Abstract

Many ornamental plant growers water excessively to reduce the risk of drought stress. Scheduling irrigation in greenhouses is challenging because there is little quantitative information about ornamental plant water requirements and how water use changes when plants are grown in varying greenhouse environmental conditions. Models to estimate the daily water use (DWU) of greenhouse crops may provide a useful tool to conserve irrigation water. Our objective was to develop a model to predict DWU based on plant age and easily acquirable environmental data. Two petunia (Petunia ×hybrida) cultivars, Single Dreams Pink and Prostrate Easy Wave Pink, were grown in different sized containers (diameter = 10, 12.5, and 15 cm) to quantify their DWU for 6 weeks. The substrate water content (θ, v/v) was maintained at 0.40 m3·m−3 using an automated irrigation system with capacitance soil moisture sensors. Every irrigation event was recorded by a data logger, and this information was used to calculate the DWU of the plants. On overcast days early in the experiment, plants used only 4.8 to 13.8 mL·d−1. The maximum DWU of ‘Single Dreams Pink’ was 63, 96, and 109 mL·d−1 in 10-, 12.5-, and 15-cm containers, respectively. Late in the experiment, ‘Prostrate Easy Wave Pink’ petunia used more water than ‘Single Dreams Pink’ because of their more vigorous growth habit. DWU was modeled as a function of days after planting (DAP), daily light integral (DLI), vapor pressure deficit (VPD), temperature, container size, and interactions between these factors and DAP (R2 = 0.93 and 0.91 for ‘Single Dreams Pink’ and ‘Prostrate Easy Wave Pink’, respectively). Days after planting and container size were the most important factors affecting DWU and are indicative of plant size. Daily light integral was the most important environmental factor affecting DWU. These models, describing the DWU as a function of the DAP and environmental conditions, may be used as guidelines for accurately watering petunias in greenhouses and may improve irrigation scheduling.

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