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Data from: Distance-based decision-making in oviposition by Tribolium castaneum Herbst (Coleoptera: Tenebrionidae) on low- and no-gluten flours

    Red flour beetles have been known to readily infest wheat flour but their likelihood to choose other types of flours is unknown. Red flour beetles will lay eggs in many types of flours but their choice to infest low- and no-gluten flours remains to be tested. Here we test a panel of 14 different commercially available flours in three different choice assays. We find that the beetles lay similar amounts of eggs in buckwheat, teff, millet, rice, and rye flours but that they show significant declines in preference for sorghum, potato, quinoa, cassava, oat, amaranth, garbanzo, spelt, and corn flours.

    Breedbase

      The Breedbase system has evolved from the Sol Genomics Network (SGN) and Cassavabase and related sites (see RTBbase.org).Breedbase is striving to be a complete breeding management system, including field management, data collection, crossing utilities, and advanced trial analysis.

      Data from: Genetic Diversity and Population Structure of the USDA Sweetpotato (Ipomoea batatas) Germplasm Collections Using GBSpoly

        Population structure and genetic diversity of 417 USDA sweetpotato (*Ipomoea batatas*) accessions originating from 8 broad geographical regions (Africa, Australia, Caribbean, Central America, Far East, North America, Pacific Islands, and South America) were determined using single nucleotide polymorphisms (SNPs) identified with a genotyping-by-sequencing (GBS) protocol, GBSpoly, optimized for highly heterozygous and polyploid species.

        Cassavabase

          The Next Generation Cassava Breeding (NEXTGEN Cassava) project aims to significantly increase the rate of genetic improvement in cassava breeding and unlock the full potential of cassava, a staple crop central to food security and livelihoods across Africa. The project will implement and empirically test a new breeding method known as Genomic Selection that relies on statistical modeling to predict cassava performance before field-testing, and dramatically accelerates the breeding cycle.