ADAPTING REDUCED TILLAGE SYSTEMS FOR ORGANIC PRODUCTION: 
UTILIZING STRIP
-
TILLAGE AND ALTERNATIVE COVER CROP SPATIAL 

ADOPTION
 
 
By 
 
 
Carolyn
 
Jane
 
Lowry
 
 
 
 
 
 
 
 
 
A DISSERTATION
 
 
Submitted to
 
Michigan S
tate University
 
in partial fulfillment of the requirements
 
for the degree of
 
 
Horticulture

Doctor of Philosophy
 
 
2015
 
 
 
 
 
ABSTRACT
 
 
ADAPTING REDUCED TILLAGE SYSTEMS FOR ORGANIC PRODUCTION: 
UTILIZING STRIP
-
TILLAGE AND ALTERNATIVE COVER CROP SPATIAL 
ARRANGEMEN

ADOPTION
 
 
By 
 
 
Carolyn Jane Lowry
 
 
Reduced tillage (RT) practices provide a number of ecological and agronomic 
benefits, but may also result in reduced inor
ganic nitrogen (N) availability
 
and greater 
weed compet
ition. These challenges are especially dif
ficult on organic farms, where 
fertilizer and herbicide options are limited
. My dissertation aims to adapt RT systems for 
organic production by addressing critical barriers to adoption: N deficiency and weeds. 
Firs

practices and 
attitudes towards RT
. Second, I evaluated the effect that one form of 
reduced tillage, strip
-
tillage (ST), has on N dynamics with
in an organic system
. Finally, I 
e
valuated how varying cover crop spatial arrangements within ST might mediate RT 
challenges; specifically how strip
-
intercropping of cereal rye and hairy vetch affects 
cover cr
op biomass and N
, s
oil and sweet corn N, and weeds
.  
 
Our survey documented a wid
e range of current tillage frequencies associated 
with produ
ction of specific organic crops.
 
Despite an overall high level of awareness
 
of 
the benefits of RT
, interest in adoption of specific RT practices among organic farmers 
was fairly low. Among RT opti
ons, vegetable farmers were most interested in 
permanent beds and strip
-
tillage and field crop producers in rotational tillage, followed 
by strip
-
till
age
. The greatest perceived barriers to RT adoption were weeds, residue 
management, crop establishment, im
pact on yields and obtaining RT equipment. 
 
 
 
Strip
-
tillage (ST) confines soil disturbance 
and incorporation of organic residues 
to a narrow str
ip directly within the crop row
. We conducted field s
tudies in s
outhwest 
Michigan from 2011 to 2014 to examine the
 
effects of tillage (ST vs full
-
width tillage 
[FWT]), on soil N and weeds under various levels of cover
 
cropping and weed 
management. 
Compared to FWT, ST had lower inorganic N availability both within and 
between crop rows. Additionally, ST increased the 
c
oncentration of N within the soil 
leachate and the 
potential for N to be lost through denitrification. Despite lower soil N, 
ST only negatively impacted sweet corn biomass and N uptake in 1 out of 3 years and 
increased
 
yields in 1 out of 3 years. ST had va
rying effects on weed emergence and 
biomass, which was largely dependent on cover crop biomass. In 20
12, ry
e
-
vetch 
biomass was between 7.0
 
and 8
.0 M
g 
ha
-
1
, and reduced weed emergence and biomass
 
within ST
, resulting in greater sweet corn yield under low we
ed management. However, 
in 2013 rye
-
vetch biomass was
 
approximately 35% lower
, weed biomass higher, and 
yields reduced in ST compared to FWT. 
 
To address the challenges of residue management and N deficiency within 
organic RT, we compared standard full
-
wid
th mixtures of rye and vetch cover crops, to a 

-

crop rows, and vetch planted only in
-
line with future crop rows. Strip
-
intercropping 
resulted in a lower C:N ratio of residu
e and higher N mineralization within the crop zone, 
but had no effect on weeds, or corn yield, biomass, or N uptake. To increase the 
potential benefits of strip
-
intercropping, future 
research 
efforts should focus on cover 
crop mixtures and termination meth
ods which minimize the movement of shoot tissue 
across zones.  
 
 
iv
 
 
ACKNOWLEDGEMENTS
 
 
This work would not have been possible without the help of many people. First, I 
must thank my advisor, Dan Brainard for putting up with me over the past 5.5 years and 
for t
eaching me, among other things, the value of a good topic sentence. From carrot 
musical instruments to carpetweed wigs, you definitely made things fun. I look forward 
to our future collaborations. 
 
Second, I must thank the members of my advisory committee,
 
Mathieu Ngouajio, 
Phil Robertson, Sasha Kravchenko, and John Kerr. I feel very fortunate to have chosen 
some of the five kindest people to comprise my committee. You have all made stressful 
situations much easier, and this work would not have been possibl
e without your 
thoughtful advice and encouragement. 
 
I owe a huge debt to the members of the Brainard lab for all their help over the 
years. Particular thanks to Erin Haramoto, for taking me under her wing and being my 
go to troubleshooter, as well as to Z
ack Hayen. You both gave me an inflated idea of 
what a graduate student should strive to accomplish, but your examples inspired me to 
work both harder and smarter. I would like to thank our lab technician, Corey Noyes, for 
laboratory and field assistance. 
This work would have been impossible without the small 
army of rotating Brainard lab undergrads that have helped me over the years. 
 
I also received both field and laboratory advice from a number of people 
throughout MSU including Joe Simmons, and the enti
re staff at the KBS Farming 
Systems Center, as well as Todd Martin, Kevin Kahrmark, and John Dahl.
 
 
v
 
 
I am also in debt to a number of graduate students for professionally and 
personally enriching my years at MSU. I would particularly like to thank Christine 
Sprunger, Bonnie McGill

Erin Hill, 
and Aaron Yoder
.
 
I would also like to thank my family, especially my parents for all their love and 
support, and for their constant reminder that at some point in my life I will eventually 
need to get a 
real job. I am fortunate to have many other family members who without 
their love and support this would not be possible, particularly my grandparents. 
 
Finally, I would like to thank my wonderful partner Jason Keagy. The best part 
about coming to MSU was 
meeting you, and I am very fortunate to have you in my life. 
 
 
 
 
vi
 
 
TABLE OF CONTENTS
 
 
 
 
LIST OF TABLES

.
..
.
.....
 
x
 
 
LIST OF FIGU

.
...
 
xiii
 
 
KEY TO ABBRE

.


.
 
xviii
 
 
INTRODUC
T


..
.
 
1
 
 
LITERATU

.
..
....
.
8
 
 
CHAPTER ONE

.

.
..
12
 
Michigan organic farmer perceptions of the barriers and benefits of reduced
-
tillag
e: 
Results from a farmer survey


 
12
 
 
ABSTRA


..
 
12
 
INTRODU

.
.
...
 
13
 
 
Study system: Organic
 

..
.
..
 
17
 
MATERIALS A


.
...
 
17
 
Survey dev


.
..
...
 
17
 
Survey con

.
...
.
..
..
18
 
Survey impleme

.

.

..
 
20
 

.

.

..
21
 


.
.
...
 
22
 
Demograph

.
..
..
 
22
 
Farm geographic distribution
 

.

..
 
23
 
Tillage and cultivat

.

.

..
 
24
 


.
.
...
 
25
 


.

.

...
 
26
 
Grower inte
rest and kn

...
 
27
 
Perceived costs and be

.

..
 
28
 
Factors associated with at

.

...
 
29
 


.
...
..
 
31
 
Survey 

.

..
 
31
 
Fa
ctors influencing interest in
 

.

..
 
32
 
Constraints to adoption and implication
s for research and extension.
...
 
32
 
Current level of tillage on
 

...
..
 
33
 


..
 
35
 
Perc
eived barriers to R

.
.....
...
 
37
 
Implications for future 

..

..
 
37
 

....
 
39
 
APPENDIX

.


..
 
41
 

.

.

.
..
5
7
 
 

..

..
 
64
 
 
vii
 
 
Strip
-
tillage influences N availability and loss
es in an organic system

 
64
 
 


.
..
...
 
64
 
INTRODUCTI

.
.
...
 
65
 
MATERIALS AND 


.

...
 
69
 
Site description and exp

.

...
 
69
 
Data collec

..

..
 
71
 
Potentially m
ine

.

...
 
71
 
Bulk d

.

...
 
71
 
Soil sampling
-
 
Inorganic N and 
g

.
.
...
 
72
 
Sweet corn biomass and N
 

.

..
 
72
 
Leachate in

.

...
 
73
 
Volumetric so

.

...
 
74
 
Denitrificatio


.
..
 
74
 
Statistical anal


...
 
75
 


..
 
76
 
Cover crop and weed biom

..
 
76
 
Potentially m
inera

..
 
76
 
Inorganic N availa


..
 
7
7
 
Gravimetric s
oil mois

..
 
78
 
Volumetric soil 

...
 
79
 
Sweet corn biomass a

.
...
 
79
 
Soil leacha

.
..
 
81
 
Denitrification pot

.
..
 
81
 
DISCUSSION




.
..
 
82
 
Potentially mineralizeable N a


..
 
82
 
Sweet corn biomass 


....
 
83
 
Soil leachate N and denitrif

.
...
 
84
 
SUMMARY AND CON




........
.
...
 
87
 
AP


........
...
 
89
 
LITERATURE CIT


......
...
 
105
 
 
 
CHAPTER THREE

...

..
..
 
113
 
Strip
-
intercropping of rye
-
vetch mixtures affects biomass, C:N ratio and spatial 
dis
tribution of cover crop 
residue

 
113
 
 


...

.
...
 
113
 
INTRODUCTIO

...


...
114
 
MATERIALS AND M

.
...
 
119
 
Site Description and Expe

.
..

..
 
119
 
Experiment 1: Cover crop
 

..

.
....
119
 
Experiment 2: Spatial distribution and C:N ratio of 
 
root and shoot t


...
 
120
 
Data collect


...
 
121
 
Cover crop abov
eground bioma


..
 
121
 
Root b


..
 
122
 
Hairy vetch percent
 

..
 
122
 
Hairy vetc


..
 
123
 
 
viii
 
 
Percent lod


..
 
123
 


.
..
 
123
 
Statistical analy

..
 
123
 


.
..
 
124
 
Growing Degree Days and 
Precipitatio

.
..
 
124
 
Experiment 1: Cover crop pr

.
..
 
125
 
Cover crop aboveground biomass


.
....
 
125
 
Vetch he


.....
 
126
 
Vetch flo


.....
 
127
 
Experiment 2: Spatial distribution and C:N ratio of root and 
 
 
           
shoot tissu


.....
 
127
 

..
.....
 
127
 
Shoot biomass spatial 


.....
 
127
 
Shoot N conte


.....
 
128
 
C:N ratio of shoot
 

.
......
.
 
128
 
C:N ratio of root t

.
......
 
129
 


..
.
.
....
 
129
 
Spatial arrangement and cove


......
 
129
 
Implications for weed s


.....
 
131
 
Implications for N ma


.....
 
131
 
Practical limitations of strip
-
i

....
.....
 
133
 
SUMM
ARY AND CONCLU


.....
 
134
 
APPE


..
.
....
 
136
 
LITERATURE CITED


.....
 
148
 
 
CHAPTER FOUR


.....
 
154
 
Rye and vetch spatial arrangement and strip
-
tillage influe
nce N availabil
ity and 
 
sweet corn root growth


 
154
 


...
...
.
.
 
154
 



.....
155
 
MATERIALS AND METH


.....
 
159
 
Site description and ex
peri
menta


.....
 
159
 
Data collection


.....
 
161
 



.....
 
161
 
Bulk densi


....
 
161
 
Soil sampling
-
 
Inorganic N and gra
vimetric soil moisture

....
 
162
 
Swe
et corn aboveground bio
mass
 


....
162
 
Sweet corn belowg


.....
 
163
 
Statistical anal


......
 
164
 



......
 
165
 
Potentially minerali


.....
.
 
165
 
Soil Inorganic
 


.....
 
165
 
Bulk densit


....
.
.....
 
167
 



.....
 
167
 
Sweet corn aboveground bio


.

.....
167
 
Sweet corn belowgr
ound bi


.....
 
168
 
 
ix
 
 
Sweet corn root morp

.....


 
169
 


...
.....
 
170
 
Strip
-
tillage effects on PMN, soil inorga


.....
.
 
170
 
Cover crop spatial arrangement effects on PMN, soi
l inorganic N, 
 
and sweet cor


.
 
172
 
SUMMARY AND CON



.....
..
 
174
 
AP


......
...
 
176
 
LITERATURE CIT


.....
 
192
 
 



..
....
198
 
Cover crop spatial arrangement and strip
-
tillage effects on weed emergence, 
 
growth and competition with organic swee
t corn
.......................................
..............
..
 
198
 


...
.....
 
198
 




.....
 
199
 
MATERIALS AND METH


.....
 
203
 
Site description and expe


.

....
 
203
 
Data collecti



......
 
206
 
Cover crop and wint


.....
 
206
 
So
il sampling
-
 
Inorganic N and gravimetric soil moisture

.
.....
 
206
 
Soil tempe

..
.....
 
207
 
Weed e


 
207
 
Weed b


 
208
 
Sweet corn biomas


.........
 
208
 
S
tatistical analy

...
.....
 
208
 


..
......
.
 
209
 
Precipitation and t

...
.....
 
209
 
Effects of cover crop spatial arrangement on cover crop and weed 
biomass


.
.....
 
210
 
Effects of cover crop spatial arrangement on soil inorganic N, soil 
moisture, weeds, and
 

.
.........
 
211
 
Effects of tillage and weed management on soil inorganic N, 
 
 
moisture, and tem


.........
 
212
 
Effects of tillage on weed eme

......
213
 
Effects of tillage on sweet corn biomass,
 
N
-


......
 
214
 



.....
 
215
 
 
Rye and vetch spatial 


...................
 
21
5
 
Strip
-
tillage effects on weed


........
 
215
 
SUMMARY AND CONCLUSI


......
.
 
218
 
APPEN

........
..
 
220
 
LITERATURE CIT


...........
 
236
 
 
x
 
 
LIST OF TABLES
 
 
 
 
Table 1
.1. STIR values used for tillage operations reported by organic farmer 
respondents for crop production.

.
.......
 
42
 
 
Table 1.2. Survey respondent demographics broken down by crop type.


.......
.
 
43
 
 
Table 1.3. Tillage and cultivation prac
tices employed for the production of field
 
 
corn, dry bean, and soybean.


 
45
 
 
Table 1.4. Tillage and cultivation practices employed for the production of winter 
 
squash and 
broccoli.


 
46
 
 
Table 1.5. Spea
rman correlation of farm and farmer characteristics, as well as 
perceived barriers and benefits of RT adoption, with expressed interest in 
adoption of specific reduced till practices.

.
.....
.
47
 
 
Table 1.6. Results from a factor analysis of th
e perceived barriers to adoption of 
 
RT, as well as correlation with maximum expressed interest in RT adoption
.
..
 
48
 
 
Table 1.7. Coding used for percent clay and percent silt soil types for correlations
 
 
in Table 1.4.



.......
 
49
 
 
Table 2.1. 
Table of Activities and Operations


........
 
90
 
 
Table 2.2. P values from a three
-
way ANOVA for inorganic N across the whole 
 
CORN and NOCORN subplots (weighted average of WR and BR) in 2012, 
 
2013, and 2014. Fixed effects incl
ude tillage (full
-
width and strip
-
tillage),
 
 
cover crop (Rye
-
vetch and NoRV), 
and date as a repeated measure.

 
91
 
 
Table 2.3. P values from a four
-
way ANOVA for inorganic N within NOCORN 
 
subplots in 2012, 2013, and 2014. Fixed effects include tillag
e (full
-
width 
 
and strip
-
tillage), cover crop (Rye
-
vetch and NoRV), zone (WR and BR), 
 
a
nd date as a repeated measure.




 
92
 
 
Table 2.4. P values from a three
-
way ANOVA for gravimetric soil moisture across 
 
the whole CORN subplot (
weighted average of WR and BR) in 2012, 2013, 
 
and 2014. Fixed effects include tillage (full
-
width and strip
-
tillage), cover 
 
crop (Rye
-
vetch and NoRV), and date as a repeated measure



.
 
93
 
 
Table 2.5. P values from a three
-
way ANOVA for volumetric
 
soil moisture within 
 
CORN subplots at three different depths (0.1 to 0.3 m, 0.4 to 0.7 m, and 0.8 
 
to 0.9 m) in 2012, 2013, and 2014. Fixed effects include tillage (full
-
width 
 
 
xi
 
 
and strip
-
tillage), cover crop (Rye
-
vetch and NoRV), and date as a 
 
repeated 
measure.

..
...............................................................................
 
94
 
 
Table 2.6. P values from a two
-
way ANOVA for sweet
 
corn nonharvested and corn 
 
ear 
biomass, as well as sweet corn N content in 2012
, 2013, and 2014. 
 
Fix
ed effects 
include tillage (full
-
width and strip
-
tillage) and cover crop 
 
(Rye
-
vetch and 
NoRV).




 
95
 
 
Table 3.1. Dates of key field operations and data collection

...
 
137
 
 
Table 3.2. Cereal rye and hairy vetch
 
spatial arrangement treatments for 
 
Experiment 1 and Experiment 2



 
138
 
 
Table 3.3. Monthly GDD accumulation during cereal rye and hairy vetch growth 
 
for both Experiments 1 and 2 at the Kellogg Biological Station in Hickory 
 
Corne
rs,
MI


.....
 
139
 
..
 
Table 3.4. Experiment 1. Significanc
e of fixed effect of cover crop 
spatial 
 
arrangement (MIX, 
SEG1, and SEG2) on vetch, rye, 
and total biomass.
.
.

140
 
 
Table 3.5. Experiment 2. Significance of fixed effect
 
of cover crop spatial 
 
arrangement (MIX vs. SEG2) on biomass, C:N, and N content of rye and 
 
vetch in mixtures within the vetch zone (VZ), rye zone (RZ) and across 
 
the whole plot (WP)
....
...................................................................
..................
 
141
 
 
Table 3.6. Experiment 2. Mean (+SEM) C:N ratio of cereal rye and hairy vetch 
 
shoots due to cover crop spatial arrangement in 2012, 2013, and 2014

..
...
 
142
 
 
Table 4.1. 
Dates of key field activities and data collection

..

...
 
177
 
 
Table 4.2. P values from a three
-
way ANOVA for inorganic N across the whole 
 
CORN and NOCORN subplots (weighted average of WR and BR) in 2012, 
 
2013, and 2014. Fixed effects include tillage (full
-
width and strip
-
tillage), 
 
rye and vetch spatial ar
rangement (Mixed and Segregated), and date 
 
as a repeated 
measure



..
......
178
 
 
Table 4.3. P values from a four
-
way ANOVA for inorganic N within NOCORN 
 
subplots in 2012, 2013, and 2014. Fixed effects include tillage (full
-
width 
 
and s
trip
-
tillage), rye and vetch spatial arrangement (Mixed and 
 
Segregated), zone (within
-
 
and between
-
row), and date as a repeated 
measure.


..
.
.............
.
.............................................................
 
179
 
 
Table 4.4. Mean (+/
-
 
SEM) 
bulk density in the top 0.25 cm of soil in 2012, 2013, 
 
and 2014. Fixed effects i
nclude tillage (full
-
width and 
strip
-
tillage) and 
 
zone (within
-
 
and between
-
row)


..

..................................
.
 
180
 
 
 
xii
 
 
Table 4.5. P values from a three
-
way ANOV
A for gravimetric 
soil moisture in 
 
CORN 
subplots. Fixed effects include tillage (full
-
width and strip
-
tillage) 
 
and rye and vetch spatial arrangement (Mixed and Segr
egated), and 
 
date as a repeated 
measure


.

181
 
 
Table 4.6. P values 
from a two
-
way ANOVA for sweet corn nonharvested and 
 
ear biomass, as well as N content from 2012, 2013, and 2014. Main 
 
effects include tillage (full
-
width and strip
-
tillage) and rye and vetch 
 
spatial arrangement (Mixed and Segregated).

..
182
 
 
Table 4.7. P values from a three
-
way ANOVA for root mass, root mass ratio, 
 
specific root length (SRL), average root diameter (Diameter), and root 
 
length density (RLD) within the within
-
row (WR
-
 
7.5 cm from plant) and 
 
between
-
row (BR
-
 
25 cm from 
plant). Main effects include tillage (full
-
 
width and strip
-
tillage), rye and vetch spatial arrangement (Mixed and 
Segregated), and year (2013 and 2014).


.
...
183
 
 
Table 5.1. 
Dates of key field activities and data collection

..
......
 
221
 
 
Table 5.2. Average temperature, GDD, and precipitation during sweet corn 
 
growth in 2012 and 2013


 
222
 
 
Table 5.3. P values from a four
-
way AN
OVA for the effects of tillage 
(fill
-
width 
 
and strip
-
tillage), cover crop spat
ial arrangement (Mixed and Segregated), 
 
weed manageme
nt (High and Low), and date on 
soil inorganic N across 
 
the whole pl
ot (weighted average of WR and 
BR) during  (weighted 
 
average o
f 
WR and BR) the 2012 and 2013 sweet corn growing 
season
.

. 
 
2
23
 
 
 
Table 5.4. P values from a four
-
way AN
OVA for the effects of tillage 
(fill
-
width 
 
and strip
-
tillage), cover c
rop spatial arrangement (Mixed 
and Segregated), 
 
weed manageme
nt (High and Low), and date on 
gravimetric soil moisture 
 
across the whole plot 
(weighted average 
WR and BR) during the 2012 
 
and 2013 sweet corn growing season
..

.
224
 
 
Ta
ble 5.5. P values for a four way ANOVA for the effects of tillage (fill
-
width and 
 
strip
-
tillage) zone (within
-
 
and between
-
row), and date on s
oil temperature 
 
from sensors buri
ed 5 cm below the soil surface.

........
....
225
 
 
Table 5.6. P values for a two
-
way ANOVA fo
r the fixed effects of tillage 
(full
-
 
width and strip
-
tillage) and zo
ne (within
-
and between
-
row) on 
percent 
 
emergence o
f sown 
Chenopodium album
 
and 
Setaria faberi
 
seeds in 
 
2012 and 2013
.


.
...............................................................................
 
226
 
 
 
 
xiii
 
 
LIST OF FIGURES
 
 
 
 
Figure 1.1. Principle crop, acreage and soil type of survey respondents by
 
 
Mich
igan 
region.




........

 
50
 
 
Figure 1.2. Percentage of organic field crop and vegetable producers in Michigan 
 
that have access to tillage and 


...
 
51
 
 
Figure 1.3. Distributions of the number
 
of tillage passes utilized for cultivation (A), 
 
field preparation (B), and cumulative total (C) for broccoli, winter squash, 
 
field corn, dry bean, and soybean. The y
-
axis is the probability density of 
 
a given number of tillage passes, and was determine
d using kernel 
 
density estimation.

...

..............................................................................
 
52
 
 
Figure 1.4. Distributions of the cumulative STIR values for tillage utilized in the 
production of broccoli, winter squash, field co
rn, dry bean, and soybean. 
 
The y
-
axis is the probability density of a given number of tillage operations, 
 
and was 
determined using kernel density 
estimation.

 
53
 
 
Figure 1.5. Distribution and median (


st 
 
in adoption (left) and perceived knowledge of specific RT practices (right) 
 
on a 0 to 7 Likert scale. The width of violin plots represents the range of 
 
interest, and the height at any point represents the proportion of 
 
respondents with that score. 0
= No interest/ knowledge, 7=Extremely 
 
interested/ knowledgeable.

...

............................................................
 
54
 
 
Figure 1.6. Distribution and median (

) of survey respondents perceptions of the 
benefits to RT adoption on a 0 to 7 
Likert scale. The width of violin plots 
represents the range of interest, and the height at any point represents the 
proportion of respondents with that score. 0= Not a likely benefit, 
 
7=Extremely likely benefit.


...

 
55
 
 
Figure 1.7. 
Distribution and median (

) of survey respondents perceptions of the 
barriers to RT adoption on a 0 to 7 Likert scale. The width of violin plots 
represents the range of interest, and the height at any point represents 
 
the proportion of respondents with th
at score. 0= Not a likely barrier, 
 
7=Extremely likely barrier.

....
.
 
56
 
 
Figure
 
2.1. Mean (+/
-
 
SEM) 
in situ
 
potentially mineralizeable N within the top 
 
25 cm of soil from planting to midseason (Early, left) and Midseas
on to 
 
Harvest (Late, right) over 2012, 2013
, and 2014 combined.
.


...

 
96
 
 
Figure
 
2.2. Mean (+/
-
 
SEM) inorganic N availability in the top 20 cm of soil across 
 
the whole subplot (weighted average of WR and BR), with Corn (top) and 
 
 
xiv
 
 
without corn (
No Corn, bottom) in 2012, 2013, and 2014. Inorganic N 
comparisons include full
-
width tillage (FWT) and strip
-
tillage (ST) 
 
combined with either no cover crop (NoRV) or a rye and vetch 
 
mixture (Rye
-
Vetch)

...

.......................................
......................
.
 
97
 
 
Figure
 
2.3. Mean (+/
-
 
SEM) inorganic N availability in the top 20 cm of soil both 
 
within (WR) and between crop rows (BR) in subplots without corn (No 
 
Corn) in 2012, 2013, and 2014. Inorganic N comparisons include full
-
width 
 
t
illage (FWT) and strip
-
tillage (ST) combined with either no cover crop 
 
(NoRV) or a rye 
and vetch mixture (Rye
-

.

 
98
 
 
Figure 2.4. Mean (+/
-
 
SEM) gravimetric soil moisture in the top 20 cm of soil 
 
across the whole subplot (weighted a
verage of IR and BR) with Corn (top) 
present in 2012, 2013, and 2014. Gravimetric soil moisture comparisons 
 
include full
-
width tillage (FWT) and strip
-
tillage (ST) combined with either 
 
no cover crop (NoRV) or a rye and vetch mixture (Rye
-
Vetch)

 
99
 
 
Figure 2.5. Mean (+/
-
 
SEM) volumetric soil moisture in subplots with corn (Corn) 
 
present in 2013, and 2014. Measurements were taken every 10 cm, and 
 
depths were combined according to: 0.1 to 0.3, 0.4 to 0.7, and 0.8 to 0.9 
 
meters. Volumetric soil mo
isture comparisons include full
-
width tillage 
 
(FWT) and strip
-
tillage (ST) combined with either no cover crop (NoRV) 
 
or a rye and vetch mixture (Rye
-
Vetch).


....
...
 
100
 
 
Figure 2.6. Mean (+/
-
 
SEM) sweet corn biomass (top) and N content (bott
om) of 
 
both nonharvested biomass and cor
n ears in 2012, 2013, and 2014.
 
Comparisons of fixed effects include full
-
width tillage (FWT) and 
 
strip
-
tillage (ST) combined with either no cover crop (NoRV) or a rye and 
 
vetch (RV) mixture

..

..............
....................................................
 
101
 
 
Figure 2.7. Precipitation in Hickory Corners, MI (top) and mean (+/
-
 
SEM) 
 
inorganic N within the soil leachate (bottom) during the sweet corn 
 
growing season in subplots with corn in 2012, 2013, an
d 2014. Sampled 
 
at a depth of 1.2 m. Tillage comparisons include full
-
width tillage (FWT) 
 
and strip
-
tillage (ST)

..

.......................................................................
 
102
 
 
Figure 2.8. Mean (+/
-
 
SEM) denitrification potential in bo
th full
-
width (FWT) and 
 
strip
-
tillage (ST). Data is a compilation of 2013 and 2014, and three time 
 
points (Planting, Midseason, and Harvest). Sampled to a depth of 20 cm

 
103
 
 
Figure 2.9. Mean (+/
-
 
SEM) denitrification potential due to tillage (A and B
) and 
 
cover crop (C and D) effects within different zones in the cropping system 
(WR=within corn row, BR=between crop rows). Tillage comparisons 
 
include full
-
width tillage (FWT) and strip
-
tillage (ST); cover crop 
 
comparisons include no cover crop (NoRV)
 
or a rye and vetch (RV) 
 
mixture. Sampled to a depth of 20 cm.

..
.
...................................................
 
10
4
 
 
xv
 
 
 
Figure 3.1. Planting arrangement of cereal rye and hairy vetch seeds among 
 
the different rye and vetch spatial arrangements in Ex
periments 1 and 2: 
 
1) the standard Mixed Biculture (MIX), 2) strip
-
intercropping with 2 rows 
 
of rye and 2 rows of vetch (SEG2), and 3) strip
-
intercropping with 1 row of 
 
vetch and 3 rows of rye (SEG1)


..
......
.
 
143
 
 
Figure 3.2. Experiment
 
1. Mean (+/
-
 
SEM) of hairy vetch, cereal rye, and total 
aboveground biomass among mixtures with varying spatial arrangements, 
compared to a rye (dotted line) and vetch (dashed line) monoculture in 
 
Year 1 (2011
-
2012), Year 2 (2012
-
2013), and Year 3 (2013
-
 
2014). 
 
Spatial arrangements include the standard MIX biculture, compared to 
 
two forms of segregation: SEG1 (3 rows of rye, 1 row of vetch), and 
 
SEG2 (2 rows of rye, 2 rows of vetch). We did not have a ry
e 
 
monoculture treatment in Year 
1.

.
.........
....................................................
144
 
 
Figure 3.3. Experiment 1. Mean (+/
-
 
SEM) of hairy vetch height within rye
-
vetch 
mixtures with varying spatial arrangements, compared to a vetch 
 
monoculture in Year 2 (2012
-
2013) and Year 3 (2013
-
20
14). Spatial 
arrangements include the standard Mix biculture, compared to two 
 
forms of segregation: SEG1 (3 rows of rye, 1 row of vetch), and SEG2 
 
(2 rows of rye, 2 rows of vetch)


......

..................................................
 
145
 
 
Figure 3
.4. Experiment 2. Mean (+/
-
 
SEM) hairy vetch and cereal rye 
 
aboveground biomass within the vetch zone (VZ) and rye zone (RZ) 
 
of a rye
-
vetch mix biculture (MIX) and segregated (SEG2) into two rows 
 
of rye and two rows of vetch in Year 1 (2011
-
2012), Year 
2 (2012
-
2013), 
 
and Year 3 (2013
-
 
2014)
.
.


..
 
146
 
 
Figure 3.5. Experiment 2. Mean (+/
-
 
SEM) C:N ratio of cereal rye and hairy 
 
vetch mixture aboveground (shoots) and belowground (root) biomass in 
 
the mix
ed (MIX) spatial arrangement and both the vetch
-
zone (VZ) and 
 
rye
-
zone (RZ)  within the segregated (SEG2) spatial arrangement in 
 
Year 1 (2011
-
2012), Year 2 (2012
-
2013), and Year 3 (2013
-
 
2014).
...

..
 
147
 
 
 
 
Figure 4.1
.
 
Mean (+/
-
 
SEM) potentially min
eralizeable N (PMN) in early and late 
 
season of sweet corn season with 2012, 2013, and 2014 combined. PMN 
comparisons include tillage (full
-
width tillage [FWT] and strip
-
tillage [ST]) 
 
and rye and vetch spatial arrangement (Mixed [MIX] and Segregated 
[SEG
])

.................................................................................
....
.
 
184
 
 
Figure 4.2. Mean (+/
-
 
SEM) potentially mineralizeable N (PMN) within both the 
 
WR and BR zones of strip
-
tillage combined with either a Mixed (MIX) or 
segr
egated (SEG) rye and vetch spatial arrangement. PMN was measured 
 
from planting to midseason (Early) and f
rom midseason to harvest (Late).
.
...
 
185
 
 
 
xvi
 
 
Figure 4.3.
 
Mean (+/
-
 
SEM) inorganic N in the top 20 cm of soil across the whole 
plot 
throughout the sweet 
corn season in 2012, 2013, and 2014 in subplots with 
 
and without corn. Inorganic N comparisons include full
-
width tillage (FWT) 
 
and strip
-
tillage (ST) combined with either a Mixed (MIX) or segregated 
 
(SEG) rye and vetch spatial arrangement



..
 
186
 
 
Figure 4.4.
 
Mean (+/
-
 
SEM) of inorganic N by zone in the top 20 cm of soil 
 
throughout the sweet corn season in 2012, 2013, and 2014 in NOCORN 
subplots. Inorganic N comparisons include full
-
width tillage (FWT) and strip
-
tillage (ST) combined w
ith either a Mixed (MIX) or Segregated (SEG) ry
e 
 
and vetch spatial arrangem

.....
.
 
187
 
 
Figure 4.5. Mean (+/
-
 
SEM) gravimetric soil moisture sampled to a depth of 20 cm 
 
in the CORN subplots in 2012, 2013, and 2014. Gravimetric soi
l moisture 
comparisons include full
-
width tillage (FWT) and strip
-
tillage (ST) 
 
combined with either a Mixed (MIX) or Segregated (SEG) rye and vetch 
 
spatial arrangement.

.
...................................................................
.
......
 
188
 
 
Figure 4.6. Mean (+/
-
 
SEM) sweet corn biomass and N content within 
 
nonharvested aboveground biomass and corn ears in 2012, 2013, and 
 
2014. Comparisons of fixed effects include full
-
width tillage (FWT) and 
 
strip
-
tillage (ST) combined with either a Mixed 
(MIX) or segregated (SEG) 
 
rye and vetch spatial arrangement.


 
189
 
 
Figure 4.7.
 
A) Mean (+/
-
SEM) sweet corn root mass in 2013 and 2014 combined. 
Samples were collected within the WR (7.5 cm) and the BR (25 cm) from 
 
the plant. B) The ra
tio of WR/BR root mass in both the 2013 and 2014 
 
combined. Comparisons of fixed effects include full
-
width tillage (FWT) 
 
and strip
-
tillage (ST) combined with either a Mixed (MIX) or segregated 
 
(SEG) 
rye and vetch spatial 
arrangement

.
 
1
90
 
 
Figure 4.8.
 
Mean (+/
-
SEM) sweet corn average root diameter (Diameter), specific 
 
root length (SRL), and root length density (RLD) in 2013 and 2014 
 
combined. Samples were collected within the WR (7.5 cm) and the BR 
 
(25 cm) from the plant. Comparisons 
of fixed effects include full
-
width 
 
tillage (FWT) and strip
-
tillage (ST) combined with either a Mixed (MIX) or 
segregated (SEG) rye and vetch spatial arrangement.

....


.....
 
191
 
 
Figure 5.1. Mean (+/
-
) SEM winter annual weed biomass in both Mixed (M
ix) and 
Segregated (SEG) rye and vetch spatial arrangements in 2012 and 2013. 
 
Within segregated rye and vetch, vetch was solely planted within the WR, 
 
and rye was planted within the BR


 
227
 
 
Figure 5.2. Mean (+/
-
 
SEM) soil inorganic 
N within the top 20 cm of soil across 
 
the whole plot in both high and low weed management in 2012 and 2013. 
Inorganic N comparisons include full width tillage (FWT) and strip tillage 
 
 
xvii
 
 
(ST), with Mixed (MIX) and Segregated (SEG) rye and vetch spatial 
arran
gements

..................................................................................

228
 
 
Figure 5.3. Mean (+/
-
 
SEM) soil inorganic N within the top 20 cm of soil in the WR 
 
and BR of strip
-
tilled treatments in low weed management subplots. S
oil 
inorganic N comparisons include mixed (MIX) and segregated (SEG) rye 
 
and vetch spatial arrangements.



 
229
 
 
Figure 5.4. Mean (+/
-
 
SEM) gravimetric soil moisture within the top 20 cm of soil 
 
across the whole plot) in both high a
nd low weed management subplots in 
 
2012 and 2013. Gravimetric soil moisture comparisons include full
-
width 
 
tillage (FWT) and strip
-
tillage (ST), and mixed (MIX) and segregated 
 
(SEG) rye and vetch spatial arrangements



 
230
 
 
Figure 5.
5. Mean (+/
-
 
SEM) gravimetric soil moisture in the top 5 cm of the soil 
 
during emergence counts of C. album and S. faberi. Comparisons include 
 
full
-
width tillage (FWT) and strip
-
tillage (ST), both within (WR) and between 
 
(BR) crop rows



.

.....
 
231
 
 
Figure 5.6. Daily maximum (top) and minimum (middle) soil temperature, as well 
 
as the temperature amplitude (maximum
-
minimum, bottom) measured 
 
from hobo sensors buried 5 cm below the soil surface both within (WR) 
 
and between crop rows
 
(BR). Comparisons include full
-
width tillage (FWT) 
 
and strip
-
tillage (ST)


.
.....
..
 
232
 
 
Figure 5.7. Mean (+/
-
 
SEM) percent emergence of sown seeds of 
C.album
 
and 
 
S.faberi
 
in 2012 and 2013. 400 seeds of each species were sown i
n 2012, 
 
and 500 seeds were sown in 2013. Emergence comparisons include full
-
 
width tillage (FWT) and strip
-
tillage (ST) within (WR) and between (BR) 
 
crop rows

.
..
..........................................................................
..
 
233
 
 
Figure 5.8. Mean (+/
-
 
SEM) cumulative weed biomass in low weed management 
subplo
ts in both 2012 and 2013. Weed biomass was summed across weed 
harvests at two time points during sweet corn growth (before each major 
 
weeding event), and then biomass was also collected at sweet corn 
 
harvest. Weed biomass comparisons include full
-
width til
lage (FWT) and 
 
strip
-
tillage (ST) within (WR) and between (BR) crop rows


...
.....
 
234
 
 
Figure 5.9. Mean (+/
-
 
SEM) sweet corn marketable yield (top), total aboveground 
biomass (middle), and total aboveground N uptake (bottom). Sweet corn 
 
yi
eld is fresh
 
weight of corn ears. Aboveground biomass is the sum of the 
 
dry weight of nonharvested biomass and marketable and nonmarketable 
 
corn ears. Total aboveground N is the sum of Kjehldahl N contained within 
nonharvested biomass and corn ears. Compar
isons include full
-
width 
 
tillage (FWT) and strip
-
tillage (ST) in both high and low weed management 
 
in 2012 and 2013

...................
...................................................
 
235
 
 
xviii
 
 
KEY TO ABBREVIATIONS
 
 
RT: Reduced tillage
 
ST:  Strip
-
tillage
 
FWT:  Full
-
width tillage
 
RV:  Rye
-
vetch mixture
 
No
RV
:  No 
ry
e
-
vetch
 
MIX:  Mixed
 
rye and vetch spatial arrangement
 
SEG:  
Rye and vetch segregated into strips
 
W
R: Within the crop row
 
BR:  Between crop rows
 
N: Nitrogen
 
 
 
 
 
 
 
 
 
1
 
INTRODUCTION
 
Reducing the frequency and intensity of tillage can lead to improvements in soil 
health, a critical objective in organic cropping systems. 
Frequent and intense tillage 
disrupts soil aggregates and macropores, resulting in increased carbon dioxide 
emissions (Grandy and Robertson, 2006), reduced infiltration rates (Franzluebbers, 
2002), 
soil crusting (Awadhwal and Thierstein, 1985), and increased risk of erosion 
(Karlen et al 1994, Triplett and Dick, 2008). 
Reduced
-
till
age
 
(RT) 
practices are also 
being promoted as an effective strategy to both mitigate and adapt to climate change 
(Pittelk
ow et al. 2014) due to their potential for C sequestration (Lal and Kimble, 1997), 
as well as increased soil water infiltration and holding capacity (Franzluebbers, 2002; 

reduce
d 
tillage (
RT
)
 
for organic production. 
 
A common critique of organic farming is that it is very tillage intensive. 
Despite 
widespread adoption
 
within conventional systems (
Triplett and Dick, 2008
), reduced
-
till 
practices have rarely been adopted by organic
 
farmers,
 
due in part to the 
challenges 
created for
 
nitrogen
 
(
N
)
 
and weeds. 
T
illage performs several important functions on 
organic farms. Primary tillage incorporates crop residues to stimulate decomposition 
and facilitate mechanical cultivation (Peigné e
t al. 2007). When left on the soil surface 
rather than incorporated, organic amendments have a slower rate of decomposition and 
mineralization, leading to reduced N availability (Dou et al
.
 
1994). Without the use of 
herbicides, tillage is the dominant form
 
of weed control in organic systems (Peigné et al
.
 
2007, Dedecker et al. 2014; Jabbour et al. 2013). Mechanical cultivation controls weeds 
 
 
 
2
 
either via burying weeds through soil inversion or by severing them at the soil surface 
(Bond and Grundy, 2001). 
 
N d
eficiency and weed competition are the two greatest challenges to achieving 
maximum yields in organic systems (Clark et al
.
 
1999; Bond and Grundy, 2001; Poudel 
et al
.
 
2002; Posner et al
.
 
2008). Cavigelli et al. (2008) found that N deficiency accounted 
for 
between 70 to 75% of the yield reductions in organically grown field corn, while weed 
competition accounted for the entire 19% yield reduction in organic soybeans. In a long
-
term comparison trial of organic and conventional production systems, mechanical 
w
eed control was sufficient to suppress weeds in 66% of site
-
years within organic 
treatments, but when wet conditions prevented cultivation the subsequent high weed 
pressure led to yield reductions (Posner et al
.
 
2008). Improving N and weed 
management in or
ganic cropping systems is essential for enhancing crop productivity. 
The cost of N inputs and labor required for mechanical weed control represent 
significant expenses within organic production (Archer et al
.
 
2007), and the challenges 
of weed control and N
 
deficiency are likely to be exacerbated by tillage reductions. 
 
This 
goal of my dissertation is to improve our understanding of tillage within 
organic systems, especially with respect to the 
critical barriers to
 
RT
 
adoption: N 
deficiency and weeds. In acc
ordance with this goal we addressed the following research 
questions. First, what is the current level of tillage being utilized on organic farms, and 
what are organic farmers perceptions regarding RT practices? Second, how does RT 
affect the N dynamics wi
thin organic production, including N losses to the environment? 
And finally, how can winter cover crops, such as rye and vetch, be more efficiently 
 
 
 
3
 
utilized within organic RT to maximize N availability, weed suppression, and crop 
yields
?
 
For research on or
ganic RT systems to be both useful and effective, it needs to 
accurately take into account the knowledge and situations curre
ntly present on organic 
farms. 
Little information is currently available on the amount and intensity of tillage 
currently used on o
rganic farms

toward reduced tillage (RT). To address these knowledge gaps, we conducted a survey 
of field crop and vegetable growers in Michigan who follow organic production 
guidelines (
Chapter 1)
. 
T
he overall goal of the survey was to evaluate attitudes and 
perspectives of organic farmers regarding RT systems in order to guide future research 
and education programs. Specific objectives were to: 1) document the extent and type 
of current tillage pract
ices employed by organic farmers; 2) determine the forms of 
reduced tillage that organic farmers are currently most interested in adopting or learning 
more about; 3) examine perceptions among organic farmers regarding the barriers and 
benefits of RT adopti
on
.  
 
Thus far, much of the research on reduced
-
till in organic production has looked at 
no
-
till or rotational till systems. However, other reduced till techniques are available that 
may be more applicable to an organic system. For example, strip
-
tillage h
as several 
distinct advantages relative to both conventional full
-
width tillage and no
-
till. Strip
-
tillage 
utilizes a combination of narrowly placed disks and rotary baskets (often attached to a 
shank), which confines tillage to a narrow strip where the cr
op will be planted (Luna and 
Staben, 2002). This divides the cropping system into two distinct yet adjacent zones: 
tilled and untilled. The tillage zone enhances soil aeration and creates a finer seedbed 
 
 
 
4
 
into which the crop will be planted (Licht and Al
-
Ka
isi, 2005). The untilled zone between
-
row (BR) maintains some of the benefits of no till, such as improved water infiltration, 
organic matter retention, and decreased erosion (Johnson and Hoyt, 1999). Strip
-
tillage 
enables a greater level of control over o
rganic matter turnover. Incorporating crop 
residue in
-
row 
(WR)
 
enables faster decomposition and mineralization of organic matter 
to increase nutrient availability to the crop. When combined with a preceding cover crop, 
residue is preserved on the soil surf
ace BR to provide a number of ecosystem services 
throughout the growing season, such as suppressing weed germination, reducing soil 
erosion and conserving soil moisture (Mohler and Teasdale, 1993; Unger and Vigil, 
1998). 
 
Few studies have examined the effe
ct that strip
-
tillage has on N availability within 
a legume
-
based system. Organic systems are already prone to N deficiency (Clark et al. 
1999; Poudel et al. 2002; Cavigelli et al. 2008), and 
previous work has shown that N 
mineralization from legume residu
e is reduced when residues are left on the surface 
compared to incorporated into the soil (
Mulvaney et al. 2010
). 
Therefore, RT may 
exacerbate the problem of N deficiency. 
Understanding how tillage influences the fate of 
N within an organic system is impor
tant to develop strategies to reduce N deficiency 
and increase N use efficiency within organic RT systems. 
To enhance our 
understanding on N dynamics within a legume based ST system, 
we examined the 
effects of strip
-
tillage and a rye and vetch cover crop o
n 1) season long inorganic N 
availability, 2) the potential for N mineralization, and 3) the potential for N losses 
through denitrification and leaching (Chapter 2). 
 
 
 
 
5
 
The final three chapters of this dissertation utilize target placement of N 
resources 
to 
address the 
critical barriers to organic
-
RT adoption: N deficiency and 
weeds. 
In conventional systems, N use efficiency has been improved with targeted 
placement of N fertilizers, such as banding N. 
Targeted placement of nitrogen has the 
potential to mitig
ate inefficiencies in nutrient management, which are profuse throughout 
agricultural production. Blanket applications of nutrient amendments incorporated 
across an entire field often place nutrients outside the access zone of crop roots. When 
nutrient avai
lability does not coincide with root location, nutrients are subject to being 
lost (Robertson, 1997). In conventional crop production, banding synthetic N fertilizers
 
WR
 
increased crop nitrogen use efficiency and reduced nitrogen losses (Maddux et al
.
 
1991
; Waddell and Weil, 2006; Nash et al. 2012). In addition to decreasing N losses, 
targeted placement of N may also have distinct advantages for weed management. For 
example, banding synthetic N has been shown to increase the crop's competiveness 
against wee
ds, and may decrease emergence and growth of annual weeds by 
decreasing their ability to access N (Kirkland and Beckie, 1998; Blackshaw et al. 2004).  
 
In organic cropping systems, few attempts have been made to improve N use 
efficiency through more precis
e placement of N sources, including legume cover crops.
 
We investigate one approach to achieve targeted N 
placement
, via strip
-
intercropping of 
functionally diverse grass and legume cover crops within an organic ST system. This 
research also evaluates pote
ntial synergistic benefits of combining strip
-
intercropping 
with strip
-
tillage. By targeting tillage to the planting zone, problems associated with hairy 
vetch re
-
growth and crop interference are minimized, while benefits associated with 
surface retention 
of rye are maximized. 
 
 
 
 
6
 
Strip
-
intercropping involves separating component species within a mixture into 
distinct alternate strips with the ultimate aim of delaying the onset of competition (Ofori 
and Stern, 1987; Midmore, 1993). Legume and cereal grass cove
r crops are utilized in 
cropping system to meet distinct objectives: legumes serve to provide N to the system, 
and cereal grasses are utilized to suppress weeds and return large quantities of carbon 
to the soil. By segregating these species into distinct z
ones, their functions can be 
targeted efficiently to maximize benefits to the crop while minimizing problems 
associated with interspecific competition in traditional full
-
width mixtures. Hairy vetch 
(
Vicia villosa 
Roth.) planted and incorporated solely in 
the
 
WR
 
zone provides N directly 
to the crop. Cereal rye (
Secale cereal
 
L.) planted only in the BR zone, reduces 
interspecific competition with vetch, creates a zone of initial N immobilization to tie up 
excess N and suppress weeds, as well as provides othe
r ecosystems services such as 
improvements in soil quality. 
 
In Chapter 3, we examine how strip
-
intercropping of rye and hairy vetch affects i) 
the overall productivity of a rye
-
vetch mixture, as well as the relative biomass of each 
component; and ii) the 
spatial distribution and C:N ratio of rye
-
vetch residues between 
zones. We hypothesized that compared to full
-
width rye
-
vetch mixtures, strip
-
intercropping of cereal rye and hairy vetch would result in a greater proportion of vetch 
biomass and a lower C:N 
ratio of cover crop residues within the vetch zone (VZ); in line 
with future crop establishment.
 
Subsequently, Chapter 4 
reports 
whether strip
-
intercropping of cereal rye and 
hairy vetch can increase potentially mineralizeable N and N availability within t
he crop 
row of strip
-
tillage, as well as the subsequent effects on sweet corn biomass, N uptake, 
 
 
 
7
 
and yields. Additionally, we examined whether rye and vetch spatial arrangement and 
strip
-
tillage effects on sweet corn were mediated through differences in ro
ot mass and 
morphology. 
 
Chapter 5 
reports 
the effect of cover crop spatial arrangement, tillage, and weed 
management intensity on weeds, soil N, and crop biomass and yields within organic 
sweet corn production. We hypothesized that soil inorganic N would 
be greater within 
the crop row 
(WR)
 
of ST when rye and vetch (RV) are segregated into strips, and lower 
between crop rows (BR). Additionally, we hypothesized that the number of weeds 
emerging BR will be lower in strip
-
tillage compared to full
-
width tillage
, and that weed 
biomass and N content, as well as sweet corn yield loss due to weeds, will be reduced 
when cereal rye and hairy vetch are segregated into strips in combination with strip
-
tillage. 
 
 
 
8
 
LITERATURE CITED
 
 
 
 
 
 
9
 
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Mohler, C. L., & J.R. Teasdale. 1993. Response of weed emergence to rate of 
Vicia 
villosa 
Roth and 
Secale cereale
 
L. residue. 
Weed Research
, 
33
: 487

499. 
 
 
Mulvaney, M. J., C.W. W
ood, K.S. Balkcom, D.A. Shannon, & J.M. Kemble. 2010. 
Carbon and Nitrogen Mineralization and Persistence of Organic Residues under 
Conservation and Conventional Tillage. Agronomy Journal, 102: 1425

1433. 
 
 
Nash, P. R., P. P. Motavalli, & K. A. Nelson. 2012
. Nitrous oxide emissions from 
claypan soils due to nitrogen fertilizer source and tillage/fertilizer placement 
practices. Soil Science Society of America Journal, 76: 983
-
993.
 
 
Peigné, J., B.C. Ball, J. Roger
-
Estrade, & C. David. 2007. Is conservation til
lage 
suitable for organic farming? A review. 
Soil Use and Management
, 
23
: 129

144. 
 
 
 
 
 
 
 
11
 
Pittelkow, C. M., X. Liang, B.A. Linquist, K.J. van Groenigen, J. Lee, M.E. Lundy, N. van 
Gestel, J. Six, R.T. Venterea, Rodney, C. van Kessel. 2014. Productivity limits a
nd 
potentials of the principles of conservation agriculture. Nature, 517: 365

368. 
 
 
Posner, J. L., J.O. Baldock, & J.L. Hedtcke. 2008. Organic and conventional production 
systems in the Wisconsin integrated cropping systems trials: I. Productivity 1990

20
02. 
Agronomy Journal
, 
100
: 253

260.
 
 
Poudel, D.D., W.R. Horwath, W.T. Lanini, S.R. Temple, and A.H.C. van Bruggen. 2002. 
Comparison of soil N availability and leaching potential, crop yields and weeds in 
organic, low
-
input and conventional farming systems 
in northern California. 
Agriculture, Ecosystems & Environment, 90: 125

137.
 
 
Ofori, F., and W. Stern. 1987. Cereal
-
legume intercropping systems. Advances in 
Agronomy, 41: 41

90.
 
 
Robertson, G.P. 1997. Nitrogen use efficiency in row crop agriculture: crop n
itrogen use 
and soil nitrogen loss. in  L. Jackson, ed. Ecology in Agriculture, Academic Press, 
NY. Pages 347
-
365.
 
 
Triplett, G. B., & W.A. Dick 2008. No
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tillage crop production: A revolution in agriculture! 
Agronomy Journal
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165.
 
 
Unger, P., & M. Vigil. 1998. Cover crop effects on soil water relationships. 
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Soil and Water Conservation
, 
53
: 200

207. 
 
 
 
 
 
 
 
 
 
12
 
C
HAPTER ONE
 
Michigan organic farmer perceptions of the barriers and benefits of reduced
-
tillage: Results from a farmer sur
vey.
 
ABSTRACT
 
A common critique of organic farming is that it relies heavily on tillage practices with 
known negative effects on soils. However, little information is currently available on 

duced tillage (RT). To 
address these knowledge gaps, we mailed a written survey to 337 Michigan farmers 
who follow organic production guidelines, and received 39 completed surveys from 
vegetable growers and 65 from field crop growers. 
Respondents
 
reported 
a wide range 
in tillage frequency and intensity, both across and within production of specific crops. 
Tillage operations were fairly evenly split between field preparation and cultivation. The 
most commonly used primary tillage was moldboard and chisel plo
w for field crops, and 
rototiller for vegetables. Interest in adoption of RT practices among respondents was 
overall fairly low, but farmers did express a high level of interest for a few forms of RT. 
Vegetable farmers reported an overall greater interest 
in adoption compared to field 
crop producers and were most interested in permanent beds and strip
-
till
age
. Field crop 
producers were most interested in rotational tillage, followed by strip
-
till
age
. The 
greatest perceived benefits to adoption were improved
 
soil quality and fuel savings. 
Vegetable producers were more likely to perceive obtaining RT equipment as a greater 
barrier to RT adoption compared to field crop producers. Both groups ranked weeds, 
impacts on yields, residue management, and crop establis
hment as high barriers to 
adoption. Survey results suggest that future research efforts should focus on: i) 
 
 
 
 
 
13
 
developing low cost adaptable RT equipment for organic vegetable producers; ii) 
identifying the optimal level of tillage that balances weed and soil
 
management 
tradeoffs; and iii) developing strategies that maximize cover crop residue weed 
suppression, while avoiding crop interference.
 
INTRODUCTION
 
A common critique of organic farming is that it is very tillage intensive.
 
Frequent 
and intense tillage 
disrupts soil aggregates and macropores, resulting in increased 
carbon dioxide emissions (Grandy and Robertson, 2006; Grandy et al. 2006), reduced 
infiltration rates (Franzlubbers, 2002), soil crusting (Awadhwal and Thierstein, 1985), 
and increased risk of
 
erosion (Karlen et al 1994, Triplett and Dick, 2008). Several long
-
term studies comparing agricultural management have found that although organic 
systems may improve soil quality indicators compared to conventional systems 
(Pimentel et al. 2005; Teasdale
 
et al. 2007), they may fail to provide soil quality 
improvements equal to no
-
till (Bhardwaj et al. 2011). For example, a long
-
term study in 
Michigan found that organically managed soils had a lower soil stability ratio and 
reduced volume of drainable pore
s compared to no
-
till (Bhardwaj et al. 2011). 
 
Although excessive tillage can be detrimental to soil health, tillage performs 
several important functions on an organic farm. Primary tillage is helpful for 
incorporation of cover crop residues to stimulate d
ecomposition and facilitate 
mechanical cultivation (Peigné et al. 2007). 
When left on the soil surface rather than 
incorporated, organic amendments have a slower rate of decomposition and 
mineralization, leading to reduced N availability (Dou et al
.
 
1994).
 
Without the use of 
herbicides, tillage is the dominant form of weed control in organic systems (Peigné et al
.
 
 
 
 
 
 
14
 
2007, Jabbour et al. 2013; Dedecker et al. 2014). Tillage is also an important tool for 
disease management because incorporating crop residues ca
n increase the mortality of 
disease propagules (
Bailey and Lazarovits, 2003
). Despite the important role of tillage 
for organic production, and the negative consequences resulting from too much tillage, 
very little information is currently available on the
 
amount and intensity of tillage 
currently used on organic farms. 
 
The type of tillage implement, as well as the number of tillage operations 
performed at any one time and across the season, vary between organic farms as well 
as by year on any one farm. In
 
certain years, limited time or wet soil conditions may 
prohibit the ideal number of tillage operations for optimum planting conditions and 
effective weed control (Posner et al. 2012). Additionally, tillage practices may vary by 
farm due to soil type, crop
s grown, or farmer preferences with respect to crops grown 
and acceptable level of weed control.
 
Access to high
-
quality and locally adapted information is a key factor that 
influences farmer adoption of conservation practices (Llewellyn, 2007; Baumgart
-
Get
z 
et al. 2012). Currently a number of researchers are working to adapt reduced tillage 
(
RT
)
 
systems for organic production (Carr, et al. 2012; Luna et al
.
 
2012; Mirsky et al
.
 
2012; Brainard et al. 2013). However, without adequate information regarding farm

perceptions of the barriers to and benefits of RT practices, it is unknown whether 
researchers are providing organic farmers with the most relevant and useful information. 
Research is unlikely to be either effective or useful if it demonstrates an ide
a or principle 
that is already well
-
known (Llewellyn 2007). For example, it is well known that a major 
benefit of RT adoption is improvements in soil quality. But common organic practices, 
 
 
 
 
 
15
 
such as cover cropping and additions of manures and compost, are al
ready meeting 
many of the same ecological objectives of no
-
till farming by promoting building of soil 

.
 
2007). Information on further improvements in soil quality 
could be a major incentive to encourage organic farmer adoption
 
of RT. If so, it would 
be important for research efforts on organic RT systems to focus on providing new 
information to farmers demonstrating other potential benefits of RT adoption. One such 
example is the role that RT systems can play in facilitating ad
aptation to the erratic 
precipitation events predicted by climate change models (Hayhoe et al. 2010) by 

Franzlubbers, 2002). 
 
For agronomic research to be effective 
at enhancing farmer decision
-
making on 

beliefs, and attitudes (Ahnström et al
.
 

attitudes influence both their likelihood of adop
tion as well the likely success of those 
practices (Ahnström et al
.
 
2009). Organic farmers tend to place a higher value on their 
own knowledge and the knowledge of other farmers compared to knowledge generated 
from scientific research (Jabbour et al. 2013)
. Incorporating farmer knowledge into 
agronomic research will assist scientist in tailoring research to address the specific 
interests and needs of farmers. Information is lacking on organic farmers knowledge 
and attitudes regarding the costs and benefits 
of RT adoption. 
 
The actual and perceived
 
costs and benefits
 
of RT adoption may be influenced 
by internal characteristics of the farm. For example, smaller farms generally have less 
access to capital to invest in machinery that may be required for some for
ms of RT 
 
 
 
 
 
16
 
(Rahm and Huffman, 1984; Fuglie, 1999). Investment in new machinery may be 
inappropriate to smaller diversified farms when RT equipment may only be suitable for a 
small portion of the total crops produced. RT practices may only be suitable to spec
ific 
crops, so which crops are utilized within a rotation may greatly impact the perceived 
costs and benefits. For example, Mirsky et al. (2013) found that the performance of field 
corn in organic no
-
till was more variable than soybean. Additionally, certa
in soil types 
and climates may be more suitable to RT (Rahm and Huffman, 1984). Other socio
-
economic factors such as farmer experience and access to information may also impact 
management decisions (Saltiel et al
.
 
1994; Knowler and Bradshaw et al
.
 
2007; Pr
okopy 
et al. 2008). While a number of studies have examined how the context of a farming 
operation impact management decisions (Beedell and Rehman, 1999; Knowler and 
Bradshaw et al
.
 
2007; Ahnström et al
.
 
2009), few studies have examined these 
relationships
 
exclusively within organic farms. 
 
The overall goal of this study was to evaluate attitudes and perspectives of 
organic farmers regarding RT systems in order to guide future research and education 
programs. Specific objectives are: 1) 
document the extent 
and type of  tillage practices 
currently employed by organic farmers, as well as their access to equipment necessary 
for RT; 2) determine the forms of reduced tillage that organic farmers are currently most 
interested in adopting or learning more about; 3)
 
examine perceptions among organic 
farmers regarding the costs and benefits of RT practices; 4) determine how farm and 
farmer characteristics influence their expressed interest in reduced tillage.
 
 
 
 
 
 
 
 
 
17
 
Study system: Organic agriculture in Michigan
 
We conduct
ed a survey of organic farmers in Michigan. Michigan has a diversity 
of soil, topography, and climate due to its unique geographical position within the Great 
Lakes region (MDARD, 2012). Due in part to proximity to major cities and vegetable and 
fruit proc
essing plants, Michigan farmers have a diversity of markets. These factors 

(MDARD, 2012; USDA 2012). Michigan contains 332 organically certified or exempt 
farms (USDA, 2014), 
up 62% from 2005 (Bingen et al. 2007). In 2012, Michigan had 
over 58,000 acres of organic production, and organic sales totaled $124 million (USDA, 
2014). Michigan is an important contributor in organic production of fruit, vegetable and 
field crops. Michi
gan ranks number 1 in the county in organic production of dry beans, 
popcorn, and tart cherry, and is a major organic producer of grain corn (#3), snap beans 
(#3), and blueberries (#4) (USDA, 2008). 
 
MATERIALS AND METHODS
 
Survey development
 
In 2014,we cond
ucted a 
written 
survey of field crop and vegetable growers in 
Michigan who follow organic production guidelines. Because a large portion of small 
farmers that follow organic guidelines are not certified, we did not make certification a 
prerequisite for sur
vey completion. Respondents were asked to verify that they meet 

return of the survey instrument. In Fall 2013, survey participants were identified through 
the NOP. The Local Ha
rvest (http://www.localharvest.org) farm database, a commonly 
used website to identify sources of local food, was utilized to identify Michigan farmers 
 
 
 
 
 
18
 
that were not certified but self
-
identified as using organic practices. We separated all 
farm participan
ts into either field crop or vegetable producer by their primary crops listed 
within NOP or Local Harvest.
 
Prior to survey administration, we obtained feedback on the survey instrument 
through an advisory group of farmers, researchers, and extension person
nel. Once the 
survey was developed it was tested on a subgroup of 5 organic farmers to check 
reliability of responses, and feedback was incorporated. This research was conducted in 
accordance with the Michigan State University Human Research Protection Pro
gram 
and was deemed exempt (IRB# x14
-
041e).
 
Survey content
 
Two forms of the survey instrument were created: one for field crop growers and 
one for veg
etable growers
 
(see Supplemental
 
File 
for survey example). 
The survey was 
organized into four sections, an
d included questions on demographic and farm 
characteristics (Section 1); crop specific questions (Section 2); and attitudes towards 
reduced tillage (Section 3); and questions related to agricultural research priorities 
(Section 4). Section 2 was the only 
section that differed between field crop and 
vegetable crop respondents. Results from Section 4 are not included. 
 
 
Section 1 included questions on respondent demographics and characteristics of 
the farm, and was the same for both survey versions. Demograp
hic questions included 
information on years farming, years farming organic, percent of the workweek spent 
farming, and percent of family income from farming. Farm characteristics included 
questions on principal crops, acreage, land tenure, soil type, organ
ic certification, an
d 
equipment access on the farm.
 
 
 
 
 
 
19
 
The focus of Section 2 was to gather specific information from each respondent 
on the practices employed for the production of a specific crop on their farm. Production 
practices vary considerably by crop
, therefore requiring respondents to answer 
questions targeted to a specific crop helped to control for crop
-
specific variability in 
responses. Field crop producers were asked to choose either field corn, soybeans, or 
dry beans. Vegetable producers were as
ked to select either winter squash or broccoli; 
but if neither of these crops were grown they could select tomato (tomato data not 
shown). These crops were selected because they were widely grown in the region, and 
because they represented crops known from
 
previous research to be relatively 
amenable to RT practices. Section 2 included both open
-
ended questions and 

and frequency of tillage used for soil preparation and weed
 
cultivation, type and quantity 
of soil amendments, organic herbicide or pesticide applications, cover crop species, and 
crop rotation. Surveys included figures illustrating different types of tillage equipment
 
(
see 
Supplemental
 
File
)
, in order to minimize
 
errors associated with equipment 
terminology (Figures obtained from Grubinger, 1999; Bowman, 2002). 
 
Section 3 consisted of a series of 0 to 7 Likert scales asking respondents to rank 
their interest in adoption of specific RT practices, knowledge of RT pr
actices, as well as 
their perceptions of potential benefits and barriers to RT adoption. For Likert scales, 0 
represented no interest or knowledge of RT, or not a potential benefit or barrier to RT 
adoption; 7 represented extremely interested in or knowled
geable about RT adoption, or 
extremely likely to be a potential barr
ier or benefit to RT adoption. 
 
 
 
 
 
 
 
20
 
Survey implementation
 
Prior to the survey we sent an announcement by mail in January 2014, providing 
basic information on the timing and purpose of the sur
vey. Survey packets were mailed 
in February 2014. Within the survey packet, we included a letter explaining the 
importance and purpose of the survey, survey instructions, a survey waiver form, as 
well as a pre
-
paid return envelope. The instruction letter a
lso emphasized that a $15 
check would be mailed following receipt of their completed survey. A reminder postcard 
was sent four weeks after the survey packet, encouraging recipients to complete the 
survey to receive their $15 check. To increase survey publi
city, we put announcements 
in local Michigan organic agriculture emailing listserves. 
 
Once a list of respondents was generated, each respondent was given a 
numbered code which corresponded to their survey instrument. The coded sheet was 
used to create a l
ist of respondents to receive compensation. The coded identifiers were 
completely separated from the return and completed surveys to ensure anonymity. 
 
The survey was sent to 337 Michigan farmers (178 field crop producers, and 159 
vegetable producers); 12 
surveys were returned unopened because they were no 
longer operating farms. 120 completed surveys were returned, and of these,1 survey 
was incomplete and 14 additional surveys were excluded because respondents were 
primarily dairy or livestock operations w
ith less than 15% of their operations dedicated 
to vegetable or field crop production. Therefore, a total of 105 surveys were included in 
analyses, for a final response rate of 34%.
 
The response rate by region varied between 
29 and 38% (Figure 1.1).
 
 
 
 
 
 
21
 
To eva
luate the soil disturbance intensity of the varying tillage practices used on 
organic farms, we calculated cumulative STIR (soil tillage intensity rating) values for the 
tillage operations reported for each respondent (USDA NRCS, 2008). A soil tillage 
inte
nsity rating (STIR) is a numerical value for a specific tillage operation used to 
estimate erosion potential in RUSLE2 (Revised Universal Soil Loss Equation Version 2). 
STIR values are calculated based on tillage type, depth, and percent of soil surface 
ar
ea disturbed, and can be found within RUSLE2. 
For each respondent, we multiplied 
the number of operations for each tillage implement by its specific STIR value (Table 
1.1), and then summed across tillage types to get a cumulative STIR value according to: 
 
 
[Equation 1.1]  
Cumulative STIR= 
















 
A distribution of cumulative STIR values for each of the five crops was then generated 
using cumulative STIR values for all respondents growing that crop.
 
Statistical analyses
 
Sur
vey data were analyzed using SPSS (Windows version 22). Descriptive 
statistics (means, medians, frequencies) were calculated for demographic data and 
summaries of production practices, as well as Likert responses. Because Likert 
responses did not follow a 
normal distribution, we reported the median responses 
instead of the mean, and nonparametric tests (Kruskal
-
Wallis) were used when 
comparing categorical groups. Spearman correlation coefficients were used to evaluate 
the relationship between continuous var
iables and Likert responses. All figures were 
made using the GGPLOT2 package in R (Wickham, 2009).
 
Factor analysis was used as a data dimensionality reduction method to evaluate 
the underlying relationships between perceived barriers, benefits, and interes
t in RT 
 
 
 
 
 
22
 
adoption. Factor analysis utilizes the patterns of covariation among measured variables 
in a data set to infer latent variables, and these latent variables represent potential 
relationships that exist within the observable variables (Johnson and Wi
chern, 2007; 

measured data variables could be reduced into a smaller number of factors: the Kaiser
-
Meyer
-

ity 
(Leech et al. 2005). We used a principal component extraction method, with list
-
wise 
deletion of missing data, Varimax rotation, and a Kaiser normalization. To determine the 
appropriate number of factors we used the following criteria: 1) theoretical 
m
eaningfulness of appropriate factors, 2) scree plots, 3) total percent variance 
explained, and 4) eigenvalues that are at or near 1. Initially the factor analysis of 
barriers to RT adoption returned three factors in the exploratory approach using the 
crite
ria of factors having eigenvalues greater than 1. This model lacked interpretability, 
however, so we included a fourth factor because it had an eigenvalue close to 1 (0.89) 
and improved separation of factors and loadings of variables onto meaningful 
constr
ucts. We selected a 0.6 cutoff for rotated factor loadings onto factor constructs. 
 
RESULTS
 
Demographics
 
Demographic characteristics of vegetable and field crop survey respondents can 
be found in Table 1.2. Field crop producers made up 62% of survey respon
dents, and 
vegetable producers made up 38%. 
 
Field crop growers tended to be more experienced, with 65% of field crop 
respondents having farmed for more than 20 years, compared to only 31% for 
vegetable growers. Among vegetable farmers, 40% had been farmin
g for 10 years or 
 
 
 
 
 
23
 
fewer. However, field crop and vegetable producers had similar levels of experience 
with organic production practices: 48% of field crop growers and 56% of vegetable 
growers had been farming organically for fewer than 10 years. 
 
Field cro
p and vegetable growers also differed in several other important 
respects. Organic field crop re
s
pondents farmed much larger acreage (70% of 
vegetable growers farmed 10 acres or less, while 63% of field crop growers farmed over 
100 acres). Field crop respo
ndents also earned a higher proportion of their income from 
the farm business; 46% of field crop producers earned over 75% of their income from 
farming, while only 39% of vegetable producers earned over 75% of their family income 
from farming. Finally, fie
ld crop respondents were more likely to be organically certified; 
90% of field crop growers were organically certified compared to only 40% of vegetable 
growers. 
 
In terms of land ownership and time dedicated to farming, field and vegetable 
crop growers we
re fairly similar. The majority of both field crop (66%) and vegetable 
(72%) 
producers
 
owned both their land and farm. Thirty percent of field crop owners 
rented some portion of their land, while only 20% of vegetable producers rented some 
portion of all o
f their land. For both vegetable and field crop producers, approximately 
60% of respondents spent over 75% of their workweek farming. All vegetable producers 
spent greater than 25% of their workweek on farming, while 15% of field crop producers 
spent less 
than 25% of their workweek on farming. 
 
Farm geograp
hic distribution and soil types
 
Regional representation of survey respondents, and regional variation of farm 
type, soil type and farm size can be found in Figure 1.1. Vegetable farms tended to 
 
 
 
 
 
24
 
have sandi
er soils, such as sandy loams, while field crop farms tended to have heavier 
textured soils such as clay loams. The regional response rate was between 29 and 
38%, and the percent of respondents from each region closely reflected the percent of 
farms within
 
the initial population. Survey respondents represented the entire state of 

1.1). The Thumb region was largely dominated by field crop growers, and is especially 
known for
 
its organic dry bean production. Approximately 10% of respondents were 
from Northern Michigan and the Upper Peninsula, and responses were dominated by 
vegetable producers. Central East and West regions comprised approximately 20% of 
respondents and were a
pproximately evenly split between vegetable and field crop 
producers. The Central 
region comprised approximately 14% of respondents and was
 
largely dominated by field crop growers. Southwest and Southeast Michigan comprised 
29% of respondents and was large
ly dominated by vegetable producers, with Southeast 
Michigan having a larger median acreage compared to Southwest. 
 
Tillage and cultivation equipment
 
Vegetable and field crop producers varied in the type of tillage equipment they 
could access (Figure 1.2).
 
Ninety
-
two percent of field crop and 70% of vegetable 
growers reported access to a tractor. The most common primary tillage implements 
available to field crop producers were a moldboard plow (83%), followed by a chisel 
plow (73%), and then rototiller (54%
). In contrast, almost all vegetable growers had 
access to a rototiller (82%), with fewer reporting access to a moldboard plow (62%) or 
chisel plow (36%). 
 
 
 
 
 
 
25
 
Field crop and vegetable growers also reported key differences in their access to 
secondary tillage 
and cultivation equipment. Field crop growers had access to a number 
of secondary tillage implements including discs (89%), field cultivators (80%), harrows 
(65%), and soil finishers (45%). Vegetable growers had lower reported access to these 
implements, i
ncluding discs (74%), harrows (51%), row cultivators (49%) and field 
cultivators (41%). However, some vegetable growers had access to bed formers (20%) 
which were less common on field crop farms. Field crop producers reported access to 
cultivation equipmen
t, including the row cultivator (86%), rotary hoe (72%), and flextine 
weeders (55%).
 
For vegetable producers, the most common cultivation tool was a row 
cultivator (49%).
 
Tillage and cultivation practices
 
Tillage practices employed for specific vegetable a
nd field crops are recorded in 
Tables 1.3 and 1.4, and generally follow the patterns seen for equipment availability. 
The most commonly used primary tillage 
implement 
was
 
the
 
moldboard plow for both 
field corn (61%) and dry bean (71%); however for soybean 
the predominant primary 
tillage
 
implement
 
was 
the 
chisel plow (47%) (Table 1.3). The most commonly used 
implements used for 
forms of secondary tillage were disc (field corn=61%, dry 
bean=14%, soybean=41%), field cultivator (field corn= 35%, dry bean=43%, 
s
oybean=29%), harrow (field corn= 22%, dry bean= 14%, soybean= 6%), and soil 
finisher (field corn= 30%, dry bean= 43%, soybean= 77%). Table 1.3 shows the most 
commonly used combinations of primary and secondary tillage implements. 
 
For both broccoli and win
ter squash, the most dominant form of primary tillage 
was rototilling (65%) (Table 1.4). In contrast, moldboard plowing was used by only 35% 
 
 
 
 
 
26
 
of winter squash producers and 29% of broccoli producers. The most commonly used 
implements for 
secondary tillage w
ere the disc (winter squash=50%, broccoli=35%), 
harrow (winter squash=30%, broccoli=24%), and bed former (winter squash=19%, 
broccoli=18%). 
 
The dominant 
implements 
used 
for
 
weed cultivation used for field crops were the 
row crop cultivator, rotary hoe, an
d flextine cultivator (Table 1.3). Row crop cultivators 
target weeds between crop rows and were used extensively (91
-
100%) in all three field 

small weeds either befor
e crop emergence or 2
-
3 weeks after emergence. For field crop 
producers, rotary hoes were more frequently used (73
-
100%) then flextine cultivators 
(45
-
71%) for this purpose. Interestingly, flextine cultivation was more common for weed 
management in beans (
71.4%) then in field corn (45.5%). 
 
For broccoli and winter squash production the dominant 
implements used for 
cultivation were the row crop cultivator (winter squash= 46%, broccoli= 24%) and the 
rototiller (18
-
19%) (Table 1.4). In contrast to field crops,
 
flextine and rotary hoe 
cultivation were very rarely used in these crops. Cultivation tools that were rarely used 
by vegetable producers included the tine cultivator (5.9% of broccoli) and the finger 


-
weeder, rolling cultivator, and tine cultivator were never used by survey respondents for 
any of the field or vegetable crops. 
 
Tillage frequency and intensity
 
The distribution for the number of tillage operations used for field prepa
ration, 
cultivation, and total tillage operations for each of the specified crops can be found in 
 
 
 
 
 
27
 
Figure 1.3. The mean number of total tillage operations was 8, 9.2, and 9.1 for field 
corn, dry beans, and soybeans, respectively. This included approximately
 
4 tillage 
operations for field preparation, and 4
-
5 operations for cultivation. The range in the total 
number of tillage operations was smallest for dry beans (5), followed by field corn (7) 
and soybean (11). 
 
For winter squash, the mean number of total t
illage operations was 6.6 with an 
average of 3.5 for field preparation, and 3.1 for cultivation. For broccoli, 7.9 total tillage 
operations were used on average, with 4.5 for field preparation and 3.4 for weed 
cultivation. Variability in tillage operations
 
was much higher for production of broccoli 
and winter squash compared to the field crops, especially for broccoli. Total tillage 
operations of broccoli ranged from 0 to 26.5, while for winter squash total operations 
ranged from 2 to 14.5.
 
The vegetable cr
ops, broccoli (mean=130, std. dev=121) and winter squash 
(mean=143, std. dev=99; Figure 1.4) had lower total STIR values compared to field 
crops, but had higher variance. Dry bean had the highest cumulative STIR with a mean 
of 224, and the lowest variance 
(std. dev=33); followed by field corn (mean=210, 
std.dev=69) and then soybean (mean=203, std.dev=65). Both vegetables crops were 
skewed right, while the field crops had more of a normal distribution. 
 
Grower interest and knowledge of RT
 
Figure 1.5 shows th
e distributions of vegetable and field crop producer interest in 
adoption of specific forms of RT. Overall, interest in adoption of RT practices among 
organic farmer respondents was fairly low, and vegetable farmers reported an overall 
greater interest in 
adoption compared to field crop producers. 
 
 
 
 
 
 
28
 
Vegetable producers were most interested in permanent beds (median=4 on 
Likert scale) and strip tillage (median=3), and least interested in ridge
-
till adoption 
(median=0) (Figure 1.5). Field crop producers were m
ost interested in adopting 
rotational tillage (median=3), followed by strip tillage (median=2). Organic field crop 
growers had little interest (median=0) in permanent beds, no
-
till, and ridge
-
till. For some 
forms of RT, the median expressed interest was lo
w or at zero, but the other 50% of 
responses were fairly evenly distributed from 1 to 7. For example, 25% of field
-
crop 
respondents answered at or above 6, 5, and 4 for rotational, strip
-
till, and no
-
till, 
respectively. And 25% of vegetable producer respon
dents expressed interest at or 
above 6, 5.5, and 5 for permanent beds, strip
-
till, and no
-
till, respectively. 
 
Organic producers reported a median knowledge of RT practices at 4 or lower 
(Figure 1.5). Field crop producers reported the greatest knowledge of
 
rotational tillage 
(median=4), followed by no
-
till (median=3) and strip
-
till (median=3). Vegetable 
producers reported the greatest knowledge of permanent beds (median=4), followed by 
no
-
till (median=3), strip
-
till (median=3), and rotational till (median=3
). Vegetable 
producers reported the lowest knowledge of ridge
-
till (median=1), and field crop 
producers reported the lowest knowledge of permanent beds (median=1). 
 
Perceived costs and benefits of RT
 
Vegetable and field crop producers had very similar perc
eptions of the benefits of 
RT adoption (Figure 1.6). The greatest perceived benefits to adoption were improved 
soil quality (reduced soil compaction, increased soil organic matter, reduced soil 
erosion, improved soil tilth, increased soil water infiltratio
n and water holding capacity) 
 
 
 
 
 
29
 
and fuel savings. Factors that scored fairly low on the perceived benefits included 
reduced labor, reduced pest pressure (from disease, insects, and weeds) and yields. 
 
Perceived barriers to adoption were also similar for vege
table and field crop 
growers, with a few notable exceptions (Figure 1.7). Vegetable producers were more 
likely to perceive obtaining RT equipment

both obtaining scale
-
appropriate equipment 
(
P
=0.001) and the cost of new equipment (
P
=0.045)

as greater barrie
rs to RT 
adoption compared to field crop producers. Both groups ranked weeds, impacts on 
yields, residue management, and crop establishment as high barriers to adoption. 
Lowest ranked barriers to adoption included labor costs, soil fertility, and learning 
new 
practices. 
 
Factors associated with attitudes towards RT
 
Interest in RT adoption was correlated with several farm characteristics and 
farmer demographics (Table 1.5). The maximum expressed interest in RT adoption 
(maximum Likert value for interest in a
ny form of RT) was negatively correlated with 
years farming and acres farmed. Greater reported knowledge of RT practices was 
positively correlated with interest in adopting no
-
till, strip
-
till, rotational till, and 
permanent beds. Demographic factors that 
did not correlate with interest in adoption 
were percent of week spent farming, land tenure, and organic certification (data not 
shown). The maximum percent clay and percent silt that respondents recorded for soil 
types that exist on their farm were both n
egatively correlated with interest in adoption. 
 
Not surprisingly, farmer perceptions of RT barriers and benefits were often 
related to interest in adoption (Table 1.5). The perceived barriers to RT adoption that 
were negatively correlated with interest in
 
adoption were weeds, insect, residue 
 
 
 
 
 
30
 
management, yields, and a lack of information. Perception of crop establishment as a 
barrier was negatively correlated with interest in adoption of no
-
till and permanent beds. 
All perceived benefits of RT were positive
ly correlated with the maximum expressed 
interest in adoption. 
 
Our analysis showed that the Likert responses of perceived benefits were all 
highly correlated with each other and with expressed interest in adoption and the factor 
reduction did not yield in

of sphericity indicate that the barriers data was amenable to factor reduction. A KMO 
value of 0.7 or greater ensures that the factors explain an adequate proportion of 
variance among the measu
red variables, and we found a KMO of 0.79 for perceived 

Square=621, 
P
<0.001), indicating that significant relationships existed between the 
perceived barriers to adopt
ion.
 
We reduced the perceived barriers to adoption of RT practices into four factors 
(Table 1.6). The four factors generated from this model explained 76% of the total 
variance. The factor loading cutoff we used was 0.6, and each variable clearly loaded 
on
to one of the four factors, with the one exception being yield, which had 0.5 onto 
Factor 1 and 0.59 onto Factor 2. The communalities were mostly all above 0.6, except 
for soil fertility, at 0.56. This indicates that the model explained a significant amoun
t of 
variation for each individual variable. Factor 1 consisted of disease and insect pressure, 
labor costs, and soil fertility. Factor 2 consisted of weed competition, residue 
management, and crop establishment. Factor 3 consisted of equipment costs and 
o
btaining scale
-
appropriate equipment. Factor 4 consisted of information lacking and 
 
 
 
 
 
31
 
challenges with learning a new practice. 
Factor 2 was negatively correlated with 
maximum expressed interest in RT adoption (
r 
= 
-
0.247). 
 
DISCUSSION
 
Survey scope
 
While the 
scope of our survey was limited to Michigan, demographic information 
shows that the respondents within our survey closely resemble organic farmers 
surveyed elsewhere. For example, compared to a national survey of organic farmers 
(Walz, 2004; Dedecker et al
. 2014), our survey respondents had similar: i) acreage 
(50% of respondents farmed 50 acres or less); ii) land tenure (approximately 65% of 
farmers owned all their farmed land); and iii) farming experience (approximately 50% 
farmed for 20 years or more) (T
able 1.2). We had a larger percentage of non
-
certified 
farmers in our study compared to other organic surveys that specifically targeted 
certified organic farmers (Walz, 2004; Dedecker et al. 2014). 
 
Our survey was different from other surveys of organic f
armers because we 
restricted it to field crop and vegetable producers, and excluded fruit, dairy, livestock, 
and other types of farming operations. We had a greater response rate of field crop 
(40%) compared to vegetable (26%) farmer respondents, so it is 
possible that field crop 
producers were slightly over represented in our sample. Alternatively, it could be that 
some of the uncertified vegetable farms we identified via farm marketing websites were 
actually not following organic guidelines, and thus did 
not participate due to this 
requirement.
 
 
 
 
 
 
 
 
32
 
Factors influencing interest in adoption of RT
 
More experienced farmers with larger acreage expressed less interest in RT 
adoption (Table 1.5). To determine whether these relationships were driven by 
differences b
etween field crop and vegetable producers, we tested these correlations 
within each farm type separately (data not shown). We did not find any significant 
relationship but we saw a similar negative trend between interest in RT and years 
farming and acres. 
This suggests that we had insufficient power to detect the negative 
relationship within each farm type (vegetable or field crop) separately (and that this 
relationship was independent of farm type. However, it is possible that field crop farmers 
are less i

on management and technology adoption rely heavily on their own experiences and 
prior held beliefs (Wilson et al. 2009; Jabbour, et al. 2013). Experienced farmers may 
have previou
sly tried reducing their tillage, or have had to miss a cultivation window due 
to wet soil conditions, and may have had negative experiences with tillage reduction. 
Therefore, experienced farmers may feel strongly that their current level of tillage is 
req
uired to maintain weed control and crop productivity. In contrast, farmers that 
expressed greater knowledge of RT practices also expressed greater interest in 
adoption. This suggests that either those interested in RT are more likely to seek out 
informatio
n, or that farmers become more interested in RT the more they learn.
 
Constraints to adoption and implications for research and extension
 
Our survey results suggest that availability of small
-
scale, affordable equipment 
is a major constraint to adoption of 
RT, especially among vegetable growers (Figures 
1.7). Research and extension efforts should thus emphasize scale
-
 
and resource
-
 
 
 
 
 
33
 
appropriate adaptations to maximize the potential adoption and impact of RT. 
Approximately 30% of vegetable respondents did not h
ave access to a tractor, and 
likely rely on walk
-
behind rototillers. Rototiller based RT practices including strip
-
tillage 
have been explored for small
-
scale resource
-
poor farmers in the developing world 
(Krupnik et al. 2013), and may have useful applicati
ons for small
-
scale organic 
producers in the US. Similarly, permanent bed systems
--
which entail wider disturbed 
and undisturbed zones compared to strip tillage (Morrison and Gerik, 1983)

may 
provide greater flexibility for RT of diverse vegetables using ex
isting farm equipment. 
Certainly, relatively strong grower interest in permanent bed systems among vegetable 
growers (Figure 1.5) reflects their perceived appropriateness.
 
Among organic field crop growers, larger
-
scale approaches to RT may be 
feasible. For
 
example, greater than 70% of field crop producers, (compared to only 35% 
of vegetable producers), reported having access to a chisel plow, and over 90% had 
access to a tractor. Large
-
scale strip
-
tillage equipment is readily available because of its 
extens
ive use on conventional field crop farms. Such equipment may also be 
appropriate for organic production of field crops with minor modifications. This may 
explain greater interest among organic field crop producers in strip
-
tillage compared to 
other forms o
f RT other than rotational tillage (Figure 1.5). 
 
Current level of tillage on organic farms
 
The type and number of tillage operations employed for crop production varied 
by crop and farm type. The dominant form of tillage for field crop producers was 
moldb
oard plow
ing
, followed by chisel plow
ing
 
and disc
ing
. These forms of tillage have 
high STIR values, which is partially why field crops had a greater cumulative STIR value 
 
 
 
 
 
34
 
compared to vegetable crops. Vegetable producers predominantly relied on the 
rototill
er, which despite b
eing fairly destructive to soil
 
has a STIR value of only 18 
because it is typically fairly shallow. Many small seeded vegetable crops, such as 
carrots, require the fine seedbed provided by rototilling. If an organic vegetable farmer 
inve
sted in any one piece of equipment, the rototiller is valuable because it can be used 
for both large and small seeded crops. Vegetable farms have more limited access to 
equipment (Figure 1.2), but typically grow a diversity of crops and require versatile 
e
quipment. 
 
Despite organic agriculture being criticized for being tillage intensive, there was 
little previous information on the number and types of tillage used on organic farms. By 
asking detailed questions about tillage practices utilized for specific 
crops, we establish 
a baseline for what routine tillage, in both number of operations and disturbance 
intensiveness, currently is on organic farms in Michigan. Soil tillage intensity ratings 
(STIR) are used within RUSLE2 to estimate the effect that soil ma
nagement operations 
have on soil, particularly with regards to increasing susceptibility to erosion. Few have 
applied STIR values as an index of soil disturbance intensity to organic or conventional 
farms. However, Karlen et al. (2008) compared various soi
l quality indicators including 
STIR values for the South Fork Watershed in Iowa. The STIR values within our study 
were significantly higher than the STIR values reported for their study, which consisted 
of conventional farms that relied predominantly on ch
isel plowing and disking. However 
their study did not include an assessment of secondary tillage. 
 
We found that a wide range of tillage frequencies and intensities are used on 
organic farms for all crops other than dry bean (Figures 1.3 and 1.4). In this 
context, 
 
 
 
 
 
35
 


employing reduced tillage compared to the general population. However, w
e currently 
have little information to compare farms that vary in tillage frequency and intensity 
(STIR adjusted tillage intensity; x
-
axis, Figure 1.4) based on weed control, crop 
establishment, and crop yields. 
 
Research has shown that despite heavy relia
nce on tillage, organic farming can 
increase soil organic matter (Teasdale et al. 2007), and improve aggregate structure 
(Pulleman et al. 2003; Papadopoulos et al. 2009; Bhardwaj et al. 2011), presumably 
due to higher inputs and quality of organic matter s
uch as compost, manures and cover 
crops. However, it is not yet clear to what extent routine tillage on organic farms impacts 
other characteristics of soil quality, such as water infiltration and soil crusting. More 
research is needed to determine the effe
ct that the routine frequency and intensity of 
tillage used on organic farms has on soil quality characteristics, particularly 
characteristics that will be important for climate change adaptation, like water infiltration 
and water holding capacity. Additio
nally, future research is needed on the balance 
between the intensity and frequency of tillage and its effectiveness for weed control. 
 
Perceptions of RT benefits
 
Determining the awareness of organic farmers regarding the benefits of RT 
adoption could poin
t to areas where more extension and education efforts are needed. 
No
-
till systems have been widely publicized because of their potential for sequestering 
carbon and reducing soil erosion (Phillips et al. 1980; Johnson and Hoyt, 1999; Triplett 
and Dick, 200
8). Traditional organic practices (ie. applying compost, manure and cover 
 
 
 
 
 
36
 
cropping) will also achieve these outcomes. Given that complete elimination of tillage in 
organic systems is likely not feasible, it is unclear to what extent RT systems that utilize
 
some periodic tillage would result in economic or ecological benefits. This lack of 
information could be responsible for low overall interest. We wondered whether organic 
farmers perceived improved water dynamics as being a likely benefit from RT adoption
. 
Our results show that improved water infiltration and increased water holding capacity 
ranked equally high as the more publicized benefits of RT, such as increased organic 
matter and decreased erosion (Figure 1.6). Therefore, education and extension effo
rts 
aimed at these issues likely will have little effect on organic farmer attitudes towards RT 
adoption. 
 
Perceived benefits to RT adoption were almost all positively correlated with 
interest in adoption. Why were some farmers more likely to perceive bene
fits from RT 
adoption than others? One explanation could be that RT is more aptly suited to certain 
soil type and climates. This is supported by the negative correlation between percent silt 
and clay with interest in adoption. Heavier soils that have poor 
drainage and take longer 
to warm in the spring experience additional problems with RT (
Peigné
 
et al. 2007). On 
the other hand, changes in physical properties associated with RT may be greater on 
silt and clay soils compared to sand (Peingé et al. 2007). Co
rrelations between soil type 
and interest in adoption may also be driven by cropping system; since field crops were 
associated with heavier soils, we cannot rule out the possibility that this correlation 
reflects lower interest among field crop growers.  S
ome farmers may not be aware of 
the benefits of RT, and these farmers may benefit from increased education and 
extension. 
 
 
 
 
 
 
37
 
Perceived barriers to 
RT 
adoption
 
The perceived barriers to adoption reduced to four factors, only one of which 
FACTOR 2 (residue man
agement, crop establishment, weeds, and to a lesser extent 
yields), was negatively correlated with interest in adoption (Table 1.6). This is consistent 

-
mulch systems that
 
are able to suppress weeds without suppressing the crop (Mirsky et 
al. 2012; Brainard et al. 2013). 
Utilizing cover crop residue as a mulch to suppress 
weeds is thought to be the key to successful implementation of organic RT (Carr et al. 
2012; Brainard e
t al. 2013). However, cover crop residue can also negatively affect crop 
establishment by interfering with planting equipment, immobilizing N, and releasing 
allelopathic compounds (Price and Norsworthy, 2013). Planting in high residue 
environments may caus
e hair pinning of residue within the seed furrow, decreasing soil
-
seed contact, and inhibiting germination (Mirsky et al. 2013). Cover crop biomass 
production is highly variable from year to year, and in years when cover crop biomass is 
low, 
there may not 
be sufficient residue for weed suppression. Low levels of residue 
used as a mulch can stimulate weed emergence (Mohler and Teasdale, 1993). High 
levels of residue create challenges for mechanical cultivation, which is likely to be 
necessary for control of 
escaped weeds. 
 
Implications for future research
 
The goals of adapting reduced tillage systems to organic farms are to improve 
soil health, decrease soil erosion, and offer organic farmers options for adapting to 
climate change. In place mulch, combined wi
th intact macropores, can buffer against 
extreme precipitation events. Complete elimination of tillage on organic farms is likely 
 
 
 
 
 
38
 

intensity that farmers are currently using, a
nd it may look very different on organic farms 
compared to the RT practices developed for conventional systems. Given the relatively 
low interest in the narrow forms of RT presented in the survey (no
-
till, rotational till, strip
-
till, permanent beds, ridge
-
till) the goal of future research and extension programs 
should perhaps be on decreasing the number of tillage operations and intensity (shifting 
the distribution to the left on the x
-
axes in Figures 1.3 and 1.4) without sacrificing weed 
control, crop yie
ld, and profitability. Given the constraints expressed by growers, the 
most promising avenues for doing so are likely identification of more effective weed 
management strategies, including cultural weed management, and identifying low cost 
adaptable cultiv
ation equipment capable of controlling weeds in high residue 
environments. 
 
Our goal in research and extension is not to promote adoption of any one specific 
practice or technology, but to improve the decision
-
making ability of farmers. We should 
strive to
 
provide them with information that is both useful and relevant. There has been 
much interest recently in the need to better frame our research and information to more 

Ahnström et al
.
 
2009
; 
W

many 
organic 
farmers 
a
re already aware of the benefits 
of
 
RT adoption, therefore to promote RT 
adoption future research efforts should likely focus more on alleviating the barriers to 
adopt
ion, rather than reiterating RT benefits. For example, future research efforts must 
evaluate the level of tillage that 
optimally balances weed control and soil quality 
 
 
 
 
 
39
 
objectives
. Additional research is needed to maximize cover crop residue weed 
suppressio
n, wh
ile avoiding crop interference.
 
SUMMARY AND CONCLUSION
 
Organic farms in Michigan exhibited a wide variation in tillage frequency and 
intensity. The number of total tillage operations was between 8 and 9 for field crops, and 
approximately 6.5 for veget
ables. Tillage operations were fairly evenly split between 
field preparation and cultivation. The most commonly used primary tillage 
implements 
were
 
moldboard and chisel plow
s
 
for field crops, and rototiller for vegetables. Vegetable 
farmers reported an ov
erall greater interest in adoption compared to field crop 
producers. Interest in adoption of RT practices among organic farmer respondents was 
overall fairly low, but farmers did express a high level of interest for a few forms of RT. 
Field crop producers 
were most interested in rotational tillage, followed by strip
-
till
age
, 
while vegetable growers were most interested in permanent beds and strip
-
till
age
. 
Future efforts in adapting RT systems for organic production would likely be more 
successful if they fo
cused on these forms of RT, or if they developed RT strategies 
specifically for organic production that targeted more incremental movements in the 
distribution and intensity of tillage to the left. 
 
This survey suggests that organic farmers in Michigan are
 
aware of the general 
benefits to RT adoption. The greatest perceived benefits to adoption were improved soil 
quality and fuel savings. Vegetable producers were more likely to perceive obtaining RT 
equipment as a greater barrier to RT adoption compared to 
field crop producers. Both 
groups ranked weeds, impacts on yields, residue management, and crop establishment 
as high barriers to adoption. Potentially fruitful avenues of future research might include 
 
 
 
 
 
40
 
identification of low cost adaptable RT equipment for 
organic producers, and 
identification of crop
-
specific levels of tillage that balance weed control and soil 
quality 
objectives, while maintaining crop yield and profitability. For example, research aimed at 
improving the efficiency of mechanical weed contr
ol (e.g. more efficient cultivation tools 
that reduce the frequency or intensity requirements) or replacing tillage and cultivation 
with non
-
mechanical forms of weed management such as cover cropping, may help 
organic growers build long
-
term soil health, w
hile avoiding short
-
term crop interference.
 
 
 
 
 
 
 
 
 
41
 
APPENDIX
 
 
 
 
 
42
 
APPENDIX
 
 
 
 
 
 
 
Table 1.1. STIR values used for tillage operations reported by organic farmer 
respondents for crop production.
 
Survey Option
 
RUSLE Tillage Operation
 
STIR 
value
 
Spader
 
Spader
 
18.00
 
Fing
erweeder
 
Weeder, fingerweeder
 
0.49
 
Striptiller
 
Subsoiler, in row strip conditioner, 40 in 
row
 
11.00
 
Bed former
 
Bed shaper
 
20.00
 
Flextine
 
Harrow coiled tine
 
16.00
 
Harrow
 
Spike tooth
 
16.00
 
Rotary hoe
 
Rotary cultivator 
 
6.60
 
Row cultivator
 
Field cultiva
tor 6
-
12 sweeps
 
26.00
 
Disc
 
Plow Disc
 
39.00
 
Field cultivator
 
Cultivator, with spike and coil tines
 
34.00
 
Rototiller
 
Rototiller (with adjusted depth)
 
18.00
 
Chisel plow
 
Chisel plow
 
53.00
 
Moldboard plow
 
Moldboard plow
 
65.00
 
Soil finisher
 
Combination of:
 
25.80
 
 
Coulter tiller and spike harrow
 
19.00
 
 
Rolling basket
 
6.80
 
Source: Revised Universal Soil Loss Equation 2
 
 
 
 
 
 
 
43
 
 
 
Table 1.2. Survey respondent demographics broken down by crop type.
 
 
 
 
Field Crop
 
Vegetable
 
 
 
 
#
 
Number of Respondents
 
 
65
 
39
 
 
 
 
 
 
Years Farming
 
 
 
%
 
 
 
5 years or less
 
4.6
 
7.7
 
 
6 to 10 years
 
10.8
 
33.3
 
 
11 to 15 years
 
7.7
 
10.3
 
 
16 to 20 years
 
7.7
 
17.9
 
 
21 to 30 years
 
12.3
 
10.3
 
 
31 to 40 years
 
32.3
 
10.3
 
 
41 to 50 years
 
12.3
 
7.7
 
 
over 50 years
 
10.8
 
2.6
 
 
 
 
 
 
Years Farming Orga
nic
 
 
%
 
 
5 years or less
 
7.7
 
17.9
 
 
6 to 10 years
 
40.0
 
38.5
 
 
11 to 15 years
 
20.0
 
12.8
 
 
16 to 20 years
 
16.9
 
10.3
 
 
21 to 30 years
 
12.3
 
5.1
 
 
31 to 40 years
 
1.5
 
7.7
 
 
41 to 50 years
 
0.0
 
5.1
 
 
 
 
 
 
Owner of Land
 
 
 
%
 
 
owner of farm, own land
 
66.2
 
71.8
 
 
own
er of farm, rented land
 
3.1
 
10.3
 
 
manager of farm
 
1.5
 
7.7
 
 
owner of farm, partial land owner
 
27.7
 
10.3
 
 
 
 
 
 
Organically Certified
 
 
%
 
 
no
 
 
10.8
 
61.5
 
 
yes
 
 
89.2
 
38.5
 
 
 
 
 
 
 
 
 
44
 
Table 1.2 (cont'd)
 
 
 
 
 
 
 
 
Field Crop
 
Vegetable
 
Percent income from farming
 
%
 
 
1
 
to 25
 
 
18.5
 
38.5
 
 
26 to 50
 
 
10.8
 
15.4
 
 
51 to 75
 
 
23.1
 
7.7
 
 
76 to 100
 
 
46.2
 
38.5
 
 
 
 
 
 
Percent of work week spent farming
 
%
 
 
1 to 25
 
 
15.3
 
0
 
 
26 to 50
 
 
9.1
 
20.5
 
 
51 to 75
 
 
13.8
 
7.7
 
 
76 to 100
 
 
59.9
 
66.7
 
 
 
 
 
 
Farm acreage breakdown in here
 
%
 
 
10 
and under
 
10.8
 
71.8
 
 
10 to 50
 
 
10.8
 
23.1
 
 
50 to 100
 
 
13.8
 
2.6
 
 
100 to 500
 
43.1
 
2.6
 
 
500 and higher
 
20.0
 
0
 
 
 
 
 
 
Percent of sales of farm 
 
 
%
 
 
vegetables
 
8.7
 
86.1
 
 
treefruit
 
 
5.9
 
2.1
 
 
small fruit
 
4.4
 
4.9
 
 
hay
 
 
4.9
 
0.3
 
 
livesotck
 
 
6.1
 
3.1
 
 
beans
 
 
21.9
 
0.3
 
 
grains
 
 
41.1
 
1.0
 
 
dairy
 
 
1.4
 
0.0
 
 
other
 
 
5.7
 
2.4
 
 
 
 
 
 
 
 
45
 
Table 1.3. Tillage and cultivation practices employed for the production of field corn, dry 
bean, and soybean.
 
 
 
 
 
 
 
 
 
 
 
46
 
Table 1.4. Tillage and cultivation practices employed for the production o
f winter squash 
and broccoli.
 
 
 
 
 
 
 
 
 
 
47
 
Table 1.5. Spearman correlation of farm and farmer characteristics, as well as 
perceived barriers and benefits of RT adoption, with expressed interest in adoption of 
specific reduced till practices.
 
 
 
 
 
 
 
48
 
Table 1.6. Results f
rom a factor analysis of the perceived barriers to adoption of RT, as 
well as correlation with maximum expressed interest in RT adoption.
 
 
 
 
 
 
 
 
49
 
Table 
1.
7.
 
Coding used for percent clay and percent silt soil 
 
types for correlations in Table 1.4.
 
 
 
 
 
 
50
 
 
 
 
 
 
 
 
Figure 1.1. 
P
rinciple crop, acreage and soil type of survey respondents by
 
Michigan
 
region. 
 
 
 
 
 
 
51
 
 
 
Figure 
1.
2. Percentage of organic field crop and vegetable producers in 
Michigan that have access to 
tillage and 
agricultural equipment. 
 
 
 
 
 
 
52
 
 
 
 
 
 
 
Figure 
1.
3. Distributions of the number of tillage passes utilized for cultivation
 
(A)
, field preparation
 
(B)
, and 
cumulative total
 
(C)
 
for broccoli, winter squash, field corn, dry bean, and soybean. 
The y
-
axis is t
he probability 
density 
of a given number of tillage passes
, and
 
was det
ermined using kernel density estimation.
 
 
 
 
 
 
 
53
 
 
 
Figure 
1.
4. Distributions of the 
cumulative STIR values for tillage utilized in the production of broccoli, winter squash, 
field corn, dry bean, and soybean. The y
-
axis is the probability 
density 
of a given number of tillage 
operations
, and 
was determined using kernel density estimation.
 
 
 
 
 
 
54
 
 
 
 
 
 
 
 
 
Figure 1.5. Distribution and median (

) of survey respondents

 
expressed 
interest in adoption (left) and perceived knowledge of specific RT practices 
(right) on a 0 to 7 
L
ikert scale. The width of violin plots represents the 
range of interest, and the hei
ght at any point represents the proportion of 
respondents with that score. 
0= No interest/ knowledge, 7=Extremely
 
interested/ knowledgeable.
 
 
 
 
 
 
55
 
 
 
 
 
Figure 
1.
6. Distribution and median (

) of survey respondents 
perceptions of the benefits to RT adoption on a 0 to
 
7 
L
ikert scale. The 
width of violin plots represents the range of interest, and the height at 
any point represents the proportion of respondents with that score. 
0= 
Not a likely benefit, 7=Extremely likely benefit.
 
 
 
 
 
 
 
56
 
 
Figure 
1.
7. Distribution and median (

) of survey respondents perceptions 
of the barriers to RT adoption on a 0 to 7 
L
ikert scale. The width of violin 
plots represents the range of interest, and the height at any point 
represents the proportion of respondents with that score. 
0= Not a likely 
b
arrier, 7=Extremely likely barrier.
 
 
 
 
 
 
57
 
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64
 
CHAPTER TWO
 
Strip
-
tillage 
influence
s
 
N availability a
nd
 
losses in an organic system
 
ABSTRACT
 
Developing reduced
-
tillage systems that increase inorganic N (IN) availability while 
decreasing N losses, is important for 
increasing nitrogen
-
use efficiency
 
within legume
-
based sy
stems. 
We 
conducted 
a
 
field study 
from
 
2011 to 2014 in 
southwest 
Michigan to 
examine the effects of strip
-
tillage and a cereal rye and hairy vetch cover crop mixture 
(RV) on 1) season long IN availability, 2) N mineralization 
potential 
(P
MN
), and 3) the 
po
tential for N losses through denitrification and leaching. Treatments consisted of 
tillage (full
-
width tillage [FWT] and strip
-
tillage [ST]) and cover crop (RV and no cover 
crop [
NoRV
]). ST had no effect on PMN in the first half of the sweet corn season, b
ut 
reduced PMN in the second half of the season when combined with RV. ST decreased 
IN availability 16 
to 
40% compared to FWT.  Within ST, IN was higher in the 
in
-
row zone 
compared to the undisturbed between
-
row zone in 1 out of 3 y
ears. The RV cover crop 
did not provide any consistent
 
increase in soil IN.  Across the three years of this study, 
ST increased the 
concentration of
 
N 
within the soil 
leach
ate
 
by approximately 50% and 
increased the potential for denitrification by 18%. However, despite lower soil
 
IN, ST 
only resulted in negative effects on corn biomass and N content in 2013, the year with 
the lowest hairy vetch biomass. ST increased soil gravimetric moisture in 2012 and 
2014, which may have compensated for the lower IN. More research is needed to 
determine strategies to increase nitrogen use efficiency and decrease N losses in 
legume
-
based reduced
-
till systems.  
 
 
 
 
 
 
 
65
 
INTRODUCTION
 
Reducing 
tillage 
can 
provide a number of ecological and agronomic benefits, 
including increased soil aggregation and organi
c matter accumulation (Wander and 
Bollero, 1999; Franzluebbers 2002; West and Post, 2002; Bhardwaj et al. 2011), as well 
as reduced soil erosion (Karlen et al 1994, Triplett and Dick, 2008) and fuel use (Phillips 
et al
.
 
1980; Gebhardt et al
.
 
1985). Reduced
-
till
age
 
(RT) 
practices are also being 
promoted as an effective strategy to both mitigate and adapt to climate change 
(Pittelkow et al. 2014) due to their potential for C sequestration (Lal and Kimble, 1997), 
as well as increased soil water infiltration an
d 
water 
holding capacity (Franzluebbers, 

services to a cropping system, such as weed control and the incorporation of plant 
residues. Reducing or eliminating tillage may resu
lt in poor crop establishment due to 

et al. 2012)
,
 
greater weed competition (Brainard et al. 2013, Mirsky et al. 2012), and 
reduced inorganic N availability (Dou et al. 1
994; Doane et al. 2009). 
 
Tillage stimulates N mineralization by incorporating plant residues (Varco et 
al.1993), increasing soil aeration, and exposing occluded soil organic matter via 
aggregate fragmentation (Grandy and Robertson, 2006). In the absence o
f tillage, 
reduced plant available N has been widely reported in both legume and fertilizer based 
systems (Dou et al. 1995; 
Johnson and Hoyt, 1999
). In legume
-
based no
-
till systems, 
residues remain on the soil surface, resulting in lower soil
-
residue conta
ct and reduced 
rates of decomposition and N mineralization (Varco et al. 1993; Dou et al. 1994; 
Drinkwater et al. 2000). 
For example, h
airy vetch residues left on the soil surface lost 
 
 
 
 
 
66
 
45% of their original weight over 30 days, compared to 77% decompositio
n when 
residues were buried in the soil (Varco et al. 1993). Lower N mineralization from legume 
residues within RT systems is especially problematic in years or sites with cool or dry 
soil conditions (Dou et al. 1995; Rannells and Wagger, 1996; Ruffo and B
ollero, 2003; 
Cook et al. 2010). 
 
Despite having lower pools of inorganic N, no
-
till systems can result in equal or 
greater N uptake and productivity (Reeves et al 1997) , and may (Drinkwater et al. 
2000; Doane et al. 2009) or may not (Cline and Silvernail
 
et al. 2002; Cook et al. 2010) 
lead to decreased yields. Sustained N recovery despite lower soil inorganic N may be 
the result of greater synchronization between N availability and crop N demand. 
Conventionally tilled legume
-
based systems generally exhibi
t a large pulse of inorganic 
N early in the season, prior to crop exponential growth (Dou et al. 1994; Kuo et al. 
1996). The slower period of N mineralization within RT may more closely reflect the 
timing of crop N demand. Slower decomposition and minerali
zation of plant residues 
may contribute to the build
-
up of organic pools of N and other nutrients (Kuo et al. 1997; 
Sainju et al. 2002). 
 
Lower pools of inorganic N in no till systems may decrease reactive N lost to the 
environment through leaching or deni
trification (Dinnes et al. 2002; Constantin et al. 
2010). A long
-
term comparison of no
-
till to conventional tillage within a corn
-
soybean
-
winter wheat rotation found that no
-
till resulted in approximately 35% lower leaching 
compared to conventional tillage
 
(Syswerda et al. 2012). However, no
-
till systems have 
greater intact soil pores, which can increase hydraulic conductivity and soil water 
drainage (Benjamin, 1993; Odgen et al. 1999). Thus, even if the concentration of N 
 
 
 
 
 
67
 
within the soil leachate is equal 
to or lower than conventional tillage, no till may increase 
N leaching when differences in water drainage are taken into account (Izaurralde at al. 
1995
; Syswerda et al. 2012
). 
 
 
 
Tillage elimination can also have varying effects on N lost to denitrificati
on. No
-
till is often associated with increased soil moisture and bulk density, factors that 
promote denitrification via reductions in oxygen levels (Smith and Tiedje, 1979; 
Groffman 1985; Palma et al. 1997). Denitrification is often carbon limited in agric
ultural 
systems (Luo et al. 1998 Kennedy et al. 2013), and when crop residues are 
incorporated into the soil they result in a pulse of high initial denitrification (Aulakh 
1991). Residue mulches often decrease soil temperatures, which may also decrease 
rat
es of denitrification (Keeney et al. 1979; Maag and Vinther, 1996). 
 
Developing reduced
-
tillage systems that increase inorganic N availability while 
decreasing N losses is essential to 
increasing nitrogen use efficiency
,
 
and may increase 
productivity,
 
espe
cially in organic or legume
-
based systems. 
Strip
-
tillage
 
(ST)
 
utilizes a 
combination of narrowly placed disks and rolling baskets (often attached to a shank) to 
confine tillage to a narrow strip where the crop will be planted (Luna and Staben, 2002). 
This 
divides the cropping system into two distinct adjacent zones: tilled and untilled. 
Tillage within the crop row 
creates a finer seedbed into which the crop will be planted 
(Licht and Al
-
Kaisi, 2005). The untilled zone between
-
row (BR) maintains the benefits
 
of 
no till, such as improved water infiltration, organic matter retention, and decreased 
erosion (Karlen et al. 1994; Johnson and Hoyt, 1999; Franzluebbers, 2002). Strip
-
tillage 
enables a greater level of control over organic matter turnover. Incorporatin
g crop 
residue 
with
in
-
row 
(WR)
 
enables faster decomposition and mineralization of organic 
 
 
 
 
 
68
 
matter to increase nutrient availability within the crop rooting zone. The 
BR 
zone is left 
undisturbed, and when combined with a preceding cover crop, residue is pres
erved on 
the soil surface for weed suppression, soil moisture conservation, and reduced soil 
erosion (Mohler and Teasdale, 1993; Unger and Vigil, 1998). 
 
Compared to no
-
till and conventional systems, strip
-
tillage effects on soil N 
dynamics have been much 
less studied. 
Haramoto and Brainard (2012) found that in 
the absence of a cover crop, ST decreased inorganic N within the crop row compared to 
full
-
width tillage; however, when an oats cover crop was present, N availability was 
equivalent in the two tillag
e systems. 
Compared to no
-
till, Sainju et al. (2007) found ST 
increased inorganic N compared to no
-
till. Al
-
Kaisi and Licht (2004) found few significant 
difference
s
 
in N concentration of soil leachate between no
-
till, strip
-
till, and conventional 
till, how
ever, they did find that like no
-
till, strip
-
tillage resulted in lower N accumulation 
within the soil profile at deeper depths. Sainju et al. (2007) did not find any difference in 
N accumulated in the soil profile between ST and 
full
-
width tillage (
FWT
)
. T
hus far, 
research on strip
-
tillage effects on inorganic N and N losses has been confined to 
conventional systems. It is not yet clear how 
ST
 
may
 
affect the fate of N in a legume
-
based organic system.
 
The objectives of this study 
we
re to examine the effects
 
that strip
-
tillage has on 
1) season long inorganic N availability, 2) the potential for N mineralization, 3) crop N 
uptake, and 4) the potential for N losses through denitrification and leaching. In addition, 
we evaluated 
 
whether these effects varied 
bet
ween zones
 
(within
-
 
and between
-
crop 
rows)
 
within the cropping system. We hypothesized that strip
-
tillage w
ould
 
have lower 
total inorganic N availability, but a greater proportion of its available N concentrated 
 
 
 
 
 
69
 
within the crop 
row
 
(WR)
 
and thus result in 
a lower potential for N losses, and greater N 
uptake by the crop. A secondary objective 
wa
s to examine how a rye and vetch cover 
crop interacts with strip
-
tillage to affect inorganic N availability, potentially 
mineralizeable N, 
sweet corn biomass and N up
take, 
and the potential for N losses.
 
 
MATERIALS AND METHODS
 
Site 
d
escription
 
and experimental design
 
The experiment was conducted between 2011
-
2014 on organically managed 
(approximately 5
-
7 years, depending on field) Kalamazoo (fine
-
loamy, mixed, mesic 
Ty
pic Hapludalfs) and Oshtemo (coarse
-
loamy, mixed, mesic Typic Hapludalfs) loams 
(Crum and Collins, 1995), at the Kellogg Biological Station in Hickory Corners, MI 
(85




 
Treatments were arranged in a split plot, randomized complete block design with 
four replicates. Main plots consisted of a 2 X 2 factorial of cover crop (Rye
-
vetch [RV] 
and 
n
o cover crop [
NoRV
]) and tillage (Strip
-
tillage [ST] and Full
-
w
idth tillage [FWT]). 
The split plot factor was crop (sweet corn [CORN] or no crop [NOCORN]). 
Zones within 
the cropping system were also treated as 
subsub
plots. 
The zone within the crop row 
(WR) was defined as the 25 cm zone where the crop was planted and w
here tillage 
occured within strip
-
tilled treatments. The between
-
row (BR) zone was defined as the 
50 cm zone between crop rows that was left untilled in strip
-
tilled treatments. The area
 
of the sub
-
plots with corn (CORN) was 27.3 m
2
 
(9.1 m X 3.0 m), 37.2 m
2
 
(6.1 m X 6.1 
m), and 41.0 m
2 
(9.1 m X 4.5 m) in 2012, 2013, and 2014 respectively. Subplots without 
corn were 9.0 m
2
 
(3.0 m X 3.0 m) in 2012 and 2013, and 20.3 m
2
 
(4.5 m X 4.5 m) in 
2014.
 
 
 
 
 
 
70
 
Field activities were performed according to Table 2.1. Both cerea
l rye and hairy 

Cereal rye was planted with a grain drill at 62.7 kg ha
-
1
. Hairy vetch was planted with a 
Jang push
-
seeder at 22.4 kg ha
-
1
. Between
-
row spacing for rye 
and vetch was 19 cm. 
No N fertilizers were added prior to cover crop establishment. Additional organic forms 
of P and K were added according to soil tests, and consisted of: 34 kg K 
and
 
P ha
-
1
 
of 
NOP certified Potassium Sulfate Plus 
and Tennessee Brown Pho
sphate in 2012 and 34 
kg K ha
-
1
 
of 
Potassium Sulfate Plus
 
in 2013. 
 
Cover crops were flail mowed twice once vetch had reached approximately 50% 
flowering in mid
-
 
to late
-
 
May of 2012, 2013, and 2014. 
Full
-
width tillage treatments
 
were chisel plowed, and th
en rototilled to break up cover crop residue. A Hiniker Model 
6000 two
-
row strip tiller (equipped with cutting
-
coulter, shank point assembly, berming 
discs, and rolling basket) was used for
 
WR
 
tillage in zone tilled treatments. To improve 
soil tilth for pl
anting in ST treatments, a walk behind rototiller was used for
 
WR
 
secondary
-
tillage. The resulting
 
WR
 
zone was approximately 25 cm wide and 25
-
30 cm 
deep. Two weeks after tillage, sweet corn was planted 
in all treatments 
using a high 
residue planter (Monos
em vacuum seeder) at a between
-
row spacing of 76 cm and in
-
row spacing of 10 cm. Once sweet corn emerged, population density was thinned to an 
in
-
row spacing of 16.5 cm
 
in C
ORN
 
treatments
, 
and
 
removed via hoeing from NOCORN
 
subplots. Weeds were controlled 
through a combination of hand weeding and hoeing. 
Sweet corn was irrigated during low
-
rainfall periods in 2012 and 2014, totaling 62.2 and 
46.8 mm of irrigated water, respectively throughout the sweet corn season. In 2013, 
rainfall was sufficient so we did
 
not provide any additional irrigation. 
 
 
 
 
 
 
71
 
Data c
ollection
 
Potentially 
mi
neralizeable N
.
 
In situ
 
N mineralization assays were employed to evaluate 
the potential for N release from cover crop residues within each tillage system 
(Robertson, et al
.
 
1999). 
In si
tu
 
or in field mineralization incubations incorporate effects 
that tillage and cover crop mulch have on soil structure, moisture, and temperature, as 
well as the subsequent effect these differences have on N mineralization. Incubations 
were initiated at tw
o time points: 7 and 35 days after tillage. 
Five 
cm internal diameter 
(24 cm depth) PVC pipes were installed 
in both the WR and BR zones 
(3 cores per 
zone and tillage combination)
 
within the sweet corn subplots
 
and capped until core 
removal. At installatio
n, we collected two soil samples directly adjacent to installed 
cores at the start of incubation and extracted for inorganic N to determine initial 
inorganic N. We removed soil cores after 30 to 35 days and measured soil for 
gravimetric moisture and inorga
nic N. 
 
Mineralization per day was calculated according to the following equation: 
 
 
[2.1]  
























 
Bulk density.
 
Upon mineralization tube extraction, soil cores 
removed from the same 
plot and zone (WR and BR) 
were pooled, homogenized, and weighed. Two 10 g 
subsamples were 
then 
removed for gravimetric soil moisture to determine soil dry 
weight (
see Equation 2.2). Bulk density was then calculated by dividing soil dry weight 
by soil volume
 
(total volume of the pooled cores)
. If soil was missing from the tube 
bottom, we measured the length of tube with soil missing to estimate the volume of 
each soi
l core for calculation of bulk density. 
 
 
 
 
 
 
72
 
Soil sampling
-
 
Inorganic N and gravimetric soil m
oisture. 
Soil samples were collected 
every 7
-
14 days (totaling 8 sampling times in 2012 and 2013, and 9 sampling points in 
2014), throughout the growing season in all
 
treatments and subplots in both the
 
WR
 
and 
BR. At each sampling date, approximately 12 cores (2.5 cm diameter and 20 cm deep) 
were taken 
from 
each plot and zone. Soil samples were dried at 38°C
 
for 2 to 3 days
, 
and ground. 
Fifty milliliters of 1M KCl were
 
used to extract soil inorganic N on 10 g of 
dry soil and samples were analyzed on a QuikChem 8500 Flow Injection Analyzer 
(Gelderman and Beegle, 1998). Two 10 g subsamples of moist soil were dried at 100°C 
and reweighed for gravimetric soil moisture. Grav
imetric water content (GWC) was 
calculated according to the following: 
 
 
[2.2] 
     























 
To calculate the soil inorganic N and gravimetric moisture across the whole plot, we 
used weighte
d means of the measured values from the WR and BR zones adjusted by 
their respective areas.  For example, for inorganic N (IN) we used: 
 
[2.3]
 
IN
whole plot
 
= IN
WR
*1/3 + IN
BR
*2/3
 
Where the within
-
row (WR) zone is the 25 cm zone where the crop is planted (an
d 
where tillage occurs within strip
-
tilled treatments), and the between
-
row (BR) zone is the 
50 cm zone between crop rows and also is untilled in strip
-
tilled treatments.
 
Sweet corn
 
biomass and N content.
 
At harvest, we removed all primary ears from sweet 
corn plants in a designated harvest area of 9.3, 14, and 22.3 m
2
 
in 2012, 2013, and 
2014, respectively. Secondary corn ears were grouped in with nonharvested biomass 
and consisted of no greater than 5% of total non
-
harvested biomass. To obtain plant dry 
we
ight and percent N, we collected the non
-
harvested aboveground portion of 5 
 
 
 
 
 
73
 
randomly sampled plants from each plot, and 5 representative corn ears. We removed a 
2.5 cm section from corn ears
 
(taken 2.5 cm 
away
 
from the base of the ear)
, dried to 
determine 
a fresh to dry weight ratio, and multiplied this ratio by total fresh weight to 
estimate total ear dry weight. Both nonharvested corn biomass and corn ear sections 

(Kalra, 1998
) was performed on 0.15 g samples of nonharvested corn biomass and corn 
ears (2 lab replicates per sample) and analyzed for total Kjehldahl nitrogen with a 
QuikChem 8500 Flow Injection Analyzer.
 
Leachate inorganic N
.
 
Suction lysimeters (Soil Moisture Corps
 
model no. 1920) were 
installed to a depth of 1.2 m to examine the effects of tillage (FWT vs ST) on 
inorganic 
N 
concentration of the soil leachate 
within sweet corn subplots
, and only within 
treatments with a rye and vetch cover crop
. In Year 1, lysimeter
s were installed in 
November 2011, during cover crop growth, 
but 
damage to cover crops was minimized. 
In both Years 2 and 3, lysimeters were installed in early Fall, prior to cover crop planting 
(Table 2.1). A 5 cm auger was used to 
hollow out 
the lysimete
r cavity.  We then inserted 
lysimeters into the 
hole
 
and a silica
-
sand slurry was used to fill the gap between the 
cavity wall and the lysimeter. Once lysimeters were in place soil was sieved and the 
hole was refilled
, and bentonite clay was added to minim
ize preferential flow.
 
Trenches 
(30 to 35 cm
 
depth
) 
were created to run sample collection tubes outside of the plot. Soil 
water samples were collected within 48 hours of every significant rainfall (when 
precipitation exceeded approximately 2 cm) or irrigat
ion event to determine the 
concentration of nitrate present in the soil water below the rooting zone of the crop. 
 
 
 
 
 
74
 
Vacuum pressure 
was applied to approximately 60 psi and left overnight; samples were 
collected within 48 hours of 
vacuum 
pressure being applie
d. 
 
Volumetric soil moisture.
 
A Sentek
TM
 
Diviner 2000 capacitance probe was used to 
estimate the volumetric water content (VWC) of the soil in 10 cm increments to a depth 
of 1 m in 2013 and 2014. Diviner tubes (5 cm diameter X 100 cm deep) were installed 
w
ithin sweet corn subplots on the border of
 
WR
/BR zone. VWC measurements were 
used to evaluate differences in water content within the soil profile of tillage and cover 
crop treatments to help determine if differences in the concentration of nitrate in soil
 
leachate
 
samples were due to differences in quantity of nitrate or 
to 
water moving 
though the soil profile. 
 
Denitrification potential.
 
 
The 
denitrification enzyme assay 
(Groffman et al. 1999) 
was 
utilized to evaluate the potential for denitrification in 
ST and FWT, with and without a RV 
cover crop, at three time points during the season in 2013 and 2014. Soil was collected 
at planting, midseason and harvest. Soil samples were collected from each tillage, zone
 
(WR and BR)
, and cover crop combination taken 
in subplots with sweet corn present. 
Separate soil cores were taken for
 
WR
 
and BR zones down to 20 cm, aggregated, and 
kept at field moisture in 4°C conditions until time of enzyme assay. Soil was passed 
through a 4 mm sieve, and 25 g of moist soil was wei
ghed into 125 ml Erlenmeyer 
flasks and used for incubation. Soil was combined with 25 mL of a 2X DEA media (1.44 
g/L KNO
3
, 0.02 g/L chloramphenicol, and 0.5 g/L dextrose), and flasks were sealed and 
flushed with N
2
 
gas. 
Thirteen
 
ml of acetylene gas was add
ed (approximately 10% of 
headspace), and flasks were placed on an orbital shaker for 90 minutes. Headspace 
samples were collected at 30, 60, and 90 min and stored in evacuated glass exetainers, 
 
 
 
 
 
75
 
Samples were analyzed using a gas chromatograph equipped with 
a 63Ni electron 

2
O dissolved in the slurry was calculated 

enzyme activity was calculated from N
2
O accumulation over time using lin
ear 
regression.
 
Statistical a
nalysis
 
All data were analyzed using mixed models ANOVA with MIXED procedures in SAS 
(Version 9.3, SAS Institute, Cary, NC). Year was initially treated as a fixed factor to 
determine if fixed effects interacted with year to aff
ect response variables, and if there 
was no difference year was pooled. In all cases, block was nested in year, and block 
and interactions of block with fixed factors were treated as random effects.  
We 
evaluated assumptions of normality and homogenous var
iances, and used unequal 
variance models where appropriate. All inorganic N data were log
-
transformed. 
 
The fixed effects of tillage (FWT vs ST), cover crop (RV vs 
NoRV
), zone (
W
R vs 
BR), crop (sweet corn vs none), and their interactions on N mineralizatio
n potential, N 
availability, and potential denitrification were analyzed.  For N leachate data, only the 
fixed effects of tillage were analyzed, since leachate was collected
 
only
 
from corn
 
subplots in treatments with
 
rye and vetch
. For corn N uptake and N 
partitioning, only the 
effects of tillage and cover crop were examined. For N leachate, IN, and gravimetric and 
volumetric soil water content, date was included as a repeated factor and repeated 
measures mixed models were performed and AIC and BIC values w
ere compared to 
determine the best model fit. When significant treatment effects were detected, means 
were separated using Fisher's protected LSD 
P
< 0.05.  In cases where P
-
values were 
 
 
 
 
 
76
 


-
value presented parenthetically. All figures were made using the GGPLOT
2 package in 
R (Wickham, 2009).
 
RESULTS
 
Cover crop and weed biomass and C:N ratio
 
The RV cover crop mixtures contained approximately 2
.7
, 1
.3
, and 3
.
0 
M
g ha
-
1
 
of 
hairy v
etch above
-
ground biomass, and 5
.
5, 4
.7
 
and 4
.7
 
M
g 
ha
-
1
 
of cereal rye above
-
ground biomass in 2012, 2013, and 2014, respectively (Table 3.5, Figure 3.4). Total 
biomass in these treatments was therefore 8
.2
, 5
.
9, and 7
.
7 
M
g ha
-
1
 
in 2012, 2013, and 
2014, res
pectively. The C:N ratio of this RV aboveground biomass was approximately 
43, 40, and 31 in 2012, 2013, 2014, respectively. Winter annual weed biomass within 
the 
NoRV
 
treatments was approximately 3
.0
, 2
.
0, and 2
.
2 
M
g ha
-
1
 
in 2012, 2013, and 
2014. 
Winter an
nual weed biomass within the RV treatments was 1.5, 0.42, and 0.58 
Mg ha
-
1
 
in 2012, 2013, and 2014, respectively. 
The mean C:N ratio of winter annual 
weeds was 37, 28, and 23 in 2012, 2013, and 2014
, respectively
. 
Winter annual weed 
C:N was only measured i
n NoRV treatments. 
 
Potentially m
ineralizable N
 
Potentially mineralizeable N 
(PMN) 
was measured from planting to mid
-
season 
(Early) and mid
-
season to harvest (Late) in 2012, 2013, and 2014 (Figure 2.1). For both 
Early and Late N mineralization potential
s
, 
there were no interactions of year with either 
the cover crop or tillage factors. RV increased Early N mineralization potential by 23% 
compared to the 
NoRV
 
control (
P
 
=0.03) but no significant tillage main effects or 
interactions were detected. For Late N 
mineralization, the effect of the RV cover crop 
 
 
 
 
 
77
 
depended on tillage system (Till*Cover, 
P
 
= 0.00
9
); RV increased Late N mineralization 
potential by 50% within FWT, but had no detectable effect within ST. 
 
Inorganic N availability
 
The NOCORN
 
subplots allow 
us to examine patterns in IN availability without 
crop interference. The effects of tillage on soil inorganic N (IN) in the absence of sweet 
corn varied with sampling time in two of three years, but rarely varied with cover crop 
(Table 2.2, Figure 2.2). In
 
2013 and 2014, ST reduced total IN availability by 
approximately 40% compared to FWT, with the greatest differences occurring after mid
-
July in both years. In 2012, a much smaller and only marginally significant (
P
=0.088) 
reduction in IN in ST compared to
 
FWT was observed (Table 2.2, Figure 2.2). 
 
 
In the presence of sweet corn, IN was also lower in ST compared to FWT in 
2013 and 2014, with the effect in both years depending strongly on sampling date 
(Table 2.2, Figure 2.2). Due to crop depletion of IN, fe
wer and smaller tillage differences 
in IN were observed, especially at later planting dates; the effect of tillage tended to 
increase until mid
-
July to August when soil inorganic N peaked, and then decrease until 
the end of the season during corn exponenti
al growth and peak N demand (Figure 2.2). 
In all three years, no differences in IN were observed at the time of crop harvest, 
suggesting that the potential for post
-
harvest differences in leaching due to different 
residual N levels were minimal. 
 
The rye a
nd vetch cover crop had minimal effects on IN, which varied by year.
 
In 
2012, in both CORN
 
and NOCORN plots, cover crop had no detectable effect on IN 
(Table 2.2, Figure 2.2). In 2013 within NOCORN subplots, there was a marginal 
interaction of cover crop, 
tillage, and sampling date (
P
=0.059). Within ST, RV increased 
 
 
 
 
 
78
 
inorganic N compared to 
NoRV
 
early in the season, but lowered IN throughout the 
month of August; however under FWT, RV only increased IN at sweet corn harvest. In 
2013 with sweet corn present, R
V increased inorganic N within FWT during the month 
of July, but RV had no effect on IN within ST. In 2014, RV increased IN at planting in 
NOCORN subplots, (Table 2.2, Figure 2.3), and at one time
-
point in early August in the 
presence of sweet corn (Figure
 
2.2). 
 
Within NOCORN subplots, tillage effects on IN availability within tillage zones 
(WR
 
vs BR) differed by year, but did not interact with cover crop and rarely interacted 
with date (Table 2.3, Figure 2.3). In 2012, we detected no significant effect of
 
tillage or 
cover crop on IN by zone. In 2013, the
 
WR
 
zone had significantly greater IN availability 
than
 
the
 
BR zone within ST plots, and both the
 
WR
 
and BR within FWT were greater 
than ST (
P
 
=0.028). In 2014, IN was higher in both the
 
WR
 
and BR of FWT th
an ST, but 
the
 
WR
 
zone of ST was higher than the BR only on the last sampling date, and this 
effect was only marginally significant (Till*Zone*Date, 
P
 
=0.094). 
 
Gravimetric soil moisture
 
In all three years we analyzed the effects of zone 
(WR
 
and BR) on gra
vimetric 
soil moisture in CORN subplots, and found no interactions with tillage, cover crop, and 
date in any year (data not shown) so the data was used to calculate gravimetric 
moisture across the whole plot. The BR zone had approximately 1 to 2% greater 
g
ravimetric moisture compared to the
 
WR
.
 
Tillage effects on gravimetric soil moisture did not interact with cover crop in any 
of the three years, but did interact with date in 2012, and there was a marginal 
interaction with date in 2013 and 2014 (Table 2.4,
 
Figure 2.4). In 2012, ST increased 
 
 
 
 
 
79
 
gravimetric soil moisture by approximately 9%, and the difference between ST and FWT 
was greatest in mid
-
July. In 2013, FWT resulted in a marginal increase in soil moisture 
at sweet corn planting, but we found no detecta
ble difference between tillage systems 
throughout the rest of the sweet corn season. In 2014, ST increased gravimetric 
moisture by an average of 7% throughout the sweet corn season, and this difference 
was greater at the end of July and end of August. 
 
Cov
er crop effects on gravimetric soil moisture interacted with date in 2012 and 
2014, but did not interact with tillage in any year (Table 2.4, Figure 2.4). RV increased 
gravimetric soil moisture by 5, 12, and 6% compared to 
NoRV
 
in 2012, 2013, and 2014, 
res
pectively. In 2012, the greatest increase in soil moisture due to RV was in mid
-
July, 
while in 2014 the difference was greatest at the end of July and end of August. 
 
Volumetric soil moisture
 
 
Volumetric soil moisture in both 2013 and 2014 can be found in 
Table 2.5 and 
Figure 2.5. ST increased soil moisture in 2013 at the 0.1 to 0.3 m depth (Till, 
P
=0.012), 
and marginally increased soil moisture in 2014 at the 0.8 to 0.9 m depth. We found no 
effect of cover crop, and no interactions of tillage or cover crop
 
with date. 
 
S
weet corn biomass and N content
 
 
Tillage effects on sweet corn ear and nonharveted biomass interacted with cover 
crop in 2012, but did not interact with cover crop in 2013 and 2014 (Table 2.6, Figure 
2.6). In 2012, tillage had no effect on sw
eet corn ear weight, however ST increased 
sweet corn nonharvested biomass by 44% within RV but not when combined with 
NoRV
. In 2013, ST decreased nonharvested biomass by 11% compared to FWT, but 
had no effect on sweet corn ear weight. In 2014, ST increased
 
sweet corn ear weight, 
 
 
 
 
 
80
 
but had no effect on nonharvested biomass. Tillage had no effect on biomass 
partitioning in 2012 and 2013, and sweet corn ear biomass constituted approximately 
46% and 32% in 2012 and 2013, respectively. In 2014, ST increased biomas
s 
partitioned to sweet corn ears, and sweet corn ear weight was 32 and 37% in ST and 
FWT, respectively. 
 
Tillage effects on total sweet corn aboveground N content interacted with cover 
crop in 2012, but not in 2013, and we found no detectable effect of til
lage in 2014. 
(Table 2.6, Figure 2.6). In 2012, ST+RV increased sweet corn N content compared to 
FWT+RV within RV, but tillage had no effect within 
NoRV
. In 2013, ST reduced N 
uptake by 25%. 
 
 
In 2012, RV increased sweet corn ear weight across both tillage
 
systems; RV 
also increased sweet corn nonharvested biomass by 70% in ST, but had no effect within 
FWT (Table 2.6, Figure 2.6). In 2013, RV increased sweet corn nonharvested biomass, 
but had no effect on sweet corn ear weight. In 2014, RV increased sweet c
orn 
nonharvested biomass and sweet corn ear dry weight. Cover crop had no effect on 
biomass partitioning in any of the three years. 
 
RV effects on sweet corn N content interacted with tillage in 2012, but not in 
2013 or 2014. In 2012, RV increased sweet co
rn N content by approximately 65% in 
ST, but only 22% in FWT. In 2013, RV increased N content of nonharvested biomass, 
but had no effect on N content of sweet corn ears or total plant N. In 2014, RV 
increased total N content by 24% compared to 
NoRV
. 
 
Neith
er tillage nor cover crop affected N partitioning to sweet corn nonharvested 
biomass or corn ears in any of the three years. In 2012, 50
-
60% of N was partitioned to 
 
 
 
 
 
81
 
the sweet corn ear. In 2013 and 2014, there was lower N partitioned to the corn ear, 
consti
tuting 41 and 33% of total aboveground sweet corn N uptake in 2013 and 2014, 
respectively.
 
Soil l
eachate N
 
Strip tillage increased the concentration of N within the soil leachate by 
approximately 50% compared to FWT (Till, 
P
=0.0113; Figure 2.7). In 2012 an
d 2014, N 
concentration within the soil leachate of ST systems tended to increase throughout the 
season, peaking at 20 mg L
-
1
 
in 2012 and 25 mg L
-
1
 
in 2014, while the FWT peaked in 
mid
-
August at 10 mg L
-
1
 
in 2012 and 17 mg L
-
1
 
in 2014. In 2013, in both FWT
 
and ST 
leachate N concentration peaked in early August at 25 mg L
-
1
 
in ST and 20 mg L
-
1
 
in 
FWT, and then sharply declined till leachate N concentration was approximately 7 mg L
-
1 
in both tillage systems. N concentration in the soil leachate was measured w
ithin the 
spring of 2013 and 2014, but leachate N concentration during cover crop growth was 
negligible so spring data was not included in the analysis. 
 
Denitrification 
p
otential
 
 
Tillage effects on denitrification potential across the whole plot did not 
interact 
with cover crop, year, or time
-
point (Figure 2.8). ST increased denitrification potential by 
18% compared to FWT (Till, 
P
=0.024). When we analyzed denitrification potential within 
zones of ST and FWT, we found that the two tillage systems showed d
ifferent patterns 
in denitrification potential between the
 
WR
 
and BR zones (Figures 2.9
-
A and 2.9
-
B). At 
planting, ST resulted in 90% greater denitrification potential in the BR compared to the
 
WR
 
zone (Figure 2.9
-
A, Till*Zone, 
P
 
<0.016), and 33% greater d
enitrification potential in 
the BR compared to the
 
WR
 
at later sampling dates (Fig 2.9
-
B). In contrast, FWT 
 
 
 
 
 
82
 
resulted in 30% greater denitrification potential within the
 
WR
 
compared to the BR at the 
late sampling points (Figure 2.9
-
B, Till*Zone, 
P
 
<0.0001).
 
 
 
Cover crop effects on denitrification potential varied with zone at late (Mid
-
Season and Harvest combined) sampling points (Figures 2.9
-
D). 
Denitrification potential 
was lower in the
 
WR
 
zone of 
NoRV
 
treatments compared to the BR zone and compared 
to the
 
RV
 
WR
 
and BR (Cover*Zone, 
P
=0.0226), but we detected no zonal differences 
between the
 
WR
 
and BR of RV. 
 
DISCUSSION
 
Potentially m
ineralizeable N and soil inorganic N
 
Residues tend to decompose slower when left on the soil surface, compared to 
when incorpor
ated into the soil (Mulvaney et al. 2010). Therefore, we expected to find 
lower PMN in ST compared to FWT. 
However, we only found 
lower PMN within ST in 
the latter part of the sweet corn season
, 
when 
RV 
offered no addition
al
 
N mineralization 
compared to th
e 
NoRV
 
within ST. This is consistent with other studies that found 
mineralization of legume
-
cereal mixtures in RT decreases 
PMN 
in second half of 
season (Rannells and Wagger, 1996).
 
Contrary to our expectations, we found no consistent benefit in IN from a 
RV 
cover crop (Figure 2.2). Legume
-
cereal mixtures are often highly variable in the 
proportion of each species within the mixture, which determines the C:N ratio of the 
mixture and ultimately the release of N (Kuo and Sainju 1998; Hayden et al. 2014) . 
Wit
hin our study, the C:N ratio of RV varied between 30 to 40, and a C:N ratio of 25 or 
below is believed to be required for net N mineralization (Allison, 1966). The 
NoRV
 
consisted of winter annual weeds which had a lower C:N ratio than the RV mixture 
 
 
 
 
 
83
 
across
 
all three years. In both 2012 and 2014, the C:N ratio of the winter weeds was 
close to 25, while in 2013 it was 38. This likely explains why we did not see more of an 
increase in inorganic N due to RV. 
 
We expected that tillage within the
 
WR
 
of ST would s
timulate N mineralization by 
incorporating RV residues and result in greater inorganic N compared to the BR. 
However, we only found greater inorganic N in one out of the three years (2013). One 
reason for the lower inorganic N within ST
-
W
R could be that st
rip
-
tillage is less intense 
disturbance compared to conventional tillage. This is consistent with Haramoto and 
Brainard (2012) who also found lower inorganic N within the crop row of ST. 
Additionally, although the BR of ST had not been tilled, there were f
requent hand
-
weeding events and the shallow disturbance resulting from weed control may have 
contributed to N mineralization. 
 
Sweet corn biomass and N uptake
 
Despite ST

 
having lower inorganic N availability across all three years, 
ST 
only 
resulted in lo
wer sweet corn yields and biomass
 
in
 
2013. Hairy vetch biomass within 
2013 was less than 50% of 2012 and 2014 levels, and within NOCORN subplots, ST 
reduced IN by approximately 65% compared to FWT when combined with RV, and ST 
reduced IN by 20% lower when 
combined with 
NoRV
. Therefore, N deficiency may 
explain lower sweet corn biomass and N uptake in ST in 2013. 2013 was the only year 
in which we did not see increased gravimetric soil moisture within ST+RV compared to 
FWT and ST+
NoRV
. It is possible that in
creased moisture compensated for lower 
inorganic N in 2012 and 2014. Alternatively, it may be that in both 2012 and 2014, rye 
and vetch cover crop biomass was sufficient so that N was not limiting within the strip
-
 
 
 
 
 
84
 
tilled system. For example, in 2014 inorga
nic N was 30% lower in ST that FWT, yet this 
had no effect on sweet corn N uptake and yields.
 
Soil leachate N and denitrification potential
 
When analyzed across all three years, ST resulted in greater concentration of N 
within the soil leachate compared to
 
FWT with a RV cover crop and equal or greater 
volumetric water content within the soil within 2013 and 2014. Greater leachate N 
concentration, combined with greater water throughout the soil profile, strongly suggests 
that ST increased the amount of N tha
t leached from the soil profile during sweet corn 
growth. This is contrary to other studies that found ST either had no effect (Al
-
Kaisi and 
Licht 2004; Haramoto, 2014) or decreased (Haramoto, 2014) N leaching potential.  
However, both these studies used i
norganic N fertilizer as the primary source of N. To 
our knowledge, ours is the first study that has examined ST effects on N leaching in a 
legume
-
based system. 
 
Numerous studies have compared N leaching within no
-
till and conventional tilled 
systems, and 
have found varying results. Some studies have found a lower 
concentration of N within the soil leachate in no
-
till, but a greater total amount of N 
leaching due to increased soil water drainage (lzaurralde et al. 1995). Other studies 
have found a greater c
oncentration of N within the soil leachate of no
-
till, with or without 
an increase in water drainage (Thomas et al. 1973; Tyler and Thomas, 1977). 
 
One
 
explanation for 
greater
 
leachate N concentration within no
-
till concerns the 
difference in soil water mo
vement through the rooting zone of undisturbed versus 
disturbed soils. No
-
till soil contains a greater proportion of macropores compared to 
conventional tillage (Thomas et al. 1973; Wu et al. 1992), resulting in greater infiltration 
 
 
 
 
 
85
 
rates and the channelin
g of water through the rooting zone
 
(
Ogden et al. 1999). When 
water travels through macropores, it bypasses much of the soil matrix and has less 
contact with micropores and lower replacement of initial soil water (Quisenberry and 
Phillips, 1976; Bandaranay
ake et al. 1998; Tyler and Thomas, 1977). In a legume
-
based no
-
till soil system, organic residues remain upon the soil surface, resulting in a 
stratification of N mineralization (Logan et al. 1991; Johnson and Hoyt, 1999). 
Mineralized N is initially concen
trated in the top few centimeters of the soil, and must 
percolate down through the soil profile. Precipitation infiltrating the soil likely captures 
recently mineralized N at the soil surface, causing mineralized N to bypass the soil 
matrix as it travels t
hrough the rooting zone through preferential flow pathways. In the 
disturbed soils of FWT, incorporated rye and vetch residues are distributed throughout 
the top soil layer. As water slowly percolates through the soil matrix within FWT, 
recently mineralize
d N has a greater chance of entering and being stored in soil 
micropores. 
 
 
In FWT, greater denitrification potential was found in the
 
WR
 
zone compared to 
the BR. 
That 
the cover crop and tillage treatments between the
 
WR
 
and BR zones of 
FWT were equivalent
 
suggests that any
 
WR
/BR differences within FWT are due to the 
presence of the crop
 
WR
. The sloughing off of corn root cells and root exudates likely 
provided a labile carbon source to enhance denitrification (Prade et al. 1988; Mahmood 
et al. 1997). Mahmo
od et al. (1997) found that maize plants stimulated denitrification at 
both high and low water
-
filled pore space, and attributed the s
t
imulative effects to 
increased C availability with maize roots present. Previous studies have found that 
denitrification 
is often limited by carbon in agricultural systems, and may be more limited 
 
 
 
 
 
86
 
by carbon than nitrogen (Kennedy et al. 2013, and Luo et al. 1998). These results 
reinforce the importance of considering
 
WR
 
and BR differences when sampling soil 
within agricultur
al systems. Often researchers take measurements across the entire 
plot, and do not 
report
 
the influence that the crop has within the crop row. 
 
ST increased denitrification potential across the whole plot, and also affected the 
zonal patterns 
(WR and BR) 
o
f denitrification differently within the two tillage systems. 
As opposed to FWT, ST had a higher denitrification potential in the BR compared to the
 
WR
. Given that the same mechanisms by which the crop enhanced denitrification 
potential within the
 
WR
 
of FW
T are also operating in the
 
WR
 
of ST, this suggests that 
the mechanisms enhancing denitrification potential 
in the BR of ST were greater
. The 
greater bulk density and increased soil moisture in 2014 likely increased water
-
filled 
pore space in the BR of ST,
 
thereby increasing denitrification potential (Aulakh et al. 
1991; Weier et al. 1993). On average, bulk density within the BR of ST was 
approximately 7% greater than tilled areas. Alternatively, Overstreet and Hoyt (2008) 
found greater microbial biomass wi
thin the untilled zone of ST. Therefore greater 
denitrification potential could be due to a greater overall microbial mass, and not to 
factors promoting denitrification 
per se
. 
 
 
Within this study w
e found that ST within a legume
-
based systems increased th
e 
potential for N losses to the environment. Increased reactive N lost through leaching 
and denitrification is concerning because of the potential for negative environmental 
ramifications. Leached N is a major contributor to hypoxic zones within marine sys
tems, 
and denitrified
-
N lost as N
2
O is a potent greenhouse gas with a CO
2
 
equivalent of 300 
(Rober
tson and Vitousek, 2009).
 
Additionally, increased N losses are especially 
 
 
 
 
 
87
 
problematic in legume
-
based organic systems, which already tend to be N deficient 
(
Clark et al. 1999; Poudel et al. 2002; Cavigelli et al. 2008). Greater leach
ate N 
concentration
 
and denitrification potential within ST may partially explain the reduced N 
availability. Early season PMN did not differ between the two tillage systems, but w
e 
found lower inorganic N availability within ST, likely due to increased N losses. Future 
research should determine the exact mechanisms responsible for differences in N 
losses between legume
-
based FWT and ST. Additionally, more research is needed to 
dete
rmine strategies to increase nitrogen use efficiency within legume
-
based ST.   
 
SUMMARY AND CONCLUSION
 
Our 
objective was to determine how strip
-
tillage affected the mineralization and 
availability of N, as well as potential for N losses, from a legume
-
base
d organically 
managed system. ST had no effect on PMN in the first half of the sweet corn season, 
but reduced PMN in the second half of the season when combined with RV. ST 
increased the 
concentration of inorganic N within the soil leachate and the 
potenti
al for 
N losses via denitrification across the three years of this study. The combined 
effects of 
reduced PMN and increased N losses explain
 
why ST had lower inorganic N availability 
compared to FWT. Tillage within the crop row increased inorganic N compar
ed to the 
undisturbed BR in only 1 out of 3 years. Additionally, we found no consistent benefit in 
N availability from the rye
-
vetch cover crop. Despite ST having lower inorganic N, it only 
had negative effects on sweet corn in one out of three years, 2013
, the year with the 
lowest hairy vetch biomass. ST increased soil gravimetric moisture in 2012 and 2014, 
which may have compensated for the lower inorganic N. 
 
 
 
 
 
 
88
 
Despite lower inorganic N, ST can produce yields equivalent to FWT in years 
with sufficient vetc
h biomass. Increased N losses likely exacerbated the 
lower soil 
inorganic N
 
within ST.
 
The greater potential for N losses within ST are concerning, not 
only because N is a limiting resource in organic systems, but also because reactive N 
lost to the enviro
nment results in negative environmental impacts. Future research 
should examine the mechanisms responsible for increased N losses in between ST, as 
well as examine strategies to increase nitrogen use efficiency and decrease N los
ses in 
legume
-
based RT syst
ems
 
 
 
 
 
89
 
APPENDIX
 
 
 
 
 
90
 
APPENDIX
 
 
 
 
Table 2.1.
 
Table of Activities and Operations.
 
 
 
 
 
 
 
91
 
Table 2.2. P values from a three
-
way ANOVA for inorganic N across the whole CORN 
and NOCORN subplots (weighted average of WR and BR) in 2012, 2013, and 2014. 
Fixed effects inclu
de tillage (full
-
width and strip
-
tillage), cover crop (Rye
-
vetch and 
NoRV), and date as a repeated measure. 
 
 
 
 
 
 
 
 
 
 
92
 
Table 2.3. P values from a four
-
way ANOVA for inorganic N within NOCORN subplots in 
2012, 2013, and 2014. Fixed effects include tillage (full
-
width and strip
-
tillage), cover 
crop (Rye
-
vetch and NoRV), zone (WR and BR), and date as a repeated measure.  
 
 
 
 
 
 
 
 
93
 
Table 2.4. P values from a three
-
way ANOVA for gravimetric soil moisture across the 
whole CORN subplot (weighted average of WR and BR) in 2012
, 2013, and 2014. Fixed 
effects include tillage (full
-
width and strip
-
tillage), cover crop (Rye
-
vetch and NoRV), 
and date as a repeated measure. 
 
 
 
 
 
 
 
 
94
 
Table 2.5. P values from a three
-
way ANOVA for volumetric soil moisture within CORN 
subplots at three diffe
rent depths (0.1 to 0.3 m, 0.4 to 0.7 m, and 0.8 to 0.9 m) in 2012, 
2013, and 2014. Fixed effects include tillage (full
-
width and strip
-
tillage), cover crop 
(Rye
-
vetch and NoRV), and date as a repeated measure.  
 
 
 
 
 
 
 
 
 
 
95
 
Table 2.6. P values from a two
-
way ANO
VA for sweet corn nonharvested and corn ear 
biomass, as well as sweet corn N content in 2012, 2013, and 2014. Fixed effects 
include tillage (full
-
width and strip
-
tillage) and cover crop (Rye
-
vetch and NoRV).
 
 
 
 
 
 
 
 
 
96
 
 
 
 
 
 
 
Figure
 
2.
1. 
Mean (+/
-
 
SEM) 
i
n situ
 
potentially mineralizeable N within the top 25 cm 
of soil from planting to m
idseason 
(Early, left) 
and Midseason to Harvest
 
(Late, 
right)
 
over 2012, 2013, and 2014 combined. 
 
 
 
 
 
 
97
 
 
 
 
 
 
Fig
ure
 
2
.
2. 
Mean (+/
-
 
SEM) 
inorganic N availability 
in the top 20 cm of soil
 
across 
the whole subplot 
(weighted average of
 
WR
 
and BR)
,
 
with Corn (top)
 
and 
without 
corn
 
(No Corn, bottom) in 2012, 2013, and 2014. 
Inorganic N comparisons 
include full
-
width tillage (F
WT) and strip
-
tillage (ST) combined with eithe
r
 
no 
cover crop (NoRV
) or 
a 
rye and vetch 
mixture (Rye
-
Vetch)
. 
 
 
 
 
 
 
98
 
 
 
 
 
 
 
 
 
Fig
ure
 
2.
3.
 
Mean (+/
-
 
SEM) 
inorganic N availability 
in the top 20 cm of soil
 
both 
within (WR) and between crop rows (BR) in subplots without corn
 
(No Corn) in 
2012, 2013, and 2014. 
Inorganic N comparisons include full
-
width tillage (FWT) 
and strip
-
tillage (ST) combined with eithe
r
 
no cover crop (NoRV
) or 
a 
rye and 
vetch 
mixture (Rye
-
Vetch)
. 
 
 
 
 
 
 
 
 
99
 
 
 
 
 
 
 
 
 
 
Figure 2.4
. 
Mean (+/
-
 
SEM) 
gravimetric soil moisture
 
i
n the top 20 cm of soil
 
across the 
whole subplot 
(weighted average of IR and BR) 
with Corn (top)
 
present in 2012, 2013, 
and 2014. Gravimetric soil moisture
 
comparisons include full
-
width tillage (FWT) and 
strip
-
tillage (ST) combined with eithe
r
 
no cover cr
op (NoRV
) or 
a 
rye and vetch 
mixture 
(Rye
-
Vetch)
. 
 
 
 
 
 
 
 
100
 
 
 
 
 
 
 
Figure 2.5
. 
Mean (+/
-
 
SEM) 
volumetric soil moisture
 
in subplots with corn (Corn)
 
present in 2013, and 2014. 
Measurements were taken every 10 cm, and depths 
were combined according to: 0.1 to 0.3, 0.4 to
 
0.7, and 0.8 to 0.9 meters. 
Volumetric soil moisture
 
comparisons include full
-
width tillage (FWT) and strip
-
tillage (ST) combined with eithe
r
 
no cover crop (NoRV
) or 
a 
rye and vetch 
mixture 
(Rye
-
Vetch)
. 
 
 
 
 
 
 
101
 
 
 
 
 
 
Figure 2.6
. 
Mean (+/
-
 
SEM) 
sweet corn biomass (top
) and N content 
(bottom) of 
both nonharvested biomass and corn ears in 2012, 2013, and 2014.
 
Comparisons of fixed effects include full
-
width tillage (FWT) and strip
-
til
lage 
(ST) combined with either no cover crop (NoRV) or
 
a
 
rye and vetch
 
(RV) 
mixture.
 
 
 
 
 
 
 
102
 
 
 
 
 
 
 
 
 
 
F
ig
ure
 
2.7
. Precipitation in Hickory Corners, MI (top) and
 
mean (+/
-
 
SEM)
 
inorganic N within the soil leachate (bottom) during the sweet corn growing season 
in subplots with corn in 2012, 2013, and 2014.
 
Sampled at a depth of 1.2 m. 
Tillage comparisons
 
incl
ude full
-
width tillage (FWT) and strip
-
til
lage (ST).
 
 
 
 
 
 
 
103
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2.8
. 
Mean 
(+/
-
 
SEM) 
denitrification potential in both full
-
width (FWT) and 
strip
-
tillage (ST)
. Data is a compilation of 2013 and 2014, and three time points 
(Planting, Midseason, and Harvest).
 
S
ampled to a depth of 20 cm.
 
 
 
 
 
 
104
 
 
 
 
 
 
 
 
Figure 2.9
. 
Mean (+/
-
 
SEM) 
denitrification potential 
due to t
illage (A and B) 
and cover crop (C and D) effects within different zones in the cropping 
system (
W
R=within corn row, BR=between crop rows). 
Tillage 
comparisons includ
e
 
full
-
width tillage (FWT) and strip
-
til
lage (ST); cover 
crop comparisons include no cover crop (NoRV) or
 
a
 
rye and vetch
 
(RV) 
mixture. Sampled to a depth of 20 cm.
 
 
 
 
 
 
105
 
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TERATURE CITED
 
 
 
 
 
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113
 
CHAPTER THREE
 
Strip
-
intercropping of rye
-
vetch mixtures affects biomass, C:N ratio and spatial 
distribution of cover crop residue
 
ABSTRACT
 
Altering the spatial arrangement of cover 
crop mixtures with strip
-
intercropping is an 
under
-
explored strategy that may enhance their performance. We hypothesized that 
strip
-
intercropping would increase overall cover crop productivity and improve N use 
efficiency in legume
-
based cropping systems b
y concentrating the low C:N vetch 
residue within the crop root zone. We conducted a field study in southwest Michigan to 
examine how strip
-
intercropping of cereal rye and hairy vetch (RV) mixtures influences: 
i) total cover crop productivity, and ii) the s
patial distribution and C:N ratio of RV 
residues in the between
-
row versus within
-
row zones of subsequent crops. Spatial 
arrangements included a full
-
width mixture (MIX) in which rye and vetch were sown 
together in the same rows; and segregated mixtures in
volving either 2 rows of rye 
alternated with 2 rows of vetch (SEG2) or 3 rows of rye alternated with 1 row of vetch 
(SEG1). Benefits of strip
-
intercropping of RV for reducing interspecific competition 
appear to have been offset by the costs of increased in
traspecific competition and/or 
reduced facilitation. Over 5 site
-
years, strip
-
intercropping either had no effect or reduced 
RV biomass or the biomass of component species relative to MIX. However, SEG2 
resulted in greater concentration of N
-
rich vetch tiss
ue and a lower C:N ratio of both 
above
-
 
and below
-
ground RV biomass in the crop
-
planting zone. Strip
-
intercropping of 
RV is a promising strategy for increasing access of future crops to mineralized N. 
 
 
 
 
 
 
 
114
 
INTRODUCTION
 
Winter cover crops can provide a number 
of useful ecosystem services to a 
cropping system, including weed suppression, reduced soil erosion, nutrient cycling, 
and increased soil organic matter (Wander and Traina, 1996; Wyland et al. 1996; 
Teasdale and Mohler, 2000; Snapp et al. 2005). Cover crop
 
services can be enhanced 
by combining species that differ in growth habit and resource capture strategies. For 
example, mixtures of legume and cereal cover crops may provide several 
complementary benefits: legumes provide N via biological N fixation, and 
cereal grasses 
suppress weeds (Mohler and Teasdale, 1993) scavenge residual nutrients (Wyland et 
al
.
 
1996), and produce large quantities of biomass. 
 
Grass and legume cover crop mixtures often produce greater biomass than 
either species in monoculture, and
 
may increase the total N supplied to the cropping 
system (Holderbaum et
 
al
.
 
1990;
 
Fujita et al
.
 
1992; Karpenstein
-
Machan and 
Stuelpnagel, 2000). By reducing soil N availability, grasses can stimulate legumes to 
increase biological N fixation, thereby incr
easing the proportion of total N derived from N 
fixation by the legume (Karpenstein
-
Machan and Stuelpnagel, 2000; Izaurralde et al
.
 
1992; Jensen, 1996)
. 
This can result in lower seed costs for a given level of N fixation, 
since legume seeds (e.g. hairy vet
ch) are typically much more expensive than grass 
seeds (e.g. cereal rye) 
(Brainard et al. 2012). Fixed N can be transferred from legumes 
to grasses (Eag
elsham et al.
 
1981; Ta
 
and Faris, 1987; Dakora et al.
 
2008), which may 
increase the N content of the gra
sses when intercro
pped with legumes (Ta and Faris
 
1987; Fuj
ita et al. 1990; Sainju et al.
 
2005). 
 
 
 
 
 
 
115
 
Hairy vetch and cereal rye are commonly used winter cover crops in 
northern 
climates and combining them in mixture can result in a number of synergistic benef
its 
compared to either species in monoculture. For example, despite hairy vetch being a 
relatively cold
-
hardy legume, vetch is susceptible to low overwinter survival and poor 
biomass in years with cold winters (Jannick et al
.
 
1997). Hairy vetch is often le
ss 
effective at weed suppression compared to cereal grains including rye (Hayden et al. 
2012). Cereal rye is quick to establish in cool fall temperatures (Shipley et al
.
 
1992) and 
acts to insulate hairy vetch from extreme frosts, thereby increasing vetch o
verwinter 
survival (Jannick et al
.
 
1997; Brainard et al
.
 
2012). Additionally, cereal rye is effective at 
suppressing winter annual weeds and acts as a nurse crop for the slow establishing 
hairy vetch. Cereal rye and hairy vetch also have 
complementary 
arch
itectures. Vining 
legumes, such as vetch, can utilize upright cereal grasses as a climbing scaffold to 
increase light interception and productivity (Keating and Carberry et al. 1993). 
 
In organically managed systems, winter cover crops such as rye and vetc
h may 
serve as the primary source of nitrogen to the subsequent crop (Gaskell and Smith, 
2007). However, N availability is often highly variable and insufficient to fully meet crop 
N demand (Kuo and Sainju, 1998; Zotarelli et al
.
 
2009). Whether a legume
-
ce
real cover 
crop mixture is able to supply the crop N requirement depends on both the N 
accumulated within cover crop biomass, as well as the timing of residue decomposition 
and N mineralization (Crews and Peoples, 2005). The ratio of the legume and grass 
b
iomass within the mixture is a major determinant of mixture C:N ratio (Hayden et al. 
2014), which is a dominant factor determining the rate and pattern of residue N 
mineralization (Allisons, 1966). Grasses are often the dominant competitor within 
 
 
 
 
 
116
 
intercrop
s, and certain growing conditions, such as high soil fertility (Brainard et al. 
2011) or late planting dates resulting in colder than average temperatures (Hayden et 
al. 2015), can enhance grass competitiveness for light and other resources. Overly 
vigorou
s grass growth results in decreased legume productivity and N fixation (Wahua 
and Miller, 1978; Nambiar et al. 1983; Brainard et al. 2011). 
 
Hairy vetch and cereal rye productivity within intercropped mixtures varies 
tremendously. Several approaches have b
een proposed to increase rye and vetch 
productivity and maximize their capacity for weed suppression and N provisioning. For 
example, utilizing earlier planting dates and later termination dates can increase vetch 
growth and N accumulation (Teasdale et al.
 
2004; Cook et al. 2010). However, cash 
crop rotations do not always accommodate adjustments in cover crop planting and 
termination. Increasing the proportion of vetch seeds within the mixture seeding rate 
may increase vetch biomass, but also increases the
 
total seed costs and decreases 
weed suppression (Hayden et al. 2014). Hayden et al. (2015) found that delayed 
seeding of rye until the time of vetch emergence resulted in up to 1
.0
 
M
g ha
-
1
 
greater 
biomass compared to co
-
seeding rye and vetch together. But
 
fall is generally a busy 
time for farmers due to the harvesting of cash crops, and they may not have time to 
employ two planting dates to establish a winter cover crop. Given these limitations, new 
strategies are needed to alleviate interspecific competit
ion in grass and legume 
mixtures, thereby ensuring adequate proportions of legume biomass and enhancing 
their capacity for provisioning N. 
 
One under
-
explored strategy that may reduce interspecific competition and 
enhance the provisioning of N in legume
-
ce
real cover crop mixtures is to alter the 
 
 
 
 
 
117
 
spatial arrangement of component species (Fujita, 1990; Lithourgidis et al
.
 
2011). Strip
-
intercropping involves separating component species within a mixture into distinct 
alternate strips with the ultimate aim of d
elaying the onset of competition (Ofori and 
Stern, 1987; Midmore, 1993). Strip
-
intercropping of grasses and legumes may provide 
the greatest benefit to the less
-
competitive legume. By reducing the space allocated to 
the dominant species, the companion spec
ies gains access to a greater share of space, 
light, and other resources. For example, strip
-
intercropping in double alternate rows 
increased yields of soybeans when grown in combination with maize or sorghum (Mohta 
and De, 1980). 
 
Several studies have exa
mined the advantages of strip
-
intercropping in cash 
crop mixtures (Li et al. 2001; Gao et al. 2009; Li et al. 2011), but this practice has yet to 
be applied to grass
-
legume cover crops. Strip
-
intercropping of rye and hairy vetch may 
reduce rye suppression 
of hairy vetch, especially in years with colder winter 
temperatures when rye competitiveness over vetch is greatly enhanced. Strip
-
intercropping of rye and vetch may also enhance N
-
use efficiency within a subsequent 
crop, much like banding of N fertilizers
 
in conventional systems (Reinersten et al. 1984; 
Malhi and Nyborg 1985; Maddux et al, 1991; Nash et al. 2013). Planting the vetch strips 
directly in
-
line with the rows of the subsequent crop concentrates N
-
rich vetch residue 
where it 
may be 
more easily ac
cessed by crop roots, and 
thus 
may increase the 
synchrony between N mineralization and crop N uptake. In the absence of carbon rich 
rye residue, in
-
row hairy vetch may increase result in higher N availability within the 
crop row due its lower C:N ratio com
pared to rye
-
vetch mixtures.
 
Planting the high 
C:N 
 
 
 
 
 
118
 
rye between future crop 
rows may initially immobilize N that can potentially serve as a 
reservoir of late
-
releasing N. 
 
Segregation of rye and vetch in distinct zones may also alleviate other 
challenges as
sociated with rye
-
vetch mixtures, especially when used in reduced tillage 
organic production systems. Previous studies have demonstrated reductions in crop 
establishment and early growth due to interference from rye residue. Rye residue may 
inhibit crop es
tablishment through allelopathy (Barnes and Putnam, 1986), N 
immobilization (Kuo and Sainju 1998) or through interference with planting equipment 
(Mirsky et al. 2012). By excluding rye from the in
-
row zone, such suppression can be 
minimized. Second, in org
anic reduced
-
tillage production systems, termination of hairy 
vetch is challenging, and hairy
-
vetch re
-
growth may interfere with crop growth (Mischler 
et al. 2010). By concentrating vetch in the in
-
row zone, strip
-
tillage equipment may be 
used to effective
ly terminate vetch while retaining the advantages of reduced tillage in 
the between row zone. 
 
The primary objectives of this study 
we
re 
to examine how strip
-
intercropping of 
rye and hairy vetch affects i) the overall productivity of a rye
-
vetch mixture, a
s well as 
the relative biomass of each component; and ii) the spatial distribution and C:N ratio of 
rye
-
vetch residues between 
the vetch and rye 
zones
 
(within
-
 
and between
-
rows of the 
future crop
, respectively
)
. A secondary objective was to evaluate the im
pact of cover 
crop spatial arrangement on the timing of flowering of vetch. We hypothesize
d
 
that 
compared to full
-
width rye
-
vetch mixtures, strip
-
intercropping of cereal rye and hairy 
vetch 
w
ould
 
result in i) increased total rye
-
vetch biomass through allev
iation of rye 
suppression of vetch; ii) a greater proportion of vetch biomass and a lower C:N ratio of 
 
 
 
 
 
119
 
cover crop residues within the vetch zone (VZ); 
and 
iii) a greater proportion of rye 
biomass and a higher C:N ratio of res
idue within the rye
-
zone (RZ). 
 
MATERIALS AND METHODS
 
Site Description and experimental d
esign
 
Two experiments were conducted between 2011
-
2014 on organically managed (5 
years) Kalamazoo (fine
-
loamy, mixed, mesic Typic Hapludalfs) and Oshtemo (coarse
-
loamy, mixed, mesic Typic Hapludalfs
) loams (Crum and Collins, 1995), at the Kellogg 
Biological Station in Hickory Corners, MI (85




Experiments 1 and 2 can be found in Table 
3.
1. Initially, in 2011
-
2012 (Year 1), 
Experiments 1 and 2 were combined withi
n one field, but were separated into two fields 
in subsequent years: 2012
-
2013 (Year 2) and 2013
-
2014 (Year 3). Dates of key field 
operations and data collection can be found in Table 3.1.
 
Experi
ment 1: Cover crop productivity
 
To determine how spatial arra
ngements of cereal rye and hairy vetch mixtures 
influenced productivity, we evaluated mixtures of rye and vetch grown in three different 
spatial arrangements compared to rye and vetch monocultures (Table 3.2). When 
considered on a whole plot basis, all bi
-
culture treatments have the same seeding rate 
for both rye and vetch, but differ in seed placement according to Figure 3.1. Between
-
row spacing for both rye and vetch was 19 cm across all the treatments. Spatial 
arrangements included a full
-
width mixture (
MIX) in which rye and vetch were sown 
together in the same rows; a segregated mixture (SEG2) in which 2 rows of hairy vetch 
were alternated with 2 rows of rye; and a second segregated mixture (SEG1) in which 3 
rows of rye were alternated with 1 row of vetc
h. All three spatial arrangements had the 
 
 
 
 
 
120
 
same seed rate on a per plot basis: 62.7 kg ha
-
1
 
for rye, and 22.4 kg ha
-
1 
for vetch. The 
rye monoculture was sown at 125.5 kg ha
-
1
 
for rye, and the vetch monoculture was 
sown at 44.8 kg ha
-
1
. 
 
In Year 1, cover cro
p treatments were part of a larger trial in which we evaluated 
effects of cover crops on a subsequent crop under two tillage systems. As such, in Year 
1, each cover crop treatment was replicated twice (for each tillage interaction) within 
each block, for a
 
total of 8 replications. In Years 2 and 3, no subsequent crop or tillage 
treatments were evaluated, and cover crop treatments were arranged in a randomized 
complete block design with 4 replications. Plot sizes were 55.5 m
2 
(18.2 m X 3.
1 
m) in 
Year 1, and 
13.5 m
2
 
(4.5 m X 3.0 m) in Years 2 and 3. A larger plot size was used in 
Year 1 to accommodate measurements of subsequent crop responses. 
 
 
Prior to cover crop planting in September, 
in 
the experimental area cereal rye 
was 
harvested for seed in July in Yea
r 1, snap beans grown from late
-
June to August in 
Year 2, and no crop in Year 3. Fields of both Experiment 1 and 2 were flail mowed, 
chisel plowed, disked, and harrowed. 
 

certi
fied across all three years. Cereal rye was planted with a grain drill with the drop 
tubes blocked as appropriate to establish rows for each treatment as described above 
(Figure 3.1). Hairy vetch was planted with a Jang push
-
seeder within the designated 
ro
ws and rates. 
 
Experiment 2: Spatial distribution and C:N ratio of root and shoot tissue
 
The focus of Experiment 2 was to evaluate in more detail the effects of cover 
crop spatial arrangement on the spatial distribution of cover crop biomass and the C:N 
 
 
 
 
 
121
 
ra
tio of cover crop tissue. For this experiment, only the effects of two of the cover crop 
spatial arrangements (MIX vs SEG2) were evaluated, with no SEG1 or monoculture 
treatments included (Table 3.1). Experiment 2 was part of a larger experiment that 
inclu
ded the effects of cover crop spatial arrangement and subsequent tillage (full width 
tillage versus strip tillage) on N dynamics and yield of subsequent sweet corn crop. The 
interactive effects of cover crop spatial arrangement and tillage on subsequent so
il and 
crop responses are reported elsewhere (Lowry 2015; Chapters 4 and 5). Because cover 
crop x tillage treatments were arranged in a randomized complete block design with 4 
replications, each cover crop treatment was replicated twice within each block, 
resulting 
in a total of 8 replications. In Year 1, Experiment 2 was imbedded in Experiment 1 
as 
described above.  Plot sizes were 55.5 m
2 
(18.2 m X 3.
1
 
m), 73.8 m
2 
(12.1 X 6.1 m), and 
62.6 m
2
 
(13.6 m X 4.6 m) in Years 1, 2, and 3, respectively. Plot sizes 
were adjusted 
each year based on availability of organically certified land and size of available 
equipment. 
 
Crops grown prior to Experiment 2 included cereal rye harvested for grain in July 
in Year 1, wheat harvested for grain in Year 2, and cereal rye i
n Year 3. As in 
Experiment 1, fallow periods were tilled frequently to minimize growth of perennial 
weeds, and to minimize volunteer rye or wheat from the previous crop.
 
Data collection
 
Cover crop aboveground biomass
.
 
We obtained cover crop aboveground bio
mass prior 
to mowing by clipping shoot tissue at ground level from two 0.25 m
2
 
quadrats 
for each 
vetch
-
zone (VZ) and rye
-
zone (RZ) area in segregated (SEG) treatments, and two 
random quadrats in mixed (MIX) bicultures. Due to lodging of the cover crops in 
Year 2 
 
 
 
 
 
122
 
in Experiment 1 it was difficult to distinguish rows so we collected three quadrats for 
segregated treatments and did not separate by rows. Fresh cover crop biomass was 
separated into hairy vetch, cereal rye, and weeds, dried at 60°C, and weighed. T
o 
determine the effect that spatial arrangement has on rye and vetch biomass across the 
whole plot, rye and vetch biomass within the vetch
-
 
and rye
-
zones were weighted by the 
relative area of that zone. 
 
Root biomass
. In Years 2 and 3 of Experiment 2, we o
btained belowground biomass of 
the cover crop mixture and associated weeds. Within one day of the cover crops being 
mowed, we excavated 3 cores (5 cm width and 25 cm depth) from the VZ and RZ within 
SEG2 (3 cores X 2 zones= 6 cores), and at three random lo
cations within the MIX (3 
cores). The three cores from each plot and zones were homogenized, weighed, and a 
subsample was taken for gravimetric moisture. The cores were then stored at 4°C until 
further processing to extract roots. To extract roots, samples
 
were sieved twice through 
a 6 mm sieve. Once roots were isolated from soil, they were washed thoroughly, 
then 
dried at 60°C. Dried roots were weighed and ground with mortar and pestle within liquid 
nitrogen, and then analyzed for elemental C and N
 
(see be
low)
. No attempt was made 
to distinguish roots by species. 
 
Hairy vetch percent flowering
.
 
We calculated percent flowering of hairy vetch at three 
time
-
points in Year 2, and two time
-
points in Year 3 in Experiment 1. We selected 10 
random vetch vines and t
raced stems to the base of the plant to ensure we removed the 
full vine length. We then removed any branches, and counted the total number of 
nodes, number of flowering nodes, and number of nodes that had set pods along the 
primary vine. 
 
 
 
 
 
 
123
 
[3.1]   




























 
Hairy vetch height.
 
Hairy vetch height was measured in Experiment 1 at three time
-
points in the spring in both Years 2 and 3. We measured the height of rye and vetch at 
10 random locations 
within each plot. For segregated spatial arrangements, at each 
location we measured the height of vetch within the VZ, vetch that had climbed the rye 
in the RZ, and rye in the RZ. We then determined the average height of each species 
across the whole plot 
by weighting the species height within each zone by the area of 
that zone. 
 
Percent lodging.
 
Visual estimates of percent lodging of rye
-
vetch mixtures were made 
prior to cover crop termination in Experiment 2. A percentage between 0 and 100 (to the 
nearest
 
5%), was assigned on a whole plot basis: 0% represent
ed
 
rye
-
vetch across the 
entire plot standing, and 100% represent
ed
 
rye
-
vetch across the whole plot lodged on 
or near the ground. Two researchers independently estimated lodging percentages, and 
then the
ir estimates were averaged.
 
Rye and vetch 
C:N ratio
.
 
Above
-
ground tissue samples of hairy vetch and cereal rye 
from Experiment 2 were analyzed for total C
 
and 
N at the University of California
-
Davis 
Stable Isotope Facility (SIF) using an elemental analyzer
 
(ANCA
-
GSL, PDZ Europa, 
Norwich, Cheshire, UK) interfaced to an isotope ratio mass spectrometer (20
-
20, 
Sercon, UK). 
 
Statistical a
nalysis
 
Due to high variability between years, as well as differences in experimental 
procedures by year, years were ana
lyzed
 
separately with one exception: years were 
pooled for analyzing the effect of cover crop spatial arrangement on the C:N ratio within 
 
 
 
 
 
124
 
rye and vetch zones, due to a lack of significant interaction with year for this response.  
All data was analyzed using Pro
c MIXED procedures in SAS with block as a random 
factor (Version 9.3, SAS Institute, Cary, NC). We evaluated assumptions of normality 
and homogenous variances, and utilized log or square
-
root transformations and 
unequal variance models where appropriate. F
or Experiments 1, the fixed effect of 
cover crop spatial arrangement was evaluated for effects on cover crop biomass and 
hairy vetch height. For Experiment 2, we analyzed the VZ and RZ separately to 
evaluate the fixed effect of cover crop spatial arrangeme
nt on cover crop biomass and 
distribution, cover crop N content, and above
-
 
and belowground C:N ratio. Treatments 
were separated using Fisher's protected LSD 
P
 
< 0.05. 
All figures were made using the 
GGPLOT
2 package in R (Wickham, 2009).
 
RESULTS
 
Growing D
e
gree Days and Precipitation 
 
Cumulative GDD accumulation during cover crop growth in Year 1 was approximately 
70 to 100 GDD higher than subsequent years in Experiment 1, and 170 and 360 GDD 
higher than Year 2 and Year 3, respectively in Experiment 2. The h
igher GDD 
accumulation in Year 1 was largely due to approximately 110 to 190 greater GDD 
accumulation in the fall of 2011 compared to 
Year 1 
and Year 2. Additionally, spring 
2012 experienced an extremely warm March with GDD accumulation approximately 20 
ti
mes greater than in spring of Year 2 and Year 3. Experiment 2 experienced low GDD 
accumulation throughout both fall and spring in Year 2. For both Experiments 1 and 2, 
precipitation was greatest in Year 2, especially during the winter and spring. Total 
pre
cipitation was 680, 870
-
900, and 630 mm in Year 1, Year 2, and Year 3, 
 
 
 
 
 
125
 
respectively. Higher precipitation in Year 2 was largely due to greater rainfall in April 
and May, when rye and vetch are in their exponential growth phase. Precipitation was 
very low i
n May 2012 (39 mm), which may have inhibited cover crop growth. 
 
Experiment 1: Cover crop productivity
 
Cover crop aboveground b
iomass
. 
Total shoot biomass was significantly affected by 
cover crop spatial arrangement in 1 of 3 years (Table 3.4). In Year 1 t
he MIX rye
-
vetch 
spatial arrangement had 16% greater total biomass production than both SEG1 and 
SEG2 segregated arrangements (Figure 3.2), however we found no detectable 
difference in total rye
-
vetch biomass in Years 2 and 3 of Experiment 1. In Year 1, to
tal 
cover crop biomass was approximately 8
.
2 
M
g ha
-
1
 
within the MIX spatial arrangement, 
and 7
.
1 
M
g ha
-
1
 
within SEG1 and 2. Total rye
-
vetch biomass was approximately 8
.
0 
M
g 
ha
-
1
 
in Year 2, and ranged between 7
.
8 to 10 
M
g ha
-
1 
in Year 3. In all three years 
the 
cover crop mixtures had greater total biomass than the vetch monoculture. Compared to 
the rye monoculture, the three mixtures had lower total biomass in Year 2, but higher 
biomass in Year 3 (Year 1 did not contain a rye monoculture). 
 
Hairy vetch shoot
 
biomass was significantly affected by cover crop spatial 
arrangement in two out of three years (Table 3.4). The MIX spatial arrangement had 
approximately 35% greater vetch biomass than SEG1 in 2012, and 45% greater 
biomass than both SEG1 and SEG2 in Year 
3. (Figure 3.2). There 
were 
no detectable 
difference
s
 
in vetch biomass within the rye
-
vetch mixtures or monoculture in Year 2. In 
Year 1, SEG2 had approximately 30% greater vetch biomass than SEG1, but there was 
no significant difference between the two se
gregated spatial arrangements in Year 2 or 
3. In Years 1 and 3, all biculture treatments had significantly lower vetch biomass than 
 
 
 
 
 
126
 
monocultures. 
Vetch within MIX
 
produced equal or greater
 
biomass per unit of vetch 
seed sown then vetch in monoculture
 
in al
l three years
. In other words, vetch in mixture 
was sown at 50% of the monoculture rate but 
always produced more than the 50% level 
of monoculture biomass
. 
On the contrary, SEG2 produced less than 50% of 
monoculture biomass in 1 out of 3 years, and SEG1 pr
oduced less than 50% of 
monoculture biomass in 2 out of 3 years. 
 
Cereal rye biomass was reduced within SEG2 by 20% compared to MIX and 
SEG1 respectively within Year 1 (Figure 3.2; Table 3.4). We found no detectable 
difference in rye biomass between cover 
crop spatial arrangements in Years 2 and 3. 
Cereal rye biomass within mixtures was approximately 5
.
5 
M
g ha
-
1
 
in SEG1 and MIX, 
and 4
.
4 
M
g ha
-
1
 
in SEG2 within Year 1; and approximately 5
.
0 
M
g ha
-
1
 
and 7
.
6 
M
g ha
-
1
 
across rye
-
vetch mixtures in Years 2 and 3, r
espectively. In Year 2, all bicultures had 
approximately 50% lower rye biomass than the rye monoculture, however, there was no 
significant difference between the rye in mixture and monoculture in Year 3 (there was 
no rye monoculture in Year 1). Rye in mixt
ures produced more biomass then rye 
monocultures per unit of rye seed sown
 
in 1
 
out 
of 3 years. 
 
Vetch h
eight.
 
In both Years 2 and 3, hairy vetch within MIX was taller than both SEG1 
and 2, as well as the vetch monoculture (Figure 3.3, 
P
=<.0001). In Year 2
, hairy vetch 
was also taller in SEG1 compared to the SEG2 arrangement, but there was no 
detectable difference between SEG1 and SEG2 in Year 3. Within Year 2, hairy vetch 
height was 74, 63, and 44 cm in the MIX, SEG1, and SEG2, respectively; and within 
Yea
r 3, hairy vetch height was 85 cm within MIX, and approximately 57 cm in the 
 
 
 
 
 
127
 
segregated spatial arrangements. In both years rye height was measured, but we saw 
no detectable differences based on rye and vetch spatial arrangement (data not shown). 
 
Vetch fl
owering. 
We found no detectable difference in vetch flowering time between rye
-
vetch spatial arrangements in any year of the study (data not shown). 
 
Experiment 2: Spatial distribution and C:N
 
ratio of root and shoot tissue
 
Cover crop lodging.
 
In both Year
s
 
1 and 3 there was substantial lodging of the rye
-
vetch 
cover crops, which likely affected the rye and vetch residue distribution. 
We found no 
detectable difference in lodging of rye and vetch between the MIX and SEG2 spatial 
arrangement in Year 1 when l
o
dging was approximately 65%; 
while in Year 3 lodging 
was marginally lower in SEG 2 (37%) compared to MIX (51%) (
P
=0.07)
. We found no 
lodging of rye and vetch in Year 2 of the study.
 
Shoot biomass spatial distribution. 
Segregation of rye and vetch into zone
s resulted in 
greater vetch biomass within the VZ of SEG2 compared to the MIX in 2 out of 3 years, 
Years 1 and 2
, but not Year 3
 
(Table 3.5, Figure 3.4). In Year 2, 74% of the vetch 
biomass was concentrated within the VZ within SEG2, and this was approxima
tely twice 
the vetch biomass within an equivalent area of the MIX. Similarly, in Year 1, 70% of 
vetch biomass within SEG2 was concentrated within the VZ, and this was 30% greater 
than the MIX
. H
owever due to high variability this difference was only margin
ally 
significant (
P
=0.06). In Year 3, we detected no difference between the spatial 
distribution of vetch biomass between MIX and SEG2. 
 
Across all three years we found significantly less rye shoot biomass within the VZ 
in SEG2 compared to an equivalent ar
ea in the MIX (Table 3.5, Figure 3.4). Similarly, 
we found significantly greater rye biomass within the RZ of SEG2 compared to an 
 
 
 
 
 
128
 
equivalent area in the MIX in 1 of 3 years; in Year 2, rye had 32% more biomass in the 
RZ compared to the MIX. Within the SEG2
 
treatment, rye biomass in the VZ 
represented 35%, 15% and 35% of total rye biomass in Years 1, 2 and 3 respectively 
(Figure 3.4). 
 
Shoot N content
. We found no significant effect of cover crop spatial arrangement on 
total N within either the VZ, RZ, or ac
ross the whole plot in Years 2 and 3. However, in 
Year 1, MIX had 16% greater N contained within the RZ than SEG2 (Table 3.5, Figure 
3.
4
). On a whole plot basis, total N was approximately 130, 100, and 160 kg 
N 
ha
-
1
 
in 
Years 1, 2, and 3, respectively. Desp
ite having a greater proportion of the N
-
rich vetch 
within the VZ, the SEG2 had only 55% of shoot N in Year 1 and Year 2, and 40 % of 
shoot N concentrated within the VZ in Year 3 due to lower vetch biomass compared to 
rye. 
 
C:N ratio of shoot tissue. 
Table
 
3.6 shows the C:N ratio of shoot tissue of hairy vetch 
and cereal rye within both the MIX and SEG2 spatial arrangements over the three 
years. We did not detect any effects of spatial arrangement on the C:N ratio of rye or 
vetch shoots, but we did see marg
inal differences 
among 
years. Hairy vetch C:N ratio 
ranged from 11 to 13.5 throughout the three years of the study. Cereal rye C:N ratio 
was more variable, and was highest in Year 1 at approximately 55, and lowest in Year 3 
at approximately 42. 
 
Across all
 
three years, the C:N ratio of rye and vetch aboveground biomass was 
lower within the VZ of SEG2 compared to MIX (
P
=0.001), but the difference between 
SEG2 and MIX was only marginal when each year was analyzed separately (0.05> 
P
>0.09). In both Years 2 and
 
3, the shoot C:N ratio within the VZ of SEG2 was below 
 
 
 
 
 
129
 
25, while the C:N in the MIX was approximately 40 and 31 in Year 2 and 3, respectively. 
However, we found no detectable difference between MIX and SEG2 in the C:N ratio of 
rye
-
vetch residue across the
 
whole plot, or shoot C:N ratio within the RZ throughout the 
three years of this study.
 
C:N ratio of root tissue
. In both Years 2 and 3 we found that strip
-
intercropping of rye 
and vetch reduced the C:N ratio of roots within the VZ from approximately 32 in
 
MIX to 
24 in SEG2 (Figure 3.5, 
P
<0.0001). However, strip
-
intercropping had no effect on the 
C:N ratio of roots in the RZ; regardless of spatial arrangement the mean C:N ratio of 
roots in the RZ ranged from approximately 30 to 35 (
we 
did not sa
m
pl
e rye
-
vet
ch roots 
in Year 1). 
 
It is likely that winter annual weed roots were also present within root 
samples. However, because weed biomass was between 4 and 9% of total 
aboveground biomass, we expect that weed contributions to root biomass were also 
small. 
 
DIS
CUSSION
 
Spatial arra
ngement and cover crop biomass
 
The hypothesis that strip
-
intercropping of rye and vetch 
will 
increase rye and 
vetch biomass due to reduced interspecific competition was not supported by the data. 
On the contrary, we found that strip
-
int
ercropping either had no effect or lowered 
biomass relative to full width mixtures (Table 3.4, Figure 3.2). In the SEG2 spatial 
arrangement, in which the rye and vetch biculture seeding rate was planted in two 
alternating rows of rye and vetch, we found lo
wer rye biomass in 3 of 5 site
-
years, and 
lower vetch biomass in 1 of 5 site
-
years compared to MIX. While in the SEG1 in which 
 
 
 
 
 
130
 
we had 1 row of vetch to 3 rows of rye, in 2 out of 3 site
-
years we found lower vetch 
biomass than MIX, and lower vetch biomass t
han SEG2 in 1 out of 3 site
-
years.
 
 
By concentrating individuals of each species into separate zones (VZ and RZ), 
we likely decreased interspecific competition at the cost of increasing intraspecific 
competition. Because mixture seed rates were 50% of mono
cultures, we might expect 
the mixtures to have 50% of the monoculture biomass if inter
-
and intraspecific 
competition was equal. Rye showed little evidence of suppression
; 
rye biomass in 
mixture across the three spatial arrangements was 46
-
53% of monocultur
e biomass in 
Year 2, and between 91
-
112% in Year 3. Within MIX we found 50% or greater vetch 
biomass as compared to the vetch monoculture across all three years, and as high as 
85% of monoculture biomass in Year 2, suggesting that there may have been some 
facilitation from the rye. These results are consistent with Hayden et al. (2014) who 
found little evidence of interspecific competition between rye and vetch across varying 
densities within a replacement series design. In contrast, in SEG1 we found eviden
ce of 
suppression of vetch in Years 1 and 3, and in SEG2 there was evidence of increased 
competition in Year 3, when SEG2 was 36% of the vetch monoculture. 
 
The reasons for reduced vetch biomass in strip
-
intercropping compared to MIX 
are unclear, but may h
ave been the result of either intraspecific competition from the 
high vetch planting density or from reduced access to light due to a loss of facilitative 
effects from proximity to rye (e.g. trellis effect).  Rye provides structural support for 
vetch, a vi
ning legume, to climb and increase its light interception. Increasing the 
distance between rye and vetch with strip
-
intercropping reduced the overall height of 
hairy vetch within the mixture (Figure 3.3). This may have been the result of vetch 
 
 
 
 
 
131
 
shoots havin
g to extend a longer distance horizontally to reach a rye plant. The 
reduction in vetch height in the segregated treatments was likely a contributing factor to 
the lower vetch biomass in both Years 2 and 3 within Experiment 1, and may have 
outweighed any p
otential benefits resulting from reduced competition with rye for soil 
resources. 
 
Implications for weed suppression
 
For organic reduced
-
till systems, it has been reported that approximately 
8.0 Mg 
ha
-
1
 
of biomass is required to sufficiently suppress weeds
 
throughout the growing 
season when used as mulch (Teasdale and Mohler, 2000). Previous studies have 
shown that it is difficult to reach those levels of biomass in 
northern 
climates (Brainard 
et al. 2013; Légère et al. 2013). We had greater success in achi
eving rye
-
vetch biomass 
that was close to 8
.
0 
Mg 
ha
-
1
 
or greater in Experiment 1 because the rye
-
vetch was 
harvested and terminated later than in Experiment 2 (see Table 3.1), and had greater 
GDD accumulation in spring. In Experiment 2 
a sweet corn crop fo
llowed the cover 
crops
, and thus we followed timing requirements for cover crop planting and termination 
that were realistic of an operating vegetable farm. Under those conditions the MIX only 
reached 8
.
0 
Mg 
ha
-
1
 
in Year 1 when we had greater GDD accumulat
ion, while SEG2 
never reached 8
.
0 
Mg 
ha
-
1
 
across the whole plot. However, SEG2 had 7400 or greater 
total biomass within the RZ, which would ultimately be between the rows of the 
subsequent crop, and therefore, may be effective at weed suppression within th
at zone. 
 
Implications for N management
 
Within Experiment 2, the MIX and SEG2 spatial arrangements did not differ in 
total N contained within the rye and vetch shoots across the whole plot in any of the 
 
 
 
 
 
132
 
three years. Total shoot N content was 130, 100, and 
160 kg ha
-
1
 
in years 2012, 2013, 
and 2014, respectively. Rye
-
vetch mixtures have been found to fully meet the N 
requirements of a subsequent sweet corn crop when they contained greater than 150 kg 
ha
-
1
 
(Cline and Silvernail, 2002). Therefore, N contained w
ithin both the MIX and SEG2 
spatial arrangements may have been insufficient to meet the full N requirements of a 
subsequent crop in two out of the three years. Nonetheless, in all cases, N contribution 
from vetch would have reduced fertilizer N requirement
s. 
 
Perhaps the most important finding of our research is that strip
-
intercropping has 
potential to improve N use efficiency in legume
-
based cropping systems by 
concentrating the low C:N vetch residue within the crop root zone (VZ, Figure 
3.
5).  We 
found t
hat segregating cereal rye and vetch into strips lowered the C:N ratio of the rye
-
vetch mixture both above
-
 
and belowground in the VZ. Strip
-
intercropping resulted in a 
reduction of C:N ratio from more than 30 to less than 25 in 2 of 3 years. Since C:N 
rat
ios of less than 25 typically result in net N mineralization (Allison, 1966), 
concentrating vetch through strip
-
intercropping resulted in a zone of mineralization (VZ) 
and a zone of immobilization (RZ). Subsequent crops planted into the zone of 
mineralizat
ion (VZ) would be expected to efficiently access N in a manner analogous to 
banding of N fertilizers. Banding of N fertilizers has been shown to increase N use 
efficiency and increase yields in many cropping systems, including field corn (Maddux et 
al. 199
1), wheat (Reinersten et al. 1984), and barley (Mahli and Nyborg 1985). N 
banding may also reduce fertilizer costs and minimize N losses to the environment 
(Maddux et al, 1991; Nash et al. 2012). However, future research is needed to 
 
 
 
 
 
133
 
determine whether simi
lar benefits might be realized through strip
-
intercropping of rye 
and vetch. 
 
Practical limitations of strip
-
intercropping
 
 
The practical use of strip
-
intercropping may be hindered by several practical 
constraints. First, the residue of cover crops sown in
to specific zones will not remain 
exclusively in those zones during cover crop growth or following mowing and 
incorporation into the soil.  As expected, vetch grew beyond the VZ into the RZ. In 
addition, both rye and vetch lodged in late spring in 2 of 3 y
ears in Experiment 2, 
resulting in movement of rye residue into the VZ, and vetch residue into the RZ. 
S
egregation of the rye and vetch residue into zones would have been greater without 
such lodging. This can be seen by comparing the extent of residue mix
ing in Year 2

when no lodging occurred

with Years 1 and 3 (Table 3.5, Figure 3.4). Lodging effects 
on cover crop distribution depends on the direction of the lodging and the orientation of 
fallen
 
cover crops 
with
in the zones. The spatial distribution of co
ver crop residues in the 
subsequent crop will also depend critically on the method used to terminate the cover 
crop and prepare the soil for planting. For example, mowing will redistribute cover crop 
shoots, and aggressive tillage methods will redistribute
 
both roots and shoots across 
zones. Therefore, the practical benefits of strip
-
intercropping are likely to be greatest 
when
 
less aggressive methods are utilized to terminate
 
cover crop
s
 
(e.g. roller
-
crimping 
in the direction of strips), and reduced tillag
e (e.g. no
-
till or strip
-
till).
 
 
 
Segregating cereal rye and hairy vetch into strips would likely be more effective 

reduce the potential for lodging would be to termina
te the cover crops before vetch 
 
 
 
 
 
134
 
flowering, when vetch is at its peak biomass and more likely to cause lodging of the rye. 
We inferred that lodging was caused by heavy vetch biomass because we found no 
relationship between spring precipitation and lodging. 
Year 2 was the year with the 
greatest precipitation in the spring (Table 3.3), but had essentially zero lodging. Year 2 
was also the year with the lowest vetch and total biomass, which 
suggests 
that lodging 
in Years 1 and 3 resulted from vetch being too he
avy for the rye to support. In 
Experiment 2, our goal was to utilize the rye and vetch cover crops for an organic 
reduced
-
till study, so we waited until vetch was between 30% flowering and early pod 
set before mowing. Terminating vetch after early pod set 
reduces chance of regrowth 
during the subsequent crop (Mischler et al. 2010). However, waiting to terminate the rye 
and vetch until after vetch podset allows vetch to continue accruing biomass, thus 
increasing the chance of lodging. Therefore, if farmers w
ere to use strip
-
intercropping of 
rye and vetch for an organic reduced
-
till system, they would face a tradeoff between 
decreasing susceptibility to eit
her lodging or vetch regrowth. 
 
SUMMARY AND
 
CONCLUSION
 
Our objectives
 
were to evaluate the effect of stri
p
-
intercropping of cereal rye and 
hairy vetch on i) cover crop productivity; ii) the distribution of rye
-
vetch biomass 
between cropping systems zones; and iii) N content and C:N ratio of the rye
-
vetch 
mixture inputs into cropping system zones. 
 
Productivit
y benefits 
of segregation for reducing interspecific competition appear 
to have been outweighed or offset by the costs of increased intraspecific competition 
and/or reduced facilitation. Rye and vetch biomass was either unaffected (4 of 5 site
-
years) or re
duced (1 of 5 site
-
years) in segregated spatial arrangements (SEG1 and 
 
 
 
 
 
135
 
SEG2) compared to the full width mixture (MIX). Vetch biomass was either unaffected 
(4 out of 5 site
-
years) or reduced (1 out of 5 site
-
years) in SEG2; and unaffected (1 out 
of 3 site
-
y
ears) or reduced (2 out of 5 site
-
years) in SEG1. Cereal rye biomass was 
reduced in 2 out of 5 site
-
years in SEG2, but not in SEG1. 
 
Despite lodging of rye and vetch, segregating rye and vetch into strips of 
alternating rows of two (SEG2), resulted in a gr
eater concentration of the N rich vetch 
and lower C:N ratio of aboveground rye
-
vetch biomass within the VZ in two out of three 
years. SEG2 also reduced C:N ratio of belowground biomass within the VZ in two out of 
two years. Therefore, strip
-
intercropping o
f cereal rye and hairy vetch is a promising 
strategy to concentrate N rich vetch residue within the row of future crops, thereby 
increasing their access to mineralized N. Future research will determine the effect that 
strip
-
intercropping of rye and vetch h
as on the subsequent crop. 
 
 
 
 
 
136
 
APPENDIX
 
 
 
 
 
137
 
APPENDIX
 
 
 
 
Table 3.1. Dates of key field operations and data collection.
 
 
 
 
 
 
 
 
 
 
 
 
138
 
Table 3.2. Cereal rye and hairy vetch spatial arrangement 
 
treatments for Experiment 1 and Experiment 2.
 
 
 
 
 
 
 
 
 
 
 
 
139
 
Table 3.3. Monthly GDD ac
cumulation during cereal rye and hairy vetch growth for both 
Experiments 1 and 2 at the Kellogg Biological Station in Hickory Corners, MI. 
 
 
 
 
 
 
 
 
 
 
 
 
 
140
 
Table 3.4. Experiment 1. Significance of fixed effect of 
 
cover crop spatial ar
rangement (MIX, SEG1, and SEG
2)
 
on vetch, rye, and total biomass.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
141
 
Table 3.5. Experiment 2. Significance of fixed effect of cover crop spatial 
 
arrangement (MIX vs. SEG2) on biomass, C:N, and N content of rye and 
 
vetch in mixtures within the vetch zone (VZ), rye zone (RZ) an
d across 
 
the whole plot (WP).   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
142
 
Table 3.6. Experiment 2. Mean (+SEM) C:N ratio of 
 
cereal rye and hairy vetch shoots due to cover crop 
 
spatial arrangement in 2012, 2013, and 2014.
 
 
 
 
 
 
 
 
143
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 
3.
1. Planting arrangement of cereal rye and hairy vetch seeds among the 
different r
ye and vetch spatial arrangements
 
in Experiments 1 and 2
: 1) the standard 
Mixed Biculture (MIX), 2) strip
-
intercropping with 2 rows of rye and 2 rows of vetch 
(SEG2), and 3) strip
-
intercropping with 1 row of vetch and 3 rows of rye (SEG1). 
 
 
 
 
 
 
144
 
 
 








































 
 
 
 
 
 
145
 
 
 
 
 
 
 


























 
 
 
 
 
 
146
 
 
 
 
 
 
 
 
 
 
 































 
 
 
 
 
 
 
 
147
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

































 
 
 
 
 
 
14
8
 
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-
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-
260.
 
 
 
 
 
 
 
 
154
 
CHAPTER FOUR
 
Rye and vetch spatial
 
arrangement 
and strip
-
tillage influence
 
N availability and 
sweet corn root growth
 
ABSTRACT
 
Strip
-
intercropping of functionally diverse cover crops, such as cereal rye and 
hairy vetch, is one mechani
sm by which N dynamics could be altered to enhance crop N 
use efficiency
 
and alleviate N deficiency
 
in organic systems. 
We established a 
field 
study in 
s
outhwest Michigan between 2011 and 2014 to examine the effects of rye
-
vetch cover crop spatial arrangem
ent (full width mixture [MIX] or segregated strips 
[SEG]) and tillage (strip
-
tillage [ST] or full width tillage [FWT]) on soil N, sweet corn N 
uptake, above
-
 
and belowground biomass, and root morphology. ST reduced Late 
season potentially mineralizeable N 
in the MIX, but not the SEG spatial arrangement. 
ST had lower soil inorganic N compared to FWT in two out of three years in both the 
MIX and SEG spatial arrangements. Despite lower N availability, ST increased sweet 
corn total biomass and N uptake in two o
ut of three years, and increased sweet corn ear 
weight in one year. ST reduced root biomass both within and between crop rows, and 
increased root diameter within the untilled 
between
-
row (
BR
)
. Segregating rye and 
vetch into strips increased inorganic N and
 
reduced root biomass within the crop row, 
but had no effect on sweet corn biomass or N content. Strip
-
tillage and strip
-
intercropping show promise in adapting reduced
-
till systems for organic production, but 
more work is needed to develop new strategies t
o band organic N within the crop row 
and increase nitrogen use efficiency. 
 
 
 
 
 
 
 
155
 
INTRODUCTION
 
Legume cover crops are a widely used source of N in organic systems (Bingen et 
al
.
 
2007), due in part to being relatively inexpensive 
sources of N 
(Gaskell and Smith,
 
2007) and because they provide soil protection and improve soil structure (McVay et al. 
1989; Cherr et al. 2006). However, legume
-
based systems are often N deficient, and 
exhibit 
reduce
d
 
crop yields compared to 
systems with 
synthetic fertilizers because o
f 
lower overall N availability (Clark et al
.
 

al
.
 
2002). Crop N deficiency resulting from a dependence on legumes as the primary N 
source is especially common in reduced tillage systems (Varco et al. 1993; Dou et al. 
1994; Doane et al. 2009). Improving ni
trogen use efficiency within legume
-
based 
systems is important for increasing crop yields as well as 
for 
maintaining ecosystem 
health and functioning.
 
Synchrony of legume N release and crop N demand depends on a number of 
factors, including the quality of 
legume residue (Kuo et al
.
 
1996; Rannells and Wager, 
1996), the timing of legume termination and incorporation (Crews and Peoples, 2005), 
as well as environmental conditions
-
 
such as soil moisture and temperature (Honeycutt 
et al
.
 
1988; Ruffo and Bollero, 
2003). Variation in the rate of legume residue 
decomposition and timing of N release complicates the predictability of legume N 
contribution to a succeeding crop. Studies have found that mineralization of N typically 
peaks anywhere between 3 to 6 weeks aft
er legume incorporation (Gaskell and Smith, 
2007). Depending on the length of time and environmental conditions between legume 
incorporation and planting of the crop, peak N availability may occur significantly earlier 
than timing of peak crop N demand (Ku
o et al
.
 
1997; Rosecrane et al
.
 
2000). On the 
 
 
 
 
 
156
 
other hand, when soil temperature and moisture are inadequate to promote 
decomposition and mineralization, organic N may be retained within legume residue, 
ultimately leading to crop N deficiency and increased 
potential for N leaching (Dou et al. 
1995; Rannells and Wagger, 1996; Pimentel et al
.
 
2005; Ruffo and Bollero, 2003; Cook 
et al. 2010). 
 
Legume based systems are prone to N losses when patterns of N release are 
asynchronous with crop N demand or when inorg
anic N is inaccessible to crop roots 
(Robertson, 1997). In organic cropping systems, efforts to improve the synchrony and 
spatial coincidence of N supply and demand have been limited. Previous research 
efforts have focused on modulating the rate of decompo
sition and inorganic N release 
from legume residues by combining them in mixtures with cereal grasses (Rannels and 
Wagger 1996; Lawson et al
.
 
2012; Hayden et al. 2014). Cereal grasses are utilized as 
cover crops because of their high capacity for suppressi
ng weeds and returning large 
quantities of carbon to the soil. Varying proportions of high carbon cereal residue with 
N
-
rich legume residue leads to alterations in the total C:N ratio, a major determinant of 
the rate and quantity of N release (Allison, 196
6). 
Rannells and Wagger (1996) grew
 
hairy vetch, a legume, in mixture with cereal rye, 
and found 
the rate of N release was 
decreased by as much as 15%. It was concluded that this slower release of N was more 
likely to be synchronized with timing of crop N 
demand. However, while mixtures of rye 
and vetch may lead to a slower release of N, the total N available to the subsequent 
crop is often reduced due to N immobilization by cereal rye (Lawson et al
.
 
2012). 
 
Rather than focus on optimizing the rate of decom
position and N mineralization 
of legume cover crops, an alternative strategy to enhanc
e
 
crop nitrogen use efficiency is 
 
 
 
 
 
157
 
to alter the placement of N. In conventional cropping systems, banded applications of N 
adjacent to the crop row can significantly impro
ve crop N uptake efficiency and yields 
(Mengel et al
.
 
1982; Maddux et al
.
 
1991), decrease N immobilization (Maddux et al
.
 
1991) and may reduce N losses (Waddell and Weil, 2006; Nash et al. 2012; Haramoto, 
2014). Studies examining changes in plant productiv
ity under controlled greenhouse 
conditions have found that concentrating forms of organic N in close proximity to crop 
roots leads to an increase in N uptake and plant biomass (Robinson et al. 1994; Hodge 
et al. 1999). For example, red clover residue incre
ased maize aboveground productivity 
when aggregated into clumps compared to uniform distribution throughout the soil 
(Loecke and Robertson, 2009). Plants stimulate root proliferation in regions of the soil 
with concentrated reserves of nutrients by diverti
ng energy for root growth away from 
nutrient poor regions (Robinson, 1994). This reduces the energy required by the plant 
for acquisition of the limiting nutrient and allows more energy to be diverted to 
aboveground productivity. The ability of a crop to b
enefit from a spatial advantage to a 
N rich resource in the soil may be greater with an organic source of N because of its 
slower, yet continuous release (Hodge, 1999). 
 
Strip
-
intercropping of functionally diverse cover crops, such as cereal rye and 
hairy 
vetch, is one mechanism by which N dynamics could be altered to enhance crop N 
use efficiency in organic systems. Legume cover crops sown solely in a strip directly in
-
row with future crop establishment may provide N directly to the crop roots and increase
 
the likelihood of crop uptake of N prior to being lost. High 
C:N 
cover crops, such as 
cereal rye, sown between crop rows may create a zone initially deficient in N that can 
potentially serve as a reservoir of late
-
releasing N. The synchrony and spatial 
 
 
 
 
 
158
 
co
incidence of N supply and demand may also be enhanced in organic cropping 
systems through use of strip
-
tillage (ST). Tillage targeted to the crop row (
with
in
-
row; 
WR
) can stimulate mineralization of soil organic N directly where the crop needs it 
(McCarthy
 
et al
.
 
1995), without unnecessarily releasing N between crop rows (BR) 
where N is not needed until later in the growing season. This could potentially decrease 
the amount of N inaccessible to crop roots leading to reductions in N losses, as well as 
increa
se the proportion of 
soil 
inorganic N in the soil taken up the crop. 
 
Our goal wa
s
 
to evaluate whether strip
-
intercropping of cereal rye and hairy 
vetch
 
within an organic ST system could
 
increase cover crop N mineralization and 
availability to the crop, 
an
d consequently
 
increase sweet corn biomass and N uptake. 
We hypothesized that 
concentration of vetch residue in the WR zone through strip
-
intercropping would increase both 
potentially mineralizeable N (PMN) and soil inorganic 
N 
in close proximity to sweet 
corn roots, hence improving N use efficiency
. 
We also 
expected that these beneficial
 
effect
s
 
of 
would
 
be more pronounced within ST than 
full
-
width tillage (
FWT
)
. 
A secondary objective was to evaluate the impact of changes in 
spatial distribution of N pools
 
on sweet corn root allocation. We expected that the 
magnitude of sweet corn response to strip
-
intercropping and strip tillage would depend 
on the plasticity of root response to differences in N spatial dynamics, and hypothesized 
that under strip
-
intercrop
ping, sweet corn would allocate a greater proportion of root 
biomass to the N
-
rich WR zone.  
 
 
 
 
 
 
 
 
 
159
 
MATERIALS AND METHODS
 
Site description and e
xperimental design
 
The experiment was conducted between 2011
-
2014 on organically managed 
(between 5
-
7 years, depend
ing on field) Kalamazoo (fine
-
loamy, mixed, mesic Typic 
Hapludalfs) and Oshtemo (coarse
-
loamy, mixed, mesic Typic Hapludalfs) sandy loams 
(Crum and Collins, 1995), at the Kellogg Biological Station in Hickory Corners, MI 
(85




e arranged in a split plot, randomized complete 
block design with four replicates. Main plots consisted of a 2 X 2 factorial of tillage (FWT 
and ST) and rye and vetch spatial arrangement (Mixed Biculture [MIX] and Segregated 
into strips [SEG]). The split p
lot factor was crop (sweet corn [CORN] or no crop 
[NOCORN]). 
Zones within the cropping system were also treated as subplots. The zone 
within the crop row (WR) is the 25 cm zone where the crop is planted (and where tillage 
occurs within strip
-
tilled treatme
nts), and the between
-
row (BR) zone is the 50 cm zone 
between crop rows and also is untilled in strip
-
tilled treatments. 
Subplot area with corn 
(CORN) was 27.3
 
(9.1 X 3.0 m), 37.2
 
(6.1 X 6.1 m), and 41.0 (9.1 X 4.5 m) m
2
 
in 2012, 
2013, and 2014 respectivel
y. Subplots without corn were 9.0 (3.0 X 3.0 m), 9.0 (3.0 X 
3.0 m), and 20.3 (4.5 X 4.5 m) m
2
 
in 2012, 2013, and 2014 respectively.
 
Field activities were performed according to Table 4.1. Both cereal rye and hairy 

organically certified across all three years. 
Spatial arrangements included a full
-
width mixture (MIX) in which rye and vetch were 
sown together in the same rows; and a segregated mixture (SEG) in which 2 rows of 
hairy vetch were alternated with 2 rows of 
rye. 
Both
 
spatial arrangements had the same 
seed rate on a per plot basis (62.7 kg ha
-
1
 
rye; 22.4 kg ha
-
1
 
vetch). In both rye and vetch 
 
 
 
 
 
160
 
spatial arrangements, between
-
row spacing for rye and vetch rows was 19.
1 
cm. Cereal 
rye was planted with a grain drill 
and every two drop tubes were blocked within SEG to 
prevent seeding rye in the vetch rows. Hairy vetch was planted with a Jang push
-
seeder. No N fertilizers were added prior to cover crop establishment or sweet corn 
planting. 
O
rganic forms of P and K were 
added according to soil tests, and consisted of: 
34 kg K 
and 
P ha
-
1
 
of 
NOP certified Potassium Sulfate Plus
 
and Tennessee Brown 
Phosphate in 2012 and 34 kg K ha
-
1
 
of Ks+ in 2013. 
 
Cover c
rops were flail mowed twice when
 
vetch had reached approximately 50% 
flowering in mid
-
 
to late
-
 
May of 2012, 2013, and 2014. Conventional tilled treatments 
were chisel plowed to a depth of approximately 20 cm, and then rototilled to break up 
cover crop residue. A Hiniker Model 6000 two
-
row strip tiller (equipped with notche
d 
row
-
cleaners, cutting
-
coulter, shank point assembly, berming discs, and rolling basket) 
was used for
 
WR
 
tillage in ST treatments. To improve soil tilth for planting in ST, a walk 
behind rototiller was used for
 
WR
 
secondary
-
tillage. The resulting
 
WR
 
zone 
was 
approximately 25 cm wide and 20
-
25 cm deep. Two weeks after tillage, sweet corn was 
planted using a high residue planter (Monosem vacuum seeder) at a between
-
row 
spacing of 76.2 cm and in
-
row spacing of 10 cm. Once sweet corn emerged, population 
densit
y was thinned to an in
-
row spacing of 16.5 cm. After corn emergence, corn was 
removed via hoeing for NOCORN subplots. Weeds were controlled through a 
combination of hand weeding and hoeing once a week for the first 5 weeks, and every 
other week thereafter.
 
Sweet corn was irrigated during low
-
rainfall periods in 2012 and 
2014, totaling 62.2 and 46.8 mm of irrigated water, respectively throughout the sweet 
 
 
 
 
 
161
 
corn season. In 2013, rainfall was sufficient so we did not provide any additional 
irrigation. 
 
Data c
ol
lection
 
Potentially Mineralizeable N
.
 
In situ
 
N mineralization assays were employed to evaluate 
the potential for N release from cover crop residues within each tillage system 
(Robertson, et al
.
 
1999). 
In situ
 
or in field mineralization incubations incorpo
rate effects 
that tillage and cover crop mulch have on soil structure, moisture, and temperature, as 
well as the subsequent effect these differences have on N mineralization. Incubations 
were initiated in treatments within the CORN subplots 7 and 35 days a
fter tillage. 
Five 
centimeter 
internal diameter (24 cm depth) PVC pipes were installed in each whole plot 
(3 cores per zone and tillage combination) and capped until core removal. At 
installation, we collected two soil samples directly adjacent to installe
d cores at the start 
of incubation and extracted for 
soil 
inorganic N to determine initial inorganic N
 
(see 
below for inorganic N)
. We removed soil cores after 30 to 35 days and measured soil for 
gravimetric moisture and inorganic N. 
 
Mineralization per da
y was calculated according to the following equation: 
 
[4.1]   























 
Bulk density. 
Upon mineralization tube extraction, soil cores were pooled, homogeniz
ed, 
and weighed. Two 10 g subsamples were removed for gravimetric soil moisture to 
determine soil dry weight (see below). Bulk density was then calculated by dividing soil 
dry weight by soil volume. If soil was missing from the tube bottom, we measured the
 
length of tube with soil missing to estimate the volume of each soil core for calculation 
of bulk density. 
 
 
 
 
 
 
162
 
Soil sampling
-
 
Inorganic N and gravimetric soil m
oisture
.
 
Soil samples were collected 
every 7
-
14 days (8 sampling times in 2012, 2013, and 9 sampli
ng points in 2014) 
throughout the growing season in all treatments and subplots in both the
 
WR
 
and BR. 
At each sampling date, 10
-
12 cores (2.5 cm diameter 
X 
20 cm deep) were taken for 
each plot. Soil samples were dried at 38°C and ground. 
Fifty milliliters
 
of 
1M KCl  
were 
used to extract soil inorganic N 
on 10 g of dry soil
 
and samples were analyzed on a 
QuikChem 8500 Flow Injection Analyzer
 
(Gelderman and Beegle, 1998)
. Two 10 g 
subsamples of moist soil were 
dried at 100°C and reweighed 
for gravimetric soi
l 
moisture. Gravimetric water content (GWC) was calculated according 
to 
the following: 
 
[4.2]   























 
To calculate the soil inorganic N and gravimetric moisture across the whole plot, we 
used weighted means of the measured values from the WR and BR zones adjusted by 
their respective areas.  For example, for inorganic N (IN) we used: 
 
[4.3]
 
IN
whole plot
 
= IN
WR
*1/3 + IN
BR
*2/3
 
Where the within
-
row (WR) zone is the 25 cm zone where the crop is
 
planted (and 
where tillage occurs within strip
-
tilled treatments), and the between
-
row (BR) zone is the 
50 cm zone between crop rows and also is untilled in strip
-
tilled treatments.
 
Sweet corn aboveground biomass
 
and N content.
 
At harvest, we removed all 
primary 
ears from sweet corn plants in a designated harvest area of 9.3, 14, and 22.3 m
2
 
in 
2012, 2013, and 2014, respectively. Harvest areas were adjusted each year based on 
available plot size and stand uniformity
,
 
which varied each year. Secondary ear w
eight 
was grouped
 
with nonharvested biomass and consisted of no more than 5% of 
nonharvested biomass. To obtain plant dry weight and percent N, we collected the 
 
 
 
 
 
163
 
nonharvested aboveground portion of 5 randomly sampled plants from each plot, and 5 
representat
ive corn ears. We removed a 2.5 cm section from corn ears, dried to 
determine a fresh to dry weight ratio, and multiplied this ratio by total fresh weight to 
estimate total ear dry weight. Both nonharvested corn biomass and corn ear sections 
were dried at 

was performed on 0.15 g samples of nonharvested corn biomass and corn ears (2 lab 
replicates per sample) and analyzed for total Kjehldahl nitrogen with a QuikChem 8500 
Flow Injection A
nalyzer. 
 
Sweet corn belowground biomass
.
 
Three cores (5 cm diameter X 25 cm depth) were 
excavated from both the
 
WR
 
and BR zones at sweet corn harvest to compare 
differences in allocation of root biomass between the
 
WR
 
and BR zones. A meter stick 
was laid 
adjacent to a sweet corn plant and perpendicular to the sweet corn row, and 
root cores were excavated 7.5 cm 
(WR)
 
and 25 cm (BR) from the plant; samples thus 
represented the mid
-
point between the edge and center of each zone. The root samples 
were weighed,
 
and a subsample was taken for gravimetric soil moisture. Samples were 
sieved twice through a 4 mm sieve and roots were collected, thoroughly washed
,
 
and 
scanned with an EPSON (V700) scanner. We used a WinRHIZO (Régent Instruments 
Inc
.
 
Québec, Canada) imag
e analysis system to analyze root images for root length and 
average root diameter (ARD). Roots were dried at 60°C for 72 hours and weighed. Root 
data was used to calculate specific root length (SRL, root length per unit root dry weight 
(m g
-
1
)) and root l
ength density (RLD, root length per unit of soil volume (cm cm
-
3
)). 
 
 
 
 
 
 
 
 
164
 
Statistical analysis
 
All data were analyzed using mixed models ANOVA with PROC MIXED 
procedures in SAS (Version 9.3, SAS Institute, Cary, NC). Year was initially treated as a 
fixed fact
or to determine if fixed effects interacted with year to affect response variables, 
and if there was no difference
s
 
data were pooled across years
. In all cases, block was 
nested in year, and block and interactions of block with fixed factors were treated a
s 
random effects. The data were checked for assumptions of normality and equal 
variance, and when necessary, unequal variance models were used selected based on 
AIC values. To increase normality, we log transformed inorganic N, specific root length, 
and sw
eet corn root mass. 
 
 
The fixed effects of tillage (FWT vs ST), cover crop spatial arrangement (MIX vs 
SEG), and zone 
(WR
 
vs BR) were analyzed for potentially mineralizeable N (PMN), 
inorganic N (IN), and gravimetric moisture
 
responses
. For IN and gravimet
ric moisture, 
the NOCORN and CORN subplots were analyzed separately. The
 
WR
 
and BR zones 
were analyzed separately for the fixed effects of tillage and cover crop spatial 
arrangement on sweet corn root mass and morphology. For soil IN and moisture, data 
wer
e analyzed using repeated measures mixed models with date included as a 
repeated factor, and AIC and BIC values were compared to determine the best model 
fit. 
 
When significant treatment effects were detected, means were separated using 
Fisher's protected 
LSD 
P
< 0.05. In cases where 
P
-
values were between 0.05 and 0.10, 

P
-
value presented 
 
 
 
 
 
165
 
parenthetically. 
All figures were made using the GGPLOT2 package in R (Wickham, 
2009).
 
RESULTS
 
Potentially mine
r
a
lizeable N
 
 
PMN was measured from planting to mid
-
season (Early) and mid
-
season to 
harvest (Late) in 2012, 2013, and 2014 (Figure 4.1). For both Early and Late N 
mineralization potential, there were no interactions of year with either cover crop spatial 
arrangement or tillage so years were pooled. We found no detectable difference in 
whole plot Early PMN due to tillage or cover crop spatial arrangement. In Late season 
PMN, within FWT, the MIX spatial arrangement had 30% greater PMN compared to the 
SEG arr
angement (Till*SA, 
P
=0.0155). ST MIX has 28% lower PMN compared to FWT
-
MIX, but we found no detectable difference between ST and FWT within SEG 
arrangements. 
 
 
Within ST, we found a marginal interaction between RV spatial arrangement and 
zone when measurin
g Early Season PMN (Figure 4.2). The BR of the MIX spatial 
arrangement had 38% greater PMN than SEG
-
BR, but there was no detectable 
difference within the
 
WR
 
between RV spatial arrangements (SA*Zone, 
P
=0.062). SEG
-
BR had greater PMN than SEG
-
W
R. We found no
 
detectable difference in Late PMN 
between the zones of ST due to cover crop spatial arrangement. 
 
Soil 
Inorganic N
 
 
The effects of tillage on soil inorganic N (IN) in the absence of sweet corn varied 
with sampling time in 2013 and 2014, but did not vary w
ith cover crop spatial 
arrangement in any year (Table 4.2, Figure 4.3). Without corn, ST decreased soil IN by 
 
 
 
 
 
166
 
an average of 33% compared to FWT in both 2013 and 2014, and the difference 
between tillage systems increased throughout the season. We found no d
etectable 
difference in IN due to tillage in either NOCORN or CORN subplots in 2012. 
 
Results with corn were very similar to those without corn: FWT had a season
-
average of 
24% and 28% greater IN compared to the ST in 2013 and 2014, respectively. The 
effec
ts of tillage on IN in the presence of sweet corn varied with sampling time in 2013 
and 2014, but did not vary with cover crop
 
spatial arrangement (Table 4.2,
 
Figure 4.3). 
IN within CORN subplots peaked in July, and then decreased throughout the month of 
A
ugust as corn was 
growing exponentially
. 
 
Figure 4.4 shows IN within the
 
WR
 
and BR zones of NOCORN subplots. 2014 
was the only year we found an interaction of tillage and zone, which was also affected 
by sampling date (Table 4.3, Figure 4.4). 
S
T
-
W
R had 20%
 
greater IN compared to the 
S
T
-
BR had 25% lower IN compared to the FWT
-
W
R/BR, and differences in soil IN 
tended to increase throughout the sweet corn season. 
 
 
We found no detectable difference in whole plot IN due to cover crop spatial 
arrangement across 
the whole plot in either FWT or ST in any of the three years (Table 
4.2, Figure 4.3). However, tillage and spatial arrangement did influence zonal availability 
of IN in subplots without corn (Table 4.3; Figure 4.4). In 2012, we found a marginal 
effect (SA*
Zone, 
P
=0.064) of cover crop spatial arrangement and zone, with SEG
-
W
R 
having 16% greater inorganic N than SEG
-
BR and 11% greater than MIX
-
W
R, 
regardless of tillage. When we analyzed the effect of cover crop spatial arrangement on 
IN within ST, we found th
at SEG increased IN within the crop row 
(WR)
 
by 11% 
compared to MIX (SA*Zone, 
P
=0.0016). 
 
 
 
 
 
 
167
 
Bulk density
 
Cover crop spatial arrangement had no effect on bulk density in any year, but 
tillage and zone influenced bulk density in 2013 and 2014 (Table 4.4). In 2
013, ST
-
BR 
had approximately 10% greater bulk density than ST
-
W
R and either zone within FWT. 
In 2014, ST
-
BR had greater bulk density than the ST
-
W
R and FWT
-
BR, but not FWT
-
W
R.
 
Gravimetric soil moisture
 
The effect of tillage on gravimetric soil moisture in 
CORN subplots interacted with 
sampling date in both 2012 and 2014, but not 2013 (Table 4.5 Figure 4.5). In 2012, ST 
had 11% greater soil moisture compared to FWT, and this difference was greatest in 
mid
-
July. In 2014, ST had 9% greater soil moisture compar
ed to FWT, and the 
difference was greatest in mid
-
July and early September. Cover crop spatial 
arrangement significantly affected gravimetric moisture in 2013, but did not interact with 
tillage or date. The MIX spatial arrangement increased gravimetric soi
l moisture by 10% 
compared to SEG. 
 
Sweet c
orn aboveground biomass
 
and 
N content
 
 
Tillage influenced 
sweet 
corn above ground biomass, but we found no 
detectable differences due to cover crop spatial arrangement, nor interactions between 
tillage and cover c
rop spatial arrangement in any of the three years (Table 4.6, Figure 
4.6).  ST increased total 
sweet 
corn biomass by 19% in 2012 and 16% in 2014 
compared to FWT. The effect of tillage was marginal in 2013 (
P
=0.09), in which ST had 
10% lower total biomass c
ompared to FWT. ST increased sweet corn nonharvested 
biomass by 37% and 19% in 2012 and 2014, respectively
,
 
compared to FWT. ST 
 
 
 
 
 
168
 
increased 
sweet 
corn ear dry weight by 18% in 2014 compared to FWT, but had no 
detectable effect on corn ear weight in 2012 and 
2013. We found no detectable 
difference in biomass partitioned to corn ears or nonharvested biomass in 2013 and 
2014. Corn ear biomass represented 35% of total biomass in both 2013 and 2014. In 
2012, we found marginally greater biomass partitioned to the c
orn ear in FWT+MIX 
compared to FWT+SEG, and ST (Till*SA, 
P
=0.082). 
 
As with corn biomass, the effect of tillage on sweet corn N uptake interacted with 
year, but not cover crop spatial arrangement. ST increased total sweet corn N uptake by 
15 and 17% compar
ed to FWT in 2012 and 2014, respectively, but decreased total 
sweet corn N uptake by 21% in 2013 (Table 4.6, Figure 4.6). Tillage had no effect on 
the N partitioned to either nonharvested biomass or sweet corn ears in any year. N 
partitioned to sweet corn 
ears was 54, 43, and 33% in 2012, 2013, and 2014, 
respectively. 
 
 
We found no detectable difference in sweet corn N uptake in nonharvested 
biomass, sweet corn ears, or total N uptake in any year. In 2012, we found a marginal 
increase in the percent of tota
l N partitioned to nonharvested biomass compared to corn 
ears within the SEG spatial arrangement (
P
=0.06), but there was no detectable 
difference in 2013 and 2014. 
 
Sweet c
orn belowground biomass
 
Within the
 
WR
 
(7.5 cm from the sweet corn plant), both tilla
ge and cover crop 
spatial arrangement affected sweet corn root mass, but there was no interaction 
between the two main effects, nor did they interact with year (Table 4.7, Figure 4.7.A). 
SEG reduced sweet corn root mass within the
 
WR
 
by 34% compared to MIX
, regardless 
 
 
 
 
 
169
 
of tillage. ST reduced sweet corn root mass in the
 
WR
 
zone by 30% compared to FWT, 
regardless of spatial arrangement. Between crops rows (at the 25 cm distance) we 
found a significant interaction between tillage and cover crop spatial arrangem
ent, but 
no interactions with year. SEG rye and vetch increased root biomass by 43% within the 
BR of FWT, but had no detectable effect on root biomass in the BR of ST.
 
Figure 4.7.B. shows the ratio of
 
WR
/BR root mass, in which we found a 
significant intera
ction between tillage and cover crop spatial arrangement. FWT+MIX 
had a significantly greater
 
WR
/BR root mass compared to FWT+SEG and ST. We found 
no detectable difference in the
 
WR
/BR mass ratio due to cover crop spatial 
arrangement within ST. 
 
Sweet c
orn
 
root
 
morphology
 
 
Within the BR, tillage and cover crop spatial arrangement interacted to affect 
both sweet corn SRL and ARD, but neither main effect interacted with year (Table 4.7, 
Figure 4.8). ST reduced SRL within the BR of the MIX, but not the SEG spa
tial 
arrangement. SEG increased ARD in the BR of FWT, but not of ST. Within the
 
WR
, we 
found a marginal increase in SRL in SEG spatial arrangements across both tillage 
systems (SA, 
P
=0.077), and a marginal decrease in root diameter but only within ST 
(Till
*SA, 
P
=0.095). 
 
 
RLD was influenced by tillage but not by cover crop spatial arrangement (Table 
4.7, Figure 4.8). The effects of tillage on RLD differed by year (Table 4.7). In 2014, ST 
decreased the RLD in both zones compared to FWT (data not shown). In 2
013, RLD 
was also lower in ST, regardless of zone, but the difference was not substantial enough 
to be significant.
 
 
 
 
 
 
170
 
DISCUSSION
 
Strip
-
tillage 
effects on PMN, 
soil 
inorganic N, and sweet 
corn
 
We expected that PMN would be greater within FWT compared to ST. H
owever, 
we only found a reduction in Late season PMN within the MIX spatial arrangement. This 
was surprising given that we found lower IN in ST compared to FWT in both cover crop 
spatial arrangements. Incorporating crop residues into the soil typically enh
ances 
decomposition and mineralization. It is possible that the lack of a tillage effect on PMN 
is due to the recalcitrant rye residue 
is 
immobilizing N as it decomposes. Rye 
immobilization of N is greatest when the C:N ratio is at its highest, typically a
fter 
anthesis
,
 
which also corresponds to the timing of rye and vetch termination in this study. 
Residues left on the soil surface have slower rate of decomposition, and are thus less 
likely to 
tie
-
up N. The discrepancy between PMN and IN may have resulted 
from greater 
N losses through leaching (See Chapter 2; Figures 2.7 and 2.8) or greater N uptake in 
ST in two out of the three years (Figure 4.6). Our results are consistent with previous 
studies that have found lower IN availability within legume
-
based red
uced
-
till systems 
(Dou et al. 1994; Mulvaney et al. 2010). 
 
It is surprising that despite lower IN in ST compared to FWT, ST only negatively 
affected corn
 
aboveground biomass and corn ear weight
 
in 
 
2013. This suggests that 
factors other than N

such as soi
l moisture

may have affected sweet corn growth in 
FWT treatments in 2012 and 2014. This hypothesis is consistent with the observation 
that soil moisture was lower in FWT then ST treatments in 2012 and 2014, but higher in 
2013 (Figure 4.5). Soil moisture, r
ather than soil IN, may have been the primary factor 
influencing tillage effects on sweet corn root growth and morphology in the
 
WR
 
zone. 
 
 
 
 
 
171
 
The lower root mass and RLD within ST suggests that sweet corn plants allocated less 
carbon to resource acquisition wi
thin the top 0.25 m compared to FWT. Plants generally 
increase root growth and RLD when nutrients or water are limiting (Bloom et al. 1985; 
Boot and Mensink, 1990), but when soil resources are sufficient carbon allocation is 
prioritized to shoots. Thus, th
e lower root mass and RLD within ST suggests a more 
efficient uptake of soil resources.   
 
Differences in bulk density between tillage systems may also have strongly 
influenced corn root growth and morphology, especially in the BR zone. We found lower 
RLD 
in both the
 
WR
 
and BR zones of ST, which is consistent with some (Chassot et al. 
2001, Qin et al. 2005) but not all (Acharya and Sharma, 1994; Ball
-
Coehlo et al. 1998) 
prior 
studies. Dal Ferro (2014) et al. found that bulk density was negatively correlated
 
with RLD and mass and attributed it to increased mechanical impedance restricting root 
growth. Within the un
-
tilled zone (BR) of ST, we found lower SRL and an increase in 
root diameter within the MIX rye
-
vetch spatial arrangements. Increased bulk density,
 
such as was found within the BR of ST, can result in roots with a greater average 
diameter (Barber, 1971; Bathke et al. 1992). Previous studies have suggested that in 
soils with greater bulk density, roots must be stronger and denser to penetrate the soil
 
(Barber, 1971; Unger and Kaspar, 1994). Increased soil moisture within untilled soils 
combined with a mulch can alleviate obstructions to root growth by increasing soil 
aggregate malleability (Unger and Kaspar, 1994). However, within our study it seems 
th
at increased soil moisture did not offset the effects of bulk density on root diameter 
and SRL. 
 
 
 
 
 
 
 
172
 
Cover crop spatial arrangement effects
 
on PMN, soil inorganic 
N, and sweet corn
 
Differences in IN due to spatial arrangement were smaller than expected, 
perha
ps 
due in part to movement of cover crop shoot tissue across zones prior to corn 
planting. Although segregating rye and vetch into strips increased IN, these differences 
were very small relative to differences due to tillage (Figure 4.4).  Cover crop lodgi
ng in 
two out of the three years likely reduced the spatial segregation of N between the
 
WR
 
and BR zones. Additionally, mowing likely contributed to additional lateral movement of 
rye and vetch between the
 
WR
 
and BR zones. Finally the chisel plowing and ro
totilling 
within FWT likely increased the lateral movement of rye and vetch residues. We may 
have seen greater differences in N availability between the
 
WR
 
and BR zones if we 
had 
used a roller crimper to terminate the cover crops and no
-
till planted the sw
eet corn. 
However, we mowed the rye and vetch to facilitate strip
-
tillage. 
 
Given these relatively small differences in IN, it is not surprising that we found no 
effect of cover crop spatial arrangement on sweet corn aboveground biomass, ear dry 
weight, or
 
N uptake in any of the three years. However, we did find SEG
-
RV effects on 
sweet corn root biomass and morphology. Segregating rye and vetch into strips reduced 
corn root biomass in the
 
WR
 
zone by 38%, and decreased root biomass in the BR zone 
of ST by 43
%. This suggests that sweet corn plants within SEG spatial arrangements 
allocated less carbon for roots within the top 25 cm of soil to support equal shoot growth 
and sweet corn yields. 
 
Fine roots with a greater surface area and larger length to mass rati
o (SRL) are 
capable of exploiting larger soil volumes, and may be more efficient at absorbing N. 
SRL 
may
 
be an indicator of the cost (root mass) to benefit (resource acquisition) ratio of 
 
 
 
 
 
173
 
obtaining soil resources (Eissenstat, 1992; Ostonen et al. 2007). 
Re
source absorption is 
directly proportional to the length of the roots, so increased SRL indicates higher 
resource absorption per carbon investment in root growth. 
Roots with a greater SRL are 
more efficient at taking up soil resources, which enables energy
 
to be diverted to 
aboveground productivity. Response of SRL to N fertilization varies considerably among 
species (Hill et al. 2006; Ryser, 2006); however previous studies have found N 
fertilization increases SRL of field corn (Anderson, 1988; Bonifas and 
Lindquist, 2009). 
This is counterintuitive, because it seems logical that the costs of building new roots, 
would be minimized when resources are limiting, thereby increasing SRL (Ryser, 2006). 
It is likely that differences in SRL do not result from any dif
ferences in absolute nutrient 
availability, but 
rather 
result from the heterogeneous distributions of resources within 
SEG
-
RV. Increased SRL within the
 
WR
 
zone of SEG
-
RV is likely a response to the
 
WR
 
enrichment with N rich organic residues, and is consist
ent with numerous studies 
that 
have found that SRL 
and N uptake 
increases in patches or zones of nutrient enrichment 
(Drew, 1975; Hodge et al. 1999; Hodge, 2004
; 
Loecke and Robertson, 2009
). 
 
 
We had expected that the greatest difference between the
 
WR
 
and
 
BR zones in 
root mass and morphology would be in the ST
-
SEG, the treatment with the greatest 
zonal differences in soil resources and physical properties. Therefore, it was surprising 
that the root
 
WR
/BR mass ratio was greatest in the most homogenous treat
ment, FWT
-
MIX. FWT loosened the soil across the plot to a depth of approximately 20 cm, and 
likely facilitated root penetration deeper into the soil profile. Compared to no
-
till, tillage 
results in a more vertical orientation of root development (Ball
-
Coel
ho et al. 1998). It is 
possible that the lower root growth in FWT
-
MIX BR is the result of a more vertical 
 
 
 
 
 
174
 
orientation of roots that penetrate more deeply but less laterally within the soil, however 
we did not sample deeper than 0.25 m so can only speculate
 
about root mass at greater 
depths. It is not clear why we detected such a large difference in root
 
WR
/BR mass ratio 
be
tween the FWT
-
MIX and FWT
-
SEG. 
 
SUMMARY AND CONCLUSION
 
Despite lower soil IN in ST compared to FWT, ST increased sweet corn total 
biomass
 
and N uptake in two out of three years, and increased sweet corn ear weight in 
one year. Our results suggest that this may have been the result of increased 
gravimetric soil moisture, and perhaps more efficient resource uptake by roots in ST. ST 
reduced c
arbon allocation to root biomass in both the
 
WR
 
and BR zones, and increased 
root diameter within the untilled BR. 
 
Segregating rye and vetch into strips increased IN and reduced root biomass 
within the crop row, but had no effect on sweet corn biomass or N
 
content. These 
results suggest that sweet corn roots are surprisingly plastic in their response to 
relatively small differences in 
the 
dis
tribution of IN within the soil.
 
Further studies are 
required to understand the mechanisms responsible for these diff
erences in carbon 
allocation to roots as well as their implications for crop productivity. 
 
Strip
-
tillage and strip
-
intercropping show promise in adapting reduced
-
till systems 
for organic production, but more work is needed to develop new strategies to ban
d 
organic N within the crop row and increase nitrogen use efficiency. Alternative 
approaches for increasing the effectiveness of strip
-
intercropping may include roller
-
crimping, earlier termination of cover crops to reduce rye and vetch susceptibility to 
l

 
 
 
 
 
175
 
targeted N placement within organic RT systems including banding of manure, compost, 
or other organic N amendments (ie. feathermeal, blood meal, etc.) within the crop row.
 
 
 
 
 
 
 
176
 
APPENDIX
 
 
 
 
 
 
 
177
 
APPENDIX
 
 
 
 
Table 4.1.
 
Dates of key field activities and data collection.
 
 
 
 
 
 
 
 
 
 
178
 
Table 4.2. P values from a three
-
way ANOVA for inorganic N across the whole CORN 
and NOCORN subplots (weighted average of WR and BR) in 2012, 2013, and 2014. 
Fixed
 
effects include tillage (full
-
width and strip
-
tillage), rye and vetch spatial 
arrangement (Mixed and Segregated), and date as a repeated measure. 
 
 
 
 
 
 
 
 
179
 
Table 4.3. P values from a four
-
way ANOVA for inorganic N within 
 
NOCORN subplots in 2012, 2013, and 2014
. Fixed effects include 
 
tillage (full
-
width and strip
-
tillage), rye and vetch spatial arrangement 
 
(Mixed and Segregated), zone (within
-
 
and between
-
row), and date 
 
as a repeated measure. 
 
 
  
 
 
 
 
 
 
 
180
 
Table 4.4. Mean (+/
-
 
SEM) bulk density in the top 0.25 cm of
 
soil in 
 
2012, 2013, and 2014. Fixed effects include tillage (full
-
width and 
 
strip
-
tillage) and zone (within
-
 
and between
-
row).
  
 
 
 
 
 
 
 
 
181
 
Table 4.5. P values from a three
-
way ANOVA for gravimetric 
 
soil moisture in CORN subplots. Fixed effects include tillage
 
 
(full
-
width and strip
-
tillage) and rye and vetch spatial arrangement 
 
(Mixed and Segregated), and date as a repeated measure.
 
 
 
 
 
 
 
 
 
182
 
Table 4.6. P values from a two
-
way ANOVA for sweet corn nonharvested and ear 
biomass, as well as N content from 2012, 2013, 
and 2014. Main effects include tillage 
(full
-
width and strip
-
tillage) and rye and vetch spatial arrangement (Mixed and 
Segregated).  
 
 
 
 
 
 
 
183
 
Table 4.7. P values from a three
-
way ANOVA for root mass, root mass ratio, specific root length (SRL), average root 
dia
meter (Diameter), and root length density (RLD) within the within
-
row (WR
-
 
7.5 cm from plant) and between
-
row (BR
-
 
25 cm from plant). Main effects include tillage (full
-
width and strip
-
tillage), rye and vetch spatial arrangement (Mixed and 
Segregated), and
 
year (2013 and 2014).
 
  
 
 
 
 
 
 
184
 
 
 
 
 
 
 
Figure 4.1
.
 
Mean (+/
-
 
SEM) potentially mineralizeable N (PMN) in early and late 
season of sweet corn season with 2012, 2013, and 2014 combined. PMN 
comparisons include tillage (full
-
width tillage [FWT] and strip
-
tillage [ST]) and rye 
and vetch spatial arra
ngement (Mixed [MIX] and Segregated [SEG]). 
 
 
 
 
 
 
185
 
 
 
 
 
 
Figure 4.2. Mean (+/
-
 
SEM) potentially mineralizeable N (PMN) within both 
the 
W
R and BR zones of strip
-
tillage combined with either a Mixed (MIX) or 
segregated (SEG) rye
 
and vetch spatial arrangement.
 
PMN was m
easured 
from planting to midseason (Early) and from midseason to harvest (Late). 
 
 
 
 
 
 
186
 
 
 
 
 
 
 
 
 
 
Figure 4.3.
 
Mean (+/
-
 
SEM) 
inorganic N 
in the top 20 cm of soil
 
across the whole 
plot throughout the sweet corn season in 2012, 2013, and 2014 in subplots with 
and without c
orn. Inorganic N comparisons include full
-
width tillage (FWT) and 
strip
-
tillage (ST) combined with either a Mixed (MIX) or segregated (SEG) rye 
and vetch spatial arrangement. 
 
 
 
 
 
 
187
 
 
 
 
Figure 4.4.
 
Mean (+/
-
 
SEM) of inorganic N by zone
 
in the top 20 cm of soil
 
thro
ughout the sweet corn season i
n 2012, 2013, and 2014 in NOCORN
 
subplots. Inorganic N comparisons include full
-
width tillage (FWT) and strip
-
tillage (ST) combined with either a Mixed (MIX) or Segregated (SEG) rye and 
vetch spatial arrangement. 
 
 
 
 
 
 
188
 
 
 
 
 
Figure 4.5.
 
Mean (+/
-
 
SEM) gravimetric soil moisture sampled to a depth of 20 
cm in the CORN subplots in 2012, 2013, and 2014. Gravimetric soil moisture 
comparisons include full
-
width tillage (FWT) and strip
-
tillage (ST) combined 
with either a Mixed (MIX) or Segregat
ed (SEG) rye and vetch spatial 
arrangement. 
 
 
 
 
 
 
189
 
 
 
 
 
 
 
Figure 4.
6
. Mean (+/
-
 
SEM) sweet corn biomass and N content within nonharvested 
aboveground biomass and corn ears in 2012, 2013, and 2014. 
Comparisons of fixed 
effects
 
include full
-
width tillage (FWT) and strip
-
tillage (ST) combined with either a 
Mixed (MIX) or segregated (SEG) rye and vetch spatial arrangement. 
 
 
 
 
 
 
190
 
 
 
 
 
 
Figure 4.
7.
 
A) 
Mean
 
(+/
-
SEM) sweet corn root mass 
in 2013 and 2014 combined. 
Samples were collected within the 
W
R (7.5 cm) and the BR (25 cm) from the
 
plant. 
B) The ratio of WR/BR root mass in both the 2013 and 2014 combined. 
Comparisons of fixed effects
 
include full
-
width tillage (FWT) and strip
-
tillage (ST) 
combined with either a Mixed (MIX) or segregated (SEG) rye and vetch spatial 
arrangement. 
 
 
 
 
 
 
191
 
 
 
 
Fig
ure 4.
8.
 
Mean (+/
-
SEM) sweet corn average
 
root
 
diameter
 
(Diameter)
, 
specific root length (SRL), and root length density (RLD) in 2013 and 2014 
combined. Samples were collected within the 
W
R (7.5 cm) and the BR (25 cm) 
from the plant. 
Comparisons of fixed e
ffects
 
include full
-
width tillage (FWT) and 
strip
-
tillage (ST) combined with either a Mixed (MIX) or segregated (SEG) rye 
and vetch spatial arrangement. 
 
 
 
 
 
 
192
 
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198
 
CHAPTER FIVE
 
Cover crop spatial arrangement and strip
-
tillage effects on weed emergence, 
growth and competition with organic sweet corn
 
ABSTRACT
 
Strip
-
intercropping of f
unctionally diverse cover cro
p mixtures including
 
cereal rye 
and hairy vetch
 
(RV)
 
is one mechanism by which N banding can be applied to an 
organic system to increase crop competitiveness over weeds. We hypothesized that by 
targeting hairy vetch, a low C:N legume, to the strip directly
 
in
-
row with future crop 
establishment, and rye, a high C:N grass, to the strip directly between future crop rows, 
that N would be preferentially available to the crop at the expense of weeds. 
We 
conducted a 
field study between 2011 to 2013 in 
southwest 
Mi
chigan to examine the 
effects of tillage (
strip
-
tillage [
ST
]
 
vs full
-
width tillage [FWT], spatial arrangement of RV 
(strip
-
intercropping vs full width mixture) and weed management intensity (low vs high) 
on 1) soil inorganic N (IN) and moisture; 2) weed em
ergence and growth; and 3) sweet 
corn N uptake and yield. 
Segregating rye and vetch into strips increased IN availability 
in 1 out of 2 years, but did not affect sweet corn yields or N uptake, nor did it have any 
detectable benefits for weed suppression. C
over crop biomass was 30 to 40% greater in 
2012 compared to 2013, resulting in very different effects of ST on weed and crop 
growth.
 
In 2012, under low weed management ST increased sweet corn yields by 
suppressing weed emergence and growth. In contrast, in
 
2013, ST decreased sweet 
corn yields under high weed management, and resulted in significantly greate
r weed 
biomass compared to FWT.
 
Complete elimination of tillage is likely not feasible for most 
organic crops in northern climates, but ST in combination 
with rye
-
vetch cover crops 
 
 
 
 
 
199
 
can suppress weeds and maintain or improve crop yields in years when cover crop 
growth is adequate. 
 
INTRODUCTION
 
 
Effective weed management is one of the greatest challenges faced by organic 
vegetable producers (Bingen et al
.
 
20
07; Lotter, 2003). Transitioning to organic 
production often increases both the diversity and density of weed seeds (Albrecht, 
2005). If not controlled effectively, weeds can lead to significant reductions in crop 
yields (Cavigelli et al
.
 
2008). Time, labo
r, and fuel required for weed management is a 
significant contributor to total costs in organic production (Archer et al
.
 
2007). For 
example, time spent hand weeding was almost double in organic fresh market tomato 
production compared to conventional produ
ction (Hillger et al
.
 
2006). Similarly, in 
organic soybean costs for hired labor were roughly ten times greater and on
-
farm fuel 
costs almost double th
ose in
 
conventional production (McBride and Greene, 2009). 
Organic farmers could greatly benefit from far
m management strategies that reduce the 
number of cultivation passes or time spent hand weeding.
 
 
Without the option of chemical herbicides, organic farmers typically rely on 
mechanical cultivation either to bury or uproot weeds through soil inversion or c
ut 
weeds at the soil surface (Bond and Grundy, 2001). Frequent and intense soil 
disturbance can result in oxidation and depletion of soil organic matter, loss of soil 
structure, increased susceptibility to soil erosion, as well as soil compaction due to 
re
current traffic (Lal, 1991). A desire to alleviate the negative effect that tillage has on 
soil quality has generated interest in adapting reduced tillage strategies to organic 
systems (Carr et al. 2012, Brainard et al. 2013). 
 
 
 
 
 
 
200
 
Reduced tillage (RT) practic
es can provide a number of economic and 
environmental benefits, including increased soil aggregation and organic matter 
accumulation (Wander and Bollero, 1999; Franzluebbers 2002; West and Post, 2002) 
as well as reduced soil erosion, compaction, and fuel u
se (Phillips et al
.
 
1980; Gebhardt 
et al
.
 
1985). Reducing tillage and leaving crop residue on the soil surface can increase 
water infiltration into the soil and reduce runoff of soil and nutrients (Franzlubbers 2002; 
Triplett and Dick, 2008). Increasing wa
ter infiltration may help mitigate the effects of 
extreme precipitation events, which are predicted to increase due to climate change 
(Hayhoe et al. 2010). 
 
 
Cultural strategies that utilize an ecological approach to weed control must be 
integrated into or
ganic systems for reduced tillage to be a viable option (Bàrberi, 2002; 
Brainard et al. 2013; Mirsky et al. 2013). Cultural weed management aims to reduce 
weed density and competition through strategies that reduce the number of viable weed 
seeds in the so
il seedbank, hinder weed seedling establishment, and prevent weed 
seed production (Anderson, 2005). Two cultural strategies that may reduce weed 
density and vigor are cover crop mulches and targeted placement of nutrient 
amendments. 
 
The inclusion of cover
 
crops into a crop rotation and use of cover crop residue as 
mulch can lead to substantial reductions in weed emergence and competition. Mulches 
can suppress weeds through a variety of physical, biological, and chemical 
mechanisms. When used as mulch, rye 
residue can decrease the number of recruited 
weed seedlings and delay weed germination (Mohler and Teasdale, 1993) by 
decreasing soil temperatures and reducing light availability. Additionally, if sufficient 
 
 
 
 
 
201
 
residue is retained on the soil surface, rye mul
ches can reduce emergence of 
germinated seedlings by exhausting seed reserves before penetration of the mulch 
layer (Teasdale and Mohler, 2000). The cool and moist environment underneath rye 
residue may also provide refuge for insects and pathogens involve
d in weed seed 
predation and decay (Chauhan et al. 2006; Pullaro et al. 2006; Menalled et al. 2007), 
which can lead to significant reductions of weed seeds in the soil seedbank. 
Allelochemicals released from rye and vetch decomposition have also been shown
 
to 
suppress emergence and growth of weeds (Barnes and Putnam, 1986; Creamer et al
.
 
1996; Hill et al. 2006). 
 
 
An additional strategy of cultural weed management is the manipulation of N 
fertilization to favor
 
crop growth over weeds (Di Tomaso, 1995). Many common 
agricultural weeds are luxurious consumers of N, and increasing N fertilizer rates can 
release 
weed seed
s from
 
dormancy and stimulate both seedling emergence and growth 
(Baskin and Baskin, 1998; Sweene
y et al
.
 
2008). The high relative growth rate and 
nutrient uptake efficiency of many problematic weeds enables them to rapidly exploit 
inorganic soil N (Harbur and Owen, 2004; Wortman et al
.
 
2011). Blackshaw et al. (2003) 
compared the growth response of 23
 
species of common agricultural weeds to wheat in 
the presence of N fertilizer and found 15 weed species exhibited a greater increase in 
shoot biomass compared to wheat. Because weed species are especially responsive to 
soil N, reducing N available to stim
ulate weed emergence and growth is a viable 
option 
for alleviating crop
-
weed competition. 
 
Targeting fertilizer placement so that nutrient inputs are in close proximity to crop 
roots increases the chance for crop N uptake and may reduce weed competition an
d 
 
 
 
 
 
202
 
increase crop yields (Di Tomaso, 1995). Banding N within the crop row or point injecting 
liquid forms of N increased wheat yields by as much as 12% (Kirkland and Beckie, 
1998) and significantly reduced weed density, biomass, and N uptake of both wild oat
 
(
Avena fatua
 
L.) and green foxtail 
(
Setaria viridis
) 
(Blackshaw et al
.
 
2004). Effective N 
placement can reduce the energy expended by the crop for nutrient scavenging, 
allowing for greater resources devoted to aboveground productivity. Hodge et al. (1999)
 
postulates that plants will gain a greater advantage from root proliferation in N rich 
patches when nutrient availability is continuous, such as from slowly decomposing 
organic matter, and when interspecific competition for that N is likely. While banding
 
of 
conventional fertilizers has been shown to enhance crop competitiveness against 
weeds (Anderson, 2008; Blackshaw et al
.
 
2004), little work has been done to apply the 
same principle to organic forms of N in agronomic systems. 
 
Strip
-
intercropping of fun
ctionally diverse cover crops, such as cereal rye and 
hairy vetch, is one mechanism by which N banding can be applied to an organic system 
to increase crop competitiveness over weeds. Rye
-
vetch (RV) cover crops are often 
planted together in full width mixt
ures (either randomly distributed by broadcasting, or 
together in drilled rows) in organic production systems. Segregating rye and vetch into 
strips has the potential for improving weed management by simultaneously targeting N 
to the crop root zone, and cr
eating a weed suppressive mulch between crop rows. Hairy 
vetch, a legume, sown in a strip directly in
-
row with future crop establishment 
could
 
provide N directly to the crop and reduce N available to competing weeds. Cereal rye 
planted between crop rows 
ma
y 
immobilize N and minimize interference with the crop. 
 
 
 
 
 
 
203
 
 
The potential benefits of strip
-
intercropping of rye
-
vetch mixtures may be 
enhanced if combined with strip
-
tillage. Incorporating hairy vetch residue in
-
row will 
result in faster mineralization of v
etch residue and greater nutrient availability to the 
crop, also reducing the potential for cereal rye to suppress crop growth. The cereal rye 
in the between
-
row zone will be left on the soil surface to suppress weeds via 
immobolizing N, reducing light pen
etration, and decreasing soil temperatures. 
 
The goal of this study was to evaluate the effect of cover crop spatial 
arrangement, strip
-
tillage, and weed management intensity on weeds, soil N, and crop 
biomass and yields within organic sweet corn productio
n. We hypothesized that strip
-
intercropping of RV would result in greater soil inorganic N within the crop row (WR) and 
less inorganic N between crop rows (BR). 
Additionally, we expected that the 
concentration of inorganic N in the WR zone would increase s
weet corn access to N, 
thereby resulting in greater N uptake and competitiveness against weeds.
 
Finally, we 
hypothesized that in the BR zone, strip
-
intercropping combined with strip
-
tillage would 
suppress weed emergence and growth due to the combined effec
ts of lower N 
availability, and cover crop surface mulch. Overall, we anticipated that sweet corn yield 
loss due to weeds would be reduced with strip intercropping and strip
-
tillage. 
 
MATERIALS AND METHODS
 
Site description and e
xperimental 
d
esign
 
The exper
iment was conducted between 2011
-
2013 on organically managed 
(approximately 5
-
7 years, depending on year) Kalamazoo (fine
-
loamy, mixed, mesic 
Typic Hapludalfs) and Oshtemo (coarse
-
loamy, mixed, mesic Typic Hapludalfs) loams 
(Crum and Collins, 1995), at the
 
Kellogg Biological Station in Hickory Corners, MI 
 
 
 
 
 
204
 
(85




block design with four replicates. Main plots consisted of a 2 X 2 factorial of tillage (Full
-
width tillage [FWT] and Strip
-
tillage [ST]) and rye and vetch spatial arrangement.
 
Spatial 
arrangements included a full
-
width mixture (MIX) in which rye and vetch were sown 
together in the same rows
, 
and a segregated mixture (SEG) in which 2 rows of hairy 
vetch were alternated with 2 rows of rye. The split plot factor consisted of two l
evels of 
weed management: i) high weed management treatments were intended to be weed 
free, and were maintained by hoeing and hand
-
weeding weekly until mid
-
July and every 
two weeks thereafter; ii) low weed management treatments were intended to have a 
rela
tively high level of weed competition and included broadcasting of supplemental 
weed seeds at planting, followed by hoeing and hand weeding twice throughout the 
sweet corn season (see Table 5.1 for weeding dates). In low weed management 
treatments, we dist
ributed 3
.0
 
g m
-
2
 
(approximately 8500 seeds m
-
2
) of stratified 
C.album
 
seeds in 2012, and 1.
8 
g m
-
2
 
(approximately 3180 seeds m
-
2
) in 2013. 
Zones 
within the cropping system were also treated as subplots. The zone within the crop row 
(WR) is the 25 cm zone 
where the crop is planted (and where tillage occurs within strip
-
tilled treatments), and the between
-
row (BR) zone is the 50 cm zone between crop rows 
and also is untilled in strip
-
tilled treatments. 
 
High weed management subplot sizes were 27.3 (9.1 X 3.0
 
m) and 37.2 m
2
 
(
6.1 
X 6.1 m) in 2012 and 2013, respectively; and low weed management subplots sizes 
were 18.6 (9.1 X 3.0 m) and 27.9 m
2
 
(6.1 X 4.5 m) in 2012 and 2013, respectively. 
Plot 
sizes were adjusted each year based on availability of organically c
ertified land and size 
of available equipment. Larger plots were used for High Weed Management subplots to 
 
 
 
 
 
205
 
accommodate more frequent and intense sampling used for other questions associated 
with this study
. 
 
Field activities were performed according to Tab
le 5.1. 
Both cereal rye and hairy 

When considered on a whole plot basis, the cover crop spatial arrangement treatments 
had the same seeding rate for both cereal rye (62
.72 kg ha
-
1
) and hairy vetch (22.4 kg 
ha
-
1
), but differed in seed placement. In both rye and vetch spatial arrangements, 
between
-
row spacing for rye and vetch rows was 19.
1 
cm. Cereal rye was planted with 
a grain drill
 
and every two drop tubes were blocked
 
within SEG to prevent seeding rye 
in the vetch rows. Hairy vetch was planted with a Jang push
-
seeder at the designated 
rates and rows. 
No N fertilizers were added prior to cover crop establishment or sweet 
corn planting. Additional organic forms of P and 
K were added according to soil tests, 
and consisted of: 34 kg K 
and
 
P ha
-
1 
of 
NOP certified Potassium Sulfate Plus 
and 
Tennessee Brown Phosphate in 2012 and 34 kg K ha
-
1 
of 
Potassium Sulfate Plus
 
in 
2013. 
 
Cover crops were flail mowed twice in mid
-
 
to late
-
 
May. In 2012, vetch had 
reached approximately 50% flowering, and in 2013 vetch had reached 30% flowering. 
Conventional tilled treatments were chisel plowed, and then rototilled to break up cover 
crop residue. A Hiniker Model 6000 two
-
row strip tiller (eq
uipped with cutting
-
coulter, 
shank point assembly, berming discs, and rolling basket) was used for
 
WR
 
tillage in 
strip
-
tilled treatments. To improve soil tilth for planting in ST treatments, a walk behind 
rototiller was used for
 
WR
 
secondary
-
tillage. The r
esulting
 
WR
 
zone was approximately 
25 cm wide and 25
-
30 cm deep. Sweet corn was planted using a high residue planter 
 
 
 
 
 
206
 
(Monosem vacuum seeder) at a between
-
row spacing of 76.2 cm and in
-
row spacing of 
10 cm. Once sweet corn emerged, population density was th
inned to an in
-
row spacing 
of 16.5 cm. 
Sweet corn was irrigated during low
-
rainfall periods in 
2012, totaling 62.2 
mm of irrigated water throughout the sweet corn season
. 
In 2013, rainfall was sufficient 
so we did not provide any additional irrigation. 
   
 
Data 
c
ollection
 
Co
ver crop and winter weed biomass.
 
Dry weights of cover crop and winter annual 
weed biomass were obtained prior to cover crop termination by clipping shoot tissue at 
ground level from two 0.25 m
2
 
quadrats 
for each
 
WR
 
and BR area in every 
plot. Fresh 
cover crop biomass was separated into hairy vetch, rye, and weeds, dried at 60°C, and 
weighed. 
 
Soil sampling
-
 
Inorganic N and gravimetric soil moisture.
 
Soil samples were collected 
every 7
-
14 days (8 sampling times per year), throughout the gr
owing season in all 
treatments and subplots in both the
 
WR
 
and BR. At each sampling date, 10 to 12 soil 
cores (5 cm diameter and 20 cm deep) were taken for each plot. Soil samples were 
dried at 38°C, and ground. A 1M KCl extraction (Gelderman and Beegle, 1
998) was 
performed and samples were analyzed on a QuikChem 8500 Flow Injection Analyzer. 
10 g of wet soil was utilized for gravimetric soil moisture, dried at 100°C, and re
-
weighed. Gravimetric water content (GWC) was calculated according to Equation 5.1 
b
elow. In addition, gravimetric water content was evaluated from separate soil samples 
taken to 5 cm depth during the period when weed germination was monitored (7/5/2012, 
7/4/2013). 
 
[5.1] 
 























 
 
 
 
 
 
207
 
To calculate the soil inorganic N and gravimetric moisture across the whole plot, we 
used weighted means of the measured values from the WR and BR zones adjusted by 
their respective
 
areas.  For example, for inorganic N (IN) we used: 
 
[5.2]
 
IN
whole plot
 
= IN
WR
*1/3 + IN
BR
*2/3
 
Where the within
-
row (WR) zone is the 25 cm zone where the crop is planted (and 
where tillage occurs within strip
-
tilled treatments), and the between
-
row (BR) zon
e is the 
50 cm zone between crop rows and also is untilled in strip
-
tilled treatments.
 
Soil temperature.
 
Soil temperature was measured using waterproof HOBO® 
Temperature/Light Pendant® Data Logger sensors (Onset Computer Corporation, 
Bourne, MA) installed 
to a depth of 5 cm both within the
 
WR
 
and BR of each plot. 
Temperature sensors were installed at corn planting only in the MIX rye
-
vetch 
treatments to compare tillage and zonal differences. We calculated mean daily minimum 
and maximum temperatures, as well
 
as the temperature amplitude across treatments. 
 
Weed emergence
.
 
Seeds of common lambsquarters (
Chenopodium album
 
L) and giant 
foxtail (
Setaria faberi 
Herrm.) were seeded into separate quadrats (0.093 m
2)
 
in both the 
in
-
 
and between
-
row strips of high wee
d management subplots of each tillage and cover 
crop mainplot. 
Four hundred 
seeds of each species were seeded in 2012 one week 
after tillage, and 500 seeds of each species were seeded 2 days after tillage in 2013. 
We collected weed seeds in the fall preced
ing the year of sowing from an organically 
managed farm approximately 35 km from the 
site 
(2012) or an adjacent field at the 
research farm (2013). Seeds of both species were separated from chaff using a rub 
board and seed cleaner, and stratified under mois

 
 
 
 
 
208
 
Emergence of each species was counted every 3
-
5 days for approximately the first 
month of the sweet corn growing season. After each count, weed seedlings were pulled. 
 
Weed biomass
.
 
Weed biomass was collected within th
e low weed management subplot 
prior to each timed weeding event (2
 
times
 
during sweet corn growth), and then again at 
harvest. Weeds in two 0.25 m
2
 
quadrats per
 
WR
 
and BR were collected, dried at 60

C, 
and weighed. In 2012, we separated weed biomass into 
C
.album
 
(which was 
overseeded) and all other weeds. Due to a large infestation of grasses and clover in 
2013, we separated weed biomass into 
C.album
, clover, grasses, and all other 
broadleaf species. 
 
Sweet 
c
orn
 
biomass and yield
.
 
Sweet corn was harvested f
rom 9.3 and 6.5 m
2
 
in high 
and low weed management subplots in 2012, respectively; and 
from 
14 m
2
 
in both high 
and low weed management subplots in 2013. At harvest, total aboveground biomass 
was collected from five random corn plants per plot. Samples were
 

weighed, and 
and ground to pass through a 1 mm screen
. To measure corn plant N 
content, a Kjeldahl digestion (Kalra, 1998) was performed on corn tissue from corn ears 
and aboveground biomass, and Kjehldahl N analysis was performed using a Q
uikChem 
8500 Flow Injection Analyzer. 
 
Statistical analysis
 
All data 
were 
analyzed using mixed models ANOVA with proc MIXED procedures 
in SAS (Version 9.3, SAS Institute, Cary, NC). Year was initially treated as a fixed factor 
to determine if fixed effects
 
interacted with year to affect response variables, and if there 
was no difference year w
ere
 
pooled. In all cases, block was nested in year, and block 
and interactions of block with fixed factors were treated as random effects. 
 
 
 
 
 
 
209
 
For cover crop and winter a
nnual weed biomass the fixed effects included cover 
crop spatial arrangement (MIX and SEG) and year. For cumulative weed biomass and 
weed emergence, the fixed effects included cover crop spatial arrangement, tillage 
(FWT vs ST), and zone 
(WR
 
vs BR). For sw
eet corn biomass, N uptake, and yield the 
fixed effects included cover crop spatial arrangement, tillage, and weed management 
intensity (High vs Low). For soil IN and moisture the fixed effects included tillage, cover 
crop spatial arrangement, weed managem
ent, and zone; and for soil temperature the 
fixed effects included tillage and zone. For soil IN, moisture, and temperature, date was 
included as a repeated factor and repeated measures mixed models were performed 
with Proc MIXED in SAS, and AIC and BIC va
lues were compared to determine the best 
model fit. When cover crop spatial arrangements were not significant, MIX and SEG 
were pooled to 
examine
 
effects of tillage within a rye
-
vetch cover crop. 
 
When significant treatment effects were detected, means wer
e separated using 
Fisher's protected LSD 
P
< 0.05. In cases where P
-
values were between 0.05 and 0.10, 

-
value presented 
parenthetically. All figures were made using the GGPLOT2 package in R (Wic
kham, 
2009).
 
RESULTS
 
Precipitation and 
t
emperature
 

during sweet corn growth in 2012 and 2013 varied widely (Table 5.2). In 2012, total 
GDD accumulation was 17% higher, and precipitation was 50% lower than in 2013. 
Average dail

 
 
 
 
 
210
 
2013. Precipitation during sweet corn growth was 128 mm greater in 2013 than in 2012, 
but with irrigation this difference was
 
in 2012
 
reduced to 66 mm. Warm temperatures 
and low precipi
tation in 2012 likely resulted in droughty conditions, especially during the 
month of July.
 
Effects of 
c
over 
c
rop 
s
patial 
a
rrangement
 
on cover crop and weed biomass
 
Cover crop spatial arrangement affected total cereal rye and hairy vetch biomass 
in one out
 
of two years, and affected cereal rye biomass in both years. In Year 1 the 
MIX rye
-
vetch spatial arrangement had 16% greater total biomass production than SEG 
(
Chapter 3: Table 3.5 Figure 3.4
), but there was no detectable difference in total 
biomass in 20
13. In both 2012 and 2013, SEG had lower cereal rye biomass compared 
to the MIX. 
Cereal rye biomass was approximately 5
.
5 and 4
.
4 
Mg 
ha
-
1
 
in MIX and SEG 
in 2012, and 4
.
7 and 3
.
6 
Mg 
ha
-
1
 
in 2013.
 
We found no detectable difference in hairy 
vetch biomass acro
ss the whole plot, but SEG did increase the vetch biomass within the
 
WR
 
in both years.
 
Hairy vetch biomass was approximately 2
.
6 
M
g ha
-
1
 
in 2012, and 1
.
5 
M
g ha
-
1
 
in 2013. More information on cover crop spatial arrangement effects on cover 
crop biomass and 
N content can be found in Chapter 
3
. 
 
 
Winter annual weed biomass in the no cover crop control was approximately 3
.
0 
and 2
.1
 
Mg 
ha
-
1
 
in 2012 and 2013, respectively (Figure 5.1). Dominant species in 2012 
included 
common chickweed (
Stellaria media
)
 
and 
corn 
chamomile (
Anthemis 
arvensis
)
. Dominant species in 2013 included red clover (
Trifolium pratense)
, 
corn 
chamomile
, and 
common chickweed
. In 2012, we found no detectable difference in 
winter annual weed biomass due to spatial arrangement in either the
 
WR
 
or 
BR zones; 
rye and vetch suppression of winter annual weeds across the whole plot was 
 
 
 
 
 
211
 
approximately 55% regardless of spatial arrangement. In 2013, we found marginally 
lower winter annual weed biomass within the
 
WR
 
(
P
=0.093) of the SEG compared to 
the MIX t
reatment, but no detectable difference BR. Across the whole plot, suppression 
of winter annual weeds by the rye and vetch cover crops was approximately 84% in MIX 
and SEG in 2013. 
 
Effects of cover crop spatial arrangement on soil inorganic N, soil moistur
e, 
weeds, and sweet corn
 
There was a significant effect of rye and vetch spatial arrangement on soil 
inorganic N (IN) across the whole plot in 2012, but not in 2013 (Table 5.3, Figure 5.2). 
Averaged across all sampling dates in 2012, SEG had 8% greater IN 
compared to MIX 
in high weed management, and 6% greater under low weed management. 
 
When analyzed by zone within ST, SEG had marginally greater IN concentrated within 
the crop row 
(WR)
 
in ST under low weed management (Figure 5.3, Cover*Zone*Date, 
P
=0.07), 
at several sampling dates within each year. Compared to the MIX spatial 
arrangement, SEG had 12% greater IN within the crop row in July of 2012, and 9% 
greater IN within the crop row in June of 2013. 
 
Spatial arrangement had few detectable effects on soil 
moisture (Table 5.4; 
Figure 5.4). In 2013, SEG resulted in 5
-
9% lower soil moisture compared to MIX, but 
this effect was only marginally significant (
P
=0.06). 
 
 
Segregating rye and vetch into strips had no effect on weed emergence or 
biomass, or sweet corn
 
biomass, N uptake, or yield in either high or low weed 
management (data not shown). 
 
 
 
 
 
 
212
 
Effects of tillage and weed management on 
soil 
inorganic 
N, 
m
oisture
, and 
temperature
 
 
Tillage influenced IN in 2013, but not 2012 (Table 5.3, Figure 5.2). In 2012, 
tilla
ge interacted with weed management, but did not interact with date. Tillage had no 
detectable effect on IN under either high or low weed management. However, low weed 
management reduced season long IN by 13% within FWT, but weed management had 
no effect on
 
IN within ST. In 2013, tillage interacted with date, but did not interact with 
weed management. ST reduced IN by 25% compared to FWT at all but the final 
sampling date, regardless of weed management. IN was lower in 2013 compared to 
2012, and in both year
s IN peaked in early July and then decreased as corn entered the 
exponential growth phase in mid
-
July to August. In August of 2012, IN plateaued 
between 30 to 40 kg N ha
-
1
, while in August of 2013 IN plateaued under 20 kg N ha
-
1
.
 
 
In 2012, ST increased gra
vimetric soil moisture (20 cm depth) across the whole 
plot 
(WR
 
and BR) during sweet corn growth by approximately 15% compared to FWT, 
with the difference being more pronounced in the later part of the growing season 
(Table 5.3, Figure 5.4). In contrast, in
 
2013, FWT had 8% greater soil moisture 
compared to ST during sweet corn growth. 
 
Gravimetric soil moisture in the top 5 cm of soil during evaluation of weed 
emergence was influenced by tillage and zone in both 2012 and 2013 (Figure 5.5). In 
2012, gravimet
ric soil moisture in the BR zone of ST+RV was 2
-
3 times greater than 
that in all other treatments and zones. In 2013, gravimetric soil moisture was 
consistently high in all treatments during the period of evaluation of weed emergence. 
 
 
 
 
 
 
213
 
 
Figure 5.6 contains
 
the maximum, minimum, and daily temperature amplitude for 
the one month period during which 
C. album
 
and 
S. faberi
 
emergence counts were 
collected in both 2012 and 2013. In both years, maximum daily temperature varied more 
between tillage treatments than 
minimum daily temperatures (Table 5.5, Figure 5.6). In 
2012 and 2013, the effect of tillage on maximum and minimum temperatures varied with 
sampling date and zone. In both years, we found no detectable difference in the 
maximum, minimum, and daily temperat
ure amplitude between the
 
WR
 
of FWT and ST. 
Maximum soil temperature was 14% and 6% lower in the BR of ST compared to FWT
-
BR in 2012 and 2013, respectively; and 13% and 1% lower than ST
-
W
R in 2012 and 
2013, respectively. The daily temperature amplitude (di
fference between daily maximum 
and minimum) across the period of emergence was reduced from 8 in FWT
-
BR to 3.5 in 
ST
-
BR in 2012, and from 8 to 5.6 in 2013. 
 
Effects of 
t
illage on 
w
eed 
e
mergence and 
b
iomass
 
 
The effect of tillage on emergence of 
C. album
 
(T
ill*Year, 
P
=0.0081) and 
S. 
faberi
 
(Till*Year, 
P
=0.0056) varied by year
. 
In 2012, the effect of tillage on emergence of 
C. album
 
and 
S. faberi
 
varied by zone (Table 5.6, Figure 5.7); in particular, emergence 
of both species was lower in the BR of ST compare
d to FWT, but we found no 
detectable effect in the
 
WR
 
zone. In 2013 we found marginally greater 
C.album 
emergence in ST compared to FWT (
P
=0.08), regardless of zone, but we found no 
detectable differences in 
S. faberi
 
emergence due to tillage or zone withi
n 2013.
 
 
In both years, the effect of tillage on weed biomass varied with zone (Figure 
5.8). In 2012, ST reduced weed biomass relative to FWT in the BR zone, but not the
 
WR
 
zone. In contrast, in 2013, ST increased weed biomass relative to FWT in the BR 
 
 
 
 
 
214
 
zon
e but not in the
 
WR
 
zone. Dominant species included within the other category 
included 
S. faberi
 
and
 
ladysthumb (
P. persicaria
)
. 
 
Effects of 
t
illage on 
s
weet 
c
orn 
b
iomass, N
-
uptake
,
 
and 
y
ield 
 
 
Tillage effects on sweet corn biomass and N uptake varied by y
ear and weed 
management. In 2012, total sweet corn aboveground biomass and N uptake were 
greater within ST compared to FWT in both high and low weed management (Figure 
5.9). ST increased total sweet corn aboveground biomass by 19% under high weed 
managemen
t, and by 71% under low weed management. Low weed management 
reduced sweet corn N uptake by 47% within FWT, but only 24% in ST (Till*Weed 
management, 
P
=0.045). In 2013, there was no detectable difference in total corn 
biomass between FWT and ST within eith
er high or low weed management. However, 
we found greater total N uptake within FWT compared to ST within high weed 
management (Till*Weed management, 
P
=0.03), and no detectable differences between 
the two tillage systems under low weed management. 
 
 
In bot
h years, the effect of tillage on sweet corn yields varied with weed 
management (Figure 5.9). In 2012, ST increased sweet corn yields by 60% compared 
to FWT under low weed management (Till*Weed Management, 
P
=0.026), but had no 
effect under high weed manage
ment. In contrast, in 2013, ST marginally reduced yields 
by 25% relative to FWT under high weed management (Till*Weed Management, 
P
=0.07), but had no effect under low weed management.
 
 
 
 
 
 
 
 
 
215
 
DISCUSSION
 
Rye and 
v
etch 
s
patial 
a
rrangement 
 
We hypothesized that se
gregating rye and vetch into strips would increase sweet 
corn access to N within ST, thereby resulting in greater N uptake and competitiveness 
against weeds. However, despite having slightly higher IN concentrated within the crop 
row in ST
-
SEG (Figure 5.3)
, we found no detectable effect of cover crop spatial 
arrangement on sweet corn or weeds. This may be partially because the increase in N 
occurred prior to sweet corn exponential growth, especially in 2013. One likely 
explanation for relatively minor effec
ts of spatial 
arrangement
 
is that above ground 
cover crop shoots were not as concentrated in their zones as we expected. While we 
anticipated some lateral mixing of rye and vetch, lateral growth of vetch, cover crop 
lodging (in 2012) and flail
-
mowing resul
ted in greater movement of shoot tissue across 
zones than anticipated. Although root tissue was unlikely to have moved laterally, it is 
thought to represent only about 18
-
30% of total cover crop tissue for rye
-
vetch mixtures 
(Mwaja et al. 1995) 
 
Strip
-
t
ill
age
 
effects on weeds and sweet corn
 
 
Cover crop residues may affect weed germination and emergence by altering 
light, moisture, nutrients and temperature (Teasdale and Mohler, 1993). Mulch effects 
on weed emergence depend on the quantity of biomass, enviro
nmental conditions, and 
target weed species. The mulch had more of an effect on maximum daily temperatures 
compared to minimum, and strongly affected the daily temperature amplitude 
(difference in max and min). The higher soil moisture, combined with reduc
ed light 
penetration from the mulch, likely reduced temperature fluctuations in ST
-
BR in 2012 
 
 
 
 
 
216
 
(Figure 5.6). 
C.album 
germination is
 
strongly
 
promoted by daily temperature
 
amplitudes 

 
(
Henson, 1970
; Murdoch et al. 1970
)
, and in 2012 the 
te
mperature amplitude in FWT periodically 
increased 

temperature amplitude was consistently below 5

 
within ST
-
BR. Additionally, the RV 
mulch created a physical barrier, which likely forced weed seedlings to exhaust seed 
reserves while p
enetrating the mulch. Our results are consistent with Teasdale and 
Mohler (2000) who found that 
C. album
 
is more sensitive to mulch suppression than 
S.faberi
, and they determined that weed sensitivity to mulch was inversely related to 
seed size. Seeds of 
S
. faberi
 
are larger than those of 
C. album
, and thus have larger 
seed reserves which may explain their higher emergence through RV mulch. 
 
In 2013, RV biomass was lower than in 2012, and we found no suppression of 
either 
S. faberi
 
or 
C. album
 
emergence in 
the BR of ST compared to FWT. On the 
contrary, ST marginally increased 
C.album
 
emergence relative to FWT. At low residue 
levels, mulches can stimulate weed emergence by increasing soil moisture, but we 
found no evidence of greater soil moisture in ST in th
e top 5 cm of soil in 2013 (Figure 
5.5). Therefore, we are unsure what soil factors could have promoted 
C.album
 
emergence within ST in 2013. 
 
 
Tillage effects on weed competition and organic sweet corn varied dramatically 
between the two years of this stud
y. 2012 was an example of how well a mulch based 
organic
-
RT system can function under the right conditions. In 2012, the weed 
community was dominated by summer annuals, and rye and vetch biomass production 
was at or near the 8
.
0 
Mg 
ha
-
1
 
level considered ne
cessary for summer annual weed 
suppression (Teasdale and Mohler, 2000). In 2012, ST decreased weed biomass within 
 
 
 
 
 
217
 
the BR throughout the sweet corn growing season compared to FWT (Figure 5.8), likely 
due primarily to suppressive effects of surface mulch on 
emergence (Figure 5.7). 
Additionally, 2012 was a hot and dry year, and despite irrigation, the sweet corn likely 
benefited from the increased soil moisture observed under ST (Figure 5.4). While we 
detected no difference in soil N between the two tillage sy
stems under either high or low 
weed management, low weed management reduced IN in FWT but not within ST. 
Therefore, competition for IN and soil moisture between sweet corn and weeds was 
likely lower under ST compared to FWT. 
 
In contrast
, 2013 was an examp
le of the challenges inherent within organic RT 
systems. Rye and vetch biomass was approximately 25% lower in 2013 compared to 
2012, and red clover, a perennial, established within the cover crops and was not 
terminated with mowing. Despite frequent weedin
g events in high weed management 
treatments, persistent red clover in the untilled BR of ST likely caused some resource 
depletion, and may explain the lower gravimetric moisture in ST compared to FWT 
(Figure 5.4). The combined effects of lower cover crop b
iomass, lower IN, and lower 
gravimetric moisture within ST in 2013, combined with the presence of a pre
-
established perennial weed, help explain the lower yields in ST compared to FWT under 
high weed management in 2013 (Figure 5.9).
 
Interestingly, despite 
lower soil IN and moisture in ST compared to FWT under 
low weed management in 2013, sweet corn yields were equal between the two tillage 
systems. The reasons for this are unclear. It is possible that factors other than IN and 
moisture (e.g. light, P or K) 
were the primary limitations to growth in 2013, and that 
weed biomass was not well correlated with those limiting factors. 
 
 
 
 
 
 
218
 
SUMMARY AND 
CONCLUSION
 
Although segregating rye and vetch into strips showed promise for increasing 
inorganic N within the crop row,
 
it did not result in improved sweet corn yields or N 
uptake. Several adaptations to strip
-
intercropping might enhance its effectiveness. First, 
use of a roller crimper (drawn in the same direction as segregated strips) rather than a 
flail mower, would 
lik
ely 
have reduced lateral movement of shoots, resulting in more 
distinct
 
WR
 
and BR environments. With roller
-
crimping, greater concentration of N
-
rich 
vetch residue in the
 
WR
 
zone, and greater concentration of rye mulch in the BR zone 
may have enhanced 
weed
 
suppression, improved N use efficiency, and reduced the 
potential interference of rye residue with crop establishment. Second, the effectiveness 
of strip
-

systems, in which residue is
 
moved from the BR to the
 
WR
 
zone for suppression of
 
WR
 
weeds, and to facilitate mechanical cultivation within the BR (Rostampour, 2011). Strip
-
intercropping may work well with a cut and carry system, because the rye roots would 
not be in the
 
WR
 
to immobil
ize N and interfere with strip
-
tillage and planting. But rye 
aboveground residue could be moved to the
 
WR
 
for weed suppression and soil 
moisture conservation, as well as to allow mechanical cultivation BR. 
 
Our study suggests that a rye
-
vetch cover crop in
 
combination with ST can result 
in similar sweet corn yields to FWT, and effective suppression of weed emergence and 
growth in years when cover crop growth is abundant (e.g. 2012). However, in years 
when cover crop growth is weak, or perennial weeds are pr
esent, the risk of yield losses 
due to weeds under ST are substantial 
due to increased weed pressure 
(e.g. 2013). 
 
 
 
 
 
 
219
 
Complete elimination of tillage in organic sweet corn production is risky in 
northern 
climates like Michigan. However, our results suggest th
at ST can be used as 
part of a rotational tillage strategy in years that are favorable for winter cover crop 
growth. Strategies to enhance cover crop growth including adjustments in fertilizer 
rates, planting dates
,
 
or planting densities have facilitated s
uccessful implementation of 
no
-
till or rotational tillage with lower risk of weed competition in field crops (Ryan et al
.
 
2011; Mirsky et al. 2013), and are likely to have similar payoffs in vegetable crops like 
sweet corn. Segregated plantings of cover cr
op mixtures may also facilitate weed 
suppression in reduced tillage systems, but greater attention is needed to identify 
species combinations, planting patterns, and termination methods to optimize this 
approach. 
 
 
 
 
 
 
 
220
 
APPENDIX
 
 
 
 
 
221
 
APPENDIX
 
 
 
 
Table 5.1. 
Dates o
f key field activities and data collection
.
 
 
 
 
 
 
 
 
 
 
222
 
Table 5.2. Average temperature, GDD, and precipitation during sweet corn growth in 
2012 and 2013. 
 
 
 
 
 
 
 
 
223
 
Table 5.3. P values from a four
-
way ANOVA for the effects of tillage 
 
(fill
-
width and strip
-
tillage), cov
er crop spatial arrangement (Mixed 
 
and Segregated), weed management (High and Low), and date on 
 
soil inorganic N across the whole plot (weighted average of WR and 
 
BR) during  (weighted average of WR and BR) the 2012 and 2013 
 
sweet corn growing season. 
 
 
 
 
 
 
 
 
 
 
 
224
 
Table 5.4. P values from a four
-
way ANOVA for the effects of tillage 
 
(fill
-
width and strip
-
tillage), cover crop spatial arrangement (Mixed 
 
and Segregated), weed management (High and Low), and date on 
 
gravimetric soil moisture across the whole plo
t (weighted average 
 
WR and BR) during the 2012 and 2013 sweet corn growing season. 
 
 
 
 
 
 
 
 
 
 
 
 
 
225
 
Table 5.5. P values for a four way ANOVA for the effects of tillage (fill
-
width 
 
and strip
-
tillage)
 
zone (within
-
 
and between
-
row), and date on soil temperature 
 
f
rom sensors buried 5 cm below the soil surface. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
226
 
Table 5.6. P values for a two
-
way ANOVA for the fixed effects of tillage 
 
(full
-
width and strip
-
tillage) and zone (within
-
and between
-
row) on 
 
percent emergence of sown 
Chenopodium album
 
and 
Setaria
 
faberi
 
 
seeds in 2012 and 2013. 
 
 
 
 
 
 
 
 
 
 
227
 
 
 
 
Figure 
5.1. Mean (+/
-
) SEM w
inter annual weed biomass in both
 
Mixed (Mix) 
and Segregated (SEG
) rye an
d vetch spatial arrangements in 2012 and 
2013. Within segregated rye and vetch, vetch was solely planted within the 
W
R, an
d rye was planted within the BR
. 
 
 
 
 
 
 
228
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2. Mean (+/
-
 
SEM) s
oil inorganic N 
within the top 20 cm of soil across 
the whole plot in
 
b
oth high and low weed management in 2012 and 2013
. 
Inorganic N comparisons include 
full width tillage (FWT) and strip tillage (ST), 
with Mixed 
(MIX) 
and Segregated 
(SEG) 
rye 
and vetch spatial arrangements.
 
 
 
 
 
 
229
 
 
 
 
 
 
Figure 5.3. Mean (+/
-
 
SEM) s
oil inorganic N withi
n the
 
top 20 cm of soil in 
the
 
W
R and BR of strip
-
tilled
 
treatments in low weed management 
subplots. Soil inorganic N comparisons include mixed (MIX) and 
segregated (SEG
) rye and vetch spatial arrangements. 
 
 
 
 
 
 
230
 
 
 
 
 
 
 
 
 
 
 
 
Figure 
5.4. Mean (+/
-
 
SEM) g
ravimetric soil moi
sture
 
within the top 20 cm of soil
 
across the whole plot) 
in both
 
high and low weed management subplots in 2012 and 
2013. Gravimetric soil moisture comparisons include
 
full
-
width tillage (FWT) and 
strip
-
tillage (ST
), and mixed (MIX) and segregated (SEG
) ry
e
 
and vetch spatial 
arrangements.
 
 
 
 
 
 
231
 
 
 
 
 
 
Figure 
5.5. Mean (+/
-
 
SEM) g
ravimetric soil moisture in the top 5 cm of the soil 
during emergence counts of 
C. album
 
and 
S. faberi
. 
Comparisons 
include
 
full
-
width 
tillage (FWT) and strip
-
tillage (ST
), both within (WR) and
 
between (BR) crop rows. 
 
 
 
 
 
 
232
 
 
 
 
 
 
Figure 
5.6
. 
Daily maximum (top) and minimum (middle) soil temperature, as well as the temperature 
amplitude (maximum
-
minimum, bottom) 
measured from hobo sensors 
buried 5 cm below the soil 
surface both within (WR) and between crop
 
rows (BR). Comparisons include full
-
width tillage (FWT) 
and strip
-
tillage (ST). 
 
 
 
 
 
 
233
 
 
 
 
 
 
 
 
Figure 
5.
7. Mean (+/
-
 
SEM) p
ercent emergence of sown seeds of 
C.album
 
and 
S.faberi 
in 2012 and 2013. 400 seeds of each species were sown in 2012, and 
500 seeds were sown in 
2013. 
Emergence
 
comparisons include
 
full
-
width tillage 
(FWT) and strip
-
tillage (ST
) within (WR) and between (BR) crop rows. 
 
 
 
 
 
 
234
 
 
 
 
 
Figure 
5.
8. Mean (+/
-
 
SEM) c
umulative weed biomass in low weed 
management subplots in both 2012 and 2013. Weed biomass was summed 
across weed harvests at two time points during sweet corn growth (before 
each major weeding event), and then biomass was also collected at sweet 
corn harvest. 
Weed biomass 
comparisons include
 
full
-
width tillage (FWT) 
and strip
-
tillage (ST
) within (WR) and 
between (BR) crop rows. 
 
 
 
 
 
 
235
 
 
 
 
 
 
 
 
Figure 
5.
9. Mean (+/
-
 
SEM) s
weet corn marketable yield (top), total aboveground 
biomass (middle), and total aboveground N uptake (bottom). Sweet corn yield is 
fresh
 
weight of corn ears. Aboveground biomass is the sum of the dry we
ight of 
nonharvested biomass and marketable and nonmarketable corn ears. Total 
aboveground N
 
is the sum of Kjehldahl N contained within nonharvested biomass 
and corn ears. 
Comparisons include
 
full
-
width tillage (FWT) and strip
-
tillage (ST
) 
in both high and
 
low weed management in 2012 and 2013. 
 
 
 
 
 
 
236
 
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