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Grain and soil were sampled across a large part of Amhara, Ethiopia in a study motivated by prior evidence of selenium (Se) deficiency in the Region's population. The grain samples (teff, Eragrostis tef, and wheat, Triticum aestivum) were analysed for concentration of Se and the soils were analysed for various properties, including Se concentration measured in different extractants. Predictive models for concentration of Se in the respective grains were developed, and the predicted values, along with observed concentrations in the two grains were represented by a multivariate linear mixed model in which selected covariates, derived from remote sensor observations and a digital elevation model, were included as fixed effects. In all modelling steps the selection of predictors was done using false discovery rate control, to avoid over-fitting, and using an α-investment procedure to maximize the statistical power to detect significant relationships by ordering the tests in a sequence based on scientific understanding of the underlying processes likely to control Se concentration in grain. Cross-validation indicated that uncertainties in the empirical best linear unbiased predictions of the Se concentration in both grains were well-characterized by the prediction error variances obtained from the model. The predictions were displayed as maps, and their uncertainty was characterized by computing the probability that the true concentration of Se in grain would be such that a standard serving would not provide the recommended daily allowance of Se. The spatial variation of grain Se was substantial, concentrations in wheat and teff differed but showed the same broad spatial pattern. Such information could be used to target effective interventions to address Se deficiency, and the general procedure used for mapping could be applied to other micronutrients and crops in similar settings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Food production is a major driver of environmental change, and unhealthy diets are the leading cause of global disease burden. In high-income countries (HICs), modelling studies suggest that adoption of healthy diets could improve population health and reduce environmental footprints associated with food production. We assessed whether such benefits from dietary change could occur in India, where under-nutrition and overweight and obesity are simultaneously prevalent. We calculated the potential changes in greenhouse gas (GHG) emissions, blue and green water footprints (WFs), and land use (LU), that would result from shifting current national food consumption patterns in India to healthy diets (meeting dietary guidelines) and to "affluent diets" (those consumed by the wealthiest quartile of households, which may represent future purchasing power and nutritional trajectories). Dietary data were derived from the 2011-12 nationally-representative household expenditure survey, and we assessed dietary scenarios nationally and across six Indian sub-regions, by rural or urban location, and for those consuming above or below recommended dietary energy intakes. We modelled the changes in consumption of 34 food groups necessary to meet Indian dietary guidelines, as well as an affluent diet representative of those in the highest wealth quartile. These changes were combined with food-specific data on GHG emissions, calculated using the Cool Farm Tool, and WF and LU adapted from the Water Footprint Network and Food and Agriculture Organization, respectively. Shifting to healthy guidelines nationally required a minor increase in dietary energy (3%), with larger increases in fruit (18%) and vegetable (72%) intake, though baseline proportion of dietary energy from fat and protein was adequate and did not change significantly. Meeting healthy guidelines slightly increased environmental footprints by about 3-5% across GHG emissions, blue and green WFs, and LU. However, these national averages masked substantial variation within sub-populations. For example, shifting to healthy diets among those with dietary energy intake below recommended guidelines would result in increases of 28% in GHG emissions, 18 and 34% in blue and green WFs, respectively, and 41% in LU. Decreased environmental impacts were seen among those who currently consume above recommended dietary energy (-6 to -16% across footprints). Adoption of affluent diets by the whole population would result in increases of 19-36% across the environmental indicators. Specific food groups contributing to these shifts varied by scenario. Environmental impacts also varied markedly between six major Indian sub-regions. In India, where undernutrition is prevalent, widespread adoption of healthy diets may lead to small increases in the environmental footprints of the food system relative to the status quo, although much larger increases would occur if there was widespread adoption of diets currently consumed by the wealthiest quartile of the population. To achieve lower diet-related disease burdens and reduced environmental footprints of the food system, greater efficiency of food production and reductions in food waste are likely to be required alongside promotion of healthy diets., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)