Food and nutrition security (FNS) rests on five pillars: availability, access, utilization, stability, and sovereignty. We assessed the potentials of local agroforestry practices (AFPs) for enabling FNS for smallholders in the Yayu Biosphere Reserve (southwestern Ethiopia). Data was collected from 300 households in a stratified random sampling scheme through semi-structured interviews and farm inventory. Utility, edibility, and marketability value were the key parameters used to determine the potential of plants in the AFPs. Descriptive statistics, ANOVA, and correlation analysis were employed to determine the form, variation, and association of local AFP attributes. Homegarden, multistorey-coffee-system, and multipurpose-trees-on-farmlands are the predominant AFPs in Yayu. Multipurpose-trees-on-farmlands are used mainly for food production, multistorey-coffee-system for income-generation, and homegarden for both. The 127 useful plant species identified represent 10 major plant utility groups, with seven (food, fodder, fuel, coffee-shade, timber, non-timber-forest-products, and medicinal uses) found in all three AFPs. In total, 80 edible species were identified across all AFPs, with 55 being primarily cultivated for household food supply. Generally, household income emanates from four major sources, multistorey-coffee-system (60%), homegarden (18%), multipurpose-trees-on-farmlands (13%), and off-farm activities (11%). Given this variation in form, purpose, and extracted benefits, existing AFPs in Yayu support the FNS of smallholders in multiple ways.

Agroforestry generally contributes to rural food and nutrition security (FNS). However, specialization on commodity-oriented agroforestry practices or management strategies can weaken local food sourcing when terms of trade fluctuate, as is the case of coffee in Ethiopia. Hence, this study assessed the trade-offs that smallholder farming households in south-western Ethiopia face between growing coffee in agroforestry systems and their food and nutrition security based on home production as well as markets. Data collected from 300 randomly selected households included: (i) attributes of agroforestry practices (AFP) and plants: structure, use type, edibility, marketability, nutritional traits, and (ii) the householders' FNS attributes: food security status, nutritional adequacy, and nutritional status. Data were collected both in food surplus and shortage seasons, during and after coffee harvesting. Within these data, the number of plant species and vegetation stories were significantly correlated with household food access security in both seasons and for all AFP identified, i.e., homegarden, multistorey-coffee-system, and multipurpose-trees-on-farmlands. The number of stories in homegardens and the richness of exotic species in multipurpose-trees-on-farmlands were significantly correlated with the biometric development of children below 5 years old during the shortage season. The richness of “actively-marketed” species in all AFP correlated with the food access security of the household, except in the multistorey-coffee-system, oriented to coffee production. Also, families that cultivate all three AFP showed significantly higher household diversity dietary during the shortage season. We conclude that no single AFP can secure FNS status of the households by itself, but the combination of all three can. Household and individual dietary scores were positively correlated with the AFP diversity-attributes, especially in the shortage season. Thus, the diversity of useful groups of plant species deserves to be promoted for instance by enriching AFP with edible and storable crops needed during the shortage season.

Background Coffee-growing communities in Yayu, Southwestern Ethiopia, are perceived to be food secure due to income from coffee production. However, while income ensures caloric sufficiency, it does not necessarily translate into complete food security, especially in terms of nutrient intake. Our study assessed the food and nutrition security of smallholder farm households in Yayu, with a particular focus on dietary diversity and hidden hunger. We found significant gaps in nutritional intake, especially for vulnerable groups such as women and children under five. Policy and Implications Ethiopia’s Productive Safety Net Program (PSNP) targets food-insecure areas based on economic status, assuming that income ensures food security. However, our findings suggest that this approach may be flawed, as households in coffee-growing regions are often labeled food secure despite significant deficiencies in essential nutrients such as heme iron. This calls for a re-evaluation of food security policies in cash-crop-dominated areas to address not only income but also access to diverse and nutrient-rich foods. Recommendations 1. Enhance Nutritional Education: Implement targeted educational programs focusing on the importance of dietary diversity, particularly in coffee-producing areas. 2. Diversify Agriculture: Encourage smallholder farmers to diversify crops, integrating nutrient-dense foods alongside coffee cultivation. 3. Improve Market Access: Strengthen local markets to ensure that nutrient-rich foods are available and affordable during both surplus and shortage seasons. 4. Revise PSNP Criteria: Use localized food and nutrition data rather than income alone to identify food insecurity in cash-crop regions. Conclusion Income from coffee production in Yayu improves caloric sufficiency but does not guarantee nutritional adequacy. To combat hidden hunger, policies must move beyond income-based indicators of food security and prioritize access to diverse, nutrient-rich foods. This will ensure a more comprehensive approach to food and nutrition security in Ethiopia’s coffee-growing regions.

Agroforestry landscapes in Ethiopia are undergoing rapid transformation driven by agricultural expansion, biomass extraction, and climate variability. Conventional monitoring approaches remain inadequate for capturing fine-scale forest dynamics in heterogeneous smallholder systems. This systematic review applies the PRISMA 2020 framework to evaluate the application of deep learning in monitoring forest dynamics and degradation in Ethiopian agroforestry landscapes. A comprehensive search across Scopus, Web of Science, Google Scholar, NASA ADS, and Semantic Scholar through 15 March 2025 identified fifteen eligible studies published between 2018 and early 2025. Results indicate that attention-based convolutional architectures applied to high-resolution imagery achieve accuracy levels between 85 and 89 percent for post-deforestation land-use classification in Ethiopia. Hyperspectral data from the PRISMA satellite combined with one-dimensional convolutional neural networks achieve over 90 percent accuracy for deforestation and degradation detection in comparable tropical systems. Neural network models using Sentinel-2 time series show moderate performance (80–85 percent) for degradation detection in Ethiopian coffee agroforestry systems. Random Forest remains a strong benchmark for tree species mapping in Eucalyptus woodlots, reaching 96.3 percent overall accuracy. Despite these advances, significant limitations persist, including spectral ambiguity in mixed systems, limited labeled datasets for the Ethiopian highlands, and insufficient validation frameworks. The review concludes that deep learning offers strong potential for operational forest monitoring and REDD+ implementation in Ethiopia but requires localized datasets, multi-sensor integration, and institutional investment in computational infrastructure.