Smart Irrigation for Improving Water Productivity in Drylands: History
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Contributor:
Zeeshan Ahmed
,
Dongwei Gui
,
Ghulam Murtaza
,
Liu Yunfei
,
Sikandar Ali

By using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind.

  • drylands
  • irrigation management
  • smart irrigation
  • sustainable development goals

1. Introduction

Drylands (hyper-arid, arid, semiarid, and dry sub-humid parts) occupy 41% of Earth’s surface, supporting 38% of global population [1][2]. Agriculture and pastoralism are the major livelihood sources for most of the population, largely dependent upon natural resources [3]. About 70% of the world’s drylands exist in developing countries where people are confronting the stark challenge of poverty, food insecurity, malnourishment, poor economic conditions, and marginalization [4][5]. Water availability and agricultural productivity are the most pressing issues associated with drylands and land degradation [6]. Globally, water scarcity is already affecting 1–2 billion people, and a majority of them are concentrated in drylands, where the supply of water is insufficient to meet the user demands [7]. Future climate projections also suggest that in coming decades more people will be facing huge shortages of water. Consequently, climate change and water management decisions will adversely affect drylands and their inhabitants [8]. As global population is increasing rapidly, agricultural productivity in drylands needs improvement to meet food security demands. Therefore, adopting smart irrigation approaches is a viable option to better utilize the available water resources and improve water productivity in drylands. Water scarcity has become one of the critical issues and threatens the sustainable development in drylands [9]. Water scarcity occurs when water demand becomes equal or even exceeds the total available freshwater resources [10]. Water scarcity should be considered from both physical and economic perspectives [11]. Physical water scarcity has two aspects: green water scarcity (soil moisture in root zone is insufficient to meet crop water demands), and blue water scarcity (both surface and ground water availability is unable to meet human water needs) [12]. The economic water scarcity occurs when water resources are physically available, but lack of institutional capacity and socioeconomic conditions limit the use of that water [13]. Water scarcity negatively impacts social integrity and sustainable economic development, especially in drylands. The primary sector, which is seriously affected, is agriculture, utilizing more than 80% of total fresh water [14]. Intensification of agricultural water scarcity could affect food production and threaten food security in drylands in the future [15].
Since the available water resources are limited and to obtain more yields with less water use, efficient management of available water with improved water productivity is direly needed to meet future food demands [16]. Managing irrigation efficiently is challenging in drylands because there are so many factors to take into account, such as crop type, climate, soil type, and irrigation methods [17]. Drylands are characterized by high potential evapotranspiration, low and erratic rainfall and high temperature [18]. Additionally, predicted extreme weather events due to climate change will further worsen the situation. Besides hostile environmental conditions, increasing water scarcity in these regions is posing serious threats to irrigated agriculture and sustained food production [19]. Although agriculture consumes about 80% of the total water utilized in the agriculture sector globally [14], this irrigation generates a lower return per unit of water used than other economic sectors [20]. The use of traditional irrigation methods and low water use efficiency (35–40%) caused by poor management are major constraints to sustainable crop production in drylands [21][22]. 
A smart irrigation system applies water in the right amount, at the right time and place, in a field [23]. Smart irrigation offers better irrigation decision-making by using several factors, including soil and climate variation, soil hydraulic properties, plant responses to water deficits, and changes in weather factors, that can help save water and increase yields [24]. By using smart irrigation systems, farmers can save precious resources without exposing plants to moisture deficiencies [25]. Smart irrigation has been argued as a way to manage soil variability and gain economic benefits by fulfilling the specific irrigation demands of individual crops [26]. It is also implied that the smart irrigation system will be managed in such a way that will enable nutrients and water to be delivered directly to the plant roots [27]. 

2. Major Constraints of Agricultural Productivity in Drylands

2.1. Land Degradation

Natural processes such as vegetation loss, wildfires, overgrazing, climate change, wind and water erosion and other adverse/destructive anthropogenic activities cause land degradation [3][28]. This causes a substantial decline in the functional capabilities of those specific areas, negatively influences agricultural activities and productivity and natural resources management, creates economic loss, and loss in biological activity [29]. Generally, land degradation is more critical or serious in dryland, semiarid and arid areas [30]. These include some parts of central Asia, China, Africa and the Mediterranean basin [31]. Land degradation in Africa highly impacts Somalia, Eritrea, and Ethiopia (horn of Africa) [32]. A prominent sign of land degradation is the occurrence of unexpected climatic conditions and vegetative stress [33].
To gain insight into land degradation intensity, statistical methods and models have been utilized using data collected over the years. These models allow precise predictions about land degradation intensity [34]. Normalized difference vegetation index (NDVI) is one of the methods used to measure the vegetation mass over a specific area [35]. NDVI represents the vegetation state of an area in terms of numerical values. If numerical values are negative, it means there is a reduction in vegetation. A study carried out by Ibrahim et al. [36] using residual trend analysis of NDVI showed substantial evidence of soil degradation in sub-Saharan West Africa. The study also highlighted the drought occurrence caused by vegetation decline in Africa. 

2.2. Water Scarcity Issues and Sustainable Development Goals

Water availability is one of the main indicators of land degradation, and upsurge in land degradation is potentially aggravating water scarcity [37][38]. Availability of water is jeopardized by lessening of ground and surface water due to reduction in biomass [39]. This results in less water available for agricultural [19] and domestic use. The scarcity of water is an indicator of safe water; hence, water scarcity is a deficiency in freshwater resources to meet the standard water demand [11]. Successful achievement of SDGs is dependent upon the water security of both human and environmental systems because water is directly linked to all SDGs [7]. The leading reasons for water scarcity are droughts, climate change, and inaccessibility and inadequate management of resources [40].
Liu et al. [12] described the common indicators employed to evaluate water scarcity, such as IWMI, (a system that evaluates the economic and physical changes influencing the availability of water within a country), the criticality ratio (a ratio of water consumption to the available water resources) and Falkenmark indicator (which compares the quantity of available water against the number of people who consume that water). Water stress and crowding indices may also be employed to measure water scarcity in a country. These indices also estimate the decrease or increase in water scarcity. Efforts are required to curtail the water scarcity issues. In this regard, implementing efficient water use practices and managing water in high-risk regions are important aspects to address [38]. These efforts could decrease water scarcity and increase access to adequate agricultural and drinking water. Moreover, by taking care of structural and social customs, governments can effectively resolve major conflicts. Hence, joint efforts are needed from governments and research institutes to resolve the water scarcity problem successfully [41].

2.3. Climate Variability

Variations/changes in climatic conditions often impact human, biological and agricultural systems through decreasing water resources, rising global temperatures, heavy precipitation, elevation in permafrost thawing, worsening water and air quality, rise in sea level, health risk, food supply and availability, intense drought, disturbing rainfall periods, and devastating effects on coastal infrastructure [42][43]. East Africa is an example of climatic anomalies, and the progression of increased rainy seasons, droughts and temperatures is detrimental to the development of this area [44]. In Tanzania, crop data techniques predicted a yield reduction of 7.6%, 8.8%, and 13% in rice, sorghum and maize by 2050, respectively [3]. The impacts of variation in climatic events are significantly negative [45], for example, the Mediterranean Basin has shown a prominent rise in average temperatures beyond global changes, with significant impacts on plant processes and water resources [46]. Climatic variation may induce a significant reduction in crop growth and productivity and increase vector-borne diseases, thus threatening food security [47]. Heavy rain and early frost may have negative effects on flowering periods and induce frost damage, while dry seasons or droughts largely decrease the decomposition of soil organic matter [48]. Such alterations induce negative impacts on biological systems. Fitness of the population is one of the main factors negatively affecting community structures and population dynamics [49].

2.4. Overexploitation of Groundwater

Increasing economic growth has resulted in continuous demand for water, leading to groundwater overexploitation, especially in big cities [50]. Both humans and the environment rely heavily upon groundwater; therefore, understanding its environmental implications is vital [51]. Over-pumping of water is a global issue, primarily caused via agricultural water use. Among its effects are the drying of wetlands and streams, phreatophytic vegetation elimination, soil subsidence, storage loss, decline in groundwater level, increased pumping cost, and soil salinization [52][53].

2.5. Socioeconomic Drivers

The adverse impacts on water availability, agricultural productivity, and economic feasibility have been correlated with the displacement of residents [54]. Countries dependent on other countries’ agricultural productive capabilities are also seriously affected. Food insecurity and hunger are common outcomes of poor agricultural production phases for millions who are dependent on agriculture [55]. The 1980s famine in Africa resulted from a decline in agricultural productivity [56][57]. Generally, droughts indicate decreased food security [58]. Between 1992 and 1995, drought periods aggravated already problematic conditions in Africa. There was income loss for farmers, a rise in unemployment, and a decline in maize export to neighboring states. Moreover, there was an emergence in incurred service debts through farmers [59].

2.6. Droughts

Drought is a condition of abnormally dry weather sufficiently prolonged, lacking water to induce hydrologic imbalance and constraining agricultural activities in the affected region [60]. Drought types such as inter-annual droughts stay longer and decrease crop productivity, whereas inter-seasonal drought may be short and controlled via efficient water management [61]. Hermans and McLeman [62] reported that repeated occurrence of drought contributed towards degradation of land. Henchiri [63] reported that sub-Saharan Africa has faced long periods of drought with greater intensity. Nijbroek et al. [64] demonstrated that Namibia is an arid country and faced drought for many years, as well as neighboring counties of South Africa being liable to droughts repeatedly triggered by El Niño—Southern Oscillation (ENSO), a system of warm seawater that passes over the Pacific about every 10 years.

2.7. Conventional Technology

In dryland regions, most farmers employ old farming techniques that result in failure to manage food for increasing populations [65]. The traditional farming techniques generate little food [3]. Agriculture conservation (crop rotation, soil cover and minimum tillage) could help to enhance crop yields with increasing profitability and decreasing soil degradation [66]. Some techniques are not used by subsistence farmers mainly because of unfamiliarity [67]. The implication of microbial-resistant varieties or seeds is less common in Africa [68]. These advancements have the capability to improve yield and increase stress tolerance. Nonetheless, most farmers in rural areas are lacking access to services and information to be effectively used in their favor [69].

3. Traditional Approaches Used for Irrigation Scheduling

3.1. Weather-Based Irrigation Scheduling

In weather-based irrigation planning, reference evapotranspiration (ET0) is calculated by measuring the weather elements that reflect the amount of water lost via plants and soil [27]. Solar radiation, humidity, air temperature and wind speed influence the quantity of water lost through evapotranspiration. In the absence of soil and plant measurements, weather attributes are used to determine irrigation schedules based on evapotranspiration [70]. Reference evapotranspiration can be calculated following the FAO Penman–Monteith equation by measuring the solar radiation, wind speed, air temperature and humidity [70][71]. Daily crop water use can be calculated by:
ETc = Kc × ET0
where ETc = crop evapotranspiration (mm day−1), Kc = crop coefficient, and ET0 = reference evapotranspiration (mm day−1).
The method is strongly dependent on (1) the accurate calculation of ET0, (2) better Kc curve development over the entire crop-growing season, (3) determination of soil water-holding capacity by analyzing soil properties, and (4) quantifying site-specific rainfall [72]. Mostly, real-time weather monitoring systems are equipped with an automatic weather station containing sensors for temperature, rainfall, wind speed, humidity, atmospheric pressure, and solar radiation [73]. These data loggers are designed to obtain data automatically at periodic intervals, and these data are transferred to an online data access portal. Data loggers communicate with remote servers using a wireless sensor network (WSN) or Internet of Things (IoT) framework [16]. WSN is one of the most popular technological methods that is used to precisely monitor the weather and environmental parameters [74][75][76][77][78]. These data finally reach smart irrigation controllers, which in combination with site-specific variables (e.g., soil type), set up the irrigation schedule. The selection and performance of a weather monitoring system depends upon different accuracy, installation, robustness, data acquisition, maintenance, and power requirements. An IoT-based weather monitoring system demonstrated by Wasson et al. [79] monitors and analyzes the crop environment in terms of wind speed, temperature, solar radiation, soil moisture, and humidity using various weather-based sensors connected through a wireless network for data transfer and web-based services.

3.2. Plant-Based Irrigation Scheduling

Plant-based irrigation scheduling mainly relies on several indices indicating plant water status [80]. The relationship between soil moisture deficit and crop water stress helps to determine irrigation scheduling. Plant-based irrigation scheduling is sensitive to measurements conducted at a specific crop stage to determine water deficit in plants [16]. Since varying plant species, plant tissues, and crop growth stages have variable sensitivity to moisture deficit, several plant-based stress measurements have been suggested for irrigation scheduling [81]. There are two principal categories based on plant variable measurements used for irrigation scheduling: firstly, plant water status-based direct measurements including leaf, stem, and xylem water potential status and indirect measurements pertinent to leaf thickness, turgor pressure, and trunk diameter [82][83]; and secondly, plant physiology-based estimates including sap flow, stomatal conductance, xylem cavitation, and thermal sensing [84]. A leaf turgor pressure sensor estimates the relative change in leaf turgor pressure to determine leaf water stress [85].
Due to advanced electronic technologies, researchers have developed small leaf sensors and tested them against cowpea (Vigna unguiculata L.) and tomato (Solanum lycopersicum L.) plants. Leaf thickness-based irrigation timing improved WUE by 25–45% compared to preset irrigation plans [86]. In another study, Afzal et al. [87] reported that leaf thickness and leaf electrical capacitance (CAP) could be employed for leaf water status monitoring. Based on energy balance and heat pulse, thermal sensors have been developed to determine sap flow from plant stems, assisting irrigation scheduling. Sap-flow methods are able to provide in situ measurements of plant water use and transpiration dynamics. The Dynagage sap-flow sensors are the latest ones used to estimate sap flow and thus the water consumption by plant. The amount of heat utilized by the sap is measured by the energy balance sensors and gives the real-time sap flow in grams or kilograms per hour. These sensors require no calibration and offer an efficient and affordable method to determine the water use of plants [27]. During transpiration, the water in the xylem subjected to tension is directly proportional to the deficit in water to the point where the water columns can rupture or cavitate [88]. This cavitation leads to the eruptive formation of a bubble that contains water vapor [89]. Audio or ultrasonic frequency signals can detect these cavitation events, and the associated embolisms can hinder water flow [82]. Detection of such ultrasonic acoustic emissions (AEs) indicates plant stress and cavitation events. Thus, the AE rate can be used as a sensor to detect plant stress.

3.3. Irrigation Scheduling Based on Soil Moisture

Soil moisture monitoring is one of the fundamental approaches used for irrigation scheduling, and it is conducted by determining the soil water content or the soil water potential [90]. Monitoring soil moisture at high spatial and temporal resolution is critical for optimal irrigation scheduling [27]. Different types of sensors, such as time-domain transmission, neutron probes, granular matrix, and capacitance, are commonly implemented for soil moisture determination [91]. Gravimetric sampling to estimate soil moisture fluxes and a tensiometer is also used to measure soil matric potential, reflecting the amount of soil water available for plant use [81]. With the advancement of technology, satellite and groundwater sensors are becoming popular as irrigation tools. Soil moisture sensors can be installed at multiple depths in the field and capture soil moisture dynamics. They enhance accuracy and improve understanding of changes appearing in soil water content pertinent to crop water use and irrigation [92]. Soil sensors also provide information about soil chemical, physical and mechanical properties obtained in the form of optical, electrical, mechanical, electromagnetic, acoustic, radiometric, and pneumatic measurements [93]. Measurement of these attributes assists in the estimation of maximum allowable depletion [94]. Soil moisture sensors estimate the volumetric moisture content (VMC) by detecting changes in soil electrical and thermal properties [95].
Frequency-domain reflectometry sensors (FDR) can estimate field soil moisture content [96]. The sensors are put near the crop roots and show a moisture content range of 0–50% with 0.1% resolution, thus optimizing water use for vegetables. Shigeta et al. [97] found that real-time soil moisture sensing can be used in practical measurements of soil moisture fluxes by correlating the VWC of the soil with the capacitance of sensors inserted in the soil. In TDR sensors, two parallel rods are inserted at the desired depth to measure the soil moisture content. The rate of the electromagnetic pulse, which radiates from the sensor into the soil and returns to the soil surface, is directly proportional to soil water content. However, this is an expensive method for farmers. 

4. Innovative Smart Irrigation Approaches

A smart irrigation system consists of firmware, software, and hardware interconnected via various computational techniques, including artificial intelligence (AI) and deep learning (DL) etc., which ensures the right amount of water at the appropriate time in crops to improve WUE, increase yield, reduce fertilizer use, reduce labor cost, and save energy [98]. Various control methods are employed to improve irrigation system efficiency by monitoring variables such as canopy and air temperature, evapotranspiration, rainfall, and solar radiation. By integrating information from multiple sources, smart irrigation systems can significantly improve crop production and resource management [99].

4.1. State-of-the-Art Smart Irrigation Technologies

4.1.1. Artificial Intelligence (AI) and Deep Learning

AI is a machine’s ability to learn and implement tasks similar to those of a human brain, and it is powered by computers [100]. When applied to a certain problem domain, AI algorithms can mimic human decision-making. Irrigation systems have been integrated with AI for adaptive decision-making through fuzzy logic, expert systems, and ANNs [101].
An artificial neural network (ANN) is an algorithm for processing information that is inspired by the working of the human brain [102]. Like human brain neurons, an ANN also contains a neural network, but synapses are substituted with biased connections and weights [103]. This facilitates the mapping of input and output relationships [104]. ANN-based control systems can learn and adapt to the variable dynamics, making them ideal for irrigation systems. Additionally, ANNs have been used as smart strategies in dealing with the issue of formulating mathematical models based on first principles. Recently, many researchers have employed ANN methods for irrigation scheduling. Using the AQUACROP model integrated with a dynamic neural network, Adeyemi et al. (2018) [101] simulated soil moisture for a potato crop.
A fuzzy logic system is an extension of Boolean logic that expresses logical values in the form of true or false and demonstrates the nonlinearity and uncertainty in real-world problems [105]. The fuzzy system uses different sets of input data to categorize data in membership classes, and then applies a decision rule to every set to produce human-like decision outputs [73]. Many researchers have recommended the use of fuzzy logic in irrigation control systems. Mendes et al. [106] designed a fuzzy inference system that can control the speed of the central pivot according to the spatial field variability. 
Expert systems can be used for problem-solving activities such as monitoring, control, planning, forecasting, prescribing, fusion, and decision-making [107]. An expert-controlled irrigation system enables farmers to quantify the water amount needed by crops at the appropriate time by considering the weather and soil conditions. Many researchers [108][109] have implemented expert systems for irrigation management. The expert system uses various knowledge-based inputs for accurate decision-making about irrigation scheduling. However, errors in knowledge-based input can seriously affect the performance and reliability of expert systems [110].
A hybrid intelligence system is another type of intelligent control system in which at least two artificial intelligence algorithms such as fuzzy logic and neural network are combined, known as “neuro-fuzzy” [101]. Other examples of such hybrid intelligence systems include fuzzy PID and GAPSO. Tsang et al. [104] employed seven different machine-learning algorithms to assess soil moisture conditions using aerial images of agricultural fields to control irrigation. The results demonstrated a 52% reduction in water consumption by reducing timing, irrigation level, and location errors.

4.1.2. Model Predictive Irrigation Systems

Development in smart agriculture through internet usage and increasing computational power facilitated large data collection from agricultural systems [111]. The model predictive system has been employed in irrigation scheduling, irrigation canal control, soil moisture, and stem water potential regulation [112]. Model predictive control (MPC) has manifested applicability to gate operation and control the canal flow. The management goal of model predictive control for canals is to maintain the level of water as close to the set-points as possible [113]. Thus, an appropriate model regulating the dynamics of canal-water levels is required. A model predictive control system has been employed to model water movement in the canals, keeping a specific level of water at different locations and the flow of water that affects these water levels [114][115][116]. The controlling instruments maintain the flow of water, by which the regulator can attain the management goals [111]. Nonetheless, attaining this goal is not straightforward, as variations in inflows and outflows interrupt the whole water system. To estimate future water flows and levels in response to control actions and disturbances, the water system (controller, canal reaches, disturbances and structures) needs to be modeled. Several authors have applied MPC in driving irrigation flows of canals.

4.1.3. Variable-Rate Irrigation (VRI)

VRI is a method of applying irrigation at variable rates in different irrigation management zones over the entire field in an optimized way [117]. Normally, the application of irrigation water is uniform in the entire field. However, owing to soil spatial variability in soil topography, hydraulic properties and vegetation condition, the soil moisture content remains nonuniform [118]. When such soil spatial variability becomes significant, the field is split into different management zones consisting of those field areas with the same soil properties and crop conditions [102]. Then, irrigation is applied at differential rates in different management zones [119]. Such variable irrigation management may enhance the economic value of irrigation by improving WUE, increasing productivity and reduction in nutrient leaching [120]. This enables an accurate and timely water application based on soil spatiotemporal properties and plant demand [121].
The speed and operation of the pivot are regulated by pivot control. The VRI controller panel governs the irrigation application based on pivot location and the prescription map. The flow of sprinkler heads is controlled by solenoid valves [25]. The pivot positioning is regulated by the GNSS system, and the control nodes attached to the pivot govern the valve opening and closing. VFD regulates the pressure by altering the irrigation rate at different points in the field [122]. The rotation speed of the pump impeller is also controlled by the VFD in response to the input communicated by the pressure switch mounted on the pump. It helps to maintain the pressure within the predefined threshold limits [123]. The use of VRI technology offers several advantages over conventional irrigation methods. VRI can substantially improve overall yields by avoiding under-irrigation and/or over-irrigation. 
Another advantage of using VRI is that irrigation can be withheld over those field areas that are not arable [124]. VRI also supports fertilizer application at variable rates that would benefit in matching the variability in crop nutrient requirements [125]. A two-year study led by Sui and Yan [126] demonstrated that crop water productivity for corn and soybean was much better under VRI than uniform rate irrigation (URI) in Mississippi. 

4.1.4. Unmanned Aerial Vehicles (UAVs) for Irrigation Management

UAVs, also called drones [127], are frequently linked with military operations, as they are used as weapons for targeting aircraft and involved in intelligence services. Recently, drones have been used in a wide range of applications, including delivery services, weather monitoring, traffic monitoring, surveillance, and rescue [128]. Several studies emphasized UAV utilization for forecasting and monitoring in agriculture to maintain crop health [129]. Drones are also useful for irrigation monitoring, as they use infrared or thermal imaging cameras in the IOT network [130]. Manual spraying of pesticides induces lethal diseases to workers globally, as described by the World Health Organization and Food and Agriculture Organization [131]. Thus, UAVs could be a potential alternative to manual pesticide spraying, reducing the potential ecological/environmental risks and health problems [129].
Several researchers examined the chances of applying IoT systems to govern crop health and irrigation monitoring. Automated water irrigation was innovated and employed by mobile applications [132]. The designed smartphone application can process and develop the soil images near the root surface of plants to ascertain sensor-less water quality. A smart drip irrigation method was established using an ARM9 processor, involving environmental conditions including CO2 amount, low moisture, and high temperature [133]. 

4.2. Forecasting Smart Irrigation Technology with DSSIS

A decision support system (DSS) is an interactive software-based system used to identify, analyze, and improve decisions based on raw data, documents, and personal knowledge [81]. Various decision support systems (DSSs) have been designed for managing irrigation water to improve WUE [134][135]. A smart and efficient DSS has to consider several factors, such as soil water status, crop type, irrigation method, weather information, and application, to develop irrigation scheduling [17]. To facilitate precise irrigation scheduling by minimizing errors in field soil moisture estimates, DSSs provide irrigation schedules not only for the current day but also to forecast irrigation events for future days.
Based on the idea of forecasting irrigation, recently a prototype of an irrigation scheduling DSS called decision support system for irrigation scheduling (DSSIS) has been developed for arid regions [136]. This DSSIS has the ability to predict irrigation events for the current day as well as forecast irrigation for the future by using the weather information of the next 4 days. The DSSIS prototype consists of irrigation pipelines, software and hardware to control irrigation and peripheral equipment The irrigation pipelines consist of a drip irrigation system, valves and polyvinyl chloride pipes. The software controlling the irrigation system includes RZWQM2 (Root Zone Water Quality Model) integrated with an irrigation scheduling software (RZ Irrsch and an online weather data acquisition system [137]. The irrigation-controlling hardware contains automatic control equipment. A peripheral equipment consists of a water reservoir, circulating pumps, and strainers. In DSSIS, the RZWQM2 model works as an engine and facilitates decision-making about irrigation scheduling. The RZWQM2 is first calibrated and validated according to site-specific experimental data (crop, weather, and soil data). The IrrSch software generates daily weather data from the nearby weather stations and also forecasts upcoming 4-day weather using a weather application program interface (API) then transfer this information to RZWQM2 [81].
Based on the information given by IrrSch, RZWQM2 predicts crop evapotranspiration, soil water stress factor (SWFAC), and soil moisture content for the current and next 4 days. When the current day’s water stress level falls below the preset threshold, an irrigation event is initiated. The amount of water to be supplied is computed by RZWQM2 using field capacity and the predicted soil water content and rooting depth. This system (DSSIS) has been tested for cotton irrigation scheduling under full, deficit, experience, and sensor-based irrigation treatments in an arid region. Under deficit irrigation, DSSIS saved 50% of irrigation water with a 4% increase in yield and up to 80% increase in water productivity over experience-based irrigation [136]. 
This irrigation forecasting DSSIS has been tested on a small scale in an arid oasis and provided satisfactory results regarding water productivity. It could be promoted over a large scale. To further improve the site-specific irrigation management, the soil textural variability could be analyzed and a local-level soil database could be developed through extensive soil sampling and analysis. This soil database could be integrated with DSSIS for scheduling irrigation for a specific field. This concept of irrigation is termed soil test-based irrigation prescription (STIP). Availability of site-specific soil information may result in potential gains in improved WUE and higher profitability in arid regions where existing irrigation strategies are poorly connected with local agronomic and biophysical settings. 

This entry is adapted from the peer-reviewed paper 10.3390/agronomy13082113

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