Variable Rate Irrigation

Crop Insights written by Jeremey Groeteke1, Laura Dotterer2 and John Shanahan3


  • Most current center pivot irrigation systems apply water uniformly to fields, yet few fields are uniform, and many possess variable soils and topography. This results in inefficient utilization of water resources.
  • Variable rate irrigation (VRI) strategies have been developed, combining various innovative precision Ag tools, as a means to better tailor applications to match field variability for more optimal water utilization.
  • The steps for implementing VRI are relatively simple and are outlined and explained in this Crop Insights.
  • Once VRI is implemented for a given field, in-season monitoring of crop condition is recommended, using tools like remote sensing.
  • In-season monitoring can identify field areas where water application issues may exist (i.e., too much or too little water) leading to crop stress and thus confirm the accuracy of the VRI prescription.


Most center pivot irrigation systems currently in use today apply water at uniform rates to entire fields, yet very few fields are uniform. A field's inherent variability is due to many factors including topography and soil type. Thus, to optimize crop production and increase water-use efficiency, a method is needed for delivering irrigation water in optimal and precise amounts tailored to the needs of each acre over entire fields. Precision agriculture is a farming management concept that can be utilized in applying the right amount of water on the right acre, resulting in increased yields as well as more efficient water use especially in areas with limited water resources.

What is Variable Rate Irrigation (VRI)?

Variable rate irrigation (VRI) can be defined as site-specific management of water. VRI encompasses a combination of innovative precision ag tools that enables a center pivot irrigation system to optimize irrigation application. VRI technology allows growers to easily apply varying rates of irrigation water based on individual management zones within fields. Management zones are field areas possessing homogenous features for landscape and soil properties. These features lead to similar crop yield potential and input-use efficiency for seed, nutrients and water (Doerge, 1998) and can be created via processes described by Zhang et al. (2002). Control systems on center pivots allow the right amount of water to be applied to these management zones within the field.

Irrigation in corn field.

Water is applied to specific management zones via a prescription (Rx) using a sector or zone control VRI pivot system as shown in Figure 1 and Figure 2, respectively. A sector control system varies the speed of the pivot in each sector or pie wedge, with faster speeds reducing and slower speeds increasing application rate (Perry and Pocknee, 2003). Pivot speed typically changes every 6 degrees, allowing 60 slices in a pivot; however, some systems are capable of changing every 2 degrees. The zone control irrigation system (Figure 2) allows the pie wedges to be further broken into smaller zones, allowing for the creation of over 5,000 management zones within a field. Water is variably applied by varying the sprinkler control valve pulse rate in each management zone according to the VRI Rx. Thus, water application rates are matched with spatial variation in soil properties for each management zone.

Variable Rate Irrigation Steps

The steps for implementing VRI for a given field are as follows:

  1. Measure field variability via a soil electrical conductivity (EC) survey.
  2. Build the VRI prescription layer.
  3. Schedule irrigation.
  4. Monitor the field during the season.

Variable rate irrigation prescription for sector control system.

Figure 1. The variable rate irrigation prescription is created by using an EC GIS data layer.
The prescription is built for a sector control system.

Variable rate irrigation prescription for zone control system.

Figure 2. The variable rate irrigation prescription is created by using an EC GIS data layer.
The prescription is built for a zone control system.

Defining Field Variability and Management Zones

Soil property measurements, such as EC, are 1 method to delineate variability and define management zones within a field. EC measures the electrical current that a soil can conduct (Gunzenhauser et al., 2012). EC provides estimates of variations in the water-holding capacity, soil texture, cation exchange capacity (CEC), drainage, subsoil properties and salinity (Kitchen et al., 2003; Grisso et al., 2009). Shallow or topsoil electrical conductivity measures at depths of 0 to 12 inches; deep or subsoil EC measures 0 to 36 inches. Deep EC measurements (Figure 3) are used to describe the variation in soil water-holding capacity to aid in developing a VRI prescription.

Building Variable Rate Irrigation Prescription Layer

Once soil EC data are collected, they are then imported into Pioneer® Field360™ Studio software to be processed into visual layers as shown in Figure 3. The EC data are then processed through an algorithm to produce the VRI Rx as displayed in Figure 1 and Figure 2. After the prescription is constructed, manual adjustments to the prescription can be made. The prescription is then uploaded into the irrigation control system. Irrigation application rates and ranges are then varied based on the EC-based management zones for the field.

Map of electrical conductivity measurements.

Figure 3. A map of electrical conductivity measurements, showing
variability within a field.

Irrigation Scheduling

VRI changes the spatial distribution of water applied to a given field without affecting the amount and timing of application. A VRI prescription is built using a base application rate determined by the target rate typically applied to a given field. How much water to apply and when to apply it can be determined by several methods for estimating soil moisture content: 1) feel and appearance, 2) measurement of soil matric potential (gypsum blocks or Watermark® probes), or dielectric constant methods (time domain reflectometry or frequency domain reflectometry).

The Feel Method and/or Soil Moisture Probes - The feel method starts with collecting soil samples at varying depths using a soil sampling tool. Then take a part of the sample and form a ball in your hand. The wetness of the soil is determined from the cohesiveness of the ball (using a guide). Another characteristic to observe is if a finger imprint occurs after squeezing the ball. Next, create a "ribbon" of soil by squeezing it tightly (while feeding it forward) between your forefinger and thumb. Use of a guide is required to estimate the soil water content by the results of the feel test characteristics noted above (Center Pivot Irrigation Management Handbook, 2012).

There are 2 types of soil moisture probes that use different methods to measure soil water content. Gypsum blocks or electrical resistance blocks buried in the soil measure soil water content by soil moisture tension. Water is absorbed from the soil by the sensor's block material, such as gypsum. The absorbed water is then measured in the block using electrical contacts. The other type is the time-domain-reflectometry (TDR) probe, which directly measures soil moisture (Figure 4). The reflected electrical signals provide the soil moisture content as a percentage at multiple depths (Heiniger, 2013).

TDR probe (with rain gauge) installed in corn field.

Figure 4. A TDR probe (with rain gauge) that measures soil moisture
content at multiple depths. The probe is connected to a communication
device to remotely monitor the soil moisture status.

Multiple Variable Rate Irrigation Prescriptions? - Some growers may question whether there is a need for multiple VRI prescriptions for different application rates as crop water demands change. Generally VRI prescriptions are built using a base application rate of 1 inch. During the growing season, irrigation scheduling may call for a different application rate than 1 inch, depending on anticipated crop water needs. If 1 inch is too much water, the base application could be reduced to 0.75 inches by increasing the speed with sector control systems. Conversely, irrigation scheduling may call for more water to be applied - for example, 1.5 inches. Thus, the base increases to 1.5 inches and slows down the pivot speed. The VRI prescription is adjusted without building a new prescription. Table 1 shows adjustment of the 0 to 30 degrees of a prescription based on 6 degree increments.

Table 1. The changes in the variable rate irrigation prescription due to varying the base application rate. The speed of the pivot in this example alters the inches applied.


Angle (°) 0.75" Base
1.0" Base
1.5" Base
Start Stop Speed Inches Speed Inches Speed Inches
0 6 21 0.757 17 0.935 9 1.767
6 12 20 0.795 16 0.994 8 1.988
12 18 19 0.837 15 1.06 8 1.988
18 24 18 0.883 14 1.136 7 2.272
24 30 20 0.795 16 0.994 8 1.988

In-Season Field Monitoring of Crop Condition

To determine if a VRI prescription is accurate for a given field, in-season monitoring of crop condition should be considered. This monitoring can identify field areas where water application issues may exist (i.e., too much or too little water) leading to crop stress. Remote sensing, or collecting information about objects from remote platforms like satellites or aircraft, is 1 technology that may prove useful for monitoring crop condition (Gunzenhauser and Shanahan, 2013). This practice involves the collection and analysis of visible and near infrared (NIR) light reflected from the crop canopy. Remotely sensed imagery can be used to identify issues in a field, such as plant water stress, nitrogen deficiency and plugged irrigation nozzles.

NDVI-Green imagery showing sandy soils under stress.

Figure 5. In-season NDVI-Green imagery showing sandy soils in the Southwest under stress.

NDVIG (Normalized Difference Vegetation Index-Green) - The NDVIG (Figure 5) is an index that is well-suited to identify characteristics in the crop canopy when there is significant green tissue present past the V9 crop growth stage (Gitelson et al., 1996). The green and NIR light bands are used to calculate NDVIG. Typically, NDVIG is correlated to water stress, where low NDVIG values indicate that the plants are experiencing water stress.

The use of remote sensing imagery to monitor your fields during the growing season does not mean that you should stop scouting your fields. However, remote sensing can be used as tool to detect and direct scouting efforts in a field.


Center Pivot Irrigation Management Handbook, 2012. University of Nebraska-Lincoln Extension Publication. Lincoln, Nebraska.

Doerge, T. 1998. Defining management zones for precision farming. Crop Insights, Vol. 8, No. 21. Pioneer Hi-Bred, Johnston, Iowa.

Gitelson, A.A., Y.J. Kaufman, and M.N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289-298.

Grisso, R., M. Alley, D. Holshouser, and W. Thomason. 2009. Precision farming tools: Soil electrical conductivity. Virginia Cooperative Extension.

Gunzenhauser, B., J. Shanahan, and E. Lund. 2012. Utilizing on-the-go soil sensing devices to improve definition of management zones. Crop Insights, Vol. 22, No. 14. Pioneer, Johnston, Iowa. 

Gunzenhauser, B. and J. Shanahan. 2013. Use of remote sensing imagery for improving crop management decisions. Crop Insights, Vol. 23, No. 12. Pioneer, Johnston, Iowa.

Heiniger, R. 2013. Sensors and monitors for measuring soil moisture. North Carolina State Extension.

Kitchen, N. R., S. T. Drummond, E. D. Lund, K. A. Sudduth, and G. W. Buchleiter. 2003. Soil electrical conductivity and topography related to yield for three contrasting soil-crop systems. Agron J 95(3): 483-495. University of Nebraska-Lincoln. 2012. Center pivot management manual. University of Nebraska-Lincoln Extension Publication.

Perry, C. and S. Pocknee. 2003. Precision pivot irrigation controls to optimize water application. In: Understanding and addressing conservation and recycled water irrigation, the Proceedings of the 2003 irrigation association meeting. San Diego, Calif., pp 86-92.

Zhang, N., M. Wang, and N. Wang. 2002. Precision agriculture-a worldwide overview. Comput. Electron. Agric. 36:113-132.

1Pioneer Encirca Services - Business Unit Lead, Northern BU, Mankato, Minn.

2Pioneer Regional Agronomist - Emerging Leader, NA Production, Dysart, Iowa.

3Pioneer Agronomy Research Manager, Johnston, Iowa.

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The foregoing is provided for informational use only. Please contact your Pioneer sales professional for information and suggestions specific to your operation. Product performance is variable and depends on many factors such as moisture and heat stress, soil type, management practices and environmental stress as well as disease and pest pressures. Individual results may vary.