Abstract South Asia is one of the most densely populated regions in the world. With 5% of the global agricultural land, South Asian farmers have to feed over 20% of the global population. South Asia is also one of the poorest regions in the word with about one-third of the world's poor living in this region. Climate change has become a pressing issue in south Asia ravaging agriculture and threatening food security. Climate change is affecting the fundamental basis of agriculture through changes in temperature, rainfall and weather, and by intensifying the occurrences of floods, droughts and heat stress. Like climate change, a pandemic is a global risk. The novel Corona virus (COVID-19) has further disrupted many activities in agriculture and supply chains in South Asia, further compounding the challenges of food and nutrition security and sustaining livelihoods. South Asian farmers are now facing double challenges of addressing the impacts of changing climate and managing the disruption arising from the Covid-19 pandemic. The unprecedented challenge posed by the COVID-19 requires very urgent and decisive actions to ensure food and nutrition security and save people's lives and livelihoods. Regional and global cooperation are also necessary to address the ripple effects of COVID-19 and climate change. South Asian countries must act collectively to share experiences and improve the disrupted agriculture supply chain. Strategies and approaches are needed to address both the coronavirus and climate crises. Currently, there is a unique opportunity to use the disruptive forces of the COVID-19 pandemic and the associated recovery policies to accelerate the transition to a more sustainable and resilient food systems. Some of the short-term support to address COVID-19 challenges can be linked to long-term sustainable food production by investing in natural capital to improve long-term productivity and resilience.
Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.
Personalized food recommendation systems (Food-RecSys) critically underperform due to fragmented component understanding and the failure of conventional machine learning with vast, imbalanced food data. While Large Language Models (LLMs) offer promise, current generic Recommendation as Language Processing (RLP) strategies lack the necessary specialization for the food domain's complexity. This thesis tackles these deficiencies by first identifying and analyzing the essential components for effective Food-RecSys. We introduce two key innovations: a multimedia food logging platform for rich contextual data acquisition and the World Food Atlas, enabling unique geolocation-based food analysis previously unavailable. Building on this foundation, we pioneer the Food Recommendation as Language Processing (F-RLP) framework - a novel, integrated approach specifically architected for the food domain. F-RLP leverages LLMs in a tailored manner, overcoming the limitations of generic models and providing a robust infrastructure for effective, contextual, and truly personalized food recommendations.
Seungmin Lee, John Hoddinott, Christopher B. Barrett
et al.
The study of food security dynamics in the U.S. has long been impeded by the lack of extended longitudinal observations of the same households or individuals. This paper applies a newly-introduced household-level food security measure, the probability of food security (PFS), to 26 waves of Panel Study of Income Dynamics (PSID) data, spanning 1979-2019, to generate a data product we describe and make newly available to the research community. We detail the construction of this unprecedentedly long food security panel data series in PSID data. Finally, we estimate key subpopulation- and national-level food security dynamics identifiable over the 40-year (1979-2019) period spanning multiple recessions and federal nutrition assistance policy changes, including disaggregated dynamics based on geography, race, sex, and educational attainment.
Muhammad Syafrudin, Ganjar Alfian, Norma Latif Fitriyani
With growing consumer health awareness, ensuring food safety and quality throughout the supply chain is crucial, particularly for perishable goods. Contamination can occur during production, processing, or distribution, making real-time monitoring essential. This study proposes an affordable Smartphone-based food traceability system (FTS) that utilizes RFID technology and smartphone sensors. A smartphone-based RFID reader tracks products, while integrated sensors monitor temperature, humidity, and location during storage and transport. The system is assessed in the kimchi supply chain in Korea, providing real-time data to both managers and consumers. It offered comprehensive product tracking, including temperature and humidity records, ensuring transparency and safety. Compared to traditional methods, the proposed system demonstrated improved efficiency in handling large volumes of data while maintaining accurate traceability. The results highlight its potential for enhancing food safety and quality across supply chains.
Food analysis has become increasingly critical for health-related tasks such as personalized nutrition and chronic disease prevention. However, existing large multimodal models (LMMs) in food analysis suffer from catastrophic forgetting when learning new tasks, requiring costly retraining from scratch. To address this, we propose a novel continual learning framework for multimodal food learning, integrating a Dual-LoRA architecture with Quality-Enhanced Pseudo Replay. We introduce two complementary low-rank adapters for each task: a specialized LoRA that learns task-specific knowledge with orthogonal constraints to previous tasks' subspaces, and a cooperative LoRA that consolidates shared knowledge across tasks via pseudo replay. To improve the reliability of replay data, our Quality-Enhanced Pseudo Replay strategy leverages self-consistency and semantic similarity to reduce hallucinations in generated samples. Experiments on the comprehensive Uni-Food dataset show superior performance in mitigating forgetting, representing the first effective continual learning approach for complex food tasks.
Pilar Ortíz Sandoval, Margarita Aguilera‐Gómez, Anna Kostka
et al.
Abstract Anthropogenic activities, such as industrial processes, urban development, intensive agriculture and waste disposal, have significantly contributed to the continuous introduction and accumulation of a wide array of xenobiotic compounds into natural ecosystems. Among them, emerging contaminants (ECs) such as pharmaceuticals, endocrine‐disrupting chemicals (EDCs), and per‐ and polyfluoroalkyl substances (PFAS) are of increasing concern due to their persistence, bioactivity and limited regulation. ECs enter ecosystems through diverse pathways including wastewater discharge, agricultural runoff and atmospheric deposition. Once released, many of these xenobiotics can bioaccumulate in organisms and enter the food chain, posing serious risks to food safety and public health. Traditional physico‐chemical remediation methods are often insufficient or environmentally taxing, prompting a shift toward bio‐based alternatives like bioremediation. These approaches, which rely on the activity of microbial communities to degrade pollutants, offer more sustainable solutions but require further interdisciplinary research to optimise their use. The One Health framework provides an effective model for addressing the complex risks posed by xenobiotics. This research programme aims to harmonise methodologies for cumulative dietary risk assessment across Europe and explore microbial strategies for xenobiotic degradation. By integrating microbiomics, toxicology, environmental science and food safety, this approach supports the development of safer food systems and more effective pollution management in line with the ‘farm to fork’ and One Health principles.
Nutrition. Foods and food supply, Chemical technology
Maryam Ahmadi-Khorram, Maryam Ahmadi-Khorram, Alireza Hatami
et al.
ObjectiveMalnutrition negatively affects mental health by altering neurotransmitter function and increasing stress responses. The gut-brain axis is pivotal in this process, and probiotics may mitigate stress. The current study examined the effects of multi-strain probiotic supplementation on stress levels in underweight individuals using the Perceived Stress Scale (PSS).MethodsA double-blind, randomized, placebo-controlled trial involved 100 underweight participants were randomized to receive either a probiotic supplement (Lactobacillus acidophilus, L. casei, L. rhamnosus; 3 × 109 CFU) or placebo for 8 weeks. Stress levels, anthropometric measures, and inflammatory markers (ESR, CRP) evaluated at baseline and post-intervention.ResultsNinety participants (mean age: 26.22 ± 7.42 years) completed the study (probiotic: n = 47; placebo: n = 43). Baseline age (p = 0.051) and gender (p = 0.101) showed no significant differences. Post-intervention, the probiotic group exhibited significant weight increases (p = 0.005), waist circumference (p = 0.038), and hip circumference (p = 0.008), and a significant reduction in Perceived Stress Scale (PSS) scores (p < 0.001) in comparison to the placebo. Inflammatory markers (ESR, CRP) also decreased significantly in the probiotic group (p < 0.001). Within-group analysis revealed improvements in anthropometric measures and inflammatory markers in both groups (p < 0.05), but stress reduction was more pronounced in the probiotic group (34% vs. 9.3%, p = 0.017). A significant time-group interaction was observed for stress scores (p < 0.001).DiscussionThe findings suggest that probiotic supplementation reduces stress levels in underweight individuals, possibly through gut microbiota modulation and inflammation reduction. Further research with larger samples and microbiome analysis is warranted.ConclusionIn conclusion, administering probiotics to underweight patients positively impacts their mental health and exhibits anti-inflammatory effects.Clinical trial registrationhttps://irct.behdasht.gov.ir/trial/69130, identifier IRCT20230310057667N1.
Mohamed Imam, Alesandros Glaros, Cheney Chen
et al.
This paper explores the potential of Building-Integrated Agriculture (BIA) as a strategy to align urban agriculture systems with building lifecycle sustainability goals. BIA systems such as indoor vertical farms, rooftop greenhouses, and soil-based urban farms promise to bolster urban food security and resource circularity. However, their environmental impacts can be further optimized via integration with building resources and strategic design, which requires a standardized framework for evaluating life-cycle metrics. This study develops a cross-industry Life Cycle Assessment (LCA) framework that harmonizes agricultural and building performance indicators, using carbon as a unifying metric to evaluate operational and embodied impacts. The research combines a meta-analysis of existing LCA studies, detailed case study evaluations, and novel paired metrics to quantify energy use, water use, and greenhouse gas emissions within a case study. Key findings identify operational carbon hotspots, infrastructure inefficiencies, and embodied carbon challenges while highlighting opportunities for integrating resource recovery strategies, such as greywater reuse and waste heat recovery. The results reveal trade-offs between productivity and environmental impact, with vertical farms demonstrating high yields but significant energy intensity, while soil-based systems excel in resource efficiency but exhibit lower output. This work introduces a structured methodology for cross-industry data integration and offers actionable insights for designers, growers and developers. By redefining system boundaries and incorporating reciprocal benefits between BIA and host buildings, this framework provides a pathway toward more sustainable urban agricultural practices and resilient urban ecosystems.
Nutrition. Foods and food supply, Food processing and manufacture
IntroductionThe global beef trade, as a critical component of the meat trade, plays an important role in balancing beef supply and demand worldwide. However, research on the evolution of its network patterns remains relatively limited. This article aims to explore the evolution of global beef trade network patterns and provide insights into its implications for sustainable development.MethodsUsing complex network theory, this paper constructs weighted and unweighted global beef trade networks based on international trade data and conducts an in-depth analysis of the evolution of global beef trade patterns from 2013 to 2022 across the overall, individual, and clustering levels.ResultsThe analysis reveals an increasing trend in connectivity, efficiency, and tightness within the global beef trade network. In the unweighted network, the core beef-importing countries are primarily concentrated in Germany, the United Arab Emirates, and the Netherlands. However, in the weighted network, the core importing countries shift to the United States, Japan, and China. Meanwhile, the core beef-exporting countries consistently remain Australia, Brazil, and New Zealand in both network types. Additionally, the analysis identifies clustering and regionalization characteristics within the global beef trade blocks.DiscussionThese findings highlight the evolving dynamics of global beef trade, emphasizing the roles of key countries and the structural shifts in the trade network. The study provides targeted recommendations for promoting sustainable development in the beef trade sector.
Nutrition. Foods and food supply, Food processing and manufacture
Current use of the food classifications: High in Fat, Salt and Sugar (HFSS) and NOVA (a scheme for food processing) for public health policy is challenging. HFSS-based policies focus on measurable nutrient content. NOVA score 4, Ultra Processed Foods (UPF) does not completely overlap with the HFSS score making integration of the two scores problematic. Analysis of the National Diet and Nutrition Survey found only just over half of foods consumed were classified as both HFSS and UPF(1). Our objective was to explore the overlap in HFSS and NOVA scores in a large, branded UK food composition database from myfood24. Identifying food categories where there was agreement and disagreement between classifications.The myfood24 branded food composition database uses back of pack nutrition information per 100g product to calculate HFSS and NOVA. HFSS classification used standard algorithms(2). NOVA classifications were defined from the NDNS source(3). Scores were allocated to each item based on their ingredient decks. Some assumptions were made across all categories such as items with preservatives were given NOVA score of 4. For items where ingredient decks are not published online, NOVA scores were assumed based on similar items in the database. Food categories and nutritional content of products were compared by both HFSS and NOVA score.89,496 branded foods had both HFSS and NOVA scores calculated. 40,032 (45%) foods were classified as being both HFSS and NOVA4. 36,256 (41%) of foods were classified as non-HFSS but NOVA4. 8,130 (9%) of foods were non-HFSS and NOVA1 (minimally processed) and 1,189 (1%) of foods were HFSS but NOVA1. Food categories with the largest disagreements between classification as non-HFSS but NOVA4 were canned/tinned foods (28%, n=192); fish/seafood (16%, n=173); fruit juices/smoothies (78%, n=406); nuts/seeds (18%, n=157); pulses (18%, n=73). Food categories with majority agreement between scores, non-HFSS and NOVA1 were fruits, pulses, rice/pasta, vegetables/potatoes. Food groups with both HFSS and NOVA4 as the majority classification were bread, breakfast cereals, cakes/sweet bakery, canned/tinned foods, confectionary, dairy/eggs, fried food, jams/honey, meat/poultry, pies/pasties, pizza/pasta, prepared meals, savoury snacks, soft drinks/squash, soups/salad/sandwich. Nutritional composition varied between classification group. HFSS and NOVA4 had nutrients/100g which were all higher than non-HFSS and NOVA4. Mean energy of 348 v. 144kcal/100g, protein 9 v. 7g/100g, carbohydrates 37 v. 17g/100g, total sugar 19g/100g; fat 18 v. 5g/100g respectively.HFSS and NOVA4 both tend to classify foods with high energy density. But some non- HFSS food categories, with very differing nutritional compositions are classified as NOVA4. Understanding where the differences and similarities lie between the scores can help interpret epidemiological research and develop public health policy to improve the quality of the UK diet.
Microbial biotechnology has a long history of producing feeds and foods. The key feature of today's market economy is that protein production by conventional agriculture based food supply chains is becoming a major issue in terms of global environmental pollution such as diffuse nutrient and greenhouse gas emissions, land use and water footprint. Time has come to re‐assess the current potentials of producing protein‐rich feed or food additives in the form of algae, yeasts, fungi and plain bacterial cellular biomass, producible with a lower environmental footprint compared with other plant or animal‐based alternatives. A major driver is the need to no longer disintegrate but rather upgrade a variety of low‐value organic and inorganic side streams in our current non‐cyclic economy. In this context, microbial bioconversions of such valuable matters to nutritive microbial cells and cell components are a powerful asset. The worldwide market of animal protein is of the order of several hundred million tons per year, that of plant protein several billion tons of protein per year; hence, the expansion of the production of microbial protein does not pose disruptive challenges towards the process of the latter. Besides protein as nutritive compounds, also other cellular components such as lipids (single cell oil), polyhydroxybuthyrate, exopolymeric saccharides, carotenoids, ectorines, (pro)vitamins and essential amino acids can be of value for the growing domain of novel nutrition. In order for microbial protein as feed or food to become a major and sustainable alternative, addressing the challenges of creating awareness and achieving public and broader regulatory acceptance are real and need to be addressed with care and expedience.
Abstract Rapidly increasing international food trade has drastically altered the global food system over the past decades. Using national scale indicators, we assess two of the resilience principles that directly reflect the effects of global trade on food systems – namely, maintaining diversity and redundancy, and managing connectivity. We perform our analysis for four nutritional components: dietary energy, proteins, fat, and quantity of vegetables & fruits – the key pillars of the WHO dietary recommendations. Our results indicate that, between 1987 and 2013, food supply diversity increased significantly for most of the world's population at the cost of an elevated dependency upon food imports. Food production diversity, particularly in terms of dietary energy and vegetables & fruits, increased for a large proportion of the world population, with the exception being major exporting countries, where it decreased. Of particular note is our finding that, despite a growing number of people being heavily dependent upon imports, the number of import partners decreased more often than it increased, except for the case of vegetables & fruits. This combination of increased dependency on imports and a reduced number of import partners indicates a potential vulnerability to disruptions in linked food systems. Additionally, it is alarming that we found many countries where the studied resilience aspects systematically declined, elevating their exposure to future shocks in the food system.
Vaida Bačiulienė, Yuriy Bilan, Valentinas Navickas
et al.
The types of artificial intelligence, artificial intelligence integration to the food value and supply chain, other technologies embedded with artificial intelligence, artificial intelligence adoption barriers in the food value and supply chain, and solutions to overcome these barriers were analyzed by the authors. It was demonstrated by the analysis that artificial intelligence can be integrated vertically into the entire food supply and value chain, owing to its wide range of functions. Different phases of the chain are affected by developed technologies such as robotics, drones, and smart machines. Different capabilities are provided for different phases by the interaction of artificial intelligence with other technologies such as big data mining, machine learning, the Internet of services, agribots, industrial robots, sensors and drones, digital platforms, driverless vehicles and machinery, and nanotechnology, as revealed by a systematic literature analysis. However, the application of artificial intelligence is hindered by social, technological, and economic barriers. These barriers can be overcome by developing the financial and digital literacy of farmers and by disseminating good practices among the participants of the food supply and value chain.
A global transformation of food systems is needed, given their impact on the three interconnected pandemics of undernutrition, obesity and climate change. A scoping review was conducted to synthesise the effectiveness of food system policies/interventions to improve nutrition, nutrition inequalities and environmental sustainability, and to identify double- or triple-duty potentials (their effectiveness tackling simultaneously two or all of these outcomes). When available, their effects on nutritional vulnerabilities and women’s empowerment were described. The policies/interventions studied were derived from a compilation of international recommendations. The literature search was conducted according to the PRISMA extension for scoping reviews. A total of 196 reviews were included in the analysis. The triple-duty interventions identified were sustainable agriculture practices and school food programmes. Labelling, reformulation, in-store nudging interventions and fiscal measures showed double-duty potential across outcomes. Labelling also incentivises food reformulation by the industry. Some interventions (i.e., school food programmes, reformulation, fiscal measures) reduce socio-economic differences in diets, whereas labelling may be more effective among women and higher socio-economic groups. A trade-off identified was that healthy food provision interventions may increase food waste. Overall, multi-component interventions were found to be the most effective to improve nutrition and inequalities. Policies combining nutrition and environmental sustainability objectives are few and mainly of the information type (i.e., labelling). Little evidence is available on the policies/interventions’ effect on environmental sustainability and women’s empowerment. Current research fails to provide good-quality evidence on food systems policies/interventions, in particular in the food supply chains domain. Research to fill this knowledge gap is needed.
Azarmidokht Gholamipour-Shirazi, Endre Joachim Lerheim Mossige
The flavor and aroma development in fermented foods is intricately tied to the mixing dynamics during fermentation. This review explores how variations in mixing influence the physical, chemical, and microbial interactions within fermentation systems, ultimately affecting sensory characteristics such as flavor and aroma. Factors like rheology, shear forces, and fluid flow patterns are critical in mass transfer, microbial activity, and the release of volatile compounds, contributing to fermented products' sensory profile. Examples from common fermented foods -- including bread, yogurt, beer, wine, and cheese -- highlight how controlled mixing can optimize the release of desirable flavor compounds, improve biosynthesis yields, and reduce technological complexity. Understanding these physical interactions is essential for advancing fermentation processes in the food industry, leading to higher product quality, better flavor retention, and enhanced consumer satisfaction.
Rui Liu, Zahiruddin Mahammad, Amisha Bhaskar
et al.
Robotic assistive feeding holds significant promise for improving the quality of life for individuals with eating disabilities. However, acquiring diverse food items under varying conditions and generalizing to unseen food presents unique challenges. Existing methods that rely on surface-level geometric information (e.g., bounding box and pose) derived from visual cues (e.g., color, shape, and texture) often lacks adaptability and robustness, especially when foods share similar physical properties but differ in visual appearance. We employ imitation learning (IL) to learn a policy for food acquisition. Existing methods employ IL or Reinforcement Learning (RL) to learn a policy based on off-the-shelf image encoders such as ResNet-50. However, such representations are not robust and struggle to generalize across diverse acquisition scenarios. To address these limitations, we propose a novel approach, IMRL (Integrated Multi-Dimensional Representation Learning), which integrates visual, physical, temporal, and geometric representations to enhance the robustness and generalizability of IL for food acquisition. Our approach captures food types and physical properties (e.g., solid, semi-solid, granular, liquid, and mixture), models temporal dynamics of acquisition actions, and introduces geometric information to determine optimal scooping points and assess bowl fullness. IMRL enables IL to adaptively adjust scooping strategies based on context, improving the robot's capability to handle diverse food acquisition scenarios. Experiments on a real robot demonstrate our approach's robustness and adaptability across various foods and bowl configurations, including zero-shot generalization to unseen settings. Our approach achieves improvement up to $35\%$ in success rate compared with the best-performing baseline. More details can be found on our website https://ruiiu.github.io/imrl.
Neris Özen, Wenjuan Mu, Esther D. van Asselt
et al.
The number of scientific articles published in the domain of food safety has consistently been increasing over the last few decades. It has therefore become unfeasible for food safety experts to read all relevant literature related to food safety and the occurrence of hazards in the food chain. However, it is important that food safety experts are aware of the newest findings and can access this information in an easy and concise way. In this study, an approach is presented to automate the extraction of chemical hazards from the scientific literature through large language models. The large language model was used out-of-the-box and applied on scientific abstracts; no extra training of the models or a large computing cluster was required. Three different styles of prompting the model were tested to assess which was the most optimal for the task at hand. The prompts were optimized with two validation foods (leafy greens and shellfish) and the final performance of the best prompt was evaluated using three test foods (dairy, maize and salmon). The specific wording of the prompt was found to have a considerable effect on the results. A prompt breaking the task down into smaller steps performed best overall. This prompt reached an average accuracy of 93% and contained many chemical contaminants already included in food monitoring programs, validating the successful retrieval of relevant hazards for the food safety domain. The results showcase how valuable large language models can be for the task of automatic information extraction from the scientific literature.
Brunella Arru, Federica Cisilino, Paola Sau
et al.
Crop and livestock farms are central to achieving the 2030 Agenda goals and a sustainable agri-food system. However, the transition toward a sustainable agri-food system requires optimizing several economic and environmental farm targets that, interacting with one another, would lead to win-win opportunities, at least as desired by the European Union (EU) policies. Indeed, in recent years, the EU has fostered sustainable development in a logic of synergy between farms’ environmental and economic performances. This work fits into the agricultural sustainability assessment with the aim of improving our understanding of the existence of synergy or a trade-off between the economic and environmental dimensions at a crop and livestock field and farm scale. Specifically, using a set of appropriate agricultural economic and environmental indicators, two composite indexes were created and used to perform trade-off analysis on 7.891 farms that participated in 2019 and 2020 in the Italian Farm Accountancy Data Network. The findings showed a trade-off between economic and environmental dimensions in all livestock sub-sectors and the cereals sector, while a synergy in the horticulture sector. Considering the new European sustainability policies on agriculture and global scenarios, the study significantly contributes to policymakers, practitioners, and academic debate on sustainability in agriculture.
Nutrition. Foods and food supply, Food processing and manufacture
Lia Nurcahyani, Dyah Widiyastuti, Wiwit Estuti
et al.
Background: Stunting leads to increased morbidity and mortality among children. To accelerate stunting reduction, family assistance teams support at-risk families, requiring engaging and accessible educational resources. However, existing educational media materials are fragmented and lack a comprehensive approach, resulting in gaps during family assistance sessions. To improve accessibility and efficacy, a comprehensive, technology-based educational tool is necessary.
Objectives: To develop a Stunting Prevention Education Media Package (PaSti PenTing) based on technology and local wisdom.
Methods: This study used a Research and Development approach conducted in Cirebon City. The stages included the formulation of basic concepts, and in-depth interviews with experts, namely the Chairman of the Central Board of the Indonesian Midwives Association, the Head of the Cirebon City Health Office, the Head of the Cirebon City Women's Empowerment, Child Protection, Population Control and Family Planning Office and lecturers with S3 backgrounds. These interviews provided input related to the materials used for designing the PaSti PenTing. The research instrument uses in-depth interview guidance and data analysis was carried out using content analysis.
Results: Based on expert input, the PaSti PenTing design was developed. The main menu consists of an introduction and a menu for target groups (teenagers, brides-to-be, pregnant women, postpartum mothers, and toddlers). Each menu contains educational materials.
Conclusions: PasTi PenTing is a comprehensive media that can be used by the assistance team and families at risk of stunting to improve knowledge, attitudes, and behaviors in stunting prevention.