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The pulse width modulation (PWM) spray system is the most advanced technology to obtain variable rate spray application without varying the operative sprayer parameters (e.g. spray pressure, nozzle size). According to the precision agriculture principles, PWM is the prime technology that allows to spray the required amount where needed without varying the droplet size spectra which benefits both the uniformity of spray quality and the spray drift reduction. However, some concerns related to the effect of on–off solenoid valves and the alternating on/off action of adjacent nozzles on final uneven spray coverage (SC) have arisen. Further evaluations of PWM systems used for spraying 3D crops under field conditions are welcomed. A tower-shaped airblast sprayer equipped with a PWM was tested in a vineyard. Twelve configurations, combining duty cycles (DC: 30, 50, 70, 100%) and forward speeds (FS: 4, 6, 8 km h−1), were tested. Two methodologies, namely field-standardized and real field conditions, were adopted to evaluate the effect of DC and FS on (1) SC variability (CV%) along both the sprayer travel direction and the vertical spray profile using long water sensitive papers (WSP), and (2) SC uniformity (IU, index value) within the canopy at different depths and heights, respectively. Furthermore, the SC (%) and deposit density (Nst, no stains cm−2), determined using short WSP, were used to evaluate the spray application performances taking into account the spray volumes applied. Under field-controlled conditions, the pulsing of the PWM system affects both the SC variability measured along the sprayer travel direction and along the vertical spray profile. In contrast, under real field conditions, the PWM system does not affect the uniformity of SC measured within the canopy. The relationship between SC and Nst allowed identification of the ranges of 200–250 and 300–370 l ha−1 as the most suitable spray volumes to be applied for insecticide and fungicide plant protection products, respectively.

BACKGROUND: Pesticide losses and uneven spray distribution should be avoided as much as possible as they reduce the effectiveness of spraying and increase environmental contamination as well as costs. Within the H2020-project OPTIMA the goal is to develop a smart sprayer for bed-grown carrots, including optimizations such as air support and variable nozzle spacing. This paper focuses on selecting the most optimal nozzle types, spacing and height for spraying bed-grown crops, while taking into account different target zone widths depending on the growth stage, based on spray distribution and droplet characterization measurements. RESULTS: The results indicate that four bed spray configurations consisting of four nozzles per bed, i.e. XR8004/XR8004/ XR8004/XR8004, AIUB8504/AI11004/AI11004/AIUB8504, AI8004/AI8004/AI8004/AI8004 and XR8002/XR8002/XR8002/ XR8002, spraying at 300 kPa and recalculated to 12.0 km h−1 forward speed, are appropriate for spraying different target zone widths (ranging from 1.2 to 2.2 m) with high uniformity (CV < 12%) and minimal losses out of the target zone ( CONCLUSION: Laboratory spray distribution and droplet characterization measurements allowed selection of the most optimal nozzle type, spacing and height for bed spray applications in terms of reduced pesticide losses compared to conventional broadcast applications.

As farming machinery size and weight increases, soil compaction continues to threaten mechanized agriculture. Controlled Traffic Farming (CTF) minimizes soil compaction in the crop zone by restricting traffic to permanent tracks. The adoption of CTF in Europe is low. This study enhances the understanding of farmers' needs and perceptions concerning the application of CTF by analyzing survey data from 103 farmers sampled from 8 European countries. The study adopted a descriptive approach to data analysis. Awareness about traffic-induced soil compaction is high among surveyed farmers and there are positive perceptions about the potential of CTF. The major factors limiting adoption of CTF appear to be: lack of compatibility in machinery and Global Navigation Satellite Systems (GNSS) by different manufacturers; expense (equipment purchase, Real-time Kinematic (RTK) signal, machinery modification); lack of demonstrated benefits under local conditions; incomplete knowledge of research findings and decision support tools; and a perception that CTF is not for small farms. The following interventions are suggested for future development and use of CTF: a shift towards lighter, simpler, adaptable, and energy effective machinery; adapted market models for cost-effective provision of basic services; demonstration of costs and benefits under local conditions in tandem with demand-driven decision support tools as well as standardization of equipment and positioning systems.

Cyperus esculentus (yellow nutsedge) is one of the world’s worst weeds as it can cause great damage to crops and crop production. To eradicate C. esculentus, early detection is key—a challenging task as it is often confused with other Cyperaceae and displays wide genetic variability. In this study, the objective was to classify C. esculentus clones and morphologically similar weeds. Hyperspectral reflectance between 500 and 800 nm was tested as a measure to discriminate between (I) C. esculentus and morphologically similar Cyperaceae weeds, and between (II) different clonal populations of C. esculentus using three classification models: random forest (RF), regularized logistic regression (RLR) and partial least squares–discriminant analysis (PLS–DA). RLR performed better than RF and PLS–DA, and was able to adequately classify the samples. The possibility of creating an affordable multispectral sensing tool, for precise in-field recognition of C. esculentus plants based on fewer spectral bands, was tested. Results of this study were compared against simulated results from a commercially available multispectral camera with four spectral bands. The model created with customized bands performed almost equally well as the original PLS–DA or RLR model, and much better than the model describing multispectral image data from a commercially available camera. These results open up the opportunity to develop a dedicated robust tool for C. esculentus recognition based on four spectral bands and an appropriate classification model.

Cyperus esculentus (yellow nutsedge) is one of the world&rsquo, s worst weeds as it can cause great damage to crops and crop production. To eradicate C. esculentus, early detection is key&mdash, a challenging task as it is often confused with other Cyperaceae and displays wide genetic variability. In this study, the objective was to classify C. esculentus clones and morphologically similar weeds. Hyperspectral reflectance between 500 and 800 nm was tested as a measure to discriminate between (I) C. esculentus and morphologically similar Cyperaceae weeds, and between (II) different clonal populations of C. esculentus using three classification models: random forest (RF), regularized logistic regression (RLR) and partial least squares&ndash, discriminant analysis (PLS&ndash, DA). RLR performed better than RF and PLS&ndash, DA, and was able to adequately classify the samples. The possibility of creating an affordable multispectral sensing tool, for precise in-field recognition of C. esculentus plants based on fewer spectral bands, was tested. Results of this study were compared against simulated results from a commercially available multispectral camera with four spectral bands. The model created with customized bands performed almost equally well as the original PLS&ndash, DA or RLR model, and much better than the model describing multispectral image data from a commercially available camera. These results open up the opportunity to develop a dedicated robust tool for C. esculentus recognition based on four spectral bands and an appropriate classification model.

Automatic detection of early blight caused by Alternaria solani could promote a drastic reduction in the consumption of plant protection agents and the related production losses. A proximal sensing platform was constructed and calibrated for acquiring high resolution hyperspectral images in the field, and used to accurately map Alternaria lesions. High resolution canopy reflectance images were obtained for 32 potato plants that had been infected with A. solani and 32 healthy reference plants. Spectral classifiers like partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) based on PCA scores were tested to discriminate affected and non-affected pixels. Both spectral classifiers performed well at pixel level with accuracies above 0.92. The NIR region (750 nm) was identified as the most discriminative part of the spectrum for detecting the lesions. As the disease pressure is typically expressed as the number of lesions per area, the accuracy was also evaluated at this level. This indicated a considerable number of false detections at the edges of the leaves and the leaf axils. Therefore, a decision tree was designed based on expert knowledge about the shape of Alternaria lesions, and used to post-process the classified images. This reduced the number of false detections, increasing the precision from 0.17 to 0.22 at the expense of a reduction in recall from 0.88 to 0.84. This leaves considerable room for improvement in the classification accuracy at the object level. We learned that (1) few, broad wavelengths are sufficient and (2) spatial context is essential for the detection of lesions caused by Alternaria infection. The application of more powerful object classification techniques such as convolutional neural networks to enhance the model performance by efficiently encapsulating the spatial context in the classifier might further improve the detection performance. This could pave the way to UAV or tractor based Alternaria mapping. ispartof: COMPUTERS AND ELECTRONICS IN AGRICULTURE vol:168 status: published

An Eulerian-Lagrangian 3D computational fluid dynamics (CFD) model of pesticide dust drift from precision vacuum planters in field conditions was developed. Tractor and planter models were positioned in an atmospheric computational domain, representing the field and its edges. Physicochemical properties of dust abraded from maize seeds (particle size, shape, porosity, density, a.i. content), dust emission rates and exhaust air velocity values at the planter fan outlets were measured experimentally and implemented in the model. The wind profile, the airflow pattern around the machines and the dust dispersion were computed. Various maize sowing scenarios with different wind conditions, dust properties, planter designs and vacuum pressures were simulated. Dust particle trajectories were calculated by means of Lagrangian particle tracking, considering nonspherical particle drag, gravity and turbulent dispersion. The dust dispersion model was previously validated with wind tunnel data. In this study, simulated pesticide concentrations in the air and on the soil in the different sowing scenarios were compared and discussed. The model predictions were similar to experimental literature data in terms of concentrations and drift distance. Pesticide exposure levels to bees during flight and foraging were estimated from the simulated concentrations. The proposed CFD model can be used in risk assessment studies and in the evaluation of dust drift mitigation measures.

This study explores the potential of a novel hyperspectral snapshot mosaic camera for weed and maize classification. The image processing, feature engineering and machine learning techniques were discussed when developing an optimal classification model for the three kinds of weeds and maize. A total set of 185 spectral features including reflectance and vegetation index features was constructed. Subsequently, the principal component analysis was used to reduce the redundancy of the constructed features, and the first 5 principal components, explaining over 95% variance ratio, were kept for further analysis. Furthermore, random forests as one of machine learning techniques were built for developing the classifier with three different combinations of features. Accuracy-oriented feature reduction was performed when choosing the optimal number of features for building the classification model. Moreover, hyperparameter tuning was explored for the optimal selection of random forest model. The results showed that the optimal random forest model with 30 important spectral features can achieve a mean correct classification rate of 1.0, 0.789, 0.691 and 0.752 for Zea mays, Convolvulus arvensis, Rumex and Cirsium arvense, respectively. The McNemar test showed an overall better performance of the optimal random forest model at the 0.05 significance level compared to the k-nearest neighbours (KNN) model.

The developments in the use of unmanned aerial vehicles (UAVs) and advanced imaging sensors provide new opportunities for ultra-high resolution (e.g., less than a 10 cm ground sampling distance (GSD)) crop field monitoring and mapping in precision agriculture applications. In this study, we developed a strategy for inter- and intra-row weed detection in early season maize fields from aerial visual imagery. More specifically, the Hough transform algorithm (HT) was applied to the orthomosaicked images for inter-row weed detection. A semi-automatic Object-Based Image Analysis (OBIA) procedure was developed with Random Forests (RF) combined with feature selection techniques to classify soil, weeds and maize. Furthermore, the two binary weed masks generated from HT and OBIA were fused for accurate binary weed image. The developed RF classifier was evaluated by 5-fold cross validation, and it obtained an overall accuracy of 0.945, and Kappa value of 0.912. Finally, the relationship of detected weeds and their ground truth densities was quantified by a fitted linear model with a coefficient of determination of 0.895 and a root mean square error of 0.026. Besides, the importance of input features was evaluated, and it was found that the ratio of vegetation length and width was the most significant feature for the classification model. Overall, our approach can yield a satisfactory weed map, and we expect that the obtained accurate and timely weed map from UAV imagery will be applicable to realize site-specific weed management (SSWM) in early season crop fields for reducing spraying non-selective herbicides and costs.

Postharvest treatment of fruit inside a cool room is gaining more interest to replace spray applications in the orchard. The efficacy of the application depends on the quality of the spray liquid and its distribution inside fruit bin. This work investigated the effect of different modes and settings of airflow delivery on the deposition amount and uniformity of the cold fogging spray deposition in a single bin. Room air circulation by means of evaporator fans was compared to an air suction configuration using a tunnel and suction fan. Deposition tests were conducted using a mineral chelate tracer solution and filter paper collectors placed on fruit. A computational fluid dynamics (CFD) model of the spraying process was developed to simulate the airflow, droplet particle tracks and spray deposits on fruits. The model predicted well the position of maximum and minimum deposit and the relative differences between the different modes and settings of airflow delivery. Generally, deposition distributions were strongly non-uniform. Spray deposits on fruit using only room air circulation were very low. Using suction airflow that directs the spray through the bin improved spray deposition and uniformity. The effects of spraying under different suction pressures (50, 167, 314 and 500 Pa) across the bin using different droplet diameters (15, 100, 200 and 300 μm) was evaluated. Depending on the size, different effects were observed. Coarse droplets perform best in terms of uniformity of deposition with more lateral dispersion, whereas fine droplets have a very limited lateral dispersion, travels deep to the stack following the high-velocity air (better axial dispersion). By implementing multiple nozzles the poor lateral dispersion of fine droplets can be improved, hence, the desirable characteristics of fine droplets in penetrating into the stack (axial distribution) can be retained. publisher: Elsevier articletitle: Experimental and numerical analysis of the spray application on apple fruit in a bin for postharvest treatments journaltitle: Journal of Food Engineering articlelink: http://dx.doi.org/10.1016/j.jfoodeng.2017.01.026 content_type: article copyright: © 2017 Elsevier Ltd. All rights reserved. ispartof: Journal of Food Engineering vol:202 pages:34-45 status: published

BACKGROUND Although considered as a safe pesticide application method, treated seeds can pose environmental risks when abraded pesticide-laden seed particles are expelled during sowing. This dust drift risk is clearly linked with seed coating quality. Seed coating quality is traditionally assessed with Heubach dust meters, and guidelines are established in terms of the ‘Heubach value’. This technique may, however, not take all drift-sensitive particles into account. In this study, results of the Heubach test are compared with two alternative set-ups: mechanical sieving and the individual sowing element. RESULTS The abrasion potential assessed with the Heubach dust meter was much lower than the total dust fraction generated by mechanical sieving and the individual sowing element. The amount of dust produced and the dust particle size distribution of both the other techniques were comparable. CONCLUSION It looks as if the Heubach dust meter underestimates the risk of dust drift. Using one of the alternative methods might be a more appropriate way to assess the abrasion potential of seeds. Given the low investment cost required, mechanical sieving seems to be a good approach for non-specialised labs. The individual sowing element set-up is the most realistic simulation of in-field dust drift generation but requires a higher initial investment. Therefore, this set-up is most suitable for specialised labs and is recommended for further research in this area. © 2017 Society of Chemical Industry

BACKGROUND Particle size is one of the most important properties affecting the driftability and behavior of dust particles abraded from pesticide dressed seeds during sowing. Three particle sizing techniques were used determine the particle size distribution of dust abraded from seeds from six different species. RESULTS Important differences in dust particle size distribution between species were observed with the finest dust for rapeseed and the coarsest dust for barley. Wet laser diffraction and sonic sieving particle size results correlated well while micro-CT is able to deliver 3D-information and additional physical particle properties (shape, porosity) CONCLUSION All particle sizing techniques have their (dis)advantages and none of them is able to perfectly describe the real size distribution of non-spherical particles. The gathered particle size information can be used in dust drift prediction models, risk assessment tools and will help to better understand the dust drift phenomenon.

BACKGROUND Apart from particle size, drift of abraded seed particles during sowing is mainly affected by two other physical properties viz. particle shape and envelope density. The impact of these abraded seed particles on the environment is highly dependable on their active ingredient content. In this study, the envelope density and chemical content of dust abraded from seeds was determined as a function of particle size for six seed species. RESULTS Envelope density and active ingredient content both change as a of function of particle size. Important differences in these physicochemical properties were observed between the six species. Functions were fitted to the collected data to describe the physicochemical properties as a function of particle size. CONCLUSION The gathered physicochemical information is essential for the CFD based dust drift prediction models and can be useful for other prediction models as well as for the ongoing risk assessment of active ingredients used for seed treatment on ecosystems and ecosystem-services. In addition, the results can help to better understand the dust drift phenomenon and to develop mitigation strategies.

Pesticides play an important role in providing high crop yields by minimising the risks associated with pests but some of the sprayed product may move beyond the intended target and result in drift. Modelling approaches can help understand the behaviour of spray drift using computer simulations. However, modelling drift from orchard spraying presents particular challenges: (1) the moving spray interacts with the canopy before drifting outside the target area; (2) the vertical wind profile in the orchard is different to neighbouring fields where there is different vegetation; (3) the moving air jet from the air-assistance cannot be ignored because the airspeed of the fan is usually higher than the wind speed.This work presents a three-dimensional (3D) computational fluid dynamics (CFD) model of spray drift from orchard sprayers that considers tree architecture, canopy wind flow and the movement of the sprayer to calculate sedimenting and airborne drift; thus tackling the challenges listed above. The model was validated against drift measurements from an apple orchard with different nozzles arrangements. The model was then used to evaluate the effect of drift reducing nozzles and fan airspeed on drift. The model predicted that drift reducing nozzles reduced the drifting distance by 50%, but increased near-tree ground deposition. This increase in ground deposition can be avoided whilst retaining the reduction in the drifting distance, by using a combination of drift reducing and standard nozzles. A reduced sprayer airflow can further reduce drift. publisher: Elsevier articletitle: Development and validation of a 3D CFD model of drift and its application to air-assisted orchard sprayers journaltitle: Biosystems Engineering articlelink: http://dx.doi.org/10.1016/j.biosystemseng.2016.10.010 content_type: article copyright: © 2016 IAgrE. Published by Elsevier Ltd. All rights reserved. ispartof: Biosystems Engineering vol:154 pages:62-75 status: published

Because of variations in environmental conditions, spray-drift field measurements following ISO 22866:2005 involve complicated and time-consuming experiments often with low repeatability. Therefore, simple, repeatable, and precise alternative drift assessment methods that are complementary to the official standards are required. One of the alternatives is the use of a drift test bench for field crop sprayers. Previous studies have demonstrated that the drift test bench can be considered an adequate complement to existing standard protocols for field drift measurements. In this study, in order to further improve the methodology and to evaluate the possibility of classifying different field-crop-sprayer settings according to drift risk using a test bench, a series of tests were performed in a test hall. A conventional mounted Delvano HD3 crop sprayer (Delvano, Kuurne, Belgium) equipped with an 800-l spray tank and a 15-m-wide stainless steel spray boom was used. Eight different sprayer setups were tested, involving three nozzle types (TeeJet XR 110 04, Agrotop TDXL 110 04 and Micron Micromax 3) and three boom heights (0.30, 0.50, and 0.70 m). For the drift classification, the reference sprayer drift behaviour was defined as that obtained using conventional flat fan TeeJet XR 110 04 nozzles operated at 0.30 MPa and at a boom height of 0.50 m. The different sprayer setups were successfully assigned to different drift reduction classes, and the results underlined the effects of nozzle type and boom height on the potential drift. The feasibility of the test-bench methodology for classifying field-crop-sprayer drift according to ISO 22369-1:2006 was demonstrated.

As an alternative method to simplify the measurement of spray drift, the University of Turin (Department of Agricultural, Forestry and Environmental Economics and Engineering (DEIAFA)) developed a drift test bench to assess the spray drift risk generated by field crop sprayers. The method is based on the principle that the potential spray drift is directly related to the amount of initial spray that remains suspended in the air after the sprayer has passed. In this study the drift potential of 16 spray boom application techniques was assessed using the drift test bench under indoor conditions using a fully automated spray track and mineral chelate tracers. By comparison with the results from a reference spraying, the resulting drift reduction potentials were compared with the results from three other drift assessment methods (field, wind tunnel, Phase Doppler particle analyser (PDPA) laser measurements). The drift test bench trials confirmed the already known effects of nozzle type, spray pressure and boom height on drift potential but not of nozzle size and application speed. In general, the drift test bench showed similar trends as the other drift assessment methods, although large variations were present and some contradictory results were observed. The test bench is therefore considered a possible alternative for measuring potential spray drift of horizontal boom sprayers, albeit future studies should focus on reducing the sources of variation and evaluating if the methodology is able to test the effects of sprayer speed.

With the implementation of integrated pest management in the European Union, growers are obliged to manage pests in a manner that minimizes health and environmental risks due to the use of plant protection products. Among other approaches, this goal can be achieved by optimizing spray application techniques. As an alternative to the predominantly used handheld equipment, such as spray guns, spray boom systems might substantially improve spray application, and thus crop protection management, in greenhouses. The aim of this proof-of-concept study was to compare different spray configurations in a spray cabin designed to spray ornamental potted plants that are moving on a conveyor belt. Seven different spray configurations were examined for optimal spray deposition in two crops (azalea and ivy) using mineral chelate tracers. The deposition tests showed that the presented prototype can satisfactorily spray potted plants up to a height of 25 cm including the pot height. The best spray deposition was found with two flat-fan nozzles oriented 35° upward, spraying at 1.0 bar and an application rate of 1047 L ha-1. This configuration increased deposition on the underside of the leaves and at the middle foliage layer compared to the other configurations that were evaluated. The spray cabin with a band spray setting has potential to mitigate the use of plant protection products and achieve a more efficient spray application compared to traditional handheld techniques and broadcast spray boom techniques. Keywords: Crop protection, Integrated pest management, Nozzle type, Spray deposition.

ispartof: GRASS AND FORAGE SCIENCE vol:74 issue:3 pages:356-369 status: published

With the increase in working widths for applicators, granular fertiliser particles spread by centrifugal spreaders have more extensive airborne trajectories. In the field, particles can be subjected to wind which can cause their trajectories to change. In this paper, a 3D ballistic model was developed describing the motion of fertiliser particles taking wind effects into account. The physical properties of eight commonly used fertiliser types were determined: particle density, size distribution and angle of repose. X-ray micro computed tomography was used to determine the shape of the particles and estimate the corresponding drag coefficient. Using these parameters in the newly derived ballistic model, the effect of wind on individual fertiliser trajectories was quantified for each fertiliser type. Three wind directions (head- tail- and crosswind) were analysed for two windspeeds: 3 and 6 m s−1. The magnitude of the effect was strongly dependent on the physical properties of the fertiliser type, the windspeed and wind direction. Simulations showed that a windspeed of 3 m s−1 already can affect the landing position of individual fertiliser particles by several metres. Generally, particles with lower density, smaller size and a more irregular shape were more sensitive to wind.

Agricultural spray characteristics determine the efficiency of a pesticide application because size and velocity affect droplet trajectory and impact behavior. At present, the relevance of different characterization techniques remains controversial since discrepancies may be significant between measurements performed in different laboratories. A digital image acquisition technique and analysis algorithm is proposed for droplet size and velocimetry measurements as an alternative to well-established techniques such as the Phase Doppler Particle Analyzer (PDPA) or laser diffraction spectrometry (LDS). The algorithm requires double exposed shadow images acquired in a back-lighted arrangement with a Particle Image Velocimetry (PIV) camera and a pulsed light emitting diode (LED). Spatial illumination heterogeneities are corrected by subtracting from each image a mean background acquired on several images without any particle. The algorithm accuracy is ensured by the rejection of out-of-focus particles using a focus parameter depending on gradient intensity at the particle edges. Thresholds for focus particle selection were determined by studying the evolution of the focus parameter and the error on particle size measurements from images containing droplets with uniform size at various distance of the object plane. Selected droplets were identified on both pairs of images to determine their size and velocity. Droplet size distributions were corrected to account for the uneven sampling probability caused by the volumetric method. Droplet size distributions of a set of reference nozzle/pressure combinations defined in the ISO/DIS 25358 were measured. The image technique was able to distinguish each of the reference sprays well. Comparison with PDPA measurements showed that the imaging technique tends to measure an equivalent Dv50, a lower Dv10 and a higher Dv90 leading therefore to a higher relative span factor. Velocity measurements showed good agreement between both techniques except for one nozzle/pressure combination.

The first CFD model of seed treatment dust dispersion is proposed.Dust particle trajectories are predicted using Lagrangian particle tracking.Model predictions were validated with wind tunnel data.Detailed description of physicochemical dust properties is necessary. Drilling of treated seeds with vacuum-based precision drills can cause emissions of pesticide-laden dust, which have been linked with declines of pollinator populations. Predicting the drift pattern of this type of dust is challenging because the particles are very irregular in terms of size, shape, density, internal porosity and chemical composition. In this work, a 3D Computational Fluid Dynamics (CFD) model of seed treatment dust drift was developed and validated with wind tunnel data. In the wind tunnel experiment, dust abraded from pesticide-treated seed was separated in three size fractions and released from a point source at a height of 0.7m at three air velocities. Dust deposition was measured at six distances on the wind tunnel floor. The physicochemical properties of the dust samples were measured and implemented in the CFD model. Lagrangian tracking was used to calculate the dust particle trajectories. The simulated dust deposition patterns agreed with those observed in the wind tunnel trials. It was demonstrated that an accurate, particle size-dependent description of the shape, chemical content and internal porosity of the dust particles was necessary to achieve good validation results. The CFD model can be used as a basis for the simulation of dust drift in the field during sowing.

Precise application of granular fertiliser requires the correct calibration of spreading equipment. Currently, the performance of spreaders is assessed by determining the transverse distribution using a row of collection trays aligned perpendicularly to the driving direction. When the homogeneity of the distribution for a given spreader with a given fertilizer is inadequate, the spreader settings need to be corrected. However, because particle deposition is measured in only one dimension, this technique does not provide an adequate insight into the spreading process as a whole. The distribution should be measured in two dimensions, which, due to the large spreading widths involved with modern spreaders, is only possible by sampling the spreading area. In this paper, two different two-dimensional methods to determine spreading pattern were evaluated, each consisting of a sampling method and a matching interpolation algorithm. Both sampling methods use a similar number of collecting trays. Experiments were executed using three commonly used types of fertiliser (CAN (Ammonium-nitrate fertiliser), NPK (Compound fertiliser), KCl (Potassium-chloride fertiliser)) with different physical properties. The resulting spreading patterns were compared with the standard one-dimensional technique. Differences were found both in the application rate and application homogeneity. The results illustrated the importance of using an adequate spreading pattern measurement technique to accurately compare the spreading patterns of granular fertiliser spreaders.

BACKGROUND: Rainfall greatly affects the retention of foliar‐applied agroformulations. Improving their resistance to wash‐off is therefore of great importance in spray applications. When developing such formulations, small‐scale laboratory assays are generally required. A set‐up for retention studies using only small amounts of agroformulations (

During sowing of pesticide-treated seeds, pesticide-laden dust and abraded seed particles may be emitted to the environment, possibly leading to environmental contamination and posing health risks. In many countries there is currently no legislation concerning the acceptable amount of dust of treated seeds. This study aimed to gain insight in the abrasion potential of available pesticide-treated seeds and its associated factors. The abrasion potential of 45 seed samples of 7 different species (viz. sugar beet, oat, barley, wheat, spelt, pea, and maize) was determined using the Heubach test and amounts of dust were expressed as g 100 kgseeds¡1, g 100,000 seeds¡1, and g ha¡1. The abrasion potential fell generally within the boundaries of maximum permissible values adopted by different countries. Species, seed treatment company, number of active ingredient (AIs) and combination of AIs had significant effects on the abrasion potential, whereas little or no effect of agitation and conservation was found. However, species were situated differently with respect to each other depending on the unit in which the abrasion potential was expressed. A standard unit that takes into account the species’ seed rate is suggested to give the fairest assessment of dust drift risk and would allow international comparison. peerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=ttpm20 ispartof: International Journal of Pest Management vol:62 issue:4 pages:1-12 ispartof: location:England status: published

Perennial ryegrass (Lolium perenne) is a perennial crop used in temperate regions as forage. In L. perenne breeding programs, persistency is an important trait. Poor persistency results in sward degradation and associated yield and nutritive value losses. Breeders assess persistency of accessions using visual scoring in field plots during the 2nd or 3rd growing season. This evaluation system is easy and cheap but is not free from human bias. In this study, the correlation between the scoring done by different breeders was only 0.243. As an alternative we have developed a methodology to assess persistency of L. perenne breeding materials based on vegetation indices (VIs) derived from Unmanned Aerial Vehicle (UAV) imagery. The VIs Excess green (ExG2), Green Leaf Index and Normalized green intensity (GCC) were found to provide consistent results for flights carried out under different light conditions and were validated by ground reference information. The correlation between the VIs and the percentage of ground cover extracted from on-ground imagery was 0.885. To test the implementation of the method we compared the ExG2 value based approach to selection with a visual score based selection methodology as applied by two breeders. The breeding decisions of Breeder A agreed well with decisions based on ExG2 values (74.6%), but those of Breeder B displayed a lower agreement (54.0%). In contrast, agreement between decisions based on different flights was very high (91.6%). The methodology was validated for general applicability. In summary, the results demonstrate that basing persistency selection in L. perenne breeding programs on ExG2 values from UAV imagery is likely to be more objective in comparison to the currently-used visual scoring method. ispartof: EUPHYTICA vol:214 issue:8 status: published

BACKGROUND: A substantial fraction of the applied crop protection products on crops is lost to the atmosphere. Models describing the prediction of volatility and potential fate of these substances in the environment have become an important tool in the pesticide authorisation procedure at the EU level. The main topic of this research is to assess the rate and extent of volatilisation of ten pesticides after application on field crops. RESULTS: For eight of the ten pesticides, the volatilisation rates modelled with PEARL (Pesticide Emission Assessment at Regional and Local scales) corresponded well to the calculated rates modelled with ADMS (Atmospheric Dispersion Modelling System). For the other pesticides, large differences were found between the models. Formulation might affect the volatilisation potential of pesticides. Increased leaf wetness increased the volatilisation of propyzamide and trifloxystrobin at the end of the field trial. The reliability of pesticide input parameters, in particular the vapour pressure, is discussed. CONCLUSION: Volatilisation of propyzamide, pyrimethanil, chlorothalonil, diflufenican, tolylfluanid, cyprodinil and E- and Z-dimethomorph from crops under realistic environmental conditions can be modelled with the PEARL model, as corroborated against field observations. Suggested improvements to the volatilisation component in PEARL should include formulation attributes and leaf wetness at the time of pesticide application.

A computational fluid dynamics (CFD) model of airflow and spray application in orchards was validated using field trials and used to assess the effect of wind and sprayer type on spray distribution in different orchard training systems. Three air-assisted orchard sprayer designs (a cross-flow sprayer, an axial sprayer and a sprayer with individual spouts) and four different training systems of apple and pear trees were used for this analysis. The CFD model integrates the tree architecture into the model geometry, rather than using a generalized canopy profile approach. Predicted vertical on-tree deposition profiles agreed well with measurements. The lower airflow rate generated by the sprayer with individual spouts resulted in a significantly larger deflection of the spray particles under the same wind conditions. A detailed assessment was made on the most common axial sprayer. An increase in the magnitude of the wind speed for flow across the tree row resulted in an increase in the amount of spray detected in the air around the trees and in the ground deposition in front of the tree row. Environmental airflow in the direction of spraying gave the largest deposition on the tree, constraining the spray in the canopy region. A wind direction opposite to the spraying direction, however, resulted in an increase of the ground deposition and the amount of spray remaining in air. The model can be used to analyze the effects of implementation of more sustainable spray application procedures taking into account wind conditions, tree and machine characteristics.

Agrosup GEAPSI; International audience; Accurate spray (droplet) characterization helps in the better understanding of the pesticide spray application process. The goal of this research was to evaluate the characteristics of a single droplet generated using a piezoelectric single droplet generator in 2 modes: Droplet-On-Demand (DOD) and continuous with 4 different orifice sizes. The droplet generator can produce a single droplet, or a continuous stream of uniformly sized droplets. Using a high speed imaging system and image processing algorithms, the effects of pulse width and pulse amplitude in DOD mode and of frequency and pulse amplitude in continuous mode on droplet characteristics (droplet size, droplet velocity and inter droplet spacing) were investigated for different orifice sizes. The system was able to produce droplet diameters within the range from 134.1 to 461.5 mm in DOD, and from 167.2 to 458.6 mm in continuous mode. In addition, droplet velocities ranged from 0.08 to 1.8 m/s in DOD mode and from 1.8 to 4.7 m/s in continuous mode. Validation using a droplet weighing method showed good agreement with the droplet image analysis results

A UK scheme to enable the protection of surface water from spray drift allows farmers to reduce the size of a buffer zone according to the drift-reducing capability of the sprayer. Recent changes to UK regulations have allowed buffer zones greater than 6 m to be included, providing that 75% drift reduction conditions are used. However, there is an implicit assumption that the level of drift reduction is independent of distance downwind, so that measurements relating to a 6 m buffer zone can be applied to 20 m. An investigation of the relationship between wind tunnel and field data was carried out with the purpose of establishing if drift reduction measured between 2 and 7 m in the Silsoe wind tunnel can be extrapolated to 20 m in the field. A computer-based spray drift model was used to explore some of the factors influencing downwind spray drift to support this extrapolation. It was concluded that spray drift reduction is dependent on distance downwind, but that wind tunnel measurements can be used to estimate this at least up to 20 m downwind. Improvements to the wind tunnel protocol were identified, which will need to take account of how the data will be used in the regulatory process before implementing. Further discussions are needed to harmonise methods for determining spray drift reduction across EU member states, but this approach of mapping the wind tunnel data onto field data is one that should be possible with other methods.