Food Security Research Articles

Automated remote sensing monitoring of cropland non-agricultural and non-grain conversion at parcel scale in complex environments through multi-source data fusion

ABSTRACT Changes in cropland use, particularly the transition from agricultural to non-agricultural and non-food crop production, can diversify rural economies but may also pose challenges to regional food security, especially in densely populated and rapidly developing regions such as China. High-precision monitoring of cropland non-agricultural and non-grain conversion is essential for balance regional food security with rural income enhancement. This study focuses on the monitoring cropland non-agricultural and non-grain conversion in the rainy and cloudy regions of southern China. We aim to develop an automated process framework that accurately extracts parcel boundaries and identifies multiple types of changes. Quantitative experiments assessed the effectiveness of various solutions for key modules in the framework, including multisource data fusion, image segmentation, sample generation, and classification feature strategies. Validation using verification samples obtained through visual interpretation and field surveys revealed the following results: (1). The use of both optical and SAR images improved classification accuracy by 1.30% compared to using optical images alone. (2) Under the constraint of vector patch data, segmentation using high-resolution images outperformed both segmentation using medium-resolution images with the same constraint and segmentation using high-resolution images without the constraint, achieving Mean Intersection over Union (MIOU) improvements of 0.28 and 0.24. (3) Samples automatically generated from vector patch data achieved classification accuracy comparable to that of manually selected samples, with only a 0.64% decrease in overall classification accuracy. (4) Classification utilizing time-series feature extraction from reconstructed data outperformed classification based on temporal feature, with an overall accuracy increase of 1.94%. The optimized automated process framework achieved an overall accuracy of 89.00% in monitoring cropland conversion in the complex planting conditions of southern China. This framework represents an effective approach for the automated monitoring of cropland non-agricultural and non-grain conversion with precise parcel boundaries, providing valuable insights for similar monitoring objectives and application scenarios.

ConvViT Driven Multi Context Feature Fusion for Sustainable Pest Monitoring in Agriculture

In the last few years, the union of modern imaging technology and AI has given rise to agriculture. Probably the most promising of its uses is AI-powered models in agricultural pest imaging, giving new meaning to pest identification, categorization, and monitoring. The world’s food security and farming yields are at risk are endangered by pests, and, too often, this necessitates undue need for pesticides that degrade the environment and the health of people. AI can be brought into play for detecting pests in a new way before they turn invasive, relying less on chemicals and perhaps even ushering in sustainable agricultural methods. Deep learning (DL), a subfield of AI especially designed for image recognition, has seemed especially promising, particularly in the highly precise and highly productive automation of pest detection. In this study, the hybrid model known as ConvViT (fusing the local detail extraction strength of Convolutional Neural Networks (CNNs) with the global contextual reasoning power of Vision Transformers (ViTs)) is introduced. To address the challenges from real-world datasets such as background clutter and image quality, viewpoint differences, as well as other exceptions, ConvViT is developed to boost pest classification performance. The proposed framework is based on a framework that shows superior accuracy than traditional models like ResNet50, EfficientNetB3, and standalone ViTs using a curated agricultural pest image dataset. This approach is an aligned, scalable, intelligent solution for next-generation crop protection by presenting a set of AI capabilities aligned with sustainable agriculture objectives.

A Generalized Deep Learning Approach for Cross-Crop Plant Disease Detection Using the Plant Village Dataset

Plant diseases continue to be one of the leading causes of reduced agricultural productivity worldwide, directly threatening food supply chains and the economic stability of farming communities. With the global population steadily increasing, the demand for intelligent, scalable, and highly accurate plant disease detection systems has never been more critical. Deep learning methods have shown promising results in this field; however, numerous conventional models cannot often generalize well across different crop species and unseen disease types. These limitations hinder their practical deployment in dynamic real-world agricultural environments. In this study, we propose a robust and generalized deep learning-based approach for cross-crop plant disease detection, using the comprehensive and diverse Plant Village dataset. Our model is built upon a custom-designed Convolutional Neural Network (CNN) architecture that incorporates a small Inception module. Unlike traditional CNNs, which primarily focus on the global features of a leaf. Our model detects and analyzes localized disease spread patterns, enhancing detection across diverse crops and adapting to novel conditions. The small Inception module plays a vital role in enabling multi-scale feature extraction from small disease-affected patches without adding excessive computational complexity. This architectural refinement allows the model to learn more discriminative features, resulting in faster convergence and higher classification accuracy. When trained and validated on the Plant Village dataset, our model achieved an impressive accuracy of 98.45%, outperforming many traditional approaches. Additionally, it demonstrated consistently high precision, recall, and F1-score, confirming its reliability and robustness. By addressing the challenges of overfitting and poor generalization, common pitfalls in many deep learning models, our method provides a scalable and effective solution for real-time agricultural disease monitoring. This work contributes to the growing field of precision agriculture by offering a model that is not only accurate but also generally efficient and practical for deployment in diverse agricultural settings. Ultimately, our research aims to support the development of smart farming technologies that ensure healthier crops and contribute to long-term global food security.

Growth is not enough: solving the global food security crisis requires investments to close gaps

ABSTRACT The escalating global hunger crisis, exacerbated by ongoing conflicts, economic shocks, and climate crises, demands comprehensive and effective solutions. Since limited research has explored the interplay between inequality and economic growth in relation to a country’s food insecurity prevalence, this study seeks to bridge this knowledge gap. Using country-level data from 113 countries in both pre- and post-pandemic periods and employing the seemingly-unrelated-regression (SUR) model, this study provides empirical evidence, highlighting the significant roles of advancing women and girls and improving incomes for the most at-risk people in addressing food insecurity. The study found that higher differences between women and men and income gaps correlate positively with food insecurity. Notably, our results indicate that economic growth can exacerbate food insecurity, particularly in the post-pandemic context. This suggests that mere economic growth is insufficient to combat food insecurity if inequalities persist.

The Current Status of Irrigated Agriculture in Cape Verde and Its Link to Water Scarcity

In arid regions with low precipitation, like most of the Cape Verde islands, irrigation is essential for maintaining agricultural production and food security. However, due to significant investment needs, it is critical to improve irrigation efficiency and reduce water losses. The aim of this study is to evaluate irrigated agriculture in Cape Verde and its relationship with water scarcity through the calculation of key indicators and the analysis of statistical and remote sensing data. Crop production data were collected from the Ministry of Agriculture and Environment, and climatic data from the National Institute of Meteorology and Geophysics of Cape Verde (INMG) and FAO’s WaPOR platform. The aridity index was calculated using the UNEP method based on data from INMG. The island of Sal showed the lowest aridity index value (0.07), while Cachaço (São Nicolau island) had the highest (0.41). Sugarcane is currently the dominant irrigated crop, covering over 3000 hectares, about 62% of irrigated land, despite its high water demands. The expansion of sugarcane threatens long-term water sustainability and food production. Promoting crops with higher water productivity and technical training are key actions to ensure the sustainability of irrigated agriculture in Cape Verde. Findings point to the urgent need to improve irrigation infrastructure, maintenance, and system design.

Indigenous coastal management in Papua New Guinea: Resilience and the revitalization of polepole stone fish traps on Lovongai Island

On Lovangai Island in Papua New Guinea, Indigenous communities have worked with Indigenous scientists and international anthropologists to document and revitalize traditional stone fish traps. These locally designed and enacted projects are an Indigenous form of coastal planning which refuses both state-planning and conservation organization planning. They are also a form of biocultural revitalization that foregrounds Indigenous sovereignty. Based on a traditional design, the Lovangai stone fish trap known as polepole works to provide pool habitat for important aquatic biota, to increase food security, to increase respect for, and interest in, traditional practices, and it is also hoped that it may contribute to prevention of coastal erosion. In this paper we describe the reciprocal collaborative process by which this project came to be, the methodology used, and the relationship between biocultural approaches to research and Indigenous-led resilience projects.

Climate change adaptation in Brazil: a scientometric analysis of key themes, gaps, and research trends

Climate change represents one of the most significant modern challenges, demanding innovative approaches to climate adaptation. This study maps Brazilian scientific production focused on adaptation to climate change through a comprehensive scientometric analysis of 629 articles. The results showed a growth in scientific production on climate adaptation in Brazil, with the predominance of research in the fields of "Climate Policy and Governance" (18.12%), "Ecology and Environmental Management" (16.85%), and "Agriculture and Climate Adaptation" (16.69%), emphasizing how important it is to devise effective policies, protect sensitive ecosystems, and achieve food security. However, there was an underrepresentation in the fields "Culture and Society" (7.00%) and "Climate Change and Land Use" (6.36%), which showed the necessity of exploring the complex interaction between cultural practices and land management. The authorship analysis showed that productivity was relatively concentrated in a few researchers, indicating that some researchers played leading roles in developing research in their areas of specialization. The country distribution emphasized the global relevance of the Brazilian context in climate adaptation studies through Brazil’s connections with the US, UK, Australia, Germany, and France. The journal trends showed that although adaptation to climate change is multidisciplinary, specific sources have consolidated themselves as primary sources for disseminating research in the area. The keyword analysis reflected research related to Brazilian adaptation to climate change, revealing the concern with the theme. Further, the paper contributes to strategic research and policy direction on a future integrated approach for Brazilian climate adaptation by highlighting current strengths and gaps within the literature.

5'UTR Dynamics: Modulating Model Plant Defense Mechanisms against Fungal Threats.

Fungal pathogens threaten global crop production, causing significant yield losses and food security concerns. Understanding plant defense mechanisms against these pathogens is essential for effective disease management. The 5'-untranslated region (5'UTR) has emerged as a key regulator of immune response genes in model plants such as Arabidopsis thaliana and Nicotiana tabacum during fungal infection. The 5'UTR, traditionally linked to mRNA translation, plays a crucial role in post-transcriptional regulation of immune responses. Fungal pathogens utilize strategies like appressoria and haustoria for host penetration, prompting immune signaling via pattern recognition receptors (PRRs), which activate PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI). Recent studies show that 5'UTRs influence immune gene expression by modulating translation initiation and mRNA stability during infection. The regulatory elements are upstream open reading frames (uORFs) and RNA-binding proteins. Understanding these dynamics of novel strategies can allow the fine-tuning of immune responses, offering potential targets to improve crop disease resistance.

Atmanirbhar Krishi for Viksit Bharat 2047: Roadmap for Self-Reliance

As India marches confidently towards its centenary of independence in 2047, the vision of a Viksit Bharat—a developed, inclusive, and empowered nation—is becoming a shared national aspiration. At the heart of this transformation lies a sector that has fed the nation, employed millions and formed the cultural and economic backbone of India: Agriculture or Krishi. But for India to truly become a developed country by 2047, its agriculture must evolve. The mantra that will drive this change is “Atmanirbhar Krishi”— self-reliant agriculture. This vision is not just about achieving food security, but about empowering farmers, modernizing practices, protecting the environment and creating a robust, technology-enabled ecosystem that brings prosperity to rural India.

Challenges and innovations for sustainable ruminant production based upon One Health principles.

Almost 12% of the human population have insufficient access to food and hence are at risk from nutrient deficiencies and related conditions, such as anaemia and stunting. Ruminant meat and milk are rich in protein and micronutrients, making them a highly nutritious food source for human consumption. Conversely, ruminant production contributes to methane (CH4) emissions, a greenhouse gas (GHG) with a global warming potential (GWP) 27-30 times greater than that of carbon dioxide (CO2). Nonetheless, ruminant production plays a crucial role in the circular bioeconomy in terms of upcycling agricultural products that cannot be consumed by humans, into valuable and nutritional food, whilst delivering important ecosystem services. Taking on board the complexities of ruminant production and the need to improve both human and planetary health, there is increasing emphasis on developing innovative solutions to achieve sustainable ruminant production within the 'One Health' framework. Specifically, research and innovation will undoubtedly continue to focus on 1) Genetics and Breeding; 2) Animal nutrition and 3) Animal Health, to achieve food security and human health, whilst limiting environmental impact. Implementation of resultant innovations within the agri-food sector will require several enablers, including large-scale investment, multi-actor partnerships, scaling, regulatory approval and importantly social acceptability. This review outlines the grand challenges of achieving sustainable ruminant production and likely research and innovation landscape over the next 15 years and beyond, specifically outlining the pathways and enablers required to achieve sustainable ruminant production within the One Health framework.

Analytical framework on climate projection and its illustration of risks to nutritional health in the Solomon Islands

The connection between agriculture and food security is well recognized, nonetheless, the long-term effects of climate change on the nutritional value of tropical produce in the Pacific are not well understood. Firstly, to understand the food and nutritional security in the Pacific, the study highlights a significant gap in existing food security frameworks between the impact of climate change, nutritional change in food crops and vegetables, and consumption. Emphasizing the need for more integrated approaches. Secondly, an analytical framework is proposed, built from systematic literature reviews, following a six-step: defining the research question, performing keyword-based searches, screening results, assessing full-text eligibility, extracting and synthesizing data, and reporting findings. Literature was sourced from academic databases, institutional repositories, and organizational websites, resulting in 73 relevant studies being included from platforms and databases such as PubMed, ScienceDirect, ProQuest, and others. This framework aims to connect climate projections with soil nutrients, crop and vegetable quality and nutrients, and dietary outcomes. Thirdly, the study stresses the importance of improving collaboration among governmental ministries and experts, as well as embracing technological innovations, to ensure effective nutrient flow from soil to crops and ultimately to consumers. It emphasizes the need to evaluate the potential nutritional consequences of climate change to safeguard nutritional security for affected populations. Finally, the framework is tailored to the Solomon Islands to inform policy recommendations that enhance food security and nutrition from the production to consumption phase. This approach highlights the interconnectedness of environmental sustainability, agricultural practices, and public health, advocating for a holistic strategy to tackle these pressing challenges.

Coupling coordination between agricultural carbon emission efficiency and food security in China: The spatial-temporal evolution and prediction.

Addressing climate change and food insecurity is crucial for achieving the Sustainable Development Goals (SDGs), with the enhancement of Agricultural Carbon Emission Efficiency (ACEE) and the assurance of Food Security (FS) playing pivotal roles. This study conducts a comprehensive evaluation of the coordination between ACEE and FS across 31 Chinese provinces from 2007 to 2021. The methodology employs the Super Slack-Based Model (SBM) and the Entropy- Weighted Technique for Order Preference by Similarity to Ideal Solution (EW-TOPSIS) for ACEE and FS assessments, respectively, while utilizing the Coupling Coordination Degree Model (CCDM) to gauge their coordination. This research applies Global spatial autocorrelation (GA), Local Indicators of Spatial Association (LISA) clustering, and Markov chain analysis to investigate the spatial correlation characteristics and dynamic evolutionary probability of Coupling Coordination Degree (CCD). Additionally, a Combination Forecasting Model predicts CCD trends through 2030. The findings indicate positive trends in both ACEE and FS, albeit with significant regional disparities and a notable lag of FS behind ACEE improvement. The evolution of China's CCD from "barely coordinated" towards "primary coordination" is evident, with the northeastern and central regions outperforming their western and eastern counterparts. Spatial analysis reveals pronounced clustering of CCD, indicating the impact of spatial spillover effects. Markov chain analysis shows that provinces have at least a 71.4% probability of maintaining their current CCD status, revealing substantial system inertia. Projections point to increasing CCD levels across various regions, with the northeastern region expected to reach 0.827 by 2030 (a 5.72% increase from 2021), followed by the central (0.742), western (0.659), and eastern regions (0.638); however, regional imbalances persist, highlighting the need for concentrated efforts in areas experiencing CCD stagnation or decline. The study concludes with a call for region-specific development strategies, enhanced resource allocation and technological support, and a comprehensive policy framework that accounts for the long-term and spatial dimensions, aiming to facilitate equitable advancements in reducing agricultural carbon emissions and enhancing food security.

The Principle "Authority over Subjects Is Contingent upon Public Interest" And It’s Role in Agricultural Food Security Data Governance: A Jurisprudential-Analytical Engineering Study

This study offers a foundational and maqasid-based analysis of the Islamic legal maxim “Authority over subjects is contingent upon public interest”, examining its alignment with modern data management technologies in the agricultural sector. The research aims to bridge classical Islamic jurisprudence with contemporary data systems such as Geographic Information Systems (GIS), Decision Support Systems (DSS), and Agricultural Database Management Systems (DBMS), with the goal of achieving food security. The study employs classical jurisprudential sources, comparative ethics, and contemporary administrative analysis to develop an integrated theoretical framework guiding national agricultural policy toward justice and efficiency. Findings suggest that when key terms—such as “public harm,” “private harm,” and “interest”—are clearly defined and operationalized within smart systems, this maxim can improve agricultural resource distribution and support food production, thereby fulfilling the objective of public welfare.

Uptake of Pharmaceuticals in Rice Plant Cultivars Through Reclaimed Water Irrigation: Impact on Food Security

Uptake of Pharmaceuticals in Rice Plant Cultivars Through Reclaimed Water Irrigation: Impact on Food Security

Ticks and tickborne diseases in Global South countries: impact and implications of environmental changes

Ticks and tick-borne diseases (TBDs) are escalating health and veterinary threats in the Global South, driven by environmental change, human activity, and socioeconomic vulnerability. Ticks transmit bacterial, viral, and protozoal pathogens, causing significant public health and economic burdens. Climate shifts and land-use changes have expanded tick habitats, intensifying disease transmission. This review examines the distribution of major tick species in the Global South and explores how ecological disruptions influence disease dynamics. Regional case studies from Africa, the Middle East, and South Asia highlight the impact on human health, livestock productivity, and food security. Addressing this growing threat requires integrated One Health strategies, improved public awareness, enhanced veterinary services, and investment in surveillance and vaccine development. International cooperation and strong policy frameworks are vital to mitigate the spread and impact of TBDs.

An Overview of Advancements in Proteomic Approaches to Enhance Livestock Production and Aquaculture

Proteomics, the large-scale study of proteins and their functions, is an essential tool in biological research, particularly in livestock production and aquaculture. This review explores the significance of proteomic techniques and technologies in enhancing agricultural practices. Key methods, including mass spectrometry, two-dimensional gel electrophoresis, and protein microarrays, enable researchers to analyze protein complexity in biological systems. In livestock production, proteomics improves animal health, growth, reproduction, and disease resistance, contributing to more efficient and sustainable practices. In aquaculture, it optimizes fish health, breeding strategies, and feed efficiency, promoting sustainable farming. Despite its potential, proteomics faces challenges, such as complexity, the need for standardized methods, and difficulties in data interpretation. However, emerging advances—including multi-omics integration, real-time monitoring, and improved understanding of protein functions under varying environmental conditions—offer promising solutions. In conclusion, proteomics is poised to transform livestock production and aquaculture, addressing key challenges in food security and sustainable agriculture.

Characterization of Lentil Genotypes for Zinc Biofortification and Soaring Grain Output

Along with food security, micronutrient malnutrition, especially zinc (Zn) deficiency, is primarily a problem in South Asia. Pulses, being an inexpensive source of protein, play a major role in giving calories to the people in poor nations. In addition to increasing profits, zinc biofortification of pulses, particularly lentils can help alleviate human zinc deficiency. The genotypes of lentils were categorized in a field experiment according to their greater grain production and Zn content. A two-factor randomized design was used to cultivate the 12 lentil genotypes at Zn concentrations of 0, 12.5, and 25 ppm with three replicates.The results showed that compared to a lower application rate and control, a 20 ppm Zn treatment greatly boosted plant growth, grain output, and grain Zn content. The large grain production of the BARI Masoor-8 genotype was exceptional. The genotypes of lentils DPL-62, BARI Masoor-3, BARI Masoor-8, and BARI Masoor-5 were identified as high grain yield cultivars and ranked as Zn receptive genotypes and was determined, these genotypes improved crop yield and grain Zn content by responding differently to Zn fertilization. However, further exploration is needed to furnish broad recommendations for the finestpedigree and application know-how while maintaining the necessary quantity of bioavailable zinc.

Heterogenization of marine catch assemblages over the past two decades in China

Abstract The trajectories of marine catch assemblages in response to anthropogenic change at large temporal and spatial scales remain elusive, posing a threat to Marine fishery biodiversity and food security. Here, we use data collected from the China Fishery Statistical Yearbook that spans 2003-2019 to systemically and quantitatively assess the trajectory change of marine catch assemblages and the drivers. Over the past two decades, we observed obvious temporal heterogenization in species taxa and structure for both occurrence and abundance-based dissimilarity in marine catch composition, with key fish species identified as the primary drivers. The heterogeneity rate is particularly pronounced in low-latitude southern regions. While overall species richness remained relatively stable, these findings highlight the critical need to shift our conservation focus from biodiversity loss to composition change as a key indicator of marine ecosystem health. These findings can aid in the creation of sustainable marine conservation and fisheries management decisions to rebuild the resilience of marine fishery stocks.

Warrantage - Opportunity to finance Agro- Forestry-Pastoral Activities on the Ruzizi Plain in the Province of South-Kivu in Democrzatic Republic of Congo

This study highlights the nature and specificities of these activities, while identifying the constraints faced by smallholder farmers, livestock breeders, and traders in accessing finance. The objective of this study is to provide a comparative overview of agro-forestry-pastoral policies and economic models in force in the Ruzizi Plain in the Democratic Republic of Congo and in rural areas of the Republic of Burundi, highlighting the crucial role of financial institutions, such as banks, microfinance institutions, and agricultural cooperatives. These institutions are vital for agricultural development and the effective implementation of the warrantage system. This study adopts a comparative method to better understand the differences and similarities between two neighboring rural realities. It allows us to assess the impact of an innovation (warrantage) in one context (Burundi) and to verify the feasibility of its implementation in another context (Ruzizi Plain, DRC). This research also aims to understand how the warrantage mechanism would influence agro-forestry-pastoral activities and the perceptions of farmers and herders regarding financial challenges in the Ruzizi Plain. It also aims to understand to what extent warrantage would improve the ability of farmers, herders, and traders to invest in agricultural activities. This is our research question. The overall hypothesis of our study is that warrantage has a significant impact on the financial and food security and productivity of farmers, herders, and traders by improving access to credit and strengthening the security of their activities.

THE ROLE OF POWER SECTOR CYBERSECURITY IN SAFEGUARDING FOOD SYSTEMS AND NATIONAL STABILITY

The increasing reliance on digital infrastructure within the power sector has significantly improved operational efficiency but also introduced critical vulnerabilities that threaten national stability. This paper examines the interconnectedness of power sector cybersecurity and food security, highlighting how disruptions in energy infrastructure—whether due to cyberattacks or systemic failures—can severely impact agricultural production, cold storage, water supply, transportation, and healthcare services. Drawing on recent global incidents, we explore how energy disruptions cascade into broader crises affecting food availability, public health, and socio-economic resilience. Particular attention is given to the vulnerabilities faced by rural communities and smallholder farmers, who often lack backup systems. We argue that ensuring cybersecurity in power systems is not only a technical imperative but also a strategic necessity for national food security and societal well-being. The paper concludes with a call for integrated policies, increased investment, public education, and cross-sector collaboration to build cyber-resilient infrastructure capable of withstanding evolving digital threats.