Every drop is evidence-based
Soil moisture sensors provide insights, QMS Water translates the data into irrigation recommendations, and drip irrigation enables targeted implementation. The integration of these components represents a significant step towards smarter and more evidence-based water management.
Precision irrigation begins with reliable measurement, but only truly delivers value when data is converted into irrigation that matches crop requirements. Within AgriDataValue, Delphy is investigating how soil moisture sensors, QMS Water and drip irrigation together form a single data-driven chain.
Using water more intelligently
The availability of fresh water is coming under increasing pressure. At the same time, the agricultural sector is expected to produce more efficiently, whilst using natural resources carefully. Precision irrigation offers opportunities in this regard. As part of the European AgriDataValue project, we are investigating how digital technology can support growers in making better irrigation decisions. The key lies not in any single technology, but in the integration of soil moisture sensors, the QMS Water decision-support system and drip irrigation. Together, these components form the basis for data-driven water management.
Soil moisture sensors: insight into the root zone
In practice, irrigation is often based on experience, field observations and an assessment of weather conditions. Soil moisture sensors provide continuous, objective information about the soil’s moisture status.
• the amount of water available to the crop;
• the rate at which the soil dries out;
• the distribution of moisture across the root zone;
• the effect of rainfall and previous water applications.
The quality of the measurements is crucial in this regard. A representative location, a suitable sensor type, correct placement and a stable data connection are required to make the measurements usable for irrigation control.
QMS Water: from measurement data to irrigation advice
Individual sensor measurements become more valuable when assessed in context. QMS Water combines soil moisture data with crop evapotranspiration, crop water requirements, rainfall and weather forecasts. This results in advice on the timing and volume of irrigation. This shifts decision-making from reacting to visible drought stress to looking ahead based on current moisture reserves and expected developments.
Drip irrigation: targeted application
Drip irrigation delivers water close to the root zone and enables small, frequent applications. This aligns well with digital recommendations that can be translated, on a plot-by-plot or irrigation-section-by-section basis, into specific system settings.
• targeted application in the root zone;
• flexible watering in small increments;
• options for section-by-section control;
• a useful basis for further automation.
The data chain at a glance
Added value is created when measurement, analysis, advice and implementation are organised as a single, coherent process.
1 Soil moisture sensors
Continuous measurement of moisture levels in the root zone.
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2 QMS Water
Combination of sensor data, crop requirements, evaporation, precipitation and weather forecast.
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3 Irrigation advice
Advice on when to irrigate and what water application is appropriate.
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4 Drip irrigation
Targeted water application in the root zone or per section.
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5 Feedback
New measurements show how the soil and crop are responding and inform the next decision.
The strength lies in the integration
A sensor alone provides a measurement. A model alone provides a calculation. An irrigation system alone supplies water. By linking these three components, a practical decision-making chain is created in which data can directly contribute to crop management.
The ultimate aim is a closed-loop control system. Soil moisture sensors provide new measurements; QMS Water processes this information into a recommendation; the drip irrigation system carries out the watering; and the next measurement shows the effect of this. Reliable data and agronomic oversight remain essential.
Learning from practice
The field trials also highlight areas for further development. When sensor data is temporarily unavailable or the system has technical limitations, additional assessment in the field remains necessary. This is precisely why validation under real-world conditions is important: not only the model, but the entire chain must function reliably.
In the AgriDataValue pilot for onions, soil moisture, weather data and the amount of irrigation applied are monitored. This data is used to test the functioning of the data chain and to further validate the model.
AgriDataValue: building practical digitalisation
The AgriDataValue project focuses on connecting and utilising agricultural data. For Delphy, this means bringing together sensor technology, crop production knowledge and practical irrigation. The aim is not to collect more data, but to enable better decisions for growers and advisers.
