Data & Analytics Platform for the Barley Agricultural Chain

01The Client

Large agribusiness company ranked in the Forbes Agro100, active in the barley production chain and connecting growers, agronomists, and industry.

02The Challenge

• Low digitalization in the field, with limited data collection and use on planting, management, and productivity
• Difficulty integrating information from growers, agronomists, and industry into a unified view
• Lack of structured data to support agronomic and operational decisions
• Low production predictability, impacting supply chain planning
• Heterogeneous performance among growers with no benchmarks or structured comparisons

03Implemented Solution

• Collection and ingestion of field data (planting, management, climate, soil, and productivity) into a centralized platform
• Development of ETL/ELT pipelines for processing and integrating agricultural chain data
• Implementation of data governance and quality practices to ensure information reliability
• Development of analytical and predictive models for harvest forecasting and agronomic recommendations
• Creation of dashboards and analysis tools for growers, agronomists, and chain managers
• Performance benchmarking across growers to identify best practices and improvement opportunities

04Strategic Differentials

• End-to-end data cycle coverage: from field collection to actionable insight generation
• Predictive models applied to the specific context of the barley chain, with high agronomic value
• Integration of multiple chain stakeholders (growers, agronomists, industry) in a single analytics platform
• Data governance from the source, ensuring quality and traceability of field information

05Results Achieved

• Greater production predictability with harvest forecast models
• More accurate agronomic decisions based on real field data and model-driven recommendations
• Reduced information asymmetry between growers, agronomists, and industry
• Identification of high-performing growers and dissemination of best practices across the chain
• Continuous improvement in quality and productivity through data-driven feedback cycles