Search Results (1,492)

The Island of Unguja in Zanzibar (Tanzania) has experienced an accelerated urban development growth since the 1990s due to a rapidly increasing population. These rapid land demands put additional stress on the country’s ability to plan urban centers, cities, and the management of natural resources. The study aimed to determine the impact of urbanization on groundwater availability in the catchment area of the Masingini–Mwanyanya forest reserves from 1992 to 2022. The study used a detection approach to determine the Land Use Land Cover (LULC) changes for three decades, starting from 1992 to 2022. Landsat remote sensed images of 1992, 2002, 2012, and 2022 were used. Additionally, a paired t-test was conducted to determine the significant changes in mean population growth, urbanization, and humidity. The aquifer recharge evolution analysis was conducted using the QGIS software (3.34.8 released version). Obtained results revealed that for these three decades, the forest areas decreased by 14.5% (i.e., from 8.3 km² in 1992 to 7.1 km² in 2022), while built-up area increased from 0 km² in 1992 to 1.7 km² in 2022. Moreover, the evolution of undesirable Land Use Land Cover (LULC) changes, particularly the persistent conversion of forested areas into built-up zones, has been detected. This trend poses a significant threat to the sustainable management of water resources and catchment forest reserves. The study also indicated a decline in the recharge of the coastal aquifer supplying Zanzibar City, which decreased from 15.5 Mm³ to 11.1 Mm³. These findings highlight that the Masingini Forest Reserve is increasingly encroached by rapid urbanization, which is a phenomenon that may jeopardize the availability and sustainability of groundwater resources in the catchment without proper urban planning. Based on these results, the study recommends further research and upscaling of the existing findings, as well as collaboration with relevant authorities to redefine the Masingini–Mwanyanya forest catchment area to ensure the sustainable use of groundwater resources. Full article

Automated vehicle (AV) technology is expected to alter travel behavior and residential location choices, yet the psychological motivations behind relocation decisions under current partial automation (Level 2) remain underexplored, as most studies focus on fully autonomous scenarios. This study explores why individuals might relocate in response to AV availability in both short-term and long-term contexts and predicts how willingness to relocate changes as automation levels advance. In a survey of Kentucky residents, data were collected on demographic and economic characteristics, travel needs, built environment attributes, AV familiarity, comfort with different automation levels, and willingness to relocate if AVs were available. Multiple machine learning models with Shapley Additive Explanations (SHAP) were used to predict and interpret changes in relocation willingness. Results indicate that greater comfort with high-level automation and higher AV familiarity increase relocation intentions, particularly among men, older adults with higher incomes, and urban residents. SHAP analysis reveals that built environment, age, and comfort with fully autonomous driving are the most influential predictors of changes in relocation willingness. Findings inform land use and housing policy by identifying where perception-driven relocation pressures are likely to emerge and by outlining adaptive tools to guide spatial growth as AV technology advances. Full article

Vast areas of natural rangelands in the Kingdom of Saudi Arabia (KSA) suffer from deterioration due to the scarcity of vegetation cover and poor soil quality. Assessing soil quality in rangelands is crucial to identifying degraded lands and to implementing proper sustainable management practices. In this study, a total data set (TDS) containing 27 physical and chemical soil indicators was generated for three rangelands (Al-Fahyhyl, Al-Sahwa, and Al-Tamryate) in KSA. Principal component analysis (PCA) and analytic hierarchy process (AHP) analysis were employed to establish a minimum data set (MDS) and to evaluate key physical and chemical properties affecting soil quality, along with the associated weight factor for each indicator. Results indicated that the MDS represented ≥70% of the total variability of the TDS and accurately estimated the soil quality index (SQI) based on determined physical and chemical soil properties in the study regions. Linear regression indicated high correlation between SQI-TDS and SQI-MDS, with the R² ranging between 0.51–0.87. On the surface layer (0–30 cm), the MDS contained seven soil indicators (sand, dispersion ratio (DR), mean weight diameter (MWD), bulk density (BD), total organic carbon (TOC), available phosphorus (Pa), and available potassium (Ka)), whereas in the sub-surface layer it contained six indicators (sand, DR, MWD, BD, TOC, Pa, and Ka). In all regions, sand had the largest weight factor (0.4514–0.4835), followed by TOC (0.2441–0.2512). Under the arid climate present in all the study sites, sand and TOC levels are crucial for nutrient retention, soil structure, and water retention. Most of the study areas had very low and low SQI (Al-Fahyhyl, 74.4%; Al-Sahwa, 61.8%; and Al-Tamryate, 81.7%), indicating an immediate need for suitable agricultural practices such as reduced tillage, increased organic amendments, and proper water management. The outcomes of this study offer valuable insights for land managers, legislators, and agricultural stakeholders to pinpoint regions in need of development, conduct comprehensive and continuous monitoring of SQI in rangeland areas, and implement land management plans for rangeland rehabilitation and environmental sustainability. Full article

Soil microorganisms play a crucial role in maintaining soil functionality and ecological balance by participating in key processes such as organic matter decomposition, nutrient cycling, soil structure formation, and plant health support. High-throughput sequencing was utilized in this study to systematically investigate the influence of different crop types, maize (Zea mays), soybean (Glycine max), and Eleutherococcus senticosus, on the communities and assembly mechanisms of soil microorganisms in a cold-temperate agroecosystem. The results reveal that cultivation practices led to significant differences in soil chemical properties compared to fallow land (CK). Total carbon (TC), total nitrogen (TN), and available nitrogen (AN) were significantly lower in CK than in cultivated soils, with the highest values observed in maize treatments among all crop types (p < 0.05). Furthermore, the alpha diversity of bacteria in the maize and soybean treatments was significantly higher than that in CK, while there was no significant difference between the Eleutherococcus senticosus treatment and CK. However, no significant differences were observed in the ACE and Chao1 indices of the soil fungal communities across the four crop types. Beta diversity of bacterial and fungal communities exhibited significant variations under different crop cultivation practices. Specifically, compared with CK, the relative abundance of Sphingomonas, which contributes to the degradation of complex organic compounds, and Gemmatimonas, which plays a role in nitrogen cycling, significantly increased, whereas the relative abundance of Clavaria, a genus capable of decomposing recalcitrant lignin and cellulose, decreased. Analysis of community assemblies revealed that both bacterial and fungal communities were predominantly influenced by deterministic processes across all crop types. This finding provides a scientific basis for maintaining soil fertility in a targeted manner, precisely protecting crop health and optimizing agricultural management efficiently, thereby supporting sustainable agricultural practices. In conclusion, by examining microbial diversity and community dynamics across different crops, along with the underlying environmental factors, this study aims to enhance our understanding of plant–microbe interactions and provide insights for sustainable agricultural practices in cold-temperate regions. Full article

Most Polish commercial dairy farms have expanded their production in recent years through herd increases and milk yield improvements. This study investigates internal and external drivers shaping farmers’ decisions regarding the future scale of milk production on family farms in Poland. The analysis is based on two sources of data. The primary input comes from a survey conducted in 2025 among 549 commercial dairy farms. To situate individual responses within a broader structural context, accounting data from 444 farms that continuously reported to the Polish FADN between 2005 and 2022 were used. Although not central to the analysis, these data illustrate long-term sectoral developments, particularly herd enlargement and resource concentration. The survey demonstrated a diversity of drivers shaping decisions to expand or stabilise milk production in the next five years. Farmers’ individual characteristics play a central role. The farmer’s perceived health and work ability (5-year horizon), as well as the availability of a successor, strongly influence the willingness to expand or maintain milk production levels. Other important factors include tangible resources, organisational capacity, and financial strength, such as herd size, agricultural land area, and investment capacity. This highlights the role of production potential and farm adaptability. External conditions such as land access, lease prices, and the market environment are not decisive by themselves but provide the background against which farmers evaluate their options. The study confirms that no single factor drives changes in dairy farms. What matters most is how farmers configure and align their available resources with external circumstances. The ability to combine human, physical, and financial capital in a coherent and strategic way is essential for shaping production strategies and ensuring the continuity of farm operations. Full article

As part of urban sustainable food strategies, reducing land and emission footprints motivates interest in edible insects (EI) as a sustainable protein source. However, research on the determinants of young consumers’ acceptance and willingness to pay for insect-based foods in Central and Eastern Europe remains limited. This study assessed whether Polish students are willing to pay more for foods containing EI when production is environmentally friendly. The analysis focused on identifying socio-demographic and product-related factors influencing willingness to pay a higher price. Data were collected in November 2023 through a nationwide Computer-Assisted Web Interview (CAWI) conducted via Google Forms among 947 Polish university students. A logistic regression model was applied to determine socio-demographic predictors, while exploratory factor analysis was used to identify latent dimensions of product attributes and food categories. Results revealed that gender and place of residence significantly affected willingness to pay, with women and urban residents showing higher readiness. Attributes related to convenience, availability, sensory appeal, health and nutrition claims, and CO₂ reduction benefits were the strongest positive correlates. The findings suggest pragmatic pathways for introducing insect-based foods into sustainable urban food systems and highlight the role of education in fostering environmentally responsible consumer behavior. Full article

Trakošćan Lake is an artificial lake created in the mid-19th century for aesthetic and economic purposes. The area around the lake has been protected as park forest. Recently, the lake has become the most famous example of eutrophication in Croatia, as by 2022, a significant amount of sediment had accumulated in it. Therefore, the lake was drained that same year, followed by mechanical removal of the sediment. The total amount of sediment removed was 204,000 m³. After the removal work, a particularly important question arose of what to do with such a large amount of sediment. The objective of this research was to gain specific insight into the chemical composition of the sediment with the aim of its possible use in agricultural production for increasing the quality of arable land. A comprehensive qualitative geochemical and agrochemical analysis of the sediment composition was carried out for the first time, including indicators of the pH value, amount of organic matter and carbon, total nitrogen, available phosphorus and potassium, amount of carbonates, and the presence of metals, metalloids, and non-metals, of which As, Cd, Hg, and Pb are toxic. Electrochemical, spectrophotometric, and titration methods were used, along with three atomic absorption spectrometry techniques. The results of the analyses were interpreted in comparison with the natural substrate, as well as with the current regulations for agricultural land in the Republic of Croatia. According to this, sediment is not harmful for the environment. Full article

This paper outlines the inception, conceptualization and primary morphological selection of a robotic system that employs raw lunar regolith for constructing protective berms and shelters on the Moon’s surface. The lunar regolith is considered the most readily available material for in situ resource utilization on the Moon. The lunar environment is characterized, and the operational task is defined, informing the development of high-level system requirements and a functional analysis through the glass-box method. The key morphological areas are identified, and candidate concepts are evaluated using the Analytic Hierarchy Process (AHP). The evaluation process employs a new approach to aggregating expert data through the ZM_II method to establish priorities of the design criteria, which eliminates the need for pairwise comparisons in data collection. Each criterion is associated with a specific and quantifiable metric, which is then used to evaluate the morphologies during the AHP. The selected morphologies are determined as: a vibrating hopper for intake (normalized decision value of 27.5% out of 5 candidate solutions), a roller system for container deployment and filling (26.2% out of 7), a magnetic RCU interface (22.6% out of 7), and a 4-DoF manipulator to place the RCUs in the environment (23.6% out of 5). The final morphology is selected by combining the decision values across the primary morphological areas into a unified decision metric. This is followed by the preliminary selection of the system’s surrounding architecture. The design conceptualization is performed within a real-life operational scenario, namely, to create a blast berm for the landing pad using the lunar regolith provided by an existing excavator. The next phase of the work will include the system’s detailed design, as well as investigations on the requirements for a variety of construction tasks on the lunar surface. Full article

This paper represents the results of ten years of monitoring using satellite imagery and aerial reconnaissance, followed by in-depth analysis utilizing LiDAR data and geomagnetic prospection techniques of the first two Neolithic rondels detected in Croatia—Markušica Brošov salaš and Gorjani Topole. Through the exclusive use of satellite and aerial image analysis, we were able to accurately determine the general size, shape, and number of ditches present at the sites under investigation. The wealth of information obtained from these images was sufficient for us to confidently interpret these formations as Neolithic rondels—meeting all the criteria commonly used. The addition of LiDAR data and geomagnetic prospection further enhanced our understanding by revealing a range of additional features and peculiarities across both sites, including within all identified ditch systems. These advanced methods allowed us to uncover details that would otherwise remain invisible through surface observation alone. Our research demonstrates the remarkable power of publicly available satellite imagery as a primary tool for archeological site detection and preliminary interpretation. The results from Markušica and Gorjani emphasize the scientific necessity of combining complementary remote sensing and geophysical techniques to overcome individual methodological limitations, providing robust documentation and interpretation of prehistoric enclosures in highly transformed landscapes. This research contributes novel insights into Neolithic social landscapes, monumentality, and land use strategies in Croatia while offering a methodological model for archeological prospection applicable across Central and Southeastern Europe. Full article

We present a stochastic method for classifying high-elevation coniferous forest coverage that includes an uncertainty estimate using Landsat images. We evaluate trends in coniferous coverage from 1986 to 2024 in a sub-basin of the Great Salt Lake basin in the western United States This work was completed before the recent release of the extended National Land Cover Database (NLCD) data, so we use the 9 years of NLCD data previously available over the period from 2001 to 2021 for training and validation. We perform 100 draws of 5130 data points each using stratified sampling from the paired NLCD and Landsat data to generate 100 Random Forest Models. Even though extended NLCD data are available, our model is unique as it is trained on high elevation dense coniferous stands and does not classify wester pinyon (Pinus edulis) or Utah juniper (Juniperus osteosperma) shrub trees as “coniferous”. We apply these models, implemented in Google Earth Engine, to the nearly 40-year Landsat dataset to stochastically classify coniferous forest extent to support trend analysis with uncertainty. Model accuracy for most years is better than 94%, comparable to published NLCD accuracy, though several years had significantly worse results. Coniferous area standard deviations for any given year ranged from 0.379% to 1.17% for 100 realizations. A linear fit from 1985 to 2024 shows an increase of 65% in coniferous coverage over 38 years, though there is variation around the trend. The method can be adapted for other specialized land cover categories and sensors, facilitating long-term environmental monitoring and management while providing uncertainty estimates. The findings support ongoing research forest management impacts on snowpack and water infiltration, as increased coniferous coverage of dense fir and spruce increases interception and sublimation, decreasing infiltration and runoff. NLCD data cannot easily be used for this work in the west, as the pinyon (Pinus edulis) and juniper (Juniperus osteosperma) forests are classified as coniferous, but have much lower impact on interception and sublimation. Full article

Red-bed desertification represents a critical form of land degradation in subtropical regions, yet the coupled soil–vegetation processes remain poorly understood. This study integrates Sentinel-2 vegetation indices with soil fertility gradients to assess vegetation–soil interactions in the Nanxiong Basin of South China. By combining Normalized Difference Vegetation Index (NDVI)-based vegetation classification with comprehensive soil property analyses, we aim to uncover the spatial patterns and driving mechanisms of degradation. The results revealed a clear gradient from intact forests to exposed red-bed bare land (RBBL). NDVI classification achieved an overall accuracy of 77.8% (κ = 0.723), with mixed forests being identified most reliably (97.1%), while Red-Bed Bare Land (RBBL) exhibited the highest omission rate. Along this gradient, soil organic matter, available nitrogen, and phosphorus declined sharply, while pH shifted from near-neutral in forests to strongly acidic in bare lands. Principal component analysis (PCA) identified a dominant fertility axis (PC1, explaining 56.7% of the variance), which clustered forested sites in nutrient-rich zones and isolated RBBL as the most degraded state. The observed vegetation–soil pattern aligns with a “weathering–transport–exposure” sequence, whereby physical disintegration and selective erosion during monsoonal rainfall drive organic matter depletion, soil thinning, and acidification, with human disturbance further accelerating these processes. To our knowledge, this study is the first to directly couple PCA-derived soil fertility gradients with vegetation patterns in red-bed regions. By integrating vegetation indices with soil fertility gradients, this study establishes a process-based framework for interpreting red-bed desertification. These findings underscore the utility of remote sensing, especially NDVI classification, as a powerful tool for identifying degradation stages and linking vegetation patterns with soil processes, providing a scientific foundation for monitoring and managing land degradation in monsoonal and semi-arid regions. Full article

Saline–alkali soils represent a significant reserve of arable land, playing a vital role in ensuring national food security. Given that saline–alkali soil has low soil organic matter (SOM) and soil nutrient contents, and that soil quality degradation poses a threat to regional high-quality agricultural development and ecological balance, this study took coastal saline–alkali land as a case study. It adopted the extreme gradient boosting (XGB) model optimized by the tree-structured Parzen estimator (TPE) algorithm, combined with in situ hyperspectral (ISH) and spaceborne hyperspectral (SBH) data, to predict and map soil organic matter and four soil nutrients: alkali nitrogen (AN), available phosphorus (AP), and available potassium (AK). From the research outputs, one can deduce that superior predictive efficacy is exhibited by the TPE-XGB construct, employing in situ hyperspectral datasets. Among these, available phosphorus (R² = 0.67) exhibits the highest prediction accuracy, followed by organic matter (R² = 0.65), alkali-hydrolyzable nitrogen (R² = 0.56), and available potassium (R² = 0.51). In addition, the spatial continuity mapping results based on spaceborne hyperspectral data show that SOM, AN, AP, and AK in soil nutrients in the study area are concentrated in the northern, eastern, southern, and riverbank and estuarine delta areas, respectively. The variability of soil nutrients from large to small is phosphorus, potassium, nitrogen, and organic matter. The SHAP (SHapley Additive exPlanations) analysis results reveal that the bands with the greatest contribution to the fitting of SOM, AN, AP, and AK are 612 nm, 571 nm, 1493 nm, and 1308 nm, respectively. Extending into realms of hierarchical partitioning (HP) and variation partitioning (VP), it is discerned that climatic factors (CLI) alongside vegetative aspects (VEG) wield dominant influence upon the spatial differentiation manifest in nutrients. Meanwhile, comparatively diminished are the contributions possessed by terrain (TER) and soil property (SOIL). In summary, this study effectively assessed the significant variation patterns of soil nutrient distribution in coastal saline–alkali soils using the TPE-XGB model, providing scientific basis for the sustainable advancement of agricultural development in saline–alkali coastal regions. Full article

The land demands of ground-mounted PV systems raise concerns about competition with agriculture, particularly in regions with limited productive farmland. Agrivoltaics, which integrates solar energy generation with agricultural use, offers a potential solution. While agrivoltaics has been extensively studied, less is known about its feasibility and impacts in complex temperate maritime climates such as Aotearoa New Zealand, in particular, the effects of PV-induced shading on ground-level light availability and vegetation. This study modelled the spatial and seasonal distribution of ground-level irradiation and Photosynthetic Photon Flux Density (PPFD) beneath fixed-tilt PV arrays at the Tauhei solar farm in the Waikato region. It quantifies and maps PPFD to evaluate light conditions and its implications for vegetation growth. The results reveal significant spatial and temporal variation over a year. The under-panel ground irradiance is lower than open-field GHI by 18% (summer), 22% (spring), 16% (autumn), and 3% (winter), and this seasonal reduction translates into PPFD gradients. This variation supports a precision agrivoltaic strategy that zones land based on irradiance levels. By aligning crop types and planting schedules with seasonal light profiles, land productivity and ecological value can be improved. These findings are highly applicable in Aotearoa New Zealand’s pasture-based systems and show that effective light management is critical for agrivoltaic success in temperate maritime climates. This is, to our knowledge, the first spatial PPFD zoning analysis for fixed-tilt agrivoltaics, linking year-round ground-light maps to crop/pasture suitability. Full article

To address the difficult problem of maintaining profitable and resilient agriculture under a changed climate, long-term prediction and planning are needed. One approach capable of helping with this endeavour is mathematical modelling and optimisation. Using a temporal framework, this paper outlines a spatio-temporal agricultural land use sequencer (STALS) model, where feasible climate-aware annual crop land uses are determined for a real-world case study region, the Murrumbidgee Irrigation Area in Australia. The results of this approach identified desirable transitions in land use and changes in the production system. The analysis revealed two differing possibilities of land use: one with a concentrated crop mix, the other more diverse. However, both suggest higher-value crops, such as horticultural species, will maximise regional economic benefit with comparable minimal water usage under climate change. To maintain regional agricultural economic benefit under reduced water availability and increased temperature, a transformation of land use is needed. Full article

Agriculture and its allied activities contribute to the primary sector in India and act as the basis for the country’s economy. Available agricultural landholdings are scattered as multiple plots across the country. Land fragmentation has led to problems achieving economies of scale and economies of scope; lower productivity, efficiency, and modernization; loss of biodiversity; and little scope for mechanization and technology. FPOs are small clusters of farmers who collaborate to enhance their bargaining strength through collective procurement, processing, and marketing efforts. To enhance the performance of FPOs at the grassroots level, the engagement of cluster-based business organizations (CBBOs) is vital. Millet FPOs are similar to voluntary farmer groups that are involved in the cultivation and promotion of millets. IIMR-promoted millet FPOs were selected purposively for the present study as they are involved in millet cultivation and farming. A total of 450 millet farmers from 15 FPOs and 3 states were randomly chosen for this action research study. The present research identified 10 key factors and collected farmers’ opinions toward member participation in millet FPOs using interpretive structural modeling. The ISM approach provided a clear understanding of how the selected factors interconnect hierarchically with each other as foundational drivers and dependent outcomes. The results from the MICMAC analysis demonstrated that foundational interventions, such as post-harvest technology availability (V2) and knowledge transfer by KVKs (V5), directly support higher-level objectives. Intermediate factors like economies of scale (V1) and market and credit linkages (V3) transform these services into operational advantages, while the outcome factors of business planning (V8), FPO branding (V7), and bargaining power (V9) emerge as dependent variables. The model demonstrates that V2 catalyzes improvements across the production, market, and institutional domains, cascading through intermediate enablers (V1, V4, V5, V6) to strengthen outcomes (V3, V7, V8, V9, V10). This hierarchy demonstrates that investing in post-harvest technology and complementary extension services is critical for building resilient millet FPOs and enhancing member participation. Full article