Y1 Progress report | Ferdinald Lubobi Shamala
Genome-Wide Identification of Resistance Loci for Napier Head Smut Disease in Napier Grass (Cenchrus purpureus)
The first year of the fellowship has focused on establishing a strong foundation for understanding the genetic basis of resistance to Napier head smut disease in Napier grass (Cenchrus purpureus). This work directly contributes to the overarching goal of developing disease-resistant Napier grass varieties that will improve forage productivity and enhance the resilience of livestock production systems in sub-Saharan Africa. Napier grass is the principal forage crop for smallholder dairy farmers across the region. Yet its productivity is severely constrained by diseases, particularly Napier head smut, which significantly reduces biomass yield, forage quality, and the longevity of planting material. Developing resistant cultivars is therefore a sustainable and cost-effective strategy for disease management and improved livestock production.
During the reporting period, the primary focus was on establishing and implementing a comprehensive phenotypic screening programme for a diverse panel of Napier grass accessions. The study evaluated 199 Napier grass accessions, including susceptible control genotypes, using a randomised complete block design with three replications. Disease response was monitored through weekly assessments over eight weeks. Data collected included disease severity scores, tiller number, and plant survival status, enabling the characterisation of disease progression and plant performance under disease pressure.
A major achievement during the first year was the successful completion of field phenotyping. Weekly disease assessments were conducted systematically to ensure consistency and reliability of the data collected. The repeated measurements yielded a valuable longitudinal dataset capturing temporal disease progression among the evaluated germplasms. This type of dataset provides greater analytical power than single-endpoint assessments because it allows disease development to be monitored over time and enables more accurate identification of resistant and susceptible genotypes.
Following completion of the field evaluations, considerable effort was devoted to data management, cleaning, and quality control. Field data often contained inconsistencies arising from manual recording, including variations in the recording of plant mortality, missing observations, and inconsistent formatting of disease scores. To address these challenges, I developed a reproducible computational workflow in Python to standardise accession identifiers, clean field observations, harmonise disease and tiller-count records, and generate a master longitudinal dataset suitable for statistical analysis. The cleaned dataset comprised 4 776 observations collected across all accessions, replications, and assessment periods. Establishing this reproducible data-processing pipeline is an important milestone, as it will support future phenotyping experiments and ensure consistency in subsequent analyses.
The cleaned phenotypic data were subsequently analysed to quantify disease progression using the Area Under the Disease Progress Curve (AUDPC). This widely accepted epidemiological measure integrates disease severity over time. Additional phenotypic parameters, including survival rate and final tiller production, were calculated to evaluate both disease resistance and plant vigour under disease pressure. Preliminary analyses revealed substantial phenotypic variation among the evaluated accessions, with some genotypes exhibiting consistently low disease progression, high survival rates, and better maintenance of tiller production. These findings provide encouraging evidence that useful genetic variation for resistance to Napier head smut disease exists within the germplasm panel and can be exploited in future breeding programmes.
Beyond phenotypic analysis, the fellowship has strengthened my skills in computational biology and bioinformatics. I gained practical experience with Python for data cleaning, data management, epidemiological analysis, and visualisation of complex phenotypic datasets. This has significantly enhanced my ability to develop reproducible analytical workflows, an increasingly important aspect of modern plant genomics research. These computational skills will be directly applied in the next phase of the project, which involves integrating phenotypic data with genome-wide marker data to identify genomic regions associated with disease resistance.
The fellowship has also provided valuable opportunities for scientific engagement and professional development. Participation in FAR-LeaF workshops, seminars, and collaborative meetings has enhanced my understanding of interdisciplinary approaches to agricultural research and expanded my professional network. These interactions have facilitated knowledge exchange with researchers working in plant genomics, molecular breeding, bioinformatics, and crop improvement, providing new perspectives that have strengthened the direction of my research. During the reporting period, I strengthened my research profile by completing international training in plant breeding and genome editing through Lanzhou University and the International Institute of Tropical Agriculture (IITA). This experience enhanced my expertise in plant genomics, bioinformatics, and modern crop improvement while expanding my international research network.
My supervisor has provided continuous scientific guidance throughout the reporting period, ensuring that the research maintains high scientific standards and remains aligned with the fellowship's objectives. Regular consultations have contributed to improvements in experimental design, data analysis, interpretation of results, and research planning. In addition, mentorship from Professor Sanushka Naidoo has broadened my exposure to international research practices. By introducing me to her research group, she created opportunities to interact with experienced researchers and postgraduate students working in plant genomics and molecular breeding, thereby enhancing my research capacity and encouraging interdisciplinary collaboration.
Although the project experienced some challenges, particularly during data management due to inconsistencies in field records and missing observations, these issues were successfully addressed through systematic quality-control procedures and computational data cleaning. The experience highlighted the importance of robust data management systems in large-scale phenotyping studies and strengthened my capacity to develop efficient and reproducible analytical pipelines.
Overall, the first year of the fellowship has achieved its primary objectives of establishing a robust phenotypic dataset and developing the analytical framework required for downstream genomic analyses. The project has generated a high-quality dataset that forms the foundation for the next phase of the research, which will involve genome-wide association studies (GWAS) to identify loci associated with resistance to Napier head smut disease. The identification of resistant accessions will contribute to the development of improved Napier grass varieties that are better adapted to disease-prone environments and capable of supporting sustainable livestock production.
During the next reporting period, I will focus on integrating the phenotypic dataset with genomic data, conducting GWAS to identify candidate resistance loci, annotating candidate genes and interpreting their functions, and preparing scientific manuscripts for publication. These activities will directly contribute to the Programme's objective of developing advanced research capacity while generating knowledge to support climate-resilient and sustainable agricultural systems in Africa.
Although my first year of research was conducted primarily under controlled experimental conditions and focused on phenotypic screening and data analysis, I have gained a greater appreciation for the role of Indigenous Knowledge (IK) in agricultural research. During interactions with farmers and agricultural stakeholders, I observed that many farmers rely on locally acquired knowledge and experience to identify disease symptoms, select planting materials, and manage Napier grass fields. This knowledge has been developed over many years through observation and practical experience and plays an important role in day-to-day farm management. One important lesson I learned is that farmers often use visual indicators such as abnormal flowering structures, reduced plant vigour, and poor tillering to recognise diseased plants and decide whether to remove or replace infected planting material. While these observations may not always be based on formal scientific diagnosis, they provide valuable insights into disease occurrence and field performance that can complement scientific research. Science provides evidence-based methods for understanding disease mechanisms and identifying genetic resistance. At the same time, Indigenous Knowledge contributes practical, location-specific experience that can guide disease monitoring and management under local farming conditions.
This experience has strengthened my appreciation of the importance of engaging farming communities as partners in agricultural research. As the project progresses to the validation and dissemination stages, I intend to engage more closely with farmers and extension personnel to ensure that research findings are communicated in ways that are practical, accessible, and responsive to local knowledge and needs. Integrating scientific evidence with farmers' experiences will be essential for promoting the adoption of improved disease-resistant Napier grass varieties and achieving sustainable impacts on smallholder livestock production.
One of the most memorable experiences during my research was observing the remarkable diversity in disease responses among the 199 Napier grass accessions. While some accessions quickly succumbed to Napier head smut disease, others remained healthy under the same conditions, reinforcing the importance of genetic diversity for breeding disease-resistant varieties. Another rewarding milestone was developing a Python-based workflow to clean and analyse thousands of phenotypic records. Although learning the programming tools was initially challenging, successfully automating the analyses greatly improved the efficiency, accuracy, and reproducibility of my research. These experiences have strengthened my confidence as a researcher and reinforced my commitment to using genomics and data science to address agricultural challenges.
Annual report submitted by Dr Ferdinald Lubobi Shamala
(summarised for publication by Heidi Sonnekus for the FAR-LeaF Programme)
