Flint presentation usa
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The FLINT project aimed to collect harmonized farm-level sustainability data in Europe, emphasizing the integration of various sustainability indicators alongside economic performance. It highlighted the importance of having a common database to analyze the impact of policy measures on farm management decisions and sustainability performance. Recommendations include adapting existing farm accountancy data networks (FADN) to facilitate better data collection and enhance policy evaluation, ultimately benefiting the food industry in monitoring sustainability.
Flint presentation usa
- 1. FLINT: Farm Level Indicators for New Topics Krijn Poppe | Wageningen Economic Research Thanks to Hans Vrolijk and the FLINT partners
- 2. Context: Sustainable Development Goals 2
- 3. Incentivise farmers on sustainability (PPP) 0 20 40 60 80 100 Cost price per 100 kg milk Income per Family Labour unit solvability (%) Energy use per euro output Water use per euro output Pesticide use per hectare Grazing days Education Surplus of Phosphate per hectare Surplus of Nitrogen per hectare PEOPLE PROFIT << PLANET >>
- 4. Context: country initiatives (example) 4
- 5. Context: company initiatives (examples) 5
- 6. Context: industry initiatives (example) 6
- 7. Challenge: the need for common indicators 7
- 8. 8 Even bigger challenge: data collection The FLINT project (Farm Level Indicators on New Topics) tested an infrastructure to collect data on farms in a harmonised way, linked to the FADN
- 9. 9 Building upon the FADN And that is already in some member states used to collect additional sustainability data (e.g. NL, Ireland, Flanders) Farm Accountancy Data Network (FADN) 85.000 farms that are monitored by EU on economics and income
- 10. Need for an Integrated data set • Measurement of different sustainability indicators on the same set of farms is essential • Allows the analysis of the full chain from: Policy objective -> policy measure -> impact on farm -> farm management decisions -> up to: sustainability performance of farms • Trade-off and jointness of performance on different sustainability measures as a consequence of policy measures – (for example is the economic performance at the expense of environmental performance, sustainability performance of large farms etc.)
- 12. 12 FLINT Indicator list (economic data already in FADN) S1: Advisory service S2: Education and training S3: Ownership management S4: Social engagement S5: Working conditions S6: Quality of life S7: Social diversification E3: Semi-natural areas E6: Soil organic matter E10: Nitrate leaching E11: Soil erosion E12: Use of legumes E4: Pesticide usage E5: Nutrient balance E7: Indirect energy use E8: Direct energy usage E9: On-farm RE prod. E1: Greening E16: Water usage, storage E17: Irrigation practices E14: GHG calculation EI1: Innovation EI2: Producing under label EI3: Market outlet EI4: Farm duration EI5: Efficiency field parcel EI7: Insurance EI8: Marketing contracts EI9: Risk exposure EI6: Modernization Environmental Economic, innovation Social sustainability
- 13. 13 Number of farms in the test of data collection # of farms FLINT data collected Finland 49 Germany 52 Ireland 63 Hungary 102 Greece 124 Poland 146 NL 155 Spain 128 France 280 Total 1099
- 14. Example of results: dairy farms in 6 MS. 14 Variable Unit ES FI FR IE NL PL Dairy cows heads 74.4 42.8 50.0 75.0 99.2 22.4 Gross farm income Euro 78,630 113,319 58,943 82,901 117,555 22,511 Farm net value added Euro 38,999 47,680 28,611 70,587 70,044 18,567 Farm net income Euro 27,205 33,057 12,643 61,299 18,692 16,719 GHG emission tonnes CO2 eq. 585.7 335.1 NA 624.0 597.3 130.8 GHG/cow tonnes CO2 per cow 7.88 7.84 NA 8.32 6.02 5.84 N-balance kg N per ha 431.1 162.4 NA 323.1 408.5 127.9 NUE output/input 6.2 6.6 NA 4.7 6.8 7.3 Advisory contacts number 25.0 13.0 21.0 11.0 24.5 21.0 Job satisfaction max 10 6.5 8.0 7.0 8.0 8.0 6.0 Quality of life max 10 5.9 8.0 6.2 7.8 7.2 6.0 Pesticides 2.1 0.4 1.6 NA 0.8 0.7 PS For illustration purposes only. These results (2015, France: 2014) are based on relatively small groups of farms which are not necessarily representative for the whole country.
- 15. Experiences in data collection • Some countries already collecting more data than required by FADN – experienced • Perceived importance of sustainability varies by MS • Many FLINT variables already indirectly available in FADN information flow (eg. quantities on invoices): environmental accounting – reduces information collected from farmers • Problematic variables tend to be country specific • Role of trusted data collector (and his know-how) important • Data collectors attitude changed from hesitant to more enthusiastic
- 16. • Policy researchers need to understand relation between policy measure and farm management with exact relation between inputs, outputs and income. • Collecting these data on the same set of farms is conceptually and empirically superior to a solution of separate panels (as illustrated in some show cases) • Collecting environmental data very often also depends on systematic recording of flows: environmental accounting is based on documents also used in financial accounting. Reduces administrative burden and increases quality Basic recommendations
- 17. • (Environmental) statistics: – Do not show relation between inputs, (bad) outputs and income at the same farms. However, relation between policies and farm management in impact analysis is needed – Can have quality problems if not based on systematic recording cross checked with financial accounts. • Separate panel on environmental accounting (and social indicators): – Same quality problems – Conceptually and empirically less good than 1 (FADN) panel – Higher costs and total administrative burden, as much more farmers are visited. • Fits in Basic act of FADN This leads to preference for FADN
- 18. 18 FLINT proposal to DG Agri: adapt FADN 1. CAP Reform and other policies demand better data for policy evaluation 2. Reduce current FADN from 85.000 farms to 75.000 to cover costs of new data 3. Collect sustainability data on 15.000 farms
- 19. Detailed recommendations (1) • Start collecting FLINT data • Including FLINT data on all FADN farms would increase total running costs with 40%. • More feasible option to collect FLINT data on a subsample of farms. • Create FADN sub-sample of 15.000 farms on which sustainability data are collected • Distribution of 15.000 over member states based on optimal allocation over the member states
- 21. Recommendations (2) • Would increase operating costs of FADN (to be paid by MS or EU?) • Alternative solution within current budget limitations: – Reduction of current sample of about 85.000 to 75.000 farms • impact on quality of estimates (at EU and MS level) of economic indicators very limited. – Large differences between MS depending on level of costs of current FADN data collection and estimated costs of FLINT data collection • Negotiation needed for countries like FR and D where this applies
- 23. Recommendations (3) • Setting up FLINT data collection requires investments (software, instructions) • DG-AGRI could support exchange of experiences and best practises • Recommendation to start a FLINT-2 project. – Could start soon – Make use of existing data in MS for policy analysis in 2018 (also from FLINT partners who will continue their data collection) – Transfer of FLINT knowledge to other MS, start testing And: – Connect with developments in IT and private sustainability schemes – Share best practises and legal arrangements in use of administrative and commercial data
- 24. In conclusion: where do we stand now ? • Data on new indicators is clearly needed for impact assessment of policies, to defend and improve the CAP • Collecting the data in FADN has clear advantages over other options (environmental statistics, separate panel) • FLINT proved that data collection is feasible. • FLINT showed how such data improves policy analysis. • FADN can keep its relevance by innovating its data collection • Next steps: – Detailed plan with FADN managers on how many farms per country depending on finance / acceptable reduction in number of FADN farms – Adapt Farm Return – Start data exchange (existing data) and data collection.
- 26. Relevance for Food Industry • Sustainability reporting and sustainability management of sourcing by the Food industry and Retail would benefit from micro-level data • Should data gathering and indicator standardisation be the sole responsibility of the EuropeanCommission in FADN or makes it sense to set up public-private initiatives or could the private sector take the lead in sustainability reporting with micro-data at a European level? – creating a monitoring targeted to farm- and industry needs • Would it make sense to involve research partners like Wageningen Economic Research for know how and a third trusted party between companies (and companies and farmers) ?
- 27. Key message The results of the FLINT project provide an excellent opportunity for food industries like dairy to install a European monitoring of their sustainability, to report to the public and governments, benchmark companies and incentivize farmers 27