Abstract Rice ( Oryza sativa ), a staple food for more than half of the world's population, is increasingly threatened by multiple abiotic and biotic stresses that interact to reduce productivity. Among these, salinity stress and insect and pest infestation are two of the most serious constraints in rice cultivation, particularly in coastal and irrigated ecosystems. Salinity impairs rice growth through osmotic stress, ionic toxicity, nutrient imbalance, and oxidative damage. Meanwhile, insect pests such as the brown planthopper, stem borer, and leaf folder further aggravate crop losses by disrupting photosynthesis, sucking plant sap, and destroying reproductive structures. Although these stresses have been studied independently, evidence suggests that their co-occurrence creates compounded damage that is greater than the sum of individual events, leading to severe instability. This dual stress interaction poses a critical challenge for global food security, especially in South and South-east Asia, where millions of smallholder farmers depend on rice for subsistence and income. Economic losses from combined salinity and pest pressure not only reduce farm profitability but also undermine resilience in already vulnerable farming communities. Addressing this issue requires integrating climate-smart and sustainable agricultural practices, including salt-tolerant and pest-resistant rice varieties, Judicious water and nutrient management, biological contents, and precision agriculture technologies. The novelty of this review lies in its focus on the combined impact of salinity stress and insect pests, an area where field-based evidence and mechanistic insights remain evident.

Electrochemical sensors revolutionize plant nitrogen monitoring: Real-time, in situ detection for precision agriculture.

Crop models: integrating systems from the molecular to global for agricultural productivity and sustainability.

Cultivated ingredients: a strategic pivot for cultivated meat?

Global food security challenges necessitate transformative approaches to enhance agricultural productivity, particularly in highland regions facing multiple production constraints. This systematic literature review examines the potential of Internet of Things (IoT) technology integration to enhance productivity of highland vegetables (potato, cabbage, and carrot) in Aceh Tengah District, Indonesia. A critical agricultural region at 1,000-2,600 m.a.s.l. Following PRISMA guidelines, we analyzed peer-reviewed publications (2020-2025) on IoT applications in vegetable production, synthesizing evidence from successful implementations across diverse geographical contexts. Empirical evidence demonstrates that precision agriculture systems incorporating soil moisture sensors, nutrient monitoring, weather stations, and disease detection algorithms achieve productivity increases of 10-20% while reducing water consumption by 20-30% and input costs by 13%. However, IoT adoption in Indonesian highland agriculture remains below 5%, constrained by infrastructure limitations, digital literacy gaps, and economic barriers. This review identifies six critical research gaps and proposes a contextualized framework for IoT implementation adapted to smallholder farming systems in highland Indonesia. The framework addresses technological, socioeconomic, and institutional dimensions essential for sustainable digital transformation of highland agriculture. A pilot project framework is proposed targeting productivity enhancement, resource efficiency, and capacity building for sustainable implementation in Aceh Tengah's unique agroecological context

Post-Harvest Management of Fruits and Vegetables: Addressing Urban Nutrition and Food Security Challenges

With the intensification of global climate change, high temperatures have emerged as a major abiotic stressor adversely affecting summer maize yields in North China. This study presents a high-resolution monitoring framework for Henan Province. First, an hourly, high-resolution (0.02° × 0.02°) near-surface air temperature dataset was generated by fusing Himawari-8 satellite observations, ERA5 reanalysis data, and ground-based measurements through a machine learning approach. Among the tested algorithms (support vector regression, random forest, and XGBoost), XGBoost achieved the best performance (R2 = 0.933 and RMSE = 0.841 °C). Second, a High-Temperature Damage Index (HTDI) was constructed using hourly temperature thresholds of 32 °C and 35 °C, respectively. The index exhibited a statistically significant but modest negative correlation with ear grain number (R2 = 0.054 and p = 0.0007). Spatial assessment revealed intensified heat damage in 2024 (average HTDI = 0.51; over 67% of the area experienced moderate or worse damage) compared to 2023 (average HTDI = 0.22), with severe damage concentrated in south–central and east–central Henan. This approach surpasses the limitations of conventional daily scale assessments by enabling refined, hourly monitoring of high-temperature heat stress. It not only advances the deep integration of remote sensing and machine learning in agricultural meteorology but also provides technical support for addressing food security challenges under climate change.

ABSTRACT Microalgal biomass has good potential as a raw material for animal feed, especially fish, being an alternative source of protein and fatty acids. Adopting microalgae for animal feed is a promising solution for addressing food security challenges. The Kingdom of Saudi Arabia (KSA) is planning a sustainable development of 100,000 ha of microalgal biomass production to be used as a raw material for animal feed. KSA is an ideal location for such production due to its abundant sunlight, flat land, CO2 resources, and nitrogen (N) and phosphorus (P) availability. This work reviews relevant information and maps industrial waste streams, mainly from thermo-power, desalination, wastewater treatment plants and petroleum refineries that could potentially be used as nutrient sources (C, N and P) in KSA. KSA has substantial potential for large-scale microalgal biomass production using industrial waste streams as nutrient sources. Analysis of the data presented here leads to the prediction that KSA could potentially produce up to 3,168,000 t year‒1 of land-based microalgal biomass and potentially uptake approximately 5,702,000 tons of CO2. This approach not only supports animal feed production but also extends carbon use, so it is not released immediately into the atmosphere, aligning with national sustainability goals.

Arid and semi-arid regions, particularly in Central Asia, face escalating food security challenges due to climate change and chronic drought, demanding innovative soil moisture management strategies for staple crops such as wheat (Triticum aestivum L.). This study introduces and validates an innovative agro-technological system that moves beyond conventional, high-consumption superabsorbent polymer (hydrogel) use to establish a highly resource-efficient and sustainable approach for dryland wheat cultivation. A split-plot field experiment was conducted across three distinct agroclimatic zones in the Turkestan Region of Kazakhstan (Kazygurt, Sairam, and the extremely arid Arys district). Eight treatments were evaluated, focusing on varying hydrogel dosages and localized co-application with reduced phosphorus and potassium (P/K) fertilizers. A novel patented slit-cutting unit was employed for the precise subsurface placement of the hydrogel–fertilizer mixture at a depth of 20 cm. Complementary laboratory experiments provided the mechanistic foundation, evaluating water absorption, retention, and vertical redistribution in the 0–20 cm and 20–40 cm soil layers. The hydrogel mixture increased total soil water retention by 14.3 %, while enhancing subsoil (20–40 cm) moisture content by 9.0 percentage points, confirming its function as an in-situ water reservoir. Field results identified Treatment 5 (Localized 30 kg ha⁻¹ hydrogel + 50 % P/K) as the optimal configuration, producing stable and significant wheat yield increases of 23.32–27.05 % across all sites compared with the control. Importantly, this precision-based method achieved 50 % fertilizer savings and 57 % reduction in hydrogel use compared to conventional broadcast application, achieving both economic efficiency and ecological sustainability. Overall, the localized subsurface co-application system establishes a new benchmark for dryland agriculture, offering a climate-resilient, input-efficient, and scalable technological platform for enhancing water use efficiency and sustaining food production under water-limited conditions.

Addressing the critical challenges of global food security and water scarcity, we introduced an Internet of Things (IoT)-driven predictive analytics framework for dynamic irrigation optimization in tomato cultivation. Our primary objective was to develop a robust model that accurately estimates daily water requirements, with the aim of minimizing water consumption while concurrently maintaining optimal soil health. This framework leverages a uniquely comprehensive, experimentally controlled dataset from multi-sensor IoT deployments, covering environmental conditions (air temperature, humidity, CO2, pressure) and key soil parameters (humidity, temperature, electrical conductivity). Through a rigorous data preprocessing pipeline and a tailored feature engineering approach, critical temporal patterns, inter-variable relationships, and insights from agronomic indicators like Growing Degree Days (GDD), alongside other dynamically derived features, were extracted. A two-part eXtreme Gradient Boosting (XGBoost) regression model, combining classification and regression, was developed and validated to precisely predict the daily water volume needed per hectare. The innovation of this work lies in its ability to harness complex historical IoT data to build a sophisticated intelligence layer for irrigation scheduling. By demonstrating the model's accuracy in identifying optimal water levels under varying conditions and achieving significant water savings in a simulated dynamic optimization, this research provides foundational data-driven insights that can inform highly effective precision irrigation strategies. The model achieved a high R2 of 0.9476 and yielded a potential water saving of 50.84% in a simulated dynamic optimization compared to the model's raw prediction. Such intelligence empowers farmers to significantly reduce water waste and prevent harmful over-irrigation, leading to more sustainable and efficient smart agriculture, which is critical for enhancing crop resilience and resource efficiency in a changing climate.

Microbial quorum sensing: Mechanisms, applications, and challenges.

Strengthening nutrient resilience in rice (Oryza Sativa L.) under elevated CO2: insights into physiological, biochemical, and transcriptomic responses to biochar and CeO2 nanoparticles.

The growing pressures of population expansion, resource depletion, and climate change are intensifying global challenges in health, food security, and environmental sustainability. Amyloid-based materials offer valuable and sustainable solutions for addressing these issues to achieve a healthier planet due to their controllable structure, exceptional adhesion stability, high mechanical robustness, and excellent environmental compatibility. This review summarizes recent advances in amyloid-based biomaterials with relevance to global health, food, agriculture, and environmental systems. We first discuss the structural characteristics and fabrication strategies of conventional amyloid fibrils and their derived materials. We then introduce a superfast amyloid-like protein aggregation strategy that enables rapid formation of robust protein films and higher-order architectures. Applications of these materials include wound healing and tissue regeneration, stabilization and protection of food systems, enhanced utilization of fertilizers and pesticides, and removal of pollutants from water. Together, these developments highlight the growing potential of amyloid-inspired assemblies as sustainable and scalable platforms for promoting human well-being and environmental resilience.

Plant biotechnology has revolutionized modern agriculture by enabling precise and efficient crop improvement strategies. This review explores the evolution of selective breeding, mutation breeding, and precision breeding, highlighting their applications in Canada's agricultural sector. Conventional selective breeding has been instrumental in developing high-yielding and disease-resistant cultivars, while mutation breeding, through physical and chemical mutagenesis, has introduced valuable genetic diversity. The advent of transgenic breeding allowed for the direct insertion of foreign genes, leading to the development of crops with herbicide tolerance, pest resistance, and improved nutritional content. However, concerns over regulatory restrictions and public acceptance have driven the rapid adoption of genome editing tools, which enable precise modifications without introducing foreign DNA. Canada has played a key role in applying these biotechnological innovations, successfully developing genetically modified canola, CRISPR-edited wheat, stress-resistant soybean, and barley and oat cultivars improved for stress resistance and yield. While each breeding approach presents distinct advantages and limitations, integrating conventional and molecular techniques is essential for maximizing genetic potential, ensuring agriculture, and effectively food security challenges.

Key messageField-based phenotyping of root system architectural (RSA) traits in a diversity panel (PI-GAP) of pigeonpea was conducted across three diverse pigeonpea growing environments along with identification of genomic regions associated with these traits through GWAS analysis.Root system architecture (RSA) plays a crucial role in plant stress tolerance mechanisms serving as the main route for water and nutrient acquisition, while also mediating plant-rhizosphere signalling. In the current study, an attempt was made to understand the genetic variability and genomic regions associated with RSA traits, as a relatively unexplored area of research in pigeonpea. The field-based “Shovelomics” approach was utilized to phenotype eight RSA traits: tap root length (TRL), lateral root length (LRL), number of lateral roots (NRL), stem diameter (SD), root diameter (RD), root angle from first and second lateral roots (RA1 and RA2) and root fresh weight (RFW) at physiological maturity. The pigeonpea international genome-wide association panel (PI-GAP) comprising of 185 genotypes from the reference set and 15 elite genotypes were used in the study. The combined ANOVA revealed significant genetic variance for all RSA traits except for RA2. Genome-wide association study was conducted using the Axiom Cajanus 56 K SNP array, leading to identification of 45 marker trait associations (MTAs) associated with RSA traits in pigeonpea. Multi-locus GWAS models detected six MTAs accounting for 4.84% to 18.73% of the phenotypic variation estimated (PVE) for TRL, 12 MTAs for LRL (4.73–13.92% PVE) and 11 MTAs for NLR (3.03–14.03% PVE value), respectively. Candidate gene analysis revealed genes associated with these traits, including BAG (Bcl-2-Associated athanogene) family molecular chaperone regulator 6 (CcLG01_17476096 and CcLG01_17476721), root cap (CcLG04_5972718) and Protein MAINTENANCE OF MERISTEMS (MAIN) (CcLG06_8242342). These genes were found to have key roles in growth and establishment of roots under stress-related conditions in model crops. Further validation of identified MTAs would provide an opportunity to develop trait-specific markers paving the way for marker-assisted breeding in pigeonpea. Based on RSA traits, pigeonpea genotypes were categorized into deep, spreading and dimorphic root system. These classifications facilitate the phenotypic selection of genotypes for breeding against drought, heat, waterlogging and salinity adaptation. Improved cultivars with an ideal root architecture designed for efficient resource uptake and high yield under diverse environments could help address food security challenges in semi-arid tropics.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00122-025-05136-y.

Food insecurity remains a recurrent issue in the Economic Community of West African States (ECOWAS), worsened by the combined constraints of climatic unpredictability and macroeconomic volatility. This study examines the interaction between climate change factors—such as precipitation, temperature, and CO₂ emissions—and macroeconomic elements, including food production, exports, imports, inflation, GDP, population, and consumer prices, to assess their impact on food security in ECOWAS member countries from 2000 to 2023. By employing panel econometric models, including Common Effects, Fixed Effects, and Random Effects, along with thorough diagnostic tests, the research reveals that food production, temperature, and exports significantly enhance food security. However, irregular rainfall and reliance on imports tend to have a detrimental effect. Interestingly, the Consumer Price Index and population growth show varied impacts, highlighting the structural and institutional differences among the member states. The findings underscore the urgent need for integrated regional policies that tackle climate change resilience, stable macroeconomic conditions, and agricultural productivity simultaneously. Strategic actions aimed at curbing inflation, encouraging climate-smart farming, facilitating trade, and improving infrastructure are essential for fostering sustainable food systems across ECOWAS. To effectively combat food insecurity in the region, comprehensive policy frameworks that focus on climate adaptation, economic stabilization, and equitable agricultural development are crucial. Additionally, regional cooperation can play a vital role in addressing food security challenges amid climate change and shifting global economic trends.

Climate change poses significant challenges to agricultural productivity and global food security, particularly in regions highly dependent on climate-sensitive farming systems. Climate-Smart Agriculture (CSA) has emerged as an integrated approach aimed at enhancing agricultural productivity, strengthening resilience to climate variability, and reducing greenhouse gas emissions. Despite the rapid growth of CSA-related studies, a comprehensive understanding of the global research landscape, thematic evolution, and collaboration patterns remains limited. This study aims to map and analyze the scientific development of CSA and food security research through a bibliometric approach. Using publication data indexed in the Scopus database, this study applies network and visualization analysis with VOSviewer to examine keyword co-occurrence, citation structures, co-authorship networks, and geographical research distribution. The findings reveal that research on CSA and food security has expanded significantly, with dominant themes focusing on climate change adaptation and mitigation, smallholder farming systems, crop resilience, sustainable land management, and technology adoption. The results also highlight strong international collaboration networks, particularly among countries in Asia, Europe, and Africa, reflecting the global relevance of CSA in addressing food security challenges. This study contributes to the literature by providing a systematic overview of research trends, influential works, and emerging themes, offering valuable insights for researchers, policymakers, and practitioners in designing evidence-based strategies for sustainable agriculture and food security under climate change.

Smart farming integrates digital technologies to optimize agricultural production and promote sustainability. Its impact depends both on technological development and adoption by farmers. Research shows significant progress, but technical and socio-behavioral gaps remain, requiring integrated approaches to strengthen its contribution to the SDGs. In this context, scientific research on smart farming has grown significantly, becoming a key axis for the fulfillment of the Sustainable Development Goals (SDGs). The aim of this study was to analyze the evolution, structure, and impact of scientific production in smart farming, identifying its main trends, authors, journals, and contributions to the SDGs. To this end, a bibliometric analysis was applied to 1580 articles indexed in the Web of Science (WoS) database, using productivity, citation, and impact indicators based on Price’s, Lotka’s, Bradford’s, and Zipf’s laws, as well as the Hirsch index. The results reveal important growth in scientific production between 2014 and 2024, with a strong concentration in high-impact journals and international collaboration networks. In conclusion, smart farming represents an engine of innovation and sustainability, integrating science, technology, and digital management to address the global challenges of food security, climate change, and sustainable development.

Climate change is one of the global environmental and socio-economic problems. In recent decades, significant impacts have been exerted by the increase in greenhouse gas levels in the atmosphere, global warming, and the growing frequency of natural disasters, which directly affect many sectors, including agriculture, as they pose a threat of reduced productivity and deterioration of land fertility. The purpose is a comprehensive study of the dynamics of carbon emissions and the determination of the impact of hydrometeorological phenomena on the performance and stability of the agrarian sector. Methods — analysis and synthesis, and the economic-statistical method to identify indicators of adaptation of agro-industrial production to various climatic conditions. The empirical basis of the research consists of data from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan on annual precipitation for 2019–2023, as well as information on carbon dioxide emissions for 2020–2024. Results indicate the relevance of introducing climate-resilient methods into AIC production processes, effective land resource management practices, and a multi-faceted approach to addressing food security challenges. The conceptual model reflects climate transformation and the effects of social and economic factors on the agrarian sphere and food systems, and demonstrates their interconnection and interdependence. This concept serves as a practical basis for developing strategies to enhance adaptability and optimize the agro-industrial complex. Conclusions – the established facts correspond to the Sustainable Development Goals (poverty eradication, combating adverse climate impacts, and environmental protection) and are aimed at creating reliable prospects for food provision.

Digital technologies are reshaping how food is produced, traded, and governed, and are increasingly framed as key to meeting the global food security challenge under climate and resource constraints. This review synthesizes recent evidences on how information technology–enabled “digital agriculture” influences the four classical dimensions of food security—availability, access, utilization, and stability—across diverse farming systems. Core technological building blocks were examined, including Internet of Things (IoT) sensing, remote sensing, artificial intelligence (AI), big data analytics, digital advisory services, and platform-based value chains, and map the main pathways through which they affect productivity, risk management, environmental outcomes, and inclusion. Empirical and review studies from both high-income and low- and middle-income countries suggest that digital agriculture can increase yields, improve input-use efficiency, reduce production risk, and enhance information flows along value chains, with positive implications for food availability and economic access. However, benefits are unevenly distributed and shaped by digital literacy, infrastructure, institutional support, and data governance regimes. Digital agriculture also carries systemic risks, including deepening power asymmetries over data, reinforcing industrial models of production, and excluding smallholders and marginalized groups. An integrated framework was proposed, that positions digital agriculture as an enabling layer within broader transitions toward sustainable and equitable food systems, rather than a technological “fix” in isolation. The review concludes with research and policy priorities focused on closing evidence gaps, governing data and platforms in the public interest, and designing inclusive, climate-resilient digital ecosystems that support global food security.