Search Results (55)

In this study, we investigated the spatial and temporal dynamics of pork carcass prices across European Union Member States, focusing on the influence of wheat prices and population income levels between 2014 and 2023. Our analysis revealed that both input costs (reflected by wheat price fluctuations) and income-driven demand factors exert significant and spatially correlated effects on pork carcass prices. The results demonstrate the existence of spatial interdependencies among neighboring countries, indicating that price changes in one region may propagate through the broader European market. By integrating spatial econometric techniques within a panel data framework, this research provides empirical evidence of the interconnected nature of EU agricultural markets, advancing the existing literature by demonstrating how input markets and consumer income dynamics jointly shape price behavior within an integrated regional economy. Our findings contribute to a deeper understanding of price transmission mechanisms in the livestock sector and offer valuable insights for policymakers seeking to enhance market efficiency and resilience within the Common Agricultural Policy context. Full article

Energy consumption and environmental pollution pose significant challenges to sustainable development. This study develops a comprehensive coupled framework model that advances the quantitative integration of carbon (C), nitrogen (N), and phosphorus (P) cycles driven by multiple anthropogenic pollution sources. This paper used Panjin city as a case study to analyze the dynamic changes and interconnections among C, N, and P. Results indicated that net anthropogenic carbon inputs (NAI_C) increased by 33% from 2016–2020, while net anthropogenic nitrogen inputs (NAI_N) and net anthropogenic phosphorus inputs (NAI_P) decreased by 14% and 28%, respectively. The primary driver of NAI_C was energy consumption, while wetlands were the dominant carbon sequestration sink. Agricultural production was identified as the primary source of NAI_N and NAI_P, and approximately 4.5% of NAI_N and 2.9% of NAI_P were discharged into receiving water bodies. We demonstrate that human activities and natural processes exhibit dual attributes, producing positive and negative environmental effects. The increase in carbon emissions drives economic growth and industrial restructuring; however, the enhanced economic capacity also strengthens the ability to mitigate pollution through environmental protection measures. Similarly, natural ecosystems, including forests and grasslands, contribute to carbon sequestration and the release of non-point source pollution. The comprehensive environmental impact assessment of C, N, and P revealed that the comprehensive environmental index for Panjin city exhibited an improved trend. The factors of energy structure, energy efficiency, and economic scale promoted NAI_C growth, with the economic scale factor alone accounting for 93% of the total increment. Environmental efficiency factor and population size factor were the primary drivers in reducing NAI_N and NAI_P discharges into the receiving water bodies. We propose a novel management model, ecological restoration, clean energy utilization, resource recycling, and pollution source reduction to achieve systemic governance of C, N, and P inputs. Full article

To explore the coupling between agricultural farming models and surface water environmental in central China’s irrigation districts, this study focuses on the Four Lakes Basin within Jianghan Plain, a key grain-producing and ecological protection area. Integrating remote sensing images, statistical yearbooks, and on-site monitoring data, the study analyzed the phased characteristics of the basin’s agricultural pattern transformation, the changes in non-point source nitrogen and phosphorus loads, and the responses of water quality in main canals and Honghu Lake to agricultural adjustments during the period 2010~2023. The results showed that the basin underwent a significant transformation in agricultural patterns from 2016 to 2023: the area of rice-crayfish increased by 14%, while the areas of dryland crops and freshwater aquaculture decreased by 11% and 4%, respectively. Correspondingly, the non-point source nitrogen and phosphorus loads in the Four Lakes Basin decreased by 11~13%, and the nitrogen and phosphorus concentrations in main canals decreased slightly by approximately 2 mg/L and 0.04 mg/L, respectively; however, the water quality of Honghu Lake continued to deteriorate, with nitrogen and phosphorus concentrations increasing by approximately 0.46 mg/L and 0.06 mg/L, respectively. This indicated that the adjustment of agricultural farming models was beneficial to improving the water quality of main canals, but it did not bring about a substantial improvement in the sustainable development of Honghu Lake. This may be related to various factors that undermine the sustainability of the lake’s aquatic ecological environment, such as climate change, natural disasters, internal nutrient release from sediments, and the decline in water environment carrying capacity. Therefore, to advance sustainability in this basin and similar irrigation districts, future efforts should continue optimizing agricultural models to reduce nitrogen/phosphorus inputs, while further mitigating internal nutrient release and climate disaster risks, restoring aquatic vegetation, and enhancing water environment carrying capacity. Full article

Land is a finite global resource supporting the growing population with food, shelter, recreation, and other environmental benefits. The United States has over 10% of global arable land, contributing to domestic and global food security. The number of farms in the United States has steadily declined with a relatively stable average farm size. Increasing population growth, pressure on food production and environmental sustainability are concerns for cropland decline and food security. This study analyzed the effects of competing land use, agricultural innovation and technology, climate change, and government policy on cropland. Seven decades (1945–2017) of United States Department of Agriculture (USDA) Census of Agriculture datasets were used as a case study to analyze drivers of cropland changes. The total amount of cropland recorded a 13% reduction in 2017 from 1945. Cropland used for pasture decreased by 72%, representing the most substantial proportional decline among the cropland categories. Competing land uses to cropland such as rural parks and wildlife increased over 1000%, urbanized land increased by 395%, and land designated for defense and industrial areas rose by 13% by 2017. The divergence between total factor productivity and farm inputs suggests that productivity gains were driven primarily by technological advancements rather than increased resource use. Linkages were drawn from several studies on climate change and population growth’s negative impact on cropland, whereas government policies and priorities can either influence cropland decline or increase, based on how the policies are structured. This study underscores a strategic planning approach that incorporates technological innovation, climate adaptation, and sustainable land management to balance agricultural output with competing land needs without compromising food security for the growing global population. Full article

Crop yield prediction (CYP) has become increasingly critical in addressing the adverse effects of abnormal climate and enhancing agricultural productivity. This review investigates the application of advanced Artificial Intelligence (AI) techniques including Machine Learning (ML), Deep Learning (DL), Ensemble Learning, and Explainable AI (XAI) to CYP. It also explores the use of remote sensing and imaging technologies, identifies key environmental factors, and analyzes the primary causes of yield reduction. A wide diversity of input features was observed across studies, largely influenced by data availability and specific research goals. Stepwise feature selection was found to be more effective than increasing feature volume in improving model accuracy. Frequently used algorithms include Random Forest (RF) and Support Vector Machines (SVM) for ML, Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for DL, as well as stacking-based ensemble methods. Although XAI remains in the early stages of adoption, it shows strong potential for interpreting complex, multi-dimensional CYP models. Hyperspectral imaging (HSI) and multispectral imaging (MSI), often collected via drones, were the most commonly used sensing techniques. Major factors contributing to yield reduction included atmospheric and soil-related conditions under abnormal climate, as well as pest outbreaks, declining soil fertility, and economic constraints. Providing a comprehensive overview of AI-driven CYP frameworks, this review offers insights that support the advancement of precision agriculture and the development of data-informed agricultural policies. Full article

This study evaluates the application of circular economy principles in the wine sector through a demonstrative case developed within the LIFE Climawin project. The initiative focuses on the local valorization of vineyard residues by producing biochar from vine pruning and using it to enrich compost derived from winemaking by-products and sheep manure. The combined application of these soil amendments aims to improve soil structure, enhance carbon sequestration, and reduce reliance on synthetic fertilizers. A systemic evaluation was conducted using the Ecocanvas methodology—a conceptual framework for mapping circular business models across environmental, economic, and social dimensions. The analysis integrated a targeted literature review, examination of technical data, direct field observations of composting and biochar production, and semi-structured interviews with key stakeholders. Results indicate multiple benefits from localized residue valorization, including improved compost quality, reduced greenhouse gas emissions, potential contributions to long-term soil health, and enhanced resource efficiency. The analysis also highlights economic opportunities, such as reduced dependency on external inputs, and social value creation through local stakeholder engagement. Furthermore, the study identifies factors that enable or constrain the replication and scaling of this model. These findings contribute to frameworks for advancing circular, economically viable, and socially inclusive climate-resilient agricultural systems. Full article

Seasonal dynamics of dissolved organic matter (DOM) in agricultural ditches significantly impact carbon cycling and water quality in connected rivers. This study aimed to characterize seasonal variations in DOM composition and dynamics within hierarchical agricultural ditch systems in Tianjin, northern China. Surface water samples were collected from river channels, main ditches, branch ditches, lateral ditches, and field ditches during wet (June 2021) and dry (December 2021) seasons. DOM characteristics were analyzed using dissolved organic carbon (DOC) quantification, ultraviolet-visible (UV-Vis) absorption spectroscopy, and three-dimensional excitation–emission matrix spectroscopy (3D-EEMs) coupled with parallel factor analysis (PARAFAC). The concentration of DOC in ditch surface water exhibited significant seasonal variations, with significantly higher levels observed during the wet season (Huangzhuang: 6.72 ± 0.7 mg/L; Weixing: 13.15 ± 3.1 mg/L) compared to the dry season (Huangzhuang: 5.93 ± 0.3 mg/L; Weixing: 9.35 ± 2.6 mg/L). Both UV-Vis spectral and EEM-PARAFAC analysis revealed that DOM in ditch systems was predominantly composed of fulvic-like and tryptophan-like components, representing the portion of organic matter in water bodies that is highly biologically active, highly mobile, relatively “fresh”, or “not fully humified”. PARAFAC identified microbial humic-like (C1: wet season 40.36%, dry season 34.42%) and protein-like (C3: wet season 40.3%, dry season 49.87%) components as dominant. DOM sources were influenced by dual inputs from terrestrial and autochthonous origins during the wet season, while primarily deriving from autochthonous sources in the dry season. This study elucidates the advances of spectroscopic techniques in deciphering the composition, sources, and influencing factors of DOM in aquatic systems. The findings support implementing riparian buffer strips and optimized fertilizer management to mitigate seasonal peaks of bioavailable DOM in agricultural ditch systems. Full article

Agriculture is fundamental to food security and environmental sustainability. Advancing its holistic ecological transformation can stimulate socioeconomic progress while fostering human–nature harmony. Utilizing provincial data from mainland China (2013–2022), this research establishes a multidimensional evaluation framework across four pillars: agricultural ecology, low-carbon practices, modernization, and productivity enhancement. Through comprehensive assessment, we quantify China’s low-carbon green agriculture (LGA) development trajectory and conduct comparative regional analysis across eastern, central, and western zones. As for methods, this study employs multiple econometric approaches: LGA was quantified using the TOPSIS entropy weight method at the first step. Moreover, multidimensional spatial–temporal patterns were characterized through ArcGIS spatial analysis, Dagum Gini coefficient decomposition, Kernel density estimation, and Markov chain techniques, revealing regional disparities, evolutionary trajectories, and state transition dynamics. Last but not least, Tobit regression modeling identified driving mechanisms, informing improvement strategies derived from empirical evidence. The key findings reveal the following: 1. From 2013 to 2022, LGA in China fluctuated significantly. However, the current growth rate is basically maintained between 0% and 10%. Meanwhile, LGA in the vast majority of provinces exceeds 0.3705, indicating that LGA in China is currently in a stable growth period. 2. After 2016, the growth momentum in the central and western regions continued. The growth rate peaked in 2020, with some provinces having a growth rate exceeding 20%. Then the growth rate slowed down, and the intra-regional differences in all regions remained stable at around 0.11. 3. Inter-regional differences are the main factor causing the differences in national LGA, with contribution rates ranging from 67.14% to 74.86%. 4. LGA has the characteristic of polarization. Some regions have developed rapidly, while others have lagged behind. At the end of our ten-year study period, LGA in Yunnan, Guizhou and Shanxi was still below 0.2430, remaining in the low-level range. 5. In the long term, the possibility of improvement in LGA in various regions of China is relatively high, but there is a possibility of maintaining the status quo or “deteriorating”. Even provinces with a high level of LGA may be downgraded, with possibilities ranging from 1.69% to 4.55%. 6. The analysis of driving factors indicates that the level of economic development has a significant positive impact on the level of urban development, while the influences of urbanization, agricultural scale operation, technological input, and industrialization level on the level of urban development show significant regional heterogeneity. In summary, during the period from 2013 to 2022, although China’s LGA showed polarization and experienced ups and downs, it generally entered a period of stable growth. Among them, the inter-regional differences were the main cause of the unbalanced development across the country, but there was also a risk of stagnation and decline. Economic development was the general driving force, while other driving factors showed significant regional heterogeneity. Finally, suggestions such as differentiated development strategies, regional cooperation and resource sharing, and coordinated policy allocation were put forward for the development of LGA. This research is conducive to providing references for future LGA, offering policy inspirations for LGA in other countries and regions, and also providing new empirical results for the academic community. Full article

Accurate assessment of green productivity is essential for advancing sustainable agriculture in ecologically fragile regions. This study examined the evolution of agricultural green total factor productivity (AGTFP) in Tibet over the period 2002–2021 by applying a super-efficiency SBM-GML model that accounts for undesirable outputs. We decompose AGTFP into technical change and efficiency change, conduct redundancy analysis to identify sources of inefficiency and explore its spatiotemporal dynamics through kernel density estimation and convergence analysis. Results show that (1) AGTFP in Tibet grew at an average annual rate of 0.78%, slower than the national average of 1.6%; (2) labor input, livestock scale, and agricultural carbon emissions are major sources of redundancy, especially in pastoral regions; (3) technological progress is the main driver of AGTFP growth, while efficiency gains have a limited impact, reflecting a technology-led growth pattern; (4) AGTFP follows a “convergence-divergence-reconvergence” trend, with signs of conditional β convergence after controlling for regional heterogeneity. These findings highlight the need for region-specific green agricultural policies. Priority should be given to improving green technology diffusion and input allocation in high-altitude pastoral areas, alongside strengthening ecological compensation and interregional coordination to enhance green efficiency and promote high-quality development across Tibet. Full article

We propose a dynamic monitoring and precision fertilization decision system for agricultural soil nutrients, integrating UAV remote sensing and GIS technologies to address the limitations of traditional soil nutrient assessment methods. The proposed method combines multi-source data fusion, including hyperspectral and multispectral UAV imagery with ground sensor data, to achieve high-resolution spatial and spectral analysis of soil nutrients. Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). The experimental validation compared the proposed system against two conventional approaches: (1) laboratory soil testing with standardized fertilization recommendations and (2) satellite NDVI-based fertilization. Field trials across three distinct agroecological zones demonstrated that the proposed system reduced fertilizer inputs by 18–27% while increasing crop yields by 4–11%, outperforming both conventional methods. Furthermore, an intelligent fertilization decision model generates tailored fertilization plans by analyzing real-time soil conditions, crop demands, and climate factors, with continuous learning enhancing its precision over time. The system also incorporates GIS-based visualization tools, providing intuitive spatial representations of nutrient distributions and interactive functionalities for detailed insights. Our approach significantly advances precision agriculture by automating the entire workflow from data collection to decision-making, reducing resource waste and optimizing crop yields. The integration of UAV remote sensing, dynamic modeling, and machine learning distinguishes this work from conventional static systems, offering a scalable and adaptive framework for sustainable farming practices. Full article

Soil available nitrogen (AN) is a critical nutrient for plant absorption and utilization. Accurately mapping its spatial distribution is essential for improving crop yields and advancing precision agriculture. In this study, 188 AN soil samples (0–20 cm) were collected at Heshan Farm, Nenjiang County, Heihe City, Heilongjiang Province, in 2023. The soil available nitrogen content ranged from 65.81 to 387.10 mg kg⁻¹, with a mean value of 213.85 ± 61.16 mg kg⁻¹. Sentinel-2 images and normalized vegetation index (NDVI) and enhanced vegetation index (EVI) time series data were acquired on the Google Earth Engine (GEE) platform in the study area during the bare soil period (April, May, and October) and the growth period (June–September). These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. The accuracy of different strategies was evaluated using 5-fold cross-validation. The research results indicate that (1) the introduction of growth information at different growth periods of soybean and maize has different effects on the accuracy of soil AN mapping. In soybean plantations, the introduction of EVI data during the pod setting period increased the mapping accuracy R² by 0.024–0.088 compared to other growth periods. In maize plantations, the introduction of EVI data during the grouting period increased R² by 0.004–0.033 compared to other growth periods, which is closely related to the nitrogen absorption intensity and spectral response characteristics during the reproductive growth period of crops. (2) Combining the crop types and their optimal period growth information could improve the mapping accuracy, compared with only using the bare soil period image (R² = 0.597)—the R² increased by 0.035, the root mean square error (RMSE) decreased by 0.504%, and the mapping accuracy of R² could be up to 0.632. (3) The mapping accuracy of the bare soil period image differed significantly among different months, with a higher mapping accuracy for the spring data than the fall, the R² value improved by 0.106 and 0.100 compared with that of the fall, and the month of April was the optimal window period of the bare soil period in the present study area. The study shows that when mapping the soil AN content in arable land, different crop types, data collection time, and crop growth differences should be considered comprehensively, and the combination of specific crop types and their optimal period growth information has a greater potential to improve the accuracy of mapping soil AN content. This method not only opens up a new technological path to improve the accuracy of remote sensing mapping of soil attributes but also lays a solid foundation for the research and development of precision agriculture and sustainability. Full article

This study employs a Super-SBM model to construct a comprehensive evaluation framework—encompassing input factors, desirable outputs, and undesirable outputs—to measure agricultural green total factor productivity (AGTFP) in the Tibet Autonomous Region in the period 2002–2021. We then apply kernel density estimation and Dagum Gini coefficient decomposition to examine its spatiotemporal evolution. The main findings are as follows: (1) AGTFP in Tibet rose overall from 0.949 in 2002 to 1.068 in 2021, with a compound annual growth rate of 0.78%, yet remained below the national average; (2) significant regional heterogeneity emerged, with three typical evolution patterns identified: continual improvement (Nagqu, Qamdo), stable fluctuation (Lhasa, Xigazê), and risk of decline (Lhoka, Nyingchi, Ngari); (3) gains in pure technical efficiency were the primary driver of AGTFP growth, while insufficient scale efficiency was a key constraint; (4) AGTFP exhibited a “convergence–divergence–reconvergence” dynamic, with interregional disparities widening but structural patterns stabilizing; and (5) interregional inequality was the main source of overall disparity—its importance grew over the study period, with the largest gap observed between agrarian and pastoral zones. On this basis, we recommend a “gradient advancement” strategy that prioritizes pure technical efficiency and regional coordination, while promoting zone-specific support tools tailored to local ecological conditions and institutional capacities to ensure inclusive green productivity growth. Full article

Achieving the twin objectives of increasing economic efficiency and lowering environmental pollution in agricultural modernization requires regions to increase agricultural energy efficiency. This study used the DEA-EBM model, an efficiency evaluation tool, to measure and analyze the agricultural energy efficiency and input redundancy of 30 Chinese regions from 2000 to 2022. It also examined the factors that influence these metrics. According to the findings, China’s agricultural energy efficiency varies greatly by region, with the eastern region performing at its best and the western region performing at a relatively low level. However, this gap is gradually closing. In the meantime, agricultural energy efficiency shows clear spatial correlation, and the energy efficiency of different regions and crop production is influenced by a combination of factors. Moreover, there is a significant degree of land and energy redundancy, and the potential for energy savings exhibits a declining and then rising tendency. In the course of advancing agricultural modernization, this study offers municipalities a valuable reference base upon which to develop unique strategies based on their unique qualities. 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

Improving comprehensive grain production capacity and ensuring food security is a critical step toward promoting national economic development and social stability. This study constructs an evaluation framework for comprehensive grain production capacity, examining five key dimensions: resource endowment, factor inputs, scientific and technological infrastructure, grain output, and sustainable development. The entropy method is employed to assess the national comprehensive grain production capacity. Subsequently, the study was performed based on the panel data from 31 provinces in China, covering the period from 2011 to 2022. This analysis was framed within the context of the Spatial Durbin Model and focused on four key aspects: the inputs of production factors, advancements in agricultural technology, national food policies, and the impact of natural disasters. The results indicate the following: (1) Grain production in the 31 provinces of the country demonstrates a significant positive correlation, with a clear space centralization effect. (2) The sown area of grain, labor inputs, fertilizer usage, and the advancement of the digital economy are the primary positive factors influencing regional grain production, but the disaster-affected area has a negative impact on grain production. As a result, this study makes policy recommendations to increase food security and sustainable agricultural development by increasing the sown area of grain, optimizing fertilizer use, and improving agricultural practice digitization. Special emphasis is being placed on reviving high-quality agricultural development by hastening the integration of the digital economy into agriculture. Full article