Abstract
Many consider tools for plant-based irrigation management methods to be the most precise way to manage irrigation in either a research or a commercial settings. Although many types of tools are available, they all measure some aspect of water movement along the soil–plant–atmosphere continuum. This article presents some of the more commonly used tools and the methods involved to properly employ them. In addition, recent literature is reviewed to provide context to the methods themselves and also to highlight each one’s specific advantages and disadvantages. Ultimately, there is no clear winner or “best” tool as all have disadvantages, either due to prohibitive cost, the amount of data output, the difficulty of data interpretation, lack of signal resolution, or lack of dynamic ability to provide decision support. Therefore, we conclude that the user should carefully weigh these varied advantages and disadvantages in the context of their production goals before deciding on a given tool for irrigation management.
Irrigation management typically starts with indirect or direct monitoring of the crop or its surrounding environment. The most common indirect methods are the monitoring of soil moisture conditions—either through measurement of soil water content (relative or absolute) or soil water tension. Another common indirect method uses estimates of crop evapotranspiration (ETc). Direct, plant-based irrigation management methods provide the irrigator with direct knowledge of the plant’s hydration state or water status. When used correctly, direct, plant-based methods can be leveraged to schedule irrigation events more precisely, thus maximizing water conservation.
For plant-based irrigation management to be effective, several requirements must be met. First, the measurements must be sensitive and consistent indicators of plant water status. In other words, the sensors or methods should accurately identify when a plant is well watered or experiencing a water deficit. Second, plant-based measurements should accurately reflect the amount of water in the soil profile. Thus, the data provided by the plant-based measurement tools should be well correlated to soil water availability. Finally, plant-based measurements should be highly correlated to other physiological processes of importance, such as vegetative and reproductive growth and, in some cases, crop quality.
In this article, we review some of the major plant-based irrigation management tools, the principles behind them, and their associated methodology. The interpretation of the data along with the advantages and disadvantages of each tool are discussed in the context of their practical utility for both researchers and commercial producers. First, however, it is necessary to briefly review the physiology behind water movement through plants.
Water potential
Water is an essential plant component, constituting the majority of the fresh weight of plant cells. Most morphological and physiological characteristics of land plants are adaptations for maintaining a high internal water status. The term “water status,” although somewhat vague, is commonly used to discuss the hydration states of plants (and also soils). Generally, well-watered plants and field capacity soils are said to have a “high” water status, and drought conditions lead to a “low” water status.
The water status of plants and soils is often defined in terms of water potential (ψ). The ψ refers to the chemical potential of water, or the ability of the water to do work, and it is expressed in pressure units (bars or megapascals). Water moves spontaneously down a chemical potential gradient (from high to low), releasing energy (and doing work) along the way. In layperson’s terms, this concept is often restated as the mnemonic “water flows downhill.”
The ψ of pure water at standard temperature and pressure is given a value of zero. The total ψ of a system (plant or soil) is made up of several components, although the detailed discussion of each is beyond the scope of this article. Briefly, the addition of solutes always lowers the total ψ of the system, so it would be negative. The application of pressure (if it is positive) to the system can raise the ψ, such as in turgid plant cells. In contrast, if the pressure is negative (i.e., tension) in the system, it would further lower the ψ, such as in the plant xylem. Ultimately, as the plant or soil becomes “drier,” the ψ value becomes more negative (lower). If they become “wetter,” the ψ becomes less negative (higher). However, the total ψ of plants and soils will never be positive under most conditions. Importantly, ψ can be used to evaluate movement in both plants and soils.
Water movement through the soil–plant–atmosphere continuum
Water flows from the soil through the plant and out into the atmosphere along what is known as the soil-plant-atmosphere-continuum (SPAC). Accordingly, most plant-based irrigation methods are correlated to detect some aspect of the SPAC. Plants are directly connected to the atmosphere through tiny microscopic pores located on the undersides of leaves called stomates (or stomata; Fig. 1). Stomata are microscopic epidermal structures on plant leaves that consist of two guard cells around a pore. Carbon dioxide (CO2) diffuses into the stomatal cavity from outside of the leaf to be used during photosynthesis, whereas water (H2O) vapor diffuses out through stomata in the process known as transpiration.
Cross-section of typical leaf showing tissues and structures including stoma (Wikipedia, 2021).
Citation: HortTechnology 31, 6; 10.21273/HORTTECH04862-21
The hydraulic and biochemical mechanisms that govern the stomatal opening and closing are influenced by many environmental factors, including changes in temperature, light intensity, atmospheric CO2 concentration, air humidity, and soil moisture content (Bertolino et al., 2019). In general, however, stomata gradually open in the morning as the sun rises, and at ≈50% of full sunlight, they are fully open, although there is variation among shade vs. sun-adapted plants. When the sun sets in the evening, stomata close, and typically little water is lost from plants at night, although this is an area of active research.
While stomata are open, water vapor passively diffuses out of the leaf. This is due to the large ψ gradient between the inside of the leaf and the atmosphere. Inside the leaf the ψ is relatively high (e.g., –0.8 MPa), while the ψ of the atmosphere is typically orders of magnitude lower (e.g., –100 MPa). This difference creates a steep gradient that drives transpiration and water loss from the leaf. A beneficial side effect of transpiration is its cooling effect on the leaf (due to the latent heat of vaporization) that can be leveraged to schedule irrigation.
Stomatal closure in response to drought is a complex process regulated by a network of hydraulic and hormonal signaling pathways, the discussion of which is beyond the scope of this paper. In general, stomatal closure in response to water deficit is a mechanism to prevent embolism of the vascular system and complete desiccation of the plant (Martin-StPaul et al., 2017). However, closure of stomata to prevent embolism bears the trade-off of potential detrimental impacts on plant growth and productivity, as well as leaf temperature regulation. Ultimately, stomatal closure reduces leaf gas exchange that can be detected and used as a signal to schedule irrigation.
The water that is lost from the inside of the leaf through the substomatal cavity must be replaced by water from the cells that surround the cavity. Water movement out of those cells lowers their ψ, establishing a ψ gradient that extends to the roots. The water lost from those cells must be replaced by water from other nearby leaf cells, and ultimately by water drawn up through the leaf xylem. Indeed, the vascular system in higher plants evolved to provide distant above-ground organs with a consistent water supply. The velocity of water transport through the plant shoot can also be measured and used as a proxy for transpiration to ultimately schedule irrigation. Shoots are connected to the roots through the entire vascular system, and so water is subsequently drawn up from the roots to replace the water that was lost from the leaf. Finally, the water that is lost from the roots is replaced by the soil water. To maintain water flux from the soil into the roots, root tissues must maintain a ψ that is lower than that of the soil, and must necessarily remain in physical contact with the soil water solution.
Plant-based methods
Pressure chamber
The most widely used tool used for plant-based irrigation management is the pressure chamber (Fig. 2A), introduced by Scholander et al. (1965). In practice, its measurement of plant ψ is considered to be the gold standard measure of plant water status (Santesteban et al., 2019). As such, it is often used as a reference value against which other emerging technologies (e.g., several of those outlined later in the article) are calibrated (King et al., 2020).
Examples of various plant-based methods in use: pressure chamber (A), infrared gas analyzer [IRGA (B)], IR thermometer [IRT (C)], and sap flow (D). These are merely examples of instruments; others of similar types are available (photos A–C by A.D. Levin; photo D courtesy of L.E. Williams).
Citation: HortTechnology 31, 6; 10.21273/HORTTECH04862-21
The pressure chamber has become so ubiquitous among plant scientists and commercial practitioners alike that detailed descriptions of the methodology used are often omitted. This has led to some disagreement among users regarding proper methodology. However, years of research have detailed the various errors that can arise from improper sampling and measurement, ultimately refining the proper techniques. Several determinations are available for use in irrigation management: predawn water potential, midday leaf water potential, and midday stem water potential.
As the name suggests, predawn leaf water potential (ψpd) determinations require leaf samples taken before sunrise, whereas midday leaf (ψleaf) and midday stem (ψstem) water potential measurements require leaf samples taken just after solar noon. These time points are critical because they represent the steady-state moments when leaf transpiration is either minimal (predawn) or maximal (midday). In other words, water flux out of the leaf is at a steady state, and the leaf’s water status is not rapidly changing during each time point.
When sampling for all determinations, selected leaves should be mature and fully expanded, with no damage. Leaves for ψpd measurements should be sampled just before sunrise, before there is any incident light on the leaf because light stimulates stomatal opening and transpiration that may reduce the ψ of the sample. However, Williams (2017) recently reported that still-shaded leaves sampled just after sunrise provide readings nearly identical to those of ψpd samples. This may extend the sampling window somewhat and provide the operator with the opportunity to take more samples. Ultimately, the total number of samples should be based on expected variability among plots and desired measurement precision.
Midday measurements should be made under clear sky conditions under full sunlight [>1500 µmol·m−2·s−1 photosynthetic photon flux density (PPFD)]. Typically, it has been recommended that ψleaf and ψstem samples be taken within an hour of solar noon (when solar elevation is highest), but minimum values are often reached somewhat later when vapor pressure deficit (VPD) is highest (Fig. 3). The timing and duration of the steady-state period are likely strongly influenced by plant water status itself because the response of ψleaf or ψstem to VPD depends on how well watered the plant is (Williams and Baeza, 2007). In addition, leaf position on the outside of the canopy and orientation to the sun may also play a role in the timing and duration of its steady-state transpiration period (Tian and Schreiner, 2021). Therefore, it is recommended that leaves for ψleaf and ψstem be sampled between the hours of 1400 and 1600 hr, but this may vary somewhat depending on time zone and/or whether the location is in daylight savings or standard time. Ultimately, best practice is to take repeated measurements across the midday period within one’s system to determine the timing and duration of steady-state values.
Diurnal response of midday leaf water potential (ψleaf) in ‘Pinot noir’ grapevine before berry ripening (23 July 2020) and during berry ripening (3 Sept. 2020). Data are means ± 1 se (n = 20). Gray area indicates 95% confidence interval of local smoothing function; 1 MPa = 10 bars.
Citation: HortTechnology 31, 6; 10.21273/HORTTECH04862-21
Generalized model of the relationship between photosynthetic rate and volumetric water content. The irrigation threshold chosen at 90% maximum predicted photosynthetic rate is shown by the dashed line. Based on research presented by Nambuthiri et al. (2017).
Citation: HortTechnology 31, 6; 10.21273/HORTTECH04862-21
For both ψpd and ψleaf, leaf samples should be covered with a plastic bag 1 to 2 s before excision from the plant (Williams and Araujo, 2002). However, it is unclear whether this step significantly changes the reading for ψpd. For example, Turner and Long (1980) show that there were no differences in ψpd between covered and uncovered leaves of sorghum (Sorghum bicolor) and sunflower (Helianthus annuus). Nevertheless, it is recommended to always cover the leaf before excision and to keep the leaf covered during pressurization to avoid any pressure-induced heating artifacts (Hsiao, 1990). Furthermore, it is critical to cover the leaf with a plastic bag before excision and during pressurization for ψleaf measurements, particularly for well-watered plants (Williams, 2017).
Midday stem water potential measurements specifically require enclosing the leaf sample in an opaque bag for at least 10 min before excision and pressurization (Fulton et al., 2001; Levin, 2019). This stops transpiration and allows the ψleaf to come into equilibrium with that of the stem (Begg and Turner, 1970). This is what gives rise to the somewhat confusing measurement name, even though it also uses leaf samples.
Once the leaf is bagged and excised from the plant, it should be placed into the chamber and pressurized within 30 s (Levin, 2019). The previously cited work demonstrated that samples could be held outside the chamber for up to 60 s with little loss in accuracy, provided they were held in the shade. However, it is impractical to hold samples in the shade considering that measurements must be made in full-sun conditions. Finally, once in the chamber, the sample should be pressurized at a constant rate of 0.025 MPa·s−1 for maximum accuracy (Naor and Peres, 2001).
Leaf gas exchange
Leaf gas exchange can be accurately measured by either an infrared gas analyzer (IRGA) or a porometer. An IRGA measures the concentration of trace gases in an air sample (e.g., CO2 and H2O) by detecting the absorption of an emitted IR light source through that sample. Different types of leaf chambers can be attached to a portable IRGA to measure CO2 and H2O fluxes from the air that has passed over a leaf surface. When the portable IRGA is paired with a microprocessor, net carbon assimilation (Anet) and transpiration (E) are calculated from the mass balance of CO2 and H2O between the air entering the sample chamber and the air leaving the chamber (Hunt, 2003). Similarly, a porometer measures leaf stomatal conductance (gS) by measuring the difference in relative humidity between incoming and outgoing air. The outgoing air has been enriched with water vapor transpired by the leaf during photosynthesis and is used as a proxy for E. These measurements can then be used to make inferences about the plant physiology and relate Anet, E, and gS rates to other factors, such as soil water content, to inform decisions about irrigation management.
Plant scientists use several commercially available portable IRGAs (Fig. 2B), as well as stationary IRGAs that can be connected to whole-plant chambers and free-air enrichment CO2 studies (Ainsworth and Long, 2005; Nackley et al., 2018). There has been great development in the commercialization of IRGA for plant science, reducing the size of the devices to equipment that can be carried around to remote locations. However, the expertise needed to operate an IRGA and the cost of these instruments have been noted as limitations to the widespread use of this method for plant physiology–based irrigation (Basiri Jahromi et al., 2020). To reduce the learning curve, detailed peer-reviewed standard operating procedures have been made available to explain the basic methodology (Evans and Santiago, 2014).
Temperature based irrigation
Leaf temperature is inversely proportional with leaf E because the conversion of liquid H2O into vapor is an endothermic process, absorbing heat energy from the air, resulting in a lower leaf temperature. In agricultural systems, assessing leaf temperature as a proxy for water stress began in the 1960s (Maes and Steppe, 2012) and has recently been popularized because of a proliferation of thermal cameras, infrared thermometers [IRTs (Fig. 2C)], and remote sensing platforms. Many commercial IRTs and thermal cameras are available for plant science, and recently, low-cost do-it-yourself systems have been applied in research and commercial applications (McCauley et al., 2021).
Sap flow
Sap flow sensors have been used in many horticultural production systems to provide continuous monitoring of plant transpiration (Nackley et al., 2020; Pearsall et al., 2014; Qiu et al., 2015; Shackel et al., 1992). Sap flow methods calculate the velocity of a short pulse of heat carried by convection in the transpirational stream (Fig. 2D) (Kirkham, 2014). The velocity of the heat convection is used as a proxy for the plant transpiration rate. The sap flow velocity is diminished when transpiration is reduced—for example, at night and during water-deficit conditions. Therefore, pairing sap flow with environmental measures is an excellent way to optimize irrigation scheduling. For example, sap flow sensors can be correlated with soil moisture levels to determine the soil moisture threshold at which plants close stomata. Similarly, sap flow sensors have been paired with IR thermometers to correlate leaf temperatures with plant transpiration to create a crop water stress index.
Carbon stable isotope ratio
Of all atmospheric CO2, 98.9% contains the stable 12C isotope, whereas the remaining 1.1% contains the stable 13C isotope. Under well-watered conditions, photosynthetic enzymes will favor fixing CO2 molecules that contain the lighter, more abundant 12C isotope (relative to the heavier 13C isotope). However, during water deficit, this isotope preference is reduced, and plants will fix a relatively higher proportion of CO2 the heavier 13C isotope. Thus, the C composition of plant tissues that have experienced water deficits will have a relatively higher ratio of 13C compared with the atmosphere and can be measured using a mass spectrometer.
Plant water use efficiency (WUE) can be easily calculated at the leaf level by measuring the rate of CO2 uptake relative to the rate of H2O loss using an IRGA as described earlier. Fundamentally, the WUE of a plant is a measure of how photosynthesis (which uses CO2 to build biomass) happens per unit of water lost. Leaf biochemical processes are incredibly dynamic and sensitive to environmental conditions at the time of measurement; therefore, WUE measurements are only useful when considering plant response over short time scales. For integration and understanding of WUE over longer time scales, analyses of stable carbon isotope composition (δ13C) in numerous plant tissues has proven to be a robust method (Farquhar et al., 1989).
Advantages and disadvantages
Each plant-based method has a slightly different interpretation, and each has its own unique set of advantages and disadvantages. The following section highlights the advantages and disadvantages of each method in the context of recent literature.
Predawn leaf water potential
The main cited advantage of ψpd is the indirect estimate of soil water availability in the root zone (Medrano et al., 2003). Because plants’ ψ is assumed to equilibrate with soil ψ overnight and there is no transpiration, these measurements are typically interpreted as an indirect measure of soil ψ. In addition, it is considered to be less sensitive to variable environmental conditions because plants are not transpiring and there is no light at the time of measurement. There have also been some reports that claim it is a more sensitive indicator of plant water status compared with other pressure chamber measurements (i.e., ψleaf and ψstem).
The main disadvantage of ψpd is that it may not accurately reflect soil water availability. This effect is magnified under heterogeneous soil moisture conditions, both in the field and in containers (Améglio et al., 1999). Plants will typically equilibrate with the wettest portion of the soil profile; thus, ψpd readings tend to underestimate the maximum water deficit experienced by plants during the day. Another disadvantage of ψpd that is not often presented is that the response signal is smaller compared with other (midday) pressure chamber determinations (described subsequently) (Williams and Trout, 2005). Indeed, the small signal of the ψpd measurement often falls within or near the gauge accuracy limit. Thus, if one were relying on ψpd measurements for irrigation management, a separate gauge with a narrowed range of pressures could be used more effectively. Finally, the last disadvantage is simply the inconvenient measurement time (0400 to 0600 hr).
Midday leaf water potential
Of the two daytime ψ measurements, midday leaf water potential (ψleaf) is the most commonly used (Levin 2019). As with any diagnostic measurements, conditions at the time of measurement must be standardized to ensure appropriate interpretation. Nevertheless, ψleaf measurements provide a robust indication of whole-plant water status (Levin et al., 2020), although they only provide direct information regarding the ψ of a particular leaf sample. Midday leaf water potential measurements have the main advantage of being quick and easy, with a single measurement requiring 45 to 90 s to complete. In addition, there is a large body of literature across crops and cropping systems that describes various plant physiological responses at various ψleaf values. For these reasons, ψleaf remains one of the most widely used plant-based management tools.
The main disadvantages of ψleaf measurements are the limited measurement time window (≈2 h at solar noon), the sensitivity of ψleaf in general to the environmental conditions during sampling, and the variability among operators. The short time window necessarily limits the number of determinations that can be made during a measurement campaign and is most often a concern of commercial practitioners compared with researchers. For example, a researcher may wish to make many determinations across replicated treatment plots located in a small area to characterize plant response or trigger an irrigation event. This might typically take ≈45 to 60 min because treatment plots are often adjacent to one another, and there is little time lost moving from plot to plot. However, a producer may wish to characterize the ψleaf of whole production blocks in a commercial farm, where fewer measurements are required per block, but the blocks are disparately located.
ψleaf is highly influenced by changing environmental conditions. Factors such as angle and intensity of light on the leaf or changing wind speed can influence ψleaf such that more measurements may be necessary to obtain an accurate average value for the measured unit (e.g., plant, plot, or block). In addition, the position of the leaf within the canopy can strongly influence the reading as well, although this may be more of an issue with assessing large plants such as trees. Finally, because instrument operator is often the largest source of error, it is important to have uniform training across all operators and/or designate operators to the same plots, sites, or fields.
Midday stem water potential
In general, ψstem is a sensitive indicator of plant water status (McCutchan and Shackel, 1992), correlates highly to soil water content (Williams and Trout, 2005) and other measures of plant ψ and gas exchange (Williams and Araujo, 2002), and has a greater ability to discriminate differences among irrigation treatments compared with ψleaf (Santesteban et al., 2019). Thus, there is increasing acceptance of ψstem as the preferred determination for plant-based measurements of ψ by both researchers and producers.
One of the primary disadvantages for ψstem determinations that have been cited is the long equilibration time of prebagged leaf samples (Williams, 2017). Indeed, there has been some disagreement in the literature regarding the length of time required to properly equilibrate the leaf sample for accurate ψstem determinations. Early studies used leaf samples that were enclosed overnight (Begg and Turner, 1970; McCutchan and Shackel, 1992). Later, Choné et al. (2001) reported no differences in grapevine ψstem values among equilibration times of 1, 2, and 6 h, thus subsequent studies typically used equilibration times ranging from 1 to 2 h (Naor et al., 2001; Williams and Araujo, 2002). However, Fulton et al. (2001) and recently Levin (2019) demonstrated that 10-min equilibration time was sufficient for ψstem across several woody perennial fruit crop species [walnut (Juglans regia), prune (Prunus domestica), almond (Prunus dulcis), and grapevine (Vitis vinifera)], and Rockwell et al. (2011) found similarly fast equilibration time for red oak (Quercus rubra). Furthermore, Hochberg (2020) computed the equilibration time for typical transpiring grapevine leaves to be only 125 s, although those calculations were based on several assumptions, and it is likely that for droughted plants, the equilibration time would be slightly longer. Nevertheless, the literature suggests that 10 min of equilibration time is more than enough for accurate ψstem determinations, essentially laying to rest the disadvantage of long equilibration time.
Gas exchange
Fulcher et al. (2012) developed a simple method that uses an IRGA to create irrigation thresholds. In this method, which has been used and modified by other researchers (Basiri Jahromi et al., 2020; Hagen et al., 2014; Nambuthiri et al., 2017), the IRGA is used to measure crop water stress responses by correlating net CO2 assimilation over a range of increasingly drier substrate moisture contents (100% to 45% of container capacity). The researchers create water stress conditions by withholding irrigation for ≈2 weeks. During the 2-week dry-down period, the researchers use the IRGA to collect midday, single-leaf, gas exchange measurements. To support photosynthetic rates the plant chambers were set equal to maximum ambient light (e.g., PPFD 2000 µmol·m−2·s−1) and ambient CO2 (e.g., 400 ppm). The substrate water content was measured by soil moisture sensors inserted into the pots. The water stress responses were used to establish the irrigation set points. For example, Nambuthiri et al. (2017) set a 10% decrease in maximum photosynthetic rate as the threshold for irrigation (Fig. 4). For boxwood (Buxus sempervirens) 10% decrease occurred at 0.28 cm·cm−1 volumetric water content (VWC), and for deutzia (Deutzia gracilis) the irrigation threshold was set to 0.33 cm·cm−1 VWC (Nambuthiri et al., 2017). The irrigation valves were then programmed to be controlled by the soil moisture sensors that could trigger sprinkler irrigation to refill the container to VWC capacity when the VWC reached the moisture level corresponding to the physiological threshold.
Compared with IRGAs, porometers are significantly less expensive and more portable, thus they may be an attractive tool for users who are only interested in basic gas exchange measurements for survey work and irrigation scheduling. Although they do not directly measure photosynthesis (i.e., Anet) like an IRGA, the gS measurement provided by the porometer remains highly valuable—and easily interpretable. First, from a physiological perspective, gS is highly correlated to Anet (Fig. 5) and has also been shown to be highly correlated to daily plant water use (Williams et al., 2012). Because stomata are highly sensitive to drought, gS measurements have been used effectively to define threshold values (Lovisolo et al., 2010), and drought response differences across cultivars (Levin et al., 2019).
Relationship between net carbon assimilation (Anet) and stomatal conductance (gS) in ‘Pinot noir’ grapevine.
Citation: HortTechnology 31, 6; 10.21273/HORTTECH04862-21
Temperature-based methods
Similar to the IRGA-based irrigation management, leaf temperature-based methods correlate plant physiological responses to decreasing soil moisture. The most common leaf temperature irrigation methods are based on the crop water stress index (CWSI) concept introduced by Idso and Jackson and others (Idso et al., 1981; Jackson et al., 1988). Simply, the CWSI is a relative comparison of plant canopy temperature to the surrounding ambient air temperature. A lower limit of this ratio is based on “healthy” unstressed plants that transpire at the potential rate for a given evaporative demand. Temperature measurements to build the CWSI are collected during solar noon across a range of increasingly drier substrate moisture contents. The upper limit represents the temperature ratio when a plant is not transpiring (Osroosh et al., 2015). CWSI can be strongly influenced by ambient weather and works best during hot and dry conditions with open skies. Therefore, CWSI is often complemented with soil moisture measurements to assist decision-making for irrigation scheduling rather than as a stand-alone technique for irrigation monitoring (Maes and Steppe, 2012).
The main advantage of temperature-based methods is that they can be scaled up to field-scale and used with remote sensing platforms. This allows a producer to scan entire fields to better understand which sections are over-or under-irrigated, and if possible, address the problem at the ground level using variable-rate irrigation management to improve field/crop uniformity. Moreover, it allows for large acreages to be assessed quickly. By the same token, the main disadvantage with these methods is that at this scale, they may not be dynamic enough for real-time management or decision-making. By the time data are retrieved (from unmanned aerial vehicles, fixed-wing aircraft, or even satellites) and analyzed, the situation on the ground may be quite different. Furthermore, if a producer is not prepared to employ a variable-rate management approach, there may be less utility in such assessments outside of a simple survey of overall field variability.
Sap flow
Like all technologies, there are trade-offs when adopting sap flow sensors for research or management. Benefits of sap flow sensors include continuous measurements, noninvasive options, and low-cost options. The great advantage of sap-flow sensors, compared with leaf gas-exchange and ψ measures, is that sap flow can provide continuous measurements that are autonomously collected. This is especially beneficial if a field site is remotely located or difficult to access. In such a situation, the sap flow sensors can be installed, connected to a data-logger, and have data wirelessly transmitted or collected periodically (e.g., weekly or monthly). Other benefits of sap-flow sensors are that they are relatively simple units and can be constructed for a low cost per sensor (Skelton, 2017), allowing for higher levels of replication. Traditionally, sap flow sensors were constructed of needle-like materials that were inserted directly into the sapwood (Granier, 1987). However, new methods are available for noninvasive, external sap flow sensors that can be attached to small diameter and herbaceous plant material (Clearwater et al., 2009; Nackley, 2019).
The main disadvantage of sap flow systems is that they are data and electronically intensive. The continuous nature of sap flow sensing means that a user can potentially have many data points per minute, creating a large data set over a month, seasons, or year. Thus, data management must either be automated or managed by a data analyst. Although this is possible for research scientists, commercial growers may need to purchase a consumer-grade sap flow system that incorporates analysis or pay for trained consultants to analyze data. An additional downside of sap flow sensors is that data can regularly be lost. The set-it-and-forget-it aspect that is beneficial for remote locations can also create gaps in data if the sensor becomes damaged or dislodged and the problem is not detected quickly. Cloud-based wireless communication can create alerts, but these types of connections may not be available or may be cost-prohibitive.
Finally, sap flow measurements are highly variable among sensors and also among installation locations on a plant. Sap flow measurements require normalization to trunk or stem cross-sectional area to determine total water flux, but this assumes that sap velocity is radially uniform. However, in woody perennial plants, this is not likely the case, as was demonstrated by Pearsall et al. (2014) in grapevine. Pearsall et al. (2014) showed that when five sensors were installed around a mature grapevine trunk, midday velocities differed by 80 to 90 cm·h−1 among sensors over a 1-week period. Additionally, the researchers showed that sensor signals varied strongly from year to year despite similarities in vine water use measured with a weighing lysimeter. Nevertheless, individual sensors were all highly correlated with measured water use. Therefore, sap flow sensors may not be reliable tools for accurate absolute measures of plant transpiration—and subsequent water volume replacement with irrigation—but rather can be used to obtain relative estimates of high or low transpiration.
Carbon stable isotope ratio
Although traditionally used as an integrated marker of WUE in leaves, the δ13C of grape must has been recently shown to be highly correlated with overall grapevine physiological function across diverse mesoclimates, soils, and cultivars in California (Brillante et al., 2020). Importantly, the aforementioned work demonstrated that δ13C reflected not only plant water status but was also a reasonable predictive proxy of plant nutrient status, photosynthesis, gS, and fruit quality. It was also related to soil electrical conductivity, a proxy for soil water content. Thus, δ13C serves as a reliable indicator of vineyard performance and WUE over the course of the season and can be used to ground truth other sensing technologies.
The main advantages of δ13C measurements are that they are inexpensive and easy data to collect. They can be collected a few times of the year and provide a useful integration of the overall water stress experienced by plants over the course of the sampled organ’s lifespan (e.g., a leaf or a fruit). The integrated nature of the measurement coupled with its temporal stability is useful for delineating management zones in the field, allowing for the application of variable rate irrigation management techniques. In addition, it can be used as an auditing tool to check on the effectiveness of other irrigation management practices.
The main disadvantages of δ13C measurements are that they are not dynamic and as such are not useful for in-season management decisions. Despite their affordability and ease of sample collection, the time from sampling to actionable data is longer than that of typical irrigation cycles (e.g., 1 week). Thus, δ13C measurements remain most useful for auditing, planning, and/or zoning.
Conclusions
All of the plant-based irrigation management tools reviewed in this article meet the requirements outlined in the introduction: 1) they are sensitive and consistent indicators of plant water status (or proxies thereof), 2) they accurately reflect soil water availability (although some better than others), and 3) they are highly correlated to other physiological processes of importance. However, each tool or method has its own unique set of advantages or disadvantages (summarized in Table 1). As the American economist Thomas Sowell famously said: “There are no solutions, there are only trade-offs; and you try to get the best trade-off you can get, that’s all you can hope for.” In other words, all tools get the job done but in different ways. Some are inexpensive and great for integrated/long-term assessments but are not dynamic enough for real-time management (e.g., δ13C or even remotely sensed CWSI), whereas others can provide useful information that can be used for real-time management (e.g., pressure chamber or sap-flow) but are expensive. Thus, the user must carefully weigh each method in the context of their specific objectives and production goals.
Summary of reviewed plant-based methods including their applications and the advantages and disadvantages for each one.
Units
Literature cited
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Wikipedia 2021 Leaf 8 June 2021. https://en.wikipedia.org/wiki/Leaf
