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AbstractStatistical matching is progressively emerging as a straightforward approach to data integration. This method of increasing importance and interest is useful to address the unsolved challenges posed by data shortage as well as the several opportunities occurring in the present data flood era. This paper offers an exhaustive review of the methodology from its early beginnings up to the most recent developments, considering also the most relevant applications. The links that statistical matching has with other integration methods are discussed, analysing how a 50-year-old method has been only recently proposed under a consistent but (yet) incomplete framework. Strengths and weaknesses of statistical matching are compared, considering different data features and sample representativeness frameworks, also, given future research ideas, always keeping an eye on uncertainty, the key problem to which statistical matching tries to answer.

In light of the ambitions of the European Union (EU) to achieve an ecological transition of its agricultural sector it is crucial to assess and continuously monitor (i) the uptake of main ecological approaches by farms and (ii) associated effects on farm performance, considering all sustainability dimensions (economic, environmental, social) jointly. Given these needs, in the present deliverable D5.1 of the LIFT project, we develop a novel indicator system, which combines the LIFT farm typology and farm performance data, covering all sustainability dimensions. The approach compares performance of farms in five ecological groups (referred to as ecological farming approaches or ecological farming systems) from the LIFT farm typology (Conservation Agriculture, Low-Input farming, Integrated/Circular farming, Organic farming, Agroecology) as well as possible combinations of these groups with a less ecological group, referred to as Standard farming. This allows us to depict whether ecological farms perform differently or have different trade-offs and synergies than standard farms. Based on this system, we carry out a farm sustainability performance assessment with the two main data sources in the LIFT project, namely Farm Accountancy Data Network (FADN) data and data from the LIFT large-scale farmer survey, covering main farm types present in the European Union (EU) in several case study regions/countries. Additionally, we present in-depth analyses of further specific aspects, namely (i) the extension of the developed indicator framework to bio-economic models, (ii) the integration of the consumption and provision of ecosystem services into the developed indicator system through composite agri-environmental performance (AEP) indicators, derived from the body of secondary literature and region-specific stakeholder input, and (iii) working conditions and employment on farms in the context of an ecological transition. Overall, our results show the importance of considering trade-offs and synergies both within and between ...

This document presents the results of Task 3.2 (farm technical-economic performance) in workpackage (WP) 3 (farm performance of ecological agriculture) of the LIFT project. The overall aim of Task 3.2 is to assess and compare technical-economic farm performance across the European Union (EU) depending on the degree of ecological approaches adopted by farms and analyse drivers, affecting their performance. This requires an approach, which allows to consider regional specifics, while still allowing comparisons between different regions and countries. The deliverable thus consists of several academic papers, focussing on a range of different case studies, applying a wide range of methods, which can most generally be divided into empirical econometric approaches and bio-economic models. At the same time, all case studies follow a similar structure and include some common elements in terms of the applied methods, in particular a set of common indicators of technical-economic farm performance was implemented in several papers. Various approaches to differentiate farms according to the degree of ecological approaches adopted were explored, including the LIFT farm typology developed in WP1 and other strategies. Overall, our results show that the wide variety of farm types and biophysical, socio-economic and political framework conditions present in the EU matter: results of comparing technical-economic farm performance depending on the degree of ecological approaches adopted, as well as with respect to drivers of farm technical-economic performance, are heterogenous and vary between the different analyses. Therefore, this heterogeneity needs to be considered by policy makers and can most likely best be addressed by providing a policy framework, which provides the necessary flexibility to adjust measures to region-specific framework conditions in order to foster economic viability of farms in the context of an ecological transition of EU agriculture. Building on the results of this deliverable and the other deliverables ...