Smart Irrigation for Climate Change Adaptation and Improved Food Security

Abstract

The global consequences of climate change cannot be ignored. The agriculture industry, in particular, has been harmed, resulting in poor production as a result of floods and droughts. One in every three people in the world’s arid and semi-arid regions lacks access to healthy food and safe drinking water. Despite the fact that irrigation development is increasing in most developing nations, it still falls short of meeting current food demand, much alone predicted need by 2050. To feed the future population while combating climate change, agricultural practices must be precise. Scarce resources such as water, land, and energy will need to be exploited more efficiently in order to produce more with less. Smart irrigation is shaping up to bring answers to these twenty-first-century concerns. This chapter discusses improvements in smart irrigation monitoring and management systems that may be used to address climate, food, and population issues. It includes an overview of smart irrigation, smart irrigation monitoring, and smart irrigation management, as well as challenges and prospects related to climate change and food security. Smart irrigation may boost water savings and agricultural production, thereby improving food security.

1. Introduction

The world presently faces challenges ranging from extreme effects of climate change i.e. droughts and floods, to a rising population [1]. This has a huge impact on the present and future food security. Presently, 1.3 billion people are residents in water-stressed areas of the world, rendering water for agricultural production insufficient as competition from other sectors of the economy increases [2]. In countries with significant amounts of fresh and groundwater resources, irrigation has substantially contributed to sustainable food production all year round [3]. Presently only 24.1% of the total agricultural area is under irrigation, yielding about 40% of the total world food and fiber. Despite the benefits of irrigation reported so far, it has been regarded as a sector that uses a lot of water depriving other sectors of the economy. The FAO state of the food report posits that of the 70% freshwater abstracted for irrigation, only 50% is beneficial to plants [4]. The pressure on freshwater and groundwater resources due to the growing food and fiber demand will further exacerbate as agricultural production will need to expand by 1.7 times by 2050. This necessitates a 15% increase in freshwater withdrawals [3]. Agriculture will be required to re-allocate a fair share of the water abstracted to meet a 25–40% increase in future water demand from higher productive and employing sectors of the economy [5, 6, 7].

Given that irrigated agriculture has proven to deliver up to two-fold more food than rainfed agriculture [8], the demand for irrigation water will inevitably continue to increase as more land is secured for irrigation. Efficient land utilization with irrigation leads to crop diversification, which later buffers against climate variability. Climate change has already impacted agriculture to the extent that water for irrigation is becoming scarce in arid and semi-arid lands. Therefore, sustainable irrigation should aim at reducing water losses, meeting crop water requirements, and maintaining ecological flows in rivers and aquifers. However, improving water management in agriculture is typically constrained by inadequate policies, major institutional under-performance, lack of technology deployment, and financing limitations [9].

Sustainable smart irrigation is an essential step towards improving the state of food security and achieving sustainable development goal number 2. Smart irrigation ensures timely, real-time water application to the plant root zone, reducing losses associated with traditional irrigation systems like evaporation, seepage, and deep percolation [10]. With effective monitoring and control in smart irrigation, water, energy and labour are saved. As the notion of more crop per drop gains attention, smart irrigation is a potential climate change adaptation strategy and an effective way to ensure sustainable food supply all year round.

2. Current status of irrigation water application methods

In the past 50 years, irrigation has undergone tremendous changes driven by increased drought indices. From 1970 to 1985, automatic control of irrigation systems emerged in the United States of America as water scarcity became prevalent [11]. As a result, researchers were interested in developing ways of optimizing irrigation to achieve maximum yields. The advent of the Internet in 1989 spiked another interest in internet-based control of irrigation systems like data storage on the web became possible. And over the years, wireless sensor networks, sensors for monitoring and control, have since been developed to facilitate precision irrigation today. Figure 1 depicts the evolution of irrigation from 1970 to 2020.

Figure 1.

Trends in irrigation since 1970s.

Eisenhauer and Martin [12] classified irrigation methods, as shown in Figure 2, ranging from micro-irrigation (surface drip irrigation, sub-surface drip irrigation, micro-spray tube irrigation, bubbler irrigation), sprinkler irrigation (solid-set system, portable sprinkler system, rain gun system, centre pivot system), surface irrigation (flood irrigation, furrow irrigation, check irrigation, border irrigation). Over the years, research has helped develop several tools and techniques for various production systems and ecologies that help save water and improve water productivity in agriculture. The advent of the Internet of Things (IoT) and wireless sensor networks has led to developing tools like soil, weather, and plant sensors that have improved monitoring that informs data-driven irrigation scheduling [13]. As depicted in Figure 3, the water use efficiency in these irrigation systems increases with the level of automation employed in the system.

Figure 2.

Irrigation methods [12].

Figure 3.

Water use efficiency and level of automation Adapted from [14].

2.1 Micro-irrigation

Water is delivered at low pressure through emitters by a distribution system located on the soil surface, beneath the surface, or suspended above the ground in micro-irrigation systems [12]. Irrigation water droplets are directed to the plant root zone via emitters, sprayers, or porous pipes, where it then infiltrates by gravity or capillary rise. The applicator’s design reduces the water pressure within the delivery lines, resulting in a low discharge.

Micro-irrigation has received a lot of attention from agriculturalists, especially for high-value crops like vegetables, fruits, and nut trees. When determining whether to invest in a costly micro-irrigation system, a producer must consider whether the increase in crop production will be sufficient to pay for the system. The other concern is, can the system be built to filter the irrigation water to avoid the emitters from clogging? Another crucial choice is selecting emitters from the broad array available are suited for the specified function. Solving these difficulties will give an excellent irrigation system for decades. Micro-irrigation is defined by water being applied: at low rates, over longer durations, near the root zone of the plants, and at constant flow rate.

Precision water application with micro-irrigation systems has been reported to save up to 35–65% more water than standard flood irrigation systems, with a commensurate increase in the production of crops. Scholars [15, 16, 17, 18, 19] have all found consistent results confirming water savings and increased yields with micro-irrigation devices.

2.2 Sprinkler irrigation

Sprinkler irrigation is a system where water is uniformly applied over the crop canopy or soil surface identical to rainfall. With a sprinkler irrigation system, water is pumped and conveyed through high-density polyethylene (HDPE) pipes eliminating water losses through seepage and evaporation as in the case of surface canals under surface irrigation. Compared to conventional flood irrigation, sprinkler irrigation is more efficient, with irrigation efficiency of up to 80–90%. The performance of a sprinkler irrigation system solely depends on the design and selection of sprinklers. In irrigation design, it is recommended to select a sprinkler whose application rate is lower than the soil infiltration rate to prevent surface ponding and runoff.

The implementation of intelligent sprinkler irrigation systems involves high control precision, high intelligence, good dependability, simple operation, wired or wireless sensor network technology, and crop water demand data collection devices [20]. Fuzzy control logic, neural networks, and expert systems and machine learning, control technologies can be built for sprinkler irrigation [21]. Furthermore, intelligent precision irrigation systems are being built with remote transmission, monitoring, decision, and control functions. Therefore, it is vital to build automatic sprinkler irrigation equipment, encompassing flow meters, solenoid valves, precision control equipment, and robotic equipment.

2.3 Surface irrigation

Surface irrigation is the oldest irrigation application method in the world. It involves the application of water over the surface of the land to supply moisture to the plant. Surface Irrigation includes furrow, border, basin and, check irrigation. Surface irrigation requires less pressure than sprinkler or micro-irrigation systems. Under surface irrigation, irrigation water is applied at the inlet end, and the water subsequently flows to the downstream end. A part of the water infiltrates as it progresses over the field. Water is frequently applied by gated pipelines, siphons, or gates. Surface irrigation may be an effective application technique provided the soils and fields are well adapted to this approach. But, it may be exceedingly inefficient if the soils and other elements are not carefully addressed while constructing and administering the system. The soil infiltration rate is very crucial in the proper functioning of surface irrigation systems. If the soil’s infiltration rate is excessively high, the depth of water that infiltrates at the entrance will be significantly bigger than the downstream end. The land slope and its regularity also considerably effect surface irrigation. Slopes that are overly steep create undue runoff and erosion. Acceptable slopes are usually less than 2%. The regularity of the slope is also crucial so that water does not gather in depressions on the surface.

To improve smart irrigation scheduling in surface irrigation systems, Supervisory Control and Data Acquisition (SCADA) software has been deployed in surface irrigation systems to improve the programming, monitoring and operation of an entire scheme from a central point [22]. Composed of field equipment, programmable logic controller (PLCs) and/or remote terminal units (RTUs) communication networks, SCADA host software and, third party systems, these components are connected to minimize human intervention but also ensure convenient operation and delivery of irrigation water with just a click of a button. SCADA systems provide real-time monitoring, remote supervisory or automatic control, troubleshooting, and automatic data reporting and archiving capabilities [22].

3. Smart irrigation scheduling approaches

Irrigation scheduling is a systematic process of determining when and how much to irrigate. This depends on various factors, including daily crop water requirement, the effective root zone, and the available soil moisture. Irrigation scheduling can be done using one or all of the following approaches: Plant-based, soil-based, and weather-based irrigation scheduling approaches. Each of these is shown in Figure 4.

Figure 4.

Smart irrigation scheduling approaches. (a) soil sensors for irrigation scheduling, (b) plant sensor for detecting sap flow in plant stems, (c) ATMOS 41 for weather monitoring.

3.1 Plant-based irrigation scheduling

Plant-based irrigation scheduling is based on the physiological and phenological status of the plant [16]. The physiological condition depicts the water stress level, which is estimated from canopy temperature depression relative to air temperature measured by infrared thermometry. The calculation of the cumulative stress degree days and crop water stress index can be used for scheduling irrigation. Phenological stages can also be used to determine when to irrigate. For example, in wheat cultivation, crown root initiation (CRI), tillering, jointing, flowering, and the grain-filling stage are critical stages of growth that need irrigation [16]. Failure to supply irrigation water at these critical stages of growth leads to low yields as water stress becomes severe. The cumulative effect of water stress is determined with this method, making it effective as a water stress indicator. This helps to capture the moisture reduction in the soil through evapotranspiration [23]. Direct and indirect measurement techniques are used to determine plant water status. Direct plant water stress detection methods include using sap flow sensors, xylem sensors, leaf sensors, and others. On the other hand, indirect methods involve thermal sensing, near-infrared spectroscopy, and aerial imagery [24].

Several authors have used plant-based approaches for irrigation scheduling [23, 25, 26, 27]. For example, King et al. [27] used data-driven models for canopy temperature-based irrigation scheduling of sugar beet and winegrape. The data-driven models developed by the authors estimated reference temperatures enabling automatic calculation of the crop water stress index for crop water stress assessment. Similarly, Meeks et al. [25] used leaf water potential monitoring system for irrigation scheduling of winter rye cover crop. The authors reported significant water savings and an improvement in crop yields.

3.2 Soil moisture-based irrigation scheduling

Soil moisture-based irrigation scheduling involves determining the soil moisture status within the root zone, and knowing the permanent wilting point [28]. Soil moisture measurements are compared to moisture thresholds to trigger irrigation. Soil moisture monitoring is done by time-domain transmission sensors, neutron probes, capacitance sensors, or granular matrix sensors. Soil moisture-based irrigation scheduling allows variable rate irrigation scheduling due to its ability to measure spatiotemporal variability in the field.

The use of soil moisture-based irrigation scheduling has been reported in the literature. For example, Pramanik et al. [28] developed an automated basin irrigation system based on soil moisture sensors for irrigation scheduling. The authors highlighted that the ideal position of sensors for shutting the system would be at 37.5 cm depth put at 25% length from the intake in larger soil moisture deficit situations and at 7.5 cm depth set at 75% length in low moisture deficit conditions. Consequently, the irrigation application efficiency was enhanced up to 86.6% using automation.

Advances in geospatial technologies like remote sensing and geographical positioning systems have made it possible to determine soil moisture from space over large land areas. Satellites in space are able to predict soil moisture by taking images and using inbuilt algorithms to assess the soil moisture deficit. This is then used to inform irrigation scheduling. Recently, Kisekka et al. [29] compared in-situ soil moisture measurements and remotely sensed measurements. The authors concluded that remotely sensed soil moisture presents an effective means of soil-moisture-based irrigation scheduling in large agricultural fields.

3.3 Weather-based irrigation scheduling

Weather-based irrigation scheduling involves the use of weather sensors to monitor and measure the parameters that affect evapotranspiration. Automatic weather stations with temperature, humidity, wind speed, rainfall, and air pressure sensors are installed in the field to collect field data around the plant. The data from the weather sensors are then used to estimate water demand using evapotranspiration models. The Penman-Monteith evapotranspiration model is used to determine the daily water demand [30]. Irrigation is scheduled after a pre-determined amount of evapotranspiration has occurred and this threshold varies with soil type, crop type, and stage of growth.

4. Enabling technologies for smart irrigation

4.1 Communication technologies

4.1.1 Wireless sensor networks (WSN)

The advent of the industrial revolution and recent advances in electronics and wireless communications have led to the development of smart sensors with low power and cost solutions [31]. WSN provides a high spatio-temporal resolution for monitoring soil and crop parameters via wirelessly-connected sensor nodes installed across the field [32]. Table 1 presents some of the common wireless sensor communication technologies used in smart irrigation.

Technology	Description	References
Wi-Fi	Wi-Fi technology enables the wireless connection to a local area network through the Internet.	[33, 34]
ZigBee	Zigbee is an IEEE 802.15.4 standard-based wireless technology developed to enable personal wireless networks with low power radio signals	[35, 36]
Bluetooth	Bluetooth is a short-range wireless communication technology of standard IEEE 802.15.1 used to exchange data based on radiofrequency.	[37]
LoRa	LoRa is a long-range wireless technology that delivers low power and secure data transfer for machine-to-machine and Internet of Things applications. Founded by Semtech in 2012, LoRa is based on chirp spread spectrum (CSS) modulation, which has low power characteristics. LoRa supports the wireless connectivity of sensors, gateways, machines, devices, animals, humans etc., to the cloud across a range of 2–10 km.	[38, 39]
General Packet Radio Service (GPRS)	GPRS is a unique non-voice, rapid, packet-switching technology for worldwide system for mobile communications (GSM) networks. It permits the transmission of brief bursts and huge amounts of data, such as email and web surfing, via a mobile telephone network.	[39]
Ethernet	Ethernet technology allows devices to connect via a local or wide area network. It lets devices to interact with one other through a protocol, which is a set of rules or common network language.	[40, 41]
Fifth-generation wireless (5G)	5G is a fresh generation of cellular technology meant to speed up the responsiveness of wireless networks. It has the capacity to transport data at multigigabit rates hitting up to 20 Gb/s. 5G is effective in applications that demand real-time monitoring and control like precision irrigation.	[42]
Low-Power Wireless Personal Area Networks. (6LoWPAN)	6LoWPAN is a communication technology that offers a strategy for routing Internet Protocol version 6 (IPv6) across low-power wireless networks. It is created and supported by the Engineering Task Force (IETF), responsible for Internet standards.	[43, 44]
Message Queuing Telemetry Transport (MQTT)	MQTT is a lightweight, publish-subscribe network protocol for exchanging data between devices. It uses TCP/IP, although any network protocol that allows for ordered, lossless, bidirectional communications may support MQTT.	[45]

Table 1.

Most utilized communication technologies in smart irrigation.

WSNs allow the surveillance of plants and soil and may enhance production, efficiency, and profitability. Effective monitoring and communication helps to reduce risks due to climate aberrations, water shortages, insect infestation, and other factors unfriendly to agricultural growth and development. WSNs are helping to attain improved reaction times owing to real-time sensing and communication in agricultural contexts. There are numerous techniques for irrigation scheduling utilizing wireless sensors. Depending on the threshold levels of temperature and soil water content, the gateway permits automatic activation of the irrigation system. However, some of the sensors, modules, and valves that are commercially available for installation in an irrigation network are very sophisticated and costly to be implemented for managing stationary irrigation systems reference.

4.1.2 Internet of Things (IoT)

The IoT technology is the evolutionary phase of the Internet that builds a global infrastructure uniting devices and people [46]. IoT has its origins in numerous previous technologies: ubiquitous information systems, sensor networks, and embedded computers. Adelodun et al. [47] identified IoT as an interesting paradigm with seamless integration of smart capabilities into physical devices for context-oriented services. It has previously been effectively utilized in agricultural systems to monitor and manage environmental variables [48, 49, 50, 51]. The Internet of Things provides a platform for precision farming, digitally integrating several soil sensing devices and, context-aware sensors, custom devices. Data analytical implementations enhance farmers’ ability to resolve intricate agricultural issues such as soil preparation, water feed estimation, yield prediction, etc., throughout the whole growing and harvesting cycle. Several mechanistic irrigation scheduling systems have been presented employing most key farmed land parameters-soil moisture content and climatic data to predict the amount of water at certain time intervals. Campos et al. [51] built an IoT framework (Figure 5) to provide services for smart irrigation, such as data monitoring, pre-processing, fusion, synchronizing, storing, and irrigation management augmented by predicting soil moisture.

Figure 5.

IoT system for smart irrigation Adapted from [51].

4.2 Decision support systems

A decision support system for smart irrigation provides a framework for incorporating various tools and techniques for site-specific irrigation decisions. Commercial precision irrigation systems will thrive with improvement in robust and optimal decision support systems [52]. Decision support systems for irrigation scheduling/control can be categorized into two, namely: open-loop irrigation and closed-loop irrigation [14, 20].

4.2.1 Open-loop irrigation control

A couple of irrigation decision support systems schedule irrigation at predefined intervals and apply predefined irrigation volumes [52]. These decisions are not based on any sensor feedback on plant status, soil moisture status, or weather parameters. The decision to initiate an irrigation action is largely dependent on historical data and heuristics. This irrigation control strategy is inefficient which may lead to over or under irrigation, thereby wasting valuable water and fertilizer.

4.2.2 Closed-loop irrigation control

Closed-loop irrigation control is a control strategy where a mathematical model is used to make predictions about the future output. This is aided by real-time feedback from sensors that monitor the process. The model of the plant can either be a physics-based or a data-driven model. Whereas physics-based models are derived from the laws of physics, such as (conservation of mass, gravity, etc.), data-driven models are derived from the real-time dynamics of a system. A combination of physics-based and data-driven models is a gray-box model. Here a mechanistic model is developed from physics and during operation data from the system is used to update the model depending on the dynamics in the plant environment. Under closed-loop irrigation, irrigation scheduling decisions are made by micro-controllers by comparing a current state with the desired state. A closed-loop control algorithm helps to initiate an action of the actuators. Figure 6 is a schematic presentation of a closed-loop control system.

Figure 6.

Closed-loop control system.

Closed-loop control is further divided into intelligent, optimal, and linear control strategies. With advances in computing power, smart irrigation systems are able to make decisions in real-time, depending on the prevailing environment of the plant. A detailed explanation of closed-loop irrigation strategies can be found in [14, 20, 53, 54].

4.3 Cloud platforms

Monitoring for smart irrigation results in enormous amounts of data that needs to be stored for processing. Data is generated by weather, soil, and plant sensors in real-time at every time step. This data is transferred to a cloud-based platform for storage and processing. Some of the common cloud platforms used in smart irrigation studies include Thinkspeak (Figure 7), (MATLAB), FIWARE, Dynamo, and MongoDB, among others [49]. Thinkspeak is a cloud-based data platform for storage, visualization, and retrieving of sensor data. A microcontroller can be connected to this platform for processing.

Figure 7.

Thinkspeak clould platform, Source [55].

5. Smart irrigation and climate-risks

Deploying smart irrigation system may lower the overall irrigation water volume necessary to cultivate field crops in two ways. First, producers may eliminate non-cropped or marginal regions from water application, and second, producers can limit application rates in low-lying areas or soils with high water-holding capacity. Field installation of smart irrigation systems has demonstrated typical savings in irrigation water consumption of 8–20% [56] compared to uniform irrigation application. Using an intelligent irrigation system may assist minimize irrigation withdrawals while still maintaining a well-watered crop. This allows effective water usage and may lessen the likelihood of transient well failures during droughts. Having the machinery and availability to water for irrigation makes irrigated croplands less exposed to climate-related threats than their dryland equivalents. Smart irrigation decreases this danger by enhancing irrigation water-use efficiency and minimizing freshwater withdrawals to allow for more predictable water supply.

6. Smart irrigation and food security

Several studies carried out in different production systems and geographical areas worldwide have demonstrated the tangible benefits of smart irrigation systems over conventional irrigation practices. In the mild climatic conditions of Prince Edward Island, Afzaal et al. [57] measured crop productivity and water saving in potato production. The authors reported on the performance of a smart fertigation system and found significantly higher irrigation efficiency of the automated fertigation (1.42 kg/m³) than for the traditional drip irrigation control system (1.19 kg/m³). Thus, an automated drip irrigation system provides up to 26% water saving and high crop productivity compared to the conventional water application methods. Another study [58] compared a soil moisture sensor-based automated drip irrigation system with a non-automated drip irrigation system for nectarine that was irrigated when soil moisture content reached 70% of field capacity; the authors reported 43% more production compared to conventional irrigation methods. Belayneh et al. [59] reported increased yields and more water savings using a sensor-based irrigation scheduling approach compared to a time-based approach for old dogwood and redmaple trees.

7. Conclusion

Smart irrigation has gained significant attention as the need to improve water use efficiency increases. Smart irrigation involves data acquisition, interpretation, analysis, and control. Monitoring and control require sensors to collect real-time information for irrigation scheduling decisions. An irrigation control strategy has to be adopted for irrigation decisions. Closed-loop irrigation strategies with feedback from sensors are widely used for real-time irrigation decisions. Smart irrigation can save irrigation water and improve yield at the farm level, consequently leading to improved food security for the global population. In all the irrigation systems, it is possible to implement smart strategies that can help in saving irrigation water and improve yields.

Acknowledgments

This publication was made possible through support provided by the West African Centre for Water, Irrigation and Sustainable Agriculture (WACWISA), University for Development Studies, Ghana with funding support from the Government of Ghana and World Bank through the African Centers for Development Impact (ACE Impact) initiative. We would like to thank the unnamed reviewers and editor for their great contributions to this book chapter. Sincere gratitude to Rita Namoe Tabi for the helpful comments offered throughout the development of this work.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

Conceptualization, EB; methodology, EB; formal analysis, EB; investigation, EB; writing-original draft preparation, EB; writing-review and editing, FKA, GKA YK; visualization, FKA; supervision, FKA, GKA YK; project administration, FKA; funding acquisition, FKA, GKA YK. All authors have read and agreed to publish the paper.

Funding

This study was financed by Institute de Recherché pour le Development (IRD), AFD and the West African Center for Water, Irrigation and Sustainable Agriculture (WACWISA), University for Development Studies, Ghana.