Forage and Feed Testing Programs – Problems and Opportunities

OUR I N D U S T R Y T O D A Y Forage and Feed Testing Programs - Problems and Opportunities C. E. COPPOCK, and C. G. WOELFEL Texas A&M University College Station 77843 R. L. BELYEA University of Missouri Columbia 65201

ABSTRACT

A survey of the 50 United States and 9 Canadian provinces showed that most offer a feed testing service. During the past 5 yr, there has been a large increase in the array of tests available to dairymen and in the number of forages and feeds submitted to laboratories for chemical analyses. However, a number of states have no specific program to help farmers use results of the forage tests. Although new infrared technology eventually may replace more expensive wet chemistry for routine forage analysis, much greater effort should be directed toward using the information now being generated. INTRODUCTION

Genetic trend, advances in milking systems, and greater control of mastitis and other infectious and metabolic diseases are directing increased attention toward nutritional constraints on higher milk production. Summaries from forage testing laboratories show great variation in forage nutrient content (Table 1), and more feeding systems are available that allow precise control of ration composition; consequently, there is an increased need for forage testing and judicious application of the results. In addition to the need for safety margins to allow for variation and avoid nutrient deficiencies, it is important to prevent gross nutrient excesses that increase metabolic stress and contribute to soil and environmental pollution. In the future it may be necessary to rely more heavily on forages and industrial by-products in ruminant diets. Knowledge of detailed nutrient profiles will be essential to their effective use.

Received October 21, 1980. 1981 J Dairy Sci 64:1625-1633

Feeding value has been defined as a product of intake, digestibility, and efficiency with which absorbed products are used; it also can be defined in terms of net energy or animal product produced. In livestock production, concern is for prediction of feeding value prior to feeding that allows more precise formulation of the total diet. Increased reliance on laboratory analyses suggests the belief that a more accurate estimate of feeding value can be obtained from chemical analyses than from a combination of less expensive indirect methods such as visual appraisal, date of cutting, etc. As one extension specialist (8) explained to a group of dairymen, "Your forage analysis is as critical an evaluation of your crop management ability as your herd average is of your herd management ability." This paper is an update of one presented 5 yr ago (4). The objectives are to present results of a forage and feed testing questionnaire, a comparison of equations to predict forage energy content from chemical analyses, and suggestions on the use of heatdamaged forage. Results of Survey

To update information on forage testing programs since 1975, a survey form was mailed to Extension Dairymen at the Land Grant universities in the 50 United States and Puerto Rico and to corresponding universities in 9 Canadian provinces. Forty-eight of the 60 forms were returned, and the results follow. Three-forths of the 48 respondents indicated that Extension Services advocated and provided a forage testing service. The majority of the testing in 22 states and provinces was by the university, in 8 by cooperatives, in 11 by private laboratories, and in 4 by others such as the state or provincial department of agriculture. The Agronomy (or Plant Sciences and Soils) Department performed this testing service in 7 cases, Animal or Dairy Science Department in 5

1625

P-

4 t~

TABLE 1. E x amples of n u t r i e n t variation in forages I . o o~ Z o

Forage

Acid detergent fiber

Crude protein

Es t i ma t e d TDN

Calcium

Phosphorus

Magnesium

(%)

(ppm)

00

Mixed main ly grass hay Mean (1,187) 2 Normal range 3 Corn silage Mean (2,434) 2 Normal range 3 Mixed mainly legume hay Mean (1,095) 2 Normal range 3 Mixed mainly legume silage Mean (1,401) ~ Normal range 3

10.5 7.2-13.7

Zinc

40.4 37.2--43.5

59.7 56.7-62.6

.65 .39-

.90

.22 .17-.26

.15 .08-.21

19 12-34

65.6

9.5

27.5 22.6-32.3

62.9-68.2

.21 .13-

.30

.20 .15-.24

.15 '11--.18

23 11-36

15.1 11.4-18.7

38.1 33.7-42.4

61.4 58.5-64.2

.99 .64-1.34

.26 .21-.30

.20 .12-.27

20

15.8 11.9-19.6

40.0 34.6-45.3

60.0

1.01 .69-1.34

.28 .22-.33

.20 .12-.27

25 2-47

O 0

8.5 7.4-

56.3-63.6

1Comp osition on a dry m a t t e r basis. From NYDHIC Forage Testing Laboratory, 1980. 2 Numb er of samples in the mean. 3 Normal range is defined as -+1 standard deviation of the mean.

13 - 26

,q

OUR INDUSTRY TODAY places, Chemistry and Biochemistry Department in 3 locations, Extension Service in 3 places, and Department of Agriculture (Provincial and State) in 6 places. The subsequent information on costs and services relates to the laboratory sponsored by the university and, if none was sponsored, to the laboratory that analyzed the greatest number of forage samples in the corresponding state or province in 1979. Charge for a "standard package" ranged from $6 to $35 with an average of $12.42 for 32 locations that indicated a price; 3 gave no price, 7 locations had no charge, and 5 had no standard package but offered comparable services priced by item. Analyses in the "standard package" were: dry matter [37], crude fiber [15], acid detergent fiber (ADF) [24], neutral detergent fiber (NDF) [5], crude protein [40], available protein [15] as part of the package, and [6] at additional cost. Acid detergent fiber nitrogen was the test of choice (15 of 17) for detection of heat damage. Other components of the standard package were energy estimates: total digestible nutrients (TDN) [32], net energy for lactation (NE1) [14], estimated net energy (ENE) [19], net energy for gain (NEg) [4], net energy for maintenance (NEm) [2], and digestible protein [13]. Five states offered an in vitro analysis, 3 by the Tilley and Terry method (17) and 2 by neutral detergent extraction. Charges for the in vitro analyses were from no charge to $10. Twenty-two locations gave equations to estimate energy concentration in forages. Thirty-four laboratories analyzed an average of 3,671 forage samples in 1979 (range from 20 to 13,153), 17 locations analyzed an average of 435 specific concentrate ingredients (range 10 to 2,000), 15 states averaged 239 samples of protein and mineral supplements (range 0 to 1000), and 20 states averaged 292 complete rations (range 1 to 148). This represents more than a 100% increase in volume for the past 5 yr. Turn around time from a farmer back to that farmer averaged 11.9 days [41]. Twentythree respondents indicated that some forage was analyzed for commercial sale (average

t The number in bracket refers to the number of locations indicating an analysis performed or used in the average.

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7.5%) with 9 stating "very low" or "very few". Twenty-eight locations offered a separate mineral package with an average cost of $11.40, [21], (6 did not charge) for from 2 to 14 elements. Six respondents indicated they included mineral elements with the "standard package", and 9 had no mineral package. Elements most widely available were calcium and phosphorus [36], magnesium [26], potassium and sodium [24], and iron, manganese, zinc, and copper at 18 places. More special analyses were available than in 1974, including urea [21] at a cost of $5.30 [14] ; nitrate was available in 36 places costing $4.69 [27] ; sulfur was included in the mineral package at 2 places while I6 offered it as a special analysis costing $4.75 [14]. Other less frequently offered analyses include ammonia [6], soluble protein, NPN [4], fat [4], pH [2], and numerous others. The accuracy of the analytical measurements was checked by use of Association of American Feed Control Officials or National Bureau of Standards standard materials [18] with a number of reporters noting internal laboratory standards were being used. Twenty states and provinces directly subsidized forage testing service; 18 locations did not. The Agricultural Experiment Station was most often responsible for support [10], followed by the Extension Service [6]. On the average farmers paid 84% [15] of the cost of the analysis. Only 17 locations provided detailed sampling instructions whereas 15 stated specifically that none was provided. Forty respondents ranked the persons responsible for sending forage samples to the laboratories in order of decreasing importance as farmers, extension agents, feed company personnel, veterinarians, Dairy Herd Improvement Association supervisors, vo-ag teachers, and others. In addition to farmers, results of forage analyses are sent routinely to extension agents [33] or to any other person who submitted the samples. Some special arrangement was made in 34 of 46 locations to interpret results of forage tests to the farmers. Respondents in 42 states and provinces ranked extension agents first in role of interpretation of forage test results, followed by university programs, feed company personnel, veterinarians, and others. Twenty-four laboratories provided interpretive information with the report form and 20 did not. For special analyses, Journal of Dairy Science Vol. 64, No. 7, 1981

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COPPOCK ET AL.

e.g., nitrate, 17 of 37 reported that interpretive information was included on the report form. Thirty-three of 46 universities provide a feed programming service which uses the results of the forage tests; 24 of 40 had available a least-cost r ration-balancing service, and one offered a maximum profit program. Nineteen used a teletype terminal system, 5 used a touchtone phone, and 8 used a mail-in batch process system to provide access to a computer. The cost of the first least cost analysis ranged from $2 to $80 averaging $26.16 [19]. Eleven states made no charge for a first analysis, and 12 charged for a second analysis ranging from $1 to $10. Fourteen institutions subsidized the feed programming service although a greater number [18] did not.

Predicting Energy Value of Forages

Among the nutrients required by cattle, energy is required in the greatest a m o u n t (with the exception of water), and its cost exceeds that of all other nutrients combined. It is ironic there is no direct laboratory analysis for digestible, metabolizable, or net energy. Precise balancing of rations for net energy requires an accurate estimate of the net energy (NE 1) content of forages. Unlike concentrates, forages vary immensely in NE1 content; many book values are generally inappropriate and testing of forages to estimate NE1 content is critical for accurate ration balancing. There are two widely used laboratory methods of deter-

mining forage net energy; both are related to fiber content. The detergent fiber system (DF) of determining forage fiber is displacing the crude fiber (CF) approach. Net energy of forage can be estimated by either neutral detergent fiber (NDF) or acid detergent fiber (ADF). Van Soest (18) reported an equation that allows prediction of NE1 from in vitro dry matter digestibility (IVDMD) and NDF; this provides reasonable estimates of NE1 and appears to rank forages accurately. The N D F - I V D M D approach is logical also in that it includes a measure of fiber (NDF) related to intake as well as a measure of digestibility (1VDMD). Because NDF was not recognized originally as an official analytical method and NDF is difficult to extract in high starch feeds, and because IVDMD is not available in many analytical facilties, the NDF--IVDMD approach has been used more in basic nutritional research than in applied work. Thus, ADF (versus NDF) has been used more extensively by analytical laboratories for practical estimates of digestibility or NE1. In addition, the ADF fraction is available for a subsequent test for heat damaged protein as ADF--N. It now generally is accepted that ADF is better for predicting digestibility while NDF is better for predicting intake (2, 16). This is true for corn and sorghum silage (7) as well as conventional grasses and legumes. The Forage Analysis Subcommittee of the Hay Marketing Task Force organized by the American Forage and Grassland Council has

TABLE 2. Examples of regression equations for predicting digestible dry matter (DDM) from acid detergent fiber (ADF) and dry matter intake (DMI) from neutral detergent fiber (NDF) in forages as proposed by Rohweder et al. (14). Forage 1. In vivo DDM (%) a. Alfalfa North and South b. Grasses Temperate With bermudagrass 2. DMI (g/kg W'Ts ) a. Alfalfa North and South b. Grasses Temperate With bermudagrass

Equation

R2

DDM = 65.5 + .975 ADF% -- .0277 ADF%2

68

DDM = 41.9 + 2.15 ADF% -- .0433 ADF%2 DDM = 59.2 + 1.32 ADF% -- .0338 ADF%2

53 48

DMI = 39 + 2.68 NDF% - .0410 NDF%2 39 89

DMI = 95.3 + 6.70 NDF% - .0668 NDF%2 DMI = 123 + 1.22 NDF% -- .00385 NDF%2

Journal of Dairy Science Vol. 64, No. 7, 1981

35

OUR INDUSTRY TODAY presented a proposal to assign relative feed values with digestible dry m a t t e r intake (DDMI) as an estimate of digestible energy intake (14). In vivo digestibility and intake data from sheep were related to corresponding values of A D F and NDF in 350 forage samples across 8 species from 4 states of diverse climates. Equations then were derived to predict digestibility from A D F and DM intake from NDF. Feed value (DDMI g/kg of body weight raised to the .75 power, W -7s) then equalled (DDM × DMI)/100. Separate equations were derived for alfalfa (north only or north plus south) and temperate as well as three subtropical grasses (see Table 2). These relative feed values are a big improvement over the former hay grades and are especially valuable in pricing forage and comparing actual feeding value. But as in (14), these values estimate the intake of digestible energy when that forage is the only source of digestible energy and protein. Therefore, it is unlikely that they can be used directly in ration formulation although the first part, the estimate of digestible energy, could be. However, they are baseline maintenances from sheep without a depression in digestibility factor because some forages fed alone, even above maintenance, suffer little depression in digestibility (21). To be used in recent dairy cattle formulation programs, a depression factor and a conversion to NE 1 are needed as in (13). From in vitro digestion and available passage, Van Soest and others (19, 20) have c o m p u t e d discount factors to use in adjusting for depressions in digestibility for a large array of individual feed ingredients. The TDN for an individual ingredient is discounted linearly according to the appropriate discount factor, and then the equation NEl(Mcal/kg DM) =

1629

.0266 TDN -- .12 is used to convert the adjusted TDN to NEI. This approach suggests that depressions in digestibility of individual feed ingredients are additive when those ingredients are fed together in a diet at production intake. If so, this approach offers increased precision over the National Research Council (NRC) (13) procedure of using a 4% depression factor for each multiple of maintenance for all feeds. The NRC (13) system begins with baseline TDN and applies the equation, NEl(Mcal/kg DM) = .0245 TDN - . 12, which incorporates an 8% reduction in TDN for an intake of three times maintenance. Another approach is to calculate NE1 directly from A D F as by Mertens (9), whose equations are being used by the NYDHIC and Penn State Forage Testing Laboratories. Examples a r e in Table 3 with an equation for each forage class. These equations seemed to give reasonable results when applied to Missouri forages at several harvest stages, but NE1 of the lower quality forages, especially grasses, was overestimated (3). Consequently, data were obtained in the following manner to improve estimates of NE1 of later cut grasses grown in Missouri and to compare detergent fiber estimates to crude fiber estimates of NEI (3). Three c o m m o n grasses (rescue, bromegrass, and orchardgrass) were grown at the University of Missouri Southwest Research Center, Mt. Vernon, and were sampled at 1 wk intervals for 8 wk. Alfalfa was grown at the University of Missouri dairy farm in Columbia and was sampled at 1 wk intervals for 8 wk. All forage samples were from first cutting and represented growth stages from early vegetative to late seed. Samples were dried to 50 C, ground through a 1-mm screen, and analyzed in duplicate for absolute dry matter, NDF, and A D F according

TABLE 3. Equations to predict net energy for lactation (NE1) from acid detergent fiber (ADF) used by NYDHIC and Penn State Forage Testing Laboratories. Legumes Grasses Mixtures (grasses and legumes) Corn silage

-- NEl(Mcal/kg DM) = (1.044 -- .0123 X ADF%) 2.2 -- NEl(Mcal/kg DM) = (1.O85 - ,0150 X ADF%) 2.2

- NEl(Mcal/kg DM) = (1.044 -- .0131 X ADF%) 2.2 _ NEl(Mcal/kg DM) = [ (.3133 × { 2.86 _

35.5 100--1.67XADF%

})] 22

1Derived by Mertens (9). Journal of Dairy Science Vol. 64, No. 7, 1981

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COPPOCK ET AL.

of the forages with corresponding equations a n d t h e i r s o u r c e s are in T a b l e 4. It a p p e a r e d

t o G o e r i n g a n d V a n S o e s t (5). All f o r a g e s w e r e d i g e s t e d in v i t r o ( I V D M D ) in d u p l i c a t e w i t h i n each o f t w o b a t c h e s o f r u m i n a l fluid i n n o c u l u m ; d i g e s t i o n w a s f o r 4 8 h, a n d e x t r a c t i o n w a s w i t h n e u t r a l d e t e r g e n t . C r u d e f i b e r c o n t e n t o f all f o r a g e s w a s d e t e r m i n e d in d u p l i c a t e (1). F i b e r c o m p o s i t i o n a n d a c o m p a r i s o n o f NE1

t h a t NE11 ( d e t e r m i n e d f r o m N D F - - I V D M D ) was reasonable and indicative of milk yields attained when the above forages were fed to dairy cows. T h e relative precision of e s t i m a t i n g N E 1 f r o m N D F - - I V D M D h a s b e e n o b s e r v e d in

TABLE 4. Composition and in vitro DM digestibility as predicted by several approaches across eight cutting dates. Cutting date

ADF l

Fescue

1 2 3 4 5 6 7 8

35 33 36 37 41 40 42 43

25 26 28 31 32 34 35 36

Bromegrass

1 2 3 4 5 6 7 8

30 30 34 35 38 39 40 41

Orchardgrass

1 2 3 4 5 6 7 8

Alfalfa

1 2 3 4 5 6 7 8

Forage

CF 2

IVDMD 3

NEI ~

NEI 2

78 79 78 77 70 65 60 57

1.27 1.20 1.18 1.01 .88 .71 .63 .63

1.23 1.30 1.20 1.17 1.03 1.07 1.00 .97

1.25 1.39 1.19 1.19 .88 .95 .84 .75

1.03 1.01 .99 .97 .97 .95 .89 .89

24 27 28 31 33 33 33 35

85 83 86 79 75 67 65 64

I. 37 1.30 1.33 1.19 1.05 .86 .81 .79

1.40 1.40 1.27 1.23 1.13 1.10 1.07 1.03

1.52 1.52 1.30 1.23 1.06 1.O1 .96 .86

1.03 1.O1 .98 .94 .92 .92 .92 .89

30 31 37 40 44 42 43 46

23 26 31 34 35 34 36 36

91 88 82 74 69 67 66 64

1.35 1.28 1.02 .86 .77 .77 .68 .45

1.40 1.36 1.17 1.07 .94 1.00 .97 .87

1.52 1.45 1.10 .97 .70 .85 .77 .61

1.O5 1.01 .95 .91 .89 .90 .89 .89

20 22 25 29 33 36 35 36

13 15 19 22 26 30 26 28

85 84 80 75 73 70 68 65

1.78 1.76 1.63 1.48 1.42 1.34 1.30 1.22

1.76 1.71 1.61 1.51 1.41 1.32 1.35 1.34

1.69 1.61 1.49 1.36 1.23 1.10 1.14 1.12

1.15 1.12 1.07 1.03 .97 .93 1.01 .99

(% of dry matter)

NEI 4

(Mcal/kg DM)

1ADF = acid detergent fiber. 2CF = crude fiber. 3 IVDMD = in vitro dry matter digestibility (NDF extraction). NE1 ~ = (.01) (TDN) (2.86 - 35.5/CS), Van Soest (18). NEI 2 = (1.085 -- .0150 X % ADF) X 2.2 for grasses, NYDHIC and Penn State. NEI 2 = (1.044 -- .O123 X % ADF) X 2.2 for legumes, NYDHIC and Penn State. NEI 3 = (1.50 -- .0267 X % ADF) X 2.2 for grasses, Belyea (3). NEI 3 = (1.09 -- .0163 X % ADF) X 2.2 for alfalfa, Belyea (3). NEI* = (.6575 X DM% - .65 × CF%) × 2.2 for all forages, Belyea (3). Journal of Dairy Science Vol. 64, No. 7, 1981

NEI 3

OUR INDUSTRY TODAY this laboratory previously and appeared to be an effective laboratory method of estimating net energy of forages. Net energy estimated from ADF by NYDHIC--Penn State equations (NE12) appeared to be adequate for early cut grasses; however, NE12 for later cut grasses was much higher than for NE11 and appeared to be overestimated. The NE12 for alfalfa was very similar to NEI 1, and thus, it seems that NE 1 of alfalfa can be predicted well with the existing NE1 equations (i.e., those used by Penn State, NYDHIC, and others). Mertens (10) is aware of the problem with low quality grass and has explained that the original data were obtained with sheep. New equations based on data from cattle are being determined. The NE13, determined by regressing NE11 upon ADF%, agreed reasonably well with NE11 for grasses; for grasses cut at later stages NE13 appeared to be more in line with practical observations in the field than NE12 . For example, fescue or orchardgrass harvested at head (dates 5 or 6) were estimated (NE12) to contain about .94 to 1.10 mcal/kg; realistically these are too high, and milk yields rarely are optimal unless grasses are supplemented extensively with concentrates. The net energy estimated by NE13 appeared closer to observed production responses. The NE14 (predicted from the CF equation) underestimated the NE1 of early cut grasses and overestimated the NE1 of late cut grasses as observed by many other workers. This equation

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had been used for determining NE1 of forages when only CF analyses were available. Limiatations o f NE1 predicted from the CF equation are obvious from data for forages other than those of average quality. Both Rohweder et al. (14) and recently Jones et al. (6) have noted that linear predictions of digestibility from A D F could be improved by equations that use quadratic functions. We do n o t know whether this applies to direct estimation of NE I from ADF, but because many factors affect digestibility, Moe et al. (12) suggested a two stage conversion: a) an estimate of digestible energy and b) conversion of digestible energy to NE1 with a factor included to account for depressed digestibility. A comparison of NE 1 estimates for c o m m o n forages is in Table 5. Only the late cut grasses are divergent in estimates of energy. The use of A D F - N to estimate heat damage in forages is now adopted widely and routinely available (4). Although the loss in protein availability is estimated directly and compensation can be made by increasing supplemental protein, there may be a substantial loss in digestible energy (11). This loss may n o t be recognized widely nor compensated even though farmers often use heat damaged feeds. Heat damage increases A D F due to increase in apparent lignin and decreases digestibility as estimated from equations based upon ADF. However, this reduction in digestibility does n o t appear to be nearly as great as in vivo data

TABLE 5. Comparison of estimates of NE 1for common forages. NRC 1 Forage

ADF

Alfalfa (1--00--054) Alfalfa (1--00--071) Bermudagrass, coastal (1-00-900) Corn silage (3-02-823) Timothy (1--04--882) Timothy (1-04-886)

NE 1 (Mcal/kg DM) Van Soest 2

NYDHIC 3

TDN

NRC 1

34 44

62 52

1.40 1.15

1.41 1.12

1.38 1.11

33 31 37 45

53 70 62 51

1.18 1.59 1.40 1.13

1.01 1.54 1.33 1.01

1.30 1.46 1.17 .90

(%)

1NRC (13). 2Van Soest et al. (20). 3Mertens (9). Journal of Dairy Science Vol. 64, No. 7, 1981

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COPPOCK ET AL,

TABLE 6. Effect of moisture content at baling and subsequent beating on composition and digestibility of alfalfa hay I . Percent moisture at baling 35.2 53.4

58.5

7.2

19.4 41.8 31.6 23.5 7.5

18.1 46.8 40.0 28.0 10.3

60.0 63.0 55.1 55.7 47.0 1.47

56.4 58.0 50.4 51.2 47.7 1.44

26.2 Composition (%) Crude protein Neutral detergent fiber Acid detergent fiber Cellulose Acid detergent lignin Digestibility (%) Dry matter Crude protein Energy Neutral detergent fiber Acid detergent fiber NEI 2(Mcal/kg DM)

18.3 44.9 30.4 22.9

20.0 46.7

39.9 27.1

10.7 51.8 40.2 47.0 52.2

53.7 1.22

45.8 27.2

34.1 48.1 49.1 1.21

t Data from Miller et al. (11). 2 Estimated from ADF by the equation of Mertens (9).

show (11). An example of a comparison is in Table 6. As protein digestibility decreased from 63.0 to 27.2% in alfalfa hay baled at increasing moisture, energy digestibility decreased from 55.1 to 34.1%. Application of the Mertens (9) equation based on ADF content gives a decrease in NE 1 from 1.47 to 1.21 Mcal/kg DM (Table 6). Prediction of energy lost through heat damage in this example appears to be depressed about .5 as much as the in rive data show. This effect could be incorporated into current prediction equations if the loss in energy shown in Table 6 is typical of many other heat damaged forages. Infrared Reflectance Spectroscopy

A promising development in forage and feedstuff analysis is infrared reflectance (IR) technology (15). Its great advantage lies in the speed of analysis; with a finely ground sample of grain or forage, multiple nutrients can be determined in less than 2 rain. It is now being used commercially for determination of protein in grains. However, application of this technology to forage evaluation is much more complex and requires much more expensive instrumentation and data processing. Even more significant is the evidence that IR can be used to predict animal response factors including Journal of Dairy Science Vol. 64, No. 7, 1981

dry matter digestibility and voluntary intake. It now appears that heat damage of protein in forages can be estimated by IR; most recent work indicated IR may have the capability for mineral analyses. It appears at this time that calcium and phosphorus can be predicted with reasonable accuracy, and potassium and boron with less accuracy (15), but accuracy of other element analyses has not been acceptable to date. Commercial instruments now are becoming available with variable wavelength settings and with ctata processing capability. Infrared reflectance offers great potential advancement in forage and feed analysis; if these capabilities are realized, they far exceed today's methodology by wet chemistry. CONCLUSIONS

During the past 5 yr the survey showed that despite increased numbers of forage analyses, there are states with no specific program to help farmers use results of the forage tests, and there is too little interpretive information available on the report forms, notably for special analyses. Greater attention to thorough sampling procedures would ensure more representative samples. In addition, greater consideration in extension to time of sampling (e.g., on the way into storage) would permit the

OUR INDUSTRY TODAY results o f t h e feed analysis t o be available f o r f o r m u l a t i o n b e f o r e t h e forages are fed. G r e a t e r precision in e s t i m a t i n g t h e digestibility a n d NE1 of forages w o u l d be h e l p f u l , b u t it seems u n l i k e l y t h a t this is feasible b y simple, fast, w e t c h e m i s t r y m e t h o d s . T h e r e also has b e e n a large increase in t h e array o f tests available to dairym e n a n d a small r e d u c t i o n in t u r n - a r o u n d t i m e f o r c o m p l e t i o n o f t h e results. A l t h o u g h volu n t a r y i n t a k e o f a single forage diet m a y be m o r e i m p o r t a n t t h a n digestibility o f t h a t forage, i n f o r m a t i o n is n o t available to p r e d i c t p e r f o r m a n c e o f high p r o d u c i n g dairy cows fed such forage in diets o f 50 to 65% c o n c e n t r a t e . S o o n IR m a y replace w e t c h e m i s t r y as t h e method for determining nutrient composition as well as giving e s t i m a t e s o f a n i m a l response. However, a n a l y t i c a l t e c h n o l o g y is n o t t h e l i m i t i n g f a c t o r f o r m o r e e f f e c t i v e use o f forage testing. R a t h e r , m o r e e f f o r t n e e d s to be d i r e c t e d t o w a r d s use o f t h e results already b e i n g generated b y forage t e s t i n g facilities in feed programming and diet formulation. Certainly the t e c h n o l o g y is available t h a t allows these results to be used m u c h m o r e e x t e n s i v e l y . ACKNOWLEDGMENTS

T h e a u t h o r s express sincere a p p r e c i a t i o n to t h e E x t e n s i o n D a i r y m e n in t h e U n i t e d S t a t e s and C a n a d a w h o t o o k t i m e to fill o u t a n d return the Forage T e s t i n g Q u e s t i o n n a i r e . T h a n k s are e x t e n d e d to K a r e n Clark f o r help in t h e s u m m a r y of t h e survey f o r m s a n d to C. C o f f m a n a n d C. E d m o n d s o f K. C. A g r i c u l t u r a l L a b o r a t o r y Service, Nevada, MO, for t h e i r support and cooperation. REFERENCES

1 Association of Official Agricultural Chemists. 1970. Official methods of analysis. AOAC, Washington, De. 2 Barnes, R. F., and G. C. Marten. 1979. Recent developments in predicting forage quality. J. Anim. Sci. 48:1544. 3 Belyea, R. L. 1980. Unpublished results. Univ. Missouri, Columbia. 4 Coppock, C. E. 1976. Forage testing and feeding

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