Magtel Highlights the Success of the KODA Project After Completing Its Final Certification

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Magtel Highlights the Success of the KODA Project After Completing Its Final Certification

Magtel Highlights the Success of the KODA Project After Completing Its Final Certification

Magtel has taken part in the certification of the final milestone of the KODA project, an initiative aimed at optimising irrigation management, fertigation and phytosanitary strategies in woody crops such as olive, almond, citrus and avocado. The technical visit, held at Drimays facilities in Seville, brought together the main partners — Magtel, Drimay and BrioAgro— along with the subcontracted entities: Instituto de Agricultura Sostenible – CSIC, Universidad Rey Juan Carlos and CITOLIVA, the latter attending remotely.

The KODA platform is built upon a technological architecture designed to maximise efficiency in agronomic management. Its design integrates an advanced network of IoT sensors that monitor the climate–soil–plant interaction with high precision, a Big Data infrastructure that manages the full lifecycle of data — from capture and storage to processing, analysis and exploitation — and Artificial Intelligence algorithms capable of analysing large volumes of information and generating highly reliable predictive models. Together, these three components enable the development of decision‑support systems that optimise irrigation and fertigation dynamically and in a data‑driven manner.

During the two certification sessions, project partners presented the progress achieved during the latest project year, as well as the degree of compliance with the objectives established at the launch of the initiative in 2021.

Results Exceeding Expectations

From a quantitative perspective, the project’s outcomes confirm a significant overachievement of the indicators set. With regard to water consumption, an improvement target of 10% had been established, ultimately reaching 31%, reflecting a substantial optimisation of water‑use efficiency in the monitored farms. Similarly, the planned reduction in fertiliser use — also set at 10% — was exceeded, reaching 32% thanks to the combined application of field sensors, advanced data analysis and more precise fertigation strategies. In terms of crop yield, the initial target of a 5% increase was amply surpassed, with results reaching 21%.
These outcomes demonstrate the effectiveness of the technological and methodological approach adopted, as well as the direct impact of KODA on data‑driven agronomic decision‑making.

Alongside these quantitative achievements, the project has generated high‑value qualitative results, reflected in scientific publications addressing topics such as vascular diseases in olive and almond crops, advanced exploratory data analysis applied to precision agriculture and the development of evapotranspiration predictive models to improve water‑planning strategies.

An Innovative Tool for Farmers and Agronomic Professionals

The completion of the project has enabled the development of a digital decision‑support tool aimed at farmers and agronomic service companies. This solution facilitates intelligent irrigation management by adjusting water inputs to the real needs of the crop, with the goal of improving efficiency, reducing costs and enhancing the sustainability of agronomic practices.

A Key Contribution to the Challenges of Water Use

KODA addresses today’s major challenges associated with water consumption and the efficient use of agricultural inputs through a dual approach. From a macroeconomic perspective, it contributes to optimising water use in a context marked by climate change and increasing pressure on water resources. In parallel, from a microeconomic perspective, the platform enhances farm profitability by enabling more precise irrigation decisions, tailoring water applications to the actual needs of the crop and reducing operational risks.

The completion of this certification marks a major milestone for Magtel and all project partners, consolidating an initiative that drives a more sustainable, efficient and data‑driven agricultural model.

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