Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors

Domínguez-Niño, Jesús María; Oliver-Manera, Jordi; Girona, J.; Casadesús, Jaume

doi:10.1016/j.agwat.2019.105880

Cited by 83 publications

(51 citation statements)

References 45 publications

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“…Nevertheless, when used for irrigation control, the criteria for selecting sensor locations would also depend on the control algorithm used. In this respect, if the control algorithm is based on thresholds for activating/deactivating irrigation pulses [ 15 , 77 ] the criteria may differ from when the algorithm is based on a water balance approach and tuned through sensor feedback [ 39 , 75 ]. Therefore, the optimal choice of sensor location will depend on the intended usage.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the difficulties described above, capacitance-type soil moisture sensors are successfully being used for irrigation management, including in scenarios of drip-irrigated orchards [ 38 , 39 ]. A better understanding of the uncertainty of these measurements and of the variability in the actual soil conditions should provide clues about how to improve their performance, their effectivity and, ultimately, their practical utility in real orchard conditions.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Variability in Soil Moisture Measurements by Capacitance Sensors in a Drip-Irrigated Orchard

Domínguez-Niño

Oliver-Manera

Arbat

et al. 2020

Sensors

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Among the diverse techniques for monitoring soil moisture, capacitance-type soil moisture sensors are popular because of their low cost, low maintenance requirements, and acceptable performance. However, although in laboratory conditions the accuracy of these sensors is good, when installed in the field they tend to show large sensor-to-sensor differences, especially under drip irrigation. It makes difficult to decide in which positions the sensors are installed and the interpretation of the recorded data. The aim of this paper is to study the variability involved in the measurement of soil moisture by capacitance sensors in a drip-irrigated orchard and, using this information, find ways to optimize their usage to manage irrigation. For this purpose, the study examines the uncertainties in the measurement process plus the natural variability in the actual soil water dynamics. Measurements were collected by 57 sensors, located at 10 combinations of depth and position relative to the dripper Our results showed large sensor-to-sensor differences, even when installed at equivalent depth and coordinates relative to the drippers. In contrast, differences among virtual sensors simulated using a HYDRUS-3D model at those soil locations were one order of magnitude smaller. Our results highlight, as a possible cause for the sensor-to-sensor differences in the measurements by capacitance sensors, the natural variability in size, shape, and centering of the wet area below the drippers, combined with the sharply defined variation in water content at the soil scale perceived by the sensors.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of the Variability in Soil Moisture Measurements by Capacitance Sensors in a Drip-Irrigated Orchard

Domínguez-Niño

Oliver-Manera

Arbat

et al. 2020

Sensors

Self Cite

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“…At just below the drip tube (0, 5), the model could estimate VWC after both irrigation and rainfall events with a RMSE of 0.021. In the other observation point (30,5), where the sensor was in the middle of two adjacent laterals at the depth of 5 cm, the RMSE between observed and simulated VWC values was 0.019. As the soil was sand, water predominantly moves downward rather than horizontally.…”

Section: Soil Water Contentmentioning

confidence: 97%

“…The soil was sand with hydraulic properties as shown in Figure 2. To check the accuracy of simulation for volumetric water content, 5TE sensors (METER Inc., USA) were installed at five observation points (x, y): (0, 5), (0, 15), (0, 45) (15,5), and (30,5), where x is the horizontal distance from drip tube and z is the soil depth in cm units. Calibration function of the 5TE sensor is shown in Figure 3.…”

Section: Field Experimentsmentioning

confidence: 99%

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Determining Irrigation Depths for Soybean Using a Simulation Model of Water Flow and Plant Growth and Weather Forecasts

2020

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A new scheme to determine irrigation depths using a two-point of predicted cumulative transpiration over irrigation interval is presented. Rather than maximizing water use efficiency, this scheme aims to maximize net income. The volumetric water price is considered to give farmers an incentive to save irrigation water. A field experiment for soybeans was carried out in the Arid Land Research Center, Tottori University, Japan in 2019. The total irrigation amount yield and net income by the proposed scheme were compared to those by a tensiometer-operated automated irrigation. The scheme could save irrigation water by 16% with a yield increment of 20%; resulting in a 22% increase in net income compared to the automated irrigation. The model simulated the volumetric water content in the effective root zone of the plant in fair agreement. These results indicate the effectiveness of the proposed scheme that may replace an automated irrigation system even considering uncertainty in weather forecast to determine irrigation depth and secure investment costs.

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Retrofitting Techniques for Agricultural Machines

Celenta

Simone

2020

Lecture Notes in Networks and Systems

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Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors

Cited by 83 publications

References 45 publications

Analysis of the Variability in Soil Moisture Measurements by Capacitance Sensors in a Drip-Irrigated Orchard

Analysis of the Variability in Soil Moisture Measurements by Capacitance Sensors in a Drip-Irrigated Orchard

Determining Irrigation Depths for Soybean Using a Simulation Model of Water Flow and Plant Growth and Weather Forecasts

Retrofitting Techniques for Agricultural Machines

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