The suitability of cloud-based irrigation technologies remains questionable due to limited information on their evaluation in the field. This study focussed on the on-field assessment of a smartphone irrigation scheduling tool—Bluleaf^®—with respect to traditional water application practices. Bluleaf^® uses weather, crop, soil, and irrigation system data to support a farmer’s decision on the timing and amounts of irrigation. The smartphone application was tested in Bekaa Valley, Lebanon, on durum wheat, a strategic Mediterranean crop, during the 2017 and 2018 growing seasons. The simulation results on soil water balance were in “acceptable to very good” agreement with the measured soil moisture values, with a root mean square error (RMSE) between 15.1 and 26.6 mm and a modelling efficiency (NSE) that ranged from 0.77 to 0.92. The appropriateness of the adopted smartphone irrigation scheduling was confirmed also by leaf water potential measurements and the Crop Water Stress Index (CWSI). A water saving of more than 1000 m³/ha (25.7%) was observed with Bluleaf^® with respect to traditional irrigation scheduling. Therefore, new technologies could bring about substantial benefits to farmers and support water saving efforts in the Mediterranean region.

The coupling of remote sensing technology and crop growth models represents a promising approach to support crop yield prediction and irrigation management. In this study, five vegetation indices were derived from the Copernicus-Sentinel 2 satellite to investigate their performance monitoring winter wheat growth in a Mediterranean environment in Lebanon’s Bekaa Valley. Among those indices, the fraction of canopy cover was integrated into the AquaCrop model to simulate biomass and yield of wheat grown under rainfed conditions and fully irrigated regimes. The experiment was conducted during three consecutive growing seasons (from 2017 to 2019), characterized by different precipitation patterns. The AquaCrop model was calibrated and validated for different water regimes, and its performance was tested when coupled with remote sensing canopy cover. The results showed a good fit between measured canopy cover and Leaf Area Index (LAI) data and those derived from Sentinel 2 images. The R² coefficient was 0.79 for canopy cover and 0.77 for LAI. Moreover, the regressions were fitted to relate biomass with Sentinel 2 vegetation indices. In descending order of R², the indices were ranked: Fractional Vegetation Cover (FVC), LAI, the fraction of Absorbed Photosynthetically Active Radiation (fAPAR), the Normalized Difference Vegetation Index (NDVI), and the Enhanced Vegetation Index (EVI). Notably, FVC and LAI were highly correlated with biomass. The results of the AquaCrop calibration showed that the modeling efficiency values, NSE, were 0.99 for well-watered treatments and 0.95 for rainfed conditions, confirming the goodness of fit between measured and simulated values. The validation results confirmed that the simulated yield varied from 2.59 to 5.36 t ha⁻¹, while the measured yield varied from 3.08 to 5.63 t ha⁻¹ for full irrigation and rainfed treatments. After integrating the canopy cover into AquaCrop, the % of deviation of simulated and measured variables was reduced. The Root Mean Square Error (RMSE) for yield ranged between 0.08 and 0.69 t ha⁻¹ before coupling and between 0.04 and 0.42 t ha⁻¹ after integration. This result confirmed that the presented integration framework represents a promising method to improve the prediction of wheat crop growth in Mediterranean areas. Further studies are needed before being applied on a larger scale.

The use of polluted water to irrigate is an increasing problem in the developing world. Lebanon is a case in point, with heavily polluted irrigation waters, particularly in the Litani River Basin. This study evaluated the potential health risks of irrigating vegetables (radishes, parsley, onions, and lettuce) using three water sources (groundwater, river water, and treated wastewater) and three irrigation methods (drip, sprinkler, and surface) over two growing seasons in 2019 and 2020. Water, crop, and soil samples were analyzed for physicochemical parameters, pathogens, and metals (Cu, Cd, Ni, Cr, and Zn). In addition, the bioaccumulation factor, estimated dietary intakes, health risk index, and target hazard quotients were calculated to assess the health risk associated with metal contamination. The study showed that, for water with less than 2 log E. coli CFU/100 mL, no pathogens (Escherichia coli, salmonella, parasite eggs) were detected in irrigated vegetables, irrespective of the irrigation method. With over 2 log E. coli CFU/100 mL in the water, 8.33% of the sprinkler-and surface-irrigated vegetables, and 2.78% of the drip-irrigated root crops (radishes and onions), showed some degree of parasitic contamination. E. coli appeared only on root crops when irrigated with water having over 3 log CFU/100 mL. The concentrations of most metals were significantly lower than the safe limits of the FAO/WHO of the Food Standards Programme Codex, except for zinc and chromium. The trends in the bioaccumulation factor and the estimated dietary intakes of metals were in the order of Cu < Cd < Ni < Cr < Zn. The target hazard quotient values for all metals were lower than 1.0. Under trial conditions, the adoption of drip irrigation with water with less than 3 log E. coli CFU/100 mL proved to be safe, even for vegetables consumed raw, except for root crops such as onions and radishes that should not be irrigated with water having over 2 log E. coli CFU/100 mL. Treated wastewater had no adverse effect on vegetable quality compared to vegetables irrigated with other water sources. These results support efforts to update the Lebanese standards for water reuse in agriculture; standards proposed in 2011 by the FAO, and currently being reviewed by the Lebanese Institution of Standards. This research will inform a sustainable water management policy aimed at protecting the Litani River watershed by monitoring water quality.