Adequate deposition in the whole canopy according to the specifications of the treatment is one of the objectives of a pesticide application. Meanwhile spray drift continues to be a major problem in applying agricultural pesticides. Drift can cause crop protection chemicals to be deposited in undesirable areas with serious consequences [1
]. Drift reduction and improvement of efficiency of pesticide application process is one of the goals of the 128/2009/CE European Directive for a Sustainable Use of Pesticides [2
]. The imminent and mandatory establishment of National Action Plans by every European Union (EU) member will include the definition, establishment and quantification of buffer zones with quantitative information about drift potential of every sprayer and configuration. According to ISO 22866:2005 [3
] drift is defined as “the quantity of plant protection product that is carried out of the sprayed (treated) area by the action of air currents during the application process”. In an orchard setting, this includes droplets which move horizontally through the orchard canopy and out the sides of the orchard, and droplets which are above the canopy (due to direct spraying into the air or diffusion up from the sprayed canopy) and move vertically into the atmosphere. Most drift involves droplets which move above the canopy for some or all of their pathways [4
A realistic representation of spray drift could, for example, not only reveals a given percentile of the spray drift expected at a given distance from a field, but it could show the entire range of spray drift that might be observed, caused by different weather conditions or the equipment (nozzle type) [5
]. Spray drift has been studied extensively [6
], in a series of field trials and for many crops. The results from these studies are currently used in pesticide registration in the EU. Specifically, the 90th percentile of all measured “drift values” (the amount of drifted residues) is commonly applied in ecotoxicological risk assessments. The data include the variability of spray drift between different fields (field trials) and the variability within fields (different Petri dishes placed at the same distance from the field border). But, despite the wide variety of collected data, not all the scenarios can be identified. Spray drift is highly influenced by many factors that may be grouped [8
] into one of the following categories: equipment and application techniques; spray characteristics; operator care and skill. Diverse methodologies [9
] developed in the last years to evaluate and quantify the effect of different parameters involved in the process, in a big effort to define a spray classification, have always resulted in great variability due to the influence of environmental conditions.
In general, arrangement of field tests for drift measurement is very difficult and expensive. The ISO 22866:2005 norm defines the procedure to quantify drift during field tests, but this method is complex, time consuming and depends heavily on external conditions such as wind, being difficult to adopt and may have poor result repeatability. These facts, together with the need to maintain the spray track perpendicular to the wind direction make the arrangement of field tests a cumbersome and difficult process. Other researchers [13
] have concluded that a sequence of experiments could last for several hours avoiding changing the line of measurements as long as the average wind deviation was in the range of ±30° from the original line.
But independent of the difficulties of field trial arrangements, the key problem in spray drift and dispersion assessment studies [14
] has been the quantification of spray droplet concentration as it cannot be accurately extrapolated from point measurements to determine spatial dispersion [4
]. It helps conclude that presently available direct and indirect methods of spray drift measurements were inadequate for measuring plumes of drifting aerosols. For these reasons, different authors have proposed different drift measurements, in an attempt to develop easy, repeatable and precise methods as an alternative to current standards. There are many methods available for sampling spray drift, and a great variety of estimates of spray drift have been published based on mathematical analysis [15
], probabilistic estimations [5
] or through the development of computational models based on indirect drift measurements [16
]. In [19
] the authors developed a drift prediction equation for reference spraying to predict the expected magnitude of sedimenting drift for various drift distances and atmospheric conditions. In [20
] a new drift test bench for measurement of drift generated by a boom sprayer in a simpler and quicker way than the ISO 22866:2005 methodology was developed. The same device was successfully used by [21
] to assess drift potential of a citrus herbicide applicator.
Sensor technology is an interesting alternative for drift evaluation purposes. Several studies [4
] were carried out using Light and Detection Ranging (LIDAR) technology to measure drift. The authors of [23
] used LIDAR to measure near-field pesticide spray movements in wing-tip vortices of a spray aircraft but not downwind drift. Stoughton et al
] adapted LIDAR technology to measure pesticide movement above an oak forest. The LIDAR system was found to be a highly useful spray plume movement measuring tool, as evidenced by the return images of spray material aloft for up to 2000 m downwind and well up into the mixing layer.
The specific scenario of spray processes in orchards is one of the most risky activities from the environmental point of view. In these cases, several researchers have selected LIDAR as an alternative device for drift measurement. In [4
] a LIDAR system developed at the University of Connecticut was used to measure the concentration of small droplets in the air above an orange orchard canopy during and after the sprayer operation. The LIDAR sensor was able to measure and evaluate airborne drift differences between stable and unstable conditions. The authors of [25
] developed a model to predict airborne drift according the target structure. The model utilizes LIDAR measurements of optical transmission to predict the characteristics of airborne drift of plant protection product's (PPP) leaving the target orchard at different growth stages and modified drift characteristic for different methods of dose adjustment. Good agreement was demonstrated between the measurements and predictions of drift from a semi-dwarf apple orchard at full-dose application rates. LIDAR systems have been used successfully to observe spray dispersion in stable [22
] and unstable atmospheric conditions [26
]. The technique has also been used for monitoring dispersion of smoke from forest fires [27
]. In [28
] a methodology to calibrate a scanning elastic backscatter LIDAR and extrapolate droplet point measurements in both space and time was developed.
The objectives of this research were to verify the use of a LIDAR sensor to measure the drift cloud during pesticide application in a vineyard and to study the effect of different working parameters (nozzle type, sprayer characteristics and air settings) on the total amount of liquid exceeding the target canopy.
In general the use of the LIDAR sensor represents an interesting and easy technique to establish the potential drift of a specific sprayer settings and environmental conditions. LIDAR system provides an idealized optical view of spray droplet escaping the canopy and its distribution away from the target. Furthermore, it allows to evaluating drift with less labor, cost and time than other current methods.
The use of test bench for drift measurement allows quantification of the amount of spray fraction escaping the canopy but the time required for the process is much higher than the one dedicated to LIDAR measurements.
In general, good correlation has been observed between the measured drift cloud with LIDAR and deposition distribution obtained on the artificial collectors placed in the test bench. However, it seems that drift measurements using LIDAR can be affected by droplet size.
The two proposed methods for drift measurement have shown potential in discriminating the effect of the different working parameters (nozzle type, air velocity and type of sprayer) on the drift. However, the results indicate a better ability of LIDAR sensor to evaluate spray drift in case of dense drift cloud. Additionally, further research should be arranged in order to assess the effect of sprayer's settings in the final droplet size in field conditions.
This technique will help the users to adjust an adequate deposition in the whole canopy according to the specifications of the treatment and could be used as a drift predictor tool depending on the target geometry, also in accordance with [25