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20 September 2021 | Story Leonie Bolleurs | Photo Supplied
Prince Matova, a PhD student in the Department of Plant Sciences, has been working on breeding a maize that can resist the fall armyworm (FAW) – a maize-eating pest. Later in September, he will receive the Young Scientist Award from the Plant Mutation Breeding Division of the International Atomic Energy Agency (IAEA) and the Food and Agriculture Organisation of the United Nations (FAO).

Prince Matova, a PhD student in Plant Breeding at the University of the Free State (UFS), received the Young Scientist Award from the Joint Food and Agriculture Organisation of the United Nations (FAO)/International Atomic Energy Agency (IAEA) Division of Nuclear Techniques in Food and Agriculture for excellence in plant mutation breeding.

The IAEA Director-General, Mr Rafael Mariano Grossi, will officially announce the award at the 65th regular session of the IAEA General Conference that will take place later in September this year.

The award is given to scientists younger than 40, who have made a significant contribution and impact in the field of mutation breeding.

Matova, a researcher, research and agronomy manager, and maize and legumes breeder at Mukushi Seeds (Pvt) Ltd in Harare, Zimbabwe, says: “People have seen the little work that I have done, and they were happy with it. That makes me happy too.”

Other contributions

In the ten years collaborating with the IAEA, practising mutation breeding, Matova – who believes innovative thinking and self-motivation to be contributing factors to a successful scientist – has also been recognised for other outstanding contributions. These include the release of a cowpea mutant variety in 2017 and its wide dissemination across Zimbabwe, as well as the modernisation of the maize and cowpea national breeding programmes. He has also contributed two publications and appeared twice at IAEA Plant Mutation Breeding symposia. Furthermore, Matova has trained other scientists and fellows across Africa and collaborated with centres of excellence in plant breeding, research, and development.

Growing up, he never guessed that he would one day become an agricultural scientist. Matova was, however, very good at biology and believes that this is one of the reasons why he ended up in crop science. “I am enjoying every moment of it. I love innovativeness and inventions and I view hybrid maize variety development as the greatest innovation in plant breeding. Working for Mukushi Seeds is inspiring; I have a young and dedicated team and the environment allows me to explore my full potential.”

“I feel science solves problems and every day as I do my breeding work, I have this desire to achieve greatness by developing a super maize hybrid,” he says.

Displaying excellence

For the past three to four years, Matova has been working to breed maize varieties that can resist fall armyworm (FAW) – a maize-eating pest. He says the pest has caused significant maize crop yield and economic losses across Africa.

More than 300 million smallholder farmers across sub-Saharan Africa rely on maize for food and livelihoods. “These farmers have limited capacities to control the pest. They are using insecticides, which we have seen to effectively provide immediate control of the pest.” However, these pesticides have environmental and health issues. “It is against this background that we, as plant breeders, felt it was important to develop varieties that are resistant to the pest. It is a more environmentally friendly, less expensive, and more sustainable solution,” explains Matova.
In his research, he evaluated the breeding potential of exotic FAW-resistant donor lines with local lines. He also investigated the resistance response and stability of local cultivars and inbred lines against FAW. 

While working at the Zimbabwean Department of Research and Specialist Services (DR&SS), Matova collaborated with the International Maize and Wheat Improvement Center (CIMMYT), the University of Zimbabwe, the UFS, and the IAEA to look into the possibility of using mutation breeding in maize crop improvement, with the intention to enhance FAW-resistance in maize genotypes.

He introgressed FAW resistance into the elite breeding materials at both DR&SS and Mukushi Seeds, where he is currently working. Matova believes that although FAW resistance is currently a nice-to-have trait, going forward, all maize varieties released should have a baseline resistance to FAW.

Ultimately, his work generated important information that can guide research and maize breeding for FAW resistance in Southern Africa. All this information is free for researchers to use for the betterment of Africa and the world.

Inspired by greatness

There are a number of people in the industry and academia who have inspired Matova. The list includes Dr Cosmos Magorokosho (CIMMYT), Prof Hussein Shimelis (University of KwaZulu-Natal), Dr Fatma Sarsu (IAEA), Dr Marilyn Warburton (Agricultural Research Service in the United States Department of Agriculture), Dr Amsal Terekegne (ZAMSEED), and Dr John MacRobert (Mukushi Seeds). They all contributed in one way or another to influence Matova in a positive way towards becoming the passionate scientist he is today.

Besides this list of prominent names, Matova says that he was more recently also motivated and encouraged by his PhD supervisor and mentor, Prof Maryke Labuschagne, Professor in Plant Sciences at the UFS. “She is a very special person doing a wonderful job. Prof Labuschagne is kind, thorough, hardworking, and a good mentor,” he states.

Prof Labuschagne is very proud of Matova for receiving this award. “He has been working really hard, and this is a wonderful recognition of the time and effort that he has invested in his research,” she says.


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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