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Water erosion is the most prevailing form of human-induced land degradation in the agricultural areas of developing countries (Scherr et al. 1996; Oldeman et al. 1991). It occurs when land [...]

Land degradation has been a persistent problem in Senegal for a long time and by now has become a serious impediment to long term development. In this paper, we quantify the impact of land degradation on crop yields using the results of a nation-wide land degradation assessment. For this, the study needs to address two issues. First, the land degradation assessment comprises qualitative expert judgments that have to be converted into more objective, quantitative terms. We propose a land degradation index and assess its plausibility. Second, observational data on soils, land use and rainfall do not provide sufficient information to isolate the impact of land degradation. We, therefore, design a pseudo-experiment that for sites with otherwise similar circumstances compares the yield of a site with and one without land degradation. This pairing exercise is conducted under a gradual refining of the classification of circumstances, until a more or less stable response to land degradation is obtained, In this way, we hope to have controlled sufficiently for confounding variables that will bias the estimation of the impact of land degradation on crop yields. A small number of shared characteristics reveal tendencies of "severe" land degradation levels being associated with declining yields as compared to similar sites with "low" degradation levels. However, as we zoom in at more detail some exceptions come to the fore, in particular in areas without fertilizer application. Yet, our overall conclusion is that yield reduction is associated to higher levels of land degradation, irrespective of whether fertilizer is being applied or not. [ABSTRACT FROM AUTHOR]

The article presents a study related to using Chemical Activated LUciferase eXpression (CALUX) bioassays as instruments to detect hormones and contaminants. Steroidal compounds from the World Anti-Doping Agency prohibited list and human plasma and urine samples were screened for androgenic, estrogenic, and glucocorticoidal activities by means of CALUX bioassays. The results confirms the capability of CALUX bioassays to detect androgenic anabolic steroids.

Objective Tremendous progress has been made in the ability to measure particular contaminants or veterinary drugs at very low concentrations. However, rare or previously unknown compounds, metabolites and mixtures are still presenting considerable analytical challenges, while this category in particular might be relevant in terms of food safety. In addition, the need for higher throughput screening strategies at lower costs also demands for methods in addition to chemical analysis. There is considerable development in methodology based on the interaction with bio-macromolecules or living cells or on a biological response in the exposed animal. The aim of this workshop is to provide an up-o-date and practical overview of the various analytical and biological strategies that are available to screen or detect (prior) exposure to drugs, contaminants and pollutants. [ABSTRACT FROM AUTHOR]

Background: The impact of diet diversity-defined as the number of different foods or food groups consumed over a given reference period-on child nutrition outcomes strongly interacts with agro-ecological, institutional, and socio-economic drivers of child food and nutrition security. Yet, the literature on the impact of diet diversity typically estimates average treatment effects, largely ignoring impact heterogeneity among different groups., Methods: In this paper, we introduce a new method of profiling to identify groups of treatment units that stand to gain the most from a given intervention. We start from the 'polling approach' which provides a fully flexible (non-parametric) method to profile vulnerability patterns (patterns in 'needs') across highly heterogeneous environments [35]. Here we combine this polling methodology with matching techniques to identify 'impact profiles' showing how impact varies across non-parametric profiles. We use this method to explore the potential for improving child nutrition outcomes, in particular stunting, through targeted improvements in dietary diversity in a physically and socio-economically diverse country, namely Zimbabwe. Complex interaction effects with agro-ecological, institutional and socio-economic conditions are accounted for. Finally, we analyze whether targeting interventions at the neediest (as identified by the polling approach) will also create the largest benefits., Results: The dominant profile for stunted children is that they are young (6-12 months), live in poorer/poorest households, in rural areas characterized by significant sloping of the terrain and with one-sided emphasis on maize cultivation and medium dry conditions. When moving from "need" to "maximal impact", we calculate both the coverage in "need" as well as the impact coverage, and find that targeting on need does not always provide the largest impact., Conclusions: Policy-makers need to remain alert that targeting on need is not always the same as targeting on impact. Estimation of heterogeneous treatment effects allows for more efficient targeting. It also enhances the external validity of the estimated impact findings, as the impact of child diet diversity on stunting depends on various agro-ecological variables, and policy-makers can relate these findings to areas outside our study area with similar agro-ecological conditions.

The identification of dryland areas is crucial for guiding policy aimed at intervening in water-stressed areas and addressing the perennial livelihood or food insecurity of these areas. However, the prevailing aridity indices (such as UNEP aridity index) have methodological limitations that restrict their use in delineating drylands and may be insufficient for decision-making frameworks. In this study, we propose a new aridity index based on based on 3 decades of soil moisture time series by accounting for site-specific soil and vegetation that partitions precipitation into the competing demands of evaporation and runoff. Our proposed aridity index is the frequency at which the dominant soil moisture value at a location is not exceeded by the dominant soil moisture values in all of the other locations. To represent the dominant spatial template of the soil moisture conditions, we extract the first eigenfunction from the empirical orthogonal function (EOF) analysis from 3 GLDAS land surface models (LSMs): VIC, MOSAIC and NOAH at 1 × 1 degree spatial resolution. The EOF analysis reveals that the first eigenfunction explains 33%, 43% and 47% of the VIC, NOAH and MOSAIC models, respectively. We compare each LSM aridity indices with the UNEP aridity index, which is created based on LSM data forcings. The VIC aridity index displays a pattern most closely resembling that of UNEP, although all of the LSM-based indices accurately isolate the dominant dryland areas. The UNEP classification identifies portions of south-central Africa, southeastern United States and eastern India as drier than predicted by all of the LSMs. The NOAH and MOSAIC LSMs categorize portions of southwestern Africa as drier than the other two classifications, while all of the LSMs classify portions of central India as wetter than the UNEP classification. We compare all aridity maps with the long-term average NDVI values. Results show that vegetation cover in areas that the UNEP index classifies as drier than the other three LSMs (NDVI values are mostly greater than 0). Finally, the unsupervised clustering of global land surface based on long-term mean temperature and precipitation, soil texture and land slope reveals that areas classified as dry by the UNEP index but not by the LSMs do not have dry region characteristics. The dominant cluster for these areas has high water holding capacity. We conclude that the LSM-based aridity index may identify dryland areas more effectively than the UNEP aridity index because the former incorporates the role of vegetation and soil in the partitioning of precipitation into evaporation, runoff and infiltration., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

The Global Assessment of Soil Degradation (GLASOD) has been the most influential global appraisal of land quality in terms of environmental policy. However, its expert judgments were never tested for their consistency and could not be reproduced at unvisited sites, while the relationship between the GLASOD assessments of land degradation and the social and economic impact of that degradation remains unclear. Yet, other methodologies that could respond to urgent calls for an updated assessment of the global environmental quality are not operational or, at best, in progress. Therefore, we evaluate the reliability and social relevance of the GLASOD approach and assess its candidacy for new global environmental assessments. The study concentrates on the African continent, capitalizing on new GIS data to delineate and define the characteristics of GLASOD map units. Consistency is tested by comparing expert judgments on soil degradation hazard for similar combinations of biophysical conditions and land use. Reproducibility is evaluated by estimating an ordered logit model that relates the qualitative land degradation classes to easily available information on explanatory variables, the results of which can be used to assess the land degradation at unvisited sites. Finally, a cross-sectional analysis investigates the relation between GLASOD assessments and crop production data at sub-national scale and its association with the prevalence of malnutrition. The GLASOD assessments prove to be only moderately consistent and hardly reproducible, while the counter-intuitive trend with crop production reveals the complexity of the production-degradation relationship. It appears that increasing prevalence of malnutrition coincides with poor agro-productive conditions and highly degraded land. The GLASOD approach can be improved by resolving the differences in conceptualization among experts and by defining the boundaries of the ordered classes in the same units as independent, quantitative land degradation data.