The productivity of smallholder dairy farms is very low in developing countries. Important genetic gains could be realized using genomic selection, but genetic evaluations need to be tailored for lack of pedigree information and very small farm sizes. To accommodate this situation, we propose a flexible Bayesian model for the genetic evaluation of milk yield, which allows us to simultaneously account for nongenetic random effects for farms and varying SNP variance (BayesR model). First, we used simulations based on real genotype data from Indian crossbred dairy cattle to demonstrate that the proposed model can separate the true genetic and nongenetic parameters even for small farm sizes (2 cows on average) although with high standard errors in scenarios with low heritability. The accuracy of genomic genetic evaluation increased until farm size was approximately 5. We then applied the model to real data from 4,655 crossbred cows with 106,109 monthly test day milk records and 689,750 autosomal SNPs. We estimated a heritability of 0.16 (0.04) for milk yield and using cross-validation, a genomic estimated breeding value (GEBV) accuracy of 0.45 and bias (regression of phenotype on GEBV) of 1.04 (0.26). Estimated genetic parameters were very similar using BayesR, BayesC, and genomic BLUP approaches. Candidate genes near the top variants, IMMP2L and ARHGEF2, have been previously associated with milk protein composition, mastitis resistance, and milk cholesterol content. The estimated heritability and GEBV accuracy for milk yield are much lower than those from intensive or pasture-based systems in many countries. Further increases in the number of phenotyped and genotyped animals in farms with at least 2 cows (preferably 3-5, to allow for dropout of cows) are needed to improve the estimation of genetic effects in these smallholder dairy farms.

Small ruminant production is vital for rural livelihoods and food security in Saudi Arabia but faces persistent constraints, including high feed costs, limited veterinary access, low reproductive efficiency, and weak extension services. To address these gaps, a Farm Service Delivery Model (FSDM) was piloted, which involved embedding trained livestock technicians into communities to deliver integrated on-farm services. This study evaluated the impact of the FSDM on 47 farms across three regions over 6–12 months. The key results showed significant improvements: flock size increased by 28%, the lambing rate per ewe doubled from 0.39 to 0.80, twin births tripled, mortality declined from 23.8% to 8.0%, and milk production more than doubled. Economic analysis revealed a benefit–cost ratio of 3.02, indicating high return on investment. Scaling the FSDM nationally could generate up to USD 4.8 billion in added meat and milk value over five years while reducing meat imports by 48%. The model aligns with Saudi Vision 2030 and offers a scalable and sustainable pathway to enhance productivity, resilience, and rural income. Policy recommendations include institutional integration, digital innovation, blended financing, and strengthened breeding programs.

Genetic resources of small ruminants are essential for food security in arid regions; however, basic data for each breed in Saudi Arabia remain incomplete. This study establishes a comprehensive national database through a systematic survey of 104 farms, covering 21,214 heads of livestock (sheep and goats) across the kingdom’s primary agro-ecological zones between January and October 2025. Although national census data indicate that major breeds of sheep such as Naeemi, Najdi, Arabi, and Harri or goats such as Ardi exceed the FAO’s numerical thresholds for “not at risk,” our analysis reveals a fundamental paradox of “genetic vulnerability,” defined as a high risk of inbreeding depression and genetic stagnation despite high census numbers. The results show significant regional variations in prolificacy (p < 0.05), with the southern region displaying a substantial productivity gap compared to the central and eastern regions, mainly due to reliance on traditional grazing (46.7%) and limited infrastructure. This vulnerability is driven by a high risk of systematic inbreeding, with 65.7% of breeders acquiring sires from their own herds, a situation worsened by a severe 80% shortage of high-quality breeding males in the central region. Furthermore, selection criteria heavily emphasize esthetic phenotypic traits (over 80%) rather than production indicators (less than 8%), hindering genetic progress. Correlation analysis showed that higher farmer education levels were negatively associated with reproductive challenges (r = −0.216), while high feed prices remained a near-universal obstacle (97.1%). To mitigate these risks, we recommend implementing region-specific sire exchange programs to break closed breeding loops and establishing a national performance recording system to shift selection focus from phenotypic traits to measurable productivity. This study provides a vital, evidence-based framework for transitioning toward data-driven, resilient conservation and breeding strategies.

Saudi Arabia’s beekeeping sector comprises traditional and modern frame-hive systems operating under diverse environmental and management conditions. This cross-sectional study characterized 98 purposively selected apiaries participating in the Sustainable Rural Agricultural Development (SRAD) Programme, jointly implemented by the Saudi Ministry of Environment, Water and Agriculture and the Food and Agriculture Organization of the United Nations, across Makkah (n = 42), Jazan (n = 34), Asir (n = 16), Al-Baha (n = 4), and Al-Madinah (n = 2). Data included hive structure, honey production, Varroa occurrence, and beekeeper-reported constraints. Given the purposive and regionally unbalanced sampling, inference was restricted to the surveyed apiaries. Among 54 apiaries maintaining both hive systems, median annual yield was higher in modern than traditional hives (2.67 vs. 1.10 kg/colony; paired Wilcoxon p < 0.001). Yield also differed among regions (Kruskal–Wallis H = 19.07, p < 0.001). In the adjusted model, the proportion of modern hives was positively associated with productivity (β = 0.019; p = 0.001), whereas Varroa percentage was not (p = 0.2644). PCA explained 68.1% of variation in the first two components and supported three exploratory apiary clusters. Among 62 apiaries reporting constraints, imports/biosecurity (25.8%), predators (22.6%), and marketing/value-chain issues (19.4%) predominated. The findings demonstrate substantial heterogeneity among surveyed apiaries and support targeted extension and biosecurity interventions, while representative longitudinal studies and standardized disease diagnostics are needed for national-level inference.

Significant regional variation was observed in colony composition, productivity, and disease prevalence. Modern colonies (colonies housed in frame hives) produced significantly higher honey yields than traditional colonies (horizontal cylindrical wooden hive), and colony composition was a stronger determinant of productivity than disease prevalence. Multiple regression indicated that the proportion of modern colonies positively influenced honey yield, while geographical region was the primary predictor of high Varroa destructor infestation. No significant predictors were identified for high Nosema sp. incidence. PCA and cluster analyses identified four distinct production system typologies differing in colony composition, productivity, and disease burden. Beekeepers identified imported bees, biosecurity risks, predator pressure, inadequate disease management, and marketing constraints as the major challenges affecting sector performance. The findings provide an evidence-based framework for developing a National Honeybee Sector Development Strategy focused on strengthening disease surveillance, biosecurity, extension, advisory and value chain interventions along with conservation and genetic improvement of Apis mellifera jemenitica. These interventions will enhance honey production, reduce colony losses, improve pollination services, and support the long-term sustainability and resilience of Saudi Arabia's apiculture sector.