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Outbreaks of whitefly, Bemisia tabaci species in East and Central Africa, have become increasingly prevalent during the previous 25 years and are responsible for driving the spread of plant-virus diseases, such as cassava mosaic disease and cassava brown steak disease. Epidemics of these diseases have expanded their ranges over the same period, spreading from Uganda into other sub-Saharan African countries. It was hypothesised that a highly abundant 'invader' population of B. tabaci was responsible for spreading these diseases from Uganda to neighbouring countries and potentially hybridising with the resident cassava B. tabaci populations. Here, we test this hypothesis by investigating the molecular identities of the highly abundant cassava B. tabaci populations from their supposed origin in Uganda, to the northern, central, eastern and coastal regions of Tanzania. Partial mitochondrial cytochrome oxidase I (mtCOI) barcoding sequences and nuclear microsatellite markers were used to analyse the population genetic diversity and structure of 2734 B. tabaci collected from both countries and in different agroecological zones. The results revealed that: (i) the putative SSA1 species is structured according to countries, so differ between them. (ii) Restricted gene flow occurred between SSA1–SG3 and both other SSA1 subgroups (SG1 and SG2), even in sympatry, demonstrating strong barriers to hybridization between those genotypes. (iii) Not only B. tabaci SSA1-(SG1 and SG2) was found in highly abundant (outbreak) numbers, but B. tabaci SSA1-SG3 and the Indian Ocean (IO) species were also recorded in high numbers in several sites in Tanzania. (iv) The SSA1-(SG1 and SG2) species was distributed in both countries, but in Tanzania, the B. tabaci IO and SSA1–SG3 species predominated. These data confirm that multiple, local Tanzanian B. tabaci species produce highly abundant populations, independent of the spread of the putative invasive B. tabaci SSA1-(SG1 and SG2) populations. [ABSTRACT FROM AUTHOR]

Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability. [ABSTRACT FROM AUTHOR]

In Burundi most small-scale farmers still grow traditional cassava landraces that are adapted to local conditions and have been selected for consumer preferred attributes. They tend to be susceptible, in varying degrees, to devastating cassava viral diseases such as Cassava Brown Streak Disease (CBSD) and Cassava Mosaic Disease (CMD) with annual production losses of US$1 billion. For long term resistance to the disease, several breeding strategies have been proposed. A sound basis for a breeding program is to understand the genetic diversity of both landraces and elite introduced breeding cultivars. This will also assist in efforts to conserve landraces ahead of the broad distribution of improved varieties which have the possibility of replacing landraces. Our study aimed at determining the genetic diversity and relationships within and between local landraces and introduced elite germplasm using morphological and single nucleotide polymorphism (SNP) markers. A total of 118 cultivars were characterized for morphological trait variation based on leaf, stem and root traits, and genetic variation using SNP markers. Results of morphological characterization based on Ward's Method revealed three main clusters and five accessions sharing similar characteristics. Molecular characterization identified over 18,000 SNPs and six main clusters and three pairs of duplicates which should be pooled together as one cultivar to avoid redundancy. Results of population genetic analysis showed low genetic distance between populations and between local landraces and elite germplasm. Accessions that shared similar morphological traits were divergent at the molecular level indicating that clustering using morphological traits was inconsistent. Despite the variabilities found within the collection, it was observed that cassava germplasm in Burundi have a narrow genetic base. [ABSTRACT FROM AUTHOR]