Acoustic Emissions to Measure Drought-Induced
Cavitation in Plants
Linus De Roo *,† , Lidewei L. Vergeynst †, Niels J.F. De Baerdemaeker and Kathy Steppe
Laboratory of Plant Ecology, Department of Applied Ecology and Environmental Biology, Faculty of Bioscience
Engineering, Ghent University, Coupure links 653, 9000 Gent, Belgium; email@example.com (L.L.V.);
firstname.lastname@example.org (N.J.F.D.B.); email@example.com (K.S.)
*Correspondence: firstname.lastname@example.org; Tel.: +32-9-264-6115
† These authors contributed equally to this work.
Academic Editor: Dimitrios G. Aggelis
Received: 30 December 2015; Accepted: 27 January 2016; Published: 3 March 2016
Acoustic emissions are frequently used in material sciences and engineering applications
for structural health monitoring. It is known that plants also emit acoustic emissions, and their
application in plant sciences is rapidly increasing, especially to investigate drought-induced plant
stress. Vulnerability to drought-induced cavitation is a key trait of plant water relations, and contains
valuable information about how plants may cope with drought stress. There is, however, no consensus
in literature about how this is best measured. Here, we discuss detection of acoustic emissions as a
measure for drought-induced cavitation. Past research and the current state of the art are reviewed.
We also discuss how the acoustic emission technique can help solve some of the main issues regarding
quantiﬁcation of the degree of cavitation, and how it can contribute to our knowledge about plant
behavior during drought stress. So far, crossbreeding in the ﬁeld of material sciences proved very
successful, and we therefore recommend continuing in this direction in future research.
Keywords: cavitation; embolism; acoustic emission detection; vulnerability curve; drought
Stress in materials or structures is often accompanied by the built up of mechanical pressures,
which, upon release, lead to elastic wave propagation away from the stressed zone [
]. These waves are
called acoustic emissions. Today, the acoustic emission technique is widely applied for material testing
and structural health monitoring on engineering materials such as concrete [
], metal alloys [
ﬁber composite materials [
]. However, the oldest reported scientiﬁcally planned acoustic emission
experiment avant la lettre was performed on wood in 1933 by Fuyuhiko Kishinouye [
], long before
the term acoustic emission (AE) was introduced by Schoﬁeld in 1961 [
]. In 1966, Milburn and Johnson
used a similar measurement set-up as Kishinouye to detect for the ﬁrst time AE signals in plants,
when they were subjected to dehydration.
Nowadays, drought associated with global warming gains increasing interest. How plants cope
with drought stress is a topic of an intense debate [
] and urges the need for a good measure
of drought stress. In this paper, the relevant literature contributing to the development of the AE
technique is reviewed in order to propose it as a promising method to measure drought-induced
cavitation in plants.
Before starting, we want to point out the different use of the concept stress between physicists
and plant scientists. As described by Lichtenthaler [
] and according to physics, stress in plants
means the state of a plant under the condition of a force applied. The response of the plant to this
stress is called strain as long as no damage occurs. This distinction is not often made in plant sciences,
where stress and strain are mostly used interchangeably, but which can be confusing in other research
Appl. Sci. 2016,6, 71; doi:10.3390/app6030071 www.mdpi.com/journal/applsci
Appl. Sci. 2016,6, 71 2 of 15
ﬁelds. Therefore, in what follows, we will use both physically deﬁned terms as an attempt to also
introduce this stress concept in plant sciences.
2. The Importance of Water
On a daily basis, plants extract water from the soil via the roots from where it is transported in
the stem towards the leaves, where it eventually transpires into the atmosphere [
]. This seemingly
wasteful process is vital for plant survival. Water is the transport medium that carries nutrients
from the soil towards the plant organs and distributes generated carbohydrates throughout the plant.
The evaporation of water in the stomata of leaves provides a cooling function, which is necessary to
prevent overheating of leaves during sunny days. The water in living cells also provides a crucial
role in the ﬁrmness and elasticity of soft tissues. The positive pressure that is exerted on the cell
walls, called turgor pressure, is essential for growth (through cell growth and cell division) and fulﬁlls
the role of backbone in non-woody tissues such as leaves and petioles. Only a minor fraction of the
transported water (<1%) is used to make new plant material through photosynthesis [
this multitude of functions, it is hence no wonder that water shortage is one of the main causes of
plant mortality [21,22].
Water is transported in the xylem tissue of the plant following a gradient in water potential. Xylem
is a porous structure of dead cells containing a network of parallel conduits, interconnected by pits [
The xylem conduits operate under negative pressure, or tension. According to the cohesion-tension
], the origin of this tension is the evaporation of water in the stomatal region. When the
stomata in the leaves are open, water is transpired due to the difference in water vapour pressure
between the atmosphere and the substomatal cavity. As water evaporates into the air spaces in the
leaf, water menisci in the small capillaries (nanometer scale) in adjacent cell walls are retracted and
capillary forces (due to strong adhesion) pull the menisci back towards the surface. The network of
many small capillaries in the cell wall acts thus as a wick for water rise [
]. Thanks to the strong
cohesion between water molecules, the tension is transmitted downwards and water can be drawn
towards the leaves. The tension in the xylem conduits may increase enormously when faced with a
dry soil or with a great transpirational demand that exceeds the rate of water supply from the roots or
from internal water reserves. This involves a risk of gas bubbles entering the conduits, which may
expand and quickly ﬁll the whole conduit. The water released from the conduits during this process
may contribute to the transpiration stream, but on the other hand, the water conducting system will be
locally interrupted. Because adjacent conduits are interconnected via pits in the conduit walls, the sap
may circumvent the embolized conduit. However, when too many conduits are embolized, this will
impair plant functioning. The formation of air emboli that block sap ﬂow in xylem conduits is currently
of high interest because it is one of the key processes leading to plant mortality during drought [
The phenomenon is called cavitation, which is the mechanical breakage of the continuous xylem water
column and occurs when the tensile strength of the column is exceeded [
]. According to the current
knowledge, the main cause of cavitation is the failure of a pit in the conduit wall to prevent gas from
entering the conduit at strong tension [
], known as the air-seeding hypothesis [
]. Recently, Schenk
et al. [
] postulated that nanobubbles are snapped off during air-seeding. These nanobubbles are
stabilized by surfactants and may exist in plant sap under tension. They may eventually result in an
embolism when the size of the nanobubble exceeds a critical threshold due to increasing tension or
when many nanobubbles coalesce.
A plant’s vulnerability to cavitation is often used as a key feature of its drought resistance [
and has been deﬁned by plotting the percentage loss of hydraulic conductivity (PLC, %) against
decreasing xylem water potential (
, MPa), which results in a vulnerability curve (VC) (Figure 1) [
The xylem water potential at 50% PLC (P50) is the most common parameter to describe a species
“drought resistance”. The cumulative number of AE, originating from cavitation events, are a good
estimate for conductivity loss [
] and thus has the potential to be used as an indirect and
non-destructive method to construct VCs and to determine drought resistance of plants.
Appl. Sci. 2016,6, 71 3 of 15
Typical vulnerability curve with P12, P50 and P88 representing the xylem water potential at
which, respectively, 12%, 50% and 88% of xylem hydraulic conductivity is lost, adapted from Domec
and Gartner  and Fichot et al. .
Because of prevailing tensions in plants, xylem is under a metastable state [
], which makes
it a difﬁcult object to study. A small intrusion in the tissue will cause the formation of emboli in the
conduits, hereby inﬂuencing the very mechanism that is about to be studied. Therefore, the current
destructive methods are under intense debate [
], triggered as well by recent evidence for possible
artefacts in these techniques [
]. The use of the non-destructive AE technique has therefore gained
renewed interest in plant sciences [
]. In particular, the question about how plants cope with
drought stress in a changing climate, and which mechanisms are underlying drought resistance are
gaining increased importance [
]. Application of AE in plant research can help with answering
these fundamental questions to increase our knowledge on drought tolerance of plants and their ability
to recover from and adapt to predicted future droughts.
3. Acoustic Emission (AE) Application to Measure Drought-Induced Cavitation: From Past
As stated in the introduction, Milburn and Johnson [
] recorded sounds in plants when they
were subjected to dehydration. They detected audible vibrations (<20 kHz) in petioles of dehydrating
leaves of diverse plant species by ﬁxing the petiole on a phonograph pick-up needle (Figure 2). Because
measurements were often disturbed by environmental noise, the step was made towards AE detection
in the ultrasonic frequency range (>20 kHz) by Tyree and Dixon [
]. This has facilitated various
experiments because the problematic ambient noise in the audible range could be electronically ﬁltered.
Based on similar sound production by the rupture of plant sap under tension in glass tubes [
it was hypothesized that the rupture of sap in plant conduits produced the observed sounds during
drying. Although extensive circumstantial evidence was provided to support this hypothesis [
it was realized that sounds may be produced by other mechanisms too. Sounds were observed during
drying of plant tissues that do not contain conduits [
], during re-watering [
], and also during
] and thawing [
]. The AE technique is also applied to monitor internal cracking of
wood during drying [
] and soaking [
]. Moreover, Gagliano et al. [
] speculated that plants may
actively produce sounds for short-distance communication. AE detection is thus an interesting tool,
applicable in a wide range of domains. Further scope of this review is, however, on its application in
the detection of drought-induced cavitation in plants.
Appl. Sci. 2016,6, 71 4 of 15
Picture of the vibration detector used by Milburn and Johnson [
] to detect drying-induced
sounds in leaf petioles.
During dehydration of fresh plant material, AE signal detection was found to be a valid
method to measure cavitation in woody branches [
], leaves [
], herbaceous stems [
] and sap
wood sections [
], while others found a poor correspondence between hydraulic and acoustic
]. Especially in angiosperm species, the continued AE activity after loss of
most of the hydraulic conductivity was a great cause of concern [
]. Other processes than
cavitation in sap-conducting elements that could cause extra AE signals during drought stress have
been suggested by various authors: cavitation of ﬁbers, wood tracheids or ray cells [
mechanical strains [
], and microscopic failure [
]. Moreover, the actual mechanism that
causes cavitation-related AE signals is not exactly known. Different processes have also been suggested:
vibration of the conduit wall after sudden pressure release due to cavitation [
], oscillation of
hydrogen bounds in water after pressure release [
], conversion from liquid water to vapour during
cavitation of the water [
], pit membrane rupture [
], torus aspiration (in gymnosperms [
the entry of a gas bubble through a pore in the pit membrane [
], and subsequent bubble
]. Moreover, as the number of detected cavitation related AE signals may be larger
than the number of cavitated conduits [
], different AE-inducing processes might be involved
Attempts have been made to use signal features in order to get more insights into the underlying
processes. Ritman and Milburn [
] suggested that vessel length has an inﬂuence on the cut-off
frequency of the detected signals. They suggested that cavitation in long vessels produced broadband
signals with frequencies down to 500 Hz, whereas cavitation in ﬁbers and wood tracheids only
produced higher frequencies (>100 kHz). Tyree and Sperry [
] observed that, when detecting AE
signals in the frequency range 50–1000 kHz, the frequency spectra changed towards higher frequencies
when the degree of cavitation increased in Thuja,Pinus, and Acer stems. However, they stayed
indecisive about the possible origin of the signals. Based on the waveforms, Laschimke et al. [
distinguished two types of AE signals in dehydrating leaves of Ulmus glabra: the abrupt disruption
of the water column and an oscillating source that was related to the vibration of gas bubbles during
sap ﬂow. It was also found that AE energy is a function of conduit size and xylem tension [
Rosner et al. [
] found that this parameter might be a better measure for hydraulic conductivity loss
than AE counts.
Although further research is necessary to develop reliable methods for AE data interpretation,
this technique has a high potential for continuous long-term cavitation monitoring in the ﬁeld [
Already in 1983, Tyree and Dixon [
] (page 1099) were looking forward to the scientiﬁc progress that
could be realized with the AE method:
Appl. Sci. 2016,6, 71 5 of 15
“If it can be proved that most ultrasonic AE are a result of cavitation events, then we will have a
powerful diagnostic tool that may give us new insight into the water relations of trees and other plants.”
However, Quarles [
], who was especially interested in AE detection of fractures in wood,
realized that deeper insights in the AE method would be essential for successful implementation:
“For successful implementation, it will be essential to understand how the propagating acoustic
wave changes as a function of factors such as distance and propagating direction between the acoustic
source and the receiving transducer.”
During the past couple of decades, rapid developments in microelectronics have resulted in great
advances in the AE technique. Currently, high performance acquisition systems are available that are
able to record and store waveforms from multiple channels at high sample frequencies [
the large amount of research concerning AE application to measure drought-induced cavitation, the full
potential of these state of the art measurement systems has not been used so far. In what follows,
current state-of-the-art and applications of AE in cavitation research will be illustrated as well as future
opportunities. These deliver important ﬁndings that contribute to the development of a powerful
diagnostic AE-tool for online and non-destructive measurement of cavitation.
4. Current State-of-the-Art and Application of AE in Plant Sciences
4.1. Endpoint Selection
Due to the many different AE sources acting during plant dehydration, AE activity can be detected
long after most of the hydraulic conductivity is lost [
]. This makes it difﬁcult to determine the
endpoint (i.e., 100% PLC) of the VC, which physiologically corresponds with complete cavitation
of the xylem vessels and, thus, the full breakdown of the plants hydraulic pathway. As a possible
solution, many studies make use of arbitrary methods. In addition, they focus more on gymnosperms,
because gymnosperms have a more uniform and plain anatomy, and, thus, a more straightforward
AE pattern compared to angiosperms [
]. Wolkerstorfer, Rosner et al. [
] ﬁltered AE measured on
dehydrating Pinus by drawing a straight line “by eye” through the point cloud of amplitude versus
time. If no clear groups can be distinguished in the point cloud, they suggest using other AE features
such as cumulative amplitude or energy until plausible VCs can be constructed. However, one has
to be cautious when using this method, because the ﬁltering has a strong effect on the estimated
vulnerability. Other arbitrary methods include setting the endpoint at a water potential value that
equals (i) the endpoint taken from parallel testing with another method [
]; (ii) a value taken from
literature; or (iii) the turgor loss point of the leaves [78,79].
Vergeynst et al. [
] developed a more mathematical approach to determine the VC endpoint.
Given that PLC should by deﬁnition be linked to cumulative AE, and that, according to
Aggelis et al. [
], a speciﬁc AE-inducing mechanism results in an AE activity, Vergeynst et al. [
recommended determining the VC endpoint by the end of the AE activity peak, which mathematically
corresponds to the local maximum of the third derivative of the curve of cumulative AE versus time
Although the AE activity may continue after the selected endpoint (Figure 3), the obtained P50
2.73 MPa) corresponded well with values found in literature for grapevine (
2.97 MPa; [
]), from which was concluded that the endpoint was accurate, and that the remaining
AE activity was related to other sources than cavitation. Strong evidence for their approach was given
by validation with X-ray micro-computed tomography (
CT), which showed quite similar patterns for
both visually and acoustically detected cavitation  (Figure 4).
Appl. Sci. 2016,6, 71 6 of 15
Time of deh
Vergeynst et al. [
] calculated the point of 100% loss of hydraulic conductivity (P100) as the
endpoint of the acoustic emission (AE) activity (ﬁrst derivative of the curve of cumulative AE) peak,
where the third derivative of the curve of cumulative AE signals in time reached a local maximum
(indicated by the vertical dashed line and the “*” symbol). The black line shows the curve of cumulative
AE. Results of measurements on three different grapevine branches are shown.
Time of deh
) Cumulative AE (cum AE, grey symbols, left y-axis) detected by four different AE sensors
(1–4) showed a pattern similar to the number of cavitated vessels (black symbols, right y-axis) counted
CT images when plotted against relative radial diameter shrinkage (
d/d), which is a measure
for decreasing xylem water potential. The
CT cross-sections of the grapevine branch are shown for
the beginning (
) and end (
) of the dehydration experiment and at the breakpoint between Phases I
and II (
). The grey zone in (
) delimits the 99.7% conﬁdence interval around the mean cumulative
AE curve .
The difﬁculty in determining the endpoint of VCs based on AEs has also been addressed by
Nolf et al. [
]. To tackle this problem, these authors hypothesized that the highest acoustic activity
Appl. Sci. 2016,6, 71 7 of 15
should occur near the steepest part of the VC, which is the inﬂection point, reﬂecting P50. They obtained
good similarity when comparing their method with hydraulic measurements of 16 species. The major
drawback of this approach is that the VC has to have a perfect sigmoidal S-shape. Any deviation from
this ideal curve might cause the steepest part to deviate from the targeted P50 value.
4.2. AE Feature Extraction
A major challenge in using AEs as an indirect measure for cavitation is determination of the source
of the detected signal [
]. The AE signal is shaped by the followed path from source to sensor, which
makes signal interpretation not straightforward because of the wood’s anisotropic characteristics [
These include differences in sound propagation in the three wood directions, wood density, xylem
water content and wood anatomy [
]. Previous AE application in cavitation research mainly focused
on the cumulative AE signal because of the assumption that the majority of the signals correspond to a
loss in hydraulic conductivity [
]. Given the many different AE sources during dehydration,
this assumption is, however, not always valid [
]. Extraction of the AE signals caused by cavitation
is therefore necessary. A method that has already proven its success is extraction of the signals based
on corresponding wave features. In industrial lumber drying, for example, the amplitude and energy
of AE signals were used to pinpointing wood checking [88–92].
Rosner et al. [
] measured AEs on juvenile and mature wood samples, taken from living Picea abies
trees, with a broadband sensor in the frequency range 100–1000 kHz. After extraction of the waveform
features amplitude, rise time, rise angle and absolute energy (Figure 5), cumulative AE energy
appeared to be a good measure for PLC and, thus, for VC development. This was conﬁrmed in
other studies on leaves of P. ponderosa,P. nigra,C. chrysophylla and P. japonica [
], and on P. abies
wood samples [
]. Mayr and Rosner [
] found a correlation between tracheid lumen area and
mean AE energy in P. abies wood samples using a 150 kHz resonant sensor, which responds most
strongly to acoustic waves in the 50–200 kHz frequency range. A comparison between samples with
different early-latewood distribution revealed that the mean energy of AEs during wood drying
increased in earlywood but decreased in samples containing more latewood. This was attributed to the
homogeneous structure of earlywood and its larger tracheid diameters, causing AE energy to increase
with increasing xylem tension. More resistant latewood tracheids, which cavitate at a more negative
xylem tension, emitted lower energy because of their smaller diameters and more heterogeneous
]. Ponomarenko et al. [
] found a similar relationship, conﬁrming that AE energy is
linked to mechanical elastic energy, which is released during cavitation in conifer tracheids. Whereas
AE feature extraction to determine cavitation-related AE has been successfully applied in conifers,
it remains a challenge for the more heterogeneous angiosperms [31,34].
0 800 kHz
PF FC WPF
0 50 s
) Frequency features peak frequency (PF), weighted peak frequency (WPF), frequency
centroid (FC) and four partial powers (PP) were used for the automated clustering algorithm;
) Waveform features rise time, amplitude and rise angle describe the shape of the AE signal [
Appl. Sci. 2016,6, 71 8 of 15
Vergeynst et al. [
] proposed clustering to determine cavitation-related AE in angiosperms.
By using state-of-the-art techniques from material sciences, ﬁnite element modelling and an automated
clustering algorithm [
], in combination with broadband point-contact sensors, they were able
to extract the AE that originated from cavitation during dehydration of grapevine branches. The ﬂat
frequency response of these sensors in a wide frequency range (20–1000 kHz) allows differentiation
between different AE sources. Instead of relating waveform features of AE signals with cavitation,
Vergeynst et al. [
] recommended to use the frequency features of AE signals. Following Sause and
], they extracted from the frequency spectrum, peak frequency (PF), frequency centroid (FC),
weighted PF (WPF, geometric mean of PF and FC) and the partial powers of the following frequency
ranges: 0–100 kHz, 100–200 kHz, 200–400 kHz and 400–800 kHz (Figure 5). The signals that typiﬁed
the cavitation phase were characterized by a high intensity at 100–200 kHz. Another signal type, with
high intensity in the range 400–800 kHz, was strongly related with branch shrinkage, and probably
originated from micro-fractures, which are small ﬁssures in the stretched cell wall or pith membrane
due to an initial volume change. Finally, a low-frequency signal type (high intensity below 100 kHz)
was identiﬁed that was attributed to macro-fractures or free drainage of water through the porous
wood medium. This clustering approach to identify cavitation-related AE signals is a great advance,
and may lead to the development of a powerful tool to also investigate cavitation in plants in their
native environment. AE sources, such as micro- and macro-fractures, will no longer disturb cavitation
measurements but may provide additional information on, for instance, use of elastic water reserves
or mechanical strains.
It has often been assumed that one AE signal represents cavitation of a single water column [
However, except from the observed one-to-one relationship by Tyree and Dixon [
] and Lewis [
on small sapwood samples of two gymnosperm species, the number of AE signals has been either
] or higher [
] than the number of cavitating conduits in further experiments.
In measurements on grapevines, the number of cavitation-related AE signals exceeded the number
of vessels in the branch by one or two size orders [
]. Moreover, the amplitude distribution with
maximum signal density near the detection threshold of 28 dB
suggested that a considerable part of
the signals could not be detected because their amplitude fell below the detection threshold, and thus
probably even more AE signals are produced during xylem cavitation [
]. The use of less sensitive
measurement systems, or higher detection threshold settings, might explain cases where less AE
signals than cavitated conduits were observed.
One reason for the higher number of signals observed by Vergeynst et al. [
] could be overlapped
between adjacent cluster types, so that part of the cavitation-related signal type possibly originated
from other co-occurring AE sources, such as micro-fractures or water drainage. This overlap might be
caused by different attenuation of the AE signals dependent on their frequency. The effect of attenuation
was also observed during a freeze-thaw experiment, where it decreased in frozen samples [
A second hypothesis is that the cavitation process generates many AE signals. According to the
nanobubble theory of Schenk et al. [
], many nanobubbles may be formed and exist in the xylem
before they coalesce and form an embolism. Coalescence of these nanobubbles and subsequent bubble
collapse may result in much more AE signals than cavitated conduits. Alternatively, ﬁssures in the
stretched pith membrane or rearrangements of the cell wall layers due to pressure redistribution after
cavitation may cause many AE signals per cavitated conduit. Further research and more detailed
modelling of the actual micro-mechanical processes at the AE source, such as bubble formation and
coalescence, may hopefully throw light on cavitation-related AE sources and, accordingly, on the
processes behind emboli formation.
4.3. In Vivo Measurements
Dealing with drought stress is a dynamic process and the resistance of a plant against drought
depends on both physiological and anatomical characteristics [
]. To obtain a good understanding of
a plant’s behavior during drought stress,
measurements on plants are necessary. Conventional
Appl. Sci. 2016,6, 71 9 of 15
methods to determine a plants’ vulnerability to cavitation are destructive, and their results are recently
under intense debate because several artefacts are reported to play a role [
]. In addition,
these methods are labor-intensive, which hampers their applicability in the ﬁeld. The call for
non-destructive methods is therefore now more urgent than ever [
]. Cochard et al. [
CT as the standard technique to measure hydraulic vulnerability, but this method is
not suitable for ﬁeld applications. Today, the AE technique is used in a destructive way and, thus,
subject to similar artefacts as the conventional methods, but in contrast to these established methods,
the AE technique has the potential to measure non-destructively, enabling automated and continuous
measurements of cavitation in the ﬁeld. Because of the difﬁcult signal interpretation of AEs, only a few
studies have focused on the possibility of
measurements of cavitation on actively growing trees
with acoustic sensors. Field measurements on P. sylvestris with a 150 kHz resonant sensor during the
growing season showed good relationships between AE activity and stem diameter variation [
sap ﬂow [
]. In addition, such continuous measurements will provide valuable information on
plant behavior during drought and, more speciﬁcally, contribute to the debated process of cavitation
When using the AE technique on living plants in lab or ﬁeld conditions, Vergeynst et al. [
] use a
broadband point-contact sensor (20–1000 kHz) with ﬂat frequency response in order to differentiate
between the different AE sources. Based on preliminary analysis and clustering, a minimum set of
signal frequency features may be selected (weighted peak frequency, frequency centroid and partial
powers) that enable discrimination between different AE sources. However, when it is sufﬁciently
demonstrated that the resonant sensor shows good results, its use might be preferred in further
practical applications because of the more straightforward data processing and interpretation. A second
point of attention for
measurements is the particular nature of cavitation detection. Contrary
to the hydraulic method and visualization methods, the AE technique measures changes in the
degree of embolism rather than the degree of embolism itself. This principle should be kept in
mind when preparing and interpreting AE measurements of cavitation. If the initial state of xylem
embolism is known, cumulative AE measurements might result in the degree of embolism if cavitation
has not been repaired in the measurement period. This will require a reference number of AE
signals at 100% embolism, which may be obtained in a VC or at the end of a drought experiment.
Combination of broadband AE measurements with other non-destructive measurements of sap ﬂow,
water potential, water content and xylem/phloem shrinkage [
] may yield the largest insight into
plant water dynamics during drought. A better knowledge of these dynamics is crucial to feed
mechanistic plant and climate models and will help with guiding mitigation of climate change impacts
on plants in natural and agricultural communities .
5. Conclusions and Future Perspectives
How plants cope with drought in a changing climate is an active area of research, but a lot of
questions remain unanswered. Mechanisms and strategies that underlie plant survival and mortality
during drought are the subject of an intense debate [
]. The main drivers of this discussion are,
however, the used methods instead of the actual mechanisms that occur in plants [
Development of a universal technique to measure cavitation, and vulnerability to it, will be essential
to bring the debate to a next level, which will change the focus towards physiology behind drought
resistance. In this review, we showed that revival in the use of AEs for detection of plants’ vulnerability
to drought is very promising. It might become a powerful non-destructive, readily automated and
online method. Although great advances were recently made in dealing with the main criticisms of
the technique, such as being indirect and endpoint selection difﬁculties, further research is needed.
The behavior of AEs in plant material has to be studied, and questions regarding wave propagation
through wood, the behavior of dehydrating wood, and the mechanisms behind cavitation need to
be answered in order to fundamentally link AEs and cavitation events. In order to achieve this,
Appl. Sci. 2016,6, 71 10 of 15
A combination of cavitation measurements with AE broadband sensors and
CT in a broad range
of plant species: interpretation of the acoustic signals in combination with visuals will govern the
largest insights in the mechanisms underlying cavitation.
Feature extraction from the AE signals: this will allow a comprehensive analysis of the AE sources,
and will deliver valuable information on cavitation and other AE producing processes that occur
in drought-stressed plants.
Detailed study of wave propagation and attenuation in plants, both in dehydrating and in
Investigation of the effects of debarking prior to acoustic measurements: as the bark can be an
additional AE source, this might inﬂuence the captured signals.
Further validation of the use of broadband point-contact AE sensors in the ﬁeld of plant hydraulics
versus the 150 kHz resonance AE sensor.
Development of an in situ AE measurement protocol for living plants, and its translation to
To conclude, the use of AEs in plant sciences to measure cavitation is promising and is gaining
increasing interest. As implementation of some of the state-of-the-art techniques from material sciences
already pushed frontiers in cavitation research, we believe that cross-fertilization between these
different scientiﬁc domains will also be beneﬁcial for both in the future.
The authors thank the Research Foundation—Flanders (FWO) for funding (research program
G.0941.15N granted to Kathy Steppe, and PhD Fellowship granted to Lidewei L. Vergeynst).
Lidewei L. Vergeynst, Linus De Roo and Niels J. F. De Baerdemaeker reviewed the
literature. Kathy Steppe supervised the work. Linus De Roo wrote most of the manuscript with the input of
Lidewei L. Vergeynst, and all authors contributed by discussing and reviewing drafts and the ﬁnal version of the
manuscript. Linus De Roo and Lidewei L. Vergeynst contributed equally to the manuscript.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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