Search Results (3,634)

The geographic distribution patterns of microorganisms and their underlying mechanisms are central topics in microbiology, crucial for understanding ecosystem functioning and predicting responses to global change. Cryoconite absorbs solar radiation to form cryoconite holes, and because it lies within these relatively deep holes, it faces limited interference from surrounding ecosystems, often being seen as a fairly enclosed environment. Moreover, it plays a dominant role in the biogeochemical cycling of key elements such as carbon and nitrogen, making it an ideal model for studying large-scale microbial biogeography. In this study, we analyzed bacterial communities in cryoconite across a transcontinental scale of glaciers to elucidate their biogeographical distribution and community assembly processes. The cryoconite bacterial communities were predominantly composed of Proteobacteria, Cyanobacteria, Bacteroidota, and Actinobacteriota, with significant differences in species composition across geographical locations. Bacterial diversity was jointly driven by geographical and anthropogenic factors: species richness exhibited a hump-shaped relationship with latitude and was significantly positively correlated with the Human Development Index (HDI). The significant positive correlation may stem from nutrient input and microbial dispersal driven by high-HDI regions’ industrial, agricultural, and human activities. Beta diversity demonstrated a distance-decay pattern along spatial gradients such as latitude and geographical distance. Analysis of community assembly mechanisms revealed that stochastic processes predominated across continents, with a notable scale dependence: as the spatial scale increased, the role of deterministic processes (heterogeneous selection) decreased, while stochastic processes (dispersal limitation) strengthened and became the dominant force. By integrating geographical, climatic, and anthropogenic factors into a unified framework, this study enhances the understanding of the spatial-scale-driven mechanisms shaping cryoconite bacterial biogeography and emphasizes the need to prioritize anthropogenic influences to predict the trajectory of cryosphere ecosystem evolution under global change. Full article

With the intensification of global climate change, frequent extreme temperature events pose increasing challenges to agricultural production. The aim of this study is to characterize the spatiotemporal evolution of county-level agricultural total factor productivity (ATFP) under extreme temperature events, reveal key driving factors and crop-specific heterogeneity, and predict potential high-risk areas, which is crucial for providing scientific basis for risk management and adaptive policy formulation in globally climate-sensitive agricultural regions. This paper selects Jiangsu Province as a typical case study, uses the DEA-Malmquist model to measure agricultural total factor productivity (ATFP), systematically analyzes the spatiotemporal dynamic evolution characteristics of ATFP at the county scale, and selects the random forest and XGBoost ensemble models with optimal accuracy through model comparison for prediction, assessing the evolution trends of ATFP under different climate scenarios. The results showed that: (1) From 1993 to 2022, the average ATFP increased from 0.7460 to 1.1063 in the province, though development showed uneven distribution across counties, exhibiting a “high in the south, low in the north” gradient pattern. (2) Mechanization, agricultural film and land inputs are the core elements driving the overall ATFP increase but there are obvious crop differences: mechanization has a more prominent role in promoting the productivity of wheat and maize, while labor inputs have a greater impact on the ATFP of rice. (3) The negative impacts of extreme climate events on agricultural production will be significantly amplified under high-emission scenarios, while moderate climate change may have a promotional effect on certain crops in some regions. Full article

Agricultural listed companies are key nodes in the agricultural industry chain, providing capital, technology and market access to upstream producers and downstream processors. When these firms face delisting risk, the resilience and sustainability of the industry chain are threatened. Using data from 897 observations of Chinese A-share listed companies in the agriculture, forestry, animal husbandry, and fishery sector over 2010–2021, this study links multidimensional firm vulnerability to subsequent delisting risk. We construct 30 internal and external indicators covering financial performance, innovation input, supply chain concentration, government support and market competitiveness. Clustering method is applied to capture heterogeneity in firms’ multidimensional structural, gradient boosting models are used to predict ST (Special Treatment) status within three years, and SHAP analysis is used to identify the main risk. The results show that a small subset of firms with high leverage, tight liquidity, weak profitability, insufficient innovation, and highly concentrated key customers and suppliers accounts for most ST cases. Strengthening capital buffers, diversifying critical supply-chain relationships, and maintaining stable innovation investment are thus crucial for reducing delisting risk and enhancing the resilience of agricultural listed companies. Full article

Crop classification of farmland is of great significance for crop monitoring and yield estimation. Airborne hyperspectral systems can provide large-format hyperspectral farmland images. However, traditional machine learning-based classification methods rely heavily on handcrafted feature design, resulting in limited representation capability and poor computational efficiency when processing large-format data. Meanwhile, mainstream deep-learning-based hyperspectral image (HSI) classification methods primarily rely on patch-based input methods, where a label is assigned to each patch, limiting the full utilization of hyperspectral datasets in agricultural applications. In contrast, this paper focuses on the semantic segmentation task in the field of computer vision and proposes a novel HSI crop classification framework named All-Attention Transformer (A²Former), which combines CNN and Transformer based on a fully attention-based mechanism. First, a CNN-based encoder consisting of two blocks, the overlap-downsample and the spectral–spatial attention weights block (SSWB) is constructed to extract multi-scale spectral–spatial features effectively. Second, we propose a lightweight C-VIT block to enhance high-dimensional features while reducing parameter count and computational cost. Third, a Transformer-based decoder block with gated-style weighted fusion and interaction attention (WIAB), along with a fused segmentation head (FH), is developed to precisely model global and local features and align semantic information across multi-scale features, thereby enabling accurate segmentation. Finally, a checkerboard-style sampling strategy is proposed to avoid information leakage and ensure the objectivity and accuracy of model performance evaluation. Experimental results on two public HSI datasets demonstrate the accuracy and efficiency of the proposed A²Former framework, outperforming several well-known patch-free and patch-based methods on two public HSI datasets. Full article

This research focuses on Henan, a key agricultural region, analyzing data from 2000 to 2022 on cropland use and agricultural input–output. It employs the Tapio decoupling model to examine the evolution and decoupling of cropland use intensity (CLUI) and cropland use carbon emissions (CUCE) in the province. The study reveals that from 2000 to 2022, CLUI in Henan Province fluctuated in a “high-low-high” pattern over time, creating a spatial distribution with high-intensity areas in the east and lower-intensity areas at the provincial boundaries. CUCE showed a “U” shaped trend, peaking around 2015 and then gradually declining. Spatially, emissions were consistently higher in the south and lower in the north. The relationship between CLUI and CUCE transitioned from a strong negative decoupling from 2000 to 2010, to a strong decoupling from 2015 to 2020, and to a recessive decoupling from 2020 to 2022. Spatially, it evolves from a state of negative decoupling across the entire region in the early stage to nearly full coverage of strong decoupling regions in the later stage. Based on these insights, the study suggests planning strategies focusing on regional management and policy alignment, providing scientific guidance for sustainable cropland use and optimized territorial planning in Henan Province. Full article

The intensification of extreme rainfall events under changing climate regimes has heightened concerns over nutrient losses from paddy agriculture, particularly nitrogen (N), a primary contributor to non-point source pollution. Despite advances in drainage management, limited studies have integrated probabilistic rainfall modeling with N transport simulation to evaluate mitigation strategies in rice-based systems. This study addresses this critical gap by coupling the Log-Pearson Type III (LP-III) distribution with the DRAINMOD-N II model to simulate N dynamics under varying rainfall exceedance probabilities and drainage design configurations in the Kunshan region of eastern China. The DRAINMOD-N II showed good performance, with R² values of 0.70 and 0.69, AAD of 0.05 and 0.39 mg L⁻¹, and RMSE of 0.14 and 0.91 mg L⁻¹ for NO₃⁻-N and NH₄⁺-N during calibration, and R² values of 0.88 and 0.72, AAD of 0.06 and 0.21 mg L⁻¹, and RMSE of 0.10 and 0.34 mg L⁻¹ during validation. Using around 50 years of historical precipitation data, we developed intensity–duration–frequency (IDF) curves via LP-III to derive return-period rainfall scenarios (2%, 5%, 10%, and 20%). These scenarios were then input into a validated DRAINMOD-N II model to assess nitrate-nitrogen (NO₃⁻-N) and ammonium-nitrogen (NH₄⁺-N) losses across multiple drain spacing (1000–2000 cm) and depth (80–120 cm) treatments. Results demonstrated that NO₃⁻-N and NH₄⁺-N losses increase with rainfall intensity, with up to 57.9% and 45.1% greater leaching, respectively, under 2% exceedance events compared to 20%. However, wider drain spacing substantially mitigated N losses, reducing NO₃⁻-N and NH₄⁺-N loads by up to 18% and 12%, respectively, across extreme rainfall scenarios. The integrated framework developed in this study highlights the efficacy of drainage design optimization in reducing nutrient losses while maintaining hydrological resilience under extreme weather conditions. Full article

Agroforestry is defined as a multifunctional approach to land management that enhances biodiversity and soil health while mitigating environmental impacts compared to intensive agriculture. The efficacy of maize cultivation in agroforestry systems is significantly influenced by nutrient competition. The factors that influence this phenomenon include the dimensions and configuration of the tree rows, as well as the availability of nutrients. This study examined the effect of nitrogen fertilization, tree line spacing, and seasonal changes on the productivity and the leaf spectral characteristics of the intercropped maize (Zea mays L.) within a willow-based agroforestry system in eastern Hungary. The experiment involved the cultivation of maize with two spacings (narrow and wide field strips) and four nitrogen levels (0, 50, 100, and 150 kg N ha⁻¹) across two growing seasons (2023–2024). The results demonstrated that yield-related parameters, including biomass, cob size and weight, and grain weight, exhibited a strong response to nitrogen level and tree line spacing. The reduction in spacing resulted in a decline in maize productivity. However, a high nitrogen input (150 kg N ha⁻¹) partially mitigated this effect in the first growing season. Vegetation indices demonstrated a high degree of sensitivity to annual variations, particularly with regard to tree competition and weather conditions. Multispectral vegetation indices exhibited a heightened responsiveness to environmental and management factors when compared to indices based on visible light (RGB). The findings of this study demonstrate that a combination of optimized tree spacing and optimized nitrogen management fosters productivity while maintaining agroecological sustainability in temperate agroforestry systems. Full article

Biochar is recognized for its ability to improve the chemical, physical, and biological properties of soil, thereby enhancing crop productivity. However, the effects of biochar on photosynthetic and transpiration traits in rice crop–soil systems, particularly through the lens of on-site data subjected to Box–Cox transformation, remain insufficiently explored. To address this, a two-factor randomized block design experiment was conducted using the rice cultivar Nangeng 9108 at the Agricultural Meteorology Experimental Station of Nanjing University of Information Science and Technology over the 2022–2023 principle phenophases. This study investigated changes in leaf stomatal conductance, photosynthetic, transpiration, and water-use efficiency (WUE) parameters under combined applications of biochar (0, 15, and 30 t/ha) and nitrogen fertilizer (0, 180, 225, and 300 kg/ha). Application of the Box–Cox transformation substantially improved data normality and variance homogeneity, enabling the development of a robust predictive model linking net photosynthetic rate to environmental factors. A two-way ANOVA further revealed that both the high nitrogen (300 kg/ha) with high biochar (30 t/ha) treatment and the conventional nitrogen (225 kg/ha) with moderate biochar (15 t/ha) treatment significantly enhanced rice photosynthetic and transpiration performance. Of particular note, the N225B15 treatment, which showed a net photosynthetic rate increase from 9.52% to 19.01%, and transpiration rate increase from 11.49% to 28.43%, is recommended as an optimal fertilization strategy for sustainable rice production. These results underscore the synergistic role of moderate biochar and nitrogen inputs in improving key physiological traits of rice, supporting higher crop yields. Full article

Coffee grounds can be used in agriculture as a bio-input to enhance soil fertility and biodiversity in the long term. Furthermore, the use of coffee grounds in agriculture is a sustainable practice because it reuses an organic waste product as natural fertilizer and minimizes the environmental impact resulting from the improper disposal of waste. This study aimed to identify the effects of coffee grounds on the growth and yield of iceberg lettuce plants. The experiment was conducted in a greenhouse using 4 kg of substrate in containers with a 5.356 dm³ capacity, following a completely randomized design in a 2 × 2 factorial arrangement. The primary treatment consisted of plants grown in two types of substrate: soil and sand (01) and soil, sand, and 10% coffee grounds (02). The secondary treatment corresponded to irrigation with water (01) and a 10% coffee ground extract solution (02). Coffee grounds incorporated into the soil increase soil fertility; however, they reduce lettuce growth due to the toxicity of the compounds present and should not be used without prior treatment. Processing coffee grounds into irrigation solutions shows promise due to its high potential for use as an agricultural bio-input in lettuce production. This solution enhances the growth and development of the species, resulting in vigorous plants with market value. Full article

In precision agriculture, the delineation of Management Zones (MZs) is essential for optimizing input use efficiency and site-specific nutrient management. MZs are established based on spatial variability derived from remote sensing data—such as Normalized Difference Vegetation Index (NDVI) from satellite or UAV-based imagery—and yield maps collected during harvest. However, the microbial community composition of the soil is often overlooked in MZ delineation. To address this gap, we investigated the soil bacterial community structure across different MZs in an arable field. The zones were delineated using NDVI data, soil profiles were described, and bulk soil samples were collected. Soil physicochemical parameters were analyzed in parallel with 16S rRNA gene amplicon sequencing to characterize bacterial community composition and diversity. The results demonstrated that soil texture and soil organic matter content were the primary drivers influencing bacterial community structure across the field. Moreover, patterns in microbial composition aligned closely with MZ delineations, indicating that microbial profiles could aid in better understanding and supporting the nutrient management practices. Our findings suggest that soil microbiological data can enhance the stability and biological relevance of MZ definitions, thereby improving resource allocation, soil health management, and overall sustainability in precision farming systems. Full article

Anaerobic digestion of straw is a crucial method for agricultural waste valorization, yet its efficiency is limited by complex factors. This study employed machine learning to predict methane yield and optimize process parameters in biochar-amended straw digestion. A comprehensive dataset integrating experimental and literature data (100 samples, 15 input variables) was constructed, incorporating operational conditions, straw characteristics, and biochar properties (e.g., dosage, particle size, specific surface area, and elemental composition). Prediction models were developed using Random Forest (RF), XGBoost, and Support Vector Machine (SVM). Results indicated that the RF model achieved the best predictive accuracy, with an R² of 0.81 and RMSE of 36.9, significantly surpassing other models. Feature importance analysis identified feeding load, biochar dosage, and biochar carbon content (C%) as the key governing factors, collectively accounting for 65.7% of the total contribution. The model-predicted optimal ranges for practical operation were 15–30 g for feeding load and 5–20 g/L for biochar dosage. This study provides data-driven validation of biochar’s enhancement mechanisms and demonstrates the utility of RF in predicting and optimizing anaerobic digestion performance, offering critical support for sustainable agricultural waste recycling and clean energy generation. Full article

Bread wheat variety development suited to organic farming conditions remains a major challenge mainly because of the high breeding costs involved and the few cultivars adapted to low-input systems. The present work explores whether early generation selection needs to take place under organic conditions for subsequent adaptation or whether conventional testing at an early stage could be adequate. A diverse set of crosses involving Greek landraces and commercial cultivars were developed and advanced by honeycomb pedigree selection under both organic and conventional environments. Subsequently, F₄ progenies and an upgraded landrace were evaluated over two years in neighboring organic and conventional trials. Both statistical and GGE biplot analyses revealed significant genotype × environment interactions. The results clearly indicate that early selection under organic conditions did not provide a consistent advantage for subsequent performance under organic management compared with conventional early selection. Genotypes derived from the Africa × Atheras cross consistently showed the highest and most stable yields across the two environments, irrespective of the early selection environment. These results indicate that genetic background and landrace-derived diversity are more important than the early selection environment for the expression of performance. A staged breeding strategy involving initial selection in conventional management followed by multi-environment testing in organic conditions can provide a cost-effective approach to developing resilient, high-yielding wheat cultivars suitable for organic farming systems, which are typically characterized by low-input management practices, and in tune with the EU targets for expanded organic farming. Full article

Arsenic contamination demands innovative, sustainable remediation. This study presents a fuzzy approach for synthesizing a magnetic biochar nanocomposite from pecan shell agricultural waste for efficient arsenic removal. Using a Multi-Input Fuzzy Rules Emulated Network (MiFREN), a systematic investigation of the synthesis process revealed that precursor type (biochar), Fe:precursor ratio (1:1), and iron salt type were the most significant parameters governing material crystallinity and adsorption performance, while particle size and N₂ atmosphere had a minimal effect. The MiFREN-identified optimal material, the magnetic biochar composite (FS7), achieved > 90% arsenic removal, outperforming the least efficient sample by 50.61%. Kinetic analysis confirmed chemisorption on a heterogeneous surface (q_e = 12.74 mg/g). Regeneration studies using 0.1 M NaOH demonstrated high stability, with FS7 retaining > 70% removal capacity over six cycles. Desorption occurs via ion exchange and electrostatic repulsion, with post-use analysis confirming structural integrity and resistance to oxidation. Application to real groundwater from the La Laguna region proved highly effective; FS7 maintained selectivity despite competing ions like ${Na}^{+}$, ${Cl}^{-},$ and ${SO}_4^{2-}$. By integrating AI-driven optimization with reusability and real contaminated water, this research establishes a scalable framework for transforming agricultural waste into a high-performance adsorbent, supporting global Clean Water and Sanitation goals. Full article

Non-invasive and intelligent technologies have been utilized to monitor agricultural systems in real time, facilitating expedient decision-making and the reduction in animal stress in diverse climatic conditions. The objective of this study was to develop a deep learning supervised model to classify Nelore cattle (Bos indicus) into two groups: those in comfort and those under thermal stress. Thirty cattle, aged between 18 and 20 months, were evaluated between June and December 2023, resulting in 676 samples collected across four daily periods (6:00, 12:00, 18:00, and 24:00). Biotic variables included rectal temperature (RT) and respiratory rate (RR), while abiotic variables included air temperature (AT) and relative humidity (RH). The neural network model exhibited an accuracy and recall of 72% but a low specificity of 42%. These metrics indicate that while the model is effective in detecting stress situations, it faces challenges in correctly identifying animals in thermal comfort, likely due to class imbalance and the need for additional input features to capture environmental adaptability. Consequently, it can be posited that supervised learning models are valuable tools for precision livestock farming, provided that discriminatory limitations are mitigated by refining input characteristics and data balancing. Full article

Biostimulants are increasingly recognized in modern agriculture as eco-friendly inputs that enhance plant growth, improve stress tolerance, and promote product quality. This study investigated the effects of fulvic acid, amino acids, and chitosan on the growth, yield, and nutritional quality of parsley grown under hydroponic greenhouse conditions. The research was conducted in two stages. In the first stage, different doses of fulvic acid (80–120 ppm), amino acids (40–80 ppm), and chitosan (0.3–0.6 mL L⁻¹) were evaluated. In the second stage, the most effective treatments were tested in combination. The results showed that all biostimulants positively influenced plant growth, productivity, and nutritional parameters. In the first experiment, the highest total yield was obtained with chitosan at 0.3 mL L⁻¹ (2068 g m⁻²; +30.1%). The greatest increase in total phenolic content was observed with AA 40 (391.1 mg GA 100 g⁻¹ FW; +64%), while the strongest nitrate reduction occurred with FA 120, reducing nitrate levels from 1048 to 405 mg kg⁻¹ (−61%). In the second experiment, the FA 80 + C 0.3 combination was the most effective treatment, increasing total yield from 493 to 856 g m⁻² (+73.7%) and reducing nitrate content from 937 to 460 mg kg⁻¹ (−50.9%). These findings suggest that fulvic acid and chitosan, applied individually and particularly in combination, may serve as effective biostimulant strategies for improving yield and nutritional quality while reducing nitrate accumulation in hydroponically grown parsley. Full article