Department of Animal Husbandry

Cypripedium franchetii, a plant of ornamental and medicinal value, is designated as a Grade II Protected Species in China. The C. franchetii population inhabiting the fragile ecosystem at Galongla Pass exhibits unclear patterns in microbial composition across rhizosphere soil, root, stem, and leaf tissues, as well as metabolite distribution and plant tissue-specific microbe-metabolite relationships. This study focused on C. franchetii, a plant growing in the fragile ecosystem of Galongla Pass in Tibet. We employed an integrated approach combining high-throughput amplicon sequencing (targeting bacterial 16S rRNA genes and fungal ITS regions) and untargeted metabolomics (LC-MS/MS) to systematically analyze the microbial community structure and metabolite distribution in its rhizosphere soil and root, stem, and leaf tissues. We further explored the associations between endophytic microorganisms and metabolites within plant tissues. The results revealed significant differences in microbial composition across plant compartments: distinct variations were observed between rhizosphere soil and plant tissues, while stem and leaf microbial communities exhibited greater similarity. At the phylum level, Pseudomonadota dominated among bacteria, while Basidiomycota and Ascomycota were the predominant fungal phyla. At the genus level, dominant taxa showed tissue specificity: Cronobacter and Lactobacillus were dominant bacterial genera in roots, whereas Acinetobacter dominated in stems, and Acinetobacter and Agrobacterium were prominent in leaves. For fungi, Tulasnella was the dominant genus in rhizosphere soil and roots, while Dioszegia prevailed in stems and leaves. Metabolite analysis indicated significant differences in metabolic profiles among tissues, with stem and leaf metabolite compositions being relatively similar. Correlation analysis further revealed statistically significant correlations between differential microorganisms and differential metabolites in roots and stems, identifying 31 microbial genera significantly correlated with 48 high-abundance metabolites. This study systematically unveils the tissue-specific microecological and metabolic characteristics of C. franchetii during its post-flowering nutrient accumulation phase. Key findings include: microbial community assembly involves cooperative mechanisms between core conserved taxa (e.g., Tulasnella) and habitat-specific taxa; microbial diversity exhibits a gradient decline from the rhizosphere into plant tissues accompanied by functional group succession; and extensive yet specific potential interaction networks (based on statistical covariation) exist between microorganisms and host metabolites, indicating potential microbial involvement in regulating plant secondary metabolism. These findings not only provide guidance for the conservation of C. franchetii (requiring consideration of both core symbiotic and habitat-specific taxa) and constructing synthetic microbial communities during artificial propagation, but also offer a new theoretical basis for the targeted regulation of medicinal active ingredient synthesis through the microbiome.

Abstract This study evaluated the effects of incorporating Leucaena leucocephala and Calliandra calothyrsus into Pakchong grass silage on its physical and chemical quality. The experiment used a completely randomized design with five treatments and replications. The treatments were: P0 (100% Pakchong), P1 (80% Pakchong + 20% L. leucocephala), P2 (60% Pakchong + 40% L. leucocephala), P3 (80% Pakchong + 20% Calliandra), and P4 (60% Pakchong + 40% Calliandra). Forages were wilted to 60–70% dry matter, ensiled in vacuum-sealed bags for 28 days, and evaluated for physical traits (color, odor, texture, mold) and chemical composition (pH, DM, CP, CF, EE, NDF, ADF). Physical quality was analyzed using ANOVA with Duncan’s test, while chemical data were descriptively presented. Results showed significant effects (P < 0.05) on color, texture, mold, and pH, but not odor. Legume inclusion increased DM and CP, reduced CF, NDF, ADF, and enhanced silage appearance and stability.

Against the backdrop of global climate change, altered precipitation patterns profoundly affect grassland ecosystems in arid and semi-arid regions. To clarify the spatiotemporal heterogeneity in the relationship between long-term precipitation variation and vegetation response, this study focuses on East Ujimqin Banner, Inner Mongolia. Utilizing 35 years of precipitation monitoring data (1983-2017) and field vegetation survey data from four key years (1983, 2009, 2013, 2017), nine Sums (townships) were categorized into three regions: northeast, central, and southwest. Methods including linear regression and ANOVA were employed to systematically analyze the long-term trends in precipitation and their relationships with the productivity and dominance of Stipa species, as well as community species richness. The results indicate that: (1) The long-term precipitation trend exhibited spatial heterogeneity, with more pronounced interannual fluctuations and a higher rate of change in the northeastern and southwestern regions compared to the central region; (2) Vegetation sensitivity to precipitation change displayed a pattern of higher responsiveness in the periphery and lower in the central region, as the response slopes of Stipa fresh weight, importance value index, and total community fresh weight per unit precipitation change were significantly greater in the northeastern and southwestern areas than in the central area. (3) Stipa populations showed a tendency to shift from the northeastern and southwestern regions toward the central area. Concurrently, species richness increased in the peripheral regions as Stipa declined, while it temporarily decreased in the central region. This study reveals the specific process of spatial range shifts in dominant grassland species and the reshaping of community structure driven by climate change, elucidating the regional differentiation in ecological sensitivity. It provides an important scientific basis for adaptive grassland management and biodiversity conservation in the region.

Abstract. Burhanuddin ANM, Hastang, Munir AR. 2025. The influence of social media marketing on Generation Z’s buying behavior of livestock products. Asian J Agric 9: 898-905. This study aims to analyze the effect of social media marketing on the purchasing decisions of Generation Z in Makassar City, South Sulawesi, Indonesia. The background of this study lies in the increasing use of social media as a primary source of information, interaction, and product evaluation among Generation Z, particularly in the context of e-commerce for processed broiler-based livestock products. The research was conducted in November 2024 using purposive sampling, targeting respondents aged 18-27 years who actively use social media and have purchased processed livestock products through online platforms. The questionnaire consisted of eight measurement items—four indicators of social media marketing and four indicators of purchase decision—evaluated using a five-point Likert scale. A descriptive and quantitative research approach was employed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method and processed with SmartPLS version 4 software. The measurement model demonstrated good reliability and validity, as reflected by Average Variance Extracted (AVE) values above 0.50, Composite Reliability exceeding 0.70, and a Standardized Root Mean Square Residual (SRMR) value within the acceptable threshold. The findings indicate that social media marketing has a positive and significant effect on purchase decisions, supported by the statistical results: an original sample (path coefficient) of 0.651, a t-statistic of 10.712—which exceeds the critical value of 1.96—and a p-value of 0.000. These results suggest that the more effective and engaging social media marketing activities are—such as informative content, consistent brand presentation, interactive communication, and accessible product information—the higher the likelihood that Generation Z consumers will purchase processed livestock products through e-commerce platforms. The study emphasizes the importance of strengthening digital marketing strategies for livestock-based SMEs and agribusiness actors seeking to compete in online food markets.

Complex interactions in soil carbon and nitrogen (C-N) synchronisation in tropical perennial orchards are highly responsive to fertiliser chemistry. However, the intensity and stage-specific dynamics of these interactions are not well quantified. Six nitrogen regimes, namely, urea (URT), ammonium (AMT), nitrate (NT), slow-release fertiliser (SRT), bio-organic fertiliser (BFT), and an unfertilised control, were assessed at the vegetative, flowering, fruit-set, and maturity stages of durian cultivated on highly weathered tropical soils. A two-way ANOVA indicated high to very high treatment × phenology interactions for almost all soil properties (p < 0.001), indicating that nutrient responses were highly stage-dependent. The highest soil organic carbon (SOC) and cation exchange capacity (CEC) values were consistently obtained with the BFT, which was often associated with significant differences compared with synthetic treatments. In contrast, the SRT showed the most consistent nutrient release behaviour, especially in flowering. On the other hand, soil pH did not differ significantly among the treatments during the vegetative and maturity stages. A significant decrease in pH was observed for the URT and NT treatments during the flowering stage, indicating temporary acidification at this stage and steep increases in nitrate nitrogen (NO3—N), indicating strong nitrification and attenuated carbon (C) stabilisation. Leaf nutrient responses were increased in phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) by 23% in response to the SRT and BFT. The NT and URT tended to enhance leaf nitrogen (N) primarily, and PCA (59–69% variance explained) clearly displayed clustering of the fertiliser effects, with the maximum difference at flowering, the peak period of nutrient demand in the crop. In general, fertiliser chemistry and phenophase jointly controlled the C-N partitioning, soil chemical paths, and nutrient yield correlations. The BFT and SRT showed the greatest significant gains in soil fertility and nutrient retention, making them the best high-performance alternatives in sustainable durian production in tropical systems.

This study aims to analyze the driving factors of millennial farmers' entrepreneurial interest in the innovative processing of cow's milk into Ambon banana yoghurt in Boyolali Regency. The local dairy farming sector faces challenges in optimising the added value of derivative products due to millennial farmers' limited knowledge. Using a descriptive quantitative approach, the study was conducted in Boyolali Regency, Central Java Province, Indonesia, from April to June 2025, with 33 respondents selected through purposive sampling. Data were collected through interviews, observations, and questionnaires, then analyzed using descriptive analysis and multiple linear regression (SPSS). The descriptive analysis showed that millennial farmers' interest was relatively high (83.83%). The regression analysis identified that simultaneously, age, education level, and experience had a very significant effect on entrepreneurial interest (R2=0.713; p<0.01). Partially, education level and experience were the main determinants, while age was not a significant factor. The effectiveness of extension services was considered very effective (81.13%). The study concludes that cognitive factors (education and experience) play a crucial role in fostering interest in innovation-based entrepreneurship among millennial farmers. The study's results recommend a structured extension program to accelerate the downstreaming of agribusiness products. Contribution to Sustainable Development Goals (SDGs):SDG 2: Zero Hunger SDG 4: Quality EducationSDG 8: Decent Work and Economic GrowthSDG 9: Industry, Innovation, and Infrastructure

ABSTRACTBackground and Aim:The global restriction on antibiotic growth promoters (AGPs) in poultry production due to antimicrobial resistance concerns has accelerated the search for effective, sustainable alternatives. Probiotics derived from agricultural by-products offer a promising strategy to enhance gut health and productivity while reducing environmental waste. Coconut-waste, including coconut water and pulp, is rich in fermentable substrates that support the growth of lactic acid bacteria (LAB) and the production of functional metabolites. This study aimed to perform an integrated in vitro characterization of indigenous microorganisms derived from coconut-waste fermentation as potential probiotic and synbiotic candidates for sustainable poultry production.Materials and Methods:Indigenous microorganism solutions (IMOS) were produced through anaerobic fermentation of coconut water and coconut pulp for 5, 10, 15, and 20 days using a completely randomized design with four treatments and five replicates. Physicochemical properties (pH, LAB counts), enzymatic activities (cellulase and mannanase), tolerance to simulated gastrointestinal conditions (acidic pH 2.5, bile salts at 0.3% and 0.5%, and thermal exposure at 42°C), cell surface hydrophobicity, antimicrobial activity against Escherichia coli, Salmonella spp., and Staphylococcus aureus, and short-chain fatty acid (SCFA) production were evaluated using standard microbiological and analytical methods.Results:Fermentation duration significantly influenced all evaluated parameters (p < 0.05). IMOS fermented for 15 days exhibited the lowest pH (3.19 ± 0.02), the highest LAB population (2.05 ± 0.13 × 10¹¹ CFU/mL), optimal cellulase (12.50 ± 0.15 U/mL) and mannanase activities (20.48 ± 0.13 U/mL), and the greatest cell surface hydrophobicity (95.09 ± 0.35%). LAB survival remained high under simulated gastrointestinal stress, reaching 80.23 ± 4.12% at pH 2.5 (6 h), 71.45 ± 0.56% in 0.5% bile salts, and 8.09 ± 0.35 × 10¹¹ CFU/mL at 42°C. Antimicrobial assays demonstrated complete inhibition of E. coli after 24 h at 15 days of fermentation. Acetate (3.34–3.43 g/L) and butyrate (0.66–0.71 g/L) were the dominant SCFAs detected.Conclusion:Coconut waste–derived IMOS demonstrates strong in vitro probiotic and synbiotic characteristics and represents a low-cost, environmentally sustainable functional feed additive for poultry. Fermentation for 15 days yielded optimal functional properties. Further in vivo validation is warranted to confirm efficacy under practical production conditions.

ABSTRACTBackground and Aim:Malignant catarrhal fever (MCF) is a highly fatal disease caused by ovine herpesvirus-2 (OvHV-2), with sheep acting as asymptomatic reservoir hosts. In Indonesia, MCF remains underdiagnosed due to its predominantly subclinical presentation, particularly in regions where mixed ruminant farming is practiced. Riau Province has a large population of Bali cattle, and sheep production is increasing, raising concerns regarding OvHV-2 spillover. This study aimed to determine the molecular prevalence of OvHV-2 infection in cattle and identify individual- and herd-level risk factors associated with the occurrence of MCF in Riau Province, Indonesia.Materials and Method:A cross-sectional study was conducted between August and December 2024 involving 274 beef cattle from 76 farms across five districts of Riau Province. Buffy coat DNA extracted from ethylenediaminetetraacetic acid-anticoagulated blood samples was examined for OvHV-2 using nested polymerase chain reaction (PCR). Epidemiological data were collected through structured questionnaires administered to farmers. The associations between potential risk factors and OvHV-2 positivity were evaluated using chi-square tests and multivariate logistic regression analysis.Result:OvHV-2 DNA was detected in 33 of 274 cattle, yielding an individual-level prevalence of 12.0% (95%CI: 8.0–16.0%). At the farm-level, 21 of 76 farms (27.6%; 95% confidence interval [CI]: 17.0%–38.0%) had at least one PCR-positive animal. Most infected cattle (n = 32/33) were clinically asymptomatic, emphasizing the silent nature of infection. The multivariable analysis identified cattle aged 2–5 years as having a significantly lower risk of infection (adjusted odds ratio [aOR] = 0.239; p = 0.004). In contrast, farms that practiced mixed rearing of cattle and sheep showed a markedly increased risk of MCF (aOR = 8.218; p = 0.015), as did farms located within 100 m of sheep holdings (aOR = 4.083; p = 0.027).Conclusio:OvHV-2 infection is endemic but largely subclinical in cattle in Riau Province. Close proximity to sheep and co-rearing practices are the primary drivers of MCF risk. These findings underscore the importance of molecular surveillance and the implementation of biosecurity measures, particularly spatial separation between cattle and sheep, to prevent OvHV-2 transmission and reduce economic losses in susceptible cattle populations.

Soil properties are vital for soil health, fertility, and crop productivity, yet traditional methods like the Partial Least Squares Regression (PLSR) predict individual properties without capturing their interactions or impacts on crop growth. This study presents the Soil Knowledge-Guided Multi-task Transformer (KGMT) model, which integrates deep learning and multi-task learning to simultaneously predict multiple soil properties and crop traits using UAV hyperspectral imagery, eliminating complex manual feature extraction. By embedding soil knowledge, KGMT improves prediction accuracy and generalization over PLSR and baseline multi-task models. Data from two agricultural plots in Bayannur City, Inner Mongolia, China, demonstrated KGMT's superior performance in predicting six soil properties (alkalinity, salinity, organic matter, potassium, sodium, calcium) and two crop traits (leaf area index, aboveground biomass), achieving up to 20% lower normalized root mean square error. For instance, KGMT yielded higher R 2 values for alkalinity (0.57) and organic matter (0.57) compared to PLSR (0.46 and 0.40, respectively). The model also enhanced cross-region generalization. Additionally, a soil early warning framework was developed, linking pre-planting soil conditions with crop traits to detect potential growth limitations early, enabling proactive management. This approach integrates spectral data with domain knowledge, offering a robust solution for soil and crop management. It contributes to precision agriculture by optimizing soil conditions and promoting sustainable land use practices, highlighting the value of advanced deep learning in agricultural applications. • Innovative Model for Multi-task Prediction : The Soil Knowledge-Guided Multi-task Transformer (KGMT) combines deep learning and domain knowledge to simultaneously predict multiple soil properties and crop growth traits directly from UAV hyperspectral imagery, eliminating the need for manual feature extraction. • Enhanced Prediction Accuracy and Generalization : KGMT outperformed traditional PLSR and baseline multi-task models in predicting six soil properties (e.g., alkalinity, salinity, organic matter, potassium, sodium, and calcium) and crop traits such as leaf area index (R 2 = 0.75) and aboveground biomass (R 2 = 0.82), achieving up to a 20% reduction in normalized root mean square error (nRMSE). • Proactive Soil and Crop Management : A novel soil early warning approach integrates soil properties from the bare soil stage with crop traits during growth, enabling early detection of potential limitations to crop growth and supporting precision agriculture for sustainable land use.