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.

The Grain-for-Green project offsets warming-induced soil organic carbon loss and increases soil carbon stock in Chinese Loess Plateau.

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.

Data on how tree planting and management practices influence tree seedling survival in Kenya and Ethiopia.

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.

Drivers of farmer-managed natural regeneration in the Sahel. Lessons for restoration.

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.

Effects of land-use change on community diversity and composition are highly variable among functional groups.

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.

Predicting the Spatial Distribution and Severity of Soil Erosion in the Global Tropics using Satellite Remote Sensing.

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.

Spatial Gradients of Ecosystem Health Indicators across a Human-Impacted Semiarid Savanna.

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.

Diversification and intensification of agricultural adaptation from global to local scales.

Smallholder farming systems are vulnerable to a number of challenges, including continued population growth, urbanization, income disparities, land degradation, decreasing farm size and productivity, all of which are compounded by uncertainty of climatic patterns. Understanding determinants of smallholder farming practices is critical for designing and implementing successful interventions, including climate change adaptation programs. We examine two dimensions wherein smallholder farmers may adapt agricultural practices; through intensification (i.e., adopt more practices) or diversification (i.e. adopt different practices). We use data on 5314 randomly sampled households located in 38 sites in 15 countries across four regions (East and West Africa, South Asia, and Central America). We estimate empirical models designed to assess determinants of both intensification and diversification of adaptation activities at global scales. Aspects of adaptive capacity that are found to increase intensification of adaptation globally include variables associated with access to information and human capital, financial considerations, assets, household infrastructure and experience. In contrast, there are few global drivers of adaptive diversification, with a notable exception being access to weather information, which also increases adaptive intensification. Investigating reasons for adaptation indicate that conditions present in underdeveloped markets provide the primary impetus for adaptation, even in the context of climate change. We also compare determinants across spatial scales, which reveals a variety of local avenues through which policy interventions can relax economic constraints and boost agricultural adaptation for both intensification and diversification. For example, access to weather information does not affect intensification adaptation in Africa, but is significant at several sites in Bangladesh and India. Moreover, this information leads to diversification of adaptive activities on some sites in South Asia and Central America, but increases specialization in West and East Africa.

Survey data of intra-household decision making and smallholder agricultural production in Northern Uganda and Southern Tanzania.

This article provides a description of intra-household survey data that were collected in Uganda and Tanzania in 2014 and 2015, respectively. The surveys were implemented using a structured questionnaire administered among 585 households in Uganda and 608 in Tanzania. Information on decision making processes in agricultural production was collected from the principal adult male and female decision-makers in each household. The survey consisted of two parts. Firstly, the decision-makers, both male and female of each household were jointly interviewed. Secondly, individual interviews were carried out, questioning the decision-makers separately. The datasets include both household and individual level data containing numeric, categorical and string variables. The datasets have been shared publicly on the Harvard dataverse.

Landscape-scale assessments of stable carbon isotopes in soil under diverse vegetation classes in East Africa: application of near-infrared spectroscopy.

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 δ13C values, particularly across smallholder farming systems in sub-Saharan Africa. This study aimed to develop predictive models for δ13C values in soil using near infrared spectroscopy (NIRS) to increase overall sample size. In addition, this study aimed to assess the δ13C 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 δ13C values, -26.1‰; followed by woodland, -21.9‰; cropland, -19.0‰; shrubland, -16.5‰; and grassland, -13.9‰. Prediction models were developed for δ13C 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 δ13C 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 δ13C in soil, which will allow for landscape-scale assessments to better understand carbon dynamics.

Effective monitoring of agriculture: a response.

The development of effective agricultural monitoring networks is essential to track, anticipate and manage changes in the social, economic and environmental aspects of agriculture. We welcome the perspective of Lindenmayer and Likens (J. Environ. Monit., 2011, 13, 1559) as published in the Journal of Environmental Monitoring on our earlier paper, "Monitoring the World's Agriculture" (Sachs et al., Nature, 2010, 466, 558-560). In this response, we address their three main critiques labeled as 'the passive approach', 'the problem with uniform metrics' and 'the problem with composite metrics'. We expand on specific research questions at the core of the network design, on the distinction between key universal and site-specific metrics to detect change over time and across scales, and on the need for composite metrics in decision-making. We believe that simultaneously measuring indicators of the three pillars of sustainability (environmentally sound, social responsible and economically viable) in an effectively integrated monitoring system will ultimately allow scientists and land managers alike to find solutions to the most pressing problems facing global food security.

Monitoring the world's agriculture.

To feed the world without further damaging the planet, Jeffrey Sachs and 24 foodsystem experts call for a global data collection and dissemination network to track the myriad impacts of different farming practices.

Depositional influences on porewater arsenic in sediments of a mining-contaminated freshwater lake.

Arsenic-containing minerals mobilized during mining activities and deposited to Lake Coeur d'Alene (CDA), Idaho sediments represent a potential source of soluble As to the overlying water. Our objective was to delineate the processes controlling porewater As concentrations within Lake CDA sediments. Sediment and porewater As concentrations were determined, and solid-phase As associations were probed using X-ray absorption near-edge structure (XANES) spectroscopy. Although maximum As in the sediment porewaters varied from 8.4 to 16.2 microM, As sorption on iron oxyhydroxides at the oxic sediment-water interface prevented flux to overlying water. Floods deposit sediment containing variable amounts of arsenopyrite (FeAsS), with majorfloods depositing large amounts of sediment that bury and preserve reduced minerals. Periods of lower deposition increase sediment residence times in the oxic zone, promoting oxidation of reduced minerals, SO4(2-) efflux, and formation of oxide precipitates. Depositional events bury oxides containing sorbed As, transitioning them into anoxic environments where they undergo dissolution, releasing As to the porewater. High Fe:S ratios limit the formation of arsenic sulfides in the anoxic zone. As a result of As sequestration at the sediment-water interface and its release upon burial, decreased concentrations of porewater As will not occur unless As-bearing erosional inputs are eliminated.