Does a whole millet grain-based diet replace whole corn grain in non-forage diets for goat kids?

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Francisca Leila Araujo dos Santos, Daniel Louçana da Costa Araújo, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7121297/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Twenty-one Anglo-Nubian goat kids (21.6 ± 2.9 kg of initial body weight) were distributed in a completely randomized design to evaluate the effects of the replacement of whole corn grain for whole millet grain in non-forage diets on the performance, ingestive behavior, physiological parameters, and carcass characteristics of goat kids finished in feedlot system. The experimental diets consisted of a control diet (CON), containing 100 g/kg of hay and 900 g/kg of concentrate, and two non-forage diets: whole corn grain-based diet (WC) or whole millet grain diet (WM), both composed of 200 g/kg of commercial pellet and 800 g/kg of respective whole grain. The WC and WM reduced (P < 0.05) dry matter (DM) intake; however, only WC reduced crude protein intake ( P = 0.001). As expected, CON showed the highest fiber intake and time spent in rumination ( P = 0.026), while WM showed the highest fat (EE) intake (P < 0.01). WC increased the digestibility of DM ( P = 0.042) and EE ( P = 0.025). As expected, WC and WM showed higher rumination efficiency of DM and fiber ( P < 0.01). Diets did not affect physiological parameters. WC reduced ( P < 0.05) average daily gain, final weight, and hot carcass weight, however, the kidney was reduced by the CON diet ( P 0.05). Millet can replace corn in non-forage diets, providing good performance and adequate carcass characteristics and leg tissue composition. average daily gain carcass Pennisetum glaucum rumination time Figures Figure 1 1. Introduction Since the 1970s, the use of a whole grain-based diet (WGD) without forage has been studied in the United States, leading to discussion about its applicability in beef cattle feedlots (Paulino et al., 2013; Contadini, 2016). The use of this diet is justified when the goal is based on high weight gain rates, better feed efficiency, and ease of daily food management (Contadini, 2016). In feedlot system production, corn is the main energy feed used in the animals' diets, and, in a WGD, is the main ingredient (Fabino Neto et al., 2022). This diet, also known as a non-forage diet, is recommended to supply 150–200 g/kg of commercial pellets and 800–850 g/kg of whole corn grain (Paulino et al., 2013). Although it is not very commonly adopted in ruminant diets, not necessarily for its economic viability, but some resistance or even fear on the part of most nutritionists. Although ruminants have evolved to consume forage-based diets, in situations where grains are available at affordable prices and there are difficulties in providing or handling roughage, forage-free diets become a viable technological alternative. Additionally, there is growing concern about the environmental impact of livestock, and the type of carbohydrates in the diet can modulate enteric methane emissions. The fermentation of highly fermentable carbohydrates, such as corn and millet starch, favors propionate production in the rumen, reducing the hydrogen available for methanogenesis, which may contribute to mitigating enteric methane emissions. Thus, the partial or total replacement of forage with grain can be a feasible strategy to reduce the environmental footprint of animal production without compromising animal performance (Rivera et al. 2021; Sun et al., 2022). Recently, some research using WGD for finishing lambs also has been conducted (Mendes et al., 2018; Carlis et al., 2021), however, there is little data on goat kids (Bilal et al., 2025). The widespread use of corn grain in non-forage diets increases feeding costs, as it is widely used in animal feed and human nutrition. Therefore, it is necessary to search for alternative ingredients, such as millet grain, which in turn has good nutritional characteristics, such as starch (63.2%), crude protein (13.6%), ether extract (7.8%), crude fiber (2.8%), ash (2.1%), and dry matter content of 92.5% (Sharma et al., 2016), and it is considered an ideal plant for feeding due to its nutritional superiority about other cereal crops (Jenipher et al., 2024). Therefore, the hypothesis in this research is that replacing whole corn grain with whole millet grain in non-forage diets improves the performance and carcass characteristics of finishing goat kids. In this sense, the objective of this study is to evaluate the replacement of whole-grain millet with whole-grain corn in non-forage diets for goat kids finished in feedlot on performance, ingestive behavior, physiological parameters, and carcass characteristics. 2. Material and Methods 2.1 Location, animals, and experimental facilities The experiment was conducted at the Animal Science Department, in Center of Agricultural Sciences of the Federal University of Piauí (5°02'30"S; 42°47'00"W; Altitude: 69.37m), located in Teresina, Piauí, Brazil. This study was approved by the Ethics Committee on Animal Use, under process number: 733/2022. Twenty-one Anglo-Nubian goat kids with an initial body weight of 21.6 ± 2.9 kg and aged 150 ± 8 days, were properly identified and individually distributed in covered pens (1.0 m × 1.3 m) with a concrete floor, metal railings on the sides, and equipped with drinker and feeder and through for mineral salt for 59 days, with the first 19 days corresponding to the adaptation of the animals to the experimental diets and management, and the remaining 40 days were intended for the finishing period of the animals. On the first day of the adaptation period, before the beginning of the experiment, the animals were properly dewormed and vaccinated against clostridiosis. 2.2 Experimental design, treatments, and management The experimental design was completely randomized design with three treatments and seven replications. The diets (NRC, 2007) were formulated to meet the requirements of growing goat kids (20 kg of body weight - BW and 150 g/d of BW gain). The chemical composition of ingredients is shown in Table 1. The treatments consisted of a control diet (CON), composed of 900 g/kg concentrate and 100 g/kg of Tifton-85 hay, and, the remaining diets, non-forage diets, were based on the use of 200 g/kg of commercial protein pellet and 800 g/kg of whole corn grain-based (WC) or whole millet grain-based (WM) (Table 2). During 19 first days, animals were submitted to the adaptation protocol as recommended by the pellet feed manufacturer, which was as follows: 1st to 3rd day: total mixed ration (TMR) equivalent to 15 g/kg of BW; 4th to 7th day: TMR equivalent to 20 g/kg of the BW; 8th to 11th day: TMR equivalent to 30 g/kg of the BW; 12th to 15th day: TMR equivalent to 35 g/kg of the fasting BW; 16th – 19th day: the access to bulky feed was removed and TMR equivalent to 35 - 40 g/kg of the fasting BW. The experimental diets were supplied twice a day, at 08:00 a.m. and 4:00 p.m., and orts were measured daily to calculate daily dry matter intake (DMI). The daily supply adjustments aimed at providing 10% of orts, depending on feed intake. Water and mineral salt were available ad libitum during the experiment. Twice a week, orts from feed supplied were collected, identified, and frozen for later determination of nutrient consumption. At the end of the experiment period, the samples were thawed and pooled by animals. The previously formulated control diet concentrate and hay was weighed separately on an electronic scale and then mixed manually in each trough. The high-grain diets were weighed in the same way, where the pellet and corn or millet whole grain were weighed separately and immediately mixed manually and fed to each animal. Due to the type of day and the impossibility of leaving the animals fasting, the goat kids were weighed for 3 consecutive days at the beginning and the end of the experimental period. The difference between the average final body weight (FBW) and initial body weight (IBW) divided by the number of experimental days was used to determine the average daily gain (ADG). After the end of the feedlot period, the animals remained for another 3 days in the stalls for total feces collection using nylon bags, as described by Santos et al. (2022). The digestibility coefficients (DC) of nutrients were calculated using the equation: DC = [(kg of nutrient ingested fraction - kg of nutrient excreted fraction)/(kg of nutrient ingested fraction)] × 100. 2.3. Sample collection and chemical analysis At the end of the feedlot period, samples of diet and orts were thawed and pooled by animals. Samples were then ground through a 1mm Wiley Mill screen (Marconi, Piracicaba, SP, Brazil) and the DM (Method 934.01), ash (Method 942.05), ether extract (Method 954.05) and total nitrogen (N; Method 968.06) were determined according to the AOAC (1990). Crude protein was calculated by multiplying the total nitrogen by 6.25. Neutral detergent fiber assayed with a heat-stable amylase and expressed as corrected for ash (aNDFom), was determined according to Mertens (2002). The total carbohydrates and non-fiber carbohydrates were determined according to Sniffen, O'Connor, Van Soest, Fox, and Russell (1992) & Mertens (2002), respectively. The mean particle size (MPS) of the diets was determined using the Penn State Particle Size Separator method (Lammers et al., 1996). Particle size was measured according to Kononoff et al. (2003), based on the proportions of the sample retained on sieves of 19, 8, 4, and 1.18 mm (Table 2). For the determination of dietary metabolizable energy (ME) contents, it was assumed that 1 kg of total digestible nutrients (TDN) is 4.409 Mcal of digestible energy (DE) and 1 Mcal of DE equals 0.82 Mcal of ME (NRC, 2007), from the data of nutrient digestibility. 2.4. Feeding behavior, physiological parameters, and water intake Individual observations of the animals were carried out on the 31st day of the experiment, according to the following activities: feeding, ruminating, idling, and other activities. The interval to evaluate feeding behavior was 5 min with continuous periods of 24 h, starting at 08:00 am, according to the Johnson and Combs (1991) method. The data were recorded by trained observers (one for every seven goat kids) positioned to interfere as little as possible. The turns were changed every 6 hours, and, at night, artificial lighting was used, following the same previous procedures adopted at the beginning of the experiment. The efficiency of feeding and ruminating DM and NDF is related to animal behavior and was calculated considering the time spent on feeding and rumination of the DM and NDF (min/day) (Azevedo et al., 2013). Thus, the rate of intake and rumination efficiency with respect to the DM and NDF were expressed as a ratio between the daily intake time and rumination and the amount of nutrient (DM or NDF) fed daily. The animals’ physiologic responses were evaluated at 06:00 am; 10:00 am; 2:00 pm and 6:00 pm, over 7 consecutive experimental days (32 to 38th of the experimental period), in which the following parameters were measured: rectal temperature (RT), body temperature (BT), heart rate (HR) and respiratory rate (RR). On these days, environmental variables (air temperature and relative humidity) also were recorded at 6:00 am, 10:00 am, 2:00 pm, and 6:00 pm during the experimental period. A veterinary clinical thermometer (Highmed, Termo KT-DT 4B, Belo Horizonte, MG, Brazil) was inserted into the rectum of the animal for 2 min to measure RT, in ° Celsius. The BT was measured with a laser thermometer (Akrom model KR380, Porto Alegre, RS, Brazil) and, was calculated as the arithmetic average of the temperature of the temple, base of the tail, and side of the lambs (Machado et al., 2020). The RR was measured through direct observation of the movements of the left flank, as described by Silva et al. (2024), counting the number of movements for 15 seconds and the value obtained was multiplied by 4 to determine the RR in movements per minute (breaths/min). The HR was measured through a veterinary stethoscope positioned in the left thoracic region (Diffay et al., 2004). For water intake data collection, the supply and ort of water were weighed in 10 L plastic buckets and weighed before delivery and weighed again after 24 h to determine water intake via drinking (WID). Water was offered daily, at 7:30 am. Two buckets containing water were distributed in the shed, to determine daily evaporation. For water intake via food (WIF) and total water intake (TWI), the values were calculated according to Silva et al. (2024). 2.5. Animal slaughter and carcass characteristics At the end of the 40th-day feeding trial, animals were subjected to fasting with access to water for 14 h, and weighed to determine the slaughter body weight (SBW) and post-slaughter hot carcass weights (HCW) were also measured. All these procedures were carried out at the commercial abattoir, in accordance with the protocols established by the Regulation of Brazilian Industrial and Sanitary Inspection of Products of Animal Origin (Brasil, 2017). Then, the carcasses were suspended by the calcaneal tendon, and a subjective evaluation of the carcass was performed, regarding finishing, in scores from 1 (very lean) to 5 (very fat), regarding conformation, in scores from 1 (poor) to 5 (excellent) and renal fat score, ranging from 1 (poor) to 3 (excellent), according to Cezar and Sousa (2007). After 24 h of cooling at 4°C, the kidneys and fat kidney were removed, and the weight was deducted from the carcass's weight. The Longissimus thoracis (LT) muscle was transversely cut and exposed between the 12th and 13th ribs and then, subcutaneous fat thickness (SFT) was measured using a digital caliper (DIGIMESS, São Paulo, SP, Brazil), on both sides of the carcass. The exposed side of the LT was measured with a plastic grid to obtain the LT area. The values from the right and left sides of the carcasses were used to calculate the arithmetic mean of SFT and LT area per carcass. 2.6. Statistical analysis The results were submitted to ANOVA using the SAS MIXED procedure, considering the experimental diet as a fixed effect. When significant ( P < 0.05), the Tukey test was performed, and the initial body weight was used as a covariate. The data were tested for normality of errors using the Shapiro-Wilk test. Physiological parameters were analyzed with repeated measures over time. The most appropriate covariance structure selection was based on Akaike’s Information Criteria Corrected (AICC) and Bayesian Information Criteria (BIC). The best model has the lowest AICC or BIC values. The covariance matrix that best dataset was a first-order autoregressive-AR(1) for HR and BT and compound symmetry CS for RT and RR. 3. Results 3.1 . Intake, growth, and nutrient utilization The whole grain diets reduced DMI, in g/day ( P = 0.002) and % BW ( P = 0.001) and, consequently, organic matter intake (OMI; P = 0.001) and total-CHO intake ( P = 0.004) compared to the control diet (Table 3). However, non-fibrous CHO intake ( P = 0.274), was not affected by diets, averaging 287 ± 68 g/day (Table 3). Due to the difference in extract content among diets, animals fed the WM diet showed the highest ether extract intake (EEI, P < 0.001), followed by WC and CON. There also was an observed effect on neutral detergent fiber intake (NDFI), which was higher ( P < 0.001) when goat kids were fed CON, followed by WM and then WC (Table 3). This reflected an increase in water intake via drinking fountain (g/day; P = 0.043) when animals fed the control diet compared to WC, but did not differ from the WM diet (Table 3). Nevertheless, the water intake via feed, total water intake, and total water intake per kg of dry matter ingested did not change ( P > 0.05). Regarding nutrient digestibility, WC increased DM digestibility ( P = 0.042) and EE digestibility ( P = 0.025) compared to CON and WM diets. The WC diet reduced crude protein intake (CPI; P = 0.001) compared to other diets and reduced ME intake compared to CON, but did not differ from the WM diet ( P = 0.012), resulting in lower ADG rates ( P = 0.006) and worst feed: gain ratio. Higher performance results were observed for the CON and WM diets (149.2 g/d and 138.5 g/d respectively), which did not differ from each other. 3.2. Ingestive behavior and physiological parameters The experimental diets did not change the RR ( P = 0.413), HR ( P = 0.904), BT ( P = 0.185), and RT ( P = 0.746; Table 4). There was an observed hour effect ( P 0.05). Regarding ingestive behavior, WM tended to reduce ( P = 0.091) the time spent in feeding and the number of feeder visits ( P = 0.091), but CON increased the time spent in rumination ( P = 0.026). Consequently, WM increased the time spent in idle ( P = 0.009) compared to CON but did not change from WC. No effects were observed for feed efficiency of DM ( P = 0.174) and feed efficiency of NDF ( P = 0.237). However, CON diet reduced the rumination efficiency of DM (P = 0.005) and NDF ( P = 0.007) compared to non-forage diets. 3.3. Carcass characteristics The WC diet reduced the slaughter weight ( P = 0.025), however, there was no observed effect on HCW ( P = 0.665), HCY ( P = 0.296), and conformation ( P = 0.542). The CON diet reduced the kidney fat score ( P = 0.004), and renal fat, g/day ( P = 0.018), compared to WC and WM diets (Table 5), but WC tended to reduce finishing ( P = 0.061), while WM tended to increase ( P = 0.054) LM area. Regarding leg tissue composition (Table 6), there was no observed difference ( P > 0.05) in weight and proportion of muscle, fat, bone, and other tissues, as muscle: fat ratio ( P = 0.383) and muscle: bone ratio ( P = 0.578). 4. Discussion Corn grain is considered as the main energy source in the diet of ruminants finished in feedlot system (Carlis et al., 2021; Pinto and Millen, 2018). However, in a whole corn-based diet for goat kids, the reduction of DMI was accompanied by reduction on ADG and BW at slaughter. Nevertheless, the data suggests that the total replacement of WC by WM in non-forage diets maintained the ADG similar to the control group. The average DMI found in this study (573 g/day) was close to that recommended by NRC (2007) for 8-month-old animals of 20 kg BW. Although there was no significant effect on ADG, offering the CON and WM diets, the latter presented ADG slightly lower (7%) than that recommended by the NRC (150 g/ day) for animals with similar category. The ADG value ​​found when the animals fed WC diet was 47% lower than the NRC recommendation. In ruminant species, the consumption of animals fed a high-concentrate diet is controlled by the energy demands of the animal and by metabolic factors (Allen, 1996). Although it had occurred with goat kids fed the WM diet, the same was not observed when animals fed the WC diet. Goat kids fed WM tended to decrease the time spent on feeding, following the reduced DMI. Nevertheless, the reduction in DMI observed for the WC diet was not accompanied by the decrease in time spent on feeding, which may evidence animal selectivity, a characteristic inherent to the caprine species. In research conducted by Ackermans et al. (2019), it was concluded that goat species are shown to be capable of avoiding coarse grit in their diet, unlike sheep, which could explain these findings. The greater DM and EE digestibility when animals fed WC, can be attributed to the prolonged retention time, and reduced rate passage which prolonged the action of microorganism’s digestive enzymes in the rumen (Allen and Mertens, 1988). Furthermore, the higher digestibility observed in the WC diet compared to the control is primarily due to the replacement of a lower-digestibility ingredient (forage) with a higher-digestibility ingredient (corn) and pellet (Table 2 ; NRC, 2001; Carlis et al., 2021). According to Van Soest (1994), diets with whole grains increase the time spent with the rumination, and consequently, more saliva is produced which will help stabilize the ruminal pH, not impairing the digestion of feed fiber. However, in this study, the time spent on rumination activity decreased, probably because of the decreased DMI. Nevertheless, the rumination efficiency of DM and the rumination efficiency of NDF increased compared to the CON. Diets with lower NDF content generally result in higher energy density and increased ruminal passage rate, which can improve feed efficiency. However, diets with very low peNDF levels may increase the risk of ruminal acidosis, especially in forage-free systems. The WM diet showed an intermediate NDF content between CON and the WC diet, which probably may indicate better modulation of ruminal fermentation. This suggests that the inclusion of whole millet grain can help balance the energy supply while reducing the risks associated with excessive fermentable carbohydrate intake, improving both digestibility and rumen health (Allen, 1996). The absence of differences in RR, HR, BT, and RT among diets suggests that the treatments did not impose metabolic or thermal challenges on the animals. According to the literature, animals exposed to high temperatures exhibit increased RR and RT to dissipate heat. However, when diets are nutritionally balanced and do not cause severe digestive challenges, physiological mechanisms remain stable, ensuring homeostasis. Additionally, the time-of-day effect on physiological parameters may be attributed to circadian rhythms and daily environmental fluctuations (Fig. 1 ), influencing thermal regulation independently of diet. These findings align with studies indicating that respiratory rate and rectal temperature are reliable indicators of heat stress, but their variation is primarily driven by ambient temperature rather than diet composition, even in diets with low forage fiber content (Berihulay et al., 2019). Regarding performance, the fat deposition occurs when the energy consumed is greater than the requirements of the animal (Robelin, 1986). Then, the lower ADG found for goat kids fed WC reflects the reduced ME intake (NRC, 2007; Parente et al., 2020; Santos e al., 2020) which also resulted in lower BW at slaughter. However, animals were slaughtered at a reduced age, which justifies the lack of effect on HCW and HCY. Leg tissue composition, which is the separation of muscles, bones, fat and other components, is determined rather than on the entire carcass for reduces time, costs and waste (Silva Sobrinho et al., 2011). The results showed which CON or non-forage diets did not affect leg components, such as muscle, bone, fat and others tissues. The absence of dietary effects on fat proportion in leg confirms the results obtained on carcass. The fat deposition in animal organisms first accumulates in visceral depots, followed by intermuscular depots, subcutaneous and finally in intramuscular depots (Schumacher et al., 2022). Moreover, the goat species tends to prioritize fat accumulation in the abdominal cavity and visceral depots (Yalcintan et al., 2024), which is used as a reserve of energy in periods of food shortages (Mtenga et al., 2005). Because of this, it is not appropriate to adopt subcutaneous fat development as an indicator of fat storage and development in growing animals (Yalcintan et al., 2024). It was expected that the increased energy intake, as observed when animals fed the CON diet, could increase kidney fat. Because, high plasma levels of acetate increase lipogenesis in extramuscular adipocytes (Ladeira et al., 2016). However, the reduced kidney fat deposition is probably a consequence of the replacement of an ingredient with less digestion potential (coastcross hay) for ingredients with greater digestion potential (corn and millet) (Carlis et al., 2021). Added to this factor, it is possible that the higher intake increased the rate of food passage in animals fed this diet and reduced the retention time of food in the rumen, as mentioned above. 5. Conclusion The whole millet grain-based diet can replace the whole corn grain in non-forage diets, providing a good performance and adequate carcass characteristics and leg tissue composition. Statements & Declarations Funding Suppor t :This work was financial supported by the National Council for Scientific and Technological Development – CNPq (406734/2022-4 Meat Production Chain) and Coordination for the Improvement of Higher Education Personnel – Brazil (CAPES; Finance Code 001). Competing Interests: The authors declare that there are no conflicts of interest issues concerning this submission. The Coordination for the Improvement of Higher Education Personnel awarded the scholarship for Francisca Leila, and The National Council for Scientific and Technological Development awarded partially the financial support for the study. Data Availability: The data that support the findings of this study are available from the corresponding author upon reasonable request. 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Meat Sci. 214, 109521. https://doi.org/10.1016/j.meatsci.2024.109521 Tables Table 1 Chemical and physical composition of the ingredients of the experimental diets (g/kg Dry Matter) Ingredients Nutrients, g/kg a Whole corn Tifton 85 Hay Whole Millet Pellet b Concentrate c Dry matter 845 865 807 813 688 Crude protein 77.7 67.3 154.5 369.1 166 Ether Extract 41.2 10.0 58.8 55.0 26.4 Ash 13.7 42.9 15.0 117.7 27.3 aNDFom 171 809 251 289 203 Total-CHO 867 879 771 458 696 NF-CHO 696 70 520 169 574 Particle size (g/kg of DM) Screen, 19 mm 0.0 0 0 0 0 Screen, 8mm 817 329 0 72.9 0 Screen, 4 mm 183 202 19 927 182 Screen,1.18mm 0 457 981 0 318 Bottom pan,<1.18mm 0 11 0 0 500 a aNDFom: Neutral detergent fiber assayed with a heat stable amylase and expressed as corrected for ash; Total-CHO: Total carbohydrates; NF-CHO: Non-fibrous carbohydrates. b Ingredients: Soybean meal, wheat bran, corn grain, urea, Dicalcium phosphate, Calcitic limestone, Sodium chloride (common salt), Livestock sulfur, Iron sulfate, Copper sulfate, Manganese sulfate, Zinc oxide, Cobalt sulfate, Calcium iodate, Sodium selenite, Vitamin A, Vitamin B3, Vitamin E, Propionic acid, Formic acid, Ammonium propionate, B.H.A., Ethoxyquinyl, Citric acid, and Phosphoric acid. c Concentrate from a control diet. Table 2 Proportion of ingredients, physical characterization and chemical composition of experimental diets. Ingredients, g/kg DM Diets d Control WC WM Ground corn 545 Soybean meal 160 Wheat bran 171 Tifton-85 hay 100 Whole corn grain 800 Whole Millet grain 800 Pellet a 200 200 Limestone 2 Ammonia Chloride 4 Sodium bicarbonate 8 Mineral salt b 10 Chemical composition, g/kg DM c Dry matter 830 838 807 Crude protein 157 136 197 Ether extract 26.4 44.0 58.0 Ash 31.6 34.5 35.5 aNDFom 282 194 258 Total-CHO 784 785 709 NF-CHO 581 590 450 ME, Mcal/kg DM 2.7 2.9 3.0 Particle size, g/kg DM Screen, 19 mm 0 0 0 Screen, 8 mm 32.9 668.2 14.6 Screen, 4 mm 184.0 331.8 200.6 Screen, 1.18 mm 331.9 0 784.8 Bottom pan, <1.18 mm 451.1 0 0 a Ingredients: Soybean meal, wheat bran, corn grain, urea, Dicalcium phosphate, Calcitic limestone, Sodium chloride (common salt), Livestock sulfur, Iron sulfate, Copper sulfate, Manganese sulfate, Zinc oxide, Cobalt sulfate, Calcium iodate, Sodium selenite, Vitamin A, Vitamin B3, Vitamin E, Propionic acid, Formic acid, Ammonium propionate, B.H.A., Ethoxyquinyl, Citric acid, and Phosphoric acid; b Guarantee levels per kilogram of the product according to the manufacturer: Sodium (min.) 147 g; Calcium (min.) 120 g; Phosphorus (min.) 87 g; Sulfur (min.) 18 g; Zinc (min.) 3800 mg; Iron (min.) 1800 mg; Manganese (min.) 1.300 mg; Fluorine (max.) 870 mg; Copper (min.) 590 mg; Molibdˆenio (Mo) 300 mg; Iodine (min.) 80 mg; Cobalt (min.) 40 mg; Chromium (min.) 20 mg; Selenium (min.) 15 mg. Phosphorus (P) solubility in 2 % citric acid (min): 95 %. c aNDFom: Neutral detergent fiber assayed with a heat-stable amylase and expressed as corrected for ash; Total-CHO: Total carbohydrates; NF-CHO: Non-fibrous carbohydrates; ME: Metabolizable energy. d WM: non-forage diets with whole millet grain; WC: non-forage diets with whole corn grain. Table 3 Effects of replacement of whole corn grain by whole millet grain on nutrient intake, digestibility coefficient and growth performance of goat kids. Item a Diets b SEM c P -value d Control WC WM Initial weight, kg 21.5 21.3 22.2 - - Final weight, kg 27.7 a 24.9 b 27.3 a 1.001 0.006 Average daily gain, g/d 149.2 a 79.1 b 138.5 a 12.045 0.006 Feed:Gain ratio 0.21 a 0.16 b 0.24 a 0.013 0.008 Intake Dry Matter, g/d 681ª 494 b 560 b 30.554 0.002 Dry Matter, %BW 2.76 a 2.10 b 2.28 b 0.084 0.001 Ash, g/d 18.18 17.47 15.57 1.205 0.689 Organic matter 663.0 a 476.7 b 544.9 b 29.837 0.001 Crude protein, g/d 103ª 61 b 98 a 5.997 0.001 Neutral detergente fiber, g/d 205 a 99 c 147 b 11.544 <0.001 Ether Extract, g/d 20.9 b 22.7 b 36.7 a 2.053 <0.001 Total-CHO, g/d 525 a 382 b 411 b 23.883 0.004 Non-fibrous CHO, g/d 318 280 268 15.221 0.274 Metabolizable energy, Mcal/d 1.84 a 1.38 b 1.69 ab 0.087 0.012 Digestibility, g/100g Dry Matter 86.15 b 91.44 a 86.69 b 0.866 0.042 Organic matter 87.62 90.27 88.07 0.655 0.282 Crude protein 85.97 81.94 86.62 0.932 0.142 Neutral detergente fiber 77.16 76.97 78.02 0.886 0.804 Ether Extract 87.81 b 90.74 a 88.53 ab 0.424 0.025 Total-CHO 87.93 91.48 88.29 0.744 0.149 Non-fibrous CHO 92.67 98.88 94.70 7.729 0.470 a Total-CHO: total carbohydrates; Non-fibrous CHO: Non-fibrous carbohydrates. b WM: non-forage diets with whole millet grain; WC: non-forage diets with whole corn grain; c SEM: Standard Error of Mean; d Values with different superscripts are different for P < .05. Table 4 Physiological parameters, feeding behavior, and water intake of goat kids fed non-forage diets containing whole millet grain as a replacement for whole corn grain. Item a Diets b SEM c Effect d Control WC WM Diet Hour D × H HR, mov/min 100.14 100.92 99.98 0.913 0.904 0.026 0.633 RR, mov/min 32.14 33.68 32.94 0.517 0.413 <0.001 0.836 RT, °C 38.88 38.86 38.92 0.036 0.746 0.048 0.843 BT, °C 36.23 36.20 36.27 0.024 0.185 <0.001 0.844 Feeding, min/d 269.59 237.28 173.79 17.738 0.091 Ruminating, min/d 135.39 a 63.14 b 65.08 b 13.332 0.026 Iddle, min/d 1023 b 1132 ab 1186 a 23.423 0.009 OA, min,d 26.34 22.20 26.34 3.490 0.696 Feeder visits, n° 53.91 47.45 34.76 3.547 0.091 Feed efficiency Dry Matter, g/h 134.33 170.75 206.57 15.300 0.174 NDF, g/h 32.52 50.63 50.63 5.267 0.237 Rumination efficiency Dry Matter, g/h 315.90 b 704.49 a 632.08 a 66.538 0.005 NDF, g/h 75.31 b 200.94 a 163.86 a 19.900 0.007 Water intake WID, g/d 3158 a 1745 b 2174 ab 255.09 0.043 WIF, g/d 331 230 299 28.69 0.291 TWI, g/d 3489 1975 2473 282.25 0.056 WI.DMI 5049 3960 4300 326.38 0.433 a HR: Heart rate; RR: Respiratory rate; RT: rectal temperature; BT: body temperature; OA: other activities; NDF: neutral detergent fiber; WID: water intake via drinking fountain; WIF: water intake via feed; TWI: total water intake; WI.DMI: Total water intake per kg of dry matter ingested; b WM: non-forage diets with whole millet grain; WC: non-forage diets with whole corn grain; c SEM: Standard Error of Mean; d D × H: Interaction between diet and hour; Values with different superscripts are different for P < .05. Table 5 Weight at slaughter and carcass characteristics of goat kids fed non-forage diets containing whole millet grain as a replacement for whole corn grain. Table 6 Leg tissue composition of goat kids fed non-forage diets containing whole millet grain as a replacement for whole corn grain. Item Diets a SEM b P -value c Control WC WM Leg. kg 1.87 1.56 1.77 0.109 0.232 Muscle Kg 1.23 1.14 1.13 g/100 g 66.25 65.50 63.38 0.834 0.384 Fat kg 0.11 0.12 0.15 0.009 0.357 g/100g 6.57 7.25 8.28 0.515 0.401 Bone kg 0.39 0.35 0.37 0.013 0.204 g/100g 21.41 20.47 21.22 0.442 0.651 Other tissues kg 0.08 0.07 0.10 0.007 0.337 g/100g 3.93 4.86 5.63 0.335 0.109 Ratios Muscle:Fat 11.87 11.46 7.92 1.275 0.383 Muscle:Bone 3.13 3.26 3.03 0.091 0.578 a WM: non-forage diets with whole millet grain; WC: non-forage diets with whole corn grain; b SEM: Standard Error of Mean; c Values with different superscripts are different for P < 0.05. 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Introduction","content":"\u003cp\u003eSince the 1970s, the use of a whole grain-based diet (WGD) without forage has been studied in the United States, leading to discussion about its applicability in beef cattle feedlots (Paulino et al., 2013; Contadini, 2016). The use of this diet is justified when the goal is based on high weight gain rates, better feed efficiency, and ease of daily food management (Contadini, 2016).\u003c/p\u003e\u003cp\u003eIn feedlot system production, corn is the main energy feed used in the animals' diets, and, in a WGD, is the main ingredient (Fabino Neto et al., 2022). This diet, also known as a non-forage diet, is recommended to supply 150\u0026ndash;200 g/kg of commercial pellets and 800\u0026ndash;850 g/kg of whole corn grain (Paulino et al., 2013). Although it is not very commonly adopted in ruminant diets, not necessarily for its economic viability, but some resistance or even fear on the part of most nutritionists.\u003c/p\u003e\u003cp\u003eAlthough ruminants have evolved to consume forage-based diets, in situations where grains are available at affordable prices and there are difficulties in providing or handling roughage, forage-free diets become a viable technological alternative. Additionally, there is growing concern about the environmental impact of livestock, and the type of carbohydrates in the diet can modulate enteric methane emissions. The fermentation of highly fermentable carbohydrates, such as corn and millet starch, favors propionate production in the rumen, reducing the hydrogen available for methanogenesis, which may contribute to mitigating enteric methane emissions. Thus, the partial or total replacement of forage with grain can be a feasible strategy to reduce the environmental footprint of animal production without compromising animal performance (Rivera et al. 2021; Sun et al., 2022). Recently, some research using WGD for finishing lambs also has been conducted (Mendes et al., 2018; Carlis et al., 2021), however, there is little data on goat kids (Bilal et al., 2025).\u003c/p\u003e\u003cp\u003eThe widespread use of corn grain in non-forage diets increases feeding costs, as it is widely used in animal feed and human nutrition. Therefore, it is necessary to search for alternative ingredients, such as millet grain, which in turn has good nutritional characteristics, such as starch (63.2%), crude protein (13.6%), ether extract (7.8%), crude fiber (2.8%), ash (2.1%), and dry matter content of 92.5% (Sharma et al., 2016), and it is considered an ideal plant for feeding due to its nutritional superiority about other cereal crops (Jenipher et al., 2024). Therefore, the hypothesis in this research is that replacing whole corn grain with whole millet grain in non-forage diets improves the performance and carcass characteristics of finishing goat kids. In this sense, the objective of this study is to evaluate the replacement of whole-grain millet with whole-grain corn in non-forage diets for goat kids finished in feedlot on performance, ingestive behavior, physiological parameters, and carcass characteristics.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cp\u003e2.1\u003cem\u003eLocation, animals, and experimental facilities\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment was conducted at the Animal Science Department, in Center of Agricultural Sciences of the Federal University of Piauí (5°02'30\"S; 42°47'00\"W; Altitude: 69.37m), located in Teresina, Piauí, Brazil. This study was approved by the Ethics Committee on Animal Use, under process number: 733/2022.\u003c/p\u003e\n\u003cp\u003eTwenty-one Anglo-Nubian goat kids with an initial body weight of 21.6 ± 2.9 kg and aged 150 ± 8 \u0026nbsp;days, were properly identified and individually distributed in covered pens (1.0 m × 1.3 m)\u0026nbsp;with a concrete floor, metal\u0026nbsp;railings on the sides, and equipped with drinker and feeder and through for mineral salt for 59 days, with the first 19 days corresponding to the adaptation of the animals to the experimental diets and management, and the remaining 40 days were intended for the finishing period of the animals. On the first day of the adaptation period, before the beginning of the experiment, the animals were properly dewormed and vaccinated against clostridiosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2 \u003cem\u003eExperimental design, treatments, and management\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental design was completely randomized design with three treatments and seven replications.\u0026nbsp;The diets (NRC, 2007) were formulated to meet the requirements of\u0026nbsp;growing goat\u0026nbsp;kids (20 kg of body weight - BW and 150 g/d of BW gain). The chemical composition of ingredients is shown in Table 1. The treatments consisted of a control diet (CON), composed of 900 g/kg concentrate and 100 g/kg of Tifton-85 hay, and, the remaining diets, non-forage diets, were based on the use of 200 g/kg of commercial protein pellet and 800 g/kg of whole corn grain-based (WC) or whole millet grain-based (WM) (Table 2).\u003c/p\u003e\n\u003cp\u003eDuring 19 first days, animals were submitted to the adaptation protocol as recommended by the pellet feed manufacturer, which was as follows: 1st to 3rd day: total mixed ration (TMR) equivalent to 15 g/kg of BW; 4th to 7th day: TMR equivalent to 20 g/kg of the BW; 8th to 11th day: TMR equivalent to 30 g/kg of the BW; 12th to 15th day: TMR equivalent to 35 g/kg of the fasting BW; 16th – 19th day: the access to bulky feed was removed and TMR equivalent to 35 - 40 g/kg of the fasting BW.\u003c/p\u003e\n\u003cp\u003eThe experimental diets were supplied twice a day, at 08:00 a.m. and 4:00 p.m., and orts\u0026nbsp;were measured daily to calculate daily dry matter intake (DMI). The daily supply adjustments aimed at providing 10% of orts, depending on feed intake. Water and mineral salt were available ad libitum during the experiment. Twice a week, orts from feed supplied were collected, identified, and frozen for later determination of nutrient consumption. At the end of the experiment period, the samples were thawed and\u0026nbsp;pooled\u0026nbsp;by animals.\u003c/p\u003e\n\u003cp\u003eThe previously formulated control diet concentrate and hay was weighed separately on an electronic scale and then mixed manually in each trough. The high-grain diets were weighed in the same way, where the pellet and corn or millet whole grain were weighed separately and immediately mixed manually and fed to each animal. Due to the type of day and the impossibility of leaving the animals fasting, the goat kids were weighed for 3 consecutive days at the beginning and the end of the experimental period. The difference between the average final body weight (FBW) and initial body weight (IBW) divided by the number of experimental days was used to determine the average daily gain (ADG).\u003c/p\u003e\n\u003cp\u003eAfter the end of the feedlot period, the animals remained for another 3 days in the stalls for\u0026nbsp;total\u0026nbsp;feces collection\u0026nbsp;using nylon bags, as described by Santos et al. (2022). The digestibility coefficients (DC) of nutrients were calculated using the equation: DC = [(kg of nutrient ingested fraction - kg of nutrient excreted fraction)/(kg of nutrient ingested fraction)] × 100.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3. Sample collection and chemical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAt the end of the feedlot period, samples of diet and orts were thawed and pooled by animals. Samples were then ground through a 1mm Wiley Mill screen (Marconi, Piracicaba, SP, Brazil) and the DM (Method 934.01), ash (Method 942.05), ether extract (Method 954.05) and total nitrogen (N; Method 968.06) were determined according to the AOAC (1990). Crude protein was calculated by multiplying the total nitrogen by 6.25.\u003c/p\u003e\n\u003cp\u003eNeutral detergent fiber assayed with a heat-stable amylase and expressed as corrected for ash (aNDFom), was determined according to Mertens (2002). The total carbohydrates and non-fiber carbohydrates were determined according to Sniffen, O'Connor, Van Soest, Fox, and Russell (1992) \u0026amp; Mertens (2002), respectively.\u003c/p\u003e\n\u003cp\u003eThe mean particle size (MPS) of the diets was determined using the Penn State Particle Size Separator method (Lammers et al., 1996). Particle size was measured according to Kononoff et al. (2003), based on the proportions of the sample retained on sieves of 19, 8, 4, and 1.18 mm (Table 2). For the determination of dietary metabolizable energy (ME) contents, it was assumed that 1 kg of total digestible nutrients (TDN) is 4.409 Mcal of digestible energy (DE) and 1 Mcal of DE equals 0.82 Mcal of ME (NRC, 2007), from the data of nutrient digestibility.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4. Feeding behavior, physiological parameters, and water intake\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIndividual observations of the animals were carried out on the 31st day of the experiment, according to the following activities: feeding, ruminating, idling, and other activities. The interval to evaluate feeding behavior was 5 min with continuous periods of 24 h, starting at 08:00 am, according to the Johnson and Combs (1991) method.\u003c/p\u003e\n\u003cp\u003eThe data were recorded by trained observers (one for every seven goat kids) positioned to interfere as little as possible. The turns were changed every 6 hours, and, at night, artificial lighting was used, following the same previous procedures adopted at the beginning of the experiment.\u003c/p\u003e\n\u003cp\u003eThe efficiency of feeding and ruminating DM and NDF is related to animal behavior and was calculated considering the time spent on feeding and rumination of the DM and NDF (min/day) (Azevedo et al., 2013). Thus, the rate of intake and rumination efficiency with respect to the DM and NDF were expressed as a ratio between the daily intake time and rumination and the amount of nutrient (DM or NDF) fed daily.\u003c/p\u003e\n\u003cp\u003eThe animals’ physiologic responses were evaluated at 06:00 am; 10:00 am; 2:00 pm and 6:00 pm, over 7 consecutive experimental days (32 to 38th of the experimental period), in which the following parameters were measured: rectal temperature (RT), body temperature (BT), heart rate (HR) and respiratory rate (RR). On these days, environmental variables (air temperature and relative humidity) also were recorded at 6:00 am, 10:00 am, 2:00 pm, and 6:00 pm during the experimental period. A veterinary clinical thermometer (Highmed, Termo KT-DT 4B, Belo Horizonte, MG, Brazil) was inserted into the rectum of the animal for 2 min to measure RT, in ° Celsius. The BT was measured with a laser thermometer (Akrom model KR380, Porto Alegre, RS, Brazil) and, was calculated as the arithmetic average of the temperature of the temple, base of the tail, and side of the lambs (Machado et al., 2020). The RR was measured through direct observation of the movements of the left flank, as described by Silva et al. (2024), counting the number of movements for 15 seconds and the value obtained was multiplied by 4 to determine the RR in movements per minute (breaths/min). The HR was measured through a veterinary stethoscope positioned in the left thoracic region (Diffay et al., 2004).\u003c/p\u003e\n\u003cp\u003eFor water intake data collection, the supply and ort of water were weighed in 10 L plastic buckets and weighed before delivery and weighed again after 24 h to determine water intake via drinking (WID). Water was offered daily, at 7:30 am. Two buckets containing water were distributed in the shed, to determine daily evaporation. For water intake via food (WIF) and total water intake (TWI), the values were calculated according to Silva et al. (2024).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5. Animal slaughter and carcass characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAt the end of the 40th-day feeding trial, animals were subjected to fasting with access to water for 14 h, and weighed to determine the slaughter body weight (SBW) and post-slaughter hot carcass weights (HCW) were also measured. All these procedures were carried out at the commercial abattoir, in accordance with the protocols established by the Regulation of Brazilian Industrial and Sanitary Inspection of Products of Animal Origin (Brasil, 2017). Then, the carcasses were suspended by the calcaneal tendon, and a subjective evaluation of the carcass was performed, regarding finishing, in scores from 1 (very lean) to 5 (very fat), regarding conformation, in scores from 1 (poor) to 5 (excellent) and renal fat score, ranging from 1 (poor) to 3 (excellent), according to Cezar and Sousa (2007).\u003c/p\u003e\n\u003cp\u003eAfter 24 h of cooling at 4°C, the kidneys and fat kidney were removed, and the weight was deducted from the carcass's weight. The \u003cem\u003eLongissimus thoracis\u003c/em\u003e (LT) muscle was transversely cut and exposed between the 12th and 13th ribs and then, subcutaneous fat thickness (SFT) was measured using a digital caliper (DIGIMESS, São Paulo, SP, Brazil), on both sides of the carcass. The exposed side of the LT was measured with a plastic grid to obtain the LT area. The values from the right and left sides of the carcasses were used to calculate the arithmetic mean of SFT and LT area per carcass.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.6. Statistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The results were submitted to ANOVA using the SAS MIXED procedure, considering the experimental diet as a fixed effect. When significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), the Tukey test was performed, and the initial body weight was used as a covariate. The data were tested for normality of errors using the Shapiro-Wilk test.\u003c/p\u003e\n\u003cp\u003ePhysiological parameters were analyzed with repeated measures over time. The most appropriate covariance structure selection was based on Akaike’s Information Criteria Corrected (AICC) and Bayesian Information Criteria (BIC). The best model has the lowest AICC or BIC values. The covariance matrix that best dataset was a first-order autoregressive-AR(1) for HR and BT and compound symmetry CS for RT and RR.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1\u003cem\u003e. Intake, growth, and nutrient utilization\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe whole grain diets\u0026nbsp;reduced DMI, in g/day (\u003cem\u003eP\u003c/em\u003e = 0.002)\u0026nbsp;and\u0026nbsp;% BW (\u003cem\u003eP\u003c/em\u003e = 0.001) and, consequently,\u0026nbsp;organic matter intake (OMI; \u003cem\u003eP\u003c/em\u003e = 0.001) and\u0026nbsp;total-CHO intake (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.004) compared\u0026nbsp;to the control diet (Table 3). However, non-fibrous CHO intake (\u003cem\u003eP\u003c/em\u003e = 0.274), was not affected by diets, averaging 287 ± 68 g/day\u0026nbsp;(Table 3).\u0026nbsp;Due to the difference in extract\u0026nbsp;content among diets, animals fed the WM diet showed the highest\u0026nbsp;ether extract intake\u0026nbsp;(EEI, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), followed by WC and CON. There also was an observed effect on neutral detergent fiber intake (NDFI), which was higher (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) when goat kids were fed CON, followed by WM and then WC\u0026nbsp;(Table 3). This reflected an increase in water intake via drinking fountain (g/day; \u003cem\u003eP\u003c/em\u003e = 0.043) when animals fed the control diet compared to WC, but did not differ from the WM diet\u0026nbsp;(Table 3). Nevertheless, the water intake via feed, total water intake, and total water intake per kg of dry matter ingested did not change (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eRegarding nutrient digestibility, WC increased DM digestibility (\u003cem\u003eP\u003c/em\u003e = 0.042) and EE digestibility (\u003cem\u003eP\u003c/em\u003e = 0.025) compared to CON and WM diets. The WC diet\u0026nbsp;reduced crude protein intake (CPI; \u003cem\u003eP\u003c/em\u003e = 0.001) compared to other diets and reduced ME intake compared to CON, but did not differ from the WM diet (\u003cem\u003eP\u003c/em\u003e = 0.012), resulting in lower ADG rates (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.006) and worst feed: gain ratio. Higher performance results were observed for the CON and WM diets (149.2 g/d and 138.5 g/d respectively), which did not differ from each other.\u003c/p\u003e\n\u003cp\u003e3.2.\u003cem\u003eIngestive behavior and physiological parameters\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental diets did not change the RR (\u003cem\u003eP\u003c/em\u003e = 0.413), HR (\u003cem\u003eP\u003c/em\u003e = 0.904), BT (\u003cem\u003eP\u003c/em\u003e = 0.185), and RT (\u003cem\u003eP\u003c/em\u003e = 0.746; Table 4). There was an observed hour effect (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.005) for all physiological parameters’ variables, but no effects of diet × hour interaction were found (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding ingestive behavior, WM tended to reduce (\u003cem\u003eP\u003c/em\u003e = 0.091) the time spent in feeding and the number of feeder visits (\u003cem\u003eP\u003c/em\u003e = 0.091), but CON increased the time spent in rumination (\u003cem\u003eP\u003c/em\u003e = 0.026). Consequently, WM increased the time spent in idle (\u003cem\u003eP\u003c/em\u003e = 0.009) compared to CON but did not change from WC. No effects were observed for feed efficiency of DM (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.174) and feed efficiency of NDF (\u003cem\u003eP\u003c/em\u003e = 0.237). However, CON diet reduced the rumination efficiency of DM (P = 0.005) and NDF (\u003cem\u003eP\u003c/em\u003e = 0.007) compared to non-forage diets.\u003c/p\u003e\n\u003cp\u003e3.3. \u003cem\u003eCarcass characteristics\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe WC diet reduced the slaughter weight (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.025), however, there was no observed effect on HCW (\u003cem\u003eP\u003c/em\u003e = 0.665), HCY (\u003cem\u003eP\u003c/em\u003e = 0.296), and conformation (\u003cem\u003eP\u003c/em\u003e = 0.542). The CON diet reduced the kidney fat score (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.004), and renal fat, g/day (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.018), compared to WC and WM diets (Table 5), but WC tended to reduce finishing (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.061), while WM tended to increase (\u003cem\u003eP\u003c/em\u003e = 0.054) LM area. Regarding leg tissue composition (Table 6), there was no observed difference (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05) in weight and proportion of muscle, fat, bone, and other tissues, as muscle: fat ratio (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.383) and muscle: bone ratio (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.578). \u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCorn grain is considered as the main energy source in the diet of ruminants finished in feedlot system (Carlis et al., 2021; Pinto and Millen, 2018). However, in a whole corn-based diet for goat kids, the reduction of DMI was accompanied by reduction on ADG and BW at slaughter. Nevertheless, the data suggests that the total replacement of WC by WM in non-forage diets maintained the ADG similar to the control group.\u003c/p\u003e\u003cp\u003eThe average DMI found in this study (573 g/day) was close to that recommended by NRC (2007) for 8-month-old animals of 20 kg BW. Although there was no significant effect on ADG, offering the CON and WM diets, the latter presented ADG slightly lower (7%) than that recommended by the NRC (150 g/ day) for animals with similar category. The ADG value ​​found when the animals fed WC diet was 47% lower than the NRC recommendation.\u003c/p\u003e\u003cp\u003eIn ruminant species, the consumption of animals fed a high-concentrate diet is controlled by the energy demands of the animal and by metabolic factors (Allen, 1996). Although it had occurred with goat kids fed the WM diet, the same was not observed when animals fed the WC diet. Goat kids fed WM tended to decrease the time spent on feeding, following the reduced DMI. Nevertheless, the reduction in DMI observed for the WC diet was not accompanied by the decrease in time spent on feeding, which may evidence animal selectivity, a characteristic inherent to the caprine species. In research conducted by Ackermans et al. (2019), it was concluded that goat species are shown to be capable of avoiding coarse grit in their diet, unlike sheep, which could explain these findings.\u003c/p\u003e\u003cp\u003eThe greater DM and EE digestibility when animals fed WC, can be attributed to the prolonged retention time, and reduced rate passage which prolonged the action of microorganism\u0026rsquo;s digestive enzymes in the rumen (Allen and Mertens, 1988). Furthermore, the higher digestibility observed in the WC diet compared to the control is primarily due to the replacement of a lower-digestibility ingredient (forage) with a higher-digestibility ingredient (corn) and pellet (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; NRC, 2001; Carlis et al., 2021). According to Van Soest (1994), diets with whole grains increase the time spent with the rumination, and consequently, more saliva is produced which will help stabilize the ruminal pH, not impairing the digestion of feed fiber. However, in this study, the time spent on rumination activity decreased, probably because of the decreased DMI. Nevertheless, the rumination efficiency of DM and the rumination efficiency of NDF increased compared to the CON. Diets with lower NDF content generally result in higher energy density and increased ruminal passage rate, which can improve feed efficiency. However, diets with very low peNDF levels may increase the risk of ruminal acidosis, especially in forage-free systems. The WM diet showed an intermediate NDF content between CON and the WC diet, which probably may indicate better modulation of ruminal fermentation. This suggests that the inclusion of whole millet grain can help balance the energy supply while reducing the risks associated with excessive fermentable carbohydrate intake, improving both digestibility and rumen health (Allen, 1996).\u003c/p\u003e\u003cp\u003eThe absence of differences in RR, HR, BT, and RT among diets suggests that the treatments did not impose metabolic or thermal challenges on the animals. According to the literature, animals exposed to high temperatures exhibit increased RR and RT to dissipate heat. However, when diets are nutritionally balanced and do not cause severe digestive challenges, physiological mechanisms remain stable, ensuring homeostasis. Additionally, the time-of-day effect on physiological parameters may be attributed to circadian rhythms and daily environmental fluctuations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), influencing thermal regulation independently of diet. These findings align with studies indicating that respiratory rate and rectal temperature are reliable indicators of heat stress, but their variation is primarily driven by ambient temperature rather than diet composition, even in diets with low forage fiber content (Berihulay et al., 2019).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegarding performance, the fat deposition occurs when the energy consumed is greater than the requirements of the animal (Robelin, 1986). Then, the lower ADG found for goat kids fed WC reflects the reduced ME intake (NRC, 2007; Parente et al., 2020; Santos e al., 2020) which also resulted in lower BW at slaughter. However, animals were slaughtered at a reduced age, which justifies the lack of effect on HCW and HCY.\u003c/p\u003e\u003cp\u003eLeg tissue composition, which is the separation of muscles, bones, fat and other components, is determined rather than on the entire carcass for reduces time, costs and waste (Silva Sobrinho et al., 2011). The results showed which CON or non-forage diets did not affect leg components, such as muscle, bone, fat and others tissues. The absence of dietary effects on fat proportion in leg confirms the results obtained on carcass.\u003c/p\u003e\u003cp\u003eThe fat deposition in animal organisms first accumulates in visceral depots, followed by intermuscular depots, subcutaneous and finally in intramuscular depots (Schumacher et al., 2022). Moreover, the goat species tends to prioritize fat accumulation in the abdominal cavity and visceral depots (Yalcintan et al., 2024), which is used as a reserve of energy in periods of food shortages (Mtenga et al., 2005). Because of this, it is not appropriate to adopt subcutaneous fat development as an indicator of fat storage and development in growing animals (Yalcintan et al., 2024).\u003c/p\u003e\u003cp\u003eIt was expected that the increased energy intake, as observed when animals fed the CON diet, could increase kidney fat. Because, high plasma levels of acetate increase lipogenesis in extramuscular adipocytes (Ladeira et al., 2016). However, the reduced kidney fat deposition is probably a consequence of the replacement of an ingredient with less digestion potential (coastcross hay) for ingredients with greater digestion potential (corn and millet) (Carlis et al., 2021). Added to this factor, it is possible that the higher intake increased the rate of food passage in animals fed this diet and reduced the retention time of food in the rumen, as mentioned above.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe whole millet grain-based diet can replace the whole corn grain in non-forage diets, providing a good performance and adequate carcass characteristics and leg tissue composition.\u003c/p\u003e"},{"header":"Statements \u0026 Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Suppor\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e:This work was financial supported by the National Council for Scientific and Technological Development – CNPq (406734/2022-4 Meat Production Chain) and\u0026nbsp;Coordination for the Improvement of Higher Education Personnel – Brazil (CAPES; Finance Code 001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that there are no conflicts of interest issues concerning this submission. The Coordination for the Improvement of Higher Education Personnel awarded the scholarship for Francisca Leila, and The National Council for Scientific and Technological Development awarded partially the financial support for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAckermans, N.L., Martin, L.F., Hummel, J., M\u0026uuml;ller, D.W.H., Clauss, M., Hatt, J.M. 2019. Feeding selectivity for diet abrasiveness in sheep and goats. Small Rumin. Res. 175, 160-164. https://doi.org/10.1016/j.smallrumres.2019.05.002\u003c/li\u003e\n \u003cli\u003eAllen, M.S. 1996.Physical constraints on voluntary intake of forages by ruminants. J Anim Sci. 74(12):3063-75. 10.2527/1996.74123063x.\u003c/li\u003e\n \u003cli\u003eAllen, M.S., Mertens, D.R. 1988. Evaluating Constraints on Fiber Digestion by Rumen Microbes. The J. Nutr. 118(2), 261-270. https://doi.org/10.1093/jn/118.2.261\u003c/li\u003e\n \u003cli\u003eAOAC, 1990. Official Methods of Analysis, 15th ed. Association of Official Analytical Chemists, Arlington, VA, USA.\u003c/li\u003e\n \u003cli\u003eAzevedo, R.A., Rufino, L.M.A., Santos, A.C.R., J\u0026uacute;nior R, Rodriguez NM, Geraseev LC. 2013. Ingestive behavior of lambs fed macauba cake. Braz. Arch. Vet. Med. Animal Sci. 65:490-496. https://doi.org/10.1590/S0102-09352013000200027\u003c/li\u003e\n \u003cli\u003eBerihulay, H., Abied, A., He, X., Jiang, L., Ma, Y. 2019. Adaptation Mechanisms of Small Ruminants to Environmental Heat Stress. \u003cem\u003eAnimals\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 75. https://doi.org/10.3390/ani9030075\u003c/li\u003e\n \u003cli\u003eBilal, M.; Malik, M.I.; Rashid, M.A.; Khurshid, M.A.; Yousaf, M.S.; Rehman, H.U. 2025. Influence of physical forms of non-forage diet on growth performance, feeding behavior, rumen and blood indices, and nutrient digestibility in fattening goats. Small Rumin. Res. 242, 107407. https://doi.org/10.1016/j.smallrumres.2024.107407\u003c/li\u003e\n \u003cli\u003eBrasil. Minist\u0026eacute;rio da Agricultura. Pecu\u0026aacute;ria e do Abastecimento (MAPA). Secretaria da Defesa Agropecu\u0026aacute;ria (SDA). Departamento de Inspe\u0026ccedil;\u0026atilde;o de Produtos de Origem Animal (DIPOA). Divis\u0026atilde;o de Normas T\u0026eacute;cnicas., 2017, 52p.\u003c/li\u003e\n \u003cli\u003eCarlis, M.S.P.; Sturion, T.U.; Silva, A.L.A.; Eckermann, N.R.; Polizel, D.M.; Assis, R.G.; Souza, T.T.; Dias Junior, P.C.G.; Vicente, A.C.S.; Santos, I.J.; Comelli, J.H.; Biava, J.K.; Pires, A.V.; Ferreira, E.M. 2021. Whole corn grain-based diet and levels of physically effective neutral detergent fiber from forage (pefNDF) for feedlot lambs: Digestibility, ruminal fermentation, nitrogen balance and ruminal pH. Small Rum. Res. 205, 106567. https://doi.org/10.1016/j.smallrumres.2021.106567Cesar, M.F., Sousa, W.H. Carca\u0026ccedil;as ovinas e caprinas: obten\u0026ccedil;\u0026atilde;o, avalia\u0026ccedil;\u0026atilde;o, classifica\u0026ccedil;\u0026atilde;o. Agropecu\u0026aacute;ria tropical. 2007; p.147.\u003c/li\u003e\n \u003cli\u003eContadini, M.A.; Ferreira, F.A.; Corte, R.R.S.; Antonelo, D.S., Gomez, J.F.M.; Silva, S.L. 2017. Roughage levels impact on performance and carcass traits of finishing Nellore cattle fed whole corn grain diets. Tropical Anim. Health Prod. 49:1709-1713. 10.1007/s11250-017-1381-x\u003c/li\u003e\n \u003cli\u003eDiffay B.C., Mckenzi, D., Wolf, C., Pugh, D.G. 2004. Approach and examination of sheep and goats. In: Pugh D. G, editor. Cl\u0026iacute;nica de caprinos e ovinos. 1st ed. S\u0026atilde;o Paulo: Roca; p. 1\u0026ndash;19.\u003c/li\u003e\n \u003cli\u003eEsteban, R.J., Julian, C. CH4 and N2O Emissions From Cattle Excreta: A Review of Main Drivers and Mitigation Strategies in Grazing Systems. Frontiers Sust. Food Systems, 5 (2021).https://doi.org/10.3389/fsufs.2021.657936.\u003c/li\u003e\n \u003cli\u003eFabino Neto, R., Pessoa, F.O., Silva, T.D., Miyagi, E.S., Santana Neto, V.V., Godoy, M.M., Lima, D.K.S., Silva, J.R.M., Bainer, M.M.A. 2022. The effect of fungal probiotics added to a high-grain diet on the gastrointestinal tract of sheep. Brazilian Animal Sci. 23, e-70605E. https://doi.org/10.1590/1809-6891v22e-70605E\u003c/li\u003e\n \u003cli\u003eJenipher, C., Santhi, V.P., Amalraj, S., Sathia Geetha, V., Gurav, S.S., Kalaskar, M.G., Ayyanar, M. 2024. A comprehensive analysis on nutritional composition, functional properties, antioxidant and enzyme inhibitory potential of selected minor millet grains. South African J. Botany, 2024. 10.1016/j.sajb.2024.05.012.\u003c/li\u003e\n \u003cli\u003eJohnson, T. R., Combs, D. K. 1991. Effects of prepartum diet, inert rumen bulk, and dietary polyethylene glycol on dry matter intake of lactating dairy cows. J. Dairy Sci. 74:933-944. https://doi.org/10.3168/jds.S0022-0302\u003c/li\u003e\n \u003cli\u003eKononoff, P.J., Heinrichs, A.J., Buckmaster D.R.2003. Modification of the Penn State Forage and Total Mixed Ration Particle Separator and the Effects of Moisture Content on its Measurements.J. Dairy Sci. 86, 5, 1858 - 1863. 10.3168/jds.S0022-0302(03)73773-4\u003c/li\u003e\n \u003cli\u003eLadeira, M.M., Schoonmaker, J.P., Gionbelli, M.P., Dias, J.C., Gionbelli, T.R., Carvalho, J.R., Teixeira, P.D. 2016. Nutrigenomics and Beef Quality: A Review about Lipogenesis. Int J Mol Sci. 17, 6. 918. 10.3390/ijms17060918.\u003c/li\u003e\n \u003cli\u003eLammers, B.P., Buckmaster, D.R., HEINRICHS, A.J. 1996. A Simple Method for the Analysis of Particle Sizes of Forage and Total Mixed Rations. J. Dairy Sci., 79. 922-928. 10.3168/jds.S0022-0302(96)76442-1\u003c/li\u003e\n \u003cli\u003eMachado, N.A.F., Barbosa Filho, J. A. D., Oliveira, K. P. L., Parente, M.O.M. 2019. Biological rhythm of goats and sheep in response to heat stress. Biol. Rhythm Res. 51(7), 1044-1052. https://doi.org/10.1080/09291016.2019.1573459\u003c/li\u003e\n \u003cli\u003eMendes, J.A.C., Parente, M.O.M., Parente, H.N., Zanine, A.M., Ferreira, D.J., Moreira Filho, M.A., Cunha, i.A.L., Landim, A.V., Rocha, K.S. 2018. Performance, ingestive behavior and cost of production of finishing lambs fed non-forage diets. Biological Rhyt. Res.https://doi.org/10.1080/09291016.2018.1535540\u003c/li\u003e\n \u003cli\u003eMertens, D.R. 2002. Gravimetric determination of amylase-treated neutral detergente fibre in feeds with refluxing beakers or crucibles: Collaborative study. \u003cem\u003eJournal of Association Chemists\u0026apos; International, 85\u003c/em\u003e, 1217\u0026ndash;1240\u003c/li\u003e\n \u003cli\u003eMtenga, G.C., Owen, L.A., Muhikambele, E., Kifaro, V.R.M. 2003. Developmental changes of fat depots in male Saanen goats. Tanzania J. Agric. Sci. 6 (2), 99-103. https://hdl.handle.net/10520/AJA0856664X_305\u003c/li\u003e\n \u003cli\u003eNRC, 2007. Nutrient Requirements of Small Ruminants: Sheep, Goats, Cervids and New World Camelids. Acad. Press, Washington, DC (2007)\u003c/li\u003e\n \u003cli\u003ePaulino, P.V.; Oliveira, T.S.; Gionbeli, M.P.; Gallo, S.B. 2013. Forage-Free Diets for Finishing Ruminant Animals. Scientific J. Animal Prod., 15, 161-172. 10.15528/2176-4158/rcpa.v15n2p161-172.\u003c/li\u003e\n \u003cli\u003ePinto, A.C.J., Millen, D. 2018. Nutritional recommendations and management practices adopted by feedlot cattle nutritionists: the 2016 Brazilian survey. Canadian J. Anim. Sci. 99(2):392-407. 10.1139/CJAS-2018-0031\u003c/li\u003e\n \u003cli\u003eRobelin J. 1986. Growth of adipose tissues in cattle; partitioning between depots, chemical composition and cellularity. A review. Livest. Prod. Sci.14:349-364. 10.1016/0301-6226(86)90014-X.\u003c/li\u003e\n \u003cli\u003eSantos, G. O., Parente, H.N., Zanine, A.M., Nascimento, T.V.C., Lima, A.G.O.V., Bezerra, L. R., Machado, N.A.F., Ferreira, D.J., Santos, V.L.F., Costa, H.H.A., Oliveira, J.S., Parente, M.O.M. 2022. Effects of dietary greasy babassu byproduct on nutrient utilization, meat quality, and fatty acid composition in abomasal digesta and meat from lambs. Animal Feed Science and Technology, 287, 115283. https://doi.org/10.1016/j.anifeedsci.2022.115283.\u003c/li\u003e\n \u003cli\u003eSchumacher M, DelCurto-Wyffels H, Thomson J, Boles J. Fat Deposition and Fat Effects on Meat Quality-A Review. Animals (Basel). 2022 Jun 15;12(12):1550. doi: 10.3390/ani12121550.\u003c/li\u003e\n \u003cli\u003eSilva, L.V., Vilela, G.K. S.M., Rocha, K.S., Cavalcanti, H.S., Gois, G.C., Santos, F.N., Campos, F.S., Parente, M.O., Zanine, A.M., Ferreira, D.J., Mariz, T.M.A., Parente, H.N. 2024. Feeding Behavior, Water Intake, and Physiological Parameters of Feedlot Lambs Fed with Diets Containing Babassu Oil Associated with Sunflower Oil Blend. Vet. Medicine Int. 8673922, https://doi.org/10.1155/2024/8673922\u003c/li\u003e\n \u003cli\u003eSharma, S., Saxena, D.C., Riar, C.S. 2016. Nutritional, sensory and in-vitro antioxidant characteristics of gluten free cookies prepared from flour blends of minor millets. J. Cereal Sci. 72, 153-161. https://doi.org/10.1016/j.jcs.2016.10.012\u003c/li\u003e\n \u003cli\u003eSilva Sobrinho, A.G.; Manzi, G.M.; Lima, N.L.L.; Almeida, F.A.; Endo, V.; Zeola, N.M.B.L. 2011. Internat. J. Biol. Life Sci. Eng. 5,7. https://doi.org/10.5281/zenodo.1328846\u003c/li\u003e\n \u003cli\u003eSniffen, C.J., O\u0026apos;Connor, J. D., Van Soest, P. J., Fox, D. G., Russell, J. B. 1992. A net carbohydrate and protein system for evaluating cattle diets: II. Carbohydrate and protein availability. J. Animal Sci.\u003cem\u003e,\u003c/em\u003e 70, 3562-3577. https://doi.org/10.2527/1992.70113562x\u003c/li\u003e\n \u003cli\u003eSun, X., Cheng, L., Jonker, A., Munidasa, S., Pacheco, D . 2022. A Review: Plant Carbohydrate Types\u0026mdash;The Potential Impact on Ruminant Methane Emissions. Front. Vet. Sci. 9. https://doi.org/10.3389/fvets.2022.880115\u003c/li\u003e\n \u003cli\u003eVan Soest, P. J. 1994. Nutritional ecology of the ruminant. Cornell University, Ithaca.\u003c/li\u003e\n \u003cli\u003eYalcintan, H., Kecici, P.D., Yilmaz, A., Ekiz, B. 2024. Carcass characteristics and meat quality of goat kids according to the Colomer \u0026ndash; Rocher carcass fatness and conformation classes. Meat Sci. 214, 109521. https://doi.org/10.1016/j.meatsci.2024.109521\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eChemical and physical composition of the ingredients of the experimental diets (g/kg Dry Matter)\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 76px;\"\u003e\n \u003cp\u003eIngredients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eNutrients, g/kg\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eWhole corn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eTifton 85 Hay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eWhole Millet\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ePellet\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eConcentrate\u003csup\u003ec\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eDry matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eCrude protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e77.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e67.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e154.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e369.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eEther Extract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e58.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eAsh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e117.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eaNDFom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eTotal-CHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eNF-CHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 38px;\"\u003e\n \u003cp\u003eParticle size (g/kg of DM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eScreen, 19 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eScreen, 8mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e72.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eScreen, 4 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eScreen,1.18mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eBottom pan,\u0026lt;1.18mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eaNDFom: Neutral detergent fiber\u0026nbsp;assayed with a heat stable amylase and expressed as corrected for ash;\u0026nbsp;Total-CHO: Total carbohydrates; NF-CHO: Non-fibrous carbohydrates.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Ingredients:\u0026nbsp;Soybean meal, wheat bran, corn grain, urea, Dicalcium phosphate, Calcitic limestone, Sodium chloride (common salt), Livestock sulfur, Iron sulfate, Copper sulfate, Manganese sulfate, Zinc oxide, Cobalt sulfate, Calcium iodate, Sodium selenite, Vitamin A, Vitamin B3, Vitamin E, Propionic acid, Formic acid, Ammonium propionate, B.H.A., Ethoxyquinyl, Citric acid, and Phosphoric acid.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eConcentrate from a control diet.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eProportion of ingredients, physical characterization and chemical composition of experimental diets. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 34px;\"\u003e\n \u003cp\u003eIngredients, g/kg DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 65px;\"\u003e\n \u003cp\u003eDiets\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eControl\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eGround corn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eSoybean meal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eWheat bran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eTifton-85 hay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eWhole corn grain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eWhole Millet grain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003ePellet\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eLimestone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eAmmonia Chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eSodium bicarbonate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eMineral salt\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003eChemical composition,\u0026nbsp;g/kg DM\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eDry matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eCrude protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eEther extract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eAsh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e31.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eaNDFom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eTotal-CHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eNF-CHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eME, Mcal/kg DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eParticle size, g/kg DM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eScreen, 19 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eScreen, 8 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e668.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eScreen, 4 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e184.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e331.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e200.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eScreen, 1.18 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e331.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e784.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eBottom pan, \u0026lt;1.18 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e451.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Ingredients:\u0026nbsp;Soybean meal, wheat bran, corn grain, urea, Dicalcium phosphate, Calcitic limestone, Sodium chloride (common salt), Livestock sulfur, Iron sulfate, Copper sulfate, Manganese sulfate, Zinc oxide, Cobalt sulfate, Calcium iodate, Sodium selenite, Vitamin A, Vitamin B3, Vitamin E, Propionic acid, Formic acid, Ammonium propionate, B.H.A., Ethoxyquinyl, Citric acid, and Phosphoric acid;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eGuarantee levels per kilogram of the product according to the manufacturer: Sodium (min.) 147 g; Calcium (min.) 120 g; Phosphorus (min.) 87 g; Sulfur (min.) 18 g; Zinc (min.) 3800 mg; Iron (min.) 1800 mg; Manganese (min.) 1.300 mg; Fluorine (max.) 870 mg; Copper (min.) 590 mg; Molibd\u0026circ;enio (Mo) 300 mg; Iodine (min.) 80 mg; Cobalt (min.) 40 mg; Chromium (min.) 20 mg; Selenium (min.) 15 mg. Phosphorus (P) solubility in 2 % citric acid (min): 95 %.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eaNDFom: Neutral detergent fiber\u0026nbsp;assayed with a heat-stable amylase and expressed as corrected for ash; Total-CHO: Total carbohydrates; NF-CHO: Non-fibrous carbohydrates; ME: Metabolizable energy.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u0026nbsp;\u003c/sup\u003eWM: non-forage diets with whole millet grain; WC: non-forage diets with whole corn grain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eEffects of replacement of whole corn grain by whole millet grain on nutrient intake, digestibility coefficient and growth performance of goat kids.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eItem\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eDiets\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eSEM\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eInitial weight, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFinal weight, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e27.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e24.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e27.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAverage daily gain, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e149.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e79.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e138.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFeed:Gain ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eIntake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDry Matter, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e681\u0026ordf;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e494\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e560\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDry Matter, %BW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.76\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.10\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.28\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAsh, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e18.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e17.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eOrganic matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e663.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e476.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e544.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e29.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eCrude protein, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e103\u0026ordf;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e61\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e5.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eNeutral detergente fiber, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e205\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e99\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e147\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e11.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eEther Extract, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e20.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e22.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e36.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eTotal-CHO, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e525\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e382\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e411\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e23.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eNon-fibrous CHO, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e15.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eMetabolizable energy, Mcal/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.84\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.38\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.69\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDigestibility, g/100g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDry Matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e86.15\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e91.44\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e86.69\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eOrganic matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e87.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e90.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e88.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eCrude protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e85.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e81.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e86.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eNeutral detergente fiber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e77.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e76.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e78.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eEther Extract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e87.81\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e90.74\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e88.53\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eTotal-CHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e87.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e91.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e88.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eNon-fibrous CHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e92.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e98.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e94.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eTotal-CHO: total carbohydrates; Non-fibrous CHO: Non-fibrous carbohydrates.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e WM: non-forage diets with whole millet grain; WC: non-forage diets with whole corn grain;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e SEM: Standard Error of Mean;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Values with different superscripts are different for \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; .05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003ePhysiological parameters, feeding behavior, and water intake of goat kids fed non-forage diets containing whole millet grain as a replacement for whole corn grain.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eItem\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eDiets\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eSEM\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eEffect\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDiet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eD \u0026times; H\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eHR, mov/min \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e100.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e100.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e99.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eRR, mov/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e32.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e33.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e32.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eRT, \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e38.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e38.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e38.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eBT, \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e36.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e36.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e36.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eFeeding, min/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e269.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e237.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e173.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e17.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eRuminating, min/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e135.39\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e63.14\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e65.08\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e13.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eIddle, min/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1023\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1132\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1186\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e23.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eOA, min,d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e26.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e22.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e26.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eFeeder visits, n\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e53.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e47.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e34.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eFeed efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDry Matter, g/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e134.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e170.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e206.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e15.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNDF, g/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e32.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e50.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e50.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eRumination efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDry Matter, g/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e315.90\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e704.49\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e632.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e66.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNDF, g/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e75.31\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e200.94\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e163.86\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e19.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eWater intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eWID, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3158\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1745\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2174\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e255.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eWIF, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e28.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eTWI, g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e282.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eWI.DMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e4300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e326.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e HR: Heart rate; RR: Respiratory rate; RT: rectal temperature; BT: body temperature; OA: other activities; NDF: neutral detergent fiber; WID: water intake via drinking fountain; WIF: water intake via feed; TWI: total water intake; WI.DMI: Total water intake per kg of dry matter ingested;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e WM: non-forage diets with whole millet grain; WC: non-forage diets with whole corn grain;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e SEM: Standard Error of Mean;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e D \u0026times; H: Interaction between diet and hour; Values with different superscripts are different for \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; .05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eWeight at slaughter and carcass characteristics of goat kids\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efed non-forage diets containing whole millet grain as a replacement for whole corn grain.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003e Leg tissue composition of goat kids\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efed non-forage diets containing whole millet grain as a replacement for whole corn grain.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eDiets\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eSEM\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLeg. kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMuscle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eKg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eg/100 g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e66.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e65.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e63.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eg/100g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e8.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eBone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eg/100g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e21.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e20.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e21.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eOther tissues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ekg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eg/100g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eRatios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMuscle:Fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e11.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMuscle:Bone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e WM: non-forage diets with whole millet grain; WC: non-forage diets with whole corn grain;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e SEM: Standard Error of Mean;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Values with different superscripts are different for \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":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":"average daily gain, carcass, Pennisetum glaucum, rumination time ","lastPublishedDoi":"10.21203/rs.3.rs-7121297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7121297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTwenty-one Anglo-Nubian goat kids (21.6 ± 2.9 kg of initial body weight) were distributed in a completely randomized design to evaluate the effects of the replacement of whole corn grain for whole millet grain in non-forage diets on the performance, ingestive behavior, physiological parameters, and carcass characteristics of goat kids finished in feedlot system. The experimental diets consisted of a control diet (CON), containing 100 g/kg of hay and 900 g/kg of concentrate, and two non-forage diets: whole corn grain-based diet (WC) or whole millet grain diet (WM), both composed of 200 g/kg of commercial pellet and 800 g/kg of respective whole grain. The WC and WM reduced (P \u0026lt; 0.05) dry matter (DM) intake; however, only WC reduced crude protein intake (\u003cem\u003eP \u003c/em\u003e= 0.001). As expected, CON showed the highest fiber intake and time spent in rumination (\u003cem\u003eP\u003c/em\u003e = 0.026), while WM showed the highest fat (EE) intake (P \u0026lt; 0.01). WC increased the digestibility of DM (\u003cem\u003eP\u003c/em\u003e = 0.042) and EE (\u003cem\u003eP\u003c/em\u003e = 0.025). As expected, WC and WM showed higher rumination efficiency of DM and fiber (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). Diets did not affect physiological parameters. WC reduced (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05) average daily gain, final weight, and hot carcass weight, however, the kidney was reduced by the CON diet (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). However, diets did not influence carcass yield, qualitative parameters, and leg tissue composition (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Millet can replace corn in non-forage diets, providing good performance and adequate carcass characteristics and leg tissue composition.\u003c/p\u003e","manuscriptTitle":"Does a whole millet grain-based diet replace whole corn grain in non-forage diets for goat kids?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 09:55:09","doi":"10.21203/rs.3.rs-7121297/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-07-23T14:17:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T08:00:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-16T11:53:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2025-07-15T15:21:56+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":"46724217-b494-4dca-a6ed-7607153c3ffe","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T13:36:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 09:55:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7121297","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7121297","identity":"rs-7121297","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}