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        <dc:title>Spatial prediction of the concentration of selenium (Se) in grain across part of Amhara Region, Ethiopia</dc:title>
        <dc:creator>Gashu, D</dc:creator>
        <dc:creator>Lark, R M</dc:creator>
        <dc:creator>Milne, A E</dc:creator>
        <dc:creator>Amede, T</dc:creator>
        <dc:creator>Bailey, E H</dc:creator>
        <dc:creator>Chagumaira, C</dc:creator>
        <dc:creator>Dunham, S J</dc:creator>
        <dc:creator>Gameda, S</dc:creator>
        <dc:creator>Kumssa, D B</dc:creator>
        <dc:creator>Mossa, A W</dc:creator>
        <dc:creator>Walsh, M G</dc:creator>
        <dc:creator>Wilson, L</dc:creator>
        <dc:creator>Young, S D</dc:creator>
        <dc:creator>Ander, E L</dc:creator>
        <dc:creator>Broadley, M R</dc:creator>
        <dc:creator>Joy, E J M</dc:creator>
        <dc:creator>McGrath, S P</dc:creator>
        <dc:subject>GIS Techniques/Remote Sensing</dc:subject>
        <dc:subject>Plant Nutrition</dc:subject>
        <dc:subject>Wheat</dc:subject>
        <dc:description>Grain and soilwere sampled across a large part of Amhara, Ethiopia in a study motivated by prior evidence of selenium&#13;
(Se) deficiency in the Region's population. The grain samples (teff, Eragrostis tef, and wheat, Triticum&#13;
aestivum) were analysed for concentration of Se and the soils were analysed for various properties, including&#13;
Se concentration measured in different extractants. Predictive models for concentration of Se in the respective&#13;
grainswere developed, and the predicted values, alongwith observed concentrations in the two grainswere represented&#13;
by a multivariate linear mixed model in which selected covariates, derived from remote sensor observations&#13;
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&#13;
to maximize the statistical power to detect significant relationships by ordering the tests in a sequence&#13;
based on scientific understanding of the underlying processes likely to control Se concentration in grain. Crossvalidation&#13;
indicated that uncertainties in the empirical best linear unbiased predictions of the Se concentration&#13;
in both grains were well-characterized by the prediction error variances obtained from the model. The predictions&#13;
were displayed as maps, and their uncertainty was characterized by computing the probability that the&#13;
true concentration of Se in grain would be such that a standard serving would not provide the recommended&#13;
daily allowance of Se. The spatial variation of grain Se was substantial, concentrations in wheat and teff differed&#13;
but showed the same broad spatial pattern. Such information could be used to target effective interventions to&#13;
address Se deficiency, and the general procedure used for mapping could be applied to other micronutrients&#13;
and crops in similar settings.</dc:description>
        <dc:publisher>Elsevier</dc:publisher>
        <dc:date>2020-05</dc:date>
        <dc:type>Article</dc:type>
        <dc:type>PeerReviewed</dc:type>
        <dc:format>application/pdf</dc:format>
        <dc:language>en</dc:language>
        <dc:identifier>http://oar.icrisat.org/11547/1/1-s2.0-S0048969720327480-main.pdf</dc:identifier>
        <dc:identifier>  Gashu, D and Lark, R M and Milne, A E and Amede, T and Bailey, E H and Chagumaira, C and Dunham, S J and Gameda, S and Kumssa, D B and Mossa, A W and Walsh, M G and Wilson, L and Young, S D and Ander, E L and Broadley, M R and Joy, E J M and McGrath, S P  (2020) Spatial prediction of the concentration of selenium (Se) in grain across part of Amhara Region, Ethiopia.  Science of The Total Environment (TSI), 733.  pp. 1-16.  ISSN 0048-9697     </dc:identifier>
        <dc:relation>https://doi.org/10.1016/j.scitotenv.2020.139231</dc:relation>
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