Matteo Serena & Bernd Leinauer New Mexico State University Las Cruces, NM

Wireless Moisture Sensor for Turfgrass Water Conservation

Wireless Moisture Sensor for Turfgrass Water Conservation

I. Introduction II. Research Study

a) Laboratory test b) Field evaluation

III. Results IV. Summary of conclusions

Strategies for Irrigation Water Conservation

1. Artificial Turf 2. Reduce area under

irrigation 3. Irrigation with

recycled/impaired water

4. Use of low water use turfgrass species

5. Accept quality reduction 6. Increase irrigation

efficiency I. Scheduling

a) Climate data b) Soil water status

II. Improve Water Distribution

A. ET Sensors

• Estimation of plant water requirements

• Accuracy can be affected by equation parameters

• Species have different crop coefficients

• Withholding irrigation is often determined as “best guess”

B. Soil Sensors

• Powerful tool as it relates directly to plant available soil moisture

• Accuracy can be affected by salinity • Dielectric sensors not only measure

moisture but also temperature and estimate salinity

Sensors

Minimum threshold

Maximum threshold

Irrigation

Soil Moisture Rain

Laboratory test • Precision soil sensors • pressure chambers • Known moisture content • Salinity

Laboratory test Test #

Water salinity (dS m-1)

Regression R2 P value

1 0.7 X= -3.64 + (0.78 * Y) 0.77 <0.001

2 0.1 X= -5.90 + (0.76* Y) 0.85 <0.001

3 0.7 X= -3.75 + (0.77 * Y) 0.89 <0.001

4 2.0 X= -9.04 + (0.91 * Y) 0.78 <0.001

y = 0.87x + 13.7 R² = 0.79

y = 0.86x + 15.6 R² = 0.78

0.0

10.0

20.0

30.0

40.0

50.0

60.0

0.0 10.0 20.0 30.0 40.0

Sensor #1

Sensor #2

• Track moisture changes accurately

• Not affected by salinity up to 4dS/m

Field evaluation • Two grasses:

Bermudagrass (Cynodon dactylon) tall fescue (Festuca arundinacea)

• Sprinkler irrigation, Toro® PrecisionTM Spray heads (7 X 7 m)

• Turfguard sensors at 5 and 20 cm (°T, %VWC ECb)

Field evaluation Four different irrigation scheduling systems: 1. Precision soil sensors 2. Climate Logic 3. Constant run time (Homeowner) 4. Historic ET

Toro PrecisionTM Series

Data collection • Turf quality, digital image analysis, Normalized

difference vegetation index (NDVI) • Water use (Flow meters) • Soil moisture • Root distribution

Results: quality (2012)

July August September October November December

Treatment Q NDVI Q NDVI Q NDVI Q NDVI Q NDVI Q NDVI

PSS 7.3 0.725 5.7 0.715 6.3 0.765 6.9 0.763 6.7 0.686 6.0 0.719

Climate Logic 6.7 0.681 5.7 0.691 6.3 0.730 7.1 0.741 7.0 0.728 7.0 0.748

Constant 7.0 0.693 6.5 0.706 7.8 0.805 6.6 0.777 7.0 0.759 7.3 0.757

ET 90% 7.0 0.716 6.0 0.724 6.7 0.764 6.3 0.818 7.0 0.804 7.7 0.783

Bermudagrass July August September October November December

Treatment Q NDVI Q NDVI Q NDVI Q NDVI Q NDVI Q NDVI

PSS 7.8 0.777 7.3 0.747 7.2 0.693 7.1 0.634 2.0 0.468 1.0 0.214

Climate Logic 7.7 0.774 7.0 0.731 7.7 0.699 6.8 0.648 2.0 0.427 1.0 0.212

Constant 7.8 0.754 7.7 0.769 8.0 0.726 7.3 0.683 1.7 0.474 1.0 0.205

ET 90% 7.5 0.788 7.7 0.750 7.5 0.740 7.2 0.655 1.3 0.468 1.0 0.207

Tall fescue

Results: water savings (2012)

- 61%

Results: water savings (2012)

- 52%

Results: water savings (2012)

- 46%

Results: water savings (2012)

- 52%

Results: soil moisture (2012)

Rain event, irrigation

withhold for 9 days Upper

threshold

Lower threshold

Results: soil moisture (2012)

Upper threshold

Lower threshold

Rain event, irrigation

withhold for 12 days

Summary 1. PSS accurately tracks soil moisture in a turf rootzone

2. Irrigation scheduled by PSS maintained both warm and

cool-season turf at a similar quality compared to the Climate LogicTM ET controller.

3. Irrigation scheduled by PSS resulted in water savings of 61% on tall fescue and 46% on bermudagrass when compared to a typical residential irrigation based on a constant run time

4. No significant differences in root biomass or root length density

In conclusion

• NMSU’s Office for Facilities and Services

NMSU’s Agricultural Experiment Station