Online Reporter
Engineer Tapuwa Justice Mashangwa
As the world changes and artificial intelligence develops, we cannot ignore the era we are in but rather acknowledge the change of wind in our agribusiness. Artificial Intelligence (AI) boosts productivity, efficiency and sustainability across the agricultural value chain. AI-enabled systems analyse data from soil sensors, weather forecasts and crop imagery to optimise irrigation, fertiliser use, and pest management, helping farmers make better real-time decisions and reduce waste. Precision agriculture powered by AI can increase crop yields while conserving resources like water and chemicals, resulting in cost savings and improved food quality. AI also supports predictive analytics for yield forecasting and market trends, enabling agribusinesses to plan more strategically and manage risks effectively. Additionally, automation through AI-driven machinery reduces labour demands and enhances operational efficiency, especially important where labour shortages are common. By integrating these technologies, agribusinesses can enhance profitability, strengthen food security and build more resilient and environmentally responsible farming systems.
At its core AI relies on data that it is fed which as previously expounded, can be classified quantitatively and qualitatively contributing to improved data sets. In the agricultural sector as with all other sectors the types of data available which need to be optimally processed to get the best results from AI can be classified into structured data, unstructured data, semi-structured data, labelled data, unlabelled data, real-time (streaming) data, synthetic data and human feedback data.
The processing of data will never come to an end as the whole development of data stems from new information accessed and developed and the posterior reactive material obtained when data is disseminated, creating a cyclical data loop. Thus the process of data collection, processing and storage must be optimally be regulated ensuring that any challenges faced are resolved instantaneously.
Methods of mitigating the challenges of data include practical solutions through establishing national data taxonomies; digitising data at the source; creating centralised or federated data platforms; enforcing data governance frameworks; using AI to fix data problems; building human capacity strategically; aligning donors to national data strategies and incentivising private sector data participation.
Establishing national data taxonomies involves defining national standards for: sector classifications; geographic coding and entity identifiers which results in data compatibility across systems and easier integration of public and private datasets.
Digitising data at the source involves replacing paper-based collection with: mobile data collection tools, offline-first digital forms and automated validation rules. The digitising process creates cleaner data, real-time categorisation and reduced human error.
Another way of mitigating data challenges is through creating centralised or federated data platforms through national data lakes or data exchanges, Application Programming Interfaces (APIs) for inter-agency access and federated models where data remains owned but interoperable. The benefits of this are reduced silos, single version of truth and AI-ready datasets.
Enforcing data governance frameworks also helps which can be done through assigning data owners, data stewards, data custodians and mandate metadata documentation. If well done this results in accountability, sustainability and long-term usability.
Using AI to fix data problems can be done through deploying AI for: automated data classification, deduplication, error detection and entity resolution. Implementation of this promotes faster data cleaning, cost reduction and continuous improvement.
Human capacity should be built strategically by shifting training focus to: data architecture, taxonomy design, governance and ethics and embed data experts in ministries and councils. Systems will then scale beyond pilots and will improve local ownership of data infrastructure.
Aligning donors to national data strategies will require donor projects to: use national standards, deposit data into national platforms and ensure post-project continuity which will reduce fragmentation and institutional memory would be retained.
Incentivising private sector data participation by providing: data-sharing tax incentives, public–private data trusts and clear IP and privacy protections would help improve the challenges being currently faced resulting in richer datasets, better economic planning and innovation growth.
In essence the strategic bottom line is that sub-Saharan Africa’s data problem is not lack of data — it is lack of structure, standards and stewardship. Solving this requires: governance before technology, standards before AI and capacity before scale.
In acknowledging the accelerating impact of artificial intelligence, we also accept our role in shaping its trajectory. The era we inhabit demands not passive observation, but active engagement leveraging AI’s capabilities while remaining critically aware of the data and values that underpin it.
As explored, the distinction between quantitative and qualitative data is more than academic; it is central to building systems that are robust, responsible and truly reflective of human nuance. Embracing AI with intention means continually refining the data that feeds it, championing transparency and ensuring that innovation serves the broadest possible benefit. Ultimately, by harmonising human insight with machine learning, we stand not only to improve the systems we create but to enrich the agribusiness sector.
As King Solomon said in Ecclesiastes 3:1, “There is a time for everything”.
The writer is Engineer Tapuwa Justice Mashangwa, GCEO Emerald Investments, CEO DataFarm, CEO Emerald Agribusiness and CEO TranslateZW. He can be contacted on +263771641714 and email: [email protected] or [email protected].
