Soil and land information: How to support decision-making?

Soils are fundamental to ensuring water, energy and food security. Within the context of sus- tainable food production, it is important to share knowledge on existing and emerging tech- nologies that support land and soil monitoring. Technologies, such as remote sensing, mobile soil testing, and digital soil mapping, have the potential to identify degraded and non- /little-responsive soils, and may also provide a basis for programmes targeting the protection and rehabilitation of soils. In the absence of such information, crop production assessments are often not based on the spatio-temporal variability in soil characteristics. In addition, uncertain- ties in soil information systems are notable and build up when predictions are used for monitor- ing soil properties or biophysical modelling. Consequently, interpretations of model-based results have to be done cautiously. As such they provide a scientific, but not always manage- able, basis for farmers and/or policymakers. In general, the key incentives for stakeholders to aim for sustainable management of soils and more resilient food systems are complex at farm as well as higher levels. The same is true of drivers of soil degradation. The decision- making process aimed at sustainable soil management, be that at farm or higher level, also in- volves other goals and objectives valued by stakeholders, e.g. land governance, improved envi- ronmental quality, climate change adaptation and mitigation etc. In this dialogue session we will share ideas on recent developments in the discourse on soils, their functions and the role of soil and land information in enhancing food system resilience.

Applying erosion damage mapping to asses and quantify off-site effects of soil erosion in Switzerland

In order to fill existing knowledge gaps in the temporal and spatial distribution of soil erosion, its sources and causes, as well as in relation to its off-site impacts, erosion damage mapping of all visible erosion features was carried out at three study sites in Switzerland. The data illustrate that about one-quarter of the cultivated land was affected by water erosion. Observed mean annual soil loss rates are considered rather low (0.7–2.3 t/ha/y) compared to other European countries. However, substantial losses of >70 t/ha were recorded on individual plots. This paper focuses on the spatial aspects of soil erosion, by observing and comparing the study areas in a 1-year period from October 2005 to October 2006. The analyses illustrate that the sites differ considerably in average soil loss rates, but show similar patterns of off-site effects. In about one-third of the damaged plots an external source of surface runoff upslope contributed to the damage (run-on). Similarly, more than 50 per cent of the soil eroded on arable land deposited downslope on adjacent plots, roads, public/private infrastructure, etc., and 20 per cent of it reached open water bodies. Large amounts of eroded soil which deposit off-site, often related to slope depressions, are considered muddy floods and were frequently observed in Switzerland. Mapping, in conclusion, helps to sheds light on some of the important challenges of today, in particular: to comprehensively assess socioeconomic and ecological off-site effects of soil erosion, to attribute off-site impacts to on-site causes, and to raise awareness of all stakeholders involved, in order to improve ongoing discussions on policy formulation and implementation at the national and international levels.

Erosion Damage Mapping: Assessing Current Soil Erosion Damage in Switzerland

Within the scope of a comprehensive assessment of the degree of soil erosion in Switzerland, common methods have been used in the past including test plot measurements, artificial rainfall simulation, and erosion modelling. In addition, mapping guidelines for all visible erosion features have been developed since the 1970s and are being successfully applied in many research and soil conservation projects. Erosion damage has been continuously mapped over a period of 9 years in a test region in the central Bernese plateau. In 2005, two additional study areas were added. The present paper assesses the data gathered and provides a comparison of the three study areas within a period of one year (from October 2005 to October 2006), focusing on the on-site impacts of soil erosion. During this period, about 11 erosive rainfall events occurred. Average soil loss rates mapped at each study site amounted to 0.7 t ha-1, 1.2 t ha-1 and 2.3 t ha-1, respectively. About one fourth of the total arable land showed visible erosion damage. Maximum soil losses of about 70 t ha-1 occurred on individual farm plots. Average soil erosion patterns are widely used to underline the severity of an erosion problem (e.g. impacts on water bodies). But since severe rainfall events, wheel tracks, headlands, and other “singularities” often cause high erosion rates, analysis of extreme erosion patterns such as maximum values led to a more differentiated understanding and appropriate conclusions for planning and design of soil protection measures. The study contains an assessment of soil erosion in Switzerland, emphasizing questions about extent, frequency and severity. At the same time, the effects of different types of land management are investigated in the field, aiming at the development of meaningful impact indicators of (un-)sustainable agriculture/soil erosion risk as well as the validation of erosion models. The results illustrate that conservation agriculture including no-till, strip tillage and in-mulch seeding plays an essential role in reducing soil loss as compared to conventional tillage.

An automated method for mapping physical soil and water conservation structures on cultivated land using GIS and remote sensing techniques

An efficient and reliable automated model that can map physical Soil and Water Conservation (SWC) structures on cultivated land was developed using very high spatial resolution imagery obtained from Google Earth and ArcGIS, ERDAS IMAGINE, and SDC Morphology Toolbox for MATLAB and statistical techniques. The model was developed using the following procedures: (1) a high-pass spatial filter algorithm was applied to detect linear features, (2) morphological processing was used to remove unwanted linear features, (3) the raster format was vectorized, (4) the vectorized linear features were split per hectare (ha) and each line was then classified according to its compass direction, and (5) the sum of all vector lengths per class of direction per ha was calculated. Finally, the direction class with the greatest length was selected from each ha to predict the physical SWC structures. The model was calibrated and validated on the Ethiopian Highlands. The model correctly mapped 80% of the existing structures. The developed model was then tested at different sites with different topography. The results show that the developed model is feasible for automated mapping of physical SWC structures. Therefore, the model is useful for predicting and mapping physical SWC structures areas across diverse areas.

Spatial Assessment of Erosion and its Impact on Soil Fertility in the Tajik Foothills

Efficient planning of soil conservation measures requires, first, to understand the impact of soil erosion on soil fertility with regard to local land cover classes; and second, to identify hot spots of soil erosion and bright spots of soil conservation in a spatially explicit manner. Soil organic carbon (SOC) is an important indicator of soil fertility. The aim of this study was to conduct a spatial assessment of erosion and its impact on SOC for specific land cover classes. Input data consisted of extensive ground truth, a digital elevation model and Landsat 7 imagery from two different seasons. Soil spectral reflectance readings were taken from soil samples in the laboratory and calibrated with results of SOC chemical analysis using regression tree modelling. The resulting model statistics for soil degradation assessments are promising (R2=0.71, RMSEV=0.32). Since the area includes rugged terrain and small agricultural plots, the decision tree models allowed mapping of land cover classes, soil erosion incidence and SOC content classes at an acceptable level of accuracy for preliminary studies. The various datasets were linked in the hot-bright spot matrix, which was developed to combine soil erosion incidence information and SOC content levels (for uniform land cover classes) in a scatter plot. The quarters of the plot show different stages of degradation, from well conserved land to hot spots of soil degradation. The approach helps to gain a better understanding of the impact of soil erosion on soil fertility and to identify hot and bright spots in a spatially explicit manner. The results show distinctly lower SOC content levels on large parts of the test areas, where annual crop cultivation was dominant in the 1990s and where cultivation has now been abandoned. On the other hand, there are strong indications that afforestations and fruit orchards established in the 1980s have been successful in conserving soil resources.

Mapping material distribution in a heterogeneous sand tank by image analysis

Water flow and solute transport through soils are strongly influenced by the spatial arrangement of soil materials with different hydraulic and chemical properties. Knowing the specific or statistical arrangement of these materials is considered as a key toward improved predictions of solute transport. Our aim was to obtain two-dimensional material maps from photographs of exposed profiles. We developed a segmentation and classification procedure and applied it to the images of a very heterogeneous sand tank, which was used for a series of flow and transport experiments. The segmentation was based on thresholds of soil color, estimated from local median gray values, and of soil texture, estimated from local coefficients of variation of gray values. Important steps were the correction of inhomogeneous illumination and reflection, and the incorporation of prior knowledge in filters used to extract the image features and to smooth the results morphologically. We could check and confirm the success of our mapping by comparing the estimated with the designed sand distribution in the tank. The resulting material map was used later as input to model flow and transport through the sand tank. Similar segmentation procedures may be applied to any high-density raster data, including photographs or spectral scans of field profiles.

Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks

Soil carbon (C) storage is a key ecosystem service. Soil C stocks play a vital role in soil fertility and climate regulation, but the factors that control these stocks at regional and national scales are unknown, particularly when their composition and stability are considered. As a result, their mapping relies on either unreliable proxy measures or laborious direct measurements. Using data from an extensive national survey of English grasslands, we show that surface soil (0–7 cm) C stocks in size fractions of varying stability can be predicted at both regional and national scales from plant traits and simple measures of soil and climatic conditions. Soil C stocks in the largest pool, of intermediate particle size (50–250 μm), were best explained by mean annual temperature (MAT), soil pH and soil moisture content. The second largest C pool, highly stable physically and biochemically protected particles (0·45–50 μm), was explained by soil pH and the community abundance-weighted mean (CWM) leaf nitrogen (N) content, with the highest soil C stocks under N-rich vegetation. The C stock in the small active fraction (250–4000 μm) was explained by a wide range of variables: MAT, mean annual precipitation, mean growing season length, soil pH and CWM specific leaf area; stocks were higher under vegetation with thick and/or dense leaves. Testing the models describing these fractions against data from an independent English region indicated moderately strong correlation between predicted and actual values and no systematic bias, with the exception of the active fraction, for which predictions were inaccurate. Synthesis and applications. Validation indicates that readily available climate, soils and plant survey data can be effective in making local- to landscape-scale (1–100 000 km2) soil C stock predictions. Such predictions are a crucial component of effective management strategies to protect C stocks and enhance soil C sequestration.