Search Results (2,432)

Antimicrobial resistance (AMR) is recognized by global health authorities as one of the foremost public health and development challenges confronting humanity today, leading the WHO to establish categories of medically important antibiotics for humans and animals. On farms with a high burden of infection, inappropriate antibiotic use, mixed bacterial infections, and environmental contamination with antibiotics from animal sources might accelerate the development of antibiotic resistance, particularly from a One Health Approach. Therefore, this study was conducted to determine correlations in AMR among antibiotics using bacteria isolated from dairy farms with a high burden of intramammary infections. In addition, antibiotic resistance percentages were compared between a smallholder dairy farm and an intensive dairy farm in Cambodia, where the dairy industry has been established for only a few years. Bacterial isolates were collected from two dairy farms: Farm A, a medium-sized local farm with approximately 30 milking cows, and Farm B, a large intensive farm with approximately 480 milking purebred Holstein-Friesian milking cows. All bacteria were tested for antimicrobial susceptibility using a disk diffusion test with 17 antibiotics from nine classes. Classifications based on CLSI interpretive criteria were distinguished from those based on study-defined, extrapolated inhibition-zone thresholds, designated expected AMR (AMR-E). The latter classifications were exploratory and were not validated measures of resistance. The antimicrobials included antibiotics from classes important in human medicine: human-used only (HU), highest-priority critically important antimicrobials (HPCIA), critically important antimicrobials (CIAs), and highly important antimicrobials (HIAs). Fisher’s exact test was used to compare AMR between farms (p < 0.05), and Spearman’s correlation adjusted by a Benjamini–Hochberg (Q < 0.05) was used to evaluate associations among antimicrobial susceptibility classifications and between these classifications and multidrug resistance (MDR) status. The results showed that the distribution of bacteria differed between the two farms. For antibiotics used only in humans, no imipenem-resistant Enterobacterales were detected, whereas three Enterobacterales isolates were classified as resistant to meropenem. Differences between farms varied by bacterial group and antimicrobial agent. Many significant correlations in AMR were found among antibiotics, especially those within the same class. Different correlations among antibiotics were found across bacterial groups, including Enterobacterales, staphylococci, enterococci, and streptococci. Phenotypic associations were observed among antimicrobial susceptibility classifications, particularly for agents within the same class. These associations do not establish shared resistance mechanisms or predict treatment outcomes. Findings based on extrapolated thresholds require validation using appropriate susceptibility testing methods. Full article

Accurate pedigree information is essential for genetic evaluation and genomic selection in dairy cattle. However, the performance of SNP-based parentage verification depends on marker informativeness, genomic distribution, and compatibility with the target population. In this study, we developed and validated a population-specific 260-SNP panel for parentage verification in Chinese Holstein cattle. The panel was selected from BovineSNP150K genotype data of 2954 Chinese Holsteins based on high marker informativeness, genotyping quality, LD-based control of marker redundancy, and genome-wide distribution. The selected SNPs had a mean minor allele frequency of 0.4981, expected heterozygosity of 0.5000, and polymorphism information content of 0.3750, with a combined probability of exclusion >0.999. Parentage verification of 867 recorded candidate parent–offspring pairs using likelihood-based and Mendelian exclusion-based approaches showed 99.54% agreement, with a Cohen’s κ of 0.981. Random SNP-subset analyses showed greater variation in pedigree concordance estimates with smaller marker sets, whereas larger subsets produced more stable estimates across replicate subsets. Independent validation using targeted-capture sequencing showed a mean SNP detection rate of 99.96% and complete genotype concordance in duplicate tests. Independent validation across six commercial dairy farms identified an overall inconsistency rate of 12.97% between recorded pedigree relationships and genotype-based parentage evidence. These results demonstrate that the population-specific 260-SNP panel provides informative and reproducible genotyping for parentage verification in Chinese Holstein cattle and offers a practical tool for pedigree quality control in dairy cattle breeding programs. Full article

The transition to low-emission (LE) dairy production is essential for mitigating agricultural greenhouse gas (GHG) emissions. However, the interannual variability and principal drivers of farm-level GHG footprints across intensive dairy farms remain insufficiently characterized, constraining targeted mitigation strategies. This study aimed to (i) quantify interannual variation in on-farm GHG emission intensity, (ii) characterize management contrasts between high- and low-emission farms, (iii) clarify the underlying mechanisms, and (iv) identify optimization pathways. A supplementary sensitivity analysis encompassing upstream emissions associated with feed production was performed using an expanded system boundary to assess the effects of system boundary selection on the estimated GHG emissions. Using 3-year (2021–2023) field data from 22 intensive dairy farms, a standardized farm-level GHG inventory was used to quantify operational GHG emission intensity per unit of fat- and protein-corrected milk (FPCM). The production-weighted mean on-farm operational GHG intensity was 0.86 kg CO₂-eq kg⁻¹ FPCM, with enteric methane constituting the dominant emission source. Incorporating upstream emissions associated with feed production contributed an additional 0.427 kg CO₂-eq kg⁻¹ FPCM, yielding a total emission intensity of 1.287 kg CO₂-eq kg⁻¹ FPCM under the expanded system boundary, representing a 49.7% increase relative to the operational inventory. Feed upstream emissions accounted for 33.2% of the total emissions estimated within the expanded system boundary. Both herd size and energy consumption exhibited increasing trends throughout the study period, whereas the proportion of lactating cows relating to the total herd declined from 46.01% in 2021 to 44.32% in 2023, suggesting that the expansion of the overall herd exceeded the corresponding growth in the lactating cow population. Under the operational system boundary, high-emission (HE) farms exhibited relatively greater contributions to their GHG emission profiles from enteric methane production, electricity consumption, and manure management activities. Conversely, LE farms attained improved performance through higher proportions of lactating cows, greater feed efficiency, and manure anaerobic digestion. Correlation analysis linked herd size, energy consumption, and dry matter intake positively to on-farm GHG emissions. Dietary starch was associated with manure N₂O emissions, while feed efficiency was associated with higher energy-related GHG emissions. Overall, herd composition, feeding management practices, energy utilization, and manure management strategies were identified as principal determinants of on-farm GHG emission performance. Furthermore, the sensitivity analysis employing an expanded system boundary demonstrated that incorporating upstream emissions associated with feed production substantially elevates the overall GHG emission intensity expressed per kg FPCM. This study helps address the lack of multi-year farm-level evidence for China’s dairy sector and proposes integrated strategies—herd structure adjustment, precision feeding, clean energy exploitation, and manure management—alongside sustained long-term monitoring, given the pronounced interannual variability. These findings highlight the roles of farm-level GHG inventories in identifying targeted on-farm emission mitigation opportunities and expanded system boundary assessments in quantifying the overall GHG burden associated with the entire dairy production supply chain. Full article

Freshwater monitoring networks are often biased toward major rivers and chemical indicators, creating blind spots in ecologically sensitive areas. This study tests whether a legacy monitoring network is positioned in the right parts of its catchment, using three lines of evidence independent of the chemical record, remote sensing to look back, biological indices to assess the present, and catchment modelling to look forward, applied to the Rangitīkei catchment, New Zealand. NDVI, NDBSI, and water quality trends were assessed with a Bayesian change-point method; a catchment-wide macroinvertebrate survey extended coverage beyond routine sites; and the InVEST Nutrient Delivery Ratio model, calibrated against measured nitrogen loads, simulated land use change scenarios. Neither remote sensing nor water quality indicated widespread deterioration, with no directional trends detected in either. Macroinvertebrate surveys identified severely degraded sites outside the existing network, including two below the national bottom line in a single survey. Converting suitable land to dairy farming predicted nitrogen export increases of up to 110% at the most exposed site, and cropping conversion up to 69%. Riparian buffers combined with a 20% reduction in nitrogen loss from high-producing grassland predicted reductions of up to 32%. Sites with the greatest predicted risk, primarily Tutaenui Stream and Kawhatau River, are not well covered by the existing network. Network design methods are largely built around statistical representativeness or accessibility rather than catchment process. This framework identified priority gaps such designs would likely miss, offering a transferable basis for auditing networks or informing new ones. Full article

Blood glucose concentration is commonly used to assess energy metabolism in dairy cows. Handheld glucose meters allow rapid measurement under farm conditions, but their performance in cattle may differ from that obtained with laboratory methods. This study evaluated the agreement between capillary whole-blood glucose measured with the Accu-Chek^® Instant handheld glucose meter and venous serum glucose measured with a laboratory biochemical analyzer. A total of 93 Holstein–Friesian animals provided 117 paired glucose measurements; 24 animals were sampled on two occasions. Capillary blood was collected from the vulvar mucosa and measured immediately, while venous blood was collected from the coccygeal vein and processed to serum for laboratory analysis. The mean glucose concentration was 3.27 ± 0.51 mmol/L in capillary whole blood and 3.44 ± 0.42 mmol/L in venous serum. The mean paired difference was 0.17 mmol/L (95% CI, 0.06–0.27 mmol/L; p = 0.0018). The correlation between methods was weak (r = 0.267), and Lin’s concordance correlation coefficient was 0.246. Bland–Altman analysis showed limits of agreement from −0.94 to 1.27 mmol/L. A significant proportional bias was also identified (β = −0.302; 95% CI, −0.562 to −0.042; p = 0.023). Categorical agreement was limited (Cohen’s κ = 0.241), and 47 of 117 paired measurements were classified into different glucose categories. These findings show that the two measurement approaches are not interchangeable for individual-cow assessment. The observed differences may reflect not only device performance but also differences in sampling site, sample matrix, and sample handling. Full article

Accurate estimation of individual herbage intake in grazing ruminants is essential for improving feeding efficiency and animal performance, yet it remains challenging under field conditions. This study evaluated the association between live weight dynamics recorded by an automated walk-over-weighing (WoW) system and equation-derived herbage dry matter intake (HDMI) in dairy ewes and whether this association changed throughout lactation. The study was conducted over two years (2023–2024) using 92 Sarda dairy ewes managed under part-time grazing with two daily pasture access times (2 or 6 h day⁻¹). Live weight was recorded daily before and after pasture access, and daily live weight change was averaged weekly. HDMI was estimated using a published prediction equation based on milk yield, herbage neutral detergent fibre concentration, and pasture access time. Live weight change was partitioned into between-animal (DW_mean) and within-animal (DW_dev) components, allowing persistent differences among animals to be distinguished from short-term within-animal fluctuations, and analysed using linear mixed-effects models. In the pooled model, both components were positively associated with equation-derived HDMI (p < 0.001), while the significant DW_dev × days-in-milk interaction (p = 0.004) indicated that the association between within-animal live weight change and equation-derived HDMI decreased as lactation advanced. However, pasture access-time-specific sensitivity analyses showed that the associations with DW_mean and DW_dev persisted within AT-6h but were not detected within AT-2h, while adjustment for milk yield attenuated the within-animal association, which was no longer statistically significant, and also removed statistical support for the DW_dev × days-in-milk interaction. The mixed-effects model explained a substantial proportion of the variability in equation-derived HDMI (marginal R² = 0.64; conditional R² = 0.94), and equation-derived and model-fitted HDMI were positively correlated (r = 0.77). These findings indicate that WoW-derived live weight dynamics provide non-invasive information associated with equation-derived HDMI under the conditions of this study, but independent validation against directly measured individual herbage intake is required before their use for individual intake estimation. Full article

Ketosis is a common metabolic disorder in dairy cows and occurs mainly during early lactation. Blood β-hydroxybutyrate (BHB) is widely used for diagnosis, but large-scale testing is limited by sampling requirements and cost. We therefore studied lactating Holstein cows from 12 large-scale dairy farms in central China. Milk samples, milk composition traits, and milk mid-infrared (MIR) spectra were collected through the Dairy Herd Improvement (DHI) testing system from April 2019 to December 2022, and ketosis diagnoses were obtained from farm health records; samples were labeled as healthy or ketotic based on the temporal relationship between diagnosis and sampling. A total of 2206 samples were used to build the calibration and validation datasets, in which five feature wavenumber selection methods and five machine learning algorithms were compared. Ketotic cows had a higher milk fat percentage and fat-to-protein ratio and a lower protein and lactose percentage (p < 0.01). The best combination was PLS-DA based on 104 CARS-selected wavenumbers, which achieved a sensitivity of 0.83, a specificity of 0.79, a balanced accuracy of 0.81 and an area under the receiver operating characteristic curve (AUC) of 0.88 in validation. MIR spectroscopy combined with machine learning can therefore support large-scale ketosis phenotyping within the DHI system. Full article

The aim of this study was to describe the prevalence and distribution of claw lesions in dairy cows from farms producing milk for Parmigiano Reggiano cheese and to evaluate factors associated with their occurrence. Data were collected during hoof-trimming visits performed by four professional hoof trimmers on 25 farms between March 2023 and October 2024 using a specifically developed digital recording application. 4410 visits from 3066 cows were included. At least one lesion was recorded in 53.5% of visits and 44.3% of cows. Sole ulcer was the most common lesion (23.8% of visits), followed by sole haemorrhage (19.3%), wall abscess (18.1%), dermatitis (16.4%), and wall lesions (10.3%). Mechanically related lesions mainly involved the lateral claws of the hind limbs. Parity was strongly associated with lesion occurrence: compared with first-parity cows, odds increased at parity 2 (OR 1.41), parity 3 (OR 2.52), and parity ≥4 (OR 3.14). Because parity and age were highly correlated (r = 0.94), this association should be interpreted as a combined parity/age effect, and their independent effects could not be distinguished. Lactation stage showed weaker associations, and seasonal and farm-level differences were observed. Overall, non-infectious lesions predominated. The marked variation among farms suggests that herd-level factors contribute to claw health. Linking these digitally recorded hoof-trimming data with national breeding and production databases may support large-scale surveillance and future epidemiological and genetic investigations. Full article

Dairy goats offer significant potential to enhance food security, promote climate resilience, and improve rural livelihoods in sub-Saharan Africa (SSA), particularly within women-led smallholder farming systems. This interdisciplinary narrative review integrates findings from nutritional science, climate adaptation studies, recent genomic innovations, and policy analyses (2010–2025) to critically evaluate dairy goats’ roles in addressing persistent malnutrition and vulnerability to climate extremes. Goat milk provides vital macronutrients and micronutrients and can contribute to improved dietary adequacy. Compared with larger ruminants such as cattle, goats—particularly indigenous and locally adapted populations—often exhibit greater tolerance to heat stress, flexible browsing behaviour, and lower water requirements. However, these adaptive advantages vary widely among dairy goat breeds, with high-yielding temperate breeds generally showing lower resilience to climatic extremes than indigenous SSA ecotypes. In addition, the per-kg GHG emissions for goat milk are typically higher than those for bovine milk, although system-level benefits may emerge in drylands where goats fit local heat/feed constraints and where species mix and management differ substantially. Recent genomic advancements, notably genome-wide association studies and selection signature analyses, have identified candidate loci for thermotolerance, disease resistance, and milk yield. Near-term improvement is most plausible where breeding is anchored in community-based programmes supported by minimal identification and phenotyping, with marker use most defensible when validated within target populations. Despite emerging opportunities through microfinance and climate finance, especially for women-centric cooperatives, dairy goats remain underrepresented in national livestock and climate policies, and life cycle assessment data to support formal carbon–credit integration are limited. We therefore recommend dedicated funding to goat-specific extension and animal health, targeted gender-inclusive training, strategic investments in genomic and data infrastructure, and enhanced data collection for carbon accounting. Overall, the evidence synthesised here indicates that scalable dairy goat development in SSA is unlikely without the coordinated implementation of climate-smart management and delivery systems (animal identification and core recording), alongside context-validated genomic approaches (e.g., MAS or genomic prediction integrated within community-based breeding where reference resources and phenotypes exist) and targeted policy and financing. Full article

Current rations are formulated on the requirements of a representative cow plus a correction factor. This is not precision feeding because in practice cows are fed as a pen, and within a pen, dry matter intake (DMI) among cows is not uniform and has skew or kurtosis. Since ration formulation depends on DMI as a binding constraint, rations formulated to the pen DMI could be more precise if the distribution of DMI is better described. The objectives of this study were to: 1. Develop an algorithm, the distribution shape model (DSM), to predict pen DMI based on the distribution of cow measures which could easily be estimated on a commercial dairy farm; and 2. Compare predictions of pen DMI from the DSM to predictions of pen DMI based on a representative cow using the National Academies of Sciences, Engineering, and Medicine (NASEM) equation model (NSM). Individual cow DMI from previously published studies was used to train an Extreme Gradient Boosting (XGB) algorithm to predict the distribution shape of pen DMI (Beta or Generalized Normal) from parity, week in milk, and descriptive statistics of milk yield. The NSM predicted DMI for a representative cow from pen-average inputs, and this prediction was multiplied by the number of cows, simulating the scenario where DMI is assumed to be uniform across a pen. Both models were tested by comparing predicted with observed total pen DMI using an independent testing dataset of 229,909 pens generated from previously collected cow observations. For pens classified as Beta, the mean squared error of prediction (MSEP) of pen DMI from the DSM and NSM was 5249 and 9736 kg², respectively (root mean squared error of prediction, RMSEP, 72.5 and 98.7 kg/d), with 3.0% and 80.2% due to mean bias, 5.4% and 8.3% due to regression slope not equal to 1, and 91.6% and 11.5% due to random variation. For pens classified as Generalized Normal, MSEP was 2086 and 6431 kg² (RMSEP 45.7 and 80.2 kg/d), with 0.8% and 73.2% due to mean bias, 7.6% and 4.7% due to slope not equal to 1, and 91.6% and 22.1% due to random variation. The NSM consistently overpredicted pen DMI, contributing to its higher error. Including the distribution shape in the prediction of DMI improved precision and accuracy of pen DMI prediction and should be considered when estimating the total daily DMI fed to a pen. Full article

This study investigates the dairy sector in the Republic of Croatia, which is characterized by pronounced structural heterogeneity. On the one hand, there are highly productive farms comparable to the leading milk producers in the European Union, and numerous small and medium-sized farms that face the challenges of maintaining competitiveness and further development. In order to design effective development and sectoral policies, it is necessary to identify and distinguish the dominant types of dairy farms and their specific development needs. The aim of the study was to develop a representative typology of Croatian dairy farms as a basis for adapting and upgrading the SiTFarm (Slovenian Typical Farm Model) model into CroTFarm (Croatian Typical Farm Model), which will enable a more comprehensive assessment of the production, economic and structural characteristics of the sector. The analysis was conducted on empirical data from the Croatian Agency for Agriculture and Food, covering 3331 dairy farms. To identify groups of farms with similar structural and production characteristics, hierarchical and non-hierarchical cluster analysis methods were applied. The analysis identified 17 clusters, ranging from smaller farms characterized by relatively extensive and diversified production to larger, more specialized farms with higher milk yields and more intensive production. A significant share of mixed farm types combining characteristics of both production systems were also identified. The resulting farm types will be used as input categorical variables in the subsequent empirical farm model, which also represents the primary purpose of their construction within this study. Full article

Enterocytozoon bieneusi is an important microsporidian pathogen with a broad host range and potential zoonotic relevance, yet molecular data from cattle in Romania are lacking. This study investigated, for the first time, the molecular prevalence, genotype diversity, and phylogenetic relationships of E. bieneusi in young dairy cattle in western Romania. A total of 190 fecal samples were collected from young dairy cattle, comprising pre-weaned calves and post-weaned cattle up to 12 months of age from 28 farms in Arad and Timiș counties and examined by nested PCR targeting the internal transcribed spacer (ITS) region of the rRNA gene. E. bieneusi DNA was detected in 27/190 cattle, corresponding to an overall prevalence of 14.2% (95% CI: 10.0–19.9%). Pre-weaned cattle showed a higher prevalence than post-weaned animals (18.1% vs. 8.1%), but the difference was not statistically significant (OR = 2.51, 95% CI: 0.96–6.54; p = 0.058). High-quality ITS sequences were obtained from 20 positive samples, revealing five established genotypes, namely BEB4 (45.0%), I (25.0%), J (10.0%), BEB8 (10.0%), and BEB17 (10.0%), among the genotyped isolates (n = 20). Phylogenetic analysis placed all Romanian isolates within Group 2, predominantly associated with ruminants but also containing genotypes reported in humans. These findings establish the first molecular baseline for E. bieneusi in young dairy cattle in western Romania and provide initial information on their epidemiological relevance within a One Health framework. Full article

Climatic variability can modify milk production through direct and indirect pathways, yet monthly farm time series also exhibit temporal dependence. A longitudinal, observational, single-case study was conducted in the San Francisco dairy system, Carchi, Ecuador, from March 2019 to December 2024. Of 70 calendar months, 64 had complete analytical information, and 58 were eligible for two-lag models. Ten model specifications were compared based on model fit, information criteria, residual diagnostics, seasonality assessment, and temporal validation. Mean daily milk production was 233.49 ± 47.90 L day⁻¹. The selected model was a second-order autoregressive model with a calendar trend and mean temperature at t − 2 (adjusted R2 = 0.827; Akaike information criterion = 519.52; Bayesian information criterion = 529.83; root mean squared error = 19.56 L day⁻¹). The temperature coefficient was 10.844 L day⁻¹ °C⁻¹ (p < 0.001 with robust standard errors). Annual harmonic terms did not improve model fit, and precipitation at t − 1 yielded only marginal improvement. The number of cows in production varied across 54 months (26.02 ± 4.11; range: 18–37). The observed temperature association should be interpreted at the system level; it does not demonstrate direct causality or individual cow productivity and requires external validation. Full article

This study evaluates the effectiveness and financial impact of three fixed timed artificial insemination (FTAI) protocols—an intensive Double Ovsynch (DO) protocol, OVS5D, a modified Ovsynch protocol incorporating GnRH administration on day 5 post-FTAI, and a standard Ovsynch (OVS) protocol, beginning with the first artificial insemination (AI) on a high-yielding dairy farm. For this study, three groups were assessed: the OVS group (n = 904), the OVS5D group (n = 830), and the DO group (n = 859). To assess the economic impacts of each protocol, the UW-Cornell DairyRepro$ decision tool was used. The results revealed that the DO group had a significantly higher conception rate (CR) at the first AI (53.08%, p < 0.05) compared to both the OVS (47.46%) and OVS5D (46.27%) groups, which showed similar results (p > 0.05). At the second AI, the CRs for the DO (45.18%) and OVS (44.97%) groups were comparable (p > 0.05), with both groups achieving higher CRs than the OVS5D group (35.67%, p < 0.05). Among the three groups, primiparous cows exhibited similar CRs (54.32 vs. 57.77 vs. 55.45, p > 0.05), while multiparous cows showed higher CRs (p < 0.05) in the DO group (52.64%) compared to the OVS (42.64%) and OVS5D (42.46%) groups. In our study, there is a clear interaction between synchronization protocols, parity, and seasonal variations on high-yielding dairy cows’ CRs. Compared to the OVS group, the net present value (NPV) per cow per year was EUR 35 higher in the DO group and EUR 11.8 lower in the OVS5D group. Seasonal analysis across the four weather seasons (spring, summer, autumn, and winter) revealed that all three protocols showed substantially reduced CRs during summer, confirming universal heat stress susceptibility in standard-cooled herds regardless of synchronization intensity. Based on these findings, the DO protocol is recommended for multiparous cows and standard OVS for primiparous cows; the OVS5D is not recommended due to inferior fertility and negative economic returns. These recommendations are specific to high-yielding Holstein herds managed under temperate continental climate conditions similar to this study farm and should not be over-generalized to other production systems or climates. Full article

Wastewater systems can serve as reservoirs and dissemination pathways for Listeria spp., yet comparative data from municipal and agricultural wastewater remain limited. The objective of this study was to isolate Listeria monocytogenes from municipal wastewater and dairy-farm runoff for use as enrichment hosts in a subsequent and separate study on the isolation of bacteriophage from the same wastewater streams. During this process, additional Listeria spp. were recovered and characterized along with L. monocytogenes. Identification of Listeria sp. was accommodated using multilocus sequencing, LIPI-1-targeted PCR, and phenotypic confirmation. Wastewater samples were filtered, enriched with Demi-Fraser broth, treated with anti-Listeria magnetic beads, and plated on MOX agar. Bead-captured Listeria were later plated on Listeria Brilliance Agar to differentiate phospholipase-positive L. monocytogenes. Putative isolates were characterized by sequencing of 16S rRNA, sigB, and iap genes, and by PCR detection of four Listeria Pathogenicity Island 1 (LIPI-1) markers (hlyA, inlA, actA, prfA). Using this combined approach, 27 Listeria isolates were recovered from 21 wastewater samples during a 6-week sampling period: 13 samples of municipal wastewater yielded 5 isolates of L. monocytogenes (38% prevalence rate) and 11 other Listeria sp. (85% overall prevalence rate; 6 L. innocua, and 5 Listeria sp.); 8 samples of dairy wastewater yielded 2 isolates of L. monocytogenes (25% prevalence) and 9 other Listeria sp. (100% overall prevalence rate; 8 L. innocua and 1 Listeria sp.). Seven isolates from municipal and dairy wastewater were confirmed as L. monocytogenes based on concordant sigB and iap gene sequence identities, LIPI-1 gene presence, and phospholipase-positive reactions on chromogenic agar. Six isolates yielded discordant results across the multilocus sequencing markers and could not be confidently assigned to a species and were designated Listeria sp. These findings highlight the value of integrating chromogenic media with multilocus genetic markers for accurate identification of L. monocytogenes in complex environmental matrices. Full article