was stirred with 50 mL 4 % sodium hexametaphosphate and 100 mL of deionized water in a mixer, to break down the aggregates into individual particles. Readings with a hydrometer (ASTM 152 H, Thermco, New Jersey, USA) were taken after letting it stand in the suspension for 30 min. The silt content was calculated by subtracting the measured proportion of sand and clay from 100 %.

The milled soils (n=94) were measured on a Bruker ALPHA DRIFT spectrometer (Bruker Optics GmbH, Ettingen, Germany), which was equipped with a ZnSe optics device, a KBr beamsplitter, and a DTGS (deuterated triglycine sulfate) detector. Mid-IR spectra were recorded between 4000 and 500 cm-1 with a spectral resolution of 4 cm-1 and a sampling resolution of 2 cm-1. Reflectance (R) spectra were transformed to apparent absorbance (A) using A=log⁡10(1/R) and corrected for atmospheric CO2 using macros within the OPUS spectrometer software (Bruker Corporation, US). The spectra were referenced to a IR-grade fine ground potassium bromide (KBr) powder spectrum, which was measured prior to the first soil sample and measured every hour again. All spectra were recorded by averaging 128 scans (internal measurements) to improve the signal-to-noise ratio for each of the three independent replicate samples of each soil.

The entire analysis was performed using the R statistical computing language and environment (version 3.6.0) . We used the pls package for PLSR, as described by . Cross-validation resampling, model tuning, and assessment was done using the caret package . Custom functions from the simplerspec package were used for spectroscopic modeling . All data and code to reproduce the results of this study are available online via Zenodo .

The distribution of soil properties of the yam fields showed a wide variation across the landscapes (Fig. ). Total C concentrations across all fields ranged from 2.4 to 24.7 g C kg-1. Total C values at the landscape scale were the lowest (median) in Léo and the highest in Tiéningboué. Soils from yam fields in the two landscapes from Côte d’Ivoire (13.0 ± 5.4 g C kg-1; mean ± standard deviation) had relatively higher total C compared with the fields in the landscapes in Burkina Faso (6.1 ± 3.6 g C kg-1). The median value and variation of CECeff exhibited similar patterns across the landscapes to total C. Total N concentrations across all fields ranged from 0.18 to 2.48 g N kg-1. Total N within and across the four landscapes exhibited a similar pattern to total C. Generally, the landscapes in Burkina Faso were low in total N compared to those from Côte d’Ivoire (0.44 ± 0.24 g N kg-1 vs. 1.09 ± 0.46 g N kg-1). Median total N concentrations were almost identical for Liliyo and Tiéningboué, with 1.1 g N kg-1. Total S concentrations varied between 41 and 242 mg S kg-1 across all fields and showed a similar pattern to total C and N. The yam fields in the landscapes of Burkina Faso had on average more than 2 times higher total S than the other landscapes. Total P concentrations were in a similar range for the landscapes Léo, Midebdo, and Liliyo. In Tiéningboué, total P values were almost 2 times higher than the other fields (817 mg S kg-1 vs. 453 mg P kg-1), with more within-landscape variation.

The concentrations of total Fe, total Al, total Ca, total Zn, and total Cu in the soil tended to be higher for the landscapes in Côte d’Ivoire than in Burkina Faso (Fig. ). To give an example, median concentrations of total Ca were 2.16 g Ca kg-1 in fields sampled from the Tiéningboué region and similar in Liliyo (i.e., 1.90 g Ca kg-1), while they were markedly lower in Léo and Midebdo (i.e., 0.90 vs. 1.26 g Ca kg-1). In general, the ranges for total micronutrient contents were more variable in the landscapes of Côte d’Ivoire (e.g., range = 14.0–57.0 mg Zn kg-1 in Liliyo; lowest range in Léo = 12.2–19.7 mg Zn kg-1). Total K concentration was highly variable within and across the landscapes (overall range = 0.5–34.1 g K kg-1) and lowest in Midebdo (range = 0.9–8.9 g K kg-1), while the highest total K median was measured in yam fields of Léo (range = 4.1–25.0 g K kg-1).

Median extractable Fe and its interquartile ranges were comparable across the landscapes (see Fig. ). However, there were some fields where extractable Fe reached values higher than 100 mg Fe kg-1. Median extractable Zn values showed a similar pattern to total C, with the highest median values and interquartile range in Tiéningboué, and had the lowest in Léo. In comparison, the highest median values and interquartile range of extractable Cu and Mn were found in Liliyo. For extractable Zn, Cu, and Mn, median values and interquartile range were higher in the two landscapes in Côte d’Ivoire than the two landscapes in Burkina Faso.

Across all samples and landscapes, soil pH varied between 4.7 and 8.4. Median pH was comparable in Tiéningboué (i.e., 6.4), Liliyo (i.e., 6.5), and Midebdo (i.e., 6.5). Median pH of yam fields in Léo (i.e., 6.0) was lower than in the other landscapes. Exchangeable K, Ca, and Mg concentrations showed similar patterns across the four landscapes. In Burkina Faso, each of the exchangeable cations showed relatively low median concentrations across the fields and less landscape-level variation than in Côte d’Ivoire. In general, the highest median and variation of exchangeable cations among the landscapes were measured in the yam field soils of Tiéningboué. Median exchangeable Al values were comparable among the landscapes, although there were some outliers with exchangeable Al > 20 mg kg-1 for Midebdo, Liliyo, and Tiéningboué. The CECeff ranged from 0.9 to 14.6 cmol(+) kg-1 across all fields and landscapes. Median CECeff tended to decrease in the following order across landscapes: Léo > Midebdo > Liliyo > Tiéningboué. The interquartile range of CECeff was also the greatest in Tiéningboué and the smallest in Léo.

Reference measurements for total N, S, exchangeable Ca, exchangeable Mg, and CECeff. were closely correlated with total C (Fig. ; 0.71≤r≤0.92 (CECeff.)). Also, total Ca, Al, and clay content correlated closely with total C (r>0.70). Clay contents were weakly related to silt (r=0.21), while sand had a markedly negative relationship with silt (r=-0.89). Bioavailable Zn (DTPA) was covarying with both CECeff. (r=0.58) and total Zn (r=0.59). Bioavailable Cu (DTPA) had a strongly positive association to total Cu (r=0.90). Exchangeable K (BaCl2) had the strongest relationship with total C and CECeff. (r=0.63, and r=0.64).

Among the measured soil properties, mid-IR PLSR models for total K (R2=0.96) and total Al (R2=0.97) performed the best (Table ). Out of a total of 27 soil attributes, 11 were well quantified by the models (Rcv2⩾0.75; Fig. ). The confidence intervals derived from cross-validation prediction were very narrow, showing that all PLSR models were stable. Within this group of stable models, four soil attributes are directly related to the mineralogy (total Fe, Al, K, and Ca), three are related to soil organic matter (total C, N, and S), one is related to texture (clay fraction), one is related to plant nutrition (exchangeable Fe), and two are related to mineralogy and plant nutrition (exchangeable Ca and CECeff). More specifically, total C was accurately predicted, with an R2 of 0.92 and a RMSE of 1.6 g C kg-1. The models were also able to predict total N well (R2=0.89; RMSE = 0.16 g N kg-1). Prediction accuracy of total S was slightly lower than for total C, but its goodness-of-fit and RMSE suggest that the model was reliable for prediction. However, exchangeable K (R2=0.28) and BSeff (R2=0.24) were poorly predicted (Table ). Predictions for percent clay were reliable (R2=0.81; RMSE=2.1 %), whereas predictions for percent sand (R2=0.45; RMSE=8.1 %) and percent silt (R2=0.41; RMSE=6.5 %) were not accurate. Finally, chosen models of all soil attributes had between one and nine PLSR components.

Timely and accurate estimates of multiple soil properties are required to better understand and predict soil constraints across the yam belt in West Africa. The soil spectral library from our study, which includes four landscapes of the yam belt, can be practical to diagnose and monitor (and eventually manage) soil fertility that is considered to be low and therefore is a major constraint to yam production in West Africa. Specifically, our results show that properties closely related to organic matter – total amount of C, (micro)nutrients, and exchangeable cations – can be accurately estimated using mid-IR spectra and in the selected yam-growing landscapes (Fig. ). Soil organic matter plays a crucial role during vegetative growth and tuber formation phases of yam, as it guarantees among many other functions the storage and availability of essential nutrients and water needed for yam and tuber growth throughout the season and prevents soil erosion as well due to its structural stabilization capacity. It promotes soil aggregation, which stabilizes soil organic matter and protects it from microbial decomposition .

Fertilizers are becoming more essential to replenish mineral nutrients for prolonged cropping. Nevertheless, soil organic matter is at high risk of depletion in these regions because of the increasing land use frequencies and shorter fallows to restore the soil organic C pools. While it is pivotal to develop innovative crop and soil management solutions to this problem , it is also crucial to perform a separate but complementary activity to give feedback on potential soil changes: developing and applying soil conventional and proximal sensing methods. When testing sustainable soil and crop management options, for example to derive region-specific and farm-adapted nutrient management strategies, putting both validated quantitative statements on the status of soil organic carbon and local farmers' soil knowledge into the equation is crucial . Inevitably, both determining the inherent soil status (i.e., soil texture and organic carbon) and measuring the chemical and physical environment that regulates nutrient availability at trial sites (e.g., pH) is of agronomic and environmental importance . Maintaining and improving soil quality attributes will be paramount to sustaining soils' ecosystem functions and crop yields over time. Activities to maintain and improve soil properties can for example be oriented towards fostering nutrient recycling.

Quick and reasonably accurate soil estimates derived from mid-IR spectra and empiric models as for example outlined in this study can inform the site-adapted timing, placing, and form of nutrient supply based on local soil conditions. To give a specific example, yam requires relatively large quantities of N and K e.g.,; on light-textured soils, yam can attain high tuber yields but at a high risk of losing large proportions of applied N and K to the environment e.g.,. Therefore, spectral estimates of texture can give an indication that applying larger amounts of N and K at once would not improve yield potential under such situations. Hence, more frequent and local mineral applications of these nutrients after crop emergence, eventually combined with organic mulch, could improve the fertilizer efficiency and mitigate negative environmental impacts under these soil conditions. To estimate the availability of specific (micro)nutrients, however, more efforts need to be made to measure them at fine temporal and spatial resolution.

The mid-IR model accurately estimated C (RMSE = 1.6 g kg-1; Table ; Fig. ). Mostly, only field-scale spectroscopic models achieve such accuracy , whereas the predictive accuracy reported for larger scale application of spectroscopic models is lower than for our model . Models covering a wide geographical range of soils often result in high prediction errors . Despite different soil types and climate regimes across a wide geographic spacing between the calibration fields, we achieved an accurate spectroscopic estimation of total C. The model was also able to reliably estimate a range of other important soil properties than total C. Specifically, other soil variables eligible for a mid-IR quantification include total N, total S, total Ca, total K, total Al, exchangeable Ca, Fe DTPA, CECeff., and clay content (R2>0.75). The close correlations of total C with N, S, exchangeable Ca, exchangeable Mg, CECeff., total Ca, Al, and clay content (Fig. ) are consistent with , who reported very similar associations of clay content and exchangeable cations (Ca, Mg, K) as well as CECeff. in soils from rice fields (0.54≤r≤0.65) – nevertheless they spectrally modeled a considerable soil variability (20 countries in sub-Saharan Africa; 42 study sites) and a larger sample size (n=285) using PLS regression. At the same time, the measured range and the error in spectral estimates of CEC were larger compared to ours (RMSE = 6.7 cmol(+) kg-1 vs. 1.4 cmol(+) kg-1; range = 1.9–66.5 cmol(+) kg-1 vs. 0.9–14.6 cmol(+) kg). Even though total K and Fe(DTPA) were poorly correlated with total C, their spectroscopic estimates were relatively accurate. This suggests that the mid-IR prediction of other soil properties is largely based on their correlation with total C as well as other absorption features of many organic and mineral soil components having a specific IR adsorption.

We also found reasonable prediction accuracy for Cu(DTPA) (R2=0.74) and Mn(DTPA) (R2=0.55), although soil nutrients that are extraction-based or dependent on surface chemistry usually have variable predictive performance . Since relationships between soil composition and soil matrix exchange processes are typically complex, some properties may not be represented in the models in a straightforward manner .

Although total elements are not necessarily a direct proxy for plant-available nutrients – with the exception of total C from organic matter – they can be related to mineralogical status, which is influenced by weathering and nutrient supply. For example, total Fe from iron oxides can be important in controlling the availability of P , and total P can be correlated to available P in other cases. For yam – which is an understudied crop with a relatively large yield gap – fertilizer response to N, P, and K is often absent on soils that have been under long fallow periods . Even more importantly, the number of thoroughly conduced yam fertilizer trials in a region and for distinct soil types is often not sufficient for the site-specific calibration of soil tests with regard to fertilizer response and recommendations .

All mid-IR spectra that we measured for soils in the four landscapes exhibited a similar pattern of absorbance (Fig. ). The O-Si-O absorptions in quartz at 1080, 800–780, and 700 cm-1 were a prominent feature in the spectra due to relatively high sand contents across the landscapes (range 30 % to 92 %, median 76 %). Our spectra further had hydroxyl (OH) absorptions, which are typical for kaolin minerals, at 3695 cm-1 (surface OH), 3620 cm-1 (inner OH), 914 cm-1 (inner OH), and 936 cm-1 (surface OH) . The spectral pattern between the hydroxyl bands at 3695 and 3620 cm-1 was relatively consistent, and the intensity ratio of these flanking peaks was close to 1. This is typical for halloysite (0.8–0.9), while the ratio for kaolinite is often higher (1.2–1.5) and dickite lower (0.6–0.8) . The two weak intermediary stretching absorptions at around 3657 and 3670 cm-1 indicate surface hydroxyls. Together with the absorption at 936 cm-1, the spectra would suggest the presence of rather well-ordered prismatic halloysite . This aligns well with the spectral patterns of soils that were assigned to the Halloysite archetype through similarity mapping (by comparison to the pure mineral spectra) by . Our spectra confirm the presence of kaolin minerals, which reflects the advanced state of mineral weathering in these tropical soil types.

Our accurate predictions, which are comparable to field-scale calibrations, are most likely because of the relatively uniform mid-IR spectra we obtained from our samples and their linear relationships to some of the key properties. This suggests a relatively homogeneous soil chemical composition, particularly with regard to the mineralogy of the sampled soils. Still, the data set presented here is relatively small, and no randomized spatial sampling strategy was used for selecting field locations. Therefore, we propose the implementation of a spectroscopy-driven approach to diagnose soils in more yam-growing areas, as an effort to broaden the library to achieve better spatial coverage of soil variability.