Suchergebnisse

The roles of government and the private sector in the provision of food safety in Russia are explored. The establishment and operation of a private food‐safety company are described and its competitiveness is analysed in the context of the evolving food‐regulatory, policy and economic environments found in a transition economy. Business success is attributed to operational efficiency, customer service and flexible strategic management. Potential threats to sustainability from inadequate regulation are identified. The creation of a viable business is viewed as an exemplar in foreign technical assistance to a country in transition to a market economy.

ObjectivesTo create an anonymised data resource of farm households in the UK to generate evidence to support policy development, implementation and evaluation; improve understanding of farm family socio-economic characteristics; and assist all stakeholders interested in understanding about the health and well-being, resilience and prosperity and spatial properties of farming communities.
ApproachThe AD|ARC resource is being created in each UK country. The core dataset comprises of linking together existing agricultural datasets (farm business activity data, Rural Payments data) the Inter Departmental Business Register and the Population Census. This hub dataset is linked to the health and education data available in each country. The research is being conducted in a separate but coordinated way across the UK, harmonising the data to allow aggregation to a UK level where possible.
Our research objectives are in four broad categories of Socio-Demographics, Health and Well-being, Prosperity and Resilience and Environment and Place.
ResultsThe presentation will report on initial findings from the socio-demographic research theme where researchers have investigated the composition and characteristics of farming households both in terms of the farm business (e.g. farm type, turnover, active farm diversification) and individual characteristics (e.g. age, educational attainment, off-farm income streams). From this analysis it will be possible to determine the degree of homogeneity that exists within the farming population for key indicators, the degree of homogeneity that exists amongst the broader group of farm households members for key indicators, and the extent to which farmers and farm household members conform to the socio-economic characteristics of the non-farming rural population. Results will be assessed for policy relevance and areas of ongoing investigation will be highlighted.
ConclusionAgriculture is currently facing a range of challenges with little known about the impact on farmers and farming households. AD|ARC introduces a new, powerful and versatile resource that will inform debate and policy decision-makers, potentially leading to better outcomes on a range of issues relevant to farmers and farming communities.

We estimate the structural effects, costs and potential efficiency gains that might arise from the introduction of an Early Retirement Scheme for farmers in Northern Ireland using data from the Farm Business Survey and a separate survey of 350 farmers aged between 50 and 65. Modelling results suggest that farm scale is a significant determinant of profit per hectare but that operator age is not. The economic gains from releasing land through a Scheme were conditional on transfers bringing about significant farm expansion and changes in land use. When these conditions were satisfied pensions payments of only about one-third the statutory maximum could be justified. Survey responses indicated that participation in the Scheme would bring forward farmers' retirement age by an average of four years. Moreover, 'deadweight' payments would equate to about 23 per cent of potential total expenditure. Overall, the economic case for the introduction of an Early Retirement Scheme to Northern Ireland is judged to be weak.

ObjectivesTo create an anonymised research-ready data resource of farm households in the UK to generate evidence to support policy development, implementation and evaluation; improve understanding of farm family socio-economic characteristics; and assist stakeholders interested in understanding the health and well-being, resilience and prosperity and spatial properties of farming communities.
MethodsThe ADARC research-ready data resource is being created in each nation of the UK. Each core dataset links agricultural datasets with individual- and household-level population data from the Census 2011. The farm business data is drawn from a number of sources (the Inter Departmental Business Register, EU Farm Structure Survey 2010 and Rural Payments data), which presented many data preparation challenges when linking to the Census of Population. Each core dataset will be linked to the health and education data available for that nation. Where possible, the ADARC datasets have been harmonised to allow federated querying across the UK.
ResultsThe ADARC core datasets are complete in Wales and near completion in England and Scotland. Work is also well advanced in Northern Ireland. To the best of our knowledge, these are the first datasets linking agricultural data to individual- and household-level data at a population level. We will report the challenges experienced in linking farm business data with data at the household and individual level. This will include a description of the data preparation steps, the challenges encountered and solutions utilised at each stage of building this complex dataset from numerous, very different 'parent' datasets.
The structure and content of the core datasets will be presented as well as the potential benefits to researchers investigating the individual, household and community dimensions of agricultural research.
ConclusionAgriculture is currently facing a range of challenges with little known about the impact on farmers and farming households. ADARC introduces a new, powerful and versatile resource that will help inform debate and potentially lead to better outcomes on a range of issues relevant to farmers and farming communities.

ObjectiveTo examine the characteristics of farming households that have no off-farm income, and how they compare to farming households containing at least one person employed in a non-farming occupation.
ApproachThis research uses the AD|ARC dataset for Wales held in the SAIL databank at Swansea University. This Research Ready Dataset, created by the research team, is comprised of population census data and existing agricultural datasets.
Individuals were split into two groups, those in households where all individuals were primarily employed in farming or unemployed, and those in households where at least one individual was in a non-farming occupation.
ResultsComparing these groups, our findings show:

There are 8135 farming households with at least one person in a non-farming occupation, and 5415 households with no off-farm income.
Households with off-farm income are on average 65% larger than households with no off-farm income.
33% of farming only households consist of a single person; 73% of these farmers are male, and the average age is 62.
The average age for farming-only households is 45, and the average age of households with non-farming occupations is 36.
We will present detailed findings on the socio-economic and farm business characteristics of these groups.

ConclusionUnderstanding differences between households with and without off-farm income provides insights on farm types and households most impacted by changes in agricultural policies and subsidies. This can inform policy design and help authorities deliver commitments on just transitions in a targeted and efficient manner.

The Administrative Data | Agricultural Research Collection (AD|ARC) is a complex research project involving four nations, a mixture of business-level, household-level and person-level data, and diverse partners, stakeholders and audiences. The project is creating independent but comparable Research Ready Datasets, using linked data for farm businesses, farmers and farm families in the four UK nations. The engagement and communications workstream is crucial to build trust, inform the programme of work, support and enhance scientific objectives, and deliver high-quality, relevant findings that meet stakeholder and funder expectations.
The project deployed a bespoke engagement and communications framework to meet these objectives. This presentation shares key elements of AD|ARC project's engagement and communications framework and strategy:

Multi-region Approach: Balancing the need for a harmonised, federated resource with respecting regional differences in data, trusted research environments, and stakeholder perspectives.
Transparency and Trust: Engaging diverse stakeholders, including farmers, researchers, and policymakers, to build trust, support and legitimacy.
Collaborative Decision-Making: Interacting with diverse partners, (data owners, researchers, research subject representatives) to refine research questions.
Outreach and Dissemination: A multi-method, iterative approach to raise awareness, share and promote the project.

Results are demonstrated by success in being selected as an ADR UK Flagship Dataset; integrating diverse perspectives into the creation of comparable Research Ready Datasets; consensus on a research programme; useful learning which is incorporated into strategy; and actionable evidence to inform policymaking.
The presentation will reflect on lessons learned, drawing out implications for research and public engagement that could apply to any complex research project.

ObjectiveTo understand the socio-demographic characteristics and structure of farming households in Wales who receive subsidies compared to non-farming rural households.
Approach We utilised the AD|ARC dataset, a comprehensive resource we created within the SAIL databank by linking de-identified records of farms receiving farming subsidies in Wales with socio-demographic information from the Census 2011. A control group was created by matching each farming household with up to three households in the 2011 Census from a similar geographical area that did not contain a farmer or household member in receipt of farming subsidies. We used Census 2011 variables to compare household size, family structure and age for 18,135 farming households with a control group of 53,365 rural non-farming households.
Results We found little difference between the age and gender distribution between cohorts, however we found significant differences in the household structure. Farming households had a larger average size compared to non-farming households. Single-occupancy farming households had a significantly higher proportion of males, farming households were more likely to have couples with children and a higher percentage of these couples were married compared to non-farming households.
Implications The agriculture sector in Wales is facing a range of challenges linked to social, economic, and environmental sustainability as it establishes a new direction after UK withdrawal from the EU. Understanding the sociodemographic characteristics of farming communities is essential for developing effective policies and interventions that address the specific needs and challenges faced by these groups.

Objective To investigate highest level of educational attainment in farming households compared to rural non-farming households in Wales.
Approach We used the AD|ARC dataset of households in Wales receiving farming subsidies in 2010.
We included farming households containing at least one member reporting an Agricultural Main Occupation (AMO) and households where all members reported Non-Agricultural Main Occupations (Non-AMO) but farming occurs.
Utilising Census 2011 variables relating to age, gender, education and occupation we conducted comparative analyses of educational attainment within AMO, Non-AMO and non-farming rural households at both an individual and household level.
ResultsFindings will be presented comparing the educational attainment for AMO individuals with matched individuals within Non-AMO and non-farming rural households. At the household level, we calculated the highest level of education attained within each household and will present results comparing the highest household level of education between each group. We will present detailed results regarding the structure and educational attainment within these households.
Full results and findings will be presented at the conference but are currently subject to information governance restrictions.
ConclusionOne indicator of resilience amongst farmers and farm households is ability to respond to economic and non-economic pressures by adopting new practices and techniques or reducing reliance on farming via diversification or off-farm employment. As educational attainment is closely correlated to acquisition of skills and is a gateway to employment, understanding relative and absolute levels of attainment is important in identifying need and devising policies which support agricultural and rural communities.