Soil: the great connector of our lives now and beyond COVID-19

Open Access Journal; Published online: 05 Nov 2020Humanity depends on the existence of healthy soils, both for the production of food and for ensuring a healthy, biodiverse environment, among other functions. COVID-19 is threatening food availability in many places of the world due to the disruption of food chains, lack of workforce, closed borders and national lockdowns. As a consequence, more emphasis is being placed on local food production, which may lead to more intensive cultivation of vulnerable areas and to soil degradation. In order to increase the resilience of populations facing this pandemic and future global crises, transitioning to a paradigm that relies more heavily on local food production on soils that are carefully tended and protected through sustainable management is necessary. To reach this goal, the Intergovernmental Technical Panel on Soils (ITPS) of the Food and Agriculture Organization of the United Nations (FAO) recommends five active strategies: improved access to land, sound land use planning, sustainable soil management, enhanced research, and investments in education and extension. The soil is the great connector of lives, the source and destination of all. It is the healer and restorer and resurrector, by which disease passes into health, age into youth, death into life. Without proper care for it we can have no community, because without proper care for it we can have no life

Lidar DEM error analyses and topographic depression identification in a hummocky landscape in the prairie region of Canada

Topographic depressions are abundant in topographically complex landscapes. A common practice with earlier, low resolution Digital Elevation Models (DEMs) was to remove all depressions to ensure that water flowed continuously to the edge of the DEM domain. The assumption was that most depressions were created due to errors in the DEMs. This practice is no longer justified with the increasing availability of high accuracy DEMs. However, very few studies have addressed how DEM processing options such as smoothing and coarsening and setting area and depth thresholds can affect depression identification. In this study, a site located in the Prairie Region of Canada was examined. The site is a hummocky glaciated landscape with many in-field wetlands. Lidar topographic data were collected and were used to generate a 1 m by 1 m square-grid DEM. Detailed error analyses of the lidar DEM were conducted. A set of DEMs were generated after different degrees of smoothing and coarsening. FlowMapR, an established terrain analysis tool, was used to identify depressions in each DEM with various user-defined area and depth thresholds. The results were validated against a field wetland survey. We determined that the problems associated with depression identification using a lidar DEM are two-fold. On one hand, artefactual depressions created due to DEM errors need to be eliminated, for which the raw lidar DEM need to be smoothed. On the other hand, it is also desirable to remove those topographic depressions that do not function as closed basins at the spatial or temporal scale of the processes of interest. Setting area and depth thresholds appeared to be the preferred choice for this. We suggested using the un-autocorrelated lidar DEM error as the criterion for DEM smoothing and considering depression connections in the selection of area and depth thresholds. Using lidar data on a hummocky landscape with loamy soils in the Prairie Region of Canada, 10 to 20 times smoothing operations with an area threshold of 200 m2 and a depth threshold of 0.1 m were recommended as guidelines for depression identification

Extracting topographic characteristics of landforms typical of Canadian agricultural landscapes for agri-environmental modeling. I. Methodology

Soil and topographic information are key inputs for many agri-environmental models and there are linkages between soil and topography at the Field scale. A major source of soil data is soil databases established based on field soil survey. Although both soil and topographic information are recorded in field soil surveys, the nominal nature of the topographic data has limited their use in agri-environmental models. in this study, we developed a methodology to extract various topographic derivatives and to classify the landscape into landform elements with distinctive topographic characteristics based on detailed analyses of fine resolution digital elevation models. Data obtained from these analyses were used to calculate a representative two-dimensional hillslope of five segments, each with a defined length and slope gradient. A set of modal hillslopes was developed to describe topographic variability. Additional topographic parameters, ratios and indices were calculated to reflect different aspects of topographic characteristics and also to build connections between different agri-environmental models. in particular, a topographic complexity index was developed as a quantitative measure of the degrees of divergence and convergence. This paper describes the methodology using one site as an example. Application of this methodology to other landforms in agricultural land of Canada is reported in a companion pape

Dub-second induction of a4 integrin clustering by immobilized chemokines stimulates luekocyte tethering and rolling on endothelial VCAM-1 under flow conditions

Item does not contain fulltex

Replication of celiac disease UK genome-wide association study results in a US population

Celiac disease is a common disease with a prevalence of ∼1%. A recent genome-wide association study (GWAS) and follow-up study identified eight loci significantly associated with celiac disease risk. We genotyped the top 1020 non-HLA single nucleotide polymorphisms (SNPs) from the GWAS study that were genotyped in the previous follow-up study. After quality control assessments, 975 SNPs were analyzed for association with 906 celiac disease cases and 3819 controls, using logistic regression. Additional genotype data were generated by imputation and analyzed across the regions showing the strongest statistical evidence for association. Twenty SNPs were associated with celiac disease with P < 0.01 in the current study as well as in the previous follow-up study, of which 16 had P < 0.001 and 11 had P < 1 × 10−11. Five of eight regions identified in the follow-up study were strongly associated with celiac disease, including regions on 1q31, 3q25, 3q28, 4q27 and 12q24. The strongest associations were at 4q27, the region most strongly associated in the GWAS and follow-up study and containing IL2 and IL21, and at 3q28 harboring LPP. In addition, we provide new evidence for an association, not previously reported, on 2q31 harboring a strong candidate gene, ITGA4. In conclusion, in this first follow-up study of celiac cases from the USA, we provide additional evidence that five of eight previously identified regions harbor risk alleles for celiac disease, and new evidence for an association on 2q31. The underlying functional mutations responsible for these replicated associations need to be identified