Cassava mosaic disease and its whitefly vector in Cameroon: Incidence, severity and whitefly numbers from field surveys

Computational models for improving surveillance for the early detection of direct introduction of cassava brown streak disease in Nigeria

Cassava is a key source of calories for smallholder farmers in sub-Saharan Africa but its role as a food security crop is threatened by the cross-continental spread of cassava brown streak disease (CBSD) that causes high yield losses. In order to mitigate the impact of CBSD, it is important to minimise the delay in first detection of CBSD after introduction to a new country or state so that interventions can be deployed more effectively. Using a computational model that combines simulations of CBSD spread at both the landscape and field scales, we model the effectiveness of different country level survey strategies in Nigeria when CBSD is directly introduced. We find that the main limitation to the rapid CBSD detection in Nigeria, using the current survey strategy, is that an insufficient number of fields are surveyed in newly infected Nigerian states, not the total number of fields surveyed across the country, nor the limitation of only surveying fields near a road. We explored different strategies for geographically selecting fields to survey and found that early and consistent CBSD detection will involve confining candidate survey fields to states where CBSD has not yet been detected and where survey locations are allocated in proportion to the density of cassava crops, detects CBSD sooner, more consistently, and when the epidemic is smaller compared with distributing surveys uniformly across Nigeria.

A labelled dataset to classify direct deforestation drivers from Earth Observation imagery in Cameroon

Understanding direct deforestation drivers at a fine spatial and temporal scale is needed to design appropriate measures for forest management and monitoring. To achieve this, reference datasets with which to design Artificial Intelligence (AI) approaches to classify direct deforestation drivers within areas experiencing forest loss in a detailed, comprehensive and locally-adapted way are needed. This is the case for Cameroon, in the Congo Basin, which has known increasing deforestation rates in recent years. Here, we created an Earth Observation dataset with associated labels to classify detailed direct deforestation drivers in Cameroon, which includes satellite imagery (Landsat and PlanetScope) and auxiliary data on infrastructure and biophysical properties. The dataset provides the following fifteen labels: oil palm, timber, fruit, rubber and other-large scale plantations; grassland/shrubland; small-scale oil palm or maize plantations and other small-scale agriculture; mining; selective logging; infrastructure; wildfires; hunting; and other.

Cassava begomovirus species diversity changes during plant vegetative cycles

Cassava is a root crop important for global food security and the third biggest source of calories on the African continent. Cassava production is threatened by Cassava mosaic disease (CMD), which is caused by a complex of single-stranded DNA viruses (family: Geminiviridae, genus: Begomovirus) that are transmitted by the sweet potato whitefly (Bemisia tabaci). Understanding the dynamics of different cassava mosaic begomovirus (CMB) species through time is important for contextualizing disease trends. Cassava plants with CMD symptoms were sampled in Lake Victoria and coastal regions of Kenya before transfer to a greenhouse setting and regular propagation. The field-collected and greenhouse samples were sequenced using Illumina short-read sequencing and analyzed on the Galaxy platform. In the field-collected samples, African cassava mosaic virus (ACMV), East African cassava mosaic virus (EACMV), East African cassava mosaic Kenya virus (EACMKV), and East African cassava mosaic virus-Uganda variant (EACMV-Ug) were detected in samples from the Lake Victoria region, while EACMV and East African mosaic Zanzibar virus (EACMZV) were found in the coastal region. Many of the field-collected samples had mixed infections of EACMV and another begomovirus. After 3 years of regrowth in the greenhouse, only EACMV-like viruses were detected in all samples. The results suggest that in these samples, EACMV becomes the dominant virus through vegetative propagation in a greenhouse. This differed from whitefly transmission results. Cassava plants were inoculated with ACMV and another EACMV-like virus, East African cassava mosaic Cameroon virus (EACMCV). Only ACMV was transmitted by whiteflies from these plants to recipient plants, as indicated by sequencing reads and copy number data. These results suggest that whitefly transmission and vegetative transmission lead to different outcomes for ACMV and EACMV-like viruses.

In Silico Identification of Cassava Genome-Encoded MicroRNAs with Predicted Potential for Targeting the ICMV-Kerala Begomoviral Pathogen of Cassava

Cassava mosaic disease (CMD) is caused by several divergent species belonging to the genus Begomovirus (Geminiviridae) transmitted by the whitefly Bemisia tabaci cryptic species group. In India and other parts of Asia, the Indian cassava mosaic virus-Kerala (ICMV-Ker) is an emergent begomovirus of cassava causing damage that results in reduced yield loss and tuber quality. Double-stranded RNA-mediated interference (RNAi) is an evolutionary conserved mechanism in eukaryotes and highly effective, innate defense system to inhibit plant viral replication and/or translation. The objective of this study was to identify and characterize cassava genome-encoded microRNAs (mes-miRNA) that are predicted to target ICMV-Ker ssDNA-encoded mRNAs, based on four in silico algorithms: miRanda, RNA22, Tapirhybrid, and psRNA. The goal is to deploy the predicted miRNAs to trigger RNAi and develop cassava plants with resistance to ICMV-Ker. Experimentally validated mature cassava miRNA sequences (n = 175) were downloaded from the miRBase biological database and aligned with the ICMV-Ker genome. The miRNAs were evaluated for base-pairing with the cassava miRNA seed regions and to complementary binding sites within target viral mRNAs. Among the 175 locus-derived mes-miRNAs evaluated, one cassava miRNA homolog, mes-miR1446a, was identified to have a predicted miRNA target binding site, at position 2053 of the ICMV-Ker genome. To predict whether the cassava miRNA might bind predicted ICMV-Ker mRNA target(s) that could disrupt viral infection of cassava plants, a cassava locus-derived miRNA-mRNA regulatory network was constructed using Circos software. The in silico-predicted cassava locus-derived mes-miRNA-mRNA network corroborated interactions between cassava mature miRNAs and the ICMV-Ker genome that warrant in vivo analysis, which could lead to the development of ICMV-Ker resistant cassava plants.