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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 (K fs ) 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 K fs . Median K fs for the five sites ranged between 23.8 and 101.8 mm h -1 . We found that K fs 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 K fs . The results further support that K fs 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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ulrik Ilstedt reports financial support was provided by Swedish Research Council. Aida Bargues Tobella reports financial support was provided by Swedish Research Council Formas., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Given the importance of soil for the global carbon cycle, it is essential to understand not only how much carbon soil stores but also how long this carbon persists. Previous studies have shown that the amount and age of soil carbon are strongly affected by the interaction of climate, vegetation, and mineralogy. However, these findings are primarily based on studies from temperate regions and from fine-scale studies, leaving large knowledge gaps for soils from understudied regions such as sub-Saharan Africa. In addition, there is a lack of data to validate modeled soil C dynamics at broad scales. Here, we present insights into organic carbon cycling, based on a new broad-scale radiocarbon and mineral dataset for sub-Saharan Africa. We found that in moderately weathered soils in seasonal climate zones with poorly crystalline and reactive clay minerals, organic carbon persists longer on average (topsoil: 201 ± 130 years; subsoil: 645 ± 385 years) than in highly weathered soils in humid regions (topsoil: 140 ± 46 years; subsoil: 454 ± 247 years) with less reactive minerals. Soils in arid climate zones (topsoil: 396 ± 339 years; subsoil: 963 ± 669 years) store organic carbon for periods more similar to those in seasonal climate zones, likely reflecting climatic constraints on weathering, carbon inputs and microbial decomposition. These insights into the timescales of organic carbon persistence in soils of sub-Saharan Africa suggest that a process-oriented grouping of soils based on pedo-climatic conditions may be useful to improve predictions of soil responses to climate change at broader scales., (© 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

The dynamics of soil organic carbon (SOC) stock is a vital element affecting the climate, and ecological restoration is potentially an effective measure to mitigate climate change by enhancing vegetation and soil carbon stocks and thereby offsetting greenhouse gas emissions. The Grain-for-Green project (GFGP) implemented in Chinese Loess Plateau (LP) since 1999 is one of the largest ecological restoration projects in the world. However, the contributions of ecological restoration and climate change to ecosystem soil carbon sequestration are still unclear. In this study, we improved a soil carbon decomposition framework by optimizing the initial SOC stock based on full spatial simulation of SOC and incorporating the priming effect to investigate the SOC dynamics across the LP GFGP region from 1982 through 2017. Our results indicated that SOC stock in the GFGP region increased by 20.18 Tg C from 1982 through 2017. Most portion (15.83 Tg C) of the SOC increase was accumulated when the GFGP was initiated, with a SOC sink of 16.12 Tg C owing to revegetation restoration and a carbon loss of 0.29 Tg C due to warming during this period. The relationships between SOC and forest canopy height and investigations on the SOC dynamics after afforestation revealed that the accumulation rate of SOC could be as high as 24.68 g C m -2 yr -1 during the 70 years following afforestation, and that SOC could decline thereafter (-8.89 g C m -2 yr -1 ), which was mainly caused by warming. This study provides a new method for quantifying the contribution of ecological restoration to SOC changes, and also cautions the potential risk of LP SOC loss in the mature forest soil under future warming., Competing Interests: Declaration of competing interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of the manuscript., (Copyright © 2022. Published by Elsevier B.V.)

Understanding which trees farmers prefer, what determines their survival and enhancing farmer knowledge of tree management is key to increasing tree cover in agricultural landscapes. This article presents data on tree seedling survival under different tree planting and management practices in Kenya and Ethiopia. Data were collected from 1600 households across three Counties in Kenya and 173 households across four Woredas in Ethiopia, using a structured questionnaire which was administered through the Open Data Kit. Data on seedling survival were collected at least six months after tree seedlings were planted. To understand how planting and management practices influence tree planting across the different socioeconomic and biophysical contexts, both household level and individual tree level data were collected. Household level data included socio-economic and biophysical characteristics of the households while tree specific data included when the tree seedling was planted, where it was planted, the management practices employed and whether surviving. The datasets described in this article help understand which options confer the best chance survival for the planted seedlings and in which socio-economic and biophysical contexts they are most successful., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2021 The Author(s). Published by Elsevier Inc.)

Farmer-managed natural regeneration (FMNR) is being promoted for restoration beyond its original range in the Sahel. FMNR involves farmers selecting and managing natural regeneration on their fields, while keeping them under the primary function of agricultural production. However, little is known about what regenerates in different contexts, even though this underlies potential restoration impact. Here we assess how human impact, land degradation and dispersal limitation affect structural and functional properties of regeneration across 316 plots in agroforestry parklands of Ghana and Burkina Faso. We found that intensity of land use (grazing and agricultural practices) and dispersal limitation inhibited regeneration, while land degradation did not. Functional composition of regenerating communities shifted towards shorter statured, small-seeded and conservative strategies with intensity of land use. We conclude that the presence of trees of desired species in the vicinity is a precondition for successfully implementing FMNR for restoration, and that regeneration needs to be protected from grazing. Assessment of regeneration potential is imperative for scaling out FMNR and where natural regeneration will be insufficient to achieve restoration targets, FMNR needs to be complemented with tree planting.

In order to understand how the effects of land-use change vary among taxa and environmental contexts, we investigate how three types of land-use change have influenced phylogenetic diversity (PD) and species composition of three functionally distinct communities: plants, small mammals, and large mammals. We found large mammal communities were by far the most heavily impacted by land-use change, with areas of attempted large wildlife exclusion and intense livestock grazing, respectively, containing 164 and 165 million fewer years of evolutionary history than conserved areas (~40% declines). The effects of land-use change on PD varied substantially across taxa, type of land-use change, and, for most groups, also across abiotic conditions. This highlights the need for taxa-specific or multi-taxa evaluations, for managers interested in conserving specific groups or whole communities, respectively. It also suggests that efforts to conserve and restore PD may be most successful if they focus on areas of particular land-use types and abiotic conditions. Importantly, we also describe the substantial species turnover and compositional changes that cannot be detected by alpha diversity metrics, emphasizing that neither PD nor other taxonomic diversity metrics are sufficient proxies for ecological integrity. Finally, our results provide further support for the emerging consensus that conserved landscapes are critical to support intact assemblages of some lineages such as large mammals, but that mosaics of disturbed land-uses, including both agricultural and pastoral land, do provide important habitats for a diverse array of plants and small mammals., (© 2019 by the Ecological Society of America.)

This research paper presents the first consistent spatial assessments of soil erosion prevalence based on Earth observation data and systematic field surveys to train models that predict soil erosion prevalence across the global tropics. The results of the study have major implications for efforts to identify land degradation hotspots and design interventions to restore degraded lands given the importance of soil erosion as a major process of land degradation globally. The paper also presents analysis of changes in soil erosion prevalence over the 15 year period from 2002 to 2017. The results of the study can also be used to better understand ecosystem health and resilience in the global tropics, for assessments of food security, and the implications of land degradation on climate change. Soil erosion has long been recognized as a major process of land degradation globally, affecting millions of hectares of land in the tropics and resulting in losses in productivity and biodiversity, decreased resilience of both marine and terrestrial ecosystems, and increased vulnerability to climate change. This paper presents an assessment of the extent of soil erosion in the global tropics at a moderate spatial resolution (500 m) based on a combination of systematic field surveys using the Land Degradation Surveillance Framework (LDSF) methodology and Earth observation data from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. The highest erosion prevalence was observed in wooded grassland, bushland, and shrubland systems in semi-arid areas, while the lowest occurrence was observed in forests. Observed erosion decreased with increasing fractional vegetation cover, but with high rates of erosion even at 50-60% fractional cover. These findings indicate that methods to assess soil erosion need to be able to detect erosion under relatively dense vegetation cover. Model performance was good for prediction of erosion based on MODIS, with high accuracy (~89% for detection) and high overall precision (AUC = 0.97). The spatial predictions from this study will allow for better targeting of interventions to restore degraded land and are also important for assessing the dynamics of land health indicators such as soil organic carbon. Given the importance of soil erosion for land degradation and that the methodology gives robust results that can be rapidly replicated at scale, we would argue that soil erosion should be included as a key indicator in international conventions such as the United Nations Convention to Combat Desertification., (© 2019 by the authors.)

Drivers of soil organic carbon (SOC) dynamics involve a combination of edaphic, human, and climatic factors that influence and determine SOC distribution across the landscape. High-resolution maps of key indicators of ecosystem health can enable assessments of these drivers and aid in critical management decisions. This study used a systematic field-based approach coupled with statistical modeling and remote sensing to develop accurate, high-resolution maps of key indicators of ecosystem health across savanna ecosystems in South Africa. Two 100-km landscapes in Bushbuckridge Local Municipality were surveyed, and 320 composite topsoil samples were collected. Mid-infrared spectroscopy was used to predict soil properties, with good performance for all models and root mean squared error of prediction (RMSEP) values of 1.3, 0.2, 5, and 3.6 for SOC, pH, sand, and clay, respectively. Validation results for the mapping of soil erosion prevalence and herbaceous cover using RapidEye imagery at 5-m spatial resolution showed good model performance with area under the curve values of 0.80 and 0.86, respectively. The overall (out-of-bag) random forest model performance for mapping of soil properties, reported using , was 0.8, 0.77, and 0.82 for SOC, pH, and sand, respectively. Calibration model performance was good, with RMSEP values of 2.6 g kg for SOC, 0.2 for pH, and 6% for sand content. Strong gradients of increasing SOC and pH corresponded with decreasing sand content between the study sites. Although both sites had low SOC overall, important driving factors of SOC dynamics included soil texture, soil erosion prevalence, and climate. These data will inform strategic land management decisions focused particularly on improving ecosystem conditions., (Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.)

Aims: Stable carbon isotopes are important tracers used to understand ecological food web processes and vegetation shifts over time. However, gaps exist in understanding soil and plant processes that influence δ 13 C values, particularly across smallholder farming systems in sub-Saharan Africa. This study aimed to develop predictive models for δ 13 C values in soil using near infrared spectroscopy (NIRS) to increase overall sample size. In addition, this study aimed to assess the δ 13 C values between five vegetation classes., Methods: The Land Degradation Surveillance Framework (LDSF) was used to collect a stratified random set of soil samples and to classify vegetation. A total of 154 topsoil and 186 subsoil samples were collected and analyzed using NIRS, organic carbon (OC) and stable carbon isotopes., Results: Forested plots had the most negative average δ 13 C values, -26.1‰; followed by woodland, -21.9‰; cropland, -19.0‰; shrubland, -16.5‰; and grassland, -13.9‰. Prediction models were developed for δ 13 C using partial least squares (PLS) regression and random forest (RF) models. Model performance was acceptable and similar with both models. The root mean square error of prediction (RMSEP) values for the three independent validation runs for δ 13 C using PLS ranged from 1.91 to 2.03 compared to 1.52 to 1.98 using RF., Conclusions: This model performance indicates that NIR can be used to predict δ 13 C in soil, which will allow for landscape-scale assessments to better understand carbon dynamics., (© The Author(s) 2017.)