Action TAPAS for Improve statistics on food pesticides residues

Transcription

1 Action TAPAS for 2004 Improve statistics on food pesticides residues

2 ABSTRACT... 3 INTRODUCTION METHODS AND ACTIVITIES DATA AND PROCEDURES AREA OF INTEREST FOR LETTUCE, OLIVE GROVE AND GRAPE ( ) CROP TREATED PESTICIDES DATA-BANK MONITORING DATA-BANK GEOGRAPHIC DATABASE BUILDING GEOREFERENCING SAMPLE POINTS INTEGRATION OF THE DATA ON SAMPLE POINTS WITH LAND USE MAP RESULTS CONCLUSION... 28

3 Abstract Italian Tapas 2004 action on Pesticides The Ministry of Agricultural and Forestry has been financed some Programmes, by 1992, with the aim: to acquire knowledge of status, in terms of residues of pesticides on fruits and vegetables, of Italian agricultural product, according to the crop protection practices adopted, to increase awareness of the importance of rigorous compliance with the regulations among farmer and agricultural technicians, to create a more extensive network of laboratories able to analyse residues on crops, to promote the quality of agricultural products. In these years more than samples have been analysed, more active ingredients have been investigated, under coordination of ISPaVe and the involvement of Italian Regions in the selection of crops, farms, laboratories, active ingredients. The most problematic crops have been analysed also to support the integrated pest management. The samples were collected on the bases of indications of ISPaVe and followed by card with the products used for controlling pests, but they had not statistical purpose. The data, , elaborated in this action are on olive, grape and lettuce, to try to obtain a better data flow for developing food safety indicators and the basis of sample organisation and localisation. All data have been organised inside a Geographical Information System, with the aim to test a methodology that allows developing a system to produce and organise data at geographical level, considering that agri-environmental problems are related to specific areas (environmentally vulnerable areas), inside specific boundaries, such as river basins. The integration of these data with different sources and typologies (industrial production and agrochemical sales, pesticide costs and uses at farm level, residues, etc.) is essential in order to correct, calibrate and validate indirect statistics, in particular, to ameliorate and redirect the existing network on pesticide residues of agricultural production towards a national statistical network, representative at Regional level, on pesticides residues (active ingredients). 3

4 Introduction The collection of a reliable set of usage statistics has value in many areas of research, legislation and agricultural support, and should not be seen as a simple statistical exercise. The following information related to pesticide use was considered important: crop treated area of crop grown product used amount used or rate of application (kg/ha) area of crop treated any biological control methods used timing of application Several methods of collection are already in use within the European Union and OECD members. Five broad methodologies are available requiring differing levels of input and organisation: 1. Personal visits to a representative sample of farmers and growers to collect information on what they have used; 2. Telephone interviews with a representative sample of farmers and growers; 3. Postal surveys of a representative sample of farmers and growers; 4. Compulsory returns of pesticide use from all farmers and growers; 5. Alternatives to surveys of usage - collation of sales statistics. The main characteristics of the programme of monitoring of residues pesticides for controlling their uses were: sampling directly in the field at the harvest before sampling, standardisation of sampling, training of Regional technicians to collect representative samples of the lot sampled, compilation of an information card on the kind and modalities of treatments applied to the crop, 4

5 compilation of a record requesting the laboratory to analyse the active substances with high risk of persistence. multiresidue methods applied make it possible to detect any undeclared substance. The origin of all samples is known only by technicians and the result of analysis return back to them. In some cases the regional institutions in charge of the surveys consider sensible information the location of the sample. In these cases, as in Campania and Calabria, it was not possible to georeferenced the sample. The obtained results, collected by ISPaVe from the regional laboratories, have been elaborated and utilised to support farmer and technicians. 5

6 1 Methods and activities Such a methodology is based on the following points: Study areas: Regions of Italy involved in the monitoring of lettuce, olive and grape in ; o Data collected for each sample: crop, variety, disease, products used, doses, date of treatments, harvest, sampling, kind of pest management; every samples was accompanied by a Sample identification documents (Annex I) filled by a technician, reporting the treatments, allowing to know the whole history of sample. o Results of residue analysis requested by regional technicians to laboratory. The samples have been given to the laboratory with the Sample identification document and with the identification of the 2 or 3 pesticide groups to be analysed by a multi-residues method (Annex II). Not all the treatments have been searched but the ones applied not so far from the harvest or with risk for residues. After one year of study only three regions have been involved: Abruzzo, Molise, Umbria, because only on these regions has been possible to find all the technicians that sampled in 2002 and that allowed to put on the map the point of sampling For each sample, we have: Number and sample code; Region and Province (but the Regional technicians know the exact place of sampling); Sampling and analysis date; Analysis report with information about active ingredient used, days from last treatment, chemical group, maxime residue level fixed (MRL), post harvest interval fixed (PHI), results of analysis and about formulations used, name, registration number, doses, volumes of distribution, number of treatments. Sample spatial referencing as indicate of Regional technicians; It was necessary to visit the technicians that picked up the samples in years, to localise the point of sampling on the map and link the treatments to the area of origin of sample. But often it has not been possible to find them 6

7 because, in the meanwhile, they didn t perform the investigation with the result to obtain only partial data on the single crops in a lot of Regions. Data bank on pesticides (ISPaVe Ministry of Agriculture), linked to the results of analysis ( (introduction paper in Annex 3), for controlling if residues were higher than fixed maxime residue level (MRL). Data on used pesticides per culture, collected samples and residues, collected and elaborated in monitoring data bank (printing of introduction data bank in Annex 4). It s important to note that a MLR is fixed on the bases of an authorised use, and in progressively increasing case, also as a consequence of the EC Regulation 396/2005, there are a lot of values of harmonised residue with uses not yet allowed in our country, but allowed in the other EU States Member. Regional Pesticides sales data (Ministry of Agriculture - National Agriculture Information System - SIAN). The national sales data of agrochemical products come straight from the seller to MIPAF and are elaborated by SIAN. They are the only official data organised for formulation of agrochemical products for Region and for Province. It s important to point out that the data from SIAN are referred at 2001 because the data elaboration is not made in real time. Anyway these data are important and show the general trend of The very high amount of sold agrochemicals is a consequence of.every single commercial step and not only of the sales to the final user. So the sales have been recorded, sent to the SIAN and so the same agrochemical product is counted more times. So an exchange coefficient was been defined: it s the amount of agrochemical normalised to the amount of the agrochemical sales (data of 2001). It is indicated exchange coefficient because the sales regard the different movement among the intermediaries, so it is not the values surely used on the crop but the values probably proportional with the uses. Moreover it s important to point out that an agrochemical product can be authorised for uses on different crops, so that the consider sales data can not always be correlated with the residues on a single crop. For these considerations the Sample identification documents (Annex I) becomes the only way to know for each used agrochemical the dose, the application time, the number of treatments and so on. For these 7

8 agrochemicals more used, it is possible to do correlation between the sales data, the residues and the application on a crop only if the product it s authorised for that specific crop. Since the 2 year the data recorded have been regarded only the agrochemical product sold for final using and so, for they, will be possible the rights correlation. Utilisation of GIS technology to perform spatial analysis of sampling point and data aggregation at geographical level; The work on residues started in December 2002 with the collection of data on land use for each sample. The final results on residues were obtained in July 2004 and the total samples organised by crop considered during , with the distribution of pesticide residue, are reported in Annex 4. The activities carried out to completely meet the methodological approach were: Defining the crop object to be monitored; Sample definition (number, kind, data organisation); the samples collection in a very important phase that needs high attention, from which depends the accuracy of data elaboration and the significance of results. The period for sampling was at the commercially grow crop, that means when the fruits and vegetables are ready to be sold. The following references have been considered, with specific adjustments, because does not exist any validate procedure of field sampling for monitoring pesticides: o C.E.E. Directive 79/100 D.P.R n 327 (G.U. n ) o D.M (G.U ) o D.M (G.U. n ) o D.M (G.U. n ) In each farm has been done one sampling for each different crop. But more sampling for the same crop has been done for not-homogeneous variety or high extension of cultivation, and in this case a mean sample has been obtained from the single ones. The same method of sampling has been always followed (es. in the diagonal segment of the plots etc.), to pick up samples that are representative of the whole yield An uniform size of fruits or vegetables has been collected. The samples were collected at least for 5 plants or multiple of 5, randomly (for vegetables) and by the inner and the outer position of the tree, in the left and right, at 8

9 man-height (for fruits). For fruits and for leave vegetable and similar, the sample collected was at least 1 kg of product with homogeneous characteristic The product with rot, mould, or any pest attach, that can influence the quality of product, has been rejected. The samples obtained have been put in plastic bags or boxes, and have been stored for lab analysis, indicated by technicians, for 1-2 day in a range of temperature between 4 and 18 C in the dark and in dry environment. Data collection of laboratory analysis results; Storing the result inside the database (ad hoc developed for analysis results processing) (Annex 5) Utilisation of GIS technology to perform spatial analysis and data aggregation at geographical level Regional Pesticides sales data collection (Ministry of Agriculture - SIAN); Connection between sales data (organized on agrochemical products) and databank on active ingredient, to know the active ingredients and the quantity used at NUTS III level. It is very important to point out that the main aim of the monitoring activity is not to check the quality of the national agricultural yields, but to investigate the critical situations of residues due to the kind of crop, the pest management, and the agricultural practices. Following these indications, the technicians can better understand the use of pesticides, which means avoid and prevent improper use of agrochemical products. The results of the monitoring activity can not be compared with other monitoring because the samples are collected not randomly but for known situations and the agrochemical products searched are the ones with highest risk for the residues. 9

10 2 Data and procedures 2.1 Area of interest for lettuce, olive grove and grape ( ) More of 50% (14 on 20) of the Italian Region are involved on data collection. In the following map are reported number (total samples ) and territorial distribution of the samples at NUTS II level (see also Annex 6 for detailed information). Figure 1 - Total number samples location 2.2 Crop treated Crop treated are shown on the following table: Crop Lettuce Olive grove Table grape Wine grape Number of samples

11 The following maps depict the location (at NUTS II level) of the samples for each crop. Figure 2 - Lettuce samples location THE FULL LIST Of ACTIVE INGREDIENTS (USES BY ACTIVE INGREDIENTS ON LETTUCE) IS REPORTED IN ANNEX 7 11

12 Lazio Figure 3 - Olive grove samples location THE FULL LIST Of ACTIVE INGREDIENTS (USES BY ACTIVE INGREDIENTS ON OLIVE GROVE) IS REPORTED IN ANNEX 8 12

13 Lazio Figure 4 - Table grape samples location THE FULL LIST Of ACTIVE INGREDIENTS (USES BY ACTIVE INGREDIENTS ON TABLE GRAPE) IS REPORTED IN ANNEX 9 13

14 Lazio Figure 5 - Wine grape samples location THE FULL LIST Of ACTIVE INGREDIENTS (USES BY ACTIVE INGREDIENTS ON WINE GRAPE) IS REPORTED IN ANNEX 10 14

15 2.3 Pesticides Data-bank The pesticides data bank has been realised by ISPaVe for MiPAF and contain the authorised quantities per culture of the different pesticides, to be used depending on the found diseases obtained by authorised label. On the bases of the document that follows the sample, the used doses and the results of analysis, it is possible to know which product was used and if gave a residue. For each active substance and for each crop is fixed a legal maxime residue level (MRL), that ensure the quality of products and the absence of risk Monitoring Data-bank All the data about samples are organised in a data base in Access that is linked to data bank on pesticide and make possible the elaboration for Region, samples, crop, active ingredient, formulation, kind of management, residues. This data bank is strictly connected with the data bank of agrochemical products to compare the uses, the results of analysis, the MRL, and permits for each sample to get Sample identification documents, containing all the information about the crop, to better understand, when the limits are overcome, the reason and to move to corrective actions.

16 3 Geographic database building A geographic information system is a system for the acquisition, management, analysis, and displaying of geographic knowledge, which is represented using a series of information sets. The information sets include: 1. Maps in numeric format: Interactive views of geographic data with which to answer questions, present results, and use as a dashboard for real work. Maps provide the advanced GIS applications for interacting with geographic data. 2. Geographic Data Sets: File bases and databases of geographic information-features, networks, topologies, terrains, surveys, and attributes. 3. Processing and Work Flow Models: Collections of geoprocessing procedures for automating and repeating numerous tasks and for analysis. 4. Data Models: GIS data sets are more than database management system (DBMS) tables. They incorporate advanced behavior and integrity like other information systems. The schema, behavior, and integrity rules of geographic data sets play a critical role in GIS. In particular, inside the TAPAS project, three steps have been realised to build the geographic database: 1. Spatial referencing of the samples, 2. Spatial database building using GIS technologies to connect geographic data (sample points location), with the alphanumeric database that store the results of the laboratory analysis on residues of pesticides, 3. Integration of the data on sample points with land use map to obtain statistics on pesticides use at Provincial level (and then with aggregation at Regional level), 4. Comparison with the residues retrieved in the sample and the Regional Pesticides sales data. 16

17 Following flow chart depict the general schema of the work. Georeferencing sample points Geographic Information System Database on sample points Link between sample points and related database Tematic layer on pesticides distribution Statistics data on pesticides use Land use map 3.1 Georeferencing sample points This has been a-posteriori activity; in fact the sample point s georeferencing is possible only with the cooperation of the Regional technicians, those have carried-out the ground truth directly on the plot. Due to the gap between the ground truth campaign and the TAPAS project starting-up, only in three Regions (Abruzzo, Molise, Umbria) we found the right cooperation of the Regional technicians. The methodology used on georeferencing sample points has been based on the use of topographic maps (1: scale), on which the Regional technicians, thanks their knowledge of the territory, have indicated the location of the sample points. The sample points have been acquired in digital format, and coded with the sample identification number. The following figures shown same example of the results of this activity. 17

18 Figure 6 - Abruzzo Region, distribution of olive grove and vineyard sample points 18

19 Figure 7 - Molise Region, distribution of olive grove sample points 19

20 Link between sample points and related database The geometric component of the geographic data base is the basis for the connection with the data base of the pesticides analysis results. The connection between the geometric and the alphanumeric component of geographic data base is based on the following relationships: relationships (join) 1 to 1 (Figure 8) relationship (link) 1 to many (Figure 9) Join and Link are ensured with a common key (sample identification number), as depict in the following figures. Sample points 223 MSA PE Related Database Figure 8 - Join relations 1 to 1 with the data base of sample points (Olive grove Chieti province, Abruzzo) 20

21 Sample point Link with data base of sample points Figure 9 - Link relation 1 to many with the data base of sample points (Vineyard - Umbria) 21

22 3.2 Integration of the data on sample points with land use map To estimate statistics about the use of pesticides, starting from the data on sample points, is determinant to know the land use of the study area. Integration between results of monitoring activity and land use map allows deriving probability maps of the pesticides use that are spatially correlated with the georeferenced sample points. Considering that the study area is composed of different Italian region, is essential to use as land use map a cartography that is homogeneous in terms of thematic and geometric accuracy, methodological principles, nomenclature system (legend). The unique product that responds to those request is represented by the CORINE land cover map, which is a part of the CORINE (Coordination of information on the environment programme), adopted on 27 June 1985 by the Council, on a Commission s proposal. This Commission work programme concerns 'an experimental project for gathering, coordinating and ensuring the consistency of information on the state of the environment and natural resources in the Community' (Official Journal L 176, ). The CORINE land cover project intends to provide consistent localized geographical information on the land cover of the Member States of the European Community. The project is necessary for the following reasons: Preliminary work on the CORINE information system showed that information on land cover, together with information on relief, drainage systems etc., was essential for the management of the environment and natural resources; information on land cover therefore provides a reference source for various CORINE database projects; In all the countries of the Community, the information on land cover available at national level is heterogeneous, fragmented and difficult to obtain. Land cover nomenclature with three levels First level: five headings Second level: 15 headings Third level: 44 headings

23 4 Results The results referred to the following two levels: 1. Italian level: sample data on residues analysis, pesticides uses and pesticides sales are aggregated at regional level, without the correlation analysis. Collection about total uses of pesticides organised by region, with the residue distribution by class: n.d. = not detected; <50% MRL; >50% and < MRL; > MRL. For the analysed uses, the ones requested to the laboratory by the technicians because considered at risk, results are as in the following tables: TABLE 1 - INTERREGIONAL PROGRAMME : Total uses by Region and residue distribution for LETTUCE Total uses N.D % N.D. <50% % <50% MRL MRL <MRL % <MRL >MRL % >MRL Abruzzo ,50% 4 12,50% 0 0,00% 0 0,00% Campania ,90% 65 18,60% 16 4,60% 38 10,90% Lazio ,50% 8 8,50% 0 0,00% 0 0,00% Lombardia ,90% 18 7,80% 3 1,30% 0 0,00% Sardegna ,50% 22 13,80% 3 1,90% 3 1,90% Toscana ,90% 1 5,60% 1 5,60% 0 0,00% Veneto ,90% ,50% 27 5,00% 8 1,50% N.D. = not detected MRL=maxime residue level TABLE 2 - INTERREGIONAL PROGRAMME : Total uses by Region and residue distribution for OLIVE Total uses N.D. % N.D. <50%MRL % <50% MRL <MRL % <MRL >MRL % >MRL Abruzzo ,10% 8 12,30% 2 3,10% 1 0,00% Basilicata ,30% 5 1,50% 2 0,60% 2 0,60% Calabria ,50% 53 15,50% 6 1,70% 1 0,30% Campania ,20% 23 14,40% 9 5,60% 6 3,70% Lombardia ,30% 2 7,70% 13 50,00% 0 0,00% Molise ,30% 28 45,90% 5 8,20% 1 1,60% Toscana ,60% 12 17,90% 1 1,50% 4 6,00% Umbria ,70% 21 70,00% 4 13,30% 0 0,00% N.D. = not detected MRL=maxime residue level TABLE 3- INTERREGIONAL PROGRAMME :Total uses by Region and residue distribution for TABLE GRAPE <50% % <50% Total uses N.D. % N.D. <MRL % <MRL >MRL % >MRL MRL MRL Basilicata ,00% 0 0,00% 0 0,00% 0 0,00% Sicilia ,80% 44 23,80% 7 3,80% 3 1,60% Valle d'aosta ,40% 25 32,10% 2 2,60% 0 0,00% N.D. = not detected MRL=maxime residue level 23

24 TABLE 4- INTERREGIONAL PROGRAMME :Total uses by Region and residue distribution for WINE GRAPE Total uses N.D. % N.D. <50% MRL % <50% MRL <MRL % <MRL >MRL % >MRL Abruzzo ,20% 21 31,80% 2 3,00% 0 0,00% Basilicata , ,70% 3 0,80% 5 1,30% Calabria , ,30% 6 2,30% 7 2,70% Campania ,40% 39 17,90% 6 2,80% 2 0,90% Lazio ,20% 9 14,80% 0 0,00% 0 0,00% Lombardia ,50% ,50% 43 8,20% 25 4,80% Marche ,60% 37 13,30% 2 0,70% 1 0,40% Molise ,40% 51 13,40% 10 2,60% 21 5,50% Toscana ,40% 6 54,50% 0 0,00% 1 9,10% Umbria ,30% 96 33,60% 8 2,80% 1 0,30% Valle d'aosta ,00% 28 30,80% 2 2,20% 0 0,00% Veneto ,00% ,50% 22 2,50% 9 1,00% N.D. = not detected MRL=maxime residue level 24

25 2. At regional level (Abruzzo, Molise, Umbria) The achieved goals are both geographic and statistical. The geographic information includes: - Cartography, representing quantities calculated for each polygon based on ISPaVe data. CLC Olive grove distribution on Chieti Province Spatial distribution of the total active ingredient The statistic information includes: - Pesticides quantities for the main categories calculated on NUTS III; - Definition of correction factor between agricultural best practices quantities and pesticides effective use on farm level (main problem for indirect statistics); - Comparison with data on sales on NUTS II level, to check the possibilities to use administrative data. Following tables illustrate same example of statistics data about pesticides quantities (total active ingredient) at NUTS III level. 25

26 TABLE 5: Olive grove - Chieti Province Agrochemical Active Ingredient N of samples % Ha Gr_ha Total_AI CHIMIGOR 40 Dimetoato ,835 DACOL L 40 Dimetoato , ,248,332 FENITROCAP Fenitrotion ,160 LEBAYCID Fention ,713 1,461 8,346,971 PERFEKTHION Dimetoato ,637 POLTIGLIA CAFFARO 20 Rame ,525 2,404,301 ROGOR L 20 Dimetoato ,835 ROGOR L 40 Dimetoato , ,940,568 DIETOL 40 Dimetoato ,967 ROGOTER 40 Dimetoato ,681 TOTAL ,184 26,558,290 Active Ingredient N of samples % Dimetoato Fenitrotion Fention Rame TOTAL TABLE 6: Olive grove - Pescara Province Agrochemical Active Ingredient N of samples % Ha Gr_ha Total_AI LEBAYCID Fention ,109 1,461 3,081,249 POLTIGLIA BORD. CAFFARO Rame di calcio solfato ,327 1,280 8,098,560 ROGOR L 40 Dimetoato , ,606,390 POLVERE CAFFARO Ossiclor.di rame e calcio ,109 1,280 2,699,520 TOTAL ,872 17,485,719 Active Ingredient N of samples % Fention Rame di calcio solfato Dimetoato Ossiclor.di rame e calcio TOTALE

27 TABLE 7: Olive grove - Teramo Province Italian Tapas 2004 action on Pesticides Active N of Agrochemical Ingredient samples % Ha Gr_ha Total_AI DIETOL 20 Dimetoato ,808 OSSICLORURO 50 WP Ossicloruro tetraramico , ,515 ROGOR L 40 Dimetoato , ,230 TOTAL , ,737,553 Active Ingredient N of samples % Ossicloruro tetraramico Dimetoato TOTALE Complete reports of the results in three Regions are described in Annex

28 5 Conclusion The project has been fully realised in terms of methodology development and procedures assessment. In three Italian Regions the results concerning the georeferenced data base development about the pesticides residues and related data on uses and sales have been fully achieved. In the other Italian regions, due to the non availability of sample location (for problems related to sensitive data collection from the regional institutions), the definition of a data base on pesticides residues, uses and sales has been realised without geographic referencing and correlation analysis. A key question arose during the work is the non exact correlation between sales, uses and residues quantities. This because the residues values are always under the MRL, and definitively less then the sales and uses amount detected. The georeferenced samples have always an MRL under the admitted limits. Since the correlation between sales, uses and residues amount has been defined, the methodological approach developed in the present TAPAS project could allow to estimate the expected residues quantities on the fields/products on the basis of the pesticides sales and uses statistics. This is particularly relevant when, in the last years, the agricultural scenario is changed, and new active ingredients are being authorised and the old one are mostly disappeared. 28