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Laure Latruffe, Yann Desjeux, Pierre Dupraz, Krijn Poppe
To cite this version:
Laure Latruffe, Yann Desjeux, Pierre Dupraz, Krijn Poppe. Usefulness of the FLINT Sustainability Data Complementing the FADN Data. [Contract] FLINT D5.3, auto-saisine. 2016, 20 p. �hal-01571904�
USEFULNESS OF THE
FLINT SUSTAINABILITY
DATA COMPLEMENTING
THE FADN DATA
Laure LATRUFFE1, Yann DESJEUX1, Pierre DUPRAZ1, Krijn POPPE2
1 INRA, SMART, 35000, Rennes, France 2 LEI Wageningen (UR), The Netherlands 31 December 2016 Public D5.3 agriXchange is funded by the European Commission’s 7th
FLINT will provide an updated data‐infrastructure needed by the agro‐food sector and policy makers to provide up to date information on farm level indicators on sustainability and other new relevant issues. Better decision making will be facilitated by taking into account the sustainability performance of farms on a wide range of relevant topics, such as (1) market stabilization; (2) income support; (3) environmental sustainability; (4) climate change adaptation and mitigation; (5) innovation; and (6) resource efficiency. The approach will explicitly consider the heterogeneity of the farming sector in the EU and its member states. Together with the farming and agro‐food sector the feasibility of these indicators will be determined.
FLINT will take into account the increasing needs for sustainability information by national and international retail and agro‐food sectors. The FLINT approach is supported by the Sustainable Agriculture Initiative Platform and the Sustainability Consortium in which the agro‐food sector actively participates. FLINT will establish a pilot network of at least 1000 farms (representative of farm diversity at EU level, including the different administrative environments in the different MS) that is well suited for the gathering of these data.
The lessons learned and recommendations from the empirical research conducted in 9 purposefully chosen MS will be used for estimating and discussing effects in all 28 MS. This will be very useful if the European Commission should decide to upgrade the pilot network to an operational EU‐wide system.
PROJECT CONSORTIUM:
1 DLO Foundation (Stichting Dienst Landbouwkundig Onderzoek) Netherlands 2 AKI ‐ Agrargazdasagi Kutato Intezet Hungary 3 MTT Agrifood Research Finland Finland 4 IERiGZ‐PIB ‐ Instytut Ekonomiki Rolnictwa i Gospodarki Zywnosciowej‐Panstwowy Instytut Badawcy Poland 5 INTIA ‐ Instituto Navarro De Tecnologias e Infraestructuras Agrolimentarias Spain 6 ZALF ‐ Leibniz Centre for Agricultural Landscape Research Germany 7 Teagasc ‐ The Agriculture and Food Development Authority of Irelan Ireland 8 Demeter ‐ Hellenic Agricultural Organization Greece 9 INRA ‐ Institut National de la Recherche Agronomique France 10 CROP‐R BV Netherlands 11 University of Hohenheim GermanyMORE INFORMATION:
Drs. Krijn Poppe (coordinator) e‐mail: krijn.poppe@wur.nl Dr. Hans Vrolijk e‐mail: hans.vrolijk@wur.nl LEI Wageningen UR phone: +31 07 3358247 P.O. Box 29703 2502 LS The Hague www.flint‐fp7.eu The NetherlandsTABLE OF CONTENTS
List of acronyms ... 5 1 Introduction ... 6 2 Some evidence of the usefulness of the data ... 7 3 Limits in the usefulness of the data ... 8 3.1 Some identified limits of the collected data ... 8 3.2 Some of the potential limits of the FLINT indicators in principle ... 9 4 Conclusions ... 11 4.1 Suggestions for data collection... 11 4.2 Representativeness of the sample ... 11 References ... 13 Appendix A : Number of farms in the FLINT sample ... 14 Appendix B : List of indicators designed in FLINT ... 15 Appendix C : Comments on the calculation of the FLINT indicators on GHG and N ... 19LIST OF ACRONYMS
CAP Common Agricultural Policy EFA Ecological focus area EU European Union FADN Farm Accountancy Data Network GHG Greenhouse gas IPCC Intergovernmental Panel on Climate Change N Nitrogen NUE Nitrogen use efficiency TF Type of farming UAA Utilised agricultural area WP Workpackage1 INTRODUCTION
There is a growing need for data on the sustainability of agriculture, not only with industry but especially also with researchers and policy makers who have to monitor and evaluate the Common Agricultural Policy (CAP), including its cross‐compliance, greening and rural development measures. The FLINT project (‘Farm Level Indicators for New Topics in policy evaluation’) has investigated options to collect such data. In nine member states of the European Union (EU), with different systems of data collection at farm level, it has collected and analysed sustainability data from 1,099 farms in the Farm Accountancy Data Network (FADN). The additional farm‐level data was collected in nine countries ‐ The Netherlands, Hungary, Finland, Poland, Spain, Ireland, Greece, France and Germany – and for eight types of farming (TF). Appendix A shows the distribution of farms within the FLINT sample. Within the FLINT project, the objective of Workpackage (WP) 5 is to analyse the added value of the newly collected farm level indicators for policy evaluation.
The data available in the FLINT project includes accountancy data from FADN, the ‘FADN data’, as well as additional data on economic, environmental and social sustainability of farms, ‘FLINT data’, and sustainability indicators computed with this data. More details about the database can be found in the first deliverable of WP5, Latruffe et al. (2016a). The data relate to accountancy year 2015, except for France and Germany for which it is 2014. Several WP5 analyses have been performed on specific topics, leading to 15 case study deliverables 5.2: Brennan et al. (2016a), Brennan et al. (2016b), Buckley et al. (2016), Eguinoa and Intxaurrandieta (2016), Herrera et al. (2016a), Herrera et al. (2016b), Kis Csatari and Keszthelyi (2016), Latruffe et al. (2016b), Latruffe et al. (2016c), Lynch et al. (2016), O’Donoghue et al. (2016), Saint‐Cyr et al. (2016), Uthes (2016), van Asseldonk et al. (2016), van der Meulen et al. (2016).
The present deliverable, the last one of WP5, uses all this to draw lessons on the usefulness of the information collected during the FLINT project and in particular whether the farm level indicators developed in FLINT (see list in Appendix B) can help improve policy evaluation.
In drawing these lessons, a distinction has to be made between the data gathered on the 1,099 farms in the FLINT project and the assessment of the usefulness of such data for a larger sample in the FADN. The data on the 1,099 farms presents numerous problems to start with. Firstly, although the data was collected on different types of farming, the number of farms is far too low to be representative for even the nine member states involved. Secondly, it was the first year in which the data was collected, and notwithstanding all the work carried out in advance in discussing indicators and testing data collection methods and software, the data across the member states is less comparable than one might expect it could be after some years of using and further harmonizing the data. Thirdly, the data collected, arrived to the policy analysts who used the data only at the end of the project (and later than planned). This ruled out options to go back to data collectors and farmers to check or correct some data. We attribute such problems with the data to the data collection on the 1,099 FLINT farms, and this can probably be solved in the future for a larger sample in the FADN. That makes it of course difficult to prove by examples that policy analysis would improve considerably if the FLINT indicators would be added to the FADN. The examples in WP5 mainly show how data can be used in such analysis and try to illustrate the hypothesis that an integrated data set provides a better insight in the management of the farm and its effects on income, environment or social aspects. And a caveat to that is that not all FLINT indicators were used in the analysis carried out in WP5. This as the two main questions of the FLINT project were (1) feasibility of data collection and (2) some illustration (and discussion) of the added value. This deliverable addresses the second point. In the next chapter some evidence on the usefulness of the data is presented, then chapter 3 discusses limits to the usefulness. Chapter 4 gives the lessons learned from this analysis in the conclusions.
2 SOME EVIDENCE OF THE
USEFULNESS OF THE DATA
From all the case study deliverables 5.2, it is evident that the wide range of FLINT data and indicators are useful in the sense that they have added value for data users.
Firstly, the FLINT data can contribute to filling gaps in terms of research methodologies. This is for example the case of the investigation of farmers’ social sustainability (e.g. working conditions, quality of life, stress) which is still largely ignored in the literature (Diazabakana et al., 2014) notably due to the lack of data. In this context, Herrera et al. (2016a) provide a valuable contribution to the literature by providing an assessment of the relevant indicators of farmers’ social sustainability at the farm level1 and of the sources.
Another example is provided in O’Donoghue et al. (2016) who analysed farm viability. The authors indicate that using the FLINT information on off‐farm income, in addition to classic FADN data, ‘opens up an important new economic viability classification’.
Secondly, the FLINT data and indicators can help better understand the sources of performance, with information that is generally not present in classic databases. For example, Brennan et al. (2016b) and Herrera et al. (2016b) explain how farmers’ extension is related to their sustainability. Kis Csatari and Keszthelyi (2016) show that the age of assets negatively influence farmers’ economic performance. Saint‐ Cyr et al. (2016) report that land fragmentation is a source of, or obstacle to, farm sustainability. Brennan et al. (2016a) indicate that not only the age of farmer (available in the FADN data) matters for sustainability but the age of starting as a decision maker (available in the FLINT data) as well.
Thirdly, the data provide additional insights into the challenges faced by farmers in terms of sustainability. Latruffe et al. (2016c) assess the tradeoffs of farms in terms of economic, environmental and social sustainability, highlighting for example: the existence of tradeoffs between economic and environmental indicators, and between different types of environmental indicators; but the absence of tradeoffs between economic and social indicators at the farm level. This is not surprising because most of these indicators describe the farmer’s well‐being in her/his enterprise. Finally, FLINT contributes to inform policy evaluation by providing more precise recommendations. One clear example is provided by Latruffe et al. (2016b) in their investigation of the effect of the Common Agricultural Policy (CAP) subsidies on farms’ technical efficiency. The latter is firstly computed in a classic way, that is to say considering all inputs and all marketed (i.e. agricultural) outputs. Then new scores of technical efficiency are calculated, considering all inputs, the marketed outputs, but also some environmental outputs. The authors show that the effect of subsidies on technical efficiency including environmental outputs may be different than the effect on classic technical efficiency, in terms of presence or absence of significant effect, but also of negative or positive sign. More precisely subsidies are found to generally decrease the technical efficiency of the production restricted to marketed output while it is not always the case when environmental outcomes are also considered as farm outputs. Another illustration is provided by the study of Kis Csatari and Keszthelyi (2016) on the role of the age of assets on economic performance. Such investigation could contribute to the understanding of the role of investment subsidies (aimed at modernising assets, and hence at lowering asset age) on economic performance. One of the difficulties in investigating the effect of investment subsidies by directly evaluating their effect on economic performance is that there may be a time difference in the receipt of investment subsidies and the effective change (modernisation or expansion) in assets. Farmers may invest before receiving subsidies, through credit that is then reimbursed in later years with the help of subsidies; or farmers may purchase the new assets the year following the receipt of the subsidies. 1 Such indicators at the farm level can only catch a small part of social sustainability which highly depends on social relationships and collective organisations that cannot be investigated at the farm level.
3 LIMITS IN THE USEFULNESS
OF THE DATA
As discussed in the Introduction, a difference should be made between the limits of the data collected in the FLINT project on 1,099 farms, and the potential a collection of such data at a larger scale in the FADN may offer. We separate these two aspects as much as possible in the next two sections.
3.1 Some identified limits of the collected data
One limit in the usefulness of the collected FLINT data and indicators is that the FLINT sample of 1,099 farms is not representative. It has been meant to be a stratified sample of FADN but in the end, for various reasons (availability of farmers’ contact details, willingness of the farmers, technical possibilities to collect data etc.), the FLINT sample is not as representative as the FADN, and hence it is not representative in economic terms, nor in environmental or social issues. The types of analysis carried out in WP5 are an example of policy analysis that might be done, but the estimated coefficients and other results would be different if a fully representative sample would be available. Hence, analyses carried out on the FLINT sample are simply illustrative: there are analyses on a selection of FADN farms and illustrate the possibilities provided by the type of information collected and developed in FLINT. This is for example stressed by Latruffe et al. (2016c) in their analysis of tradeoffs between economic, environmental and social sustainability. Hence, by no means should the conclusions drawn in the FLINT case study deliverables or later drawn from further analyses, should be taken as a basis to draw policy recommendations.
This is linked to the representativeness of the FADN for environmental or social issues. The FADN is representative in economic terms, and that raises the question if the sample is also representative in terms of environmental and social issues. In WP6 it is argued that the current stratification variables (TF, size class, region) would most likely also be the first ones used in an environmental network. If in an analysis it turns out that other aspects, like soil or altitude, would be very important, a post‐stratification could be used to create more precise estimations. But there is an issue of efficiency: if one would be interested in for instance the nitrate leaking problem, it makes sense to collect these data in regions where it is a problem and not in those where the issue is not relevant, as this would only add to costs.
Another limit to the usefulness of the FLINT data and indicators, in their current form, is that they relate to one year only (2014 or 2015). This generates three problems as discussed in several case study deliverables (Brennan et al. (2016b), Herrera et al. (2016a), Herrera et al. (2016b), Kis Csatari and Keszthelyi (2016), Latruffe et al. (2016b), Latruffe et al. (2016c), O’Donoghue et al. (2016), Saint‐Cyr et al. (2016), van Asseldonk et al. (2016), van der Meulen et al. (2016). (i) Firstly, one year is not sufficient to control for external events that may affect farmers’ behaviour or outcomes, e.g. climatic extreme events, pest outburst, economic shocks. Hence, the significant relationships identified in the various case study deliverables may in fact simply reflect the role of the specific events that occurred in 2014 or 2015. (ii) Secondly, analyses performed on one year only do not allow to fully understand causalities; the significant relationships may simply capture correlations between various variables and may not fully inform on which variable impacts the other. (iii) Thirdly, one year of data cannot capture fully the delays in responses. While farmers may immediately (i.e. within the same year) react to external drivers (e.g. subsidies, climatic variability) in terms of change in inputs that can be modified over the short term (e.g. fertilisers, pesticides), the responses with respect to other inputs or to outputs may take longer. This is the case of adjustment in capital (investment or disinvestment) such as buildings, lumpy equipment or some livestock; such adjustment is not instant. This is also the case of the level of production of some environmental outputs or services, e.g. relating to soil fertility or soil erosion.
A third shortcoming to the FLINT data and indicators relates to the number of valid observations (see Latruffe et al., 2016a, for a discussion on the extent of missing observations in the database). When a large part of observations is missing, it is impossible to carry out statistical analyses. Latruffe et al. (2016b) for example report in their descriptive statistics (their table 1) that the number of valid observations for the indicator of the N balance is 14 farms in the type of farming of mixed cropping farms, compared to the total number of observations of 21 for this type of farming. The consequence is that the authors could not assess the average treatment effect of subsidies on technical efficiency accounting for N balance, as the number of observations in this type of farming is too low (their table 3). Brenann et al. (2016a) also underline in their conclusion that a larger sample size would increase the accuracy of results as regard the effect of farmers’ starting date on sustainability. In addition, Brennan et al. (2016b) report that they could not account for self‐selection bias in their analysis of the effect of extension on farmers’ sustainability, due to the too small sample size.
3.2 Some of the potential limits of the FLINT
indicators in principle
The analyses carried out in WP5 have also brought to light some weaknesses that should be taken into account in the case that the data collection would be pursued on a larger group of FADN farms. An important issue is that some indicators have weaknesses that limit the scope of analyses, and hence their usefulness. Some examples of the weaknesses (not exhaustive) are presented as below: (i) Firstly, the choice was made to calculate some indicators with simplifying assumptions so as not to be too data demanding but this means that they are not as informative and precise as they could be. This is for instance the case of greenhouse gases (GHG) (E_14_1) and nitrogen (N) indicators (E_5_1 and E_5_3), as explained in Appendix C. In addition, these indicators rely on data that were also recorded with simplifying assumptions, such as the animals’ weight (Z8_LS_*_*). As farmers do not record animal weights in their bookkeeping, they had to estimate average weights and multiplied them by the number of animals. Hence, the information most probably is highly correlated to the number of animals recorded in the FADN data.
All this may explain why the figures for the N indicators calculated in FLINT, reported for example in Latruffe et al. (2016b), are so different than the ones calculated with national extensions of FADN in Ireland and the Netherlands, as reported in Buckley et al. (2016). Appendix C provides avenues to improve the design of N and GHG indicators.
One could also mention the indicator of pesticide usage (E_4_1). In FLINT this indicator is in kg per hectare and is simply calculated as the sum of the quantities of active substances related to the farm UAA. However, this does not reflect the risk factors, that are influenced by issues as the persistence of the substances and the weather conditions at the time of spraying.
(ii) Secondly, some indicators provide an incomplete view for some farm types, some due to simplistic calculation rules in the software, others due to lack of data availability or improper calculations (e.g. missing values considered as zero values). For example, the FLINT computation of the quantity of GHG (indicator E_4_1) considers only NO2 and CH4 (and not CO2), and thus this indicator mainly relates to
livestock activities and is not useful for specialist crop farms. Another example relates to the data about pesticides. Indeed the provided list was not fully complete and accurate, as for instance it did not encompass adjuvants and boosting additives. Moreover, some organic farmers did not feel concern about this pesticide‐oriented table (see Latruffe et al., 2016a – Appendix A) and therefore did not provide any data on that although they use some plant protection products (like micronutrients). Hence, the current FLINT indicator on pesticide usage (E_4_1) should not be used for organic farms. (iii) Thirdly, some indicators do not accurately capture what they are meant to capture. Let’s take again the example of the pesticide usage indicator (E_4_1). It is calculated with the information collected as regard the quantity of active substances per each crop. However, some active substances used by the farmers were not in the list of active substances provided to the farmers and used to calculate the
does not fully capture the pesticide usage on farms.
The last example relates to the information related to the manager or the owner. While the manager was the person to be interviewed as regard to personal information (FLINT tables Z1 and Z2), some questions related to the owner, namely regarding personal insurance (Z6_IN_1030_ST) and off‐farm employment (Z6_AM_3020_ST and Z6_AM_3020_H). However, management and ownership may be separated. In some cases, the manager had not this information regarding the owner.
(iv) Finally, some indicators could not be linked to FADN data. This is the case of the manager’s personal information in FLINT tables Z1 and Z2. The objective was to link them to the numerous information provided in FADN as regard the manager: date of birth (C_*_*_B_*), gender (C_*_*_G_*), agricultural training (C_*_*_T_*), number of annual units (C_*_*_W1_*), share of work for other gainful activities (C_*_*_W2_*), annual time worked (C_*_*_Y1_*). However, in the FADN these data are available for several managers: up to five paid regular managers (e.g. for gender: C_PR_70_G_1, C_PR_70_G_2, C_PR_70_G_3, C_PR_70_G_4, C_PR_70_G_5) and up to eight unpaid regular holders/managers (e.g. for gender: C_UR_10_G_1, C_UR_10_G_2, … C_UR_10_G_8). It was not possible to understand which one had answered the FLINT survey. The above list of potential limits is not exhaustive, but some of these issues may be ‘repaired’ in the future by better definitions, deepened reflexions on calculation designs, more complete calculation rules in the software, and more harmonised instructions. Some are typically the problems that are encountered in a in the first year of collection (and is therefore also an argument to collect such data on a permanent basis). Others might be a problem of any type of dataset, where some others (like the risk of pesticides depending on the weather) are perhaps more problematic in gathering this data via an accounting system than via management software.
4 CONCLUSIONS
4.1 Suggestions for data collection
For future data collection some indicators (and the variables behind them) might be adjusted, and sufficient resources and time should be allocated to such adjustment, as suggested in section 3.2. Some variables need to be collected at the same time of FADN, e.g. sub‐categories of areas such as extensive grassland, in order to avoid inconsistencies between the sum of sub‐categories and the full variable collected in FADN. Personal information linked to the manager (e.g. perceived satisfaction, social engagement) should be collected at the same time as FADN as well so that they could accurately be linked to the FADN data of this specific manager. It is necessary to collect some information on a long period of time and not in a specific year only. This would enable control for specific (climatic, sanitary, economic, etc.) events that occurred during the year considered and that could bias some conclusions if not accounted for. It would also ensure that causalities could be investigated. With one year of observation, only correlations can be highlighted. In addition, it would help accounting for delay in responses. This is particularly true for farmers’ responses to some policy support, such as investment aids. Investment may have an effect on performance several years after it has been realised. This is also important for some type of environmental sustainability: implementing more or less ecological focus areas (EFA) on the farm or seeing changes in soil erosion, require time.
As the current data set has missing observations (e.g. non‐reporting farmers) for some variables (and by extension, indicators), although this data is relevant for those farms, like the issue of water use or succession (see Latruffe et al., 2016a), there is a valid question if it makes sense to reduce the data collection in terms of the number of indicators (and therefore in collected variables). A way of tackling this problem may be to opt for a reduced set of data. This would also reduce the burden placed on farms and member states in terms of data collection (time, cost, infrastructure requirement etc.).
One suggestion is that some indicators are not relevant for some types of farming, e.g. GHG related to animals are not relevant for arable farms, EFA are not relevant for off land specialisations (pigs and poultry). This means that some indicators could be collected for only a few types of farms; and only a small selection of indicators could be collected for all types of farms. This would reduce the burden on farms and member states. Similarly, some indicators may not be relevant for the whole EU. This may be the case for soil erosion information, which might be collected in area at stake only, thus reducing the burden on some farms and member states.
In addition, it is not useful to collect information where it is evident that farmers have difficulties to answer (e.g. land subject to erosion), or which is sensitive (e.g. water consumption). There will be too many observations for which the information is missing and this would prevent any analysis. By contrast, the repeated data collection over time will improve data collectors’ and farmers’ skills and would reduce the errors due to misunderstanding.
4.2 Representativeness of the sample
The representativeness of the sample deserves a discussion, as set out in section 3.1. The FADN sample is representative for ‘commercial farms’ in terms of main productions and farm size, but the sub‐sample used for the FLINT pilot test is not. It is hard to determine to what extent the FADN sample, stratified for farm size, TF and region, is representative for environmental and social issues on commercial farms.
like e.g. well‐being of farmers.
However, the question of representativeness may depend of the objective of collecting the additional information to FADN. (i) One objective may be to evaluate the effectiveness and efficiency of the policy measures through a better understanding of the behaviour of the farmers and the choices that they make in a tradeoff between economic, social and different (sometimes contradicting) environmental goals. Complementing the economic information with environmental and social information would fit this purpose. (ii) By contrast, if the objective is to assess the environmental or social sustainability of EU farmers, including those outside the definition of ‘commercial farms’ (notably small farms), then a specific sample should be selected in order to be representative of the environmental or social themes considered. The basic regulation for the FADN state that the purpose of the FADN is the first objective: analyse the CAP. But of course it is totally valid to be interested in the environmental and social sustainability of EU farmers in line with (ii). In that case the FADN data collection could help, but is not sufficient.
ACKNOWLEDGEMENTS
We thank John Lynch for his comments on the N balance and GHG indicators (Appendix C).REFERENCES
Brennan, N., Ryan, M., Hennessy, T., Cullen, P. 2016a. The impact of farmer age on indicators of agricultural sustainability. FLINT Deliverable D5.2H. December.
Brennan, N., Ryan, M., Hennessy, T., Dillon E. J., Cullen, P. 2016b. The role of extension in agricultural sustainability. FLINT Deliverable D5.2I. December.
Buckley, C., Daatselaar, C., Hennessy, T., Vrolijk, H. 2016. Using the Farm Accountancy Data Network to develop nutrient use efficiency indicators for milk production. FLINT Deliverable D5.2K. December. Diazabakana, A., Latruffe, L., Bockstaller, C., Desjeux, Y., Finn, J., Kelly, E., Ryan, M., Uthes, S. 2014. A review of farm level indicators of sustainability with a focus on CAP and FADN. FLINT Deliverable D1.2. December. Eguinoa, P., Intxaurrandieta, J. M. 2016. Water usage, source and sustainability: Examples from the region of Navarra (Spain) and Greece. FLINT Deliverable D5.2O. December. Herrera, B., Gerster‐Bentaya, M., Knierim, A. 2016a. Social indicators of farm‐level sustainability. FLINT Deliverable D5.2E. December.
Herrera, B., Gerster‐Bentaya, M., Tzouramani, I., Knierim, A. 2016b. Advisory services and farm level sustainability. FLINT Deliverable D5.2M. December.
Kis Csatari, E., Keszthelyi, S., 2016. Effect of age of assets on farm profitability and labour productivity. FLINT Deliverable D5.2F. December.
Latruffe, L., Desjeux, Y., Dupraz, P. 2016a. Database used for FLINT WP5 activities: description and quality assessment. FLINT Deliverable D5.1. December.
Latruffe, L., Dakpo, H., Desjeux, Y., Justinia Hanitravelo, G. 2016b. Subsidies and technical efficiency including environmental outputs: The case of European farms. FLINT Deliverable D5.2B. December. Latruffe, L., Desjeux, Y., Justinia Hanitravelo, G., Hennessy, T., Bockstaller, C., Dupraz, P., Finn, J. 2016c. Tradeoffs between economic, environmental and social sustainability: The case of a selection of European farms. FLINT Deliverable D5.2L. December. Lynch, J., Finn, J., Ryan, M. 2016. Investigation of indicators for greening measures: Permanent grassland and semi‐natural area. FLINT Deliverable D5.2J. December.
O’Donoghue, C., Devisme, S., Ryan, M., Conneely, R., Gillespie, P., Vrolijk, H. 2016. Farm economic sustainability in the EU: A pilot study. FLINT Deliverable D5.2G. December.
Saint‐Cyr, L., Latruffe, L., Piet, L. 2016. Farm fragmentation, performance and subsidies in the European Union. FLINT Deliverable D5.2D. December.
Uthes, S., 2016. Deriving indicators for soil organic matter management from FLINT data. FLINT Deliverable D5.2N. December.
van Asseldonk, M., Tzouramani, I., Ge, L., Vrolijk, H. 2016. Adoption of risk management strategies in European agriculture. FLINT Deliverable D5.2A. December.
van der Meulen, H., van Asseldonk, M., Ge, L. 2016. Adoption of innovation in European agriculture. FLINT Deliverable D5.2C. December.
APPENDIX A: NUMBER OF
FARMS IN THE FLINT SAMPLE
Farms specialist in: Arable and mixed cropping Horti‐ culture Permanent crops excl. wine & olives
Wine Olives Dairy livestock Beef cattle Sheep and goats Granivores (pigs & poultry) Mixed livestock, mixed crops‐ livestock Whole sample Type of farming # 15, 16, 61 21, 22, 23 36, 38 35 37 45 46, 47 48 51, 52, 53 73, 74, 83, 84 The Netherlands 40 33 53 27 2 155 Hungary 38 4 4 1 22 33 102 Finland 1 26 19 3 49 Poland 33 26 25 1 22 39 146 Spain 53 3 1 2 28 15 25 1 128 Ireland 35 24 3 1 63 Greece 25 38 31 30 124 France 76 61 45 51 1 7 39 280 Germany 11 1 5 13 4 3 6 9 52 Total 277 36 66 68 31 229 118 63 84 127 1,099
APPENDIX B: LIST OF
INDICATORS DESIGNED IN
FLINT
Economic indicators EI_1_1 Product Innovation at farm level (0/1) EI_1_2 Process innovation at farm level (0/1) EI_1_3 Market and organisational innovation at farm level (0/1) EI_1_4 Innovation at farm level (0/1) EI_2_1 Farm under label EI_2_2 Experience in label production EI_2_3 Degree of certified organic label (minimum across activities) EI_2_4 Degree of certified organic label (weighted average of activities) EI_2_5 Share of UAA under certified organic label EI_2_6 Share of UAA under EU Public quality label EI_2_7 Share of UAA under other collective quality label EI_3_1_* Number of markets outlets – for each crop # EI_3_2_* Exclusive outlets – for each crop # EI_3_3_* Diversified outlets – for each crop # EI_3_4_* Main outlet – for each crop # EI_4_1 Experience in decision making of the interviewed manager on the farm EI_4_2 Age of starting as a decision maker, for individual farms only EI_4_3 Succession EI_5_1 Number of reference parcels EI_5_2 Average size of reference parcels EI_5_3 Average distance of reference parcels EI_5_4 Normalized average distance of reference parcels EI_5_5 Maximum distance of reference parcels EI_5_6 Standardised maximum distance of reference parcels (Grouping index)EI_5_7 Normalised maximum distance of reference parcels Or Doubly‐normalised maximum distance of reference parcels (Structural index) EI_5_8 Minimum distance of reference parcels EI_5_9 Normalised range of reference parcels distances EI_5_10 Furthest reference parcel management EI_5_11 Stated favourability of the field pattern EI_6_1 Average age of machinery EI_6_2 Average age of dairy assets
EI_6_4 Average age of pig farm assets EI_6_5 Average age of winery farm assets EI_6_6 Average age of agricultural buildings EI_7_1 Adoption of crop insurance at farm level EI_7_2 Adoption of building insurance at farm level EI_7_3 Adoption of livestock insurance at farm level EI_7_4 Adoption of disability insurance at farm level EI_7_5 Number insured categories at farm level EI_8_1 Adoption of contracts at farm level EI_8_2 Contracts with price specification at farm level EI_8_3 Contracts with quantity specification at farm level EI_8_4 Contracts with duration specification at farm level EI_8_5 Contracts with delivery specification at farm level EI_8_6 Contracts with quality specification at farm level EI_8_7 (Approximation of) share of turn over under contract EI_8_14 Average share of turnover by contracts EI_9_1 Adoption of farm diversification EI_9_2 Adoption of farm processing / sales EI_9_3 Adoption of off farm investments EI_9_4 Adoption of credit avoidance EI_9_5 Adoption of hedging EI_9_6 Adoption of financial reserves EI_9_7 Adoption of production contracts EI_9_8 Off‐farm employment EI_9_9 Other gainful activities (with possible subcategories) EI_9_10 Count of other income sources at farm level Environmental indicators E_1_1 Share of permanent grassland under intensive management E_1_2 Share of permanent grassland that is extensively managed with semi‐natural vegetation E_1_3 Share of permanent grassland that is extensively managed with semi‐natural vegetation under nature protection E_2_1 Preferred EFA element on farms with arable area E_2_2 Share of potential EFA area on farms with arable area E_3_1 Share of semi‐natural habitat area E_4_1 Pesticide usage E_5_1 Farm gate N‐balance E_5_3 Nitrogen use efficiency (NUE) E_10_1 Percentage of farm UAA with early catch crop E_10_2 Percentage of farm UAA with late catch crop E_10_3 Weighted percentage of catch crop within farm UAA E_10_4 Percentage of farm UAA with nitrate risk E_11_1 Percentage of farm UAA associated with erosion risk E_11_2 Percentage of erosion risk area not ploughed
E_11_3 Percentage of erosion risk area with catch crop incorporated before winter E_11_4 Percentage of erosion risk area with catch crop incorporated after winter E_11_5 Percentage of erosion risk area with soil cover in every second row for vineyards or orchard E_11_6 Percentage of erosion risk area with soil cover in every row for vineyards or orchard E_11_7 Weighted percentage of erosion risk area with soil cover E_11_8 Percentage of farm area with erosion mitigation E_14_1 GHG emissions, at farm level E_16_1 Direct blue water footprint(kg): Water consumption / kg of product E_16_2 Direct blue water footprint (CU): Water consumption / CU of product E_16_3 Water consumption metering: % of measured consumption E_16_4 Water price: Water cost / water consumption E_16_5 Main source of water E_17_1 Water governance E_17_3 Water consumption (m3) / irrigated area (ha) E_17_4 Water irrigation system E_17_5 Energy dependence E_18_1 Crop species diversity Social indicators S_1_1 Advisory contacts per year per holding S_1_2_1 Advisory contacts per year per holding: Accountancy, bookkeeping, taxes S_1_2_2 Advisory contacts per year per holding: Management, business planning, and marketing S_1_2_3 Advisory contacts per year per holding: Crop production S_1_2_4 Advisory contacts per year per holding: Livestock production S_1_2_5 Advisory contacts per year per holding: Animal products and services S_1_2_6 Advisory contacts per year per holding: Other gainful activities directly related to the farm S_1_2_7 Advisory contacts per year per holding: Investments S_1_2_8 Advisory contacts per year per holding: Other S_1_4 Number of main information sources about CAP S_2_1 Degree of agricultural training of the manager S_2_2 Training days for manager S_2_3 Total days for training S_2_4 Share of participation in training S_2_5 Number of persons participating in training events S_3_6 Financial involvement S_3_7 Technology use S_4_1 Social engagement S_4_2 Degree of social engagement S_4_3 Degree of agricultural engagement S_4_4 Degree of environmental engagement S_4_5 Degree of societal engagement S_5_1 Total labour in Annual Working Units S_5_2 Share of unpaid labour of total labour
S_5_3 Labour force directly employed by the holding annual working hours (A+B+C+D) (total labour in hours)
S_5_5 Non‐family labour force employed on a non‐regular basis annual working hours (D) S_5_6 Family labour force annual working hours (A+B) (unpaid labour input in hours) S_5_7 Non family regularly employed labour force annual working hours (C ) S_5_8 Holder annual working hours (A) S_5_9 Member of the holder’s family annual working hours (B) S_5_13 Share of other gainful work in total work
S_5_14 On farm annual working units for manager and family labour / Unpaid labour input in working units S_5_15 Off farm annual working hours owner S_5_16 Off farm annual work spouse S_5_17 Annual working hours manager S_5_18 Average weekly working hours of manager S_5_19 Length peak season in days S_5_20 Annual working hours during peak season S_5_21 Average day working hours during peak season S_5_22 Holidays per year S_5_23 Regular days‐off per week S_5_24 Working conditions: Replacement during illness S_5_25 Working conditions: Replacement other than in case of illness S_5_27 Length of peak season in months S_5_28 Share of total manager working hours worked during peak season S_5_29 Workload seasonality index during peak months S_5_30 Workload seasonality index during non‐peak months S_6_1 Satisfaction with job S_6_2 Satisfaction with work life balance S_6_3 Satisfaction with being a farmer S_6_4 Satisfaction with quality of life S_6_5 Satisfaction with freedom of making decision S_6_6 Stress perception S_6_7 Change in freedom S_6_8 Change in stress S_6_9 Perception of farming S_6_10 Overall quality of life S_7_1 Local participation S_7_2 Social diversification index (real sum)
APPENDIX C: COMMENTS ON
THE CALCULATION OF THE
FLINT INDICATORS ON GHG
AND N
N balance N balances were calculated based on annual accounting of produce and inputs at the farm‐gate level. The N content of mineral fertiliser brought onto the farm used values provided directly in FADN. N content of crops, livestock, and manures moving into and out of the farm (including opening and closing inventories either end of the year) were estimated by multiplying the weight of a given input/output by a relevant N content coefficient. Different countries could update coefficients for accuracy if different standard values were used locally. Providing livestock weights was a common difficulty across the countries in the project, and most used estimates based on animal numbers and/or prices. This was especially difficult for opening and closing values, where for some livestock categories even financial values may still be a rough estimate. It may be possible to refine these weights in the future, but will remain a difficult area of data collection. Different countries are also at different stages on the path towards collecting N application volumes, and particularly transfers of manure and slurry. Although this data can be challenging to collect, efforts should be maintained to do so, as it is a highly important element of agricultural management, and essential for N balance calculations. To add to the nitrogen use efficiency (NUE) indicator in the future, two additional N inputs would be important additions, but were beyond the scope of the initial N balance calculations: atmospheric N deposition and biological N fixation. Estimates of these two processes could be generated from data already available in FLINT and FADN. N deposition could be estimated by adding typical national (or sub‐national, depending on data and model availability) N deposition rates per hectare for each farm. Biological N fixation could be added by using typical N fixation rates per unit area of each relevant crop on a given farm. GHG Agricultural GHG emissions were estimated based on livestock inventories and N fertiliser applications following Intergovernmental Panel on Climate Change (IPCC) methodologies. Tier 2 calculations were used with each nation supplying its own emissions factors. Numbers of each livestock category were multiplied by relevant methane emissions factors to estimate CH4. Numbers of each livestock category weremultiplied by relevant organic N excretion rates and allocations (to manure or slurry storage, or excreted directly to pasture), and each manure category was then multiplied through the appropriate national emissions factors to estimate NO2 and CH4 emissions. For the purposes of reporting these were converted
into CO2, but the individual components can also be isolated for further analysis.
As the livestock categories supplied were those used in FADN, they are more restrictive than those used in the IPCC methodologies. Some countries may already collect animal numbers in more detailed categories before submitting to FADN, and so this detail is also lost in the emissions estimates. In updating this methodology in the future, it may be beneficial for some more of the calculations to be done in individual countries using the more specific livestock categories and emissions factors.
factors to the N applied, as reported in FADN. As noted above for the N balance calculations, this is dependent on accurate N data being supplied. In the current methodology excretion based emissions are all applied to the farm on which this manure was generated, in the year of their generation. In future if manure transfers are recorded in sufficient detail these could be assigned to where and in which year manures are applied, although arguments could be made for assigning them to either location, and implications should be considered beyond the methodological feasibility.
At present several agricultural emissions categories are not included, which it would be useful to add in the future. CO2 emissions from lime and urea are not currently included due to a lack of relevant data
supplied on these inputs. Emissions related to rice production are not currently as this only covered a small number of farms in the FLINT pilot survey. Emissions resulting from land use and soil carbon stock changes are not currently included due to the scale of regional complexities in land use and tillage practices. In the first instance tier 1 estimates could be used based on land use types encountered on farms. Similarly, N inputs from crop residues and soil N mineralisation that contribute to N2O emissions
from managed soils could be added in the future at either a tier 1 level, or with estimates based on regional differences in cropping and soil practices.
The GHG emissions in the FLINT methodology currently only include emissions the IPCC agriculture category, not those resulting from on‐farm energy and fuel use. FLINT data collected on energy usage (based on information in FLINT table Z9) could be used to reliable estimates these additional emissions in the future.
