Irrigation Scheduling Methods: Overview and Recent Advances

Abstract

Applying irrigation at the right time and the correct amount is a challenge. Irrigation scheduling is a method of determining the appropriate amount of water to be applied to a crop at the correct time to achieve full crop production potential. Scheduling irrigation based on the weather, soil moisture, and plant data are reviewed. The advantages and challenges of each irrigation scheduling method are also discussed. In addition, innovative irrigation scheduling methods such as internet of things (IoT)-based on using wireless communication and smartphone app-based are described. In conclusion, the irrigation scheduling method has been continuously developed to be more accurate and precise. Integration of innovative technologies and techniques, such as IoT and machine learning, could be used to take the scheduling method to the next level.

1. Introduction

Water is an essential component of growing crops. Even in humid climates, precipitation is not enough to meet plant water requirements. Thus, additional water is applied through irrigation. Irrigation management can be complicated with unpredictable precipitation patterns. Not watering at the right time and correct amount can result in plant water stress and reduce the quality and yields of crops. On the other hand, over-watering can increase the risk of nutrients leaching below the root zone, waste resources (water, energy, and nutrients), and environmental impacts. Therefore, it is important to apply irrigation at the right timing and correct amount. Determining the appropriate amount of irrigation and the optimal timing of irrigation are challenging due to unpredictable weather conditions and climate changes.

Irrigation scheduling is a method of determining the appropriate amount of water to be applied to a crop at the correct time to achieve full crop production potential. Scheduling irrigation water has been based on the soil moisture measurement and/or weather data that are estimates of evapotranspiration.

This chapter reviews the various existing and recent advances in irrigation scheduling methods. The irrigation scheduling methods are:

• Feel and appearance.

• Gravimetric Method.

• Weather-based irrigation scheduling.

• Sensor-based irrigation scheduling.

• Plant-based irrigation scheduling.

• IoT sensor technology.

• Smartphone APP.

2. Irrigation scheduling method

2.1 Feel and appearance

The most popular and quickest method is based on the feel and appearance of the soil. A soil probe is typically used to take soil samples. Table 1 shows the soil moisture and appearance relationship. Table 1 shows an approximate relationship between field capacity and wilting point. The top of each soil type corresponds to the condition of zero soil moisture deficiency, also known as field capacity. The bottom of each soil type corresponds to the condition of maximum soil moisture deficiency, also known as wilting point. The soil moisture deficiency also presents the available moisture range of the soil. The table provides general numbers for a specific group of soils and may not apply to all soil groups. This method is not quantitative and is judged by the individual, which lacks precision.

Moisture deficiency in/ft	Loamy sand	Sandy loam	Loam	Clay loam
0	Leaves wet outline on hand when squeezed (field capacity).	Appears very dark, leaves wet outline on hand; makes a short ribbon (field capacity).	Appears very dark; leaves a wet outline on hand; will ribbon out about one inch (field capacity).	Appears very dark; leaves slight moisture on hand when squeezed; will ribbon out about two inches (field capacity).
0.2	Appears moist; makes a weak ball.	Quite dark color; makes a hard ball.	Dark color; forms a plastic ball; slicks when rubbed.	Dark color; will slick and ribbon easily.
0.4	Appears slightly moist sticks together slightly.	Fairly dark color, makes a good ball.	Quite dark, forms a hard ball.	Quite dark, will make a thick ribbon; may slick when rubbed.
0.6	Very dry, loose; flows through fingers. (Wilting point)	Slightly dark color, makes a weak ball.	Fairly dark, forms a good ball.	Fairly dark, makes a good ball.
0.8		Lightly colored by moisture, will not ball.	Slightly dark, forms a weak ball.	Will ball, small clods will flatten out rather than crumble.
1		Very slight color due to moisture. (Wilting point)	Lightly colored; small clods crumble easily.	Slightly dark, clods crumble.
1.2			Slight color due to moisture, small clods are hard (Wilting point)	Some darkness due to unavailable moisture, clods are hard, cracked. (Wilting point)

Table 1.

Relationship between soil moisture and appearance [1].

2.2 Gravimetric method

Soil moisture content is an important parameter for understanding the water movement in the soil. Taking soil samples is the direct method to measure the actual soil moisture level. This method requires weighing a sample of a known volume of soil and then reweighing it after drying in an oven at 105°C to calculate the mass of water lost by drying [2]. This method allows for calculating gravimetric water content (g/g) and soil bulk density (g/cm³). Multiplying the gravimetric water content by the soil bulk density allows for calculating the volumetric water content (cm³/cm³) [3]. The equation of volumetric water content is described in (Eq. (1)). Soil sample collection method is accurate, but it requires intense labor, time, and soil disturbance. Therefore, continuous soil moisture monitoring through soil sample collection on farmland can be difficult and limited.

$${\theta }_v=\frac{{\theta }_g\ast {\rho }_{\mathit{\text{soil}}}}{{\rho }_{\mathit{\text{water}}}}=\frac{\left(\left(\frac{\left(M_{\mathit{\text{water}}}-M_{\mathit{dry}}\right)}{M_{\mathit{dry}}}\right)\ast \left(\frac{M_{\mathit{dry}}}{V_{\mathit{\text{soil}}}}\right)\right)}{{\rho }_{\mathit{\text{water}}}}$$E1

Where,

${\theta }_v$ = Volumetric water content (cm³/cm³).

${\theta }_g$ = Gravimetric water content (g/g).

${\rho }_{\mathit{\text{soil}}}$ = Soil bulk density (g/cm³).

${\rho }_{\mathit{\text{water}}}$ = Density of water = 1 (g/cm³).

$M_{\mathit{\text{water}}}$ = Weight of wet soil (g).

$M_{\mathit{dry}}$ = Weight of dry soil (g).

$V_{\mathit{\text{soil}}}$ = Volume of soil sample (cm³).

2.3 Weather-based irrigation scheduling method

Weather-based irrigation scheduling method is based on the weather condition. Four major weather parameters determine evapotranspiration (ET), which drives the weather-based irrigation scheduling method. The weather parameters are solar radiation, air temperature, relative humidity, and wind speed. Higher the solar radiation, the greater ET. This is because sunlight is the main energy source for evaporating water. The warmer the air, the greater ET, because it can hold more water vapor. The drier the air, the greater the ET, because there is less water vapor it already holds. The greater wind, the greater the ET. In humid climate regions, solar radiation and air temperatures play a significant role in determining daily ET.

ET can be estimated in several ways. One method that is accepted as an international standard is the Penman–Monteith Equation, which is used to calculate the reference potential ET (rPET) using comprehensive weather data. The weather data includes net radiation, soil heat flux, average air temperature, wind speed, saturation vapor pressure, actual vapor pressure, the slope of vapor pressure curve, and psychrometric constant. rPET assumes a four grass-covered surface that is well-watered and unshaded. The actual ET of a crop at any given time depends on the amount of leaf area and the developmental stage, so to calculate the ET for a specific crop type, at a specific developmental stage, the rPET values must first be multiplied by a crop. Kc changes with crops as they grow, for example, the Kc of fruit trees increases rapidly in the spring as the trees leaf out to full canopy. Figure 1 shows the change of Kc as a soybean grows. To estimate actual crop ET, the rPET is multiplied by the crop coefficient Kc to determine the actual water lost from the crop via ET (see (Eq. (2)).

Figure 1.

Crop coefficient (kc) changes as the soybean grows.

$$E{T}_C=K_C\ast \mathit{\text{rPET}}$$E2

Where,

$E{T}_C=$Actual Crop Evapotranspiration (in/day).

$K_C=$Crop Coefficient (unitless multiplier).

$\mathit{\text{rPET}}=$Reference Potential Evapotranspiration (in/day).

Based on each day of the actual crop evapotranspiration, the suggested irrigation amount can be calculated to ensure that the soil has adequate moisture for plant growth and improve irrigation water use efficiency. For example, if last week’s cumulative actual crop evapotranspiration was 2.5 cm, the farmer should apply 2.5 cm of irrigation to maximize crop production and minimize environmental impacts.

2.4 Soil moisture sensor-based irrigation scheduling method

An alternative way to measure soil moisture content is using a soil moisture sensor. A typical soil moisture sensor estimates the volumetric water content (cm3/cm3) in soils. Soil moisture sensors allow monitoring the changes of soil moisture level over time without disturbing the soil. The sensors can be installed at multiple depths of soil to monitor the water flow in soil. In general, there are two types of soil moisture sensors. Soil tension sensors measure the required force for roots to pull water out of the soil. Volumetric water content sensor based on the electrical properties of the soil to estimate the soil moisture level. Knowing some common terminologies used in soil moisture sensor-based irrigation scheduling would be helpful in interpreting these sensor data.

The descriptions of some useful terminologies follow:

Saturation: All soil pore spaces are filled with water.

Field Capacity (FC): Maximum amount of water that soil can hold after drainage.

Wilting point (WP): Soil moisture level where there is no available water for the crop.

Available Water Capacity (AWC): The difference between FC and WP, is often expressed in inches of water per foot of soil. AWC of a soil is primarily related to the soil texture, organic matter content, and bulk density. The equation of AWC is shown in (Eq. (3)).

$$\mathit{AWC}=\frac{{\rho }_{\mathit{\text{soil}}}\ast T\ast P_W}{{\rho }_{\mathit{\text{water}}}\ast 100}$$E3

Where,

AWC = Available water capacity in inches.

${\rho }_{\mathit{\text{soil}}}$= Soil bulk density.

T = thickness of soil horizon under consideration in inches.

$P_W$ = Moisture content between field capacity and wilting point in percentage by weight.

${\rho }_{\mathit{\text{water}}}$ = Density of water = 1 (g/cm³).

Maximum Allowable Depletion (MAD): The amount of available water that can be safely depleted without causing drought stress, which depends on the crop and the growth stage.

Soil Matric Potential (SMP): Physical force required for the plant to move water into its root system.

2.4.1 Type of soil moisture sensors: Soil tension sensors

Soil moisture can be estimated by electrical resistance instruments. As the soil moisture content changes, the electrical properties of the soil also change. Electrical resistance sensing devices are sensitive to salts and fertilizer in the soil. A typical soil tension sensor is a solidstate electrical resistance sensing device that also measures soil matric potential (SMP). This type of sensor reads the resistance changes as the soil tension changes, which depends on the soil moisture content. A common soil tension sensor is WATERMARK, manufactured by the IRROMETER company (Riverside, CA, USA). The sensor measures from 0 to 239 kPa. The value of 0 kPa indicates that the soil reached saturation. The measurement of 239 kPa indicates that the soil is dry. Based on the tension (kPa) values, depletion in water holding capacity can be estimated. An example of depletion in water holding capacity for different soil types is described in Table 2. From the depletion values, irrigation recommendations and timing can be estimated to ensure the soil moisture level is at optimal condition for plant growth.

Soil texture	Depletion in water holding capacity (kPa)
	30%	50%	70%
Sand	20	30	60
Loamy Sand	25	40	67
Sandy Loam	28	50	80
Silt Loam	80	150	250

Table 2.

Soil matric potential (SMP) for 30%, 50%, and 70% of soil water depletion for different soil types [4].

2.4.2 Type of soil moisture sensors: Frequency domain reflectometry

Frequency domain reflectometry (FDR) sensors estimate soil water level using the dielectric properties of soil, which are highly dependent on moisture content. The changes in the dielectric permittivity correlate with changes in the circuit frequency, which also correlates with soil moisture level. As soil moisture content increases, the dielectric permittivity increases. Table 3 shows an example of dielectric permittivity for different materials.

Material	Dielectric permittivity
Air	1
Soil Minerals	3–7
Organic Matter	2–5
Ice	5
Water	80

Table 3.

Example of dielectric permittivity.

Common FDR sensors are the EC-5 and 10-HS manufactured by Meter Group (Pullman, WA, USA), which provide the value as volumetric water content (cm³/cm³). This sensor comes with factory calibration, which should be validated with the site-specific soil condition. A previous study described the correction equations of soil moisture sensors for different types of soils to improve the accuracy of the reading [5]. Most of the cases, the sensors provide the trends of moisture level changes, which allows understanding of how the water flows in soils. This information is helpful to determine the field capacity of the field and evaluate the irrigation practice, such as under- or over-irrigation.

2.4.3 Soil moisture sensor: Placement and installation

There are several considerations when installing soil moisture sensors. Sensors should be installed between plants in a representative area within a row. For the drip irrigation system, ensure to install the sensors in the wetting zone, as shown in Figure 2. The figure clearly shows the wetting zone in the soil. For center pivot irrigation systems, avoid placing the soil moisture sensors close to a wheel track or lane edge. Additionally, sensor locations that are not representative of most of the field, such as the top of the hill, low areas, and the edge of the field should be avoided. The placement of the soil depth of the sensors is important. The user should be considered based on the crop’s root depth (Figure 3). For example, the corn root system typically grows up to 36-inch soil depth. The recommended soil moisture placements are 6-, 18-, 24-, and 36-inch depths. Typical effective root zone moisture extraction depths for crops are described in Table 4 [6]. It is important to monitor these effective moisture extraction soil depths to improve the accuracy and precision of irrigation scheduling. Soil moisture sensors measure only a small volume of the soil surrounding sensor. Therefore, the sensor installation technique is critical in obtaining accurate readings.

Figure 2.

Demonstrated wetting zone under the drip irrigation system using a blue dye (Benton Harbor, MI, USA).

Figure 3.

Installed FDR soil moisture sensors in a blueberry field (west olive, MI, USA).

Crop	Effective root zone depth (inches)	Crop	Effective root zone depth (inches)
Alfalfa	36	Peppers	18
Asparagus	36	Potatoes	18
Apples	30	Pumpkins	24
Beets	18	Radish	6
Blueberries	18	Strawberries	6
Carrots	18	Sorghum	24
Corn	24	Soybean	24
Cucumber	18	Snap beans	18
Eggplant	18	Spinach	6
Grapes	36	Squash	24
Lettuce	6	Sweet Potatoes	18
Melons	24	Tomatoes	24
Onions – bunch	6	Watermelons	24
Peas	18	Wheat	24

Table 4.

Effective root zone water extraction depth in unrestricted soils [6].

2.5 Plant-based irrigation scheduling method

A common method of plant-based irrigation scheduling is using an sap flow sensor. Sap flow is the measurement of the water, nutrients, hormones, and anything else in the water that flows through the stem of a plant. The sensors use a heater and thermocouples to measure the amount of heat carried by the sap. This can then be converted to sap flow in units of grams per hour. Once the sensors are installed and the parameters are set, the system will record and calculate the sap flow, which can be downloaded from the system at any time. The sap flow sensor has been previously used in woody plants or other herbaceous plants [7, 8]. A common sap flow sensor is Dynagage Flow 32-1 K Sap Flow system, manufactured by Dynamax (Houston, TX). Figure 4 shows the installed Flow 32-1 K Sap Flow system in a potato field (Lakeview, MI). An example of sap flow sensor data is shown in Figure 5. The result shows that the transpiration started at 9:30 am and stopped at 8:00 pm. Based on the total amount of water used by the plant, irrigation scheduling can be developed to maintain adequate soil moisture levels for plant growth. This approach is similar to weather-based irrigation scheduling methods, but uses directly measured values of water uptake from the plant.

Figure 4.

Installed Dynagage flow 32-1 K sap flow system in a potato field (Lakeview, MI, USA).

Figure 5.

SAP flow data result from a potato field (Lakeview, MI, USA).

2.6 IoT (internets of things) sensor technology

Agricultural technology industry is moving toward Agriculture 4.0, which includes the internet of things (IoT) and the utilization of big data to improve practices and efficiencies. Many microcontroller systems, such as Arduino and ESP 32, can be used in agricultural fields. Analog or digital soil moisture sensors can be connected to a microcontroller system to measure soil conditions. In addition to the soil conditions, other irrigation information, including water pressure, energy usage, irrigation system uniformity, and environmental conditions, can be measured using a microcontroller system. Many microcontroller systems allow sending data to a web server using Wi-Fi, cellular, or long range radio (LoRa) network system. The advantages of the remote monitoring system are that it allows monitoring the performance of data loggers and sensors and detecting any problems without having to visit the field. It also allows farmers to make timely farm management decisions. Michigan State University team has developed LOCOMOS (IoT-based Low-Cost Sensor Monitoring System), which measures soil and environmental conditions in irrigated fields, including multiple depths soil moisture levels, leaf wetness duration, temperature, humidity, soil temperature, and precipitation. The data is collected every 15 minutes and sent to the LOCOMOS IoT cloud web service. Then the data output is displayed on the IoT dashboard website and a smartphone app. IoT-based irrigation management has been very positive to most farmers as they can see soil moisture status in realtime without visiting the field.

2.7 Smartphone APP-based irrigation scheduling

Numbers of smartphone apps are available for irrigation scheduling. A large number of irrigation scheduling decision support tools have been developed in recent decades, many of them available via mobile apps. For example, water irrigation scheduling for efficient application (WISE) was developed by Colorado State University as an irrigation scheduling mobile app that uses evapotranspiration data and the water balance method [9]. WISE collects weather data from Colorado Agricultural Meteorological Network (CoAgMet) and Northern Colorado WATER Conservation District (NCWCD) weather stations. Cotton SmartIrrigation App (Cotton App), developed by the University of Georgia and the University of Florida, is an evapotranspiration-based irrigation scheduling tool that estimates root zone. Soil water deficits based upon weather data, soil parameters, crop phenology, crop coefficients, and irrigation rates [10]. In addition to cotton, SmartIrrigation offers separate apps for vegetables, soybean, turf, avocado, strawberry, citrus, and blueberry [11]. The apps obtain meteorological data from Florida Automated Weather Network (FAWN) and Georgia Automated Environmental Monitoring Network (GAEMN). While these and other similar systems improve the ability to operationally monitor crop water needs, they primarily weather data-based and do not take in situ soil moisture observations or data into account. Within growing season, observations of soil moisture can be extremely useful in monitoring crop water usage and plant available water in the rooting zone. LCOOMOS-APP is developed by Michigan State University’s Irrigation Team, which is an easy-to-use smartphone app that accounts for in-field sensor data for determining irrigation management decisions. LOCOMOS- APP connects with a IoT cloud web server, which collects sensor data from each in-field LOCOMOS station. LOCOMOS-APP requires username and password to log-in. Once a user signs in, the app displays all registered devices to the specific user. When the user clicks a device, it will show available soil water (%) and recommended irrigation amount (in). Available soil water (%) value can help farmers when to irrigate. Recommended irrigation amount (in) value can help farmers how much to irrigate. Additionally, the app shows daily disease severity value (DSV) and cumulative DSV, and precipitation data. Cumulative DSV values can help farmers to apply fungicide in a timely manner. Overall, the advantage of the smartphone app-based irrigation scheduling tool is that it is an easy-to-use scheduling tool for farmers. This may increase the adoption of irrigation scheduling tools.

3. Conclusion

In conclusion, irrigation scheduling will be more important as the water resource is limited. Irrigation scheduling is critical to ensure optimum irrigation application. The benefits of applying the correct amount of water at the correct time are improved yield, reduced pumping costs, reduced nitrate leaching into groundwater or streams, improved soil health, and maximized return on investment. In the future, the incorporation of IoT sensor technology with AI Machine Learning techniques will be expected to increase the precision and accuracy of irrigation recommendations for different crop types.

Acknowledgments

Thank you to Michigan State University Irrigation Team, including Steve Miller, Lyndon Kelley, Brenden Kelley, Catherine Christenson, Allison Smith, and Hunter Hansen for their support on research and extension activities.

Conflict of interest

The authors declare no conflict of interest.