The principles and control procedures necessary for a theoretical and practical understanding of irrigation control are described below.
The scientific principle behind the system
The scientific principle behind the system The principle of operation is best illustrated on the water retention diagram (Figure 1). In scientific works in Hungary, the water retention curve is referred to as the pF curve.
- Fig. pF curve is the relationship between volumetric water content and tensiometry (Várallyay, 1998).
The vertical axis represents the suction force (water potential) that the plant must exert to pull the water molecule off the soil structure, the horizontal axis represents the water content of the soil in % volume (dm3/dm3) called soil moisture.
The soil type specific (sand, loam, clay) curves show the difference in water dynamics between soils.
The green bar shows the water-stress-free range for most plants, and the yellow bar shows the water-stress tolerance for short periods, but this can vary greatly from plant to plant.
If we measure the working points of both axes in two dimensions simultaneously, i.e. using both a tensiometer and volumetric soil moisture sensors, we can delimit the area of the pF curve that is the common intersection of the plant-favourable water uptake range and the soil-favourable water retention range.
By controlling irrigation to keep the water status within this range, you can create water and nutrient-efficient and water-stress-free production conditions from a water-holding perspective.
- Figure 1.1 Theoretical representation of the essence of AgriSmartGreen irrigation control
In the case of onions, the pilot measurements provided sufficient data to establish a plant-specific irrigation control algorithm.
For all economically relevant irrigated crops, the tensiometric and soil moisture limits should be determined and pilot experiments should be used to adjust the production and water use indices to the optimum ranges.
Demonstration of the application of the control principle
The following 3. The diagrams in Figure 3 show the parametric changes and possible control events in an onion-growing period.
- Figure 1 Control events in onion irrigation
The top green “Tensiometry kPa 15cm” diagram illustrates the force (tensiometry) that the plant needs to exert to absorb water from the soil matrix. The lower the value of the green curve, the greater the force needed to absorb water, the drier the soil. When the value of tension downwards crosses the “Stress Limit” threshold indicated by the dashed line, it is time to start the deficit irrigation without deficit watering. The red “Deficit Events” sections indicate when a deficit of irrigation water has occurred from the plant’s point of view because irrigation has not started when the stress threshold has been exceeded.
It is the sum of the length and frequency of these red stages that gives the irrigation deficit value of the growing season, which, with the right skills and knowledge of the crop, can be used to produce a higher quality crop with increased yields from less irrigation water. If there was too much deficit sum for the plant or it didn’t fit in with the other production conditions you will get the experience on how (not) to adjust the control in the future.
The black “Precipitation” curve shows the amount of water applied during each irrigation and the amount of precipitation that fell.
The dark blue “Moisture 20cm” curve shows that the soil moisture rises steeply during irrigation, and when it reaches the water content limit set by the soil properties, it is recommended to stop irrigation to allow the soil to retain the applied water in the root zone.
By capturing the decision events of the described algorithm and qualifying the results obtained, a self-learning decision support application can be developed using a machine learning (ML) algorithm. With the continuously collected information, local production decisions can be refined over time and the collected data can be used to build a central crop-specific database and to achieve a high level of operational accuracy.
