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Background and aims Abandonment of agricultural land is a common type of land-use change worldwide. Nevertheless, there is currently no consensus on how soil aggregates and aggregate-associated organic carbon (C) vary with agricultural land abandonment on a global scale. Methods We synthesized the global responses and controlling factors of distribution, stability, and associated organic C concentration of soil water-stable aggregates under the influence of agricultural land abandonment using meta-analysis. Results On average, agricultural land abandonment significantly enhanced the mass proportion of large macroaggregates (LMA) and mean weight diameter (MWD) by 89.9% and 51.1%, respectively, while leading to a significant reduction in the proportion of silt-clay particles (SC) (−26.6%). By contrast, the proportions of both small macroaggregates (SMA) and microaggregates (MIA) showed no response to agricultural land abandonment. Overall, agricultural land abandonment significantly increased the aggregate-associated organic C concentrations by 23.3–24.8%, and the highest increase was observed for LMA. In most cases, the responses of soil aggregates to agricultural land abandonment did not differ significantly between subgroups of mean annual temperature, mean annual precipitation, soil texture, and abandonment duration (AD). We found that the dynamics of MWD and associated organic C concentrations were positively related to AD according to redundancy analysis. Conclusion Our findings suggested that the formation and C accrual of LMA, which could be improved with the increase of AD due to a more favorable environment for plant and microbial growth, played crucial roles in both soil structural rehabilitation and soil C sequestration during agricultural land abandonment.

Decarbonization strategies aim at increasing renewable energy source (RES) capacity, including new photovoltaic (PV) systems. Utility-scale PV installations are often placed in agricultural areas, resulting in a reduction in agricultural land and affecting the environment. To balance agricultural and energy policies, PV development should not limit agricultural purposes, allowing sustainable exploitation under specific technological and environmental conditions, particularly in areas of actual or potential abandonment. Studying agricultural abandonment is complex due to its multifaceted nature, the lack of a clear definition, and challenges in acquiring cartographic data. This study introduces and compares two methodologies to identify abandoned agricultural areas, aiming to delineate macro-areas of potential abandonment and examine patterns for conversion to energy use, with a focus on Toscana, a region (NUTS-2) in central Italy, which has experienced cropland reduction unrelated to urbanization. The first, simplified approach analyses land cover changes from 2000 to 2018, while the second method provides a more detailed abandonment detection by means of medium spatial resolution satellite imagery from the Harmonized Landsat and Sentinel-2 dataset. A Random Forest classifier combined with Object-Based Image Analysis (OBIA) is applied to satellite data to map annual active/non-active croplands. Annual maps are then validated with a trajectory-based approach to detect agricultural land abandonment. This second methodology can help in providing spatially and timely meaning estimates of abandoned agricultural areas to be recovered for energy purposes and promote a sustainable growth of PV systems.

Precisely determining agricultural land abandonment (ALA) in an area is still difficult, even with recent progress in data collection and analysis. It is especially difficult in fragmented areas that need more tailor-made methods. The aim of this research was to determine ALA using airborne laser scanning (ALS) data, which are available in Poland with 4 to 6 points per square metre resolution. ALS data were processed into heat maps and modified with chosen kernel functions: triweight and Epanechnikov. The results of ALS data processing were compared to the control method, i.e., visual interpretation of an orthophotomap. This study shows that ALS data modelled with kernel functions allow for a good identification of ALA. The accuracy of results shows 82% concordance as compared to the control method. When comparing triweight and Epanechnikov functions, higher accuracy was achieved when using the triweight function. The research shows that ALS data processing is a promising method of detection of ALA and could provide an alternative to well-known methods such as the analysis of satellite images.

Food security remains a major issue for developing countries. Reducing arable land abandonment (ALA) is crucial to ensuring food security. In China, the ‘decline in both quantity and quality’ of arable land resources, especially in major grain-producing areas, has become increasingly serious. This study uses fuzzy set qualitative comparative analysis (fsQCA) to explore the core conditions and combinations of paths leading to explicit and implicit abandonment using 30 typical cases in the main grain-producing areas of Hubei Province. The results show that (1) three combined pathways lead to explicit ALA (EALA) and that two pathways lead to implicit ALA (IALA); (2) laborer health (LH) is the core condition leading to EALA; and (3) LH, agricultural laborer (AL), per capita income (PCI) and social relationships (SRs) are the core conditions leading to IALA. To effectively alleviate ALA, the government should improve production conditions, pay attention to laborer health issues, improve agricultural returns and strengthen food security publicity and guidance, thereby promoting the rational use of arable land in these areas. The findings in this study link the changes in arable land use and provide a reference for other developing countries in ensuring food security.

The abandonment of agricultural land is now considered one of the primary land use changes driven by complex interactions between social, economic, and environmental factors. To understand and manage this process, a holistic approach that integrates multidimensional methodologies and interactions is essential. This study examines the key driving factors behind agricultural land abandonment in Lithuania using two methodological approaches. First, seventeen highly qualified land management experts were surveyed, and their insights were analysed using in-depth qualitative interviews, focusing on agricultural land abandonment and its underlying factors. Second, the development of agricultural land abandonment in a representative Lithuanian municipality was modelled using Markov chain models, incorporating freely available geographic data as factors influencing land use transformation. Actual areas of abandoned agricultural land were mapped using orthophotos from 2012, 2018, and 2021, for both model development and validation. The importance of predictors in the model was then assessed in relation to their significance as drivers of agricultural land abandonment. The findings indicate that natural factors, such as the proximity of forests and topographical constraints, play a significant role in explaining land abandonment processes. Additionally, agricultural land abandonment is influenced by social, economic, and legal factors, including land ownership structures, migration, and infrastructure accessibility. The importance of soil quality, productivity, and the presence of nearby arable land was found to vary depending on data accuracy and local environmental conditions, highlighting the complexity of agricultural land use patterns. The chosen mixed-method approach, combining qualitative surveys with numerical spatial modelling, demonstrates potential for identifying critical land use areas and providing insights to improve land management policies and decision making.

Agricultural land abandonment is a globally significant threat to the sustenance of economic, ecological, and social balance. Although the driving forces behind it can be multifold and versatile, rural depopulation and urbanization are significant contributors to agricultural land abandonment. In our chosen case study, focusing on two locations, Ruen and Stamboliyski, within the Plovdiv region of Bulgaria, we use aerial photographs and satellite imagery dating from the 1950s until 1980, in connection with official population census data, to assess the magnitude of agricultural abandonment for the first time from a remote sensing perspective. We use multi-modal data obtained from historical aerial and satellite images to accurately identify Land Use Land Cover changes. We suggest using the rubber sheeting method for the geometric correction of multi-modal data obtained from aerial photos and Key Hole missions. Our approach helps with precise sub-pixel alignment of related datasets. We implemented an iterative object-based classification approach to accurately map LULC distribution and quantify spatio-temporal changes from historical panchromatic images, which could be applied to similar images of different geographical regions.

The problem of arable land abandonment has become increasingly prominent in China as an important hidden danger of regional and national grain security. Therefore, it is necessary to fully understand its developmental mechanism in order to improve land protection policies and maintain the sustainable use of arable land. This study took Pingyang County in the Yangtze River Delta Economic Zone as an example. Based on remote sensing image data in 2000, 2010, and 2018, the landscape pattern index was used to reveal the changes in the landscape pattern of abandoned land in the study area, and the FLUS model was used to simulate the spatial and temporal distribution changes in abandoned land in the study area in 2028. The results showed that the abandoned areas in the study area spread rapidly from 2000 to 2018, the area of abandoned land increased nearly 12 times in the past 18 years, and the areas with a high abandonment rate were concentrated in the western and northwestern mountainous areas of the study area. In the view of the landscape pattern, the areas with a high fragmentation degree of abandoned land gradually shifted to the western mountainous areas from 2000 to 2018, and the areas with high landscape complexity of abandoned land gradually shifted from the middle to the northern and western areas. The simulation results of abandoned land showed that the high-value areas of abandoned land rate in the study area would be more concentrated by 2028. Among them, the abandoned land rate of arable land in the northwest would increase to 15.76~24.89%, while the landscape fragmentation and complexity of abandoned land would be slightly lower than that in 2018. Finally, some countermeasures were proposed for the protection and sustainable utilization of cultivated land resources.

Based on the hypothesis of individual-bounded rationality, this study analyzes the mechanisms of farmers’ cultivated land abandonment behavior, theoretically and empirically, by integrating the theory of planned behavior (TPB) and a structural equation model (SEM). On the basis of the TPB’s logical analysis framework of farmers’ abandonment behavior, combined with social psychology, behavioral economics, and a household behavior model, this study analyzes the influence of attitude on behavior, the subjective norm, and perceived behavioral control on farmers’ abandonment actions, then verifies it via an SEM Model. The research shows that farmers’ abandonment behavior accords with the theory of planned behavior. Farmers’ recognition of the negative impacts of abandonment, the intervention of important other persons, and the obstacles encountered in the process of abandonment can effectively restrain farmers’ abandonment behavior. Finally, by considering the determinants for farmers’ abandonment decisions, this study proposes to curb abandonment practices through measures that include strengthening publicity about abandonment, creating a favorable atmosphere for farming, and improving tillage conditions.

Under suitable conditions, deforested land used for agricultural crops or pastures can revert to forest through the assisted or unassisted process of natural regeneration. These naturally regenerating forests conserve biodiversity, provide a wide array of ecosystem goods and services, and support rural economies and livelihoods. Based on studies in tropical and temperate forest ecosystems, we summarize cases where natural regeneration is occurring in agricultural landscapes around the world and identify the socio-ecological factors that favor its development and affect its qualities, outcomes and persistence. We describe how the economic and policy context creates barriers for the development, persistence, and management of naturally regenerating forests, including perverse outcomes of policies intended to enhance protection of native forests. We conclude with recommendations for specific economic and policy interventions at local, national, and global scales to enhance forest natural regeneration and to promote the sustainable management of regrowth forests on former agricultural land while strengthening rural communities and economies.

To improve predictions of the future of ecosystems in a changing world, it is necessary to consider fine-scale processes. We propose that for the Mediterranean region (a hotspot of climate change and biodiversity), there are three local processes that have often been overlooked in predictive models and that are key to understanding vegetation changes: rural abandonment that increases wildlands, population changes that boost fire ignitions, and coastal degradation that enhances drought. These processes are not directly driven by global warming and act in different directions (greening and browning). The current balance is still toward greening, because land abandonment is buffering the browning drivers; however, it is likely to switch with increasing warming. The challenge is to mitigate the browning processes. Given that climatic warming is not directly driving these processes, local management can make a difference in reducing the overall impact on the landscape and society.