The Royal Eswatini Sugar Corporation: Shaping the future of farming with intelligent agriculture
Explore The Royal Eswatini Sugar Corporation’s journey with SAP
The Royal Eswatini Sugar Corporation (RES) produces more than two-thirds of Eswatini’s sugar, 35 million liters of ethanol, and other renewable products. To enable its growers to farm more efficiently and sustainably, RES turned to the SAP Intelligent Agriculture solution for greater visibility into growing conditions and processes using AI and machine learning.
| Industry | Region | Company Size |
| Agribusiness | Simunye, Eswatini, Africa | >4,000 employees |
increase in yield per year.
savings per season.
increase in forecast accuracy.
Group IT Manager, The Royal Eswatini Sugar Corporation
Navigating climate change, regulations, and the need for greater efficiency
Sugar accounts for almost 25% of Eswatini exports, making it a critical part of its economy. With more than 4,000 employees and thousands of independent farmers, or outgrowers, working 67,000 acres of land, The Royal Eswatini Sugar Corporation (RES) is the largest sugar cane producer in Eswatini, Africa. Operating since 1950, RES must continually seek ways to operate more efficiently and sustainably as challenges to efficiency come from multiple directions.
On one hand, pressure from climate change, geopolitical conflict, and growing regulatory restrictions requires continual optimization of resource usage. The company also faces the ever-present challenge of coordinating information across system silos, seeking data synchronization to increase the yield from its outgrowers. With thousands of independent farming operations to oversee and coordinate, RES knew it was important for the company and its farmers to share real-time data on planting, growing, and harvesting processes. This demanded a digital solution that was not only intuitive but also able to handle the complexity required to support the successful production of large volumes of sugar cane across a vast region.
To meet these challenges head on, RES sought an intelligent digital solution that would provide visibility into resource needs and usage on the ground, enabling efficient use of land, water, fertilizer, and transportation. The goal was to optimize resource consumption and production processes to help RES and its outgrowers get maximum crop yield with the minimum amount of time and resource investment.
Leveraging AI and machine learning to optimize sugar cane production
Facing global and economic pressure from many directions, RES chose to implement the SAP Intelligent Agriculture solution, combining it with satellite information, maps, and direct crop data. Additionally, the company is also using SAP Business Technology Platform to build integrations and extensions for SAP Intelligent Agriculture, enabling the use of AI data to support a targeted approach to farming. Using AI and machine learning capabilities, RES is enhancing its efforts to meet growers’ requirements for seed, fertilizer, water, and transport, as well as improving decision-making on ripening through crop modeling, leading to increased overall yields.
Integrating SAP Intelligent Agriculture with geographical information system and GPS data helps improve the efficiency with which RES plans, grows, and harvests sugar cane. To create a sustainable system, the project also brings together external intelligence, such as water and weather information and global supply chain information for fertilizer, equipment, and logistics. This provides farmers with greater support to know when, what, and where to plant as well as where and how much to water and fertilize.
Group IT Manager, The Royal Eswatini Sugar Corporation
Benefiting local populations with better yields using intelligent agriculture
In the effort to meet challenges such as climate change, geopolitical conflict, and the increasing costs of crop production, RES leveraged intelligent digital technology from SAP to aid farmers and outgrowers in the quest to grow sugar cane as efficiently and sustainably as possible. Machine learning capabilities combined with agronomic data and satellite and weather modeling for harvest optimization have led to a 5.29% increase in sugar cane yield, or an estimated yield increase of €4.8 million each year. Additionally, improved decision-making in relation to crop ripening has saved RES and outgrowers an estimated €150,000 each season.
RES and its outgrowers now plan and manage every aspect of the crop with greater precision and support, leading to better yield that benefits local populations, families, and community health and sustainability.
Want to know more about RES?
Driving Sustainable Sugar and Renewables with SAP Intelligent Agriculture (SAP Innovation Awards 2024 Entry Pitch Deck)
RES: How Can Cultivating a Data-Driven Approach Empower Farmers to Grow? (SAP Business Transformation Study)