Yam (Dioscorea spp.) is a tuber crop grown for food security, income generation, and traditional medicine. This crop has a high cultural value for some of the groups growing it. Most of the production comes from West Africa where the increased demand has been covered by enlarging cultivated surfaces while the mean yield remained around 10 t tuber ha−1. In West Africa, yam is traditionally cultivated without input as the first crop after a long-term fallow as it is considered to require a high soil fertility. African soils, however, are being more and more degraded. The aims of this review were to show the importance of soil fertility for yam, discuss barriers that might limit the adoption of integrated soil fertility management (ISFM) in yam-based systems in West Africa, present the concept of innovation platforms (IPs) as a tool to foster collaboration between actors for designing innovations in yam-based systems and provide recommendations for future research. This review shows that the development of sustainable, feasible, and acceptable soil management innovations for yam requires research to be conducted in interdisciplinary teams including natural and social sciences and in a transdisciplinary manner involving relevant actors from the problem definition, to the co-design of soil management innovations, the evaluation of research results, their communication and their implementation. Finally, this research should be conducted in diverse biophysical and socio-economic settings to develop generic rules on soil/plant relationships in yam as affected by soil management and on how to adjust the innovation supply to specific contexts.

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.

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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.

We need measurements of soil water retention (SWR) and available water capacity (AWC) to assess and model soil functions, but methods are timeconsuming and expensive. Our aim here was to investigate the modelling of AWC and SWR with visible-near-infrared spectra (vis-NIR) and the machinelearning method CUBIST. We used soils from 54 locations across Australian agricultural regions, from three depths: 0-15 cm, 15-30 cm and 30-60 cm. The volumetric water content of the samples and their vis-NIR spectra were measured at seven matric potentials from À1 kPa to À1500 kPa. We modelled the following: (i) AWC directly with the average spectra of the samples measured at different water contents, (ii) water contents at field capacity and permanent wilting point and calculated AWC from those estimates, (iii) AWC with spectra of air-dried soils, and (iv) parameters of the Kosugi and van Genuchten SWR models, then reconstructed the SWR curves to calculate AWC. We compared the estimates with those from a local pedotransfer function (PTF) and an established Australian PTF. The accuracy of the spectroscopic approaches varied but was generally better than the PTFs. The spectroscopic methods are also more practical because they do not require additional soil properties for the modelling. The root-mean squared-error (RMSE) of the spectroscopic methods ranged from 0.033 cm 3 cm À3 to 0.059 cm 3 cm À3 . The RMSEs of the PTFs were 0.050 cm 3 cm À3 for the local and 0.077 cm 3 cm À3 for the general PTF.Spectroscopy with machine learning provides a rapid and versatile method for estimating the AWC and SWR characteristics of diverse agricultural soils. Highlights• Soil available water capacity can be estimated with vis-NIR specta.• Parameters of water retention models can be estimated with vis-NIR spectra.• vis-NIR spectroscopy performed better than pedotransfer functions.• The results apply to a diverse range of soils.