# Acoustic Emissions to Measure Drought-Induced Cavitation in Plants
## 3 Acoustic Emission (AE) Application to Measure Drought-Induced Cavitation: From Past to Present
However, they stayed indecisive about the possible origin of the signals. Based on the waveforms, [PERSON] _et al._[68] 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 [34] and [PERSON] _et al._[66] 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 [69; 70; 71; 72; 73]. Already in 1983, [PERSON] and [PERSON] [43] (page 1099) were looking forward to the scientific progress that could be realized with the AE method:  
Figure 2: Picture of the vibration detector used by [PERSON] and [PERSON] [42] to detect drying-induced sounds in leaf petioles. \"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, [PERSON] ([PERSON], 2016), 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 (K  
The difficulty in determining the endpoint of VCs based on AEs has also been addressed by [PERSON] _et al._[84]. To tackle this problem, these authors hypothesized that the highest acoustic activity  
Figure 4: **(a)** 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 on \(\upmu\)CT images when plotted against relative radial diameter shrinkage (\(\Delta d/d\)), which is a measure for decreasing xylem water potential. The \(\upmu\)CT cross-sections of the grapevine branch are shown for the beginning (**d**) and end (**b**) of the dehydration experiment and at the breakpoint between Phases I and II (**c**). The grey zone in (**a**) delimits the 99.7% confidence interval around the mean cumulative AE curve [33]. Figure 3: [PERSON] _et al._[80] 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 \({}^{\star\star\star}\) symbol). The black line shows the curve of cumulative AE. Results of measurements on three different grapevine branches are shown. 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.