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Plant root growth affects FDR soil moisture sensor calibration
Scientia Horticulturae 252 (2019) 208–211
Contents lists available at ScienceDirect
Scientia Horticulturae journal homepage: www.elsevier.com/locate/scihorti
Short communication
Plant root growth affects FDR soil moisture sensor calibration a
b
Seonghwan Kang , Marc W. van Iersel , Jongyun Kim a b c
c,⁎
T
Department of Biosystems and Biotechnology, Korea University, Seoul, 02841, Republic of Korea Department of Horticulture, University of Georgia, Athens, GA, 30602, USA Division of Biotechnology, Korea University, Seoul, 02841, Republic of Korea
ARTICLE INFO
ABSTRACT
Keywords: Frequency domain reflectometry Capacitance sensor Lettuce Automated irrigation Water management
To acquire accurate volumetric water content (VWC) measurements from horticultural substrates using dielectric sensors, a substrate-specific calibration is critical. Calibrations typically are conducted with a substrate without plants, but water in the root system may affect soil moisture sensor readings. We investigated the effect of root growth on the measured VWC. Lettuce seedlings (Lactuca sativa L. ‘Joek Chi Ma’) were grown in 10 cm round containers (440 mL) filled with soilless substrate. Four EC-5 soil moisture sensors (Decagon Devices Inc., Pullman, WA, USA) were used to determine the effect of root system size on sensor calibration over an eight week period. Both calibration coefficients (slope and intercept) decreased (P < 0.001) with increasing root system size. The actual VWC at 8 weeks after transplanting (WAT) was 9% lower at 65% VWC compared to the estimated VWC from the substrate-only calibration. Multiple regression models indicated that various root size indicators (WAT, root fresh weight, root dry weight, and root water content) all had a negative effect on the estimation of VWC. The effect of root system size on estimated VWC may be tolerable in production, but should be considered in research applications. Careful interpretation is needed when using FDR soil moisture sensors to monitor substrate VWC in the presence of a growing root system.
1. Introduction Soil moisture sensors enable precise irrigation of plants based on their actual water needs (Chappell et al., 2013; Lichtenberg et al., 2013). The sensors, along with a datalogger and solenoid valves, can provide growers with information like the number of irrigation events, amount of water used by irrigation, and the current level of soil moisture (Kim et al., 2014; Lea-Cox et al., 2013). Dielectric sensors, and FDR (frequency domain reflectometry) sensors in particular, can provide an estimate of the volumetric water content (VWC) of the soil and/ or substrates, and are suitable for horticultural use due to their low cost, easy maintenance, durability, and reliability (Jones, 2004; van Iersel et al., 2013). Therefore, FDR sensor-based, automated irrigation systems have been used for research into efficient irrigation (Bayer et al., 2013; Burnett and van Iersel, 2008; Nemali and van Iersel, 2008; Thompson et al., 2007). FDR sensors measure the dielectric constant of substrates, which is a function of water content (Cobos and Chambers, 2010). To get accurate readings, it is essential to calibrate the sensor to convert the measured dielectric constant to the actual VWC (Sakaki et al., 2011). Sensor calibration may depend on sensor and substrate type, bulk density, elec-
⁎
trical conductivity (EC), and soil temperature (Kizito et al., 2008; Limsuwat et al., 2009; Nemali et al., 2007; Rhie and Kim, 2017). FDR sensor calibration is generally done using the substrates without a plant. However, the dielectric constant depends on the amount of water in the sphere of influence of the sensor and is affected both by water in the substrate and the roots. Thus, the size of the root system may affect the FDR sensor readings and this effect can change as roots grow. We investigated how FDR sensor calibration changes in the presence of a growing lettuce root system. We compared the actual VWC with estimated VWC from the substrate-only calibration to quantify the effect of root system growth on the calibration coefficients. 2. Materials and methods 2.1. Plant material Lettuce (Lactuca sativa L. ‘Joek Chi Ma’, Asia Seed Company, Seoul, Korea) seeds were sown in two 128-cell plug trays with a germinating substrate (Sunshine Mix #5, Sun Gro Horticulture, Agawam, MA, USA). During germination, air temperature, relative humidity, and daily light
Corresponding author. E-mail address: [email protected] (J. Kim).
https://doi.org/10.1016/j.scienta.2019.03.050 Received 30 November 2018; Received in revised form 26 February 2019; Accepted 23 March 2019 Available online 01 April 2019 0304-4238/ © 2019 Elsevier B.V. All rights reserved.
Scientia Horticulturae 252 (2019) 208–211
S. Kang, et al.
integral (DLI) were 14.3 ± 1.1 °C, 41.8 ± 9.5%, and 10.6 ± 2.3 mol·m2·d−1, respectively. After a month, 100 seedlings of similar size were transplanted into 10-cm round plastic containers (440 mL) filled with a soilless substrate (Sunshine Mix #4, SunGro Horticulture) and grown in a glass greenhouse for eight weeks. Air temperature, relative humidity, and DLI in the greenhouse averaged 22.4 ± 2.7 °C, 49.0 ± 9.0%, and 12.5 ± 5.0 mol·m2·d−1. Plants were irrigated daily with tap water. 2.2. FDR soil moisture sensor calibration Four FDR soil moisture sensors (EC-5, Decagon Devices) were used to conduct the VWC calibrations in the presence of plant roots. Each sensor was connected to a CR1000 datalogger (Campbell Sci.), with an excitation voltage of 2.5 VDC. Twenty plants were used biweekly to conduct sensor calibrations. To achieve different VWC levels in the containers, plants were irrigated with different amounts of water (from 20 to 400 mL per pot) 2 or 3 d before the calibration procedure, inducing a range of VWC from 10% to near saturation. At the time of calibration, the shoots of the plants were removed. Then, four soil moisture sensors were inserted one after another toward the center of each pot with the intact root system. Sensor voltage output was recorded by the datalogger. Then, the wet substrate plus root weight of each pot was measured. To measure root fresh weight, roots were separated from the substrates. The substrates were dried at 105 °C in a drying oven, and dry substrate weights were measured to calculate VWC. To calibrate the sensors, the VWC (%, v/v) was calculated as:
VWC including root system (%)=
[Wet substrate weight (g)
Dry substrate weight (g)
Root dry weight (g)] Container volume (mL)
× 100
These values were then plotted against sensor output and calibration coefficients were determined using linear regression. Calibration with the substrate only was performed using without roots:
VWC (%) =
Fig. 1. Root system size (root fresh weight, root dry weight, and root water content) of Lactuca sativa L. ‘Jeok Chi Ma’ during the eight week growing cycle.
Wet substrate weight (g) Dry substrate weight (g) × 100 Container volume (mL)
3. Results 3.1. FDR sensor VWC calibration with different levels of root system growth
followed by linear regression. At each harvest, root fresh and dry weight were measured. Root water content was calculated by subtracting root dry weight from root fresh weight.
Root fresh weight, root dry weight, and root water content increased as WAT increased, and the root growth parameters of lettuce showed a quadratic relationship with WAT values (Fig. 1). The soil moisture sensor outputs and VWC of the substrates had a strong linear relationship regardless of the WAT (P < 0.001, r2 > 0.97) (Table 1). When data from all WAT were combined in the multiple regression, the interaction between the slope and WAT value was significant (P = 0.005) (Fig. 2), indicating a change in calibration
2.3. Experimental design and statistical analysis The experiment was conducted with a completely randomized design. The relationships between WAT value and lettuce root growth were analyzed using regression analysis with SAS 9.4 (SAS Inst., Cary, NC, USA). To acquire each FDR soil moisture sensor’s calibration coefficients for different root system sizes, regression analysis between sensor output (mV) and actual VWC (in %VWC) was conducted for each sampling time. Multiple regression analysis was conducted to determine the relationship of WAT value, root fresh weight, root dry weight, and root water content on VWC calibration with different levels of root growth (proc REG in SAS 9.4; SAS Inst.). In multiple regression analyses, VWC was the dependent variable, and the FDR sensor output and a potential root growth indicator (WAT, root fresh weight, root dry weight, or root water content), and their interaction were independent variables. Partial R2 values were acquired from the multiple regression analysis to quantify the contribution of each variable to the calibration equation.
Table 1 EC-5 soil moisture sensor calibration coefficients for horticultural substrate in the presence of a lettuce root system at 2, 4, 6, and 8 weeks after transplanting and without plant root system. Volumetric water content (v/v, %) = β1 × sensor output (mV) + β2.
209
Weeks after transplanting
β1
β2
r2
Substrate only 2 4 6 8
0.1771 0.1763 0.1747 0.1741 0.1701
−52.968 −53.317 −53.695 −55.228 −57.561
0.993 0.983 0.972 0.984 0.994
Scientia Horticulturae 252 (2019) 208–211
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multiple regression was conducted using variables that indicate the size of the root system (WAT, root fresh weight, dry weight, or root water content) along with sensor output (V) as a variable. In all these regression analyses, sensor output was positively correlated with VWC (P < 0.001) and explained 95.8% of the variability in the data (Table 3). The interaction between root size indicators and sensor output was significant (P < 0.001), but had a low partial R2. The interaction between sensor output and WAT value had the highest partial R2 (0.024), while the interaction between sensor output and root dry weight had the lowest partial R2 (0.015). These significant interactions indicated that root growth affected the soil moisture sensor reading. 4. Discussion 4.1. VWC calibration with root growth over time Root system-induced errors in VWC estimates may be due to two reasons. Firstly, an increase in bulk density and decrease in total porosity due to an increase in root system volume may affect sensor readings. The bulk density of substrates affects FDR soil moisture sensors (Mendes et al., 2014) and the total porosity (air space + water space) in peat substrate may be decreased by root growth (Cannavo et al., 2011). Secondly, the water in the root system can directly affect FDR readings, because these sensors measure total dielectric within their sphere of influence and cannot distinguish between water in the root system and water in the substrate. Calibration equations obtained from substrate without plants have been used in many previous studies that used automated irrigation system-based soil moisture sensors (Burnett and van Iersel, 2008; Kim et al., 2012; Rhie and Kim, 2017). Bell et al. (1987) reported that a capacitance sensor created anomalous results when contact was introduced between the sensor prong and the roots, but they did not quantify the effect of root system growth on sensor measurement. We showed that the calibration slope and y-intercept decreased with increasing root system size (Table 1 and Fig. 2). When the substrate VWC was estimated as 50% with substrate-only calibration, the actual VWCs estimated from the adjusted calibrations for 2, 4, 6, and 8 WAT were 49.2, 47.9, 46.0, and 41.3%, respectively, indicating likely overestimation of the VWCs with substrate-only calibration. Comparison between estimated VWC and actual VWC at specific WATs showed a similar result with the slope and y-intercept decreasing as the WAT increased (Table 2 and Fig. 3). The difference between estimated VWCs from substrate-only calibration and actual VWCs becomes larger as the WAT increases. Although we only tested FDR sensors, it seems likely that time-domain reflectometry and time-domain transmissivity sensors would respond similarly to the presence of roots, since all these sensors measure the dielectric within their sphere of influence. Likewise, neutron probes, whose readings depend on the amount of water molecules in the volume of influence of the sensor, will likely be affected by water in the roots. This should be considered when precise measurements of VWC are required. Since the presence of roots increases the measured VWC values, this can create problems when irrigation is based on FDR sensor readings. Overestimated VWC values may negatively influence crop production due to unwanted and undetected drought.
Fig. 2. Substrate volumetric water content calibration of horticultural substrates with EC-5 soil moisture sensor at 2, 4, 6, and 8 weeks after transplanting of lettuce and substrate only without plant root system. EC-5 soil moisture sensors were connected to a CR1000 datalogger with 2.5-VDC excitation voltage. See Table 1 for regression coefficients. Table 2 Calibration coefficients of the regression analyses between actual volumetric water content (VWC, %) and VWC estimation from substrate-only calibration at 2, 4, 6, and 8 weeks after transplanting of lettuce seedlings. Actual volumetric water content (v/v, %) = β1 × VWC from substrate-only calibration (%) + β2. Weeks after transplanting
β1
β2
r2
2 4 6 8
0.9953 0.9866 0.9828 0.9604
−0.5964 −1.4383 −2.9261 −6.6913
0.983 0.972 0.984 0.994
Fig. 3. Comparison of estimated VWC from substrate-only calibration with actual VWC at 0, 2, 4, 6, and 8 weeks after transplanting lettuce. See Table 2 for regression coefficients.
over time. As the WAT value increased from 0 to 8, both the slope and y-intercept of the calibration equation decreased. To understand the error in VWC values induced by root growth, the estimated VWC values from the substrate-only calibration equation and the actual VWC were compared through regression analyses (Table 2 and Fig. 3). These analyses indicated that errors in the estimated VWC from substrate only calibration increased over time (P = 0.004).
4.2. Results of multiple regression with sensor output and root growth The sensor output was affected by the size of the root system, as quantified by WAT, root fresh weight, root dry weight, or root water content. An increase in root system size results in increasingly larger underestimations of the true VWC, presumably because of the water in the roots. In the multiple regression analyses, the interaction between sensor output and root growth indicators that include the moisture in the roots (root fresh weight and root water content) had a higher partial
3.2. Root impact on VWC calibration with multiple regression To investigate the effect of root growth on estimating actual VWC, 210
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Table 3 Multiple regression coefficients (β) and partial R2 values for variables used in estimating substrate volumetric water content (VWC) with lettuce roots. Variables were sensor output (V), a root growth indicator (either weeks after transplanting, root fresh weight, root dry weight, or root water content), and their interaction. VWC (%) = β var1 × var1 + β var2 × var2 + β interaction × var1 × var2 + β intercept. Variables Var1
Var2
β
Sensor output (V)
Week after transplanting
185.4 *** 183.3 *** 181.2 *** 183.3 ***
Root fresh weight (g) Root dry weight (g) Root water content (mL) ***
Partial R2
Regression coefficients and P-value var1
β
var2
– – – –
β
interaction
−2.268 *** −0.823 *** −6.916 *** −0.917 ***
β
intercept
−55.06 *** −55.60 *** −54.47 *** −55.69 ***
Adjusted R2
Var1
Var2
Interaction
0.958
–
0.024
0.981
0.958
–
0.020
0.978
0.958
–
0.015
0.973
0.958
–
0.020
0.978
indicates significance at P < 0.001, - indicates no significance.
R2 than root dry weight. FDR soil moisture sensors take advantage of the high dielectric constant of water (˜80.4) compared to that of air (˜1) or soil/substrate (˜2 to 8) (Nemali et al., 2007), but they cannot distinguish between the water in the soil/substrate and that in the root. Although the partial R2 of the interaction term between the sensor output and root growth indicator was low, not accounting for this effect resulted in an error in the calculated VWC of up to 8.7%.
Bell, J.P., Dean, T.J., Hodnett, M.G., 1987. Soil moisture measurement by an improved capacitance technique, part II. Field techniques, evaluation and calibration. J. Hydrol. (Amst) 93 (1), 79–90. Burnett, S.E., van Iersel, M.W., 2008. Morphology and irrigation efficiency of Gaura lindheimeri grown with capacitance sensor-controlled irrigation. HortScience 43 (5), 1555–1560. Cannavo, P., Hafdhi, H., Michel, J.-C., 2011. Impact of root growth on the physical properties of peat substrate under a constant water regimen. HortScience 46 (10), 1394–1399. Chappell, M., Dove, S.K., van Iersel, M.W., Thomas, P.A., Ruter, J., 2013. Implementation of wireless sensor networks for irrigation control in three container nurseries. HortTechnology 23 (6), 747–753. Cobos, D.R., Chambers, C., 2010. Calibrating ECH2O Soil Moisture Sensors. Application Note. Decagon Devices, Pullman, WA. Jones, H.G., 2004. Irrigation scheduling: advantages and pitfalls of plant-based methods. J. Exp. Bot. 55 (407), 2427–2436. Kim, J., Malladi, A., van Iersel, M.W., 2012. Physiological and molecular responses to drought in Petunia: the importance of stress severity. J. Exp. Bot. 63 (18), 6335–6345. Kim, J., Lea-Cox, J., Chappell, M., van Iersel, M.W., 2014. Wireless sensors networks for optimization of irrigation, production, and profit in ornamental production. Acta Hortic. 1037, 643–650. Kizito, F., Campbell, C.S., Campbell, G.S., Cobos, D.R., Teare, B.L., Carter, B., Hopmans, J.W., 2008. Frequency, electrical conductivity and temperature analysis of a low-cost capacitance soil moisture sensor. J. Hydrol. (Amst) 352 (3), 367–378. Lea-Cox, J.D., Bauerle, W.L., van Iersel, M.W., Kantor, G.F., Bauerle, T.L., Lichtenberg, E., King, D.M., Crawford, L., 2013. Advancing wireless sensor networks for irrigation management of ornamental crops: an overview. HortTechnology 23 (6), 717–724. Lichtenberg, E., Majsztrik, J., Saavoss, M., 2013. Profitability of sensor-based irrigation in greenhouse and nursery crops. HortTechnology 23 (6), 770–774. Limsuwat, A., Sakaki, T., Illangasekare, T.H., 2009. Experimental quantification of bulk sampling volume of ECH2O soil moisture sensors. Proc. Annual American Geophysical Union Hydrology Days Vol. 29, 39–45. Mendes, L.B., Li, H., Xin, H., do Nascimento, J.W.B., 2014. Evaluation of EC-5 soil moisture sensors for real-time determination of poultry manure or litter moisture content. Appl. Eng. Agric. 30 (2), 277–284. Nemali, K.S., van Iersel, M.W., 2008. Physiological responses to different substrate water contents: screening for high water-use efficiency in bedding plants. J. Am. Soc. Hortic. Sci. 133 (3), 333–340. Nemali, K.S., Montesano, F., Dove, S.K., van Iersel, M.W., 2007. Calibration and performance of moisture sensors in soilless substrates: ECH2O and Theta probes. Sci. Hortic. 112 (2), 227–234. Rhie, Y.H., Kim, J., 2017. Changes in physical properties of various coir dust and perlite mixes and their capacitance sensor volumetric water content calibrations. HortScience 52 (1), 162–166. Sakaki, T., Limsuwat, A., Illangasekare, T.H., 2011. A simple method for calibrating dielectric soil moisture sensors: laboratory validation in sands. Vadose Zone J. 10 (2), 526–531. Thompson, R.B., Gallardo, M., Valdez, L.C., Fernández, M.D., 2007. Using plant water status to define threshold values for irrigation management of vegetable crops using soil moisture sensors. Agric. Water Manage. 88 (1), 147–158. van Iersel, M.W., Chappell, M., Lea-Cox, J.D., 2013. Sensors for improved efficiency of irrigation in greenhouse and nursery production. HortTechnology 23 (6), 735–746.
5. Conclusion The effect of the root system size variables on measured VWC was significant. In commercial production settings with a soil moisturebased automated irrigation system, the size of the root system is not easy to estimate. However, a difference of 8.7% VWC between actual VWC and measured VWC might be tolerable in a production area, because most growers program their irrigation system to ensure crops are safe from drought. Alternatively, a setpoint may be carefully adjusted within the range of water level easily attainable from the substrates considering the growth of the root system, and irrigation managers may then change the irrigation setpoint depending on crop growth. However, providing accurate VWC levels in research could be troublesome if the researcher relies only on the equation from substrateonly calibration. If the difference between VWC estimated from the sensor and actual VWC were 8.7%, this would represent considerable error. Therefore, providing accurate VWCs with FDR soil moisture sensors in research with growing plants is difficult. Thus, careful interpretation is required, or some indication of the limitations in using substrate-only calibration in VWC estimation would be needed. Acknowledgments This study was supported by the Korea University Grant supported by Korea University and Export Promotion Technology Development Program (315041-05) supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry, and Fisheries. References Bayer, A., Mahbub, I., Chappell, M., Ruter, J., van Iersel, M.W., 2013. Water use and growth of Hibiscus acetosella ‘Panama Red’ grown with a soil moisture sensor-controlled irrigation system. HortScience 48 (8), 980–987.
211
Contents lists available at ScienceDirect
Scientia Horticulturae journal homepage: www.elsevier.com/locate/scihorti
Short communication
Plant root growth affects FDR soil moisture sensor calibration a
b
Seonghwan Kang , Marc W. van Iersel , Jongyun Kim a b c
c,⁎
T
Department of Biosystems and Biotechnology, Korea University, Seoul, 02841, Republic of Korea Department of Horticulture, University of Georgia, Athens, GA, 30602, USA Division of Biotechnology, Korea University, Seoul, 02841, Republic of Korea
ARTICLE INFO
ABSTRACT
Keywords: Frequency domain reflectometry Capacitance sensor Lettuce Automated irrigation Water management
To acquire accurate volumetric water content (VWC) measurements from horticultural substrates using dielectric sensors, a substrate-specific calibration is critical. Calibrations typically are conducted with a substrate without plants, but water in the root system may affect soil moisture sensor readings. We investigated the effect of root growth on the measured VWC. Lettuce seedlings (Lactuca sativa L. ‘Joek Chi Ma’) were grown in 10 cm round containers (440 mL) filled with soilless substrate. Four EC-5 soil moisture sensors (Decagon Devices Inc., Pullman, WA, USA) were used to determine the effect of root system size on sensor calibration over an eight week period. Both calibration coefficients (slope and intercept) decreased (P < 0.001) with increasing root system size. The actual VWC at 8 weeks after transplanting (WAT) was 9% lower at 65% VWC compared to the estimated VWC from the substrate-only calibration. Multiple regression models indicated that various root size indicators (WAT, root fresh weight, root dry weight, and root water content) all had a negative effect on the estimation of VWC. The effect of root system size on estimated VWC may be tolerable in production, but should be considered in research applications. Careful interpretation is needed when using FDR soil moisture sensors to monitor substrate VWC in the presence of a growing root system.
1. Introduction Soil moisture sensors enable precise irrigation of plants based on their actual water needs (Chappell et al., 2013; Lichtenberg et al., 2013). The sensors, along with a datalogger and solenoid valves, can provide growers with information like the number of irrigation events, amount of water used by irrigation, and the current level of soil moisture (Kim et al., 2014; Lea-Cox et al., 2013). Dielectric sensors, and FDR (frequency domain reflectometry) sensors in particular, can provide an estimate of the volumetric water content (VWC) of the soil and/ or substrates, and are suitable for horticultural use due to their low cost, easy maintenance, durability, and reliability (Jones, 2004; van Iersel et al., 2013). Therefore, FDR sensor-based, automated irrigation systems have been used for research into efficient irrigation (Bayer et al., 2013; Burnett and van Iersel, 2008; Nemali and van Iersel, 2008; Thompson et al., 2007). FDR sensors measure the dielectric constant of substrates, which is a function of water content (Cobos and Chambers, 2010). To get accurate readings, it is essential to calibrate the sensor to convert the measured dielectric constant to the actual VWC (Sakaki et al., 2011). Sensor calibration may depend on sensor and substrate type, bulk density, elec-
⁎
trical conductivity (EC), and soil temperature (Kizito et al., 2008; Limsuwat et al., 2009; Nemali et al., 2007; Rhie and Kim, 2017). FDR sensor calibration is generally done using the substrates without a plant. However, the dielectric constant depends on the amount of water in the sphere of influence of the sensor and is affected both by water in the substrate and the roots. Thus, the size of the root system may affect the FDR sensor readings and this effect can change as roots grow. We investigated how FDR sensor calibration changes in the presence of a growing lettuce root system. We compared the actual VWC with estimated VWC from the substrate-only calibration to quantify the effect of root system growth on the calibration coefficients. 2. Materials and methods 2.1. Plant material Lettuce (Lactuca sativa L. ‘Joek Chi Ma’, Asia Seed Company, Seoul, Korea) seeds were sown in two 128-cell plug trays with a germinating substrate (Sunshine Mix #5, Sun Gro Horticulture, Agawam, MA, USA). During germination, air temperature, relative humidity, and daily light
Corresponding author. E-mail address: [email protected] (J. Kim).
https://doi.org/10.1016/j.scienta.2019.03.050 Received 30 November 2018; Received in revised form 26 February 2019; Accepted 23 March 2019 Available online 01 April 2019 0304-4238/ © 2019 Elsevier B.V. All rights reserved.
Scientia Horticulturae 252 (2019) 208–211
S. Kang, et al.
integral (DLI) were 14.3 ± 1.1 °C, 41.8 ± 9.5%, and 10.6 ± 2.3 mol·m2·d−1, respectively. After a month, 100 seedlings of similar size were transplanted into 10-cm round plastic containers (440 mL) filled with a soilless substrate (Sunshine Mix #4, SunGro Horticulture) and grown in a glass greenhouse for eight weeks. Air temperature, relative humidity, and DLI in the greenhouse averaged 22.4 ± 2.7 °C, 49.0 ± 9.0%, and 12.5 ± 5.0 mol·m2·d−1. Plants were irrigated daily with tap water. 2.2. FDR soil moisture sensor calibration Four FDR soil moisture sensors (EC-5, Decagon Devices) were used to conduct the VWC calibrations in the presence of plant roots. Each sensor was connected to a CR1000 datalogger (Campbell Sci.), with an excitation voltage of 2.5 VDC. Twenty plants were used biweekly to conduct sensor calibrations. To achieve different VWC levels in the containers, plants were irrigated with different amounts of water (from 20 to 400 mL per pot) 2 or 3 d before the calibration procedure, inducing a range of VWC from 10% to near saturation. At the time of calibration, the shoots of the plants were removed. Then, four soil moisture sensors were inserted one after another toward the center of each pot with the intact root system. Sensor voltage output was recorded by the datalogger. Then, the wet substrate plus root weight of each pot was measured. To measure root fresh weight, roots were separated from the substrates. The substrates were dried at 105 °C in a drying oven, and dry substrate weights were measured to calculate VWC. To calibrate the sensors, the VWC (%, v/v) was calculated as:
VWC including root system (%)=
[Wet substrate weight (g)
Dry substrate weight (g)
Root dry weight (g)] Container volume (mL)
× 100
These values were then plotted against sensor output and calibration coefficients were determined using linear regression. Calibration with the substrate only was performed using without roots:
VWC (%) =
Fig. 1. Root system size (root fresh weight, root dry weight, and root water content) of Lactuca sativa L. ‘Jeok Chi Ma’ during the eight week growing cycle.
Wet substrate weight (g) Dry substrate weight (g) × 100 Container volume (mL)
3. Results 3.1. FDR sensor VWC calibration with different levels of root system growth
followed by linear regression. At each harvest, root fresh and dry weight were measured. Root water content was calculated by subtracting root dry weight from root fresh weight.
Root fresh weight, root dry weight, and root water content increased as WAT increased, and the root growth parameters of lettuce showed a quadratic relationship with WAT values (Fig. 1). The soil moisture sensor outputs and VWC of the substrates had a strong linear relationship regardless of the WAT (P < 0.001, r2 > 0.97) (Table 1). When data from all WAT were combined in the multiple regression, the interaction between the slope and WAT value was significant (P = 0.005) (Fig. 2), indicating a change in calibration
2.3. Experimental design and statistical analysis The experiment was conducted with a completely randomized design. The relationships between WAT value and lettuce root growth were analyzed using regression analysis with SAS 9.4 (SAS Inst., Cary, NC, USA). To acquire each FDR soil moisture sensor’s calibration coefficients for different root system sizes, regression analysis between sensor output (mV) and actual VWC (in %VWC) was conducted for each sampling time. Multiple regression analysis was conducted to determine the relationship of WAT value, root fresh weight, root dry weight, and root water content on VWC calibration with different levels of root growth (proc REG in SAS 9.4; SAS Inst.). In multiple regression analyses, VWC was the dependent variable, and the FDR sensor output and a potential root growth indicator (WAT, root fresh weight, root dry weight, or root water content), and their interaction were independent variables. Partial R2 values were acquired from the multiple regression analysis to quantify the contribution of each variable to the calibration equation.
Table 1 EC-5 soil moisture sensor calibration coefficients for horticultural substrate in the presence of a lettuce root system at 2, 4, 6, and 8 weeks after transplanting and without plant root system. Volumetric water content (v/v, %) = β1 × sensor output (mV) + β2.
209
Weeks after transplanting
β1
β2
r2
Substrate only 2 4 6 8
0.1771 0.1763 0.1747 0.1741 0.1701
−52.968 −53.317 −53.695 −55.228 −57.561
0.993 0.983 0.972 0.984 0.994
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multiple regression was conducted using variables that indicate the size of the root system (WAT, root fresh weight, dry weight, or root water content) along with sensor output (V) as a variable. In all these regression analyses, sensor output was positively correlated with VWC (P < 0.001) and explained 95.8% of the variability in the data (Table 3). The interaction between root size indicators and sensor output was significant (P < 0.001), but had a low partial R2. The interaction between sensor output and WAT value had the highest partial R2 (0.024), while the interaction between sensor output and root dry weight had the lowest partial R2 (0.015). These significant interactions indicated that root growth affected the soil moisture sensor reading. 4. Discussion 4.1. VWC calibration with root growth over time Root system-induced errors in VWC estimates may be due to two reasons. Firstly, an increase in bulk density and decrease in total porosity due to an increase in root system volume may affect sensor readings. The bulk density of substrates affects FDR soil moisture sensors (Mendes et al., 2014) and the total porosity (air space + water space) in peat substrate may be decreased by root growth (Cannavo et al., 2011). Secondly, the water in the root system can directly affect FDR readings, because these sensors measure total dielectric within their sphere of influence and cannot distinguish between water in the root system and water in the substrate. Calibration equations obtained from substrate without plants have been used in many previous studies that used automated irrigation system-based soil moisture sensors (Burnett and van Iersel, 2008; Kim et al., 2012; Rhie and Kim, 2017). Bell et al. (1987) reported that a capacitance sensor created anomalous results when contact was introduced between the sensor prong and the roots, but they did not quantify the effect of root system growth on sensor measurement. We showed that the calibration slope and y-intercept decreased with increasing root system size (Table 1 and Fig. 2). When the substrate VWC was estimated as 50% with substrate-only calibration, the actual VWCs estimated from the adjusted calibrations for 2, 4, 6, and 8 WAT were 49.2, 47.9, 46.0, and 41.3%, respectively, indicating likely overestimation of the VWCs with substrate-only calibration. Comparison between estimated VWC and actual VWC at specific WATs showed a similar result with the slope and y-intercept decreasing as the WAT increased (Table 2 and Fig. 3). The difference between estimated VWCs from substrate-only calibration and actual VWCs becomes larger as the WAT increases. Although we only tested FDR sensors, it seems likely that time-domain reflectometry and time-domain transmissivity sensors would respond similarly to the presence of roots, since all these sensors measure the dielectric within their sphere of influence. Likewise, neutron probes, whose readings depend on the amount of water molecules in the volume of influence of the sensor, will likely be affected by water in the roots. This should be considered when precise measurements of VWC are required. Since the presence of roots increases the measured VWC values, this can create problems when irrigation is based on FDR sensor readings. Overestimated VWC values may negatively influence crop production due to unwanted and undetected drought.
Fig. 2. Substrate volumetric water content calibration of horticultural substrates with EC-5 soil moisture sensor at 2, 4, 6, and 8 weeks after transplanting of lettuce and substrate only without plant root system. EC-5 soil moisture sensors were connected to a CR1000 datalogger with 2.5-VDC excitation voltage. See Table 1 for regression coefficients. Table 2 Calibration coefficients of the regression analyses between actual volumetric water content (VWC, %) and VWC estimation from substrate-only calibration at 2, 4, 6, and 8 weeks after transplanting of lettuce seedlings. Actual volumetric water content (v/v, %) = β1 × VWC from substrate-only calibration (%) + β2. Weeks after transplanting
β1
β2
r2
2 4 6 8
0.9953 0.9866 0.9828 0.9604
−0.5964 −1.4383 −2.9261 −6.6913
0.983 0.972 0.984 0.994
Fig. 3. Comparison of estimated VWC from substrate-only calibration with actual VWC at 0, 2, 4, 6, and 8 weeks after transplanting lettuce. See Table 2 for regression coefficients.
over time. As the WAT value increased from 0 to 8, both the slope and y-intercept of the calibration equation decreased. To understand the error in VWC values induced by root growth, the estimated VWC values from the substrate-only calibration equation and the actual VWC were compared through regression analyses (Table 2 and Fig. 3). These analyses indicated that errors in the estimated VWC from substrate only calibration increased over time (P = 0.004).
4.2. Results of multiple regression with sensor output and root growth The sensor output was affected by the size of the root system, as quantified by WAT, root fresh weight, root dry weight, or root water content. An increase in root system size results in increasingly larger underestimations of the true VWC, presumably because of the water in the roots. In the multiple regression analyses, the interaction between sensor output and root growth indicators that include the moisture in the roots (root fresh weight and root water content) had a higher partial
3.2. Root impact on VWC calibration with multiple regression To investigate the effect of root growth on estimating actual VWC, 210
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Table 3 Multiple regression coefficients (β) and partial R2 values for variables used in estimating substrate volumetric water content (VWC) with lettuce roots. Variables were sensor output (V), a root growth indicator (either weeks after transplanting, root fresh weight, root dry weight, or root water content), and their interaction. VWC (%) = β var1 × var1 + β var2 × var2 + β interaction × var1 × var2 + β intercept. Variables Var1
Var2
β
Sensor output (V)
Week after transplanting
185.4 *** 183.3 *** 181.2 *** 183.3 ***
Root fresh weight (g) Root dry weight (g) Root water content (mL) ***
Partial R2
Regression coefficients and P-value var1
β
var2
– – – –
β
interaction
−2.268 *** −0.823 *** −6.916 *** −0.917 ***
β
intercept
−55.06 *** −55.60 *** −54.47 *** −55.69 ***
Adjusted R2
Var1
Var2
Interaction
0.958
–
0.024
0.981
0.958
–
0.020
0.978
0.958
–
0.015
0.973
0.958
–
0.020
0.978
indicates significance at P < 0.001, - indicates no significance.
R2 than root dry weight. FDR soil moisture sensors take advantage of the high dielectric constant of water (˜80.4) compared to that of air (˜1) or soil/substrate (˜2 to 8) (Nemali et al., 2007), but they cannot distinguish between the water in the soil/substrate and that in the root. Although the partial R2 of the interaction term between the sensor output and root growth indicator was low, not accounting for this effect resulted in an error in the calculated VWC of up to 8.7%.
Bell, J.P., Dean, T.J., Hodnett, M.G., 1987. Soil moisture measurement by an improved capacitance technique, part II. Field techniques, evaluation and calibration. J. Hydrol. (Amst) 93 (1), 79–90. Burnett, S.E., van Iersel, M.W., 2008. Morphology and irrigation efficiency of Gaura lindheimeri grown with capacitance sensor-controlled irrigation. HortScience 43 (5), 1555–1560. Cannavo, P., Hafdhi, H., Michel, J.-C., 2011. Impact of root growth on the physical properties of peat substrate under a constant water regimen. HortScience 46 (10), 1394–1399. Chappell, M., Dove, S.K., van Iersel, M.W., Thomas, P.A., Ruter, J., 2013. Implementation of wireless sensor networks for irrigation control in three container nurseries. HortTechnology 23 (6), 747–753. Cobos, D.R., Chambers, C., 2010. Calibrating ECH2O Soil Moisture Sensors. Application Note. Decagon Devices, Pullman, WA. Jones, H.G., 2004. Irrigation scheduling: advantages and pitfalls of plant-based methods. J. Exp. Bot. 55 (407), 2427–2436. Kim, J., Malladi, A., van Iersel, M.W., 2012. Physiological and molecular responses to drought in Petunia: the importance of stress severity. J. Exp. Bot. 63 (18), 6335–6345. Kim, J., Lea-Cox, J., Chappell, M., van Iersel, M.W., 2014. Wireless sensors networks for optimization of irrigation, production, and profit in ornamental production. Acta Hortic. 1037, 643–650. Kizito, F., Campbell, C.S., Campbell, G.S., Cobos, D.R., Teare, B.L., Carter, B., Hopmans, J.W., 2008. Frequency, electrical conductivity and temperature analysis of a low-cost capacitance soil moisture sensor. J. Hydrol. (Amst) 352 (3), 367–378. Lea-Cox, J.D., Bauerle, W.L., van Iersel, M.W., Kantor, G.F., Bauerle, T.L., Lichtenberg, E., King, D.M., Crawford, L., 2013. Advancing wireless sensor networks for irrigation management of ornamental crops: an overview. HortTechnology 23 (6), 717–724. Lichtenberg, E., Majsztrik, J., Saavoss, M., 2013. Profitability of sensor-based irrigation in greenhouse and nursery crops. HortTechnology 23 (6), 770–774. Limsuwat, A., Sakaki, T., Illangasekare, T.H., 2009. Experimental quantification of bulk sampling volume of ECH2O soil moisture sensors. Proc. Annual American Geophysical Union Hydrology Days Vol. 29, 39–45. Mendes, L.B., Li, H., Xin, H., do Nascimento, J.W.B., 2014. Evaluation of EC-5 soil moisture sensors for real-time determination of poultry manure or litter moisture content. Appl. Eng. Agric. 30 (2), 277–284. Nemali, K.S., van Iersel, M.W., 2008. Physiological responses to different substrate water contents: screening for high water-use efficiency in bedding plants. J. Am. Soc. Hortic. Sci. 133 (3), 333–340. Nemali, K.S., Montesano, F., Dove, S.K., van Iersel, M.W., 2007. Calibration and performance of moisture sensors in soilless substrates: ECH2O and Theta probes. Sci. Hortic. 112 (2), 227–234. Rhie, Y.H., Kim, J., 2017. Changes in physical properties of various coir dust and perlite mixes and their capacitance sensor volumetric water content calibrations. HortScience 52 (1), 162–166. Sakaki, T., Limsuwat, A., Illangasekare, T.H., 2011. A simple method for calibrating dielectric soil moisture sensors: laboratory validation in sands. Vadose Zone J. 10 (2), 526–531. Thompson, R.B., Gallardo, M., Valdez, L.C., Fernández, M.D., 2007. Using plant water status to define threshold values for irrigation management of vegetable crops using soil moisture sensors. Agric. Water Manage. 88 (1), 147–158. van Iersel, M.W., Chappell, M., Lea-Cox, J.D., 2013. Sensors for improved efficiency of irrigation in greenhouse and nursery production. HortTechnology 23 (6), 735–746.
5. Conclusion The effect of the root system size variables on measured VWC was significant. In commercial production settings with a soil moisturebased automated irrigation system, the size of the root system is not easy to estimate. However, a difference of 8.7% VWC between actual VWC and measured VWC might be tolerable in a production area, because most growers program their irrigation system to ensure crops are safe from drought. Alternatively, a setpoint may be carefully adjusted within the range of water level easily attainable from the substrates considering the growth of the root system, and irrigation managers may then change the irrigation setpoint depending on crop growth. However, providing accurate VWC levels in research could be troublesome if the researcher relies only on the equation from substrateonly calibration. If the difference between VWC estimated from the sensor and actual VWC were 8.7%, this would represent considerable error. Therefore, providing accurate VWCs with FDR soil moisture sensors in research with growing plants is difficult. Thus, careful interpretation is required, or some indication of the limitations in using substrate-only calibration in VWC estimation would be needed. Acknowledgments This study was supported by the Korea University Grant supported by Korea University and Export Promotion Technology Development Program (315041-05) supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry, and Fisheries. References Bayer, A., Mahbub, I., Chappell, M., Ruter, J., van Iersel, M.W., 2013. Water use and growth of Hibiscus acetosella ‘Panama Red’ grown with a soil moisture sensor-controlled irrigation system. HortScience 48 (8), 980–987.
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