giuseppe pellizzi prize 2018 - clubofbologna.org · strategies for the optimization of the...

Strategies for the optimization of the efficiency in the plant protection product

applications in olive canopies

Giuseppe Pellizzi Prize 201828th Members’ Meeting of the Club of Bologna

November 10, 2018

Antonio Miranda-Fuentes –[email protected] / [email protected]

Antonio Miranda-Fuentes - Strategies for the optimization of the efficiency in the plant protection product applications in olive canopies

PRESENTATION INDEX

IntroductionObjectives

Materials and methodsResearch

Conclusions


OLIVE IN THE WORLD AND EUROPE

Source: FAO (2018)

Olive harvested area (a) and olive production (b) in the main growing countries in 2016


OLIVE IN SPAIN

Olive plantation systems in Spain(AEMO, 2012)

74 %

2 %

24 %

Superintensive(hedgerow)

Intensive(isolated)

Traditional(isolated)


OLIVE IN SPAIN

Main problems regarding pesticide use in olive farmers

Lack of knowledge about the optimal operation parameters

Popular belief: maximise spray volume and air flow rate

Special characteristics of the olive orchards

Wide tree and row spacing, irregular tree crown shape

Lack of specificity of the spraying equipment

Usually airblast sprayer or handgun sprayer

Lack of specific dosing criteria

Volume to point of runoff


FARMER’S KNOWLEDGE ABOUT THE MAIN SPRAYING PARAMETERS

Main adjustable parameters in any airblast sprayer(Cross et al., 2001)

Cross, J.V.,Walklate, P.J.,Murray, R.A., Richardson, G.M., 2001. Spray deposits and losses in different sized apple trees from an axial fan orchard sprayer: 1.Effects of spray liquid flow rate. Crop Protection, 20: 13-30

Liquid flow rateSpray quality

Positioning of nozzlesAir flow rate

Forward speed


OFFICIAL DOSING SYSTEMS


OLIVE CANOPY CHARACTERIZATION

Canopy characterization methods

Light Detection and Ranging (LiDAR)

(Rosell and Sanz, 2012)

MANUAL METHODS ELECTRONIC METHODS (Rosell and Sanz, 2012)

Stereovision(Rovira-Más et al., 2005)

Ultrasonic sensors(Escolà et al., 2011)

Ellipsoid method(Zaman and Salyani, 2004)

Vertical projected area(Iniesta et al., 2009)

Light sensors(Giuliani et al., 2000)


SPRAYING EQUIPMENT


PRESENTATION INDEX



Conclusions


OBJECTIVES

MAIN OBJECTIVE ─ to develop different strategies to improve the efficiencyof pesticide applications in olive orchards, by adjusting the volume rateaccording canopy characteristics, reducing drift and losses to the ground andavoiding environmental damage.

#1 ─ To find optimal ranges for the operation parameters

#2 ─ To check the accuracy of different manual canopy characterization methods todefine appropriate values for pesticide dosing.

#3 ─ To find the optimal liquid specific volume in accordance to the canopy volumefor isolated olive trees.

#4 ─ To develop and test adapted air-assisted sprayers to improve applicationefficiency in olive orchards.


PRESENTATION INDEX



Conclusions


RESEARCH

PAPER AInfluence of liquid-volume and airflow

rates on spray application quality

and homogeneity in super-intensive

olive tree canopies

PAPER BTowards an optimized

method of olive tree crown volume measurement.

PAPER CAssessing the optimal liquid volume to be

sprayed on isolated olive trees according

to their canopy volume

PAPER DImproving plant

protection product applications in traditional and

intensive olive orchards through the

development of new air-assisted sprayer

prototypes


PAPER I




olive tree canopies










prototypes

The intention of this research was todetermine the effect of air assistance and liquid volume

rate onde- position (uniformity and penetration) in olive trees, trying to obtain an

objective relationship between those fluids and canopy characteristics.

Objective of the research work:

To determine the effect of air assistanceand liquid volume rate on deposition(uniformity and penetration) in olive trees.


MATERIALS AND METHODS

Experimental plots and canopy characterization

AFR trialVR trial



VR trialParameter HV MV LV

Nozzle type and colour Albuz ATR Green Albuz ATR Orange Albuz ATR Brown

Number of open nozzles 14 (2 × 7) 14 (2 × 7) 14 (2 × 7)

Pressure (bar) 24.5 11.9 4.7

Liquid flow rate (l min-1) 58.0 22.4 6.6

Spray volume (l ha-1) 1603 619 182

Forward speed (km h-1) 6.04 6.02 5.99

Airflow rate (m3 s-1) 11.90 11.90 11.90 AFR trialParameter HF MF LF

Nozzle type and colour Albuz ATR Orange Albuz ATR Orange Albuz ATR Orange

Number of open nozzles 14 (2 × 7) 14 (2 × 7) 14 (2 × 7)

Pressure (bar) 15.0 15.0 15.0

Liquid flow rate (l min-1) 24.01 23.11 24.71

Spray volume (l ha-1) 768.2 744.2 778.2

Forward speed (km h-1) 4.90 4.98 5.03

Airflow rate (m3 s-1) 11.93 8.90 6.15

Operational parameters of spray application


RESULTS

Volume rate trial

TREATMENTPARAMETER HV MV LV

Mean absolute deposit d (µg cm-2) 5.08 ± 0.42 a 2.98 ± 0.17 b 1.74 ± 0.10 cMean normalised deposit dn (µg cm-2) 0.58 ± 0.05 a 0.88 ± 0.05 b 1.74 ± 0.10 cCoefficient of penetration Cp (%) 79.40 ± 21.39 113.60 ± 6.58 123.80 ± 12.67

Deposit homogeneity CV (%) 54.00 ± 5.00 39.80 ± 2.03 36.40 ± 5.90Upper side coverage SC up (%) 51.36 ± 4.08 a 29.17 ± 2.92 b 9.72 ± 1.36 cLower side coverage SC lo (%) 15.12 ± 3.22 a 9.66 ± 1.54 a 3.49 ± 0.84 b

Mean coverage SC (%) 33.25 ± 2.86 a 19.42 ± 1.73 b 6.60 ± 0.80 cCoverage homogeneity HSC (%) 29 33 36

Upper side impacts Ni up (cm-2) 81.19 ± 10.37 a 96.22 ± 7.43 a 116.03 ± 11.01 bLower side impacts Ni lo (cm-2) 92.86 ± 8.84 a 100.03 ± 9.46 a 59.62 ± 8.14 a

Impacts homogeneity HNi (%) 86 96 51


RESULTSAir flow rate trial

TREATMENTPARAMETER HF MF LF

Mean normalised deposit dn (µg cm-2) 10.5 ± 0.61 a 12.96 ± 0.73 b 11.08 ± 0.70 ab

Coefficient of penetration Cp (%) 73.25 ± 15.79 94.75 ± 17.12 82.25 ± 8.83Deposit homogeneity CV (%) 32.2 ± 5.67 34.5 ± 5.04 37.0 ± 4.37 Upper side coverage SC up (%) 77.4 ± 4.61 a 66.8 ± 5.15 a 57.11 ± 5.82 aLower side coverage SC lo (%) 71.0 ± 4.91 a 57.0 ± 5.68 ab 34.3 ± 4.93 bMean coverage SC (%) 74.2 ± 4,10 a 61.9 ± 3,93 a 45.71 ± 3,82 aCoverage homogeneity HSC (%) 92 85 60Upper side impacts (cm-2) Ni up (cm-2) 64.4 ± 12.65 a 89.0 ± 14.22 a 80.44 ± 11.67 a

Lower side impacts (cm-2) Ni lo (cm-2) 74.7 ± 12.55 a 93.1 ± 14.15 a 116.1 ± 11.31 a



RESULTS

General overview

𝑅𝑅 =𝐿𝐿𝐿𝐿𝑅𝑅( �𝐿𝐿 𝑚𝑚𝑚𝑚𝑚𝑚)

𝐴𝐴𝐿𝐿𝑅𝑅( �𝑚𝑚3𝑠𝑠)


RESEARCH




olive tree canopies










prototypes


PAPER II



and homogeneity in super-intensive olive

tree canopies

PAPER BTowards an optimized method of olive tree

crown volume measurement.

PAPER CAssessing the optimal

liquid volume to be sprayed on isolated

olive trees according to their canopy

volume


protection product applications in

traditional and intensive olive orchards through

the development of new air-assisted sprayer

prototypes





Determine the accuracy of differentmanual canopy characterization methodsin comparison with LiDAR scannermeasurements.



Characteristics of Selected Fields and Tree Plantations

Field number Plot number Tree Structure rs x ts(m)

Number of trees

1 1 Intensive 7 x 5 18

1 2 Traditional (1 trunk) 10 x 12 12

2 3 Traditional (> 1 trunks) 12 x 12 25



Manual canopy characterization methodologies

Vertical Crown Projected Area method (VCPA)

Ellipsoid Volume method (VE) Tree Silhouette Volume method (VTS)

Mean Vector method (MV)



Point projection plane

Convex-hull algorithm(Xu et al., 2013)*

* Xu, W., Su, Z., Feng, Z., Xu, H., Jiao, Y., Yan, F., 2013. Comparison of conventional measurement and LiDAR-based measurement for crown structures. Computers and Electronics in Agriculture, 98: 242–251.

∆ℎ

∆ℎ

Ai


RESULTS

Mean and Standard Error

Tree type

Intensive Adapted Traditional

HT (m) 3.91 ± 0.09 4.52 ± 0.11 4.58 ± 0.05

TD (m) 0.19 ± 0.01 0.41 ± 0.04 0.75 ± 0.08

Ea (m) 1.82 ± 0.05 2.09 ± 0.06 2.10 ± 0.03

Eb (m) 2.07 ± 0.09 2.33 ± 0.09 3.24 ± 0.08

Ec (m) 1.96 ± 0.09 2.25 ± 0.07 3.07 ± 0.10

𝑴𝑴𝑴𝑴 (m) 2.03 ± 0.07 2.31 ± 0.07 3.27 ± 0.08

VE (m3) 31.90 ± 3.00 46.74 ± 3.52 87.64 ± 3.79

APA (m2) 11.88 ± 0.83 15.39 ± 0.96 35.58 ± 2.06

VTS (m3) 29.69 ± 2.32 45.11 ± 3.92 100.26 ± 5.21

VL (m3) 24.60 ± 2.19 33.49 ± 3.58 98.08 ± 5.21

APA VEVLVTS


RESULTSComparison between LiDAR and Manual Methods for Tree Volume Estimation

Intensive Adapted Traditional TraditionalVL (m3)

APA (m2) 0.860** 0.835** 0.242*VE (m3) 0.755** 0.760** 0.399**VTS (m3) 0.792** 0.903** 0.275**


RESULTS

Correlation between LiDAR Volume and Simple Canopy Parameters MV

a)

b)

c)

d)


RESEARCH




olive tree canopies










prototypes


PAPER III







prototypes

The intention of this research was todeterminethe effect of air assistance and liquid volume




To define the optimal relationshipbetween the canopy volume and theapplied liquid volume.


MATERIALS AND METHODSLaboratory trial

Top view

6 treatments x 15 sampling positions x 3 sampling profiles x 2 WSP pieces x 3 replications = 1,620 WSP pieces



Spray Parameter Value

Treatment ID 1 2 3 4 5 6

CV (L · m-3) 0.05 0.08 0.10 0.12 0.15 0.20

Spray Volume Rate (L · ha-1) 270.88 433.41 541.76 650.11 812.64 1083.52

Liquid Flow Rate (L · min-1) 15.80 25.28 31.60 37.92 43.34 63.20

Albuz ATR nozzle colour Yellow Red Green Green Blue Blue

Pressure (bar) 12 9 8 12 8 18



𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑚𝑚𝑣𝑣 �𝐿𝐿 𝑡𝑡𝑆𝑆𝑣𝑣𝑣𝑣 = 𝑉𝑉𝐿𝐿 𝑚𝑚3 × 𝐶𝐶𝑣𝑣 �𝐿𝐿 𝑚𝑚3

Studied trees

Studied row

N

Field trial



Spray Parameter ValueCV (L · m-3) 0.08 0.10 0.12

Spray Volume Rate (L · ha-1) 433 541 650Liquid Flow Rate (L · min-1) 25.28 31.60 37.92

Albuz ATR nozzle colour Red Green GreenPressure (bar) 9 8 12

Dependent variables

d

Deposition and normalized deposition

dn

Spray coverage and impacts

SC SC up SC lo

Ni Ni up Ni lo

Spray homogeneity

Cp CV

HSC HNi


RESULTS

Target coverageGroup ‘a’ coverageGroup ‘b’ coverageGroup ‘c’ coverage

Laboratory trial


RESULTSField trial

ParameterSpecific spray volume, CV

0.10 0.12 0.08

Mean absolute deposit d (µg · cm-2) 15.49 ± 1.57 a 20.51 ± 1.42 b 12.04 ± 1.01 a

Mean normalized deposit dn (µg · cm-2) 12.39 ± 1.25 a 13.74 ± 0.95 a 12.04 ± 1.01 a

Coefficient of penetration Cp (%) 60.34 69.18 64.38

Deposit homogeneity CV (%) 71.16 61.70 71.96

Upper side coverage SC up (%) 20.77 ± 2.54 ab 29.15 ± 2.87 b 20.21 ± 2.18 a

Lower side coverage SC lo (%) 14.31 ± 1.71 a 22.19 ± 2.34 b 11.91 ± 1.37 a

Mean coverage SC (%) 17.54 ± 1.55 a 25.67 ± 1.87 b 16.06 ± 1.33 a

Coverage homogeneity HSC (%) 69 76 59

Upper side impacts Ni up (cm-2) 74.36 ± 4.51 a 102.15 ± 5.88 b 96.36 ± 4.71 b

Lower side impacts Ni lo (cm-2) 100.76 ± 6.36 a 143.35 ± 8.08 b 100.29 ± 6.76 a



RESEARCH




olive tree canopies










prototypes


PAPER IV







prototypes





To test the performance of newlydeveloped prototype air-assisted sprayersspecifically designed to work in traditionaland intensive olive orchards.



Prototype development: Mecaolivar project

Public administration

Public research organism

Main aim to involve bothmanufacturers and researchers in a continuous and collaborativeprototype development process.

Olive oil sector



Prototype calibration and preliminary tests

Spray coverage and impacts

SC SC up SC lo

Ni Ni up Ni lo

Spray homogeneity

Cp CV

HSC HNi

Dependent variables

3 replications (trees)


RESULTS

Prototype P1 Prototype P2Prototype P3


RESULTSP1 prototype


RESULTSP2 prototype


RESULTSP3 prototype


RESULTS

Field test

Conventional P1 P2 P3


PRESENTATION INDEX



Conclusions


CONCLUSIONS (I)

Pesticide applications in olive orchards can be markedly improved by increasingtheir efficiency without compromising their biological efficacy, what has thepotential to generate important environmental, personal and food securityoutcomes.

Both volume rate (VR) and airflow rate (AFR) have optimal values that are lowerthan those used by the farmers. A reduction in these parameters could lead to anincrease in the spraying efficiency by reducing spray drift and runoff from leavesand, in addition, to a reduction in the fuel consumption and the power needs ofthe tractors performing the treatments. The environmental save is, therefore,closely linked to the economic one.

Manual canopy characterization methods can contribute to estimate the crownvolume accurately. The different methods vary their accuracy depending on thecultivation system. The Mean Vector (MV) method showed to fit reasonably wellto the three evaluated orchard types, and can be considered as an accurateestimator to be used in any general dosing system for olive.


CONCLUSIONS (II)

The laboratory approach for multiple spray volumes testing resulted very usefulas it reduced the number of treatments that were taken to the field. The samplingstructure made the work easy as it allowed the researchers to easily change thesamples by respecting the coordinates of each one. This setup can makeresearchers to save time and money, and to optimize the working settings in thefield trials.

It was shown that improving spraying efficiency is possible through the use ofadapted equipment. The difficulties present in the crop make necessary to adaptthe spraying machinery, what makes possible to significantly reduce spray lossesand, therefore, to perform treatments in a safer way.

It is possible to find an optimal specific spray volume for isolated olives thatrelates the spray volume to the tree crown volume. This parameter can be thebasis on which others could be added to improve pesticide applications in oliveorchards.


THANK YOU VERY MUCH FOR YOUR ATTENTIONGRAZIE A TUTTI PER L’ATTENZIONE


ACKNOWLEDGEMENTS


BIBLIOGRAPHY

Bibliography

Escolà, A., Planas, S., Rosell, J., Pomar, J., Camp, F., Solanelles, F., Gracia, F., Llorens, J., Gil, E., 2011.Performance of an Ultrasonic Ranging Sensor in Apple Tree Canopies. Sensors, 11: 2459–2477.

Giuliani, R., Magnanini, E., Fragassa, C., Nerozzi, F., 2000. Ground monitoring the light shadowwindows of a tree canopy to yield canopy light interception and morphological traits. Plant CellEnvironment, 23: 783–796.

Iniesta, F., Testi, L., Orgaz, F., Villalobos, F.J., 2009. The effects of regulated and continuous deficitirrigation on the water use, growth and yield of olive trees. European Journal of Agronomy, 30: 258–265.

Rosell, J.R., Sanz, R., 2012. A review of methods and applications of the geometric characterization oftree crops in agricultural activities. Computers and Electronics in Agriculture, 81: 124–141.

Rovira-Más, F., Zhang, Q., Reid, J., 2005. Creation of Three-dimensional Crop Maps based on aerialstereoimages. Biosystems Engineering, 90 (3): 251–259.

Zaman, Q.U., Salyani, M., 2004. Effects of foliage density and ground speed on ultrasonicmeasurement of citrus tree volume. Applied Engineering in Agriculture, 20: 173–178.

Strategies for the optimization of the efficiency in the plant protection product

applications in olive canopies

Giuseppe Pellizzi Prize 201828th Members’ Meeting of the Club of Bologna

November 10, 2018

Antonio Miranda-Fuentes –[email protected] / [email protected]

giuseppe pellizzi prize 2018 - clubofbologna.org · strategies for the optimization of the...

Documents