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The rational fertilisation of olive trees, based on adding exclusively the nutrients that are actually needed, is important from both the economic and environmental sustainability points of view. This paper employs UAV-obtained multispectral data collected from five different orchards located in Southern Spain to build a set of models for the prediction of the leaf nutrient content of olive trees using Support Vector Regression. The paper shows the convenience of addressing the problem as a Multiple Instance Regression, and compares two strategies of data aggregation and different choices of feature vectors derived from the raw multispectral data. The models provided good results for N, P and K (r 2 = 0.76, r 2 = 0.87 and r 2 = 0.91, respectively for the Hojiblanca model, and r 2 = 0.79, r 2 = 0.80 and r 2 = 0.80 for the Picual model). The rest of nutrients studied also offered good results for both the Picual and Hojiblanca models, ranging from r 2 = 0.69 for B to r 2 = 0.93 for Cu. The results indicate a robust performance of the models and a potential for improvement with the addition of more data, along with an advantage of considering individual models for each cultivar variety. Overall, these results are very promising for the estimation of the leaf nutrient content of olives trees and the detection of spatial variability in the fertilisation needs of orchards. [Display omitted] • Leaf nutrient content of olive trees is predicted from UAV multispectral data. • Multiple Instance Regression is applied and its benefits discussed. • Models provide good results for N, P and K in the Hojiblanca and Picual models. • The rest of nutrients also show acceptable results. • Results are promising for detecting spatial variability in fertilisation needs. [ABSTRACT FROM AUTHOR]

This paper addresses the need to understand the structure of sustainability change in the face of the unpredictability of modern civilization. The aim of the paper is to examine the structure of sustainability change in relation to smaller spatial units such as the regions of the European Union and to compare these results with those achieved at the country level. For this purpose, the sustainable development indicators available in the Eurostat database were analyzed. Studies published to date have tended to focus on the performance of national economies or selected regions. In this article, the available data on regions are also analyzed. The study aims to compare the level of sustainable development achieved at the level of EU countries and regions located within them. Methodologically, the study uses advanced methods of comparative analysis, and the synthesis of the data enables the construction of a synthetic measure using taxonomic methods, facilitating the classification of EU countries and regions into clusters. The results highlight significant divergences in sustainability achievements between EU countries and regions. The study reveals the importance of studying sustainable development in smaller geographical cross-sections. The results highlight the need to expand access to data at the regional level (sustainability indicators) to facilitate more comprehensive analyses and inform targeted policy interventions. Looking to the future, the study recommends further research into the inter-linkages between sustainability and other critical areas in EU regions, such as innovation and competitiveness. • A new approach to studying sustainable development from a regional perspective has been proposed. • An in-depth analysis of the current level of sustainability in EU regions was carried out. • The diversity of sustainable development in the countries of the EU has been described. [ABSTRACT FROM AUTHOR]