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Physical and chemical characterization of sediments from an Andean river exposed to mining and agricultural activities: The Moquegua River, Peru

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International Journal of Sediment Research 2022年第6期37卷 780-793页

作者： Luis de Los Santos Valladares Juan Luis Ccamapaza Renato Arturo Valencia-Bedregal Luis Enrique Borja-Castro Jose Velazquez-Garcia Dehiwalage Harshani Nimalika Perera Adrian Ionescu David Arvidsson Eustace Peregrine Barnes Peter Newton Hugo Lepage patrick byrne Angel Guillermo Bustamante Dominguez Crispin H.W.Barnes Cavendish Laboratory Department of Physics University of Cambridge Escuela Acad emico Profesional de Ingeniería Ambiental Universidad Nacional de Moquegua Provincia de Mariscal Nieto Laboratorio de Cer amicos y Nanomateriales Facultad de Ciencias Físicas Universidad Nacional Mayor de San Marcos School of Biological and Environmental Science Liverpool John Moores University

This study presents an analysis of sediments in an Andean river impacted by both natural conditions and anthropogenic activities. Fifty samples were collected from selected sites throughout the Moquegua River drainage basin, and Tambo River headwaters at Pasto Grande, in Peru, and analysed with X-ray diffraction, energy dispersive X-ray spectroscopy, scanning electron microscopy, Fourier-transform infrared spectroscopy and thermogravimetric analysis. Si, Ca, Al, Fe, and O, common constituents of soils, were dominant, along with K, N, and P. The latter originating as primary macronutrients or chemical residuals from fertilizers used in agriculture. Higher concentrations of macronutrients and organic components were found in agricultural and urban areas, respectively. Arsenic minerals were also detected, which occur naturally, but possibly at levels augmented by anthropogenic activity. The application of cluster analysis revealed clustering between arsenic, arsenolite, and potassium but no significant geospatial correlation between sample sites in the drainage basin.

关键词： Andean river Moquegua river Sediments Fourier-transform infrared spectroscopy TG-DTG/DTA Clustering analysis

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Genomic prediction of seasonal forage yield in perennial ryegrass

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Grassland Research 2023年第3期2卷 167-181页

作者： Agnieszka Konkolewska Steffie Phang patrick Conaghan Dan Milbourne Aonghus Lawlor Stephen byrne Crop Science Department TeagascCarlowCo.CarlowIreland Insight SFI Research Centre for Data Analytics University College DublinDublinIreland VistaMilk SFI Research Centre FermoyCo.CorkIreland Grassland Science Research Department Animal and Grassland Research and Innovation CentreTeagascCarlowIreland

Background:Genomic selection has the potential to accelerate genetic gain in perennial ryegrass breeding,provided complex traits such as forage yield can be predicted with sufficient ***:In this study,we compared modelling approaches and feature selection strategies to evaluate the accuracy of genomic prediction models for seasonal forage yield ***:Overall,model selection had limited impact on predictive ability when using the full data *** a baseline genomic best linear unbiased prediction model,the highest mean predictive accuracy was obtained for spring grazing(0.78),summer grazing(0.62)and second cut silage(0.56).In terms of feature selection strategies,using uncorrelated single-nucleotide polymorphisms(SNPs)had no impact on predictive ability,allowing for a potential decrease of the data set *** a genome-wide association study,we found a significant SNP marker for spring grazing,located in the genic region annotated as coding for an enzyme responsible for fucosylation of xyloglucans—major components of the plant cell *** also presented an approach to increase interpretability of genomic prediction models with the use of Gene Ontology enrichment ***:Approaches for feature selection will be relevant in development of low-cost genotyping platforms in support of routine and cost-effective implementation of genomic selection.

关键词： forage yield genomic selection perennial ryegrass breeding

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