Using Soil Moisture Sensors for Automated Irrigation Scheduling in a Plum Crop
2. Materials and Methods
2.1. Study Area and Irrigation System
2.2. Automatic Irrigation System
- To monitor the soil moisture content in the experiment, fifteen 10HS capacitance probes (Decagon Devices Inc., Pullman, WA, USA) were installed in three representative trees. Five probes were installed in each tree: two probes under the emitter at 0.3 m depth, two probes under the emitter at 0.6 m depth and one probe situated between emitters, approximately at a distance of 0.15 m horizontally under the dripper line. The IRRIX software evaluated every probe and established the reference levels.
- An air temperature sensor (CS2015, Campbell Scientific Inc., Logan, UT, USA) at a height of 3 m (in line with a tree row).
- Energy source: next to the temperature probe, a solar panel was installed with a 12V-7Ah lead battery and voltage regulator (BlueSolar PWM-Pro, Victron Energy Blue Power, De Paal, The Netherlands).
- Sensors for irrigation water application control: a 24V-50Hz solenoid valve (Rain Bird Europe SCN, Aix-en-Provence, France), a digital water meter (MTK, Zenner, Madrid, Spain) and a relay controller (SMD-CD16AC, Campbell Scientific Inc., Logan, UT, USA).
- Logger: all the above components were connected to a datalogger (CR1000, Campbell Scientific Inc., Logan, UT, USA) which stored the data once every 5 min.
- Captures data from the sensors installed in the field. The data were uploaded 4 times a day via IPv4/IPv6 to the IRRIX server.
- Data processing and interpretation: IRRIX analyzes all the input data to detect anomalies or important events in the system (irrigation, rain). As an indicator of the evolution of the soil’s water status, IRRIX is set for each sensor at the driest value recorded every day, soil water content (SWC), daily driest SWC (SWCd). Then, to reduce the variability between sensors, IRRIX normalizes these values specifically for each sensor, such as:NSWCd = (SWCd − SWCWP)/(SWCFC − SWCWP)
- Irrigation scheduling decision-making. IRRIX analyzes the dataset on a daily basis to determine and adjust the irrigation dose based on the information provided by the soil moisture sensors. IRRIX uses a control algorithm which combines a water-balance-based estimation of crop water needs (feed-forward control) with readjustment based on sensor readings (feedback control).
- Irrigation scheduling: IRRIX sends the updated irrigation doses to the datalogger. Then, the order is given to activate the other devices (relay, valve, pumps, etc.) to apply the required irrigation dose.
- User interaction: IRRIX is an automated system whose main purpose is to allow the user to carry out other tasks. The main function of the user is to verify that the system has operated correctly and that the irrigation campaign has been implemented as planned. If there is any anomaly in the system, the user must resolve it. To facilitate this process, a simplified interactive display panel is available with warning alerts.
- Irrigation strategy: In this case, it was decided to use an RDI strategy recommended for this cultivar in accordance with previous studies in the same area . This strategy consists of avoiding a situation of water stress during the preharvest period (100% ETc) and reducing irrigation inputs during the postharvest period to replace 40% of the ETc and thereby induce a moderate water deficit.
- Seasonal plan: This was based on a seasonal water consumption curve for the irrigation campaign determined on the basis of a calendar adjusted according to previous campaigns for the crop or plot, and in this case integrating the RDI strategy. Curves above and below this initial curve were established to determine the admissible bounds for the irrigation scheduling. These bounds were to ensure that irrigation inputs above those available for the campaign were not used and to ensure that a situation of sever water deficit did not arise.
- Soil comfort zone in relation to the dimensionless capacitive sensor readings: This soil comfort zone was used to determine the acceptable range for the soil moisture sensor measurements. The control system modulates the irrigation dose to maintain the soil moisture capacitive sensor measurements within this comfort zone. The system determines this comfort zone on an individual basis for each sensor and modifies it over the course of the season depending on the irrigation strategy employed.
2.3. Irrigation Treatments and Experimental Design
2.4.1. Applied Water and Water Status
2.4.2. Trunk Cross-Sectional Area and Winter Pruning
2.4.3. Phenology and Yield
2.5. Statistical Analysis
3. Results and Discussion
3.1. Automatic Irrigation
3.2. Meteorological Conditions and Applied Water
3.3. Tree Water Status
3.4. Vegetative and Reproductive Growth
3.5. Yield and Number of Fruit/Tree
Conflicts of Interest
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|Bud burst||27 January 2016||30 January 2017|
|Flowering||29 February 2016||02 March 2017|
|Harvest||08 June 2016||29 May 2017|
|Leaf fall||20 November 2016||15 November 2017|
|Pre 1||Post 2||Annual 3||Pre 1||Post 2||Annual 3|
|T mean (°C)||12.59||19.20||15.89||13.41||19.69||16.55|
|RH mean (%)||80.14||65.54||72.84||71.42||58.05||64.73|
|C||6.20 ± 0.35||a||8.68 ± 0.87||a|
|Winter pruning of new wood|
|RDI||3.84 ± 0.49||b||4.46 ± 0.60||b|
|A||4.73 ± 0.34||b||5.38 ± 0.58||b|
|C||8.35 ± 1.51||a||2.53 ± 0.44|
|Winter pruning of old wood|
|RDI||5.11 ± 1.03||ab||2.52 ± 0.19|
|A||4.09 ± 0.65||b||2.27 ± 0.32|
|C||11.48 ± 0.45||a||9.94 ± 0.42||a|
|RDI||9.56 ± 0.78||b||8.43 ± 0.66||b|
|A||10.55 ± 0.44||ab||9.02 ± 0.25||ab|
|C||26.04 ± 1.63||a||21.15 ± 1.14||a|
|RDI||18.52 ± 1.83||b||15.42 ± 1.19||b|
|A||19.38 ± 0.96||b||16.67 ± 0.86||b|
|C||65.54 ± 3.77||69.59 ± 5.31|
|RDI||55.47 ± 4.62||69.39 ± 10.54|
|A||59.72 ± 6.11||67.41 ± 5.71|
|C||15158 ± 2114.80||4076 ± 414.82||b||14491 ± 1090.55||b|
|RDI||14240 ± 2081.19||6229 ± 587.07||a||16448 ± 1538.96||ab|
|A||13697 ± 1652.65||7228 ± 818.03||a||19908 ± 1447.29||a|
|C||721 ± 113.62||130 ± 14.31||b||404 ± 29.08|
|Number of fruit/trees||RDI||681 ± 99.08||203 ± 19.21||ab||456 ± 50.78|
|A||629 ± 88.67||237 ± 27.50||a||485 ± 34.86|
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Millán, S.; Casadesús, J.; Campillo, C.; Moñino, M.J.; Prieto, M.H. Using Soil Moisture Sensors for Automated Irrigation Scheduling in a Plum Crop. Water 2019, 11, 2061. https://doi.org/10.3390/w11102061
Millán S, Casadesús J, Campillo C, Moñino MJ, Prieto MH. Using Soil Moisture Sensors for Automated Irrigation Scheduling in a Plum Crop. Water. 2019; 11(10):2061. https://doi.org/10.3390/w11102061Chicago/Turabian Style
Millán, Sandra, Jaume Casadesús, Carlos Campillo, María José Moñino, and Maria Henar Prieto. 2019. "Using Soil Moisture Sensors for Automated Irrigation Scheduling in a Plum Crop" Water 11, no. 10: 2061. https://doi.org/10.3390/w11102061