Survey description

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To cite this version:

Meri Raggi, Laura Sardonini, Fabio Bartolini, Davide Viaggi, Nico Polman, et al.. Survey description. [Research Report] n.D2.13-23, auto-saisine. 2010, 133 p. �hal-01462653�

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Department of Agricultural Economics and Engineering, University of Bologna Viale Fanin, 50 ‐ 40127 BOLOGNA (ITALY)

Deliverable n.D2.13‐23

S

URVEY

D

ESCRIPTION

Authors

M. Raggi, L. Sardonini, F. Bartolini, D. Viaggi,

L.N. Polman, D. Roberts, B. Manos, E. Majewski,

S. Piotr, J. Berbel, D. Nikolov, L. Latruffe, Y. Desjeux,

A. Piorr, G. Giannoccaro, T. Bournaris, A. Lange.

Version n.3

18 November 2010

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1 MAIN CHARACTERISTICS OF SURVEY A

CSA Sample Way

Response rate (or numbers of contacts) Sample selection Other information 1. Emilia‐Romagna (Italy) 300 (only beneficiaries in 2007) Telephone 62% Random sample, proportional stratification by location (mountains, hill, plain) and by amount of payment received in 2007 (higher or lower of the average) 2. Noord‐Holland (Netherlands) 300 Postal 21% Random sample 3. Macedonia and Thrace (Greece) 300 (only beneficiaries in 2007) Telephone (243) / Face to face (57) 55% Random sample, proportional stratification by Greek regions and prefectures and by amount of payment received in 2007 4. Podlaskie (Poland) 250 Face to face ~95% Random selection in specified clusters of farms (because of the heterogeneity of farming sector in Poland we decided that the survey should reflect the structure of farms and structure of land use in the region ‐ there is a high disproportion between number of farms and their share in the use of agricultural land in specified area groups). 5. North East of Scotland (UK) 168 Telephone 68% Random sample, stratification by amount of payment received in 2005 Population of 5036 farmers. Estimated beneficiaries based Beneficiaries of CAP payments in 2007. We sampled about 25% of population. about 83000 beneficiaries in2008 3090 beneficiaries in 2005. Target population 7606 beneficiaries in 2007. We sampled about 4% of population.

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Andalusia, i.e. Cordoba, Seville, Jaen). interview by payments (amount of payments (Euro): < 5 000; 5 000‐20 000; 20 000‐50 000; >50 000) and by location (mountains, hill, plain), as well main farm activities (crops and livestock). 7. South‐East Planning Region (Bulgaria) 273(only beneficiaries in 2007) Face‐to‐face 92% Random sample, proportional stratification by location (mountains, hill, plain) and by production specialisation. Mainly focus on rural farm households

8. Centre (France) 140 Face‐to‐face 400 contacts

were provided Individual contacts were drawn from the 2008 contact joint database of the farmers’ social security (i.e. MSA) and of the regional body helping farmers setting up farms or implementing investments (i.e. ARASEA Centre). The draw was done in order to be representative of the main regional farm specialisations: 300 farms in Types of Farming 13, 14 or 81, and 100 farms in Types of Farming 41, 42, 43 or 44. Non‐physical farmers were not considered. Besides these restrictions, the draw was random. Interviews were sub‐ contracted to an agricultural school. The final sample is not as random as it originally was, due to many farmers’ refusals: students conducting the interviews replaced the refusals by some farmers they personally knew. The original sample seems to be biased towards young farmers. 8582 beneficiaries in 2007. We sampled about 3% of the beneficiaries. Regional statistics: ‐ 21,976 SFP beneficiaries in 2007 (Agreste, 2007a). ‐ 21,425 professional farms in 2007 (Agreste, 2007b). No contacts could however be obtained from this target population and another contact source was used. (SPF and RDP) at regional level NUT 2. 178 509 beneficiaries in 2007 (SPF and RDP) at NUT 3 level (3 Provinces).

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(France) were provided 2008 contact database of the Public Payment Agency for Second Pillar. Non‐physical farmers were not considered. Besides this restriction, the draw was random. contracted to an agricultural school. The final sample is not as random as it originally was, due to many farmers’ refusals: students conducting the interviews replaced the refusals by some farmers they personally knew. The original sample seems to be biased towards young farmers. Complete coverage of target population; contact information from federal statistics and from LPN partners within the region. Reduced number in study population due to data inconsistencies in federal statistics on CAP beneficiaries 11/1 Ostprignitz‐ Ruppin (Germany) (NUTS 3) 62 (beneficiaries in 2008) written questionnaire 16.8% complete coverage of target population; reduced number in study population due to data inconsistencies in federal statistics on CAP beneficiaries 11/2 North‐East Brandenburg (Germany) (NUTS 2) 73 (beneficiaries in 2008) written questionnaire 12.2% proportional stratified random sample; stratified by location (according to districts on NUTS 3 level, excluding Ostprignitz‐ Ruppin) and amount of CAP payment received in 2008 (according to quartiles in data distribution) more responses expected to come in until end of November 2615 beneficiaries in region, about 23% surveyed population of 536 beneficiaries in 2008 – surveyed: about 69% 10. Lahn‐Dill‐District (Germany) 117 (CAP beneficiaries in 2007) written questionnaire population of 659 beneficiaries in 2007 ‐ surveyed: about 85% 20% SFP beneficiaries in 2007 (Agreste, 2007a). ‐ 36,399 professional farms in 2007 (Agreste, 2007b). No contacts could however be obtained from this target population and another contact source was used.

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2 NOTE ON THE SURVEY A DATABASE

Please when you analyze data in the survey A database, note that: LFA CSA2 (The Netherland) used a different codification: 1=yes and partially, 2=no Question 2.05 (unemployed) CSA1 (Italy): for Italy that is not the number of official longterm unemployees, but only the number of no‐workers in the households: it means that includes retired, students,…

Question 3.01 (legal status)

CSA7 (Bulgaria): all the cases with farmers as juridical status physical person are in the line "others" (with the specification “physical person” in the 3_01_txt). It is very close to "sole trader" because they are unlimited responsible for the business.

Question 3.14 (contract for selling)

Note by Edward Majewski only for CSA4 (Poland): I realized that all products were considered as a subject for contracting except MILK, which is the key product in the region in general. However, in the vast majority of farms milk producers are co‐op members and sign usually contracts on deliveries of milk to dairies. This is simply the normal practice, that is why it was not treaded as "contracts".

We decided to keep the original answers and not consider as a real contract the sell to a cooperative of which the farmer is a members. In the additional survey on the web, you could find both the version of this question for CSA4. Question 3.17a_txt and 3.17b_txt (sfp and other payments) There are some amount that have comma as decimal and some have dot. Still, there are some text. Question 3.09db and 3.09dc(Number of household with land rent out to relatives or other)

These questions were omitted in the written German questionnaire due to capacity reasons.

Question 4.15A and 4.15B (use of water)

Question 4.25Aa, 4.25Ab, 4.25Ba and 4.25Bb (change who sell outpuy)

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3 COMPARISON OF MAIN DESCRIPTIVE FROM SURVEY A AND CHARACTERISTICS OF CSA

AND BENEFICIARIES

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Note Farm specialization: from the question Q3.03 of survey A we aggregate as: Arable farms: Modalities 01 + 02 + 03 Permanent farms: Modalities 04 + 05 + 06 + 07

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UK: The Survey A responses were self‐classifications of farm types which may not be consistent with official classifications. Data for total land is based on old NUTS3 area (which includes Moray) as no figures are available for new NUTS3 definition. FR1 and FR2: Note: Collected data at the CSA or at the beneficiary level all relate to the year 2007 (Source: Agreste, Structural Survey, 2007). Total land (mean) CSA: UAA only DE2: Data relates to survey of the total area, i.e. CSA11/1+CSA11/2. % of arable, permanent, livestock and mixed farms of survey A: data relates to the number of farms not to share of UAA

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4 ITALY (CSA 1)

4.1 Household

Question 2.03 (Table 3) Question 6.04 (Table 122) The most frequent highest educational level in the household is the upper secondary (45%) followed by lower and secondary (18%). The category none and primary has a relevant frequency (17%) showing a lower level of farmers’ education. In particular, the percentages of respondents with low educational attainment show that most of the interviewed have none and primary educational attainment (36.3%) and lower and secondary (27%). Question 2.09a, b, c (Tables 12, 13, 14)

The most part of farmers does not participate to social network. Only the farmers union has a large frequency of yes (89.7%), instead all the other organizations (sports clubs, recreation or other social organization, natural conservation organization or other environmental organization) have a little relevance because only respectively 19% and 5% of the farmers are involved. Question 2.05 (Table 5) The high value of the number of the unemployed has not to be misunderstood; in this case it is not the official long‐term unemployed but only the number of no‐workers in the household. Then it includes also the retired, students, etc …

4.2 Farm

Questions 3.03, 3.04, 3.09 (Tables 19‐27, 38‐40, 42) For more than half of the farms (53%), the main specialization results to be ‘specialist cereals, oilseed and protein crops’, the other farms are distributed over the other specializations without showing a rank of importance.

In the Italian case study the mean of owned land results 20.28ha, the mean of the rent‐out is 11.06ha and the rent‐in 20.20ha. About the land rent‐in almost the 40% are from relatives.

Question 3.15 (Table 55)

The use of internet is not frequent, only 4% uses this way for buying or selling products.

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Only 9% of the agricultural holdings’ leaders are engaged in some other activities different from crop cultivations and livestock breading, mainly recreational service (11 farms).

4.3 Reaction

Question 4.01 (Tables 59‐60) The percentage of those quitting the farm activity is 15% in the baseline scenario and the percentage increases in the No‐Cap scenario (almost 30%). Question 4.02 (Tables 61‐62) The main reason of the exit in the Baseline is the lack of successor with family (30.4%) and the not enough profitability (28.3%). In the No‐Cap scenario the main reasons are the same but the rank is the opposite. Question 4.04 4.05 (Tables 65‐68) The percentage of those will move from present location to live on farm are 11.3% in the Baseline and 6.3% in the No‐Cap scenario. The percentage of those will move from farm to live off farm are 3.4% in the Baseline and 6.8% in the No‐Cap scenario. Question 4.09 4.10 4.11 (Tables 75‐80) About the land the main intention is to maintain the same amount in both scenarios for the owned, rent‐in and rent‐out land. Question 4.14 (Tables 85‐88)

About the use of pesticides and of water, the no change intention has the highest frequency in both scenarios, followed by the intention to decrease in the use of pesticides and then intention to increase in the use of water.

Question 4.20 4.21 (Tables 97‐100)

In the Italian case study, there is no intention in changing to who sell output, in fact only around 10% (for both scenarios) have intention to modify it. For those changing, the buyer category that would be important is the private (around 50% for both scenarios). Question 4.26 (Tables 109‐116) The farmers’ intention to adopt innovation is not common in both scenarios. The more relevant innovation is the energy crops: 23% in Baseline and 14.3% in No‐Cap.

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5 THE NETHERLANDS (CSA 2)

5.1 Summary

On the whole, most of the data found in the survey don’t come as a surprise. A few results do warrant further consideration. These are: Specialisation, Workers full time and other, and Other activities.

5.2 Household

Education (question 2.03 and 6.04) Remarkable is the the relatively high level of education compared to other case study areas. Given that 3% of the labour force has an agricultural and/or environmental education with only 1,7% working in the agricultural, forestry or fishery sector (according to CBS, 2010), the 74,3% of households where at least one member has a formal Agricultural education is not surprising.

Participation to organization (2.09a,b,c)

2.09a: The results of the survey show that 50% of households have at least one person who is a member of a sport, recreation or other social organization. According to the CBS (2010) about 30% of people are member of sports club, with a slightly lower percentage in highly urbanized areas, and about 10% are a member of a hobby organization. While these may partially overlap (i.e. some people are both a member of a sports club and a member of a hobby organization), a participation rate where 50% of the households have at least one member is plausible.

2.09b: According to the CBS (2010) the participation level of non‐urban people in labour unions is 20‐22% and in trade organizations 13‐14%. According to the survey 78,7% is a member of a farmers union or another farming pressure group. However, LTO Nederland (the national organization for the agricultural sector) claims to “represent almost 50.000” farmers (LTO, 2010) on a total of 73.009 farms (preliminary number for 2009, (CBS, 2010)) (almost 70%). The results from the survey are therefore plausible.

2.09c: According ot the survey the membership of nature conservation or other environmental organizations is 31,7%. This higher than according to the numbers of the CBS (2010) indicating that about 25% of non‐urban people are member of such an organization..

Unemployement (2.05 ) According to the CBS (2010) the long‐term unemployment in

Noord‐Holland was in 2008 about 1,5%. The long‐term unemployment for the agricultural sector according to the survey is about zero. The difference is relatively small

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5.3 Farm

Size (3.09) The mean size in the CAP‐IRE survey is 33 ha. According to the CBS (2010), the average farm‐size in Noord‐Holland is about 30 ha. For dairy farms it is about 40 ha. With almost 50% of respondents being dairy farmers, a mean of 33 ha. per farm seems plausible. Specialization (3.03, 3.04)

The CAP‐IRE survey indicates that 49,7% of farms have as a specialization in dairy farming. However, according to Polman et al. (2008) 19% of all farms (instead of just those receiving subsidies) in Noord‐Holland is a dairy farm. According to the CBS (2010), in 2009 about 28% of farms in Noord‐Holland are dairy and cattle‐raising farms. Given this results, the 49,7% according the CAP‐IRE survey seems to be high. However, given the focus in sampling on farmers receiving CAP payments this is acceptable Internet use for buying and selling (3.15) – About 85% of households in low and non‐ urbanised areas in the Netherlands have access to internet, and about 60% of the people in those areas have access to internet at home (CBS, 2010). According to the survey 38,7 and 12,0% of the respondents buy respectively sell online. Workers full time and other (3.10, 3.11)

According to the survey the average farm has two full‐time and two part‐time male workers; and one full‐time and three part‐ time female workers. (It seems to be a lot, however, it could be possible) According to Polman et al. (2008) there are about twice as many full‐time workers as part‐time workers, and almost twice as many male as female workers. These numbers don’t seem to match.

Other activities (3.05, 3.06)

According to the survey 45,3% of households have beside crop cultivation and animal farming other activities. Compared to other studies it seems very high. According to CBS (2010), of the total of 5114 farms in Noord‐Holland in 2008, there are 1607 farms with other activities.

For the four categories mentioned in the survey the number of farms with other activities drops to 708, and it is conceivable that some farms have multiple activities, such as food processing and manufacturing as well as retailing.

The individual activities show comparable percentages, with the exception of recreation. Recreational activities are less than half of what they are in the CBS‐ data.

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(PM not enough knowledge to comment)

5.4 Policy scenario

Abandonment and motivation (4.01, 4.02)

Succession in the Netherlands shows a declining trend: in 2004 only 15% of farmers had a successor ready, whereas in 1996 it was over 23% (CBS, 2006). Nationwide, of the 40.000 farms where succession was an issue in the coming ten years, 70% had no successor yet. It is therefore not surprising that 35‐40% of the respondents have no successor within the household. The main reason for not having a successor in the household, i.e no succession within the family, fits with the statement of CBS (2006): that succession is more and more the result of a rational calculation rather than tradition. Change in suppliers and buyers (4.20, 4.21) (PM not enough knowledge to comment) Innovation (4.26)

According to the Innovatiemonitor 2008 (Van Galen en Gé, 2009), in 2007 12,7% of farmers “carried out a modernization in produced or marketed products and/or the technological production methods”, but in 2004 it was only 6,5%. Results from the survey are in between: increasing e‐commerce and robotisation and/or precision farming in 9% of the cases; innovation in energy crops and new irrigation systems are outliers at around 15 and 1%, respectively. Movement from/to farm (4.04, 4.05) (PM not enough knowledge to comment) Change in size (4.09, 4.10, 4.11) According to the CLO (2010a) and CBS (2010) the average size of a farm is increasing. The survey‐results are therefore not surprising: the number of households with owned and rented in land are both increasing, while the number of households that rent land out are decreasing. Use of water and pesticides (4.14, 4.15) Use of pesticides (quantity) is declining according to CBS (2010) and CLO (2010b). This is in accordance with the tables 85 and 86 of the survey, showing a mild decrease in the use of pesticides in the future in both Baseline and NO CAP scenarios.

Use of water is hard to predict or compare, because of the large effect the seasonal weather has . The survey shows a future increase in use of water in both Baseline and No‐CAP scenarios, but use of water is largely dependent on available water during the growing season (CLO, 2010c).

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References CBS (2006) ‘Animo om boerderij over te nemen daalt’ http://www.cbs.nl/nl‐ NL/menu/themas/landbouw/publicaties/artikelen/archief/2006/2006‐1927‐ wm.htm CBS (2010) Statline, Statistical database http://statline.cbs.nl/statweb/

CLO, Compendium voor de Leefomgeving (2010a) ‘Ontwikkeling kaveloppervlak en bedrijfsoppervlak grondgebonden landbouw; periode 1980‐ 2005’

http://www.compendiumvoordeleefomgeving.nl/indicatoren/nl1529‐Ontwikkeling‐ kavel‐‐en‐bedrijfsoppervlak‐grondgebonden‐landbouw.html?i=11‐61 (version 01)

CLO, Compendium voor de Leefomgeving (2010b) ‘Afzet van chemische gewasbeschermingsmiddelen in de landbouw, 1985‐2008’

http://www.compendiumvoordeleefomgeving.nl/indicatoren/nl0015‐Afzet‐van‐

chemische‐gewasbeschermingsmiddelen‐in‐de‐landbouw.html?i=11‐61 (version 10)

CLO, Compendium voor de Leefomgeving (2010c) ‘Watergebruik in de land‐ en tuinbouw, 2001‐2007’

http://www.compendiumvoordeleefomgeving.nl/indicatoren/nl0014‐Watergebruik‐ landbouw.html?i=11‐61 (version 06)

Galen, M van & L. Gé Innovatiemonitor 2008; Vernieuwing in de land‐ en tuinbouw

ontcijferd Den Haag, LEI, Rapport 2009‐027 LTO Nederland (2010) ‘LTO Organisatie’ http://www.lto.nl/nl/5140887‐LTO_Organisatie.html Polman, N., R. Michels and L. Slangen (2008) CAP‐IRE ‐ Case area description Bologna: Università di Bologna

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6 GREECE (CSA 3)

6.1 Household

It is well assessed that the educational attainment is one of the most crucial factors concerning the ability of a person to obtain adequate income and to avoid unemployment. The picture resulting from the percentages of respondents with low educational attainment shows that –as it was expected‐ most of the adults living in predominantly rural areas posses low educational attainment (40.7% lower and secondary and 37.7% none and primary). This finding may reveal the fact that people in rural areas quit more often the nine years compulsory education in comparison to young people of other areas.

The low education level of rural areas in Macedonia and Thrace (GR) is considered a serious developmental problem. As the educational level of employed in primary sector is concerned, 7.0% are elementary school graduates, 2.3% have not completed elementary school or are illiterate and 45.7% are graduates of secondary education.

Question 2.09a, b, c (Tables 12, 13, 14)

The participation of the agricultural holdings in any Social Network is low in Macedonia and Thrace (GR). The half of the agricultural holdings are members of farmers unions or any other farming pressure group, some of them are members of sports clubs, recreation or other social organization but only a few of them are members of some natural conservation organization or other environmental organization. The rates are low because the farmers are not official members of social networks, but they informally participate in local activities or events.

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6.2 Farm

Questions 3.03, 3.04, 3.09 (Tables 19‐27, 38‐40)

Despite the importance of the primary sector in the economy of Macedonia and Thrace (GR), its role tends to be a diminishing one, while the main characteristic of it remain more or less unchanged: small size of agricultural holdings (land owned: 8.15 hectares, land rent in: 10.74 hectares, land rent out: 2.86 hectares), land parceling and agricultural holdings without any specialization (various crops and livestock combined, mixed livestock, field crops – grazing livestock combined, or cereals).

The use of Internet for buying production means or for selling products in Macedonia and Thrace (GR) has revealed an upward trend during the last years, but it still lags behind in comparison to the rest CSA’s. The respective percentages for buying production means or selling are 8% and 3.3%. Questions 3.10, 3.11 (Tables 43‐49) The primary sector of Macedonia and Thrace (GR) covers a considerable part of labour force. It must be also noted that the great majority of people living in mountainous and less favoured areas is employed in primary sector’s economic activities. Despite the importance of the primary sector for the economy its role tends to be a diminishing one, while the main characteristic of it remain more or less unchanged: women working full time or part time present a lower average in comparison with the rest CSA’s. On the other hand, the results show a higher employment engagement of male workers, both as full time and part time.

As regards the immigrants, 12.7% of them have emigrated from a relatively limited number of other European countries, while the vast majority (63.7%) comes from non European countries (mainly Albania).

Questions 3.05, 3.06 (Tables 28‐32)

A serious structural change envisaged in rural areas in Macedonia and Thrace (GR) regards the expansion of multi – functionality in these areas. Only 12% of the agricultural holdings’ leaders are engaged in some other activities different from crop cultivations and livestock breading. These activities include contract work using farm labour and/or machinery, or retailing for the most of the agricultural holdings and food processing and manufacturing for the rest of them. Question 3.13 (Tables 51‐53) In Macedonia and Thrace (GR) most of the agricultural holdings sell their products to private wholesalers/retailers. Some of them prefer to sell directly to final consumer,

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and even fewer prefer processing industries or cooperatives. This is due to the low participation of the farmers in cooperatives.

6.3 Reactions to Policy (Policy Scenario)

Questions 4.01, 4.02 (Tables 61, 62)

The motivation of abandon the farm in the Baseline Scenario for the most of the farmers in Macedonia and Thrace (GR) is that there will be no successor within the family, or because of the high risk of farming. Most of the farmers in Macedonia and Thrace (GR) would abandon farm in the No Cap Scenario because farming will be no profitable enough. Only a few of them would abandon the farm because of the high risk of farming or because there will be too many constraints. Questions 4.04, 4.05 (Tables 65‐68) All the farmers in Macedonia and Thrace (GR) prefer to live in small towns and villages, and they would not move to live on the farm or off farm, in both Scenarios. Questions 4.09, 4.10, 4.11 (Tables 75‐80) Most of the farmers in Macedonia and Thrace (GR) would not change their farm size both in the Baseline and in the No Cap Scenario. What is more important though, is that most of the farmers who would increase the owned land in the Baseline Scenario, they would increase it also in the No Cap Scenario. Additionally, more farmers would decrease the owned land in the No Cap Scenario, than in the Baseline Scenario. Their reactions as regards the land rent in or out by the farm are about the same.

The vast majority of the farmers in Macedonia and Thrace (GR) would not change the use of water or pesticides in the farm. On the other hand more farmers would decrease the pesticides in the Baseline Scenario, than in the No Cap Scenario and none of those who would increase the use of water in the Baseline Scenario would increase it in the No Cap Scenario.

Over the half of the farmers in Macedonia and Thrace (GR) would not change their suppliers and buyers both in the Baseline and in the No Cap Scenario. In the Baseline Scenario, from the farmers that would change their suppliers, most of them prefer private buyers and only a few prefer cooperatives or prefer to sell directly to final consumers. On the other hand, in the No Cap Scenario all the farmers that would change their suppliers prefer to sell directly to final consumers.

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Question 4.26 (Tables 109‐116)

None of the farmers in Macedonia and Thrace (GR) would adopt innovations in robotisation or precision farming, and in e‐commerce, both in Baseline and in No Cap Scenario. Only a few of them would adopt innovations in new irrigation system, in both Scenarios. Finally, more farmers would adopt innovations in energy crops in the Baseline Scenario, than in the No Cap Scenario.

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7 POLAND (CSA 4)

7.1 Methodology of the sample selection and its

representativeness

Because of the heterogeneity of farming sector in Poland we decided that criteria for sampling should reflect the structure of farms and structure of land use in the region. Taking into account a high disproportion between number of farms and their share in the use of agricultural land in specific clusters of farm size there were weights attached to those criteria allowing for avoiding over representing the sample by small farms, which outnumber the general population of farms in the region.

The following formula for each of the five farm size clusters that were distinguished was applied:

Z= [0,25 (weight) * X + 0,75 (weight)*Y] * 250 (targeted total number of farms to

interview), where: X – share of specific size cluster in total number of farms in the region Y – share of specific size cluster in total agricultural land in the region Z ‐ number of farms to interview in specific size cluster The structure of farms in the region and structure of the sample is presented below: Farm size clusters Structure of farms (num-ber) [%]* Structure of land use [%]* Number of farms for interviewi ng Structure of the sample – number of farms [%] Structure of farms applying for direct payments** 1-5 ha 29% 7% 32 13% 6% 5-10 ha 28% 16% 47 19% 16% 10-20 ha 29% 35% 84 34% 36% 20-50 ha 14% 31% 67 27% 32% >50 ha 1% 10% 19 8% 10% Total 100% 100% 250 100% 100% *own estimates based of Central Statistical Office data

**according to the data of the Agency for Restructuring and Modernization of Agriculture (paying agency)

In the result of using the weighting procedure the structure of the sample is very similar to the structure of farms applying for payments in the Podlaskie region.

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At the final stage of selecting farms for interviewing the production orientation of farms was taken into account. It was assumed that in the clusters of farms above 10 ha of UAA the sample should consist of: - farms with milking cows ‐ about 45%; - farms with pigs – about 25%; - farms without animals or with animals only for own consumption – about 30%. Although there was no formal estimation of representativeness of the sample it can be stated that farms selected for interviewing reflect fairly well both, the farm as well as production structure in the region.

7.2 Comments on selected aspects of the survey

7.2.1 Age of the respondents (table 121) The age of farmers in the Polish sample is the lowest (33 years) of all CSAs, comparable with FR1 only. It is below the country’s average which is about 45 years (in the year 2007). This suggest that the sample is slightly biased, however there are some features of the region that give an explanation:

- agriculture is the main industry in the region and an average size of farms is one of the largest across regions of Poland. Due to this, the number of subsistence or semi‐ subsistence farms (about 20% of farms in Poland), which are often run by elderly farmers, is relatively small;

- a significant number of farmers at the age of over 55 years took an advantage of early retirement scheme passing the farm over to younger successors;

- an additional incentive to take farms over by farmers below the age of 40 is an opportunity to apply for grants “support for young farmers” which is another IInd pillar policy instrument.

7.2.2 Education (tables 3, 4 and 122)

Relatively high percentage of respondents with the upper secondary education level (79,1%) is to a large extent a consequence of the low age in the sample. It is becoming a standard in Poland that this level of education is a minimum satisfying ambition of young people as well as their parents. Those young people from farmer’s families who finish education at this level frequently stay on farm. The majority of those who continue their education leave farms.

Also the high share of farmers with agricultural education (93,2% of the whole sample) should be explained (table 4). Because possessing an agricultural education is on of the conditions for applying for IInd pillar subsidies those farmers who are not graduates of agricultural schools may document required education with the diploma of so called “agricultural courses” providing basic knowledge on the key

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aspects of agricultural production – this applies to 32% of all farmers declaring agricultural education.

7.2.3 Participation in farmer’s unions and other organizations (tables 12,13,14)

Very low number of respondents engaged in any kind of social activities is typical not only for farmers, but nowadays also for the entire Polish society. Recent sociological studies show that most of the people are cautious in contacts with other people (81% according to CEBOS 2007 survey, which was one of the lowest scores in Europe). It results with a weak social ties and the lack of willingness to co‐ operate (participate in organizations).

In the opinion of sociologists this attitude is typical for societies in the periods of transformation, when people concentrate on activities that contribute in a direct way to their welfare. In Podlaskie region, which is one the poorest in Poland, the well‐being of the region’s inhabitants is relatively low. It might be expected that in line with an increase of personal incomes of the regions population social capital will be strengthen.

7.2.4 Specialization (table 12)

Specialized dairy farms dominate the sample (36,1%). This corresponds with the leading position of the region in Poland in milk production and processing – dairy co‐operatives in Podlaskie belong to the largest and strongest of all milk processors in the country scale.

Second largest in the sample is a group of arable farms (25%). This reflects the most recent specialization and concentration trends in Poland. Livestock farms tend to increase scale of animal productions, whilst a growing number of farms, very often significantly increasing the area of agricultural land, tend to specialize in crop production.

7.2.5 Sales to another farm (table 51)

Percentage of farms selling to other farms is relatively high (39%). It should be emphasized, however, that the amounts of agricultural produce traded with other farms probably are rather small. Most likely farmers selling small numbers of piglets or cereals to small scale, mixed farms answered “yes” to this question (this hypothesis might be checked, if required).

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7.2.6 Contract to sell (table 54) The low figure on the percentage of farmers contracting their produce relates to other products then milk (4%). If the milk was also considered, this figure goes up to 40,9%. Probably this correction should be made in the survey results. Probably all farmers in the sample deliver their milk to co‐operative dairies, being also members of the co‐ops. It is quite natural that farmers supply their “own” dairies and we didn’t consider the arrangements between farmers and co‐ops as contracting. However, although specific, the agreement between those parties have a nature of contracting. Please decide how to classify. 7.2.7 Continuation of agricultural activity (table 59) Baseline scenario No‐CAP scenario % of „yes” 96,4% (average76%) 84,3% (average 45%)

Different than in Rother CSAs and very high percentage of farmers declaring continuation of farming under No‐CAP scenario can be explained by the following:

- Podlaskie is a typical agricultural region and other branches of the region’s economy don’t offer attractive Job opportunities;

- due to relatively large size of farm holdings and ongoing concentration processes farmers are fairly well‐off relying even entirely on incomes from agricultural production (so far milk production, which is the dominating activity in the region, belongs to most profitable. Having experienced farming without subsidies not a long time ago, at the beginning of the economic transformation in Poland, such scenario is probably easier to “imagine” and accept for Polish farmers.

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8 UNITED KINGDOM (CSA 5)

8.1 Introduction

The following comments aim to provide both background and interpretation of the results from the descriptive analysis of Survey A for the UK Case study area of North East Scotland. Survey A collected information on a large number of different household and farm characteristics, as well as the stated behaviour of respondents to two contrasting policy scenarios. Rather than discuss each individual result in turn, the comments focus on those results which are of particular interest to the project and which are to be analysed further in the project’s thematic work packages.

In the case of some variables (for example, farm characteristics), the results are compared with information drawn from secondary sources including Deliverable D2.5 North East Scotland CSA Description. In this way some idea of the representativeness of the Survey A sample of farms can be gained. In other cases, the comments focus on explaining differences in results in North East Scotland to those of the other CSAs in the project.

8.2 Household characteristics

Table 2.1 indicates that the size of farm households in NE Scotland is small. On average there are only 2.73 members per household – the smallest of all study areas. Only 43 of the 168 (approximately 25%) households in the sample had members less than 18 years old, slightly more, 48 of the sample, had members older than 65.

While there are no comparator figures on the demographic characteristics of the population of farm households in North East Scotland, they results are consistent with general socio‐economic trends of small (and decreasing) household sizes in the UK and the fact that many of the farms do not have a successor (see section 4 below).

The most common highest education level attained by a member of the farm household is upper secondary education (40.5%). Attendance in education to this level of training has been compulsory in the UK for over 50 years so the fact that only 1 respondent had lower attainment than this is as expected. Of the remaining households, the split was equal between post‐secondary non‐tertiary attainment (ie diploma level) and first‐stage tertiary (ie degree level). The two households with respondents with PhDs is surprising. This, and the relatively high level of tertiary education, may reflect the presence of two Universities within the study area. Unlike in some study areas eg France), there is no requirement that farmers have a

formal agricultural education in order to receive farm subsidies, therefore the fact

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educational qualifications of household members beyond secondary level is not necessarily in an agricultural subject area.

Table 11 indicates that a large proportion of farm households in North East Scotland have income from sources other than from farming activities. In particular, more than half of the same had 10% or more of their total household income from non‐

farming sources. Thirteen percent received less than 30% of total household

income from farming. The data are consistent with figures from the Scottish FADN‐ based data which suggests many farms have income from non‐farming activities but that this varies considerably according to farm type and farm size (Scottish Government, 2009a). In relation to the other case study areas, the percentage of total household income from farming in North East Scotland is within the middle of the distribution of results.

Of the alternative type of organisations detailed in Survey A, farm households in North East Scotland were most likely to be involved in a farm union or other farming pressure group than they were in a recreational group or environmental organisation. Just over half of the sample responded that they were in a farm‐ related organisation. This is relatively low compared to the other CSAs from EU15 member states.

As expected, almost all farm households (98%) lived on the farm holding. This reflects the historical nature of farming in the UK and succession patterns. It may also reflect the fact that, in the recruitment process, those farmers receiving SFPs but who no longer actively farmed often refused to participate in the survey. It is possible that such farm households may have moved away from the holding but there is no empirical evidence to support this.

8.3 Holding characteristics

The majority of farms in North East Scotland (the highest of all CSAs) have the legal

status of partnerships (65%) with most of the remaining holdings in sole

proprietorship. As shown in Table 17, it is common for partnerships in the CSA to be with relatives.

The average area farmed in the North East Scotland sample was 249 hectares. This is the second largest of all CSAs in the project. The majority of farm households in the area own land (86%), some rent land out (16%), more than half rent land in (55%). In the case of renting, very few arrangements (only 2) are connected with relatives.

The average area of land owned, 179.33 ha, is the highest of all CSAs as is the median area owned, 103.95 ha. The size of holding in the sample is larger than that based on census data for the region which is 72.71 ha, (Cook, 2008) and reflects the sampling frame of Survey A. In particular, a large number of small holdings were not included in the sampling frame for the project due to the fact they did not receive support payments. Farm size in the region has been falling over the last

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decade, while farm numbers have been increasing reflecting a trend towards “hobby farming” in the region. (Cook, 2008).

Farm type (tables19 – 27). The vast majority of farms (74%) in the North East Scotland

CSA were classified as livestock or mixed farms of various types. Compared with secondary sources (Entwistle, 2008), the sample over‐represents livestock farms and under‐represents more specialised cereals or general cropping farms. However some of the differences may be attributable to respondents classifying their holdings in a different way to that used by government agencies (based on proportions of revenue attributable to different types of output).

Almost 62 farm holdings, representing 37% of farms in the North East Scotland sample had an “other activity” on the holding, that is some form of diversification that is not classified as a farming activity. The majority of these (71%) were found to be associated with contracting. Very few holdings were involved with food processing, manufacturing or retailing with the other most common form of diversification being into some form of recreational services such as tourism or horse livery. These findings are consistent with expectations. Although no data on income from diversification was collected as part of Survey A, secondary sources suggest that in general, farm diversification activities contribute a relatively low percentage of total non‐farm income (less than 10%) compared to the off‐farm sources (Scottish Government, 2009)

Most farms in the North East Scotland sample (74%) have no farm employees. Of those that do, the average number of part‐time employees, particularly female part time employees is heavily influenced by a single farm which employees over 100 workers on a seasonal basis (in this case migrants from another EU country). The low level of farm employment is consistent with secondary sources: employment in the sector has been in long term decline with full time staff falling by 23% between 2000 and 2007. (Entwistle, 2008)

In common with all but one of the other 11 CAP‐IRE CSAs, the results in Table 55 suggest that farmers in North East Scotland are more likely to use the internet to buy inputs than sell output, however, in both cases, the use of the internet seems limited. Feedback from interviewees suggested that the closed nature of the question in Survey A masked the fact that many farmers use the internet for research purposes, for example to find compactor prices for inputs/output etc, even if they did not use it for actual trade purposes. Finally the level of SFP received by holdings in the North East Scotland sample was high relative to other CSAs and skewed by a few large recipients. The figures however are reflective of the region (Entwistle, 2008; Scottish Government, 2009b)

8.4 Stated behavior responses

From table 59, just over 85% of the sample of farms in the North East study region stated that under the baseline scenario, they would continue farming activity on the holding. The remainder was split between those that stated they did not know

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if they would continue (8.5%), and those that said they would not continue (6.5%). The proportion stating they would remain farming is third highest of all the CSAs in the project.

Under the alternative, No‐CAP scenario (table 60), the proportion that stated they would continue farming fell to 44.6%. However, a large proportion of the others (58 farms in total) stated they did not know rather than that they would leave the sector. In fact the proportion stating they do not know was highest of all the CSAs. The total stating they would stop farming rose from 6.5% in the baseline to 20.8% in the no‐CAP scenario, a sizeable increase.

The reasons given for the exit from the sector were very different between the baseline and no‐CAP scenarios (tables 61 and 62). In the former, of the 11 farms choosing to exit, the main reason given was no successor. In comparison, of the 35 farms stating they would exit under the No‐CAP scenario, the majority (71%) stated it would be because farming would not be sufficiently profitable. This highlights the importance of the CAP for the commercial viability of many farms in the study area. Again, however, a lack of successor was highlighted as a problem for some farms.

One particularly interesting findings from the reactions to the baseline scenario was that slightly more farmers suggested, under the baseline, that they would decrease the amount of off farm work they were doing than those who stated that such work would increase (table 71). Given the importance of off farm income, this was unexpected. The only other CSA with the same pattern (although in this case much stronger) was in Bulgaria. Under the no‐CAP scenario, as would be expected, off farm work was expected to increase on more farms than it was expected to decrease, with majority (76.5%) predicting no change from current levels.

Given that almost all farm households in North East Scotland reside on the holding, the responses to questions on whether the household would move to the holding under the two contrasting policy scenarios was irrelevant for most of the sample (see tables 65 and 66). More interesting were the responses to the questions as to whether the household would move from the farm under the two scenarios. The results suggesting they would move away from the holding were surprisingly high: under the baseline 14 of those continuing (8.6%) would move, and, under the no‐ CAP scenario, 29 (17.8%) would move despite continuing farming. This has potential implications for the more rural parts of North East Scotland and deserves further analysis.

In relation to farm size, under the baseline scenario, of those staying in farming, most said they expected to own the same area or increase land owned (Table 75). The results also suggested that land rented in under the baseline is likely to increase in more cases than land rented out. Thirteen of the 75 farms stating they would remain in farming under the no‐CAP scenario said they would increase the area of land owned, 28 increase area rented in. This compares to 5 and 6 respectively who said they would decrease these variables, the others either not knowing or stating they would not change from existing levels. None of the stated behaviour

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responses with respect to farm size appear very different from those given in the other CSAs.

A high percentage (25.2%) of those remaining in farming under the baseline scenario suggested they planned to reduce pesticide use. In the No‐CAP scenarios the number reducing their use of pesticides was even higher (26%). In comparison very few suggested their use of pesticides would increase relative to current levels : 1.4 % in the baseline, 2.7% in the no‐CAP scenario. The latter are low compared to the other CSAs and may reflect the fact that pesticide usage levels were relatively high initially and/or farm types in the area.

Changes in terms of water use are stronger in the no‐CAP scenario than baseline scenario but in general terms are muted compared to the other CSAs, presumably because water use is less of an issue for North East farmers for both climatic reasons and because of the type of agriculture.

The proportion of farmer suggesting they would change who they sell their output to was low under both the baseline and no‐CAP scenario compared to some of the other CSAs. Instead, the majority ‐ 81% (baseline) and 76% (No‐CAP) ‐ stated they would stay with the same seller, most of the remainder suggesting they did not know who they would sell to in future.

The results in relation to innovation are more interesting with 25 farmers (17.5% of those remaining under the baseline scenario) suggesting that they planned to innovate in terms of robotisation and/or precision farming while 54 farms (37.8%) said they would innovate in terms of introducing energy crops. These were far larger increases than in the other forms of innovation explored in survey A (new irrigation and e‐commerce). The same types of innovation were identified as important by those farmers continuing in the no‐CAP scenario, with the increase in energy crops even higher (41.3%), the highest of all CSAs. This is consistent with current trends in the sector and the emphasis being given to renewable energy issues by the Scottish Government (Scottish Executive, 2005)) References Cook, P. (2008) Agriculture in Aberdeenshire – Looking to the Future. A Study for NESAAG, Aberdeenshire Council and Scottish Enterprise Grampian. October 2008. Entwistle, G. (2008) North East Scotland Case Study Area Description, UKM50. Deliverable D2.5. CAP‐IRE project. Scottish Government (2009a) Economic Report on Scottish Agriculture 2009 Edition Rural and Environment Research and Analysis Directorate, Rural and Environment Analytical Services, ISBN 978 0 7559 8058 1 Scottish Government (2009b) Rural payments and Inspections Directorate Annual Report 2008‐2009.

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9 SPAIN (CSA 6)

2.03 What is the highest education level attained by one member of your household? (Table 3) In the CSA Andalusia, the percentage with none and primary education level is higher than other CSA. In general, the level of training and education in rural areas is lower than in urban ones. 54.6% of the population in Andalusia has no studies or primary studies (PDR base 2001 data). SURVEY CSA

None and primary

(elementary school) 26.4 % 54.6% Lower Secondary (primary school) 2.0 % 38.7% Upper secondary education High school 23.4 % Post‐secondary non‐ tertiary education (professionalizing master) 20.9 %

First stage of tertiary education (degree)

24.9 % 6.8%

Second stage of tertiary education (PhD)

2.5 %

6.04 Education level of respondents (Table 122)

SURVEY

None and primary

(elementary school) 53.7 % Lower Secondary (primary school) 2.0 % Upper secondary education High school 24.9 % Post‐secondary non‐ tertiary education (professionalizing master) 10.9 %

First stage of tertiary education (degree)

7.5 % Second stage of tertiary

education (PhD)

1.0 %

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The survey shows that the educational level of those in the survey is similar to the general rural population in Andalusia. 2_09 a,b,c Are anyone in your household members of one of the organizations listed below? Participation to sports club recreation or other organization (Table 12) SURVEY YES 13.9 % NO 85.6 % MISSING 0.5% Participation to farmer union or any other farming pressure group (Table 13) SURVEY YES 55.7 % NO 44.3 %

Participation to nature conservation organization or other environmental organization (Table 14) SURVEY YES 2.0 % NO 98.0 % The results show that most of observations in survey belong to “union of farmers” or any other farming pressure group.

2_05 How many long‐term unemployed are there in your household (including yourself)? (Table 5)

On average, Andalusia is the second value of all study cases after Italy. This reflects the high level of unemployment in rural areas of Andalusia and the great problem it conveys. Female employment is higher than male one. Females Males Long‐term unemployment rate 5.7 2.2

3_09 Please indicate how many hectares the farm owns or operates (Table 38, 39, 40) SURVEY Mean Median Land owned 73.48 15.00 Land rent out 13.25 10.00 Land rent in 80.05 20.00

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Yes No surface To have land owned 89.6% 10.0% To have land rent out 2.0% 97.5% To have land rent in 42.3% 56.7% The average farm area in the CSA is 73.48 ha and the percentage of land ownership is 89.6%. In the survey 50% approximately rent the land. 3_03 Specialization (Table 19, Table 20) SURVEY

Specialist cereals, oilseed and protein crops 18.9% General field cropping 25.9% Specialist horticulture 1.0% Specialist vineyards ‐ Fruit and citrus fruits 4.5% Olives 30.3% Permanent crops combined 6.0% Dairying 1.5% Cattle‐rearing and fattening ‐ Cattle‐dayring, reairng and fattening combined 0.5%

Sheeps, goats and other grazing livestock

0.5%

Granivores ‐

Mixxed cropping ‐

Mixed livestock, mainly grazing livestock

4.5% Mixed livestock, mainly

granivores

‐ Field crops‐grazing

livestock combined ‐

Various crops and

livestock combined

6.5%

Not clasificable ‐

The most frequent farming orientation is olives (30%), general mixed herbaceous cropping and cereal, oilseed and protein crops (18.9%). In Andalusia, the most extended crop is the olive tree which occupies an area of 1.504.884 ha (Instituto Estadístico de Andalucía, 2007).

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3_04 If specialisation includes livestock, please specify how many animals are kept on your farm (Table 21‐27)

SURVEY TOTAL

Mean Median Mean Median

Dairy cows 57 60 71 42

Beef cows 82 80 55 20

Fattening cattle 79 100 74 30

Sows and hogs 168 32 60 4

Fattening pigs 316 200 216 18

Adult sheep or goats 433 220 236 140 Poultry ‐ ‐ 3,152 30 In Andalusia, livestock farming or farming with crops and animals are around 10‐15% of farming. The most important are goats and sheep (6.5% UAA, PDR 2007‐2013). 3_15 Is the holding using Internet for one of the following activities…? (Table 55) Buying Selling

Yes No Yes No

ES 4.5% 95.5% 2.0% 98.0% Total 16.8% 82.5% 8.2% 90.9% The results show a low use of the internet regarding these activities. 3_10 How many employees does the agricultural holding have (excluding household members)? (Table 6‐9) Full time worker Part time worker

None Yes None Yes

ES 48% 52% 39% 61%

Yes ‐ Mean 1 2

3_11 How many of the employees are citizens of another country? (Table 47‐50)

Workers from EU countries Workers from non EU countries

Yes None Yes None

ES 83.6% 15.9% 95.5% 4.0%

Mean 10 4

Median 4 4

The existence of immigrant workers farmers is common in Andalusia, especially in specific periods of great labour demanding, such as olive collection or strawberry collection.

3_05 Does the agriculture holding do any other activity different from crop cultivation and animal rearing? (Table 28)

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SURVEY

Yes No

ES 3.0% 97.0%

In most cases, the agricultural activity in rural areas is not adapted to any other activity. 3_06 Does these activities include any of the following? (Table 29‐32) SURVEY Yes No Contract work 50.0% (6) 50.0% (6) Food processing ¬ manufacturing 33.3% (2) 66.7% (4 Retailing 0% 100% (6) Recreational services 16.7% (1) 83.3% (5)

The second most usual activity is contract work, following by food processing and finally other recreational services. 3_13 To whom do the holding sells its products? (Table 51‐53) SURVEY Yes No Processor 9.0% 91.0% Private wholesaler/retailer 27.9% 72.1% Cooperative 84.6% 15.4% Direct to final consumer 1.5% 98.5% Another farm 0.05% 99.5% Farmers normally sell their products to cooperatives, since this type of activity is very well implemented. In the second place stands private wholesaler or retailer, leaving in a far place the other sale activities.

4_01 Would someone in your household continue the farming activity as owner/entrepreneur in this holding? (Table 59‐60)

Yes No Other Do not

know Do not answer Baseline‐scenario (CSA) 72.1% 22.4% ‐ 5.5% ‐ No‐Cap scenario (CSA) 33.8% 56.7% ‐ 9.5% ‐

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The percentage of ‘abandoning’ is higher in Baseline scenario, which implies a relation between CAP payments and the possibility to continue the activity. 4_02 If 4_01=No, What is the main motivation? (Table 61‐62) Not profita ble enoug h Too many contraints High risk of farmin g No successo r within the family Other Do not kn ow Baseline‐scenario (CSA) 53.3% ‐ 2.2% 42.2% 2.2% ‐ No‐Cap scenario (CSA) 85.1% ‐ 0.9% 13.2% 0.9% ‐

In Baseline scenario, the main reason is the option “not profitable enough” (53.3%), followed by “no successor” (42.2%). On the other hand in No‐Cap scenario clearly is the option “not profitable enough” (85.1%).

4_20 Would you change who you sell your output to? (Table 97‐98)

Yes No Other Do not

know Do not answer Baseline‐scenario (CSA) 31.7% 48.1% ‐ 17.3% 2.9% No‐Cap scenario (CSA) 23.7% 52.6% ‐ 17.8% 5.9%

In both scenarios, there is a tendency not to change the buyer and being more emphasized in the No‐Cap scenario.

4_21 If 4_20=Yes, Which category of buyers do you expect would gain more importance (Table 99‐100)

Processor Private Cooperative Direct to final consume r Other far ms Do no t kn ow Do not ans wer Baseline‐ scenario (CSA) 77.3% 4.5% 12.1% 4.5% ‐ ‐ 1.5% No‐Cap scenario (CSA) 75.0% 3.1% 3.1% 6.3% ‐ ‐ 12.5% In both scenarios the results are with the option “processor”. 4_26 Would there be any of the following major innovations? (109‐116) Robotisation and/or Yes No Other Do not Do not