This is to certify that the
dissertation entitled

ASSESSING PERSISTENCE, PRODUCTIVITY, ANIMAL PREFERENCE, FORAGE
QUALITY AND BOTANICAL COMPOSITION OF ROTATIONALLY GRAZED
PERENNIAL COOL SEASON GRASSES AND CLOVERES GROWN IN
MONOCULTURE AND BINARY MIXTURES IN MICHIGAN

Presented by

Nasser S. Al-Ghumaiz

has been accepted towards fulfillment
of the requirements for the

Ph.D degree in Crop and Soil Sciences

KEN/K V150

Major lyrofessor t 1gnature

///.2aj/a (a

Date

 

 

MS U is an Affirmative Action/Equal Opportunity Institution

 

 

LIBRARY
Michigan State
University

 

 

PLACE IN RETURN BOX to remove this checkout from your record.
To AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE DATE DUE DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2/05 p:/CIRC/DateDueiindd-p.1

 

ASSESSING PERSISTENCE, PRODUCTIVITY, ANIMAL PREFERENCE, FORAGE
QUALITY AND BOTANICAL COMPOSITION OF ROTATIONALLY GRAZED
PERENNIAL COOL SEASON GRASSES AND CLOVERS GROWN IN
MONOCULTURE AND BINARY MIXTURES IN MICHIGAN

By

Nasser S. Al-Ghumaiz

A DISSERTATION

Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of

DOCTOR OF PHILOSOPHY
Department of Crop and Soil Sciences

2006

ABSTRACT
ASSESSING PERSISTENCE, PRODUCTIVITY, ANIMAL PREFERENCE, FORAGE
QUALITY AND BOTANICAL COMPOSITION OF ROTATIONALLY GRAZED
PERENNIAL COOL SEASON GRASSES AND CLOVERS GROWN IN
MONOCULTURE AND BINARY MIXTURES IN MICHIGAN
By
Nasser S. Al-Ghumaiz
Most perennial cool season grasses and clovers introduced to Michigan have not been
evaluated in binary mixtures. The objectives of this study, which was conducted over
five growing seasons at three different latitudes in Michigan were: (i) to determine
persistence and productivity of perennial grass and clover species grown in monoculture
and binary mixtures under three different latitudes in Michigan; (ii) to determine
botanical composition of grass-clover binary mixtures over time ; (iii) to determine
animal preference and forage quality of these species under rotational grazing, and (iv) to
evaluate the use of Near-Infrared Reflectance Spectroscopy (N IRS) to predict the species
composition using three calibration equations. Grass-clover binary mixtures resulted in
greater persistence, increased ground cover, greater animal preference and higher forage
quality than grass or clover growing in monoculture. However, persistence of both
perennial grass and clover was related to the site of the study. Kura clover provided the
highest dry matter (DM) yield (tones acre") and exceptional persistence under grazing
particularly at southern part of Michigan. NIRS had higher prediction accuracy with R2
ranging from 0.67 to 0.72 and SEP from 6.9 to 12.8 respectively using equations
generated from hand separations of several grass and clover species. NIRS can be used

to replace the hand separation method to determine botanical composition pasture species

using a calibration equation developed from a large data set of hand-separated samples.

DEDICATION

To My Parents, My Wife And My Children

iii

ACKNOWLEDGMENTS

First I want to thank ALLAH, my lord who enabled me to succeed in this work.
Secondly, I sincerely express my gratitude to the people who helped in this work, my
major advisor Dr. Richard Leep for giving me the opportunity to be his student at
Michigan State University. I would like to thank him for encouraging me to express
confidence, gain new skills and trusting me to get the job done. A special thanks to the
other members of my guidance committee: Dr. Donald Penner for providing his expertise
and advice to improve my research project, Dr. Herbert Bucholtz, Department of Animal
Science, for his support, sharing knowledge and research ideas, that help to improve my
research project and last but not least Dr. Shasha Kravchenko for her assistance in my
program, particularly, her great inputs on statistical analyses.

I am grateful to my colleague and fiiend Timothy Dietz for his valuable
contribution in the field and lab works throughout my program until I accomplished this
project. My thanks goes also to forage research crew namely, Deborah Warnock, James
De Young, Daniel Hudson and the summer student employees including Joseph Brooks,
Stephanie Little, Matthew Smith, Garrett Laurain-Marushia, Mildred Lyon, and Sherri
Weisbeck, for their assistance in data collection and lab analyses. I acknowledge
Christian Kapp for his technical support and effort managing the plots at Upper Peninsula
Experiment Station. My appreciation is extended to all the staffs at Upper Peninsula
Experiment Station, Lake City Experimental Station, Kellogg Biological Research
Station at Hickory Comers and Crop and Soil Sciences Research and Teaching Station at
East Lansing for their cooperation in providing facilities and support in my field research.

I wish to think all graduate students, faculty and staff in Department of Crop and Soil

iv

Sciences at Michigan State University, as they were always supportive during my
academic program. I am especially indebted to Calvin Bricker, Research Assistance, for
his technical assistance and consultation in computer software.

I want to thank Rood Trust Fund who provided funding for this research. My
sincere appreciation goes to Saudi Arabia Government who was behind all the financial
support for entire my graduate studies at Michigan State University. To my colleagues in
college of Agriculture in Qassem University in Saudi Arabia for offering all possible help
throughout my academic program.

I am especially gratefirl to my beloved father and mother for their years of
patience, encouragement and prayers that I achieve my goal during my study abroad.
Another heartfelt thanks to my wife, Nourah Al-Homaid, and my children, Saleh, Shahad
and Abdulaziz, for their strong support and patience throughout my academic program.

To all my relatives and friends who they were always thinking about me.

TABLE OF CONTENTS Page

LIST OF TABLES ............................................................................. v11
LIST OF FIGURES ............................................................................ xi
GENERAL INTRODUCTION ................................................................. 1
Literature Cited .................................................................................. 5
CHAPTER 1

EVALUATION OF THE PERSISTENCE AND PRODUCTIVITY OF
PERENNIAL COOL SEASON GRASSES AND CLOVERS GROWN IN
MONOCULTURE AND BINARY MIXTURES IN MICHIGAN

Abstract ........................................................................................... 6
Introduction And Background .................................................................. 7
Materials And Methods ........................................................................... 15
Results And Discussion. ......................................................................... 20
Conclusions ........................................................................................ 41
Literature Cited ................................................................................... 44
CHAPTER 2

ANIMAL GRAZING PREFERENCE AND FORAGE QUALITY OF
PERENNIAL COOL SEASON GRASSES AND CLOVERS GROWN IN
MONOCULTURE AND BINARY MIXTURES UNDER ROTATIONAL
GRAZING

Abstract .......................................................................................... 76

Introduction And Background ................................................................ 77
Materials And Methods ........................................................................ 83

Results And Discussion. .......................................................................... 87
Conclusions ....................................................................................... 94
Literature Cited ................................................................................. 95
CHAPTER 3

PREDICTING BOTANICAL COMPOSITION OF GRASS-CLOVER PASTURE
MIXTURES USING NEAR-INFRARED REFLECTANCE SPECTROSCOPY

Abstract .............................................................................................. 107
Introduction And Background ................................................................ 109
Materials And Method8112
Results And Discussion ........................................................................ 117
Conclusions ....................................................................................... 119
Literature Cited .................................................................................. 121
GENERAL SUMMARY ........................................................................ 125
APPENDICES ................................................................................... 127

vi

LIST OF TABLES
Page

CHAPTER I
Table 1. The mean values of winter injury rating of 16 grass cultivars
established in monoculture at Kellogg Biological Research Station (KBS), Lake
City (LC) and Upper Peninsula Experiment Station (UPES) over a three-year
period ....................................................................................... 51

Table 2. The mean values of winter injury rating of 67 grass-clover binary

mixture treatments established at Kellogg Biological Research Station (KBS),

Lake City (LC) and Upper Peninsula Experiment Station (UPES) over a three-

year period .................................................................................... 52

Table 3. The mean values of spring and fall ground cover rating of 16 grass

cultivars established in monoculture at Kellogg Biological Research Station

(KBS), Lake City (LC) and Upper Peninsula Experiment Station (U PES) over a
three-year period ............................................................................. 54

Table 4. The mean values of spring (S) and fall (F) ground cover rating of 67

binary mixtures treatments established at Kellogg Biological Research Station

(KBS), Lake City (LC) and Upper Peninsula Experiment Station (UPES) over a
three-year period ............................................................................. 55

Table 5. The mean values of winter injury rating of eight clover cultivars

established in monoculture at Kellogg Biological Research Station (KBS), Lake

City (LC) and Upper Peninsula Experiment Station (UPES) over a three-year

period .......................................................................................... 58

Table 6. The mean values of spring and fall ground cover rating of eight clover
cultivars established in monoculture at Kellogg Biological Research Station

(KBS), Lake City (LC) and Upper Peninsula Experiment Station (UPES) over a
two-year period .............................................................................. 59

Table 7. The mean values of spring (S) and fall (F) percentage clover rating of
festulolium-clover binary mixtures established at Kellogg Biological Research
Station (KBS), Lake City (LC) and Upper Peninsula Experiment Station (U PES)

over a three -year period .................................................................... 60

Table 8. The mean values of spring (S) and fall (F) percentage clover rating of
orchardgrass-clover binary mixtures established at Kellogg Biological Research
Station (KBS), Lake City (LC) and Upper Peninsula Experiment Station (UPES)
over a three-year period ...................................................................... 61

vii

Table 9. The mean values of spring (S) and fall (F) percentage clover of
perennial ryegrass-clover mixtures in three locations ...................................

Table 10. The mean values of spring (S) and fall (F) percentage clover rating of
tall fescue-clover binary mixtures established at Kellogg Biological Research
(KBS), Lake City (LC) and Upper Peninsula Experiment Station (UPES) over a
three-year period ............................................................................

Table 11. Dry matter yield (tons acre") of 16 grass cultivars established in

monoculture at Kellogg Biological Research (KBS) over a two-year period ........

Table 12. Dry matter yield (tons acre") of 16 grass cultivars established in
monoculture at Lake City (LC) over a two-year period .................................

Table 13. Dry matter yield (tons acre'l) of 16 grass cultivars established in
monoculture at Upper Peninsula Experiment Station (UPES) over a two-year
period ..........................................................................................

Table 14. Dry matter yield (tons acre") of 67 grass-clover binary mixtures
treatments established at Kellogg Biological Research (KBS) over a two-year
period ...........................................................................................

Table 15. Dry matter yield (tons acre") of 67 grass- clover binary mixtures
treatments established at Lake City (LC) over a two-year period .....................

Table 16. Dry matter yield (tons acre'l) of 67 grass- clover binary mixtures
treatments established at Upper Peninsula Experiment Station (U PES) over a
two-year period ...............................................................................

Table 17. Dry matter yield (tons acre!) of eight clover cultivars established in
monoculture at Kellogg Biological Research Station (KBS) over a two-year
period ..........................................................................................

Table 18. Dry matter yield production (tons acre") of eight clover cultivars
established in monoculture at Lake City (LC) over a two-year period ...............

Table 19. Dry matter yield (tons acre") of eight clover cultivars established in

monoculture at Upper Peninsula Experiment Station (UPES) over a two-year
period .........................................................................................

viii

62

64

65

66

67

68

70

72

74

74

75

CHAPTER II

Table 1. Animal preference score of 16 grass cultivars established in
monoculture treatments over eight grazing events ....................................

Table 2. Animal preference score of eight clover cultivars established in
monoculture over seven grazing events ................................................

Table 3. Animal preference score of 67 grass-clover binary mixtures over eight
grazing events ..................................................................................

Table 4. The mean values (%) of forage quality components (CP, ADF and
NDF) for pasture species of grass, clover and binary mixtures over 2004-2005
growing seasons in three different locations ..............................................

Table 5. Average forage quality parameters (CP, ADF and NDF) of 16 grass
cultivars established in monoculture and binary mixtures in three locations. . ..

Table 6. Average forage quality parameters (CP, ADF and NDF) of eight clover
cultivars established in monoculture in three locations ................................

CHAPTER III

Table 1. Calibration and validation statistics for NIRS determination of
botanical composition of grass-clover mixture samples collected over 2003-2005
in KBS, LC and UPES using calibration equations developed from artificial
mixed samples of 2004 Lake City data ....................................................

Table 2. Calibration and validation statistics for NIRS determination of
botanical composition of grass- clover mixture samples collected over 2003-
2005 in KBS, LC and UPES using calibration equations developed from hand-
separation samples of 2004 Lake City data .............................................

Table 3. Calibration and validation statistics for NIRS determination of

botanical composition of grass-clover mixture and weed samples collected over
2003-2005 in KBS, LC and UPES using calibration equations developed from
the three years and three locations hand separation samples

ix

101

102

103

105

105

106

123

123

123

APPENDICES
Table 1A. Soil type, soil pH, cutting schedule over 2004 and 2005 growing
seasons at Kellogg Biological Research Station (KBS), Lake City (LC) and
Upper Peninsula Experiment Station (UPES) and the grass and clover cultivars
established in this study ........................................................................................... 131

Table 2A. Total monthly precipitation (mm) at Kellogg Biological Research
Station (KBS), Lake City (LC) and Upper Peninsula Experiment Station (UPES)
during the growing season of 2004-2005 comparing with 30-year average. . . 132

Table 3A. Average daily maximum air temperatures (°C) at Kellogg Biological
Research Station (KBS), Lake City (LC) and Upper Peninsula Experiment

Station (UPES) during the growing season of 2004-2005 comparing to 30-year
average ........................................................................................ 133

Table 4A. Average daily minimum air temperatures (°C) and snow fall (cm) at
Kellogg Biological Research Station (KBS), Lake City (LC) and Upper

Peninsula Experiment Station (UPES) from January through April of 2003-2006
comparing with 30-yr average ............................................................. 134

Table 5A. Average animal weight (kg), and the number of animals in each
grazing event during 2004—2006 grazing season .......................................... 135

Table 6A. ANOVA for winter injury, spring and fall ground cover and spring
and fall clover for binary mixture data fi'om years 2004-2006, locations (KBS,
LC and UPES) and 67 binary mixtures(treatments) ..................................... 136

Table 7A. ANOVA for winter injury, spring and fall ground cover for grass
monoculture data from years 2004-2006, locations (KBS, LC and UPES) and 16
grass varieties (Cultivars) .................................................................. 137

Table 8A. ANOVA for winter injury, spring, and fall ground cover for clover
monoculture data from years 2004-2006, locations (KBS, LC and UPES) and
eight clover varieties (Cultivars) ........................................................... 138

Table 9A. ANOVA for dry matter yield, animal preference and forage quality
(CP, ADF and NDF) for binary mixtures data from years 2004-2005, locations
(KBS, LC and UPES), cutting and 67 binary mixture (treatments) .................. 139

Table 10A. ANOVA for dry matter yield, animal preference and forage quality
(CP, ADF and NDF) for grass monoculture data from years 2004-2005, locations
(KBS, LC and UPES), cutting and 16 grass varieties (Cultivars) .................... 140

Table 11A. ANOVA for dry matter yield, animal preference and forage quality
(CP, ADF and NDF) for clover monoculture data from years 2004-2005,
locations (KBS, LC and UPES), cutting and eight clover varieties (Cultivars) ...... 141

LIST OF FIGURES
Page
CHAPTER I

Figure 1. Comparing average winter injury (WI) at Kellogg Biological Research
Station (KBS), Lake City (LC) and Upper Peninsula Experimental Station

(U PBS) for grass-clover binary mixtures (BM), grasses, and clovers grown in
monoculture .................................................................................. 50

CHAPTER II

Figure 1. The differences in animal grazing preference for the binary mixture
(BM), clover, and grass treatments during 2004 grazing season ...................... 100

Figure 2. The differences in animal grazing preference for the binary mixture
(BM) clover, and grass treatments during 2005 grazing season ...................... 100

CHAPTER 111

Figure 1. Relationship of NIRS predicted percentage to hand-separated of

grasses (a), clovers (b) and weeds (c) using a calibration equation of the hand
separation of each component over three years at three experimental research
stations. .......................................................................................................... 124

APPENDICES

Figure 1A. Michigan map showing the three experimental locations representing

three different latitudes in Michigan: Kellogg Biological Research Station at
Hickory Comers (KBS) (1), Lake City Research Station (LC) (2), and Upper
Peninsula Experiment Station at Chatham (UPES) (3) ................................. 128

Figure 2A. The map design of grass-clover binary mixtures and grass and clover
monoculture treatments ...................................................................... 129

Figure 3A. The experimental area grazing arrangement ............................... 130

xi

GENERAL INTRODUCTION

Forage crops, by definition, are crops grown primary for feeding livestock. Forage
crops can be harvested by machines or grazed by animals. Grazed forage is defined as
forage, which is consumed directly by the grazing animals (Pasturage) (V allentine, 2001).
Pasture refers to the fenced area of domesticated forages on which animals are grazed. In
Michigan more than 1.5 million hectares of farmland is dedicated to forage crops used for
hay, silage, green chop, and over 500 thousand hectares are pastureland (2002 Census of

Agriculture).

The goal of pasture management is to maximize forage quality and yield, while
ensuring stand persistence. Stand persistence depends in part on management practices
such as controlling weeds, maintaining proper soil fertility, and using an appropriate
harvest or grazing schedule, but it also depends on the innate compatibility and winter

hardiness of the forage species or cultivars used in pastures.

Severe winter conditions can cause damage to perennial forage crops in northern
latitudes (Bélanger et al., 2002). Introducing a new species requires knowledge of the
species tolerance to local or regional environmental conditions. Pasture managers in
northern temperate climates could improve their production and lengthen stand life by
selecting high yielding winter hardy cultivars for pasture production. Since most pastures
are planted with both legume and perennial grass, it is important that species selection is
based on their performance in binary mixtures. However, there is limited commercial
information available on pasture species performance in binary mixtures because seed

companies mainly test their species in a monoculture.

Plant performance in pastures cannot be understood without reference to
animals. Under gazing, animals tend to prefer the most palatable plants first, which
consequently causes overgazing of the most palatable species (V allentine, 2001 ).
Consumption of the more palatable species can also have an impact on the mixture
composition. Hence, animal preference is a factor that may influence the relationship
between legume and gasses in the pasture system. Forage nutritive evaluation provides
the final assessment of pasture in terms of nutritional values, which ultimately can be
vital in a pasture forage evaluation program.

The botanical composition need to be determined to estimate the percentage of
actual legume and gass content in a pasture. Several methods have been used for
estimating botanical composition of gass and legume mixtures. The most common
method involves manually separating the mixtures into gass, legume, and weed
components. However, this strategy is not practical since it is laborious, time-consuming,
costly, and prone to operator error, especially when a large numbers of samples are being
processed.

Near-Infrared Reflectance Spectroscopy (N IRS) can lead to more efficient
determination of various components of forage quality. NIRS might be used successfully
for analyzing forage components as a rapid and inexpensive technique to replace the hand
separation approach. Historically, the first application of NIRS was reported in literature
in 1939 (Gordy and Martin, 1939). The potential of NIRS for solving analytical
problems was developed by Kay (1954). In 1968, Ben-Gera and Norris applied NIRS to
analysis of ag'icultural products. NIRS was first shown by Norris et al. (1976) to be a

rapid method to determine the chemical composition of forages.

This research was conducted to assess the persistence of gass and clover species
and cultivars gown in monoculture and binary mixtures at three different latitudes in
Michigan. Animal gazing preferences and forage quality were used in assessing the
nutritive values and palatability of species and cultivars in this study. In addition, this
study evaluated the use of NIRS for providing rapid and accurate results for predicting

the botanical composition of gass and clover species.

This dissertation is divided into three studies. The objective of the first study,
Chapter 1, was to assess the persistence and productivity of gass and clover cultivars
gown in monoculture and binary mixtures established in three latitude locations in
Michigan. Several variables were used for this evaluatation including: total forage yield,
winter hardness, estimation of the gass and clover botanical composition, determining

clover and gass gound cover and the percent clover in gass-clover mixtures over time.

The objective of the second study, Chapter 2, was to determine animal
preferences and forage nutritive evaluation of perennial gass and clover species and
cultivars established in monocultures and binary mixtures. The gazing trial was
conducted in a single location. However, the nutritive evaluation was completed on all

perennial gasses and clovers from three locations.

The objective of the third study, Chapter 3, was to determine whether NIRS can
be applied as an alternative technique to the hand separation method for estimating gass-
clover botanical composition. In this study, three calibration equations were created.

The first equation was developed from pure samples obtained from a single location and

year, which were mixed artificially to different proportions; the second equation was
developed from hand separated samples collected from a single location and year and the
third equation created from hand separated samples from three locations over three years
with an additional constituent, weeds. All the three equations were developed to predict
gass-clovers botanical composition collected from samples from three locations during
2003-2005 seasons. Calibration and validation equations were reported along with

coefficients of determination (R2) and standard errors of prediction (SEP).

LITERATURE CITED
Belanger, G, P. Rochette, Y. Castonguay, A Bootsma, D. Mongain, and D.A.J. Ryan.

2002. Climate. change and winter survival of perennial forage crops in eastern
Canada. Agr. J. 94: 1120-1130.

Ben-Gera, 1., and K.H. Norris.l968. Direct spectrophotometric determination of fat and
moisture in meat products. J .Food Sci. 33: 64-67.

Gordy, W., and PC. Martin 1939. The infrared absorption of HLC in solution J. Chem.
Phys. 7:99-102.

Kaye,W.l954. Near infrared spectroscopy. 1. Spectral identification and analytical
application. Spectrochim . Acta 6:257-287.

Norris, K.H., R.F.Bames, J .E. Moore, and 1.8. Shenk. 1976. Predicting forage quality by
infrared reflectance spectroscopy .J. Anim. Sci. 43: 889- 897.

USDA, National Agiculture Statistics Service. 2002 Census of Ag'iculture-State data.

Vallentine, J .F. 2001. Grazing management, 2“d ed. Harcourt Science and Technology
company. San Diego, CA.

Chapter 1
EVALUATION OF THE PERSISTENCE AND PRODUCTIVITY OF PERENNIAL
COOL SEASON GRASSES AND CLOVERS GROWN IN MONOCULTURE AND
BINARY MIXTURES IN MICHIGAN
ABSTRACT
Persistence of gass-legume binary mixture components is important for pasture

management, yet one species is often lost from the stand within a few gowing seasons.
Forage and livestock producers seek species and cultivars, which provide the geatest
persistence and dry matter (DM) yield over the longest time. The objective of this study
was to evaluate the persistence and DM yield of several gass and clover cultivars
established in monoculture and binary mixtures across three locations representing
different latitudes in Michigan. The evaluation criteria were based upon winter injury,
percent gound cover, percentage clover in the gass-clover binary mixtures and total DM
yield. Binary mixtures resulted in increased DM yield and resistance to winter injury
compared to monoculture treatments. Even non-winter hardy gass species had higher
persistence when associated with clovers compared with gass monoculture. Sigrificant
differences in performance among gass cultivars were observed with tall fescue shown
sigrificantly higher persistence and yield stability at all locations. Variations between
latitude locations were observed. Among clover monoculture species, kura clover
(Endura) had the geatest persistence and DM yield at the southern latitude in both
monoculture and binary mixtures. This study demonstrated the importance of proper
species and cultivars selection based upon location for persistence and dry matter

production.

INTRODUCTION AND BACKGROUND

Several temperate forage gasses are commonly gown in binary mixtures with
alfalfa (Medicago sativa L.), for hay or silage production (Casler, 1988). Most forage
gasses gown in Michigan pastures can be classified as cool season plants (C3 plant),
which have an optimum gowth temperature of 18°-24 °C (Rohweder and Albrecht,
1995). During the mid to late summer, both heat and rainfall deficits often exceed the
optimal gowth range for cool season gasses, causing them to go into a drought-induced
dormancy. During this period, there is often a shortage of soil moisture available for the
shallow rooted cool season gasses (Penn State University Agonomy Guide, 2005-2006).
Thus, many producers include legumes such as clovers in their pasture mixtures since the
deeper taproots of clovers are able to utilize the soil moisture and minerals in the sub soil.
Clovers species are often preferred for pasture over alfalfa as they are more tolerant to

frequent gazing than alfalfa.

Forage gass-legume binary mixtures are important components of dairy and
livestock diets, which offer many advantages over pure stands (Vough, et.al., 1995).
Grasses provide a sod that can be helpful for increasing water infiltration and reducing
runoff and soil erosion (Wadleight, et al., 1974; Wischmeier and Smith, 1978). Binary
mixtures are more competitive with weeds than pure stands (Drolsom and Smith, 1976).
Moreover, gasses benefit from fixing the atmosphere nitrogen (N) provided by legumes,
which reduces the need for supplemental nitrogen fertilization (Vough, et al., 1995).
Grass-legume binary mixtures may yield geater dry matter (DM) than legume
monoculture (Chamblee and Collins, 1988). In contrast, alfalfa-gass binary mixtures did

not yield more forage than an alfalfa monoculture at high seeding rates (Wilsie, 1949). In

addition, legume-gass binary mixtures had higher in vitro DM digestibility (IVDMD),
crude protein (CP) and better seasonal yield distribution than monoculture treatments.
Binary mixed stands can reduce the risk of bloat, which is a very common problem with
ruminants associated with gazing exclusively legume pastures (Casler and Walgenbach,
1990). In this study, we evaluated the persistence and DM yield of cool season gasses

and clover species and cultivars under Michigan gowing conditions.

Cool season grasses

Perennial ryegass (Lolium perenne L.) is one of the most common cool season
gasses gown in binary mixtures. It is considered a temperate perennial gass and is
gown throughout the world including North and South America, Europe, New Zealand
and Australia. It gows well in early spring and fall. However, during excessive high
temperatures in summer months, it becomes dormant even with irrigation or abundant
summer rainfall (Leep, 2004). Perennial ryegass is suitable for pasture due to its high
forage quality (Balasko et al., 1995). It can tolerate close frequent gazing, which makes
it useful in an intensive gazing system (Jung et al., 1996). Perennial ryegass has a
higher IVDMD than other temperate perennial gass species (Pysher and F ales, 1992).

Orchard gass (Dactylis glomerata L.) is a major gass species commonly
recommended for pastures in the Northeastern and North Central United States, (Van
Santen and Sleper, 1996) due to its geater drought tolerance and winter hardiness as
compared to perennial ryegass (Christie and McElroy, 1995). It is also well suited for
mixtures with one or more legume species because of its fast gowth and tillering ability

(Hoveland, 1992). Tillering occurs almost continuously throughout the gowing season

with large number of tillers within a clump (the elongated stem). These remain
vegetative, retaining the gowth point close to the gound, which produce only leaves.
Constant tiller production results in a rapid recovery after gazing (Christie and Mcelroy,
1995). Thus, orchard gass can persist under frequent gazing or clipping more than other
gass species such as ryegass, timothy (Phleum pretense L.), or tall fescue (Davies,
1988)

F estulolium (F estulolium braunii, K.A.) is derived from crosses between meadow
fescue (F estuca pratensis Huds) or tall fescue (F estuca arundinacea Schreb.), and either
Italian ryegass (Lolium multiflorum Lam.) or perennial ryegass. This cross combines
the palatability of perennial or Italian ryegass with the persistence of meadow or tall
fescue. However, festulolium is less palatable and forms a more open sward as compared
to perennial ryegass (Wit, 1959). Festulolium combines the disease resistance and
winter hardiness of meadow fescue with the high crude protein (CP) and good season-
long productivity of Italian ryegass. Winter hardiness of some festulolium varieties may

approach that of perennial ryegass (Ohio State University Extension bulletin).

Tall fescue (F estuca amndinacea Schreb.) is used widely for forage, turf, and
conservation purposes (Collins and Hannaway, 2003). It is the predominant cool-season
pasture gass in many southern states. It is tolerant of continuous close gazing and
superior to many other cool-season gasses in livestock-carrying capacity. Tall fescue
may be used as a hay crop, but needs to be harvested as the first seed heads begin to
appear. Tall fescue is also gown as a turf gass species and its use has increased since
the introduction of turf-type cultivars (Funk et al., 1981). Some tall fescue cultivars are

infected with a fungal endophyte, Neotyphodium coenophr’alum , Margan-J ones & Garns,

which produces alkaloids that are toxic to gazing animals (Ju et al., 2006) resulting in
reduction of feed intake, weight gain, and milk production . However, non-endophyte and
nontoxic endophyte-infected cultivars have been developed for livestock pastures. The
nontoxic infected plants have shown improved tolerance to some environmental stresses

such a drought and some insects (Collins and Hannaway, 2003).

Cool season clovers

Red clover (T rifolium pratense L.) is gown as a monoculture or mixed with
gasses. Red clover may be gouped into three types: early-flowering, late-flowering and
wild red clover. Most red clover gowing in the US is the early-flowering type (Taylor
and Smith, 1995). It is a very important legume hay crop in the Northeastern US. The
relative performance of red clover cultivars may be different when monoculture and
binary mixtures are compared. It is considered a short-lived perennial legume (Nelson
and McGraw, 2003). Red clover is most often gown with a companion gass for hay,
silage, or gazing. However, little information is available on the performance of red
clover cultivars gown with gass species in rotationally gazed pastures. The
performance of red clover cultivars is influenced by location, cutting system, and the

presence or absence of gass.

White clover (Trifolium repens L.) (Common and ladino types) is one of the most
important legumes used for gazing in the US. White clover is classified in three general
cultivars goups: small, intermediate and large. The small cultivars are usually native and
are referred to as wild white clover. Most white clover marketed in the US belongs to the

intermediate type. Large cultivars referred to as Ladino, were introduced from Italy

10

(Henning and Wheaton, 1993). White clover should be gown in binary mixtures with
gasses to prevent bloat in livestock. Under adequate soil moisture, white clover can
gow in every state of the US and province in Canada (Pederson, 1995). White clover is
very palatable and is high in forage quality (higher CP and lower fiber) and tolerates

continuous, heavy gazing pressures (Spitaleri et al., 2003).

Kura clover (T rifolium ambiguim Bieb.) is a long-lived, perennial, rhizomatous
legume (Bryant,l974; Taylor and Smith,1998) that tolerates fi'equent defoliation in
monoculture (Peterson et al., 1994) or binary mixtures with gass (Kim,1996). It can be
used for hay or pasture production (Sheaffer and Marten, 1991; Sheaffer et al., 1992).
Kura clover is very compatible with gasses and suitable for mechanical harvest system
(Kim, 1996). Kura clover gows slowly during the establishment year (Speer and
Allinson, 1985). Slow establishment have limited widespread use of kura clover. This is
related in part to the kura clover devoting most of its fixed C to roots and rhizomes
during establishment and its slow nodulation, which limits N2 fixation in the seeding year
(Peterson etal., 1994; Seguin et al., 2001). However, with its rhizomatous root system, it
is able to survive harsh environmental condition at northern latitudes (Pederson, 1995).
DM yield of kura clover is lower than alfalfa but similar to red clover and birdsfoot
trefoil (Lotus comiculatus L.) (Sheaffer and Marten, 1991). However, kura has higher
forage quality (higher CP, lower acid detergent fiber [ADF] and neutral detergent fiber
[NDF]) than other legumes (Allinson et al., 1985; Sheaffer and Marten, 1991). Kura
clover has a higher risk of causing bloating in gazing animals when compared to white
clover or alfalfa (Sheaffer et al., 1992). Because of this, it should be gown in

combination with perennial gass to reduce incidence of bloat (Mourifio et al., 2003).

ll

Grass and legume compatibility in binary mixtures is an important aspect of
pasture systems. There are several factors that influence the ecology of gass-legume
binary mixtures and many researchers have reviewed this subject. Grass and legumes
compete for water and soil minerals when gown in mixtures (Jones, et al., 1988).
However, competing for irradiance is often considered to be most critical among all the
competition factors (Donald, 1961). Palatability of species can have a geat impact on
binary mixture composition. Animals tend to gaze the most palatable species when
multiple species are offered (Chapter 2). In addition, gass and legumes have different
gowth habits, regowth, and physiological gowth requirements, which make
management more difficult for the binary mixtures compared to monocultures of the
same species (Smith et al., 1986).

Forage gass species vary in their ability to persist with legumes in binary
mixtures (Camlin, 1981). For instance, orchardgass is more competitive in binary
mixtures with alfalfa than smooth bromegasses (Bromus inermis.Leyss) (Schmidt and
Tenpas, 1965; Smith et al., 1973). This criterion is important when evaluating gass-
legume associations (Zannone et al., 1986). Unlike alfalfa and red clover, white clover
can tolerate both continuous and rotational gazing due to the proliferation of stolon
segnents that can gow and spread by forming new plants (Pederson, 1995). Under both
rotational and continuous gazing, it is generally expected that DM yield of pastures will
decrease to a certain extent during gazing season. Many factors can cause yield
reduction. For example, changing botanical composition with a loss of legume can result
in nitrogen deficiency. Soil structure and compaction can cause poor gaseous exchange

at the root resulting in stand declines especially with legumes. In addition, soil nitrogen

12

and potassium deficiencies as well as disease can also lead to DM yield reduction
(Carnlin and Stewart, 1976). It has also been reported that gass-legume balance is very
susceptible to changes in respect to environmental conditions (Camlin, 1981). Therefore,
for accurate assessment of gass-legume binary mixtures persistence, it is important for
this type of research to be conducted under different soil types and environmental
conditions.

Environmental conditions can regulate gass-legume interaction (Snaydon 1987).
Low temperatures during the winter are an important factor in determining geogaphical
distribution and persistence of forage species (Lorenzetti et al., 1971; Shimada et al.,
1993). Winter injury occurs in most perennial gass and legume species and is related to
the length and intensity of the cold weather conditions coupled with the effects of snow
cover, soil heaving, and ice encasement. Bélanger et a1. (2002) reported that severe
winters in eastern Canada caused recurrent damage to perennial forage crops. For a crop
such as alfalfa, winter injury often occurs due to repeated freezing and thawing causing
death of plants. Non-winter hardy species such as rye gass (Lolium sp L.) and
festuloilum (some cultivars) have limited use in binary mixtures because of their poor
cold persistence (Elissa et al., 1995). However, snow cover has a geat impact on
increasing forage survival. Leep et a1. (2001) in Michigan concluded that 10 cm of snow
cover is sufficient to protect alfalfa from winter injury. The ability to survive freezing
temperatures depends upon adequately managing soil fertility, especially N and K, which
can increase cold resistance (Nelson and Volenec, 1995). Winter survival of Coastal
bermudagass (Cynodon dactylon) decreased with increasing levels of applied nitrogen

(N) fertilizer (Adams and Twersky, 1960). Genetic backgound may also influence the

13

severity of winter injury (McKenzie et al., 1988). Past research indicates that tetraploid
perennial ryegass cultivars had sigrificantly higher competitive abilities during summer
and lower cold tolerances in winter than the diploid cultivars (Dvorak and Fowler, 1978;
Sugiyama, 1998). Although the lower cold tolerance of tetraploid cultivars may lead to
low persistence in pasture in northern latitudes, their lower survival rate in a cold winter
is somewhat counterbalanced by high competitiveness during the summer (Sugiyama,
1998). Tetraploid cultivars of other forage species such as red clover and meadow fescue

have been shown to have lower cold tolerance than diploids (Tyler et al., 1978).

In recent years, both the gass and legume seed industry have begun marketing
new species and cultivars of perennial gasses and clovers to Michigan gowers. For
example, orchardgass cultivars have been introduced with value-added traits such as
increased tillering, later maturity, and geater winter hardness. Tall fescue has been
recently offered endophyte free with smoother leaf blades. Most seed companies have
information on how monocultures of their cultivars perform but they do not provide
information on how the species perform as binary mixtures in long-term pastures. In
addition, the ability of new cultivars to survive the severe winter conditions could be an
issue, especially when gown in northern latitudes (44°—460 N). No previous research has
been done to evaluate multiple gass and clover cultivars in binary mixtures at different
latitudes in Michigan. Therefore, more research is needed to determine how these

cultivars perform under Michigan gowing conditions at latitudes 44°-46°.

In this study, several improved cool season gass and clover species and cultivars

established in monoculture and binary mixtures were evaluated to determine their

14

suitability for Michigan climatic conditions. Several aspects need to be considered for
evaluating gass and clover species, including dry matter yield, winter injury, pasture

gound cover, and clover composition in the mixtures.

MATERIALS AND METHODS

Experiment Establishment and Maintenance:

Monocultures and binary mixtures experiments were established in 2001 to be
evaluated over a 5-year period (2002-2006) at three Michigan State University (MSU)
experimental stations: (i) Kellogg Biological Research Station at Hickory Comers (KBS)
(42° 24' N, 85° 24' W); (ii) The Beef Cattle and Forage Research Station at Lake City (LC)
(44°19' N, 85°12' W) and (iii) Upper Peninsula Experiment Station at Chatham (UPES)
(46°33' N, 86°55' W). The locations map is illustrated in Appendix Fig 1A. This research
focuses on the period of 2004-2006.

The study area was separated into three portions: gass only, legume only (clover),
and gass-clover binary mixtures (Appendix Fig. 2A). Each portion was a RCBD with three
replications. For the gass-clover binary mixtures, each replication consisted of 67 entries of
different combinations of gass and clover cultivars seeded in 1.8 by 5 m plots. Grass and
clover monocultures experiments consisted of 16 and 8 gass and clover entries, respectively
with plots of the same size as these of the binary mixtures portion.

Eight clover cultivars were established in this study included three red clover (VNS',

Star Fire, and Start), four white clover (KopulI, Ladino, Alice, and Jumbo), and one kura

 

‘ Variety Not Stated

15

clover (Endura). The 16 gass cultivars were included two festuloliurns (Duo and Hykor),
four orchardgass (Amba, Niva, Sparta and Tekapo), seven perennial ryegass (Aries,
Maverick Gold, Quartet, Tonga, Barfort, Mara and Calibra) and three endophyte free tall
fescues cultivars (Bronson, K5666V, and Barolex). Appendix Table 1A lists information on
soil type, soil pH, harvest schedule, and gass and clover species and cultivars.

Soil was analyzed at the MSU soil-testing laboratory; fertilizers and lime were
applied as recommended by soil test results. All tested soil parameters were at or above
optimal levels (average: P: 60 ppm, K: 139 ppm, and pH: 6.5). In each location, nitrogen
(N) was applied to the gass only plots following each cutting in the form of ammonium
sulfate at a rate of 45.4 kg ha’1 in four applications through the gowing season.

However, since nitrogen deficiency appeared during 2005 in some gass species due to
the poor clover stands in the binary mixture treatments, nitrogen was applied in a single
application of (45.4 kg ha’l) to the binary mixture treatments in all locations. Weeds
were controlled with an herbicide in the gass monoculture treatments when necessary.
Weather record:

For each experimental location, total monthly precipitation (mm), average daily
maximum and minimum air temperatures (°C) during the months of gowing season and

the total monthly snowfall (cm) during winter months were recorded.

16

Data Collection and Sampling Procedure:

Three to four gazing (cutting) events were obtained from each location with 30 to 35
days interval between each event depending on weather conditions. The following data were
collected:

Qualitative Measurements (Visual Estimates):

In all locations, qualitative measurements were recorded during 2004-2006.
However, the clover monoculture portion was excluded in 2006 due to poor stand and
weed contamination. Data on winter injury, percent living gound cover, and percent
clover in the binary mixtures were obtained by visual rating estimates. The rating
estimate was based on the average of the ratings generated by two trained people in each
plot. The ratings were based on a 1 to 5 scale (Brummer and Moore, 2000). The scale
system used for winter injury data collection is inversed from the one used either for
gound cover or percentage clover. For examples, score 1 in winter injury is considered
good (survive) and score 5 is poor (highly injured), while for gound cover and
percentage clover data collection, score 1 is considered poor (low cover) and score 5 is
good (highly covered).

Winter Injury: Winter injury ratings were taken during the early season gowth (usually
end of April to early May) for both monoculture and binary mixture treatments at all
locations. A general percentage score of plots affected with winter injury was placed on
the following scale: 1: less than 20% of the vegetation killed; 2: 20-40%; 3 = 40-60%; 4
= 60-80% killed; and 5 = geater than 80% of the vegetation was killed.

The cultivars that have low winter injury ranking over time are those that have

high cold resistance.

17

Vegetative ground cover: Ground cover was recorded in the spring when the gasses
were actively gowing— two weeks after winter injury ratings were taken— and again in
the fall before the dormancy period (at the end of October). Ground cover was visually
scored by assessing the amount of vegetation cover based on the following scale: 1 =
geater than 80% of the vegetation missing; 2 = 60-80%; 3 = 40-60%; 4 = 40 —20% and;
5 = less than 20% of the vegetation missing. The cultivars that have low gound cover
ranking over time are those that have less persistence.

Percentage clover cover: Clover stands were visually rated for the percentage of clover
versus gass in each plot of the binary mixture treatments. The ratings were conducted in
the spring (early May) and fall (at the end of October) according to the following scale:
1: less than 20% of the clover stand appears in the mixture; 2 = 20- 40%; 3 = 40-60%; 4
= 60-80%; and 5 = 80% or geater of the binary mixture was covered with the clover
stand. The clover cultivars that have low cover ranking over time are those that are less

compatible with gasses.

Quantitative Measurements:

Dry matter yield: Forage biomass of monoculture and binary mixture treatments were
collected from all locations. When the average plant height was 20 cm, samples were
hand clipped at 8 cm height within a 0.25 m2 quadrate in each plot and dried at 60 °C for

48 hr. Dry samples were then weighted for DM yield.

18

Statistical analyses: All the statistical computations were performed using SAS (SAS
Institute, 2000). Analysis of variance (ANOVA) was performed separately for binary
mixtures, gass monoculture and clover monoculture using PROC GLM. The data from
all years and locations were analyzed together. The interactions between years, locations,
and cultivars (treatments) were examined and when found sigrificant, mean separations
were conducted using Fischer’s LSD at p<0.05. Normality assumption was checked
using stem and leaf and normal probability plots in PROC UNIVARIATE. Homogeneity
of variance among years and locations was examined and found acceptable. Coefficient
of variation (CV) was listed to measure the precision of the experiment. Winter injury
was prevalent at LC in the spring of 2005 due to ice-sheeting in the lower portion of the
experimental area, which resulted in serious damage in some plots. The affected plots

were eliminated from the analyses and reported as missing data.

19

RESULTS AND DISCUSSION

1. Climatic Observations

Precipitation: In general, there was a high amount of precipitation recorded in 2004,
especially during the months of May and August at the KBS and UPES locations. At LC,
there was a higher amount of precipitation during the months of April and May and lower
during the months of June, July, August and September. However, in 2005, lower
precipitation was observed during most of the gowing season at three locations with
some higher amounts recorded in the month of July at KBS, August and September at LC
and July and September at the UPES (Appendix Table 2A).

Average maximum and minimum daily air temperatures: At both KBS and LC
locations, the maximum air temperature during 2004 was above the 30-year average only
in April and September and in UPES, the maximum air temperature of 2004 was below
the 30-year average throughout the gowing season with the exception of September
(Appendix Table 3A).

In 2005 gowing season, the average maximum air temperatures at KBS, LC and
UPES were above the 30-year average during the entire gowing season except for the
month of May (Appendix Table 3A).

The lack of precipitation along with above normal temperature in 2005 may have
affected the DM yield for all cutting events. Average minimum air temperature (°C) and
snowfall (cm) data for the months of January through April of 2003-2006 is listed in

Appendix Table 4A.

20

II. Persistence of Grass and Clover Cultivars Grown in Monoculture and Binary
Mixtures

Persistence results include data obtained from winter injury and gound cover for

each monoculture gass and clover cultivar and binary mixtures in all locations over

2004-2006.

Si grificant years X location X treatment interactions were present in winter
injury, spring and fall gound cover for gass-clover binary mixtures (Appendix Table
6A). In addition, sigrificant interactions were also present in gass monoculture
treatment (varieties) for winter injury, spring and fall gound cover (Appendix Table 7
A), and similarly for clover monoculture treatment (varieties) (Appendix Table 8 A). We
attribute these interactions primarily to weather conditions varying from locations to

location and fi'om year to year.

Grass-clover binary mixtures had less winter injury compared to monocultures of
gass and clover (Figl ). Differences in winter injury score within cultivars and among
locations were observed. Greater winter injury impact on gass cultivars occurred at the
UPES as it is farther north than KBS and LC (Appendix Table 4 A). In 2006, the gass
and clover species at LC were affected by ice-sheeting, resulting in some native gass

such as Kentucky blue gass (Poa pratensis) to dominate the plots.

Analyses of winter injury of the gass monoculture showed there were sigrificant
differences (p<0.05) among the monoculture gass cultivars at KBS in all years (Tablel).
Grass cultivars displayed severe winter injury at the UPES location in the spring of 2006.

Even winter hardy species like orchardgass and tall fescue exhibited sigrificant winter

21

injury (Tablel ). Nitrogen applied the previous fall may have accentuated the winter
injury. Adams and Twersky (1960) concluded that winter survival of coastal
berrnudagass (Cynodon dactylon) decreased with increasing levels of applied nitrogen

(N) fertilizer.

Cultivars that expressed severe winter injury (score 5) displayed less gound
cover in their plots. Ground cover data of gass monoculture, gass-clover binary
mixtures and clover monoculture are presented in Tables 3, 4 and 6, respectively.
Winter injury and gound cover results will be discussed in more detail for each

individual gass species.

Persistence of Grass Species

F estuloliums: There was no apparent winter injury with the two monoculture
festulolium cultivars (Duo and Hykor) at the KBS. There was sigrificant winter injury
in Duo in 2006 (Tablel). Duo resulted in significantly more winter injury (p<0.05) than

Hykor, at the UPES site (4.3 and 5 in 2005 and 2006, respectively).

Analysis of festulolium-clover binary mixtures winter injury showed that
sigrificant winter injury occurred at the UPES during 2004-2006 compared to the same
years at KBS and LC (Table 2). These data showed that persistence of Duo did not
improve when associated with clover in binary mixtures at the UPES, while at KBS and

LC, Duo showed improved winter survival when gown with clover in binary mixtures.

Analyses of gound cover of the monoculture festulolium cultivars (Duo and

Hykor) showed both had excellent gound cover ratings (score 5) at KBS and LC.

22

However, gound cover was different between Hykor and Duo at the UPES with Duo

resulting in sigrificantly (p<0.05) less gound cover than Hykor during 2005 (Table3).

In conclusion, the results of winter injury and gound cover from the two
festuloliums cultivars (Duo and Hykor) established in monoculture and binary mixtures
showed Duo was less persistent and more vulnerable to winter injury when gown in the
Upper Peninsula of Michigan. Duo has a ryegass backgound (a hybrid resulting from a
cross of meadow fescue and a tetraploid perennial ryegass (AMPAC seed company web
site). Ryegass parents generally reduce winter survival of festuloliums hybrids and their
derivatives (Casler et al., 2002), which helps explain the lower winter hardness of Duo.
The lack of the adaptation of Duo in the upper peninsula of Michigan limits its use in

pasture systems although it was preferred more by animals than Hykor (Chapter 2).

Orchardgrass: Winter injury data analyses showed that orchardgass monoculture
cultivars (Tekapo, Amba, Niva, and Sparta) had good winter survival at KBS and LC
(Average score 1.3) (Table 1), which was similar to results reported by Christie and
McElroy (1995). No sigrificant differences in winter injury appeared at KBS and LC
experimental locations, at the UPES, however, sigrificant differences in winter injury
between the four orchardgass cultivars occurred during 2004-2006. Tekapo and Sparta
demonstrated sigrificant winter injury (p<0.05) at UPES (scores 4 and 5 respectively)

when compared to the cultivars Amba and Neva.

Winter injury analyses of orchardgass-clover binary mixtures showed a
sigrificant difference in winter injury at KBS with one treatment of Amba-red clover

(VNS) in 2004 (Table 2). Higher winter injury was observed in Tekapo-clover binary

23

mixtures at LC during 2006. The highest winter injury occurred in the Tekapo-Star Fire
red clover binary mixture (score 3.7) (Table 2). Tekapo resulted in sigrificantly higher
winter injury when gown with all clover cultivars particularly with white clover K0pu II
and Ladino compared to all other orchardgass cultivars in 2006 at the UPES (Table2).
These results indicate that Tekapo had sigrificantly lower winter hardiness than all other

orchardgass cultivars even when gown with clover in binary mixtures.

Ground cover of the monoculture orchardgass cultivars showed geat vegetative
cover at both KBS and LC (average score 4) compared to the same cultivars at the UPES
in 2006 where scores ranged fi'om 1 to 3 (Table 3). Tekapo resulted in sigrificantly
lower gound cover (score of 1 out of 5) than all other orchard gass cultivars due to

winter injury.

Analysis of gound cover in orchardgass-clover binary mixtures showed
sigrificantly lower gound cover for Tekapo when gown with white clover cultivars
(K0pu II and Ladino) at UPES in 2006 (Table4). There were no sigrificant differences in
gound cover between orchardgass cultivars gown in binary mixtures at the KBS, LC

locations in 2004 and fall of 2005 at the UPES.

Orchardgass is quite winter hardy when compared to perennial ryegass (Christie
and McElroy, 1995). In this study, four orchard gass cultivars gown in monoculture
and binary mixtures exhibited good gound cover and persistence when gown at the
KBS and LC experimental stations. At the UPES location, however, there were

differences in persistence and gound cover among cultivars as a result of colder

24

temperatures. Tekapo resulted in sigrificantly lower persistence at the UPES site whether

gown in monoculture or binary mixtures.

Perennial ryegrass: Perennial ryegass cultivars were more susceptible to winter injury.
However, analysis of perennial ryegass monoculture showed there was no sigrificant
winter injury in the first two years of the study at KBS, but Maverick Gold had
sigiificantly higher winter injury in 2006 (Tablel). At LC, there were sigrificant
differences between perennial rye gass cultivars in all years. Calibra showed
sigrificantly higher persistence in each year at LC compared to other cultivars. Calibra
resulted in sigrificantly higher winter hardiness and better persistence at the UPES
location compared to other cultivars; however, all cultivars including Calibra were
completely killed in 2006. Aries, Maverick Gold, and Quartet were all completely killed
in 2005 with an injury rating of 5 while Calibra resulted in an injury rating of 3. The
remaining cultivars were not significantly different in winter injury. The cultivars, Mara,

Tonga, Calibra and Barfort had slightly less winter injury at LC and UPES in 2004.

Perennial ryegass-clover binary mixtures resulted in less winter injury compared
to the same cultivars gown in monoculture. Maverick Gold-red clover (VNS) binary
mixtures at KBS had sigrificantly geater winter injury than other entries (Table 2).
Similarly, data from the LC site showed sigrificantly higher winter injury (p<0.05) for
the binary mixture of Aries-kura (Endura). All binary mixtures of Maverick Gold-
clovers and Quartet-kura (Endura) resulted in significantly higher winter injury (p<0.05)
at LC (Table 2). At the UPES, the perennial ryegass-clover mixtures of Maverick Gold

had sigrificantly less persistence than all other treatments in 2004. Some treatments of

25

Calibra, Mara, and Tonga gown in binary mixtures resulted in sigrificantly less winter
injury compared to other binary mixtures in 2005, however, there was considerable

winter injury of those cultivars in 2006 (Table 2).

Ground cover analysis of the perennial ryegass cultivars showed better
persistence of cultivars established at the KBS and LC sites compared to UPES site
(Table 3). Aries had sigrificantly higher in gound cover than Maverick Gold (p<0.05) at
KBS in fall 2005 and spring 2006 (Table 3). At LC, lower gound cover observed for
Maverick Gold, Aries and Mara in spring of 2005 and Calibra and Quartet resulted in
sigrificantly higher gound cover in spring of 2006 compared to all other perennial
ryegass cultivars (Table 3). At the UPES, sigrificant lower gound cover was found in
Aries, Maverick Gold and Quartet during spring 2005 and sigrificantly lower for all

cultivars in 2006 (Table 3).

Ground cover analysis of perennial ryegass-clover binary mixtures presented in
Table 4 show high gound cover at KBS and LC in 2004-2005 even with less winter-
hardy cultivars. There was no sigrificant difference found for gound cover at KBS.
However, at LC and UPES, sigrificant differences were found between cultivars in 2005.
Analysis of the data from the UPES site showed good gound cover ratings in 2004,
however, there were low gound cover ratings for all binary mixtures of perennial
ryegass cultivars in 2005 (Table 4). In spring, 2005, all perennial-clover binary mixtures
were sigrificantly lower in gound cover compared to binary mixtures of Calibra (Table

4). By fall 2005, all cultivars recovered from winter injury and were higher in gound

26

cover. However, in the spring of 2006, all cultivars were again lower in gound cover

with ratings of less than 50 % of the fall, 2005 ratings.

In summary, there was geater persistence when perennial ryegass cultivars were
gown in binary mixtures compared to the same cultivars gown in monoculture.
Perennial ryegass cultivars such as Maverick Gold, Aries, and Quartet had sigrificant
higher winter injury at LC and UPES, where colder winter temperatures prevail

compared to the KBS location.

Tall fescue: Winter injury analyses of the three tall fescue monoculture cultivars
(Bronson, K5666V and Barolex) showed Bronson to have sigrificantly less winter injury
than Barolex at LC in 2005. No sigrificant differences were found between tall fescue
cultivars at LC in 2006. However, in 2004, Bronson was sigrificantly lower winter
injury than K5666V and Barolex (Table 1). There was more winter injury at the UPES
site for all cultivars in 2006 with scores ranging from 3 for Bronson and 5 for K5666V
and Barolex. Bronson resulted in sigrificantly less winter injury than K5666V and
Barolex at the UPES in 2006 (Table 1). These data show Bronson has significantly
higher winter hardiness than the other cultivars in this study. Bronson is well adapted to

all latitudes in Michigan.

Winter injury analyses of tall fescue-clover binary mixtures from at locations
showed little winter injury (Table 2). However, K5666V resulted in sigrificantly higher

winter injury than Bronson when gown with 3 of the 5 clover species and cultivars in

2005 at LC and UPES.

27

Ground cover of all tall fescue monoculture showed excellent cover across all
locations (Table 3). However, at LC, Barolex was sigtificantly higher in gound cover
than K5666V and Bronson in spring 2005. K5666V was sigrificantly lower in gound
cover compared to Bronson at UPES in 2005 while both K5666V and Barolex were
sigrificantly lower in gound cover than Bronson in the spring of 2006 at the UPES

(Table 3).

Ground cover analysis of tall fescue-clover binary mixtures is presented in Table
4. There was higher gound cover for all years and locations (score 4 to 5) with binary
mixtures compared to monoculture of tall fescue. Bronson tall fescue gown in binary
mixtures of Ladino white clover and VNS red clover was sigrificantly higher in gound
cover in 2005 at the UPES compared to other binary mixtures and tall fescue cultivars.
There were no differences in gound cover between cultivars and species of clovers in
any other treatments and years. These data indicate better gound cover when tall fescue

is gown with clovers compared to tall fescue gown in a monoculture.

In conclusion, the endophyte free tall fescue cultivars used in this study
demonstrated an excellent persistence whether gown in monoculture or binary mixtures
at KBS, LC for all years and 2004-2005 at UPES. Binary mixtures of tall fescue —clover
showed geater winter hardiness and gound cover at all locations, which is likely due to
better distribution of available nitrogen throughout the gowing season for tall fescue

gowing in association with clovers.

28

Persistence of Clover Species

Kura clover had sigrificantly less winter injury than some red and white clovers
cultivars at KBS. No data were recorded at LC during 2005-2006 due to poor stands with
the exception of kura clover (Endura), which did not have winter injury (Table 5).
However, there was little winter injury in all cultivars at LC during 2004. Star Fire
resulted in sigrificantly higher winter injury in 2004 compared to other cultivars. At
UPES, kura clover had the geatest winter injury (score 3.8) among all clover cultivars
(Table 5). However, this may be an anomaly as kura clover has been shown to have a
high winter hardness. It is possible these results were attributed to poor establishment.
Overall, there was less winter injury in the clover monocultures compared to the

perennial gass monoculture.

Ground cover of all clover cultivars was high (scored 3.5 to 4) at KBS in all years
Table 6). There were sigrificant differences in gound cover between species or cultivars
at KBS. At LC site, there were sigrificant differences between treatment means in
gound cover in fall of 2004. The gound cover of kura clover at LC in the spring of
2005 resulted in a high score (3.2) (Table 6). In contrast, kura clover had low gound
cover in fall of 2004 at LC, which is likely related to an initial poor stand of kura clover
at seeding time due to slower establishment (Cuomo et al., 2003). At the UPES, data
analyses showed sigrificantly higher gound cover scores for all white clover cultivars
(K0pu 11, Alice, Jumbo and Ladino), which was likely due to excellent stolon production
resulting in new plants forming. However, diseases such as root rots eliminated red
clover stands (Taylor and Smith, 1995), which helped explain the low gound cover

scored at the UPES (scored of 1.8) (Table 6) in spite of their persistence to cold winter.

29

At LC, higher gound cover for all the eight cultivars was found during 2004. Red and
white clovers did not persist and resulted in no gound cover in the last two years (2005-

2006)

III. Grass-Clover Composition in Binary Mixtures

Sigrificant year X location X treatment (binary mixtures) interactions were
present for spring and fall clover content (Appendix Table 6A). These interactions
resulted primarily from changes in mixture compositions that occurred from year to year
due to low persistence of some clovers. Statistical analysis was preformed for the entire
67 binary mixture treatments in this study. However, results were reported separately by
gass species. Results presented in Tables 7, 8, 9, and 10 are the mean values of
percentage clover gown with festuloliums, orchardgass, perennial ryegass and tall

fescue respectively.

Clover Composition in F estuloliutn -Clover Binary Mixtures: There was a sigrificant
difference (p<0.05) in clover composition between the clover cultivars gown with Duo
(Table 7). The clover species and cultivars gown in association with festulolium
cultivars resulted in different responses from year to year and location to location. In the
fall of 2004 (three years after establishment), kura clover cultivar (Endura) composition
surpassed 80% (scored 5) when gown with Duo at KBS. However, kura clover
composition gowing with festulolium (Duo) was sigiificantly less at both LC and UPES
locations (scored 2.4 and 1.9 respectively) (Table 7). Analysis of the data showed that
red clover cultivars (Star Fire, VNS) at KBS had a high clover composition (scored 4 to

5) when associated with the two festulolium cultivars during the first three years of

30

production followed by a progessive decline in their composition (scored 1 to 1.7)

(Table 7).

The LC site showed a high clover composition in the fall of 2005, however, much
of this was from volunteer white clover, which filled in after the red clover plants died.
As mentioned previously, red clover is susceptible to root rot diseases, which cause a
decline in their stands (Taylor and Smith, 1995). There was low clover composition of
white clover cultivars (Jumbo and Ladino) in all locations during spring 2005 and spring
2006 when associated with Hykor (scored 1 to 1.7) (Table 7). Hykor was more dominant

in the binary mixture, which contributed to the lower clover botanical composition.

In conclusion, Endura kura clover botanical composition increased each year
when gown with festulolium (Duo) binary mixtures at the KBS location. However, this
was not the case at LC or UPES locations where kura clover stands were not as
competitive as kura established at KBS. A possible reason may be related to the poor
establishment at LC and UPES, which resulted in lower stands over time. It may also be
attributed to the highly competitive festuloliums, which gow more vigorously at northern

locations.

Clover Composition in Orchardgass-Clover Binary Mixtures: There was a sigrificant
difference (p<0.05) in clover botanical composition between orchardgass-clover binary
mixture treatments (Table 8). Previous research has suggested that orchardgass is well

suited for mixtures with one or more legume species (Hoveland, 1992). Orchardgass is

also characterized by the extensive tillering that occurs throughout the gowing season,

31

enabling the species to have quick re-gowth following harvest or gazing. This vigorous

gowth makes this species highly competitive in binary mixtures (Casler, 1988).

Red and white clover cultivars in binary mixtures with the orchardgass cultivars
were lower in composition during the final two years of the study at KBS and UPES
(scored 1 to 1.7) (Table 8). This may be due to less persistence in red clover caused by
root rots or winter injury in white clover. At LC, however, there was moderate clover
composition in the mixtures of red (VNS) and white clover (Ladino) stands when gown
with Niva and red clover (Star Fire) when gowing with Tekapo (Table 8). In contrast,
kura clover (Endura) resulted in sigrificantly higher composition at the KBS location
when gown with Tekapo, which persisted throughout all years in this study (scored 4.3)
(Table 8). These results are supported by the observations of Hoveland (1992) and
Casler (1988), which concluded that orchardgass is more competitive in binary mixtures
with clovers. The quick re-gowth following harvest enables orchardgass cultivars to be
more dominant in the binary mixtures. In addition, with adequate moisture, kura clover is
considered an excellent choice for Southern Michigan with orchardgass in binary
mixtures. However, slow establishment of kura clover made it less competitive with
orchardgass at LC and UPES. Perhaps if kura was seeded by itself and allowed to

establish first followed by seeding the perennial gass, it would result in good stands.

Clover Composition in Perennial Ryegrass-Clover Binary Mixtures: Perennial
ryegass- clover binary mixtures were sigrificantly different (p<0.05) in clover
composition between the seven perennial ryegass cultivars used in this study at KBS in

the fall 2005 and spring 2006 (Table 9). Sigrificant differences in the clover composition

32

of perennial ryegass- clover was found in LC, spring and fall, of 2004 at UPES. There
were sigrificant differences in clover composition between perennial-clover binary
mixtures for all years. At KBS, kura clover (Endura) had the geatest clover composition
among perennial ryegass cultivars over all years (average score 4). These results were

different at the other locations.

At LC, the clover composition during spring 2005 of white clover (Kopu II and
Ladino) was higher when gown with Tonga. Red clover (Star Fire) composition was
geater than other clovers when gown with Quartet. Clover composition of white clover
(Ladino) was geater than other clovers when gown with Barfort, Tonga, Maverick Gold
and Quartet at the UPES site. Alice and Kopu 11 white clover clover composition was
higher when gown with Mara and Maverick Gold perennial ryegass respectively (Table
9). All red clovers gowing with perennial ryegass cultivars at KBS and UPES persisted
well until the fall of 2005 but nearly disappeared by the spring of 2006 with average
percentage cover rating of 1.5 (Table 9). This was likely due to root rots, which red
clovers are susceptible to. On the other hand, the increase of clover composition in red
clover observed in LC binary mixtures was likely related to the appearance of volunteer
native legume from the soil seed bank. White clover cultivars persisted similarly to red
clovers at KBS (Ladino, Kopu 11, Alice and Jumbo) with good clover cover until the

spring of 2006.

In conclusion, kura clover’s persistence was excellent with all perennial ryegass
cultivars at the KBS location and though to a lesser extent at the LC site. A study

conducted by Cuomo et al. (2003) in Minnesota showed that kura clover was able to

33

compete, persist, and spread under intermittent gazing and has the potential to be an
important and persistent component of cool-season gass pasture in north-central USA.
However, white clovers, in particular, Kopu II and Ladino were higher in clover
composition (scored 3 to 4) at the UPES site compared to red clover and kura clover
cultivars. The initial stand of kura clover at the UPES site was not as good as the other
two locations, which may have contributed to the lower botanical composition with

perennial gasses at this site.

Clover Composition in T all F escue-Clover Binary Mixtures: Tall fescue cultivars
(Barolex, K5666V and Bronson) were more competitive with all clover species at all
locations (average scores from 1.3 to 3) (Table 10). Results showed kura clover
composition was sigrificantly geater than all other clovers when gown in binary

mixtures with K5666V cultivar at the KBS location (average score 3.9) (Table 10).

Tall fescue was more aggessive when gown with all clover species compared to
some cultivars of festulolium, orchardgass, and perennial ryegass. Tall fescue resulted
in good stand persistence at all locations. This species should be considered when
developing new pastures. However, according to this study, white and red clover
cultivars may need to be reseeded more often when associated with tall fescue with the
exception of kura clover, which showed better compatibility than other species when

gown with tall fescue (K5666V) at KBS (Table 10).

In conclusion, binary mixtures behaved differently depending on the gass and
clover gowing in the mixtures. There was variation in clover composition among gass

cultivars and years. For example, some gass cultivars that had low clover composition

34

in LC in the first two years showed excellent clover composition in the fall of 2005 such
as festuloliums (Duo)-red clover (Star Fire) (Table 7), orchardgass (N iva)-red clover
(VN S) (Table 8), and most perennial ryegass cultivars (Table 9). This was likely due to
winter injury in gass cultivars resulting in little or no gass competition and higher
composition of clover in these binary mixtures.

IV. Dry Matter Yield of Grass And Clover Cultivars Grown in Monoculture And
Binary Mixtures.

Seasonal differences in DM production of species and cultivars occurred across
gazing events and locations. There was also an overall decline in DM production in time
across species. Sigrificant year X location X cut X treatment (binary mixtures)
interactions were observed in DM yield (Appendix Table 9 A). In addition, sigrificant
year X location X cut X treatment (varieties) interactions in DM yield was found in
clover monocultures (Appendix Table 11 A). These interactions are primarily due to
changes in DM yield production resulting from different weather conditions, locations
and variation among cultivars. DM yield (tons acre") analyses were reported for each
individual location, year, and cutting event. However, there was no interaction between
gass monoculture treatments (Appendix Table 10 A). These data are presented by
location, where Tables 11, 12 and 13 present the mean values of the gass monoculture
yield at KBS, LC and UPES respectively; Tables 14, 15 and 16 give the mean yield
values of the gass-clover binary mixtures at KBS, LC and UPES respectively and Tables
17, 18, and 19 provide the mean yield values of clover monoculture at KBS, LC and

UPES locations respectively.

35

Dry Matter Yield of Grass Monocultures

Yield (tons acre ") analyses of gass monocultures are presented in Tables 11,12 ,
and 13. Cultivar and species varied in DM yield. DM yield for each individual gass

type within a cutting event are presented in details as follows:

Festulolium: The monoculture Duo and Hykor were sigrificantly different in DM yield.
Hykor had sigrificantly higher (p<0.05) DM yield than Duo in four of eight cutting
events at KBS during 2004 and 2005 gowing season (Table 11). LC dry matter yield
analysis showed Hykor was sigrificantly higher (p<0.05) than Duo in three of six cutting
events during 2004-2005 (Table 12). Hykor was also sigrificantly higher at two of eight
gazing events during 2004-2005 at UPES (Table 13). Total two year DM yield of Hykor
was sigrificantly higher than Duo at all locations. Total DM yield production of Duo
festulolium at the UPES site was geater than either KBS or LC (Tables. 11, 12 and 13),

which indicates that Duo performs better in a cooler climate such as Northern Michigan.

Orchardgass: There was a sigrificant difference in DM yield between monoculture
orchardgass cultivars in one of eight cutting events at KBS (Table 11). There was a
sigrificant difference in DM yield in two cutting events between orchardgass cultivars at
LC and UPES (Table 12 and 13 respectively). Total two-year DM yield was numerically
geater (tons acre ") in each monoculture orchardgass cultivar at UPES compared to

KBS and LC.

Perennial ryegrass: DM yield ranged from 2.37 tons acre 4 for Maverick Gold to 5.92

tons acre ‘1 for Mara at the UPES location (Table 13). Lower DM yield resulted from

36

Maverick Gold since it was severely affected by winter injury at the UPES location in
2005 (Table 13). Some of the Maverick Gold stands were recovered, but did not
contribute sigrificantly to yield. Statistically, there was a sigrificant difference in DM
yield between monoculture perennial ryegass cultivars in three of eight cutting events at
KBS (Table 11). At the LC site, a sigrificant difference in DM yield occurred in two
cutting events (Table 12). There was a sigrificant difference in DM yield in six of seven
cutting events at UPES (Table 13). DM yield of perennial ryegass was excluded from
the fourth cutting event (October) in 2004 at UPES due to lack of moisture that occurred

in perennial ryegass cultivars at the UPES (Appendix Table 2A).

Tall fescue: Monoculture tall fescue resulted in the highest DM yield among all gass
species. DM yield ranged from 3.95 with K5666V at the KBS site (Table 11) to 6.88 tons
acre '1 with Barolex at the UPES (Table 13). High DM yield productivity (average of
1.41 tons acre '1) for all gass cultivars was obtained at the second cutting event at UPES
in 2004 (Table 13). There was a sigrificant difference in DM yield between monoculture
tall fescue cultivars in four of eight cutting events at KBS (Table 11). At LC, a
sigrificant difference in DM yield occurred in one of six cutting events (Table 12). At the
UPES, there was a sigrificant difference in DM yield in two cutting events during 2004-

2005 (Table 13).
Dry Matter Yield of Grass-Clover Binary Mixtures

There were sigtificant differences in DM yield of festulolium Hykor- clover
binary mixtures compared with Duo-clover mixtures and results varied between

locations. For example, Hykor-Ladino binary mixtures were sigtificantly higher in DM

37

yield compared to Duo-Ladino mixtures in four of eight cutting events at KBS (Table
14), while there was no significant difference between these mixtures at the LC site
(Table 15). At the UPES there was a sigrificant difference in two cutting events (Table
15). In addition, Hykor gown in binary mixture with white clover (Jumbo) and red
clover (VNS) resulted in a sigrificantly higher DM yield at KBS site (Table 14). There
were sigrificantly higher DM yield in Hykor binary mixtures at KBS (Table 14) and LC
sites (Table 15) compared to Duo-binary mixtures. However, Duo- Star Fire red clover
binary mixture was sigrificantly higher in DM yield than Hykor-red clover (VNS) and

white clover (Ladino) at the UPES site (Table 16).

Orchardgass- clover binary mixtures were higher in DM yield at KBS (Tables
14) and LC (Tables 15) compared to the UPES site (Tables 16). Sparta gown with VNS
red clover was significantly higher yielding than other orchardgass-clover binary
mixtures at the KBS location (Tablel4). Tekapo gown with VNS red clover had a
sigrificantly higher DM yield at the LC location (Table 15). Orchard gass (Sparta)-
jumbo white clover binary mixtures were the highest in DM yield. Tekapo associated
with Endura kura clover was the lowest yielding among the orchardgsas-clover binary
mixtures at the UPES location (Table 16). Even though orchardgass is considered an
excellent cool season gass for binary mixtures due to its fast re-gowth and tillering
ability (Hoveland, 1992), there are some factors, which might limit its success. For
instance, winter injury of some orchardgass-clover binary mixtures such as Tekpao-kura
(Endura), and Tekapo gowing with white clover (Kopu II and Ladino) were lower in
DM yield at the UPES site (Table 16). Since orchardgass is an aggessive gass, it may

crowd out associated clovers gowing in binary mixtures, thus causing reduction in clover

38

content with resultant nitrogen deficiency from lack of fixed nitrogen from lower clover

content.

Perennial ryegass- clover binary mixtures were highly in DM yield compared to
monoculture treatments of perennial ryegass at all locations. In all locations, the total
DM yield ranged from 2.05 tons acre'1 in Maverick Gold- kura (Endura) at UPES (Table

16) to 5.36 tons acre" in Calibra-red clover (VNS) at LC (Table 15).

The UPES site resulted in lower DM yield of perennial ryegass-clover binary
mixtures. Winter injury of non hardy perennial ryegass cultivars was likely the reason
for the lower yield at this location. The low yield of the Maverick Gold-Endura kura
clover binary mixture in the UPES was likely related to a combination of winter injury to
the Maverick Gold perennial ryegass and poor establishment of Endura kura clover.
Likewise, at the UPES the higher yield of Tonga-Star Fire red clover was likely due to
geater winter hardiness of Tonga perennial ryegass and more vigorous gowth of Star
Fire red clover (Table 16). Mara perennial ryegass gown with VNS red clover had
sigrificantly higher DM yield than Aries-Endura kura clover, Quartet-Endura kura

clover, and Quartet-Kopu II at the KBS location.

Tall fescue- clover binary mixtures ranged from 3.90 to 6.29 tons acre"1 in DM
yield for K5666V- Koppu II and Bronson-Endura kura clover treatments, respectively at
the KBS location (Table 14). DM yield of Barolex-Start was sigrificantly higher than
Barolex-Alice but not different than other Barolex-clover binary mixtures at KBS (Table

14).

39

In general, KBS, tall fescue cultivars gown in binary mixtures with clovers
performed similarly to orchardgass- clover cultivars in yield but were higher than both
perennial ryegass and festulolium- clover mixtures. For LC, DM yield of tall fescue-
clover binary mixtures ranged from 3.96 to 5.33 tons acre'1 for K5666V-Star Fire red
clover and Bronson-VNS red clover mixes (Table 15) and for the UPES, DM yield of tall
fescue- clover binary mixtures ranged from 2.81 to 4. 90 tons acre'1 with Bronson-Endura
kura clover and Barolex-Start red clover mixtures (Table 16).

The average total DM yield of tall fescue- clover binary mixtures was geatest at
the KBS location followed by LC and UPES locations. Tall fescue-clover binary
mixtures appear to be a good choice for all the locations in this study. These data
indicate good yield stability for tall fescue-clover binary mixtures in Michigan
However, as mentioned previously, it is important to select the proper cultivars of both

tall fescue and clovers as each performed differently depending on location.

Dry Matter Yield of Clover Monocultures

Dry matter yield of clover monoculture at KBS is presented in Table 17, at LC in
Table18 and at UPES in Table 19. There was a sigrificant difference (p<0.05) in DM
yield observed among clover species and cultivars within cutting events at KBS, UPES
2004-2005 and LC 2004. Endura kura clover resulted in sigrificantly higher total DM
yield than red clover (VNS) and white clover (Kopu II, Alice and Ladino) KBS (Table
17). At LC, DM yield ranged from 1.95 to 3.24 tons acre'l at LC with Endura kura clover
being also the highest in DM yield (Table 18). At UPES, no siglificant difference was

found in total two year DM yield between clover species and cultivars (Table 19).

40

These data clearly showed Endura kura clover to be well adapted to southern
Michigan where it had the highest yield of all clover cultivars in this study. Some
cultivars of clover such as red clover (VNS) and white clover (Kopu II) had higher total
one-year production at LC (Table 18) than the same cultivars produced in two years at

the UPES site (Table 19).
CONCLUSIONS

Grass-clover binary mixtures resulted in increased DM yield and had higher
resistance to winter injury compared to gasses and clovers in monoculture. However,

this study demonstrated the importance of proper species and cultivar selection.

Grass and clover species in binary mixtures showed sigrificant differences
between species and cultivars. In the early part of this study, there was a balance in
botanical composition between gass and clover content. However, this balance
disappeared after two seasons with either the gass or clover soon becoming more or less
dominant. Vigorous gass species such as tall fescue and orchardgass tended to be more
dominating in the mixtures resulting in decreased clover content. In contrast, some
clovers persisted better than gasses. Environmental conditions are a factor that changes
the gass-clover balance (Camlin, 1981). Thus, winter injury leading to a loss in
vegetative gound cover of some gasses, especially perennial ryegass provided an
opportunity for the associated clover to dominate the mixtures. When clover disappeared
from the binary mixtures, there was limited nitrogen (N) fixation, which resulted in
nitrogen deficiency and subsequent loss of DM yield of the associated gass species.

Therefore, when the clover content becomes low in the mixtures, nitrogen application is

41

necessary to maintain high DM yield. This study demonstrated the importance of
compatible clovers and gasses species in binary mixtures. Non-winter hardy cultivars of
gass such as perennial ryegass (Maverick Gold and Aries) used in this study showed

geater persistence when they were gown in binary mixtures due to reduced winter

injury.

Nitrogen fixation by clovers has a positive effect upon winter survival of
perennial gasses. In binary mixtures, available nitrogen from N fixation of clover is
provided to gasses in the critical spring and summer months in a steady supply.
However, in the fall months, N fixation slows in clovers with subsequent less N available
to gasses resulted in hardening of gasses and better winter hardness. Insulation from
snow cover also increases gass survival in the winter (Leep et al., 2001). Growing gass

with clover in binary mixtures can help to intercept snow resulting in less winter injury.

Binary mixtures of clover and gass resulted in a higher total DM yield and more
uniform dry matter distribution through the season. These results are similar to those

observed by Charnblee and Collins (1988).

Tall fescue resulted in excellent yield stability and broad adaptation to different
environments compared to other gass species in this study, while perennial ryegass
yield was found to be unstable. However, there were siglificant differences in the

performance of perennial ryegasses cultivars due to differences winter hardiness.

Kura clover (Endura) provided the highest DM yield and exceptional persistence

under gazing compared to other clover cultivars in this study, particularly at 42° N

42

latitude zone. Thus, the influence of latitude on the performance of clover was observed
in this study. Kura clover (Endura) demonstrated the highest persistence, which is
similar to results found by Woodman et al., (1992). However, the establishment of kura
is somewhat difficult, as it gows more slowly during the establishment year than other
clover species (Speer and Allinson, 1985). For northem latitudes, kura clover dry matter
yield was low due to the poor establishment, which indicates that improved establishment
of this species need more attention for it to be successful in binary mixtures. Interseeding
cool-season gass species into established kura clover provides an opportunity for kura to

be well established prior to gass establishment.

43

LITERATURE CITED

Allinson, D.W., G.S. Speer, R.W. Taylor, and K. Guillard. 1985. Nutritional
characteristics of kura clover (T rifolium ambiguum Bieb.) compared with other
forage legumes. J. Agic. Sci. 104:227—229.

Adams, W.E., and M. Twersky. 1960. Effect of soil fertility on winter killing of Coastal
bermudagass. Agon j. 52:325-326.

Balasko, J .A. and C.J.Nelson. 2003. Grasses for northem areas. In Barnes et al. (ed.).
Forages volume I: An introduction to gassland agiculture, 6‘h ed. Iowa State
University Press. Ames. IA.

Balasko, J. A., G.W. Evers, and R.W. Duel]. 1995. Bluegasses, Ryegasses, and
Bentgasses. In Barnes et al. (ed.). Forages volume I: An introduction to gassland
agiculture, 5th ed. Iowa State University Press. Ames. IA.

Bélanger, G., P. Rochette, Y. Castonguay, A. Bootsma, D. Mongain, and D.A.J. Ryan.
2002. Climate change and winter survival of perennial forage crops in eastern
Canada. Ag. J. 94: 1120-1130.

Baker, M.J., W. M.Williams. 1987. White clover. Wallingford CAB International.

Bryant, W.G.l974. Caucasian clover (T rifolium ambiguim. Bieb): A review. J. Aust.
Inst. Agic. Sci. 40: 11-19.

Brummer, EC, and K.J.Moor. 2000. Persistence of perennial cool-season gass and
legume cultivars under continuous gazing by beef cattle. Agon.J. 92:466-471.

Christie, BR, and AR. McElroy.1995. Orchardgass. In Barnes et al. (ed.). Forages
volume I: An introduction to gassland agiculture, 5th ed. Iowa State University
Press. Ames, IA.

Chamblee, D.S., and M. Collins. 1988. Relationships with other species in a mixture.
P.439-461. In A.A. Hanson et al.(ed.). Alfalfa and alfalfa improvement.
Agonomy Monog. 29., ASA, Madison, WI.

Cuomo, G.J., P.R. Peterson, A. Singh, D.G. Johnson, W.A. Head, jr, and M.H Reese.
2003. Persistence and spread of kura clover in cool-season gass pasture. Agon.J
95:1591-1594.

Collins, M. and DB. Hannaway. 2003. Forage-related animal disorders. In Barnes et al.
(ed.). Forages volume I: An introduction to gassland agiculture, 6th ed. Iowa
State University Press. Ames. IA.

44

Casler, M.D., P. R. Peterson, L. D. Hoffman, N. J. Ehlke, E. C. Brummer, J. L. Hansen,
M. J. Mlynarek, M. R. Sulc, . J. C. Henning, D. J. Undersander, P. G. Pitts, P. C.
Bilkey, and C. A. Rose-Fricker. 2002. Natural Selection for Survival Improves
Freezing Tolerance, Forage Yield, and Persistence of festuloliums. Crop Sci.
42:1421-1426.

Casler, M.D.1988. Performance of orchardgass, smooth bromegass, and ryegass in
binary mixtures with alfalfa. Agon. J. 80:509-514.

Casler, M.D., and RP. Walgenbach.l990. Ground cover potential of forage gass
cultivars mixed with alfalfa at divergent locations. Crop. Sci. 30: 825-831.

Camlin, MS, and RH. Stewart.1976. The assessment of persistence its application to the
evaluation of early perennial ryegass cultivars. J Br. Grassl.Soc.31:1-6.

Camlin, MS. 1981. Competitive effects between ten cultivars of perennial ryegass and
three cultivars of white clover gowing in association. Grass Forage Sci.36:169-
178.

Davies, A. 1988. The regowth of gass swards. In M.B Jones and A Lazenby (ed.) The
Grass Crop. London: Chapman and Hall, 85-127.

Donald, C.M.196l. Competition for light in crops and pastures. Symp.Soc. Exp.Biol.
15:282-313.

Dvorak, J ., and D. B. Fowler. 197 8. Cold hardiness potential of triticale and tetraploid
rye.Crop Science. 18 (3):477-478.

Drolsom, RN, and D. Smith.l976. Adapting species for forage mixtures. P.223-232. In
R.I. Papendick, et al. (ed.) Multiple cropping. ASA Spec. Publ.27. ASA, CSSA,
and SSSA, Madison, WI.

Elissa, M.N., M.D. Casler, and RR Hill, Jr. 1995. Selection for persistence of tetraploid
ryegasses and festulolium in mixtures with perennial legumes.

Funk, C.R., R.E. Engle, W.K. Dickson, and RH. Hurley. 1981. Registration of Rebel. tall
fescue. Crop Sci. 21:632.

Gilliland, TJ. 1996. Assessment of perennial ryegass variety compatibility with white
clover under gazing. Plant var. Seeds 9:65-75.

Hoveland, GS. 1992. Grazing systems for humid regions. J .prod. Agic. 5:23-27.
Henning, J .C., and Wheaton. 1993. White, Ladino and Sweet Clover. University of

Missouri extension. Available on line at:
http://muextensionmissouri.edu/explorg/ggguides/crops/go4639.htm.

45

Jensen, K.B., K.H. Asay, and BL. Waldron. 2001. Dry matter production of
orchardgass and perennial ryegass at five irrigation levels. Crop Sci. 41: 47 9-
487.

Ju' H, J ., N. S. Hill, T. Abbott and K. T. Ingarn. 2006. Temperature Influences on
Endophyte Growth in tall fescue. Crop Sci. 46:404-412.

Jung, G.A., J .P. Van Wijk, W.F.Hunt, and EB. Watson.l996. Ryegass. P. 605-41. In
L.e.Moser, et al.(ed.) Cool- Season Forage gasses ASA Monogaph 34. ASA,
Madison, WI.

Jones, T.A., I.T.Carlson, and D.R.Buxton. 1988. Reed Canarygass binary mixtures with
alfalfa and birdsfoot trefoil in comparison to monocultures. Agon. J 80:49-55.

Kim, B.W. 1996. Kura clover development and performance of kura clover/gass
mixtures. Ph.D diss.Univ. of Wisconsin, Madison (Diss Abster. 97-08277).

Leep, RH. 2004. Perennial Ryegass and festulolium potential in Michigan. Michigan
hay and gazing conference. East Lansing, MI.

Leep, R.H., J.A. Andersen, and P. J eranyarna. 2001. Fall Dorrnancy and snow depth
effects on winterkill of alfalfa. Agon. J. 93:1142-1148.

Lorenzetti, F., B.F. Tyler, J .P. Cooper, and EL. Breese. 1971. Cold tolerance and winter
hardiness in Lolium perenne. 1. Development of screening techniques for cold
tolerance and survey of geogaphical variation. J Agic Sci (Cambridge) 76: 199—
209.

Murphy, J .S., and DD. Briske. 1992. Regulation of tillering by apical dominance:
Chronology, interpretive value and current perspectives J. Range Managez452419-
429.

Mourifio‘, F., K. A. Albrecht, D. M. Schaefer, and P. Berzaghi. 2003. Steer Performance
on Kura Clover—Grass and Red Clover—Grass Mixed Pastures. Agon. J. 95:652-
659.

McKenzie, J .S., R. Paquin, and SH. Duke. 1988. Cold and heat tolerance. P.259-302. In
A.A. Hanson et al. (ed.) Alfalfa and alfalfa imporvrnent. Agon. Monog. 29.
ASA,CSSA, and SSSA, Madison, WI.

Nelson, C.J., and R.L.McGraw. 2003. Legumes for Northern Areas. In Barnes et al.
(ed.). Forages volume I: An introduction to gassland agiculture, 6‘“ ed. Iowa
State University Press. Ames. IA.

Nelson, C. J ., and J .J . Volenec. 1995. Environmental and physiological aspects of forage

management. In Barnes et al. (ed.). Forages volume I: An introduction to
gassland agiculture, 5th ed. Iowa State University Press. Ames. IA.

46

Pysher, D., and S Fales.1992. Production and quality of selected coo-season gasses
under intensive rotational gazing by dairy cattle. In proc. Am. Forage and gassl.
Conf., Grand Rapids, Mich 32-36.

Pederson, GA. 1995. White clover and other perennial clovers. In Barnes et al. (ed.).
Forages volume I: An introduction to gassland agiculture, 5th ed. Iowa State
University Press. Ames. IA.

Pederson, G.A., and GE Brink. 1988. Compatibility of five white clover and five tall
fescue cultivars gowing association. Agon. J. 80:755-758.

Peterson, P.R., C.C. Sheaffer, R.M. Jordan, and G]. Christians.1994. Response of kura
clover to sheep gazing and clipping: 1. Yield and forage quality. Agon. J 86:655-
660.

Peterson, P.R., C.C. Sheaffer, R.M. Jordan, and G]. Christians. 1994. Responses of Kura
clover to sheep gazing and clipping: II. Below-gound morphology, persistence,
and total nonstructural carbohydrates. Agon. J. 86:660—667.

Penn State University Agonomy guid 2005-2006. Part 1, section 8, Forages. Aavailable
at URL. httpflgguideggonomv.psu.edu/cm/sec8/sec810b.cfm.

Rohweder, D.A., and K.A.Albrecht. 1995. Permanent pasture Ecosystems. In Barnes et
al. (ed.). Forages, volume 11: The Science of Grassland Agiculture, 5th ed. Iowa
State University. Ames, IA.

Smith, D., A.V.A.Jacques, and J .A.Balasko.l973. Persistence of several temperate
gasses gown with alfalfa and harvested two, three, and four times annually at
two stubble height. Crop Sci. 13:553-556.

Smith, D. R.J.Bula, and RP. Walgenbach. 1986. Forage management. Kendall/Hunt,
Dubuque, IA.

Sleugh, B., K.J.Moore, J .R.George, and E.C.Brummer. 2000. Binary legume-gass
mixtures improved forage yield, quality, and seasonal distribution. Agon. J.
92:24-29.

Sheaffer, CG, and G.C. Marten.l991. Kura clover forage yield, forage quality, and stand
dynamics. Can. J. Plant Sci. 71:1169-1172.

Sheaffer, C.C., G.C. Marten, R.M. Jordan., and EA. Ristau. 1992. Forage potential of
kura clover and birdsfoot trefoil when gazed by sheep. Agon. J. 84: 176-180.

Seguin, P., C.C. Sheaffer, N.J. Ehlke, M.P. Russelle, and PH. Graham. 2001. Nitrogen

fertilization and rhizobial inoculation effects on Kura clover gowth. Agon. J.
93:1262—1269.

47

Schmidt, DR, and G.H. Tenpas. 1965. Seasonal response of gasses fertilized with
nitrogen compared to a legume-gass mixtures. Agon.J. 571428-431.

Speer, GS, and D.W. Allinson. 1985. Kura clover (T rifolium ambiguum): Legume for
forage and soil conservation. Econ. Bot. 39:165-76 .nb.

Shimada, T., S. Shibata & I. Masuyama. 1993. Meteorological factors responsible for
winter injury of orchardgass. J Japan Grasl Sci 39: 77—85.

Sanderson, M.A., and G.F Elwinger. 1999. Grass species and cultivars effect on
establishment of gass-white clover mixture. Agon. J. 91: 889-897.

Snaydon, R.W.l987. Population response to environmental disturbance. P.15-31. In J.
van Andel et al. (ed). Disturbance in gassland. Causes, effect, and processes.
Dr.W. Publ., Dordrecht, The Netherlands.

Spitaleri, R.F. J .C. Henning, G.D. Lacefield, N.L. Taylor, and E. Vanzant, 2003. 2002
red and white clover gazing tolerance report. Collage of Agi. University of
Kentucky. Electronic publication available at URL:
http://www.cankLedu/agc/pubs/Qr/pr48 1/PR48 1 .PDF.

Sugiyama, S. 1998. Differentiation in competitive ability and cold tolerance between
diploid and tetraploid cultivars in loliurn perenne. Euphytica. 103: 55-59.

SAS Institute. 2000. The SAS system, version 8. The SAS Institute, Cary, NC.

Thomley, J .H.M. 2001. Simulating Grass-Legume Dynamics: a Phenomenological
Submodel. Annals of Botany 88:905-913.

Tyler, B.F.,M.Borrill,.and D.Chorlton. 1978. Studies in Festuca x. Observations on
germination and seedling cold tolerance in diploid F estuca pratensis and

tetraploid F. pratensis var. appennina in relation to their altitude distribution. J.
Appl. Ecol. 15:219-226.

Taylor, N.L., and RR Smith. 1995. Red clover. In Barnes et al. (ed.). Forages volume I:
An introduction to gassland agiculture, 5th ed. Iowa State University Press.
Ames. IA.

Taylor, N.L., and RR Smith.l998. Kura clover (Trifolium ambiguim.M.B) breeding,
culture, and utilization. Adv. Agon. 63: 1 53-178.

Vough, L.R., A.M., Decker, and T.H.Taylor. 1995. Forage Establishment and

Renovation. In Barnes, Miller and Nelson (ed). Forages, Volume II: The Science
of Grassland Agiculture, 5“1 ed. Iowa State University. Ames, IA.

48

Van Santen, E., Sleper DA. 1996. Orchardgass In: Moser et a1 L.E., ed. Cool-season
forage gasses. Madison, WI: ASA, CSSA, and SSSA :503-534 Agon. Mong.
34.

Van Soest, R]. 1982. Nutritional ecology of the ruminant. O & B Book, Corvallis, OR.

Wadleight, C.H., L.M. Glymph, and H.N. Holton. 1974. Grasslands in relation to water
resources. P. 1 5-42. In H.B. Sprague(ed) Grasslands of the united Sates. Their
economic and ecologic importance. Iowa State Univ. press. Ames. Al.

Wischmeier, W.H., and DD. Smith. 1978. Predicting rainfall erosion losses- a guide to
conservation planning. USDA Agic. Hand book. 53 7. US. Gov. Print. Office,
Washington DC.

Wit, F. 1959. Hybrids of ryegasses and meadow fescue and their value for gass
breeding. Euphytica 8: 1—12.

Wilsie, C.P.l949. Evaluation of gass-legume associations, with emphasis on yield of
bromegass varieties. Agon.J 41 .412-420.

Woodman, R.F., J .M.Keoghan, and BE. Allan. 1992. Pasture species for drought-prone
lower slopes in the south island high country. Proc. N.Z. Grass]. Assoc.54:115-
120.

Zemenchik, R.A., K.A. Albrecht, and MK. Schultz. 2001. Nitrogen replacement value of
kura clover and birdfoot trefoil in binary mixtures with cool-season gass.
Agon.J.93:451-458.

Zannone, L., .P.Rotili, R.Faoletti, and C. Scotti. 1986. Experimental studies of gass-
legume associations. Agonomie (Paris) 6:931-940.

49

 

 

IHVIEJGassDvaa

 

 

 

3 1C UPB
location

Fig.1. Comparing average winter injury (W I) at Kellogg Biological Research
(KBS), Lake City (LC) and Upper Peninsula Experiment Station (U PBS)
for gass-clover binary mixtures (BM), gasses, and clovers gown in

monocultures.

1= less than 20% of the vegetation was killed.
5= Greater than 80% of the vegetation was killed.

50

 

Table 1. The mean values of winter injury rating of 16 gass cultivars established in
monoculture at Kellogg Biological Research Station (KBS), Lake City (LC) and Upper
Peninsula Experiment Station (U PES) over a three-year period.

 

Grass Grass KBS LC UPES

Species cultivars 20041200512006 3-yr ave 20041200512006 3-yr ave120041200512006 3-yr ave
Duo 1.6 1.0 1.0 1.2 1.6 2 2.3 2.0 1.8 4.3 5.0 3.7
Fest Hykor 1.3 1.0 1.0 1.1 1.0 1.0 1.0 1.0 1.3 1.0 2.0 1.4

 

 

 

 

 

 

 

 

 

Tekapo 1.5 1-0 1-0 1.2 1.0 2.7 1.0 1.6 1.3 3.0 5.0 3.1
Amba 1.1 1-0 1-0 1.0 1.0 1.4 1.0 1.1 2.0 2.0 2.8 2.3
OR Niva 1-0 1-0 1-0 1.0 1.1 1.5 1.0 1.2 1-0 1.6 3.3 2.0
Sparta 1-0 1-0 1.0 1.0 1.6 1.4 1-0 1.3 1-0 1.3 4.0 2.1

 

 

 

 

 

 

 

 

 

 

 

 

 

Aries 1.8 1.2 1.2 1.4 3.0 4.4 5.0 4.1 4.1 5.0 5.0 4.7
MvrckGld 2.0 1.8 4.0 2.6 4.0 3.7 4.3 4.0 4.0 5.0 5.0 4.7
Quartet 2.1 1.0 1.0 1.4 3.0 3.7 4.0 3.6 2.6 5.0 5.0 4.2
PR Tonga 1.3 1.0 1.0 1.1 1.8 3.0 3.0 2.6 2.1 4.0 5.0 3.7
Barfort 1.5 1.0 1.0 1.2 2.0 3.0 3.0 2.7 2.0 4.0 5.0 3.7
Mara 1.5 1.0 1.0 1.2 2.6 4.4 3.3 3.4 2.0 4.0 5.0 3.7
Calibra 1.3 1.0 1.0 1.1 2.0 2.7 1.3 2.0 1.8 3.3 5.0 3.4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Bronson 1.0 1.0 1.0 1,0 1.0 1.0 1.0 1.0 1.0 1,3 3,5 1,9
TF K5666V 1.5 1.0 1.0 1.2 1.1 1.4 1.0 1.2 1.5 2.8 5.0 3.1
Barolex 1.6 1-0 1-0 1.2 2.0 2.5 1.3 1.9 2.0 2.3 5.0 3.1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mean 1.4 1.1 1.2 1.6 2.5 2.2 1.8 3.1 4.4
cv 27.2 9.9 6.0 35.9 33.0 23.9 34.8 15.4 5.9
LSD (0.05) 0.6 0.2 0.2 0.9 1.4 0.8 1.1 0.8 0.4

 

1= less than 20% of the vegetation was killed.
5= Greater than 80% of the vegetation was killed.
Fest=Festulolium ; OR=Orchardgrass, PR=Perennial ryegass and TF= Tall fescue.

51

Table 2. The mean values of winter injury rating of 67 gass-clover binary mixture
treatments established at Kellogg Biological Research Station (KBS), Lake City (LC) and
Upper Peninsula Experiment Station (U PES) over a three-year period (continued on the

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

next page).

Grass Clover KBS LC UPES
Cultivars Cultivars ‘041 ‘051 ‘06 AVE ‘04 I ‘051‘06 AVE 04105 I ‘06 AVE
Duo Endura 1.6 1.0 1.0 1.2 1.5 1.3 2.3 1.7 2.0 4.0 4.7 3.6
Duo StarFire 1.6 1.0 1.0 1.2 1.0 2.8 1.7 1.8 2.1 4.3 4.7 3.7
Duo VNS 1.6 1.0 1.0 1.2 1.6 1.0 2.7 1.8 1.8 3.7 4.5 33
Duo Ladino 2.0 1.0 1.0 13 1.3 2.5 3.5 2.4 1.5 4.7 4.7 3.6
Duo Kopu 11 1.6 1.0 1.0 1.2 1.2 2.0 2.7 2.0 1.3 4.3 4.8 3.5
Hykor VNS 1.0 1.0 1.0 1.0 1.3 1.0 1.0 1.1 1.0 1.3 1.0 1.1

 

 

 

Hykor Jumbo 1.5 1.0 1.0 1.2 1.5 1.6 1.3 1.5 1.0 1.2 1.0 1.1
Hykor Ladino 1.0 1.0 1.0 1.0 1.1 1.0 1.0 1.0 1.0 1.5 1.0 1.2

 

 

 

Amba VNS 1.8 1.0 1.0 1.3 1.3 1.1 2.3 1.6 1.0 1.0 1.0 1.0
Amba Jumbo 1.6 1.0 1.0 1.2 1.0 1.3 1.0 1.1 1.0 1.2 1.7 1.3
Amba Ladino 1.0 1.0 1.0 1.0 1.1 1.0 1.3 1.1 1.1 1.0 1.3 1.1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Niva VNS 1.1 1.0 1.0 1.0 1.1 1.1 2.0 1.4 1.0 1.0 1.0 1.0
Niva Jumbo 1.1 1.0 1.0 1.0 1.0 1.5 2.3 1.6 1.0 1.0 1.7 1.2
Niva Ladino 1.0 1.0 1.0 1.0 1.7 1.6 2.3 1.9 1.0 1.0 1.3 1.1
Sparta VNS 1.0 1.0 1.0 1.0 1.5 1.1 2.3 1.6 1.3 1.0 1.5 1.3

 

 

 

Sparta Jumbo 1.1 1.0 1.0 1.0 1.0 1.1 2.3 1.5 1.0 1.0 2.2 1.4
Sparta Ladino 1.1 1.0 1.0 1.0 1.0 2.1 2.7 1.9 1.3 1.0 2.2 1.5
Tekapo Endura 1.1 1.0 1.0 1.0 1.3 1.0 1.0 1.1 1.3 2.0 3.3 2.2
Tekapo VNS 1.3 1.2 1.2 1.2 1.3 1.2 2.3 1.6 1.1 2.0 2.5 1.9
Tekapo Star Fire 1.5 1.0 1.0 1.2 1.5 2.5 3.7 2.6 1.1 2.0 2.8 2.0
Tekapo Kopu II 1.6 1.0 1.0 1.2 1.1 1.1 2.6 1.6 1.6 3.0 4.0 2.9
Tekapo Ladino 1.3 1.0 1.0 1.1 1.0 2.0 1.0 1.3 1.8 2.8 3.8 2.8

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Aries Endura 1.6 1.0 1.0 1.2 2.6 3.6 4.0 3.4 2.1 4.7 4.8 3.9
Aries VNS 2.3 1.5 1.5 1.8 2.1 2.5 3.7 2.8 3.0 4.7 5.0 4.2
Aries Star Fire 2.1 1.0 1.0 1.4 1.8 1.2 2.3 1.8 3.0 4.7 5.0 4.2
Aries Kopu II 1.5 1.0 1.0 1.2 2.3 3.0 3.5 2.9 2.1 5.0 5.0 4.0
Aries Ladino 1.5 1.0 1.0 1.2 1.6 2.3 2.7 2.2 1.8 5.0 5.0 3.9
Barfort VNS 1.5 1.0 1.0 1.2 1.8 1.6 2.7 2.0 2.1 3.3 3.8 3.1
Barfort Start 1.6 1.0 1.0 1.2 2.0 1.5 3.0 2.2 1.6 3.0 4.5 3.0
Barfort Alice 1.6 1.0 1.0 1.2 2.0 2.3 2.7 2.3 1.3 4.3 5.0 3.5
Barfort Ladino 1.3 1.0 1.0 1.1 1.8 2.3 2.8 2.3 1.0 4.3 5.0 3.4
Calibra VNS 1.5 1.2 1.2 1.3 1.0 1.7 2.7 1.8 1.3 1.6 3.8 2.2
Calibra Jumbo 1.1 1.0 1.0 1.0 1.6 1.8 1.0 1.5 1.1 3.3 4.7 3.0
Calibra Ladino 1.6 1.0 1.0 1.2 1.6 3.1 2.3 2.3 1.0 3.0 4.2 2.7

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mara VNS 1.3 1.0 1.0 1.1 1.8 2.0 1.0 1.6 1.5 3.0 3.0 2.5
Mara Start 1.6 1.0 1.0 1.2 2.3 2.5 3.0 2.6 1.3 3.8 4.3 3.1
Mara Alice 1.1 1.0 1.0 1.0 2.0 2.0 1.8 1.9 1.5 4.2 5.0 3.6

 

52

Table 2 continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Orchardgass (Amba, Niva, Sparta and Tekapo)

Perennial ryegass (Aries, Barfort, Calibra, Mara, Maverick Gold, Quartet, and Tonga,)
Tall fescue (Bronson, K5666V, and Barolex)

Clover cultivars:
Red clover (Start Fire, Start and VNS); white clover (Ladino, Alice, Kopu II and Jumbo); Kura (Endura)

53

Grass Clover KBS LC UPES
Cultivars Cultivars ‘04 1 ‘05 1 ‘06 AVE ‘04 1 ‘05] ‘06 AVE ‘04 1 ‘05 I ‘06 AVE
Mara Ladino 1.0 1.0 1.0 1.0 2.1 1.3 1.7 1.7 1.3 3.3 4.0 2.9
Mvck G Endura 2.0 1.4 1.3 1.6 2.3 3.3 4.0 3.2 4.6 5.0 5.0 4.9
Mvck G VNS 2.3 2.2 2.2 2.2 2.1 3.0 4.0 3.0 4.1 5.0 5.0 4.7
Mvck G Star Fire 2.0 1.4 1.3 1.6 2.6 4.0 4.3 3.6 3.6 5.0 5.0 4.5
Mvck G Kopu II 2.3 1.0 1.0 1.4 2.5 3.6 3.7 3.3 2.8 5.0 5.0 4.3
Mvck G Ladino 2.1 1.0 1.0 1.4 2.8 3.0 4.3 3.4 2.8 5.0 5.0 4.3
Quartet Endura 1.3 1.0 1.0 1.1 1.5 3.5 4.0 3.0 1.3 3.7 5.0 3.3
Quartet VNS 2.0 1.0 1.0 1.3 1.6 3.1 4.3 3.0 2.0 3.7 5.0 3.6
Quartet Ladino 1.5 1.0 1.0 1.2 1.5 1.1 3.0 1.9 2.0 5.0 5.0 4.0
Quartet Star Fire 2.0 1.0 1.0 1.3 1.3 1.3 2.7 1.8 2.0 4.2 4.8 3.7
Quartet Kopu II 2.0 1.0 1.0 1.3 1.5 1.8 3.0 2.1 2.0 4.5 4.7 3.7
Tonga Endura 1.3 1.0 1.0 1.1 1.1 2.4 3.3 2.3 1.0 2.7 4.0 2.6
Tonga VNS 1.6 1.0 1.0 1.2 1.0 2.4 3.7 2.4 1.3 3.0 4.7 3.0
Tonga Star Fire 2.0 1.0 1.0 1.3 1.0 1.6 2.3 1.6 1.0 3.7 4.7 3.1
Tonga Kopu II 1.3 1.0 1.0 1.1 1.1 1.0 1.7 1.3 1.1 4.0 4.7 ‘ 3.3
Tonga Ladino 1.5 1.0 1.0 1.2 1.1 1.1 2.7 1.6 1.3 5.0 5.0 3.8
Bronson VNS 1.3 1.0 1.0 1.1 1.0 1.2 1.0 1.1 1.1 1.3 1.0 1.1
Bronson Endura 1.5 1.0 1.0 1.2 1.0 1.2 1.0 1.1 1.0 2.3 1.0 1.4
Bronson Kopu II 1.5 1.0 1.0 1.2 1.1 1.1 2.0 1.4 1.0 1.3 1.0 l l
Bronson Ladino 1.1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.3 1.0
Bronson Star Fire 1.1 1.0 1.0 1.0 1.1 1.3' 1.3 1.2 1.3 1.6 1.0 1.3
K5666V VNS 1.6 1.0 1.0 1.2 1.3 2.4 2.0 1.9 1.8 2.3 1.3 1.8
K5666V Endura 1.1 1.0 1.0 1.0 1.5 2.4 1.7 1.9 1.6 2.0 1.0 1.5
K5666V Kopu II 1.8 1.0 1.0 1.3 1.1 2.1 2.2 1.8 1.8 2.0 1.3 1.7
K5666V Ladino 1.3 1.0 1.0 1.1 1.1 1.1 1.2 1.1 2.0 1.8 1.3 1.7
K5666V Star Fire 1.3 1.0 1.0 1.1 1.6 1.0 1.0 1.2 1.5 2.0 1.7 1.7
Barolex Alice 1.6 1.0 1.0 1.2 1.8 1.4 1.0 1.4 1.0 1.6 1.0 1.2
Barolex VNS 1.5 1.0 1.0 1.2 1.0 1.0 1.3 1.1 1.3 1.6 1.3 1.4
Barolex Ladino 1.3 1.0 1.0 1.1 1.0 1.0 1.3 1.1 1.1 1.8 1.0 1.3
Barolex Start 1.5 1.0 1.0 1.2 1.3 1.7 2.0 1.7 1.3 1.5 1.3 1.4
Mean 1.5 1.0 1.0 1.5 I 1.8 2.4 1.6 2.9 33
CV 29 20 20 28 1 47 51 23 l9 l4
LSD(0.05) 0.7 0.3 0.4 0.7 1 0.0 0.0 0.6 0.9 0.8
_ 1= less than 20% of the vegetation killed; 5= Greater than 80% of the vegetation was killed
Grass cultivars:
Festulolium (Duo and Hykor)

Assam c8 .383 .8386 88a =fi

398,—. was 43:30 .200 c.0552 .982 dim—no J.E.—am auras Rama».— 38:83
ESE c5 screw .22 SEC 883.35

Coo—am 98 can: 83—238,,“

55?an $80

magma 8:50»? on. he $8 55 82 n m ”Manama nozfiomg 05 .«o e\e cm 55 88on n u

 

 

 

v... _ m... E. S. m... a... a... 3 m... 3 E _ S. v... _ E _ a... 5.3%.—
n.: _ S n2 2: we .2..— mé «.3 S. 3.. SN _ 4...: a... _ cs _ 3. >0
3 _ 3. 3 an a... 3. a... 3. c... 3. 3. _ 3. a... _ m... _ 3. as:

 

 

 

 

 

 

 

 

 

ad o.— Qm Em v.v 9% av o6 o6 o.m ow w.v Ev Ev o6 9m v.v _.v x2055

 

 

 

9n o4 o.m EN m.v w.v Ev 06 En Em 9m v.v v.v m.v wv ad ad _.v >083—

 

 

 

n.v EN o.m m.v v.v o6 Ev Qm ed Ev o6 w.v Ev o.m 9m 9w o6 v.v "525.5

 

QM o4 ow o.N _.v m.v =.v ow o6 MN _.v 9m N.v o.v Em ow v.v 9m 952.6

 

 

 

 

 

 

 

 

 

N.n o4 o6 o.N _.v _.v ed m.m m.v mg m.v 9v Ev Ev Ev ofi 06 _.v E32
N.n A: wv o.N o.v o.v :.v o.v ow m.N w.v w.m N.v Wm Wm 36 Ev m.v totem—
e.N o; v.N E~ Em _.v Em o.v ow m.N _.v Em En Em EN o6 v.v _.v wmcoh

 

 

 

N.N Q— o.m o._ wN mum —.v o6 o6 m.N mv mgr. EN EN w.N o.n o.v w.m 35:0

 

 

 

v.— o; N; o._ E~ m.N EN Em Wm o.N 9m M: m.N m.— 04 Gem new mac. 200 395—2

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

E— o._ oN o4 N.N ~.N N.n EN Wm E_ fiv _.v —.v Em m6. wv 9v o.v more
—.v m.N ow o.v v.v xv v.v Em od o.v 9v wv 9v Ev mv wv c6 _.v 53mm
Ev EN o6 Em m.v o.m v.v m.v o6 Em Ev v.v Ev Ev 06 ed v.v m.v «32
—.v o6 ow Em Em ow v.v o.v o.m o.v 9v n.v Qv Ev Em wv m.v _.v was
m.m o; o.m EN m.v v.v —.v Ev Ev m.N v.v _.v v.v Ev wv Ev v.v o.v 093.0“.
e.v ma o6 o6 v.v o.n Em ad ad w.v od 06 av o.n o.w o6 v.v m.v cox»:
m.N c; m._ E— Em o.v N.v m.v o6 QM m.v Ev v.v o.v o.v o6 Ev m.v 0:0

 

c>< egN mgu mgN vch vch o>< ecaN mch mceN vch 33 o>< ech mecN mag 33 vch 3.3230

 

er... «seem as 5am =£ scum rem macaw =£ scam =£ macaw arm 3% .3 macaw an Era see

 

 

 

 

 

 

 

 

 

 

 

 

 

 

max—D U4— mav—

 

 

 

 

 

 

 

eecfl access e 66 GEE cease. 888.5 geese 6%: e5 65 ace 83 .65: 8:3 Essex 38.65
30:02 E 833888 E Exam—£38 mugs—3 macaw 3 mo meta b>oo 958w =£ EB 95% .«o 332$ :38 oFH. .m 2%...

54

 

 

En m.N 9m mN m.v mv N.v Ev o.m Wm o.v m.v Ev Ev xv o.m v.v m.v 0593 REV—oh
06 ON o6 m.N v.v _.v N.v m.v ofi o.v w.m m.v v.v v.v N.v Ev v.v _.v 3 REM OAS—oh
En QM xv Wm Ev o.v —.v Ev n.v m.N o.v wv m.v o.v wv Ev v.v o.v oh...— gm ans—oh
—.v Wm o6 Em v.v _.v m.v Ev w.v m.v _.v v.v m.v N.v o.v av wv m.v mZ> ens—oh
c. w.N o.m Em o.v m.v 9v Ev Qm o6 9m wv Ev Qm wv ow 9v _.v 835m 03:3.
m.v w.m 9w Ev N.v o6 v.v mv wv Wm m.v o6 Ev wv mv o.m o6 m.v oats gmm

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

m... 5. 3. od 3. on S. S. m... 3. 2. o.“ 5 3. 3. ca ow 3. 883 $an
5. 3. ca od 2. 3. m... 3. ed a... 3. 3. m... w... 3. 3 m... 3. 9.3 «5%
5. m... ca 3 S. on m.» m... ca 3 3. 3. 3. 3. 3. ed 3. 3 863 22
E. 2. ca 5. 3. ca 2. 9m 2. 3. 3. o.“ 5. ca 3 ca 2. 3. Ba: 22
a... ow on S. 3. ca 5. 5. ca 06 3. 9m 3. 3. S. 3. 3. m... 95 22
a... 3. 3 3. 3. ca m... 3. 3 a... 3. 2. m... 2. 2. w... 3. 2. 863 £5.
3. 3. 3 3. 3. 3 m... S. o... m... 3. 2. N... S. S. 3. S. 3. 883 35.
9... ca 3 ed m... an 3. 3. ed on 2. 3. 3. 3 2. ed 3. 3 mz> 3.5.

9v Ev 9m m.v N.v od v.v 9m ow o.m v.v o6 Ev Qm o6 o6 m.v m.v 2&5..— “833
Ev o.m Qm N.v _.v c6 a.v o6 9m v.v mv ow Ev 0% 9n ow m.v m.v 383. you—>3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5. od ca 5. 2. 3. a... 3 ca ca 2. an a... c.“ on 3 o.“ S. mz> .93:
an 3 3. 3 S. 3. n... 3 S. E. 3. 3. an 3 3 3 o... 2. :33 25
3 o.~ E S 3. 2. N... 3. 3. Z 3. 3. 3. 2 S“ on f. 2. 863 8a
an 3 ma 5 3. 2. N... 3. S. v... 3. o... N... 3. on on 3 2. $5 25
a." 2 2 2 3. 3. 3. o... S. 3 2. 3. 3. 3. 3 3. M3. 3. 05.5 8a
3. E 3. 2 mm 3. S. 2. 2. o... 3 3. 3. ca 3. on 2. 2. 952m 25

 

 

 

 

3. 8. m? m? 3. 3. 22 8. 3. m? 2.. 3. .3. 8. 3. me. 3. 3. paging :«éau
5m m a m m m ma m m m m m .3 m m m n. m :26 3:0
3:5 3 we.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

.Nmoww .38: 05 so 325:8
v2.8a Suzbfifi a 8>o Ammma aouaam “Eatomxm «Swanson .833 95 A01: .36 8:5 .Ammva :osfim scammed Rflwfiafi
326% as 35388 355805 8.5328 .985 S .«o mesa 5.60 23on Oh :3 v5 Amv watam mo 82g :88 05. .v 035.

55

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3 3 3 S 3 3 3 3 3 3 3 3 3 5 3 3 3 3 :83 .880
3 S 3 3 3 3 3 3 3 3 3 3 3 f 3 3 3 3 3 8:83 3:80
3 3 3 o: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 8:8: .820
3 o: 3 3.: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 95 8:80
3 o: 3 S 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2:8: .880
3 3 3 o: 3 3 3 3 3 3 3 3 3 S 3 3 3 3 8:3 5:82
3 3 3 o: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 :83 8:82
3 2 3 c: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 8:83 8:82
3 o: 3 o: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 $3 8:82
3 3 3 o: 3 2 3 3 3 3 3 3 3 3 3 3 3 3 8:8: 8:82
3 3 3 S 3 3 3 3 3 3 3 3 3 3 3 3 3 3 8:3 .82
3 3 3 o: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 83 «a:
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 :3 as:
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 $5 8:2
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 83: 885
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 88: 85:6
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 mz> «5:8
3 3 3 o: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 8:3 83m
3 3 3 o: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 83 team
3 M: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 Sam 2&8:
3 w: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 93 team
3 3 3 o: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 8:3 $3
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 :83 8.2
3 o: 3 o: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 amusm 83
3 o: 3 o: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 mz> 83
3 M: 3 o: 3 3 3 3 3 3 3 3 3 3 3 3 3 3 .88: 83
8< 8. 2. m... 2.. 2.. 93 8. m... 3. 3. 3. 22 8. 3. _ 3. 3. 2.. 882.6 2.32.6
:3 m m m m m :3. m u. m m m :3 m m _ m n: m 82.5 8.5
3.5 0,. we.

 

 

 

 

 

 

 

 

3:558 v 23%

56

ESE—m: 0.5M :5 A0083. ::0 .= 008— .00=< 65:3: 0903 BE? AmZ> :00 gm .ohm 0.80m: 09,20 :3. ”figs—:0 0306

Axe—0.8m :00 .>:::mv~ .08086 0:80: :8. .30ng :00 .8500 .200 0.2.0262

.802 65:00 ficfam .850: 080on 3000.6: ”308—3. :00 5.80m .02 Z 628$ map—0:80 50th :00 03: 80:01:00,,— “Emzzzo $80
9:33. 00:80»? 2: :0 gow as: 002 u m 358:: 00:80w0> 2: :0 ..\o cm 05: 020on n —

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

~.— 5.: :.: :.: :.: :.: :.: :.: :.: 5.: :.— :.: :.: _ 6.: :.: 3:5: Gmd

NAN :.: :.:N :.: :.: :.: :.: :.: :.n— m.:— m.m~ v.3 :.m _ :.:~ ~.: >0

fin v.v :.N —.v N.v m.N fl: :.: fie :.: :.m :.m :.: _ mi N.: 000—2
:.: m6. :.w Em m6 3‘ :.: :.m m6 v.v :.v né 5.: 5.: :.m :.v :.m _.v Sam x208m
m6. :.m :.m Nam :.v :.v :.: :.m :.w v.v m6. :.v 5.: v.v :.m :.n :.m :.: 05:3 5.08m
v.v v.v :.m “an :.v m6 :.: :.m :.m v.v :.v :.n :.v :.: :.m :.m :.m :.: mZ> 5.08m
m6 :.m :.m Em 2. :6 :.: :.n :.m v.v :.v :.v m6 v.v N.v :.n :6 :.v 02? 5.03m

 

 

 

N.v N.v :.n mum m6 fiv. v.v :.m :.m :.m n...» :.v :.: m6. :.: :.m :.: M6. 005.8% >:::wv~

 

 

 

3. 2. ca 2 2. S. a... 3 ca 5. 3. 3 5. we 3. on ow 3. 8:3 >089

 

 

 

n... v.v :.m men :.v I. m6 5.: :.m Wm w.v :.v :.: N.v 5.: :.v :.m :.v =000¥ >353—

 

 

 

N... :.m w: m.m :.v :.v v.v :.m :.m Wm _.v M: :.: :.w 0% :.m 0.: m.v 93:5: >cocmM

 

 

 

N.v v.v :.n :.m _.v :.v mi :.m v.v :6 :.v :.v 5.: wé 5.: 0.: :.m _.v mZ> >:::m¥

 

 

 

m... ow ca 3. 3. 3 3 3 3 ca 3. 2. 5 3 3 ca 3. 3 255m 8880

 

 

 

ha. :.m :.w mé v.v :.v 5.: 5.: :.m :.m :V :.v :.m :.m :.m :.m :.m w.: 05:3 comaoum

 

 

 

:.: :.m :.m w.m v.v :.v :.: :.n :.: :.m :.m w: :.: w: :.m :.: :6. :.: :.EOM aomcoum

 

 

 

«.9. :.m :.: :.m :.v mé :.: :.m :.n v.v :.v :.n :.v :.m :.m :.n :.v 9v «BEG 000000m

 

 

 

:.v :.m :.m m4. v.v m6 :.: :.m :.m v.v :.m :.m :.: :.m :.m :.m :.v :.: mZ> 0030.5

Wm 5N EM :3 n6 :.v N.: m4... m6. m6 m6 m6 :.: Nam w.~ :.m :.v mé 05:3 098:.

 

 

 

—.n :.N :.: :4 :.v mé n... :.: :.: v.v :.v —.v :.m EN EN 9: 0.: m6 ==qov~ smack

 

 

 

WM x; Q: :.N :.v mé :.: 5.: :.m «an _.v mé Wm EN EN v.v :.m 0.: ohmHSm «mach

 

 

 

9.: Nu— m.».. mum m6 :6 Q: m: :.w EN mé né —.v N.v NM :.v :.v :.v mZ> «mach
:.n w.m :.v Wm WM :.: :.m 5m v.v VA.” _.v 3‘ n6. v.v w.m :.m WM 0.: 8.6—5 «mach

 

0>< ::. m:. m:. 3. v:. u>< e:. m:. m:. v:. v:. 0>< ::. m? m? v:. :.:. 9:33—50 9:25—50

 

:.n m m m 0 m :-n m m m 0 m :-m m 0 m h. m .320 :20

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mam: Du— mav—

 

:o:=::00 v 030,—.

57

Table 5. The mean values of winter injury rating of eight clover cultivars established in
monoculture at Kellogg Biological Research Station (KBS), Lake City (LC) and Upper
Peninsula Experiment Station (UPES) over a three-year period

 

Clover Clover KBS *LC #UPES

Species Cultivars 200412005] 2006 Ave 2004] 2005 12006 2004] 2005 Ave
Kura clover Endura 1.0 1.0 1.0 1.0 1.5 1.0 1.0 3.8 1.0 2.4

 

 

 

 

 

 

Star Fire 3.1 2.0 2.0 2.4 1.8 NA NA 2.6 1.0 1.8
Red Start 2.8 1.5 1.5 1.9 1.5 NA NA 2.6 1.0 1.8
Clover VNS 3.0 1.8 2.0 2.3 1.1 NA NA 2.5 1.0 1.8

 

 

 

 

 

 

 

 

Kopu II 2.3 1.3 1.4 1.7 1.0 NA NA 2.0 1.0 2.0
White Alice 2.1 1.0 1.0 1.4 1.0 NA NA 1.0 1.0 1.0
Clover Jumbo 2.0 1.5 1.5 1.7 1.0 NA NA 1.6 1.0 1.3
Ladino 2.5 1.0 1.0 1.5 1.0 NA NA 1.6 1.0 1.3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mean 2.4 1.4 1.4 1.25 1.0 1.0 2.2 1.0
CV 18.9 23.9 23.4 0.0 0.0 0.0 26.8 0.0
LSD (0.05) 0.8 0.6 0.6 0.6 NS NS 1.05 NS

 

l= less than 20% of the vegetation killed.

5= Greater than 80% of the vegetation was killed.

"' LC 2005 and 2006 white and red clovers data are not included.
# UPES 2006 data are not included.

NA= Not applicable.

58

.3305 .8 an 08 38. .8 :0 Eu
.08 .96: e3 8300 9% 28 00 _.
903:0 00:80mo> 2: .«0 $8 005 002 u m
wfimmma 000.0%? 2: :0 .x. :w 05: 023% u _

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

S E. .2 a... a... 3. E. 0... a... a... 5. $0.00 03

:.NN 0.3 m6: N.v— :.: :.: mdu W: Qua v.2 v.3 >0

fin EN :.N QM :.: :.: :.N :.: and H: :.n 0002
fl: mé Nam :v :.v Q: <2 <2 Wm :.m fie :6 :.m :.m 05:3
6.: QM :.v _.m :.v :.m <2 <2 Wm :.m N.v :.v :.v :.v 0980—. 0050—0
«.9. WV 5.: sum 0.: :.: <2 <2 :.m :.m v.v 5.: m: mé 00=< 2.33
:.: m6 Nam :.: :.v :.: <2 <2 NA.“ :6 «.9 N.v :.v w.m = 090M
:4 w._ o._ :3 Wm WM <2 <2 EN M: :.m Wm :.m :.m m2>
:.N 5.2 :.: :.N fin Wm <2 <2 5N N.v :.m Wm w.m :.v gm 095—0
b4 m3 m2 mg :.m fin <2 <2 :.m N.v :.m Wm fin WM 0.0.2 08m :32
:.N min. :.N v.v m._ :.N 5N NM :3 N.v :.v :.v :.v _.v 0.595 02,020 «BM
0>< meow moom voom voom 0>< moom moom voom voom 0>< moom voom voom 00020—00 0300mm

:0: wanna =5 wfiumm =0.— wfiumm =0.— wfiflmw wit: :0.— wfiumm 095—0 096—0

wan—D 01—... wav—
.:0.:00 80%

-22 a 03 $0.50 803 005530 03080 0&0 2a 60 06 8.3 .35: Scam 0.5080 @0220 002.30

00 83.000005 5 2005:0800 0.82300 026—0 230 .«0 mafia: 00.60 :000.& :0: :00 M00000 :0 830.» 00000 2:. 0 030.2.

59

€00.05 003. ”.0000; 000 00.001. ... 0000.. 090.0 20.3 5.5m .00 mZ> .00.". 08m. 090.0 .03. 0030.00 005.0
00.00.00. .00 000 80.. coon 01. ..
0080 030.0 05 5.3 00>0o w. 0000.00 05 .0 000000» 00 $0M. u m ”00000.00 05 0. E00000 000% 030.0 05 .0 $8 008 000. N.

$008.02. 000000.00 C005 no 0.0.00 0... .0 0.9.0000 05 E80500 000.0> >0 000 Cm. .0003.

 

 

 

n... N— 0.. Q. x... m7. m2 w... ... 0.: 0.. h... a... 5... Ga... .Gmw.
.n on on mm 0. mm mv 3. cm mN an a. n. .. >0
m.— m.N ..N ed m6 ad Y. 9.. «an m.. .m— vé N... m... 000.).

 

 

 

 

 

 

 

 

 

 

 

 

Wu 0.. EN 5N 0am Wm @— m. 0.. 0.. 0.. fin o. 0.. 0.0 m6. m... 00.001. 09$...

 

 

 

md o.. 0.. EN wN Wm m.. 5.. 0.. 0.. mum QN 0.. 0.. 0.0 w.m 0.0 2.05.. 000.0....

 

 

 

N... 0.. o.~ o. Nd m.m 0.. ON 0.. m. ON md 0.. o.. Wm o.m .40 m2> 000.3.

 

 

 

 

 

 

v.m m.m ma Wm Om 0.0 ..~ Wm 0.. 0.. m.m ad m. o.m ow Qw WV ..000v. 00h.

 

 

 

9m wd w.m 0.0 NM o0 «gm m6 o.m M... 0..» Wm m. N. oh 0.0 0.0 00.001. 00m.

 

 

 

9N 0.. EN o. in ..m 0d ed o.~ ..N mgm NM N... N. N.v 0.0 w.» w2> 00m.

 

 

 

EN m. m.m 0N 9m ..m ed v.v N... 0.. 9N ..m m. m. N.v ..v 0.0 00.08% 00m.

 

 

 

0.. N... Nd o.N ..N 0.. TN w.m ON 0.. ..N 0... Qm m6 ow of mé 00000”. 00h.

 

 

 

 

 

 

 

0: 09m 8.0 8.13.0430 03 30700—30700 0: 80700—3073.;30 030.0 080.
.ié :-n

 

 

mHhD 0‘: wav—

 

 

 

 

00000 000»- 000.. 0 00>0 .mMmD.
8000 0888000 0358.. 5&0 05 .00 0.0 8.3 .8000 8030 02880 030220 0033. a 0203st
00000.00 E000. 00>0.0-0S..0.Bmo.. ..0 M0000 003.0 0m8000000 a. :0. .000 Am. w0..=.m ,.0 000.0> 000.0 00% .N. 030...

60

2052.0. 83. H32:... 05 8.03 .0 2.30 .22"v 223 ”.000 02. 02> .00.... .50. 2:20 03. H2.22:0 .320

00.00.00. .00 000 0.0.. ocom 0.. .0
000.0 00>0.0 05 5.3 00>00 m. 0000...... 05 .0 00.000» 00 $3 n w

000.00.... 05 .0. 0000000 000.0 00>0.0 05 .0 o\oom 005 000.. n.

0.008.000. 0000.000. .000... 00 00000 05 .0 0.02.000 05 000000000 m00.0> >0 .000 Gm. 000.).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0... 0.. 0.. 00. 0... 02 02 0... ... 0... 0.. E. 0... 5. $0.800.

.0 00 S 00 0. .0. my 3. :0 mu .0 0. 0. .. >0

0.. m.” .0 0.. 0.0 0.. 0.. 0.. 0.. v... 0.. v... a... v.v :82
0.. 0.. N. 0.. 0.. o... 0.. 2 0.. 0.. 2 0.0 0.. 0.. 0.. 0.. n... 8.00.. 2.900
0.0 o. 0.. 0.. 0.. 0.0 0.. 0.. o. ... 0.. 0.0 0.. 0.. 0.0 m... 0... 0 2.3. 2.9.0.0
0.. 0.. 0.. 0.. 2. m0 0.. 0.. o. 0. 0.0 2 0.. 0.. N... m... 0.. 8.0.0.0 8200
0.. 0.. 0.. 0.. 0.. 0.0 .0 0.0 0.. 0.. 0.. 5 0. o. N... .0 .0 02> 2.9.00
.... 0.. E o... o. 0.. 0.. Z I o. 0... 0... m... o... S. m... 2. 0.22.0 2.800
3 0.. S 0.. .0 m... 0.. «.0 I 0.. .0 0.0 0.. 0. m... 0... 0... 8.00.. 0.2.0
0.. o. E 0.0 .0 m... 0.. 0.0 0.. ... 0.. 0.... 0.. 0.. 0.0 0.0 m... 2.2:. 2.2.0
0.. 0.. 0.. o. 0.. 0.. 0.. .0 Z 2 0.. 0.0 0.. o. 0... o... o... 02> 2.2.0
.0 0.. 0.. 0.0 0.. 0... 0.. 3. 2 0. 0.0 0.0 0.. 0.. 0.0 m... n... 2.3 022
m.” 0.. 0.. o... 0.. 2. 0.. 0.. 0.. 0.. 0.. .0 0.. o. 0.0 o... m... 2.2:. 022
0.. o. S 0.. 3 0.0 0.. 3. 0.. 0.. 0.. 0.. 0.. o. 2. 0.0 o... 02> 022
0.. 0.. m... o. 0.. 0.0 0.. 0.. 0.. 2 0.0 00 o. o. o... 0.0 m... 02.3 2.8..
0.. 0.. 0.. 0.. 0.. .0 0.. 0.0 0.. 0.. 0.. 0.0 0.. 0. 0.0 m... m... 2.2... 025.
0.. 0.. 0.. 0.. 0.0 0.0 h. 0.. 0.. 0.. 0.. 0.0 o. n. m... o... o... 02> 3:2
2. com 30 new 30 vom 9:. mo”. .8 30 30 a: com 80 80 3. 3m 2.3.2.6 2.3.2.6
0....” 0....N 0..-.” 00>0.0 8003000000

0000 0.... 000.
00.00

000-82. 0 .26 60.5. 02.20 0825.00 2328.. .200 2a .0... 0.0 8.0.. .3000 802m 2288.. 020220 002.3.
.0 0000.30.00 00.0.0.0. >000... 00>0.0-mm00w.000..000 .0 w0..00 00>0.0 00300000.. A... :0. .000 Am. w0.000 .0 m00.0> 0000. 0.... .w 0.0.0...

61

 

6.M QM N6 QM MN .6 QN QN 6.N M.. ..6 QM Q. QN QM QM QM 00.00.. 8.0000

 

 

 

—.N Q. QN Q. M.M ..M QM 6.6 6.. MN M.M QM M.. M. N6 .6 M6 MZ> 8.0000

 

 

 

Q. Q. N.N QN Q. Q. —.N NM 6.. ... QN M6 QM QM QM M6 M6 00000m. 8.0000

 

 

 

Q6 Q6 QM QM 6.6 Q6 N.N QM Q. ... ..M M.M Q. Q. Q6 Q6 M6 00.00... 0.00.003).

 

 

 

6.6 N6 QM Q6 Q6 Q6 —.N QM Q. ... QN 6.M M.. M.N Q6 Q6 M6 ..0003 0.00.952

 

 

 

N.M Q. M6 QN QM M6 M.N QM Q. QN M.M QM M.. M.. QM Q6 Q6 00.00am 0.00.003...

 

 

 

QN M.. M.M Q. M6 M6 QN 6.M Q. ... Q6 QN M.. M.. QM Q6 M6 mZ> 0.00.003).

 

 

 

QN M.N M.M QN Q. QM 6.N N.M 6.N Q. QM M6 QM QM M6 QM M6 00000m 0.00.003).

 

 

 

 

 

 

 

 

 

 

 

 

QN M.. N.M QM M.M Q6 Q— 6.N Q. Q. Q. QM Q. Q. N6 Q6 M6 00.00.. 0003.
MM Q. M.M Q6 QN M6 Qu QN Q. Q. M.N —.M Q. M.. QM Q6 Q6 00..< 0002
M.N Q. N.N Q. QM ..M QN N.N 6.. M.N QN QN Q. Q. M.M M6 Q6 .00.m 0003.
Q. Q. Q. Q. Q. QN M.N QM Q. ..N M.N QN Q. N.. M.M MM .6 mZ> 000$.

 

 

 

..M Q. QM QM 6.M Q6 QN NM 6.. ... M.N QM M.. M.N QM QM Q6 00.00.. 00....00

 

 

 

..M QN QM Q6 6.N .6 ..N QM Q. ... M.N QM Q. Q. QM QM Q6 00.00... 00....00

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

..N Q. M.N Q. ..M QM QN 6.M Q. QN M.M QN M.. M.. Q6 Q6 .6 MZ> 00....00
QM QM Q6 Q6 Q6 .6 M. N Q6 Q. Q. M.N 6.M Q. M.. QM QM Q6 00.00.. .00..00m
..6 MM M6 Q6 QM M6 6.N 6.M 6.. M.. ..M QM Q. QN QM QM QM 00.? .00000m
M.N Q. M.N QN Q6 M.M 6.N 6.M 0.. Q. M.N QM M.. Q. Q6 Q6 Q6 .00.m .00....0m
QN Q. N.N Q. QN M.M QN Q6 6.N QN ..M QM M.. Q. M6 Q6 Q6 wZ> .00....0m
QM QM QM QN Q. Q6 M.N QN QN Q. QN QM M.. Q. QM QM Q6 00.00... 00.0<

 

 

 

QN N.N M.M QN N.N QM 6.N QM QN Q. QN QM Q. N.N M6 QM Q6 =000v. 00.06.

 

 

 

QN M.. M.M QN 6.M QM 6.N QM 0.. Q. QN ..M Q. Q. N6 .6 M6 00.0005 00.0.0.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.. Q. QN Q. QN QM QN 6.M 0.. Q. ..M QN Q. M.. M6 QM Q6 MZ> 00.0<
N.N N.N QM QN QN QN QN QN QN Q. ..N QM Q6 M.M Q6 M.M QM 00000m. 00...<
02. com Mo... Mom 6o... 68 0>0 Mo... Mom 60... 60m 02. com Mom Mom 69. 6cm 003.0 000M000 .0
0.0M 0.?N 0.0M
was 0a mav—

 

 

 

 

 

00m00 .000 0... 00 00000000.
000.0000. 000... 0. 00.0.0.8 00>0.0-0000w0b .0.000000 ..0 00>0.0 090000000 P... :00 000 Am. w0.000 ..0 000.0> 00000 0:... .0 0.0.0...

62

 

@328 «SM MARSH Ea 8:< .863 .= as: 533 233 5.5 c5 mz> .ea 5% $63 Bx ”53:6 586

vows—05 .0: 0.8 8% coom DA ..
Exam ago—0 05 55 .850 m_ 2:33: 05 we 888» .5 $3 n m

8858 05 E magnum v53 hog—o 05 .3 $8 56 33 n—

.3cozbmob 3558 E35 no 935 05 mo man—£8 05 28832 mos—g >0 can qu :32

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

a... «A A: v.— a... mz mz a... fin a... 9.. . >6 a... he Gaga—ma
nn an an nn 3 ma mv vn an mu an an n u — — >0
m4 m.~ fiu e." n.n ad v.— v4 v." m.— Nu— vé N.v v.v 53:
fin N.n fin o6 fin n6 v." m.n v; o; fin an n; w.~ ed on on 9:23 %
fin N.N an on fim of ed on o._ o._ an an 5; EN an on on = flex «week
a." m; En on oé on b." fiv n; n.~ w.~ v.v. o; N.N on oé 3‘ 2E 2am «much
N.N o; On o; m.n n.n md n6 fi~ o; m.n c.n o._ n._ n.v on v.v mz> «mach
v.” on n.n o.~ 0; n4 m.n ed on m._ Wm ma. on 04. o.n 9v 9v «Baum «week
QN o.~ on on on fiv EN an. Wm n._ fin an n; on on v.v mi 3 ago! 6530
=.n QN N.v od fin m.n a.” v.v Wm 04 fin ~.n Nu— »; 0v 9n v.v 2E 5m 85:0
93 com mam now won vow 93 now now vou— vom 95 com new mom 3:.— vom .355 8&9? ..—
...0& in in
mam: Oa— mav—

 

 

 

 

 

 

VOSGGGOO Q o-Dah

63

9.528 83. ”A883 23 8:3 a as: 583 223 x55 25 32> .em 58 .253 Ba 5325 .266

noun—oi 8: 0.3 8% ooom DA ..

38m ago—0 05 55 828 w_ 2858 05 no .8303 .5 $3 u m

9.8me on. E @8255 986 56—0 2: :8 $3 35 33 u—

.ficogmob 853:: 3:3 no 2:3 05 mo man—3 on. “58.52 833 >0 c5 93 .582

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3 3 3 _ 3 _ a... mz mz 3 2 3. S 2. 3 E. @353

3 an a _ 33 _ 3 ma 3. 3 3 mu 3 S 2 = >0

3 m3 3 _ 3 _ 3. 3 I I 3 3 S 3. N... a... :32
3 3 E 3 3 3 5 3 3 3 3 3 3 S 3 3 3 new 53:5
2 3 3 3 E 3 2 E 3 S 3 3 3 3 3. 3 3 863 53.8
3 3 S 3 N.N 3 3 E E S 3 3 3 3 Z 3 3 mz> 533m
3 3 M: 3 3 3 2 2 3 2 2 3 3 3 2. 3 3 8?. 328
3 3 3 3 3 3 3 3 S 2 3 3 3 S 3 3 3 em 33 >883
3 3 2 3 3 3 3 3 3 S 3 3. 3 2 3 3 3 9&3 >882
3 3 2 3 S 3 3 3 3 2 3 3 3.. .2 3 3 3 = .53 >803
3 2 3 3 3 M: S 3 E 3 3 3 3 3 3 3 3 Sam >833
3 S M: 3 3 3 3 3 S 3 3 3 3 3 3 3 3 93 >39
3 3 3 3 3 3 3 .3 E 2 3 3 3 S 3 3 3 am am 835
3 S 3 3 3 3 3 E S S 3 3 3 3 3 3 3 8:3 888m
3 S 2 3 3 3 E E 3 3 3 3 3 S 3 3. 3 = 25 838m
3 E 3.. 3 3 2 2 E 3 3 3 3 E 3 3 3 3 835 828m
E 3 2 3 3 3 3 3 E 3 2 3 3 S 3 3 3 $3 $2on
23 8m 83 wow 3m vow 9:. new mom XE vow 0: com 38 now 3p. Em 2.32.6 32:5
in ..mé in

3.5 .04 m5. .356 «38. H

 

 

.32 3.0325 n 55 3:3 8:3 “88533 235an 5&3 as 6436 8335: .333 30365 23
3 332988 8:5me 3:5 “PS—0-088% :8 .«o mcufi B>£o owficooba EV =£ was Amv wctam mo $23 308 05. d— 035.

64

Table 11. Dry matter yield (tons acre") of 16 grass cultivars established
in monoculture at Kellogg Biological Research (KBS) over a two-year period.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2004 2005
Grass 24- 24- 26- 9- 4- 14- 2- 13- Total
Species / Cultivars May Jun Jul Sep Max Jun Aug Oct 2-yr
DM tons acre”1

Duo 0.75 0.52 0.27 0.29 0.41 0.16 0.31 0.13 2.84

Fest Hykor 1.08 0.84 0.82 0.39 1.16 0.56 0.51 0.30 5.66
Tekapo 1.04 0.56 0.61 0.27 0.60 0.73 0.50 0.26 4.57

OR Amba 0.85 0.82 0.46 0.20 0.88 0.33 0.39 0.22 4.15
Niva 0.99 0.72 0.62 0.24 0.84 0.54 0.40 0.28 4.63

Sparta 0.95 0.84 0.73 0.23 0.91 0.55 0.49 0.30 5.00

Aries 0.87 0.64 0.47 0.63 0.83 0.38 0.34 0.26 4.42

Maverick Gold 0.79 0.95 0.46 0.16 0.63 0.38 0.38 0.21 3.96

PR Quartet 0.40 0.43 0.24 0.33 0.70 0.70 0.35 0.26 3.41
Tonga 0.81 0.34 0.50 0.16 0.82 0.24 0.25 0.20 3.32

Barfort 0.64 0.41 0.54 0.28 0.75 0.20 0.28 0.20 3.30

Mara 0.77 0.58 0.50 0.23 0.74 0.45 0.37 0.53 4.17

Calibra 0.60 0.56 0.43 0.63 0.65 0.47 0.25 0.22 3.81

Bronson 0.69 0.70 0.90 0.74 0.86 0.65 0.78 0.47 5.79

TF K5666V 0.79 0.52 0.83 0.42 0.58 0.29 0.30 0.22 3.95
Barolex 0.82 1.02 1.03 0.46 0.68 0.38 0.54 0.30 5.23

Mean 0.8 0.6 0.6 0.4 0.8 0.4 0.4 0.3 4.3

CV 46.2 38.9 39.0 92.3 48.9 42.3 35.6 36.1 18.4

LSD (0.05) 0.6 0.4 0.4 0.5 0.6 0.3 0.2 0.2 1.3

 

 

 

 

 

 

 

 

 

65

 

Table 12. Dry matter yield (tons acre'l) of 16 grass cultivars established
in monoculture at Lake City (LC) over a two-year period.

 

 

 

 

 

 

 

 

 

 

 

 

Grass 2004 2005 Total

Species / Cultivars 19-May I 21-Jun I S-Aug 23-Mafl 7-Jul I 9-Aug 2-yr
DM tons acre'

Duo 0.51 0.88 0.51 0.37 0.44 0.44 3.15

Fest Hykor 0.91 0.74 0.74 0.55 0.87 0.89 4.70

Tekapo 0.73 0.69 0.85 0.18 0.57 0.27 3.29

OR Amba 0.89 0.56 0.69 0.57 0.47 0.24 3.42

Niva 0.71 0.70 0.77 0.46 0.26 0.34 3.24

Sparta 1.12 0.71 0.86 0.61 0.52 0.51 4.33

Aries 0.35 0.72 0.54 0.35 0.33 0.25 2.54

Maverick Gold 0.14 0.45 0.62 0.69 0.35 0.42 2.67

PR Quartet 0.42 0.84 0.43 0.33 0.34 0.43 2.79

Tonga 0.50 0.44 0.32 0.44 0.47 0.32 2.49

Barfort 0.28 1.25 0.61 0.20 0.48 0.29 3.11

Mara 0.24 1.24 0.51 0.18 0.47 0.37 3.01

Calibra 0.52 0.84 0.34 0.23 0.55 0.33 2.81

Bronson 0.91 0.93 0.94 0.48 0.52 0.46 4.24

TF K5666V 1.05 0.96 0.79 0.70 0.53 0.43 4.46

Barolex 0.63 0.87 0.78 0.65 0.50 0.74 4.17

Mean 0.7 0.9 0.6 0.4 0.5 0.4 3.4

CV 36.6 25.1 33.4 55.7 43.2 51.3 17.0

LSD( 0.05) 0.4 0.4 0.3 0.4 0.3 0.4 1.2

 

 

 

 

 

 

 

 

 

66

 

Table 13. Dry matter yield (tons acre'l) of 16 grass cultivars established in
monoculture at Upper Peninsula Experiment Station (UPES) over a two-
year period.

 

2004 2005
Grass 7- 12- 22- 17- 1- 10- 23- 29- Total
Species / Cultivar Jun Jul Aug Oct Jun Jul Aug Sep 2-yr

 

 

 

 

 

 

 

 

DM tons acre'l

 

 

 

 

 

 

 

 

 

 

 

 

Duo 0.56 1.67 0.48 0.30 0.63 0.47 0.45 0.65 5.21
Fest Hykor 0.86 1.12 1.30 0.27 0.80 0.56 0.87 0.79 6.57
Tekapo 0.94 1.33 0.96 0.31 0.72 0.64 0.60 0.77 6.27
OR Amba 1.06 1.25 0.70 0.30 1.04 0.37 0.46 0.60 5.78
Niva 0.78 1.09 0.93 0.28 0.97 0.66 0.70 0.57 5.98
Sparta 0.82 1.11 1.01 0.30 1.01 0.56 0.62 0.54 5.97
Aries 0.23 1.27 0.43 0.00 0.10 0.31 0.00 0.15 2.49
Maverick Gold 0.36 0.78 0.58 0.00 0.00 0.37 0.00 0.28 2.37
PR Quartet 0.42 1.76 0.35 0.00 0.16 0.33 0.12 0.42 3.56
Tonga 0.59 1.44 0.74 0.00 0.60 0.42 0.48 0.54 4.81
Barfort 0.75 1.93 0.59 0.00 0.67 0.30 0.54 0.77 5.55
Mara 0.86 2.03 0.92 0.00 0.58 0.36 0.62 0.55 5.92
Calibra 0.74 1.71 0.62 0.00 0.58 0.58 0.51 0.85 5.59
Bronson 0.82 1.23 1.19 0.38 0.65 0.65 0.92 0.71 6.55
TF K5666V 0.80 1.31 0.96 0.19 0.32 0.51 0.72 0.74 5.55
Barolex 1.02 1.47 1.09 0.25 0.78 0.62 0.84 0.81 6.88
Mean 0.7 1.4 0.8 0.3 0.6 0.5 0.5 0.6 . 5.3
CV 34.2 25.4 31.6 17.78 23.4 44.9 24.6 23.1 10.7
LSD(0.05) 0.4 0.6 0.4 0.1 0.2 0.4 0.2 0.2 0.9

 

 

 

 

 

 

 

 

Data of Perennial ryegrass cultivars in last cutting event of 2004 were not included

67

 

Table 14. Dry matter yield (tons acre") of 67 grass- clover binary mixtures treatments
established at Kellogg Biological Research (KBS) over a two-year period (continued on

 

 

 

 

the next page).
2004 2005
Grass Clover Total
Cultivars Cultivars 24-Maj 24-Jun 26—Ju1 9-Sep 4-May 14-Jun Z-Qg l3-Oct 2-yr
DM tons acre‘I

Duo Endura 0.91 0.53 0.52 0.72 0.48 0.55 0.28 0.28 4.27
Duo Star Fire 0.57 0.67 0.77 0.63 0.75 0.71 0.30 0.24 4.64
Duo VNS 0.65 0.70 0.62 0.71 0.85 0.65 0.30 0.38 4.86
Duo Ladino 0.83 0.60 0.51 0.57 0.35 0.57 0.24 0.22 3.89
Duo Kopu II 0.60 0.48 0.54 0.53 0.76 0.57 0.32 0.25 4.05
Hykor VNS 0.92 0.84 0.66 0.61 0.99 0.65 0.68 0.43 5.78
Hykor Jumbo 0.75 0.78 1.06 0.96 1.07 0.64 0.62 0.60 6.48
Hykor Ladino 0.85 0.62 0.93 0.85 1.34 0.41 0.78 0.54 6.32
Amba VNS 0.54 0.62 0.52 1.32 0.54 0.56 0.60 0.24 4.94
Amba Jumbo 0.22 0.91 0.74 0.62 0.60 0.40 0.50 0.26 4.25
Amba Ladino 0.66 0.61 0.75 0.79 0.74 0.40 0.54 0.34 4.83
Niva VNS 0.51 0.64 0.66 0.82 0.60 0.48 0.55 0.36 4.62
Niva Jumbo 0.75 0.64 0.66 0.77 0.50 0.45 0.55 0.30 4.62
Niva Ladino 0.61 0.71 0.71 0.44 0.85 0.62 0.60 0.35 4.89
Sparta VNS 0.73 0.75 1.02 0.70 0.83 0.48 0.50 0.36 5.37
Sparta Jumbo 0.75 0.75 0.91 0.49 0.63 0.32 0.40 0.24 4.49
Sparta Ladino 0.55 0.75 0.56 0.56 0.61 0.44 0.48 0.32 4.27
Tekapo Endura 0.80 0.69 0.62 0.64 0.76 0.68 0.42 0.38 4.99
Tekapo VNS 0.64 0.89 0.59 0.99 0.66 0.73 0.47 0.28 5.25
Tekapo Star Fire 0.79 0.81 0.60 0.67 0.58 0.35 0.53 0.41 4.74
Tekapo Kopu II 0.60 0.62 0.54 0.72 0.60 0.38 0.43 0.37 4.26
Tekapo Ladino 0.94 0.60 0.73 0.57 0.73 0.70 0.45 0.34 5.06
Aries Endura 0.53 0.61 0.44 0.37 0.59 0.40 0.30 0.32 3.56
Aries VNS 0.62 0.48 0.47 0.65 0.48 0.26 0.43 0.26 3.65
Aries Star Fire 0.78 0.44 0.83 0.92 0.53 0.55 0.38 0.30 4.73
Aries Kopu H 0.62 0.61 0.40 0.52 0.65 0.51 0.40 0.28 3.99
Aries Ladino 0.72 0.69 0.75 0.74 0.51 0.43 0.36 0.18 4.38
Barfort VNS 0.99 0.56 0.69 0.55 0.40 0.37 0.35 0.34 4.25
Barfort Start 0.64 0.58 0.56 0.68 0.55 0.96 0.35 0.22 4.54
Barfort Alice 0.65 0.76 0.51 0.51 0.48 0.43 0.25 0.21 3.80
Barfort Ladino 0.78 0.70 0.42 0.38 0.44 0.48 0.31 0.25 3.76
Calibra VNS 0.54 0.44 0.46 0.98 0.78 0.37 0.32 0.36 4.25
Calibra Jumbo 0.83 0.58 0.59 0.58 0.66 0.44 0.41 0.30 4.39
Calibra Ladino 0.71 0.58 0.34 0.47 0.70 0.48 0.39 0.24 3.91
Mara VNS 0.69 0.54 0.89 0.85 0.53 0.53 0.44 0.31 4.78
Mara Start 1.05 0.73 0.32 0.65 0.65 0.37 0.51 0.33 4.61
Mara Alice 0.74 0.66 0.57 0.62 0.43 0.50 0.43 0.40 4.35
Mara Ladino 0.29 0.71 1.03 0.77 0.45 0.51 0.50 0.37 4.63
Mav. Gold Endura 0.74 0.60 0.42 0.65 0.76 0.64 0.34 0.00 4.15
Mav. Gold VNS 0.64 0.58 0.73 0.82 0.59 0.46 0.19 0.00 4.01
Mav. Gold Star Fire 0.71 0.71 0.95 0.64 0.80 0.62 0.52 0.00 4.95
Mav. Gold Kopu II 0.81 0.57 0.52 0.69 0.47 0.76 0.40 0.00 4.22
Mav. Gold Ladino 0.65 0.66 0.44 0.61 0.48 0.75 0.39 0.00 3.98
Quartet Endura 0.47 0.47 0.62 0.51 0.50 0.53 0.26 0.21 3.57
Quartet VNS 0.99 0.40 0.63 0.50 0.52 0.38 0.30 0.36 4.08

 

 

 

 

 

 

68

 

Table 14 continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2004 2005

Grass Clover Total
Cultivars Cultivars 24-May 24-Jun 26-Jul 9-Sep 4-May 14-Jun 2-Aug 13-Oct 2- r
Quartet Ladino 0.78 0.61 0.53 0.65 0.50 0.55 0.28 0.22 4.12
Quartet Star Fire 0.75 0.71 0.63 0.77 0.45 0.61 0.35 0.22 4.49
Quartet Kopu II 0.89 0.60 0.44 0.59 0.24 0.33 0.25 0.25 3.59
Tonga Endura 0.70 0.57 0.54 0.60 0.90 0.48 0.38 0.25 4.42
Tonga VNS 0.52 0.60 0.69 0.89 0.78 0.50 0.31 0.20 4.49
Tonga Star Fire 0.42 0.60 0.67 0.59 0.50 0.51 0.40 0.23 3.92
Tonga Kopu II 0.60 0.65 0.84 0.64 0.43 0.45 0.33 0.18 4.12
Tonga Ladino 0.59 0.60 0.67 0.59 0.58 0.34 0.26 0.12 3.75
Bronson VNS 0.99 0.64 0.68 0.83 0.82 0.80 0.47 0.65 5.88
Bronson Endura 0.73 0.59 0.87 0.95 1.20 0.84 0.51 0.60 6.29
Bronson Kopu II 0.55 0.67 0.82 0.86 0.81 0.43 0.55 0.28 4.97
Bronson Ladino 0.84 0.74 0.75 0.61 0.73 0.70 0.40 0.54 5.31
Bronson Star Fire 0.64 0.67 0.44 0.86 1.03 0.64 0.55 0.58 5.41
K5666V VNS 0.57 0.55 0.86 0.91 1.30 0.40 0.42 0.66 5.67
K5666V Endura 0.97 0.72 0.55 0.54 0.64 0.52 0.27 0.40 4.61
K5666V Kopu II 0.61 0.52 0.59 0.44 0.54 0.50 0.32 0.38 3.90
K5666V Ladino 0.86 0.72 0.79 0.74 0.67 0.54 0.41 0.47 5.20
K5666V Star Fire 0.59 0.54 0.65 0.88 0.71 0.68 0.36 0.47 4.88
Barolex Alice 0.73 0.99 0.68 0.59 0.46 0.56 0.44 0.28 4.73
Barolex VNS 0.73 0.77 0.84 0.69 0.64 0.75 0.69 0.51 5.62
Barolex Ladino 0.51 0.85 1.07 0.79 0.74 0.76 0.61 0.40 5.73
Barolex Start 0.93 0.70 0.99 0.85 0.69 0.74 0.51 0.56 5.97

Mean 0.7 0.6 0.7 0.7 0.6 0.5 0.4 0.3 4.6

CV 33 30 37 41 36 36 26 41 16

LSD(0.05) 0.4 0.3 0.4 0.4 0.4 0.3 0.2 0.2 1.2

 

 

 

 

 

 

 

 

Grass cultivars:
F estulolium (Duo and Hykor)
Orchardgrass (Amba, Niva, Sparta and Tekapo)

Perennial ryegrass (Aries, Barfort, Calibra, Mara, Maverick Gold, Quartet, and Tonga,)

Tall fescue (Bronson, K5666V, and Barolex)

Clover cultivars:

Red clover (VNS', Star Fire, and Start)

White clovers (KopuII, ladino, Alice, and Jumbo)
Kura clover (Endura)

 

‘ Variety Not Stated

69

 

Table 15. Dry matter yield (tons acre") of 67 grass- clover binary mixtures treatments

 

 

 

 

 

established at Lake City (LC) over a two -year period (continued on the next page).
2004 2005
Grass Clover Total
Cultivars Cultivars l9-May 21-Jun 5-Aug 7-Sep 23-May 7-Ju1 9-Aug 2-Oct 2-yr
DM tons acre'I

Duo Endura 0.64 0.99 0.27 0.41 0.23 0.63 0.29 0.28 3.74
Duo Star Fire 1.00 0.81 0.36 0.71 0.46 0.38 0.35 0.31 4.38
Duo VNS 0.74 1.00 0.67 0.83 0.41 0.38 0.48 0.34 4.85
Duo Ladino 0.78 1.01 0.34 0.53 0.59 0.39 0.47 0.45 4.56
Duo Kopu II 0.75 0.92 0.21 0.42 0.24 0.39 0.25 0.22 3.40
Hykor VNS 1.08 0.71 0.59 0.76 0.66 0.23 0.33 0.54 4.90
Hykor Jumbo 1.15 0.83 0.48 0.55 0.38 0.49 0.30 0.43 4.61
Hykor Ladino 1.09 0.68 0.35 0.66 0.40 0.30 0.58 0.58 4.64
Amba VNS 0.93 0.68 0.47 0.83 0.66 0.43 0.38 0.42 4.80
Amba Jumbo 0.91 0.69 0.51 0.70 0.77 0.61 0.40 0.38 4.97
Amba Ladino 0.83 0.67 0.36 0.78 0.72 0.63 0.37 0.34 4.70
Niva VNS 1.00 0.58 0.51 0.91 0.56 0.37 0.33 0.49 4.75
Niva Jumbo 0.72 0.70 0.35 0.85 0.58 0.54 0.22 0.45 4.41
Niva Ladino 0.87 0.75 0.43 0.70 0.44 0.74 0.39 0.35 4.67
Sparta VNS 0.83 0.69 0.54 0.65 0.40 0.44 0.26 0.38 4.19
Sparta Jumbo 0.8 0.65 0.44 0.82 0.50 0.27 0.31 0.31 4.10
Sparta Ladino 0.94 0.8 0.40 0.63 0.81 0.57 0.26 0.42 4.83
Tekapo Endura 0.69 0.68 0.42 0.63 0.38 0.53 0.38 0.37 4.08
Tekapo VNS 0.81 0.93 . 0.45 1.01 0.89 0.60 0.38 0.42 5.49
Tekapo Star Fire 0.96 0.62 0.52 0.83 0.53 0.44 0.20 0.42 4.52
Tekapo Kopu II 1.08 0.72 0.55 0.65 0.29 0.43 0.29 0.28 4.29
Tekapo Ladino 0.78 0.62 0.59 0.54 0.46 0.65 0.21 0.22 4.07
Aries Endura 0.65 0.68 0.21 0.00 0.56 0.36 0.25 0.43 3.14
Aries VNS 0.54 0.97 0.97 0.00 0.79 0.48 0.23 0.34 4.32
Aries Star Fire 0.81 0.96 0.48 0.00 0.51 0.40 0.21 0.34 3.71
Aries Kopu II 0.47 0.92 0.49 0.00 0.26 0.33 0.22 0.45 3.14
Aries Ladino 0.63 0.75 0.42 0.00 0.35 0.31 0.19 0.28 2.93
Barfort VNS 0.83 1.14 0.55 0.00 0.42 0.36 0.25 0.32 3.87
Barfort Start 0.75 1.05 0.51 0.00 0.23 0.31 0.35 0.27 3.47
Barfort Alice 0.52 1.02 0.19 0.00 0.23 0.53 0.27 0.25 3.01
Barfort Ladino 0.72 l .06 0.35 0.00 0.42 0.39 0.40 0.42 3.76
Calibra VNS 0.59 1.40 0.68 0.00 1.07 0.92 0.30 0.40 5.36
Calibra Jumbo 0.71 0.97 0.31 0.00 0.18 0.39 0.31 0.26 3.13
Calibra Ladino 0.65 1.05 0.30 0.00 0.25 0.55 0.24 0.41 3.45
Mara VNS 0.65 1.16 0.48 0.00 0.54 0.63 0.53 0.47 4.46
Mara Start 0.54 1.25 0.48 0.00 0.44 0.39 0.49 0.46 4.05
Mara Alice 0.62 1.22 0.42 0.00 0.23 0.40 0.41 0.61 3.91
Mara Ladino 0.56 0.97 0.44 0.00 0.25 0.33 0.25 0.32 3.12
Mav. Gold Endura 0.76 1.01 0.34 0.00 0.58 0.68 0.30 0.21 3.88
Mav. Gold VNS 0.61 0.81 0.77 0.00 0.45 0.40 0.31 0.20 3.55
Mav. Gold Star Fire 0.56 0.86 0.95 0.00 0.34 0.29 0.30 0.27 3.57
Mav. Gold Kopu II 0.79 0.70 0.46 0.00 0.46 0.46 0.18 0.20 3.25
Mav. Gold Ladino 0.45 1.00 0.47 0.00 0.70 0.42 0.14 0.26 3.44
Quartet Endura 0.74 1.03 0.29 0.00 0.41 0.40 0.52 0.37 3.76
Quartet VNS 0.75 0.89 0.79 0.00 0.36 0.37 0.49 0.46 4.11
Quartet Ladino 0.69 1.01 0.55 0.00 0.58 0.35 0.35 0.14 3.67
Quartet Star Fire 0.88 0.85 0.63 0.00 0.45 0.40 0.37 0.42 4.00

 

 

 

 

 

7O

 

Table 15 continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2004 2005

Grass Clover Total
Cultivars Cultivars l9-May 21-Jun 5-Atg 7-Sep 23-May 7-Jul 9-Aig 2-Oct 2-yr
Quartet Kopu II 0.68 1.11 0.42 0.00 0.51 0.56 0.41 0.35 4.04
Tonga Endura 0.76 0.78 0.33 0.00 0.67 0.40 0.27 0.20 3.41
Tonga VNS 0.83 0.62 0.70 0.00 0.29 0.80 0.37 0.35 3.96
Tonga Star Fire 0.99 0.87 0.45 0.00 0.80 0.42 0.34 0.39 4.26
Tonga Kopu II 0.74 0.67 0.27 0.00 0.78 0.32 0.26 0.29 3.33
Tonga Ladino 0.62 0.79 0.35 0.00 0.78 0.43 0.32 0.66 3.95
Bronson VNS 0.76 1.10 0.51 0.74 0.7 5 0.58 0.30 0.59 5.33
Bronson Endura 0.79 0.72 0.45 0.80 0.39 0.53 0.48 0.46 4.62
Bronson Kopu II 0.88 1.00 0.51 0.60 1.03 0.45 0.37 0.34 5.18
Bronson Ladino 0.87 0.65 0.47 0.67 0.46 0.40 0.39 0.43 4.34
Bronson Star Fire 0.78 0.82 0.55 1.11 0.69 0.32 0.39 0.43 5.09
K5666V VNS 0.78 0.92 0.38 0.66 0.49 0.35 0.32 0.38 4.28
K5666V Endura 0.89 0.86 0.48 0.47 0.55 0.39 0.46 0.50 4.60
K5666V Kopu II 0.74 0.90 0.41 0.57 0.83 0.55 0.24 0.35 4.59
K5666V Ladino 0.78 0.76 0.38 0.68 0.32 0.34 0.31 0.56 4.13
K5666V Star Fire 0.76 0.73 0.55 0.59 0.34 0.29 0.28 0.42 3.96
Barolex Alice 0.75 1.34 0.60 0.65 0.39 0.33 0.37 0.43 4.86
Barolex VNS 0.86 1.16 0.80 0.76 0.74 0.37 0.20 0.39 5.28
Barolex Ladino 0.66 0.97 0.49 0.80 1 .03 0.22 0.40 0.49 5.06
Barolex Start 0.61 0.69 0.54 0.97 0.74 0.37 0.24 0.34 4.50

Mean 0.8 0.9 0.5 0.7 0.5 0.4 0.3 0.4 4.5

CV 30 28 42 33 53 43 52 37 14

LSD (0.05) 0.4 0.4 0.3 0.4 NS NS NS NS NS

 

Data of Perennial ryegrass cultivars in last cutting event of 2004 were not included

Grass cultivars:
Festulolium (Duo and Hykor)
Orchardgrass (Amba, Niva, Sparta and Tekapo)

Perennial ryegrass (Aries, Barfort, Calibra, Mara, Maverick Gold, Quartet, and Tonga,)

Tall fescue (Bronson, K5666V, and Barolex)

Clover cultivars:
Red clover (VNS', Star Fire, and Start)
White clovers (KopuII, ladino, Alice, and Jumbo)
Kura clover (Endura)

 

' Variety Not Stated

71

 

Table 16. Dry matter yield (tons acre") of 67 grass- clover binary mixtures treatments
established at Upper Peninsula Experiment Station (U PES) over a two-year period
(continued on the next page).

 

2004 2005

 

Grass Clover 2-yr
Cultivars Cultivars 7-Jun 12-Jul 22-Aug l7-Oct l-Jun lO-Jul 23-Aug 29-Sep Total

 

DM tons aere'I

 

 

Duo Endura 0.37 0.31 0.64 0.23 0.58 0.35 0.34 0.31 3.13
Duo Star Fire 0.79 0.72 0.81 0.40 0.91 0.64 0.83 0.30 5.40
Duo VNS 0.69 0.72 0.37 0.28 0.70 0.50 0.50 0.37 4.13
Duo Ladino 0.95 0.81 0.76 0.32 0.60 0.44 0.18 0.12 4.18
Duo Kopu II 0.87 0.67 0.58 0.31 0.55 0.33 0.24 0.20 3.75
Hykor VNS 0.71 0.47 0.43 0.29 0.74 0.33 0.52 0.38 3.87
Hykor Jumbo 1.11 0.77 0.88 0.28 0.86 0.50 0.44 0.25 5.09
Hykor Ladino 0.75 0.65 0.39 0.30 0.67 0.50 0.42 0.42 4.10

Amba VNS 0.66 0.40 0.43 0.30 0.74 0.27 0.52 0.43 3.75
Amba Jumbo 0.72 0.65 0.66 0.30 0.75 0.34 0.28 0.34 4.04
Amba Ladino 0.77 0.35 0.44 0.31 0.77 0.20 0.28 0.37 3.49
Niva VNS 0.68 0.49 0.63 0.27 0.70 0.37 0.38 0.40 3.92
Niva Jumbo 0.64 0.71 0.57 0.29 0.56 0.40 0.27 0.27 3.71
Niva Ladino 0.63 0.52 0.36 0.30 0.68 0.33 0.24 0.50 3.56
Sparta VNS 0.76 0.48 0.51 0.30 0.57 0.24 0.34 0.35 3.55
Sparta Jumbo 0.78 0.83 0.79 0.29 0.70 0.30 0.35 0.53 4.57
Sparta Ladino 0.83 0.71 0.41 0.24 0.70 0.26 0.30 0.33 3.78
Tekapo Endura 0.36 0.40 0.32 0.23 0.66 0.32 0.26 0.38 2.93
Tekapo VNS 0.62 0.51 0.57 0.34 0.67 0.32 0.33 0.38 3.74
Tekapo Star Fire 0.51 0.65 0.71 0.36 0.83 0.46 0.37 0.48 4.37
Tekapo Kopu II 0.57 0.67 0.63 0.31 0.67 0.38 0.24 0.41 3.88
Tekapo Ladino 0.60 0.73 0.59 0.26 0.66 0.30 0.25 0.41 3.80

Aries Endura 0.31 0.42 0.77 0.00 0.50 0.40 0.24 0.1 l 2.75
Aries VNS 0.51 0.46 0.83 0.00 0.51 0.60 0.60 0.13 3.64
Aries Star Fire 0.35 0.76 0.79 0.00 0.73 0.44 0.40 0.31 3.78
Aries Kopu II 0.55 0.62 0.46 0.00 0.55 0.34 0.30 0.10 2.92
Aries Ladino 0.54 0.73 0.55 0.00 0.51 0.30 0.13 0.15 2.91
Barfort VNS 0.64 0.74 0.51 0.00 0.62 0.70 0.64 0.36 4.21
Barfort Start 1.26 0.74 0.55 0.00 0.62 0.66 0.53 0.28 4.64
Barfort Alice 0.94 1.40 0.67 0.00 0.62 0.42 0.30 0.25 4.60
Barfort Ladino 0.78 1.02 0.47 0.00 0.57 0.35 0.25 0.18 3.62
Calibra VNS 0.58 0.55 0.51 0.00 0.61 0.26 0.44 0.30 3.25
Calibra Jumbo 0.71 1.39 0.68 0.00 0.64 0.20 0.36 0.20 4.18
Calibra Ladino 0.90 0.81 0.62 0.00 0.60 0.30 0.33 0.30 3.86

Mara VNS 0.70 0.60 0.60 0.00 0.62 0.27 0.46 0.53 3.78
Mara Start 0.74 0.86 0.80 0.00 0.70 0.64 0.50 0.52 4.76
Mara Alice 0.72 1.16 0.72 0.00 0.62 0.31 0.21 0.25 3.99

Mara Ladino 0.78 0.79 0.70 0.00 0.55 0.34 0.24 0.36 3.76
Mav. Gld Endura 0.13 0.32 0.25 0.00 0.58 0.38 0.24 0.15 2.05
Mav. Gld VNS 0.32 0.46 0.82 0.00 0.62 0.63 0.65 0.13 3.63
Mav. Gld Star Fire 0.60 0.66 1.02 0.00 0.77 0.90 0.34 0.21 4.50
Mav. Gld Kopu II 0.50 0.69 0.53 0.00 0.54 0.22 0.23 0.17 2.88
Mav. Gld Ladino 0.52 0.86 0.61 0.00 0.55 0.15 0.17 0.12 2.98

Quartet Endura 0.37 0.35 0.34 0.00 0.50 0.50 0.20 0.30 2.56
Quartet VNS 0.58 0.73 0.56 0.00 0.65 0.55 0.38 0.32 3.77

 

 

 

 

 

72

 

Table 16 continued

 

 

 

 

 

 

 

 

 

 

 

 

2004 2005

Grass Clover 2-yr
Cultivars Cultivars 7-Jun 12-Jul 22-Aug 17-Oct l-Jun 10-Jul 23-Aig 29-Sep Total
Quartet Ladino 0.70 0.83 0.66 0.00 0.65 0.27 0.15 0.20 3.46
Quartet Star Fire 0.65 0.88 1.02 0.00 0.82 0.50 0.52 0.40 4.79
Quartet Kopu 11 0.83 0.88 0.75 0.00 0.58 0.36 0.14 0.18 3.72
Tonga Endura 0.40 0.44 0.37 0.00 0.68 0.46 0.30 0.33 2.98
Tonga VNS 1.07 0.62 0.53 0.00 0.72 0.64 0.41 0.60 4.59
Tonga Star Fire 0.88 0.73 0.84 0.00 0.80 0.54 0.70 0.34 4.83
Tonga Kopu II 0.75 0.70 0.91 0.00 0.50 0.22 0.24 0.25 3.57
Tonga Ladino 1.01 0.66 1.13 0.00 0.58 0.36 0.23 0.20 4.17
Bronson VNS 0.75 0.38 0.52 0.25 0.60 0.47 0.62 0.43 4.02
Bronson Endura 0.55 0.31 0.31 0.22 0.50 0.31 0.36 0.25 2.81
Bronson Kopu II 0.83 0.67 0.55 0.29 0.63 0.42 0.38 0.32 4.09
Bronson Ladino 0.77 0.64 0.57 0.44 0.68 0.22 0.40 0.30 4.02
Bronson Star Fire 0.80 0.54 0.63 0.29 0.71 0.37 0.50 0.37 4.21
K5666V VNS 0.55 0.45 0.54 0.28 0.70 0.41 0.45 0.46 3.84
K5666V Endura 0.39 0.45 0.51 0.29 0.40 0.78 0.27 0.25 3.34
K5666V Kopu II 0.63 0.54 0.70 0.32 0.55 0.33 0.36 0.26 3.69
K5666V Ladino 0.60 0.83 0.80 0.16 0.58 0.40 0.35 0.40 4.12
K5666V Star Fire 0.77 0.58 0.80 0.35 0.65 0.67 0.63 0.34 4.79
Barolex Alice 0.69 0.58 0.58 0.32 0.70 0.58 0.47 0.50 4.42
Barolex VNS 0.68 0.43 0.56 0.28 0.58 0.46 0.28 0.48 3.75
Barolex Ladino 0.59 0.64 0.60 0.38 0.50 0.34 0.33 0.42 3.80
Barolex Start 0.79 0.73 1.04 0.36 0.80 0.43 0.40 0.35 4.90

Mean 0.7 0.6 0.6 0.3 0.6 0.4 0.4 0.3 3.8

CV 26 27 34 25 18 46 48 36 15

LSD(0.05) 0.3 0.3 0.3 0.1 0.2 0.3 0.3 0.2 0.9

 

 

 

 

 

 

 

Data of Perennial ryegrass cultivars in last cutting event of 2004 were not included

Grass cultivars:
Festulolium (Duo and Hykor)
Orchardgrass (Amba, Niva, Sparta and Tekapo)
Perennial ryegrass (Aries, Barfort, Calibra, Mara, Maverick Gold, Quartet, and Tonga,)
Tall fescue (Bronson, K5666V, and Barolex)

Clover cultivars:

Red clover (VNS', Star Fire, and Start)

White clovers (KopuII, ladino, Alice, and Jumbo)
Kura clover (Endura)

 

‘ Variety Not Stated

73

 

 

Table 17. Dry matter yield (tons acre!) of eight clover cultivars established in
monoculture at Kello

Biological Research Station (KBS) over a two-year period.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Clovers 2004 2005 Total
Species / Cultivars 24-May I 24-Jun I 26-Jul I 9-Sep 4-May I 14-Jun I 2-Au 2-yr
DM tons acre'l
Kura Endura 1.53 0.64 0.68 0.60 0.57 0.63 0.50 5.15
Clover
Red Star Fire 094 0.52 0.51 0.69 0.53 0.43 0.27 3.89
Clover Start 0.64 0.57 0.64 0.61 0.53 0.48 0.08 3.55
VNS 0.65 0.50 0.70 0.70 0.56 0.42 0.16 3.69
White Kopu II 0.74 0.38 0.40 0.51 0.36 0.32 0.38 3.09
Clove, Alice 0.56 0.60 0.63 0.45 0.42 0.37 0.36 339
Jumbo 0.88 0.91 0.69 0.69 0.41 0.43 0.08 4.09
Ladino 0.79 0.67 0.43 0.44 0.40 0.36 0.15 3.24
Mean 0.8 0.6 0.6 0.6 0.5 0.4 0.3 3.8
CV 51.8 43.6 44.9 38.5 66.1 45.4 78.4 20.8
LSD L005) 0.8 0.5 0.5 0.4 0.5 0.3 0.3 1.4
Table 18. Dry matter yield production (tons acre'l) of eight clover cultivars
established in monoculture at Lake City (LC) over a two-year period.
Clovers 2004 2005 T088]
Species/Cultivar l9-May I 21-Jun I 5-Aug 23-May I 7-Jul I 9-Aug I 2-Oct 2-yr
DM tons acre'l
’Kura Endura 0.57 0.90 0.25 0.38 0.82 0.21 0.11 3.24
Clover
Red Star Fire 0.80 0.58 0.70 0.00 0.00 0.00 0.00 2.08
Clover Start 0.71 0.68 0.80 0-00 0.00 0.00 0.00 2.19
VNS 0.64 0.72 1.20 0-00 0.00 0.00 0.00 2.56
White
Kopu II 0.59 0.88 0.59 0.00 0.00 0.00 0.00 2.06
Clove, Alice 0.73 0.78 0.44 0.00 0.00 0.00 0.00 1.95
Jumbo 0.73 0.79 0.55 0-00 0-00 0-00 0-00 2.07
Ladino 0.63 0.84 0.49 0.00 0.00 0.00 0.00 1.96
Mean 0.7 0.8 0.6 0.0 0.0 0.0 0.0 0.0
CV 22.2 29.6 27.4 0.0 0.0 0.0 0.0 0.0
LSD ( 0.05) 0.3 0.4 0.3 0.0 0.0 0.0 0.0 0.0

 

 

 

 

 

 

 

 

 

 

LSD is not available due to missing data
* Kura clover (Endura) is the only cultivar reported in 2005.

74

 

 

 

Table 19. Dry matter yield (tons acre") of eight clover cultivars established in
monoculture at Upper Peninsula Experiment Station (U PES) over a two-year

 

 

 

 

 

 

 

 

eriod.
Clover, 2004 2005 Total
Species/Cultivar 7-Jun I 12-Jul I 22-Aug r-JuriI 10-Ju1 I 23-Aug 2-yr
DM tons acre‘l
Kura Endura 0.52 0.23 0.28 0.69 0.40 0.02 2.14
clover
Star Fire 0.44 0.77 0.75 0.70 0.04 0.04 2.74
Red Start 0.70 0.71 0.46 0.58 0.00 0.00 2.45
Clover VNS 0.29 0.50 0.68 0.38 0.05 0.00 1.90
White Kopu 11 0.39 0.32 0.45 0.61 0.24 0.03 2.04
Clove, Alice 0.40 0.32 0.55 0.53 0.25 0.08 2.13
Jumbo 0.46 0.32 0.52 0.69 0.21 0.02 2.22
Ladino 0.41 0.46 0.45 0.59 0.20 0.16 2.27
Mean 0.5 0.5 0.5 0.6 0.2 0.1 2.3
cv 36.7 32.4 44.3 28.9 99.4 163.9 23.6
LSD (0.05) 0.3 03 0.4 0.3 0.3 0.1 0.9

 

 

 

 

 

 

 

 

75

 

Chapter 2
ANIMAL GRAZING PREFERENCE AND FORAGE QUALITY OF PERENNIAL

COOL SEASON GRASSES AND CLOVERS GROWN IN MONOCULTURE AND
BINARY MIXTURES UNDER ROTATIONAL GRAZING

ABSTRACT
Grass-legume forage pasture mixtures are important to animal production in the
temperate United States. The objective of this study, which was conducted from 2004-06
at Hickory Comers, MI, was to assess animal grazing preferences and forage quality of
introduced grass and clover cultivars grown in monoculture and binary mixtures. Results
indicated that grazing preference depends on the type of pasture species and the species
within the mixtures; binary mixtures were preferred over the monocultures. Higher crude
protein (CP), lower acid detergent fiber (ADF), and neutral detergent fiber (NDF) were
found in binary mixture treatments compared to grass and clover monoculture treatments.
Perennial rye grass cultivars demonstrated higher forage quality and animal preferences
when associated with clover cultivars. Tall fescue was less preferred by animals
compared to perennial rye grass, particularly when grown in a monoculture. The results
showed that animal preference of festulolium (Duo) monoculture was similar to that of
perennial ryegrass cultivars. The binary mixtures of festulolium (Duo)-clover and
perennial ryegrass-clover enhanced preferences and forage quality compared to
monoculture festulolium and perennial ryegrass, which indicate these mixtures are a good
choice for livestock producers to use for grazing. Total dry matter yield and animal

preferences were not always positively correlated.

76

INTRODUCTION AND BACKGROUND
The most important forage legumes in the North-Central US are alfalfa (Medicago
sativa L.), red clover (T rifolium pratense L.), birdsfoot trefoil (Lotus corniculatus L.),
and white clover (T rifolium repens L.) (Knight, 1985). However, their ability to remain
in the pasture under grazing is often limited (F orde et al., 1989; Van Keuren and
Matches, 1988). Kura clover (T rifolium ambiguim Bieb.) is a perennial legume that has
extensive rhizomes with a wide range of adaptation, which makes it potentially more

suitable as a pasture crop and for soil conservation purposes (Bryant, 1974; Speer and

Allison, 1985).

White clover (common and ladino types) is one of the most important legumes,
which can be used for grazing. Beside its palatability and high forage quality, white
clover also has the ability to tolerate continuous, heavy grazing (Spitaleri et al., 2003).
This characteristic makes it successfully adapted for rotational grazing. White clover
may be grazed continuously or rotationally and may be grazed closely (2.54 to 5.08 cm)

(Penn State University Agronomy guide 2005-2006).

Plant performance in pastures cannot be fully understood without reference to
animals. Animals tend to prefer and consume the most palatable plants first if given a
choice from various forage species. Hence, animals are always selective in what they eat.

The word “select” is defined as the action of choosing in preference to others (Cruz and

Ganskopp, 1998).

77

Animal preferences refer to the selective response made by animals, which is
mostly behavioral (Vallentine, 2001). Relative preference indicates a proportional
choice among two or more feeds (Heady, 1964). Preference is important because it
afi‘ects animal intake and any factor limiting intake can impact animal performance
(McCaughey, 1998). On the other hand, consumption of the more palatable species can
have a great impact on grass-legume binary mixture composition. Hence, animal
preference is a factor that influences the pasture community as a result of overgrazing of

the most preferred species in the pasture.

Animal grazing preference has been researched for many years. Several studies
have investigated the factors that make animals prefer certain plant species over others.
Some of these factors are plant-related while others are animal-related. Plant growth
stage is a major factor that influences animal preference. Advanced grth stage has
been indicated as a factor, which is correlated with decreasing preferences (Heady, 1964)
Fresh immature forages is highly palatable and livestock will selectively graze those
forages (Rohweder and Albrecht, 1995). The high preferences for rough fescue appeared
to be determined by the accessibility of the large tufted plants to cattle (Willma and Rode,
1998). Animals graze the most palatable species when multiple species are offered. If no

alternative is available, they will consume the specific feed even if it is less palatable.

Preference can be associated with plant physical characteristics. Within plant
parts, young leaves and stems are higher in crude protein and lower in lignin than older
stems and leaves (Heady, 1964). Thus, the younger leaves and stems are more likely to

be preferred by animals. Animals avoid consuming plants with spines, pubescence or

78

poisonous leaves. MacAdam and Mayland (2003) found that there was a negative
correlation between tall fescue leaf strength and preference. They concluded that the
cultivars that have the highest tensile strength would have the most fiber making it

difficult to break down by grazing.

Plant chemical composition also influences animal preference. Forage species
high in sugar content tend to be preferred by cattle (Plice, 1952), calves (Kare and
Halpem, 1961), and deer (Mitchell and Hosley, 1936). Additionally, animals are able to
preferentially select aftemoon cut alfalfa hay over morning cut hay due to higher sugar
content. Three ruminant species, sheep (Ovis aries), goats (Capra hircus), and cattle
(Bos taurus) preferred alfalfa hay cut in the afiemoon over hay cut in the morning (Fisher
et al., 2002). High positive correlation between protein content and preference by cattle
and sheep has been reported (Saltonstall, 1948; Cook, 1959; Blaser et al., 1960). In
addition, lignin and crude fiber were observed to be negatively correlated with animal
preference (Collins and Fritz, 2003). In most recent study, Smit et al., (2006) concluded
that dairy cows selection among six perennial ryegrass cultivars was positively related to

high water soluble carbohydrates (WSC) concentration and negatively to ash and fiber.

Past experience of animal diet selection influences animal preference. Vallentine
(2001) stated in his book (Grazing Management) “Animals can acquire preferences for
familiar food, first from their dams and second from their peers, but also by trial and

’9

error .

Several methods have been reported to measure animal preferences. In general,

the food that is eaten has been defined as the most preferable (Smit et al., 2006).

79

Preferences can be measured indoor by weighing the offered herbage and the residual
then preferences is expressed as the food that has been consumed (offered- residual)
(Aderibigbe et al., 1982; Provenza et al., 1996; Tolkamp et al., 1998). However, in a
grazing system, preferences can be measured using the sward-cutting method (Meijs,
1981; Macoon., 2003). Animal preference can also be determined visually by estimating
the herbage yield before and after grazing using preference scoring of 0 to 10 (O to 100%
of forage eaten) (Shewmaker et al., 1997) or a scale of l to 5 (1=least preferred ; 5 =
most preferred ) described by McCaughey (1998).

Forage quality is extremely important for livestock producers. The term forage
quality is defined as the capacity of forage to supply animal nutrient requirements. Crude
protein (CP), acid detergent fiber (ADF) and neutral detergent fiber (N DF) are the most
common criteria that can be used to evaluate forage quality. Forage species with low
NDF or ADF content are considered higher in quality than one with a high NDF or ADF
content. In addition, as CP increases in forage, livestock perform greater (i.e., gain more
weight, produce more milk, etc.) (Clemson, University Cooperative Extension Service).
Buxton and Mertens (1995) defined forage quality in terms of performance of animals
when fed herbage. It also refers to the physical and chemical characteristics of forage
that make it valuable to animals as a source of nutrients (Balasko and Nelson, 2003).
Collins and Fritz (2003) found that forage species vary in their quality components. They
stated that “Legumes usually have 15% to 20% crude protein, while tropical forage

grasses have about half as much and cool season grass had intermediate concentration “.

Differences in forage quality within grass and legume species may exist. Collins

and Fritz (2003) reported that cool season grasses averaged about 13% higher in

80

digestibility than warm season gasses. Leaf anatomy contributes to differences in forage
quality. It has been found from leaf cross sections that bermudagass (a C4 plant) is
generally higher in the low digestible vascular bundle, epidermis and sclerechyma tissues
than tall fescue (a C3 plant) (Akin and Burdick, 1975). Forage species can also differ in
forage quality based on weather conditions. For instance, high temperatures decrease
forage quality due to increased plant ligrin content (Castle and Halley, 1953; Corbett,
1953). Anti-quality components are considered a major factor that decreases forage

quality and animal preferences (Collins, and Hannaway, 2003).

Grass-legume mixtures vary in fiber depending on the Species. Pastures
consisting of kura clover with gasses had lower levels of fiber, greater protein
concentration, and higher digestibility than the pasture consisting of red clover with
gasses (Mourifio et al., 2003). It has been concluded that in a temperate pasture, white
clover is selectively gazed in preference to perennial ryegass (Curll and Wilkins, 1982).
It was also observed that plant species selection was influenced by the relative maturities
of the gass and legume (Grant.et al., 1985). Animals are able to select the legume from

the lower quality gasses in a mixed stand of legume and gasses (Laidlaw, 1983).

Forage quality of the gass-legume binary mixtures appears to be higher than
gass monocultures. Binary mixtures tend to have geater CP and lower NDF than pure
stands of gass (Collins and Hannaway, 2003). In contrast, few studies reported that
forage quality of gass-legume mixture is more likely to be lower in quality than the pure

legume due to the addition of the gass component (Van Soest, 1982).

81

In recent years, both the gass and legume seed industry has began marketing new
perennial gass and clover species and cultivars to Michigan gowers. For example,
orchardgass has been introduced with value-added traits such as increased tillering, later
maturity, and better winter hardness. Tall fescue has been recently offered with smoother
leaf blades, endophyte free, and geater palatability. Because of the potential high forage
dry matter yield of these species and cultivars, dairy and livestock producers may benefit

by adapting these species and cultivars in their gazing systems.

Currently, there has been no research done to evaluate animal preferences and
forage quality for introduced gass cultivars of festuloliums, orchardgass, tall fescue and
perennial ryegass cultivars and clover cultivars of red clover, white clover and kura
clover gown in a binary mixture. Therefore, more research is needed to determine
animal preference and forage quality when gown in a mixture. Thus in this research,
different gass and clover cultivars combinations were tested for their animal preference

and forage quality under rotational gazing.

The objectives of this study were to determine animal preference of introduced
gass and clover Species and cultivars under rotational gazing, and determine forage
quality based 0 NDF, ADF and CP, of the same gass and legume gowing in

monoculture and binary mixture under rotational gazing.

82

MATERIALS AND METHODS

Experiment Establishment and Maintenance:

The gazing trial was conducted in the Kellogg Biological Research Station at
Hickory Corners, M1 at the same experimental area established for evaluating persistence and
productivity trial mentioned in Chapter 1.

The study area was separated into three portions: gass only, legume only (clover),
and gass- clover binary mixtures (Appendix Fig. 2A). Each portion was arranged in a
randomized complete block desigi (RCBD) with three replications. Each replication of the
binary mixture portion consisted of 67 entries of different combinations of gass and clover
mixtures. Each replication of clover and gass portion consisted of 8 and 16 monocultures of
clover or gasses entries respectively (Chapter 1). Soil was analyzed at Michigan State
University (MSU) soil laboratory for fertilizer recommendation as indicated in the first
chapter. Appendix Table 1A presents soil information and the gass and clover cultivars
used in this study.

Grazing procedure and data collection: All treatments were rotationally gazed during the
2004-2006 seasons. The gazing season began in late April and continued until early to late
September, depending on the weather conditions. Grazing was started when the average
gass height ranged from 20 to 30 cm. Four gazing events were typically obtained at the site
of the study (Hickory Comers, MI). A total of eight gazing events were taken over the three
years of the study. Four gazing events in 2004, three gazing events in 2005 and a single
gazing event in 2006 were conducted.

For animal gazing, the whole area (binary mixture portion, gass and clover

monoculture portions) was divided into two sections using electric fencing. One section

83

consisted of replications 1 and 2 of the binary mixtures portion. The second section
consisted of the replication 3 of the binary mixtures and all three replications of the
monoculture portions (gasses and clovers). For all gazing events, animals began
gazing in section 1 for 48 hr then they were moved to section 2 for another 48 hr. The
gazing map of the arrangement used in this trial is illustrated in Appendix Fig 3A.

The number of cattle released to gaze depended upon their body weight. In each
year, approximately 10 Holstein steers, each weighting 226 kg was used for the first
gazing event (Holstein heifers were used for 2006 gazing). However, for the
subsequent gazing events, the number of animals was reduced to maintain the same
stocking density (Appendix Table 5A). After each gazing event, plots were
mechanically mowed to a uniform height (8 cm) and cattle were kept in different
paddocks until the next gazing event. Adequate water and minerals were available
during the gazing time in the gazing area.

Preference scores were visually assigred to assess the degee to which each of the
plots had been gazed off after allowing the animals to gaze each section for 48 hr. The
preference rating was conducted by assessing the amount of remaining vegetation on a
scale of (1-5) (McCaughey, 1998) where: 1 = less than 20% of the plot gazed; 2 = 20-
40%; 3 = 40 - 60 %; 4 = 60 - 80 %; and 5 = 80% or geater of the plot was gazed. The
cultivars that have high preference score over the course of the study are potentially more

palatable.

84

Forage quality analyses: Prior to each gazing event, forage samples were hand clipped
within 0.25 m2 quadrate in each plot of the gass, clover and binary mixtures portions.
Additional samples were also collected from Lake City Experiment Station (LC) and the
Upper Peninsula Experiment Station at Chatham (UPES), MI.

Samples were dried at 60 °C for 48 hr for dry matter determination (Data on dry
matter yield presented in Chapter 1). For forage quality analyses, dried samples were
gound to pass through a 2 mm screen using a Wiley Grinding Mill (Authur H. Thomas
Co., Philadelphia, PA) and then passed through a 1mm screen using a UDY Cyclone
Mill, (Udy Mill Corp., Fort Collins, CO.).

1n the crude protein analysis, total N was determined for the subset by the Hach
modified Kjeldahl procedure (Watkins et al., 1987), and CP was estimated by multiplying
total N by 6.25 because N makes up about 16% of the protein in the plant (Collins and
Fritz, 2003). The Goering and Van Soest (1970) method was used for NDF and ADF
determination with the addition of one ml of alpha-amylase to the neutral detergent
solution for the breakdown of starch.

A sub-sample of ~20 gams of each sample was retained for forage quality
analysis using Near-Infi'ared Reflectance Spectroscopy (N IRS). Each sample was
scanned with a 6500 near-infrared spectrophotometer (FOSS NIRSystems, Inc., Eden
Prairie, MN) with wavelengths between 400 and 2500 nm. Reflected wavelengths were
recorded. CP, ADF and NDF were determined fi'om equations developed by the NIRS
consortium (Madison, WI). All the chemical analyses and NIRS procedures were

performed at the MSU Forage and Physiology Lab.

85

Statistical analyses: All the statistical computations were performed using SAS (SAS
Institute, 2000). Analysis of variance was conducted using PROC GLM procedure.
Experimental units were the individual plots. The data for 2004 and 2005 were analyzed
separately. Experiment type and gazing event were the fixed factors and blocks
(replication) nested within type was a random factor used as an error term to test the
effect of type. Grazing by blocks by type interaction term was used as an error term to
test the effect of gazing and the effect of gazing by type interaction. When the
interaction between gazing and type was found to be significant (p<0.05), mean
separations between the types were conducted separately within each gazing.

Mean separation of animal preference scores and CP, ADF and NDF was
achieved using Fischer’s LSD at p<0.05. Normality assumption was checked using stem
and leaf and normal probability plots in PROC UNIVARIATE. Coefficient of variation

(CV) was listed to measure the precision of the experiment.

86

RESULTS AND DISCUSSION

Animal preferences

Sigiificant years X gazing events X treatment (binary mixtures) interactions were
found in animal preferences for gass-clover binary mixtures (Appendix Table 9A).
Similar interactions were observed for gass monoculture (Appendix Table 10A). These
interactions were primarily due to changes in plant maturity from one gazing event to the
other and also from year to year. However, these interactions were not sigiificant for
clover monoculture (Appendix Table 11A).

In general, there was a trend toward higher preference scores of early gazing
events compared to later gazing events in 2004 (Figl) and 2005 (Fig2). During the early
season, animals had less experience selecting among the forage species and as the season
progessed, they started to acquire preferences for their diet selection (Vallentine, 2001).
This may also be a result of lower plant ligrin and/or higher sugar levels in the early
spring.

Animals in this study showed varying degees of preference between
monocultures and binary mixtures. Results showed binary mixtures were preferred by
the animals over monoculture treatments (Figl and 2). Binary mixtures were
sigiificantly higher in animal preference compared to gass monoculture (p<0.01) in
three of four gazing events in 2004 and significantly (p<0.05) higher than clover
monoculture in two gazing events in the same year (Fig 1). However, in 2005, gass-
clover binary mixtures were significantly (p<0.05) higher than gass monoculture in one

of three gazing events and higher than clover monoculture in two gazing events (Fig 2).

87

No sigrificant differences were found between monoculture and binary mixtures in the
single gazing event of 2006 (data not shown).

Animals showed a preference between gasses and clovers gown separately as
monocultures. Clovers were significantly (p<0.05) geater in animal preference than the
gasses in two gazing events in 2004 (Figl). The gass monoculture was significantly
preferred over the clover monoculture (p<0.05) in only the first gazing event in 2005
(Fig. 2). These data provide evidence of the importance of the binary mixtures as well as
clovers for increased animal preference.

Grass monoculture data presented in Table 1 shows a sigiificant difference in
animal preference (p<0.05) between gass species and cultivars, while the preferences
among clover species (Table 2) were less distinguishable than for gass species. Only
one gazing event was Si grificantly different in preferences rating among eight clover
cultivars in 2004 (Table 2). In 2005, even though there were sigtificant differences
among the clovers cultivars across all the gazing events, the scores were lower for all
gazing events compared to 2004 (Table 2), which is likely due to the higher maximum
temperature causing increased lignification in gass and clover species. In addition,
animal dry matter intake decreases with heat stress. In 2006, there was no data recorded
on clovers due to the insufficient gowth of the clover portion at the gazing event.

There was a Sigiificant difference in animal preference for the binary mixture
treatments. The preference was more dependent upon the gass species than the
associated clovers (Table 3). The animal preferences results of each gass species gown

in monocultures or in binary mixtures are described as follows:

88

Festulolium: Duo gown in m0noculture was sigiificantly (p<0.05) higher in animal
preference than Hykor in five of eight gazing events during the three year period (Table
1). When gown with clover in a binary mixture, Duo was sigiificantly higher in animal
preference (p<0.05) in six gazing events especially when associated with white clover
(Kopu II) and kura clover (Endura) (Table 3). Duo was most likely preferred over Hykor
because it has finer leaf blades, thus making it more desirable for animals (MacAdam and
Mayland, 2003). In addition, since festuloliums are derived from a cross between Italian
or perennial ryegass and either meadow or tall fescue, it is likely that the genetic make
up was a factor that influenced the difference in preference. Tall fescue has thicker and
more course leaf blades than that of meadow fescue. Duo is the result of a cross of
Meadow fescue and a tetraploid perennial ryegass, with leaf blades and sugar content
more like a ryegass (AMPAC seed company web site). Thus, with the average of three
gazing years, animal preference of Duo was similar to perennial ryegass cultivars
(Tablel ). These data confirm that Duo and Hykor festulolium cultivars differed in their
animal preference and animals distinguish between them either when gown in
monoculture or a binary mixture with a clover. However, even though Hykor appeared to
be less palatable and had not been selected over Duo when they were gown in
monocultures, it was gazed relatively well when gown in binary mixtures with clovers
(Table 3). This would indicate that clovers in a binary mixture of somewhat unpalatable
gasses can increase the consumption of the gasses which may not be normally preferred
by animals due to the reduction of NDF in the binary as it associated with animal intake.
There was no sigrificant difference in animal preference between the three Hykor-clover

binary mixtures (Table 3).

89

Orchardgrass: Hoveland, (1992) found that orchardgass is a well suited perennial cool
season gass for using in binary mixtures. Orchardgass cultivars were less preferred by
animals when gazed as a monoculture compared to binary mixtures of clover.
Preference scores of four orchardgass cultivars (Tekapo, Amba, Niva, and Sparta) were
sigrificantly (p<0.05) different in one of eight gazing events (Table 1). However, the
binary mixture analysis presented in Table 3 showed geater preference for some
cultivars in 2004 versus 2005 and 2006. For example, Tekapo had a very high preference
score when gown with kura clover during the 2004 gowing season and a low preference
score for all the gazing events of 2005 and the single gazing event of 2006. As
indicated earlier, the reduction of 2005 preference ratings may be due to weather related
stress causing higher ligiification and heat stress in animals. Orchardgass cultivars
Tekapo, Sparta, and Niva had lower preference scores when gown with certain white
and red clover cultivars (Table 3). Over the three year gazing period, the best
orchardgass-clover combination was Tekapo with kura clover and with the white clover
cultivar, Kopu II. Based on these data, Tekapo orchardgass would be best suited as a
binary mixture with clovers for Michigan producers under rotational gazing although
Tekapo had lower persistence at the northern latitude whether gown in monoculture or
binary mixtures (Chapter 1), which indicated that cultivar selection Should be based on

both the persistence and animal preference.

Perennial ryegrass: Perennial ryegass is considered one of the most desirable species
for gazing livestock because of its high palatability and forage quality (Balasko et al.,
1995). Results showed that perennial ryegass cultivars in the monoculture trial were

high in animal preference in the first gazing event of 2004 and 2005 gazing seasons

90

(Tablel). There was a sigrificant difference in animal preference (p<0.05) among the
seven perennial ryegass cultivars when gown in monoculture (Aries, Maverick Gold,
Quartet, Tonga, Barfort, Mara, and Calibra) at only two of eight gazing events (Table 1).
Animals showed higher (p<0.05) selectivity for perennial rye gass cultivars in all
gazing events over the three years period when gown in binary mixtures with clovers.
For example, Aries-kura (Endura), Quartet-white clover (Kopu II)‘, Quartet-kura (Endura)
and Tonga -white clover (Ladino) binary mixtures resulted in the highest preference
among perennial ryegass-clover mixtures (Table 3)

Perennial ryegass does not tolerate heat stress or drought and its production
during hotter and drier summer months will be lower (Balasko and Nelson, 2003). This
explains the decline in average preference during the second, third, and fourth gazing
events of 2004 (Table 1). The preference of forages varies with seasonal conditions and

stage of gowth (Am.Soc.Range Mgnt, 1962).

Tall fescue: Tall fescue is considered less preferred by animals compared to perennial
rye gass particularly when gown in monoculture. There was one gazing event which
resulted in a sigrificant difference (p<0.05) among the three monoculture tall fescue
cultivars in this study (Bronson, K5666V, and Barolex) (Table 1). Tall fescue cultivars
resulted in higher preference scores when gown in binary mixtures. There were
sigiificant differences between the tall-fescue binary mixtures (Table 3). Bronson and
Barolex had Sigiificantly lower animal preferences when gown with red clover. Kura
clover gown with K5666V resulted in the highest preference among the tall fescue

mixtures.

91

Tall fescue cultivars demonstrated geater yield production, better compatibility
with clovers, better winter hardness and good gound cover compared to other gass
species in this study (Chapter 1), yet they were less preferred by animals compared to the
other gass species even when gown in binary mixtures. The reason for the decreased
preference for tall fescue cultivars was likely due to increased leaf width and thickness as
explained by MacAdam and Mayland (2003). However, if animals are only offered tall
fescue cultivars in pastures, they will likely consume it. Some of the cultivars in this
experiment displayed leaf blades, which were narrower and less thick than others.
However, it would be still more useful for Michigan gowers to use tall fescue cultivars
with clovers to enhance preference.

Forage Quality

Significant years X location X cutting (gazing) X treatment (binary mixtures)
interactions were present for CP, ADF and NDF (Appendix Table 9A). These
interactions were due primarily to change in sward composition of the mixtures, which
occurred from one gazing event to another and from year to year, which was similar to
results found by Zemenchik et al. (2001). Significant years X location X cutting
(gazing) X treatment interactions were found for CP, ADF and NDF for gass and clover
monoculture treatments (Appendix Tables 10A and 11A, respectively), which was due to
the change in plant forage quality that occurred between gazing events and year to year.
However, there was no interaction for (N DF) in gass treatments (Appendix Table 10A)
and in (CP) clover (Appendix Table 11A). Results from forage quality analysis have
proven the hypothesis, which states that binary mixtures have higher forage quality than

monocultures (gass or clover). Results of forage quality Show that clovers had highest

92

CP, lowest NDF and ADF compared to gass monocultures and binary mixtures (Table
4). Grass monoculture had lower forage quality (Greater ADF and NDF and lower CP)
than binary mixtures. Combining clover with any of the cool-season gasses sigrificantly
reduced concentration of NDF and ADF and increased CP compared to gass
monocultures (Table 5). These results concur with Zemenchick et al., (2002) in which
they found that legume proportions in binary mixtures were positively correlated to CF

concentrations and negatively correlated to NDF concentrations.

There was a Sigiificant difference in forage quality among species as well as
between cultivars of each gass. In 2003, the first gazing event resulted in low ADF and
NDF content in perennial ryegass (Barfort) and orchardgass (Tekapo) and higher CP
than other gass cultivars when gown in binary mixtures. Wamock (2004) concluded
that Barfort perennial ryegass and Tekapo orchardgass had lower NDF and ADF values
than other cultivars such as perennial ryegass (Aries). The 2004-2005 analysis of data
revealed higher forage quality in all perennial ryegass cultivars especially Maverick
Gold, Quartet and Tonga. The forage quality of festulolium (Duo) was higher in CP and
lower in ADF and NDF than that of Hykor festulolium. In addition, tall fescue (Bronson)
and festulolium (Hykor) cultivars were lower in forge quality compared to the other
cultivars within their species (Table 5). In the orchard gass monocultures, Sparta had the

highest CP compared to Amba, Niva and Tekapo.

Clover species also were significantly different in forage quality. Kura clover
(Endura) and white clover (Kopu 11, Alice, Jumbo and Ladino) were higher in CP and

lower in ADF and NDF compared to all red clover cultivars (Table 6). However, all red

93

clover cultivars were much higher in CF and lower in ADF and NDF compared to

perennial gass species.

Total yield and animal preferences were not positively correlated. For example,
white clover (J umbo)- festulolium (Hykor) mixtures that produced the highest total dry
matter yield were less preferred by animals than perennial ryegass-clover mixtures,
which had lowest yield and a higher animal preferences rating (Data not shown). These
results concur with a study conducted by Shewmaker et al. (1997).

CONCLUSIONS

Livestock producers should consider adapting forage species and cultivars,
which show higher animal preferences and have higher forage quality for profitable
systems. Thus, cultivar selection should be based upon animal preference, persistence and
yield. The binary mixtures of festulolium (Duo)-clover increased preferences and forage
quality compared to monoculture festulolium. In addition, perennial ryegass cultivars
demonstrated good forage quality and animal preferences when associated with clover
cultivars, which indicate these mixtures are a good choice for livestock producers to use
for gazing. However, their ability to survive the harsh winter could be an issue
especially, when gow in northern latitudes (44°-46° N). Cultivars that have shown a
higher preference to animals with higher forage quality (Aries, Maverick Gold) were
more vulnerable to the winter injury. Therefore, areas where winter injury is problem,

these cultivars would not be a good choice (Chapter 1).

94

LITERATURE CITED

Aderibigbe, A.O., D.C. Church, R.V. Frakes, and R.G. Petersen. 1982. Factors
determining palatability of ryegass to cattle. J. Anim. Sci. 54:164—172.

Akin, DE, and D Burdick. 1975. Percentage of tissue types in tropical and temperate
gass leaf blades and degadation of tissues by rumen microorganisms. Crop Sci.
15:661-668.

Am. Soc. Range Mgnt. 1962. Basic problems and techniques in range research. Nat
Acad. Sci. Pub.890.

AMPAC seed company. A F estulolium facts. Aavailable at URL:
http://www.ampaseed.com/festfacts.htm.

Balasko, J .A., and C.J.Nelson. 2003. Grasses for northern areas. In Barnes et al. (ed.).
Forages volume I: An introduction to gassland agiculture, 6th ed. Iowa State
University Press. Ames, IA.

Balasko, J. A., G.W. Evers and R.W Duell. 1995. Bluegasses, Ryegasses, and
Bentgasses. In Barnes et al. (ed.). Forages volume I: An introduction to gassland
agiculture, 5th ed. Iowa State University Press. Ames, IA.

Buxton, DR, and DR. Mertens. 1995. Quality-related characteristics of forages. In
Barnes et al. (ed). Forages, Volume II: The Science of Grassland Agiculture, 5th
ed. Iowa State University. Ames, IA.

Blaser, R.E., R.C. Hames J R, H.T. Bryant, W.A.Hardison, J .P. Fontenot and R.W. Engel.
1960. The effect of selective gazing on animal output. Proc.8th Intern. Grassl.
Cong. 8: 601-606.

Bryant, W.G.l974. Caucasian clover (T rifolium ambiguim. Bieb): A review. J. Aust.
Inst. Agic. Sci. 40: 11-19.

Castle, M.E., and R. J. Halley. 1953. The gazing behavior of dairy cattle of the national
institute for research in dairying. Brit.J.Anim. Behav. 1:139-143.

Corbett, J .L. 1953. Grazing behaviour in New Zealand. Brit.J.Anim. Behav. 1: 67-71.
Collins, M., and J .O.Fritz. 2003. Forage quality. In Barnes et al. (ed.). Forages volume I:

An introduction to gassland agiculture, 6th ed. Iowa State University Press.
Ames, IA.

Collins, M., and DB. Hannaway.2003. Forage related animal disorder. In Barnes et al.
(ed.). Forages volume I: An introduction to gassland agriculture, 6th ed. Iowa
State University Press. Ames, IA.

95

Christie, BR, and AR McElroy1995. Orchardgass. In Barnes et al. (ed.). Forages
volume I: An introduction to gassland agriculture, 5th ed. Iowa State University
Press. Ames, IA.

Cruz, R., and D. Ganskopp. 1998. Seasonal preferences of steers for prominent northern
geat basin gasses. J. Range Manage. 51:557-565.

Cook, C. W.1959. The effect of site on the palatability and nutritive content of seeded
wheatgasses. J. Range Manage. 12:289-292.

Curll, M.L., and R.J.Wilkins. 1982. Frequency and severity of defoliation of gass and
clover by sheep at different stocking rates. Grass Forage Sci. 37: 291-297.

Clemson University Cooperative Extension Service Web Based Electronic Forage. 1997.
Forage Quality. Aavailable at URL
http://www.clemson.edu/agonomy/stlands/basic/foragehtml.

Davies, A. 1988. The regowth of gass swards. In M.B Jones and A Lazenby (ed.). The
Grass Crop. London: Chapman and Hall, 85-127.

Forde, M.B., M.J.B. Hay, and J .L. Brock. 1989. Development and gowth characteristics
of temperate perennial legumes. p. 91-109. In G.C. Marten et al. (ed.) Persistence
of forage legumes. Proc. Australian/New Zealand/United States Workshop,
Honolulu, HI. 18—22 July 1988. ASA, CSSA, and SSSA, Madison, WI.

Fisher, D.M., H. F. Mayland, and J. C. Burns. 2002. Variation in ruminant preference
for alfalfa hays cut at sunup and sundown. Crop Sci 42:23 1-23 7.

Goering, H.K., and P. Van Soest. 1970. Forage fiber analysis: Apparatus, application.
USDA. Agic. Handb 379. US.Gov. Print Office. Washington. DC.

Grant, S.A., D.F. Suckling, H.K.Smith, L. Torvell, T.D.A. Forbes, and J .Hodgson. 1985.
Comparative studies of diet selection by sheep and cattle. The hill gasslands.
J.Ecol. 73:987-1004.

Heady, HF. 1964. Palatability of herbage and animal preferences. I. Range Manage.
17:76-82.

Hoveland, CS. 1992. Grazing systems for humid regions. J.prod. Agic. 5:23.

Kare, MR .,and B.P. Halpem. 1961. Physiological and behavioral aspects of taste. Univ.
Chicago press 149. pp.

Knight, W.E. 1985. The distribution and use of forage legumes in the United States. p.
34—46. In R.F Barnes et al. (ed.). Forage legumes for energy efficient animal

96

production. Proc. Trilateral Workshop, Palmerston North, New Zealand. 30 Apr.—
4 May 1984. USDA-ARS. US. Gov. Print. Office, Washington, DC.

Laidlaw, A.S. 1983.Grazing by sheep and the distribution of species through the canopy
of a red clover-perennial ryegass sward. Grass Forage Sci. 38:317-321.

Macoon, B., L.E. Sollenberger, J .E. Moore, C.R. Staples, J .H. Pike, and KM. Portier.
2003. Comparison of three techniques for estimating the forage intake of lactating
dairy cows on pasture. J. Anim. Sci. 81:2357—2366.

MacAdam, J .W., and HF. Mayland. 2003. The relationship of leaf strength to cattle
preference in tall Fescue cultivars. Agon.J 95:414-419.

Mitchell, H.L., and N.W.Hosley, 1936. Differential browsing by deer on plots variously
fertilized. Black Rock Forest Papers: 1 24-27.

McCaughey, W.P.1998. Herbage yield and cattle preference for dryland pasture gasses.
Can. J. Anim. Sci. 78:237-239.

Mourifio, F., K.A. Albrecht, D. M. Schaefer and P Berzaghi. 2003. Steer Performance on
Kura Clover—Grass and Red Clover—Grass Mixed Pastures. Agon. J. 95:652-659.

Meijs, J.A.C. 1981. Herbage intake by gazing dairy cows. Wageningen Univ.,
Wageningen, the Netherlands.

Provenza, F.D., C.B. Scott, T.S. Phy, and J .J . Lynch. 1996. Preference of sheep for foods
varying in flavors and nutrients. J. Anim. Sci. 74:2355—2361.

Plice, M. J. 1952. Sugar versus the intuitive choice of foods by livestock. J. Range
Manage 5: 69-75.

Pederson, G.A.1995. White clover and other perennial clovers. In Barnes et al. (ed.).
Forages volume I: An introduction to gassland agriculture, 5th ed. Iowa State
University Press. Ames, IA.

Penn State University Agonomy guid 2005-2006. Part 1, section 8, Forages.
Aavailable at URL. http://agguide.agronomvpsu.edu/cm/sec8/sec8lOb.cfm.

Rohweder, D.A., and K.A.Albrecht. 1995. Permanent pasture Ecosystems. In Barnes et
al. (ed.). Forages, volume 11: The Science of Grassland Agiculture, 5th ed. Iowa
State University. Ames, IA.

Speer, GS, and D.W Allinson.1985. Kura clover (Trifolium ambiguum): Legume for
forage and soil conservation. Econ. Bot. 39:165-76 .nb.

97

Sleugh, B., K.J.Moore, J .R.George, and E.C.Brummer. 2000. Binary legume-gass
mixtures improved forage yield, quality, and seasonal distribution. Agon. J.
92:24-29.

Spitaleri, R.F., J .C. Henning, G.D. Lacefield, N.L. Taylor, and E. Vanzant. 2003. 2002
red and white clover gazing tolerance report. Collage of Agi. University of
Kentucky. Electronic publication available at URL:
http://wwwcauky.edu/ach/pubs/pr/pr48l/PR481.PDF.

Smit, H.J ., S. Tamminga, and A. Elgersma. 2006. Dairy Cattle Grazing Preference among
Six Cultivars of Perennial Ryegass. Agon. J. 98: 1213-1220.

Shewmaker, G.E., H.F. Mayland, and SB Hansen. 1997. Cattle gazing preference
among eight endophyte-free tall fescue cultivars. Agon. J. 89:695-701.

Saltonstall, JR.L. 1948. The measurement of quantity and quality of pasture herbage
consumed by sheep: A technique study. Ph.D. Thesis, Cornell University, Ithaca,
NY.

Tolkamp, B.J., R.J. Dewhurst, N.C. Friggens, I. Kyriazakis, R.F. Veerkamp, and J .D.
Oldharn. 1998. Diet choice by dairy cows: 1. Selection of feed protein content
during the first half of lactation. J. Dairy Sci. 81 :2657—2669.

Vallentine, J .F. 2001. Grazing management, 2"d ed. Harcourt science and Technology
company. San Diego, CA.

VanKeuren, R.W., and AG. Matches. 1988. Pasture production and utilization. p. 515—
538. In A.A. Hanson et al. (ed.) Alfalfa and alfalfa improvement. Agon.
Monog. 29. ASA, CSSA, and SSSA, Madison, WI.

Van Soest, P.J. 1982. Nutritional ecology of the ruminant. O & B Book, Corvallis, OR.

Wamock, D.L. 2004. Adaptation of diploid and tetraploid cultivars of perennial
ryegass throughout Michigan as gowing in association with or without ladino
white clover. Master Thesis, Michigan State University. East Lansing, MI.

Watkins, KL. 1987. Total nitrogen determination of various sample types: A
comparison of the Hach, Kjeltec and Kjeldahl methods. J. Assoc. of Anal. Chem
70:3.

Willms, W.D., and L.M. Rode. 1998. Forage selection by cattle on fescue prairie in
summer or winter. J. Range Manage. 51 :496-500.

98

Zemenchik, R.A., K.A. Albrecht, and MK. Schultz. 2001. Nitrogen replacement value of
kura clover and birdfoot trefoil in binary mixtures with cool-season gass.
Agon.J.93:451-458.

Zemenchik, R.A., K.A. Albrecht, and RD. Shaver.2002. Improved nutritive value of

Kura clover —and birdsfoot trefoil-gass mixtures compared with gass
monoculture. Agon.J. 94: 1131-1138.

99

 

 

 

 

 

 

 

 

 

Fig.1. The differences in animal grazing preference for the binary mixture (BM), clover,
and grass treatments during 2004 grazing season (Bars with different letters within
grazing event are significantly different at p<0.05).

 

lGrassuOou—rrlBM

 

 

 

 

 

 

 

Grazirg evert

 

 

 

Fig 2. The differences in animal gazing preference for the binary mixture (BM), clover,
and grass treatments during 2005 grazing season (Bars with different letters within
grazing event are significantly different at p<0.05).

100

Table 1. Animal preference score of 16 gass cultivars established in monoculture
treatments over eigrt gazing events.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Grass 2004 2005 2006
Species Cultivars 28-May 26-Jun 28-Ju1 9-Sep 6-May l6-Jun 4-Aug lZ-May
Duo 4.8 3.3 1.2 1.1 4.7 1.0 2.7 3.0

Fest Hykor 1.5 2.1 1.7 1.1 1.7 1.7 1.0 1.2
Tekapo 2.0 1.5 1.3 1.0 1.5 1.0 1.2 2.3

Amba 2.3 3.5 2.6 1.0 2.7 1.0 1.0 1.7

0R Niva 1.5 3.5 3.3 1.8 2.0 1.0 1.0 3.2
Sparta 3.0 3.0 3.3 1.8 1.9 1.3 1.0 3.2

Aries 3.3 3.6 1.5 1.0 4.5 1.5 1.7 1.8
Maverick Gold 4.8 3.5 2.0 1.5 4.2 1.0 2.2 1.2
Quartet 4.0 3.0 1.8 1.0 3.5 1.0 2.0 1.5

PR Tonga 4.0 3.5 1.5 1.0 4.8 2.0 2.4 3.3
Barfort 4.8 3.6 1.5 1.3 4.7 1.3 1.7 3.3

Mara 5.0 3.5 1.6 1.1 4.4 1.0 1.0 3.0

Calibra 4.1 3.1 1.6 1.0 4.7 1.0 2.4 3.3
Bronson 1.3 2.6 3.0 1.0 1.2 1.3 1.0 1.0

TF K5666V 1.3 2.6 3.3 1.0 2.4 1.5 1.0 1.2
Barolex 1.0 2.8 2.0 1.0 2.4 1.0 1.0 1.3

Mean 3.1 3.1 2.1 1.2 3.2 13 1.5 2.2

cv 21 30 38 34 20 54 42 47
LSD(0.05) 1.1 1.6 13 . 0.7 1.0 NS 1.0 1.7

 

1 = less than 20% of the plot grazed

5 = 80% or geater of the plot was grazed.

Grass cultivars:

Fest=Festulolium ; OR=Orchardgass, PR=Perennial ryegrass and TF= Tall fescue

101

 

 

Table 2. Animal preference score of eight clover cultivars established in monoculture
over seven gazing events.

 

 

 

 

Clover 2004 2005

Species Cultivars 28-May 26-Jun 28-Jul 9- Sep 6-May l6-Jun 4-Aug

Kura Endura 3.8 3.0 3.3 1.3 1.0 3.7 2.5

Red Star Fire 3.6 4.1 4.1 1.8 1.0 1.4 1.0

Clover Start 2.8 3.0 3.6 1.6 1.2 1.5 1.0

VNS 2.6 2.8 2.5 2.5 1.0 1.7 1.0

Kopu II 3.8 3.6 3.8 3.3 1.7 1.9 1.2

White Alice 3.8 3.0 3.5 2.5 1.0 1.0 1.0

Clover Jumbo 3.0 2.3 3.6 2.1 1.7 3.2 1.0

Ladino 3.3 4.3 3.8 2.8 1.2 1.0 1.0

Mean 3.3 3.2 3.5 2.2 1.2 1.9 1.2

CV 37 48 37 41 32 57 18

LSD (0.05) NS NS NS 1.6 0.7 1.9 0.4

 

 

 

 

 

l = less than 20% of the plot grazed

5 = 80% or greater of the plot was gazed.

102

Table 3.Animal preference score of 67 gass-clover binary mixtures

over eight gazing events (continue next page).

 

 

 

 

 

 

 

Grass Clover 2004 2005 2006
Cultivars Cultivars 28-May 26-Jun 28-Jul 9-Sep 6-May 16-Jun 4-Aug l2-May
Duo Endura 4.8 3.3 3.0 2.8 3.2 2.5 4.8 5.0
Duo Star Fire 4.5 3.3 2.5 2.8 4.4 1.0 2.5 1.3
Duo VNS 4.8 3.1 2.3 2.1 3.0 1.2 1.9 2.7
Duo Ladino 4.6 2.8 2.8 3.1 4.5 1.0 3.7 2.7
Duo Kopu 11 4.8 4.6 3.1 4.0 4.7 1.0 2.0 3.0
Hykor VNS 2.3 2.3 2.3 1.6 3.6 1.5 1.0 1.0
Hykor Jumbo 2.3 2.3 3.5 2.0 3.3 1.0 1.0 1.3
Hykor Ladino 2.6 2.3 3.3 1.8 3.5 1.3 1.5 1.3
Amba VNS 3.8 3.8 3.8 3.0 3.3 3.0 1.3 1.0
Amba Jumbo 3.8 4.5 4.1 4.1 3.8 1.8 2.2 1.0
Amba Ladino 4.6 4.6 4.1 3.5 4.1 1.7 1.7 1.0
Niva VNS 3.5 3.5 3.8 3.0 2.0 2.0 1.5 2.3
Niva Jumbo 4.3 3.6 3.5 3.6 1.4 1.7 1.4 2.5
Niva Ladino 3.3 4.3 4.3 3.3 2.7 2.0 2.0 2.7
Sparta VNS 3.8 3.8 3.5 3.6 2.7 1.0 1.0 2.0
Sparta Jumbo 3.8 4.5 4.5 4.0 3.4 3.0 1.9 3.2
Sparta Ladino 3.8 2.8 4.5 3.3 2.9 1.5 1.9 2.2
Tekapo Endura 3.8 4.0 5.0 4.5 2.5 3.9 3.5 2.2
Tekapo VNS 4.0 2.8 3.6 2.8 1.4 1.7 1.4 2.7
Tekapo Star Fire 3.8 2.1 2.0 3.5 2.0 3.4 2.2 1.2
Tekapo Kopu 11 4.1 4.5 4.5 3.6 2.4 3.0 3.5 2.0
Tekapo Ladino 4.6 3.1 4.8 3.8 1.7 2.5 2.2 1.2
Aries Endura 4.6 4.5 4.0 4.8 4.2 3.7 4.5 3.3
Aries VNS 4.1 3.8 3.5 4.0 3.2 2.8 3.0 2.2
Aries Star Fire 3.8 3.8 3.8 3.0 4.4 2.2 3.7 2.0
Aries Kopu 11 3.6 4.6 4.5 4.8 3.4 1.8 3.7 1.2
Aries Ladino 4.8 3.5 3.1 3.0 4.2 1.5 3.0 2.3
Barfort VNS 4.5 3.1 1.3 2.3 3.8 1.8 3.0 2.0
Barfort Start 4.5 3.6 4.1 3.0 3.83 1.0 3.7 1.7
Barfort Alice 4.0 3.8 3.6 3.8 3.7 1.2 3.7 2.3
Barfort Ladino 4.6 4.3 4.1 3.1 4.0 1.5 2.5 2.5
Calibra VNS 4.8 3.3 2.1 3.0 4.4 1.5 1.9 1.8
Calibra Jumbo 4.3 4.3 3.3 3.8 5.0 1.4 4.0 2.0
Calibra Ladino 5.0 4.1 4.1 2.6 2.7 2.7 4.7 2.7
Mara VNS 4.0 2.0 1.5 1.5 3.5 1.5 2.5 4.3
Mara Start 4.3 2.6 1.8 2.6 4.5 1.0 1.4 2.5
Mara Alice 5.0 3.1 2.1 2.6 4.5 1.0 2.9 3.3
Mara Ladino 4.5 2.1 2.0 2.3 4.5 1.0 2.4 3.5
Mavrick Gold Endura 4.8 3.8 3.6 4.0 3.7 2.4 2.7 2.2
Mavrick Gold VNS 4.1 2.8 2.1 2.3 3.0 2 2.9 1.5
Mavrick Gold Star Fire 4.1 3.3 3.1 2.6 3.7 1.2 2.4 1.0
Mavrick Gold Kopu 11 4.5 4.0 3.8 4.5 4.4 1.0 2.7 2.2
Mavrick Gold Ladino 4.3 4.0 2.6 3.8 4.0 1.9 3.0 1.5
Quartet Endura 4.8 4.0 4.3 4.5 4.5 3.9 4.7 2.5

 

 

 

 

 

 

 

103

 

Table 3 continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Grass Clover 2004 2005 2006
Cultivars Cultivars 28-May 26-Jun 28-Jul 9-Sgp 6-May 16-Jun 4-Au 12-May

Quartet VNS 4.6 4.5 4.1 4.5 2.9 2.2 3.2 2.0
Quartet Ladino 4.6 4.6 3.5 3.6 3.5 1.0 3.9 1.5
Quartet Star Fire 4.8 4.6 4.6 3.3 3.5 1.4 3.5 1.5
Quartet Kopu 11 5.0 4.5 4.8 2.6 3.4 3.7 4.5 2.0
Tonga Endura 4.6 3.6 2.6 2.5 3.5 1.9 4.2 3.2
Tonga VNS 4.0 3.5 2.8 1.8 4.0 1.0 4.0 2.0
Tonga Star Fire 4.3 4.5 3.5 3.3 3.5 2.9 3.2 1.8
Tonga Kopu 11 4.5 3.8 3.6 3.1 4.5 1.6 2.7 2.5
Tonga Ladino 4.8 4.6 4.6 3.3 4.0 3.2 4.7 2.3
Bronson VNS 2.5 1.6 3.0 1.1 3.4 1.7 1.0 1.0
Bronson Endura 2.8 2.1 3.1 1.8 3.7 3.5 2.7 1.0
Bronson Kopu 11 2.6 2.1 4.0 2.0 3.2 1.5 1.4 1.0
Bronson Ladino 3.6 2.5 4.1 3.1 4.0 2.9 1.4 1.0
Bronson Star Fire 3.5 2.0 3.1 1.6 2.9 1.0 1.0 1.3
K5666V VNS 3.6 3.3 4.5 2.6 3.7 1.0 1.7 1.7
K5666V Endura 4.0 3.8 4.6 4.0 4.2 2.5 4.7 2.3
K5666V Kopu 11 3.8 4.1 5.0 4.5 3.9 1.3 2.9 1.8
K5666V Ladino 4.1 3.1 3.8 2.1 3.7 2.4 2.7 1.3
K5666V Star Fire 3.3 2.5 3.5 2.6 3.4 1.7 3.2 1.0
Barolex Alice 3.3 4.0 3.8 3.1 4.8 1.5 2.2 1.7
Barolex VNS 2.5 3.5 2.6 1.6 4.2 1.8 1.5 1.2
Barolex Ladino 3.5 3.5 3.1 1.6 3.7 1.2 1.0 1.0
Barolex Start 2.5 2.6 3.0 1.5 2.5 1.2 1.0 1.3
Mean 4.1 3.5 3.5 3.1 3.5 1.9 2.6 1.9

CV 18.1 26.1 30.5 33.3 26.6 53.5 37.3 51.7
LSD (0.05) 1.2 1.5 1.7 1.6 1.5 1.6 1.6 1.7

 

1 = less than 20% of the plot grazed

5 = 80% or geater of the plot was gazed

Grass cultivars:

Festulolium (Duo and Hykor)
Orchardgass (Amba, Niva, Sparta and Tekapo)
Perennial ryegass (Aries, Barfort, Calibra, Mara, Maverick Gold, Quartet, and Tonga,)
Tall fescue (Bronson, K5666V, and Barolex)

Clover cultivars:

Red clover (Start Fire, Start and VNS)
White clover (Ladino, Alice, Kopu II, and Jumbo)

Kura (Endura)

104

 

Table 4 . The mean values (%) of forage quality components (CP, ADF
and NDF) for pasture species of gass, clover and binary mixtures over
2004-2005 gowing seasons in three different locations.

 

 

 

 

 

 

2004 2005
pasture Type CP" I ADF” | NDF' CP I ADF I NDF
Grass 16.9 28.2 45.6 20.2 28. 2 54.2
Clover 20.2 26.4 33.4 20.6 20.9 29.8
Binary Mixtures 20.1 25.9 43.9 17.7 25.8 42.4

 

 

 

 

Values based on the (Lsmeans) of all locations and cuts

x Crude protein

y Acid detergent fiber (ADF)
2 Neutral detergent fiber (NDF)

Table 5. Average forage quality parameters (CP, ADF and NDF) of 16 gass
cultivars established in monoculture and binary mixtures in three locations.

 

 

 

 

 

 

 

 

 

 

 

 

Grass Grass Monoculture Binary Mixtures
Species Cultivars CF I ADF I NDF CP I ADF I NDF
%
Duo 19.4 25.6 48.2 23.6 22.1 37.1
Fest Hykor 17.6 29.7 51.5 20.5 25.2 44.6
Tekapo 19.2 27.8 52.2 22.5 24.3 42.7
Amba 18.9 26.1 49.3 21.7 23 .7 41.9
OR Niva 19.3 25.2 50.7 22.4 24.3 42.9
Sparta 20.2 26.4 50.2 22.3 23.8 42.4
An'es 19.1 24.9 48.8 23.7 22.2 36.7
Maverick Gold 20.6 23.9 49.1 23.5 22.0 373
Quartet 20.1 25.1 45.7 24.3 21.4 35.7
PR Tonga 20.1 23.4 46.2 24.0 21.5 35.5
Barfort 19.8 23.7 46.9 23.7 21.7 36.4
Mara 18.8 25.8 49.5 22.3 23.2 39.7
Calibra 19.4 25.8 47.4 23.6 22.0 36.9
Bronson 18.6 29.3 54.3 21.3 24.5 43.8
TF K5666V 18.5 28.1 50.3 22.4 23 .2 40.2
Barolex 17.5 28.9 52.8 21.6 24.1 42.5

 

F est= Festuloliums.
OR= Orchardgass.
PR=Perennial ryegass.
TF= Tall fescue.

105

 

Table 6. Average forage quality parameters (CP, ADF and NDF)
of eight clover cultivars established in monoculture in three locations.

 

 

 

 

 

 

 

 

Clover Forage Quality Parameters

Species Cultivars CP ADF NDF
%

Kura Endura 24.3 2 l .7 27 .9
Red Star Fire 23.7 23.1 29.8
Clover Start 21.6 21.9 28.5
VNS 23.9 22.9 29.3
Kopu II 24.5 20.6 26.0
White Alice 25.3 20.3 26.1
Clover Jumbo 25.3 20.2 25.1
Ladino 25.0 20.1 26.1

 

 

 

106

 

Chapter 3

PREDICTING BOTANICAL COMPOSITION OF GRASS-CLOVER PASTURES
MIXTURES USING NEAR-INFRARED REFLECTANCE SPECTROSCOPY

ABSTRACT
Hand separation is a common method used to determine the botanical species
composition of gass-legume swards. However, this method is very laborious and is time
consuming. This study was conducted to evaluate the use of Near-Infrared Reflectance

Spectroscopy (N IRS) to predict the species composition of several gass-clover binary

 

mixtures with samples collected from three locations and over three-year period.

Samples were taken during 2003-2005 gowing season from clover-gass binary mixture
experiments at Kellogg Biological Research Station at Hickory Comers (KBS), Lake City
Research Station (LC) and Upper Peninsula Experiment Station at Chatham (UPES), MI.
Second cutting event from each location was hand separated into gass, clover and weeds.
All samples were dried, gound and scanned with NIRS. Three calibration equations
were developed to predict the Species composition. The first equation was developed
using artificially mixed samples in 10% increments of pure gass and clover samples
collected from monoculture plots at LC location in 2004. The second equation was
created from hand-separated samples clipped at LC location in 2004. The third equation
was created from selected subsets of hand-separated samples collected from all three
locations and three years. The equations developed based on artificially mixed samples
and the hand-separated samples from the Single location and year resulted in a poor
prediction of gass and clover components. Prediction coefficients of determination (R2)

of gass and clover were, 0.24 and 0.31, respectively for artificially mixed samples

107

equation and were equal 0.25 and 0.37 for the hand-separation equation. Standard errors
of prediction (SEP) were relatively high for both calibration equations. However, the
equations developed based on selected hand-separated samples from all three locations
and all three years had higher prediction accuracy with R2 ranging from 0.67 to 0.72 and
SEP from 6.9 to 12.8, respectively. These results suggest that using either artificially
mixed or hand separated samples from a single location in a Single year is unsuitable to
predict species composition at different locations and years. It can be concluded that
NIRS can be applied to replace the hand separation method using the calibration equation
developed from hand separation data from different locations and years with some

limitations.

108

INTRODUCTION AND BACKGROUND

Legume content in a binary mixture is a key parameter for the quantification of N2
fixation and diet quality. Pastures with less than 30% legume should either be fertilized
with up to 168 kg of N ha'l or improved by introducing legumes in the sward.
Determination of botanical composition is important, since animal performance is
dependent on the proportion of desired plant species in the diet (Petersen et al., 1987).

The development of a reliable and rapid approach for determining botanical
composition has been a research goal for many years. There are several indirect methods
for estimating botanical composition of gass and legume mixtures including visual
estimates (Marten, 1964; Tanner et al., 1966; Tiwari et al., 1963), point quadrate methods
(Leasure, 1949; Vankeuren and Ahl gen 1957), dry-weight rank method (Mannetij and
Haydock, 1963; Walker, 1970) and chemical composition based method (Cooper et al.,
1957). The method that is commonly used for determining botanical composition is
hand-separation (Vankeuren and Ahl gen, 1957). Hand separation requires manually
separating the mixture into its components such as legumes, gasses, and weeds and the
component percentage is then determined by dry matter weight. However, hand-
separation is not practical since it is laborious, time-consuming, costly, and prone to
operator errors, especially when a large number of samples are being processed. Up to 2
to 3 hr may be required for identification and separation of sown pasture samples (Grant,
1981). Therefore, Near Infrared Reflectance Spectroscopy (N IRS) may be able to replace
hand-separation as a rapid and more convenient technique.

The primary use of NIRS in forages is to determine nutrient composition values

of feedstuffs (Norris et al., 1976). NIRS is an inexpensive method, which allows the

109

 

dried forage samples to be stored and then processed. Hand separation requires the
samples to be fresh and processed as soon as possible after harvest (Shaffer et al., 1990).

NIRS instruments use light at different wavelength in the NIR region to make
measurements. The light is either absorbed or reflected by the sample and the
wavelengths are determined at intervals between 700-2500 nm by a sensor. The
absorption of the light in the NIRS region is primarily due to the fi'equency and
arrangement of X-H bounds (Shenk and Westerhaus, 1994). Based on the absorption of
light, the forage composition may be predicted. Calibration equations are developed
from samples with known entities of interest (i.e acid detergent fiber or crude protein).
These entities are associated with their spectral values. The most accurate calibration
equations are developed from samples that represent all the variables that affect the
NIRS. Shenk et al. (1979) demonstrated that the percent legume in gass-legume
mixtures could be predicted within i 10 % by NIRS.

Several researchers have attempted to use NIRS as an approach to determine
botanical compositions. Some researchers have already proven the capability of NIRS to
determine legume content in legume-gass mixtures (Petersen et al., 1987; Pitrnan et al.,
1991 ). Coleman et al. (1985) concluded that with proper calibration (R2 from 0.95 to
0.99 and SEP from 1.9 to 6.9), NIRS could accurately determine species composition.
Petersen et al. (1987) reported a high R2 when predicting species composition in tall
fescue (F estuca arundinacea Schreb.) and white clover (T rifolium repens L) mixtures.
Wachendorf et.al.(1999) successfully predicted red clover (T rifolium pratense L.) and
white clover-gass mixtures content by using calibration equations from samples that

included gass-clover mixtures collected at different years and locations. Coleman et al.

110

(1990) predicted the botanical composition of legumes and gasses mixtures using a
calibration equation from a goup of pure samples, each sample consisting of one of three
botanical components to be validated by hand separation. They reported excellent
prediction of the legume component with prediction coefficient of determination R2 of
0.96 compared with caucasian bluestem [Andropogon caucasicus (Trin) C.E. Hubb] and
cheatgass (Bromus tectorum L.) that had R2 of 0.77 and 0.84, respectively. Shaffer et a1.
(1990) predicted alfalfa (Medicago sativa L.) and ryegass (Lolium perenne L.) in
mixtures using an equation developed based on data from different locations and years.
However, higher SEP was observed in prediction botanical composition of alfalfa-
ryegass from locations not included in the calibration equation. This may infer that
calibration equations should be derived from samples representing all the variables that
might influence the NIRS results, such as climate, soil type and species. Shaffer et al.
(1990) concluded that approximately 200 samples were required to provide sufficiently
accurate botanical composition prediction. Locher et al. (2005) obtained an accurate
prediction of legume content over a broad variation of multispecies clover-gass, plant
age, and Site conditions.

It is generally believed that NIRS will accurately quantify the botanical
composition of binary mixtures of vegetative gasses and legumes. However, no work
has been done to develop calibration equations from several vegetative pasture gass-
clover mixtures established at various latitudes over multiple— years period under North-
central US gowing conditions.

The objectives of this study were (i) to determine if NIRS could accurately predict

species composition of several vegetative pasture gass-clover mixtures in three different

11]

locations over three-year period and (ii) to compare prediction accuracy of two
calibration equations, one developed from pure laboratory-mixed samples and the other

developed from hand separation samples, both clipped from a Single location and year.

MATERIALS AND METHODS
Plant Material and Locations: Cool-season gass and clover binary mixtures were
established in summer of 2001 at three Michigan State University (MSU) experimental
stations: (i) Kellogg Biological Research Station at Hickory Comers (KBS) (42° 24' N,
85° 24' W); (ii) The Beef Cattle and Forage Research Station at Lake City (LC) (44°19'
N, 85°12' W), and (iii) Upper Peninsula Experiment Station at Chatham (U PES) (46°33'
N, 86°55' W). Three to five cutting events, depending on the location, were used to
obtain samples during 2003-2005 gowing seasons with 30 to 35 days interval between
cutting events. More cuttings were taken at the KBS site since it is located filrther south
than LC and UPES sites and has a longer gowing season.

Twenty-four clover and gass cultivars were used for this study. Clovers included
three red clover cultivars (VNS', Star Fire, and Start), four white clover (Kopull, Ladino,
Jumbo, and Alice), one kura clover cultivar (Endura). The gasses included two
festuloliums cultivars (Duo and Hykor), four orchardgass (Tekapo, Amba, Niva, and
Sparta), seven perennial ryegass (Aries, Maverick Gold, Quartet, Tonga, Barfort, Mara
and Calibra) and three tall fescue cultivars (Bronson, K5666V, and Barolex).

At each location, the experiment was arranged in a randomized complete block
design (RCBD) with three replications. Each replication consisted of 67 entries of different

combinations of the above mentioned gass and clover cultivars seeded in 1.8 by 5 m plots.

 

' Variety Not Stated

112

In addition, the gass and clover cultivars were established as monocultures in plots of the
same size as these of the binary mixtures portion as previously explained in chapter 1.
Samples were hand clipped within a 0.25 m2 quadrate in each plot when plants
were 20 cm in height. Clipped samples were dried at 65 °C in a forced-air oven for 48 hr.
All samples were gound to pass through a 1mm-screen in a Christy-Norris cyclone mill

(Christy-Norris, lnc., Ipswitch, UK).

NIRS procedure: Two gams of each dried sample was packed into a sample holder
with a 30 mm diameter quartz window and scanned with a NIRS monochromator (FOSS
6500). The wavelength range of 400-2500 nm was scanned at every 2 nm giving a total
of 1050 data points. All the spectral data were recorded as log R], where R is
reflectance. Reflectance data were regesses on each constitute of the binary mixtures,
that is, measured percentages of gass, legume and weed components to develop
calibration equations. Standard deviation of calibration (SD), standard error of
calibration (SEC) and calibration coefficient of determination (R2) were used to evaluate
the calibration equation.

The calibration equation was validated by comparing the reference standards
(hand separated or artificially pure samples) with NIRS predicted values. Prediction
coefficient of determination (R2), standard error of prediction (SEP), Slope, and bias were
used as validation criteria. Calibration equations were developed using two different

strategies described below.

113

1. Calibration Equation Developed From Artificially Pure Mixed Samples of 2004
Lake City data.

Pure gass and clover samples were clipped from monoculture plots at the Lake
City site during the 2004 season. The samples were dried and gound as previously
indicated. Artificially pure samples were hand mixed at 10% increments to a total of 4 g
providing 11 possible combinations between each individual gass (Perennial ryegass,
orchardgass, festuloliums and tall fescue) and clover species (white, red and kura
clover). For instance, one mixture might contain 90% of perennial rye gass with 10%
white clover, where a second mixture may include 80% of perennial rye gass with 20%

white clover and so on). A total of 132 artificially mixed samples were made from the 4

 

gass and 3 clover species combinations. Mixed samples were packed into sample
holders and scanned by NIRS as described previously. The calibration equations were
developed (wavelengths 400 to 2492 nm) using an equation development progam within
the NIRS software from WinISI ver. 1.5 (FOSS NIRSystems, Inc., Eden Prairie, MN).
The partial least-squares method was used to eliminate outliers. Five outliers were
eliminated and remaining 127 samples were used to generate the calibration equation.

Weeds were not included in the calibration equation. I

 

II. Calibration Equation Developed From Hand-Separated Mixed Samples :—
In each location, a total of 201 (67 entries at three replications) samples were
clipped from binary mixtures treatments and hand separated into three components:
gasses, clovers, and undefined weeds species, mostly dandelion (T araxacum oflicinale).
The three separated components were dried, weighed separately, recombined and then

gound using the same ginding method as the pure mixed samples. Each sample was

114

scanned using NIRS following the previously described protocol. Two calibration
equations were developed from hand-separated samples.
a. Calibration equation developed from hand-separation of 2004 Lake City data

This equation was derived from selected hand-separated samples from the 201
binary mixtures samples collected from LC during 2004 (weeds not included). Two
outliers were eliminated from gass data and remaining 199 gass samples were used to
develop the calibration equation. In addition, 60 outliers were eliminated from clover
data and remaining 141 samples were used to generate the calibrating equation. The
geater number of outliers eliminated fi'om clover samples was due to the poor
persistence of the clover over a period of years causing reduction in clover portion in the
mixed samples (Chapterl ). The purpose was to determine whether this calibration
equation, which created from hand separation at single location and year, could provide a
higher prediction accuracy of the binary mixtures composition than the calibration
equation developed from artificial mixed samples obtained from the same single location
and year. Validation statistics (R2 and SEP) values obtained from artificial pure mixed

and hand-separation equations were reported.

b. Calibration equation developed from multiple locations and years

This calibration equation was obtained based on the hand-separated mixtures
samples collected from the three locations (KBS, LC and UPES) over 2003-2005
seasons. The reason for creating this equation was to determine if the prediction of the
binary mixtures improved when more samples from different locations and years were

included. A total of 1809 samples (201 *3 locations *3 years) were clipped from the

115

second cutting events in the three locations over three years. The samples were hand-
separated as explained earlier. Half of the samples was randomly selected (904 samples)
to create an equation to predict gass-clover species composition. The reflectance data
from 471 gass, 455 clover and 381 weed samples were selected after outliers were
eliminated using the Select progam from WinISI, v. 1.5 software (Infrasofl International,
LLC, Silver Springs, MD). Reflectance data were regessed on the proportion of each
botanical component to develop three equations: one for gasses, one for clovers and one

for weeds. All the equations were developed to predict the gass-clover botanical

 

composition samples collected from all cutting events during 2003 -2005 in three

locations (KBS, LC, and UPES). i

 

116

RESULTS AND DISCUSSION
1. Artificially pure mixed samples method: Table 1 lists the results of the NIRS
calibration and validation statistics for estimating gass and clover content using the
artificial pure mixed method. Data presented in Table 1 shows a very strong calibration
equation with R2 of 0.99 and standard error of calibration (SEC) of 1.4. The high R2 of
this equation is likely due to accurate handling of the pure mixtures samples with high
precision in sample preparation and weighing samples using a sensitive scale. However,
regession analyses of the spectral data with botanical composition resulted in prediction
coefficient of determination (R2) of 0.31 and 0.24 and SEP of 28.1 and 26.7 for gass and
clover respectively (Tablel ). This indicated that this equation failed to predict the actual
botanical composition of the pasture species. The reason for poor performance is that all
artificially mixed samples were collected from one location and a single year.
Additionally, the variation in plant maturity and weed content observed in the field
samples were not represented in the lab mixed samples. Thus, the calibration equation
developed from the artificially mixed samples of several gass and clover Species
collected from one location in a Single year did not accuracy predict the species
composition in field samples clipped fiom three different locations over three gowing
seasons. A higher prediction may obtain if the calibration equation was derived from
samples representing all locations and years (Shaffer et. al., 1990).
11. Hand separation methods:
a. Equation developed from Hand-separation of LC 2004: Table 2 represents the
calibration equation and validation statistics for predicting the botanical composition

from an equation developed from hand separated samples collected in the single location

117

and year. Calibration equations had R2 of 0.97 for gass and 0.95 for clover. However,
when this equation was applied to predict the botanical composition of samples collected
from three locations and three years, similar prediction results were obtained to the
results of the artificial pure mixed samples with validation R2 of 0.25 for gass with
somewhat improvement prediction for clovers with R2 of 0.37 (Table 2). This clearly
reflects the problem of obtaining enough representative samples that can include all
Spectra data from other locations.
b. Equation developed from multiple locations and years: Table 3 lists the calibration
equations developed fiom hand-separated samples collected from three locations over
three years. Calibration equations had R2 of 0.82 for gass and 0.84 for both clovers and
weeds (Table 3). The validation statistics for these equations resulted in an R2 of 0.67 to
0.72, SEP of 6.9 to 12.8, and a bias of 0.3 to 0.6% (Table 3). The regession of each
component is illustrated in Fig. 1. Slopes were close to one for the gass and clover.
Calibration samples comprised a wide range of values from 0 to 100 (g g’1 DM) for gass,
0 to 95 (g g‘1 DM) for clover, and 0 to 85 (g g'1 DM) for weed (Figl). Unlike the previous
methods, this result suggests that using the same gass and clover species for both
. calibration and validation Should improve the accuracy of NIRS predictions of these
species. In spite of there being multiple species in the weeds portion, SEP for weeds was
lower than gass and clover components. It is possible the weed species were more
similar in reflectance values than the different Species of gasses.

In conclusion, even though we had some success in predicting gass-clover
mixtures based on multiple locations and years as compared with the equation created

from a single location and year, this study did not result in as high R 2 as those observed

118

in other studies (Locher et al., 2005). The lower R2 observed in this study might be
related to various factors. The calibration equations were derived from a large number of
samples including different gass and clover cultivars in three different locations.
Different cultivars, environmental conditions, the grinding processing and storing the
samples can affect NIRS prediction (Shaffer, et al., 1990). In addition, clover and gass
samples used in developing calibration equations were less mature than samples used in
other studies. This may increase the difficulty of differentiation by NIRS since fiber
content (hydrogen bonds arrangement and quantity) of immature gass and clover species
were very similar. Coleman et al. (1985) indicated that the errors found with hay species
were associated with (i) composition error (ii) machine error (iii) sampling error and (iv)
methodology error. Hence, it is possible to generate a calibration equation developed
from hand separation samples if the handing of the samples were controlled to reduce the
variability among the samples.
CONCLUSIONS

Previously conducted research has Shown success in predicting the botanical
composition of gass-legume mixtures using artificial mixed pasture samples. This study
showed that NIRS did not accuracy determine botanical composition of pasture gass-
clover mixtures based on equation generated from artificial pure mixtures or hand-
separated samples collected from a single location and year, since it may not represented
the maturities of samples to be predicted. However, equations generated from hand
separations of several gasses and clovers across all locations and years resulted in a more
accurate prediction with some limitations. Some pasture gasses and clovers that are

harvested at earlier maturities have similar fiber content (Chapter 2). This may explain

119

 

the lower R2 obtained in this study compared to other studies conducted using more
mature hay samples. Environmental conditions may also cause variation in spectral
properties of samples. Therefore, it is necessary to select diverse samples for calibration
in order to obtain the best prediction from the pure sample equations.

This study, which presented two different strategies for developing a calibration
equation, has shown that creating an equation from representative samples of a larger
database of previously hand-separated mixtures can increase the prediction accuracy.
Thus, NIRS can replace the hand separation method for botanical composition
determination to a prediction accuracy (SEP) of d: 12%, and R2 ranging from 0.67 to

0.72.

120

 

l
l:-

LITERATURE CITED

Coleman, S.W., F .E. Barton 11, and RD. Meyer. 1985. The use of near- infrared
reflectance to predict species composition of forage mixture. Crop Sci. 25: 834-
837.

Coleman, S.W., S.Christiansen, and J .S.Shenk.1990. Prediction of botanical composition

using NIRS calibration developed fi'om botanically pure samples. Crop Sci.
30:202-207.

Cooper, C.S., D.N. Hyder, R.G. Petersen, and RA. Sneva. 1957.The constituent
differential method of estimating species composition in mixed hay. Agon. J.
49:190-193.

Grant, S.A.1981. Sward components. p. 71-92. In J. Hodgson et al.(ed) Sward
measurement handbook. British Grassland Society, Berkshire, UK.

Locher, F ., H. Heuwinkel, R.Gutser, and U. Schmidhalter. 2005. Development of near
infrared reflectance spectroscopy calibration to estimate legume content of
multispecies legume-gass mixtures. Agon. J. 97:11-17.

Leasure, L.K. 1949. Determining the species composition of swards. Agon. J .41 :204-
206.

Mannetij, LT, and K.Haydock. 1963. The dry-weight-rank method for botanical
analysis of pasture. J .Br.Grassl. Soc. 1 8:263-275.

Marten, G.C. 1964. Visual estimation of botanical composition in simple legume-gass
mixtures. Agn. J. 56:549-552.

Norris, K.H., R.F.Bames, J .E. Moore, and J .S. Shenk. 1976. Predicting forage quality by
infrared reflectance spectroscopy .J. Anim. Sci. 43: 889- 897.

Petersen, J .C., F .E Barton, 11, W.R. Windham, and CS. Hoveland. 1987. Botanical
composition definition of tall fescue-white clover mixture by near infrared
reflectance spectroscopy. Crop Sci. 27: 1077-1080.

Pitman, W.D., C.K. Piacitelli., G.E Aiken., and RE. Barton 11. 1991. Botanical
composition of tropical gass-legume pasture estimated with Near-Infrared
Reflectance spectroscopy. AgnJ . 83: 103-107.

Shaffer, J. A., J .S. Shenk., and SM Abrams. 1990. Estimation of botanical composition
in alfalfa/ryegass mixtures by Near Infrared Spectroscopy. Agon. J 82:669-673.

121

 

 

Shenk, J. S., and M .O Westerhaus.. 1994. The application of Near infrared reflectance
spectroscopy (NIRS) pp. 406-449. In Fahey, G. C., Jr. et al. (Eds) Forage quality,
evaluation and utilization. American Society of Agonomy, Madison, WI.

Shenk, J. S., MD. Westerhaus and MR. Hoover. 1979. Analysis of forage by near
infrared reflectance. J. Dairy Sci. 62: 807-812.

Tanner, J .W., E.E. Gamble, and WE. Tossel. 1966. Determination of botanical
composition of two-component forage mixtures. Can. J. Plant Sci. 40:225-234.

Tiwari, D.K., J .A. J ackobs, and S.G.Garmer.l963. Statistical technique for correcting
botanical or floristic estimates in pasture research. Agon. J. 55:226-228. r

Vankeuren, R.W., and H.L. Ahlgen. 1957. A statistical study of several methods used in _
determining the botanical composition of a sward: I.A study of established .j
pastures. Agon.J.49z532-536.

 

Walker, B.H. 1970. An evaluation of eight methods of botanical analysis on gasslands in h
Rhodesia. J. Appl . Ecol. 7:403-416.

Wachendorf, M., B.Ingwersen and F. Taube. 1999. Prediction of the red clover-and
white clover-gass mixtures by near-infrared reflectance spectroscopy. Grass
Forage Sci. 541287-90.

 

122

Tablel. Calibration and validation statistics for NIRS determination of botanical composition of gass-
clover mixture samples collected over 2003-2005 in KBS, LC and UPES using calibration equations

developed from artificial mixed samples of 2004 Lake City data.

 

 

 

 

 

 

Calibration Validation
Mixtures type n+ SD: SECrr R211 n Bias Slope SEP§ R244
Grass 127 31.93 1.14 0.99 1440 -14.1 0.44 28.1 0.31
Clover 127 31.93 1.14 0.99 1375 6.7 0.40 26.7 0.24

 

Tab1e2. Calibration and validation statistics for NIRS determination of botanical composition of gass-
clover mixture samples collected over 2003-2005 in KBS, LC and UPES using calibration equations

developed from hand-separation samples of 2004 Lake City data.

 

 

 

 

 

 

Calibration Validation
Mixtures type n+ SD: SECrt R211 n Bias Slope SEP§ R24:
Grass 199 19.13 3.22 0.97 1437 -20.81 0.36 27.41 0.25
Clover 141 20.53 4.17 0.95 1375 -0.88 0.56 20.95 0.37

 

Table.3. Calibration and validation statistics for NIRS determination of botanical composition of gass-
clover mixture and weed samples collected over 2003-2005 in KBS, LC and UPES using calibration
equations developed from the three years and three locations hand separation samples.

 

 

 

 

 

 

 

Calibration Validation
Mixtures type n+ SD: SECn RZ1| n Bias Slope SEP§ R24!
Grass 471 21.8 9.3 0.82 946 0.53 0.90 12.8 0.67
Clover 455 22.1 8.9 0.84 902 0.35 0.94 12.22 0.72
Weed 381 10.0 4.0 0.84 763 0.64 1.04 6.99 0.68

 

+ Number of samples.

1: Standard deviation of calibration sample set.
21:1 Standard error of calibration sample set.

1] Calibration coefficient of determination.

§ Standard error of prediction.

# Prediction coefficient of determination.

123

 

 

 

 

 

 

A

 

 
  

 

 

 

50. ° 3‘
40« . :g
20 . Sltzipe: 0.90
10 R = 0.67
0 . . r . .
0 20 40 60 80 100

NIRS predicted grass content %
8

 

(a) Hand separated % grasses

 

 

 

A

888883888

 

10

 

NIRS predicted clover content %

 

 

100

(b) Hand separated "/o clovers

 

 

 

 

100

 

401

 
  

 

Slope: 1.04
if = 0.68

 

 

T r

O 20 40 60 80 100
(c) Hand separated % weeds

N IRS predicted weed content “/e

 

 

 

Figl. Relationship of NIRS predicted percentage to hand-separated of gasses (a), clovers (b),
and weeds (c) using a calibration equation of the hand separation of each component
over three years at three experimental research stations. The solid line represents the
regession with a slope and the coefficient of determination R2

124

 

 

GENERAL SUMMARY

This research was conducted to evaluate the effects of gowing pastures as binary
mixtures of perennial gass and clovers upon animal preference, forage quality, plant
persistence, botanical composition, and dry matter production.

Growing clovers with perennial gasses in binary mixtures resulted in increased
pasture dry matter yield, persistence, forage quality, and animal preference. Grass
monoculture treatments resulted in lower persistence and sigiificantly more winter injury
compared to the same gass species and cultivars gown in binary mixtures. Differences
in persistence and winter injury due to location were observed. At northern latitudes
(44°-46° N), where cold temperature predominate, higher incidence of winter injury
occurred with less winter hardy cultivars. Tall fescue and orchardgass cultivars resulted
in better persistence than perennial ryegass and festulolium. Orchardgasses dry matter
yield was slightly less than tall fescue but higher in animal preference. Among clover
species, kura clover Showed a high persistence at KBS and increased in clover content
over time in all binary mixtures, which would indicate its potential as an excellent clover
for gazing. Limited precipitation and high temperatures in 2005 can explain the
reduction in pasture productivity at all locations.

There were significant differences in animal preferences among monoculture
perennial gass species and cultivars. Forage quality of binary mixtures was higher in
CP, and lower in ADF, and NDF than gass monoculture, which resulted in higher animal
preferences for binary mixtures compared to monocultures of gass.

Perennial ryegass-clover binary mixtures resulted in significantly higher forage

quality and animal preference compared to perennial ryegass monocultures. However,

125

 

“M.H-Dir-.. l' -

 

some perennial ryegass cultivars that demonstrated a higher animal preference was less
winter hardy. Binary mixtures of festulolium (Duo)-clover and perennial ryegass-clover
increased animal preference and forage quality compared to monoculture festulolium and
perennial ryegass, indicating these mixtures are a good choice for livestock producers to
use for gazing. Total dry matter yield and animal preferences were not always positively
correlated.

The study of Near-Infrared Reflectance Spectroscopy (NIRS) showed that using
either pure mixed samples or hand separation from single location and year resulted in
low prediction accuracy in determining gass-clover binary mixtures samples collected
from KBS, LC and UPES locations during 2003-2005 gowing season. In contrast, NIRS
prediction accuracy was improved when the calibration equation was derived from hand-
separated samples collected from the same locations and years.

In summary, this research provided a better understanding of the importance of
species and cultivar selection for performance in gazing systems across three latitudes in
Michigan. It also provided helpful information on gass-clover relationships in pastures
over time under rotational gazing. In addition, the study documented the positive
benefits of proper gass-clover cultivar and species selection for higher animal preference
and plant persistence. Finally, NIRS has the potential to replace hand-separation in
determining pasture species composition when calibration equation was developed from a

large data set, which represents environments between locations and years.

126

 

 

 

APPENDICES

 

127

 

  
   

 

 

 
       
 
 
 

I I,Supe Ito'r‘.‘ I" I
..... -‘ ‘4. .

E“~ "r
:1? I £T'IOquMon I \ ‘ ‘~ . ‘7:
:f 3 00111064190? , nMarquette I .
H O R I ‘

_ . Hunting"
=2

  

. . boygan *

 
 

Iriilpenasi '

 

44°N

  

 

 

 

 

 

 

 

Fig 1A. Michigan map Showing the three experimental locations
representing different latitudes in Michigan: Kellogg Biological
Research Station at Hickory Comets (KBS) (1), Lake City Research
Station (LC) (2), and Upper Peninsula Experiment Station at Chatham
(UPES) (3).

 

128

 

Grass-clover binary mixture plots

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Plot trt Plot trt Plot trt Plot trt Plot trt Plot tl‘t Plot trt Plot trt
101 1 110 10 119 19 128 28 137 37 146 46 155 55 164 64
102 2 111 11 120 20 129 29 138 38 147 47 156 56 165 65
103 3 112 12 121 21 130 30 139 39 148 48 157 57 166 66
104 4 113 13 122 22 131 31 140 40 149 49 158 58 167 67
R1 105 5 114 14 123 23 132 32 141 41 150 50 159 59 168 68*
106 6 115 15 124 24 133 33 142 42 151 51 160 60 169 69*
107 7 116 16 125 25 134 34 143 43 152 52 161 61 170 70"
108 8 117 17 126 26 135 35 144 44 153 53 162 62 171 71*
109 9 118 18 127 27 136 36 145 45 154 54 163 63 172 72*
201 56 210 31 219 63 228 36 237 64 246 43 255 61 264 55
202 48 211 27 220 29 229 9 238 41 247 40 256 3 265 35
203 21 212 39 221 18 230 15 239 54 248 37 257 2 266 57
R2 204 72* 213 16 222 26 231 50 240 11 249 59 258 47 267 4
205 53 214 60 223 8 232 30 241 22 250 14 259 49 268 65
206 70* 215 44 224 52 233 17 242 6 251 25 260 45 269 38
207 1 216 33 225 62 234 23 243 34 252 24 261 51 270 69*
208 67 217 58 226 5 235 66 244 12 253 20 262 42 271 71*
209 10 218 28 227 7 236 13 245 68* 254 32 263 46 272 19
301 7 310 17 319 50 328 45 337 35 346 23 355 3 364 67
302 28 311 27 320 12 329 71* 338 43 347 47 356 46 365 44
303 58 312 59 321 53 330 72* 339 18 348 24 357 48 366 22
R3 304 70* 313 15 322 6 331 20 340 40 349 9 358 51 367 32
305 2 314 42 323 63 332 19 341 38 350 57 359 8 368 68*
306 54 315 69* 324 39 333 62 342 60 351 25 360 16 369 10
307 31 316 4 325 1 334 29 343 30 352 11 361 61 370 37
308 52 317 21 326 33 335 34 344 49 353 65 362 13 371 56
309 14 318 66 327 41 336 5 345 26 354 55 363 64 372 36
Clover lots
R1 101 73 102 74 103 75 104 76 105 77 106 78 107 79 108 80
R2 201 79 202 77 203 75 204 80 205 78 206 73 207 74 208 76
R3 301 76 302 78 303 74 304 73 305 80 306 79 307 77 308 75
Grassplots
101 81 103 83 105 85 107 87 109 89 111 91 113 93 115 95
R1 102 82 104 84 106 86 108 88 110 90 112 92 114 94 116 96
201 89 203 83 205 85 207 93 209 88 211 81 213 96 215 95
R2 202 87 204 94 206 90 208 92 210 86 212 82 214 91 216 84
301 96 303 92 305 89 307 87 309 82 311 90 313 85 315 84
R3 302 91 304 88 306 95 308 94 310 93 312 86 314 81 316 83

 

Fig. 2 A. The design map of grass-clover binary mixtures and grass and clover

monoculture treatments. (* Trt.# 68,69,70,7l and 72 in the binary mixtures plots are blanks)

129

 

 

 

 

 

 

Binary mixtures

portion (Rep 1)

Binary mixtures

portion (Rep2)

Grazing area “ Section 1”

 

Binary mixtures

portion (Rep 3)

 

 

Clover monocultures portion

 

 

Grazing area “section 2” f

Grass monocultures portion

 

 

 

Fig. 3A. The experimental area grazing arrangement.

 

130

 

 

 

 

 

 

 

 

 

E, L5
eoasm 82 55> .
x0355 00:80.0 :5.
>ooomM 838$ :3.
5305 8380.: fig
05:00 mew—w?»— 3500:
0.8.2 $.25».— 35:23
team magma: 38:20:
05:0— :0>20 0:55 mwfidr mafiwob 358$ ugmém .80-:
BEE. :03? 0:55 000.830 mmfiwPC 3:50: ws< -mm .m:<-m~ avenue—amt 29?
8:< 633 833 Boo 3.85.2 3:09: 3:880 .23 -2 337$ 20$ .858 552
= :moM :0>o_0 0:55 005 mag.— EE:0:0& 0:3. A 0:341 0.5 00:38 :82 ~A:Em mmmD
:00..N E0m -5 9233320 050me
8.8mm mamawcbfiouo wa< -a ws< -m Emma 6200383
.mZ> :05? com «3 Z 00mmflwusao5 bi. -b 0:3. -3 £058 65%
team :05? 00% BE 8mmfiwubw€5 32 -mm 52.2 :0 :82 30:3 0302 04
2E gm 005—0 3m came—0... 00mmfiwubfio5 000$: Bomd £35133 0E3.
.w:<-m 33.6w 200:. 638388
.803: 9:23:50”— 0:3.-E 0:3... cm 605:: $802-0:E
820R.“ :05? 83— 0:9 meg—238m .32 -v >037 em 06 :82 8meme mmx
20.5—:0 0200mm 235—=0 momoomw mccu SEN In 09:. =em 25
820—0 3380 033:5 wfitsu

 

 

 

 

 

 

 

Seam £5 :_ “00:02:88 mugs—:0 02:0 0:: $8» 05 :5 AmmmDv :ozfim #:085me 235:0: 0.52 b5 65 55 00—04
.6va :038m £8008”: Romwgomm wwo=0¥ «a 8808 wEBEw moom EB 38 :0>o 033:8 wfizso .3: :8 .089 mom .< 330:.

131

Table 2 A. Total monthly precipitation (mm) at Kellogg Biological
Research Station (KBS), Lake City (LC) and Upper Peninsula Experiment
Station (UPES) during the growing season of 2004-2005 comparing with

30-year average.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

KBS
Month of growing 2004 2005 1 30-yr Ave.
season mm
April 19.8 6.3 97.0
May 248.4 50.8 89.0
June 85.2 124 98.0
July 76.1 122.4 93.7
Aug. 131 11.4 100.1
Sep. 44.1 69.9 108
Total 604.6 384.8 585.8
Seasonal Average 100.8 64.2 97.7

LC

Month of growing 2004 2005 1 30-yr Ave.
season mm
April 100.6 26.4 70.4
May 161.3 65.5 70.8
June 61.7 52.1 75.0
July 46.7 93.7 72.6
Aug. 53.0 113.8 92.9
Sep. 21.8 103.1 94.5
Total 445.1 454.6 476.2
Seasonal Average 74.2 75.8 79.4

UPES
Months of growing 2004 2005 I 30-yr Ave.
season mm
April 60.7 36.8 40.13
May 107.4 51.0 72.4
June 75.9 41.4 77.7
July 92.9 98.5 90.6
Aug. 145.8 61.4 78.1
Sep. 50.5 123.4 93.7
Total 533.2 412.5 452.6
Seasonal Average 88.9 68.8 75.5

 

 

 

 

132

 

 

 

Table 3 A. Average daily maximum air temperatures (°C) at Kellogg
Biological Research Station (KBS), Lake City (LC) and Upper Peninsula
Experiment Station (UPES) during the growing season of 2004-2005
comparing to 30-year average.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

KBS
Months of growing 2004 I 2005 I 30—yr Ave.
season ................ ° C
April 17.3 18.2 15.3
May 22.0 19.9 22.3
June 25.2 30.0 27.1
July 27.5 30.4 29.2
Aug. 25.7 29.4 27.9
8g). 26.7 27.3 23.8 E"
LC
Months of growing 2004 I 2005 I 30-yr Ave.
season ----------------- ° C
April 13.1 14.8 11.4
May 17 16.4 19.3
June 22.7 27.5 24.2 _
July 25.5 27.4 26.6
Aug. 23.5 26.0 25.1 F
Sep. 24.9 23.8 20.3
UPES
Months of growing 2004 I 2005 I 30—yr Ave.
season ----------------- ° C
April 7.7 13.0 10.4
May 15.4 15.7 18.9
June 19.9 25.5 24.3
July 23.6 27.2 25.6
Aug. 22.4 26.4 24.9
Sep. 23.3 23.3 20.6

 

 

 

 

 

133

Table 4 A. Average daily minimum air temperatures (°C) and snow fall (cm) at Kellogg

Biological Research Station (KBS), Lake City (LC) and Upper Peninsula Experiment

Station (UPES) from January through April of 2003-2006 comparing with 30-yr. average

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

KBS
2003 2004 2005 2006 30-yr Ave.
Temp Snow Temp Snow Temp Snow Temp Snow Temp Snow
Month °C cm °C cm °C cm °C cm °C cm
Jan -9.8 18.9 -8.4 30.09 -9 17.6 -2 19.8 ~8.8 20.5
Feb -10.1 10.4 -7.7 11.6 -5 6.4 -6 9.2 -8.1 13.5
Mar -3.2 1.7 0.2 1.5 -5 1.2 -2 4.2 -3.2 10.2
Apr 3.3 0.5 2.3 0 4 0 4 0.3 2.4 2.9
L C
Jan -12.6 l8 -14 19 -13 15 -5 14 -13 19.3
Feb -15.4 13 -11 12 -8 14 -11 16 -12.8 15.4
Mar -9.3 12 -4 11 -10 11 -6 13 -7.9 12.2
Apr -2.3 4 0 2 0 5 0 2 -0.9 4.4
UPES
Jan -15.6 35 -15.8 32 -14 37 -7 31 -12.7 35.9
Feb -l8.4 26 -l4.0 29 -10 24 -13 28 —12.4 29
Mar -12.8 28 -7.4 22 -10 21 -6 22 -8.3 18.9
Apr -5.8 10 -2.7 8 -1 ll 0 7 -2.3 6.6

 

 

134

 

 

Table.5 A. Average animal weight (kg), and the number of animals in each grazing event

during 2004-2006 grazing seasons.

 

 

 

 

 

 

 

 

 

2004 2005 2006*
Animal Info 28-May 26-Jun 28-Jul 9-Sep 6-May l6-Jun 4-Aug 12-May
Ave animal _
weight (kg) 266.22 31 1.85 384.42 41 l .11 243.46 365.60 403. 10 443.72
Number of
Animals used 10 4 4 3 6 3 3 16
Breed: Holstein Steers Steers Steers Steers Steers Steers Steers Heifers

 

* Single grazing was taken during 2006 season

135

 

 

 

 

8822: so: a 3% Sea 356 ea "EN

ego—U wfiim ”Umz

8822: so: a as 83 53o 2:55 =£ noon:

830 9580 wqtam HUOm»

has .253 us, F

 

 

 

 

 

 

 

 

 

88¢ 42. 58v 03 88v :._ 88v Ra 88v :.N Em 3.8.1.:
58... won 38v :.m 88.0 NS 58v 2 .o 58v «3 N: :52
88¢ 03 58v 2...“ 88v 8... 88v :2 58v 2a m: 5.3:
585 v: 58v 2.: 58v 3.: 58v 3% 88v 3. _ m 8 E
88¢ 3.3 58v 3.3 Ego N2 88v 3.2.. 58v no.2 a. 8.18»
886 a? 386 03m 2de 3m 58v 8.8 88¢ 32 N 8.
886 as 58v E: 203 a: 886 3.2 38v 218 N .3»
n— A un— 03_m>ou— nm A Man 03—fl>..n~ ..m A ham 03—M>unw n— A ham 03—M>un~ ”— A ham 03—M>nnm ”—9 #00:”
.8 .8 .08 . E5

 

 

 

 

 

 

 

 

 

.Aacoésaafiae 5:3 3 Ba $95 Ba 3 we: 25:82 608-3% e8»
Set 83 0.3328 .935 Sm ago—o =£ can macaw Ea 8>oo 933w zfl .98 watam 55.? SE?» 8% <>OZ< .< o 035.

136

Table 7 A. ANOVA for winter injury, spring, and fall ground cover for grass

monoculture data from years 2004-2006, locations (KBS, LC and UPES) and 16 grass

varieties (Cultivars)

 

 

 

 

 

WI‘ soc‘ FGC"
Effect DF F-value Pr > F F-value Pr > F F-value Pr > F
year 2 121.83 <.0001 83.55 <.0001 9.19 0.0387
Ice 2 152.05 <.0001 171.50 <.0001 13.93 0.0158
year*loc 4 133.52 <.0001 125.36 <.0001 49.22 0.0022
var 15 50.09 <.0001 43.27 <.0001 30.55 <.0001
year*var 30 2.23 <.0007 1.98 <.0034 2.85 <.0022
loc*var 30 8.74 <.0001 5.30 <.0001 4.97 <.0001
year*loc*var 60 4.47 <.0001 4.42 <.0001 4.02 <.0001

 

 

 

 

 

 

 

 

1] WI= Winter Injury

*SGC= Spring Ground Cover

xFGC= Fall Ground Cover (2006 data is not included)

137

 

 

 

 

Table 8 A. ANOVA for winter injury, spring, and fall ground cover for clover

monoculture data from years 2004-2006, locations (KBS, LC and UPES) and eight clover

varieties (Cultivars).

 

 

 

 

 

WIx SGCy FGCz

Effect DF F-value Pr > F F-value Pr>F F-value Pr>F

year 2 78.68 <.0001 9.57 0.0059 0.01 0.9381
loc 2 70.25 <.0001 26.32 0.0011 32.13 0.0006
year*loc 4 27.33 <.0001 21.99 0.0002 2.86 0.1343
var 7 6.67 0.0001 25.77 <.0001 26.32 <.0001
year*var 14 5.55 0.0004 12.53 <.0001 2.17 0.0682
loc*var 14 2.73 0.01 14 5.84 <.0001 3.79 0.0013
year*loc*var 28 3.74 0.0055 5.85 0.0003 4.17 0.0029

 

 

 

 

 

 

 

 

 

x W]: Winter injury.

y SGC =Spring ground cover

zFGC =Fall ground cover (2006 data is not included)

138

 

 

 

La... - ... a...

633:3“ 8: a San

"<2

.89.“ «cowbsv 35qu u ”52 N
..3c .5928 22 u B...»

580.5 830

H mUK

3:8 mm! “a 385:8 888893 3863 mooaeomoa $83 "ea 38:2...

 

 

 

 

 

 

 

 

 

 

~8O.V on; ~oocV ac; moocd OVA <2 <2 ~ooo.v 2.; wNm «beau—Utoc—ihaoh
owcod DNA ~OOO.V vm ._ ocood ~N._ <2 <2 _ooo.v $0.2 wmm 95.423106—
~OOOV :2 25¢.V E..— —OOO.V cw.— ~8O.V N¢.— 28¢.V and 3N «beggar—no.»
Zucov - —OS.V N.N.N _ooo.V GYN ~8O.V wag ~§.V N— .m VON abet—o
cubed aqm —OOO.V Nnd _OS.V WWW“ <2 <2 ~ooc.v _w.mw w nausea—«...?»
~ooo.V mwdm 28¢.V ma. _ O2 _ooo.v NNdVN <2 <2 _OOO.V m _ .3 m 25.28—
~ooo.v NQOON ~08.V ONd— w —ooo.v nN6¢m ~08.V NW2: _§.V mm. 20 v 25.1.39»
mecov . ~8O.V Vmémoh _§.V chmm ~8O.V Om. ~a ~8O.V o2 . _a v «:9
~0Oo.v c—N _oooV a0; _GOO.V Va; <2 <2 ~ooc.v ca; NM— «bag—thaw»
_COO.V OWN ~OOO.V Go; —ooo.v coN <2 <2 ~ooo.V _O.N N: 95.43—
~OOO.V Nmé _ooo.V 2mm _ooo.v evd _OCO.V cw; nosed 3..— 00 «.5139»
_OOO.V .. ~08.V NO.MN _Oco.v vafi— _OOO.V @wfi _OOO.V No6 cc «.5
cNmod ové Nvocd hmd— mmvod find <2 m2 vmood ato— N 02.4.39»
ONOOd wde ~ooo.V ovdb ~oooV oa.~n— <2 <2 avomd God N 3—
avod no.0 vm _ ed de ~ooo.v oNdN— _coo.v NOAH— :oad cod — 53>
“7.5 0397,.— mAE eagi ..M—A5 02.3-...— mAum o=_a>-m m A 5 0%?» ma «oatm—
aaz a9. :6 ”a 3&5... 22>

 

 

 

 

 

 

 

 

Aesoésaafifie has 3 as. 3:8 $95 as 3 we: 25:82 .8838 as» see as 228
.623 82 EQZ 98 mg .mov 3:36 owfiom was 8:288; 2255 .22» 8358 be com <>OZ< .< a 2an

139

 

 

95:23:: 8: 2 San

"<2

.3: .8938 382 n .52 N
:25 Bow—9.29 29< u .22»

 

 

 

 

 

 

 

 

 

2205 23.5 n mUx
920 ma 8 293:8 39:29.22: Ego 89:29.22: 385 No.5 3:22...
Smoo um; moooo co; omooo on; <2 <2 Sumo N. .2 o2 guru—992213.»
woooo no; voood $2 ~ooo.o 3.2 <2 <2 good So o2 ...—339:9:—
~ooo.v wm.~ vmooo on; ooooo 3. 2 _ooo.v m _ .m 386 No. _ oo 9:32.913.»
~ooo.V mm.m _ooo.v 5N _ooo.v ovd _ooo.v o5: _ooo.v mn.m oo .3339
_ooo.v 2.3. _ooo.v 3.3 _ooo.v mod <2 <2 _ooo.v oNd— w E92213.»
_ooo.v no.2. _ooo.v om.mo_ ~ooo.v omdw <2 <2 _ooo.v o_ .m: w 39:92
.ooo.v wooe— _ooo.v wtmv ~ooo.v 3.9: not .o 32 _ooo.v 3.3 v 9:91:92
_ooo.v mmoov _ooo.v ovSS _ooo.v 2 .3 _ooo.v E .om— fiooov mm. _ n v Agn.—UV “:9
~ooo.v Sum mmooo 2 .m omooo mod <2 <2 no _ o.o an; on 99392139.»
wfl _.o ow; moooo mod 386 on; <2 <2 886 $8 om .3392
_ooo.v m5. _ooo.v ow.” 02 o.o ooN _ooo.v mad Sooo 5o 2 .3329»
_ooo.v no.2 _ooo.v mo. 3 _ooo.v o_ .w moooo wo.v _ooo.v 3nd— 2 99>
voNoo no.» ovooo 5.2 _ooo.v mm .NS <2 <2 good 3.». N 92.239.»
_ooo.v Same voooo vmdm voooo 36m <2 <2 oo_o.o vo.o~ m 92
_ooo.v oode 2.36 wmo _ooo.v $63. _ooo.v oméo _ooo.v oodw _ .39.»
2A.—m 929?..— .mA:& 92872 ”HA5 9297..— mAE 929.72 .2 A 5 9299-2 “a gamma
.32 £9. ..6 ea 35...... 22>

 

 

 

 

 

 

 

 

 

Aagsaoo was...» we.» 2 Ba 3.98 68.5 E... 3 .89: 22.82 .8338 was; so: 9%
82:90:08 38» :2 $52 28 mD< .228 32:3 93:2 28 99:89.28: BEE: .22.» .632: .Go :2 < >0 2< .<.o_ 2an

140

 

 

 

938:3: 8: 2 San

“<2

:25 9:9»:8929 28392 n ”52 N

:3: 259.2% 22 n :9;

298:: 92980 n 20x

92:: mg 3 39:29:09 «99:29.28: 2:852 89:29.22: 29:25 n8: 232$...

 

 

 

 

 

 

 

 

 

 

Rood w¢.m good 2%.? Omaod No. 2 <2 <2 meod 00. 2 no ua>t=ot82tuaoh
m2vwd N56 V2.20 cod 222 Md 222.2 <2 <2 m2mN.o 2.2 .2 no 2:33:93:—
hcohd 00.0 ”2 mod whd cavwd cod 2 2nd mmd mcb2 .O on. 2 wN unortflofao»
om2od 05.2 2OOO.V OWN 535.0 90.2 22N2.0 22.».2 hoood 2N.N wN 2.3.4.259
2ooo.v oNd omocd Nwé m2co.o VMd <2 <2 2ooc.v 2.0N2 w 259.42.24.30»
2oco.V N.N.o 2ooo.v omN— 289V NodN <2 <2 2ooo.V Raw w 259.82
2ooc.v v0. 2 m 2ooo.V OWVN 2ooo.v wodw 222.96 wm.m 2ooo.v cod v 2:99:39»
2ooo.v MN.Nm2 2ooo.v EMwON 2ooo.v vafic 2ooo.v Nm.22 2ooo.v :62 v AQNQLUV :3
oocod NMN no 2 o.o h 2 . m own 2 .o no. 2 <2 <2 wooed co. m #2 ua>ig2tuno>
oo2od avd Nmood Owd Ohomd bod <2 <2 ©w2od 2M.N v2 25,982
ov2 0.0 2.5.0 oowmd 2 wd wvood 2&6. wNm 2 .o no. 2 o2oo.O and b 2.3.9.23.2
2ooo.v Omd 20oo.v c 2 d2 2ooc.v 02mm good find wvmod a2 .N h ha>
hwcod mmé hNNod 00.2. chod 2 m.v2 <2 <2 Noood awflv N 292:260.2
2OOO.V Nm6¢2 2ooo.v oo.¢NN 2OOO.V MNdQV <2 <2 booed ocdm N 062
No2o.o 00.222 2OOO.V 00. 202 owWNd mm. 2 @256 292 .mm 2ooc.v bwfi 2 m 2 .392
2A.—n2 92:?2 "2A5 929?..2 2A.—.2 923.2 «7:2 92:?2 ..2 A :2 92:>-..2 “20 209.—mm
...Ez an... .8 ea 35.. 22>

 

 

 

 

 

 

 

 

$32938 82%? 58: Ems Ba 95:8 .695 as 3 .89: 8882 .mooméom $2,. 88: 8%
9:323:88 :9>29 .82 2.202 29:: na< .202 5:26 $89.2 29:: 99:89.28: 2982:: .292» .839: 229 8.2 <>OZ< .< 22 939.2.

141

T

(I{TflzfliflifllflismllflflflllflfliflniIjjfljnlfijg‘fiu