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Using scenarios to examine Using scenarios to examine farmer knowledge networks for farmer knowledge networks for bovine TB bovine TB Damian Maye¹, Rhiannon Naylor², Gareth Enticott 3 & James Kirwan¹ ¹ Countryside and Community Research Institute, University of Gloucestershire ² Royal Agricultural University, Cirencester 3 School of Planning and Geography, Cardiff University RGS-IBG Annual Conference. RGRG Session: Rural Animal and Plant Health. RGS, London, 27 th -29 th August, 2014

Bovine TB & Farmer Knowledge Networks

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Presentation at the 2014 annual international conference of the Royal Geographical Society - Institute of British Geographers, held in London on August 27th-29th. The paper provided an analysis of farmer knowledge networks in relation to bovine TB and argues that understandings of farmers’ knowledge networks relating to animal disease control are weak. TB is used as a case study and scenario analysis to determine the networks that farmers would draw upon in particular situations. The research team developed four different scenarios to control TB in the future: a badger cull in hot spot areas, an oral badger vaccine, a cattle vaccine, and a range of measures. The findings confirmed the importance of certain so-called ‘influencers’, such as private vets and the NFU, as well as Defra. The influence of other farmers is also notable but the findings raise interesting questions about how farmers are influenced by their peers – typically more to compare practise / reactions than to obtain information. At the end of the paper these specific findings are related to more general questions about the merits of using scenarios and influence maps to inform TB and other complex policy areas and wider debates about ‘stakeholdership’ and ‘partnership’ governance.

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Page 1: Bovine TB & Farmer Knowledge Networks

Using scenarios to examine farmer Using scenarios to examine farmer knowledge networks for bovine TBknowledge networks for bovine TB

Damian Maye¹, Rhiannon Naylor², Gareth Enticott3 & James Kirwan¹

¹ Countryside and Community Research Institute, University of Gloucestershire

² Royal Agricultural University, Cirencester3 School of Planning and Geography, Cardiff University

RGS-IBG Annual Conference. RGRG Session: Rural Animal and Plant Health. RGS, London, 27th-29th August, 2014

Page 2: Bovine TB & Farmer Knowledge Networks

Context: new styles of animal Context: new styles of animal health governancehealth governance

• Knowledge co-production: interaction process between actors from different knowledge domains (Edelenbos et al., 2011).

• Neoliberalisation of animal health governance (Enticott et al., 2011; Maye et al., in press):– Bluetongue outbreak in 2008– Strategies of privatisation– ‘Cost & responsibility sharing’

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Context: bTB and farmer Context: bTB and farmer knowledge networksknowledge networks

• For bTB discourses of cost and responsibility sharing now popular.

• Partnership governance (Defra, 2005).• Disease responsibility (BVDP – 2008;

wildlife control companies – 2011).• Centrality of farmers to these new models.• Need to understand farmer knowledge

networks (first step).3

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Farmer knowledge networksFarmer knowledge networks• Farmers’ knowledge networks explored to

understand attitudes towards various issues (e.g. Sligo and Massey, 2007; Morris, 2010).

• Value of local knowledge (Enticott, 2011).Oreszczyn et al. (2010) GM crop study:• Communities of practice• Networks of practice• Boundaries, shared objects, boundary agents

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Case

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Case

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Scenarios & influence mappingScenarios & influence mapping

• To understand CoPs/NoPs need to examine how farmers view their own networks.

• Combine scenarios with influence maps.• Scenarios can elicit attitudes and beliefs

about situations (e.g. Quine et al., 2011); they are not forecasts / predictions but “plausible descriptions of possible (alternative) futures” (Quine, 2008).

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bTB scenariosbTB scenarios

• Scenario 1: Rolling out a national badger cull in high risk areas (2014).

• Scenario 2: Oral badger vaccine (2019).• Scenario 3: Cattle vaccination (2023).• Scenario 4: A range of measures (badger

cull, badger vaccination, cattle vaccination) are available (2025).

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Influence mapsInfluence maps

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Influence scoresInfluence scoresInfluencer Foreground Mid-ground Background TotalPrivate vet 125 31 13 169Other farmers 82 49 33 164Defra 69 36 32 137Farming press 38 60 35 133NFU 69 41 13 123AHVLA 45 36 28 109Family 76 9 4 89Public 30 21 16 67Wildlife groups 19 20 27 66Buyers 25 22 12 59Internet 22 23 14 59Farmer discussion groups 22 27 7 56Supermarkets 19 16 10 45Scientists 19 23 3 45Politicians 1 21 18 40Fera 14 14 4 32Natural England 12 11 8 31Non-farming friends 9 10 9 28Press 3 10 10 23CLA 7 6 0 13Gamekeeper 5 1 0 6EU 5 1 0 6Landlord 2 2 0 4Workers 3 0 0 3Marksman 2 1 0 3National trust 1 0 1 2Total 724 491 297 1512

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bTB scenarios & key influencersbTB scenarios & key influencers

Scenario 1 – national badger cull•Did not feel need to proactively seek information; first contact would be local NFU office; neighbouring farms – influential but not as a source of information:

“I would probably go along with it, especially if all my neighbours were doing it. I wouldn’t want to be the only one not doing it” (GT1087).

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bTB scenarios & key influencersbTB scenarios & key influencers

Scenario 3: cattle vaccination•Private vet most influential source; some farmers influenced by actions of farming neighbours but not all; drug companies not trusted.

“I think the vet would be quite important. They know about the data and things and injections and all that, so they are quite important...” (GT1023).

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Scenario differencesScenario differencesScenario

1 2 3 4 1 2 3 4 1 2 3 4

Influencer Foreground Mid-ground Background Total

Private vet 19 34 37 35 8 3 12 8 9 2 0 2 169

Other farmers 20 23 15 24 14 10 8 17 8 11 7 7 164

Defra 14 15 20 20 4 9 11 12 12 12 4 4 137

Farming press 7 13 10 8 13 11 15 21 10 13 6 6 133NFU 19 21 12 17 10 8 10 13 7 5 1 0 123

AHVLA 8 11 15 11 3 8 16 9 10 12 3 3 109

Family 22 19 17 18 1 3 1 4 0 1 2 1 89

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Influence totals: 147 186 192 199 80 103 140 168 104 101 53 39 1512

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ConclusionsConclusions• Influence networks can change depending

on the scenario.• Private vet - farmers’ most influential

contact; however, influence of peers is less clear (see also Heffernan et al., 2008).

• Tailor partnerships depending on strategy; key actors as future brokers.

• Need for localised knowledge production.• Plans for further data analysis. 13

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Thank you for your attention