Search Results (1,109)

Female empowerment in post-conflict rural contexts is strategic for food security and socioeconomic resilience. This study analyzed the relationship between women’s productive empowerment and food security in 40 rural women involved in pig production in Santa Rosa del Sur, Bolívar, Colombia. A mixed approach with a descriptive–exploratory design and longitudinal scope was used. Data collection employed adapted versions of the Women’s Empowerment in Agriculture Index (A-WEAgI) and the Household Food Insecurity Access Scale (HFIAS), alongside participant observation and reflective thematic analysis. Quantitative data were analyzed via descriptive statistics and Spearman correlation. The baseline revealed low empowerment regarding income, resources, technical capacities, and time. The global A-WEAgI reached 21%, while HFIAS showed moderate food insecurity in 52% of households. Spearman analysis (CS) indicated moderate negative correlations between food insecurity and income (CS = −0.56), access to resources (CS = −0.51), and technical capacities (CS = −0.49), suggesting that greater women´s empowerment was associates with lower food insecurity. Post-intervention, improvements occurred in technical skills, leadership, and organizational participation. Qualitative findings showed increased confidence in Agroindustry activities, though limitations in economic autonomy, commercialization, and domestic workloads persisted. Gender-focused rural strategies enhance productive capacities and food resilience; however, structural barriers related to economic autonomy and gender inequality persist. Full article

Background: Early childhood nutrition is strongly associated with neurodevelopmental outcomes, particularly in socially vulnerable settings. Limited evidence is available describing the relationship between nutritional status, food security, and neurodevelopment among preschool children in low-income urban areas of Colombia. This study aimed to evaluate nutritional status, household food insecurity, and neurodevelopmental outcomes in children attending early childhood centers in El Codito, Bogotá, and to explore the association between anthropometric indicators and neurodevelopmental performance. Methods: A cross-sectional study was conducted in children enrolled in community childcare centers. Nutritional status was assessed using anthropometric indicators according to World Health Organization growth standards, including weight for age, height for age, and body mass index for age. Neurodevelopment was evaluated using the Escala Abreviada de Desarrollo (EAD). Household food insecurity was measured through a validated questionnaire. Descriptive statistics were performed, and associations between variables were analyzed using correlation tests and group comparisons according to data distribution. Results: Most participants presented adequate nutritional status; however, a proportion of children showed risk of stunting or excess weight. Neurodevelopmental scores were generally within expected ranges, although variability was observed across developmental domains. Significant associations were identified between certain anthropometric indicators and neurodevelopmental outcomes. Moderate to severe household food insecurity was identified in 21.4% of participating households. Conclusions: Nutritional status and household food insecurity represent important contextual factors for child health in vulnerable urban populations. These findings highlight the importance of integrated nutritional and developmental monitoring strategies within early childhood programs. Further longitudinal studies are required to clarify causal pathways and to guide targeted public health interventions in similar contexts. Full article

Reliable, high-quality rainfall data are vital for soil and water management, crop forecasting, and risk assessment. These applications are essential for food security, climate resilience, biodiversity monitoring, and rural livelihoods. Rainfall monitoring in Thailand is challenging due to the limited density of official stations and the inconsistent quality of data from multiple sources, compounded by calibration issues. This study introduces a comprehensive quality control (QC) approach tailored for the Thai context, presenting a systematic pipeline that clarifies the hierarchy and sequence of operations. The method uses rainfall data from 3075 stations of the Thai Meteorological Department (TMD) and the Thaiwater network. It includes basic QC for data completeness and advanced QC using a quality (Q) index to assess station reliability, diving the stations into five groups: poor (<50), moderate (50–80), acceptable (80–85), good (85–90), and excellent (>90). The results indicate that Thaiwater consistently achieved moderate to excellent Q index values, exceeding 70% annually, with values surpassing 90% in 2023. In contrast, the TMD maintained excellent quality, with values above 90% for all years. Out of over one million daily entries, 87% were verified as correct, though the Thaiwater data for 2024 showed only 70% accuracy. The QC procedures significantly improved data reliability, reducing the root mean square error for GSMaP and IMERG by 1.7% and 1.5%, respectively, and lowering the false alarm rate by approximately 0.001–0.002 without compromising heavy rainfall detection. A systematic QC framework is essential for ensuring high-quality datasets in rainfall applications. Full article

The coastal zone exhibits complex resource constraints and environmental pressures, with marked industrial structure differentiation and considerable spatial stress on agriculture. This study enhances industrial cluster resilience by employing shift-share analysis to delineate industrial structure and constructing a multi-objective optimization model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The model encompasses industrial cluster-driven development, economic benefits, social food security, ecological advantages, and land use efficiency, integrating coastal-specific constraints including soil salinity, tidal influence, and aquaculture competition. An empirical study in Nantong City, Jiangsu Province, China, demonstrates that optimized land allocation achieves a 3.13% reduction in cropland area while maintaining 42.56% coverage, increases forest land by 0.28% to 75.3847 km², and enhances other land uses by 2.21% to 2169.6563 km². The multi-objective optimization successfully balances five competing objectives with an overall improvement index of 0.847, validating both scientific robustness and practical feasibility. This research provides a scientific basis for agricultural space reconstruction and rural revitalization in coastal regions. Full article

Enhancing agricultural economic resilience (AER) is essential for global food security. As a key policy tool for stabilizing agricultural production, policy-based agricultural insurance lacks rigorous causal evidence on its impact on resilience. In this study, AER is operationalized as a composite index capturing resistance and recovery capacities across pressure, state, and response dimensions. Using 2012–2023 provincial panel data from China (31 provinces × 12 years = 372 observations), we measure AER via the entropy method and identify policy effects using a staggered multi-timepoint difference-in-differences (DID) model. We find that policy-based staple crop insurance significantly increases AER by approximately 2.5 percentage points, primarily by promoting agricultural technological innovation (ATI) and regional industrial structure upgrading (RIS). The improvement effects are more pronounced in central and western regions, non-major staple-crop producing areas, and regions with higher natural risks. Robustness is confirmed via event study, alternative weighting schemes (PCA and equal weighting), and placebo tests. This study provides reliable causal evidence for the resilience-enhancing effect of agricultural insurance and clarifies its internal transmission mechanisms, offering empirical support for the optimization of agricultural risk governance policies. Limitations include the use of provincial-level aggregate data and the lack of analysis of spatial spillover effects between regions. Our findings suggest that differentiated policy implementation can support more sustainable and targeted agricultural risk governance. Full article

Transport infrastructure is widely viewed as a key lever for integrating lagging rural regions into broader economic systems. Western China, marked by vast territory, complex topography, and historically severe spatial market frictions, offers a particularly informative setting for examining this question within the sustainable rural development agenda. Exploiting the staggered rollout of China’s National Highway Expansion Program across 276 prefectures from 2003 to 2018, we combine high-frequency wholesale prices for 93 agricultural commodities, geocoded expressway network data, and the China Family Panel Studies. A staggered difference-in-differences design is supplemented by a time-varying minimum spanning tree instrument capturing network-efficiency considerations, alongside event-study and recently developed robust estimators for staggered treatments. Two-stage least squares estimates indicate that expressway connection raises the agricultural price integration index by 0.071, reduces within-prefecture price volatility by approximately 0.040 (about 13% of baseline), raises agricultural household income per capita by roughly 16%, and improves the household food-security index by 0.571 points. Event-study results show no pre-trends, with effects materializing over three to four years post-connection. Mechanism analysis highlights expanded market linkages, and the gains are stronger in nationally designated poverty counties and prefectures with rugged terrain. Partial-equilibrium welfare accounting implies annual gains of roughly USD 4.92 billion, and unconditional quantile regressions reveal a progressive distribution across farm incomes. These findings underscore the role of transport infrastructure in alleviating spatial frictions, integrating lagging regions, and advancing sustainable rural development while warranting careful attention to the environmental externalities of large-scale infrastructure. Full article

Background: Household food insecurity (HFI), defined as the lack of reliable access to adequate food because of limited money or resources, may influence children’s nutritional status. This study aimed to examine the association between HFI risk, based on a single screening item, and underweight and obesity among kindergarten children in Nevada. Methods: Cross-sectional data from the Kindergarten Health Survey (KHS) collected across three school years (2022–2023, 2023–2024, and 2024–2025) were analyzed using a pooled sample of 7267 children. HFI risk was assessed using one item from the Hunger Vital Sign. Weight status was determined using Body Mass Index (BMI) guidelines from the Centers for Disease Control and Prevention (CDC). Descriptive statistics and multinomial logistic regression examined associations between HFI risk and underweight and obesity, adjusting for confounders. Results: Across the pooled sample, 16.3% were at risk for HFI, 16.0% were underweight, and 21.9% had obesity. In pooled analysis, HFI risk was associated with higher odds of obesity (Adjusted Odds Ratio [AOR] 1.29; 95% Confidence Interval [CI]: 1.05–1.59), but not underweight, compared with food-secure children. In year-specific analyses, higher odds of underweight were observed in 2023–2024 (AOR 1.74; 95% CI: 1.14–2.66) and 2024–2025 (AOR 1.58; 95% CI: 1.04–2.38). Conclusions: HFI risk was associated with obesity among kindergarten children in Nevada, while associations with underweight were observed only in certain school years and should be interpreted cautiously. These findings suggest HFI risk as an important early childhood health concern and support the need for nutrition support, family assistance, and longitudinal research. Full article

Digital technologies provide effective tools for formulating sustainable, evidence-based policies; however, this field has so far lacked a cohesive and practical framework to guide their application. Providing comprehensive answers to six primary research questions, this study aims to address this critical gap concerning the prerequisites, challenges, opportunities, key technologies, policy areas, and critical success factors (CSFs) for applying digital technologies in environmental sustainability policymaking. In this study, 39 articles were analyzed from 293 documents indexed in the Web of Science as of 19 August 2025, in accordance with the PRISMA 2020 guidelines. The prerequisites are categorized into the following themes: fiscal incentives, a culture of innovation and sustainability, effective regulations, robust digital infrastructures, participation, and reliable and accessible data. We identified significant challenges, including financial constraints, human resource deficits, infrastructural and regulatory gaps, and the adverse environmental impacts of digital technologies themselves. Opportunities emerged under two main domains: effective policymaking and enhanced environmental management. Our study indicates that pioneering technologies at the core of this transformation include artificial intelligence, big data, blockchain, the Internet of Things, machine learning, and robots. Their applications are predominant in key policy areas, including the environment, energy, climate change, urban sustainability, agriculture, industry, and food security. The analysis identifies four CSFs: the policy–digital–sustainability nexus, fundamental processes, soft capacities, and hard capacities. Full article

Earthquake-induced infrastructure disruption can delay emergency response and prolong recovery, yet many post-earthquake damage studies either focus primarily on superstructures or examine individual infrastructure sectors separately. This study presents a questionnaire-based expert assessment of earthquake-induced damage factors and mitigation measures in urban infrastructure systems. Fourteen damage factors and seventeen mitigation measures were identified through a structured literature review and evaluated by 424 technical experts using a five-point Likert scale. The responses were analyzed using reliability analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), the Relative Importance Index (IRI), and Pearson correlation analysis. The dataset showed high internal consistency (Cronbach’s alpha = 0.926), with KMO = 0.941 and a significant Bartlett’s test (p < 0.001), confirming its suitability for factor analysis. EFA and CFA grouped the damage factors into three dimensions: Post-Earthquake Intervention Challenge (PEIC), Health Food Water Security (HFWS), and After-Earthquake Preparedness (AEP). IRI results ranked PEIC as the highest-priority expert-perceived factor group (average IRI = 90.61%), followed by HFWS (88.32%) and AEP (85.10%). Pearson correlations indicated that resilient network and pipeline infrastructure, resource diversification and redundant distribution capacities, regular maintenance and inspection, strategic stockpiles, site-selection reassessment, slope stabilization, and early warning systems were strongly associated with one or more factor groups (r > 0.60; p < 0.001). The findings should be interpreted as expert-perceived priorities rather than objective damage probabilities; nevertheless, they provide a structured basis for preliminary prioritization of urban infrastructure resilience measures in earthquake-prone contexts. Full article

Enhancing mariculture’s green total factor productivity (GTFP) is essential to balance industrial growth with ecology, safeguard global food security, and meet UN Sustainable Development Goal 14 amid mounting marine stress. As a global leading mariculture producer, China provides a typical research sample. This study constructs a mariculture GTFP measurement index system, estimates GTFP in China’s coastal provinces via the global Super-SBM model, identifies root causes of efficiency loss, and explores influencing factors and spatial spillover effects using a spatial econometric model. The results show that the overall mariculture GTFP of China’s coastal provinces exhibits a fluctuating upward trend with significant regional heterogeneity, specifically presenting a distribution pattern of “the highest in the South China Sea Region, followed by the East China Sea Region, and the lowest in the Yellow Sea and Bohai Sea Region”. Meanwhile, mariculture GTFP shows significant positive spatial autocorrelation, with distinct High-High and Low-Low agglomeration characteristics. Excessive resource consumption and undesirable output discharge are the core drivers of efficiency loss. For direct effects, industrial scale, industrial structure, fishermen’s income, transportation accessibility, internet development, technology adoption, and environmental regulation significantly boost local GTFP, while fishery disasters exert a significant negative impact. For spatial spillovers, industrial scale, industrial structure, and internet development show significant positive effects, while fishermen’s income and urbanization present negative effects. Based on these findings, this study proposes targeted multi-stakeholder optimization paths, providing decision support for China’s mariculture green development and replicable experience for global coastal countries. Full article

Agrotextiles are critical for enhancing climate resilience and food security in modern agriculture. This scoping review maps the global research landscape to identify primary functions, applications, and emerging sustainability challenges. Following the Arksey and O’Malley framework and PRISMA-ScR guidelines, 206 studies published between 2000 and 2025 and indexed in Scopus and WoSCC were systematically analysed using a hybrid qualitative–quantitative approach. Results demonstrate that pest exclusion (37.4%) and solar radiation management (34.5%) are the dominant functional roles, with research heavily concentrated in high-value crops such as tomato (22.2%) and pepper (13.8%). Although synthetic polymers prevail, a substantial reporting gap remains, as 51.9% of studies do not explicitly specify base materials. Nevertheless, a clear shift toward sustainability is emerging, with environmental themes accounting for 77.8% of publications in 2025, particularly focusing on biodegradable materials and pesticide reduction. Overall, while applied performance research in agrotextiles is relatively mature, the field remains fragmented in terms of material transparency and structural standardisation. Future advances should integrate circular economy principles, establish technical reporting standards, and expand applications into extensive and tropical cropping systems to support global agricultural resilience. Full article

Rapid urbanization, climate change, and uneven regional development have increasingly intensified spatial heterogeneity in food security. As one of China’s major commercial grain-producing areas, the Main Grain-Producing Region in the Middle Reaches of the Yangtze River (MGPR-MRYR) plays a critical role in ensuring national food security. However, existing studies have paid limited attention to spatial heterogeneity and driving mechanisms at the urban agglomeration scale. Taking the Wuhan (WUA), Changsha–Zhuzhou–Xiangtan (CZXUA), and Poyang Lake (PYLUA) urban agglomerations as analytical units, this study constructs a multidimensional food security evaluation framework covering supply security, production resource security, and circulation–consumption security. Based on panel data from 2013 to 2023, the entropy weight method, kernel density estimation (KDE), Theil index decomposition, spatial autocorrelation analysis, and the optimal-parameter geographical detector (OPGD) model were employed. Food security levels in the MGPR-MRYR exhibited an overall upward trend, particularly after 2020, although significant spatial heterogeneity persisted among urban agglomerations. A spatial pattern of “higher in the west than east, and inland over lakeside” emerged, with significant positive clustering gradually expanding westward. Intra-agglomeration disparities—especially within the WUA—contributed more to regional inequality than inter-agglomeration differences. Agricultural machinery power and rural population remained the dominant driving factors, while the influence of urbanization and annual precipitation increased over time. All factor interactions showed enhancement effects, indicating that food security is shaped by the synergistic interplay of natural, socioeconomic, and agricultural production factors. This study reveals the transition of driving mechanisms from traditional factor dependence to multi-factor system synergy. These findings suggest that food security governance in rapidly urbanizing grain-producing regions should shift from uniform policies to differentiated, synergy-oriented strategies tailored to each urban agglomeration’s development stage and resource constraints. Full article

In response to increasingly complex risks and challenges and to safeguard national agricultural product supply security, this study constructs a four-dimensional evaluation index system encompassing “Resistance-Adaptation-Recovery-Innovation”. Utilizing panel data from 30 provincial-level regions in China from 2017 to 2023, and employing a comprehensive methodology including the entropy method, Dagum Gini coefficient, Markov chain, kernel density estimation, and convergence models, this research measures the resilience of China’s agricultural product supply chain and investigates its spatiotemporal evolution patterns. The findings are as follows: Firstly, the resilience level of the national agricultural product supply chain shows overall steady improvement, but regional development is uneven, presenting a pattern of eastern regions leading, central regions maintaining steady progress, and western regions catching up. Secondly, the overall resilience difference is strongly correlated with regional variability, with the most pronounced internal disparity observed in the western region. Thirdly, the evolution of resilience exhibits path dependency characterized by the coexistence of a “low-level trap” and “high-level stability”, and less developed regions demonstrate a significant “catch-up effect” towards their more developed counterparts. Based on these findings, this study proposes countermeasures such as implementing targeted policies for different regions, establishing cross-regional coordination mechanisms, strengthening dynamic monitoring and early warning systems, and promoting innovation-driven development and structural upgrading. These efforts aim not only to enhance China’s capacity to respond to risks in its agricultural product supply chain and ensure national food security, but also to provide valuable insights for other countries facing similar challenges in building resilient agricultural systems in an increasingly uncertain global environment. Full article

Sustainable agriculture has been a focus of research for over three decades, gaining particular urgency with the escalation of global conflicts, especially the Russian–Ukrainian war. Selecting appropriate parameters for objectively assessing sustainable agricultural development remains challenging, with limited studies addressing the aggregation of all relevant indicators into a single analytical framework. Given that these indicators and their quantitative values change annually, continuous updating and analysis are essential. This study was guided by selected SALSA/PRISMA principles to structure the indicator-selection process for examining Ukraine’s agricultural sector, which is vital to both national and global food security and accounts for approximately 10% of GDP, more than 50% of exports, and nearly 17% of employment. Alongside climate change pressures, the sector faces severe disruption from military aggression, undermining its economic contribution and stability. This research identifies and selects the most relevant economic, ecological, and social indicators to assess sustainable agricultural development in Ukraine, comparing values before and during the war. Based on these, this study proposes the Sustainable Agriculture Index (ISA), an aggregated measure that integrates multiple dimensions of sustainability. The ISA was calculated using a normalized weighted aggregation approach across economic, environmental, and social indicators. This approach enables a comprehensive evaluation of Ukraine’s agricultural resilience and its capacity to contribute to sustainable development under crisis conditions. Full article

Food security serves as a vital cornerstone for social stability. As one of the most important staple crops globally, the quality and geographical origin of rice are directly associated with consumer health. Traditional methods for classifying rice by origin and variety rely on sensory evaluation and manual inspection, which are subject to uncertainty and human error. To address this, this paper proposes a method for classifying rice by origin and variety based on terahertz time-domain spectroscopy. Terahertz technology features the advantages of non-destructive, high-sensitivity and non-contact detection, making it well-suited for food detection. This study employs terahertz time-domain spectroscopy combined with machine learning modeling methods, using 20 types of rice as the subject of investigation, with a focus on modeling and analyzing four representative samples. Refractive index and absorption coefficient were extracted through preprocessing methods including Savitzky–Golay convolution smoothing, wavelet denoising and moving average smoothing. Modeling, classification, and detection were implemented using principal component analysis, partial least squares discriminant analysis, and least-squares support vector machine. The experimental results indicate that principal component analysis (PCA) alone performs poorly in classification tasks. However, a classification model combining PCA for dimensionality reduction with a least-squares support vector machine (SVM), following Savitzky–Golay smoothing, demonstrated the best performance, achieving a prediction accuracy of 93.3%. In an extended test involving 20 samples, the model achieved an identification accuracy of 89.6%. Quantitative metrics demonstrate the feasibility of using terahertz technology combined with optimized machine learning algorithms for classifying rice by origin and variety. Full article