• Situation and Outlook in Agriculture 2008/09

      Breen, J.; Connolly, Liam; Donnellan, Trevor; Hanrahan, Kevin; Hennessy, Thia; Kinsella, Anne; Martin, Michael; Ryan, Michael; Thorne, Fiona (Teagasc Rural Economy Research Centre, 2008-12)
      CONTENTS: (1)Farm Incomes 2007; (2) Investment in Agriculture 2008/09: Dairying, Cattle, Sheep, Pigs, Tillage, Forestry
    • Teagasc National Farm Survey 2016 Estimates

      Dillon, Emma; Moran, Brian; Donnellan, Trevor (Teagasc, 2017-07-26)
      Background Notes: The National Farm Survey (NFS) has been conducted by Teagasc on an annual basis since 1972. The survey is operated as part of the Farm Accountancy Data Network of the EU and fulfils Ireland’s statutory obligation to provide data on farm output, costs and income to the European Commission. A random, nationally representative sample is selected annually in conjunction with the Central Statistics Office (CSO). Each farm is assigned a weighting factor so that the results of the survey are representative of the national population of farms. These preliminary estimates are based on a sub sample of 805 farms which represents 83,377 farms nationally. Farms are assigned to six farm systems on the basis of farm gross output, as calculated on a standard output basis. Standard output measures are applied to each animal and crop output on the farm and only farms with a standard output of €8,000 or more, the equivalent of 6 dairy cows, 6 hectares of wheat or 14 suckler cows, are included in the sample. Farms are then classified as one of the six farm systems on the basis of the main outputs of the farm. Farms falling into the Pigs and Poultry System are not included in the survey, due to the inability to obtain a representative sample of these systems. Due to the small number of farms falling into the Mixed Livestock system these farms are not reported here.
    • Teagasc National Farm Survey Preliminary Estimates 2016

      Dillon, Emma; Moran, Brian (Teagasc, 2017-05-31)
      This presentation provides an overview of the preliminary results of the National Farm Survey for 2016
    • Work-related musculoskeletal disorders among Irish farm operators

      Osborne, Aoife; Blake, Catherine; Meredith, David; Kinsella, Anne; Phelan, James; McNamara, John G.; Cunningham, Caitriona; Health and Safety Authority, Ireland; Teagasc (Wiley Periodicals Inc., 10/07/2012)
      Background- To establish prevalence, risk factors and impact of work-related musculoskeletal disorders (WMSDs) among farmers in Ireland. Methods- In summer 2009, a questionnaire was appended to the Teagasc (Irish Agricultural and Food Development Authority) National Farm Survey (n=1110) to obtain data on the prevalence, risk factors and impact of WMSDs amongst farm operators in Ireland. Data were collected by trained recorders and analyzed using chi-square tests, t-tests, Mann-Whitney tests and binary logistic regression. Results- The prevalence of WMSDs in the previous year was 9.4% (n=103), with the most commonly affected body region being the low back 31% (n=32). Nearly 60% (n=57) of farmers reported missing at least a full day’s work as a consequence of their WMSD. Personal factors evaluated using bivariate regression analysis, were found not to influence whether or not a farmer experienced a WMSD. However, work-related factors such as larger European Size Units (ESUs) (OR=1.007, CI=1.002-1.012), greater number of hectares farmed (OR=2.50, CI=1.208-4.920), higher income (OR=1.859, CI=1.088-3.177), dairy enterprise (OR=1.734, CI=1.081-2.781), and working on a fulltime farm (OR=2.156, CI=1.399-3.321) increased the likelihood of experiencing a WMSD. The variable ‘fulltime farm’ which was associated with a higher labour unit requirement to operate the farm, was the only factor found to independently predict WMSDs in the multivariate regression analyses. Conclusions- This study suggests that the prevalence of WMSDs can be reduced by the application of improved farm management practices. A more detailed examination of the risk factors associated with WMSDs is required to establish causality and hence, effective interventions.