Characterising source intensity

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The text on this page is taken from an equivalent page of the IEHIAS-project.

As part of the EU-funded INTARESE project, which contributed to the development of this Toolbox, a case study was carried out to assess health impacts associated with agricultural land use change in two study areas, in Greece and England.

In order to provide a basis for exposure assessment, data were required on a range of land use activities, including pesticide usage and livestock farming, both for a baseline year (2000) and for future years (2020, 2050) under two policy scenarios.

Pesticides usage data

In England, a national database of pesticide usage is maintained, based on sample surveys of farms. Data are collated at the regional level, though provided for this assessment at county (NUTS 3) level in the form of the total area and amount applied, by crop and pesticide type. Pesticide types are categorised on the basis both of functional and chemical group and active ingredient (ca. 350). Because of the coarse scale of these data, modelling was done to disaggregate the statistics to a more local (ward = LAU2) level, using GIS techniques (see Spatial disaggregation).

In the study regions in Greece (Thessaly and Central Macedonia), there is no legal requirement nor systematic procedure for reporting of pesticide use. Purpose-designed data were therefore collected for the assessment. For the Region of C. Macedonia, pesticide sales data for the ytears 200 and 2004 were obtained from relevant sale points. The data were checked and adjusted against related data obtained from the Directorate of Agricultural Development and the Directorate of Production and Development of Tobacco and Cotton of Thessaloniki (local government authorities), and advice was also taken from expert agronomists to verify and interpret the information. A similar approach was taken for the Region of Thessaly, where pesticide sales data for the Prefecture of Larisa were collected from local sale points; these were checked and enhanced with data from the Directorate of Agricultural Development of Larisa for the reference year 2000. Data comprise the amount (kg) of each active substance (AS) that was applied to the major crops, the application rate (kg/km2 or l/km2) and the number of applications. Data for approximately 60 active substances were collected for Thessaly and 50 for C. Macedonia. For the purpose of the assessment, the active substances were classified on the basis of their action (herbicides, fungicides, insecticides and plant growth regulators) and chemical class (e.g. carbamates, organophosphorus compounds etc.).

In both cases, estimation of future pesticide usage involved the application of models. These had to take account not only of how land use might change under the different scenarios, but also changes in regulation of pesticides, and their effects on pesticide practice. At present, two general categories of active substances can be recognised - those approved for use and those under evaluation (pending approval), as discussed in Karabelas et al. (2009). For the purpose of the Greek case study, a list was developed for the major crops (for 2020 and 2050), identifying the active substances likely still to be used under these restrictions, based on the list of marketed pesticides in the Prefecture of Thessaloniki for the year 2004. A database of active substances for energy crops has also been developed.

Animal husbandry data

In England, annual data on farming activities are reported as part of an annual census of agriculture (the so-called 'June Returns'). The data include information on a wide range of agricultural activities, including details of employment, crop areas and livestock numbers. Livestock data are defined to a high level of specification (e.g. breed, function and age of animals). Data are collated and made available at agricultural ward (~LAU-2) level, and spatial aggregations thereof (e.g. county). Ward-level data are in some cases suppressed, however, to maintain confidentiality. Where data suppression created gaps in the data required for the case study, therefore, livestock numbers were estimated by disaggregating data from the next available level by area-weighting.

In Greece, data on livestock numbers (including dairy cows, beef cattle, fattening pigs, sows, laying hens and other poultry, horses, sheep and goats) are provided by the National Statistical Service of Greece (ESYE) at NUTS 3 level. These were disaggregated to LAU-2 level by area-weighting.

As with pesticides, future livestock numbers had to be modelled to provide estimates for the two policy scenarios in the years 2020 and 2050. Since ruminant animals (dairy cows, beef cattle, horses, sheep and goats) spend time on pastures grazing, it was assumed that numbers are strongly dependent on pasture area. Modelling was therefore done on the assumption that grazing intensities (per unit area of pasture) remained the same, but the spatial distribution changed with land use. In Greece, granivore (pigs and poultry) numbers also had to be modelled. These are more-or-less independent of cropping systems, so trends had to be estimated by alternative methods; in this case, using the GAINS Model (Greenhouse Gas and Air Pollution Interactions and Synergies Model) (IIASA: GAINS model, 2010).


See also

Integrated Environmental Health Impact Assessment System
IEHIAS is a website developed by two large EU-funded projects Intarese and Heimtsa. The content from the original website was moved to Opasnet.
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