The Adoption of 4.0 Agriculture for Wine Production in Order to Improve Efficiency, Sustainability and Competitiveness

The imperative to feed a global population of approximately 10 billion people within the next 30 years, amid challenges like resource scarcity and environmental concerns, has catalysed the emergence of Agriculture 4.0. This paradigm shift involves integrating emerging technologies such as robotics, artificial intelligence, and the Internet of Things into traditional agri-food systems, and precision agriculture technologies. In the wine industry, deeply rooted in tradition, Agriculture 4.0 presents a promising solution to enhance efficiency, sustainability, and competitiveness. By leveraging data-driven decision-making, automation, and precision agriculture techniques, vineyard management, grape cultivation, and wine production could undergo a fundamental transformation. The thesis explores the multifaceted aspects of Agriculture 4.0 adoption, exploring technologies, strategies, and case studies to assess its impact on efficiency. Additionally, it addresses challenges and barriers hindering the widespread adoption of these technologies. However, the adoption of precision viticulture, a crucial component of Agriculture 4.0, introduces a differentiated management approach to vineyards. This approach aims to enhance efficiency, quality, and sustainability by utilizing tools such as georeferencing, remote sensing, and wireless sensor networks. Despite the increasing prevalence of Industry 4.0 technologies, a notable research gap exists in understanding their nuanced application within viticulture. Unique challenges, including data compatibility and environmental sustainability, necessitate focused investigation. Therefore, the thesis seeks to fill this gap by examining the distinctive challenges and opportunities associated with the adoption of 4.0 Agriculture in viticulture, providing valuable insights for stakeholders, policymakers, and researchers. The primary research objectives of the thesis centre around investigating how the adoption of agricultural innovations, particularly precision agriculture, affects the technical efficiency of Italian farm production. The study explores the impact of precision agriculture on input use and technical efficiency, employing a Data Envelopment Analysis (DEA) method. By contributing to the existing literature on technology adoption, the research aims to provide valuable insights for policymakers, investors, and farmers, fostering a more sustainable and prosperous future for the agri-food sector. The thesis evolves across seven chapters, each contributing to a comprehensive understanding of the adoption of Agriculture 4.0 in vineyards and agricultural produces in general. Each chapter attempts to lay the groundwork for answering the research question. In particular, Chapter 01 introduces the background, the problem, and the research question, while chapter 02 describes the trends of recent innovation in agriculture where agriculture 4.0 and precision viticulture technologies were discussed and reviewed in detail along with their potential applications. Chapter 03 describes the diffusion process of agricultural innovation and different models and theories behind this process. Chapter 04 reviews the literature on the different factors which affect the adoption of precision agriculture technologies in the context of Italy. In particular, a scoping review is performed to investigate the determinants which can potentially be triggering or hindering farmers to adopt agricultural innovations. Chapter 05 describes the theoretical background related to efficiency analysis empirical work. First, the production function and the key performance indicators to evaluate farm performances, such as productivity and technical efficiency, are introduced. Subsequently, methods used to evaluate technical efficiency are reviewed. Chapter 06 first presents the methodology implemented in this thesis, based on a model developed in the recent literature. In particular, it describes the Data envelopment approach and Ordinary least squares (OLS) regression. Then, the descriptive statistics of the Farm Accountancy Data Network sample are reported, as well as the results of the estimated model. Ultimately, the findings of this analysis are discussed, providing some conclusions. Finally, Chapter 7 discusses the results, summarizes the main conclusion of the thesis, and highlights the limitations and future research directions.