Georgia produces more than 3800 acres of tomato valued at more than $56 million annually (Wolfe and Stubbs, 2016). Tomato in Georgia are grown almost exclusively using plastic mulch with drip irrigation. In southwest Georgia, where considerable production occurs, groundwater resources are relatively abundant and growers often over irrigate. However, over irrigating tomato not only wastes water but also can reduce yields (Locascio, 2005).
Current recommendations for drip-irrigated tomato in Georgia and Florida are based on variations of the WB method (Harrison, 2009). The WB method estimates daily crop water use based on historic evapotranspiration (ETo) values for a region adjusted with a crop coefficient (Kc) (Allen et al., 1998). Current recommendations use a range of Kcs based on five stages of tomato growth (Dukes et al., 2015). An advantage of using the WB method is that it allows growers to anticipate crop water requirements at certain times during the growing season and plan irrigation accordingly. Irrigating solely based on predicted ETc values can be inaccurate because of changes in annual weather patterns and differences in production practices for which Kc were developed (Amayreh and Al-Abed, 2005).
In lieu of using the WB method, some growers may use a form of soil moisture–based irrigation. Personal observations by the authors suggest that the most common soil moisture–based method used is the “feel method,” where irrigation is started when the soil “feels” dry (Maynard and Hochmuth, 2007). Other methods of soil moisture–based irrigation may use tensiometers, granular matrix, or resistance-based sensors to determine thresholds for irrigation management (Cardenas-Lailhacar et al., 2010; Munoz-Carpena et al., 2005). Soil moisture sensor-based irrigation has been shown to be more efficient than a time-based system (Zotarelli et al., 2009a, 2009b, 2011). However, proper placement of sensors to accurately reflect conditions experienced by the plant can be challenging (Dabach et al., 2015). Furthermore, placement of sensors within an irrigation zone can be problematic for growers with heterogeneous soils or variable topography within a field. Irrigation thresholds may also be affected by factors such as soil type and depth of drip tubing (Coolong, 2016).
Recently, smartphone applications have been developed that scheduled irrigation using real-time weather data to calculate ETo [Smartirrigation App (University of Florida, 2012)]. These tools use meteorological parameters to determine irrigation schedules based on ETc calculated, using a Kc and ETo in the following relationship: ETc = ETo × Kc. The suite of applications can schedule irrigation for avocado (Persea americana), citrus (Citrus sp.), strawberry (Fragaria ×ananassa), cotton (Gossypium hirsutum), and several vegetables. The cotton smartphone irrigation–scheduling application resulted reductions in irrigation water use of 40% to 75% with concomitant 10% to 25% increases in yield in Georgia when compared with the WB method recommended for cotton (Vellidis et al., 2015).
The VegApp currently can be used to schedule irrigation for four vegetables, cabbage (Brassica oleracea), squash (Cucurbita pepo), tomato, and watermelon (Citrullus lanatus). Weather data are retrieved from the Florida Automated Weather Network or the University of Georgia Weather Network and are used to calculate ETo from air temperature, solar radiation, wind speed, and relative humidity measurements using the Penman–Monteith equation (Migliaccio et al., 2016). Each new field registered in the VegApp by a user is automatically associated with the closest weather station; however, the user has the option to select any of the other available weather stations. The VegApp uses ETo from the prior 5 d to calculate an average ETo. Then ETc is estimated using Kc curves developed by the University of Florida based on a days-after-transplanting (DAT) model of crop maturity (Dukes et al., 2015; Stanley and Clark, 2009). The Kc curve for tomato was developed for drip-irrigated crop grown with plastic mulch (Dukes et al., 2015; Stanley and Clark, 2009). The VegApp can provide an irrigation schedule for the subsequent week or longer. The irrigation schedule is provided to the user as an irrigation run time per day. Additional model variables used by the VegApp to schedule irrigation include crop, row spacing, irrigation rate, irrigation system efficiency, and planting date. The VegApp uses an ETo replacement model. It does not account for precipitation or soil types. It was designed for use with vegetables grown in a drip-irrigation and raised-bed plastic mulch production system (Migliaccio et al., 2016). Because the performance of the VegApp had not been evaluated in southern Georgia, the objective of this study was to determine the utility of the VegApp for tomato growers in Georgia. Water usage, tomato yield, and fruit quality were evaluated using the VegApp and accepted irrigation scheduling methods.
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