In citrus fruits, phases I and II of fruit growth are sensitive to water deficit, and for this reason, deficit irrigation (DI) has been usually restricted to the final ripening phase. However, the optimal timing and intensity of stress during sensitive phases have not been clearly defined. The main objective was to determine the sensitivity of the second stage of fruit growth to water deficit in adult mandarin trees, and to explore the suitability of different soil and plant water status indicators, including the leaf-scale spectrum, according to the water stress level. Four irrigation treatments were tested: a control (CTL) irrigated at ~ 80% of ET_c during the entire crop cycle, and three irrigation suppression treatments, in which no water was applied during the end of phase I and the beginning of phase II (DI₁), the second half of phase II (DI₂), and phase III of fruit growth (DI₃), respectively. Phase II of fruit growth can be considered as a non-critical phenological period until the fruit reaches approximately 60% of its final size, with the application of a water deficit using an irrigation threshold of midday stem water potential of − 1.8 MPa, and a cumulative water stress integral close to 28 MPa day. The novel visible infrared ratio index (VIRI) showed a high sensitivity for trees subjected to moderate and severe water stress and can be complementarily used to estimate on a larger temporal and spatial scale the plant water status. Wavelengths in the short-wave infrared (SWIR) region allowed differentiation between non-stressed, moderately, and severely water-stressed trees, and can be considered as an initial basis for determining the water status of mandarin trees at various stress intensities by remote sensing.

Crop sustainability is essential for balancing economic development and environmental care, mainly in strong and very competitive regions in the agri-food sector, such as the Region of Murcia in Spain, considered to be the orchard of Europe, despite being a semi-arid area with an important scarcity of fresh water. In this region, farmers apply efficient techniques to minimize supplies and maximize quality and productivity; however, the effects of climate change and the degradation of significant natural environments, such as, the “Mar Menor”, the most extent saltwater lagoon of Europe, threatened by resources overexploitation, lead to the search of even better irrigation management techniques to avoid certain effects which could damage the quaternary aquifer connected to such lagoon. This paper describes the Irriman Platform, a system based on Cloud Computing techniques, which includes low-cost wireless data loggers, capable of acquiring data from a wide range of agronomic sensors, and a novel software architecture for safely storing and processing such information, making crop monitoring and irrigation management easier. The proposed platform helps agronomists to optimize irrigation procedures through a usable web-based tool which allows them to elaborate irrigation plans and to evaluate their effectiveness over crops. The system has been deployed in a large number of representative crops, located along near 50,000 ha of the surface, during several phenological cycles. Results demonstrate that the system enables crop monitoring and irrigation optimization, and makes interaction between farmers and agronomists easier.

For three consecutive years (2015–2017), two deficit irrigation (DI) strategies were used in a 12-year old vineyard (cv. ‘Crimson Seedless’) to implement a sustainable irrigation protocol according to the available water for the farmer. Four different irrigation treatments were assessed: (i) Control (CTL), irrigated to satisfy the maximum crop water requirements throughout the entire growing season; two DI treatments irrigated as CTL except during post-veraison, when the vines were irrigated at 50% CTL: (ii) Regulated Deficit Irrigation (RDI); and (iii) Partial Root Drying (PRD), alternating the wet and dry sides of the root zone, and (iv) irrigated according to the criteria followed by the farmer (FARM), and conditioned by the availability of water each season. The DI strategies resulted in a 50% increase in water use efficiency in the first two years and 81% during the third year. Weekly deficit irrigation protocols are proposed, which specify a maximum difference of 0.22 MPa of midday stem water potential with respect to well-watered vines for a range of irrigation water availabilities between 4000 and 7000 m³ ha⁻¹. An applied water prediction model based on the Gaussian regression using day of the year and maximum temperature of the day is also proposed.

This work assessed the effects of the sustained (during the whole berry growth) and regulated (at post-veraison) practices of deficit irrigation on water relations, yield components and berry quality in a commercial vineyard of ‘Crimson Seedless’ table grapes. For this, five irrigation treatments were established during a complete irrigation season (from April to October): (i) Control (CTL) irrigated to 110% crop evapotranspiration (ET_c); (ii) Regulated Deficit Irrigation (RDI) irrigated at 50% of CTL during the non-critical period of post-veraison; (iii) Sustained Deficit Irrigation (SDI), irrigated at 50% of CTL throughout the entire berry growing season; (iv) Partial Root-Zone Drying (PRD), irrigated similar to RDI but alternating the irrigation applied on the dry side every 10–14 days; (v) Sustained Partial Root-Zone Drying (SPRD), irrigated as SDI but alternating the irrigation on the dry side every 10–14 days. RDI and PRD received 24% and 28% less water than CTL, respectively. These reductions were higher in SDI and SPRD (65% and 53%, respectively). Total yield was not affected by any DI strategy. Only significantly lower productive values were observed in the weight and height of the berries as compared to CTL. However, the color parameters evaluated increased in all the DI treatments, being slightly higher in SDI and SPRD as compared with RDI and PRD. In addition, total soluble solids (TSS) were significantly higher in SDI, compared to other irrigated counterparts. Our findings showed that the application of water deficit during the entire period of berry growth using SDI and SPRD can be considered for irrigation scheduling in ‘Crimson Seedless’ table grapes when the aim is to solve the trouble of insufficient reddish color of the berries.

The aims of this work were modelling the effect of water stress intensity during post-veraison on table grape quality and yield, as well as predicting berry quality at harvest using a machine learning algorithm. The dataset was obtained by applying different irrigation regimes in two commercial table grape vineyards during seven growing seasons. From these data, it was possible to train and validate the predictive models over a wide range of values for the independent (water stress intensity and fruit load) and dependent (firmness and berry color) variables. The supervised learning algorithm Gaussian Process Regression allowed us to predict the variables with high accuracy. It was also determined that a reduction in irrigation of up to 40% during post-veraison, compared to vines without water limitations, and the accumulation of the water stress integral of up to 30 MPa per day, linearly increase the irrigation water use efficiency (IWUE) and promote higher berry color and firmness. The severe water scarcity and the increasing uncertainty about the irrigation water availability for the season that farmers are facing highlight the advantage of incorporating these validated techniques into agricultural decision making, as they allow for the planning of cultural practices and criteria to increase the IWUE and crop sustainability.