Organic Agriculture

, Volume 9, Issue 3, pp 295–303 | Cite as

Estimating missing data for organic farming by multiple imputation: the case of organic fruit yields in Italy

  • Francesco SolfanelliEmail author
  • Danilo Gambelli
  • Daniela Vairo
  • Raffaele Zanoli


The availability of timely and good-quality data on the organic farming sector is a crucial factor for the development of the organic food market. While data on hectares and farms are now widely available in Europe, data on organic yields are still relatively sparsely reported by official statistical sources for most European countries, including Italy. Information on organic yields is crucial to determine the volumes of organic production and supply. Issues such as the potential of organic farming for feeding the world, the understanding of the optimal conditions for conversion and the appropriate policy measures for supporting the organic sector are all dependent on the knowledge of organic productivity. In this study, we show how a statistical method known as multiple imputation can contribute to the improvement of the availability of organic data, through systematic exploitation of data from different sources. We apply the method to estimate missing data on organic fruit crop yields for the central regions of Italy, based on data from official national statistics and expert assessments. The results illustrate the advantages and limitations of such methods for estimating missing data on organic crops.


Organic market data Organic yields Missing data Multiple imputation 



This study was undertaken as part of the research project ‘Data network for better European Organic Market Information’ (OrganicDataNetwork). This project received funding from the European Union Seventh Framework Programme for Research, Technological Development and Demonstration, under grant agreement no. 289376. The opinions expressed in this contribution are those of the authors and do not necessarily represent the views of the European Commission.


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© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Università Politecnica delle MarcheAnconaItaly

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