Blood biochemical, mineral, and electrolyte responses of cattle to indigenous forage-based and locally formulated concentrate rations in Southern Ethiopia

Blood biochemical, mineral, and electrolyte responses of cattle to indigenous forage-based and locally formulated concentrate rations in Southern Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Blood biochemical, mineral, and electrolyte responses of cattle to indigenous forage-based and locally formulated concentrate rations in Southern Ethiopia Tesfaye Edjem¹²* Tkapel, Yisehak Kechero²* Kebede, Asrat Guja Amejo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8851272/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study evaluated the effects of indigenous forage-based diets supplemented with locally formulated concentrate rations on serum biochemical, enzymatic, mineral, and electrolyte profiles of cattle managed under pastoral and mixed crop-livestock systems in Southern Ethiopia. The experiment was conducted in Nyangatom (pastoral) and Salamago (mixed farming) districts using thirty clinically healthy indigenous cattle assigned to five dietary treatments: free grazing only (control) and four increasing levels of supplementation in a 2 × 5 factorial arrangement. Blood samples were collected before and after a 21-day feeding period and analyzed for key serum metabolites, liver enzymes, minerals, and electrolytes using standard laboratory procedures. Dietary supplementation significantly improved serum total protein, globulin, glucose, cholesterol, and urea concentrations, with higher responses observed at moderate to high supplementation levels (P < 0.05). Activities of hepatic enzymes (AST, ALT, and ALP) increased with dietary treatment, indicating enhanced metabolic activity without pathological deviation. Serum calcium, sodium, and potassium concentrations increased significantly with supplementation, while magnesium and phosphorus were primarily influenced by location. Treatment × location interactions were generally non-significant, suggesting consistent physiological responses across production systems. Correlation analyses revealed strong positive associations among protein, energy, lipid, and liver metabolism indicators, highlighting the integrated nature of metabolic adaptation to improved nutrition. Overall, the findings demonstrate that indigenous forage-based diets complemented with locally available concentrate resources effectively enhance metabolic and mineral status of cattle. This approach offers a sustainable, context-specific feeding strategy to improve cattle health and productivity in pastoral and agro-pastoral systems of Southern Ethiopia. Indigenous forages Blood biochemical parameters Mineral and electrolyte profiles Cattle nutrition Pastoral and agro-pastoral systems 1. Introduction Livestock production plays a central role in the livelihoods, food security, and socio-cultural systems of rural communities in Ethiopia, particularly in pastoral and agro-pastoral areas (FAO, 2023 ). Cattle contribute substantially to household income, draft power, milk production, and risk buffering, yet overall productivity remains low due to chronic feed shortages, poor feed quality, and seasonal variability in forage availability (Yisehak et al., 2020 ; Gizaw et al., 2024 ). In arid and semi-arid regions such as the Lower Omo Valley, livestock production depends largely on natural rangelands dominated by indigenous grasses, shrubs, and browse species. These feed resources are often inadequate in crude protein and metabolizable energy during the dry season, leading to negative energy balance and impaired physiological performance (Alemayehu et al., 2021 ). Previous studies have demonstrated that strategic supplementation using protein- and energy-rich feeds can improve animal performance and health, but the use of locally available indigenous forages and concentrate resources remains insufficiently documented under pastoral conditions (Bekele et al., 2022 ). Blood biochemical and mineral parameters are widely recognized as sensitive indicators of nutritional status, metabolic efficiency, and health of livestock (Deribe et al., 2020 ). Changes in serum metabolites such as glucose, total protein, urea, cholesterol, and liver enzymes reflect dietary adequacy, rumen fermentation efficiency, and hepatic function (Mekuriaw et al., 2021 ). Likewise, serum mineral and electrolyte profiles provide insight into mineral balance, homeostasis, and adaptation to environmental stressors (Zewdie et al., 2022 ). Nyangatom and Salamago districts represent contrasting livestock production systems within South Omo Zone, where pastoral and mixed crop–livestock practices coexist under varying agro-ecological conditions. Despite the abundance of indigenous forage species and local concentrate ingredients, limited empirical evidence exists on their combined effects on cattle metabolic responses across these systems. Therefore, this study aimed to evaluate the effects of indigenous forage-based and locally formulated concentrate rations on serum biochemical, enzymatic, mineral, and electrolyte profiles of cattle in pastoral and mixed farming systems of Southern Ethiopia, and to compare responses across agro-ecological contexts. 2. Materials and Methods 2.1 The Study Area The study was conducted in two distinct districts of the South Omo Zone, Southern Ethiopia: Nyangatom (pastoral system) and Salamago (mixed crop–livestock system). Nyangatom District is located in the arid to semi-arid lowland plains of the Lower Omo Valley, with an altitude ranging from approximately 350 to 700 m above sea level. The climate is characterized by high mean annual temperatures (often exceeding 30°C, reaching up to 38°C) and low, erratic bimodal rainfall averaging 350–600 mm annually. Frequent droughts and prolonged dry periods shape a predominantly transhumant pastoral livelihood system (CSA, 2021). The sparse population relies on livestock (cattle, goats, and sheep), with seasonal mobility dictated by pasture and water availability. Feed resources are primarily from natural rangelands of indigenous grasses, browse species, and shrubs. Salamago District occupies lowland to mid-altitude zone (approximately 800–1,800 m above sea level) with a milder climate. Mean annual temperatures range from 20–28°C, and annual rainfall is higher (700–1,200 mm) with a unimodal to weak bimodal pattern. These conditions support a mixed crop–livestock farming system (Yisehak et al., 2020 ). The district has a higher population density, with livelihoods combining crop cultivation (e.g., maize, sorghum) and livestock rearing. Feed resources include natural pasture, crop residues, and limited local concentrates, with indigenous forage remaining crucial during dry seasons. 2.2. Experimental Animals and Design Thirty clinically healthy indigenous cattle (15 from each district) of similar age, sex, breed, and initial body weight were selected, following matching procedures recommended for tropical nutrition studies (Deribe et al., 2020 ). Animals were individually tagged and randomly assigned to one of five dietary treatments in a completely randomized design with a 2 × 5 factorial arrangement (location × diet), with six animals per treatment group. 2.3. Dietary Treatments and Formulation Five dietary treatments were evaluated: T1 Free grazing only (control) T2 Low level of supplementary ration (e.g., 0.5% of body weight) T3 Moderate level of supplementary ration (e.g., 1.0% of body weight) T4 High level of supplementary ration (e.g., 1.5% of body weight) T5 Very high level of supplementary ration (e.g., 2.0% of body weight) Note The exact inclusion levels (e.g., % body weight, dry matter intake, or specific nutrient density) were specified based on the experimental design. 2.4. Feeding Protocol Animals were individually fed for 28 days. The supplementary ration was offered once daily in the morning, followed by free grazing. Clean drinking water was available ad libitum. Daily feed intake was recorded throughout the experimental period. 2.5. Blood Sample Collection and Processing Blood samples were collected from the jugular vein before the trial (day 0) and after 21 days of feeding, following standard veterinary procedures (Deribe et al., 2020 ). Samples were drawn into vacutainer tubes containing acid citrate dextrose as anticoagulant, at a consistent morning hour to minimize diurnal variation (Zewdie et al., 2022 ). Serum was separated by centrifugation at 4,000 rpm for 5 min and stored at − 20°C until analysis. 2.6. Biochemical and Mineral Analysis Serum biochemical parameters were analyzed using standard spectrophotometric methods described previously (Mekuriaw et al., 2021 ): Metabolites : total protein, albumin, globulin, glucose, creatinine, urea, total bilirubin, triglycerides, cholesterol, as well as Enzymes : aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP). Minerals (calcium, magnesium, phosphorus) and electrolytes (sodium, potassium, chloride) were measured using atomic absorption spectrometry and ion-selective electrodes, respectively. Analyses were performed at Sodo Animal Health Laboratory. 2.7. Statistical Analysis Data were checked for normality and homogeneity of variance. A two-way analysis of variance (ANOVA) was used to evaluate the effects of location, dietary treatment, and their interaction, consistent with analytical approaches in similar nutrition studies. Mean separation was performed using Tukey’s HSD test at P < 0.05. Pearson’s correlation coefficients were calculated to evaluate relationships among serum parameters. All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). 3. Results 3.1. Serum biochemical metabolites and enzyme activities The results of Table 1 below indicated that Dietary treatment levels significantly influenced several serum biochemical parameters of cattle in both Nyangatom (NY) and Salamago (SL) districts. Total protein (TP) concentrations increased progressively with increasing dietary treatment levels, with the highest values recorded in T4 and T5, particularly in Nyangatom cattle (P < 0.01). Albumin (Alb) concentrations were significantly affected by location (P < 0.05), with generally higher values observed in Salamago compared with Nyangatom, while treatment effects were not significant. Globulin (Glb) levels showed a significant response to dietary treatment (P < 0.01), with higher concentrations observed at intermediate to higher inclusion levels (T3–T5) in both locations. Serum glucose concentrations were significantly affected by treatment (P < 0.01), with higher values recorded in T3 -T5 compared with T1 and T2, particularly in Salamago cattle. Creatinine concentrations increased with dietary treatment level in Nyangatom cattle (P < 0.01), whereas no consistent trend was observed in Salamago. Activities of hepatic enzymes, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP), were significantly influenced by dietary treatments (P < 0.05), with higher activities generally observed at higher treatment levels. Cholesterol concentrations increased progressively with dietary treatment level in both districts (P < 0.05). Serum urea and total bilirubin concentrations showed moderate increases with increasing dietary treatment levels; however, treatment × location interactions were not significant for most biochemical parameters, indicating similar response patterns across districts. Table 1 Effects of dietary treatment inclusion levels on serum metabolites and enzymatic profiles of cattle in Nyangatom and Salamago districts Parameters L Treatment, Mean P value T1 T2 T3 T4 T5 SEM L T L×T TP NY 5.79 c 6.4 b2 6.7 ab2 6.97 a2 6.98 a 0.9 0.011 0.004 0.774 SL 6.38 b 7.17 a1 7.32 a1 7.30 a1 7.07 ab Alb NY 3.18 3.15 3.2 3.2 3.58 0.58 0.01 0.397 0.563 SL 3.7 3.72 3.72 3.78 3.37 Glb NY 2.78 b 3.25 ab 3.12 ab 3.77 a 3.37 ab 0.07 0.507 0.004 0.563 SL 2.68 b 3.45 a 3.60 a 3.52 a 3.52 a Glucose, mg/dl NY 56.83 ab 47.67 b 65.83 a 70 a 66.67 a 1.68 0.26 0.001 0.990 SL 65.00 ab 56.83 b 75.83 a 76.33 a 71.67 a Creatin, g/dl NY 0.83 2 0.84 2 0.97 2 1.03 2 1.09 0.04 0.000 0.403 0.085 SL 1.49 1 1.09 1 1.23 1 1.34 1 1.07 AST, g/dl NY 36.83 b2 53.33 a2 50.67 a2 61 a2 56.5 a2 1.72 0.001 0.021 0.376 SL 71.67 ab1 76.00 ab1 64.33 b1 82.17 a1 74 ab1 Cholesterol NY 51.67 c2 57.83 bc2 68.67 bc2 85.33 ab 91 a2 3.08 0.002 0.014 0.350 SL 73.17 b1 97.83 a1 93 ab1 85.33 ab 104.67 a1 Urea, mg/dl NY 27 b 31.5 ab 33.83 ab 37 a 37 a 0.94 .417 0.40 0.913 SL 29.83 35.17 36.17 37.17 35.67 Total Bilirubin, g/dl NY 1.55 b 2.11 ab 4.14 a 2.53 ab 2.64 ab 0.18 0.336 0.035 0.568 SL 2.79 2.35 3.48 3.01 3.05 Triglycerides NY 26.18 35.94 38.41 39.98 35.49 1.35 0.598 0.096 0.547 SL 31.22 44.31 37.41 34.81 35.41 ALT NY 21.77 b 33.87 ab 40.6 ab 49.03 a 45.47 a 1.67 0.025 0.001 0.385 SL 23.27 22.20 39.63 32.27 34.90 ALP NY 54.17 b2 85.67 ab 118.96 a2 108.7 ab2 134.83 a2 4.09 0.01 0.001 0.351 SL 90.33 b1 75.17 b 153.8 a1 126.3 ab1 165.83 a1 TP: Total Protein; Alb: Albumin; Glb: Globulin; Creatin: Creatinine; AST: Aspartate Aminotransferase; ALT: Alanine Aminotransferase; ALP: Alkaline Phosphatase; SEM: Standard Error of the Mean; L: Location (NY: Nyangatom, SL: Salamago); T: Treatment; L×T: Interaction between Location and Treatment; P values correspond to the main effects of Location (L) and Treatment (T), and their interaction (L×T). 3.2. Serum mineral and electrolyte profiles The research findings in Table 2 below shown that dietary treatments significantly influenced serum mineral and electrolyte concentrations of cattle in both Nyangatom and Salamago districts. Serum calcium concentrations increased progressively with increasing dietary treatment levels (P < 0.01), with the highest values observed in cattle receiving T4 and T5 diets in both locations. This pattern indicates a consistent response of calcium status to dietary treatment, regardless of production system. Magnesium concentrations, however, were primarily influenced by location (P < 0.01), with higher mean values recorded in Nyangatom cattle compared with those from Salamago. Serum phosphorus concentrations differed significantly between locations (P < 0.01) but were not affected by dietary treatment level. Sodium and potassium concentrations responded significantly to dietary treatments (P < 0.01), with potassium showing a marked increase at higher treatment levels, particularly in Salamago cattle. Chloride concentrations were also significantly affected by dietary treatment (P < 0.01) and exhibited a declining trend as dietary inclusion levels increased. Treatment × location interactions were not significant for most mineral and electrolyte parameters, suggesting comparable response patterns across districts. Table 2 Effects of dietary treatments on serum mineral and electrolyte profiles of cattle in Nyangatom and Salamago districts Parameters L Treatment, Mean P value T1 T2 T3 T4 T5 SEM L T L×T Calcium NY 7.04 7.35 8.03 9.50 8.18 0.16 0.01 0.000 0.815 SL 6.29 b 5.77 b 7.52 ab 8.57 a 7.68 ab Magnesium NY 3.54 3.62 3.48 4.08 3.38 0.09 0.006 0.076 0.860 SL 2.88 3.43 3.03 3.50 2.58 Phosphorus NY 4.68 4.90 4.78 4.88 4.43 0.079 0.003 0.389 0.832 SL 3.96 4.28 4.57 4.30 4.12 Sodium NY 132.8 149.5 144.3 136.2 135.00 0.86 0.107 0.001 0.793 SL 128.0 146.5 a 139.7 ab 133.2 136.33 Potassium NY 3.52 5.55 5.63 6.83 4.91 0.14 0.563 0.001 0.444 SL 3.39 b 5.27 a 5.02 ab 6.17 a 5.78 a Chloride NY 104.3 98.8 109.8 101.5 87.83 1.73 0.288 0.002 0.953 SL 104.5 ab 96.3 ab 106.7 a 93.00 ab 83.17 b L: Location (NY: Nyangatom, SL: Salamago); T: Treatment; L×T: Interaction between Location and Treatment; SEM: Standard Error of the Mean. P values correspond to the main effects of Location (L) and Treatment (T), and their interaction (L×T). 3.3. Correlation among serum biochemical parameters Pearson’s correlation analysis of serum biochemical parameters in Salamago cattle is presented in Table 3 . Total protein (TP) showed significant positive correlations with globulin (r = 0.845, P < 0.05), glucose (r = 0.576, P < 0.01), cholesterol (r = 0.542, P < 0.01), urea (r = 0.738, P < 0.01), and total bilirubin (r = 0.572, P < 0.01). TP also exhibited weaker, non-significant correlations with creatinine, AST, triglycerides, ALT, and ALP. Albumin had limited associations, showing a significant positive correlation only with total bilirubin (r = 0.421, P < 0.05), whereas correlations with other metabolites and enzymes were generally weak or non-significant. Globulin was positively correlated with glucose (r = 0.616, P < 0.01), cholesterol (r = 0.588, P < 0.01), urea (r = 0.674, P < 0.01), and total bilirubin (r = 0.421, P < 0.05). Glucose was positively correlated with urea (r = 0.494, P < 0.01), total bilirubin (r = 0.492, P < 0.01), ALT (r = 0.534, P < 0.01), and cholesterol (r = 0.382, P < 0.05). Creatinine showed a significant correlation with total bilirubin (r = 0.401, P < 0.05) only, while AST was positively correlated with cholesterol (r = 0.461, P < 0.05). Cholesterol was significantly associated with urea (r = 0.493, P < 0.01) and ALT (r = 0.392, P < 0.05). Urea was positively correlated with total bilirubin (r = 0.431, P < 0.05). Other parameters, including triglycerides, ALT, and ALP, showed limited and non-significant associations. Table 3 Pearson's correlation coefficient among serum biochemical parameters of Salamago cattle Alb Glb Glu Creatin AST Chol. Urea Total B. Trigly. ALT ALP TP 0.456* 0.845* 0.576** 0.74 0.277 0.542** 0.738** 0.572** 0.294 0.274 0.224 Alb -0.43 .105 0.130 0.12 0.044 0.269 0.421* 0.046 -0.029 -0.254 Glb 0.616** 0.036 0.358 0.588** 0.674** 0.421* 0.334 0.243 0.39 Glu 0.384* 0.339 0.382* 0.494** 0.492** 0.218 0.534** 0.392* Creatin -0.049 -0.145 0.092 0.401* -0.144 0.1 0,012 AST 0.461* 0.313 -0.076 0.089 0.096 -0.119 Chol. 0.493** 0.311 0.392* 0.309 0.218 Urea 0.431* 0.223 0.296 0.242 Total B. 0.273 0.235 0.247 Trigly. 0.205 -0.084 ALT 0.182 3.4 Correlation among serum biochemical parameters in Nyangatom cattle Pearson’s correlation coefficients among serum biochemical parameters of Nyangatom cattle are presented in Table 4 below. Total protein (TP) showed significant positive correlations with most serum metabolites and enzymes, including glucose (r = 0.82, p < 0.01), AST (r = 0.80, p < 0.01), cholesterol (r = 0.85, p < 0.01), urea (r = 0.85, p < 0.01), and total bilirubin (r = 0.63, p < 0.01). TP was also moderately correlated with albumin, globulin, triglycerides, ALT, and ALP (p < 0.05–0.01). Albumin was significantly correlated with glucose (r = 0.61, p < 0.01), AST (r = 0.41, p < 0.05), cholesterol (r = 0.44, p < 0.05), urea (r = 0.54, p < 0.01), and total bilirubin (r = 0.59, p < 0.01), whereas its correlations with creatinine, triglycerides, ALT, and ALP were not significant. Globulin showed significant positive correlations with glucose, creatinine, AST, cholesterol, urea, ALT, and ALP (p < 0.05–0.01). Glucose was strongly correlated with cholesterol (r = 0.85, p < 0.01), urea (r = 0.71, p < 0.01), total bilirubin (r = 0.64, p < 0.01), creatinine (r = 0.59, p < 0.01), and AST (r = 0.54, p < 0.01), and moderately correlated with ALT and ALP (p < 0.05–0.01). Creatinine showed significant associations with AST, cholesterol, urea, total bilirubin, and ALT (p < 0.05–0.01), but not with triglycerides or ALP. AST was positively correlated with cholesterol (r = 0.65, p < 0.01), urea (r = 0.83, p < 0.01), total bilirubin (r = 0.53, p < 0.01), triglycerides (r = 0.63, p < 0.05), and ALT (r = 0.49, p < 0.01). Cholesterol exhibited significant correlations with urea, total bilirubin, triglycerides, ALT, and ALP (p < 0.05–0.01). Urea was moderately correlated with total bilirubin, triglycerides, ALT, and ALP (p < 0.05–0.01). Total bilirubin showed significant correlations with triglycerides and ALT (p < 0.05–0.01), while triglycerides were not significantly correlated with ALT or ALP. A strong positive correlation was observed between ALT and ALP (r = 0.65, p < 0.01). Table 4 Pearson's correlation coefficient among serum biochemical parameters of Nyangatom cattle Alb Glb Glu Creatin AST Chol. Urea Total B. Trigly. ALT ALP TP 0.62** 0.67* 0.82** 0.6** 0.80** 0.85** 0.85** 0.63** 0.66** 0.60** 0.47** Alb 0.04 0.61** 0.21 0.41* 0.44* 0.54** 0.59** 0.28 0.31 0.21 Glb 0.48** 0.38* 0.58** 0.62** 0.48** 0.23 0.49* 0.39* 0.29 Glu 0.59** 0.54** 0.85** 0.71** 0.64** 0.49* 0.59** 0.45* Creatin 0.51** 0.53** 0.66** 0.39* 0.54* 0.34 0.26 AST 0.65** 0.83** 0.53** 0.63* 0.49** 0.34 Chol. 0.72** 0.51* 0.58** 0.59** 0.59** Urea 0.51* 0.48** 0.49** 0.47** Total B. 0.55** 0.42* 0.32 Trigly. 0.27 0.14 ALT 0.65** 3.5. Correlation among serum mineral and electrolyte profiles of Salamago cattle The results in Table 5 described below indicated that Pearson’s correlation analysis among serum mineral and electrolyte parameters of Salamago cattle revealed mostly weak to moderate associations. Potassium showed a significant positive correlation with sodium (r = 0.485, P < 0.01), indicating coordinated variation between these two electrolytes. Magnesium was positively correlated with sodium (r = 0.399, P < 0.05) and chloride (r = 0.459, P < 0.05). Calcium showed weak, non-significant correlations with all measured electrolytes and minerals, including magnesium, phosphorus, sodium, potassium, and chloride. Phosphorus was weakly correlated with sodium and potassium and showed a negative, non-significant association with chloride. Potassium exhibited a negative but non-significant correlation with chloride. Table 5 Pearson's correlation coefficient among mineral and electrolyte profiles of Salamago cattle Magnesium Phosphorus Sodium Potassium Chloride Calcium 0.082 0.284 -0.224 0.246 -0.265 Magnesium 0.293 0.399* 0.279 0.459* Phosphorus 0.185 0.273 -0.198 Sodium 0.485** 0.172 Potassium -0.340 3.6. Correlation among serum mineral and electrolyte profiles of Nyangatom cattle In Nyangatom cattle, Pearson’s correlation analysis demonstrated several moderate associations among serum mineral and electrolyte parameters shown in Table 6 below. Calcium was positively correlated with magnesium (r = 0.499, P < 0.01), while its associations with phosphorus, sodium, potassium, and chloride were weak and non-significant. Magnesium showed a significant positive correlation with potassium (r = 0.415, P < 0.05). Sodium exhibited a moderate positive correlation with potassium (r = 0.392, P < 0.05) and a weak positive association with chloride. Phosphorus showed weak, non-significant correlations with sodium, potassium, and chloride. Potassium was weakly and non-significantly correlated with chloride. Table 6 Pearson's correlation coefficient among mineral and electrolyte profiles of Nyangatom cattle Magnesium Phosphorus Sodium Potassium chloride Calcium 0.499** 0.115 -0.070 0.328 -0.1 Magnesium 0.257 -0.061 0.415* 0.114 Phosphorus 0.203 0.264 -0.080 Sodium 0.392* 0.329 Potassium 0.093 4. Discussion 4.1: Effects of dietary treatments on serum metabolites and enzymatic profiles The results indicate significant effects of dietary treatment levels and location on serum biochemical parameters in cattle from Nyangatom (NY) and Salamago (SL) districts. Total protein (TP) showed higher levels in SL across all treatments, which may reflect better nutritional status or metabolic adaptation in this region, consistent with findings that regional forage quality directly influences protein synthesis in grazing ruminants (Kaneko et al., 2008 ; Alemayehu et al., 2021 ). The increase in TP with treatment level suggests improved protein metabolism with dietary supplementation, aligning with recent studies on strategic feed supplementation in tropical cattle systems (Dereje et al., 2023 ). Glucose levels were generally higher in SL and increased with treatment level, particularly in T4 and T5, indicating enhanced energy availability, possibly due to improved gluconeogenesis or carbohydrate intake (Humann-Ziehank et al., 2022 ). AST and ALT, markers of hepatic function, were elevated in SL, which may be attributed to higher metabolic demand, dietary composition, or even subclinical responses to environmental stressors common in certain agro-ecological zones (Mekonnen et al., 2021 ). ALT showed a significant treatment effect in NY, suggesting that dietary interventions may influence liver enzyme activity more prominently in certain regions, highlighting the importance of location-specific nutritional management (Gelaye et al., 2023 ). Cholesterol levels increased with treatment in both districts, reflecting improved lipid metabolism and energy status, though values were consistently higher in SL, a pattern also observed in cattle with access to diverse browse species (Tekle et al., 2022 ). Urea levels remained stable across treatments, indicating consistent nitrogen metabolism and efficient urea recycling, which is often maintained under varying protein intakes in adapted indigenous breeds (Asmare, 2020 ). ALP, associated with bone and liver metabolism, showed marked increases with treatment, especially in SL, which may indicate enhanced metabolic or growth activity, as ALP is a recognized biomarker for bone turnover and hepatic function in growing cattle (Payne & Payne, 1987 ; Worku et al., 2024 ). Overall, the dietary treatments positively influenced several metabolic parameters, with location-specific responses underscoring the critical interaction between nutrition, environment, and genotype in cattle physiology. 4.2. Effects of dietary treatments on serum mineral and electrolyte profiles Dietary treatments significantly affected serum mineral and electrolyte profiles, with notable differences between districts. Calcium levels were higher in NY, particularly in T4, which may reflect better calcium absorption or supplementation efficacy, potentially linked to differences in soil pH, forage calcium content, or vitamin D status, as highlighted in recent studies on mineral bioavailability in tropical forages (McDowell, 2003 ; Solomon et al., 2022 ). Magnesium and phosphorus levels were generally stable, though phosphorus was lower in SL, possibly due to inherent soil mineral deficiencies or antagonistic interactions with other minerals in the local forages, a common challenge in extensive grazing systems (Mousa & Alhidary, 2023 ). Sodium and potassium showed significant treatment effects, with T2 and T3 resulting in higher sodium levels in both districts. This aligns with findings that supplemental mineral mixes can effectively correct transient electrolyte imbalances in cattle (Shenkoru et al., 2021 ). Potassium increased with treatment in NY, especially in T4, which may support better electrolyte balance, muscle function, and rumen buffering capacity (Khalid et al., 2022 ). Chloride levels decreased with higher treatment levels in both districts, which could indicate altered acid-base balance or renal adaptation to maintain homeostasis, a physiological response documented in cattle under dietary electrolyte manipulation (Bayissa et al., 2023 ). These findings suggest that while dietary supplementation can enhance mineral status, regional differences in soil geochemistry, water quality, and forage composition remain dominant factors that must be integrated into nutritional planning for sustainable cattle production (Desta & Beyene, 2024 ). 4.3. Pearson’s correlation among serum biochemical parameters in Salamago cattle The correlation analysis for Salamago cattle revealed a network of significant relationships among serum biochemical parameters, reflecting interconnected metabolic and physiological pathways. Total protein (TP) exhibited strong positive correlations with globulin (Glb) and moderate associations with glucose, cholesterol, urea, and total bilirubin. This pattern suggests that protein metabolism is closely linked to energy homeostasis, lipid metabolism, and nitrogen excretion in this population, a finding consistent with systems biology approaches that reveal co-regulation of these pathways under nutritional modulation (Gebreyohannes et al., 2022 ). Glucose demonstrated significant positive correlations with creatinine, cholesterol, urea, total bilirubin, ALT, and ALP. These associations indicate that glucose metabolism is not only central to energy provision but also interrelated with renal function (creatinine), lipid regulation, and hepatic activity, supporting the concept of a tightly regulated "metabolic engine" in ruminants (Huang et al., 2021 ). The strong correlation between glucose and ALT (r = 0.534) may reflect enhanced gluconeogenic activity or liver involvement in glucose regulation, as ALT is increasingly recognized for its role beyond mere liver damage, including in gluconeogenesis (Tadesse & Reta, 2023 ). AST showed a notable positive correlation with cholesterol (r = 0.461), supporting the established role of liver enzymes in lipid metabolism and export (Kraft & Dürr, 2005 ). Cholesterol itself was positively associated with urea and triglycerides, suggesting coordinated lipid and protein metabolic pathways, possibly mediated by insulin and other regulatory hormones (Mekuriaw et al., 2024 ). Urea was further correlated with total bilirubin, which may point to shared regulatory mechanisms in nitrogen metabolism and liver function, particularly in the context of the urea cycle and heme catabolism (Yokus & Cakir, 2006 ). Overall, the correlation matrix underscores a tightly coupled metabolic system in Salamago cattle, where dietary or physiological changes affecting one parameter may have cascading effects on others, emphasizing the need for a holistic view in nutritional interventions. 4.4. Pearson’s correlation among serum biochemical parameters in Nyangatom cattle In Nyangatom cattle, the correlation analysis revealed a highly integrated metabolic network, with particularly strong associations among protein, energy, and lipid metabolism indicators. Total protein (TP) was significantly correlated with nearly all parameters, especially glucose, cholesterol, urea, and liver enzymes, highlighting its central role as a marker of overall metabolic and nutritional status, a pattern often seen in well-nourished and metabolically active herds (Ayele et al., 2023 ). Glucose showed robust positive correlations with cholesterol, urea, total bilirubin, and liver enzymes (ALT and ALP), reinforcing its pivotal role as the primary energy currency and its interaction with hepatic and renal functions. The exceptionally strong link between glucose and cholesterol (r = 0.85) suggests a coordinated regulation of energy and lipid metabolism in this group, possibly driven by insulin sensitivity and lipogenic pathways that are highly active in certain cattle ecotypes (Getachew et al., 2022 ). Cholesterol was positively associated with urea, total bilirubin, triglycerides, ALT, and ALP. These relationships point to a close interplay between lipid metabolism, protein catabolism, and liver function, where the liver serves as the central hub orchestrating the synthesis, degradation, and interconversion of these metabolites (Piccione et al., 2012 ). The correlation between urea and total bilirubin further supports the interconnectedness of nitrogen metabolism and hepatobiliary function, reflecting shared metabolic fates of amino acids and heme (Sartin et al., 1985 ). Additionally, the strong positive correlation between ALT and ALP (r = 0.65) indicates parallel activity of these liver enzymes, possibly reflecting adaptive or responsive hepatic mechanisms under dietary influence, and may be indicative of hepatocellular activity related to both protein turnover and bone metabolism (Woldehanna et al., 2024 ). These findings illustrate a coherent and responsive metabolic network in Nyangatom cattle, suggesting that dietary interventions can simultaneously influence multiple, interconnected physiological pathways. 4.5. Pearson’s correlation among mineral and electrolyte profiles of Salamago cattle The correlation analysis of mineral and electrolyte profiles in Salamago cattle indicates distinct physiological interactions, with a moderate number of significant associations. The strong positive correlation between sodium and potassium (r = 0.485, p < 0.01) highlights their well-documented synergistic role in maintaining osmotic balance, neuromuscular function, and acid-base homeostasis (Goff, 2018 ). Recent studies in grazing cattle have reinforced that sodium and potassium levels are closely co-regulated, especially under conditions of varied forage mineral content (Neves et al., 2021 ). Notably, magnesium exhibited positive correlations with sodium (r = 0.399) and chloride (r = 0.459), which may reflect its involvement in electrolyte transport and cellular ion balance. Emerging research suggests that magnesium status can influence sodium retention and chloride distribution, particularly in ruminants fed region-specific diets (Menta et al., 2022 ). The absence of a strong calcium-phosphorus correlation, contrary to classical expectations, may indicate independent regulatory mechanisms or environmental influences such as soil mineral composition and forage type (Pegorer et al., 2023 ). Overall, these correlations suggest that mineral homeostasis in Salamago cattle is influenced by both physiological interactions and local nutritional factors. 4.6. Pearson’s Correlation among Mineral and Electrolyte Profiles of Nyangatom Cattle The inter-mineral relationships in Nyangatom cattle reveal a more integrated network than observed in Salamago, with several significant correlations underscoring key metabolic linkages. The strong positive correlation between calcium and magnesium (r = 0.499, p < 0.01) suggests co-regulation in absorption or excretion, possibly mediated by shared transport mechanisms or dietary interactions. Recent studies indicate that calcium and magnesium homeostasis can be closely linked in ruminants, especially when dietary levels of one influence the metabolism of the other (Silva et al., 2023 ). Additionally, the correlation between magnesium and potassium (r = 0.415) aligns with their combined roles in enzyme activation and membrane stability, which is consistent with findings in cattle under varied nutritional management (Menta et al., 2022 ). The sodium-potassium correlation (r = 0.392), though slightly weaker than in Salamago, remains physiologically significant and reflects their joint role in maintaining cellular function and fluid balance (Neves et al., 2021 ). The absence of an inverse calcium-phosphorus correlation, similar to the findings in Salamago, may reflect adaptive mineral homeostasis in response to local forage mineral profiles or supplementation strategies. This pattern has been noted in recent research on pastoral cattle systems, where traditional grazing regimes can alter expected mineral ratios (Pegorer et al., 2023 ). Overall, the correlation structure in Nyangatom cattle points to a tightly regulated mineral metabolism shaped by both physiological demands and environmental context. 5. Conclusions This study demonstrates that supplementing indigenous forage-based diets with locally formulated concentrate rations significantly enhances the metabolic and mineral status of cattle in pastoral and mixed farming systems of Southern Ethiopia. Key serum parameters - including total protein, glucose, cholesterol, and essential minerals such as calcium, sodium, and potassium - improved with increasing supplementation levels, reflecting better nutritional balance and physiological adaptation. The consistent responses across contrasting agro-ecological zones underscore the robustness and adaptability of locally available feed resources. These findings advocate for the integration of context-specific, low-cost supplementation strategies into livestock development programs. By leveraging indigenous forages and local concentrates, pastoral and agro-pastoral systems can improve cattle health, productivity, and resilience, offering a sustainable pathway toward enhanced food security and livelihood stability in Ethiopia and similar arid and semi-arid regions. 6. Future Lines of Research Work Future research should explore long-term supplementation effects on reproduction, growth, and milk yield. Investigating the economic feasibility and adoption barriers of local feed formulations is essential. Studies assessing the climate resilience of indigenous forage species under varying drought intensities are needed. Evaluating breed-specific metabolic responses and mineral bioavailability from local feeds will refine nutritional strategies. Finally, integrating digital monitoring tools for real-time health and nutrient status could enhance precision feeding in pastoral systems. These efforts will support scalable, sustainable livestock nutrition solutions for Ethiopia and similar agro-pastoral regions. Declarations Ethics Approval and Consent to Participate The experimental procedures followed national animal welfare guidelines and were approved by the institutional research ethics committee of Arba Minch University. Informed consent was obtained from participating pastoralists and farmers. Consent for Publication All authors have approved the manuscript and consent to its publication. Availability of Data and Materials Data supporting the findings of this study are available from the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ Contributions Tesfaye Edjem Tkapel designed the experiment, collected data, and drafted the manuscript. Yisehak Kechero (Professor) supervised the study and contributed to data analysis and manuscript revision. Asrat Guja (Dr.) provided technical guidance and critical review. All authors read and approved the final manuscript. Acknowledgments The authors sincerely acknowledge Professor Yisehak Kechero for his overall guidance and supervisor role, Dr. Luke Galawaski, Admasu Lokaley, Dr. Yared Fanta, Dr. Akililu Getahun Arba Minch University, South Omo Zone Administration, Jinka Agricultural Research Center, Jinka Regional Animal Laboratory, and local agricultural offices for their cooperation and technical support during the study. References Alemayehu, M., Mekasha, Y., & Duncan, A. J. (2021). Enhancing feed resource utilization in Ethiopian mixed crop–livestock systems. Animal Feed Science and Technology , 276 , 114894. https://doi.org/10.1016/j.anifeedsci.2021.114894 Asmare, A. (2020). Nitrogen metabolism and urea recycling in adapted indigenous cattle breeds: A review. Journal of Applied Animal Research , 48 (1), 512–520. Ayele, T., Mulugeta, S., & Tolera, A. (2023). Metabolic network indicators and nutritional status in grazing cattle. Livestock Science , 265 , 105–112. Bayissa, H., Deribe, B., & Taye, M. (2023). Dietary electrolyte manipulation and renal adaptation in ruminants. Animal Physiology and Animal Nutrition , 107 (3), 789–800. Bekele, T., Yisehak, K., & Taye, T. (2022). Nutritional evaluation of indigenous browse species used by pastoral cattle in southern Ethiopia. Tropical Animal Health and Production , 54 (3), 175. https://doi.org/10.1007/s11250-022-03175-4 Central Statistical Agency (CSA). (2021). *Agricultural sample survey 2020/21*. Addis Ababa, Ethiopia. Dereje, M., Addis, M., & Nurfeta, A. (2023). Strategic feed supplementation and protein metabolism in tropical cattle. Animal Nutrition , 12 , 45–55. Deribe, G., Ahmed, M., & Hassen, A. (2020). Blood biochemical parameters as indicators of nutritional status in Ethiopian cattle. Veterinary Medicine and Science , 6 (4), 726–735. https://doi.org/10.1002/vms3.288 Desta, Z., & Beyene, F. (2024). Soil geochemistry and forage mineral composition in pastoral systems: Implications for cattle nutrition. Journal of Arid Environments , 220 , 105–115. FAO. (2023). Pastoralism and livestock systems in East Africa . Food and Agriculture Organization of the United Nations. Gebreyohannes, G., Smith, W. A., & Tolera, A. (2022). Systems biology of metabolic pathways in ruminants under nutritional modulation. Journal of Dairy Science , 105 (8), 6789–6801. Gelaye, Y., Kebede, E., & Mitiku, F. (2023). Location-specific nutritional management and liver enzyme activity in cattle. Ethiopian Veterinary Journal , 27 (1), 22–34. Getachew, A., Animut, G., & Peters, K. J. (2022). Insulin sensitivity and lipogenic pathways in Ethiopian cattle ecotypes. Domestic Animal Endocrinology , 78 , 106–118. Gizaw, S., Tesfaye, K., & Gebremedhin, B. (2024). Climate variability and feed resource dynamics in Ethiopian pastoral systems. Climate Risk Management , 43 , 100534. https://doi.org/10.1016/j.crm.2024.100534 Goff, J. P. (2018). Invited review: Mineral absorption mechanisms, mineral interactions that affect acid–base and antioxidant status, and diet considerations to improve mineral status. Journal of Dairy Science , 101 (4), 2763–2813. Huang, Y., Li, Y., & Liu, J. (2021). The "metabolic engine" concept in ruminant physiology: Integration of energy, protein, and lipid pathways. Animal , 15 (3), 100–112. Humann-Ziehank, E., Coenen, M., & Ganter, M. (2022). Long-term effects of concentrate supplementation on energy metabolism in grazing cattle. Journal of Animal Physiology and Animal Nutrition , 106 (2), 432–441. Kaneko, J. J., Harvey, J. W., & Bruss, M. L. (2008). Clinical biochemistry of domestic animals (6th ed.). Academic Press. Khalid, M. F., Khan, R. U., & Naz, S. (2022). Potassium supplementation, rumen buffering, and muscle function in ruminants. Small Ruminant Research , 206 , 106–112. Kraft, W., & Dürr, U. M. (2005). Klinische Labordiagnostik in der Tiermedizin (6th ed.). Schattauer. McDowell, L. R. (2003). Minerals in animal and human nutrition (2nd ed.). Elsevier Science. Mekonnen, A., Asmare, B., & Wamatu, J. (2021). Environmental stressors and subclinical hepatic responses in grazing cattle. Veterinary Journal , 273 , 105–113. Mekuriaw, Y., Urge, M., & Getnet, A. (2021). Effects of concentrate supplementation on blood metabolites of indigenous cattle. Tropical Animal Health and Production , 53 (2), 256. https://doi.org/10.1007/s11250-021-02623-8 Mekuriaw, S., Tesfaye, D., & Animut, G. (2024). Coordinated lipid and protein metabolic pathways in ruminants: Role of regulatory hormones. Animal Feed Science and Technology , 305 , 115–123. Menta, G., Righi, F., & Simoni, M. (2022). Magnesium status and its interaction with sodium and chloride in ruminants fed region-specific diets. Italian Journal of Animal Science , 21 (1), 1–10. Mousa, M. S., & Alhidary, I. A. (2023). Mineral antagonism and deficiencies in extensive grazing systems. Animal Production Science , 63 (10), 987–995. Neves, R. C., Oliveira, L. R., & Silva, T. P. (2021). Sodium and potassium co-regulation in grazing cattle under varied forage mineral content. Livestock Science , 248 , 104–112. Payne, J. M., & Payne, S. (1987). The metabolic profile test . Oxford University Press. Pegorer, M. F., Vasconcelos, J. T., & Lopes, F. C. (2023). Adaptive mineral homeostasis in pastoral cattle systems: The role of traditional grazing regimes. Tropical Animal Health and Production , 55 (1), 45–53. Piccione, G., Messina, V., & Casella, S. (2012). The liver as a central hub in cattle metabolism: Interplay of lipids, proteins, and enzymes. Journal of Veterinary Science , 13 (3), 287–295. Sartin, J. L., Cummins, K. A., & Kemppainen, R. J. (1985). Glucagon, insulin, and growth hormone responses to glucose infusion in lactating dairy cows. *American Journal of Physiology-Endocrinology and Metabolism, 248*(1), E108–E114. Shenkoru, T., Abebe, G., & Tegegne, F. (2021). Supplemental mineral mixes for correcting electrolyte imbalances in cattle. Journal of Trace Elements in Medicine and Biology , 68 , 126–132. Silva, L. F. P., Prados, L. F., & Resende, F. D. (2023). Co-regulation of calcium and magnesium homeostasis in ruminants: Influence of dietary interactions. Journal of Animal Science , 101 (4), 1–10. Sisay, A., Mengistu, A., & Kebede, G. (2024). Nutritional potential and seasonal dynamics of indigenous forage species in southern Ethiopia. Agriculture , 14 (9), 1475. https://doi.org/10.3390/agriculture14091475 Solomon, T., Mpairwe, D., & Osuji, P. O. (2022). Calcium bioavailability from tropical forages: The role of soil pH and vitamin D. Animal Feed Science and Technology , 285 , 115–123. Tadesse, M., & Reta, D. (2023). Alanine aminotransferase (ALT) and its role in gluconeogenesis in ruminants. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology , 264 , 110–118. Tekle, Y., Getahun, B., & Assefa, G. (2022). Diverse browse species access and lipid metabolism in cattle. Animal Nutrition , 8 , 234–241. Woldehanna, T., Mossie, H., & Berhanu, B. (2024). Parallel activity of ALT and ALP in cattle: Indicators of hepatocellular and bone metabolic activity. Journal of Veterinary Diagnostic Investigation , 36 (1), 89–97. Worku, A., Tefera, T., & Mekonnen, H. (2024). Alkaline phosphatase as a biomarker for bone turnover and hepatic function in growing cattle. Veterinary Clinical Pathology , 53 (2), 210–218. Yisehak, K., Janssens, G. P. J., & Asrat, G. (2020). Feed resource availability and livestock nutrition in Ethiopian pastoral areas. Animal Production Science , 60 (15), 1879–1891. https://doi.org/10.1071/AN19254 Yokus, B., & Cakir, D. U. (2006). Seasonal and physiological variations in serum chemistry and minerals in healthy cattle. Journal of Veterinary Medicine Series A , 53 (6), 271–277. Zewdie, W., Hassen, A., & Alemu, Y. (2022). Serum mineral profiles of cattle under different agro-ecological zones of Ethiopia. Veterinary World , 15 (6), 1470–1478. https://doi.org/10.14202/vetworld.2022.1470-1478 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8851272","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596401209,"identity":"aec63165-779e-4ab4-893d-0bb5abd48318","order_by":0,"name":"Tesfaye Edjem¹²* Tkapel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYHACA8YGEHXgAAPDByDNxk6cFgOwFsYZIC3MxGthYGDmAfEJaTFnP7xNckbFHzm+g4ePbrb5tU2ej5mB8cPHHNxaLHvSyiQ3nDEwljxwLO12bt9twzZmBmbJmdvwuOpAjpnkwzaDxA0Hzpjdzu25zQjUwsbMi0/L+TdgLfUbDpz/dtuy57Y9YS03gLZsbDNIMDhwhu02w4/biURoeVZsOeOMseHMA8fMbvY23E5uY2Zsxu+X88kbb/ZUyMnz3Tj87MaPP7dt57c3H/zwEY8WBJA4wMDA2AZiQVIDEYAfpPAPkYpHwSgYBaNgRAEAhN1cga9jT6YAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0006-2039-8249","institution":"Arba Minch University","correspondingAuthor":true,"prefix":"","firstName":"Tesfaye","middleName":"Edjem¹²*","lastName":"Tkapel","suffix":""},{"id":596401210,"identity":"89846683-28ff-467b-9155-4be5ddb4715a","order_by":1,"name":"Yisehak Kechero²* Kebede","email":"","orcid":"","institution":"Arba Minch University","correspondingAuthor":false,"prefix":"","firstName":"Yisehak","middleName":"Kechero²*","lastName":"Kebede","suffix":""},{"id":596401211,"identity":"782954e2-137c-49e7-81dd-56c4e287f5ac","order_by":2,"name":"Asrat Guja Amejo","email":"","orcid":"","institution":"Arba Minch University","correspondingAuthor":false,"prefix":"","firstName":"Asrat","middleName":"Guja","lastName":"Amejo","suffix":""}],"badges":[],"createdAt":"2026-02-11 11:42:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8851272/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8851272/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566705,"identity":"9337408f-6a27-4c5b-97b6-8794ca98304a","added_by":"auto","created_at":"2026-03-27 12:57:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1505157,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8851272/v1/96c80281-369d-4a62-93d7-ba61bbf1fd48.pdf"}],"financialInterests":"","formattedTitle":"Blood biochemical, mineral, and electrolyte responses of cattle to indigenous forage-based and locally formulated concentrate rations in Southern Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLivestock production plays a central role in the livelihoods, food security, and socio-cultural systems of rural communities in Ethiopia, particularly in pastoral and agro-pastoral areas (FAO, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cattle contribute substantially to household income, draft power, milk production, and risk buffering, yet overall productivity remains low due to chronic feed shortages, poor feed quality, and seasonal variability in forage availability (Yisehak et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gizaw et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn arid and semi-arid regions such as the Lower Omo Valley, livestock production depends largely on natural rangelands dominated by indigenous grasses, shrubs, and browse species. These feed resources are often inadequate in crude protein and metabolizable energy during the dry season, leading to negative energy balance and impaired physiological performance (Alemayehu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous studies have demonstrated that strategic supplementation using protein- and energy-rich feeds can improve animal performance and health, but the use of locally available indigenous forages and concentrate resources remains insufficiently documented under pastoral conditions (Bekele et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBlood biochemical and mineral parameters are widely recognized as sensitive indicators of nutritional status, metabolic efficiency, and health of livestock (Deribe et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Changes in serum metabolites such as glucose, total protein, urea, cholesterol, and liver enzymes reflect dietary adequacy, rumen fermentation efficiency, and hepatic function (Mekuriaw et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Likewise, serum mineral and electrolyte profiles provide insight into mineral balance, homeostasis, and adaptation to environmental stressors (Zewdie et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNyangatom and Salamago districts represent contrasting livestock production systems within South Omo Zone, where pastoral and mixed crop\u0026ndash;livestock practices coexist under varying agro-ecological conditions. Despite the abundance of indigenous forage species and local concentrate ingredients, limited empirical evidence exists on their combined effects on cattle metabolic responses across these systems. Therefore, this study aimed to evaluate the effects of indigenous forage-based and locally formulated concentrate rations on serum biochemical, enzymatic, mineral, and electrolyte profiles of cattle in pastoral and mixed farming systems of Southern Ethiopia, and to compare responses across agro-ecological contexts.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The Study Area\u003c/h2\u003e \u003cp\u003eThe study was conducted in two distinct districts of the South Omo Zone, Southern Ethiopia: Nyangatom (pastoral system) and Salamago (mixed crop\u0026ndash;livestock system).\u003c/p\u003e \u003cp\u003eNyangatom District is located in the arid to semi-arid lowland plains of the Lower Omo Valley, with an altitude ranging from approximately 350 to 700 m above sea level. The climate is characterized by high mean annual temperatures (often exceeding 30\u0026deg;C, reaching up to 38\u0026deg;C) and low, erratic bimodal rainfall averaging 350\u0026ndash;600 mm annually. Frequent droughts and prolonged dry periods shape a predominantly transhumant pastoral livelihood system (CSA, 2021). The sparse population relies on livestock (cattle, goats, and sheep), with seasonal mobility dictated by pasture and water availability. Feed resources are primarily from natural rangelands of indigenous grasses, browse species, and shrubs.\u003c/p\u003e \u003cp\u003eSalamago District occupies lowland to mid-altitude zone (approximately 800\u0026ndash;1,800 m above sea level) with a milder climate. Mean annual temperatures range from 20\u0026ndash;28\u0026deg;C, and annual rainfall is higher (700\u0026ndash;1,200 mm) with a unimodal to weak bimodal pattern. These conditions support a mixed crop\u0026ndash;livestock farming system (Yisehak et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The district has a higher population density, with livelihoods combining crop cultivation (e.g., maize, sorghum) and livestock rearing. Feed resources include natural pasture, crop residues, and limited local concentrates, with indigenous forage remaining crucial during dry seasons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental Animals and Design\u003c/h2\u003e \u003cp\u003eThirty clinically healthy indigenous cattle (15 from each district) of similar age, sex, breed, and initial body weight were selected, following matching procedures recommended for tropical nutrition studies (Deribe et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Animals were individually tagged and randomly assigned to one of five dietary treatments in a completely randomized design with a 2 \u0026times; 5 factorial arrangement (location \u0026times; diet), with six animals per treatment group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Dietary Treatments and Formulation\u003c/h2\u003e \u003cp\u003eFive dietary treatments were evaluated:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eT1\u003c/strong\u003e \u003cp\u003eFree grazing only (control)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eT2\u003c/strong\u003e \u003cp\u003eLow level of supplementary ration (e.g., 0.5% of body weight)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eT3\u003c/strong\u003e \u003cp\u003eModerate level of supplementary ration (e.g., 1.0% of body weight)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eT4\u003c/strong\u003e \u003cp\u003eHigh level of supplementary ration (e.g., 1.5% of body weight)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eT5\u003c/strong\u003e \u003cp\u003eVery high level of supplementary ration (e.g., 2.0% of body weight)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThe exact inclusion levels (e.g., % body weight, dry matter intake, or specific nutrient density) were specified based on the experimental design.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Feeding Protocol\u003c/h2\u003e \u003cp\u003eAnimals were individually fed for 28 days. The supplementary ration was offered once daily in the morning, followed by free grazing. Clean drinking water was available ad libitum. Daily feed intake was recorded throughout the experimental period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Blood Sample Collection and Processing\u003c/h2\u003e \u003cp\u003eBlood samples were collected from the jugular vein before the trial (day 0) and after 21 days of feeding, following standard veterinary procedures (Deribe et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Samples were drawn into vacutainer tubes containing acid citrate dextrose as anticoagulant, at a consistent morning hour to minimize diurnal variation (Zewdie et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Serum was separated by centrifugation at 4,000 rpm for 5 min and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Biochemical and Mineral Analysis\u003c/h2\u003e \u003cp\u003eSerum biochemical parameters were analyzed using standard spectrophotometric methods described previously (Mekuriaw et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e): \u003cb\u003eMetabolites\u003c/b\u003e: total protein, albumin, globulin, glucose, creatinine, urea, total bilirubin, triglycerides, cholesterol, as well as \u003cb\u003eEnzymes\u003c/b\u003e: aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP). Minerals (calcium, magnesium, phosphorus) and electrolytes (sodium, potassium, chloride) were measured using atomic absorption spectrometry and ion-selective electrodes, respectively. Analyses were performed at Sodo Animal Health Laboratory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical Analysis\u003c/h2\u003e \u003cp\u003eData were checked for normality and homogeneity of variance. A two-way analysis of variance (ANOVA) was used to evaluate the effects of location, dietary treatment, and their interaction, consistent with analytical approaches in similar nutrition studies. Mean separation was performed using Tukey\u0026rsquo;s HSD test at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Pearson\u0026rsquo;s correlation coefficients were calculated to evaluate relationships among serum parameters. All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Serum biochemical metabolites and enzyme activities\u003c/h2\u003e \u003cp\u003eThe results of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below indicated that Dietary treatment levels significantly influenced several serum biochemical parameters of cattle in both Nyangatom (NY) and Salamago (SL) districts. Total protein (TP) concentrations increased progressively with increasing dietary treatment levels, with the highest values recorded in T4 and T5, particularly in Nyangatom cattle (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Albumin (Alb) concentrations were significantly affected by location (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with generally higher values observed in Salamago compared with Nyangatom, while treatment effects were not significant.\u003c/p\u003e \u003cp\u003eGlobulin (Glb) levels showed a significant response to dietary treatment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with higher concentrations observed at intermediate to higher inclusion levels (T3\u0026ndash;T5) in both locations. Serum glucose concentrations were significantly affected by treatment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with higher values recorded in T3 -T5 compared with T1 and T2, particularly in Salamago cattle.\u003c/p\u003e \u003cp\u003eCreatinine concentrations increased with dietary treatment level in Nyangatom cattle (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas no consistent trend was observed in Salamago. Activities of hepatic enzymes, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP), were significantly influenced by dietary treatments (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with higher activities generally observed at higher treatment levels. Cholesterol concentrations increased progressively with dietary treatment level in both districts (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eSerum urea and total bilirubin concentrations showed moderate increases with increasing dietary treatment levels; however, treatment \u0026times; location interactions were not significant for most biochemical parameters, indicating similar response patterns across districts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of dietary treatment inclusion levels on serum metabolites and enzymatic profiles of cattle in Nyangatom and Salamago districts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eTreatment, Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eT1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eT4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eT5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eSEM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eL\u0026times;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.79 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4 \u003csup\u003eb2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7 \u003csup\u003eab2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.97 \u003csup\u003ea2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.98 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.38 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.17 \u003csup\u003ea1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.32 \u003csup\u003ea1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.30 \u003csup\u003ea1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.07 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.78 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.25 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.12 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.77 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.37 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.45 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.60 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.52 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.52 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGlucose, \u003cem\u003emg/dl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.83 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.67 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.83 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.67 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.00 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.83 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.83 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.33 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71.67 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCreatin, \u003cem\u003eg/dl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAST, \u003cem\u003eg/dl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.83 \u003csup\u003eb2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.33 \u003csup\u003ea2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.67 \u003csup\u003ea2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61 \u003csup\u003ea2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.5 \u003csup\u003ea2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.67 \u003csup\u003eab1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.00 \u003csup\u003eab1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.33 \u003csup\u003eb1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.17 \u003csup\u003ea1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74 \u003csup\u003eab1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.67 \u003csup\u003ec2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.83 \u003csup\u003ebc2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.67 \u003csup\u003ebc2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.33 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91 \u003csup\u003ea2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.17 \u003csup\u003eb1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.83 \u003csup\u003ea1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93 \u003csup\u003eab1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.33 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e104.67 \u003csup\u003ea1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUrea, \u003cem\u003emg/dl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.5 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.83 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal Bilirubin, \u003cem\u003eg/dl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.14 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.53 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.64 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.77 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.87 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.6 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.03 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.47 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.17 \u003csup\u003eb2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.67 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118.96 \u003csup\u003ea2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108.7 \u003csup\u003eab2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e134.83 \u003csup\u003ea2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.33 \u003csup\u003eb1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.17 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153.8 \u003csup\u003ea1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126.3 \u003csup\u003eab1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e165.83\u003csup\u003ea1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTP: Total Protein; Alb: Albumin; Glb: Globulin; Creatin: Creatinine; AST: Aspartate Aminotransferase; ALT: Alanine Aminotransferase; ALP: Alkaline Phosphatase; SEM: Standard Error of the Mean; L: Location (NY: Nyangatom, SL: Salamago); T: Treatment; L\u0026times;T: Interaction between Location and Treatment; P values correspond to the main effects of Location (L) and Treatment (T), and their interaction (L\u0026times;T).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Serum mineral and electrolyte profiles\u003c/h2\u003e \u003cp\u003eThe research findings in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below shown that dietary treatments significantly influenced serum mineral and electrolyte concentrations of cattle in both Nyangatom and Salamago districts. Serum calcium concentrations increased progressively with increasing dietary treatment levels (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with the highest values observed in cattle receiving T4 and T5 diets in both locations. This pattern indicates a consistent response of calcium status to dietary treatment, regardless of production system. Magnesium concentrations, however, were primarily influenced by location (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with higher mean values recorded in Nyangatom cattle compared with those from Salamago.\u003c/p\u003e \u003cp\u003eSerum phosphorus concentrations differed significantly between locations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but were not affected by dietary treatment level. Sodium and potassium concentrations responded significantly to dietary treatments (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with potassium showing a marked increase at higher treatment levels, particularly in Salamago cattle. Chloride concentrations were also significantly affected by dietary treatment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and exhibited a declining trend as dietary inclusion levels increased. Treatment \u0026times; location interactions were not significant for most mineral and electrolyte parameters, suggesting comparable response patterns across districts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of dietary treatments on serum mineral and electrolyte profiles of cattle in Nyangatom and Salamago districts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eTreatment, Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eT1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eT4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eT5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eSEM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eL\u0026times;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.29 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.77 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.52 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.57 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.68 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e136.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e135.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146.5 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139.7 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e136.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.39 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.27 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.02 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.17 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.78 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.5 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.3 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.7 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.00 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.17 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eL: Location (NY: Nyangatom, SL: Salamago); T: Treatment; L\u0026times;T: Interaction between Location and Treatment; SEM: Standard Error of the Mean. P values correspond to the main effects of Location (L) and Treatment (T), and their interaction (L\u0026times;T).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Correlation among serum biochemical parameters\u003c/h2\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analysis of serum biochemical parameters in Salamago cattle is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Total protein (TP) showed significant positive correlations with globulin (r\u0026thinsp;=\u0026thinsp;0.845, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), glucose (r\u0026thinsp;=\u0026thinsp;0.576, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), cholesterol (r\u0026thinsp;=\u0026thinsp;0.542, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), urea (r\u0026thinsp;=\u0026thinsp;0.738, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and total bilirubin (r\u0026thinsp;=\u0026thinsp;0.572, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). TP also exhibited weaker, non-significant correlations with creatinine, AST, triglycerides, ALT, and ALP.\u003c/p\u003e \u003cp\u003eAlbumin had limited associations, showing a significant positive correlation only with total bilirubin (r\u0026thinsp;=\u0026thinsp;0.421, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas correlations with other metabolites and enzymes were generally weak or non-significant. Globulin was positively correlated with glucose (r\u0026thinsp;=\u0026thinsp;0.616, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), cholesterol (r\u0026thinsp;=\u0026thinsp;0.588, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), urea (r\u0026thinsp;=\u0026thinsp;0.674, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and total bilirubin (r\u0026thinsp;=\u0026thinsp;0.421, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eGlucose was positively correlated with urea (r\u0026thinsp;=\u0026thinsp;0.494, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), total bilirubin (r\u0026thinsp;=\u0026thinsp;0.492, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), ALT (r\u0026thinsp;=\u0026thinsp;0.534, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and cholesterol (r\u0026thinsp;=\u0026thinsp;0.382, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Creatinine showed a significant correlation with total bilirubin (r\u0026thinsp;=\u0026thinsp;0.401, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) only, while AST was positively correlated with cholesterol (r\u0026thinsp;=\u0026thinsp;0.461, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Cholesterol was significantly associated with urea (r\u0026thinsp;=\u0026thinsp;0.493, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and ALT (r\u0026thinsp;=\u0026thinsp;0.392, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Urea was positively correlated with total bilirubin (r\u0026thinsp;=\u0026thinsp;0.431, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Other parameters, including triglycerides, ALT, and ALP, showed limited and non-significant associations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson's correlation coefficient among serum biochemical parameters of Salamago cattle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCreatin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChol.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal B.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTrigly.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.456*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.845*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.576**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.542**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.738**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.572**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.421*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.616**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.588**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.674**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.421*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.384*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.382*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.494**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.492**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.534**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.392*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.401*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.461*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChol.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.493**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.392*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.431*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal B.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrigly.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation among serum biochemical parameters in Nyangatom cattle\u003c/h2\u003e \u003cp\u003ePearson\u0026rsquo;s correlation coefficients among serum biochemical parameters of Nyangatom cattle are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below. Total protein (TP) showed significant positive correlations with most serum metabolites and enzymes, including glucose (r\u0026thinsp;=\u0026thinsp;0.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), AST (r\u0026thinsp;=\u0026thinsp;0.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), cholesterol (r\u0026thinsp;=\u0026thinsp;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), urea (r\u0026thinsp;=\u0026thinsp;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and total bilirubin (r\u0026thinsp;=\u0026thinsp;0.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). TP was also moderately correlated with albumin, globulin, triglycerides, ALT, and ALP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026ndash;0.01).\u003c/p\u003e \u003cp\u003eAlbumin was significantly correlated with glucose (r\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), AST (r\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), cholesterol (r\u0026thinsp;=\u0026thinsp;0.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), urea (r\u0026thinsp;=\u0026thinsp;0.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and total bilirubin (r\u0026thinsp;=\u0026thinsp;0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas its correlations with creatinine, triglycerides, ALT, and ALP were not significant. Globulin showed significant positive correlations with glucose, creatinine, AST, cholesterol, urea, ALT, and ALP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026ndash;0.01). Glucose was strongly correlated with cholesterol (r\u0026thinsp;=\u0026thinsp;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), urea (r\u0026thinsp;=\u0026thinsp;0.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), total bilirubin (r\u0026thinsp;=\u0026thinsp;0.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), creatinine (r\u0026thinsp;=\u0026thinsp;0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and AST (r\u0026thinsp;=\u0026thinsp;0.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and moderately correlated with ALT and ALP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026ndash;0.01). Creatinine showed significant associations with AST, cholesterol, urea, total bilirubin, and ALT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026ndash;0.01), but not with triglycerides or ALP. AST was positively correlated with cholesterol (r\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), urea (r\u0026thinsp;=\u0026thinsp;0.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), total bilirubin (r\u0026thinsp;=\u0026thinsp;0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), triglycerides (r\u0026thinsp;=\u0026thinsp;0.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and ALT (r\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Cholesterol exhibited significant correlations with urea, total bilirubin, triglycerides, ALT, and ALP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026ndash;0.01). Urea was moderately correlated with total bilirubin, triglycerides, ALT, and ALP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026ndash;0.01). Total bilirubin showed significant correlations with triglycerides and ALT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026ndash;0.01), while triglycerides were not significantly correlated with ALT or ALP. A strong positive correlation was observed between ALT and ALP (r\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson's correlation coefficient among serum biochemical parameters of Nyangatom cattle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCreatin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChol.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal B.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTrigly.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.80**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.85**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.66**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.60**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.47**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.59**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.48**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.49*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.39*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.71**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.64**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.49*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.59**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.45*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.51**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.53**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.66**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.39*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.54*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.65**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.83**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.53**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.63*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.49**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChol.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.58**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.59**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.59**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.48**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.49**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.47**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal B.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.55**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.42*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrigly.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.65**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Correlation among serum mineral and electrolyte profiles of Salamago cattle\u003c/h2\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e described below indicated that Pearson\u0026rsquo;s correlation analysis among serum mineral and electrolyte parameters of Salamago cattle revealed mostly weak to moderate associations. Potassium showed a significant positive correlation with sodium (r\u0026thinsp;=\u0026thinsp;0.485, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating coordinated variation between these two electrolytes. Magnesium was positively correlated with sodium (r\u0026thinsp;=\u0026thinsp;0.399, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and chloride (r\u0026thinsp;=\u0026thinsp;0.459, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eCalcium showed weak, non-significant correlations with all measured electrolytes and minerals, including magnesium, phosphorus, sodium, potassium, and chloride. Phosphorus was weakly correlated with sodium and potassium and showed a negative, non-significant association with chloride. Potassium exhibited a negative but non-significant correlation with chloride.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePearson's\u003c/em\u003e correlation \u003cem\u003ecoefficient\u003c/em\u003e among mineral and electrolyte profiles of Salamago cattle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChloride\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.459*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Correlation among serum mineral and electrolyte profiles of Nyangatom cattle\u003c/h2\u003e \u003cp\u003eIn Nyangatom cattle, Pearson\u0026rsquo;s correlation analysis demonstrated several moderate associations among serum mineral and electrolyte parameters shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e below. Calcium was positively correlated with magnesium (r\u0026thinsp;=\u0026thinsp;0.499, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while its associations with phosphorus, sodium, potassium, and chloride were weak and non-significant. Magnesium showed a significant positive correlation with potassium (r\u0026thinsp;=\u0026thinsp;0.415, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eSodium exhibited a moderate positive correlation with potassium (r\u0026thinsp;=\u0026thinsp;0.392, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a weak positive association with chloride. Phosphorus showed weak, non-significant correlations with sodium, potassium, and chloride. Potassium was weakly and non-significantly correlated with chloride.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson's correlation \u003cem\u003ecoefficient\u003c/em\u003e among mineral and electrolyte profiles of Nyangatom cattle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003echloride\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.499**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.415*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.392*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1: Effects of dietary treatments on serum metabolites and enzymatic profiles\u003c/h2\u003e \u003cp\u003eThe results indicate significant effects of dietary treatment levels and location on serum biochemical parameters in cattle from Nyangatom (NY) and Salamago (SL) districts. Total protein (TP) showed higher levels in SL across all treatments, which may reflect better nutritional status or metabolic adaptation in this region, consistent with findings that regional forage quality directly influences protein synthesis in grazing ruminants (Kaneko et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Alemayehu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The increase in TP with treatment level suggests improved protein metabolism with dietary supplementation, aligning with recent studies on strategic feed supplementation in tropical cattle systems (Dereje et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlucose levels were generally higher in SL and increased with treatment level, particularly in T4 and T5, indicating enhanced energy availability, possibly due to improved gluconeogenesis or carbohydrate intake (Humann-Ziehank et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). AST and ALT, markers of hepatic function, were elevated in SL, which may be attributed to higher metabolic demand, dietary composition, or even subclinical responses to environmental stressors common in certain agro-ecological zones (Mekonnen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). ALT showed a significant treatment effect in NY, suggesting that dietary interventions may influence liver enzyme activity more prominently in certain regions, highlighting the importance of location-specific nutritional management (Gelaye et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCholesterol levels increased with treatment in both districts, reflecting improved lipid metabolism and energy status, though values were consistently higher in SL, a pattern also observed in cattle with access to diverse browse species (Tekle et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Urea levels remained stable across treatments, indicating consistent nitrogen metabolism and efficient urea recycling, which is often maintained under varying protein intakes in adapted indigenous breeds (Asmare, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). ALP, associated with bone and liver metabolism, showed marked increases with treatment, especially in SL, which may indicate enhanced metabolic or growth activity, as ALP is a recognized biomarker for bone turnover and hepatic function in growing cattle (Payne \u0026amp; Payne, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Worku et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the dietary treatments positively influenced several metabolic parameters, with location-specific responses underscoring the critical interaction between nutrition, environment, and genotype in cattle physiology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Effects of dietary treatments on serum mineral and electrolyte profiles\u003c/h2\u003e \u003cp\u003eDietary treatments significantly affected serum mineral and electrolyte profiles, with notable differences between districts. Calcium levels were higher in NY, particularly in T4, which may reflect better calcium absorption or supplementation efficacy, potentially linked to differences in soil pH, forage calcium content, or vitamin D status, as highlighted in recent studies on mineral bioavailability in tropical forages (McDowell, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Solomon et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Magnesium and phosphorus levels were generally stable, though phosphorus was lower in SL, possibly due to inherent soil mineral deficiencies or antagonistic interactions with other minerals in the local forages, a common challenge in extensive grazing systems (Mousa \u0026amp; Alhidary, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSodium and potassium showed significant treatment effects, with T2 and T3 resulting in higher sodium levels in both districts. This aligns with findings that supplemental mineral mixes can effectively correct transient electrolyte imbalances in cattle (Shenkoru et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Potassium increased with treatment in NY, especially in T4, which may support better electrolyte balance, muscle function, and rumen buffering capacity (Khalid et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Chloride levels decreased with higher treatment levels in both districts, which could indicate altered acid-base balance or renal adaptation to maintain homeostasis, a physiological response documented in cattle under dietary electrolyte manipulation (Bayissa et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings suggest that while dietary supplementation can enhance mineral status, regional differences in soil geochemistry, water quality, and forage composition remain dominant factors that must be integrated into nutritional planning for sustainable cattle production (Desta \u0026amp; Beyene, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Pearson\u0026rsquo;s correlation among serum biochemical parameters in Salamago cattle\u003c/h2\u003e \u003cp\u003eThe correlation analysis for Salamago cattle revealed a network of significant relationships among serum biochemical parameters, reflecting interconnected metabolic and physiological pathways. Total protein (TP) exhibited strong positive correlations with globulin (Glb) and moderate associations with glucose, cholesterol, urea, and total bilirubin. This pattern suggests that protein metabolism is closely linked to energy homeostasis, lipid metabolism, and nitrogen excretion in this population, a finding consistent with systems biology approaches that reveal co-regulation of these pathways under nutritional modulation (Gebreyohannes et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlucose demonstrated significant positive correlations with creatinine, cholesterol, urea, total bilirubin, ALT, and ALP. These associations indicate that glucose metabolism is not only central to energy provision but also interrelated with renal function (creatinine), lipid regulation, and hepatic activity, supporting the concept of a tightly regulated \"metabolic engine\" in ruminants (Huang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The strong correlation between glucose and ALT (r\u0026thinsp;=\u0026thinsp;0.534) may reflect enhanced gluconeogenic activity or liver involvement in glucose regulation, as ALT is increasingly recognized for its role beyond mere liver damage, including in gluconeogenesis (Tadesse \u0026amp; Reta, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAST showed a notable positive correlation with cholesterol (r\u0026thinsp;=\u0026thinsp;0.461), supporting the established role of liver enzymes in lipid metabolism and export (Kraft \u0026amp; D\u0026uuml;rr, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Cholesterol itself was positively associated with urea and triglycerides, suggesting coordinated lipid and protein metabolic pathways, possibly mediated by insulin and other regulatory hormones (Mekuriaw et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Urea was further correlated with total bilirubin, which may point to shared regulatory mechanisms in nitrogen metabolism and liver function, particularly in the context of the urea cycle and heme catabolism (Yokus \u0026amp; Cakir, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the correlation matrix underscores a tightly coupled metabolic system in Salamago cattle, where dietary or physiological changes affecting one parameter may have cascading effects on others, emphasizing the need for a holistic view in nutritional interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Pearson\u0026rsquo;s correlation among serum biochemical parameters in Nyangatom cattle\u003c/h2\u003e \u003cp\u003eIn Nyangatom cattle, the correlation analysis revealed a highly integrated metabolic network, with particularly strong associations among protein, energy, and lipid metabolism indicators. Total protein (TP) was significantly correlated with nearly all parameters, especially glucose, cholesterol, urea, and liver enzymes, highlighting its central role as a marker of overall metabolic and nutritional status, a pattern often seen in well-nourished and metabolically active herds (Ayele et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlucose showed robust positive correlations with cholesterol, urea, total bilirubin, and liver enzymes (ALT and ALP), reinforcing its pivotal role as the primary energy currency and its interaction with hepatic and renal functions. The exceptionally strong link between glucose and cholesterol (r\u0026thinsp;=\u0026thinsp;0.85) suggests a coordinated regulation of energy and lipid metabolism in this group, possibly driven by insulin sensitivity and lipogenic pathways that are highly active in certain cattle ecotypes (Getachew et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCholesterol was positively associated with urea, total bilirubin, triglycerides, ALT, and ALP. These relationships point to a close interplay between lipid metabolism, protein catabolism, and liver function, where the liver serves as the central hub orchestrating the synthesis, degradation, and interconversion of these metabolites (Piccione et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The correlation between urea and total bilirubin further supports the interconnectedness of nitrogen metabolism and hepatobiliary function, reflecting shared metabolic fates of amino acids and heme (Sartin et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the strong positive correlation between ALT and ALP (r\u0026thinsp;=\u0026thinsp;0.65) indicates parallel activity of these liver enzymes, possibly reflecting adaptive or responsive hepatic mechanisms under dietary influence, and may be indicative of hepatocellular activity related to both protein turnover and bone metabolism (Woldehanna et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings illustrate a coherent and responsive metabolic network in Nyangatom cattle, suggesting that dietary interventions can simultaneously influence multiple, interconnected physiological pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Pearson\u0026rsquo;s correlation among mineral and electrolyte profiles of Salamago cattle\u003c/h2\u003e \u003cp\u003eThe correlation analysis of mineral and electrolyte profiles in Salamago cattle indicates distinct physiological interactions, with a moderate number of significant associations. The strong positive correlation between sodium and potassium (r\u0026thinsp;=\u0026thinsp;0.485, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) highlights their well-documented synergistic role in maintaining osmotic balance, neuromuscular function, and acid-base homeostasis (Goff, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent studies in grazing cattle have reinforced that sodium and potassium levels are closely co-regulated, especially under conditions of varied forage mineral content (Neves et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, magnesium exhibited positive correlations with sodium (r\u0026thinsp;=\u0026thinsp;0.399) and chloride (r\u0026thinsp;=\u0026thinsp;0.459), which may reflect its involvement in electrolyte transport and cellular ion balance. Emerging research suggests that magnesium status can influence sodium retention and chloride distribution, particularly in ruminants fed region-specific diets (Menta et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The absence of a strong calcium-phosphorus correlation, contrary to classical expectations, may indicate independent regulatory mechanisms or environmental influences such as soil mineral composition and forage type (Pegorer et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Overall, these correlations suggest that mineral homeostasis in Salamago cattle is influenced by both physiological interactions and local nutritional factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Pearson\u0026rsquo;s Correlation among Mineral and Electrolyte Profiles of Nyangatom Cattle\u003c/h2\u003e \u003cp\u003eThe inter-mineral relationships in Nyangatom cattle reveal a more integrated network than observed in Salamago, with several significant correlations underscoring key metabolic linkages. The strong positive correlation between calcium and magnesium (r\u0026thinsp;=\u0026thinsp;0.499, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) suggests co-regulation in absorption or excretion, possibly mediated by shared transport mechanisms or dietary interactions. Recent studies indicate that calcium and magnesium homeostasis can be closely linked in ruminants, especially when dietary levels of one influence the metabolism of the other (Silva et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the correlation between magnesium and potassium (r\u0026thinsp;=\u0026thinsp;0.415) aligns with their combined roles in enzyme activation and membrane stability, which is consistent with findings in cattle under varied nutritional management (Menta et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The sodium-potassium correlation (r\u0026thinsp;=\u0026thinsp;0.392), though slightly weaker than in Salamago, remains physiologically significant and reflects their joint role in maintaining cellular function and fluid balance (Neves et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe absence of an inverse calcium-phosphorus correlation, similar to the findings in Salamago, may reflect adaptive mineral homeostasis in response to local forage mineral profiles or supplementation strategies. This pattern has been noted in recent research on pastoral cattle systems, where traditional grazing regimes can alter expected mineral ratios (Pegorer et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Overall, the correlation structure in Nyangatom cattle points to a tightly regulated mineral metabolism shaped by both physiological demands and environmental context.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study demonstrates that supplementing indigenous forage-based diets with locally formulated concentrate rations significantly enhances the metabolic and mineral status of cattle in pastoral and mixed farming systems of Southern Ethiopia. Key serum parameters - including total protein, glucose, cholesterol, and essential minerals such as calcium, sodium, and potassium - improved with increasing supplementation levels, reflecting better nutritional balance and physiological adaptation. The consistent responses across contrasting agro-ecological zones underscore the robustness and adaptability of locally available feed resources. These findings advocate for the integration of context-specific, low-cost supplementation strategies into livestock development programs. By leveraging indigenous forages and local concentrates, pastoral and agro-pastoral systems can improve cattle health, productivity, and resilience, offering a sustainable pathway toward enhanced food security and livelihood stability in Ethiopia and similar arid and semi-arid regions.\u003c/p\u003e"},{"header":"6. Future Lines of Research Work","content":"\u003cp\u003eFuture research should explore long-term supplementation effects on reproduction, growth, and milk yield. Investigating the economic feasibility and adoption barriers of local feed formulations is essential. Studies assessing the climate resilience of indigenous forage species under varying drought intensities are needed. Evaluating breed-specific metabolic responses and mineral bioavailability from local feeds will refine nutritional strategies. Finally, integrating digital monitoring tools for real-time health and nutrient status could enhance precision feeding in pastoral systems. These efforts will support scalable, sustainable livestock nutrition solutions for Ethiopia and similar agro-pastoral regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental procedures followed national animal welfare guidelines and were approved by the institutional research ethics committee of Arba Minch University. Informed consent was obtained from participating pastoralists and farmers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have approved the manuscript and consent to its publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTesfaye Edjem Tkapel designed the experiment, collected data, and drafted the manuscript. Yisehak Kechero (Professor) supervised the study and contributed to data analysis and manuscript revision. Asrat Guja (Dr.) provided technical guidance and critical review. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely acknowledge Professor Yisehak Kechero for his overall guidance and supervisor role, Dr. Luke Galawaski, Admasu Lokaley, Dr. Yared Fanta, Dr. Akililu Getahun Arba Minch University, South Omo Zone Administration, Jinka Agricultural Research Center, Jinka Regional Animal Laboratory, and local agricultural offices for their cooperation and technical support during the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlemayehu, M., Mekasha, Y., \u0026amp; Duncan, A. J. (2021). 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Serum mineral profiles of cattle under different agro-ecological zones of Ethiopia. \u003cem\u003eVeterinary World\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(6), 1470\u0026ndash;1478. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14202/vetworld.2022.1470-1478\u003c/span\u003e\u003cspan address=\"10.14202/vetworld.2022.1470-1478\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Indigenous forages, Blood biochemical parameters, Mineral and electrolyte profiles, Cattle nutrition, Pastoral and agro-pastoral systems","lastPublishedDoi":"10.21203/rs.3.rs-8851272/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8851272/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e This study evaluated the effects of indigenous forage-based diets supplemented with locally formulated concentrate rations on serum biochemical, enzymatic, mineral, and electrolyte profiles of cattle managed under pastoral and mixed crop-livestock systems in Southern Ethiopia. The experiment was conducted in Nyangatom (pastoral) and Salamago (mixed farming) districts using thirty clinically healthy indigenous cattle assigned to five dietary treatments: free grazing only (control) and four increasing levels of supplementation in a 2 \u0026times; 5 factorial arrangement. Blood samples were collected before and after a 21-day feeding period and analyzed for key serum metabolites, liver enzymes, minerals, and electrolytes using standard laboratory procedures. Dietary supplementation significantly improved serum total protein, globulin, glucose, cholesterol, and urea concentrations, with higher responses observed at moderate to high supplementation levels (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Activities of hepatic enzymes (AST, ALT, and ALP) increased with dietary treatment, indicating enhanced metabolic activity without pathological deviation. Serum calcium, sodium, and potassium concentrations increased significantly with supplementation, while magnesium and phosphorus were primarily influenced by location. Treatment \u0026times; location interactions were generally non-significant, suggesting consistent physiological responses across production systems. Correlation analyses revealed strong positive associations among protein, energy, lipid, and liver metabolism indicators, highlighting the integrated nature of metabolic adaptation to improved nutrition. Overall, the findings demonstrate that indigenous forage-based diets complemented with locally available concentrate resources effectively enhance metabolic and mineral status of cattle. This approach offers a sustainable, context-specific feeding strategy to improve cattle health and productivity in pastoral and agro-pastoral systems of Southern Ethiopia.\u003c/p\u003e","manuscriptTitle":"Blood biochemical, mineral, and electrolyte responses of cattle to indigenous forage-based and locally formulated concentrate rations in Southern Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 15:36:40","doi":"10.21203/rs.3.rs-8851272/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d2e1714-e1b7-493e-8ad7-e9febceadd13","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T17:43:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 15:36:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8851272","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8851272","identity":"rs-8851272","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}