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Publication - Research Publication

Impact of diversity of ownership scale on social, economic and environmental outcomes

Published: 29 Jul 2016
Part of:

Report on the impact of diversity of ownership on the socioeconomic outcomes for rural areas.

119 page PDF


119 page PDF


Impact of diversity of ownership scale on social, economic and environmental outcomes

119 page PDF



In all case studies, the focus group participants felt that the local environment and landscapes had not changed considerably, in nature, over the last 50 years, and that where it had (2b, 3a) this was due to forestry planting or housing development (1a and 1b). It was considered in case study areas 1 and 2 (where there is better-quality farmland) that the long-term intensification of agriculture has resulted in farm landscape changes through changed cropping patterns, and emphasis on silage production in livestock areas. The recent de-coupling of agricultural payments, increased support for environmental measures, and greater (or threatened) environmental regulation has increased land mangers' recognition of the potential pollution effects from their activities and is reported to have led to changing local land management practices. At a landscape scale, removal of stane dykes (1b), the erection of feed stores and processing buildings (2a), alongside new-build housing (all), were noted as having impacts. In 3a some respondents felt that that the local community had little say in local land use decisions due to the control that the estate had over large amounts of the locality.

Public Benefits from Environment

Figure 19 Agricultural land use 1982-2012


Using data from the June Agricultural Census ( JAC) [45] , Figure 19 shows how agricultural land is used in each of the case studies. This reveals broadly similar land uses within the case study pairs. In case study pair 1, about 30% to 40% of the farmland is used for cropping, with 40% to 45% used for pasture and a relatively small proportion of rough grazing (with some recent planting of farm woodland in the rough grazing area). In case study pair 2, there is very little cropping land with about 45% to 50% of farmland used as pasture and 50% to 60% as rough grazing. Paired case study 3 is largely dominated by rough grazing, with only small amounts of pasture and cropping land.

Feedback from the focus groups revealed few changes to farming systems in the mountainous case studies (case study pair 3), where the focus remains on beef and sheep production, albeit under more mechanised systems than in the past. However, in paired case study 1 there have been more significant changes, particularly to the types of crops grown (e.g. moves out of potatoes and turnips, winter barley introduced) and changed emphasis towards crops from cattle as a result of the introduction of CAP in the 1970s and the switch of dairy farms to beef. In case study pair 2, there has also been some switching from dairy to beef whilst the remaining dairy farms have significantly specialised and intensified, with greater stocking densities: on some farms indoor feeding means increased silage cutting, whilst on others grazing-based systems has required on-farm infrastructure investment.

Analysis of agri-environmental payments undertaken for the Ex-post Evaluation of the Scotland Rural Development Programme (2000-2006) was reassessed to reveal agri-environmental scheme (Environmentally Sensitive Area ( ESA) Scheme, the Farm Woodland Premium Scheme and the Rural Stewardship Scheme) uptake [46] in each case study. [47] Table 9 shows the average annual number of payments and amount paid over the life of the Scotland Rural Development Programme (2000-2006) for various schemes.

Table 9 Agri-environment scheme claimants and payments, 2000-2006

Case Study Annual Average 2000-2006
Environmentally Sensitive Area Farm Woodland Premium Scheme Rural Stewardship Scheme
Annual Total Claimants Annual Total Claimants Annual Total Claimants
1a - Unfragmented N/A N/A £27,550 3.4
1b - Fragmented N/A N/A £717 1.7 £6,114 3.0
2a - Unfragmented £39,274 8.6 £24,741 9.0
2b - Fragmented £16,348 2.2 £1,821 2.2 £9,456 3.7
3a - Unfragmented £72,452 13.4 £10,249 5.5 £46,487 6.2
3b - Fragmented £85,939 19.2 £5,474 5.5 £11,610 4.5

Focus group participants suggested that farming has become much more environmentally aware than in the past, and whilst participation in agri-environmental schemes can benefit farm profitability, farmers increasingly understand the intrinsic (i.e. non- monetary) importance of protecting / enhancing habitats and species. This drive, and changed societal interests in habitat and species protection, has meant that there are now more people engaged in land-based work with an environmental focus (3b) such as on environmental reserves, ranger services, etc.

Healthy environment

Table 10 shows the number and area of key environmental designations within each case study and these environmental designations may restrict land management practices to some extent.

As a relatively intensive agricultural area in case study pair 1, it is perhaps unsurprising that very little land is so designated. In case study 2a there is a higher proportion of the land (31%) designated as a Special Site of Scientific Interest ( SSSI) and Special Protection Area ( SPA) than in its paired case study 2b (4% SSSI and only 2.5% Special Area of Conservation ( SAC)) due, in part, to the former having more upland (rough grazing) habitats. Both case studies 3a and 3b have a number of environmental designations, e.g. 21 SSSIs covering nearly 20% of 3a and 8 SSSIs covering 15% of the land in 3b. Without further investigation into the condition of individual designated areas, it is difficult to make conclusions about the impact of land ownership scale, fragmentation, or historic land management practices on environmental designations (and hence condition) of the land in the case studies.

Table 10 Proportion of case study covered by selected environmental designations, 2014 [48]

Case Study Designation
1a- Unfragmented 0.4% (1) - -
1b - Fragmented - - -
2a - Unfragmented 31.5% (3) - 31.3% (2)
2b - Fragmented 4.4% (1) 2.5% (3) -
3a - Unfragmented 19.2%(21) 22.2%(5) 39.1%(3)
3b - Fragmented 15.1%(8) 22.7% (2) -

Using SEPA's interactive River Basin Management Planning website [49] , water quality was assessed for each case study, as shown in Table 11. It should be noted that water body condition is influenced by a number of factors, and that water abstractions and flow regulations for renewable energy companies have an influence on status, particularly in case study pair 3, whereas in the more intensively farmed case study pairs 1 and 2 diffuse pollution is the primary cause of a 'less than good' water body classification. With so few observations, it is difficult to derive any conclusions, but in both case study areas 1 and 2 the fragmented case studies have marginally higher water quality.

Table 11 Water bodies classified as less than good, 2015

Loch River
Case Study Less than good Total Less than good Total
1a - Unfragmented 1 1 6 6
1b - Fragmented 0 0 2 3
2a - Unfragmented 3 3 6 8
2b - Fragmented 2 2 4 8
3a - Unfragmented 1 6 12 28
3b - Fragmented 0 3 12 21

While the development investment in case studies was generally considered by focus groups as positive (e.g. creating jobs, enhancing the local built environment), concerns were raised about potential new developments having a negative environmental impact (3b, 1a, 1b), and, in particular, issues around sewerage service provision had been a restricting factor for some housing developments (1a, 1b). The sewerage (and industrial effluent) problem in 1b had led to pollution problems in a local loch, and to regulatory restrictions until the problem was resolved.

Carbon Footprint

Data from the National Forest Inventory Scotland shows the extent of woodland coverage and the proportion of young stock in each case study. Table 12 shows that apart from case study pair 1 each of the paired areas have very similar levels of woodland coverage, perhaps reflecting regional similarities. It is particularly noticeable that nearly a quarter of all the woodland in 3a is under 15 years of age, and this reflects a major planting regime by one of the major landowners in recent years.

Table 12 Total woodland area, and estimated recent woodland planting, 2014 [50]

Case Study % Land under woodland % Woodland under 15 years old
1a - Unfragmented 26.0% 2.9%
1b- Fragmented 17.4% 6.0%
2a - Unfragmented 17.0% 8.8%
2b - Fragmented 18.5% 8.8%
3a - Unfragmented 12.6% 22.9%
3b- Fragmented 13.2% 16.7%

Focus group feedback reported that timber extraction in woodlands has become increasingly mechanised, meaning fewer workers (chainsaw squads, planters, etc.) and also heavier machinery on afforested land, and greater requirement for access roads, turning points, etc. that can affect the landscape and create run-off. The focus groups reported that areas under farm tenancies were less likely to have woodland plantations or areas under woodland cover. It was felt that where estates remain there is greater woodland cover due to an emphasis on commercial forestry, native/mixed woodlands for sporting interests, and landscaped gardens / policies.

A noticeable recent environmental change in many case studies has been the landscape impacts of wind turbines and associated infrastructure, driven by Government incentives. For some case study residents, the renewables revolution is seen as negative due to local landscape changes ( NIMBYism [51] ) although for others there is no issue, particularly in places such as 3b where the community receives an annual payment from a nearby windfarm development.

The increase in traffic (particularly in 1a, 1b and 2a) was considered to have been negative for the environment and local residents, although where the village has been bypassed (1b) this was seen as an improvement through reduced congestion and traffic accidents. The demise of public transport facilities and increased use of cars as a means of transport in the case studies was considered to be negative from a carbon perspective, but lack of public transport services were seen by most case studies as a barrier to changing local behaviours and attitudes.

Main drivers of change

A Multi Criteria Analysis ( MCA) exercise was utilised to elicit conclusions from the fieldwork participants as to the main drivers of change in their communities that affect the chosen outcomes. Identifying the effects that the identified drivers of change had on each of the individual "ingredients" for a healthy and resilient community through the MCA exercise was considered too complex for many participants. Nonetheless, the exercise did evoke discussion and conclusions as to the five main drivers of change influencing local outcomes in each case study. These key drivers, shown in Table 13, were classified [52] as being: directly related to land ownership; indirectly related to land ownership; discrete one-off events; or background societal change effects.

Table 13 Key influencing factors in achieving local outcomes identified by case study participants using multi criteria analysis.

Key Drivers of Change Community Land Managers
Unfragmented Fragmented Unfragmented Fragmented
Agriculture- larger units / amalgamation 1a, 2a 2b 3a
Fragmentation of land ownership

2b, 3b
Land tenure changes 3a 1b 1a,2a 1b
Housing - second home ownership growth
2b, 3b
Housing development - village growth
Tourism - Landmark investment 3a

Transport Infrastructure - Landmark event 2a

Agricultural change - mechanisation 2a 2b 1a, 2a, 3a 1b, 2b
Common Agricultural Policy

1a, 2a 2b
Centralisation of services
1b, 3b
Community - changing aspirations 1a

Community vibrancy decline
2b 2a
Demography- ageing / diversity / migration 1a, 2a 2b, 3b
Demography - population mobility 1a

Demography - population growth 1a 1b
Farm diversification increase

Farm profitability decrease

2b, 3b
Farm regulations

Infrastructure decline - transport 2a, 3a

Infrastructure development - transport/electricity 2a, 3a 1b 1a
Modern transport

3a 1b
Regional economic development 1a 1b
Rural business decline
1b, 2b 1a, 2a
Tourism - diversification / growth 3a 3b 3a
Direct landownership (e.g. tenure) Discrete local events (e.g. business investment)
Indirect landownership (e.g. housing) Background trends (e.g. mechanisation)

Table 13 highlights that direct land ownership issues were not frequently raised by fieldwork participants as a driver of change. In particular, land fragmentation was only mentioned in 2b and 3b (both fragmented) with changes in land tenure arrangements and amalgamation of units (including all of the unfragmented case studies, 1a, 2a, 3a) being seen as a driver of change. Indirect land ownership drivers, through second home ownership (remote: 2b, 3b) or housing development (accessible: 1b), were prevalent as perceived drivers of change in each of the fragmented case studies. There were very few discrete local events that were considered important drivers of change, but, where these had occurred, the impacts were considered to be quite significant. The majority of the drivers of change identified were therefore general societal changes, such as mechanisation of farming and forestry, CAP support, demographic change, and infrastructural developments. It was particularly interesting that the community focus groups did not consider the background farm-level drivers (e.g. CAP, farm profitability) as important drivers of change in the wider community, suggesting that they now consider agriculture to be peripheral to community success.


Email: Graeme Beale,