Agricultural Information Institute

Purpose This study analyzes the mechanism by which capitalization impacts green total factor productivity (GTFP) in pig production, revealing its operational pathways. The findings provide empirical evidence relevant for future research and support for green development in the pig farming industry. Design/methodology/approach Based on the provincial panel data of large-, medium- and small-scale pig farms from 2016 to 2023, the capitalization level and GTFP of different-sized pig farms were measured. The impact mechanism and path of capitalization and GTFP of pig farming were empirically examined using fixed effect models, moderated effect models and panel threshold models. Findings Capitalization has significantly enhanced the GTFP of large-scale and medium-scale pig farms. This conclusion remains valid after undergoing multiple robustness tests, but the impact on small-scale farms failed to pass the significance test. The results of the mechanism analysis show that in large-scale pig farms, industrial agglomeration and government support significantly enhance the positive impact of capitalization on the GTFP of pig farming. The panel threshold effect test reveals that in medium-sized livestock farms, the positive impact of capitalization on the GTFP of pig farming exhibits a nonlinear marginal increase. Originality/value This article puts forward policy suggestions such as improving the extensive development model, implementing differentiated and precise measures, and steadily advancing the process of pig farming capitalization.

Polyphenols are a diverse group of plant-derived bioactives that have been investigated as multi-target candidates for the potential prevention and management of neurodegenerative diseases (NDDs). We conducted an integrated bibliometric and mechanistic scoping review covering 12 polyphenol classes and seven major NDDs using records from PubMed, Embase, the Cochrane Library, and Web of Science (1940-2024). Research landscapes and emerging themes were mapped using keyword co-occurrence, clustering analyses, and BERTopic modeling. Mechanistic evidence was synthesized across core pathways, including oxidative stress, neuroinflammation, proteostasis (amyloid/tau and α-synuclein), mitochondrial dysfunction, and cholinergic modulation, to link preclinical findings with clinical outcomes. Publication output increased markedly after 2000, with China and the United States contributing the most records. Four persistent hotspots were identified: (1) antioxidant and neuroprotective effects (e.g., resveratrol, curcumin); (2) anti-inflammatory activity and intracellular signaling; (3) cognition, aging, and sex-specific responses in clinical research; and (4) animal models of memory impairment. Clinically investigated interventions include epigallocatechin gallate, Ginkgo biloba extracts, olive/cocoa polyphenols, and flavonoid-rich mixtures; however, limited bioavailability and heterogeneous trial designs constrain the strength of effect estimates. Advances in delivery systems, computational screening, and precision nutrition may improve translation. Overall, polyphenols show multi-target neuroprotective potential, but larger and more standardized clinical trials are needed to support evidence-based nutritional strategies for NDD prevention and management.

ABSTRACT We investigate whether having more classmates with ECE experience affects junior high school studentsʼ non‐cognitive/socio‐emotional skills. We draw on panel data from the China Education Panel Survey (CEPS) and leverage the exogenous variation in classroom ECE experience composition brought by the random assignment of students when they entered junior high schools. Results from a quasi‐experimental research design show that having more classmates with ECE experience helps to improve studentsʼ non‐cognitive/socio‐emotional skills. Possible mechanisms underlying these relationships include having more classmates with ECE experience is associated with improved classroom peer environment, the enhanced parent and teacher behaviours. Heterogeneity analyses indicate that the positive spillover effects of ECE are more prominent among girls, students without ECE experience, those with better‐educated parents, those in classes with lower ECE enrolment or smaller size, as well as those in urban or public schools.

Against the backdrop of rapid urbanization, the human–land relationship in the mountainous regions of Southwest China (Sichuan, Yunnan, Guangxi, Chongqing, and Guizhou) confronts dual pressures from terrain constraints and development demands, shaping a uniquely complex evolutionary pattern. To clarify the evolutionary laws of the regional human–land system, this study focuses on the period of 2000–2020, integrating land use, socioeconomic, and topographic data to construct a comprehensive analytical framework of “Human Activity Intensity (HAI)–Land Use Dynamic Degree (LUDD)–decoupling model–geographic detector.” This framework is employed to explore the spatio-temporal evolution characteristics of the human–land pattern, the differentiation of decoupling modes, and the underlying driving mechanisms. The key findings are as follows: Human Activity Intensity (HAI) presents a stable spatial pattern of “agglomeration in low-altitude areas and dispersion in high-altitude areas,” undergoing a three-stage temporal evolution of “terrain anchoring–policy constraint–all-round expansion.” Land use dynamics are predominantly governed by terrain: low-altitude river valley plains exhibit significant changes, while high-altitude karst regions remain relatively stable, with an overall policy-responsive fluctuation of “rise–fall–rebound.” Human–land decoupling forms a continuous spectrum encompassing four modes: “collaborative optimization–extensive transition–rigid stagnation–advantageous aggregation,” with strong negative decoupling dominating low-altitude favorable areas and recessive decoupling prevailing in high-altitude mountainous areas. In terms of driving mechanisms, terrain factors serve as the rigid foundation of the human–land relationship, while the urban–rural population structure, urbanization level, and land use intensity act as core human drivers. Additionally, the interaction of factors such as “terrain–economy–transportation” plays a crucial role in the differentiation of decoupling modes. This study clarifies the evolutionary logic of “terrain laying the foundation and human factors shaping the pattern” for the human–land relationship in Southwest China’s mountainous regions, providing scientific support for the coordinated advancement of regional economic development and ecological protection, as well as a Chinese case study for global research on human–land coordination in ecologically fragile mountainous areas.

Fruit recognition and ripeness detection are crucial steps in selective harvesting. To better address the difficulties of fruit recognition and ripeness detection techniques in complex facility environments, a novel lightweight tomato ripeness detection network model based on an improved YOLO v8s is proposed (called TRD-Net). Here, a tomato dataset including 3,330 images from real scenarios was constructed, and an accurate lightweight tomato ripeness detection model trained on the captured images was developed. The TRD-Net model achieves efficient detection of tomatoes affected by overlapping occlusions, lighting variations, and capture angles, offering swifter detection speeds and lower computational demands. Specifically, the feature extraction module of YOLO v8s was refactored by employing spatial and channel reconstruction convolution (SCRConv) and adding the SimAM attention mechanism. The CIoU loss function was replaced by the MPDIoU loss function. The performance of the novel TRD-Net was comprehensively investigated. The proposed TRD-Net achieved an mAP@0.5 of 0.9581 with an improvement of 4.32 percentage points, and the model size decreased from 22.5 M to 17.6 M with an inference time of 8.7 ms per image. The number of model parameters and floating-point operations per second (FLOPs) decreased by 19.69% and 22.03%, respectively. Compared with state-of-the-art models, the proposed TRD-Net is notably promising for real-time tomato recognition and maturity detection. The study contributes to the establishment of a machine vision sensing system for a selective harvesting robot in a complex gardening environment.

A low-carbon economy serves as a core pathway and pivotal engine for advancing the SDGs. Drawing on provincial panel data across 30 Chinese administrative regions spanning 2011–2023, the present study empirically examines how new infrastructure interacts with low-carbon economic development levels and their underlying transmission mechanisms by building an econometric model. Empirical results demonstrate that “new infrastructure” generates a notably positive facilitating impact on low-carbon economic development, with this influence being more pronounced in the central and western regions of China and policy pilot zones, while a rebound effect is identified in eastern China. Among various types of new infrastructure, information infrastructure and innovation infrastructure play particularly prominent roles, while integrated infrastructure shows a positive yet statistically insignificant impact. Mechanism analysis reveals that new infrastructure advances low-carbon economic progress primarily by curbing capital factor misallocation, while the elevation of the population urbanization level can amplify the facilitative impact of new infrastructure on the low-carbon economy. On this basis, it is imperative to raise investment in new infrastructure and enhance its systematic coordination with traditional infrastructure; implement differentiated layout strategies aligned with regional features; rationally steer the population urbanization process; and effectively facilitate the decoupling of carbon emissions from economic growth, thereby furnishing a robust underpinning for the full attainment of SDGs.

Spodoptera frugiperda (fall armyworm, FAW) has rapidly spread across Asia and Africa in recent years, with its seasonal long-distance migration ability serving as the biological basis driving its region-wide outbreaks. Although the migratory biology of FAW has been extensively studied, it remains unclear whether there is stable differentiation between migratory and non-migratory individuals. In this study, we revealed the significant differences in morphological parameters between migratory populations and laboratory-reared populations. The migratory populations exhibited a greater body length and width and forewing size, as well as a lower body weight, compared to the laboratory colony. After three generations of indoor rearing, the migrants' morphology and flight capacity converged to the laboratory phenotype, indicating the existence of a migratory ecotype in FAW. Through further investigation, a method for identifying the migratory ecotype of FAW was proposed based on the corrected wing loading (WL) and forewing aspect ratio (FA), which was successfully applied to distinguish individuals of the migratory ecotype in field populations. Our results confirm that FAWs exhibit stable differentiation into a migratory ecotype, and using WL and FA provides a robust, field-deployable tool for regional FAW monitoring, early warning systems, and targeted FAW control.