Working Group Agriculture and Environment March 2011 Agri-environmental data needs

Transcription

1 Working Group Agriculture and Environment Agri-environmental data needs March 2011

2 Content Data types Building blocks Present data sources Data collection scenarios

3 Data types Statistics Collected from specified population Common methodology and harmonised definitions Quality and reliability requirements Aim to producing long time series Gives comparable and coherent data

4 Data types (2) Other data Can be collected for AEI purposes, but not necessarily Sources can be very varied No common methodology or harmonised definitions Quality and reliability requirements less rigid Often reflect local conditions that are difficult to harmonise They can also reflect new, emerging trends that are not yet possible to follow in statistics, for example emission mitigation techniques or changing agricultural practices Can be very important for AEI

5 Data types (3) Coefficients and parameters Often result of scientific or desk-top research Allow showing complex issues that are difficult to measure directly or where data collection would be too difficult Sources can be very varied, statistics important for better quality and reliability Should be based on common international methodology and harmonised definitions if to be used in international comparisons Quality and reliability requirements should be rigid Can also reflect emerging trends, but in an nontransparent manner if underlying data are not available Are very important for AEI

6 Building blocks Crop related data Livestock related data Nutrient data Manure application techniques Livestock housing Manure storage Irrigation Energy use

7 Crop related data Arable and permanent crops Area Yield Irrigated areas Temporary and permanent grasslands and rough grazing Area Yield Irrigated areas Available in crop statistics and FSS Available in crop statistics Available in SAPM for major crops, formerly part of FSS Available in crop statistics and FSS Not available Available in SAPM, formerly part of FSS

8 Crop related data (2) Input to crop production N and P input per crop N and P fertilised area Pesticide use per crop Water use per crop Not available Will be available Not available Additional data needed related to crop production Winter crops Catch crops Crop residues returned to field Crop residues burnt Tillage practices Available in older FSS and SAPM Not available Available in SAPM only

9 Livestock related data Average livestock numbers Dairy cattle Beef cattle Calves Young cattle Other cattle Sows, incl. piglets Fattening pigs Boars Horses Other animals Other pigs Sheep Goats Laying hens Broilers Ducks Turkey Other poultry Available at specific data in livestock production statistics or FSS. Not available as average number present during the year in Eurostat statistics. Available partly in FSS, but only for farms. Most horses outside FSS coverage. Not available, locally important

10 Livestock related data (2) Grazing/time on pasture Dairy cattle Beef cattle Young cattle Calves Sheep Goats Horses Other grazing livestock Available only partly in SAPM Not available Feeding situation: confined, grazing, pasture conditions Dairy cattle Other cattle Sheep Not available

11 Nutrient data Nutrient input: kg nutrient per country per year Use of ammonium nitrate, calcium ammonium nitrate, anhydrous ammonia, ammonium phosphate urea, urea-ammonium nitrate solution, other N fertilizers; P fertilizers (all types), national level Not available in statistics Nutrient input: kg nutrient per ha per year Use of ammonium nitrate, calcium ammonium nitrate, anhydrous ammonia, ammonium phosphate urea, urea-ammonium nitrate solution, other N fertilizers; P fertilizers (all types), per ha Not available in statistics

12 Nutrient data (2) Nutrient input: kg nutrient per ha per year N from mineral fertilizers, animal manures, composts and sludge P from mineral fertilizers, animal manures, composts and sludge Not available in statistics Manure flow Manure imported to the region Manure exported from the region Not available Not available

13 Nutrient data (3) Feed input Total feed uptake Purchased feed Not available Fertiliser application techniques Immediate incorporation of urea Not available

14 Manure application techniques Manure application techniques Manure band spread on grassland and arable land Band spread with incorporation <1 day on bare arable land Band spread with - incorporation <2hrs on bare arable land Broadcast with incorporation <1 day on Arable land Broadcast with - incorporation <2hrs on Arable land Broadcast - no incorporation on grassland and arable land Deep injection on grassland and arable land Shallow injection on grassland and arable land Surface spreading on grassland and arable land Reduced ammonia application on grassland and arable land Partly available in SAPM

15 Manure application techniques Manure spreading time Autumn spread manure Spring spread manure Manure spread year round Not available

16 Housing Percentage of livestock in specific housing Cattle housing: Liquid (slurry + urine Liquid with scrubbers/biofilters loose housing partially slatted floor loose housing partially slatted floor with scrubbers/biofilters loose housing with fully-slatted floor loose housing with fully-slatted floor with scrubbers/biofilters tied housing partially slatted floor tied housing partially slatted floor with scrubbers/biofilters tied housing with fully-slatted floor tied housing with fully-slatted floor with scrubbers/biofilters Farmyard manure (solid) Partly available in SAPM

17 Housing (2) Percentage of livestock in specific housing Poultry housing: Aviary house Battery cages Battery cages with drying Free range Liquid (slurry + urine) Farmyard manure (solid) Pig housing: Liquid (slurry + urine Liquid with scrubbers/biofilters Farmyard manure (solid) Other animal housing Farmyard manure (solid) Partly available in SAPM Partly available in SAPM Not available

18 Manure storage Percentage of cattle, pig and poultry manure stored in: Liquid with anaerobic digestion with supplements Liquid with anaerobic digestion without supplements Slurry stored in covered tanks Slurry stored in lagoons Slurry stored in open tanks Slurry stored in underfloor pits Solid manure composted Solid manure incinerated Solid manure stored in manure heaps Partly available in SAPM

19 Manure storage (2) Percentage of manure stored in system Other animal manure: Solid manure composted Solid manure incinerated Solid manure stored in manure heaps Not available

20 Irrigation Irrigation Irrigable area Irrigation equipment and systems Source of irrigation water Available in FSS Available in SAPM, formerly in FSS Available in SAPM, formerly in FSS Irrigation water Water volume Available for the farm in SAPM

21 Energy use Energy use in agriculture Oil products Natural gas Electricity Derived heat Renewable energy Not available in requested detail

22 DireDate data collection scenario 1 Normal FSS in 2010, 2013 and 2016, and SAPM in Data on fertilizers, pesticides, energy, animal feeding, irrigation, housing, and manure storage and application techniques to be collected in existing or new surveys + Continues the current surveys + FSS regulation would stay as it is - Efforts needed to create new surveys or amending the legislation for other surveys - The no renewal of the SAPM causes extra problems. - The new surveys would not be linked to the micro data of the FSS

23 DireDate data collection scenario 2 Re-combine the FSS and SAPM characteristics supplemented with questions derived from the building blocks so that two new lists of variables result. One would address the operational and tactical farm management aspects and should be carried out once in ~3 years. The other would address the farm structural management aspects and should be carried out once in ~5 years. Would require changes to the FSS basic regulation, for which a decision has been taken that this will be done for earliest the FSS 2016

24 DireDate data collection scenario 2 (cont.) + Would create a common and harmonized collection of key farm data for accurate characterization of the agrienvironmental interactions + Would give a good access to the data to analysts. - Would not necessary lower the data collection burden, especially as the FSS is supposed to meet certain reliability standards at regional level, implying quite a big sample size. - Variables of not structural character would be added to a structure survey, potentially creating problems in the data collection phase, causing lower quality data.

25 DireDate data collection scenario 3 Combine key aspects of FSS and SAPM with key questions on animal feeding into a highly condensed new questionnaire to be carried out once in around each 3 years Derive key data related to farm inputs and management from the annual surveys of the FADN and sales data of market organizations + Lower farm data collection burden - Requires coordinated efforts by institutions across the EU for establishing harmonized relationships between data of the FSS-SAPM and data derived from FADN - Risk of loss of accuracy, depending on the size of the sample

26 Eurostat data collection scenario 1 Very detailed survey to be set up, all (or most) of the required data is collected, together with those that have not been identified as having the highest priority by the DireDate project Information on for example local soil conditions, and other similar issues could also be included Small sample size, potentially a panel of farms, potentially area frame sample Micro-data to be sent to Eurostat FSS list of characteristics to be adapted to include key questions that could be used to bridge the two surveys

27 Eurostat data collection scenario 1 (cont.) + Would allow more detailed analyses of the environmental impacts + Flexible approach, easier to change than FSS + Easy access to data + Could also give data on mitigation actions - Sample could not be very large, meaning that very detailed analyses on regional level cannot be done - Potentially costly and resource consuming, especially in initial phase

28 Eurostat data collection scenario 2 Data warehouses (either one in Eurostat fed by the Member States or one in each country to which Eurostat would link) where each Member State would save all data surveyed (HUB approach). If data is available on a certain topic, Eurostat would have access to creating statistics from it. The basis for this approach could be for example the "June surveys" carried out in many countries for collecting not only annual crop statistics, but also many other data

29 Eurostat data collection scenario 2 + Rather free hands to the Member States to set up their data collection system + Would not only feed agri-environmental statistics, but also other domains, as the idea is that all statistical surveys related to farms would be stored in the hub. + Would fit the requirements of a modern information society on the access to information - Would require good planning and coordination - Initial workload could be substantial

30 Eurostat data collection scenario 3 A "traditional" statistical system, which means only identifying statistical data tables that should be transmitted to Eurostat + Would give free hands to the Member States to set up their surveys, as long as the data transmitted would meet certain quality criteria - Very rigid in construction - Makes it difficult to do any further analyses of the data - Users wanting to know more would have to address each Member State separately - Outdated in this age of internet and new technologies, and would not be appropriate as a long-term solution for the future

31 Next steps The DireDate reports are not yet finalised and need to be analysed together with all stakeholders to identify a final set of data requirements. These will be combined with other user needs to create a set of building blocks that can then be further analysed. Final scenarios to be discussed in future WG s will be based not only on the data needs, but also on the expert advice presented by statisticians. This can be done in Task Forces, by exchange of s, country visits, etc. Finally, Eurostat will create a strategy and identify a timetable for future actions for creating a long-term data collection system