22ndnd European ModellingEuropean Modelling Workshop Workshop -- Lisbon, June 6 Lisbon, June 6 –– 7 20027 2002

A decision support system for A decision support system for

a sustainable use of pesticidesa sustainable use of pesticides

SuSAPSuSAP

Stefano Brenna Stefano Brenna Carlo Riparbelli Carlo Riparbelli Regional Board for Agriculture Development of LombardyRegional Board for Agriculture Development of LombardySoil ServiceSoil Serviceriparbelli@ersal.lombardia.ithttp://www.ersal.lombardia.it/life98/inglese/default.htm

ERSAL

ERSAL ERSAL Regional Board for Agriculture Development of Lombardy

Agro-meteorological ServiceMeteorological monitoring and

weather forecastApplied climatology

Crop survey

Agriculture and food ServiceTypical and local food products

promotion Studies and projects on food quality

and food safety

Zootechnics ServiceTechnical and marketing support to

stock-farmersStudies and projects on Zootechnics

Soil ServiceSoil survey

Soil cartographySoil applications for environmental and

agriculture purpose

r

ERSAL – Soil ServicePalazzo CanovaMilano 2 - 20090 Segrate (Milano) ItalyTel. ++390221762442 - Fax ++390226410459http://www.ersal.lombardia

SuSAP - the project

The project has been proposed by ERSAL and financed by the

European Commission - LIFE Environment European

instrument and by Regione Lombardia DG Agricoltura.

The organisations involved in the project together with ERSAL are:

• International Centre for Pesticide Safety (ICPS)

• Università Cattolica di Piacenza

• European Soil Bureau (ESB) of the Joint Research Centre of Ispra.

Software implementation: TXT e-solutions - Milano

SuSAP SuSAP

““Supplying Sustainable Agriculture ProductionSupplying Sustainable Agriculture Production””

SuSAP - the project

Allegato VII parte B: zone vulnerabili ai prodotti fitosanitari

la valutazione della “vulnerabilità intrinseca degli acquiferi” è da effettuarsi tenendo conto anche della “capacità di attenuazione del suolo”.

la valutazione della “vulnerabilità specifica” è da effettuarsi attraverso “l’uso di opportuni modelli di simulazione”.

Legislative backgroundLegislative background

The D.lvo 194/95, which receipt in Italy the Dir. 91/414/ECC, gives the possibility at regional level to limit the use of approved pesticides, if vulnerable areas are identified.

The D.lvo 152/99, indicate that how to identify vulnerable areas.

SuSAP - the project

SuSAP is a decision support system working at different spatial scales allowing the users:

•at regional (1:250,000) and local (catchments -1:50,000) level to map the soil vulnerability to pesticide leaching

•at farm level (1:10,000) to identify the most sustainable crop protection strategies

What is What is SuSAPSuSAP

SuSAP – spatial level

SuSAP allows to:

• build soil vulnerability maps to pesticide leaching at scale 1:250,000 (regional level) and 1:50,000 (local level);

• define a wide range of scenarios, selecting crop, field treatment conditions and pesticide of interest;

• look up data and maps concerning soil properties related to the soil protective function, climate, pesticides and land use.

SuSAPSuSAP regional and local levelregional and local level

SuSAP – spatial level

Area selection

Crop selection

Regional and local levelRegional and local level

Irrigation Yes-No Selection of

pesticide and mode of application

Models run for the assessement of pesticide leaching potential

PELMOother crops

PESTLA-SWAPrice

OUTPUT

TABLES – pesticides amount at different depths in soil

GRAPHS – pesticide amount at the bottom of the soil at different times

THEMATIC MAPS – sensitivity of soil to the leaching of pesticides

SuSAP – Data

Database Structure

SOIL SOIL InformationInformationSystem System

at scale at scale 1:1:250,000250,000

SuSAP – Data

The cartographic base has been developed in Arc/Info - ArcView environment:

• it is characterized by a single and continuous cover, without interruption of no-soil areas (lakes, rivers, urban area, rocks ,…..)

• it is congruent with administrative limits and regional officialtopographic base at scale 1:10,000 (Carta Tecnica Regionale – CTR).

SOIL scale 1:250,000: cartographySOIL scale 1:250,000: cartography

SuSAP – Data

In SuSAP model simulations are performed using data of all mapped soils (every polygons) in order to totally respect the geographic variability of soil database at this scale 1:250,000.

SOIL scale 1:250,000: SOIL scale 1:250,000: SuSAPSuSAP

SuSAP – Data

SuSAP – Data

SuSAP – Data

Database and soil covers at semidetailed scale (1:50,000).

Soil information at this scale has been organized with the same criteria used at scale 1:250,000, in order to preserve the complete integration between local, regional and farm level.

Each mapping units is associated with tho kind of soils (simple mapping units and compound mapping units) classified at “phase series” according to Soil Taxonomy (USDA – ’94).

SUSAP local level presents two main functions:

- describing the soil behaviour of the area at a higher detailedlevel that 1:250,000;

- giving the reference information in SuSAP for the applications at farm scale.

SOIL scale 1:50,000: generalitySOIL scale 1:50,000: generality

SuSAP – Data

SuSAP – Data

10 representative soil profiles were characterised by field and laboratory analysis.

Physical and hydrological measured data were compared with data calculated by pedotransferfunctions (PTF).

SOIL SCENARIOS for calibration of SOIL SCENARIOS for calibration of pedotransferpedotransfer function & calculation function & calculation of irrigation practicesof irrigation practices

SuSAP – Data

Sito Localizzazione Comune Provincia Riferimento tassonomico (USDA) Uso del suolo

1 C.na Ruga ovest Groppello Cairoli PV Argic Udipsamment, mixed mesic risaia

2 C.na Mantovazza Garlasco fraz. S. Biagio PV Typic Hapludalf, coarse-lomy over sandy, mixed, mesic cereali tipo mais

3 C.na Croce Nuova S. Benedetto Po MN Vertic Ustifluvent, fine, mixed (calcareus), mesic seminativo

4 Ospedale Treviglio Treviglio BG Typic Hapludalf, loamy-skeletal, mixed, mesic prato permanente

5 C.na S. Naborre nord-ovest Masate MI

Glossic Fragiudalf, fine-silty, mixed, mesic prato permanente

6 C.na Costa Travacò Siccomario PV Typic Udifluvent, fine-silty, mixed (calacareus), mesic pioppeto

7 C.na Paradiso Rodano MI Aquic (Albaquic) Hapludalf, fine-silty,mixed,mesic seminativo a set-aside

8 S. Maria dei Sabbioni Cappella Cantone CR

Thapto-Histic Fluvaquent, coarse-loamy?, mixed,mesic

suolo nudo (prato irriguo)

9 C.na Cantarane Soresina CR Aquic Haplustalf, fine, mixed, mesic suolo nudo (risaia)

10 Bivio per Cimbro sud Vergiate VA Pachic Haplumbrept, coarse-lomy over sandy-skeletal, mixed, mesic prato permanente

Spatial profile Spatial profile distributiondistribution

SuSAP – Data

Soil profilesSoil profiles

SuSAP – Data

SITO Quota Orizzonte da a Sabbia Limo Argilla Classe pH C. org. S. org. CSC m slm cm cm 2-0.05 mm 0.05-0.002 <0.002 mm tessit. USDA in H2O % %

2 91Ap 0 32 68.6 28.0 3.4 FS 5.4 0.87 1.5

BE 32 53 53.6 35.4 11.0 FS 6.2 0.17 0.3

Bt 53 73 78.2 13.8 8.0 SF 6.3 0.14 0.2

BC 73 105 90.4 7.7 1.9 S 6.6 0.14 0.2

CB&BT 105 200 95.5 1.0 3.5 S 6.7 ----- ----- -----

SITO orizzonte da a Velocità di infiltrazione (ms-1) (cm) (cm) min. max. media

2 Ap 0 32 8.0E-06 2.0E-05 1.4E-05

BE 32 53 1.2E-06 1.5E-05 8.1E-06

Bt 53 73 6.0E-05 1.0E-04 8.0E-05

SITO Orizzonte Da

a

Densità apparente (gcm-3)

(cm) (cm) con scheletro senza scheletro 2 Ap 0 32 1.33 1.33 Bt 53 73 1.74 1.74 BCt 73 105 1.51 1.51

SITO Orizzonte da a Permeabilità satura (ms-1) (cm) (cm) min. max. media

2 Ap 0 32 4.8E-05 1.1E-04 7.9E-05

Bt 53 73 1.4E-07 1.4E-05 7.1E-06

BC 73 105 2.2E-05 3.7E-05 3.0E-05

Sito 4 Bt1

0

0.1

0.2

0.3

0.4

0.5

0.6

1 10 100 1000 10000

MisuratiMisurati 2°Misurati mediaRawls1SaxtonPELMO

Sito 4 Bt1

0

0.1

0.2

0.3

0.4

0.5

0.6

1 10 100 1000 10000

MisuratiMisurati 2°Misurati mediaRawls1SaxtonPELMO

Soil profiles Soil profiles datadata

SuSAP – Data

Author Pedotransfer function (PTF)Rawls FC = 0.2576 + (-0.002*Sand) + (0.0036*Clay) + (0.0299*Organic Matter)

WP = 0.026 + (0.005*Clay) + (0.0158* Organic Matter)Saxton FC = (φFC / A)1/B ( φ Matric pressure at FC and WP )

WP = (φWP / A)1/B (A and B parameters function of Sand and Clay amount in Klein FC = (20*Sand + 60*Clay + 40*Silt) / 100

WP = (3*Sand + 40*Clay + 7*Silt) / 100 (Clay < 50 %)WP = (3*Sand + 30*Clay + 7*Silt) / 100 (Clay > 50 %)

PTFPTF

Soil Unit

Horizon n. Horizon From

(cm)To

(cm) FC % WP % Bd (g/cm3)

GTA2 1 A 5 25 31.61 16.39 1.05GTA2 2 Bw 25 40 22.95 12.47 1.28GTA1 1 A 5 25 31.61 16.39 1.05GTA1 2 Bw 25 40 22.95 12.47 1.28BNI2 1 Ap 5 30 33.53 16.40 0.84BNI2 2 Bw 30 80 14.29 5.69 1.39BNI2 3 C 80 90 3.63 1.56 1.58GVN1 1 A 0 10 33.80 19.92 1.06GVN1 2 Bw 10 100 30.57 16.40 1.18GVN1 3 C 100 130 21.13 12.24 1.54

ESTIMATED SOIL PROPERTIESESTIMATED SOIL PROPERTIES

The best fitting PTF was used to derive field capacity, wilting point and bulk density, to add to basic soil data

Soil properties estimation: Soil properties estimation: Field Capacity and Wilting PointField Capacity and Wilting Point

SuSAP – Data

The monthly rainfall data of 125 meteo stations of Lombardy have been colleted (30 to 40 year data) in order to define the geographical distribution and variability of this parameter.

Punctual data interpolated with geostatistical methodologies (Linear Kriging – Arcview, Spatial Analyst, ESRI inc.) obtaining the rainfall maps:

• Average (year with average rain – standard, leaching potential);• 10 percentile (year with few rain – lower leaching potential);• 90 percentile (year with a lot of rain – higher leaching potential).

METEOROLOGICAL DATAMETEOROLOGICAL DATA

SuSAP – Data

METEOROLOGICAL DATA METEOROLOGICAL DATA INTERPOLATIONINTERPOLATION

SuSAP – Data

METEOROLOGICAL MACROAREASMETEOROLOGICAL MACROAREAS

The analysis of soil and meteorological data allowed to identify 20 macro-areas defined by a reference meteorological station (daily data for at list 12 years) and characterised by homogeneous climatic conditions.

SuSAP – Data

SuSAP – Data

Amounts, dates and type of irrigation have been calculated by means of CropSyst 2.02 (Stöckle et al., 1999) applied to 10 soilscenarios with reference to a standard maize cultivation.

IRRIGATION ScenariosIRRIGATION Scenarios

SuSAP – Data

Features of major crops of the region and the photographs of most diffused weeds and pests complete the SuSAP database.

wheat

rice

maize

CROPSCROPS

SuSAP – Data

Crop coefficients Kc for the potential evapotranspiration calculation have been daily calculated for every cultivation following a linear interpolation of crop grow curve.

CROPS CROPS fisiology fisiology

SuSAP – Data

215 active substances and related plant protection products, including their chemico-physical properties, ecotoxicologicaldata, date, number and rate of application together with the efficacy against pests have been stored in SuSAP database.

Nome pKa He nry Koc DT50suolo EC50da phnia EC50a lghe2,4-D 2,64 4,42E-05 60 7 2.35E+02 2.50E+012,4-DB 4,80 2,17E-11 437 7 2.35E+02 1.00E+00ABAMECTIN 5,00 2,46E-02 5000 28 3.40E-04 1.00E-01ACEPHATE 5,00 5,06E-08 2 3 6.72E+01 9.80E+02ACIFLUORFEN 3,86 3,01E-05 44 200 7.70E+01 2.60E+02ACLONIFEN 5,00 3,03E-03 5318 80 2.50E+00 6.90E-03ACRINATHRIN 5,00 1,19E-03 127500 100 5.70E-01 8.20E-01ALACHLOR 0,62 2,20E-03 170 15 1.00E+01 1.20E-02ALPHA-CYPERMETHRIN 5,00 9,58E-01 100000 91 1.00E-04 1.00E+02AMIDOSULFURON 3,58 5,34E-04 18 29 3.60E+01 4.70E+01AMITRAZ 4,20 1,00E+00 1000 1 3.50E-02 1.20E+01ATRAZINE 1,70 2,88E-04 100 77 8.70E+01 4.30E-03AZIMSULFURON 3,60 5,00E-10 61 3 1.00E+03 1.20E-02AZINPHOS METHYL 5,00 3,17E-04 407 100 1.10E-03 7.15E+00AZOCYCLOTIN 5,36 7,00E-02 81805 100 4.00E-02 1.60E-01AZOXYSTROBIN 5,00 7,00E-09 130 28 4.00E-02 3.60E-01 . . . . . .

Nome pKa He nry Koc DT50suolo EC50da phnia EC50a lghe2,4-D 2,64 4,42E-05 60 7 2.35E+02 2.50E+012,4-DB 4,80 2,17E-11 437 7 2.35E+02 1.00E+00ABAMECTIN 5,00 2,46E-02 5000 28 3.40E-04 1.00E-01ACEPHATE 5,00 5,06E-08 2 3 6.72E+01 9.80E+02ACIFLUORFEN 3,86 3,01E-05 44 200 7.70E+01 2.60E+02ACLONIFEN 5,00 3,03E-03 5318 80 2.50E+00 6.90E-03ACRINATHRIN 5,00 1,19E-03 127500 100 5.70E-01 8.20E-01ALACHLOR 0,62 2,20E-03 170 15 1.00E+01 1.20E-02ALPHA-CYPERMETHRIN 5,00 9,58E-01 100000 91 1.00E-04 1.00E+02AMIDOSULFURON 3,58 5,34E-04 18 29 3.60E+01 4.70E+01AMITRAZ 4,20 1,00E+00 1000 1 3.50E-02 1.20E+01ATRAZINE 1,70 2,88E-04 100 77 8.70E+01 4.30E-03AZIMSULFURON 3,60 5,00E-10 61 3 1.00E+03 1.20E-02AZINPHOS METHYL 5,00 3,17E-04 407 100 1.10E-03 7.15E+00AZOCYCLOTIN 5,36 7,00E-02 81805 100 4.00E-02 1.60E-01AZOXYSTROBIN 5,00 7,00E-09 130 28 4.00E-02 3.60E-01 . . . . . .

PESTICIDEPESTICIDE

SuSAP – Data

Active ingredient are related to commercial products to define condition, time and amounts of applications

2,4-D BROMOPROPYLATE CYPERMETRHIN2,4-DB BROMOXYNIL OCTANOATE DALAPON SODIUMABAMECTIN BUPROFEZIN DELTAMETHRINACEPHATE CAPTAN DESMEDIPHAMACIFLUORFEN CARBARYL DIAZINONACLONIFEN CARBENDAZIM DICAMBAACRINATHRIN CHLORIDAZON DICHLOBENILALACHLOR CHLOROTOLURON DICHLOFLUANIDALPHA-CYPERMETHRIN CHLORPROPHAM DICLOFOP METHYLAMIDOSULFURON CHLORPYRIFOS DICHLORPROPAMITRAZ CHLORPYRIFOS METHYL DICHLORVOSATRAZINE CHLORSULFURON DICOFOLAZIMSULFURON CYFLUTHRIN DIFENOCONAZOLEAZINPHOS METHYL CYMOXANIL DIFLUBENZURONAZOCYCLOTIN CINOSULFURON DIFLUFENICANAZOXYSTROBIN CYPRODINIL DIMEPIPERATEBENALAXYL CLETHODIM DIMETHENAMIDBENFURACARB CLODINAFOP-PROPARGYL DIMETHOATEBENOMYL CLOFENTEZINE DIMETHOMORPHBENSULFURON METHYL CLOPYRALID DINOCAPBENTAZONE CLOQUINTOCET MEXYL DIQUATBENZOXIMATE CYHALOFOP BUTYL DITHIANONBIFENOX CYCLOATE DIURONBIFENTHRIN CYCLOXYDIM DODINE

ENDOSULFAN FOMESAFEN MEFENPYR-DIETHYLHEXACONAZOLE GLUFOSINATE AMMONIUM METALAXYLESFENVALERATE GLYPHOSATE METHAMIDOPHOSETHOFUMESATE GLYPHOSATE TRIMESIUM METAMITRONETHOXYSULFURON HALOXYFOP-ETOTYL METHOMYLETHIOFENCARB IMAZAMETHABENZ METHIDATHIONETOFENPROX IMAZETHAPYR METHIOCARBHEXYTHIAZOX IOXYNYL METIRAMFAMOXADONE IOXYNYL OCTANOATE METOBROMURONFENARIMOL IPRODIONE METOLACHLORFENAZAQUIN ISOPROTURON METOSULAMFENBUCONAZOLE ISOXABEN METRIBUZINFENBUTATIN-OSSIDO ISOXAFLUTOLE METSULPHURON METHYLFENCLORIM KRESOXIM METHYL MYCLOBUTANILFENITROTHION LAMBDA-CYHALOTHRIN MOLINATEFENOXAPROP-P-ETHYL LENACIL NICOSULFURONFENPROPATHRIN LINURON NUARIMOLFENPYROXIMATE LUFENURON OMETHOATEFLUAZIFOP-P-BUTYL MALATHION OSSICLORURO DI RAMEFLUCYTHRINATE MANCOZEB OXADIAZONFLUFENOXURON MANEB OXADIXYLFLUROXYPYR MCPA OXYDEMETON METHYLFLUSILAZOLE MCPB OXYFLUORFENFOLPET MECOPROP PARAQUAT

PARATHION PROPARGITE TERBUTRYNPARATHION-METHYL PROPICONAZOLE TETRACONAZOLEPENCONAZOLE PROPINEB TETRADIFONPENDIMETHALIN PROPOXUR THIFENSULFURON-METHYLPERFLUIDONE PROPYZAMIDE THIOBENCARBPERMETHRIN PROSULFURON TIOCARBAZILPHENMEDIPHAM PYRAZOXYFEN THIOPHANATE-METHYLPHORATE QUINALPHOS THIRAMPHOSALONE QUINCLORAC TRALKOXYDIMFOSETYL ALUMINIUM QUIZALOFOP-ETHYL TRIADIMEFONPICLORAM RAME TRIADIMENOLPYRETHRINS RAME DA IDROSSIDO TRIASULFURONPYRIDABEN RAME DA SOLFATO TRIBENURON-METHYLPYRIDAPHENTION RAME DA SOLFATO NEUT.CALCE TRICLOPYRPYRIFENOX RIMSULFURON TRICHLORFONPYRIMETHANIL ROTENONE TRIFLUMURONPIRIMICARB SETHOXYDIM TRIFLURALINPIRIMIPHOS-METHYL SIMAZINE TRIFLUSULFURON METHYLPRETILACHLOR SULCOTRIONE TRIFORINEPRIMISULFURON-METHYL TEBUCONAZOLE VINCLOZOLINPROCYMIDONE TEBUFENOZIDE ZINEBPROPACHLOR TEBUFENPYRAD ZIRAMPROPANIL TEFLUBENZURON ZOLFOPROPAQUIZAFOP TERBUTHYLAZINE

PESTICIDEPESTICIDE

SuSAP – farm level

SuSAP allows agronomists and farmers to:

•Define the best crop protection strategies on the

basis of the pesticides impact on the environment

(e.g.: groundwater, surface water, air and soil)

•Consider the best crop protection strategies in

relation with the plant protection products costs

•Build a farm database

SuSAPSuSAP farm levelfarm level

SuSAP – farm level

Fate of pesticide in the environmentFate of pesticide in the environment

SOIL

CROPS

PESTICIDE APPLICATION

LATERAL WATER FLUX

LEACHING

GROUNDWATER

RUNOFF

AQUATIC ECOSYSTEM

TERRESTRIAL ECOSYSTEM

VOLATILIZATION

DRIFT

DRAINAGE

SuSAP – farm level

Digitalization of Landscape Farm Unit (LFU) and soil/crop input data

Crop selection

Pest recognising by means of photo

and tables

Pest selection

Irrigation Yes-No

Selection of pesticide treatment efficiency

MODELS RU

N

• PELMO• PESTLA-SWAP

• Indexes forSurface water

and soilPesticides environmental assessment with the risk

index EPRIP

Productstreatment rank on

the basis of environmental hazard classes

Treatmentscosts

Selection of sustainable pesticide

treatments

Farm levelFarm level

GIMMI

GIMMI: project

Geographic Information and Mathematical Models Inter-operability

Framework: Fifth European ResearchProgrammeIST – Information Society and Technology(IST-2001-34245)Duration 30 Months - starting date April 1st 20027 partners from 4 countries: I, D, E, EC

GIMMI

GIMMI: partners

TXT e-solutions (coordinator, on-line and off-line web services, workflow, MS-Office and e-commerce integration);

FhG-AIS (GIS developments, CommonGIS);

EIG (inter-operability architecture, data mining);

USR-LABSITA (AI applications);

ERSAL (Italian test case - Lombardy);

SARA (Spanish test case - Catalunya)

INAMHI (Ecuadorian observatory)

GIMMI

GIMMI: aim

Data Providers - soil, meteorology, agronomy, pesticide

Scientists - chemists, agronomists, geologists, modellers and academic institutions

Service Providers - local and central governments, public administration bodies, chemical industries manufacturing pesticides

Contribute to bridging the gap in the pesticide evaluation domain among:

GIMMI

GIMMI: architecture in progress – June 2002

Final Users

GIMMICentre

WEB/WAP Interface

Off-line QBuilderOn-line Analyser

GIS-based GUI System

E-commerce Engine

Meta-DataMeta-Service

Repository

Middleware Layer

INTERNET

DATA and SERVICE PROVIDERS

Middleware Layer

Soil, Meteo, Agr.Data Providers

Middleware Layer

PesticideData Providers

Middleware Layer

Math. ModelsDSS SuSAPs

Existing GI DBs Existing GI DBs Existing Models DSS

Workflow Engine

Datamining Engine

GIMMI

GIMMI: web services

On-line data access, when the user seeks to "drill down" into the huge amount of information distributed in different formats and in different sites.On-line simulation, like the activation of very simple web-based mathematical models or expert systems.Off-line study, when the requested services require huge amounts of data to be inter-operated, long time or the attendance of human experts.

SuSAP REFERENCES

Council Directive 91/414/EEC concerning the placing of plant protection products on the market. (1991) Official Journal of the European Communities. L230 vol. 34, 1-32.D.lvi 152/99 and 258/00 Testo Unico tutela della acque (1999 – 2000).ERSAL – Regione Lombardia (2000) – EU. SuSAP – Supplying Sustainable Agriculture Production – Manuale metodologico. Progetto LIFE ENV98/IT/00010.Klein M. & Jene B. (1995). PELMO version 2.01 / 3.00 Staatliche Lehr und Forschungsanstalt für Landwirtschaft, Weinbau und Gartenbau Fachbereich Ökologie - Neustadt Germany.Kroes J.G., van Dam J.C., Huygen J., Vervoort R.W. (1999). User’s Guide of SWAP version 2.0 - Technical Document 53 SC-DLO Wageningen, The Netherlands.Results of the European CAPER Project (1999). Comparing environmental risk indicators for pesticides – Centre for Agriculture & Environment Utrecht, The Netherlands.Stöckle C. & Nelson R. CropSyst version 2.02.30 (1999). Department of Biological Systems Washington State University USA.Van Den Berg & J.J.T.I. Boesten. (1998). PESticide Leaching and Accumulation model PESTLA version 3.4 – Technical Document 43 SC-DLO Wageningen The Netherlands.

ReferencesReferences

A DECISION SUPPORT SYSTEM FOR A SUSTAINABLE USE OF PESTICIDES IN AGRICULTURE

1S. Brenna, 1C. Riparbelli, 2E. Capri, 2M. Trevisan, 3D. Auteri, 4S. Gusmeroli

1Ente Regionale di Sviluppo Agricolo della Lombardia Segrate MI – Italy riparbelli@ersal.lombardia.it

2Istituto di Chimica Agraria ed Ambientale, Università Cattolica, Piacenza – Italy 3International Centre for Pesticide and Health Risk Prevention - Busto Garolfo MI – Italy

4TXT E-solutions MI - Italy ABSTRACT Pesticide contamination may affect the quality of different environmental compartments as soil, water and air and, consequently, may pose a risk to humans, flora and fauna. The current environmental and agricultural policies of the European Union (e.g.: Dir. 91/414) are aimed at the development of “sustainable agriculture production”. Therefore the decision makers need instruments to plan the pesticide use and farmers are often lacking in really helpful information to address their fight against pests in a sustainable way. A prototype of a decision support system windows based called SuSAP– Supplying Sustainable Agriculture Production – was developed in an intensive agricultural area of Lombardy (North Italy) to promote a sustainable use of pesticides at regional, local and farm level. Key words: sustainable agriculture, soil vulnerability, pesticide. INTRODUCTION Crop protection is essential to maintain the agricultural production level sufficient to ensure food needs of a continuously increasing world population. In this context pesticides are essential for the modern agriculture; more than 2 million tons of pesticides derived from 900 active ingredients are used annually world-wide. Pesticide contamination may affect the quality of different environmental compartments as soil, water and air and, consequently, may pose a risk to humans, flora and fauna. Hence the need to assess the nature and degree of the risk and to take at the same time preventive measures aimed at minimising possible damages. The current environmental and agricultural policies of the European Union (e.g.: Dir. 91/414, EEC Reg. 2078/92) are aimed at the development of sustainable agriculture production methods. Therefore the decision makers need instruments to plan the pesticide use and farmers are often lacking in really helpful information to address their fight against pests in a sustainable way. A prototype of a decision support system called SuSAP– Supplying Sustainable Agriculture Production – was developed in an intensive agricultural area of Lombardy (North Italy) to promote a sustainable use of pesticides at different scale of study and management. METHODS AND RESULTS SuSAP is a software running on personal computer, integrating existing database (soil, climate, crop, pesticides) and mathematical models to help the decision making to assess the environmental risk of pesticides at three different levels as follows:

• regional level - scale 1:250,000 - addressing programs related to specific European regulations and promoting the introduction of sustainable agricultural production methods;

• local level - scale 1:50,000 - (i.e. watershed), addressing prevention, control and monitoring activities of potentially hazardous effects resulting from the use of pesticides;

• farm level - scale 1:10,000 - directing the farmers, in the use of plant protection products and in planning sustainable agricultural practices (Fig. 1).

Fig. 1: General flow chart of SuSAP. Basic soil data are surveyed at scale 1:250,000 and at scale 1:50,000 according to the different level of study. Several representative soil profiles were characterised by field and laboratory analysis; physical and hydrological measured data were compared with data calculated by pedo-transfer functions (PTF). The best fitting PTF was used to derive field capacity, wilting point and bulk density, to add to basic soil data. Data of 125 meteorological stations (rainfall, temperature, wind, solar radiation, relative humidity, evapotranspiration) were collected and interpolated with geostatistical methods to obtain spatial patterns of rainfall and temperature. The analysis of soil and meteorological data allowed to identify 20 macro-areas defined by a reference meteorological station and characterised by homogeneous climatic conditions (Fig. 2a, b, c).

Fig. 2a: Soil map units. Fig. 2b: Average rainfall per year.

Fig. 2c: Meteorological macro-areas.

215 active substances and related plant protection products, including their chemico-physical properties, ecotoxicological data, date, number and rate of application together with the efficacy against pests have been stored in SuSAP database and could be evaluated using the system. The features of major crops of the region and the photographs of most sprayed weeds and pests complete the SuSAP database. Amounts, dates and type of irrigation have been calculated by means of CropSyst 2.02 (Stöckle et al., 1999) applied to 10 soil scenarios with reference to a standard maize crop. At regional and local level the model PELMO 3.0 (PEsticide Leaching MOdel - Klein et al., 1995) was integrated in SuSAP to estimate the leaching potential of pesticides through distinct soil horizons based on an extended cascade model; processes include estimating of soil temperatures, pesticide degradation, sorption, volatilisation, and potential evapotranspiration. To simulate pesticide behaviour in soil-water system for rice flooded cultivation, the combination of PESTLA 3.4 (PESTicide Leaching and Accumulation - Van Den Berg et al., 1998) with SWAP 2.07d (Soil Water Atmosphere Plant - Kroes et al., 1999) was adopted. The above models have been selected among those indicated and validated by the FOCUS (acronym for the FOrum for the Co-ordination of pesticide fate models and their USe), jointly established since 1993 by the European Commission and the European Crop Protection Association to provide guidance to the Member States in EU pesticide registration process. These models were integrated in SuSAP to simulate pesticide degradation and transport for each combination of soil map units and meteorological macro-areas. After defined the studying area and selected the pesticide of interest, PELMO or PESTLA automatically run for a period of 12 years, so describing a panel of different meteorological conditions, potentially occurring in the area. From the resulting concentration values expressed in µg/l estimated at 1 m depth, calculated for each year, the 80th percentile is extracted by the system. Results, ranked in five concentration classes, are visualised as soil vulnerability maps to pesticide leaching, using the Geographic Information System Arcview (ESRI inc.) (Fig.3).

Fig. 3: Maps of soil vulnerability to pesticide leaching at regional and local scale resulting, from SuSAP run. At farm scale SuSAP defines the best crop strategies on the basis of the pesticides impact on the environment (e.g.: groundwater, surface water, soil and air). Predicted Environmental Concentrations (PECs) are calculated for each compartment by both mathematical models and simple algorithms. The ratio between PECs and the detection limit of 0.1 µg/l for groundwater and the toxicological endpoints for non target organisms for surface water (LC50 or EC50 for algae, fish and crustaceous), soil (LC50 for heart worms) and air (LC50 inhalation rats) defines the exposure toxicity values (TER). The EPRIP index (Environmental Potential Risk Indicator for Pesticides CAPER, 1999) using these data, estimates a global environmental risk for each plant protection product, according to the site-specific characteristics of the farm. TER values are transformed into risk points (RP) using a scale from 1 to 5 and EPRIP score is obtained by multiplying the RP values corresponding to each compartment. The final EPRIP score, so obtained, is ranked in environmental potential risks classes on the basis of expert judgement. In SuSAP is also implemented a specific tool that, using the topographic base at scale 1:10,000, assists the user to digitise farm boundaries and to define homogeneous farm areas with respect to crop practices and general soil characteristic, called Landscape Farm Unit (LFU). At this scale the user, following a number of user-friendly interfaces, can choose between the possibility to select a single pest or a group of them between Gramineae or Dycotiledones together with the desired level of efficacy, or directly test a specific treatment, according the site specific characteristic of each LFU previously identified. With the first option a table comparing and ranking in six classes - none, negligible, small, present, large, very large - the environmental risk for the different strategies extracted is prepared after models simulation and EPRIP calculation (Fig

4); with the second option the risk is estimated for the selected treatment. Finally a table containing the costs per hectare of each plant protection product helps to carry out the sustainable crop protection strategies.

Small MAIZE Dual Vegoil April 5th 1 70 70 60Small MAIZE Acconem March 15th 1 95 80 95

Negligible MAIZE Antigram March 15th 1 95 95 70

Environmental risk

Level of efficacy agaist pests (%)

Crop Commercial product Application date Number of

applications

Dig

itaria

sa

ngui

nalis

Echi

nocl

oa

crus

-gal

li

Pani

cum

sp

p.

LFU E

Fig. 4: Environmental risk for different plant protection products (table), calculated for a specific LFU identified in the map by capital letters.

At regional and local level SuSAP allows the local authorities to assess the aquifer vulnerability to soil at pesticide leaching. For this purpose, SuSAP provides:

• the possibility to look up data and maps concerning soil properties (e.g. soil texture, soil permeability) related to the soil protective function (groundwater protection from pollution), climate, pesticides and land use;

• soil vulnerability maps to pesticide leaching at scale 1:250,000 and 1:50,000; • the possibility to define a wide range of scenarios, selecting crop, field treatment conditions and pesticide of

interest. At farm level SuSAP allows agronomists and extension services to carry out crop protection strategies fully aware of the potential environmental impact. For this purpose, SuSAP provides:

• tools to compare the environmental impact of different crop protection strategies, taking in account at farm level soil characteristics, weed and pest occurrence, effectiveness degree of the treatments, crops and farm practices;

• tools to select the plant protection product according to the environmental impact and their costs; • files helpful to upgrade and modify the scenarios, to store information about farm soils and crops, to record data

about the crop protection treatments performed. DISCUSSION AND CONCLUSIONS SuSAP should be considered a demonstrative pilot experience. Such an experience, besides being characterised by a highly innovative nature in terms of both contents and actors involved, is specifically aimed at integrating environmental concerns within the land use planning at different decision levels. SuSAP stores a huge amount of complex data combining and integrating them according to the desired use. Generally, such sophisticated data types are used independently from one to other, thereby causing severe loss of efficiency and co-ordination in the resulting activities. Providing a solution for this problem, SuSAP can satisfy significant support needs both for Local Authorities, carrying out environmental monitoring and land regulation tasks, and Farmers, planning sustainable agricultural practices. With respect to Local Authorities, it could be envisaged that the adoption of SuSAP system could provide decision makers with all the necessary information for an optimised management of the monitoring networks and campaigns. Such saved resources could be the flywheel to intensify the control activities in real risky areas and to extend the monitoring survey to other potential risky zones, evidenced by SuSAP simulations. Actually to correctly plan water resources, it would be very important to know well in advance, by means of proper simulations, what the future status of the water resources will be, in particular the groundwater, in order to take timely the proper corrective and recovery actions. With respect to Farmers, SuSAP can be considered an integrated system which could offer solutions to many of the real problems encountered on farms throughout Europe today. Not all the agricultural production could be “biological”, so a consistent market share could be always at disposal of traditionally managed farms with a certain sensitivity with

respect to environmental and health protection issues. SuSAP could help farmer in certification of eco-compatibility production, where pesticides are used in reduced amounts, only whenever necessary and at defined and controlled time intervals. In SuSAP, the fundamental concepts of Integrated Crop Management will be promoted by means of eco-compatible treatment plans compared with traditional ones and by the demonstration of the economical sustainability of such practices. The methodology here presented is conceived to reproduce in any geographic area, where data on soil, climate, crops and pesticides are available. Further development and extensions to other crops, areas and environmental pollutants (e.g.: nitrogen, heavy metals) should be planned in the future. REFERENCES • Klein M. & Jene B. 1995. PELMO version 2.01 / 3.00 Staatliche Lehr und Forschungsanstalt für Landwirtschaft,

Weinbau und Gartenbau Fachbereich Ökologie - Neustadt Germany. • Kroes J.G., van Dam J.C., Huygen J., Vervoort R.W. 1999. User’s Guide of SWAP version 2.0 - Technical

Document 53 SC-DLO Wageningen, The Netherlands. • Results of the European CAPER Project 1999. Comparing environmental risk indicators for pesticides – Centre for

Agriculture & Environment Utrecht, The Netherlands. • ERSAL – Regione Lombardia. 2000. SuSAP – Supplying Sustainable Agriculture Production –

Life98/ENV/IT/00010. Manuale Metodologico. • Van Den Berg and J.J.T.I. Boesten. 1998. PESticide Leaching and Accumulation model PESTLA version 3.4 –

Technical Document 43 SC-DLO Wageningen The Netherlands.