SPRAY ADJUVANT CHARACTERISTICS AFFECTING AGRICULTURAL SPRAYING DRIFT

This study defined the main adjuvant characteristics that may influence or help to understand drift formation process in the agricultural spraying. It was evaluated 33 aqueous solutions from combinations of various adjuvants and concentrations. Then, drifting was quantified by means of wind tunnel; and variables such as percentage of droplets smaller than 50 μm (V50), 100 μm (V100), diameter of mean volume (DMV), droplet diameter composing 10% of the sprayed volume (DV0.1), viscosity, density and surface tension. Assays were performed in triplicate, using Teejet XR8003 flat fan nozzles at 200 kPa (medium size droplets). Spray solutions were stained with Brilliant Blue Dye at 0.6% (m/ v). DMV, V100, viscosity cause most influence on drift hazardous. Adjuvant characteristics and respective methods of evaluation have applicability in drift risk by agricultural spray adjuvants.


INTRODUCTION
Adjuvants are products added to the spray solution with specific functions.Activator adjuvants directly improve pesticide efficiency increasing plant absorption rate.Special purpose adjuvants reduce drift negative effects, without acting directly on pesticide efficiency (HAZEN, 2000;McMULLAN, 2000;PENNER, 2000).Previous studies with products other than Brazilian ones have shown physical properties changes in spray and reduced drift risk by adjuvant addition.BECK et al. (2013) found increased effective foliar application of entomopathogenic nematodes Rone B. de Oliveira, Ulisses R. Antuniassi, M arco A. Gandolfo Eng.Agríc., Jaboticabal, v.35, n.1, p.109-116, jan./fev. 2015 110 with adjuvant and spray nozzle combinations.These effects were confirmed both in experiments in which drift was measured by wind tunnels and directly in the field (OLIVEIRA et al., 2013).Potential drift risk have been satisfactory estimated by means of wind tunnel method (MOREIRA JÚNIOR & ANTUNIASSI, 2010).Although real drift conditions might only be obtained in field experiments, the wind tunnel experiments have a great advantage over those, once wind tunnels determine the potential drift risk of different application systems.These experiments could not be repeated and compared under field conditions due to weather variations (NUYTTENS et al., 2009;CHECHETTO et al., 2013;GANDOLFO et al., 2013;FRITZ et al., 2014;GANDOLFO et al., 2014).
Droplet spectrum has been recognized as the most important variable to be controlled to reduce spraying drifts, especially in aerial applications.Spraying produces drops of different sizes and, therefore, it is required technical criteria to analyze and quantify comparing droplet sizes produced by other equipment.Thus, many researchers have used the laser diffraction method to study and analyze the droplet spectrum of different equipment (MOTA et al., 2010;CHECHETTO et al. 2013;BUENO et al., 2013;OLIVEIRA et al., 2013).
The present study aimed to define the main adjuvant characteristics that influence and might contribute to understand drift formation process during agricultural spraying.

MATERIAL AND METHODS
The experiment was performed by spraying 33 aqueous solution of 18 adjuvant types at different doses and drift measurements were made by means of the wind tunnel under controlled conditions (Table 1).Adjuvants were chosen by their market acceptability at ma nufacturer recommended doses or simulating field conditions.These products belong to surfactant, mineral and vegetal oil and drift reducers, representing the commonly used functional groups of adjuvants in Brazil (Table 1).The water used in the experiment was distilled and had a surface tension of 72.6 mN m -1 .
Drift assays were performed by means of designed wind tunnel, which was developed and validated by MOREIRA JÚNIOR & ANTUNIASSI (2010).The tunnel has an open circuit and a 4.8-m-long closed test section, with a square test section of 0.56 m x 0.56 m (0.31 m 2 ) in 2.5 m useful length.The device is made of wood and produces a uniform and laminar airflow of 2.0 m s -1 , generated by a fan with a 180 W power engine.After formulated, solutions were placed in a 15-L stainless steel tank for storage and CO 2 pressurization by compressed gas cylinder.In addition, it was installed an anti-drip valve nozzle and a Teejet XR8003 VK nozzle to generate a jet perpendicular to the tunnel length, subjected to 200 kPa pressure, forming medium sized droplets.All solutions were stained with Brilliant Blue Dye at 0.6% (v/ m).To collect spray water deposit, polyethylene yarns with 2.0 mm in diameter and 0.56 m effective length (wind tunnel width) were used; these yarns were horizontally and perpendicularly placed to the tunnel length through wall holes and fixed by clamps.Polystyrene yarns were positioned at 1.0; 1.5; 2.0 to 2.5 m distant from spray nozzle along the tunnel length.At all points, yarns were fixed at 0.10 and 0.20 m high from tunnel floor.The environmental conditions were monitored and assays were only carried out under temperatures higher than 30 °C and 50% relative humidity.The wind tunnel test method was as described by MOREIRA JÚNIOR & ANTUNIASSI (2010).Droplet spectrum analyzes were performed using a drop analyzer in real time based on laser diffraction technique (Mastersizer S ®, version 2.15).This analysis quantifies variables such as DMV, droplet diameter that composes 50% of the spray volume; DV 0.1 , droplet diameter of 10% of the spray volume; part of the spray volume composed by droplets with diameter smaller than 50 μm (V50) and the part that comprise droplet sizes greater than 100 μm (V100).
A Brookfield LVDV-III + viscometer measured solution viscosity.This instrument is equipped with different diameter cylinders (spindles), which are adequate to fluid viscosity.For this research, a cylinder of 100-mm external diameter (spindle # S-28) at 60-rpm rotation as recommended by manufacturer.
Solution density was assessed by weighing a 1-L solution deposited in a volumetric flask in a 0.01-g precision scale.
Solution surface tension was determined by gravimetric method by weighing sets of 25 drops per replicate (four replicates), using an analytical balance accurate to 0.1 mg, an approximate average time of 27 seconds.Drops were placed into a beaker placed on the scale.They were obtained with the help of a 5 ml syringe and a capillary (used in chromatography), which allowed horizontal position at a predetermined constant speed, increasing droplet uniformity.Droplet weight data were converted to surface tension, assuming an average drop weight of distilled water near 0.0726 m N -1 .
The experiment was performed in triplicate.Data normality was analyzed by Shapiro-Wilk test (p <0.05).Moreover, Pearson correlations (P <0.01) were made to verify the relationship between drift and other variables.Finally, multivariate analysis was applied to treat all variables simultaneously, summarizing the data and showing its structure to avoid loss of information.

RESULTS AND DISCUSSION
Correlation significant results between drift and physical parameters and the droplet spectrum (p <0.01) are shown in Figure 1.In brief, it appears that the drift is more influenced by inversely proportional variables.Surface tension has not showed significant correlation with drifting.The greatest correlation was in DMV (-0.59), followed by DV 0.1 (r = -0.49),density (r = -0.47)and viscosity (r = -0.46).Positive correlation was only observed between drift and V100 (r = 0.46).Such results highlight spray solution viscosity as an anti-drift agent, notably on basis of viscosity correlation with DMV and V100, using method of adjuvant evaluation regarding drift potential risk (Table 2).This correlation gradient supports the idea that adjuvant addition raises droplet sizes; moreover, spray formation comes from interaction between nozzle model and fluid properties (SPANOGHE et al., 2007).In this research, there was significant correlation between drift and liquid viscosity, and it was assessed that viscosity increases provided drift reduction.FIGURE 1. Drift general correlation (%) with significant physical variables (p<0.01), for all treatments (adjuvant types and concentrations).
Table 2 presents the correlations among physical variables and droplet spectrum.The greatest correlation was between DMV and DV 0.1 (r = 0.86); which indicates that a droplet spectrum with larger DV 0.1 has also larger DMV.Viscosity and DMV had a directly proportional correlation (r = 0.67).Raise on spray liquid viscosity increase DMV and, consequently, reducing droplet sizes that are prone to drift (SPANOGHE et al., 2007).It is noteworthy that greater viscosity generates larger droplets, this way acting in drift potential (Table 2 and Figure 1).By adding polymer-based adjuvants, drift was significantly decreased due to increased spray viscosity.Liquid viscosity affects the formation of smaller droplets by resistance to airflow due to extensional viscosity modifying the spray liquid in the tank (SCHAMPHELEIRE et al., 2008).
Relationships and interactions of the variables with the treatments and contributions of F1 and F2 factors are shown in Figure 2.There is a formation of four distinct groups, with adjuvant grouping or remoteness characterized by high or low values of the variables evaluated.
V100 was the variable that most influenced treatment variability, followed by V50.The T12 (Define ® -0.12%) had the highest remoteness from other treatments, which was characterized by high values of viscosity, DV 0.1 and DMV.It was noted a greater influence of surface tension on T11 (Define ® -0.06%), T26 (Nutrifix ® -0.1%) and T32 (Tac-Tic ® -0.26%).This research results contribute to standard methodology for simple, direct and independent assays, which would be able to prove effectiveness on drift reduction of a large number of adjuvants from Brazilian market.

CONCLUSIONS
The diameter of mean volume, percentage of droplets smaller than 100 µm and viscosity have great influence on drift risk potential.
The evaluated characteristics and their respective determination methods are applicable on adjuvant assessment concerning drift risk potential.

FIGURE 2 .
FIGURE 2. Graphics of the relationship between physical variables and droplet spectrum variables and interactions with treatments.

Figure 3
Figure 3 presents the principal component analysis of the variables, showing their relationships and contributions to F1 and F2.It is observed that all variability of the variables, with respect to their correlations among each other, was summed up in two factors that explain 71.49% all data variability.The DV 0.1 (r = 0.90) and DMV (r = 0.85) provided the highest contributions

FIGURE 4 .
FIGURE 4. General correlation between drift and diameter of mean volume (DMV), percentage of droplets smaller than 100 μm (V100) for the tested adjuvants and concentrations.

TABLE 1 .
Evaluated treatments, main component chemical composition and concentration.
1/ Composition quote does not indicate authors' recommendation and approval.

TABLE 2 .
jan./fev.2015 113 Correlation among physical variables within the droplet spectrum analysis for all treatments.