DATA MANAGEMENT IN AGRICULTURE
February 2024
Image taken from Transforming Africa Agriculture Blog
Agriculture stands as the cornerstone of Rwanda’s economy, constituting 29% of the total GDP, with 66.5% of the workforce engaged in agriculture and related fields, making it the largest employment sector as reported by Food and Agriculture Organization (FAO) in 2020. The adoption of digital technology and investments in data-centric agriculture are anticipated to boost Rwanda’s agricultural output and efficiency, aiding in adapting to and mitigating climate change impacts. This approach aims to promote economic and resource utilization efficiency, minimize risks, enhance farming resilience, and streamline agri-food market chains, ultimately contributing to food and nutrition security and alleviating hunger in Rwanda.
Technological Solutions and Types of Agricultural Data needed by private companies in agriculture
The use of technology in agriculture such as drones, Geographic Information System(GIS), and sensors to collect data such as weather data, soil data, crop data, machinery data, market and economic data, is considered as important in agriculture sector development. The integration of technology is expected to boost yields, optimize farming practices, minimize post-harvest losses, and enhance the overall quality of agricultural products.
Weather data, including temperature, precipitation, and wind speed, aids in planning activities like planting and harvesting while also supporting disease and pest management. Soil data, encompassing composition and nutrient levels, informs decisions on fertilization and irrigation, fostering optimal soil health. Crop yield data plays an important role in assessing productivity, guiding choices on planting density, irrigation, and crop varieties. Market and economic data help farmers align production with market demand, contributing to informed decision-making for improved profitability and sustainability.
Optimizing Agriculture Operations: Program Interventions and Data Management in Private Sector Companies
Under the Rwanda Economic Digitalisation Program, executed by Cenfri in collaboration with MINICT and the MasterCard Foundation, the Private Sector Data Acceleration Program was facilitated by the Rwanda ICT Chamber. As part of this initiative, participating companies underwent a data maturity assessment to evaluate their current data management capabilities.
This assessment highlighted significant gaps in data collection to data analysis and using information from analysis. This indicates the necessity for improvement in agriculture businesses to become data-centric entities. Over the period of the programme, Rwanda ICT Chamber has assisted agro-businesses in utilizing data insights to increase their economic standing. One of the interventions was to create a Data Collection Tool for Afrifarmers Market that was developed to gather information about farmers, including their total number, contact details, location, plot size, and cultivated crops and other data points.
The data collected from the aforementioned system, and other systems like sales and finance, is consolidated into a centralized repository, a data warehouse. This strategic adoption facilitates improved data quality management, enhances data security, and ensures scalability and accessibility of the aggregated data. The integrated data is then linked to analytics tools such as Power Bi and Google Looker Studio, playing an important role in uncovering patterns, trends, and correlations within datasets. This analytical approach empowers agri-business companies participating in the program to make well-informed decisions regarding crop selection in specific regions, considerations related to weather patterns, and market prices. It also enables them to identify opportunities for improvement, optimize operations, and proactively anticipate future trends and challenges.
Case study: Agriculture sector private companies in Data Acceleration programme
Similar to other private companies, East Africa Exchange(EAX) company, a company involved in the agriculture and trade sector, collects data in different types and formats. The company was unable to fully utilize the data they have access to in order to make informed business decisions.
“Implementation of data management practices has resulted in large positive impacts after joining the program. The organization now leverages data to make informed decisions, such as predicting future price trends based on historical data and identifying top customers and popular commodity types and grades. This data-driven approach has proven instrumental in determining optimal times for buying or selling products. Moreover, the benefits extend beyond the company’s walls, reaching farmers and stakeholders in the agriculture chain. This is through weekly market insights delivered via SMS and the website, farmers gain valuable information on pricing dynamics, empowering them to make strategic decisions about when to sell their produce. Similarly, stakeholders benefit from accurate and timely information, influencing the company’s overall performance and strategy positively.” Kevin NDIZIHIWE, IT,e-WR & Trade Associate at EAX
Future Trends and Innovations: the use of artificial intelligence, machine learning, and blockchain in agriculture
The technological revolution in the agriculture sector is transforming the industry into smart agriculture through the emergence of innovative technologies such as artificial intelligence (AI), machine learning, and blockchain. These advancements have the potential to significantly enhance efficiency, sustainability, productivity, and transparency throughout the entire food system. AI plays a crucial role in automating processes and extracting meaningful insights from extensive datasets. By employing machine learning algorithms on weather data, soil sensor data, and historical crop yields, AI systems can accurately forecast outcomes and future trends. This predictive analytics capability empowers farmers to make more informed decisions regarding crop selection, optimal planting times, expected yields, and precise irrigation requirements. Moreover, the integration of blockchain applications in agriculture brings about improvements, particularly in supply chain and internet of things (IoT) based systems. Blockchain ensures the traceability and provenance of agricultural products, effectively addressing concerns related to food safety and maintaining the integrity of the supply chain. These technological innovations collectively contribute to a more sophisticated and data-driven approach in agriculture, fostering a future characterized by increased productivity, sustainability, and transparency across the entire food production and distribution network.
Lessons learned:
Image taken from J-PAL Blog
In contrast to various industries, the agricultural sector in Rwanda has lagged behind in the realm of data management when compared with sectors like tourism, education, and retail. Agri-businesses within the region have encountered a deficiency in the adoption of advanced tools for tasks such as data collection, storage, and analysis. This shortfall retards their ability to make well-informed decisions, allocate resources with precision, and optimize operational practices effectively.
By collecting and analyzing data related to weather, soil, crop yields, and machinery, farmers can make strategic choices in crop selection, irrigation, and fertilization, leading to increased productivity and reduced environmental impact. Data-driven insights also enhance risk management, allowing for proactive responses to challenges such as pests and diseases.
Moreover, the adoption of advanced technologies like AI, machine learning, and blockchain, supported by robust data management, facilitates precision agriculture, traceability in the supply chain, and overall sustainability. By leveraging data, the agriculture sector can achieve resource efficiency, minimize waste, and contribute to a resilient and environmentally conscious approach, ensuring a more sustainable and efficient future for global agriculture.