Tomato production in the United States is dominated by the states of California and Florida (Costa and Heuvelink, 2005). In Florida, tomato total harvested area was 11.34 thousand ha with the production value of US$382 million in 2016 (USDA, 2017). In 2015, Florida fresh-market tomato was 36% of the total production value in the United States (FDACS, 2017). In 2015, tomato ranked fourth in value among all agricultural commodities (crops) in Florida, with orange, sugarcane, and floriculture in the first three positions, respectively (USDA, 2017).
Vegetable production requires adequate water supply throughout the production cycle for maximum yield and quality. Inadequate water management causes water stress for crop, resulting in a reduced plant growth and consequently reduced yield and postharvest quality in tomato (Kirda et al., 2004) and agronomic crops (Rowland et al., 2012). Nutrient supply is a key factor in crop production (Hochmuth and Hanlon, 2014); however, excessive irrigation application may reduce crop nutrient supply and increase contaminations (Zegbe et al., 2006; Zegbe-Dominguez et al., 2003, Zotarelli et al., 2009a). Excessive irrigation increases percolation, reducing water use efficiency and nutrient retention in sandy soils (Zotarelli et al., 2009b).
Irrigation scheduling methods are established procedures to determine the adequate irrigation volume and timing for a specific crop stage (Morgan et al., 2010) and can have a significant impact on the water and nutrient uptake and use efficiencies in tomato production (Zotarelli et al., 2009a). Therefore, proper irrigation scheduling could contribute to increase in crop yield as well as improve the economic viability of crop production.
There are several irrigation scheduling methods used in vegetable production in Florida, such as time-based scheduling (Migliaccio et al., 2008), schedule based on soil moisture (Zotarelli et al., 2009b), and ET scheduling based on weather information (Migliaccio et al., 2016; Morgan et al., 2006). Time-based scheduling results in lower water and energy use efficiencies compared with soil moisture- and ET-based scheduling because soil moisture conditions and real-time weather data are not considered in this schedule (Dukes, 2012; Kisekka et al., 2010; Migliaccio et al., 2008). One of the commonly adopted ET-based irrigation schedules is the use of long-term historical ET (historic ET-based schedule) by averaging weather data for a specific time period and area or location (Davis and Dukes, 2010). Because this scheduling method does not use real-time weather data, scheduling may not accurately represent the actual water requirement of a particular crop in a specific season and location.
SI is one of the most recent ET-based irrigation decision support systems that uses real-time weather data to estimate irrigation schedules for several crops grown in Florida (Migliaccio et al., 2014). SI Apps are ET-based models designed as smartphone applications using reference evapotranspiration (ETo) from FAO Penman-Monteith (Allen et al., 1998) procedure and crop coefficient (Kc) to determine crop water requirement (Migliaccio et al., 2016). In addition, SI Apps (SI App for several crops in Florida and Georgia are available for download at http://smartirrigationapps.org/) have the ability to reduce user calculation error or misplaced irrigation records and timing. The smartphone irrigation apps are not only effective in reducing crop irrigation volume but also can significantly increase crop yield (Vellidis et al., 2014).
Although the effects of irrigation amount, frequency, and scheduling methods on tomato have been extensively studied, none of these focused on determining the effects of real-time, site-specific irrigation on productivity (Kirda et al., 2004; Patanè and Cosentino, 2010; Zegbe-Dominguez et al., 2003; Zotarelli et al., 2009a). In recent years, there has been an increasing trend of more studies focusing on improved tomato water use without compromising yield and fruit quality (Nangare et al., 2016; Patanè et al., 2011; Topcu et al., 2007). Therefore, the objective of this study was to compare the efficacy of an ET-based, real-time, and location-specific irrigation scheduling to irrigation schedules base on historic ET information in open-field, fresh-market tomato production. Tomato plant biomass accumulation, nutrient uptake and accumulation, and yield were used to determine the efficacy of the two ET-based irrigation scheduling methods. This study hypothesized that based on crop performance and irrigation water savings, irrigation based on location-specific and real-time weather information improves irrigation scheduling accuracy in relation to actual crop water requirement in tomato crop compared with a schedule that is based on historical weather data.
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