The promise of one health for improved soil and food security in Papua New Guinea.

Poverty and food security are two closely interrelated global issues and are top priorities in the Sustainable Development Goals (SDGs) agenda, particularly SDG 1 (no poverty) and SDG 2 (no hunger). This study aims to analyze the spatial correlation between poverty and food security in Central Java in the 2023–2024 period. The research method used is a quantitative descriptive approach with spatial analysis using Moran's I and Local Indicator of Spatial Association (LISA). Secondary data were obtained from the Central Statistics Agency (BPS), the National Food Agency, and administrative maps in shapefile form. The analysis was conducted using GeoDa software, by examining univariate and bivariate spatial autocorrelation patterns, as well as mapping High-High, Low-Low, High-Low, and Low-High clusters. The results show that the distribution of poverty and food security indices in Central Java is not random, but rather forms a clustered pattern. Bivariate analysis shows a negative spatial correlation, where areas with high poverty rates tend to be associated with low food security. The LISA Bivariate Map identifies clusters of High-High areas concentrated in the southern and coastal regions, while urban areas tend to be in the Low-Low category with relatively better socio-economic conditions. The implication of these findings is the importance of spatially based development policies that integrate poverty alleviation programs with improving food security. Therefore, spatially integrated policy interventions are crucial. Local governments are recommended to prioritize targeted programs in High–High areas, including improving rural food logistics and distribution infrastructure, expanding community-based microfinance and agricultural innovation programs.

Objectives. To expand the understanding of food security among a racially and ethnically diverse sample of US adults living below 200% of the federal poverty line (FPL). Methods. I performed a cross-sectional analysis of the National Health Interview Survey (2019-2023) that included 37 748 respondents. I estimated unadjusted proportions and adjusted probabilities of food security for non-Hispanic White, Black, American Indian/Alaska Native (AIAN), Asian, and Hispanic adults living in severe (< 50% FPL), moderate (50% to < 100% FPL), and near poverty (100% to < 200% FPL). Results. The study reveals important nuances in food security by race and ethnicity. Non-Hispanic Asian adults in severe poverty experienced higher food security than their counterparts in less dire economic situations, non-Hispanic Whites and Hispanics were more similar than previously reported, and non-Hispanic AIAN adults reported extremely low probabilities of food security. Conclusions. Thoroughly documenting and expanding on these patterns of food security for lower-income adults could lead to better understanding of policy mechanisms that could alleviate food insecurity and reduce health disparities by racial and ethnic identity in the United States. (Am J Public Health. 2026;116(6):841-850. https://doi.org/10.2105/AJPH.2026.308508).

Nigeria, a key agricultural nation in West Africa, faces persistent food insecurity due to limited access to improved farming practices, with cassava production playing a pivotal role in addressing hunger and supporting rural economies. In Ekiti State, mobile phone technologies offer a potentially impactful solution to enhance cassava production and food security, yet their utilization remains underexplored. This study investigates the socio-economic characteristics of cassava farmers in Ekiti State, Nigeria, and identifies factors influencing their use of mobile phone technologies to improve agricultural productivity and food security. Conducted across three agricultural zones (Aramoko, Ikere, and Isan), the study employed a multistage sampling technique to select 376 cassava farmers from nine local government areas. Data were collected using structured questionnaires via the Open Data Kit (ODK) and focus group discussions (FGD) with a Multifunctional Rechargeable Device (MRD), a software app for data collection. Factor analysis, chi-square tests, and linear regression were applied to analyze quantitative data, while qualitative data were processed using Atlas. Ti. Four key factors emerged from factor analysis influencing mobile phone technology utilization: economic factors (27.267%), perceived benefits (20.517%), technology enablers (15.033%), and constraints (10.921%). Significant relationships were found between utilization and technology enablers, alongside socio-economic variables like education, farming experience, and farmland size. Qualitative data from FGDs revealed inadequate electricity and poor network coverage as major barriers to mobile phone technology utilization. The study underscores that the mobile technology adoption by cassava farmers is driven by economic factors and perceived benefits but hindered by infrastructural constraints. To improve utilization and boost productivity, it is recommended to invest in rural electricity and internet, design inclusive applications with local languages, and enhance farmers' access to affordable credit.

Effect of livelihood diversification on households’ food security: A propensity score analysis

Community-based interventions to improve tuberculosis treatment outcomes: A meta-analysis.

Like many oceanic regions in the world, the Southwestern Indian Ocean (SWIO) is facing challenges to sustain economic growth, livelihoods and food security, while preserving essential habitats to safeguarding the marine ecosystems. Reaching the Global Biodiversity Framework targets and implementing the Blue Economy roadmaps developed in the SWIO region require reliable data and science-based strategies to manage the marine space as well as knowledge-based governance schemes at both national and regional levels. Monaco Explorations, as a platform committed to foster knowledge, sustainable management and protection of the ocean, organised a multidisciplinary expedition in 2022, in partnership with Mauritius and Seychelles, to advance the state of knowledge of unique sites in the SWIO and contribute to setting harmonised ocean governance in the region. This paper sets the general framework of the Monaco Explorations-Indian Ocean (ME-IO) Expedition and its grand objectives. It provides an outline of the projects developed during the expedition and summarizes the main results for an integrated understanding of the biophysical processes and ecological features in the Mascarene Basin, its underwater plateaus and the waters surrounding the islands.

Farmers’ perceptions and government-farmer value co-creation: The roles of artificial intelligence adoption and government support

Optimizing import patterns to reduce the environmental effects and risks of China’s grain imports

Reframing livestock antimicrobial use as a global public good.

Leveraging genomic technologies in amaranth for stress resilience and food security: advancements, challenges, and future perspectives

Toward an integrative framework for monitoring biodegradation of environmental contaminants across scales.

Healthmaking in the past: Using the social determinants of health in paleopathology.

Grain energy intensity and food security in Africa

Macroeconomic stability, fiscal management and food security in Saudi Arabia: A regime-switching analysis

Cowpea (Vigna Unguiculata), a vital legume for suitable agriculture and food security in sub-Saharan Africa, plays a crucial role in improving soil health through intricate plant-microbe interactions in the rhizosphere. This review synthesizes current knowledge on the microbial interactions in the rhizosphere, focusing on soil health, microbial diversity, and their contributions to nutrient cycling and plant growth. Cowpea roots foster a diverse microbial consortium, including nitrogen-fixing rhizobia, phosphate-solubilizing bacteria and organic matter decomposers, which enhance soil fertility and structure. The microbial community in the cowpea rhizosphere is shaped by complex soil physiochemical properties, such as potential of hydrogen (pH), nutrient availability, and salinity, which significantly influence plant-microbe interactions. However, contradictions persist regarding pH's effect on microbial diversity, with unresolved questions about how specific environmental conditions regulate microbial taxa. Advanced techniques, including metagenomic analyses, have provided deeper insights into the taxonomic and functional composition of rhizosphere microbiomes, uncovering both abundant and rare microbial taxa involved in these processes. Despite these advancements, gaps remain in understanding the dynamic responses of microbial communities to environmental stresses. Bridging these gaps through integrative multi-omics approaches will enable the development of microbiome-informed strategies to improve cowpea productivity and promote sustainable agricultural practices, ensuring resilience in the face of climate variability.

Accurate crop yield forecasting under fluctuating agro-climatic conditions is critical for sustainable agriculture and food security. This study presents a climate-resilient, data-driven framework for soybean (Glycine max L.) yield prediction using Artificial Neural Networks (ANNs), to capture delayed and nonlinear agro-climatic effects relevant to smallholder farming systems. The model synthesizes a diverse set of agro-meteorological and environmental variables, including temperature, rainfall, humidity, wind speed, fertilizer inputs, soil characteristics, and atmospheric pressure, captured between June and October 2022 to support robust predictive analysis. The ANN architecture was trained and evaluated using standardized agro-climatic datasets, achieving R² of 0.9439, MSE of 0.3253 t 2 /ha², and MAE of 0.1432 t/ha, RSME of 0.1804 t/ha, demonstrating strong predictive capability under variable environmental conditions. Comparative analysis showed that the ANN outperformed Multiple Linear Regression and Random Forest models, particularly in representing nonlinear rainfall–temperature–fertility interactions. Lag-time sensitivity analysis further revealed that rainfall and humidity shifts exert the most influential delayed effects on soybean yield. Principal Component Analysis and feature ranking identified fertilizer input and rainfall as primary yield drivers, with relative humidity and temperature playing secondary roles. These findings are consistent with established agronomic patterns in legumes and highlight the ANN’s ability to capture complex environmental interactions. The findings highlight the ANN model’s applicability for resource-limited, smallholder contexts, providing a practical decision-support tool for climate-smart agronomy and adaptive planning in semi-arid regions .

Seasonality of employment and food security in rural sub-Saharan Africa

A review on the impact of biochar applications on soil health and fertility, plant growth, and food security: advancing agricultural sustainability

Addressing the double burden of malnutrition in Bangladesh: Evidence from National Policy Documents and gaps in policy integration.