Cowpea witchweed [Striga gesnerioides (Willd.) Vatke] is a primary constraint of cowpea [Vigna unguiculata (L.) Walp.] production in West Africa. Previously, seven S. gesnerioides races were classified based upon host specificity and genotypic profiling. Because race number and distribution are dynamic systems influenced by gene flow, genetic drift, and natural selection, a thorough investigation of S. gesnerioides diversity and the effectiveness of known sources of resistance in cowpea is needed to develop varieties with durable and broad-spectrum Striga resistance. In this study, we screened seven cowpea lines against 58 unique S. gesnerioides populations collected from across nine West African countries. Individuals from 10 S. gesnerioides populations were genotyped with simple sequence repeat (SSR) markers. We identified six races of S. gesnerioides based on their parasitism of the seven cowpea lines with known differential resistance genotypes. No cowpea line was resistant to all 58 Striga populations and none of the Striga populations were able to overcome the resistance of all seven lines. A novel race, SG6, of the parasite collected from Kudu, Nigeria, was found to overcome more cowpea resistance genes than any previously reported race. SSR analysis indicates that Striga populations are highly differentiated and genetic relatedness generally corresponds with geographic proximity rather than their host compatibility. Due to the dearth of broad-spectrum resistance found among Striga-resistant cowpea lines, there exists a need to stack multiple Striga resistance genes in order to confer broad-spectrum and durable resistance.

Cowpea is one of the most important crops in West Africa and is essential for the region’s food and nutrition security and economic development. Consequently, improving its agronomic performance and yield is a desirable goal. Brown blotch disease, caused by the fungal pathogen Colletotrichum capsici, is an important constraint of cowpea productivity, and at present, only limited genetic resources are available for breeding improved brown blotch-resistant varieties. The current study has characterized the genetic basis for brown blotch resistance conferred by the cowpea cultivar KN1 and identified a major dominant quantitative trait locus (QTL) for resistance on chromosome Vu02. A segregating F₂ population (n = 200), derived from a cross between KN1 and brown blotch-susceptible Tiligre (KVx775-33-2G), was developed and scored for disease severity following controlled inoculation. A subset of the population (n = 94) was genotyped with 99 newly developed allele-specific polymerase chain reaction (AS-PCR) markers, and multiple interval mapping was performed. One major and three minor QTL were identified. This is the first reported mapping of QTL conferring resistance to C. capsici in cowpea, and it is expected that the markers identified here will be a valuable resource for developing elite cowpea cultivars with resistance to brown blotch.

Maize lethal necrosis is a destructive virus disease of maize caused by maize chlorotic mottle virus (MCMV) in combination with a virus in the family Potyviridae. Emergence of MLN is typically associated with the introduction of MCMV or its vectors and understanding its spread through seed is critical for disease management. Previous studies suggest that although MCMV is detected on seed, the seed transmission rate of this virus is low. However, mechanisms influencing its transmission are poorly understood. Elucidating these mechanisms is crucial for informing strategies to prevent spread on contaminated seed. In this study, we evaluated the rate of MCMV seed transmission using seed collected from plants that were artificially inoculated with MCMV isolates from Hawaii and Kenya. Grow-out tests indicated that MCMV transmission through seed was rare, with a rate of 0.004% among the more than 85,000 seed evaluated, despite detection of MCMV at high levels in the seed lots. To understand factors that limit transmission from seed, MCMV distribution in seed tissues was examined using serology and immunolocalization. The virus was present at high levels in maternal tissues, the pericarp and pedicel, but absent from filial endosperm and embryo seed tissues. The ability to transmit MCMV from seed to uninfected plants was tested to evaluate virus viability. Transmission was negatively associated with both seed maturity and moisture content. Transmission of MCMV from infested seed dried to less than 15% moisture was not detected, suggesting proper handling could be important for minimizing spread of MCMV through seed.

One of the most important and widespread corn/maize virus diseases is maize dwarf mosaic (MDM), which can be induced by sugarcane mosaic virus (SCMV). This study explores a machine learning analysis of five-band multispectral imagery collected via an unmanned aerial system (UAS) during the 2021 and 2022 seasons for SCMV disease detection in corn fields. The three primary objectives are to (i) determine the spectral bands and vegetation indices that are most important or correlated with SCMV infection in corn, (ii) compare spectral signatures of mock-inoculated and SCMV-inoculated plants, and (iii) compare the performance of four machine learning algorithms, including ridge regression, support vector machine (SVM), random forest, and XGBoost, in predicting SCMV during early and late stages in corn. On average, SCMV-inoculated plants had higher reflectance values for blue, green, red, and red-edge bands and lower reflectance for near-infrared as compared to mock-inoculated samples. Across both years, the XGBoost regression model performed best for predicting disease incidence percentage (R² = 0.29, RMSE = 29.26), and SVM classification performed best for the binary prediction of SCMV-inoculated vs. mock-inoculated samples (72.9% accuracy). Generally, model performances appeared to increase as the season progressed into August and September. According to Shapley additive explanations (SHAP analysis) of the top performing models, the simplified canopy chlorophyll content index (SCCCI) and saturation index (SI) were the vegetation indices that consistently had the strongest impacts on model behavior for SCMV disease regression and classification prediction. The findings of this study demonstrate the potential for the development of UAS image-based tools for farmers, aiming to facilitate the precise identification and mapping of SCMV infection in corn.

Maize lethal necrosis (MLN) is a devastating synergistic viral disease of maize caused by maize chlorotic mottle virus (MCMV) plus a virus in the Potyviridae family. The disease has severely impacted maize production in East Africa, Asia, and South America over the past decade, and the presence of MCMV and maize infecting potyviruses in the United States is an ongoing concern. In this study, we performed a genome‐wide association study (GWAS) using the Maize 282 Association Mapping Panel (AMP) to characterize the genetic architecture of host response to MCMV and to identify disease‐tolerant lines. The GWAS analysis detected genomic variants on chromosomes 3, 4, 5, and 8 that were significantly associated with response to MCMV. Several lines in the 282 AMP were highly tolerant to infection, including CML333, one of the parental lines used in the maize nested association mapping population. Quantitative trait locus analysis of the B73 × CML333 recombinant inbred line population revealed an MCMV tolerance locus on chromosome 10. These findings further our understanding of the genetic architecture of MCMV tolerance and will facilitate the development of maize lines, hybrids, and cultivars with improved resistance to MLN and MCMV.