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Water security represents a major challenge in East Africa, affecting the livelihoods of millions of people and hindering sustainable development. Predicted increases in rainfall intensity and variability are expected to exacerbate water insecurity and land degradation. Improving soil infiltrability is an effective strategy for addressing water insecurity and land degradation. Research on soil infiltrability is often highly localized; therefore, scientific understanding of the drivers of infiltrability on larger spatial scales is limited. The aim of this study was to understand the main drivers of infiltrability across five contrasting landscapes in Kenya. We measured field-saturated hydraulic conductivity (Kfs) in 257 plots and collected data for variables representing soil properties (sand content, soil organic carbon (SOC) and pH), land degradation (grazing pressure and presence of erosion), vegetation quantity (woody aboveground biomass), and vegetation quality (functional properties and diversity). We used generalized mixed-effects models to test for the effects of these variables on Kfs. Median Kfs for the five sites ranged between 23.8 and 101.8 mm h−1. We found that Kfs was positively associated with sand content (standardized effect 0.39), SOC content (0.15), and functional diversity of woody vegetation (0.09), while it had a negative relationship with the presence of erosion (−0.24) and grazing pressure (−0.09). Subsequently, we conclude that infiltrability can be enhanced through using land restoration strategies which specifically target parameters that affect Kfs. The results further support that Kfs is not solely dictated by inherent soil properties, and that management interventions which boost SOC, reduce erosion, and minimize unsustainable grazing can help address water scarcity by restoring soil hydrological function.

Soil infiltration is critical for water security and related ecosystem services. This infiltration, the ability of soils to absorb water at their surface, is controlled by the soil hydraulic conductivity. Despite recent efforts in assembling measurements of soil hydraulic conductivity, global databases and derived pedotransfer functions lack coverage in the tropics. Here, we present soil infiltration measurements and other indicators of soil and land health collected systematically in 3,573 plots from 83 100 km2 sites across 19 countries in sub-Saharan Africa. We use these data to (a) determine field-saturated hydraulic conductivity (Kfs) and (b) explore which variables best predict variation in Kfs. Our results show that sand content, soil organic carbon (SOC), and woody cover had a positive relationship with Kfs, whereas grazing intensity and soil pH had a negative relationship. Our findings highlight that, despite soil texture being important, structure also plays a critical role. These results indicate considerable potential to improve soil hydrological functioning through management and restoration practices that target soil structure. Enhancing SOC content, limiting animal stocking, promoting trees, shrubs, and other vegetation cover, and preventing soil erosion can increase Kfs and improve water security. This data set can contribute to improving Earth system and land surface models for applications in Africa.

Land degradation is a major threat to food security in Sub Saharan Africa. Low infiltration rates in degraded soils increase the risk of surface runoff and decrease soil and groundwater recharge, resulting in further loss of soil fertility, water scarcity and crop failure. Increasing woody vegetation typically enhances soil infiltrability but little is known about how species may have differential effects on the soil hydrological properties. The aim of this study is to understand how woody vegetation and its functional properties affect soil fertility and infiltrability.1. We measured field-saturated soil hydraulic conductivity (Kfs) and soil organic carbon (SOC) in 38 plots across agricultural landscapes in Muminji, Kenya. Woody vegetation and land use inventories took place and species functional traits were measured on the 63 most abundant species. We systematically tested the effects of vegetation quantity (aboveground woody biomass and vegetation cover) and quality (functional properties and diversity) on soil health (Kfs as a proxy for soil infiltrability and SOC for soil fertility).2. We found that both vegetation quantity and quality affected soil health. Aboveground woody biomass increased the Kfs and we found a nearly significant positive effect of vegetation cover on SOC. Woody plants with a low leaf thickness positively affected Kfs and a nearly significant negative effect of wood moisture content on SOC was found.3. Synthesis and applications. This study demonstrates that the systematic assessment of vegetation can lead to evidence-based recommendations to guide land restoration. We found that avoiding bare soil and promoting woody plants, while favouring species with thin leaves and avoiding species with a very low wood density and water storage strategy, is beneficial for soil health across agricultural landscapes in East African drylands.

We measured field-saturated soil hydraulic conductivity (Kfs) and soil organic carbon (SOC) in 38 plots across agricultural landscapes in Muminji, Kenya. Woody vegetation and land use inventories took place and species functional traits were measured on the 63 most important species. We systematically tested the effects of vegetation quantity (aboveground woody biomass and vegetation cover) and quality (functional properties and diversity) on soil health (Kfs as a proxy for soil infiltrability and SOC for soil fertility). This dataset contains the data that underlies the analysis and outcomes of this study, described for each dataset below.

Several forest change detection algorithms are available for tracking and quantifying deforestation based on dense Landsat and Sentinel time series satellite data. Only few also capture regrowth after clearing in an accurate and continuous way across a diversity of forest types (including dry and seasonal forests) and are thus suitable to address the need for better information on secondary forest succession and for assessing forest restoration activities. We present a new change detection algorithm that makes use of the flexibility of kernel density estimations to create a forest reference phenology, taking into account all historical phenological variations of the forest rather than smoothing these out by curve fitting. The AVOCADO (Anomaly Vegetation Change Detection) algorithm allows detection of anomalies with a spatially explicit likelihood measure. We demonstrate the flexibility of the algorithm for three contrasting sites using all available Landsat time series data; ranging from tropical rainforest to dry miombo forest ecosystems, with different time series data densities, and characterized by different forest change types (e.g. selective logging, shifting cultivation). We found that the approach produced in general high overall accuracies (> 90%) across these varying conditions, but had lower accuracies in the dry forest site with a slight overestimation of disturbances and regrowth. The latter was due to the similarity of crops in the time series NDMI signal, causing false regrowth detections. In the moist forest site the low producer accuracies in the intact forest and regrowth class was due to its very small area class (most forest disappeared by the nineties). We showed that the algorithm is capable of capturing small-scale (gradual) changes (e.g. selective logging, forest edge logging) and the multiple changes associated to shifting cultivation. The performance of the algorithm has been shown at regional scale, but if larger scale studies are required

This easy-to-use App shows you data on the distribution of indigenous tree species in different natural vegetation types, combined with information on the products and services that the tree species can provide. It arms you – local community members, government agencies, private sector owners, and other land managers – with the information you need to select the best tree species for your landscape restoration or agroforestry effort.

This dataset underlies the statistical analyses central to the publication Lohbeck et al 'Drivers of farmer-managed natural regeneration in the Sahel. Lessons for restoration', published in Scientific Reports (DOI: 10.1038/s41598-020-70746-z68689791-6eec-4311-a341-bcf0cf5b1e06). The data is based on a field inventory using the Land Degradation Surveillance Framework (LDSF) methodology (see LDSF fieldguide under 'related publications', and methods in the publication in scientific reports). In addition, a detailed inventory of the trees (in the plot) and the regeneration (in the subplot) has been carried out. In addition to that functional traits have been measured for the most common species among the regeneration. Functional traits measurements followed standardized protocols (See Perez-Harguindeguy et al 2013 and methods in the publication). Functional traits at the species level were scaled to the plot level using community-weighted means, those are the values represented in the data table (see methods in publication for details). Soil variables are predicted using MIR spectroscopy (see methods in publication for details)., This dataset underlies the statistical analyses central to the publication Lohbeck et al 'Drivers of farmer-managed natural regeneration in the Sahel. Lessons for restoration', published in Scientific Reports (DOI: 10.1038/s41598-020-70746-z68689791-6eec-4311-a341-bcf0cf5b1e06). The data is based on a field inventory using the Land Degradation Surveillance Framework (LDSF) methodology (see LDSF fieldguide under 'related publications', and methods in the publication in scientific reports). In addition, a detailed inventory of the trees (in the plot) and the regeneration (in the subplot) has been carried out. In addition to that functional traits have been measured for the most common species among the regeneration. Functional traits measurements followed standardized protocols (See Perez-Harguindeguy et al 2013 and methods in the publication). Functional traits at the species level were scaled to the plot level using community-weighted means, those are the values represented in the data table (see methods in publication for details). Soil variables are predicted using MIR spectroscopy (see methods in publication for details).