EVALUATION OF BLACK OAT (AVENA STRIGOSA SCHREB.) GERMPLASM  
 
 
 
 
Except where reference is made to the work of others, the work described in this thesis is 
my own or was done in collaboration with my advisory committee. This thesis does not 
include proprietary or classified information.  
 
 
 
 
________________________________________ 
Thomas Antony 
 
 
 
 
 
 
 
Certificate of Approval:  
 
 
_________________________                                   _________________________ 
David B. Weaver               Edzard van Santen, Chair 
Professor                                                                               Professor 
Agronomy and Soils               Agronomy and Soils 
 
 
 
_______________________             _________________________ 
Andrew J. Price                Joe F. Pittman  
Assistant Professor                    Interim Dean 
Agronomy and Soils               Graduate School  
                                      
 
 
 
 
 
EVALUATION OF BLACK OAT (AVENA STRIGOSA SCHREB.) GERMPLASM  
 
 
 
 
 
Thomas Antony  
 
 
 
 
 
 
 
 
 
 
A Thesis  
Submitted to  
the Graduate Faculty of  
Auburn University  
in Partial Fulfillment of the  
Requirement for the  
Degree of  
Master of Science  
 
Auburn, Alabama  
December 17, 2007 
 
 
 
 
  iii 
EVALUATION OF BLACK OAT (AVENA STRIGOSA SCHREB.) GERMPLASM  
 
 
 
 
 
 
 
Thomas Antony 
 
 
 
Permission is granted to Auburn University to make copies of this thesis at its discretion, 
upon the request of individuals or institutions and at their expense. The author reserves 
all publication rights.  
 
 
 
 
 
      
 ___________________________________ 
  Signature of Author  
                 
___________________________________ 
  Date of Graduation  
 
 
 
 
 
 
 
 
 
 
 
 
  iv 
THESIS ABSTRACT 
EVALUATION OF BLACK OAT (AVENA STRIGOSA SCHREB.) GERMPLASM  
 
 
 
 
Thomas Antony 
Master of Science, December 17, 2007  
(B.S. (Agriculture), Kerala Agricultural University, India, 2002)  
(B.S. (Botany), Mahatma Gandhi University, India, 1995) 
 
156 Typed Pages  
Directed by Edzard van Santen 
 
  Black oat has become an important winter cover crop in subtropical and temperate 
regions. Originating in the northern parts of Spain and Portugal, black oat cultivation has 
spread to different parts of the globe. Even though different in ploidy level, diploid black 
oat has been used in many hexaploid common oat (A. sativa L.) breeding programs as a 
donor parent for some desirable characters such as rust resistance. Black oat is an 
emerging cover crop for the Southeastern US. The only commercially available black oat 
cultivar in US is ?SoilSaver? released by Auburn University and USDA-ARS-NSDL in 
2002. Even though SoilSaver is superior for some traits (e.g. maturity and biomass yield), 
some traits need further improvement. Over 100 black oat accessions are available from 
the USDA-ARS Small Grains Germplasm Unit at Aberdeen, Idaho, but a detailed study 
  v 
of this collection is needed before they can be used in a breeding program. The objective 
of the study was to evaluate the entire USDA-NPGS black oat germplasm collection in 
the field for morphological traits and maturity and a subset for biomass and grain yield. 
We used 103 black oat accessions available from USDA and SoilSaver for the 
morphology and maturity study and 18 accessions selected based on their relative 
maturity compared to SoilSaver for plot biomass and grain yield trials. Among the 14 
response variables measured, 12 were used for the ?Canonical Discriminant Analysis? 
(CDA) in morphology and maturity study. In CDA the first four canonical variates were 
responsible for 84 % of the total variation and when plotted the first two axes, the 
accession CIav 9015 was separated farthest from the rest of the accessions. This 
accession is extremely early maturing, has short culms but long and broad leaves. So we 
suspect that it may not belong to Avena strigosa Schreb., but to some other Avena 
species. Further karyotypic studies may be needed to ascertain our findings in this regard. 
For the yield trials we compared the biomass and grain yield and test weight of the 
selected accessions to SoilSaver at a standard seeding rate. None of the tested accessions 
performed better than SoilSaver at standard seeding rate consistently in all locations. The 
allelopathy study identified seven accessions having significantly higher radish radicle 
suppressive ability than SoilSaver. 
 
 
 
 
 
  vi 
ACKNOWLEDGMENTS 
 
 
 
 The author is thankful to God for all the blessings He showered upon him. The 
author would like to express his sincere thanks to Dr. Edzard van Santen for his guidance 
and encouragement during the course of study. The author also would like to extend his 
sincere thanks to the members of advisory committee, Dr. David B Weaver and Dr. 
Andrew J. Price for their help and support. He is also grateful to Dr. Steven L Noffsinger, 
for his day to day guidance and help in the course of research. The author also would like 
to acknowledge the helps of Dr. Ludovic J.A. Capo-chichi, Maria Stoll, staff of Alabama 
Agricultural Experiment Station and student workers who helped him in lab and field and 
fellow graduate students Reji, Janaki, Lakshmi, Monika and Jessica. Most of all, he 
would like to express his deep appreciation to his wife Manju for her love, support and 
encouragement.  
 
 
 
 
 
 
 
  vii 
Style manual or journal format used: Handbook and Style Manual of the American 
Society of Agronomy, Crop Science Society of America, and Soil Science Society of 
America  
Computer software used:  Microsoft Office 2003; SAS v.9 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  viii 
TABLE OF CONTENTS 
 
LIST OF TABLES.............................................................................................................. x  
 
LIST OF FIGURES .......................................................................................................... xii  
 
I. LITERATURE REVIEW 
 Importance of cover crops ...........................................................................1 
Classification and Botany ..........................................................................12 
Cytogenetics...............................................................................................15 
Molecular Markers.....................................................................................21  
Breeding Programs.....................................................................................23 
References..................................................................................................26 
 
II. MORPHOLOGY AND MATURITY OF BLACK OATS (Avena strigosa  Schreb.) 
Abstract .....................................................................................................37 
Introduction................................................................................................38  
Materials and Methods...............................................................................39 
Statistical Analysis.....................................................................................41
Results and Discussion ..............................................................................43 
  ix
Summary and Conclusions ........................................................................48 
References..................................................................................................50 
 
III. PLOT TRIALS IN BLACK OATS FOR BIOMASS, GRAIN YIELD AND TEST     
WEIGHT 
Abstract......................................................................................................69  
Introduction................................................................................................70  
Materials and Methods...............................................................................71 
Statistical Analysis.....................................................................................73 
Results and Discussion ..............................................................................74 
Conclusions................................................................................................77  
References..................................................................................................79 
IV. ALLELOPATHY OF BLACK OAT ACCESSIONS 
Abstract....................................................................................................111 
Introduction..............................................................................................112  
Materials and Methods.............................................................................113 
Results and Discussion ............................................................................114 
References................................................................................................115 
V. APPENDIX.................................................................................................................118 
 
 
 
 
 
  x
LIST OF TABLES 
 
Table 2-1: Materials used for the morphology and maturity study ...............................53 
Table 2-2: Response variables studied...........................................................................59 
Table 2-3: Response variables studied and their range..................................................60 
Table 2-4: Loading of Canonical Discriminant Analysis (CDA) ..................................61 
Table 2-5:  Variance analysis of the traits identified by canonical discriminant  
 analysis .........................................................................................................62 
 
Table 2-6:  Traits with high correlation to CAN1 among different accessions..............63 
Table 2-7:  Traits with high correlation to CAN 2 among different accessions.............64 
Table 2-8:  Traits with high correlation to CAN 3 among different accessions.............65 
Table 2-9:  Traits with high correlation to CAN 4 among different accessions.............66 
Table 3-1: Materials used for the Biomass and grain yield studies ...............................81 
Table 3-2: Variance analysis of the biomass, grain yield and test weight of  
               2004-05 studies ............................................................................................82 
 
Table 3-3: Variance analysis of the biomass, grain yield and test weight of 
            2004-05 studies ............................................................................................83 
Table 3-4: LS means of biomass study 2004-05 at four locations.................................84 
Table 3-5: LS means of biomass study 2006-07 at four locations.................................85
Table 3-6: LS means of grain yield and test weight study 2004-05 at four locations ...86 
Table 3-7: LS means of grain yield study 2006-07 at four locations ............................87 
Table 3-8: LS means of test weight study 2006-07 at four locations ............................88 
 xi
Table 4-1: The allelopathic potential of different black oat accessions in  
           comparison to SoilSaver.............................................................................117 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 xii
LIST OF FIGURES 
 
Figure 1-1: Erosion in Alabama........................................................................................3 
Figure 1-2: Erosion in Alabama........................................................................................3 
Figure 2-1: Primary and secondary centers of origin of black oats ................................67 
Figure 2-2: Canonical discriminant analysis scatter plot of morphology and maturity                               
study .............................................................................................................68 
 
Figure 3-1: Biomass study 2004-05at Gulf Coast Research and Extension center, 
        Fairhope........................................................................................................89 
 
Figure 3-2: Biomass study 2006-07at Gulf Coast Research and Extension center, 
             Fairhope........................................................................................................90 
 
Figure 3-3: Biomass study 2004-05 at Plant Breeding Unit, Tallassee ..........................91 
Figure 3-4: Biomass study 2006-07 at Plant Breeding Unit, Tallassee ..........................92 
Figure 3-5: Biomass study 2004-05 at Tennessee Valley Research and Extension 
  Center, Belle Mina, Alabama.......................................................................93 
 
Figure 3-6: Biomass study 2006-07 at Tennessee Valley Research and Extension 
 Center, Belle Mina, Alabama........................................................................94 
 
Figure 3-7: Biomass study 2004-05 at Wiregrass Research and Extension Center, 
      Headland, Alabama ......................................................................................95 
 
Figure 3-8: Biomass study 2006-07 at Prattville Agricultural Research Unit, 
      Prattville, Alabama.......................................................................................96 
 
Figure 3-9: Grain yield study 2004-05 at Prattville Agricultural Research Unit,                                           
Prattville, Alabama .......................................................................................97 
 
Figure 3-10: Grain yield study 2006-07 at Prattville Agricultural Research Unit,             
Prattville, Alabama ......................................................................................98
  xiii 
Figure 3-11: Grain yield study 2004-05 at Plant Breeding Unit.......................................99 
Figure 3-12: Grain yield study 2006-07 at Plant Breeding Unit.....................................100 
Figure 3-12: Grain yield study 2006-07 at Gulf Coast Research and 
 Extension center, Fairhope.........................................................................101 
 
Figure 3-13: Grain yield study 2006-07 at Tennessee Valley Research and Extension        
Center, Belle Mina......................................................................................102 
 
Figure 3-14: Grain yield study 2006-07 at Wiregrass Research and Extension 
 Center, Headland........................................................................................103 
 
Figure 3-16: Test weight study 2004-05 at Prattville Agricultural Research Unit,  
            Prattville .....................................................................................................104 
 
Figure 3-17: Test weight study 2006-07 at Prattville Agricultural Research Unit,  
                   Prattville ...................................................................................................105 
 
Figure 3-18:  Test weight study 2004-05 at Plant Breeding Unit, Tallassee ...................106 
Figure 3-19: Test weight study 2006-07 at Plant Breeding Unit, Tallassee ...................107 
Figure 3-20: Test weight study 2006-07 at Gulf Coast Research and 
               Extension center, Fairhope.........................................................................108 
 
Figure 3-21: Test weight study 2006-07 at Tennessee Valley Research and  
              Extension Center, Belle Mina ...................................................................109 
 
Figure 3-22: Test weight study 2006-07 at Wiregrass Research and Extension 
             Center, Headland........................................................................................110
  1 
I. LITERATURE REVIEW 
 
Introduction 
In modern sustainable agricultural systems cover crops occupy a pivotal role in 
no-tillage and reduced tillage crop management. They are grown to protect and improve 
the physical and chemical properties of soils. These crops can be utilized for various 
purposes such as winter cover or green manure, supplemental forage covering periods of 
shortages for regular crops, living mulch, and as a catch crop. Cover crops may be 
classified based on their taxonomic status (leguminous or non-leguminous) or growing 
season (winter vs. summer).   
Leguminous cover crops currently in use worldwide and those that show potential 
include white lupin (Lupinus albus L.) (Mask et al., 1993; Noffsinger et al., 1998), 
velvetbean [Mucuna deeringiana (Bort) Merr], jackbean [Canavalia ensiformis (L.) 
DC.], jumbie-bean [Leucaena leucocephala (Lam.) de Wit], wild tamarind [Lysiloma 
latisiliquum (L.) Benth] (Caamal-Maldonado et al., 2001), crimson clover [Trifolium 
incarnatum L.] and other Trifolium species, and hairy vetch [Vicia villosa Roth subsp. 
villosa]. Other leguminous crops are not considered cover crops per se but play an 
important role in crop rotations, e.g., alfalfa [Medicago sativa L.] and soybean [Glycine 
max. (L.) Merr]. Alfalfa, called the ?queen of the forages,? is cultivated for forage 
purposes as well as soil bioremediation (Anonymous, 2000). It was reported that a corn-
soybean rotation enhances corn yield without additional nitrogen. (Maloney, 1999). 
  2 
Another corn-soybean crop rotation study produced corn yields equivalent to the yield 
produced by the application of 144 kg N ha-1 (Omay, 1998). Some of the non-leguminous 
cover crops used extensively in the southeastern USA are rye (Secale cereale L.), wheat 
(Triticum aestivum L.), and oat (Avena sativa L.) (Bauer and Reeves, 1999). Non-
leguminous cover crops with worldwide importance are black oat (Avena strigosa Shreb.)  
(Ceretta et al., 2002; Federizzi and Mundstock, 2004), white mustard (Sinapis alba L. 
subsp. alba), and rapeseed (Brassica napus L.) (Weinert et al., 2002). 
 
Benefits of growing cover crops 
Cover crops have been a part of agriculture for thousands of years, but they have 
received the serious attention of many farmers quite recently.  Subtropical climates such 
as the southeastern USA can support plant growth 12 months of the year, provided 
moisture is available. Yet, the main agronomic crops such as corn (Zea mays subsp. 
mays), cotton (Gossypium hirsutum L.), and peanut (Arachis hypogaea L.) are grown 
during spring to early autumn. This leaves the ground uncovered for up to eight months 
of the year, particularly during the winter months with rainfalls in excess of 322 mm 
month-1  (NCDC, 2005) leading to severe erosion. 
In the southeastern USA, winter cover crops are essential in conservation tillage 
systems to protect soils from erosion and for improving soil productivity (Schomberg et 
al., 2005). They are of particular benefit in colder winter months to prevent the loss of 
nutrients (Ditsch et al., 1993; Kinyangi et al., 2001).  A study conducted at the E.V. 
Smith Research Center showed that cotton yield was higher in a conservation system than 
in the conventional system of soil management. (Terra et al., 2005).   
  3 
 
Figure 1-1: Erosion in Alabama (http://newdeal.feri.org/library/s25.htm) 
 
Figure 1-2: Erosion in Alabama (http://newdeal.feri.org/library/s24.htm) 
  4 
Improving Physical Properties of Soil 
 
Cover crops are important in increasing the physical properties of the soil in many 
ways. Cover crops increase the soil organic carbon (SOC) content (Hermawan and 
Bomke, 1997; Liua et al., 2005) and dilute acid-extractable polysaccharides (Liua et al., 
2005) in the soil. The dilute-acid-extractable polysaccharides act as active binding agents 
in the soil. The SOC (Hermawan and Bomke, 1997; Liua et al., 2005) and acid-
extractable polysaccharides (Liua et al., 2005) increase aggregate stability of the soil, 
which is expressed as the increase in mean weight diameter (MWD). Winter wheat was 
found to increase vesicular-arbuscular mycorrhiza (VAM) inoculum potential, soil 
aggregation and yield of cash crops (Kabir and Koide, 2000).  Glomalin, which is a 
glycoprotein produced by arbuscular mycorrhizal (AM) fungi, is positively correlated 
with the aggregate stability of the soil. Aggregate stability is measured as resistance to 
breakdown by wet sieving of air-dried soil samples (Wright and Upadhyaya, 1998). 
Living cover crop mulches which can reduce the daily maximum soil temperature may 
have both positive (in tropical conditions) and negative (in temperate conditions) effects 
on main crop growth (Chassot et al., 2001).  Rainfall pattern and/or water availability 
may be other factors that need to be taken into consideration before starting any 
conservation system or cover cropping. Stored soil water was found to be reduced 
slightly after the incorporation of winter cover crops, which necessitates proper water 
budgeting if planted in arid and semi-arid areas (Mitchell et al., 1999). However, the 
water storage capacity of the soils was increased by the incorporation of winter cover 
crops into soil before the next season. 
  5 
Cover crops can improve water quality by decreasing the amount of pesticides 
and nutrients percolating from the agricultural fields to the water sources. The reduced 
seepage of nutrients from agricultural land also reduces microbial contamination of water 
bodies. Increased phosphorus input can accelerate fresh water eutrophication (Carpenter 
et al., 1999; Sharpley et al., 2001). Nitrate leaching from the soil is a potential problem 
for water sources near the agricultural fields (Sainju et al., 1998). The leached nitrate can 
pollute groundwater by precipitation in fall and winter seasons. One of the methods to 
control nitrate leaching from the crop field is to plant a cover crop with an extensive root 
system that can translocate the available nitrogen into their roots. In a comparative study 
for the effectiveness of preventing nitrate leaching, cereal rye (Secale cereale L.) was 
found to be more effective in reducing residual NO3- and potential leaching than hairy 
vetch (Vicia villosa Roth) and crimson clover (Trifolium incarnatum L.) (Sainju et al., 
1998). To prevent nitrate leaching effectively, it is important for a cover crop to have an 
extensive root system. In the experiment conducted by Sainju, the  total mini rhizotron 
root count (MRC; no. roots cm-2 soil profile) of cover crops at 0 to 50 cm  depth showed 
a positive correlation to N uptake and a negative correlation to soil NO3-  concentration. 
(Sainju et al., 1998).  The amount of nitrate leached into water bodies can be controlled 
by cultivating winter cereal grain cover crops in the field (Staver and Brinsfield, 1998). 
Soil cover can reduce the amount of pesticides reaching soil during spraying and, thus, 
can reduce the runoff loss of pesticides and eventual contamination of water bodies. 
(Silburn et al., 2002).  
 
  6 
 Cover crops can absorb P from the upper layers of soils and transport it in their 
roots to subsoil layers. Pea (Pisum sativum L. subsp. arvense), black oat (Avena strigosa 
Schreb) and narrow leafed lupin (Lupinus angustifolius L) were the most efficient cover 
crops for translocation of soil phosphorus for a 0 to 55 cm depth in oxisol soils. For the 
10 to 55 cm depth pea, black oat and narrow leafed lupin were most efficient. Lupin had 
higher root phosphorus content without P-fertilizer application. This is due to the fact that 
lupin can absorb phosphorus from soil using specialized proteoid roots if the soil is 
deficient in phosphorus. These cluster roots release citric acid to mobilize the sparingly 
available P in the rhizosphere (Neumann et al., 1999). With fertilizer application, 
common vetch followed by hairy vetch and black oat has the highest content of root P.  
White lupin has the highest capacity of P accumulation in the aerial parts without P 
application. In the presence of P fertilizer, black oat accumulated more than 20 kg ha-1 of 
P on the aerial parts. Without P application black oat had the highest root dry matter 
content among the ten cover crops (Avena strigosa, Avena sativa, Secale cereale, Pisum 
sativum subsp arvense, Pisum sativum, Vicia villosa, Vicia sativa, Lupinus angustifolius 
L., Lupinus albus, and Triticum aestivum) studied (Franchini et al., 2004). 
Cover crops may play a role in integrated pest management (IPM) practices by 
either altering the life cycle of the insect pest or by producing some inhibitory substances. 
Velvetbean [Mucuna deeringiana (Bort) Merr] has a strong inhibitory effect on the gall 
index of M. incognita in the roots of tomato (Caamal-Maldonado et al., 2001). Various 
cover crops are used to suppress weeds in different parts of the world. The canopy cover 
produced by the cover crops is important in controlling weeds by photosynthetic 
suppression. Apart from the cover crop biomass, there are some other factors that are 
  7 
helpful in suppressing weeds effectively, including the quickness in establishment of the 
cover crop (Barberi and Mazzoncini, 2001) and the ability to demobilize available 
nutrients from the field in fallow. Fibrous rooted cereal cover crops can scavenge the 
excess nutrients present in the soil after the harvest of the cash crop and make them 
available later; thus, this may decrease the weed population. The leguminous cover crops 
release symbiotic nitrogen to the soil, which decreases the effectiveness of them in 
suppressing the weeds population. Also, allelopathic chemicals released by the active 
crop or cover crop residues are important in weed suppression. 
A cover crop management system has a clear advantage in weed control 
compared to the winter fallow system (Reeves et al., 2005). Velvetbean [Mucuna 
deeringiana (Bort)Merr] is used in tropical regions of Mexico to suppress spiny amaranth 
(Amaranthus spinosus L.), smooth pigweed (A. hybridus L.), field sandbur (Cenchrus 
insertus M. A. (Curtis), and bitterweed (Parthenium hysterophorus L.) (Caamal-
Maldonado et al., 2001). In a field trial conducted in the northern Guinea savanna of 
Nigeria, it was shown that velvetbean combined with 40,000 corn plants ha-1, and 
weeding three times gave higher corn grain yields and efficient weed control than a 
farmer's control consisting of a single weeding at 4 weeks after planting (WAP) and low 
corn density. (Chikoye et al., 2004). Even though cover crops can be used to suppress 
weeds, even cover crops may be considered weedy in certain situations.  
The botanical characteristics that predispose a cover crop to become a potential 
weed are: 1) the ability of the plants to produce seeds at an early stage of its life cycle and 
continue seed production till the end of its life cycle, 2) the easy shattering of seeds, 3) 
production of a large number of seeds, 4) high seed dormancy even after long exposure to 
  8 
harsh environmental conditions or no dormancy at all, 5) quick germination and 
establishment, and 6) the ability to propagate vegetatively. The non-shattering nature of 
seeds in black oat compared to its wild relatives and the low survival of volunteer seeds 
in harsh environmental conditions during the summer, where high moisture and high 
temperature lead to a rapid degradation of seeds without a hard seed coat, reduces the 
potential threat of black oat as a weed in actual field conditions in southeastern USA. 
 
Allelopathy 
The allelopathic effect of a cover crop may negatively affect the crop growth in 
some cases. The allelopathic effects of some plant residues on the yield parameters of 
other crops might be reduced by proper tillage and other practices that promote rapid 
decomposition of the plant materials (Roth et al., 2000). On the other hand it is an 
efficient method for controlling the weed without chemical application. The allelopathic 
potential of a cover crop can be estimated by growing the plants in greenhouse conditions 
and conducting in-vitro germination and radicle growth bio-assays using aqueous 
leachates collected from them at a standard concentration (1%) (Caamal-Maldonado et 
al., 2001). In velvet bean L-3-(3,4-dihydroxyphenyl) alanine (L-DOPA) is mainly 
responsible for the allelopathic action (Nishihara et al., 2005). The use of genetic 
engineering to impart allelopathic potential to cover crops has been a discussion among 
scientists. Many, however, are skeptical about the effectiveness of such a strategy (Duke 
et al., 2001). Farmers in Mexico use leguminous species like Mucuna spp. and Canavalia 
spp. to control weeds in their fields (Caamal-Maldonado et al., 2001). 
 
  9 
BLACK OAT 
 
Biomass Production 
 Recently black oat (A. strigosa Schreb.) has become very important in subtropical 
and temperate regions as a winter cover crop and forage (Suttie, 2004). In South America 
black oat is grown on more than 3 million hectares as a cover crop or forage (Federizzi 
and Mundstock, 2004). It has the potential to produce a considerable amount of biomass 
in comparison with other non-leguminous or leguminous cover crops. In addition to that, 
it has many desirable qualities over the other cover crops. In a study conducted by 
Schomberg et al. (2005) results showed that the biomass production and soil N 
mineralization dynamics of black oat was similar to crimson clover, indicating the 
potential of black oat as a cover crop in the southeast USA. The amount of N mineralized 
in 90 days (Nmin90) measured with in situ soil cores was 1.3 to 2.2 times greater 
following black oat, crimson clover, and oilseed radish than following rye (Schomberg et 
al., 2005). Black oat can be used as a forage crop and produce a comparable dry matter 
yield to that of ryegrass (Lolium perenne L. cv. Kangaroo Valley) and it can also thrive 
well with other legume forage crops like barrel medic (Medicago truncatula L.) (Lowe 
and Bowdler, 1998). A study conducted at Instituto Agron?mico do Paran? IAPAR, 
Brazil demonstrated the ability of cover crop residues of black oat to decrease manganese 
toxicity in well-aerated, acid soils by lowering the Mn solubility (Andrade et al., 2002). 
Another advantage of black oat is the relative ease of field establishment. Black oat only 
needs N as a top dressing; neither P nor K is needed (Federizzi and Mundstock, 2004). In 
South America, black oat cultivated for cover crop purpose is desiccated in August 
  10 
(Federizzi and Mundstock, 2004). Black oat reached maximum biomass at anthesis (8579 
kg ha -1), while rye (Secale cereale L.) and wheat (Triticum aestivum L.) continued to 
increase biomass significantly through soft dough (9497 kg ha -1 and 10460 kg ha -1, 
respectively) (Ashford and Reeves, 2003). Therefore, black oat can be terminated at an 
earlier stage compared to the other two cover crops. Early termination will reduce 
depletion of the available nutrients and moisture in the field.  This is a useful trait since it 
can be planted as a fall-sown winter cover crop. If the winter is severe it will all be killed 
so there is no need for the use of herbicides to kill the cover crop. Since black oat has the 
C3 photosynthetic pathway for Carbon fixation (Tesar, 1984), it can contain more 
available NH4+ per dry matter mass unit compared to C4 cover crops such as Pennisetum 
glaucum (Waller, 1979), Sorghum vulgare (Waller, 1979), and Brachiaria decumbens 
(Rosolem CA, 2005). Cotton lint yield following black oat was higher than that following 
rye, even though rye produces more biomass than black oat (Bauer and Reeves, 1999). 
Even though black oat can thrive well with other cover crops in mixed culture with wheat  
black oat performs poorer than in monoculture in terms of biomass production (Cousens 
et al., 2003).  
 
Weed Control 
Black oat can suppress the weeds cutleaf evening primrose (Oenothera laciniata 
Hill) and common chickweed (Stellaria media (L) Vill) (Reeves et al., 2005). In years 
without any freezing injury, black oat provides better weed control than rye (Reeves et 
al., 2005). Black oat has a greater inhibitory effect on root elongation of radish than rye 
suggesting the suitability of black oat as mulch for weed control (Bauer, 1999). Black oat 
  11 
can perform well even in mixtures with other cover crops. Saini et al., (2005) reported a 
40% winter weed biomass reduction by mixed covers of lupin (80%) and black oat (20%) 
preceding corn, but these mixed covers were less effective preceding cotton. Weed 
biomass produced is less in cotton planted in black oat or rye covers compared to that 
planted in wheat or fallow covers. Without herbicides, black oat gave greater sickle pod 
[Senna obtusifolia (L.) H. S. Irwin & Barnaby] and palmer amaranth control than rye or 
wheat, showing the suitability of black oat as an effective cover crop for weed 
suppression (Patterson et al., 1996).  
  
Management Practices 
 Black oat has a low C/ N ratio of 34:1 compared to 42:1 for oat and wheat and 
45:1 for rye (Bauer and Reeves, 1999). This is desirable for the main crop in regions with 
high rainfall and temperatures during the growing season because the decomposition of 
the cover crop residue is slowed down. However, it may also sequester the nitrogen 
available to the main crop and, hence, application of starter N to the main crop may be 
warranted (Ceretta et al., 2002). Obviously, the kill date has an effect on biomass yield 
and available nitrogen and maximizing the days between cover crop establishment and 
kill is a desirable practice. A delayed kill date of hairy vetch for two weeks can improve 
N accumulation significantly (from 104 to 113 kg ha-1) (Sainju and Singh, 2001). Using 
chemicals to kill the cover crop may be advantageous for the following main crop (Vyn 
et al., 2000). The most effective and economical method to kill black oat at anthesis is the 
combination of roller and herbicide (88% with roller+paraquat and 91% with 
roller+glyphosate) (Ashford and Reeves, 2003). The use of a roller also facilitates 
  12 
planting by reducing hairpinning of residue when the planter runs parallel to the roller 
(Ashford and Reeves, 2003).  
 There may be a difference in the response of cover crop performance based on the 
planting date (Bauer and Reeves, 1999). Black oat had higher N concentration than other 
winter cereals (oat, rye and wheat) for October and November planting dates (Bauer and 
Reeves, 1999). The date of planting affects the cover crop dry matter yield, N content and 
C/N ratio (Odhiambo and Bomke, 2001). In a study conducted in Britain, cover crops 
performed better in terms of dry matter yield and N accumulation if planted in late 
August compared with late September (Odhiambo and Bomke, 2001). However, the 
planting date of the cover crop had no influence on cotton lint yield in the southeastern 
United States (Bauer and Reeves, 1999). Considering all these aspects, black oat, which 
is widely grown in Brazil and Paraguay as a cover crop, can be used as a winter cover 
crop in the cotton belt of southeastern USA due to the climatic similarity between these 
places (Bauer and Reeves, 1999).  
 
Classification and Botany 
Black oat (Avena strigosa Schreb.) is a member of genus Avena of the Aveneae 
tribe within the Pooideae subfamily of the grass family Poaceae. The Latin binomial is 
particularly useful in communication because (i) the common name black oat is also 
sometimes applied to other oat species and (ii) in the English speaking regions of the 
world Avena strigosa Schreb. is referred to by different common names such as bristle 
oat, sand oat, or small oat. Common names for A. strigosa in other languages are avoine 
  13 
rude in French, Rauhhafer and Schwarzhafer in German, and aveia-preta in Portuguese 
(USDA GRIN Taxonomy 1997). 
Avena strigosa can be confused with two closely related diploid species viz. 
Avena hispanica and Avena brevis. The conclusive identification can be made with the 
inspection of the unique strigosa-type lodicules in which the side lobe is larger than that 
of sativa and fatua type lodicules and is fused partly or almost completely to the conical 
part of the lodicules above the level of troughing, sometimes near the very tip of the 
lodicules (Baum, 1977).  
Black oat is an annual (Baum, 1977; Legget, 1992) and juvenile growth and 
flowering stems are either prostrate or erect. The height of flowering stems ranges from 
80 to 200 cm. (Legget, 1992). Ligules are obtuse and sometimes pointed (Baum, 1977). 
Panicles are equilateral and spikelets are short measuring 20 to25 mm long without the 
awns, each of which consists of two to three florets. The glumes are equal or nearly equal 
in length (the length may range from 16 to 24 mm) and are non-disarticulating at 
maturity. Awns inserted at the middle of the lemma and tips of lemmas are bisetulate-
biaristulate or sometimes biaristulate only. Lemmas are glabrous or not and sometimes 
they bear only a few hairs around the point of awn insertion. The palea has 1-2 rows of 
cilia along the edge of the keel and the back of the palea is often beset with prickles, 
rarely without. The lodicules are with prickles (Baum, 1977). There is not much 
information available about the phenological changes other than a brief account given by 
Baum (1977) regarding the flowering of black oat from June to September and rarely in 
October.  
  14 
 From very early times black oat has been cultivated for different purposes in 
different parts of the world. Prior to the 17th century Avena strigosa was the most 
common oat cultivated in Scotland (Murphy and Hoeffman, 1992). Black oat is used as 
an animal forage, green manure and cover crop and in erosion control (Anonymous). In 
Brazil much of the fodder oat is Avena strigosa rather than Avena sativa. (Martinelli, 
2004). In Japan Avena sativa and Avena strigosa are the cultivated fodder oats (Katsura, 
2004). Avena strigosa Schreb, which formerly was a minor cereal of poor soils, is now 
grown on a large scale in southern Brazil, Chile, Uruguay and Paraguay (Anonymous, 
2004). In southern Brazil, Black oat is cultivated as winter forage, cover crop and for 
grain production. (Ceretta et al., 2002).  
 
Center(s) of diversity 
Coffman (1961) citing Sampson (1954) who in turn cited Tackholm et al. (1941) stated 
that ?What are probably the oldest known oat grains were found in Egypt associated with 
the remains belonging to the 12th dynasty [2000 to 1788 BC]. Similar grains have been 
found among Egyptian cereals of the 2nd and 3rd century AD. These Egyptian oats were 
originally identified as Avena strigosa, but Tackholm et al. believe they are either A. 
fatua or A. sterilis.? 
 Avena strigosa is distributed in Austria, Belgium, Corsica, Czech Republic, 
Slovakia, Denmark, Finland, France, Germany, Hungary, Lithuania, Luxemburg, 
Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, and USSR (Baum, 
1977). Presently it is grown commercially in South America as a cover crop or for grain 
to be sown as cover crop (Federizzi and Mundstock, 2004) and to a limited extend in 
  15 
North America also. Even though black oat was introduced to the United States from 
Europe, it has become naturalized in California (Magness et al., 1971).The strigosa group 
is seen in vast areas from the Iberian Peninsula, considered to be the center of origin, to 
the plains of Afghanistan occupying a wide range of ecological niches (Weibull et al., 
2005). Black oat is cultivated in France, Belgium, Portugal, Spain, northwest Germany, 
and Great Britain (Coffman, 1961). Black oat is cultivated in Wales to a limited extent on 
poor upland soils and to a greater extent in Germany, the United Kingdom, and various 
parts of Eastern Europe (Baum, 1977). In Latin America the cultivated black oats are 
introductions from Europe (Federizzi and Mundstock, 2004). A more detailed account of 
the present geographical distribution of black oat is found in the Flora Europeae 
(Anonymous).  
In many of these regions different cultivars have been developed as a result of 
cultivation or naturalization. The main black oat cultivars used in Peru are Vilcanota, 
Mantaro and Pastos (Federizzi and Mundstock, 2004). The United States Department of 
Agriculture National Germplasm Resources Information Network (USDA-GRIN) lists 
121 accessions stored at the National Small Grains Collection (NSGC), Aberdeen, Idaho, 
of which 119 are available to researchers around the world (http://www.ars-grin.gov/cgi-
bin/npgs/html/tax_acc.pl, verified on June 18, 2007).  
 
Cytogenetics 
Like the genus Triticum, species in the genus Avena constitute a polyploid series 
with a basic chromosome number of x = 7. There are 23 species belonging to the three 
genome groups with three ploidy levels. All diploid species contain either the A or C 
  16 
genome, the tetraploid species contain the A and C genomes, and the hexaploids have the 
ACD genomic constitution. Thus, the A and C genomes occur at diploid, tetraploid and 
hexaploid levels, but the D genome is present at the hexaploid only (Leggett and 
Markhand, 1995). 
Avena strigosa is a diploid species with the A genome and a chromosome number 
of 2n = 2x = 14 (Baum, 1977; O'Mara, 1961; Thomas, 1992),  Avena strigosa weistii is 
another diploid  with As genome. A. ventricosa Bal. ex. Coss is a diploid with CvCv and 
both A.caudate Dur. and A.eriantha Dur. are diploids with the  CpCp genome. A.barbarate 
Pott ex Link., A.abyssinica Hochst and A.vavilovania(Malz.) Mordv. are tetraploids with 
AABB genomes and  A.murphyi Ladiz. and A.maroccana Gdgr. are tetraploids with 
AACC genomes. There are six hexaploid species identified with AACCDD genome viz.  
A.sativa L., A.byzantina C.Koch., A.sterilis L., A. fatua L., A. hybrida Petern., 
A.atherantha Presl., A.occidentalis Dur., and A. trichophylla C.Koch. (Thomas, 1992). 
 The C genome is quite different from both the A and D genomes. Based on the 
Giemsa C-banding technique, it can be seen that the heterochromatin in genome A is 
located at the telomeres, whereas it is located at the centromeric and interstitial regions in 
the C genome and is spread throughout the chromosome in the D genome (Linares et al., 
1992). The species with A genome have symmetrical chromosomes with low 
heterochromatin content, whereas those with C genome have asymmetrical 
heterochromatic chromosomes (Fominaya et al., 1988). Failed attempts to distinguish 
between the A and D genomes may be an indication of the possible role of A genome as a 
donor of both A and D genomes of the hexaploid oat. In order to find the role of diploid 
oat species in the evolution of the hexaploid species, a clear understanding of evolution 
  17 
of various genomes and intergenomic translocations is considered to be significant 
(Linares et al., 1998).  
The cytogenetic map which is a visual representation of chromosomes when 
stained and examined under a microscope provides valuable information in  cytogenetic 
studies. With the advancement of technology, a high resolution cytogenetic map obtained 
from Fluorescence in situ Hybridization (FISH) has provided important biological 
information on genome organization and functions. FISH is a molecular cytogenetic 
technique that can be used to identify and localize the presence or absence of specific 
DNA sequences on chromosomes. It uses fluorescent probes of desired length and 
sequence, which hybridize only to the complementary sequences in the chromosomes. 
The probe is actually a single stranded denatured DNA mixed with a fluorescent dye and 
it is mixed with the denatured target DNA (combed DNA) for hybridization. The specific 
tagged sequence can then be visualized using a fluorescence microscope. On the other 
hand GISH (Genomic in situ hybridization) is another molecular cytogenetic technique 
used to find a genomic relationship among different species or genus. Using this 
technique chromosomes or genomes from different parents or ancestors can be identified 
by means of differential hybridization of entire genomic probes. 
The highly irregular chromosome pairing in a cross between A. macroccana 
(AACC) and the autotetraploid plant produced by A. strigosa Schreb.(AsAs) and A. 
eriantha Dur.( CpCp) (Legget, 1998) discards the chance that A. strigosa and A. eriantha 
participated in the formation of the tetraploid A. macroccana (AACC) (Legget, 1998). 
The low chromosome pairing in a hybrid between A. strigosa and A. insularis excludes A. 
strigosa as a diploid progenitor of A. insularis (Ladizinsky, 1999). A cross between 
  18 
tetraploid A. abyssinica and A. strigosa can form a triploid (Dilkova et al., 2000b). The 
genome of Avena longiglumis is different from the A genome of all other Avena species 
and it is designated as Al. The total length of the chromosomes of Avena strigosa Schreb. 
is 17 units shorter than that of Avena longiglumis, which is equal to the longest arm in the 
strigosa set. This indicates a substantial loss of chromatin material from the strigosa 
group. This may be due to the elimination of acentric fragments due to translocations in 
the strigosa genome rather than a chromatin gain due to duplication in the Avena 
longiglumis genome. The F1 produced from crosses between Avena longiglumis, Avena 
strigosa and Avena hirtula are sterile due to chromosome incompatibility among 
themselves. (Rajhathy, 1961). 
Avena insularis Ladizinsky was found to be the tetraploid progenitor of the 
regular oat Avena sativa after studying the F1 between these two species. It contains two 
pairs of satellite chromosomes and one pair of subterminal chromosome more 
morphologically similar to Avena magna than any other tetraploids. However, in A. 
magna three pairs of satellite chromosomes are present. Avena insularis Ladizinsky 
forms pentaploid hybrids with Avena sativa but seed set happens only if the Avena sativa 
is used as the female parent; meiosis is irregular in these hybrids. The mean number of 
chiasmata per cell in the hybrid formed from a cross between Avena sativa and Avena 
insularis is higher (88%) than the mean number of chiasmata in hybrids formed from 
crosses involving Avena sativa with other tetraploids such as Avena magna (75%), Avena 
murphyi (62%) and Avena barbata (42%). This suggested a closer resemblance of Avena 
insularis to Avena sativa than any other oat species (Ladizinsky, 1998). 
  19 
Zhou et al. (1999) suggested Avena sterilis L. as the putative progenitor of the 
cultivated hexaploid oat Avena sativa L. and Avena byzantina C. Koch. based on the 
cluster analysis of 248 polymorphic RAPD (Random Amplified Polymorphic DNA) 
markers and the studies of 7C-17 intergenomic chromosomal translocation among the 
accessions of these three species. They also proposed a dichotomy or divergence in 
speciation during the period of domestication that leads to the formation of both Avena 
sativa L. and Avena sterilis L. 
Avena barbarata was introduced to California from Spain during the seventeenth 
and eighteenth centuries. The gene pool  present in California is similar to the present day 
Spanish gene pool based on allelic and single-locus genotype composition, but different 
on a multilocus genotype basis. (Garcia et al., 1989). 
Avena agardiriana Baum et. Fedak is a recently discovered tetraploid (2n= 4x = 
28).  Its C-banding pattern revealed that it shows resemblance to A/B/D genomic groups 
of chromosomes of Avena species rather than the C genomic group (Jellen and Gill, 
1996). Avena strigosa, Avena wiestii and Avena hirtula are karyotypically similar with 
two metacentric, two submetacentric, one subacrocentric, and two morphologically 
different satellite (SAT) chromosomes (Badaeva et al., 2005). 
 
Pairing inhibitor genes 
In allopolyploids such as wheat (Triticum aestivum) diploid-like pairing occurs 
during meiotic division. This means that homologous chromosomes pair as bivalents 
rather than homoeologous chromosomes pairing as multivalent. Homoeologous are 
partially homologous chromosomes originating from different ancestral genomic groups. 
  20 
This mechanism is governed by the gene Ph1, which is a trans-acting gene affecting 
centromere-microtubules interaction (Vega and Feldman, 1998). This kind of genetic 
control of meiotic pairing is observed in other allopolyploids as well, e.g., Avena sativa 
L. and Festuca arundinacea (tall fescue) (Jenczewski and Alix, 2004). In wheat another 
gene (Ph2) controlling the pairing of homoeologous chromosomes is present on 
chromosome 3D, but it is a rather weaker suppresser than Ph1 (Mello-Sampayo, 1971).  
Hexaploid oat has three genomes that might have derived from different ancestral 
genomes (or three sets of seven chromosome pairs).  Each chromosome is capable of 
pairing with five other related chromosomes, one homologue and four homoeologous, 
during meiosis, but actually pairs only with the homologous chromosome. This pairing 
behavior is controlled by pairing control genes (PCG). It is hypothesized that the grass 
genome might have originated from an ancestor with holocentric chromosomes. 
Holocentric chromosomes have diffuse centromeres and possess multiple cites for 
microtubule attachment (Moore, 1998). Newly formed allopolyploids may display 
homoeologous pairing with multivalent formation resulting in reduced fertility, but as 
generations advance, the formation of multivalent decreases sharply due to natural 
selection of plants having more pairing control genes (Jenczewski and Alix, 2004). Avena 
barbata Pott ex Link formed from the polyploidization of Avena hirtula Lag. and Avena 
wiestii Steud. complex showing bivalent pairing during meiosis. This shows that the 
homeologous pairing is suppressed in this tetraploid (Allard et al., 1993). Thomas and 
Rajhathy (1966) observed that initial pairing of chromosomes in an F2 population of a 
cross between Avena abyssinica Hochst. and Avena barbarata took place at early 
prophase, but desynapsis occurred and was associated with a high incidence of univalents 
  21 
at Metaphase I. They suggested the role of a single recessive gene ds2 for this pairing 
control. 
Molecular Markers 
Even though different plant species are morphologically different, they might 
have arisen from a common ancestor during the course of evolution. The ubiquitous grass 
family (Poaceae) is one of the most studied plant groups at all aspects from 
morphological to molecular level. This family is further divided into six or seven 
subfamilies with about 40 tribes and 600 to 700 genera (Mathews et al., 2000). In order 
to classify the vast number of grasses, earlier scientists employed the classical approach 
of studying the morphology of flowering parts and the plant as a whole. Most of the 
recent studies on grass phylogeny are based on data from chloroplast genomes and a few 
from the information based on the data from nuclear genomes. The phylogenetic analysis 
of data obtained from the partial phytochrome B nuclear DNA sequences (nuclear 
PHYB) is a modern technique in phylogenetic studies (Mathews et al., 2000). 
The genome co-linearity or synteny among different species is the basis of 
comparative mapping. Closely related species show convergence in genome structure, 
whereas distant ones show greater divergence. In a study conducted at Cornell University 
comparing the oat genome with Triticeae species, rice and maize genomes revealed the 
conservation of certain regions of homologous segments among these groups (Deynze et 
al., 1995). The integrated grass genome map consists of species of six different tribes and 
three different subfamilies: viz. Bambusoideae (rice etc), Pooideae (oats and Triticeae) 
and Panicoideae (Devos and Gale, 1997). In an oat linkage group 134 DNA sequences 
  22 
were assigned to 10 chromosomes associated with the syntenic group using nullisomics 
of hexaploid oats (Kianian et al., 1997).   
A genetic linkage map is a useful tool for the localization of qualitative and 
quantitative traits loci for marker assisted selection and breeding for agronomically 
important traits.  Even though different linkage groups are present in oat species, the 
correspondence of individual A. strigosa chromosomes to these linkage groups or loci is 
still unknown. Genetic linkage can be explained as the association of genes located on the 
same chromosomes. In a linkage map, map distance is a statistical estimate of the 
crossover and physical distance is the number of DNA pairs between two linked genes. 
Aneuploids are used for linkage mapping because of the ease of assigning the gene 
families, monomorphic Restriction fragment length polymorphism (RFLP) sequences, 
and oat linkage groups to chromosomes. Aneuploids can be produced by various 
techniques such as X-ray irradiation at doses varying from 75r to 600r (Andrews and 
McGinnis, 1964). 
The direct approach for assigning linkage groups to individual chromosomes 
would be to hybridize genetically mapped RFLP probes to chromosomes. An alternative 
approach is to directly amplify sequence-tagged site (STS) markers, derived from 
genetically mapped RFLP clones in the DNA of microdissected chromosomes. Based on 
the two or four RFLP-derived STS markers, A. strigosa chromosomes 2 and 3 were found 
to be homologous to the oat linkage groups C and E, respectively. Chromosome 7 
corresponds to linkage group F and was most probably involved in an A. strigosa-specific 
chromosomal translocation relative to the diploid species A. atlantica and A. hirtula 
(Loarce et al., 2002). Based on Amplified fragment length polymorphism (AFLP) and 
  23 
RAPD dendrograms, A. prostrata and A. longiglumis have the most divergent A genomes 
and are considered to be the most ancient, while the As ( A. strigosa) genome is the most 
recently evolved (Drossou, 2004).  
Zhu and Kaeppler (2003) compared the hexaploid oat linkage map produced from 
a mapping population of a recombinant inbred line derived from the F5:6 lines of a cross 
?Ogle/MAM17-5? (OM) to the previous linkage map produced from a mapping 
population of ?Kanota/Oagle? (Wight et al., 2003). They identified three putative 
homoeologous groups 5 cM or longer out of the 28 linkage group determined. Group one 
includes OM7, OM8 and OM18, group two includes OM2 and OM23, and group 3 
includes OM13 and OM16. Nine linkage groups identified were homologous to the 
linkage groups in the KO map. The relevance of the MO mapping population is that it is 
segregating for a number of agronomically important traits (Zhu and Kaeppler, 2003). 
 
Breeding Programs 
Even though black oat has these promising features as a cover crop it has some 
drawbacks that need to be corrected by the implementation of a proper breeding program. 
Biomass production of black oat is reduced compared to rye if low night temperature 
persist for long periods of time (Reeves et al., 2005). This is due to freezing injury. Even 
though there are six oat-breeding programs active in South America there is no true 
breeding program in Brazil for A. strigosa (Federizzi, 2004). Cold hardiness of black oat 
needs to be improved through breeding or selection (Schomberg et al., 1995; Bauer 
1999). Black oat (A. strigosa cv. Saia) was found to be susceptible to root knot nematode 
(Meloidogyne marylandi, M. javanica and M. incognita in a study in Israel), while A. 
  24 
sativa was resistant or a non-host (Oka et al., 2003).  This indicates that we need to 
concentrate on this aspect also in a breeding program. However another study conducted 
in Brazil, using five cultivars of black oat demonstrated that all of them are resistant to 
Meloidogyne incognita and M. paranaensis. (Moritz et al., 2003). In southeastern USA 
nowadays reniform nematode (Rotylenchulus reniformis Linford & Oliveira) is more 
problematic than Meloidogyne. 
 A. strigosa has been used in many oat breeding programs to impart disease 
resistance to cultivated oats as it is an important source of resistance to crown rust 
(Puccinia coronata f. sp. A.e) (Weibull et al., 2005). The crown rust resistance gene is a 
complex with 9 genes involved (Dilkova et al., 2000; Rayapati et al., 1994). The stem 
rust (caused by Puccinia graminis Pers f.sp avenae Erikss. and Henn) resistance gene is 
also present in Avena strigosa strains C.D. 3820 and C.I. 3078. The gene is a single 
dominant gene (Dyck, 1966).  However the hybridization incompatibility of the diploid 
black oat with the natural hexaploid has been a problem. Successful hybridization 
between A. strigosa cv. Saia (2n = 14) and A. magna (2n = 28) and the induction of a 
hexaploid form using colchicine treatment of the triploid hybrid were reported when A. 
strigosa was used as the female parent. However, this hexaploid failed to retain its barley 
yellow dwarf virus (BYDV) resistance even though it retained its crown rust resistance 
(Ladizinsky, 2000). For transferring the desirable traits from a diploid to a hexaploid 
species, the first step is a cross between diploid Avena strigosa and a tetraploid such as 
Avena abyssinica to form a triploid. Then, the 6x amphiploid is induced by colchicine 
treatment of the triploid and it will be hybridized with the hexaploid oat to transfer the 
desired characteristics. C- banded karyotyping can be used to verify the substitution of 
  25 
the desired gene (Dilkova et al., 2000). According to Forsberg ?several barriers to inter-
specific gene transfer must be overcome to transfer genes for resistance from diploid 
species such as A. strigosa to A. sativa? (Forsberg and Shands, 1969).  
Based on the preceding literature review it is clear that cover crops are important 
for conservation practices and that black oat is a suitable cover crop for the Southeastern 
USA. The only commercially available black oat cultivar in United States is SoilSaver, 
released in 2002 by USDA-ARS and The Alabama Agricultural Experiment Station. 
Even though SoilSaver is superior in many traits like maturity and biomass yield than 
many other accessions, some traits need to be improved. In order to start a breeding 
program the foremost important thing needed to be done is the evaluation of available 
germplasm, since without genetic variation selection for superior characters can not be 
made. There are 120 accessions of black oat accessions available from ?USDA-ARS 
Small Grains Germplasm Unit? at Aberdeen, Idaho, but a detailed study of this 
germplasm collection for maturity and morphology needs to be done before starting a 
breeding program. The objective of my research is the characterization of the entire 
USDA-GRIN Avena strigosa Schreb. germplasm collection based on reproductive 
maturity and morphological traits. 
 
 
 
 
 
 
  26 
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  37 
II. MORPHOLOGY AND MATURITY OF BLACK OAT (AVENA STRIGOSA  
SCHREB.) 
 
Abstract 
 
Black oat (Avena strigosa Schreb.) is a potential cover crop for the southeastern 
United States. We evaluated the entire USDA-ARS Germplasm collection for identifying 
the accessions suitable for cover crop purpose in southeastern US conditions. One 
hundred four accessions were planted at Shorter, Alabama on a Norfolk sandy loam 
(coarse-loamy, siliceous, subactive, thermic, Plinthic, Paleudults) during 2003-04 and 
2004-05. Agronomic traits and maturity were studied. Relevant traits that can 
differentiate the accessions identified after the forward stepwise selection procedure 
(STEPDISC) were used for canonical discriminant analysis (CDA). The first four 
canonical discriminate variates (CAN) had Eigenvalues ? 1 and are responsible for 84 % 
of the total variation among the entire population for both years. Plotting CAN1 vs. 
CAN2 differentiated Accession CIav 9015 from all other accessions in this analysis. We 
speculate that accession CIav 9015 may not be Avena strigosa Schreb., the species under 
consideration, but  some other species of Avena. Cytogenetic and molecular marker study 
may be needed for a conclusive identification of this accession. CDA also separated four 
  38 
accessions from Europe and one from Australia to the extreme right of the first quadrant 
with high positive values for both CAN1 and CAN2 variates. The accessions studied are 
significantly different based on individual traits among and within countries and 
continents. South American accessions are with prostrate habit and are suitable for cover 
crop purpose in the southeastern US. Accessions from Europe and Australia are late 
maturing and may not be suitable for the southeastern US condition, but may be suitable 
for the colder parts where winter kill is employed for the termination of the cover crop.  
 
Introduction 
Black oat (Avena strigosa Schreb.) is a diploid species with As As genome and a 
chromosome number of 2n = 2x = 14 (Baum, 1977; O'Mara, 1961; Thomas, 1992). 
Originating in the northern parts of Spain and Portugal (Weibull et al., 2005), black oat 
has spread to different parts of the globe (Fig. 2-1.) (Baum, 1977; Coffman, 1977; 
Federizzi and Mundstock, 2004; Weibull et al., 2005). Even though different in ploidy, 
diploid black oat has been used in many hexaploid common oat (A. sativa L.) breeding 
programs as a donor parent for some desirable characters such as crown rust (Dilkova et 
al., 2000) and stem rust  resistance (Dyck, 1966). 
Black oat has become an important winter cover crop in subtropical and temperate 
regions (Suttie and Reynolds, 2004) and is cultivated on more than 3 million hectares in 
South America (Federizzi and Mundstock, 2004), especially in southern Brazil (Ceretta et 
al., 2002). Experiments conducted in the southeastern United States revealed its potential 
to become a major cover crop in that region (Bauer and Reeves, 1999). Black oat has 
biomass production and nitrogen dynamics comparable to crimson clover (Schomberg et 
  39 
al., 2005). It also has the potential to be grown as a forage and fodder crop and can thrive 
well with leguminous forage crops (Katsura, 2004; Lowe and Bowdler, 1998; Martinelli, 
2004). Cotton lint yield following black oat was higher than that following rye. Black oat 
has a low C/ N ratio of 34:1 compared to 42:1 for common oat and wheat and 45:1 for 
rye (Bauer and Reeves, 1999). Thus the availability of nutrients through degradation of 
plant residues when used as a cover crop will be faster in black oat than other small grain 
cover crops. Studies in Brazil revealed black oats also has the potential to decrease 
manganese toxicity in well aerated acidic soils (Andrade et al., 2002).  
The only commercially available black oat cultivar in the United States is 
?SoilSaver?, which was released by Auburn University and USDA in 2002. Even though 
SoilSaver is superior to other black oat accessions for some traits such as maturity and 
biomass yield (van Santen, unpublished data, 2005), some traits need further 
improvement. Over 100 black oat accessions are available from the USDA-ARS Small 
Grains Germplasm Unit at Aberdeen, Idaho, but a detailed study of this collection is 
needed before accessions can be used in a breeding program. In this paper we examine 
the agronomic traits of the entire USDA black oat accessions that are important for use as 
a cover crop and the potential of these accessions for cover crop purposes in the 
southeastern United States. 
 
 Materials and Methods 
SoilSaver and 103 Avena strigosa Schreb accessions (Table 2-1) obtained from 
the USDA-ARS National Small Grains Collection Unit at Aberdeen, ID, USA 
(http://www.ars-grin.gov/cgi-bin/npgs/html/site.pl?NSGC) were used for the study. Two 
  40 
seeds per cone-tainer (Ray Leach Cone-tainers, Washougal, WA; 2.5 cm diameter by 12 
cm depth) were planted in 1:1 sand and PRO-MIX medium (Sun Gro Horticulture 
Distribution Inc., Bellevue, WA) at the Auburn University Plant Sciences Research 
Center (PSRC) greenhouse. Approximately 2 weeks after seeding, seedlings were thinned 
to one per cone-tainer. The seedlings were transplanted on 90 x 90 cm centers at the Field 
Crops Unit of the E.V. Smith Research Center, Shorter, Alabama (32?24.5'N, 85?57'W) 
on October 31, 2003, and November 16, 2004. The soil type of the field is a Norfolk 
sandy loam (coarse-loamy, siliceous, subactive, thermic, plinthic Paleudult). The 
experimental design was an RCBD with three replicates. Six seedlings were transplanted 
per year x block x entry combination. Nitrogen was applied during the 3rd week of 
February of each year at a rate of 36 kg ha-1. Occasional manual and mechanical weeding 
was done. 
 
Response variables 
Twelve traits were measured in this study viz. heading date, tiller angle, tiller 
diameter, tiller length, tiller node number, tiller number, panicle length, panicle node 
number, flag leaf length and width, seed yield and average seed mass (Table 2-2). 
Heading date was defined as the day of the year when the first tiller of a given plant had 
emerged 75% of its length from its leaf sheath. When all the plants in an accession were 
headed out, the outermost tiller from each plant was removed and observations taken for 
length and width of flag leaf, tiller and panicle length, tiller and panicle node number and 
tiller diameter.  
  41 
The freezing temperature experienced in January 2005 killed many plants in the 
2004-05 study, and this resulted in a delayed heading and harvesting dates and lower seed 
yield. In 2003- 2004 study plants were harvested from mid May to early June, whereas in 
the 2004-05 study plants were harvested from 10 to 20 June. Tiller angle was measured 
on one tiller per plant before harvesting using a protractor attached to a wooden ruler. 
Each plant was harvested separately, dried to reduce the moisture to < 10%, and stored in 
a paper bag until further processing. The tiller number per plant was counted and tillers 
were threshed using a belt thresher and cleaned with South Dakota Blower (Seedburo 
Equipment Co., Chicago, IL) and meshed sieves of different sizes. Total seed yield per 
plant was determined from cleaned seed and 1000 seed mass from a sample of 50 seeds 
per plant.  
 
Statistical Analysis 
Mixed model methodology as implemented in PROC MIXED of SAS? was used 
to analyze the data because of observations missing at random. Since the data are not 
balanced, instead of using simple arithmetic mean, there is the need to use adjusted 
means (least square means) for optimal representation. The method for variance 
component estimation is also different for unbalanced data than a balanced one where we 
can use ANOVA for estimating the variance of the random variable by invoking the 
Type3 option of PROC MIXED (http://support.sas.com/onlinedoc/913/docMainpage.jsp; 
verified 9th February 2007); instead in an unbalanced data we need to use ?Restricted 
Maximum Likelihood? (REML) method of variance estimation. The Kenward-Roger 
method was used for the estimation of degrees of freedom.  
  42 
For each of the 12 response variables (Table 2-2) we calculated year x accession 
least squares interaction means listed in Appendix 1.Block within year was the only 
random effect in the model. Year, accession, and the year x accession interaction were 
treated as fixed effects. These interaction means were then used in discriminant analysis 
to investigate multivariate differences among accessions. As a first step PROC 
STEPDISC (SAS Institute, 1999) procedure was used for stepwise forward selection of 
quantitative variables discriminating among accessions. This procedure eliminated tiller 
number and seed yield from other response variables since they had the least canonical 
correlation among the 12 response variables studied. The 10 selected variables were 
further analyzed by canonical discriminant analysis (CDA) using PROC CANDISC (SAS 
Institute, 1999) with accession as class variable. 
Unlike fixed and pseudo-random models, where one design matrix is used for the 
entire model, mixed models use two design matrices say X to describe the fixed effects in 
the model, and Z to describe the random effects in the model. The fixed effects design 
matrix X has dimension n * p, where n is the number of observations in the data set and p 
is the number of fixed effect parameters in the model and Z has dimension n * q, where q 
is the number of random effect coefficients in the model. In the mixed model analysis, 
block nested within year was taken as the random factor and interaction means of 
accession x year were used for further analysis. The analyses of individual traits were 
based on loadings from CDA.   
 
  43 
Results and Discussion 
The underlying assumptions of ANOVA are (1) normality of distribution of data 
within a group with mean ? and standard deviation ?, i.e., the data should be 
symmetrically distributed. A histogram is a good indicator to check this assumption; (2) 
homogeneity of variances, which assumes that the variances in different groups of 
variables are similar. The box and whisker plot can provide a visual estimation of the 
homogeneity of variance of observations within a group; and (3) independence of 
observations. The SAS? GLIMMIX procedure (http://support.sas.com/rnd/app/papers/ 
glimmix.pdf; verified February 7, 2007) was used to evaluate these assumptions for all 
response variables. For each response variable this procedure creates a graph of 
studentized residuals with four panels: (1) residuals vs. linear predictor, (2) histogram, (3) 
quantile-quantile plots, and (4) box and whisker plots. 
A residual is the difference between the observed value of the variable (Yi) and 
the predicted value of the variable Y^i. Because the variances of the residuals differ, even 
though the variances of the true errors are equal to each other, it is necessary to do  the 
studentization of the residuals. When the residual is adjusted by dividing it with the 
standard deviation we call it as a studentized residual.  
   ,  
1?
?residual  dStudentize
ii
i
h?
=
?
? where h
ii   is the ?leverage? (a measure of the influence 
of the ith observation in the matrix) ranges from 0 to 1. The leverage helps to identify the 
influential observation. Studentized residuals will approximate a normal distribution with 
mean 0 and variance 1 when residual degrees of freedom for a model get large. The 
studentization of residuals helped to detect the outliers in each response variable.   
  44 
Heading date data were long tailed with some extreme values. The heading dates 
were calculated as the interval between the date of transplanting of the seedling to the 
field and the date 75% of the panicle had emerged from the leaf sheath. The tiller angle 
and tiller diameter were normally distributed with few outliers. The tiller length data was 
distributed within a narrow range with few extreme values. The panicle length and 
panicle node numbers were normally distributed, but the panicle node number had a 
narrow distribution. The seed yield residual plot was funnel shaped so that one must 
assume some kind of trend in the data. The seed yield was much higher in year 2003-04 
than 2004-05, the result of the freezing injury experienced in January 2005. The average 
seed mass is distributed in a very narrow range with some extreme values. 
 All phenotypic response traits measured had higher values for the year 2003 than 
2004 except for the tiller node number, panicle node number and the tiller angle (Table 2-
3). The maximum tiller angle remained the same for two years. The average and 
maximum heading date were more for the 2nd year of the study, likely due to the fact that 
the plants showed temporary dormancy in growth after exposure to the freezing 
temperature of January 2005. All other growth parameters like tiller diameter, length, 
panicle length, flag leaf length and flag leaf width were affected as well by the low 
temperature but seed yield and seed mass were most affected by freezing injury in 2004-
05. 
 
Multivariate analysis 
The first four variates from the canonical discriminant analysis all had 
Eigenvalues ?1 and were responsible for 84% of the total variation in the entire 
  45 
population (Table 2-4). Heading date (r = 0.72) and tiller node number (r = 0.78) had the 
highest correlation with CAN 1 and tiller length (r = 0.54), tiller diameter (r = 0.55) and 
leaf length(r = -0.64) with CAN 2. Tiller angle (r = 0.54) and panicle length (r = -0.59) 
had highest correlation with CAN3 and tiller angle (r = 0.47), panicle node number (r = 
0.59) and leaf width (r = 0.77) with CAN4.  
Accession CIav 9015 from Canada was clearly different from all other accessions 
in this analysis (Fig. 2-2). It is characterized by an extreme early heading date, short 
tillers with few nodes, yet flag leaves that are longer than most accessions evaluated 
(Appendix 1). This accession may in fact not belong to A. strigosa, the species under 
consideration, but to another member of the genus Avena. Cytogenetic and molecular 
studies may be needed to confirm the identity of the accession CIav 9015.  Because of the 
large difference caused by a single accession, the relationship among the remaining 103 
accessions is severely compressed (Fig. 2-2).  
Plotting CAN1 vs. CAN2, separated five accessions grouped together to the first 
quadrant of the graph with all positive values for CAN1 (tiller node number and heading 
date) and both positive and negative values for CAN2 (tiller diameter, tiller length and 
flag leaf length). 
  While plotting CAN3 vs. CAN4, the separation of accession CIav 9015 is not 
prominent as in CAN1 vs. CAN2, even though the accession is distinct from all other 
North American accessions. The five accessions that grouped together in the previous 
plotting of CAN1 vs. CAN2 are dispersed in second and fourth quadrants while plotting 
CAN3 vs. CAN4. However the accession PI 306419 from Romania is quite distinct with 
  46 
high positive values for CAN3. This accession is most erect (73o) with a short panicle and 
high tiller node number. This accession is among the ones that headed very late. 
 
Analysis of individual traits 
A variance analysis of the traits identified by canonical discriminant analysis as 
contributing significantly to the differences among accessions indicated that there were 
significant differences among continents, countries within continents, and accessions 
within countries (Table 2-5). For the user searching for suitable accession the practical 
question arises where to look for such accessions. As indicated earlier, heading date and 
the number of tiller nodes were traits highly correlated with canonical variate 1 (Table 2- 
4, Fig. 2-2). As a group, accessions from Australia and Europe matured significantly later 
than accessions from the Mid-East and North America although individual accessions 
within geographic region vary greatly in heading date (Table 2-5). Some accessions from 
Australia and Europe headed very late, in fact too late to be useful for cover crop 
purposes in the southeastern USA. South American accessions on the average did not 
differ significantly from Australian and European accessions. Except for accession that 
headed before the January 2005 freeze (e.g., accession CIav 9015 from Canada) heading 
date among the accessions was delayed in the second year. For both years heading date 
was latest for accessions from Australia (data not shown). Heading date has the narrowest 
and widest range among the accessions from Australia and North America, respectively. 
Tiller node number, the second trait with a high correlation to CAN1, was 
consistent among the accessions from different continents. Accessions from Australia and 
Europe had higher tiller node numbers, consistent with their later maturity (Table 2-6). 
  47 
There were no significant differences in tiller node number among the Middle East and 
the Americas and between North and South America. 
Tiller length, tiller diameter, and leaf length were the traits with the highest 
correlation with canonical variate 2 (Fig. 2-2, Table 2-4). The significance of differences 
among continents for tiller length (Table 2-7) mirrored those observed for heading date 
(Table 2-6). The tallest accession came from Australia (PI 83720) and the shortest from 
North America (CIav 9015). These are also the accessions with the latest and earliest 
heading date, respectively. The largest range in tiller length was observed among 
accessions from North America (Table 2-7). Based on tiller length, accessions from 
North and South Americas were significantly different from that of Mid-East and Europe. 
For tiller diameter, accessions from Australia were again significantly different from 
accessions from all other continents. Because of accessions CIav 9015, North America 
had the largest among accession difference for tiller diameter. The same accession also 
had the longest leaves, which were 18% (3.6 cm) longer than the leaves of the accession 
with the second-longest leaves. The trait means discussed thus far CIav 9015 underscore 
the uniqueness of this accession within the collection evaluated. 
Tiller angle (deviation from horizontal) and panicle length had the highest 
correlation (r = 0.54 and -0.59, respectively) with CAN3 (Table 2-4). Tiller angle was 
fairly consistent between the two years. As a group, South American accessions had the 
most prostrate growth habit (Table 2-8), although PI 401793 from Spain had the least 
tiller angle among all accessions studied (data not shown). Tiller angle varied widely 
within European (50?), North American (40?), and South American accessions (45?). 
Within country variation for tiller angle among accessions was quite narrow for Brazil 
  48 
and Uruguay with most accessions being quite prostrate (data not shown). This may 
simply reflect the fact that black oat is primarily used as a cover crop in those countries. 
Selection for cover crop purposes would tend to favor those accession that exhibit 
potential to suppress weeds by denying them access to light. Tiller angle among 
accessions from North America was significantly different from South America. North 
American accessions as a group also had the largest range among accessions for panicle 
length (Table 2-8). Based on panicle length accessions from South America is 
significantly different from Europe and North America. 
Panicle node number and leaf width were the discriminating traits most important 
for CAN4 (Table 2-4, Fig. 2-2). Again accession CIav 9015 from North America was 
unique with respect to all accessions studied, having the least number of panicle nodes 
(data not shown). European accession as a group differed from all other continents except 
Australia (Table 2-9). As a group, Australian accessions had the broadest leaves and were 
significantly different from all other continents. Mid-eastern accessions not only had the 
narrowest leaves as a group but also the smallest range among accessions (2.1 mm). The 
broadest-leaved accession came from Europe (Romania, PI 361912) and the narrowest 
one is from South America (PI 436108). 
 
Summary and Conclusion 
Significant difference can be observed for traits of agronomic importance among 
and within countries and continents and the reason why these difference exist may be the 
selection of black oat for different purposes. Late heading European and Australian 
accessions may not be useful as cover crop in southeastern US conditions, but may be 
  49 
useful in colder parts of the country as winter kill can be used for terminating the crop. 
Mostly South American accessions are with prostrate habit and are suitable for cover 
crop purpose in the southeastern US.  
The first conclusion that can be drawn from this evaluation is that there is 
sufficient genetic variation to begin a hybridization and selection program. The second 
conclusion is that the accession CIav 9015 from Canada may not belong to A. strigosa 
Schreb., the species under consideration, but to another member of the genus Avena. 
Cytogenetics studies may be useful for the conclusive identification of this accession with 
a doubtful identity. The third conclusion is that some traits like tiller angle are highly 
heritable. So this evaluation of morphology and maturity is the beginning of future 
breeding programs and field plot trials of the selected accessions. 
 
 
 
 
 
 
 
 
 
 
 
 
  50 
References 
Andrade, E., M. Miyazawa, M. A. Pavan, and E. L. d. Oliveira. 2002. Effect of organic 
matter on manganese solubility. Brazilian Archives of Biology And Technology 
45:17 - 20. 
Bauer, P. J., and D. W. Reeves. 1999. A comparison of winter cereal species and planting 
dates as residue cover for cotton grown with conservation tillage. Crop Sci. 
39:1824-1830. 
Baum, B. R. 1977. Oats: wild and cultivated: A monograph of the genus Avena L. 
(Poaceae), p. 185-193, Vol. 14. Canada department of agriculture, Research 
Branch,Ontario, Canada. 
Ceretta, C. A., C. J. Basso, J. Diekow, C. Aita, P. S. Pavinato, F. C. B. Vieira, et al. 2002. 
Nitrogen fertilizer split-application for corn in no-till succession to black oats. 
Scientia Agricola 59:549-554. 
Coffman, F. A. 1977. Origin and History, p. 15-40, In F. A. Coffman, ed. Oats and Oat 
Improvement. American Society of Agronomy, Madison. 
Dilkova, M., E. N. Jellen, and R. A. Forsberg. 2000. C-banded karyotypes and meiotic 
abnormalities in germplasm derived from interploidy crosses in Avena. Euphytica 
111:175-184. 
Dyck, P. L. 1966. Inheritance of stem rust resistance and other characteristics in diploid 
oats, Avena strigosa. Can. J. Genet. Cytol. 8:444-450. 
Federizzi, L. C., and C. M. Mundstock. 2004. Fodder oats: An overview for South 
America, p. 37-51, In J.M.Suttie and S.G.Reyynolds, eds. Fodder oats: A world 
overview, Vol. 33. Food and Agriculture organizations of the United Nations. 
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Katsura, M. 2004. Fodder oats in Japan, p. 145-152, In J.M.Suttie and S.G.Reynolds, eds. 
Fodder oats: a world overview, Vol. 33. Food and Agriculture organizations of 
the United Nations. 
Lowe, K. F., and T. M. Bowdler. 1998. Effects of height and frequecy of defoliation on 
the productivity of irrigated oats (Avena stigosa cv.Saia) and perennial ryegrass 
(Lolium  perenne cv. Kangaroo Valley), grown alone or with barrel medic 
(Medicago truncatula cv.Jemalong). Australian journal of experimental 
Agriculture, 28:57-67. 
Martinelli, J. A. 2004. Oat diseases and their control, p. 197-214, In J.M.Suttie and 
S.G.Reyynolds, eds. Fodder oats: a world overview, Vol. 33. 
O'Mara, J. G. 1961. Cytogenetics-The fatuoid and steriloid mutations, p. 112-124, In F. 
A.Coffman, ed. Oats and Oat improvement. Agron. Monogr. 8. ASA, Madison, 
WI. 
Schomberg, H. H., D. M. Endale, A. Calegari, R. Peixoto, M. r. Miyazawa, and M. L. 
Cabrera. 2005. Influence of cover crops on potential nitrogen availability to 
succeeding crops in a Southern Piedmont soil. Biology and Fertility of Soils 
42:299-307. 
Suttie, J. M., and S. G. Reynolds. 2004. Back ground to fodder oats worldwide, p. 1-7, In 
J.M.Suttie and S.G.Reyynolds, eds. Fodder oats: a world overview. Food and 
Agriculture organizations of the United Nations. 
Thomas, H. 1992. Cytogenetics of Avena, p. 473-507, In H.G.Marshall and M.E.Sorrells, 
eds. Oat Science and Technology-Agronomy monograph, Vol. 33. American 
Society of Agronomy, Madison, WI, U.S.A. 
  52 
Weibull, J., L. Lyng, J. Bojensen, and V. Rasomavicius. 2005. Avena strigosa in 
Denmark and Lithuania: Prospects for in situ conservation. Plant Genetic 
Resources Newsletter 131:1 - 6. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
53
Table 2-1: Materials used for the morphology and maturity study 
 
 
    Geographic origin   
NPGS no. Plant ID Country State/Province Continent Vernalization 
CIav 1782  Russian Federation Leningrad Europe Spring 
CIav 2520 26-389 France Cote-d'Or Europe Spring 
CIav 2521 26-390 France Cote-d'Or Europe Spring 
CIav 2523 26-391 France Cote-d'Or Europe Spring 
CIav 2524 26-398 United Kingdom Wales Europe Spring 
CIav 2525 26-381 United Kingdom England Europe Spring 
PI 78821 Glabrota Australia New South Wales Australia Spring 
PI 83720 C. 3758 Australia New South Wales Australia Unknown 
PI 83721 C. 3758 Australia New South Wales Australia Unknown 
PI 83722 C. 3593 Australia New South Wales Australia Unknown 
PI 83723 C. 3756 Australia New South Wales Australia Unknown 
CIav 2894  United States Washington North America Spring 
CIav 2920 S 77 United Kingdom Wales Europe Spring 
CIav 2921 S 76 United Kingdom Wales Europe Spring 
CIav 3214 Reed's No. 590 United States New York North America Spring 
PI 111261 CIav 3280 Romania Cluj Europe Spring 
CIav 3372 S-171 Unknown  South America Spring 
  
54
Table 2-1: (cont.)  
 
PI 131695 5081 Poland Krakow Europe Spring 
PI 131640 7513 Poland Krakow Europe Spring 
PI 131641 9411 Poland Krakow Europe Spring 
PI 131642 9249 Poland Krakow Europe Spring 
CIav 4639 Saia Brazil Rio Grande do Sul South America Facultative 
PI 158244 typica Russian Federation Smolensk Europe Spring 
PI 158246 WIR 5201/1 Spain Lugo Europe Winter 
PI 274610 Glabrata Poland  Europe Winter 
PI 287315 Rauhhafer aus Neustadt Germany  Europe Winter 
PI 291990 Saia 2 Israel  Mid-East Facultative 
PI 291991 Saia 4 Israel  Mid-East Facultative 
PI 292226  Israel Tel Aviv Mid-East Facultative 
PI 304557 4659 United Kingdom Wales Europe Spring 
PI 436080 353 Chile Los Lagos South America Spring 
PI 436082 361 Chile Los Lagos South America Spring 
PI 436103 1 Chile Bio-Bio South America Spring 
PI 436104 35 Chile La Araucania South America Spring 
PI 436105 113 Chile La Araucania South America Spring 
PI 436106 117 Chile La Araucania South America Winter 
  
55
Table 2-1: (cont.)  
 
PI 306419 2582 Romania  Europe Facultative 
PI 361910  Romania Brasov Europe Spring 
PI 361911  Romania Brasov Europe Spring 
PI 361912  Romania Brasov Europe Spring 
PI 401793 Avena strigosa nuda 7 United Kingdom  Europe Spring 
PI 401794 Avena strigosa nuda 8 United Kingdom  Europe Winter 
PI 436107 146 Chile Bio-Bio South America Spring 
PI 436108 197 Chile La Araucania South America Spring 
PI 436109 202 Chile La Araucania South America Spring 
PI 436110 209 Chile La Araucania South America Spring 
PI 436111 218 Chile La Araucania South America Spring 
PI 436112 266 Chile Los Lagos South America Spring 
PI 436119 355 Chile Los Lagos South America Winter 
PI 436120 364 Chile Los Lagos South America Spring 
PI 436121 367 Chile Los Lagos South America Spring 
PI 436122 394 Chile Los Lagos South America Spring 
PI 436124 401 Chile Los Lagos South America Spring 
PI 436125 420 Chile Los Lagos South America Spring 
PI 436127 425 Chile Los Lagos South America Spring 
  
56
Table 2-1: (cont.) 
 
PI 436126 423 Chile Los Lagos South America Spring 
PI 436130 448 Chile Los Lagos South America Spring 
PI 436131 449 Chile Los Lagos South America Spring 
PI 436132 454 Chile Los Lagos South America Spring 
PI 436133 507 Chile La Araucania South America Spring 
PI 436134 509 Chile La Araucania South America Spring 
PI 436113 280 Chile Los Lagos South America Spring 
PI 436114 302 Chile Los Lagos South America Winter 
PI 436115 333 Chile Los Lagos South America Winter 
PI 436116 335 Chile Los Lagos South America Winter 
PI 436117 346 Chile Los Lagos South America Spring 
PI 436118 354 Chile Los Lagos South America Winter 
CIav 9019 CD 3642 United Kingdom Wales Europe Winter 
CIav 9020 CD 3819 Argentina  South America Mixed 
CIav 9021 CD 3820 Canada Ontario North America Facultative 
CIav 9022 CD 3916 Netherlands  Europe Facultative 
CIav 9024 CD 4481 Germany  Europe Facultative 
CIav 9030 CD 7497 Canada Ontario North America Winter 
CIav 8089 Autotetraploid of Saia United States Pennsylvania North America Winter 
  
57
Table 2-1: (cont.) 
 
CIav 9007 CD 1002 Romania  Europe Spring 
CIav 9011 CD 1025A Denmark  Europe Spring 
CIav 9012 CD 1576 Bulgaria  Europe Spring 
CIav 9014 CD 2050 Canada Ontario North America Winter 
CIav 9015 CD 2108 Canada Ontario North America Winter 
CIav 9066 CD 8088 Canada Ontario North America Facultative 
CIav 9110 GA 23 Canada Ontario North America Mixed 
CIav 9112 GA 33 Canada Ontario North America Spring 
CIav 9116 GA 74 Canada Ontario North America Winter 
PI 274608 Glabrescens Cambrica Poland  Europe Facultative 
PI 274609 Oreadensis Arguta Poland  Europe Spring 
CIav 9031 CD 7497A Canada Ontario North America Facultative 
CIav 9035 CD 7847 Russian Federation Leningrad Europe Facultative 
CIav 9038 CD 7853 United Kingdom Northern Ireland Europe Facultative 
CIav 9043 CD 7954 Argentina  South America Winter 
CIav 9064 CD 8086 Canada Ontario North America Facultative 
CIav 9065 CD 8087 Canada Ontario North America Facultative 
PI 158245 WIR 5199 Spain Lugo Europe Spring 
PI 158247 WIR 5288/1 Portugal  Europe Winter 
  
58
Table 2-1: (cont.) 
 
CIav 5057 C.D. 3686 Russian Federation Former Soviet Union Europe Spring 
CIav 5082 C.D. 3381 Uruguay Colonia South America Spring 
CIav 6858  Uruguay  South America Spring 
PI 186606 Saia Brazil Rio Grande do Sul South America Unknown 
CIav 6956 C.D. 3820 Canada Ontario North America Facultative 
CIav 7010 Saia Selection Brazil Rio Grande do Sul South America Unknown 
CIav 7121 C.D. 1007 Canada Ontario North America Winter 
CIav 7122 C.D. 920 Canada Ontario North America Winter 
CIav 7280  United States Maryland North America Winter 
CIav 8087 5201 Spain  Europe Facultative 
SoilSaver  United States Alabama North America   
 
 
 
 
 
 
 
 
 
 
 
  59 
 
     Table 2-2: Response variables studied 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
No. Response variable Unit 
1 Heading date Julian date 
 Tiller  
2 angle Degree from vertical 
3 diameter mm 
4 length cm 
5 node number count tiller-1 
6 number count plant-1 
 Panicle  
7 length cm 
8 node number count panicle-1 
 Flag leaf  
9 length mm 
10 width mm 
11 Seed yield g plant-1 
12 Average seed mass g 1000 seed-1 
  60 
      Table 2-3: Response variables studied and their range. 
 
 
 
 
 
 
 
No. Response variable Unit Year Max Min Average 
1 Heading date Julian date 2003 195 86 170 
   2004 224 29 193 
 Tiller      
2 angle Degree from vertical 2003 73.33 23.33 48.16 
   2004 73.33 34.67 54.10 
3 diameter mm 2003 6.78 2.54 4.22 
   2004 5.76 1.27 3.71 
4 length cm 2003 147.05 62.19 115.00 
   2004 140.02 49.88 96.98 
5 node number count tiller-1 2003 7.73 2.21 4.72 
   2004 7.18 2.72 4.29 
6 number count plant-1 2003 48.94 0.17 22.25 
   2004 28.46 1 12.04 
 Panicle      
7 length cm 2003 44.13 18.6 30.00 
   2004 39.72 15.33 25.84 
8 node number count panicle-1 2003 11.49 4.5 9.11 
   2004 10.99 3.37 8.49 
 Flag leaf      
9 length mm 2003 220.04 60.85 108.23 
   2004 202.48 62.5 106.94 
10 width mm 2003 1.52 0.34 0.76 
   2004 1.21 0.29 0.66 
11 Seed yield g plant-1 2003 51.54 -0.63 8.96 
   2004 6.39 -0.14 1.71 
12 Average seed mass g 1000 seed-1 2003 34.8 0 11.84 
   2004 25.47 -0.15 12.22 
  61 
    Table 2-4: Loading of Canonical Discriminant Analysis (CDA). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Trait CAN1 CAN2 CAN3 CAN4 
Angle -0.30 0.10 0.54 0.47 
Heading 0.72 0.22 -0.16 0.01 
Tiller length -0.10 0.54 0.18 0.33 
Tiller diameter -0.47 0.55 0.06 0.12 
Tiller node number 0.78 0.15 0.39 -0.11 
Panicle length 0.37 0.37 -0.59 0.14 
Panicle node number 0.20 0.17 0.18 0.59 
Leaf length 0.00 -0.64 0.26 0.38 
Leaf width 0.02 -0.06 0.26 0.77 
Seed mass -0.40 0.01 0.38 0.31 
Eigenvalues 22.59 10.53 8.73 4.04 
Cumulative proportion of Eigenvalues 0.41 0.61 0.77 0.84 
  
62
Table 2-5: Variance analysis of the traits identified by canonical discriminant analysis. These traits are contributing 
significantly to the differences among accessions among continents, countries within continents, and accessions  
within countries. 
 
    Tiller   Panicle   Flag leaf 
Source Heading angle diameter length 
node 
number   length 
node 
number   length width 
  --------------------------------------------- P-values --------------------------------------- 
Year 0.0001 0.001 0.0002 0.0001 0.0002  0.0001 0.0001  0.9728 0.0001 
Continent 0.0001 0.0001 0.0001 0.0001 0.0001  0.0114 0.0005  0.0039 0.0001 
Country 
(Continent) 0.0001 0.0001 0.0001 0.0001 0.0001  0.0001 0.0001  0.0001 0.0001 
Accession 
(Country*Cont) 0.0001 0.0001 0.0001 0.0001 0.0001   0.0001 0.0001   0.0001 0.0001 
 
 
 
 
 
 
 
  
63
Table 2-6: Traits with high correlation to CAN1 among different accessions. CAN 1 is responsible for 41 % of total variation.  
Pair-wise comparisons identified the significant difference among accessions based on these traits among different continents 
 
          Pair-wise differences and P-values 
Continent Mean SE Range   Australia Europe 
Mid-
East 
North 
America 
South 
America 
Heading date          
Australia 10-May 12.3 27-Apr-28 May   6 25 18 9 
Europe 4-May 11.7 5-Mar-13-Jun  0.602  19 12 3 
Mid-East 15-Apr 12.7 27-Mar-11-May  <0.001 0.002  -7 -16 
North America 22-Apr 11.8 30-Nov-1-Jun  <0.001 <0.001 0.646  -9 
South America 1-May 11.7 27-Mar-11-Jun  0.150 0.418 0.018 0.004  
Tiller node number         
Australia 5.6 0.22 4-7   0.94 1.65 1.49 1.36 
Europe 4.7 0.20 3-8  <0.001  0.71 0.55 0.42 
Mid-East 4.0 0.22 4-4  <0.001 <0.001  -0.16 -0.28 
North America 4.1 0.20 2-6  <0.001 <0.001 0.639  -0.12 
South America 4.3 0.20 3-6   <0.001 <0.001 0.075 0.218   
 
  
64
Table 2-7: Traits with high correlation to CAN 2 among different accessions. CAN 2 is responsible for 20% of total variation. 
   The significance of differences among continents for tiller length is similar to those observed for heading date (Table 2-6). 
          Pair-wise differences and P-values 
Continent Mean SE Range   Australia Europe 
Mid-
East 
North 
America 
South 
America 
Tiller length, cm          
Australia 108 10.5 85-147   -7.64 -9.11 2.19 -0.32 
Europe 116 10.3 78-143  0.003  -1.47 9.83 7.32 
Mid-East 117 10.5 98-133  0.021 0.969  11.30 8.79 
North America 106 10.3 50-132  0.850 <0.001 <0.001  -2.51 
South America 109 10.3 66-144  1.000 <0.001 0.002 0.259  
Tiller diameter, mm         
Australia 3.5 0.21 2.9-5.0   -0.42 -1.06 -0.47 -0.77 
Europe 4.0 0.19 2.4-7.0  0.001  -0.64 -0.05 -0.35 
Mid-East 4.6 0.22 3.9-5.0  <0.001 <0.001  0.59 0.29 
North America 4.0 0.19 1.3-6.0  <0.001 0.947 <0.001  -0.3019 
South America 4.3 0.19 2.4-6.0  <0.001 <0.001 0.088 <0.001  
Leaf length, mm          
Australia 113 3.5 88-165   4.94 20.63 -3.49 13.16 
Europe 108 1.4 61-186  0.645  15.70 -8.43 8.22 
Mid-East 93 4.0 85-103  <0.001 0.001  -24.13 -7.47 
North America 117 1.9 63-220  0.885 0.001 <0.001  16.65 
South America 100 1.3 76-169   0.002 <0.001 0.346 <0.001   
 
  
65
Table 2-8: Traits with high correlation to CAN 3 among different accessions. CAN 3 is responsible for 16 % of total  
variation. European accessions have broadest range for tiller angle among themselves. Tiller angle among accessions 
 from North America was significantly different from South America.  
          Pair-wise differences and P-values 
Continent Mean SE Range   Australia Europe 
Mid-
East 
North 
America 
South 
America 
Tiller angle, ? from horizontal        
Australia 56 3.8 43-68   4.92 -3.74 0.63 7.55 
Europe 51 3.0 23-73  0.338  -8.67 -4.29 2.63 
Mid-East 60 4.3 57-67  0.878 0.064  4.37 11.30 
North America 56 3.1 32-73  0.999 0.065 0.702  6.92 
South America 49 3.0 27-72  0.035 0.245 0.006 <0.001  
Panicle length, cm         
Australia 30 2.3 24-39   0.56 1.86 0.84 2.04 
Europe 29 2.3 21-40  0.790  1.29 0.28 1.47 
Mid-East 28 2.3 24-31  0.063 0.131  -1.01 0.18 
North America 29 2.3 15-44  0.493 0.882 0.397  1.19 
South America 28 2.3 17-35   0.000 <0.001 0.997 0.001   
 
  
66
Table 2-9: Traits with high correlation to CAN 4 among different accessions. CAN 4 is responsible for 7 % of total variation. 
Pair-wise comparisons identified the significant difference of European accessions from accessions of all other continents  
except Australia.  
          Pair-wise differences and P-values 
Continent Mean SE Range   Australia Europe 
Mid-
East 
North 
America 
South 
America 
Panicle node number         
Australia 9.2 0.28 8.0-11.0   0.13 0.86 0.77 0.46 
Europe 9.1 0.25 7.0-11.0  0.871  0.73 0.64 0.33 
Mid-East 8.4 0.29 8.0-9.0  <0.001 <0.001  -0.09 -0.40 
North America 8.5 0.26 3.0-11.0  <0.001 <0.001 0.977  -0.31 
South America 8.8 0.25 7.0-11.0  0.006 <0.001 0.062 0.001  
Leaf width, mm          
Australia 8.62 0.51 5.9-12.3   1.186 1.84 1.10 1.97 
Europe 7.44 0.45 3.1-15.2  <0.001  0.656 -0.081 0.782 
Mid-East 6.78 0.53 6.1-8.2  <0.001 0.200  -0.737 0.126 
North America 7.52 0.46 3.3-14.2  0.001 0.986 0.139  0.864 
South America 6.66 0.45 2.9-12.5   <0.001 <0.001 0.993 <0.001   
 
  67 
 
  
 
Figure 2-1: Primary (circle) and secondary (rectangle) centers of origin of black oats. 
 
 
 
 
 
 
 
 
 
 
 
 68 
-17
-12
-7
-2
3
-12 -7 -2 3 8
CAN1 (41 % of total variation)
CA
N2
 (2
0 %
 of
 to
tal
 va
ria
tio
n) .
Australia Europe Mid-East North America South America
-17
-12
-7
-2
3
-12 -7 -2 3 8
CAN3 (16 % of total variation)
CA
N4
 (7
 %
 of
 to
tal
 va
ria
tio
n) .
Australia Europe Mid-East North America South America
  
Figure 2-2: Canonical discriminant analysis scatter plot separates the accessions 
originated in different geographical locations into meaningful groups. In the first panel 
accession CIav 9015 separated to the extreme left of second quadrant. CAN1 vs. CAN2 
separates five accessions to the extreme right of the first quadrant. 
 
 
 69 
III. PLOT TRIALS IN BLACK OAT (AVENA STRIGOSA SCHREB.) FOR 
BIOMASS, GRAIN YIELD AND TEST WEIGHT 
 
 
Abstract 
 
 
 
  Even though more than 100 black oat accessions are available from USDA for 
research purpose the only commercially available black oat cultivar in US is the 
SoilSaver. In this pioneering study we compared the biomass, grain production and test 
weight of SoilSaver to the other selected black oat accessions. Eighteen accessions were 
selected for biomass, grain yield and test weight study based on their relative maturity to 
that of SoilSaver and the availability of enough seeds from 15 to18 plants. The studies 
were conducted in 2004-05 and 2006-07. The biomass study  was conducted in four 
locations with two replications in each location and grain yield and test weight study was 
conducted in two locations in the first year and five locations in the second year with 
three replicates at each location. Mixed model method by invoking PROC MIXED 
procedure of SAS? (SAS Institute, Cary, NC) was used for the analysis of variance. The 
differences of the least squares means of accessions and the SoilSaver control calculated 
with the ?Dunnett? adjustment gave a clear comparison of the performance of different 
accessions based on biomass and grain yield and test weight to that of SoilSaver control. 
 
 70 
The study revealed the superiority of SoilSaver in biomass and grain yield production, 
but identified many accessions with higher test weight. 
 
Introduction 
 
 Black oat (A. strigosa Schreb) has recently emerged as an important winter cover 
crop and forage crop suitable for subtropical and temperate regions of the world (Suttie 
and Reynolds, 2004). In South America black oat is grown extensively for cover crop and 
forage purposes (Federizzi and Mundstock, 2004). It has the potential to produce 
comparable amount of biomass to other leguminous and non-leguminous cover crops. 
Biomass production and soil N mineralization dynamics of Black oat are on par with that 
of crimson clover (Schomberg et al., 2005). A study conducted in Brazil demonstrated 
the ability of cover crop residues of black oat to decrease the manganese toxicity in well 
aerated acid soils by lowering the Mn solubility (Andrade et al., 2002). Obviously, the 
kill date has an effect on biomass yield and nitrogen availability of cover crops and 
maximizing the window between establishment and kill may increase the biomass 
production and nutrient accumulation. Delayed kill date of hairy vetch for two weeks 
improved N accumulation significantly (Sainju and Singh, 2001). Black oat reached 
maximum biomass at anthesis compared to rye (Secale cereale L.), and wheat (Triticum 
aestivum L.) that continued to increase biomass significantly through soft dough stage 
(Ashford and Reeves, 2003). Cotton lint yield following black oat was higher than that 
following rye, even though rye produces more biomass than black oat (Bauer and Reeves, 
1999). But low night temperature for a longer period of time has a negative influence on 
biomass production in black oat compared to rye (Reeves et al., 2005). 
 
 71 
Even though more 100 black oat accessions are available from USDA germplasm 
unit for research purpose the only commercially available black oat cultivar in the United 
States is ?SoilSaver?, which was released by Auburn University and USDA in 2002. In 
this paper we are comparing the biomass production, grain yield and test weight of 
selected black oat accessions based on the preliminary results from a previous study of 
the entire USDA black oat accessions for agronomic and morphological traits to that of 
SoilSaver. 
. 
Materials and Methods 
 
Accessions were selected based on their relative heading date to that of SoilSaver 
(? 2 weeks) and adequate seed yield from 15-18 plants. The 18 accessions selected have 
9 different countries of origin (Brazil, Bulgaria, Canada, Chile, Denmark, Israel, Poland, 
Romania and United States). For the 2004-05 study the accessions were planted as 5' X 
10' row plots on last week of October and first week of November, 2004. The plots were 
harvested on first week of April, 2005. For the 2004-05 biomass study the entire plot 
weight was taken in a once-over harvesting scheme when the majority of entries were 
fully headed. A plot combine was used for grain harvest. Harvested grain was dried to < 
10% moisture and cleaned with an Airblast Cleaner (ALMACO Inc., Nevada, Iowa) and 
meshed sieves of different sizes. For the biomass study the accessions were planted at 
four locations in Alabama: 1) Plant Breeding Unit (PBU), Tallassee, Alabama, 
(32?24.5'N, 85?57'W), where the soil type is fine sandy loam, 2) Tennessee Valley 
Research and Extension Center (TVS), Belle Mina, Alabama (34?41'N, 86?53'W) where 
 
 72 
the soil type is Decatur silt loam (fine, kaolinitic, thermic, Rhodic, Paleudults.),                 
3) Wiregrass Research and Extension Center (WGS), Headland (31?21'N, 85?21' uniF020W,) 
where the major soil type is Dothan sandy loam, and 4) Gulf Coast Research and 
Extension Center (GCS), Fairhope (30?33' N, 87?81 ' W) where the soil type is Malbis 
sandy loam. Two replicates were planted in each location for the biomass study. The 
locations for grain yield and test weight study are 1) Plant Breeding Unit, Tallassee, 
Alabama and      2) Prattville Agricultural Research Unit (PEF), Prattville, Alabama 
(32?42' 40'' N, 86?44' 38'' W) where the major soil type is Lucedale sandy loam. Three 
replicates were planted in each location for grain yield and test weight studies. 
The experiments were repeated in 2006, but this time with more locations for 
grain yield and test weight studies. The biomass study was conducted at four locations 1) 
Gulf Coast Research and Extension Center (GCS, 2) Plant Breeding Unit (PBU), 3) 
Tennessee Valley Research and Extension Center (TVS) and 4) Prattville Agricultural 
Research Unit (PEF). Two replicates were planted at each location for the biomass study. 
The grain yield and test weight studies were conducted at five locations of Alabama viz. 
1) Gulf Coast Research and Extension Center (GCS), 2) Plant Breeding Unit, (PBU) 3) 
Tennessee Valley Research and Extension Center (TVS), 4) Prattville Agricultural 
Research Unit (PEF) and 5) Wiregrass Research and Extension Center (WGS). Three 
replicates were planted at each location for the grain yield and test weight studies. The 
plots were harvested during the last week of May, 2007 and processed as described 
earlier. 
 
 
 
 73 
Statistical Analysis 
Mixed model methodology as implemented in PROC MIXED of SAS?(SAS 
Institute, Cary, NC) was used to analyze the data using a nearest neighbor analysis 
model. First we calculated the residuals using the Proc Mixed procedure. A residual is the 
difference between the observed value of the variable (Yi) and the predicted value of the 
variable Y^i. The covtest option in proc mixed produces asymptotic standard errors and 
Wald Z-tests for the covariance parameter estimates. The Wald Z is a common likelihood-
based statistic, which is computed as the parameter estimate divided by its asymptotic 
standard error. The asymptotic standard errors are computed from the inverse of the 
second derivative matrix of the likelihood with respect to each of the covariance 
parameters. A mean nearest neighbor distance ( d  ) was calculated by taking the mean of 
residuals of the four plots surrounding each plot and used in the mixed model analysis 
using PROC MIXED procedure of SAS? (SAS institute, Cary, NC) for nearest neighbor 
adjustment. The mean nearest neighbor distance d = N
di
N
i
?
=1 , where N is the number of 
points, di is the nearest neighbor distance for point i. The Kenward-Roger method was 
used for the estimation of degrees of freedom. For each of the three response variables we 
calculated least squares interaction means listed in (Tables 3-4, 3-5, 3-6, 3-7 & 3-8). 
Block within location, mean nearest neighbor distance, location, accession and location x 
accession interactions were taken as the fixed effects in the model for the 2004-05 study. 
Block within location, location, accession and location x accession interactions were 
taken as the fixed effects in the model for the 2006-07 study. The differences of the least 
squares means of accessions and the SoilSaver control were calculated with the ?Dunnett? 
 
 74 
adjustment. The Dunnett?s test is useful when the only pair-wise comparisons of interest 
are comparisons with a control. The Dunnett?s test is an exact test, because it?s family-
wise error rate (FWE) is exactly equal to? , for balanced as well as unbalanced one-way 
designs and hence it reduces the type I error. The yield of SoilSaver at standard seeding 
rate (90 lbs acre-1) was taken as the reference point or control in this experiment. The 
difference in least square means with that of the control were plotted for biomass, grain 
yield and test weight.  
 
Results and Discussion 
 
Biomass Yield 
 
SoilSaver at standard seeding rate (90 lbs acre-1) was used as a control for this 
evaluation. SoilSaver at standard seeding rate produced 8769 kg ha-1, 11683 kg ha-1, 3997 
kg ha-1 and 4088 kg ha-1 of biomass at Gulf Coast Research and Extension Center, Plant 
Breeding Unit, Tennessee Valley Research and Extension Center and Wiregrass Research 
and Extension Center respectively (Table 3-4) during 2004-05 study. Accessions CIav 
8089 produced significantly higher biomass yield than SoilSaver at standard seeding rate 
at Tennessee Valley (P= 0.0167) and Wiregrass (P=0.001). Accessions PI 111261 except 
at Wiregrass, CIav 9110 except at Tennessee Valley, and CIav 9112 except at Wiregrass, 
performed poorer than SoilSaver at standard seeding rate. 
 During the 2006-07 study SoilSaver at standard seeding rate produced 6562 kg 
ha-1, 6552 kg ha-1, 1473 kg ha-1 and 10433 kg ha-1 of biomass at Gulf Coast Research and 
Extension Center, Plant Breeding Unit, Tennessee Valley Research and Extension Center 
 
 75 
and Prattville Agricultural Research Unit, respectively (Table 3-5). None of the 
accessions are significantly different from SoilSaver in biomass production at Gulf Coast 
Research and Extension Center, Plant Breeding Unit and Prattville Agricultural Research 
Unit. At Tennessee Valley Research and Extension Center Accession CIav 2520 from 
France (P = 0.02) produced higher biomass yield than the control. 
 
Grain yield 
 
Grain yield was not consistent across the two locations (Plant Breeding Unit, 
Tallassee, Alabama and Prattville Agricultural Research Unit, Prattville, Alabama) for 
the 2004-05 study.  SoilSaver at standard seeding rate (90 lbs acre-1) was used as a 
control for this evaluation. SoilSaver at standard seeding rate produced 1013 kg ha-1 and 
652 kg ha-1 grain yield at Plant Breeding Unit (PBU) and Prattville Agricultural Research 
Unit respectively (Table 3-6). The severe lodging experienced in Prattville was 
responsible for the low grain production there during 2004-05. Accessions PI 436103, PI 
436104 and CIav 7010 performed better than the control in both locations. At PBU, 
accessions CIav 2520 (P < 0.001), CIav 9007 (P < 0.001), CIav 9012 (P < 0.001) and 
CIav 9112 (P = 0.001) performed poorer than the control and accessions PI 291991 (P < 
0.01), PI 436103 (P < 0.001), PI 436104 (P = 0.05), PI 274608 (P < 0.001) and CIav 
7010 (P = 0.02) performed better than the control. At PEF accessions PI 436103 (P = 
0.03), PI 436104 (P < 0.001), PI 436105 (P = 0.02), PI 436114 (P = 0.003) and CIav 
7010 (P = 0.001) performed better than the control. 
 
 76 
During the 2006-07 study, SoilSaver at standard seeding rate produced 1662 kg 
ha-1, 1650 kg ha-1, 822 kg ha-1, 1130 kg ha-1, and 344 kg ha-1 of grain yield at Gulf Coast 
Research and Extension Center (GCS), Plant Breeding Unit (PBU), Prattville 
Agricultural Research Unit (PEF), Tennessee Valley Research and Extension Center 
(TVS) and Wiregrass Research and Extension Center (WGS) respectively (Table 3-7). 
During 2006-07 study at GCS accessions PI 274608 (P = 0.02) and CIav 9112 (P = 0.07) 
produced significantly lower grain yield than control. At PBU all the accessions except PI 
436080 and CIav 9011 differed significantly from SoilSaver in grain production. Among 
those significantly different accessions, all but SoilSaver_45 (SoilSaver at half the 
standard seeding rate) produced lower grain yield than SoilSaver at standard seeding rate. 
At PEF and TVS none of the accessions differ significantly from control. At WGS 
accession at SoilSaver_45 (P = 0.01) produced significantly higher grain yield than 
control. 
 
Test weight 
 
During the 2004-05 study SoilSaver at standard seeding rate has test weight of 
27.5 lbs bu-1and 32.1 lbs bu-1 at Plant Breeding Unit (PBU), and Prattville Agricultural 
Research Unit (PEF), respectively (Table 3-6). Accessions PI 291991, PI 436103, PI 
436104, PI 436105, PI 436110, PI 436114, CIav 8089, PI 274608, and CIav 7010 had 
higher test weight than the control at both locations. Accessions PI 111261 performed 
poorer than the control at the two locations. 
During the 2006-07 study SoilSaver at standard seeding rate had a test weight of 
18.6 lbs bu-1, 25.3 lbs bu-1, 25.8 lbs bu-1, 24.1 lbs bu-1 and 19.0 lbs bu-1 at Gulf Coast 
 
 77 
Research and Extension Center (GCS), Plant Breeding Unit (PBU), Prattville 
Agricultural Research Unit (PEF), Tennessee Valley Research and Extension Center 
(TVS), and Wiregrass Research and Extension Center (WGS) respectively (Table 3-8). 
At GCS accession CIav 8089 (P = 0.08) has significantly higher test weight than 
SoilSaver. Accession PI 436103 (P = 0.06) has significantly lower test weight than 
SoilSaver. At PEF accessions PI 291991 (P = 0.01), PI 436103 (P = 0.01), PI 436104 (P 
= 0.01), PI 436110 (P = 0.05), PI 436114 (P = 0.04), CIav 8089 (P = 0.01) and CIav 
7010 (P = 0.01) differ significantly from SoilSaver at standard seeding rate. At TVS 
accessions CIav 2520 (P < 0.001), PI 291991 (P < 0.001), PI 436103 (P < 0.001), PI 
436104 (P < 0.001), PI 436105 (P = 0.07), PI 436110 (P < 0.001), PI 436114 (P < 
0.001), CIav 8089 (P < 0.001), CIav 9007 (P = 0.005), CIav 9110 (P = 0.06), CIav 9112 
(P < 0.001), PI 274608 (P = 0.012) and CIav 7010 (P < 0.001) show significantly higher 
test weight than SoilSaver at standard seeding rate. At WGS accessions PI291991 (P < 
0.001), PI 436103 (P < 0.001), PI 436104 (P < 0.001), PI 436105 (P < 0.001), PI 36110 
(P < 0.001), PI436114 (P < 0.001), CIav 8089 (P < 0.001), PI 274608 (P = 0.03) and 
CIav 7010 (P < 0.001) has significantly higher test weight than control. 
 
Conclusions 
Based on the 2004-05 and 2006-07 study it is found that none of the accessions 
performed better than SoilSaver in biomass production consistently at all the four 
locations. None of the accessions performed significantly better than SoilSaver in grain 
yield production consistently in all locations during 2006-07 study. This evaluation 
 
 78 
identified many accessions having higher test weight than SoilSaver. Overall SoilSaver 
performed better than most of the accessions studied in all the aspects. 
 
 79 
References 
Andrade, E., M. Miyazawa, M. A. Pavan, and E. L. d. Oliveira. 2002. Effect of organic 
matter on manganese solubility. Brazilian Archives Of Biology And Technology 
45:17 - 20. 
Ashford, D. L., and D. W. Reeves. 2003. Use of a mechanical roller-crimper as an 
alternative kill method for cover crops. American Journal of Alternative 
Agriculture 18(1):37-45. 
Bauer, P. J., and D. W. Reeves. 1999. A comparison of winter cereal species and planting 
dates as residue cover for cotton grown with conservation tillage. Crop Science 
39:1824-1830. 
Federizzi, L. C., and C. M. Mundstock. 2004. Fodder oats: An overview for South 
America, p. 37-51, In J.M.Suttie and S.G.Reyynolds, eds. Fodder oats: A world 
overview, Vol. 33. Food and Agriculture organizations of the United Nations. 
Reeves, D. W., A. J. Price, and M. G. Patterson. 2005. Evaluation of three winter cereals 
for weed control in conservation-tillage nontransgenic cotton. Weed Technology 
19:731-736. 
Sainju, U. M., and B. P. Singh. 2001. Tillage, Cover Crop, and Kill-Planting Date Effects 
on Corn Yield and Soil Nitrogen. Agron. J. 93:878-886. 
Schomberg, H. H., D. M. Endale, A. Calegari, R. Peixoto, M. Miyazawa, and M. L. 
Cabrera. 2005. Influence of cover crops on potential nitrogen availability to 
succeeding crops in a Southern Piedmont soil. Biology and Fertility of Soils 
42:299-307. 
 
 80 
Suttie, J. M., and S. G. Reynolds. 2004. Back ground to fodder oats worldwide, p. 1-7, In 
J.M.Suttie and S.G.Reyynolds, eds. Fodder oats: a world overview. Food and 
Agriculture Organizations of the United Nations. 
 
 
 
 
 
 
 
 
 
 
 
 
81
Table 3-1: Materials used for the Biomass and grain yield studies 
 
  Geographic origin 
NPGS no. Plant ID Country State/Province Continent 
CIav 2520 26-389 France Cote-d'Or Europe 
CIav 7010 Saia Selection Brazil Rio Grande do Sul South America 
CIav 8089 Autotetraploid of Saia United States Pennsylvania North America 
CIav 9007 CD 1002 Romania  Europe 
CIav 9011 CD 1025A Denmark  Europe 
CIav 9012 CD 1576 Bulgaria  Europe 
CIav 9110 GA 23 Canada Ontario North America 
CIav 9112 GA 33 Canada Ontario North America 
CIav 9116 GA 74 Canada Ontario North America 
PI 111261 CIav 3280 Romania Cluj Europe 
PI 274608 Glabrescens Cambrica Poland  Europe 
PI 291991 Saia 4 Israel  Mid-East 
PI 436080 353 Chile Los Lagos South America 
PI 436103 1 Chile Bio-Bio South America 
PI 436104 35 Chile La Araucania South America 
PI 436105 113 Chile La Araucania South America 
PI 436110 209 Chile La Araucania South America 
PI 436114 302 Chile Los Lagos South America 
SoilSaver  United States Alabama North America 
 
 
82
 
 Table 3-2: Variance analysis of the biomass, grain yield and test weight studies of 2004-05 studies 
 
 
 
 
 
 
 
 
 
 
 
 
 
Source Biomass  Grain yield  Test weight 
 GCS PBU TVS WGS  PBU PEF  PBU PEF 
 ---------------------------------------------------P-value----------------------------------------------- 
Rep 0.0002 0.0045 0.8761 0.2346  0.0601 0.0079  0.0053 0.0001 
Nearest  
neighbor 
distance 
0.0001 0.0001 0.0001 0.0001  0.0001 0.0001  0.0001 0.0001 
Accession 0.0001 0.0001 0.0001 0.0001  0.0001 0.0001  0.0001 0.0001 
 
 
83
 
 
Table 3-3: Variance analysis of the biomass, grain yield and test weight studies of 2006-07 studies 
 
 
  Biomass   Grain yield   Test weight 
Location Rep Accession   Rep Accession   Rep Accession 
 .-------------------------------P P value----------------------------- 
GCS 0.004 0.237  0.335 0.000  0.000 0.147 
PBU 0.026 0.797  0.260 0.000  0.001 0.531 
TVS 0.563 0.004  0.004 0.025  0.511 0.000 
PEF 0.031 0.213  0.276 0.028  0.765 0.000 
WGS -   -   0.443 0.008   0.060 0.000 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
84
 
Table 3-4: LS means of biomass study 2004-05 at four locations. 1) Gulf Coast Research and Extension Center, 
 Fairhope 2) Plant Breeding Unit, 3) Tennessee Valley Research and Extension Center, and 4) Wiregrass Research  
and Extension Center. 
 
  GCS EVS TVS WGS 
Accession Estimate SE Estimate SE Estimate SE Estimate SE 
SoilSaver_90 8769 174 11683 375 3997 133 4088 94 
As_016 7067 250 8653 524 2843 187 3803 133 
As_028 9566 248 11199 526 4505 187 4179 136 
As_031 8188 247 13436 524 3834 188 4756 109 
As_033 8974 249 10638 526 4688 188 4362 109 
As_034 9568 249 11260 524 4638 188 4497 112 
As_035 9514 247 9913 537 4734 188 4374 133 
As_046 7618 247 10360 524 4836 187 4189 112 
As_063 10788 250 11144 525 4370 209 4304 134 
As_074 8926 247 12014 524 4873 188 5117 135 
As_075 7238 249 12338 524 2802 187 3456 133 
As_076 7954 250 10140 525 3025 188 2871 143 
As_077 6574 248 10172 526 3740 188 4373 137 
As_081 7745 247 8674 525 3728 188 3303 134 
As_082 7373 247 8705 524 3154 188 3693 133 
As_083 8004 248 10828 524 3580 188 3463 133 
As_084 10385 247 12053 527 4150 190 4549 133 
As_099 8606 247 11783 524 4947 187 4623 133 
SoilSaver_45 9320 248 10790 524 3288 188 4221 134 
 
 
85
 
 
 Table 3-5: LS means of biomass study 2006-07 at four locations. 1) Gulf Coast Research and Extension Center, Fairhope 
 2) Plant Breeding Unit, Tallassee, 3) Prattville Agricultural Research Unit and 4) Tennessee Valley Research and  
 Extension Center.
  GCS PBU PEF TVS 
Accession Estimate SE Estimate SE Estimate SE Estimate SE 
SoilSaver_90 6562 971 6552 1846 10433 1366 1473 104 
CIav 2520 6807 971 5567 1846 11010 1366 2005 104 
PI 111261 6270 971 7624 1846 9414 1366 1314 104 
PI 291991 7279 971 6951 1846 12690 1366 1447 104 
PI 436080 6527 971 9458 1846 11387 1366 1571 104 
PI 436103 6656 971 10211 1846 10124 1366 1489 104 
PI 436104 6401 971 5773 1846 13049 1366 1509 104 
PI 436105 6199 971 7347 1846 12896 1366 1448 104 
PI 436110 7935 971 8265 1846 9229 1366 1536 104 
PI 436114 7960 971 5314 1846 11461 1366 1564 104 
CIav 8089 7536 971 6071 1846 9312 1366 1400 104 
CIav 9007 7107 971 9781 1846 9841 1366 1356 104 
CIav 9011 3957 971 8509 1846 8638 1366 1514 104 
CIav 9012 7264 971 9273 1846 10173 1366 1249 104 
CIav 9110 5947 971 8260 1846 6600 1366 1157 104 
CIav 9112 4571 971 7919 1846 9666 1366 1657 104 
CIav 9116 6673 971 10168 1846 10486 1366 1278 104 
PI 274608 4194 971 8313 1846 11790 1366 1319 104 
CIav 7010 7142 971 6562 1846 12423 1366 1687 104 
SoilSaver_45 5150 971 8145 1846 8822 1366 1767 104 
 
 
86
 Table 3-6: LS means of grain yield and test weight study 2004-05 at 1) Plant Breeding Unit, and 2) Prattville 
Agricultural Research Unit.  
  Grain yield   Test weight 
 PBU PEF  PBU PEF 
Accession Estimate SE Estimate SE  Estimate SE Estimate SE 
SoilSaver_90 1013 73 652 91  27.5 0.4 32.1 0.4 
As_002 488 85 652 91  24.9 0.4 37.9 0.6 
As_016 738 85 1089 140  25.2 0.4 28.9 0.8 
As_028 1675 84 392 138  36.3 0.4 37.1 0.6 
As_031 1147 84 468 137  24.7 0.4 31.6 0.8 
As_033 1490 85 480 137  39.0 0.4 38.5 0.6 
As_034 1353 84 1199 137  37.9 0.4 39.8 0.6 
As_035 1175 84 1571 137  37.1 0.4 37.1 0.6 
As_046 1353 84 1227 137  36.8 0.4 36.9 0.6 
As_063 1154 84 949 137  35.1 0.4 37.6 0.6 
As_074 1240 84 1325 137  35.9 0.4 36.3 0.6 
As_075 555 85 986 137  24.3 0.4 31.4 0.6 
As_076 921 84 266 137  27.1 0.4 30.8 0.6 
As_077 563 73 867 138  24.8 0.4 31.2 0.8 
As_081 709 84 671 169  24.9 0.4 28.8 0.6 
As_082 513 104 543 137  23.5 0.5 29.6 0.8 
As_083 720 85 724 170  23.7 0.4   
As_084 1447 73 783 137  31.7 0.4 35.8 0.6 
As_099 1381 73 1392 137  36.9 0.4 37.5 0.6 
SoilSaver_45 1080 84 949 138   26.8 0.4 31.5 0.6 
 
 
87
Table 3-7: LS means of grain yield study 2006-07 at five locations. 1) Gulf Coast Research and Extension Center, Fairhope 
 2) Plant Breeding Unit, Tallassee 3) Prattville Agricultural Research Unit and 4) Tennessee Valley Research and  
 Extension Center 5) Wiregrass Research and Extension Center. 
 
 
  GCS PBU PEF TVS WGS 
Accession Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE 
SoilSaver_90 1662 142 1650 80 822 164 1130 227 344 144 
CIav 2520 1661 142 748 80 850 164 1567 227 259 144 
PI 111261 1410 142 800 80 712 164 1043 227 246 144 
PI 291991 1142 142 421 80 1343 164 1553 227 554 144 
PI 436080 1933 142 1475 80 857 202 776 227 893 144 
PI 436103 1424 142 193 80 1113 164 1640 227 724 144 
PI 436104 1469 142 277 80 1301 164 1696 227 518 178 
PI 436105 1670 142 665 80 1097 164 1768 227 527 144 
PI 436110 1386 142 247 80 1177 164 1337 227 535 144 
PI 436114 1202 142 282 80 1091 164 1303 227 576 144 
CIav 8089 1394 142 257 80 1331 164 1690 227 773 144 
CIav 9007 1567 142 924 80 771 164 1341 227 570 144 
CIav 9011 1415 142 1360 80 698 164 1073 227 360 144 
CIav 9012 1123 142 879 99 811 164 984 227 379 144 
CIav 9110 1406 142 859 80 720 164 687 227 285 144 
CIav 9112 1082 142 710 80 846 164 1260 227 269 144 
CIav 9116 1368 142 790 80 790 164 773 227 304 144 
PI 274608 962 142 282 80 1053 164 987 227 462 144 
CIav 7010 1253 142 317 80 1316 164 1251 227 517 144 
SoilSaver_45 1979 142 1655 80 658 164 1067 227 1116 144 
 
 
88
Table 3-8: LS means of test weight study 2006-07 at five locations. 1) Gulf Coast Research and Extension Center, Fairhope 
 2) Plant Breeding Unit, Tallassee, 3) Prattville Agricultural Research Unit and 4) Tennessee Valley Research and  
 Extension Center 5) Wiregrass Research and Extension Center. 
  GCS PBU PEF TVS WGS 
Accession Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE 
SoilSaver_90 18.6 2.3 25.4 1.1 25.8 2 24.1 0.9 19.1 0.6 
CIav 2520 20.2 2.3 24.4 1.1 30.3 2 30.7 0.9 20.9 0.8 
PI 111261 19.1 2.3 24.7 1.1 27.1 2 25.7 0.9 19.7 0.8 
PI 291991 22.8 2.3 23.3 1.1 36.1 2 32.3 0.9 27.6 0.6 
PI 436080 18.5 2.3 24.0 1.1 25.8 2.5 23.2 0.9 20.0 0.6 
PI 436103 22.7 2.3 19.8 1.4 36.6 2 33.7 0.9 26.2 0.6 
PI 436104 23.2 2.3 23.6 1.4 37.0 2 34.3 0.9 27.5 0.8 
PI 436105 21.8 2.3 23.7 1.1 32.6 2 28.0 0.9 26.4 0.6 
PI 436110 23.0 2.3 22.4 1.4 34.5 2 34.7 0.9 27.8 0.6 
PI 436114 22.9 2.3 23.6 1.1 34.8 2 34.5 0.9 27.8 0.6 
CIav 8089 28.1 2.3 23.4 1.4 36.0 2 32.9 0.9 27.8 0.6 
CIav 9007 20.2 2.3 24.6 1.1 28.9 2 29.3 0.9 21.3 0.8 
CIav 9011 18.7 2.3 24.7 1.1 25.6 2 24.8 0.9 19.5 0.6 
CIav 9012 23.5 2.3 23.8 1.4 27.3 2 26.7 0.9 20.5 0.6 
CIav 9110 24.0 2.3 22.3 1.1 25.6 2 28.0 0.9 20.2 0.6 
CIav 9112 19.0 2.3 22.6 1.1 28.6 2 30.9 0.9 21.7 0.8 
CIav 9116 23.3 2.3 23.3 1.1 26.2 2 26.6 1.1 20.3 0.6 
PI 274608 20.3 2.3 23.3 1.4 29.7 2 28.8 0.9 21.9 0.6 
CIav 7010 27.8 2.3 25.0 1.1 36.0 2 32.3 0.9 25.0 0.6 
SoilSaver_45 18.5 2.3 24.6 1.1 18.1 2 23.7 0.9 19.7 0.6 
 
 
89
-2500
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Figure 3-1: Biomass study 2004-05 at Gulf Coast Research and Extension Center, Fairhope. The biomass yield of 
SoilSaver at standard seeding rate is 8769 kg ha-1 with a SED of 174. 
 
 
 
 
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Figure 3-2: Biomass study 2006-07 at Gulf Coast Research and Extension Center, Fairhope. The biomass yield of 
SoilSaver at standard seeding rate is 6562 kg ha-1 with a SED of 971. 
 
 
 
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Figure 3-3: Biomass study 2004-05 at Plant Breeding Unit, Tallassee, Alabama. The biomass yield of SoilSaver at standard 
seeding rate is 11683 kg ha-1 with a SED of 374. 
 
 
 
 
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Figure 3-4: Biomass study 2006-07 at Plant Breeding Unit, Tallassee, Alabama. The biomass yield of SoilSaver at standard 
seeding rate is 6552 kg ha-1 with a SED of 1846. 
 
 
 
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Figure 3-5: Biomass study 2004-05 at Tennessee Valley Research and Extension Center, Belle Mina. The biomass yield 
of SoilSaver at standard seeding rate is 3997 kg ha-1 with a SED of 133 
 
 
 
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CIa
v 70
10
Soi
lSa
ver_
45
 
Figure 3-6: Biomass study 2006-07 at Tennessee Valley Research and Extension Center, Belle Mina. The biomass yield 
of SoilSaver at standard seeding rate is 1473 kg ha-1 with a SED of 104. 
 
 
 
95
-1500
-1000
-500
0
500
1000
1500
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
 
Figure 3-7: Biomass study 2004-05 at Wiregrass Research and Extension Center, Headland. The biomass yield of 
SoilSaver at standard seeding rate is 4088 kg ha-1 with a SED of 94. 
 
 
 
 
96
-10000
-8000
-6000
-4000
-2000
0
2000
4000
6000
8000
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
 
Figure 3-8: Biomass study 2006-07 at Prattville Agricultural Research Unit. The biomass yield of SoilSaver at standard 
seeding rate is 10433 kg ha-1 with a SED of 1366.
 
 
97
-600
-400
-200
0
200
400
600
800
1000
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
  
Figure 3-9: Grain yield study 2004-05 at Prattville Agricultural Research Unit, Prattville, Alabama. The grain yield of 
SoilSaver at standard seeding rate is 652 kg ha-1 with a SED of 91. 
 
 
 
 
98
-200
-100
0
100
200
300
400
500
600
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
 
 
Figure 3-10: Grain yield study 2006-07 at Prattville Agricultural Research Unit, Prattville, Alabama. The grain yield of 
SoilSaver at standard seeding rate is 822 kg ha-1 with a SED of 164. 
 
 
 
 
99
-600
-400
-200
0
200
400
600
800
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
 
Figure 3-11: Grain yield study 2004-05 at Plant Breeding Unit, Tallassee, Alabama. The grain yield of SoilSaver at 
standard seeding rate is 1013 kg ha-1 with a SED of 73. 
 
 
 
 
100
-1600
-1400
-1200
-1000
-800
-600
-400
-200
0
200
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
 
Figure 3-12: Grain yield study 2006-07 at Plant Breeding Unit, Tallassee, Alabama. The grain yield of SoilSaver at 
standard seeding rate is 1650 kg ha-1 with a SED of 80. 
 
 
 
 
101
-800
-600
-400
-200
0
200
400
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
 
 
Figure 3-13: Grain yield study 2006-07 at Gulf Coast Research and Extension Center, Fairhope. The grain yield of 
SoilSaver at standard seeding rate is 1662 kg ha-1 with a SED of 142. 
 
 
 
102
-600
-400
-200
0
200
400
600
800
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
    
Figure 3-14: Grain yield study 2006-07 at Tennessee Valley Research and Extension Center, Belle Mina. The grain yield of 
SoilSaver at standard seeding rate is 1130 kg ha-1 with a SED of 227. 
 
 
 
 
 
103
-200
-100
0
100
200
300
400
500
600
700
800
900
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
  
 
Figure 3-15: Grain yield study 2006-07 at Wiregrass Research and Extension Center, Headland. The grain yield of 
SoilSaver at standard seeding rate is 344 kg ha-1 with a SED of 144. 
 
 
 
104
 
-4
-2
0
2
4
6
8
10
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
  
Figure 3-16: Test weight study 2004-05 at Prattville Agricultural Research Unit, Prattville. The grain yield of SoilSaver 
at standard seeding rate is 32.1 lbs bu-1 with a SED of 0.4. 
 
 
 
105
 
-10
-5
0
5
10
15
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
 
Figure 3-17: Test weight study 2006-07 at Prattville Agricultural Research Unit, Prattville. The grain yield of SoilSaver 
at standard seeding rate is 25.8 lbs bu-1 with a SED of 2.3 
  
 
 
 
106
 
 
-6
-4
-2
0
2
4
6
8
10
12
14
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
  
Figure 3-18: Test weight study 2004-05 at Plant Breeding Unit, Tallassee. The grain yield of SoilSaver at standard 
seeding rate is 27.5 lbs bu-1 with a SED of 0.4. 
 
 
107
-6
-5
-4
-3
-2
-1
0
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
 
Figure 3-19: Test weight study 2006-07 at Plant Breeding Unit, Tallassee. The grain yield of SoilSaver at standard 
seeding rate is 25.3 lbs bu-1 with a SED of 1.1. 
 
 
 
 
108
-2
0
2
4
6
8
10
12
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
 
Figure 3-20: Test weight study 2006-07 at Gulf Coast Research and Extension Center, Fairhope. The grain yield of 
SoilSaver at standard seeding rate is 18.6 lbs bu-1 with a SED of 2.3. 
 
 
 
 
 
109
-2
0
2
4
6
8
10
12
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
 
Figure 3-21: Test weight study 2006-07 at Tennessee Valley Research and Extension Center, Belle Mina.. The grain 
yield of SoilSaver at standard seeding rate is 24.1 lbs bu-1 with a SED of 0.9. 
 
 
 
110
0
1
2
3
4
5
6
7
8
9
10
CIa
v 25
20
PI 1
112
61
PI 2
919
91
PI 4
360
80
PI 4
361
03
PI 4
361
04
PI 4
361
05
PI 4
361
10
PI 4
361
14
CIa
v 80
89
CIa
v 90
07
CIa
v 90
11
CIa
v 90
12
CIa
v 91
10
CIa
v 91
12
CIa
v 91
16
PI 2
746
08
CIa
v 70
10
Soi
lSa
ver_
45
  
Figure 3-22: Test weight study 2006-07 at Wiregrass Research and Extension Center, Headland. The grain yield of 
SoilSaver at standard seeding rate is 19 lbs bu-1 with a SED of 0.6.
 
 111 
IV. ALLELOPATHY OF BLACK OAT (AVENA STRIGOSA SCHREB.) 
ACCESSIONS 
 
Abstract 
 
Black oat (Avena strigosa Schreb.) has been considered to have allelopathic 
potential in suppressing weedy species in the crop field when grown as a cover crop. 
Various bio-assays have been used by many researchers in screening plant species based 
on their allelopathic potential. In this study we compared the allelopathic potential of 
eighteen black oat accessions and SoilSaver using the modified Pederson bio-assay. All 
the black oat accession and SoilSaver performed better than the control in suppressing 
radicle elongation of pre-germinated radish (Raphanus sativus L.) seedlings. Accession 
CIav 2520 expressed the maximum suppression of radicle elongation of the indicator 
species. .
 
 112 
Introduction 
Black oat has been found to be effective in suppressing different weeds in field 
condition (Price et al., 2006; Reeves et al., 2005), and this may be due to the release of 
allelopathic chemicals to the soil. The allelopathic potential of a cover crop can be 
estimated by in-vitro germination and radicle growth bio-assay of the target species using 
aqueous leachates collected from the donor species at a standard 1% concentration 
(Caamal-Maldonado et al., 2001). Earlier researchers used the aqueous leachates from 
seeds (Cope, 1982), roots (Peters, 1968) or stems on a germination paper to test the 
germination of the indicator species, but Carlson et al (1983) used the extract agar 
medium for testing germination. Carlson et al (1983) took only the germination 
percentage data, which were transformed with arcsine square root transformation in order 
to equalize among treatment variances. Cope (1982) measured the root length of the 
leguminous species and shoot length of the grassy species to assess allelopathic potential. 
According to Pederson (1986) the germination using extract agar is more precise than 
using germination paper since the germination of indicator species on a germination 
paper may not be even. He also suggested that root length measurement will give better 
indication of allelopathic effects than the germination method. But some times the 
dormancy that varies differently among different seeds is a problem in bio-assay and may 
result in higher experimental error, but the use of pre-germinated seeds will reduce 
experimental error (Ben-Hammouda et al., 1995; Wardle et al., 1993; Wu et al., 2001). 
Even though the germination bio-assay is a good way to test the allelopathic effects of 
donor plants, some researchers argue that findings of the extract screening bioassay may 
not be extrapolated to real agronomic situation because the allelopathic interactions may 
 
 113 
be different in crop fields (Romeo and J.D.Weidenhamer, 1999). But as a preliminary 
way to select the allelopathic potential of a given species, researchers use germination 
bio-assay extensively. In this study we used a modified method of Pederson (Pederson, 
1986) described by Stoll et al (2006) for the screening of black oat accessions for their 
allelopathic potential. 
 
Materials and Methods 
Eighteen black oat accessions and SoilSaver were selected based on the results of 
a previous study of morphology and maturity of black oat (Table 3-1). The entire 
evaluation for allelopathic potential was repeated three times. Seeds were sown in 7.6 L 
plastic containers in 1:1 sand and PRO-MIX medium (Sun Gro Horticulture Distribution 
Inc., Bellevue, WA) at the Auburn University Plant Sciences Research Center (PSRC) 
greenhouse and watered daily. Thirty seeds were sown and thinned to 15 seedlings per 
container after emergence. Three replicattes were planted on 1) last week of December, 
2006, 2) third week of January and 3) first week of April 2007. Aboveground biomass 
was harvested by cutting at soil level 5 weeks after planting, shredded into 15 mm pieces 
and mixed well. From each sample 10 g plant material was soaked in 50 mL of distilled 
water in opaque plastic containers. Extracts were filtered after 24 hours using coffee 
filters into another plastic container and 20 ml of filtered extracts were transferred into 
separate test tubes. As a control we used 20 mL of distilled water instead of filtered plant 
extract. Agar medium (12g L-1) was prepared by autoclaving the granulated agar (12 g) 
mixed with distilled water (1 L) at 121? C for 15 minutes. The medium was cooled down 
to 50 ?C, mixed with the filtered extract in the test tubes, transferred into Petri plates (15 
 
 114 
x 100mm) and allowed to solidify. Radish seeds (Raphanus sativus L.) pre-germinated 
for 24 hours with a radicle length < 2 mm were planted at 5 seedlings/ Petri-plate, sealed 
with parafilm and kept in dark around 22 ?C. The response variable was the radicle 
lengths 48 h post placement. 
We calculated the mean radicle length per Petri plate and analyzed the data using 
a mixed models approach as implemented in SAS Proc Mixed. Treatment (18 accessions 
and SoilSaver plus untreated control) was the sole fixed effect and run run x trt effects 
were considered to be random effects. Accession means were compared to either the 
untreated control or SoilSaver using Dunnett?s test. 
 
Result and Discussion 
Accessions CIav 2520, PI 111261, PI 291991, PI 436103, PI 436104, PI 436105 
and PI 436110 have significantly greater radicle suppressive ability of the indicator 
species than SoilSaver. Accession CIav 2520 showed the most radicle suppressive ability 
among all accession studied. Even though this screening based on bioassay is an 
indication of the effectiveness of black oat in suppressing weed growth, in real crop field 
the interaction of different factors may play their role, influencing the actual expression 
of the allelopathic potential. Also quantification of the allelopathic chemicals using 
chromatographic method may give more precise information about the antagonistic 
chemicals present in these accessions. 
 
 
 
 
 115 
References 
 
Ben-Hammouda, M.R.J. Kremer, H.C. Minor, and M.Sarwar. 1995. A chemical basis for 
the differential allelopathic potential of sorghum hybrids on wheat. J. Chem. Ecol. 
21:775-786. 
Caamal-Maldonado, J.A., J.J. Jime?nez-Osornio, A. Torres-Barragan, and A.L. Anaya. 
2001. The use of allelopathic legume cover and mulch species for weed control in 
cropping systems. Agron. J. 93:27-36. 
Carlson, J.R., R.L. Ditterline, J.M. Martin, D.C. Sands, and R.E. Lund. 1983. Alfalfa seed 
germination in anitibiotic agar containing NaCl. Crop.Sci. 23:882-885. 
Cope, W.A. 1982. Inhibition of germination and seedling growth of eight forage species 
by leachates from seeds. Crop Sci. 22:1109-1111. 
Fay, P.K., and W.B. Duke. 1977. An assessment of allelopathic potential in Avena 
germplasm. Weed science 25:224-228. 
Pederson, G.A. 1986. White clover seed germination in agar containing tall fescue leaf 
extracts. Crop Sci. 26:1248-1249. 
Peters, E.J. 1968. Toxicity of tall fescue to rape and birdsfoot trefoil seeds and seedlings. 
Crop Sci. 8:650-653. 
Price, A.J., D.W. Reeves, and M.G. Patterson. 2006. Evaluation of weed control provided 
by three winter cereals in conservation-tillage soybean. Renewable Agriculture 
and Food Systems 21(3):159-164(6). 
Reeves, D.W., A.J. Price, and M.G. Patterson. 2005. Evaluation of three winter cereals 
for weed control in conservation-tillage nontransgenic cotton. Weed Technology 
19:731-736. 
 
 116 
Romeo, J.T., and J.D.Weidenhamer. 1999. Bioassay for allelopathy in terrestrial plants, 
p. 179-211, In K. F. Haynes and J. G. Millar, eds. Methods in chemical ecology. 
Kluwer Academic Publishing, Boston. 
Stoll, M.E., A.J. Price, and J.R. Jones. 2006. Cover crop extract effects on radish radicle 
elongation, p. 184-186 Southern Conservation Systems Conference, Amarillo TX. 
Wardle, D.A., K.A. Nicholson, and A. Rahman. 1993. Influence of plant age on 
allelopathic potential of nodding thistle(Carduus nutans L.) against pasture 
grasses and legumes. Weed research 33:69-78. 
Wu, H., J. Pratley, D. Lemerle, T. Haig, and M. An. 2001. Screening methods for the 
evaluation of crop allelopathic potential. Botanical Review 67(3):403-415. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 117 
Table 4-1: The allelopathic potential of different black oat accessions in comparison to 
SoilSaver.  
 
 
 
   P > 0 (Dunnett's) 
Treatment Country of origin Radicle elongation vs. Control vs. SoilSaver 
   ---- mm ----   
Control  65.6   
PI 274608 Poland 25 <0.0001 1.000 
CIav 9116 Canada 23.8 <0.0001 0.962 
SoilSaver United States 22.5 <0.0001  
CIav 8089 United States 22.2 <0.0001 1.000 
CIav 9112 Canada 21.7 <0.0001 0.473 
CIav 7010 Brazil 21.3 <0.0001 0.436 
PI 436114 Chile 20.6 <0.0001 0.999 
CIav 9007 Romania 20 <0.0001 0.472 
CIav 9012 Bulgaria 19.4 <0.0001 0.163 
CIav 9011 Denmark 19.3 <0.0001 0.394 
CIav 9110 Canada 19.3 <0.0001 0.268 
PI 436080 Chile 19.1 <0.0001 0.161 
PI 111261 Romania 17.3 <0.0001 0.015 
PI 436110 Chile 17.1 <0.0001 0.021 
PI 291991 Israel 16.8 <0.0001 0.002 
PI 436103 Chile 16.1 <0.0001 0.002 
PI 436105 Chile 15.3 <0.0001 0.0005 
PI 436104 Chile 14.8 <0.0001 <0.0001 
CIav 2520 France 14.3 <0.0001 <0.0001 
  SE 3.7   
 
 
118
Appendix 1. Year x accession least squares interaction means. 
 
 
 
 
 
      Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 CIav 1782 120 5.03 26.67 5.95 2.76 0.24 107.54 4.34 5.33 0.22 29.39 3.49 
2004 CIav 1782 138 5.03 69.62 10.24 2.36 0.42 94.37 8.27 5.20 0.41 5.01 4.80 
2003 CIav 2520 105 5.03 50.00 5.95 4.40 0.23 132.62 4.34 5.21 0.22 28.59 3.49 
2004 CIav 2520 136 5.03 53.00 5.95 3.56 0.26 123.42 4.65 5.60 0.23 9.17 3.49 
2003 CIav 2521 120 5.03 48.33 5.95 3.53 0.24 140.69 4.34 6.01 0.22 30.67 3.49 
2004 CIav 2521 137 5.03 49.67 5.95 3.24 0.26 123.54 4.65 5.71 0.23 15.20 3.67 
2003 CIav 2523 116 5.03 41.67 5.95 3.60 0.42 106.88 8.29 5.32 0.41 25.50 3.49 
2004 CIav 2523 141 5.03 47.67 5.95 3.57 0.30 84.86 5.55 4.21 0.28 6.09 4.19 
2003 CIav 2524 122 5.03 43.33 5.95 3.64 0.25 133.48 4.48 5.28 0.23 29.22 3.49 
2004 CIav 2524 136 5.03 65.47 7.26 2.77 0.38 100.83 7.41 4.50 0.37 13.48 6.36 
2003 CIav 2525 120 5.03 45.00 5.95 3.03 0.24 122.40 4.21 5.94 0.21 40.00 3.49 
2004 CIav 2525 139 5.03 57.34 7.26 3.25 0.48 105.71 9.53 6.06 0.47 7.31 5.42 
2003 PI 78821 120 5.03 53.33 5.95 2.93 0.24 124.81 4.34 6.26 0.22 28.61 3.49 
2004 PI 78821 147 5.03 43.33 5.95 4.29 0.27 85.33 5.04 7.18 0.25 5.53 3.58 
 
 
119
Appendix 1. Year x accession least squares interaction means. 
. 
 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 PI 83720 117 5.03 58.33 5.95 4.55 0.27 147.05 5.05 6.06 0.25 32.67 3.49 
2004 PI 83720 138 5.03 68.34 7.26 2.96 0.33 91.79 6.28 4.40 0.31 6.63 5.41 
2003 PI 83721 120 5.03 65.00 5.95 3.11 0.25 117.29 4.48 5.52 0.23 28.89 3.49 
2004 PI 83721 138 5.03 65.67 5.95 3.61 0.31 106.69 5.88 4.93 0.29 11.33 3.49 
2003 PI 83723 120 5.03 51.67 5.95 3.90 0.23 112.80 4.09 5.58 0.21 22.56 3.49 
2004 PI 83723 141 5.03 53.33 5.95 2.78 0.27 90.55 5.04 5.11 0.26 13.12 3.78 
2003 CIav 2894 127 5.03 50.00 5.95 3.70 0.30 98.34 5.55 4.66 0.28 10.89 3.49 
2004 CIav 2894 139 5.03 40.05 10.24 2.05 0.35 54.67 6.76 3.50 0.33 2.36 4.80 
2003 CIav 2920 120 5.03 41.67 5.95 3.51 0.27 107.44 4.83 4.59 0.24 21.94 3.49 
2004 CIav 2920 139 5.03 52.67 5.95 3.49 0.31 92.77 5.88 4.37 0.29 8.06 3.49 
2003 CIav 2921 122 5.03 46.67 5.95 4.32 0.25 126.20 4.48 6.15 0.23 23.72 3.49 
2004 CIav 2921 140 5.03 53.33 5.95 3.05 0.30 99.25 5.55 5.10 0.28 12.89 3.78 
2003 CIav 3214 107 5.03 38.33 5.95 4.41 0.28 106.71 5.28 4.76 0.26 24.33 3.49 
2004 CIav 3214 137 5.03 48.67 5.95 3.42 0.24 96.88 4.34 4.28 0.22 12.06 3.49 
 
 
120
Appendix 1. Year x accession least squares interaction means. 
 
 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 PI 111261 103 5.03 65.00 5.95 4.07 0.23 141.46 4.09 4.89 0.21 35.61 3.49 
2003 CIav 3372 132 5.03 31.67 5.95 4.38 0.48 109.60 9.53 5.27 0.47 3.44 3.49 
2004 CIav 3372 151 5.03 48.33 5.95 3.35 0.80 67.18 16.45 4.14 0.81 2.02 4.37 
2003 PI 131695 85 5.03 68.33 5.95 4.45 0.23 127.39 3.98 3.83 0.20 31.00 3.49 
2004 PI 131695 127 5.03 60.97 7.26 3.48 0.28 103.32 5.28 4.17 0.26 11.81 4.20 
2003 PI 131640 85 5.03 56.67 5.95 4.78 0.23 127.72 3.98 3.67 0.20 25.89 3.49 
2004 PI 131640 116 5.03 55.67 5.95 4.30 0.24 103.84 4.21 3.33 0.21 11.72 3.49 
2003 PI 131641 86 5.03 58.33 5.95 4.81 0.25 134.30 4.34 3.89 0.22 35.16 3.58 
2004 PI 131641 109 5.03 57.33 5.95 4.41 0.24 109.05 4.21 3.62 0.21 20.28 3.49 
2003 PI 131642 85 5.03 56.67 5.95 5.00 0.23 107.87 4.09 3.65 0.21 27.22 3.49 
2004 PI 131642 113 5.03 40.00 5.95 3.79 0.26 86.50 4.65 3.29 0.23 5.78 3.49 
2003 CIav 4639 89 5.03 50.00 5.95 5.28 0.23 132.83 3.98 4.33 0.20 34.67 3.49 
2004 CIav 4639 111 5.03 56.67 5.95 5.07 0.24 117.25 4.21 4.25 0.21 20.89 3.49 
2003 PI 158244 120 5.03 38.33 5.95 3.20 0.26 122.77 4.65 6.39 0.24 27.83 3.49 
 
 
121
Appendix 1. Year x accession least squares interaction means. 
 
 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2004 PI 158244 139 5.03 65.47 7.26 2.50 0.28 95.61 5.27 4.97 0.26 7.65 4.37 
2003 PI 158246 132 5.03 23.33 5.95 5.11 0.48 112.36 9.53 4.65 0.47 2.94 4.20 
2004 PI 158246 152 5.03 39.84 7.26 4.14 0.35 90.70 6.78 3.41 0.34 4.60 4.57 
2004 PI 274610 140 5.03 54.84 7.26 2.76 0.31 89.83 5.88 4.24 0.29 8.82 5.07 
2003 PI 287315 116 5.03 33.33 5.95 3.09 0.24 124.45 4.21 5.12 0.21 35.00 3.49 
2004 PI 287315 131 5.03 58.33 5.95 2.93 0.25 112.52 4.48 5.13 0.23 25.65 3.58 
2003 PI 291990 91 5.03 56.67 5.95 4.78 0.23 132.89 3.98 4.22 0.20 30.50 3.49 
2004 PI 291990 130 5.03 58.00 5.95 4.90 0.27 111.34 5.04 4.34 0.25 16.26 3.67 
2003 PI 291991 92 5.03 60.00 5.95 4.60 0.23 125.75 4.09 4.12 0.21 30.94 3.49 
2004 PI 291991 119 5.03 61.67 5.95 4.66 0.23 107.99 4.09 3.99 0.21 16.72 3.49 
2003 PI 292226 85 5.03 66.67 5.95 4.75 0.24 127.27 4.21 3.63 0.21 32.94 3.49 
2004 PI 292226 108 5.03 57.67 5.95 3.87 0.26 97.64 4.65 3.70 0.23 13.73 4.04 
2003 PI 304557 120 5.03 46.67 5.95 6.78 0.80 116.17 16.45 5.90 0.81 4.00 3.49 
2004 PI 304557 139 5.03 52.33 5.95 4.13 0.25 106.08 4.48 4.16 0.23 7.66 3.78 
 
 
122
Appendix 1. Year x accession least squares interaction means. 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 PI 436080 102 5.03 33.33 5.95 3.98 0.23 114.68 4.09 4.63 0.21 22.11 3.49 
2004 PI 436080 136 5.03 45.33 5.95 3.33 0.24 113.11 4.34 4.18 0.22 19.89 3.58 
2003 PI 436082 134 8.71 35.00 5.95 5.01 0.48 74.69 9.53 4.32 0.47 0.61 3.49 
2004 PI 436082 136 5.03 48.97 7.26 3.85 0.38 79.96 7.40 3.97 0.37 1.00 5.08 
2003 PI 436103 92 5.03 63.33 5.95 4.63 0.24 130.34 4.21 4.31 0.21 28.61 3.49 
2003 PI 436104 95 5.03 61.67 5.95 4.97 0.24 122.89 4.34 3.88 0.22 26.56 3.49 
2004 PI 436104 111 5.03 56.00 5.95 3.43 0.23 101.44 3.98 3.94 0.20 14.61 3.49 
2003 PI 436105 94 5.03 55.00 5.95 4.85 0.24 137.21 4.21 4.75 0.21 35.50 3.49 
2004 PI 436105 112 5.03 55.00 5.95 4.68 0.24 112.55 4.21 4.12 0.21 28.46 3.78 
2003 PI 436106 91 5.03 53.33 5.95 4.58 0.23 133.46 4.09 4.36 0.21 35.22 3.49 
2004 PI 436106 112 5.03 55.33 5.95 4.53 0.23 107.28 4.09 3.70 0.21 22.80 3.58 
2003 PI 306419 122 5.03 61.67 5.95 5.06 0.48 120.73 9.53 7.73 0.47 2.28 3.49 
2004 PI 306419 151 5.03 73.33 5.95 3.79 0.58 112.55 11.66 6.97 0.57 1.47 4.04 
2003 PI 361910 122 5.03 53.33 5.95 4.62 0.48 97.07 9.53 5.06 0.47 6.00 3.49 
2004 PI 361910 150 5.03 52.33 5.95 3.83 0.42 140.02 8.29 4.88 0.41 14.89 3.91 
 
 
123
Appendix 1. Year x accession least squares interaction means. 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 PI 361912 117 5.03 60.00 5.95 5.68 0.48 106.60 9.53 4.61 0.47 5.94 3.49 
2004 PI 361912 150 5.03 61.33 5.95 5.10 0.35 86.64 6.78 4.92 0.34 4.42 4.04 
2003 PI 401793 134 5.03 23.33 5.95 3.91 0.35 115.32 6.77 7.67 0.33 31.17 3.49 
2004 PI 401793 158 5.03 34.67 5.95 3.64 0.58 100.55 11.66 6.47 0.57 6.57 3.67 
2003 PI 401794 134 5.03 46.67 5.95 3.43 0.38 102.72 7.40 6.99 0.37 13.85 4.20 
2004 PI 401794 163 5.03 43.00 5.95 2.67 0.33 80.09 6.28 5.71 0.31 12.56 4.04 
2004 PI 436107 140 5.03 42.33 5.95 4.15 0.24 93.99 4.34 4.06 0.22 11.06 3.49 
2003 PI 436108 107 5.03 31.67 5.95 3.60 0.35 91.26 6.78 3.88 0.34 10.72 3.49 
2004 PI 436108 136 5.03 49.67 5.95 2.35 0.24 89.83 4.34 4.08 0.22 14.56 3.49 
2003 PI 436109 88 5.03 71.67 5.95 4.85 0.24 124.04 4.21 3.69 0.22 27.78 3.49 
2004 PI 436109 112 5.03 50.33 5.95 5.64 0.23 111.23 4.09 3.65 0.21 15.28 3.49 
2003 PI 436110 92 5.03 60.00 5.95 4.68 0.24 144.09 4.21 4.75 0.21 40.39 3.49 
2004 PI 436110 112 5.03 55.33 5.95 5.19 0.25 107.69 4.34 3.88 0.22 26.44 3.49 
2003 PI 436111 89 5.03 63.33 5.95 4.61 0.23 112.22 3.98 4.39 0.20 39.78 3.49 
2004 PI 436111 116 5.03 56.67 5.95 3.70 0.24 102.52 4.34 3.66 0.22 28.04 3.67 
 
 
124
Appendix 1. Year x accession least squares interaction means. 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 PI 436112 134 6.16 45.00 5.95 3.80 0.48 106.09 9.53 5.04 0.47 3.48 4.04 
2004 PI 436112 161 6.16 52.34 7.26 3.49 0.38 76.78 7.43 4.34 0.37 2.90 4.57 
2003 PI 436119 127 5.03 26.67 5.95 4.21 0.33 105.34 6.27 5.30 0.31 11.09 4.20 
2004 PI 436119 151 5.03 37.67 5.95 3.85 0.25 87.63 4.48 4.63 0.23 7.39 3.49 
2003 PI 436120 125 5.03 48.33 5.95 4.11 0.48 93.60 9.53 3.94 0.47 8.83 3.49 
2004 PI 436120 151 5.03 49.33 5.95 3.11 0.28 72.98 5.27 4.59 0.26 11.58 3.67 
2003 PI 436121 125 5.03 36.67 5.95 3.99 0.31 97.58 5.88 5.12 0.29 14.00 3.49 
2003 PI 436122 127 5.03 26.67 5.95 4.67 0.42 112.36 8.26 5.26 0.41 0.87 4.37 
2004 PI 436122 134 5.03 65.00 5.95 3.16 0.30 119.99 5.55 4.20 0.28 17.61 3.91 
2003 PI 436124 125 5.03 51.67 5.95 4.10 0.30 109.87 5.55 5.54 0.28 21.11 3.49 
2004 PI 436124 147 5.03 48.00 5.95 3.26 0.25 80.38 4.48 4.88 0.23 12.52 3.90 
2003 PI 436125 122 5.03 35.00 5.95 4.35 0.42 92.49 8.27 4.43 0.41 8.44 3.49 
2004 PI 436125 137 5.03 51.00 5.95 3.73 0.25 99.88 4.48 4.23 0.23 12.58 3.78 
2003 PI 436127 120 5.03 53.33 5.95 4.07 0.30 119.93 5.55 5.68 0.28 24.94 3.49 
2004 PI 436127 148 5.03 52.33 5.95 3.68 0.28 91.30 5.27 4.60 0.26 13.77 3.58 
 
 
125
Appendix 1. Year x accession least squares interaction means. 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 PI 436126 131 6.16 35.10 7.26 4.40 0.42 86.13 8.27 4.99 0.41 0.17 3.49 
2004 PI 436126 156 5.03 47.00 5.95 3.34 0.35 77.15 6.77 4.14 0.33 2.75 3.91 
2003 PI 436130 134 5.03 45.00 5.95 3.58 0.31 96.60 5.88 5.76 0.29 14.17 3.49 
2004 PI 436130 149 5.03 56.00 5.95 2.97 0.24 83.86 4.34 5.52 0.22 16.18 3.67 
2003 PI 436131 131 6.16 33.33 5.95 3.68 0.48 120.60 9.53 5.61 0.47 9.39 3.49 
2004 PI 436131 161 5.03 38.33 5.95 2.74 0.48 65.86 9.53 3.58 0.47 3.95 3.67 
2003 PI 436132 122 5.03 45.00 5.95 3.97 0.31 112.04 5.88 5.60 0.29 25.89 3.49 
2004 PI 436132 137 5.03 44.33 5.95 2.99 0.31 83.18 5.88 4.20 0.29 9.74 4.80 
2004 PI 436133 159 5.03 53.00 5.95 2.96 0.33 67.03 6.29 4.39 0.31 2.92 3.78 
2003 PI 436134 127 5.03 35.00 5.95 4.00 0.38 125.96 6.28 4.74 0.31 4.06 3.49 
2004 PI 436134 150 5.03 35.67 5.95 2.96 0.30 70.06 4.48 3.78 0.23 5.06 3.58 
2003 PI 436113 109 5.03 35.00 5.95 4.52 0.38 94.22 7.41 3.25 0.37 7.33 3.49 
2004 PI 436113 139 5.03 49.67 5.95 3.47 0.25 81.49 4.48 3.47 0.23 9.95 3.67 
2003 PI 436114 95 5.03 53.33 5.95 4.43 0.23 133.00 3.98 4.22 0.20 37.22 3.49 
2004 PI 436114 111 5.03 57.33 5.95 5.29 0.23 105.11 3.98 3.67 0.20 23.72 3.49 
 
 
126
Appendix 1. Year x accession least squares interaction means. 
 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 PI 436115 125 5.03 35.00 5.95 4.04 0.38 84.62 7.41 4.07 0.26 3.94 3.49 
2004 PI 436115 151 5.03 42.33 5.95 3.51 0.26 72.87 4.65 3.66 0.23 5.55 3.58 
2003 PI 436116 114 5.03 31.67 5.95 4.85 0.38 94.80 7.41 3.40 0.37 4.80 3.49 
2004 PI 436116 124 5.03 50.33 5.95 3.36 0.27 90.83 4.64 4.06 0.24 8.83 3.58 
2003 PI 436117 86 5.03 65.00 5.95 4.96 0.23 127.17 3.98 4.22 0.20 33.28 3.58 
2004 PI 436117 112 5.03 55.67 5.95 4.80 0.23 105.87 4.09 3.82 0.21 18.39 3.49 
2003 PI 436118 107 5.03 30.00 5.95 4.60 0.31 96.35 5.89 3.73 0.29 18.26 4.20 
2004 PI 436118 135 5.03 46.67 5.95 3.40 0.25 84.69 4.48 4.17 0.23 12.61 3.49 
2003 CIav 9019 120 5.03 28.33 5.95 4.32 0.27 124.51 5.03 5.49 0.26 19.00 3.49 
2003 CIav 9020 89 5.03 51.67 5.95 4.67 0.23 137.61 3.98 4.78 0.20 38.83 3.49 
2004 CIav 9020 112 5.03 56.67 5.95 4.57 0.24 111.11 4.21 3.68 0.21 12.78 3.49 
2003 CIav 9021 88 5.03 55.00 5.95 4.87 0.23 131.00 3.98 4.33 0.20 43.00 3.49 
2004 CIav 9021 128 5.03 61.67 5.95 4.33 0.26 96.35 4.65 3.20 0.23 18.11 3.49 
2003 CIav 9022 113 5.03 61.67 5.95 3.55 0.31 132.30 5.89 5.07 0.47 34.33 3.58 
2004 CIav 9022 136 5.03 72.00 5.95 2.98 0.24 109.79 4.21 4.42 0.21 23.45 3.67 
 
 
127
Appendix 1. Year x accession least squares interaction means. 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 CIav 9024 122 5.03 48.33 5.95 3.88 0.24 126.60 4.34 5.53 0.22 28.06 3.49 
2004 CIav 9024 141 5.03 67.00 5.95 3.00 0.28 95.71 5.27 5.27 0.26 5.88 3.67 
2003 CIav 9030 127 5.03 50.00 5.95 3.34 0.31 96.83 5.88 4.87 0.29 9.44 3.49 
2004 CIav 9030 137 5.03 73.33 5.95 4.18 0.27 100.65 4.83 4.56 0.24 18.17 3.49 
2003 CIav 8089 92 5.03 61.67 5.95 4.66 0.23 132.11 4.09 4.30 0.21 37.00 3.49 
2004 CIav 8089 111 5.03 61.33 5.95 5.49 0.23 111.00 4.09 3.65 0.21 26.17 3.49 
2003 CIav 9007 106 5.03 71.67 5.95 4.43 0.23 142.78 3.98 4.72 0.20 41.17 3.49 
2004 CIav 9007 132 5.03 68.67 5.95 3.60 0.25 129.49 4.48 4.70 0.23 12.56 3.49 
2003 CIav 9011 105 5.03 68.33 5.95 4.20 0.23 140.56 4.09 4.70 0.21 39.89 3.49 
2004 CIav 9011 136 5.03 61.00 5.95 3.41 0.27 126.39 4.83 4.91 0.24 14.08 3.58 
2004 CIav 9012 135 5.03 70.00 5.95 3.67 0.25 136.44 4.48 5.22 0.23 19.88 3.67 
2003 CIav 9014 108 5.03 50.00 5.95 5.30 0.31 122.47 5.88 4.89 0.29 8.11 3.49 
2004 CIav 9014 144 5.03 56.67 5.95 4.04 0.30 103.72 5.55 4.51 0.28 9.21 3.67 
2003 CIav 9015 25 5.03 73.33 5.95 2.54 0.25 62.19 4.48 2.21 0.23 12.28 3.49 
2004 CIav 9015 -32 5.03 65.32 10.24 1.27 0.38 49.88 7.41 2.72 0.37 2.03 5.84 
 
 
128
Appendix 1. Year x accession least squares interaction means. 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 CIav 9066 131 6.16 31.67 5.95 3.73 0.38 126.85 7.43 4.83 0.37 1.44 3.49 
2004 CIav 9066 151 5.03 45.69 7.26 4.38 0.31 90.76 5.88 3.49 0.29 4.60 5.41 
2003 CIav 9110 105 5.03 70.00 5.95 4.03 0.24 126.61 4.34 4.81 0.23 33.61 3.49 
2004 CIav 9110 137 5.03 66.34 7.26 3.32 0.31 103.88 5.88 4.40 0.29 12.19 4.04 
2003 CIav 9112 110 5.03 70.00 5.95 3.35 0.35 116.34 6.80 4.40 0.34 39.33 3.49 
2004 CIav 9112 139 5.03 65.00 5.95 2.99 0.27 111.24 4.83 5.07 0.24 12.83 3.49 
2003 CIav 9116 105 5.03 73.33 5.95 4.05 0.24 116.33 4.34 4.36 0.23 48.94 3.49 
2004 CIav 9116 136 5.03 70.67 5.95 3.59 0.27 125.48 4.83 4.83 0.24 20.89 3.67 
2003 PI 274608 103 5.03 58.33 5.95 5.01 0.26 140.47 4.65 4.35 0.23 37.11 3.49 
2004 PI 274608 120 5.03 54.67 5.95 4.69 0.23 107.40 4.09 4.05 0.21 18.78 3.49 
2003 PI 274609 85 5.03 43.33 5.95 5.14 0.23 108.50 3.98 3.78 0.20 26.28 3.49 
2003 CIav 9031 117 5.03 43.33 5.95 4.48 0.38 79.24 7.41 4.44 0.37 4.78 3.49 
2004 CIav 9031 143 5.03 65.67 5.95 3.90 0.26 80.75 4.64 4.07 0.23 4.00 3.49 
2003 CIav 9035 114 5.03 43.33 5.95 3.28 0.80 84.17 16.45 3.90 0.81 7.06 3.49 
2004 CIav 9035 131 5.03 47.00 5.95 4.84 0.25 94.07 4.48 3.57 0.23 10.50 3.49 
 
 
129
Appendix 1. Year x accession least squares interaction means. 
 
 
 
   Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 CIav 9038 114 5.03 38.33 5.95 5.03 0.58 78.17 11.66 4.40 0.57 2.78 3.49 
2004 CIav 9038 152 5.03 47.00 5.95 4.37 0.30 100.13 5.55 4.35 0.28 6.76 4.04 
2003 CIav 9043 120 5.03 38.33 5.95 3.99 0.31 130.42 5.89 5.76 0.29 27.39 3.49 
2004 CIav 9043 136 5.03 56.47 7.26 3.31 0.28 100.14 5.28 4.94 0.26 9.59 4.57 
2003 CIav 9064 65 5.03 53.33 5.95 2.95 0.23 100.95 4.21 3.62 0.21 24.94 3.49 
2004 CIav 9064 119 5.03 63.47 7.26 2.70 0.27 88.50 5.27 2.81 0.25 3.98 4.20 
2003 PI 158247 86 5.03 66.67 5.95 4.46 0.23 139.89 3.98 3.83 0.20 38.39 3.49 
2004 PI 158247 113 5.03 68.67 5.95 5.76 0.24 113.65 4.34 3.19 0.22 13.29 3.67 
2003 CIav 5057 109 5.03 28.67 5.95 4.41 0.31 119.54 5.89 4.93 0.29 12.44 3.49 
2004 CIav 5057 146 5.03 36.67 5.95 3.47 0.27 93.21 5.03 4.26 0.25 5.92 4.04 
2003 CIav 5082 89 5.03 60.00 5.95 4.66 0.23 128.80 4.09 3.94 0.21 34.13 3.58 
2004 CIav 5082 115 5.03 54.67 5.95 5.37 0.24 105.31 4.34 3.65 0.22 17.80 3.58 
2004 CIav 6858 112 5.03 57.67 5.95 5.58 0.23 106.52 4.09 3.52 0.21 19.21 3.58 
2003 PI 186606 85 5.03 53.33 5.95 4.98 0.24 121.40 4.21 4.25 0.21 24.56 3.49 
2004 PI 186606 109 5.03 56.00 5.95 5.22 0.23 106.89 3.98 3.67 0.20 19.17 3.49 
 
 
130
Appendix 1. Year x accession least squares interaction means. 
 
 
 
 
 
 
      Tiller 
Year NPGS no. Heading SE Angle SE Diameter SE Length SE Node number SE Number SE 
2003 CIav 6956 88 5.03 46.67 5.95 4.82 0.23 123.34 4.09 4.30 0.21 26.22 3.49 
2004 CIav 6956 112 5.03 54.67 5.95 5.60 0.24 112.93 4.21 3.75 0.21 18.83 3.49 
2003 CIav 7010 94 5.03 55.00 5.95 4.79 0.24 127.15 4.21 4.19 0.21 28.97 3.49 
2004 CIav 7010 116 5.03 58.33 5.95 3.97 0.26 97.23 4.64 3.46 0.23 18.01 3.67 
2003 CIav 7121 120 5.03 43.33 5.95 3.10 0.26 111.90 4.65 5.01 0.23 23.02 3.49 
2004 CIav 7121 137 5.03 52.34 7.26 2.82 0.38 105.05 7.41 5.54 0.37 8.27 4.37 
2003 CIav 7122 122 5.03 33.33 5.95 3.44 0.24 105.81 4.34 4.28 0.22 22.72 3.49 
2004 CIav 7122 139 5.03 54.97 7.26 2.46 0.28 92.78 5.28 3.92 0.26 12.82 4.37 
2003 CIav 7280 85 5.03 63.33 5.95 5.12 0.23 126.50 3.98 4.33 0.20 27.78 3.49 
2004 CIav 7280 109 5.03 57.67 5.95 4.78 0.23 112.47 4.09 3.87 0.22 20.39 3.49 
2003 CIav 8087 105 5.03 38.33 5.95 4.75 0.35 105.21 6.77 4.69 0.33 9.33 3.49 
2004 CIav 8087 150 5.03 43.33 5.95 4.58 0.28 103.41 5.27 4.77 0.26 2.34 4.19 
2003 SoilSaver 102 5.03 36.67 5.95 4.48 0.25 105.88 4.49 3.89 0.23 14.39 3.49 
2004 SoilSaver 112 5.03 48.67 5.95 4.29 0.23 96.64 4.09 4.05 0.21 15.89 3.49 
 
 
131
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 CIav 1782 29.82 1.14 9.40 0.29 112.94 7.87 0.77 0.06 2.11 2.28 11.00 1.29 
2004 CIav 1782 26.54 2.16 9.73 0.57 110.80 15.15 0.90 0.12 -0.14 2.73 6.92 2.87 
2003 CIav 2520 33.08 1.14 9.60 0.29 108.60 7.40 0.97 0.06 14.95 2.28 8.85 1.18 
2004 CIav 2520 28.09 1.22 9.38 0.31 92.94 8.45 0.69 0.07 0.88 2.28 8.59 1.57 
2003 CIav 2521 27.94 1.14 9.53 0.29 83.33 8.14 0.85 0.07 6.81 2.28 10.64 1.25 
2004 CIav 2521 25.59 1.22 9.01 0.31 94.34 8.45 0.69 0.07 3.43 2.28 7.32 1.38 
2003 CIav 2523 23.86 2.49 8.51 0.57 82.03 15.19 0.64 0.12 7.38 2.28 13.09 1.21 
2004 CIav 2523 24.94 1.46 8.55 0.38 109.88 10.12 0.70 0.08 0.46 2.28 11.65 2.22 
2003 CIav 2524 31.34 1.18 9.50 0.30 113.24 8.15 0.87 0.07 4.86 2.28 10.46 1.33 
2004 CIav 2524 26.39 1.94 9.56 0.51 113.89 13.57 0.75 0.11 1.43 2.73 8.91 2.48 
2003 CIav 2525 33.16 1.11 10.25 0.28 110.42 7.63 0.91 0.06 6.73 2.28 10.56 1.21 
2004 CIav 2525 27.13 2.49 10.36 0.65 88.33 17.47 0.73 0.14 0.31 2.73 10.23 2.86 
2003 PI 78821 29.35 1.14 9.46 0.29 89.47 7.87 0.67 0.06 2.99 2.28 9.61 1.25 
2004 PI 78821 31.93 1.32 7.73 0.34 149.73 9.61 1.07 0.08 0.43 2.28 17.12 2.03 
 
 
 
 
 
 
 
132
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 PI 83720 39.47 1.33 10.81 0.34 115.11 9.63 0.93 0.08 5.85 2.28 11.16 1.21 
2004 PI 83720 26.09 1.64 9.28 0.43 101.03 11.47 0.59 0.09 0.51 2.73 8.18 2.22 
2003 PI 83721 27.80 1.18 9.64 0.30 88.01 8.15 0.82 0.07 4.94 2.28 10.06 1.29 
2004 PI 83721 32.14 1.54 8.28 0.40 164.59 10.74 0.86 0.09 1.98 2.28 17.58 1.76 
2003 PI 83723 30.44 1.08 9.59 0.28 114.19 7.40 0.92 0.06 2.43 2.28 10.08 1.18 
2004 PI 83723 23.96 1.38 8.92 0.34 105.42 9.17 0.75 0.07 1.27 2.28 7.06 1.50 
2003 CIav 2894 26.14 1.46 10.22 0.38 97.68 10.12 0.57 0.08 1.49 2.28 6.78 1.50 
2004 CIav 2894 15.33 1.77 7.00 0.46 62.50 12.37 0.33 0.10 0.35 2.73 4.83 3.51 
2003 CIav 2920 27.68 1.27 9.58 0.33 74.71 8.78 0.46 0.07 2.34 2.28 9.88 1.33 
2004 CIav 2920 28.34 1.54 9.12 0.40 94.32 10.74 0.56 0.09 1.03 2.28 10.32 1.76 
2003 CIav 2921 31.93 1.18 10.29 0.30 114.47 8.14 0.84 0.07 1.82 2.28 8.73 1.38 
2004 CIav 2921 26.94 1.46 8.11 0.38 106.32 10.12 0.76 0.08 2.27 2.28 9.58 1.76 
2003 CIav 3214 25.63 1.39 9.39 0.36 75.31 9.63 0.67 0.08 4.65 2.28 9.15 1.18 
2004 CIav 3214 21.99 1.14 8.94 0.29 75.34 8.14 0.48 0.07 2.35 2.28 10.24 1.29 
 
 
 
 
 
 
 
 
133
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 PI 111261 34.43 1.08 9.06 0.28 93.39 7.20 0.92 0.06 19.73 2.28 9.69 1.18 
2003 CIav 3372 29.04 2.49 9.99 0.65 122.86 17.47 0.76 0.14 0.61 2.54 10.34 4.95 
2004 CIav 3372 17.10 4.29 8.06 1.13 105.36 30.22 0.49 0.23 -0.02 2.33 9.55 4.95 
2003 PI 131695 29.50 1.05 8.56 0.27 116.22 7.20 0.71 0.06 21.88 2.28 16.70 1.18 
2004 PI 131695 24.86 1.39 7.69 0.36 94.41 9.62 0.59 0.08 1.83 2.73 16.27 1.88 
2003 PI 131640 33.39 1.05 8.78 0.27 137.95 7.40 0.90 0.06 17.03 2.28 14.01 1.18 
2004 PI 131640 25.29 1.11 8.57 0.28 104.72 8.44 0.62 0.07 1.78 2.28 13.61 1.33 
2003 PI 131641 32.60 1.14 8.80 0.29 132.86 7.40 0.86 0.06 24.84 2.33 17.01 1.29 
2004 PI 131641 26.98 1.11 8.81 0.28 127.45 7.63 0.98 0.06 1.83 2.28 13.43 1.39 
2003 PI 131642 30.61 1.08 9.69 0.28 141.93 7.63 0.69 0.06 11.78 2.28 14.61 1.21 
2004 PI 131642 25.09 1.22 9.23 0.31 104.66 8.78 0.65 0.07 0.60 2.28 11.73 1.66 
2003 CIav 4639 30.06 1.05 8.83 0.27 90.17 7.20 0.78 0.06 24.34 2.28 16.65 1.21 
2004 CIav 4639 28.34 1.11 9.13 0.29 118.34 7.63 0.98 0.06 5.13 2.28 14.89 1.21 
2003 PI 158244 33.62 1.22 10.46 0.31 150.77 8.45 0.71 0.07 8.65 2.28 10.84 1.38 
 
 
 
 
 
 
 
134
 
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2004 PI 158244 25.28 1.38 9.69 0.36 111.77 10.12 0.46 0.08 0.56 2.28 9.40 2.03 
2003 PI 158246 34.10 2.49 10.99 0.65 149.11 21.38 0.85 0.17 0.00 2.73 0.00 0.00 
2004 PI 158246 27.53 1.77 9.47 0.46 145.47 12.39 0.67 0.10 -0.08 2.73 11.78 2.48 
2004 PI 274610 28.16 1.54 10.62 0.40 114.44 10.74 0.68 0.09 0.34 2.28 9.05 2.03 
2003 PI 287315 28.23 1.11 9.25 0.28 79.43 7.87 0.62 0.06 5.98 2.28 10.93 1.18 
2004 PI 287315 26.35 1.18 9.78 0.30 97.94 8.45 0.48 0.07 3.89 2.28 10.20 1.38 
2003 PI 291990 30.56 1.05 8.78 0.27 89.19 7.40 0.82 0.06 13.41 2.28 15.09 1.21 
2004 PI 291990 26.41 1.32 8.35 0.34 88.51 9.17 0.67 0.07 2.93 2.28 14.64 1.50 
2003 PI 291991 30.20 1.08 8.65 0.28 90.39 7.40 0.67 0.06 14.71 2.28 13.52 1.18 
2004 PI 291991 24.08 1.08 8.62 0.28 84.77 7.63 0.61 0.06 3.29 2.28 16.26 1.29 
2003 PI 292226 30.85 1.11 8.31 0.28 101.79 8.14 0.65 0.07 26.18 2.28 18.20 1.21 
2004 PI 292226 24.21 1.22 7.54 0.31 102.88 8.45 0.65 0.07 1.18 2.28 15.60 1.38 
2003 PI 304557 30.65 4.29 11.00 1.13 157.35 30.22 0.76 0.23 0.00 2.28 0.00 0.00 
2004 PI 304557 24.69 1.18 7.72 0.30 96.79 8.45 0.58 0.07 0.71 2.28 13.98 1.88 
 
 
 
 
 
 
135
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 PI 436080 33.20 1.08 9.41 0.28 88.37 7.40 0.72 0.06 8.65 2.28 10.28 1.18 
2004 PI 436080 31.04 1.14 8.53 0.29 109.39 7.87 0.49 0.06 3.83 2.28 12.95 1.33 
2003 PI 436082 26.43 2.49 10.66 0.65 109.37 17.47 0.52 0.14 0.03 2.28 3.11 3.50 
2004 PI 436082 25.18 1.94 9.19 0.51 111.73 13.54 0.60 0.11 -0.11 2.73 0.24 4.95 
2003 PI 436103 30.24 1.11 9.00 0.28 92.74 7.63 0.63 0.06 14.91 2.28 14.18 1.21 
2003 PI 436104 30.01 1.14 8.87 0.29 97.10 7.87 0.65 0.06 16.30 2.28 12.98 1.33 
2004 PI 436104 23.33 1.05 7.56 0.27 84.17 7.20 0.52 0.06 1.35 2.28 13.89 1.33 
2003 PI 436105 30.60 1.11 9.07 0.29 76.23 7.87 0.82 0.06 17.54 2.28 14.15 1.21 
2004 PI 436105 25.14 1.11 7.81 0.28 95.39 7.63 0.71 0.06 4.02 2.28 16.39 1.29 
2003 PI 436106 30.02 1.08 8.76 0.28 78.86 7.40 0.61 0.06 12.54 2.28 15.99 1.18 
2004 PI 436106 23.49 1.08 7.62 0.28 78.57 7.40 0.49 0.06 3.09 2.28 15.86 1.21 
2003 PI 306419 38.96 2.49 8.01 0.65 135.14 17.47 0.91 0.14 0.31 2.28 19.42 1.88 
2004 PI 306419 39.72 3.04 6.98 0.80 132.87 21.41 1.00 0.17 0.75 2.73 23.54 2.86 
2003 PI 361910 23.96 2.49 9.67 0.65 98.81 17.47 0.81 0.14 1.83 2.28 15.71 1.88 
2004 PI 361910 37.43 2.17 9.21 0.57 134.28 15.18 0.86 0.12 1.99 2.28 13.70 1.88 
 
 
 
 
 
 
136
 
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 PI 361912 31.71 2.49 7.99 0.65 139.52 17.47 1.52 0.14 4.17 2.28 23.23 1.50 
2004 PI 361912 28.64 1.77 6.70 0.46 186.19 12.39 1.21 0.10 0.84 2.28 22.07 2.48 
2003 PI 401793 27.95 1.77 9.34 0.46 126.82 12.38 0.39 0.10 0.14 2.28 5.15 2.22 
2004 PI 401793 21.22 3.04 7.98 0.80 85.37 21.41 0.30 0.17 0.00 2.28 0.24 4.95 
2003 PI 401794 22.49 1.94 8.20 0.51 87.64 13.54 0.34 0.11 -0.25 2.73 4.13 4.95 
2004 PI 401794 22.29 1.65 7.72 0.43 80.88 11.48 0.30 0.09 0.08 2.28 3.95 2.48 
2004 PI 436107 26.27 1.14 9.26 0.29 95.13 7.87 0.63 0.06 0.60 2.28 11.62 1.66 
2003 PI 436108 27.06 1.77 8.16 0.46 88.01 12.39 0.51 0.10 0.99 2.28 7.02 1.44 
2004 PI 436108 25.09 1.14 7.67 0.29 77.41 7.87 0.29 0.06 2.05 2.28 13.83 1.44 
2003 PI 436109 29.61 1.11 8.63 0.28 92.33 7.63 0.64 0.06 14.41 2.28 15.89 1.25 
2004 PI 436109 26.58 1.08 8.63 0.28 112.45 7.40 0.79 0.06 3.70 2.28 15.86 1.29 
2003 PI 436110 31.43 1.11 9.25 0.28 92.54 7.87 0.84 0.06 28.38 2.28 15.33 1.18 
2004 PI 436110 28.63 1.14 9.21 0.29 120.21 7.87 1.05 0.06 6.38 2.28 15.16 1.21 
2003 PI 436111 29.06 1.05 9.18 0.28 90.11 7.20 0.67 0.06 32.95 2.28 15.49 1.29 
2004 PI 436111 25.14 1.14 9.06 0.29 85.20 7.87 0.57 0.06 5.62 2.28 12.72 1.25 
 
 
 
 
137
 
 
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 PI 436112 27.39 2.49 9.33 0.65 97.51 17.47 0.73 0.14 0.62 2.63 14.36 4.95 
2004 PI 436112 23.30 1.94 10.46 0.51 98.16 13.61 0.41 0.11 0.02 2.73 22.93 2.49 
2003 PI 436119 30.36 1.64 10.43 0.43 80.25 11.46 0.53 0.09 0.19 2.73 11.13 2.48 
2004 PI 436119 23.78 1.18 8.64 0.30 106.19 8.14 0.53 0.07 0.92 2.40 8.87 2.03 
2003 PI 436120 34.04 2.49 7.66 0.65 169.11 21.38 1.25 0.17 1.98 2.28 13.71 1.44 
2004 PI 436120 29.37 1.38 7.90 0.36 163.83 9.61 0.80 0.08 1.05 2.28 16.49 1.76 
2003 PI 436121 33.56 1.54 8.50 0.40 163.03 10.73 0.89 0.09 1.87 2.28 15.09 1.44 
2003 PI 436122 28.46 2.16 10.00 0.56 108.97 15.13 0.59 0.12 0.02 2.83 9.47 2.03 
2004 PI 436122 25.90 1.46 7.77 0.38 99.67 10.12 0.79 0.08 2.02 2.28 14.49 1.76 
2003 PI 436124 25.79 1.46 9.89 0.38 109.06 10.12 0.56 0.08 1.88 2.28 7.21 1.50 
2004 PI 436124 22.41 1.18 9.08 0.30 84.48 8.14 0.41 0.07 1.48 2.33 7.87 1.76 
2003 PI 436125 28.69 2.16 10.75 0.57 97.23 15.15 0.56 0.12 1.38 2.28 11.04 2.03 
2004 PI 436125 26.80 1.18 8.72 0.30 93.19 8.14 0.66 0.07 1.41 2.28 12.77 1.76 
2003 PI 436127 30.05 1.46 9.00 0.38 113.18 10.13 0.75 0.08 4.71 2.28 6.77 1.38 
2004 PI 436127 23.17 1.38 8.80 0.36 94.68 10.12 0.40 0.08 0.96 2.28 8.62 1.66 
 
 
 
138
 
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 PI 436126 29.53 2.16 11.49 0.57 100.49 15.15 0.47 0.12 0.00 2.28 0.00 0.00 
2004 PI 436126 27.52 1.77 9.32 0.46 103.17 12.38 0.49 0.10 0.08 2.28 10.66 3.50 
2003 PI 436130 23.67 1.54 7.88 0.40 120.96 10.73 0.79 0.09 0.78 2.28 18.14 1.50 
2004 PI 436130 22.47 1.14 7.46 0.29 137.00 7.87 0.73 0.06 1.12 2.33 25.47 1.38 
2003 PI 436131 29.04 2.49 9.99 0.65 104.19 17.47 0.56 0.14 0.50 2.28 34.80 2.22 
2004 PI 436131 20.86 2.49 6.63 0.65 113.28 21.40 0.61 0.17 0.00 2.28 -0.15 4.95 
2003 PI 436132 27.54 1.54 9.50 0.40 112.96 10.73 0.64 0.09 1.48 2.28 7.11 1.44 
2004 PI 436132 24.81 1.54 8.60 0.40 87.45 10.74 0.48 0.09 0.02 2.28 9.43 3.50 
2004 PI 436133 21.09 1.65 7.99 0.43 114.16 11.49 0.74 0.09 0.00 2.28 -0.09 4.95 
2003 PI 436134 31.14 1.64 10.28 0.43 89.16 10.74 0.58 0.09 0.11 2.28 12.36 4.95 
2004 PI 436134 21.99 1.18 8.92 0.30 99.26 8.14 0.49 0.07 0.04 2.28 7.85 1.88 
2003 PI 436113 27.90 1.94 8.60 0.51 124.60 15.15 0.68 0.12 0.34 2.28 8.04 1.66 
2004 PI 436113 24.32 1.18 8.99 0.30 91.94 8.45 0.55 0.07 0.41 2.28 11.08 1.57 
2003 PI 436114 29.50 1.05 9.06 0.27 82.24 7.63 0.57 0.06 24.44 2.28 13.65 1.21 
2004 PI 436114 24.97 1.05 8.36 0.28 89.59 7.87 0.71 0.06 3.47 2.63 15.55 1.21 
 
 
 
 
 
139
 
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 PI 436115 31.78 1.38 8.50 0.36 156.83 13.56 0.86 0.11 0.00 2.28 12.29 4.95 
2004 PI 436115 22.71 1.22 9.53 0.31 102.63 8.45 0.61 0.07 0.02 2.28 7.88 2.86 
2003 PI 436116 35.19 1.94 8.99 0.51 133.97 13.57 0.97 0.11 -0.63 2.73 8.62 1.88 
2004 PI 436116 25.21 1.22 8.77 0.31 101.79 8.44 0.72 0.07 0.45 2.28 12.71 1.66 
2003 PI 436117 29.33 1.05 9.11 0.27 94.30 7.63 0.73 0.06 17.02 2.33 15.94 1.18 
2004 PI 436117 23.91 1.08 8.37 0.28 102.86 7.40 0.73 0.06 2.85 2.28 15.56 1.44 
2003 PI 436118 29.76 1.54 9.87 0.40 128.05 10.75 0.74 0.09 1.59 2.28 8.26 1.33 
2004 PI 436118 22.62 1.18 10.08 0.30 84.63 8.15 0.51 0.07 0.78 2.28 11.62 1.44 
2003 CIav 9019 25.04 1.32 8.82 0.34 107.42 8.78 0.89 0.07 0.43 2.28 11.28 1.38 
2003 CIav 9020 32.44 1.05 8.94 0.27 100.47 7.63 0.67 0.06 28.67 2.28 15.75 1.18 
2004 CIav 9020 25.26 1.11 8.25 0.28 92.33 7.87 0.65 0.07 2.29 2.28 14.73 1.44 
2003 CIav 9021 30.33 1.05 9.39 0.27 92.33 7.20 0.74 0.06 23.64 2.28 16.20 1.21 
2004 CIav 9021 25.15 1.22 7.91 0.31 109.15 9.17 0.81 0.08 4.27 2.28 16.15 1.44 
2003 CIav 9022 36.20 2.49 8.35 0.65 98.21 10.75 1.00 0.09 6.86 2.28 9.60 1.25 
2004 CIav 9022 26.55 1.11 8.26 0.29 85.83 7.63 0.51 0.06 2.09 2.28 9.49 1.29 
 
 
 
 
 
140
 
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 CIav 9024 29.07 1.14 8.93 0.29 74.07 7.87 0.55 0.06 4.06 2.28 11.38 1.25 
2004 CIav 9024 24.78 1.38 9.59 0.36 81.88 10.13 0.59 0.08 0.39 2.28 19.76 1.66 
2003 CIav 9030 29.81 1.54 6.87 0.40 152.15 10.73 1.13 0.09 1.29 2.28 17.01 1.44 
2004 CIav 9030 34.27 1.27 8.82 0.33 183.75 8.79 0.99 0.07 1.53 2.28 21.90 1.76 
2003 CIav 8089 29.73 1.08 8.94 0.28 86.67 7.63 0.74 0.06 21.14 2.28 13.89 1.18 
2004 CIav 8089 27.43 1.08 8.50 0.28 91.45 7.40 0.85 0.06 6.39 2.28 14.92 1.18 
2003 CIav 9007 34.17 1.05 8.89 0.27 99.89 7.20 0.93 0.06 28.23 2.28 10.43 1.18 
2004 CIav 9007 29.06 1.18 9.71 0.30 85.05 8.14 0.64 0.07 3.43 2.28 9.98 1.33 
2003 CIav 9011 35.95 1.08 8.88 0.28 112.05 7.63 0.97 0.06 27.29 2.33 9.90 1.18 
2004 CIav 9011 28.93 1.32 8.83 0.33 93.97 9.17 0.75 0.07 3.22 2.28 9.52 1.38 
2004 CIav 9012 31.38 1.18 9.50 0.30 86.48 8.14 0.65 0.07 4.68 2.28 10.56 1.38 
2003 CIav 9014 44.13 1.54 8.75 0.40 146.96 9.17 1.42 0.07 3.35 2.28 11.27 1.38 
2004 CIav 9014 36.08 1.46 7.76 0.38 200.55 10.13 1.04 0.08 1.35 2.28 18.46 1.66 
2003 CIav 9015 18.60 1.18 4.50 0.30 220.04 8.78 0.72 0.07 6.24 2.28 16.86 1.33 
2004 CIav 9015 17.70 1.94 3.37 0.51 171.31 17.47 0.51 0.14 0.62 3.77 13.22 4.95 
 
 
 
 
141
 
Appendix 1. Year x accession least squares interaction means.  
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 CIav 9066 36.42 1.94 9.79 0.51 123.07 13.60 1.24 0.11 0.00 2.28 6.79 3.50 
2004 CIav 9066 28.80 1.54 10.99 0.43 122.34 10.73 0.51 0.09 0.33 2.84 7.87 3.51 
2003 CIav 9110 37.73 1.14 9.13 0.29 113.42 7.40 0.91 0.06 16.48 2.28 9.44 1.21 
2004 CIav 9110 31.08 1.54 8.26 0.40 104.20 10.74 0.47 0.09 1.63 2.28 9.07 2.03 
2003 CIav 9112 36.65 1.78 8.16 0.46 141.35 12.44 0.95 0.10 12.53 2.28 9.02 1.18 
2004 CIav 9112 29.59 1.27 8.16 0.33 125.08 8.78 0.65 0.07 2.21 2.28 12.89 1.57 
2003 CIav 9116 35.13 1.14 9.47 0.29 123.33 7.63 0.95 0.06 30.97 2.28 9.51 1.18 
2004 CIav 9116 31.79 1.27 9.75 0.33 98.24 8.78 0.58 0.07 4.56 2.28 10.26 1.50 
2003 PI 274608 35.52 1.22 10.00 0.31 141.79 8.80 0.99 0.07 31.96 2.28 15.73 1.38 
2004 PI 274608 27.64 1.08 9.00 0.28 134.63 7.40 0.87 0.06 2.34 2.28 13.53 1.44 
2003 PI 274609 31.06 1.05 10.78 0.27 143.72 7.20 0.68 0.06 13.07 2.28 14.33 1.18 
2003 CIav 9031 25.36 1.94 7.99 0.51 128.05 13.57 0.79 0.11 1.81 2.28 19.75 1.66 
2004 CIav 9031 26.21 1.22 7.69 0.31 202.48 8.44 0.89 0.07 1.12 2.28 20.75 1.57 
2003 CIav 9035 21.65 4.29 10.00 1.13 68.35 30.22 0.46 0.23 0.37 2.28 7.47 1.57 
2004 CIav 9035 27.77 1.18 8.43 0.30 125.67 7.87 0.79 0.06 0.76 2.28 12.94 1.58 
 
 
 
 
 
142
Appendix 1. Year x accession least squares interaction means. 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 CIav 9038 21.15 3.05 11.00 0.80 60.85 21.41 0.46 0.17 0.05 2.28 11.70 2.87 
2004 CIav 9038 26.65 1.46 8.34 0.38 85.55 10.12 0.48 0.08 0.12 2.28 11.23 1.88 
2003 CIav 9043 32.45 1.54 9.76 0.40 113.99 10.75 0.82 0.09 1.84 2.28 8.57 1.25 
2004 CIav 9043 27.50 1.39 8.57 0.36 116.23 9.63 0.74 0.08 0.75 2.73 5.41 1.76 
2003 CIav 9064 30.35 1.11 5.56 0.28 126.93 7.87 0.66 0.06 4.99 2.28 12.28 1.25 
2004 CIav 9064 24.81 1.32 5.36 0.34 122.33 9.17 0.62 0.07 0.21 2.73 11.02 2.48 
2003 PI 158247 28.50 1.05 8.11 0.27 120.74 7.63 0.73 0.06 51.54 2.28 19.60 1.18 
2004 PI 158247 27.89 1.11 7.93 0.30 116.02 8.45 1.13 0.07 3.07 2.28 15.87 1.29 
2003 CIav 5057 31.30 1.54 10.25 0.40 108.20 10.75 0.69 0.09 1.18 2.28 6.91 1.38 
2004 CIav 5057 27.17 1.32 9.09 0.34 87.60 9.17 0.49 0.07 0.20 2.28 14.05 2.22 
2003 CIav 5082 30.54 1.08 8.94 0.28 105.07 7.40 0.76 0.06 17.82 2.28 16.33 1.21 
2004 CIav 5082 25.81 1.14 8.59 0.29 118.09 8.45 0.78 0.07 2.83 2.28 16.37 1.38 
2004 CIav 6858 25.20 1.11 8.37 0.28 91.77 7.63 0.69 0.06 3.30 2.28 16.62 1.29 
2003 PI 186606 28.86 1.11 8.87 0.28 85.24 7.63 0.62 0.06 11.86 2.28 14.36 1.21 
2004 PI 186606 22.84 1.05 7.65 0.28 87.71 7.63 0.59 0.06 4.79 2.28 16.55 1.29 
 
 
 
 
 
 
143
 
Appendix 1. Year x accession least squares interaction means. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
    Panicle  Flag  leaf Seed 
Year NPGS no. Length SE Node number SE Length SE Width SE Yield SE Mass SE 
2003 CIav 6956 29.73 1.08 9.06 0.28 94.33 7.20 0.63 0.06 10.19 2.28 16.03 1.29 
2004 CIav 6956 26.89 1.11 8.44 0.28 100.58 7.63 0.80 0.06 3.48 2.28 15.58 1.21 
2003 CIav 7010 29.85 1.11 9.00 0.28 93.83 7.40 0.64 0.06 12.06 2.28 12.35 1.21 
2004 CIav 7010 24.48 1.22 8.08 0.33 104.41 8.44 0.76 0.07 2.04 2.28 14.77 1.38 
2003 CIav 7121 32.08 1.22 9.23 0.31 111.72 8.45 0.74 0.07 3.72 2.28 10.96 1.38 
2004 CIav 7121 28.17 1.94 9.98 0.51 124.49 13.57 0.68 0.11 0.79 2.73 9.88 2.22 
2003 CIav 7122 27.66 1.14 9.60 0.29 80.12 8.14 0.70 0.07 3.28 2.28 9.31 1.25 
2004 CIav 7122 25.18 1.39 9.57 0.36 86.61 9.63 0.62 0.08 1.51 2.73 10.07 1.58 
2003 CIav 7280 31.39 1.05 9.00 0.27 88.96 7.63 0.61 0.06 13.50 2.28 13.59 1.18 
2004 CIav 7280 25.50 1.05 8.44 0.27 108.72 7.20 0.81 0.06 5.22 2.28 15.55 1.18 
2003 CIav 8087 25.36 1.77 9.50 0.46 86.75 12.38 0.68 0.10 1.54 2.28 9.54 1.66 
2004 CIav 8087 25.48 1.38 9.54 0.38 115.13 9.61 0.73 0.08 0.06 2.28 15.01 2.22 
2003 SoilSaver 28.31 1.18 9.33 0.33 117.11 8.15 0.63 0.07 5.02 2.28 9.96 1.29 
2004 SoilSaver 25.41 1.08 9.76 0.28 114.52 7.63 0.69 0.06 1.12 2.28 13.28 1.50