Estimation and modeling true metabolizable energy of sorghum grain for poultry

Sedghi, Mohamad; Ebadi, M. R.; Golian, Abolghasem; Ahmadi, Hamed

doi:10.3382/ps.2010-01005

Cited by 18 publications

(13 citation statements)

References 17 publications

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“…Amino acid digestibilities, AME and TME of the tannin varieties were low in the present study. Condensed tannins have been reported to reduce feed intake (Oduhu & Baker, 2005;Bryden et al, 2009), metabolizable energy (Bryden et al, 2009;Perez-Maldonado & Rodrigues, 2009;Sannamani et al, 2010;Sedghi et al, 2011) and amino acid digestibility (Selle et al, 2010;Ebadi et al, 2011) in broilers. Such limiting factors are the result of the poor nutritional quality of the protein (kafirins and glutelin) and subsequently attributed to low solubility and deficiencies in amino acids (Sastry et al, 1986;Selle et al, 2010).…”

Section: Resultsmentioning

confidence: 99%

“…The adaptive agronomic characteristics of sorghum make it suitable for cultivation in different environmental conditions. Sorghum varieties have been used for many years for poultry and human consumption, especially in Africa and Asia, and have been shown to vary in physical and chemical characteristics (Neucere & Sumrell, 1980;Sedghi et al, 2011). While some authors have reported anti-nutritional factors in sorghum grain that have adverse effects on the utilization of sorghum protein and metabolizable energy by poultry, such as tannins (Gous et al, 1982;Boren & Waniska, 1992), others have stated that these factors do not always reduce the performance of poultry (Nyachoti et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

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Chemical Composition and Nutritive Value of South African Sorghum Varieties as Feed for Broiler Chickens

Mabelebele¹,

Siwela²,

Gous³

et al. 2015

SA J. An. Sci.

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Sorghum (Sorghum bicolor (L.) Moench) is the fifth most important grain crop after wheat, rice, maize and barley. It is cultivated for food and feed in America, Asia, Australia and Africa. Newly developed sorghum varieties should be evaluated for their suitability as food and feed. The physical characteristics and proximate composition, total phenolic content, mineral content, amino acid profile and digestibility, and true metabolizable energy of four sorghum varieties were studied. The condensed tannin sorghum varieties PAN8625 and NS5511 had higher total phenolic content and antioxidant activity than the non-tannin varieties PAN8816 and PAN8906. Crude protein content diverged narrowly across the varieties, ranging from 81.2 to 95.4 g/kg DM. Starch and gross energy contents also differed, though varieties that were used had similar total and individual mineral contents. The threonine, leucine, phenylalanine, valine, proline and alanine contents of PAN8625 and PAN8906 were higher than those of NS5511 and PAN8816, which had similar contents. Amino acid digestibility and metabolizable energy of the tannin varieties were generally lower than those of the non-tannin ones. These results indicate that chemical and nutrient composition of sorghum varieties should be considered when selecting for broiler chicken feeding.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chemical Composition and Nutritive Value of South African Sorghum Varieties as Feed for Broiler Chickens

Mabelebele¹,

Siwela²,

Gous³

et al. 2015

SA J. An. Sci.

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“…Model performance was assessed after generating output by the models. Previous studies have shown the ability of ANN to estimate the TME n of poultry feedstuffs based on their chemical composition (Ahmadi et al, 2008;Sedghi et al, 2011). The results of our study reveal that the SVR approach offers a competitive alternative to existing powerful ANN models.…”

Section: Calibrationmentioning

confidence: 57%

“…Recently, artif icial neural network (ANN) models have received attention among poultry nutritionists, e.g. for estimating the ME of poultry offal meal (Ahmadi et al, 2008) and sorghum grain (Sedghi et al, 2011) …”

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confidence: 99%

Predicting the metabolizable energy content of corn for ducks: a comparison of support vector regression with other methods

Faridi

Golian

Mottaghitalab³

et al. 2013

Span. j. agric. res.

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IntroductionCorn (Zea mays L.) is the main energy source in diets for intensively reared avian species (broilers and ducks), therefore accurate information on its effective energy content is of importance to nutritionists. A number of studies have been conducted to estimate the metabolizable energy (ME) content of corn based on its physical characteristics and chemical composition (e.g. Leeson et al., 1993;Zhao et al., 2008). The energy content of feedstuffs depends strongly on their chemical composition. Nutritionists are interested in using models that predict the nutritive value of poultry feedstuffs accurately. Recently, artif icial neural network (ANN) models have received attention among poultry nutritionists, e.g. for estimating the ME of poultry offal meal (Ahmadi et al., 2008) and sorghum grain (Sedghi et al., 2011) AbstractSupport vector regression (SVR) is used in this study to develop models to estimate apparent metabolizable energy (AME), AME corrected for nitrogen (AME n ), true metabolizable energy (TME), and TME corrected for nitrogen (TME n ) contents of corn fed to ducks based on its chemical composition. Performance of the SVR models was assessed by comparing their results with those of artificial neural network (ANN) and multiple linear regression (MLR) models. The input variables to estimate metabolizable energy content (MJ kg -1 ) of corn were crude protein, ether extract, crude fibre, and ash (g kg -1 ). Goodness of fit of the models was examined using R 2 , mean square error, and bias. Based on these indices, the predictive performance of the SVR, ANN, and MLR models was acceptable. Comparison of models indicated that performance of SVR (in terms of R 2 ) on the full data set (0.937 for AME, 0.954 for AME n , 0.860 for TME, and 0.937 for TME n ) was better than that of ANN (0.907 for AME, 0.922 for AME n , 0.744 for TME, and 0.920 for TME n ) and MLR (0.887 for AME, 0.903 for AME n , 0.704 for TME, and 0.902 for TME n ). Similar findings were observed with the calibration and testing data sets. These results suggest SVR models are a promising tool for modelling the relationship between chemical composition and metabolizable energy of feedstuffs for poultry. Although from the present results the application of SVR models seems encouraging, the use of such models in other areas of animal nutrition needs to be evaluated.Additional key words: maize; poultry; nutritive value; chemical composition; artificial neural network; multiple linear regression. Abbreviations used: AME (apparent metabolizable energy); AME n (apparent metabolizable energy corrected for nitrogen); ANN (artificial neural network); CF (crude fibre); CP (crude protein); EE (ether extract); ME (metabolizable energy); MLR (multiple linear regression); SVM (support vector machine); SVR (support vector regression); TME (true metabolizable energy); TME n (true metabolizable energy corrected for nitrogen); VIF (variance inflation factor).

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“…Flavonoids in sorghums include phenolic compounds, of which, tannins and anthocyanins are the most important flavonoids isolated from sorghum (Rooney and Waniska, 2000;. Tannin content in the pericarp is one of the most important factors affecting the feed value of sorghum grain, since it adversely affects the metabolizable energy in grain and protein utilization by poultry (Sedghi et al, 2011). The most common anthocyanins in sorghum are the 3-deoxyanthocyanidins, which include luteolinidin and apigeninidin ).…”

Section: Introductionmentioning

confidence: 99%

Chemical and genetic diversity of high-seed-yield sorghum (Sorghum bicolor M.) germplasms

Ryu

Kwon³

et al. 2016

Genet. Mol. Res.

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ABSTRACT. This study evaluated the chemical and genetic diversity of high-seed-yield sorghum germplasms from Korea, the United States, and South Africa. We identified significant differences in the chemical contents of whole plants at the heading stage in all cultivars, including differences in crude protein, fat, fiber, ash, neutral detergent fiber, acid detergent fiber, mineral, and fatty acid contents. Our results suggest that Banwoldang is the most appropriate cultivar for roughage because of its high protein yield. We identified significant differences in the tannin, flavonoid, amylose, mineral, crude fat, fatty acid, and 3-deoxyanthocyanin contents in the whole grain from all cultivars, but not in the mineral or crude fat contents. Tannin levels were generally low. IS645 contained the highest levels of flavonoids and linolenic acid compounds, and Moktak had the highest amylose and deoxyanthocyanidin content in the grain. To assess genetic diversity, we used 10 simple sequence repeat (SSR) primer sets to identify 38 2 J. Ryu et al. Genetics and Molecular Research 15 (3): gmr.15038677 alleles with 3-8 alleles per locus. Based on phylogenetic analysis of the SSR markers, the sorghum cultivars were divided into three major groups. Comparison of clusters based on chemical compositions with those based on SSRs showed that the groups formed by the three native Korean cultivars clustered similarly in molecular dendrograms. Association analysis was conducted for the 10 SSR marker; 48 chemical and growth traits were present for two marker traits (seed color and whole plant fatty acid content) with significant marker-trait associations. These markers could be used to select sorghum cultivars for breeding programs.

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Estimation and modeling true metabolizable energy of sorghum grain for poultry

Cited by 18 publications

References 17 publications

Chemical Composition and Nutritive Value of South African Sorghum Varieties as Feed for Broiler Chickens

Chemical Composition and Nutritive Value of South African Sorghum Varieties as Feed for Broiler Chickens

Predicting the metabolizable energy content of corn for ducks: a comparison of support vector regression with other methods

Chemical and genetic diversity of high-seed-yield sorghum (Sorghum bicolor M.) germplasms

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