This paper presents a conceptual model of land as a coupled human–environment system. Land use and land cover are incorporated as elements of the human and environment system respectively. Drivers and associated processes that influence land use, land cover, and land system dynamics are incorporated within a set of sub-systems. The model includes consideration of driving sub-systems as a set of capital funds and flows, and how these are influenced by linkages between processes in the human (socio-economic) and environment systems and sub-systems. The model is consistent with existing models of the biophysical earth system used by the land change, earth system sciences, and socio-ecological systems communities. The purposes of the model are to provide (i) a holistic framework within which descriptions, models and analyses that focus on various components of land can be placed to describe and explain land systems and land system changes; and (ii) a guide for the development of more fully integrated and interdisciplinary understanding, analysis and study of land use and land cover dynamics, with explicit focus on relationships between human and natural systems.

The rank aggregation problem can be summarized as the problem of aggregating individual preferences expressed by a set of judges to obtain a ranking that represents the best synthesis of their choices. Several approaches for handling this problem have been proposed and are generally linked with either axiomatic frameworks or alternative strategies. In this paper, we present a new definition of median ranking and frame it within the Kemeny’s axiomatic framework. Moreover, we show the usefulness of our approach in a practical case about triage prioritization.

We address understanding of whole-system and landscape-based approaches to the ecosystem services framework by considering the supply of provisioning services and the dynamics of agricultural land use in Scotland between 1940 and 2016.

Even if large historical dataset could be available for monitoring key quality features of a process via multivariate control charts, previous knowledge may not be enough to reliably identify or adopt a unique model for all the variables. When no specific parametric model turns out to be appropriate, some alternative solutions should be adopted and exploiting non‐parametric methods to build a control chart appears a reasonable choice. Among the possible non‐parametric statistical techniques, data depth functions are gaining a growing interest in multivariate quality control. Within the literature, several notions of depth are effective for this purpose, even in the case of deviation from the normality assumption. However, the use of the L^p depth for constructing non‐parametric multivariate control charts has been surprisingly neglected so far. Hence, the goal of this work is to investigate the behavior the L^p depth in the statistical process control and to compare its performances to those of the Mahalanobis depth, which is often adopted to build depth‐based control charts.

Plants of Nephrolepis cordifolia (L.) C. Presl were grown on soil samples collected in a mine site located in Central Italy and on soil samples from uncontaminated soils to test the ability of this species to accumulate inorganic contaminants under semi-natural conditions. The plants were kept under observation for monitoring the growth and the appearance of any stress symptoms. The concentrations of inorganic ions were determined in the substrates and in different plant organs. The results indicated that N. cordifolia is able to grow vigorously on soils contaminated by inorganic ions that are potentially toxic for living organisms and that this species is able to accumulate several inorganic contaminants mainly in its underground parts. Concentrations of aluminium, iron and lead >1000 mg/kg in the underground parts were detected in plants grown on the contaminated substrate. N. cordifolia is, therefore, potentially useful as a tool for phytostabilization of contaminated soils.