Canopy structure, herbage quality and performance of Holstein × Gyr cows (Bos taurus taurus L. × Bos taurus indicus L.) under rotational stocking in Urochloa spp. pastures

Canopy structure, herbage quality and performance of Holstein × Gyr cows (Bos taurus taurus L. × Bos taurus indicus L.) under rotational stocking in Urochloa spp. pastures | 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 Canopy structure, herbage quality and performance of Holstein × Gyr cows ( Bos taurus taurus L. × Bos taurus indicus L.) under rotational stocking in Urochloa spp. pastures Natalia Avila Soares, Carlos Augusto de Miranda Gomide, Patrícia do Rosário Rodrigues, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9361311/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Pasture-based dairy systems require well-adapted forage cultivars and appropriate grazing management to sustain milk production while maintaining favorable sward structure. The objective of this study was to evaluate the performance of Holstein × Gyr crossbred cows and the productive and structural characteristics of Paiaguás grass pasture ( Urochloa brizantha cv. BRS Paiaguás) and Ipyporã grass pasture ( Urochloa brizantha × Urochloa ruziziensis cv. BRS Ipyporã), under rotational stocking during two rainy season. For the agronomic variables, a completely randomized design was adopted in a split-plot design: two grasses and three experimental phases, with twelve paddocks evaluated per phase. The arrangement for milk yield and milk composition was the complete switchback trial, and for dry matter intake, it was the cross-over. Paiaguás grass showed greater herbage mass, accumulation and herbage accumulation rate. Ipyporã grass showed a greater leaf:stem ratio (1.54 vs 1.02) and greater volumetric density (127.45 vs 108.15 kg DM cm ha − 1 ). Milk yield did not differ between cultivars or experimental phases with an average value of 107.65 L ha − 1 day − 1 ). Stocking rate was higher in Paiaguás grass pasture compared with Ipyporã grass (7.71 vs 6.90 AU ha − 1 ). Dry matter intake differed between cultivars only in phase 2, when it was higher for Ipyporã grass compared with Paiaguás grass (2.21 vs 1.97% of body weight, respectively). Both cultivars supported a great milk yield when managed with 41 cm pre-grazing height for cv. BRS Ipyporã and 63 cm for cv. BRS Paiaguás, and 50% defoliation. Dry matter intake herbage production leaf:stem ratio milk composition milk yield volumetric density Figures Figure 1 Figure 2 Figure 3 Introduction Pasture-based dairy cow production systems are used worldwide, with different levels of intensification (Neal and Roche 2019 ; Boddey et al. 2020 ; Wilkinson et al. 2020; Oliveira et al. 2026 ). The use of these systems is associated with lower production costs, greater sustainability, higher product quality, and improved animal welfare (Hanrahan et al. 2018 ; Merino et al. 2019; Kogima et al. 2022 ). The low productivity is partly a consequence of intense monoculture, poor adaptation of the herbage to the growing environment, and inadequate management practices (Euclides et al. 2010 ; Souza et al 2025 ). Under rotational grazing management, the adoption of a rest period based on reaching approximately 95% light interception by the herbage canopy (Parsons and Penning 1988 ; Da Silva and Nascimento Jr. 2007 ), combined with 50% pasture reduction (Sbrissia et al. 2018 ), results in high net herbage production and low restriction to dry matter intake (DMI) by grazing animals. The search for diversification and increased pasture productivity has led research to develop grass and legume cultivars with superior characteristics (Valle et al. 2009 ). Two recently released cultivars of great importance are Urochloa brizantha cv. BRS Paiaguás (syn. Brachiaria brizantha cv. BRS Paiaguás), which shows great persistence during the dry season, and Urochloa hibrida [ U. brizantha x U. ruziziensis (syn. Brachiaria híbrida)] cv. BRS Ipyporã, the first Urochloa hybrid released by Embrapa, and standing out for its tolerance to pasture spittlebugs, the main insect pests of tropical pastures (Valle et al. 2013 ; Echeverria et al. 2016 ; Euclides et al. 2016 ; Valle et al. 2017 ; Euclides et al. 2018 ). Choosing the appropriate herbage, coupled with proper annual planning and growth management, is critical for pasture-based dairy systems (Santos and Fonseca 2016 ), as these factors directly affect sustainability and land-use efficiency (Macdonald et al. 2017 ). The present study hypothesizes that morphological and nutritional differences between the cultivars result in variations in herbage production and milk productivity. Therefore, the objective of this study was to evaluate milk yield and DMI of Girolando cows, as well as the structural characteristics of Paiaguás grass and Ipyporã grass pastures under rotational stocking, managed at 93–95% of light interception. Materials and Methods Experimental area The experiment was conducted in the Mata Atlântica biome at the José Henrique Bruschi Experimental farm of Brazilian Agricultural Research Corporation – Dairy Cattle (21°33'22''S and 43°06'15''W, altitude 410 m), from December 2017 to June 2019. The region climate in classified as Cwa , humid subtropical, following the classification proposed by Köeppen-Geiger (Alvares et al. 2013 ). Climate data were collected from the meteorological station of the National Institute of Meteorology (INMET) situated 700 m from the experimental site and are presented in the Fig. 1 . The soil of the experimental area is a dystrophic Ferralsol (IUSS Working Group WRB, 2015) with a clayey texture. Soil samples were collected from the 0–20 cm layer for chemical characterization and to establish the experiment. Soil chemical analysis showed: pH (water), 5.25; phosphorus (Mehlich-1), 14.2 mg dm -3 ; potassium, 188 mg dm -3 ; calcium, 2.5 cmolc dm -3 ; magnesium, 1.1 cmolc dm -3 ; aluminum, 0.05 cmolc dm -3 ; H + Al, 4.62 cmolc dm -3 ; base saturation, 44.5%; and organic matter, 3.1 mg dm -3 . Pastures of Urochloa brizantha cv. BRS Paiaguás (Paiaguás grass) and Urochloa brizantha × Urochloa ruziziensis cv. BRS Ipyporã (Ibiporã grass) were established in December 2016, with one hectare of each cultivar. After soil preparation with plowing, harrowing, and liming, sowing was carried out by broadcast seeding using 5 kg ha − 1 of pure live seeds concomitantly with phosphorus fertilization, with 80 kg ha − 1 of P2O5 (single superphosphate). In February 2017, a conditioning grazing was performed with Holstein × Gyr crossbred dairy heifers ( Bos taurus taurus L. × Bos taurus indicus L.) with an average body weight of 200 ± 15 kg. Subsequently, the experimental paddocks were subdivided (ten paddocks of 1.000 m² for each cultivar) for pasture management and conditioning under rotational stocking (with dry cows) throughout the year 2017. During the rainy season, the pastures were broadcast-fertilized with the equivalent of 40 kg ha − 1 of N per grazing cycle, using urea (46% N) as the source, immediately after the animals left the paddocks. Experimental Treatments and Statistical Design The experiment was divided into three experimental phases: phase 1, from December 04, 2017 to January 28, 2018; phase 2, from February 05 to April 03, 2018; and phase 3, from December 16, 2018 to March 01, 2019. Each experimental phase consisted of three grazing cycles (each grazing cycle corresponded to the period in which the animals grazed from paddock 1 to paddock 10). Treatments consisted of the two cultivars and the three evaluation phases. For milk yield and milk composition, a complete switchback trial was conducted with five testers cows arranged in three periods. For agronomic evaluations, a completely randomized design was used in a 2 (two cultivars under study) × 3 (experimental phases) factorial arrangement, with four paddocks evaluated for each treatment per grazing cycle. The paddock was considered as the replicate for the treatments. Dry matter intake was evaluated using a cross-over design, and was conducted in two evaluations, the first (intake 1) between February 28 and March 28, 2018, and the second (intake 2) between January 08 and February 25, 2019, with 5 replicates per treatment. Grazing Method, Growth Characteristics, and Morphological Composition of the Pasture The grazing method used was the rotational stocking. The criterion adopted to determine the time of animal entry into the paddocks was the canopy reaching 95% light interception (LI) (Silva and Nascimento 2007 ). Light interception was measured weekly and was estimated as the mean of ten points in each paddock using the Accupar LP 80 device from DECAGON (USA). The post-grazing canopy residual height for each cultivar corresponded to 50% defoliation of the pre-grazing height (Sbrissia et al. 2018 ). To achieve the residual height, stocking adjustments were performed using the put-and-take technique so that defoliation was achieved within two days of occupation (Allen et al. 2011 ). Thus, when necessary, extra dry cows were placed in the paddocks to ensure the residual canopy height within the predetermined period. Canopy height was evaluated using a centimeter-graduated ruler, with 30 random points measured per paddock, both in pre-grazing and post-grazing conditions. Total herbage mass (HM), under pre- and post-grazing conditions, was estimated using the direct (destructive) method. For this, a 1.0 × 0.5 m (0.50 m²) metal frame was used and placed at four points representative of the average canopy height in each evaluated paddock. The material within each quadrat was cut at ground level and placed in plastic bags. The harvested herbage was then taken to the laboratory for weighing and subsequent processing. Herbage accumulation (HA) was calculated as the difference between herbage mass in the previous post-grazing and the current pre-grazing. To determine the herbage accumulation rate (HAR), accumulation values were divided by the number of rest days between each grazing cycle. Total herbage accumulation (THA) over the experimental period was calculated by summing the accumulations of all grazing cycles. For the morphological evaluation of the herbage components, a subsample of approximately 400 g was taken from the samples collected to determine HM under pre and post-grazing conditions. This subsample was divided into leaf blade, stem (stem + sheath), and dead material fractions, which were weighed and dried in a forced-air oven set at 55°C for 72 h, or until constant weight. Herbage mass values were converted to kg DM ha − 1 , and morphological components were expressed as a proportion (%) of total HM. Determination of Chemical Composition To determine the herbage chemical composition, the samples were collected above the residual height (50% of the pre-grazing height), using the simulated grazing technique. Samples of the energy supplement were collected weekly. After collection, herbage samples were pre-dried in a forced-air oven (55°C; until constant weight). Subsequently, herbage and supplement samples were ground in a Wiley mill equipped with 1 mm screens and then stored in properly identified containers. Bromatological analyses were performed according to INCT-CA (Detmann et al. 2021 ) to determine dry matter at 105°C, total nitrogen, ether extract, minerals, and ash contents. Cell wall components were determined according to INCT-CA methods F-001/1 (neutral detergent fiber), F-003/1 (acid detergent fiber), and F-005/1 (lignin) (Detmann et al. 2021 ). In vitro dry matter digestibility was also analyzed (Tilley and Terry 1963 ). Milk production and composition To evaluate the productive performance of lactating cows, 10 Holstein × Gyr cows were used shortly after peak lactation, with mean and standard deviations for milk yield, days in milk, and body weight of 17.38 ± 1.81 kg day − 1 of milk, 85 ± 10 days, and 510 ± 8 kg, respectively. Cows were allocated to the treatments according to these characteristics in order to obtain two homogeneous groups. During the experimental period, cows were supplemented with concentrate twice a day during milking, with 2 kg day − 1 per cow (Table 1 ) in the morning (07:00 h) and 1 kg day − 1 per cow in the afternoon (14:00 h), totaling 3 kg day − 1 per cow (as fed basis). Between milking, the animals remained in a resting area. Animals underwent ectoparasite control and weighing at the end of each grazing cycle between milking. Table 1 Nutritional composition of the concentrate in each experimental phase Item Phase 1 Phase 2 Phase 3 Dry matter (DM), g kg − 1 953.0 951.8 951.5 In vitro dry matter digestibility, g kg − 1 of DM 863.7 884.1 884.0 Ether stract, g kg − 1 da DM 36.2 34.4 35.2 Neutral detergent fiber, g kg − 1 da DM 129.6 117.5 120.5 Acid detergent fiber, g kg − 1 da DM 48.2 43.9 46.5 Crude protein, g kg − 1 da DM 202.0 214.5 210.2 Individual milk yield was measured at the end of each milking and added together to obtain milk yield per cow/day, subsequently, milk yield per hectare/day was calculated. To determine milk composition, milk samples were collected individually, refrigerated, and then sent to the Milk Quality Laboratory of Embrapa Dairy Cattle, in Juiz de Fora, MG. Estimation of Dry Matter Intake Dry matter intake (DMI) was estimated using the external marker titanium dioxide (TiO 2 ) associated with in vitro dry matter digestibility (IVDMD) of the pasture and the supplement, over the three grazing cycles. 10 g cow − 1 day − 1 of TiO 2 were supplied in two daily doses of 5 g each, administered orally, for 12 consecutive days. The first six days were considered an adaptation period to stabilize marker excretion flows, and the last six days were used for fecal collections. IVDMD was determined according to the protocol described by Tilley and Terry ( 1963 ). Fecal samples were dried in a forced-air oven at 55°C, ground in a Willey mill equipped with a 1 mm screen. Then, the samples were subjected to acid digestion with 15 mL of sulfuric acid (H 2 SO 4 ), followed by the addition of 10 mL of 30% v/v hydrogen peroxide (H 2 O 2 ), and subsequent quantification of TiO2 content by spectrophotometry, according to INCT-CA method M-007/1 (Detmann et al. 2021 ). Fecal output (FO, kg cow − 1 day − 1 of DM) was calculated using Eq. 1, as follows: Equation 1: FO = daily marker dose (g day − 1 ) / marker concentration in fecal DM (g kg − 1 ). Daily pasture DMI was estimated using Eq. 2: Equation 2: DMI (kg cow − 1 day − 1 ) = ((FO – FO conc ) / (1 − (IVDMD pasture /100)) Where: FO conc (kg cow − 1 day − 1 ) = fecal output attributable to concentrate intake (FO conc = concentrate DM intake, kg cow − 1 day − 1 × IVDMD conc /100); IVDMD conc = IVDMD of the concentrate; IVDMD pasture = IVDMD of the pasture. Statistical Analyses The assumptions of normal probability distribution and homogeneity of variances for the use of the statistical model were verified using the Shapiro–Wilk and Bartlett tests, respectively. Analyses of variance (ANOVA) for the agronomic variables were performed according to the following model: Y ijk = µ + C i + θ ik + F j + ( F/C ) ij + α ijk . Where: Y ijk = observation of cultivar i , phase j , and replicate k ; µ = overall mean; C i = fixed effect of cultivar i ; θ ik = random error associated with the observation of cultivar i and replicate k , with θ ik ~ N( µ, σ 2 ); Fj = fixed effect of phase j ; ( F/C ) ij = interaction effect between cultivar i and phase j ; α ijk = random error associated with the subplot of cultivar i in phase j of replicate k , with α ijk ~ N( µ, σ 2 ). For milk yield variables, analyses of variance (ANOVA) were performed according to the following model: Y ijk = µ + P j + T i + ε ijk . Where: Y ijk = observation in adjusted treatment i , in period j of replicate k ; µ = overall mean effect; P j = effect of period j ; T i = effect of adjusted treatment i ; ε ijk = random error of adjusted treatment i in period j of replicate k , with ε ijk ~ N( µ, σ 2 ). Analyses of variance (ANOVA) for DMI were performed according to the following model: Y ijk = µ + P j + A k + T i + ε ijk . Where: Y ijk = observation in adjusted treatment i , in period j of animal k ; µ = overall mean effect; P j = effect of period j ; A k = effect of animal k ; T i = effect of treatment i ; ε ijk = random error of treatment i in period j of animal k , with ε ijk ~ N( µ, σ 2 ). To compare the means of experimental groups, orthogonal contrasts were used by the Fisher’s analysis, with an error rate lower than 5% as the criterion for statistical significance. For unfolding interactions and comparing phases, Tukey’s test was used. All analyses were performed using R Core Team (2019) software. Results No significant difference (P > 0.05) was observed between the rest period of the cultivars in any of the experimental phases; the observed mean was 18 days. There was no cultivar × phase interaction effect (P > 0.05) for the variables canopy height, morphological components under pre and post-grazing conditions, leaf/stem ratio, herbage accumulation, herbage accumulation rate and volumetric density (Table 2 ). Table 2 Canopy height, herbage mass (HM), morphological components at pre and post-grazing, herbage accumulation (HA), herbage accumulation rate (HAR), and volumetric density (VD) of two cultivars (C) – BRS Ipyporã and BRS Paiaguás – and three experimental phases (P) Variables BRS Ipyporã BRS Paiaguás SEM P-value Phase 1 Phase 2 Phase 3 Phase 1 Phase 2 Phase 3 C P C*P Pre-grazing Height (cm) 41.55 43.35 39.98 67.38 65.95 57.13 2.03 < 0.0001 0.0047 0.1050 HM (Kg DM ha − 1 ) 5153.19 5917.09 5059.4 8104.99 7075.39 6232.35 537.71 < 0.0001 0.0156 0.0184 L/S 1.54 1.41 1.67 0.9 0.96 1.21 0.06 < 0.0001 0.0009 0.4569 Leaf (%) 49.78 50.21 50.87 38.19 39.17 44.48 1.92 < 0.0001 0.1330 0.3371 Stem (%) 33.77 36 30.96 42.98 41.02 37.11 1.35 < 0.0001 0.0017 0.2867 Dead material (%) 16.44 13.78 18.16 18.43 18.91 18.2 2.14 0.1773 0.6929 0.4915 Pos-grazing Height (cm) 22.22 20.87 20.48 35.75 33.19 30.44 0.92 < 0.0001 0.0014 0.1538 HM (Kg DM ha − 1 ) 2747.05 2236.62 1992.65 3781.2 3228.99 2767.01 176.4 < 0.0001 0.0005 0.7834 Leaf (%) 19.76 22.18 18.18 14.43 13.92 11.61 1.24 < 0.0001 0.0412 0.5036 Stem (%) 46.28 43.85 44.4 47.83 45.36 37.72 1.76 0.4041 0.0046 0.0319 Dead material (%) 33.95 33.96 37.4 37.73 40.71 50.66 2.18 < 0.0001 0.0008 0.0939 HA (kg DM ha − 1 phase − 1 ) 4714.95 7180.35 6453.99 8645.07 8982.35 9207.19 426.21 0.0018 0.0042 0.0543 HAR (kg DM ha − 1 day − 1 ) 78.58 119.67 85.81 144.08 157.69 118.77 9.76 0.0009 0.0016 0.2121 VD (kg DM cm ha − 1 ) 117.07 141.14 125.29 108.39 103.88 112.36 8.1 0.0029 0.5209 0.1680 L/S = Leaf/Stem ratio; SEM = Standard error of the mean The canopy height differed (P < 0.05) between cultivars under pre and post-grazing conditions (Table 2 ). Paiaguás grass showed a 1.52 times greater management height than Ipyporã grass at pre-grazing and 1.57 times at post-grazing. In addition, pre and post-grazing herbage mass differed significantly between cultivars (P < 0.05). Under pre-grazing conditions, HM of BRS Paiaguás grass exceeded that of Ipyporã grass by 1,761 kg DM ha − 1 . Under post-grazing conditions, HM was 933.41 kg DM ha − 1 higher in Paiaguás grass. The leaf/stem ratio (L/S) was 33.76% higher in Ipyporã grass (1.54 vs 1.02; P < 0.05). The leaf proportion was also higher in Ipyporã than in Paiaguás grass (P < 0.05), both at pre-grazing (50.3 vs 40.6%, respectively) and post-grazing (20 vs 13.3%, respectively; Table 2 ). At pre-grazing, stem proportion was 16.81% higher (P < 0.05) in Paiaguás grass, whereas at post-grazing no difference was observed between cultivars. The proportion of dead herbage mass did not differ between the cultivars (P = 0.17), but it was higher (P < 0.05) in the residue of Paiaguás grass. Herbage accumulation (HA) and herbage accumulation rate (HAR) differed between the evaluated cultivars (P < 0.05; Table 2 ). Both variables were higher in Paiaguás grass: HA was 31.61% and HAR was 32.43% higher than in Ipyporã grass. For volumetric density (VD), there was a significant difference between cultivars (P < 0.05). VD was 15% higher in Ipyporã grass compared with Paiaguás. Among the phases, a significant difference (P < 0.05) was observed for HM, canopy height, L/S, and stem percentage at pre-grazing, being lower in the phase three. Under post-grazing conditions, canopy height, HM, and leaf and stem percentages were higher in phase 1 (P < 0.05). Herbage accumulation and HAR also differed among the experimental phases (P < 0.05), being higher in phase two (8,081.35 kg DM ha − 1 phase − 1 and 138.68 kg DM ha − 1 day − 1 , respectively) (Table 3 ). Volumetric density did not differ among phases (P = 0.52), and the observed mean was 117.96 ± 7.71 kg DM cm ha − 1 . Table 3 Canopy height, herbage mass (HM), leaf/stem ratio (L/S), morphological components, herbage accumulation (HA), herbage accumulation rate (HAR), and volumetric density (VD) at pre and post-grazing and three experimental phases Variables Phase SEM P-value 1 2 3 Pre-grazing Height (cm) 54.46 a 54.65 a 48.56 b 1.43 0.0047 HM (Kg DM ha − 1 ) 6629.09 a 6496.22 a 5645.88 b 380.22 0.0156 L/S 1.22 b 1.18 b 1.44 a 0.56 0.0045 Leaf (%) 43.98 a 44.69 a 47.68 a 1.35 0.1330 Stem (%) 38.38 a 38.51 a 34.04 b 0.96 0.0017 Dead material (%) 17.43 a 16.34 a 18.18 1.15 0.6929 Pos-grazing Height (cm) 28.98 a 27.03 ab 25.46 b 0.65 0.0013 HM (Kg DM ha − 1 ) 3262.45 a 2732.80 b 2379.83 b 150.63 0.0005 Leaf (%) 17.09 ab 18.05 a 14.89 b 0.88 0.0412 Stem (%) 47.05 a 44.61 ab 41.06 b 1.24 0.0046 Dead material (%) 35.84 b 37.33 b 44.03 a 1.54 0.0008 Acumulation HA (kg DM ha − 1 Phase − 1 ) 6680.01 b 8081.35 a 7830.59 a 301.58 0.0042 HAR (kg DM.ha − 1 dia − 1 ) 111.33 b 138.68 a 102.29 b 6.90 0.0016 Volumetric density VD (kg DM cm ha − 1 ) 112.57 a 122.51 a 118.82 a 7.71 0.5209 Lowercase letters compare phases by Tukey’s test (P < 0.05). SEM = Standard error of the mean The stocking rate (SR) (AU ha − 1 ) differed between cultivars (P < 0.05), being 11% higher in Paiaguás grass (Fig. 2 ). Lowercase letters compare cultivars by Fisher’s test (P < 0.05); AU = animal unit (450 kg); ha = hectare; SEM = 0.23; P = 0.03. For fiber composition and IVDMD, a significant interaction (P < 0.05) was observed between the factors under study (Table 4 ). Neutral detergent fiber (NDF) and acid detergent fiber (ADF) were higher in Paiaguás grass in all evaluated phases. Regarding comparisons among phases, Paiaguás grass showed higher NDF and ADF in phase three, whereas Ipyporã grass showed higher values in phase two. The lignin content was higher in Paiaguás grass than in Ipyporã grass. When comparing phases, there was a difference only for Ipyporã grass, for which the lowest lignin content was observed in phase three. IVDMD differed between cultivars in experimental phase three, being higher in Ipyporã grass. Among phases, for both cultivars the highest digestibility occurred in phase three. Table 4 Fiber composition and in vitro dry matter digestibility (IVDMD) of the cultivars BRS Paiaguás and BRS Ipyporã across three experimental phases Cultivar Phase SEM P-value 1 2 3 NDF (g kg − 1 ) BRS Ipyporã 566.73 bB 590.55 bA 566.03 bB 7.01 0.0008 BRS Paiaguás 626.85 aAB 614.85 aB 644.28 aA ADF (g kg − 1 ) BRS Ipyporã 260.57 bB 283.35 bA 262.93 bB 3.76 0.0001 BRS Paiaguás 314.81 aAB 310.68 aB 325.69 aA Lignin (g kg − 1 ) BRS Ipyporã 22.88 bA 23.29 bA 19.7 bB 0.87 0.0478 BRS Paiaguás 30.59 aA 28.05 aA 28.56 aA IVDMD (g kg − 1 ) BRS Ipyporã 700.80 aB 710.50 aB 830.77 bA 5.64 0.0500 BRS Paiaguás 700.00 aB 714.94 aB 808.56 aA Lowercase letters compare cultivars and uppercase letters compare phases by Tukey’s test (P < 0.05). NDF = neutral detergent fiber; ADF = acid detergent fiber; SEM = standard error of the mean Lowercase letters compare phases by Tukey’s test (P < 0.05); a: SEM-cultivar = 2.89; P-cultivar = 0.416; b: SEM-phase = 3.58; P-Phase 0.05) with a mean value of 144.44 g kg − 1 . Among phases, the lowest crude protein content was observed in phase 1, with an observed mean of 129.60 g kg − 1 (Fig. 3 ). The herbage DMI differed between cultivars only in the first intake evaluation (P < 0.05), when it was higher for Ipyporã grass (Table 5 ). In the second intake evaluation, there was no difference between cultivars, and the observed mean was 11.68 ± 0.42 kg cow − 1 day − 1 . Table 5 Herbage dry matter intake (DMI) of Holstein × Gyr cows managed under grazing on BRS Ipyporã and BRS Paiaguás pasture Variable Intake 1 SEM P-value Intake 2 SEM P-value BRS Ipyporã BRS Paiaguás BRS Ipyporã BRS Paiaguás DMI (kg cow − 1 day − 1 ) 11.11 a 10.02 b 0.22 0.0125 12.05 a 11.31 a 0.42 0.2541 DMI (% of BM) 2.21 a 1.97 b 0.08 0.0122 2.43 a 2.28 a 0.13 0.3251 Lowercase letters compare cultivars by Fisher’s test (P < 0.05); SEM = standard error of the mean; BW = body weight No significant difference was observed in daily milk production per area (L ha − 1 day − 1 ) nor in the analyzed milk composition variables (fat, protein, lactose, and total solids) (Table 6 ). Table 6 Milk yield and milk composition of Holstein × Gyr cows managed under grazing on BRS Ipyporã and BRS Paiaguás pasture across three experimental phases Phase Cultivar BRS Ipyporã BRS Paiaguás SEM P-value Milk yield (L ha − 1 day − 1 ) 1 106.33 109.66 4.49 0.43 2 102.94 108.31 3.79 0.13 3 109.09 109.56 5.68 0.92 Fat (g kg − 1 ) 1 37.2 32.7 2.2 0.18 2 31.9 31.9 0.8 0.96 3 31.0 31.7 0.5 0.40 Protein (g kg − 1 ) 1 28.7 27.9 0.7 0.45 2 29.2 25.0 3.0 0.33 3 33.8 29.5 1.8 0.11 Lactose (g kg − 1 ) 1 46.7 46.8 0.74 0.98 2 47.0 46.9 0.78 0.93 3 45.3 46.6 0.78 0.23 Total solids (g kg − 1 ) 1 122.0 126.0 3.7 0.37 2 117.0 113.0 2.8 0.33 3 120.0 118.0 1.9 0.47 SEM = Standard error of the mean Discussion The goals for animal entry and exit from paddocks chosen in the study (95% LI for animal entry and a 50% reduction in height) aimed at the most efficient use of the herbage and they were achieved (Da Silva and Nascimento Jr. 2007 ; Sbrissia et al. 2018 ). The management height of the cultivars was higher than those observed in other studies. Echeverria et al. ( 2016 ) found a mean height of 30 cm when Ipyporã grass was managed at 95% LI under intermittent grazing, a value similar to that observed by Paraiso et al. ( 2019 ) of 29 cm under continuous stocking. In a study with Paiaguás grass, the height observed by Gobbi et al. ( 2018 ) was 34 cm in plots managed at 95% LI. Under continuous stocking management, Euclides et al. ( 2016 ) proposed a management height of 30 cm. Among the experimental phases, the difference in heights is associated with management adjustment throughout the experiment (Table 3 ). The greater canopy height found in phase one can be explained by the pre-experimental pasture conditions, which presented high HM, stem, and dead material, mainly for Paiaguás grass (Table 2 ). These results corroborate the findings of Euclides et al. ( 2019 ), who observed that a greater management height of Marandu grass ( Urochloa brizantha cv. Marandu) resulted in a higher stem percentage and, consequently, a lower L/S ratio. As the experimental phases progressed, heights were adjusted, and in phase three the pre-grazing height was 11% lower than those observed in phases 1 and 2 (Table 3 ). Under Brazilian semiarid conditions, Rodrigues et al. ( 2021 ) reported that a canopy height of 46.3 cm resulted in 78.7% LI during the establishment of Paiaguás grass. Establishing pastures have greater spacing between clumps and, consequently, insufficient canopy density to cover the soil. In this situation, canopies do not have sufficient density to determine a management height associated with the recommended LI. As grazing occurs, the tillers that emerge tend to be smaller than those that were grazed (Gastal and Lemaire 2015 ) but the tiller density increase (Hodgson, 1990 ). This explains the greater heights in the first phase, decreasing up to the third phase for both Urochloa cultivars (Table 2 ). A 50% reduction in canopy height through grazing is considered moderate, ensuring high herbage intake rates with great nutritive value (Fonseca et al. 2012 ; Mezzalira et al. 2014 ; Sbrissia et al. 2018 ). Thus, DMI is expected to be higher (Zanine et al. 2018 ) and animals consume more leaf, since 90% of stem mass is concentrated in the lower half of the herbage canopy (Zanini et al. 2012 ). One of the factors that most affects the profitability of pasture-based dairy systems is the productivity (Hanrahan et al. 2018 ). Thus, the greater the quantity and the quality of available herbage, the more promising pasture-based milk production systems tend to be (Elgesma et al. 2015). The HM observed in the pastures was within the range reported in the scientific literature, varying from 3,095 to 6,110 kg DM ha − 1 for Ipyporã grass (Echeverria et al. 2016 ; Euclides et al. 2018 ; Paraiso et al. 2019 ) and from 2,605 to 8,115 kg DM ha − 1 for Paiaguás grass (Euclides et al. 2016 ; Germani et al. 2018; Gobbi et al. 2018 ). Ipyporã grass showed lower HM compared with Paiaguás grass (Table 2 ), which may be related with the lower productivity of the Ipyporã grass progenitor, Urochloa ruziziensis , compared with other Urochloa species (Euclides et al. 2018 ; Paraiso et al. 2019 ). This resulted in a lower SR than that of Paiaguás grass pasture (Fig. 2 ). However, Ipyporã grass showed a higher leaf percentage, a lower stem percentage and, consequently, a higher L/S ratio (Table 3 ), highlighting it compared to other Urochloa spp. Cultivars (Valle et al. 2017 ). Gurgel et al. ( 2022 ) also found a greater leaf blade mass and L/S ratio in Ipyporã grass during establishment in the Cerrado biome, concluding that the stem elongation rate of Paiaguás grass was higher than that of Ipyporã grass (1.2 and 0.52, respectively). The greater fiber fraction and the lower digestibility observed in Paiaguás grass are explained by its higher stem percentage (Table 4 ). The stem is the structural component of the plant, in which a greater amount of fibrous tissues is concentrated (Van Soest 1994 ). Crude protein content, however, did not differ between cultivars (Fig. 3 ). The management of canopy height directly affects the morphological components of the herbage canopy (Silva Neto et al. 2020 ; Mezzalira et al. 2014 ). An increase in canopy height results in competition for light among plants, promoting stem elongation in order to take advantage of higher light incidence (Moura et al. 2017 ). During the phase three, the management had already been stabilized, therefore, a lower pre-grazing height was maintained (Table 3 ). A consequence of the management adjustment was the higher L/S ratio and the lower stem percentage in this phase (Table 3 ). Similar behavior was observed by Echeverria et al. ( 2016 ), in which a greater management height resulted in a decrease in the L/S ratio in Marandu grass. The reduction in pre-grazing height influenced the chemical composition of the cultivars (Euclides et al. 2019 ), showing higher IVDMD (Table 4 ) and crude protein (Fig. 3 ) values compared with experimental phases one and two. The potential of the herbage production for each grass is genetically determined, but it depends on environmental conditions (water, light, temperature, and nutrient availability). Grazing management is also fundamental for the productive potential to be achieved (Sbrissia et al. 2018 ). Herbage accumulation after defoliation results from the flow of new tissues (Hodgson 1990 ; Fagundes et al. 2005), which occurs both in defoliated but not decapitated tillers and in new tillers. The high HAR observed in the study (between 94.69 and 140.15 kg DM ha -1 day -1 ) reflects the favorable climatic conditions during the experimental period (Fig. 1 ), as well as the level of fertilization applied and the great response of the cultivars to fertilization. In intensive production systems, rapid regrowth, represented by a short interval between grazing events, allows higher herbage quality and a reduction in the number of paddocks in the rotational stocking method (Gomide and Paciullo 2014 ). Therefore, it is necessary to highlight the rapid attainment of grazing readiness of the cultivars (95% LI) during the rainy season, resulting in a mean rest period of 18 days. The lower herbage accumulation (HA) observed in phase one may be associated with the greater management height and lower volumetric density (VD) during this phase (Table 3 ). Pastures managed at greater heights tend to undergo stem elongation, reducing the canopy density and, consequently, the herbage accumulation (Euclides et al. 2019 ; Moura et al. 2017 ). This explains the higher HA and L/S observed in the phases in which management was already established (phases two and three). During phase three, an extended dry spell occurred, increasing the days required for the herbage to reach its optimal grazing point (95% LI), and significantly reducing the HAR (Table 3 ) (Beloni et al. 2018 ). The higher volumetric density (kg DM cm - ¹ ha - ¹) observed in Ipyporã grass (Table 2 ) can be explained by its lower average height, since this cultivar was managed at a height 35% lower than that of Paiaguás grass, while its herbage mass was only 25% lower (Table 2 ). Volumetric density and the HM are important variables that directly affects the accessibility of herbage to be consumed by animals (Hodgson, 1990 ). The higher VD of Ipyporã grass, combined with a higher leaf percentage, higher L/S ratio, and lower fibrous fraction, explains the greater DMI observed in the first intake evaluation (intake 1) (Table 5 ) compensating for its lower HM compared to Paiaguás. Stocking rate adjustment was performed based on the HM produced, which explains the higher SR observed for Paiaguás grass (Fig. 2 ), due to its greater dry matter (DM) yield. A similar relationship was reported by Moura et al. ( 2017 ), who found a higher SR in treatments with Marandu grass managed at 95% light interception compared with treatments managed under fixed rest periods. The higher SR at Paiaguás pasture was not enough to impact daily milk productivity (L ha -1 day -1 ). Morphological and chemical composition indicated a better structural and nutritional conditions at pre-grazing in Ipyporã grass pastures (Table 2 and Table 4 ), directly affecting the DMI (Fonseca et al. 2013 ). Under grazing conditions, DMI is directly influenced by non-nutritional factors, which vary according to the structure of the herbage canopy (Da Silva et al. 2012 ). Grasses with a higher leaf proportion, greater VD, and lower fibrous fraction are preferentially more consumed (Mezzalira et al. 2014 ; Moura et al. 2017 ). Adjustments in herbage management can result in improvements in bromatological composition and in herbage intake (Anjos et al. 2016; Pereira et al. 2015 ). These arguments are supported by the values observed in the present study, since adjustments in the management height of the cultivars during the third experimental phase (intake 2) resulted in a higher L/S ratio, greater in vitro dry matter digestibility (IVDMD) (Table 4 ) and equalized DMI between cultivars. The values observed for DMI were close to those reported for other tropical grasses, with an average of 13 kg cow - ¹ day - ¹ in Tanzania grass pasture ( Megathyrsus maximus cv. Tanzania), 11.3 kg cow - ¹ day - ¹ in stargrass cv. Africana ( Cynodon nlemfuensis ) and 11.7 kg cow - ¹ day - ¹ in Marandu grass. Fukumoto et al. ( 2010 ) found a DMI of 10.1 and 12.3 kg cow - ¹ day - ¹ in pastures of elephant grass cv. Cameroon ( Cenchrus purpureus ) managed under maximum light interception and 95% light interception, respectively, showing the detrimental effect of stem accumulation on DMI by grazing animals. The milk yield observed in this study (Table 6 ) is within the range reported in the literature for cows grazing tropical grass pastures. In a study with elephant grass cv. Cameroon, milk production values ranged from 15.7 to 18.1 L day - ¹ (Congio et al. 2018 ). Demski et al. ( 2019 ) reported milk yields between 17.34 and 13.73 L cow - ¹ day - ¹ for the Mulato II hybrid ( U. brizantha × U. ruziziensis × U. decumbens ) and between 17.24 and 11.96 L cow - ¹ day - ¹ for Marandu grass. When evaluating Marandu grass at 30 days of regrowth and 95% light interception, milk production ranged from 100.3 to 139.7 L ha - ¹ day - ¹ (Moura et al. 2017 ). However, it is worth noting that this study used cows with higher production potential, as well as a higher level of concentrate supplementation (6 kg cow - ¹ day - ¹). To date, there are no data in the literature regarding milk production in pastures of BRS Ipyporã and BRS Paiaguás. Milk solids content (Table 6 ) was similar to the values reported by Demski et al. ( 2019 ), who evaluated grazing on Marandu grass and Mulato II grass and observed mean values of 39.15 g kg - ¹ for fat, 34.75 g kg - ¹ for protein, 42.7 g kg - ¹ for lactose, and 126.45 g kg - ¹ for total solids. The similarity between the values observed in this study and those reported in the literature indicates the potential of Paiaguás and Ipyporã grasses to be used in pasture-based dairy production systems. These values are also close to those observed in studies with elephant grass by Voltolini et al. ( 2010 ), as well as in studies with Tanzania grass, Marandu grass, and star grass (Porto et al. 2009 ). The lack of differences in milk yield and milk composition between the cultivars can be explained by the fact that they belong to the same genus, were managed under the same conditions (95% light interception and 50% post-grazing height reduction), which allowed greater leaf selection by the cows, and received the same amount of concentrate supplementation (3 kg cow - ¹ day - ¹). Conclusion The cultivars evaluated show potential for use in pasture-based dairy production systems when managed at 95% light interception (41 cm pre-grazing height for cv. BRS Ipyporã and 63 cm for cv. BRS Paiaguás) with a 50% post-grazing height reduction. The BRS Ipyporã cultivar showed a higher leaf proportion and a greater leaf-to-stem ratio, resulting in herbage with lower fiber and lignin content. However, BRS Paiaguás produced a greater herbage mass, resulting in a higher stocking rate. The average milk productivity of BRS Paiaguás and BRS Ipyporã pastures under rotational grazing during the rainy season is approximately 107 L ha − 1 day − 1 . Declarations Acknowledgements The authors wish to thank the CAPES, CNPq, UNIPASTO and the Embrapa Gado de Leite for the support in conducting this research Funding The author Soares NA has received research support from CNPq (Grant number 454711/2014-0). Disclosure statement The authors report there are no competing interests to declare Ethical Approval All experimental procedures were approved by the Ethics Committee on Animal Use of the Embrapa (Protocol number: 6231210316). Data availability Data sets generated during the current study are available from the corresponding author on reasonable request. References Allen VG, Batello C, Berretta EJ et al (2011) An international terminology for grazing lands and grazing animals. 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Ciência Rural 42:882–887. https://doi.org/10.1590/S0103-84782012000500020 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 25 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 13 Apr, 2026 First submitted to journal 08 Apr, 2026 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-9361311","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628832882,"identity":"08a17f0c-2226-4af2-8acd-aeec4aef30f0","order_by":0,"name":"Natalia Avila Soares","email":"","orcid":"","institution":"Federal University of Minas Gerais Veterinary School: Universidade Federal de Minas Gerais Escola de Veterinaria","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"Avila","lastName":"Soares","suffix":""},{"id":628832883,"identity":"9052349b-b1fe-4430-b363-1688f2685520","order_by":1,"name":"Carlos Augusto de Miranda Gomide","email":"","orcid":"","institution":"Embrapa Dairy Cattle: Embrapa Gado de Leite","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"Augusto de Miranda","lastName":"Gomide","suffix":""},{"id":628832884,"identity":"e13ea3dc-bd21-4bfe-8222-e4c2927fed83","order_by":2,"name":"Patrícia do Rosário Rodrigues","email":"","orcid":"","institution":"Federal University of Minas Gerais Veterinary School: Universidade Federal de Minas Gerais Escola de Veterinaria","correspondingAuthor":false,"prefix":"","firstName":"Patrícia","middleName":"do Rosário","lastName":"Rodrigues","suffix":""},{"id":628832885,"identity":"b841f945-a7bf-4d0e-988c-ff24462dd598","order_by":3,"name":"Domingos Sávio Campos Paciullo","email":"","orcid":"","institution":"Embrapa Dairy Cattle: Embrapa Gado de Leite","correspondingAuthor":false,"prefix":"","firstName":"Domingos","middleName":"Sávio Campos","lastName":"Paciullo","suffix":""},{"id":628832886,"identity":"93e1a40e-2615-420b-8bac-04b387c061df","order_by":4,"name":"Mirton José Frota Morenz","email":"","orcid":"","institution":"Embrapa Dairy Cattle: Embrapa Gado de Leite","correspondingAuthor":false,"prefix":"","firstName":"Mirton","middleName":"José Frota","lastName":"Morenz","suffix":""},{"id":628832887,"identity":"2c061cd8-3cc4-4ebb-80d2-21ce57b0ea67","order_by":5,"name":"Angelo Herbet Moreira Arcanjo","email":"","orcid":"","institution":"EPAMIG: Empresa de Pesquisa Agropecuaria de Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Angelo","middleName":"Herbet Moreira","lastName":"Arcanjo","suffix":""},{"id":628832888,"identity":"f35fd145-7ac0-42e8-b6fa-4e75de6775dd","order_by":6,"name":"Fernanda de Kássia Gomes","email":"","orcid":"","institution":"EPAMIG: Empresa de Pesquisa Agropecuaria de Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Fernanda","middleName":"de Kássia","lastName":"Gomes","suffix":""},{"id":628832889,"identity":"c8a535ef-efbb-42e0-814e-20cd66be26f6","order_by":7,"name":"Pedro Drummond Rodrigues","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYHACAwYGNiiTB0SwN4AELUjQwsNzACQoQYoWiQQQE7cWc/bmbR8+lN1LXDvt8LEHbxgOy9lLPr+64UeBBAN/e3cCNi2WPceKZ844V5y47XZauuEchsPGPNI5ZTd7gA6TOHN2A1ZX3cgxZuZtSwBqyTGT5mFIS+yRzkm7wQPUYiCRi13L/TeoWup7JM+k3fyDT8sNHhQtNgk8EuzHbuOzxbInrZhxxrkEY6Bf0iTnGNgY9pzJYbstYyDBg8sv5uyHNzN8KEuQ3XY7+ZjEmwoJefb2489uvvljI8ff3ovdYVi4PBASm3IsWsCA/QEu1aNgFIyCUTAyAQC1Kl7Nd7RhrQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-0005-4674","institution":"Federal University of Minas Gerais Veterinary School: Universidade Federal de Minas Gerais Escola de Veterinaria","correspondingAuthor":true,"prefix":"","firstName":"Pedro","middleName":"Drummond","lastName":"Rodrigues","suffix":""},{"id":628832890,"identity":"bc908d49-6f02-4102-91c2-32b13e300af1","order_by":8,"name":"Ângela Maria Quintão Lana","email":"","orcid":"","institution":"Federal University of Minas Gerais Veterinary School: Universidade Federal de Minas Gerais Escola de Veterinaria","correspondingAuthor":false,"prefix":"","firstName":"Ângela","middleName":"Maria Quintão","lastName":"Lana","suffix":""}],"badges":[],"createdAt":"2026-04-08 22:36:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9361311/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9361311/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108517579,"identity":"8571c729-aec6-4acc-b567-199d29e4ca3b","added_by":"auto","created_at":"2026-05-05 13:45:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92004,"visible":true,"origin":"","legend":"\u003cp\u003ePrecipitation, maximum and minimum temperature throughout the experimental period, (Data collected from Inmet, Station: Coronel Pacheco - A557)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9361311/v1/57f702096ed39b302c907d28.png"},{"id":108804159,"identity":"51a546c7-3062-4929-8a62-f9a602766e50","added_by":"auto","created_at":"2026-05-08 15:16:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29422,"visible":true,"origin":"","legend":"\u003cp\u003eStocking rate of Paiaguás and Ipyporã grass pastures\u003c/p\u003e\n\u003cp\u003eLowercase letters compare cultivars by Fisher’s test (P \u0026lt; 0.05); AU = animal unit (450 kg); ha = hectare; SEM = 0.23; P = 0.03.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9361311/v1/a39411542427b0cb5c80d82f.png"},{"id":108517581,"identity":"1dae09fa-c885-4773-ae7c-3b4d89f7d8a4","added_by":"auto","created_at":"2026-05-05 13:45:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46722,"visible":true,"origin":"","legend":"\u003cp\u003eCrude protein content of BRS Paiaguás and BRS Ipyporã across three experimental phases\u003c/p\u003e\n\u003cp\u003eLowercase letters compare phases by Tukey’s test (P\u003cem\u003e \u003c/em\u003e\u0026lt; 0.05); SEM-cultivar = 2.89; P-cultivar = 0.416; SEM-phase = 3.58; P-Phase \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9361311/v1/ce9a1f3f0da7e2c85a1e183c.png"},{"id":108809303,"identity":"eaf845ed-c11f-41e7-b6e8-54488225c6f4","added_by":"auto","created_at":"2026-05-08 15:51:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":851022,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9361311/v1/124192b0-d1d7-46ba-be00-06f16a62bc59.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eCanopy structure, herbage quality and performance of Holstein × Gyr cows (\u003cem\u003eBos taurus taurus\u003c/em\u003e L. × \u003cem\u003eBos taurus indicus\u003c/em\u003e L.) under rotational stocking in \u003cem\u003eUrochloa \u003c/em\u003espp. pastures\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePasture-based dairy cow production systems are used worldwide, with different levels of intensification (Neal and Roche \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boddey et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wilkinson et al. 2020; Oliveira et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The use of these systems is associated with lower production costs, greater sustainability, higher product quality, and improved animal welfare (Hanrahan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Merino et al. 2019; Kogima et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The low productivity is partly a consequence of intense monoculture, poor adaptation of the herbage to the growing environment, and inadequate management practices (Euclides et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Souza et al \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Under rotational grazing management, the adoption of a rest period based on reaching approximately 95% light interception by the herbage canopy (Parsons and Penning \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Da Silva and Nascimento Jr. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), combined with 50% pasture reduction (Sbrissia et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), results in high net herbage production and low restriction to dry matter intake (DMI) by grazing animals.\u003c/p\u003e \u003cp\u003eThe search for diversification and increased pasture productivity has led research to develop grass and legume cultivars with superior characteristics (Valle et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Two recently released cultivars of great importance are \u003cem\u003eUrochloa brizantha\u003c/em\u003e cv. BRS Paiagu\u0026aacute;s (syn. \u003cem\u003eBrachiaria brizantha\u003c/em\u003e cv. BRS Paiagu\u0026aacute;s), which shows great persistence during the dry season, and \u003cem\u003eUrochloa\u003c/em\u003e hibrida [\u003cem\u003eU. brizantha\u003c/em\u003e x \u003cem\u003eU. ruziziensis\u003c/em\u003e (syn. \u003cem\u003eBrachiaria\u003c/em\u003e h\u0026iacute;brida)] cv. BRS Ipypor\u0026atilde;, the first \u003cem\u003eUrochloa\u003c/em\u003e hybrid released by Embrapa, and standing out for its tolerance to pasture spittlebugs, the main insect pests of tropical pastures (Valle et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Echeverria et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Euclides et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Valle et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Euclides et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChoosing the appropriate herbage, coupled with proper annual planning and growth management, is critical for pasture-based dairy systems (Santos and Fonseca \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), as these factors directly affect sustainability and land-use efficiency (Macdonald et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study hypothesizes that morphological and nutritional differences between the cultivars result in variations in herbage production and milk productivity. Therefore, the objective of this study was to evaluate milk yield and DMI of Girolando cows, as well as the structural characteristics of Paiagu\u0026aacute;s grass and Ipypor\u0026atilde; grass pastures under rotational stocking, managed at 93\u0026ndash;95% of light interception.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental area\u003c/h2\u003e \u003cp\u003eThe experiment was conducted in the Mata Atl\u0026acirc;ntica biome at the Jos\u0026eacute; Henrique Bruschi Experimental farm of Brazilian Agricultural Research Corporation \u0026ndash; Dairy Cattle (21\u0026deg;33'22''S and 43\u0026deg;06'15''W, altitude 410 m), from December 2017 to June 2019. The region climate in classified as \u003cem\u003eCwa\u003c/em\u003e, humid subtropical, following the classification proposed by K\u0026ouml;eppen-Geiger (Alvares et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Climate data were collected from the meteorological station of the National Institute of Meteorology (INMET) situated 700 m from the experimental site and are presented in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe soil of the experimental area is a dystrophic Ferralsol (IUSS Working Group WRB, 2015) with a clayey texture. Soil samples were collected from the 0\u0026ndash;20 cm layer for chemical characterization and to establish the experiment. Soil chemical analysis showed: pH (water), 5.25; phosphorus (Mehlich-1), 14.2 mg dm\u003csup\u003e-3\u003c/sup\u003e; potassium, 188 mg dm\u003csup\u003e-3\u003c/sup\u003e; calcium, 2.5 cmolc dm\u003csup\u003e-3\u003c/sup\u003e; magnesium, 1.1 cmolc dm\u003csup\u003e-3\u003c/sup\u003e; aluminum, 0.05 cmolc dm\u003csup\u003e-3\u003c/sup\u003e; H\u0026thinsp;+\u0026thinsp;Al, 4.62 cmolc dm\u003csup\u003e-3\u003c/sup\u003e; base saturation, 44.5%; and organic matter, 3.1 mg dm\u003csup\u003e-3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePastures of \u003cem\u003eUrochloa brizantha\u003c/em\u003e cv. BRS Paiagu\u0026aacute;s (Paiagu\u0026aacute;s grass) and \u003cem\u003eUrochloa brizantha\u003c/em\u003e \u0026times; \u003cem\u003eUrochloa ruziziensis\u003c/em\u003e cv. BRS Ipypor\u0026atilde; (Ibipor\u0026atilde; grass) were established in December 2016, with one hectare of each cultivar. After soil preparation with plowing, harrowing, and liming, sowing was carried out by broadcast seeding using 5 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of pure live seeds concomitantly with phosphorus fertilization, with 80 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of P2O5 (single superphosphate).\u003c/p\u003e \u003cp\u003eIn February 2017, a conditioning grazing was performed with Holstein \u0026times; Gyr crossbred dairy heifers (\u003cem\u003eBos taurus taurus\u003c/em\u003e L. \u0026times; \u003cem\u003eBos taurus indicus\u003c/em\u003e L.) with an average body weight of 200\u0026thinsp;\u0026plusmn;\u0026thinsp;15 kg. Subsequently, the experimental paddocks were subdivided (ten paddocks of 1.000 m\u0026sup2; for each cultivar) for pasture management and conditioning under rotational stocking (with dry cows) throughout the year 2017. During the rainy season, the pastures were broadcast-fertilized with the equivalent of 40 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of N per grazing cycle, using urea (46% N) as the source, immediately after the animals left the paddocks.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental Treatments and Statistical Design\u003c/h3\u003e\n\u003cp\u003eThe experiment was divided into three experimental phases: phase 1, from December 04, 2017 to January 28, 2018; phase 2, from February 05 to April 03, 2018; and phase 3, from December 16, 2018 to March 01, 2019. Each experimental phase consisted of three grazing cycles (each grazing cycle corresponded to the period in which the animals grazed from paddock 1 to paddock 10). Treatments consisted of the two cultivars and the three evaluation phases. For milk yield and milk composition, a complete switchback trial was conducted with five testers cows arranged in three periods. For agronomic evaluations, a completely randomized design was used in a 2 (two cultivars under study) \u0026times; 3 (experimental phases) factorial arrangement, with four paddocks evaluated for each treatment per grazing cycle. The paddock was considered as the replicate for the treatments. Dry matter intake was evaluated using a cross-over design, and was conducted in two evaluations, the first (intake 1) between February 28 and March 28, 2018, and the second (intake 2) between January 08 and February 25, 2019, with 5 replicates per treatment.\u003c/p\u003e\n\u003ch3\u003eGrazing Method, Growth Characteristics, and Morphological Composition of the Pasture\u003c/h3\u003e\n\u003cp\u003eThe grazing method used was the rotational stocking. The criterion adopted to determine the time of animal entry into the paddocks was the canopy reaching 95% light interception (LI) (Silva and Nascimento \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Light interception was measured weekly and was estimated as the mean of ten points in each paddock using the Accupar LP 80 device from DECAGON (USA). The post-grazing canopy residual height for each cultivar corresponded to 50% defoliation of the pre-grazing height (Sbrissia et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To achieve the residual height, stocking adjustments were performed using the put-and-take technique so that defoliation was achieved within two days of occupation (Allen et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, when necessary, extra dry cows were placed in the paddocks to ensure the residual canopy height within the predetermined period.\u003c/p\u003e \u003cp\u003eCanopy height was evaluated using a centimeter-graduated ruler, with 30 random points measured per paddock, both in pre-grazing and post-grazing conditions.\u003c/p\u003e \u003cp\u003eTotal herbage mass (HM), under pre- and post-grazing conditions, was estimated using the direct (destructive) method. For this, a 1.0 \u0026times; 0.5 m (0.50 m\u0026sup2;) metal frame was used and placed at four points representative of the average canopy height in each evaluated paddock. The material within each quadrat was cut at ground level and placed in plastic bags. The harvested herbage was then taken to the laboratory for weighing and subsequent processing.\u003c/p\u003e \u003cp\u003eHerbage accumulation (HA) was calculated as the difference between herbage mass in the previous post-grazing and the current pre-grazing. To determine the herbage accumulation rate (HAR), accumulation values were divided by the number of rest days between each grazing cycle. Total herbage accumulation (THA) over the experimental period was calculated by summing the accumulations of all grazing cycles.\u003c/p\u003e \u003cp\u003eFor the morphological evaluation of the herbage components, a subsample of approximately 400 g was taken from the samples collected to determine HM under pre and post-grazing conditions. This subsample was divided into leaf blade, stem (stem\u0026thinsp;+\u0026thinsp;sheath), and dead material fractions, which were weighed and dried in a forced-air oven set at 55\u0026deg;C for 72 h, or until constant weight. Herbage mass values were converted to kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and morphological components were expressed as a proportion (%) of total HM.\u003c/p\u003e\n\u003ch3\u003eDetermination of Chemical Composition\u003c/h3\u003e\n\u003cp\u003eTo determine the herbage chemical composition, the samples were collected above the residual height (50% of the pre-grazing height), using the simulated grazing technique. Samples of the energy supplement were collected weekly. After collection, herbage samples were pre-dried in a forced-air oven (55\u0026deg;C; until constant weight). Subsequently, herbage and supplement samples were ground in a Wiley mill equipped with 1 mm screens and then stored in properly identified containers. Bromatological analyses were performed according to INCT-CA (Detmann et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to determine dry matter at 105\u0026deg;C, total nitrogen, ether extract, minerals, and ash contents. Cell wall components were determined according to INCT-CA methods F-001/1 (neutral detergent fiber), F-003/1 (acid detergent fiber), and F-005/1 (lignin) (Detmann et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In vitro dry matter digestibility was also analyzed (Tilley and Terry \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1963\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMilk production and composition\u003c/h3\u003e\n\u003cp\u003eTo evaluate the productive performance of lactating cows, 10 Holstein \u0026times; Gyr cows were used shortly after peak lactation, with mean and standard deviations for milk yield, days in milk, and body weight of 17.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81 kg day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of milk, 85\u0026thinsp;\u0026plusmn;\u0026thinsp;10 days, and 510\u0026thinsp;\u0026plusmn;\u0026thinsp;8 kg, respectively. Cows were allocated to the treatments according to these characteristics in order to obtain two homogeneous groups.\u003c/p\u003e \u003cp\u003eDuring the experimental period, cows were supplemented with concentrate twice a day during milking, with 2 kg day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e per cow (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in the morning (07:00 h) and 1 kg day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e per cow in the afternoon (14:00 h), totaling 3 kg day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e per cow (as fed basis). Between milking, the animals remained in a resting area. Animals underwent ectoparasite control and weighing at the end of each grazing cycle between milking.\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\u003eNutritional composition of the concentrate in each experimental phase\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhase 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhase 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry matter (DM), g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e953.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e951.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e951.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn vitro dry matter digestibility, g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e863.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e884.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e884.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEther stract, g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e da DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral detergent fiber, g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e da DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcid detergent fiber, g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e da DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude protein, g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e da DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIndividual milk yield was measured at the end of each milking and added together to obtain milk yield per cow/day, subsequently, milk yield per hectare/day was calculated. To determine milk composition, milk samples were collected individually, refrigerated, and then sent to the Milk Quality Laboratory of Embrapa Dairy Cattle, in Juiz de Fora, MG.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of Dry Matter Intake\u003c/h2\u003e \u003cp\u003eDry matter intake (DMI) was estimated using the external marker titanium dioxide (TiO\u003csub\u003e2\u003c/sub\u003e) associated with in vitro dry matter digestibility (IVDMD) of the pasture and the supplement, over the three grazing cycles. 10 g cow\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of TiO\u003csub\u003e2\u003c/sub\u003e were supplied in two daily doses of 5 g each, administered orally, for 12 consecutive days. The first six days were considered an adaptation period to stabilize marker excretion flows, and the last six days were used for fecal collections. IVDMD was determined according to the protocol described by Tilley and Terry (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1963\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFecal samples were dried in a forced-air oven at 55\u0026deg;C, ground in a Willey mill equipped with a 1 mm screen. Then, the samples were subjected to acid digestion with 15 mL of sulfuric acid (H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e), followed by the addition of 10 mL of 30% v/v hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e), and subsequent quantification of TiO2 content by spectrophotometry, according to INCT-CA method M-007/1 (Detmann et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fecal output (FO, kg cow\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of DM) was calculated using Eq.\u0026nbsp;1, as follows:\u003c/p\u003e \u003cp\u003eEquation 1: FO\u0026thinsp;=\u0026thinsp;daily marker dose (g day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) / marker concentration in fecal DM (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eDaily pasture DMI was estimated using Eq.\u0026nbsp;2:\u003c/p\u003e \u003cp\u003eEquation 2: DMI (kg cow\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) = ((FO \u0026ndash; FO\u003csub\u003econc\u003c/sub\u003e) / (1 \u0026minus; (IVDMD\u003csub\u003epasture\u003c/sub\u003e/100))\u003c/p\u003e \u003cp\u003eWhere: FO\u003csub\u003econc\u003c/sub\u003e (kg cow\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) = fecal output attributable to concentrate intake (FO\u003csub\u003econc\u003c/sub\u003e = concentrate DM intake, kg cow\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026times; IVDMD\u003csub\u003econc\u003c/sub\u003e/100); IVDMD\u003csub\u003econc\u003c/sub\u003e = IVDMD of the concentrate; IVDMD\u003csub\u003epasture\u003c/sub\u003e = IVDMD of the pasture.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eThe assumptions of normal probability distribution and homogeneity of variances for the use of the statistical model were verified using the Shapiro\u0026ndash;Wilk and Bartlett tests, respectively.\u003c/p\u003e \u003cp\u003eAnalyses of variance (ANOVA) for the agronomic variables were performed according to the following model:\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e \u003csub\u003e \u003cem\u003eijk\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;C\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;θ\u003c/em\u003e\u003csub\u003e\u003cem\u003eik\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;F\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+\u003c/em\u003e (\u003cem\u003eF/C\u003c/em\u003e)\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ α\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e = observation of cultivar \u003cem\u003ei\u003c/em\u003e, phase \u003cem\u003ej\u003c/em\u003e, and replicate \u003cem\u003ek\u003c/em\u003e; \u003cem\u003e\u0026micro;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;overall mean; \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e = fixed effect of cultivar \u003cem\u003ei\u003c/em\u003e; \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003eik\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;random error associated with the observation of cultivar \u003cem\u003ei\u003c/em\u003e and replicate \u003cem\u003ek\u003c/em\u003e, with \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003eik\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;~\u0026thinsp;N(\u003cem\u003e\u0026micro;, σ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e); \u003cem\u003eFj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;fixed effect of phase \u003cem\u003ej\u003c/em\u003e; (\u003cem\u003eF/C\u003c/em\u003e)\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e = interaction effect between cultivar \u003cem\u003ei\u003c/em\u003e and phase \u003cem\u003ej\u003c/em\u003e; \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;random error associated with the subplot of cultivar \u003cem\u003ei\u003c/em\u003e in phase \u003cem\u003ej\u003c/em\u003e of replicate \u003cem\u003ek\u003c/em\u003e, with \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;~\u0026thinsp;N(\u003cem\u003e\u0026micro;, σ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eFor milk yield variables, analyses of variance (ANOVA) were performed according to the following model:\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e \u003csub\u003e \u003cem\u003eijk\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;P\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ T\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e = observation in adjusted treatment \u003cem\u003ei\u003c/em\u003e, in period \u003cem\u003ej\u003c/em\u003e of replicate \u003cem\u003ek\u003c/em\u003e; \u003cem\u003e\u0026micro;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;overall mean effect; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e = effect of period \u003cem\u003ej\u003c/em\u003e; \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e = effect of adjusted treatment \u003cem\u003ei\u003c/em\u003e; \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;random error of adjusted treatment \u003cem\u003ei\u003c/em\u003e in period \u003cem\u003ej\u003c/em\u003e of replicate \u003cem\u003ek\u003c/em\u003e, with \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;~\u0026thinsp;N(\u003cem\u003e\u0026micro;, σ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eAnalyses of variance (ANOVA) for DMI were performed according to the following model:\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e \u003csub\u003e \u003cem\u003eijk\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;P\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ A\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ T\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eWhere: Y\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e = observation in adjusted treatment \u003cem\u003ei\u003c/em\u003e, in period \u003cem\u003ej\u003c/em\u003e of animal \u003cem\u003ek\u003c/em\u003e; \u003cem\u003e\u0026micro;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;overall mean effect; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e = effect of period \u003cem\u003ej\u003c/em\u003e; \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e = effect of animal \u003cem\u003ek\u003c/em\u003e; \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e = effect of treatment \u003cem\u003ei\u003c/em\u003e; \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;random error of treatment \u003cem\u003ei\u003c/em\u003e in period \u003cem\u003ej\u003c/em\u003e of animal \u003cem\u003ek\u003c/em\u003e, with \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;~\u0026thinsp;N(\u003cem\u003e\u0026micro;, σ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eTo compare the means of experimental groups, orthogonal contrasts were used by the Fisher\u0026rsquo;s analysis, with an error rate lower than 5% as the criterion for statistical significance. For unfolding interactions and comparing phases, Tukey\u0026rsquo;s test was used. All analyses were performed using R Core Team (2019) software.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eNo significant difference (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was observed between the rest period of the cultivars in any of the experimental phases; the observed mean was 18 days. There was no cultivar \u0026times; phase interaction effect (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for the variables canopy height, morphological components under pre and post-grazing conditions, leaf/stem ratio, herbage accumulation, herbage accumulation rate and volumetric density (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eCanopy height, herbage mass (HM), morphological components at pre and post-grazing, herbage accumulation (HA), herbage accumulation rate (HAR), and volumetric density (VD) of two cultivars (C) \u0026ndash; BRS Ipypor\u0026atilde; and BRS Paiagu\u0026aacute;s \u0026ndash; and three experimental phases (P)\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBRS Ipypor\u0026atilde;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eBRS Paiagu\u0026aacute;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhase 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhase 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhase 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePhase 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePhase 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eC*P\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003ePre-grazing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHM (Kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5153.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5917.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5059.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8104.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7075.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6232.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e537.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL/S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.3371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead material (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003ePos-grazing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHM (Kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2747.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2236.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1992.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3781.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3228.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2767.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e176.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.7834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.5036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead material (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHA (kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e phase\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4714.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7180.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6453.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8645.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8982.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9207.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e426.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAR (kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e157.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e118.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVD (kg DM cm ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eL/S\u0026thinsp;=\u0026thinsp;Leaf/Stem ratio; SEM\u0026thinsp;=\u0026thinsp;Standard error of the mean\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe canopy height differed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between cultivars under pre and post-grazing conditions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Paiagu\u0026aacute;s grass showed a 1.52 times greater management height than Ipypor\u0026atilde; grass at pre-grazing and 1.57 times at post-grazing. In addition, pre and post-grazing herbage mass differed significantly between cultivars (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Under pre-grazing conditions, HM of BRS Paiagu\u0026aacute;s grass exceeded that of Ipypor\u0026atilde; grass by 1,761 kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Under post-grazing conditions, HM was 933.41 kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e higher in Paiagu\u0026aacute;s grass. The leaf/stem ratio (L/S) was 33.76% higher in Ipypor\u0026atilde; grass (1.54 \u003cem\u003evs\u003c/em\u003e 1.02; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe leaf proportion was also higher in Ipypor\u0026atilde; than in Paiagu\u0026aacute;s grass (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), both at pre-grazing (50.3 \u003cem\u003evs\u003c/em\u003e 40.6%, respectively) and post-grazing (20 \u003cem\u003evs\u003c/em\u003e 13.3%, respectively; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At pre-grazing, stem proportion was 16.81% higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in Paiagu\u0026aacute;s grass, whereas at post-grazing no difference was observed between cultivars. The proportion of dead herbage mass did not differ between the cultivars (P\u0026thinsp;=\u0026thinsp;0.17), but it was higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the residue of Paiagu\u0026aacute;s grass.\u003c/p\u003e \u003cp\u003eHerbage accumulation (HA) and herbage accumulation rate (HAR) differed between the evaluated cultivars (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both variables were higher in Paiagu\u0026aacute;s grass: HA was 31.61% and HAR was 32.43% higher than in Ipypor\u0026atilde; grass. For volumetric density (VD), there was a significant difference between cultivars (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). VD was 15% higher in Ipypor\u0026atilde; grass compared with Paiagu\u0026aacute;s.\u003c/p\u003e \u003cp\u003eAmong the phases, a significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was observed for HM, canopy height, L/S, and stem percentage at pre-grazing, being lower in the phase three. Under post-grazing conditions, canopy height, HM, and leaf and stem percentages were higher in phase 1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Herbage accumulation and HAR also differed among the experimental phases (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), being higher in phase two (8,081.35 kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e phase\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 138.68 kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Volumetric density did not differ among phases (P\u0026thinsp;=\u0026thinsp;0.52), and the observed mean was 117.96\u0026thinsp;\u0026plusmn;\u0026thinsp;7.71 kg DM cm ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCanopy height, herbage mass (HM), leaf/stem ratio (L/S), morphological components, herbage accumulation (HA), herbage accumulation rate (HAR), and volumetric density (VD) at pre and post-grazing and three experimental phases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePre-grazing\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.46 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.65 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.56 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHM (Kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6629.09 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6496.22 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5645.88 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e380.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL/S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.98 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.69 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.68 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.38 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.51 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.04 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead material (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.43 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.34 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePos-grazing\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.98 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.03 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.46 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHM (Kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3262.45 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2732.80 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2379.83 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.09 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.05 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.89 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.05 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.61 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.06 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead material (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.84 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.33 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.03 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAcumulation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHA (kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Phase\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6680.01 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8081.35 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7830.59 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e301.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAR (kg DM.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dia\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111.33 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.68 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.29 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eVolumetric density\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVD (kg DM cm ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.57 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.51 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118.82 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eLowercase letters compare phases by Tukey\u0026rsquo;s test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). SEM\u0026thinsp;=\u0026thinsp;Standard error of the mean\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe stocking rate (SR) (AU ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) differed between cultivars (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), being 11% higher in Paiagu\u0026aacute;s grass (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLowercase letters compare cultivars by Fisher\u0026rsquo;s test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); AU\u0026thinsp;=\u0026thinsp;animal unit (450 kg); ha\u0026thinsp;=\u0026thinsp;hectare; SEM\u0026thinsp;=\u0026thinsp;0.23; P\u0026thinsp;=\u0026thinsp;0.03.\u003c/p\u003e \u003cp\u003eFor fiber composition and IVDMD, a significant interaction (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was observed between the factors under study (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Neutral detergent fiber (NDF) and acid detergent fiber (ADF) were higher in Paiagu\u0026aacute;s grass in all evaluated phases. Regarding comparisons among phases, Paiagu\u0026aacute;s grass showed higher NDF and ADF in phase three, whereas Ipypor\u0026atilde; grass showed higher values in phase two. The lignin content was higher in Paiagu\u0026aacute;s grass than in Ipypor\u0026atilde; grass. When comparing phases, there was a difference only for Ipypor\u0026atilde; grass, for which the lowest lignin content was observed in phase three. IVDMD differed between cultivars in experimental phase three, being higher in Ipypor\u0026atilde; grass. Among phases, for both cultivars the highest digestibility occurred in phase three.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFiber composition and in vitro dry matter digestibility (IVDMD) of the cultivars BRS Paiagu\u0026aacute;s and BRS Ipypor\u0026atilde; across three experimental phases\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCultivar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c6\" namest=\"c5\" rowspan=\"2\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eNDF (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS Ipypor\u0026atilde;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e566.73 bB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e590.55 bA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e566.03 bB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c6\" namest=\"c5\" rowspan=\"2\"\u003e \u003cp\u003e7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS Paiagu\u0026aacute;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e626.85 aAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e614.85 aB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e644.28 aA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eADF (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS Ipypor\u0026atilde;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260.57 bB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283.35 bA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262.93 bB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c6\" namest=\"c5\" rowspan=\"2\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS Paiagu\u0026aacute;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e314.81 aAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310.68 aB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e325.69 aA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eLignin (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS Ipypor\u0026atilde;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.88 bA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.29 bA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.7 bB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c6\" namest=\"c5\" rowspan=\"2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS Paiagu\u0026aacute;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.59 aA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.05 aA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.56 aA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eIVDMD (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS Ipypor\u0026atilde;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e700.80 aB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e710.50 aB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e830.77 bA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c6\" namest=\"c5\" rowspan=\"2\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS Paiagu\u0026aacute;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e700.00 aB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e714.94 aB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e808.56 aA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eLowercase letters compare cultivars and uppercase letters compare phases by Tukey\u0026rsquo;s test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). NDF\u0026thinsp;=\u0026thinsp;neutral detergent fiber; ADF\u0026thinsp;=\u0026thinsp;acid detergent fiber; SEM\u0026thinsp;=\u0026thinsp;standard error of the mean\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLowercase letters compare phases by Tukey\u0026rsquo;s test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); a: SEM-cultivar\u0026thinsp;=\u0026thinsp;2.89; P-cultivar\u0026thinsp;=\u0026thinsp;0.416; b: SEM-phase\u0026thinsp;=\u0026thinsp;3.58; P-Phase\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003cp\u003eCrude protein content did not differ between cultivars (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) with a mean value of 144.44 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Among phases, the lowest crude protein content was observed in phase 1, with an observed mean of 129.60 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe herbage DMI differed between cultivars only in the first intake evaluation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), when it was higher for Ipypor\u0026atilde; grass (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the second intake evaluation, there was no difference between cultivars, and the observed mean was 11.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 kg cow\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHerbage dry matter intake (DMI) of Holstein \u0026times; Gyr cows managed under grazing on BRS Ipypor\u0026atilde; and BRS Paiagu\u0026aacute;s pasture\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIntake 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eIntake 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRS Ipypor\u0026atilde;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBRS Paiagu\u0026aacute;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBRS Ipypor\u0026atilde;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBRS Paiagu\u0026aacute;s\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMI\u003c/p\u003e \u003cp\u003e(kg cow\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.11 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.02 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.05 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.31 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMI\u003c/p\u003e \u003cp\u003e(% of BM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.43 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.28 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eLowercase letters compare cultivars by Fisher\u0026rsquo;s test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); SEM\u0026thinsp;=\u0026thinsp;standard error of the mean; BW\u0026thinsp;=\u0026thinsp;body weight\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo significant difference was observed in daily milk production per area (L ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) nor in the analyzed milk composition variables (fat, protein, lactose, and total solids) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMilk yield and milk composition of Holstein \u0026times; Gyr cows managed under grazing on BRS Ipypor\u0026atilde; and BRS Paiagu\u0026aacute;s pasture across three experimental phases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCultivar\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRS Ipypor\u0026atilde;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBRS Paiagu\u0026aacute;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMilk yield (L ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eFat (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eProtein (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eLactose (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eTotal solids (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSEM\u0026thinsp;=\u0026thinsp;Standard error of the mean\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe goals for animal entry and exit from paddocks chosen in the study (95% LI for animal entry and a 50% reduction in height) aimed at the most efficient use of the herbage and they were achieved (Da Silva and Nascimento Jr. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sbrissia et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The management height of the cultivars was higher than those observed in other studies. Echeverria et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found a mean height of 30 cm when Ipypor\u0026atilde; grass was managed at 95% LI under intermittent grazing, a value similar to that observed by Paraiso et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) of 29 cm under continuous stocking. In a study with Paiagu\u0026aacute;s grass, the height observed by Gobbi et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was 34 cm in plots managed at 95% LI. Under continuous stocking management, Euclides et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) proposed a management height of 30 cm.\u003c/p\u003e \u003cp\u003eAmong the experimental phases, the difference in heights is associated with management adjustment throughout the experiment (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The greater canopy height found in phase one can be explained by the pre-experimental pasture conditions, which presented high HM, stem, and dead material, mainly for Paiagu\u0026aacute;s grass (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results corroborate the findings of Euclides et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who observed that a greater management height of Marandu grass (\u003cem\u003eUrochloa brizantha\u003c/em\u003e cv. Marandu) resulted in a higher stem percentage and, consequently, a lower L/S ratio. As the experimental phases progressed, heights were adjusted, and in phase three the pre-grazing height was 11% lower than those observed in phases 1 and 2 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnder Brazilian semiarid conditions, Rodrigues et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that a canopy height of 46.3 cm resulted in 78.7% LI during the establishment of Paiagu\u0026aacute;s grass. Establishing pastures have greater spacing between clumps and, consequently, insufficient canopy density to cover the soil. In this situation, canopies do not have sufficient density to determine a management height associated with the recommended LI. As grazing occurs, the tillers that emerge tend to be smaller than those that were grazed (Gastal and Lemaire \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) but the tiller density increase (Hodgson, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). This explains the greater heights in the first phase, decreasing up to the third phase for both \u003cem\u003eUrochloa\u003c/em\u003e cultivars (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA 50% reduction in canopy height through grazing is considered moderate, ensuring high herbage intake rates with great nutritive value (Fonseca et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mezzalira et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sbrissia et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, DMI is expected to be higher (Zanine et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and animals consume more leaf, since 90% of stem mass is concentrated in the lower half of the herbage canopy (Zanini et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the factors that most affects the profitability of pasture-based dairy systems is the productivity (Hanrahan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, the greater the quantity and the quality of available herbage, the more promising pasture-based milk production systems tend to be (Elgesma et al. 2015).\u003c/p\u003e \u003cp\u003eThe HM observed in the pastures was within the range reported in the scientific literature, varying from 3,095 to 6,110 kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for Ipypor\u0026atilde; grass (Echeverria et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Euclides et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paraiso et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and from 2,605 to 8,115 kg DM ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for Paiagu\u0026aacute;s grass (Euclides et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Germani et al. 2018; Gobbi et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ipypor\u0026atilde; grass showed lower HM compared with Paiagu\u0026aacute;s grass (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which may be related with the lower productivity of the Ipypor\u0026atilde; grass progenitor, \u003cem\u003eUrochloa ruziziensis\u003c/em\u003e, compared with other \u003cem\u003eUrochloa\u003c/em\u003e species (Euclides et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paraiso et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This resulted in a lower SR than that of Paiagu\u0026aacute;s grass pasture (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, Ipypor\u0026atilde; grass showed a higher leaf percentage, a lower stem percentage and, consequently, a higher L/S ratio (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), highlighting it compared to other \u003cem\u003eUrochloa\u003c/em\u003e spp. Cultivars (Valle et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Gurgel et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also found a greater leaf blade mass and L/S ratio in Ipypor\u0026atilde; grass during establishment in the Cerrado biome, concluding that the stem elongation rate of Paiagu\u0026aacute;s grass was higher than that of Ipypor\u0026atilde; grass (1.2 and 0.52, respectively).\u003c/p\u003e \u003cp\u003eThe greater fiber fraction and the lower digestibility observed in Paiagu\u0026aacute;s grass are explained by its higher stem percentage (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The stem is the structural component of the plant, in which a greater amount of fibrous tissues is concentrated (Van Soest \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Crude protein content, however, did not differ between cultivars (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe management of canopy height directly affects the morphological components of the herbage canopy (Silva Neto et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mezzalira et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). An increase in canopy height results in competition for light among plants, promoting stem elongation in order to take advantage of higher light incidence (Moura et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the phase three, the management had already been stabilized, therefore, a lower pre-grazing height was maintained (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A consequence of the management adjustment was the higher L/S ratio and the lower stem percentage in this phase (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similar behavior was observed by Echeverria et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), in which a greater management height resulted in a decrease in the L/S ratio in Marandu grass. The reduction in pre-grazing height influenced the chemical composition of the cultivars (Euclides et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), showing higher IVDMD (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and crude protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e) values compared with experimental phases one and two.\u003c/p\u003e \u003cp\u003eThe potential of the herbage production for each grass is genetically determined, but it depends on environmental conditions (water, light, temperature, and nutrient availability). Grazing management is also fundamental for the productive potential to be achieved (Sbrissia et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Herbage accumulation after defoliation results from the flow of new tissues (Hodgson \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Fagundes et al. 2005), which occurs both in defoliated but not decapitated tillers and in new tillers.\u003c/p\u003e \u003cp\u003eThe high HAR observed in the study (between 94.69 and 140.15 kg DM ha\u003csup\u003e-1\u003c/sup\u003e day\u003csup\u003e-1\u003c/sup\u003e) reflects the favorable climatic conditions during the experimental period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), as well as the level of fertilization applied and the great response of the cultivars to fertilization. In intensive production systems, rapid regrowth, represented by a short interval between grazing events, allows higher herbage quality and a reduction in the number of paddocks in the rotational stocking method (Gomide and Paciullo \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, it is necessary to highlight the rapid attainment of grazing readiness of the cultivars (95% LI) during the rainy season, resulting in a mean rest period of 18 days.\u003c/p\u003e \u003cp\u003eThe lower herbage accumulation (HA) observed in phase one may be associated with the greater management height and lower volumetric density (VD) during this phase (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Pastures managed at greater heights tend to undergo stem elongation, reducing the canopy density and, consequently, the herbage accumulation (Euclides et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Moura et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This explains the higher HA and L/S observed in the phases in which management was already established (phases two and three). During phase three, an extended dry spell occurred, increasing the days required for the herbage to reach its optimal grazing point (95% LI), and significantly reducing the HAR (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Beloni et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe higher volumetric density (kg DM cm\u003csup\u003e-\u003c/sup\u003e\u0026sup1; ha\u003csup\u003e-\u003c/sup\u003e\u0026sup1;) observed in Ipypor\u0026atilde; grass (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) can be explained by its lower average height, since this cultivar was managed at a height 35% lower than that of Paiagu\u0026aacute;s grass, while its herbage mass was only 25% lower (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVolumetric density and the HM are important variables that directly affects the accessibility of herbage to be consumed by animals (Hodgson, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). The higher VD of Ipypor\u0026atilde; grass, combined with a higher leaf percentage, higher L/S ratio, and lower fibrous fraction, explains the greater DMI observed in the first intake evaluation (intake 1) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) compensating for its lower HM compared to Paiagu\u0026aacute;s.\u003c/p\u003e \u003cp\u003eStocking rate adjustment was performed based on the HM produced, which explains the higher SR observed for Paiagu\u0026aacute;s grass (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e), due to its greater dry matter (DM) yield. A similar relationship was reported by Moura et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who found a higher SR in treatments with Marandu grass managed at 95% light interception compared with treatments managed under fixed rest periods.\u003c/p\u003e \u003cp\u003eThe higher SR at Paiagu\u0026aacute;s pasture was not enough to impact daily milk productivity (L ha\u003csup\u003e-1\u003c/sup\u003e day\u003csup\u003e-1\u003c/sup\u003e). Morphological and chemical composition indicated a better structural and nutritional conditions at pre-grazing in Ipypor\u0026atilde; grass pastures (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), directly affecting the DMI (Fonseca et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Under grazing conditions, DMI is directly influenced by non-nutritional factors, which vary according to the structure of the herbage canopy (Da Silva et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Grasses with a higher leaf proportion, greater VD, and lower fibrous fraction are preferentially more consumed (Mezzalira et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Moura et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdjustments in herbage management can result in improvements in bromatological composition and in herbage intake (Anjos et al. 2016; Pereira et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These arguments are supported by the values observed in the present study, since adjustments in the management height of the cultivars during the third experimental phase (intake 2) resulted in a higher L/S ratio, greater in vitro dry matter digestibility (IVDMD) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and equalized DMI between cultivars.\u003c/p\u003e \u003cp\u003eThe values observed for DMI were close to those reported for other tropical grasses, with an average of 13 kg cow\u003csup\u003e-\u003c/sup\u003e\u0026sup1; day\u003csup\u003e-\u003c/sup\u003e\u0026sup1; in Tanzania grass pasture (\u003cem\u003eMegathyrsus maximus\u003c/em\u003e cv. Tanzania), 11.3 kg cow\u003csup\u003e-\u003c/sup\u003e\u0026sup1; day\u003csup\u003e-\u003c/sup\u003e\u0026sup1; in stargrass cv. Africana (\u003cem\u003eCynodon nlemfuensis\u003c/em\u003e) and 11.7 kg cow\u003csup\u003e-\u003c/sup\u003e\u0026sup1; day\u003csup\u003e-\u003c/sup\u003e\u0026sup1; in Marandu grass. Fukumoto et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found a DMI of 10.1 and 12.3 kg cow\u003csup\u003e-\u003c/sup\u003e\u0026sup1; day\u003csup\u003e-\u003c/sup\u003e\u0026sup1; in pastures of elephant grass cv. Cameroon (\u003cem\u003eCenchrus purpureus\u003c/em\u003e) managed under maximum light interception and 95% light interception, respectively, showing the detrimental effect of stem accumulation on DMI by grazing animals.\u003c/p\u003e \u003cp\u003eThe milk yield observed in this study (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) is within the range reported in the literature for cows grazing tropical grass pastures. In a study with elephant grass cv. Cameroon, milk production values ranged from 15.7 to 18.1 L day\u003csup\u003e-\u003c/sup\u003e\u0026sup1; (Congio et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Demski et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported milk yields between 17.34 and 13.73 L cow\u003csup\u003e-\u003c/sup\u003e\u0026sup1; day\u003csup\u003e-\u003c/sup\u003e\u0026sup1; for the Mulato II hybrid (\u003cem\u003eU. brizantha\u003c/em\u003e \u0026times; \u003cem\u003eU. ruziziensis\u003c/em\u003e \u0026times; \u003cem\u003eU. decumbens\u003c/em\u003e) and between 17.24 and 11.96 L cow\u003csup\u003e-\u003c/sup\u003e\u0026sup1; day\u003csup\u003e-\u003c/sup\u003e\u0026sup1; for Marandu grass. When evaluating Marandu grass at 30 days of regrowth and 95% light interception, milk production ranged from 100.3 to 139.7 L ha\u003csup\u003e-\u003c/sup\u003e\u0026sup1; day\u003csup\u003e-\u003c/sup\u003e\u0026sup1; (Moura et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, it is worth noting that this study used cows with higher production potential, as well as a higher level of concentrate supplementation (6 kg cow\u003csup\u003e-\u003c/sup\u003e\u0026sup1; day\u003csup\u003e-\u003c/sup\u003e\u0026sup1;). To date, there are no data in the literature regarding milk production in pastures of BRS Ipypor\u0026atilde; and BRS Paiagu\u0026aacute;s.\u003c/p\u003e \u003cp\u003eMilk solids content (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) was similar to the values reported by Demski et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who evaluated grazing on Marandu grass and Mulato II grass and observed mean values of 39.15 g kg\u003csup\u003e-\u003c/sup\u003e\u0026sup1; for fat, 34.75 g kg\u003csup\u003e-\u003c/sup\u003e\u0026sup1; for protein, 42.7 g kg\u003csup\u003e-\u003c/sup\u003e\u0026sup1; for lactose, and 126.45 g kg\u003csup\u003e-\u003c/sup\u003e\u0026sup1; for total solids.\u003c/p\u003e \u003cp\u003eThe similarity between the values observed in this study and those reported in the literature indicates the potential of Paiagu\u0026aacute;s and Ipypor\u0026atilde; grasses to be used in pasture-based dairy production systems. These values are also close to those observed in studies with elephant grass by Voltolini et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), as well as in studies with Tanzania grass, Marandu grass, and star grass (Porto et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe lack of differences in milk yield and milk composition between the cultivars can be explained by the fact that they belong to the same genus, were managed under the same conditions (95% light interception and 50% post-grazing height reduction), which allowed greater leaf selection by the cows, and received the same amount of concentrate supplementation (3 kg cow\u003csup\u003e-\u003c/sup\u003e\u0026sup1; day\u003csup\u003e-\u003c/sup\u003e\u0026sup1;).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe cultivars evaluated show potential for use in pasture-based dairy production systems when managed at 95% light interception (41 cm pre-grazing height for cv. BRS Ipypor\u0026atilde; and 63 cm for cv. BRS Paiagu\u0026aacute;s) with a 50% post-grazing height reduction.\u003c/p\u003e \u003cp\u003eThe BRS Ipypor\u0026atilde; cultivar showed a higher leaf proportion and a greater leaf-to-stem ratio, resulting in herbage with lower fiber and lignin content. However, BRS Paiagu\u0026aacute;s produced a greater herbage mass, resulting in a higher stocking rate.\u003c/p\u003e \u003cp\u003eThe average milk productivity of BRS Paiagu\u0026aacute;s and BRS Ipypor\u0026atilde; pastures under rotational grazing during the rainy season is approximately 107 L ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the CAPES, CNPq, UNIPASTO and the Embrapa Gado de Leite for the support in conducting this research\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author Soares NA has received research support from CNPq (Grant number 454711/2014-0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures were approved by the Ethics Committee on Animal Use of the Embrapa (Protocol number: 6231210316).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllen VG, Batello C, Berretta EJ et al (2011) An international terminology for grazing lands and grazing animals. 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Ci\u0026ecirc;ncia Rural 42:882\u0026ndash;887. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/S0103-84782012000500020\u003c/span\u003e\u003cspan address=\"10.1590/S0103-84782012000500020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Dry matter intake, herbage production, leaf:stem ratio, milk composition, milk yield, volumetric density","lastPublishedDoi":"10.21203/rs.3.rs-9361311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9361311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePasture-based dairy systems require well-adapted forage cultivars and appropriate grazing management to sustain milk production while maintaining favorable sward structure. The objective of this study was to evaluate the performance of Holstein \u0026times; Gyr crossbred cows and the productive and structural characteristics of Paiagu\u0026aacute;s grass pasture (\u003cem\u003eUrochloa brizantha\u003c/em\u003e cv. BRS Paiagu\u0026aacute;s) and Ipypor\u0026atilde; grass pasture (\u003cem\u003eUrochloa brizantha\u003c/em\u003e \u0026times; \u003cem\u003eUrochloa ruziziensis\u003c/em\u003e cv. BRS Ipypor\u0026atilde;), under rotational stocking during two rainy season. For the agronomic variables, a completely randomized design was adopted in a split-plot design: two grasses and three experimental phases, with twelve paddocks evaluated per phase. The arrangement for milk yield and milk composition was the complete switchback trial, and for dry matter intake, it was the cross-over. Paiagu\u0026aacute;s grass showed greater herbage mass, accumulation and herbage accumulation rate. Ipypor\u0026atilde; grass showed a greater leaf:stem ratio (1.54 \u003cem\u003evs\u003c/em\u003e 1.02) and greater volumetric density (127.45 \u003cem\u003evs\u003c/em\u003e 108.15 kg DM cm ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Milk yield did not differ between cultivars or experimental phases with an average value of 107.65 L ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Stocking rate was higher in Paiagu\u0026aacute;s grass pasture compared with Ipypor\u0026atilde; grass (7.71 \u003cem\u003evs\u003c/em\u003e 6.90 AU ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Dry matter intake differed between cultivars only in phase 2, when it was higher for Ipypor\u0026atilde; grass compared with Paiagu\u0026aacute;s grass (2.21 \u003cem\u003evs\u003c/em\u003e 1.97% of body weight, respectively). Both cultivars supported a great milk yield when managed with 41 cm pre-grazing height for cv. BRS Ipypor\u0026atilde; and 63 cm for cv. BRS Paiagu\u0026aacute;s, and 50% defoliation.\u003c/p\u003e","manuscriptTitle":"Canopy structure, herbage quality and performance of Holstein × Gyr cows (Bos taurus taurus L. × Bos taurus indicus L.) under rotational stocking in Urochloa spp. pastures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 13:45:00","doi":"10.21203/rs.3.rs-9361311/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-25T15:03:04+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T08:26:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T09:53:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2026-04-08T18:36:19+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":"7b020e61-be29-4df7-a0a8-7be9b91c2e04","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T13:45:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 13:45:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9361311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9361311","identity":"rs-9361311","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}