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27 September 2021 | Story Leonie Bolleurs | Photo Supplied
Dr Frikkie Maré is serving as one of the directors of the non-profit organisation, the Agri Relief Foundation (ARF).

The agricultural sector is used to facing events of abnormal impact, including floods, droughts, veld fires, and disease outbreaks. Even if it is possible to prepare against any of these risks by taking proper measures, for instance by having a farm emergency plan in place or by securing property properly, there are times when it is not possible or practical for the modern-day South African farmer to proactively manage all the risks they are facing.

It is in times like these that the newly established body, the Agri Relief Foundation (ARF), provides an invaluable service to the agricultural sector. 

Dr Frikkie Maré, Senior Lecturer in the Department of Agricultural Economics at the University of the Free State (UFS), is one of the directors of this non-profit organisation, which focuses on assisting agricultural producers in need. 

This initiative is the brainchild of a number of businesses in the agricultural sector.

He says although there are many institutions in South Africa assisting farmers, most of the current initiatives are geared towards large-scale disasters, such as severe droughts, floods, unpreventable pests and diseases, and veld fires that affect many producers.  

Benefiting the wider society

According to Dr Maré, the ARF will focus on helping individual agricultural producers who are in need; both financially and otherwise.  This may include elements such as the loss of grazing due to brown locust, assistance after a farm attack or murder to ensure the day-to-day running of the farm, and localised natural disasters such as floods, hail, severe cold, or fire.

The group of directors plays a key role in screening the applications for assistance and deciding, based on merit and the availability of resources, who they can assist.

Besides the direct benefit to the farmer, this initiative also adds value to the wider society. “When the sustainability of an agricultural producer is under threat, it also threatens the livelihoods of his/her workers and their families, the rural economy of the nearest town where they purchase production inputs and general groceries, as well as society at large, as less food and/or fibre will be produced.  The assistance of the ARF will therefore ripple out to a much larger level than only the agricultural producer,” explains Dr Maré. 

A learning experience

There is also a benefit for the university. In the classroom, Dr Maré will be able to share any knowledge he is gaining in this process with his students. “Agricultural Economics is fundamentally about ensuring the long-term sustainability of agricultural production through concepts, including but not limited to, production economics, natural resource economics, agricultural management, and marketing.  My involvement in the ARF will provide examples of what can go wrong in terms of primary production that threatens the sustainability of the enterprise and what can be done to assist,” he says. 

Any business or individual can contribute to this noble cause. Financial contributions as well as physical products such as transport, fuel, animal feed, and legal services are welcome. 

Dr Maré says they have already received contributions from companies such as Zoetis (animal health), which sponsor a part of their profit from certain products to the foundation on a continuous basis. Lavendula (animal feed) also sponsored the proceeds of a farmers’ information day.

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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