TY - JOUR
T1 - Applications and challenges of digital soil mapping in Africa
AU - Nenkam, Andree M.
AU - Wadoux, Alexandre M.J.C.
AU - Minasny, Budiman
AU - Silatsa, Francis B.T.
AU - Yemefack, Martin
AU - Ugbaje, Sabastine Ugbemuna
AU - Akpa, Stephen
AU - Zijl, George Van
AU - Bouasria, Abdelkrim
AU - Bouslihim, Yassine
AU - Chabala, Lydia Mumbi
AU - Ali, Ashenafi
AU - McBratney, Alex B.
PY - 2024/9
Y1 - 2024/9
N2 - The mapping of soils in Africa is at least a century old. We currently have access to various maps depicting mapping units locally and for the continent. In the past two decades, there has been a growing interest in alternatives for generating soil maps through digital soil mapping (DSM) techniques. There are, however, numerous challenges pertaining to the implementation of DSM in Africa, such as the unavailability of appropriate covariates, age and positional error in the measurements, low sampling density, and spatial clustering of the soil data used to fit and validate the models. This review aims to investigate the current state of DSM in Africa, identify challenges specific to implementing DSM in Africa and the ways it has been solved in the literature. We found that nearly half of African countries had an existing digital soil map covering either a local or national area, and that most studies were performed at a local extent. Soil carbon was the most common property under study, whereas soil hydraulic variables were seldom reported. Nearly all studies performed mapping for the topsoil up to 30 cm and calculated validation statistics using existing datasets but without collecting a post-mapping probability sample. Few studies (i.e., 11%) reported an estimate of map uncertainty. Half of the studies had in mind a downstream application (e.g., soil fertility assessment) in the map generation. We further correlated the area of study and sampling density and found a strong negative relationship. About 30% of the studies relied on legacy soil datasets and had a lack of sufficient spatial coverage of their area of study. From this review, we highlight some research opportunities and suggest improvements in the current methodologies. Future research should focus on capacity building in DSM, new data collection, and legacy data rescue. New initiatives, that should be initiated and led from within the continent, could support the long-term monitoring of soils and updating of soil information systems while ensuring their contextualised usability. This pairs with better delivery of existing DSM studies to stakeholders and the generation of a value-added proposition to governmental institutions.
AB - The mapping of soils in Africa is at least a century old. We currently have access to various maps depicting mapping units locally and for the continent. In the past two decades, there has been a growing interest in alternatives for generating soil maps through digital soil mapping (DSM) techniques. There are, however, numerous challenges pertaining to the implementation of DSM in Africa, such as the unavailability of appropriate covariates, age and positional error in the measurements, low sampling density, and spatial clustering of the soil data used to fit and validate the models. This review aims to investigate the current state of DSM in Africa, identify challenges specific to implementing DSM in Africa and the ways it has been solved in the literature. We found that nearly half of African countries had an existing digital soil map covering either a local or national area, and that most studies were performed at a local extent. Soil carbon was the most common property under study, whereas soil hydraulic variables were seldom reported. Nearly all studies performed mapping for the topsoil up to 30 cm and calculated validation statistics using existing datasets but without collecting a post-mapping probability sample. Few studies (i.e., 11%) reported an estimate of map uncertainty. Half of the studies had in mind a downstream application (e.g., soil fertility assessment) in the map generation. We further correlated the area of study and sampling density and found a strong negative relationship. About 30% of the studies relied on legacy soil datasets and had a lack of sufficient spatial coverage of their area of study. From this review, we highlight some research opportunities and suggest improvements in the current methodologies. Future research should focus on capacity building in DSM, new data collection, and legacy data rescue. New initiatives, that should be initiated and led from within the continent, could support the long-term monitoring of soils and updating of soil information systems while ensuring their contextualised usability. This pairs with better delivery of existing DSM studies to stakeholders and the generation of a value-added proposition to governmental institutions.
KW - Digital soil assessment
KW - Legacy soil data
KW - Predictive models
KW - Sampling density
KW - Soil information
U2 - 10.1016/j.geoderma.2024.117007
DO - 10.1016/j.geoderma.2024.117007
M3 - Article
AN - SCOPUS:85202016129
SN - 0016-7061
VL - 449
JO - Geoderma
JF - Geoderma
M1 - 117007
ER -