Determinant factors of high performing agricultural regions in the European Union

The agricultural sector is considered as one of the most strategic sectors in Europe. It supplies goods for the market and environmental services and biodiversity as public goods. Agricultural productivity differs across European regions. This can be explained by the disparity in the competitive conditions, which are influenced by spatial factors and policies. The main objective of this research is to assess the drivers of agricultural performance in the European regions, that is to say, to identify which factors make one region more productive than others. The question is not obvious given the debate on the role of farm structures, innovation policies and CAP support as sources of agricultural income. Regional data at NUTS 2 level were collected from several sources. Data related to farm structures, direct support to farmers (CAP expenditure), natural conditions of production (soil erosion), and public investment in R and D were obtained from Eurostat databases. Other data have been worked out by the author, such as those related to the excellence in academic agricultural research at the university level (National Taiwan Ranking) and the public innovation effort at the regional level in Rural Development Programs. Explaining agricultural performance requires a spatial and multidimensional approach that offers a qualitative and quantitative perspective of the pathways that promote agricultural productivity. The question refers to groups of conditions that can be combined to lead to higher agricultural incomes, measured as Gross Value Added per Annual Working Unit. Regional data, was analyzed by using the fuzzy set-Qualitative Comparative Analysis (fs-QCA) methodology that identifies necessary and sufficient conditions for a region to become highly productive. Our findings suggest that the methodology is useful for the qualitative assessment of regional competitive conditions. There is more than one pathway that leads to high agricultural performance. Fs-QCA analysis illustrates that farm structures is a relevant factor that leads regions to be high performing, especially where it is combined with R and D and innovation efforts. This study also highlights the key role of the technological dimension (R plus D plus i) as a condition included in several pathways for high productive regions. Agricultural R and D and public rural innovation effort appeared as relevant conditions that lead to high agricultural productivity, but combined with the presence of strong farm structures or high CAP support. CAP support also appeared as a condition for a high performing agricultural region. It has been included in a set of competitive pathways, (even in low innovative regions) but there are other pathways where CAP payments are not present. Moreover, the soil characteristic of the region seems to be relevant but not a constraining handicap for agricultural productivity if efforts to promote innovation in the agricultural sector are made.