Unveiling innovation imperatives in agriculture: A data-driven framework for identifying latent needs and regional priorities

Abstract

Innovation within a company, sector, or territorial context can arise from identifying specific problems or needs. Performance analysis provides valuable insights, potentially highlighting areas where innovation may be necessary. This paper outlines a methodology designed to identify latent innovation needs at regional, sectoral, and dimensional levels. This methodology is based on critical issues emerging from comparing economic, environmental, and social sustainability indicators of farms. An iterative approach is used for this purpose. Mixed matching algorithms are employed to form the comparison groups, while G-computation is used to calculate the differences. Logistic regression is adopted to calculate the propensity scores, and several generalized linear models are used to estimate the impact of regional localization. Hypothesis tests are then conducted to confirm the statistical significance of the impacts, from which criticality levels are derived. This methodology is applied to data collected by the Italian Farm Accountancy Data Network, which includes over 64,000 observations from two consecutive three-year periods (2016-2018 and 2019-2021). The results highlight the existence of widespread critical issues in Italian agriculture. These problems primarily concern the efficient use of water, whose costs, relative to revenue, exceed, on average, around 40 %, and greenhouse horticulture, which shows an increased criticality level in terms of sustainability of over 50 %. From a regional perspective, Puglia in Southern Italy exhibits the most evident critical issues, with an average criticality level of 30 % that has increased over time. The emerging criticalities are analyzed in relation to the interventions introduced by the 2023-2027 Common Agricultural Policy to verify their coherence and identify possible innovative actions that can be implemented.