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This is to certify that the
thesis entitled

CLIMATOLOGICAL CONSTRAINTS OF A WHEATZSOYBEAN
DOUBLE-CRQPPING SYSTEM IN THE GREAT LAKES REGION
presented by

Colleen Marie Garrity

has been accepted towards fulfillment
of the requirements for

M.A. degree in Geography

    

Major professor

Date ,0 AUG 1%

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[DATE DUE DATE DUE DATE DUE
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me chlRCIDumepGS-p.“

CLIMATOLOGICAL CONSTRAINTS OF A WHEAT-SOYBEAN DOUBLE-
CROPPING SYSTEM IN THE GREAT LAKES REGION

By

Colleen Marie Garrity

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF ARTS

Geography

1999

ABSTRACT

CLIMATOLOGICAL CONSTRAINTS OF A WHEAT SOYBEAN DOUBLE
CROPPING SYSTEM IN THE GREAT LAKES REGION

BY
COLLEEN MARIE GARRITY

Double cropping soybeans following winter wheat has traditionally been
limited to areas hundreds of miles south of the Great Lakes Region. Primary
climatological constraints for the secondary soybean crop include dry topsoil for
germination and establishment, lack of available moisture during vegetative and
reproductive stages due to dry subsoil layers, and limited frost-free growing
season length. In the study, the potential for successful wheat-soybean double
cropping across the region is examined. Historical risk of the cropping system is
assessed using the DSSAT crop simulation system given weather data from
stations across the region, 1895-1996. Given the potential for future climate
change, the cropping system is also evaluated given weather data derived from
the HadCM2 transient general circulation model to the year 2099. Simulated
yield potentials increased from north to south across the region, and yields
improved with earlier planting dates, greater mean seasonal precipitation, and
greater plant extractable soil water. Future simulated yield potentials increased
over historical levels in response to warmer, wetter growing seasons, and 002
enrichment further boosted yields. Mean yields increased significantly in the
latter half of the 21’”t century; by 2099, mean yields under C02 enrichment

improved by at least 50% over the historical mean.

Copyright by
Colleen Marie Garrity
1 999

for Mark

ACKNOWLEDGMENTS

Thanks to my dedicated advisor, Dr. Jeff Andresen, and to my committee
members Dr. Jay Harman, Dr. Julie Winkler, Dr. Joe Ritchie for their guidance
and support throughout this process. Thank you to Dr. Alagarswamy, Brian
Baer, Jim Brown, and Andy LeBaron for providing technical assistance in a
pinch. Special thanks to Tracy Aichele, Steve Aichele, Peter Harsha, and Jill
Hallden for graphical and editing assistance, and to my roommates Julie Colby,

Mark Bowersox, and Yushuang Zhou for their patience and understanding.

TABLE OF CONTENTS

LIST OF TABLES
LIST OF FIGURES
INTRODUCTION

CHAPTER 1
BACKGROUND

CHAPTER 2
METHODS
Agronomic Assumptions
Model Requirements
Weather Data
Soils Data
Sequential Experiment Files
and Study Treatments
Model Output Analysis

CHAPTER 3
RESULTS AND DISCUSSION
Historical Analysis
HadCM2 Future Climate Scenario Simulation

CHAPTER 4
CONCLUSIONS

APPENDIX A

DSSAT Input File Samples
Weather File Sample
Soil File Sample
Sequential File Sample

APPENDIX B

DSSAT Output File Samples
Growth Aspects File Sample
Simulation Overview File Sample
Summary File Sample
Water Balance File Sample
Water Balance Summary File Sample

LIST OF REFERENCES

vi

vii

viii

11
13
15
15
18

21
23

24
24
45

68

72
72
73
74

77
77
80
86
87
92

95

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

Table 9

Table 10

Table 11

LIST OF TABLES

Stations and Weather Record lnforrnation
used in the study.

Soil series, texture and taxonomy at each site.

Mean simulated historical double-crop soybean
yields by site and planting date for dryland and
irrigated simulations, 1895-1996.

Historical mean season length and yield for
soybeans at Adrian, 1895-1996.

Simulated mean historical dryland soybean
water balance by site and planting date
(1895-1996).

Historical probability of attaining simulated
soybean yields greater than 1000 kg/ha at
different planting dates (1895-1996).

Summary of mean number of irrigation applications
and mean seasonal amounts of irrigation applied

in historical soybean double crop simulations by
site and planting date (1895-1996).

Simulated mean historical irrigated soybean water
balance by site and planting date (1895-1996).

Comparison of mean future (2001-2099) and
historical (1896-1996) simulated soybean yields
by site for the July 15 planting date.

Future simulated mean seasonal water balance
for the July 15 planting date (2001-2099).

Historical and future probability of attaining yields
greater than 1000 kg/ha for July 15 planting date.

vii

17

20

24

25

29

36

37

39

45

49

61

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7a

Figure 7b

Figure 8

Figure 9

Figure 10a

LIST OF FIGURES

Seasonal Chronology of Wheat and Soybean
Cropping Systems 5

Stations used in the study. 16

Simulated historical cumulative probability
distribution of dryland soybean yields by planting
date at Ft. Wayne (1897-1996). 26

Simulated historical cumulative probability

distribution of dryland soybean yields for a north-

south transect of sites for the July 1 planting date
(1896-1996). 27

Seasonal precipitation totals vs. simulated dryland
Soybean yields for a June 15 planting date at
Findlay (1896-1996). 31

Simulated plant extractable soil moisture for three
different layers at Circleville from 1 October 1907
through 30 September 1908. Soil type is Coloma
Loamy Sand. 32

Simulated historical cumulative probability distribution
of dryland soybean yields by planting date at
Coldwater (1897-1996). 34

Simulated historical cumulative probability distribution
of irrigated soybean yields by planting date at
Coldwater (1897-1996). 34

Simulated historical cumulative probability distribution
of irrigated soybean yields for a north-south transect
of sites at the July 1 planting date (1896-1996). 35

Simulated historical cumulative probability distribution
of dryland soybean yields by soil texture at Adrian for
July 1 planting date (1895-1996). 41

Simulated historical dryland yields and moving 9-year
average by year at Allegan for June 15 planting date
(1895-1996). 42

viii

Figure 10b Simulated historical dryland yields and moving 9-year
average by year at Greencastle for June 15 planting
date (1895-1996). 43

Figure 113 Mean simulated dryland soybean yields by site,
without future CO2 enrichment, for the July 15
planting date. 46

Figure 11b Mean simulated dryland soybean yields by site,
with future CO2 enrichment, for the July 15
planting date. 47

Figure 12a Future cumulative probability distribution of
simulated dryland soybean yields with constant
CO2 levels for a north-south transect of sites
(2001-2099). 48

Figure 12b Future cumulative probability distribution of
simulated dryland soybean yields with enhanced
CO2 levels for a north-south transect of sites
(2001-2099). 48

Figure 13a Comparison of future (2001-2099) and historical
(1895-1996) mean seasonal evapotranspiration
for dryland soybean yields for July 15 planting date. 51

Figure 13b Comparison of future (2001-2099) and historical
(1895-1996) mean seasonal precipitation for
dryland soybean yields for July 15 planting date. 51

Figure 14 Mean soybean growing season precipitation for
the July 15 planting date. 52

Figure 15 Mean evapotranspiration for simulated dryland
soybeans, with transient future 00,, for the
July 15 planting date. 53

Figure 16a Future simulated cumulative probability distribution
for irrigated soybean yields under constant 002
levels (2001-2099) for the July 15 planting date. 55

Figure 16b Future simulated cumulative probability distribution
for irrigated soybean yields under enriched CO2
levels (2001-2099) for the July 15 planting date. 55

ix

Figure 173

Figure 17b

Figure 183

Figure 18b

Figure 19a

Figure 19b

Figure 20a

Figure 20b

Figure 21a

Figure 21b

Figure 22a

Comparison of future and historical mean irrigation
application frequency for the July 15 planting date.

Comparison of future and historical mean simulated
seasonal irrigation requirements for a cross-section
of sites in the Great Lakes region for the July 15
planting date.

Mean seasonal number of irrigation applications,
without CO2 enrichment, for the July 15 planting
date.

Mean seasonal number of irrigation applications,
with CO2 enrichment, for the July 15 planting
date.

Mean seasonal irrigation application amounts,
without CO2 enrichment, for the July 15 planting
date.

Mean seasonal irrigation application amounts,
with CO2 enrichment, for the July 15 planting
date.

Historical and future cumulative probability
distributions of dryland soybean yields at Big
Rapids for planting date July 15.

Historical and future cumulative probability
distributions of irrigated soybean yields at Big
Rapids for planting date July 15.

Future simulated dryland soybean yields and nine-
year moving average at Salem without CO2
enrichment for the July 15 planting date
(2001-2099).

Future simulated dryland soybean yields and nine-
year moving average at Salem with CO2 enrichment
for the July 15 planting date (2001-2099).

Future simulated dryland soybean yields and nine-
year moving average at Coldwater without CO2
enrichment for the July 15 planting date
(2001-2099).

56

57

58

59

6O

60

62

62

66

Figure 22b Future simulated dryland soybean yields and nine-
year moving average at Coldwater with CO2
enrichment for the July 15 planting date
(2001 -2099).

xi

66

INTRODUCTION

Net cash farm income (gross cash income minus gross cash expenses)
for agricultural operations in the United States has decreased in recent years and
further decreases are likely in the future, attributable largely to changes in
economic factors, such as low commodity prices and the phase-out of
governmental support programs (Collins, 1999). Continued low commodity
prices in coming years will create further financial stress for producers who are
already financially leveraged.

In the face of such trends, farmers retain a limited number of adaptive
strategies. One managerial option that allows farmers the opportunity to
increase profits with only a small increase in risk is a double-cropping system, in
which two crops are produced in overlapping or succeeding order. Double-
cropping systems allow farmers to increase potential income, to diversify by
spreading financial risks over two crops, and to make more efficient use of land
resources. The practice of double cropping winter wheat followed by soybeans
has been commonplace in the US. from the Ohio River Valley southward for
much of the past few decades. In more northerly areas of the central U.S.,
however, double cropping has generally been possible only in certain years and
only with significant production modifications due to climatological limitations
such as growing season length.

During the 1998 growing season, farmers in southern Michigan reported
success at double cropping wheat and soybeans. The 1998 season was

characterized by an abnormally mild, early spring (the warmest January - May

on record for the Great Lakes region) which led to a very early winter wheat
harvest, followed by timely late summer rainfall, mild fall temperatures, and a
delayed first killing freeze of the fall season (NCAA, 1999; NCDC 1998; MASS
1998). Growers who attempted a secondary soybean crop reported yields as
high as 2.5 ton/ha, which translated into cash receipts up to $500/ha with total
production cost of $175lha or less (Ned Birkey, MSU Extension, personal
communication; Mike Staton, MSU Extension, personal communication).

This success raises climatological questions as to the potential for this
type of agricultural practice in the Great Lakes region: How often do extended
growing seasons conducive to double cropping occur? Is the recent long
growing season indicative of a larger change of climate, with increasing
probabilities of success for double cropping in the region? Or are longer growing
seasons only an intermittent phenomenon, with limited long-term possibilities for
successful double cropping? Assuming current levels of technology, what is the
historical potential for double cropping winter wheat and soybean in Michigan?

How might anticipated future climate change affect wheat-soybean
double-cropping potential in the Great Lakes region? Global climate change and
its potential impacts on weather and climate dependent processes are under
investigation worldwide. Increasing concentrations of 002 and other atmospheric
trace gases have been projected to lead to global increases in temperature on
the order of 0.9-3.5°C by the end of the next century (Houghton et al., 1996), with
even further increases possible due to future reductions in sulfur dioxide

emissions (Wrgley, 1999). Climate change projected for the Great Lakes region

during the next century includes trends toward a warmer and potentially wetter
climate (Wigley, 1999). Will this projected climate change result in increased
opportunities for double cropping in the region? VVIth the potential for a warmer
climate and increasing levels of atmospheric carbon dioxide in the future, what
might the potential for double-cropping systems be, based on current levels of
technology, for producers in the Great Lakes region? The purpose of this thesis
is to examine the historical and future potential of wheat-soybean double-

cropping systems in the Great Lakes region.

Chapter 1

BACKGROUND

Definitions

Double cropping refers to the planting and harvesting of a second crop
after the harvest of the first crop in the same growing season at the same
location. The seasonal chronology of a typical wheat-soybean double-cropping
system in the Ohio Valley is illustrated in Figure 1.

Several variations of the double-cropping system described above are
commercially used in the US. Chief among these is relay intercropping, in
which the second crop is planted directly into the first crop while it is still actively
growing, with subsequent harvest of both crops during the same growing season.
This cropping method is an adaptation of double cropping and is practiced in
regions with cooler climates that cannot otherwise produce two crops in a single

season .

 

Typical Midwestern Winter Wheat and Soybean Cropping Systems

 

Winter Wheat Monocropping System .

Soybean Monocropping System .
Fallow ‘ V ; . , f Soybeans

Double-cropping System
Wheat Soybeans

Relay lntercropping System

Wneat Soybeans

 

 

 

Fall Winter Spring Summer Fall
Oct Dec Feb Apr Jun Aug Oct

 

 

 

Figure 1 Seasonal Chronology of Wheat and Soybean Cropping Systems

Fall—Wheat planting

Winter—Vernalization, soil water accumulation (when/if snowmelt occurs)
Spring—Wheat growth and development

Early Summer—Wheat harvest/soybean planting

Summer—Soybean growth and development

Fall—Soybean harvest, wheat planting

aerate

The most common justification for the use of a double-cropping system is
potential economic advantage. Other potential advantages include soil nitrogen
credits (from the leguminous soybean crop) for future crops, crop rotational
benefits related to insect and disease pressure, and the potential for use as
animal forage if the secondary soybean crop isn’t successful (LeMahieu &
Brinkman, 1990).

Constraints

 

Shapiro et al (1992) demonstrated that risk perception is key to explaining
adoption of wheat-soybean double-cropping practices. Producers in northern
regions of the US. face climatological constraints that render the prospect of
adopting the practice less profitable and more risky. What risks do producers
face in making the decision to double crop wheat and soybeans?

The potential for success with two crops in a single season involves a
number of constraints, but three factors related to climatology are most
significant. The first constraint is length of frost-free growing season. If the
season is too short, the soybean crop won’t have enough time to mature before
the first killing freeze of the fall. Soybean killed prior to maturity may suffer
significant reductions in yields, test weights, and quality (Halvorson et al, 1995).
In Indiana, Schweitzer (1981) found that a minimum 90-day frost-free growing
season was necessary for double-cropped soybean to reach maturity and still
maintain a yield potential of 1881 kg/ha (30 bushels/acre) or higher. Timing of

the soybean planting is also critical. For every day after June 15th that soybean

planting is delayed, Jeffers (1987) found yield potential to decrease 31 to 47
kglha (1/2 to 3/4 bushels/acre) and recommends that double crop soybeans not be
planted after July 10‘“.

The second constraint is adequate soil moisture for soybean germination
and establishment. The adage “if June is dry, do not try,” is based on the
requirement of adequate moisture for germination of the soybean seed (Jeffers,
1995). If moisture in the upper several centimeters of the soil profile is
insufficient to facilitate germination at planting time, the soybean seeds will
remain dormant until sufficient moisture occurs, possibly resulting in poor stand
establishment and subsequent delays in phenological development and progress
(Pearce et al, 1993; Jeffers, 1995).

A third climatological limitation to wheat-soybean double cropping is the
lack of available moisture to the soybean crop during vegetative and reproductive
phenological stages. Total evapotranspiration from the primary wheat crop
typically ranges from 350 to 700 mm between emergence and maturity (Musick &
Porter, 1990). This moisture must usually come from a combination of
precipitation and antecedent soil water. In Midwestern climates, wheat crops
generally leave the secondary soybean crop with a moisture-depleted soil profile
and at risk of moisture shortages should precipitation not be sufficient to meet the
secondary crop needs.

Due to climatological and other factors, yields in double-cropping systems
are generally lower than those of single-season crops. In Indiana, double-crop

soybean yields are generally 60% of full-season yields (Schweitzer, 1981), while

Mississippi State University data (taken in a warmer and wetter climate) indicate
that, on average, double-crop yields are 15-30% lower than full-season yields
(Blaine, 1998). Jeffers (1987) found well-managed double-crop soybean yields
in Ohio to average 50% of single-crop yields, while intercrcp wheat and soybean
yields produce roughly 85% and 75% of monocrop yields, respectively.

Once the decision to plant a double crop has been made, a number of
tactical concerns must also be addressed. The most critical tactical decisions
concern the timing of wheat harvest and soybean planting in the summer. The
farmer can opt for the conventional practice—harvest the wheat, till the field, and
plant the soybeans. However, this method is time-consuming and can create soil
moisture problems for the soybeans, as tilling tends to dry the soil. Tilling and
planting at night can reduce soil moisture losses, but any tilling practice will still
cause more moisture losses than other methods of preparing the field for planting
soybeans (Crabtree et al, 1990).

A second option is no-till planting the soybean seed directly into the wheat
stubble. This option allows maximum moisture retention in the soil, which
promotes soybean germination. Standing wheat stubble also prevents soil
erosion and can encourage the soybean plants, once established, to grow taller
and flower more quickly, competitively accelerating growth in an already short
growing season. While benefiting growth and development to some degree, the
wheat residue, if left on the field, still poses some problems, as it is difficult for
the planter to cut through, can impede soybean harvest, can harbor diseases and

limit weed control practices, and may hinder initial establishment of the soybean

plants (Blaine, 1998). To alleviate some of these problems and advance
soybean emergence, the wheat residue can be chopped, shredded, or baled as
straw after the wheat harvest, but these reductions in the amount of straw
present were associated with lower volumetric soil moisture content (Vyn et al,
1998). Because soybean establishment is commonly difficult in stubble, a
seeding rate 25% greater than normal was suggested by Schweitzer (1981).

Nc-till is a common practice with several possible variations. The
standard no-till method is to harvest the wheat and plant the soybeans into wheat
stubble. An alternative no-till method is intercropping, where soybeans are
planted into a standing wheat crop that will be harvested shortly after soybean
establishment (Jeffers, 1995; Moomaw and Powell, 1990). A related alternative
method is aerially seeding the soybean seed into a standing wheat crop. In both
of the latter cases, wheat can be harvested above young soybean plants if the
cutter bar on the combine harvester is set to a level high enough to avoid
damaging the soybean plants.

Still another alternative no-till method is to harvest the wheat, burn the
wheat stubble in a controlled manner, and then plant the soybeans in the burned
stubble. Research in Mississippi demonstrated that while fast-burning fires in
wheat stubble can be dangerous, the burning practice tends to result in higher
yields than both conventional and nc-till practices (Blaine, 1998).

The double-crop production system generally requires a high level of
management and a restricted time budget (Jeffers, 1995). Given climatological

restrictions, conventional double-cropping methods have traditionally been

restricted to the Ohio River Valley, the Lower Mississippi Valley, and the
Southeastern United States. Modifications of traditional double-cropping
strategies have allowed the practice to spread to formerly marginal regions, such
as the Southern Great Plains, where the availability of water has historically
limited double-crop potential. Other specialized double-cropping practices are
adapted to colder climates in the northern US. For instance, in Wisconsin,
winter small grains are sometimes planted and used as forage in the spring
before a soybean crop is planted (LeMahieu & Brinkman, 1990).

Intensive management and innovative strategies are required of farmers
wishing to successfully double crop in areas north of the climatic optimum. One
option is early wheat harvest at high moisture levels. At high moisture levels, the
farmer can artificially dry the wheat from 20-25% moisture, windrow the wheat at
30-40% moisture, or remove the wheat as silage (Schweitzer, 1981).

The climate of the Great Lakes region traditionally has not been suited for
wheat-soybean double cropping, though success has been reported in
exceptional growing seasons such as 1998. Based on climatology, what is the
potential for a wheat-soybean double-cropping system in areas north of the

traditional double-cropping region?

10

Chapter 2

METHODS

Ultimate determination of the potential feasibility of double cropping
systems in the Great Lakes region will likely require many seasons of traditional
agronomic field experimentation with data taken at a number of locations. One
less time-intensive methodology available to investigators that can provide an
initial assessment of the potential for this system in the region is the crop
simulation model, which is a quantitative, deterministic simulation of the
physiological processes that govern crop growth, development, and yield. In this
study, the DSSAT (Decision Support System for Agrotechnology Transfer) v.3.5
(T suji et al., 1994) crop modeling system was employed to assess the historical
and potential viability of double-cropping practices for eighteen stations across
the Ohio Valley and Great Lakes regions. The DSSAT modeling system contains
more than 12 different crop simulations and has been used for a range of
agronomic simulations and impact assessment studies in the past (e.g. Adams
et al, 1995; Chipanshi et al, 1997; Lal et al, 1998; Landau et al, 1998; Meams et
al, 1996; Meams et al, 1999; Parsch et al, 1991).

For this study, the CERES-Wheat (Godwin et al, 1989; Ritchie et al, 1985)
and SOYGRO (Jones et al, 1989; Wilkerson et al, 1985) crop models were used
to simulate wheat and soybean, respectively, in a double cropping system under
a variety of environmental conditions. CERES-Wheat and SOYGRO have been

successfully utilized in many past modeling studies and production areas, and

11

have been found to compare well with observed data and with other crop models
(Meams et al., 1997, Meams et al., 1999; Pickering et al, 1995). In this study,
the primary focus is on the constraints of the secondary soybean crop, so the
wheat simulation was used essentially to initialize soil moisture conditions for the
second crop. Because winter wheat parameters in double cropping are largely
congruent with those of full-season crops, the CERES-Wheat model verifications
by other authors were assumed to be sufficient for this study. Double-crop
soybean parameters, however, differ from those of full-season soybeans. Yield
data are not officially recorded for double-crop soybeans, so verification for this
cultivation practice is not possible. One past simulation study of full-season
soybeans found SOYGRO to adequately simulate crop growth phenology and
yields in the midwestern U.S., though it performed better in southern than
northern sections of the region (Kunkel and Hollinger, 1991).

The area chosen for the present study encompasses the Ohio River
Valley, where double cropping is commonly practiced, and sections of the Great
Lakes Region including the Lower Peninsula of Michigan, where double cropping
is atypical. In the first phase of the project, a wheat-soybean double cropping
system was simulated at 18 locations across the region using approximately 100
years of historical daily weather data (1895-1996). This necessitated the
development of weather, soil, and other agronomic data sets for each station
location. In the second phase of the project, the double cropping system was
modeled at 6 of the 18 historical station locations using simulated future daily

weather data for the next century (2001-2099) from the Hadley Centre HadCM2

12

general circulation model (Mitchell et al, 1995; Johns et al, 1997; Viner & Hulme,
1998). Extensive, detailed output from the crop models with a large number of
historical and potential future scenarios provided a means of analyzing the
potential for wheat-soybean double cropping in this region. In addition, a variety
of sensitivity analyses were also performed on the model simulations to identify

and characterize important climatological constraints.

Agronomic Assumptions

In order to run any model, assumptions must be made regarding input
variables to simplify the vast array of decisions involved in the modeled process.
Accordingly, some agronomic assumptions were made to represent wheat-
soybean double-cropping tactical decisions in the DSSAT sequential analysis
files. First, agronomic input variables were chosen as typical of current (i.e. late
1990’s) technology. This includes cultivar selection, planting row width, and
seeding population specifications. While soybean cultivars should vary as
operators adapt to a changing climate, for example, the cultivars are kept
constant here for the purpose of consistency in the simulation. Second, fertility
was assumed to be non-limiting in all simulations and there was no consideration
of the potentially negative impacts of weeds, pests, or diseases. These factors,
while possibly important on local scales, are beyond the scope of this
investigation, which focuses on the climatological constraints of a double
cropping system. Third, soil profiles chosen for the analysis were assumed to

generally represent typical agricultural areas in the vicinity of each station.

13

Finally, an important component frequently overlooked in past agronomic impact
studies is the effect of ambient COz concentrations, which can significantly
increase plant water use efficiency, dry matter production rates, and yield
(Adams et al, 1990; Rosenzweig, 1985) . In many of the studies that have taken
CO; concentration into account, the future scenarios were adjusted by an
equilibrium doubling of 002 concentration relative to global pre-industrial
revolution levels (e.g. Rosenzweig & Parry, 1994; Meams et al, 1996). However,
comparing the effects of 1x00; scenarios with those of 2xCOz scenarios may not
provide a realistic transition from current concentrations to projected future
concentrations of 002. Researchers have cited the need for transient models to
be incorporated into impacts assessment modeling, as this approach offers a
more realistic picture of climate change than equilibrium models allow
(Rosenzweig et al, 1993). With advances in climate modeling capabilities,
transient climate models have recently been made available for use in impact
studies. In this study, projected atmospheric CO; concentrations for the years
2001-2099 were taken from the transient IPCC 92a scenario as outlined by Joos
et al. (1995) and combined with the HadCM2 daily weather data. The crop
models respond to the increases in CO; with increased photosynthetic rates and
decreased transpiration for a net increase in plant water use efficiency. The
combination of transient C02 levels with crop models able to respond to
changing ambient 002 levels, such as the DSSAT model suite, provides a more

accurate picture of the potential effects of projected climate change scenarios.

14

Model Requirements

The DSSAT crop model requires a wide variety of weather, soil, and other
agronomic data as inputs for simulations and experiment files. The Sequential
Analysis component of DSSAT is used to model multiple-year and double-
cropping sequences. Sequential experiment files drive the crop model
subroutines, CERES-Wheat and SOYGRO, and specify the parameters of each
simulation. Parameters specified in the sequential experiment files include crop
model subroutines, cultivars, field and soils information, initial conditions at the
start of simulation, planting details, irrigation and water management, harvest
details, and simulation controls. An example of sequential experiment files is

given in Appendix A.

Weather Data

Weather variables used in the DSSAT modeling process include daily
maximum and minimum temperatures, precipitation totals, and solar radiation
totals. Historical daily temperature and precipitation data for the period 1895-
1996 (or as close as possible to these years) were obtained from the Midwestern
Regional Climate Center for eighteen stations across the Great Lakes Region
and Ohio River Valley. Stations were chosen on the basis of series quality,

continuity, and length, completeness of record, and by geographic location

15

relative to other stations in the study area to ensure roughly equal spatial

representation across the region. A map of the stations is given in Figure 2.

  

* Station location

Figure 2. Stations used in the study.

16

The DSSAT model simulation framework requires serially complete weather files.
Missing daily data in each of the station series in this study were estimated at the
Midwest Regional Climate Center with a kriging objective analysis, which weights
estimated values heavily on those of the nearest available neighbor stations (Ken
Kunkel, personal communication). A list of the stations with periods of records
and percentage of complete data is given in Table 1. To complete the historical
weather data, daily solar radiation totals were synthetically generated based on
historical statistics using the WGEN (Richardson and Wright, 1984) stochastic
weather generation program. Prior to model simulation runs, the raw weather
files were converted into DSSAT-compatible weather input files (*.WTH files). An
example of the *.WTH file format is given in Appendix A.

Table 1 Stations and Weather Record lnfon'nation used in the study. Percent

complete refers to the percent of total weather observations during the period of
record that were not missing or estimated.

 

 

 

 

 

IState Station Period of Record % Complete I
IMI Adrian 1895 - 1996 99
Allegan 1895 - 1996 99
Bay City 1896 - 1996 90
Big Rapids 1896 - 1996 99
Coldwater 1897 - 1996 99
Owosso 1896 - 1996 95
Pontiac 1895 - 1996 99
IN Cambridge City 1896 - 1996 91
Ft. Wayne 1897 - 1996 99
Greencastle 1896 - 1996 85
Salem 1896 - 1996 94
South Bend 1896 - 1996 99
OH Circleville 1896 - 1996 97
Findlay 1896 - 1996 99
Wooster 1896 - 1996 99'
IL Jacksonville 1896 - 1996 99
Mt. Vernon 1895 - 1996 98
Ottawa 1895 - 1996 97

 

 

17

In the second phase of the project, feasibility of double cropping in a future
climate is explored. Simulated daily weather data from the HadCM2 transient
general circulation model for the next century were obtained in conjunction with
the US. Global Change Research Program National Assessment (Great Lakes
Regional Assessment). The original 2.5° X 3.75°-resolution HadCM2 general
circulation model data, in the form of monthly mean departures from historical
averages, were converted into a gridded 0.5° x 0.5° and daily time step format for
VEMAP (Kittel et al., 1997; Kittel et al., 1995) using stochastic weather
generation techniques. The future daily weather series were obtained from the
nearest model grid on land for each of six stations in north to south and east to
west transects across the study area and converted to DSSAT-compatible

weather input file (*.WTH) format.

Soils Data

Because DSSAT models simulate plant growth and development above
and below the surface, detailed soil profile data input files are required for each
site. Representative agricultural soils were chosen for each site using USDA
County Soil Survey publications (USDA—NRCS County Soil Survey Series). For
each site, the two most prevalent agricultural production soils in the county (by
percentage of area) were selected as representatives of soils potentially suitable

for double cropping. Many, but not all, of these soils have sample profile data

18

available, which are required by DSSAT to run the crop simulations. Final soil
series selection for each site was determined first by the degree of agricultural
potential and second by availability of soil profile data. A listing of soil series,
texture, and taxonomy by site is given in Table 2. Detailed soil series profile data
were obtained from the National Soil Survey Center (NSSC, 1999).

When a representative soil was chosen for each site, soil data files (*.SOL) were
created in DSSAT for use in crop simulations. lnforrnation required to create soil
data files includes texture of uppermost horizon in the soil profile, number of
horizons in the profile and the depth of each, coarse fraction, bulk density,
saturated hydraulic conductivity, total nitrogen, pH 1:1 in water, cation exchange
capacity, and root quantity for each horizon. A sample soil data file is given in

Appendix A.

19

Table 2. Soil series, texture, and taxonomy at each site.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Station Soil Series Texture Taxonomy
Adrian Hoytville Clay Loam Fine illitic mesic mollic ochraqualf
Allegan Blount Loam Fine illitic mesic aeric ochraqualf
Sandy over loamy mixed frigid alfic
Bay City losco Loamy Sand haplorthod
Sandy mixed mesic psammentic
Big Rapids Coloma Loamy Sand hapludalf
Cambridge City Crosby Silt Loam Fine mixed mesic aeric ochraqualf
Fine-loamy mixed mesic typic
Circleville Brookston Loam agiaquoll
Fine loamy over sandy mixed
Coldwater Fox Loam mesic typic hapludalf
Findlay Blount Loam Fine illitic mesic aeric ochraqualf
Fine-loamy mixed mesic typic
Ft. Wayne Miami Silt Loam hapludalf
Fine-silty mixed superactive mesic
Greencastle Fincastle Silt Loam aeric epiaqualf
Fine montmorillonitic mesic aquic
Jacksonville lpava Silty Clay Loam argiudoll
Fine-silty mixed mesic aeric
Mt. Vernon Bluford Silt Loam ochraqualf
Fine-silty mixed mesic typic
Ottawa Catlin Silty Clay Loam Li‘giudoll
Owosso Conover Sandy Loam Fine montmorillonitic eutroboralf
Pontiac Conover Sandy Loam Fine montmorillonitic eutroboralf
Fine-silty over clayey mixed mesic
Salem Crider Silt Loam typic paleudalf
Fine-loamy mixed mesic typic
South Bend Brookston Loam argiaquoll
Wooster Bennington Silt Loam Fine illitic mesic aeric ochraqualf

 

20

 

Sequential Experiment Files and Study Treatments

 

Once weather and soil data input files were complete, sequential
experiment files (*.SQX) were created in the DSSAT program framework. For
both the 100-year historical and future double cropping simulations, sequential
program experiment files were created to simulate each multi-year sequence of
CERES-Wheat followed by SOYGRO simulations. Eight sequential experiment
files were created for each of the eighteen sites in the historical analysis to
evaluate the effects of different soybean planting dates and water availability.
Sequential experiment files for four planting dates (June 1, June 15, July 1, and
July 15) were created for each site using both dryland and irrigated situations.
These dates were chosen to represent a range of possible soybean planting
dates across the regions, with mid-July considered the latest possible planting
date in the region (Jeffers, 1987). Simulations for each planting date were run
twice, one for dryland (water limiting) and one for irrigated (water non-limiting)
conditions.

Future sequential experiment files were created for six sites using the July
15 soybean planting date to simulate the potential effects of water stress and
carbon dioxide enrichment. Experiment files were created to compare the
potential effects of both dryland and irrigated CO2-enriched and non-CO2-
enriched future scenarios on double cropping. To account for the effects of the
transient climate model CO2 enrichment, sequential files were created by
decade, beginning with 2001-2010, using the median CO2 value for each decade

given in the Joos et al (1995) series.

21

In all of the sequential experiment files, both historical and future, the
simulation beginning date was January 15‘. For the wheat simulations, a generic
U.S. Soft Red Winter variety was used at all sites. The CERES-Wheat sequence
begins using the automatic planter feature, which plants the wheat within a
specified window of dates, weather, and soil moisture conditions. In all
simulations, the planting window was defined as the 30-day period after the
Hessian fly—free date for each site, with planting generally occurring in early
October. Seeding populations were set to 300 seeds per square meter and row
spacing was set to 100m. CERES-Wheat model harvest details were set to
automatically harvest the day before the scheduled soybean planting date.

For the soybean simulations, generic soybean cultivars of differing
maturity group were selected for each site according to latitude, ranging from
maturity Group 0 in the northern region of the study area to maturity Group 3
near the Ohio River, the southern boundary of the study area. The SOYGRO
sequences were set to plant soybeans on a specified date for each model run,
June 1, June 15, July 1, or July 15. Seeding rates were set to 50 seeds per
square meter and row spacing was set at 380m. Irrigated simulations were set to
automatically water the soybean plants with 25mm of water when a threshold of
50% of maximum available soil moisture was reached. SOYGRO harvest details

were set to harvest the soybeans automatically at maturity.

22

Model Output Analysis

DSSAT created five output files for each of the simulations (wbal.out,
water.out, summarycut, growth.out, and overview.out). Because of the large
volume of data produced in 156 century-long historical and 120 decade-long
future series of daily simulations, output generation frequency was restricted to
once every 10 days. For examples of the output files, refer to Appendix B. To
analyze the model run results, text output files were converted to Microsoft Excel
97 (*.XLS) format. The analysis focuses primarily on the double-cropped
soybeans. Answers to the research questions in this study were addressed first
for the historical analysis and second for the future scenario.

In the assessment, it was necessary to define a breakeven soybean yield
level, at which economic input costs equal output costs for the secondary
soybean crop. Based on estimated current production costs, including
seed/technology fees, planting costs, one herbicide application, harvesting costs,
and the market price for soybeans, the breakeven yield is estimated at 1000
kglha (15 bushels/acre). Because production costs may vary by location, this
breakeven yield is not exact, but can be considered a liberal cost, conservative
yield estimate as some of the actual costs may be cheaper or may not be

applicable in some cases.

23

Chapter 3

RESULTS AND DISCUSSION

Historical Analysis

On average, later planting dates resulted in lower simulated soybean

yields than earlier planting dates. Table 3 lists mean simulated double crop

soybean yields for each of the eighteen sites, for both dryland and irrigated crops

across four different planting dates.

Table 3. Mean simulated historical double-crop soybean yields (kg/ha) by site

and planting date for dryland and irrigated simulations, 1895-1996.

 

 

 

 

 

 

State Site Dryland Irrigated

L 1-Jun 15-Jun 1-Jul 15-Jul 1-Jun 15-Jun 1-Jul 15-Jul

Ml Adrian 549 3'7 260 188 3758 3365 2651 1638
Allegan 1009 716 491 329 3719 3140 2580 1611
Bay City 420 279 200 264 3488 3071 2346 1398
Big Rapids 950 802 506 210 3245 2675 1645 582
Coldwater 1 124 986 815 621 3697 3295 2609 1624
Owosso 723 458 307 209 3589 3175 2428 1371
Pontiac 789 539 426 320 3739 3344 2645 1736

IN Cambridge City 1982 1593 1149 861 3888 3518 2823 1821
Ft. Wayne 1350 960 694 592 3890 3535 2915 2056
Greencastle 2088 1741 1521 1393 4049 3731 3159 2435
Salem 1995 1723 1609 1469 4146 3832 3315 2595
South Bend 1667 1232 905 721 3881 3509 2865 1993

OH Circleville 1714 1332 1192 1101 3984 3666 3115 2384
Findlay 1386 952 668 518 3953 3597 2940 2011
Wooster 1785 1306 891 592 3749 3356 2626 1512

IL Jacksonville 1517 1379 1236 1164 4181 3837 3270 2454
Mt. Vernon 1596 1461 1472 1467 4204 3914 3469 2834
Ottawa 1254 1069 911 887 3798 3476 2914 2234

 

 

 

 

 

24

At Coldwater, mean yield decreased from 986 kglha at June 15 dryland
planting dates to 621 kglha for the July 15 planting dates, a 37% decrease.
Irrigated yields decreased by 51% for the same planting dates at Coldwater.
Exceptions occurred at Bay City, where the simulated mean dryland soybean
yield increased slightly (by 64 kglha) from July 1“"t to July 15‘“, and at Mt. Vernon,
where the mean dryland yields remained relatively constant from June 15th

through July 15‘“.

Table 4. Historical mean season length (days) and yield (kg/ha) for soybeans at
Adrian, 1895-1996.

 

 

 

 

 

 

 

 

Dryland Irrigated
PDAT Mean season Length Mean Yield Mean season Length Mean Yield
1-Jun 1 13 549 111 3758
1 5-Jun 1 10 377 105 3365
1-Jul 105 260 99 2651
1 5-Jul 97 188 94 1638]

 

 

 

 

 

Mean season length and mean yield at Adrian given in Table 4 further
illustrate this point. For both dryland and irrigated simulations, mean yield
decreased with later planting dates. Dryland mean yields decreased by 66%
between the June 1 and July 15 planting dates with a 14% (16-day) decrease in
mean season length. Irrigated mean yields decreased by 56% with a 15% (17-
day) decrease in mean season length.

One method of analyzing the character of a given series, including both
frequency and magnitude, is estimation of the probability distribution of the
series, which can be obtained empirically by rank ordering the data and then

calculating frequency/probability. Probability distributions are useful in illustrating

25

risk involved in decision-making processes (Chipanshi et al., 1997). The

cumulative probability distributions of soybean yields for the four different

planting dates given in Figure 3 at Ft. Wayne illustrate the risks involved with

later soybean planting dates through a lower probability of attaining a given yield

level, such as the breakeven 1000 kglha yield.

 

 

 

 

 

 

 

4500
4000 4— 2 . ., , - g __ .
3500 .——. -2 ,2 2 2 2 ;_i‘
.2 3000 -2-” 2 2 2 , , 2 4 g g u
.3 ‘fl A1-Jun
E 2500 W T 77 T T N T i _TT..8“:. i -15-Jun
2 2000 ~- —— _ _ _ _ _ __2 ‘ M .- - - _1_Jul
'2 “w -15-.iui
> 1500-
10004
500 4-
o T I T r T I 1 .
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Probability

 

 

 

Figure 3. Simulated historical cumulative probability distribution of dryland
soybean yields by planting date at Ft. Wayne (1897-1996).

A planting date of June 1 is associated with approximately a 63% chance of
achieving a breakeven 1000 kglha yield. The probability of reaching breakeven
yields decreases with later planting dates; 43% at June 15, 27% at July 1, and
22% at July 15.

Frequency of breakeven growing seasons also varied by location from

north to south across the study region, with generally greater simulated yields at

26

sites in the southern portion of the study area. For example, the July 1St dryland
simulation at Mt. Vernon averaged 561 more kglha yield than the analogous
simulation at Ottawa. This result was expected, as Mt. Vernon and other sites in
the Ohio Valley are traditionally double-cropping areas with warmer climates,
longer growing seasons, and greater precipitation than northern sites, such as
Ottawa, which are primarily monocultural production regions. The probability

distribution for the July 1St planting date in Figure 4 illustrates

 

 

4000
3500 4» # L 22 2 #34
3000 -2 - f -2, 22 fig;
5 2500 ‘T‘ ‘ m i 7 Fwd-“’3' ’— ----- Salem
2 2000 2 2 éffl?’ __fi . Jacksonville
2 ,..--v""' .o» . - Wooster
a 1500- --“ e 2 Owosso
1000 »
500 »

 

 

 

0

 

Probability

 

 

 

Figure 4. Simulated historical cumulative probability distribution of dryland
soybean yields for a north-south transect of sites for the July 1 planting date
(1896-1996).

the increasing potential for dryland double-crop soybean yields from north to

south across the study area. Salem, the southernmost site in the region, shows

a near-70% probability of attaining 1000 kglha yields. Jacksonville, Wooster, and

27

Owosso show progressively lower probabilities of attaining 1000 kglha yields with
increasing latitude.

Why does the potential for successful double cropping change from north
to south across the study region? Climatic limitations are often cited as reasons
why double cropping is generally not practiced in northern areas of the US. The
most important variable is moisture availability (Jeffers, 1987; Beuerlein, 1987).
On a seasonal or annual basis, the overall bulk water balance of the soil profile
can be described as the balance between precipitation and the sum of
evapotranspiration (plant transpiration plus soil evaporation), runoff, and
drainage out of the profile. The DSSAT simulations provide estimates of each of
the components of the soil water balance, which in turn provides an opportunity
to investigate the relationship between crop performance and moisture
availability in greater detail. Simulated mean historical dryland water balance by

site and planting date is given in Table 5.

28

Table 5. Simulated mean historical dryland soybean water balance (mm) by site

and planting date (1895-1996). ET=evapotranspiration, R0=runoff,

DR=drainage, and PR=precipitation.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1-Jun 15-Jun

State Station ET R0 DR PR ET R0 DR PR

MI Adrian 260 61 1.4 305 229 58 1.1 377
Allegan 295 43 3.2 316 257 41 2.7 305
Bay City 237 50 1.8 279 210 49 1.9 270
Big Rapids 279 2.9 31 324 249 2.8 26 302
Coldwater 306 19 10 332 272 18 9.2 315
Owosso 274 33 3.2 296 236 32 3.2 280
Pontiac 274 33 3.4 291 238 31 3.3 274

IN Cambridge City 377 26 13 360 328 25 10 338
Ft. Wayne 330 38 4.4 326 278 35 3.9 301
Greencastle 378 27 1 0 367 329 25 7.6 345
Salem 392 27 7.3 377 341 26 5.4 349
South Bend 357 38 5.1 356 305 36 4 339

OH Circleville 357 36 5.5 351 313 34 4.6 330
Findlay 342 48 2.9 351 290 45 2.1 324
Wooster 370 51 4.5 374 317 47 3.6 348

IL Jacksonville 372 38 7.6 387 323 35 5.2 354
Mt. Vernon 368 27 8.9 364 324 26 6.4 337
Ottawa 307 32 5.7 31 7 269 31 3.7 303

1-Jul 15-Jul

State Station ET R0 DR PR ET R0 DR PR

MI Adrian 201 54 1.1 201 183 50 1.1 248
Allegan 224 38 2.8 289 202 38 3.1 281
Bay City 191 47 2 259 189 29 4.3 256
Big Rapids 212 2.5 24 274 178 2.3 25 252
Coldwater 238 17 10 291 214 16 12 274
Owosso 203 28 3.3 254 183 25 3.4 234
Pontiac 21 1 30 3.3 259 193 27 3.6 241

IN Cambridge City 277 22 8.1 309 246 20 9.7 278
Ft. Wayne 237 34 3.7 280 216 31 4 256
Greencastle 287 22 8.2 313 262 22 9.4 288
Salem 305 25 7.5 319 273 24 10 292
South Bend 259 35 3.7 319 233 34 4.4 303

OH Circleville 278 32 4.9 301 250 29 6.1 271
Findlay 247 41 2.2 297 219 37 2.5 266
Wooster 264 43 2.9 31 1 230 38 3.5 275

lL Jacksonville 281 33 6.1 319 254 31 8.4 294
Mt. Vernon 292 24 7.9 306 268 23 10 283
Ottawa 237 30 4 286 222 28 5.4 267

 

29

 

In general, the southern sites have higher seasonal mean
evapotranspiration values, which are indicative of their warmer, wetter climate.
Runoff and drainage values in Table 5, however, vary across the region, likely
reflecting the physical characteristics of different soils. For example, Big Rapids
has very low runoff values and very high drainage values (e.g. 2.9 mm runoff and
31 mm drainage for the June 1 planting date), likely indicative of the coarse-
textured, porous upper layers of the Coloma sand soil used in the simulation. In
contrast, for a fine-textured soil with lower infiltration rates, such as Hoytville clay
loam at Adrian, the magnitude of the runoff and drainage totals is symmetrically
opposite (61mm runoff and 1.4 mm drainage for the June 1 planting date).

Seasonal precipitation totals vs. simulated yield, illustrating the importance
of seasonal moisture on yield outcome, is given in Figure 5. All of the sites for all
of the planting dates show a positive correlation between total seasonal

precipitation and yield.

30

 

 

4000

3500 +7-

3000 «v -

2500 22 — - 2

2000 ——~ - - - -
1500 7* - 2
1000 ,2, -- - —
500 %

YIeId (kglha)

 

 

 

 

 

0 100 200 300 400 500 600 700

Seasonal Preclpltatlon (mm)

 

 

 

Figure 5. Seasonal precipitation totals vs. simulated dryland soybean yields for a
June 15 planting date at Findlay (1896-1996).

Double cropping has special soil moisture constraints. The amount of soil
water used by the primary wheat crop may leave insufficient moisture for the
soybean crop. This point is illustrated in Figure 6, which depicts soil moisture
conditions of three soil layers for a 12-month simulation at Circleville. The high
variability of available soil moisture in the upper 0-15 cm layer relative to the
lower layers is evident, as are differences in the rates of moisture drawdown and

recharge.

31

 

0.14

 

  
  

    
 

(112 ' ' ll I

l l I: l .
I ' I. I l

s 1 ”ill". 2111171" 1"
Il' 11V.)';,‘\tl

g 0.08 III .-

E :

3

5, 0.06 ~

Lu

0.

0.04

   

----- 45-60 cm

0.02 —90-120 cm ‘- 1,

 

 

 

 

1 31 61 91 121 151 181 211 241 271 301 331 361
Days After Wheat Planting

 

 

 

Figure 6. Simulated plant extractable soil moisture for three different layers at
Circleville from 1 October 1907 through 30 September 1908. Soil type is Coloma
Loamy Sand.

However, as wheat progresses through the grain-fill period towards maturity
(indicated in Figure 6 between day 211 and 267), plant extractable soil moisture
is severely depleted for both topsoil and subsoil layers simultaneously. This is a
critical consideration for planting double-crop soybeans, as the seeds require
moist soil for germination and delays in germination may negatively impact yields

(Jeffers, 1987).

32

How much of a constraint is soil moisture on the double-crop soybeans?
Simulations with irrigation serve to illustrate yield potential when moisture is not a
limiting factor in plant growth and development. For simulations with the same
planting dates, irrigation significantly improved mean yields. In these irrigated
simulations, most sites exhibited mean yields more than double those of dryland
simulations. For example, in the simulation at Cambridge City, dryland yields
planted on June 15th averaged 1593 kglha while irrigated yields averaged 3518
kglha. Big Rapids more than tripled its yield for the same simulation, from 802
kglha yield under dryland conditions to 2675 kglha under irrigation. These
results underscore the importance of moisture availability in determining the
success of double crop soybeans. The effects of irrigation can be seen by
comparing Figures 7a & 7b, which illustrate yield probabilities at Coldwater for
dryland and irrigated simulations across all planting dates. For the July 15
planting date, the probability of attaining a 1000 kglha yield jumped from 20% in

the dryland simulation to approximately 82% in the irrigated simulation.

33

 

 

.‘ - 1-Jun
- 15-Jun
A 1-Jul
- 15-Jul

Yield (kglha)
k:
c:
c:
c:

  

 

 

 

 

1 0.8 0.6 0.4 0.2 0
Probability

 

 

 

Figure 7a. Simulated historical cumulative probability distribution of dryland
soybean yields by planting date at Coldwater (1897-1996).

 

4500
4000 -———~fin_—___--___n -
3500

 

2500

 

3000 —

2000 1
1500 - --
1000 -

500 —

 

 

 

 

 

1 0.8

0.6

0.4

0.2

- 1-Jun
. 15-Jun
4 1-Jul
.. 15-Jul

 

Figure 7b. Simulated historical cumulative probability distribution of irrigated

soybean yields by planting date at Coldwater (1897-1996).

 

With irrigation, does the yield potential still differ from north to south? Figure 8
illustrates the probability distribution of simulated soybean yields for a north-

south transect of sites.

 

 

 

E ----- Salem

3’ . Jacksonville
5 . Wooster

5 Owosso

 

 

 

 

0 A , . . T . .
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Probability

 

 

 

Figure 8. Simulated historical cumulative probability distribution of irrigated
soybean yields for a north-south transect of sites at the July 1 planting date
(1896-1996).

The distributions still reflect differences in yield potential from north to south, with
Salem and Jacksonville, the southern sites, achieving substantially higher yields
and potential yields than Wooster and Owosso in the northern region.

As in the dryland simulations, the probability of obtaining yields of 1000
kglha or better increased from north to south in the study area. Probabilities of
attaining 1000 kglha yields for all simulations at all sites are given in Table 6.
With irrigation, nearly 100% of the time for all sites, simulated double-crop
soybean yields were over 1000 kglha. The exception was the July 15th planting

date, at which point the frequency of 1000 kglha yields at many northern sites

35

dropped off significantly. This is likely the effect of limited growing season on the

second crop.

Table 6. Historical probability of attaining simulated soybean yields greater than
1000 kglha at different planting dates (1895-1996).

 

 

 

 

 

 

 

State Station Dryland Irrigated
1-Jun 15-Jun 1-Jul 15~Iul 1-Jun 154m 1-Jul 15-Jul
Ml Adrian Ofi 0.09 0.05 0.05 1.00 1.00 1.00 0ffi
Allegan 0.40 0.25 0.15 0.12 1.00 1.00 1.00 0.79
Bay City 0.11 0.05 0.04 0.03 1.00 1.00 0.99 0.73
Big Rapids 0.41 0.33 0.22 0.05 1.00 0.98 0.76 0.24
Coldwater 0.52 0.41 0.34 0.22 1.00 1.00 1.00 0.82
Owosso 0.24 0.13 0.08 0.03 1.00 1.00 0.98 0.74
Pontiac 0.31 0.19 0.16 0.08 1.00 1.00 1.00 0.90
IN Cambridge City 0.80 0.63 0.50 0.41 1.00 1.00 1.00 0.93
Ft. Wayne 0.62 0.40 0.26 0.21 1.00 1.00 1.00 0.99
Greencastle 0.84 0.70 0.60 0.63 1.00 1.00 1.00 1.00
Salem 0.79 0.70 0.66 0.66 1.00 1.00 1.00 1.00
South Bend 0.70 0.49 0.41 0.34 1.00 1.00 1.00 1.00
OH Circleville 0.63 0.43 0.50 0.51 1.00 1.00 1.00 1.00
Findlay 0.58 0.37 0.24 0.20 1.00 1.00 1.00 094'
Wooster 0.75 0.49 0.37 0.26 1.00 1.00 1.00 0.76
IL Jacksonville 0.61 0.59 0.49 0.51 1.00 1.00 1.00 0.98
Mt. Vernon 0.68 0.62 0.63 0.67 1.00 1.00 1.00 100]
Ottawa 0.46 0.39 0.33 0.38 1.00 1.00 1.00 1.00

 

 

How much moisture is necessary to eliminate moisture stress on the
double-cropped soybeans? Table 7 lists the mean seasonal number and amount
(in millimeters) of irrigation applications for each planting date. In the irrigated
simulations, a large amount of water was required to keep soil moisture above
stressful levels. For many of the northern sites, the average seasonal irrigation
amounts were comparable to the mean seasonal precipitation amounts. For

example, at Adrian on the June 15‘ planting date, an average of 315 mm of

36

irrigation is applied in a season while the total mean seasonal precipitation is only
305 mm. At the same planting date in Ft. Wayne, an average of 257 mm of
irrigation is applied per season while mean seasonal precipitation is 326 mm.

For all sites, mean seasonal irrigation amounts and average seasonal number of

irrigation applications were highest with earlier planting dates.

Table 7. Summary of mean number of irrigation applications (No. Apps.) and
mean seasonal amounts of irrigation (Amt.) applied in historical soybean double
crop simulations by site and planting date (1895-1996).

 

 

 

 

 

1-Jun 15-Jun 1-Jul 15-Jul

State Station No. Amt. No. Amt. No. Amt. No. Amt.
Apps. (mm) Apps. (mm) Apps. (mm) Apps. (mm)
Ml Adrian 12.4 315 11.6 294 9.8 246 7.7 189
Allegan 12.5 307 1 1.3 278 10.7 261 8.7 209
Bay City 12.2 311 11.4 289 9.9 248 7.6 189
Big Rapids 13.6 260 12 227 9.8 184 7.5 138
Coldwater 10 244 9.4 225 7.8 185 5.9 140
Owosso 11.7 283 10.9 262 9.2 219 7 164
Pontiac 11.4 275 10.8 259 9.3 222 7.3 170
IN Cambridge City 7.3 199 7.1 193 6 162 4.7 124
Ft. Wayne 9.6 257 8.9 239 7.5 199 6 157
Greencastle 7.9 221 7.2 202 6 165 4.7 129
Salem 8.5 239 7.7 217 6.2 172 4.3 120
South Bend 8.9 233 8.4 217 7.1 183 5.3 136
0H Circleville 8.8 228 8.2 212 6.8 176 5.5 141
Findlay 10 251 9.6 237 8.3 202 6.5 158
Wooster 7.7 207 7.4 197 6.3 167 4.9 129
IL Jacksonville 10.6 266 9.8 244 7.9 194 6 147
Mt. Vernon 11.2 280 10 250 8 195 6.1 149
Ottawa 9.6 266 9.1 252 7.7 211 6.3 169

 

 

 

 

 

 

 

Potential yield under simulated non-limiting moisture conditions is clearly
higher than dryland simulations across the region, but the mean seasonal

amounts of irrigation applied to eliminate moisture stress are substantial. If used,

37

irrigation would substantially increase the soybean production cost and the
resulting breakeven yield level. However, the irrigated simulations used here are
intended to show overall yield potential and to illustrate moisture deficiencies
across the region. The numbers indicate that northern sites, especially in
Michigan, generally have greater potential risk of moisture stress than other sites
in the study area, possibly due to lower average growing season precipitation.
Looking at the seasonal water balances, the model results indicate that
seasonal cumulative evapotranspiration decreased with later planting dates.
Cumulative runoff also decreased with later planting dates, while cumulative
drainage varied—no predictable increases or decreases with planting dates.
This was likely caused by increased water uptake due to plant growth, drying the
subsoil so that the soil soaks up more of the precipitation or irrigation. A
comparison of mean seasonal evapotranspiration for dryland (Table 5) and
irrigated double-crop soybeans (Table 8) reveals that potential
evapotranspiration in an unlimited moisture simulation far exceeds actual
evapotranspiration in the dryland simulation. Moisture availability plays a major

role in double crop success.

38

Table 8. Simulated mean historical irrigated soybean water balance (mm) by site
and planting date (1895-1996). ET=evapotranspiration, RO=runoff,
DR=drainaE and PR=precipitation.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1-Jun 15-Jun

State Station ET R0 DR PR ET RO DR PR

MI Adrian 473 75 1.4 299 427 70 1.1 279
Allegan 471 53 3.5 316 414 51 2.9 305
Bay City 451 61 2.2 269 410 59 1.8 259
Big Rapids 425 3.6 48 314 373 3.4 42 294
Coldwater 455 24 29 326 412 23 26 305
Owosso 462 42 4.5 284 416 41 3.7 269
Pontiac 459 42 4.5 282 414 39 4.1 262

IN Cambridge City 481 34 19 359 434 32 15 331
Ft. Wayne 478 47 7.5 322 428 43 5.8 294
Greencastle 496 34 18 366 445 32 14 341
Salem 523 36 9.9 373 466 34 8.3 341
South Bend 488 50 6.6 351 438 46 5.2 327

OH Circleville 489 47 9.4 348 442 44 7.3 325
Findlay 499 60 3.8 346 448 55 2.7 316
Wooster 479 63 6.2 370 433 58 4.5 340

lL Jacksonville 540 49 12 380 480 45 1 1 352
Mt. Vernon 544 35 10 356 484 32 8.6 328
Ottawa 472 41 12 313 428 40 10 296

1-Jul 15-Jul

State Station ET RO DR PR ET RO DR PR

MI Adrian 366 63 1 .2 256 305 59 1 .2 243
Allegan 370 46 3.8 288 316 45 4.3 280
Bay City 356 56 2.5 248 302 54 3.7 243
Big Rapids 307 2.9 40 268 244 2.7 39 250
Coldwater 353 21 28 285 295 19 36 269
Owosso 353 36 4.5 246 290 31 5.6 227
Pontiac 359 36 3.9 245 304 34 5 235

IN Cambridge City 371 28 14 303 312 25 18 275
Ft. Wayne 366 40 6.9 273 310 36 9.3 251
Greencastle 380 28 17 309 327 25 24 285
Salem 400 32 13 316 336 29 29 289
South Bend 373 44 5.8 307 313 41 9.3 294

OH Circleville 382 39 8.1 298 327 34 12 269
Findlay 383 50 3.1 289 321 43 4.7 261
Wooster 369 53 4.2 308 305 44 5.3 272

IL Jacksonville 403 42 1 3 315 336 37 20 291
Mt. Vernon 41 1 30 13 300 351 27 19 281
Ottawa 368 38 12 279 317 35 18 264

 

39

 

Because available soil moisture can be related to soil texture, a sensitivity
analysis was performed at Adrian, MI. To explore soybean yield variability as a
function of soil type, simulations contrasting three soil textures were run at Adrian
keeping all other variables constant. While total seasonal precipitation was
constant across the three simulations, water balance (i.e. runoff, drainage, and
evapotranspiration) and PESW varied by soil texture. The sandy textured soil
was associated with the greatest ET and DR and the clay loam, the least (data
not shown). Simulated potential yield differences across the differing soil
textures are given in Figure 9. The sandy soil, most permeable of the three,
produced the highest yield potential while clay loam produced the worst. For
example, at the 1000 kglha yield level, the frequency for the sand was 0.40, for
the loam, 0.18, and for the clay loam, only 0.05. This was unexpected as loamy
textured soils are often preferred soils for agriculture. This result could be a
function of the model, but more likely indicates that more permeable soils, in
which a relatively greater fraction of growing season precipitation reaches the
rooting zone, may actually be an advantage in this type of double cropping

system.

40

 

 

 

3500
3000 J
2500 ~4 . .'

2000 '-°
' - - Sand

...... '32" - Loam
- Clay Loam

Yield (kglha)

1500 ,J-

1 000 III-I‘ll.

500 ..... .mn . ._... ------ .

 

 

Il. ...

..I

In" ...-Inonou
..............

1 0.8 0.6 0.4 0.2 0
Probability

 

 

Figure 9. Simulated historical cumulative probability distribution of dryland
soybean yields by soil texture at Adrian for July 1 Planting Date (1895-1996).

41

Given the constraints that affect the frequency of historical success at
double cropping, is there a time trend in the simulated double-crop yield data?
Time series of simulated soybean yields for Allegan in the northern part of the
study area and Greencastle in the south are given in Figures 10a and 10b.
Allegan has lower overall yields and a smaller overall yield range than
Greencastle. The moving average at Allegan depicts a variable yield trend over
the past century, with a marked increase in mean yield from 1965-1975. The
moving average at Greencastle reflects a flatter distribution of simulated yields in
the early part of the 20th century, with steady increases for much of the latter half

of the century. The reasons for the periods of relatively higher and lower yield

 

3500

 

3000 - 2,, *7.

2500 - .. . . _ 22“.. _

2000

 

1500 -. A 2 .

10mj,I I . -,- ”I I\ AI
2 M " L

 

 

 

Yield (kglha)

 

 

 

 

 

 

 

 

 

500 . \L o-

1895 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995
Year

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 10a. Simulated historical dryland yields and moving 9-year average by
year at Allegan for June 15 planting date (1895-1996).

42

 

4500
4000
3500
3000
2500

Yield (kglha)
8

1 500
1 000
500

 

0
1896 1906 1916 1926 1936 1946 1956 1966 1976 1986 1996»

Year

 

 

 

Figure 10b. Simulated historical dryland yields and moving 9-year average by
year at Greencastle for June 15 planting date (1896-1996).

trends are unknown, but may be linked to long-term increases in precipitation
(Karl et al., 1994) and precipitation frequency (Andresen, 1999).

Historical analysis of a wheat-soybean double-cropping system in the
region resulted in several consistent trends and associations. First, mean
double-crop soybean yields positively correlated with mean season length and
later planting dates resulted in lower simulated soybean yields than earlier
planting dates. The frequency of simulated breakeven yields increased with
earlier planting dates. Yield potential increased from north to south across the
study area.

Overall, however, the most important climatological factor for the Great
Lakes and Ohio Valley regions is precipitation, with simulated yield potentials

found to be strongly and positively correlated with plant available water. In

43

general, the Ohio Valley sites have higher mean seasonal precipitation and
evapotranspiration than the Great Lakes sites, which explained a major portion of
the differences in regional yield potential. Soil moisture limitations for soybean
are related to high rates of evapotranspiration and resulting depletion of subsoil
moisture by the primary wheat crop during grain-fill.

Finally, some increase in simulated soybean yields is evident in time
series plots of historical yield simulations for the past century, which are possibly

linked to long-term changes in precipitation.

44

HadCM2 Future Climate Scenario Simulation

Simulating double cropping with data or output from a climate model
scenario can serve as a tool to explore a range of potential effects of climate
change on agricultural production. Using the HadCM2 scenario, one possible
outcome on double cropping wheat and soybeans was explored for 2001-2099.
The results are compared with the results of the historical analysis in the
previous section.

How do mean double-crop soybean yields compare between historical
and future scenarios? A comparison of simulated soybean yields at a subset of
sites for each of the scenarios modeled: both dryland and irrigated, for future
002 - enriched, future non-CO2 — enriched, and historical scenarios is given in

Table 9 for the July 15 planting date.

Table 9. Comparison of mean future (2001-2099) and historical (1896-1996)
simulated soybean yields by site for the July 15 planting date.

 

 

 

 

 

Station Dryland Irrigated
Future Future Historical Future Future Historical
no C02 002 no 002 002
Big Rapids 655 1272 210 1282 2004 582
Cambridge City 1247 2346 861 2003 3148 1821
Coldwater 868 1 726 621 1630 2569 1624
Ottawa 1 143 2122 887 1767 2793 2234
Salem 1577 2751 1469 2274 3495 2595
Wooster 738 1 608 592 1 71 7 2686 1512

 

 

Mean yields for dryland soybeans without carbon dioxide enrichment increase

more than 200% over dryland historical mean yields at Big Rapids, while Salem

45

increased only 7% for the same scenario. Vlfith carbon dioxide enrichment, mean
yield values nearly double those without carbon dioxide enrichment.

Figures 11a and 11b illustrate a comparison of changes in mean
simulated dryland soybean yields for two decades in the future and the historical
mean, with constant and transient CO2 concentrations, respectively. Vlfithout
C02 enrichment, mean yields in the 20403, a decade of relatively low
precipitation, are far lower than those in the 20905. Mean yields for the 2040s
are comparable to those of the historical mean, with the exceptions of Salem and

Ottawa, which decrease markedly from the historical mean.

 

 

2500

2000

I 1895-1996

I 2041 -2050
I 2091 -2099

1 500

1000

Yleld (kglha)

500

 

 

 

 

 

 

Figure 11a. Mean simulated dryland soybean yields by site, without future CO2
enrichment, for the July 15 planting date.

46

 

4000
3500
3000
2500
2000
1500
1000

 

I 1895-1996
I 2041-2050
I 2091-2099

Yleld (kglha)

 

 

 

 

 

 

Figure 11b. Mean simulated dryland soybean yields by site, with future 002
enrichment, for the July 15 planting date.

V\fith CO2 enrichment, both future decades reflect an increased simulated
mean yield over the historical mean at all sites. The 20905 had the highest

simulated mean yields, with all six sites averaging above 2500 kglha.

Does the yield potential still reflect a north-south gradient with climate
change? Cumulative probability distribution functions of simulated future soybean
yields at Big Rapids, Ottawa, and Salem, (a north-south transect of the study
area) for constant and enriched CO2 levels are given in Figures 12a & 12b. The
distributions indicate that the north-south gradient is still prominent, with greater
yield potential at the southern sites than at the northern sites. For example,

yields at Salem are 45% more likely to exceed 1000 kglha than at Big

47

 

 

‘6

5 ----- , Salem

a

5 —0ttawa

'D

5 - Big Rapids
>.

 

 

 

 

oI . . . , , . . -
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

Probability

 

 

 

Figure 12a. Future cumulative probability distribution of simulated dryland
soybean yields with constant 00., levels for a north-south transect of sites (2001-

2099)

 

 

5000
4500
4000-
3500 ~
3000-
2500-
2000 -
1500-
1000 a

500-

 

----- Salem
Ottawa
- Big Rapids

 

Yield (kglha)

 

 

 

 

 

Probability

 

 

 

Figure 12b. Future cumulative probability distribution of simulated dryland
soybean yields with enhanced CO2 levels for a north-south transect of sites
(2001-2099).

48

Rapids, where breakeven yields occurred less than 30% of the time. Yield
potential increases for all three sites with CO2 enrichment, but the north-south
gradient remains evident across both constant and enriched CO2 scenarios.
While the probability of achieving breakeven yields at Big Rapids increased to
52% with CO2 enrichment, the same probability at Salem increased to 85%.

Is water stress in the future as much of a limitation for the Great Lakes
region as it was for the historical simulation? How does the future water balance
compare with the historical water balance? Mean seasonal water balance for the
six sites for both CO2 enriched and constant CO2 scenarios in the future are
listed in Table 10 for the July 15 planting date.

Table 10. Future simulated mean seasonal water balance (mm) for the July 15

planting date (2001-2099). ET=evapotranspiration, RO=runoff, DR=drainage,
and PR=precipitation.

 

Dryland

no 002 002
ET R0 DR PR ET R0 DR PR

226 2.5 40 326 210 3 ‘ 48 325
284 20 13 319 268 22 19 321
250 18 22 318 233 19 27 317
261 35 1 8 336 244 37 23 335
307 28 40 353 293 30 46 356
260 37 1.3 315 243 40 2 314

 

 

Irrigated

no COZ C02
ET RO DR PR ET RO DR PR

272 2.7 53 324 244 3 61 323
328 23 20 318 299 25 27 321
297 20 50 316 266 21 55 316
305 40 38 337 273 42 44 337
348 31 52 353 321 33 59 356
317 42 4.9 315 286 44 9 312

 

 

 

 

 

49

The southernmost site, Salem, still has the highest mean
evapotranspiration value, while the northernmost site, Big Rapids, retains the
lowest values across all simulations. Compared with historical dryland
evapotranspiration values, the mean at Big Rapids in the future simulation
increased by 48 mm without 002 enrichment and by 32 mm with CO2
enrichment. Mean evapotranspiration values for Salem increased by 34 mm
without CO2 enrichment and by 20 mm with CO2 enrichment. The difference
between mean evapotranspiration in the irrigated simulation and the dryland
simulation decreased from the historical to the future scenario at Big Rapids by
20 mm and at Salem by 22 mm, suggesting less potential water stress on
double-crop soybeans in the future for both the Great Lakes and Ohio Valley
regions.

A comparison between future and historical mean evapotranspiration and
precipitation is given in Figures 13a & 13b. In all cases, mean seasonal
evapotranspiration increases from the levels in the historical simulation to those
in the future simulation, likely due to increased mean precipitation indicated in
Figure 13b. While precipitation is greater than evapotranspiration for both future
and historical simulations, yields in the future are significantly greater, likely
because of reduced plant water stress (data not shown). Evapotranspiration in
the future is reduced with CO2 enhancement, reflecting increased water use
efficiency. Evapotranspiration values are greatest at southern sites and

decrease to the north.

50

 

 

El Historical
I Future no C02
I Future C02

mm H20

 

 

 

 

 

 

Figure 13a. Comparison of future (2001-2099) and historical (1895-1996) mean
seasonal evapotranspiration for dryland soybean yields for July 15 planting date.

 

 

El Historical
I Future

mm H20

 

 

 

 

 

 

Figure 13b. Comparison of future (2001-2099) and historical (1895-1996) mean
seasonal precipitation for dryland soybean yields for July 15 planting date.

51

To illustrate decadal changes in precipitation in the future, refer to Figure
14. Mean seasonal precipitation increases over the historical mean for both the
20405 and the 2090s. Changes simulated at Big Rapids show mean

precipitation to double in the 20903 over historical means.

 

 

E
E
5 I 1895-1996
3 I2041-2050
§ I2091-2099
0
2
a.

 

 

 

 

 

 

Figure 14. Mean soybean growing season precipitation (mm) for the July 15
planting date.

52

Changes in mean evapotranspiration for two decades of future simulated
dryland soybeans are given in Figure 15 for each site under transient 00;;
conditions. In all cases, future evapotranspiration exceeds mean historical
evapotranspiration levels, likely reflecting increased precipitation in the future.

On average, evapotranspiration levels in the 20905 equaled or exceeded those
of the 20405. For example, the 20905 simulation at Coldwater had approximately
252 mm of mean evapotranspiration, while the 20405 had approximately 235

mm.

 

0)
0|
0

 

NO)
010
CO

I 1895-1996
I 2041-2050
I 2091-2099

evapotransplratlon (mm)
.4 _| N
o a: o
o o o

01
O

 

 

 

 

 

 

Figure 15. Mean evapotranspiration for simulated dryland soybeans, with
transient future C02, for the July 15 planting date.

Irrigated scenarios used to illustrate water non-limiting situations in a
north-south transect with both constant and enriched CO; levels are given in
Figures 16a and 16b. Yield potential increased significantly with C02 enrichment

when water was not limiting. For instance, Big Rapids jumps from a 57% to a

53

69% probability of attaining breakeven yields with C02 enhancement. The north-
south transect is still evident in yield potential, despite the effects of 002
enhancement, as Salem, the southernmost site in the graph has a greater yield

potential than both Ottawa and Big Rapids, the northernmost site.

54

 

 

4000
3500«~ -5- _- w , e ,, -7 , V 7 .7. - --.. 2 ._..-1
3000 -- ,
2500 .
----- Salem

Ottawa
- Big Rapids

 

2000 -.

Yleld (kglha)

1500 «
1000 ‘-
500 -.

 

 

 

0

 

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Probability

 

 

 

Figure 16a. Future simulated cumulative probability distribution for irrigated
soybean yields under constant 002 levels (2001-2099) for the July 15 planting
date.

 

 

6000

5000 ~ ~—

 

4000
----- Salem

Ottawa
- Big Rapids

 

 

 

 

3000 “I --

Yield (kglha)

 

2000 ~ --

 

1000 5'

 

 

 

 

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

Probability

 

 

 

Figure 16b. Future simulated cumulative probability distribution for irrigated
soybean yields under enriched C02 levels (2001-2099) for the July 15 planting
date.

55

A comparison of historical and future mean frequencies and amounts of irrigation
applications is illustrated in Figures 17a and 17b. Because the irrigated
simulations were designed to eliminate water stress, the changes in irrigation
application frequency and amounts through time are assumed to represent
changes in water stress levels for the soybeans. Both frequency and amount of
irrigation applied decreased from the historical simulations relative to the future
simulations, indicating decreased water stress on the soybeans. Carbon dioxide
enhancement scenarios exhibited less frequent and smaller amounts of irrigation.
The greatest decrease was found at Ottawa, where the amount of irrigation
applied decreased by more than 50% from historical to future COz-enhanced

simulations.

 

 

 

 

D Historical
I Future no 002
I Future COZ

 

 

no. applications
o _. N no -h 01 a) ‘1 on

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 17a. Comparison of future and historical mean irrigation application
frequency for the July 15 planting date.

56

 

 

El Historical
I Future no COZ
I Future COZ

 

 

 

 

Irrigation applied (mm)
A d
M A a; on o N
o o o o o o o

 

 

 

Figure 17b. Comparison of future and historical mean simulated seasonal
irrigation requirements for a cross-section of sites in the Great Lakes region for
the July 15 planting date.

57

A comparison of the mean number of irrigation applications for two future
decades with the historical simulated mean across static and transient C02 levels
is given in figures 18a and 18b. For all sites, the frequency of irrigation
applications for the future decades was less than those of the historical
simulations. On average, the frequency declines from the 2040s to the 20905.
Under transient 002 levels in the future, the frequency of irrigation applications

decreases even more.

 

 

 

I 1895-1996
I 2041-2050
I 2091-2099

no. appllcatlons
J5

  

 

 

, l
0 ' i" « ' .1

Big Rapids Cambridge Coldwater Ottawa Salem Wooster
City

 

 

Figure 18a. Mean seasonal number of irrigation applications, without C02
enrichment, for the July 15 planting date.

58

 

 

7
6

2

¢_: 5

g I 1895-1996

‘=°l 4 .2041-2050

it .2091-2099

0'

C

  

 

 

Big Rapids Cambridge Coldwater Ottawa Salem Wooster
City

 

 

 

Figure 18b. Mean seasonal number of irrigation applications, with C02
enrichment, for the July 15 planting date.

Mean seasonal amounts of irrigation applied are given in Figures 193 and
19b for static and transient C02 levels. Total applied irrigation decreases from
historical to future levels. On average, the amounts also decreased through time
in the future simulations from the 20405 to the 20905. For example, at Coldwater
in the non-COz-enriched scenario, mean irrigation decreased from 140 mm in the
historical simulation to 90 mm in the 20405, to 67 mm in the 20905. With C02
enrichment, future irrigation amounts decreased even more. At Coldwater under
transient C02, an average 67mm of irrigation was applied in the 20405 and 40

mm in the 20905.

59

 

 

I 1895-1996
I 2041-2050
I 2091-2099

Irrigation (mm)

 

 

 

 

 

 

Figure 19a. Mean seasonal irrigation application amounts, without CO;
enrichment, for the July 15 planting date.

 

 

E
E I 1895-1996
:5 I2041-2050
g I2091-2099
':
L

 

 

 

. Q
<59 6.90

 

 

 

Figure 19b. Mean seasonal irrigation application amounts, with CO; enrichment,
for the July 15 planting date.

60

An overall comparison of the probability of simulated breakeven yields for
each of the six sites (historical vs. future) is given in Table 11. The probability of
reaching soybean yields greater than 1000 kglha changes between historical and
future scenarios, but results vary by site. Probabilities at Big Rapids, Coldwater,
and Wooster all increased for future simulations vs. historical simulations, while
Cambridge City, Ottawa, and Salem exhibited increased potential yields for

dryland simulations in the future, but decreases for the irrigated simulation.

Table 11. Historical and future probability of attaining yields greater than 1000
kglha for July 15 planting date.

 

 

 

 

 

 

Dryland Irrigated
Station Future Future Historical Future Future Historical
no C02 C02 no C02 C02
Big Rapids 0.25 0.53 0.05 0.56 0.70 0.24
Cambridge City 0.60 0.80 0.41 0.87 0.89 0.93
Coldwater 0.44 0.71 0.22 0.83 0.86 0.82
Ottawa 0.59 0.76 0.38 0.85 0.87 1.00
Salem 0.70 0.86 0.66 0.88 0.91 1.00
Wooster 0.32 0.60 0.26 0.80 0.84 0.76

 

Dryland and irrigated yield probability distribution functions comparing
historical and future yield potentials at Big Rapids for the July 15 planting date
are given in Figures 20a & 20b. Both future simulations at Big Rapids reflect

decreased risk versus the historical levels. The future scenario without C02

61

 

 

4500

 

4000 W ~ ~ *- , . 7— - -_ 7 I -fl
I
3500 b I" 7 . 7 7. 7.. ""g'
I.
3 3000 — - — — — . _- , If-.-
g I. .....
El 2500 , ~ ,, . , -~ -. —7— f1--- - -, Future+Coz
v ' Future
a .
g 2000 " Historical

1500 *

1000"

500 -

o« . .

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Probability

 

 

 

 

 

 

 

Figure 20a. Historical and future cumulative probability distributions of dryland
soybean yields at Big Rapids for planting date July 15.

 

 

4500

4000 —# f~—.w-=~

 

3500 ._ * , W , , ,2‘“ WW
3000 *r ,' WW
2500-
2000
1500 ,
1000
500 -
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Probability

 

""" Future + 002
Future
' Historical

 

 

Ylold (kglha)

 

 

 

 

 

 

 

 

 

Figure 20b. Historical and future cumulative probability distributions of irrigated
soybean yields at Big Rapids for planting date July 15.

62

reflects changing temperatures and precipitation amounts, and the yields
increase over the historical simulations as a result.

Finally, time trends of yields in the future scenarios at Salem were plotted
in Figures 21a and 21b. An evenly-weighted nine—year moving average was
fitted to the distribution to illustrate decadal-scale trends. While the mean yields
are greater in the COz-enriched scenario, the trends through time are similar.
Both graphs reflect an overall upward trend through the next century, with a
phase shift in mean yields occurring in mid-century. Periods of relatively low
yields on the graphs correspond to periods of lower precipitation, including the

20405.

63

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3500
3000 ,. ~ e - . - ..V. - A _ ___
2500— —«. . ._. A A

£2000 [N22, , f - - UH»

gm \ - [VI III
1000

II
500 4—4 — -2 -_- __ -__,
0 V I

 

 

 

2001 2011 2021 2031 2041 2051 2061 2071 2081 2091

Year

 

 

 

Figure 21a. Future simulated dryland soybean yields and nine-year moving
average at Salem without CO2 enrichment for the July 15 planting date (2001-
2099).

 

5000
4500
4000
3500
3000
2500
2000
1500
1000

500

0
2001 2011 2021 2031 2041 2051 2061 2071 2081 2091

Year

Ylald (kglha)

 

 

 

 

Figure 21b. Future simulated dryland soybean yields and nine-year moving
average at Salem with CO2 enrichment for the July 15 planting date (2001-2099).

64

For comparison, the same future simulated dryland soybean yield time
series are given for Coldwater in Figures 22a and 22b. In general, the trends are
similar to those at Salem. Yields decrease slightly in the first decade of the 21St

century, but increase overall through the rest of the century.

65

 

 

2500

2000 ~- . .. . .. _ __. __,._._

15002,. i I M i I A IA

\l _ I n ' '

...i l , II I
I "

2001 2011 2021 2031 2041 2051 2061 2071 2081 2091
Year

 

 

 

 

 

 

 

Yleld (kglha)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 22a. Future simulated dryland soybean yields and nine-year moving
average at Coldwater without CO2 enrichment for the July 15 planting date
(2001-2099).

 

 

4500
4000~~‘~.-95~7 ~_~-_-- _---_ -U ___.
3500._,_ - --,- M f ‘_ .
3000 . . l I I I PI
2500 »~—e_~ -
2000 . ,____W I n
1500 II
1000 l -
500- TIII
0 . I. U . . , I
2001 2011 2021 2031 2041 2051 2061 2071 2031 2091

Year

 

 

 

 

 

 

 

 

 

 

 

 

 

Yield (kglha)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

#~——4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 220. Future simulated dryland soybean yields and nine-year moving
average at Coldwater with CO2 enrichment for the July 15 planting date (2001-

2099).

66

Overall, the simulations with potential future climate appear to suggest an
increased probability of successful double cropping in the region. For the six
sites modeled, mean yields increased for all future scenarios vs. historical
scenarios. Yields increased most with irrigation and when carbon dioxide
enrichment was taken into account. The future yield potentials still reflected a
north-south gradient, with overall yields greater at southern sites vs. those in the
nonh.

Water stress in the future scenario was not as much of a limitation for sites
in the Great Lakes region as it was in the historical scenario. Mean seasonal
evapotranspiration totals increased for all future scenarios, especially for those
including C02 enrichment. Future yield potential was enhanced by increasing
ambient C02 levels. Frequency and amounts of irrigation required to eliminate
water stress decreased for the future models, especially for higher

concentrations of C02.

67

Chapter 4

CONCLUSIONS

Double cropping wheat and soybeans has been historically possible to
some degree, according to research and simulations for the Great Lakes region.
For model simulations at 18 sites throughout the region, later planting dates
resulted in lower soybean yields than earlier planting dates and mean double-
crop soybean yields and mean season length were positively correlated. The
probability of attaining breakeven yields increased with earlier planting dates.
Geographically, yield potential increased from north to south across the study
area.

Soil moisture and precipitation are the most important climatological
factors influencing double cropping in the Great Lakes region. In general, the
Ohio Valley sites have higher mean seasonal precipitation and
evapotranspiration than the Great Lakes sites. Soil moisture may also limit
soybean emergence and growth due to high rates of wheat evapotranspiration
and resulting depletion of subsoil moisture during grain-fill.

Even when moisture is non-limiting, the probability of reaching breakeven
yields is still greater for sites in the southern portion of the study area, likely an
impact of limited growing season length on the secondary soybean crop.

Increasing yields through time are evident in time series plots of historical

yield simulations, especially in the past 50 years. Climate change, particularly

68

Increasing yields through time are evident in time series plots of historical
yield simulations, especially in the past 50 years. Climate change, particularly
with increasing levels of atmospheric C02, longer growing seasons, warmer
temperatures, and increasing precipitation, may help to boost yields in the future.
Time series plots of simulated double-crop soybean yields from the future
HadCM2 scenario indicate a significant increase in yields by the end of the 21St
century.

The results of this model analysis should be tested and proven in the field
before widespread adoption of double cropping occurs. Given both crop model
and general circulation model limitations, this is just one in a range of possible
scenarios that might occur in a future climate. This study explores one possible
farm-level adaptation to climate change. It does not include the effects of fertility,
pest, or disease problems that may occur or develop in the future. It also does
not factor in any technological advances that may occur, such as seed genetics,
new management techniques, or advances in tillage, planting, and harvesting
equipment.

Model limitations are the major constraint to this sort of analysis. Some
research and development for the DSSAT model suite that might be useful to this
analysis would include the implementation of relay lntercropping simulations.

The DSSAT model does have some inherent limitations. Currently,
DSSAT is capable of handling only one crop at a time, meaning that soybeans
cannot be planted into standing wheat in a simulation, making it impossible to

simulate relay intercropping. Only standard double cropping practices can

69

currently be modeled using DSSAT, with the proper adjustments to the
sequential experiment files.

Future research might include other general circulation models,
particularly those with transient CO; scenarios, to further assess potential
impacts of changing climate and ambient carbon dioxide levels on crop

production.

70

APPENDICES

71

APPENDIX A

DSSAT INPUT FILE SAMPLES

Weather (*.WTH) File Sample: Coldwater, Michigan, 1901

*WEATHER DATA : Coldwater, MI

@ INSI LAT LONG ELEV TAV AMP REFHT WNDHT
COMI 41.950 -85.000 1000 9.1 13.4 -99.0 -99.0

@DATE SRAD TMAX TMIN RAIN
01001 3.7 ~5.6 —15.0 0.0
01002 2.3 -5.6 -16.7 0.0
01003 5.2 -5.0 ~19.4 0.0
01004 2.4 1.1 -9.4 0.0
01005 3.9 0.0 -10.6 0.0
01006 5.6 3.9 ~8.9 0.0
01007 2.1 2.2 -1.1 3.6
01008 2.1 9.4 -1.7 2.5
01009 2.1 6.1 -3.9 5.1
01010 2.1 4.4 -2.2 22.9
01011 2.1 0.6 -1.7 10.2
01012 6.4 0.0 -3.3 0.0
01013 8.5 1.1 -11.1 0.0
01014 2.2 1.7 -3.3 5.1
01015 9.5 5.6 -3.3 0.0
01016 8.2 5.6 -2.2 0.0
01017 7.1 -1.7 -8.9 0.0
01018 8.9 -6.7 —10.0 0.0
01019 2.3 -5.6 -17.8 5.1
01020 4.8 10.6 -12.2 0.0
01021 6.1 7.2 -1.1 0.0
01022 10.6 1.7 -3.9 0.0
01023 11.8 1.7 -3.3 0.0
01024 2.4 2.2 —6.1 2.5
01025 7.5 -2.2 -7.8 0.0
01026 2.4 -3.9 -11.1 8.9
01027 10.6 -0.6 -4.4 0.0
01028 9.2 -3.3 -8.3 0.0
01029 8.9 -5.6 -12.8 0.0
01030 2.8 -4.4 -10.0 2.5
01031 3.6 -5.6 —17.2 2.5
01032 10.2 -7.2 -l2.8 0.0
01033 9.7 —3.3 -17.8 0.0
01034 2.7 -2.8 -6.1 15.2
01035 2.7 -1.7 -8.3 3.8
01036 10.5 -5.6 —10.0 0.0
01037 9.9 -7.2 -15.6 0.0
01038 13.8 -5.0 -17.2 0.0
01039 9.0 -5.6 -17.8 0.0
01040 6.8 -5.6 -11.1 12.7
01041 7.2 -5.0 -18.3 0.0 u

72

Soil File Sample: Catlin Silty Clay Loam, Ottawa, IL.

*SOILS
*M800910002 SCS SICLL 114 FINE-SILTY MIXED MESIC
@SITE COUNTRY LAT LONG SCS FAMILY
OTTAWA USA 41.320 88.920 MOLLISOL
@ SCOM SALB SLUl SLDR SLRO SLNF SLPF SMHB SMPX
BN 0.13 10.8 0.40 76 1.00 1.00 18001 IBOOl
@ SLB SLMH SLLL SDUL SSAT SRGF SSKS SBDM SLOC
SLCF SLNI SLHW SLHB SCEC

28 AP 0.189 0.329 0.388 1.00 —99.0 1.50 2.90
99.0 -99 5.9 -99.0 25.4

41 AB 0.203 0.341 0.387 0.75 -99.0 1.50 1.72
99.0 -99 5.6 -99.0 24.2

66 ET 0.193 0.332 0.387 0.50 -99.0 1.47 0.67
99.0 -99 5.2 -99.0 22.7

104 ET 0.056 0.200 0.389 0.35 -99.0 1.50 0.40
99.0 -99 5.8 -99.0 15.6

114 BC 0.178 0.307 0.399 0.35 -99.0 1.30 0.24
3.0 -99 7.4 -99.0 10.0

73

TYPIC ARGIUDOLL

SMKB
18001
SLCL
34.5
37.5
35.4
25.5

32.6

SLSI

63.0

60.6

62.2

71.1

47.3

Sequential file sample: Coldwater, Michigan, historical irrigated simulation for

planting date 1 June.

*EXP.DETAILS: COM12004SQ COLDWATER 19008 PDAT=01JUN IRR

*TREATMENTS ------------- FACTOR LEVELS ----------
@N R O C TNAME .................. CU FL SA IC MP MI MF MR MC MT ME MH SM
1 1 1 0 soybean 1 l 0 1 l 1 0 0 0 0 O 1 1
l 2 1 0 wheat 2 1 O 2 2 0 0 0 O 0 O 2 2
*CULTIVARS

@C CR INGENO CNAME
1 SB 990001 M GROUP 1
2 WH 990003 WINTER-US

*FIELDS
@L ID_FIELD WSTA.... FLSA FLOB FLDT FLDD FLDS FLST SLTX SLDP
ID_SOIL

1 COMI COMI —99.0 0 IBOOO 0 0 00000 -99 150
MSOOOOOOOl
@L ........... XCRD ........... YCRD ..... ELEV ............. AREA .SLEN
.FLWR .SLAS

1 0.00000 0.00000 0.00 0.0 0
0.0 0.0

*INITIAL CONDITIONS
@C PCR ICDAT ICRT ICND ICRN ICRE ICWD ICRES ICREN ICREP ICRIP
ICRID

1 SB 1001 100 0 1.00 1.00 180.0 1000 0.80 0.00 100
15
@C ICBL SH2O SNH4 SNO3

1 20 0.201 1.0 1.3

1 28 0.233 1.0 1.1

1 71 0.224 1.0 1.1

1 152 0.223 1.0 1.1
@C PCR ICDAT ICRT ICND ICRN ICRE ICWD ICRES ICREN ICREP ICRIP
ICRID

2 01001 0 0 1.00 1.00 -99.0 0 0.00 0.00 100
15
@C ICBL SH2O SNH4 SNO3

2 10 0.338 1.0 4.5

2 23 0.340 1.0 4.9

2 43 0.340 0.5 1.4

2 69 0.315 0.5 0.8

2 89 0.304 0.5 1.0

2 114 0.338 0.5 1.1

2 152 0.408 0.5 1.4

*PLANTING DETAILS
@P PDATE EDATE PPOP PPOE PLME PLDS PLRS PLRD PLDP PLWT PAGE
PENV PLPH SPRL .

1 1152 -99 50.0 50.0 S R 38 0 4.0 -99 -99 -
99.0 -99.0 0.0

74

2 01280
99.0 ~99.0

-99 300.0 300.0
0.0

S

*IRRIGATION AND WATER MANAGEMENT

@I EFIR IDEP ITHR IEPT
l 1.00 30 50 100
*HARVEST DETAILS
@H HDATE HSTG HCOM HSIZE
1 1354
2 02151
*SIMULATION CONTROLS
@N GENERAL NYERS NREPS
1 GE 96 1
@N OPTIONS WATER NITRO
1 OP Y N
@N METHODS WTHER INCON
1 ME M M
@N MANAGEMENT PLANT IRRIG
1 MA R A
@N OUTPUTS FNAME OVVEW
DIOUT LONG CHOUT OPOUT
1 OU N Y
N Y N N
@ AUTOMATIC MANAGEMENT
@N PLANTING PFRST PLAST
1 PL 01152 01196
@N IRRIGATION IMDEP ITHRL
1 IR 30 50
@N NITROGEN NMDEP NMTHR
1 NI 30 50
@N RESIDUES RIPCN RTIME
1 RE 100 1
@N HARVEST HFRST HLAST
1 HA 0 01360
@N GENERAL NYERS NREPS
2 GE 95 l
@N OPTIONS WATER NITRO
2 OP Y N
@N METHODS WTHER INCON
2 ME M M
@N MANAGEMENT PLANT IRRIG
2 MA A A
@N OUTPUTS FNAME OVVEW
DIOUT LONG CHOUT OPOUT
2 OU N Y
N Y N N
@ AUTOMATIC MANAGEMENT
@N PLANTING PFRST PLAST
2 PL 1275 1360
@N IRRIGATION IMDEP ITHRL
2 IR 30 50
@N NITROGEN NMDEP NMTHR

IOFF
GSOOO

HPC

100.0

100.0

START

SYMBI

LIGHT

FERTI

SUMRY

PHZOL

ITHRU
100
NAMNT
25
RIDEP
20
HPCNP
100

START

SYMBI

LIGHT

FERTI

SUMRY

PH2OL

ITHRU
100
NAMNT

IAME
IR001

SDATE

1001
PHOSP
EVAPO
RESID
FROPT

30

PHZOU

99
IROEE
IBOOl
NCODE
IBOOl

HPCNR

SDATE

1001
PHOSP
EVAPO
RESID
FROPT

30

PHZOU

99
IROFF
IBOOl
NCODE

75

10

IAMT
10

RSEED

1157
POTAS
INFIL
HARVS

GROUT

PHZOD
IMETH
IR004

NAOFF
IBOOl

RSEED

1157
POTAS
INEIL
HARVS

GROUT

PH2OD

IMETH
IR004
NAOFF

-99

-99 _

SNAME ....................
COLDWATER SB IRR

DISES
N
PHOTO
C

CAOUT

N

PSTMX
40
IRAMT
25

CHEM
N
HYDRO
R
WAOUT

Y

PSTMN

IREEF
0.75

TILL
N

NIOUT MIOUT

N

N

SNAME ....................

COLDWATER WH

DISES
N
PHOTO
C

CAOUT

PSTMX
40
IRAMT
25

CHEM
N
HYDRO
R
WAOUT

Y

PSTMN

IREFE
0.75

IRR

TILL
N

NIOUT MIOUT

N

N

NI
RESIDUES
RE
HARVEST
HA

30 50 25 18001 18001
RIPCN RTIME RIDEP

100 1 20
HFRST HLAST HPCNP HPCNR
0 02195 100 0

76

*GROWI'H ASPECTS OUTPUT FILE

*RUN 1
MODEL
EXPERIMENT
TREATMENT 1
CROP :

MATURITY GROUP 1
STARTING DATE
PLANTING DATE
38.cm
WEATHER
SOIL
SOIL INITIAL C
.Okg/ha
WATER BALANCE
IRRIGATION
50.%]

NITROGEN BAL.
N-EERTILIZER
RESIDUE/MANURE
ENVIRONM. OPT.

.00

.00
SIMULATION OPT
ET :R
MANAGEMENT OPT
WTHzM

@DATE
GWGD HIAD PWAD
PWTD SLAD CHTD
RL7D RL8D RL9D
1152 0 0.
0 0.000 0

192.9 0.00 0
.00 0.00 0.00
1181 29 3.
0 0.000 0

310.9 0.21 0
00 0.00 0.00
1211 59 11.
0.0 0.000 419

419 258.3 0.68

0.14 0.00 0.00

0.
0
0

0.
0
0.

CDAY L#SD GSTD LAID

APPENDIX B

DSSAT OUTPUT FILE SAMPLES

soybean
CRGRO980 - SOYBEAN

 

 

ALM12004 SB ALLEGAN 1900s WH-SB PDAT=01JUN IRR
soybean r7
SOYBEAN CULTIVAR M GROUP 1 -
JAN 1 1901
JUN 1 1901 PLANTS/m2 50.0 ROW SPACING

'I
ALMI 1901 g ;
M800890001 TEXTURE lo — BLOUNT i;
DEPTH:152cm EXTR. H20:l88.1mm NO3: .Okg/ha NH4:

AUTOMATIC IRRIGATION - REFILL PROFILE
AUTOMATIC - PLANTING -> MATURITY [ SOIL DEPTH:30.00m

NOT SIMULATED NO N-STRESS

I
I

DAYL= .00 SRAD= .00 TMAX= .00 TMIN=
RAIN: .00 C02 = R330.00 DEW = .00 WIND=
WATER :Y NITROGEN:N N-FIX:N PESTS :N PHOTO :C
PLANTING:R IRRIG :A FERT :N RESIDUE:N HARVESTzM

LWAD SWAD GWAD RWAD CWAD G#AD
P#AD WSPD WSGD NSTD EWSD LN%D SH%D HIPD PWDD
CWID NWAD RDPD RLlD RL2D RL3D RL4D RL5D RL6D

 

RL10 CDAD LDAD SDAD
0 0 0.00 0 0 0 0 0 0
0 0.000 0.000 0.000 0.000 0.00 0.00 0.00 0
.00 0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00 0 0 0
8 0 0.51 166 74 0 191 239 0
0 0.000 0.000 0.000 0.000 5.31 0.00 0.00 0
.20 0.0 0.62 0.30 0.38 0.35 0.17 0.08 0.00
0.00 5 3 2
6 5 4.13 1599 1731 0 1106 3749 0
680 0.000 0.000 0.000 0.000 5.17 0.00 0.11 0
0.38 0.0 1.23 1.11 1.45 1.38 0.96 0.73 0.41
0.00 158 100 58

77

 

1241 89 14.8 5 2.83 1125 1564 2912 799 7073 2232
130.5 0.412 4384 1015 0.000 0.000 0.000 0.000 3.17 66.43 0.62 0
4384 251.3 0.88 0.38 0.0 1.43 0.81 1.05 1.01 0.70 0.53 0.31
0.13 0.02 0.00 0.00 1154 370 215

1259 107 14.8 8 0.17 67 925 3916 654 6288 2232
175.4 0.623 5296 1015 0.000 0.000 0.000 0.000 2.19 73.94 0.84 0
5296 258.4 0.88 0.38 0.0 1.50 0.65 0.87 0.83 0.58 0.44 0.26
0.11 0.01 0.00 0.00 2707 1354 785
*RUN 2 wheat

MODEL GECER980 - WHEAT

EXPERIMENT ALM12004 WH ALLEGAN 19008 WH-SB PDAT=01JUN IRR
TREATMENT 1 wheat

CROP WHEAT CULTIVAR WINTER-US -

STARTING DATE JAN 1 1901

PLANTING DATE OCT 27 1901 PLANTS/m2 :300.0 ROW SPACING

10.cm

WEATHER ALMI 1901

SOIL M800890001 TEXTURE lo — BLOUNT

SOIL INITIAL C DEPTH:152cm EXTR. H20:188.1mm N03: .Okg/ha NH4:
.Okg/ha

WATER BALANCE RAINFED

IRRIGATION NOT IRRIGATED

NITROGEN BAL. NOT SIMULATED ; NO N-STRESS

N-FERTILIZER

RESIDUE/MANURE

ENVIRONM. OPT. DAYL= .00 SRAD= .00 TMAX= .00 TMIN=
.00

RAIN: .00 C02 = R330.00 DEW = .00 WIND=

.00

SIMULATION OPT WATER :Y NITROGEN:N N-FIX:N PESTS :N PHOTO :C
ET :R

MANAGEMENT OPT PLANTING:A IRRIG :N FERT :N RESIDUE:N HARVEST:R
WTH:M

IYR Days Leaf Grow Dry Weight Grain
Kern. Pod Phot. Grow Leaf Shell Spec Canopy
Root 3 Root Length Density 3

! and after Num Stage LAI Leaf Stem Grain Root Crop per
wght HI Wgt. No. Water Nit. Nit -ing Leaf tht Brdth
Depth 3 cm3/cm3 of soil 3

I DOY plant 3< ————————— kg/Ha --------- >3 m2 mg
Kg/Ha 3<Stress (0-1)>3 % % Area m m m
3< ______________________________________________________ >3
@DATE CDAY L#SD GSTD LAID LWAD SWAD GWAD RWAD CWAD G#AD

GWGD HIAD EWAD E#AD WSPD WSGD NSTD LN%D SH%D SLAD CHTD CWID
EWSD RDPD RLlD RL2D RL3D RL4D RL5D RL6D RL7D RL8D RL9D RL10
1283 0 .0 .00 0 0 0 0 0 0
.000 0 0 .000 .000 .00 .00 .0 .00 .00
.04 .00 .00 .00 .00 .00 .00 .00 .00 .00
7 0 0 0 0 0
.000 .000 .00 .0 .00
.00 .00 .00 .00 .00

0
.000
.00
.00
.000
.00

.0
.000
1290
.000
.09

0
.00
.00

0
.00
.00

.0
.000

0
.00

0
.00

78

1320 37 3.0 1 .21 137 0 0 69 137 0
.0 .000 0 0 .000 .000 .000 .00 .00 152.7 .00 .00
.000 .28 .39 .47 .23 .00 .00 .00 .00 .00 .00 .00
1350 67 4.0 1 .23 159 0 0 109 159 0
.0 .000 0 0 .000 .000 .000 .00 .00 142.5 .00 .00
.000 .32 .62 .70 .46 .01 .00 .00 .00 .00 .00 .00
2015 97 4.0 1 .02 154 0 0 99 167 0
.0 .000 0 0 .000 .000 .000 .00 .00 10.2 .00 .00
.000 .33 .56 .63 .43 .01 .00 .00 .00 .00 .00 .00
2045 127 4.0 1 .02 154 0 0 99 167 0
.0 .000 0 0 .000 .000 .000 .00 .00 10.2 .00 .00
.000 .33 .51 .57 .39 .01 .00 .00 .00 .00 .00 .00
2075 157 5.0 1 .12 226 0 0 112 226 0
.0 .000 0 0 .000 .000 .000 .00 .00 53.9 .00 .00 l
.000 .43 .51 .60 .45 .06 .00 .00 .00 .00 .00 .00
2105 187 7.0 1 1.36 1106 0 0 742 1106 0
.0 .000 0 0 .000 .000 .000 .00 .00 123.3 .00 .00
.000 .61 1.62 2.95 2.72 1.75 1.00 .00 .00 .00 .00 .00
2135 217 12.0 2 5.13 5685 1117 0 1933 6802 0
.0 .000 0 0 .000 .000 .000 .00 .00 90.2 .00 .00
.000 .91 3.54 4.00 4.00 4.00 3.16 .73 .00 .00 .00 .00 ;
2151 233 12.0 4 4.68 5928 5057 0 2272 10985 0 l
.0 .000 0 0 .000 .000 .000 .00 .00 78.9 .00 .00 I

 

.000 1.10 4.00 4.00 4.00 4.00 3.64 1.19 .19 .00 .00 .00

79

*SIMULATION OVERVIEW FILE

*RUN 1
MODEL
EXPERIMENT
TREATMENT

CROP

1

MATURITY GROUP 1

STARTING DATE
PLANTING DATE

38.cm

WEATHER
SOIL

SOIL INITIAL C
.Okg/ha
WATER BALANCE

IRRIGATION

50.%]

NITROGEN BAL.
N-FERTILIZER
RESIDUE/MANURE
ENVIRONM. OPT.

.00

.00

SIMULATION OPT

ET :R

MANAGEMENT

WTH:M

OPT

soybean
CRGRO980 - SOYBEAN

ALM12004 SB ALLEGAN 19008 WH—SB PDAT=01JUN IRR
soybean

SOYBEAN CULTIVAR : M GROUP 1 -

JAN 1 1901

JUN 1 1901 PLANTS/m2 50.0 ROW SPACING
ALMI 1901

MSOO890001 TEXTURE lo - BLOUNT

DEPTH:152cm EXTR. H20:188.1mm N03: .Okg/ha NH4:

AUTOMATIC IRRIGATION
AUTOMATIC - PLANTING

NOT SIMULATED ; NO N

DAYL= .00 SRAD=

RAIN= .00 C02

WATER :Y NITROGEN

PLANTING:R IRRIG

- REFILL PROFILE
-> MATURITY [ SOIL DEPTH:30.00m

-STRESS

.00 TMAX= .00 TMIN=
R330.00 DEW = .00 WIND=
:N N-FIX:N PESTS :N PHOTO :C

:A FERT :N RESIDUE:N HARVEST:M

*SUMMARY OF SOIL AND GENETIC INPUT PARAMETERS

SOIL LOWER UPPER SAT EXTR INIT

NH4 ORG

DEPTH LIMIT LIMIT SW SW SW
C

cm cm3/cm3 cm3/cm3 cm3/cm3

ugN/g

0- 5 111 .233 .366 .122 .233
.50 1.23

5— 15 111 .233 .366 .122 .233
.50 1.23

15- 30 186 .305 .380 .119 .305
.50 .77

30- 45 244 .363 .411 .119 .363
.50 .51

45- 60 .216 .336 .399 .121 .336
.50 .44

60- 90 .191 .317 .386 .126 .317
.50 .35

90-120 .191 .317 .386 .126 .317
.50 .35

80

ROOT BULK pH N03
DIST DENS
g/cm3 ugN/g
.50 1.41 6.30 4.50
.50 1.41 6.30 4.50
.48 1.38 6.50 2.89
.35 1.38 7.24 1.94
.28 1.36 7.73 1.67
.20 1.33 8.10 1.40
.20 1.33 8.10 1.40

 

 

120-136 .191 .317 .386 .126 .317 .20 1.33 8.10 1.40
.50 .35
136-152 .191 .317 .386 .126 .317 .20 1.33 8.10 1.40
.50 .35
TOT-152 28.9 47.7 58.8 18.8 47.7 <--cm - kg/ha--> .0
.0 0
SOIL ALBEDO .13 EVAPORATION LIMIT 9.40 MIN.
FACTOR 1.00
RUNOEF CURVE # 384.00 DRAINAGE RATE .20 FERT.
FACTOR 1.00
SOYBEAN CULTIVAR :990001*M GROUP 1 ECOTYPE :880101- Ea
MATURITY GROUP 1 g
CSDVAR :13.84 PPSEN .20 EMG-FLW:17.00 PLW-FSD:13.00 FSD-PHM ;
:32.00 S
WTPSD .190 SDPDVR : 2.20 SDFDUR 223.00 PODDUR £10.00 XFRUIT ;
1.00
*SIMULATED CROP AND SOIL STATUS AT MAIN DEVELOPMENT STAGES i?

RUN NO.

LAI

12

12

20

21

11

20

30

13

19

19

1 soybean

CROP GROWTH BIOMASS
STRESS
AGE STAGE kg/ha
H20 N

0 Start Sim 0
.00.000.00

0 Sowing 0
.00.000.00

11 Emergence 26
.20.000.09

11 End Juven. 26
.20.000.09

19 Unifoliate 45
.80.000.46

20 Flower Ind 54
.70.000.00

40 First Flwr 1025
.20.000.00

49 First Pod 2203
.90.000.00

59 First Seed 3749
.70.000.00

73 End Pod 5393
.50.000.00

79 End Msnode 6058
.60.000.00

79 End Leaf 6058
.60.000.00

95 Phys. Mat 7492
.90.000.00

.00

.00

.04

.04

.08

.11

.83

.30

.13

.70

.34

.34

.64

LEAF ET RAIN IRRIG SWATER CROP
NUM. mm mm mm mm kg/ha
0.0 0 0 0 188 0
0.0 123 199 0 176 0
0.1 128 200 0 172 1
0.1 128 200 0 172 1
1.1 142 216 0 174 2
1.3 143 216 0 173 3
6.5 234 267 47 163 43
8.9 287 281 73 142 87

11.6 346 327 121 151 137

14.1 412 327 170 122 190

14.8 447 327 197 107 217

14.8 447 327 197 107 217

14.8 515 363 245 108 290

81

16 SEP 107 Harv. Mat 6288 0.17 14.8 558 390 295 124 278
4.40.000.00

16 SEP 107 Harvest 6288 0.17 14.8 558 390 295 124 278
4.40.000.00

*MAIN GROWTH AND DEVELOPMENT VARIABLES

@ VARIABLE PREDICTED MEASURED
Anthesis Date (dap) 40 -99
First Pod (dap) 49 —99
First Seed (dap) 59 -99
Physiological Maturity (dap) 95 -99
Pod Yield (kg/ha;dry) 5296 -99
Seed Yield (kg/ha;dry) 3916 -99
Shelling Percentage (%) 73.94 -99
Weight Per Seed (g;dry) 0.175 -99
Seed Number (Seed/m2) 2232 -99
Seeds/Pod 2.20 -99
Maximum LAI (m2/m2) 4.14 -99
Biomass (kg/ha) at Anthesis 1025 -99
Biomass (kg/ha) at Harvest Mat. 6288 -99
Stalk (kg/ha) at Harvest Mat. 925 —99
Harvest Index (kg/kg) 0.623 -99
Final Leaf Number (Main Stem) 14.83 —99
Canopy Height (m) 0.88 -99
Seed N (kg N/ha) 253 -99
Biomass N (kg N/ha) 278 -99
Stalk N (kg N/ha) 6 -99
Seed N (%) 6.47 -99
Seed Lipid (%) 19.25 -99

*ENVIRONMENTAL AND STRESS FACTORS

------------------------------------ ENVIRONMENT------—----------STRESS-
l--DEVELOPMENT PHASE—-|-TIME-| ------- WEATHER ———————— I I—-—WATER--| I-
NITROGEN-I
DURA TEMP TEMP SOLAR PHOTOP PHOTO GROWTH
PHOTO GROWTH
TION MAX MIN RAD [day] SYNTH
SYNTH

Emergence —First Flower 29 30.36 17.46 23.17 15.00 0.000 0.000
0.000 0.128

First Flower-First Seed 19 32.04 18.92 24.64 14.60 0.000 0.000
0.000 0.000

First Seed Phys. Mat. 36 27.48 14.15 21.35 13.58 0.000 0.000
0.120 .000

Emergence Phys. Mat. 84 29.51 16.37 22.72 14.30 0.000 0.000
0.052 .044

OIOI

82

(0.0 = Minimum

Stress
1.0 = Maximum
Stress)
SOYBEAN YIELD : 3916 kg/ha [DRY WEIGHT]
*RUN 2 : wheat
MODEL : GECER980 - WHEAT
EXPERIMENT : ALM12004 WH ALLEGAN 19008 WH-SB PDAT=01JUN IRR
TREATMENT 1 : wheat
CROP : WHEAT CULTIVAR : WINTER-US -
STARTING DATE : JAN 1 1901
PLANTING DATE : OCT 27 1901 PLANTS/m2 :300.0 ROW SPACING
10.cm
WEATHER : ALMI 1901
SOIL : MS00890001 TEXTURE : lo - BLOUNT
SOIL INITIAL C : DEPTH:152cm EXTR. H20:188.1mm NO3: .Okg/ha NH4:
.Okg/ha
WATER BALANCE : RAINFED
IRRIGATION : NOT IRRIGATED
NITROGEN BAL. : NOT SIMULATED ; NO N—STRESS
N-FERTILIZER
RESIDUE/MANURE .
ENVIRONM. OPT. : DAYL= .00 SRAD= .00 TMAX= .00 TMIN=
.00
RAIN= .00 C02 = R330.00 DEW = .00 WIND=
.00
SIMULATION OPT : WATER :Y NITROGEN:N N-FIX:N PESTS :N PHOTO :C
ET :R
MANAGEMENT OPT : PLANTING:A IRRIG :N FERT :N RESIDUE:N HARVEST:R
WTH:M
*SUMMARY OF SOIL AND GENETIC INPUT PARAMETERS
SOIL LOWER UPPER SAT EXTR INIT ROOT BULK pH NO3
NH4 ORG
DEPTH LIMIT LIMIT SW SW SW DIST DENS
C
cm cm3/cm3 cm3/cm3 cm3/cm3 g/cm3 ugN/g
ugN/g %
0- 5 .111 .233 .366 .122 .338 .50 1.41 6.30 4.50
1.00 1 23
5- 15 .111 .233 .366 .122 .339 .50 1.41 6.30 4.70
1.00 1 23
15— 30 .186 .305 .380 .119 .340 .48 1.38 6.50 3.27
77 .77
30- 45 .244 .363 .411 .119 .337 .35 1.38 7.24 1.32
50 .51
45- 60 .216 .336 .399 .121 .315 .28 1.36 7.73 .80
50 44

83

60- 90 .191 .317 .386 .126 .308 .20 1 33 8.10 .94
.50 .35
90-120 .191 .317 .386 .126 .352 .20 1 33 8.10 1.16
.50 .35
120-136 .191 .317 .386 .126 .408 .20 1 33 8.10 1.40
.50 .35
136-152 .191 .317 .386 .126 .408 .20 1.33 8.10 1.40
.50 .35
TOT-152 28.9 47.7 58.8 18.8 52.8 <--cm - kg/ha--> .0
.0 0
SOIL ALBEDO EVAPORATION LIMIT 9.40 MIN.
FACTOR 1.00
RUNOFF CURVE # :84.00 DRAINAGE RATE .20 FERT.
FACTOR : 1.00
WHEAT CULTIVAR :990003-WINTER-US ECOTYPE : -
PlV :6.000000 P1D :2.500000 P5 -5.00
G1 5.000 G2 1.200 G3 1.400 PHINT 80.000

*SIMULATED CROP AND SOIL STATUS AT MAIN DEVELOPMENT STAGES

RUN NO. 2 wheat
DATE CROP GROWTH BIOMASS LAI LEAF ET RAIN IRRIG SWATER CROP
N STRESS
AGE STAGE kg/ha NUM. mm mm mm mm kg/ha

% H20 N

17 SEP 0 Start Sim 0 .00 .0 3 0 0 122 0
.0 .00 .00

10 OCT 0 Sowing O .00 .0 18 22 0 127 0
.0 .00 .00

10 OCT 0 Emergence 0 .00 .0 18 22 0 127 0
.0 .00 .00

11 OCT 1 Germinate O .00 .0 21 22 0 125 0
.0 .00 .00

24 OCT 14 Emergence 37 .01 2.0 40 80 0 151 0
.0 .00 .00

28 APR 200 Term Spklt 3291 3.78 9.0 247 337 0 147 0
.0 .00 .00

19 MAY 221 End Veg 7781 4.93 12.0 329 447 0 157 O
.0 .00 .00

31 MAY 233 End Ear Gr 10985 4.68 12.0 390 468 0 114 O
.0 .00 .00

31 MAY 233 Harvest 10985 4.68 12.0 390 468 0 114 0
.0 .00 .00

*MAIN GROWTH AND DEVELOPMENT VARIABLES

@ VARIABLE PREDICTED MEASURED
FLOWERING DATE (dap) ~99 -99
PHYSIOL. MATURITY (dap) —99 -99

84

GRAIN YIELD (kg/ha;dry) 0 ~99

WT. PER GRAIN (g;dry) .0000 ~99
GRAIN NUMBER (GRAIN/m2) 0 ~99
GRAINS/EAR .0 ~99
MAXIMUM LAI (m2/m2) 4.96 ~99
BIOMASS (kg/ha) AT ANTHESIS 10985 ~99
BIOMASS N (kg N/ha) AT ANTHESIS 0 ~99
BIOMASS (kg/ha) AT HARVEST MAT. 10985 ~99
STALK (kg/ha) AT HARVEST MAT. 0 ~99
HARVEST INDEX (kg/kg) .000 ~99
FINAL LEAF NUMBER 12.00 ~99
GRAIN N (kg N/ha) 0 ~99
BIOMASS N (kg N/ha) 0 ~99
STALK N (kg N/ha) 0 ~99
SEED N (%) .00 ~99

*ENVIRONMENTAL AND STRESS FACTORS

------------------------------------ ENVIRONMENT~~~~~~~~~~~~~~~~~STRESS~
|~~DEVELOPMENT PHASE—~l-TIME-I ——————— WEATHER -------- I I~~~WATER——| I-
NITROGEN—I
DURA TEMP TEMP SOLAR PHOTOP PHOTO GROWTH
PHOTO GROWTH
TION MAX MIN RAD [day] SYNTH
SYNTH

Emergence - Term Spiklt 186 5.46 ~4.93 10.17 10.45 .000 .000

.000 .000
End Veg-Beg Ear Growth 21 20.61 6.85 20.97 14.10 .000 .000
.000 .000
Begin Ear-End Ear erth 12 24.88 11.26 24.49 14.65 .000 .000
.000 .000
End Ear Grth-Beg Grn F1 1 26.70 12.20 24.00 14.82 .000 .000
.000 .000
Linear Grain Fill Phase 0 .00 .00 .00 .00 .000 .000
.000 .000
(0.0 = Minimum
Stress
1.0 = Maximum
Stress)
WHEAT YIELD : 0 kg/ha [DRY WEIGHT ]

85

*SUMMARY : ALM|200480 ALLEGAN 19008 WH-SB PDAT=01JUN

IIDENTIFIERS ..............................
DATES .........................

DRY

WEIGHTS ....................................
WATER ....................................

NITROGEN .......................................

HWAM
DRCM
ONAM

3916
79
0

37

3412

180

3261

113

3603

66

3842

0

101

IRR
PHOSPHORUS ............
@RP TN ROC CR TNAM

HDAT DWAP CWAM
PRCM ETCM ROCM
CNAM GNAM RECM

1 1 110 SB soybean
1259 119 6288
390 558 37
278 253 0

2 1 210 WH wheat
2151 85 10985
468 390 50
0 0 0

3 1 110 SB soybean
2268 119 5951
339 503 33
254 225 0

4 1 210 WH wheat
3151 85 10917
565 394 75
0 0 0

5 1 110 SB soybean
3272 119 5652
403 441 83
233 209 0

6 1 210 WH wheat
4151 85 6866
396 308 30
0 0 0

7 1 110 SB soybean
4264 119 6029
223 466 33
259 233 0

8 1 210 WH wheat
5151 85 10858
567 409 96
0 0 0

9 1 110 SB soybean
5263 119 6146
324 458 40
272 248 0

10 1 210 WH wheat
6151 85 8046
461 357 35
0 0 O

FNAM
HWAH BWAH
SWXM NI#M
OCAM PO#M

ALMI
3916 0
124 0

0 0

ALMI
3842 0
123 0
0 0

ALMI

91 0

86

ORGANIC MATTER...

SDAT PDAT ADAT MDAT
HWUM H#AM H#UM IR#M IRCM
NICM NFXM NUCM NLCM NIAM
POCM CPAM SPAM

1001 1152 1192 1247
175 2232 2.20 12 295
0 0 0 0 0
0 0 0
1260 1283 ~99 ~99
0 0 0 0 0
0 0 0 0 0
0 0 0
2152 2152 2199 2256
145 2347 2.20 12 292
0 0 0 0 0
0 0 0
2269 2275 3151 ~99
0 0 0 0 0
0 0 0 0 0
O 0 0
3152 3152 3202 3260
159 2046 2.20 11 272
0 0 0 0 0
0 0 0
3273 3275 ~99 ~99
0 0 0 0 0
0 0 0 0 0
0 0 0
4152 4152 4196 4252
163 2215 2.20 17 429
0 O 0 0 0
0 0 0
4265 4275 ~99 ~99
0 0 0 0 0
0 0 0 0 0
0 0 0
5152 5152 5196 5251
161 2382 2.20 9 231
0 0 0 0 0
0 0 0
5264 5284 ~99 ~99
0 0 0 0 0
0 0 0 0 0
0 0 0

*WATER BALANCE OUTPUT FILE

*RUN 1 : soybean

MODEL : CRGRO980 ~ SOYBEAN

EXPERIMENT : ALM12004 8B ALLEGAN 19008 WH—SB PDAT=01JUN IRR
TREATMENT 1 : soybean

CROP : SOYBEAN CULTIVAR : M GROUP 1 ~

MATURITY GROUP 1

STARTING DATE : JAN 1 1901

PLANTING DATE : JUN 1 1901 PLANTS/m2 : 50.0 ROW SPACING
38.cm

WEATHER : ALMI 1901

SOIL : M800890001 TEXTURE : lo - BLOUNT

SOIL INITIAL C : DEPTH:152cm EXTR. H20:188.1mm N03: .Okg/ha NH4:
.Okg/ha

WATER BALANCE : AUTOMATIC IRRIGATION - REFILL PROFILE

IRRIGATION : AUTOMATIC ~ PLANTING -> MATURITY [ SOIL DEPTH:30.00m
50.%]

NITROGEN BAL. : NOT SIMULATED ; NO N-STRESS

N-FERTILIZER

RESIDUE/MANURE
ENVIRONM. OPT. : DAYL= .00 SRAD= .00 TMAX= .00 TMIN=
.00
RAIN= .00 CO2 = R330.00 DEW = .00 WIND=
.00

SIMULATION OPT : WATER :Y NITROGEN:N N-FIX:N PESTS :N PHOTO :C
ET :R

MANAGEMENT OPT : PLANTING:R IRRIG :A FERT :N RESIDUE:N HARVEST:M
WTH:M

@DATE CDAY EPAA ETAA EOAA SWXD ROFC DRNC PREC IRRC SRAA
TMXA TMNA DAYD ESAA EPAC ESAC ETAC IR#C DTWT SW1D SW2D
SW3D SW4D SW5D SW6D SW7D SW8D SW9D SW10 TSlD TS2D TS3D
TS4D TS5D TSOD TS7D TS8D TS9D T810
1001 0 0.00 0.04 0.04 187.9 0.0 0 0 0 2.0 -
3.9 ~10.6 9.01 0.04 0.0 0.0 0.0 0 152 0.232 0.233

87

1121

1151

1152

1181

1211

1241

0.305 0.

2.8 5.0

0 0.00
0.4 ~8.0 9
0.308 0.
2.6 4.7

0 0.00
3.6 ~13.4 11
0.304 0.
2.1 4.0

0 0.00
4.4 -4.4 12
0.312 0.
4.1 5.1

0 0.00
14.3 1.6 1

0.295 0.361 0.338 0.320 0.320 0.320 0.320 0.

14.7

19.4

0.285 0.356 0.

9.8

16.1

0.285 0.356 0.

9.9

27.8

0.255 0.339 0.

18.7

31.6

0.279 0.315 0.267 0.256 0.308 0.319 0.320 0.

18.3

27.5

0.263 0.309 0.256 0.236 0.263 0.306 0.315 0.

18.7

0

0

29

59

89

12.8
0.00
7.4 1

9.0
0.00
1.1 1

9.1
0.42
14.5 1

15.7
3.85
18.6 1

15.6

3.88

14.7 1

16.2

7.1
0.36
.76 0

6.8
0.21
.03 0

6.1
1.38
.47 1

6.5
1.11
3.83

11.2
1.01
82
334
8.5
0.51
84
334
8.5
1.73
02
328
12.8
5.76
4.33

4.

4.

5.

12.8
4.96
3.08

13.4

8.5 8.9 9.0
0.36 187.9 0.3
.36 0.0 10.9 1

8.3 8.9 9.1
0.21 192.7 0.3
.21 0.0 17.1 1

7.9 8.6 8.9
1.39 189.3 9.2
.38 0.0 58.6 5

7.9 8.5 8.8
3.49 179.0 9.3
1.11 0.0 91.8

10.3 10.0 9.9 0.0

5.21 176.8 9.3 79
1.01 0.0 122.2 122.2
0.320 0.320 0.320 0.320 0.
8.5 8.7 8.9 0.0

4.91 176.3 9.3 79
0.51 0.0 122.7 122.7
0.320 0.320 0.320 0.320 0.
8.5 8.7 8.9 0.0

5.70 169.1 9.9 79
1.31 12.3 160.6 172.9
0.318 0.320 0.320 0.320 0.
10.8 10.1 9.8 0.0

5.97 151.4 29.1 79

0.0

0.9

0.0

7.1

0.0

8.6

0.0

91.8

1.91 127.7 218.0 345.7

10.7

4.96 110.4

10.0

9.7
33.2

0.0

1.08 244.2 250.3 494.5

11.1

10.2

9.8

88

0.0

363 0.336 0.317 0.317 0.317 0.317 0.

11

366 0.336 0.317 0.317 0.317 0.317 0.

11

365 0.336 0.317 0.317 0.317 0.317 0.

77

368 0.340 0.320 0.320 0.320 0.320 0.

79

79

000 ~5.9 ~3.6 ~0.3
22 0 6.1
0 152 0.222 0.230
000 ~4.3 ~2.6 0.0
34 0 7.6 -
0 152 0.310 0.242
000 ~3.1 ~1.9 0.0
147 0 14.3
0 152 0.153 0.237
000 2.4 2.6 3.1
171 0 18.8
0 152 0.096 0.201
000 24.0 21.2 17.7
199 0 25.1
0 152 0.133 0.190
000 14.3 13.0 11.2
199 0 26.5
0 152 0.127 0.189
000 14.5 13.2 11.4
243 0 23.6
0 152 0.126 0.200
000 30.4 27.3 22.8
327 121 24.5
5 152 0.307 0.245
000 27.8 25.4 21.8
363 222 22.5
9 152 0.122 0.203
000 26.5 24.6 21.7

1259 107 1.75 3.55 3.59 124.5 37.3 79 390 295 16.6
24.8 11.8 12.23 1.80 275.7 282.8 558.5 12 152 0.127 0.274
0.304 0.339 0.257 0.234 0.258 0.300 0.310 0.000 20.3 19.4 17.9
16.1 14.5 12.5 10.7 9.9 9.6 0.0

*RUN 2 : wheat

MODEL : GECER980 - WHEAT

EXPERIMENT : ALM12004 WH ALLEGAN 19008 WH-SB PDAT=01JUN IRR

TREATMENT 1 : wheat

CROP : WHEAT CULTIVAR : WINTER-US ~

STARTING DATE : JAN 1 1901

PLANTING DATE : OCT 27 1901 PLANTS/m2 :300.0 ROW SPACING
10.cm

WEATHER : ALMI 1901

SOIL : MSOO890001 TEXTURE : lo ~ BLOUNT

SOIL INITIAL C : DEPTH:152cm EXTR. H20:188.1mm N03: .Okg/ha NH4:
.Okg/ha

WATER BALANCE : RAINFED

IRRIGATION : NOT IRRIGATED

NITROGEN BAL. : NOT SIMULATED ; NO N—STRESS

N—FERTILIZER

RESIDUE/MANURE

ENVIRONM. OPT. : DAYL= .00 SRAD= .00 TMAX= .00 TMIN=
.00

RAIN= .00 C02 = R330.00 DEW = .00 WIND=

.00

SIMULATION OPT : WATER :Y NITROGEN:N N-FIX:N PESTS :N PHOTO :C
ET :R

MANAGEMENT OPT : PLANTING:A IRRIG :N FERT :N RESIDUE:N HARVEST:R
WTH:M

IYR Days Daily Evapotran. PESW Cumulative Ave
Temp. Temp Day Day Cumu. Evap. No.
Soil water in Layer Soil

Temperature in Layer

89

I and after Plant Total Pot. RunOff Drain Prcip Irr 801 Max

Min Len Soil Plant Soil Total of l 2 3
4 5 6 7 8 9 10 1 2 3 4
5 6 7 8 9 10

! DOY Plant 3<--~- mm ~~~~>3 mm 3< ~~~~~~~~ mm --------- >3MJ/m2 C
C hr Evap 3< ~~~~~~ mm ~~~~~ >3 irr 3< ~~~~~~~~~~~~~~~~
~~~~~~~ cm3/cm3 --~~~—~~~~--~~-~~~----~>3 3<~~~~~~~~~~~~~~~~~~~~~
_____ C ___________________-______>3

@DATE CDAY EPAA ETAA EOAA SWXD ROFC DRNC PREC IRRC SRAA
TMXA TMNA DAYD ESAA EPAC ESAC ETAC IR#C DTWT SW1D SW2D
SW3D SW4D SW5D SW6D SW7D SW8D SW9D SW10 TSlD TS2D TS3D
TS4D TSSD TS6D TS7D TS8D TS9D T810

1260 0 .00 2.65 3.27 121.8 .0 0 0 0 17.2
15.6 4.4 12.18 2.65 .0 2.7 2.7 0 152 .086 .259
.304 .343 .257 .234 .258 .300 .310 .000 19.4 18.6 17.3
15.7 14.2 12.3 10.6 9.9 9.6 .0

1283 0 .00 .68 3.05 127.4 .3 0 22 0 14.7
20.6 7.0 11.08 .68 .0 18.4 18.4 0 152 .208 .246
.298 .338 .273 .236 .261 .296 .305 .000 17.8 17.4 16.6
15.4 14.2 12.6 11.0 10.2 9.8 .0

1290 7 .00 1.47 1.47 162.7 13.1 0 80 0 7.8
13.9 5.4 10.76 1.47 .0 28.7 28.7 0 152 .234 .295
.338 .379 .335 .262 .262 .295 .303 .000 9.6 10.3 11.1
11.4 11.3 10.8 10.0 9.6 9.4 .0

1320 37 .08 .99 1.52 164.5 13.3 0 112 0 8.8
13.4 1.2 9.55 .91 2.4 56.0 58.3 0 152 .259 .248
.305 .363 .336 .300 .270 .292 .298 .000 4.0 5.4 7.2
8.6 9.4 9.9 9.8 9.5 9.4 .0

1350 67 .06 .62 .62 204.7 23.2 0 180 0 6.5
3.5 ~5.0 8.96 .56 4.1 72.7 76.9 0 152 .297 .301
.343 .374 .339 .317 .317 .317 .310 .000 ~1.9 .0 2.7
5.1 6.8 8.3 9.1 9.2 9.2 .0

2015 97 .00 .38 .38 182.6 23.2 18 188 0 6.2
.2 ~9.1 9.27 .38 4.3 84.0 88.3 0 152 .165 .207
.301 .365 .339 .319 .317 .317 .317 .000 ~1.8 -.2 2.2
4.5 6.2 7.9 8.9 9.2 9.3 .0

2045 127 .00 .22 .22 180.5 23.2 18 192 0 7.2 -
2.4 ~11.2 10.31 .22 4.3 90.5 94.8 0 152 .180 .204

90

 

 

    
  
  

I and after Plant Total Pot. RunOff Drain Prcip Irr 801 Max
Min Len Soil Plant Soil Total of l 2 3
4 5 6 7 8 9 10 1 2 3 4
5 6 7 8 9 10
I DOY Plant 3<———— mm ————>3 mm 3< -------- mm ————————— >3MJ/m2 C
C hr Evap 3< ------ mm ----- >3 irr 3< ————————————————
~~~~~~~ cm3/cm3 >3 3<
_____ C )3
@DATE CDAY EPAA ETAA EOAA SWXD ROFC DRNC PREC IRRC SRAA
TMXA TMNA DAYD ESAA EPAC ESAC ETAC IR#C DTWT SW1D SW2D
SW3D SW4D SW5D SW6D SW7D SW80 SW9D SW10 TSlD TSZD TS3D
TS4D TSSD TS6D TS7D TS8D TS9D T810
1260 0 .00 2.65 3.27 121.8 .0 O 0 O 17.2
15.6 4.4 12.18 2.65 .0 2.7 2.7 0 152 .086 .259
.304 .343 .257 .234 .258 .300 .310 .000 19.4 18.6 17.3
15.7 14.2 12.3 10.6 9.9 9.6 .0
1283 O .00 .68 3.05 127.4 .3 0 22 0 14.7
20.6 7.0 11.08 .68 .0 18.4 18.4 0 152 .208 .246
.298 .338 .273 .236 .261 .296 .305 .000 17.8 17.4 16.6
15.4 14.2 12.6 11.0 10.2 9.8 .0
1290 7 .00 1.47 1.47 162.7 13.1 0 80 0 7.8
13.9 5.4 10.76 1.47 .0 28.7 28.7 0 152 .234 .295
.338 .379 .335 .262 .262 .295 .303 .000 9.6 10.3 11.1
11.4 11.3 10.8 10.0 9.6 9.4 .0
1320 37 .08 .99 1.52 164.5 13.3 0 112 0 8.8
13.4 1.2 9.55 .91 2.4 56.0 58.3 0 152 .259 .248
.305 .363 .336 .300 .270 .292 .298 .000 4.0 5.4 7.2
8.6 9.4 9.9 9.8 9.5 9.4 .0
1350 67 .06 .62 .62 204.7 23.2 0 180 3 6.5
3.5 —5.0 8.96 .56 4.1 72.7 76.9 0 152 297 .301
.343 .374 .339 .317 .317 .317 .310 000 ~1.9 .0 2.7
5.1 6.8 8.3 9.1 9.2 9.2 .0
2015 97 .00 .38 .38 182.6 23.2 18 185 0 6.2
.38 4.3 84.0 88.3 C 152 165 .2“
.317 .317 2:3 ~1.8 -,2 : I
9.3 .C
23.2 :E 192 C ‘_: ‘
.5 94.5 C 152 .153 :-

 

 

.292 .359 .337 .319 .317 .317 .317 .000 ~3.7 -2.3 .0

2.4 4.4 6.5 8.2 8.8 9.0 .0

2075 157 .04 1.21 1.40 191.9 27.1 32 257 0 11.7
5.3 ~6.1 11.70 1.18 5.4 125.8 131.2 0 152 .214 .257
.311 .366 .339 .319 .317 .317 .317 .000 7.8 7.4 7.1
7.3 7.7 8.4 9.1 9.4 9.4 .0

2105 187 .74 2.23 2.47 167.2 28.0 37 306 0 16.3
10.2 -1.5 13.13 1.49 27.6 170.5 198.1 0 152 .078 .185
.274 .343 .327 .320 .317 .317 .317 .000 8.3 7.6 7.0
6.8 7.0 7.6 8.4 8.9 9.1 .0

2135 217 3.22 3.68 3.80 167.1 49.1 37 437 0 20.7
19.2 5.5 14.36 .45 124.4 184.2 308.5 0 152 .293 .306
.308 .313 .255 .280 .314 .317 .317 .000 16.1 14.5 12.4
10.7 9.7 9.0 9.0 9.1 9.2 .0

2151 233 4.46 5.12 5.12 114.4 50.4 37 468 0 24.7
25.2 11.2 14.82 .66 195.7 194.7 390.4 0 152 .135 .160
.226 .284 .248 .246 .307 .315 .317 .000 20.9 18.7 15.7
13.2 11.5 10.1 9.4 9.3 9.3 .0

91

*WATER BALANCE SUMMARY FILE

*RUN 1
MODEL
EXPERIMENT
TREATMENT 1

CROP

STARTING DATE
PLANTING DATE
38.cm
WEATHER
SOIL
SOIL INITIAL C
.Okg/ha
WATER BALANCE
IRRIGATION
50.%]
NITROGEN BAL.
N-FERTILIZER
RESIDUE/MANURE
ENVIRONM. OPT.
.00

.00
SIMULATION OPT
ET :R
MANAGEMENT OPT
WTH:M

soybean

CRGRO980 - SOYBEAN

ALMI2004 SB ALLEGAN 19008 WH—SB PDAT=01JUN IRR
soybean

SOYBEAN CULTIVAR : M GROUP 1 -

MATURITY GROUP 1

JAN 1 1901
JUN 1 1901 PLANTS/m2 : 50.0 ROW SPACING

ALMI 1901
M800890001 TEXTURE : lo - BLOUNT
DEPTH:152cm EXTR. H20:188.1mm NO3: .Okg/ha NH4:

AUTOMATIC IRRIGATION ~ REFILL PROFILE
AUTOMATIC - PLANTING -> MATURITY [ SOIL DEPTH:30.00m

NOT SIMULATED ; NO N-STRESS

DAYL= .00 SRAD= .00 TMAX= .00 TMIN=
RAIN= .00 CO2 = R330.00 DEW = .00 WIND=
WATER :Y NITROGEN:N N-FIX:N PESTS :N PHOTO
PLANTING:R IRRIG :A FERT :N RESIDUE:N HARVEST:

WATER BALANCE PARAMETERS

======================== ~-mm~~
Soil H20 (start) on day 1001 477.19
Soil H20 (final) on day 1259 413.73
Irrigation 295.22
Effective Irrigation 221.42
Irrigation Lost 73.81
Precipitation 389.70
Drainage 78.76
Runoff 37.35
8011 Evaporation 282.79
Transpiration 275.68
Evapotranspiration 558.48
Potential ET 882.50
Final Balance 0.0001

*RUN 2
MODEL

wheat
GECER980 ~ WHEAT

92

:C

M

EXPERIMENT : ALM12004 WH ALLEGAN 19008 WH-SB PDAT=01JUN IRR
TREATMENT 1 : wheat

CROP : WHEAT CULTIVAR : WINTER-US -

STARTING DATE : JAN 1 1901

PLANTING DATE : OCT 27 1901 PLANTS/m2 :300.0 ROW SPACING
10.cm

WEATHER : ALMI 1901

SOIL : M800890001 TEXTURE : 10 ~ BLOUNT

SOIL INITIAL C : DEPTH:152cm EXTR. H20:188.1mm NO3: .Okg/ha NH4:
.Okg/ha

WATER BALANCE : RAINFED

IRRIGATION : NOT IRRIGATED

NITROGEN BAL. : NOT SIMULATED ; NO N-STRESS

N-FERTILIZER

RESIDUE/MANURE .

ENVIRONM. OPT. : DAYL= .00 SRAD= .00 TMAX= .00 TMIN=
.00

RAIN= .00 C02 = R330.00 DEW = .00 WIND=

.00

SIMULATION OPT : WATER :Y NITROGEN:N N-FIX:N PESTS :N PHOTO :C
ET :R

MANAGEMENT OPT : PLANTING:A IRRIG :N FERT :N RESIDUE:N HARVEST:R
WTH:M

WATER BALANCE PARAMETERS

======================== ~~mm~~
Soil H20 (start) on day 1260 413.73
8011 H20 (final) on day 2151 403.63
Irrigation .00
Effective Irrigation .00
Irrigation Lost .00
Precipitation 467.50
Drainage 36.76
Runoff 50.40
Soil Evaporation 194.75
Transpiration 195.68
Evapotranspiration 390.43
Potential ET 478.06
Final Balance .0001

93

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94

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