Land Degradation and Mitigation Policies in the Mediterranean Region: A Brief Commentary

Application of a novel remote sensing ecological index (RSEI) based on geographically weighted principal component analysis for assessing the land surface ecological quality

In evaluating the integrated remote sensing-based ecological index (RSEIPCA), principal component analysis (PCA) has been extensively utilized. However, the conventional PCA-based RSEI (RSEIPCA) cannot accurately evaluate component indicators' spatially shifting relative significance. This study presented a novel RSEI evaluation strategy based on geographically weighted principal component analysis (RSEIGWPCA) to address this deficiency. Second, compared to the classic RSEIPCA, RSEIGWPCA was tested at English Bazar and surrounding areas using two-fold validation. In this regard, the Jaccard test from a different setting and correlation analysis were utilized to examine the geographical distribution of RSEI derived by PCA and GWPCA. The validation output revealed better effectiveness of GWPCA over PCA in assessing the RSEI. The findings revealed that (i) in RSEI assessment, the spatial heterogeneity of the dataset helped to formulate individual weights by GWPCA that was not performed by PCA; and (ii) the areas having higher RSEI were primarily located around the Chatra wetland of this study area, and the areas with lower RSEI were located mainly in the industrial part. It has been concluded that RSEIGWPCA is a helpful approach in the RSEI evaluating for the regional and local scale like English bazaar city and its neighbourhood.

Mapping of population growth influence on land use transformation from 1994 to 2015 in Madaba Governorate, Jordan

The Madaba Governorate, as the second-largest wheat producer in Jordan, holds a crucial position in safeguarding regional food security. Its evolving landscape, marked by changes in land use, presents environmental and socio-economic challenges that necessitate sustainable urban planning and land management practices. This study delves into the intricate relationship between the conversion of agricultural lands into urban areas and the concurrent rise in population within the Madaba Governorate. Utilizing a Markov model, this research employs land use and land cover (LULC) data from 1994, 2004, and 2015 to project future changes in 2025 and 2035 with an impressive 80% accuracy (kappa coefficient). The findings reveal a projected 6% increase in urban areas over the next decade and a notable 11.81% decline in rural lands, signifying a substantial urbanization trend. In response to these population-driven LULC dynamics, there is an urgent need for the implementation of sustainable land use planning and management solutions. Given the constraints of limited water resources in the region, this study also places emphasis on water resource management. Recommendations include measures such as restricting urban sprawl, preserving agricultural lands, managing population growth, and implementing water conservation strategies. These insights provide invaluable information for stakeholders in the Madaba Governorate, including policymakers and land use planners, fostering a comprehensive understanding of the complex interplay between regional water resources, population expansion, and land use changes.

Delineating the Intrinsic, Long-Term Path of Land Degradation: A Spatially Explicit Transition Matrix for Italy, 1960-2010

Vulnerability to land degradation in southern Europe has increased substantially in the last decades because of climate and land-use change, soil deterioration, and rising human pressure. The present work focuses on a quantitative evaluation of changes over time in the level of vulnerability to land degradation of a Mediterranean country (Italy) using a composite indicator, the environmentally sensitive area index (ESAI), which is the final outcome of a complex model conceived to assess land vulnerability on the basis of climate, soil, vegetation, and human pressure. Considering four different levels of vulnerability to land degradation (not affected, potentially affected, fragile, and critical), the main trajectories of this index were highlighted in a long-time perspective (1960-2010), discriminating dynamics over two sub-periods (1960-1990 and 1990-2010). The empirical results at a very detailed spatial scale (1 km² grid) reflect spatial consolidation of degradation hot-spots over time. However, aggregated trajectories of change indicate an overall improvement in the environmental conditions between 1990 and 2010 compared with what is observed during the first period (1960-1990). Worse environmental conditions concerned southern Italian regions with a dry climate and poor soil conditions in the first time interval, large parts of northern Italy, traditionally recognized as a wet and affluent agricultural region, experienced increasing levels of land vulnerability in the second time interval. Being classified as an unaffected region according with the Italian national action plan (NAP), the expansion of (originally sparse) degradation hot-spots in northern Italy, reflective of an overall increase in critical areas, suggests a substantial re-thinking of the Italian NAP. This may lead to a redesign of individual regional action plans (RAPs) implementing place-specific approaches and comprehensive measures to be adopted to mitigate land degradation.

Urbanisation-driven land degradation and socioeconomic challenges in peri-urban areas: Insights from Southern Europe

Climate change and landscape transformation have led to rapid expansion of peri-urban areas globally, representing new 'laboratories' for the study of human-nature relationships aiming at land degradation management. This paper contributes to the debate on human-driven land degradation processes by highlighting how natural and socioeconomic forces trigger soil depletion and environmental degradation in peri-urban areas. The aim was to classify and synthesise the interactions of urbanisation-driven factors with direct or indirect, on-site or off-site, and short-term or century-scale impacts on land degradation, focussing on Southern Europe as a paradigmatic case to address this issue. Assuming complex and multifaceted interactions among influencing factors, a relevant contribution to land degradation was shown to derive from socioeconomic drivers, the most important of which were population growth and urban sprawl. Viewing peri-urban areas as socio-environmental systems adapting to intense socioeconomic transformations, these factors were identified as forming complex environmental 'syndromes' driven by urbanisation. Based on this classification, we suggested three key measures to support future land management in Southern European peri-urban areas.

Influence of the Choice of Cultivar and Soil Fertilization on PTE Concentrations in Lactuca sativa L. in the Framework of the Regenerative Agriculture Revolution

Evaluating the relative weight of the choice of cultivar and soil fertilization on potentially toxic elements (PTEs) accumulation is crucial in promoting informed decisions in the framework of regenerative agriculture. To this end, 11 PTEs (Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Zn) were determined in both leaves and roots of six cultivars (Stylist, Xanadu, Aljeva, Bacio, Analena, Vincenzo) of lettuce (Lactuca sativa L.) grown side by side on mineral fertilized or biowaste compost amended soils, under greenhouse conditions. The use of multivariate and linear modelling approaches indicated that the organ and cultivar primarily account for the variability in PTE concentrations. In terms of PTE partitioning between organs, Cd and Mg were preferentially accumulated in leaves, whereas Cu, Pb, K and Zn in roots. As for the cultivar, Xanadu showed the highest concentrations of several PTEs, with Cd reaching concerning levels. Fertilization had a detectable contribution only on Cd accumulation, slightly increased in leaves by compost. Findings highlight the key role of cultivar choice in guaranteeing food safety and grant the possibility to adopt biowaste compost in regenerative agriculture without concerns about PTE accumulation enhancements in lettuce, but demand a cautionary approach in the case of Cd.

Evolutionary Overview and Prediction of Themes in the Field of Land Degradation

Land degradation has become one of the major global environmental problems threatening human well-being. Whether degraded land can be restored has a profound effect on the achievement of the 2030 UN Sustainable Development Goals. Therefore, the ways by which to identify the current research status and potential research topics in the massive scientific literature data in the field of land degradation is a crucial issue for scientific research institutions in various countries. In view of the shortcomings in the current research on the thematic evolution and thematic and thematic prediction, such as the ignorance of random features during scientific innovation, the defects of manual classification, and the difficulty of identifying technical terms, this research proposes a new combined method. First, the Latent Dirichlet Allocation (LDA) algorithm in machine learning is used to capture the potential clustering of themes in the literature sample set of land degradation research. The distribution characteristics and evolution of themes in each period are then analyzed. The method is combined with the Hidden Markov Model (HMM), which contains double stochastic process to quantitatively predict the trend of future thematic evolution. Finally, the above-mentioned combined method is used to analyze the evolution characteristics and future development trends of the themes in the field of land degradation. Comparative experiments show that the method in this study is effective and practical. The research results show that rangeland degradation, surface temperature, island, soil degradation, water quality, crop productivity and restoration are important research topics in the field of land degradation in the future. In addition, based on the advantages of this model, this model can be widely used in the thematic evolution and prediction analysis of different research fields in land use science.