Controls on timescales of soil organic carbon persistence across sub-Saharan Africa

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

Equity in ecosystem restoration

The importance of equity has been emphasized in climate change, biodiversity loss, land degradation, and ecosystem restoration. However, equity implications are rarely considered explicitly in restoration projects. Although the role of equity has been studied in the context of biodiversity conservation and environmental governance, environmental variables are often ignored in equity studies, and spatial analyses of equity are lacking. To address these gaps, we use a mixed methods approach, integrating spatially explicit ecological and social data to evaluate, through an equity lens, a restoration project in a semi-arid rangeland socioecological system in Kenya. We use questionnaires and semi-structured key informant interviews to explore four dimensions of equity: distributional, procedural, recognitional, and contextual. Our results show that restoration employment and distance to the restoration site strongly influence perceived distributional and procedural equity. Employment and distance to restoration site can interact in counterintuitive ways in their influence on aspects of perceived equity, in this case, the fairness of site selection. Our findings exemplify that equity dimensions are intimately linked, and trade-offs can occur between equity dimensions, across socio-temporal scales, and in choosing the ethical framework to apply. Our work demonstrates how restoration is influenced by different dimensions of equity and we opine that incorporating equity in project planning and implementation processes can improve restoration outcomes. We emphasize the importance of respecting plurality in the values systems and ethical frameworks that underlie what is considered equitable, while negotiating trade-offs between diverse ethical positions in the design and implementation of ecosystem restoration projects

Bringing evidence to bear for negotiating tradeoffs in sustainable agricultural intensification using a structured stakeholder engagement process

Sustainable agricultural intensification (SAI) has the potential to increase food security without detrimental effects on ecosystem services. However, adoption of SAI practices across sub-Saharan Africa has not reached transformational numbers to date. It is often hampered by lack of context-specific practices, sub-optimal understanding of tradeoffs and synergies among stakeholders, and lack of approaches that bring diverse evidence sources together with stakeholders to collectively tackle complex problems. In this study, we asked three interconnected questions: (i) What is the accessibility and use of evidence for SAI decision making; (ii) What tools could enhance access and interaction with evidence for tradeoff analysis; and (iii) Which stakeholders must be included? This study employed a range of research and engagement methods including surveys, stakeholder analysis, participatory trade-off assessments and co-design of decision dashboards to better support evidence-based decision making in Zambia, Tanzania and Ethiopia. At the inception, SAI evidence was accessible and used by less than half of the decision makers across the three countries and online dashboards hold promise to enhance access. Many of the stakeholders working on SAI were not collaborating and tradeoff analysis was an under-utilized tool. Structured engagement across multiple stakeholder groups with evidence is critical

A Disease-Mediated Trophic Cascade in the Serengeti and its Implications for Ecosystem C

The removal of rinderpest had cascading effects on herbivore populations, fire, tree density, and even ecosystem carbon in the Serengeti ecosystem of East Africa

Prediction of Soil Fertility Properties from a Globally Distributed Soil Mid-Infrared Spectral Library

Globally applicable calibrations to predict standard soil properties based on infrared spectra may increase the use of this reliable technique. The objective of this study was to evaluate the ability of mid-infrared diffuse reflectance spectroscopy (4000-602 cm(-1)) to predict chemical and textural properties for a globally distributed soil spectral library. We scanned 971 soil samples selected from the International Soil Reference and Information Centre database. A high-throughput diffuse reflectance accessory was used with optics that exclude specular reflectance as a potential source of error. Archived data on soil chemical and physical properties were calibrated to first derivative spectra using partial least-squares regression. Good predictions for the spatially independent validation set were achieved for pH value, organic C content, and cation exchange capacity (CEC) (n = 291, r(2) of linear regression of predicted against measured values >= 0.75 and ratio of standard deviation of measured values to root mean square error of prediction (RPD) >= 2.0). The root mean square errors of prediction (RMSEP) were 0.75 pH units, 9.1 g organic C kg(-1) and 5.5 cmol(c) CEC kg(-1). Satisfactory predictions (r(2) = 0.65-0.75, RPD = 1.4-2.0) were obtained for exchangeable Mg concentration and clay content. The respective RMSEPs were 4.3 cmol(c) kg(-1) and 126 g kg(-1). Poorer predictions (r2 = 0.61 and 0.64) were achieved for sand and exchangeable Ca contents. Although RMSEP values are large relative to laboratory analytical errors, our results suggest a marked potential for the global spectral library as a tool for advice on land management, such as the classification of new samples into basic soil fertility classes based on organic C and clay contents, CEC, and pH. Further research is needed to test the stability of this global calibration on new data sets

Trait‐based approaches for guiding the restoration of degraded agricultural landscapes in East Africa

Functional ecology provides a framework that can link vegetation characteristics of various land uses with ecosystem function. However, this application has been mostly limited to [semi‐]natural systems and small spatial scales. Here, we apply functional ecology to five agricultural landscapes in Kenya, Uganda and Ethiopia, and ask to what extent vegetation characteristics contribute to soil functions that are key to farmers’ livelihoods. We used the Land Degradation Surveillance Framework (LDSF), a multi‐scale assessment of land health. Each LDSF site is a 10 × 10 km landscape in which vegetation cover and erosion prevalence were measured, a tree inventory was carried out, and topsoil (0–20 cm) samples were collected for organic carbon (SOC) analysis in approximately 160 × 1,000 m2 plots. Land degradation is a recurring phenomenon across the five landscapes, indicated by high erosion prevalence (67%–99% of the plots were severely eroded). We used mixed models to assess if vegetation cover, above‐ground woody biomass and the functional properties of woody vegetation (weighted‐mean trait values, functional diversity [FD]) explain variation in SOC and erosion prevalence. We found that the vegetation cover and above‐ground biomass had strong positive effects on soil health by increasing SOC and reducing soil erosion. After controlling for cover and biomass, we found additional marginal effects of functional properties where FD was positively associated with SOC and the abundance of invasive species was associated with higher soil erosion. Synthesis and applications. This work illustrates how functional ecology can provide much‐needed evidence for designing strategies to restore degraded agricultural land and the ecosystem services on which farmers depend. We show that to ensure soil health, it is vital to avoid exposed soil, maintain or promote tree cover, while ensuring functional diversity of tree species, and to eradicate invasive species

Wet chemistry data for a subset of AfSIS: Phase I archived soil samples, World Agroforestry - Research Data Repository

This dataset contains a subset of the samples collected during the AfSIS Phase I project and was a collaborative effort between World Agroforestry (ICRAF) and Rothamsted Research. The soil samples were retrieved from ICRAF Soil Archive: https://worldagroforestry.org/output/icraf-soil-archive-physical-archive-systematically-collected-soil-samples and subject to wet chemical analysis at Rothamsted Research in the UK under a Global Challenges Research Fund project, "BBS/OS/GC/000014B: Chemical and Biological Assessment of AfSIS soils" funded through the UK Biotechnology and Biological Sciences Research Council. This dataset includes the Site, Cluster, Plot as well as the GPS coordinates and wet chemistry data from 2002 samples collected from 18 countries and 51 LDSF sites. The original data collection was part of the AfSIS Phase I project, funded by the Bill and Melinda Gates Foundation (BMGF) and took place between 2009-2013. ICRAF and CIAT contributed the Site, Cluster, Plot and GPS coordinates for the soil samples, ICRAF organized the sub-sampling of the soil samples from the ICRAF physical archive in Nairobi and Rothamsted analysed the soil samples in the UK in 2017 and 2018. Visit our websites here: https://worldagroforestry.org/landhealth and https://www.rothamsted.ac.uk/. The AfSIS Phase I project funded by the Bill and Melinda Gates Foundation (BMGF) from 2009-2013, aimed to provide a consistent baseline of soil information across sub-Saharan Africa (SSA). Led by CIAT-TSBF, partners included: ISRIC, CIESIN, The Earth Institute at Columbia University and World Agroforestry (ICRAF). ICRAF led the systematic assessments of soil health using the Land Degradation Surveillance Framework (LDSF), which was developed at ICRAF, http://landscapeportal.org/blog/2015/03/25/the-land-degradation-surveillance-framework-ldsf/. LDSF sites were randomized using spatial stratification based on Koeppen-Geiger Climate zones across 19 countries in SSA. In total 60 LDSF sites were sampled. Soil samples were collected using the LDSF at two depths, 0-20 cm (labelled Topsoil) and 20-50 cm (labelled Subsoil). In each LDSF site, approximately 320 standard soil samples were collected. All of these were also scanned using MIR Spectroscopy and are available on Dataverse here: Vågen, Tor-Gunnar;Winowiecki, Leigh Ann;Desta, Luseged;Tondoh, Ebagnerin Jérôme;Weullow, Elvis;Shepherd, Keith;Sila, Andrew, 2020, "Mid-Infrared Spectra (MIRS) from ICRAF Soil and Plant Spectroscopy Laboratory: Africa Soil Information Service (AfSIS) Phase I 2009-2013", https://doi.org/10.34725/DVN/QXCWP1, World Agroforestry - Research Data Repository, V1

Assessing drivers of soil properties and classification in the West Usambara mountains, Tanzania

Geoderma Regional 2017; Vol. 11:141 - 154Improved soil information in tropical montane regions is critical for conservation, sustainable agricultural management, and land use planning, but is often challenged by topographic and land-use heterogeneity. The West Usambara mountains are a part of the Eastern Arc chain of mountains of Tanzania and Kenya, a globally important tropical montane ecoregion made up of isolated fault-block mountain complexes characterized by high biological endemism, population density, and agronomic productivity. We synthesized novel and legacy soil data from published and unpublished studies to better understand the drivers of soil property distributions and soil diversity in the West Usambaras, and to serve as a foundation for improved soil mapping efforts across the Eastern Arc. Analysis of the resulting dataset of 468 sites (ranging in elevation from 1040 to 2230 m.a.s.l.) revealed that soil properties varied more significantly by land use and topography than by soil type, suggesting that future mapping efforts in the region should focus primarily on soil property prediction and secondarily on soil classification. Sites under cultivated land uses had the lowest topsoil soil organic carbon (SOC) concentrations and highest pH values, and SOC generally increased with increasing elevation. Valley soils had significantly lower surface SOC concentrations but higher exchangeable bases and pH values than all other landscape positions. Soil pH decreased by an average of 3.5 units across the entire elevation gradient and decreased by 1 unit with elevation even after SOC, land use and landscape position were included in multiple regression models. The relationship of cation exchange capacity (CEC) to SOC and clay content varied by landscape position. Therefore, particularly in montane regions where soils can vary significantly over short distances, multiple functions may be necessary to produce improved estimates of parameters such as CEC. Soil classification was driven most strongly by topography, with Acrisols (WRB Reference Group) and Ultisols (U.S. Soil Taxonomy (ST)) as the dominant soil types, located primarily on mid slope, upper slope and crest landscape positions, making up 47% and 75% of observed profiles, respectively. However, five ST Orders and seven WRB Reference Groups were present in the dataset, with the highest soil diversity occurring at lower slope landscape positions. Conclusions drawn from this large dataset support previous work in the West Usambaras and provide a conceptual foundation from which to build improved soil maps across the Eastern Arc and in other tropical montane systems throughout the world