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07 August 2025 | Story Martinette Brits | Photo Stephen Collett
Prof Willem Boshoff
Prof Willem Boshoff shares insights from decades of rust disease research during his inaugural lecture at the University of the Free State.

Rust diseases of food crops remain one of agriculture’s most enduring and evolving challenges. In his inaugural lecture on 23 July 2025 at the University of the Free State (UFS), Prof Willem Boshoff shared how these complex pathogens continue to pose a significant threat to South Africa’s staple crops – and why continued research is more critical than ever.

Titled Battling rust diseases of food crops in South Africa, the lecture reflected on decades of rust research and recent developments in pathogen virulence. Prof Boshoff, from the Department of Plant Sciences, emphasised that the threat posed by rust fungi today stems from their “mechanisms of variability, their ease of long-distance spore dispersal, and subsequent foreign race incursions”.

 

A shifting disease landscape

Rust fungi are biotrophic organisms that cannot be cultured on artificial growth media. This makes rust research a technically demanding field that requires living pathogen collections, seed sources, skilled researchers, and specialised infrastructure. Prof Boshoff noted that for more than 35 years, the UFS has been at the forefront of this work, monitoring rust pathogens on wheat, barley, oats, maize, and sunflower.

While wheat remains the most extensively studied type, recent rust outbreaks across a range of crops point to a worrying trend. A localised outbreak of stem rust on spring wheat in the Western Cape has been linked to race BFGSF, which carries a previously unknown combination of virulence genes affecting both wheat and triticale. In 2021, leaf rust race CNPSK was detected, showing virulence to the highly effective Lr9 resistance gene.

More recently, stripe rust race 142E30A+ – first reported in Zimbabwe – was found in wheat cultivars from the Free State and northern irrigation areas. “Results revealed increased susceptibility of especially spring irrigation wheat cultivars,” Prof Boshoff explained, particularly due to its virulence to the Yr9 and Yr27 resistance genes.

Rust pathogens affecting other crops are also evolving. In maize, only a few lines with mostly stacked resistance gene combinations were effective against all tested isolates. In sunflower, just four of 30 Agricultural Research Council national trial hybrids showed resistance to local rust races.

 

Building better resistance

A key strategy in rust control lies in identifying and understanding resistance in host plants. This, Prof Boshoff stressed, requires optimised phenotyping systems for both greenhouse and field conditions, along with a solid understanding of available resistance sources. At the UFS, several recent studies have contributed valuable data to both local and international plant breeding programmes.

“Continued local and regional rust research is critical,” he said. “It supports early detection of new races, alerts to producers through updated cultivar responses, and enables efficient breeding strategies and other sustainable methods of rust management.”

The rust programme at the UFS has not only supported varietal release and on-farm risk management, but also strengthened collaboration between plant scientists, industry partners, and international researchers. With South Africa’s strategic location and history of rust surveillance, the programme continues to play a pivotal role in continental and global food security efforts.

 

About Prof Willem Boshoff

Prof Willem Boshoff is a plant pathologist with a strong background in wheat breeding and rust disease control. He holds four degrees from the University of the Free State, all awarded cum laude: a BScAgric (1994), BScAgric Honours (1995), MScAgric (1997), and PhDAgric (2001). His doctoral research focused on the control of foliar rusts in wheat.

Between 2001 and 2016, he worked as a wheat breeder and contributed to the release of several commercial cultivars. He joined the UFS Department of Plant Sciences in 2017 and has since been actively involved in national and international research projects, capacity development, and advancing disease resistance in food crops.

News Archive

Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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