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.

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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. Identifying spatial patterns of soil and plant properties can be an efficient method for site-specific management in areas with homogeneous characteristics (i.e., management zones, MZs). In this study, the use of soil apparent electrical conductivity (ECa) is proposed as the main information source for evaluating the spatial variability of soil and plant properties when using this variability to determine potential MZs. This study was conducted in a commercial hedgerow olive grove. Spatial distribution maps of the main soil properties and normalized difference vegetation index (NDVI) were generated by regression-kriging in which ECa was used as a secondary variable. According to the results obtained by the validation process, all maps were accurate. Soil and plant properties and ECa were subjected to principal component analysis (PCA). Two MZs were determined using a fuzzy cluster classification. The MZ map was validated using data related to soil samples, yield, and NDVI. Establishing different MZs was useful for adapting the irrigation strategies to the soil conditions of the plot, which resulted in increased productivity of the hedgerow olive grove. Keywords: Fuzzy c-means, Principal components analysis, Regression-kriging, Spatial prediction.

Advances in electromagnetic sensor technologies in recent years have made automated irrigation scheduling a reality through the use of state-of-the-art soil moisture sensing devices. However, correct sensor positioning and interpretation of the measurements are key to the successful implementation of these management systems. The aim of this study is to establish guidelines for soil moisture sensor placement to support irrigation scheduling, taking into account the physiological response of the plant. The experimental work was carried out in Vegas Bajas del Guadiana (Extremadura, Spain) on a drip-irrigated experimental orchard of the early-maturing Japanese plum cultivar “Red Beaut”. Two irrigation treatments were established: control and drying. The control treatment was scheduled to cover crop water needs. In the drying treatment, the fruit trees were irrigated as in control, except in certain periods (preharvest and postharvest) in which irrigation was suspended (drying cycles). Over 3 years (2015–2017), a series of plant parameters were analyzed in relation to the measurements provided by a battery of frequency domain reflectometry probes installed in different positions with respect to tree and dripper: midday stem water potential (Ψstem), sap flow, leaf stomatal conductance, net leaf photosynthesis and daily fraction of intercepted photosynthetically active radiation. After making a comparison of these measurements as indicators of plant water status, Ψstem was found to be the physiological parameter that detected water stress earliest. The drying cycles were very useful to select the probe positions that provided the best information for irrigation management and to establish a threshold in the different phases of the crop below which detrimental effects could be caused to the crop. With respect to the probes located closest to the drippers, a drop in the relative soil water content (RSWC) below 0.2 would not be advisable for “non-stress” scheduling in the preharvest period. When no deficit irrigation strategies are applied in the postharvest period, the criteria are similar to those of preharvest. However, the probes located between the dripper at 0.15 and 0.30 m depth provide information on moderate water stress if the RSWC values falls below 0.2. The severe tree water stress was detected below 0.1 RSWC in probes located at 60 cm depth from this same position.