Reconnaissance soil maps at 1:250,000 scale are the most detailed source of soil information for large parts of France. For many environmental applications, however, the level of detail and accuracy ...of these maps is insufficient. Funds are lacking to refine and update these maps by traditional soil survey. In this study we investigated the merit of digital soil mapping to refine and improve the 1:250,000 reconnaissance soil map of a 1580km2 area in Haute-Normandie, France. The soil map was produced in 1988 and distinguishes nine soil class units. The approach taken was to predict soil class from a large number of environmental covariates using regression techniques. The covariates used include DEM derivatives, geology and land cover maps. Because very few soil point observations were available within the area, we calibrated the regression model by sampling the soil map on a grid. We calibrated three models: classification tree (CT), multinomial logistic regression (MLR) and random forests (RF), and used these models to predict the nine soil classes across the study area. The new and original maps were validated with field data from 123 locations selected with a stratified simple random sampling design. For MLR, the estimate of the overall purity was 65.9%, while that of the reconnaissance map was 55.5%. The difference between the purity estimates of these maps was statistically significant (p=0.014). The significant improvement over the existing soil map is remarkable because the regression model was calibrated with the existing soil map and uses no additional soil observations.
•We refined a reconnaissance soil map by using digital soil mapping techniques.•Regression models were calibrated with the existing soil map.•Maps were validated with independent field data.•The overall purity of the new map significantly increased.
Soils are back on the global political agenda. Renewed interest of the soil resource is fuelled by an increasing awareness about the importance of sustainable soil management to secure production of ...food and fiber for a quickly growing world population, and about the major role of soils in the global carbon cycle. With this has come great demand for accurate, up-to-date and detailed geographical soil information. The current generation soil information systems typically store data from conventional surveys. Besides soil data at points, these systems contain soil maps that are often restricted to soil type; thematic soil maps are mostly missing. The maps are frequently outdated, lack detail and quantitative information on accuracy, or have no full spatial coverage. Consequently, these data are of limited use in today´s soil data applications.The Dutch soil information system BIS is no exception to this situation. The main source of soil information in the Netherlands, the nationwide 1:50 000 soil map, is becoming outdated, particularly for the areas with peat soils, and needs to be updated. Furthermore, maps of basic soil properties with quantified accuracy are lacking. Such maps are essential input for environmental process models that predict the effect of policy measures on for example soil acidification, pesticide leaching and greenhouse gas emission. Now the urgent need is felt to update the national soil map and to extend BIS with full-coverage thematic maps of all major soil properties with quantified accuracy. Efficient, quantitative methods for (geo)statistical modelling of soil maps, referred to as digital soil mapping (DSM), might be very useful for this purpose. Yet, despite growing global popularity DSM has not been applied in an operational way in the Netherlands so far. The main objective of this thesis is therefore to investigate and evaluate the merit of DSM for updating soil information in the Netherlands. Research focuses on DSM methods for updating soil type maps as well as maps of continuous soil properties. The province of Drenthe with large areas of peat soils is selected as case study area to illustrate and evaluate the developed methods.After the general introduction in Chapter 1, Chapter 2 describes a study on the possibility of updating the 1:50 000 soil map using a simple generalized linear regression Summary model and legacy soil point data from BIS. Map unit-specific multinomial logit models (MLM) were used to predict probability distributions of soil types within ten map units of the simplified soil map 1:50 000. For this purpose a framework for selecting an MLM was taken from the literature and adapted for soil mapping. Updating not only focused on peat soils but also on mineral soils to investigate if the purity of these map units could be increased through disaggregation with high-resolution covariates. Validation showed a modest 6% improvement in map purity compared to the existing, outdated soil map. This improvement was mainly attributed to better representation of soil distribution within the peat map units of the simplified map. However, map unit purities and class representations of the four peat soils as depicted on the updated map were still small.Digital soil type maps offer new possibilities for mapping individual soil properties. Chapter 3 describes the development of a model that exploits the information from such soil type map for spatial prediction of continuous soil properties. This model has important advantages compared to the conventional geostatistical model. First, actual (observed) soil type at sampling locations can be used as covariate instead of the mapped soil type. This has the advantage that the relationship between soil property and soil type is not confounded by impurities in the map units. Second, using actual soil type as covariate in the model makes it possible to quantify the proportion of the prediction variance that arises from uncertainty of the actual soil type at prediction locations. The developed model is applied to map the soil organic matter (SOM) content using the digital soil type map created in Chapter 2. Validation showed that the prediction performance of the proposed model was slightly better than that of the conventional geostatistical model.In Chapter 4 a method is proposed for three-dimensional mapping of SOM that combines general pedological knowledge with geostatistical modelling. A conceptual SOM depth profile was constructed by stacking building blocks (model horizons) for each soil type depicted on the updated digital map from Chapter 2. The vertical distribution of SOM within each building block was described by a function. The combination of building blocks—stacked in pre-defined order—with their associated parameters (thickness, average SOM content, exponential decay parameters) describes a soil type-specific depth profile. The parameters of each of these depth profiles were spatially predicted by geostatistical interpolation with covariates. A probability distribution of soil type-specific depth functions was then obtained by combining these predictions with the digital soil type map from Chapter 2. The depth functions and their associated probabilities were used to map the SOM stock for four depth intervals using the methodology described in Chapter 3. Validation of the predicted stocks with an independent probability sample showed accurate results for the topsoil. Results for deeper soil layers, however, were modest. Prediction performance of pedometric depth functions was comparable to that of conventional depth functions.The main drawback of the MLM, which was applied for soil type mapping in Chapter 2, is that spatial dependency in the data is not exploited for spatial prediction. Chapter 5 addresses this issue and investigates if a soil type map predicted by a spatial model is more accurate than one predicted by a non-spatial model. As spatial model the generalized linear geostatistical model (GLGM) was chosen. The GLGM is central to the methodological framework of model-based geostatistics, which is considered state-of-the-art in DSM. A pragmatic approach was adopted in which each of the five soil types in the case study area in the cultivated peatlands was modelled separately with a binomial logit-linear GLGM. Predictions with the soil type-specific GLGMs resulted in five binomial probabilities at each prediction location, which were scaled to multinomial probabilities so that they sum to one. Validation showed that use of a spatial model for digital soil type mapping did not result in more accurate predictions of soil type than those with the non-spatial MLM.Chapter 6 compares the efficiency of DSM methods with that of conventional soil mapping (CSM) methods for updating soil type and property maps. In addition, the effect of mapping effort (expressed in a monetary unit per ha) on accuracy is assessed for digital soil type and property maps. For digital soil type mapping the GLGM was used. For soil property mapping (SOM content en peat thickness) two methods are considered for both DSM and CSM. For DSM these are the method from Chapter 3 and the conventional geostatistical method (universal kriging). For CSM these are the representative profile descriptions (RPD) and map-unit-means (MUM) methods. For DSM both methods gave similar results in terms of accuracy. The MUM method gave better results than the RPD. For CSM the MUM method gave better results than the RPD. Validation results further showed that DSM produced soil type and property maps that were of similar accuracy as those produced by CSM. Furthermore, DSM maps were produced much more efficiently than the CSM maps: costs per hectare were a factor three to four smaller without compromising accuracy. This shows that for future updating of soil information DSM can be an attractive alternative to CSM. Finally, Chapter 7 presents a synthesis of the results and the main findings of Chapters 2 to 6. Implications of the results for the soil information system BIS and future updating of soil information in the Netherlands are discussed and an outlook on future research is given. It is argued that soil survey is shifting from conventional, qualitative soil survey to quantitative soil survey. This means that a toolbox with quantitative, state-of-the-art methods for soil mapping is not sufficient for effective and successful operational use of DSM. It requires the development of a next-generation soil information system based on new strategies and methods for collecting, storing, processing, visualizing and disseminating soil information. This thesis presents a first step on the road towards such system.
80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management ...practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management-organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data.
Background and purpose
Inflammatory disease activity in multiple sclerosis (MS) decreases with advancing age. Previous work found a decrease in contrast‐enhancing lesions (CELs) with age. Here, we ...describe the relation of age and magnetic resonance imaging (MRI) measures of inflammatory disease activity during long‐term follow‐up in a large real‐world cohort of people with relapse onset MS.
Methods
We investigated MRI data from the long‐term observational Amsterdam MS cohort. We used logistic regression models and negative binomial generalized estimating equations to investigate the associations between age and radiological disease activity after a first clinical event.
Results
We included 1063 participants and 10,651 cranial MRIs. Median follow‐up time was 6.1 years (interquartile range = 2.4–10.9 years). Older participants had a significantly lower risk of CELs on baseline MRI (40–50 years vs. <40 years: odds ratio OR = 0.640, 95% confidence interval CI = 0.45–0.90; >50 years vs. <40 years: OR = 0.601, 95% CI = 0.33–1.08) and a lower risk of new T2 lesions or CELs during follow‐up (40–50 years vs. <40 years: OR = 0.563, 95% CI = 0.47–0.67; >50 years vs. <40 years: OR = 0.486, 95% CI = 0.35–0.68).
Conclusions
Greater age is associated with a lower risk of inflammatory MRI activity at baseline and during long‐term follow‐up. In patients aged >50 years, a less aggressive treatment strategy might be appropriate compared to younger patients.
Background:
Extended interval dosing (EID) of natalizumab treatment is increasingly used in multiple sclerosis. Besides the clear anti-inflammatory effect, natalizumab is considered to have ...neuroprotective properties as well.
Objectives:
This study aimed to study the longitudinal effects of EID compared to standard interval dosing (SID) and natalizumab drug concentrations on brain atrophy.
Methods:
Patients receiving EID or SID of natalizumab with a minimum radiological follow-up of 2 years were included. Changes in brain atrophy measures over time were derived from clinical routine 3D-Fluid Attenuated Inversion Recovery (FLAIR)-weighted magnetic resonance imaging (MRI) scans using SynthSeg.
Results:
We found no differences between EID (n = 32) and SID (n = 50) for whole brain (−0.21% vs −0.16%, p = 0.42), ventricular (1.84% vs 1.13%, p = 0.24), and thalamic (−0.32% vs −0.32%, p = 0.97) annualized volume change over a median follow-up of 3.2 years. No associations between natalizumab drug concentration and brain atrophy rate were found.
Conclusion:
We found no clear evidence that EID compared to SID or lower natalizumab drug concentrations have a negative impact on the development of brain atrophy over time.
In 2008, the Ministry of Health, Welfare and Sport commissioned the National Care for the Elderly Programme. While numerous research projects in older persons' health care were to be conducted under ...this national agenda, the Programme further advocated the development of The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS) which would be integrated into all funded research protocols. In this context, we describe TOPICS data sharing initiative (www.topics-mds.eu).
A working group drafted TOPICS-MDS prototype, which was subsequently approved by a multidisciplinary panel. Using instruments validated for older populations, information was collected on demographics, morbidity, quality of life, functional limitations, mental health, social functioning and health service utilisation. For informal caregivers, information was collected on demographics, hours of informal care and quality of life (including subjective care-related burden).
Between 2010 and 2013, a total of 41 research projects contributed data to TOPICS-MDS, resulting in preliminary data available for 32,310 older persons and 3,940 informal caregivers. The majority of studies sampled were from primary care settings and inclusion criteria differed across studies.
TOPICS-MDS is a public data repository which contains essential data to better understand health challenges experienced by older persons and informal caregivers. Such findings are relevant for countries where increasing health-related expenditure has necessitated the evaluation of contemporary health care delivery. Although open sharing of data can be difficult to achieve in practice, proactively addressing issues of data protection, conflicting data analysis requests and funding limitations during TOPICS-MDS developmental phase has fostered a data sharing culture. To date, TOPICS-MDS has been successfully incorporated into 41 research projects, thus supporting the feasibility of constructing a large (>30,000 observations), standardised dataset pooled from various study protocols with different sampling frameworks. This unique implementation strategy improves efficiency and facilitates individual-level data meta-analysis.
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