Rurbanity: a concept for the interdisciplinary study of rural-urban transformation.

Along with climate change, population growth, and overexploitation of natural resources, urbanisation is among the major global challenges of our time. It is a nexus where many of the world's grand challenges intersect, and thus key to sustainable development. The widespread understanding of urbanisation as a successive and unidirectional transformation of landscapes and societies from a rural to an urban state is increasingly questioned. Examples from around the globe show that 'the rural' and 'the urban' are not only highly interdependent, but actually coexist and often merge in the same space or livelihood strategy. Our concept of rurbanity provides an integrated theoretical framework which overcomes the rural-urban divide and can be operationalised for empirical research. Rurbanity is the next stringent step following the gradual widening of previous concepts from urban-centred approaches through the emphasis on urban peripheries to attempts of abolishing any distinction of a rural environment and acknowledging the highly dynamic nature of globalising urbanisation. Building on complex systems theory and assemblage thinking, our concept explores complementary aspects of the distinct epistemic worldviews dominating the natural and social sciences. Within this theoretical frame, we derive four analytical dimensions as entry points for empirical research: Endowments and Place, Flows and Connectivity, Institutions and Behaviour, and Lifestyles and Livelihoods. Two examples illustrate how these dimensions apply, interact, and together lead to a comprehensive, insightful understanding of rurban phenomena. Such understanding can be an effective starting point for assessing potential contributions of rurbanity to long-term global sustainability.

Data on spatial and temporal modelling of soil water storage in the Guinea savannah zone of Northern Ghana.

In this article, we present the space-time variability of soil moisture (SM) and soil water storage (SWS) from key agricultural benchmark soil types measured across the Guinea savannah zone of Ghana (n ≈ 2,000 measurements) in a single cropping season (Nketia et al., 2022). From 36 locations, SM measurements were obtained with a PR2/60 moisture probe calibrated for a 0-100 cm soil depth interval (at six depths). We further introduce a new pedotransfer model that was developed in deriving the SWS for the same depth interval of 0-100 cm. Assessing information on the space-time variability of SM and SWS is essential for agricultural intensification efforts, especially in semi-arid landscapes of sub-Saharan Africa (SSA), where there is the need and the potential to increase food-crop production. This dataset spans the main topographic units of the Guinea savannah zone and covers dominant vegetation types and land uses of the region, which is similar to most parts of West Africa. The comprehensive dataset and the customized machine learning models can be used to support crop production with respect to water management and optimized agricultural resource allocation in the Guinea savannah landscapes of Ghana and other parts of SSA.

A new method for selecting sites for soil sampling, coupling global weighted principal component analysis and a cost-constrained conditioned Latin hypercube algorithm.

Analysing spatial patterns of soil properties in a landscape requires a sampling strategy that adequately covers soil toposequences. In this context, we developed a hybrid methodology that couples global weighted principal component analysis (GWPCA) and cost-constrained conditioned Latin hypercube algorithm (cLHC). This methodology produce an optimized sampling stratification by analysing the local variability of the soil property, and the influence of environmental factors. The methodology captures the maximum local variances in the global auxiliary dataset with the GWPCA, and optimizes the selection of representative sampling locations for sampling with the cLHC. The methodology also suppresses the subsampling of auxiliary datasets from areas that are less representative of the soil property of interest. Consequently, the method stratifies the geographical space of interest in order to adequately represent the soil property. We present results on the tested method (R2 = 0.90 and RMSE = 0.18 m) from the Guinea savannah zone of Ghana. •It defines the local structure and accounts for localized spatial autocorrelation in explaining variability.•It suppresses the occurrence of model-selected sampling locations in areas that are less representative of the soil property of interest.