SLU news

Livestock in the blood

Published: 09 March 2022
Photo of Ugandan man in a dry area.

Ronald Ayahura started learning his profession already as a child. “During my childhood I used to take part in grazing, milking, spraying and deworming of the cattle, goats and sheep owned by my family.” Today he is a PhD student in the Drylands Transform Project, working on co-developing sustainable rangeland options with local communities in the setting of knowledge sharing hubs (livestock cafes). His research will mainly focus on animal production and productivity aspects of livestock in the drylands.

When visiting the project sites it was a dry season, and most of the areas had little or no pastures for the animals. Movement of animals over long distances for grazing and access to water was a common sight. The rangelands were heavily degraded as shown by loss of vegetation and gullies due to erosion in some areas. It was evident that the rangelands had lost their capacity to support the pastoral communities and their livestock in a sustainable way. Without strategic intervention, the degradation would continue causing total loss of livelihoods of the pastoralists inhabiting some of these areas.

Improved feeding strategies for better livestock performance

The poor performance of the animals in these areas is mainly caused by inadequate nutrition in terms of quality and quantity of the feed available. Ronald believes that this can be addressed through restoration of the pastures and ensuring proper utilisation of all the available feed resources including crop residues and tree foliage. This would consequently result in increased milk production and growth rates of slaughter animals, which in turn can be enhancing food and income security and the general livelihoods of the pastoralists.

"I look forward to the development of feeding strategies for improved production of livestock in the drylands."

Cattle grazing in drylands in East Africa.
Beef cattle grazing during the dry season in Rupa near one of the project sites in Uganda. Photo: Ronald Ayahura

The feeding strategies will be developed and evaluated together with the local communities in livestock cafes. Beef cattle are the most important livestock species in these areas and animal performance trials will be established at the livestock cafés to assess various feeding strategies that can be used to improve the performance of grazing beef cattle. The effects of pasture restoration on grazing intake of beef animals will also be studied.

Background within animal science

Before becoming a PhD student in Drylands Transform, Ronald got his Master of Science degree in Animal Science from Makerere University in 2016. Earlier he had obtained a Bachelor of Science in Agriculture majoring in Animal Science.

“During my MSc research, I worked with urban and peri-urban dairy farmers in the Lake Victoria crescent focusing on the utilization of locally available feed resources for improved productivity of dairy cattle.”

During the special project research at undergraduate level he focused on the effect of reseeding and cattle manure application on the nutritive quality (chemical composition and degradability) and mineral composition of rehabilitated rangeland pastures, which is closely related to the current research field.

Ronald was quick to apply when PhD positions were advertised as he learnt about the Drylands Transform project by Professor Denis Mpairwe, the country coordinator of the project in Uganda. Denis was his academic supervisor for Ronald’s research projects both at Bachelors and Masters level.

More information about the work with livestock in Drylands Transform can be found here.

Facts:

Logotype for the project Drylands Transform

Drylands Transform

Drylands Transform is a 4-year research project funded by Formas that started up during the Covid-19 pandemic in October 2020. It includes an interdisciplinary research team representing SLU and seven other universities and international organisations from Sweden, Kenya and Uganda. 

Visit the website for Drylands Transform.