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Systematic Review on Comparing Calculated and Laboratory Determined Crude Protein Estimates for Animal Feedstuffs and Diets 1Kosshak

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Systematic Review on Comparing Calculated and Laboratory Determined Crude Protein Estimates for Animal Feedstuffs and Diets 1Kosshak, A.A., 2Goshit, T. D., 3Dauda, G., 1Ameh, D.A. and 1Ochai, D. 1 Department of Agricultural Technology, Federal College of Land Resources Technology, Kuru, Plateau State, Nigeria. 2Department of Agricultural Science Education, College of Education, Gindiri, Plateau State, Nigeria. 3Department of Animal Breeding and Physiology, College of Animal Science, University of Agriculture, Makurdi, Benue State, Nigeria Received 00 Feb, 2015 Accepted 00 Feb, 2015 The author(s) 2014. Published with open access at www.questjournals.org ABSTRACT Feed is a major determinant of the profitability and sustainability of any animal enterprise. The feed must be nutritionally balanced and economically formulated to meet the purpose of production. Crude protein is a parameter that is often used in the assessment of the quality state of feed and feedstuff. The crude protein can either be calculated or chemically determined using various methods. Calculated crude protein is easier and quicker to carry out than laboratory assay of feed composition. Differences between the estimates of calculated crude protein and the chemically determined composition have been reported. A survey of agricultural, veterinary, biological and evolutionary literature yielded 107 animal feeding trial studies in which the author(s) reported crude protein estimates for calculated composition and/or chemically determined compositions. Using suitable statistical tools and reliability tests the study was conducted to provide a basis for use of calculated methods in animal feeding trials. It was determined from these studies that the calculated crude protein composition is a true reflection of the chemically determined estimate and hence be used where laboratory assay is not readily available. Keywords calculated chemical composition comparison crude protein feeding trial methods INTRODUCTION Feed has been reported to play an important role in economics of animal production as it constitutes about 60 70 percent in cost of production of eggs and poultry meat (North and Bell 1990).The ultimate goal of feed analysis is to predict the productive response of animals when they are fed rations of a given composition. Chemical analysis of formulated feeds is been used to obtain crude protein for feed (Gul and Safdar, 2009 Houndonougbo et al., 2012). Other researchers have tended to use the calculated crude protein of feed because either lack of possibility to determine the actual compositional data or there is insufficient time to obtain an analysis (Stanton and LeValley, 2010). The main quality factors of feeds are the energy value, the amount of crude fibre (CF being very important in regard to digestibility), crude protein (CP important for the balance and digestibility of essential amino acids) and the ether extract (EE), together with the different additives that may be present (Pavlova et al., 2011). Crude protein percentage is used as a method to determine the protein content of an animal feed. It measures the total nitrogen content of a feed or feedstuff. Crude protein measures both nitrogen from proteins as well as from non-protein nitrogen sources in the feedstuff such as creatinine and urea. Crude protein differs from true protein measurement that quantifies the actual protein content and excludes non-protein nitrogen (Annigan 2011). The methods available for crude protein determination includes Kjeldahl, Dumas, applications of Ultra-Violet (UV) visible spectroscopy, Nuclear Magnetic Resonance (NMR) spectroscopy and Infrared (IR) techniques (McClements, 2003 Stanton and LeValley, 2010 Krotz et al.,2014 Van Saun, 2014). Factors which determines what method of determination of crude protein to use include the intended use of obtained information, the equipment available, ease of operation, the desired accuracy, whether or not the technique is non-destructive, the sample preparation, method characteristics (e.g. sensitivity and specificity), speed (time required per analysis) and the number of samples analysed per batch (McClements, 2003). A peruse through studies using varying feed and different animals that involve both chemically analyzed and calculated values for crude protein showed differences in the components (Davis et al., 1962 Ashraf, 1981 Iyeghe-Erakpotobor et al., 2006 Ahmed et al., 2013 Alikwe et al., 2014). No study reports the justification for the use of calculated crude protein (CCP) composition in place of chemically determined crude protein (DCP). The present study was undertaken to establish similarities and correlation between calculated crude protein of feed and chemically analyzed crude protein of feed. Hypothesis H0 the calculated crude protein value is equal to chemically determined value and that similar and/or dissimilar crude corresponding CCP and DCP are not different HAthe calculated crude protein value is not equal to chemically determined value and that similar and/or dissimilar corresponding CCP and DCP are different. MATERIALS AND METHODS In carrying out feeding trials, animal scientists, biologists and veterinarians formulate diets usually at graded levels to determine the effect of such ingredients or feeds on certain selected growth, productive and reproductive parameters of animals. The ingredients and feed are calculated and analyzed to ensure that they meet the nutrient requirement of the animals that are to be tested. An internet (using Google Scholar and Yippy search engines) and manual (printed materials) search for animal experiments in which feed for animals were formulated was carried out (Table 1). Table 1 Journal type and number of Calculated and laboratory determined estimates and the number selected from each category. Source Journal CCP DCP SEL Agricultural 53 7 7 Veterinary 20 2 2 Biological 27 2 2 Evolutionary 7 – – Total 107 11 11 CPcalculated crude protein, DCP determined crude protein. SEL selected A survey of agricultural, veterinary, biological and evolutionary literature yielded 107 animal feeding trial studies in which the author(s) reported crude protein estimates for calculated composition and/or chemically determined compositions. Out of 107 studies, eleven (11) were selected as they reported crude protein for both chemically analyzed and calculated compositions. From the 11 selected studies, a data set consisting of 62 pairs of calculated composition and chemically determined composition matrices was obtained. The final data encompass more than 10 years of research involving 5 different species of animals including rabbits, pigs, Japanese quails, broilers and laying hens (Table 2). Table 2 Source of calculated and laboratory determined crude protein Authors CCPDCPAni 200716.7515.6516.8214.6516.9015.6517.0116.50Onyimonyi and Okeke 200717.9018.92Ari et al.,201122.8221.9321.3020.5621.3321.6019.6922.3219.5521.67Idiong et al.,200720.0020.8020.0220.2520.1622.2020.2019.8020.3021.4420.4020.60Ahmed et al.,201322.5025.9022.0025.4022.2022.1022.2021.0021.6020.00Akade et al.,201220.3021.0020.1120.4020.2820.1820.4520.41Oresanya 200525.2825.3730.1130.44Amaefule et al., 201117.1415.6317.4415.4017.4413.6418.0413.6518.3417.23Rashid et al.,200419.0020.5715.2916.0919.2820.3415.1615.81Sun 200722.0019.6222.0020.0222.0020.7922.0020.2820.0019.3120.0018.8320.0018.9420.0018.2017.5014.8817.5016.9817.5015.3217.5016.6316.5015.1216.5015.6316.5014.5816.5014.91Alikwe et al.,201421.3723.3521.2122.8720.1821.7019.2021.2318.1321.0623.1023.3523.2522.8723.3121.7023.3021.2323.2421.06 CCPcalculated crude protein, DCP determined crude protein III. Data Analysis Any two crude protein values within the same row of a matrix from the original data set exhibit dependence (Waitt and Levin, 1998). Hence, randomization tests are useful because they require no prior assumptions regarding the distribution of the test statistic. A web based number tables generator (HYPERLINK http//tools.perceptus.ca/number-tables.phphttp//tools.perceptus.ca/number-tables.php) was used to randomize the estimates of the calculated crude protein and the chemically analyzed for the original dataset of this study. The original dataset was randomized for both values four times. The values from the randomization process were then used to obtain disparity and the mean of the between corresponding estimates of calculated crude protein values and chemically determined crude protein values or subsets thereof to assess how close were the estimates of calculated crude protein and chemically analysed. The original and randomized data were subjected to statistical analysis using formulas of Cheverud (1988) and Roff (1998). EMBED CorelDRAW.Graphic.12 EMBED CorelDRAW.Graphic.12 EMBED CorelDRAW.Graphic.12 Where DCD is the mean disparity between the calculated crude protein and the chemically determined crude protein obtained from the original data. N is the number of observations used in the study. CCPi and DCPi are the calculated crude protein and chemically determined crude protein from the original data respectively. CCP1 and CCP2 are the randomized data for four times and DCP1 and DCP2 are the corresponding randomized estimates for the chemically determined crude protein. The data were tested for normality of distribution using Kolmogorov-Smirnov test (Willis et al., 1991 Waitt and Levin, 1998). Pearson, Kendall-Tau and Spearman correlation coefficients (Akanno and Ibe, 2005 Visscher et al., 2008) were determined using the bivariate correlation protocol of SPSS and Cohens Kappa(Cohen, 1968 Landis and Koch, 1977 Viera and Garrett, 2005) was run to determine the relationship and level of agreement between the calculated crude protein and the chemically determined crude protein. The level of agreement was determined using the interpretation of Kappa (Landis and Koch, 1977 Table 3). Mantel (1967) test was used to test similarity and dissimilarity amongst the corresponding CCP and DCP matrix. Table 3 Landis and Koch (1977) Interpretation of Kappa Kappa Agreement 0 Less than chance agreement 0.010.20 Slight agreement 0.21 0.40 Fair agreement 0.410.60 Moderate agreement 0.610.80 Substantial agreement 0.810.99 Almost perfect agreement Table 4 Descriptive statistics of CCP and DCP from the original data set obtained from 11 studies from year 2004 to year 2014. N Range Min Max Mean SEM Var CV CCP 62 14.95 15.16 30.11 19.93 0.34 7.1 13.37 DCP 62 16.80 13.64 30.44 19.61 0.43 11.27 17.21 N number of observation, min minimum estimate, max maximum estimate, var variance, CVcoefficient of variation, SEMstandard error of the mean, CCP calculated crude protein, DCP determined crude protein Table 5 Descriptive statistics of randomized CCP and DCP from the original data set obtained from 11 studies from year 2004 to year 2014N Range Min Max Mean SEM Var CV CCP1 62 14.95 15.16 30.11 19.93 0.34 7.1 13.37 CCP2 62 14.95 15.16 30.11 19.93 0.34 7.1 13.37 CCP3 62 14.95 15.16 30.11 19.93 0.34 7.1 13.37 CCP4 62 14.95 15.16 30.11 19.93 0.34 7.1 13.37 DCP1 62 16.80 13.64 30.44 19.61 0.43 11.27 17.21 DCP2 62 16.80 13.64 30.44 19.61 0.43 11.27 17.21 DCP3 62 16.80 13.64 30.44 19.61 0.43 11.27 17.21 DCP4 62 16.80 13.64 30.44 19.61 0.43 11.27 17.21 N number of observation, min minimum estimate, max maximum estimate, var variance, CVcoefficient of variation, SEMstandard error of the mean, CCP calculated crude protein, DCP determined crude protein Differences between the CCP and DCP for the original as well as the randomized data are presented in Table 7. The mean value for all the disparity between calculated crude protein and their corresponding laboratory determined crude protein was 0.32. The variance amongst the disparity for the original data had the least estimate when compared with the other randomized estimates. The result obtained for Kolmogrov-Smirnov test for normality indicated no significance and hence disparities for all the various subsets are normally distributed. Although the mean disparity for the original data subset obtained was0.32 corresponds to that obtained for data of the randomization, the original data had the narrower estimate of variance, standard deviation and range of values. The analysis of variance of the disparity for both data subsets demonstrated no difference Figure1. Scatter plot and regression line of calculated crude protein (CCP) on chemically determined crude protein (DCP) Table 7 Disparity between CCP and DCP from original and randomized dataset obtained from 11 studies from year 2004 to year 2014 N Range Min Max Mean SEM Var CCP-DCP 62 7.79 -3.40 4.39 0.32 0.21 2.80 CCP1-DCP1 62 19.95 -11.44 8.51 0.32 0.51 16.37 CCP2-DCP2 62 22.33 -10.42 11.91 0.32 0.56 19.44 CCP3-DCP3 62 23.16 -10.28 12.88 0.32 0.52 16.45 CCP4-DCP4 62 22.85 -13.30 9.55 0.32 0.56 19.28 CCP calculated estimate of crude protein, DCP chemically determined estimate of crude protein, Min minimum, Max maximum, SEM standard error of the mean, Var variance. V. DISCUSSION Feed manufacturing involves the processing of mixtures of feedstuffs and feed additives into a usable form to increase profits of animal production by maximizing the nutritional value of a feedstuff or a mixture of feedstuffs. Nutrient requirements as established by research conducted at various agencies are continually being used as the basis for feed formulation for animals. Nutrient requirement data are updated frequently to ensure current data are available for formulating least cost feeds. Nutrient profiles of feedstuffs sometimes supplied by different suppliers are continually updated based on actual assays conducted over a number of years. When formulating diet for animals, a safety margin is used to account for variations in the nutrient content of feed ingredients (Robinson and Li, 1996). A variety of biologic, chemical, enzymatic, and other sophisticated analytical and computational methods are used to evaluate nutrient content of feeds. Chemical methods can directly measure quantities of compounds associated with an essential nutrient however, they tell us nothing about digestibility and absorbability. Biologic, enzymatic, and other sophisticated methods provide a more nutritional perspective to feed analysis thus helping to better understand just how the animal will interact with its diet (Van Saun, 2014). The problem with using this method is that feeds vary in their composition and the organic constituents (e.g., crude protein, ether extract, crude fibre, acid detergent fibre and neutral detergent fibre) can vary as much as 15 percent, the mineral constituents as much as 30 percent, and the energy values at least 10 percent from published and commonly used tables values (NRC, 1994 Aduku, 2004 Stanton and LeValley, 2010). Most balanced diet formulations are currently based on proximate nutrient values. Increasing evidence suggests that nutrient values of dietary ingredients are also affected by active components such as enzyme inhibitors (Hall et al., 2009 Alu, 2012 Dashe, 2015). Variations from commonly used table values in the levels of crude protein contents would be explained in terms of processing methods, geographical condition of the areas in which cereals and legumes are cultivated and to formulation in compound feeds (Bhatti et al., 2002). Method of storage also influences the crude protein content due to certain metabolic activities during storage, composition of the feed (e.g. fibrous plant components are retained other dry matter lost). A slight amount of crude protein is lost during storage. However, the protein is lost at a slower rate than carbohydrates. Thus, due to drying off of the feedstuffs, crude protein concentration increases slightly during storage (Buckmaster et al., 1989 Hall et al., 2009). The correlation for the original data set for CCP and DCP shows a strong and positive correlation (87). A large positive matrix correlation indicates that correlations vary in similar directions, not that the magnitudes of individual correlations are identical (Waitt and Levin, 1998). Furthermore, with a very low and non-significant estimate of Cohens-Kappa (which shows that the agreement between the CCP and the laboratory determined crude estimates are largely due to chance), we can hypothesize that the corresponding estimates are similar. The sample size in this study was 62 and with a large sample size, the results will change as P values and confidence intervals are sensitive to sample size, and with a large enough sample size, the result can become statistically significant (Viera and Garrett, 2005). Again, variables from two similar sources can be expressed in the form of dissimilarity matrices (distance apart for sample composition), leading to a consistent analytic framework that will allow answer the question without requiring the data to conform to particular distributions or assumptions (Goslee and Urban, 2007). The simple Mantel statistic is effectively the correlation between two dissimilarity matrices. This is a normalized version of the original Mantel statistic (Mantel, 1967).The hypothesis of a Mantel test is that the degree of dissimilarity in one dataset corresponds to the degree of dissimilarity in another independently-derived dataset (Goslee and Urban, 2007). Since the Monte Carlo value obtained for the test gave a non-significant value, it holds true that the similarities and dissimilarities between CCP and DCP are the same. Both, the related samples sign test and the related samples Wilcoxon signed rank test resulted in a non-significant estimates of 0.162 and 0.092 respectively, the null hypothesis and that the Mantel test for dissimilarity between corresponding CCP and DCP are non-significant, it is save to conclude that although the corresponding CCP and DCP are different in terms of value, they are the same. Furthermore, the related samples Friedmans Two Way Analysis of Variance by Ranks and the Kendalls coefficient of concordance gave also non-significant values of 0.128 and 0.128 respectively the null hypothesis that the corresponding CCP and DCP are different was rejected. VI. CONCLUSION Feed manufacturing involves the processing of mixtures of feedstuffs and feed additives into a usable form to increase profits of animal production by maximizing the nutritional value of a feedstuff or a mixture of feedstuffs. Nutrient requirements as established by research conducted at various agencies are continually being used as the basis for feed formulations. This study has shown that the estimates of calculated crude protein composition as being used currently are similar to chemically determined composition of crude protein. Therefore, where running a chemical analysis of feedstuffs or diet is not possible in required time-frame, the calculated composition is a good alternative. References Aduku, A.O. 2004. Animal nutrition in the tropics Feeds and feeding, pasture management, monogastric and ruminant nutrition Pp. 17-18. Ahmed, M.A, Dousa, B.M and Abdel Atti, K.A 2013. Effect of Substituting Yellow Maize for Sorghum on Broiler Performance J. Worlds Poult. Res.3(1)13-17 Akade, F.T., Antyev. M, Mufwa, B.J., Nyameh. J. and Zaklag, D.U 2012. 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PAGE Arabic MERGEFORMAT 10 Page Quest Journals Journal of XXXXXXXXXXXXXXXXXXXXX Volume 2 Issue x (2014) pp 00-00 ISSN(Online) xxxx-xxxx ISSN (Print)xxxx-xxxx HYPERLINK http//www.questjournals.org www.questjournals.org Corresponding Author 1Kosshak, A. S. PAGE Arabic MERGEFORMAT 1 Page Department of Agricultural Technology, Federal College of Land Resources Technology, Kuru-Jos, Plateau State Nigeria. Email HYPERLINK [email protected] [email protected] Research Paper / BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU / BUU / BUU a va BUU CCPi-DCPj BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU . v. BUU CCPi-CCPj BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU BUU v BUU DCPi-DCPj s j/q_lvsDI7egC .ltNc0lpkSlNNl-Nf /l3 WlKCLeK/.g_p nu/[email protected]/_ nUBS Eq,[email protected] kclTZMkGFd aqypjM7TFy0g gqnih zGa
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