Big Food, Food Systems, and Global Health

In an article that forms part of the PLoS Medicine series on Big Food, guest editors David Stuckler and Marion Nestle lay out why more examination of the food industry is necessary, and offer three competing views on how public health professionals might engage with Big Food

Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria

Rainfall induced landslides are a common threat to the communities living on dangerous hill-slopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence (WoE) method was applied to calculate the positive (presence of landslides) and negative (absence of landslides) factor weights. A combination of analytical hierarchical process (AHP) and fuzzy membership standardization (weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren’s algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of WoE, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively

Control of style-of-faulting on spatial pattern of earthquake-triggered landslides

Predictive mapping of susceptibility to earthquake-triggered landslides (ETLs) commonly uses distance to fault as spatial predictor, regardless of style-of-faulting. Here, we examined the hypothesis that the spatial pattern of ETLs is influenced by style-of-faulting based on distance distribution analysis and Fry analysis. The Yingxiu–Beichuan fault (YBF) in China and a huge number of landslides that ruptured and occurred, respectively, during the 2008 Wenchuan earthquake permitted this study because the style-of-faulting along the YBF varied from its southern to northern parts (i.e. mainly thrust-slip in the southern part, oblique-slip in the central part and mainly strike-slip in the northern part). On the YBF hanging-wall, ETLs at 4.4–4.7 and 10.3–11.5 km from the YBF are likely associated with strike- and thrust-slips, respectively. On the southern and central parts of the hanging-wall, ETLs at 7.5–8 km from the YBF are likely associated with oblique-slips. These findings indicate that the spatial pattern of ETLs is influenced by style-of-faulting. Based on knowledge about the style-of-faulting and by using evidential belief functions to create a predictor map based on proximity to faults, we obtained higher landslide prediction accuracy than by using unclassified faults. When distance from unclassified parts of the YBF is used as predictor, the prediction accuracy is 80%; when distance from parts of the YBF, classified according to style-of-faulting, is used as predictor, the prediction accuracy is 93%. Therefore, mapping and classification of faults and proper spatial representation of fault control on occurrence of ETLs are important in predictive mapping of susceptibility to ETLs

Mapping and linking supply- and demand-side measures in climate-smart agriculture. A review

Climate change and food security are two of humanity’s greatest challenges and are highly interlinked. On the one hand, climate change puts pressure on food security. On the other hand, farming significantly contributes to anthropogenic greenhouse gas emissions. This calls for climate-smart agriculture—agriculture that helps to mitigate and adapt to climate change. Climate-smart agriculture measures are diverse and include emission reductions, sink enhancements, and fossil fuel offsets for mitigation. Adaptation measures include technological advancements, adaptive farming practices, and financial management. Here, we review the potentials and trade-offs of climate-smart agricultural measures by producers and consumers. Our two main findings are as follows: (1) The benefits of measures are often site-dependent and differ according to agricultural practices (e.g., fertilizer use), environmental conditions (e.g., carbon sequestration potential), or the production and consumption of specific products (e.g., rice and meat). (2) Climate-smart agricultural measures on the supply side are likely to be insufficient or ineffective if not accompanied by changes in consumer behavior, as climate-smart agriculture will affect the supply of agricultural commodities and require changes on the demand side in response. Such linkages between demand and supply require simultaneous policy and market incentives. It, therefore, requires interdisciplinary cooperation to meet the twin challenge of climate change and food security. The link to consumer behavior is often neglected in research but regarded as an essential component of climate-smart agriculture. We argue for not solely focusing research and implementation on one-sided measures but designing good, site-specific combinations of both demand- and supply-side measures to use the potential of agriculture more effectively to mitigate and adapt to climate change

The relationship between geology and rock weathering on the rock instability along Mugling-Narayanghat road corridor, Central Nepal Himalaya

The present study was conducted along the Mugling–Narayanghat road section and its surrounding region that is most affected by landslide and related mass-movement phenomena. The main rock types in the study area are limestone, dolomite, slate, phyllite, quartzite and amphibolites of Lesser Himalaya, sandstone, mudstone and conglomerates of Siwaliks and Holocene Deposits. Due to the important role of geology and rock weathering in the instabilities, an attempt has been made to understand the relationship between these phenomena. Consequently, landslides of the road section and its surrounding region have been assessed using remote sensing, Geographical information systems and multiple field visits. A landslide inventory map was prepared and comprising 275 landslides. Nine landslides representing the whole area were selected for detailed studies. Field surveys, integrated with laboratory tests, were used as the main criteria for determining the weathering zones in the landslide area. From the overall study, it is seen that large and complex landslides are related to deep rock weathering followed by the intervention of geological structures as faults, joints and fractures. Rotational types of landslides are observed in highly weathered rocks, where the dip direction of the foliation plane together with the rock weathering plays a principle role. Shallow landslides are developed in the slope covered by residual soil or colluviums. The rock is rather fresh below these covers. Some shallow landslides (rock topples) are related to the attitude of the foliation plane and are generally observed in fresh rocks. Debris slides and debris flows occur in colluviums or residual soil-covered slopes. In few instances, they are also related to the rock fall occurring at higher slopes. The materials from the rock fall are mixed with the colluviums and other materials lying on the slope downhill and flow as debris flow. Rock falls are mainly related to the joint pattern and the slope angle. They are found in less-weathered rocks. From all these, it is concluded that the rock weathering followed by geological structures has prominent role in the rock slope instability along Mugling–Narayanghat road section and its surrounding regions