1. Introduction
Sustainable irrigation management requires reliable and easy-to-use methods and tools to support real-time scheduling with respect to the availability of water, specific soil and weather conditions, a crop’s water requirements, and a crop’s response to stress. Studies, conducted in recent years in various parts of the world, have shown that the use of innovative technologies, management approaches, and modelling tools can improve irrigation scheduling, save water, enhance a farmer’s income, and reduce the environmental burden [
1,
2,
3,
4,
5,
6,
7,
8]. In this context, real-time automatized irrigation scheduling, based on reliable low-cost sensors and simple water balance models, is receiving a growing amount of attention [
9].
A plethora of innovative technological solutions in the agricultural sector is emerging worldwide. However, the commercialization of these products is intricate due to difficulties in demonstrating the on-field applicability and the effectiveness of proposed innovations and in creating direct contact with potential users. Over the past decade, numerous web-based irrigation scheduling tools have been developed that integrate real-time weather data and simple water balance models for irrigation scheduling [
10,
11,
12,
13]. However, their suitability in practice has been limited due to the need to use desktop computers (or laptops) and, therefore, scarce user–tool interaction. In contrast, the smartphone applications that have been developed for a new generation of mobile phones offer continuous user–tool interaction, increase operational flexibility, and permit the update of data in real-time during field inspection activities.
In the last few years, a huge number of studies have reported advances in ICT (Internet and Communication Tools) applications in agriculture, and, in particular, in irrigation at different scales [
14,
15,
16,
17,
18,
19]. They include the latest cloud-based technologies for on-field data acquisition, transmission, and management, monitoring of the soil-plant-atmosphere continuum and irrigation network performance, satellite- and ground-based remote-sensing applications, soil water balance and crop growth models, and the remote control of the irrigation process.
In Florida, smart irrigation applications (apps) were developed to provide real-time irrigation scheduling for selected crops (i.e., avocado, citrus, cotton, peanut, strawberry, and vegetables). Irrigation scheduling is based on crop evapotranspiration (ETc) or a water balance methodology using real-time weather data [
15]. The apps were customized for different users considering the adopted irrigation systems, water conservations options, and other management practices.
In Italy, a new smartphone application, called Bluleaf
®, was developed through collaboration between research institutions and the private sector [
17]. Bluleaf
® is based on a Decision Support System (DSS) platform that integrates weather and soil sensors with soil water balance and irrigation scheduling models that are fully adapted to the specific conditions of irrigated plots and describe in detail the crop’s phenological stages and the characteristics of the on-farm irrigation systems. This permits the optimization of irrigation inputs and the enhancement of the irrigation application’s efficiency. Bluleaf
® was tested in southern Italy, and the results confirmed the robustness of the approach and its capacity to save water and energy with respect to traditional irrigation practices [
17]. However, further investigations are needed to examine its performance under different soil and weather conditions and management practices.
The application of technological advances should be investigated more in the Mediterranean region in order to provide a step toward improved agricultural water use in terms of both increased economic benefits and a reduced environmental impact [
20]. This is particularly relevant for the Middle East and North Africa (MENA) region, where water resources are scarce and agriculture is primarily based on irrigation. Therefore, it is important to consider the efficiency of adopted technological innovations in the context of specific hydrological realities and agronomic constraints [
21,
22] and to apply adequate indicators of water use performance and productivity for the sustainable conservation of resources [
23].
The Bekaa valley is considered to be the food basket of Lebanon, where winter cereals are produced under supplemental irrigation and spring/summer vegetables are cultivated under full or deficit irrigation (depending on the availability of water). The increasing water scarcity in the valley constitutes the main driver threatening farmers to use less water on food production. In the valley, traditional irrigation scheduling, based on the farmer’s knowhow, is the norm. This means that the irrigation scheduling is performed according to a time-set calendar schedule, the number of days that has elapsed since the last irrigation, visual detection of a change in crop color or wilting leaves, and/or according to how dry the soil feels. However, none of these traditional methods can provide information on ‘how much’ water to apply.
The main objective of the study was to assess the performance of the Bluleaf® smart irrigation scheduling application and to determine whether it could save water and improve yield water productivity over traditional irrigation scheduling practices in the Bekaa valley. The study focused on durum wheat, a strategic Mediterranean crop, cultivated during the 2016–2017 and 2017–2018 growing seasons.
This investigation is important at the local and regional scales to improve farmers’ irrigation practices and to disseminate the benefits of use of innovative technologies that can increase water productivity, save resources, and reduce pressure on freshwater withdrawal.
3. Results and Discussion
3.1. Irrigation Water Supply and Saving
Irrigation dates and amounts of water supply considering two management approaches (Bluleaf
® and the farmer’s approach) are presented in
Table 4 for both seasons. In the first season (2016–2017), the Bluleaf
® irrigation was scheduled three times, each with 67.5 mm of net irrigation, which accounted for a total of 202.5 mm of net irrigation supply and 270 mm of gross irrigation input. The farmer’s irrigation strategy assumed four irrigations of 67.5 mm each, with a total of 270 mm of net irrigation and 360 mm of gross irrigation. Therefore, a total water saving of 90 mm was obtained when the Bluleaf
® application was used.
In the second season (2017–2018), the Bluleaf® irrigation was scheduled four times with different water inputs, which accounted for 248 and 331 mm of net and gross irrigation, respectively. In the case of the farmer’s irrigation strategy, five irrigation events occurred, each with 67.5 mm, which meant 337.5 mm of net irrigation supply and 450 mm of gross irrigation input. Thus, in the second year, a water saving of 119 mm was observed with the Bluleaf® application.
Overall, I-Bluleaf received 25.7% less water than I-farm independently on the growing season. In Lebanon, the area cropped with wheat represents about 30,000 ha, of which almost 50% is being irrigated [
35]. Therefore, the annual water saving could reach about 15 million m
3 provided that, in all irrigated areas, a real-time irrigation scheduling tool is applied.
The difference in irrigation amounts between the two growing seasons was mainly due to the different soil characteristics of the two experimental seasons (the soil water holding capacity was about 25% greater in the first year with respect to the second year) and the distribution of precipitation throughout the growing season, which was more uniform in the first than in the second year. In fact, in the second growing season, the precipitation was higher than in the first year by about 51 mm; however, it was distributed mainly in January and February, when both the rooting depth and the crop evapotranspiration were low. Thus, in the second season, most of precipitation was lost mainly through deep percolation. The difference in net irrigation requirements estimated by Bluleaf® was about 45 mm. Nevertheless, the difference in net irrigation applications adopted by the farmer’s approach was 67 mm. This confirms the suitability of smart irrigation technologies for irrigation scheduling, which permit us to have better knowledge of the soil-plant-atmosphere continuum and a more accurate irrigation supply.
3.2. Assessment of the Soil Water Content Estimation in the Root Zone
The results of soil water balance estimated on a daily basis are given by the values of soil water depletion in the root zone in
Figure 2 and
Figure 3 for the three water treatments (I-Bluleaf, I-farm, and I-rainfed) in the 2016–2017 and 2017–2018 growing seasons. In both seasons, the plants were kept under optimal water conditions from the booting until the grain-filling stage. The irrigation was always applied before the root zone’s soil water content went below the allowable depletion threshold. Consequently, no apparent water stress was experienced by the crops during the irrigation period. When irrigation was stopped, at the dough stage, the soil water content dropped below the readily available water threshold. The total net irrigation amounts of I-farm were 270 mm and 338 mm in the 2016–2017 and 2017–2018 growing seasons, respectively. Irrigation was performed every 15 days according to the common farmer’s practice in the region. Accordingly, irrigation started at the early vegetative stage. For I-rainfed, water stress started at the booting stage and it was increased progressively until the ripening stage.
The testing of Bluleaf
® to predict soil water content in the root zone demonstrated a good agreement with the measured values considering the soil water dynamics and the soil spatial variability and heterogeneity. The goodness-of-fit indicators are presented in
Table 5. The RMSE ranged from 15.12 mm to 26.64 mm with a CV(RMSE) between 0.14 and 0.61 mm. The d
IA ranged from 0.77 to 0.98. The NSE showed that the model can be classified from acceptable to very good [
19], with values ranging from 0.77 to 0.92. The differences between the simulated and observed soil water contents could be due to the fact that the soil water balance considers a one-dimensional flow of water through the soil, while ignoring lateral and preferential flow [
36,
37,
38]. In addition, since soil properties are generally highly heterogeneous, the simulations were accepted as an average representation of soil water variations within the root zone. Trends in soil water content dynamics and not exact values of soil water content were reproduced by the model, as was also obtained in the study for the validation of a smartphone application for avocado [
19].
3.3. Biomass, Yield, and Water Productivity
The main examined variables, particularly the final aboveground dry biomass (AGDB), the grain yield, and the yield water productivity (Y-WP), are reported in
Table 6 for all treatments and both seasons. In addition, the grain yield, as affected by year (i.e., specific weather conditions) and treatment application, is presented in
Figure 4.
Considering “year” as the source of variance, there was not a significant difference in terms of biomass production. However, the grain yield and the Y-WP were significantly different with higher mean values (4.81 t/ha and 1.01 kg/m3, respectively, in the 2016–2017 growing season) than in 2017–2018 (3.22 t/ha and 0.59 kg/m3). The reduction of yield in the second growing season could be explained by a higher air temperature during the crop development phase (an average of about 3.0 °C) in 2017–2018 than in 2016–2017, and a lack of precipitation in December (immediately after sowing) and in March. The high temperatures accelerated crop development and reduced biomass and yield in 2017–2018. Moreover, in the second season, the booting stage, which is sensitive to water stress, was anticipated in March when the precipitation was low and there was no irrigation. Thus, it could be an additional factor affecting the grain yield in that year.
Considering “water” as the source of variance, the total dry biomass of wheat was significantly influenced by the water regime. In fact, I-Bluleaf and I-farm produced 41.76% and 41.37% higher biomass than I-rainfed, respectively. In addition, yield varied significantly in relation to water practice, with the values for I-Bluleaf and I-farm respectively 67.14% and 67.50% higher than I-rainfed. It should be mentioned that I-Bluleaf and I-farm were not significantly different in terms of final biomass and yield, and the values were very close.
Concerning the Y-WP that was expressed on the basis of grain yield, the results revealed that, although not significant, I-Bluleaf had a 13.5% higher Y-WP than I-farm. In fact, despite I-Bluleaf and I-farm presenting similar values of AGDB and grain yield, it is of great importance to emphasize that I-Bluleaf received 24.8% and 26.6% less water than I-farm, respectively, in seasons 2016–2017 and 2017–2018, which lead to a greater Y-WP.
The results confirmed that the application of supplemental irrigation and a limited amount of water increased the crop yield and water productivity of durum wheat [
39,
40,
41,
42].
3.4. Stress Indicators: Leaf Water Potential (LWP) and the Crop Water Stress Index (CWSI)
The midday leaf water potential was measured as illustrated in
Figure 5, and corresponding to the time interval between the booting and grain-filling stages. In season 2016–2017, for the first measurement, the midday LWP presented almost similar values, between −24.5 and −25 bar in all treatments. However, for the second and third measurement, the LWP showed lower values (more negative), between −27.67 and −32 bar for rainfed treatments, which indicated water stress. In the case of irrigated treatments, the LWP was higher (around −20 bar) for the second measurement, and it was lower (around 26.5 bar) for the third measurement. The values of LWP were slightly higher for I-Bluleaf than for I-farm treatment. In season 2017–2018, the LWP was lower for rainfed treatment (between −28.67 and −33.17 bar) than for irrigated treatments (between −19.50 and −25.33 bar). There was a clear difference of midday LWP values between rainfed and irrigated treatments. Finally, the treatments I-Bluleaf and I-farm presented the same range of midday LWP, and no clear difference was noticed between the two treatments that mainly exhibited similar trends of LWP. The results obtained in this study are in agreement with the findings of [
43], who reported similar LWP values for durum wheat grown under optimal and water-stress conditions in Avignon, France. These findings are also in agreement with the study of [
42], who investigated the LWP variation under different water regimes for durum wheat and barley crops. This confirmed the validity of the Bluleaf
® application for the irrigation scheduling of wheat, revealing that the plants were well-watered during the irrigation season.
In
Figure 6a, the canopy temperature minus air temperature (Tc – Ta) values are plotted against the corresponding values of air vapor pressure deficit (VPD), and delimited by the estimated upper and lower baselines.
The variation of the empirical CWSI under the three water regimes in 2016–2017 is shown in
Figure 6b. Since the irrigation supply started late in the season (April), the first measurements showed a common stress (with a CWSI between 0.4 and 0.55). Later on, the CWSI decreased as a result of irrigation in I-Bluleaf and I-farm. The irrigated treatments followed a similar trend of CWSI in agreement with the adopted irrigation strategy. The CWSI threshold for irrigation can be adopted as 0.5, which is in agreement with other studies [
44]. Therefore, the results on CWSI variation confirmed the validity of the Bluleaf
® irrigation app for use in the irrigation management of durum wheat.
4. Conclusions and Perspectives
Field crops, such as wheat and other cereals, are important for stabilizing food security at the national level in the most of the MENA countries. In most cases, these crops are irrigated in order to reach a satisfactory level of production. Although water authorities are trying to develop regulations to limit water abstraction (especially from groundwater), their effective application requires support to help farmers in the implementation of new water-efficient technologies. For this reason, a user-friendly smartphone application for irrigation scheduling is of great importance in rationalizing water quantities.
The application of Bluleaf®, which was tested in this study, can provide daily customized irrigation scheduling for each farm at the irrigation sector scale using local meteorological data on a real-time basis, weather forecasting, soil, and crop data, and the hydraulic characteristics of the irrigation system. The results of the test indicated a considerable water saving of at least 1000 m3/ha, which confirmed that the irrigation practices adopted by farmers are not efficient, cause a waste of water, nutrients, and energy, and trigger other environmental burdens. The assessment of Bluleaf®’s performance revealed that the presented tool could constitute a promising solution for irrigation scheduling with an “acceptable to very good” simulation of the soil water balance in the root zone.
Nowadays, there is an increasing demand for user-friendly platforms in the agricultural sector that should be able to provide relevant information for farmers by means of various types of sensors (local and remote) and modeling tools. Additionally, these applications could be used to produce reliably traceable records of farm activities and for the estimation of eco-efficiency [
45], which is increasingly being demanded by the market.
The presented tool is one such platform, based on a smartphone application that allows for easy and instantaneous interaction with users. This provides additional insight into real-time irrigation management and permits more efficient water, nutrient, and energy use. Certainly, proper use of this and other similar tools depends on a concerted capacity-building effort and strong collaboration between researchers, extension service staff, and farmers. Further testing and calibration of other crop, soil, weather, and management conditions is needed.