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Publication #AE458

Evapotranspiration-Based Irrigation for Agriculture: Implementing Evapotranspiration-Based Irrigation Scheduling for Agriculture1

Isaya Kisekka, Kati W. Migliaccio, Michael D. Dukes, Jonathan H. Crane, and Bruce Schaffer2

This article is part of a series on ET-based irrigation scheduling for agriculture. The rest of the series can be found at http://edis.ifas.ufl.edu/topic_series_ET-based_irrigation_scheduling_for_agriculture.

Introduction

This publication is part of a series on ET-based irrigation scheduling in agriculture and outlines step-by-step procedures to follow when implementing a "smart" evapotranspiration (ET)-based irrigation schedule and a do-it-yourself irrigation schedule. A numerical example is provided for how to implement a do-it-yourself ET-based irrigation schedule. General information about the equations referenced in evaluating the different parameters for do-it-yourself irrigation scheduling are listed and explained in Evapotranspiration-Based Irrigation Scheduling for Agriculture at http://edis.ifas.ufl.edu/ae457.

Criteria for Selecting Smart ET-Based Controllers for Agricultural Applications

The first consideration when selecting an ET-based irrigation controller is the financial capital costs of some of the systems. Do-it-yourself ET irrigation systems require no additional up-front costs, but may require more capital with time, as effort must be given to obtaining ET data and updating the irrigation timer. Smart ET controllers, which use automated features for modifying ET, cost about $500 to install but require less effort over time because they automatically update their irrigation schedule.

If a smart ET controller is selected as the ideal choice, another decision must be made: smart ET controllers may be fully automated so that real-time updates are received through an annual subscription service (signal-based technology) ($50/year) or may be connected to on-site weather sensors.

Steps to Follow in Implementing a Signal-Based ET Irrigation Controller

Step 1: Choose a brand and have a supplier test the strength of the communication signal at the installation site. If the signal is weak or poor, purchase an antenna to boost reception.

Step 2: Before programming the ET controller, collect the following data.

  • Soil series name (this information can be obtained from the USDA-National Resources Conservation Service web soil survey at http://websoilsurvey.nrcs.usda.gov/app/HomePage.htm).

  • Management allowable depletion (MAD) based on local management practices. (If no specific information is available for MAD, 50% is typically used in Florida).

  • Plant type and crop coefficient Kc (these data are available from a nearby UF/IFAS Research and Education Center, provided the center is researching your crop). If no locally developed Kc exists for your crop of interest, you could use typical average values listed in Evapotranspiration-Based Irrigation for Agriculture: Crop Coefficients of Commercial Agricultural Crops in Florida at http://edis.ifas.ufl.edu/ae456.

  • Root depth measured on site.

  • Irrigation system characteristics (e.g., irrigation rate, irrigation system efficiency, and emitter type); some of this information can be obtained from the manufacturer's catalog.

  • Location-specific information, such as slope, microclimate (shaded or sunny all day), and ZIP code.

Step 3: Install an on-site rain sensor.

Step 4: Seek the services of a professional to properly install the ET-based controller.

Steps to Follow in Implementing an Onsite Weather-Based ET Irrigation Controller

Step 1: Choose a brand.

Step 2: Purchase and install controller system, including mini weather station.

Step 3: Before programming the controller, collect data noted in step 2 for signal-based ET controllers.

Step 4: Seek the services of a professional to properly install the ET-based controller.

Steps to Follow in Implementing a Historical Weather-Based ET Controller

Step 1: Choose a brand and purchase the ET controller.

Step 2: Before programming the controller, collect data noted in step 2 for signal-based ET controllers.

Step 3: Seek the services of a professional to properly install the ET controller.

Step-by-Step Guide for Implementing Do-It-Yourself ET-Based Irrigation Scheduling

  1. Obtain historical average monthly daily ETo and total average monthly precipitation (data is available from Florida Automated Weather Network or FAWN at http://fawn.ifas.ufl.edu/). See Evapotranspiration-Based Irrigation for Agriculture: Sources of Evapotranspiration Data for Irrigation Scheduling in Florida at http://edis.ifas.ufl.edu/ae455.

  2. Determine crop coefficient (Kc) for your crop. See Evapotranspiration-Based Irrigation for Agriculture: Crop Coefficients for Some Commercial Crops in Florida (http://edis.ifas.ufl.edu/ae456) for steps 2 and 3.

  3. Calculate crop evapotranspiration (ETc).

  4. Calculate effective precipitation (Pe). More information on the variables identified in steps 4 through 7 is provided in Evapotranspiration-Based Irrigation Scheduling for Agriculture at http://edis.ifas.ufl.edu/ae457.

  5. Calculate the net irrigation requirement (I).

  6. Calculate gross irrigation requirement (GI).

  7. Calculate the precipitation rate (PR) of the irrigation system by determining the flow rate and dividing it by the wetted area, soil water-holding capacity (SWHC), and root depth. (Typical SWHC values for some common soils in Florida can be found in Irrigation Scheduling for Tropical Fruit Groves in South Florida at http://edis.ifas.ufl.edu/tr001).

  8. Estimate irrigation run time per cycle or event, and adjust run times accordingly in the timer.

Example Do-It-Yourself ET-Based Irrigation Schedule

Consider an avocado orchard in South Florida (Homestead) with the following soil, plant type, weather data, irrigation system, and local management characteristics.

Soil characteristics: The soil series was obtained from the NRSC web soil survey at http://websoilsurvey.nrcs.usda.gov/app/HomePage.htm.

  • Krome gravely loam with a SWHC of 1.2 inches of water per foot of soil.

Plant characteristics: Plant data were obtained from UF/IFAS Tropical Research and Education Center:

  • Crop development stage: Assume mid-stage (September to December).

  • Crop coefficient Kc = 0.80.

  • Root depth: Effective root depth, assume 6 inches.

Weather data: These data were obtained from the closest FAWN weather station (http://fawn.ifas.ufl.edu/).

  • Historical monthly ETo averages (in/day) for Homestead FAWN station.

  • Historical average total monthly rainfall Pm (in/month).

Irrigation system characteristics: These data were obtained from a manufacturer's catalog and onsite measurements (assume the orchard is irrigated with micro-sprinklers).

  • Flow rate 24 gal/h (this should be measured on site).

  • Assume effective wetted diameter of 118 in (this should be measured on site).

  • Irrigation system efficiency, assume 80%.

Local management practices:

  • Assume a management allowable depletion (MAD) of 50%.

Step-by-step calculation

Step 1: Calculate historical average monthly and daily ETo and total average monthly precipitation (assume a 10-year data record from 1998 to 2008).

  • September ETo = 0.13 in/day and Pm = 4.2 in.

  • October ETo = 0.12 in/day and Pm = 3.5 in.

  • November ETo = 0.09 in/day and Pm = 2.4 in.

  • December ETo = 0.08 in/day and Pm = 1.9 in.

Step 2: Calculate crop evapotranspiration using Equation ETc = ETo*Kc.

  • September ETc = 0.11 in/day.

  • October ETc = 0.1 in/day.

  • November ETc = 0.09 in/day.

  • December ETc = 0.07 in/day.

Step 3: Calculate effective precipitation (P). General information on estimating P can be found in Evapotranspiration-Based Irrigation Scheduling for Agriculture at http://edis.ifas.ufl.edu/ae457.

  • September Pe = 2.8 in/month or 0.09 in/day.

  • October Pe = 1.8 in/month or 0.04 in/day.

  • November Pe = 0.7 in/month or 0.02 in/day.

  • December Pe = 0.83 in/month or 0.03 in/day.

Step 4: Calculate the net irrigation requirement I using the following equation:

Equation 1. 

  • September I = 0.04 in/day.

  • October I = 0.08 in/day.

  • November I = 0.08 in/day.

  • December I = 0.04 in/day.

Step 5: Calculate the gross irrigation requirement GI using the following equation and assuming 80% efficiency:

Equation 2. 

  • September GI = 0.05 in/day.

  • October GI = 0.1 in/day.

  • November GI = 0.1 in/day.

  • December GI = 0.05 in/day.

Step 6: Calculate irrigation run time per cycle/event.

Assuming the effective root zone is 6 inches deep and a 0.1 inch per inch soil water-holding capacity, the water storage capacity is 0.6 inches.

Irrigation precipitation rate = flow rate/effective wetted area:

Equation 3. 

Assuming a MAD of 50% or 0.3 inches irrigation depth:

  • September irrigation schedule = run irrigation system for ((0.3 inches/0.5 inches per hour) * 60 minutes per hour) is 36 minutes every (0.3 inches/0.05 inches per day) = 6 days

  • October irrigation schedule = run irrigation system for 36 minutes every 3 days.

  • November irrigation schedule = run irrigation system for 36 minutes every 3 days.

  • December irrigation schedule = run irrigation system for 36 minutes every 6 days.

Step 7: Adjust irrigation run times per month in the irrigation controller accordingly.

Conclusion

Based on the overall production goal and availability of resources (equipment and funds), either of the above approaches can optimize irrigation water use in agriculture. However, smart ET-based irrigation scheduling controllers minimize both labor and maintenance requirements.

Footnotes

1.

This document is AE458, one of a series of the Department of Agricultural and Biological Engineering, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. Original publication date January 2010. Revised February 2013. Visit the EDIS website at http://edis.ifas.ufl.edu.

2.

Isaya Kisekka, graduate student, Department of Agricultural and Biological Engineering, Tropical Research and Education Center, Homestead FL; Kati W. Migliaccio, associate professor, Department of Agricultural and Biological Engineering, Tropical Research and Education Center, Homestead, FL; Michael D. Dukes, interim chair, Environmental Horticulture Department, and professor, Department of Agricultural and Biological Engineering; Jonathan H. Crane, professor, Department of Horticultural Sciences, Tropical Research and Education Center, Homestead, FL; Bruce Schaffer, professor, Department of Horticultural Sciences, Tropical Research and Education Center, Homestead, FL; Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611.


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U.S. Department of Agriculture, UF/IFAS Extension Service, University of Florida, IFAS, Florida A & M University Cooperative Extension Program, and Boards of County Commissioners Cooperating. Nick T. Place, dean for UF/IFAS Extension.