A Total Factor Productivity Index for Scottish Agriculture 1973-2004

Thirtle et al (2003) have provided a Total Factor Productivity (TFP) index for UK agriculture. This note follows a similar methodology to construct a TFP index for Scottish agriculture beginning in 1973 and ending in 2004. Essentially, Scottish agricultural growth grew strongly during the 1970s but then fell to negative levels over the period 1984-2004. In comparison to the UK Index, Scotland has performed poorly and is only showing signs of a positive recovery from 2000 onwards.Productivity Analysis,

Technical Efficiency Estimates of Scottish Agriculture: Evidence from the dairy, sheep and cereals sector

Technical efficiency, the ratio of physical inputs to outputs, is a popular means of assessing agricultural performance. Benchmarking of these efficiencies is a fundamental tool for the farming industry. More sophisticated techniques have been developed recently which offer a greater degree of complexity for measuring technical efficiency. This paper adopts a parametric approach, referred to as stochastic production frontiers (SPF), to study three major sectors the Scottish agricultural economy, namely i) cereals, ii) dairy, and iii) sheep, over the period 1989 to 2004.Crop Production/Industries, Livestock Production/Industries, Productivity Analysis,

A Total Social Factor Productivity Index for the UK Food Chain Post-Farm Gate

The UK post-farm gate food chain comprises manufacturing, wholesaling , retailing and catering. Current turnover is around £250 billion per annum. Total factor productivity measures the ratio of inputs to outputs. However, most studies have only included the marketable inputs and outputs within the system. Following criticisms of the negative effects of the food chain this paper adopts a n index based approach to measuring Total Social Factor Productivity, which includes the major externalities within the food chain. Generally, whilst TFP growth rates are low over the period 1998-2002, these have reduced even further when negative externalities are included.Food Chain, Total Factor Productivity, Total Social Factor Productivity, Externalities, Industrial Organization, Productivity Analysis, Q56,

Measuring the Sustainability of the UK Food Chain

Recent policy interest has been directed at the sustainability of food industries, in particular the post-farm gate food chain. This comprises of manufacturing, wholesaling, retailing and catering. In order to measure sustainability Byerlee and Murgai (2001) have argued that productivity measures, alongside key indicators of resource quality trends, should be used to indicate sustainable growth. This paper adopts this approach by presenting Fisher indexes of both Total Factor Productivity (TFP) index and for prominent externalities emerging from the food chain over the period 1998 to 2002. TFP shows an average annual growth rate of –0.52% per annum. Input growth, in particular intermediate purchases, has outstripped output growth over the entirety of this period. In addition, major externalities of environmental and social costs have increased over this period. Consequently, both sets of indicators give a somewhat bleak assessment of the sustainability of the UK food chain.Total Factor Productivity, Externalities, Sustainable Growth, Agribusiness,

A Metafrontier Analysis of Technical Efficiency of Selected European Agricultures

Farm Management,

A Metafrontier Analysis of Technical Efficiency of Selected European Agricultures

Technical efficiency refers to the situation where it is impossible for a firm to produce, with the given know-how, (1) a larger output from the same inputs or (2) the same output with less of one or more inputs without increasing the amount of other inputs. In practice, the interest is on the relative position in terms of efficiency of a particular firm with respect to others. Therefore, technical efficiency is characterised by the relationship between observed production and some ideal or potential production (Greene, 1993). Although the beginning of the efficiency work can be traced to the 1950s (Farrell, 1957), there have been a growing interest on its use in benchmarking performance, predominantly as a means of identifying best practice and improving the efficiency of resource use within the agricultural industry (e.g., Defra 2004, SAC 2009). This paper deals with the estimation of technical efficiency for the agricultural sectors in several European countries and moreover, it aims to compare the efficiency amongst them using a metafrontier analysis. The use of this type of analysis is justified because a frontier, which represents the best available technology within a particular region/country cannot be strictly compared across other regions/countries, unless they operate under the same production set. The metafrontier analysis has been developed in a number of studies (Battese and Rao, 2002; Nkamleu et al., 2006; Chen and Song, 2006; O‟Donnell et al., 2008.) The metafrontier analysis in this paper, which uses data from the Farm Accountancy data Network (FADN), was focused on four farm types: two specialised farming types (i.e., specialist cereals, oilseed and protein crops and specialist dairying) and two more mixed farming sets (i.e., general field cropping and mixed farms), and was applied to a total of 11 countries namely Belgium, Denmark, France, Germany, Hungary, Ireland, Italy, Netherlands, Poland, Spain and the UK. For most of the countries the information was available from 1995 until 2007, excepting Hungary and Poland, for which it was available only since 2004. Also note that not all the farm types were available for all the countries. The structure of the paper is as follows: it starts presenting an overview of the metafrontier analysis used to compare technical efficiency amongst the European countries. It is followed by the empirical work, which comprises a description of the data used, the estimation and discussion of the results. Finally we present conclusions.Research and Development/Tech Change/Emerging Technologies,

A metafrontier approach to measuring technical efficiencies across the UK dairy sector

A regional approach is applied to measure technical efficiencies on dairy farms which employs the deterministic metafrontier approach. We construct six super regions for the UK, i.e. Eastern, Western, Northern England, Wales, Scotland and Northern Ireland. Data are collected through three different administrative systems, all be it under the same FADN guidance. We find for dairy farming comparative indicators of performance in all three data sets. The stochastic frontier approach is applied to construct 6 regional frontiers and a pooled (UK) dataset for comparison. A likelihood ratio test rejects the null hypothesis that these regions operate under a common frontier which may indicate bias in previous attempts to measure dairying efficiency at the country level. Mean technical efficiencies are high for the period 2005 to 2008, though there is some indication that little technical progress has occurred since decoupling of CAP payments from production in all regions. The metafrontier presents estimates against a common technology and mean scores range from below 0.50 for the English regions and Northern Ireland, 0.52 for Wales and 0.56 for Scotland. This paper promotes the application of the deterministic metafrontier approach for similar sub-country studies.Stochastic Production Frontiers, Metafrontiers, UK Farm Account Data, Dairy farming., Agricultural and Food Policy, Q12, D24, C23, C51,

Community environment, cognitive impairment and dementia in later life: results from the Cognitive Function and Ageing Study

Background: Few studies have investigated the impact of the community environment, as distinct from area deprivation, on cognition in later life. This study explores cross-sectional associations between cognitive impairment and dementia and environmental features at the community level in older people. Method: The postcodes of the 2424 participants in the year-10 interview of the Cognitive Function and Ageing Study in England were mapped into small area level geographical units (Lower-layer Super Output Areas) and linked to environmental data in government statistics. Multilevel logistic regression was conducted to investigate associations between cognitive impairment (defined as MMSE3 in GMS-AGECAT) and community level measurements including area deprivation, natural environment, land use mix and crime. Sensitivity analyses tested the impact of people moving residence within the last two years. Results: Higher levels of area deprivation and crime were not significantly associated with cognitive impairment and dementia after accounting for individual level factors. Living in areas with high land use mix was significantly associated with a nearly 60% reduced odds of dementia (OR: 0.4; 95% CI: 0.2, 0.8) after adjusting for individual level factors and area deprivation, but there was no linear trend for cognitive impairment. Increased odds of dementia (OR: 2.2, 95% CI: 1.2, 4.2) and cognitive impairment (OR: 1.4, 95% CI: 1.0, 2.0) were found in the highest quartile of natural environment availability. Findings were robust to exclusion of the recently relocated. Conclusion: Features of land use have complex associations with cognitive impairment and dementia. Further investigations should focus on environmental influences on cognition to inform health and social policies

Marginal abatement cost curves for UK agriculture, forestry, land-use and land-use change sector out to 2022

Greenhouse gas emissions from agriculture, land use, land use change and forestry (ALULUCF) are a significant percentage of UK industrial emissions. The UK Government is committed to ambitious targets for reducing emissions and all significant industrial sources are coming under increasing scrutiny. The task of allocating shares of future reductions falls to the newly appointed Committee on Climate Change (CCC), which needs to consider efficient mitigation potential across a range of sectors. Marginal abatement cost curves are derived for a range of mitigation measures in the agriculture and forestry sectors over a range of adoption scenarios and for the years 2012, 2017 and 2022. The results indicate that in 2022 around 6.36 MtCO2e could be abated at negative or zero cost. Further, in same year over 17% of agricultural GHG emissions (7.85MtCO2e) could be abated at a cost of less than the 2022 Shadow Price of Carbon (£34tCO2e).Environmental Economics and Policy,