## EVALUATION OF SWITCHGRASS, M. X GIGANTEUS

, AND SORGHUM AS BIOMASS

CROPS: EFFECTS OF ENVIRONMENT AND FIELD MANAGEMENT PRACTICES

BY

MATTHEW W. MAUGHAN

DISSERTATION

Submitted in partial fulfillment of the requirements

for the degree of Doctor of Philosophy in Crop Sciences

in the Graduate College of the

University of Illinois at Urbana-Champaign, 2011

Urbana, Illinois

Doctoral Committee:

Professor Germán A. Bollero, Chair

Assistant Professor DoKyoung Lee, Director of Research

Associate Professor Thomas B. Voigt

Professor Donald G. Bullock

Assistant Professor Fernando E. Miguez, Iowa State University

## ii 

## ABSTRACT

Switchgrass ( Panicum virgatum L.), Miscanthus x giganteus ( M. x giganteus ), and

sorghum ( Sorghum bicolor L.) have been proposed as potential bioenergy feedstock crops. This

study evaluates how these crops performs in different environments under different crop

management practices, particularly nitrogen (N) fertilizer rates. Chapter 1 provides the rationale

of this research and a general discussion of the unique characteristics of these three crops. In

Chapter 2, an extensive database of switchgrass biomass yields from 106 sites and 45 field

studies in eastern two thirds of the USA and southeastern Canada is evaluated using descriptive

statistics, and using a random coefficients model. Switchgrass has been researched extensively

in North America as a biomass crop and data reported since the 1990’s reveal large variability in

dry biomass yields which are related to multiple environment and field management practices.

This analysis describes switchgrass biomass N response, and shows that in addition to N

fertilizer rate the most important factors affecting switchgrass dry biomass yields are growing

region, spring precipitation, growing season, ecotype, and harvest timing. Chapter 3 remarks

that studies reporting M. x giganteus dry biomass yields to date in the USA are few in number

and little information is available to suggest a suitable growing region. This study investigates

M. x giganteus in four Midwest and Atlantic Coast environments under three N rates.

Establishment success, plant growth, morphology, and dry biomass yields were evaluated and

results reveal no response to N rate during the establishment years, large biomass yield

differences among environments, and decreased yield when the crop experienced a combination

of high heat and dry conditions. Chapter 4 introduces two types of sorghum, forage sorghum and

biomass sorghum (referred to as energy sorghum) which have been proposed as crops with high

biomass production potential although prior to this study no research had evaluated these

## iii



## sorghum types grown for biomass in IL. This field study evaluated two forage sorghum and two

en ergy sorghum hybrids in four IL environments under different N rates. Measurements of

morphology and crop growth were measured throughout the growing season, and dry biomass

yields revealed significant differences betw een the two sorghum types. The e nerg y sorghum

hybrids achieved the greatest biomass yields in each environment with the effects of

environment and N rate affecting the biomass yields. The results of these studies provide

valuable information for stakeholders, producers, and scientists regar ding the impact of

environment and management practices on biomass yields of switchgrass, M. x giganteus , and

sorghum. It is necessary that these factors be evaluated prior to making decisions as to which

crop species and which cultivar or hybrid to plant in a given location. In most cases, no regional

recommendations for species selection and N fertility rates are adequate and most field

management practices must be made on a site

- by

- site basis.

## iv 

## ACKNOWLEDGMENTS

It has been a pleasure and honor being a graduate student in the Dept. of Crop Sciences at

the University of Illinois Urbana

- Champaign. My time as a graduate student has been a time of

growth and learning. The knowledge and experience I have gained are invaluable stepping

stones in my life.

First I would like to thank my wife, Julie, for her continued sacrifice and support in my

behalf. Her encouragement and confidence in me have been a source of strength and joy to me,

and I could not have come this far without her love, companionship, and friendship. She has

tirelessly and lovin gly cared for our four children Timothy, Porter, Emmett, and Amelia, who

have also been a wonderful support to me, helping me to maintain a balance in life.

I express many thanks to my dissertation committee membe rs for their support and

guidance. I am grateful for the opportunity I had to meet Dr. Germán Bollero at an Agronomy

Society of America meeting several years ago in Salt Lake City when I was applying to graduate

school. I was fortunate to have been offer ed an assistantship and co

- advised by him as a

Master’s student and then later offered a position by him and Dr. Tom Voigt to stay at Illinois to

pursue a Doctoral degree. His guidance as an advisor has been tremendous, and his sincerity and

concern for m e as a person and as a student have truly shaped me into a better and more qualified

person and agronomist. Serving as a teaching assistant for his course, Crop Science 440,

Applied Statistical Methods I, truly laid a statistical foundation for me that ha s been invaluable in

my research and evaluation of experiments. I thank Dr. Do K young (D.K.) Lee for the

opportunity to be involved in multiple research projects that have taught me many things about

applied agronomy research and how to set up field experi ments so that planting, management,

and harvesting of the crops are straight forward. Additionally, his knowledge and passion of

## v



## biomass crops has been a tremendous asset when considering each of my experiments. We have

had had many fruitful conversation s about field experiments, challenges, and future

opportunities. I express gratitude for the opportunity of working with Dr. Tom Voigt. Through

him I was fortunate to be the recipient of a PhD research assistantship funded by the Sun

Grant/U.S. Departmen t of Energy Regional Biomass Feedstock Partnership. We have had s ome

enjoyable trips together traveling to field sites in IN, NE, SD, and KY. Our conversations about

research and life have been helpful in my work. I also thank him for the many editing v ersions

of manuscripts that he has provided to help improve my writing and flow of the manuscripts. I

am thankful to Dr. Fernando Miguez for his i nvaluable modeling skills, statistical skills, and R

software skills. His help with statistical modeling and abilities to use R as a statistical modeling

and graphical tool have been invaluable in my research. He provided a great deal of R code and

example R code that have given me the opportunity to become experienced with two statistical

software’s, R and SAS . His published research has also been a valuable resource in my research

and writing. One of the greatest opportunities I have had as a graduate student is being a

teaching assistant for Dr. Don Bullock. He gave me the opportunity to be a teaching assi stant for

two courses, Crop Science 542, Applied Statistical Methods II, and Crop Science 541,

Regression Analysis , which was very challenging and a huge learning experience. This gave me

the opportunity to be mentored by him, and to work with excellent t eaching assistants and

students. These experiences not only helped me to learn the statistical material very well, but

also helped become more comfortable teaching and presenting to audiences. The statistical skills

and resources I gained from these cour ses have been invaluable in my research, which I have

used over and over. Because of Dr. Bullock’s passion for analyzing data in the most appropriate

and correct way, I often evaluated my own work to make sure that it would measure up.

## vi



## Last but not least I thank the many collaborators, field crews, and undergraduate students

that have made these exper iments possible. Allen Parrish, in particula r, was very helpful in

setting up experiments and harvesting of biomass.

## vi i 

## TABLE OF CONTENTS

CHAPTER 1 Rationa le for Research

................................. ... ..........................1

CHAPTER 2 M anagement and Environmental Factors that Affect Switchgrass B iomass

P roduction................................................

............ ...............9

CHAPTER 3 Miscanthus x giganteus Productivity: The Effects of Management in

Different Environment s..............................

.. ........................... 5 2

CHAPTER 4 Management of Forage and Energy Sorghum for Biomass P roduction in

Illinois ...............................................................................8 6

CHAPTER 5 Supplementary Information to Ch. 4

.................. ............ . . . . . . ... ... .11 5

CONCLUDING RE MARKS...........................................................................

. .12 7

APPENDIX A Sel ected SAS and R code for Ch. 2

..................... ............ ... ... . . . . . ..13 2

APPENDIX B Sel ected SAS and R code for Ch. 3

.................. ...... ......... ... . . . . ... .14 3

APPENDIX C Selected SAS and R code for Ch. 4

..................... ..... ............ ... . ...15 6

APPENDIX D Selected SAS code for Ch. 5

... .. .......................................... . . . . . .1 7 0

CURRICULUM VITAE..............................................................................

. . . . . .18 3

## 1 

## CHAPTER 1

R ationale for Research

The need to produce high yielding biomass crops has become an increasingly important

priority in the USA because of finite energy supplies , increasing energy prices, and volatile

foreign oil and gas markets (Perlack et al., 2005; Asif and Muneer, 2007; U.S. Chamber of

Commerce, 2010). The USA accounts for 25% of the world’s oil consumption , primarily to fuel

the transportation and shipping sectors of its economy ( Biomass Research and Development

Board, 2008 ). These reasons have urged the USA to attempt shift ing its reliance on oil and gas

to more renewable sources of energy and liquid transportation fuel , especially biomass based

energy (He aton et al., 2008a)

. Continued research efforts and the scaling up of a plant and

biomass based energy industry is necessary to meet the USA’s vision of replacing 30% of

current petroleum consumption by 2030 with biofuels (Perlack et al., 2005). These ef forts will

also help to meet the government Renewable Fuels S tandard (RFS) mandates of producing 136

billion liters of biofuels by the year 2022 (Energy Independence and Security Act, 2007). Of this

136 billion liters

, 79 must be from advanced b iofuels (n ot corn starch) including 60 must come

from cellulosic feedstock sources. In response, several herbaceous crops are being researched as

potential sources of bio energy feedstock that can be utilized as energy sources. Three of these

crops are switchgrass ( Panicum virgatum L.)

, Miscanthus x giganteus ( M. x giganteus ) and

sorghum ( Sorghum bicolor L.), and each has been proposed as potential bioenergy crops for the

USA. These crops provide an opportunity to grow and utilize a renewable source of energy, and

can help to meet the mandate. Biomass yields and biomass yield potential of these crops has

been reported but varies greatly between and within them. Since the early 1990’s, switchgrass

has been researched extensively in North America as a biomass feedst ock crop (Wright and

## 2



## Turhollow, 2010; Wullschleger et al., 2010), while less research has evaluated M. x giganteus

and sorghum as biomass feedstock crops in North America. This dissertation research (Ch.2, Ch.

3, Ch. 4, and Ch. 5) focuses on the growth, m anagement, dry biomass yield, and statistical

modeling of these crops. The main objective of this research is to evaluate reported biomass

yields of switchgrass in North America, describe the factors that most influence these yields, and

implement field studies to evaluate M. x giganteus and sorghum in different environments, and

evaluate field management practices, in particular N rate, that influence biomass yield in these

environments.

No direct field comparisons among these three crops are made in th e research cont ained

in Chapters 2, 3, and 4 , although this chapter describes some differences between the crops that

may favor one over the other in certain situations or environments. Particular emphasis is placed

on the Midwest, although the area that is covered by each research project is different. Chapter 2

covers the largest area investigating switchgrass biomass yields across t he eastern two thirds of

the U.S. and southern Canada. Chapter 3 evaluates M. x giganteus in three Midwest

environments a nd one Atlantic Coast environment. Finally, Chapter 4 is confined to evaluating

sorghum in four IL environments. Literature review for each of these crops and their specific

research objectives are primarily contain ed in their respective chapters

. Commo nalities that are

focused on in each chapter relate to the biomass yield and N response of the crops. Nitrogen

response in these crops has varied significantly from site

- to

- site as described in Chapter’s 2, 3,

and 4, while biomass yield is arguably the mo st important characteristic to consider when

selecting a biomass crop. Additionally, these crops are all C 4 

## photosynthetic grasses and as such

exhibit higher photosynthetic carbon uptake rates than C 3 

## crops (Hopkins and Hüner, 2004)

.

When considering the reaching effects of a biomass based energy industry it is necessary to take

## 3



## a skeptical or investigative approach when evaluating these crops. This is necessary to fully

understand the impacts that such production could have on current cropping systems, and the

difficulty of incorporating such crops into current cropping systems, in particular Midwest

cropping systems.

Large scale bioenergy production of M

. x giganteus , switchgrass, and sorghum in the

Midwest, may or may not be adopted by farmers in the f uture given the dominance of corn ( Zea

mays L.) and soybean [ Glycine max (L.) Merr.] production. Also, if market prices of corn,

soybean, and wheat ( Triticum aestivum L.) remain high as they have during 2010 and 2011,

much leverage would be required to co nvince farmers to grow bioenergy crops that have an

uncertain market and future. One of the purposes of this research is to develop useful agronomic

information for stakeholders, producer s , and researchers that will help them make this decision.

Currentl y, little infrastructure or industry in the Midwest exists for processing and handling

dedicated biomass energy crops. On the other hand, the infrastructure will likely not be

established until there is a reasonable amount of biomass production already oc curring.

Feedstock composition will likely not favor one crop over the other, since research has shown

that most biomass feedstocks have similar chemical composition when harvested at

physiological maturity (Lee et al., 2007). The decision on which crop to produce will likely

depend on a decision of which crop will best fit int o a producer’s crop rotation, provide reliable

and stable biomass yields , and produce significant positive net returns . Table 1.1 summarizes

some of the major advantages and limita tions to producing these three crops and provides a

framework for helping to select the right crop for the right situation. It must be noted that a

similar summary table has been published comparing C 4 

## rhizomatous grasses, switchgrass, and

M. x giganteus (Heaton et al., 2004).

## 4



## One major distinction between these crops is that M. x giganteus and switchgrass are

perennial crops and sorghum is an annual crop. For Midwest farmers, an annual crop like

sorghum may be a front runner candidate, at least in the sh ort term because it will more easily fit

into current corn and soybean crop rotations and does not require committing land to production

for multiple years like perennial switchgrass and M. x giganteus . As energy markets and a stable

bioenergy industry in frastructure are developed, perennial switchgrass and M. x giganteus may

provide long term options, where contracts can support committing farm ground to perennial

crop production. One major advantage of these two perennial crops is that they do not requi re

planting every year and may be well suited to soil that is less productive for corn and soybean

production and highly erodible for annual row crop production

. There are also other advantages

and limitations associated with perennial vs. annual bioenerg y crop production. One of the

limitations of switchgrass and M. x giganteus production is the duration period required to reach

maturity and become fully productive. Often, establishment year yields are non harvestable,

meaning that establishment year yi elds are minimal and harvesting is not justified. In most

cases, M. x giganteus takes at least three years to become fully productive (Miguez et al., 2008),

while switchgrass may become fully productive in as few as two years ( Grassini et al., 2009 ;

Schme r et al., 2010), but it usually requires three years ( Parrish and Fike, 2005; Heaton et al.,

2004; Schmer et al., 2010). Because establishment year yields of M. x giganteus and switchgrass

are usually non

- harvestable, a compromise between waiting for full productivity and receiving

some economic benefit during establishment year is the use of a companion crop. Studies

involving corn and sorghum

- sudangrass companion cropped with switchgrass have

simultaneously resulted in harvestable corn and sorghum

- sudan grass crops and successfully

established switchgrass stands (Hintz et al., 1998 ; Cossar and Baldwin, 2002) . Sorghum requires

## 5



## planting every year but energy sorghum hybrids can be planted on the same row spacing as corn

(0.76 m) using corn planting equipme nt. This provides an advantage over switchgrass which

generally requires narrower spacing and the need for equipment that can plant small seeds on

narrow row spacing, and M. x giganteus which requires vegetative propagation (Pyter et al.,

2009).

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, D

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( Ed .). Omnipress, Chapel Hill, NC.

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## 6



## Grassini, P., E. Hunt, R.B. Mitchell, and A. Weiss. 2009. Simulating Switchgrass Growth and

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Hopkins , W.G., and N

. P.A. Hüner. 2004. Energy Conservation in Photosynthesis: CO 2 

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## Perlack, R.D., L.L. Wright, A.F. Turnhollow, R.L. Graham, B.J. Stokes, and D.C. Erbach. 2005.

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Sorghum bicolor (L.) Moench. Crop Sci. 39:397

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Rooney

, W.L., J. Blumenthal, B. Bean, and J.E. Mullet. 2007. Designing sorghum as a dedicated

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Effects on Switchgrass Stands and Yield in the Great Plains. Bioenerg. Res. 3:159

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costs for eight switchgrass varieties in Alabama. Biomass Bioenerg. 1:119

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Thomason, W.E., W.R. Raun, G.V. Johnson, C.M. Tatiaferro, K.W. Freeman, K.J. W ynn, and

R.W. Mullen. 2004. Switchgrass Response to Harvest Frequency and Time and Rate of

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27 June 2011 ).

Wright, L., A. Turhollow. 2010. Switchgrass selection as a "model" bioenergy crop: A h istory of

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Production in Switchgrass across the United States: Database Description and Determinants

of Yield. Agron. J. 102:11 58

- 1168.

## 8



## TABLES

Table 1 .1

. Summary † 

### of several major advantages and limitations of growing and using switchgrass, M. x

giganteus , and sorghum (forage sorghum or energy sorghum) as biomass feedstock crops. Emphasis is placed on

growing these crops in th e Midwest. A positive sign (+) represents a positive characteristic or advantage, while a

negative sign (

- ) represents a negative characteristic or limitation. A 0 represents a neutral or undecided basis.

Superscripts are included where particular advan tages and limitations are referenced.

Characteristics/attributes Switchgrass M. x giganteus Sorghum

High yielding biomass potential 

# + b, j, k, l 

# + a, b, e 

# + g, p, q



### Low energy inputs (fertilizer) 0 d 

### 0 s, t , u



# - r



### Environmental benefits 

# + c 

# +

-

Annual nutri ent recycling 

# + b 

# + b



# -

Breeding improvement potential 

# + c, i, m 

# + e 

# + g



### Population genetic diversity 

# + c, n



# - a 

# + g



### Market prices



# - f



# - f 0



### Ease of establishment 0 o



# - a 

# +

Non Invasiveness 

# + + +

Perennial 

# + i 

# +

-

Annual



# - - +

Broad adaptation 

# + i



# - + h



### C 4 

### phot osynthetic 

# + + +

† A similar summary table was published in Heaton et al., 2004, and some similar information is reported here.

a) Lewandowski et al. (2000)

b) Heaton et al. (2004)

c) Sanderson et al. (2006)

d) Parrish and Fike, 2005

e) Heaton et al. (2 008b)

f) Khanna et al. (2008) g) Rooney et al. (2007)

h) Rooney and Aydin, 1999

i) Gonzalez et al. (2009)

j) Thomason et al. (2004)

k) Sladden et al. (1991)

l) Kiniry et al. (1999)

m) Bhandari et al. (2011) n) Narasimhamoorthy et al. (2008)

o) Sanderson e t al. (1996)

p) McCollum et al. (2005)

q) Miller and McBee, 1993

r) Sorghum Management Guide, 2010

s) Christian et al. (2008)

t) Ercoli et al. (1999)

u) Cadoux et al. (2011)

## 9 

## CHAPTER 2

Management and Environmental Factors that Affect Switchgrass Biom ass P roduction

ABSTRACT

S witchgrass ( Panicum virgatum L.) dry biomass yields can be largely impacted by

multiple environmental factors and field management practices factors that must be understood

so that when switchgrass is grown as a bioenergy crop, p roper management practices are

implemented. The objective of this study was to compile an extensive database regarding

switchgrass biomass production and perform a meta

- analysis to evaluate the effects of growing

region, spring precipitation, growing seaso n, ecotype , harvest frequency, harvest timing , and

nitrogen rate (N) rate on dry biomass yields. Data from 106 sites and 45 studies were gathered

from t he eastern two thirds of the U.S. and southeastern Canada. A random coefficients model

was implemented to describe switchgrass biomass N response and other environmental and

management factors were incorporated into the model. The linear N rate ( P < .0001), quadratic

N rate ( P = 0.0363), region ( P < .0001), spring precipitation ( P = 0.0001), growing seaso n ( P =

0.0466), ecotype ( P < .0001), harvest timing ( P = 0.0771), spring precipitation x ecotype ( P =

0.0092), spring precipitation x growing season ( P = 0.0007), linear N rate x ecotype ( P = 0.064),

and region x harvest timing ( P = 0.0817) effects were al l significant. Results revealed that N

response varies from site

- to

- site and the critical N fertilizer rate (Max N) required for maximum

biomass yield may be quite higher than the economical optimum N rate (EONR). Switchgrass

grown in the lower and upper central U.S. regions produce d greater biomass yields than

switchgrass grown in the north or south, and the lowland ecotype yield ed better than the upland

ecotype, but this depended on the region

.

## 10



## INTRODUCTION

Volatile energy markets, high prices, and fi nite supplies are reasons for the USA to shift its

reliance on oil and gas to more renewable sources of energy and liquid transportation fuel

,

especially biomass based energy (Heaton et al., 2008) . The USA accounts for 25% of the

world’s oil consumption

, primarily to fuel the transportation and shipping sectors of its economy

( Biomass Research and Development Board, 2008 ). As part of the U.S. Energy Indep endence

Security Act of 2007 a Renewable Fuels S tandard (RFS) mandates the production of 136 billion

l iters of biofuels by the year 2022 (Energy Independence and Security Act, 2007). Of this 136

billion liters

, 79 must be from advanced biofuels (not corn starch) and 60 must come from

cellulosic feedstock sources. Advanced biofuels or cellulosic biofuel c rops like switchgrass

( Panicum virgatum L.) provide an opportunity to grow and utilize a renewable source of energy,

and can help to meet the mandate.

S witchgrass has received a great deal of attention among researchers and producers

because of its use a s a forage crop and its potential as an herbaceous bioenergy crop.

Switchgrass is a warm

- season perennial grass that is native throughout much of North America

east of the Rocky Mountains, and is most abundant east of 100°W (Vogel, 2004; Samson and

Omelia n, 1994). It has been the subject of much research in the US A since the early 1990s when

the Department of Energy evaluated a wide variety of herbaceous species across a range of

environments and identified it as a ‘model’ bioenergy feedstock (McLaughlin and Kszos, 2005;

Wright and Turhollow, 2010). Switchgrass has many desirable characteristics that make it

suitable for feedstock production . Among them, the ability to grow on marginal and disturbed

sites , provide environmental and ecosystem services, re quire relatively low moisture and nutrient

inputs , and produce relatively high and reliable dry biomass yields across a wide range of

## 11



## environments (Wright and Turhollow, 2010; Parrish and Fike, 2005; Mitchell et al., 2008;

Bransby et al., 1998; Sanderson e t al., 1996; Sanderson et al. 2006; Lemus and Parrish, 2009).

Moreover, Gonzalez et al. ( 2009 ) described several traits that herbaceous bioenergy crop species

should posses

, including not directly competing with food crops for land and resources,

producin g reliable yields in a broad range of environments under limited inputs, be ing perennial,

and having the potential for improvement through breeding

. Switchgrass possesses each of these

characteristics.

Several review s (Bransby et al., 1998; Parrish and Fi ke, 2005; Lemus and Parrish, 2009;

Mitchell et al., 2008; Sanderson and Adler, 2008; Sanderson et al., 1996; Sanderson et al., 2006a;

Wright and Turnhollow, 2010) and meta

- analytical (Heaton et al., 2004; Wang et al., 2010;

Wullschleger et al., 2010) artic les have been published regarding switchgrass production . From

a production perspective, dry biomass yields among switchgrass cultivars are highly variable. In

a database compilation of 39 studies ranging across much of the eastern two

- thirds of the US,

Wullschleger et al. ( 2010 ) reported yields ranging from 1 Mg ha

- 1 

## to 39.1 Mg ha

- 1



## . B iomass

yields can be influenced by the climate, location and geography of the experiment, management

practices

, and site

- to

- site conditions (Johnston et al., 2009 ; Wang et al., 2010 ). These factors

must be considered to maximize biomass yields and maintain stand longevity. From the early

1990s through the present

, much research has focused on cultivar and ecotype adaptation and

evaluation, harvest management, and nitrogen and fertilizer response (Table 2. 1).

The overall purpose of this study was to perform a meta

- analysis of important factors in

switchgrass production that influence dry biomass yield and was accomplished by collecting and

synthesizing data from the liter ature of independent studies (Cooper et al., 2009; Gurevitch and

Hedges, 1999; Gates, 2002 ). A random coefficients model was then implemented to account for

## 12



## the variability among sites (St

- Pierre, 2002) and describe the effects of different factors on

swi tchgrass biomass yield. Wullschleger et al. ( 2010 ) recently performed a statistical analysis of

switchgrass and various factors affecting biomass yields, covering 39 different studies from

across the USA. Th eir study evaluated site location, stand age, p lot size, cultivar, crop

management, biomass yield, temperature, precipitation, and ecotype , and concluded that the most

important predictors of biomass yield were ecotype, temperature, precipitation, and N

fertilization. To further add to the understandi ng of the these factors

, we compile a more

extensive database of 1 06 sites from 45 studies throughout North America , which includes many

o f the studies that were part of previous meta

- analysis (Heaton et al., 2004; Wang et al., 2010;

Wullschleger et al.

, 2 010 ) as well as additional studies that were eithe r missed or published

during the revi ew process of these meta analyse s (Table 2. 3)

. A quadratic function is the most

common functional form that has been evaluated in switchgrass N response trials (Muir et al.,

2001; Heggenstaller et al., 2009; Lemus et al., 2008a; Haque et al., 2009; Vogel et al., 2002) and

we suspected that a quadratic function would best describe the relationship between biomass

yield and N rate, but that environmental and site differenc es would result in variation in the

strength and magnitude of the N response observed from site

- to

- site (i.e. intercept and slope

would vary from site

- to

- site). We hypothesized that selection of proper ecotype (and specific

cultivars within ecotype) would significantly affect biomass yield potential in different regions of

the USA and southern Canada. We also hypothesized that harvest timing and number of annual

harvest s play an important role in affecting dry biomass yields. The database resulting from our

literature search contains information on region of growth, spring precipitation, growing season

,

ecotype, number of annual harvests, harvest timing, and annual N application rate. Following

the compilation of this database, the specific objectives of the analysis were to develop a random

## 13



## coefficients model that best describes the curvilinear relationship between biomass yield and

annual N application rate and then incorporate other selected factors into the model that have a

significant influence on d ry biomass yield. The approach in our statistical analysis that

distinguishes it from previous analysis (Heaton et al., 2004; Wang et al., 2010; Wullschleger et

al., 2010) is that it includes a quantification of the curvilinear N fertilization response of

switchgrass. The N responses were quantified for specific regions of the USA and southern

Canada that were categorized into groups of relatively similar growing season length,

temperatures, day length, and winter freezing conditions. Spring (Apr. throug h Jun) precipitation

rather the growing season or annual precipitation was used as a covariate in the model because

early season water availability can have an important impact on biomass yield (Cassida et al.,

2005) and early season precipitation has been shown to be a good predictor of biomass yield

( Fike et a l., 2006b; Lee and Boe, 2005).



# MATERIALS AND METHODS

Database C ompilation and D escription

A literature search of primary research was conducted using the ISI Web of Knowledge

(ISI, Philadelphia, PA ) electronic database, and through location of studies included in the

references. Switchgrass is a multi

- purpose crop and has been grown for livestock forage, for

biomass, as vegetative filter or streamside buffers, and as prairie or restorative planting s (Parrish

and Fike, 2005). Switchgrass grown for these different purposes can be managed quite

differently and in order make more meaningful comparisons , only data coming from studies that

focused on switchgrass grown as a biomass crop or that were manag ed similar to switchgrass

grown for biomass were included. Only literature published since 1990 measuring switchgrass

dry biomass yield s were included in the database using d ata gathered from the USA and southern

## 14



## Canada. Switchgrass literature published before 1990 focused primarily on switchgrass grown

as a forage crop, whereas most literature on switchgrass production since 1990 has focused on

switchgrass grown as a biomass crop. In addition to the criteria just described studies were

excluded from our database if dry biomass yields were averaged across N rates or ecotype, if

biomass was harvested more than twice a year or studies were un

- replicated. Studies that

averaged biomass yields harvested at different times, meaning biomass harvested at peak

bi omass (before physiological maturity) or at the end of the growing season once the crop

reached physiological maturity or following a post killing frost, were also excluded. Studies in

which switchgrass plants were transplanted rather than seeded were exc luded from the database

unless the plants were in the second growing season or beyond, given the crop ’ s ability to spread

out, fill in stand gaps, and achieve similar yields across a wide range of plant spacing s (Parrish

and Fike, 2005). Also, b efore the statistical model and predictions were developed, several

biomass yield value s from Thomason et al., 2004, and Sanderson et al., 2001 receiving annual N

application rates ranging from 322 to 896 kg N ha

- 1 

## were removed. There were very relatively

few obser vations in this N application range, and the N application rates from all of the other

studies were less than 300 kg N ha

- 1 

## , thus annual N applicat ion rates for each site were limited to

a range between 0 and 300 kg N ha

- 1



## .

In the database, studies and exp eriments were separated into individual sites in order to

disaggregate varying responses to annual N application. In some cases the reported biomass

values specified for a particular site were averaged across more than one location within a

particular stu dy, where it was not possible to separate the studies by location. Cultivars

,

varieties, experimental lines , and synthetic lines (each referred to as cultivars) were classified

into two categories, upland and l owland ecotype ( Table 2.2 ). The assignment o f most upland or

## 15



## lowland ecotype classifications were obtained from references cited in Table 2. 2. The reported

harvest tim e varied from study to study, so we classified the final biomass harvest into two

categories: peak standing crop (PSC) or end of sea son (ES). These classifications were assigned

according to harvest and morphology information reported in the individual studies. Peak

standing crop refers to harvests at or near peak biomass, which usually occurred at or near

anthesis and before physiol ogical maturity . The ES harvest timing is associated with harvests

occurring after a killing frost or once the crop had begun to senesce and field dry.

Where possible, s pring precipitation (April through June) values were obtained from the

studies when reported, or were estimated from the long

- term averages when specific seasons of

precipitation were not reported. In cases, where no precipitation data was reported, spring

precipitation was obtained from the North American Regional Reanalysis (NARR) data base

(Mesinger et al. 2006)

. A few observations that utilized irrigation in any specific year were

excluded from the database and statistical analysis so that recorded spring precipitation levels

were not confounded with applied irrigation in the statisti cal analysis.

Growing seasons were divided into 3 categories: establishment ( growing season 1),

maturation (growing season 2) , and production (growing seasons >=3 )

. Growing s easons >=3

were grouped together, primarily because the number of growing seasons >=3 ranged from 3 to

26 year old stands, and insufficient data were available to provide reliable estimates for each of

these individual growing seasons . It was assumed that by the time the crop reaches three years

of growth, the crop is fully productive and potentially achieving its maximum yields (Parrish and

Fike, 2005; Heaton et al., 2004; Schmer et al., 2010) . In some studies included in the database,

biomass yield was reported as an average o f growing seasons 2 and 3 ( Casler et al., 2004; Vogel

et al., 2002, Hopkins et al., 1995) and sometimes as an average of growing seasons 2, 3, and 4

## 16



## (Casler et al., 2007 ; Haque et al., 2009 ). Rather than disregard these data, we categorized these

data into growing seasons >=3. In these cases we assumed that th e authors were reporting data

from stands that were fully or near fully productive during their second season (Grassini et al.,

2009) as it has been shown that switchgrass stands can reach their yield potential in as few as

two years (Schmer et al., 2010)

.

Within the database, the data was divided into 4 regions or areas of growth that were

classified as: North Region (NR), Upper

- Central Region (UCR), Lower

- Central Region (LCR),

and South Region (SR) (Fig. 2. 1 and Table 2.3 ). Thes e regions were created bas ed on

in formation from the USDA plant hardiness zone map and latitude

. Fig. 2.1 shows the sites

categorized by region overlaid on the USDA plant hardiness zone map. The allocation of the

studies into the 4 regions was fairly balanced with the NR, UCR, LC R , and SR containing data

from 29, 24, 31, and 22 sites and locations, respectively (Table 2. 3). In this way we simplified

the range of production into groups of data originating from areas with relatively similar growing

season lengths, day lengths and p hotoperiods, and winter freezing conditions (Fig. 2. 1). Data

from some regional scale experiments (Heaton et al., 2008; Casler et al., 2004; Casler et al.,

2007; Adler et al., 2006; Schmer et al., 2010) were split into 2 or more regions depending on the

g eographical spread of the study sites (Table 2. 3). Several data points in the NR, coming from

Casler et al., 2004 and Madakadze et al., 1998b, were excluded from the analysis because they

were the only studies reporting lowland ecotype yields in this regi on, producing little information

for prediction of lowland ecotype biomass yields in the NR. Most other studies in the NR, did

not plant lowland cultivars , most likely because of their susceptibility to winter kill in northern

environments.

## 17



## In total there were 106 sites from 45 studies totaling 1476 observations in the database.

Forty

- one of these studies came from t he eastern two thirds of the U.S. with the remaining four

studies coming from southeastern Canada. All studies originated from peer

- reviewed journal

articles , except Fuentes and Taliaferro ( 2002 ) which was published in a conference proceeding.

Factors considered in the statistical analysis were: region (NR, UCR, LCR, SR), spring

precipitation (April through June)

, growing seasons (1, 2, >=3), ecotype (lowland or upland),

number of annual harvests (1 or 2), harvest timing (PSC or ES), and annual N application rate.

Preliminary Statistical Analysis

Cultivar means, standard error of the mean (SEM), minimum, and maximum dry biomass

yields were ca lculated using the MEANS procedure of SAS ( SAS Institute, 2007) for growing

season 2 and growing seasons >=3 categorized by region (Table 2. 4). D ata exploration,

graphing, and data manipulation was performed using the graphical interface and descriptive

s tatistical packages of R statistical software (R Core, v. 2.12.1, 2010), in particular the ‘graphics’

v.2.12.1 and ‘lattice’ v.0.19

- 13 packages. Before statistical analysis and development of model

,

biomass yields were averaged across their respective eco type within region, site, spring

precipitation

, growing season, and number of annual harvests

. Based upon these averages, mean

ecotype values and their respective SEM were then calculated

.

Statistical A nalysis and M odel D evelopment

A ‘random coefficient m odel’ was used to describe the response of mean switchgrass dry

biomass yield (averaged across ecotype within region, site, spring precipitation

, growing season,

and number of annual harvests) to N rate . This allowed unique N response curves to be

estimat ed for each site and was necessary due to site

- to

- site conditions that created variation in

the response of dry biomass to N rate (Aitkin et al., 2009; Littell et al., 2006; St

- Pierre, 2002 ).

## 18



## The first step was to develop the basic model structure by cons idering the linear and quadratic

components of N rate. Three models were tested each model included fixed linear and quadratic

components of N rate (

. In addition, t he first model also had a random intercept. The second

model also had a random intercept and random linear and quadratic components of N rate. The

third model also had a random intercept and a random linear component of N rate , but no random

quadratic component of N rate

. Akaike Information Criterion (AIC) was used to compare the s e

three mode ls, and favored the third model, which had lower a lower AIC value (3480) than the

first (3491) and second (3486) models. Each of these models resulted in significant fixed linear

( P <.0001) and fixed quadratic ( P 0.0674 ) N rate components. The third model that was

selected takes on the following basic form :

, [eq. 1]

, ,

where dry biomass yield ( depends on N rate (

, the mean biomass value within the

site and is a function of the fixed ( and random ( intercept, fixed ( and random ( linear

N rate effect s , and fixed quadratic N ra te effect ( . The random intercept ( depends on site

( and the random linear N rate ( effect which enters linearly into the model. The model

assumes the random effects have a mean of zero and a general variance

- covariance matrix, (i .e.

for random intercept and for the random linear N rate effect), and that the errors ( have

a mean of zero and common variance. Once the basic form of this model was developed, a

backward elimination method was implemented to inv estigate the fixed main effects of region,

spring precipitation , ecotype, growing season, number of annual harvests

, and harvest timing

,

and interaction effects of region x spring precipitation, ecotype x spring precipitation, growing

season x spring preci pitation, linear N rate x ecotype, quadratic N rate x ecotype and region x

## 19



## harvest timing. Backward stepwise searches are recommended over forward stepwise searches

when the number of potential predictor variables is relatively small as is the case here a nd allows

each potential predictor variable to be considered under the premise that it has been adjusted for

all other potential predictor variables (Kutner et al., 2004). In the first step each of the variable

and interactions listed above were simultane ously investigated. The following non

- significant

terms were then removed if their p

- values were greater than α = 0.1: n umber of annual harvests

( P = 0.6591), region x spring precipitation ( P = 0.1951), and quadratic N rate x ecotype ( P =

0.6411), and growing season x ecotype ( P = 0.6429)

. The next step involved reanalyzing those

variables that were significant in step one less the non

- significant variables in step one. No

further terms were eliminated, and b ased upon these results a final model was established (Table

2.5 ) from which model predictions were calculated. Other susp ected interactions (e.g. region x

ecotype) were not included in the analysis because when investigated, resulted in model

singularity, due to the elimination of the lowland ecotype from the NR. Statistical analysis was

performed using a linear mixed

- effec t model in the ‘nlme’ v.3.1

- 97 package of R statistical

software (R Core, v. 2.12.1, 2010). Residuals were examined for normality and the assumption

of common variance by inspection of normal quantile and residual plots. Individual site

predictions based upon the final model were plotted to show visual agreement between observed

yield and model predictions (Fig. 2. 3). Several unusually high mean biomass yields were

deemed outliers, when visually assessing the residual plots. These values were not remove d

from the analysis because they did not modify the results of the model (Table 2. 5), when the

model was rerun without them. Furthermore, they may represent the potential of the switchgrass

crop or potential sampling error , and were retained in the model. The critical rate of N fertilizer

required to achieve maximum dry biomass yield (Max N) and ex post economic optimum N rates

## 20



## were calculated only for sites which reported biomass yields across at least four N rates and this

included 14 sites. Coefficien ts were extracted from eq. 1, and do not reflect the fixed main

effects or investigated interactions shown in Table 2. 5 other than the linear N rate and quadratic

N rate effects. The critical rate of N fertilizer (Max N) required to achieve maximum biomas s

yield was calculated using the following equation:

[eq. 2]

Where depends on the site and is the random linear N rate coefficient from eq. 1,

and is the fixed quadratic coefficient from eq. 1. Based upon the price of Mar. urea (46% N)

from 2006 to 2011 (USDA/ERS, 2011), three prices of urea ($362 ton

- 1 

## ($328.3 Mg

- 1 

## ), $448 ton

1 

## ($406.3 Mg

- 1 

## ), and $552 ton

- 1 

## ($500.6 Mg

- 1 

## ) and respective unit prices of a kg of N ($0.87 kg

- 1



## N, $1.07 kg

- 1 

## N, and 1.32 kg

- 1 

## N)

, economical optimum N rates (EONR) w ere calculated by

considering three theoretical prices of a Mg of dry biomass: $35 Mg

- 1 

## , $50 Mg

- 1 

## , and $65 Mg

- 1



## (Table 2. 6). These EONR were calculated ex post (after the fact) using the following equation:

[eq. 3]

where is the cost of N fertilizer ($ kg

- 1 

## ), is the price of dry biomass ($ Mg

- 1 

## ), is the

random linear N rate coefficient from eq. 1, and is the fixed quadratic coefficient from eq. 1.



# RESULTS AND D ISCUSSION

Preliminary Results

M ean biomass yields (± SEM) across all regions and both ecotypes were the lo west in

growing season 1 (6.6 ± 3 Mg ha

- 1 

## ), which increased t o 9.1 ± 5.5 Mg ha

- 1 

## in growing season 2

and 10.9 ± 5.2 Mg ha

- 1 

## in growing season s >=3. T his is in agreement with the literature

, which

reports that the necessary time for switchgrass to achieve full productivity varies

, but is generally

## 21



## three years (Parrish and Fike, 2005; Heaton et al., 2004; Schmer et al., 2010) , although

switchgrass stands may become fully or near

- fully productive during their second season

(Gra ssini et al., 2009; Schmer et al., 2010). Fa ctors that influence establishment, including seed

quality, seedbed preparation, planting date, planting rate, and planting depth , can ef fect the time

needed to reach full productivity (Masters et al., 2004) . Moreover, l ow stand densities achieved

during growing season 1 tend ed to result in lower biomass yields in growing season 2 than those

having higher stand densities (Schmer et al., 20 06).

Overall, the lowland ecotype produce d greater yields than the upland ecotype. In

growing season 2, ecotype m ean biomass yields (± SEM) were greater for the lowland ecotype

(11.1 ± 6.1 Mg ha

- 1 

## ) than for the upland ecotype (6.7± 3.2 Mg ha

- 1 

## ). Similarl y, in growing

seasons >=3, ecotype m ean biomass yields (± SEM) were greater for the lowland ecotype (12.6 ±

5.4 Mg ha

- 1 

## ) than for the upland ecotype (9.3 ± 4.3 Mg ha

- 1 

## ).

Switchgrass yield s were the greatest when grown in the two central regions (Fig. 2.2 )

with mean biomass yields ( ± SEM ) averaged across growing season 2 and growing seasons >=3

the greatest in the LCR (13.4 ± 4.5) and UCR (10.1 ± 4.3) when compared to the SR (9.3 ± 5.7)

and NR (7.3 ± 3.1). Mean biomass yield values for the different regio ns were highly variable

with large ranges: NR (1.1 to 14.5 Mg ha

- 1 

## ), UCR (2.5 to 26 Mg ha

- 1 

## ), LCR (4.6 to 33.4 Mg ha

1 

## ), and SR (0 to 30.4 Mg ha

- 1 

## ). Mean biomass yields of zero in the SR came from Cassida et al.

(2005) which reported negligible yields at or near zero in LA in which stand densities of the

upland ecotype had declined to zero or near zero after two years of production resulting in

biomass yields equal to 0 Mg ha

- 1 

## . In the NR, mean yields were less variable than in the other

regions and the g reatest mean yields in this region, reaching yields greater than 13 Mg ha

- 1 

## were

## 22



## achieved in SD, WI, and Montreal, QC (Boe, 2007; Casler and Boe, 2003; Madakadze et al.,

1998b).

Although mean biomass yields among the four regions were quite variable (Fig . 2.2), the

lowland ecotype produce d greater yields than the upland ecotype, especially in the LCR, where

mean yields greater than 20 Mg ha

- 1 

## were achieved by lowland cultivars Alamo and Kanlow in

TN, VA, and OK (Fike et al., 2006a; Fike et al., 2006b; Tho mason et al., 2004). In the UCR,

there were relatively few lowland mean biomass values, although lowland yields reached greater

than 14.5 Mg ha

- 1 

## (Vogel and Mitchell, 2008) and up to 21 Mg ha

- 1 

## (Casler et al., 2004) in Mead,

NE.

Mean, SEM, and median calc ulations of individual cultivars indicate that biomass yields

were greatly impacted by the region in which they were grow n (Table 2. 4 ). For example, Cave

in

- Rock (Table 2. 2) is a commonly planted cultivar that has been evaluated in experiments in

each of the region s described in this analysis

. Cave

- in

- Rock mean yields

, while quite variable

,

were greatest when grown in the two central regions (UCR and LCR) , achieving mean yields (±

SEM) of 11.2 ± 5 .3 in the UCR and 12.5 ± 3.9 in the LCR for growing season s >=3 (Table 2. 4).

However when grown in the NR or SR (both distant from its origin) , it s mean yields decrease d

,

dropping to 4.4 ± 3 in the SR and 8.5 ± 5 in the NR (Table 2.4).

Ecotype, R egion, and Growing S eason

The effects of region, ecotype, and growi ng season were each significant (Table 2. 5).

The significant effect of growing season is related to the time it takes for switchgrass to reach

full productivity (maximum biomass yields), while the effect of ecotype appears to be dependent

on region and th e cultivar or ecotype being produced. Due to this we suspected that an ecotype x

season interaction was also significant. However, when the ecotype x season interaction was

## 23



## included in the model it resulted in model singularity (an indication of the unba lanced nature of

the data and potential model over parameterization), so we were unable to estimate its effect.

Overall, the lowland ecotype had significantly greater biomass yields than the upland ecotype ( P

< .0001) (Table 2. 5).

The overall higher bio mass yields of the lowland ecotype compared to the upland ecotype

likely has to do with the morphological differences between the two . Compared to the upland

ecotype

, the lowland ecotype is adapted to longer growing seasons with reduced photoperiod and

lo wland cultivars generally produce taller plants with thicker leaves and stems, have fewer tillers

with greater tiller diameter, and generally have more of a ‘bunch form’ growth pattern as

opposed to a ‘sod formation’ growth pattern (Parrish and Fike, 2005; Cassida et al., 2005 ).

Although not observed in this study, variation in the morphology of switchgrass and the impact

of environment has tended to produce an ecotype x environment interaction in experimental

studies (Boe and Casler, 2005; Cassida et al., 2005; Casler and Boe, 2003; Casler, 2005).

Moving cultivars south of their origin hastens reproductive development and the end of

vegetative growth

, while moving populations north of their origin can prolong vegetative growth

(Parrish and Fike, 2005; Vog el, 2004). This potentially increase s biomass yield s , but Casler et

al. (2007) indicated that movement of switchgrass populations north or south of their origin

should be limited to no more than one USDA plant hardiness zone due to risk of winter kill.

T wo switchgrass cultivars, Cave

- in

- Rock, originating from ~38° N in Southern IL and OK

- NU

- 2

originating from 35° N latitude in Oklahoma, grown in Mandan, ND (46°48’ N) were severely

affected by the northern movement from their origin (Berdahl et al., 2005). In the span of 4

years their survival percentages decreased from near 100% to 20 or 30%. Other studies have

also evaluated switchgrass cultivars in regions where adaptation is unlikely, resulting in large

## 24



## yield variation, and often low yields. For exam ple, a TX study evaluated several cultivars

,

including Alamo (a cultivar adapted to Texas) and Summer (a cultivar originating in the northern

Great Plains) (Sanderson et al., 1999b) . Summer is not well suited to biomass production in TX

producing dry yiel ds as low as 1.75 Mg ha

- 1



## , while Alamo produced 17.59 Mg ha

- 1 

## in the same

study.

Spring Precipitation

The spring precipitation x growing season, spring precipitation x ecotype, and

precipitation effects were all significant (Table 2. 5)

. The l owland ecoty pe generally produced

greater yields than the upland ecotype under similar spring precipitation levels (Fig. 2.4). This

can be partially explained by the adaptation differences of the different ecotypes. Cultivars

within the lowland ecotype are best adap ted to more southern latitudes and lower wetter areas,

while cultivars within the upland ecotype are better suited to more northern latitudes and mesic

environments with moderate soil moisture conditions . Because cultivars within the upland

ecotype are ge nerally more adapted to drier environments, they are typically less sensitive to dry

conditions th an cultivars within the l owland ecotype (Vogel, 2004; Parrish and Fike, 2005;

Porter, 1966).

As the crop matures from growing season 1 to growing season s >= 3, similar precipitation

levels result in higher biomass yields

. Precipitation has limited switchgrass growth in the

Northern Great Plains where annual levels have been as low as 193 mm (Lee and Boe, 2005),

and in droughty TX conditions where stand loss h as been threatened (Cassida et al., 2005). Total

annual precipitation is probably not as important as the timing and number of the precipitation

occurrences (Wullschleger et al., 2010). Fike et al. ( 2006b ) show ed that biomass yields are

## 25



## strongly influenc ed by early season rainfal l, while Lee and Boe ( 2005 ) reported that early season

(April

- May) rainfall is a good predictor of biomass yield.

Nitrogen R esponse

T he biomass yield response to applied N was quite variable (Fig. 2. 3 and Table 2. 5) and

conseque ntly t he linear N rate x ecotype, linear N rate, and quadratic N rate effects were all

significant (Table 2. 5). When predictions were made on a regional level (Fig. 2. 5), the predicted

N response curves for lowland and upland ecotypes were slightly differ ent, with a steeper linear

component for the lowland ecotype than the upland ecotypes. This is likely because of the

greater number of high mean biomass yields at N rates greater than 100 kg ha

- 1 

## for the lowland

ecotype in the SR and LCR compared to the u pland ecotype, which caused the linear relationship

between biomass yield and N rate to be slightly more positive (Fig. 2. 5). In Fig.’s 2. 5 and 2. 6,

s everal mean values stand out in the SR and LCR with particularly high biomass yields (>= 28

Mg ha

- 1 

## ). Th ese mean values (33.4, 29.5, 28.6, 30.4, and 29.4) come from studies with reported

biomass yields reaching up to 36.7, 34.06, and 39.5 Mg ha

- 1 

## respectively from Thomason et al.

( 2004 ) , Sladden et al. ( 1991 ) , and Kiniry et al. ( 1999 ) . These data represent N rates ranging from

0 to 224 kg N ha

- 1 

## , 2 and >=3 growing season s, 1 or 2 annual harvests , different final harvest

timing, and were for lowland cultivars, Kanlow and Alamo. T hese high yield values data do not

appear to be associated with any particular m anagement factor other than being from two high

yielding lowland cultivars, and were not necessarily associated with higher N application rates

,

although they do represent the high biomass yield potential of switchgrass.

Optimal N rates and associated fert ilizer recommendations have varied greatly in the

literature and maximum yields have been achieved at a wide range of N rates. Most observed

maximum biomass yield have been achieved at rates between 112 and 270 kg N ha

- 1 

## (Haque et

## 26



## al., 2009; Heggenstaller et al., 2009; Vogel et al., 2002; Lemus et al., 2008a; Lemus et al.,

2008b; Muir et al., 2001; Mooney et al., 2009; Mulkey et al., 2006), although maximum biomass

yields have been achieved at lower rates (67 kg N ha

- 1 

## ) (Mooney et al., 2009) and much highe r

rates (448 kg N ha

- 1 

## and greater) (Thomason et al., 2004; Sanderson et al., 2001). Recommended

or optimum N rates have ranged between 56 and 168 kg N ha

- 1 

## (Haque et al., 2009;

Heggenstaller et al., 2009; Vogel et al., 2002; Lemus et al., 2008b; Muir et al., 2001; Mulkey et

al., 2006), while the most

- efficient N rates, in terms of N use (calculated as the percentage of N

removed from the crop) have ranged between 56 an and 112 kg N ha

- 1 

## (Lemus et al., 2008a;

Lemus et al., 2008b; Muir et al., 2001). As th e literature suggests, achieving maximum biomass

yields may not be the best way to optimize fertilizer application, and in many cases, biomass

yields slightly lower than the maximum can be achieved from much lower N rates. This further

implies that the EON R is generally lower than the N rate required to achieve maximum biomass.

Even though general response curves were generated from the results of this analysis for

a specific set of management practices and spring precipitation levels (Fig. 2. 5), it must be noted

that it is difficult to make any general or even regional recommendation regarding an optimal N

rate for switchgrass, due the random variation from site

- to

- site which was high (Fig. 2. 3) and a

95% confidence interval for the linear N rate coeffici ents was 0.019 to 0.055. Nitrogen

application decisions are best made by considering individual site conditions, and individual site

predictions may provide some useful information for making these decisions (Fig. 2. 3). In most

cases the sites exhibited good agreement between observed values and predicted N response

(Fig. 2. 3). Due to the nature of the quadratic model, it has been shown to predict a decline in

crop yield with increasing levels of N fertilizer beyond Max N (Bullock and Bullock, 1994 a ).

T hese yield decreases are often associated with excessive amounts of N application that cause

## 27



## problems such as lodging. The N application range for this study was constrained to a range of 0

to 300 kg N ha

- 1 

## and at most sites higher N rates did not result in observed or predicted biomass

decreases (Fig. 2.3)

. There were 14 sites in the statistical analysis that reported biomass yields

across at least four N rates. These sites were 31a, 32a, 32b, 60a, 64a, 65a 65c, 65f, 65g, 65h, 65i,

65j, 69a, and 73a, an d come from several studies (Vogel et al., 2002; Muir et al., 2001; Mooney

et al., 2001; Sanderson et al., 2001; Schmer et al., 2010; Haque et al., 2009; Heggenstaller et al.,

2009) (Table 2. 3 and Fig. 2. 3). Specifically, sites 31a (Vogel et al., 2001) an d 73a

(Heggenstaller et al., 2009), showed a reliable fit to the data (Fig. 2. 3) with maximum biomass

yields being achieved at 259 kg N ha

- 1 

## and 260 kg N ha

- 1 

## , respectively (Table 2. 6). Max N for

these sites were calculated by setting the first derivative of the quadratic function equal to zero

and solving for N rate. At some of these sites predicted Max N was beyond the highest level of

actual N applied in the studies . Due to this the observed maximum yields reported in the studies

were sometimes lower than Max N predicted in this analysis . For example, in a two

- year study

in Boone County, IA (site 73a), maximum observed biomass yields were achieved at the highest

N rate applied in the study (220 kg N ha

- 1 

## ), while Max N calculated in this analysis was a t 260

kg N ha

- 1 

## (Fig. 2. 3 and Table 2. 6). It is likely that if higher N rates had been applied in this

study, higher observed maximum yields would also have been observed. In 13 out of these 14

sites, the N rate (Max N) required to achieve maximum biomas s ranged from 214 kg N ha

- 1 

## to

287 kg N ha

- 1 

## , with the exception of site 32a at 413 kg N ha

- 1 

## (Table 2. 6). Economical optimum

N rates, calculated ex post (after the fact) (Bullock and Bullock, 1994b) for these 14 sites showed

a wide range of values, depen ding on the N rate price and dry biomass price ratio (Table 2. 6).

As this ratio decreased, or as N rate prices decreased and the price of dry biomass increased,

EONR increased.

## 28



## Harvest T iming

Even though the number of annual harvests was not significant in the modeling process

,

it should not be concluded that this is of little importance . Harvest frequency can have a m ajor

impact on N use and removal, and is likely best considered in combination with harvest timing.

Harvest timing and its associated reg ion x harvest timing interaction were both significant (Table

2. 5). Results of the region x harvest timing interaction are variable, especially in the LCR and

UCR (Fig. 2. 6). However, i n the SR , PSC harvests tend ed to result in greater biomass yields

tha n harvests at ES, but this was not the case in the NR. In fact, the opposite occurred in the NR,

with ES harvests yielding greater than PSC harvests. In the SR, a longer growing season and

mild winter temperatures are likely to create an environment in w hich PSC harvests will not pose

a significant threat to the survival of the crop as long as increased N removal at early harvests are

balanced with N fertilization. In the north, PSC harvest s are more likely to injure the switchgrass

stands likely because harvesting early at PSC remov es carbohydates needed for winter survival

that would have been allocated below ground to the rhizome biomas s had the crop been

harvested later at ES ( Mulkey et al., 2006; Smith and Nelson, 1985)

. This is consistent with

reco mmended harvest practices. Harvesting switchgrass once at ES or after a killing frost is th e

recommend ed harvest practice (Parrish and Fike, 2005) unless higher levels of fertilizer

,

especially N, are applied to compensate for greater nutrient removal

, si nce harvesting twice per

year removes greater amounts of N than a single annual harvest (Fike et al., 2006a ; Lemus et al.,

2009; Lemus et al., 2008b ). Harvesting at ES allows the switchgrass plants to prepare for winter

by remobilizing carbohydrates and n utrients to the rhizomes, thus preparing t he plant to survive

the winter, and increasing the N recycling process

. This is probably most important in northern

environments where inadequate levels of carbohydrates may result in poor winter hardiness.

## 29



## H arve sting early at PSC or multiple times per year can result in decreased stand longevity and

lowered biomas s yields (Casler and Boe, 2003; Monti et al., 2008; Sanderson et al., 1999a),

unless N recommendations are made to account for N removal rates associate d with these

practices (Parrish and Fike, 2005).

CONCLUSIONS

As the literature suggests, and this analysis confirms, switchgrass yields are highly

variable, being influenced greatly by multiple factors. In this analysis, we found that the region

,

spring p recipitation (April through June)

, growing season, ecotype

, harvest timing , and annual N

application rate were factors that significantly affected yields. In addition, we found that

biomass yields can also depend on multiple environment and management fac tors. Switchgrass

grown in the LCR and UCR tended to produce greater biomass yields than switchgrass grown in

the NR and SR. The lowland ecotype tended to yield better than the upland ecotype, but this

depended on the region. Spring precipitation had a positive effect on biomass yield and

switchgrass biomass yields had a quadratic response to N application that was similar from site

to

- site, but vary in its intercept and linear components. As large

- scale production of switchgrass

commences, it becomes n ecessary for stakeholders and producers to have a solid understanding

of the effects these factors can have on biomass yields, especially when selecting the most

appropriate ecotype or cultivar for a specific region and then basing N application rates on s ite

conditions, local recommendations, and implemented harvest practices.

Acknowledgements

The authors acknowledge Bhupinder Farmaha for his aide in creating Fig. 2. 1.

## 30



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-

337. In M.E. Heath et al. (ed.) Forages the Science of Grassland Agriculture. Fourth edition.

Iowa State University Press, Ames, IA.

## 37



## Staley, T.E., W.L. Stout, and G.A. Jung. 1991. Nitrogen Use by Tall Fescue a nd Switchgrass on

Acidic Soils of Varying Water Holding Capacity. Agron. J. 83:732

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Stork, J., M. Montross, R. Smith, L. Schwer, W. Chen, M. Reynolds, T. Phillips, T. Coolong,

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http://www.ers.usda.gov/Data/FertilizerUse/ (accessed 15 May 2011; verified 22 June 2011).

Vogel, K.P. 2004. Sw itchgrass. p. 561

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Warm

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Vogel, K.P., and R.A. Masters. 2001. Frequency Grid: A Simple Tool for Measuring Grassland

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Vogel, K.P., and R.B. Mitchell. 2008. Heterosis in Switchgrass: Biomass Yield in Swards. Crop

Sci. 48:2159

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Vogel, K.P., M.R. Schm er, and R.B. Mitchell. 2005. Plant Adaptation Regions: Ecological and

Climatic Classification of Plant Materials. Rangeland Ecol. Mange. 58:315

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Wang, D ., D.S. Lebauer, and M.C. Dietze. 2010. A quantitative review comparing the yield of

switchgrass in monocultures and mixtures in relation to climate and management factors.

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Wright, L., A. Turhollow. 2010. Switchgrass selection as a "model" bioenergy crop: A history of

the process. Biomass Bioenerg. 34:851

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## 38



## Wullschleger, S.D. , E.B. Davis, M.E. Borsuk, C.A. Gunderson, L.R. Lynd. 2010. Biomass

Production in Switchgrass across the United States: Database Description and Determinants

of Yield. Agron. J. 102:1158

- 1168.

## 39



## TABLES

Table 2. 1

. List † 

### of publications on switchgrass cove ring several important and highly

studied agronomic topics.

Cultivar and ecotype

adaptation/evaluation Harvest management ‡ 

### Nitrogen and fertilizer

response

Alexopoulou et al., 2008 Monti et al., 2008 Boehmel et al., 2008

Berdahl., et al., 2005 Adler et al., 2006 Christian et al., 2002

Boe and Casler, 2005 Casler and Boe, 2003 Christian, 1994

Boe and Lee, 2007 Fike et al., 2006a Haque et al., 2009

Boe, 2007 Fike et al., 2006b Heggenstaller et al., 2009

Casler and Boe, 2003 Haque et al., 2009 Lee et al ., 2007

Casler et al., 2004 Heaton et al., 2008 Lee et al., 2009

Casler et al., 2007 Hopkins et al., 1995 Lemus et al., 2008a

Casler, 2005 Lee and Boe, 2005 Lemus et al., 2008b

Cassida et al., 2005 Lee et al., 2007 Ma et al., 2001

Christian et al., 20 02 Lee et al., 2009 Madakadze et al., 1999a

Christian, 1994 Lemus et al., 2008b Mooney et al., 2009

Fike et al., 2006a Lemus et al., 2009 Muir et al., 2001

Fike et al., 2006b Ma et al., 2001 Piscioneri et al., 2001

Fuentes and Taliaferro , 2002 Madakadz e et al., 1999a Sanderson and Reed, 2000

Garten Jr et al., 2010 Madakadze et al., 1999b Sanderson et al., 2001

Hopkins et al., 1995 Reynolds et al., 2000 Shinners et al., 2010

Lee and Boe, 2005 Sanderson et al., 1999a Staley et al., 1991

Lemus et al., 2002 Sanderson et al., 2004 Stout, 1992

Madakadze et al., 1998a Sladden et al., 1991 Thomason et al., 2004

Madakadze et al., 1998b Staley et al., 1991 Vogel and Masters, 1998

Madakadze et al., 1998c Stork et al., 2009 Vogel et al., 2002

Madakadze et al ., 1999a Thomason et al., 2004

Madakadze et al., 1999b Vogel and Masters, 1998

Piscioneri et al., 2001 Vogel et al., 2002

Sanderson et al., 1999b

Sharma et al., 2003

Sladden et al., 1991

Stork et al., 2009

Vogel and Masters, 1998

Vogel and Mitchell, 2008

† This is an incomplete list of publications on these topics.

‡ Harvest management encompasses harvest timing (i.e. peak biomass, after killing frost, etc...)

and multiple harvests per year

.

## 40

Table 2. 2. Names of 41 different swit chgrass cultivars, varieties, experimental lines, and synthetic lines (each referred to as cultivars) used in analysis,

their ecotype, upland (U) or lowland (L), and their latitude or location of origin.

Cultivar Ecotype Latitude/Location of origin Refere nce(s)

Alamo L near George West, TX (Southern Texas) Cassida et al., 2005; Alderson and Sharp, 1995; Sanderson et al., 1999b

Blackwell U 36°N, Northern Oklahoma; Blackwell, OK Hopkins et al., 1995; Casler et al., 2004; Alderson and Sharp, 1995;

Sanderso n et al., 1999b

Caddo U Stillwater, OK Cassida et al., 2005; Sanderson et al., 1999b

Cave

- in

- Rock U 38°28’N, Near Cave

- In

- Rock, IL (Southern Illinois) Berdahl et al., 2005; Casler et al., 2004; Alderson and Sharp, 1995

Cave

- in

- Rock high yield

- DMD C1

( CIR HY DDM C1) U Hopkins et al., 1995

Dacotah U 46°30’N, Near Breien, North Dakota Boe and Casler, 2005; Alderson and Sharp, 1995 ; Berdahl et al., 2005

Dakota U 47°N, North Dakota Madakadze et al., 1998b

EY x FF LDMDC1 U same base population as Trailbl azer Redfearn et al., 1997

EY x FF LDMDC3 U same base population as Trailblazer Redfearn et al., 1997

Ey x FF high yield C3 U Hopkins et al., 1995

Ey x FF low IVDMD C1 U Hopkins et al., 1995

Forestburg U 44°N, Forestburg, South Dakota Madakadze et al ., 1998 b; Casler et al., 2004 ; Alderson and Sharp, 1995;

Christian et al., 2002

GA992 and GA993 L Derived from Kanlow and Alamo, respectively Garten Jr. et al., 2010

Kanlow L 35°N, Central Oklahoma (Wetumka, OK) Hopkins et al., 1995; Casler et al., 2004 ; Ald erson and Sharp, 1995;

Sanderson et al., 1999b

Kanlow N1 L Vogel and Mitchell, 2008

Kansas Native U Sladden, 1991

KYPV 9504 U West Virginia Stork et al., 2009

KYPV 9505 U West Virginia Stork et al., 2009

KYPV 9506 U West Virginia Stork et al., 2009

late synthetic U Experimental breeding line from Nebraska Sanderson et al., 1999b

Late synthetic high yield (Late syn HY) U Fuentes and Taliaferro, 2002

Late syn high yield C3 (Late syn HY C3) U Hopkins et al., 1995

Late syn high yield

- DMD C2

(Late syn HY DMD C2) U Hopkins et al., 1995

## 41

Table 2. 2

. continued

Cultivar Ecotype Latitude/Location of origin Reference(s)

NCSU

- 1 L Sanderson et al., 1999b

NCSU

- 2 L Sanderson et al., 1999b

ND3743 U 49°N, Near Upham, ND Berdahl et al., 2005; Mada kadze et al., 1998b

Nebraska 28 U 42°N, Holt Co, NE Alderson and Sharp, 1995

NJ50/Carthage L North Carolina Madakadze et al., 1998b

NL 94

- 2 , NL 93

- 1 L Synthetic lines from OK and s outhern K S Cassida et al., 2005

NU 94

- 2 U Synthetic line from OK and s ou thern K S Cassida et al., 2005

OK NU

- 2 U 35°N, Oklahoma Agric. Exp. Stn. Berdahl et al., 2005

Pathfinder U Nebraska and Kansas Hopkins et al., 1995; Alderson and Sharp, 1995; Madakadze et al., 1998b

Pathfinder HYDMDC2 U experimental line from Pathfinder Redfearn et al., 1997

PMT

- 279 L Collected in south Texas Sanderson et al., 1999b

PMT

- 785 L Collected in south Texas Sanderson et al., 1999b

Shawnee U Southeastern Illinois, selected from Cave in Rock Boe and Casler, 2005, Berdahl et al., 2005

Shelter U 39°N, Near St. Mary's, West Virginia Madakadze et al., 1998b ; Casler et al., 2004; Alderson and Sharp, 1995;

Sanderson et al., 1999b

SL 93

- 1, SL 93

- 2, SL 93

- 1 L Synthetic lines from central and southern TX

SU 94

- 2 U Synthetic line from central and so uthern T X Cassida et al., 2005

Summer U 40°40’N, south of Nebraska City, NE Gunter et al., 1996; Casler et al., 2004; Alderson and Sharp, 1995; Berdahl

et al., 2005

Sunburst U 43°N to 42°.40’N, Union County, South Dakota Berdahl et al., 2005; Madakadze e t al., 1998b ; Casler et al., 2004

Trailblazer U 43°N to 37°N, Nebraska and Kansas collections Berdahl et al., 2005; Vogel et al., 1991; Casler et al., 2004; Alderson and

Sharp, 1995

WS

- SB U Sterling Barrens State National Area, WI Casler et al., 2007

WS

- IP U Ipswitch Prairie State Natural Area, WI Casler et al., 2007

## 42

Table 2. 3

. Publications from the database included in the analysis, their Country, Experiment number, site

number(s) and location(s), and their Region of classification (North, Upper Ce ntral, Lower Central, and South).

For each region, information is sorted by site.

Authors Country Experiment Site number(s) and l ocations

---------------------------------------------------------------- ------------- ------ North

-------------------- ------- --------------------------- ----------------------------------- Boe, 2007 USA 0 7 0 7a

– South Shore, SD

Boe and Lee, 2007 USA 0 8 0 8a

– Aurora, SD

Berdahl et al., 2005 USA 0 9 0 9a

– Mandan 1, ND, 0 9b

– Mandan 2, ND, 0 9c

– Dickinson, ND

Lee and Boe, 2005 U SA 15 15a

– Dakota Lakes 1 , SD, 15b

– Da kota Lakes 2, SD

Boe and Casler, 2005 USA 19 , 20 19a

– Bristol, SD, 19b

– South Shore, SD

, 20a

– Pierre, SD

Casler et al., 2004 USA 23 23a

– Spooner, WI, 23b

– Arlington, WI

Casler and Boe, 2003 USA 25 25a

– Brook ings, SD, 25b

– Arlington, WI

Madakadze et al., 1998c Canada 37 37a

– Montreal, QC

Madakadze et al., 1999a Canada 43 43a

– Montreal, QC

Madakadze et al., 1998b Canada 45 45a

– Montreal, QC

Madakadze et al., 1999b Canada 49 49a

– Montreal, QC

Schmer et al., 20 10 USA 65 65a

– Munich, ND, 65b

– Streeter, ND, 65c

– Bristol, SD, 65d

– Highmore, SD,

65e

– Huron, SD, 65f

– Ethan, SD, 65g

– Crofton, NE, 65h

– Atkinson, NE.

Casler et al., 2007 USA 68 68a

– Spooner, WI, 68b

– Mandan, ND, Rosemont, MN, Marshfie ld,

Lancaster, & Arlington, WI

Shinneres et al., 2010 USA 74 74a

– Arlington, WI

------------------------------------------------------------------- ------- ---- Upper Central

---------- -------------------- --------------------------------------------------- -- Adler et al., 2006 USA 11 11b

– Ligonier, PA

Casler et al., 2004 USA 23 23c

– Mead, NE, 23d

– Manhattan, KS

Vogel et al., 2002 USA 31 31a

– Ames, IA

Heaton et al., 2008 USA 44 44a

– Shabbona, IL, 44b

– Urbana, IL

Hopkins et al., 1995 USA 46 46a

– M ead, NE, 46b

– Ames, IA, 46c

– West Lafayette, IN

Vogel and Mitchell, 2008 USA 48 48a

– Mead, NE

Staley et al., 1991 USA 58 58a

– northern Appalachian ridge and valley province 1, PA,

58b

– northern Appalachian ridge and valley province 2, PA,

58c

– no rthern Appalachian ridge and valley province 3, PA

Cuomo et al., 1996 USA 59 59a

– Mead, NE

Redfearn et al., 1997 USA 61 61a

– Ames, IA, 61b

– Mead, NE

Lemus et al., 2008 a USA 63 63a

– Lucas County, IA, 63b

– Wayne County, IA

Schmer et al., 20 10 USA 65 65i

– Douglas, NE, 65j

– Lawrence, NE

Casler et al., 2007 USA 68 68c

– Mead, NE, Ames, IA, Columbia, MO, Dekalb, IL

Propheter and Staggenborg, 2010 USA 71 71a

– Troy, KS, 71b

– Manhattan, KS

Heggenstaller et al., 2010 USA 73 73a

– Boone County, IA

-- ------------------------------------------------------------------- ----------- - Lower Central

--- --------------------------------------------- ------------------------------- Adler et al., 2006 USA 11 11a

– Rock springs, PA

Fike et al., 2006b USA 13 13a

– Princeton, KY, 13b

– Raleigh, NC, 13c

– Jackson, TN, 13d

– Knoxville, TN,

13e

– Blacksburg A, VA, 13f

– Blacksburg B, VA, 13g

– Orange, VA,

13h

– Morgantown, WV

Fike et al., 2006a USA 14 14a

– Princeton, KY, 14b

– Raleigh, NC, 14c

– Jackson, TN, 14d

– Knoxville, TN,

14e

– Blacksburg A, VA, 14f

– Blacksburg B, VA, 14g

– Orange, VA,

14h

– Morgantown, WV

Evanylo et al., 2005 USA 21 21a

– Wise County, VA)

Casler et al., 2004 USA 23 23e

– Stillwater, OK

Thomason et al., 2004 USA 24 24a

– Chickasha, OK, 24b

– Perkins, OK

Fuentes and Taliaferro, 2002 USA 41 41a

– Chickasha, OK, 41b

– Haskell, OK

Heaton et al., 2008 USA 44 44c

– Simpson, IL

Mooney et al., 2009 USA 60 60a

– Milan, TN

Lemus et al., 2008 b USA 62 62a

– Blacksburg & Orange, VA

Sanderson et al., 2004 USA 66 66a

– Rock Springs, PA

Casler et al., 2007 USA 68 68d

– Stillwater, OK & Fayetteville, AR

Haque et al., 2009 USA 69 69a

– Stillwater, OK

Garten Jr. et al., 2010 USA 70 70a

– Milan, TN

Stork et al., 2009 USA 72 72a

– Lexington, KY

--- -------------------------------------------- ----------------------------------- --- South

--- -- --- ------------ ------------------------------------------------------------------- Bow et al., 2008 USA 0 4 0 4a

– Stephenville, TX

Cassida et al., 2005 USA 10 10a

– College Station, TX, 10b

– Dallas, TX, 10c

– Stephenville, TX,

10d

– Hope, AK, 10e

– Clinton, LA

Ma, 2001 USA 30 30a

– Shorter, AL, 30b

– Piedmont substation, AL

Muir et al., 2001 USA 32 32a

– Stephenville, TX, 32b

– Beeville, TX

Sanderson et al., 1 999b USA 35 , 36 35a

– Beeville, TX, 35b

– College Station, TX, 5c

– Dallas, TX, 35d

– Stephenville,

TX, 35e

– Temple, TX

, 36a

– Stephenville, TX, 36b

– College Station, TX

Sladden et al., 1991 USA 40 40a

– East Central, AL

Sanderson et al., 1999a USA 42 42a

– Dallas, TX, 42b

– Stephenville, TX

Sanderson et al., 200 1 USA 64 64a

– Stephenville, TX

Kiniry et al., 1999 USA 67 67a

– Temple, TX

## 43

Table 2.4

. Frequency/sample size (n), mean, standard error of mean (SEM), minimum (min), and maximum

(max) bio mass yields of switchgrass cultivars in growing seasons >=3 categorized by region. Values are sorted by

mean from largest to smallest.

cultivar n/freq † 

### mean ‡ 

### SEM § 

### min ‡ 

### max ‡ 

### cultivar n/freq mean SEM min max

--------------- --- north region, growing seaso ns >=3

- ------- -------- - ------- ---- upper central region, growing seasons >=3

------ -- -- NJ50/Carthage 2 13.9 0.9 13.2 14.5 Pathfinder HYDMDC2 3 16.7 6.4 12.8 24.1

Shelter 2 11.5 0.3 11.3 11.8 NL 2 15.6 7.5 10.3 20.9

Shawnee 26 10 4.6 2.9 18.9 CIR HY

- DMD C1 2 14.2 1 13.5 14.9

WS

- SB 2 10 1.2 9.2 10.8 SL 2 13.4 5.8 9.3 17.5

Pathfinder 13 9.8 3 4 14.4 late syn HY C3 2 12.8 2.2 11.2 14.4

Blackwell 5 9.7 3.3 4.6 13.3 EY x FF LDMDC1 2 12.7 0.8 12.2 13.3

SU ¶ 

### 2 9.4 2.9 7.4 11.4 EY x FF LDMDC3 2 11.7 4. 3 8.7 14.8

NU ¶ 

### 2 8.8 2.1 7.3 10.3 SU 2 11.5 5.0 8 15

WS

- IP 2 8.7 1.3 7.7 9.6 Cave

- in

- Rock 38 11.2 5.3 4.2 26

Sunburst 34 8.6 3.7 2.6 17.1 Pathfinder 3 11.2 3.7 7.1 14.2

Cave

- in

- Rock 47 8.5 5 1.4 20.6 Sunburst 3 10.4 3.2 6.9 13.3

Trailblazer 19 8 3 .7 2.9 14.1 NU 2 9.9 4 7.1 12.7

NL ¶ 

### 2 7.6 4.2 4.6 10.6 Blackwell 2 9.9 1.8 8.6 11.2

Forestburg 21 7.1 3.2 2.3 13.5 NJ50/Carthage 16 9.6 3.0 4.4 14.1

ND 3743 2 6.6 0.6 6.1 7 Trailblazer 7 8.4 3.9 3.3 13.4

Summer 4 5.6 0.6 4.8 6.1 Shelter 4 3.2 1.3 2.1 5.1

Dakota # 

### 2 5.4 0.2 5.3 5.6 Pathfinder HYDMDC2 3 16.7 6.4 12.8 24.1

Dacotah 36 5.2 2.6 1.5 10.3 NL 2 15.6 7.5 10.3 20.9

SL ¶ 

### 2 5.1 1.9 3.8 6.5 CIR HY

- DMD C1 2 14.2 1 13.5 14.9

---------- -- lower central region, growing seasons >=3

- ----------- - ------------- -- south region, growing season s >=3

----- -----------

Kanlow 105 15 3.8 7.1 26.4 SL 13 15.7 4.9 9.2 27.8

Alamo 107 15 4.8 4.6 27.4 NL 13 14.6 3.9 9 20.9

PMT

- 279 12 14.2 4.6 7.1 21.1 Alamo 110 11.1 7 1.3 39.5

Cave

- in

- Rock 106 12.5 3.9 5 26 .4 PMT

- 785 17 10.3 4.9 2.4 17.6

Shelter 70 11.9 3.4 5.3 22.5 PMT

- 279 19 9.9 5.8 1.8 18.9

Blackwell 13 11 3.5 6.3 20.3 NCSU

- 1 16 9.6 4.8 1.8 18

KYPV 9504 7 10.9 3.4 7.2 16.4 NCSU

- 2 16 9.6 5.2 1.9 18.7

late syn HY 12 10.9 4.0 5.8 19.2 Kanlow 20 9.4 6.4 1 23.2

Caddo 12 10.6 3.5 5.2 16.9 Summer 2 6.3 6 2.1 10.6

KYPV 9505 7 10.5 3.7 6.6 16.6 Blackwell 3 6.3 2 4.0 8

KYPV 9506 7 10.2 3.3 6.8 15.8 SU 94

- 2 13 5.7 3.4 0 11.3

Summer 6 9.5 3.2 5.2 14.6 NU 13 5.6 3.3 0 10.2

Shawnee 2 7.6 1.2 6.7 8.5 L ate synthetic 2 5.3 2.8 3.3 7.3

Trailblazer 9 7.2 3 4.7 12.9 Caddo 18 5.2 3.1 1.3 11

Cave

- in

- Rock 19 4.4 3 0.6 10.4

Shelter 2 2.8 0 2.8 2.8

† Only cultivars with frequencies or sample size (n) >=2 were included.

‡ Mean, min, and max wer e calculated within region across nitrogen rates (0 to 300 kg N ha

- 1 

### ).

§ SEM of cultivars within region and sample size, n.

¶ NU, NL, SU, and SL represent northern upland, northern lowland, southern upland, and southern lowland values, which are

reported in the literature in most cases as mean values of similar cultivars.

# D akota was likely spelled incorrectly in the literature, and the correct spelling of cultivar name should be Dacotah. Rather

than make the assumption that the name was misspelled, two names, and consequently two cultivars are distinguished.

## 44

Table 2. 5

. ANOVA table of significant management and environmental effects influencing dry biomass yield s of

switchgrass obtained from the literature from across the eastern two thirds of the U.S. and southeastern Canada

. Effect/ Factor Num. df Den. df F

- value P

- value

(Intercept) 1 494 900 <.0001

Linear N rate 1 494 35 <.0001

Quadratic N rate 1 494 4 0.0363

Region 3 93 16 <.0001

Spring precipitation 1 494 15 0.0001

Growing season 2 494 3 0.0466

Ecotype 1 494 105 <.0001

Harvest timing 1 494 3 0.0771

Spring precipitation x E cotype 1 494 7 0.0092

Spring precipitation x Growing season 2 494 7 0.0007

Linear N rate x Ecotype 1 494 3 0.064

Region x H arvest timing 3 494 2 0.0817

## 45

Table 2. 6

. Site random intercept, random linear N rate, and fixed quadratic N rate coefficients for sites that reported switchgrass biomass yields across at

least four N rates. N rate required to achieve maximum biomass (Max N), and e x post economic optimum N rat es based upon three prices of a unit of N

($0.87 kg

- 1 

### N, $1.07 kg

- 1 

### N, and 1.32 kg

- 1 

### N) and three prices for a Mg of dry biomass ($35 Mg

- 1 

### , $50 Mg

- 1 

### , and $65 Mg

- 1 

### ). These calculations do not take

into acco unt the significant effects in T able 2. 5 other than the intercept, linear N rate, and quadratic N rate effects. The coefficients were extracted from the

basic model described in materials and methods before other effects were incorporated.

----------------------------- N rate price/dry biomass price ratio ($ kg - 1 

### N:$ Mg

- 1 

### )

----------------------------

sites intercept linear N

rate

coefficient quadratic N

rate

coefficient Max

N 0.87 / 35

=0.025 0.87/50

=0.017 0.87/65

=0.013 1.07/35

=0.031 1.07/50

=0.021 1.07/65

=0.016 1.32/35

=0.038 1.32/50

=0.026 1.32/ 65

=0.020

31a 8.286677 0.0394

- 0.0000761 259 96 145 171 58 118 151 11 85 125

32a 5.819987 0.062863

- 0.0000761 413 250 299 325 212 273 305 165 240 280

32b 5.371916 0.036925

- 0.0000761 243 79 128 155 42 102 135 0 † 

### 69 109

60a 10.37737 0.036313

- 0.0000761 239 75 124 151 38 98 130 0 † 

### 65 105

64a 3.066783 0.042905

- 0.0000761 282 119 168 194 81 141 174 34 108 149

65a 3.837839 0.043695

- 0.0000761 287 124 173 199 86 147 179 39 114 154

65c 7.430547 0.032598

- 0.0000761 214 51 100 126 13 74 106 0 † 

### 41 81

65f 3.32 399 0.039886

- 0.0000761 262 99 148 174 61 122 154 14 89 129

65g 4.196071 0.041798

- 0.0000761 275 111 160 187 74 134 167 27 101 141

65h 3.621642 0.043413

- 0.0000761 285 122 171 197 84 145 177 37 112 152

65i 5.160255 0.040322

- 0.0000761 265 102 151 177 64 124 157 17 92 132

65j 3.420913 0.04271

- 0.0000761 281 117 166 193 80 140 173 33 107 147

69a 7.878856 0.039197

- 0.0000761 258 94 143 170 57 117 149 10 84 124

73a 8.540619 0.039492

- 0.0000761 260 96 145 172 59 119 151 12 86 126

† Economic optimum N rate s calculated were less than zero.

## 46



## FIGURES

Figure 2. 1. Sites and locations included in the statistical analysis of switchgrass biomass yields grouped by region and overlaid on the USDA plant hardiness

zones for the US A (excluding AK and HI), and two Canadian provinces (Ontario & Quebec). Description of the USDA plant hardiness zones (labeled 1 to 11 on

map) follows that explained by Vogel et al., 2005 and the average annual minimum temperature ranges (°C) are as follows: zone 1, less than

- 45.6, zone 2,

- 45.5

to

- 40.0; zone 3,

- 39.9 to

- 34.5; zone 4,

- 34.5 to

- 28.9; zone 5,

- 28.8 to

- 23.4; zone 6,

- 23.3 to

- 17.8; zone 7,

- 17.7 to

- 12.3; zone 8,

- 12.2 to

- 6.7; zone 9,

- 6.6 to

- 1.2; zone 10,

- 1.1 to 4.4; zone 11, greater than 4.5. Sites from Stout et al ., 1992, Staley et al., 1991, and Lemus et al., 2008a were not included on map, because

no detailed information regarding latitude and longitude of sites were provided in the papers.

## 47

Figure 2. 2

. Boxplots of mean dry biomass switchgrass yield (Mg ha

- 1 

### ) for each growing season, region, and ecotype. From left to right the

plots represent growing seasons 1, 2, and >=3. The L and U in the upper part of each panel represent lowland and upland ecotypes,

respectively, with upland and lowland ecotypes resp ectively in the upper and lower panels. The black dot in each boxplot represents the

median, while open circles represent outlying values.

## 48

Figure 2. 3. Individual model predictions for each site included in the statistical analysis. Open circles rep resent

mean switchgrass dry biomass values plotted against N rate. Solid lines represent fixed effects for individual site

predictions, and dashed lines represent the random effect of individual sites (experimental units). The number in

each panel cor respo nds to the site number in T able 2



## . 3.

## 49

Figure 2. 4

. Switchgrass d ry biomass yield (Mg ha

- 1 

### ) plotted against spring precipitation (April through June)

fitted with simple linear regression lines. Key showing L and U, represent lowland and upland ecotypes

, respectively

.

## 50

Figure 2. 5

. Observed l owland and upland ecotype mean switchgrass dry biomass yield values (Mg ha

- 1 

### ) plotted

against nitrogen rate (kg N ha

- 1 

### ) for each region. Observed mean values are only for growing seasons >=3 are

subsetted f

or PSC harvest (upper figure) and ES harvest (lower figure). Dry bi omass yield predictions are based

on the model in Table 2.5 for growing season s >=3 , harvested at PSC (upper figure) or ES (lower figure), with a

spring precipitation (April

– June) level o f 300 mm. Open circles represent the lowland ecotype while solid

triangles represent the upland ecotype . The darker dashed prediction line represents the lowland ecotype while the

lighter solid prediction line represents the upland ecotype

.

## 51

Figure 2. 6

. M ean switchgrass dry biomass yield values (Mg ha

- 1 

### ) plotted against nitrogen rate (kg N ha

- 1 

### ) for each

region, and 80% confidence ellipses grouped by harvest timing: peak standing crop (PSC) and end of season (ES).

Solid squares and solid lines repre sent PSC, and open triangles and dashed lines represent ES.

## 52 

## CHAPTER 3

Miscanthus x giganteus Productivity: The Effects of Management in Different

Environments

ABSTRACT

Little research has been published in the USA investigating Miscanthus x giganteu s ( M. x

giganteus )

, a C 4 

## perennial grass showing great potential as a high yielding biomass crop.

Understanding the growth and biomass yield potential of M. x giganteus in different

environments in the USA is critical to identifying a suitable growing reg ion. This study

investigated the establishment success, plant growth, and dry biomass yield of M. x giganteus

during its first three seasons at four locations (Urbana, Illinois; Lexington, Kentucky; Mead,

Nebraska; Adelphia, NJ, referred to as IL, KY, NE, and NJ) in the USA. Three nitrogen rates (0,

60, and 120 kg ha

- 1 

## ) were applied at each location each year. Good survival of M. x giganteus

during its first winter was observed at KY, NE, and NJ (79

- 100%), and poor survival at IL

(17%), due to late plant ing and cold winter temperatures that dropped to

- 27°C. Site soil

conditions, and growing season precipitation and temperature had the greatest impact on dry

biomass yield between season two (2009) and season three (2010). This was especially true at

NE, where ideal weather conditions, resulted in significant yield increases (p<.0001), 15.63 Mg

ha

- 1 

## to 27.32 Mg ha

- 1 

## . Biomass yield decreases of 17.02 to 13.21 Mg ha

- 1 

## in KY and 13.46 to

11.37 Mg ha

- 1 

## in NJ were observed between season two and three. These decreases were

primarily related to excessive spring rain and hot dry conditions later in the growing season at

KY, and hot dry weather, and poor soil conditions in NJ. Season three yields were positively

correlated with end

- of

- season plant height ( = 0.91) and tiller density ( = 0.76) . N fertilization

had no significant effect on plant height, tiller density, or dry biomass yield at any of the sites

during seasons two or three.

## 53



## INTRODUCTION

Current worldwide energy instability has resulted in t he investigation of alternative energy

sources . In response, several herbaceous crops are being researched as potential sources of

biomass feedstock that can be utilized as energy sources. One of these crops, Miscanthus x

giganteus

, is a sterile and rhiz omatous perennial C 4 

## photosynthet ic plant (Lewandowski et al

. ,

2000) that has potential for producing high dry biomass yields (Heato n et al., 2004), exhibits

efficient conversion of solar radiation to biomass, efficient use of nitrogen and water, and

posse sses good pest and disease management (Beale and Long, 1995 ; Beale et al., 1999 )

. This

long

- lived perennial grass is a cross between M. sinensis and M

. sacchariflorus (Hodkinson and

Renoize, 2001) as confirmed by Hodkinson et al. ( 2002 ) and Swaminathan et al. (2010). M. x

giganteus was first collected in Japan in 1935 (Hodkinson et al., 2002) and was initially planted

as a landscape ornamental, and later a bioenergy feedstock in Europe. From the time a new crop

of M. x giganteus is planted, 3

- 5 seasons a re necessary for the crop to become fully established

and be considered a mature crop that can achieve ceiling biomass yields (Miguez et al., 2008). A

high

- yielding feedstock, mature stands of M. x giganteus have been close to 40 Mg ha

- 1 

## p eak

biomass yiel ds in some European locations (Miguez et al., 2008). A quantitative review of

mature M. x giganteus stands across Europe reported a mean peak biomass yield of 22 Mg ha

- 1



## ,

averaged across nitrogen rates and precipitation levels (Heaton et al., 2004), while harvestable

yields up to 25 Mg ha

- 1 

## from mature stands of M. x giganteus have been reported in areas

between central Germany and southern Italy (Lewandowski et al., 2000)

. M. x giganteus is

typically harvested during the winter or early spring once signi ficant drying of stems and leaves,

and translocation of nutrients from above ground plant tissue to rhizomes, has occurred

. There is

a tradeoff between harvesting early, at the end of the growing season, or late, during the winter

## 54



## or in the early spring

. End of season or ‘peak biomass’ harvests have higher yields, but higher

moisture levels and higher content of undesirable minerals, particularly nitrogen . W aiting to

harvest until winter or early spring reduces yield s , but the need for fertilization is r educed and

feedstock quality is improved (Heaton et al., 2009; Lewandowski et al., 2000; Beale and Long,

1997; Clifton

- Brown and Lewandowski, 2002) . Waiting to harvest until winter or early spring is

the recommended practice and results in approximately a 33% reduction from peak biomass

yields (Lewandowski et al., 2003), lost primarily from drying tissue and senesced leaves that

have dropped from the stems.

More extensive study of M. x giganteus as a bioenergy feedstock in the USA has taken

place over the past decade, and relatively little information regarding plant growth, biomass yield

and its response to agronomic treatments such as nitrogen, are available. At present, M. x

giganteus biomass yields in the USA have been reported in only four publicatio ns from Illinois

and Kansas ( Heaton et al., 2008; Dohleman and Long, 2009; Propheter et al., 2010 ; Propheter and

Staggenborg, 2010). Propheter et al. (2010) reported mean harvestable biomass yield increases

of 2.7 to 11.8 Mg ha

- 1 

## and 4.0 to 13.7 Mg ha

- 1 

## fr om season 1 to season 2 for Manhattan, Kansas

and Troy, Kansas, respectively. Heaton et al. (2008) and Dohleman and Long ( 2009 ) reported

mean M. x giganteus , biomass yields ranging from 17.9 to 34.6 Mg ha

- 1 

## in Illinois from mature

stands

.

Several current studies are in progress that will significantly expand the number of M. x

giganteus evaluation sites throughout the US A and include much of the Midwest, areas of the

Great Plains, and Atlantic coast regions. Data from these studies will provide valuable

i nformation that can be used to identify the optimal growing region for M. x giganteus

. Such

data will also provide valuable information about establishment success, responses to new and

## 55



## varying environments, growth patterns from season to season , and resp onse to N fertilizer

.

Because there are limited USA sites growing M. x giganteus

, scant information is available to

suggest a suitable growing region for the grass.

It is believed that M. x giganteus should be grown in temperate climates, as a frost perio d

is needed in order to mark the end of the growing season and the beginning of dormancy, which

in turn promotes plant senescence (Pyter et al., 2009)

. M. x giganteus possesses winter hardiness

traits obtained from M

. sinensis (Clifton

- Brown and Lewandows ki, 2000) , however there are still

concern s about the ability of this crop to withstand harsh winter environments ( low and

f luctuating winter temperatures) ( Clifton

- Brown and Lewandowski, 2000 ). In northern Europe,

testing has shown that M. x giganteus rh izomes are severely affected by temperatures <

- 3.4°C

(Clifton

- Brown and Lewandowski, 2000 ). Additionally, late

- spring frosts have proven to

negatively affect emerging and young M. x giganteus tillers (Farrell et al., 2006). The risk of

growing this crop in some colder environments may be confined primarily to the establishment

years, because mature stands of M. x giganteus have survived winters in IL with air temperatures

lower than

- 26°C (

- 15 °F ) ( personal observations ).

Beyond the need to identify the o ptimal M. x giganteus growing regions in the USA,

additional research is needed regarding its response to applied N. The literature shows varied

response to applied N ( Miguez et al., 200 8 ; Lewandowski et al., 2000 ; Heaton et al., 2004 )

. For

example in It aly over 4 years M. x giganteus responded favorably to applied N up to 200 kg N

ha

- 1 

## (179 lb N acre

- 1 

## ) (Ercoli et al., 1999)

. At Rothamsted , England, M. x giganteus grown for 14

years on a silty clay loam did not respond to cumulative applications of 60 a nd 120 kg N ha

- 1



## ( Christian et al., 2008 ) . Across 14 years there was only a 5% difference in biomass yield

between N treatments. In their study Christian et al. (2008) suggested that annual applications of

## 56



## 7 kg P ha − 1 

## and 100 kg K ha − 1 

## were important for soil maintenance. One major difference

between these two studies, aside from site differences, is that the Italian study was harvested at

peak biomass, while the English study was harvested in winter, the recommende d harvest timing.

In general it appears that yield response to applied N will occur on a site

- by

- site basis. It has

also been suspected that some N may be made available from biological N

- fixation (Davis et al.,

2010). Other factors that may impact N re sponse include: soil type and quality (i.e. texture, bulk

density, rooting depth), % soil organic matter which influences the amount of annual N

mineralization, harvest timing, status of other soil nutrients, and the length of time since planting.

As part of the Sun Grant/ U.S. Department of Energy Regional Biomass Feedstock

Partnership, M. x giganteus was planted in replicated trials in four locations through the central

region of the eastern half of the US. The locations ranging from west to east were Mea d, NE;

Urbana, IL; Lexington, KY; and Adelphia, NJ (referred to as NE, IL, KY, and NJ). This

experiment began in 2008 and will be ongoing for five or more years, allowing sufficient time

for the crop to reach maturity at each of the sites. This experimen t involves various investigators

from multiple institutions, and multiple aspects of production and sustainability are being

evaluated. The overall objective for this experiment is to develop and analyze data regarding

sustainability, soil C sequestration , growth, morphology, and biomass yield under different N

rates for assessing the potential expansion of M. x giganteus as a bioenergy feedstock resource.

This specific study focuses predominately on aboveground plant material. The first three

years of pr oduction as establishment years for the crop were considered. The specific objectives

of this study were to: 1) e stablish M. x giganteus at these four locations and assess their

overwintering capability, and 2) collect morphological, plant growth, and bio mass yield data that

## 57



## can b e used to assess the impact of temperature and precipitation, season of growth, and N rate

on the productivity of M. x giganteus at each of these locations.

MATERIALS AND METHODS

Site D escriptions

This field study was conducted at four university field sites: University of Illinois

Urbana

- Champaign, Urbana, IL (40°06’20” N, 88°19’18 W); University of Kentucky,

Lexington, KY (38°07’45” N, 84°30’08 W); University of Nebraska

- Lincoln, Mead, NE

(41°10’07” N, 96°28’10” W); and Rutgers , The State University of New Jersey, Adelphia, NJ

(40°13’31” N, 74°14’54 W). At IL, the soil is classified as a very deep, well

- drained Wyanet silt

loam ( loamy, mixed, active, mesic Typic Argiudolls ). The upper 30 cm of soil at this site is

dominated by a sandy loam soil texture that transitions into a silty clay loam soil texture at the 50

to 100 cm depth (Table 3.1). The water table at this site ranges between 61 and 107 cm in depth.

Organic matter levels are relatively low ranging from 1.9% at the 0

- 10 cm depth down to 1.2% at

the 50

- 100 cm depth (Table 3.1). This is a non

- typical site for the east

- central IL area; typical

soils for this area are usually Drummer (very deep, poorly drained, silty clay loam soils) or

Flanagan (very deep, somewhat poor ly drained, silt loam soils) series. At KY, the soil is

classified as very deep, well drained Maury silt loam ( Fine , mixed, active, mesic Typic

Paleudalfs) with a water table deeper than 200 cm. Percent organic matter levels at this site

range from 4.7% at the 0

- 10 cm depth to 1.8% at the 50

- 100 cm depth. At NE, the soil is

classified as a very deep well drained Tomek silt loam (Fine, smectitic, mesic Pachic

Argiudolls) , however this specific site is dominated by a silty clay loam soil texture (Table 3.1 ).

The water table at this site is greater than 200 cm in depth and percent organic matter levels

range from 5.1% at the 0

- 10 cm depth down to 1.4% at the 50

- 100 cm depth. At NJ

, the soil is a

## 58



## Holmdel sandy loam ( Fine

- loamy, mixed, active, mesic Aquic Ha pludults) with a relatively high

water table ranging between 15 and 91 cm in depth. Percent organic matter levels at this site

range from 2.1% at the 0

- 10 cm depth down to 0.8% at the 50

- 100 cm depth. At this site there is

a restrictive soil layer or rat her a bedrock layer between 50 cm and 80 cm depth, depending on

the plot.

Crop E stablishment

In fall 2007, rhizomes were harvested from a demonstration planting of M. x giganteus at

the University of Illinois Landscape Horticulture Research Center, Urbana, IL . This

demonstration was planted in 1988 using rhizomes obtained from the Chicago Botanic Garden

(Glencoe, IL, USA) . Propagation took place in University of Illinois greenhouses where

rhizomes of approximately 25 g were planted into 9

- cm square pots d uring winter and spring

2008, in artificial soil mixes and grown in the greenhouse. In early

- to

- mid summer 2008, 1200

potted plants were shipped to each location for hardening and transplanting. At each location,

twelve 10 m x 10 m plots comprised of 100 M. x giganteus plants per plot were transplanted at a

density of 1 plant m

- 2 

## , with 5 m alleys between the plots. Throughout this study, the stand

density of 1 plant m

- 2 

## was maintained, since M. x giganteus produces sterile seed and the plants

during the duration of this study are slowly filling in the gaps between plants. Transplanting

dates were 24 July, 20 June, 18 June, and 19 June, respectively in IL, KY, NE, and NJ. Late

planting at the IL site occurred due to the time necessary to propagate additi onal M. x giganteus

plants. The plots at each location were arranged in a randomized complete block design with

four replicates . Three N treatments (0, 60, and 120 kg ha

- 1 

## ) were applied at each location in each

replicate each year beginning with establis hment year (2008). During the planting year,

irrigation and mechanical weed control were provided where necessary at each location to

## 59



## promote establishment of M. x giganteus

. In spring 2009, the number of surviving plants from

those transplanted in 2008 at each location was counted to determine % winter survival . Percent

survival across all N rates at each location was 17, 99, 79, and 100% respectively at IL, KY, NE,

and NJ. Poor survival at Urbana, IL during the first winter required replanting in 2009 to bring

the plots to a fully

- planted status. Percent winter survival was measured again in each plot at

each location in spring 2010.

Soil Sampling and W eather

Three 1

- m deep soil cores from each plot at each location were sampled in 2008 on 17

July in IL, 5 Aug. in KY, 14 Aug. in NE, and 3 Sept. in NJ. Each core sample was split into 5

segments: 0

- 10, 10

- 20, 20

- 30, 30

- 50

, and 50

- 100 cm depths. Table 3.1 summarizes selected

variables from these soil data at each location averaged across plot and treatm ent. W eather data

from stations nearby Urbana, IL ; Lexington, KY ; Mead, NE ; and Adelphia, NJ were obtained

from the Midwestern Regional Climate Center, a cooperative program of the Illinois State Water

Survey and the National Climatic Data Center , or dire ctly from the National Climatic Data

Center

. Data from each location were collected from stations nearby Urbana, IL ; Lexington,

KY ; Mead, NE ; and Adelphia, NJ, respectively with station name and cooperative identification

number Urbana, IL (118740), Lexin gton Bluegrass AP, KY (154746), Mead 6S, NE (255362),

Hightstown 2W, NJ ( 283951). Monthly weather data for 2008, 2009, and 2010 are summarized

in Tables 3.2 and 3.3. Accumulated thermal time (aka growing

- degree days) was calculated with

a base temperatur e of 0°C as has been done in other studies (Miguez et al., 2008; Miguez et al.,

2009; Hastings et al., 2009 ).

## 60



## Plant Measurements

Throughout the second (2009) and third (2010) growing seasons, plant growth and

morphological measurements were collected at KY, NE, and NJ. These data were not collected

in IL until 2010 due to 2009 replanting. Average emergence date was determined in the spring

when approximately the first 10 plants in each plot had emerged. Date of full

- headed flowering

(R3) (Moore et al., 1991) was determined when approximately 50% of the plants in each plot

were fully flowered. In 2009 at KY, N, and NJ, average plant height and leaf number per tiller

were measured throughout the season. Average plant height was determined by measuring t he

height of the upper most fully expanded leaf of the tallest tiller on five randomly selected plants

in each plot each month in 2009 and 2010. In 2009 and 2010, average tiller density (tillers m

- 2 

## )

was determined by counting the number of tillers per pl ant on at least five random plants in each

plot after the end of the growing season. These sampling dates are shown in Table 3.4. In 2010

when the tiller density measurements were collected the average number of phytomers tiller

- 1



## ,

and average tiller dia meter were also determined. This was done by randomly selecting a total of

10 representative tillers from each of five plants. Tiller diameter was determined by measuring

each tiller at the center of the first full internode above the ground level of the tiller. Phytomer

number was determined by recording the number of nodes on each tiller. Average tiller diameter

and phytomer number were determined by first calculating the average within plants and then

across all plants within a plot.

In 2009 and 2010 dry biomass yield estimates were obtained at each location after the

end of the growing season. Harvest dates for the 2009 are shown in Table 3.4. Harvesting at all

locations in 2009 and 2010, with exception of NE, employed the following protocol. A

re presentative plant in each plot was selected , avoiding plants on the border rows of the plot

. A

## 61



## 1 m

- 2 

## quadrat was centered on the middle of the plant and all standing tillers within the meter

squared area were cut at

~ 10 cm above the ground . No ground li tter was included in the sample.

After harvesting the first plant, the 1 m

- 2 

## quadrat was flipped directly to the north and the

adjacent meter

- square area (i.e. single plant) was harvested

. If the adjacent sampling area

happened to fall on edge of the plo t, the quadrat was flipped to the south and that adjacent meter

square area was harvested

. This process was repeated a second time by selecting a second

representative M. x giganteus plant in each plot. This resulted in a total of 4 harvested plants

,

eac h representing a 1 m

- 2 

## area from each M. x giganteus plot

. Fresh weight was determined by

weighing a subsample , and d ry biomass (Mg ha

- 1 

## ) was determined by calculating the percent

moisture of a subsample dried in the oven at 60° C for at least 48 hours

. The NE plots were

harvested using a mechanical forage plot harvester (Carter MFG Co., Inc. Brookston, IN) by

harvesting one row of plants (10 plants) for a total area of 10 m

- 2 

## in each plot. A subsample from

each plot was collected, weighed, and dried to calculate % moisture and determine dry biomass

yield.

Statistical M odeling and A nalysis

Data from IL were not statistically analyzed with the other locations for any of the

analyzed variables, because this location (as of 2010) possessed a mixture of two and three year

old plants, and the stand was non

- uniform. When applicable, mean values from IL were reported

in the results to provide some information regarding this location, and these mean values were

calculated using the MEANS procedure of SAS ( SAS In stitute, 2007)

.

Dry biomass yield was analyzed in the MIXED procedure of SAS (SAS Institute, 2007)

while tiller density, which follow a Poisson distribution (aka count data), was analyzed in the

GLIMMIX procedure of SAS ( SAS Institute, 2007)

. Locations ( KY, NE, and NJ) and years

## 62



## (2009 and 2010) were combined to create six environments: KY

- 2009, KY

- 2010, NE

- 2009, NE

2009, NE

- 2010, NJ

- 2009, and NJ

- 2010. Environments, blocks, and subsamples were considered

random effects while nitrogen rate was declared a f ixed effect. The mixed model was described

as follows

w here dry biomass yield or tiller density ( ) depends on the random subsample of the

nitrogen rate in the block nested in the environment , having an intercept ( , and being

influenced by the random environment ( ), random block ( ), random interaction between

environment and nitrogen rate ( ), and fixed nitrogen rate( ) effects. The model assumes

that

, , and are independent normal random variables with expectations zero and

respective variances σ e 2



## , σ b(e ) 2



## , σ eα 2



## , and that the errors ( ) and subsamples ( ) have

means of zero and common variances. Environments were considered random to account for

different weather and other environmental conditions at each site which could not be controlled.

Significance of random eff ects were calculated using the COVTEST option in the MIXED and

GLIMMIX procedure s of SAS (SAS Institute, 200 7). Best linear unbiased predictions of

random effects (aka means of random effects) and their interactions were calculated using

estimate statemen ts with appropriate degrees of freedom and standard errors. Residuals were

examined for normality and the assumption of common variances by inspection of residual plots.

The non

- linear function used to model the increase in M. x giganteus plant height

thr oughout the growing season was the logistic growth function,

[eq. 1]

## 63



## Here, is M. x giganteus plant height (meter units) measured throughout the growing season

and x is the day of year (DOY). Three parameters describe the shape and spread of the function:

1) asymptote ( asym ) or maximum height achieved by the crop, 2) scale ( scal ) or the elapsed time

between the crop achieving half and three quarters of it maximum height, and 3) inflection poin t

( xmid ) or DOY at which the crop achieves half of its maximum height. A nonlinear mixed

model was used to implement the logistic growth function and investigate the effects of

environment, N rate, and there interaction. This was accomplished by consider ing asym

, scal

,

and xmid for each environment and each N rate as fixed components, and individual plots

(experimental units) as random. The modeling process followed principles in Pinheiro and Bates

(2000) and was implemented with ‘nlme’ package (Pinheiro et al., 2009) of R statistical software

(R Core, v. 2.12.1, 2010). Residuals were checked for patterns by plotting standardized residuals

against their fitted values. Parameter estimates and their 95% confidence intervals were obtained

using the ‘summar y’ and ‘interval’ functions of R (R Core, v. 2.12.1, 2010). Prediction plots and

all other graphics were obtained using the ‘graphics’ and ‘lattice’ packages of R (R Core, v.

2.12.1, 2010).

Pearson correlation coefficients and their respective P

- values we re calculated using the

CORR procedure of SAS ( SAS Institute, 200 7) to evaluate the linear association of dry biomass

yield, end of season plant height, tiller density, phytomers tiller

- 1 

## , and tiller diameter among

environments during the 2010 growing seas on. End of season plant height measurements were

determined by selecting the last set of plant height (between Sept. and Nov.) measurements

collected in each environment. For each variable the mean value for each plot was determined

using the MEANS proce dure of SAS ( SAS Institute, 200 7), resulting in 12 observations, n=12 (4

blocks x 3 N rates) for assessing the effect of environment on these variables. Matrix scatter

## 64



## plots were obtained using the SGSCATTER procedure of SAS ( SAS Institute, 200 7) to visua lly

assess correlations among environments within a growing season.

RESULTS

Winter S urvival

The percent of M. x giganteus plants that survived the first winter was very dependent

upon the site. Only 17% of the M. x giganteus plants survived the first wint er at IL which

required replanting to bring the plots to a fully

- planted status during the 2009 season. The other

three sites had adequate to excellent winter survival. KY, NE, and NJ had 99%, 79%, and 100%,

respectively. Poor winter survival at IL was related to late transplanting in summer 2008 which

did not occur until 24 July. In addition to being planted late, winter temperatures at IL dropped

to

- 27°C in the middle of Jan. 2009. Percent winter survival at the beginning of the 2010 season

was near 100% at each site.

Plant H eight

T he N rate x environment interaction was investigated and found non

- significant for the

asym ( P = 0.1249)

, xmid ( P = 0.5954 ) and scal ( P = 0.9369 ) parameters. The N rate main effect

was also non

- significant for the asym ( P = 0.4392)

, xmid ( P = 0.8832 ) and scal ( P = 0.5247)

parameters, while environment was significant for each parameter ( P < .0001 ). P lant height

increases throughout the growing season showed very similar patterns among different

environments and N rates (F ig. 3.1). The estimated asym attained in the different environments

ranged between 3 and 3.79 m, and there were significant increases in the asym in the KY and NE

environments from 2009 to 2010 (Table 3.6). The parameters xmid and scal were quite differe nt

from environment to environment ranging from 143.5 to 171.5 for xmid and 15.38 to 24.23 for

## 65



## scal

. These differences were environment dependent and appear to be related to the length of the

growing season and weather conditions in each environment.

Bio mass Y ield

The environment x N rate interaction did not contribute a significant amount of variation

and was not significant ( P = 0.2066). The main effect of N rate was not significant ( P = 0.7223)

,

but the environment main effect was significant ( P = 0.0 628). There were significant decreases

in biomass yield at KY ( P = 0.0200 ) and NJ ( P = <.0001 ) from 2009 to 2010 (Table 3.7). These

yield decreases represent 22% and 16% decreases, respectively for KY and NJ. Conversely,

biomass yields at NE increased 7 5% from 2009 to 2010 ( P = <.0001 ) (Table 3.7). Mean biomass

yields at IL in 2009 were 1.1, 3.8, and 4 Mg ha

- 1 

## for 0, 60, and 120 kg N ha

- 1 

## , respectively, while

2010 mean biomass yields at IL were 14.8, 16.1, and 16 Mg ha

- 1 

## for 0, 60, and 120 kg N ha

- 1



## ,

re spectively.

Tiller D ensity

There was no significant tiller

- density response to applied N in any of the environments

and the only significant effects was the environment main effect ( P = 0.0624). There were

significant increases in tiller densities in the KY ( P = <.0001 ) and NE ( P = <.0001 )

environments from 2009 to 2010 (Table 3.7). To put this in perspective, tiller densities in 2010

were approximately 52% and 56% greater than in the 2009 seasons for KY, and NE, respectively

(Table 3.7). Tiller densitie s were relatively constant ( P = 0.7820 ) between the NJ

- 2009 and NJ

2010 environments. Mean tiller densities at IL in 2010 were 37.3, 32.3, and 37.6 tillers m

- 2



## respectively for 0, 60, and 120 kg N ha

- 1



## .

## 66



## Corr elations A mong V ariables

In 2010 there was a s trong positive correlation between end of season plant height and

dry biomass yield ( = 0.91) ( P <.0001). There were also strong positive correlations between

dry biomass and tiller density ( = 0.76) ( P <.0001), and between plant height and tiller density

( = 0.88) ( P <.0001). There were moderate positive correlations between plant height and tiller

diameter ( = 0.59) ( P = 0.0002), and between tiller density and tiller diameter ( = 0.63)

( P < .0001). These relationships are summarized with matrix scatter plots in Fig. 3.2 and suggest

that higher biomass yields were prima rily related to higher tiller densities and taller plant heights.

NE stands out in the consistency of the relationships among these variables, having the highest

biomass yields, highest tiller densities, and tallest plant heights in 2010. NJ tended to ha ve the

lowest amount of each of these three variables, with KY lying in between NE and NJ. Tiller

diameter and phytomers tiller

- 1 

## did not produce any noteworthy relationships to biomass yield.

DISCUSSION

The percent of M. x giganteus plants that survived the first winter was very dependent

upon the site, and was primarily related to late planting and cold winter temperatures. The

European literature reports that r hizomes from newly planted M. x giganteus are severely

affected by soil temperatures less tha n

- 3.4°C (26°F) , providing a lethal dose ( 50% kill) to the

rhizomes exposed to these temperatures (Clifton

- Brown and Lewandowski, 2000) . Generally,

winterkill is primarily an issue during the establishment year. This is considering that mature

stands of M. x giganteus have survived winters in IL with temperatures lower than

- 26°C (

- 15 °F )

( personal observations )

. Clifton

- Brown and Lewandowski ( 2000 ) suggests that larger plants

having rhizomes with low moisture content that initiate dormancy on their own b efore the first

fall frost will tend to overwinter better than M. x giganteus plants that are shorter, have high

## 67



## rhizome moisture content and that do not begin dormancy until the first fall frost occurs. To

promote overwintering capability, M. x giganteus should be planted in mid

- to

- late spring,

providing ample time for the crop to develop and prepare for winter by developing larger

rhizome biomass.

M. x giganteus is impressive in its ability to begin growth early in the growing season and

it is interestin g to note, that approximately half of the crops plant height is achieved by late May

in KY and by mid June in NE (Table 3.6). This implies that this crop may reach 1.5 to 2 m by

early June. This early growth habit, allows the crop to close its canopy rel atively early in the

growing season, allowing it to intercept more photosynthetic active radiation. M. x giganteus

has been shown to intercept about 95% of photosynthetic active radiation between June and the

later end of the growing season, when senescen ce begins (Heaton et al., 2008). M. x giganteus

achieves greater amounts of seasonal canopy

- level photosynthesis than some other C 4



## photosynthetic grasses such as perennial switchgrass ( Panicum virgatum L.) (Dohleman et al.,

2009) and maize ( Zea mays ) (Do hleman and Long, 2009). In addition to being able to develop a

leaf canopy early in the season it also has the ability to remain vegetative late into the season,

when grown in northern environments, resulting in a long canopy duration (Dohleman and Long,

2009). The sites selected for this study, generally allowed the crop to remain green and

vegetative until at least early September (Table 3.5). The ability of this crop to remain

vegetative late in the season provides time for the crop to intercept more light and potentially

produce more biomass. This generally should be true as long the crop is not grown too far north

where longer day lengths in late summer and early fall could potentially keep the crop from

flowering and properly entering dormancy. In environments where winter kill is a concern, there

## 68



## should be ample time between flowering and the first killing frost, allowing carbohydrates to

translocate to storage organs and build up of these carbohydrates for winter survival.

The dry biomass yields at each location in 2009 were slightly higher than other second

season biomass yields that have been achieved in the USA. Second season biomass yields in this

study were 17, 15.6, and 13.5 Mg ha

- 1 

## in KY, NE, and NJ, respectively. These values are higher

than second season mean biomass yields in a study in Kansas, USA that ranged from 11.8 Mg

ha

- 1 

## to 13.7 Mg ha

- 1 

## depending on the site (Propheter and Staggenborg, 2010) . The growing

conditions were adequate for growth in each environment in 2009. This was especially true in

KY which had more growing

- season precipitation (April through September) (863 mm) than

either NE (527 mm) or NJ (829 mm) in 2009 (Table 3.2). These precipitation levels are reflected

in the yields that were attained in 2009, except for NJ where the yields were lowest; this is most

likely due to the shallow, sandy soils at that site (Table 3.1). Tiller densities at each site in 2009

were similar at 38 tillers m

- 2 

## in KY, 44.5tillers m

- 2 

## in NE, and 43.8 tillers m

- 2 

## in NJ. It was

expected that tiller densities and biomass yields would have increased from 2009 to 2010 at each

site (Miguez et al ., 2008), but this was not the case.

Dry biomass yield were surprisingly low at KY in 2010 and this was due to abnormal

weather conditions that result ed in a highly stressed crop. The crop emerged on 2 April (Table

3. 5), and a late frost on 19 April resulted in some frost damage

. The effects of this frost damage

were not quantified, but it is likely that some of the actively growing tillers were eithe r killed or

stunted

. Farrell et al. ( 2006 ) showed that newly emerging shoots exposed to freezing

temperature s can have a negative impact on M. x giganteus biomass yields

. Early season

precipitation was much higher than normal, with 253 mm precipitation i n May, 132 mm more

than the long term average

. The latter end of the growing season was very dry, with

## 69



## precipitation levels of 15 mm in bot h Aug. and Sept

. The normal precipitation levels for these

months are 96 mm and 79 mm

, respectively . Limited preci pitation in Aug. and Sept. was

couple d with hot summer temperatures resulting in 44 days w ith the temperature greater than or

equal to 32 .2 °C between May and Sept. Generally, high summer temperatures should not have a

large negative impact on this C 4 

## crop , but when combined with low precipitation, it appears to

generate highly stressful growing conditions. The combination of conditions present at KY in

2010 caused the M. x giganteus plants to go dormant earlier than normal while the crop was still

in a ve getative growth stage. This resulted in the M. x giganteus crop not flowering in 2010

(Table 3.5).

Similar weather patterns were observed in NJ in 2010. New Jersey weather was very

warm in 2010 accumulating 4601 thermal heating units compared to 3459 hea ting units in 2009.

In 2010, there were 49 days between May and Sept. when the temperature was greater than or

equal to 32.2°C (90°F). High temperatures were combined with less than normal precipitation

(399 mm) between April and September compared to th e average for this period (670 mm).

Growing

- season precipitation has been shown to be extremely important for M. x giganteus to

achieve its biomass yield potential (Heaton et al., 2004). At one mature M. x giganteus site in

England, variables relating av ailable water capacity and soil moisture to biomass yield during the

growing season accounted for 70% of annual biomass yield variation (Richter et al., 2008).

Areas of low precipitation or prolonged periods of drought are not suitable for growing M. x

gi ganteus

, as it lacks drought tolerance or the capacity to survive under low precipitation

environments, and will begin to senesce under water stressed conditions (Clifton

- Brown et al.,

2002) . In NJ in 2010, not all of the plots reached full maturity with only half of the plots

flowering. Those that flowered were the plots fertilized with 0 kg N ha

- 1 

## or 60 kg N ha

- 1 

## . There

## 70



## was limited flowering in the 60 kg ha

- 1 

## plots and none of the 120 kg N ha

- 1 

## plots flowered. The

NJ plots that did flower did so on 30 Oct., and the season’s first fall frost on 1 Nov., limited

flowering of any remaining plants. Similar results have reported that nitrogen

- fertilized plots of

M. x giganteus retain their leaves and potentially remain vegetative longer than unfertilized pl ots

(Himken et al., 1997); however, a difference in the yields of these plots was not observed once

leaf senescence was complete and the plots were harvested in the winter. Lower yields at NJ in

2010 were was due to several reasons including weather condi tions that were both warmer and

drier than normal. Also, the standing residue left behind following the winter harvest from the

2009 season was not mowed until early April 2010, around the time the crop was beginning to

green up. This may have damaged so me emerging shoots and consequently affected the 2010

biomass yields. The shallow and sandy soils at that site may have also contributed to the low

yields. In particular, the bedrock layer that is present between 50 and 80 cm is possibly

impeding M. x gi ganteus root development and restricting the plant’s ability to obtain water at

deep soil depths. In a mature stand of M. x giganteus in Germany, roots have been shown to

reach down to 250 cm in the soil, with almost half of the roots deeper than 90 cm (N eukirchen et

al., 1999).

In 2010, NE growing conditions were favorable for crop growth, and this is reflected in

the high biomass yields which were highly correlated with end of season plant height (Table 3.7,

Fig. 3.2). Similar results relating biomass to plant height have been reported in production in

central Italy (Angelini et al., 2009). Between Apr. and Sept. there was 814 mm of precipitation,

295 mm greater than the normal rainfall for this period at the NE site in 2010. This amount of

rainfall w as greater than any of the other sites in 2010, which received 570, 613, and 399 mm

respectively in IL, KY, and NJ. Additionally, there were 25 days during the growing season at

## 71



## NE when the tempera ture was greater than or equal to 32.2°C (90°F). This com bination of warm

temperatures and adequate precipitation levels spread throughout the growing season promoted

an ideal set of growing conditions. Optimal growing temperatures for M. x giganteus are

between 30 and 35°C (Naidu et al., 2003).

Even though the re was no significant effect of applied N on any of the measured

variables in any of the environments, it does not suggest, however that M. x giganteus does not

require N fertilizer. As the literature suggests, N response varies from site

- to

- site. To ful ly

understand the N needs of M. x giganteus , various aspects of the crop and the site where it is

being grown require consideration. Since M. x giganteus has the ability to recycle its nutrients

from year to year, much of the crop’s nutrient needs are lik ely met on an annual basis without

annual applications of N fertilizer provided, harvesting takes place in the winter after nutrients

from above

- ground plant parts have been recycled via translocation from shoots to rhizomes,

fallen leaves, and leaching fr om stems (Cadoux et al., 2011). In a review, Cadoux et al. (2011)

reported that t hree year old stands of M. x giganteus harvested during the winter remove

approximately 4.9 g N kg

- 1 

## dry matter, and maximum N fertilization rates should be 49, 73.5,

and 98 kg N ha

- 1 

## , respectively, for harvestable dry biomass yields of 10, 15, and 20 Mg ha

- 1



## .

This study also recommended that fertilizer should not be applied during the first two years after

M. x giganteus is planted, unless it is planted in poor soils. This recommendation is supported

by a recent meta

- analysis repor ting that there was little response to N fertilizer during the

establishment years (years 1

- 3) of M. x giganteus but once the crop reached maturity, a response

to N fertilizer was detected (Miguez et al., 2008). A lack of N fertilizer response is doubly

supported when M. x giganteus is planted in locations where soil N is abundant, or where high

levels of soil organic matter can meet the N needs of the crop through N mineralization from soil

## 72



## organi c matter. At this reporting, our study is still in its ‘yield building’ or establishment years

( Clifton

- Brown et al., 2007) , however, a response to N may occur in future years of production at

some locations as the biomass yields more or less stabilize. It can be anticipated that if any site

shows a response to N in the future, it will occur at the IL or NJ sites where soil texture is coarse

and there are relatively low amounts of soil organic matter. With rooting depths reaching down

to 250 cm in mature stands, roots can explore large areas of soil for nutrients (Neukirchen et al.,

1999). An extensive root system tends to limit the amount of N that is leached from the soil

profile. It is also important to consider how much N is being removed from the s ystem at harvest

and how much is being recycled back into the system. Christian et al. (1997), reported that of

the 117 kg N ha

- 1 

## that the crop acquired from the soil during the growing season, about 20%

came from the labeled N source when 60 kg N ha

- 1 

## of labeled N was applied to M. x giganteus

during the second season of growth. The authors suspected that the majority of the actual N

taken up by the crop came from mineralized soil organic matter.

CONCLUSIONS

This study increases our understanding of how different environments impact M. x

giganteus morphology, growth, and biomass yield. Not surprisingly, increases in biomass yield

from season two to season three rely on good growing conditions. Significant biomass yield

decreases from 2009 (season 2) to 2010 (season 3) were related to a late spring frost and

excessively wet spring, followed by a hot and excessively dry late summer in KY, and hot, dry

weather, and poor soil conditions in NJ. Significant increases in dry biomass yields between

season two and season three are highly correlated with taller plants and greater tiller densities,

which are a function of adequate precipitation and warm summer temperatures, as was the case

for NE in 2010. Also of importance, nitrogen fertilization had no signific ant effects on M. x

## 73



## giganteus biomass in season two or three at any site. As the crop matures and becomes fully

productive, this experiment will shed important light on the capacity of M. x giganteus to

provide stable and reliable biomass yields at these locations.

Acknowledgements

The authors acknowledge the Sun Grant/ U.S. Department of Energy Regional Biomass

Feedstock Partnership for funding this study, as well as the Energy Biosciences Institute Energy

Farm at the University of Illinois Urbana

- Champai gn for providing land and labor support for

this study.

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## 77



## TABLES

Table 3. 1. Selected soil variables from each site sampled during summer 2008 at five soil depths (0

- 10, 10

- 20, 23

- 30, 30

- 50,

and 50

- 100 cm).

Total (%)

----- Extractable (mg/kg)

----- -

Location d epth,

cm %

sand %

silt %

clay pH CEC, cmol

c



### kg

– 1 

### % SOM C N P K Ca Mg S BD , g

cm - 3



### Urbana, IL 0

- 10 55 30 16 5.7 10.6 1.9 1.14 0.11 39.1 110 1390 154 15.8 1.52

10

- 20 54 30 15 5.9 10.7 1.9 1.11 0.11 47.1 144 1613 154 17.1 1.69

20

- 30 52 32 16 6.0 10.9 1 .9 1.08 0.11 39.5 130 1686 177 17.4 1.66

30

- 50 36 43 21 6.0 12.0 1.6 0.75 0.08 11.3 75 1722 325 13.4 1.59

50

- 100 11 53 35 6.5 20.9 1.2 0.41 0.05 1.4 126 2659 840 11.4 1.64

Lexington, KY 0

- 10 9 64 27 5.1 18.6 4.7 2.49 0.31 322.2 229 18 60 214 45.5 1.38

10

- 20 9 63 28 5.8 16.2 3.2 1.64 0.21 302.8 117 2122 174 25.2 1.56

20

- 30 8 60 32 5.9 16.0 2.4 1.08 0.15 321.7 91 2145 144 16.3 1.57

30

- 50 10 52 38 6.0 17.8 1.8 0.66 0.10 383.4 92 2405 133 12.5 1.71

50

- 100 14 38 48 5.9 25.6 1.8 0.46 0.09 391.8 106 3453 146 17.3 1.83

Mead, NE 0

- 10 4 59 36 6.1 22.4 5.1 2.99 0.33 108.3 667 3053 610 19.6 1.23

10

- 20 4 58 39 6.7 22.6 3.6 1.98 0.24 74.7 672 3082 634 16.2 1.39

20

- 30 4 55 42 6.7 23.3 3.2 1.70 0.20 60.3 614 3098 715 14.9 1 .40

30

- 50 3 54 43 6.9 23.8 2.5 1.18 0.14 28.4 575 3006 834 13.7 1.45

50

- 100 4 56 40 7.0 23.2 1.4 0.53 0.07 24.9 414 2861 879 11.6 1.53

Adelphia, NJ 0

- 10 52 35 13 5.3 9.5 2.1 1.23 0.11 219.2 119 800 154 19.3 1.34

10

- 20 53 35 12 5.5 9 .6 2.2 1.24 0.12 220.5 95 874 158 18.1 1.52

20

- 30 53 35 13 5.5 9.1 2.0 1.14 0.11 202.3 92 816 155 17.4 1.57

30

- 50 55 30 15 5.5 5.8 1.1 0.48 0.04 43.4 69 627 177 15.4 1.68

50

- 100 70 15 16 5.4 5.0 0.8 0.24 0.03 14.7 70 643 141 27.9

- *



### Abbreviations ar e as follows: cation exchange capacity (CEC), soil organic matter (SOM), and bulk density (BD).

* 

### a restrictive soil feature ranging between 50 and 80 cm precluded the calculation of bulk density at this NJ site.

## 78

Table 3.2

. Total monthly precipita tion (mm) at each location during 2008,

2009, and 2010, and their 30

- yr normal averages.

------------------ IL

----- ------ ------- ----------------- KY

-- ------- --------

Month 2008 2009 2010 30

- year

normal 2008 2009 2010 30

- year

normal

Jan 59 17 31 48 11 2 110 76 85

Feb 151 43 41 51 146 65 41 83

Mar 72 67 74 82 160 61 29 112

Apr 76 176 53 93 150 121 59 93

May 154 145 87 122 112 153 253 121

Jun 163 112 212 107 91 132 117 116

Jul 200 160 95 119 87 192 154 122

Aug 20 143 42 111 55 115 15 96

Sep 207 20 81 82 36 150 15 79

Oct 75 223 28 71 39 147 31 69

Nov 33 100 98 88 64 24 113 87

Dec 124 96 65 70 153 102 63 102

Annual 1336 1302 906 1043 1205 1372 966 1166

------------------ NE

---- -------- ----- ------------------ NJ

-- ------ ---------

M onth 2008 2009 2010 30

- year

normal 2008 2009 2010 30

- year

normal

Jan 6 7 23 12 69 71 67 95

Feb 10 12 17 13 110 15 110 70

Mar 17 8 41 47 83 47 229 100

Apr 118 41 102 70 62 99 67 100

May 151 30 68 106 116 112 82 112

Jun 251 165 249 101 107 187 7 8 100

Jul 95 67 183 84 89 159 75 126

Aug 26 185 64 85 39 172 20 123

Sep 110 39 148 73 178 100 77 109

Oct 129 110 6 55 90 119 82 87

Nov 45 0 0 40 84 64 50 93

Dec 30 67 0 18 151 166 74 95

Annual 987 732 902 704 1177 1311 1012 1211

## 79

Table 3.3

. Average minimum and maximum air temperature (°C) at each location (IL, KY, NE, and NJ) during 2008, 2009, and 2010, and their 30

- yr

normal averages.

--------------------------- ----- -------- IL

-- ----- -------------------------------- ---------- - ----- ----- - ---------------- KY

------ ------ -------------------------

------- average minimum (°C)

------- ------- average maximum (°C)

------- ------- average minimum (°C)

------- ------- average maximum (°C)

-------

Month 2008 2009 2010 30

- year

normal 2008 2009 2010 30

- yea r

normal 2008 2009 2010 30

- year

normal 2008 2009 2010 30

- year

normal

Jan

- 8.6

- 12.3

- 10.1

- 8.8 1.8

- 2.4

- 3.4 0.0

- 4.4

- 6.5

- 5.8

- 4.4 4.7 2.6 1.7 4.4

Feb

- 7.9

- 5.7

- 8.0

- 6.1 0.7 4.7

- 0.4 3.1

- 2.3

- 2.1

- 5.6

- 2.4 6.2 8.5 2.1 7.3

Mar

- 1.1 0.1 1.1

- 0.9 8.5 1 2.6 11.9 9.7 1.2 3.4 2.9 2.2 12.3 14.3 13.4 12.9

Apr 4.7 5.0 7.6 4.4 16.6 16.3 21.3 16.8 6.8 7.4 8.5 6.7 18.2 18.7 22.0 18.4

May 8.9 11.4 12.5 10.6 20.4 23.3 23.7 23.1 10.5 13.1 14.4 12.0 22.0 23.1 24.3 23.3

Jun 17.2 18.1 18.6 15.8 28.6 29.2 29.0 28.1 1 7.4 17.9 19.5 16.8 29.0 28.3 30.1 27.9

Jul 17.6 16.0 19.4 18.0 28.8 26.2 30.5 29.6 18.2 17.6 20.3 19.1 30.2 26.8 30.8 29.9

Aug 16.5 15.8 18.8 16.9 28.0 26.9 31.3 28.4 17.3 17.9 19.4 18.3 30.0 27.9 31.8 29.2

Sep 14.1 13.9 12.9 12.4 25.3 24.7 26.3 25.3 15 .5 15.6 14.2 14.4 28.4 24.9 28.8 25.6

Oct 6.1 5.3 6.2 6.1 19.2 14.4 20.8 18.4 7.4 6.9 7.4 8.0 20.6 16.6 22.2 19.4

Nov

- 0.6 2.7

- 0.7 0.3 8.9 13.1 12.1 9.7 0.9 3.7 2.1 2.9 11.2 14.1 14.4 12.5

Dec

- 7.8

- 6.1

- 8.8

- 5.6 1.3 1.9

- 1.4 2.7

- 3.3

- 2.4

- 5.8

- 2.0 7. 6 5.8 0.8 6.8

--------- ------------------------------- NE

----------------------- ----- ------------ ------------------------- -------------- NJ

----------- ----- ----------------------

------- average minimum (°C)

------- ------- average maximum (°C)

------- - ------ average minimum (°C)

------- ------- average maximum (°C)

-------

Month 2008 2009 2010 30

- year

normal 2008 2009 2010 30

- year

normal 2008 2009 2010 30

- year

normal 2008 2009 2010 30

- year

normal

Jan

- 12.9

- 12.4

- 13.4

- 12.4

- 1.6 0.4

- 5.6

- 0.7

- 3.9

- 7.6

- 4 .9

- 5.8 6.4 1.3 4.1 3.7

Feb

- 11.6

- 8.4

- 11.7

- 9.1

- 0.4 6.5

- 2.7 2.8

- 3.4

- 4.8

- 5.0

- 4.8 7.4 7.2 3.4 5.1

Mar

- 5.0

- 4.2

- 1.8

- 3.0 9.6 10.2 8.8 9.4

- 0.2

- 1.1 2.4

- 0.4 11.2 10.4 13.4 10.1

Apr 0.7 1.1 5.2 3.4 13.4 16.1 20.3 16.7 5.7 5.4 6.3 4.1 18.1 17.4 20. 1 16.1

May 7.9 9.1 8.9 9.8 21.5 23.7 21.4 22.8 8.6 10.6 10.8 9.4 20.8 22.1 24.9 22.0

Jun 14.9 15.0 15.9 15.3 27.8 27.3 28.4 28.6 16.1 14.7 17.1 14.4 29.4 24.9 30.0 26.8

Jul 17.3 14.8 18.4 17.9 29.9 27.2 29.6 30.8 18.4 15.7 19.8 17.3 31.0 28.8 32.3 29.4

Aug 15.2 14.3 17.4 16.4 29.4 27.5 31.1 29.3 14.4 17.6 18.4 16.4 28.9 29.5 29.9 28.4

Sep 10.4 10.3 11.8 11.1 24.6 24.1 25.5 25.3 13.4 11.7 14.1 12.2 25.6 23.7 27.8 24.6

Oct 4.2 1.8 3.8 4.1 18.2 12.4 21.7 18.5 3.7 5.4 7.4 5.7 18.6 17.0 19.5 18.4

Nov

- 2.2

- 0.7

. - 2.9 10.1 13.3

. 8.6 1.3 4.8 0.9 1.4 10.9 14.4 13.3 12.3

Dec

- 12.3

- 12.5

. - 9.3

- 0.3

- 1.9

. 1.3

- 3.0

- 3.1

- 5.4

- 2.9 7.8 6.3 4.1 6.4

## 80

Table 3. 4

. Sampling dates from the 2009 and 2010 growing seasons for plant height, tiller density, and biomass yield

harvest at each location (IL, KY, NE, and NJ)

.

----------------------------------------------------------- 2009 growing season

--------------------------------------------------------

location plant height

---------------------------------- measureme nts

---------------------------------- tiller density

measurements biomass yield

harvest

IL Jan. 2010 †



### KY 24 Apr., 18 May, 19 June, 27 July, 4 Sept. 4 Sept. 16 Mar. 2010 †



### NE 10 May, 10 June, 15 July, 17 Aug., 15 Sept. 15 Sept. 1 April 2010 †



### NJ 15 May, 17 June, 22 July, 20 Aug.,18 Sept., 23 Oct. 23 Oct. 16 Dec. 2009

--------------------------------------------------------- 2010 growing season

----------------------------------------------------------

location plant height

----------------------------- ----- measurements

---------------------------------- tiller density

measurements biomass yield

harvest

IL 29 Nov. 2010 29 Nov. 2010

KY 9 Apr., 10 May, 11 June, 17 July, 9 Aug., 14 Sept., 12 Oct. 11 Nov 11 Nov. 2010

NE 28 Apr., 26 May, 28 June, 28 July, 24 Aug., 27 Sept., 26 Oct., 22 Nov. 2 Dec 2 Dec 2010

NJ 17 May, 17 June, 15 July, 18 Aug., 24 Sept., 13 Oct., 2 Nov. 9 Dec 9 Dec 2010

† 

### Harvesting for the 2009 growing season in IL, KY, and NJ actually occurred early in 2010 before the start of the 2010

g rowing season.

## 81

Table 3. 5

. Season length, accumulated thermal time, average emergence date, and flowering date(s) for each

growing season and location (KY, NE, and NJ)

.

location season length † 

### accumulated

thermal time ‡ 

### emergence date § 

### flowering

date(s ) ¶



### ----------------------------------------------------------- 2009 growing season

------------------------------------------------------

KY 193 days (8 Apr.

– 18 Oct.) 3783 31 Mar. 18 Sept.

NE 172 days (15 Apr.

– 4 Oct.) 3196 26 Apr.

– 1 May 23 Sept.

N J 185 days (17 Apr.

– 19 Oct.) 3459 27 Apr. ~25

- 30 Sept.

----------------------------------------------- ------------ 2010 growing season

------------------------------------------------------

IL 196 days (10 Apr.

– 23 Oct.) 3911 10 Apr. 1 Oct.

KY 217 days (27 Mar.

– 30 Oct.) 4531 2 Apr. did not flower

NE 167 days (19 Apr.

– 3 Oct.) 3402 2 week Apr. 27 Sept.

NJ 218 days (28 Mar.

– 1 Nov.) 4601 11 Apr. 30 Oct. #

† 

### season lengths were calculated as the number of days between the last frost in the spring to the first frost in the fall.

One exception is in KY in 2010, where a late frost on 19 Apr. was not used as the beginning of the growing season

since there had already been two

- to

- three weeks of above

- freezing weather since the previous frost on 27 March. In

this case, 27 Mar. was marked as the last frost in the spring.

‡ 

### accumulated thermal time was calculated with a base temperature of 0°C, by determining the average of the

minimum (when greater than 0°C) and maximum daily (no limit) temperatures, and su mming these values across

time

. In calculating accumulated thermal time for individual days, if the average temperature for that day did not

exceed 0°C, no TT was accumulated

§ 

### emergence date was determined in the spring when approximately the first 10 pla nts in each plot had emerged.

¶ 

### date of full

- headed flowering was determined when approximately 50% of the plants in each plot were fully

flowered.

# 

### only plots that flowered at Adelphia, NJ in 2010 were those plots applied with 0 kg N ha

- 1 

### and some that w ere

applied with 60 kg N ha

- 1. 

### Those that did flower, flowered on 30 Oct. First fall frost was 1 Nov.

## 82

Table 3. 6. 95% confidence intervals (upper and lower) of the parameters estimates of the logistic growth function

for each environment (KY

- 2009, KY

- 2010, NE

- 2009, NE

- 2010, NJ

- 2009, and NJ

- 2010) fit for M. x giganteus plant

height

. Parameter environment lower estimate upper

asym KY

- 2009 2.99 3.08 3.17

KY

- 2010 3.19 3.39 3.59

NE

- 2009 2.62 2.85 3.07

NE

- 2010 3.59 3.79 3.99

NJ

- 2009 3.17 3.37 3.57

NJ

- 2010 2.80 3.00 3.20

xmid KY

- 2009 149.1 151.5 153.8

KY

- 2010 138.3 143.5 148.7

NE

- 2009 165.4 171.5 177.6

NE

- 2010 156.7 161.7 166.8

NJ

- 2009 155.1 160.2 165.4

NJ

- 2010 141.8 146.8 151.9

scal KY

- 2009 19.71 21.75 23.79

KY

- 2010 16.42 20.86 25.3 0

NE

- 2009 19.08 24.23 29.37

NE

- 2010 17.53 21.84 26.15

NJ

- 2009 15.31 19.82 24.33

NJ

- 2010 10.96 15.38 19.81

asym = maximum height (m), xmid = day of year at which crop achieves half of its maximum height, scal = time (in

terms of days) between half and three quarters maximum height.

## 83

Table 3. 7

. M. x giganteus d ry biomass yield (Mg ha

- 1 

### ) and tiller density (number tillers

m - 2 

### ) estimated for each environment at each of three N rates ( kg N ha

- 1 

### ) and their means

averaged across N rates.

----- N r ate, kg N ha - 1



### ------

Environment 0 60 120 mean † 

### p

- value ‡



### --------------- dry biomass, Mg ha

- 1



### ----------------

KY

- 2009 16.7 § 

### 17.5 16.9 17 0.0200 KY

- 2010 12.8 13.2 13.6 13.2

NE

- 2009 15.7 15.9 15.2 15.6 <.0001 NE

- 2010 27 27.8 27.4 27.3

NJ

- 20 09 12.2 14.3 13.8 13.5 0.1000 NJ

- 2010 11.6 11.7 10.7 11.4

----------------- - number tillers m

- 2



### ------------------

KY

- 2009 38.8 37.4 38.5 38.2 <.0001 KY

- 2010 58.8 56.8 58.4 58

NE

- 2009 45.2 43.6 44.8 44.6 <.0001 NE

- 2010 70.4 67.9 69.8 69.3

N J

- 2009 44.7 43.2 44.3 44.1 0.78 2 0 NJ

- 2010 44.0 42.4 43.6 43.3

† 

### mean values are averaged across N rate.

‡ 

### P values for contrast statements comparing mean environment values within a location

(KY

- 2009 vs. KY

- 2010, NE

- 2009 vs. NE

- 2010, and NJ

- 2009 vs. N J

- 2010).

§ 

### no contrasts among N rates were made because the environment x N rate and N rate

effects were non

- significant for both dry biomass and tiller density.

## 84



## FIGURES

Figure 3.1

. M. x giganteus plant height (m) plotted against day of year (DOY) and fit with logistic growth functions

for each N rate (0, 60, 120 kg N ha

- 1 

### ) in each of six environments ( KY

- 2009, KY

- 2010, NE

- 2009, NE

- 2009, NE

2010, NJ

- 2009, and NJ

- 2010).

## 85

Figure 3.2

. Matrix scatter plots showing relationships between M. x giga nteus d ry biomass yield (Mg ha

- 1 

### ) end of

season plant height (m), tiller density (number tillers m

- 2 

### ), average number of phytomers tiller

- 1 

### , and average tiller

diameter (cm) grouped by different 2010 environments: KY

- 2010, NE

- 2010, and NJ

- 2010

.

## 86 

## CHAPTER 4

Management of F orage and E nergy Sorghum for Biomass P roduction in Illinois

ABSTRACT

No research has been published in IL evaluating forage and energy sorghum as biomass

feedstock crops. This study was conducted to evaluate the growth and biomass poten tial of these

sorghum types in different IL environment under different nitrogen (N) rates. Two forage

sorghum and two energy sorghum hybrids each were evaluated in 2009 in one environment

(Urbana 2009) under four N rates (0, 50, 100, and 150 kg N ha

- 1 

## ), and in 2010 in three

environments (Urbana 2010, Dixon Springs 2010, and Perry 2010) under five N rates (0, 56, 112,

168, and 224 kg N ha

- 1 

## ). Measurements of biomass yield, plant height, leaf development, and

leaf area index (LAI) were collected to evaluat e the effects of environment, N rate, sorghum

type, and sorghum hybrid. Environment had a major impact on plant growth and total biomass

yields. Forage sorghum harvested twice annually (summer and fall) yielded less than energy

sorghum harvested once in the fall in both 2009 ( P < 0.0114) and 2010 ( P < 0.0734). Total

energy and forage sorghum biomass yields averaged across all 2010 environments were 30.1 Mg

DM ha

- 1 

## and 19.2 Mg DM ha

- 1 

## , respectively. Biomass yield, LAI, and plant height response to N

rate were observed up to 150 kg N ha

- 1 

## in 2009 and up to at least 168 kg N ha

- 1 

## in 2010. Energy

sorghum reached plant heights of 3.7 m, 4.1 m, and 3.8 m in the Dixon Springs 2010, Perry

2010, and Urbana 2010 environments, respectively. Leaf development was l inearly related to

GDD and in the warmest environments the average number of fully expanded leaves reached 28

on the energy sorghum hybrids. Photoperiod

- sensitive energy and forage sorghum hybrids

possess great potential in IL as biomass crops because of their ability to remain vegetative until

late Sept., and therefore produce large amounts of biomass.

## 87



## INTRODUCTION

The need to produce large quantity of bioenergy feedstock has become an increasingly

important priority in the US as energy prices continue to increase and foreign oil and gas become

more volatile (Perlack et al., 2005; Asif and Muneer, 2007; U.S. Chamber of Commerce, 2010).

Continued research efforts and the scaling up of a plant and biomass based energy industry is

necessary to meet the US ’s vision of replacing 30% of current petroleum consumption by 2030

with biofuels (Perlack et al., 2005). These efforts will also help to meet the government mandate

of producing 136 billion liters of renewable fuels by 2022, of which 60 billion liters mu st come

from cellulosic sources (Energy Independence and Security Act of 2007). Forage ( Sorghum

bicolor L.) and biomass (referred to as energy sorghum) sorghums are sorghum types that can

help meet this need because these grasses produce large amounts of structural carbohydrates

(cellulosic feedstock) in their biomass (McBee and Miller, 1990; Rooney et al., 2007; Hartley et

al., 2009). Sorghum is a multipurpose C 4 

## photosynthetic, annual grass that has been grown as a

grain crop for food and feed and as a forage crop. More recently, forage and energy sorghum

hybrids have been developed and identified as promising biomass crops. These sorghum types

possess potential because of potentially high yields, efficient water

- use efficiency, drought

tolerance, seed reproduction, wide adaptability, and potential for genetic improvement (Rooney

et al., 2007; Miller and McBee, 1993).

Forage sorghum includes sorghum x sudangrass and brown midrib hybrids that have been

used primarily in the livestock industry (McCollum et al., 2005; Beyaert and Roy, 2005 ).

Recent research and hybrid improvements, however, suggest that brown midrib forage sorghum

and certain photoperiod

- sensitive forage sorghum hybrids possess great biomass yield potential

(Rooney et al., 2007; Venuto a nd Kindiger, 2008; Hallam et al., 2001). Because brown midrib

## 88



## hybrids have relatively low levels of lignin, the cellulosic ethanol conversion process may be

more efficient than with other sorghum hybrids containing higher levels of lignin (Propheter et

al ., 2010). Photoperiod

- sensitive forage sorghum hybrids (as well as energy sorghums) will

continue vegetative growth until day lengths are 12 hrs 20 min (Rooney and Aydin, 1999;

McCollum et al., 2005). This ability to remain vegetative late into the gro wing season is also an

advantage in dryland or rainfed agriculture because sorghums’ tolerance to drought is greater

while growing vegetatively (Rooney et al., 2007) than reproductively when significant stress can

occur (Prasad et al., 2008). Forage sorgh um hybrids also have the ability to ‘rattoon’ or regrow

after being harvested and can produce multiple cuttings in one season ( Beyaert and Roy, 2005 ;

Ketterings et al., 2007 ). Photoperiod

- sensitive forage sorghum hybrids have reached mean dry

biomass yiel ds of 24.0 Mg dry matter (DM) ha

- 1 

## with only one cutting in Bushland, TX

(McCollum et al., 2005), and 31.9 Mg DM ha

- 1 

## in College Station, TX (Miller and McBee,

1993). In a study in El Reno, OK a double cutting (Aug. and Nov.) of several forage sorghum

hyb rids yielded an average of 25.5 Mg DM ha

- 1 

## dry biomass whereas a single cutting of the same

hybrids in Sept. yielded 27.0 Mg DM ha

- 1 

## (Venuto and Kindiger, 2008). In this study the

highest yield was achieved from a single Sept. harvest of a photoperiod

- se nsitive forage sorghum

hybrid that produced 40.3 Mg DM ha

- 1



## . However, multi

- cut sorghum biomass total yields are

usually greater than single cut sorghum biomass yields in tropical and subtropical regions

(Rooney et al., 2007).

Energy sorghum hybrids have been developed from photoperiod

- insensitive sorghum lines,

are photoperiod

- sensitive and similar to forage sorghum hybrids, but have been designed to be

harvested once per year and possess even greater biomass yield potential than forage sorghum

hybrids ( Rooney and Aydin, 1999; Rooney et al., 2007; Sorghum Management Guide, 2010).

## 89



## These energy sorghum hybrids contain large amounts of structural carbohydrates such as

cellulose and hemicellulose in the leaves and stalks that can be converted to energy via

c ombustion or into ethanol using cellulosic ethanol conversion methods (McBee and Miller,

1990; Hartley et al., 2009). Energy sorghum hybrids are photoperiod

- sensitive, remain

vegetative late into the growing season, have been shown to grow for more than 2 00 days in TX,

and may grow 6 m tall(Sorghum Management Guide, 2010), while forage sorghum hybrids may

reach heights greater than 4.3 m (Marsalis, 2011). An advantage of growing these photoperiod

sensitive forage and energy sorghum hybrids in more norther n environments such as the upper

Midwest, allows vegetative growth to continue until late in the growing season. Panicle and

flower initiation triggers the end of vegetative biomass accumulation. As long as vegetative

growth continues, biomass will conti nue to accumulate, which is ideal for a bioenergy crop that

will be used as dry biomass feedstock (Katrin et al., 2009). No literature coming from IL has

been published on forage and energy sorghum produced for biomass feedstock purposes.

However, it is expected that when photoperiod

- sensitive forage and energy sorghum hybrids are

grown in IL, vegetative biomass accumulation will continue until mid or late Sept. because

before this time period day lengths are longer than 12 hrs 20 min and the average date of the first

fall frost ranges from 21 Oct. in the southwestern region to 7 Oct. in the northern region (IL State

Water Survey, 2011, http://www.isws.illinois.edu/atmos/statecli/Frost/first_fall_frost.htm ).

There have been studies that have investigated the N response of sorghum when grown for

hay and silage, but information is scant regarding the response of forage and energy sorghum to

N fertilizer when grown as a biomass feedstock crop. Studies evaluating N rates for sorghum

have produced variable re sults, and as expected, are due in great part to site

- to

- site variation and

different management practices that have been implemented. A management guide for forage

## 90



## sorghum suggests that in irrigated environments with high yield potential, N rates as high as 269

kg N ha

- 1 

## may be needed, but under dryland conditions little to no N fertilizer may be required

(Marsalis, 2011). In ON, Canada sorghum x sudangrass cut three times in July, Aug., and Sept.

for hay reached maximum yields of 5.95 Mg DM ha

- 1 

## with an N application rate of 125 kg N ha

1 

## , although economical optimum N rates were calculated at between 83 kg N ha

- 1 

## and 107 kg N

ha

- 1 

## depending on the prices of N fertilizer and hay ( Beyaert and Roy, 2005). Sweet sorghum

and forage sorghum reached near maxi mum yields at 140 kg N ha

- 1 

## yr - 1 

## when harvested once in

Sept. and between 1988 and 1992 where forage sorghum mean biomass yields ranged from 14.6

to 16.7 Mg DM ha

- 1 

## at Ames, IA, and 12.6 to 21.8 Mg DM ha

- 1 

## at Chariton, IA (Hallam et al.,

2001). In a study in NY, economic optimum N rates based upon a quadratic plus plateau model

ranged between 137 and 192 kg ha

- 1 

## per cutting in four out of six site

- years for brown midrib

sorghum x sudangrass cut two times per year in July or Aug. and then again in Sept . ( Ke tterings

et al., 2007). The other two sites had lower economical optimum N rates; 120 kg N ha

- 1 

## where a

manure application in addition to the synthetic N fertilizer treatments had been applied, and < 56

kg N ha

- 1 

## when following a spring plow down of a lon g term grass and legume stand. In a KS

study that evaluated several annual and perennial crops for biofuel feedstock production,

photoperiod

- sensitive sorghum reached dry biomass yield of 26.8 Mg DM ha

- 1 

## when fertilized

with 180 kg N ha

- 1 

## , while three oth er forage sorghum hybrids reached mean biomass yields

ranging from 14.8 Mg DM ha

- 1 

## to 20.7 Mg DM ha

- 1 

## also with 180 kg N ha

- 1 

## (Propheter et al.,

2010). Under limited irrigation in Lubbock, TX the optimum N rate for ethanol production from

sweet sorghum an d photoperiod

- sensitive sorghum was 108 kg N ha

- 1 

## but the economical

optimum N rate ranged between 59 and 101 kg N ha

- 1 

## (Tamang et al., 2011).

## 91



## The overall objective of this study was to evaluate forage and energy sorghum grown for

biomass feedstock produc tion in IL

. To meet this objective, two forage sorghum hybrids, and

two energy sorghum hybrids were grown in four IL environments under different nitrogen rates.

Measurements of biomass yield, plant height, leaf development, and leaf area index (LAI) wer e

collected and the effects of environment, N rate, sorghum type, and sorghum hybrids were

evaluated.

MATERIALS AND METHODS

This field study was conducted in Urbana, IL (40°04’ N, 88°12’W) in 2009 and 2010 and

in Dixon Springs, IL (37°27’ N, 88°43’ W), an d Perry, IL (39°48’ N, 90°49’W) in 2010,

resulting in four environments in which the effects of N on the growth and yield of sorghum

grown for biomass were evaluated. These sites are referred to as Urbana 2009, Urbana 2010,

Dixon Springs 2010, and Perry 2 010. The soils were a Flanagan series silt loam (Fine, smectitic,

mesic Aquic Argiudolls) at Urbana, a Grantsburg silt loam ( Fine

- silty, mixed, active, mesic

Oxyaquic Fragiudalfs) at Dixon Springs, and a Menfro silt loam (Fine

- silty, mixed, superactive,

m esic Typic Hapludalfs) at Perry. The experimen t was arranged as a split

- split

- plot arrangement

in a randomized complete block design with four replications with sorghum type as whole plots,

sorghum hybrids as sub plots, and N fertilizer treatments as sub

- sub plots. Two forage sorghum

hybrids and two energy sorghum hybrids were planted each spring using a small plot drill (Great

Plains No

- Till Drill (606NT), Salina, KS). Planting dates ranged between 13 May and 9 June

depending on the environment (Table 4.3). The energy hybrids, TAMUXH08001 and TX09007,

both photoperiod

- sensitive, were planted at 185,250 seeds ha

- 1 

## spaced at 0.76 m. The forage

hybrids, Graze All (a photoperiod

- insensitive hybrid) and Graze. n. Bale (and photoperiod

sensitive hybrid) wer e planted at 247,000 seeds ha

- 1 

## spaced at 0.19 m. All hybrids were obtained

## 92



## from Dr. William Rooney’s breeding program at Texas A&M University. Prior to planting, the

seeds had been treated with a herbicide safener, Concep II (Syngenta, Macquarie, NSW). In

2009, four N fertilizer treatments were applied at 0, 50, 100, and 150 kg N ha

- 1 

## , while in 2010,

five N fertilizer treatments were applied at 0, 56, 112, 168, and 224 kg N ha

- 1 

## . The nitrogen

fertilizer source, urea, was surface applied in each environ ment on the same day as planting

(Table 4. 3). The sub

- sub plots were 5 m wide x 5 m long in 2009 and 5 m wide x 6.7 m long in

2010.

Preemergence herbicide, 2.2 kg a.i. ha

- 1 

## of atrazine ( 6

- chloro

- N

- ethyl

- N'

- isopropyl

- 1,3,5

-

triazine

- 2,4

- diamine ) at Urbana and Perry, and 2.2 kg a.i. ha

- 1 

## of atrazine and 1.4 kg a.i. ha

- 1 

## s

Metolachor (Acetamide, 2

- chloro

- N

- (2

- ethyl

- 6

- methylphenyl)

- N

- (2

- methoxy

- 1

- methylethyl]

-

,(S)) at Dixon Springs were applied to help control weeds. In addition, some manual weed

removal was necessary at each site to minimize weed pressure and ensure proper establishment

when weather conditions likely reduced the effectiveness of the pre

- emergence herbicide. These

conditions also resulted in slow, prolonged emergence. Weather data at Urbana were obtained at

an on farm weather station for 2009 and 2010 growing season observations, while data for Dixon

Springs, Perry, and 30

- year weather averages were obtained from nearby weather stations from

the Midwestern Regional Climate Center, a cooperat ive program of the Illinois State Water

Survey and the National Climatic Data Center (Table 4. 2). Accumulated growing

- degree days

were calculated with a base temperature of 11°C (Hammer et al., 1993).

Energy sorghum plant height was determined by measurin g the height of the upper fully

most expanded leaf on the main tiller of one

- to

- three randomly selected plants on four of five

dates throughout the growing season (Table 4. 3). On the same tillers, the number of fully

expanded leaves was also determined by counting the number of nodes or visible leaves present

## 93



## on each tiller. These measurements were collected during GS I (vegetative growth from

emergence to initiation of reproductive structures) and GS II (panicle initiation through booting

and heading) gr owth stages (Gerik et al., 2003; Sorghum Management Guide, 2010). The focus

of these measurements was on the photoperiod

- sensitive energy sorghum hybrids

( TAMUXH08001

, and TX09007) which remained in the GS I stage throughout most of the

growing season and only reached the GS II stage near the end of Sept. For the energy sorghum

hybrids ( TAMUXH08001

, and TX09007), leaf area index (LAI) and the proportion of

photosynthetic active radiation (PAR, 400

- 700 nm) intercepted by the crop canopy were

measured on fo ur dates during the 2009 and 2010 growing seasons at Urbana. Sampling dates in

2009 were 13 July, 29 July, 29 Aug, and 18 Sept., while sampling dates in 2010 were 25 June, 14

July, 12 Aug., and 31 Aug. The measurements were collected by measuring the PAR above the

crop canopy using an external sensor (Model Quantum, LI

- COR Biosciences) connected to a

linear ceptometer (Model PAR

- 80, Decagon Devices, Inc.) which was used to measure the

amount of PAR not intercepted by the crop canopy. These measurements w ere taken beneath the

crop canopy between 10:00 and 14:00 hrs on sunny or mostly sunny days. Two to three

subsamples were taken in each plot for each measurement date and each subsample was the

average of ~20 independent readings. Light interception was determined by calculating the

proportion of PAR intercepted by the crop canopy. Leaf area index was calculated for each

subsample using the observations of radiation interception beneath and above the canopy, and

zenith angle and leaf angle distribution ( Decagon, 1994). For LAI, only readings with above

canopy PAR that were at least 1400 nm were used in the statistical analysis.

The forage sorghum hybrids had the capability of ratooning (regrowth after harvesting)

and were harvested twice each season, o nce near the end of summer and a second time in the fall

## 94



## (Table 4. 3). This was done per recommendation of Dr. William Rooney at Texas A&M

University, plant breeder of hybrids used in this experiment, who suggested that forage sorghum

be harvested two time s each season and energy sorghum harvested one time in the fall or after

the end of the growing season. In 2009 at Urbana, a second harvest for the forage sorghum

hybrids was not obtained because of late planting (9 June), a late first harvest (3 Sept), and a

relatively short and cool growing season (Table 4. 3) that resulted in a lack of harvestable

biomass for a second harvest in the fall. Harvesting was done by cutting a 1.216 m wide pass

down the middle of each plot using a small plot biomass harveste r with an electronic scale for

determining harvestable wet biomass ( Wintersteiger Cibus S harvester mounted with a Kemper

forage chopper, Ames, IA ), expect for Perry 2010 where harvesting was done by hand by cutting

a 0.76 m pass down the middle of each en ergy hybrid plot and a 1.1216 m pass down the middle

of each forage sorghum hybrid plot. Subsamples were collected from the wet biomass of each

plot to determine percent moisture and dry biomass. Subsamples were dried in a forced air oven

for at least 48 hr at 60°C and % moisture was used to calculate dry biomass.

Statistical A naly s is

In each environment, the experimen t was arranged as a split

- split

- plot arrangement in a

randomized complete block design with four replications with sorghum type as whole plots,

sorghum hybrids as sub plots, and N fertilizer treatments as sub

- sub plots. Data from Urbana

2009 were analyzed separately from the data obtained in 2010. Environments and blocks were

considered random while the effects of sorghum type, hybrid, an d N rate were considered fixed.

The non

- linear function used to model the increase in energy sorghum plant height

throughout the growing season was the logistic growth function,

## 95 

## [eq. 1]

Here, is energy sorghum plant height (meter units) measured throughout the growing

season and x is the day of year (DOY). Three parameters describe the shape and spread of the

functio n: 1) asymptote ( asym ) or maximum height achieved by the crop, 2) scale ( scal ) or the

elapsed time between the crop achieving half and three quarters of it maximum height, and 3)

inflection point (

xmid ) or DOY at which the crop achieves half of its maximum height.

Parameters for each experimental unit (i.e. plot) were derived by fitting a logistic function to the

data for each environment using the ‘nlme’ package (Pinheiro et al., 2009) of R statistical

software (R Core, v. 2.12.1, 2010) by following princi ples in Pinheiro and Bates (2000). The

logistic curves fitted to the data in each experimental unit were plotted visually to evaluate the

agreement between the observed and fitted values and to identify possible outliers. Residuals

were checked for patte rns by plotting standardized residuals against their fitted values and plots

were obtained using the ‘graphics’ and ‘lattice’ packages of R (R Core, v. 2.12.1, 2010). Once

the parameter estimates were derived they were analyzed in the GLIMMIX procedure of SAS

( SAS Institute, 2007) to evaluate the effects of environment, hybrid, and N rate on each logistic

growth function parameter.

Leaf area index, forage sorghum summer and fall harvest biomass yields, and total biomass

yield analyzed in the GLIMMIX proc edure of SAS ( SAS Institute, 2007)

. Leaf area index

measurements collected throughout the 2009 and 2010 growing seasons at Urbana were only

used in if the above canopy PAR was at least 1400 nm. Before analysis, LAI values for each

plot were averaged acro ss subsamples before statistical analysis. Day of year was analyzed

using a repeated measures technique with appropriate variance

- covariance structure (Littell et al.,

## 96



## 2000, 2006). Least square means from 2009 and 2010 were then plotted against DOY for e ach N

rate (Fig. 4.2). Forage sorghum summer and fall biomass yields were analyzed to evaluate the

effect of environment, N rate, and hybrid on the biomass yield of each cutting. Total dry

biomass yield achieved during the growing season (summer + fall h arvest for forage sorghum,

and fall harvest for energy sorghum) were analyzed to consider the effects of environment,

sorghum type, hybrid, and N rate on the seasons total biomass productivity.

For each variable that was analyzed in the GLIMMIX procedure o f SAS ( SAS Institute,

2007) , it was assumed that the random effects of block and environment were independent

normal random variables with expectations zero and their own respective variances

, and that the

errors had means of zero and common variances. En vironments were considered random to

account for different weather and other environmental conditions at each site which could not be

controlled. Because of the split

- split

- plot arrangement sorghum hybrids were considered to be

nested within sorghum type. Similarly, the blocks in each environment were unique to their

associated environment and were thus considered to be nested in environment. Significance of

random effects were calculated with a Wald Z test statistic using the COVTEST option in the

GLIMM IX procedure of SAS (SAS Institute, 200 7). Residuals were examined for normality and

the assumption of common variances by inspection of residual plots. Preplanned comparisons

and best linear unbiased predictions (i.e. means of random effects) and their interactions were

calculated using estimate and contrast statements with appropriate degrees of freedom and

standard errors in the GLIMMIX procedure of SAS (SAS Institute, 200 7). Fisher’s least

significant differences (LSD) were determined at the α = 0.5 level using appropriate degrees of

freedom and standard errors. Pearson correlation coefficients and their respective P

- values were

calculated using the CORR procedure of SAS ( SAS Institute, 200 7)

. Number of fully expanded

## 97



## leaves accumulated thro ughout the growing season before harvesting of energy sorghum hybrids

exhibited a linear relationship when plotted against GDD. Simple linear regression lines were fit

to these data for each environment (Fig. 4. 3) using the ‘xyplot’ function of R (R Core, v. 2.12.1,

2010). All other figures were created using the SGPLOT procedure of SAS ( SAS Institute,

200 7) and SigmaPlot (v. 11.2, Systat Software, inc.). Pearson correlations coefficients were

calculated to evaluate end of season plant height, end of sea son expanded leaf number, and dry

biomass yields of energy sorghum. This was done by correlating the average of the final plant

height and expanded leaf number measurements collected in each environment (Table 4. 3) with

energy sorghum biomass yield.

RESUL TS AND DISCUSSION

Plant Height

For energy sorghum plant height in Urbana 2009, the main effect of N rate was

significant for the asym ( P = 0.0007)

, xmid ( P = 0.0036 ) and scal ( P = 0.0012 ) parameters but

not for the hybrid x N rate interaction ( P 0.3442 ) and main effect of hybrid ( P 0.2259 )

.

This suggests that both energy sorghum hybrids ( TAMUXH08001 and TX09007) accumulated

plant height similarly throughout the growing season and were influenced in the same manner by

N rate. As the season pr ogressed, the differences in plant height due to N rate became more and

more apparent, and by the end of the season there was a large difference between the maximum

height ( asym ) of plots receiving 0 kg N ha

- 1 

## and the plots receiving 50, 100, or 150 kg N h a - 1



## (Fig. 4. 1). When the average of the 50, 100, and 150 kg N ha

- 1 

## rates were contrasted against the

0 kg N ha

- 1 

## rate, there was a significant difference for each logistic function parameter ( P

0.0018 ) . More specifically, the asym or maximum height increased significantly from 0 kg N ha

-

## 98

1 

## to 150 kg N ha

- 1 

## ( P

= 0.0002 ) but the higher N rates (100 kg N ha

- 1 

## and 150 kg N ha

- 1 

## ) were not

significantly different from each other ( P

= 0.3124).

For e nergy sorghum plant height in the 2010 environments, the effects of environment, N

rate, and energy sorghum hybrid were evaluated for each logistic function parameters (eq. 1). It

was found that the effect of environment was non

- significant for the asym ( P = 0.1753)

, xmid ( P

= 0.2326 ) and scal ( P = 0.1642 ) parameters, and that the hybrid x N rate interaction ( P 0.5854 )

and the main effect of hybrid ( P 0.5785 ) were also non

- significant for each logistic function

parameter. Plant height responded less favorably in 2010 than in 2009 to N rate. The main

effect of N rate in 2010 was only significant for the asym ( P < .0001) but not for the xmid ( P =

0.7286 ) and scal ( P = 0.8253 ) parameters. The greatest plant heights achieved of the three 2010

environments occurred at the 168 kg N ha

- 1 

## rate in each environment achieving 3.7 m in Dixon

Springs 2010, 4.1 m in Perr y 2010, and 3.8 m in Urbana 2010 (Fig. 4. 1). This is contrasted with

the greatest plant height in the Urbana 2009 environment which reached only 3 m at the 150 kg

N ha

- 1 

## rate (Fig. 4. 1). The sorghum plants in 2010 likely grew taller than in 2009 because they

were planted at least 2 weeks earlier ( T able 4. 3) and had a longer growing season. Additionally,

the 2010 growing season was much warmer and had more precipitation during June through

Sept. than the 2009 growing season ( T able 4. 2 ). Energy sorghum, c an grow for 200 days in the

right environment (typically southern environments like TX) and reach heights of 6 m ( 20 ft)

(Sorghum Management Guide, 2010), however its ability to produce average heights up to 4.1

meters in Perry, IL is quite remarkable, con sidering that Perry, IL is over 1300 km northeast of

College Station, TX (driving distance), the area where these hybrids were developed.

## 99



## Leaf Area Index

Leaf area index expresses the ratio of surface leaf area to the ground area occupied by the

crop. For LAI of the energy sorghum hybrids in the Urbana 2009 environment the N rate x DOY

interaction ( P = 0.0058 ) , N rate ( P = 0.0042 ) , and DOY effects were significant ( P < .0001 ) . As

the energy sorghum hybrids grew throughout the growing season, LAI increa sed significantly

from the first sampling date on 13 July to the third sampling date on 29 Aug. ( P < .0001 ) but

then leveled off between the third sampling date and the fourth sampling date on 18 Sept. ( P

=

0 .7748 ) (Fig. 4. 2). Throughout the growing seaso n, LAI was lower for the 0 kg N ha

- 1 

## rate than

for the other N rates (50 kg N ha

- 1 

## , 100 kg N ha

- 1 

## , and 150 kg N ha

- 1 

## ( P

= 0 .0014 ) . In the Urbana

2010 environment, the only significant main effect was DOY ( P < .0001 ) . Just as in 2009, LAI

significantly in creased in 2010 from the first sampling date on 25 June to the third sampling date

on 12 Aug. ( P < .0001 ) but then leveled off between the third sampling date and the fourth

sampling date on 31 Aug. ( P

= 0 .7985 ) (Fig. 4. 2). The pattern of LAI responses to N rate was

very similar to that of plant heights as the response of plant height was lower in 2010 than in

2009. Leaf area index values were highly correlated with the proportion of PAR intercepted by

the crop canopy in 2009 ( = 0.93) ( P < .0001) and in 2010 ( = 0.95) ( P < .0001).

Leaf E xpansion and D evelopment

The number of fully expanded leaves produced by the photoperiod

- sensitive energy

sorghum hybrids ( TAMUXH08001

, and TX09007 ) was linearly associated with GDD, with

greater a greater number of fully expanded leaves produced with greater heating units or GDD.

The warmest environments, Dixon Springs 2010 and Perry 2010 accumulated 1906 and 1631

GDD, respectively (Table 4. 3). These were the two environments which accu mulated the

greatest number of fully expanded leaves, each accumulating an average of 28 leaves on the

## 100



## energy sorghum hybrids during the growing season. This is compared to typical grain sorghum

hybrids (which are not photoperiod

- sensitive) that accumulat e between 15 and 19 leaves per

plant depending on maturity group (Gerik et al., 2003). E ven though the number of fully

expanded leaves was similar for Dixon Springs 2010 and Perry 2010, plant height and biomass

yield between the two environments were qu it e different (Fig. 4. 1 and Table 4. 4 ).

Dry B iom ass Y ields

For total dry biomass yields in the Urbana 2009 environment, the effects of type ( P

=

0 .0114 ) and N rate ( P < .0001 ) were significant. The effect of hybrid was non

- significant ( P

=

0.5445 ) and est imates of dry biomass yield were calculated only for sorghum type (Table 4. 4).

In Urbana 2009, dry biomass yields were significantly greater for energy sorghum than for

forage sorghum ( P = 0.0114). Preplanned comparisons showed that an N linear response w as

significant ( P < 0.0001) and the linear slope was the same both sorghum types ( P = 0.5924).

F orage sorghum was first harvested on 3 Sept. and a lack of harvestable biomass limited a

second cutting in the fall. The energy sorghum hybrids were allowed t o grow the entire season

and were not harvested until 12 Nov. It appears that energy sorghum hybrids had greater overall

biomass potential than forage sorghum hybrids even if forage sorghum hybrids were harvested

both in summer and fall as occured in 2010 (Tables 4. 4 and 4. 5). Increasing N application from

0 kg N ha

- 1 

## to 150 kg N ha

- 1 

## improved energy sorghum biomass yields by 10.6 Mg ha

- 1 

## and

forage sorghum by 9.6 Mg ha

- 1



## .

B iomass yields for forage sorghum in 2010 showed that summer harvest accounted for

approximately 67%, 66%, and 79% of the total biomass (summer + fall harvest) yield in the

Urbana 2010, Dixon Springs 2010, and Perry 2010 environments, respectively. For the summer

forage sorghum harvest, the N rate effect was significant ( P = 0.0272) bu t not for the fall forage

## 101



## sorghum harvest ( P = 0.2844). There was no difference among forage sorghum hybrids at the

summer harvest ( P = 0.7812), but there was a significant hybrid difference for the fall harvest ( P

= 0.0240) with Graze All re

- growing appr oximately 0.65 Mg DM ha

- 1 

## more biomass than Graze.

n. Bale since the summer harvest.

For total dry biomass yields in the 2010 environments, the effects of type ( P

= 0.0734 )

and N rate ( P

= 0.0062 ) were significant, and again as in the Urbana 2009 environm ent, the

hybrid main effect was non

- significant ( P

= 0.2489 ) . Estimate statements showed that energy

sorghum yields were significantly greater than forage sorghum yields in the Perry 2010 ( P

=

0.0018 ) and Urbana 2010 ( P

= 0.0311 ) environments but not in t he Dixon Springs 2010

environment ( P

= 0.2104 ) . Forage sorghum yields in each 2010 environment were quite similar

and their response to increasing levels of N tended to follow a similar pattern (Table 4. 4) and

when contrasting forage sorghum dry biomass y ields among environments the only significant

difference was between Perry 2010 and Urbana 2010 ( P

= 0.0121 ) . Overall in 2010, there was a

significant increase in dry biomass yield for forage sorghum between the 0 kg N ha

- 1 

## rate and the

168 kg N ha

- 1 

## rate , but dry biomass yields did not increase from the 168 kg N ha

- 1 

## rate to the 224

kg N ha

- 1 

## rate (Table 4. 4). Energy sorghum dry biomass yields were more influenced by

environment than forage sorghum, and were less stable from environment to environment (T able

4. 4). Energy sorghum yields were the highest in the Perry 2010 environment when compared

with Urbana 2010 ( P

= 0.0811 ) and Dixon Springs 2010 ( P

= 0.0072 ) which was the lowest.

The soil at Dixon Springs (southern IL) is generally of lower quality ( i .e. lower organic matter

,

total N ) than Urbana (east central IL) and Perry (west central IL) (Wander and Bollero, 1999).

Also this environment had relatively low levels of P and K at planting compared to the other

environments, and the precipitation in mi d

- to

- late summer was very low totaling 74 mm

## 102



## precipitation in July and Aug. In addition to being dry, the temperature was also very high

during this time period with 15 days > 35°C (Table 4. 2). It appears that this combination of

lower soil quality and h ot and dry conditions created an environment in which energy sorghum

hybrids were unable to grow as well as in Perry 2010 and Urbana 2010. In addition to lower dry

biomass yields, plant height was shorter for energy sorghum in the Dixon Springs 2010

envir onment (Fig. 4. 1).

Correlations of end of season plant height, end of season fully expanded leaf number and

energy sorghum biomass yield (Mg DM ha

- 1 

## ) in 2009 showed positive linear relationships. In

the Urbana 2009 environment end of season plant height a nd biomass yield was highly correlated

( = 0.87) ( P < .0001), while biomass yield and end of season fully expanded leaf number ( =

0.31) ( P = 0.0815), and end of season plant height and end of season fully expanded leaf number

( = 0.35) ( P = 0. 0477) showed less positive relationships. The strong positive relationship end

of season plant height and biomass yield in 2009 is likely related to the strong N rate response

observed for both plant height and biomass yield (Fig. 4. 1 and Table 4. 4). In the 2010

environments, the relationship between these two variables was much less pronounced than in

2009, with the following correlations between end of season plant height and biomass yield:

Urbana 2010 ( = 0.19) ( P = 0.2903), Dixon Springs 2010 ( = 0.39) ( P = 0.0137), and Perry

2010 ( = 0.19) ( P = 0.2422). However, end of season plant height and end of season fully

expanded leaf number in 2010 was generally more positive than in 2009: Urbana 2 010 ( = 0.65)

( P < .0001), Dixon Springs 2010 ( = 0.36) ( P = 0.0243), and Perry 2010 ( = 0.51) ( P < .0007).

Plant height accumulation and biomass yield response to N in each of the 2010

environments were less pronounced than in the Urbana 2009 environment likely because the

residual soil NO 3 - 

## present in the soil in the Urbana 2009 environment at planting was very low

## 103



## (5.5 mg kg

- 1 

## in the upper 23 cm of soil) . This was due to a wetter than normal spring (Table 4. 2)

which limited early planting an d probably caused some N loss via leaching. T hus

, the plants in

the Urbana 2009 environment responded very favorably to N fertilizer application , thus creating

a larger disparity between shorter plants with lower yields and taller plants with greater yiel ds

.

In 2010, each environment had higher residual NO 3 - 

## levels than Urbana 2009, which likely

limited the N response from being greater than it was. Due to the strong linear response of dry

biomass yield to N rate in the Urbana 2009 environment and the va riability in dry biomass yields

among the 2010 environments it is difficult to suggest an optimal N rate recommendation when

growing forage or energy sorghum for biomass in IL. These results suggest the need to continue

evaluating sorghum grown for biomas s in these IL sites as well as other sites to increase the

number of site

- years to predict dry biomass yield potential and recommended N rates.

CONCLUSIONS

The results of this study show that forage and energy sorghum possess great potential as

biomass cr ops for IL. Environment had a major impact on plant growth and total biomass yields,

and the degree to which sorghum responded to N fertilizer rate. In some cases these differences

were explained by differences in leaf area index and plant height accumul ation. Even though

forage sorghum was harvested twice annually (summer and fall), energy sorghum harvested once

in the fall had greater biomass yields in each environment. Biomass yield, LAI, and plant height

response to N rate were observed up to 150 kg N ha

- 1 

## in 2009 and up to at least 168 kg N ha

- 1 

## in

2010. Photoperiod

- sensitive forage and energy sorghum allow vegetative growth to continue

late into until late Sept. in IL and therefore continue accumulating biomass until late Sept.

Because forage and energy sorghum are annual crops they provide a unique crop option to IL

producers and stakeholders interested in biomass crop production.

## 104



## Acknowledgments

The authors acknowledge the Energy Biosciences Institute and the Energy Biosciences

Institute Energ y Farm at the University of Illinois Urbana

- Champaign for providing funding,

land, and labor support for this study.

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## 107



## TABLES

Table 4



## . 1. Soil properties from environments in 2009 and 2010 (Urbana 2009, Urbana 2010, Dixon Springs 2010,

and Perry 2010) . Samples were obtained either with in the plots before fertilization occurred or adjacent to the plots

at planting or early in the growing season and are expressed as mean values ± standard deviation.

Environment depth pH SOM † 

### P K NH 4 

### - N NO 3 

### - N

cm %

---------------------------- mg kg

- 1



### --- ------------------------

Urbana 2009 0

- 23 6.5 (0.3) ‡ 

### 2.8 (0.6) 38 (12) 207 (41)

- 5.5 (1.7)

Urbana 2010 0

- 23 6.4 (0.6) 3.3 (0.5) 51 (29) 200 (412) 5.6 (1) 23.8 (6.2)

Dixon Springs 2010 0

- 15 7.1 (0.1) 2.4 (0.2) 41 (15) 110 (8) 10.6 (2) 30.5 (7.7)

15

- 30 6 (0.5) 1.4 (0.2) 13 (3) 81 (4) 11.4 (2.2) 22.6 (5.8)

Perry 2010 0

- 10 6.1 (0.2) 2.2 (0.1) 24 (2) 175 (13) 11.2 (1.8) 23.3 (8.7)

10

- 20 6.5 (0) 1.7 (0.2) 14 (3) 130 (20) 9.9 (1.6) 7.9 (1.4)

20

- 30 6.2 (0.2) 1.4 (0.2) 16 (3) 160 (4) 8.9 (1.1) 4.9 (0.8)

† 

### soil organic matter (SOM)

‡ 

### standard deviation in parenthesis, and n=4, 5, 3, and 4 respectively for Urbana

- 2009, Urbana

- 2010, Dixon

Springs

- 2010, and Perry

- 2010.

## 108

Table 4



## . 2. Monthly a verage minimum (min.) and maximum (max.) air temperature, 30

- yr te mperature averages, number of

days with temperature > 35°C, and precipitation in each environment (Urbana 2009, Urbana 2010, Dixon Springs 2010, and

Perry 2010) during the growing season (May

- October).

Environment weather May June July Aug. Sept. Oct. ave rage/total

Urbana 2009 average min. (°C) 11.1 16.7 15.7 15.5 12.9 5.3 12.9

30

- yr average min. (°C) 10.6 15.8 18.0 16.9 12.4 6.1 13.3

average max. (°C) 22.5 27.7 25.2 26.3 24.0 14.8 23.4

30

- yr average max. (°C) 23.1 28.1 29.6 28.4 25.3 18.4 25.5

no . days temp. > 35°C 0 0 0 0 0 0 0

precipitation, mm 145 92 137 94 22 201 692

30

- year average precipitation, mm 122 107 119 111 82 71 612

Urbana 2010 average min. (°C) 12 18.1 18.6 17.9 12.3 5.6 14.1

30

- yr average min. (°C) 10.6 15.8 18 16. 9 12.4 6.1 13.3

average max. (°C) 23.3 28.4 29.6 30.2 26.1 21 26.4

30

- yr average max. (°C) 23.1 28.1 29.6 28.4 25.3 18.4 25.5

no. days temp. > 35°C 0 0 0 0 0 0 0

precipitation, mm 75 199 82 55 75 34 519

30

- year average precipitation, mm 122 107 1 19 111 82 71 612

Dixon Springs 2010 average min. (°C) 16.1 19.4 20.4 18 14.2 6.7 15.8

30

- yr average min. (°C) 12.8 17.3 19.6 18.4 14.5 8.2 15.1

average max. (°C) 26.7 32.8 34 34.4 29.8 25 30.4

30

- yr average max. (°C) 26.1 30.4 32.4 32 28.6 22.8 28.7

no. days temp. > 35°C 0 0 3 12 0 0 15

precipitation, mm 18 104 51 23 86 25 306

30

- year average precipitation, mm 131 104 95 91 83 80 585

Perry 2010 average min. (°C) 11.4 17.8 19.2 18.1 13.1 4.9 14.1

30

- yr average min. (°C) 11 .4 16.7 18.7 17.2 12.4 6.2 13.8

average max. (°C) 24.2 31.4 32.4 31.8 26.8 21.9 28.1

30

- yr average max. (°C) 23.1 27.8 30.2 29.3 25.4 19.2 25.8

no. days temp. > 35°C 0 0 1 6 0 0 7

precipitation, mm 143 292 301 26 62 52 876

30

- year average precipi tation, mm 104 84 103 77 82 72 523

## 109

Table 4



## . 3

. Planting date, dates of plant height and fully expanded leaf number measurements, accumulated growing

degree days, and harvest dates for each environment (Urbana 2009, Urbana 2010, Dixon Springs 2010, and Perry

2010)

. plant height and fully expanded leaf

number measurements ----- harvest dates

-----

Environment planting date GDD † 

### summer ‡ 

### fall §



### Urbana 2009 9 June 13 July, 29 July, 19 Aug., 18 Sept., 7 Oct. 1129 3 Sept. 12 Nov

Urbana 2010 24 May 25 Ju ne, 21 July, 31 Aug., 27 Sept. 1517 12 Aug. 9 Nov.

Dixon Springs 2010 13 May 30 June, 19 July, 5 Aug., 8 Dec

. ¶ 

### 1906 5 Aug. 8 Dec.

Perry 2010 26 May 1 July, 23 July, 17 Aug., 23 Nov. ¶ 

### 1631 17 Aug. 23 Nov.

† 

### growing degree days (GDD) with a base temperatu re of 11°C accumulated since planting until fall harvest or first

fall frost, whichever occurred first.

‡ 

### forage sorghum was cut two times, once in summer and once in the fall.

§ 

### energy sorghum was only cut one time in the fall.

¶ 

### measurement s w ere taken d uring the fall harvest, but it was assumed that th ese final plant height s and number of

fully expanded leaves were achieved the day before the first killing frost . The first killing frost for these two

environments occurred on 5 Oct. and 4 Oct. for Dixon S prings 2010 and Perry 2010, respectively.

## 110

Table 4



## . 4

. Total sorghum b iomass yield (Mg DM ha

- 1) obtained from each environment in

2009 (Urbana 2009) and 2010 (Urbana 2010, Dixon Springs 2010, and Perry 2010) for each

N rate (kg N ha

- 1) and their respect ive ANOVA. B iomass yields were obt ained using least

square means and in the case of the 2010 environments were estimated as best linear unbiased

predictors (aka means of random variables).

environment sorghum

----------------- kg N ha

- 1



### ---------------- LSD †



### type 0 50 100 150

Urbana 2009 energy 13.1 15.4 21.6 23.7 3.63

forage 7.0 9.7 14.1 16.7 3.63

---------------------- kg N ha

- 1



### ----------------------

0 56 112 168 224

Urbana 2010 energy 22.6 24.8 25.9 27.2 30.1

forage 16.9 18.9 19.4 20.4 20.5

Dixon Springs 2010 energy 17.8 20.0 21.2 22.4 25.3

forage 15.7 17.7 18.2 19.2 19.3

Perry 2010 energy 27.6 29.8 31.0 32.2 35.1

forage 14.4 16.4 16.9 17.9 18.0

all 2010 environments energy 22.7 24.9 26.0 27.2 30.1 2.96

forage 15.7 17.7 18.2 19.2 19.2 2.84

ANOVA and P

- values

-------------- Urbana 2009 ANOVA

------------- ---------- 2010 environments ANOVA

---------- Type 0.0114 environment ‡ 

### > 0.99

hybrid 0.5445 Type 0.0734

N rate < .0001 hybrid 0.2489

type x N rate 0.9056 N rate 0.0062

N rate x hybrid 0.6613 type x N rate 0.2311

N rate x hybrid 0.324 0

† LSD values for comparing dry biomass yield values across N rate within sorghum t ype. Differences greater than LSD values are significantly different at the α = 0.05 level.

‡ environment is a random effect and when its covariance parameter estimate was equal to zero it’s p

value (based upon a Wald Z statistic) was set to > 0.99.

## 111

Table 4



## . 5. Forage sorghum biomass yields for summer harvest and

fall harvests for each 2010 environment (Urbana 2010, Dixon

Springs 2010, and Perry 2010) and ANOVA for each harvest.

Biomass yields were obtained using least square means and in the

case of individual environments were estimated as best linear

unbiased predictors (aka means of random variables).

Environment/source N rate

kg ha - 1 

### summer

Mg DM ha - 1 

### fall

Mg DM ha - 1



### Urbana 2010 0 10.5 6.3

56 12.6 6.4

112 13.1 6.0

168 14.1 6.5

224 14.0 6.7

Dixon Springs 2010 0 10.2 5.9

56 12.0 6.0

112 11.9 5.7

168 13.0 6.2

224 13.0 6.4

Perry 2010 0 10.9 3.2

56 12.7 3.4

112 13.0 3.0

168 14.2 3.5

224 14.0 3.7

--------------------------------------------

all 2010 environments 0 10.6 5.1

56 12.4 5.3

112 12.7 4.9

168 13.8 5.4

224 13.6 5.6

LSD † 

### 1.91 0.64

ANOVA

------------ P

- values

------------

environment 0.2986 0.1632

hybrid 0.7812 0.0240

N rate 0.0272 0.2844

hybrid x N rate 0.1743 0.7145

† LSD values for comparing dry bioma ss yield of the 2010

environments grouped together by N rate.

## 112



## FIGURES

Figure 4.1

. Energy sorghum plant height (m) plotted against day of year (DOY) and fit with logistic growth functions averaged across

hybrids for each N rate at each locatio n in each year.

## 113

Figure 4.2

. Energy sorghum leaf area index (LAI) and interception of photosynthetic active radiation (PAR) plotted against day of year

(DOY) and averaged across hybrids for each N rate at Urbana in 2009 and 2010.

## 114

Figure 4

. 3. Number of fully expanded leaves of photoperiod

- sensitive energy

sorghum hybrids plotted against growing degree days (GDD) with a base

temperature of 11°C accumulated since planting , and simple linear regression lines

for each environment (Urbana 2009, Urbana 2010, Dixon Springs 2010, and Perry

2010)

.

## 115 

## CHAPTER 5

Supplementary Information to Ch. 4

This chapter summarizes some additional analysis from the forage and energy sorghum

field study in Ch. 4.

In an effort to develop an understanding of the N response that sorghum exhibits,

quadratic plus plateau curves were fit to the total biomass yield data when possible. A quadratic

plus plateau as opposed to a quadratic model, was implemented because of research in corn ( Zea

mays L.) that has shown rec ommendations based on and quadratic model result in biases and

over predictions (Bullock and Bullock, 1994 a ). The quadratic function tends to predict a decline

in crop yield with increasing levels of N beyond the optimal N rate (Bullock and Bullock,

1994 a ), calculated by setting the first derivative equal to zero . This prediction is generally not

true unless extremely excessive N rates are applied that may ultimately cause lodging or other

crop problems due to excessive N application. Additionally, the q uadratic response model may

overestimate yields for the predicted optimum N rate (Cerrato and Blackmer, 1990).

The quadratic plus plateau model has been shown to best describe yield responses to N in

corn when compared to several other N response functions (linear

- plus

- plateau, quadratic,

exponential, and square root) (Cerrato and Blackmer, 1990). Extending this model to sorghum

grown for biomass is reasonable due to the similarities between the two crops. In the context of

dry biomass production the quad ratic plus

- plateau model can be described as:

[eq. 2]

## 116



## where is the biomass yield (Mg DM ha

- 1 

## ) and is the N application rate (kg N ha

- 1 

## ), is the

intercept, is the linear coefficient, is the quadratic coefficient, and is the rate of N

fertilization which occurs at the intersection of the quadratic response and the plateau ( )

portions of the model. The critical rate of fertilizer can be defined as the N rate required to

achieve maximum dry biomass yield (Max N)

.

Where model convergence was possible, a quadratic plus plateau curve was fit to each

sorghum type in each 2010 environment using the NLMIXED procedure of SAS (SAS Institute,

200 7). Plateau dry biomass yields and Max N rates were derived from each respon se function

and are shown in Table 5.1. Max N rates are assumed to be the N rates that maximize dry

biomass yields, and not profits, since they do not consider input costs, risk, or other stochastic

events that can optimize or minimize a farmer’s producti on objective (Bullock and Bullock,

1994b). Where convergence of a quadratic plus plateau model could not be achieved, or it was

observed that the relationship between dry biomass yield and N rate was linear, a linear model

was fit to the data. This was t he case for both sorghum types in the Urbana 2009 environment

and for energy sorghum in the Perry 2010 environment (Fig. 5.1). Residuals (observed yields

minus predicted yields) were plotted against N rate for each environment (Fig. 5.1). Based upon

the price of Mar. urea (46% N) from 2006 to 2011 (USDA/ERS, 2011), three prices of urea

($362 ton

- 1 

## ($328.3 Mg

- 1 

## ), $448 ton

- 1 

## ($406.3 Mg

- 1 

## ), and $552 ton

- 1 

## ($500.6 Mg

- 1 

## ) and respective

unit prices of a kg of N ($0.87 kg

- 1 

## N, $1.07 kg

- 1 

## N, and 1.32 kg

- 1 

## N)

, eco nomical optimum N

rates (EONR) were calculated by considering three theoretical prices of a Mg of dry biomass:

$35 Mg

- 1 

## , $50 Mg

- 1 

## , and $65 Mg

- 1 

## (Table 5.1). These economical optimum N rates were

calculated ex post (after the fact) (Bullock and Bullock, 19 94b) using the following equation:

## 117 

## [eq. 3]

where is the cost of N fertilizer ($ kg

- 1 

## ), is the price of dry biomass ($ Mg

- 1 

## ), is the linear

coefficient from eq. 2, and is the quadratic coefficient from eq. 2. Gene rally, it is

recommended that an ex ante (expected before the event) economical optimum N rate be

calculated based upon several years of ex post rates calculated for a given site or location

(Bullock and Bullock, 1994b), but this was not possible due to th e limited amount of data

available for each site. The variation within and among environments, shown by the varying

levels of plateau yields, Max N, EONR, and the difference in functional form (linear vs.

quadratic plus plateau) fit to the data suggest th e need to continue researching sorghum biomass

production in IL so that sufficient data is available to make reliable predictions of yield and

economical optimum N rates that should be applied to meet farmers objectives. In addition,

plant breeding effort s may continue to improve dry biomass yield potential, but also increase the

ability of the crop to respond favorably and more consistently to agronomic inputs such as N

fertilizer. It is apparent, however that the price of a Mg of dry biomass and a the p rice of N

fertilizer can alter the rate of N that should be applied to meet an economic goal. As the price of

N increased, the EONR decreased, however as the price of a Mg of dry biomass increased, the

EONR also increased (Table 5.1).

Measurements of lea f greenness for all hybrids were taken using a SPAD meter (Minolta

Camera Co., Ltd., Japan) at 3 different time periods (14

- 15 July, 30 July, and 20 Aug.)

heretofore referred to as early

- summer, mid

- summer, and late

- summer, respectively. In 2010,

SPAD rea dings were taken at two different time periods in the Urbana 2010 (25 June, and 21

- 22

July), Dixon Springs 2010 (30 June, and 19 July), and Perry 2010 (11 July, and 23 July),

heretofore referred to as early

- summer and mid

- summer, respectively. The samplin g time

## 118



## periods in 2009 and 2010 each occurred during the GSI and GSII growth stages for photoperiod

sensitive hybrids and the into the GSIII stage for the single photoperiod

- insensitive hybrid

(Graze All) (Gerik et al., 2003; Sorghum Management Guide, 2010 ). On each sampling date,

eight SPAD reading were taken from each plot on eight separate tillers from eight representative

plants and then averaged to produce one SPAD reading per plot. Readings were taken from the

upper fully most expanded leaf of each selected tiller. From approximately the same location the

SPAD readings were collected, three

- one cm diameter leaf disc samples were taken across the

leaf (one on the midrib and two on either side of the midrib) using a leaf puncher (Precision

Machine Co. , Lincoln, NE). This resulted in 24 leaf disks that were aggregated for each plot and

were weighed wet and dried in a forced air oven at 48°C for at least 48 hours. Dried samples

were ground and TN and TC were determined us a CHN analyzer (Costech Analyt ical Elemental

Combustion System, Costech 4010 model, Valencia, CA). Leaf TC and leaf TN were

determined by combustion in the presence of oxygen and appropriate standard curves and

verification of proper analysis were determined using acetanilide and appl e leaf samples

(National Institute of Standards and Technology, Gaithersburg, MD). Subsamples that had been

collected at harvest were dried in a forced air oven were then ground and used to determine

harvest TN and TC following the same protocol as descri bed above for the leaf punch samples.

For forage sorghum, only samples coming from the first harvest were used in the statistical

analysis.

SPAD, leaf TN, leaf TC, harvest TN, and harvest TC were each analyzed in the GLIMMIX

procedure of SAS ( SAS Institut e, 2007)

. For SPAD, leaf N, and leaf C, statistical analysis, ti me

of sampling (sampling period) was considered a fixed variable and was modeled as a split in

time, because in 2010 there were only two sampling periods in each environment, reducing the

## 119



## nee d to consider this variable as a repeated measurement. In the Urbana 2009 environment, the

first sampling period (14

- 15 July) for leaf TN and leaf TC was not used in the analysis because

of errors resulting from the CHN analysis. Each of these variables were analyzed in the

GLIMMIX procedure of SAS ( SAS Institute, 2007) , and statistical analysis followed the same

protocol as in Ch. 4.

SPAD meter readings allow the relative comparison of plants showing varying levels of

leaf greenness and chlorophyll conce ntration (Bullock and Anderson, 1998; Markwell et al.,

1995). SPAD readings can indicate plant N stress (Ruiz Diaz et al., 2008) and have been related

to yield response to N in corn ( Zea mays. L) (Scharf et al., 2006) and leaf TN in switchgrass

( Panicum v irgatum L.) (Madakadze et al., 1999). In the Urbana 2009 environment there was a

moderate, yet positive significant correlation between SPAD and N rate ( = 0.69) ( P < .0001)

throughout the growing season. The correlation between SPAD and leaf TN duri ng the last two

sampling periods (mid

- summer and late

- summer) was also significant ( P < .0001) but the

correlation was much lower ( = 0.41). Correlations between SPAD meter readings and leaf TN

have also been shown in corn, although correlations were low early in the season and increased

as the crop moved into the reproductive stage (Bullock and Anderson, 1998). For SPAD, there

was a significant N rate x time period x type interaction ( P = 0.035) and a significant type x time

period interaction ( P < . 0001), although readings generally declined from the early

- summer to

late summer for both sorghum types and almost all hybrids (Table 5.2). Each hybrid responded

similarly to N, but was slightly different for each time period (Table 5.2). SPAD meter read ings

and leaf TN increased significantly from 0 kg N ha

- 1 

## to 100 kg N ha

- 1 

## but then leveled off

between 100 kg N ha

- 1 

## and 150 kg N ha

- 1 

## (Table 5.2). SPAD meter readings also differed among

sorghum hybrids (Table 5.2) which has also been shown in corn (Bul lock and Anderson, 1998).

## 120



## There was a significant N rate x time period interaction for leaf TN ( P < .0001) which increased

from mid

- summer to late

- summer for each hybrid (Table 5.2). The only significant effect for

leaf TC was time period ( P = 0.0051) an d leaf TC increased significantly for the energy sorghum

hybrids from mid

- summer to late

- summer, but not for the forage sorghum hybrids (Table 5.2).

For the 2010 environments, there were moderate, yet positive significant correlations

between SPAD and TN ( = 0.49) ( P < .0001), and SPAD and N rate ( = 0.60) ( P < .0001) at

the early

- summer measurements. The correlation between SPAD and TN ( = 0.30), and SPAD

and N rate ( = 0.51) decreased in mid

- summer although the correlation were still signi ficant ( P

< .0001). The effect of environment was non

- significant for SPAD ( P > .99), leaf TN ( P =

0.2817), and leaf TC ( P = 0.4285). For SPAD, there was a significant time period x hybrid

interaction ( P = 0.0925) and SPAD measurements increased from earl y

- summer to mid

- summer

for each hybrid although there were differences between hybrids (Table 5.3). SPAD meter

readings and leaf TN increased significantly from 0 kg N ha

- 1 

## to 112 kg N ha

- 1 

## but then leveled

off between 112 kg N ha

- 1 

## and 168 kg N ha

- 1 

## (Tabl e 5.3). Leaf TN also differed at each sampling

period and decreased from early

- summer to late

- summer for each sorghum type and each

sorghum hybrid. All leaf TC effects were non

- significant (Table 5.3).

Harvest TN levels were at least half of the leaf TN levels taken during the growing

season (Tables 5.2, 5.3, and 5.4). Harvest TN samples were a composite sample of the entire

plant including stems, leaves, and in some cases panicles, while leaf TN samples only contained

leaf material. Harvest TN was sig nificant only for N rate ( P = 0.0876) in the Urbana 2009

environment, while only type was significant for the 2010 environments ( P = 0.0589). Harvest

TN increased significantly from 0 kg N ha

- 1 

## to 150 kg N ha

- 1 

## in the Urbana 2009 environment

and from 0 kg N ha

- 1 

## to 150 kg N ha

- 1 

## in the 2010 environments. The effect of environment was

## 121



## non

- significant for the 2010 environments, but energy sorghum had significantly lower harvest

TN levels than forage sorghum. This is likely because the forage sorghum harves t samples came

from the summer harvest when the plants were still green, while the energy sorghum harvest

samples came from the fall harvest, which, for Dixon Springs and Perry, had begun field drying

at harvest time.

REFERENCES

Bullock, D.G., and D.S. An derson. 1998. Evaluation of the Minolta SPAD

- 502 cholorphyll

meter for nitrogen management in corn. J. Plant Nutr. 21:741

– 755.

Bullock, D.G., and D.S. Bullock. 1994a. Quadratic and Quadratic

- Plus

- Plateau Models for

Predicting Optimal Nitrogen Rate of Corn : A Comparison. Agron. J. 86: 191

- 195.

Bullock, D.S., and D.G. Bullock. 1994b. Calculation of optimal nitrogen fertilizer rates. Agron.

J. 86: 921

- 923.

Gerik, T. B. Bean, and R. Vanderlip. 2003. Sorghum Growth and Development. Texas AgriLife

Extension pu blication, Texas A&M University.

Madakadze, I.C., K.A. Stewart, R.M. Madakadze, P.R. Peterson, B.E. Coulman, and D.L. Smith.

1999. Field Evaluation of the Chlorophyll Meter to Predict Yield and Nitrogen Concentration

of Switchgrass. J. Plant Nutr. 22:100 1

- 1010.

Markwell, J., J.C. Osterman, and J.L. Mitchell. 1995. Calibration of the Minolta SPAD

- 502 leaf

chlorophyll meter. Photosynth. Res. 46:467

– 472.

Ruiz Diaz, D.A., J.A. Hawkins, J.E. Sawyer, and J.P. Lundvall. 2008. Evaluation of In

- Season

Nitrogen Management Strategies for Corn Production. Agron. J. 100:1711

– 1719

.

Scharf, P.C., S.M. Brouder, and R.G. Hoeft. 2006. Cholorphyll Meter Readings Can Predict

Nitrogen Need and Yield Response of Corn in the North

- Central USA

. Agron. J. 98:655

–

665

.

Sorghum Management Guide. 2010. Managing High

- Biomass Sorghum as a Dedicated Energy

Crop. Bl ade Energy Crops. Available online at

http://www.bladeenergy.com/Bladepdf/Blade_SorghumMgmtGuide2010.pdf ( accessed 2

May 2011; verified 22 June 2011 ).

USDA/ERS. 2011. Aver age U.S. farm prices of selected fertilizers. Available at

http://www.ers.usda.gov/Data/FertilizerUse/ (accessed 15 May 2011; verified 22 June 2011).

## 122



## TABLES

Table 5. 1. Plateau biomass yields based upon quadratic plus plateau response

curves and associa ted N rate (max N) required to achieve plateau biomass yields at

each 2010 environment for each sorghum type. Ex post economic optimum N rates

using based upon three prices of a unit of N ($0.87 kg

- 1 

### N, $1.07 kg

- 1 

### N, and 1.32 kg

1 

### N) and three prices for a Mg of dry biomass ($35 Mg

- 1 

### , $50 Mg

- 1 

### , and $65 Mg

- 1 

### ).

plateau biomass yields ( Mg DM ha - 1 

### )

environment sorghum

type plateau yield

Mg DM ha - 1 

### max N

kg N ha - 1



### Urbana 2010 energy 28.4 187.4

forage 21.1 193.7

Dixon Springs 2010 energy 21.2 41.8

forage 18.3 47.8

Perry 2010 energy †



### - -

forage 17.9 214.9

economic optimum N rate (kg N ha - 1 

### )

----------- $ Mg

- 1 

### dry biomass

---------

environment sorghum

type $ kg

- 1 

### N $35 $50 $65

Urbana 2010 energy $ 0.87 115.42 137.02 148.65

$ 1.0 7 98.87 125.43 139.74

$ 1.32 78.18 110.95 128.60

forage $ 0.87 96.63 125.75 141.43

$ 1.07 74.32 110.13 129.41

$ 1.32 46.43 90.61 114.39

Dixon Springs 2010 energy $ 0.87 35.66 37.48 38.47

$ 1.07 34.25 36.50 37.72

$ 1.32 32.50 35.28 36.77

forage $ 0.87 35.66 37.48 38.47

$ 1.07 34.25 36.50 37.72

$ 1.32 32.50 35.28 36.77

Perry 2010 energy $ 0.87

- - - $ 1.07

- - - $ 1.32

- - - forage $ 0.87 62.16 107.97 132.65

$ 1.07 27.05 83.40 113.74

$ 1.32 na ‡ 

### 52.68 90.11

† a quadratic plus plateau response curve was not fit for the energy sorghum type at

Perry 2010.

‡ economical optimum N rate calculated was less than 0.

## 123

Table 5. 2

. SPAD, leaf TN, and leaf TC measurements from Urbana 2009 showing

results by N rate, for the type x time period, an d time period x hybrid(type) interaction.

A nalysis of variance (ANOVA) for each variable is shown at bottom of table.

SPAD leaf TN leaf TC

N rate, kg N ha - 1 

### no units g kg - 1 

### g kg - 1



### 0 34.5 1.87 38.5

50 38.1 1.91 40.0

100 43.0 2.37 41.4

150 44.9 2.23 42.6

LSD † 

### 1.96 0.354 4.25

type x time period

Energy, early

- summer 42.5

- - Energy, mid

- summer 41.2 1.96 38.6

Energy, late

- summer 39.2 2.17 44.0

Forage, early

- summer 41.6

- - Forage, mid

- summer 37.5 2.06 38.6

Forage, late

- summer 38.6 2.19 41.3

LSD ‡ 

### 3.37 0.561 4.32

time period x hybrid

TAMUXH08001, early

- summer 42.3

- - TAMUXH08001, mid

- summer 41.9 2.04 40.2

TAMUXH08001, late

- summer 39.7 2.29 42.8

TX09007, early

- summer 42.8

- - TX09007, mid

- summer 40.6 1.88 37.0

TX09007, late

- summer 38.7 2.05 45.3

Graze All, early

- summer 42.5

- - Graze All, mid

- summer 39.0 2.30 38.1

Graze All, late

- summer 42.2 2.59 40.3

Graze. n. Bale, early

- su mmer 40.8

- - Graze. n. Bale, mid

- summer 36.0 1.82 39.0

Graze. n. Bale, late

- summer 35.0 1.79 42.2

LSD § 

### 2.97 0.599 5.87

ANOVA

----------------------- P

- values

------------------------ Type 0.175 0.7542 0.3855

hybrid 0.0008 0.00 16 0.7816

N rate <.0001 0.0136 0.245

type x N rate 0.3166 0.327 0.7843

N rate x hybrid 0.3994 0.3042 0.4382

time period <.0001 0.1469 0.0051

type x time period <.0001 0.7276 0.3297

time period x hybrid <.0001 0.6014 0.35 5

N rate x time period <.0001 0.01 0.5687

type x Nrate x time period 0.035 0.6463 0.3943

N rate x time period x hybrid 0.3697 0.1118 0.8613

† LSD values for comparing within N rate.

‡ LSD values for comparing values within type x time pe riod interaction.

§ LSD values for comparing values within time period x hybrid(type) interaction.

Differences greater than LSD values are significantly different at the α = 0.05 level

## 124

Table 5. 3

. SPAD, leaf TN, and leaf TC measurements from all 2010 environments

(Urbana 2010, Dixon Springs 2010, and Perry 2010) showing results by N rate, for the

t ype x time period, and time period x hybrid(type) interaction. A nalysis of variance

(ANOVA) for each variable is shown at bottom of table.

SPAD leaf TN leaf TC

N rate, kg N ha - 1 

### no units g kg - 1 

### g kg - 1



### 0 38.6 1.94 34.5

56 42.2 2.12 35.0

112 45.0 2.44 35.3

168 46.0 2.61 34.6

224 46.3 2.60 35.2

LSD † 

### 1.78 0.205 2.39

type x time period

Energy, early

- summer 43.9 2.83 37.1

Energy, mid

- summer 46.1 1.97 34.0

Forage, early

- summer 41.0 2.41 35.5

Forage , mid

- summer 43.4 2.14 33.1

LSD ‡ 

### 4.18 0.714 19.95

time period x hybrid

TAMUXH08001, early

- summer 43.4 2.81 37.7

TAMUXH08001, mid

- summer 46.1 2.00 34.7

TX09007, early

- summer 44.4 2.85 36.5

TX09007, mid

- summer 46.2 1.95 33.3

Graze All, early

- summer 42.0 2.49 35.4

Graze All, mid

- summer 45.5 2.46 33.1

Graze. n. Bale, early

- summer 39.9 2.33 35.6

Graze. n. Bale, early

- summer 41.3 1.83 33.0

LSD § 

### 4.15 0.527 20.39

ANOVA

----------------------- P

- values

- -----------------------

environment ¶ 

### >0.99 0.2817 0.4285

Type 0.002 0.0532 0.2024

hybrid <.0001 <.0001 0.3691

N rate <.0001 <.0001 0.9458

type x N rate 0.4057 0.7051 0.9824

N rate x hybrid 0.5058 0.9563 0.932

time period 0.196 0.0666 0.6069

type x time period 0.8334 <.0001 0.7202

time period x hybrid 0.0925 0.0325 0.9821

N rate x time period 0.3085 0.1908 0.6689

type x Nrate x time period 0.8907 0.9649 0.7227

N rate x time period x hybr id 0.6106 0.8876 0.8173

† LSD values for comparing within N rate.

‡ LSD values for comparing values within type x time period interaction.

§ LSD values for comparing values within time period x hybrid(type) interaction.

Differences greater than LSD values are significantly different at the α = 0.05 level.

¶ environment is a random effect and when its covariance parameter estimate was equal

to zero it’s p

- value (based upon a Wald Z statistic) was set to > 0.99.

## 125

Table 5. 4

. Harvest TN and harvest TC samp les from Urbana 2009 and all 2010 environments (Urbana 2010,

Dixon Springs 2010, and Perry 2010) showing results by N rate and for the type x N rate interaction. A nalysis of

variance (ANOVA) for each variable is shown at bottom of table.

--------------- ---------- Urbana 2009

------------------------- ---------------------- 2010 environments

---------------------

harvest TN harvest TC harvest TN harvest TC

N rate, kg N ha - 1 

### g kg

- 1 g kg

- 1 N rate, kg N ha - 1 

### g kg

- 1 g kg

- 1

0 0.79 33.3 0 0.91 39 .7

50 0.80 32.3 56 0.97 39.9

100 0.82 34.9 112 1.09 41.7

150 0.99 34.1 168 1.04 40.7

LSD † 

### 0.180 4.07 224 1.13 41.9

type x N rate LSD † 

### 0.194 3.22

e nergy, 0 0.76 31.6 type x N rate

e nerg y, 50 0.77 32.1 e nergy, 0 0.68 39.6

e nergy, 100 0.67 34.2 e nergy, 56 0.67 39.4

e nergy, 150 0.86 35.0 e nergy, 112 0.74 41.3

f orage, 0 0.81 35.1 e nergy, 168 0.73 40.2

f orage, 50 0.83 32.6 e nergy, 224 0.74 40.1

f orage, 100 0.96 35.5 f orage, 0 1.14 39.8

f orage, 150 1.12 33.2 f orage, 56 1.27 40.3

LSD ‡ 

### 0.322 6.04 f orage, 112 1.45 42.1

ANOVA

-------- P

- values

-------- f orage, 168 1.35 41.1

Type 0.1838 0.6199 f orage, 224 1.52 43.7

Hybrid 0.9024 0.9451 LSD ‡ 

### 0.604 4.73

N rate 0.0876 0.6038 ANOVA

-------- P

- values

-------- type x Nrate 0.4032 0.621 environment 0.4035 0.1641

N rate x hybrid 0.8204 0.2918 type 0.0589 0.2007

hybrid 0.5728 0.1358

N rate 0.1389 0.505

type x Nrate 0.1773 0.845

N rate x hybrid 0.3918 0.5652

† LSD values for comparing within N rate

. ‡ LSD values for comparing values of type x N rate interaction

. Differences g reater than LSD values are significantly different at the α = 0.05 level.

## 126



## FIGURES

Figure 5.1

. Biomass yields (Mg DM ha

- 1 

### ) and residuals of energy and forage sorghum in each

environment fit with quadratic plus plateau response curve s and linear response curves where quadratic

plus plateau response curves could not be fitted to the data. Both sorghum types at Urbana 2009 and

energy sorghum at Perry 2010 were fitted with linear response curves, while the others were fit with

quadratic plus plateau response curves.

## 127 

## CONCLUDING REMARKS

When considering biomass production of switchgrass, M. x giganteu s , and forage and

energy sorghum, the effects of the environment and field management practices are very critical

elements that can grea tly affect biomass yield and productivity. Because these crops were not

evaluated in the same experiment no direct comparisons about these crops can be discussed,

although each individual study provides valuable information about these three crops. F utur e

research could also benefit from a comparison of these crops in s ide

- by

- side experiments

.

In Ch. 2, an evaluation of data from 106 sites and 45 studies from the eastern two thirds

of the USA and southeastern Canada revealed that N rate, region, spring pr ecipitation, growing

season, ecotype, and harvest timing each had a significant effect on biomass yields of

switchgrass. Results of this study also revealed that N response varies from site

- to

- site, that the

lower and upper central USA regions tend to pro duce greater biomass yields than the northern or

southern regions of the USA , and that the lowland ecotype tends to yield better than the u pland

ecotype, although this depends on the region in which it is grown

. Several issues made the

compilation of the database for this study and its statistical analysis c h a l l e n g i n g . First,

management practices and methods of reporting biomass yields were different from study

- to

study a n d p r o v i d e d a c h a l l e n g e i n classif y i n g data int o a discrete set of categories

. T h i s often

resulted in the exclusion of valuable data. In several cases, data from certain studies were

excluded from the database because rather than reporting biomass yield for specific mana gement

practices (that were of interest in this study) such as N rate and harvest timing, biomass yields

were often reported as averages across these variables. A random coefficients model was

implemented that allowed several variables of interest to be c onsidered together in a single

model. Because the number of variables of interest was relatively high, it was not possible to

## 128



## estimate the variance (i.e. standard error of the differences of means, confidence intervals,

standard deviations, coefficient of variation) within and among studies in way that was consistent

or standardized from study

- to

- study. Based upon the results of this study, there are several areas

of that could benefit from future research, some of which are already in progress. First,

c ontinued development of new switchgrass cultivars with improved biomass yield potential and

broad adaptation is necessary to provide reliable and high biomass yielding switchgrass crops.

Secondly, one of the most important decisions a producer can make is selecting the proper

ecotype and cultivar suited for biomass production in his or her area. For example, Cave

- in

Rock is a cultivar that originates from southern IL. This cultivar may be a front runner candidate

for biomass production in northern IL, but other cultivars such as Kanlow may possess greater

biomass yield potential in southern and central IL. It is necessary that local scientists and

agronomists evaluate potential cultivars for a specific area and develop recommendations that

will help produ cers make informed cultivar selection decisions.

In Ch. 3, M. x giganteus was grown for three years (establishment years) in four

environments ranging from the western Corn Belt in Mead, NE to the Atlantic Coast in

Adelphia, NJ. It was observed that late planting and cold winter temperatures may limit the

production range for this clone. M. x giganteus biomass yields did not respond significantly to N

application during this study, but according to the literature, response to N application in future

prod uction years (beyond establishment) is more likely. Biomass yields were greatly impacted

by environment, with low yields associated with poor soil conditions and hot dry conditions, and

high biomass yields associated with high levels of well distributed r ainfall (throughout the

growing season) and warm growing conditions as were observed in NE in 2010. One of the

major limitations of this study was the limited number of sites that were established and the

## 129



## broad range of geography that was evaluated. This limitation was due to a lack of sufficient M. x

giganteus rhizome material available for establishing replicated experiments at more sites. Four

areas of potential production were evaluated (NE, IL, KY, and NJ), but only one location in each

of these are as was established with a replicated experiment. This made the evaluation of these

specific production areas difficult, because in each year, production area was confounded with

environment, due to a lack of replication within area. For example, it m a y h a v e b een better for

IL if replicated experiments had been set up at six locations at the main University of Illinois

research and extension centers (Urbana, Brownstown, Dixon Springs, Perry, Monmouth, and

Dekalb). A further step would have been to set up multi ple replicated experiments at or near

each of these research and education centers so that different soil types and potentially different

environment effects could have been observed. This would have provided a hierarchical

experimental design which inclu des the effects of area, location within area, and site within

location. Because there is still limited published research available on M. x giganteu s biomass

production in the USA, many more sites and years of production are needed to suggest an

optimal production region and how it performs under different management practices such as

harvest timing and response to fertilizer application. One specific management practice that

needs further evaluation is the effect of N fertilization on fully productive s tands of M. x

giganteu s , or M. x giganteu s stands that are at least 3 to 5 years old.

In Ch. 4, forage and energy sorghum hybrids were grown in four IL environments under

different N rates. It was observed that e nvironment had a major impact on plant gr o wth and total

biomass yields, and in each environment f orage sorghum harvested twice annually (summer and

fall) yielded les s than energy sorghum harvested once in the fall

. Biomass yield, leaf area index,

and plant height response to N rate were observed up to 150 kg N ha

- 1 

## in 2009 and up to at least

## 130



## 168 kg N ha

- 1 

## in 2010. Photoperiod sensitivity allowed the energy sorghum hybrids to co ntinue

vegetative growth until late Sept. which allowed them to continue accumulating plant height and

biomass throughout the majority of the growing season. Future research would benefit from

more sites and more years of production. This study was limited to three locations, two years,

and a total of four environments. M ultiple years of production for a give n s i t e w o u l d p r o v i d e

m o r e u s e f u l i n f o r m a t i o n f

o r c a l c u l a t i n g o p t i m a l N r a t e s t h a n a s i n g l e y e a r o f production a t t h a t

s i t e ( B u l l o c k a n d B u l l o c k

, 1 9 9 4 )

. If agronomists were to continue evaluating sorghum in IL for

biomass production, a couple of research areas should be pursued. First, continue evaluating

e nergy and forage sorghum hybrids under a wide range of N rates, and establish trials at several

locations (i.e. six main research and extension centers in IL) for two or three or even more years.

Of course, this should be done under soil conditions that h ave adequate levels phosphorous,

potassium and other essential macro and micro nutrients. One major foreseen challenge s in

dealing with sorghum biomass is the high moisture content of the biomass material. Even when

harvested in Nov., the biomass still ha d approximately 60% moisture. The process of baling and

hauling this material to a conversion facility would require that this biomass be dried down to a

level that would allow long term storage. Baled biomass with high moisture content, stored for

even a short period of time, can result in rapid decomposition and even spontaneous combustion.

Biomass harvesters with conditioners could potentially provide a means to crack and break apart

the sorghum stems so that significant field drying would occur. Als o, harvesting earlier in the

season could provide sufficient time for the crop to dry in the field when there is still plenty of

warm late summer and early fall weather. In each case, the biomass would likely need to be

spread out evenly on the field (for drying purposes) or windrowed in a way that allows the

## 131



## biomass to be turned over with equipment such as a hay rake that can expose the underside of the

windrow to the sun and air.

R E F E R E N C E S

Bullock, D.S., and D.G. Bullock. 1994b. Calculation of optimal nitrogen fertilizer rates. Agron.

J. 86: 921

- 923.

## 132 

## APPENDIX A

Sel ected SAS and R code for Ch. 2

Cultivar m eans, SEM, min , max

- Ch.2, Table 2.4.

options ps = 100 ls

= 100 nodate nocenter nonumber formdlim

= '*' ;

data SW;

length Cultivar$ 30 ;

infile 'C: \ Documents and Settings \ maughan2 \ My Documents \ My Dropbox \ Switchgrass Meta

- Analysis \ SW

database June 18, 2011, no Europe, no Irrigat ion.csv'

dlm

= "," firstobs

= 2 ;

input PubName$ Category$ Publication$ Experiment sites$ Country$ Location$ Region$ Latitude

Longitude

soil$ establishment$ SeedRate$ Precip spring Irrigation$ year$ season$ Cultivar$

Ecotype$ Cuttings

harvest$ Nrate bio mass notes$;

if Nrate > 300 then delete ;

cards ;

run ;

proc print ; run ;

data sw; set sw;

if cultivar= "mean" then delete ;

if cultivar= "NA" then delete ;

if cultivar= "unknown" then delete ;

if cultivar= "average" then delete ;

if cultivar= "Mean" then delete ;

if cultivar= "average of 3 varieties" then delete ;

*if cuttings=2 then delete;

if season= 1 then delete ;

*if season=2 then delete;

run ; proc print ; run ;

proc sort data =sw; by season region cultivar; run ;

proc means data =sw noprint mean std min max ; var biomas s; by season region cultivar;

output out =cultmean mean =meanbiomass std =stdbiomass min =minbiomass max =maxbiomass;

run ; proc print data =cultmean; run ;



## Descriptive statistics, model development

- Ch.2, Fig. 2.2, Fig. 2.3, Fig. 2.4, Fig. 2.5, Fig. 2.6,

Table 2. 5, Table 2.6 in part.

## Load packages

library ( nlme ) #library(help = nlme)

library (Hmisc)

library (lattice) #library(help = lattice)

library (graphics) #library(help = graphics)

#library(help = nlme)

## Load file

sw <

- read.csv ( "./SW database June 1 8, 2011, no Europe, no Irrigation.csv" )

head (sw)

sw.NR <

- sw[sw$Region == "North" ,]

unique (sw.NR[,5])

sw.UCR <

- sw[sw$Region == "Upper

- Central" ,]

unique (sw.UCR[,5])

sw.LCR <

- sw[sw$Region == "Lower

- Central" ,]

unique (sw.LCR[,5])

sw.SR <

- sw[sw$Region == "South " ,]

unique (sw.SR[,5])

unique (sw[,3]) #45 studies

unique (sw[,5]) #106 site or location combinations

sw #1476 observations

##which varieties are yielding greater than 26? Kanlow, Alamo.

## 133

sw[sw$biomass.Mg.ha.1. > 28, "variety" ]

##which va rieties are yielding greater than 30? Kanlow, Alamo.

sw[sw$biomass.Mg.ha.1. > 30, "variety" ]

##which varieties are yielding greater than 35? Kanlow, Alamo.

sw[sw$biomass.Mg.ha.1. > 35, "variety" ]

## Removing data with N rates higher than 300.

##can cause lodging and most recommended N rates have been much lower.

sw[sw$N.rate.kg.ha. > 300,23]

sw[sw$N.rate.kg.ha. > 300,23] <

- NA

## Removing lowland upper north

#sw[sw$Region == "north" & sw$Ecotype == "L",19]

#sw[sw$Region == "north" & sw$Ecotype == "L",19 ] <

- NA

## Get rid of SeedRate

sw <

- sw[,

- 13]

## getting rid of Precip.annual.mm.

head (sw)

sw <

- sw[,

- 13]

head (sw)

length ( unique (sw$sites))

head (sw)

unique (sw$sites)

sw$sites <

- factor (sw$sites, levels = unique (sw$sites))

## I will average over varie ties before I do the analysis

## Averaged switchgrass biomass over varieties

sw.b <

- with (sw,summarize(biomass.Mg.ha.1.,

llist(sites, Region, Apr.June.precip..mm., season, Ecotype, Cuttings,

harvest.timing, N.rate.kg.ha.),

FUN= mean , data = sw))

n ms <

- names (sw.b)

nms[8:9] <

- c ( "Nrate"

, "biomass" )

names (sw.b) <

- nms

sw.b$sites <

- as.factor (sw.b$sites)

sw.b$Region <

- factor (sw.b$Region,

levels

= c ( "South"

, "Lower

- Central"

, "Upper

- Central"

, "North" ))

sw.b$season <

- as.factor (sw.b$season)

sw.b$Ecoty pe <

- as.factor (sw.b$Ecotype)

sw.b$Cuttings <

- as.factor (sw.b$Cuttings)

sw.b$harvest.timing <

- as.factor (sw.b$harvest.timing)

sw.b$Apr.June.precip..mm. <

- as.numeric (sw.b$Apr.June.precip..mm.)

sw.b$biomass <

- as.numeric (sw.b$biomass)

sw.b$Nrate <

- as.numeric (sw.b$Nrate)

write.csv(sw.b, "./sw

- meta June 18, 2011.csv"

, row.names = FALSE )

rm ( list

= ls ())

sw.b <

- read.csv ( "./sw

- meta June 18, 2011.csv" )

#manually removing observations from NA's associated with N rates higher than

#280 if the above code is rerun, thi s will have to be done again.

unique (sw.b$sites)

head (sw.b)

sw.b.NR <

- sw.b[sw.b$Region == "North" ,]

sw.b.UCR <

- sw.b[sw.b$Region == "Upper

- Central" ,]

sw.b.LCR <

- sw.b[sw.b$Region == "Lower

- Central" ,]

sw.b.SR <

- sw.b[sw.b$Region == "South" ,]

range (sw.b$biom ass) #0 to 33.4

#season 2 yields by ecotype

mean (sw.b[sw.b$season == "2" & sw.b$Ecotype == "L"

, "biomass" ]) #11.1

## 134

sd (sw.b[sw.b$season == "2" & sw.b$Ecotype == "L"

, "biomass" ]) #6.1

mean (sw.b[sw.b$season == "2" & sw.b$Ecotype == "U"

, "biomass" ]) #6.7

sd (s w.b[sw.b$season == "2" & sw.b$Ecotype == "U"

, "biomass" ]) #3.2

#seasons >=3 yields by ecotype

mean (sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "L"

, "biomass" ]) #12.6

sd (sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "L"

, "biomass" ]) #5.4

mean (sw.b[sw.b$se ason == ">=3" & sw.b$Ecotype == "U"

, "biomass" ]) #9.3

sd (sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "U"

, "biomass" ]) #4.3

#biomass yield ranges by region

range (sw.b[sw.b$Region == "South"

, "biomass" ]) #0 to 30.4

range (sw.b[sw.b$Region == "Lower

- Centra l"

, "biomass" ]) #4.6 to 33.4

range (sw.b[sw.b$Region == "Upper

- Central"

, "biomass" ]) #2.5 to 26

range (sw.b[sw.b$Region == "North"

, "biomass" ]) #1.1 to 14.5

mean (sw.b[sw.b$Region == "South" & sw.b$season != "1"

, "biomass" ]) #9.3

sd (sw.b[sw.b$Region = = "South" & sw.b$season != "1"

, "biomass" ]) #5.7

mean (sw.b[sw.b$Region == "Lower

- Central" & sw.b$season != "1"

, "biomass" ]) #13.4

sd (sw.b[sw.b$Region == "Lower

- Central" & sw.b$season != "1"

, "biomass" ]) #4.5

mean (sw.b[sw.b$Region == "Upper

- Central" & sw.b$s eason != "1"

, "biomass" ]) #10.1

sd (sw.b[sw.b$Region == "Upper

- Central" & sw.b$season != "1"

, "biomass" ]) #4.3

mean (sw.b[sw.b$Region == "North" & sw.b$season != "1"

, "biomass" ]) #7.3

sd (sw.b[sw.b$Region == "North" & sw.b$season != "1"

, "biomass" ]) #3.1

#biomass yield ranges by season

range (sw.b[sw.b$season == "1"

, "biomass" ]) #2.2 to 12.6

range (sw.b[sw.b$season == "2"

, "biomass" ]) #1.1 to 33.4

range (sw.b[sw.b$season == ">=3"

, "biomass" ]) #0 to 30.4

#biomass yields by season

mean (sw.b[sw.b$season == " 1"

, "biomass" ]) #6.6

sd (sw.b[sw.b$season == "1"

, "biomass" ]) #3

mean (sw.b[sw.b$season == "2"

, "biomass" ]) #9.1

sd (sw.b[sw.b$season == "2"

, "biomass" ]) #5.5

mean (sw.b[sw.b$season == ">=3"

, "biomass" ]) #10.9

sd (sw.b[sw.b$season == ">=3"

, "biomass" ]) #5.2

#yield increases from season 2 to seasons >=3

mean (sw.b[sw.b$season == "2" & sw.b$Ecotype == "L"

, "biomass" ]) ## 11.1

mean (sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "L"

, "biomass" ]) ## 12.6

mean (sw.b[sw.b$season == "2" & sw.b$Ecotype == "U"

, "biomass" ]) ## 6.7

mean (sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "U"

, "biomass" ]) ## 9.3

median (sw.b[sw.b$season == "2" & sw.b$Ecotype == "L"

, "biomass" ]) ## 10.1

median (sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "L"

, "biomass" ]) ## 12.8

median (sw.b[sw.b$season == "2" & sw.b$Ecotype == "U"

, "biomass" ]) ## 6.7

median (sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "U"

, "biomass" ]) ## 9.1

## What are the mean values by region

mean (sw.b[sw.b$Region == "South" & sw.b$Ecotype == "L"

, "biomass" ])

## The mean for the south lowlan d is 10.3

mean (sw.b[sw.b$Region == "South" & sw.b$Ecotype == "U"

, "biomass" ])

## The mean for the south upland is 5.2

mean (sw.b[sw.b$Region == "Lower

- Central" & sw.b$Ecotype == "L"

, "biomass" ])

## The mean for the lower central lowland is 14.4

mean (sw. b[sw.b$Region == "Lower

- Central" & sw.b$Ecotype == "U"

, "biomass" ])

## The mean for the lower central upland is 11.9

mean (sw.b[sw.b$Region == "Upper

- Central" & sw.b$Ecotype == "L"

, "biomass" ])

## The mean for the upper central lowland is 9.9

mean (sw.b[ sw.b$Region == "Upper

- Central" & sw.b$Ecotype == "U"

, "biomass" ])

## The mean for the upper central upland is 9.7

mean (sw.b[sw.b$Region == "North" & sw.b$Ecotype == "L"

, "biomass" ])

## The mean for the north lowland is 10.23

mean (sw.b[sw.b$Region == "N orth" & sw.b$Ecotype == "U"

, "biomass" ])

## The mean for the north upland is 7

## Removing lowland upper north, b/c few observation exist in the north region

##for lowland ecotypes

sw.b[sw.b$Region == "North" & sw.b$Ecotype == "L" ,5]

## 135

sw.b[sw.b$Region = = "North" & sw.b$Ecotype == "L" ,5] <

- NA

unique (sw.b$Ecotype)

sw.b$Region <

- factor (sw.b$Region,

levels = c ( "South"

, "Lower

- Central"

, "Upper

- Central"

, "North" ))

bwplot (biomass ~ Region | Ecotype*season, data = sw.b,

xlab

= "Region"

, ylab

= "Dry Biomass Yield (Mg/ha)"

,

fill

= c ( "yellow" ),

panel = function (...) {

panel.grid (v = 0, h

=

- 1)

panel.bwplot (...)

})

## This suggests that yield is higher in the lower central region vs the

## upper central for lowland ecotypes but they are about the same for upland

## ecotypes

##boxplots by season

##FIGURE 3

- PART 1

png ( "./manuscript figures/season1.png"

, width =600, height =1200, res=120)

sw.b.one <

- sw.b[sw.b$season == "1" ,]

print ( bwplot (biomass ~ Region | Ecotype, data = sw.b.one,

xlab

= "Region"

, ylab

= list ( expression ( paste ( "Dry biomass yield "

, "(Mg " ,ha^

- 1, ")" ))),

ylim

= c (0,35),

main = "growing season 1"

,

fill

= c ( "ivory2" ),

panel = function (...) {

panel.grid (v = 0, h

=

- 1)

panel.bwplot (.. .)

},

layout = c (1,2)))

dev.off ()

##FIGURE 3

- PART 2

png ( "./manuscript figures/season2.png"

, width =600, height =1200, res=120)

sw.b.two <

- sw.b[sw.b$season == "2" ,]

print ( bwplot (biomass ~ Region | Ecotype, data = sw.b.two,

xlab

= "Region"

, ylab

= list ( expression ( paste ( "Dry biomass yield "

, "(Mg " ,ha^

- 1, ")" ))),

ylim

= c (0,35),

main = "growing season 2"

,

fill

= c ( "ivory3" ),

panel = function (...) {

panel.grid (v = 0, h

=

- 1)

panel.bwplot (. ..)

},

layout = c (1,2)))

dev.off ()

mean (sw.b.two[sw.b.two$Region == "South" & sw.b.two$Ecotype == "L"

, "biomass" ])

## The mean for the south lowland is 9.3

mean (sw.b.two[sw.b.two$Region == "South" & sw.b.two$Ecotype == "U"

, "biomass" ])

## The mean for the south upland is 5.5

mean (sw.b.two[sw.b.two$Region == "Lower

- Central" & sw.b.two$Ecotype == "L"

, "biomass" ])

## The mean for the lower central lowland is 14.3

median (sw.b.two[sw.b.two$Region == "Lower

- Central" & sw.b.two$Ecotype == "L"

, "biomass" ])

## The median for the lower central lowland is 10.3

mean (sw.b.two[sw.b.two$Region == "Lower

- Central" & sw.b.two$Ecotype == "U"

, "biomass" ])

## The mean for the lower central upland is 12.9

mean (sw.b.two[sw.b.two$Region == "Upper

- Centr al" & sw.b.two$Ecotype == "L"

, "biomass" ])

## The mean for the upper central lowland is 11.1

median (sw.b.two[sw.b.two$Region == "Upper

- Central" & sw.b.two$Ecotype == "L"

, "biomass" ])

## The mean for the upper central lowland is 11.2

mean (sw.b.two[sw.b. two$Region == "Upper

- Central" & sw.b.two$Ecotype == "U"

, "biomass" ])

## The mean for the upper central upland is 8.2

mean (sw.b.two[sw.b.two$Region == "North" & sw.b.two$Ecotype == "L"

, "biomass" ])

## 136 ## The mean for the north lowland is NA

mean (sw.b.two[s w.b.two$Region == "North" & sw.b.two$Ecotype == "U"

, "biomass" ])

## The mean for the north upland is NA

##FIGURE 3

- PART 3

png ( "./manuscript figures/season3.png"

, width =600, height =1200, res=120)

sw.b.three <

- sw.b[sw.b$season == ">=3" ,]

print ( bwplot (biomass ~ Region | Ecotype, data = sw.b.three,

xlab

= "Region"

, ylab

= list ( expression ( paste ( "Dry biomass yield "

, "(Mg " ,ha^

- 1, ")" ))),

ylim

= c (0,35),

main = "growing seasons >=3"

,

fill

= c ( "ivory4" ),

panel = function (. ..) {

panel.grid (v = 0, h

=

- 1)

panel.bwplot (...)

},

layout = c (1,2)))

dev.off ()

mean (sw.b.three[sw.b.three$Region == "South" & sw.b.three$Ecotype == "L"

, "biomass" ])

## 10.8

mean (sw.b.three[sw.b.three$Region == "South" & sw.b.three$Ecotype == "U"

, "biomass" ])

## 5.1

mean (sw.b.three[sw.b.three$Region == "Lower

- Central" & sw.b.three$Ecotype == "L"

, "biomass" ])

## 14.6

median (sw.b.three[sw.b.three$Region == "Lower

- Central" & sw.b.three$Ecotype == "L"

, "biomass" ])

## 14.6

mean (sw.b.three[sw.b.three$Region == "Lower

- Central" & sw.b.three$Ecotype == "U"

, "biomass" ])

## 11.9

mean (sw.b.three[sw.b.three$Region == "Upper

- Central" & sw.b.three$Ecotype == "L"

, "biomass" ])

## 10.4

median (sw.b.three[sw.b.three$Region == "Upper

- Central" & sw.b.three$Ecotype == "L"

, "biomass" ])

## 10.7

mean (sw.b.three[sw.b.three$Region == "Upper

- Central" & sw.b.three$Ecotype == "U"

, "biomass" ])

## 10

mean (sw.b.three[sw.b.three$Region == "North" & sw.b.three$Ecotype == "L"

, "biomass" ]) ## NA

mean (sw.b.three[sw.b.three$Region == "North" & sw.b.three$Ecotype == "U"

, "biomass" ]) ## NA

library (latticeExtra)

sw.b$harvest.timing <

- factor (sw.b$harvest.timing, levels = c ( "PSC"

, "ES" ))

png ( "./manuscript figures/harvesttiming

- ellipse.png"

, widt h =1500, height =600, res=120)

xyplot (biomass ~Nrate | factor (Region), groups=harvest.timing, data = sw.b,

xlab

= list ( expression ( paste ( "N rate "

, "(kg " ,ha^

- 1, ")" )), cex =1.2),

ylab

= list ( expression ( paste ( "Dry biomass yield "

, "(Mg " ,ha^

- 1, ")" )), cex =1.2),

col

= c ( "Green"

, "Brown" ), layout

= c (4,1), auto.key= TRUE

, panel = function (x,y,...){

panel.xyplot (x,y, type = c ( "p" ), pch

= c (15,25), fill

= "transparent"

,

cex =1.2, ...)

panel.ellip se(x, y, lwd = 2, lty

= c (1,2), center.pch= NULL , level=0.80, ...)

},

key= list ( text

= list ( c ( "PSC"

, "ES" )), x=0.8, y=.9, cex.title=1.2, pch

= c (15,25),

points

= TRUE

, cex =1.2, lty

= c (1, 2), lwd =2, lines

= TRUE

,

col

= c ( "Green"

, "Brown" )))

dev.off ()

#yield increases from season 2 to seasons >=3

mean (sw.b[sw.b$Region == "South" & sw.b$harvest.timing == "PSC"

, "biomass" ]) ## 11.1

sd (sw.b[sw.b$Region == "South" & sw.b$harvest.timing == "PSC"

, "biomass" ]) ## 6.3

mean (sw.b[sw.b$Region == "South" & sw.b$harvest.timing == "ES"

, "biomass" ]) ## 8.2

sd (sw.b[sw.b$Region == "South" & sw.b$harvest.timing == "ES"

, "biomass" ]) ## 5

mean (sw.b[sw.b$Region == "North" & sw.b$harvest.timing == "PSC"

, "biomass" ]) ## 6.4

sd (sw .b[sw.b$Region == "North" & sw.b$harvest.timing == "PSC"

, "biomass" ]) ## 3

mean (sw.b[sw.b$Region == "North" & sw.b$harvest.timing == "ES"

, "biomass" ]) ## 7.8

sd (sw.b[sw.b$Region == "North" & sw.b$harvest.timing == "ES"

, "biomass" ]) ## 3.2

##it appears that i n the south, PSC yields better than ES, but in the North it is

##the other way around. This may have to do with early harvesting injuring the crop

## 137

##long term in the north, being unprepared for winter due to early harvest.

##The south appears to have s ome recovery time due to lack of temperatures excessively

##below freezing

##precipitation

png ( "./manuscript figures/spring precip.png"

, width =1200, height =600, res=120)

xyplot (biomass ~ Apr.June.precip..mm., group=Ecotype, data = sw.b,

ylab

= lis t ( expression ( paste ( "Dry biomass yield "

, "(Mg " ,ha^

- 1, ")" )), cex =1.2),

xlab = list ( "April

- June Precipitation (mm)"

, cex =1.2),

pch

= c (1,17), col

= c ( "black"

, "grey" ), fill

= "transparent"

, lty

= c (2,1), lwd = 2,

type = c ( "p"

, "r" ), cex =1,

xlim

= c (50,1020),

key= list (x=0.5, y=1, cex.title=1.2,

pch

= c (1,17), cex =1.2,

lty

= c (2,1), lwd =2, points

= TRUE

, lines

= TRUE

,

col

= c ( "black"

, "grey" ),

text

= list ( c ( "Lowland"

, "Upland" ))))

dev.off ()

#biomass yields tend to be higher with greater amounts of early season precipitation

#lowland yield better than upland

sw.b[sw.b$biomass > 34,]

#sw.b[sw.b$biomass > 34,10] <

- NA

sw.b.n <

- na.omit (sw.b)

sw.b.nG <

- groupedData (biomass ~ Nrate | sites, dat a = sw.b.n)

unique (sw.b.n$sites)

xyplot (biomass ~ Nrate | sites, type = c ( "p"

, "a" ), data = sw.b.n)

##MODEL BUILDING

## model without random Nrate effects

quad.lme <

- lme (biomass ~ Nrate + I (Nrate^2) ,

random = ~ 1 | sites, data = sw.b. nG,

na.action

= "na.omit" )

quad.lme

anova (quad.lme)

plot ( ranef (quad.lme))

plot ( intervals (quad.lme))

## model with random effect for the quadratic term

fitquad.lme <

- lme (biomass ~ Nrate + I (Nrate^2) ,

random = ~ 1 + Nrate + I (N rate^2) | sites, data = sw.b.nG,

na.action

= "na.omit" )

fitquad.lme

anova (fitquad.lme)

plot ( ranef (fitquad.lme))

plot ( intervals (fitquad.lme))

## A simpler model does not have random effect for the quadratic term

fitsimple.lme <

- lme (biomass ~ Nrate + I (Nrate^2) ,

random = ~ 1 + Nrate | sites, data = sw.b.nG,

na.action

= "na.omit" )

fitsimple.lme

anova (quad.lme,fitquad.lme,fitsimple.lme)

# Model df AIC BIC logLik Test L.Ratio p

- valu e

#quad.lme 1 5 3490.786 3512.788

- 1740.393

#fitquad.lme 2 10 3485.885 3529.888

- 1732.943 1 vs 2 14.901160 0.0108

#fitsimple.lme 3 7 3480.126 3510.928

- 1733.063 2 vs 3 0.241361 0.9707

anova (fitsimple.lme)

p lot ( ranef (fitsimple.lme))

## The simpler model is better, rather parsimony is better here.

## 138

coef (fitsimple.lme)

plot ( augPred (fitsimple.lme, level=0:1, data =sw.b.nG))

##NOW WE ADD IN VARIABLES and use a backward elimination method. IF p

- value larger

##th an 0.1 then drop. adding all main effects and suspected interactions that

##could have valid interpretations, based upon literature and xyplots.

fit.lme <

- lme (biomass ~ Nrate +

I (Nrate^2) +

Region +

Apr.June.p recip..mm. +

season +

Ecotype +

harvest.timing +

Cuttings +

Apr.June.precip..mm.:Region +

Apr.June.precip..mm.:Ecotype +

Apr.June.precip..mm.:season +

Nrate:Ecotype +

I (Nrate^2):Ecotype +

Region:harvest.timing +

season:Ecotype,

random = ~ 1 + Nrate | sites,

data = sw.b.nG)

#other potential interacations that result in singularity or don't makes sense.

#Region:Ecotype clearly is significant in reality but it is suspected that the

##unbalanced nature of the data render this interaction a singularity problem.

#harvest.timing:Cuttings interact ion comes out significant if left in, but I

##don't think there is a logical explanation for it, and Cuttings alone is non significant.

##I just tried this, but don't think it is logical.

anova (fit.lme)

# numDF denDF F

- val ue p

- value

#(Intercept) 1 487 918.5267 <.0001

#Nrate 1 487 34.8070 <.0001

#I(Nrate^2) 1 487 4.3686 0.0371

#Region 3 93 16.7507 <.0001

#Apr.June .precip..mm. 1 487 14.7192 0.0001

#season 2 487 3.1355 0.0444

#Ecotype 1 487 104.6983 <.0001

#harvest.timing 1 487 3.1562 0.0763

#Cuttings 1 487 0.1949 0.6591

#Region:Apr.June.precip..mm. 3 487 1.5727 0.1951

#Apr.June.precip..mm.:Ecotype 1 487 8.7157 0.0033

#Apr.June.precip..mm.:season 2 487 7.6962 0.0005

#Nrate:Ecotype 1 487 3.362 1 0.0673

#I(Nrate^2):Ecotype 1 487 0.2175 0.6411

#Region:harvest.timing 3 487 2.2997 0.0766

#season:Ecotype 2 487 0.4422 0.6429

#now removing non

- significant terms

fit2.lme <

- lme (biomass ~ Nra te +

I (Nrate^2) +

Region +

Apr.June.precip..mm. +

season +

Ecotype +

harvest.timing +

Apr.June.precip..mm.:Ecotype +

Apr.June.precip..mm .:season +

Nrate:Ecotype +

Region:harvest.timing,

random = ~ 1 + Nrate | sites,

data = sw.b.nG)

anova (fit2.lme)

## 139

# numDF denDF F

- value p

- value

#(Intercept) 1 494 899.7931 <.0001

#Nrate 1 494 34.9935 <.0001

#I(Nrate^2) 1 494 4.4053 0.0363

#Region 3 93 16.2564 <.0001

#Apr.June.precip..mm. 1 494 14.7079 0.0001

#season 2 494 3.0845 0.0466

#Ecotype 1 494 104.9683 <.0001

#harvest.timing 1 494 3.1382 0.0771

#Apr.June.precip..mm.:Ecotype 1 494 6.8370 0.009 2

#Apr.June.precip..mm.:season 2 494 7.4240 0.0007

#Nrate:Ecotype 1 494 3.4461 0.0640

#Region:harvest.timing 3 494 2.2497 0.0817

coef (fit2.lme)

summary (fit2.lme)

fixed.effects (fit2.lme)

intervals (fit2.lme)

## Spring precipitation seems to interact strongly with season, this means

## that biomass responds differently to precipitation depending on the

## season. As the crop matures from season 1 to season 2 and seasons >=3,

## similar precipitation levels result in higher biomass yields

#also spring precipitation interacts with ecotype, and this makes since since lowland ecotypes

#tend to come from wetter environments than upland ecotypes, and likely do better under wetter

#conditions than upland ecotypes.

#Lowland and upland ecotypes may have slightly different N requirements. This has been suggested

#in the old literature

##in the south, PSC harvests tend to result in greater biomass yields, but not so in the north.

##In the north, PSC har vest likely injured the stands by removing nutrients needed for winter

##survival, causing yields to decline over time. This is just one possible explanation for

##this interaction

plot (fit2.lme)

qqnorm (fit2.lme, abline

= c (0,1))

## final model

unique (s w.b.nG$sites)

## Sort the sites

sw.b.nG$sites <

- factor (sw.b.nG$sites, levels = unique (sw.b.nG$sites))

unique (sw.b.nG$sites)

augpred <

- augPred (fit2.lme, level=0:1, group=Region, data =sw.b.nG)

augpred$.groups <

- factor (augpred$.groups, levels = unique (sw.b .nG$sites))

png ( "./manuscript figures/site_predictions.png"

, width =1200, height =1600, res=140)

plot (augpred,

xlab

= list ( expression ( paste ( "N rate "

, "(kg " ,ha^

- 1, ")" )), cex =1.2),

ylab

= list ( expression ( paste ( "Dry biomass yield "

, "(Mg " ,ha^

- 1, ")" )), cex =1.2),

as.table = TRUE

, layout = c (8,13),

lty

= c (1,2), col

= "black"

, key= list ( text

= list ( c ( "fixed"

, "experimental unit" )), lty

= c (1,2), lines

= T ))

dev.off ()

#plotting predictions 300mm SPRING PRECIPITATION, PSC, growing seasons >=3

rgn <

- c ( "South"

, "Lower

- Central"

, "Upper

- Central"

, "North" )

nd <

- expand.grid (Region = rgn, season = ">=3" , Nrate = c (0,50,100,150,200,250),

Ecotype = c ( "L"

, "U" ), Apr.June.precip..mm.=300,

harvest.timing = "PSC" )

pp <

- predict (fit2.lme, newdata = nd, level = 0)

preds <

- data.frame (pp=pp, nd)

preds$ type <

- "predicted"

names (preds) <

- c ( "biomass"

, "Region"

, "season"

, "Nrate"

, "Ecotype"

, "Precip"

, "harvest.timing"

, "type" )

sw.psc <

- sw.b.nG[sw.b.nG$harvest.timing == "PSC" & sw.b. nG$season == ">=3" ,]

# sw.b.nG$Apr.June.precip..mm. >=200 & sw.b.nG$Apr.June.precip..mm. <=400,]

## 140

obs <

- with (sw.psc, data.frame (biomass = biomass, Region = Region, season = season,

Nrate = Nrate, Ecotype = Ecotype,

Precip = Apr.June.precip..mm., harvest.timing = harvest.timing, type = "observed" ))

pred.obs <

- rbind (preds, obs)

pred.obs$Region <

- factor (pred.obs$Region,

levels

= c ( "South"

, "Lower

- Central"

, "Upper

- Central"

, "North" ))

pred.obs$ type <

- factor (p red.obs$ type )

pred.obs$type.Ecotype <

- with (pred.obs, type :Ecotype)

pred.obs[pred.obs$ type == 'predicted' & pred.obs$Region == 'North'

& pred.obs$Ecotype == 'L'

, 'biomass' ] <

- NA

png ( './manuscript figures/predicted

- and

- observed

- PSC

- 300mm.png'

, width =1500, height =600, res=120)

xyplot (biomass ~ Nrate | Region,

data = pred.obs, main

= "growing seasons >=3, PSC harvest, 300mm spring precipitation"

, cex.title=1.2,

pch = c (1,17), fill

= "transparent"

,

cex =1.2

, groups = type.Ecotype,

layout = c (4,1),

xlab

= list ( expression ( paste ( "N rate "

, "(kg " ,ha^

- 1, ")" )), cex =1.2),

ylab

= list ( expression ( paste ( "Dry biomass yield "

, "(Mg " ,ha^

- 1, ")" )), cex =1.2),

col = c ( "black"

, "grey" ),

ylim

= c (0,35),

panel = function (x,y,...){

panel.superpose.2 (x,y, type

= c ( 'p'

, 'p'

, 'l'

, 'l'

, 'l'

, 'l' ), lty

= c (2,1), lwd = 2,...)

},

key = list (x=0.8, y=.9, cex.title=1.2,

pch

= c (1,17), cex =1.2,

lty

= c (2,1), lwd =2, p oints

= TRUE

, lines

= TRUE

,

col

= c ( "black"

, "grey" ),

text

= list ( c ( "Lowland"

, "Upland" ))))

dev.off ()

#plotting predictions 300mm SPRING PRECIPITATION, ES, growing seasons >=3

rgn <

- c ( "South"

, "Lower

- Central"

, "Upper

- Central"

, "North" )

nd <

- expand.grid (Region = rgn, season = ">=3" , Nrate = c (0,50,100,150,200,250),

Ecotype = c ( "L"

, "U" ),

Apr.June.precip..mm.=300,

harvest.timing = "ES" )

pp <

- predict (fit2.lme, newdata = nd, level = 0)

preds <

- data.frame (pp=pp, nd)

preds$ type <

- "pr edicted"

names (preds) <

- c ( "biomass"

, "Region"

, "season"

, "Nrate"

, "Ecotype"

, "Precip"

,

"harvest.timing"

, "type" )

sw.es <

- sw.b.nG[sw.b.nG$harvest.timing == "ES" & sw.b.nG$season == ">=3" ,]

# & sw.b.nG$Apr.June.precip..mm. >=200 & sw .b.nG$Apr.June.precip..mm. <=400,]

obs <

- with (sw.es, data.frame (biomass = biomass, Region = Region, season = season,

Nrate = Nrate, Ecotype = Ecotype,

Precip = Apr.June.precip..mm., harvest.timing = harvest.timing, t ype = "observed" ))

pred.obs <

- rbind (preds, obs)

pred.obs$Region <

- factor (pred.obs$Region,

levels

= c ( "South"

, "Lower

- Central"

, "Upper

- Central"

, "North" ))

pred.obs$ type <

- factor (pred.obs$ type )

pred.obs$type.Ecotype <

- with (pred.obs, type :Ecotype)

pred.obs[pred.obs$ type == 'predicted' & pred.obs$Region == 'North'

## 141 & pred.obs$Ecotype == 'L'

, 'biomass' ] <

- NA

png ( './manuscript figures/predicted

- and

- observed

- ES

- 300mm.png'

, width =1500, height =600, res=120)

xyplot (biomass ~ Nrate | Region,

data = pred.obs, main

= "growing seasons >=3, ES harvest, 300mm spring precipitation"

, cex.title=1.2,

pch = c (1,17), fill

= "transparent"

,

cex =1.2,

groups = type.Ecotype,

layout = c (4,1),

xlab

= list ( expression ( paste ( "N rat e "

, "(kg " ,ha^

- 1, ")" )), cex =1.2),

ylab

= list ( expression ( paste ( "Dry biomass yield "

, "(Mg " ,ha^

- 1, ")" )), cex =1.2),

col = c ( "black"

, "grey" ),

ylim

= c (0,35),

panel = function (x,y,...){

panel.superpose.2 (x,y, type

= c ( 'p'

, 'p'

, ' l'

, 'l'

, 'l'

, 'l' ), lty

= c (2,1), lwd = 2,...)

},

key = list (x=0.8, y=.9, cex.title=1.2,

pch

= c (1,17), cex =1.2,

lty

= c (2,1), lwd =2, points

= TRUE

, lines

= TRUE

,

col

= c ( "black"

, "grey" ),

text

= list ( c ( "Lowland"

, "Upla nd" ))))

dev.off ()

##removing outliers for supplementary results

## beginnning with final model

fit2.lme <

- lme (biomass ~ Nrate +

I (Nrate^2) +

Region +

Apr.June.precip..mm. +

season +

Ecotype +

harvest.timing +

Apr.June.precip..mm.:Ecotype +

Apr.June.precip..mm.:season +

Nrate:Ecotype +

Region:harvest.timing,

random = ~ 1 + Nrate | sites,

data = sw.b.nG)

anova (fit2.lme)

# numDF denDF F

- value p

- value

#(Intercept) 1 494 899.7931 <.0001

#Nrate 1 494 34.9935 <.0001

#I(Nrate^2) 1 494 4.4053 0.0363

#Region 3 93 16.2564 <.0001

#Apr.June.precip..mm. 1 494 14.7079 0.0001

#season 2 494 3.0845 0.0466

#Ecotype 1 494 10 4.9683 <.0001

#harvest.timing 1 494 3.1382 0.0771

#Apr.June.precip..mm.:Ecotype 1 494 6.8370 0.0092

#Apr.June.precip..mm.:season 2 494 7.4240 0.0007

#Nrate:Ecotype 1 494 3.4461 0.0640

#Regi on:harvest.timing 3 494 2.2497 0.0817

plot (fit2.lme) ##residuals look OK, some outliers

## checking the residuals for outliers

rnm <

- row.names ( na.omit (sw.b.nG[,

- 3]))

plot (fit2.lme, id = 0.08, idLabels = rnm)

qqnorm (fit2.lme, abline

= c (0, 1))

## Outliers 233, 387, 29, 617, 235

sw.b.nmd <

- na.omit (sw.b.nG)

## 142

sw.b.n2 <

- sw.b.nmd[! row.names (sw.b.nmd) %in% c (233, 387, 29, 617, 235),]

## What are these outliers?

unique (sw.b.nmd[! row.names (sw.b.nmd) %in% c (233, 387, 29, 617, 235),]$sites)

sw.b.nm d[ row.names (sw.b.nmd) %in% c (233, 387, 29, 617, 235),]

# sites Region Apr.June.precip..mm. season Ecotype Cuttings harvest.timing Nrate biomass

#29 10a South 393 >=3 L 1 ES 150 24.36441

# 233 24a Lower

- Central 379 2 L 2 ES 0 33.40000

#235 24a Lower

- Central 379 2 L 2 ES 224 29.50000

#387 40a South 243 >=3 L 2 ES 84 28.63500

#617 72a Lower

- Central 475 >=3 L 2 ES 67 25.97100

fit2b.lme <

- lme (biomass ~ Nrate +

I (Nrate^2) +

Region +

Apr.June. precip..mm. +

season +

Ecotype +

harvest.timing +

Apr.June.precip..mm.:Ecotype +

Apr.June.precip..mm.:season +

Nrate:Ecotype +

Region:harvest.timing,

random = ~ 1 + Nrate | sites,

data = sw.b.n2)

anova (fit2b.lme)

# numDF denDF F

- value p

- value

#(Intercept) 1 489 938.0432 <.0001

#Nrate 1 4 89 38.0413 <.0001

#I(Nrate^2) 1 489 7.6106 0.0060

#Region 3 93 17.2381 <.0001

#Apr.June.precip..mm. 1 489 18.0897 <.0001

#season 2 489 3.8675 0.0216

#Ecotype 1 489 110.0736 <.0001

#harvest.timing 1 489 3.8717 0.0497

#Apr.June.precip..mm.:Ecotype 1 489 5.9813 0.0148

#Apr.June.precip..mm.:season 2 489 8.7673 0.0002

#Nrate:Ecotype 1 489 4.7955 0.0290

#Region:harvest.timing 3 489 2.5163 0.0576

plot (fit2b.lme) ##residuals look pretty good

plot ( augPred (fit2b.lme, level=0:1))

qqnorm (fit2b.lme, abline

= c (0,1))

## 143 

## APPENDIX B

Sel ect ed SAS and R code for Ch. 3

Plant height

- Ch.3

, Table 3.6, Fig. 3.1

library ( nlme )

library (Hmisc)

library (lattice)

library (graphics)

#2009 & 2010 ht data

grwth <

- read.csv ( file = "../2009 2010 ht data.csv"

, skip = 2, na.strings

= "." )

head (grwth)

grwth

grwt h <

- grwth[

- 373:

- 426,] #getting rid of Illinois

head (grwth)

unique (grwth$Year)

grwth$Year <

- as.factor (grwth$Year)

head (grwth)

xyplot (grwth[,8] ~ grwth[,10],

ylab = "Height"

,

xlab = "day of year"

, groups = grwth[,1],

auto.ke y= TRUE )

grwth2 <

- data.frame (env=grwth[,11],

doy = grwth[,10],

Nrate = grwth[,6],

height = grwth[,8],

TT = grwth[,9])

## developing a model

expu <

- with (gr wth, Environment: factor (Rep): factor (N.rate.kg.ha))

grwthG <

- groupedData ( height ~ doy | expu, data = grwth2)

head (grwthG)

fit.nls <

- nlsList ( height ~ SSlogis (doy, Asym, xmid, scal), data = grwthG)

fit.nlme <

- nlme (fit.nls)

anova (fit.nlme)

plot ( augPred (fi t.nlme, level = 0:1))

fix2 <

- fixef (fit.nlme)

fix2

fit2.nlme <

- update (fit.nlme, fixed = Asym + xmid + scal ~ Nrate,

start = c (fix2[1], 0, fix2[2], 0, fix2[3], 0))

anova (fit2.nlme)

# numDF denDF F

- value p

- value

#Asy m.(Intercept) 1 379 6453.716 <.0001

#Asym.Nrate 1 379 0.001 0.9725

#xmid.(Intercept) 1 379 14449.984 <.0001

#xmid.Nrate 1 379 0.001 0.9717

#scal.(Intercept) 1 379 2782.442 <.0001

#scal.Nrate 1 379 0.155 0.6940

## Add the effect of env

fix3 <

- fixef (fit2.nlme)

fix3

fit3.nlme <

- update (fit.nlme, fixed = Asym + xmid + scal ~ Nrate + env,

start = c (fix3[1:2], 0, 0, 0, 0, 0, fix3[3:4], 0, 0, 0, 0, 0, fix3[5:6], 0, 0, 0, 0, 0))

anova (f it3.nlme)

## 144

## Add the interaction of Nrate*loc

fix4 <

- fixef (fit3.nlme)

fix4

fit4.nlme <

- update (fit3.nlme, fixed = Asym + xmid + scal ~ Nrate * env,

start = c (fix4[1:7], 0, 0, 0, 0, 0, fix4[8:14], 0, 0, 0, 0, 0, fix4[15:21], 0, 0, 0, 0, 0))

anova (fit4. nlme)

# numDF denDF F

- value p

- value

#Asym.(Intercept) 1 349 14528.41 <.0001

#Asym.Nrate 1 349 1.21 0.2713

#Asym.env 5 349 1039.42 <.0001

#Asym.Nrate:env 5 349 3.08 0.0099

#xmid.(Int ercept) 1 349 167106.08 <.0001

#xmid.Nrate 1 349 0.02 0.8942

#xmid.env 5 349 96.82 <.0001

#xmid.Nrate:env 5 349 0.74 0.5913

#scal.(Intercept) 1 349 3552.38 <.0001

#scal.Nrate 1 349 0.40 0.5276

#scal.env 5 349 11.61 <.0001

#scal.Nrate:env 5 349 0.25 0.9382

## Simplify error structure

##looking for more negative values of BIC

fit5.nlme <

- update (fit4.nlme, random = pdDiag ( list (Asym + xmid + scal ~ 1)))

fit5.nlme #the std. deviations for xmid and scale are very small here and could be eliminated

anova (fit4.nlme, fit5.nlme)

## Simpler model is better

fit6.nlme <

- update (fit5.nlme, random = pdDiag (Asym ~ 1))

anova (fit5.nlme, fit6.nlme)

#th is simplification is best

fit7.nlme <

- update (fit6.nlme, weights = varPower ())

fit7.nlme

anova (fit6.nlme, fit7.nlme)

# Model df AIC BIC logLik Test L.Ratio p

- value

#fit6.nlme 1 38

- 422.4554

- 265.8007 249.2277

#fit7.nlme 2 39

- 420.4606

- 259.6834 249.2303 1 vs 2 0.005209665 0.9425

#does not seem to be any better since BIC values are not more negative for fit7.nlme

#I will stick with fit6.nlme

## Investigating the effect, especially the int eraction, now that our error structure is settled?

anova (fit6.nlme)

# numDF denDF F

- value p

- value

#Asym.(Intercept) 1 349 7945.70 <.0001

#Asym.Nrate 1 349 0.60 0.4392

#Asym.env 5 349 827.98 <. 0001

#Asym.Nrate:env 5 349 1.74 0.1249

#xmid.(Intercept) 1 349 168312.32 <.0001

#xmid.Nrate 1 349 0.02 0.8832

#xmid.env 5 349 97.36 <.0001

#xmid.Nrate:env 5 349 0.74 0.5954

#scal.(Inte rcept) 1 349 3537.96 <.0001

#scal.Nrate 1 349 0.41 0.5247

#scal.env 5 349 11.60 <.0001

#scal.Nrate:env 5 349 0.26 0.9369

#the interaction is only somewhat sifnificant for the asymptote,

#the eff ect of N is inconsistent from location to location, and appears generally to not be

important

## 145

#I will reduce down to just Nrate + env

fit8.nlme <

- update (fit6.nlme, fixed = Asym + xmid + scal ~ Nrate + env,

start = c (fix3[1:2], 0, 0, 0, 0, 0, fix3[3:4], 0, 0, 0, 0, 0, fix3[5:6], 0, 0, 0, 0, 0))

plot (fit8.nlme)

qqnorm (fit8.nlme, abline

= c (0,1))

#residuals look are not too bad

fit8.ss <

- summary (fit8.nlme)

fit8.ss$tTable

# Value Std.Error DF t

- value p

- v alue

#Asym.(Intercept) 3.079769e+00 0.0491411709 364 62.67186833 4.178050e

- 197

#Asym.Nrate

- 1.515855e

- 05 0.0002937831 364

- 0.05159775 9.588775e

- 01

#Asym.envKY

- 2010 3.110370e

- 01 0.0569491855 364 5.46165884 8.755655e

- 08

#Asym.envNE

- 2009

- 2.33 6358e

- 01 0.0698799919 364

- 3.34338555 9.136056e

- 04

#Asym.envNE

- 2010 7.111425e

- 01 0.0558585500 364 12.73113115 5.792894e

- 31

#Asym.envNJ

- 2009 2.901095e

- 01 0.0574492388 364 5.04984017 7.005466e

- 07

#Asym.envNJ

- 2010

- 7.862059e

- 02 0.0550161745 364

- 1.42904501 1.538489e

- 01

#xmid.(Intercept) 1.514565e+02 1.2454308733 364 121.60972985 7.878221e

- 297

#xmid.Nrate

- 2.097660e

- 03 0.0075318111 364

- 0.27850673 7.807815e

- 01

#xmid.envKY

- 2010

- 7.984292e+00 1.4510276017 364

- 5.50250872 7.073680e

- 08

#xm id.envNE

- 2009 2.000777e+01 1.9235455543 364 10.40150308 2.334178e

- 22

#xmid.envNE

- 2010 1.028235e+01 1.3937302031 364 7.37757353 1.100905e

- 12

#xmid.envNJ

- 2009 8.788309e+00 1.4479067746 364 6.06966470 3.220649e

- 09

#xmid.envNJ

- 2010

- 4.613128e+00 1.3988466305 364

- 3.29780847 1.070451e

- 03

#scal.(Intercept) 2.174922e+01 1.0618779263 364 20.48184901 1.446951e

- 62

#scal.Nrate 9.255571e

- 04 0.0067791685 364 0.13652959 8.914781e

- 01

#scal.envKY

- 2010

- 8.905391e

- 01 1.2483827914 364

- 0.713354 23 4.760837e

- 01

#scal.envNE

- 2009 2.477236e+00 1.6164154659 364 1.53254920 1.262556e

- 01

#scal.envNE

- 2010 8.951886e

- 02 1.1837697880 364 0.07562185 9.397615e

- 01

#scal.envNJ

- 2009

- 1.927412e+00 1.2862738772 364

- 1.49844575 1.348842e

- 01

#scal.envNJ

- 2010

- 6.366202e+00 1.2409978986 364

- 5.12990539 4.723678e

- 07

intervals (fit8.ss, level=0.95, which

= "fixed" )

# lower est. upper

#Asym.(Intercept) 2.985384e+00 3.079769e+00 3.174153826

#Asym.Nrate

- 5.7 94241e

- 04

- 1.515855e

- 05 0.000549107

#Asym.envKY

- 2010 2.016554e

- 01 3.110370e

- 01 0.420418615

#Asym.envNE

- 2009

- 3.678534e

- 01

- 2.336358e

- 01

- 0.099418124

#Asym.envNE

- 2010 6.038557e

- 01 7.111425e

- 01 0.818429349

#Asym.envNJ

- 2009 1.797674e

- 01 2.901 095e

- 01 0.400451512

#Asym.envNJ

- 2010

- 1.842895e

- 01

- 7.862059e

- 02 0.027048292

#xmid.(Intercept) 1.490644e+02 1.514565e+02 153.848595643

#xmid.Nrate

- 1.656392e

- 02

- 2.097660e

- 03 0.012368596

#xmid.envKY

- 2010

- 1.077126e+01

- 7.984292e+00

- 5.1973 21360

#xmid.envNE

- 2009 1.631323e+01 2.000777e+01 23.702295046

#xmid.envNE

- 2010 7.605427e+00 1.028235e+01 12.959267334

#xmid.envNJ

- 2009 6.007332e+00 8.788309e+00 11.569285179

#xmid.envNJ

- 2010

- 7.299876e+00

- 4.613128e+00

- 1.926380928

#scal.(Int ercept) 1.970969e+01 2.174922e+01 23.788759082

#scal.Nrate

- 1.209511e

- 02 9.255571e

- 04 0.013946222

#scal.envKY

- 2010

- 3.288292e+00

- 8.905391e

- 01 1.507214158

#scal.envNE

- 2009

- 6.273929e

- 01 2.477236e+00 5.581865317

#scal.envNE

- 2010

- 2.18413 3e+00 8.951886e

- 02 2.363170778

#scal.envNJ

- 2009

- 4.397942e+00

- 1.927412e+00 0.543118617

#scal.envNJ

- 2010

- 8.749771e+00

- 6.366202e+00

- 3.982632569

unique (grwth2$env)

grwth2$env <

- factor (grwth2$env,

levels

= c ( "NJ

- 2009"

, "NJ

- 2010"

, "NE

- 2009"

, "NE

- 2010"

, "KY

- 2009"

, "KY

- 2010" ), order

= TRUE )

unique (grwth2$env)

newdat <

- expand.grid (Nrate = unique (grwth2$Nrate), env = unique (grwth2$env), doy = 50:290)

preds <

- predict (fit6.nlme , newdata = newdat, level = 0)

## 146

prds <

- cbind (newdat, preds)

obs <

- with (grwthG, data.frame (Nrate = Nrate, env = env, doy = doy, height = height ))

pred.obs <

- merge (prds, obs, by

= c ( "Nrate"

, "env"

, "doy" ), all

= TRUE )

## Do one plot at a time and then combine them

## Kentucky 2009

obs.KY.2009 <

- obs[obs$e nv == 'KY

- 2009' ,]

prds.KY.2009 <

- prds[prds$env == 'KY

- 2009' ,]

plot1 <

- xyplot ( height ~ doy, data = obs.KY.2009, groups = Nrate,

col = 'black'

, fill = 'transparent'

, panel = function (x,y,...){

panel.xyplo t (x,y, type = 'p'

, pch

= c (17,19,22), ...)

panel.xyplot (prds.KY.2009[prds.KY.2009$Nrate == 0,]$doy,

prds.KY.2009[prds.KY.2009$Nrate == 0,]$preds, type

= 'l'

, lty = 1,...)

panel.xyplot (prds.KY.2009[prds.KY .2009$Nrate == 60,]$doy,

prds.KY.2009[prds.KY.2009$Nrate == 60,]$preds, type

= 'l'

, lty = 2,...)

panel.xyplot (prds.KY.2009[prds.KY.2009$Nrate == 120,]$doy,

prds.KY.2009[prds.KY.2009$Nrate == 120,]$pre ds, type

= 'l'

, lty = 3,...)

panel.text (170,4, labels = 'KY

- 2009'

, cex = 1.2)

}, ylab = list ( 'height, m'

, cex =1), ylim = c (0,4.2),

xlim = c (50,290), xlab = list ( 'DOY'

, cex =1), cex =1.2

)

## Kentucky 2010

obs.KY.2010 <

- obs[obs$env == 'KY

- 2010' ,]

prds.KY.2010 <

- prds[prds$env == 'KY

- 2010' ,]

plot2 <

- xyplot ( height ~ doy, data = obs.KY.2010, groups = Nrate,

col = 'black'

, fill = 'transparent'

, panel = function (x,y,...){

panel.xyplot (x,y, type = 'p'

, pch

= c (17,19,22), ...)

panel.xyplot (prds.KY.2010[prds.KY.2010$Nrate == 0,]$doy,

prds.KY.2010[prds.KY.2010$Nrate == 0,]$preds, type

= 'l'

, lty

= 1,...)

panel.xyplot (prds.KY.2010[prds.KY.2010$Nrate == 60,]$doy,

prds.KY.2010[prds.KY.2010$Nrate == 60,]$preds, type

= 'l'

, lty = 2,...)

panel.xyplot (prds.KY.2010[prds.KY.2010$Nrate == 120,]$doy,

prds.KY.2010[prds.KY.2010$Nrate == 120,]$preds, type

= 'l'

, lty = 3,...)

panel.text (170,4, labels = 'KY

- 2010'

, cex = 1.2)

}, ylab = list ( 'height, m'

, cex =1), ylim = c (0,4.2),

xlim = c (50,2 90), xlab = list ( 'DOY'

, cex =1), cex =1.2

)

## Nebraska 2009

obs.NE.2009 <

- obs[obs$env == 'NE

- 2009' ,]

prds.NE.2009 <

- prds[prds$env == 'NE

- 2009' ,]

plot3 <

- xyplot ( height ~ doy, data = obs.NE.2009, groups = Nrate,

col = 'b lack'

, fill = 'transparent'

, panel = function (x,y,...){

panel.xyplot (x,y, type = 'p'

, pch

= c (17,19,22), ...)

panel.xyplot (prds.NE.2009[prds.NE.2009$Nrate == 0,]$doy,

prds.NE.2009[prds. NE.2009$Nrate == 0,]$preds, type

= 'l'

, lty = 1,...)

panel.xyplot (prds.NE.2009[prds.NE.2009$Nrate == 60,]$doy,

prds.NE.2009[prds.NE.2009$Nrate == 60,]$preds, type

= 'l'

, lty = 2,...)

panel.xyplot (prds.NE. 2009[prds.NE.2009$Nrate == 120,]$doy,

prds.NE.2009[prds.NE.2009$Nrate == 120,]$preds, type

= 'l'

, lty = 3,...)

panel.text (170,4, labels = 'NE

- 2009'

, cex = 1.2)

}, ylab = list ( 'height, m'

, cex =1), ylim = c (0,4.2),

xlim = c (50,290), xlab = list ( 'DOY'

, cex =1), cex =1.2

)

## Nebraska 2010

## 147

obs.NE.2010 <

- obs[obs$env == 'NE

- 2010' ,]

prds.NE.2010 <

- prds[prds$env == 'NE

- 2010' ,]

plot4 <

- xyplot ( height ~ doy, data = obs.NE.201 0, groups = Nrate,

col = 'black'

, fill = 'transparent'

, panel = function (x,y,...){

panel.xyplot (x,y, type = 'p'

, pch

= c (17,19,22), ...)

panel.xyplot (prds.NE.2010[prds.NE.2010$Nrate == 0,]$ doy,

prds.NE.2010[prds.NE.2010$Nrate == 0,]$preds, type

= 'l'

, lty = 1,...)

panel.xyplot (prds.NE.2010[prds.NE.2010$Nrate == 60,]$doy,

prds.NE.2010[prds.NE.2010$Nrate == 60,]$preds, type

= 'l'

, lty = 2,. ..)

panel.xyplot (prds.NE.2010[prds.NE.2010$Nrate == 120,]$doy,

prds.NE.2010[prds.NE.2010$Nrate == 120,]$preds, type

= 'l'

, lty = 3,...)

panel.text (170,4, labels = 'NE

- 2010'

, cex = 1.2)

}

, ylab = list ( 'height, m'

, cex =1), ylim = c (0,4.2),

xlim = c (50,290), xlab = list ( 'DOY'

, cex =1), cex =1.2

)

## New Jersey 2009

obs.NJ.2009 <

- obs[obs$env == 'NJ

- 2009' ,]

prds.NJ.2009 <

- prds[prds$env == 'NJ

- 2009' ,]

pl ot5 <

- xyplot ( height ~ doy, data = obs.NJ.2009, groups = Nrate,

col = 'black'

, fill = 'transparent'

, panel = function (x,y,...){

panel.xyplot (x,y, type = 'p'

, pch

= c (17,19,22), ...)

panel.x yplot (prds.NJ.2009[prds.NJ.2009$Nrate == 0,]$doy,

prds.NJ.2009[prds.NJ.2009$Nrate == 0,]$preds, type

= 'l'

, lty = 1,...)

panel.xyplot (prds.NJ.2009[prds.NJ.2009$Nrate == 60,]$doy,

prds.NJ.2009[prds.NJ. 2009$Nrate == 60,]$preds, type

= 'l'

, lty = 2,...)

panel.xyplot (prds.NJ.2009[prds.NJ.2009$Nrate == 120,]$doy,

prds.NJ.2009[prds.NJ.2009$Nrate == 120,]$preds, type

= 'l'

, lty = 3,...)

panel.text (170,4, lab els = 'NJ

- 2009'

, cex = 1.2)

}, ylab = list ( 'height, m'

, cex =1), ylim = c (0,4.2),

xlim = c (50,290), xlab = list ( 'DOY'

, cex =1), cex =1.2

)

## New Jersey 2010

obs.NJ.2010 <

- obs[obs$env == 'NJ

- 2010' ,]

prd s.NJ.2010 <

- prds[prds$env == 'NJ

- 2010' ,]

plot6 <

- xyplot ( height ~ doy, data = obs.NJ.2010, groups = Nrate,

col = 'black'

, fill = 'transparent'

, panel = function (x,y,...){

panel.xyplot (x,y, type = 'p'

, pc h

= c (17,19,22), ...)

panel.xyplot (prds.NJ.2010[prds.NJ.2010$Nrate == 0,]$doy,

prds.NJ.2010[prds.NJ.2010$Nrate == 0,]$preds, type

= 'l'

, lty = 1,...)

panel.xyplot (prds.NJ.2010[prds.NJ.2010$Nrate == 60,]$d oy,

prds.NJ.2010[prds.NJ.2010$Nrate == 60,]$preds, type

= 'l'

, lty = 2,...)

panel.xyplot (prds.NJ.2010[prds.NJ.2010$Nrate == 120,]$doy,

prds.NJ.2010[prds.NJ.2010$Nrate == 120,]$preds, type

= 'l'

, lty = 3 ,...)

panel.text (170,4, labels = 'NJ

- 2010'

, cex = 1.2)

}, ylab = list ( 'height, m'

, cex =1),

ylim = c (0,4.2),

xlim = c (50,290),

xlab = list ( 'DOY'

, cex =1), cex = 1.2,

key= list (x=0.5, y=0.25, cex.title=1.2,

pch = c (17, 19, 22),

lty = c (1,2,3), points = TRUE

, lines = TRUE

,

text

= list ( c ( "0 N"

, "60 N"

, "120 N" ))))

png ( '../figs/ht 2009 2010.png'

, width =2200, he ight =2800, res=225)

print (plot1, c (0, 0.666, 0.5, 1), more = TRUE )

print (plot2, c (0.5, 0.666, 1, 1), more = TRUE )

print (plot3, c (0, 0.333, 0.5, 0.666), more = TRUE )

## 148

print (plot4, c (0.5, 0.333, 1, 0.666), more = TRUE )

print (plot5, c (0, 0, 0.5, 0.333), more = TRUE )

print (plot6, c (0.5, 0, 1, 0.333))

dev.off ()

## Plot predictions

png ( '../figs/augPred

- final 2009 2010 ht.nlme.png'

, width =1600, height =1600, res=145)

plot ( augPred (fit6.nlme, level = 0:1),

lty

= c (1,2), col

= c ( "black" ), as.table

= T

, key= list ( text

= list ( c ( "fixed"

, "random" )), lty

= c (1,2), lines

= T ),

ylab = list ( 'height, m'

, cex =1.2),

xlab = list ( 'day of year'

, cex =1.2))

dev.off ()



## Biomass yields

- Ch.3

, Table 3.7

*DOE miscanthus manuscript, 2nd and 3rd year biomass yield;

optio ns ls

= 120 ps = 100 nodate nocenter formdlim

= "

- " ;

data biomass;

length location$ 10 ;

length harvest$ 10 ;

infile 'F: \ PhD \ Miscanthus fertility study \ DOE 2009 2010 growth and yield manuscript \ 2009 2010

season biomass yields, NE, KY, NJ, IL.csv'

dlm

= "," firstobs

= 4 ;

input year location$ block subsample Nrate biomass harvest$;

label Nrate= "N rate (kg N/ha)" ;

label biomass= "Dry Biomass (Mg/ha)" ;

*if location="Illinois" then delete;

*if location="Illinois" and year="2009" then Env="IL09";

*if location="Illinois" and year="2010" then Env="IL10";

if location= "Kentucky" and year= "2009" then Env= "KY09" ;

if location= "Kentucky" and year= "2010" then Env= "KY10" ;

if location= "Nebraska" and year= "2009" then Env= "NE09" ;

if location= "Nebraska" and year= "2010" then Env= "NE10" ;

if location= "New Jersey" and year= "2009" then Env= "NJ09" ;

if location= "New Jersey" and year= "2010" then Env= "NJ10" ;

*dropping IL, b/c in poor winter survival in 2008

- 2009 required ~80% replanting. As of 2010 the

IL

stand is a mixture of 2 and 3 year old pla nts. This is not on par with the other 3 sites, all of

which are 3 year old stands as of 2010.;

cards ;

run ; proc print ; run ;

data Il; set biomass;

if location ne "Illinois" then delete ;

run ;

proc sort data =IL; by year Nrate; run ;

proc means data =IL mea n noprint ; var biomass; by year Nrate;

output out =ILmeans mean =biomass; run ; proc print data =ILmeans; run ;

*running a model with location and year combined into an environment effect.

model:

random: Environment(E) = KY09 KY10 NE09 NE10 NJ09 NJ10 i=1,2,3

random: block(B) = 1,2,3,4 j=1,2,3,4

fixed: N rate

- (N) = 0,60,120 k=1,2,3

random: subsample

- (S) = 1 to 4 depending on site l=1 to 4

*main questions:

Is there an N response, what type (linear/quadratic), what locations?

Is there a signif icant increase or decrease in yield from 2009 to 2010 at each site?;

proc mixed data =biomass covtest method =REML;

class Env Nrate block;

model biomass=Nrate/ ddfm =kr outpred =temp;

random Env block(Env) Env*Nrate Nrate*block(Env);

*should block be nested in Env? We know it is nested in location.;

estimate "KY09 vs KY10" |Env 1

- 1 0 0 0 0 ;

estimate "NE09 vs NE10" |Env 0 0 1

- 1 0 0 ;

estimate "NJ09 vs NJ10" |Env 0 0 0 0 1

- 1 ;

## 149

*BLUP Env lsmeans;

estimate "KY09" intercept 1 |Env 1 0 0 0 0 0 ;

estimate "KY10" intercep t 1 |Env 0 1 0 0 0 0 ;

estimate "NE09" intercept 1 |Env 0 0 1 0 0 0 ;

estimate "NE10" intercept 1 |Env 0 0 0 1 0 0 ;

estimate "NJ09" intercept 1 |Env 0 0 0 0 1 0 ;

estimate "NJ10" intercept 1 |Env 0 0 0 0 0 1 ;

*BLUP Env*Nrate lsmeans;

estimate "KY09 Nrate 0" interc ept 1 Nrate 1 |

Env 1 Env*Nrate 1 ;

estimate "KY09 Nrate 60" intercept 1 Nrate 0 1 |

Env 1 Env*Nrate 0 1 ;

estimate "KY09 Nrate 120" intercept 1 Nrate 0 0 1 |

Env 1 Env*Nrate 0 0 1 ;

estimate "KY10 Nrate 0" intercept 1 Nrate 1 |

Env 0 1 Env*Nrate 0 0 0 1 ;

estimate "KY10 Nrate 60" intercept 1 Nrate 0 1 |

Env 0 1 Env*Nrate 0 0 0 0 1 ;

estimate "KY10 Nrate 120" intercept 1 Nrate 0 0 1 |

Env 0 1 Env*Nrate 0 0 0 0 0 1 ;

estimate "NE09 Nrate 0" intercept 1 Nrate 1 |

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 1 ;

est imate "NE09 Nrate 60" intercept 1 Nrate 0 1 |

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 0 1 ;

estimate "NE09 Nrate 120" intercept 1 Nrate 0 0 1 |

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 1 ;

estimate "NE10 Nrate 0" intercept 1 Nrate 1 |

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 1 ;

estimate "NE10 Nrate 60" intercept 1 Nrate 0 1 |

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "NE10 Nrate 120" intercept 1 Nrate 0 0 1 |

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "NJ09 Nrate 0" intercept 1 Nrate 1 |

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "NJ09 Nrate 60" intercept 1 Nrate 0 1 |

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "NJ09 Nrate 120" intercept 1 Nrate 0 0 1 |

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "NJ10 Nrate 0" intercept 1 Nrate 1 |

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "NJ10 Nrate 60" intercept 1 Nrate 0 1 |

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "NJ10 Nrate 120 " intercept 1 Nrate 0 0 1 |

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

*Nlinear effect within environment;

estimate "KY

- 09 linear" Nrate 1 0

- 1 |Env*Nrate 1 0

- 1 ;

estimate "KY

- 10 linear" Nrate 1 0

- 1 |Env*Nrate 0 0 0 1 0

- 1 ;

estimate "N E

- 09 linear" Nrate 1 0

- 1 |

Env*Nrate 0 0 0 0 0 0 1 0

- 1 ;

estimate "NE

- 10 linear" Nrate 1 0

- 1 |

Env*Nrate 0 0 0 0 0 0 0 0 0 1 0

- 1 ;

estimate "NJ

- 09 linear" Nrate 1 0

- 1 |

Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 0

- 1 ;

estimate "NJ

- 10 linear" Nrate 1 0

- 1 |

Env *Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

- 1 ;

*Nquad effect within environment;

estimate "KY

- 09 quad" Nrate 1

- 2 1 |Env*Nrate 1

- 2 1 ;

estimate "KY

- 10 quad" Nrate 1

- 2 1 |Env*Nrate 0 0 0 1

- 2 1 ;

estimate "NE

- 09 quad" Nrate 1

- 2 1 |

Env*Nrate 0 0 0 0 0 0 1

- 2 1 ;

estimate "NE

- 10 quad" Nrate 1

- 2 1 |

Env*Nrate 0 0 0 0 0 0 0 0 0 1

- 2 1 ;

estimate "NJ

- 09 quad" Nrate 1

- 2 1 |

Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1

- 2 1 ;

estimate "NJ

- 10 quad" Nrate 1

- 2 1 |

Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

- 2 1 ;

run ;

## 150

goptions r eset

= all ;

symbol v =plus color =blue h

= 1.5 ;

proc gplot data =temp;

plot resid*pred/ vref

= 0 vaxis

=

- 10 to 10 by 2 ;

run ;

symbol v =circle color =blue h

= 2 ;

proc univariate data =temp normal plot ; var resid;

probplot resid/ normal ( mu =est sigma =est color =black w = 3 );

i nset n mean median min max skewness kurtosis var probn ;

run ;



## Tiller density

- Ch.3, Table 3.7.

*2009 end of season stems;

options ls

= 100 ps = 100 nodate nocenter formdlim

= "

- " ;

data grwth09;

length loc$ 10 ;

length month$ 10 ;

infile 'F: \ PhD \ Miscanthus fertilit y study \ DOE manuscript, 2nd year growth \ 2009 growth data \ 2009

growth data, NE, KY, NJ.csv'

dlm

= "," firstobs

= 4 ;

input year$ loc$ block month$ date$ Nrate Nlb ht stems leaves TT;

*keeping final sampling dates at each location for stem #;

*KY=Sept 4, 2009, N E=Sept 15, 2009, NJ=Oct 23, 2009;

if loc= "Nebraska" and month= "May" then delete ;

if loc= "Nebraska" and month= "June" then delete ;

if loc= "Nebraska" and month= "July" then delete ;

if loc= "Nebraska" and month= "August" then delete ;

if loc= "Kentucky" and month= " April" then delete ;

if loc= "Kentucky" and month= "May" then delete ;

if loc= "Kentucky" and month= "June" then delete ;

if loc= "Kentucky" and month= "July" then delete ;

if loc= "New Jersey" and month= "May" then delete ;

if loc= "New Jersey" and month= "June" then de lete ;

if loc= "New Jersey" and month= "July" then delete ;

if loc= "New Jersey" and month= "August" then delete ;

if loc= "New Jersey" and month= "September" then delete ;

drop date Nlb ht leaves TT;

sqstems=sqrt(stems);

run ; proc print data =grwth09; run ;

data shoo t09; set grwth09;

drop month; run ; proc print data =shoot09; run ;

*2010 stem number for KY, NE, IL, NJ;

Data stemnum;

length loc$ 10 ;

Infile 'F: \ PhD \ Miscanthus fertility study \ DOE 2009 2010 growth and yield manuscript \ 2010 stem

number.csv'

firstobs

= 4 dl m = ',' ;

input location$ year$ plotID$ block grass Nrate harvest$ plant_sub stems biomass;

drop grass plotID harvest plant_sub;

if location= "IL" then delete ;

if location= "KY" then loc= "Kentucky" ;

if location= "NE" then loc= "Nebraska" ;

if location= "NJ" then loc= "New Jersey" ;

sqrtstems=sqrt(stems);

run ; proc print data =stemnum; run ;

/*

data ILstems; set stemnum;

if location ne "IL" then delete;

run; proc print data=ILstems; run;

proc sort data=ILstems; by Nrate; run;

proc means data=ILstems mean; by Nrate; v ar stems; run;

*/

## 151

*means of stem number;

proc sort data =stemnum; by loc year block Nrate; run ;

proc means data =stemnum noprint mean ; var stems sqrtstems;

by loc year block Nrate;

output out =shoots mean =stems sqstems; run ;

proc print data =shoots; run ;

da ta shoot10; set shoots;

drop _TYPE_ _FREQ_;

run ; proc print data =shoot10; run ;

*combining 2009 2010 shoot number;

proc sort data =shoot10; by loc year block Nrate; run ;

proc sort data =shoot09; by loc year block Nrate; run ;

data shoot; merge shoot10 shoot 09;

by loc year block Nrate; run ; proc print data =shoot; run ;

data stem; set shoot;

if loc= "Kentucky" and year= "2009" then Env= "KY09" ;

if loc= "Kentucky" and year= "2010" then Env= "KY10" ;

if loc= "Nebraska" and year= "2009" then Env= "NE09" ;

if loc= "Nebraska" and year= "2010" then Env= "NE10" ;

if loc= "New Jersey" and year= "2009" then Env= "NJ09" ;

if loc= "New Jersey" and year= "2010" then Env= "NJ10" ;

run ; proc print data =stem; run ;

ods html ;

ods graphics on ;

proc glimmix data =stem plots =(residualpanel pearsonpan el studentpanel);

class Env Nrate block;

model stems=Nrate/ dist =Poisson ddfm =kr;

random Env block(Env) Env*Nrate Nrate*block(Env);

covtest glm/wald;

estimate "KY09 vs KY10" |Env 1

- 1 0 0 0 0 ;

estimate "NE09 vs NE10" |Env 0 0 1

- 1 0 0 ;

estimate "NJ09 vs NJ10 " |Env 0 0 0 0 1

- 1 ;

*BLUP Env lsmeans;

estimate "KY09" intercept 1 |Env 1 0 0 0 0 0 / ilink ;

estimate "KY10" intercept 1 |Env 0 1 0 0 0 0 / ilink ;

estimate "NE09" intercept 1 |Env 0 0 1 0 0 0 / ilink ;

estimate "NE10" intercept 1 |Env 0 0 0 1 0 0 / ilink ;

estimate "NJ0 9" intercept 1 |Env 0 0 0 0 1 0 / ilink ;

estimate "NJ10" intercept 1 |Env 0 0 0 0 0 1 / ilink ;

*BLUP Env*Nrate lsmeans;

estimate "KY09 Nrate 0" intercept 1 Nrate 1 |

Env 1 Env*Nrate 1 / ilink cl ;

estimate "KY09 Nrate 60" intercept 1 Nrate 0 1 |

Env 1 Env*Nrate 0 1 / ilink cl ;

estimate "KY09 Nrate 120" intercept 1 Nrate 0 0 1 |

Env 1 Env*Nrate 0 0 1 / ilink cl ;

estimate "KY10 Nrate 0" intercept 1 Nrate 1 |

Env 0 1 Env*Nrate 0 0 0 1 / ilink cl ;

estimate "KY10 Nrate 60" intercept 1 Nrate 0 1 |

Env 0 1 Env*Nrate 0 0 0 0 1 / ilink cl ;

estimate "KY10 Nrate 120" intercept 1 Nrate 0 0 1 |

Env 0 1 Env*Nrate 0 0 0 0 0 1 / ilink cl ;

estimate "NE09 Nrate 0" intercept 1 Nrate 1 |

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 1 / ilink ;

estimate "NE09 Nrate 60" intercept 1 Nrate 0 1 |

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 0 1 / ilink ;

estimate "NE09 Nrate 120" intercept 1 Nrate 0 0 1 |

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 1 / ilink ;

estimate "NE10 Nrate 0" intercept 1 Nrate 1 |

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 1 / ilink ;

estimate "NE10 Nrate 60" intercept 1 Nrate 0 1 |

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 1 / ilink ;

estimate "NE10 Nrate 120" intercept 1 Nrate 0 0 1 |

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1 / ilink ;

## 152

estimate "NJ09 Nrate 0" intercept 1 Nrate 1 |

Env 0 0 0 0 1 Env* Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 / ilink ;

estimate "NJ09 Nrate 60" intercept 1 Nrate 0 1 |

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1 / ilink ;

estimate "NJ09 Nrate 120" intercept 1 Nrate 0 0 1 |

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 / ilink ;

estimate "NJ10 Nrate 0" intercept 1 Nrate 1 |

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 / ilink ;

estimate "NJ10 Nrate 60" intercept 1 Nrate 0 1 |

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 / ilink ;

estimate "NJ10 N rate 120" intercept 1 Nrate 0 0 1 |

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 / ilink ;

run ;

ods graphics off ;

ods html close ;



## Correlations among variables and scatter plot

- Ch.3, Fig. 3. 2

*2009 end of season ht, stems;

options ls

= 100 ps = 100 nodate nocenter formdlim

= "

- " ;

data grwth09;

length loc$ 10 ;

length month$ 10 ;

infile 'F: \ PhD \ Miscanthus fertility study \ DOE manuscript, 2nd year growth \ 2009 growth data \ 2009

growth data, NE, KY, NJ.csv'

dlm

= "," firstobs

= 4 ;

input year$ loc$ block m onth$ date$ Nrate Nlb ht stems leaves TT;

*keeping final sampling dates at each location for stem #;

*KY=Sept 4, 2009, NE=Sept 15, 2009, NJ=Oct 23, 2009;

if loc= "Nebraska" and month= "May" then delete ;

if loc= "Nebraska" and month= "June" then delete ;

if loc= "Nebraska" and month= "July" then delete ;

if loc= "Nebraska" and month= "August" then delete ;

if loc= "Kentucky" and month= "April" then delete ;

if loc= "Kentucky" and month= "May" then delete ;

if loc= "Kentucky" and month= "June" then delete ;

if loc= "Kentucky" and month= "July" then delete ;

if loc= "New Jersey" and month= "May" then delete ;

if loc= "New Jersey" and month= "June" then delete ;

if loc= "New Jersey" and month= "July" then delete ;

if loc= "New Jersey" and month= "August" then delete ;

if loc= "New Jersey" and mont h= "September" then delete ;

run ; proc print data =grwth09; run ;

proc sort data =grwth09; by loc year block Nrate;

proc means data =grwth09 noprint mean ; var ht stems;

by loc year block Nrate;

output out =height09 mean =ht_mean shoot_mean; run ;

proc print data

= height09; run ;

data htstem09; set height09;

drop _TYPE_ _FREQ_;

run ; proc print data =htstem09; run ;

*2010 end of season ht;

data grwth10;

length loc$ 10 ;

length month$ 10 ;

infile 'F: \ PhD \ Miscanthus fertility study \ DOE 2009 2010 growth and yield manuscr ipt \ 2010 growth

data, KY, NE, NJ, IL.csv'

dlm

= "," firstobs

= 4 ;

input year$ loc$ block month$ date$ Nrate Nlb ht TT;

*keeping final sampling dates at each location for stem #;

*KY=Oct 12, 2010, NE=Nov 22, 2010, NJ=Nov 2, 2010;

if loc= "Illinois" then delete ;

if loc= "Kentucky" and month= "April" then delete ;

if loc= "Kentucky" and month= "May" then delete ;

## 153

if loc= "Kentucky" and month= "June" then delete ;

if loc= "Kentucky" and month= "July" then delete ;

if loc= "Kentucky" and month= "August" then delete ;

if loc= "Kent ucky" and month= "September" then delete ;

if loc= "Nebraska" and month= "April" then delete ;

if loc= "Nebraska" and month= "May" then delete ;

if loc= "Nebraska" and month= "June" then delete ;

if loc= "Nebraska" and month= "July" then delete ;

if loc= "Nebraska" and m onth= "August" then delete ;

if loc= "Nebraska" and month= "September" then delete ;

if loc= "Nebraska" and month= "October" then delete ;

if loc= "New Jersey" and month= "May" then delete ;

if loc= "New Jersey" and month= "June" then delete ;

if loc= "New Jersey" and mo nth= "July" then delete ;

if loc= "New Jersey" and month= "August" then delete ;

if loc= "New Jersey" and month= "September" then delete ;

if loc= "New Jersey" and month= "October" then delete ;

run ; proc print data =grwth10; run ;

proc sort data =grwth10; by loc year block Nrate;

proc means data =grwth10 noprint mean ; var ht;

by loc year block Nrate;

output out =height10 mean =ht_mean; run ;

proc print data =height10; run ;

data ht10; set height10;

drop _TYPE_ _FREQ_;

run ; proc print data =ht10; run ;

*2010 end of season KY, NE, IL, NJ;

Data stem10;

length loc$ 10 ;

Infile 'F: \ PhD \ Miscanthus fertility study \ DOE 2010 yield components \ 2010 stem number.csv'

firstobs

= 4 dlm

= ',' ;

input location$ year$ plotID$ block grass Nrate harvest$ plant_sub shoots;

drop grass;

if location= " IL" then delete ;

if location= "KY" then loc= "Kentucky" ;

if location= "NE" then loc= "Nebraska" ;

if location= "NJ" then loc= "New Jersey" ;

run ; proc print data =stem10; run ;

*means of stem number;

proc sort data =stem10; by loc year block Nrate; run ;

proc mean s data =stem10 noprint mean ; var shoots;

by loc year block Nrate;

output out =shoots mean =shoot_mean; run ;

proc print data =shoots; run ;

data shoot10; set shoots;

drop _TYPE_ _FREQ_;

run ; proc print data =shoot10; run ;

*2010 stem number;

Data stemnum;

Inf ile 'F: \ PhD \ Miscanthus fertility study \ DOE 2010 yield components \ 2010 stem number.csv'

firstobs

= 4 dlm

= ',' ;

input loc$ year plotID$ rep grass Nrate harvest$ plant_sub shoots biomass;

drop grass;

run ; proc print data =stemnum; run ;

*combining 2010 ht sho ot number;

data comb10; merge shoot10 ht10;

by loc year block Nrate; run ; proc print data =comb10; run ;

* 2nd and 3rd year biomass yield;

## 154

options ls

= 120 ps = 100 nodate nocenter formdlim

= "

- " ;

data biomass;

length loc$ 10 ;

length harvest$ 10 ;

infile 'F: \ Ph D \ Miscanthus fertility study \ DOE 2009 2010 growth and yield manuscript \ 2009 2010

season biomass yields, NE, KY, NJ, IL.csv'

dlm

= "," firstobs

= 4 ;

input year$ loc$ block subsample Nrate biomass harvest$;

label Nrate= "N rate (kg N/ha)" ;

label biomass= "Dry Bio mass (Mg/ha)" ;

if loc= "Illinois" then delete ;

if loc= "Kentucky" and year= "2009" then Env= "KY09" ;

if loc= "Kentucky" and year= "2010" then Env= "KY10" ;

if loc= "Nebraska" and year= "2009" then Env= "NE09" ;

if loc= "Nebraska" and year= "2010" then Env= "NE10" ;

if lo c= "New Jersey" and year= "2009" then Env= "NJ09" ;

if loc= "New Jersey" and year= "2010" then Env= "NJ10" ;

cards ;

run ; proc print ; run ;

data biomass09; set biomass;

if year= 2010 then delete ;

run ; proc print data =biomass09; run ;

*means of biomasss 2009;

proc s ort data =biomass09; by loc year block Nrate; run ;

proc means data =biomass09 noprint mean ; var biomass;

by loc year block Nrate;

output out =bio09 mean =bio_mean; run ;

proc print data =bio09; run ;

data biom09; set bio09;

drop _TYPE_ _FREQ_;

run ; proc print data =biom09; run ;

data biomass10; set biomass;

if year= 2009 then delete ;

run ; proc print data =biomass10; run ;

*means of biomass 2010;

proc sort data =biomass10; by loc year block Nrate; run ;

proc means data =biomass10 noprint mean ; var biomass;

by loc yea r block Nrate;

output out =bio10 mean =bio_mean; run ;

proc print data =bio10; run ;

data biom10; set bio10;

drop _TYPE_ _FREQ_;

run ; proc print data =biom10; run ;

*combining 2009 ht, shoot, biomass;

data all2009;

label loc=location;

label ht_mean=plant ht (m);

label shoot_mean=tiller density (tillers/ sq. m);

label bio_mean=dry biomass (Mg/ha);

merge htstem09 biom09;

by loc year block Nrate; run ;

proc print data =all2009; run ;

data all2009; set all2009;

if loc= "Kentucky" and year= "2009" then Env= "KY

- 2009" ;

if loc= "Nebraska" and year= "2009" then Env= "NE

- 2009" ;

if loc= "New Jersey" and year= "2009" then Env= "NJ

- 2009" ;

label env=environment;

run ; proc print data =all2009; run ;

proc sgscatter data =all2009;

title "2009 season" ;

matrix ht_mean shoot_mean bio_mean/ gr oup =env;

run ;

proc corr data =all2009;

var ht_mean shoot_mean bio_mean; run ;

## 155

*combining 2010 ht, shoot, biomass;

data all2010;

label loc=location;

label ht_mean=plant ht (m);

label shoot_mean=tiller density (tillers/ sq. m);

label bio_mean=dry biomass (M g/ha);

merge comb10 biom10;

by loc year block Nrate;

run ; proc print data =all2010; run ;

data all2010; set all2010;

if loc= "Kentucky" and year= "2010" then Env= "KY

- 2010" ;

if loc= "Nebraska" and year= "2010" then Env= "NE

- 2010" ;

if loc= "New Jersey" and year= "20 10" then Env= "NJ

- 2010" ;

label env=environment;

run ; proc print data =all2010; run ;

proc sgscatter data =all2010;

title "2010 season" ;

matrix ht_mean shoot_mean bio_mean/ group =env;

run ;

proc corr data =all2010;

var ht_mean shoot_mean bio_mean; run ;

## 156 

## APPENDI X C

Selected SAS and R code for Ch. 4

Plant h eight and expanded leaf number

- Ch.4, Fig. 4.1, Fig. 4.3

library ( nlme )

library (Hmisc)

library (lattice)

library (graphics)

#2009 & 2010 sorghum ht, leafno data

htleaf <

- read.csv ( file = "./2009 & 2010 analysis for manuscript 1/sorghum ht

- leafno 2009 &

2010.csv"

, skip = 4, na.strings

= "." )

htleaf$ height <

- htleaf$ht..cm. / 100

head (htleaf)

htleaf <

- htleaf[,

- 2] #getting rid of Date

htleaf <

- htleaf[,

- 4] #getting rid of sample

htleaf <

- htleaf[,

- 7] #getting rid of nitrogen lb/acre

htleaf <

- htleaf[,

- 7] #getting rid of plot

htleaf <

- htleaf[,

- 8] #getting rid of height expressed in cm

htleaf <

- htleaf[,

- 8] #getting rid of comments

htleaf <

- na.omit (htleaf) #getting rid of observations with missing ht and leafno d ata

names (htleaf) <

- c ( 'Environment'

, 'doy'

, 'block'

, 'type'

, 'variety'

, 'Nrate'

, 'leafno'

, 'GDD10'

,

'GDD11'

, 'height' )

head (htleaf)

UrbE09 <

- htleaf[htleaf$Environment == "Urbana

- 2009" & htleaf$ type == "Energy" ,]

unique (UrbE09$doy) #194 210 231 261 280

Ur bE10 <

- htleaf[htleaf$Environment == "Urbana

- 2010" & htleaf$ type == "Energy" ,]

unique (UrbE10$doy) #176 202 243 270

DSE10 <

- htleaf[htleaf$Environment == "Dixon Springs

- 2010" & htleaf$ type == "Energy" ,]

unique (DSE10$doy) #181 200 217 277

PerE10 <

- htleaf[htlea f$Environment == "Perry

- 2010" & htleaf$ type == "Energy" ,]

unique (PerE10$doy) #182 204 229 276

#plotting for leaf number

energy <

- htleaf[htleaf$ type == "Energy" ,]

forage <

- htleaf[htleaf$ type == "Forage" & htleaf$variety == "Graze. n. Bale" ,] #dropping

phot period insensitive variety, Graze All

forage[forage$doy > 242,2]

forage[forage$doy > 242,2] <

- NA #getting rid of leafno beyond the first harvest

forage <

- na.omit (forage)

head (forage)

leafno <

- rbind (energy, forage)

head (leafno)

unique (leafno$Environment) #Dixon Springs

- 2010 Perry

- 2010 Urbana

- 2009 Urbana

- 2010

xyplot (leafno ~ GDD11, data = energy, type

= c ( "p"

, "r" ), groups = Environment, auto.key= TRUE )

xyplot (leafno ~ GDD11, data = forage, type

= c ( "p"

, "r" ), groups = Environment, auto.key= TRUE )

png ( "./2009 & 2010 analysis for manuscript 1/leafno.png"

, width =600, height =600, res=80)

xyplot (leafno ~ GDD11, data = energy, groups = Environment,

xlab

= list ( "GDD"

, cex =1.2),

ylab

= list ( "expanded leaf number"

, cex =1.3),

pch

= c (17, 19, 22, 8), col

= c ( "darkgreen"

, "darkred"

, "midnightblue"

, "black" ),

fill = 'transparent'

, lty = c (1,2,3,4), lwd =2,

type

= c ( "p"

, "r" ), cex = 1.2,

key= list (x=0.3, y=1, cex.title=1.3,

pch = c (17, 19, 22, 8), cex =1.3,

lty = c (1,2,3,4), points = TRUE

, lines = TRUE

, lwd =2,

col

= c ( "darkgreen"

, "darkred"

, "midnightblue"

, "black" ),

text

= list ( c ( "Dixon Springs 2010"

, "Perry 2010"

, "Urbana 2009"

, "Urbana

2010" ))))

dev.off ()

mean (leafno[leafno$doy == "277" & leafno$Environment == "Dixon Springs

- 2010"

, "leafno" ]) #28.25

mean (leafno[leafno$doy == "276" & leafno$Environment == "Perry

- 2010"

, "leafno" ]) #28.42

## 157

## dataframe for Urbana Energy 2009

UrbE09.1 <

- data.frame (env = UrbE09[,1],

doy = UrbE09[,2],

variety = UrbE09[,5],

Nrate = UrbE09[,6],

leafno = U rbE09[,7],

GDD10 = UrbE09[,8],

GDD11 = UrbE09[,9],

height = UrbE09[,10])

head (UrbE09.1)

## developing a model Urbana Energy 2009 height

expu <

- with (UrbE09, Environment: factor (block): factor (va riety): factor (Nrate))

UrbE09G <

- groupedData ( height ~ doy | expu, data = UrbE09.1)

head (UrbE09G)

UrbE09ht.nls <

- nlsList ( height ~ SSlogis (doy, Asym, xmid, scal), data =UrbE09G)

UrbE09ht.nlme <

- nlme (UrbE09ht.nls)

anova (UrbE09ht.nlme)

plot ( augPred (UrbE09ht. nlme, level = 0:1))

coef (UrbE09ht.nlme)

## dataframe for Urbana Energy 2010

UrbE10.1 <

- data.frame (env = UrbE10[,1],

doy = UrbE10[,2],

variety = UrbE10[,5],

Nrate = UrbE10[,6],

leafno = UrbE10[,7],

GDD10 = UrbE10[,8],

GDD11 = UrbE10[,9],

height = UrbE10[,10])

head (UrbE10.1)

## developing a model Urbana Energy 2010 height

expu <

- with (UrbE10, Environment: factor (block): factor (variety): factor (Nrate))

UrbE10G <

- groupedData ( height ~ doy | expu, data = UrbE10.1)

head (UrbE10G)

UrbE10ht.nls <

- nlsList ( height ~ SSlogis (doy, Asym, xmid, scal),

data =UrbE10G,

start = c (Asym = 3.5, xmid = 220, scal = 25))

UrbE10ht.nlme <

- nlme (UrbE10ht.nls)

anova (UrbE10ht.nlme)

plot ( augPred (UrbE10ht.nlme, level = 0:1))

coef (UrbE10ht.nlme)

## dataframe for Dixon Springs Energy 2010

DSE10.1 <

- data.frame (env = DSE10[,1],

doy = DSE10[,2],

variety = DSE10[,5],

Nrate = DSE10[,6],

leafno = DSE10[,7],

GDD10 = DSE10[,8],

GDD11 = DSE10[,9],

height = DSE10[,10])

head (DSE10.1)

## developing a model Dixon Springs Energy 2010 height

expu <

- with (DSE10, Environment: factor (block): factor (variety): factor (Nrate))

DSE10G <

- groupedData ( height ~ doy | expu, data = DSE10.1)

head (DSE10G)

xyplot ( height ~ doy, data = DSE10G, groups = Nra te, auto.key= TRUE )

DSE10ht.nls <

- nlsList ( height ~ SSlogis (doy, Asym, xmid, scal),

data =DSE10G,

start = c (Asym = 3.5, xmid = 230, scal = 20))

DSE10ht.nlme <

- nlme (DSE10ht.nls)

anova (DSE10ht.nlme)

plot ( augPred (DSE10ht.nlme, lev el = 0:1))

coef (DSE10ht.nlme)

## dataframe for Perry Energy 2010

## 158

PerE10.1 <

- data.frame (env = PerE10[,1],

doy = PerE10[,2],

variety = PerE10[,5],

Nrate = PerE10[,6],

leafn o = PerE10[,7],

GDD10 = PerE10[,8],

GDD11 = PerE10[,9],

height = PerE10[,10])

head (PerE10.1)

## developing a model Perry Energy 2010 height

expu <

- with (PerE10, Environment: factor (block): facto r (variety): factor (Nrate))

PerE10G <

- groupedData ( height ~ doy | expu, data = PerE10.1)

head (PerE10G)

xyplot ( height ~ doy, data = PerE10G, groups = Nrate, auto.key= TRUE )

PerE10ht.nls <

- nlsList ( height ~ SSlogis (doy, Asym, xmid, scal),

data =P erE10G,

start = c (Asym = 3.9, xmid = 230, scal = 30))

PerE10ht.nlme <

- nlme (PerE10ht.nls)

anova (PerE10ht.nlme)

plot ( augPred (PerE10ht.nlme, level = 0:1))

coef (PerE10ht.nlme)

*Bringing in logistic fitted paramteres for energy sorghum height

o ptions ls=120 ps=150 nodate nocenter formdlim="

- ";

data locyr;

length loc$ 14 ;

length hybrid$ 11 ;

infile 'C: \ Documents and Settings \ maughan2 \ My Documents \ My Dropbox \ sorghum fertility trial \ 2009 &

2010 analysis for manuscript 1 \ Energy sorghum logistic fits, LOC

- YR.csv'

dlm

= "," firstobs

= 3 ;

input loc$ yr block hybrid$ Nrate Asym xmid scal;

if loc= "Urbana" and yr= 2010 then env= "Urb2010" ;

if loc= "Dixon Springs" and yr= 2010 then env= "DS2010" ;

if loc= "Perry" and yr= 2010 then env= "Per2010" ;

cards ;

run ; proc print ; run ;

*Urbana 2009 analyzed alone;

data Urbana09; set locyr;

if loc ne "Urbana" then delete ;

if yr ne 2009 then delete ;

run ; proc print data =Urbana09; run ;

*Asym;

ods html ;

ods graphics on ;

proc glimmix data =Urbana09 plots =RESIDUALPANEL;

class block hy brid Nrate;

model Asym=hybrid Nrate hybrid*Nrate/ dist =normal ddfm =kr;

random block block*hybrid;

covtest glm/wald;

lsmeans Nrate/ pdiff ;

contrast "0 vs all N" Nrate 1

- .33333

- .33333

- .33333 ;

contrast "0 vs 50" Nrate 1

- 1 ;

contrast "0 vs 100" Nrate 1 0

- 1 ;

contrast "0 vs 150" Nrate 1 0 0

- 1 ;

contrast "50 vs 100" Nrate 0 1

- 1 0 ;

contrast "50 vs 150" Nrate 0 1 0

- 1 ;

contrast "100 vs 150" Nrate 0 0 1

- 1 ;

run ;

ods graphics off ;

ods html close ;

*xmid;

ods html ;

ods graphics on ;

## 159

proc glimmix data =Urbana09 plots =R ESIDUALPANEL;

class block hybrid Nrate;

model xmid=hybrid Nrate hybrid*Nrate/ dist =normal ddfm =kr;

random block block*hybrid;

covtest glm/wald;

lsmeans Nrate/ pdiff ;

contrast "0 vs all N" Nrate 1

- .33333

- .33333

- .33333 ;

contrast "0 vs 50" Nrate 1

- 1 ;

contra st "0 vs 100" Nrate 1 0

- 1 ;

contrast "0 vs 150" Nrate 1 0 0

- 1 ;

contrast "50 vs 100" Nrate 0 1

- 1 0 ;

contrast "50 vs 150" Nrate 0 1 0

- 1 ;

contrast "100 vs 150" Nrate 0 0 1

- 1 ;

run ;

ods graphics off ;

ods html close ;

*scal;

ods html ;

ods graphics on ;

proc g limmix data =Urbana09 plots =RESIDUALPANEL;

class block hybrid Nrate;

model scal=hybrid Nrate hybrid*Nrate/ dist =normal ddfm =kr;

random block block*hybrid;

covtest glm/wald;

lsmeans Nrate/ pdiff ;

contrast "0 vs all N" Nrate 1

- .33333

- .33333

- .33333 ;

contrast "0 vs 50" Nrate 1

- 1 ;

contrast "0 vs 100" Nrate 1 0

- 1 ;

contrast "0 vs 150" Nrate 1 0 0

- 1 ;

contrast "50 vs 100" Nrate 0 1

- 1 0 ;

contrast "50 vs 150" Nrate 0 1 0

- 1 ;

contrast "100 vs 150" Nrate 0 0 1

- 1 ;

run ;

ods graphics off ;

ods html close ;

*2010 data considering environments random;

data env2010; set locyr;

if yr= 2009 then delete ;

run ; proc print ;

*Asym;

ods html ;

ods graphics on ;

proc glimmix data =env2010 plots =RESIDUALPANEL;

class env block hybrid Nrate;

model Asym=hybrid Nrate hybrid*Nrate/ dist =n ormal ddfm =kr;

random env block(env) env*hybrid hybrid*block(env) env*Nrate env*hybrid*Nrate;

covtest glm/wald;

estimate "DS 0" intercept 1 Nrate 1 |env 1 env*Nrate 1 ;

estimate "DS 56" intercept 1 Nrate 0 1 |env 1 env*Nrate 0 1 ;

estimate "DS 112" intercept 1 Nrate 0 0 1 |env 1 env*Nrate 0 0 1 ;

estimate "DS 168" intercept 1 Nrate 0 0 0 1 |env 1 env*Nrate 0 0 0 1 ;

estimate "DS 224" intercept 1 Nrate 0 0 0 0 1 |env 1 env*Nrate 0 0 0 0 1 ;

estimate "PE 0" intercept 1 Nrate 1 |env 0 1 env*Nrate 0 0 0 0 0 1 ;

estimate "P E 56" intercept 1 Nrate 0 1 |env 0 1 env*Nrate 0 0 0 0 0 0 1 ;

estimate "PE 112" intercept 1 Nrate 0 0 1 |env 0 1

env*Nrate 0 0 0 0 0 0 0 1 ;

estimate "PE 168" intercept 1 Nrate 0 0 0 1 |env 0 1

env*Nrate 0 0 0 0 0 0 0 0 1 ;

estimate "PE 224" intercept 1 Nra te 0 0 0 0 1 |env 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 0" intercept 1 Nrate 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 56" intercept 1 Nrate 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 112" intercept 1 Nrate 0 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

## 160

estimate "UR 168" intercept 1 Nrate 0 0 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 224" intercept 1 Nrate 0 0 0 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

contra st "0N vs. Napp" Nrate 1

- .25

- .25

- .25

- .25 ;

contrast "0 vs 56" Nrate 1

- 1 ;

contrast "0 vs 112" Nrate 1 0

- 1 ;

contrast "0 vs 168" Nrate 1 0 0

- 1 ;

contrast "0 vs 224" Nrate 1 0 0 0

- 1 ;

contrast "56 vs 112" Nrate 0 1

- 1 0 0 ;

contrast "56 vs 168" Nrate 0 1 0

- 1 0 ;

contrast "56 vs 224" Nrate 0 1 0 0

- 1 ;

contrast "112 vs 168" Nrate 0 0 1

- 1 0 ;

contrast "112 vs 224" Nrate 0 0 1 0

- 1 ;

contrast "168 vs 224" Nrate 0 0 0 1

- 1 ;

run ;

ods graphics off ;

ods html close ;

*xmid;

ods html ;

ods graphics on ;

proc glimmix dat a =env2010 plots =RESIDUALPANEL;

class env block hybrid Nrate;

model xmid=hybrid Nrate hybrid*Nrate/ dist =normal ddfm =kr;

random env block(env) env*hybrid hybrid*block(env) env*Nrate env*hybrid*Nrate;

covtest glm/wald;

estimate "DS 0" intercept 1 Nrate 1 |env 1 env*Nrate 1 ;

estimate "DS 56" intercept 1 Nrate 0 1 |env 1 env*Nrate 0 1 ;

estimate "DS 112" intercept 1 Nrate 0 0 1 |env 1 env*Nrate 0 0 1 ;

estimate "DS 168" intercept 1 Nrate 0 0 0 1 |env 1 env*Nrate 0 0 0 1 ;

estimate "DS 224" intercept 1 Nrate 0 0 0 0 1 |e nv 1 env*Nrate 0 0 0 0 1 ;

estimate "PE 0" intercept 1 Nrate 1 |env 0 1 env*Nrate 0 0 0 0 0 1 ;

estimate "PE 56" intercept 1 Nrate 0 1 |env 0 1 env*Nrate 0 0 0 0 0 0 1 ;

estimate "PE 112" intercept 1 Nrate 0 0 1 |env 0 1

env*Nrate 0 0 0 0 0 0 0 1 ;

estimate "PE 168" intercept 1 Nrate 0 0 0 1 |env 0 1

env*Nrate 0 0 0 0 0 0 0 0 1 ;

estimate "PE 224" intercept 1 Nrate 0 0 0 0 1 |env 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 0" intercept 1 Nrate 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 56" intercept 1 Nrate 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 112" intercept 1 Nrate 0 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 168" intercept 1 Nrate 0 0 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

e stimate "UR 224" intercept 1 Nrate 0 0 0 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

contrast "0N vs. Napp" Nrate 1

- .25

- .25

- .25

- .25 ;

contrast "0 vs 56" Nrate 1

- 1 ;

contrast "0 vs 112" Nrate 1 0

- 1 ;

contrast "0 vs 168" Nrate 1 0 0

- 1 ;

contr ast "0 vs 224" Nrate 1 0 0 0

- 1 ;

contrast "56 vs 112" Nrate 0 1

- 1 0 0 ;

contrast "56 vs 168" Nrate 0 1 0

- 1 0 ;

contrast "56 vs 224" Nrate 0 1 0 0

- 1 ;

contrast "112 vs 168" Nrate 0 0 1

- 1 0 ;

contrast "112 vs 224" Nrate 0 0 1 0

- 1 ;

contrast "168 vs 224" Nrat e 0 0 0 1

- 1 ;

run ;

ods graphics off ;

ods html close ;

*scal;

ods html ;

ods graphics on ;

proc glimmix data =env2010 plots =RESIDUALPANEL;

class env block hybrid Nrate;

model scal=hybrid Nrate hybrid*Nrate/ dist =normal ddfm =kr;

## 161

random env block(env) env*hybrid hybrid*block(env) env*Nrate env*hybrid*Nrate;

covtest glm/wald;

estimate "DS 0" intercept 1 Nrate 1 |env 1 env*Nrate 1 ;

estimate "DS 56" intercept 1 Nrate 0 1 |env 1 env*Nrate 0 1 ;

estimate "DS 112" intercept 1 Nrate 0 0 1 |env 1 env*Nrate 0 0 1 ;

estimate "DS 168" intercept 1 Nrate 0 0 0 1 |env 1 env*Nrate 0 0 0 1 ;

estimate "DS 224" intercept 1 Nrate 0 0 0 0 1 |env 1 env*Nrate 0 0 0 0 1 ;

estimate "PE 0" intercept 1 Nrate 1 |env 0 1 env*Nrate 0 0 0 0 0 1 ;

estimate "PE 56" intercept 1 Nrate 0 1 |env 0 1

env*Nrate 0 0 0 0 0 0 1 ;

estimate "PE 112" intercept 1 Nrate 0 0 1 |env 0 1

env*Nrate 0 0 0 0 0 0 0 1 ;

estimate "PE 168" intercept 1 Nrate 0 0 0 1 |env 0 1

env*Nrate 0 0 0 0 0 0 0 0 1 ;

estimate "PE 224" intercept 1 Nrate 0 0 0 0 1 |env 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 0" intercept 1 Nrate 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 56" intercept 1 Nrate 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 112" intercept 1 Nrate 0 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 168" intercept 1 Nrate 0 0 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 224" intercept 1 Nrate 0 0 0 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

contrast "0N vs. Napp" Nrate 1

- .25

- .25

- .25

- .25 ;

contrast "0 vs 56" Nrate 1

- 1 ;

contrast "0 vs 112" Nrate 1 0

- 1 ;

contrast "0 vs 168" Nrate 1 0 0

- 1 ;

contrast "0 vs 224" Nrate 1 0 0 0

- 1 ;

contrast "56 vs 112" Nrate 0 1

- 1 0 0 ;

contrast "56 vs 168" Nrate 0 1 0

- 1 0 ;

contrast "56 vs 224" Nrate 0 1 0 0

- 1 ;

contrast "112 vs 168" Nrate 0 0 1

- 1 0 ;

contrast "112 vs 224" Nrate 0 0 1 0

- 1 ;

contrast "168 vs 224" Nrate 0 0 0 1

- 1 ;

run ;

ods graphics off ;

ods html close ;



## Leaf area index

- Ch.4, Fig. 4. 2

*Urbana 2009 and Urbana 2010 LAI;

options ls

= 120 ps = 150 nodate n ocenter formdlim

= "

- " ;

data lai;

length yr$ 11 ;

length hybrid$ 14 ;

infile 'C: \ Documents and Settings \ maughan2 \ My Documents \ My Dropbox \ sorghum fertility trial \ 2009 &

2010 analysis for manuscript 1 \ sorghum LAI 2009 & 2010.csv'

dlm

= "," firstobs

= 7 ;

input yr$ d ay$ doy block type$ hybrid$ Nrate sub plot abovePAR Par1 tao1 lai zenith hours$ month$

comment$ GDD11 intpar;

penetr= 100

- intpar;

drop day sub plot Par1 tao1 zenith hours month comment;

if type= "Forage" then delete ;

if abovePAR le 1400 then delete ;

cards ;

r un ; proc print ; run ;

*averaging subsamples;

proc sort data =lai; by yr doy block hybrid Nrate GDD11;

proc means data =lai mean noprint ; by yr doy block hybrid Nrate GDD11; var lai intpar ;

output out =laimean mean =lai intpar; run ; proc print data =laimean; r un ;

data laimean; set laimean;

drop _TYPE_; drop _FREQ_; run ; proc print data =laimean; run ;

data lai09; set laimean;

if yr ne "Urbana 2009" then delete ;

## 162

run ; proc print ;

proc corr data =lai09;

var lai intpar; run ;

data lai10; set laimean;

if yr ne "Urba na 2010" then delete ;

run ; proc print ;

proc corr data =lai10;

var lai intpar; run ;

*2009 LAI;

ods html ;

ods graphics on ;

proc glimmix data =lai09 plots =all;

class block hybrid Nrate doy;

model lai = hybrid Nrate hybrid*Nrate doy hybrid*doy Nrate*doy hybr id*Nrate*doy/ dist =normal

ddfm =KR;

random block block*hybrid block*hybrid*Nrate;

random doy/ type =arh( 1 ) subject =block*hybrid*Nrate residual;

covtest glm/wald;

lsmeans Nrate*doy/plot=meanplot(sliceby=Nrate ilink join);

lsmeans Nrate*doy/ pdiff ;

contrast "DOY July 13 vs Aug. 29" DOY 1 0

- 1 0 ;

contrast "DOY Aug. 29 vs Sept 18" DOY 0 0 1

- 1 ;

contrast "Nrate 0 vs. rest" Nrate 1

- .33333

- .33333

- .33333 ;

run ;

ods graphics off ;

ods html close ;

proc sort data =lai09; by Nrate doy;

proc means data =lai09 mean noprint ; b y Nrate doy; var lai intpar;

output out =lai09mean mean =lai intpar; run ; proc print data =lai09mean; run ;

*2010 LAI;

ods html ;

ods graphics on ;

proc glimmix data =lai10 plots =all;

class block hybrid Nrate doy;

model lai = hybrid Nrate hybrid*Nrate doy hybrid *doy Nrate*doy hybrid*Nrate*doy/ dist =normal

ddfm =KR;

random block block*hybrid block*hybrid*Nrate;

random doy/ type =arh( 1 ) subject =block*hybrid*Nrate residual;

covtest glm/wald;

lsmeans Nrate*doy/plot=meanplot(sliceby=Nrate ilink join);

lsmeans Nrate*doy/ pd iff ;

contrast "DOY 25 June vs Aug. 12" DOY 1 0

- 1 0 ;

contrast "DOY Aug. 12 vs Aug. 31" DOY 0 0 1

- 1 ;

run ;

ods graphics off ;

ods html close ;

proc sort data =lai10; by Nrate doy;

proc means data =lai10 mean noprint ; by Nrate doy; var lai intpar;

output out =la i10mean mean =lai intpar; run ; proc print data =lai10mean; run ;



## Biomass yields

- Ch.4, Table 4.4, Table 4.5.

options ls

= 150 ps = 150 nodate nocenter formdlim

= "

- " ;

data biom10;

length env$ 18 ;

length hybrid$ 14 ;

infile 'C: \ Users \ maughan2 \ Documents \ My Dropbox \ so rghum fertility trial \ 2009 & 2010 analysis for

manuscript 1 \ sorghum dry biomass yield 2010.csv'

dlm

= "," firstobs

= 7 ;

input env$ block type$ hybrid$ Nratelbs Nrate plot summer fall tons biomass comments$;

drop Nratelbs tons comments;

*if env ="Urbana 2010" and block=1 and hybrid="TX09007" then biomass=.; *poor planting in 106

- 110

and some flooding;

if env = "Dixon Springs 2010" and plot= 301 then biomass=

. ; *no second harvest;

*if env ="Perry 2010" and plot=401 then biomass=.;

early=summer* 2.24 ;

## 163

late=fall* 2.24 ;

run ; proc print ; run ;

proc sort data =biom10; by env type hybrid block Nrate; run ; proc print ;

*means by cutting and total biomass production;

proc sort data =biom10; by env type Nrate; run ;

proc means data =biom10 noprint mean ; var early; by env type Nrat e;

output out =summer mean =summerbiom; run ; proc print data =summer; run ;

data summer; set summer;

drop _TYPE_ _FREQ_; run ;

proc means data =biom10 noprint mean ; var late; by env type Nrate;

output out =fall mean =fallbiom; run ; proc print data =fall; run ;

data fall; set fall;

drop _TYPE_ _FREQ_; run ;

data all; merge summer fall;

by env type Nrate; run ; proc print data =all; run ;

data all1; set all;

if summerbiom=

. then summerbiom= 0 ;

total=summerbiom+fallbiom; run ;

proc print data =all1; run ;

*modeling 2010 t otal dry biomass yields;

*blup estimate statements calculating dry biomass yield in each environment by type and Nrate;

ods html ;

ods graphics on ;

proc glimmix data =biom10 plots =all;

class env block type hybrid Nrate;

model biomass=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/ dist =normal ddfm =kr ;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type);

covtest glm/wald;

*lsmeans type*Nrate/plot=meanplot(sliceby=type joi n);

estimate "DS energy 0"

intercept 1 type 1 0 Nrate 1 0 0 0 0 type*Nrate 1 |env 1 0

env*type 1 0 0 0 0 0 env*type*Nrate 1 ;

estimate "DS energy 56"

intercept 1 type 1 0 Nrate 0 1 0 0 0 type*Nrate 0 1 |env 1 0

env*type 1 0 0 0 0 0 env*type*Nrat e 0 1 ;

estimate "DS energy 112"

intercept 1 type 1 0 Nrate 0 0 1 0 0 type*Nrate 0 0 1 |env 1 0

env*type 1 0 0 0 0 0 env*type*Nrate 0 0 1 ;

estimate "DS energy 168"

intercept 1 type 1 0 Nrate 0 0 0 1 0 type*Nrate 0 0 0 1 |env 1 0

env*type 1 0 0 0 0 0 env*type*Nrate 0 0 0 1 ;

estimate "DS energy 224"

intercept 1 type 1 0 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 1 |env 1 0

env*type 1 0 0 0 0 0 env*type*Nrate 0 0 0 0 1 ;

estimate "DS forage 0"

intercept 1 type 0 1 Nrate 1 0 0 0 0 type*Nrate 0 0 0 0 0 1 |e nv 1 0

env*type 0 1 0 0 0 0 env*type*Nrate 0 0 0 0 0 1 ;

estimate "DS forage 56"

intercept 1 type 0 1 Nrate 0 1 0 0 0 type*Nrate 0 0 0 0 0 0 1 |env 1 0

env*type 0 1 0 0 0 0 env*type*Nrate 0 0 0 0 0 0 1 ;

estimate "DS forage 112"

intercept 1 type 0 1 Nrate 0 0 1 0 0 type*Nrate 0 0 0 0 0 0 0 1 |env 1 0

env*type 0 1 0 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 1 ;

estimate "DS forage 168"

intercept 1 type 0 1 Nrate 0 0 0 1 0 type*Nrate 0 0 0 0 0 0 0 0 1 |env 1 0

env*type 0 1 0 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 1 ;

estimate "DS forage 224"

intercept 1 type 0 1 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 0 0 0 0 0 1 |env 1 0

env*type 0 1 0 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 1 ;

estimate "PE energy 0"

intercept 1 type 1 0 Nrate 1 0 0 0 0 type*Nrate 1 |env 0 1

env*type 0 0 1 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "PE energy 56"

intercept 1 type 1 0 Nrate 0 1 0 0 0 type*Nrate 0 1 |env 0 1

## 164 env*type 0 0 1 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "PE energy 112"

intercept 1 type 1 0 Nrate 0 0 1 0 0 type*Nrate 0 0 1 |env 0 1

env*type 0 0 1 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "PE energy 168"

intercept 1 type 1 0 Nrate 0 0 0 1 0 type*Nrate 0 0 0 1 |env 0 1

env*type 0 0 1 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "PE energy 224"

intercept 1 type 1 0 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 1 |env 0 1

env*type 0 0 1 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "PE forage 0"

intercept 1 type 0 1 Nrate 1 0 0 0 0 type*Nrat e 0 0 0 0 0 1 |env 0 1

env*type 0 0 0 1 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "PE forage 56"

intercept 1 type 0 1 Nrate 0 1 0 0 0 type*Nrate 0 0 0 0 0 0 1 |env 0 1

env*type 0 0 0 1 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "PE forage 112"

intercept 1 type 0 1 Nrate 0 0 1 0 0 type*Nrate 0 0 0 0 0 0 0 1 |env 0 1

env*type 0 0 0 1 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "PE forage 168"

intercept 1 type 0 1 Nrate 0 0 0 1 0 type*Nra te 0 0 0 0 0 0 0 0 1 |env 0 1

env*type 0 0 0 1 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "PE forage 224"

intercept 1 type 0 1 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 0 0 0 0 0 1 |env 0 1

env*type 0 0 0 1 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR energy 0"

intercept 1 type 1 0 Nrate 1 0 0 0 0 type*Nrate 1 |env 0 0 1

env*type 0 0 0 0 1 0 env*type*Nrate

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR energy 56"

intercept 1 type 1 0 Nr ate 0 1 0 0 0 type*Nrate 0 1 |env 0 0 1

env*type 0 0 0 0 1 0 env*type*Nrate

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR energy 112"

intercept 1 type 1 0 Nrate 0 0 1 0 0 type*Nrate 0 0 1 |env 0 0 1

env*type 0 0 0 0 1 0 env*type*Nrate

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR energy 168"

intercept 1 type 1 0 Nrate 0 0 0 1 0 type*Nrate 0 0 0 1 |env 0 0 1

env*type 0 0 0 0 1 0 env*type*Nrate

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR energy 224"

intercept 1 type 1 0 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 1 |env 0 0 1

env*type 0 0 0 0 1 0 env*type*Nrate

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR forage 0"

intercept 1 type 0 1 Nrate 1 0 0 0 0 type*Nrate 0 0 0 0 0 1 |env 0 0 1

env*type 0 0 0 0 0 1 env*type*Nrate

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR forage 56"

intercept 1 type 0 1 Nrate 0 1 0 0 0 type*Nrate 0 0 0 0 0 0 1 |env 0 0 1

env*type 0 0 0 0 0 1 env*type*Nrate

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR forage 112"

intercept 1 type 0 1 Nrate 0 0 1 0 0 type*Nrate 0 0 0 0 0 0 0 1 |env 0 0 1

env*type 0 0 0 0 0 1 env*type*Nrate

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR forage 1 68"

intercept 1 type 0 1 Nrate 0 0 0 1 0 type*Nrate 0 0 0 0 0 0 0 0 1 |env 0 0 1

env*type 0 0 0 0 0 1 env*type*Nrate

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR forage 224"

intercept 1 type 0 1 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 0 0 0 0 0 1 |env 0 0 1

env*type 0 0 0 0 0 1 env*type*Nrate

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "N 0 vs 56" Nrate 1

- 1 0 0 0 ;

estimate "N 0 vs 112" Nrate 1 0

- 1 0 0 ;

estimate "N 0 vs 168" Nrate 1 0 0

- 1 0 ;

esti mate "N 0 vs 224" Nrate 1 0 0 0

- 1 ;

estimate "N 56 vs 112" Nrate 0 1

- 1 0 0 ;

## 165

estimate "N 56 vs 168" Nrate 0 1 0

- 1 0 ;

estimate "N 56 vs 224" Nrate 0 1 0 0

- 1 ;

estimate "N 112 vs 168" Nrate 0 0 1

- 1 0 ;

estimate "N 112 vs 224" Nrate 0 0 1 0

- 1 ;

estimate "N 1 68 vs 224" Nrate 0 0 0 1

- 1 ;

estimate "energy 0" intercept 1 type 1 Nrate 1 type*Nrate 1 ;

estimate "energy 56" intercept 1 type 1 Nrate 0 1 type*Nrate 0 1 ;

estimate "energy 112" intercept 1 type 1 Nrate 0 0 1 type*Nrate 0 0 1 ;

estimate "energy 168" inter cept 1 type 1 Nrate 0 0 0 1 type*Nrate 0 0 0 1 ;

estimate "energy 224" intercept 1 type 1 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 1 ;

estimate "forage 0" intercept 1 type 0 1 Nrate 1 type*Nrate 0 0 0 0 0 1 ;

estimate "forage 56" intercept 1 type 0 1 Nrate 0 1 typ e*Nrate 0 0 0 0 0 0 1 ;

estimate "forage 112" intercept 1 type 0 1 Nrate 0 0 1 type*Nrate 0 0 0 0 0 0 0 1 ;

estimate "forage 168" intercept 1 type 0 1 Nrate 0 0 0 1 type*Nrate 0 0 0 0 0 0 0 0 1 ;

estimate "forage 224" intercept 1 type 0 1 Nrate 0 0 0 0 1 type *Nrate 0 0 0 0 0 0 0 0 0 1 ;

estimate "energy 0 vs 56" Nrate 1

- 1 type*Nrate 1

- 1 ;

estimate "energy 0 vs 112" Nrate 1 0

- 1 type*Nrate 1 0

- 1 ;

estimate "energy 0 vs 168" Nrate 1 0 0

- 1 type*Nrate 1 0 0

- 1 ;

estimate "energy 0 vs 224" Nrate 1 0 0 0

- 1 typ e*Nrate 1 0 0 0

- 1 ;

estimate "energy 56 vs 112" Nrate 0 1

- 1 0 0 type*Nrate 0 1

- 1 0 0 ;

estimate "energy 56 vs 168" Nrate 0 1 0

- 1 0 type*Nrate 0 1 0

- 1 0 ;

estimate "energy 56 vs 224" Nrate 0 1 0 0

- 1 type*Nrate 0 1 0 0

- 1 ;

estimate "energy 112 vs 168" Nra te 0 0 1

- 1 0 type*Nrate 0 0 1

- 1 0 ;

estimate "energy 112 vs 224" Nrate 0 0 1 0

- 1 type*Nrate 0 0 1 0

- 1 ;

estimate "energy 168 vs 224" Nrate 0 0 0 1

- 1 type*Nrate 0 0 0 1

- 1 ;

estimate "forage 0 vs 56" Nrate 1

- 1 type*Nrate 0 0 0 0 0 1

- 1 ;

estimate "forage 0 vs 112" Nrate 1 0

- 1 type*Nrate 0 0 0 0 0 1 0

- 1 ;

estimate "forage 0 vs 168" Nrate 1 0 0

- 1 type*Nrate 0 0 0 0 0 1 0 0

- 1 ;

estimate "forage 0 vs 224" Nrate 1 0 0 0

- 1 type*Nrate 0 0 0 0 0 1 0 0 0

- 1 ;

estimate "forage 56 vs 112" Nrate 0 1

- 1 0 0 type*Nrat e 0 0 0 0 0 0 1

- 1 0 0 ;

estimate "forage 56 vs 168" Nrate 0 1 0

- 1 0 type*Nrate 0 0 0 0 0 0 1 0

- 1 0 ;

estimate "forage 56 vs 224" Nrate 0 1 0 0

- 1 type*Nrate 0 0 0 0 0 0 1 0 0

- 1 ;

estimate "forage 112 vs 168" Nrate 0 0 1

- 1 0 type*Nrate 0 0 0 0 0 0 0 1

- 1 0 ;

estimate "forage 112 vs 224" Nrate 0 0 1 0

- 1 type*Nrate 0 0 0 0 0 0 0 1 0

- 1 ;

estimate "forage 168 vs 224" Nrate 0 0 0 1

- 1 type*Nrate 0 0 0 0 0 0 0 0 1

- 1 ;

estimate "Dixon Springs 2010 type difference" type 1

- 1 |env 1 env*type 1

- 1 ;

estimate "Perry 2 010 type difference" type 1

- 1 |env 0 1 env*type 0 0 1

- 1 ;

estimate "Urbana 2010 type difference" type 1

- 1 |env 0 0 1 env*type 0 0 0 0 1

- 1 ;

estimate "DS vs PE energy vs. forage" type 1

- 1 |env 1

- 1 0 env*type 1

- 1

- 1 1 ;

estimate "DS vs UR energy vs. forage " type 1

- 1 |env 1 0

- 1 env*type 1

- 1 0 0

- 1 1 ;

estimate "PE vs UR energy vs. forage" type 1

- 1 |env 0 1

- 1 env*type 0 0 1

- 1

- 1 1 ;

estimate "DS vs PE energy" |env 1

- 1 0 env*type 1 0

- 1 0 0 0 ;

estimate "PE vs UR energy" |env 0 1

- 1 env*type 0 0 1 0

- 1 0 ;

output out =out1 residual=resid predicted=pred;

run ;

ods graphics off ;

ods html close ;

*checking outliers;

data out1out; set out1;

if resid le 5 then delete ;

run ; proc print data =out1out; run ;

*removing outliers;

data new; set out1;

if resid ge 10 then delete ;

run ;

ods html ;

ods graphics on ;

proc glimmix data =new plots =all;

class env block type hybrid Nrate;

model biomass=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/ dist =normal ddfm =kr ;

random env block(env) env*type type*block(env) env*hybri d(type) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type);

## 166

covtest glm/wald;

output out =out1 residual=residnew predicted=prednew;

run ;

ods graphics off ;

ods html close ;

*Running the model without these outliers did not change the sign ificance of any of the

factors or parameter estimates in a way that would warrant leaving them out. The residuals

looked better but the model did not change significantly so I will keep them in the model.

*Based upon the results of the model only the e ffect of Nrate and Type are significant at

the alpha=.1 level, ;

*2010 forage sorghum harvest 1, harvest 2 analysis;

data forage; set biom10;

if type = "Energy" then delete ;

run ; proc print data =forage; run ;

*2010 summer forage sorghum harvest analysis ;

ods html ;

ods graphics on ;

proc glimmix data =forage plots =all;

class env block hybrid Nrate;

model early=hybrid Nrate hybrid*Nrate/ dist =normal ddfm =kr ;

random env block(env)env*hybrid hybrid*block(env) env*Nrate env*Nrate*hybrid;

covtest glm/wald;

lsmea ns Nrate/ pdiff ;

estimate "DS 0" intercept 1 Nrate 1 0 0 0 0 |env 1 0 0 env*Nrate 1 ;

estimate "DS 56" intercept 1 Nrate 0 1 0 0 0 |env 1 0 0 env*Nrate 0 1 ;

estimate "DS 112" intercept 1 Nrate 0 0 1 0 0 |env 1 0 0 env*Nrate 0 0 1 ;

estimate "DS 168" intercep t 1 Nrate 0 0 0 1 0 |env 1 0 0 env*Nrate 0 0 0 1 ;

estimate "DS 224" intercept 1 Nrate 0 0 0 0 1 |env 1 0 0 env*Nrate 0 0 0 0 1 ;

estimate "PE 0" intercept 1 Nrate 1 0 0 0 0 |env 0 1 0 env*Nrate 0 0 0 0 0 1 ;

estimate "PE 56" intercept 1 Nrate 0 1 0 0 0 |env 0 1 0 env*Nrate 0 0 0 0 0 0 1 ;

estimate "PE 112" intercept 1 Nrate 0 0 1 0 0 |env 0 1 0 env*Nrate 0 0 0 0 0 0 0 1 ;

estimate "PE 168" intercept 1 Nrate 0 0 0 1 0 |env 0 1 0 env*Nrate 0 0 0 0 0 0 0 0 1 ;

estimate "PE 224" intercept 1 Nrate 0 0 0 0 1 |env 0 1 0 env*Nrate 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 0" intercept 1 Nrate 1 0 0 0 0 |env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 56" intercept 1 Nrate 0 1 0 0 0 |env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 112" intercept 1 Nrate 0 0 1 0 0 |env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 168" intercept 1 Nrate 0 0 0 1 0 |env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0

1 ;

estimate "UR 224" intercept 1 Nrate 0 0 0 0 1 |env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 ;

run ;

ods gr aphics off ;

ods html close ;

*2010 fall forage sorghum harvest analysis;

ods html ;

ods graphics on ;

proc glimmix data =forage plots =all;

class env block hybrid Nrate;

model late=hybrid Nrate hybrid*Nrate/ dist =normal ddfm =kr ;

random env block(env)env*hybrid hybrid*block(env) env*Nrate env*Nrate*hybrid;

covtest glm/wald;

lsmeans Nrate/ pdiff ;

estimate "DS 0" intercept 1 Nrate 1 0 0 0 0 |env 1 0 0 env*Nrate 1 ;

estimate "DS 56" intercept 1 Nrate 0 1 0 0 0 |env 1 0 0 env*Nrate 0 1 ;

estimate "DS 112" intercept 1 Nrate 0 0 1 0 0 |env 1 0 0 env*Nrate 0 0 1 ;

estimate "DS 168" intercept 1 Nrate 0 0 0 1 0 |env 1 0 0 env*Nrate 0 0 0 1 ;

estimate "DS 224" intercept 1 Nrate 0 0 0 0 1 |env 1 0 0 env*Nrate 0 0 0 0 1 ;

estimate "PE 0" intercept 1 Nrate 1 0 0 0 0 |env 0 1 0 env *Nrate 0 0 0 0 0 1 ;

estimate "PE 56" intercept 1 Nrate 0 1 0 0 0 |env 0 1 0 env*Nrate 0 0 0 0 0 0 1 ;

estimate "PE 112" intercept 1 Nrate 0 0 1 0 0 |env 0 1 0 env*Nrate 0 0 0 0 0 0 0 1 ;

estimate "PE 168" intercept 1 Nrate 0 0 0 1 0 |env 0 1 0 env*Nrate 0 0 0 0 0 0 0 0 1 ;

estimate "PE 224" intercept 1 Nrate 0 0 0 0 1 |env 0 1 0 env*Nrate 0 0 0 0 0 0 0 0 0 1 ;

## 167

estimate "UR 0" intercept 1 Nrate 1 0 0 0 0 |env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 56" intercept 1 Nrate 0 1 0 0 0 |env 0 0 1 env*Nrat e 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 112" intercept 1 Nrate 0 0 1 0 0 |env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "UR 168" intercept 1 Nrate 0 0 0 1 0 |env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0

1 ;

estimate "UR 224" intercept 1 Nrate 0 0 0 0 1 |env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;

estimate "graze all" intercept 1 hybrid 1 0 ;

estimate "graze. n. bale" intercept 1 hybrid 0 1 ;

estimate "graze all vs graze. n. bale" hybrid 1

- 1 ;

run ;

ods graphics off ;

ods html close ;

* 2009 biomass data;

data biom09;

length env$ 18 ;

length hybrid$ 14 ;

infile 'C: \ Users \ maughan2 \ Documents \ My Dropbox \ sorghum fertility trial \ 2009 & 2010 analysis for

manuscript 1 \ sorghum dry biomass yield 2009.csv'

dlm

= "," firstobs

= 5 ;

input env$ block type $ hybrid$ Nrate plot tons;

biomass=tons* 2.24 ;

run ; proc print ; run ;

proc sort data =biom09; by env type hybrid block Nrate; run ; proc print ;

proc sort data =biom09; by env type Nrate; run ;

proc means data =biom09 noprint mean ; var biomass; by env type Nrate;

output out =mean09 mean =biomass; run ; proc print data =mean09; run ;

ods graphics on ;

proc glimmix data =biom09 plots =RESIDUALPANEL;

class block type hybrid Nrate;

model biomass=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type);

random block block*type b lock*hybrid(type);

covtest glm/wald;

*lsmeans type*Nrate/pdiff;

estimate "energy 0 vs 50" Nrate 1

- 1 type*Nrate 1

- 1 ;

estimate "energy 0 vs 100" Nrate 1 0

- 1 type*Nrate 1 0

- 1 ;

estimate "energy 0 vs 150" Nrate 1 0 0

- 1 type*Nrate 1 0 0

- 1 ;

estimate "en ergy 50 vs 100" Nrate 0 1

- 1 0 type*Nrate 0 1

- 1 0 ;

estimate "energy 50 vs 150" Nrate 0 1 0

- 1 type*Nrate 0 1 0

- 1 ;

estimate "energy 100 vs 150" Nrate 0 0 1

- 1 type*Nrate 0 0 1

- 1 ;

estimate "forage 0 vs 50" Nrate 1

- 1 type*Nrate 0 0 0 0 1

- 1 ;

estimate "fo rage 0 vs 100" Nrate 1 0

- 1 type*Nrate 0 0 0 0 1 0

- 1 ;

estimate "forage 0 vs 150" Nrate 1 0 0

- 1 type*Nrate 0 0 0 0 1 0 0

- 1 ;

estimate "forage 50 vs 100" Nrate 0 1

- 1 0 type*Nrate 0 0 0 0 0 1

- 1 0 ;

estimate "forage 50 vs 150" Nrate 0 1 0

- 1 type*Nrate 0 0 0 0 0 1 0

- 1 ;

estimate "forage 100 vs 150" Nrate 0 0 1

- 1 type*Nrate 0 0 0 0 0 0 1

- 1 ;

contrast "linear N rate" Nrate

- 3

- 1 1 3 ;

contrast "linear N rate energy vs forage" type*Nrate

- 3

- 1 1 3 3 1

- 1

- 3 ;

run ;

ods graphics off ;



## Correlations among plant height, expanded leaf number, and biomass yield

*2010 energy sorghum biomass yields;

options ls

= 150 ps = 150 nodate nocenter formdlim

= "

- " ;

data biom10;

length env$ 18 ;

length hybrid$ 14 ;

infile 'C: \ Users \ maughan2 \ Documents \ My Dropbox \ sorghum fertility tria l \ 2009 & 2010 analysis for

manuscript 1 \ sorghum dry biomass yield 2010.csv'

dlm

= "," firstobs

= 7 ;

## 168

input env$ block type$ hybrid$ Nratelbs Nrate plot summer fall tons biomass comments$;

if type= "Forage" then delete ;

drop Nratelbs summer fall tons comments;

r un ; proc print data =biom10; run ;

*2009 energy sorghum biomass data;

data biom09;

length env$ 18 ;

length hybrid$ 14 ;

infile 'C: \ Users \ maughan2 \ Documents \ My Dropbox \ sorghum fertility trial \ 2009 & 2010 analysis for

manuscript 1 \ sorghum dry biomass yield 2 009.csv'

dlm

= "," firstobs

= 5 ;

input env$ block type$ hybrid$ Nrate plot tons;

biomass=tons* 2.24 ;

if type= "Forage" then delete ;

drop tons;

run ; proc print data =biom09; run ;

*2009 & 2010 leaf number and height;

data htleafno;

length env$ 18 ;

length hybrid$ 14 ;

infile 'C: \ Users \ maughan2 \ Documents \ My Dropbox \ sorghum fertility trial \ 2009 & 2010 analysis for

manuscript 1 \ sorghum ht

- leafno 2009 & 2010 for correlations.csv'

dlm

= "," firstobs

= 6 ;

input env$ date$ doy block sample type$ hybrid$ Nrate Nratelbs plot l eaf htcm GDD10 GDD11

comment$;

htm=htcm/ 100 ;

if type= "Forage" then delete ;

drop date sample Nratelbs htcm GDD10 GDD11 comment;

run ; proc print data =htleafno; run ;

*Urbana 2009 end of season ht and leaf number;

data htleaf09; set htleafno;

if env ne "Urba na 2009" then delete ;

if doy ne 280 then delete ;

run ; proc print ; run ;

proc sort data =htleaf09; by env block type hybrid Nrate plot; run ;

proc means data =htleaf09 mean noprint ; var leaf htm; by env block type hybrid Nrate plot;

output out =new09 mean =leaf htm; run ; proc print data =new09; run ;

data new09; set new09;

drop _TYPE_ _FREQ_; run ; proc print data =new09; run ;

*2010 end of season ht and leaf number;

data htleaf10; set htleafno;

if env = "Urbana 2009" then delete ;

if env= "Urbana 2010" and doy ne 270 then delete ;

if env= "Dixon Springs 2010" and doy ne 277 then delete ;

if env= "Perry 2010" and doy ne 276 then delete ;

run ; proc print ; run ;

proc sort data =htleaf10; by env block type hybrid Nrate plot; run ;

proc means data =htleaf10 mean noprint ; var leaf htm; by env block type hybrid Nrate plot;

output out =new10 mean =leaf htm; run ; proc print data =new10; run ;

data new10; set new10;

drop _TYPE_ _FREQ_; run ; proc print data =new10; run ;

*merging 2009 info;

proc sort data =biom09; by env block type hybrid Nrate plot; run ;

data all09; merge biom09 new09;

by env block type hybrid Nrate; run ;

proc print data =all09; run ;

*Correlations among biomass, leaf number, and height for Urbana 2009;

## 169

proc corr data =all09;

var biomass leaf htm; run ;

proc sgscatter data

= all09;

title "Urbana 2009" ;

matrix htm leaf biomass/ group =Nrate;

run ;

*merging 2010 info;

proc sort data =biom10; by env block type hybrid Nrate plot; run ;

data all10; merge biom10 new10;

by env block type hybrid Nrate; run ;

proc print data =all10; run ;

* Correlations among biomass, leaf number, and height for 2010;

proc corr data =all10;

var biomass leaf htm;

by env; run ;

proc sgscatter data =all10;

title "2010 environments" ;

matrix htm leaf biomass/ group =env;

run ;

## 170 

## APPENDIX D

Selected SAS code for Ch. 5

Nitrogen response curves, EONR

- Ch.5, Fig. 5.1 , Table 5. 1

. options ls

= 150 ps = 150 nodate nocenter formdlim

= "

- " ;

data biom10;

length env$ 18 ;

length hybrid$ 14 ;

infile 'C: \ Users \ maughan2 \ Documents \ My Dropbox \ sorghum fertility trial \ 2009 & 2010 analysis fo r

manuscript 1 \ sorghum dry biomass yield 2010.csv'

dlm

= "," firstobs

= 7 ;

input env$ block type$ hybrid$ Nratelbs Nrate plot summer fall tons biomass comments$;

drop Nratelbs summer fall tons comments;

run ; proc print ; run ;

proc sort data =biom10; by env type hybrid block Nrate; run ; proc print ;

*URBANA 2010 ENERGY SORGHUM;

data urbenergy; set biom10;

if type ne "Energy" then delete ;

if env ne "Urbana 2010" then delete ;

*if block=1 and hybrid="TX09007" then delete; *poor planting in 106

- 110 and some floodin g;

X=Nrate;

X2=X*X;

run ; proc print data =urbenergy; run ;

proc sort data =urbenergy; by env hybrid block type Nrate;

proc print data =urbenergy; run ;

proc sort data =urbenergy; by block; run ; proc print ;

proc nlmixed data =urbenergy;

parms alpha= 22.5 beta= .0 75 gamma=

- .0001 /*s2blk=1*/ s2e= 45 ;

x0 =

- .5 *beta / gamma;

y = (x <x0)*(alpha+beta*x + gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0) /* + blk */ ;

plateau =alpha + beta*x0 + gamma*x0*x0;

EONR1a=(( .87 / 35 )

- beta)/( 2 *gamma);

EONR2a=(( .87 / 50 )

- beta)/( 2 *gamma);

EONR3a=(( .87 / 65 )

- beta)/( 2 *gamma);

EONR1b=(( 1.07 / 35 )

- beta)/( 2 *gamma);

EONR2b=(( 1.07 / 50 )

- beta)/( 2 *gamma);

EONR3b=(( 1.07 / 65 )

- beta)/( 2 *gamma);

EONR1c=(( 1.32 / 35 )

- beta)/( 2 *gamma);

EONR2c=(( 1.32 / 50 )

- beta)/( 2 *gamma);

EONR3c=(( 1.32 / 65 )

- beta)/( 2 *gamma);

model biomas s ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau;

estimate "$35a" EONR1a;

estimate "$50a" EONR2a;

estimate "$65a" EONR3a;

estimate "$35b" EONR1b;

estimate "$50b" EONR2b;

estimate "$65b" EONR3b;

e stimate "$35c" EONR1c;

estimate "$50c" EONR2c;

estimate "$65c" EONR3c;

predict y out=response1;

run ;

proc print data =response1; run ;

data response1; set response1;

residuals=biomass

- pred; run ; proc print data =response1; run ;

data ce;

do i= 0 to 224 by 0.5 ;

do n= 187.42 ;

yielde = (i <n)*( 22.3700 + 0.06470 *i

- 0.00017 *i*i) +

## 171

(i>=n)*( 22.3700 + 0.06470 *n

- 0.00017 *n*n);

output ;

end ;

end ;

proc print data =ce; run ;

*URBANA 2010 FORAGE SORGHUM;

data urbforage; set biom10;

if type ne "Forage" then delete ;

if env ne "U rbana 2010" then delete ;

X=Nrate;

X2=X*X;

run ; proc print data =urbforage; run ;

proc sort data =urbforage; by env hybrid block type Nrate;

proc print data =urbforage; run ;

proc sort data =urbforage; by block; run ; proc print ;

proc nlmixed data =urbforage;

pa rms alpha= 16.25 beta= .049 gamma=

- .0001 /*s2blk=1*/ s2e= 45 ;

x0 =

- .5 *beta / gamma; *estimate of joint point;

y = (x <x0)*(alpha+beta*x +gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0) /* + blk */ ;

plateau =alpha + beta*x0 + gamma*x0*x0;

EONR1a=(( .87 / 35 )

- be ta)/( 2 *gamma);

EONR2a=(( .87 / 50 )

- beta)/( 2 *gamma);

EONR3a=(( .87 / 65 )

- beta)/( 2 *gamma);

EONR1b=(( 1.07 / 35 )

- beta)/( 2 *gamma);

EONR2b=(( 1.07 / 50 )

- beta)/( 2 *gamma);

EONR3b=(( 1.07 / 65 )

- beta)/( 2 *gamma);

EONR1c=(( 1.32 / 35 )

- beta)/( 2 *gamma);

EONR2c=(( 1.32 / 50 )

- beta)/( 2 *gamma) ;

EONR3c=(( 1.32 / 65 )

- beta)/( 2 *gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau;

estimate "$35a" EONR1a;

estimate "$50a" EONR2a;

estimate "$65a" EONR3a;

estimate "$35b" EONR1b;

estimate "$50b" EONR2b;

estimate "$65b" EONR3b;

estimate "$35c" EONR1c;

estimate "$50c" EONR2c;

estimate "$65c" EONR3c;

predict y out=response2;

run ;

proc print data =response2; run ;

data response2; set response2;

residuals=biomass

- pred; run ; proc print da ta =response2; run ;

data cf;

do j= 0 to 224 by 0.5 ;

do n= 193.69 ;

yieldf = (j <n)*( 16.2596 + 0.0496 *j

- 0.00013 *j*j) +

(j>=n)*( 16.2596 + 0.0496 *n

- 0.00013 *n*n);

output ;

end ;

end ;

proc print data =cf; run ;

*PLOTTING RESPONSE CURVES FOR URBANA 2010;

data all; mer ge urbenergy urbforage;

by env hybrid block type Nrate biomass X X2;

run ; proc print data =all; run ;

proc sort data =all; by type; run ; proc print data =all; run ;

data urb; merge all ce cf; run ; proc print data =urb; run ;

## 172

proc sort data =urb; by i j; run ; pr oc print data =urb; run ;

proc sgplot data =urb noautolegend ;

title "Urbana 2010" ;

yaxis label

= "Biomass (Mg/ha)" values =( 0 to 50 by 10 );

xaxis label = "N rate (kg/ha)" values =( 0 to 225 by 25 );

scatter y =biomass x =x/ group =type markerattrs =( size

= 8 ) name = "obs" NO MISSINGGROUP;

series y =yielde x =i/ lineattrs =( pattern

= longdash thickness

= 2 px color =blue) name = "E"

legendlabel

= "Energy" ;

series y =yieldf x =j/ lineattrs =( pattern

= solid thickness

= 2 px color =darkred) name = "F"

legendlabel

= "Forage" ;

discretelegend "obs" "E" "F" / acr oss = 1

position =topleft location

= inside ;

run ;

*plotting residuals;

data Urb10; merge response1 response2; by type;

run ; proc print data =Urb10; run ;

proc sgplot data =Urb10 noautolegend ;

title "Urbana 2010" ;

yaxis label

= "Residuals (observed yield

- predict ed yield)" ;

xaxis label = "N rate (kg/ha)" values =( 0 to 225 by 25 );

scatter y =residuals x =x/ group =type markerattrs =( size

= 8 ) name = "obs" NOMISSINGGROUP;

refline 0 / axis

= y ;

run ;

*DIXON SPRINGS 2010 ENERGY SORGHUM;

data dsenergy; set biom10;

if type ne " Energy" then delete ;

if env ne "Dixon Springs 2010" then delete ;

*if plot=311 then delete;

*if plot=109 then delete;

*if plot=204 then delete;

X=Nrate;

X2=X*X;

run ; proc print data =dsenergy; run ;

proc sort data =dsenergy; by env hybrid block type Nrate;

p roc print data =dsenergy; run ;

proc sort data =dsenergy; by block; run ; proc print ;

proc nlmixed data =dsenergy;

parms alpha= 17 beta= .18 gamma=

- .00247 /*s2blk=1*/ s2e= 8.5 ;

x0 =

- .5 *beta / gamma;

y = (x <x0)*(alpha+beta*x + gamma*x*x) + (x>=x0)*(alpha+beta*x0 +gamma*x0*x0) /* + blk */ ;

plateau =alpha + beta*x0 + gamma*x0*x0;

EONR1a=(( .87 / 35 )

- beta)/( 2 *gamma);

EONR2a=(( .87 / 50 )

- beta)/( 2 *gamma);

EONR3a=(( .87 / 65 )

- beta)/( 2 *gamma);

EONR1b=(( 1.07 / 35 )

- beta)/( 2 *gamma);

EONR2b=(( 1.07 / 50 )

- beta)/( 2 *gamma);

EONR3b=(( 1.07 / 65 )

- beta)/( 2 *gamma);

EONR1c=(( 1.32 / 35 )

- beta)/( 2 *gamma);

EONR2c=(( 1.32 / 50 )

- beta)/( 2 *gamma);

EONR3c=(( 1.32 / 65 )

- beta)/( 2 *gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau;

estimate "$ 35a" EONR1a;

estimate "$50a" EONR2a;

estimate "$65a" EONR3a;

estimate "$35b" EONR1b;

estimate "$50b" EONR2b;

estimate "$65b" EONR3b;

estimate "$35c" EONR1c;

## 173

estimate "$50c" EONR2c;

estimate "$65c" EONR3c;

predict y out=response3;

run ;

proc print data =respo nse3; run ;

data response3; set response3;

residuals=biomass

- pred; run ; proc print data =response3; run ;

data ce;

do i= 0 to 224 by 0.5 ;

do n= 41.7536 ;

yielde = (i <n)*( 17.6916 + 0.1702 *i

- 0.00204 *i*i) +

(i>=n)*( 17.6916 + 0.1702 *n

- 0.00204 *n*n);

output ;

end ;

en d ;

proc print data =ce; run ;

*DIXON SPRINGS 2010 FORAGE SORGHUM;

data dsforage; set biom10;

if type ne "Forage" then delete ;

if env ne "Dixon Springs 2010" then delete ;

if plot= 301 then delete ; *no second harvest;

*if plot=114 then delete;

*if plot=115 then delete;

*if plot=306 then delete;

*if plot=403 then delete;

X=Nrate;

X2=X*X;

run ; proc print data =dsforage; run ;

proc sort data =dsforage; by env hybrid block type Nrate;

proc print data =dsforage; run ;

proc sort data =dsforage; by block; run ; proc pr int ;

proc nlmixed data =dsforage;

parms alpha= 16 beta= .09 gamma=

- .0008 /*s2blk=1*/ s2e= 3.5 ;

x0 =

- .5 *beta / gamma;

y = (x <x0)*(alpha+beta*x + gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0) /* + blk */ ;

plateau =alpha + beta*x0 + gamma*x0*x0;

EONR1a=(( .87 / 35 )

- beta)/( 2 *gamma);

EONR2a=(( .87 / 50 )

- beta)/( 2 *gamma);

EONR3a=(( .87 / 65 )

- beta)/( 2 *gamma);

EONR1b=(( 1.07 / 35 )

- beta)/( 2 *gamma);

EONR2b=(( 1.07 / 50 )

- beta)/( 2 *gamma);

EONR3b=(( 1.07 / 65 )

- beta)/( 2 *gamma);

EONR1c=(( 1.32 / 35 )

- beta)/( 2 *gamma);

EONR2c=(( 1.32 / 50 )

- beta)/( 2 * gamma);

EONR3c=(( 1.32 / 65 )

- beta)/( 2 *gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau;

estimate "$35a" EONR1a;

estimate "$50a" EONR2a;

estimate "$65a" EONR3a;

estimate "$35b" EO NR1b;

estimate "$50b" EONR2b;

estimate "$65b" EONR3b;

estimate "$35c" EONR1c;

estimate "$50c" EONR2c;

estimate "$65c" EONR3c;

predict y out=response4;

run ;

proc print data =response4; run ;

data response4; set response4;

residuals=biomass

- pred; run ; proc pr int data =response4; run ;

## 174

data cf;

do j= 0 to 224 by 0.5 ;

do n= 47.8261 ;

yieldf = (j <n)*( 16.6889 + 0.06675 *j

- 0.00070 *j*j) +

(j>=n)*( 16.6889 + 0.06675 *n

- 0.00070 *n*n);

output ;

end ;

end ;

proc print data =cf; run ;

*PLOTTING RESPONSE CURVES FOR Dixon springs 201 0;

data allds; merge dsenergy dsforage;

by env hybrid block type Nrate biomass X X2;

run ; proc print data =allds; run ;

proc sort data =allds; by type; run ; proc print data =allds; run ;

data ds; merge allds ce cf; run ; proc print data =ds; run ;

proc sort dat a =ds; by i j; run ; proc print data =ds; run ;

proc sgplot data =ds noautolegend ;

title "Dixon Springs 2010" ;

yaxis label

= "Biomass (Mg/ha)" values =( 0 to 50 by 10 );

xaxis label = "N rate (kg/ha)" values =( 0 to 225 by 25 );

scatter y =biomass x =x/ group =type markerat trs =( size

= 8 ) name = "obs" NOMISSINGGROUP;

series y =yielde x =i/ lineattrs =( pattern

= longdash thickness

= 2 px color =blue) name = "E"

legendlabel

= "Energy" ;

series y =yieldf x =j/ lineattrs =( pattern

= solid thickness

= 2 px color =darkred) name = "F"

legendlabel

= "Forage" ;

discre telegend "obs" "E" "F" / across = 1

position =topleft location

= inside ;

run ;

*plotting residuals;

data DS10; merge response3 response4; by type;

run ; proc print data =DS10; run ;

proc sgplot data =DS10 noautolegend ;

title "Dixon Springs 2010" ;

yaxis label

= "Res iduals (observed yield

- predicted yield)" ;

xaxis label = "N rate (kg/ha)" values =( 0 to 225 by 25 );

scatter y =residuals x =x/ group =type markerattrs =( size

= 8 ) name = "obs" NOMISSINGGROUP;

refline 0 / axis

= y ;

run ;

*PERRY 2010 ENERGY SORGHUM;

data perenergy; se t biom10;

if type ne "Energy" then delete ;

if env ne "Perry 2010" then delete ;

*if plot=310 then delete;

*if plot=110 then delete;

*if plot=104 then delete;

*if plot=206 then delete;

*if plot=301 then delete;

*if plot=420 then delete;

*if plot=108 then delete;

*if plot=309 then delete;

X=Nrate;

X2=X*X;

run ; proc print data =perenergy; run ;

proc sort data =perenergy; by env hybrid block type Nrate;

proc print data =perenergy; run ;

/*

proc sort data=perenergy; by block; run; proc print;

## 175

proc nlmixed data=p erenergy;

parms alpha=18 beta=.01 gamma=

- .0001 s2e=10;

x0 =

- .5*beta / gamma;

y = (x <x0)*(alpha+beta*x + gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0);

plateau =alpha + beta*x0 + gamma*x0*x0;

EONR1=((1.1638/35)

- beta)/(2*gamma);

EONR2=((1.1638/50)

- beta) /(2*gamma);

EONR3=((1.1638/65)

- beta)/(2*gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau;

estimate "$35" EONR1;

estimate "$50" EONR2;

estimate "$65" EONR3;

predict y out=respo nse;

run;

proc print data=response; run;

data ce;

do i=0 to 224 by 0.5;

do n=41.7536;

yielde = (i <n)*(17.6916+0.1702*i

- 0.00204*i*i) +

(i>=n)*(17.6916+0.1702*n

- 0.00204*n*n);

output;

end;

end;

proc print data=ce; run;

*/

*running it as a linear equatio n;

proc sort data =perenergy; by block; run ; proc print ;

proc nlmixed data =perenergy;

parms alpha= 28 beta= .03 s2e= 42 ;

y=alpha + beta*x;

model biomass ~ normal(y, s2e);

predict y out=response5;

run ;

proc print data =response5; run ;

data response5; set respo nse5;

residuals=biomass

- pred; run ; proc print data =response5; run ;

data ce;

do i= 0 to 224 by 0.5 ;

yielde = 27.1121 + 0.04319 *i;

output ;

end ;

proc print data =ce; run ;

*/

*Perry 2010 FORAGE SORGHUM;

data perforage; set biom10;

if type ne "Forage" then de lete ;

if env ne "Perry 2010" then delete ;

*if plot=401 then delete; *extremely high for 0 N, something not correct;

X=Nrate;

X2=X*X;

run ; proc print data =perforage; run ;

proc sort data =perforage; by env hybrid block type Nrate;

proc print data =perforage; run ;

proc sort data =perforage; by block; run ; proc print ;

proc nlmixed data =perforage;

parms alpha= 14 beta= .05 gamma=

- .0001 /*s2blk=1*/ s2e= 9 ;

x0 =

- .5 *beta / gamma;

y = (x <x0)*(alpha+beta*x + gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0) /* + blk */ ;

plateau =alpha + beta*x0 + gamma*x0*x0;

## 176

EONR1a=(( .87 / 35 )

- beta)/( 2 *gamma);

EONR2a=(( .87 / 50 )

- beta)/( 2 *gamma);

EONR3a=(( .87 / 65 )

- beta)/( 2 *gamma);

EONR1b=(( 1.07 / 35 )

- beta)/( 2 *gamma);

EONR2b=(( 1.07 / 50 )

- beta)/( 2 *gamma);

EONR3b=(( 1.07 / 65 )

- beta)/( 2 *gamma);

EONR1c= (( 1.32 / 35 )

- beta)/( 2 *gamma);

EONR2c=(( 1.32 / 50 )

- beta)/( 2 *gamma);

EONR3c=(( 1.32 / 65 )

- beta)/( 2 *gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau;

estimate "$35a" EONR1a;

estimate "$ 50a" EONR2a;

estimate "$65a" EONR3a;

estimate "$35b" EONR1b;

estimate "$50b" EONR2b;

estimate "$65b" EONR3b;

estimate "$35c" EONR1c;

estimate "$50c" EONR2c;

estimate "$65c" EONR3c;

predict y out=response6;

run ;

proc print data =response6; run ;

data respons e6; set response6;

residuals=biomass

- pred; run ; proc print data =response6; run ;

data cf;

do j= 0 to 224 by 0.5 ;

do n= 214.88 ;

yieldf = (j <n)*( 14.1348 + 0.03497 *j

- 0.00008 *j*j) +

(j>=n)*( 14.1348 + 0.03497 *n

- 0.00008 *n*n);

output ;

end ;

end ;

proc print data =cf; run ;

*PLOTTING RESPONSE CURVES FOR Perry 2010;

data allper; merge perenergy perforage;

by env hybrid block type Nrate plot biomass X X2;

run ; proc print data =allper; run ;

proc sort data =allper; by type; run ; proc print data =allper; run ;

data per; merg e allper ce cf; run ; proc print data =per; run ;

proc sort data =per; by i j; run ; proc print data =per; run ;

proc sgplot data =per noautolegend ;

title "Perry 2010" ;

yaxis label

= "Biomass (Mg/ha)" values =( 0 to 50 by 10 );

xaxis label = "N rate (kg/ha)" values =( 0 to 225 by 25 );

scatter y =biomass x =x/ group =type markerattrs =( size

= 8 ) name = "obs" NOMISSINGGROUP;

series y =yielde x =i/ lineattrs =( pattern

= longdash thickness

= 2 px color =blue) name = "E"

legendlabel

= "Energy" ;

series y =yieldf x =j/ lineattrs =( pattern

= solid thickness

= 2 px color =darkred) name = "F"

legendlabel

= "Forage" ;

discretelegend "obs" "E" "F" / across = 1

position =topleft location

= inside ;

run ;

*plotting residuals;

data PE10; merge response5 response6; by type;

run ; proc print data =PE10; run ;

proc sgplot data =PE10 n oautolegend ;

title "Perry 2010" ;

yaxis label

= "Residuals (observed yield

- predicted yield)" ;

## 177

xaxis label = "N rate (kg/ha)" values =( 0 to 225 by 25 );

scatter y =residuals x =x/ group =type markerattrs =( size

= 8 ) name = "obs" NOMISSINGGROUP;

refline 0 / axis

= y ;

run ;

*2009 biomass data;

data biom09;

length env$ 18 ;

length hybrid$ 14 ;

infile 'C: \ Users \ maughan2 \ Documents \ My Dropbox \ sorghum fertility trial \ 2009 & 2010 analysis for

manuscript 1 \ sorghum dry biomass yield 2009.csv'

dlm

= "," firstobs

= 5 ;

input env$ bloc k type$ hybrid$ Nrate plot tons;

biomass=tons* 2.24 ;

run ; proc print ; run ;

proc sort data =biom09; by env type hybrid block Nrate; run ; proc print ;

*Urbana 2009 ENERGY SORGHUM;

data urbenergy; set biom09;

if type ne "Energy" then delete ;

X=Nrate;

X2=X*X;

run ; proc print data =urbenergy; run ;

proc sort data =urbenergy; by env hybrid block type Nrate;

proc print data =urbenergy; run ;

*running it as a linear equation;

proc sort data =urbenergy; by block; run ; proc print ;

proc nlmixed data =urbenergy;

parms al pha= 13 beta= .07 s2e= 22 ;

y=alpha + beta*x;

model biomass ~ normal(y, s2e);

predict y out=response7;

run ;

proc print data =response7; run ;

data response7; set response7;

residuals=biomass

- pred; run ; proc print data =response7; run ;

data ce;

do i= 0 to 150 by 0.5 ;

yielde = 12.7240 + 0.07618 *i;

output ;

end ;

proc print data =ce; run ;

*Urbana 2009 FORAGE SORGHUM;

data urbforage; set biom09;

if type ne "Forage" then delete ;

X=Nrate;

X2=X*X;

run ; proc print data =urbforage; run ;

proc sort data =urbforage; by env hy brid block type Nrate;

proc print data =urbforage; run ;

*running it as a linear equation;

proc sort data =urbforage; by block; run ; proc print ;

proc nlmixed data =urbforage;

parms alpha= 7 beta= .07 s2e= 5 ;

y=alpha + beta*x;

## 178

model biomass ~ normal(y, s2e);

pr edict y out=response8;

run ;

proc print data =response8; run ;

data response8; set response8;

residuals=biomass

- pred; run ; proc print data =response8; run ;

data cf;

do j= 0 to 150 by 0.5 ;

yieldf = 6.8105 + 0.06752 *j;

output ;

end ;

proc print data =cf; run ;

*PL OTTING CURVES FOR Urbana 2009;

data allur; merge urbenergy urbforage;

by env hybrid block type Nrate plot biomass X X2;

run ; proc print data =allur; run ;

proc sort data =allur; by type; run ; proc print data =allur; run ;

data ur; merge allur ce cf; run ; pr oc print data =ur; run ;

proc sort data =ur; by i j; run ; proc print data =ur; run ;

proc sgplot data =ur noautolegend ;

title "Urbana 2009" ;

yaxis label

= "Biomass (Mg/ha)" values =( 0 to 50 by 10 );

xaxis label = "N rate (kg/ha)" values =( 0 to 225 by 25 );

scatter y = biomass x =x/ group =type markerattrs =( size

= 8 ) name = "obs" NOMISSINGGROUP;

series y =yielde x =i/ lineattrs =( pattern

= longdash thickness

= 2 px color =blue) name = "E"

legendlabel

= "Energy" ;

series y =yieldf x =j/ lineattrs =( pattern

= solid thickness

= 2 px color =darkred) name = " F"

legendlabel

= "Forage" ;

discretelegend "obs" "E" "F" / across = 1

position =topleft location

= inside ;

run ;

*plotting residuals;

data UR09; merge response7 response8; by type;

run ; proc print data =UR10; run ;

proc sgplot data =UR09 noautolegend ;

title "Urbana 2009" ;

yaxis label

= "Residuals (observed yield

- predicted yield)" ;

xaxis label = "N rate (kg/ha)" values =( 0 to 225 by 25 );

scatter y =residuals x =x/ group =type markerattrs =( size

= 8 ) name = "obs" NOMISSINGGROUP;

refline 0 / axis

= y ;

run ;



## SPAD, leaf TN, and leaf TC

- Ch.5, Table 5.2, Table 5.3.

*SPAD, leaf TN, leaf TC;

options ls

= 150 ps = 150 nodate nocenter formdlim

= "

- " ;

data TN;

length env$ 18 ;

length date$ 16 ;

length hybrid$ 14 ;

length time$ 12 ;

infile 'C: \ Users \ maughan2 \ Documents \ My Dropbox \ sorghum fertility trial \ 2 009 & 2010 analysis for

man. 2 \ 2009 2010 TN TC SPAD.csv'

dlm

= "," firstobs

= 14 ;

input env$ date$ doy time$ block sample type$ hybrid$ Nrate plot TN TC SPAD SLA;

drop date sample plot doy;

*if env ="Urbana 2009" and doy=232 then delete;

run ; proc print ; run ;

proc sort data =TN; by env time type hybrid block Nrate SLA; run ; proc print ;

proc sort data =TN; by env time type hybrid Nrate;

## 179

proc means data =TN noprint mean std ; var TN TC SPAD SLA; by env time type hybrid Nrate;

output out =means mean =mean_TN mean_TC m ean_SPAD mean_SLA; run ; proc print data =means; run ;

*URBANA 2009;

data Urb2009; set TN;

if env ne "Urbana 2009" then delete ;

if time = "early

- summer" then time= 1 ;

if time = "mid

- summer" then time= 2 ;

if time = "late

- summer" then time= 3 ;

run ; proc sort da ta =Urb2009; by env time type hybrid Nrate; run ; proc print data =Urb2009; run ;

*SPAD;

ods html ;

ods graphics on ;

proc glimmix data =Urb2009 plots =residualpanel;

class block type hybrid Nrate time;

model SPAD=type hybrid(type) Nrate type*Nrate Nrate*hybrid(t ype) time type*time time*hybrid(type)

Nrate*time type*Nrate*time Nrate*time*hybrid(type)/ dist =normal ddfm =kr;

random block block*type block*hybrid(type) block*Nrate*hybrid(type);

covtest glm/wald;

lsmeans time*hybrid(type)/ pdiff ;

lsmeans time*type/ pdiff ;

lsmeans Nrate*time/ pdiff ;

lsmeans Nrate/ pdiff ;

estimate "linear N" Nrate

- 3

- 1 1 3 ;

estimate "quad N" Nrate 1

- 1

- 1 1 ;

lsmeans time*type*Nrate/ pdiff plot=meanplot(sliceby=type*time join ilink );

run ;

ods graphics off ;

ods html close ;

proc corr data =Urb2009 ;

var SPAD Nrate; run ;

data TotalN; set Urb2009;

if time = 1 then delete ; *this run for total N is mest up;

run ; proc print data =totalN; run ;

proc corr data =totalN;

var SPAD TN Nrate; run ;

*TN;

ods html ;

ods graphics on ;

proc glimmix data =TotalN plots =r esidualpanel;

class block type hybrid Nrate time;

model TN=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type) time type*time time*hybrid(type)

Nrate*time type*Nrate*time Nrate*time*hybrid(type)/ dist =normal ddfm =kr;

random block block*type block*hybrid( type) block*Nrate*hybrid(type);

covtest glm/wald;

lsmeans time*hybrid(type)/ pdiff ;

lsmeans time*type/ pdiff ;

lsmeans Nrate*time/ pdiff ;

lsmeans Nrate/ pdiff ;

estimate "linear N" Nrate

- 3

- 1 1 3 ;

estimate "quad N" Nrate 1

- 1

- 1 1 ;

run ;

ods graphics off ;

ods ht ml close ;

*TC;

ods html ;

ods graphics on ;

proc glimmix data =TotalN plots =residualpanel;

class block type hybrid Nrate time;

model TC=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type) time type*time time*hybrid(type)

Nrate*time type*Nrate*time Nrate* time*hybrid(type)/ dist =normal ddfm =kr;

random block block*type block*hybrid(type) block*Nrate*hybrid(type);

covtest glm/wald;

## 180

lsmeans time*hybrid(type)/ pdiff ;

lsmeans time*type/ pdiff ;

lsmeans Nrate*time/ pdiff ;

lsmeans Nrate/ pdiff ;

estimate "linear N" Nrate

- 3

- 1 1 3 ;

estimate "quad N" Nrate 1

- 1

- 1 1 ;

run ;

ods graphics off ;

ods html close ;

*URBANA 2010;

data Urb2010; set TN;

if env ne "Urbana 2010" then delete ;

run ; proc print data =Urb2010; run ;

proc corr data =Urb2010;

var SPAD TN Nrate; run ;

*Dixon S prings 2010;

data DS2010; set TN;

if env ne "Dixon Springs 2010" then delete ;

run ; proc print data =DS2010; run ;

proc corr data =DS2010;

var SPAD TN; run ;

*Perry 2010;

data PE2010; set TN;

if env ne "Perry 2010" then delete ;

run ; proc print data =PE2010; run ;

proc corr data =PE2010;

var SPAD TN; run ;

data data2010; set TN;

if env= "Urbana 2009" then delete ;

run ; proc print data =data2010; run ;

*SPAD;

ods html ;

ods graphics on ;

proc glimmix data =data2010 plots =residualpanel;

class env block type hybrid N rate time;

model spad=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)

time type*time time*hybrid(type) Nrate*time type*Nrate*time Nrate*time*hybrid(type)/ dist =normal

ddfm =kr;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybri d(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type) block*hybrid*Nrate(env type)

env*time env*type*time env*time*hybrid(type) env*Nrate*time env*type*Nrate*time

env*Nrate*time*hybrid(type);

covtest glm/wald;

lsmeans time*hybrid(type)/ pdiff ;

lsmeans time*type/ pdiff ;

lsmeans Nrate/ pdiff ;

estimate "linear N" Nrate

- 2

- 1 0 1 2 ;

estimate "quad N" Nrate 2

- 1

- 2

- 1 2 ;

run ;

ods html ;

ods graphics on ;

*TN;

ods html ;

ods graphics on ;

proc glimmix data =data2010 plots =residualpanel;

class env block type hybrid Nrate time;

model TN=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)

## 181

time type*time time*hybrid(type) Nrate*time type*Nrate*time Nrate*time*hybrid(type)/ dist =normal

ddfm =kr;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybri d(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type) block*hybrid*Nrate(env type)

env*time env*type*time env*time*hybrid(type) env*Nrate*time env*type*Nrate*time

env*Nrate*time*hybrid(type);

covtest glm/wald;

lsmeans time*hybrid(type)/ pdiff ;

lsmeans time*type/ pdiff ;

lsmeans Nrate/ pdiff ;

estimate "linear N" Nrate

- 2

- 1 0 1 2 ;

estimate "quad N" Nrate 2

- 1

- 2

- 1 2 ;

run ;

ods html ;

ods graphics on ;

*TC;

ods html ;

ods graphics on ;

proc glimmix data =data2010 plots =residualpanel;

class env block type hybri d Nrate time;

model TC=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)

time type*time time*hybrid(type) Nrate*time type*Nrate*time Nrate*time*hybrid(type)/ dist =normal

ddfm =kr;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybr id(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type) block*hybrid*Nrate(env type)

env*time env*type*time env*time*hybrid(type) env*Nrate*time env*type*Nrate*time

env*Nrate*time*hybrid(type);

covtest glm/wald;

lsmeans time*hybrid(type)/ pdiff ;

lsmean s time*type/ pdiff ;

lsmeans Nrate/ pdiff ;

estimate "linear N" Nrate

- 2

- 1 0 1 2 ;

estimate "quad N" Nrate 2

- 1

- 2

- 1 2 ;

run ;

ods html ;

ods graphics on ;

*early

- summer correlations;

data early; set data2010;

if time= "mid

- summer" then delete ;

run ; proc prin t data =early; run ;

proc corr data =early;

var SPAD TN Nrate; run ;

*mid

- summer correlations;

data mid; set data2010;

if time= "early

- summer" then delete ;

run ; proc print data =mid; run ;

proc corr data =mid;

var SPAD TN Nrate; run ;



## Harvest TN and harvest TC

- Ch.5, Table 5.4.

options ls

= 150 ps = 150 nodate nocenter formdlim

= "

- " ;

data harvest;

length env$ 18 ;

length hybrid$ 14 ;

infile 'C: \ Users \ maughan2 \ Documents \ My Dropbox \ sorghum fertility trial \ 2009 & 2010 analysis for

man. 2 \ 2009 2010 TN TC harvest samples.csv'

dlm

= "," firstobs

= 9 ;

input env$ block type$ hybrid$ Nrate plot position wt pos wt TN TC;

drop plot position wt pos;

run ; proc print ; run ;

## 182

*URBANA 2009;

data Urbana2009; set harvest;

if env ne "Urbana 2009" then delete ;

run ; p roc print data =Urbana2009; run ;

*TN;

ods html ;

ods graphics on ;

proc glimmix data =Urbana2009 plots =residualpanel;

class block type hybrid Nrate;

model TN=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/ dist =normal ddfm =kr;

random block block*type bl ock*hybrid(type);

covtest glm/wald;

lsmeans type*Nrate/ pdiff ;

lsmeans Nrate/ pdiff ;

run ;

ods graphics off ;

ods html close ;

*TC;

ods html ;

ods graphics on ;

proc glimmix data =Urbana2009 plots =residualpanel;

class block type hybrid Nrate;

model TC=type hybrid (type) Nrate type*Nrate Nrate*hybrid(type)/ dist =normal ddfm =kr;

random block block*type block*hybrid(type);

covtest glm/wald;

lsmeans type*Nrate/ pdiff ;

lsmeans Nrate/ pdiff ;

run ;

ods graphics off ;

ods html close ;

*2010;

data envs2010; set harvest;

if env = "Urbana 2009" then delete ;

run ; proc print data =envs2010; run ;

*TN;

ods html ;

ods graphics on ;

proc glimmix data =envs2010 plots =residualpanel;

class env block type hybrid Nrate;

model TN=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/ dist =normal ddfm =kr;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type);

covtest glm/wald;

lsmeans type*Nrate/ pdiff ;

lsmeans Nrate/ pdiff ;

run ;

ods graphics off ;

ods html close ;

*TC;

ods html ;

ods graphics on ;

proc glimmix data =envs2010 plots =residualpanel;

class env block type hybrid Nrate;

model TC=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/ dist =normal ddfm =kr;

random env block(env) env*type type*block(env) env*hybrid(type ) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type);

covtest glm/wald;

lsmeans type*Nrate/ pdiff ;

lsmeans Nrate/ pdiff ;

run ;

ods graphics off ;

ods html close ;

## 183 

## CURRICULUM VITAE



# Matthew W. Maughan , Ph.D. candidate

Education

University of Illinois Urbana

- Champaign Urbana, IL

Ph.D. in Crop Sciences, summer 2011 (expected)

University of Illinois Urbana

- Champaign Urbana, IL

M.S. in Crop Sciences, May 2008

Brigham Young University

- Idaho Rexburg, ID

B.S. in Agricultural Business Science & Technology, May 2006

Work Experience

Research Assistant (while Ph.D. student) Aug. 2008

– present

Department Crop Sciences, University of Illinois Urbana

- Champaign

Supervised by Dr. Germán Bollero, Dr. D.K . Lee and Dr. Tom Voigt.

Major research projects:

•Meta

- analysis of switchgrass: management and environmental factors that affect

biomass yields in the US and southern Canada.

•Field experimentation of Miscanthus x giganteus productivity in Nebraska, Illin ois,

Kentucky, and New Jersey, and the impacts of nitrogen rate, season of growth, and

environmental conditions on morphology, plant growth, and biomass yield.

•Management of biomass sorghum hybrids for biomass feedstock production under

different nitrogen rates in four Illinois environments.

Research Assistant (while M.S. student) Aug. 2006

– May 2008

Department Crop Sciences, University of Illinois Urbana

- Champaign

Supervised by Dr. Germán Bollero and Dr. Benjamin Tracy.

Replicated farm scale study i nvestigating the impact of an integrated crop

- livestock

system that includes perennial pastures and winter cover crops on soil physical and

biological properties, and corn grain yield.

Teaching Assistant 2007

- 2010

Supervised by Dr. Germán Bollero and Dr. Donald Bullock.

Teaching assistant for following courses:

1) Applied Statistical Methods I

- Introductory Applied Statistics (Fall 2007/Spring

2008/Fall 2008).

2) Applied Statistical Methods II

- Experimental Design and Analysis (Spring

2009/ Spring 2010).

3) Regression Analysis (Fall 2009).

Responsibilities for these three courses included:

## 184 1) Preparing teaching material used in weekly laboratory sessions where I taught and

reviewed lecture material, and provided statistical examples ap plicable to

assigned homework, exams, and quizzes.

2) Taught procedures and coding in Statistical Analysis Software (SAS).

3) Administered and graded scheduled quizzes, graded homework assignments, and

helped to grade midterms and final exam.

4) Held weekly office hours, and interacted and taught students through Web CT

(Compass).

Farm Hand/Supervisor, Maughan Farms, Inc. 1996

- 2006

Maughan Farms Inc. is a seed potato , corn, small grain, and hay farm near Fort Benton,

MT. This is a family farm operation on which I have worked at different times between

1996 and 2006. Responsibilities and tasks related to this farm business have included:

•Planting and harvesting of seed potatoes, wheat, and corn.

•Fertilizer and chemical ap plication.

•Center pivot, wheel line, and hand line irrigation of crops

•Solid set irrigation of potatoes on corners of center pivots.

• Supervision and participation in potato rogueing crew for disease identification and

removal.

•Wheat rogueing, primaril y for wild oat and rye.

•Haying, raking, baling, and stacking hay.

•Extensive field and farm equipment operation including tractors, implements, and

harvesters.

•Various other jobs and responsibilities included: feeding and tending livestock, and

light mechanical work on farm equipment (i.e. oil changes and general maintenance).

Publications, peer reviewed

Tracy, B.F., M. Maughan , N. Post, D.B. Faulkner. 2010. Integrating Annual and

Perennial Warm

- season Grasses in a Temperate Grazing System. Crop Sci. 50: 2171

- 2177.

Maughan, M.W. , G. Bollero, J.P.C. Flores, B.F. Tracy, and F.G. Fernández. 2009. Soil

Quality and Corn Yield under Crop

– Livestock Integration in Illinois. Agron. J. 101:1511

-

1521.

Anderson, E., R. Arundale, M. Maughan , A.Oladeinde, A. Wyci slo, and T. Voigt.

Growth and Agronomy of Miscanthus x giganteus for Biomass Production in the US.

Biofuels 2:167

- 183.

## 185

Manuscripts in preparation or submitted

Maughan, M. , G. Bollero, D.K. Lee, R. Darmody, S. Bonos, L. Cortese, J. Murphy, R.

Gaussoin , M. Sousek, D. Williams, L. Williams, F. Miguez, and T. Voigt. 2010. Miscanthus

x giganteus Productivity: The Effects of Management in Different Environments. GCB

Bioenergy ( in review ).

Maughan, M.W. , G. Bollero, J.P.C. Flores, B.F. Tracy, and F.G. Fern ández. Multivariate

Analysis of Soil Properties in an Integrated Crop

– Livestock System in Illinois ( in

preparation ).

Maughan, M. , F. Miguez, G. Bollero, D. Wang, T. Voigt, and D.K. Lee. 

## Management

and environmental factors that affect switchgrass biomass production. ( in

preparation ).

Maughan, M. , A. Parrish, G. Bollero, T. Voigt, D. Bullock, W. Rooney, and D.K. Lee.

Management of forage and energy sorghum for biomass production in Illinois. ( in

preparation ).

Invited Presentations

Maughan, M.W. 2011. Mis canthus x giganteus Productivity: The Effects of Management

in Different Environments. Eighth annual Bioenergy Feedstock Symposium, Jan 11

- 12,

2011. Champaign, IL.

Maughan, M.W. 2008. Soil properties and corn yield response in the management of an

integr ated crop

- livestock system. Univ. of Illinois Dudley Smith Farm Beef Cow

- Calf

Field Day, June 2008. Pana, IL.

Maughan, M.W. 2007. Research on soil properties at the Dudley Smith farm. Univ. of

Illinois Dudley Smith Farm Beef Cow

- Calf Field Day, June 2007. Pana, IL.

Abstracts at Professional meetings

Maughan, M., D.K. Lee, A. Parrish, G. Bollero, and T. Voigt. 2010. Management of

forage and energy sorghum in Illinois for biomass feedstock production. Long Beach,

CA. In annual meeting abstracts. ASA

- CSSA

- SSSA, Madison, WI.

Maughan, M., F. Miguez, T. Voigt, S. Bonos, L. Cortese, J. Murphy, R. Gaussoin, M.

Sousek, D. Williams, and G. Bollero. 2010. Field experimentation of Miscanthus x

giganteus growth in KY, NE, and NJ, and validation of biomass crop model , BioCro.

Long Beach, CA. In annual meeting abstracts. ASA

- CSSA

- SSSA, Madison, WI.

## 186

Maughan, M., F. Miguez, G. Bollero, D.K. Lee, and T. Voigt. 2009. Meta

- analysis of

Switchgrass: Management and Environmental Factors that Affect Biomass Production.

Pittsbu rgh, PA. In annual meeting abstracts. ASA

- CSSA

- SSSA, Madison, WI.

Maughan, M., F. Miguez, T. Voigt, S. Bonos, J. Murphy, R. Gaussoin, D. Williams, and

G. Bollero. 2009. Miscanthus x giganteus growth and survival in IL, IN, KY, NE, and NJ.

Pittsburgh, PA. In annual meeting abstracts. ASA

- CSSA

- SSSA, Madison, WI.

Maughan, M.W., G.A. Bollero, and B.F. Tracy. 2007. Integrated crop

- livestock system:

effect on soil compaction, microbial biomass, and grain yield. New Orleans, LA. In

annual meeting abstracts. ASA

- CSSA

- SSSA. Madison, WI.

Awards/Fellowships

•2009

- 2010 Lawrence E. Schrader and Elfriede Massier Plant Physiology Fellowship

, Dept. of Crop Sciences, Univ. of Illinois.

•2008

- 2009 University Fellowship, Block Grant , Univ. of Illinois.

•Recognized at th e Univ. of Illinois on the “ Incomplete List of Teachers Ranked as

Excellent by their Students ” during 4 semesters (Fall 2007, Spring 2009, Fall 2009,

Spring 2010).

•2006 Rutger

- Harper Walker Agriculture Leadership Award , BYU

- Idaho.

•2005 Outstanding Agricu ltural Science Award & Grant , BYU

- Idaho.

• Eagle Scout Award. 2000.

Leadership/Service

•Crop Science Graduate Organization, Vice President (2009

- 2010), and Treasurer (2007

- 2009), Dept. of Crop Sciences, Univ. of Illinois.

• Boy Scout leader (2006

- 2010).V arsity team 333. Prairielands Council, Champaign, IL.

International Experience & Languages

• Citizen Ambassador (October 9

- 19, 2009). Participated in an Agronomy Delegation

Trip to the People’s Republic of China representing the Agronomy Society of Amer ica

to participate in bicultural exchanges and discussions with Chinese Agronomists and

Ag Professionals regarding various aspects of Chinese agriculture including soils, crop

geography, and agricultural production practices.

•Lived and served in Brazil fo r 2 years (2001

- 2003) as a volunteer missionary , becoming

immersed in Brazilian culture and the Portuguese language.

•Bilingual

- Portuguese . Speaking & Conversation

– advanced level. Reading & Writing

–

intermediate level.

## 187

Computer and statistical softwar e skills

Microsoft office (Word, Excel, PowerPoint)

Statistical Analysis Software (SAS)

R

- software for statistical computing

SigmaPlot

- data analysis and graphing software

Professional & Honor Societies

American Society of Agronomy (2007

- present)

C rop Science Society of America (2007

- present)

Soil Science Society of America (2007

- present)

Gamma Sigma Delta (2008

- present)

Phi Theta Kappa Society (2005

- present)

Certificates

Completed a Certificate in Business Administration

Spring 201 1 program (http://www.business.illinois.edu/cib/) at the Univ. of Illinois

Urbana

- Champaign in which I strengthened my understanding and abilities in the

functional areas of business.

References

Germán Bollero. Department Head and Professor of Biometry and Cropping Systems.

Dept. of Crop Sciences. Univ. of Illinois. Mail: W

- 201 Turner Hall, 1102 S. Goodwin Ave,

Urbana, IL 61801. E

- mail: gbollero@illinois.edu. Phone: (217) 333

- 9475.

DoKyoung "D.K." Lee. Assistant Professor of Biomass and Bioenergy Crop P roduction.

Dept. of Crop Sciences. Univ. of Illinois. Mail: S

- 320 Turner Hall, 1102 S. Goodwin Ave,

Urbana, IL 61801. E

- mail: leedk@illinois.edu. Phone: (217) 333

- 7736.

Thomas B. Voigt. Associate Professor and Extension Turfgrass Specialist, Dept. of Crop

Sciences. Univ. of Illinois. Mail S

- 416 Turner Hall, 1102 S. Goodwin Ave, Urbana, IL

61801. E

- mail: tvoigt@illinois.edu. Phone: (217) 333

- 7847.