FGRAGE EVALUATEON USMC VAMOUS BABGRATORY TECEN‘RQEES A DMMPMHM {ta-r Hm 53:29?“ a?E M}. D. HiCBifiék’ STATE UNIVERSETY' Parnich Tinnimit 3W4 This ls to certify1 that the ' V thesis entitled ' FORAGE EVALUATION USING VARIOUS LABORATORY TECHNIQUES presented by Parnich Tinnimit has been accepted towards fulfillment of the requirements for Ph.D. degreein Dairy Science _“ _ | A J.“- ABSTRACT FORAGE EVALUATION USING VARIOUS LABORATORY TECHNIQUES BY Parnich Tinnimit Forages from tropical and temperate regions were evaluated by several laboratory methods. Samples were analyzed for crude protein, cell walls, acid-detergent fiber, lignin, cellulose, silica, and other chemical compo- nents by standard AOAC methods and by the Van Soest system of feed analysis. Various in_vi£rg fermentations, and extents of solubility by enzymes (cellulase, pepsin, amylase) and acidic buffers were determined on these forages. Simple and multiple correlations and regressions among various laboratory estimates and their relationships to in zizg measurements were conducted to determine and select the most precise prediction equations for in vivo parameters. Temperate grasses and legumes had higher levels of crude protein and i2.XiE£2.drY matter disappearance than did tropical forages but tropical forages had higher levels of cell walls, acid-detergent fiber, cellulose and ash than Parnich Tinnimit did temperate forages. The rates of decline in crude protein and ig_yitrg dry matter disappearance with advancing maturity for temperate forages were much greater than those for tropical forages and conversely the rate of increase in cell walls of temperate forages was greater than that for tropical forages. Generally, crude protein in forages was positively correlated with ash but negatively correlated with cell walls, acid-detergent fiber, cellulose, lignin and silica and all fibrous fractions were positively and mutually correlated. For temperate forages in y_i_lg dry matter digesti— bility, total digestible nutrients, digestible energy, dry matter intake, and digestible dry matter intake had positive correlation coefficients (r = 0.07 to 0.92) with crude protein, ash, in yitrg fermentations, or enzymatic incubations but negative correlations (r = -.07 to -.82) with cell walls, acid-detergent fiber, cellulose, hemi- cellulose and lignin. Water-soluble carbohydrates, total nonstructural carbohydrates and total available carbohydrates after cellulase plus amylase incubations had low correlations with in yiyg_parameters and these chemical components as well as total ash could not be used as single predictors 0f any in yiyg_parameters. Acid—detergent fiber and lignin could predict in yiyg dry matter digestibility of forages with moderate to high accuracy (r = -.70 to -.93, SEE = 2.9 to 5.6). Enzymatic incubation values, cellulase, Parnich Tinnimit amylase, pepsin or a sequential hydrolysis by two enzymes predicted digestible dry matter with an accuracy similar to that for the chemical components. With some forage species these enzymes predicted dry matter digestibility with useable accuracy having standard errors of 2.3 to 6.1 and correlation coefficients of 0.52 to 0.93. Two- stage in yi2£9_fermentation (IVDMD or IVOMD) was the method of choice for predicting in_yiyg dry matter digesti- bility of both grasses and legumes with small standard errors of estimate (SEE 1.8 to 4.4). Dry matter intake of forages could be predicted more precisely from cellulase, amylase or cellulase plus amylase than from chemical components or the two-stage }2_yitrg fermentation. Total digestible nutrient content could be predicted from acid—detergent fiber, cellulase plus pepsin, cellulase or the two—stage in_yit£2|fermenta- tion with standard errors of 1.8 to 6.5. Digestible energy content might be predicted from cellulase, cellulase plus pepsin or the two-state in yitrg fermentation with more accuracy than that from chemical components. The best predictors of in yiyg parameters for various types of forages were not the same and the predic- tion equations using the same predictor were different for each forage species. Multiple correlation and regression technique using combinations of chemical components did not significantly improve the precision of prediction for digestible dry Parnich Tinnimit matter and intake. However, combinations of 36-hour or two-stage $2.!iEEQ fermentations with these chemical com— ponents significantly improved the precision of prediction for digestibility and intake of legumes or grasses. Com— binations of the two-stage in_zitrg fermentation plus crude protein and ash accurately predicted total digestible nutrients of legumes whereas 36-hour in_yit£g_fermentation plus acid-detergent fiber accurately predicted total digestible nutrients of grasses. The combination of ether extract plus nitrogen-free extract or crude protein plus crude fiber plus ether extract accurately predicted total digestible nutrients of silages. FORAGE EVALUATION USING VARIOUS LABORATORY TECHNIQUES BY Parnich Tinnimit A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Dairy Science 1974 ACKNOWLEDGMENTS The author wishes to express his sincerest thanks to Dr. C. A. Lassiter, Chairman of the Department of Dairy Science, for granting him an assistantship to complete his degree at Michigan State University. Thanks and grateful appreciation are due to Dr. J. W. Thomas who served as an academic adviser and has given an untiring assistance throughout the course of these studies. Dr. M. B. Tesar, Dr. M. E. Miller, Dr. H. D. Hafs, Dr. M. G. Yang, Dr. J. H. Britt, Dr. D. R. Romsos and Dr. C. H. Wamhoff have kindly served as members of the thesis committee. The author also thanks Dr. Henry B. Kayongo-Male for providing some samples and information on forages from Puerto Rico and Dr. Roger Neitzel for assistance in sta— tistical and computer work. Finally, his wife, Mayuree Tinnimit, has not only given her firm encouragement but also helped in typing the manuscript of this thesis. ii ‘1 ‘h ‘4 5 ACKNOWLEDGME TABLE OF CONTENTS NTS O O O O O O 0 LIST OF TABLES . . . . . . . . LIST OF FIGU RES 0 O O O O 0 INTRODUCTION . . . . . . . . . . . . . REVIEW OF LITERATURE . . . . . . . . . . . . I. Proximate Analysis System . . . . . . II. Van Soest System . . . . . . . . . III. Fonnesbeck and Harris System . . . . . IV. Other Methods Used in Forage Evaluation . a. Digestion Trial . . . . . . . . b. In Vitro Fermentations . . . . . . c. Solubility and Turbidity Test . . . d. Forage Evaluation Using Enzymes . . . e. Soluble and Nonstructural Carbohydrates f. Energy System . . . . . . . . . g. Nutritive Value Index . . . . . . V. Considerations in Forage Evaluation . . . a. Goals and Precaution . . . . . . b. Sampling Techniques . . . . . . . c. Data Collection and Source Form . . . VI. Factors Influencing Nutritive Value of Forages . . . . . . . . . . . a. Chemical Composition . . . . . . b. Voluntary Intake . . . . . . . . c. Forage Digestibility . . . . . . VII. Relationships Among Chemical Components, Laboratory Value and In Viyg_Data . . . VIII. Some Characteristic Differences Between Tropical and Temperate Forages . . . . IX. Prediction Equations . . . . . . . . EXPERIMENTAL PROCEDURES . . . . . . . . I. Materials . . . . . . . a. b. Forages . . . . . . . . Enzymes . . . . . . . iii Page ii ix 12 14 14 15 18 19 22 23 24 24 25 26 26 27 27 29 32 35 56 58 74 74 74 76 Page II. Methods of Analysis . . . . . . . . 77 a. Chemical Analysis . . . . . . . 77 b. Two— stage In Vitro Fermentations . . 77 c. In Vitro Cell Wall and True D1gestibilities (IVCWD and IVTDMD) . . 79 d. Cellulase Digestion . . . . 79 e. Cellulase and Amylase Digestion . . . 80 f. Cellulase and Pepsin Digestion . . . 81 g. Pepsin Digestion . . . . . . . 81 h. Amylase Digestion and TNC . . . . . 82 i. Water- -solub1e Carbohydrates and Turbidity Test . . . . . . . . 82 III. Statistical Analysis . . . . . . . . 83 RESULTS AND DISCUSSION . . . . . . . . . . . 84 I. Studies on Enzyme Activities . . . . . 84 II. Results of a Comparative Study on Temperate and Tropical Forages . . . . 91 III. Results of Studies on Forages from Purdue University and Department of Dairy Science, Michigan State University . . . 95 IV. Results of Studies on Grasses from Michigan State University . . . . . . 108 V. Results of Studies on Forages from Thailand and Puerto Rico . . . . . . 114 VI. Results of Studies on Grasses and Legumes . 121 VII. Predictions of Nutritive Value from Laboratory Estimates and Stage of Maturity . . . . . . . . . . . . 127 VIII. Predictions of Forage Nutritive Value Using Multiple Regression Technique . . . 142 CONCLUSIONS . . . . . . . . . . . . . . . 172 BIBLIOGRAPHY o o o o o o o o o o o o o o 180 APPENDIX . . . . . . . . . . . . . . . . 194 10. ll. 12. 13. 14. LIST OF TABLES Page The effects of stage of maturity on intake, DDM and NVI of two forages . . . . . . . 34 Some simple correlation coefficients among various chemical components . . . . . . . 36 Some correlations between CWC and measures of forage nutritive value . . . . . . . 37 Simple correlations between ADF and other nutritive values of forages . . . . . . . 39 Some correlations between lignin and forage nutritive values 0 O O O O O O O O O 41 Some correlations between cellulose and measures of forage nutritive value . . . . 42 Some correlations between HC and other nutritive values of forages . . . . . . . 44 Some correlations between CP and other measures of forage nutritive value . . . . 46 Some correlations between IVDMD and other measures of forage nutritive value . . . . 48 Some correlations between CWD and other measures of forage nutritive value . . . . 51 Some correlations between IVCD and measures of nutritive value . . . . . . . . . . 52 Some correlations between DMS and other measures of forage nutritive value . . . . 53 Some correlations between cellulose solubility in CED and other measures of nutritive value . 55 Equations used to predict ig_vivo digestible dry matter (DDM) . . . . . . . . . . 59 Table 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. Page Equations proposed for use in predicting in vivo true digestible dry matter (TDDM) . . 62 Equations proposed for predicting in vivo organic matter digestibility (OMD) and digestible organic matter (DOM) . . . . . 64 Equations used to predict cellulose digestibility . . . . . . . . . . . 65 Equations proposed for predicting digestible protein . . . . . . . . . . 66 Equations proposed for predicting TDN . . . 68 Equations proposed to predict energy digestibility . . . . . . . . . . . 69 Equations used for predicting voluntary intake 72 Equations used to predict nutritive value index . . . . . . . . . . . . . . 73 Composition and activities of four different cellulases on two substrates . . . . . . 85 Effects of pH levels on activities of two cellulases, an amylase and pepsin . . . . . 86 Effects of length of incubation on various enzyme activities . . . . . . . . . . 88 Effects of enzyme concentrations on dry matter disappearance (DMD) of various substrates . . . . . . . . . . . . 89 Chemical composition and in_vitro digestibility of temperate and trepical forages . . . . . . . . . . . . . 92 Comparisons of chemical composition and digestibilities between tropical and temperate grasses with advancing maturity (30 to 90 days) . . . . . . . . . . . 94 Correlation coefficients among various chemical components and enzymatic incubation values . . . . . . . . . . 96 vi ‘(U A111 at! '5; 7‘. O Table 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. Page Correlation coefficients of in vivo parameters with chemical components and enzymatic incubation values . . . . . 99 Correlation coefficients of in_vivo parameters with in Vitro fermentations and enzymatic inEfibat1ons for forages from two sources . . . . . . . . . . 104 Correlation coefficients between age and chemical composition and digestibilities for MSU grasses during 30 to 135 days of growth . . . . . . . . . . . . . 109 Correlation coefficients among chemical components for MSU grasses during 30 to 135 days of growth . . . . . . . . . . 111 Correlation coefficients between chemical components and digestibilities for MSU grasses during 30 to 135 days of growth . . . 113 Correlation coefficients between maturity and measures of nutritive value for Thai forages during 30 to 90 days of growth . . . . . . 115 Correlation coefficients among chemical components for Thai and Puerto Rican forages . . . . . . . . . . . . . 117 Correlation coefficients among measures of nutritive value for Thai and Puerto Rican forages . . . . . . . . . . . . . 120 Correlation coefficients among chemical components for grasses and legumes from temperate and tropical regions . . . . . . 123 Correlation coefficients between digestibility or intake and measures of nutritive value in temperate forages . . . . . . . . . . 125 Regression equations for estimating in_vivo digestibility and in_vitro true dry matter disappearance from laboratory analytical values . . . . . . . . . . . . . . 129 Regression equations for estimating dry matter intake from laboratory analytical values 0 O O O O O O O C O O O O O 135 vii . 741' .. Table Page 42. Regression equations for estimating total digestible nutrients, digestible dry matter and digestible energy from laboratory analytical values . . . . . . . . . . 137 43. Regression equations for estimating forage nutritive value from stage of maturity . . . 139 44. Multiple regression equations for estimating in vivo digestibility and intake of temperate grasses and legumes from laboratory analytical values (Purdue University and MSU forages) . . 144 45. Multiple regression equations for estimating in vivo digestibility from laboratory analytical values (Michigan forages) . . . . 152 46. Multiple regression equations for estimating dry matter intake and digestible dry matter intake from laboratory analytical values (Michigan forages) . . . . . . . . . . 158 47. Multiple regression equations for estimating total digestible nutrients from laboratory analytical values (Michigan forages) . . . . 162 48. Multiple regression equations for estimating digestible energy from laboratory analytical values (Michigan forages) . . . . . . . 165 49. Multiple regression equations for estimating weight gain from laboratory analytical values (Michigan forages) . . . . . . . 170 viii LIST OF FIGURES Figure Page 1. Diagrammatic representation of major feed compounds from chemical analysis . . . . . 10 ix 1 a INTRODUCTION Forages are an important source of feeds for ruminants and other animals. Horses, cattle, sheep, goats, deer, rabbits, termites, or their associated intestinal microflora are able to digest cellulose to a varying degree. Cellulose and hemicellulose in forage crops can be broken down by the rumen microflora and provide the host animals with a source of energy. Fifty percent or more of the potentially useful energy of forages can be obtained from cellulose and hemicellulose fractions. Cellulose is the most abundant carbohydrate in the plant kingdom and can become the cheapest source of energy for ruminant animals under some conditions. In many geographical areas, the economic develop— ment of a livestock industry parallels the development of grassland farming or a good forage production program. There are about 10,000 Species of grasses (Gramineae) in the world and only 40 species are used to any large extent in the development of the cultivated pastures (73). In many areas, grass when farmed at its highest potential, yields more energy and protein equivalent per acre than any other crop. In humid temperate regions, grass yields over 11,000 kilograms dry matter per hectare per year m\~ . 6.» a5 *8 N\H whereas in the tropics it may yield 22,400 kilograms dry Hatter per hectare per year. Tropical forages are important to future world food supplies. In the trOpics, there are probably 10,000 million acres of land which provide grazing, food and shelter to animals. The overall contribution of these tropical grasslands is to sustain about half the domestic animals of the world and to produce one—third of the meat and one-fifth of the milk products produced globally (32). Besides, more than half of the cattle of the world are raised in the tropics with their plane of nutrition being very low. Forages may not supply sufficient energy but do provide sufficient crude protein for most ruminant animal enterprises. The greater part of energy provided by forages comes from the carbohydrates and its value depends on the quantity and digestibility of these carbohydrate fractions. The nutritive value of forages varies with species, cultivars, age, stage of cutting, environmental factors, fertilization, cultural practices, etc. Proper evaluation of forage nutritive value is useful to animal feeders, forage producers, and researchers such as animal nutritionists, forage breeders, and forage management specialists. The best method to determine quality and nutritive value of any forage is to feed that forage to animals. However, a digestion trial is time-consuming and needs considerable quantities of material and equipment. Recently, many laboratory procedures have been developed for estimating forage nutritive value. The objectives of the present study were: 1. To further evaluate and verify some new laboratory methods with additional samples of grasses and legumes from both temperate and trOpical regions; To develop and evaluate some new techniques for forage evaluation using cellulase, amy- 1ase, pepsin and a combination of these enzymes; To determine correlations among laboratory estimates and in_yiyg data and to develop prediction equations for various parameters. é It. REVIEW OF LITERATURE TERMINOLOGY OF FORAGE EVALUATION As in many disciplines, terms and definitions used are important in communicating ideas. However, many terms are peculiar to some disciplines and readily understood only by those familiar with.the subject matter. Several of the terms and abbreviations used throughout this presentation are given in Appendix Table l. SYSTEMS AND METHODS OF FORAGE EVALUATION I. PROXIMATE ANALYSIS The Weende system of proximate analysis which was developed over 100 years ago is universally used by many laboratories. Moisture, crude protein, crude fiber, ether extract, nitrogen—free extract and ash in feedingstuffs are determined and from these values an evaluation of the feed can be made. a. Variables Used l. Nitrogen (N). Nitrogen is a basic and charac- teristic constituent of all proteins and many other com- Pounds and total nitrOgen is determined by the Kjeldahl method. On the average, crude protein (CP) in common 4 9‘ r—o (I In a. (I) _ A T? V. feedingstuffs contains about 16% N. Therefore, a factor of 6.25 (100/16) is generally used to convert % N in feeds to % CP (4,130). In fact, CP also contains non-protein nitrogen, amides, amines and amino acids. 'The term true protein is used in certain situations. 2. Crude Fiber (CF). As originally proposed, CF represents an indigestible, fibrous fraction of the feed. It contains 50 to 80% of cellulose (C), 15 to 25% of hemi- cellulose (HC), 10 to 50% of lignin (L) and some insoluble substances (42,123). Crude fiber is determined by alter- nately boiling the sample with dilute H2504 and dilute NaOH (4,130). 3. Ether Extract (EE). Ether extract represents crude fat which contains both triglycerides, fatty acids and many non-triglyceride components such as chlorOphyll, sterols, anthocyanin, waxes, etc. Ether extract is obtained by percolating ether over the sample for 8-16 hours and evaporating ether to obtain ether extract (4,130). I 4. Ash. This fraction represents the mineral residue of feeds. It is obtained by igniting the sample at 600 C and contains various major and trace elements and/or other oxides (4,130). 5. Nitrogen-free Extract (NFE). This fraction represents the highly digestible carbohydrates of the feed. It contains starch, sugars, pentosans, fructosans, hexosans and some impurities as well as errors resulting from previous determinations. Nitrogen-free extract is obtained )1 ‘C mi ‘u. T. ‘A 'I.’ ll ‘0. by subtracting percentages of moisture, CP, CF, EE and ash from 100 (4,130). 6. Moisture. The water content of feeds can be determined by drying the sample at 100 to 103 C for 4 to 12 hours depending on type of sample, etc. (4,130). b. Usefulness of Proximate Analysis The system of proximate analysis has been used in human, nonruminant and ruminant nutrition studies for more than a century. The determinations of chemical constituents in this system are simple and less time consuming than methods that more precisely identify nutrients. The measurement of food energy expressed as total digestible nutrients (TDN) is derived from this proximate analysis. The use of TDN in feeds and feeding is internationally accepted and many TDN values exist so that this system will likely continue to be used for many years. Data using TDN and proximate analysis values have been used in the develOp— ment of feeding standards (71,83). In addition, many scientists have used chemical constituents from this system to predict in_giyg_performances. Bredon §t_al, (19) used CP, CF and NFE to predict in yiyg dry matter digestibility and TDN of tropical forages whereas Adams §£;§l, (1) used CP, CF to predict TDN values of many temperate forages. Finally, many prediction equations for digestible protein (DP) have been developed using CP (19,23,59). c. Shortcomings of Proximate Analysis The proximate analysis does not properly separate plant carbohydrates into discrete chemical entities based on their biological availability (121,122). The CF residue does not include all HC, L and acid—insoluble ash (42,123). Nitrogen-free extract which is supposed to contain soluble carbohydrates, contains variable amounts of HC, L, C, and acid-insoluble ash. The method of determining NFE by difference, therefore, accumulates errors from previous determinations. Ether extract or crude fat does not recover protein—bound lipids and contains many non—nutritive impurities as discussed earlier (42). The TDN system resulting from these methods is therefore based on inaccurate assumptions. Another drawback of the system is related to nutrient digestibility. In many cases, CF is more diges— tible than NFE because the latter contains L, HC and some C. Butterworth (22) reported that the digestion coeffi- cients of CF for Para grass, Bermuda, Guinea and Spear- grass were 57, 66, 72 and 74% whereas the coefficients of NFE for the same grasses were only 51, 59, 67 and 57%, respectively. One final drawback of the proximate analysis system is that the chemical estimates are poorly related to the in_!izg_data. They are poor predictors of forage quality and no useable prediction of digestible energy could be made from these chemical constituents (21,80,81). mu; ~\~ 6. 'A nan. ntk II. VAN SOEST SYSTEM In the early sixties Van Soest proposed a new system of feed partitioning which overcomes some of the shortcomings of the TDN and proximate analysis system (115, 116,119,120,126), and uses detergents to differentially solubilize forages and partition forage dry matter into high digestible and low digestible components. With this system, feed dry matter is partitioned into 2 parts, namely, cell wall constituents and cell contents and the details of which are discussed below. Diagrammatic repre— sentation of major feed compounds from chemical analysis is shown in Figure l. a. Variables Used 1. Cell Wall Constituent (CWC or CW). This frac- tion represents the total fiber of forages. It is composed of HC, C, L, attached protein, lignified nitrogenous com- pounds, heat-damaged protein, keratin and silica. This fraction is determined by boiling forages with neutral detergent (sodium lauryl sulphate) solution for 1 hour (43). The components of CW can only be digested by the microorganisms. The digestibility of CW fraction is variable but could be calculated from the extent of ligni- fication of the acid-detergent fiber (ligno-cellulose) fraction. 2. Acid-Detergent Fiber (ADF). This fibrous fraction is composed of C, L, acid—insoluble ash (silica) and some cutin. Acid-detergent fiber is determined by boiling forages for 1 hour with acid—detergent solution (43). 3. Lignin (L). This component is a polymer of phenyl propane units found in plants and forages. Basic- ally, L in feeds is indigestible and has no nutritional value to the animals. Lignin is determined by either solubilizing C in ADF with 72% H2804 or by oxidizing L in ADF by permanganate solution (43). 4. Cellulose (C). This is a B-D-Glucose polymer. Cellulose in this system is determined by the difference between the ADF and L content (43) or as the residue after boiling in an acetic-nitric acid mixture. 5. Hemicelluloses (HC). They are amorphous mixed polysaccharides found in plant cell walls. A simple deter— mination used for HC is the difference between CW and ADF (43). 6. Silica (Si)and Cutin. Plant Si has no nutri— tional value to animals. Silica builds up in plant tissues through plant metabolism or from soil contamination. Silica in this system is obtained by oxidizing acid- insoluble ash with hydrobromic acid (43). Cutin is an aliphatic compound composed of fatty acids, hydrocarbons, alcohol and aldehydes. The amount is obtained by treating permanganate C with 72% H SO4 and 2 calculating cutin after ashing (43). lO Feed I J I Dry Matter (DM) Moisture I l fl Organic Matter (OM) Inorganic Matter (Ash) Nitrogenous Compounds Non—Nitrogenous Compounds l I Fat or ET] Ether Extract (EE) Carbohydrates 4 1 Non-Protein Nitrogen1 Total Fiber as Water—soluble Cell Walls (CW) or Crude Fiber (CF) Carbohydrates (WS-CHO) Acid Detergent Fiber (ADF) Hemicellulose (HC) | ' I T Cellulose (C) Crude Protein (CP) (NPN) 1 Cutin Lignin (L) Silica (Si) 2 FiSure l. Diagrammatic representation of major feed compounds from chemical ana1y51s. l'2Not determined in this study. 11 7. Cell Contents (CC). This fraction representing the highly digestible portion of plant cells is composed of lipids, protein, amino acids, sugars, starch, organic acids, non-protein nitrogen (NPN), pectin and other water- soluble material. They are assumed to be completely digestible by animals without the aid of microbial fermen- tation. The digestibility of CC is assumed to be 98% (120). Cell contents are equal to 100-CWC (43). b. Advantages of Van Soest System Feed partitioning and analysis by the Van Soest system has been accepted by scientists all over the world because it classifies feeds and forages according to nutritional functions by monogastric and ruminant animals (43). The results of chemical analysis have been satisfac- torily correlated with in_yiyg data and accurate prediction equations have been developed. For example, the ratio between L and ADF or log L/ADF is found to be highly correlated with CW digestibility (118,120). Another advantage of this system is a recent development reported by Van Soest and Robertson (125) who developed techniques to recover some analytical reagents from the, otherwise, discarded detergent solutions. c. Shortcomings of Van Soest System Even though Van Soest's system is one of the best procedures, criticisms have been made by others (36,42). Firstly, about 30% of the total protein remains in the CW 12 fraction and most of the silica is extracted into CC (42). Secondly, the acid-detergent solution dissolves a consider— able amount of lignin and ADF also contains some nitrogen. Thirdly, hemicelluloses and C are determined by differences so they may contain errors from previous determinations. Fourthly, Van Soest's procedures with high energy cereal feeds, protein supplements and mixed diets filter very slowly and the resulting CWC values are erroneously high. Finally, many scientists found that Van Soest's system (summative equation) used to estimate forage digestibility did not agree with in_yiyg digestibility (36) or dry matter disappearance (DMD) by the Tilley and Terry method (64). At present, scientists are attempting to deveIOp, refine or alter this system in order to improve its rela- tionship to in vivo data. III. FONNESBECK AND HARRIS SYSTEM A modification of the Van Soest's system has been proposed by Fonnesbeck and Harris (42) and this is outlined as follows. a. Variables Used The chemical constituents and major partitioning are similar to those of the Van Soest system. However, the methods for determining some constituents are different and the system requires quantitative determination of more 13 constituents than does the Van Soest or proximate analysis system. 1. Cell Wall Constituents (CWC). These fractions are further partitioned into HC, C, L, acid-insoluble ash (Si). Cellulose and HC are partially nutritive matters which can be digested only by microbial enzymes. Lignin and acid-insoluble ash (mostly Si) are non—nutritive residue. 2. Cell Contents (CC). These fractions are further partitioned into soluble carbohydrates, protein, solvent extract, and soluble ash. The solvent extract is further separated into fats plus fatty acids and non-fat extract. The cell contents include nutritive matters that are diges- tible by enzymes secreted in the GI tract or are otherwise soluble enough for absorption. The non-nutritive solvent extract is the non—saponified fraction composed mostly of chlor0phyll, sterols, carotenoids, waxes, etc. This new system of feed partitioning requires analyses for DM, CWC, HC, L, acid-insoluble ash (Si), N, ~ash, solvent extract and nutritive solvent extract. Other fractions Can be calculated as follows: 1. CC = 100 - CWC 2. Soluble carbohydrate = CC - (protein + solvent extract + soluble ash) 3. Soluble ash = Total ash — Acid insoluble ash 4. Cellulose = CWC - (HC + L + Acid insoluble ash) 5. Non-nutritive solvent extract = Solvent extract - nutritive solvent extract 14 b. Advantages of the System In this system, CWC are free from CP due to the use of pepsin and the CWC value is higher than that of Van Soest's because of increased recovery of HC and Si. There is no necessity to determine ADF. Cell wall constituents, HC, L and acid insoluble ash are determined directly and C is obtained by difference as indicated earlier. Lignin can be determined using the CWC residue and there is no need to add asbestos to the crucible when determining lignin. These new methods provide for the determination of solvent extract, nutritive and non—nutritive solvent extract, soluble ash and soluble carbohydrates. IV. OTHER METHODS USED IN FORAGE EVALUATION a. Digestion Trial Results of a digestion trial are usually considered one of the best methods for evaluation of different forages. It is also used in studying nutrient requirements, diges- tibility, intake, energy metabolism, mineral utilization, body weight gain and toxic substances with many animal species. The techniques and procedures in digestion trials have been fully described in Bulletin 45, Commonwealth Agricultural Bureau (28), and by Lindahl (68), Maynard and Loosli (71). Although most investigators use a 7-day collection period, a 5-day trial was found sufficient for tropical forages (46). Comparative digestibilities for various forages by sheep, rabbits and heifers have been discussed by Ingalls et al. (52,53). 15 b. In Vitro Fermentation 1. Types of In_yitrg_5ystems There are many types of systems and procedures for performing in_yit£2 rumen fermentations. These measure the disappearance of any of the following constituents, dry matter, cellulose, cell walls, other carbohydrates or the production of acids or gas (55). Generally Speaking, short-time fermentations are superior in predicting dry matter intake (DMI), digestible dry matter intake (DDMI), nutritive value index (NVI) and weight gain (24,51) whereas long-time or two-stage fermentations are superior for predicting apparent digestibility (ll,55,57,73). Techni— ques and procedures for one- or two-stage rumen fermenta- tions can be found in the papers by Tilley and Terry (112), Barnes (10,11), Johnson (55,57), Troelsen (113), Goering and Van Soest (43), Mellenberger §t_al. (76) and Minson and McLeod (80) and those for in yitrg true dry matter digestibility (IVTDMD) or in_yitrg_cell wall digestibility (IVCWD) by Van Soest §t_§l, (126) and Goering and Van Soest (43). Many scientists presently use a two-stage 12.!1EE2 rumen fermentation (48-hour with buffer plus rumen fluid plus a 24 or 48-hour pepsin digestion) as the most practical technique to predict forage digestibility with a small residual standard deviation. This method is also satisfac- tory for evaluating tropical forages even though the mean éky matter in Vitro digestion coefficient of tropical 16 grasses was 2.6 percentage units lower than in_yiyg value (98). However, this method may not be satisfactory for silage samples (90). From this two-stage in yitrg technique, in_yit£g_ organic matter digestibility (IVOMD) or in_yit£g_digestible organic matter (IVDOM) can be obtained after ashing the residue. [In yitrg OMD is preferred to IVDMD because it can eliminate the variation in IVDMD when the samples or feces are contaminated with dirt and sand (81). 2. Factors Affecting In Vitro Fermentation A detailed discussion on factors affecting in vitro rumen fermentations is found in a paper by Johnson (57). However, some important points will be discussed here. 2.1 Inocula Bezeau (14) reported that the activity of inocula from an Ayrshire cow was significantly higher than that from a Holstein cow when fed alike. Troelsen (113) indi- cated that there were no differences due to use of inoculum from either sheep, cattle or goats. In general, the IVCD using inocula from an alfalfa-fed cow is greater than that when using inocula from the one fed grass (14). Robertson and Van Soest (100) found the inocula from a forage-fed donor (timothy hay) to digest greater amounts of CW from forage and concentrate substrates than did inocula from a concentrate-fed animal. 17 2.2 Forage Species and Cuttings There are significant differences in in_yi£rg DMD between genera and Species of grasses and legumes (11,98). Forages from a 4-cutting system have higher IVDMD values than those of a 3—cutting, 2-cutting and l-cutting system (3). 2.3 Effects of Drying and Temperature Treatment There are no significant differences in rate and extent of IVDMD, CWD for samples which have been freeze— dried or oven-dried at 100 C. However, heating and drying at 100 C for over 4 days will decrease the IVDMD of forages (63,112). Johnson gt_al. (59) reported that undried samples of forages had higher digestibility values and correlation coefficients between IVCD and DDM than did dried samples. Forages grown under a high temperature regime have lower IVDMD than those exposed to a cool temperature (106). 2.4 Effects of Grinding The particle size does not affect dry matter dis- appearance because finely ground or coarsely ground samples from the same herbage have identical values (112). For mature forages, grinding slightly increased IVDMD. Grind- ing through a 1 mm screen is recommended (113) because grinding more finely does not improve prediction accuracy (74). 18 2.5 Effects of Sample Size McLeod (74) reported that the use of 0.1 gm sample for fermentation will increase the residual standard error of prediction to 3.4% units as compared to 2.5 units when using 0.5 gm sample. Others found that increasing the size of the sample led to a decrease in the 12.XEE£2 digesti- bility value and, therefore, 0.5 gm sample has been recommended (80,113). 2.6 Minerals and Other Substances The in yitrg fermentation values have increased variation and differences when urea and glucose are omitted from the media and especially when the inocula are from different donors. With ample urea and glucose, differ— ences in IVDMD due to inocula are decreased (87). Many trace minerals stimulate IVCD when used at low concentrations. These are Co, I, Fe, Mn, Mo, Rb, Zn, Cd, Cr, Sr. Minerals that depress IVCD are Ba, B, Cd, Cr, Co, Cu, F, Fe, Mn, Ni, Se, Sr, V, and Zn (70). Other substances that reduce IVDMD values are Si, L, high level of fat, tannin, alka- loids and other plant inhibitors. c. Solubility and Turbiditerests The extent of solubility of forages in dilute acids and other chemicals can be used as a rapid and inexpensive Hmthod of forage evaluation. Dry matter solubility of forages in 1 N HZSO4 and cellulose solubility in 1 M cuPriethylene diamine have been determined and the 19 results are highly correlated with many in_vivo parameters (34,35,60). A simple procedure of measuring turbidity of a forage extract was found to correlate well with some chemical components (5,13). d. Forage Evaluation using Enzymes Hydrolysis of forages by enzymes was prOposed over 10 years ago by Donefer et a1. (39) as an evaluation technique. They used the amount of hydrolysis by cellulase, pepsin and a mixture of these enzymes to estimate forage DDM. Tilley and Terry (112) used pepsin after fermentation with rumen fluid. Smith (105,106) and Grotelueschen and Smith (47) used takadiastase (amylase) to determine total available carbohydrates (TAC) and total nonstructural carbohydrates (TNC) in plant tissues. Later, Jarrige et a1. (54) used cellulase hydrolysis to estimate OMD and DOM. Guggolz et a1. (48) pr0posed cellulase plus pronase to evalute forages and crop or woody residues, while Moore et a1. (82) proposed another cellulase preparation (Onozuka) for this purpose. Recently, McQueen and Van Soest (75) used a cellulase from Trichoderma viride (fungi) to evaluate forages. 1. Cellulases Available cellulase preparations are crude enzymes. The cellulase complex contains Cl enzyme, 8-1-4 Glucanases (= Ox) and B-Glucosidases (66). Cellulase can hydrolyze 20 HC, C, some starch and other nitrogenous compounds and may yield 30% glucose from cellulose digestion (48,54,103). The Onozuka product is obtained from fungi Trichoderma viride and contains hemicellulase, lactose, galactose, glucose and arabinose. It solubilizes pure cellulose to the extent of 10 and 46% for Solka-floc (48, 82). This preparation was the most active preparation investigated. Cellulase used by French investigators contains 19% CP and 60% of the powder is water soluble. It contains other enzymes which attack HC, CP, starch and some nitro— genous compounds (54). Jarrige gtgalj (54) reported that the residue remaining after cellulase digestion was highly correlated (r = -.921**) with in_yiyg_OMD using 100 samples of hays and herbages and the standard error of prediction was 3.22%. The correlation coefficient between total solubles after cellulase (TSAC) and digestible organic matter (DOM) was highly significant (r'= 0.922**) and the standard error of prediction was only 2.82%.* They concluded that cellulase digestion appears to be a better predictor of in_vivo digestibility than ADF and the Tilley-Terry method as far as time, equipment and manipulations are concerned. In addition, the correlation coefficient between cellulase residue and DMI was highly significant (r = -.70) and that between cellulase residue and DOM intake was ~.81 (P < .01). The prediction of DMI and DOM intake from 21 cellulase residue was not very precise but satisfactory with standard errors of 8.93 and 6.32 g/kg 0.75, respectively (54). Guggolz §E_§l. (48) reported that the correlation coefficient between total solubles after enzymes (TSAE) and in xiyg DMD was 0.900 (P < .01) and that between TSAE and TDN was also highly significant. Results from these two groups of investigators clearly indicate that the new cellulase technique can predict in_yiyg_DDM with sufficient precision thus eliminating any necessity for rumen fluid from a donor animal. 2. Pepsin Forages have been incubated with pepsin alone or pepsin has been used as a second-stage incubation after cellulase or rumen fluid fermentation (39,40,65,123). The addition of pepsin or pronase as a second-stage incubation will increase DMD by 4 to 5% units (48,82). This type of second incubation may not be necessary for samples low in protein. Moore and Mott (81) reported the DMD values of tropical grasses (Panicum spp.) by pepsin digestion to have a low correlation with 12.YEYQ.DDM and DMI with standard errors of 3% and 8 g/kgo'75 , respectively. On the contrary, Donefer et al. (39,40) found highly significant correlations between pepsin DMD and £2 vivo data for different forages as shown below: 22 £_ Samples DMD (pepsin) vs. NVI 0.95** All forages DMD (pepsin) vs. RI 0.87** to o.94** " DMD (pepsin) vs. ED 0.68** to o.73** " (** P < .01) Furthermore, Wilkins and Minson (129) reported that OM solubility in pepsin was significantly correlated with 12 vivo OMD or in_vivo CD with standard errors of prediction of 5 and 6%, respectively. 3. Takadiastase Takadiastase is a crude amylase which also contains some sucrase, maltase, oligo-l, 6-glucosidase and traces of hemicellulase or cellulase and is used to determine TAC in plants (47). There is meager evidence concerning the use of takadiastase to estimate forage digestibility and DMI by farm animals. e. Soluble and Nonstructural Carbohydrates Some scientists use water-soluble carbohydrates (WS-CHO) and nonstructural carbohydrates (TAC, TNC) in their forage evaluation programs (37,47,61,105,106). The significance of WS-CHO in ruminant nutrition is unclear because Ingalls (51) found negative and non—significant correlations between soluble carbohydrate and certain in 2132 measurements. On the other hand, TAC has been used to predict in inQ_OMD and cellulose digestibility satisfac- torily (129). 23 f. Energy System 1. Gross Energy Most feedingstuffs, forages and even feces contain approximately 4.40 KCal/g of dry matter and thus gross energy values are not useful in evaluating feeds or forages. 2. Digestible Energy (DE) Apparent digestible energy represents energy intake minus fecal energy and is a measure of the portion of food energy that can be used by the animals (71). 3. Metabolizable Energy (ME) Metabolizable energy represents actual energy absorbed and utilized by the animal and is obtained by subtracting urinary and gaseous energy from DE (71). 4. Net Energy (NE) During energy metabolism a portion of energy is used for metabolic processes and called heat increment. Net energy is therefore obtained by subtracting heat increment from ME and represents energy used for maintenance, growth, Production of meat, milk, eggs, etc. (71). 5. Total Digestible Nutrients (TDN) Total digestible nutrients represent an expression 0f the energy content of feedingstuffs. Chemical components measured in the proximate analysis system were used to 24 formulate the TDN system by Henry and Morrison in 1910 (42) and TDN represents the sum of digestible CP, digestible CF, digestible NFE, digestible EE (2.25) expressed as a percent. 9. Nutritive Value Index (NVI) Nutritive value index (NVI) is the mathematical product of forage digestibility and intake (31). However, these two factors may not be closely related. Nutritive value index is relatively useless in practical ration formulation (57) but may be useful in evaluating forages. Nutritive value index of alfalfa is higher than that of bromegrass which is higher than that of reed canary grass (51). For tropical forages, NVI is highest at 4-5 weeks of regrowth indicating that grazing or feeding should be done at this stage of growth and the high correlation (r = 0.91, p <.01) between NVI and body weight gain supports this idea (46). V. CONSIDERATIONS IN FORAGE EVALUATION Forage evaluation is truly an interdisciplinary science involving groups of investigators such as the live- stock feeders, nutritionists, forage producers, and plant breeders, etc. Emphasis by each group may be different but many considerations should be recognized by all so that proper forage evaluation can be successfully accom- plished. 25 a. Goals and PreCautions The objectives and systems used in forage evaluation may vary among groups. For example, livestock feeders may be interested only in particular constituents (i.e., CP, TDN, etc.) in forages whereas the nutritionist may be interested in interrelationships among chemical components, mineral contents, vitamins, digestibility and nutrient metabolism. Therefore, each discipline should clearly out— line its goals of forage evaluation so that the results and interpretation will be clear to all concerned (56). A feeding trial is expensive in terms of animals, feeds, labor, time and equipment. Therefore, investigators attempt to replace the feeding trial with laboratory or chemical methods. The major goal of forage evaluation is to develop and utilize laboratory methods for determining forage quality that is related to animal performance. A useful laboratory procedure for routine evaluation of forages should have these characteristics: 1. Require a small sample of the forage under study; 2. Simple enough to permit rapid evaluation with minimum equipment and reagents; 3. Must produce repeatable results with high degree of accuracy in predicting forage nutritive value. The most economical approach would be to analyze a series of forages having known animal data and then to correlate animal with laboratory data followed by estab— lishment of prediction equations (90,123). 26 Many animal and laboratory variables may influence the results and relationship obtained in one location so that a somewhat different relationship may be obtained under different conditions. Without similar techniques, standard— ization and other precautions, prediction equations should be used with caution. b. Sampling Technigges The best laboratory estimates may be useless if the sample obtained for analysis is not representative of the entire lot of material. Also, the results of a forage analysis will be reliable and useful only if the sample taken is representative of what the animal consumes (102). Larsen (67) describes sampling techniques for baled hay, loose hay, haylage, grass silage, corn silage, etc. Grier (45) described the preparation of plant materials for chemical analysis. Troelsen (113) suggested sampling techniques for forages including the methods of collection, morphological fractionation, drying, grinding and weighing. Goering and Van Soest (43) describe some excellent sampling techniques for dry and wet feeds. Other details on sampling are presented in Bulletin 45, Commonwealth Agricultural Bureau (28). c. Data Collection and Source Form Some standardization for reporting forage data would facilitate data collection, tabulation and use by investi- gators. Harris et a1. (49) have developed an excellent 27 computerized source form for reporting data that should be considered by all feed and forage investigators. VI. FACTORS INFLUENCING NUTRITIVE VALUE OF FORAGES The nutritive value of forages usually refers to chemical compositon of feeds, their digestibility, animal intake and the nature of the digested products (85). a. Chemical Composition Chemical composition is the most simple and the generally accepted criterion used in feed evaluation and is influenced by a number of factors, some of which are briefly discussed below. 1. Species and Cultivars All chemical fractions of trOpical grasses differed between species (30,64). Generally, legumes contain more CP and less CW than do grasses. Legume CW contains less HC and is more lignified than grass CW (107). A cultivar of alfalfa named Vernal had only 17% CP whereas Du Puits alfalfa contained 22% CP when out at the same age. Rohweder and Henderson (101) also reported that different cultivars of oats had different chemical composition. Cultivars of Goodfield, Portal, Rodney varieties contained 22, 17, 14% CPI respectively. 2. Age or Stage of Maturity As forages get older, CP, DP, ash, EE, TDN, soluble carbohydrates, P, K and carotene contents decrease whereas 28 CW, ADF, CF, L, C, methoxyl, pentosan, and hexosan contents increase while NFE and Ca contents may remain unchanged depending on the Species (3,15,16,19,37,64,72,73,92,101, 104,114,120,128). 3. Leaf-Stem Ratio Forage legumes have a different leaf/stem ratio from that of grasses and usually leaves contain more nutrients than stems. McIlroy (73) found the CP content in leaves of several forages was higher than that in stems. 4. Nitrogen Fertilization Nitrogen fertilization increases CP content of grasses while maintaining CWC, C, L, DE at the same level (16,73,122). With corn silage, 179 Kg N/ha increased CP from 6 to 9% and TDN from 65 to 66% whereas ADF decreased from 34 to 26% (101). 5. Climatic Conditions Important environmental factors that cause changes in forage nutritive value are light, temperature and fertility level. An increase in light intensity will increase WS-CHO and DM but decrease CP, ash, CWC, C and L without materially altering digestibility. Van Soest (122) further reported that an increase in temperature will cause an increase in CWC, C, L, DM with a decrease in CF and WS-CHO. Therefore, the increase in both light and temperature will lower the nutritive value of the forages. The above statement may be true when the temperature 29 increases beyond 32 C. On the contrary, alfalfa grown in chambers maintained under cool (18 C) or warm (32 C) temperatures, showed an increase in CF but a decrease in WS-CHO and DM at 32 C. Crude fiber was relatively unchanged (106,107). b. Voluntary Intake Animal performance is more related to voluntary intake than to digestibility (10,51) and feed intake varies much more than does the latter (81). McIlroy (73) reported that intake of legumes is greater than that of grasses. This is in agreement with Ingalls (51) who showed that the DMI of 4 forages were in the following order: birdsfoot trefoil Z alfalfa > bromegrass > reed canary grass. In addition, lactating cows consume much more feed in relation to body size than do other animals. Therefore, some standardization is necessary when comparing intake data. 1. Expression of Feed Intake Since voluntary intake is much influenced by forage species and body size, Crampton §E_al. (31) have developed a method to eliminate the variation in forage intake due to different sizes. They found that the coefficient of variation (CV) in expressing feed intake per unit of meta- bolic size was only 13% but CV was 20% when based on con- sumption per animal per day. They arbitrarily suggested that DMI of a standard forage by sheep was 80 g/BWKgo‘75 30 and further suggested that the metabolic size and relative intake terms be used regularly in all intake studies. Expressions of feed intake are illustrated below (31). Term Cgefficient of Variation gm or Kg/animal/d r 20 % gm or Kg/100 1b(Kg) BW/d i 14 % gm or Kg/ngé75/d i 13 % Relative Intake (RI) gm dally forage‘DMI x 100 0.75 80 x (BWKg ) Intakes of good temperate grasses are normally higher than 80 g/d (81). However, most tropical grasses have lower maximum intake values with excellent forages having a value of 70 g/Kgo’75. Yet maximum intake of chopped tropical forages by sheep was 83 g DM/Kg metabolic weight/day with most values generally below 80 g/d (81). Grieve and Osbourn (46) indicated that expressing voluntary intake of tropical forages based on metabolic size is valid since only 0.2% of the variation in feed intake was due to differences in metabolic size of the wethers. 2. Factors Controlling Voluntary Intake A more complete discussion on factors influencing intake can be found in the reports by Balch and Campling (9), Conrad (29), Van Soest (117), Ingalls (51). Short statements about main factors that control forage intake follow. 2.1 2.2 2.6 2.8 2.9 2.10 2.11 2.12 31 Central nervous system (CNS) and the hypo- thalamus may control overall responses of feed intake and hunger drive. Thermostatic regulation; warm temperature (2 40 C) will decrease feed intake. Chemostatic regulation; blood or rumen meta- bolites act on sensory mechanism so that high levels of ruminal VFA, quinine, NaCl or blood glucose will decrease voluntary intake. Lipostatic regulation; increased body fatness decreases intake. Oropharyngeal regulation; mouth is a metering system. Caloric density; intake stops when enough energy is consumed. Reticulo-ruminal size; gut fill limits intake. Cell walls; 50—60% or more CW decreases intake. Rate of digestion and passage; rapid digestion and passage will increase feed intake. Activities and level of production; increased activities and milk production will increase intake. Physical forms of feeds; grinding and pelleting increases intake; silage decreases DMI. Protein and Mg; low Mg and CP (S 7%) decrease intake. Highly fertilized forages (with high NPN) decrease intake. 2.13 2.14 2.16 2.17 2.18 32 Additives; urea decreases intake; molasses increases intake. Water; there is a positive relation between water and dry matter intake. Contamination; mold, feces, sand, hairy feeds tend to decrease intake. Relative humidity; high temperature plus high humidity decrease intake. Parasites decrease intake and digestibility. Hormones; thyroxine and growth hormone increases feed intake. c. Forage Digestibility The common ig_vivo digestibility terms are DMD and OMD. Digestibility of forages is governed by many factors and some are discussed below. 1. Species and Cultivars In general, temperate forages are more digestible than tropical forages due to lower CWC, ADF and lignin. Mean digestibility of tropical forages was found to be 12.8 units lower than that of temperate grasses (73,81). At a comparable age, alfalfa is more digestible than orchard grass (3). Leafy species are more digestible than stemmy varieties (73). 2. Age and Maturity Digestibility decreases with advancing maturity (31). Immature forages an d those with high CC are more 33 digestible because CC might supply readily available nutrients to the microbial population (39). Digestible dry matter of temperate grasses decreases steadily at the rate of 0.4-0.5 percentage unit per day from initial date of growth in the spring (3,51,93,94). The rate of change in DMD of tropical grasses ranges from a decrease of 0.7% to an increase of 1.3% units per day from initial growth (81). Minson and McLeod (80) reported that the digestibi— lity of trOpical grasses in Australia decreased at 0.2 percentage unit per day in summer regrowth compared with 0.1 unit/day in autumn regrowth. However, Grieve and Osbourn (46) reported that trOpical grasses in Trinidad showed an increase in DMD and GE up to 5 weeks of regrowth but the digestibility decreased rapidly after that period. The effect of stage of maturity on nutritive value of forages is presented in Table l. 3. Chemical Composition Chemical composition appears to be more related to digestibility than to intake (81). A decrease in CP, ash, soluble carbohydrates along with an increase in CWC, ADF, In methoxyl and Si results in decreased digestibility. 4. Fertility Level The effect of fertilization on digestibility is variable. The digestibility of fertilized "improved" tropical grasses is similar to that of the temperate 34 TABLE 1. The effects of stage of maturity on intake, DDM and NVI of two forages. Forage and Maturity DDM DMI NVI Timothy Hayg(Temperate)a % Early Bloom 65 1593C 75 Half Bloom 57 1487 60 Full Bloom 51 1242 46 Post Bloom 48 1079 37 Bermuda (Tropical)b 3-Week 59 70d 52 4-Week 65 88 73 6-Week 55 77 49 DDM = Digestible dry matter; DMI = Dry matter intake; NVI = Nutritive value index. aLloyd et a1., 1961. J. Anim. Sci. 20:468. bGrieve and Osbourn, 1965. J. Agr. Sci. 65:411. Cg/animal/d. d . g/Kgo 75/d- grasses (98). Fertilization with zero to 448 Kg N/ha increased protein digestibility but not DMD or CD of first and second cut timothy hay (97). 5. Feed Preparation The digestibility of forages is greatly affected by particle size. The reduction of particle size by grinding or pelleting will enhance voluntary intake but decreases its digestibility (81,128). 35 6. Level of Feeding The digestibility of forage is decreased when the level of feeding is increased. Forages probably do not remain in the rumen sufficiently long for maximum fermenta— tion and degradation of CWC by rumen microorganisms (81). 7. Animal Species The animals themselves may have different effi- ciencies for forage digestion. Sheep digest concentrate more efficiently than cattle whereas cattle digest dry roughage to a greater extent than do sheep (99). Butter- worth (20) reported that the digestion of ruminants under tropical conditions may differ from that found in the temperate regions. VII. RELATIONSHIPS AMONG CHEMICAL COMPONENTS, LABORATORY VALUES AND IN VIVQ DATA a. Relationships Among Chemical Components of Forages The relationships among chemical constituents are influenced by stage of maturity, fertilization, heat, light, etc. as discussed previously. By definition, there is a negative relationship between CWC and CC and thus when CWC, ADF, C, L increase CP, sugars, lipids, vitamins, minerals and other soluble materials will decrease. The negative nature of the CP:CW relationship and the positive nature of relations among fractions of CW are given in Table 2 for both tropical and temperate forages. 36 TABLE 2. Some simple correlation coefficients among various chemical components. Temperatel Tropical2 Items Forages Forages CWC vs. CP - .78** - .63** CWC vs. C 0.71** 0.68** CWC vs. L 0.17 0.45** L vs. C 0.48** 0.38** L vs. ADF 0.65** 0.65** 1Van Soest. 1965. J.A.S. 24:834. 2Kayongo-Male et al., 1972. **p < .01 For abbreviations, see Appendix Table l. b. Relationships Between Cell Walls and Measures of Nutritive Value of Forages Cell wall contents not only have a great influence on concentrations of other components, but also have signi- ficant effects on forage digestibilities, DMI and ADG as shown in Table 3. An increase in CWC definitely decreases ig_yiyg‘ DDM, OMD, CWD, ED, IVDMD, IVTDMD of both tropical and temperate forages but with differing magnitudes of depres- sion. High CWC will significantly lower the NVI of grasses and legumes. Dry matter intake and RI are significantly depressed by high CWC and finally ADG is reduced due to low DMI caused by high CW concentrations. 37 TABLE 3. Some correlations between CWC and measures of forage nutritive value. Factors correlated r Forage Type Reference CWC vs. £2.21X2 DDM -.48** Grasses 88 " " —.74** Legumes 88 " " —.86** Alfalfa 88 " " -.47** Gra. + Leg. 88 " " -.50** Low ADF diet 8 " " -.32** Gra. + Leg. 58 " " —.45** Forages 123 " " -.20 " 51 CWC vs. In_Vitro DMD -.84** Trop. gra. 81 " " -.14 Corn plant 12 “ " -.22* Trop. gra. 64 CWC vs. I2 Vitro TDMD -.69** " 81 CWC vs. 12.2129 DCW 0.85** Low ADF diet 8 CWC vs. CWD -.67** Trop. gra. 81 CWC vs. DCW 0.73** All forages 120 CWC vs. OMD -.8l** TrOp. gra. 81 CWC vs. ED -.38** Gra. + Leg. 58 CWC vs. NVI -.63** " 58 CWC vs. RI -.56** " 58 CWC vs. DMI —.70** All forages 51 " -.66** " 51 " —.76** " 123 " -.77** “ 77 " —.65** " 117 CWC vs. ADG —.80** Trop. gra. 81 *P < .05 **P < .01 For abbreviations, see Appendix Table 1. 38 c. Relationships Between ADF and Nutritive Values of Forages Acid-detergent fiber has a negative relationship to CP but positive relationships to CWC, C and L (64,117) and has significant negative effects on forage digestibili- ties, consumption and nutritive value as illustrated in Table 4. There was a significant and negative relation between ADF and ig_yiyg DDM, CD, DP, ED, IVDMD and IVTDMD for all forages studied (Table 4). This indicates that ADF alone can be used to predict forage digestibilities with moderate accuracy. The variation in correlation indicates that prediction equations should be developed for each species at each location. High concentrations of ADF decreased NVI of forages and depressed forage DMI except in one case. d. Relationships Between MADF and In Vivg Data of Forages Modified acid—detergent fiber (MADF) was highly correlated with in_yiyg DDM (r = -.85, P < .001) whereas the correlation coefficient between ADF and DDM was only -.70 (P < .001). The prediction of i§_yiyg DDM from MADF had an error of 5.63% compared with 8.99% when using ADF. Besides, MADF was also highly correlated (r = -.82, P < .001) with DMI with about 7% standard error of estimate. Apparently MADF may be preferable to ADF to predict both intake and digestibility of forages and this method can be adapted to any routine forage evaluation system (25). 39 See Appendix Table l for abbreviations. TABLE 4. Simple correlations between ADF and other nutritive values of forages. Factors correlated r Forage Type Reference ADF vs. In Viyg DDM —.39** Grasses 88 " “ —.76** Legumes 88 " " —.84** Alfalfa 88 " " —.80** Orchard 88 " " -.53** Gra. + Leg. 88 " " -.85** Forages 131 " " -.78** Gra. + Leg. 115 " " -.75** “ 123 " " -.74** All forages 58 " " —.70** Gra. + Leg. 25 " " -.66** " 51 ADF vs. IVDMD -.90** Corn Plant 12 " " -.38** Trop. grasses 64 ADF vs. IVTDMD -.82** " 81 ADF vs. £2.YEYQ.CD -.89** Forages 131 ADF vs. Dig. ADF +.50** All forages 120 ADF vs. DP -.85** Forages 131 ADF vs. ED -.76** A11 forages 58 ADF vs. RI -.31** " 58 .ADF vs. NVI -.61** " 53 ADF vs. DMI —.64** " 123 .. .. _,53** " 117 " " +.37 " 51 *P < .05 **P < .01 40 e. Relationships Between Lignin and Measures of Forage Nutritive Value Legumes contain up to 2-3 times more lignin than do the grasses (51,95,119,122). Lignin in grasses is more alkali-soluble than that of legumes (72,119). Lignin itself is not digestible but inhibits the digestibility of CWC, C and HC. However, L does not affect the digestibility of CC (122). Lignin probably decreases digestibility by forming incrustations and complexes with CHO, C, HC, etc. (115). The use of L as a predictor of digestibility is excellent within the same forage species (123). Usually lignin is negatively related to other measures of ig_vivo nutritive value such as intake and digestibility as can be seen from Table 5. As lignin content increased, there were significant decreases in CD, in_!iyg_DDM, DP, ED, IVDMD and IVTDMD of all forages studied. The NVI of forages decreased slightly with an increase in L content. Also lignin had a low correlation with DMI but in one case L was positively correlated with DMI (51) and in another case the correla- tion between L and RI was positive (58). The positive correlation between lignin and intake when all forages are combined may be somewhat complicated by the greater intake Of legumes than grasses and the greater lignin content of legumes as compared to grasses. With high correlation coefficients between L and DDM in either grasses, forage legumes or within one spec1es, 41 TABLE 5. Some correlations between lignin and forage nutritive values. Factors correlated r Forage Type Reference L vs. 12 Viyg DDM —.62** Grasses 88 " " -.81** Legumes 88 " " -.95** Reed Canary 88 " " -.46** Gra. + Leg. 88 " " -.72** Low ADF diet 8 " " —.50* Forages 51 " " -.88** " 131 " " -.80** A1f.,B,Tim. 88 " " —.82** Grasses 115 " " -.74** Leg. 115 " " —.40** Gra. + Leg. 115 " " —.95** Mixed Forages 111 " " —.64** All Forages 53 L vs. IVDMD -.76** Trop. grasses 81 " " —.69** Corn plant 12 u n —,92** All forages 84 " " —.96** Dried grasses 84 u u —,16 Trop. grasses 64 L vs. IVTDMD -.80** Trop. grasses 81 L vs. £2.YEYQ.CD -.88** Forages 131 L vs. DP ‘-84** " 131 L vs. ED -.60** " 58 L vs. DMI +-78** " 51 " " -.13 " 117 ,, ', —.10 n 123 L vs. RI +-21 " 58 L vs. NVI —.11 " 58 *P < .05 **p < .01 For abbreviations, see Appendix Table l. 42 L can be used to predict forage digestibilities with higher accuracy than using CWC or ADF alone. Forages Cellulose content differs somewhat among grass species but on the and legumes is similar. average the content in both grasses Relationships Between Cellulose and In_Vivo Data of Alfalfa C has greater resistance to hydrolysis by cellulase than C from grasses of similar digestibility (54). ig_vivo parameters are shown in Table 6. TABLE 6. of forage nutritive value. The relationships between C and other Some correlations between cellulose and measures Factors correlated r Forage Type Reference C vs. 12 yiyg DDM -.60** Low ADF diet 8 " " -.62** Forages 131 " " —.40** Gra. + Leg. 60 " " —.81** " " 25 C vs. IVDMD -.75** Corn plant 12 " " -.35** Trop. grasses 64 C VS. IVTDMD -.64** " " 81 C vs. £2_XAXQ.CD -.60** Forages 131 " " 0.25* Gra. + Leg. 60 C vs. DP -.64* Forages 131 C vs. DC 0.95** Low ADF diet 8 " " 0.67** All forages 120 C vs. ED —.46** Gra. + Leg. 60 C vs. DMI -.59** All forages 117 C vs. RI —.75** Gra. + Leg. 60 C vs. NVI —.78** “ “ 60 *P < .05 **P < .01 For abbreviations, see Appendix Table l. 43 All data indicate a negative relationship between c and i_r_1_ 1113 DDM, CD, DP, ED, IVDMD and IVTDMD of both grasses and legumes. An increase in C content would significantly reduce the NVI of forages and result in a significant decrease in both RI and DMI. The predicta- bility of i§_yiyg_DDM using C may not be satisfactory for some combined forages due to low correlation coefficients between these 2 factors but in many cases C content can predict DDM of each forage species with moderate to high accuracy. 9. Relationships Between HC and Other Nutritive Values of Forages Grasses may contain up to 4 times the amount of HC found in legumes. Legume HC is less digestible than HC from grass species (120). Hemicellulose is neither chemi— cally nor nutritionally uniform since it contains variable proportions of pentose, hexose and their derivatives as well as pectin (120,121). Its relationship to digestibility is low (Table 7). Therefore, HC may not be useful as a sole predictor for ig vivo digestibility of forages. h. Relationships Between Silica and Digestibility or Weight Gain In temperate grasses, plant silica causes a decline in digestibility of about 3 units per 1 unit of Si (43). The correlation coefficient between Si and DDM in reed canary grass was highly significant (r = “.86, P < .01) Whereas that between L and DDM was only *.58 (122)- 44 TABLE 7. Some correlations between HC and other nutritive values of forages. Factors correlated r Forage Type Reference HC vs. i3 vivo DMD 0.02 Low ADF diet 8 HC vs. IVDMD -.13 Corn plant 12 " " 0.03 Trop. grasses 64 HC vs. Dig. HC 0.94** All forages 120 BC vs. CP -.26** TrOp. grasses 64 HO vs. IVTDMD —.45** " 62 **P < .01 For abbreviations, see Appendix Table 1. Coward-Lord gE_gi. (30) reported that plant Si caused a decline in ig_yiyg_dry matter digestibility of 3-5 units per 1 unit of silica in trOpical grasses. However, Si did not significantly depress IVDMD and IVTDMD of tropical grasses (64,81). In addition, body weight gains of growing finishing lambs were significantly affected by adding soluble Si (sodium silicate) to their drinking water at a concentration of 800 mg/l (108). Feed efficiency of these lambs was also decreased. Silica may exist in forages in various forms thus quantitative relations between total Si and nutritive value may not be high. 1. Relationships Between CP and Other Measures of Forage Nutritive value Five to 10% of total N is bound with lignin in CWC and fisindigestible (27,119). When CP level in forage is 45 less than 6%, digestibility of total carbohydrates is markedly decreased (73). There are highly positive corre- 1ations between CP and several measures of nutritive value but negative correlations are also noted for CWC and other fibrous constituents (Table 8). An increase in CP level is normally followed by increases ig.yiyg DDM, CD, DP, ED, NVI, RI, DMI, IVDMD, IVTDMD and IVCWD. However, the relationships between CP and forage digestibilities are variable depending on types of forages and other factors. The correlations between CP and intake or NVI are rather low and the use of CP as a sole predictor of ig.yiyg_intake or digestibility may not be satisfactory. The use of CP to predict fiber fractions or vice versa provides only moderate accuracy. j. Relationships Between Two-stage In Vitro Fermentation Value and Other Measures of Forage Nutritive Value The two-stage i§_yit£9_fermentation procedure (IVDMD or IVOMD) has been widely accepted as a useful technique to predict forage digestibility or its nutritive value. In tropical grasses, ig_yiyg_OMD can be predicted from IVOMD with a correlation of 0.93 (P < .01) and a standard error of 3.98%. The correlation coefficient between IVOMD and IVDMD in mixed forages was 0.98 (P < .01) (81) . The relationships between two-stage i§_yiyg_fermen— tation values and other measures of nutritive value are shown in Table 9. Digestible DM, true digestibility and 46 TABLE 8. Some correlations between CP and other measures of forage nutritive value. Factors correlated r Forage Type Reference CP vs. i3 yiyg DMD 0.21 Grasses 88 " " 0.76** Legumes 88 " " 0.11 Low ADF diet 8 " " 0.85** Forages 131 " " 0.58** Gra. + Leg. 60 CP vs. IVDMD -.24 Corn plant 12 " " 0.20** Trop. grasses 64 CP vs. IVTDMD 0.46** " " 64 " " 0.82** 81 CP vs. lg vivo CD 0.83** Forages 131 " " 0.23* Gra. + Leg. 60 CP vs. IVCWD 0.27** Trop. grasses 64 CP vs. DP 0.97** Low ADF diet 8 " " 0.99** All forages 120 CP vs. ED 0.61** Gra. + Leg. 60 CP vs. NVI 0.62** " " 60 CP vs. RI 0.47** " " 60 CP vs. DMI 0.54** All forages 117 CP vs. EDDM 0.38** Trop. grasses 64 CP vs. ETD 0.27** " " 64 CP vs. CWC -.63** " “ 64 CP vs. ADF -.68** " " 64 CP vs. C -.69** “ " 64 CP vs. L -.48** “ “ 64 CP vs. Si +.l7 " " 64 *P < .05 **P < .01 For abbreviations, see Appendix Table l. 47 ED of different feeds and forages can be satisfactorily predicted from IVDMD. With these high correlation coeffi- cients, one prediction equation may be satisfactory to predict ig vivo DDM of both grasses and legumes as squested by Tilley and Terry (112). The correlations between IVDMD and estimated digestibilities (summative equations) in tropical grasses are very low (r = 0.05 to 0.06) indicating that the summa- tive equation developed from temperate forages may not be satisfactory with tropical forages. The method (IVDMD) may be used to predict ADG and NVI of forages with moderate accuracy but it is not satis— factory to routinely predict either RI or DMI. k. Relationships Between In Vitro True Dry Matter Digesfibility (IVTDMD) and Forage NutritIVe Value Van Soest et al. (126) developed the method for IVTDMD and reported that ig_vivo apparent digestibility was positively and significantly related to IVTDMD (r = 0.96) and the prediction of apparent digestibility had a standard error of 2.8%. The correlation between IVTDMD and ig'vivo true digestibility was exceptionally high (r = 0.98) and the prediction of the latter had a standard error of only 1.7%. The relationship between IVDMD (Tilley and Terry) and IVTDMD was also high (r = 0.95) with a standard error of prediction of 3%. Regarding chemical composition, Johnson and Pezo (62) reported that only CWC showed a high TABLE 9. Some correlations between IVDMD and other measures 48 of forage nutritive value. Factors correlated r Forage Type Reference IVDMD vs. £2.X£XQ.DDM 0.83** Grasses 88 " " 0.97** Legumes 88 " " 0.89** Forages 131 " " 0.93** " ll " " 0.73** Tall Fescue 18 " " 0.95** Grasses 76 " " 0.97** Alfalfa 76 " " 0.99** Hi-roug. diet 76 " " 0.99** Lo-roug. diet 76 " " 0.96** Gra. + Leg.+woods 76 " " 0.97** Forages 10 " " 0.93** " 126 " " 0.90** Gra. + Leg. 58 IVDMD vs. IVTDMD 0.95** Forages 126 IVDMD vs. .3331 yiyg TDMD o.92** " 126 IVDMD vs. IVTDMD 0.65** Trop. grasses 64 IVDMD VS. EDDM 0.05 " " 64 IVDMD vs. ETD 0.06 “ " 64 IVDMD vs. ED 0.89** Tall fescue 18 IVDMD vs. IVCWD 0.66** Trop. grasses 64 IVDMD vs. VFA 0.81** “ " 2 IVDMD vs. ADG 0.78** “ “ 81 IVDMD vs. DMI 0.51* Forages 10 IVDMD vs. RI 0.08 All forages 58 IVDMD vs. NVI 0.46** “ " 58 " " 0.76** “ “ 10 *P < .05 **P < .01 For abbreviations, see Appendix Table l. 49 but negative correlation with IVTDMD for all grasses and legumes in temperate and tropical regions. Therefore, IVTDMD might be predicted from either CWC or the two-stage IVDMD. Since the numerical values for IVTDMD are close to or equal to actual i§_yiyg_true digestibilities, this method was proposed as the most accurate one to predict 12. vivo digestibilities (126). The method requires less time than the two-stage IVDMD and it gives satisfactory results when used to evaluate both temperate and trOpical forages (30,62,126). 1. Relationships Between Cell Wall Digestibility (CWD)_and Other Measures of Nutritive Value of Forages. The digestibility of CWC or ADF in grasses is higher than that in legumes due to a lower lignin content in grass CWC. Cell wall digestibility decreases with advancing maturity along with the lowering of HC and C digestibilities (91). The rate of CWD was positively correlated (r = 0.77, P < .05) with cell contents even though CC did not contribute directly to a faster rate of CWD (110). On the other hand, rate of CWD or total CWD was negatively correlated with CWC, ADF, L, C, Si and I/ADF, L/C and L/HC ratios. In tropical grasses, an increase in ADF was followed by a significant decrease in CWD whereas ratios of L/ADF, L/C did not have any significant effect on CWD. However, increasing L/HC seemed to decrease CWD in tropical forages. 50 In temperate forages, CWD was significantly decreased by an increase in L and L/C ratio whereas L/ADF ratio did not have significantly negative effect on CWD. Some data in Table 10 do not agree with that of Goering and Van Soest (43) who found a significantly negative correlation between L/ADF and CWD and used logarithm of L/ADF when calculating CWD. ig_yiE£g_CWD was highly correlated with two-stage IVDMD, i2_yiE£g_true digestibility and OMD. This indicates that CWD is one of the main factors controlling i§_yiyg forage digestibilities. In fact, the actual value of IVCWD is similar to that for 12.XAYQ.CWD (36). m. Relationships Between In Vitro Cellulose Digesti- bility (IVCD) and Other Nutritive Values of Forages Cellulose digestibility ig_yi££g is another labora— tory technique used to estimate DMI, digestibility and NVI of forages. IVCD (12-hr) measurements are highly corre— lated with ig_yiyg data for grasses (60). Cellulose diges- tibility also decreases with advancing maturity and the rate of CD is rapid within the first 12 hours then decreases (38). The correlations between IVCD and other measure- ments in Table 11 indicate that ig yiyg DDM has a moderate relationship to IVCD. The correlation coefficients between IVCD and in vivo CWD or DE are sufficiently high to assure that these two parameters could be accurately predicted from IVCD. Dry matter intake of forages had a high correlat1on 51 TABLE 10. Some correlations between CWD and other measures of forage nutritive value. Factors correlated r Forage Type Reference IVCWD vs. IVTDMD 0.92** Trop. grasses 64 IVCWD vs. IVDMD 0.66** " 64 IVCWD vs. EDDM 0.14 " 64 IVCWD vs. OMD 0.97** " 81 IVCWD vs. CP 0.27** " 64 IVCWD vs. CWC -.21* " 64 IVCWD vs. ADF -.36** " 64 IVCWD vs. L —.26** " 64 IVOWD vs. L/ADF -.1l " 64 IVCWD vs. C -.28** " 64 IVCWD vs. L/C -.16 " 64 IVCWD vs. HC 0.02 “ 64 IVCWD vs. L/HC -.23* " 64 IVCWD vs. Si -.13 " 64 IVCWD vs. L -.88** Temp. Gra. + Leg. 109 IVCWD vs. L/ADF -.60 " 109 IVCWD vs. L/C -.82** " 109 IVCWD vs. cc o.77* " 110 *P < .05 **P < .01 For abbreviations, see Appendix Table 1. with short-time, lZ-hr IVCD. Longer time Showed high correlations with NVI and TDN IVCD (36-48 hrs) and might be used to predict NVI and TDN of both tropical and temperate forages with reasonable accuracy. TABLE 11. nutritive value. 52 Some correlations between IVCD and measures of Factors correlated r Forage Type Reference IVCD vs. 12.2122 DMD 0.75** Gra. + Leg. 88 " " 0.61** All forages 58 " " 0.72** Gra. + Leg. 60 " " 0.49** Tall Fescue 18 IVCD(36-hr) vs. i2_Viyg_DMD 0.95** TrOp. grasses 7 IVCD vs. ig_!iyg CD 0.88** Forages 131 " " 0.89** Tall Fescue 18 " “ 0.48** Gra. + Leg. 60 IVCD(24-hr) vs. EE.ZEYQ.CD 0.93** Non-legumes 86 IVCD(24—hr) vs. DE 0.90** " 86 " " 0.73** All forages 86 " " 0.87** " 38 IVCD(36-hr) vs. DE 0.94** Trop. grasses 7 IVCD vs. ED 0.76** Gra. + Leg. 60 " " 0.64** All forages 58 IVCD(lZ-hr) vs. DMI 0.83** Forages 38 IVCD vs. RI o.50** " 60 IVCD vs. NVI o.71** " 60 IVCD(48-hr) vs. NVI 0.85** Trop. grasses IVCD(36-hr) vs. TDN 0.94** " **P < .01 For abbreviations, see Appendix Table 1. n. Relationshi 5 Between Dr Matter Solubilit (DM8 and Other Measures of Nutritive Value of Forages The mere solubility of the dry matter under stan- dardized conditions has been proposed as a simple but useful technique for evaluating several forages. lations with many in vivo parameters such as DDM, It has high corre- CD, ED 53 and intake (Table 12). As with many other laboratory techniques, the relationships are greater within one plant species than when species are combined. Most of the corre- lations between DMS and forage digestibilities are not as great as those for two—stage IVDMD. Therefore, the predic- tion of ig vivo digestibilities using DMS may not be satisfactory. However, forage NVI might be estimated from DMS in both grasses and legumes since the correlation coefficients are 0.67 to 0.83. TABLE 12. Some correlations between DMS and other measures of forage nutritive value. Factors correlated r Forage Type Reference DMS vs. £2.21X2 DMD 0.60** Grasses 88 " " 0.71** " 60 " " 0.76** Legumes 88 " " 0.87** Alfalfa 60 " " 0.54** Gra. + Leg. 88 DMS vs. ED 0.52** Mixed forages 58 " " 0.71** Grasses 60 .. .. o. 87** Alfalfa 60 DMS vs. CD 0.68** Grasses 60 " " 0.40 Alfalfa 60 DMS vs. RI 0.52** Mixed forages 58 " u 0.79** Grasses 60 " " 0.55** Alfalfa 50 DMS vs. NVI 0.83** " 60 " " 0.81** Grasses 60 " " 0.67** Mixed forages 58 *p < .05 **P < .01 For abbreviations, see Appendix Table 1. 54 0. Relationships Between Cellulose Solubility in CED and Other Measures of NutritiVe Val e’of Forages The solubility of cellulose or other plant material in cupriethylene diamine (1.0 M) has been used to measure forage nutritive value. There are significant correlations between cellulose solubility in CED and ig_yiyg_DDM, CD, ED, intake and NVI. The relationships between CED solubility and digestibilities were slightly higher than those between DMS and digestibilities. Again, the correlations within one plant species were greater than when species were com- bined. The relationships between CED and RI or CED and NVI were variable and low, so the use of CED as a predictor may not be satisfactory. p. Relationships Between Turbidity Test and Other Components Bennett and Archibald (13) and Archibald §E_§i. (5) found highly significant correlations between turbidity in forage extracts and some chemical constituents. With various forage samples in two studies, the relationships between turbidity test and CP were positive (r = 0.86 and 0.52, P < .01) and negative with CF (r = -.82 and -.45, P < .01) and also positive with total ash (r = 0.65 and 0-53. P < .01). These relationships have never been used for prediction purposes. q. Relationship Between Intake and Other Factors Voluntary intake is not highly correlated with digestibility except in a limited number of forage species (10:31,ll7). However, DMI is highly correlated with 55 TABLE 13. Some correlations between cellulose solubility in CED and other measures of nutritive value. Factors correlated r Forage Type Reference CED vs. ig_yiyg_DMD 0.69** Grasses 88 " “ 0.92** “ 34 " " 0.57*- Alfalfa 60 " “ 0.69** Legumes 88 " " 0.67** Gra. + Leg. 88 " " 0.52** All forages 58 CED vs. i2 Vivo CD 0.92** Grasses 34 " " 0.50* Alfalfa 60 CED vs. i§_yiyg_ED 0.90** Grasses 34 " “ 0.55** Alfalfa 60 “ “ 0.46** All forages 58 CED vs. RI -:16 " 58 " “ 0.71** Alfalfa 60 " " 0.60* Grasses 34 CED vs. NVI 0.76** " 34 " " 0.76** Alfalfa 60 " " 0.08 All forages 58 *P < .05 **P < .01 For abbreviations, DDMI, and DEI (51). see Appendix Table 1. Among the chemical components, CWC seems to be one of the best predictors of intake (117). However, for tropical grasses ADF may be the better pre— dictor of OM intake (81)- Dry matter disappearance (DMD) after 6 hours of incubation with rumen fluids or rumen fluids plus pepsin gave very high correlations with DMI (0.83), DDMI (0.85), DEI (0.90). (10,51,77). Thus a 6-hr DMD value can be used to predict DMI with reasonable accuracy. 56 r. Relationship Between Digestibility and Other Factors The relationships between i2_yiyg digestibility and chemical composition have been previously discussed. Acid- detergent fiber, Si, L have significantly negative correla- tions with digestibility whereas IVDMD, IVOMD, IVCD, IVTDMD, TSAE, CP have significantly positive correlations with the digestibility (11,48,117,126). Digestibility and intake are positively correlated in some cases but are negatively correlated in others. VIII. SOME CHARACTERISTIC DIFFERENCES BETWEEN TROPICAL AND TEMPERATE FORAGES a. Histochemical Differences In their review, Moore and Mott (81) tabulated the following differences between trOpical and temperate grasses: 1. The carbon pathways for photosynthesis are different. 2. Photorespiration is lower in tropical than in temperate grasses. 3. The maximum level of photosynthesis is higher in tropical than in temperate grasses. 4. TranSpiration may be less in trOpical than in temperate grasses and tropical grasses use less water per gram of DM produced during growth. 5. Leaf anatomy differs eSpecially with reference to the develOpment and distribution of vascular bundles. 57 b. Differences in Chemical Composition When compared with temperate grasses cut at similar stages of growth, trOpical grasses, on the average, have lower levels of CP, TDN but higher CWC, ADF, CF, L, C and Si (30,73,81,98). Hemicellulose content of temperate grasses increases slightly with maturity (127) but it does not change with maturity in tropical grasses (30). For trOpical grasses, the contents of CWC, ADF, L and Si are higher than those of temperate grasses as illus- trated below (2,30,64,8l,122,123): Temperate grasses Tropical grasses CWC 34 - 73 45 - 83 ADF 18 - 46 21 - 57 L l — ll 2 - 12 Si 0.5 - 4 1 — 5 Some of these workers reported that CWC of most tropical grasses exceeds 65% while that for temperate grasses may be lower than this value. Grasses of temperate origin accumulate fructosans while common biennial and perennial legumes accumulate starch and sucrose (105). Grasses of subtropical and tropical origin accumulate starch and sugars such as sucrose, glucose, fructose and traces of glucofructosans (15,105). Besides, tannic acid is higher in many tropical forages such as Desmodium, Paspalum, and Digitaria (96). Large changes in chemical composition occur between 30 to 60 days for tropical forages. CWC. C, ADF, L increase with advancing maturity from 30 to 180 days of age (30). 58 c. Digestibilityy Intake and Yield Tropical grasses have lower digestibility and intake than temperate grasses cut at similar stages of growth (30,73,81,98). Dry matter and energy digestibility of tropical forages tends to increase up to 4 to 5 weeks of age then declines gradually thereafter and the rate of decline is less than that of temperate forages (30,46,81). Many tropical grasses have higher DM yields than temperate grasses (73). IX. PREDICTION EQUATIONS In order to have accurate prediction equations, i3 yiyg data used in deriving such equations must also be accurate. Also any prediction equation is valid only for the type and Species of forages used in its development. One desirable test before universal acceptance of any developed equation is to apply the equation to another or different set of samples having known animal values and thus verify the prediction equation (123). At present, there are hundreds of prOposed prediction equations using different components and ig_yi££g_data and these are pre- sented in Tables 14 to 22. a. Prediction of_;g_¥iygLDigestible Dry Matter (DDM) From Table 14, ig.yiyg DDM can be predicted from neny chemical components and ig_yi3£g data. Crude protein, CF and CF plus NFE show high correlations with DDM but the standard errors of prediction are too large (8 — 10%) to guarantee their application. Lignin and MADF may be better 59 sue s.m .4mm.o sop oo.s + ozo>H om.o u s 0H m.a «shm.o Ema om.v I Q2Q>H wo.a n S was m.m .4sm.o awe os.os : azes>s sm.o u s mm m.m ssmm.o :09 ozo>H vs.o + o>.ma u w NHH w.H II QOB hm.m + DED>H mm.o n M was m.m .1 amp ao.a u QSQ>H mm.o u s 5 II II OMB AQU>H Hfilwmvmm.a + vm.NH u w om II «sHh.o Ema Amie Wowo>Hv om.H + om.m¢ u w ooa . . 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The use of multiple variables as combinations of CC, CWC, L/ADF (118), L, HC, CP, C (27), ADF, ADF-P, TP-ADFP (132) to predict DDM did not Significantly increase the magnitude (If the correlation coefficient nor decrease the standard error of estimate. For instance Van Soest's equation using CC, CW, L/ADF gave standard error of estimate Similar to that for the equation using L alone (compare Equations for Ref. 111 with that for 118, Table 14). Cellulose solubility in CED or CED x DMS may be satisfactory to predict DDM for grasses but the prediction of DDM from IVCD x DMS gave lower accuracy for mixed forages than did previously mentioned factors. The two-stage IVDMD has proven to be an excellent method to predict i2_yiyg_DDM Since it gives high predic— tability for DDM in temperate forages with standard errors ranging from 1.3 to 3.7%. However, the method gives low but acceptable predictability for DDM in tropical forages and in Silages with standard errors ranging from 3.3 to 4.4%. b. Prediction of In Viyg True Digestibility ig‘yiyg_true digestibility (Table 15) can be pre— cisely predicted from i§_yiE£g_TDMD with small standard errors (1.7 to 2.0%). The two-stage IVDMD procedure can also be used to predict ig_vivo true digestibility 62 me we >o .Ism.o any 030 Hm.o I oo.oHH n ezee>H ms was >0 «sas.o Sue use ms.o I os.oaa u ozoe>H ms was >o .Iom.o Esp senses .smm mmm.a I om mmm.o I o Hos.o I o~.saa u asee>H ms was >0 Isms.o Sue A ssm.a I so mam.o I om.saa n osoe>H ems o.m .4mm.o awe o~.mH + eso>H ~m.o n esee>H ms s.oa «Ism.o Ems Ammmesaemov meme Hm.o + os.~s u a mu o.N temm.o SUB DZQB>H Nm.o + om.m n N was s.H 44mm.o Ems oe.m + ozoe>H mm.o u w ems s.m .Ima.o Ems os.mm + azs>H am.o u s mma m.H ssmm.o Ema om.am + Amen .mam o>w> mmv mm.o u w some mosmnmmmm saw u mmmnom asee>H no\csm Ass some o>e>.mm use manwummmflp coup o>fl>.flm UGHDOHDDHQ SH own How oomOQOHm msoaumoom .ASQQBV kuumfi .mH mqmdfi 63 but with a larger standard error (3.4%) than the first method. Other laboratory methods such as TSAE gave low predictability for true digestibility with a very large standard error (10.4%). True digestibility ig_vitro has been predicted from a two-stage IVDMD procedure with reasonable accuracy. The use of cell wall constituents to predict IVTDMD in tropical legumes gave moderate accuracy whereas combinations of CWC, L or C, HC, L still gave low predictability for IVTDMD. C. Prediction of In livg Organic Matter Digestibility (OMDY Like apparent DDM, i§_vivo OMD (Table 16) can not be satisfactorily predicted from CP or CF + NFE because of large standard errors, but OMD i§_vivo can be predicted more precisely from ig_vitro OMD with a low standard error of about 2%. Lignin in OM can be satisfactorily used to predict i2 vivo OMD in either grasses or legumes but L gives larger standard error for mixed forages. A summative equation for i§_vivo OMD using CC, CWC, L/ADF ratio gives satisfactory predictability with a 3% standard error. Total residue after cellulase (TRAC) and TSAE can be used to predict i3 vivo OMD or DOM of forages with reasonable accuracy. For Silages, i3 vivo OMD or DOM can be predicted from i§_vitro OMD with about a 4% standard error. II III III (II T “I! ll‘l. I’l‘l 7‘ V :ttlxrfly AU) 4.) Hi i hdn~ .H U r» m. HDIIVIIN. N I~ AL Ii Evils at. Aida ~59 I~ GIN IINI HIV-AF M4 ~.~A.IM H Id «Iv: N041 I‘ll QOIllI 64 .H MHDMB xaocomom mom .mc0flumfi>cnnnm Dom Ho. V max mm m.m ..mm.o Hem Asoe>Hc Hm.o + om.me I son 4m m.~ Ismm.o Ems leases smm.o + ~m.om a zoo am ~.s «4am.o Hem AoSo>HV ms.o + om.aa u oso sm ~.m «.mm.I Ems losses mmo.s I sm.sms u are 5H o.m sssm.o Ema Hm.m I loos x me4\e mos smm.o I sm.ec use + comm.o I use NH m.N ##mmIO SOB OH.m + QSO>H Nam.o H 920 SH m.a «swm.I A09 A20 cflvq mas.m I mm.om u 920 he o.m «.ms.I use 120 secs smm.m I sm.em u use he m.s .Imm.I Ems A20 sacs smo.m I oo.ss a s20 mas m.~ ..sm.o ems lszo>sc ~m.o + mm.ee I use as on 44mm.o use Amsz + soc mso.a I om.~sa u ago as saw «.mm.o was so mes.s + mo.sv I use mosmummmm mmm H some h a a sandman mNMM IN omcuom D.H.n. III omonm unoflumpom DH“ @GM . zoos pounce oasmmuo manflumm . . mamas AQEOV Suwafinwummmflo Hmuumfi Oflcmmho O>fl>.m% mGHuOflcwhm HON emu ma 65 d. Prediction of IE ziyg Cellulose Digestibility Cellulose digestibility ig_viVo for grasses can be precisely predicted from either IVCD or cellulose solubility in CED with a small standard error of about 2%. TABLE 17. Equations used to predict cellulose digestibility. ig_Vivo CD (Y) Forage r SEE Reference Type Y = 8.84 + 0.807 (CED) TeG 0.92** --- 34 Y = 6.64 + 1.02 (IVCD) TeG 0.97** 2.0 129 e. Prediction of Digestible Protein Digestible protein in both trOpical and temperate forages can be predicted from CP or CP plus CF with very high correlations. These prediction equations may be accurate for forages having no heat damage such as green and new forages. The equation proposed by Holter and Reid (50) has been verified for both fertilized and non-fertilized forages. Many scientists criticize these equations because CP may vary with N fertilization and high CP level does not always give high DP value probably due to over heating and drying of forages. Goering 23.21: (44) reported high corre— lations between DN and ADF—N, acid-detergent soluble N, Pepsin-soluble N, AD insoluble N, pepsin-insoluble N and many equations have been proposed to predict DN in heat- damaged forages. ‘Along with this new idea, some scientists suggested the use of ADF—protein, total protein and their II 1 I... I1 IIII II III F (A v IQHIIHP.‘ _ PL CHE IDUIIU HmUClflfiu LHAJIW wIIvItIClnDL .IW muses file uU~J.~v..~ I .«..~ s~< a. Q) 1 66 Gflmmmmv as m o .«sm.o Ewe Hm.o I Az.aom cemwwowOMMmo + mm.o u 2o as m.o ..sa.o awe 12.H0mss use os.o I lz.aom may ms.o + mm.o I ze av m.o ssmm.o Ema no.0 I AZ.HOm cammmmv hm.o a 29 we m.o «avm.o ZmB mN.o I AZ.HOm adv mh.o n ZQ vv m.m «amm.o EOE Aooa x Z ”Mdv No.H I mm.Nb u ZOO ma III .4mm.o one so sm~.s + mmm.m u moon as III .Imm.o one so se.m u soon as III ..sm.o one ms.ss I so mos mm.ooa I moon om III III SHE mUm.H I mo moH H.om + o.HN n moon om III III SHE H.NN I mu 00H m.Hm u @009 as III ..ms.o use mm.m I so smmm.o u an om m.o 44mm.o awe ms.m I so mmm.o u so mm s.a .4om.o one sm.am I so use mm.sm u so consummmm mmm m Ho H mwmmwm mEoDH .swmuonm manflumomwc mcwuoflowum How venomoum mc0flumsom .mH wands 67 .H canoe xflcsommd mom .sfiououm Hmuoe u we «:mmonuflc Hmuoa u 28 “z masasmmose u za “mu mo .mooo .mwa u mUUQ .mcowumfl>mubnm Honuo Mom ucflououm unconcuocIcfiom u mend “somouuflc unmoumumcIcHod u zmnd “z ammoa mo .mooo mac n zoo “cemuoum mabflumomfia n ma III . me mma Issm 0 amp Assess as.o I Assess Hm.m I Amps mo.a + ma.os u zoo N I." 0 me «sea 0 Sop mioe\mme¢c Ho.o + “m9\mmo«c m.a I ms.~m u zoo mama mocmuwumm mmm m Ho M mmmuom mEouH .UODCHHCOU .mH mnmfia 68 ratio to estimate the digestion coefficient of N in normal and heated forages (132). f. Prediction of TDN From Table 19, all prediction equations for TDN using proximate analysis were based on CP + CF and CF + NFE. Even though the correlation between TDN and these components is high, prediction of TDN using these components may not be satisfactory due to the large standard error. However, TDN can be successfully predicted from 36-hr i2 vitro cellulose digestibility for tropical grasses. TABLE 19. Equations proposed for predicting TDN. TDN (Y) Forage r SEE Ref. Type Y = 7.76 + 0.8192 (i2_vivo DDM) TrG 0.99** 4.0 19 y = 129.39 - 0.9419 (CF + NFE) TrG 0.93** 9.0 19 Y = 74.43 + 0.35 op - 0.73 CF TeL —-- --— 1 Y = 50.41 + 1.04 cp - 0.07 CF TeG --- ——- 1 Y = 65.14 + 0.45 cp - 0.38 cp TeM —-— --- 1 Y = 77.07 - 0.75 cp - 0.07 CF Sil --- --- 1 Y = 17.79 + 0.906 (36-hr IVCD) TrG --- '7“ 7 9. Prediction of Energy Digestibility Digestible energy (Table 20) can be precisely Predicted from either acid—insoluble lignin or two-stage IVDMD values with a standard error of prediction of 2.3%. (II(|I I, I | n, I‘leI-IIIIIH“ I\A..~.fi In. .nndfiUmunUmwmmJ DNwIHmUF~U III II I I HEIVE. Hilllllul Hill I IIII I'll. ll UnUufifiveUIHnH 0U ”VIIIVIMMOAINOHAIN mconIanvvuqu IIJII. II,“ .. I lullll “hull! 1”...“ MIN ION 69 mm m.~ III 209 os.m + Aezo>Ho me.o n me A III III one os.HH I Aoo>H usIomv om.a n me am s.~ «Imm.o owe Aoo>H ssIomv ms.o + m.m~ n me am III 44mm.o one Assoc soso.o + mms.o u Am\smosc so am III ..om.o one hoses maso.o + mH~.o n Am\amosc on em III ssom.o woe Aomov voo.o + om.ma n mm as s.m III ass leave .oso >HV m.omm + m.msma I A.Hmoc me as m.m III Sue Asm>c m.~mm + m.oama n A.Hmoo mo om m.m III 2H9 Aoo>H HcIvmv m.mH + H.vmsa u A.Hmuv no sea m.m «svm.I Sop A.mae .HOmsHIoHoav H~.o I o.mm n me come ll mosmummmm mmm H mmmuom ssseansummmes o>s> CH .ssaaaneummmse smumsm Dowcmum on oomOQOHm msowumoom .om mamas IMMvav~I-I~N JCAHHV IQ?! .I.IN.I~m~/\.~. 70 .H magma Xawcwmmm mmm .mcoflpMH>mHan Mom Hoe. v mIII Ho.v mII m m.ma «IIH wma Azo may a mH.o I mm.~ Am\amomv «z m m.m «IIM woe Azo may a ma.o I vo.m Am\amoxv 82 m o.~a «II» was Azo :HV mo moo.o + mm.o Amxamomv w"z m m.m III“ ems Azo aflv mu mmo.o + om.H Am\amuxv ez m o.m III“ ume A20 gay mo “mo.o + mm.a Am\Hmumv m: o n.v «««u ems A20 cflvq mna.o I mm.m Am\Hmumv m: «w ¢.¢ «Iom.o Ema Azmn¢v mm.ov ~m.~h n ma mm m.¢ «gmn.o Ham u.~H + Azon>Hv mm.o n ma mama II. mocwnmwmm mmw H mmmuom huwawnwumwmwn O>H> cH .wmsnflucou .ON mqm¢fi T r p. was or ad DE from ( PEPSi: C. 0513 71 Cellulose solubility in CED or 30—hr IVCD can be used to predict DB of grasses with reasonable accuracy. Digestible energy in silage could be predicted from IVDOM but volatile fatty acid and organic acid production in_yit£g are not useful predictors for DE. Metabolizable energy in temperate grasses can be predicted from L (in CM) with moderate accuracy. In addi- tion, GP or L in forage organic matter can be used to predict ME or NE of temperate grasses but with relatively large standard errors. h. Prediction of Voluntary Intake From Table 21, 6—hr in_vitrg_fermentation (IVDMD) was one of the more accurate items used to predict DMI, DEI and DDMI. Besides, DMI can be satisfactorily predicted from CWC, MADF, 18—hr IVCD or a combination of buffer + PePSin and IVDMD. Relative intake can be predicted from C, DMS, CED, CED x DMS or IVCD x DMS with moderate accuracy. i. Prediction of Nutritive Value Index (NVI) Data in Table 22 indicate that NVI was predicted from CED, CED x DMS or 18—hr IVCD with moderate accuracy. The precision for predicting NVI was improved when DMS, Pepsin DMD, lZ-hr IVCD, IVCD x DMS, 18-hr IVCD x 30-hr IVCD, or a combination of 48-hr buffer + rumen fluid and IVDMD values were used as predictors. Dry matter NVI was satis— factorily predicted from DDMI, DEI, or 6-hr IVDMD. 1 II N. IIIIII‘ "ll F‘l.’ VII: ‘I I. L T Firiifi AXI- HO!‘ mwv md HIM nJ RI». who fl 1* H15]. H.394 HIM”. .JJINI.N a 5“.” 72 .H magma xfloswmm< mom .msofipwfl>mnnnm mom Ho. v m«« mo. v ms om He.m «Imm.o awe o Hm.~ I n.mma I Hm om III IIon.o Ema Amzoomaoo>Hv mm.m + >.mm I Hm om III «Inv.o Ema Amzoomaomov mH.~ + A.mm I Hm mm III «Imp.o SIB mzo ~¢.m + mm.m I Hm mm mo.h «Imm.o qua Aamov mo.a + mm.om I Hm vm III «om.o owe Aomov oo.a + Ho.oH I Hm Hm mH.o Igmm.o Ema Anza>H Hanv avo.o + mm¢.o I pzo\Hzoa Hm va.o «gem.o awe Anza>H ugImv Hmo.o + omm.o I uzo\Hma Hm om.o «Ian.o zma Anza>H HsImv moo.o + omo.H I usotza me III «Imm.I awe ooonnH I «.0HH I Hzo OH III «Iam.o Ems Aozo>Hv mm.OI Amos + msmv ~5.H + m.mm I Hzo mm oo.n «amm.I Ema AmoH ugImHv mm.H + H.mv I Hza mama wocmuwmmm mmw H Gannon mMmusH mo cowuoflcmhm .mxmusw humussao> msfluowwmnm How poms msowumnwm .HN mumfis P‘ .u.‘ I.“ f..- Crfi ave E1. Mun fl~0 .fiI My 5‘1. NLE . FL ME ‘H n N <. Np 73 .H magma xflocwam4 00m N Ho. V was Hm a.m .Iam.o awe Anza>H InImc mam.H + m.mH I H>zwm Hm a.~ IImm.o zma Hmn Ho.mm + nm.m I H>zzo Hm H.N «tum.o Ema HEQQ Hm.m~ + mm.N H H>ZSD 0H III «tvm.o Emfi AQSD>HV mm.m + Ahm + mam Halmvv mv.~ I w.Nwl M % o¢ III «cmm.o 206 mh.o I AGED Cflmmwmv om.H u N mm III IIHm.o zme m.n I Aoo>H unINHV «Hm.a I x cm H.m «Ion.o owe Aoo>H unImHV om.H + m.m~ I m em o.m «Imm.o owe Aoo>H unIomv x Aoo>H unImHv v~.o + «.mm I w ow III IImm.o Ema Amzoomaoo>HV mo.m + o.¢~ I w on III «Iva.o Ema Ammnomaomov ~m.~ + «.ma I » mm III «Imm.o Ema mH.m I mzo mao.~ I w an III Igmn.o owe hm.om I Aomov mNH.H I » mama mocmuomom mmm n common AMV H>z .xmvca 05Hm> m>wuwhu9c DOflUmHm ou 0mm: mGOHumswm .NN 3&8 EXPERIMENTAL PROCEDURES I. MATERIALS a. Forages Forage samples were obtained from both tropical and temperate regions. There were 24 alfalfa (Medicago sativa) 10 bromegrass (Bromus inermis), 9 tall fescue (Festuca arundinacea) samples with known in vivo data and some chemical estimates from Purdue University and 6 alfalfa, 6 bromegrass, and 6 reed canary (Phalaris arundinacea) grass samples with known in vivo data from the Department of Dairy Science, Michigan State University. Also, 40 samples of 5 grasses (8 for each of bromegrass, orchard (Dactylis glomerata), reed canary grass, Kentucky bluegrass (Poa pratensis), tall fescue) grown at Department of Crop Science fields, Michigan State University were cut at 30, 45, 60, 75, 90, 105, 120, 135 days of age after regrowth in the Spring with the first cutting on May 20, 1972. There were five para grass (Brachiaria mutica), 5 Napier grass (Pennisetum purpureum), 5 speargrass (Imperata cylindrica), 5 centrosema (Centrosema pubescens) and 3 mung bean (Phaseolus aureus) samples cut at 30, 45, 60, 75, 90 days of age after initial growth from Thailand. Forty grass samples from various species in genera 74 75 Pennisetum, Panicum, PaSpalum, Brachiaria, Cenchrus, Digitaria, Andropogon, Tripsacum,‘Cynodon,‘sorghastrum and Eriochloa were from Mayaguez, Puerto Rico and were cut after 30 days of regrowth. In addition, some statistical analyses of forage samples from previous experiments at the Department of Dairy Science were used in this study. Using data obtained from 1961 to 1970 from digestion trials and laboratory analyses on forages at Michigan State University, multiple correlations and regression equations were calculated to more accurately predict in yiyg_dry matter digestibility, dry matter intake, digestible dry matter intake, total digestible nutrients, digestible energy, weight gain. Forage samples were composed of alfalfa, birdsfoot trefoil, Siberian reed canary grass, bromegrass, ryegrass, timothy and legume silage. These samples had been analyzed for CP, CF, EE, ash, NFE by standard methods outlined in AOAC (4) and by Woodman (130). Cell walls, ADF, L were determined accord- ing to Goering and Van Soest (43). Dry matter solubility followed the method outlined by Dehority and Johnson (35). }g_yitrg dry matter disappearance (6 and 36-hr) for samples and standard was the same as that reported by Ingalls (51) and Allinson et_al. (3). Two—stage in_yit£2_fermentation was according to Tilley and Terry (112) with some modifi— cations developed by various personnel at the Department of Dairy Science, Michigan State University. Various sequential combinations of variables selected from these laboratory estimates (CP, CF, EE, ash, h: 76 NFE, CW, ADF, L, CC, DMS, 6-hr DMD, 36*hr DMD, TT DMD, L/ADF ratio and DMD of standard) were used to develOp multiple regression equations by means of a LSD program on a CDC 3600 computer. b. Enzymes Samples of different cellulases (Cellulase 36, Marschall, Novo, Onozuka) were obtained from commercial companies. Takadiastase (amylase) was obtained from Miles Laboratories sold under the name of "Clarase 900." Pepsin (1:10.000) was purchased from Nutritional Biochemicals Corporation, Cleveland, Ohio. c. Methods for Studies on Enzyme Activities Several commercial cellulases, Clarase 900 (amylase) and pepsin were used to determine apprOpriate pH levels, ratio of enzyme to substrate, length of incubation and the kind of cellulase that would give the greatest solubilizing activity on forages. Two hundred mg of Whatman cellulose powder, 300 mg of high (H) or low (L) strain or common alfalfa hays and unbeaten paper ground to pass a 40smesh screen in a Wiley mill were incubated with these enzymes. Different concentrations of cellulases and amylase were dissolved in sodium acetatei acetic acid buffer having different pH levels from 3.0 to 6.0. Various con- centrations of pepsin were suspended in HCl with different pH levels (1.5 to 3.0). A mixture of cellulase was filtered through Whatman filter paper No. l to remove the residue £1. 77 whereas clear solutions of amylase and pepsin were used directly. The samples plus enzymes were kept in 50—ml Erlenmyer flasks with rubber stoppers and the flasks incubated at 38-39 C for different intervals of time with occasional shaking by hand. At the end of incubation time, the mixture was filtered using a Millipore apparatus and tared prefilter paper. The residue was dried and weighed to determine total residue after enzyme and dry matter loss after enzymatic incubation. II. METHODS OF ANALYSIS a. Chemical Analysis Forages were analyzed for moisture content, CP, total ash by standard methods outlined in AOAC (4). Neutral-detergent fiber or CW, ADF, ADL, permanganate lignin, insoluble ash, silica values were determined accord- ing to the procedures of Goering and Van Soest (43). Hemi- cellulose was calculated as the difference between neutral— detergent fiber and ADF and cellulose as the weight loss upon ashing the permanganate treated ADF residue or after treating ADF residue with 72% H SO . 2 4 b. Two-stage In xitgg Fermentation (IVDMD) This procedure was adapted from that of the Tilley— Terry method (112) with some modifications made by various personnel in this laboratory. Half a gram of sample as well as alfalfa standard was placed into SO—ml centrifuge 78 tubes. Several samples were done simultaneously along with separate determinations for dry matter content. PhOSphate buffer was used and composed of 4.08 g KH2P04, 8.72 g NaZHPO4, 25 mg NaZS .9 H 1.5 g M9804.7 H O, 0.5 9 KCl, 0.1 g CaCl 2 2’ 20, 10 m1 urea (8% solution), 20 ml Na2C03 (15.73% solution) per liter. The buffer was warmed up in a water bath (38-39 C) and bubbled with CO2 until the solution became clear and the pH was about 6.8. Rumen fluid was squeezed out through 5 layers of cheesecloth from a fistulated cow that had been fed good quality alfalfa hay 2 hours previously, and 1 hour pre- viously the remaining hay removed and access to water pre- vented. The rumen fluid was allowed to settle at 37-38 C for a short time and the bottom layer which was free from feed particles was drawn into a flask kept warm at the same temperature. Carbon dioxide was then bubbled into rumen fluid constantly. Ten m1 of buffer were added to the sample in the tubes 15-40 minutes before the rumen fluid and the tubes kept in water bath at 38-39 C. Twelve ml of rumen fluid were added to the tubes followed by a flush of CO2 after which tubes were capped immediately with rubber stoppers fitted with a bunsen valve. The sample tubes were incubated in a water bath (38-39 C). This bath was covered with a plastic sheet and CO2 bubbled into the enclosure. Tubes were incubated for 48 hours and shaken twice per day. After 48 hours, 0.9 m1 of 6 N HCl was added to each tube to stop microbial fermentation and bring a pH to 1.7 to 2.0 and then 0.5 m1 of 20% pepsin solution was added, 79 and tubes were incubated in the water bath for another 48 hours with occasional shaking by hand. After 96 hours, the fermentation mixture was filtered through a tared sintered glass crucible with dry matter remaining determined gravi- metrically and the loss of dry matter calculated as percent of initial dry matter and called IVDMD. c. I Vitrg_Cell Wall and True Digestibilities fivm IVTDMD) The method of in_yit£g_fermentation was the same as the first-stage IVDMD. After 48 hours of incubation, the fermentation mixture was transferred into a beaker and boiled for 1 hour with neutral—detergent solution according to the procedures outlined by Van Soest §t_al, (126) or Goering and Van Soest (43). In yi££9_CWD and IVTDMD were calculated as percentages of original CW or DM lost during the fermentation or the boiling procedure. d. Cellulase Digestion The methods for cellulase digestion were similar to those described by Jarrige gt_gl. (54), Guggolz gt_al, (48) and Moore §t_al, (82) with modifications. A buffer used for cellulase digestion was a mixture of sodium acetate and acetic acid prepared and adjusted to have a pH of 3.85 to 3.90 according to the techniques out— lined by Dawson gt_al. (33). Three hundred mg of air-dry samples ground to pass a 40-mesh screen in a Wiley mill, were placed in 50-ml Erlenmyer flasks followed by an addi— tion of 10 ml distilled water to moisten the samples. Ten 80 ml of the buffer containing 300 mg cellulase were added to the flasks which were stoppered and placed in an incubator at 38-39 C for 60 hours with occasional stirring. At the end of the incubation time, the mixture was filtered using Millipore apparatus and tared prefilter paper.1 The residue was dried and weighed to determine total residue after cellulase (TRAC) and dry matter loss from cellulase digestion. e. Cellulase and Amylase Digestion The digestion of forages by cellulase was the same as that discussed above. At the end of 30-hr incubation, pH of the mixture was adjusted to 5.5 using saturated sodium acetate. Immediately, 10 ml of 2% Takadiastase solution in water were added to the flasks which were stoppered and incubated at 38-39 C for another 30 hours. Since cellulase and Takadiastase contained some carbohy— drates, an enzyme blank treated-as sample was necessary for proper calculations. At the end of the incubation time (60 hours in total), the mixture was filtered using Millipore apparatus and prefilter paper as described above. The filtrate was received in a suction flask for carbohydrate determination (TACAE). The residue was dried and weighed to determine total solubles after enzymes (TSAE) and total residues after k 1Obtained from Millipore Corporation, Bedford, Massachusettes, 01730, Cat. No. AP 2504700. 81 enzymes (TRAE). .The filtrate was transferred to 500~ml volumetric flask and treated with 2*3 ml of 10% lead acetate to precipitate excess protein or enzyme. Subsequent pro— cedures were according to Smith (105). For the determination of sugars, 0.5 ml of aliquot was used for Nelson's test according to procedures outlined by Clark (26) using 10 to 100 ug glucose as standard. Total sugars minus sugars in the blank were expressed as total available carbohydrates after enzymes (TACAE). f. Cellulase and Pepsin Digestion The digestion of forages by cellulase was the same as that described earlier. At the end of 30ehr incubation, pH of the mixture was adjusted to 1.75 to 1.85 using 6 N HCl. Immediately, 1 ml of 20% pepsin solution in water was added to the flasks which were stoppered and incubated at 38-39 C for another 30 hours with occasional agitation. At the end of incubation time (60 hrs in total), the mixture was filtered using Millipore apparatus and prefilter paper as discussed earlier. The residue was dried and weighed to determine total DMD due to these enzymes. g. Pepsin Digestion The method of pepsin digestion by Donefer §E_al, (39) was used with some modifications. Three hundred mg of sample ground to pass a 40-mesh.screen were placed in 50-ml Erlenmyer flasks followed by an addition of 35 ml of 0.075 N HCl containing 200 mg pepsin. The flasks were stoppered and incubated at 38—39 C for 60 hours with 82 occasional agitation. At the end of incubation the mixture was filtered using the Millipore apparatus and prefilter paper. The residue was dried and weighed to determine total DMD due to pepsin digestion. h. Amylase Digestion and TNC A buffer solution for amylase digestion was a mixture of sodium acetate~acetic acid prepared and adjusted to have a pH of 5.5 according to Dawson et_al. (33). Three hundred mg of samples ground to pass a 40—mesh screen were placed in 50-ml Erlenmyer flasks to which were added 10 ml of distilled water. The rest of procedures were the same as outlined by Smith (105) except that 1% Takadiastase solution in water was used and the residue was filtered using the Millipore apparatus and prefilter paper. The residue was dried and weighed to determine total DMD due to this enzyme. The filtrate (0.5 ml) was used for the determination of total nonstructural carbohydrates (TNC) according to procedures outlined by Clark (26) using 10 to 100 ug glucose as standard. i. Water-soluble Carbohydrates and Turbidity Test Half a gm of sample ground to pass a l-mm screen was placed into screwcap test tubes followed by an addition of 25 ml distilled water and 2 drops of concentrated acetic acid. The tubes were placed on a shaker for 30 minutes at about 200 strokes/minute. The mixture was filtered through glasswool and the extract was used for turbidity test according to the procedures outlined by Bennett and Archibald 83 (13). Another portion of the mixture was filtered through Whatman filter paper No. 30 and the filtrate was analyzed for water—soluble carbohydrates according to the procedures described by Johnson et a1. (61). III. STATISTICAL ANALYSIS Simple and multiple correlations as well as regres- sions among in yiyg_data, in yitrg fermentations, enzyme digests, and chemical components were calculated. Some prediction equations for in_yiyg_parameters from selected laboratory estimates were also calculated using a CDC 3600 computer. RESULTS AND DISCUSSION I. STUDIES ON ENZYME ACTIVITIES a. Comparisons of Cellulases Existing techniques using cellulases, pepsin and amylase for forage evaluation have considerable variation in procedural details especially in terms of buffer compo— sition, pH, incubation time, kinds and concentration of enzymes and incubation techniques (39,48,54,63,75,82,105). The cellulolytic activity of four enzyme preparations using pure cellulose and alfalfa hays as substrate is shown in Table 23. Onozuka enzyme digested the greatest amount of pure cellulose (8%) compared to only approximately 2% for cellulase 36, Marschall and Novo enzymes. For the alfalfa sample, the Marschall enzyme solubilized 50% of the DM compared with 46 to 48% for the other three enzymes. The DMD for Marschall enzyme was statistically greater (P < .05) than values for the other 3 enzymes. Also, DMD of alfalfa hay due to cellulase alone was greatest for Marschall enzyme. Due to its slightly greater DMD value and enzyme availability, Marschall enzyme was used in subsequent studies. 84 I ha; 85 TABLE 23. Composition and activities of four different cellulasesa on two substrates. Alfalfa (L) 300 mg Pure Cellulose Total Due to Enzymes DM CP 200 mg solubility Enzymeb % % of DM % DMDC Cellulase 36 (300mg) 91.86 15.43 1.64 46.11 8.20 Marschall (300mg) 87.43 32.93 2.47 50.07 12.16 Novo (300mg) 94.48 54.87 1.80 47.94 10.03 Onozuka (300mg) 94.00 6.25 8.16 46.37 8.46 aCellulase 36: obtained from Rohm and Haas, Philadelphia, Pennsylvania. Marschall: obtained from Miles Labs., Elkhart, Indiana. Novo: obtained from Novo Enzyme Corporation, Omaha, Nebraska. Onozuka: obtained from All Japan Biochemicals, Co., Ltd., 8-21 Shingikancho, Nishinomiya, Japan. Each enzyme was dissolved in the buffer (pH 4.8) and incubated at 38-39 C for 64 hrs. bDMD of enzyme treated alfalfa minus buffer treated alfalfa. cEach value was average of duplicate samples. DM = Dry Matter; CP = Crude protein; DMD = Dry matter disappearance. b. pg Levels for Maximum Enzymic Activity The amount of enzymic activity at various pH levels for cellulases, Clarase 900 (amylase) and pepsin is Presented in Table 24. Both Marschall and Novo enzymes showed the greatest activity at a pH between 3.5 to 4.0 with both alfalfa and cellulose as substrates. On the other hand, Clarase 900 had maximum solubilizing activity at a pH 86 .cmsflfiuwumw #02 n DZ .sflmmmm How mm: mm cam mmmamfim .mmmmasaamo How my: em .0 mmlmm cm UmpmnsosH .Amfimmn ZDV m0 wwm.ma .za wmm.am pmcHMDsOO Usm mcmecH ..mnmq mmaflz EOHM AmmmHhfimv OmMDmMHpmxme ma oom mmmumao oo.hm DZ oz oz o.m ms.om mm.oa oa.Ha oz m.m mm.mm HH.H~ Hm.me km.ee o.m om.mm oo.m mo.mm ma.a~ No.me mk.om m.e am.em om.~ em.mm mm.om eq.ma om.mm 0.4 mm.ae mm.s oz mk.mm mm.Hm Hm.mm m.m am.mm om.o oz m¢.m~ mm.me me.nm o.m 98 w on oso it com Momma mm roomy mmH .oz .Hmoao oom Amc.amoaa omnmmooo com nov.ommaa com sz.amoao loony osmomo looms com Immumao com o>oz com o>oz oom Hamoomomz Ame CH owns mussoamv mmumuumnsm can mmfimusm .sdmmmm cam OmMHMEm so .mmmmasflawu 03¢ Ho mmHuH>Huom so mH0>mH mm mo muommmm .vm mamfia 87 above 5.5. Pepsin could maximally solubilize alfalfa dry matter at a pH of 1.85. Therefore, a pH of 3.85 to 4.0 was adopted for subsequent cellulase incubations and that of 5.5 for Takadiastase and 1.85 for pepsin studies. c. Incubation Time The effects of length of incubation times on enzymatic activities are shown in Table 25. All three enzymes namely cellulase, Clarase 900 and pepsin appeared to have maximum activity near 60 hours of incubation. Therefore, an incubation time of 60 hours was used for single enzyme incubations and for a sequential hydrolysis by 2 enzymes (30 hrs for first enzyme followed by 30 hrs for second enzyme). d. Concentrations of Enzymes Used The ratios of enzymes to substrates for maximum enzymatic activity are presented in Table 26. Both Novo and Marschall enzymes solubilized relatively large amounts of alfalfa hay and cellulose powder at concentrations between 200 to 600 mg cellulase per 200 to 300 mg cellulose Powder or hay. Clarase 900 and pepsin solubilized large amounts of alfalfa hay at a concentration of 200 mg enzyme per 300 mg hay. Therefore, 300 mg of Marschall, 200 mg Clarase 900 and 200 mg pepsin per 300 mg substrates were adopted for enzymic evaluation of forages. Cellulase action on cellulose showed a curvilinear resPOnse but cellulase on alfalfa did not have a decrease in Substrate solubilization at a high enzyme concentration. .mmHmEMm mucosamso mo mommum>m mumz mosam> .U mmnmm um omumnoocfl Has .mm.a mm as Hmmmsn Hum as UO>HOmmHU cflmmmm .m.m mm um Hmmwsn ofiom owumomnmumsmom ESHUOm ca pm>HOmmHo oom mmmumHU .mm.m me an summon oflom OHDOOMIwumumom Endem ca pm>a0wma© mmmasaamo 88 oa.mm He.me ~m.me as om.sm ma.ea as.m¢ so om.km m~.m4 No.m¢ om «4.5m mo.ma e~.ma em mm.mm mk.me 40.54 as mo.om ao.me mm.ma me om.em no.me mm.ee em mm.am Na.~¢ 4H.oa om ma.em mm.aa Hm.ee am me.mm ma.oe em.ma as QZQ m Amunv Aosoomv mma .oz moamoao xosoomv o .oz moammaa “meoomv as .oz moammao mafia Ame come Ass come ooflumooooH ooo.oH"H osmomo Ame ooav com Immomoo mmmHoHHmo Hamoomumz .mmwua>wuom mfihnso msowhm> so GOHHMQSOGH mo cumcma mo mpommmm .mN wands 89 .om.m .mw.m mm um Hmmmsn @Hom .Umswaumump uoz u 92 .mnn ow How U mmImm um wmumnsosfl Has .mm.H mm um Hmmmnn Hum CH wm>HOmmH© sawmmm mm um Hmmmsn pave 0Humomloumumom ESAGOm ca ©O>Hommwp com wmmeHU owpmomsmumumom EUH©On SH ©0>H0mmHU mommHsaamo Hamsomhmz can 0>oz oz oz oz mm.~ em.em as com oz oz oz mm.m mm.em as con oz oz oz mm.~ em.mm as com mm.mm mm.ma mm.Hm ha.~ no.4m as com Ho.mm Ho.ms ma.Hm ma.m mm.mm ms oov ma.mm mm.~e e~.om om.m mo.mm as com mm.mm mm.ma om.ee aH.m om.mm as com mo.mm mk.He em.ee oz mm.ma as gas oz oz oz oz No.ke as om oz oz oz oz mm.mm as ea ozo m as com as com as com as com as com .oooo on .oz .Hmmam on .oz .Hmmas on .oz .Hmmad mmoHsHamu AmV.Hme¢ msmncm sflmmmm com mmmumau Hamsomnmz o>oz o>oz mmucnpmnsm can mmemncm .mODMHquDm m50fihm> mo AQSQV mosMHMOQQMmfio HODDME hum so macaumuusmosoo weaned mo muommmm .mm mamas 90 Continuous agitation for 60-64 hours was not neces- sary because there was no significant difference between DMD values (52.34% versus 53.33%) for shaken and non—shaken samples, reSpectively. A mixture of 300 mg cellulase (Marschall) and 200 mg Takadiastase incubated with low- strain alfalfa samples at pH 3.85 for 60 hours gave 47.8% DMD and this value was not significantly different from 46.7% DMD when using cellulase alone. Probably the pH (3.85) used for this incubation was not appropriate for Takadiastase activity. However, when the pH was raised to 5.5 using saturated sodium acetate solution at end of a 30-hr cellulase incubation and then Takadiastase added and incubated for another 30 hours, an increase of about 4-7% DMD over that when using cellulase alone (60 hrs) was obtained. A mixture of 300 mg Marschall cellulase (in sodium acetate-acetic acid buffer, pH 3.85) and 70 mg pepsin (in HCl, pH 1.85) was incubated with alfalfa hay for 60 hours and gave higher DMD (52.5%) compared with 35.5 or 50.3% DMD when using pepsin or cellulase alone for the same incubation time. Since all 3 enzymes have a different pH for maximum activity, a sequential incubation with pH adjustments was made when any two enzymes were used in subsequent experiments- In this study, maximum cellulolytic activity was not obServed when incubated at pH 4.5 to 4.8 as proposed by many workers (48,75,82). The use of pH 4.45 for Takadiastase as proposed by Smith (105) was also not observed 91 to be maximal. An incubation time of 72 hours for cellu- lase as proposed by others (48,75) was unnecessary. II. RESULTS OF A COMPARATIVE STUDY ON TEMPERATE AND TROPICAL FORAGES a. Chemical Composition and Digestibility in Temperate and Tropical Forages Literature values indicate differences between temperate and tropical forages in many chemical components as well as in digestibility (2,81,98). Tabulations showing differences for forages of the present study are given in Tables 27 and 28. All forages from Purdue University, Departments of Dairy Science, Crop and Soil Sciences, Michigan State University, Thailand and Puerto Rico were used in comparing digestibility and chemical composition and only Thai and MSU grasses were used for comparing the effects of maturity on forage nutritive value. On the average, temperate forages had 1.05 times greater in_yiE£ngMD than tropical forages (54.54% vs. 51.87%). However, this difference may not be significant due to large standard deviations for both groups of forages. Both trOpical grasses and legumes had lower IVDMD values than those for their temperature counterparts. Crude protein content in tropical forages was also lower than that for temperate forages (12.63 vs. 15.67%). This difference (a factor of 0.80) was due to lower concentra- tion of CP in both tropical grasses and legumes. However, tropical forages were greater than temperate forages by a factor of 1.11, 1.05, 1.21 for CWC, ADF and ash, .’ III. IL! .5! 1! PM .rI. IF wk .1. > AH WU-F~ A III.II I FHA”. H. .0 IN T IVAN-CAIVHV I IN. nuvIIIv IN..:FJ ONIIV I. Nth -,~.~II‘<.U 92 .mmmmuom smeam ounmsm can Hone HMOHQOHB “mommuom sz Ham can msonsm u ODMHOQEOB N .COADMH>mp UHMUGMDn cum cams H .cwcmflq u men “Hmnam ucmmumquIMH04 u and “mHHMB HHOU u “cwmuonm mnsuu u mu “OOGMHMOQQMmflU HODDME who onuw> CH QEQ>H Amnsv Aomnsv Ammusv Adhusv Ammusv “Hoaucv em.HHom.m hm.awmm.h mm.~ amm.m wm.a Haa.m mm.m “vs.m ww.a “mo.m And Amuse Aomucv Ammncv Aahusv Ammucv Afloausv mv.HHmH.n o>.HHmn.h mH.H Ana.m bH.N “mm.m hm.H Hmv.m hm.m va.m mag Amuse Aomuov Ammucv Aahuov immune Afloaucv mm.vnmm.am mm.mnm¢.mm nm.m www.mm mm.m Ham.>m om.v “mm.mm mm.m Hbm.mm has Amuse Aomuov Ammuov Ammuov Ammuov immune mm.mhmm.mv mm.hflmm.vv mh.m www.5m mH.w “mm.mm vh.m Hmm.¢m 5H.Hahmm.>m 30 Amuse homage Ammucv Aaeuov Ammnov AHoHuov ov.mnmh.ma Hm.MH>m.mH nm.m Hom.aa em.m Hmm.MH mm.v Hmm.ma mo.m Hum.ma mo Amuse finance Ammnov Aomuov Ammuov Ameuov mm.mfiom.mm om.m«mm.om No.0HMwn.om em.NHHoH.mm mo.oaflnm.am no.aawvm.vm QEQ>H umuumfi mum mo w Hmowmoua mpmummama amoamona mumummfima HMUHQOHB mpmummsma mEmuH mmasmmq mommmuw pmswnfioo N.mOUMHom Hmowmonu can mumnmmsmu mo muflaflnflummmflp OHDM> mm use coauflmomeoo Hmowfimnu .nm mqmH “mosmummmmmmwp “ounce who ouuw> sH n Q2Q>H “maams Hamu u 30 "camuonm OUDHU n no .smmE 30mm How m u s .psmawmaa Eoum mucumummmm .Hmwmmz .mmmum chum mo monam> acme mnmS monsmwm HH4 .smmE comm How m u s .mufimnm>wcs oumvm smmH:Ofl2 .mosmfiom mono mo unmfiunmamo Scum mmmumwan hxosucwx .wsommw Hana .mumcmo town .UHMQOHO .mmmummaoun mo mosam> some mHm3 mmusmflm Ham "mwmmmuw .moue ”mommmnm .mEmB . . 0H.I b.0m m.vm m.mm m.hm v.mm ¢.mm mmmum .QOHB 4 om vo m 4 om.I m.om m.~m e.am «.mm H.4o m.os Imago .osma ozoe>H 9 . . mo.I e.mv e.ke m.m¢ m.om m.am 4.me Immoo .oouo OH vo m a ma.I m.mm H.54 m.mm m.km m.~m m.me Imago .osms ozo>H . . Ho.+ m.am m.os m.mm m.mm a.mm a.mm mmmnm .oouo Ho vo m s mH.+ m.mm k.pm m.4s k.mm m.mm m.mm Imago .osmo zo . . mo.l m.> v.m m.n h.@ 5.5 m.oa mmmum .moua mo vo m m mH.I N.MH a.m H.0H a.Ha m.aH H.5H muons .oamo mo umuumfi amp «0 w Hm>mo .mmao oxmmomoo NH as m m e IImzmoz msmuo .mam cums mmmum>< .m>¢ om me om mv oMIImmmc .Ammmw cm 0» omv muwhsumfi msflosm>om nufl3 nmmmmno wumummfimu can Hmowmoup smm3umn mmHDHHHQHumOmHU was GOHDHnomEoo HMOAEOSU mo msomwnmmeoo .mm Hands 95 §E_§1. (30) and Grieve and Osbourn (46) also reported that CW fractions of tropical forages in Puerto Rico increased rapidly between 4-5 weeks of age with small changes there- after. Tropical grasses are low in nutritive value and this is due to an integrated action of low CP, high CW and ADF as well as probably the presence of some inhibitors. III. RESULTS OF STUDIES ON FORAGES FROM PURDUE UNIVERSITY AND DEPARTMENT OF DAIRY SCIENCE, MICHIGAN STATE UNIVERSITY a. Correlations AmoanVarious Chemical Components Correlation coefficients between several chemical components and enzyme values for forages from Purdue and Michigan State University are presented in Table 29. Forages from Purdue University contained 56% legume (alfalfa) whereas those from MSU contained only 33% legume (alfalfa) and the rest were grasses. There were negative relation- ships between CP and all cell wall fractions for the Purdue forages but MSU forages showed positive and non—significant correlations between CP and CW or HC. An increase in ADF, C and L would take the space of other nutrients and there- fore produced a decrease in CP content. Ash and CP were positively correlated with both groups of forages. Cell walls had negative correlations with L and ash but positive correlations with C and HC. High correlation between CW and HC (r = 0.96, P < .01) in the MSU forages indicates that HC accounted for most of the fiber in CW probably because 67% of the samples were grasses and 33% of them were legumes. These grasses contained higher levels of HC (25.62%) than 96 TABLE 29. Correlation coefficients among various chemical components and enzymatic incubation values. Purdue Univ. Dairy, MSU Factors correlated n r n r CP vs. CWC 43 -.91** 12 0.10 CP vs. ADF 43 —.83** 18 -.83** CP vs. L 43 —.12 18 —.53* CP vs. C 43 -.92** 18 -.78** CP vs. HC 43 -.67** 12 0.34 CP vs. Ash 43 0.64** 18 0.30 CP vs. TNC 14 —.02 ——— —-- CP vs. TACAE 39 0.17 -__ *—- CWC vs. ADF 43 0.74** 12 -.25 CWC vs. L 43 —.09 12 -.66** CWC vs. C 43 0.90** 12 0.44 CWC vs. HC 43 0.87** 12 0.96** CWC vs. Ash 43 -.44** 12 -.02 CWC vs. TNC 14 0.17 -—— ——— CWC vs. TACAE 39 0.08 ——- --- ADF vs. L 43 0.55** 18 0.74** ADF vs. C 43 0.93** 18 0.85** ADF vs. HC 43 0.31* 12 —.50 ADF vs. Ash 43 -.70** 18 -.24 ADF vs. TNC 14 0.06 —-— ___ ADF vs. TACAE 39 —.42** -—- -—— L vs. C 43 0.20 18 0.28 L vs. HC 43 —.53** 12 —.81** L vs. Ash 43 —.60** 18 —.35 97 TABLE 29. Continued. Purdue Univ. Dairy, MSU Factors correlated n r n r L vs. TNC 14 0.27 ——— ___ L vs. TACAE 39 —.64** ——— -__ C vs. HC 43 0.59** 12 0.22 C vs. Ash 43 —.56** 18 -.07 C vs. TNC 14 -.01 5—_ -__ C vs. TACAE 39 —.22 ——- -—— HC vs. Ash 43 -.10 12 0.12 HC vs. TNC 14 0.45 ——— __- HC vs. TACAE 39 0.43** —-— ——— Ash vs. TNC 14 —.48 ——— --— Ash vs. TACAE 39 0.35* --~ ——— TNC VS. TACAE 14 0.14 ——— ___ CP = Crude protein; CW = Cell walls; ADF = Acid-detergent fiber; L = Lignin; C = Cellulose; HC = Hemicellulose; TNC = Total nonstructural carbohydrates; TACAE = Total available carbohydrates after enzymes. *P < .05 **P <.01 did legumes (9.20% HC) and the percentages of BC in CW for these grasses and legumes (alfalfa) were 41.4 and 20.6, respectively. High concentrations of HC were related to low concentrations of ADF (r = —.50) and consequently the correlation between ADF and CW was negative (r = —.25) in the.MSU forages. A negative relationship between CW and L for both groups of forages indicates that L tends to decrease when CW increases. 98 Total nonstructural carbohydrates (TNC) had low correlations (r = +0.45 to —0.48) with other chemical com- ponents. Correlations between TACAE and CP, CW, HC, Ash, TNC were low and positive (0.08 to 0.43**) but those between TACAE and ADF, L, C were slightly higher and negative {—.22 to -.64**). The negative relationships between GP or ash to CW and other fibrous fractions are in accord with those reported by Kayongo-Male §E_§l.(64) and Van Soest (117). The positive changes between CP and ash are in agreement with the report by Smith (104) and Van Riper and Smith (114). Low correlations between TNC, TACAE and GP or CW in these forages indicate that changes in GP or CW were not related to changes in these nonstructural or available carbohydrates. Similarly, Van Riper and Smith (114) reported that TAC (or TNC) was variable and did not show definite trends with changes in other chemical constituents or with advancing maturity. High levels of CP are generally accepted as an indication of good quality in forages. Therefore, an increase in CW and other fibrous fractions with correlated decrease in CP would definitely result in a decrease of forage nutritive value. b. ;g_y;yg Dry Matter Digestibility (DDM) vs. Chemical Components The relationships between chemical components and five in vivo parameters are presented in Table 30. In vivo DDM was positively and significantly correlated with.CP (r = 0.66 to 0.76) or ash (r = 0.46 to 0.64) for both MSU 99 Ho. v mic mo. v m« .msoHHMH>mHnnw Hmsuo How mN mHnma muosuoom wmm .mmnmsw mHnHummmHQ n ma “mandamus: mHnHumOmap Hmuoa I . . . . . . I ZQB .OxmuGH ll. Hmuumfi mac mHnHumomHa u HZQQ NoxmucH pounce who u Hza “Mousse mus mHnHunmmHn u 290 NH.o mm Ho.o mm om.o mm m¢0<5 mo.o «H mo.| «H mH.o «H 028 0N.0 0 Nm.I 0 Nm.0 NH Inm.I me 00.0 NH IIom.I 00 00.0 NH 00.I 00 o\oz hN.I m om.o m mm.l NH «aN«.o m« m«.I NH c¢mm.o m« mH.I NH NH.o N« um\H m«.| «H m«.I NH 0N.I NH mH.o m« No.1 mH «mm.o m« «>«.I mH HH.I m« O\H m«.I «H m«.l NH HN.I mH NH.o m« mo.I mH Imm.o m« «n«.1 mH NH.I m« ma¢\H Imm.o «H m«.o NH om.o mH ««m«.o m« mH.o NH ««m.o m« Im«.o mH «I«m.o m« cm< no.0 m mm.1 m Nm.o NH «Imm.l m« o«.o NH «««m.l m« Ho.o NH «mm.| m« or m«.I «H «mm.I NH mo.o mH «Imm.n m« mN.o mH «Imm.I m« «om.| mH «I«N.I m« U ««m.I «H mm.I NH oN.I mH mH.I m« No.0 mH mo.o m« I«m.l mH ««m«.l m« H «Nm.l «H «on.| NH no.1 mH «Imm.l m« HN.o mH ««h«.l m« «amm.| mH «INm.I m« mad om.I m om.| m mH.o NH IIMh.o m« Hm.o NH Igon.l m« NH.I NH «Iwm.u m« 30 IHm.o «H «mm.o NH oH.o mH «Imn.o m« .HH.I NH «Imm.o m« «awm.o mH «Imh.o m« so u s H s H c H s H c H s H s H s mEmuH sz pm: sz .>HcD sz .>HsD sz .>HcD mHHma mHHmo mnHma oswusm NHHMQ mocusm NHHMQ mousse mo zos Hzoo o>H>.mH Hzo o>H>_mm zoo o>H> mm .mmDHm> sOHumnsosH OHuMEMNsm can musmsomfioo HmoHEmco £9H3 mumumemumm O>H> sH mo mucmHonmmoo GOHHMHOHHOU .om mqmda 100 and Purdue forages. These were consistent and signifi- cantly negative correlations between DDM and ADF, L, C, and CW. Lignin-fiber ratios had relatively low correlations with DDM for both groups of forages. Total nonstructural carbohydrates and TACAE had positive but low correlations with ig_yiyg DDM. The significant and negative relation (r = —.65 to -.82) between ADF content and ip_yiyg_DDM is in agreement with other studies (25,88,115,ll7,131). Lignin did not have as marked depressing effect (r =-.49 to -.54) on DDM as that reported in the other studies mentioned above. Based on the present study the use of ADF as a single pre- dictor of i§_yiyg_DDM may be the most preferable since it had a higher correlation than any other chemical constitu— ents with DDM. Total nonstructural carbohydrates and TACAE had such low correlations (r = 0.18 to 0.30) that they can not be used as useful predictors of DDM. This finding is not in agreement with that of Wilkins and Minson (129) who reported good relationship between TAC (TNC) and 12.3122. OMD and actually suggested TAC as a useful single predictor of forage digestibility. c. Drpratter Intake (DMI) vs. Chemical Components Forage dry matter intake (DMI) for both groups showed opposite signs of relationships (+ vs. -) to all chemical components (CP, CW, ADF, C, HC, L-fiber ratios) except L and ash (Table 30). For Purdue forages, there 101 were significant, positive correlations between DMI and CP (r = 0.68) or L/HC ratio (r = 0.56) but negative corre- lations (P < .01) with CW (r = -.70), ADF, C, HC and HC/C ratio. All chemical components had low and non-significant relationships to DMI for MSU forages. The most important components depressing DMI for Purdue and MSU forages were CW and L/HC, reSpectively. For Purdue forages, CW would be the most appropriate single predictor for estimating forage intake. This finding is in agreement with that reported by Van Soest (117) who found that CW had signifi- cant correlation with DMI and developed a prediction equation for DMI using a reciprocal of lOO—CW. Neither TNC nor TACAE could be used to predict DMI due to their extremely low correlations with forage intake. The differences in trends and magnitude of correla- tion coefficients for these two groups of forages emphasize the fact that differences do exist between any two popula— tions and in this case the differences might be due to unequal proportions of grasses and legumes in each forage group and that MSU forages consisted of only 18 samples and not all had complete chemical analysis. d. Digestible Dry Matter Intake (DDMI) vs. Chemical Components The correlations between digestible dry matter intake (DMI x DDM) and chemical components were not con— sistent for both groups of forages (Table 30). Crude Protein, ash and L/HC ratio had positive and significant correlations with DDMI whereas CW, ADF, C, HC and HC/C 102 ratio had significantly negative correlations with DDMI for Purdue forages. Total nonstructural carbohydrates and TACAE showed very low correlations with DDMI. All chemical components had low and non—significant correlations with DDMI for MSU forages. For Purdue forages, an increase in CW followed by a decrease in CP would result in a signi— ficant decrease in DDMI. Either crude protein or CW seemed to be the appropriate single predictor for estimating DDMI of Purdue forages but no single chemical components would be satisfactory to predict DDMI for MSU forages. e. Total Digestible Nutrients (TDN) vs. Chemical Components Total digestible nutrients were significantly correlated (r = 0.65, P <.05) with CP but had low and posi- tive correlations with ash and L/HC ratio for MSU forages. All other CW fractions and fiber ratios had negative correlations (r = —.32 to -.70) with TDN. Acid-detergent fiber, C and CP seemed to be the three important factors controlling the concentration of TDN in forages. Acid~ detergent fiber showed a reasonably high correlation with TDN (r = -.70, P < .05) and this value might be sufficient to use ADF as a single predictor for mixed forages. In this case, ADF might predict TDN more accurately than GP or C alone. Adams £2.21- (1) proposed the use of CP and CF to predict TDN in various forages (Table 19). Probably a combination of factors (ADF, CP, C, etc.) in a multiple regression equation may be able to predict TDN more 103 precisely than using single predictors. At present, there are no reports using these three factors. f. Digestible Energy (DE) vs. Chemical Components The correlations of digestible energy with chemical components for MSU forages followed a pattern similar to that for TDN. There were significant correlations between DE and CP or ash but negative correlations with ADF, L, CW, C, and L—fiber ratios. Among chemical components, ADF tended to be the most important single constituent (r = -.62, P < .05) controlling energy digestibility. This finding is in agreement with that reported by Johnson and Dehority (58) who found a high correlation between ADF and energy digestibility (r = —.76, P < .01). In addition, lignin also Showed a marked depressing effect on DE. Sullivan (111) used acid-insoluble L to predict DE with reasonable accuracy (r = -.94, P < .01, SEE = 2.3). How- ever, there are no useable prediction equations using ADF or a combination of ADF, CP, L, ash, etc. to predict diges- tible energy. 9. m Dry Matter Digestibility (DDM) vs. IQ. giggg Fermentations and Enzymatic Incubations £2.3iyg DDM was significantly and positively correlated (r = 0.76 to 0.88) with EEHXEE£Q DMD by the Tilley-Terry method for both groups of forages (Table 31). Ifl_!iE£g organic matter and dry matter disappearance had similar correlation coefficients with DDM. 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MN mad 440m.0 Q30>H .m> QEQ>H 44N«.I 0« MN.0 MN 44>m.t 0« «4M0.I MN 0M.I 0« 44HM.I MN 30 0N.0 0« 440m.| MN 440«.0 0« «m«.0 MN MH.0 00 40«.0 MN mo H s H s H s H s H s H c mamuH AmmmmMHo 000m OOHM OOHm HmOHmosu HHmV mch> ounmsm tsmHHmse ounmsm UQMHHmna ouuwsm UGMHHMSB m>HuHHusz mo mmusmmmz _ moose mooHumHmusoo oso>H ozos>H ozo>H .mmmmuom smOHm ousmsm cam Hana How msHm> m>HuHHpss mo mensmmwfi mcoEm musmHonmwoo QOHDMHmHHOU .0M mqmOHQQm HON mN UHQMB MO THOGHOOMVUMM .Mm u c« «m u GM 0mm H CN 0cm H CH «smm.0 0m.0 «0M.0 mN.0 0« 111 MN.0 0« mm.0 m «HN.0 mm *«N.0 MOH £m¢ .w> Hm ««h«.1 0N.0 asmm.l ««««.I HOH a««0.1 ««N«.1 HM ««M«.l MM «¥H«.1 0NH ««««.1 «0H £w¢ .m> H «0.0 M«.0 m0.0 «amm.0 0« 111 «smm.0 0« M«.0 m «amN.0 mm sMN.0 MOH Hm .m> H mH.1 NM.0 ««h«.1 ««m«.1 HOH atH0.1 ««N«.1 H5 «00.1 0M «s0N.1 0NH ssHN4l «0H EmH $3.h0¢ «*0M.0 m0.o «N.0 «svm.0 0« 111 «a«m.0 0« m0.0 m ss«M.0 mm ««N«.0 MOH Hm .m> haw 0H.0 «MN.0 «sN0.0 «chm.0 HOH «¢«M.0 «smm.0 Hh «0MM.0 MM «sm0.0 0NH «sHm.0 «0H H .m> hoe MN.I no.1 ssMb.1 ssbN.1 m0 «««0.1 «com.1 mm ««h«.1 0M ««N«.1 0NH 00.1 0MH Smfl .m> BU 0N.0 «00.0 00.1 MN.0 0« 111 MN.0 0« «00.0 0 No.0 mm «HN.0 MOH Hm .m> 30 0H.I 0m.0 semM.0 00.0 mm «sm>.0 ««Mb.0 m0 «amn.0 MM «««m.l 0NH 00.1 MMH H .m> 3U «aN0.0 ««MM.0 ««Mh.0 «sNh.o mm «s«m.0 «aNm.0 m0 «000.0 MM «««m.0 0NH ssmn.0 MMH mad .m> BU «N.0 MM.I «smm.0 4«0«.0 HOH «4N0.0 ««m«.0 HM «0M.0 MM «c0M.0 0NH «0H.0 «0H fiwd .m> mu «*0M.1 00.1 «0N.I ssmm.1 0« 111 «smm.1 0« 00.1 w ««0«.1 m0 ««m«.1 MOH Hm .m> mo 00.1 MN.1 «sN0.I «sNM.1 HOH «s00.1 «smm.1 HM «smm.1 MM «sN0.1 0NH «*0N.1 «0H H .m> m0 «000.1 NM.0 «s00.1 «smh.1 HOH «emm.1 «st.1 HM «sm0.1 MM 4400.1 0NH ashh.1 «0H mad .m> m0 «tNh.1 hM.0 asbm.1 ssHm.1 mm *«Hm.1 «chm.1 m0 «cmn.1 MM «««0.1 0NH «s00.1 MMH 30 .m> mu H H s H c H H s H s H s H c mewuH «.HEOO M.mmH N.muw cmGHQEoo H.me mmmmmhu mmEdmmH mmmmmsw pmanEoo wwmmuom HMUHQOHB mmmmuom mumnmefime memMHOh HHch>o .msOHmmH HMOHQOH» tam mumsmefimu Home messmmH 0cm mommmum HOH mucmsomfioo HMUHEmno macaw musmHonmmOU COHuMHthOU .MM MHmme 124 and negative correlations with CW and ADF but the corre— lations were positive and nonosignificant in tropical legumes. Lignin and Si were significantly and negatively correlated with CP in grasses but nonesignificant in legumes. The correlation between CP and ash was positive for tropical grasses but negative for the legumes. The correlation between CW and Si was slightly negative in tropical grasses but significantly positive in legumes. Cell walls had a significant, negative correlation with ash in grasses but practically no relationship for tropical legumes. In tropical grasses, ash had significant and negative correlations with ADF and L but non-significant and positive correlations in tropical legumes. Evidently there were many opposite relationships among chemical components for grasses and legumes. Besides, the rates of change in chemical composition and digestibilities were slower for trOpical forages (Table 28). Tropical legumes had only two species of forages and a small number of samples which might not be representative of a larger population of tropical legumes. All these reasons might also contribute to overall differences between temperate and tropical forages. b. Lg XiXQ Parameters vs. Laboratory Estimates The relationships between ip_vivo digestibility, DMI and laboratory estimates are presented in Table 39. In temperate forages, ig vivo DDM had significant but low correlations with DMI, CP, ash, and all enzymatic incubations 125 TABLE 39. Correlation coefficients between digestibility or intake and measures of nutritive value in temperate forages. Combined Grasses Legumes (Alfalfa) Factors correlated n r n r n r I_n mg DDM vs. DMI 61 0.44** 31 0.41* 30 0.46** Ip.yiy2_DDM vs. IVDMD 33 O.85** 20 0.92** 13 0.86** Ig_yiyg_DDM vs. IVOMD 15 0.89** 8 0.98** 7 0.95** Ip'yiyg DDM vs. Cell 32 0.63** 26 0.85** 6 0.59 12 yiyg DDM vs. Amy 20 0.53* 14 0.78** 6 0.52 IE 2122 DDM vs. Buf 57 0.62** 29 0.61** 28 0.74** Ip_yiyg DDM vs. CP 61 0.69** 31 0.65** 30 O.79** Ip_yiyg DDM vs. CW 55 -.62** 25 —.7l** 30 -.86** Ip.yiyg DDM vs. ADF 61 -.74** 31 -.70** 30 —.79** 12.2122 DDM vs. L 61 -.40** 31 -.75** 3O -.70** £p_yiyg DDM vs. Ash 61 0.55** 31 0.65** 30 0.48** Ip_yiyg DMI vs. IVDMD 33 0.64** 20 0.61** 13 0.62* lg vivo DMI vs. IVOMD 15 0.74** 8 0.51 7 0.74 £p_yiyg_DMI vs. Cell 32 0.53** 26 0.52** 6 0.22 Ig’yiyg DMI vs. Amy 20 0.69** 14 0.88** 6 0.08 £g_yiyg DMI vs. Buf 57 0.42** 29 0.45* 28 .0.10 £2_yiyg DMI vs. CP 61 0.52** 31 0.63** 30 0.26 }§_vivo DMI vs. CW 55 -.45** 25 -.45* 30 -.22 £§.yiyg DMI vs. ADF 61 —.37** 31 —.60** 30 —.22 £g_vivo DMI vs. L 61 —.04 31 -.64** 30 -.28 fig yiyg_DMI vs..Ash 61. 0.33**. 31..0-53fi* 30 .0.20 See footnotes of Tables 29, 30, and 31 for abbreviations. *P < .05 **P < .01 126 (r = 0.44 to 0.69). Digestibility was significantly and negatively correlated with CW, ADF and lignin. Two-stage i2_yi£59 fermentations (IVDMD or IVOMD) had highly signi— ficant correlations with $2 yiyg DDM (r = 0.85 to 0.89). In this study, ADF seemed to be an important factor depres— sing forage digestibility. Due to low correlations, all enzymatic values would not be satisfactory to estimate i§.yiyg DDM for combined forages. Even buffer extract was as good as the two enzymatic incubations. The two-stage ip yiggg fermentation was an excellent technique and IVOMD was better than IVDMD in predicting ip.yiyg_DDM. The relationships between in_yiyg DDM and DMI or other measures of nutritive value for temperate grasses were similar to those for temperate legumes in terms of plus and minus signs. However, the correlations between DDM and cellulase or amylase incubations were highly significant (r = 0.78 to 0.85) for grasses but non-signifi— cant for legumes. Many laboratory methods had high rela— tionships to DDM within either grasses or legumes indicating that these measurements can be used to predict ip_yiyngDM of that particular forage type. Forage DMI had significant but low correlations with IVDMD, enzymatic incubations, buffer extract, CP and ash (r = 0.33 to 0.69) whereas it had negative relation— ships to CW, ADF and L when forages were combined. Ig_ giggg OMD seemed to be a better predictor of DMI for combined forages than other laboratory estimates. Amylase incubation was slightly better than cellulase in predicting DMI. Among 127 chemical components, CP was better than CW and this finding is not in agreement with that by Van Soest (117) who found CW to be a better predictor of DMI than CP. Relationships between DMI and laboratory estimates for temperate grasses and legumes were similar but the magnitudes of correlation coefficients for both forages were very different. Amy— lase incubation seemed to be a good predictor of DMI for grasses but not for legumes. £2.21EEQ DMD might be used to predict DMI for either grasses, legumes or combined forages but with low accuracy. Cell walls were not satis— factory (r = -.45) to predict DMI of forages even though grasses contain higher amounts of CW than do legumes. On the other hand, L was a better predictor of DMI than CP, CW, ADF in grasses. VII. PREDICTIONS OF NUTRITIVE VALUE FROM LABORATORY ESTIMATES AND STAGE OF MATURITY In the present study, there were different relation- ships among in vivo parameters and laboratory values for the various groups of forages studied. Therefore, some laboratory methods may be useful in predicting nutritive value of certain forages but not for other forages. To illustrate this point, several selected laboratory values that could be used to predict i§_vivo nutritive value of specified forage types are presented in Tables 40 to 42. Prediction equations for nutritive value from stage of maturity are shown in Table 43. Various aspects of these prediction equations will now be discussed. 128 a. Predictions of ;p giyg Dry Matter Digestibility 112241 The in’yiyg_dry matter digestibility of alfalfa and some temperate grasses can be precisely predicted from several values obtained by laboratory procedures (Table 40). Cell walls could be used to predict DDM of alfalfa more accurately (r = -.86, P < .01, SEE = 3.9) than for temperate grasses (r = -.71, P < .01, SEE = 5.6). Oh et a1. (88) also reported a high correlation (r = -.86, P < .01) between CW and DDM for alfalfa. £2 yiyg DDM of alfalfa and temperate grasses could also be predicted by using ADF, L and CP with moderate to high accuracy (Items 3 to 13, Table 40) with standard error of estimate of 2.9 to 5.6. Acid-detergent fiber or L could predict ip_yiyngDM of reed canary grass more accurately than they could predict DDM for alfalfa, bromegrass, tall fescue and mixed grasses. Many scientists have also reported that ADF and L had high correlations with DDM (58,88,115,131) and that DDM.of SEE = temperate grasses was precisely predicted (r — -.94, 2.1) from their lignin content (111). Crude protein could predict DDM of alfalfa with only moderate accuracy (SEE = 4.7) in this study. Bredon et a1. (19) reported that CP was not a reliable predictor of DDM for mixed tropical grasses (SEE S 10.0). Generally sPeaking, all these chemical components (CW, ADF, L,.CP) could be used to predict DDM of a specified forage type but with large standard errors of estimate. An acceptable standard error for predicting DDM should be kept below 3 TABLE 40. 129 Regression equations for estimating in vivo digestibility and in vitro true dry matter disappearance from_ laboratory analytical values. DDM or IVTDMD vs. Laboratory Forage Estimates Type n r SEE Predictions of DDM 1%) 1. DDM = 98.840-0.790CW Alfalfa 30 —.86** 2. DDM = 108.900-0.760CW Temp. gra. 25 —.7l** . 3. DDM = 97.800-0.966ADF Alfalfa 30 -.79** . 4. DDM = 96.025-0.959ADF Brome 16 -.70** . 5. DDM = 112.426-1.635ADF Reed Can. 6 -.84* . 6. DDM = 103.000-1.115ADF Tall Fes. 9 -.86** . 7. DDM = 93.819—0.912ADF Temp. gra. 31 -.70** . 8. DDM = 86.665-2.970L Alfalfa 30 -.70** . 9. DDM = 79.317 - 3.584L Brome 16 -.79* . 10. DDM = 87.499-8.029L Reed Can. 6 -.89* . ll. DDM = 83.038-5.115L Tall Fes. 9 —.93** . 12. DDM = 76.746-3.444L Temp. gra. 31 -.75** . 13. DDM = 34.300+l.460CP Alfalfa 30 O.79** 4.7 14. DDM = 1.3691VDMD-23.923 Alfalfa 12 0.88** .4 15. DDM = 0.960 IVDMD+4.579 Brome 11 0.93** .8 l6. DDM = l.1381VDMD—7.786 Reed Can. 6 0.88* .1 l7. DDM = 0.934IVDMD+5.716 Temp. gra. 20 0.92** .6. 18. DDM = l.5251VOMD-30.411 Alfalfa 0.96** . l9. DDM = 1.077IVOMD+1.621 Temp. gra. 0.98** . 20. DDM = 29.460+0.650Ce11 Alfalfa 6 0.58 .4 21. DDM = 38.570+0.660Cell Brome 13 0.83** .8 22. DDM = 33.810+0.810Ce11 Reed Can. 0.75 -1 23. DDM = 31.340+0.880Ce11 Tall Fes. 0.89** .6 24. DDM = 35.660+0.740Cell Temp. gra. 26 0.85** .9 25. DDM = 28.257+0.865Amy Alfalfa 6 0.52 5. 26. DDM = 43.062+0.723Amy Brome 7 0.78* 5.4 130 TABLE 40. Continued. DDM Or IVTDMD vs. Laboratory Forage Estimates Type n r SEE Predictions of DDM (%) 27. DDM = 35.283+0.961Amy Tall Fes. 7 0.79* . 28. DDM = 39.229+0.836Amy Temp. gra. 14 0.78** . 29. DDM = 10.264+l.506 Pep Alfalfa 5 0.90* . 30. DDM = 39.934+0.734 Pep Temp. gra. 10 0.74* 31. DDM = 20.090+0.904(Cell+Amy) Alfalfa 22 0.77** 32. DDM = 35.784+0.678(Ce11+Amy) Brome 0.85** . 33. DDM = 32.230+0.806(Cell+Amy) Tall Fes. 0.89** . 34. DDM = 34.684+0.722(Ce11+Amy) Temp. gra. 17 0.86** 35. DDM = 23.526+0.729(Cell+Pep) Alfalfa 6 0.63 . 36. DDM = 39.106+0.584(Cell+Pep) Brome 6 0.87* . 37. DDM = 23.589+0.960(Cell+Pep) Reed Can. 6 0.93** .3 38. DDM = 32.449+0.743(Ce11+Pep) Temp. gra. 12 0.88** .4 39. DDM = 30.498+l.267Buf Alfalfa 22 0.75** . 40. DDM = 38.788+l.lOlBuf Tall Fes. 8 0.74* . 41. DDM = 40.584+l.035Buf Temp. gra. 17 0.64** Predictions of IVTDMD (%) 42. IVTDMD = 19.760+0.7OOIVDMD Temp. gra. 40 0.97** 2.4 43. IVTDMD = 31.230+0.930Cell Trop. gra. 31 0.83** 5.5 44. IVTDMD = 26.086+l.548Amy Trop. gra. 31 0.74** 6.5 45. IVTDMD = 26.400+1.721Buf Trop. gra. 31 0.84** 5.3 DDM = In vivo dry matter digestibility; IVTDMD = I§_vitro true dfy matter disappearance; = Acid-detergent fiber; L IVDMD = In vitro dry matter disappearance ADF protein; (Tilley-Terry methodT; digestibility; Amylase incubation; Buffer extract; r = SEE *P < CW = Cell walls; = Lignin; = Standard error of estimate. .05 **P < .01 CP Cell = Cellulase_IncuEation; Pep = Pepsin incubation; Correlation coefficients; Crude IVOMD = In vitro organic matter Amy = Buf = 131 digestibility units (112,117). A prediction equation for a given forage type was usually unsuitable for another forage type. For example, prediction equations in items 8 and 9 using lignin to predict DDM of alfalfa and bromegrass might be different because they had different intercepts or constants (86.7 vs. 79.3) and different slopes (3.0 vs. 3.6) for alfalfa and bromegrass, respectively. Two-stage ip.yiE£g fermentations (IVDMD and IVOMD, Items 14 to 19, Table 40) could predict ip_yiyg DDM of alfalfa and temperate grasses more accurately than could chemical components discussed above. The respective standard errors of estimate ranged from 1.8 to 4.4 compared to 2.9 to 5.6. Ip.yiggg OMD was better than IVDMD in predicting DDM of both alfalfa and grasses. Several scientists also reported that the two—stage fermentations were excellent techniques to predict DDM of grasses or legumes (10,58,88,112,126). Cellulase, amylase, pepsin and a sequential hydro— 1ysis by these enzymes (Items 20 to 38, Table 40) could be used to predict ig yiyg DDM of alfalfa and temperate grasses with standard errors of estimate ranging from 2.3 to 6.1. Cellulase was efficient in predicting DDM of bromegrass and/or combined temperate grasses but it was not a reliable predictor of DDM for alfalfa. Amylase could predict DDM of temperate grasses slightly better than it could for alfalfa, but pepsin could predict DDM of alfalfa better than for grasses. A sequential hydrolysis by 132 cellulase plus amylase could predict DDM of alfalfa and grasses with similar accuracy whereas the hydrolysis by cellulase plus pepsin could predict DDM of grasses much more accurately than it could for alfalfa. The amount of material solubilized by only buffer could predict DDM of alfalfa more accurately than it could for temperate grasses. However, the standard errors of estimate for buffer incubations were too large (4.53 to 6.86) for acceptable prediction purposes. In temperate grasses, IVDMD could be used to predict ip_yi3£g true dry matter disappearance (IVTDMD) accurately (SEE = 2.4) whereas cellulase, amylase and buffer incubations could predict IVTDMD of tropical grasses but with larger standard errors (5.3 to 6.5). In this study, the two—stage ip_yiE£g_fermentations (IVDMD or IVOMD) were the most accurate methods to predict i§.yiyg DDM of legumes as well as grasses. Chemical com- ponents (CW, ADF, L, CP) were as accurate predictors as enzymatic incubation values in predicting DDM. Among these three enzymes, cellulase and pepsin were comparable and were slightly better than amylase in predicting DDM. Cellulase tended to be the preferred enzyme for predicting DDM of grasses while pepsin was the preferred enzyme for legumes. This may be related to the fact that legumes (alfalfa) contain higher level of CP (20.0% compared with 13.8%) which may serve as substrate for pepsin while grasses contain more cellulose than alfalfa (32.0 compared with 133 27.7, Table 27, C = ADF — L). A sequential hydrolysis by cellulase plus amylase did not measurably improve the accuracy of DDM prediction over the use of one enzyme alone. Yet, in some cases, the sequence of two enzymes increased precision of estimation over that of only one enzyme. For example, a sequence of cellulase plus pepsin greatly improved the prediction of DDM for grasses by reducing standard errors from 3.9 (for cellulase alone) and 3.3 (for pepsin alone) to 2.4 digestibility units. Similarly cellulase plus amylase reduced standard errors of predicting DDM for alfalfa from 5.4 (cellulase alone) and 5.6 (amylase alone) to 4.4. The regression analysis in Table 40 indicates that CW and ADF had a smaller effect on digestibility of grasses than for alfalfa but lignin had a larger effect in grasses than in legumes (compare the regression coefficients (b-values) for Equations 1 vs. 2, 3 vs. 7 and 8 vs. 12, Table 40). The intercept values (constants) for these equations indicate that the digestibility of alfalfa having no ADF or L (97.8, 86.7) exceeds that of grasses having no ADF or L (93.8, 76.7). The intercept values for Equations 1 and 2 approximate 100 indicating that CW may be responsible for DDM varying from 100. b. Predictions of Dry Matter Intake (DMI) Dry matter intake (gm/ngé75 ) of temperate grasses predicted from CW or L content had standard errors of estimate of 13.4 and 10.2 and were too large to be acceptable 134 (Table 41). Other workers also reported that CW had low but significant correlations (r = -.65 to -.77) with DMI (51,77,117). Two-stage IVDMD was not a reliable predictor of DMI with standard errors of prediction of 14.3 and 12.0 for alfalfa and temperate grasses, respectively. This finding is in agreement with studies by others (10,51). Cellulase incubation values were an excellent predictor of DMI for tall fescue and bromegrass (SEE = 3.2 to 4.1) but not for mixed grasses. Amylase incubation was also a reliable predictor of DMI for bromegrass, tall fescue and mixedgrasses with standard errors of 4.3 to 5.2. A sequential hydrolysis by cellulase then amylase could predict DMI of bromegrass, tall fescue and mixed grasses more precisely than using amylase alone. Buffer extract could predict DMI of bromegrass, tall fescue and mixed grasses more accurately than could CW, L or IVDMD as buffer incubation values had greater correlations and lower standard errors of estimate. In this study, enzymatic incubations were more accurate than chemical components (CW, L), IVDMD or buffer extract in predicting forage DMI. Even incubation with buffer only was slightly better than IVDMD or chemical components. All three types of enzymatic incubations had similar degrees of accuracy for predicting forage intake with cellulase preferable for the individual grasses. In practice, a single enzymatic incubation would be simpler TABLE 41. 135 Regression equations for estimating dry matter intake from laboratory analytical values. £2 Vivo DMI vs. Laboratory Forage Estimates Type n r SEE . . 0.75 Predictions of DMI (gm/BWKg ) l. DMI = 123.070-0.940CW Temp. gra. 24 —.46* 13.4 2. DMI = 87.790-5.240L Temp. gra. 31 -.64** 10.2 3. DMI = 2.063IVDMD-47.948 Alfalfa 13 0.62* 14.3 4. DMI = l.409IVDMD-14.746 Temp. gra. 20 0.61** 12.0 5. DMI = 1.070Cell+16.770 Brome 0.94** 4.1 6. DMI = O.800Ce11+30.650 Tall Fes. 7 0.93** 3.2 7. DMI = 0.910Ce11+32.900 Temp. gra. 26 0.51** 12.0 8. DMI = 1.203Amy+22.701 Brome 7 0.91** 5.2 9. DMI = 0.922Amy+32.899 Tall Fes. 7 0.87* 4.3 10. DMI = 1.061Amy+27.951 Temp. gra. 14 0.88** 4.7 11. DMI = 0.910(Cell+Amy)+21.365 Brome 0.96** 3.4 12. DMI = 0.778(Cell+Amy)+30.166 Tall Fes. 0.93** 3.4 13. DMI = O.836(Cell+Amy)+26.006 Temp. gra. 17 0.92** 3.8 14. DMI = 1.587Buf+24.104 Brome 9 0.77* 7.4 15. DMI = 1.053Buf+36.702 Tall Fes. 8 0.76* 5.7 16. DMI = 1.307Buf+30.579 Temp. gra. 17 0.75** 6.4 DMI = Dry matter intake. For other abbreviations, see Table 40. *P < .05 **P < .01 than a sequence of two enzyme incubations. Cellulase or amylase incubation values would be a satisfactory predictor of DMI for several temperate grasses. For rapid and inexpensive screening of grasses, incubations with buffer only could be used to estimate dry matter intake. 136 c. Predictions Of Tetal Digestible Nutrients (TDN)L Qigestible Drquatter'(DDM) and Digestible Ene£927(DE) Since only a few values for TDN of legumes were available, the following discussion will serve as an exploration to the use of some laboratory estimates to predict TDN. Crude protein, two-stage IVDMD, cellulase, pepsin and a sequential hydrolysis by cellulase then pepsin had positive and non-significant correlations with TDN for legumes. Data in Table 42 indicate that these laboratory values might not be reliable predictors of TDN because some of them gave large standard errors of estimate (4.4 to 6.5). Acid-detergent fiber might be an accurate predictor of TDN for legumes since it had high correlation with TDN and gave small standard error of estimate (SEE = 1.8). The same samples of temperate grasses were used to compare the predictability of DDM, TDN and DE from similar laboratory values (Items 7 through 24, Table 42). A sequential hydrolysis of cellulase plus pepsin tended to be a more accurate predictor of DDM than chemical components, IVDMD, cellulase or pepsin alone (SEE = 2.9 for cellulase + pepsin compared with 3.3 to 4.2 for the others). In this study, the two—stage IVDMD and enzymatic incubations did not excel chemical components (CP, ADF) in predicting TDN of temperate grasses. Acid—detergent fiber tended to be a reliable predictor of TDN with a standard error of 2.6. TABLE 42. 137 Regression equations for estimating total digestible nutrients, digestible dry matter and digestible energy from laboratory analytical values. In Vivo Parameters vs. Forage LEbEEEEory Estimates Type n r SEE a. Predictions of TDN'(%) l. TDN = l3.722+2.724CP Alfalfa 4 0.87 .4 2. TDN = 269.077-5.633ADF " 4 -.98*I .8 3. TDN = l.334IVDMD—22.329 " 4 0.84 .9 4. TDN = 0.898Ce11+15.920 " 4 0.69 . 5. TDN = 1.250Pep+13.795 “ 4 0.69 . 6. TDN = 0.946(Ce11+Pep)+10.401 " 4 0.72 . b. Predictions of DDM (%) 7. DDM = 34.937+1.236CP Temp. gra. 7 0.86* . 8. DDM = 115.658—l.724ADF " 8 -.84** . 9. DDM = l.0421VDMD—0.271 " 8 0.84** . 10. DDM = 0.858Ce11+32.797 " 8 0.83** . 11. DDM = 0.955Pep+32.742 " 8 0.77* . 12. DDM = 0.786(Cell+Pep)+31.306 “ 8 0.90** . c. Predictions of TDN (%) 13. TDN = 37.974+0.971cp Temp. gra.l 7 0.77* 6 14. TDN = 106.305—l.506ADF " 8 -.87** 6 15. TDN = 0.829IVDMD+9.841 " 8 0.80* .3 16. TDN = 0.711Cell+35.194 " 8 0.82* .2 17. TDN = 0.715 Pep+37.430 " 8 0.69 .0 18. TDN = 0.615(Cell+Pep)+35.350 " 8 0.84** .0 d. Predictions of DE (%) 19. DE = 39.164+0.914CP Temp. gra. 7 0.70 .2 20. DE = 100.125-1.309ADF " 8 -.74* .8 21. DE = 0.904IVDMD+5.402 “ 8 0.85** .9 22. DE = 0.773Cell+33.135 " 8 0.87** .8 138 TABLE 42. Continued. In Vivo Parameters vs. Forage n r SEE Laboratory Estimates Type 23. DE = 0.723Pep+37.217 Temp. gra. 8 0.68 4.1 24. DE = 0.650(Cell+Pep)+34.035 " 8 0.87** 2.8 TDN = Total Digestible nutrients; DDM = Digestible dry matter; DE = Digestible energy. See Table 40 for other abbreviations. 1Same samples for all three parameters (DDM, TDN, DE). *P < .05 **P < .01 Digestible energy of grasses could be satisfactorily predicted from either cellulase, cellulase plus pepsin or the two—stage IVDMD with standard errors ranging from 2.8 to 2.9. Pepsin was not as efficient as cellulase in pre- dicting DE of grasses. In this study, IVDMD, cellulase and cellulase plus pepsin excelled chemical components (CP, ADF) in predicting digestible energy. This finding is in accord with those by Butterworth (21), Johnson and Dehority (58). Even though a sequence of cellulase plus pepsin was also excellent, cellulase alone might be sufficient for predicting DE because a cellulase incubation has less manipulations than the two-enzyme sequence. d. Predictions of Crude Protein and I V't Digestibilities from Stages of Maturity Correlation coefficients and prediction equations for CP levels and i2_yi2£g digestibilities based on stages of maturity of grasses are presented in Table 43. Crude TABLE 43. 139 Regression equations for estimating forage nutritive value from stage of maturity. Nutritive Value vs. Forage n r SEE Maturity Type Crude Protein (CP, %) 1. CP = l7.309-0.093X Brome 8 -.96** .l 2. CP = 18.414-0.096X Orchard 8 -.89** .9 3. CP = 20.810-0.120X Reed Canary 8 -.93** .9 4. CP = 19.695-0.103X Tall Fescue 8 -.9l** .9 5. CP = 20.034-0.071X Ky. Bluegrass 8 -.78* .3 6. CP = 19.252-0.096X Temp. grasses 4O -.83** 3 7. CP = 11.162-0.066X Para grass 5 -.74 . 8. CP = 14.032-0.092X Napier 5 -.83 . 9. CP = 10.880-0.062X Speargrass 5 -.99** . 10. CP = 12.024-0.073X Trop. grasses 15 —.77** . 11. CP = 19.470+0.035X Centrosema 5 -.30 In gitgg Dry Matter Disappearance (IVDMD, %) 12. IVDMD = 72.854-0.228X Brome 8 -.93** . 13. IVDMD = 86.999—0.422X Orchard 8 -.99** 14. IVDMD = 74.680-0.214X Reed Canary 8 -.82* . 15. IVDMD = 82.889-0.423X Tall Fescue 8 -.98** . 16. IVDMD = 81.304-0.457X Ky. Bluegrass 8 -.98** . l7. IVDMD = 79.745-0.349X Temp. grasses 40 -.87** . 18. IVDMD = 55.358-0.013X Para grass 5 -.14 . l9. IVDMD = 60.764-0.052X Napier 5 -.82 . 20. IVDMD = 39.278-0.018X Speargrass 5 -.21 . 21. IVDMD = 60.102-0.063X Centrosema 5 -.36 22. IVDMD = 51.133-0.028X Trop. grasses 15 —.07 . InhxigggyTrue Drszatter Disappearance (IVTDMD, %) 23. IVTDMD = 73.028-0.l75X Brome 8 -.90** . 24. IVTDMD = 81.764-0.312X Orchard 8 -.98** . 25. IVTDMD = 75.190-0.163X Reed Canary 8 -.85** . 26. IVTDMD = 77.164-0.295X Tall Fescue 8 -.97** . 27. IVTDMD = 7o.954-0.262x Ky. Bluegrass 3 '-93** ° 140 TABLE 43. Continued. Nutritive Value vs. Forage n r SEE Maturity Type 28. IVTDMD = 75.620-0.241X Temp. grasses 4O -.83** 5.7 29. IVTDMD 58.538-0.030X Trop. grasses 15 -.07 9.6 X = No. of days elapsing from first regrowth (April 15 through September 4, 1972 for temperate grasses; October 10, 1971 through January 15, 1972 for tropical forages). r = Correlation coefficients; SEE = Standard error of estimate. *P < .05 **P < .01 protein levels in bromegrass, orchard grass, reed canary, tall fescue and Kentucky bluegrass decreased significantly (P < .05 to < .01) with advancing maturity. From the day of first cutting in the spring (May 20) throuqh September 4, CP levels of reed canary and tall fescue decreased in excess of 0.10% per day whereas that for Kentucky bluegrass decreased only 0.07% per day. On the average, CP in temperate grasses decreased at the rate of 0.10% per day and this was statistically significant (P < .01). There was greater precision for estimating percentage of CP in bromegrass than in Kentucky bluegrass or all grasses com- bined from maturity data. Regression equations to predict CP from maturity for orchard grass, reed canary and tall fescue had similar standard errors (SEE = 1.9). For the tropical grass samples, all had a decrease in protein with advancing maturity but only speargrass and 141 combined tropical grasses had statistically significant, negative correlations (Table 43). The rate of decline in CP for Napier grass was greatest (0.09% per day) compared with 0.06 and 0.07% per day for Speargrass and Para grass, respectively. On the other hand, CP level in Centrosema, a tropical legume, tended to increase probably due to only 5 cuttings where CP increased from 17% (week 4, first cutting) to 24, 23, 23, 20% for second, third, fourth, and fifth cutting, respectively. The rate of change was obviously non-linear. On the average, CP in tropical grasses declined at a slower rate (0.07 vs. 0.10% per day) and this was not statistically different from that for temperate grasses. Crude protein in speargrass could be precisely predicted from Equation 9 with only 0.2% standard error and,Equation 10 could predict CP in all tropical grasses with a standard error of only 1.4%. .Ig yitgg DMD of both temperate and tropical forages decreased with advancing maturity. The rates of decline in IVDMD for five temperate grasses ranged from 0.21 to 0.46% unit per day with an average of 0.35 (P < .01). The IVDMD of orchard grass could be predicted with the least standard error of any grass whereas the IVDMD levels in other four grasses could be predicted with standard errors of 3.1 to 5.8. £2 yitgg DMD of all tropical grasses and the legume decreased with advancing maturity but the correlations and regression coefficients were not statistically significant. The rates of decline in IVDMD for tropical forages ranged 142 from 0.01 to 0.06 percentage unit per day with an average rate of 0.03% unit per day and this was significantly different (P < .01) from a value of 0.35 for temperate grasses. I§_yit£g true dry matter disappearance (IVTDMD) of temperate grasses was significantly decreased (P < .01) with advancing maturity while that for tropical grasses was not affected. The rate of decline in IVTDMD for orchard grass was greatest (0.31) and that for reed canary grass was the least (0.16% unit per day). On the whole, the rate of digestibility decline for temperate grasses was greater (P < .05) than that for tropical grasses (0.24 vs. 0.03% unit per day). Regression equation for Kentucky bluegrass could accurately predict its IVTDMD at any stage of growth with 2.1% standard error. Equations for other temperate grasses gave larger standard errors (2.8 to 4.1). VIII. PREDICTIONS OF FORAGE NUTRITIVE VALUE USING MULTIPLE REGRESSION TECHNIQUE In many cases, simple correlation and regression was not satisfactory for predicting ip_yiyg forage nutri- tive value due to the low relationships and predictability obtained between the laboratory estimators and the 12.2122 parameters. In many cases, inclusion of two or more variables in a multiple regression equation can greatly increase the accuracy of the prediction equation. 143 a. Predictions cf In yiyg Digestibility and Intake for Purdue and Michigan State UniVersity Forages Multiple regression equations for predicting ig yiyg DDM and DMI of Purdue and MSU forages from laboratory analytical values are presented in Table 44. Combinations of chemical components used to predict DDM and DMI of temperate grasses (Equations 1,2) gave large standard errors (5.0 to 5.3 for DDM and 9.9 to 7.5 for DMI prediction). The multiple correlation coefficients for DDM in Equations 1,2 were only slightly greater than their individual simple correlation coefficients (see Table 44) and low partial correlation coefficients indicate that these predictor com- binations were little or no more reliable predictors of DDM than were CW, ADF or L alone. The latter three each used alone had r value of 0.70 or more and could predict DDM of grasses with standard errors of estimate similar to that for the multiple regression (Table 40). However, prediction of DMI from combined chemical predictors (Equations 1 or 2, Table 44) was slightly more accurate than using CW or L alone (Table 41). For multiple correlation and regression analysis in this case, the multiple correlation coefficient (R) measures the closeness of relationship between i3 vivo DDM or DMI and combined effects of two or more variables. The numerical value of an R lies between zero (no relation— ship) and +1.0 (greatest relationship) and generally the value is always at least as large as that of any simple or partial correlation coefficient. Partial correlation is a TABLE 44. 144 Multiple regression equations for estimating im vivo digestibility and intake of temperate grasses and legumes from laboratory analytical values Items Equation 1 . (n = 31) bo Constant X1 CP X2 ADF X3 Lig X4 Ash Equation 2. (n = 25) bo Constant X1 CP X2 CW X3 ADF X4 Lig X5 Ash Equation 3. (n - 23) bo Constant Xl Buffer X2 CP X3 CW X4 ADF £2 Vivo Dry Matter Digestibility (%) (Purdue University and MSU forages). Dry Matter Intake 0 . 75 (gm/BWKg ) Temperate Grasses R = 0.77** SEE = 5.0 76.36 0.14 (0.65**,10.072) -0.20 (-.70**, -.08) -2.41 (-.75**, -.33) 0.05 (0.65**, 0.01) R = O.79** SEE = 5.3 87.18 0.41 (0.76**, 0.14) -0.33 (".7l**, -.17) 0.16 (-.75**, 0.04) -2.48 (-.75**, -.24) -0.81 (0.63**, -.14) R = 0.81** SEE = 5.3 162.29 -1.17 (0.61**, -.34) 0.59 (0.75**, 0.16) -1.01 (-.71**, -.36) -0.01 (-.74**, -.00) .R = 0.70** SEE = 9.9 40.42 1.33 (0.63**3'o.332) 0.97 (-.60**, 0.13) —5.36 (-.54**, -.351 -l.l9 (0.52**, -.12) R = 0.83** SEE = 7.5 13.34 1.34 (0.77**, 0.40) 1.31 (-.45*, 0.56**) —1.41 (-.67**, -.24) -5.44 (-.73**, -.36) -2.40 (0.54**, 0.291 R = 0.92** SEE = 7.0 126.48 -O.83 (0449*, -.19) ‘0417 (0.78**, '403) 2.77 (-.46*, 0.62**) -4.58 (—.68**, -.51*) 145 TABLE 44. Continued. Items In Vivo Dry Matter Dry Matter Intake fifg'e‘sTIbllity (%) (gm/BWEJS) ~ g X5 Lig -2.14 (-.73**, -.21) -7.27 (-.76**, —.48*) X6 Ash -l.58 (0.61**, -.26) -1.72 (0.55**, -.22) Equation 4. (n = 20) R = 0.90** SEE = 3.7 R = 0.95** SEE = 5.8 bo Constant -52.64 90.87 Xl Cellulase 1.31 (0.86**, 0.77**) -0.73 (0.54*, -.40) X2 CP 0.37 (0.68**, 0.15) 1.29 (0.80**, 0.32) X3 CW -0.09 (-.69**, -.05) 2.63 (-.46*, 0.69**) X4 ADF 1.47 (-.70**, 0.38) -3.67 (-.68**, -.55*) X5 Lig 0.48 (-.65**, 0.07) -7.18 (-.77**, —.56*) X6 Ash 1.41 (0.49*, 0.31) —2.08 (0.54*, -.29) Equation 5. (n = 14) R = 0.98** SEE = 1.7 R = 0.98** SEE = 6.1 bo Constant 0.36 124.17 X1 IVDMD 1.37 (0.94**, 0.95**) -1.49 (0.71**, -.71**) X2 CP —1.07 (0.68**, -.73**) 4.56 (0.93**, O.79**) X3 CW 0.15 (—.38, 0.25) 1.08 (-.67**, 0.46) X4 ADF -0.31 (—.62*, —.23) -2.26 (—.88**, -.44) X5 Lig —0.52 (—.56*, —.19) -0.49 (-.84**, -.06) X6 Ash —0.02 (0.36, -.01) —2.91 (0.64*, -.45) Egan-3131381; 6. R = 0.93** SEE = 3.4 R = 0.96** SEE = 5.7 bo Constant 40.41 133-75 X1 Cellulase 1.23 (0.86**, O.79**) —0.81 (0.54*, -.45) 146 TABLE 44. Continued. Items In Vivo Dry Matter Dry Matter Intake BIgEEEIbility (%) (gm/ng.75) 9 x2 Buffer -1.03 (0.53**, -.49) -1.13 (0.43*, -.33) x3 cp 0.03 (0.68**, 0.01) 0.92 (0.30**, 0.24) x4 cw —0.43 (-.69**, —.27) 2.22 (-.45*, O.62**) x5 ADF 0.64 (~.70**, 0.13) —4.55 (-.68**, -.62**) X6 Lig -0.04 (-.65**, -.011 —7.73 (—.77**, -.60*) x7 Ash 0.79 (0.49*, 0.20) —2.72 (0.54*, -.33) Equation 7. (n = 12) R = 0.99** SEE = 1.7 R = 0.93** SEE = 6.2 bo Constant -36.74 287.92 x1 IVDMD 1.29 (0.94**, 0.96**) -1.40 (0.71**, -.72) x2 Buffer 0.32 (0.26, 0.29) -2.09 (0.72**, -.43) x3 cp —0.23 (0.67*, —.22) 2.30 (0.93**, 0.45) x4 cw 0.03 (-.36, 0.03) 1.39 (-.69*, 0.49) x5 ADF 0.32 (-.56, 0.22) -4.53 (—.92**, —.66) X6 Lig 1.11 (-.43, 0.36) —5.86 (—.90**, -.49) x7 Ash 0.07 (0.23, 0.06) -3.11 (0.70*, —.56) Ignitii? 8. R = 0.99** SEE = 1.0 R = 0.99** SEE = 4.0 bo Constant -42.19 94-21 x1 IVDMD 0.93 (0.96**, 0.96**) -2.44 (0.67*, —.9l*) x2 Cellulase 0.64 (0.33**, 0.37*) 2.52 (0.78**. 0.86*) x3 CP —0.11 (0.56, -.16) 2.92 (0.94**, 0.73) x4 cw —0.13 {-.30, —.48) 1.51 (-.67*, 0.74) x5 ADF 0.53 (-.43, 0.53) -3-45 (—.91**. '-57) 147 TABLE 44. Continued. Items Im_yiyg Dry.Matter Dry Matter Intake Digestibility (%) (gm/ng'75) g . X6 Lig 2.18 (-.31, 0.82) 2.60 (-.90**, 0.38) X7 Ash 0.93 (-.04, 0.68) 0.65 (0.66*, 0.16) Equation 9. (n = 17) R - 0.93** SEE = 2. R = 0.63 SEE = 13.2 bo Constant 14.30 -7.71 X1 IVDMD 0.83 (0.90**, 0.75**) 0.31 (0.56,* 0.08) X2 Cellulase 0.33 (0.80,** 0.35) 1.18 (0.61,** 0.24) X3 Buffer -0.65 (0.34, -.55) 0.85 (0.49,* 0.16) Equation 10. (n = 30) bo Constant X1 CP X2 CW X3 ADF X4 Lig X5 Ash Equation 11. (n = 28) bo Constant Temperate Legumes R = 0.88** SEE = 3.8 89.03 0.49 (O.79**, 0.18) -o.77 (-.86**, -.43) 0.47 (-.79**, 0.23) -1.32 (-.70**, -.26) -o.91 (0.43**, -.22) R = 0.87** 83.30 SEE = 4.0 R = 0.40 -7.21 2.80 (0.26, 0.29) 0.88 (-.22, 0.15) 1.10 (-.22, 0.15) -5.38 (-.28, -.30) -1.35 (0.20, -.10) SEE = 13.3 X1 X2 Buffer CP 0.82 (0.81**. -0.19 (0.74**, -.09) 0.25) X3 CW X4 ADF X5 Lig X6 Ash Equation 12. (n = 13) bo Constant X1 IVDMD -0.74 (-.84**, -.37) 0.58 (-.76**, 0.26) -1.41 (-.65**, .28) -0.95 (O.49**, -.22) 5.0 R = 0.91* SEE 55.59 0.27 (0.85**, 0.12) R = 0.54 SEE = 12.3 109.35 -2.43 (0.10, -.33) 3.00 (0.23, 0.29) 0.32 (—.24, 0.05) 0.63 (-.26, 0.09) —6.54 (—.36, -.39) —2.02 (0.26, -.15) R = 0.33 SEE 13.3 -217.29 2.69 (O.62*, 0.40) 148 TABLE 44. Continued. Items 'In Vivo Dry Matter Dry Matter Intake b‘i‘gEE'E‘fbillty (%) ( gm /BW10§.J75) X2 CP 1.56 (0.72**, 0.39) 4.42 (0.54, 0.40) X3 CW -0.17 (-.85**, 0.11) 3.65 (-.40, 0.67) X4 ADF —0.07 (-.80**, -.03) -1.86 (-.51, -.26) X5 Lig —2.14(—.56*, —.38) —4.34 (—.32, -.28) X6 Ash —l.47 (0.47, -.27) —2.57 (0.26, -.l7) Equation 13. (n = 11) R = 0.90 SEE = 7.0 R = 0.90 SEE = 14.2 bo Constant 55.12 ~22.77 Xl IVDMD 0.50 (0.85**, 0.18) 4.77 (0.62*, 0.65) X2 Buffer 0.04 (0.71*, 0.01) -2.43 (0.35, -.37) X3 CP 1.04 (O.79**, 0.19) —1.74 (0.56, —.16) X4 CW -O.12 (-.83**, -.07) 3.18 (-.39, 0.67) X5 ADF -0.37 (-.81**, —.11) —6.17 (-.64*, —.65) X6 Lig —l.80 (—.50, -.31) -1.13 (-.42, -.10) X7 Ash -1.36 (0.50, -.25) —l.96 (0.35, -.18) Equation 14. (n = 11) R = 0.86** SEE = 5.0 R = 0.64 SEE = 15.9 bo Constant -26.06 -62.35 X1 IVDMD 1.19 (0.85**, 0.69*) 2.81 (0.62*, 0.58) X2 Buffer 0-55 (0.71*, 0.26) -l.29 (0.35, —.19) R = Multiple correlation coefficients; SEE = Standard error of estimate; CP = Crude protein; CW = Cell walls; ADF = Acid—detergent fiber; Lig = Lignin; IVDMD = Two- stage in vitro fermentation (Tilley-Terry); Cell = Cellul§§e incubation; Buf = Buffer extract. 1Simple correlation coefficient with in vivo parameter. Partial correlation coefficient with—Zp_v1vo parameter. *P < .05 ** P < .01 149 measure of association of im;yiyg DDM and one variable with a fixed value of the second or third variable. Partial regression coefficient also indicates the magni— tude of contribution of each variable to the im_yiyg digestibility. A study of an R, simple correlation (r), partial correlation coefficient, and partial regression coeffi- cient will give the degree of contribution of each variable in multiple regression analysis. Generally, multiple correlation and regression give more accurate prediction and more precise relationships among the variables than does a series of simple regression analysis. The inclusion of the value for buffer solubility to various chemical components gave a multiple regression with an R value of only 0.81 which is not considered satisfactory for good precision of predicting DDM but buffer value added slightly improved the precision for DMI (Equation 3, Table 44). The inclusion of cellulase alone or cellulase plus buffer to these chemical components did reduce the standard error of estimate for both DDM and DMI predictions (R 2 0.90 for Equations 4 and 6). The addition of the two-stage IVDMD value alone or IVDMD and buffer solubility values to various chemical components greatly improved the prediction of DDM and DMI (Equations 5 and 7, R 3 0.97 and SEE = 1.7 and 6.1, respectively). One of the most reliable combinations to predict DDM and DMI of grasses was to include both IVDMD and cellulase 150 values to values for chemical components (Equation 8). This gave an R value of 0.99 and a standard error of only 1.0 for DDM and 4.0 for DMI prediction which is excellent for prediction purposes. A combination of values for IVDMD, cellulase, buffer solubility (Equation 9) could accurately predict DDM (R of 0.93 and SEE = 2.4) but was not sufficiently useful for predicting DMI of grasses with an R of only 0.63. For temperate legumes, a combination of chemical components (Equation 10) tended to predict DDM more accurately than did chemical components plus values for buffer solubility or IVDMD (Equations 11 to 13). The combination of IVDMD and buffer solubility was unsatisfac- tory (Equation 14). However, all these combinations when used to predict DDM had R values of above 0.86 with rather large standard errors (SEE = 3.8 to 7.0) similar to those for CW, ADF, lignin, CP, IVDMD or enzymatic incubation values alone (Table 40). No combinations of items were satisfactory to predict DMI of legumes due to large standard errors of estimate (SEE = 12.8 to 15.9). In this study, no significant improvement in pre- dicting DDM was noted by using multiple correlation coefficients when compared to that of the best individual predictors. Oh gt_gl. (88) also found similar results that multiple regression or correlations did not improve the relationship above that of simple correlation for 151 combined forages but greatly improved the correlations for some particular species. b. Predictions of IQ yiyo Dry Matter Digestibility (DDM) for Micfiigan Forages Several multiple regression equations were developed using a large number of samples and different predictor combinations for predicting im_yiyg parameters of temperate legumes, grasses and Silages are arranged in the order of increasing standard errors of estimate (SEE) in Tables 45 through 49. The samples comprise a series of data collected during the past 10 years (1961 to 1970). However, not all laboratory determinations had been conducted on every sample. Thus the number of samples (n) for equations in the following tables might not be the same. Dry matter digestibility ifl.Y£XQ.°f temperate legumes could be satisfactorily predicted from a combina— tion of CP, ADF and 36-hr IVDMD with or without ash giving a standard error of estimate of 2.2 percentage units (Equations 1 and 2, Table 45). The multiple correlation coefficient (R = 0.80, P < .01) was much greater than that for simple correlation coefficients using individual variables. Each of these variables had moderately high partial correlation coefficients with DDM and these vari- ables accounted for 64% (R2 = 0.64) of the variation in DDM. However, ash contributed little to this combination. A combination of ADF and TTDMD (Equation 3) was also an 152 QNm.I .MH.0 0H.0 0.00.1 .0H.0 0M.I MNmMoO qumol nmm.I 0.0M.I .mN.0 HN.0 Q.N«.I Q.Nv.1 .MN.0 350.0 Q.Nm.1 .HH.0 Qm«.0 £.«0.I w¢m.o n.h«.1 m 0M.0 mmv.o £00m41 sm0.1 b.0«.0 flwm.0 Q.m0.1 n.m«.0 0M.0 0M.0 D.M0.I . . 0...”- 00 b m0 0 gvmoo Q§mmol .HH.I Q.m«.0 fl«m.0 Q.m0.1 n m«.0 QMm.0 Q.bm.l . wom.0 Q 00 1 H mm 0 000.0 o.00.I N.N 00. M« N.N 05. HM 0.N MN. «« hQH HSIMMVMN.0+ N.N 00. N« mQ¢«M.0ImUM«.0+hM.Hm n W .N Aozo>H HEI0000N.0+ 0N.I o.H0.0 00.I 0.00.0 N.N oH0. N0 so000.0Is000N.0Iso00.0+00.0m I 0 . mmzoomH mGOHanmHHoo msOHHMHmHHoo mmm m s 0 Amy :00 O>H> mm mo coHuOHemum HMHHHcm mHmEHm meanHm> £003 200 m0 .mmmoo .HHOU .kmwmmsom smmHSOHSV mwnHm> HmothHmsc hsoumHoQEH Eoum NuHHHnHummmHU O>H> CH msHHmfiHpmm How EQOHumsvm sOHmmmnmmH onHuHsz . mv NHQANB 153 hv.l mN.o nsm50‘ ”\Nmo‘ Q mmOI \mv ' m N MHm NH uoquO'quOm'hnmmmOH'NmohmH " m omH amn.o m.mw.o non.o .mm.o m.m mam. em Aooaxwzum\nzaeavom.mm+mom~.o+mm.m I m .na «2.. . . .3- .. . . a.Hm.o mv.o mm o Ha o w m nmm NH madmm mumuam.a+momm.o+ma.mh I w .ma no>.o . mmnno . . Aooachum\azoaevm¢.mv+ «mm.u .nq.o .mv.u ”mm.n . . mnH usnmmvmm.o+ma¢mn.onom.oe I » .ma mv.| n.mm.o mm.o n.om.o o.a mum. NH Ha.mnm>na.osAooaquum\azaasvmm.am I w .HH mmmmémw mmv.l no.1 n.om.u nH.o Q.Nm.| 9.0m.o o.m amm. vm sm¢mb.otmomm.oumohm.o+-.mb I w .OH mGOflumHmHHoo macaumamnuoo MMm m a w va zoo 0>H>.mm mo aowuowvmnm Hmwuumm mamfiam mmHQMwnm> spas zoo mo .mmmoo .HHOU .wmscwucoo .mv mqmdfi 154 “mumfiflumw mo Houum Unaccmum I mmm Ho. v m I Q “mo. v m I m umfluomnwmsm uucmfloflmmmoo cowumamunoo mamflaanz I m «mHHMB Hamo I 30 mum£3 .Aooa x ma<\q moq m.mh I m.>¢H~BU +IMBU I ooa.mm.o I MW “uosumfi MHHmBImmHHflB an mozmumeQMmfiU Hmuumfi who OHuH> ca mmmumlnmm I DEG «mamfimm wumvcmum mo QSQ I wcum “mocmummmmmmflw Hmppmfi mum onufl> qH I QSQ>H “muflawnsaom Hmuumfi mun I mzn “nomuuxm mmnmnnmmwuuflm I mmz «mucmuqoo HHwU I 00 .cwc an I q “Hmnwm mwsuo I mu “Hmnfim unmmHmumclcfiwM I mn¢ ucflmuonm mosuu I m0 “muwafinflummmflv Hmuumfi muv o>fl> CH I San wmh.l mm.l . . . n.m>.o m mm.o vm.o .mm.o N.N m~m. ma mo¢\qqm.mmHImmsz.o+mvmm o+am «w I w mm mq.o n.nn.u vo.o .mm.a mnaflmuo+ . .om.I .mm.o .m¢.I .mm.o N.N mm. NH ammmv.mlm0mm.oImomm.o+mm mm I m Hm m m m mmw«qu mom.o amm.o . n.mm.s mH.I .om.I .mm.o m.~ app. vm Anzaeevom.o+mn¢mo.HImUmH.oIom.mm I » om amp.o m.~ amp. vm Aooaxnnum\azoeavva.vm+om.s I w .mH mzowumamunoo mzofiumamuuoo mmm m c w ANV 209 o>w>.mm mo :0wuuawmum Hmwunmm mHmEMm mmanmfium> spas EDD NO .mmmoo .MHOU .cmsqflucou .mv mqmdfi 155 accurate predictor of DDM (R = 0.76, P < .01, SEE = 2.4) and both these two variables contributed significantly to the variation in DDM as evidenced by their partial corre- lations. The addition of GP to ADF plus TTDMD (Equation 4) did not improve accuracy and the partial correlations indicate that CP contributed little to this equation. Combinations of chemical components (CP, ash, ADF; CP, ADF; CP, CF, ash) in Equations 6 to 10 had large standard errors (2.6 to 3.0) and small multiple correla- tion coefficients (0.73 to 0.67). Prediction equation including CP in combination with ADF or CF tended to have low multiple correlations with DDM and low predictability. In these equations protein had a relatively low partial correlation coefficient yet protein itself had higher simple correlations with DDM than many other constituents. For legume forages, a combination of chemical components plus ig_yiE£g fermentation values was superior to a combination of chemical components alone in predicting $2.!ivg DDM. Using a combination of predictors from the proximate analysis scheme (CP, CF, ash) was less accurate in predicting DDM than using predictors from Van Soest‘s system of analysis when either was used with an in_!iE£9_ rumen value. Any acceptable multiple regression should have great accuracy and includes predictors that can be determined easily or obtained in a sequential analysis. Therefore, Equation 3 with an R of 0.76 might be preferable 156 to Equations 1 or 2 (R = 0.80 to 0.81) which uses values from three systems of analysis. For temperate grasses, combinations of artificial rumen fermentation values and values from Van Soest's scheme of analysis (Equations 11 and 12, Table 45) were reliable predictors of in_yiyg DDM with R2 of 0.84 to 0.94. However, the term TTDMD/Standard as a single pre— dictor was highly correlated with DDM (r = 0.96, P < .01) suggesting the use of only this laboratory estimate to predict in_yixg DDM for grasses. The low simple and partial correlations indicate that digestibility estimated from Van Soest's summative equation ("VS" in Equation 11) had low relationship to in_yiyg_DDM and could be omitted. The inclusion of ADF, CP, ash or 36—hr DMD to TTDMD/ Standard (Equations 12, 13, 15, 17) did not improve multi— ple correlation coefficients or decrease the standard errors when compared with Equation 11, but TTDMD/Standard alone (Equation 19) gave a larger standard error and smaller R2 than the combinations discussed above. Com- binations of chemical components alone (CP, CF, ash, ADF; CP, CF, ADF; ADF, L, CC) as in Equations 14, 16, 18 gave standard errors of 2.2 to 2.8 with R2 of 0.80 to 0.65. Equations 14 and 16 use terms from both the Van Soest and proximate systems of analysis whereas Equation 18 uses only terms from Van Soest's system. Prediction of DDM from Equation 18 may be accomplished faster but with less precision than that for Equation 14. For grasses, a 157 combination of an artificial rumen value and some chemical components (Equations 11, 12, 13) gave more accurate prediction of DDM than combinations of chemical components alone. A combination of artificial rumen value and chemi— cal components (TTDMD, CP, ADF) had small R2 and large SEE and CP did not contribute significantly in this combi- nation (Equation 20). Digestible dry matter in yiyg_for silages could be predicted from combinations of CP, CF, ash, ADF or CP, NFE, L/ADF with a standard error of 2.2 and R2 of 0.73 to 0.68. Most variables used had significant partial corre- lations with DDM. Artificial rumen values alone or in combination with chemical components did not give satis- factory predictions for silage DDM in this study. On the whole, multiple regression technique improved the correlations between predictor combinations and DDM and increased precision of prediction for each category of forages. These findings are in agreement with that by Oh §_t__a_l_. (88). c. Predictions of Drngatter Intake (DMI) and Digestible Dry Matter Intake (DDMI) for Michigan Forages Multiple regression equations for predicting DMI and DDMI are presented in Table 46. Multiple regression equations using combinations of chemical components did not satisfactorily predict DMI for temperate legumes. The most satisfactory equation to predict dry matter intake (lb/cwt) of legumes was a combination of DMS, 36-hr DDM and 158 vm.H+ mv.| b.mh.o mo.I m.mm.o m¢.o Mme. NH mzona.ounooaxccum\ozolunmvmm.m I Au .5 nvh.o wmh.o .Hm.o .om.o .mm.| m¢.o hm.o now. ma em.mlmmmm.o+mono.o+mubo.o I fly .m Qmm.o Amh.o amm.l n.ho.o mm.I .HN.I mm.aummmo.a+qmm.olzooo.o+ n.mb.l b.nm.o m.mm.o .m¢.o ha.o bum. ma A920 Hsnmvma.olaozo ucnmmvwo.o I as .m mmmm4M0 no.s ma.o .mo.o n.mm.o .NH.I n.mm.o mv.o 3mm. we mm.HImomH.o1mQ¢Ho.o+Aazoanmo.o I a» .e HH.I n.mm.o NH.o A.mm.o mv.o new. we mm.aumo~o.onknzoaevmo.o I as .m mmm.o mow.o mo.ml n.m¢.o n mv.o Q.mm.o om.o mm.o now. me Aozoaevoo.o+aozo Helmmvvo.o+qma.o I aw .m mam.u bom.o HB.HIAooncnum\ozqur mmvmm.an n.om.o n.m¢.o n.ms.o .m~.o mm.o bum. mm Anzauaummvaa.o+msnvo.o I as .H mmzsumq mcoflumamuuoo mcowumHoHHoo mmm m c um I Hmfluumm mamsam A3.035v Hana I m» moanwfinm> zufl3 can Ausc\nav Hza .xmz I a» mxmucfl mo .mmmoo .Huoo .Aoomcnom cmownowzv modam> Hmoauhamcm whoumnonma scum oxmucfl Houumfi hut manwummmwm ccm oxmucfl “ounce mud msflumEHumo How macaumcww scammmnmom mamwuacz .mv mqmmunnm Hocuo Mom «mxmucw Monuma who I Hzn om.o .mwnm . . .mo.o 9 mm I m mm.o m.HH mam. NH mm.momIAmm.m+ma zuwz . I @v—MUCH M0 .mHmOU .HHOU @Gm AH3U\QHv HEQ NM: l HM .cmsnaucou .mv mamas 160 DMD/Standard with a SEE of 0.35 and R of 0.82. A combina— tion of L, 36—hr DMD, TTDMD (Equation 2, Table 46) could predict DMI just as accurately as Equation 1. Combina~ tions of TTDMD with GP or ADF (Equations 3 or 4) had smaller multiple correlation coefficients and larger standard errors than the first two combinations. Crude protein or ADF had very low partial correlations with DMI indicating that these two variables were not important for predicting intake of legumes. values from artificial rumen fermentations alone could be used to predict DMI of legumes with r value of about 0.65 to 0.73. One of the reliable predictors of DMI for temperate grasses was a combination of 36-hr DMD, 6—hr DMD, CW, L, EE with an R of 0.97. Each variable in this combination had significant and high partial correlations with DMI (Equation 5, Table 46). However, this equation involved values from the Van Soest system, the proximate analysis system and two i§_!i2£g fermentations which are excessive for practical purposes. Combinations such as CP + CF + EE or 6-hr DMD/Standard + DMS might be more convenient but had smaller R2 and larger standard errors (compare Equa- tions 6 and 7 with No. 5, Table 46). Dry matter intake of silages could not be satis— factorily predicted from any combinations of laboratory estimates. Digestible dry matter intake (DDMI) of temperate legumes and grasses could be predicted from combinations 161 E chemical components alone or chemical components plus 3_yi§rg fermentations with R ranging from 0.72 to 0.84 nd standard errors of 18.7 to 28.8. Crude protein in quation 9 had a very low partial correlation with DDMI nd could be excluded from TTDMD, CP combination and the wo-stage DMD alone could be used to predict DDMI of egumes with an r of 0.72. Two prediction equations for DMI of silages are given in Table 46 with the equation sing ADF, DMS, 6-hr DMD/standard having a much greater ,and smaller SEE than the equation using CC, ADF, and ignin. d. Predictions of Total Digestible Nutrients (TDN) for Michigan Forages .Multiple regression equations for predicting TDN f legumes are in Table 47 and the best equation utilized combination of two-stage in_yit£9 fermentation value, P and ash with a standard error of 2.9 and an R of 0.73. owever, this predictor combination accounted for only 3% (R2 = 0.53) of the variation in TDN. Combinations f predictors from the proximate analysis or the Van Soest gstem (Equations 2 through 5, Table 47) gave still smaller Jltiple correlation coefficients (R = 0.67 to 0.47). For temperate grasses, TDN could be precisely redicted from a combination of various in yitrg_rumen ermentations or a combination of ADF and one in vitro Imen fermentation with R's of 0.91 to 0.93 and SEE of 7. All these variables in Equations 6 and 7 had 162 Aoonoaum\nsaasvms.om+ . Hv.o amm.o . I . I s.ms I s .m m mm.I .m~.I n.Hs.I .mm.o m.~ nHm. mH moamH H moon o m o n o QBBV GH o O”...- nmo o om I nmo.o .Hs.I Hoonnn¢m\oz . . I s .m m.Hm.I .o~.o .H~.In.mm.o m.~ new. mH mammm.oIrm«¢H HIEUmH o+mm am . a .l . .I . . l . +hQ¢mo.Hle.m5 H w .b aHm o n mm «mm o n.Hs a H nHm mH Assn Ha mmvsm o . . coaxcnpm\azn Hclmmvma.am+ . . mam o «as o A . . I . I s .m .I . . . . . +.QZG Halmmvom a «m m.lllllll 2 mm o n mm m mm o mm o s H nmm mH Aosassvmm o A mmmmamo m¢.I .Hs.I SN.I .H~.I v.¢ , as. NH mEme.0Imomm.OIom.moH I w .m amm.I m~.I . .sm.I m.eo.I .vo.o .H~.I 0.4 as. «H mEZav.HIrm¢mw.mImom¢.HIms.mmH I s v m¢.I ms.I n.4m.o m.mm.o .vH.I .mm.o m.m moo. mH NH.vIst.HImnmmH.H+oon.o I s .m nsm.I n.5m.o Ho.I 6.44.0 H.m has. an Hm.mv+ammsm.HImomm.H I w .m 65¢.I Ho.I n.vo.o .mm.o m.av.o m.m¢.o m.m nms. em oo.m~+nmmom.HImomm.H+Anzoeeom.o I s .H mmzoomq mGOHDMHOHHOU mGOthHOHHOU mmm m G AWV HMfluHmm onEHm mmHanHm> cuw3 299 MO .mmooo .uuoo mucmflnusz mHQHumomHQ Hmuoa mo coHuowmem .Amommnom cmmHQOsz mmSHm>IHm0Hu>Hmcm muoumnoan 163 Ho. v m I a «mo. v m I c umfluomummsm .chHDMH>mHnnm Mom Ll .mv can me moanme mom mUHm.oI¢m.mm I w .bH mmb.I «.m can. m 0 0|. 0 “wOOH m¢.I .Nm.l MH.I m.m>.I m.m cmm. m nmfimo almovm a pm mm mh>.o Hm.o . . I . .sm.I .mo.I m.ms.I A$2.5 m.H «mm. m mumm.m+mooq.oImo¢o OImo so I a mH . . . . . . . . mm2mv.o+mmmm.m+mm.Hm I w . H nmm 0 em 0 om 0 cm o o H 3mm b macaqu mo.I Hm.I . . A.mm.I .vm.I .H>.I .mm.o m.m awn. mH ammmH.oImn¢hw.HImU~m.oumm mHH I w MH om.o NN.0 n.vn.I .m¢.I .Hn.I .mm.o >.m awn. mH mmHH.H+mQ¢mm.HImUu¢.OIm~.¢HH I w .NH nmn.u .Hq.I QH>.I .mm.o >.m awn. mH mammm.HImUmm.oIoo.mHH I w .HH mm.o vv.o n.mwf .mm.I .Hn.I .mm.o n.m Amp. mH ADSQBBVmH.o+mn4mm.HImUHm.OImh.mm.NIMIDDH mmmmmmo mcoHumamHHoo chHuMHouuoo mam m c va HMHuHmm mHmEHm mucmwnusz mHnHummmHo Hmuoe Mo cowpofivmum moaanHm> cuH3 208 m0 .mmmoo Junoo .303‘1111-“DJ O a II 164 ignificant partial correlations with TDN. Combinations f rumen fermentation values with other chemical components Equations 8, 9, 10) were less accurate but yet satisfac— ory for predicting TDN with standard errors of 2.3 to .7. As with legumes, combinations of chemical components CP + ADF: CP + ADF + EE: CP + ADF + ash) gave larger tandard errors than did the chemical components plus in itro fermentation values or combinations of the two in itro systems. Yet combinations of chemical components ould predict TDN of grasses with moderate accuracy having tandard errors under 3 percentage units. Unlike grasses and legumes, TDN of silages could e precisely predicted from EE + NFE or CP + CF + EE with tandard errors of 1.0 to 1.8 and R values of 0.92 to .98 (Equations 14, 15). However, there were only data n 7 to 9 silage samples but both ether extract and NFE ad significant and high partial correlations with TDN nlike CP in the CP, CF, EE combination. e. Predictions of Digestible Energy (DE) for Michigan Forages .Multiple regression equations for predicting DE re in Table 48. Digestible energy of temperate legumes Duld be satisfactorily predicted from a combination of TDMD, 6-hr DMD/Standard with a standard error of 2.5 1d these two variables accounted for 75% of the variation 1 DE. Predictor combinations such as CP + EE + ADF or ? + EE + CF could predict DE of legumes with similar 165 nm>.I m.~ am». om hammm.I~m.mm I w .oH ol- 5 0 0|. s o o o O I O o u 0 now mm 0 Amp n Ho 0 m N nun Hm modem 0 muhm o+mm Nb w a 955.0 m.~ nun. om m~.o+AO0Hx©sum\azoBBVeH.oo I w .m nmn.I .nm.I nmn.I .mm.o m.m has. om madmm.0IMMvo.~Imo.¢m I w .h vm.o who.o m.Hm.I .-.o n.mh.I n mm.o m.m nHm. om AQEQBBVoq.o+mndmv.0ImUm~.o+om.mw I w .w AOOH x nmm.o .mm.o nus.o n.mm.o n.~ now. on ocum\ozoasvmm.bq+momm.o+mm.m I w .m mm.o mph.o Aoonocum\azassvem.Hm+ mm.I .m~.o n.mn.I Q mm.o h.m nHm. om madmN.OImoom.o+ow.mm I w .v Hm.I «mm.I .Hm.I .mm.o .mo.o m mm.o n.m new. 0H MUmm.0Immom.mImomm.o+hv.H> I w .m amm.I mm>.I ‘ .mv.I .nm.o .mm.o n mm.o m.m 3mm. om masmm.oImmmH.mImom¢.o+oH.Hm I w .m om.vm I AOOH x sm.I m.Hm.o mm.o m.ms.o m.m mom. a caum\nsa unImvmm.o¢IAosassvsH.~ I s .H mmzowmq chHDMHoHHoo mcowumHouuoo mmm m c Amy Hmwuhmm mHmEHm mmumcm manflpmmmwa How mcoaumsvm GOHpcfiomHm mmHQMHHm> cuHS no mo .mmmoc .HHOU .Ammmmnom cmchOsz moDHm> HMOHDmHmcm >H0pmnoan EOHH NOHDZD D4Q4unvm43 m24DUE4DnD 434 0334J03TD 334.0004fi04. 04:91... 4.34.4 o )F 1]}‘4 166 “somol svHo‘ nsm.o mmm.o smmolu a mN.o m.N emu. «N HQSQBBVmv.o+momHm.oImomH.oIom.om I w .mH mm.w+ hm.o A.mn.o mN.o n.vh.o m.N am». vm mUNN.o+HOOHxUGum\QSOBBVmo.wv I w .mH mmh.l m¢.I n.ms.o ov.o .mN.o .OH.o N.N mNm. NH mamas.HImomq.H+moqe.o+sm.so I w .nH mv.I 5N.o n.mh.l m.mm.I Q.Hb.l .m¢.I N.N Mmm. NH UUmv.o:Hmw.wlhnmvH.HImm.HmH I w .wH amm.I .om.o mv.| .hN.I mndNN.NI n.~m.o .h¢.o .mm.o .OH.o o.N chm. NH Smde.H+m0mm.H+mUh¢.o+mh.mm I w .mH cmm.I m.Hm.I cam.l .bN.o AmQ¢\HVm¢.mmMI .He.o n.mh.l n.Hh.I .mv.I m.H pom. NH 00mm.OIHmm.m+mQ¢Hm.NI¢N.mmH I w .eH nwm.o N.H nvm. NH mc.0IHoOchcum\anBBvmH.mm I w .mH mmmwdmw Nb.I N.m mNb. m hom>.0INo.Nm I w .NH th.o H.m amp. m mm.o+HQ2QBBme.o I » .HH mGOHHMHmHHoo mQOHDMHmHHOU mmm m c Hwy HMHunmm mHmEHm mmHannn> auHs mo «0 .mmmoo .Huoo mmuocm oHQHummmHn Hem mGOHumcwm QOHUOHUmHm 167 Ho. v m I 9 .mo. v m I m umHHomHomcm omV UQM m¢ mGHQMB 0mm umCOflpmkamHQgM HOW . . . mUOH.HImomN.mImv.hmH I w .NN o s 0|. “0|. sNN o N N om has I m we N m «mmuo .ms.I .mw.m S.H mom. magmm.o+mom~.HImon.NIma.m~H I s .Hm n em I n «sumo .Ns.I «MM.W m.H mm. mmva.H+momm.OImon.mINo.mmH I w .om .om.I mm I m mmuHHHm Q 9 II. 00 mm m Hwy mcoHDMHMMMOU msowwmwwmn m mouocm mHQHummmHo How mCOHumcUm GOHuoHcmHm HMH» . II II mmHQMHHm> cuHs ma no .mmooo .HMOU 03’34fl4lv 41)) I )lw Ill'l 168 accuracy (R = 0.82 to 0.84, SEE = 2.6 to 2.7). Combination of chemical components with either TTDMD/Standard or TTDMD (Equations 4, 5, 6) could predict DE of legumes with essentially the same accuracy (SEE = 2.7 to 2.8, R = 0.80 to 0.81). Prediction of DE from TTDMD/Standard alone (Equation 8) gave a standard error of 2.8 whereas that from TTDMD had a standard error of 3.1. Addition of EE or GP to ADF as predictors did little to improve accuracy of prediction (Equations 7 and 9 vs. 10) and ADF alone was more accurate than CF (Equations 10 vs. 12). For temperate grasses, TTDMD/Standard alone seemed to be an excellent predictor of DE (R = 0.94, P < .01, SEE = 1.2). The inclusion of CP with TTDMD/Standard did not improve accuracy of DE prediction (Equations 13 vs. 18, Table 48). Combinations of chemical components such as ADF + L + L/ADF + CC, CP + CF + ash + ADF, ADF + L + CC, CP + CF + ADF could be used to predict DE of grasses with R‘s ranging from 0.90 to 0.82 and standard errors from 1.8 to 2.2. There were only data on seven silage samples and digestible energy could be precisely predicted from the three predictor combinations, CP + CF + BE: CP + CF + ADF or CP + CF with R's ranging from 0.96 to 0.90 and standard errors of 1.6 to 2.2. All chemical components used had significant partial correlations with DE indicating that these chemical components contributed significantly to the variation in DE of silages. 169 f. Predictions of Bodijeiq t Gain for Michigan Forages Data in Table 49 indicate that body weight gain of sheep consuming temperate legumes could be predicted from a combination of 6-hr DMD, 36-hr DMD, TTDMD and ADF with a standard error of 0.08 and R of 0.84. However, use of this equation would become tedious since it involved three systems of in_yitrg fermentations and one chemical analysis. Combinations of chemical components were not satisfactory (R S 0.52) to predict weight gain of sheep fed these legumes and none of these equations are presented. Body weight gain for temperate grasses could be predicted from a combination of ADF, DMS, 6-hr DMD/ Standard (Equation 2) with a small standard error (0.07) and large R of 0.85. However, the exclusion of ADF from this combination (Equation 3) did not significantly reduce the R but maintained the same standard error. Combinations of TTDMD or TTDMD/Standard with chemical components such as TTDMD + CP + ADF; TTDMD/Standard + CP + ash + ADF or TTDMD + CP could predict weight gain for grasses with essentially the same accuracy (SEE = 0.08, R = 0.79 to 0.78). Surprisingly, the addition of CP to TTDMD did not improve the correlation over that for TTDMD alone (Equa- tion 6). Combinations of some selected chemical compo- nents alone, CP + CF + EE or EE + CW + ADF gave an accuracy similar to that for combination of TTDMD plus chemical components in predicting weight gain for grasses and silages (Equations 7 and 8 vs. 4, 5, 6). 170 m I m umHHumummcm .mq can mv mmHnma mom .mGOHHMH>mHnnm Mom Ho. v m I 2 .mo. v I om.I NN.I . . . I . +mmHH.o I HIwm .ms.o n.mN.o .HN.I m.mm.o No.0 INN. NH 6N QIEQNNQ 0 some 0 mmomHHm com.o mom.o . . . I . .NN.o m.Nm.o .NH.I «.om.o mo.o ass. mH ammo o+moHo o+moNo o s .N oH.o n.ms.o mN.o n.Ns.o No.0 amp. NN NH.HImoNoo.o+AozosaVNo o I N m NH.o .mv.o NH.I .mm.o Hm.HImosHo.o+nm4mo.m+ . .HH.o M.mm.o .NN.o N.Nm.o mo.o has. NN mosoo.o+AOOercum\osassvso H I H m «N.o NH.I . . .VN.o n.NN.o NN.0 n.Ns.o No.0 has. NN mm.HImomHo.o+moHo.o+iozoesvNo o I H q HooH x nvm.o N.Nm.I nNN.o .OH.I so.o mam. NH ccum\asn HnIovms.H+mono.oI0H.o I N .m mom.o nNN.o HOOH x cc»m\nza HsISiHm.H+ .Nm.I MN.I .OH.I .mv.l ho.o Mmm. NH mEDvo.OIhQ¢Ho.0Imm.o N M .N mummamo NN.NIAooH x «No.I n.HN.o No.o n.am.o equm\oza HnImem.oIAosnssvuo.o+ o om.o n.0m.o .mH.I n.vm.o mo.o new. em MQ4N0.0+HO0Hx©cum\on Halmmvhm.o I w .H mmsoomq mcoHumHmuuoo chHumHmuuoo mmm m a Ao\nH I as cho urmHos Ho coHHOHomHm HMHHHmm mH EHm moHanHm> nqu chm mo .mmmoo .HHOU .HmmmmHOH smmHnonv mmch> HMOHumHmcm muoumuoan Eonm sHmm uanms mcHumfiHumm How mCOHumcvm :0Hmmmnmmn mHmHuHsE .mv names 171 In multiple regression technique, the use of two terms obtained from similar analysis (i.e. 36*hr DMD, TTDMD or CW, ADF, CF) having a high correlation between them is not considered desirable. One of these predictors may contribute little to the predictability of in_yiyg. parameters. Much more precision is usually accomplished by using only one of these two terms plus another term having a low correlation with these two terms. In order to achieve meaningful improvement in correlations and precision of prediction, several values from many systems of analysis (i.e. chemical, microbiolo- gical, enzymatic incubations) would have to be used. Such a practice may be too time—consuming and laborious to be suitable for a routine forage evaluation program. Therefore, use of one good laboratory analytical value from one analysis with an excellent simple correlation coefficient would appear preferable for many forage species where a very high degree of predictability is not needed. CONCLUS IONS The studies on forage evaluation using various laboratory techniques yielded the following conclusions: 1. Appropriate buffer, pH level, enzyme concentra- tion and length of incubation for various enzymes used in forage evaluation follow: Quantity of Substrate: Incub. Time Enzyme Buffer pH Enzyme (mg) (hrs) Cellulase Sodium acetate: 3.85 300:300 60 Acetic acid Amylase " 5.50 300:200 60 Pepsin HCl 1.85 300:200 60 2. The solubility of forages incubated with three enzymes, Marschall's cellulase, Clarase 900 (amylase) and pepsin could be used to predict in yiyg parameters with correlation coefficients (r) of 0.51 to 0.96. 3. Temperate forages had 1.05 times greater in_yitrg_ dry matter diappearance, 1.28 times greater crude protein (CP), 1.14 times greater lignin (L) than tropical forages but tropical forages were greater than temperate forages by a factor of 1.11, 1.05, 172 173 1.21 for cell walls (CW), acid-detergent fiber (ADF) and ash, respectively. Quality of tropical forages was lower than that for temperate forages primarily due to lower levels of CP, digestibility and higher levels of CW, ADF, cellulose (C), hemicellulose (HC) in tropical forages. Values for the two—stage in_yi£rg_fermentation (IVDMD), in_yitrg true dry matter disappearance (IVTDMD) and CP of temperate grasses decreased with advancing maturity at the rates of 0.35, 0.24 and 0.10 percentage unit per day, reSpec- tively whereas those for trOpical grasses decreased at the lower rates of 0.03, 0.03 and 0.07% unit per day, respectively. Crude protein had a positive correlation with ash but negative correlations with CW, ADF, C, L, silica (Si) and these "fibrous" fractions were mutually and positively correlated. In yitrg digestibilities (IVDMD, IVTDMD) were positively correlated with enzymatic incubation values and both sets had positive correlations with CP and ash but negative correlations with fibrous fractions. lg_yiyg dry matter digestibility (DDM), total digestible nutrients (TDN), digestible energy (DE), dry matter intake (DMI), digestible dry matter 10. 174 intake (DDMI) had positive correlations with CP, ash, in_yitrg fermentation values and enzymatic incubations but negative correlations with CW, ADF, C, HC and lignin. Water~soluble carbohydrates, total nonstructural carbohydrates (TNC), total available carbohydrates after enzymes (TACAE) had low correlations with in yiyg_measurements and these components as well as total ash could not be used as useful single predictors of any in yiyg parameters. In_yiyg_DDM of forages could be predicted by using any of these predictors: a. CP, CW, ADF, L with r values of 0.79 to 0.86 for CP and r values of -.70 to -.93 for CW, ADF, L and standard errors of estimate (SEE) of 2.9 to 5.6. b. IVDMD or IVOMD with r values of 0.88 to 0.98 and SEE of 1.8 to 4.4. c. Cellulase incubation with r values of 0.58 to 0.89 and SEE of 3.8 to 5.4. d. Amylase incubation with r values of 0.52 to 0.79 and SEE of 5.4 to 6.1. e. Pepsin incubation with r values of 0.74 to 0.90 and SEE of 3.0 to 4.2. f. Cellulase plus amylase with r values of 0.77 to 0.89 and SEE of 4.3 to 5.2. 11. 12. Dry matter intake (gm/BW 175 Cellulase plus pepsin with r values of 0.63 to 0.93 and SEE of 2.3 to 5.0. Buffer incubation with r values of 0.64 to 0.75 and SEE of 4.5 to 6.9. 0.75 Kg ) of forages could be predicted by using: a. CW or L with r values of —.46 to -.64 and SEE of 10.2 to 13.4. IVDMD with r values of 0.61 to 0.62 and SEE of 12.0 to 14.3. Cellulase incubation with r values of 0.51 to 0.94 and SEE of 3.2 to 12.0. Amylase incubation with r values of 0.87 to 0.91 and SEE of 4.3 to 5.2. Cellulase plus amylase with r values of 0.92 to 0.96 and SEE of 3.4 to 3.8. Buffer incubation with r values of 0.75 to 0.77 and SEE of 5.7 to 7.4. Total digestible nutrients of forages could be predicted by using: a. CP or ADF with r values of 0.77 to 0.87 for CP and -.87 to -.98 for ADF with SEE values of 1.8 to 4.4. IVDMD with r values of 0.80 to 0.84 and SEE of 3.3 to 4.9. Cellulase incubation with r values of 0.69 to 0.82 and SEE of 3.2 to 6.5. 13. 14. 15. 16. 176 d. Pepsin incubation with r value of 0.69 and SEE of 4.0 to 6.5. e. Cellulase plus pepsin with r values of 0.72 to 0.84 and SEE of 3.0 to 6.3. Digestible energy of grasses could be predicted by using: a. CP or ADF with r values of 0.70 for CP and -.74 for ADF and SEE of 3.8 to 4.2. b. IVDMD with r value of 0.85 and SEE of 2.9. c. Cellulase incubation with r value of 0.87 and SEE of 2.8. d. Pepsin incubation with r value of 0.68 and SEE of 4.1. e. Cellulase plus pepsin with r value of 0.87 and SEE of 2.8. Two-stage in_yitrg fermentation (IVDMD OR TTDMD) was excellent to predict DDM of grasses and legumes whereas cellulase plus pepsin was efficient to predict DDM of grasses. The enzyme cellulase, amylase, cellulase plus amylase were excellent for predicting dry matter intake of grasses whereas cellulase plus pepsin was not acceptable. Regression equations developed from the same seven to eight samples of grasses reveal that DDM of grasses was most accurately predicted from cellulase plus pepsin (r = 0.90, SEE = 2.9); TDN 17. 18. 19. 20. 177 from ADF (r = -.87, SEE = 2.6); DE from cellulase, cellulase plus pepsin or IVDMD (r = 0.85 to 0.87, SEE 2.8 to 2.9). Laboratory estimates could predict in yiy9~para- meters much more accurately on a within-species basis than for all forages combined. The best predictors of in_yiyg_parameters for various types of forages were not the same and the prediction equations using the same predictors were different for each forage Species. Multiple regression equations using combinations of the chemical components such as CP, CW, ADF, L, crude fiber (CF), ether extract (EE), nitrogen- free extract (NFE) and ash did not significantly improve the precision of predicting DDM or DMI. Multiple regression equations using combinations of the 36—hr DMD or the two-stage in yit£g_DMD values with various chemical components signifi- cantly improved the precision of predicting DDM (SEE = 1.0 to 2.9) and in some cases improved the prediction for DMI. Combinations of the two-stage IVDMD + CP + ash or 36-hr DMD + ADF accurately predicted TDN of legumes (SEE = 2.9) and grasses (SEE = 1.7) whereas combinations of EB + NFE or CP + CF + EE predicted TDN of silages with standard errors of 1.0 and 1.8, reSpectively. 178 21. Combinations of chemical components alone such as CP + EE + ADF; ADF + L + L/ADF + cell contents; CP + CF + EE could satisfactorily predict DE of legumes, grasses and silages with standard errors of 2.6, 1.8 and 1.6, respectively. 22. Body weight gain of sheep fed_grasses might be predicted from dry matter solubility (DMS) + 6-hr DMD/Standard or two-stage IVDMD + CP + ADF with standard errors of 0.07 to 0.08 lb/d. 23. Some simple and useful prediction equations for different forages were as follows: Alfalfa DDM = 1.369 IVDMD - 23.923 (r = 0.88**, SEE = 4.4) DDM = 1.525 IVOMD - 30.411 (r = 0.96**, SEE = 2.8) DDM = 1.506 Pepsin + 10.264 (r = 0.90*, SEE = 3.0) TDN = 269.077 - 5.633 ADF (r = -.98*, SEE = 1.8) Bromeqrass DDM = 0.960 IVDMD + 4.579 (r = 0.93**. SEE = 2.8) DDM = 0.584 (Ce11+Pep) + 39.106 (r = 0.87*, SEE = 2.4) DMI = 0.910 (Cell+Amy) + 21.365 (r = 0.96**, SEE = 3.4) Reed Canarnyrass DDM = 0.960 (Ce11+Pep) + 23.589 (r = 0.93**. SEE = 2.3) DDM = 87.499 - 8.029 L (r = -.89*. SEE = 2.9) Tall Fescue DDM = 83.038 - 5.115 L (r = —.93**. SEE = 3.3) DMI = 30.650 + 0.800 Cell (r = 0.93**, SEE = 3.2) 179 Temperate Grasses Combined DDM = 0.934 IVDMD + 5.716 (r = 0.92**. SEE DDM = 1.077 IVOMD + 1.621 (r = 0.98**, SEE DDM = 0.743 (Cell+Pep) + 32.449 (r = 0.88**. SEE DMI = 0.836 (Cell+Amy) + 26.006 (r = 0.92**, SEE TDN = 106.305 - 1.506 ADF (r = -.87**. SEE DE = 0.773 Cell + 33.135 (r = 0.87**. SEE Crude Protein from Age (X = days of regrowth) CP = 19.252 — 0.096 X —.83**: CP = 12.024 - 0.073 X -.77**I Temp. grass: (r = SEE Trop. grass: SEE (r = LI; Vitgg Dry Matter Disappearance (IVDMD) from Age Temp. grass: IVDMD = 79.745—0.349 X (r = -.87**. SEE Trop. grass: IVDMD = 51.133-0.028 X (r = —.07, SEE In giggg True Dry Matter Disappearance from IVDMD Temp. grass: IVTDMD = 19.760 + 0.700 IVDMD (r = 0.97**. SEE 2.6) 1.8) 2.4) 3.8) 2.6) 2.8) 2.3) 1.4) 7.0) 9.0) BIBLIOGRAPHY BIBLIOGRAPHY Adams, R. S., J. H. Moore, E. M. Kesler and G. Z. Stevens. 1964. 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Yu, Yu and J. W. Thomas. 1973. Estimation of forage nutritive value. J. Animal Sci. 37:361. APPENDIX APPENDIX TABLE 1 Terminology of Forage Evaluation Following are technical terms and abbreviations commonly used in feed and forage evaluation and in animal nutrition. ADF Acid-detergent fiber; obtained after boiling for 1 hour and filtering using the acid— detergent solution devised by Van Soest. ADG Average daily gain. ADF-N(ADN) Acid detergent nitrogen; insoluble nitrogen remaining in the ADF fraction expressed as % of total N or of DM. ADL Acid detergent lignin; obtained by dissolv- ing ADF in 72% H2804 and ashing the residue to determine ADL as weight loss. AE Available energy; amount of energy an animal can extract per unit of dry matter consumed. . . . _ _ 100L AI Ava11ab111ty Index — 100 ADF(100-CWC) Apparent Nutrient Intake — Nutrient in Feces x 100 Digestibility Nutrient Intake Ash Mineral residue left after igniting sample at 600 C. Buffer Extract Solubility or disappearance of dry matter in a buffer solution. C Cellulose, a polymer of B—D- glucose units; B-glucosan. 194 CC CD CED Cellulase Residue CF CP CWC or CW Cutin DCW DDMI DE DEI Digestibility Digestion Coefficient Digestible Nutrient DM 195 Cell contents = 100— cell walls. Cellulose digestibility; amount of C digested, usually expressed as a percentage of total C. Cupriethylenediamine; chemical used to solubilize dry matter, and supposedly selectively solubilize cellulose. Residue left after cellulase digestion. Crude fiber; residue left after boiling sample with dil. acid and dil. alkali by AOAC standard procedure. Crude protein; calculated from N x 6.25. Cell wall constituent; residue left after boiling sample for 1 hour in Van Soest's neutral detergent solution. Cell wall digestibility; amount of CWC digested and expressed as percent of total CWC. Acid-detergent cutin; aliphatic cutin composed of polymerized hydroxyl fatty acids, monomeric hydrocarbons, alcohol and aldehydes. Digestible cell wall; amount of digested CWC expressed as % of total D.M. Digestible dry matter intake. Digestible energy; energy in feed minus energy in feces expressed as % of energy intake. Digestible energy intake. The chemical, physical and enzymatic break- down of feed followed by absorption. Amount of digested feed divided by total feed consumed and expressed as a percentage. Amount of digested nutrient divided by total nutrient ingested. Dry matter; moisture free feed. DMD DDM DMD DMI DMNVI DMS DOM DP EAD ,ED EDMD, EDDM EE ETD Forage Quality GE HC Holocellulose 196 Dry matter digestibility = DM Intake 1 Fecal DM DM Intake x 100 Digestible dry matter; same as DMD. Dry matter disappearance; term normally used with in_vitro system, as dry matter solubilized for a given in_vitro system. Dry matter intake normally calculated as amount consumed per animal, per unit weight or per unit metabolic weight. Dry matter nutritive value index = Relative Intake x DMD. Dry matter solubility in 1.0 N H2804. Digestible organic matter; digested OM as percentage of total DM. Digestible crude protein. Estimated apparent digestibility = 0.98 CC + CWC (1.81 - .97 log L/ADF x 100) — 3 (Si) - 12.9. Energy digestibility; same as DE. Estimated dry matter digestibility = 0.98CC + CWC (1.81 - .97 log L/ADF x 100). Ether extract; crude fat obtained from proximate analysis. Estimated true digestible dry matter = 0.98CC + cwc (1.81 — .97 log L/ADF 100) .5 300 (Si). Includes voluntary intake, digestibility and output per animal. Gross energy; obtained from burning sample in bomb calorimeter. Hemicelluloses; amorphous polysaccharides composed of glucans, polymers of xylose, arabinose, mannose, galactose plus mixed sugar and uronic acid polymers. A combination of cellulose plus hemicellulose. Insoluble Ash I§_Vitro In Vivo Inoculum DMD IVCD IVDMD IVTDMD IVOMD Lignocellulose Maillard Reaction MADF MCF ME Metabolic Size Methoxyl NC NDF NE 197 Ash residue containing silica. In an artificial container, in glass etc.; outside of life. In, on or with the animal; within life. Inoculum dry matter disappearance used by Barnes (1969) to check the reliability of the in_vitro fermentation. In_vitro cellulose digestibility. In vitro dry matter disappearance; usually that determined by the Tilley and Terry method. In_vitro true dry matter digestibility; determined by boiling the 48~hr fermenta- tion product with neutral detergent. In vitro organic matter disappearance. Lignin, an aromatic substance composed of phenylpropane polymers. A complex of lignin, cellulose and hemi- cellulose. Non—enzymic browning reaction, a complex condensation of carbonyls and amino ac1d. Modified acid detergent fiber; obtained after boiling sample for 2 hrs. with 1% CTAB in 1 N H2804 without antifoamant. Modified crude fiber; developed by California workers. Includes ash. Metabolizable energy; DE minus gas and urinary losses. Standardized weight = ngé75 Chemical radical attached to L molecule; used as a predictor of L content and L complexity. Nutrient concentration in a feed. Neutral-detergent fiber; same as CWC. Net energy = ME - Heat increment; real _ energy used for maintenance and production. NFE Non—nutritive Residue NPN Nutritive Value NVI NVI (In_Vitro) Nylon Bag Technique OM OMD Palatability Permanganate Lignin Prediction Equation RI Si Solubility Test Soluble Carbohydrates Soluble Ash 198 Nitrogenefree Extract; presumably soluble sugars, starch, dextrin. ' Chemical components of feedstuffs that can not be completely digested. Non—protein nitrogen such as urea, amino ac1ds, amines, etc. Quality of feed including chemical composi- tion, voluntary intake and digestibility of feed. Nutritive value index = Relative intake x Digestible energy. Nutritive value index (I2 Vitro) = 6-hr DMD x 36-hr DMD/100 (Ingalls, 1964). Ig_vivo fermentation by suspending sample bags in the rumen of a fistulated animal. Organic matter; 100 - Total ash. Organic matter digestibility; amount of digested OM expressed as % of total OM in feed. The degree to which a food is attractive to animals under defined conditions of choice. Lignin obtained from weight loss after oxidizing it from ADF by potassium permanganate. Equation based on laboratory or in vitro estimates to predict in_vivo performances. gm daily forage DMI 0.75 80 (ng ) Relative Intake = 100 x Sandy residue composed of $102. Mixing or incubating feeds with water, buffer, solvents or enzymes to determine DMD. Those carbohydrates which are soluble in water or alcohol. Total ash — insoluble ash. Summative Equation TAC Tannin TDN TEE TNC TRAC TRAE TSAE True Digestibility TTDMD Turbidity Test VFA VI WS-CHO 199 Scheme for the estimation of in vivo digestibility by combining thE—digesti- bility of CC, CWC and metabolic fecal losses. Total available carbohydrates; plant component hydrolyzable by amylolytic enzyme to simple sugars. Tannic acid (C76H52046); an amorphous polyphenol, strongly astringent substance found in plants. Total digestible nutrients = DP + Dig. CF + Dig. NFE + Dig. fat x 2.25 Total enzyme extract; soluble DM due to enzyme alone. Total nonstructural carbohydrates; readily available CHO; sugars, starch, fructosans; similar to TAC. Total residue after cellulase; obtained from incubating sample with cellulase. Total residue after enzyme(s). Total soluble after enzyme(s); same as total enzyme extract. Overall digestibility of feeds by taking into account both bacterial and endogenous losses. Tilley—Terry; 12 vitro rumen fermentation with rumen fluids followed by pepsin digestion. Checking forage quality by measuring opaqueness of a suspension of the sample in water. Volatile fatty acids; acids produced in the rumen (acetic, propionic, butyric, valeric acids). Voluntary intake; unit of intake per unit of body weight normally expressed as gm of 0.75 feed/BWKg Water—soluble carbohydrates, mainly mono-and disaccharides. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII IIIIIIIIIIIIIIII ! \ in. I s r I ... I I .. I I... a... \ I .\ I II ._\... i __ l Hal-IIHIIIIII lfll I I-..» I III-I