Search Results (402)

Understanding the spatio-temporal evolution of farmland carbon emissions, disentangling their underlying driving forces, and exploring the decoupling relationship between these emissions and economic development are pivotal to advancing low-carbon and high-quality agricultural development in Shandong Province, China. Using the Logarithmic Mean Divisia Index (LMDI) and Tapio decoupling model, this study conducted a comprehensive analysis of panel data from 16 cities in Shandong Province spanning 2004–2023. This research reveals that the total farmland carbon emissions in Shandong Province followed a trajectory of “initial fluctuating increase and subsequent steady decline” during the study period. The emissions peaked at 29.4 million tons in 2007 and then declined to 20.2 million tons in 2023, representing a 26.0% reduction compared to the 2004 level. Farmland carbon emission intensity in Shandong Province showed an overall downward trend over the period 2004–2023, with the 2023 intensity registering a 68.9% decline compared to 2004. The carbon emission intensity, agricultural structure, and labor effects acted as inhibiting factors on farmland carbon emissions in Shandong Province, while the economic development effect exerted a positive driving impact on the growth of such emissions. Over the 20-year period, these four factors cumulatively contributed to a reduction of 2.1 × 10⁵ tons in farmland carbon emissions. During 2004–2013, the farmland carbon emissions in Zaozhuang, Yantai, Jining, Linyi, Dezhou, Liaocheng, and Heze showed a weak decoupling state, while in 2014–2023, the farmland carbon emissions and economic development in all cities of Shandong Province showed a strong decoupling state. In the future, it is feasible to reduce farmland carbon emissions in Shandong Province by improving agricultural resource utilization efficiency through technological progress, adopting advanced low-carbon technologies, and promoting the transformation of agricultural industrial structures towards “high-value and low-carbon” designs. Full article

The intensifying global climate warming caused by human activities poses severe challenges to ecosystem stability. Constructing an ecological security pattern can identify ecological land supply and an effective spatial distribution baseline and provide a scientific basis for safeguarding regional ecological security. This study analyzes land-use data from 2000 to 2020 for Golog Tibetan Autonomous Prefecture. The PLUS model was utilized to project land-use potential for the year 2030. The InVEST model was employed to conduct a comprehensive assessment of habitat quality in the study area for both 2020 and 2030, thereby pinpointing ecological sources. Critical ecological restoration zones were delineated by identifying ecological corridors, pinch points, and barrier points through the application of the Minimum Cumulative Resistance model and circuit theory. By comparing ecological security patterns (ESPs) in 2020 and 2030, we proposed a dynamic restoration framework and optimization recommendations based on habitat quality changes and ESPs. The results indicate significant land-use changes in the eastern part of Golog Tibetan Autonomous Prefecture from 2020 to 2030, with large-scale conversion of grasslands into bare land, farmland, and artificial surfaces. The ecological security pattern is threatened by risks like the deterioration of habitat quality, diminished ecological sources as well as pinch points, and growing barrier points. Optimizing the layout of ecological resources, strengthening barrier zone restoration and pinch point protection, and improving habitat connectivity are urgent priorities to ensure regional ecological security. This study offers a scientific foundation for the harmonization of ecological protection and economic development and the policy development and execution of relevant departments. Full article

High-standard farmland fertility enhancement projects can lead to the sustainable utilization of arable land resources. However, due to difficulties in project implementation and uncertainties in costs, resource allocation efficiency is constrained. To address these challenges, this study first analyzes the impact of geography and engineering characteristics on cost indicators and applies principal component analysis (PCA) to extract key influencing factors. A hybrid prediction model is then constructed by integrating the Northern Goshawk Optimization (NGO) algorithm with a Backpropagation Neural Network (BP). The NGO–BP model is compared with the RF, XGBoost, standard BP, and GA–BP models. Using data from China’s 2025 high-standard farmland fertility enhancement projects, empirical validation shows that the NGO–BP model achieves a maximum RMSE of only CNY 98.472 across soil conditioning, deep plowing, subsoiling, and fertilization projects—approximately 30.74% lower than those of other models. The maximum MAE is just CNY 88.487, a reduction of about 32.97%, and all R² values exceed 0.914, representing an improvement of roughly 5.83%. These results demonstrate that the NGO–BP model offers superior predictive accuracy and generalization ability compared to other approaches. The findings provide a robust theoretical foundation and technical support for agricultural resource management, the construction of projects, and project investment planning. Full article

Due to prolonged irrigation from the Yellow River, a large area of farmland in the Hetao Oasis has undergone different degrees of salinization and alkalization, leading to reduced crop yields and incapable soil for plant growth. To enhance the productivity of the farmland with saline-alkali soils, it is important to select salt-tolerant economic plant species that are capable of growing under the local climate and soil conditions in the Hetao Oasis. We conducted the experiment by planting Ziziphus jujuba var. spinose, Elaeagnus angustifolia, Hippophae rhamnoides and Lycium chinense in the Bayan Taohai Farm of the Hetao Oasis. Changes of plant growth (the survival rate, plant height, canopy, basal diameter and new branch length) and soil physicochemical properties (soil organic carbon, total carbon, total nitrogen, pH, electrical conductivity and particle size distribution) were continuously monitored during two growing seasons. Results indicated that, by the end of the first growing season, the survival rate of the Z. jujuba was less than 10%, making it unsuitable for plantation in the saline-alkali soils of the Hetao Oasis. In terms of plant growth, the E. angustifolia exhibited the highest survival rate (94.71%) and the fastest growth rate, indicating that E. angustifolia is adapted in the saline-alkali soils of the Hetao Oasis. The survival rates for L. chinense and H. rhamnoides were 86.46% and 65.64%, respectively, indicating that these species could grow in the saline-alkali soils, but at a slower rate. From the perspective of soil improvement, E. angustifolia, H. rhamnoides and L. chinense could reduce the soil pH, and E. angustifolia could significantly increase soil nutrients. In conclusion, it is not recommended to plant Z. jujuba, while the E. angustifolia is recommended as a proper economic species to be widely planted in the saline-alkali soils of the Hetao Oasis. H. rhamnoides could be selectively planted in areas with better soil conditions, and the L. chinense could be planted following soil improvement measurements. The research enhanced the effective utilization of the saline-alkali farmland and provided proper economic plant species for sustainable agriculture management in the Hetao Oasis of Inner Mongolia. Full article

As the global population continues to rise, the impact of urbanization on land utilization and ecosystems are growing more pronounced, particularly within the expanding area of Asia. The land use/land change (LULC) brought by urban expansion directly impacts plant growth and ecological productivity, altering the carbon cycle and climate regulation functions of the region. This research focuses on Harbin City as a case study, employing an enhanced version of the Carnegie–Ames–Stanford Approach (CASA) model to analyze the spatial–temporal variations in vegetation Net Primary Productivity (NPP) across the area from 2000 to 2020. The findings indicate that Net Primary Productivity (NPP) in Harbin exhibited notable interannual variability and spatial heterogeneity. From 2000 to 2005, a decline in NPP was observed across 60.75% of the area. This reduction was predominantly concentrated in the central and eastern areas of the city, where forested landscapes are the dominant feature. In contrast, from 2010 to 2015, 92.12% of the region saw an increase in NPP, closely related to the overall improvement in NPP across all land-use types. Land-use change significantly influenced NPP dynamics. Between 2000 and 2005, 54.26% of NPP increases stemmed from the transition of farmland into forest, highlighting the effectiveness of the “conversion of farmland back to forests” policy. From 2005 to 2010, 98.6% of the area experienced NPP decline, mainly due to forest and cropland degradation, especially the unstable carbon sink function of forest ecosystems. Between 2010 and 2015, NPP improved across 96.86% of the area, driven by forest productivity recovery and better agricultural management. These results demonstrate the profound and lasting impact of land-use transitions on the spatiotemporal dynamics of NPP. Full article

Soil heavy metal pollution threatens agricultural safety and human health, with Cd exceeding standards being the most common problem in contaminated farmland. The development of hyperspectral remote sensing technology has provided a novel methodology of quickly and non-destructively monitoring heavy metal contamination in soil. This study aims to explore the potential of an interpretable Stacking ensemble learning model for the estimation of soil Cd contamination in farmland hyperspectral data. We assume that this method can improve the modeling accuracy. We chose Zhangjiagang City, Jiangsu Province, China, as the study area. We gathered soil samples from wheat fields and analyzed soil spectral data and Cd level in the lab. First, we pre-processed the spectra utilizing fractional-order derivative (FOD) and standard normal variate (SNV) transforms to highlight the spectral features. Second, we applied the competitive adaptive reweighted sampling (CARS) feature selection algorithm to identify the significant wavelengths correlated with soil Cd content. Then, we constructed and compared the estimation accuracy of multiple machine learning models and a Stacking ensemble learning method and utilized the Optuna method for hyperparameter optimization. Ultimately, the SHAP method was used to shed light on the model’s decision-making process. The results show that (1) FOD can further highlight the spectral features, thereby strengthening the correlation between soil Cd content and wavelength; (2) the CARS algorithm extracted 3.4–6.8% of the feature wavelengths from the full spectrum, and most of them were the wavelengths with high correlation with soil Cd; (3) the optimal estimation precision was achieved using the FOD1.5-SNV spectral pre-processing combined with the Stacking model (R² = 0.77, RMSE = 0.05 mg/kg, RPD = 2.07), and the model effectively quantitatively estimated soil Cd contamination; and (4) SHAP further revealed the contribution of each base model and characteristic wavelengths in the Stacking modeling process. This research confirms the advantages of the interpretable Stacking model in hyperspectral estimation of Cd contamination in farmland wheat soil. Furthermore, it offers a foundational reference for the future implementation of quantitative and non-destructive regional monitoring of heavy metal contamination in farmland soil. Full article

The arid and semi-arid regions of Northwest China, as major agricultural production zones, have long faced dual challenges: increasing water resource pressure and severe supply–demand imbalances caused by the expansion of cultivated land. The crop water footprint, an effective indicator for quantifying agricultural water use, plays a crucial role in supporting sustainable development in the region. This study adopted a multi-scale spatiotemporal analysis framework, combining the CROPWAT model with Geographic Information System (GIS) techniques to investigate the spatiotemporal evolution of crop water footprints in Northwest China from 2000 to 2020. The Logarithmic Mean Divisia Index (LMDI) model was used to analyze spatial variations in the driving forces. A multidimensional evaluation system—encompassing structural, economic, ecological, and sustainability dimensions—was established to comprehensively assess agricultural water resource utilization in the region. Results indicated that the crop water footprint in Northwest China followed a “decline-increase-decline” trend, it increased from 90.97 billion m³ in 2000 to a peak of 133.49 billion m³ in 2017, before declining to 129.30 billion m³ in 2020. The center of the crop water footprint gradually shifted northward—from northern Qinghai to southern Inner Mongolia—mainly due to rapid farmland expansion and increasing water consumption in northern areas. Policy and institutional effect, together with economic development effect, were identified as the primary drivers, contributing 49% in total. Although reliance on blue water has decreased, the region continues to experience moderate water pressure, with sustainable use achieved in only half of the study years. Water scarcity remains a pressing concern. This study offers a theoretical basis and policy recommendations to enhance water use efficiency, develop effective management strategies, and promote sustainable water resource utilization in Northwest China. Full article

Autonomous navigation technology plays a crucial role in advancing smart agriculture by enhancing operational efficiency, optimizing resource utilization, and reducing labor dependency. With the rapid integration of information technology, modern agricultural machinery increasingly incorporates advanced techniques such as high-precision positioning, environmental perception, path planning, and path-tracking control. This paper presents a comprehensive review of recent advancements in these core technologies, systematically analyzing their methodologies, advantages, and application scenarios. Despite notable progress, considerable challenges persist, primarily due to the unstructured nature of farmland, varying terrain conditions, and the demand for robust and adaptive control strategies. This review also discusses current limitations and outlines prospective research directions, aiming to provide valuable insights for the future development and practical deployment of autonomous navigation systems in agricultural machinery. Future research is expected to focus on enhancing multi-modal perception under occlusion and variable lighting conditions, developing terrain-aware path planning algorithms that adapt to irregular field boundaries and elevation changes and designing robust control strategies that integrate model-based and learning-based approaches to manage disturbances and non-linearity. Furthermore, tighter integration among perception, planning, and control modules will be crucial for improving system-level intelligence and coordination in real-world agricultural environments. Full article

In the face of increasingly complex urban challenges, a critical question arises: can urban ecosystems maintain resilience, vitality, and sustainability when confronted with external threats and pressures? Taking Kunming—a plateau-mountainous city in China—as a case study, this research constructs an urban ecosystem resilience (UER) assessment model based on the DPSIR (Driving forces, Pressures, States, Impacts, and Responses) framework. A total of 25 indicators were selected via questionnaire surveys, covering five dimensions: driving forces such as natural population growth, annual GDP growth, urbanization level, urban population density, and resident consumption price growth; pressures including per capita farmland, per capita urban construction land, land reclamation and cultivation rate, proportion of natural disaster-stricken areas, and unit GDP energy consumption; states measured by Evenness Index (EI), Shannon Diversity Index (SHDI), Aggregation Index (AI), Interspersion and Juxtaposition Index (IJI), Landscape Shape Index (LSI), and Normalized Vegetation Index (NDVI); impacts involving per capita GDP, economic density, per capita disposable income growth, per capita green space area, and per capita water resources; and responses including proportion of natural reserve areas, proportion of environmental protection investment to GDP, overall utilization of industrial solid waste, and afforestation area. Based on remote sensing and other data, indicator values were calculated for 2006, 2011, and 2016. The entire-array polygon indicator method was used to visualize indicator interactions and derive composite resilience index values, all of which remained below 0.25—indicating a persistent low-resilience state, marked by sustained economic growth, frequent natural disasters, and declining ecological self-recovery capacity. Forecasting results suggest that, under current development trajectories, Kunming’s UER will remain low over the next decade. This study is the first to integrate the DPSIR framework, entire-array polygon indicator method, and Grey System Forecasting Model into the evaluation and prediction of urban ecosystem resilience in plateau-mountainous cities. The findings highlight the ecosystem’s inherent capacities for self-organization, adaptation, learning, and innovation and reveal its nested, multi-scalar resilience structure. The DPSIR-based framework not only reflects the complex human–nature interactions in urban systems but also identifies key drivers and enables the prediction of future resilience patterns—providing valuable insights for sustainable urban development. Full article

The sole function of cultivated land of agricultural production is insufficient to meet the diverse demands of modern agriculture. To address land-use conflicts and achieve the United Nations Sustainable Development Goals (SDGs) of zero hunger and reduced carbon emissions by 2030, this study introduces the theory of land sparing and sharing, uses landscape indices to identify spatially fragmented areas, employs a four-quadrant model to assess the matching status of functional supply and demand, and applies correlation analysis to determine the trade-off/synergy relationships between functions. The results indicate the following: (1) Zhengzhou’s farmland landscape exhibits characteristics of low density, low continuity, and high aggregation, with separation zones and sharing zones accounting for 77% and 23% of the total farmland area, respectively. (2) The multifunctional supply (high in the northeast, low in the southwest) and demand (high in the west, low in the east) of farmland show significant mismatches, with PF and EF exhibiting the most pronounced supply–demand mismatches. The “LS-LD and HS-LD” types of farmland account for the largest proportions, at 39% and 35%, respectively. (3) The study area is divided into four primary types: “PCZ, RLZ, BDZ, and MAZ” to optimize supply–demand relationships and utilization patterns. This study enriches the application of land sparing and sharing in related fields, providing important references for policymakers in optimizing land-use allocation and balancing food and ecological security. Full article

The Soil and Water Assessment Tool (SWAT) was utilized to analyze the spatiotemporal distribution patterns of composite non-point source pollution in the Yapu Port Basin, China, and to quantify the pollutant load contributions from various sources. Scenario-based simulations were designed to assess the effectiveness of different mitigation strategies, focusing on both agricultural and urban non-point source pollution control. The watershed was divided into 39 sub-watersheds and 106 hydrologic response units (HRUs). Model calibration and validation were conducted using the observed data on runoff, total phosphorus (TP), and total nitrogen (TN). The results demonstrate good model performance, with coefficients of determination (R²) ≥ 0.85 and Nash–Sutcliffe efficiencies (NSEs) ≥ 0.84, indicating its applicability to the study area. Temporally, pollutant loads exhibited a positive correlation with precipitation, with peak values observed during the annual flood season. Spatially, pollution intensity increased from upstream to downstream, with the western region of the watershed showing higher loss intensity. Pollution was predominantly concentrated in the downstream region. Based on the composite source analysis, a series of management measures were designed targeting both agricultural and urban non-point source pollution. Among individual measures, fertilizer reduction in agricultural fields and the establishment of vegetative buffer strips demonstrated the highest effectiveness. Combined management strategies significantly enhanced pollution control, with average TN and TP load reductions of 22.18% and 22.70%, respectively. The most effective scenario combined fertilizer reduction, improved urban stormwater utilization, vegetative buffer strips, and grassed swales in both farmland and orchards, resulting in TN and TP reductions of 67.2% and 56.2%, respectively. Full article

By constructing a comprehensive evaluation index system for the development level of Agricultural Machinery Socialization Services (AMSS) in China’s hilly and mountainous areas, the article adopts the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) entropy weight method to carry out a comprehensive evaluation of the development level of AMSS in China’s 17 major hilly mountainous provinces, and utilizes the obstacle degree model and the Dagum Gini coefficient decomposition method to deeply explore the developmental constraints and regional differences in characteristics. The results of the study show that the development level of AMSS in all provinces is generally on the rise, and the overall development level of the Southwest region is relatively lagging behind, with significant differences from other regions. The obstacle degree model shows that industrial development, Government funding, and farmland construction are the main factors constraining AMSS in hilly and mountainous areas, specifically, the degree of coverage of AMSS, the percentage of agricultural machinery professional cooperatives, the degree of land fragmentation, and the level of agricultural machinery extension inputs have a greater impact on the level of development of AMSS. Dagum Gini coefficient calculations show that the overall relative differences in development levels have a tendency to decrease, but the level of development of agricultural machinery socialization in the southwestern hilly and mountainous second-maturity areas is still low, with an imbalance in development within the region and a more significant gap with the development levels of other hilly and mountainous regions. Full article

Anthropogenic activities on soil layers contribute to reworking and eventual modification, which, in most cases, are detrimental to the soil. Going by the significance of soil to life in many ramifications, it is imperative that its consistent assessment enhances and guides management practices. This study focuses on delineating soil structure alterations using ground-penetrating radar (GPR), a geophysical survey method. The principle of operation and the simplicity of the technique have attracted the choice of the non-destructive testing (NDT) method with a view that it could circumvent the drawbacks that characterized the conventional methods hitherto used for such evaluation. Furthermore, the technique allows for the spatial investigation of the concealing sub-layer of the soil and, thus, informs its choice. A test site was selected on a plain farmland in Kraków, Poland, where some parts of the soil structure distortions were induced using tractor movement, which exerted normal stress from the soil surface layer. Subsequently, GPR measurements were acquired via pre-established profiles on the test site, and soil samples were taken for the laboratory evaluation of some of the soil’s physical properties. An analysis of the field data revealed that zones of distorted soil structures have lower attenuation effects on the GPR signal, with corresponding lower amplitude values compared with the unaltered soil structure zones. Evaluated physical properties such as bulk density and state variables like moisture water contents also show a declining trend from the unaltered soil structure zone to the altered zones. The results have revealed characteristic signatures of the zone of soil structure alterations from GPR scanning that can enhance its identification and characterization in the field and, thus, promote decision making toward the effective utilization and management of soil. Full article

The low availability of phosphorus (P) in soil has become a critical factor limiting crop growth and agricultural productivity. This study aimed to isolate and evaluate a bacterial strain with high phosphate-solubilizing capacity to improve soil phosphorus utilization and promote crop growth. A phosphate-solubilizing bacterium, designated as YS-13, was isolated from farmland soil in Henan Province, China, and identified as Brevibacillus laterosporus based on morphological characteristics, physiological and biochemical traits, and 16S rDNA sequence analysis. Qualitative assessment using plate assays showed that strain YS-13 formed a prominent phosphate solubilization zone on organic and inorganic phosphorus media containing lecithin and calcium phosphate, with D/d ratios of 2.28 and 1.57, respectively. Quantitative evaluation using the molybdenum–antimony colorimetric method revealed soluble phosphorus concentrations of 21.24, 6.67, 11.73, and 17.05 mg·L⁻¹ when lecithin, ferric phosphate, calcium phosphate, and calcium phytate were used as phosphorus sources, respectively. The fermentation conditions for YS-13 were optimized through single-factor experiments combined with response surface methodology, using viable cell count as the response variable. The optimal conditions were determined as 34 °C, 8% inoculum volume, initial pH of 7.55, 48 h incubation, 5 g L⁻¹ NaCl, 8.96 g L⁻¹ glucose, and 8.86 g L⁻¹ peptone, under which the viable cell count reached 6.29 × 10⁸ CFU mL⁻¹, consistent with the predicted value (98.33%, p < 0.05). The plant growth-promoting effect of YS-13 was further validated through a pot experiment using Triticum aestivum cv. Jinchun 6. Growth parameters, including plant height, fresh biomass, root length, root surface area, root volume, and phosphorus content in roots and stems, were measured. The results demonstrated that YS-13 significantly enhanced wheat growth, with a positive correlation between bacterial concentration and growth indicators, although the growth-promoting effect plateaued at higher concentrations. This study successfully identified a high-efficiency phosphate-solubilizing strain, YS-13, and established optimal culture conditions and bioassay validation, laying a foundation for its potential application as a microbial inoculant and providing theoretical and technical support for reducing phosphorus fertilizer inputs and advancing sustainable agriculture. Full article

To address the issues of high computational complexity and boundary feature loss encountered when extracting farmland information from high-resolution remote sensing images, this study proposes an innovative CNN–Transformer hybrid network, MAMNet. This framework integrates a lightweight encoder, a global–local Transformer decoder, and a bidirectional attention architecture to achieve efficient and accurate farmland information extraction. First, we reconstruct the ResNet-18 backbone network using deep separable convolutions, reducing computational complexity while preserving feature representation capabilities. Second, the global–local Transformer block (GLTB) decoder uses multi-head self-attention mechanisms to dynamically fuse multi-scale features across layers, effectively restoring the topological structure of fragmented farmland boundaries. Third, we propose a novel bidirectional attention architecture: the Detail Improvement Module (DIM) uses channel attention to transfer semantic features to geometric features. The Context Enhancement Module (CEM) utilizes spatial attention to achieve dynamic geometric–semantic fusion, quantitatively distinguishing farmland textures from mixed ground cover. The positional attention mechanism (PAM) enhances the continuity of linear features by strengthening spatial correlations in jump connections. By cascading front-end feature module (FEM) to expand the receptive field and combining an adaptive feature reconstruction head (FRH), this method improves information integrity in fragmented areas. Evaluation results on the 2022 high-resolution two-channel image dataset from Chenggong District, Kunming City, demonstrate that MAMNet achieves an mIoU of 86.68% (an improvement of 1.66% and 2.44% over UNetFormer and BANet, respectively) and an F1-Score of 92.86% with only 12 million parameters. This method provides new technical insights for plot-level farmland monitoring in precision agriculture. Full article