Development and Validation of Equations for Predicting the Metabolizable Energy Value of Double-Low Rapeseed Cake for Growing Pigs.
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ABSTRACT:
SUBMITTER: Wang L
PROVIDER: S-EPMC8073417 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE - 0.3926; r2 = 0.99, root mean square prediction error [RMSPE] = 0.04, and P < 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = -0.567 to -0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE - 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH4, Mcal/kg = 0.3344 - 0.05639 × multiple of maintenance; r2 = 0.536, RMSPE = 0.0245, and P < 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.
| S-EPMC8892684 | biostudies-literature
Project description:This study was performed to update and generate prediction equations for converting digestible energy (DE) to metabolizable energy (ME) for Korean Hanwoo beef cattle, taking into consideration the gender (male and female) and body weights (BW above and below 350 kg) of the animals. The data consisted of 141 measurements from respiratory chambers with a wide range of diets and energy intake levels. A simple linear regression of the overall unadjusted data suggested a strong relationship between the DE and ME (Mcal/kg DM): ME = 0.8722 × DE + 0.0016 (coefficient of determination (R2) = 0.946, root mean square error (RMSE) = 0.107, p < 0.001 for intercept and slope). Mixed-model regression analyses to adjust for the effects of the experiment from which the data were obtained similarly showed a strong linear relationship between the DE and ME (Mcal/kg of DM): ME = 0.9215 × DE - 0.1434 (R2 = 0.999, RMSE = 0.004, p < 0.001 for the intercept and slope). The DE was strongly related to the ME for both genders: ME = 0.8621 × DE + 0.0808 (R2 = 0.9600, RMSE = 0.083, p < 0.001 for the intercept and slope) and ME = 0.7785 × DE + 0.1546 (R2 = 0.971, RMSE = 0.070, p < 0.001 for the intercept and slope) for male and female Hanwoo cattle, respectively. By BW, the simple linear regression similarly showed a strong relationship between the DE and ME for Hanwoo above and below 350 kg BW: ME = 0.9833 × DE - 0.2760 (R2 = 0.991, RMSE = 0.055, p < 0.001 for the intercept and slope) and ME = 0.72975 × DE + 0.38744 (R2 = 0.913, RMSE = 0.100, p < 0.001 for the intercept and slope), respectively. A multiple regression using the DE and dietary factors as independent variables did not improve the accuracy of the ME prediction (ME = 1.149 × DE - 0.045 × crude protein + 0.011 × neutral detergent fibre - 0.027 × acid detergent fibre + 0.683).
| S-EPMC8228101 | biostudies-literature
Project description:ObjectiveThe objective of this study was to determine net energy (NE) of expeller-press (EP-RSM) and solvent-extracted rapeseed meal (SE-RSM) and to establish equations for predicting the NE in rapeseed meal (RSM) fed to growing pigs.MethodsThirty-six barrows (initial body weight [BW], 41.1±2.2 kg) were allotted into 6 diets comprising a corn-soybean meal basal diet and 5 diets containing 19.50% RSM added at the expense of corn and soybean meal. The experiment had 6 periods and 6 replicate pigs per diet. During each period, the pigs were individually housed in metabolism crates for 16 days which included 7 days for adaption to diets. On day 8, pigs were transferred to respiration chambers and fed their respective diet at 2,000 kJ metabolizable energy (ME)/kg BW0.6/d. Feces and urine were collected, and daily heat production was measured from day 9 to 13. On days 14 and 15, the pigs were fed at 890 kJ ME/kg BW0.6/d and fasted on day 16 for evaluation of fasting heat production (FHP).ResultsThe FHP of pigs averaged 790 kJ/kg BW0.6/d and was not affected by the diet composition. The NE values were 10.80 and 8.45 MJ/kg DM for EP-RSM and SE-RSM, respectively. The NE value was positively correlated with gross energy (GE), digestible energy (DE), ME, and ether extract (EE). The best fit equation for NE of RSM was NE (MJ/kg DM) = 1.14×DE (MJ/kg DM)+0.46×crude protein (% of DM)-25.24 (n = 8, R2 = 0.96, p<0.01). The equation NE (MJ/kg DM) = 0.22×EE (% of DM)-0.79×ash (% of DM)+14.36 (n = 8, R2 = 0.77, p = 0.018) may be utilized to quickly determine the NE in RSM when DE or ME values are unavailable.ConclusionThe NE values of EP-RSM and SE-RSM were 10.80 and 8.45 MJ/kg DM. The NE value of RSM can be well predicted based on energy content (GE, DE, and ME) and proximate analysis.
| S-EPMC7888491 | biostudies-literature
Project description:BackgroundIn the nutrition literature, there are several reports on the use of artificial neural network (ANN) and multiple linear regression (MLR) approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM) method as a new alternative approach to MLR and ANN models is still not fully investigated.MethodsThe MLR, ANN, and SVM models were developed to predict metabolizable energy (ME) content of compound feeds for pigs based on the German energy evaluation system from analyzed contents of crude protein (CP), ether extract (EE), crude fiber (CF), and starch. A total of 290 datasets from standardized digestibility studies with compound feeds was provided from several institutions and published papers, and ME was calculated thereon. Accuracy and precision of developed models were evaluated, given their produced prediction values.ResultsThe results revealed that the developed ANN [R2 = 0.95; root mean square error (RMSE) = 0.19 MJ/kg of dry matter] and SVM (R2 = 0.95; RMSE = 0.21 MJ/kg of dry matter) models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR (R2 = 0.89; RMSE = 0.27 MJ/kg of dry matter).ConclusionThe developed ANN and SVM models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR; however, there were not obvious differences between performance of ANN and SVM models. Thus, SVM model may also be considered as a promising tool for modeling the relationship between chemical composition and ME of compound feeds for pigs. To provide the readers and nutritionist with the easy and rapid tool, an Excel® calculator, namely, SVM_ME_pig, was created to predict the metabolizable energy values in compound feeds for pigs using developed support vector machine model.
| S-EPMC5491901 | biostudies-literature
Project description:Microbial crude protein (MCP) produced in rumen could be estimated by a variety of protocols of experimental sampling and analysis. However, a model to estimate this value is necessary when protein requirements are calculated for small ruminants. This model could be useful to calculate rumen degradable protein (RDP) requirements from metabolizable protein (MP). Then, our objective was to investigate if there is a difference in MCP efficiency between sheep and goats, and to fit equations to predict ruminal MCP production from dietary energy intake. The database consisted of 19 studies with goats (n = 176) and sheep (n = 316), and the variables MCP synthesis (g/day), total digestible nutrients (TDN), and organic matter (OM) intakes (g/day), and OM digestibility (g/kg DM) were registered for both species. The database was used for two different purposes, where 70% of the values were sorted to fit equations, and 30% for validation. A meta-analytical procedure was carried out using the MIXED procedure of SAS, specie was considered as the fixed dummy effect, and the intercept and slope nested in the study were considered random effects. No effect of specie was observed for the estimation of MCP from TDN, digestible Organic Matter (dOM), or metabolizable energy (ME) intakes (P > 0.05), considering an equation with or without an intercept. Therefore, single models including both species at the same fitting were validated. The following equations MCP (g/day) = 12.7311 + 59.2956 × TDN intake (AIC = 3,004.6); MCP (g/day) = 15.7764 + 62.2612 × dOM intake (AIC = 2,755.1); and MCP (g/day) = 12.7311 + 15.3000 × ME intake (AIC = 3,007.3) presented lower values for the mean square error of prediction (MSEP) and its decomposition, and similar values for the concordance correlation coefficient (CCC) and for the residual mean square error (RMSE) when compared with equations fitted without an intercept. The intercept and slope pooled test was significant for equations without an intercept (P < 0.05), indicating that observed and predicted data differed. In contrast, predicted and observed data for complete equations were similar (P > 0.05).
| S-EPMC8222605 | biostudies-literature
Project description:Rapeseed meal is a sustainable feed ingredient that can be used as an alternative to imported soybean meal in European pig production. The gut microbiota plays an important role on pig physiology and health but the impact on microbiota of using rapeseed in diets is still not well known. In this study, 84 purebred Norwegian Landrace pigs with average initial weight of 25 kg were divided into two groups and fed for approximately three months with either a control diet containing soybean meal (CON) or a high-fiber experimental diet where 20% rapeseed meal (RSF) was included as an alternative to soybean meal in CON. The composition and function of microbiome in gut digesta samples were analyzed by performing 16S rRNA gene sequencing and culturing of bacteria. The microbiota diversity and composition were similar between the dietary treatments; however, relative abundance of a variety of bacterial groups and imputed functions of microbiome in the ileum and large intestine were altered when the pigs were fed with a rapeseed-based diet. It was notable that the immune-inducing bacterial group Mucispirillum and anti-inflammatory stimulating bacteria Lachnospira were more abundant in the ileum and large intestine of the RSF group, respectively. Moreover, there was a higher abundance of major amino acid fermenters and amylolytic bacteria in the CON group and a high abundance of putative short chain fatty acid producers in RSF group. In comparison with the CON group, the gut microbiome of RSF group possessed an enhanced potential for carbohydrate and energy metabolism and a reduced potential for bacterial pathogenicity-related pathways.
| S-EPMC7287078 | biostudies-literature
Project description:The experiment aimed to study effects of dietary metabolizable energy (ME) and crude protein (CP) levels alone and in interaction on performance, pectoral muscle composition and gut microbiota in native growing chickens. A total of 648 10-wks-old Beijing-You Chicken (BYC) female chickens were randomly allocated to 9 groups with 6 replicates per group and 12 chickens per replicate, and the chickens were fed with a 3 × 3 factorial diets (3 levels of dietary ME: 11.31 MJ/kg, 11.51 MJ/kg, 11.71 MJ/kg; and 3 levels of dietary CP: 14%, 15%, 16%). The results showed that dietary ME and CP levels didn't affect average feed intake (AFI), body weight gain, feed gain ratio (P > 0.05), but ME level significantly affected the AFI (P < 0.05); mortality rate of 11.31 MJ/kg group was the highest (P < 0.05). Dietary ME, CP levels, and the interaction significantly affected pectoral CP and crude fat (CF) content of the growing chickens (P < 0.01). Dietary CP level had opposite effects on pectoral CP and CF content (P < 0.01). The 16% CP increased the pectoral CF content, which may have a negative impact on meat flavor. Dietary ME level affected 11 types of pectoral free amino acids (FAA) contents, including aspartic acid, L-threonine (P < 0.05), also amino acid classification, for example, total amino acid (TAA) and essential amino acid (EAA) content (P < 0.05). The 11.51 MJ/kg group had the highest TAA, EAA, delicious amino acid (DAA) content and EAA percentage (P < 0.05), while 11.31 MJ/kg group had the lowest bitter amino acid (BAA) content and BAA percentage and the highest fresh and sweet amino acid (FSAA) percentage (P < 0.05). Dietary CP level significantly affected glutamine and tyrosine content (P < 0.05). The interaction of dietary ME and CP level affected C20:3n6 content, saturated fatty acid (SFA), and unsaturated fatty acid (UFA) percentage (P < 0.05). The CP level significantly affected SFA percentage (P < 0.05). The 16% CP level increased the diversity of gut microbiota, but at the same time increased the relative abundance of Proteobacteria (P < 0.05), which is a sign of microbiota disorder. The increase of dietary ME level resulted in a gradual decrease in the diversity and relative abundance of gut microbiota. In conclusion, the present study suggested that the medium dietary ME (11.51 MJ/kg) and low CP (14-15%) levels can be helpful for enhancing pectoral muscle composition, increase meat quality such as flavor and nutritional value, and benefit for gut microbiota in native growing chickens.
| S-EPMC9720343 | biostudies-literature
Project description:To explore the effects of fermented rapeseed meal (FRSM) on growth performance and intestinal health, a total of 30 growing pigs were randomly allotted to three treatments consisting of corn-soybean meal diet (CSD), rapeseed meal diet (RSD), and fermented rapeseed meal diet (FRSD). Results showed that compared with RSD, FRSD feeding increased the average daily gain and final body weight in pigs (P < 0.01). Compared with RSD feeding, FRSD feeding elevated the apparent digestibility of crude protein, acid detergent fiber, and ether extract in pigs (P < 0.01). Moreover, the FRSD group exhibited greater apparent ileal digestibility of His, Thr, Lys, and Ser than the RSD group (P < 0.01). The digestible energy, metabolic energy, and nitrogen utilization were higher in the FRSD and CSD groups than in the RSD group (P < 0.01). As compared to the RSD, FRSD feeding decreased the serum concentration of leptin but significantly increased the concentrations of immunoglobulin (Ig) A, IgG, ghrelin, and enzyme activities of amylase, lipase, and trypsin in the pancreas (P < 0.05). Interestingly, the villus height, the ratio of villus height to crypt depth, and the activities of brush border enzymes (e.g., maltase and sucrase) in the small intestine were higher in the CSD and FRSD groups than in the RSD group (P < 0.05). As compared to the RSD, the FRSD feeding not only increased the expression level of the occludin in the small intestinal epithelium (P < 0.05) but also elevated the expression levels of claudin-1, MUC1, and PepT1 genes in the duodenum, and elevated the expression levels of SGLT1 and CAT1 genes in the jejunum (P < 0.05). Importantly, FRSD feeding significantly decreased the abundance of Escherichia coli, but increased the abundance of Lactobacillus and the content of butyrate in the cecum and colon (P < 0.05). These results indicated that compared with rapeseed meal, fermented rapeseed meal exhibited a positive effect on improving the growth performance and intestinal health in growing pigs, and the results may also help develop novel protein sources for animal nutrition and the feed industry.
| S-EPMC10696392 | biostudies-literature
Project description:BackgroundsEvaluating the growth performance of pigs in real-time is laborious and expensive, thus mathematical models based on easily accessible variables are developed. Multiple regression (MR) is the most widely used tool to build prediction models in swine nutrition, while the artificial neural networks (ANN) model is reported to be more accurate than MR model in prediction performance. Therefore, the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this study.ResultsBody weight (BW), net energy (NE) intake, standardized ileal digestible lysine (SID Lys) intake, and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate variables. In the training phase, MR models showed high accuracy in both ADG and F/G prediction (R2ADG = 0.929, R2F/G = 0.886) while ANN models with 4, 6 neurons and radial basis activation function yielded the best performance in ADG and F/G prediction (R2ADG = 0.964, R2F/G = 0.932). In the testing phase, these ANN models showed better accuracy in ADG prediction (CCC: 0.976 vs. 0.861, R2: 0.951 vs. 0.584), and F/G prediction (CCC: 0.952 vs. 0.900, R2: 0.905 vs. 0.821) compared with the MR models. Meanwhile, the "over-fitting" occurred in MR models but not in ANN models. On validation data from the animal trial, ANN models exhibited superiority over MR models in both ADG and F/G prediction (P < 0.01). Moreover, the growth stages have a significant effect on the prediction accuracy of the models.ConclusionBody weight, NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs, with trained ANN models are more flexible and accurate than MR models. Therefore, it is promising to use ANN models in related swine nutrition studies in the future.
| S-EPMC9102637 | biostudies-literature
Project description:BackgroundRapeseed cake is an important agricultural waste. After enzymatic fermentation, rapeseed cake not only has specific microbial diversity but also contains a lot of fatty acids, organic acids, amino acids and their derivatives, which has potential value as a high-quality organic fertilizer. However, the effects of fermented rapeseed cake on tea rhizosphere microorganisms and soil metabolites have not been reported. In this study, we aimed to elucidate the effect of enzymatic rapeseed cake fertilizer on the soil of tea tree, and to reveal the correlation between rhizosphere soil microorganisms and nutrients/metabolites.ResultsThe results showed that: (1) The application of enzymatic rapeseed cake increased the contents of soil organic matter (OM), total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), and available phosphorus (AP); increased the activities of soil urease (S-UE), soil catalase (S-CAT), soil acid phosphatase (S-ACP) and soil sucrase (S-SC); (2) The application of enzymatic rapeseed cake increased the relative abundance of beneficial rhizosphere microorganisms such as Chaetomium, Inocybe, Pseudoxanthomonas, Pseudomonas, Sphingomonas, and Stenotrophomonas; (3) The application of enzymatic rapeseed cake increased the contents of sugar, organic acid, and fatty acid in soil, and the key metabolic pathways were concentrated in sugar and fatty acid metabolisms; (4) The application of enzymatic rapeseed cake promoted the metabolism of sugar, organic acid, and fatty acid in soil by key rhizosphere microorganisms; enzymes and microorganisms jointly regulated the metabolic pathways of sugar and fatty acids in soil.ConclusionsEnzymatic rapeseed cake fertilizer improved the nutrient status and microbial structure of tea rhizosphere soil, which was beneficial for enhancing soil productivity in tea plantations. These findings provide new insights into the use of enzymatic rapeseed cake as an efficient organic fertilizer and expand its potential for application in tea plantations.
| S-EPMC10483718 | biostudies-literature