Determining irrigation run times that minimize water use while sustaining optimal production is a difficult task for container nursery managers. This is particularly true for production in small containers as conditions in the irrigated area that affect evapotranspiration (ET) rates (e.g., plant growth, pruning, spacing) can change rapidly compared with microirrigated plants grown in large containers. Furthermore, the capture of sprinkler irrigation water can be greatly affected by the plant canopy so that crops with similar ET rates may require different irrigation rates (Million and Yeager, 2015). The dynamic natures of ET and irrigation capture provide great challenges for ET-based irrigation scheduling.
Several systems are being used to implement ET-based irrigation in container nurseries. One approach is to directly monitor substrate moisture content with sensors (Kohanbash et al., 2013; van Iersel et al., 2013). Sensors can be programmed to open solenoid valves when substrate moisture falls below critical values and then to close valves once favorable substrate moisture thresholds are reached. Sensor-based irrigation has the distinct advantage of directly monitoring the water status of the container substrate. A disadvantage of sensor-based irrigation is the cost for purchasing, installing, and maintaining a sensor network in the nursery. However, as the technology improves, this approach will become less expensive and easier to use and manage (Belayneh et al., 2013).
Another means for implementing ET-based irrigation, and one used in this research, is a software approach whereby substrate water loss is estimated with ET functions. A general function used to estimate ET for a wide range of agricultural crops entails multiplying potential ET (ETo) by a crop coefficient to determine actual ET (Schuch and Burger, 1997). Because crop coefficients depend on rapidly changing growing conditions, continuous functions have been developed to estimate actual crop ET rates (ETc) from ETo (Beeson, 2005, 2010; Grant et al., 2012; Irmak, 2005; Pardossi et al., 2008). For example, Beeson (2010) developed ET functions that use projected plant area and a growth index to estimate ETc from ETo. Million et al. (2011), using a partial cover function developed by Ritchie (1972) for field crops, estimated ETc as a function of ETo and leaf area index. Unlike field crop situations, partial cover in container nurseries can result in significant temperature increases when solar radiation not intercepted by the plant canopy heats nonradiating black container sidewalls and ground-cloth surfaces. To account for the heating effect, Million et al. (2011) used a biased maximum daily temperature in their ETo calculation.
Another source of variation when estimating the irrigation requirement using an indirect approach is the influence that the plant canopy can have on the capture of sprinkler irrigation water. We define the capture factor (CF) as the amount of sprinkler irrigation water captured by the container with a plant relative to that amount of water that would be captured without a plant. CF >1 indicates that the plant canopy is augmenting irrigation capture so that irrigation amounts can be reduced accordingly. Similarly, CF <1 indicates that the plant canopy is reducing irrigation capture and irrigation amounts would need to be increased accordingly. Functions for estimating CF based on plant size, container size and spacing, and the plant species’ water-capturing ability were reported by Million and Yeager (2015).
Using a software approach for indirectly estimating the irrigation requirement for sprinkler-irrigated container crops, daily irrigation run times can be output by estimating ETc and CF and then calculating the irrigation run time that will deliver the required amount of water to the container, taking into account any rain received. The advantage of a software approach is that no hardware is required except a weather station. A disadvantage of the software approach compared with a sensor-based system is that irrigation run times are indirectly estimated and therefore a degree of uncertainty always exists. Both approaches require labor to either monitor sensor function (sensor approach) or to monitor plant conditions in the irrigated area that affect ET and CF estimation (software approach).
The purpose of this study was to evaluate an irrigation management program, CIRRIG, for implementing ET-based irrigation in a container nursery. In the first section, we describe CIRRIG and discuss the required inputs to be monitored by nursery staff. In the second section, we describe how the program was used to automatically control an irrigation valve in a nursery and compare water savings of the program with the nursery’s traditional irrigation practice. Using results from the trial, we also evaluated the benefit of adopting a weather-based irrigation management program that adjusts run times daily compared with a periodically adjusted irrigation management practice.
Beeson R.C. Jr 2010 Modeling actual evapotranspiration of Viburnum odoratissimum during production from rooted cuttings to market size plants in 11.4-L containers HortScience 45 1260 1264
Belayneh, B.E., Lea-Cox, J.D. & Lichtenberg, E. 2013 Costs and benefits of implementing sensor-controlled irrigation in a commercial pot-in-pot nursery HortTechnology 23 760 769
Burt, C.M., Clemmens, A.J. & Strelkoff, K.H. 1997 Irrigation performance measures: Efficiency and uniformity J. Irrig. Drain. Eng. 123 423 442
GNU Image Manipulation Program 2015 GIMP V2.8. 30 June 2015. <http://www.gimp.org/>
Grant, O.M., Davies, M.J., Longbottom, H. & Harrison-Murray, R. 2012 Evapotranspiration of container ornamental shrubs: Modelling crop-specific factors for a diverse range of crops Irrig. Sci. 30 1 12
Kohanbash, D., Kantor, G., Martin, T. & Crawford, L. 2013 Wireless sensor network design for monitoring and irrigation control: User-centric hardware and software development HortTechnology 23 725 734
Million, J.B., Yeager, T.H. & Albano, J.P. 2010 Evapotranspiration-based irrigation scheduling for reducing runoff during production of Viburnum odoratissimum (L.) Ker Gawl HortScience 45 1741 1746
Million, J.B., Ritchie, J.T., Yeager, T.H., Larsen, C.A., Warner, C.D. & Albano, J.P. 2011 CCROP - Simulation model for container-grown nursery plant production Sci. Hort. 130 874 886
Pardossi, A., Incrocci, L., Incrocci, G., Tognoni, F. & Marzialetti, P. 2008 What limits and how to improve water use efficiency in outdoor container cultivation of ornamental nursery stocks Acta Hort. 843 73 80
Playan, E., Salvador, R., Faci, J.M., Zapata, N., Martınez-Cob, A. & Sanchez, I. 2005 Day and night wind drift and evaporation losses in sprinkler solid-sets and moving laterals Agr. Water Mgt. 76 139 159
Schuch, U.K. & Burger, D.W. 1997 Water use and crop coefficients of woody ornamentals in containers J. Amer. Soc. Hort. Sci. 122 727 734
University of Florida 2015 CIRRIG. 6 June 2015. <http://www.bmptoolbox.org/cirrig/>
van Iersel, M.W., Chappell, M. & Lea-Cox, J.D. 2013 Sensors for improved efficiency of irrigation in greenhouse and nursery production HortTechnology 23 735 746