‘One drop at a time’, part 3: When the irrigation system learns to listen

Sensors, weather data and automated controls are giving potato growers a clearer picture of what is happening inside the ridge. The real advance, however, will come from combining those signals with sound agronomy—not from surrendering decisions to an algorithm.

By Lukie Pieterse, Editor/Publisher of Potato News Today

Editor’s note: Lukie Pieterse is a former South African potato grower who produced drip-irrigated potatoes for several of the country’s major processors at the time, including McCain Foods, Frito-Lay and Heinz. His interest in drip irrigation is therefore grounded in first-hand farming experience.

Instalment one and instalment two in this Potato News Today series examined the agronomic case for drip irrigation and the practical realities of installing and operating it on commercial potato farms.

The next question is equally important: How does a grower know when to turn the system on, how long to run it and whether the water is reaching the part of the ridge where it is needed?

Drip irrigation gives growers the ability to apply relatively small amounts of water frequently and accurately. That precision is valuable only when the underlying decisions are correct.

A highly efficient delivery system following an inaccurate schedule will simply apply the wrong amount of water more efficiently.

The emerging frontier in potato irrigation is therefore not drip tape alone. It is the combination of soil-moisture sensors, local weather information, crop-development data, evapotranspiration estimates, remote monitoring and automated controls.

Used together, these technologies can help growers understand what is happening between the irrigation line and the crop. Used carelessly, they can create a misleading impression of certainty.

A new British trial brings the question into focus

The subject has acquired particular relevance in the UK.

In August 2026, the Department for Environment, Food and Rural Affairs highlighted a farmer-led smart-irrigation project involving potatoes and onions at Waldersey Farms in Cambridgeshire and Elveden Estate in Suffolk. The farms are working with Water Resources East through the ADOPT Fund under the Farming Innovation Programme.

According to Defra’s account of the project, conventional hose-reel irrigation normally applies water uniformly despite substantial differences in soil drainage within a field. Heavier areas can receive too much, while sandier sections may receive too little.

The advanced system being tested uses information on soil conditions, crop growth and weather to determine water requirements for individual areas measuring 20 by 20 metres.

The two-season trial is comparing conventional and advanced irrigation systems across different soil types. Yield, crop quality and costs are being measured.

Defra says previous evidence indicates that the approach could produce water and energy savings of 20% to 25%, together with yield improvements. Those figures should not yet be reported as results from the current trial. The purpose of the project is precisely to determine whether the anticipated benefits can be reproduced under everyday commercial conditions.

Although the trial concerns smart irrigation more broadly and should not be presented as a drip-only project, it illustrates the direction in which drip management is moving. The delivery hardware is becoming part of a larger information system.

Why the ridge cannot be managed by rainfall totals alone

Potato growers have always used weather, experience and physical examination of the soil to guide irrigation. Those observations remain valuable, but rainfall totals alone reveal only part of the picture.

A rain gauge records how much water fell at one location. It does not show how much entered the ridge, ran off, evaporated, moved beneath the active root zone or remained available to the crop.

Two fields receiving the same rainfall may behave very differently because of soil texture, compaction, organic matter, slope, canopy development and previous moisture conditions.

Variation can occur within the same field as well. Sandy sections may dry rapidly, while heavier areas remain wet. A uniform irrigation application can therefore produce simultaneous under-irrigation and over-irrigation.

Drip irrigation can potentially respond to that variation through separate management zones and shorter, more frequent applications. The difficulty is identifying the zones correctly and obtaining measurements that represent them.

Three ways of asking whether the crop needs water

A 2025 scoping review in the American Journal of Potato Research groups advanced potato-irrigation scheduling into three broad approaches: soil-based, weather or evapotranspiration-based, and plant-based.

Each asks a slightly different question.

Soil-based monitoring asks how much water remains in the root zone or how tightly that water is held by the soil.

Weather-based scheduling estimates how much water has been lost through evaporation from the soil and transpiration by the crop.

Plant-based monitoring looks for evidence that the crop itself is experiencing water stress, often through canopy temperature or related measurements.

None provides a complete answer on its own. The review concludes that sustainable irrigation will require the integration of monitoring technologies with scheduling methods appropriate to the climate, soil and irrigation system.

That conclusion is especially relevant to drip-irrigated potatoes, where only part of the ridge may be wetted and small errors can persist unnoticed beneath the soil surface.

What soil-moisture sensors actually measure

The term “soil-moisture sensor” covers several types of instrument.

Some measure volumetric water content—the proportion of soil volume occupied by water. Others measure soil-water tension, sometimes called matric potential: how strongly the soil holds the remaining water and, consequently, how much effort the plant must make to extract it.

These measurements are related, but they are not interchangeable.

A given volumetric water content may represent readily available moisture in one soil and significant crop stress in another. Clay, loam and sand have different relationships between water content and water availability.

Tensiometers and granular-matrix sensors measure soil-water tension. Capacitance and time-domain reflectometry probes estimate volumetric water content. Each has advantages, limitations and calibration requirements.

New Oregon State University Extension guidance on soil-moisture monitoring stresses that reliable information depends on proper installation, placement and interpretation.

Good soil contact is essential. Air gaps around a probe can produce misleading readings. Soil disturbance during installation can also affect measurements until the soil settles.

Sensor accuracy does not rescue a poor location. A perfectly functioning probe placed in an unrepresentative part of the field will provide accurate information about the wrong place.

One sensor cannot speak for an entire field

The temptation is to install a sensor in a convenient location and treat its reading as representative of the entire irrigation block.

That may be sufficient in an unusually uniform field. It is a risky assumption where texture, slope, rooting depth or water-holding capacity changes significantly.

Oregon State recommends defining management zones according to soil texture and depth, topography, irrigation uniformity, and historic yield or crop-vigour patterns. Sensors should then be installed in representative areas rather than at field margins, wheelings, depressions or visibly abnormal patches.

The guidance recommends at least two monitoring locations within each management zone and measurements at more than one depth. For shallow-rooted crops, two depths will often reveal whether the upper ridge is drying while water is moving below the principal root zone.

Drip irrigation requires an additional spatial consideration: distance from the emitter.

A sensor placed immediately beside the tape may remain wet while roots farther across the ridge experience stress. A sensor positioned too far away may suggest that irrigation is inadequate even though the principal root zone is receiving sufficient water.

For this reason, growers should examine soil moisture both vertically and laterally. Sensor data should be checked periodically against an auger, spade or soil probe. Technology should make the grower more observant, not less.

Evapotranspiration estimates supply another piece

Evapotranspiration, normally shortened to ET, combines water lost through evaporation with water transpired by the crop.

Weather stations or regional weather networks estimate reference evapotranspiration using variables such as solar radiation, temperature, humidity and wind. A crop coefficient is then applied to estimate water use by potatoes at a particular stage of development.

The resulting water-balance calculation begins with the water stored in the root zone, subtracts estimated crop use and adds effective rainfall and irrigation.

This approach can be highly useful, especially where weather information is reliable and crop development is recorded accurately. It also has limitations.

Crop coefficients are generalisations. A field with incomplete emergence, disease, premature senescence or an unusually dense canopy may not use water at the predicted rate. Recorded rainfall may not equal water stored in the ridge. Irrigation uniformity may also differ from the figure assumed by the model.

ET-based scheduling is consequently strongest when checked against field measurements.

What the crop itself can reveal

Plant-based methods attempt to detect the crop’s response to declining water availability.

One approach uses canopy temperature. A well-watered plant normally cools itself through transpiration. As stomata close under water stress, transpiration declines and leaf temperature may rise relative to the surrounding air.

Thermal cameras mounted on handheld devices, drones or other platforms can help identify patterns of crop stress. Multispectral imagery may also reveal differences in canopy vigour.

These tools offer an important advantage: they can show spatial variation across an entire field rather than reporting conditions at one sensor point.

However, they do not measure root-zone moisture directly. Canopy temperature is affected by sunlight, wind, humidity, disease, nutrient status, canopy structure and time of day.

Satellite-based soil-moisture products have another limitation. Oregon State notes that these systems usually represent conditions in approximately the upper 5 to 10 centimetres of soil. That may not accurately describe moisture through the crop’s complete rooting depth.

Remote sensing is therefore best used alongside field-level measurements and water-balance calculations.

Automation has shown promise—but not infallibility

Automated control can connect irrigation decisions directly to pumps and valves. Once a defined threshold is reached, the system can start an irrigation event, deliver a predetermined amount and record what happened.

A two-year experiment conducted in 2018 and 2019 at Oregon State University’s Malheur Experiment Station compared two automated scheduling approaches for drip-irrigated Clearwater Russet potatoes.

One used soil-water tension to trigger irrigation when a threshold of 25 kilopascals was reached, applying 1.5 millimetres of water. The other used an evapotranspiration-based model.

According to the published research summary, there were no statistically significant differences between the two methods in tuber yield, grade or quality.

The ET-based system averaged 99.0 tonnes per hectare, with 96.1% No. 1 tubers. Soil-water-tension scheduling averaged 95.1 tonnes per hectare and 92.7% No. 1 tubers. The tension-based treatment used less water and achieved higher water-use efficiency.

The findings do not prove that one method is universally preferable. They show that more than one automated approach can support high-performing drip irrigation—and that the system producing the numerically larger yield may not be the one that uses water most efficiently.

The danger of false precision

A digital dashboard may display soil moisture to several decimal places, but this does not mean the underlying representation of the field is equally precise.

Sensors drift or fail. Batteries run down. Communications are interrupted. Weather stations require maintenance. Flow meters and pressure gauges can fall out of calibration. Models may continue making calculations from incomplete or incorrect data.

There is also a difference between measuring that a valve opened and confirming that water reached every emitter. A control platform may report a completed irrigation event even when pressure was inadequate, a lateral was damaged or part of the system was blocked.

Meaningful automation therefore requires safeguards:

Flow and pressure should be compared with expected values.
Sensor readings should be checked for implausible changes.
Rainfall data should be verified locally.
Manual operation should remain available.
Alerts should distinguish between a recommendation and a confirmed system action.
Field observations should be used to test whether the digital picture is credible.

The goal is not to remove the farmer from irrigation management. It is to direct limited human attention toward the places where intervention is most valuable.

The next step is integration

The most useful system is unlikely to depend entirely on a single sensor, satellite image or model.

A stronger approach combines:

Soil characteristics and mapped management zones.
Moisture measurements at several positions and depths.
Local rainfall and weather data.
Crop growth stage and canopy development.
Evapotranspiration estimates.
Irrigation flow and pressure records.
Forecast rainfall and temperature.
Field observations and inspection of the wetted ridge.
Yield, quality and water-use records from previous seasons.

When these information streams agree, the irrigation decision becomes more defensible. When they conflict, the disagreement is itself useful: it tells the grower where to investigate.

Artificial intelligence may eventually help identify patterns, predict faults and refine schedules. But agriculture’s familiar “garbage in, garbage out” problem remains. A sophisticated algorithm cannot compensate for badly positioned sensors, incorrect soil information or an irrigation system that distributes water unevenly.

The scarce resource is not only water

England’s 2026 report on agriculture and the water environment says that irrigation accounts for a relatively small proportion of annual licensed abstraction nationally. During summer in south-east England, however, daily agricultural demand can become comparable with public water use.

The same report points to hotter and drier summers, including those experienced in 2018, 2022 and 2025, as evidence of the growing climate challenge.

Under those conditions, the scarce resource is not simply water. It is water available at the correct time, in the correct field and under the restrictions governing its use.

Smart scheduling cannot create water where none exists. What it can do is help growers extract greater value from an available allocation, identify losses and provide stronger evidence for operational and investment decisions.

From irrigation equipment to irrigation intelligence

The evolution of drip irrigation is entering a new phase.

The first advance was physical: delivering water directly into the ridge. The second was operational: improving filtration, installation, retrieval and commercial-scale management. The third is informational: building a reliable picture of soil, weather, crop and system performance.

This does not mean every potato field requires a dense network of expensive sensors. The appropriate level of technology must reflect field variability, crop value, water cost and the financial consequences of getting irrigation wrong.

A few carefully positioned and properly interpreted probes may provide more value than a large collection installed without a clear purpose.

The central principle remains remarkably simple. Before applying water, a grower needs to know how much remains available, how quickly the crop is using it, where the next application will move and what will happen if irrigation is delayed.

Drip irrigation provides the control. Sensors and models provide evidence. The grower must still provide judgement.

That human judgement—supported rather than displaced by technology—will determine whether smart irrigation becomes a practical climate-adaptation tool or merely another expensive layer of equipment in the field.