Tor-Gunnar Vågen

Digital Soil Map of the World

Increased demand and advanced techniques could lead to more refined mapping and management of soils. Soils are increasingly recognized as major contributors to ecosystem services such as food production and climate regulation (1, 2), and demand for up-to-date and relevant soil information is soaring. But communicating such information among diverse audiences remains challenging because of inconsistent use of technical jargon, and outdated, imprecise methods. Also, spatial resolutions of soil maps for most parts of the world are too low to help with practical land management. While other earth sciences (e.g., climatology, geology) have become more quantitative and have taken advantage of the digital revolution, conventional soil mapping delineates space mostly according to qualitative criteria and renders maps using a series of polygons, which limits resolution. These maps do not adequately express the complexity of soils across a landscape in an easily understandable way.

Mapping of soil organic carbon stocks for spatially explicit assessments of climate change mitigation potential

Current methods for assessing soil organic carbon (SOC) stocks are generally not well suited for understanding variations in SOC stocks in landscapes. This is due to the tedious and time-consuming nature of the sampling methods most commonly used to collect bulk density cores, which limits repeatability across large areas, particularly where information is needed on the spatial dynamics of SOC stocks at scales relevant to management and for spatially explicit targeting of climate change mitigation options. In the current study, approaches were explored for (i) field-based estimates of SOC stocks and (ii) mapping of SOC stocks at moderate to high resolution on the basis of data from four widely contrasting ecosystems in East Africa. Estimated SOC stocks for 0?30?cm depth varied both within and between sites, with site averages ranging from 2 to 8?kg?m?2. The differences in SOC stocks were determined in part by rainfall, but more importantly by sand content. Results also indicate that managing soil erosion is a key strategy for reducing SOC loss and hence in mitigation of climate change in these landscapes. Further, maps were developed on the basis of satellite image reflectance data with multiple R-squared values of 0.65 for the independent validation data set, showing variations in SOC stocks across these landscapes. These maps allow for spatially explicit targeting of potential climate change mitigation efforts through soil carbon sequestration, which is one option for climate change mitigation and adaptation. Further, the maps can be used to monitor the impacts of such mitigation efforts over time.

Land Health Surveillance: Mapping Soil Carbon in Kenyan Rangelands

Land health surveillance is a methodological framework for measuring and monitoring land health—the capacity of land to sustain delivery of ecosystem services—for the purpose of targeting agroforestry and other sustainable land management in landscapes, and assessing their impacts. It is modelled on scientific principles used in surveillance in the public health sector, which has a long history of evidence-informed policy and practice. Key elements of the science methodological framework are (1) probability-based sampling of well-defined populations of sample units; (2) standardized protocols for data collection to enable statistical analysis of patterns, trends, and associations; and (3) multilevel statistical modelling of land health attributes at different scales, including in relation to satellite imagery for spatial interpolation. The framework was applied in assessing soil carbon in Kenyan rangelands in Laikipia. Systematic probability-based field sampling provided a robust baseline on condition in the study area. Infrared spectroscopy was used in the laboratory as a rapid low-cost tool for estimating soil carbon concentration. The georeferenced soil carbon values were modelled to reflectance values of fine resolution (2 m) satellite imagery and spatially interpolated over the 100-km2 sampling block. The combination of methods makes soil carbon baselines feasible at a landscape level in land management projects and provides much additional information on soil and vegetation health for targeting interventions. The land health surveillance approach could form the basis for evidence-based decision making on land management at project, national, and even continental levels.

Improvement of spatial modelling of crop suitability using a new digital soil map of Tanzania

Climate change is projected to have widespread impacts on the climate suitability and geographical distribution of agricultural crops. Simulations were conducted on the suitability of common beans (Phaseolus vulgaris L.) in Tanzania under progressive climate change, taking into account a soil fertility constraint. The results were used to assess the effects of incorporating information on soil fertility, more specifically soil organic carbon (SOC) content, into the niche-based EcoCrop model, which was previously based only on climate data. Extending the model improved the correlation between predicted suitability and production statistics at the regional level. Simulated suitability was highly sensitive to SOC-related model parameters, implying that it is critical to incorporate these parameters in order to improve estimates of crop suitability. Simulations using the best parameterisation identified showed that low SOC is currently more limiting for common bean suitability than climate in 51% of the Tanzanian land area (protected areas excluded). However, future projections suggest that climate will be more limiting for the geographic distribution of common beans than SOC in the near future (2030). Spatial data on predicted SOC levels and other soil properties in future scenario modelling are needed for better identification of suitable areas for common bean production.

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.

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

AimsStable 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.MethodsThe 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.ResultsForested 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.ConclusionsThis 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.