Influence of micrometeorological factors on pesticide loss to the air during vine spraying : Data analysis with statistical and fuzzy inference models

Upward spray losses over vines were assessed during a typical air-assisted application using a fluorescent tracer dye and PVC lines as samplers. Linear multiple regression and fuzzy logic inference models were used to evaluate the effects of micrometeorological conditions on pesticide application for two spray qualities (fine and very fine). For the fine spray application (volume median diameter [VMD] 134 μm), the significant variables for the multiple regression were wind speed, air temperature and wet bulb temperature depression, with a coefficient of determination of 0.70. For the very fine spray application (VMD 65 μm), atmospheric stability was also significant, with a coefficient of determination of 0.82. Spray losses were also predicted using fuzzy inference systems, and high coefficients of determination were obtained (R2=0.72 for the fine spray and 0.66 for the very fine spray). Interpretable rules were established for the characterisation of micrometeorological parameters using the two sprays. Both analysis tools can be combined with mathematical modelling in order to evaluate air pollution and spray drift from simplified field tests.

[1]  M. Raupach,et al.  Endosulfan transport: II. Modeling airborne dispersal and deposition by spray and vapor. , 2001, Journal of environmental quality.

[2]  D. Aylor,et al.  Modeling spore dispersal in a barley crop , 1982 .

[3]  Enrique H. Ruspini,et al.  A New Approach to Clustering , 1969, Inf. Control..

[4]  C. S. Parkin,et al.  The Use of Computational Fluid Dynamic Code for Modelling Spray from a Mistblower , 1993 .

[5]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[6]  T. B. Curbishley,et al.  AgDrift®: A model for estimating near‐field spray drift from aerial applications , 2002, Environmental toxicology and chemistry.

[7]  José Valente de Oliveira,et al.  Semantic constraints for membership function optimization , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[8]  S. Weisberg,et al.  Residuals and Influence in Regression , 1982 .

[9]  Hidetomo Ichihashi,et al.  Neuro-fuzzy ID3: a method of inducing fuzzy decision trees with linear programming for maximizing entropy and an algebraic method for incremental learning , 1996, Fuzzy Sets Syst..

[10]  G. M. Richardson,et al.  Stochastic modelling of turbulent spray dispersion in the near-field of orchard sprayers , 1998 .

[11]  R. B. Brown,et al.  Simulation of spray dispersal and deposition from a forestry airblast sprayer - Part II: Droplet trajectory model , 2001 .

[12]  R. C. Derksen,et al.  AIRBORNE SPRAY COLLECTION EFFICIENCY OF NYLON SCREEN , 2004 .

[13]  D. Dubois,et al.  Fundamentals of fuzzy sets , 2000 .

[14]  C. Sinfort,et al.  Emission of pesticides to the air during sprayer application: A bibliographic review , 2005 .

[15]  P. J. Walklate,et al.  A simulation study of pesticide drift from an air-assisted orchard sprayer , 1992 .

[16]  R. C. Derksen,et al.  EVALUATION OF A PNEUMATIC–SHIELDED SPRAYING SYSTEM BY CFD SIMULATION , 2002 .

[17]  William Ocampo-Duque,et al.  Assessing water quality in rivers with fuzzy inference systems: a case study. , 2006, Environment international.

[18]  Yves Brunet,et al.  Atmospheric loss of pesticides above an artificial vineyard during air-assisted spraying , 2007 .

[19]  D. B. Smith,et al.  PREDICTING GROUND BOOM SPRAY DRIFT , 2000 .

[20]  Sébastien Destercke,et al.  Building an interpretable fuzzy rule base from data using Orthogonal Least Squares - Application to a depollution problem , 2007, Fuzzy Sets Syst..

[21]  G. M. Richardson,et al.  Spray deposits and losses in different sized apple trees from an axial fan orchard sprayer: 3. Effects of air volumetric flow rate , 2001 .

[22]  Measurements and computational fluid dynamic simulations of the capture of drops by spray drift samplers. , 2000 .

[23]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[24]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..

[25]  Claudio M. Ghersa,et al.  Evaluation of environmental impact indicators using fuzzy logic to assess the mixed cropping systems of the Inland Pampa, Argentina , 2003 .

[26]  Angel R. Martinez,et al.  Computational Statistics Handbook with MATLAB , 2001 .

[27]  C. E. Goering,et al.  Paired Field Studies of Herbicide Drift , 1975 .

[28]  H. Ganzelmeier,et al.  The International (BCPC) spray classification system including a drift potential factor , 1998 .

[29]  A. J. Hewitt,et al.  Drift Management Using Modeling and GIS Systems , 2002 .

[30]  R. Stull.  An Introduction to Boundary Layer Meteorology , 1988 .