Productive performance and estimations of urine and faecal nitrogen excretions of Brown Swiss cows in small dairy farms in the highlands of México

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IF and SIF were based on temperate pastures, whereas the EF was based on tropical pastures. Farms were visited every month (experimental period (EP)), to recorded productive response variables, as well as feeds samples. Urine and Faecal nitrogen excretions (g/day/cow) (UNE and FNE, respectively), were estimated using equations from the literature that used nitrogen intake as a predictor. Experimental design was a complete random analysis, using a Mixed model procedure in SAS. There were significant differences (P < 0.05) of cows performance due to the farm type of management. The diets of the cows in the farms in the study provided net high energy of lactation (NE L ), that would allowed higher milk yields (20.7 kg/cow/day) than observed (17.2 kg/cow/day), being the dietary crude protein (DCP) the limiting factor that determined the cows performance. These low DCP levels (14.6%), determined low urine nitrogen excretions (UNE, 131.6 g/cow/day) and faecal nitrogen excretions (FNE, 139.5 g/cow/day). Out of the total manure nitrogen excretions (TMNE), 49 and 51% of nitrogen (N) were excreted in urine and faeces, respectively. Mean nitrogen utilization efficiency (NUE) was 20.3%. When expressing nitrogen excretions per kg of milk produced cows in the study excreted 51% more nitrogen per kg of milk compared to highly intensive dairy systems from North America. Conclusion. The estimated excretions of nitrogen excreted in urine and faeces (g/cow/day) from dairy cows in small farms in the Highlands of Mexico were low; however much work is needed to increase nitrogen utilization efficiency of cows in small dairy farms. One option could be to increase the productivity of the cows diluting nitrogen manure excretion per kg of milk produced. nitrogen excretions small dairy farms crude protein efficiency Introduction Feeding practices in small dairy farms in Mexico have been reported as deficient and imbalanced causing predisposition to diseases, low performance of cows and reduced profitability (Absalón-Medina et al., 2012). Unbalanced diets are caused by variations of the nutritional composition of ingredients along with farmers lack of basic nutritional knowledge (Deen and Tyagi 2019 ). Excesses of dietary crude protein (CP) is one of the more frequent examples of unbalanced rations. Powell et al. ( 2014 ) found that half of lactating cows in the state of Wisconsin U.S. were over with CP. Overfeeding CP is usually associated with higher feeding cost and higher N excretion in the urine as urinary urea nitrogen (UUN; Powell and Rotz 2015 ). Dairy farms contribute to environment N pollution from forage production (fertilizers) and excretions from cows in manure and urine. From the total N farm input, only about 15% is transformed into animal protein as meat and milk (Tamminga 1992 ). Dairy cow transforms between 26 to 30% of the total dietary N into milk protein and, the rest is excreted in faeces (33%) and urine (24%) (Spek et al. 2013b ; Barros et al. 2017 ). This low milk N efficiency is primarily due to overfeeding of dietary crude protein in the diet. A positive relationship between crude protein intake and N excreted in urine has been established (Kebreab et al. 2002 ). While faecal N is relatively stable and can be incorporated to cropland and recycle through crops, UUN is rapidly transformed into ammonium (NH4), and then, lost as ammonia (NH3) during manure storage and application. Then, it can be transformed into nitrate (NO3) and nitrogen dioxide (N2O) contributing to greenhouse gasses emitted to the atmosphere (Powell and Rotz 2015 ). Urea in blood is the main end-product of nitrogen metabolism in ruminants. This metabolite has been used as an indicator of the efficiency of N utilization; however, it cannot be routinely measure in farm (Huhtanen et al. 2015a ). Apart from urine and faeces urea excretion, a second route of nitrogen excretion is milk, which is closely correlated with blood urea-N (BUN) (Broderick and Clayton 1997 ). Studies have reported that the measurement of milk urea N (MUN) concentration, is a useful indicator of efficiency of N utilization and urinary N output (Jonker et al. 1998 ; Kauffman and St-Pierre 2001 ). Crude protein in the diet has been identified as the main factor influencing concentration of urea N in the milk and urea N in urine (Nousiainen et al. 2004 ; Spek et al. 2013b ). Strong correlation among MUN, UUN and dietary crude protein have been established (Spek et al. 2012 ). It has been suggested that MUN can be a useful diagnostic tool to assess the efficiency of N utilization, based on averages values from experiments (Jonker et al. 2002 ; Nousiainen et al. 2004 ; Spek et al. 2013a ). The objective of the study was to use MUN as predictor of nitrogen excretions in urine and faeces of cows from three small dairy farms representing intensive, semi-intensive and extensive management in Central México. Materials and methods Location The study was conducted in two locations in the southwest of the Estado de Mexico. The first location included two dairy farms in Telpintla community, municipality of Temascaltepec at latitude 19°3’59’’ N and longitude 100°3’ 6’’ W, at 1,882 meters above sea level. The weather is classified as Cwb subtropical highland, according to the Köppen climate classification; with average temperatures of 15.7°C and average annual rainfall of 1,159 mm. The second location was in the municipality of Zacazonapan between 19º 00’ 17’’ and 19º 16’ 17’’ north latitude and between 100º 12’ 55’’ and 100º 18’ 13’’ west longitude, at an altitude of 1,470 meters above sea level. The climate in this region is semi-hot classified as A(C) (w2), with a mean temperature of 23°C and 1,115 mm of annual rainfall, with summer rains and a marked dry season from November to May, (SMN 2020). The participating farms were classified as of intensive, semi-intensive and extensive managed farms, visited for two consecutive days once a month, from February to July of 2021 to record the productive performance of cows and sampling of milk and feeds. Farms description The intensive managed farm (IF) had an extension of 50 ha of which, ten were cultivated with Italian Ryegrass ( Llolium multiflorum ) established in the earliest days of October 2020. Grass from pastures was cut every 30 to 45 days and let dry on pastures tossing the grass every other day before collection and storage. The rest of the land extension was pastures of native species and exotic grasses like Bahia grass ( Paspalum notatum ) and Africa Star ( Cynodon plechtostachyus ), where growing calves and replacements grazed. There were 80 Brown Swiss animals, of which 33 were lactating cows and the rest were calves at different stages of development, under semi-confinement in free stall barns. Cows were machine milked in a milking parlour twice a day at 7:00 a.m. and 3:00 p.m. Cows were supplemented with 7 kg/day of commercial concentrate with 18% crude protein (CP) split twice a day after each milking at 9:00 a.m. and 4:00 pm. Cows received ryegrass hay after consuming the concentrate, they had free access to water in barns. The semi-intensive (SI) farm comprised of 11 lactating cows plus three heifers and replacements. Three cows were Holstein, six Brown Swiss breed and five were crossbred Holstein x Brown Swiss. The farmer did not own land; therefore, he rented 3 ha of pasture contiguous to the barn for grazing and the forage surplus were preserved as hay and, 5 ha to crop maize at separate location were harvested grounding the whole plant to be used as a supplementary feed. Cows grazed a ryegrass ( L. multiflorum ) pasture after morning milking from 8:00 a.m. until 3:00 p.m., and posterior to the afternoon milking had a second grazing period from 4:30 until 6:30 pm., from December until April (dry season). Due to high temperatures during the rainy season (above 23°C) the ryegrass doesn´t grow; so, cows consume herbage available in the pastures that consist mainly of Africa Star ( C. plechtostachyus ) and Bahia grass ( P. notatum ). Cows had free access to water in water troughs at all times. Cows were kept at night in a free stall barn. Cows were milked twice a day at 6:00 a.m. and 3:00 p.m. in a rustic milking parlour using a portable milking machine. Cows were supplemented with 8 kg/day, split in two meals, while being milked with a mixture of grounded maize (12%), soybean meal (4%), commercial concentrate (75%) and wheat brand (9%). This mix contained 16% of CP. The third farm had dual-purpose cattle under extensive agrosilvopastoral management (EF). The herd comprised of 60 Brown Swiss animals, of which 35 were lactating cows and the rest were calves (male and female 50:50). Female calves were kept as replacements and male calves were sold as six-months old bulls. The farm had 100 ha of which 30 ha were used for cropping maize ( Zea mays ) and 70 ha were for pastures dominated by Africa Star ( C. plectostachyus ), Bahia grass ( P. notatum ) and other native grasses, with scattered trees. Cows remained in the paddocks for 24 h a day and, were milked once a day (7:00 a.m.) with presence of the calf as a support to let the milk down and, tied to the cow’s neck (usual management in the region). During milking the cows received 5.0 kg DM in a bag tied to the cow´s neck, that consisted of cracked-maize (94%), soybean meal (6%) and urea (100 g/cow/day), This mix contained 14% of CP. After milking, the calf remained with his mother until 2:00 p.m., then was separate and remained enclosed until next morning milking. Cows had access to water throughout channels that crossed the grazing land. Cow measurements Six lactating cows from the IF and EF farms were randomly selected every month to record milk production. In the SIM farm the same six cows were measured every month. Milk yield (MY; kg/cow/day) was recorded for two consecutive days (i.e. four milkings at the intensive and semi-intensive farms; whereas two milkings at the extensive farm), using a digital hanging scale of 20 kg capacity (Rhino®). Milk samples from the cows were collected after milking using a plastic container of 100 ml capacity to determine fat, true protein and lactose concentration (g/kg) using a portable ultra-sound Lactoscan Milk Analyzer®, serial 9414, Milkotronic, Bulgaria, 2008. Milk subsamples of 50 ml per cow were frozen in the laboratory for the determinations of MUN using enzymatic colorimetry. Body weight was recorded for two consecutive days after the morning milking, with a portable Smart Scale 200 Gallagher® of 1,500 kg capacity. Body condition score was assessed on a 1 to 5 points scale according to Wildman et al. ( 1982 ). Feed sampling and analysis Feed samples were collected at each farm visit. A sample of approximately 100 g of the supplementary feed offered during the morning meal was stored in a plastics flask for chemical analysis. A sample of pasture grazed was collected by the hand-pluck technique in a zig zag pattern taking 5 samples every month from a paddock close to where the cows were grazing (Hernandez-Mendo and Leaver 2006 ). Additionally, samples of ryegrass hay were taken at five different points of the bale and mixed to obtain a composite sample every month. Feed chemical analysis The chemical composition of the supplements and forages was determined in the laboratory of nutrition of Instituto de Ciencias Agropecuarias y Rurales of Universidad Autónoma del Estado de México. Diet ingredients were subject to dry matter determination by drying at 60°C in a forced-air oven and ash was obtained by incineration at 550°C for 6 h. Crude protein (CP) was estimated by the Kjeldahl method (AOAC, 1995), and neutral detergent fibre (NDF) and acid detergent fibre (ADF) by the Ankom micro-bag technique (ANKOM Technology, Macedon, New York, USA). Estimations of nitrogen excretions and nitrogen use efficiency Dry matter intake of cows (kg of DM/cow/day) were based on the requirements of net energy for lactation and metabolizable protein estimated using the NASEM Dairy-8 program. Data on individual cow performance and live weight, and live weight change and composition of the diet were entered into the program, which calculated dry matter requirements. Nitrogen excretions were estimated using the equations developed by Bougouin et al. ( 2022 ) (equations number 2, 8 and 17) based on nitrogen intake was as follows: Faecal nitrogen (FN) (g/day per cow) = 32.0 + 0.27×N-intake; Urine nitrogen (UN) (g/day per cow) = 32.9 + 0.25×N-intake, and total manure nitrogen (TN) (g/day per cow) = 49.0 + 0.56×N-intake. Nitrogen intake (g/day/cow) was calculated based on CP intake (g/day/cow) divided by 6.25. Nitrogen use efficiency (NUE) was calculated as secretion of nitrogen in the milk divided by nitrogen intake (Aguirre-Villegas et al. 2018 ). Milk nitrogen output (MNO) was estimated as milk protein (g/kg) divided by 6.38 (Bougouin et al. 2022 ). Experimental design and statistical analysis Data were analysed using SAS MIXED model procedure of the statistical package SAS University version (SAS Institute Inc. 2013, Cary, NC, USA), using the following model. Y ijk = µ + F i + P j +C(F i ) k + e ijkl where: µ = overall mean, F i was the fixed effect of the farm ( i = intensive, semi-intensive and extensive), P j was the fixed effect of experimental evaluation periods (month) ( j = 1, 2…6), C k was the random effect of cow within farm (k = 1, 2…6) and e ijk was the residual effect of the random term. Significant differences between means were compared by Tukey test (P < 0.05). Results Table 1 shows the means and standard deviations for parity, days in milk, body weight and body condition score of cows sampled from each of the farms considered in this study. Table 1 Mean and standard deviation for parity number, days in milk (DIM), body weight (BW) and body condition score (BCS) of cows from the intensive, semi-intensive and extensive farms. Type of farm Parity DIM BW (kg) BCS Intensive 3.1 ± 2.7 108 ± 83 550 ± 77 2.5 ± 0.1 Semi-intensive 2.8 ± 1.6 159 ± 92 594 ± 43 2.7 ± 1.0 Extensive 3.3 ± 2.6 128 ± 109 450 ± 41 2.7 ± 0.3 The nutritional composition of feeds in the diet for lactating cows in the farms monitored is shown in Table 2 . There were significant differences in all nutritional composition variables of supplements and forages due to farm and experimental period. Dry matter of supplements in SIF and EF were statistically similar with a mean of 945 (g/kg of DM), and statistically higher (P = 0.01) than IF with a mean of 897.9 (g/kg of DM). Mean of crude protein content of commercial concentrate supplemented in the IF was 143 (g/kg of DM), being statistically similar to EF with 143.3 (g/kg of DM), whereas CP mean of SIF was 168.3 (g/kg of DM), that was statistically higher (P < 0.01), than IF and EF. Neutral detergent and acid detergent fibre of EF were statistically higher (P < 0.01) than in IF and SIF. The roughage of the diets was comprised of grazed pasture in SIF and EX, whereas in the IF was annual rye grass hay, with significant differences among farms on dry mater, NDF and ADF. Table 2 Nutritional composition (g/kg of DM) of supplements and roughage included in diets for lactating cows from intensive (IF), semi-intensive (SIF) and extensive (EF) dairy farms at different experimental periods. Experimental farm (F) Experimental period 1 (EP) Item IF SIF EF EP1 EP2 EP3 EP4 EP5 EP6 P-value Supplement F EP DM 897.9 a 948.6 b 941.4 b 936.8 a 912.1 a 935.3 a 959.0 a 968.6 a 864.2 b 0.01 < 0.01 CP 143.2 a 168.3 b 143.3 a 166.6 a 140.8 b 163.3 a 139.0 b 166.6 a 133.3 b < 0.01 < 0.01 NDF 292.8 a 191.6 b 356.0 c 274.0 a 264.2 a 280.3 a 326.9 b 270.0 a 265.3 a < 0.01 0.02 ADF 104.8 a 46.8 b 140.6 c 97.8 a 79.7 a 96.3 a 124.3 b 92.7 a 93.7 a < 0.01 < 0.01 Roughage DM 632.3 a 416.6 b 543.8 c 770.1 a 367.0 b 461.1 c 222.0 b 756.5.4 a 608.6 a < 0.01 < 0.01 CP 156.8 a 133.3 b 113.2 b 101.4 a 125.5 a 175.8 b 125.4 a 153.1 b 125.4 a < 0.01 < 0.01 NDF 460.9 b 406.8 a 623.8 c 445.6 a 458.9 ab 504.3 bc 514.3 c 596.9 c 463.2 ab < 0.01 < 0.01 ADF 266.6 a 238.0 b 331.0 c 237.1 a 222.5 b 266.9 a 294.8 b 335.8 b 314.3 b < 0.01 < 0.01 1 Experimental periods were EP1, etc Cow performance Cow performance in each production system is shown in Table 3 . There were significant differences (P < 0.001) of estimated DMI among farms, where cows in the SIF had the highest estimated DM intakes with 19.6 (kg DM/day), followed by IF with 18.0 (kg DM/day), and cows in the EF had the lowest intakes with 13.2 (kg of DM/day). Milk yields followed the same pattern as DMI, having 19.6, 18.0 and 7.8 (kg/cow/day) for the SIF, IF and EF, respectively (P < 0.001). Energy corrected milk was statistically the same between IF and SIF with 17.4 and 20.0 (kg/day), respectively, being both higher than EF with 6.4 (kg/day). Fat-protein corrected milk (kg/day) was the highest in SIF with 22.7 (kg/day), followed by IF with 19.4 (kg/day), whereas EF had the lowest with 6.9 (kg/day). Milk fat content (g/kg) and fat yield (kg/day) were statistically the same between IF and SIF with 41.0 and 37.7 (g/kg) and 0.78 and 0.87 (kg/day), respectively, being higher than EF that had 29.3 (g/kg) and 0.23 (kg/day) of fat contentment and fat yield, respectively. Milk true protein content (g/kg) was statistically the same between the IF and EF with 33.6 and 32.5 (g/kg), whereas milk true protein content of EF was statistically the same as in the SIF with 31.5 (g/kg). True milk protein yield (kg/day) was statistically the same between IF and SIF with 0.14 and 0.12 kg/day, respectively, being higher than EF (0.10 kg/day). No differences for lactose content and yield among farms (P > 0.05) were observed, with a mean of 47.2 (g/kg) and 0.15 (kg/day), respectively. Milk urea nitrogen content (mg/dL) was statistically higher (P < 0.001) in the IF with 7.5 (mg/dL) compared with 3.8 and 4.2 (mg/dL) for SIF and EF, respectively, being statistically the same among the last two. There were differences for milk urea nitrogen yield (kg/day), where IF and SIF were statistically the same with 1.45 and 0.90 (g/day), respectively, being statistically higher (P < 0.001) than EF with 0.35 (g/day). Body weight change of cows from SIF and EF (0.01 and 0.34 kg/day, respectively) were statistically the same being lower than in cows from the IF with 0.8 (kg/day); whereas there were no significant differences of body condition score change between farms. Cows from the IF and SIF had a feed conversion efficiency (MY/DMI, kg/kg) of 1.1 and 1.2, respectively, being statistically the same but higher than 0.6 estimated for cows from the EF. Table 3 Cow performance of lactating cows from intensive (IF), semi-intensive (SIF) and extensive (EF) dairy farms. Farm Item IF SIF EF P-value SE DMI (kg/day) 18.0 a 19.6 b 13.2 c < 0.001 0.37 Milk yield (kg/day) 19.2 a 24.5 b 7.8 c < 0.001 1.07 ECM yield (kg/day) 17.4 a 20.0 a 6.4 c < 0.001 0.89 FPCM (kg/day) 19.4 a 22.7 b 6.9 c < 0.001 0.98 Fat concentration (g/kg) 41.0 a 37.7 a 29.3 b < 0.001 1.75 Fat yield (kg/day) 0.78 a 0.87 a 0.23 b < 0.001 0.04 True protein concentration (g/kg) 33.6 a 31.5 b 32.5 ab 0.03 0.58 True protein yield (kg/day) 0.14 a 0.12 a 0.10 b < 0.001 0.005 Lactose concentration (g/kg) 46.2 47.5 47.8 0.16 0.61 Lactose yield (kg/day) 0.15 0.15 0.16 0.13 0.004 MUN (mg/dl) 7.5 a 3.8 b 4.2 b < 0.001 0.45 MUN yield (g/day) 1.45 a 0.90 b 0.35 c < 0.001 0.06 BW (kg) 513 a 585 b 431 c < 0.001 12.53 BWc (kg/day) 0.80 a 0.10 b 0.34 b 0.02 0.30 BCS 2.5 a 3.1 b 2.5 a < 0.001 0.10 BCSc 0.001 0.006 0.002 0.17 0.002 FE (MY/DMI) 1.1 a 1.2 a 0.6 b < 0.001 0.05 ECM = Energy corrected milk yield, FPCM = Fat and protein corrected milk yield, MUN = Milk urea nitrogen, BW = Body weight, BCS = Body condition score, UUN = Urine urea nitrogen, and FE = Feed efficiency. In Table 4 are shown cow performance across the experimental periods. In general, there were significant differences for most of the animal performance variables across the experimental periods, with the exception of milk fat and lactose content (g/kg), lactose yield (kg/day), and BCS (P > 0.05). A trend shows a decline in values in some of the most important variables like DMI, MY, ECM and FPCM from EP1 to EP6, with the exception of EP5 where a significant increase was observed (P < 0.001). Table 4 Cow performance of lactating cows from intensive (IF), semi-intensive (SIF) and extensive (EF) dairy farms, according to experimental period. Item EP1 EP2 EP3 EP4 EP5 EP6 P = E.E. DMI (kg/day) 17.2 a 17.6 a 17.5 a 17.3 a 16.6 a 15.3 b < 0.001 0.36 Milk yield (kg/day) 18.1 a 19.0 a 17.0 ab 16.1 b 18.1 a 14.8 b < 0.001 0.92 ECM yield (kg/day) 15.0 abc 15.3 abc 14.0 abd 13.6 abd 16.0 c 12.9 d 0.001 0.83 FPCM (kg/day) 16.9 abc 17.3 ac 15.6 abd 15.0 bd 18.5 c 14.6 d 0.001 0.90 Fat concentration (g/kg) 34.4 32.7 34.4 36.0 40.8 38.0 0.06 2.04 Fat yield (kg/day) 0.64 a 0.65 a 0.59 a 0.58 a 0.77 b 0.55 a 0.006 0.04 True protein concentration (g/kg) 32.4 a 31.4 a 31.3 a 32.2 a 31.9 a 35.4 b 0.008 0.83 True protein yield (kg/day) 0.11 a 0.10 a 0.11 a 0.12 ab 0.13 b 0.13 b 0.006 0.001 Lactose concentration (g/kg) 48.7 47.9 46.8 47.3 46.8 46.9 0.59 0.07 Lactose yield (kg/day) 0.16 0.15 0.15 0.15 0.15 0.16 0.06 0.004 MUN (mg/dL) 6.4 a 4.5 b 4.7 b 4.6 b 6.7 a 4.4 b < 0.001 0.40 MUN yield (g/day) 1.1 a 0.90 b 0.81 b 0.71 b 1.22 a 0.65 b < 0.001 0.09 BW (kg) 530 a 524 a 529 a 538 a 488 b 446.5 c < 0.001 13.39 BWc (kg/day) 1.4 a -0.2 bc 0.2 b 0.4 ab -1.2 c -1.3 c 0.03 0.30 BCS 2.6 2.6 2.6 2.7 2.8 2.8 0.12 0.07 BCSc 0.006 0.001 0.005 -0.001 0.002 -0.000 0.10 0.002 FE (MY/DMI) 0.97 ac 1.02 a 0.92 c 0.87 bc 1.04 a 0.96 abc 0.003 0.04 ECM = Energy corrected milk yield, PFCP = Fat and protein corrected milk yield, MUN = Milk Urea Nitrogen, BW = Body weight, BWc = Body weight change, BCS = Body condition score, BCSc = Body condition score change Table 5 shows the estimations of nitrogen excretions in cows from the different farms. Urinary nitrogen excretions of the cows from SIF were 146.3 (g/day) were significantly higher (P < 0.00) than cows from the IF and EF with 137.9 and 110.8 (g/day), respectively, being statistically different between the last two. Cows from the SIF had the highest (P < 0.05) faecal nitrogen excretion with 155.3, followed by cows from the IF with 146.3 and EF with 117.0 (g/day) the lowest excretion. Effect of farm production system was significant on total manure nitrogen excretion (P < 0.001). Cows from the SIF had the highest values with 304.8 (g/day), followed by IF cows with 286 (g/day) and EF cows with had the lowest excretions with 225.3 (g/day). Estimated nitrogen intake (NI, g/day) of SIF cows was 456.8 being the highest among farms (P < 0.001), whereas IF had the second highest values with 423.2 and, EF had the lowest values with 314.8. Milk nitrogen output and nitrogen utilization efficiency were statistically the same between IF and SIF with 100.9 and 112.2 (g/day) and 0.24% in both farms, respectively, being significantly higher than EF with 28.9 (g/day) and 0.13%, respectively. Total nitrogen excretions per kg of milk yield were statistically higher with 28.9 (g/kg) (P < 0.001), compared with IF and SIF that had 14.9 and 12.4, being statistically the same among the last two. Table 5 Estimations of urinary nitrogen excretions (UNE, g/day), faecal nitrogen (FN, g/day) and total manure nitrogen excretions (TMNE, g/day) of cows in intensive (Int), semi-intensive (Sint) and extensive (Ext) dairy farms. Item IF SIF EF P = S.E. UNE 137.9 a 146.3 b 110.8 c < 0.001 2.2 FNE 146.3 a 155.3 b 117.0 c < 0.001 2.15 TMNE 286.0 a 304.8 b 225.3 c < 0.001 2.77 Nitrogen intake 423.2 a 456.8 b 314.8 c < 0.001 8.0 Milk nitrogen output 100.9 a 112.2 a 44.0 b < 0.001 5.3 NUE (%) 24 a 24 a 13 b < 0.001 0.001 TMNE/MY (g/kg) 14.9 a 12.4 a 28.9 b < 0.001 2.1 In Table 6 can be observed nitrogen excretions (g/day) estimates as well as nitrogen intake (g/day) and nitrogen utilization efficiencies of experimental periods. Urinary nitrogen excretions, FN and TMNE were statistically the same from EP1 to EP5, being statistically higher (< 0.001) that values of EP6. Urine nitrogen excretion from EP1 to EP5 averaged 33.5 g/day, whereas EP6 had 122.6 g/day. Faecal nitrogen excretions (g/day) from EP 1 to EP 5 averaged 141.5 being higher than EP6 with 129.7 (g/day). Total nitrogen excretions averaged 276.1 g/day, that were higher that 251.7 g/day from EP6. Table 6 Estimations of urinary nitrogen excretions (UNE, g/day), faecal nitrogen (FN, g/day) and total manure nitrogen excretions (TMNE, g/day) of cows in intensive (Int), semi-intensive (Sint) and extensive (Ext) dairy farms, according to experimental period (EP) Item EP1 EP2 EP3 EP4 EP5 EP6 P = E.E. UNE (g/day) 133.3 a 135.0 a 134.7 a 134.0 a 130.7 a 122.6 b < 0.001 2.24 FN (g/day) 141.3 a 143.1 a 142.8 a 141.8 a 138.4 a 129.7 b < 0.001 2.15 TMNE (g/day) 275.7 a 279.4 a 278.8 a 276.8 a 269.8 a 251.7 b < 0.001 2.49 Nitrogen intake (g/day) 404.8 a 411.4 a 410.4 a 406.8 a 394.2 a 361.9 b < 0.001 7.98 Milk nitrogen output (g/day) 89.6 91.5 82.2 78.5 88.0 84.4 0.19 5.33 NUE (%) 21 ab 21 ab 19 ab 18 a 22 b 23 b 0.02 0.01 TMNE/MY (g/kg) 23.0 a 21.9 a 24.3 a 28.6 b 21.4 a 24.7 a < 0.001 1.87 Nitrogen intake (g/day) mean was 405.5 from EP1 to 5, being statistically higher than 361.9 from EP6 (Table 6 ). There were of milk nitrogen output due to EP, with an average of 86.0. Nitrogen utilization efficiency (NUE) (%) was significantly different among EP, with decreasing values from EP1 of 0.21 to EP4 0.18 where the lowest value was observed and then increasing to 22 and 23 in EP5 and 6, respectively. The amount of nitrogen excreted per kg of milk produced had the highest value (P < 0.001) in EP4 with 28.6 g of nitrogen per kg of milk produced, whereas for the rest of the EP, there were no differences with an average of 23.1 g of nitrogen per kg of milk. Table 7 shows the NASEM Dairy-8 ( 2021 ) predictions of energy, CP, metabolizable protein supplied and required by cows within the participating farms. Net energy of lactation was 1.8, 1.8 and 1.6 (Mcal/day), of IF, SIM and EF. In the three farms, there were a positive energy and metabolizable protein balance. Predictions of diets crude protein ranged from 13.6 of EF to15.3% of IF, whereas percentages of RDP ranged from 9.1% in the EF to 10.6% in the IF, with an average of 68% of the CP in the diets. Table 7 NASEM Dairy-8 ( 2021 ) model predictions and observed cow performance of intensive, semi-intensive and extensive farms Dietary CP, % of Dry matter Item IF SIF EF NE L , (Mcal/day) 1.8 1.8 1.6 Supplied 31.9 35.5 32.1 Required 30.6 32.8 27.0 Balance 1.3 2.7 5.1 CP 15.3 14.8 13.6 RDP, % of DM 10.6 10.0 9.1 RUP, % of DM 4.8 4.8 4.5 MP supply (g/day) 1607 1711 1174 MP Required 1583 1676 838 Balanced 23 35 336 Milk yield, kg/day (A) 19.2 24.5 7.8 1 NE L allowable milk (B) 21.0 28.2 13.0 2 MP allowable milk (C) 19.7 25.3 15.0 3 A - B -1.8 -3.7 -5.2 4 A - C -0.5 -0.8 -7.2 1 NASEM DAIRY-8 ( 2021 )-predicted milk production based on supply of NE L . 2 NASEM DAIRY-8 ( 2021 )-predicted milk yield based on supply of MP. 3 Difference between observed milk yield and NASEM DAIRY-8 ( 2021 )-predicted MP allowable milk. 4 Difference between observed milk yield and NASEM DAIRY-8 ( 2021 )-predicted MP allowable milk. Estimates of net energy of lactation (NE L ) allowable milk yield were 21 and 28.2 kg/cow/day) which were 8 and 13% higher than observed milk yields (MY) (19.2 and 24.5 kg/cow/day) of intensive and semi-intensive farms, respectively; whereas for the EF observed MY (7.8 kg/cow(day) only represented 52% of the NE L allowable milk yield 13.0 kg/cow/day). Regarding metabolizable protein (MP), observed MY 19.2 24.2 and 7.8 (kg/cow/day) represented 92, 87 and 60% of the MP allowable milk estimates 19.7 25.2 and 15.0 (kg/cow/day) for the IF, SIF and EF, respectively. Discussion The nutritional composition of the feeds used in the participating farms variated during the study period. Crude protein of commercial concentrates labelled as 18% of CP in the IF ranged from 137 to 174 (g/kg of DM), with an average of 148 g/kg of DM, which was below of what was stated in the label. Similar variations were determined in the concentrate mixes of the SIF and EF, elaborated by the farmers. Forages sources also showed nutritional variations throughout the study period. These variations apparently contributed to temporary nutritional imbalances during the study period. However, is has been reported that milk yield and milk components of mid to late lactation cows (128 ± 12 days in milk) subjected to oscillations of low (13.8%) and high (15.5%) dietary crude protein every 48 h were not affected, due to stimulation of nitrogen recycling to the rumen; suggesting that performance of lactating cows is resilient to dietary CP variations over periods of time no larger than 43 h (Erickson et al. 2023 ). If dietary crude protein is sustained for longer periods a reduction in milk yields occur (Brown 2014 ). According to NASEM Dairy-8 ( 2021 ), the diets supplied energy could had allowed higher milk yields than yields observed in the three farms studied. Whereas MP allowable milk was close to the observed MY in IF and SIF; indicating that dietary CP was the limiting factor for higher milk yields. In the case of EF, observed milk yields were far below of NASEM Dairy-8 energy and metabolizable protein allowable milk. These differences were due to the fact that cows were partially milked (morning milking), leaving the remnant milk to be suckled by the calf right after milking and until 2:00 p.m. (five hours period). So, observed milk yields did not reflected the cow’s milk potential. Low dietary CP levels have been reported as the main factor to reduced N excretions of dairy cattle (Hristov et al. 2015 ; Huhtanen et al. 2015b ). However, this is associate with reduced productive potential of the cows (Barros et al. 2017 ). Therefore, according to the results in the present study, a two-way approach could be implemented in IF and SIF farms. First, increased dietary crude protein to match net energy allowable milk; or second, reduce dietary energy supply to match MP allowable milk. There first approach may increase milk yields, along with feeding cost and nitrogen excretions to the environment; whereas the second approach may reduce feeding cost but, also impairing cows to achieve their milk yield potential in the long term, by impairing cows to replenish body condition score (Leaver 1985 ). Therefore, cost-benefit analysis must be performed in order to recommend the best productive, economic and environmental alternative under the study circumstances. It has been reported that cows in grazing systems tend to have low NUE ranging from 13 to 33%), due to the fact that temperate pastures are high in rumen degradable protein which often exceeds cows requirements, in comparison with total mixed ratios fed in confine dairy systems (Waghorn and Clark 2004 ). Perdana-Decker et al. ( 2024 ) reported that under organic management, NUE of dairy cows ranged from low 11.5 to high 39.5%, under low intensity (low supplementary feeds, low stoking rate and labour) and high intensity (higher supplementary feed and high labour) fresh pasture base systems in New Zealand. The efficiency of crude protein utilization was 20.1 and 27.7%, respectively. The nitrogen or crude protein efficiencies mentioned above are similar to the ones reported in the present study and, may be more comparable since the diets of the cows in farms studied are based on grazing fresh forage and grass hay. Milk urea nitrogen levels were lower than the benchmark of 12 mg/dL reported in the literature as indicator that the cow is not being overfeed with crude protein (Kohn et al. 2002 ). This was due to the low crude protein levels in the diets of the cows in the farms monitored, that ranged from 13.6 (EF) to 15.3% (IF) of dietary CP. The maximum recommended dietary CP for a cows of similar milk yield potential and weight as in our study is 18.0% as indicated in the NASEM Dairy-8, ( 2021 ) guidelines. The mentioned before confirmed that the diets cows in our study were fed with low dietary crude protein in their diets. Mean estimate of UNE in the present study was 131.6, which was lower than mean value of 166.5 (g of N/day) reported by Correa-Luna et al. ( 2020 ) from low and high intensive grazing systems in New Zealand (199 and 134 g of N/day, respectively). The intensity of the systems was determined according to the level of supplements received by the cows. The low-intensive system consisted of once-daily milking, grazing, and low supplementary feed inclusion (304 kg pasture silage/cow); whereas high-intensity system consisted of twice-daily milking, (429 kg pasture silage and 1,695 kg concentrate/cow). The study mentioned before found that cows eating higher proportions of fresh pasture had higher UNE than cows receiving higher levels of supplementation (silages and concentrates). The high-intensity system was similar to the IF and SIF according to the management intensity, supplementation level and milk yields. Thus, the mean value of UNE of IF and SIF was 142.1 that was 29% lower than the value reported by Correa-Luna et al. ( 2020 ) from high-intensity system under grazing conditions in New Zealand. These lower excretions of nitrogen in urine in the present study could be due to the lower crude protein content of the forage in the diets, in comparison of the pastures utilized in the above-mentioned study. Perdana-Decker et al. ( 2024 ), assessed the nitrogen utilization and excretion of grazing cows in semi-natural grasslands-based on organic dairy farms in the south of Germany. The mean value of UNE reported by them was 263.5 (g/cow/day), which was 50% higher than the mean value reported in the present study, and 24% higher than the mean value reported by Correa-Luna et al. ( 2020 ). As a comparison, dairy cows manage under intensive stall barns with total mixed ratios from the U.S., reported UNE range from 238 (adequate dietary crude protein, 16.5% ) to 187 (low dietary crude protein, 14.6%) (g/day) (Zanton 2019 ) which are below the values reported by Perdana-Decker et al. ( 2024 ), and similar to the values reported by (Correa-Luna et al. 2020 ). The mean UNE of a cow from milk production system representative of the Midwest USA, (Zanton 2019 ) was 209.3 (g cow/day), that was higher than mean of 131.7 (g cow/day) of the cows from the three farms evaluated in this study. This difference may seem large, however, if the excretions are express as UNE per kg of milk produced, the cows from the Midwest farm excreted 3.8 vs 7.8 g of N/kg milk, that represents 51% higher UNE that a cow from Midwest USA, farm. The main explanation between the two systems could be the large differences in milk production of a cow from very intensive production systems. In the present study faecal nitrogen excretions mean value was 139.5 (g/day) which was slightly higher than 127.5 (g/day) reported by Perdana-Decker et al. ( 2024 ), and similar to 143 reported by (Correa-Luna et al. 2020 ), from dairy systems based on pastures. On the contrary, cows from the Midwest of USA faecal N excretions reported mean was 303 (g/d) (Zanton 2019 ), that was 51% higher than the mean value reported in the present study. This large difference could be due to the higher body weight and dry matter intake of the USA Midwest cows. Out of the total nitrogen intake, total manure nitrogen excretion in our study represented 69% which coincides with the same value reported as average by Reed et al. ( 2015 ), whereas the nitrogen utilization efficiency in our study are within the low range reported by Calsamiglia et al. ( 2010 ) of 21%. Of the total manure nitrogen excretions, on average nitrogen excreted in urine represented 49%, whereas in faeces was 51%, in the present study, which is desirable since N in urine is more susceptible to leaching and volatilization losses, whereas N in faeces is more stable reducing the risk of environmental losses (Dijkstra et al. 2013 ). In comparison, in the study by Perdana-Decker et al. ( 2024 ) the proportion of N excreted in urine and faeces were 33 and 67%, respectively; whereas in the study by Correa-Luna et al. ( 2020 ) 55% of N was excreted in urine and 45% in faeces. According to Olmos Colmenero and Broderick ( 2006 ) N in manure is excreted almost equally via faeces and urine, although the proportion are affected by CP levels, ratio of RDP to RUP in the diet, and metabolizable energy and NDF of pastures herbage (Perdana-Decker et al. 2024 ). Conclusion The excretions of urine urea nitrogen and nitrogen in faeces of dairy cows in the present study, were considered low and similar to reports in the literatures from pasture based-systems. Low dietary crude protein may be the main determinant of the low nitrogen excretions estimated in the study. However, when expressing nitrogen excretions per kg of milk produced cows in the study excreted 51% more nitrogen per kg of milk compared to highly intensive dairy systems from North America, which is confirmed by the low NUE estimated in this study that is in the bottom ranged reported in the literature. Declarations Acknowledgements The authors would like to thank the participating farmers for his cooperation towards the project. Our gratitude also to Universidad Autónoma del Estado de México for funding the project (grant UAEM 6156/2020CIF); and Secretaría de Ciencia, Humanidades, Tecnología e Innovación. Funding The Universidad Autónoma del Estado de México funded this work via the project gran UAEM 6156/2020CIF. Conflicts of interest/Competing interests The other authors declare that they have no conflict of interest. Ethics approval Participating farmers were thoroughly informed about the research work and gave their consent to participate in the project. Animal handling and procedures followed guidelines accepted and institutionally approved by the Ethics and Anima Welfare of Centro Universitario UAEM Temascaltepec, Universidad Autónoma del Estado de México. Consent for publication Not applicable. Availability of data and material (data transparency) The data that support the findings of this study are available from the corresponding author upon reasonable request. Code availability (software application or custom code) Not applicable Authors’ contributions Benito Albarrán-Portillo: Conceptualization, investigation, data analyses, formal analyses, writing – original draft, review and editing. Anastacio García-Martínez: Conceptualization, methodology, supervision, writing – review and editing. Nicolás López-Villalobos: Data analysis, writing – review and editing. María Danae Celis Álvarez: Laboratorio analysis and supervision. Carlos M. Arriaga-Jordán: Supervision. Sherezada Esparza-Jiménez: Supervision and editing. References Aguirre-Villegas HA, Wattiaux MA, Larson RA, Chase L, Ranathunga SD, Ruark MD (2018) Dairy Cow Nitrogen Efficiency. 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J Dairy Sci 102:5094–5108. doi: 10.3168/jds.2018-15730 Cite Share Download PDF Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Tropical Animal Health and Production → Version 1 posted Reviewers agreed at journal 10 Apr, 2025 Reviewers invited by journal 26 Mar, 2025 Editor assigned by journal 27 Feb, 2025 First submitted to journal 27 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6123406","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434402262,"identity":"1ba6ce73-e024-4569-92a4-1f116fa61d86","order_by":0,"name":"Benito Albarrán-Portillo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACAwbmNiiT+QCxWhhhWtgSSNbCY0CcFnP2g20Pfu65J28u3fNN4kfFNjmDA9xpEvi0WPYkthv2PCs23Dnn7DbJnjO3jSUbeDfjtc/gQGKbBM+BBMYNN3K3SfC23U7sZ+Dd+ACvlvMP2yT/HEiw33Aj55nk33+369sYeDccwKvlRmKbNNCWRKAWNmnehtsJ/IRssZzxsN1Y5kBC8oY7x4ytZY7dNpzZTMAv5vzJxx6+OZBgu+F288Obb2puyxsc792GN8QQAK6MmTj1yFpGwSgYBaNgFKABAIQOUU/Ro1+OAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-9807-8452","institution":"Universidad Autónoma del Estado de México. Centro Universitario UAEM Temascaltepec","correspondingAuthor":true,"prefix":"","firstName":"Benito","middleName":"","lastName":"Albarrán-Portillo","suffix":""},{"id":434402263,"identity":"117c5bdc-458b-4800-bde0-ace5219c97e3","order_by":1,"name":"Anastacio García-Martínez","email":"","orcid":"","institution":"Universidad Autónoma del Estado de México. Centro Universitario UAEM Temascaltepec - Centro Universitario Temascaltepec","correspondingAuthor":false,"prefix":"","firstName":"Anastacio","middleName":"","lastName":"García-Martínez","suffix":""},{"id":434402264,"identity":"7a2243a4-4569-42cd-81a4-16650cf59ee3","order_by":2,"name":"Nicolás López-Villalobos","email":"","orcid":"","institution":"Te Kunenga ki Purehuroa: Massey University","correspondingAuthor":false,"prefix":"","firstName":"Nicolás","middleName":"","lastName":"López-Villalobos","suffix":""},{"id":434402265,"identity":"8c07f3e4-1fc7-4140-bd4b-9974f68477a1","order_by":3,"name":"María Danae Celis Álvarez","email":"","orcid":"","institution":"Universidad Autonoma del Estado de Hidalgo Instituto de Ciencias Agropecuarias","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Danae Celis","lastName":"Álvarez","suffix":""},{"id":434402266,"identity":"bdbfa99f-fec4-4a2d-acab-4531005faa1e","order_by":4,"name":"Carlos M. Arriaga-Jordán","email":"","orcid":"","institution":"Universidad Autonoma del Estado de Hidalgo Instituto de Ciencias Agropecuarias","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"M.","lastName":"Arriaga-Jordán","suffix":""},{"id":434402267,"identity":"dca0aff9-3008-486f-9db1-6e79790e1ccb","order_by":5,"name":"Sherezada Esparza-Jiménez","email":"","orcid":"","institution":"Universidad Autónoma del Estado de México. Centro Universitario UAEM Temascaltepec","correspondingAuthor":false,"prefix":"","firstName":"Sherezada","middleName":"","lastName":"Esparza-Jiménez","suffix":""}],"badges":[],"createdAt":"2025-02-27 18:47:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6123406/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6123406/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11250-025-04693-0","type":"published","date":"2025-10-13T15:58:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93956074,"identity":"7208e75f-2f94-4264-b6cd-b11d09768cf9","added_by":"auto","created_at":"2025-10-20 16:10:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1148486,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6123406/v1/598014f2-b249-4ca0-bf1e-171fe06ef613.pdf"}],"financialInterests":"","formattedTitle":"Productive performance and estimations of urine and faecal nitrogen excretions of Brown Swiss cows in small dairy farms in the highlands of México","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFeeding practices in small dairy farms in Mexico have been reported as deficient and imbalanced causing predisposition to diseases, low performance of cows and reduced profitability (Absal\u0026oacute;n-Medina et al., 2012). Unbalanced diets are caused by variations of the nutritional composition of ingredients along with farmers lack of basic nutritional knowledge (Deen and Tyagi \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExcesses of dietary crude protein (CP) is one of the more frequent examples of unbalanced rations. Powell et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that half of lactating cows in the state of Wisconsin U.S. were over with CP. Overfeeding CP is usually associated with higher feeding cost and higher N excretion in the urine as urinary urea nitrogen (UUN; Powell and Rotz \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDairy farms contribute to environment N pollution from forage production (fertilizers) and excretions from cows in manure and urine. From the total N farm input, only about 15% is transformed into animal protein as meat and milk (Tamminga \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Dairy cow transforms between 26 to 30% of the total dietary N into milk protein and, the rest is excreted in faeces (33%) and urine (24%) (Spek et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e; Barros et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This low milk N efficiency is primarily due to overfeeding of dietary crude protein in the diet. A positive relationship between crude protein intake and N excreted in urine has been established (Kebreab et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). While faecal N is relatively stable and can be incorporated to cropland and recycle through crops, UUN is rapidly transformed into ammonium (NH4), and then, lost as ammonia (NH3) during manure storage and application. Then, it can be transformed into nitrate (NO3) and nitrogen dioxide (N2O) contributing to greenhouse gasses emitted to the atmosphere (Powell and Rotz \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUrea in blood is the main end-product of nitrogen metabolism in ruminants. This metabolite has been used as an indicator of the efficiency of N utilization; however, it cannot be routinely measure in farm (Huhtanen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e). Apart from urine and faeces urea excretion, a second route of nitrogen excretion is milk, which is closely correlated with blood urea-N (BUN) (Broderick and Clayton \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Studies have reported that the measurement of milk urea N (MUN) concentration, is a useful indicator of efficiency of N utilization and urinary N output (Jonker et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Kauffman and St-Pierre \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCrude protein in the diet has been identified as the main factor influencing concentration of urea N in the milk and urea N in urine (Nousiainen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Spek et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e). Strong correlation among MUN, UUN and dietary crude protein have been established (Spek et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It has been suggested that MUN can be a useful diagnostic tool to assess the efficiency of N utilization, based on averages values from experiments (Jonker et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Nousiainen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Spek et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e). The objective of the study was to use MUN as predictor of nitrogen excretions in urine and faeces of cows from three small dairy farms representing intensive, semi-intensive and extensive management in Central M\u0026eacute;xico.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLocation\u003c/h2\u003e \u003cp\u003eThe study was conducted in two locations in the southwest of the Estado de Mexico. The first location included two dairy farms in Telpintla community, municipality of Temascaltepec at latitude 19\u0026deg;3\u0026rsquo;59\u0026rsquo;\u0026rsquo; N and longitude 100\u0026deg;3\u0026rsquo; 6\u0026rsquo;\u0026rsquo; W, at 1,882 meters above sea level. The weather is classified as Cwb subtropical highland, according to the K\u0026ouml;ppen climate classification; with average temperatures of 15.7\u0026deg;C and average annual rainfall of 1,159 mm. The second location was in the municipality of Zacazonapan between 19\u0026ordm; 00\u0026rsquo; 17\u0026rsquo;\u0026rsquo; and 19\u0026ordm; 16\u0026rsquo; 17\u0026rsquo;\u0026rsquo; north latitude and between 100\u0026ordm; 12\u0026rsquo; 55\u0026rsquo;\u0026rsquo; and 100\u0026ordm; 18\u0026rsquo; 13\u0026rsquo;\u0026rsquo; west longitude, at an altitude of 1,470 meters above sea level. The climate in this region is semi-hot classified as A(C) (w2), with a mean temperature of 23\u0026deg;C and 1,115 mm of annual rainfall, with summer rains and a marked dry season from November to May, (SMN 2020).\u003c/p\u003e \u003cp\u003e The participating farms were classified as of intensive, semi-intensive and extensive managed farms, visited for two consecutive days once a month, from February to July of 2021 to record the productive performance of cows and sampling of milk and feeds.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFarms description\u003c/h3\u003e\n\u003cp\u003eThe intensive managed farm (IF) had an extension of 50 ha of which, ten were cultivated with Italian Ryegrass (\u003cem\u003eLlolium multiflorum\u003c/em\u003e) established in the earliest days of October 2020. Grass from pastures was cut every 30 to 45 days and let dry on pastures tossing the grass every other day before collection and storage. The rest of the land extension was pastures of native species and exotic grasses like Bahia grass (\u003cem\u003ePaspalum notatum\u003c/em\u003e) and Africa Star (\u003cem\u003eCynodon plechtostachyus\u003c/em\u003e), where growing calves and replacements grazed. There were 80 Brown Swiss animals, of which 33 were lactating cows and the rest were calves at different stages of development, under semi-confinement in free stall barns. Cows were machine milked in a milking parlour twice a day at 7:00 a.m. and 3:00 p.m. Cows were supplemented with 7 kg/day of commercial concentrate with 18% crude protein (CP) split twice a day after each milking at 9:00 a.m. and 4:00 pm. Cows received ryegrass hay after consuming the concentrate, they had free access to water in barns.\u003c/p\u003e \u003cp\u003eThe semi-intensive (SI) farm comprised of 11 lactating cows plus three heifers and replacements. Three cows were Holstein, six Brown Swiss breed and five were crossbred Holstein x Brown Swiss. The farmer did not own land; therefore, he rented 3 ha of pasture contiguous to the barn for grazing and the forage surplus were preserved as hay and, 5 ha to crop maize at separate location were harvested grounding the whole plant to be used as a supplementary feed. Cows grazed a ryegrass (\u003cem\u003eL. multiflorum\u003c/em\u003e) pasture after morning milking from 8:00 a.m. until 3:00 p.m., and posterior to the afternoon milking had a second grazing period from 4:30 until 6:30 pm., from December until April (dry season). Due to high temperatures during the rainy season (above 23\u0026deg;C) the ryegrass doesn\u0026acute;t grow; so, cows consume herbage available in the pastures that consist mainly of Africa Star (\u003cem\u003eC. plechtostachyus\u003c/em\u003e) and Bahia grass (\u003cem\u003eP. notatum\u003c/em\u003e). Cows had free access to water in water troughs at all times. Cows were kept at night in a free stall barn.\u003c/p\u003e \u003cp\u003eCows were milked twice a day at 6:00 a.m. and 3:00 p.m. in a rustic milking parlour using a portable milking machine. Cows were supplemented with 8 kg/day, split in two meals, while being milked with a mixture of grounded maize (12%), soybean meal (4%), commercial concentrate (75%) and wheat brand (9%). This mix contained 16% of CP.\u003c/p\u003e \u003cp\u003eThe third farm had dual-purpose cattle under extensive agrosilvopastoral management (EF). The herd comprised of 60 Brown Swiss animals, of which 35 were lactating cows and the rest were calves (male and female 50:50). Female calves were kept as replacements and male calves were sold as six-months old bulls. The farm had 100 ha of which 30 ha were used for cropping maize (\u003cem\u003eZea mays\u003c/em\u003e) and 70 ha were for pastures dominated by Africa Star (\u003cem\u003eC. plectostachyus\u003c/em\u003e), Bahia grass (\u003cem\u003eP. notatum\u003c/em\u003e) and other native grasses, with scattered trees. Cows remained in the paddocks for 24 h a day and, were milked once a day (7:00 a.m.) with presence of the calf as a support to let the milk down and, tied to the cow\u0026rsquo;s neck (usual management in the region). During milking the cows received 5.0 kg DM in a bag tied to the cow\u0026acute;s neck, that consisted of cracked-maize (94%), soybean meal (6%) and urea (100 g/cow/day), This mix contained 14% of CP. After milking, the calf remained with his mother until 2:00 p.m., then was separate and remained enclosed until next morning milking. Cows had access to water throughout channels that crossed the grazing land.\u003c/p\u003e\n\u003ch3\u003eCow measurements\u003c/h3\u003e\n\u003cp\u003eSix lactating cows from the IF and EF farms were randomly selected every month to record milk production. In the SIM farm the same six cows were measured every month. Milk yield (MY; kg/cow/day) was recorded for two consecutive days (i.e. four milkings at the intensive and semi-intensive farms; whereas two milkings at the extensive farm), using a digital hanging scale of 20 kg capacity (Rhino\u0026reg;). Milk samples from the cows were collected after milking using a plastic container of 100 ml capacity to determine fat, true protein and lactose concentration (g/kg) using a portable ultra-sound Lactoscan Milk Analyzer\u0026reg;, serial 9414, Milkotronic, Bulgaria, 2008. Milk subsamples of 50 ml per cow were frozen in the laboratory for the determinations of MUN using enzymatic colorimetry.\u003c/p\u003e \u003cp\u003eBody weight was recorded for two consecutive days after the morning milking, with a portable Smart Scale 200 Gallagher\u0026reg; of 1,500 kg capacity. Body condition score was assessed on a 1 to 5 points scale according to Wildman et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eFeed sampling and analysis\u003c/h3\u003e\n\u003cp\u003eFeed samples were collected at each farm visit. A sample of approximately 100 g of the supplementary feed offered during the morning meal was stored in a plastics flask for chemical analysis. A sample of pasture grazed was collected by the hand-pluck technique in a zig zag pattern taking 5 samples every month from a paddock close to where the cows were grazing (Hernandez-Mendo and Leaver \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Additionally, samples of ryegrass hay were taken at five different points of the bale and mixed to obtain a composite sample every month.\u003c/p\u003e\n\u003ch3\u003eFeed chemical analysis\u003c/h3\u003e\n\u003cp\u003eThe chemical composition of the supplements and forages was determined in the laboratory of nutrition of Instituto de Ciencias Agropecuarias y Rurales of Universidad Aut\u0026oacute;noma del Estado de M\u0026eacute;xico. Diet ingredients were subject to dry matter determination by drying at 60\u0026deg;C in a forced-air oven and ash was obtained by incineration at 550\u0026deg;C for 6 h. Crude protein (CP) was estimated by the Kjeldahl method (AOAC, 1995), and neutral detergent fibre (NDF) and acid detergent fibre (ADF) by the Ankom micro-bag technique (ANKOM Technology, Macedon, New York, USA).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEstimations of nitrogen excretions and nitrogen use efficiency\u003c/h2\u003e \u003cp\u003eDry matter intake of cows (kg of DM/cow/day) were based on the requirements of net energy for lactation and metabolizable protein estimated using the NASEM Dairy-8 program. Data on individual cow performance and live weight, and live weight change and composition of the diet were entered into the program, which calculated dry matter requirements.\u003c/p\u003e \u003cp\u003eNitrogen excretions were estimated using the equations developed by Bougouin et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (equations number 2, 8 and 17) based on nitrogen intake was as follows: Faecal nitrogen (FN) (g/day per cow)\u0026thinsp;=\u0026thinsp;32.0\u0026thinsp;+\u0026thinsp;0.27\u0026times;N-intake; Urine nitrogen (UN) (g/day per cow)\u0026thinsp;=\u0026thinsp;32.9\u0026thinsp;+\u0026thinsp;0.25\u0026times;N-intake, and total manure nitrogen (TN) (g/day per cow)\u0026thinsp;=\u0026thinsp;49.0\u0026thinsp;+\u0026thinsp;0.56\u0026times;N-intake.\u003c/p\u003e \u003cp\u003eNitrogen intake (g/day/cow) was calculated based on CP intake (g/day/cow) divided by 6.25. Nitrogen use efficiency (NUE) was calculated as secretion of nitrogen in the milk divided by nitrogen intake (Aguirre-Villegas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Milk nitrogen output (MNO) was estimated as milk protein (g/kg) divided by 6.38 (Bougouin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental design and statistical analysis\u003c/h3\u003e\n\u003cp\u003eData were analysed using SAS MIXED model procedure of the statistical package SAS University version (SAS Institute Inc. 2013, Cary, NC, USA), using the following model.\u003c/p\u003e \u003cp\u003eY\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;F\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e + P\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e +C(F\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e)\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e + e\u003csub\u003e\u003cem\u003eijkl\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere:\u003c/p\u003e \u003cp\u003e\u0026micro;\u0026thinsp;=\u0026thinsp;overall mean, F\u003cem\u003ei\u003c/em\u003e was the fixed effect of the farm (\u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;intensive, semi-intensive and extensive), P\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e was the fixed effect of experimental evaluation periods (month) (\u003cem\u003ej\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, 2\u0026hellip;6), C\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e was the random effect of cow within farm (k\u0026thinsp;=\u0026thinsp;1, 2\u0026hellip;6) and e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e was the residual effect of the random term. Significant differences between means were compared by Tukey test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the means and standard deviations for parity, days in milk, body weight and body condition score of cows sampled from each of the farms considered in this study.\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\u003eMean and standard deviation for parity number, days in milk (DIM), body weight (BW) and body condition score (BCS) of cows from the intensive, semi-intensive and extensive farms.\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of farm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDIM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBCS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e108\u0026thinsp;\u0026plusmn;\u0026thinsp;83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e550\u0026thinsp;\u0026plusmn;\u0026thinsp;77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-intensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e159\u0026thinsp;\u0026plusmn;\u0026thinsp;92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e594\u0026thinsp;\u0026plusmn;\u0026thinsp;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e128\u0026thinsp;\u0026plusmn;\u0026thinsp;109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e450\u0026thinsp;\u0026plusmn;\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\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\u003eThe nutritional composition of feeds in the diet for lactating cows in the farms monitored is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There were significant differences in all nutritional composition variables of supplements and forages due to farm and experimental period. Dry matter of supplements in SIF and EF were statistically similar with a mean of 945 (g/kg of DM), and statistically higher (P\u0026thinsp;=\u0026thinsp;0.01) than IF with a mean of 897.9 (g/kg of DM). Mean of crude protein content of commercial concentrate supplemented in the IF was 143 (g/kg of DM), being statistically similar to EF with 143.3 (g/kg of DM), whereas CP mean of SIF was 168.3 (g/kg of DM), that was statistically higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), than IF and EF. Neutral detergent and acid detergent fibre of EF were statistically higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) than in IF and SIF.\u003c/p\u003e \u003cp\u003eThe roughage of the diets was comprised of grazed pasture in SIF and EX, whereas in the IF was annual rye grass hay, with significant differences among farms on dry mater, NDF and ADF.\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\u003eNutritional composition (g/kg of DM) of supplements and roughage included in diets for lactating cows from intensive (IF), semi-intensive (SIF) and extensive (EF) dairy farms at different experimental periods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eExperimental farm (F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c10\" namest=\"c5\"\u003e \u003cp\u003eExperimental period\u003csup\u003e1\u003c/sup\u003e (EP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSIF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEP1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEP2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eEP3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eEP4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eEP5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eEP6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSupplement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eEP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e897.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e948.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e941.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e936.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e912.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e935.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e959.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e968.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e864.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143.2 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e163.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e139.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e166.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e133.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356.0\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e274.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e264.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e280.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e326.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e270.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e265.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140.6\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e124.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e93.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRoughage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e632.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e543.8\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e770.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e367.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e461.1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e222.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e756.5.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e608.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e175.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e125.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e153.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e125.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e460.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e406.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e623.8\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e445.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e458.9\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e504.3\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e514.3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e596.9\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e463.2\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e266.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e331.0\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e237.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e222.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e266.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e294.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e335.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e314.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003e1\u003c/sup\u003eExperimental periods were EP1, etc\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCow performance\u003c/h2\u003e \u003cp\u003eCow performance in each production system is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There were significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of estimated DMI among farms, where cows in the SIF had the highest estimated DM intakes with 19.6 (kg DM/day), followed by IF with 18.0 (kg DM/day), and cows in the EF had the lowest intakes with 13.2 (kg of DM/day). Milk yields followed the same pattern as DMI, having 19.6, 18.0 and 7.8 (kg/cow/day) for the SIF, IF and EF, respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Energy corrected milk was statistically the same between IF and SIF with 17.4 and 20.0 (kg/day), respectively, being both higher than EF with 6.4 (kg/day). Fat-protein corrected milk (kg/day) was the highest in SIF with 22.7 (kg/day), followed by IF with 19.4 (kg/day), whereas EF had the lowest with 6.9 (kg/day). Milk fat content (g/kg) and fat yield (kg/day) were statistically the same between IF and SIF with 41.0 and 37.7 (g/kg) and 0.78 and 0.87 (kg/day), respectively, being higher than EF that had 29.3 (g/kg) and 0.23 (kg/day) of fat contentment and fat yield, respectively. Milk true protein content (g/kg) was statistically the same between the IF and EF with 33.6 and 32.5 (g/kg), whereas milk true protein content of EF was statistically the same as in the SIF with 31.5 (g/kg). True milk protein yield (kg/day) was statistically the same between IF and SIF with 0.14 and 0.12 kg/day, respectively, being higher than EF (0.10 kg/day). No differences for lactose content and yield among farms (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were observed, with a mean of 47.2 (g/kg) and 0.15 (kg/day), respectively.\u003c/p\u003e \u003cp\u003eMilk urea nitrogen content (mg/dL) was statistically higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the IF with 7.5 (mg/dL) compared with 3.8 and 4.2 (mg/dL) for SIF and EF, respectively, being statistically the same among the last two. There were differences for milk urea nitrogen yield (kg/day), where IF and SIF were statistically the same with 1.45 and 0.90 (g/day), respectively, being statistically higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than EF with 0.35 (g/day).\u003c/p\u003e \u003cp\u003eBody weight change of cows from SIF and EF (0.01 and 0.34 kg/day, respectively) were statistically the same being lower than in cows from the IF with 0.8 (kg/day); whereas there were no significant differences of body condition score change between farms. Cows from the IF and SIF had a feed conversion efficiency (MY/DMI, kg/kg) of 1.1 and 1.2, respectively, being statistically the same but higher than 0.6 estimated for cows from the EF.\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\u003eCow performance of lactating cows from intensive (IF), semi-intensive (SIF) and extensive (EF) dairy farms.\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eFarm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMI (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk yield (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.8\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECM yield (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPCM (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat concentration (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat yield (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrue protein concentration (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.5\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrue protein yield (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactose concentration (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactose yield (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUN (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUN yield (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e513\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e431\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBWc (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFE (MY/DMI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eECM\u0026thinsp;=\u0026thinsp;Energy corrected milk yield, FPCM\u0026thinsp;=\u0026thinsp;Fat and protein corrected milk yield, MUN\u0026thinsp;=\u0026thinsp;Milk urea nitrogen, BW\u0026thinsp;=\u0026thinsp;Body weight, BCS\u0026thinsp;=\u0026thinsp;Body condition score, UUN\u0026thinsp;=\u0026thinsp;Urine urea nitrogen, and FE\u0026thinsp;=\u0026thinsp;Feed efficiency.\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\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e are shown cow performance across the experimental periods. In general, there were significant differences for most of the animal performance variables across the experimental periods, with the exception of milk fat and lactose content (g/kg), lactose yield (kg/day), and BCS (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). A trend shows a decline in values in some of the most important variables like DMI, MY, ECM and FPCM from EP1 to EP6, with the exception of EP5 where a significant increase was observed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eCow performance of lactating cows from intensive (IF), semi-intensive (SIF) and extensive (EF) dairy farms, according to experimental period.\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\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEP1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEP2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEP3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEP4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEP5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEP6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP =\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eE.E.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMI (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk yield (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.0\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECM yield (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.3\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0\u003csup\u003eabd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.6\u003csup\u003eabd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.0\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.9\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPCM (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.9\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.3\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.6\u003csup\u003eabd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.0\u003csup\u003ebd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.6\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat concentration (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.4\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\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.0\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\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat yield (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrue protein concentration (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrue protein yield (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactose concentration (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactose yield (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\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\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUN (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUN yield (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e530\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e524\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e529\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e538\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e488\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e446.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBWc (kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFE (MY/DMI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eECM\u0026thinsp;=\u0026thinsp;Energy corrected milk yield, PFCP\u0026thinsp;=\u0026thinsp;Fat and protein corrected milk yield, MUN\u0026thinsp;=\u0026thinsp;Milk Urea Nitrogen, BW\u0026thinsp;=\u0026thinsp;Body weight, BWc\u0026thinsp;=\u0026thinsp;Body weight change, BCS\u0026thinsp;=\u0026thinsp;Body condition score, BCSc\u0026thinsp;=\u0026thinsp;Body condition score change\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the estimations of nitrogen excretions in cows from the different farms. Urinary nitrogen excretions of the cows from SIF were 146.3 (g/day) were significantly higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.00) than cows from the IF and EF with 137.9 and 110.8 (g/day), respectively, being statistically different between the last two. Cows from the SIF had the highest (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) faecal nitrogen excretion with 155.3, followed by cows from the IF with 146.3 and EF with 117.0 (g/day) the lowest excretion. Effect of farm production system was significant on total manure nitrogen excretion (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Cows from the SIF had the highest values with 304.8 (g/day), followed by IF cows with 286 (g/day) and EF cows with had the lowest excretions with 225.3 (g/day).\u003c/p\u003e \u003cp\u003eEstimated nitrogen intake (NI, g/day) of SIF cows was 456.8 being the highest among farms (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas IF had the second highest values with 423.2 and, EF had the lowest values with 314.8. Milk nitrogen output and nitrogen utilization efficiency were statistically the same between IF and SIF with 100.9 and 112.2 (g/day) and 0.24% in both farms, respectively, being significantly higher than EF with 28.9 (g/day) and 0.13%, respectively. Total nitrogen excretions per kg of milk yield were statistically higher with 28.9 (g/kg) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared with IF and SIF that had 14.9 and 12.4, being statistically the same among the last two.\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\u003eEstimations of urinary nitrogen excretions (UNE, g/day), faecal nitrogen (FN, g/day) and total manure nitrogen excretions (TMNE, g/day) of cows in intensive (Int), semi-intensive (Sint) and extensive (Ext) dairy farms.\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP =\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110.8\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.0\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e304.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225.3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e423.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e456.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e314.8\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk nitrogen output\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNUE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMNE/MY (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\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\u003eIn Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e can be observed nitrogen excretions (g/day) estimates as well as nitrogen intake (g/day) and nitrogen utilization efficiencies of experimental periods. Urinary nitrogen excretions, FN and TMNE were statistically the same from EP1 to EP5, being statistically higher (\u0026lt;\u0026thinsp;0.001) that values of EP6. Urine nitrogen excretion from EP1 to EP5 averaged 33.5 g/day, whereas EP6 had 122.6 g/day. Faecal nitrogen excretions (g/day) from EP 1 to EP 5 averaged 141.5 being higher than EP6 with 129.7 (g/day). Total nitrogen excretions averaged 276.1 g/day, that were higher that 251.7 g/day from EP6.\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\u003eEstimations of urinary nitrogen excretions (UNE, g/day), faecal nitrogen (FN, g/day) and total manure nitrogen excretions (TMNE, g/day) of cows in intensive (Int), semi-intensive (Sint) and extensive (Ext) dairy farms, according to experimental period (EP)\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\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEP1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEP2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEP3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEP4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEP5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEP6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP =\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eE.E.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUNE (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e130.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e122.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFN (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e129.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMNE (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e279.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e278.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e276.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e269.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e251.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen intake (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e404.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e411.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e410.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e406.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e394.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e361.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk nitrogen output (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNUE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMNE/MY (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.87\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\u003eNitrogen intake (g/day) mean was 405.5 from EP1 to 5, being statistically higher than 361.9 from EP6 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). There were of milk nitrogen output due to EP, with an average of 86.0. Nitrogen utilization efficiency (NUE) (%) was significantly different among EP, with decreasing values from EP1 of 0.21 to EP4 0.18 where the lowest value was observed and then increasing to 22 and 23 in EP5 and 6, respectively. The amount of nitrogen excreted per kg of milk produced had the highest value (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in EP4 with 28.6 g of nitrogen per kg of milk produced, whereas for the rest of the EP, there were no differences with an average of 23.1 g of nitrogen per kg of milk.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the NASEM Dairy-8 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) predictions of energy, CP, metabolizable protein supplied and required by cows within the participating farms. Net energy of lactation was 1.8, 1.8 and 1.6 (Mcal/day), of IF, SIM and EF. In the three farms, there were a positive energy and metabolizable protein balance. Predictions of diets crude protein ranged from 13.6 of EF to15.3% of IF, whereas percentages of RDP ranged from 9.1% in the EF to 10.6% in the IF, with an average of 68% of the CP in the diets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNASEM Dairy-8 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) model predictions and observed cow performance of intensive, semi-intensive and extensive farms\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDietary CP, % of Dry matter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNE\u003csub\u003eL\u003c/sub\u003e, (Mcal/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupplied\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\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRequired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDP, % of DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRUP, % of DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMP supply (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMP Required\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk yield, kg/day (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eNE\u003csub\u003eL\u003c/sub\u003e allowable milk (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eMP allowable milk (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e3\u003c/sup\u003eA - B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e4\u003c/sup\u003eA - C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e NASEM DAIRY-8 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)-predicted milk production based on supply of NE\u003csub\u003eL\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003e NASEM DAIRY-8 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)-predicted milk yield based on supply of MP.\u003c/p\u003e \u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDifference between observed milk yield and NASEM DAIRY-8 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)-predicted MP allowable milk.\u003c/p\u003e \u003cp\u003e\u003csup\u003e4\u003c/sup\u003eDifference between observed milk yield and NASEM DAIRY-8 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)-predicted MP allowable milk.\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\u003eEstimates of net energy of lactation (NE\u003csub\u003eL\u003c/sub\u003e) allowable milk yield were 21 and 28.2 kg/cow/day) which were 8 and 13% higher than observed milk yields (MY) (19.2 and 24.5 kg/cow/day) of intensive and semi-intensive farms, respectively; whereas for the EF observed MY (7.8 kg/cow(day) only represented 52% of the NE\u003csub\u003eL\u003c/sub\u003e allowable milk yield 13.0 kg/cow/day). Regarding metabolizable protein (MP), observed MY 19.2 24.2 and 7.8 (kg/cow/day) represented 92, 87 and 60% of the MP allowable milk estimates 19.7 25.2 and 15.0 (kg/cow/day) for the IF, SIF and EF, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e The nutritional composition of the feeds used in the participating farms variated during the study period. Crude protein of commercial concentrates labelled as 18% of CP in the IF ranged from 137 to 174 (g/kg of DM), with an average of 148 g/kg of DM, which was below of what was stated in the label. Similar variations were determined in the concentrate mixes of the SIF and EF, elaborated by the farmers. Forages sources also showed nutritional variations throughout the study period. These variations apparently contributed to temporary nutritional imbalances during the study period. However, is has been reported that milk yield and milk components of mid to late lactation cows (128\u0026thinsp;\u0026plusmn;\u0026thinsp;12 days in milk) subjected to oscillations of low (13.8%) and high (15.5%) dietary crude protein every 48 h were not affected, due to stimulation of nitrogen recycling to the rumen; suggesting that performance of lactating cows is resilient to dietary CP variations over periods of time no larger than 43 h (Erickson et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). If dietary crude protein is sustained for longer periods a reduction in milk yields occur (Brown \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to NASEM Dairy-8 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the diets supplied energy could had allowed higher milk yields than yields observed in the three farms studied. Whereas MP allowable milk was close to the observed MY in IF and SIF; indicating that dietary CP was the limiting factor for higher milk yields. In the case of EF, observed milk yields were far below of NASEM Dairy-8 energy and metabolizable protein allowable milk. These differences were due to the fact that cows were partially milked (morning milking), leaving the remnant milk to be suckled by the calf right after milking and until 2:00 p.m. (five hours period). So, observed milk yields did not reflected the cow\u0026rsquo;s milk potential.\u003c/p\u003e \u003cp\u003eLow dietary CP levels have been reported as the main factor to reduced N excretions of dairy cattle (Hristov et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Huhtanen et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e). However, this is associate with reduced productive potential of the cows (Barros et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, according to the results in the present study, a two-way approach could be implemented in IF and SIF farms. First, increased dietary crude protein to match net energy allowable milk; or second, reduce dietary energy supply to match MP allowable milk. There first approach may increase milk yields, along with feeding cost and nitrogen excretions to the environment; whereas the second approach may reduce feeding cost but, also impairing cows to achieve their milk yield potential in the long term, by impairing cows to replenish body condition score (Leaver \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Therefore, cost-benefit analysis must be performed in order to recommend the best productive, economic and environmental alternative under the study circumstances.\u003c/p\u003e \u003cp\u003eIt has been reported that cows in grazing systems tend to have low NUE ranging from 13 to 33%), due to the fact that temperate pastures are high in rumen degradable protein which often exceeds cows requirements, in comparison with total mixed ratios fed in confine dairy systems (Waghorn and Clark \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Perdana-Decker et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that under organic management, NUE of dairy cows ranged from low 11.5 to high 39.5%, under low intensity (low supplementary feeds, low stoking rate and labour) and high intensity (higher supplementary feed and high labour) fresh pasture base systems in New Zealand. The efficiency of crude protein utilization was 20.1 and 27.7%, respectively. The nitrogen or crude protein efficiencies mentioned above are similar to the ones reported in the present study and, may be more comparable since the diets of the cows in farms studied are based on grazing fresh forage and grass hay.\u003c/p\u003e \u003cp\u003eMilk urea nitrogen levels were lower than the benchmark of 12 mg/dL reported in the literature as indicator that the cow is not being overfeed with crude protein (Kohn et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This was due to the low crude protein levels in the diets of the cows in the farms monitored, that ranged from 13.6 (EF) to 15.3% (IF) of dietary CP. The maximum recommended dietary CP for a cows of similar milk yield potential and weight as in our study is 18.0% as indicated in the NASEM Dairy-8, (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) guidelines. The mentioned before confirmed that the diets cows in our study were fed with low dietary crude protein in their diets.\u003c/p\u003e \u003cp\u003eMean estimate of UNE in the present study was 131.6, which was lower than mean value of 166.5 (g of N/day) reported by Correa-Luna et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) from low and high intensive grazing systems in New Zealand (199 and 134 g of N/day, respectively). The intensity of the systems was determined according to the level of supplements received by the cows. The low-intensive system consisted of once-daily milking, grazing, and low supplementary feed inclusion (304 kg pasture silage/cow); whereas high-intensity system consisted of twice-daily milking, (429 kg pasture silage and 1,695 kg concentrate/cow). The study mentioned before found that cows eating higher proportions of fresh pasture had higher UNE than cows receiving higher levels of supplementation (silages and concentrates). The high-intensity system was similar to the IF and SIF according to the management intensity, supplementation level and milk yields. Thus, the mean value of UNE of IF and SIF was 142.1 that was 29% lower than the value reported by Correa-Luna et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) from high-intensity system under grazing conditions in New Zealand. These lower excretions of nitrogen in urine in the present study could be due to the lower crude protein content of the forage in the diets, in comparison of the pastures utilized in the above-mentioned study.\u003c/p\u003e \u003cp\u003ePerdana-Decker et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), assessed the nitrogen utilization and excretion of grazing cows in semi-natural grasslands-based on organic dairy farms in the south of Germany. The mean value of UNE reported by them was 263.5 (g/cow/day), which was 50% higher than the mean value reported in the present study, and 24% higher than the mean value reported by Correa-Luna et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a comparison, dairy cows manage under intensive stall barns with total mixed ratios from the U.S., reported UNE range from 238 (adequate dietary crude protein, 16.5% ) to 187 (low dietary crude protein, 14.6%) (g/day) (Zanton \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) which are below the values reported by Perdana-Decker et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and similar to the values reported by (Correa-Luna et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mean UNE of a cow from milk production system representative of the Midwest USA, (Zanton \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was 209.3 (g cow/day), that was higher than mean of 131.7 (g cow/day) of the cows from the three farms evaluated in this study. This difference may seem large, however, if the excretions are express as UNE per kg of milk produced, the cows from the Midwest farm excreted 3.8 vs 7.8 g of N/kg milk, that represents 51% higher UNE that a cow from Midwest USA, farm. The main explanation between the two systems could be the large differences in milk production of a cow from very intensive production systems.\u003c/p\u003e \u003cp\u003eIn the present study faecal nitrogen excretions mean value was 139.5 (g/day) which was slightly higher than 127.5 (g/day) reported by Perdana-Decker et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and similar to 143 reported by (Correa-Luna et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), from dairy systems based on pastures. On the contrary, cows from the Midwest of USA faecal N excretions reported mean was 303 (g/d) (Zanton \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), that was 51% higher than the mean value reported in the present study. This large difference could be due to the higher body weight and dry matter intake of the USA Midwest cows.\u003c/p\u003e \u003cp\u003eOut of the total nitrogen intake, total manure nitrogen excretion in our study represented 69% which coincides with the same value reported as average by Reed et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), whereas the nitrogen utilization efficiency in our study are within the low range reported by Calsamiglia et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) of 21%.\u003c/p\u003e \u003cp\u003eOf the total manure nitrogen excretions, on average nitrogen excreted in urine represented 49%, whereas in faeces was 51%, in the present study, which is desirable since N in urine is more susceptible to leaching and volatilization losses, whereas N in faeces is more stable reducing the risk of environmental losses (Dijkstra et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In comparison, in the study by Perdana-Decker et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) the proportion of N excreted in urine and faeces were 33 and 67%, respectively; whereas in the study by Correa-Luna et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) 55% of N was excreted in urine and 45% in faeces. According to Olmos Colmenero and Broderick (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) N in manure is excreted almost equally via faeces and urine, although the proportion are affected by CP levels, ratio of RDP to RUP in the diet, and metabolizable energy and NDF of pastures herbage (Perdana-Decker et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe excretions of urine urea nitrogen and nitrogen in faeces of dairy cows in the present study, were considered low and similar to reports in the literatures from pasture based-systems. Low dietary crude protein may be the main determinant of the low nitrogen excretions estimated in the study. However, when expressing nitrogen excretions per kg of milk produced cows in the study excreted 51% more nitrogen per kg of milk compared to highly intensive dairy systems from North America, which is confirmed by the low NUE estimated in this study that is in the bottom ranged reported in the literature.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participating farmers for his cooperation towards the project. Our gratitude also to Universidad Aut\u0026oacute;noma del Estado de M\u0026eacute;xico for funding the project (grant UAEM 6156/2020CIF); and Secretar\u0026iacute;a de Ciencia, Humanidades, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Universidad Aut\u0026oacute;noma del Estado de M\u0026eacute;xico funded this work via the project gran UAEM 6156/2020CIF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe other authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipating farmers were thoroughly informed about the research work and gave their consent to participate in the project. Animal handling and procedures followed guidelines accepted and institutionally approved by the Ethics and Anima Welfare of Centro Universitario UAEM Temascaltepec, Universidad Aut\u0026oacute;noma del Estado de M\u0026eacute;xico.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material (data transparency)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability (software application or custom code)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBenito Albarr\u0026aacute;n-Portillo: Conceptualization, investigation, data analyses, formal analyses, writing \u0026ndash; original draft, review and editing.\u003c/p\u003e\n\u003cp\u003eAnastacio Garc\u0026iacute;a-Mart\u0026iacute;nez: Conceptualization, methodology, supervision, writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003cp\u003eNicol\u0026aacute;s L\u0026oacute;pez-Villalobos: Data analysis, writing \u0026ndash; review and editing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMar\u0026iacute;a Danae Celis \u0026Aacute;lvarez: Laboratorio analysis and supervision.\u003c/p\u003e\n\u003cp\u003eCarlos M. Arriaga-Jord\u0026aacute;n: Supervision.\u003c/p\u003e\n\u003cp\u003eSherezada Esparza-Jim\u0026eacute;nez: Supervision and editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAguirre-Villegas HA, Wattiaux MA, Larson RA, Chase L, Ranathunga SD, Ruark MD (2018) Dairy Cow Nitrogen Efficiency. Madison\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarros T, Quaassdorff MA, Aguerre MJ, Colmenero JJO, Bertics SJ, Crump PM, Wattiaux MA (2017) Effects of dietary crude protein concentration on late-lactation dairy cow performance and indicators of nitrogen utilization. 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J Dairy Sci 102:5094\u0026ndash;5108. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3168/jds.2018-15730\u003c/span\u003e\u003cspan address=\"10.3168/jds.2018-15730\" 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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"nitrogen excretions, small dairy farms, crude protein, efficiency","lastPublishedDoi":"10.21203/rs.3.rs-6123406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6123406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThree small dairy farms of intensive (IF), semi-intensive (SIF) and extensive (EF) management (six cows per farm) were monitored during six months. IF and SIF were based on temperate pastures, whereas the EF was based on tropical pastures. Farms were visited every month (experimental period (EP)), to recorded productive response variables, as well as feeds samples. Urine and Faecal nitrogen excretions (g/day/cow) (UNE and FNE, respectively), were estimated using equations from the literature that used nitrogen intake as a predictor. Experimental design was a complete random analysis, using a Mixed model procedure in SAS. There were significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of cows performance due to the farm type of management. The diets of the cows in the farms in the study provided net high energy of lactation (NE\u003csub\u003eL\u003c/sub\u003e), that would allowed higher milk yields (20.7 kg/cow/day) than observed (17.2 kg/cow/day), being the dietary crude protein (DCP) the limiting factor that determined the cows performance. These low DCP levels (14.6%), determined low urine nitrogen excretions (UNE, 131.6 g/cow/day) and faecal nitrogen excretions (FNE, 139.5 g/cow/day). Out of the total manure nitrogen excretions (TMNE), 49 and 51% of nitrogen (N) were excreted in urine and faeces, respectively. Mean nitrogen utilization efficiency (NUE) was 20.3%. When expressing nitrogen excretions per kg of milk produced cows in the study excreted 51% more nitrogen per kg of milk compared to highly intensive dairy systems from North America. Conclusion. The estimated excretions of nitrogen excreted in urine and faeces (g/cow/day) from dairy cows in small farms in the Highlands of Mexico were low; however much work is needed to increase nitrogen utilization efficiency of cows in small dairy farms. One option could be to increase the productivity of the cows diluting nitrogen manure excretion per kg of milk produced.\u003c/p\u003e","manuscriptTitle":"Productive performance and estimations of urine and faecal nitrogen excretions of Brown Swiss cows in small dairy farms in the highlands of México","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 12:01:52","doi":"10.21203/rs.3.rs-6123406/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-10T22:05:28+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-26T15:42:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-28T00:31:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2025-02-27T19:27:57+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":"27ca005e-2a97-4e1d-a31c-5dbad891637a","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:03:55+00:00","versionOfRecord":{"articleIdentity":"rs-6123406","link":"https://doi.org/10.1007/s11250-025-04693-0","journal":{"identity":"tropical-animal-health-and-production","isVorOnly":false,"title":"Tropical Animal Health and Production"},"publishedOn":"2025-10-13 15:58:15","publishedOnDateReadable":"October 13th, 2025"},"versionCreatedAt":"2025-04-14 12:01:52","video":"","vorDoi":"10.1007/s11250-025-04693-0","vorDoiUrl":"https://doi.org/10.1007/s11250-025-04693-0","workflowStages":[]},"version":"v1","identity":"rs-6123406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6123406","identity":"rs-6123406","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}