Predicting Chemical Body Composition Using Body Part Composition in Boer × Saanen Goats
by
, , ,
Izabelle A. M. A. Teixeira
1,*,
Adrian F. M. Ferreira
2,
José M. Pereira Filho
3
,
Luis O. Tedeschi
4 and
Kleber T. Resende
2 1
Department of Animal, Veterinary, and Food Sciences, University of Idaho, Twin Falls, ID 83301, USA
2
Department of Animal Science, School of Agricultural and Veterinarian Sciences, Universidade Estadual Paulista (UNESP), Jaboticabal 14884-900, SP, Brazil
3
Animal Science Department, Universidade Federal de Campina Grande, Patos 58700-970, PB, Brazil
4
Department of Animal Science, Texas A&M University, College Station, TX 77843, USA
*
Author to whom correspondence should be addressed.
Ruminants 2024, 4(4), 543-555; https://doi.org/10.3390/ruminants4040038
Submission received: 22 August 2024
/
Revised: 8 November 2024
/
Accepted: 13 November 2024
/
Published: 19 November 2024
Simple Summary
In this study, we wanted to find the best way to predict the overall chemical composition of Boer × Saanen male kids by evaluating different parts of their bodies. We conducted two experiments where goats were fed different intake levels and slaughtered at various weights. We used various body parts such as the neck, ribs, leg, shoulder, loin, hide, head + feet, and organs to see which ones best estimate the whole body composition regarding nutrients such as fat, protein, and minerals. We found that the neck, loin, and 9–11th ribs could precisely predict the body composition. However, using the loin or 9–11th ribs to do this can lower the price one can obtain from selling the meat because they are valuable parts of the carcass. Our study suggests that the neck can be used as effectively as the 9–11th ribs to estimate the chemical body composition. This finding is useful for farmers, nutritionists, and meat processors as it helps them choose a cost-effective method for evaluating body composition without sacrificing valuable parts of the goat carcass.
Abstract
Two experiments were conducted to determine which part of the empty body of Boer × Saanen intact male kids can be used to predict the chemical composition of the whole body. In the first experiment, kids were fed ad libitum and slaughtered at 5, 10, and 15 kg body weight (BW). Eighteen animals were group-fed at three intake levels (ad libitum or restricted to 30% and 60% of the ad libitum level). When the ad libitum animal in the group reached 15 kg BW, all animals in the group were slaughtered. In the second experiment, kids were fed ad libitum and slaughtered at 15, 20, and 25 kg BW. Twenty-one animals were group-fed at three intake levels and slaughtered when the ad libitum animal within the group reached 25 kg BW. Analyzed body parts included head + feet, hide, organs, neck, shoulder, ribs, loin, leg, 9–11th ribs, and half carcass. Principal component and cluster analyses showed that the neck, 9–11th ribs, and loin had the highest frequency of grouping with the empty body. These body parts were used to develop prediction models for estimating body composition. The neck, loin, and 9–11th ribs accurately and precisely predicted the dry matter, ash, fat, protein, and energy body composition of goats, with most models also incorporating BW as a predictor variable. The equations showed root mean squared error (RMSE) lower than 13.5% and a concordance correlation coefficient (CCC) greater than 0.84. Fat and protein concentrations in the loin and neck were also reliable predictors of empty body energy composition (RMSE = 2.9% of mean and concordance correlation coefficient = 0.93). Removing the loin and 9–11th ribs could reduce the carcass retail price. Using the neck to estimate body composition in growing Boer × Saanen goats provides a valuable alternative for nutrition studies, given its low commercial value.
1. Introduction
Understanding body composition is important in nutrition studies for several reasons. It ensures that muscle and fat deposition meet consumer market demands [1,2]. It also helps evaluate the impacts of diet modification or management practices on animal growth and efficiency. Additionally, the knowledge of body composition is essential for estimating the nutritional requirements of animals [3,4]. Body composition information allows the assessment of growth changes based on age, breed, body weight (BW), sex, and diet composition [5,6]. It also helps in evaluating the effects of metabolism-modifying compounds [7].
The most accurate and precise method to determine chemical body composition is the direct method, which involves chemically analyzing the whole body after removing the contents of the gastrointestinal tract and bladder [8]. However, this method is expensive, time-consuming, and labor-intensive. Due to these challenges, various indirect methods have been proposed for predicting carcass or body composition, such as deuterium oxide dilution [8,9], rib section composition [1,10,11], composition of body parts [12,13], ultrasound [8,14,15], bioelectrical impedance spectroscopy [8,16,17], computer tomography [8,15], dual-energy X-ray absorptiometry (DXA) [11,15], and others. While each of these methods have shown satisfactory results under specific conditions, they also present limitations, such as high operational cost, low accuracy, the need for specific conditions to be implemented, and applicability in practical situations.
Among indirect methods, the use of rib section composition, specifically the 9–11th rib section, has been widely accepted for predicting carcass composition in bovines due to its ease of obtention at slaughterhouses and reliable results [1,10]. Although primarily recommended for estimating carcass composition, this section has also been used to estimate overall body composition [18]. Numerous studies have applied or modified the 9–11th rib section [10] prediction equation for cattle of various breeds and nutritional regimes [1,19].
However, there is limited information on estimating body composition from the chemical composition of body parts in small ruminants. For instance, loin and 9–11th ribs were suggested as potential body parts for predicting body composition in goat kids [20]. Similarly, organs + blood and 9–11th ribs can accurately estimate the body composition of meat goat kids [12]. Another study found a strong correlation between the composition of carcass non-components and body composition in lambs [21]. On the other hand, it was found that the 9–11th ribs were not a good indicator of mineral body composition in lambs [22].
These different results indicate a significant gap in the literature regarding which body part can be used for estimating body composition in small ruminants. Therefore, the objective of this study was to determine which part of the body best predicts the chemical composition of the empty body in Boer × Saanen kids.
2. Materials and Methods
Two experiments were conducted at the School of Agriculture and Veterinary Sciences (FCAV/UNESP) in Jaboticabal, São Paulo, Brazil, located at 21°15′22″ S and 48°18′58″ W, with an altitude of 595 m. The Boer breed and its crossbreds are recognized for enhancing meat production. In Brazil, especially in the southeast region where goat milk production is a priority, the Boer × Saanen crossbred is common, making it a suitable choice for this study. In the first experiment, the body weight (BW) of the Boer × Saanen goat kids ranged from 5 to 15 kg, while in the second experiment, the BW ranged from 15 to 25 kg.
Humane animal care and handling procedures were followed according to the university’s animal care committee guidelines. The slaughter procedures were completed following instructions from the Ministry of Agriculture in Brazil (instruction number 03/2000).
2.1. Animals and Housing
In experiment 1, a total of 30 dehorned, intact male Boer × Saanen kids with an initial average BW of 5 kg were used. Six kids were slaughtered at the beginning of the experiment. Six more kids were fed ad libitum and slaughtered at 10 kg BW. The remaining kids were randomly allocated to one of three nutritional levels (0%, 30%, and 60% feed restriction), with 6 kids per nutritional level, and were group-fed. The feed intake of the animal fed 0% feed restriction determined the amount of ration offered to the 30% and 60% feed restriction animals within the same group. When the animal on the 0% feed restriction reached 15 kg BW, all animals (i.e., three in total) in that group were slaughtered.
In experiment 2, a total of 34 dehorned intact male Boer × Saanen kids with an initial average BW of 15 kg were used. Seven kids were slaughtered at the beginning of the experiment. Six kids were fed ad libitum and slaughtered at 20 kg BW. The remaining kids were group-fed and, within their groups, were randomly allocated to one of three nutritional levels (0%, 30%, and 60% feed restriction) with 7 kids per nutritional level. As in experiment 1, the feed intake of the animal on 0% feed restriction determined the ration offered to the 30% and 60% feed restriction animals within the same group. When the animal on the 0% feed restriction reached 25 kg BW, it was slaughtered along with the other animals in their group.
In both experiments, kids were housed individually in indoor pens measuring 0.5 × 1.0 m.
2.2. Feed
In both experiments, the pre-experimental period was defined as the period from birth until the animals reached the predetermined initial BW (5 kg for experiment 1 and 15 kg for experiment 2). During this period, all feed was offered ad libitum. For the first two days after birth, the kids received cow’s colostrum, followed by 1.50 L/day of cow’s milk. A solid diet, similar to that offered during the experiments (see Table 1), was introduced at 7 days of age and continued until the start of the experiments.
In experiment 1, the maximum amount of cow’s milk offered to kids was 1.50, 1.05, and 0.60 L/day for the 0%, 30%, and 60% feed restriction levels, respectively. All animals were weaned at 50 days of age.
In both experiments, rations were offered daily at approximately 07:00 a.m. and weighed on an electronic balance with a precision of 0.1 g. Before feeding, feed leftovers from the previous day were measured to determine actual daily intake. The amount of ration offered to the 30% and 60% feed restriction levels was calculated based on the intake of the animals on the 0% feed restriction from the previous day. Animals in the 0% feed restriction level received enough feed to ensure that, on average, about 20% of the ration was left over each day. This was achieved by offering approximately 20% more ration than each animal had consumed the previous day. Drinking water was available ad libitum for all animals.
Animals in both experiments received a similar ration consisting of 46.9% dehydrated whole corn plant and 53.1% concentrate (25.9% corn grain, 19.3% soybean meal, 4.3% sugar cane molasses, 2.0% mineral premix, 0.8% soy oil, and 0.8% limestone). The chemical composition of the ration for each experiment is presented in Table 1.
2.3. Slaughter and Body Composition
The procedures outlined in this subsection were similar for both experiments. Animals were weighed weekly before feeding, using an electronic balance with a precision of 0.1 kg. Prior to slaughter, all kids were fasted from feed for 24 h and water for 16 h. Immediately before slaughter, the kids were weighed, and the shrunk body weight (SBW) was recorded. Kids were electrically stunned for two periods of 15 s before being slaughtered by exsanguination using conventional humane procedures. Blood was collected and weighed. The gastrointestinal tract was removed and weighed before and after its contents were removed to determine the content of the gastrointestinal tract. Empty body weight (EBW) was obtained as the difference between SBW and the contents of the gastrointestinal tract and bladder.
After skinning and evisceration, the carcass was obtained by separating the feet at the carpal-metacarpal and tarsal-metatarsal joints and the head at the atlanto-occipital joint. The kidneys, heart, liver, lungs, empty gastrointestinal tract, abdominal fat, and diaphragm were also removed from the carcass. The carcasses were kept in a cold room for 24 h at 5 °C. After this period, each carcass was sectioned longitudinally with an electric saw into two halves. The left half of the carcass was sectioned into five primal cuts [25]: the leg included the ilium, ischium, tibia, femur, pubis, and sacral vertebrae, and the first two coccygeal vertebrae; the loin included all lumbar vertebrae; the ribs included thoracic vertebrae and the upper portion of the ribs; the shoulder included the scapula, humerus, ulna, radius, and carpus; and the neck included the seven cervical vertebrae (Figure 1).
The 9–11th ribs were obtained from the right half of the carcass, consisting of the ninth to the eleventh thoracic vertebrae and the upper half of the corresponding ribs.
Ten parts of the body were separated: head + feet, hide, organs (all viscera, blood, and abdominal fat), neck, shoulder, ribs, loin, leg, 9–11th ribs, and right half carcass. These body parts were weighed on an electronic balance to the nearest 0.1 kg and then frozen. Subsequently, these samples were ground and homogenized, and a representative subsample (100 g) was freeze-dried for 84 h, as this was found to be the minimum time required for drying the samples. The freeze-dried samples were used to determine dry matter (DM), fat, ash, crude protein (CP), and gross energy (GE) based on a previous study [26].
After determining the chemical composition of each body part, both individual body parts and the entire empty body were considered as treatments to evaluate which body part can be used to predict the chemical composition of the whole empty body.
2.4. Statistical Analyses
Initially, the chemical composition data obtained for the empty body and body parts, in each slaughter weight (5, 10, 15, and 20 kg) and nutritional levels (0%, 30%, and 60% feed restriction), were subjected to principal component analysis and cluster analysis. These multivariate analyses were performed using the R Core Team (version 2023.12.1+402) [27]. The following packages were used for the principal component analysis: readxl, FactoMineR, factoextra, tidyverse, and grid. This analysis provided the percentage of data variability explained by each principal component, allowing for the assessment of data variability based on the identified components. The graph generated in this step helped to identify variables influencing and explaining animal growth and body composition. The tidyverse, cluster, factoextra, NbClust, and gridExtra packages were used for cluster analysis, which allowed for a hierarchical cluster analysis using the Euclidean distance measure. This analysis identified the body parts that clustered with the empty body.
Considering the data from both experiments, we developed empirical models using backward stepwise selection, focusing on body parts that showed a higher frequency of grouping with the empty body. Predictor variables included chemical composition (dry matter, ash, fat, protein, and energy) of the selected body parts (9–11th ribs, loin, and neck), expressed as a percentage on a dry matter basis. Intake level (0%, 30%, and 60% feed restriction) and BW (kg) were also included as predictors. The backward stepwise involved removing nonsignificant predictor variables until all remaining variables in the model had a p-value < 0.05. We also considered models with the lowest AICc and ensured that independent variables had variance inflation factors (VIF) < 10. All predictor variables in the model had a VIF of less than 8, indicating minimal multicollinearity problems. The analysis was conducted using the lme4 and lmerTest packages in R [28,29,30]. In addition to AICc, models were evaluated using root mean squared error (RMSE), expressed as a percentage of the mean [31], and Lin’s concordance correlation coefficient (CCC) [32].
3. Results and Discussion
The summary of descriptive statistics of the chemical composition of the empty body and body parts is presented in Table 2.
The body composition and body parts data for each slaughter weight and nutritional level were analyzed separately using cluster analyses (Figure 2). These analyses revealed that across various scenarios, the neck, 9–11th ribs, loin, and half carcass consistently grouped with the empty body (Figure 2). This indicates that these body parts have similar chemical composition to the empty body. These findings are supported by the mean values of dry matter, fat, protein, energy, and ash in the empty body and body parts across all body weights and nutritional levels (experiments 1 and 2, Table 2).
Conversely, various body parts rarely grouped with the empty body, including the hide, organs, head + feet, shoulder, and leg. The lack of grouping of the hide with the empty body was expected because it is mainly composed of structural proteins, such as collagen, elastin, and keratin [33]. Despite this, the protein percentage in the hide remained constant at approximately 95.1%, regardless of body weight. This pattern differs from the empty body, where the protein percentage decreases with increasing body weight [34]. As a result, the chemical composition of the hide may lead to an overestimation of body protein composition and an underestimation of the composition of other nutrients.
Similarly, the head + feet did not group with the empty body, likely because it overestimated the ash body composition. While the ash percentage of the empty body decreases with increasing body weight [34], it remains relatively constant in the head + feet. Additionally, head + feet underestimated the protein body composition. The lack of grouping for the leg with the empty body was unexpected, as the physical composition of this body part has been previously used to predict carcass physical composition [25,35] and chemical body composition [13].
The body parts with the highest grouping frequency with the empty body (neck, loin, half carcass, and 9–11th ribs) were further analyzed using cluster analyses and principal component analysis (Figure 3), considering all slaughter weights and nutritional levels. This additional multivariate analysis aimed to identify which of the pre-selected body parts most closely align with the chemical composition of the empty body. Our findings showed that the neck had the highest grouping frequency with the empty body (100%), indicating that its chemical composition closely resembles that of the empty body. This is consistent with previous studies that found similar dry matter, protein, and fat percentages in both the neck and the empty body [20]. On the other hand, the half carcass showed a lower grouping frequency (67%) compared to the other pre-selected body parts (88% for loin and 9–11th ribs), suggesting it is less representative of the chemical composition of the empty body. This lower grouping frequency of half carcass may be related to the fat distribution across different body regions in goats, as the largest fat depot is found in internal fat [14]. Given these results and the high retail value of the half carcass, we will focus on developing prediction models of body composition using the neck, loin, and 9–11th ribs.
Regarding the principal component analysis, the first two principal components have eigenvalues of 4.1 (81.5%) and 0.5 (10.2%), respectively. Together the first two principal components capture 91.7% of the variance, allowing us to confidently reduce the dimensionality of our dataset while retaining most of the important information. Consequently, for plotting purposes, only the first two principal components were used (Figure 3).
The first component primarily reflects variations in growth, accounting for a substantial portion of the variance (Figure 3). Data from experiment 1 were mainly positioned in the left quadrants relative to the principal component, indicating a negative correlation with protein and ash (Table 3). Animals with a slaughter weight of 15 kg were positioned in the intermediate strip between the two groups. In contrast, data from experiment 2 were predominantly positioned in the right quadrants, showing a positive correlation to dry matter, fat, and energy (Table 3). This pattern aligns with the established understanding of animal tissue development, where bone is an early-maturing tissue and adipose tissue matures later [34,36]. Therefore, the higher fat influence in older animals is expected, as fat concentration increases with increasing body weight. Similarly, the higher influence of protein and minerals (ash) in younger animals is expected, as their proportion decreases with increasing body weight [7,37,38].
The coefficients of principal component 1 were highly correlated with all tested variables (Table 3), particularly with fat and energy, which showed the highest correlation coefficients.
The dominance of the first principal component indicates that the primary variation in our data is driven by animal growth and tissue development. This insight is important for understanding the underlying patterns of predicting body composition based on the composition of body parts. Considering the influence of the first principal component on data structure and its reflection of growth, we will also explore the effect of body weight in the prediction models for body composition based on the composition of body parts.
Regression models were employed to determine the relationship between empty body composition and the chemical composition of pre-selected body parts in Boer × Saanen goats (Table 4). Across the 17 models presented (Table 4 and Table 5), nutritional level was not a significant predictor for any body composition component (i.e., dry matter, ash, fat, protein, and energy). On the other hand, body weight was a significant predictor in all models except for estimating body fat composition based on the fat composition of the 9–11th ribs (model 7). Our findings indicated that using the 9–11th ribs, loin, or neck presents viable alternatives for predicting body composition across various body weights and nutritional levels in growing goats.
Overall, irrespective of the body part used, all models demonstrated desirable goodness of fit (i.e., high R2 adjusted, low RSME and high CCC, Table 4). This suggests that the composition of any of the pre-selected body parts (9–11th ribs, loin, and neck) can precisely estimate body dry matter, ash, fat, protein, and energy composition (Table 4).
If the nutrient content in one body part can be used to estimate the energy concentrations in the empty body, there are potential savings in labor and laboratory costs. This is advantageous, especially for laboratories without bomb colorimeters for determining energy content. Therefore, our models for estimating body energy composition accounted for the fat and protein composition in the body parts. Our findings show that body energy can be predicted by using protein composition (i.e., loin; Model 14) and/or fat composition (i.e., loin and neck; models 14 and 16, respectively) (Table 5). Conversely, using 9–11th ribs for predicting body energy composition requires energy composition in the 9–11th ribs as a predictor (Model 13, Table 5).
In bovines, the 9–11th rib section is widely used to predict carcass composition [10,18] and body composition [1,11,39]. Similarly, previous studies in goats [12] and sheep [13,40] have found that the 9–11th ribs can be used to estimate body composition, corroborating our findings. However, the lack of standardization for this cut in small ruminants presents a challenge and may contribute to some contradictory results in the literature. For example, it was reported that the 9–11th ribs were not a good indicator of mineral body composition in lambs [22]. Unlike in bovines, where this section is well defined by measuring 61.5% of the distance between the first and last points of the rib bone [10], using a 9–11th rib section in small ruminants is less effective. The smaller size of the rib section in lighter animals can limit the sample available for chemical analysis.
There is a lack of studies indicating that the composition of the loin can be used to estimate body composition, especially in small ruminants. Although, in the present study, the 9–11th ribs and loin were as precise as the neck, the cluster analysis revealed that the loin and 9–11th ribs had a lower grouping frequency with the empty body compared to the neck. Additionally, obtaining these body parts may result in carcass depreciation because they are among the most valuable parts of the carcass.
From an economic perspective, it is preferable to use body parts that are not highly commercially valuable to predict body composition. Therefore, the neck, which is a low-value commercial cut, presents a good alternative. There is limited information in the literature about using the neck to predict body composition in ruminants, making this finding particularly noteworthy.
Boer crossbreeds are widely recognized for enhancing meat production [41,42,43]. In Brazil, the Boer × Saanen crossbred is commonly used in regions where milk production is a priority. Although our models were developed using data from Boer × Saanen goat kids with varying body weights (i.e., from 5 to 25 kg) and nutritional levels (i.e., from maintenance level to ad libitum), their accuracy and precision may vary across different situations due to differences in body composition among breeds, sex, and physiological categories. Therefore, further studies are needed to evaluate this technique under conditions different from those used in this study.
4. Conclusions
Our study demonstrates that the loin, 9–11th ribs, and neck can be effectively used as indirect methods for estimating body composition in Boer × Saanen goats. Fat and protein concentrations in the loin and neck also serve as reliable predictors of empty body energy composition.
Utilizing the neck for estimating empty body composition provides a valuable alternative in nutrition studies, given its low commercial value. Further studies are needed to assess the applicability of this technique across different contexts, such as physiological categories, sex, and breeds.
Author Contributions
Conceptualization, I.A.M.A.T. and K.T.R.; methodology, I.A.M.A.T., J.M.P.F., and K.T.R.; validation, I.A.M.A.T. and K.T.R.; formal analysis, I.A.M.A.T., A.F.M.F., J.M.P.F., and K.T.R.; investigation, I.A.M.A.T., J.M.P.F., and K.T.R.; data curation, I.A.M.A.T. and K.T.R.; writing—original draft preparation, I.A.M.A.T.; writing—review and editing, I.A.M.A.T., A.F.M.F., L.O.T., and K.T.R.; visualization, I.A.M.A.T. and A.F.M.F.; supervision, I.A.M.A.T. and K.T.R.; project administration, I.A.M.A.T. and K.T.R.; funding acquisition, I.A.M.A.T. and K.T.R. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by São Paulo Research Foundation—FAPESP, grant number 2000/00778-1.
Institutional Review Board Statement
The animal study protocol was approved by the Animal Ethics and Welfare Commission of São Paulo State University, Jaboticabal, SP, Brazil, under protocol # 03/2000 in 2000.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available upon request to the corresponding author.
Acknowledgments
The authors appreciate the labor of the undergraduate and graduate students and the laboratory staff, specifically the help of R.C. Canesin and A.P. Sader.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Paulino, P.V.R.; Valadares Filho, S.C.; Costa, M.A.L.; Paulino, M.F.; Magalhães, K.A.; Detmann, E.; Valadares, R.F.D.; Porto, M.O.; Andreatta, K. Validation of the 9–11th rib cut to estimate the chemical composition of the dressed carcass and of the whole empty body of Zebu cattle. Livest. Prod. Sci. 2005, 93, 245–253. [Google Scholar] [CrossRef]
- Schumacher, M.; DelCurto-Wyffels, H.; Thomson, J.; Boles, J. Fat Deposition and Fat Effects on Meat Quality—A Review. Animals 2022, 12, 1550. [Google Scholar] [CrossRef] [PubMed]
- Agricultural Research Council. The Nutrient Requirements of Ruminant Livestock; Commonwealth Agricultural Bureaux: Slough, UK, 1980; p. 351. [Google Scholar]
- Teixeira, I.A.M.A.; Almeida, A.K.; Fernandes, M.H.M.R.; Resende, K.T. Applying the California net energy system to growing goats. Transl. Anim. Sci. 2019, 3, 999–1010. [Google Scholar] [CrossRef]
- Véras, A.S.C.; Valadares Filho, S.C.; Silva, J.F.C.; Paulino, M.F.; Cecon, P.R.; Valadares, R.F.D.; Ferreira, M.A.; Silva, C.M.; Silva, B.C. Predição da composição química corporal de bovinos Nelore e F1 Simental × Nelore a partir da composição química da seção Hankins e Howe (Seção HH). Rev. Bras. Zootec. 2001, 30, 1112–1119. [Google Scholar] [CrossRef]
- Lewis, R.M.; Emmans, G.C. Feed intake of sheep as affected by body weight, breed, sex, and feed composition. J. Anim. Sci. 2013, 88, 467–480. [Google Scholar] [CrossRef] [PubMed]
- Greenhalgh, J.F. Recent studies on the body composition of ruminants. Proc. Nutr. Soc. 1986, 45, 119–130. [Google Scholar] [CrossRef]
- Lerch, S.; Torre, A.D.; Huau, C.; Monziols, M.; Xavier, C.; Louis, L.; Le Cozler, Y.; Faverdin, P.; Lamberton, P.; Chery, I.; et al. Estimation of dairy goat body composition: A direct calibration of eight methods. Methods 2021, 186, 68–78. [Google Scholar] [CrossRef]
- Martin, R.A.; Ehle, F.R. Body composition of lactating and dry Holstein cows estimated by deuterium dilution. J. Dairy Sci. 1986, 69, 88–98. [Google Scholar] [CrossRef]
- Hankins, O.G.; Howe, P.E. Estimation of the Composition of Beef Carcasses and Cuts; Technical Bulletin No. 926; US Department of Agriculture: Washington, DC, USA, 1946; pp. 1–19.
- Xavier, C.; Driesen, C.; Siegenthaler, R.; Dohme-Meier, F.; Cozler, Y.L.; Lerch, S. Estimation of empty body and carcass chemical composition of lactating and growing cattle: Comparison of imaging, adipose cellularity, and rib dissection methods. Transl. Anim. Sci. 2022, 6, txac066. [Google Scholar] [CrossRef]
- Fernandes, M.H.M.R.; Resende, K.T.; Tedeschi, L.O.; Fernandes, J.S.; Teixeira, I.A.M.A.; Carstens, G.E.; Berchielli, T.T. Predicting the chemical composition of the body and the carcass of ¾ Boer × ¼ Saanen kids using body components. Small Rumin. Res. 2008, 75, 90–98. [Google Scholar] [CrossRef]
- Barcelos, S.S.; Vargas, J.A.C.; Mezzomo, R.; Gionbelli, M.P.; Gomes, D.I.; Oliveira, L.R.S.; Luz, J.B.; Maciel, D.L.; Alves, K.S. Predicting the chemical composition of the body and the carcass of hair sheep using body parts and carcass measurements. Animal 2021, 15, 100139. [Google Scholar] [CrossRef] [PubMed]
- Härter, C.J.; Silva, H.G.O.; Lima, L.D.; Castagnino, D.S.; Rivera, A.R.; Boaventura Neto, O.; Gomes, R.A.; Canola, J.C.; Resende, K.T.; Teixeira, I.A.M.A. Ultrasonographic measurements of kidney fat thickness and Longissimus muscle area in predicting body composition of pregnant goats. Anim. Prod. Sci. 2014, 54, 1481–1485. [Google Scholar] [CrossRef]
- Scholz, A.M.; Bünger, L.; Kongsro, J.; Baulain, U.; Mitchell, A.D. Non-invasive methods for the determination of body and carcass composition in livestock: Dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: Invited review. Animal 2015, 9, 1250–1264. [Google Scholar] [CrossRef]
- Berg, E.P.; Marchello, M.J. Bioelectrical impedance analysis for the prediction of fat-free mass in lambs and lamb carcasses. J. Anim. Sci. 1994, 72, 322–329. [Google Scholar] [CrossRef]
- Moro, A.B.; Pires, C.C.; Silva, L.P.; Dias, A.M.O.; Simões, R.R.; Pilecco, V.M.; Mello, R.O.; Aguiar, L.K. Prediction of lamb body composition using in vivo bioimpedance analysis. Meat Sci. 2019, 150, 1–6. [Google Scholar] [CrossRef] [PubMed]
- Alleoni, G.F.; Leme, P.R.; Boin, C.; Nardon, R.F.; Demarchi, J.J.A.A.; Vieira, P.F.; Tedeschi, L.O. Avaliação da composição química e física dos cortes da costela para estimar a composição química corporal de novilhos Nelore. Rev. Bras. Zootec. 1997, 26, 382–390. [Google Scholar]
- Fonseca, M.A.; Tedeschi, L.O.; Valadares Filho, S.C.; Paula, N.F.; Silva, L.D.; Sathler, D.F.T. Evaluation of equations to estimate body composition in beef cattle using live, linear and standing-rib cut measurements. Anim. Prod. Sci. 2017, 57, 378–390. [Google Scholar] [CrossRef]
- Medeiros, A.N. Body Composition and Net Energy and Protein Requirements for Maintenance and Weight Gain of Goats Saanen in the Initial Growth. Ph.D. Dissertation, Universidade Estadual Paulista/UNESP, Jaboticabal, Brazil, 2001. [Google Scholar]
- Wilkinson, R.G.; Greenhalgh, J.F.D. Prediction of the body composition of lambs from the composition of their non-carcass components. Anim. Sci. 1995, 61, 265–268. [Google Scholar] [CrossRef]
- Trindade, I.A.C.M. Composição corporal e exigências nutricionais em macrominerais de ovinos lanados e deslanados, em crescimento. Master’s Thesis, Universidade Estadual Paulista/UNESP, Jaboticabal, Brazil, 2000. [Google Scholar]
- Blaxter, K.L. The Energy Metabolism of Ruminants, 1st ed.; Hutchinson: London, UK, 1962; p. 329. [Google Scholar]
- Teixeira, I.A.M.A.; Fernandes, M.H.M.R.; Pereira Filho, J.M.; Canesin, R.C.; Gomes, R.A.; Resende, K.T. Body composition, protein and energy efficiencies, and requirements for growth of F1 Boer × Saanen goat kids. J. Anim. Sci. 2017, 95, 2121–2132. [Google Scholar] [CrossRef]
- Pereira Filho, J.M.; Resende, K.T.; Teixeira, I.A.M.A.; Silva Sobrinho, A.G.; Yáñez, E.A.; Ferreira, A.C.D. Características da carcaça e alometria dos tecidos de cabritos F1 Boer × Saanen. Rev. Bras. Zootec. 2008, 37, 905–912. [Google Scholar] [CrossRef]
- AOAC. Official Methods and Analysis, 15th ed.; Association of Official Analytical Chemists: Washington, DC, USA, 1990. [Google Scholar]
- Charrad, M.; Ghazzali, N.; Boiteau, V.; Niknafs, A. NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. J. Stat. Softw. 2014, 61, 1–36. [Google Scholar] [CrossRef]
- Pinheiro, J.; Bates, D.; DebRoy, S.; Sarkar, D.; R Core Team. nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1-145. 2020. Available online: https://CRAN.R-project.org/package=nlme (accessed on 18 October 2021).
- Bartoń, K. MuMIn: Multi-Model Inference. 2023. Available online: https://CRAN.R-project.org/package=MuMIn (accessed on 18 October 2021).
- Fox, J.; Weisberg, S.; Price, B.; Adler, D.; Bates, D.; Baud-Bovy, G.; Bolker, B.; Ellison, S.; Firth, D.; Friendly, M.; et al. Car: Companion to Applied Regression. 2019. Available online: https://CRAN.R-project.org/package=car (accessed on 18 October 2021).
- Bibby, J.; Toutenburg, H. Prediction and Improved Estimation in Linear Models, 1st ed.; Wiley: Chichester, UK, 1977; p. 188. [Google Scholar]
- Lin, L.I. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989, 45, 255–268. [Google Scholar] [CrossRef] [PubMed]
- Henrickson, R.L.; Ranganayaki, M.D.; Asghar, A.; Bailey, D.G. Age, species, breed, sex, and nutrition effect on hide collagen. CRC Crit. Rev. Food Sci. Nutr. 1984, 20, 159–172. [Google Scholar] [CrossRef]
- Almeida, A.K.; Resende, K.T.; Tedeschi, L.O.; Fernandes, M.H.M.R.; Regadas Filho, J.G.L.; Teixeira, I.A.M.A. Using body composition to determine weight at maturity of male and female Saanen goats. J. Anim. Sci. 2016, 94, 2564–2571. [Google Scholar] [CrossRef]
- Yáñez, E.A.; Resende, K.T.; Ferreira, A.C.D.; Pereira Filho, J.M.; Silva Sobrinho, A.G.; Teixeira, I.A.M.A.; Medeiros, A.N. Restrição alimentar em caprinos: Rendimento, cortes comerciais e composição de carcaça. Rev. Bras. Zootec. 2006, 35, 2093–2100. [Google Scholar] [CrossRef]
- Owens, F.N.; Dubeski, P.; Hanson, C.F. Factors that alter the growth and development of ruminants. J. Anim. Sci. 1993, 71, 3138–3150. [Google Scholar] [CrossRef] [PubMed]
- Ferreira, A.C.; Yáñez, E.A.; Medeiros, A.N.; Resende, K.T.; Pereira Filho, J.M.; Fernandes, M.H.M.R.; Almeida, A.K.; Teixeira, I.A.M.A. Protein and energy requirements of castrated male Saanen goats. Small Rumin. Res. 2015, 123, 88–94. [Google Scholar] [CrossRef]
- Resende, K.T.; Fernandes, M.H.M.R.; Hentz, F.; Teixeira, I.A.M.A.; Garcia, J.A. Methods of body composition estimative of growing goats. Acta Sci.—Anim. Sci. 2017, 39, 189–194. [Google Scholar] [CrossRef]
- Marcondes, M.I.; Tedeschi, L.O.; Valadares Filho, S.C.; Chizzotti, M.L. Prediction of physical and chemical body compositions of purebred and crossbred Nellore cattle using the composition of a rib section. J. Anim. Sci. 2012, 90, 1280–1290. [Google Scholar] [CrossRef]
- Sousa, A.R.; Campos, A.C.N.; Silva, L.P.; Bezerra, L.R.; Furtado, R.N.; Oliveira, R.L.; Pereira, E.S. Prediction of the chemical body composition of hair lambs using the composition of a rib section. Small Rumin. Res. 2020, 191, 106189. [Google Scholar] [CrossRef]
- Medeiros, E.J.L.; Mendonça, F.H.O.; Queiroga, R.C.R.E.; Madruga, M.S. Meat quality characteristics of exotic and SPRD crossbred goats from the semiarid region. Food Sci. Technol. 2012, 32, 768–774. [Google Scholar] [CrossRef]
- Shrestha, J.N.B.; Fahmy, M.H. Breeding goats for meat production: 2. Crossbreeding and formation of composite population. Small Rumin. Res. 2007, 67, 93–112. [Google Scholar] [CrossRef]
- Gawat, M.; Boland, M.; Singh, J.; Kaur, L. Goat Meat: Production and Quality Attributes. Foods 2023, 12, 3130. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Primal cuts obtained from the left carcasses of goats: 1—leg, 2—loin, 3—ribs, 4—shoulder, and 5—neck.
Figure 1.
Primal cuts obtained from the left carcasses of goats: 1—leg, 2—loin, 3—ribs, 4—shoulder, and 5—neck.
Figure 2.
Cluster dendrogram of the body parts and empty body of Boer × Saanen goat kids at different slaughter weights and nutritional levels ((A)—goat kids fed ad libitum and slaughtered at 25 kg BW, (B)—goat kids subjected to 30% of feed restriction, experiment 2—15–25 kg BW, (C)—goat kids subjected to 60% of feed restriction, experiment 2—15–25 kg BW, (D)—goat kids fed ad libitum and slaughtered at 15 kg BW, (E)—goat kids subjected to 30% of feed restriction, experiment 1—5–15 kg BW, (F)—goat kids subjected to 60% of feed restriction, experiment 1—5–15 kg BW, (G)—goat kids fed ad libitum and slaughtered at 20 kg BW, (H)—goat kids fed ad libitum and slaughtered at 10 kg BW, and (I)—goat kids fed ad libitum and slaughtered at 5 kg BW).
Figure 2.
Cluster dendrogram of the body parts and empty body of Boer × Saanen goat kids at different slaughter weights and nutritional levels ((A)—goat kids fed ad libitum and slaughtered at 25 kg BW, (B)—goat kids subjected to 30% of feed restriction, experiment 2—15–25 kg BW, (C)—goat kids subjected to 60% of feed restriction, experiment 2—15–25 kg BW, (D)—goat kids fed ad libitum and slaughtered at 15 kg BW, (E)—goat kids subjected to 30% of feed restriction, experiment 1—5–15 kg BW, (F)—goat kids subjected to 60% of feed restriction, experiment 1—5–15 kg BW, (G)—goat kids fed ad libitum and slaughtered at 20 kg BW, (H)—goat kids fed ad libitum and slaughtered at 10 kg BW, and (I)—goat kids fed ad libitum and slaughtered at 5 kg BW).
Figure 3.
Principal component analysis loading plot of chemical body composition and chemical composition of pre-selected body parts (9−11th ribs, loin, and neck) of Boer × Saanen goats at different slaughter weights and nutritional levels. The percentage of total variance accounted for by each of the first 2 principal components (Dim) is shown in parentheses. Experiment 1 is represented by pink circles, and experiment 2 is represented by blue triangles. This biplot shows the orientation of the test samples relative to the principal components and the orientation of the nutrients and energy relative to the principal components.
Figure 3.
Principal component analysis loading plot of chemical body composition and chemical composition of pre-selected body parts (9−11th ribs, loin, and neck) of Boer × Saanen goats at different slaughter weights and nutritional levels. The percentage of total variance accounted for by each of the first 2 principal components (Dim) is shown in parentheses. Experiment 1 is represented by pink circles, and experiment 2 is represented by blue triangles. This biplot shows the orientation of the test samples relative to the principal components and the orientation of the nutrients and energy relative to the principal components.
Table 1.
Chemical composition of milk and rations, on a dry matter basis, for each experiment.
| Experimental Rations | |||
|---|---|---|---|
| Nutrient | Milk | 1 | 2 |
| Dry matter (%) | 10.72 | 89.10 | 89.85 |
| Crude protein (%) | 31.33 | 16.31 | 15.50 |
| Metabolizable energy (Mcal/kg MS) 1 | 5.34 | 2.86 | 2.88 |
| Ash (%) | 6.86 | 7.65 | 8.00 |
| Fat (%) | 2.14 | 2.79 | |
| Neutral detergent fiber (%) | 38.19 | 38.86 | |
| Acid detergent fiber (%) | 16.60 | 17.87 | |
| Lignin (%) | 2.41 | 2.36 | |
Table 2.
Descriptive statistics of body composition and composition of body parts (dry matter basis) of Boer × Saanen goats weighing 5 to 25 kg.
Table 2.
Descriptive statistics of body composition and composition of body parts (dry matter basis) of Boer × Saanen goats weighing 5 to 25 kg.
| Composition | Mean | Standard Deviation | Minimum | Maximum |
|---|---|---|---|---|
| Empty body | ||||
| Dry matter (%) | 30.51 | 3.16 | 21.4 | 37.5 |
| Ash (% DM) | 13.35 | 2.3 | 9.12 | 20.7 |
| Fat (% DM) | 23.05 | 8.39 | 4.29 | 42.27 |
| Protein (% DM) | 62.06 | 6.71 | 46.44 | 77.93 |
| Energy (kcal/kg DM) | 5560.51 | 472.25 | 4410.86 | 6510.14 |
| Half carcass | ||||
| Dry matter (%) | 29.47 | 2.35 | 23.04 | 35.87 |
| Ash (% DM) | 15.78 | 3.11 | 10.74 | 27.09 |
| Fat (% DM) | 23.84 | 9.01 | 2.77 | 43.62 |
| Protein (% DM) | 59.6 | 6.95 | 45.02 | 73.53 |
| Energy (kcal/kg DM) | 5504.94 | 553.73 | 4211.38 | 6712.63 |
| 9–11th ribs | ||||
| Dry matter (%) | 31.51 | 3.45 | 22.49 | 40.28 |
| Ash (% DM) | 13.84 | 3.3 | 8.8 | 23.99 |
| Fat (% DM) | 21.23 | 10.27 | 2.39 | 42.58 |
| Protein (% DM) | 64.47 | 7.58 | 47.32 | 76.97 |
| Energy (kcal/kg DM) | 5477.9 | 614.5 | 3847.66 | 6605.02 |
| Loin | ||||
| Dry matter (%) | 30.16 | 3.54 | 21.44 | 39.81 |
| Ash (% DM) | 12.53 | 3.06 | 8.28 | 22.96 |
| Fat (% DM) | 22.28 | 9.71 | 3.24 | 45.22 |
| Protein (% DM) | 63.76 | 8.86 | 43.42 | 82.82 |
| Energy (kcal/kg DM) | 5679.99 | 541.27 | 4316.58 | 6758.22 |
| Leg | ||||
| Dry matter (%) | 31.13 | 2.55 | 25.32 | 36.72 |
| Ash (% DM) | 13.86 | 3.33 | 8.58 | 25.58 |
| Fat (% DM) | 21.18 | 7.08 | 4.59 | 36.51 |
| Protein (% DM) | 64.04 | 6.81 | 50.56 | 80.18 |
| Energy (kcal/kg DM) | 5576.42 | 530.41 | 4188.97 | 6413.07 |
| Neck | ||||
| Dry matter (%) | 30.25 | 2.88 | 24.22 | 37.61 |
| Ash (% DM) | 14.52 | 2.87 | 9.4 | 21.7 |
| Fat (% DM) | 21.95 | 8.88 | 5.41 | 39.4 |
| Protein (% DM) | 63.23 | 7.52 | 46.48 | 79.44 |
| Energy (kcal/kg DM) | 5545.74 | 528.98 | 4529.03 | 6489.33 |
| Ribs | ||||
| Dry matter (%) | 31.77 | 3.45 | 24.2 | 41.01 |
| Ash (% DM) | 13.75 | 2.86 | 9.8 | 26.14 |
| Fat (% DM) | 24.32 | 9.6 | 3.97 | 41.85 |
| Protein (% DM) | 58.41 | 8.19 | 42.59 | 72.46 |
| Energy (kcal/kg DM) | 5725.82 | 528.92 | 4251.61 | 6654.11 |
| Shoulder | ||||
| Dry matter (%) | 31.73 | 2.37 | 27 | 36.45 |
| Ash (% DM) | 18.9 | 3.47 | 13.81 | 30.89 |
| Fat (% DM) | 19.9 | 8.14 | 0.08 | 33.61 |
| Protein (% DM) | 59.87 | 5.29 | 49.5 | 68.49 |
| Energy (kcal/kg DM) | 5203.96 | 555.66 | 3778.31 | 5970.13 |
| Organs | ||||
| Dry matter (%) | 23.19 | 4.48 | 14.04 | 36.63 |
| Ash (% DM) | 4.09 | 0.87 | 2.14 | 5.95 |
| Fat (% DM) | 29.34 | 12.77 | 3.05 | 60.98 |
| Protein (% DM) | 63.28 | 12.77 | 32.02 | 92.83 |
| Energy (kcal/kg DM) | 6392.94 | 576.17 | 5031.37 | 7683.04 |
| Head + Feet | ||||
| Dry matter (%) | 37.58 | 4.7 | 25.15 | 52.64 |
| Ash (% DM) | 22.01 | 2.58 | 16.89 | 28.89 |
| Fat (% DM) | 23.63 | 6.24 | 5.38 | 33.05 |
| Protein (% DM) | 51.98 | 5.01 | 45.29 | 69.52 |
| Energy (kcal/kg DM) | 4944.4 | 372.03 | 3632.83 | 5848.57 |
| Hide | ||||
| Dry matter (%) | 33.16 | 3.49 | 25.56 | 42.71 |
| Ash (% DM) | 2.27 | 0.35 | 1.41 | 3.23 |
| Fat (% DM) | 6.3 | 2.12 | 3.46 | 12.73 |
| Protein (% DM) | 95.12 | 4.25 | 82.15 | 99.91 |
| Energy (kcal/kg DM) | 5205 | 157.33 | 4745.52 | 5502.83 |
Table 3.
Correlations between the first two principal components and the nutrients and energy composition (% on a dry matter basis).
Table 3.
Correlations between the first two principal components and the nutrients and energy composition (% on a dry matter basis).
| Variable | Principal Component 1 | Principal Component 2 |
|---|---|---|
| Dry matter | 0.833 | 0.325 |
| Minerals (ash) | −0.846 | 0.503 |
| Fat | 0.976 | 0.032 |
| Protein | −0.884 | −0.341 |
| Energy (kcal/kg DM) | 0.964 | −0.184 |
Table 4.
Prediction of empty body composition (% on a dry matter basis) using body weight (BW, kg) and composition of 9–11th ribs, loin, and neck in Boer × Saanen goats.
Table 4.
Prediction of empty body composition (% on a dry matter basis) using body weight (BW, kg) and composition of 9–11th ribs, loin, and neck in Boer × Saanen goats.
| Model | AICC | R2 | RMSE (% Mean) | CCC | |
|---|---|---|---|---|---|
| Models for estimating body dry matter (%) | |||||
| 1 | Dry MatterBody (%) = 13.55 (±2.59 ***) + 0.27 (±0.067 ***) BW (kg) + 0.44 (±0.101 ***) DM9–11th ribs (%) | 227.18 | 0.74 | 5.23 | 0.851 |
| 2 | Dry MatterBody (%) = 13.92 (±2.24 ***) + 0.26 (±0.063 ***) BW (kg) + 0.44 (±0.091 ***) DMLoin (%) | 223.50 | 0.76 | 5.06 | 0.861 |
| 3 | Dry MatterBody (%) = 10.23 (±3.44 **) + 0.22 (±0.078 **) BW (kg) + 0.58 (±0.14 ***) DMNeck (%) | 227.98 | 0.74 | 5.26 | 0.848 |
| Models for estimating body ash (% DM 1) | |||||
| 4 | AshBody (%) = 10.56 (±1.29 ***) − 0.19 (±0.041 ***) BW (kg) + 0.37 (±0.063 ***) Ash9–11th ribs (%) | 176.06 | 0.80 | 7.69 | 0.887 |
| 5 | AshBody (%) = 9.43 (±1.14 ***) − 0.16 (±0.036 ***) BW (kg) + 0.47 (±0.061 ***) AshLoin (%) | 162.03 | 0.84 | 6.81 | 0.913 |
| 6 | AshBody (%) = 11.33 (±1.64 ***) − 0.23 (±0.046 ***) BW (kg) + 0.33 (±0.081 ***) AshNeck (%) | 188.88 | 0.75 | 8.59 | 0.855 |
| Models for estimating body fat (% DM) | |||||
| 7 | FatBody (%) = 6.61 (±0.81 ***) + 0.77 (±0.034 ***) Fat9–11th ribs (%) | 282.83 | 0.90 | 11.39 | 0.947 |
| 8 | FatBody (%) = 4.29 (±1.10 ***) + 0.62 (±0.15 ***) BW (kg) + 0.51 (±0.0802 ***) FatLoin (%) | 304.14 | 0.86 | 13.43 | 0.926 |
| 9 | FatBody (%) = 3.19 (±0.97 **) + 0.32 (±0.15 *) BW (kg) + 0.73 (±0.084 ***) FatNeck (%) | 286.06 | 0.90 | 11.49 | 0.947 |
| Models for estimating body protein (% DM) | |||||
| 10 | ProteinBody (%) = 41.92 (±6.30 ***) − 0.64 (±0.12 ***) BW (kg) + 0.43 (±0.079 ***) Protein9–11th ribs (%) | 272.45 | 0.88 | 3.80 | 0.933 |
| 11 | ProteinBody (%) = 55.99 (±3.99 ***) − 0.87 (±0.087 ***) BW (kg) + 0.26 (±0.050 ***) ProteinLoin (%) | 274.75 | 0.87 | 3.87 | 0.930 |
| 12 | ProteinBody (%) = 47.89 (±5.34 ***) − 0.76 (±0.1004 ***) BW (kg) + 0.37 (±0.068 ***) ProteinNeck (%) | 273.30 | 0.87 | 3.82 | 0.932 |
1 DM = dry matter. Significance codes: *** = 0.001, ** = 0.01, * = 0.05.
Table 5.
Prediction of empty body energy composition (kcal/kg DM 1) using body weight and 9–11th ribs, loin, and neck composition in Boer × Saanen goats.
Table 5.
Prediction of empty body energy composition (kcal/kg DM 1) using body weight and 9–11th ribs, loin, and neck composition in Boer × Saanen goats.
| Model | AICC | R2 | RMSE (% Mean) | CCC | |
|---|---|---|---|---|---|
| 13 | EnergyBody (kcal/kg DM) = 2205.58 (±265 ***) + 22.68 (±7.33 **) BW (kg) + 0.56 (±0.061 ***) Energy9–11th ribs (kcal/kg DM) | 752.09 | 0.90 | 2.65 | 0.948 |
| 14 | EnergyBody (kcal/kg DM) = 2790.76 (±441 ***) + 27.99 (±8.31 **) BW (kg) + 48.15 (±6.72 ***) FatLoin (%) + 21.33 (±5.41 ***) ProteinLoin (%) | 766.31 | 0.88 | 2.93 | 0.935 |
| 15 | EnergyBody (kcal/kg DM) = 2044.72 (±369 ***) + 30.57 (±8.35 ***) BW (kg) + 0.55 (±0.079 ***) EnergyLoin (%) | 769.17 | 0.87 | 3.07 | 0.929 |
| 16 | EnergyBody (kcal/kg DM) = 4453.74 (±59.5 ***) + 19.02 (±8.96 *) BW (kg) + 39.97 (±5.17 ***) FatNeck (%) | 763.52 | 0.88 | 2.92 | 0.934 |
| 17 | EnergyBody (kcal/kg DM) = 2320.53 (±444 ***) + 34.27 (±10.16 **) BW (kg) + 0.51 (±0.098 ***) EnergyNeck (kcal/kg DM) | 783.11 | 0.83 | 3.46 | 0.908 |
1 DM = dry matter. Significance codes: *** = 0.001, ** = 0.01, * = 0.05.
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Teixeira, I.A.M.A.; Ferreira, A.F.M.; Pereira Filho, J.M.; Tedeschi, L.O.; Resende, K.T. Predicting Chemical Body Composition Using Body Part Composition in Boer × Saanen Goats. Ruminants 2024, 4, 543-555. https://doi.org/10.3390/ruminants4040038
AMA Style
Teixeira IAMA, Ferreira AFM, Pereira Filho JM, Tedeschi LO, Resende KT. Predicting Chemical Body Composition Using Body Part Composition in Boer × Saanen Goats. Ruminants. 2024; 4(4):543-555. https://doi.org/10.3390/ruminants4040038
Chicago/Turabian StyleTeixeira, Izabelle A. M. A., Adrian F. M. Ferreira, José M. Pereira Filho, Luis O. Tedeschi, and Kleber T. Resende. 2024. "Predicting Chemical Body Composition Using Body Part Composition in Boer × Saanen Goats" Ruminants 4, no. 4: 543-555. https://doi.org/10.3390/ruminants4040038
APA StyleTeixeira, I. A. M. A., Ferreira, A. F. M., Pereira Filho, J. M., Tedeschi, L. O., & Resende, K. T. (2024). Predicting Chemical Body Composition Using Body Part Composition in Boer × Saanen Goats. Ruminants, 4(4), 543-555. https://doi.org/10.3390/ruminants4040038
