Search Results (13,884)

Winter fallow fields are widely distributed in southern China, where legume–grass mixtures are considered a useful approach for improving land-use efficiency and forage productivity while maintaining agroecosystem functions. Previous studies on the use of winter fallow fields have generally focused either on monoculture or on crops subjected to a single cut. These studies have primarily assessed aboveground productivity, whereas soil bacterial community responses have received comparatively little attention. Consequently, limited information is available on how different seeding combinations and cutting times affect aboveground biomass and soil bacterial community structure in mixed-cropping systems. A fixed-site field experiment involving annual ryegrass (Lolium multiflorum L.) and Chinese milk vetch (Astragalus sinicus L.) was conducted on yellow clay soil. Five annual ryegrass-Chinese milk vetch seeding combinations (100:0, 75:25, 50:50, 25:75, and 0:100) were established as proportions of their respective monoculture seeding rates. The same plots were maintained for five consecutive winter growing seasons, from autumn 2021 to spring 2026. Plant and soil samples were collected only during the final growing season (2025–2026), at four cutting times in January, March, April, and May. These samples were used to assess the effects of seeding combination and cutting time on forage productivity, root traits, soil fertility, and soil bacterial community structure. Among all groups, the 75% annual ryegrass + 25% Chinese milk vetch group (R75M25) exhibited the best overall performance, with cumulative yields after four cuttings of 17,274.21 kg·ha⁻¹ dry matter, 2717.02 kg·ha⁻¹ crude protein, and 12,220.46 kg·ha⁻¹ digestible dry matter. In addition, the R75M25 group maintained relatively high contents of alkali-hydrolyzable nitrogen, available phosphorus, and available potassium in soil. The top three phyla of soil samples were Proteobacteria, Actinobacteria, and Acidobacteria, with a total relative abundance of >60%. Among these, Proteobacteria showed the greatest relative abundance in monoculture, whereas the seeding combination had a reduced proportion of it. In addition, its relative abundance rose steadily with cutting time. At the genus level, RB41, Gemmatimonas, and Sphingomonas dominated the soil bacterial community. The seeding combinations did not alter soil bacterial alpha diversity. Cutting times significantly reduced the phylogenetic diversity index and drove the temporal differentiation of taxa such as Actinobacteria and Bacteroidetes. The inclusion of an appropriate proportion of Chinese milk vetch improved forage nutritional value while maintaining high biomass accumulation in southern China, with the R75M25 group showing the best overall performance in winter fallow fields. Full article

Anaerobic digestion (AD) plays a key role in the circular bioeconomy by converting organic waste into renewable energy and facilitating the sustainable utilisation of waste materials. Agricultural and agro-industrial by-products are increasingly recognised as valuable AD feedstocks due to their widespread availability and significant bioenergy potential. However, many of these substrates contain plant-derived bioactive compounds, such as polyphenols, tannins, flavonoids and terpenes, which can influence microbial communities and process performance. Depending on their concentration and chemical characteristics, these compounds may inhibit microbial activity, impair process stability, and ultimately decrease methane production. This review critically synthesises current knowledge on the occurrence, bioavailability and biological activity of plant-derived bioactive compounds in agricultural feedstocks used for anaerobic digestion, with particular emphasis on their implications for process performance and reactor stability. The principal mechanisms through which phytochemicals influence anaerobic digestion include enzyme inhibition, membrane disruption, interference with syntrophic interactions and trace metal chelation. The available evidence demonstrates a pronounced dose-dependent response, whereby low concentrations may exert neutral or selective modulatory effects. In contrast, elevated concentrations disrupt microbial activity, leading to volatile fatty acid accumulation, prolonged lag phases and reduced methane production. Current mitigation strategies include substrate pretreatment, co-digestion, microbial adaptation, adsorbent-assisted detoxification and the use of DIET-promoting materials. An integrated evidence matrix is proposed to link phytochemical composition with reactor configuration, operational parameters and mitigation strategies, thereby providing a practical framework for feedstock-specific process optimisation. Overall, the available evidence demonstrates that reliable evaluation of agricultural feedstocks should extend beyond conventional biochemical methane potential assessment to incorporate phytochemical composition, microbial functional responses and key operational parameters. Such an integrated approach can improve the prediction of methane recovery and support evidence-based optimisation of anaerobic digestion within circular bioeconomy systems. Full article

To address inaccurate weed segmentation under crop overlap, occlusion, and complex field backgrounds, this study developed a combined method integrating DViT-YOLOv8-seg with confidence-guided SLIC voting. The dataset contained 1872 field images (800 × 600 pixels) of Guangzhou soft-stem lettuce and four common weed species: Eleusine indica, Digitaria sanguinalis, Portulaca oleracea, and Amaranthus blitum. All weed species were merged into one weed class, while lettuce, soil, and other field regions were treated as non-weed. Real-ESRGAN and data augmentation enhanced the training samples; Dual-ViT strengthened global–local feature interaction; GSConv reduced redundant computation; BiFPN improved multi-scale fusion; and SLIC refined ambiguous boundaries. After super-resolution preprocessing and three-fold expansion, baseline mPA increased by 10.9 percentage points. The improved network achieved 88.3% mPA at 7.9 GFLOPs, corresponding to +3.6 percentage points and -1.0 GFLOPs relative to the baseline. SLIC voting increased FWIoU to 95.6%, 4.1 percentage points above the network without SLIC. Compared with YOLOv5-seg and Fast-SCNN, mPA improved by 2.0 and 6.5 percentage points, respectively; GFLOPs were 87.7% and 95.5% lower than those of YOLOv5-seg and DeepLabv3+, respectively. The method therefore provides a favorable trade-off between segmentation accuracy and theoretical network computation for complex lettuce field imagery. Full article

To address uneven temperature and relative humidity distributions, localized heat accumulation, and insufficient air velocity in an enclosed stacked-cage laying hen house, a three-dimensional computational fluid dynamics (CFD) model of the laying hen house was developed using field-measured structural and environmental data, and a porous-media model was established for the cage zone. Model validation showed that the normalized mean square error (NMSE) values for temperature, relative humidity, and air velocity were all below 0.25, confirming the reliability of the CFD model. Through visualization analysis of the contour maps, the problems of uneven airflow distribution in the original ventilation system and significant heat accumulation at the fan end were identified. On this basis, numerical simulations were conducted for six air-inlet configurations by varying two key parameters: air-inlet spacing and air-inlet number. The simulation results showed that, compared with the original model, the configuration with an air-inlet spacing of 1.14 m and a total of 32 air inlets on the two gable walls improved the uniformity of temperature, air velocity, and relative humidity by 18.00%, 10.54%, and 18.38%, respectively, while reducing the mean effective temperature index (ETI) in the cage zone by 0.5 °C. This configuration effectively alleviated localized heat accumulation and improved air-velocity uniformity. These findings provide a theoretical basis and technical support for the structural optimization and environmental regulation of enclosed stacked-cage laying hen houses. Full article

Crops cultivated on trellis systems, such as kiwifruit, grapes, et al., create a partially structured workspace; this environment is highly suitable for Cartesian robotic harvesting. However, limited extension range of current Cartesian manipulators necessitates a large vertical space, which directly conflicts with the height constraints of trellis canopies. This paper presented a hollow telescopic Cartesian manipulator with a belt-driven cascaded differential transmission for single-degree-of-freedom kiwifruit operations. The two-stage nested carbon fiber structure offers an extension ratio of 1.83:1 and a 549 mm retracted length, requiring 45.36% less vertical space than its single-stage architecture. The hybrid model–vision error compensation (HMVEC) strategy is proposed to address nonlinear positioning errors inherent in the cantilever telescopic configuration. It combines a polynomial–Fourier kinematic model for feedforward correction with YOLO11n AprilTag detection. The results showed the HMVEC strategy reduced the positioning root mean square error (RMSE) by 42.28% (from 7.90 mm to 4.56 mm). The designed manipulator achieved a 95% kiwifruit-transfer success rate at a mean cycle time of 4.8 s per fruit. These results demonstrate the feasibility of the proposed structural design and HMVEC strategy for precise manipulator positioning and post-detachment fruit transfer. Full article

Uncaria rhynchophylla is an important medicinal plant widely used for treating neurological and cardiovascular diseases. However, current utilization primarily focuses on its hooked stems (Uncariae Ramulus cum Uncis), while its by-products remain underutilized. A systematic understanding of its cultivation and whole-plant utilization is lacking, limiting its industrial and ecological potential. We comprehensively evaluated the cultivation, processing, by-product utilization, and ecological value of U. rhynchophylla within an industrial crop framework. This review synthesizes recent advances in agronomic practices, phytochemical composition, environmental influences, and product development strategies by integrating findings from experimental and applied studies. The hooked stems are rich in alkaloids such as rhynchophylline, while other plant parts contain valuable active compounds suitable for bio-based products, including functional foods and medicinal materials. Environmental factors (light, soil, altitude) and cultivation strategies significantly influence yield and active compound accumulation. Whole-plant utilization and by-product valorization can enhance resource-use efficiency and economic returns. U. rhynchophylla has significant potential as an industrial medicinal crop. Integrating optimized cultivation, processing technologies, and by-product utilization strategies supports sustainable production and rural development. However, limitations include standardization and limited large-scale validation. Future research should focus on mechanistic studies, industrial-scale processing, and development of high-value products to promote a circular bioeconomy. Full article

Climate change presents significant challenges for smallholder dairy farmers, especially in developing countries where production relies heavily on natural resources. This study examined factors linked to climate change adaptation among smallholder dairy farmers in Plateau State, Nigeria. A cross-sectional survey was conducted with 400 farmers selected through multistage sampling across eight major dairy-producing Local Government Areas. Data collection involved structured questionnaires, key informant interviews, and focus group discussions. Analysis included descriptive statistics, Pearson’s chi-square test, one-way ANOVA, and multiple linear regression. The results showed that dairy farming is mostly practised by economically active male farmers. Many respondents noticed changes in rainfall and rising temperatures, but awareness of climate change and its impact on dairy production was generally low. Farmers identified seasonal feed shortages, declining forage quality, water scarcity, heat stress, and reduced feed intake as key challenges. While some adaptation measures, such as adjusting grazing practices and water management, had been adopted, many found them to be ineffective. Regression analysis indicated that education, climate change awareness, access to extension services, and feed availability significantly influenced adaptation outcomes, with the model explaining 61.0% of the variation (R² = 0.610). Access to extension services had the strongest impact. The results highlight the need to strengthen institutional support to boost smallholder farmers’ adaptive capacity. Enhancing livestock extension programmes, raising awareness about climate change, improving access to information, and promoting climate-smart dairy management and sustainable feed practices can improve the resilience and sustainability of dairy systems in Plateau State and similar regions. Full article

Excessive use of chemical fertilizers degrades soil health and threatens sustainable crop production, which can be mitigated by partially substituting chemical fertilizers with Chinese milk vetch (Astragalus sinicus L., MV, as green manure). However, the mechanisms through which MV incorporation alters soil microbial community structure and function, enhances soil quality and crop productivity, as well as its long-term effects in paddy soils, are still not fully understood. In this study, we investigated the responses of soil physical, chemical, and biological properties (to comprehensively evaluate soil quality); microbial community structure and function; rice productivity; and the sustainable yield index (SYI) to five fertilizer treatments based on a 13-year field experiment in a paddy’s soil in Henan, China. The treatments included: CK (no chemical fertilizer and no MV), F100 (100% chemical fertilizer), MVF80, MVF60 and MVF40 (80%, 60%, and 40% of the chemical fertilizer rate combined with MV, respectively). Compared with the F100 treatment, MVF60 slightly increased rice yield by 1.71% and significantly improved SYI by 5.10%. All MV treatments significantly increased soil organic carbon (SOC, by 14.4–16.3%) and microbial biomass carbon (MBC, by 16.7–20.1%). MVF60 and MVF40 significantly reduced bulk density, and increased macroaggregate content and mean weight diameter (MWD). MVF80 significantly enriched soil total phosphorus (TP), total potassium (TK), mineral nitrogen (Nmin), and urease (UE). The improvement in these soil properties resulted in a marked increase (by 11.6–20.1%) in the soil quality index (SQI) under all MV treatments. Random forest analysis identified MBC and Nmin as the most important predictors of SQI. Moreover, MV incorporation increased the relative abundance of beneficial taxa (Firmicutes, Clostridium_sensu_stricto_1, Bradyrhizobium, and Nigrospora), which were positively correlated with SQI (p < 0.05), while reducing the relative abundance of pathogenic fungal genera such as Fusarium. Furthermore, regression analysis revealed strong positive correlations between SQI and both rice yield and SYI. In summary, long-term MV incorporation with a 40% reduction in chemical fertilizer (MVF60) constitutes an effective and sustainable nutrient management approach for rice production in southern China. This practice enhances soil quality through improved physical structure, nutrient cycling, and microbial community structure and function, ultimately resulting in higher and more stable yields. Full article

Accurate short-term agricultural commodity price forecasting is important for market monitoring, procurement planning, inventory management, and price–risk awareness. This study develops a data-driven forecasting framework, namely CMACS–ARO–LSTM–Attention (CALA), which combines an LSTM–Attention predictor with an improved Artificial Rabbits Optimization algorithm. The original reported CALA results are retained unchanged. This minimal revision adds persistence/random-walk and ARIMA references on the archived comparison blocks. For wheat, the reported CALA result is $R^2=0.995$, RMSE $=0.008$, MAE $=0.004$, and MAPE $=0.021\%$; the newly added persistence/random-walk and ARIMA references have RMSE values of 0.102 and 0.092, respectively. The soybean application is presented as supplementary mixed evidence rather than proof of broad generalization. The high goodness of fit is interpreted as short-horizon price-level approximation in a persistent series, not as proof of directional trading profitability. Repeated CALA runs, formal predictive-accuracy tests, equal-budget ablations, and multi-step interval forecasts remain outside this minimal revision. Full article

This paper systematically reviews the emerging field of smart urban agriculture, defined as the integration of information and communication technologies (ICTs) into food production practices within and around built-up urban areas. Although urban agriculture and digital agriculture have each generated substantial scholarship, their intersection remains conceptually fragmented and empirically uneven. Following the PRISMA 2020 guidelines, this study reviews 143 English-language articles indexed in Web of Science and Scopus between 2016 and 2026, combining bibliometric mapping with structured thematic coding. The analysis shows that smart urban agriculture has expanded rapidly since 2021, but remains geographically concentrated and disciplinarily dispersed. Current research is organized mainly around IoT and sensor networks, machine learning, soilless cultivation, vertical farming, plant factories, and controlled-environment agriculture. ICT applications are most mature in enclosed, data-rich, and technically controllable systems, where they support monitoring, prediction, automation, and resource optimization. By contrast, community-based, open-space, and governance-oriented forms of urban agriculture remain underexplored. By systematically linking ICT families with different urban agriculture production settings, this review clarifies the field’s emerging knowledge structure and demonstrates that technological development remains uneven across agricultural forms and socio-institutional contexts. Full article

Adequate nutrition is essential for honey bee colony development and may influence pollination services provided to agricultural crops. In contrast, fungicides commonly used in agriculture have been associated with physiological and behavioral alterations in individual bees, although their consequences at the colony level remain poorly understood. This study evaluated the effects of colony nutritional status and exposure to a commercial fungicide containing bixafen, prothioconazole, and trifloxystrobin on honey bee colony development and soybean pollination efficiency. Twelve honey bee colonies were maintained under nutritional supplementation or restriction (n = 6) for seven weeks. Subsequently, each group was subdivided according to fungicide exposure or no exposure while colonies were confined in soybean pollination cages. Colony weight, food reserves, hygienic behavior, brood area, and Varroa destructor infestation were monitored throughout the experiment, and soybean yield was assessed at harvest. Nutritional supplementation improved colony performance, increasing weight gain, food reserves, hygienic behavior efficiency, and soybean yield, which increased by 31.8% (3076.7 vs. 2334.2 kg ha⁻¹) compared with pollinator-excluded plots. Fungicide exposure did not affect colony development, food reserves, hygienic behavior, or mite infestation, regardless of nutritional status. Confinement reduced colony weight and brood area, but supplemented colonies resumed weight gain after returning to the apiary, whereas food restricted colonies maintained stable weights. These findings demonstrate that nutritional status is a key determinant of honey bee colony performance and pollination efficiency, whereas no measurable adverse effects of fungicide exposure on colony performance were detected. Full article

Exceptionally, this space is now dedicated to a tribute to Prof. Dr. Danilo Florentino Pereira (in memoriam), who passed away on 30 May 2026, during the organization period for the Special Issue “Application of Antimicrobial Peptides and Natural Products as Feed Additives in Animal Production” of the journal Agriculture, in which he was contributing as a Guest Editor [...] Full article

The natural disasters caused by climate change are increasingly threatening the livelihood sustainability of rural households. How to enhance the adaptability of farmers has become a major issue that urgently needs to be addressed in rural development. An increasing amount of research suggests that the digital transformation in rural areas may provide solutions to this problem. This study selected large sample data from the China Family Panel Studies (CFPS) from 2014 to 2020 to empirically test the role of digital economy in mitigating the adverse impact of natural disasters on livelihood resilience of rural households in China. The empirical results indicate that although natural disasters have a significant negative impact on the livelihood resilience of rural households, the embedding of digital technology can alleviate this negative impact to some extent by expanding non-agricultural employment opportunities for rural households, enhancing households’ access to credit, and improving agricultural production strategies. During this process, the buffering capacity, self-organization ability, and learning ability of rural families have been significantly improved. Moreover, the mitigating effect of the digital economy exhibits heterogeneity. It is more pronounced in the central and western regions than in the eastern regions, and also more evident among high-income families relative to low-income families. The key to improving the benefits of digital technology may lie in tailored policy interventions and digital skills training for farmers. Finally, this study provides empirical evidence from rural China on the role of the digital economy in strengthening farmers’ adaptive capacity against disaster risks. While these findings are context-specific, they may still provide valuable insights for other agriculture-dependent developing countries facing persistent climate risks. Full article

Coffee leaf diseases impair photosynthesis, thereby degrading the chemical composition and flavor quality of coffee beans. However, these resulting quality defects are often not visible from the appearance of green beans alone, compelling e-commerce quality control to trace back to leaf disease detection at the production origin. This focus on “quality traceability” imposes requirements on computer vision technologies that fundamentally differ from those of conventional pesticide-application-oriented detection: prioritizing high precision over high recall, replacing simple classification with severity grading, and necessitating the integration of variety and origin metadata. This paper conducts a systematic review of 53 relevant studies published from January 2020 to March 2026, examining existing data resources, model architectures, and industrial adaptability through the lens of e-commerce quality traceability. Our review highlights three major findings: (1) existing datasets could be further enriched in variety labeling, severity scoring, and origin metadata; (2) current models, predominantly focused on classification, have room for closer alignment with traceability requirements regarding optimization objectives, task definitions, and output formats; and (3) cutting-edge technologies, including semantic segmentation, multimodal fusion, and visual foundation models, offer viable pathways to bridge these gaps. This review provides standardized technical evaluation criteria and clear optimization directions for origin inspection, batch grading and whole-chain traceability management of coffee agricultural e-commerce platforms. Full article

Understanding how the spatiotemporal variability of precipitation affects ecosystem respiration (RE) is central to carbon–climate feedback in climate-smart agriculture, yet remains unresolved for the alpine agricultural region of the southern Qinghai–Tibet Plateau, where flux observations are sparse. Using 25 years (2000–2024) of monthly gridded climate and remote sensing data for the Yarlung Zangbo River Basin and Its Two Tributaries Basin, we developed a flux tower-constrained reference–respiration (R_ref) environment-matching model in which R_ref varies with the enhanced vegetation index (EVI) and land surface temperature (LST) to correct the Lloyd–Taylor parameterization. The correction reduced the RE root mean square error by 54.8% (1.04 → 0.47 gC·m⁻²·month⁻¹) and eliminated systematic bias (+0.80 → −0.001) relative to an independent gridded RECO product. We then constructed a multidimensional index of precipitation variability (intra-annual concentration, interannual variability, long-term trend, spatial clustering) and combined random forest, spatial regression, lag analysis, and structural equation modeling (SEM) to disentangle direct and indirect pathways from precipitation variability to RE. The central finding is an indirect-conduction mechanism: precipitation concentration (PCI) affects RE almost entirely through vegetation productivity (PCI → GPP → RE, indirect effect −0.676) rather than directly (direct effect +0.076, opposite in sign), because low temperature and high soil water holding capacity buffer the immediate soil moisture response. The basin functions as a net carbon source (mean NEP = −0.550 gC·m⁻²·month⁻¹) with a significant warming-driven interannual RE increase (Sen’s slope = 0.0025 yr⁻¹, p = 0.022) that is independent of the stable precipitation total. The framework offers a transferable paradigm for carbon flux attribution in alpine regions under sparse observation. Full article