Large-scale Land Research Articles

Globalization and Conflicts: the Good, the Bad, and the Ugly of Corporations in Africa

Abstract Using a novel georeferenced datataset on the affiliates and headquarters of multinational enterprises between 2007 and 2018 together with georeferenced conflict data for the African continent, this work establishes a causal link between the activities of multinational enterprises and violent conflicts: multinationals’ activity increases the number of conflicts. This applies particularly to sectors intense in scarce resources, especially land. As farming is the primary source of food and income for Africans, land-intensive activity on the part of the multinationals increases local grievance, escalating to violent actions. These effects are magnified in areas targeted for large-scale land acquisitions.

The roles of habitat isolation, landscape connectivity and host community in tick-borne pathogen ecology.

Habitat loss and forest fragmentation are often linked to increased pathogen transmission, but the extent to which habitat isolation and landscape connectivity affect disease dynamics through movement of disease vectors and reservoir hosts has not been well examined. Tick-borne diseases are the most prevalent vector-borne diseases in the United States and on the West Coast, Ixodes pacificus is one of the most epidemiologically important vectors. We investigated the impacts of habitat fragmentation on pathogens transmitted by I. pacificus and sought to disentangle the effects of wildlife communities and landscape metrics predictive of pathogen diversity, prevalence and distribution. We collected pathogen data for four co-occurring bacteria transmitted by I. pacificus and measured wildlife parameters. We also used spatial data and cost-distance analysis integrating expert opinions to assess landscape metrics of habitat fragmentation. We found that landscape metrics were significant predictors of tick density and pathogen prevalence. However, wildlife variables were essential when predicting the prevalence and distribution of pathogens reliant on wildlife reservoir hosts for maintenance. We found that landscape structure was an informative predictor of tick-borne pathogen richness in an urban matrix. Our work highlights the implications of large-scale land management on human disease risk.

Urban Land Use Classification Model Fusing Multimodal Deep Features

Urban land use classification plays a significant role in urban studies and provides key guidance for urban development. However, existing methods predominantly rely on either raster structure deep features through convolutional neural networks (CNNs) or topological structure deep features through graph neural networks (GNNs), making it challenging to comprehensively capture the rich semantic information in remote sensing images. To address this limitation, we propose a novel urban land use classification model by integrating both raster and topological structure deep features to enhance the accuracy and robustness of the classification model. First, we divide the urban area into block units based on road network data and further subdivide these units using the fractal network evolution algorithm (FNEA). Next, the K-nearest neighbors (KNN) graph construction method with adaptive fusion coefficients is employed to generate both global and local graphs of the blocks and sub-units. The spectral features and subgraph features are then constructed, and a graph convolutional network (GCN) is utilized to extract the node relational features from both the global and local graphs, forming the topological structure deep features while aggregating local features into global ones. Subsequently, VGG-16 (Visual Geometry Group 16) is used to extract the image convolutional features of the block units, obtaining the raster structure deep features. Finally, the transformer is used to fuse both topological and raster structure deep features, and land use classification is completed using the softmax function. Experiments were conducted using high-resolution Google images and Open Street Map (OSM) data, with study areas on the third ring road of Shenyang and the fourth ring road of Chengdu. The results demonstrate that the proposed method improves the overall accuracy and Kappa coefficient by 9.32% and 0.17, respectively, compared to single deep learning models. Incorporating subgraph structure features further enhances the overall accuracy and Kappa by 1.13% and 0.1. The adaptive KNN graph construction method achieves accuracy comparable to that of the empirical threshold method. This study enables accurate large-scale urban land use classification with reduced manual intervention, improving urban planning efficiency. The experimental results verify the effectiveness of the proposed method, particularly in terms of classification accuracy and feature representation completeness.

Maize-Straw Biochar Enhances Soil Properties and Grain Yield of Foxtail Millet in a Newly Reclaimed Land

Large-scale land reclamation has become common in northwestern China; however, low soil fertility and poor soil water-holding capacity limit agricultural production on these reclaimed lands, requiring increased fertilizer and irrigation inputs. Biochar, produced from agricultural waste, has shown potential in improving soil quality and water-holding capacity. In this two-year field study (2021 and 2022), we investigated the effects of biochar produced from maize straw on soil properties and grain yield of foxtail millet grown on newly reclaimed land. Three biochar treatments (3000, 4500, and 6000 kg ha−1) were compared to a control (CK) with no biochar application. Biochar application resulted in increased soil organic matter, total phosphorus, total nitrogen, soil enzyme activity, and soil organic acid content. It also significantly decreased soil pH and bulk density. Compared with the CK, biochar increased available nitrogen from 29.7% to 108% in 2021 and 37.0% to 88.4% in 2022. Similarly, biochar increased available phosphorus from 64.7% to 143% in 2021 and 41.9% to 96.5% in 2022. Grain yields ranged from 3092 to 4753 kg ha−1. Biochar treatments increased grain yield compared to the CK, ranging from 12.2% to 24.6% in 2021 and 27.1% to 53.7% in 2022. Correlation analysis revealed that soil pH was negatively related to soil oxalic acid content, phosphorus content, and sucrase activity. Available nitrogen and phosphorus contents were negatively related to soil bulk density and positively related to catalase activity. Soil water content was negatively correlated with soil bulk density and positively correlated with organic matter. In conclusion, biochar improved the rhizosphere soil pH and the effectiveness of soil fertility in the newly reclaimed soil, resulting in an enhanced grain yield of foxtail millet.

Harvest Initiated Volatile Organic Compound Emissions from In-Field Tall Wheatgrass.

While crop and grassland usage continues to increase, the full diversity of plant-specific volatile organic compounds (VOCs) emitted from these ecosystems, including their implications for atmospheric chemistry and carbon cycling, remains poorly understood. It is particularly important to investigate VOCs in the context of potential biofuels: aside from the implications of large-scale land use, harvest may shift both the flux and speciation of emitted VOCs. To this point, we evaluate the diversity of VOCs emitted both pre and postharvest from "Alkar" tall wheatgrass (Thinopyrum ponticum), a candidate biofuel that exhibits greater tolerance to frost and saline land compared to other grass varieties. Mature plants grown under field conditions (n = 6) were sampled for VOCs both pre- and postharvest (October 2022). Via hierarchical clustering of emitted VOCs from each plant, we observe distinct "volatilomes" (diversity of VOCs) specific to the pre- and postharvest conditions despite plant-to-plant variability. In total, 50 VOCs were found to be unique to the postharvest tall wheatgrass volatilome, and these unique VOCs constituted a significant portion (26%) of total postharvest signal. While green leaf volatiles (GLVs) dominate the speciation of postharvest emissions (e.g., 54% of unique postharvest VOC signal was due to 1-penten-3-ol), we demonstrate novel postharvest VOCs from tall wheatgrass that are under characterized in the context of carbon cycling and atmospheric chemistry (e.g., 3-octanone). Continuing evaluations will quantitatively investigate tall wheatgrass VOC fluxes, better informing the feasibility and environmental impact of tall wheatgrass as a biofuel.

A “Region-Specific Model Adaptation (RSMA)”-Based Training Data Method for Large-Scale Land Cover Mapping

An accurate and historical land cover monitoring dataset for Alaska could provide fundamental information for a range of studies, such as conservation habitats, biogeochemical cycles, and climate systems, in this distinctive region. This research addresses challenges associated with the extraction of training data for timely and accurate land cover classifications in Alaska over longer time periods (e.g., greater than 10 years). Specifically, we designed the “Region-Specific Model Adaptation (RSMA)” method for training data. The method integrates land cover information from the National Land Cover Database (NLCD), LANDFIRE’s Existing Vegetation Type (EVT), and the National Wetlands Inventory (NWI) and machine learning techniques to generate robust training samples based on the Anderson Level II classification legend. The assumption of the method is that spectral signatures vary across regions because of diverse land surface compositions; however, despite these variations, there are consistent, collective land cover characteristics that span the entire region. Building upon this assumption, this research utilized the classification power of deep learning algorithms and the generalization ability of RSMA to construct a model for the RSMA method. Additionally, we interpreted existing vegetation plot information for land cover labels as validation data to reduce inconsistency in the human interpretation. Our validation results indicate that the RSMA method improved the quality of the training data derived solely from the NLCD by approximately 30% for the overall accuracy. The validation assessment also demonstrates that the RSMA method can generate reliable training data on large scales in regions that lack sufficient reliable data.

Analysis of the Spatial Characteristics and Influencing Factors of Large-Scale Land Acquisition Projects in Southeast Asia

Southeast Asia is an essential region for companies carrying out large-scale land acquisitions (LSLAs). Exploring the distribution patterns and influencing factors of LSLA projects in this region is of great practical significance for summarizing the characteristics of LSLA projects in Southeast Asia, for gaining a thorough understanding of LSLA project development rules, and for formulating reasonable policies to guide local LSLA projects. This study explores the spatial distribution and influencing factors of LSLA projects in Southeast Asia using the mean center method, the kernel density estimation method, and the grey correlation method. The findings indicate the following: Firstly, the majority of LSLA projects in Southeast Asia are located in the Indo-China Peninsula, Cambodia, Myanmar, Laos, and other countries, which represent significant regions of interest for LSLA projects in this region. Secondly, the spatial distribution of LSLA intention projects and LSLA contract projects in Southeast Asia is similar, whereas LSLA production projects differ from the former two. Thirdly, the scale of LSLA projects in Southeast Asia is closely related to the host country’s natural resources, socio-economic conditions, governance, and market environment. The total GDP, per capita arable land area, net foreign direct investment inflow, and political stability have been identified as exerting a significant influence on investment corporations’ selection of LSLA host countries.

Response of hydrodynamic environment to land reclamation in Sanmen Bay, China over the last half-century

Sanmen Bay (SMB) is one of the important harbors in Zhejiang Province. It is a semi-enclosed shallow bay that has undergone large-scale land reclamation activities. Long-term reclamation has changed the hydrodynamic conditions of SMB, such as tide, residual current, tidal prism, water exchange capacity, and tidal asymmetry. In this study, three typical periods of numerical models, based on historical charts and remote sensing, were established to investigate the influence of reclamation activities on the hydrodynamic conditions of SMB from 1971 to 2020. These model results reveal that the amplitude and phase of M2, the main tidal components in SMB, decreased by ~0.1–0.3 m and ~ 5°–15°, respectively, over the last half-century. Additionally, under the influence of ~200 km2 reclamation, many hydrodynamic conditions in SMB also changed. This includes the reduction of a residual current and tidal prism, an increase in residence time, and a change in tidal asymmetry characteristics. The residence time in nearby Xiayangtu exhibited a downward trend from 2003 to 2020, because land reclamation squeezed, and thus, enhanced the residual current eddy. The water-exchange capacity of the bay became weaker with the reduction of tidal prism to one-third and an increase in residence time. The tidal asymmetry characteristic of SMB changed from half of flood dominant to fully flood dominant by the influence of shoreline and bathymetry, which raised the flood risk. Research on the response of the hydrodynamic environment to reclamation activities in SMB reminds the local government to reassess the impact of land reclamation on the hydrodynamic environment.

Large-scale agro-industries and host community land restitution demands: understanding the land-use conflicts between the CDC and Fako elites in the south west region, Cameroon

Large-scale land acquisition has been frequent in most parts of Africa. This frequently led to extensive dispossession among other pitfalls of violation of indigenous land ownership rights. A significant proportion of Fako division’s fertile slopes and plains were expropriated by the Germans for implantation and development of plantations. Ownership rights of indigenous community lands were transferred without the legal consent of the rightful native authorities. Against the backdrop of this colonial intrusion and consequential violations of local customary tenure rights, the growing need for justice and restoration of dignity has been a shared value among a preponderant proportion of the local Bakweri ethnic group. This culminated to a restitution decision of CDC leased lands to the Bakweri by the African Commission on Human and People’s Rights. Nonetheless, the proportion of restituted lands has not been significant and satisfactory enough to conciliate the expropriated host communities of the extensive CDC leaseholds. As such, landownership contestations and its consequential socio-political tensions still persist in Fako. This paper is centered on yet a more frequently expressed contestation: encroachment into and occupancy of CDC leaseholds by natives of Fako as a whole and Ndongo of Tiko sub-division in particular. Prior extensive documentary review was done which pointed to the locality of Ndongo as a suitable case of interest. Contrary to the general land grabbing tendencies of agro-businesses, community members here are accused of unauthorized intrusion into and tenancy of lands with legal ownership rights belonging to the CDC. The study was ethnographic comprising of individual in-depth interviews and observations.

Sub-national scale mapping of individual olive trees integrating Earth observation and deep learning

The olive tree holds great cultural, environmental, and economic significance in the Mediterranean region. In particular, Morocco has been making dedicated investments over $10 billion since 2008 to fuel the transition from cereal to olive production. Understanding the spatial extent of this large-scale land conversion is critical for a variety of socioeconomic purposes. In response to this demand, we conducted a study to map individual olive trees in northern Morocco using satellite imagery and deep learning techniques at a sub-national scale. This study utilized cloud-free, very-high-resolution DigitalGlobe imagery collected between 2018 and 2022 to identify each individual olive tree in six northern Morocco provinces. We compared various deep learning models, including both transformer-based and CNN-based models, to generate patch-level spatial constraints and pixel-level tree identification. We found that transformer-based models outperformed CNN-based models in both tasks. Additionally, spatially constraining the pixel-level results improved olive tree mapping accuracy to varying degrees, depending on the initial performance of the model. The evaluation of the olive map generated from this study shows high accuracy in both surveyed and unsampled regions. This research represents the first-of-its-kind individual olive tree mapping at the sub-national scale that can help monitor the large-scale land conversions such as about 110,000 ha of olive plantings in the six Moroccan provinces studies here. Meanwhile it demonstrates a cost-effective and efficient prototype approach that can be adapted to identify similar tree crop expansion occurring in other parts of the world.

Totalinė melioracija Lietuvoje 1966–1990 m.: poveikis žemės ūkio ir kaimo sovietinei modernizacijai

The paper examines the impact of total land reclamation on the Soviet modernisation of agriculture and the countryside, analyses the political, economic, and social aspects of the Soviet reconstruction of the homestead settlement system, and attempts to assess the economic effect of land reclamation and its impact on the countryside. The leadership of the Central Committee of the Lithuanian Communist Party managed to use the implementation of the all-Union large-scale land reclamation programme quite effectively for Lithuania’s economic interest, industrial development, and modernisation of the agricultural sector. The destruction of the farmstead settlement structure during total land reclamation accelerated the internal and social urbanisation of the countryside, which contributed to the formation of a homogeneous Soviet society and eliminated socio-cultural and material-lifestyle disparities between the city and the country. The radical Soviet reconstruction (modernisation) of the countryside, which continued for over two decades, was mainly coercive, revolutionary, and therefore painful for the rural society; it deformed the evolutionary processes in the countryside.

Community perceptions on land and water acquisitions in the Okavango Delta: implications for rural livelihoods

ABSTRACT Large-scale land acquisitions in Africa are increasing, reported often as the transfers of land for food and biofuel crop production. Only reporting agricultural acquisitions underplays potential impacts of other forms of acquisitions like tourism and conservation, which are new engines for economic growth in Southern Africa. While this shift has complex social-ecological implications, there is limited evidence of the multiple ways that land acquisitions unfold in wetland ecosystems, and implications for people and nature. This study aims to investigate local perceptions of implications of land and water acquisitions on local livelihoods in the Okavango Delta, Botswana, using in-depth interviews with 116 local respondents in Etsha 6, Khwai and Tubu villages. Findings revealed that the primary drivers of land acquisitions in the Okavango Delta were tourism and subsistence agriculture, and a new and unique land exchange (we termed land borrowing) was prevalent in Tubu, involving the borrowing of farmland in flood recessions between locals. Concessions, borrowings, and rentals were key perceived land acquisition types. Both positive and negative impacts of land acquisitions on livelihoods surfaced. The diversity of cultural grouping influenced locals’ intricate connection with riparian waters and affected how land was exchanged and governed. The disparities in benefits from land resources have negative implications for equitable resource distribution and natural resource governance, in policy and practice. This research highlights the importance of an expanded view of acquisitions and associated impacts with closer attention to power dynamics which can facilitate more nuanced implementation of targets of the Kunming–Montreal Global Biodiversity framework.

Juridical Review of The Impact of Unlicensed Mining (PETI) on Environmental Damage in South Kalimantan Province

Environmental law regulates the protection and management of the natural environment. Its goal is to preserve the environment and prevent pollution and damage through management, utilization, development, maintenance, restoration, supervision, and control. These regulations govern the relationship between humans and their living environment. The ineffectiveness of environmental law is due to a lack of strict law enforcement. Therefore, strong law enforcement is needed to deter environmental pollution and damage, to achieve sustainable development and societal well-being. This research aims to analyze environmental law, forms of law enforcement against environmental issues such as unlicensed mining, and the importance of law enforcement in achieving sustainable development. The method used in this research is a normative juridical approach. The study found that Indonesia has regulations that impose sanctions on unlicensed mining activities, with penalties proportional to the severity of the violations. Cases include the excavation of natural gravel by Muhammad Sauqi and large-scale land clearing by Saipul Rahman, which have caused significant environmental damage in South Kalimantan. The impacts include water and air pollution and risks of landslides, mine collapses, and earthquakes, which harm the surrounding environment.

Large-Scale Land Cover Mapping Framework Based on Prior Product Label Generation: A Case Study of Cambodia

Large-Scale land cover mapping (LLCM) based on deep learning models necessitates a substantial number of high-precision sample datasets. However, the limited availability of such datasets poses challenges in regularly updating land cover products. A commonly referenced method involves utilizing prior products (PPs) as labels to achieve up-to-date land cover mapping. Nonetheless, the accuracy of PPs at the regional level remains uncertain, and the Remote Sensing Image (RSI) corresponding to the product is not publicly accessible. Consequently, the sample dataset constructed through geographic location matching may lack precision. Errors in such datasets are not only due to inherent product discrepancies, and can also arise from temporal and scale disparities between the RSI and PPs. In order to solve the above problems, this paper proposes an LLCM framework for generating labels for use with PPs. The framework consists of three main parts. First, initial generation of labels, in which the collected PPs are integrated based on D-S evidence theory and initial labels are obtained using the generated trust map. Second, for dynamic label correction, a two-stage training method based on initial labels is adopted. The correction model is pretrained in the first stage, then the confidence probability (CP) correction module of the dynamic threshold value and NDVI correction module are introduced in the second stage. The initial labels are iteratively corrected while the model is trained using the joint correction loss, with the corrected labels obtained after training. Finally, the classification model is trained using the corrected labels. Using the proposed land cover mapping framework, this study used PPs to produce a 10 m spatial resolution land cover map of Cambodia in 2020. The overall accuracy of the land cover map was 91.68% and the Kappa value was 0.8808. Based on these results, the proposed mapping framework can effectively use PPs to update medium-resolution large-scale land cover datasets, and provides a powerful solution for label acquisition in LLCM projects.

The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm

In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of China traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people’s awareness of the importance of local culture, awakens people’s sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in China, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.

Związek między wzorem krajobrazu a zakłóceniami powodowanymi przez człowieka w latach 1990-2018 w Portugalii kontynentalnej

The land use/land cover pattern of landscapes as the main element of the landscape structure has an important role in landscape management, nature conservation, and preservation, considering its ability to provide valuable information about the spatial distribution and changes in land cover that occur on the Earth's surface. On the other hand, human activities have caused significant impacts on ecosystems, resulting in a loss of biodiversity, increased risks of erosion and soil degradation, and wildfires. In the last few decades, the growing trend of occurrence of negative effects on land use/land cover has not contributed to the achievement of sustainable development goals (SDGs), considering that the relationship between humankind and the environment is an extremely fragile equilibrium. In recent decades, large-scale land use and land cover (LULC) patterns have shown stability, but there have been significant dynamics observed specifically in forest areas and agricultural land. Among these changes, the conversion of maritime pine to eucalyptus stands out as the most relevant transition in terms of forest and agroforestry use classes. This conversion is a result of economic incentives available, which have contributed to the gain in forested areas. This study intends to verify how the alteration of the landscape configuration, represented by different metrics of configuration and diversity, is related to the intensity of human disturbance. The objectives of the study are: (1) to quantify the change in land use/land cover (LULC) patterns and the degree of human disturbance in Mainland Portugal between 1990 and 2018, and (2) to study the relationship between LULC configuration and the impact resulting from human disturbance under different levels of intensity, to understand how changing trends in landscape pattern can serve as indicators to estimate landscape changes resulting from human actions. The Hemeroby index (HI) was calculated to quantify the impacts on ecosystems resulting from disturbance caused by human actions. Based on the Hemeroby index value analysis for the period between 1990 and 2018, the level of naturalness increased in 58% of the country, mainly in the south and east of Portugal. The landscape pattern was quantified using a set of metrics, namely Mean Patch Size (MPS), Mean Shape Index (MSI), Total Edge (TE), Mean Patch Fractal Dimension (MPFD), and Mean Perimeter-Area Ratio (MPAR). To identify the existing statistical correlations between the geometric parameters of the landscape and the HIs values, the Spearman method was used. At the landscape level, the metrics TE and MSI have a strong negative correlation with HI. This suggests that landscapes with greater structural complexity are good indicators of low levels of hemeroby. At the class level, edge density (ED) and mean patch size (MPS) showed a significant correlation with the Hemeroby index for seminatural areas (all metrics), artificial surfaces and forests (TE, MSI, and MPS), agricultural areas (TE, MPS, and MPFD), and Bare Ground (TE, MSI, and MPFD).

Past and future land use change dynamics: assessing the impact of urban development on agricultural land in the Pantura Jabar region, Indonesia.

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017-2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87-88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29-86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual.

Assessing effects of climate and technology uncertainties in large natural resource allocation problems

Abstract. The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible, investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. In this paper, we apply the SCEQ algorithm (Cai and Judd, 2023) to solve a large-scale dynamic stochastic global land resource use problem with stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters and bounded shocks, the range of land conversion is considerably smaller for the dynamic stochastic model than for deterministic scenario analysis.

Overview of Landslide Trends in the World and in Ukraine

Purpose. The main purpose of the study is to systematize and analyse in-depth current trends in the field of landslide prevention and development in different countries of the world, especially in Ukraine, as well as to consider and highlight the main factors that cause landslides, and to study in detail the methods of their prediction and minimization of relevant risks. Methodology. Landslides are a serious geological hazard. Currently, there is a tendency to increase the frequency of landslides due to anthropogenic impact worldwide. The natural processes of the geological environment, together with the development of mineral deposits, massive and large-scale land development, lead to the intensification of landslide processes. The cause of landslide formation is an imbalance between the shear force of gravity and the retaining forces. Findings. The number of landslides in recent years has been analysed and their distribution on different continents has been considered in detail. The causes of landslides in Ukraine and other countries of the world are identified and systematized. The urgency of the problem of landslide phenomena in Ukraine is considered, and the need for progressive study of landslide formation processes in order to prevent them is emphasized. The importance of continuous monitoring of slope stability is emphasized to reduce risks and improve safety. Originality. A comprehensive study of current trends in the occurrence and development of landslides in different countries of the world and Ukraine in particular was carried out, taking into account the latest advances in the field of geology, geomechanics and construction. Based on the analysis, conclusions were drawn about the need for progressive study of landslide processes in order to prevent them. Practical value. The results of the review of landslide prevention trends can be effectively used to improve natural risk management strategies, as well as to develop new and improve existing engineering measures aimed at preventing landslides. This, in turn, will help to improve the safety of both the population and critical infrastructure.

Those with constant property rights have perseverance: China’s land tenure reform promotes environment-friendly farming practices

Few studies have used large-scale household-level panel data to quantify the impact of land tenure reform on environment-friendly farming practices from a chemical fertiliser reduction perspective. This study attempts to fill this gap by exploring the relationship between land certification and farmers’ chemical fertiliser use. It employs nationally representative panel data from the Chinese Family Database and a propensity score matching and difference-in-difference approach. The results show that land certification significantly reduces the usage of chemical fertilisers, mainly by increasing long-term investment, promoting land renting, and inhibiting off-farm employment. This effect varies greatly between farmers with and without land certification experience before the new round of land certification, although no significant difference exists based on whether farmers have previous land adjustment experience. Furthermore, land certification also helps increase the probability of straw recycling and farm manure use. Accordingly, developing countries can combine land tenure reform with environment-friendly farming practices. Through large-scale land certification reforms, and the continuous enhancement of land tenure security, a solid foundation for a land tenure system for sustainable agricultural development can be established.