Growth performance, feeding behaviour, serum biochemical and meat quality traits of Karayaka lambs fed pastures consisting of different relative forage quality

Growth performance, feeding behaviour, serum biochemical and meat quality traits of Karayaka lambs fed pastures consisting of different relative forage quality | 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 Growth performance, feeding behaviour, serum biochemical and meat quality traits of Karayaka lambs fed pastures consisting of different relative forage quality Ahmet Akdağ, İbrahim Aydın, Nuh Ocak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7230525/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Tropical Animal Health and Production → Version 1 posted 4 You are reading this latest preprint version Abstract This study aimed to evaluate how pastures with four relative forage quality (RFQ) indices, 89 (89RFQ), 105 (105RFQ), 121 (121RFQ) and 147 (147RFQ), affect the growth, feeding behaviour, serum biochemistry and meat quality of Karayaka male lambs. Thirty-six lambs (90 days old, 22.8 ± 0.14 kg body weight) were assigned to graze on one of the pastures, established with different forage compositions to achieve the target RFQ indices, with three replicates for 60 days. The 121RFQ lambs had higher body weight and gain than the 105RFQ lambs ( p < 0.05). The 121RFQ lambs had the highest, the 89RFQ and 105RFQ lambs had intermediate and the 147RFQ lambs had the least DMI ( p < 0.05). The 121RFQ and 89RFQ lambs had a better feed conversion ratio than the 105RFQ lambs ( p < 0.05). The 121RFQ and 89RFQ lambs had a better feed conversion ratio compared to 105RFQ ( p < 0.05). Water intake was higher in the 105RFQ lambs than in the 121RFQ and 147RFQ lambs ( p < 0.05). The grazing history influenced the feeding behaviour, with lambs generally preferring forages from pastures they had previously grazed. The Longissimus dorsi muscle b* value was higher in the 121RFQ lambs than in the 147RFQ lambs ( p < 0.05). The 89RFQ and 105RFQ lambs had higher meat fat and also serum triglyceride contents compared to the 121RFQ and 147RFQ lambs, respectively ( p < 0.05). The 121RFQ pasture could enhance the growth performance without compromising carcass yield and meat quality traits, except for muscle b* value and fat content. Furthermore, prior grazing experience influences subsequent forage selection in lambs. Forage quality grazing history growth rate lamb production metabolic profile nutritional value Figures Figure 1 Introduction Pasture-based sheep production plays an important economic and environmental role in many subtropical countries (Kemp et al. 2010 ; Poli et al. 2020 ). Furthermore, pastured-lamb production reduces input costs, promotes ecological and animal welfare and yields potentially healthier meat (Elizalde et al. 2021 ). However, lamb production remains far below its potential, primarily due to a lack of knowledge regarding pasture quality and the nutrition of pastured lambs in subtropical countries (Poli et al. 2020 ). Also, wide variations in legume and grass content established by parameters such as plant growth rate, seed ratio, establishment, management and persistence, makes it challenging to regulate the quality and quantity of forage grazed by lambs (Poli et al. 2020 ). Accordingly, these systems can lead to slower lamb growth and thus delay timely target weight achievement, impacting production efficiency (De Brito et al. 2017 ). In pastoral systems, increasing animal performance, producing healthy, high-quality products and maintaining the persistence and productivity of legume and grass forages are key challenges. To overcome these challenges, there is an increasing awareness of incorporating different forages into pasture establishment (Ates et al. 2013 , 2015 ; Jimenez et al. 2019 ). Papadopoulos et al. ( 2001 ) demonstrated that the inclusion of white clover ( Trifolium repens ) in orchardgrass ( Dactylis glomerata ) pastures resulted in improved lamb performance. Fraser et al. ( 2004 ) reported that grazing lambs on forage legume swards, such as red clover ( Trifolium pratense ) and lucerne ( Medicago sativa ), increases individual lamb performance without compromising carcass and meat quality except for muscle unsaturated fatty acid profiles compared to perennial ryegrass ( Lolium perenne ) swards. Chicory ( Cichorium intybus ), plantain ( Plantago lanceolata ), red clover ( Trifolium pratense ) and white clover ( Trifolium repens ) based pastures can result in a more significant live weight gain in lambs compared to perennial ryegrass ( Lolium perenne ) based pastures (Kemp et al. 2010 ). Incorporating subterranean clover ( Trifolium subterraneum ) into orchardgrass ( Dactylis glomerata ) and perennial ryegrass ( Lolium perenne )-based pastures has a positive impact on pasture production (Ates et al. 2013 ) and lambs’ growth rates (Ates et al. 2015 ). Based on the above research findings, animal productivity in grazing systems is contingent on forage species (such as legumes, grasses and other botanical families) and characteristics (nutrient composition, quality and quantity). Pasture forage quality, defined as the capacity of forage to supply the requisite nutrients for grazing animals, including lambs (Tesk et al. 2018 ), is a critical determinant of effective lamb production. Consequently, achieving optimal lamb performance is closely associated with the quantity of specific nutrients and key quality indicators in the forage (Tesk et al. 2018 ; Aydın et al. 2019), such as crude protein (CP), total digestible nutrients (TDN), digestible dry matter (DDM) and metabolizable energy (ME). One of the main constraints in grazing systems is the limited dry-matter intake (DMI) by grazing animals, resulting in the inability to meet their nutrient (energy and protein) requirements for high performance (Fernandez-Turren et al. 2020 ). Relative forage quality (RFQ), used as a quality indicator (Aydin et al. 2019 ), is a crucial index for evaluating the nutritional value of ruminant feeds (Favre et al. 2019 ). Since the RFQ index relies on neutral detergent fibre (NDF), NDF digestibility (NDFD), TDN, CP, fatty acids and ash (Favre et al. 2019 ), a pasture established according to this index can be an alternative to overcome this constraint in pasture quality. To our knowledge, the effects of pastures comprised of species with different RFQ indices on the grazing lambs' growth performance, carcass and meat quality have not been investigated. Also, no information is available regarding the effect of such pasture-based diet on some blood parameters and the meat quality of growing lambs. It has been indicated that the negative experiences (Pedernera et al. 2022 ) with forage species enhance risk-aversion during future encounters with novel feeds, reducing grazing pressure on novel plants and thus the degradation of pasture through overgrazing on familiar forages (Launchbaugh and Howery 2005 ). Moreover, a preference for unfamiliar and potentially low-quality forages has negatively impacted their nutrient intake and thus compromised their productive performance and feeding behaviour (Pedernera et al. 2022 ). However, the influence of prior experience or grazing history on food preference and feeding behaviour of lambs exposed to unfamiliar pasture forages with different RFQ indices has not yet been quantified. Accordingly, the aims of the study reported here were to quantify the production benefits in terms of body weight gain (BWG), dry matter intake (DMI), feed conversion ratio (FCR) and carcass weight and yield of Karayaka male lambs grazed on pastures with different RFQ indices and to investigate the pH, colour traits and nutrient content of the meat produced by growing lambs, as well as the effects of dietary history on plant preferences and feeding behaviour of lambs exposed to unfamiliar pasture forages with different RFQ indices. Materials and methods Sward, animal and grazing management The lambs in this study were managed, cared for and fed following the Ondokuz Mayıs University Local Ethics Committee Animal Experimental Guidelines (Protocol No: 2016/44). Replicate (n = 3) plots (0.136 ha) with four subplots (0.034 ha) of pasture forages with four different RFQ indices that were established in May 2019 at the Research and Application Farm, Bafra Station, Faculty of Agriculture, Ondokuz Mayıs University (40°59'40"N, 35°54'27"E, 8 m above sea leve) were used in a two-year experiment between 2019 and 2020. Each subplot was planned to meet the dry matter (DM) requirement (3.5% of body weight, NRC, 2007 ) of three lambs during a 60-day grazing period after the swards reached grazing height. Seeds were drilled synchronously on 5 May 2019, after grasses and/or legume seeds were thoroughly mixed for mixed cropping. Before sowing, all plots were treated with herbicides and insecticides to prevent pests and weeds. Irrigation was applied twice during tillering and stem elongation stages and when needed in later periods. The primary soil characteristics of the experimental site were a loamy texture, a pH of 7.73 and workability of 55%, 0.60% organic matter, 0.009% total salt (as NaCl), 10.9% lime (CaCO 3 ), 0.66 kg/da of phosphorus (as P 2 O 5 ) and 25 kg/da of potassium (as K 2 O). Based on the soil test results, a similar amount of fertiliser (20 kg ha − 1 of each N, P and K) was applied manually to all plots. Because herbicides were not used after swarding the pasture plots, weeding was done continuously and uniformly by hand in all plots. During the experiment, the monthly average temperature, precipitation and humidity were 14.6°C, 716.7 mm and 73.1%, respectively, whereas during the grazing period, corresponding values were 21.0 ℃, 15.0 mm and 69.5%. Thirty-six intact male Karayaka lambs, each 3 months old and weighing 22.8 ± 0.14 kg, were assigned to completely randomized design with four RFQ treatments, each with three replications, for 74 days, comprising 14 days of adaptation to the experimental condition and 60 days of data collection. The subplots, divided by temporary fencing, were grazed rotationally by three lambs in 10-day intervals from the beginning of May until the beginning of July 2020 (Fig. 1 ). Self-measuring drinkers (plastic calibrated buckets) and mineral blocks were positioned and suspended within the shelter cabins to ensure ad libitum access to potable water and to satisfy the animals' mineral requirements, respectively. A three-sided enclosed wooden shelter (1.5 m × 2 m × 2 m) that served as the lambs' resting and overnight area was relocated to the subsequent grazing subplot to coincide with rotational grazing management. Species identified as having high forage yield and persistency in monoculture and/or polyculture plantings in a previous study at the same station (Aydın et al. 2022 ) were used to establish pastures with different RFQ indices in this study. The RFQ treatments were: 89.3 (89RFQ, comprising a blend of meadow fescue ( Festuca pratensis Population), tall fescue ( Festuca arundinacea cv. Starlett) and cocksfoot grass ( Dactylis glomerata cv. Lidacta) at an equal rate of 33.3% for each; 105.2 (105RFQ, comprising a blend of 40% alfalfa ( Medicago sativa cv. Emiliana) and meadow fescue ( Festuca pratensis Population), tall fescue ( Festuca arundinacea cv. Starlett) and cocksfoot grass ( Dactylis glomerata cv. Lidacta) at an equal rate of 20% each; 121.4 (121RFQ, comprising a blend of 80% white clover ( Trifolium repens cv. Rivandel) and 20% perennial ryegrass ( Lolium perenne cv. Belida) and 147.0 (147RFQ, comprising 100% bird's foot trefoil ( Lotus corniculatus Population). Aydın et al. ( 2022 ) have determined that the RFQs of bird's foot trefoil, alfalfa, white clover, perennial ryegrass, tall fescue, meadow fescue and cocksfoot grass were 147, 129, 126, 103, 92, 91 and 85, respectively, at this study location. Grazing behaviour In a free-choice trial, conducted to determine the influence of grazing history on forage preference by lambs exposed to unfamiliar forages, 500 g of all RFQ forages were offered simultaneously in separate troughs to the lambs in the individual pens (with six and two lambs per treatment and replicate, respectively). Observations on feeding behaviour were conducted (Rice et al. 2016 ) recorded from 07:30 to 09:30 hrs, across three consecutive days. Then the rank of the lambs' unfamiliar forage preference was determined by examining camera records. Thus, the total time spent by lambs from each plot in optional feeders containing different RFQ feeds and first-choice feeds from each RFQ pasture, was determined. Time spent in the optional feeders was calculated as the difference between the time each lamb spent seeking feed until feed intake was definite. Then, each of these parameters is expressed as a percentage of the relevant total. Measurements To monitor BWG, all lambs were weighed at the beginning and end of the 60-day grazing period, at 10-day intervals, based on changes in the BW on a scale of 100 g sensitivity, to determine lambs’ DM requirements (IMMAX EB 600). Before weighing, lambs were fasted (14 h) to exclude the effect of empty/full gastrointestinal tract condition on their body weight. The BWG of each lamb was calculated individually by subtracting the final BW from the initial weight and dividing it by sixty. The forage DMI of the lambs per replicate subplot was estimated using the cage technique as explained by Undi et al. ( 2008 ). Pre- and post-grazing herbage mass at 10-day grazing intervals were determined on each RFQ sward (Fraser et al. 2004 ). Because lambs in each subplot were grazed as a group, BW, BWG and DMI data are presented as the means of each subplot to calculate the feed conversion ratio (FCR). Thus, the FCR was calculated as average DMI divided by average BWG. Drinking water intake (DWI) was recorded daily as the difference by comparing the water offered and left in the plastic calibrated buckets. The forage samples collected by hand-harvesting at the soil level from each subplot at 10-day intervals were air-dried at room temperature until a constant weight and then ground to pass through a 1 mm sieve for proximate analysis. The analysis of DM (method 930.15), ash (method 942.05) and CP (method 954.01) of the samples was performed according to the methods approved by AOAC ( 2005 ). The NDF and ADF of the samples were analysed sequentially in an ANKOM A200/220 Fibre Analyser (ANKOM Technology Corp., located in Fairport, NY, USA). In vitro dry matter digestibility (IVDMD) was performed as described by Hervás et al. ( 2004 ). The TDN and ME values were calculated using equations from Aydin et al. ( 2019 ) and Aydın et al. ( 2022 ), respectively. One forage sample per subplot was analysed for nutritive value, with the analysis replicated three times. To assess serum biochemical parameters related directly to animal health, 10 ml blood samples were collected from the jugular vein of each lamb selected for slaughter into a clean, dry EDTA tube. Blood samples were centrifuged at 3,000 × g at 4°C for 15 min. Serum was separated into 2 ml clean, dried Eppendorf tubes and frozen at − 20°C until analysis. All blood serum samples were analysed using a biosystem autoanalyser (Roche Integra® 400 plus) to determine the concentrations of glucose, blood urea nitrogen (BUN), triglycerides, calcium (Ca) and phosphorus (P) using specific kits following the manufacturer's instructions. Also, the enzyme activities such as alkaline phosphatase (ALP), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were determined using the UV kinetic method (International Federation of Clinical Chemistry). At the end of the 60-day grazing period, all lambs were removed from their RFQ treatments, fasted for 12 h and again weighed to determine slaughter weights (SW) before transport to slaughter. One lamb with the BW closest to the treatment mean from each replication (n = 3 per treatment) was slaughtered humanely by severing the throat and major blood vessels in the neck at a nearby commercial abattoir (Bafra Commodity Exchange Slaughterhouse Limited). After we removed internal organs, carcass weights were measured immediately. Carcass yield (dressing percentages) was calculated as the ratio of carcass weight to the SW. After slaughtering, rumen pH was measured using a glass electrode pH meter (Hanna Instruments HI 8521) without opening the rumen to determine whether rumen pH was affected by the forages of different RFQ indices. The pH of the Longissimus dorsi muscle (LDM) at the interface of the 12th and 13th rib was measured using a Testo 205 pH meter with a solid glass probe at 1 hour (pH 1 ) and 24 hours (pH 24 ) after slaughter. At these times, meat colour was assessed by the CIE L* (lightness), a* (redness) and b* (yellowness) system using the Minolta CR 400 colourimeter (Minolta Camera Co., Osaka, Japan), with three repetitions in the LDM. The meat samples kept at 4°C for 24 hours were subjected to analytical procedures. A change (%) in the weight of the LDM over the subsequent 24 h was taken as the drip loss, as described by Honikel ( 1998 ). The dry matter (method 930.15), ash (method 942.15), crude protein (method 990.03) and ether extract (EE, method 920.39) contents of the LDM were determined in triplicate by the approved methods (AOAC, 2005 ). Statistical analysis All data, with subplots (n = 3 subplots per treatment) serving as the experimental units, were analysed using the mean of measurements within each subplot in the SPSS software v.21.0 using the MIXED procedure (SPSS Inc., Chicago, IL, USA). The normality of the data and the homogeneity of variances were evaluated with the Kolmogorov-Smirnov test and the Levene test, respectively. No outliers were detected in the variables. Percentage data that did not show a normal distribution were subjected to arcsin transformation. Because lambs in each subplot did not represent completely independent replicates and because subplots were nested within main plots, the mathematical model for a standard analysis of variance (ANOVA) was used as follows: Yij​ = µ + αi​ + βj(i)​ + ϵij​ Wherein Yij = value referring to the observation of repetition i of treatment j; µ = the overall mean; αi​ = the effect of the ith main plot (i = A, B, C); βj(i) = the effect of the jth subplot within nested within the ith main plot (j = 1, 2, 3, 4; RFQ treatments); ϵij = the random error associated with the kth observation in the jth subplot within the ith main plot. The treatment means were compared by Tukey's HSD test at a significance level of p ≤ 0.05. Results Nutritional value The nutritional values of forages from pastures with different RFQ values during the first and the last 30 days of the grazing period are shown in Table 1 . The 105RFQ forage exhibited the highest CP content during the first 30-day grazing period, followed by 147RFQ and 121RFQ pastures, with 89RFQ displaying the lowest. In the subsequent 30 days, the CP content ranked 147RFQ > 121RFQ > 105RFQ > 89RFQ. In the fibre components (NDF and ADF), the studied RFQ pastures ranked similarly (89RFQ > 121RFQ > 105RFQ > 147RFQ) in both grazing periods. The IVDMD decreased with increasing pasture RFQ. TDN generally increased with RFQ, except at 121RFQ, where the TDN level was lower than that at 105RFQ in the first period and higher than that at 147RFQ in the second. In both grazing periods, the ME values of the forages were in the following order: 105RFQ > 121RFQ > 89RFQ > 147RFQ. Table 1 Chemical composition and some quality indicators of pastures with different relative forage quality (RFQ) during the first (days 1 to 30) and the last (days 31 to 60) 30 days of the grazing period Item 1 Days 1 to 30 Days 31 to 60 89RFQ 105RFQ 121RFQ 147RFQ 89RFQ 105RFQ 121RFQ 147RFQ DM, % 29.78 24.30 26.29 22.09 32.40 28.45 22.45 25.02 Chemical composition, % of DM CP 12.14 20.30 17.09 19.49 12.66 18.91 19.27 19.59 ADF 32.24 29.25 30.66 28.37 36.37 33.23 29.80 32.11 NDF 58.09 48.91 49.71 44.16 66.49 50.02 47.20 48.91 Ash 10.62 11.79 9.94 10.58 11.89 11.25 10.58 11.79 Quality indicator, % of DM IVDMD 45.36 49.15 48.78 53.00 39.07 48.56 51.14 53.11 TDN 58.93 65.36 64.49 68.04 58.26 62.45 66.24 64.70 ME, Mj/kg DM 8.91 9.52 9.08 8.30 8.59 9.16 8.90 8.36 89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. DM: Dry matter, CP: Crude protein, ADF: Acid detergent fibre, NDF: Neutral detergent fibre, IVDMD: In vitro dry matter digestibility, TDN: Total digestible nutrients, ME: Metabolizable energy. 1 The values represent the means of the analyses and calculations performed with the samples obtained at 10-day intervals during the 60-day grazing period. Growth performance As in Table 2 , the BW and BWG of the 121RFQ lambs were higher than those of the 105RFQ lambs ( p < 0.05). The 121RFQ lambs had the highest DMI. Intermediate values observed for the 89RFQ and 105RFQ lambs, while the 147RFQ lambs had the least intake ( p 0.05). The 121RFQ and 89RFQ pastures exhibited a better feed conversion ratio than the 105RFQ pasture ( p < 0.05). The DWI of the 105RFQ lambs was higher than that of the 121RFQ and 147RFQ lambs ( p < 0.05). Table 2 Growth performance of Karayaka male lambs grazing on pastures with different relative forage quality (RFQ) Variable 1 89RFQ 105RFQ 121RFQ 147RFQ SEM p value Body weight (BW), kg Initial 22.93 22.90 22.66 22.86 0.136 0.928 Final 27.41 ab 26.18 b 28.28 a 26.53 b 0.390 0.047 BW gain, g/day 78.05 ab 54.55 b 95.83 a 63.33 b 6.397 0.031 Dry matter intake, g/day 834.16 b 815.74 b 926.10 a 719.69 c 24.835 0.004 Feed conversion ratio, g/g 11.16 b 14.89 a 9.88 b 11.74 ab 0.725 0.040 Water intake, L/day 1.09 ab 1.30 a 0.97 b 0.84 b 0.062 0.029 89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. SEM: Standard error of the mean. a,b,c Mean values in the same row with different superscripts differ ( p < 0.05). 1 Values are means of three subplots with three lambs per treatment. Carcass and meat quality The slaughter weight (SW) and carcass weight and yield of lambs (Table 5 ) were not affected by the RFQ treatment ( p > 0.05). The rumen pH of the 147RFQ lambs was lower than that of 89RFQ, 105RFQ and 12RFQ lambs ( p < 0.05; Table 5 ). The RFQ treatment did not affect the studied meat quality traits, except muscle b* values and fat content of the LDM (Table 6 ). While the b* values of the LDM measured 1 hour after slaughter were higher in the 89RFQ lambs than in the 105RFQ and 147RFQ lambs ( p < 0.05), the corresponding value at 24 hours was higher in the 121RFQ lambs than in the 147RFQ lambs ( p < 0.05). The 89RFQ and 105RFQ pastures increased the meat fat content compared to the 121RFQ and 147RFQ pastures ( p < 0.05). Table 5 The slaughter and carcass weights, carcass yield and rumen pH value of Karayaka male lambs grazing on pastures with different relative forage quality (RFQ) Variable 1 89RFQ 105RFQ 121RFQ 147RFQ SEM p value Slaughter weight (SW), kg 27.61 26.28 28.41 26.46 0.390 0.167 Carcass weight, kg 10.70 10.32 11.18 10.37 0.164 0.236 Carcass yield, % of SW 38.76 39.30 39.36 39.23 0.001 0.517 Rumen pH 6.37 ab 6.09 ab 6.55 a 5.70 c 0.109 0.004 89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. SEM: Standard error of the mean. a,b,c Mean values in the row with different superscripts differ ( p < 0.05). 1 Values are means of three replicate pens with one lamb per treatment. Table 6 Meat quality characteristics of the longissimus dorsi muscle of lambs grazing on pastures with different relative forage quality (RFQ) Variable 1 89RFQ 105RFQ 121RFQ 147RFQ SEM p value One hour after slaughter pH 1 5.94 6.11 5.86 6.12 0.067 0.488 CIELab values Lightness (L*) 38.42 38.76 38.09 38.42 0.549 0.982 Redness (a*) 15.28 14.36 13.72 13.93 0.278 0.202 Yellowness (b*) 5.42 a 4.04 b 4.50 ab 3.86 b 0.203 0.023 24 hours after slaughter pH 24 5.64 5.66 5.58 5.65 0.023 0.659 CIELab values Lightness (L*) 42.29 43.77 44.65 44.68 0.546 0.379 Redness (a*) 14.58 14.91 13.62 14.03 0.233 0.213 Yellowness (b*) 7.57 ab 7.75 ab 9.38 a 7.83 b 0.288 0.040 Drip loss, % 0.62 0.54 0.80 0.47 0.113 0.813 Chemical composition, % of dry matter Dry matter, % 24.42 24.53 23.36 22.76 0.392 0.346 Ash 1.53 1.61 1.71 1.69 0.044 0.539 Protein 20.41 20.51 19.90 19.98 0.206 0.717 Fat 2.47 a 2.40 a 1.08 b 1.73 b 0.086 0.031 89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. SEM: Standard error of the mean a,b,c Mean values in the same row with different superscripts differ ( p < 0.05). 1 Values are means of three replicate pens with one lamb per treatment. Serum biochemical parameters The serum metabolic profile, including glucose, triglyceride, BUN, Ca and P and enzyme activities such as ALP, ALT and AST, is presented in Table 4 . The RQF level did not affect the serum glucose and Ca content and the studied enzyme activities ( p > 0.05). The 105RFQ lambs had a higher serum triglyceride level than the 147RFQ lambs ( p < 0.05). The 89RFQ pasture decreased the BUN concentration and increased serum P content compared to the 105RFQ and 121RFQ pastures ( p < 0.05). Table 4 Serum biochemical parameters of Karayaka male lambs grazing on pastures with different relative forage quality (RFQ) Variable 1 89RFQ 105RFQ 121RFQ 147RFQ SEM p value Serum metabolic profile, mg/dl Glucose 64.66 60.00 66.33 71.00 1.892 0.239 Trigliserides 27.66 ab 38.00 a 23.66 ab 19.33 b 2.976 0.012 Blood urea nitrogen 11.33 b 20.66 a 19.66 a 17.66 ab 1.257 0.008 Ca 9.60 9.53 9.76 9.20 0.141 0.612 P 5.80 a 4.33 b 4.53 b 4.76 ab 0.232 0.018 Enzyme activity, U/l Alkaline phosphatase 124.00 106.00 126.00 136.00 16.313 0.234 Alanine aminotransferase 10.00 8.33 10.66 11.33 0.743 0.585 Aspartate aminotransaminase 75.00 80.00 84.33 75.00 3.412 0.833 89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. SEM: Standard error of the mean. a,b,c Mean values in the same row with different superscripts differ ( p < 0.05). 1 Values are means of three subplots with one lamb per treatment. Grazing behaviour Table 3 shows that lambs previously grazed on 89RFQ pastures spent a similar percentage of time in the 89RFQ troughs as in the 105RFQ troughs and significantly more time in both of these compared to the 121RFQ and the 147RFQ troughs ( p < 0.05). Lambs with a history of the 105RFQ grazing spent the most time in 105RFQ troughs and a similar amount of time in the 121RFQ troughs, but significantly more time in both compared to the 89RFQ and 147RFQ troughs ( p < 0.05). Lambs from the 121RFQ group spent the most time in the 105RFQ troughs, followed by the 121RFQ, 89RFQ and 147RFQ troughs ( p < 0.05). A similar pattern was observed for the 147RFQ lambs, which spent the most time in the 105RFQ troughs, followed by time spent in the 147RFQ, 121RFQ and 89RFQ troughs ( p < 0.05). The first feeding preferences of lambs demonstrated a clear influence of their prior dietary experience (Table 6 ). Lambs with a dietary history of 89RFQ overwhelmingly preferred the 105RFQ forage over the familiar 89RFQ. Similarly, lambs previously fed 105RFQ exhibited a strong preference for the 89RFQ forage over the 147RFQ forage. Lambs with a history of consuming 121RFQ showed a strong preference for the familiar 121RFQ forage. Finally, lambs with a history of 147RFQ preferred the familiar 147RFQ and unfamiliar 105RFQ forages. Table 3 The influence of grazing history on forage preference by lambs exposed to unfamiliar forages and the percentage of first-choice forages from each relative forage quality (RFQ) pasture Grazing history Lamb 89RFQ 105RFQ 121RFQ 147RFQ SEM p value Time spent in the optional troughs, % 2 89RFQ 26.27 ab (22.2) 27.59 a (77.8) 24.81 b 21.52 c 0.617 0.024 105RFQ 21.74 bc 34.41 a 25.00 b (44.4) 18.84 c (55.6) 1.192 0.006 121RFQ 5.92 d 43.18 a 33.64 b (100) 17.27 c 2.692 < 0.001 147RFQ 8.01 c 45.24 a 23.71 b (22.2) 22.61 b (77.8) 2.504 < 0.001 89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. SEM: Standard error of the mean a,b,c Mean values in the same row with different superscripts differ ( p < 0.05). 1 Values are the average of three replicates with two lambs per treatment selected at the end of the growth trial. 2 Percentage of the total time spent by the lambs in the troughs. Values ​​in parentheses indicate the percentage of first-choice forages from the RFQ pastures. Discussion Since this is the first study on lambs grazed on pastures with different RFQs for the parameters examined, our findings for the parameters examined were interpreted with results from studies involving lambs grazing under extensive feeding systems and those fed forage and supplementation. While the study aimed to elucidate the predictive power of RFQ, the findings reveal a complex interplay of nutritional, physiological and behavioral factors that necessitate a cautious interpretation of RFQ's sole utility in lamb production models. Accordingly, integrating feed quality indices such as RFQ with actual nutritional value parameters can provide more accurate and practical tools for pastured-growing lambs. The results of the present study reveal a complex interplay between forage quality indices and lamb responses, highlighting the limitations of relying solely on RFQ as a comprehensive predictor of animal performance and metabolic status. This aligns with established literature indicating that while forage mass influences intake and performance (Turner et al. 2014 ), the specific botanical composition, particularly the inclusion of legumes, significantly impacts dietary protein utilisation and growth rates due to faster digestion kinetics (Howes et al. 2015 ; Kaithwas et al. 2020 ). The comparable growth observed with different forage species in other studies (Fraser et al. 2004 ; Ates et al. 2013 , 2015 ), attributed to their similar quality, further supports the notion that specific forage characteristics beyond a broad index like RFQ are critical determinants of animal performance. This result, when compared with the findings of previous studies using the same breed (Olfaz et al. 2005 ; Sen et al. 2011 ; Yıldırım et al. 2013 ), may explain why Karayaka lambs perfomed poorly in this study. Poor lamb performance may mainly be attributed to nutritional deficiency from consuming roughage as the sole source (Elizalde et al. 2021 ). Uzun and Ocak ( 2019 ) noted that pastured-animal productivity is contingent on grazing management (frequency, intensity, pressure) and forage characteristics (botanical composition, quality, quantity). Variations in forage quality are mainly associated with fibre and CP content (Fernandez-Turren et al. 2020 ), while the limiting factor for DMI is NDF intake (Forbes, 2007 ). Indeed, when grazing animals were fed a legume-based pasture, they tended to produce higher BWG and carcass weights than those on a grass-based pasture (Fraser et al. 2004 ; Kemp et al. 2010 ). Therefore, future models that predict lamb performance in pasture-based systems should integrate detailed forage compositional data alongside quality indices to enhance accuracy and practical applicability. Lamb growth performance was more directly linked to specific RFQ treatments, with 121RFQ lambs demonstrating superior BW, BWG and the highest DMI. This suggests that the blend of white clover and perennial ryegrass in the 121RFQ pasture provided a palatable and nutritionally balanced diet conducive to growth performance, even though its nutrient values were not uniformly the highest across all nutritive parameters. Papadopoulos et al. ( 2001 ) and Kemp et al. ( 2010 ) reported that white clover and wheat mixtures had an effect on lamb growth performance similar to the one in the present study. The better FCR in 121RFQ and 89RFQ lambs compared to 105RFQ, highlights that efficient nutrient utilisation is not solely determined by estimated energy content (Birkett and de Lange, 2001 ). Moreover, ryegrass-white clover mixtures (up to 50%), as opposed to monoculture ryegrass, resulted in superior performance outcomes (Niderkorn et al. 2017 ). This supports the idea that polyculture pastures are advantageous in terms of DMI and preservation of forage quality (Lüscher et al. 2014 ; Ates et al. 2015 ; Jimenez et al. 2019 ). This situation may be related to increasing nutrient utilisation and reducing environmental impacts by associative effects between plant chemical substances (Mueller-Harvey et al. 2019 ). The lower DMI that observed in the 147RFQ lambs, despite the high crude protein content of late-season bird's foot trefoil, suggests the potential influence of palatability or other intake-limiting factors associated with pure bird's foot trefoil. This is further supported by the finding that the 147RFQ group exhibited a lower rumen pH, likely due to the distinct fermentation profile (Bach et al. 2005 ) of sole bird's foot trefoil compared to other RFQ treatments, suggesting that while RFQ indicated high quality (Aydın et al. 2019), it did not fully predict intake behaviour. However, our results challenge the finding that tannin-containing birdsfoot clover increases BWG by improving protein utilisation compared to alfalfa or white clover-perennial ryegrass pastures in lambs (Girard et al. 2016 ). These discrepancies underscore the complex interplay between forage quality indices, specific plant characteristics and animal performance, highlighting the need to consider factors beyond RFQ when evaluating the nutritional value and utilisation of forages like pure bird's foot trefoil. Contrary to the expectation that higher forage quality, characterised by elevated protein and fibre content, would induce a physiological demand for increased DWI, as suggested by previous research Malan et al. ( 2020 ), the findings of the current study do not support this premise. Instead, the observed higher DWI in lambs grazing 105RFQ pastures appears to be more closely associated with the immediate water status of the pasture vegetation, specifically its DM content and the presence of canopy surface moisture (Sun et al. 2014 ). This outcome suggests that environmental factors related to pasture conditions may exert a more immediate influence on lamb water consumption than the inherent nutritional quality of the forage alone (Malan et al. 2020 ). Consequently, the present results regarding DWI hold potentially significant implications for our understanding of the complex interplay among forage RFQ levels and DMI and DWI in grazing lambs. Further in-depth investigation is warranted to elucidate the underlying osmotic and metabolic mechanisms driven by specific forage compositional attributes that may ultimately influence water intake patterns. The nutritional results of forages across the grazing period demonstrated temporal dynamics in key nutrients, particularly CP. The shift in the highest CP content from 105RFQ in the initial phase to 147RFQ in the later phase underscores the importance of considering forage maturity and botanical composition beyond a static RFQ value. The nutritive value and nutrient degradation kinetics of pastures are affected by botanical composition, harvest date and the interaction between type of pasture and date of harvest (Keim et al. 2013 ; Ma et al. 2021 ). Although there is a consistent inverse relationship (Roukos et al. 2011 ; Aydin et al. 2019 ) between RFQ and fibre fractions (NDF, ADF), the observed decrease in IVDMD with increasing RFQ suggests that the specific forage mixes at higher RFQs might have contained components hindering in vitro digestibility (Moore and Jung, 2001 ). Because of the non-linear relationship between RFQ and ME (Aydın et al. 2019), it is difficult to estimate energy availability for growing lambs based on this index alone. Serum biochemical analysis revealed a selective sensitivity to RFQ, with triglycerides, BUN and phosphorus showing significant differences across groups while general metabolic indicators remained stable. These findings suggest that varying forage compositions elicited distinct metabolic responses, highlighting RFQ's limitations in predicting specific nutrient absorption and utilisation at a biochemical level. The observed positive correlation between CP and BUN aligns with previous reports (Li et al. 2025 ), indicating that increased nitrogen intake leads to higher BUN. However, elevated BUN, as suggested by Kohn et al. ( 2005 ), might also point to reduced nitrogen utilisation efficiency. These selective biochemical responses underscore the need to consider forage composition beyond RFQ to fully understand nutrient dynamics in grazing lambs. In our study, the elevated serum P in 89RFQ lambs, coupled with their higher DMI and performance, suggests that the high P content of the wheatgrass pasture likely contributed to these levels (Zhang et al. 2016 ). The selective impact of RFQ on meat quality, precisely b* values and intramuscular fat, suggests subtle forage-induced alterations in muscle metabolism. The higher fat in lambs with lower RFQ levels, such as 89RFQ and 105RFQ lambs warrants further investigation into fatty acid profiles and their quality implications. The muscle nutrient content aligned with a previous Karayaka lamb study (Aksoy and Ulutaş, 2016). The significant effect of RFQ treatments on fat content did not reflect in the meat colour characteristics, despite the positive correlation between lower fat content and higher b* values (Calnan et al. 2017 ). This suggests that the consistent pH values observed, rather than fat content variations, likely explain the lack of significant differences in meat colour (Montelli et al. 2021 ). Grazing behaviour in lambs demonstrates learned preferences, with familiarity and positive post-ingestive feedback significantly influencing feed selection (Pedernera et al. 2022 ). Lambs preferred forages with RFQ values similar to their grazing history, suggesting a role for familiarity and gradual changes in forage quality. Consistent with Fitzsimons et al. ( 2017 ), feeding behaviour is driven by forage quantity and quality, mediated by physical and metabolic mechanisms shaped by novel forage characteristics. While trough-seeking time remained consistent (4.35 ± 0.426) across the RFQ groups, the time spent at troughs with different RFQ levels indicated selective feeding decisions based on prior experience and forage characteristics (Baumont et al. 2000 ; Neave et al. 2018 ). This highlights that feeding behaviour is influenced by both immediate availability and past experience. Dietary diversity or complementary forage combinations may better support lamb growth than RFQ alone (Pedernera et al. 2022 ). However, initial palatability can sometimes override established familiarity. The findings confirm that prior experience enhances foraging efficiency (Launchbaugh & Howery, 2005 ; Neave et al. 2018 ) and varying time at RFQ troughs may reflect nutritional rebalancing (Baumont et al. 2000 ). The introduction of grazing ruminants to novel environments containing unfamiliar or undesirable plant species can precipitate a decline in pasture quality and health, driven by the selective overconsumption of habitual forage (Launchbaugh and Howery 2005 ; Neave et al. 2018 ; Pedernera et al. 2022 ). It is imperative to further investigate, recognizing the implications of these observations for the sustainability and resilience of pastoral systems. Limitations An important limitation of this study is the use of a relatively low number of animals in each treatment (n = 3 lambs/subplot). This reduces statistical power and increases the sensitivity of the results to deviations from the assumptions of normality and homogeneity of variance. Therefore, low statistical power may have led to the failure to detect present effects in this study. The absence of a digestibility trial in the evaluations rendered the findings challenging to discuss and interpret meaningfully. Furthermore, the observed outcomes are context-dependent, necessitating cautious extrapolation to extended durations or disparate climatic regimes. One of the limitations of our study is that metabolic and rumen metabolites were determined only at certain times, such as during the entire experiment or at lesat during the 30-day grazing period. Therefore, the presented results should be interpreted cautiously and the observed trends should be confirmed in future studies with larger sample sizes or more extended periods or different climatic conditions. Conclusion In conclusion, our hypothesis that the studied variables, especially growth performance, would improve as the pasture was established with high RFQ forages, was not consistently supported. Indeed, our study demonstrated that the RFQ of pasture significantly influenced several aspects of lamb production, including forage nutritional value, growth performance and specific carcass and meat quality traits, while having limited effects on serum biochemical parameters. Higher RFQ pastures did not consistently translate to improved outcomes across all variables. While some nutritional components (e.g., fibre) varied predictably with RFQ, others (e.g., TDN, ME) showed more complex relationships. Lamb growth performance was optimised at the 121RFQ treatment, characterized by the highest DMI and feed conversion ratio. RFQ did not affect the carcass weight and yield, but some meat quality traits (muscle b* values, fat content) were affected. Notably, grazing history influenced feeding behaviour, with lambs generally preferring forages from pastures they had previously grazed. These findings highlight the importance of considering the multifaceted effects of RFQ in pasture management for lamb production, suggesting that optimising RFQ for specific production goals may require a nuanced approach rather than simply maximising RFQ. Further research is needed to elucidate the underlying mechanisms driving these relationships and to determine optimal RFQ ranges for various lamb production systems. Declarations Acknowledgements The authors would like to thank the Ondokuz Mayıs University (Samsun, Turkiye) for providing the facilities for this study and the MSc. B. Bilik for his valuable contribution to the fieldwork. Author contributions İA and NO conceived and designed the research. AA conducted experiments. AA and NO collected and analysed data. AA wrote the manuscript. NO edited the manuscript. All authors read and approved the manuscript. Data availability The data for this study are available from the corresponding author upon reasonable request. Funding The data in this study were collected as a part of the first author’s doctoral thesis. The research was funded through the Scientific and Technological Research Council of Turkiye (TUBITAK) (Grant/Award Number: 118 O 197). Code availability Not applicable. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ondokuz Mayıs University Local Ethics Committee following the Animal Experimental Guidelines (Protocol No: 2016/44) Consent to participate All authors consented to participation. Consent for publication All authors consented to submit the manuscript to the journal. Conflict of interest All authors declare that there are no actual or potential conflicts of interest. References Aksoy, Y., Ulutas, Z., 2016. Meat production traits of local Karayaka sheep in Turkey 1. The meat quality characteristic of lambs. Italian Journal of Food Science, 28(1), 131–138. AOAC, 2005. Official Method of Analysis. 18th Edition, Association of Official Analytical Chemists, Washington, DC. Ates, S., Lucas, R. J., Edwards, G. R., 2015. Stocking rate effects on liveweight gain of ewes and their twin lambs when grazing subterranean clover–perennial grass pastures. Grass and Forage Science, 70(3), 418–427. Ates, S., Lucas, R. J., Edwards, G. R., 2013. Effects of stocking rate and closing date on subterranean clover populations and dry matter production in dryland sheep farms. New Zealand Journal of Agricultural Research, 56, 22–36. Aydin, I., Algan, D., Pak, B., Ocak, N., 2019. Similarity analysis with respect to some quality indicators and quality categories based on relative forage quality ranges of desirable rangeland forages. Fresenius Environmental Bulletin, 28(8): 5926–5936. Aydın, İ., Pak, B., Ocak, N., 2022. Comparison of cultivated and wild relatives of several forage species in mixed rangeland based on some nutritional characteristics. Black Sea Journal of Agriculture, 5(2), 91–99. Bach, A., Calsamiglia, S., Stern, M. D., 2005. Nitrogen metabolism in the rumen. Jorunal of Dairy Science, 88, E9–E21. Baumont, R., Prache, S., Meuret, M., Morand-Fehr, P., 2000. How forage characteristics influence behaviour and intake in small ruminants: a review. Livestock Production Science, 64(1), 15–28. Birkett, S., de Lange, K., 2001. Limitations of conventional models and a conceptual framework for a nutrient flow representation of energy utilization by animals. British Journal of Nutrition, 86(6), 647–659. Calnan, H. B., Jacob, R. H., Pethick, D. W., Gardner, G. E., 2017. Selection for intramuscular fat and lean meat yield will improve the bloomed colour of Australian lamb loin meat. Meat Science, 131, 187–195. De Brito, G. F., Ponnampalam, E. N., Hopkins, D. L., 2017. The effect of extensive feeding systems on growth rate, carcass traits, and meat quality of finishing lambs. Comprehensive Reviews in Food Science and Food Safety, 16(1), 23–38. Elizalde, F., Hepp, C., Reyes, C., Tapia, M., Lira, R., Morales, R., Sales, F., Catrileo, A., Silva, M., 2021. Growth, carcass and meat characteristics of grass-fed lambs weaned from extensive rangeland and grazed on permanent pastures or alfalfa. Animals, 11(1), 52. Favre, J. R., Castiblanco, T. M., Combs, D. K., Wattiaux, M. A., Picasso, V. D., 2019. Forage nutritive value and predicted fiber digestibility of Kernza intermediate wheatgrass in monoculture and in mixture with red clover during the first production year. Animal Feed Science and Technology, 258, 114298. Fernandez-Turren, G., Repetto, J. L., Arroyo, J. M., Pérez-Ruchel, A., Cajarville, C., 2020. Lamb fattening under intensive pasture-based systems: a review. Animals, 10(3), 382. Fitzsimons, C., McGee, M., Keogh, K., Waters, S. M., Kenny, D. A., 2017. Molecular physiology of feed efficiency in beef cattle. In Biology of domestic animals (ed. CG Scanes and RA Hill), 120–163. CRC Press, FL, Boca Raton, USA. Fraser, M. D., Speijers, M. H., Theobald, V. J., Fychan, R., Jones, R., 2004. Production performance and meat quality of grazing lambs finished on red clover, lucerne or perennial ryegrass swards. Grass and Forage Science, 59(4), 345–356. Forbes, J. M., 2007. A personal view of how ruminant animals control their intake and choice of food: Minimal total discomfort. Nutrition Research Reviews, 20, 132–146. Girard, M., Dohme-Meier, F., Silacci, P., Kragten, S. A., Kreuzer, M., Bee, G., 2016. Forage legumes rich in condensed tannis may increase n-3 fatty acid levels and sensory quality of lamb meat. Journal of the Science of Food and Agriculture, 96: 1923–1933. Hervás, G., Ranilla, M. J., Mantecón, Á. R., Bodas, R., Frutos, P., 2004. Comparison of in vitro digestibility of feedstuffs using rumen inoculum from sheep or red deer. Journal of Animal and Feed Science, 13 (1):91–94. Honikel, K. O., 1998. Reference methods for the assessment of physical characteristics of meat. Meat Science, 49, 447–457. Howes, N. L., Bekhit, A. E. D. A., Burritt, D. J., Campbell, A. W., 2015. Opportunities and implications of pasture-based lamb fattening to enhance the long‐chain fatty acid composition in meat. Comprehensive Reviews in Food Science and Food Safety, 14(1), 22–36. Jimenez, L. E. R., Naranjo, A., Hernandez, J. C. A., Ovalos, J. O., Ortega, O. C., Ronquillo, M. G., 2019. A meta-analysis on the effect of the feeding type and production system on the carcass quality of lambs. Italian Journal of Animal Science, 18, 423–434. Kaithwas, M., Singh, S., Prusty, S., Mondal, G., Kundu, S. S., 2020. Evaluation of legume and cereal fodders for carbohydrate and protein fractions, nutrient digestibility, energy and forage quality. Range Management and Agroforestry, 41(1), 126–132. Keim, J. P., Valderrama, X., Alomar, D., López, I. F., 2013. In situ rumen degradation kinetics as affected by type of pasture and date of harvest. Scientia Agricola, 70, 405–414. Kemp, P. D., Kenyon, P. R., Morris, S. T., 2010. The use of legume and herb forage species to create high performance pastures for sheep and cattle grazing systems. Revista Brasileira de Zootecnia, 39, 169–174. Kohn, R. A., Dinneen, M. M., Russek-Cohen, E., 2005. Using blood urea nitrogen to predict nitrogen excretion and efficiency of nitrogen utilization in cattle, sheep, goats, horses, pigs, and rats. Journal of Animal Science, 83, 879–889. Malan, J. A. C., Flint, N., Jackson, E. L., Irving, A. D., Swain, D. L., 2020. Environmental factors influencing cattle's water consumption at offstream watering points in rangeland beef cattle. Livestock Science, 231, 103868. Launchbaugh, K.L., Howery, L.D., 2005. Understanding landscape use patterns of livestock as a consequence of foraging behavior. Rangel. Ecol. Manag. 58, 99–108. Li, B., Hou, P., Liu, L., Zhao, L., Zhang, X., Yang, C., Huang, X., Ge, T., Zheng, Y., Wen, Y., Zhang, E., 2025. Effects of dietary protein level on growth performance, nitrogen metabolism, serum biochemical index, and meat quality of Suffolk x Hu F1 lambs. Journal of Agriculture and Food Research, 21, 101808. Lüscher, A., Mueller-Harvey, I., Soussana, J. F., Rees, R. M., Peyraud, J. L., 2014. Potential of legume-based grassland-livestock systems in Europe: A review. Grass and Forage Science, 69, 206–228 Ma, Y., Khan, M. Z., Liu, Y., Xiao, J., Chen, X., Ji, S., Li, S., 2021. Analysis of nutrient composition, rumen degradation characteristics, and feeding value of Chinese rye grass, barley grass, and naked oat straw. Animals, 11(9), 2486. Montelli, N. L. L. L., Alvarenga, T. I. R. C., Almeida, A. K., Alvarenga, F. A. P., Furusho-Garcia, I. F., Greenwood, P. L., Pereira, I. G., 2021. Associations of feed efficiency with circulating IGF-1 and leptin, carcass traits and meat quality of lambs. Meat Science, 173, 108379. Moore, K. J., Jung, H. J., 2001. Lignin and fiber digestion. Journal of Range Management 2001;54:420–430. Mueller-Harvey, I., Bee, G., Dohme-Meier, F., Hoste, H., Karonen, M., Kolliker, R., Lüscher, A., Niderkorn, V., Pellikaan, W. and Salminen, J. P., 2019. Benefits of condensed tannins in forages fed to ruminants: importance of structure, concentration and diet. Crop Science, 59, 1–25. Neave, H. W., Weary, D. M., von Keyserlingk, M. A. G., 2018. Review: Individual variability in feeding behaviour of domesticated ruminants. Animal 12: s419–s430. Niderkorn, V., Martin, C., Le Morvan, A., Rochette, Y., Awad, M., Baumont, R., 2017. Associative effects between fresh perennial ryegrass and white clover on dynamics of intake and digestion in sheep. Grass and Forage Science, 72, 691–699. NRC, 2007. Nutrient Requirements of Small Ruminants: Sheep, Goats, Cervids, and New World Camelids, 6th. ed, National Academy Press Washington, DC, USA. Olfaz, M., Ocak, N., Erener, G., Cam, M. A., Garipoglu, A. V., 2005. Growth, carcass and meat characteristics of Karayaka growing rams fed sugar beet pulp, partially substituting for grass hay as forage, Meat Science, 70, 7–14. Pedernera, M., Vulliez, A., Villalba, J. J., 2022. The influence of prior experience on food preference by sheep exposed to unfamiliar feeds and flavors. Applied Animal Behaviour Science, 246, 105530. Papadopoulos, Y. A., Charmley, E., McRae, K. B., Farid, A., Price, M. A., 2001. Addition of white clover to orchardgrass pasture improves the performance of grazing lambs, but not herbage production. Canadian Journal of Animal Science, 81(4), 517–523. Rice, M., Jongman, E. C., Butler, K. L., Hemsworth, P. H., 2016. Relationships between temperament, feeding behaviour, social interactions, and stress in lambs adapting to a feedlot environment. Applied Animal Behaviour Science, 183, 42–50. Roukos, C., Papanikolaou, K., Karalazos, A., Chatzipanagiotou, A., Mountousis, I., Mygdalia, A. 2011. Changes in nutritional quality of herbage botanical components on a mountain side grassland in North-West Greece. Animal Feed Science and Technology, 169(1–2), 24–34. Poli, C. H. E. C., Monteiro, A. L. G., Devincenzi, T., Albuquerque, F. H. M. A. R. D., Motta, J. H. D., Borges, L. I, et al. 2020. Management Strategies for Lamb Production on Pasture-Based Systems in Subtropical Regions: A Review. Frontiers in Veterinary Science, 7:543. Sen, U., Sirin, E., Ulutas, Z., Kuran, M., 2011. Fattening performance, slaughter, carcass and meat quality traits of Karayaka lambs. Tropical Animal Health and Production, 43(2), 409–416. Sun, L. Z., Auerswald, K., Wenzel, R., Schnyder, H., 2014. Drinking water intake of grazing steers: The role of environmental factors controlling canopy wetness. Journal of Animal Science, 92(1), 282–291. Tesk, C. R. M., Pedreira, B. C., Pereira, D. H., Pina, D. S., Ramos, T. A., Mombach, M. A., 2018. Impact of grazing management on forage qualitative characteristics: a review. Scientific Electronic Archives, 11(5), 188–197. Undi, M., Wilson, C., Ominski, K. H., & Wittenberg, K. M., 2008. Comparison of techniques for estimation of forage dry matter intake by grazing beef cattle. Canadian Journal of Animal Science, 88(4), 693–701. Uzun, F., Ocak, N., 2019. Some vegetation characteristics of rangelands subjected to different grazing pressures with single-or multi-species of animals for a long time (A case of Zonguldak province, Turkey). Anadolu Tarım Bilimleri Dergisi, 34(3), 360–370. Turner, K. E., Belesky, D. P., Cassida, K. A., Zerby, H. N., 2014. Carcass merit and meat quality in Suffolk lambs, Katahdin lambs, and meat-goat kids finished on a grass–legume pasture with and without supplementation. Meat Science, 98(2), 211–219. Yıldırım, A., Ulutaş, Z., Ocak, N., Kaptan, M., 2013. Effects of birth weight and feeding system on fattening performance and feeding behaviour of Karayaka male lambs. Italian Journal of Animal Science, 12(4), e89. Zhang, B., Wang, C., Wei, Z., Sun, H., Liu. H.J.A.-A.J. o A.S., 2016. The Effects of Dietary Phosphorus on the Growth Performance and Phosphorus Excretion of Dairy Heifers. Asian-Australasian Journal of Animal Science, 29 (7), 960–964. Cite Share Download PDF Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Tropical Animal Health and Production → Version 1 posted Reviewers agreed at journal 16 Aug, 2025 Reviewers invited by journal 12 Aug, 2025 Editor assigned by journal 30 Jul, 2025 First submitted to journal 27 Jul, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7230525","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499566143,"identity":"8308ecb8-81e2-4d1f-ba07-11212f82aa18","order_by":0,"name":"Ahmet Akdağ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDACCTBpA+HwEK3lAEMaXIsEsVoOk6CFf3Z34uMPNefl5CMSGB+8bWOoM28gZMmds5sNDhy7bWx4I4HZcG4bg4TMAULW3MjdJnGA7XbixhkJbNK8QC0EXSZ/I3f7jwP/zoG0sP8mSosB0BaGg20HEudLJLAxE6XF8EbuZomzfcnGBjwPmyXnnJOQnEFIi9yN3I0fKr7Zycm3Jx/88KbMhp+IiIG58ABjAwNRMQkH8g0kKB4Fo2AUjIKRBQDYbj+5E4FYYgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0154-7839","institution":"Eskisehir Osmangazi University: Eskisehir Osmangazi Universitesi","correspondingAuthor":true,"prefix":"","firstName":"Ahmet","middleName":"","lastName":"Akdağ","suffix":""},{"id":499566144,"identity":"c30f14f8-a898-4fb9-942b-229236f7caf2","order_by":1,"name":"İbrahim Aydın","email":"","orcid":"","institution":"Ondokuz Mayis University: Ondokuz Mayis Universitesi","correspondingAuthor":false,"prefix":"","firstName":"İbrahim","middleName":"","lastName":"Aydın","suffix":""},{"id":499566145,"identity":"71c7a2c5-4510-462a-b6da-2f9e37e23522","order_by":2,"name":"Nuh Ocak","email":"","orcid":"","institution":"Ondokuz Mayis University: Ondokuz Mayis Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Nuh","middleName":"","lastName":"Ocak","suffix":""}],"badges":[],"createdAt":"2025-07-28 06:42:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7230525/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7230525/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11250-026-04956-4","type":"published","date":"2026-03-02T16:00:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89484390,"identity":"2e3e51e3-b19c-47b5-928c-74dd7e462747","added_by":"auto","created_at":"2025-08-20 12:32:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42404,"visible":true,"origin":"","legend":"\u003cp\u003eThe main plot and subplots of pastures that were established according to different relative forage quality and grazing management practices\u003c/p\u003e\n\u003cp\u003eA, B and C represent the main plots; 1, 2, 3 and 4 indicate the relative feed quality (RFQ) treatments; ■ denotes the shelters; a, b, c, d, e and f represent the subplots served as replicates and a 10-day grazing pattern implemented in each subplot.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7230525/v1/fb67c95fa919cc363f93ecc3.png"},{"id":104251812,"identity":"4900f9f8-3639-4306-aa77-c84cb8bb8bd3","added_by":"auto","created_at":"2026-03-09 16:15:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1043479,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7230525/v1/b3a8cc72-341a-4ebb-a99f-e0d6d6dfd10b.pdf"}],"financialInterests":"","formattedTitle":"Growth performance, feeding behaviour, serum biochemical and meat quality traits of Karayaka lambs fed pastures consisting of different relative forage quality","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePasture-based sheep production plays an important economic and environmental role in many subtropical countries (Kemp et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Poli et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, pastured-lamb production reduces input costs, promotes ecological and animal welfare and yields potentially healthier meat (Elizalde et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, lamb production remains far below its potential, primarily due to a lack of knowledge regarding pasture quality and the nutrition of pastured lambs in subtropical countries (Poli et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Also, wide variations in legume and grass content established by parameters such as plant growth rate, seed ratio, establishment, management and persistence, makes it challenging to regulate the quality and quantity of forage grazed by lambs (Poli et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Accordingly, these systems can lead to slower lamb growth and thus delay timely target weight achievement, impacting production efficiency (De Brito et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn pastoral systems, increasing animal performance, producing healthy, high-quality products and maintaining the persistence and productivity of legume and grass forages are key challenges. To overcome these challenges, there is an increasing awareness of incorporating different forages into pasture establishment (Ates et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Jimenez et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Papadopoulos et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) demonstrated that the inclusion of white clover (\u003cem\u003eTrifolium repens\u003c/em\u003e) in orchardgrass (\u003cem\u003eDactylis glomerata\u003c/em\u003e) pastures resulted in improved lamb performance. Fraser et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) reported that grazing lambs on forage legume swards, such as red clover (\u003cem\u003eTrifolium pratense\u003c/em\u003e) and lucerne (\u003cem\u003eMedicago sativa\u003c/em\u003e), increases individual lamb performance without compromising carcass and meat quality except for muscle unsaturated fatty acid profiles compared to perennial ryegrass (\u003cem\u003eLolium perenne\u003c/em\u003e) swards. Chicory (\u003cem\u003eCichorium intybus\u003c/em\u003e), plantain (\u003cem\u003ePlantago lanceolata\u003c/em\u003e), red clover (\u003cem\u003eTrifolium pratense\u003c/em\u003e) and white clover (\u003cem\u003eTrifolium repens\u003c/em\u003e) based pastures can result in a more significant live weight gain in lambs compared to perennial ryegrass (\u003cem\u003eLolium perenne\u003c/em\u003e) based pastures (Kemp et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Incorporating subterranean clover (\u003cem\u003eTrifolium subterraneum\u003c/em\u003e) into orchardgrass (\u003cem\u003eDactylis glomerata\u003c/em\u003e) and perennial ryegrass (\u003cem\u003eLolium perenne\u003c/em\u003e)-based pastures has a positive impact on pasture production (Ates et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and lambs\u0026rsquo; growth rates (Ates et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on the above research findings, animal productivity in grazing systems is contingent on forage species (such as legumes, grasses and other botanical families) and characteristics (nutrient composition, quality and quantity). Pasture forage quality, defined as the capacity of forage to supply the requisite nutrients for grazing animals, including lambs (Tesk et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), is a critical determinant of effective lamb production. Consequently, achieving optimal lamb performance is closely associated with the quantity of specific nutrients and key quality indicators in the forage (Tesk et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Aydın et al. 2019), such as crude protein (CP), total digestible nutrients (TDN), digestible dry matter (DDM) and metabolizable energy (ME). One of the main constraints in grazing systems is the limited dry-matter intake (DMI) by grazing animals, resulting in the inability to meet their nutrient (energy and protein) requirements for high performance (Fernandez-Turren et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Relative forage quality (RFQ), used as a quality indicator (Aydin et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), is a crucial index for evaluating the nutritional value of ruminant feeds (Favre et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Since the RFQ index relies on neutral detergent fibre (NDF), NDF digestibility (NDFD), TDN, CP, fatty acids and ash (Favre et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a pasture established according to this index can be an alternative to overcome this constraint in pasture quality.\u003c/p\u003e\u003cp\u003eTo our knowledge, the effects of pastures comprised of species with different RFQ indices on the grazing lambs' growth performance, carcass and meat quality have not been investigated. Also, no information is available regarding the effect of such pasture-based diet on some blood parameters and the meat quality of growing lambs. It has been indicated that the negative experiences (Pedernera et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with forage species enhance risk-aversion during future encounters with novel feeds, reducing grazing pressure on novel plants and thus the degradation of pasture through overgrazing on familiar forages (Launchbaugh and Howery \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Moreover, a preference for unfamiliar and potentially low-quality forages has negatively impacted their nutrient intake and thus compromised their productive performance and feeding behaviour (Pedernera et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the influence of prior experience or grazing history on food preference and feeding behaviour of lambs exposed to unfamiliar pasture forages with different RFQ indices has not yet been quantified. Accordingly, the aims of the study reported here were to quantify the production benefits in terms of body weight gain (BWG), dry matter intake (DMI), feed conversion ratio (FCR) and carcass weight and yield of Karayaka male lambs grazed on pastures with different RFQ indices and to investigate the pH, colour traits and nutrient content of the meat produced by growing lambs, as well as the effects of dietary history on plant preferences and feeding behaviour of lambs exposed to unfamiliar pasture forages with different RFQ indices.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eSward, animal and grazing management\u003c/b\u003e\u003c/p\u003e\u003cp\u003e The lambs in this study were managed, cared for and fed following the Ondokuz Mayıs University Local Ethics Committee Animal Experimental Guidelines (Protocol No: 2016/44). Replicate (n\u0026thinsp;=\u0026thinsp;3) plots (0.136 ha) with four subplots (0.034 ha) of pasture forages with four different RFQ indices that were established in May 2019 at the Research and Application Farm, Bafra Station, Faculty of Agriculture, Ondokuz Mayıs University (40\u0026deg;59'40\"N, 35\u0026deg;54'27\"E, 8 m above sea leve) were used in a two-year experiment between 2019 and 2020. Each subplot was planned to meet the dry matter (DM) requirement (3.5% of body weight, NRC, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) of three lambs during a 60-day grazing period after the swards reached grazing height. Seeds were drilled synchronously on 5 May 2019, after grasses and/or legume seeds were thoroughly mixed for mixed cropping. Before sowing, all plots were treated with herbicides and insecticides to prevent pests and weeds. Irrigation was applied twice during tillering and stem elongation stages and when needed in later periods. The primary soil characteristics of the experimental site were a loamy texture, a pH of 7.73 and workability of 55%, 0.60% organic matter, 0.009% total salt (as NaCl), 10.9% lime (CaCO\u003csub\u003e3\u003c/sub\u003e), 0.66 kg/da of phosphorus (as P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e) and 25 kg/da of potassium (as K\u003csub\u003e2\u003c/sub\u003eO). Based on the soil test results, a similar amount of fertiliser (20 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of each N, P and K) was applied manually to all plots. Because herbicides were not used after swarding the pasture plots, weeding was done continuously and uniformly by hand in all plots. During the experiment, the monthly average temperature, precipitation and humidity were 14.6\u0026deg;C, 716.7 mm and 73.1%, respectively, whereas during the grazing period, corresponding values were 21.0 ℃, 15.0 mm and 69.5%.\u003c/p\u003e\u003cp\u003eThirty-six intact male Karayaka lambs, each 3 months old and weighing 22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 kg, were assigned to completely randomized design with four RFQ treatments, each with three replications, for 74 days, comprising 14 days of adaptation to the experimental condition and 60 days of data collection. The subplots, divided by temporary fencing, were grazed rotationally by three lambs in 10-day intervals from the beginning of May until the beginning of July 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Self-measuring drinkers (plastic calibrated buckets) and mineral blocks were positioned and suspended within the shelter cabins to ensure ad libitum access to potable water and to satisfy the animals' mineral requirements, respectively. A three-sided enclosed wooden shelter (1.5 m \u0026times; 2 m \u0026times; 2 m) that served as the lambs' resting and overnight area was relocated to the subsequent grazing subplot to coincide with rotational grazing management.\u003c/p\u003e\u003cp\u003eSpecies identified as having high forage yield and persistency in monoculture and/or polyculture plantings in a previous study at the same station (Aydın et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were used to establish pastures with different RFQ indices in this study. The RFQ treatments were: 89.3 (89RFQ, comprising a blend of meadow fescue (\u003cem\u003eFestuca pratensis\u003c/em\u003e Population), tall fescue (\u003cem\u003eFestuca arundinacea\u003c/em\u003e cv. Starlett) and cocksfoot grass (\u003cem\u003eDactylis glomerata\u003c/em\u003e cv. Lidacta) at an equal rate of 33.3% for each; 105.2 (105RFQ, comprising a blend of 40% alfalfa (\u003cem\u003eMedicago sativa\u003c/em\u003e cv. Emiliana) and meadow fescue (\u003cem\u003eFestuca pratensis\u003c/em\u003e Population), tall fescue (\u003cem\u003eFestuca arundinacea\u003c/em\u003e cv. Starlett) and cocksfoot grass (\u003cem\u003eDactylis glomerata\u003c/em\u003e cv. Lidacta) at an equal rate of 20% each; 121.4 (121RFQ, comprising a blend of 80% white clover (\u003cem\u003eTrifolium repens\u003c/em\u003e cv. Rivandel) and 20% perennial ryegrass (\u003cem\u003eLolium perenne\u003c/em\u003e cv. Belida) and 147.0 (147RFQ, comprising 100% bird's foot trefoil (\u003cem\u003eLotus corniculatus\u003c/em\u003e Population). Aydın et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have determined that the RFQs of bird's foot trefoil, alfalfa, white clover, perennial ryegrass, tall fescue, meadow fescue and cocksfoot grass were 147, 129, 126, 103, 92, 91 and 85, respectively, at this study location.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGrazing behaviour\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn a free-choice trial, conducted to determine the influence of grazing history on forage preference by lambs exposed to unfamiliar forages, 500 g of all RFQ forages were offered simultaneously in separate troughs to the lambs in the individual pens (with six and two lambs per treatment and replicate, respectively). Observations on feeding behaviour were conducted (Rice et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) recorded from 07:30 to 09:30 hrs, across three consecutive days. Then the rank of the lambs' unfamiliar forage preference was determined by examining camera records. Thus, the total time spent by lambs from each plot in optional feeders containing different RFQ feeds and first-choice feeds from each RFQ pasture, was determined. Time spent in the optional feeders was calculated as the difference between the time each lamb spent seeking feed until feed intake was definite. Then, each of these parameters is expressed as a percentage of the relevant total.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurements\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo monitor BWG, all lambs were weighed at the beginning and end of the 60-day grazing period, at 10-day intervals, based on changes in the BW on a scale of 100 g sensitivity, to determine lambs\u0026rsquo; DM requirements (IMMAX EB 600). Before weighing, lambs were fasted (14 h) to exclude the effect of empty/full gastrointestinal tract condition on their body weight. The BWG of each lamb was calculated individually by subtracting the final BW from the initial weight and dividing it by sixty. The forage DMI of the lambs per replicate subplot was estimated using the cage technique as explained by Undi et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Pre- and post-grazing herbage mass at 10-day grazing intervals were determined on each RFQ sward (Fraser et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Because lambs in each subplot were grazed as a group, BW, BWG and DMI data are presented as the means of each subplot to calculate the feed conversion ratio (FCR). Thus, the FCR was calculated as average DMI divided by average BWG. Drinking water intake (DWI) was recorded daily as the difference by comparing the water offered and left in the plastic calibrated buckets.\u003c/p\u003e\u003cp\u003eThe forage samples collected by hand-harvesting at the soil level from each subplot at 10-day intervals were air-dried at room temperature until a constant weight and then ground to pass through a 1 mm sieve for proximate analysis. The analysis of DM (method 930.15), ash (method 942.05) and CP (method 954.01) of the samples was performed according to the methods approved by AOAC (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The NDF and ADF of the samples were analysed sequentially in an ANKOM A200/220 Fibre Analyser (ANKOM Technology Corp., located in Fairport, NY, USA). \u003cem\u003eIn vitro\u003c/em\u003e dry matter digestibility (IVDMD) was performed as described by Herv\u0026aacute;s et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The TDN and ME values were calculated using equations from Aydin et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Aydın et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), respectively. One forage sample per subplot was analysed for nutritive value, with the analysis replicated three times.\u003c/p\u003e\u003cp\u003eTo assess serum biochemical parameters related directly to animal health, 10 ml blood samples were collected from the jugular vein of each lamb selected for slaughter into a clean, dry EDTA tube. Blood samples were centrifuged at 3,000 \u0026times; g at 4\u0026deg;C for 15 min. Serum was separated into 2 ml clean, dried Eppendorf tubes and frozen at \u0026minus;\u0026thinsp;20\u0026deg;C until analysis. All blood serum samples were analysed using a biosystem autoanalyser (Roche Integra\u0026reg; 400 plus) to determine the concentrations of glucose, blood urea nitrogen (BUN), triglycerides, calcium (Ca) and phosphorus (P) using specific kits following the manufacturer's instructions. Also, the enzyme activities such as alkaline phosphatase (ALP), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were determined using the UV kinetic method (International Federation of Clinical Chemistry).\u003c/p\u003e\u003cp\u003eAt the end of the 60-day grazing period, all lambs were removed from their RFQ treatments, fasted for 12 h and again weighed to determine slaughter weights (SW) before transport to slaughter. One lamb with the BW closest to the treatment mean from each replication (n\u0026thinsp;=\u0026thinsp;3 per treatment) was slaughtered humanely by severing the throat and major blood vessels in the neck at a nearby commercial abattoir (Bafra Commodity Exchange Slaughterhouse Limited). After we removed internal organs, carcass weights were measured immediately. Carcass yield (dressing percentages) was calculated as the ratio of carcass weight to the SW.\u003c/p\u003e\u003cp\u003eAfter slaughtering, rumen pH was measured using a glass electrode pH meter (Hanna Instruments HI 8521) without opening the rumen to determine whether rumen pH was affected by the forages of different RFQ indices. The pH of the \u003cem\u003eLongissimus dorsi\u003c/em\u003e muscle (LDM) at the interface of the 12th and 13th rib was measured using a Testo 205 pH meter with a solid glass probe at 1 hour (pH\u003csub\u003e1\u003c/sub\u003e) and 24 hours (pH\u003csub\u003e24\u003c/sub\u003e) after slaughter. At these times, meat colour was assessed by the CIE L* (lightness), a* (redness) and b* (yellowness) system using the Minolta CR 400 colourimeter (Minolta Camera Co., Osaka, Japan), with three repetitions in the LDM. The meat samples kept at 4\u0026deg;C for 24 hours were subjected to analytical procedures. A change (%) in the weight of the LDM over the subsequent 24 h was taken as the drip loss, as described by Honikel (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The dry matter (method 930.15), ash (method 942.15), crude protein (method 990.03) and ether extract (EE, method 920.39) contents of the LDM were determined in triplicate by the approved methods (AOAC, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll data, with subplots (n\u0026thinsp;=\u0026thinsp;3 subplots per treatment) serving as the experimental units, were analysed using the mean of measurements within each subplot in the SPSS software v.21.0 using the MIXED procedure (SPSS Inc., Chicago, IL, USA). The normality of the data and the homogeneity of variances were evaluated with the Kolmogorov-Smirnov test and the Levene test, respectively. No outliers were detected in the variables. Percentage data that did not show a normal distribution were subjected to arcsin transformation. Because lambs in each subplot did not represent completely independent replicates and because subplots were nested within main plots, the mathematical model for a standard analysis of variance (ANOVA) was used as follows:\u003c/p\u003e\u003cp\u003e\u003cem\u003eYij​ = \u0026micro;\u0026thinsp;+\u0026thinsp;αi​ + βj(i)​ + ϵij​\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWherein \u003cem\u003eYij\u003c/em\u003e\u0026thinsp;=\u0026thinsp;value referring to the observation of repetition i of treatment j; \u0026micro;\u0026thinsp;=\u0026thinsp;the overall mean; αi​ = the effect of the ith main plot (i\u0026thinsp;=\u0026thinsp;A, B, C); βj(i)\u0026thinsp;=\u0026thinsp;the effect of the jth subplot within nested within the ith main plot (j\u0026thinsp;=\u0026thinsp;1, 2, 3, 4; RFQ treatments); ϵij\u0026thinsp;=\u0026thinsp;the random error associated with the kth observation in the jth subplot within the ith main plot. The treatment means were compared by Tukey's HSD test at a significance level of p\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eNutritional value\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe nutritional values of forages from pastures with different RFQ values during the first and the last 30 days of the grazing period are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The 105RFQ forage exhibited the highest CP content during the first 30-day grazing period, followed by 147RFQ and 121RFQ pastures, with 89RFQ displaying the lowest. In the subsequent 30 days, the CP content ranked 147RFQ\u0026thinsp;\u0026gt;\u0026thinsp;121RFQ\u0026thinsp;\u0026gt;\u0026thinsp;105RFQ\u0026thinsp;\u0026gt;\u0026thinsp;89RFQ. In the fibre components (NDF and ADF), the studied RFQ pastures ranked similarly (89RFQ\u0026thinsp;\u0026gt;\u0026thinsp;121RFQ\u0026thinsp;\u0026gt;\u0026thinsp;105RFQ\u0026thinsp;\u0026gt;\u0026thinsp;147RFQ) in both grazing periods. The IVDMD decreased with increasing pasture RFQ. TDN generally increased with RFQ, except at 121RFQ, where the TDN level was lower than that at 105RFQ in the first period and higher than that at 147RFQ in the second. In both grazing periods, the ME values of the forages were in the following order: 105RFQ\u0026thinsp;\u0026gt;\u0026thinsp;121RFQ\u0026thinsp;\u0026gt;\u0026thinsp;89RFQ\u0026thinsp;\u0026gt;\u0026thinsp;147RFQ.\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\u003eChemical composition and some quality indicators of pastures with different relative forage quality (RFQ) during the first (days 1 to 30) and the last (days 31 to 60) 30 days of the grazing period\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eItem\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eDays 1 to 30\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eDays 31 to 60\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e89RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e105RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e121RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e147RFQ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e22.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e25.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eChemical composition, % of DM\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e19.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e29.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e32.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e50.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e47.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e48.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e11.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eQuality indicator, % of DM\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVDMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e51.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e53.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTDN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e62.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e66.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e64.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eME, Mj/kg DM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e8.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. DM: Dry matter, CP: Crude protein, ADF: Acid detergent fibre, NDF: Neutral detergent fibre, IVDMD: \u003cem\u003eIn vitro\u003c/em\u003e dry matter digestibility, TDN: Total digestible nutrients, ME: Metabolizable energy.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003e1\u003c/sup\u003e The values represent the means of the analyses and calculations performed with the samples obtained at 10-day intervals during the 60-day grazing period.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGrowth performance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the BW and BWG of the 121RFQ lambs were higher than those of the 105RFQ lambs (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The 121RFQ lambs had the highest DMI. Intermediate values observed for the 89RFQ and 105RFQ lambs, while the 147RFQ lambs had the least intake (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). Corresponding variables of the DMI of the 89RFQ and 105RFQ lambs were similar (\u003cem\u003ep\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05). The 121RFQ and 89RFQ pastures exhibited a better feed conversion ratio than the 105RFQ pasture (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The DWI of the 105RFQ lambs was higher than that of the 121RFQ and 147RFQ lambs (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\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\u003eGrowth performance of Karayaka male lambs grazing on pastures with different relative forage quality (RFQ)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e121RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e147RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eBody weight (BW), kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e22.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e22.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.41\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.18\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e28.28\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e26.53\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBW gain, g/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.05\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.55\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e95.83\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e63.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e6.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry matter intake, g/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e834.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e815.74\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e926.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e719.69\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e24.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeed conversion ratio, g/g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e9.88\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e11.74\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater intake, L/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.30\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.97\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.84\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. SEM: Standard error of the mean.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea,b,c\u003c/sup\u003e Mean values in the same row with different superscripts differ (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e1\u003c/sup\u003e Values are means of three subplots with three lambs per treatment.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCarcass and meat quality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe slaughter weight (SW) and carcass weight and yield of lambs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e) were not affected by the RFQ treatment (\u003cem\u003ep\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05). The rumen pH of the 147RFQ lambs was lower than that of 89RFQ, 105RFQ and 12RFQ lambs (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The RFQ treatment did not affect the studied meat quality traits, except muscle b* values and fat content of the LDM (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e6\u003c/span\u003e). While the b* values of the LDM measured 1 hour after slaughter were higher in the 89RFQ lambs than in the 105RFQ and 147RFQ lambs (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05), the corresponding value at 24 hours was higher in the 121RFQ lambs than in the 147RFQ lambs (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The 89RFQ and 105RFQ pastures increased the meat fat content compared to the 121RFQ and 147RFQ pastures (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\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 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe slaughter and carcass weights, carcass yield and rumen pH value of Karayaka male lambs grazing on pastures with different relative forage quality (RFQ)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlaughter weight (SW), kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarcass weight, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarcass yield, % of SW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRumen pH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.37\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.09\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.55\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.70\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. SEM: Standard error of the mean.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea,b,c\u003c/sup\u003e Mean values in the row with different superscripts differ (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003e Values are means of three replicate pens with one lamb per treatment.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\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 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeat quality characteristics of the \u003cem\u003elongissimus dorsi\u003c/em\u003e muscle of lambs grazing on pastures with different relative forage quality (RFQ)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne hour after slaughter\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.488\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIELab values\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLightness (L*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRedness (a*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYellowness (b*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.50\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.86\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24 hours after slaughter\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003csub\u003e24\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.659\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIELab values\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLightness (L*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRedness (a*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYellowness (b*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.57\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.75\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.83\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrip loss, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eChemical composition, % of dry matter\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry matter, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.717\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.40\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.73\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. SEM: Standard error of the mean\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea,b,c\u003c/sup\u003e Mean values in the same row with different superscripts differ (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003e Values are means of three replicate pens with one lamb per treatment.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSerum biochemical parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe serum metabolic profile, including glucose, triglyceride, BUN, Ca and P and enzyme activities such as ALP, ALT and AST, is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The RQF level did not affect the serum glucose and Ca content and the studied enzyme activities (\u003cem\u003ep\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05). The 105RFQ lambs had a higher serum triglyceride level than the 147RFQ lambs (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The 89RFQ pasture decreased the BUN concentration and increased serum P content compared to the 105RFQ and 121RFQ pastures (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSerum biochemical parameters of Karayaka male lambs grazing on pastures with different relative forage quality (RFQ)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147RFQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSerum metabolic profile, mg/dl\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrigliserides\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.66\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.66\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood urea nitrogen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.66\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.66\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.66\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.80\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.53\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.76\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnzyme activity, U/l\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlkaline phosphatase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e136.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlanine aminotransferase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspartate aminotransaminase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. SEM: Standard error of the mean.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea,b,c\u003c/sup\u003e Mean values in the same row with different superscripts differ (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003e Values are means of three subplots with one lamb per treatment.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGrazing behaviour\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that lambs previously grazed on 89RFQ pastures spent a similar percentage of time in the 89RFQ troughs as in the 105RFQ troughs and significantly more time in both of these compared to the 121RFQ and the 147RFQ troughs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Lambs with a history of the 105RFQ grazing spent the most time in 105RFQ troughs and a similar amount of time in the 121RFQ troughs, but significantly more time in both compared to the 89RFQ and 147RFQ troughs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Lambs from the 121RFQ group spent the most time in the 105RFQ troughs, followed by the 121RFQ, 89RFQ and 147RFQ troughs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A similar pattern was observed for the 147RFQ lambs, which spent the most time in the 105RFQ troughs, followed by time spent in the 147RFQ, 121RFQ and 89RFQ troughs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The first feeding preferences of lambs demonstrated a clear influence of their prior dietary experience (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Lambs with a dietary history of 89RFQ overwhelmingly preferred the 105RFQ forage over the familiar 89RFQ. Similarly, lambs previously fed 105RFQ exhibited a strong preference for the 89RFQ forage over the 147RFQ forage. Lambs with a history of consuming 121RFQ showed a strong preference for the familiar 121RFQ forage. Finally, lambs with a history of 147RFQ preferred the familiar 147RFQ and unfamiliar 105RFQ forages.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe influence of grazing history on forage preference by lambs exposed to unfamiliar forages and the percentage of first-choice forages from each relative forage quality (RFQ) pasture\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eGrazing history\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLamb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89RFQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105RFQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121RFQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147RFQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTime spent in the optional troughs, %\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e89RFQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.27\u003csup\u003eab\u003c/sup\u003e (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.59\u003csup\u003ea\u003c/sup\u003e (77.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.81\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.52\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e105RFQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.74\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.41\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.00\u003csup\u003eb\u003c/sup\u003e (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.84\u003csup\u003ec\u003c/sup\u003e (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e121RFQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.92\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.64\u003csup\u003eb\u003c/sup\u003e (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.27\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e147RFQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.71\u003csup\u003eb\u003c/sup\u003e (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.61\u003csup\u003eb\u003c/sup\u003e (77.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e89RFQ: 33.3% meadow fescue, 33.3% tall fescue, 33.3% cocksfoot grass; 105RFQ: 40% alfalfa, 20% meadow fescue, 20% tall fescue, 20% cocksfoot grass; 121RFQ: 80% white clover, 20% perennial ryegrass; 147RFQ: 100% bird's-foot trefoil. SEM: Standard error of the mean\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea,b,c\u003c/sup\u003e Mean values in the same row with different superscripts differ (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003e Values are the average of three replicates with two lambs per treatment selected at the end of the growth trial.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e2\u003c/sup\u003e Percentage of the total time spent by the lambs in the troughs. Values ​​in parentheses indicate the percentage of first-choice forages from the RFQ pastures.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSince this is the first study on lambs grazed on pastures with different RFQs for the parameters examined, our findings for the parameters examined were interpreted with results from studies involving lambs grazing under extensive feeding systems and those fed forage and supplementation. While the study aimed to elucidate the predictive power of RFQ, the findings reveal a complex interplay of nutritional, physiological and behavioral factors that necessitate a cautious interpretation of RFQ's sole utility in lamb production models. Accordingly, integrating feed quality indices such as RFQ with actual nutritional value parameters can provide more accurate and practical tools for pastured-growing lambs. The results of the present study reveal a complex interplay between forage quality indices and lamb responses, highlighting the limitations of relying solely on RFQ as a comprehensive predictor of animal performance and metabolic status. This aligns with established literature indicating that while forage mass influences intake and performance (Turner et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the specific botanical composition, particularly the inclusion of legumes, significantly impacts dietary protein utilisation and growth rates due to faster digestion kinetics (Howes et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kaithwas et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The comparable growth observed with different forage species in other studies (Fraser et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ates et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), attributed to their similar quality, further supports the notion that specific forage characteristics beyond a broad index like RFQ are critical determinants of animal performance. This result, when compared with the findings of previous studies using the same breed (Olfaz et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sen et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yıldırım et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), may explain why Karayaka lambs perfomed poorly in this study. Poor lamb performance may mainly be attributed to nutritional deficiency from consuming roughage as the sole source (Elizalde et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUzun and Ocak (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) noted that pastured-animal productivity is contingent on grazing management (frequency, intensity, pressure) and forage characteristics (botanical composition, quality, quantity). Variations in forage quality are mainly associated with fibre and CP content (Fernandez-Turren et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while the limiting factor for DMI is NDF intake (Forbes, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Indeed, when grazing animals were fed a legume-based pasture, they tended to produce higher BWG and carcass weights than those on a grass-based pasture (Fraser et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Kemp et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, future models that predict lamb performance in pasture-based systems should integrate detailed forage compositional data alongside quality indices to enhance accuracy and practical applicability.\u003c/p\u003e\u003cp\u003eLamb growth performance was more directly linked to specific RFQ treatments, with 121RFQ lambs demonstrating superior BW, BWG and the highest DMI. This suggests that the blend of white clover and perennial ryegrass in the 121RFQ pasture provided a palatable and nutritionally balanced diet conducive to growth performance, even though its nutrient values were not uniformly the highest across all nutritive parameters. Papadopoulos et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and Kemp et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) reported that white clover and wheat mixtures had an effect on lamb growth performance similar to the one in the present study. The better FCR in 121RFQ and 89RFQ lambs compared to 105RFQ, highlights that efficient nutrient utilisation is not solely determined by estimated energy content (Birkett and de Lange, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Moreover, ryegrass-white clover mixtures (up to 50%), as opposed to monoculture ryegrass, resulted in superior performance outcomes (Niderkorn et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This supports the idea that polyculture pastures are advantageous in terms of DMI and preservation of forage quality (L\u0026uuml;scher et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ates et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Jimenez et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This situation may be related to increasing nutrient utilisation and reducing environmental impacts by associative effects between plant chemical substances (Mueller-Harvey et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe lower DMI that observed in the 147RFQ lambs, despite the high crude protein content of late-season bird's foot trefoil, suggests the potential influence of palatability or other intake-limiting factors associated with pure bird's foot trefoil. This is further supported by the finding that the 147RFQ group exhibited a lower rumen pH, likely due to the distinct fermentation profile (Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) of sole bird's foot trefoil compared to other RFQ treatments, suggesting that while RFQ indicated high quality (Aydın et al. 2019), it did not fully predict intake behaviour. However, our results challenge the finding that tannin-containing birdsfoot clover increases BWG by improving protein utilisation compared to alfalfa or white clover-perennial ryegrass pastures in lambs (Girard et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These discrepancies underscore the complex interplay between forage quality indices, specific plant characteristics and animal performance, highlighting the need to consider factors beyond RFQ when evaluating the nutritional value and utilisation of forages like pure bird's foot trefoil.\u003c/p\u003e\u003cp\u003eContrary to the expectation that higher forage quality, characterised by elevated protein and fibre content, would induce a physiological demand for increased DWI, as suggested by previous research Malan et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the findings of the current study do not support this premise. Instead, the observed higher DWI in lambs grazing 105RFQ pastures appears to be more closely associated with the immediate water status of the pasture vegetation, specifically its DM content and the presence of canopy surface moisture (Sun et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This outcome suggests that environmental factors related to pasture conditions may exert a more immediate influence on lamb water consumption than the inherent nutritional quality of the forage alone (Malan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, the present results regarding DWI hold potentially significant implications for our understanding of the complex interplay among forage RFQ levels and DMI and DWI in grazing lambs. Further in-depth investigation is warranted to elucidate the underlying osmotic and metabolic mechanisms driven by specific forage compositional attributes that may ultimately influence water intake patterns.\u003c/p\u003e\u003cp\u003eThe nutritional results of forages across the grazing period demonstrated temporal dynamics in key nutrients, particularly CP. The shift in the highest CP content from 105RFQ in the initial phase to 147RFQ in the later phase underscores the importance of considering forage maturity and botanical composition beyond a static RFQ value. The nutritive value and nutrient degradation kinetics of pastures are affected by botanical composition, harvest date and the interaction between type of pasture and date of harvest (Keim et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although there is a consistent inverse relationship (Roukos et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Aydin et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) between RFQ and fibre fractions (NDF, ADF), the observed decrease in IVDMD with increasing RFQ suggests that the specific forage mixes at higher RFQs might have contained components hindering in vitro digestibility (Moore and Jung, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Because of the non-linear relationship between RFQ and ME (Aydın et al. 2019), it is difficult to estimate energy availability for growing lambs based on this index alone.\u003c/p\u003e\u003cp\u003eSerum biochemical analysis revealed a selective sensitivity to RFQ, with triglycerides, BUN and phosphorus showing significant differences across groups while general metabolic indicators remained stable. These findings suggest that varying forage compositions elicited distinct metabolic responses, highlighting RFQ's limitations in predicting specific nutrient absorption and utilisation at a biochemical level. The observed positive correlation between CP and BUN aligns with previous reports (Li et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), indicating that increased nitrogen intake leads to higher BUN. However, elevated BUN, as suggested by Kohn et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), might also point to reduced nitrogen utilisation efficiency. These selective biochemical responses underscore the need to consider forage composition beyond RFQ to fully understand nutrient dynamics in grazing lambs. In our study, the elevated serum P in 89RFQ lambs, coupled with their higher DMI and performance, suggests that the high P content of the wheatgrass pasture likely contributed to these levels (Zhang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe selective impact of RFQ on meat quality, precisely b* values and intramuscular fat, suggests subtle forage-induced alterations in muscle metabolism. The higher fat in lambs with lower RFQ levels, such as 89RFQ and 105RFQ lambs warrants further investigation into fatty acid profiles and their quality implications. The muscle nutrient content aligned with a previous Karayaka lamb study (Aksoy and Ulutaş, 2016). The significant effect of RFQ treatments on fat content did not reflect in the meat colour characteristics, despite the positive correlation between lower fat content and higher b* values (Calnan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This suggests that the consistent pH values observed, rather than fat content variations, likely explain the lack of significant differences in meat colour (Montelli et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGrazing behaviour in lambs demonstrates learned preferences, with familiarity and positive post-ingestive feedback significantly influencing feed selection (Pedernera et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Lambs preferred forages with RFQ values similar to their grazing history, suggesting a role for familiarity and gradual changes in forage quality. Consistent with Fitzsimons et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), feeding behaviour is driven by forage quantity and quality, mediated by physical and metabolic mechanisms shaped by novel forage characteristics. While trough-seeking time remained consistent (4.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.426) across the RFQ groups, the time spent at troughs with different RFQ levels indicated selective feeding decisions based on prior experience and forage characteristics (Baumont et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Neave et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This highlights that feeding behaviour is influenced by both immediate availability and past experience. Dietary diversity or complementary forage combinations may better support lamb growth than RFQ alone (Pedernera et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, initial palatability can sometimes override established familiarity. The findings confirm that prior experience enhances foraging efficiency (Launchbaugh \u0026amp; Howery, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Neave et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and varying time at RFQ troughs may reflect nutritional rebalancing (Baumont et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The introduction of grazing ruminants to novel environments containing unfamiliar or undesirable plant species can precipitate a decline in pasture quality and health, driven by the selective overconsumption of habitual forage (Launchbaugh and Howery \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Neave et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pedernera et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is imperative to further investigate, recognizing the implications of these observations for the sustainability and resilience of pastoral systems.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAn important limitation of this study is the use of a relatively low number of animals in each treatment (n\u0026thinsp;=\u0026thinsp;3 lambs/subplot). This reduces statistical power and increases the sensitivity of the results to deviations from the assumptions of normality and homogeneity of variance. Therefore, low statistical power may have led to the failure to detect present effects in this study. The absence of a digestibility trial in the evaluations rendered the findings challenging to discuss and interpret meaningfully. Furthermore, the observed outcomes are context-dependent, necessitating cautious extrapolation to extended durations or disparate climatic regimes. One of the limitations of our study is that metabolic and rumen metabolites were determined only at certain times, such as during the entire experiment or at lesat during the 30-day grazing period. Therefore, the presented results should be interpreted cautiously and the observed trends should be confirmed in future studies with larger sample sizes or more extended periods or different climatic conditions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our hypothesis that the studied variables, especially growth performance, would improve as the pasture was established with high RFQ forages, was not consistently supported. Indeed, our study demonstrated that the RFQ of pasture significantly influenced several aspects of lamb production, including forage nutritional value, growth performance and specific carcass and meat quality traits, while having limited effects on serum biochemical parameters. Higher RFQ pastures did not consistently translate to improved outcomes across all variables. While some nutritional components (e.g., fibre) varied predictably with RFQ, others (e.g., TDN, ME) showed more complex relationships. Lamb growth performance was optimised at the 121RFQ treatment, characterized by the highest DMI and feed conversion ratio. RFQ did not affect the carcass weight and yield, but some meat quality traits (muscle b* values, fat content) were affected. Notably, grazing history influenced feeding behaviour, with lambs generally preferring forages from pastures they had previously grazed. These findings highlight the importance of considering the multifaceted effects of RFQ in pasture management for lamb production, suggesting that optimising RFQ for specific production goals may require a nuanced approach rather than simply maximising RFQ. Further research is needed to elucidate the underlying mechanisms driving these relationships and to determine optimal RFQ ranges for various lamb production systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Ondokuz Mayıs University (Samsun, Turkiye) for providing the facilities for this study and the MSc. B. Bilik for his valuable contribution to the fieldwork.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eİA and NO conceived and designed the research. AA conducted experiments. AA and NO collected and analysed data. AA wrote the manuscript. NO edited the manuscript. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data in this study were collected as a part of the first author\u0026rsquo;s doctoral thesis. The research was funded through the Scientific and Technological Research Council of Turkiye (TUBITAK) (Grant/Award Number: 118 O 197).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ondokuz Mayıs University Local Ethics Committee following the Animal Experimental Guidelines (Protocol No: 2016/44)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consented to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consented to submit the manuscript to the journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that there are no actual or potential conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAksoy, Y., Ulutas, Z., 2016. Meat production traits of local Karayaka sheep in Turkey 1. The meat quality characteristic of lambs. Italian Journal of Food Science, 28(1), 131\u0026ndash;138.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAOAC, 2005. Official Method of Analysis. 18th Edition, Association of Official Analytical Chemists, Washington, DC.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAtes, S., Lucas, R. J., Edwards, G. R., 2015. Stocking rate effects on liveweight gain of ewes and their twin lambs when grazing subterranean clover\u0026ndash;perennial grass pastures. Grass and Forage Science, 70(3), 418\u0026ndash;427.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAtes, S., Lucas, R. J., Edwards, G. R., 2013. Effects of stocking rate and closing date on subterranean clover populations and dry matter production in dryland sheep farms. New Zealand Journal of Agricultural Research, 56, 22\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAydin, I., Algan, D., Pak, B., Ocak, N., 2019. Similarity analysis with respect to some quality indicators and quality categories based on relative forage quality ranges of desirable rangeland forages. Fresenius Environmental Bulletin, 28(8): 5926\u0026ndash;5936.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAydın, İ., Pak, B., Ocak, N., 2022. Comparison of cultivated and wild relatives of several forage species in mixed rangeland based on some nutritional characteristics. Black Sea Journal of Agriculture, 5(2), 91\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBach, A., Calsamiglia, S., Stern, M. D., 2005. Nitrogen metabolism in the rumen. Jorunal of Dairy Science, 88, E9\u0026ndash;E21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaumont, R., Prache, S., Meuret, M., Morand-Fehr, P., 2000. How forage characteristics influence behaviour and intake in small ruminants: a review. Livestock Production Science, 64(1), 15\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBirkett, S., de Lange, K., 2001. Limitations of conventional models and a conceptual framework for a nutrient flow representation of energy utilization by animals. British Journal of Nutrition, 86(6), 647\u0026ndash;659.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalnan, H. B., Jacob, R. H., Pethick, D. W., Gardner, G. E., 2017. Selection for intramuscular fat and lean meat yield will improve the bloomed colour of Australian lamb loin meat. Meat Science, 131, 187\u0026ndash;195.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Brito, G. F., Ponnampalam, E. N., Hopkins, D. L., 2017. The effect of extensive feeding systems on growth rate, carcass traits, and meat quality of finishing lambs. Comprehensive Reviews in Food Science and Food Safety, 16(1), 23\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElizalde, F., Hepp, C., Reyes, C., Tapia, M., Lira, R., Morales, R., Sales, F., Catrileo, A., Silva, M., 2021. Growth, carcass and meat characteristics of grass-fed lambs weaned from extensive rangeland and grazed on permanent pastures or alfalfa. Animals, 11(1), 52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFavre, J. R., Castiblanco, T. M., Combs, D. K., Wattiaux, M. A., Picasso, V. D., 2019. Forage nutritive value and predicted fiber digestibility of Kernza intermediate wheatgrass in monoculture and in mixture with red clover during the first production year. Animal Feed Science and Technology, 258, 114298.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFernandez-Turren, G., Repetto, J. L., Arroyo, J. M., P\u0026eacute;rez-Ruchel, A., Cajarville, C., 2020. Lamb fattening under intensive pasture-based systems: a review. Animals, 10(3), 382.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFitzsimons, C., McGee, M., Keogh, K., Waters, S. M., Kenny, D. A., 2017. Molecular physiology of feed efficiency in beef cattle. In Biology of domestic animals (ed. CG Scanes and RA Hill), 120\u0026ndash;163. CRC Press, FL, Boca Raton, USA.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFraser, M. D., Speijers, M. H., Theobald, V. J., Fychan, R., Jones, R., 2004. Production performance and meat quality of grazing lambs finished on red clover, lucerne or perennial ryegrass swards. Grass and Forage Science, 59(4), 345\u0026ndash;356.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eForbes, J. M., 2007. A personal view of how ruminant animals control their intake and choice of food: Minimal total discomfort. Nutrition Research Reviews, 20, 132\u0026ndash;146.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGirard, M., Dohme-Meier, F., Silacci, P., Kragten, S. A., Kreuzer, M., Bee, G., 2016. Forage legumes rich in condensed tannis may increase n-3 fatty acid levels and sensory quality of lamb meat. Journal of the Science of Food and Agriculture, 96: 1923\u0026ndash;1933.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerv\u0026aacute;s, G., Ranilla, M. J., Mantec\u0026oacute;n, \u0026Aacute;. R., Bodas, R., Frutos, P., 2004. Comparison of in vitro digestibility of feedstuffs using rumen inoculum from sheep or red deer. Journal of Animal and Feed Science, 13 (1):91\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHonikel, K. O., 1998. Reference methods for the assessment of physical characteristics of meat. Meat Science, 49, 447\u0026ndash;457.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHowes, N. L., Bekhit, A. E. D. A., Burritt, D. J., Campbell, A. W., 2015. Opportunities and implications of pasture-based lamb fattening to enhance the long‐chain fatty acid composition in meat. Comprehensive Reviews in Food Science and Food Safety, 14(1), 22\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJimenez, L. E. R., Naranjo, A., Hernandez, J. C. A., Ovalos, J. O., Ortega, O. C., Ronquillo, M. G., 2019. A meta-analysis on the effect of the feeding type and production system on the carcass quality of lambs. Italian Journal of Animal Science, 18, 423\u0026ndash;434.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaithwas, M., Singh, S., Prusty, S., Mondal, G., Kundu, S. S., 2020. Evaluation of legume and cereal fodders for carbohydrate and protein fractions, nutrient digestibility, energy and forage quality. Range Management and Agroforestry, 41(1), 126\u0026ndash;132.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeim, J. P., Valderrama, X., Alomar, D., L\u0026oacute;pez, I. F., 2013. In situ rumen degradation kinetics as affected by type of pasture and date of harvest. Scientia Agricola, 70, 405\u0026ndash;414.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKemp, P. D., Kenyon, P. R., Morris, S. T., 2010. The use of legume and herb forage species to create high performance pastures for sheep and cattle grazing systems. Revista Brasileira de Zootecnia, 39, 169\u0026ndash;174.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKohn, R. A., Dinneen, M. M., Russek-Cohen, E., 2005. Using blood urea nitrogen to predict nitrogen excretion and efficiency of nitrogen utilization in cattle, sheep, goats, horses, pigs, and rats. Journal of Animal Science, 83, 879\u0026ndash;889.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalan, J. A. C., Flint, N., Jackson, E. L., Irving, A. D., Swain, D. L., 2020. Environmental factors influencing cattle's water consumption at offstream watering points in rangeland beef cattle. Livestock Science, 231, 103868.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaunchbaugh, K.L., Howery, L.D., 2005. Understanding landscape use patterns of livestock as a consequence of foraging behavior. Rangel. Ecol. Manag. 58, 99\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, B., Hou, P., Liu, L., Zhao, L., Zhang, X., Yang, C., Huang, X., Ge, T., Zheng, Y., Wen, Y., Zhang, E., 2025. Effects of dietary protein level on growth performance, nitrogen metabolism, serum biochemical index, and meat quality of Suffolk x Hu F1 lambs. Journal of Agriculture and Food Research, 21, 101808.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026uuml;scher, A., Mueller-Harvey, I., Soussana, J. F., Rees, R. M., Peyraud, J. L., 2014. Potential of legume-based grassland-livestock systems in Europe: A review. Grass and Forage Science, 69, 206\u0026ndash;228\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa, Y., Khan, M. Z., Liu, Y., Xiao, J., Chen, X., Ji, S., Li, S., 2021. Analysis of nutrient composition, rumen degradation characteristics, and feeding value of Chinese rye grass, barley grass, and naked oat straw. Animals, 11(9), 2486.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontelli, N. L. L. L., Alvarenga, T. I. R. C., Almeida, A. K., Alvarenga, F. A. P., Furusho-Garcia, I. F., Greenwood, P. L., Pereira, I. G., 2021. Associations of feed efficiency with circulating IGF-1 and leptin, carcass traits and meat quality of lambs. Meat Science, 173, 108379.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoore, K. J., Jung, H. J., 2001. Lignin and fiber digestion. Journal of Range Management 2001;54:420\u0026ndash;430.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMueller-Harvey, I., Bee, G., Dohme-Meier, F., Hoste, H., Karonen, M., Kolliker, R., L\u0026uuml;scher, A., Niderkorn, V., Pellikaan, W. and Salminen, J. P., 2019. Benefits of condensed tannins in forages fed to ruminants: importance of structure, concentration and diet. Crop Science, 59, 1\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNeave, H. W., Weary, D. M., von Keyserlingk, M. A. G., 2018. Review: Individual variability in feeding behaviour of domesticated ruminants. Animal 12: s419\u0026ndash;s430.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiderkorn, V., Martin, C., Le Morvan, A., Rochette, Y., Awad, M., Baumont, R., 2017. Associative effects between fresh perennial ryegrass and white clover on dynamics of intake and digestion in sheep. Grass and Forage Science, 72, 691\u0026ndash;699.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNRC, 2007. Nutrient Requirements of Small Ruminants: Sheep, Goats, Cervids, and New World Camelids, 6th. ed, National Academy Press Washington, DC, USA.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlfaz, M., Ocak, N., Erener, G., Cam, M. A., Garipoglu, A. V., 2005. Growth, carcass and meat characteristics of Karayaka growing rams fed sugar beet pulp, partially substituting for grass hay as forage, Meat Science, 70, 7\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePedernera, M., Vulliez, A., Villalba, J. J., 2022. The influence of prior experience on food preference by sheep exposed to unfamiliar feeds and flavors. Applied Animal Behaviour Science, 246, 105530.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePapadopoulos, Y. A., Charmley, E., McRae, K. B., Farid, A., Price, M. A., 2001. Addition of white clover to orchardgrass pasture improves the performance of grazing lambs, but not herbage production. Canadian Journal of Animal Science, 81(4), 517\u0026ndash;523.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRice, M., Jongman, E. C., Butler, K. L., Hemsworth, P. H., 2016. Relationships between temperament, feeding behaviour, social interactions, and stress in lambs adapting to a feedlot environment. Applied Animal Behaviour Science, 183, 42\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoukos, C., Papanikolaou, K., Karalazos, A., Chatzipanagiotou, A., Mountousis, I., Mygdalia, A. 2011. Changes in nutritional quality of herbage botanical components on a mountain side grassland in North-West Greece. Animal Feed Science and Technology, 169(1\u0026ndash;2), 24\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePoli, C. H. E. C., Monteiro, A. L. G., Devincenzi, T., Albuquerque, F. H. M. A. R. D., Motta, J. H. D., Borges, L. I, et al. 2020. Management Strategies for Lamb Production on Pasture-Based Systems in Subtropical Regions: A Review. Frontiers in Veterinary Science, 7:543.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSen, U., Sirin, E., Ulutas, Z., Kuran, M., 2011. Fattening performance, slaughter, carcass and meat quality traits of Karayaka lambs. Tropical Animal Health and Production, 43(2), 409\u0026ndash;416.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun, L. Z., Auerswald, K., Wenzel, R., Schnyder, H., 2014. Drinking water intake of grazing steers: The role of environmental factors controlling canopy wetness. Journal of Animal Science, 92(1), 282\u0026ndash;291.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTesk, C. R. M., Pedreira, B. C., Pereira, D. H., Pina, D. S., Ramos, T. A., Mombach, M. A., 2018. Impact of grazing management on forage qualitative characteristics: a review. Scientific Electronic Archives, 11(5), 188\u0026ndash;197.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUndi, M., Wilson, C., Ominski, K. H., \u0026amp; Wittenberg, K. M., 2008. Comparison of techniques for estimation of forage dry matter intake by grazing beef cattle. Canadian Journal of Animal Science, 88(4), 693\u0026ndash;701.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUzun, F., Ocak, N., 2019. Some vegetation characteristics of rangelands subjected to different grazing pressures with single-or multi-species of animals for a long time (A case of Zonguldak province, Turkey). Anadolu Tarım Bilimleri Dergisi, 34(3), 360\u0026ndash;370.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurner, K. E., Belesky, D. P., Cassida, K. A., Zerby, H. N., 2014. Carcass merit and meat quality in Suffolk lambs, Katahdin lambs, and meat-goat kids finished on a grass\u0026ndash;legume pasture with and without supplementation. Meat Science, 98(2), 211\u0026ndash;219.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYıldırım, A., Ulutaş, Z., Ocak, N., Kaptan, M., 2013. Effects of birth weight and feeding system on fattening performance and feeding behaviour of Karayaka male lambs. Italian Journal of Animal Science, 12(4), e89.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, B., Wang, C., Wei, Z., Sun, H., Liu. H.J.A.-A.J. o A.S., 2016. The Effects of Dietary Phosphorus on the Growth Performance and Phosphorus Excretion of Dairy Heifers. Asian-Australasian Journal of Animal Science, 29 (7), 960\u0026ndash;964.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Forage quality, grazing history, growth rate, lamb production, metabolic profile, nutritional value","lastPublishedDoi":"10.21203/rs.3.rs-7230525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7230525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to evaluate how pastures with four relative forage quality (RFQ) indices, 89 (89RFQ), 105 (105RFQ), 121 (121RFQ) and 147 (147RFQ), affect the growth, feeding behaviour, serum biochemistry and meat quality of Karayaka male lambs. Thirty-six lambs (90 days old, 22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 kg body weight) were assigned to graze on one of the pastures, established with different forage compositions to achieve the target RFQ indices, with three replicates for 60 days. The 121RFQ lambs had higher body weight and gain than the 105RFQ lambs (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The 121RFQ lambs had the highest, the 89RFQ and 105RFQ lambs had intermediate and the 147RFQ lambs had the least DMI (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The 121RFQ and 89RFQ lambs had a better feed conversion ratio than the 105RFQ lambs (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The 121RFQ and 89RFQ lambs had a better feed conversion ratio compared to 105RFQ (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). Water intake was higher in the 105RFQ lambs than in the 121RFQ and 147RFQ lambs (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The grazing history influenced the feeding behaviour, with lambs generally preferring forages from pastures they had previously grazed. The \u003cem\u003eLongissimus dorsi\u003c/em\u003e muscle b* value was higher in the 121RFQ lambs than in the 147RFQ lambs (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The 89RFQ and 105RFQ lambs had higher meat fat and also serum triglyceride contents compared to the 121RFQ and 147RFQ lambs, respectively (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The 121RFQ pasture could enhance the growth performance without compromising carcass yield and meat quality traits, except for muscle b* value and fat content. Furthermore, prior grazing experience influences subsequent forage selection in lambs.\u003c/p\u003e","manuscriptTitle":"Growth performance, feeding behaviour, serum biochemical and meat quality traits of Karayaka lambs fed pastures consisting of different relative forage quality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 12:24:37","doi":"10.21203/rs.3.rs-7230525/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-16T10:40:33+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-12T18:54:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T04:37:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2025-07-28T02:41:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"823da9e9-9630-4e38-abbb-706057795b43","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:11:33+00:00","versionOfRecord":{"articleIdentity":"rs-7230525","link":"https://doi.org/10.1007/s11250-026-04956-4","journal":{"identity":"tropical-animal-health-and-production","isVorOnly":false,"title":"Tropical Animal Health and Production"},"publishedOn":"2026-03-02 16:00:01","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2025-08-20 12:24:37","video":"","vorDoi":"10.1007/s11250-026-04956-4","vorDoiUrl":"https://doi.org/10.1007/s11250-026-04956-4","workflowStages":[]},"version":"v1","identity":"rs-7230525","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7230525","identity":"rs-7230525","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}