The benefits of integrated crop-livestock systems (ICLS) have been widely discussed, but their application remains limited. The effects of agricultural characteristics and spatial distribution in a landscape on the development of ICLS are not well understood. This study aimed to better understand the current specialization of farming systems to support ICLS development, by capturing the diversity of farms and their spatial distribution patterns. It developed a spatially explicit farm typology and map of the proportion of types throughout the study area, using a 300-households survey data set from Quzhou, a typical agricultural production county on the North China Plain. Also, it identified six distinct farm types characterized by the degree of specialization, management and farm size. Environmentally and socioeconomically oriented variables were used to further quantify farm types. Three features in these farm types were identified as being relevant in the context of ICLS, that is overuse of fertilizer, the decoupling of crop and livestock production, and a strong dependence of specialized livestock farms on feed import. Farm types were unevenly distributed across the study area, indicating regional specialization and a spatial decoupling of crop and livestock production. The paper discusses driving forces behind the different farm types and their implications for ICLS. New guiding policies are needed to limit strong regional specialization and facilitate ICLS to ensure a balanced crop-to-livestock ratio and distribution at a subregional scale. Overall, this study may help to contextualize future ICLS designs to local conditions and support agricultural transition policies and rural development on the North China Plain.
The agronomic use of plastic film mulching (PFM) has been hugely successful in promoting food security and improving rural livelihoods. PFM has also enabled substantial increases in food production, farmer incomes, reduced pesticide use and improved water and nutrient use efficiencies. However, the resulting plastic pollution in soils (including macro-, micro and nano-sized plastics) caused by PFM has attracted significant attention from scientists, regulators, policymakers and plastic manufacturers. While many articles have been published on the amount, behavior and fate of microplastics in soils, most of these studies are based on laboratory- and plot scale experiments with extremely high concentrations of macro- and microplastics, resulting in over-exaggerated conclusions about the environmental risk of PFM use. Therefore, there remains a critical need to determine the effect of legacy PFM contamination in soil ecosystems at realistic field loading rates. Also, although biodegradable plastic film represents a promising way to help resolve the plastic pollution caused by conventional plastic film, the effect of biodegradable plastic film on soil microplastic concentration, soil microbial communities and the plant growth remains poorly understood. Therefore, the 10 articles in this special issue focus on the abundance and distribution of macro- and microplastics in soils, the effect of conventional and biodegradable plastic films on the soil environment and plant growth as well as the policy for plastic sustainable management.
Aquaculture is increasingly important in global food production; however, its environmental impacts, particularly greenhouse gas (GHG) emissions, are subject to increasing scrutiny. This literature review synthesizes current research on GHG emissions from aquaculture, identifying key emission sources, species-specific emission patterns, geographical research trends and mitigation approaches. A systematic search was performed using the Web of Science, using search terms associated with aquaculture and GHGs. The search yielded 1821 publications. Subsequent analysis indicated a marked rise in academic interest since 2000, reaching a peak of 222 publications in 2023. Geographically, China has dominated publication output, followed by the USA, Australia and Norway. Major themes have included quantifying emissions of CO2, CH4 and N2O across species, such as mussels, salmon, shrimp, and tilapia. Seaweed and bivalves have often been identified as low-emission or carbon-sequestering organisms, whereas intensive production of shrimp and catfish tended to be associated with elevated emission levels. Notable mitigation measures included optimized feed composition, integrated multi-trophic aquaculture and adoption of renewable energy technologies. This review also highlights the lack of research in regions such as Africa and stresses the importance of adopting standardized methodologies for emission measurement and life cycle assessment. This work offers research-informed and policy-relevant guidance to advance low-carbon aquaculture systems in line with global climate objectives.
Soil acidification models are useful for evaluating measures to mitigate soil acidification under various agronomic practices. However, the appropriate modeling approaches for simulating the soil acidification process have not been adequately studied across soils with distinct buffering mechanisms. This study evaluated the performance differences between a process-based soil acidification model (VSD+) and four machine learning models, including random forest (RF), support vector machine, extreme gradient boosting and decision tree, in simulating pH dynamics of neutral and acidic soils. Two longterm experimental sites were selected with distinct buffering mechanisms on purple soil as an example for the development, calibration and validation of soil acidification models. Results from the RF importance factor analysis indicated that soil background pH was the primary factor influencing the dynamic changes in purple soil pH, followed by meteorological conditions and agronomic practices. pH was then chosen as an essential input variable to developing machine learning models for simulating soil acidification patterns. Machine learning models achieved higher accuracy in neutral soil than the VSD+ model. The RF model gave the best simulation performance, outperforming other machine learning models at both sites, with the highest R-2 of 0.70 and 0.47 and the lowest MAE of 0.19 and 0.17 for neutral and acidic soils, respectively. In contrast, the VSD+ model exhibited excellent accuracy with acidic soil (R-2 = 0.95, RMSE = 0.05 and MAE = 0.02) compared to the other machine learning models (R-2 = 0.20-0.47, RMSE = 0.15-0.23 and MAE = 0.14-0.20). These findings provide information for selecting the most suitable modeling approach to simulate soil acidification process with distinct buffering mechanisms, supporting informed decision-making for restoring soil health and quality. (c) The Author(s) 2025. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)
Agricultural intensification, to meet the nutritional needs of the growing world population, has been made possible through the extensive use of agrochemicals, such as synthetic fertilizers and pesticides. However, these practices pose significant health and environmental risks, including groundwater contamination, soil degradation and microbial resistance. Also, predictions indicate that relying solely on synthetic chemicals to boost production may not be enough to meet the future global need for food. Sustainable agricultural intensification involves the use of novel tools to enhance production while addressing environmental concerns using eco-friendly strategies, such as microbial inoculants. These can improve soil fertility, nutrient cycling and crop yield, while enhancing stress tolerance and overall crop fitness. This review outlines the key aspects of the global presence of plant diseases, plant defense responses and disease management strategies, and examines bacterial endophytes as crop biostimulants and biocontrol agents for sustainable control of mycotoxigenic fungi. It also proposes strategies to increase microbial product adoption by addressing technical limitations, such as field stability, delivery precision and shelf-life.
This study investigated the antiviral activity and molecular mechanisms of oligochitosan against potato virus Y (PVY) in Nicotiana benthamiana. The results demonstrate that oligochitosan exhibits significant anti-PVY activity, achieving a preventive efficacy of 54.7%. Biochemical analyses revealed that oligochitosan treatment enhances the activities of defense-related enzymes and stimulates hydrogen peroxide accumulation in N. benthamiana. Integrated transcriptomic and proteomic analyses identified key differentially expressed genes associated with reactive oxygen species signaling and the mitogen-activated protein kinase pathway, including PYL1, PP2C, OXI1, NDPK4, MAPKKK21 and POD4. Functional characterization demonstrated that oligochitosan specifically upregulates OXI1 expression while enhancing MAPKKK21 and NDPK4 transcript levels, thereby conferring enhanced PVY resistance. These findings establish that oligochitosan-induced plant defense against PVY operates primarily through ROS-mediated activation of the mitogen-activated protein kinase signaling cascade. This work provides novel insights into the molecular basis of the antiviral activity of oligochitosan in plant protection.
The rapid spread of animal diseases and the evolution of associated pathogens underscore the urgent need for improved diagnostic techniques. Established nucleic acid detection methods typically rely on expensive and complex machinery, which requires specialized expertise and is time-consuming to operate. As a result, these methods are not well-suited for the monitoring and preliminary screening of epidemics in highly-intensive livestock operations. Therefore, there is a pressing need for the development of on-site rapid nucleic acid detection technologies that offer both high sensitivity and specificity. The clustered regularly interspaced short palindromic repeats and associated proteins (CRISPR-Cas) system is notable for its simplicity, precision and high-efficiency gene-editing capabilities. Recent investigations into CRISPR-Cas-based nucleic acid detection methods have demonstrated considerable potential for advancing diagnostic technology in this field. This paper provides a comprehensive review of CRISPR-Cas-based nucleic acid detection principles and their application in diagnosing animal diseases. It aims to serve as a valuable reference for researchers and practitioners involved in the development and implementation of CRISPR-Cas technologies for animal pathogen detection.
Efficient nutrient management is essential for mitigating nutrient losses from farmland in the Erhai Lake Basin (ELB). This 2-year field study (2021-2022) in the northern ELB investigated the effects of different fertilizer application methods on nitrogen and phosphorus losses. The four fertilizer treatments included: no fertilizer, farmer practice of solely organic fertilizer application (FP), mineral fertilizer, and a combination of organic and mineral fertilizers (OMC). Over the study period, total N (TN) losses ranged from 17 to 34 kgha-1 and total P (TP) losses from 1.0 to 1.4 kgha-1. Peak N and P losses occurred during June and July, with N lost primarily as nitrate and P lost primarily in dissolved forms. Compared with the FP treatment, the OMC treatment significantly reduced nutrient losses throughout the tobacco season; TN runoff decreased by 2.7 kgha-1, TP runoff by 0.1 kgha-1, TN leaching by 21% and TP leaching by 17%. Also, the OMC treatment increased the average tobacco yield by 3.8% (to 2.55 tha-1) compared to the FP treatment, which in turn enhanced the gross value. Fertilizer treatments significantly affected soil properties. These altered soil properties, particularly alkaline hydrolysis N and soil organic matter levels, subsequently regulated N and P loss dynamics. These results provide a scientific basis for mitigating nutrient loss from farmland in the ELB through optimized fertilizer application.
The dynamic variation issues of variable-load unmanned aerial vehicle (UAV) used in agricultural plant protection activities was addressed by a disturbance-resistant control system based on PD (proportional-derivative) sliding mode control. First, a time-varying dynamic model was developed by analyzing the variations in mass, center of gravity and moment of inertia across time. Then a trajectory tracking control approach based on PD sliding mode control was designed to develop an inner-loop attitude controller and an outer-loop trajectory controller to accomplish precise and closely coupled trajectory tracking. Numerical simulations were conducted to verify the trajectory tracking performance, demonstrating accurate tracking of the desired trajectory with standard deviations of 0.0507, 0.1613 and 0.0002 m in the horizontal, lateral and vertical directions, respectively. In terms of attitude control, the system exhibited favorable performance on the roll, pitch and yaw axes, with small transient errors and rapid convergence. Flight experiments further demonstrated that the UAV accurately followed the specified path, and errors in both straight and twisting segments satisfied control criteria. This control system ensured efficient and steady trajectory tracking, offering theoretical and application references for intelligent and precise agricultural plant protection activities.
China has made considerable effort to address methane emissions in the agricultural sector. This paper analyzes the trends in China’s agricultural methane emissions using national greenhouse gas inventory data from 1994 to 2021, identifies key emission sources and reviews relevant policies, while summarizing the practical challenges currently faced in mitigation efforts. The findings reveal that China’s agricultural methane emissions remain high. Although a turning point emerged in 2017, emissions rebounded slightly in 2021 with the recovery of pork production. Rice production, enteric fermentation and manure management are the key agricultural methane emission sources. China has made progress in reducing agricultural methane emissions by integrating climate change policies with green agricultural development initiatives. However, the implementation of these policies must overcome several challenges. The growing food demand will further intensify the pressure on methane reduction. Low adoption rates of existing technologies and limited development of innovative solutions hinder progress toward emission reduction targets. The measurement, reporting and verification (MRV) system remains inadequately developed. Inadequate policy support and financial incentives compromise the sustainability of current efforts. This paper proposes several pathways to promote agricultural methane reduction and support the transition to low-carbon agricultural development. These include strengthening the MRV system, enhancing policy and financial support for emission reduction, advancing research and development, establishing compensation mechanisms for emission reduction, encouraging low-carbon and healthy dietary habits among consumers and strengthening international cooperation.
To address the challenges faced in real-world tomato ripeness detection, such as variable lighting conditions, complex backgrounds, and the trade-off between accuracy and the model being effectively lightweight, this study proposes a lightweight YOLOv11-MHS model. The improvements of the proposed model are reflected in three aspects: (1) the C3k2_MSCB module is designed, which integrates a multiscale convolutional block (MSCB) for multiscale feature extraction and fusion, thereby enhancing detection accuracy; (2) the neck of the model is redesigned as a high-level feature screening-fusion pyramid structure, which fuses key features to improve robustness in cluttered environments while reducing model size; and (3) the C2PSA module is enhanced by introducing the spatial and channel synergistic attention mechanism to improve the ability of the model to handle complex scenes. Experimental results on the same data set show that, compared to the baseline model YOLOv11n, YOLOv11-MHS achieves improvements of 1.7% in mAP0.5 and 2.9% in mAP0.5-0.95, while reducing parameters and model size by 35.2% and 32.7%, respectively. These results demonstrate that YOLOv11-MHS achieves both outstanding accuracy and lightweight performance in tomato ripeness detection, providing technical support for agricultural applications.
To address challenges in crop grasping tasks for agricultural robots, specifically, poor crop background segmentation and limited adaptability in grasp point localization, this paper proposes a saliency guided segmentation approach. This method improves both object recognition and grasp point detection, thereby optimizing robot grasping performance and increasing success rates, even under complex environmental conditions. The proposed network uses a boundary aware detection strategy built on an encoder decoder architecture with an improvement module. First, standard convolutions are replaced by dynamic convolution to improve feature representation. Second, a Haar wavelet downsampling module is introduced to improve multi scale feature extraction. Finally, the standard squeeze and excitation attention block is improved with edge enhancement, which is embedded at each decoding stage to emphasize boundary information. In benchmark tests, the proposed model achieved a mean absolute error of 10.9%, with F-, E- and S-measures of 97.0%, 98.4%, and 96.8%, respectively. When deployed on an agricultural robot platform, it achieved a 78.0% grasping success rate, processing images at 35 frames per second. These results demonstrate that the proposed network reliably identifies and localizes optimal grasp points under real world conditions.
Plant growth-promoting rhizobacteria enhance plant growth and stress resilience, but the metabolite-based mechanisms behind these effects remain insufficiently characterized. This study aimed to assess the metabolite profiles of three rhizobacterial treatments (RK1, RT2 and RT3) and evaluate their effects on drought tolerance in lettuce (Lactuca sativa). The strains were cultured under standard laboratory conditions, and their intracellular metabolites were analyzed using gas chromatography-mass spectrometry. Results showed production of key compounds such as proline, glycine, glutamine, niacin, riboflavin, biotin, pantothenic acid, luteolin and apigenin 7-glucoside, with proline being the most abundant across strains. Lettuce plants were grown under controlled conditions and inoculated by soil drenching at transplantation. Drought stress was imposed 5 days after inoculation by withholding water for 7 days. Survival rate and fresh weight were measured after rewatering. Plants treated with rhizobacterial strains, particularly RT3, had significantly higher survival rates and fresh weight compared to the uninoculated control. These findings highlight the distinct contribution of specific rhizobacterial metabolites to drought tolerance and demonstrate their potential as microbial bioinoculants for improving plant performance under water-limited conditions.
Nitrous oxide (N2O) is a potent greenhouse gas with about 60% of its emissions are attributed to agricultural activities. Its fluxes are influenced by a range of crop-specific factors, such as nitrogenous fertilizer inputs, soil N availability, tillage practices, temperature, pH and soil moisture. These factors interact in complex, nonlinear ways, creating the need for predictive modeling of N2O emissions to both improve understanding and estimation and identify mitigating strategies. This proposes proposes data-driven machine learning techniques, particularly multilayer perceptron and random forest (RF) algorithms, for estimating soil N2O fluxes in a sugarcane plantation under different irrigation regimes and to contrast machine learning results with conventional analytical methods. The findings indicate that RF modeling achieved a coefficient of determination of 87.4% for N2O emission prediction, and identified ammonium, nitrogen nitrate, soil temperature, and water-filled pore space as the most influential predictors, in that order. The results open new possibilities for integrating machine learning to study N2O fluxes in sugarcane and other major crops. All data and code used in this study are provided openly to support further research.
Wastewater from livestock production is characterized by a complex composition, high pollutant load and the presence of emerging contaminants. These properties lead to critical challenges in conventional treatment processes, including excessive energy consumption, low treatment efficiency and incomplete pollutant removal. Photocatalytic oxidation is an advanced oxidation process that uses light energy to generate reactive oxygen species to degrade pollutants. It has gained significant attention due to its advantages of high efficiency, environmental friendliness and the ability to mineralize organic pollutants into water, carbon dioxide and other small molecules without consuming fossil energy. However, despite its potential, photocatalytic oxidation has not been widely applied in wastewater treatment. This is mainly due to the large band gap, low utilization of visible light and fast carrier recombination of photocatalyst. To address these issues, this paper comprehensively reviews the current technical developments of the photocatalytic oxidation process and suggests potentially productive future studies. Despite significant progress, several critical challenges remain to be addressed in photocatalytic material applications, including low visible light utilization, complex synthesis process, expensive material costs, poor practical performance and insufficient mechanism understanding. This review will help design high-efficiency visible-light-driven photocatalysts and promote the application of photocatalysts in the treatment of wastewater from livestock production.
Plastic film mulching (PFM) significantly enhances crop yield and quality by increasing soil temperature, reducing water evaporation and optimizing nutrient cycling. However, improper management of plastic film residues has led to microplastic pollution in farmland, posing a major challenge to sustainable agricultural development. The accumulation of microplastics in soil not only affects soil structure but also profoundly impacts crop growth and ecosystem stability by altering nitrogen-related microbial activities and nitrogen (N) cycling processes. This review synthesizes the effects of PFM and microplastics on soil N pools and cycling, exploring their mechanisms in plant N uptake, microbial immobilization, gaseous emissions (e.g., NH3 and N2O), and N transformation processes (e.g., N fixation, assimilation, mineralization, nitrification and denitrification). Research indicates that PFM and microplastics significantly influence N processes by modifying soil physicochemical properties and microbial community structure, although their effects vary depending on plastic type, environmental conditions and crop growth stages. Future studies should further investigate the long-term ecological impacts of microplastics in complex natural environments and employ advanced statistical methods and models to quantify their dynamic effects on N cycling.
Biodegradable plastic film (BF) has been widely used in agriculture owing to concerns over microplastic (MP) contamination and its potential risks to agricultural sustainability. Elucidating the distribution of MP and the role of microbial communities in their biodegradation is crucial for evaluating the effectiveness of BF in paddy soil. In this study, soil samples were collected from typical paddies in southern China. The MP composition was analyzed by Fourier-transform infrared spectroscopy. Metagenomic sequencing was conducted to identify MP degradation genes and characterize microbial communities. The results revealed that BF-mulched soil had significantly higher MP abundance in the 0.25–0.1 mm size range than soil with a history of no film use as a comparator (CK) (P < 0.05). Similarly, BF had a significantly higher abundance of particular MP types than the CK (P < 0.05). Five main types of MP biodegradation pathways were identified in both BF and CK samples. Over 26 functional genes and 10 genera were associated with the biodegradation of the top five polymer types. However, only a subset of genes and genera significantly differed between BF and CK samples, particularly in the degradation of di(2-ethylhexyl) phthalate, polyethylene and other polymers (P < 0.05). At the functional level, similar genera contributed to MP degradation. However, the relative contributions of these genera varied depending on the polymer type. Overall, BF use led to more efficient MP degradation into simpler structures than in CK. Although the total MP content did not significantly differ between BF and CK samples, BF use had altered the composition and abundance of MP-degrading bacterial communities in the sampled paddy soil. This enhanced biodegradation efficiency under BF use further supports agricultural sustainability in paddy systems.
The global use of agricultural plastic film has severely impacted the ecosphere due to their non-biodegradability, unsafe disposal and limited recyclability. This study aimed to investigate how farmers use agricultural plastic film, including plastic mulch and plastic covers and manage their disposal. The study was conducted in three governorates of Egypt: Dakhalia, Giza and Minya. Data were collected through stratified proportional random sampling based on farm size, surveying 300 farmers managing four plasticulture systems: plastic mulch in open fields, net houses, low tunnels and high tunnels. The data were collected through face-to-face interviews using a structured questionnaire. The study findings reveal that plastic mulch residuals are often burnt in the open or plowed back into soil matrix, whereas plastic film covers are typically collected for recycling. Also, farmers identified the lack of recycling facilities and the absence of fixed disposal locations as the main obstacles for proper plastic waste management. The regression analysis findings also showed the main factors positively affecting the plastic mulch recycling behavior of farmers, including farm size, farm ownership, family member participation in agriculture, and years of experience with plasticulture systems. The findings provide valuable insights for policymakers to develop collaborative management strategies for plastic disposal and recycling, aiming to reduce the environmental risks of microplastics in terrestrial ecosystems.
Microplastic accumulation caused by traditional plastic mulching can disturb plant nutrient-mining strategies. Biodegradable plastics may reduce these risks. However, the different effects of traditional and biodegradable microplastics on agroecosystems and optimal microplastic type for crop-soil systems remain largely unknown. A pot experiment was performed to identify the mechanisms underlying the effects of traditional [polypropylene (PP) and polyethylene (PE)] and biodegradable [polycaprolactone (PCL) and polyadipate/butylene terephthalate (PBAT)] microplastics at 0%, 0.1% and 1% (w/w) in a pea-soil ecosystem. Traditional microplastics caused greater carbon allocation to shoots, while PBAT did not significantly alter dissolved organic-carbon content. NH4+-N increased with 1% (w/w) PP whereas NO3–-N decreased owing to enhanced N-acetylglucosaminidase activity with 0.1% and 1% PP and PE, and 1% PBAT during pea growth. Biodegradable microplastics enhanced microbial biomass carbon, nitrogen and phosphorus, whereas traditional microplastics gave inconsistent results. Microplastics increased the complexity of bacterial and fungal networks and impacted ecosystem functions because they may serve as labile carbon resources for soil microorganisms, stimulating organic matter decomposition. However, once labile carbon in native soils is depleted, inadequate fresh labile carbon from root exudates fails to alleviate microbial carbon limitations, resulting in peas competing with microorganisms for scarce nitrogen resources to promote its growth.
In the context of food security and the greening of agriculture, the sustainable management of agricultural plastics is critical. Although essential to agricultural production, agricultural plastics pose significant environmental risks, particularly in the waste management phase, requiring urgent intervention. Source reduction and effective recycling are key solutions. Based on a systematic review of policy documents, this study analyzed the evolution of China’s agricultural plastics management policies. This policy framework has progressed through three stages. The first stage focused on increasing production and promoting technology, leading to widespread adoption of agricultural plastics but weak environmental regulation. In the second stage, policies began balancing production growth with environmental protection, with stronger environmental regulations and regional successes, but nationwide systematic management remains inadequate. The third stage emphasizes agricultural sustainability, promoting life-cycle management and regionally differentiated management. Although recycling rates have improved significantly, a long-term sustainable management mechanism is still lacking, and there are many challenges to source reduction and effective recycling. Based on this analysis and incorporating international experience, a set of key strategies is proposed for sustainable management, including establishing a unified national framework with region-specific programs, advancing technological innovation and adoption, and integrating environmental regulations with market mechanisms.
