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Abstract: Judicious irrigation necessitates development of technologies that apply the precise amount of water demanded by crops. The goal is to schedule irrigation according to the particular requirements of each orchard and to the variability encountered during the season caused by weather conditions, canopy development and other factors. For supporting a precise and low labour management of irrigation here we propose and depict an algorithm that coordinates seven automatable tasks: (1) estimation of irrigation needs, (2) adaptation to a particular irrigation setup, (3) execution of the schedule, (4) soil and/or plant monitoring, (5) interpretation of sensor data, (6) reaction to occasional events and (7) tuning the model of irrigation needs. This is illustrated with three examples from a peach orchard where a software tool implementing the algorithm was configured to manage irrigation under different strategies. They included two alternative methods for estimating the crop water needs: a typical water balance based on reference evapotranspiration (ETo) and a variation based on the measurement of solar radiation intercepted by the canopy. They also included two types of sensors for feedback: soil water probes and dendrometers, the latter for applying regulated deficit irrigation. The results show that the proposed algorithm provided a common framework that could be embraced under different irrigation strategies to support autonomous control through the season, requiring no more data than those acquired and processed by the same system and enabling some degree of tolerance to failures. Overall, some advantages were the fast response to changes in factors such as weather conditions whose effect is rather predictable. At the same time the algorithm self-adjusted through feedback to variation in orchard-specific factors related with crop development and phenology which would be hard to model. The separation between agro-meteorological and hydraulic concerns in different steps of the algorithm facilitated the adaptation to the particularities of each irrigation setup, simplifying the use of commercial equipment from different manufacturers. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Although surface drip irrigation allows an efficient use of water in agriculture, the heterogeneous distribution of soil water complicates its optimal usage. Mathematical models can be used to simulate the dynamics of water in the soil below a dripper and promote: a better understanding, and optimization, of the design of drip irrigation systems, their improved management and their monitoring with soil moisture sensors. The aim of this paper was to find the most appropriate configuration of HYDRUS-3D for simulating the soil water dynamics in a drip-irrigated orchard. Special emphasis was placed on the source of the soil hydraulic parameters. Simulations parameterized using the Rosetta approach were therefore compared with others parameterized using that of HYPROP + WP4C. The simulations were validated on a seasonal scale, against measurements made using a neutron probe, and on the time course of several days, against tensiometers. The results showed that the best agreement with soil moisture measurements was achieved with simulations parameterized from HYPROP + WP4C. It further improved when the shape parameter n was empirically calibrated from a subset of neutron probe measurements. The fit of the simulations with measurements was best at positions near the dripper and worsened at positions outside its wetting pattern and at depths of 80 cm or more. [ABSTRACT FROM AUTHOR]

Recent technological advances have made possible automated irrigation scheduling using decision-support tools. These tools help farmers to make better decisions in the management of their irrigation system, thus increasing yields while preserving water resources. The aim of this study is to evaluate in a commercial plot an automated irrigation system combined with remote-sensing techniques and soil mapping that allows the establishment of regulated deficit irrigation (RDI) strategies. The study was carried out over 3 years (2015–2017) in a commercial hedgerow olive orchard of the variety 'Arbequina' located in Alvarado (Extremadura, Spain). An apparent electrical conductivity (ECa) map and a normalized difference vegetation index (NDVI) map were generated to characterize the spatial variability of the plot and classify the zones in homogeneous areas. Then, reference points were selected to monitor the different irrigation sectors. In 2015, the plot was irrigated according to the farmer's technical criteria throughout the plot. In 2016 and 2017, two different areas of the plot were irrigated applying an RDI strategy, one under expert supervision and the other automatically. The results show that in a heterogeneous plot the use of new technologies can be useful to establish the ideal location for an automatic irrigation system. Furthermore, automatic irrigation scheduling made it possible to establish an RDI strategy recommended by an expert, resulting in the homogenization of production throughout the plot without the need for human intervention. [ABSTRACT FROM AUTHOR]

The growing scarcity and competition for water resources requires the urgent implementation of measures to ensure their rational use. Farmers need affordable irrigation tools that allow them to take advantage of scientific know-how to improve water use efficiency in their common irrigation practices. The aim of this study is to test under field conditions, and adjust where required, an automated irrigation system that allows the establishment of regulated deficit irrigation (RDI) strategies in a stone fruit orchard. For this, an automated device with an algorithm which combines water-balance-based irrigation scheduling with a feedback adjustment mechanism using 15 capacitive sensors for continuous soil moisture measurement was used. The tests were carried out in 2016 and 2017 in Vegas Bajas del Guadiana (Extremadura, Spain) on an experimental plot of 'Red Beaut', an early-maturing Japanese plum cultivar. Three irrigation treatments were established: control, RDI and automatic. The control treatment was scheduled to cover crop water needs, a postharvest deficit irrigation (40% crop evapotranspiration (ETc)) strategy was applied in the RDI treatment, while the Automatic treatment simulated the RDI but without human intervention. After two years of testing, the automated system was able to "simulate" the irrigation scheduling programmed by a human expert without the need for human intervention. [ABSTRACT FROM AUTHOR]

Soil water content (SWC) monitoring is often used to optimize agricultural irrigation. Commonly, capacitance sensors are used for this task. However, the factory calibrations have been often criticized for their limited accuracy. The aim of this paper is to test the degree of improvement of various sensor- and soil-specific calibration options compared to factory calibrations by taking the 10HS sensor as an example. To this end, a two-step sensor calibration was carried out. In the first step, the sensor response was related to dielectric permittivity using calibration in media with well-defined permittivity. The second step involved the establishment of a site-specific relationship between permittivity and soil water content using undisturbed soil samples and time domain reflectometry (TDR) measurements. Our results showed that a model, which considered the mean porosity and a fitted dielectric permittivity of the solid phase for each soil and depth, provided the best fit between bulk permittivity and SWC. Most importantly, it was found that the two-step calibration approach (RMSE: 1.03 vol.%) provided more accurate SWC estimates compared to the factory calibration (RMSE: 5.33 vol.%). Finally, we used these calibrations on data from drip-irrigated almond and apple orchards and compared the factory calibration with our two-step calibration approach. [ABSTRACT FROM AUTHOR]

• Capacitance sensors provide automated feedback to the scheduling algorithm. • Sensors analysis is focused at the trend of the driest measurement of the day. • Without using information of tree vigour, the algorithm apply a differential irrigation. • Irrigation doses fixed by the algorithm are aligned with the ET of the lysimeter. • Irrigation based on water balance can be complement with soil moisture sensors. Automated software tools are required to undertake the routine tasks and decision-making involved in scheduling irrigation. A key issue in this topic is how to integrate sensors in the scheduling approach. The objectives of this research were to test, in the context of drip-irrigated orchards: (a) the suitability of FAO's water balance method, locally adjusted by sensors, as the basis for the scheduling algorithm, (b) the suitability of capacitance-type soil moisture sensors, and an approach for their automated interpretation, for providing feedback to the scheduling algorithm, and (c) the performance of these combined approaches in the autonomous scheduling of irrigation in an apple orchard with heterogeneous vigour. The trial consisted of applying for two years the proposed approaches using an experimental web application, IRRIX, which scheduled irrigation of two irrigation sectors, which differed in tree size. The automated system was compared with manual scheduling by a classical water balance and with the actual evapotranspiration determined by a weighing lysimeter located in the same orchard. Results show that the irrigation applied by the automated approach in the sector of larger trees agreed with the ET determined by the lysimeter and, overall, with the scheduling by an experienced irrigator using a classical water balance. Meanwhile, as a result of a different feedback from soil moisture sensors, the same system reduced irrigation in the sector of smaller trees by a similar amount to that expected from the differences between the two sectors in the fraction of photosynthetically active radiation. This study illustrates that the method of water balance complemented with capacitance-type soil moisture sensors provides a sound basis for automated irrigation scheduling in orchards. [ABSTRACT FROM AUTHOR]