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Innovation feeding agri industry

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With one-quarter of the world’s arable land, Africa is expected to play a leading role in ensuring the global population – expected to grow to 9 billion by 2050 – has access to a secure source of food. This has led the World Bank to predict that Africa’s agricultural sector will grow to $1-trillion by 2030.

However, despite 65% of the continent’s labour force being engaged in agriculture, and 32% of the continent’s GDP stemming from this sector, Africa only contributes 10% of global agricultural output. Key factors contributing to this is a lack of access to markets and financing, as well as productivity levels that are well below developed world standards.

For Africa’s agricultural sector to reach its potential and meet the food needs of a growing global population, improvements in four key areas need to be achieved, namely:

  • Increased productivity, including access to financing and education, integration of end-to-end processes to track farm-to-fork, and affordable access to machinery;
  • Improved food quality and safety through track-and-trace of the origin of products at every step of the logistics chain, as well as reducing the use of pesticides;
  • Better international go-to-market by professionalising the marketing of African agricultural products, and enabling access to regional and global markets for smallholder farmers, who constitute the majority of Africa’s agricultural sector; and
  • Improved government steering, aimed at guiding production and export, and prioritising securing food and nutrition needs of local populations.

The challenge of productivity

Africa’s agri sector consists mostly of a large number of subsistence farmers, who generally sell or trade their produce locally. Their output is often limited by their access to equipment and information: only 5% of the cultivated land on the continent makes use of irrigation, compared to 38% in Asia, while the spare use of fertiliser – as little as 7.4kg per hectare in Ghana compared to 100kg in South Asia – contributes to further underperformance. Many have no access to farming machines to automate some of the more time-consuming and physically demanding work.

There is also a prevailing disconnect between smallholder farmer production and real-time market needs, which hampers government efforts to steer the industry strategically to serve local economic and food security needs. One free trade agreement in the east of the continent was suspended after one of the governments involved identified that the food being exported was much-needed locally.

On the topic of exports: even Africa’s leading agricultural producers have limited access to global markets. Egypt and Nigeria may produce one-third of the continent’s agricultural output, but due to a lack of monitoring and unclear origin, the produce from these two agricultural powerhouses often fail to inspire confidence in global buyers, leaving smallholder farmers with only local market access to sell their goods.

This has created an urgent need to develop a holistic technology-led approach to addressing productivity and quality concerns across the entire agri value chain. Encouragingly, a number of powerful new technologies are emerging to digitise Africa’s agricultural sector and bring a slew of new advances in productivity and quality.

Digitising Africa’s agri-industry

Digital farming combines several key technologies to make farming more efficient and sustainable, and to create opportunities for rural farmers to gain access to the global marketplace.

The megatrend of hyperconnectivity, driven by IoT and mobile phones, is connecting every market participant and machine, from farmers and seed producers to equipment manufacturers, commodity markets, governments and other stakeholders. Smallholder farmers in Africa are enjoying the benefits of hyperconnectivity through mobile applications that enable farmers to get SMS notification of weather information, market prices, and best practice.

Connected sensors for crops and livestock are generating huge amounts of agricultural data that is processed by precision agriculture algorithms to optimise production activities such as irrigation, fertiliser use, and crop protection. This enables farmers to increase yields and maintain global quality standards while saving input resources, minimising any negative effects on the environment.

Improving access through innovation

By increasing transparency, digital agriculture also enables smallholder farmers to have end-to-end track-and-trace for certification requirements to fully integrate them into the supply chain. The Rural Sourcing Management solution developed by SAP in partnership with the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), the German Federal Ministry for Economic Cooperation and Development (BMZ), and other private sector partners, is integrating smallholder farmers into regional and global value chains.

It not only connects them to global markets, but a built-in e-learning component helps to deliver critical information and best practice to enhance the quality of their produce. By tracing produce from farm to fork, the Rural Sourcing Management solution also enables smallholder farmers to sell produce at market related prices, increasing their revenue and opening up new markets in the process.

A new digital agriculture think-tank is also leveraging SAP’s start-up initiatives on the continent – such as the MakeIT initiative in Nigeria conducted in partnership with GIZ – to develop new solutions for Africa’s agri-industry. The aim is to find niche solutions such as Hello Tractor, which functions as a sort of Uber for tractors by provisioning on-demand tractor services to smallholder farmers, or Ghana’s AgroCenta, a fair-trade initiative aimed at improving access to markets among the region’s smallholder farmers.

By equipping Africa’s smallholder farmers with productivity and quality -boosting technology tools, and ensuring they have access to market opportunities beyond their immediate environment, the continent’s agricultural sector can start delivering on its potential to feed the world. As the global population expands and food demand increases, Africa’s smallholder farmers are set to become key players in the global economy of the future.

Africa News

Smart grids needed for Africa’s utilities

Power utilities across Africa should rethink their business models and how they manage and monetise their assets to keep pace with the changing energy ecosystem, says COLIN BEANEY, Global Industry Director for Asset-intensive and Energy and Utilities at IFS.

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Africa’s abundant natural resources and urgent need for power mean that it is one of the most exciting and innovative energy markets in a world that is moving rapidly towards clean, renewable energy sources. The continent’s energy industry is taking new approaches to providing unserved and underserved communities with access to power, with an emphasis on smart technologies and greener energy sources.

Power systems are evolving from centralised, top-down systems as interest in off-grid technology grows among African businesses and consumers. And according to PwC, we will see installed power capacity rise from 2012’s 90GW to 380GW in 2040 in sub-Saharan Africa. Power utilities are needing to rethink their business models and how they manage and monetise their assets to keep pace with the changing energy ecosystem.

Energy and utilities providers are transforming from centralised supply companies to more distributed, bi-directional service providers. They can only achieve this through the evolution of “smart grids” where sensors and smart meters will be able to provide the consumer with a more granular level of detail of power usage. This shift from an energy supplier to “lifestyle provider” will require a much more dynamic and optimised approach to maintenance and field service.

African companies must thus embrace digital transformation as an imperative. This transformation begins by embracing enterprise asset management to improve asset utilisation. The subsequent steps are enhancing upstream and downstream supply chain management; resource optimisation; introducing enterprise operational intelligence; embracing new technologies such as the Internet of Things, machine learning, and predictive maintenance; and becoming a smart utility.

Embracing mobility to drive ROI

Getting it right is about putting in place an enterprise backbone that accommodates asset and project management, multinational languages and currencies, new energies and markets, visualisation of the entire value chain, and mobility apps. Mobile technologies that support the field workforce have a vital role to play in driving better ROI from utilities’ investments in enterprise asset management and enterprise resource planning solutions.

Today’s leading enterprise asset management solutions feature powerful functionality for mobile management of the complete workflow of work orders – from logging status changes and updates, from receiving and creating new orders to concluding the job and reporting time, material and expenses. Such solutions are easy to deploy and intuitive for end users to learn and use.

Importantly for organisations operating in parts of the continent with poor telecoms infrastructure, connectivity is not an issue. The solutions work offline and synchronises when network connectivity is available. Users can work on any device—laptops, tablets, and smartphones—commercial or ruggedised.

By ensuring that field technicians have easy access to information and processes, the mobile solution enables technicians and maintenance engineers to easily do the following tasks:

·         Create a new work order on the fly and log new opportunities

·         Access both historical and planned work information when requested

·         Permit customers to sign when the job is completed

·         Capture measurements and inspection notes on route work orders

·         Create new fault reports on routing

·         Facilitate documentation through photo capturing

·         Provide easy access to technical data and preventive actions.

The power of mobility allows the engineer to be the origin of all data capture on a service event. They can easily inquire on asset history, record parts used or parts needed for repair, record labour hours, and expenses as they occur, and any notes of repairs performed. When coupled with workforce management tools, such solutions unlock significant productivity gains for utilities who are trying to get the most from their workforce and assets.

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Africa News

How machine learning can save your life

Over 11000 people died during the 2014-2016 Ebola outbreak in West Africa.The virus hopped between Guinea, Leone, Nigeria and Liberia, before making its way to the UK and US. But what would have happened if analysis and machine learning stepped in to help solve the problem, asks ANESHAN RAMALOO of SAS.

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Over 11000 people died during the 2014-2016 Ebola outbreak in West Africa.The virus hopped between Guinea, Leone, Nigeria and Liberia, before making its way to the UK and US. But what would have happened if analysis and machine learning stepped in to help solve the problem, asks ANESHAN RAMALOO of SAS.

But what if we could have predicted the outbreak months before it happened, buying us time to take proactive measures to contain it and curb its spread?

With access to overwhelming volumes of data, the computational power needed to store and analyse this data in real time, and sophisticated algorithms that can find patterns in the data and alert authorities to health problems before they become, well, problems, pandemics don’t have to be as devastating as they have been in the past.

In fact, with advanced data analytics, we can better manage any disease – long-term, short-term or pandemic – resulting in better patient treatment, more efficient use of resources and cost savings.

It’s been done before.

By analysing data from social media, blogs, online forums and keyword searches, we were able to predict the 2012-2013 US flu season three months before the Center for Disease Control (CDC) issued its first official warning.

Imagine the impact if the same analytical power was applied across the entire healthcare spectrum – not only on a national and global level, but right down to the individual level.

Data evolution

In the past, health workers relied on manually intensive, paper-based systems to record infections and deaths during disease outbreaks. Not only was it easy for errors to slip through but because the data was anecdotal and historical, authorities did not get a complete understanding of the reach and impact of the outbreak.

During the Ebola outbreak, the CDC adopted a mobile data collection system that enabled health workers to instantly submit information to a database via text messages. This low-cost method of information gathering not only resulted in fewer errors but also allowed analysts to draw up detailed maps of population movements, which made it easier to understand how the disease was likely to spread, and where to set up treatment centres.

While this was certainly an improvement on the paper-based systems of old, the drawback was that mobile data was historic and did not provide researchers with the ability to track developments and population movements in real time.

Data-driven action

But mobile phones are just one source of data. Today, health authorities can overlay thousands of data sources – including social media, health and physician reports, keyword searches, media reports, transactional data from retailers and pharmacies, airline ticket sales, geospatial data and more – to not only better manage diseases and outbreaks when they do happen, but to see them coming months in advance – and what could happen if we don’t act on the information.

By mining structured and unstructured data, we can track the movements of infected populations and who they come into contact with; we can measure the success of containment policies, education campaigns and treatments – and what to do if they’re not working; we can determine the effect of weather and other environmental factors on the spread of diseases.

Never before have we been able to act on information to save lives, not just during pandemics but through better understanding and treatment of diseases.

Personalised treatment

Until now, standard treatments for diseases such as cancer and HIV have been applied to all patients, regardless of their unique profiles and with little understanding as to why some people respond well to certain treatments and others don’t.

But by analysing and creating ‘medical maps’ of individuals that take into account their anatomy, physiology, DNA, RNA and chemical composition, doctors can prescribe personalised treatments that have a greater chance of success.

There are many other benefits of data analysis in healthcare:

·        Personalised treatment can result in fewer hospital admissions and can produce faster results and better experiences for patients;

·        By better understanding the impact of lifestyle and diet on health, medical aid providers can educate their members with the aim of improving their health, which could result in cost savings for both the provider and the member;

·        Governments can use data to develop proactive approaches to protecting and promoting public health, to prioritise services and to find ways to cut costs so that they can provide healthcare to more citizens.

·        By sharing data and results from clinical trials and combining that data with academic, patient and industry data, medical researchers can better understand the genetics of viruses, why some strains are more deadly than others, and why some people are more resistant to viruses. This could spark innovation and generate new insights that ultimately improve treatment and outcomes.

AI and machine learning

As the use of intelligent algorithms, machine learning and natural language processing becomes more entrenched in advanced data analytics, technology will increasingly supplement the skills of humans to produce faster and more accurate medical diagnoses.

We’re already seeing successful applications of artificial intelligence (AI) in predicting relapse in leukaemia patients and in distinguishing between different types of cancer.

Machine learning can extract valuable insights from unstructured data like clinical notes and academic journals to provide even larger datasets that will transform the medical industry into a proactive front against diseases.

There are plenty of doomsday theories about how machines will supersede our intelligence and rise against us. But there aren’t enough stories about the potential of data analytics, AI and machine learning to supplement human skills and knowledge to drastically changes lives for the better – and even save them. Right now, it’s looking more likely that machines will actually help us to live longer – and I don’t know many people who would object to that.

 

  • ANESHAN RAMALOO, ‎Data Scientist and Senior Business Solutions Manager at SAS.
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