Comparing Smart Irrigation Controllers for Turfgrass Landscapes

Evans, Shane R.; Kopp, Kelly L.; Johnson, Paul G.; Hopkins, Bryan G.; Dai, Xin; Schaible, Candace

doi:10.21273/horttech04985-21

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…This is a general figure and it is assumed that less water is wasted by trained sports field managers than uneducated residential lawn owners. Temporal misapplication of irrigation water can be reduced by the use of smart sprinkler systems, which consider local weather conditions in the scheduling of irrigation [22][23][24][25][26][27][28]. Precision turfgrass irrigation could also employ soil moisture sensors in existing sprinkler zones to determine when to apply water.…”

Section: Introductionmentioning

confidence: 99%

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Kerry,

Ingram,

Sanders

et al. 2024

Agronomy

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Turfgrass irrigation consumes a large amount of the scarce freshwater in arid/semi-arid regions. Approximately 50% of this irrigation water is wasted. It has been suggested that determining patterns of spatial variability in soil moisture to modify applications with valve-in-head sprinkler technology can greatly reduce waste. Variable rate irrigation (VRI) studies in traditional agricultural settings have shown that VRI zones do not stay static temporally and need to be frequently redetermined. Electrical conductivity (ECa) data from Geonics EM38 surveys and data from Red, Green, Blue (RGB) and Thermal Infra-Red (Th.IR) drone surveys are less time-consuming and therefore expensive to collect than a dense field survey of soil moisture and grass health to produce accurate geostatistical maps. Drone flights and ECa surveys are compared here for their ability to accurately estimate spatial patterns of soil volumetric water content (VWC) using simple linear regression and z-score transformations for prediction—non-geostatistical approaches that require less data. Overall, ECa readings collected in the horizontal mode were the most consistent at capturing spatial patterns in soil moisture. Predictions from regression produced lower root mean squared errors (RMSEs) for the larger datasets. However, z-score transformation produced lower RMSEs when the sample number was very small and preserved the scale of values better than the regression approach. The results suggested that predictions from ECa and drone data were useful for capturing key features in soil moisture patterns for 2–3 weeks, suggesting that a periodic reassessment of zones is needed. Using ECa and drone data in an urban environment is more labor-intensive than in an agricultural field, so it is likely that automating periodic re-surveying of ECa data for zone definition would only be cost-effective for golf courses or high-income sports fields. Elsewhere, using static zones with variable rates applied to each zone may be more efficient.

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Section: Introductionmentioning

confidence: 99%

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Kerry,

Ingram,

Sanders

et al. 2024

Agronomy

View full text Add to dashboard Cite

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“…Reference evapotranspiration (ET O ) calculated using local weather station data may be used to schedule irrigation frequency or determine the amounts of irrigation water applied to residential landscapes (Evans et al 2022). However, when the irrigation frequency and amounts of irrigation water decrease, the visual quality of landscape plants may be impaired because of an increase in the number of necrotic leaves and a reduction in floral abundance (Hartin et al 2018;Zollinger et al 2006) October 2023 33( 5)…”

mentioning

confidence: 99%

Climate-ready Landscape Plants: Garden Roses Trialed at Reduced Irrigation Frequency in Utah, USA

Chen,

Sun,

Oki

et al. 2023

hortte

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Increased urban and suburban populations in the arid western United States have resulted in more water demand; however, water availability in the region has become limited because of inadequate precipitation. Recent droughts have led to restrictions on irrigating landscape plants. Garden rose (Rosa ×hybrida) is commonly used as flowering plants in residential landscapes, but its drought tolerance has not been widely studied. The objective of this study was to determine the impact of reduced irrigation frequency on visual quality, plant growth, and physiology of five garden rose cultivars, including ChewPatout (Oso Easy® Urban Legend®), Meibenbino (Petite Knock Out®), MEIRIFTDAY (Oso Easy® Double Pink), Overedclimb (Cherry Frost™), and Radbeauty (Sitting Pretty™). Twenty-four plants of each rose cultivar were established in a trial plot at Utah Agricultural Experiment Station Greenville Research Farm (North Logan, UT, USA) in Summer 2021. Plants were randomly assigned to one of three deficit irrigation treatments for which irrigation frequencies were calculated using 80% reference evapotranspiration (ETO) (high), 50% ETO (medium), and 20% ETO (low). The total volumes of irrigation water applied to each plant were 345.6, 172.8, and 43.2 L for the high, medium, and low irrigation frequencies, respectively, during the deficit irrigation trial from 12 May to 30 Sep 2022. Root zones were wetted more frequently as irrigation frequency increased from low to high irrigation frequencies. Decreased irrigation frequency increased the number of visibly wilted and damaged leaves on all rose cultivars. However, only ‘Meibenbino’ and ‘MEIRIFTDAY’ exhibited a reduction in overall appearance under decreased irrigation frequency. The relative growth indices of both ‘Meibenbino’ and ‘MEIRIFTDAY’ decreased by 6%, whereas the dry weights of their leaves decreased by 37% and 36%, respectively, as irrigation decreased from high to low frequencies. Roses in this study appeared to decrease stomatal conductance up to 51% when irrigation decreased from high to low frequencies, or when air temperature increased. ‘Meibenbino’ and ‘MEIRIFTDAY’ exhibited unacceptable overall appearance, growth reduction, and higher leaf–air temperature differences, and they were less tolerant to reduced irrigation. Although the ‘Radbeauty’ maintained plant growth under the reduced irrigation frequency, the large leaf size led to a more visibly wilted appearance and the potential for heat stress, thus impairing visual quality. ‘ChewPatout’ and ‘Overedclimb’ were most tolerant to deficit irrigation at 20% ETO and maintained plant growth with acceptable visual quality and lower leaf temperatures when they received one irrigation during the growing season.

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Comparing Smart Irrigation Controllers for Turfgrass Landscapes

Cited by 2 publications

References 10 publications

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Climate-ready Landscape Plants: Garden Roses Trialed at Reduced Irrigation Frequency in Utah, USA

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