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.

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.

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.

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 long-term 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 R² 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 (R² = 0.95, RMSE = 0.05 and MAE = 0.02) compared to the other machine learning models (R² = 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.

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.

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.

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 kg·ha⁻¹ and total P (TP) losses from 1.0 to 1.4 kg·ha⁻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 kg·ha⁻¹, TP runoff by 0.1 kg·ha⁻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 t·ha⁻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.

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.

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.

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.