Search Results (1,919)

The composition and structure of understory plants are crucial for forest ecosystem succession and stability. This study examined the impact of various Cunninghamia lanceolata mixed plantation patterns on understory biodiversity, aiming to provide a theoretical foundation for sustainable management. Six patterns were evaluated using sample plots at Guanshan Forest Farm in Jiangxi Province, China. Understory vegetation diversity, biomass, and soil properties—including total nitrogen, available nitrogen, total phosphorus, available phosphorus, total potassium, available potassium, soil organic matter, and pH—were quantitatively analyzed. Significant differences in diversity among the patterns were revealed. The ‘Cunninghamia lanceolata + Phoebe bournei (Hemsl.) Yen C. Yang + Schima superba Gardner & Champ’ mixed plantation exhibited the most pronounced enhancement of understory plant diversity, whereas the ‘C. lanceolata + Liquidambar formosana Hance’ pattern demonstrated the least significant effects among all treatments. Significant correlations were detected between soil nutrients and diversity indices. Mixed patterns enhance diversity through expanded ecological niches and optimized microenvironments, thereby strengthening ecological functions and management efficiency. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article

To explore the characteristics of species diversity and phylogenetic diversity, as well as the dominant processes of community construction, in different forest types (deciduous broad-leaved forest, mixed coniferous and broad-leaved forest, and Chinese fir plantation) in subtropical regions, analyze the specific driving patterns of soil nutrients and other environmental factors on the formation of forest diversity in different forest types, and clarify the differences in response to environmental heterogeneity between natural forests and plantation forests. Based on 48 fixed monitoring plots of 50 m × 50 m in Shouchang Forest Farm, Jiande City, Zhejiang Province, woody plants with a diameter at breast height ≥5 cm were investigated. Species diversity indices (Margalef index, Shannon–Wiener index, Simpson index, and Pielou index), phylogenetic structure index (PD), and environmental factors were used to analyze the relationship between diversity characteristics and environmental factors through variance analysis, correlation analysis, and generalized linear models. Phylogenetic structural indices (NRI and NTI) were used, combined with a random zero model, to explore the mechanisms of community construction in different forest types. Research has found that (1) the deciduous broad-leaved forest had the highest species diversity (Margalef index of 4.121 ± 1.425) and phylogenetic diversity (PD index of 21.265 ± 7.796), significantly higher than the mixed coniferous and broad-leaved forest and the Chinese fir plantation (p < 0.05); (2) there is a significant positive correlation between species richness and phylogenetic diversity, with the best fit being AIC = 70.5636 and R² = 0.9419 in broad-leaved forests; however, the contribution of evenness is limited; (3) the specific effects of soil factors on different forest types: available phosphorus (AP) is negatively correlated with the diversity of deciduous broad-leaved forests (p < 0.05), total phosphorus (TP) promotes the diversity of coniferous and broad-leaved mixed forests, while the diversity of Chinese fir plantations is significantly negatively correlated with total nitrogen (TN); (4) the phylogenetic structure of three different forest types shows a divergent pattern in deciduous broad-leaved forests, indicating that competition and exclusion dominate the construction of deciduous broad-leaved forests; the aggregation mode of Chinese fir plantation indicates that environmental filtering dominates the construction of Chinese fir plantation; the mixed coniferous and broad-leaved forest is a transitional model, indicating that the mixed coniferous and broad-leaved forest is influenced by both stochastic processes and ecological niche processes. In different forest types in subtropical regions, the species and phylogenetic diversity of broad-leaved forests is significantly higher than in other forest types. The impact of soil nutrients on the diversity of different forest types varies, and the characteristics of community construction in different forest types are also different. This indicates the importance of protecting the original vegetation and provides a scientific basis for improving the ecological function of artificial forest ecosystems through structural adjustment. The research results have important practical guidance value for sustainable forest management and biodiversity conservation in the region. Full article

The scarab species Protaetia brevitarsis, an edible insect, has been used in traditional medicine, as animal feed, and for converting agricultural organic wastes into biofertilizer. The intestinal tract, which contains a diverse array of microbiota, including viruses, plays a critical role in animal health and homeostasis. We previously conducted a comparative analysis of the gut microbiota of third-instar larvae of P. brevitarsis obtained from five different farms and found significant differences in the composition of the gut bacterial microbiota between farms. To better understand the gut microbiota, the composition of DNA viruses in the hindgut contents of P. brevitarsis larvae obtained from five farms was investigated using metagenomic sequencing in this study. The β-diversity was significantly different between metagenomic data obtained from the five farms (PERMANOVA, pseudo-F = 46.95, p = 0.002). Family-based taxonomic analysis indicated that the relative abundance of viruses in the gut overall metagenome varied significantly between farms, with viral reads comprising approximately 41.2%, 15.0%, 4.3%, 4.0%, and 1.6% of metagenomic sequences from the farms Tohamsan gumbengi farm (TO), Secomnalagum gumbengi (IS), Gumbengi brothers (BR), Kyungpook farm (KB), and Jhbio (JH), respectively. More than 98% of the DNA viruses in the hindgut were bacteriophages, mainly belonging to the Siphoviridae family. At the species level, Phage Min1, infecting the genus Microbacterium, was detected in all farms, and it was the most abundant bacteriophage in intestinal microbiota, with a prevalence of 0.9% to 29.09%. The detected eukaryotic DNA viruses accounted for 0.01% to 0.06% of the intestinal microbiota and showed little or no relationship with insect viruses. Therefore, they most likely originated from contaminated feed or soil. These results suggest that the condition of substrates used as feed is more important than genetic factors in shaping the intestinal viral microbiota of P. brevitarsis larvae. These results can be used as reference data for understanding the hindgut microbiota of P. brevitarsis larvae and, more generally, the gut virome of insects. Full article

Soil respiration (Rt), consisting of heterotrophic (Rh) and autotrophic respiration (Ra), plays a vital role in terrestrial carbon cycling and is sensitive to soil temperature and moisture. In dryland agriculture, plastic film mulching (PM) is widely used to regulate soil hydrothermal conditions, but its effects on Rt components and their temperature sensitivity (Q₁₀) across regions remain unclear. A two-year field study was conducted at two rain-fed maize sites: Anding (warmer, semi-arid) and Yuzhong (colder, drier). PM significantly increased Rt, Rh, and Ra, especially Ra, due to enhanced root biomass and improved microclimate. Yield increased by 33.6–165%. Peak respiration occurred earlier in Anding, aligned with maize growth and soil temperature. PM reduced Q₁₀ of Rt and Ra in Anding, but only Ra in Yuzhong. Rh Q₁₀ remained stable, indicating microbial respiration was less sensitive to temperature changes. Structural equation modeling revealed that Rt and Ra were mainly driven by soil temperature and root biomass, while Rh was more influenced by microbial biomass carbon (MBC) and dissolved organic carbon (DOC). Despite increased CO₂ emissions, PM improved carbon emission efficiency (CEE), particularly in Yuzhong (+67%). The application of PM is recommended to enhance yield while optimizing carbon efficiency in dryland farming systems. Full article

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions. Full article

The somatic cell count (SCC) and differential somatic cell count (DSCC) are proxies for the udder health of dairy cattle, regarded as the criterion of mastitis identification with healthy, suspicious mastitis, mastitis, and chronic/persistent mastitis. However, SCC and DSCC are tested using flow cytometry, which is expensive and time-consuming, particularly for DSCC analysis. Mid-infrared spectroscopy (MIR) enables qualitative and quantitative analysis of milk constituents with great advantages, being cheap, non-destructive, fast, and high-throughput. The objective of this study is to develop a dairy cattle udder health status diagnostic model of MIR. Data on milk composition, SCC, DSCC, and MIR from 2288 milk samples collected in dairy farms were analyzed using the CombiFoss 7 DC instrument (FOSS, Hilleroed, Denmark). Three MIR spectral preprocessing methods, six modeling algorithms, and three different sets of MIR spectral data were employed in various combinations to develop several diagnostic models for mastitis of dairy cattle. The MIR diagnostic model of effectively identifying the healthy and mastitis cattle was developed using a spectral preprocessing method of difference (DIFF), a modeling algorithm of Random Forest (RF), and 1060 wavenumbers, abbreviated as “DIFF-RF-1060 wavenumbers”, and the AUC reached 1.00 in the training set and 0.80 in the test set. The other MIR diagnostic model of effectively distinguishing mastitis and chronic/persistent mastitis cows was “DIFF-SVM-274 wavenumbers”, with an AUC of 0.87 in the training set and 0.85 in the test set. For more effective use of the model on dairy farms, it is necessary and worthwhile to gather more representative and diverse samples to improve the diagnostic precision and versatility of these models. Full article

Culicoides (Diptera, Ceratopogonidae) are small biting midges and are known as vectors for many arboviruses, including bluetongue virus (BTV) and epizootic hemorrhagic disease virus (EHDV). Tengchong County of Yunnan Province, China, which borders Myanmar, has many private farms with goats, sheep, and cattle. To estimate the risk of Culicoides-borne viral diseases such as bluetongue (BT) and epizootic hemorrhagic disease (EHD) in this area, an investigation of the diversity and abundance of Culicoides in Tengchong between May 2024 and April 2025 was performed. As a result, 70 collections totaling approximately 93,000 Culicoides were carried out at five farms (cattle + Asian buffaloes, goats, and sheep, respectively). Nineteen species were identified, and eight potential cryptic species were found. A total of 13 cox1 sequences and 4 28S sequences for 13 specimens were generated. The most dominant species were Obsoletus (44.1%), C. homotomus (23.3%), and C. arakawae (12.9%) at the bovine farm; C. tainanus (68.0%), C. orientalis (12.6%), and C. newsteadi (Asia) (6.3%) at the goat farm; and C. tainanus (73.6%), C. fenggangensis (7.3%), and C. sp. nr palpifer (6.3%) at the sheep farm. In this investigation, C. tainanus, Obsoletus, and C. orientalis were the most dominant potential BTV vectors, and the period between July and October may be the main period for epidemics of Culicoides-borne viruses in Tengchong. Full article

As the primary biological risk threatening safe dairy production, bovine mastitis control highly relies on environmental disinfection measures. However, the mechanisms by which chemical disinfectants influence host–environment microbial interactions remain unclear. This study systematically investigated the disinfection efficacy and regulatory effects on microbial community composition and diversity of glutaraldehyde-benzalkonium chloride (BAC) and glutaraldehyde-didecyl dimethyl ammonium bromide (DAB) at recommended concentrations (2–5%), using 80 environmental samples from intensive dairy farms in Xinjiang, China. Combining 16S rDNA sequencing with culturomics, the results showed that BAC achieved a disinfection rate of 99.33%, higher than DAB’s 97.87%, and reduced the environment–gut microbiota similarity index by 23.7% via a cationic bacteriostatic film effect. Microbiome analysis revealed that BAC selectively suppressed Fusobacteriota abundance (15.67% reduction) and promoted Bifidobacterium proliferation (7.42% increase), enhancing intestinal mucosal barrier function through butyrate metabolism. In contrast, DAB induced Actinobacteria enrichment in the environment (44.71%), inhibiting pathogen colonization via bioantagonism. BAC’s long-acting bacteriostatic properties significantly reduced disinfection costs and mastitis incidence. This study first elucidated the mechanism by which quaternary ammonium compound (QAC) disinfectants regulate host health through “environment-gut” microbial interactions, providing a critical theoretical basis for developing precision disinfection protocols integrating “cost reduction-efficiency enhancement-risk mitigation.” Full article

Enterocytozoon bieneusi, a microsporidian protozoan parasite, infects diverse hosts, including humans and livestock. Transmission occurs primarily through the fecal–oral route or exposure to contaminated environmental sources, such as water and soil. While its prevalence in animals is well documented, data on environmental contamination—particularly in areas surrounding livestock farms—remain limited. Therefore, this study aims to investigate the presence of E. bieneusi in environmental sources near cattle farms in Korea, evaluating potential risks for zoonotic transmission. Overall, 364 environmental samples (soil and water) were collected from areas surrounding cattle farms and analyzed using nested PCR targeting the internal transcribed spacer region of E. bieneusi. One positive sample (0.3%) was identified in surface water near a shed housing Korean native cattle during autumn. Genotyping and phylogenetic analysis identified the sequence as originating from genotype BEB1, a Group 2 genotype commonly associated with ruminants and recognized for its zoonotic potential. While the detection rate was low, this represents the first report of E. bieneusi contamination in water near cattle housing and the first identification of BEB1 in environmental water in Korea. These findings highlight the potential for environmental transmission, emphasizing the need for further research and monitoring to inform strategies for public health and livestock biosecurity. Full article

Organic farming is widely recognized as a promising practice for sustainable agriculture, yet its long-term ecological impacts remain insufficiently investigated. In this study, we evaluated these impacts by comparing heavy metal concentrations, soil invertebrate communities, and microbial profiles between long-term organic and conventional farming systems. A comparative analysis was conducted on 24 plot soils from two paired organic and conventional farm systems in Beijing, each managed continuously for over 20 years. Our results revealed that soils under organic management consistently contained 10.8% to 73.7% lower heavy metals, along with reduced geo-accumulation indices (I_geo, a standardized metric for soil contamination assessment), indicating decreased contamination risks. In terms of soil fauna, while conventional soils showed higher Collembola abundance, organic farming significantly enhanced Collembola richness and diversity by 20.6% to 55.0%. Microbial sequencing likewise revealed enhanced richness and diversity of bacteria and fungi in organic soils. These microbial communities also displayed shifts in dominant taxa and more stable co-occurrence networks under organic management. Principal component analysis and Mantel tests identified soil pH and nutrients as key drivers of soil biodiversity, while heavy metals also imposed negative influences. Collectively, these findings demonstrate that long-term organic farming not only mitigates environmental risks associated with soil contaminants but also promotes belowground ecological integrity by supporting biodiversity of soil fauna and microbiota. This study highlights the ecological significance of sustained organic practices and provides critical insights for advancing sustainable agricultural developments. Full article

The primary aim of this study was to explore the influence of abiotic factors on weed development in maize fields, with the goal of informing more effective weed management practices. We focused on identifying key environmental, edaphic, and agricultural variables that contribute to weed infestations, particularly before the application of spring herbicide treatments. Field investigations were conducted from 2018 to 2021 across selected maize-growing regions in Hungary. Over the four-year period, a total of 51 weed species were recorded, with Echinochloa crus-galli, Chenopodium album, Portulaca oleracea, and Hibiscus trionum emerging as the most prevalent taxa. Collectively, these four species accounted for more than half (52%) of the total weed cover. Altogether, the 20 most dominant species contributed 95% of the overall weed coverage. The analysis revealed that weed cover, species richness, and weed diversity were significantly affected by soil properties, nutrient levels, geographic location, and tillage systems. The results confirm that the composition of weed species was influenced by several environmental and management-related factors, including soil parameters, geographical location, annual precipitation, tillage method, and fertilizer application. Environmental factors collectively explained a slightly higher proportion of the variance (13.37%) than farming factors (12.66%) at a 90% significance level. Seasonal dynamics and crop rotation history also played a notable role in species distribution. Nutrient inputs, particularly nitrogen, phosphorus, and potassium, influenced both species diversity and floristic composition. Deep tillage practices favored the proliferation of perennial species, whereas shallow cultivation tended to promote annual weeds. Overall, the composition of weed vegetation proved to be a valuable indicator of site-specific soil conditions and agricultural practices. These findings underscore the need to tailor weed management strategies to local environmental and soil contexts for sustainable crop production. Full article

The value realization of ecological products is an important part of rural and agricultural development. As a significant force for protecting traditional agricultural systems and promoting rural revitalization, agricultural heritage systems (AHSs) have formed diverse value realization paths of ecological products in the process of dynamic protection and adaptive management. Through theoretical research, this article analyzed the characteristics of ecological products in AHS sites (EPAHSSs) and summarized the framework of value realization paths of EPAHSSs. Then, the Wannian Rice Culture System in China was selected as a case for conducting empirical research. The results showed that EPAHSSs exhibit obvious uniqueness in terms of climate environment, germplasm resources, farming and breeding models, and cultural heritage. The value realization paths of EPAHSSs mainly include industrial development support, such as the extension of agricultural industrial chains and the development of tourism, as well as fiscal transfer payments. The case analysis results indicated that Wannian County contains a rich variety of ecological products and developed a value realization pathway mainly based on the integration of industries and supplemented by fiscal transfer payments during the process of protection and development. However, further optimization is needed to promote the development of tourism and other paths. This study not only contributes to the sustainable development of the Wannian Rice Culture System, but the proposed framework is also applicable to other heritage systems and similar regions. Full article

Achieving optimal agricultural yields and promoting sustainable farming relies on accurate crop recommendations. However, the applicability of many current systems is limited by their considerable computational requirements and dependence on comprehensive datasets, especially in resource-limited contexts. This paper presents HOLISTIQ RS, a novel crop recommendation system explicitly designed for operation on low-specification hardware and in data-scarce regions. HOLISTIQ RS combines collaborative filtering with a Markov model to predict appropriate crop choices, drawing upon user profiles, regional agricultural data, and past crop performance. Results indicate that HOLISTIQ RS provides a significant increase in recommendation accuracy, achieving a MAP@5 of 0.31 and nDCG@5 of 0.41, outperforming standard collaborative filtering methods (the KNN achieved MAP@5 of 0.28 and nDCG@5 of 0.38, and the ANN achieved MAP@5 of 0.25 and nDCG@5 of 0.35). Significantly, the system also demonstrates enhanced recommendation diversity, achieving an Item Variety (IV@5) of 23%, which is absent in deterministic baselines. Significantly, the system is engineered for reduced energy consumption and can be deployed on low-cost hardware. This provides a feasible and adaptable method for encouraging informed decision-making and promoting sustainable agricultural practices in areas where resources are constrained, with an emphasis on lower energy usage. Full article

This study explores the role of community-centered farm-based hospitality in promoting sustainable rural development, with a focus on South Tyrol, Italy. A survey of 461 local residents assessed perceptions of agritourism’s impact on agricultural heritage, environmental sustainability, and community well-being. Factor analysis identified two main constructs—Agroheritage Sustainability and Empowered Eco-Tourism—which together capture the multifaceted benefits of agritourism. Agroheritage Sustainability reflects the preservation of traditional farming practices, cultural landscapes, and intergenerational knowledge, emphasizing the role of tourism in maintaining cultural identity and preventing land abandonment. Empowered Eco-Tourism highlights the socio-economic benefits of sustainable tourism, including community empowerment, environmental stewardship, and the creation of new economic opportunities. The study’s findings indicate that local residents view agritourism as a holistic approach that supports rural livelihoods while preserving cultural heritage and promoting ecological resilience. The analysis further supports the potential of farm-based hospitality as a model for sustainable rural development, aligning closely with EU policies and global best practices. The Roter Hahn initiative in South Tyrol serves as a practical example of this approach, demonstrating the value of certification programs in enhancing transparency, quality, and sustainability. These insights provide valuable guidance for policymakers and tourism developers seeking to promote sustainable rural tourism globally. The contribution of this research lies in its empirical validation of a dual-construct model that links community engagement with agroecological and cultural sustainability, offering a transferable framework for evaluating agritourism as a lever for sustainable rural development in diverse regional contexts. Full article