Irrigation scheduling in ornamental plant production is complex due to the large number of species grown by individual growers and the need to consider plant, environment, and substrate conditions to make correct irrigation decisions on a daily or more frequent basis. The engineering team in our project has developed a smart wireless sensor node that is capable of integrating outputs from a range of soil moisture and environmental sensors to schedule irrigation events. In addition, an advanced monitoring and control software enables growers to manage irrigation based on set-point or model-based protocols, which are then independently executed by the nodes, enhancing or replacing human decision making. During 2012, we implemented a sensor-controlled vs. grower-controlled irrigation study at a pot-in-pot nursery in Tennessee. Sensor networks were installed in two separate production blocks of 3-year-old dogwood (Cornus florida ‘Cherokee Brave’) and 2-year-old red maple (Acer rubrum ‘Autumn Blaze’) trees grown in 15- and 30-gal containers, respectively. One row of trees in each block was irrigated based on the average reading of soil moisture sensors inserted in individual trees using micropulse irrigation, i.e., sensor controlled. Trees in the adjacent row and the rest of the block were independently irrigated by the grower using standard practices, i.e., grower controlled. Sensor volumetric water content (VWC) readings and irrigation volumes were logged by nodes on a 15-min basis and were relayed to a base station on the farm. For the study period between Mar. 2012 and Nov. 2012, average daily water applied by the grower-controlled irrigation to the dogwood block was 0.92 gal/tree, compared with 0.34 gal/tree applied using sensor-controlled irrigation; for red maple, these values were 1.72 gal/tree and 1.13 gal/tree, respectively. No significant differences in tree caliper or quality were noted between the two irrigation treatments in either species over the year. The cost of water for this particular operation was negligible consisting only of pumping costs, as water is drawn from a perennial stream with excellent water quality. Consequently, a conservative return on investment for a wireless sensor network capable of covering the entire operation was 37.5%, corresponding to a payback period of 2.7 years, associated almost entirely from a reduction in irrigation management time. Pricing in a nominal cost for water of $326 per acre-foot ($1 per 1000 gal) increased annualized net savings 9-fold, reducing the payback period to less than 4 months. This analysis did not factor in additional economic benefits such as reductions in production time, losses due to disease, or increased plant quality, which have been associated with the use of sensor-based irrigation control in other studies.

Recycling irrigation reservoirs (RIRs) are an emerging aquatic ecosystem and water resource of global significance. This study investigated the vertical distribution of water temperature, dissolved oxygen (DO), and pH in eight RIRs at two nurseries each in Virginia and Maryland from 2011 to 2014. Monomictic thermal stratification was observed from April to October in all RIRs, despite their shallow depths (0.75-3.89 m). The strongest stratification had a top-bottom temperature difference of 21.53°C. The top-bottom temperature difference was positively correlated with water column depth, air temperature, and daily light integral (p < 0.05). Wind speed did not impact the thermal stratification, likely due to their relatively small surface areas. Thermal stratification affected the vertical distribution of DO and pH. The top-bottom differences in DO and pH were greater during stratification periods than nonstratification periods. Water pH in all RIRs was higher at the top than at the bottom with the greatest difference of 4.16 units. Discovery and characterization of thermal stratification in RIRs helps understand water quality dynamics in this novel ecosystem and promote safe and productive water reuse for irrigation. Specifically, water withdrawal depths should be adjusted according to variations in temperature, DO, and pH during the stratification and nonstratification periods to mitigate pathogen risk and improve water treatment efficacy and crop production.(KEY TERMS: recycling irrigation reservoir; thermal stratification; recycled water quality; dissolved oxygen; pH; water management.)

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Recycling irrigation reservoirs (RIRs) are an emerging aquatic ecosystem of critical importance, for conserving and protecting increasingly scarce water resources. Here, we compare water quality between runoff entrance and middle of four RIRs in nurseries in Virginia (VA) and Maryland (MD). Surface water temperature (T) and oxidation-reduction potential (ORP) were lower in the middle than at the entrance, while the trend was opposite for dissolved oxygen (DO), pH and chlorophyll a (Chla). The magnitude of these differences between the entrance and middle decreased with increasing depth. These differences were magnified by water stratification from April to October. Minimum differences were observed for electrical conductivity (EC), total dissolved solids (TDS) and turbidity (TUR). Cluster analyses were performed on water quality difference data to evaluate whether the differences vary with respect to reservoirs. Two clusters were OPEN ACCESSWater 2015, 7 3862 formed with one consisting primarily of VA reservoirs, and the other consisting mostly of MD reservoirs in both years. Water quality in the middle and at the entrance of RIRs was expected to vary greatly because of runoff inflow. The two-point water quality differences observed here, although statistically significant, are not large enough to cause significant impact on crop health and productivity for most water quality parameters except pH. Additional analysis of outlet data shows that the range and magnitude of water quality difference between the middle and the outlet are comparable to those between the middle and entrance of RIRs. These results indicate that monitoring at a single point is sufficient to obtain reliable water quality estimates for most water quality parameters in RIRs except pH. This is important when considering the cost of labor and equipment necessary for documenting water quality in agricultural production systems. However, additional pH measurements are still necessary to make practical water quality management decisions.