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VERS SITY Ll BRAR

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3 1293 00914 99

 

 

 

 

 

 

 

 

This is to certify that the

dissertation entitled

Modelling tillage effects on soil physical
properties and maize (zea mays, L.)
development and growth.

presented by

Frederic A. Dadoun

has been accepted towards fulfillment
of the requirements for

Ph.D. degreein Crop & Soil Sciences

JW

Major professor

Date 3-23-93

 

MS U is an Affirmative Action/Equal Opportunity Institution 0-12771

 

 

 

 

 

 

 

l LIBRARY
Michigan State
University

 

 

 

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MSU Is An Affirmative Action/Equal Opportunity Imitation
cmma-DJ

MODELLING TILLAGE EFFECTS ON SOIL PHYSICAL PROPERTIES AND
MAIZE (ZEA MAYS, L.) DEVELOPMENT AND GROWTH

By

Frédéric Antoine Dadoun

A DISSERTATION

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

DOCTOR OF PHILOSOPHY

Department of Crop and Soil Sciences

1993

ABSTRACT

MODELLING TILLAGE EFFECTS ON son. PHYSICAL PROPERTIES AND
MAIZE (ZEA MAYS, L.) DEVELOPMENT AND GROWTH

By

Frédéric Dadoun

Conservation tillage controls soil erosion by keeping residue cover at the soil surface.
The presence of a residue cover in a humid temperate climate affects soil
environment by increasing soil surface water content, thus decreasing soil temperature
and plant development. The efl'ects of four residue covers on soil temperature, soil
water content, plant development and growth, and final biomass were studied at East
Lansing, Michigan, on a Conover loam (Fine-loamy, mixed, mesie Udollic
Ochraqualf). Soil temperature on the fully covered plots were on the average 2 °C
colder than the bare surface. In 1990, due to a cold spring, maize development was
delayed by two leaves on the 100% residue covered plots. In 1991, temperatures in
the spring were warmer, reducing the period when the meristem was below ground,
thus maize development delay was smaller. Soil surface water content was higher on
the covered plots than the bare plots. The effect of the residue cover on soil water
content was negligible below 50 cm on the fallow plots. Leaf growth was delayed but
not reduced. Leaves senesced faster in 1991 on the 100% covered plots. Plant water
content was higher at harvest on the covered plots than on the bare plots. In 1991,

differences in growth were smaller. Plant development prediction using soil

temperature while the meristem was below ground gave better results than using air
temperature for the whole growing season. The CERES models leaf development
prediction was improved by changing the soil temperature prediction model to
account for residue cover and using soil temperature at 2.5 cm to calculate thermal
time. A model of soil surface properties changes due to rainfall intensity and residue
cover was added to better predict water infiltration, soil surface bulk density, and
ponding capacity. The model was run for four sites in Michigan to evaluate the
consequences of residue management on rainfed maize final yield, and annual runofi.
The no-till strategy increased the length of the vegetative stage and plant water
availability; this increased yield and economic return for rainfed maize. The annual

runofl’ was higher on the no-till strategy.

 

ACKNOWLEDGEMENTS

I would like to thank Dr. J .T. Ritchie for having me come to Michigan State
University and work with his international team. Meeting so many people from
different countries and addressing different problem opened my mind to agricultural
problems faced by other countries. I thank Dr Ritchie for letting me direct my own
research and thus teaching me how to face and solve different types of problems. I
would like also to thank Dr. F. J. Pierce for his friendship and support throughout
this project. I would like to thank him for letting me use his lab, and equipment for
all my research. I thank Dr. K. Poff for the most thought provoking course I took on
campus and for his participation on my committee. Iwould also like to thank Dr. G.
Merva for being in my committee. I would like to thanks Dr M-C Fortin for making
some soil temperature data available and for her friendship and help at the beginning
of my stay at MSU. A special thank to Sharlene Rhines for her friendship, for editing
this dissertation and giving me good writing advice. I have to thank all the friends I
made at Michigan State University for their support and friendship even when I was

not always in a good mood.

 

TABLE OF CONTENTS

LIST OF FIGURES ............................................ v
LIST OF TABLES ........................................... viii
LIST OF SYMBOLS .......................................... xii
Chapter 1: Residue management and soil environment .................. 1
Introduction ............................................. 1
The erosion process ....................................... 2
Splash erosion ...................................... 2
Runoff erosion ..................................... 3
Wind erosion ....................................... 4
Prevention of soil erosion ................................... 4
Soil aggregate stability ................................ 5
Splash erosion ...................................... 6
Runoff erosion ..................................... 6
Wind erosion ....................................... 7
Reducing soil displacement ............................ 7
Residue cover as means to control soil erosion ................... 8
Tillage systems creating a soil cover ........................... 8
Residue management and crops ............................. 10
Simulation of crop production system ......................... 11
Objectives of this thesis ................................... 12
Bibliography ........................................... 14
Chapter 2: Crop residue influence on thermal time and maize (Zea Mays,
L)
development ........................................... 17
Abstract .............................................. 17
Introduction ............................................ 18
Material and methods .................................... 21
Results and discussion .................................... 26
Soil temperature ................................... 29
Plant development .................................. 34

Errors in prediction .................................
Conclusion .............................................
Bibliography ...........................................

Chapter 3: Surface crop residue influence on maize (Zea Mays, L.) growth
and soil water content. ....................................
Abstract ..............................................
Introduction ............................................
Material and methods ....................................
Results and discussion ....................................
Conclusion .............................................
Bibliography ...........................................

Chapter 4: CERES-Till, a model to predict the influence of crop residue
cover on soil surface properties and plant development ...........
Abstract ..............................................
Introduction ............................................
Model description .......................................

Surface residue dynamics .............................

Residue incorporation in the soil profile ............

Surface Residue decomposition ...................

Residue coverage .............................

Mulch thickness ..............................

Impact of a residue cover on the soil water balance .........

Rainfall interception ...........................

Potential soil evaporation .......................

Soil temperature ...................................

Soil surface albedo ............................

Soil surface temperature ........................

Plant development prediction ....................

Surface properties ..................................

Precipitation intensity ..........................
Change in surface properties ....................
Model input modifications ...........................
Model Validation .......................................
Residue decomposition model ........................
Soil water balance .................................
Soil temperature model and leaf development ............
Demonstration of the model of soil surface property dynamics .....
Conclusion ............................................
Bibliography ..........................................

Chapter 5: Use of CERES-Till to evaluate conservation tillage strategies in
four areas of Michigan ...................................

41
45
46

43
48
49
51
53
70
71

73
73
74
76
76
78
80
85
88
89
9O
90
93
95
97
98
99
99
101
103
105
105
1 10
113
119
121
122

125

 

Abstract ............................................. 125

Introduction ........................................... 126
Methodology .......................................... 127
Results .............................................. 133
Huron site ....................................... 133

Tuscola site ...................................... 135

Ingham site ...................................... 137

Kalamazoo site ................................... 141

Discussion ............................................ 147
Conclusion ............................................ 153
Bibliography .......................................... 154
Conclusion and recommendations ................................ 155
Appendix 1: Weather and soil data ............................... 157
Appendix 2: Modified subroutines ............................... 165
Program MAIN ........................................ 165
Subroutine CALEO ..................................... 172
Subroutine DYNTILL ................................... 173
Subroutine IPN IT ...................................... 174
Subroutine IPSOIL ..................................... 177
Subroutine MULCHE ................................... 178
Subroutine OMINIT .................................... 179
Subroutine OMupdate ................................... 180
Subroutine PONDIN G .................................. 181
Subroutine PROGRI .................................... 182
Subroutine SOILNI ..................................... 184
Function TILLTIME .................................... 185
Subroutine SOLT ....................................... 185
Subroutine UPFLOW ................................... 186
Subroutine WATBAL ................................... 187
Appendix 3: Input files to run CERES ........................... 191
MZEXPDIR: Directory of files for each experiment ............ 191
WTHDIR: Daily weather data ............................. 191
SPROFILE.MZZ: File 2, soil profile properties ................. 191

FILE 4: Soil nitrogen dynamics ............................ 191

File 5: Soil profile initial condition .......................... 192

File 6: Irrigation management data .......................... 193

File 7: Nitrogen Fertilizer data ............................. 194

File 8: Crop management data ............................. 194

File 9: Genetic coefficients ................................ 194

vii

LIST OF FIGURES

Figure 2-1: Estimated thermal time for Spring 1990 calculated using air
temperature and soil temperature under four soil cover treatment
with a base temperature of 8 °C. ............................ 27

Figure 2-2: Thermal time for Spring 1991 calculated using air temperature
and soil temperature under four residue covers with a base
temperature of 8 °C. ..................................... 28

Figure 2-3: Variation of air temperature and soil temperature under a bare
surface at 2.5, 10, and 30 cm, between 24 June and 26 June 1990 ..... 30

Figure 2-4: Hourly soil temperature variation at 2.5 cm under four residue
covers from 24 June to 26 June 1990. ......................... 31

Figure 2-5: Maize early leaf development under four residue covers versus
day of the year 1990. ..................................... 35

Figure 2-6: Maize leaf development under four residue covers versus day of
the year 1991. .......................................... 36

Figure 2-7: Number of leaf tips in the whorl (total leaf tip number minus ‘
total collar number) versus day of the year 1990. ................ 37

Figure 2-8: 1990 early leaf development versus thermal time from planting
calculated from air and soil temperature at 2.5 cm under four
residue covers using a base temperature of 8 OC. ................ 38

Figure 2-9: 1991 leaf development versus thermal time from planting
calculated from air and soil temperature at 2.5 cm under four
residue covers using a base temperature of 8 °C. ................ 39

Figure 2-10: Thermal time using air temperature with a base temperature
of 6 °C and soil temperature with a base temperature of 8 °C. ...... 42

Figure 3.1: Surface volumetric water content for the 0-2.5 cm and 0—10 cm
layers under for residue cover on fallow plots during summer 1990. . . . 54

Figure 3.2: Surface volumetric water content from 0 to 20 cm and 0 to 40
cm under four residue cover during summer 1990 on fallow plots. . . . . 55

viii

Figure 3.3: Surface volumetric water content from 0 to 7 cm and 0 to 15

cm under four residue covers on fallow plots during summer 1991. . . .

Figure 3.4: Volumetric water content from 0 to 26 cm and O to 40 cm

under four residue covers on fallow plots during summer 1991. ......

Figure 3.5: Surface volumetric water content from 0 to 2.5 cm and 0 to 10
cm under four residue covers on maize cropped plots during

summer 1990. ..........................................

Figure 3.6: Surface volumetric water content from 0 to 20 cm and 0 to 40
cm under four residue covers on maize cropped plots during

summer 1990. ..........................................

Figure 3.7: Soil water content variation (cm) in the top 20 cm and 100 cm
under four residue covers, on maize planted plots, during summer

1991. .................................................

Figure 3.8: Plant leaf surface area for four surface residue cover treatments

versus day of year 1990. ...................................

Figure 3.9: Plant leaf surface area versus thermal time calculated from soil

temperature in 1990. .....................................

Figure 3.10: Plant leaf surface area for maize plants grown under four

residue covers versus day of the year 1991. .....................

Figure 3.11: Plant green leaf surface area for maize grown under four

residue covers in 1991. ....................................

Figure 3.12: Total leaf surface area for maize plants grown under four

residue cover treatments in 1991. ............................

Figure 4.1: CERES model version 2.1 structure, and added (double line) or
modified subroutines (thick line) to include residue management and

tillage. Unmodified subroutines are boxed with a single line. .......

Figure 4.2: Temperature and water content factors affecting residue

decomposition. One represents optimal conditions and 0 no activity. .

Figure 4.3: Remaining surface residue for three crops representing different

C:N ratio, and assuming optimal conditions. ....................

57

58

62

63

64

65

66

67

83

84

Figure 4.4: Fraction of soil covered versus amount of residue at the surface
for three different crops. .................................. 86

Figure 4.5: Bare soil surface albedo versus soil water content factor from O
to 2 cm. Adapted from Idso et al., 1975, soil water content was
changed to a relative water content factor (SW(1)-0.03)/(0.3-0.03). . . . 96

Figure 4.6: Model predictions and observed data from the Pullman,
Washington, site (site A and B) for 1983 and 1984. Data are from
Stott et al., 1990 . ....................................... 106

Figure 4.7: Model predictions and observed values for Bushland, Texas
1984. Data are from Stott et al., 1990. ....................... 107

Figure 4.8: Model predictions and Observed data from West Lafayette,
Indiana, 1984 (site D). Data are from Stott et al., 1990. .......... 108

Figure 4.9: Model prediction and observed data for Pullman, Washington,
1985 (Site B). Data are from Stott et al. (1990). ................ 109

Figure 4.10: Surface water balance of a bare surface and a fully covered
surface on a fallow plot for the upper four layers ................ 111

Figure 4.11: Surface water balance of a bare and fully covered soil for the
upper four layers on a fallow plot during summer 1991. .......... 112

Figure 4.12: Observed and predicted daily average soil temperature at 2.5
cm at East Lansing, Michigan, 1988 for an uncovered and 100%
covered soil surface. Data from Fortin (1990). ................. 114

Figure 4.13: Observed and predicted daily average soil temperature at 2.5
cm at East Lansing, Michigan, 1990 for an uncovered and 100%
covered soil surface. ..................................... 115

Figure 4.14: Observed and predicted daily average soil temperature at East
Lansing, Michigan, 1991 for an uncovered and 100% covered soil
surface. .............................................. 116

Figure 4.15: Observed and predicted leaf tip number at East Lansing,
Michigan, 1990. ........................................ 117

Figure 4.16: Observed and predicted leaf tip number at East Lansing,
Michigan, 1991. ........................................ 118

 

Figure 4.17: Predicted effect of a 100% residue cover on soil surface
properties. Data set of 1990, East Lansing, Michigan, on a Conover
soil. ................................................. 120

Figure 5.1: Cumulative distribution of economic return (3 ha'l) and annual
runoff (cm) at the Huron site for the best planting dates for
conventional tillage, reduced tillage, and no-till. ................ 136

Figure 5.2: Water deficit during vegetative and reproductive stage for three
tillage strategies at optimal planting date at the Huron site. ....... 138

Figure 5.3: Cumulative distribution of economic return (3 ha'l) and annual
runoff (cm) at the Tuscola site for the best planting dates for
conventional tillage, reduced tillage, and no-till. ................ 140

Figure 5.4: Water deficit during vegetative and reproductive stage for three
tillage strategies at optimal planting date at the Tuscola site. ...... 142

Figure 5.5: Cumulative distribution of economic return ($ ha’l) and annual
runoff (cm) at the Ingham site for the best planting dates for
conventional tillage, reduced tillage, and no-till. ................ 145

Figure 5.6: Water deficit during vegetative and reproductive stage for three
tillage strategies at optimal planting date at the Ingham site. ....... 146

Figure 5.7: Cumulative distribution of economic return ($ ha'l) and annual
runoff (cm) at the Kalamazoo site for the best planting dates for
conventional tillage, reduced tillage, and no-till. ................ 149

Figure 5.8: Water deficit during vegetative and reproductive stage for three
tillage strategies at optimal planting date at the Kalamazoo site. . . . . 150

 

LIST OF TABLES

Table 2.1: Minimum, maximum, average and amplitude of soil temperature
at 2.5 cm under four residue covers and air temperature on 24 June
1990 and 26 June 1990. ................................... 29

Table 2.2: Minimum, maximum, average and amplitude of air and soil
temperature at 2.5 cm, 10 cm, and 30 cm on June 24, 1990 and June
26, 1990. .............................................. 33

Table 2.3: Correlation coefficient for leaf tip number prediction from
thermal time. ........................................... 40

Table 3.1: Stover (leaves and stem) and cob water content at harvest in
1990 and 1991 under four residue cover treatments. .............. 68

Table 3.2: Grain number per plant , kernel weight (g), and total grain

weight per plant (g) for four residue cover treatments in 1990 and

1991. ................................................. 69
Table 4.1: Influence of field operations on surface residue (Shelton,1990) . . 79

Table 4.2: Values of average mass to area conversion for residues. ....... 87

Table 4.3: Default tillage input and tillage coding for the tillage version of

the CERES model. ..................................... 104
Table 5.1: Geographical location of the Michigan areas and their major soil

type. ................................................ 127
Table 5.2: Description of the three tillage systems compared for four

Michigan sites. ......................................... 129
Table 5.3: Genetic coefficient chosen to run CERES-Till for four Michigan

sites. ................................................ 130
Table 5.4: Cost associated with each tillage system ($ ha'l). ............ 131

Table 5.5: Yield (T ha'l), return (3 ha'l) for three tillage strategies and
four planting dates at the Huron site. ........................ 134

Table 5.6: Number of days from sowing to anthesis and anthesis to maturity
for three tillage strategies and four planting dates at the Huron site. .

Table 5.7: Yield (T ha‘l), return (5 ha'l) for three tillage strategies and
four planting dates at the Tuscola site. .......................

Table 5.8: Number of days from sowing to anthesis and anthesis to maturity
for three tillage strategies and four planting dates at the Tuscola
site. .................................................

Table 5.9: Yield (T ha'l), return (3 ha’l) for three tillage strategies and
four planting dates at the Ingham site. .......................

Table 5.10: Number of days from sowing to anthesis and anthesis to
maturity for three tillage strategies and four planting dates at the
Ingham site. ...........................................

Table 5.11: Yield (T ha’l), return ($ ha'l) for three tillage strategies and
four planting dates at the Kalamazoo site. ....................

Table 5.12: Number of days from sowing to anthesis and anthesis to
maturity for three tillage strategies and four planting dates at the
Kalamazoo site. ........................................

Table 5.13: Five day average air temperature in the spring at the Huron,
Tuscola, Ingham and Kalamazoo sites. .......................

Table 5.14: Annual runoff (cm) at four sites for three different tillage
strategies at the optimum yield. ............................

Table 1.1: Conover loam volumetric water content at 3 different depth and

different suction and bulk density at the Box Farm, East Lansing,
Mich” USA. ...........................................

xiii

137

139

141

142

144

147

148

151

152

164

FOM(L)
FRACI'ILL
KE

K,

K,
KSMACRO
KSMTRX
MULCH

MULCHI

MULCHSW
OC(L)
POND
PONDMAX
PRECIP
RAIN

Rstl

Salb
Salbedo

LIST OF SYMBOLS

. Field albedo
. . Delay In maximum temperature due to geographic location
. Residue cover capacity (ha kg 1)
. . Yearly air temperature amplitude
. . Aggregate water stability (%)
. . Air dry soil water content
. Bulk density of layer L
. . Canopy cover
. Tillage code number
. . Fraction of easily decomposable residue
. . Depth of tillage (cm)
. Day of the year
. Canopy evaporative demand reduction coefficient
. Surface residue evaporative demand reduction coefficient
. Potential evaporation
. Bare soil potential evaporation
. Fraction of soil covered by surface residue
. Water factor to calculate change in soil albedo
. Fresh organic matter content of layer L
. Fraction of residue incorporated at tillage (%)
. . Precipitation intensity
. Decomposition rate of the labile pool (day'l)
. Decomposition rate of the resistant pool (day 1)
. Saturated water conductivity with macropores
. . Saturated water conductivity without macropores
. . . . Amount of residue left at the surface (kg ha 1)
MULCHCOV . . .

Residue albedo
Residue cover

. Amount of surface residue in the labile pool (kg/ ha)

MUIJCI-IT . . . . Amount of surface residue in the resistant pool (kg ha 1)
MULCHSAT . . . .

Residue water content at saturation (cm)

. Residue water content (cm)
. Organic carbon content of layer L
. Amount of surface water ponding (cm)
. Maximum surface water ponding capacity (cm)
. Amount of precipitation or irrigation (cm)
. Precipitation amount (cm)
. . Surface property reduction coefficient
. . Wet soil albedo
. Dry soil surface albedo

xiv

SAT(L)
SCN
SOILCOV
Solrad
ST(L)
SUMD'IT
SUMKE
SW(L)

TA

TAV
TEMPFAC
TEMPM
TEMPMN
TEMPMX
TILLBD
TILLPOND

. . Soil water content at saturation
. Residue C:N ratio
. . Fraction of soil covered
. Daily solar radiation (MJ m’2)
. Layer L soil temperature
. . Thermal time from emergence
. Cumulative precipitation intensity
. . Soil water content of layer L
. Normalized soil temperature
. Yearly air average temperature
. Temperature factor affecting residue decomposition
. Daily air average temperature
. Daily air minimum temperature
. . Daily air maximum temperature
. . Bulk density at time of tillage
. . Ponding capacity at time of tillage
. Interval length (hr)
. Total leaf number
. Soil surface temperature
. . Water factor affecting residue decomposition
. Soil temperature damping coefficient

Chapter 1: Residue management and soil environment

Introduction

"Typically each hectare in the US. is losing soil and producing an average
sediment of 6.7 Mg/ha. Croplands are the source of nearly 50% of the sediment
and erosion; these lands erode about 30% more than the overall erosion average
for all lands" (Miller and Donahue, 1990).

The increasing demand for food production pushed farmers to use lands
less suitable for agriculture. The rapid need for new land and economic
productivity requirements prevented farmers fiom adequately preparing the land
and using conservation techniques. Because of diminished soil cover and
decreased soil aggregate stability, soil erosion increased on land prepared for
agriculture or construction. Water and wind remove the surface layer of soil that
is rich in organic matter and nutrients necessary for crop growth and brings to the
surface a coarser and poorer material. The land then becomes less productive,
increasing the need for external inputs such as water and fertilizers. The
rebuilding of soil productivity is a slow process. Hence soil erosion impairs
agricultural land use for the present and the future (Wolman, 1985).

Soil erosion also has other deleterious consequences upon the environment.
The soil removed from one location by erosion is translocated to other
environments where its high levels of nutrients and fine particles are undesirable.

For example, these nutrients, when added to streams and lakes, favor

2

eutrophication, and the presence of fine particles increases the turbidness of the
aquatic habitat (Frere, 1976).

Conservation tillage has been developed to control soil erosion. However,
we need to evaluate the performance of new techniques in different environments
and their consequences on yields and costs of production. To better control and
evaluate soil erosion we need to understand the processes involved. The following

is a brief description of processes involved in soil erosion.

The erosion process

Erosion occurs in three stages: detachment, transport, and deposition. A
soil particle or aggregate is detached from the soil surface when a force with
higher energy than its binding forces is exerted on the soil surface. Wind or water
with energy higher than the gravity force exerted on soil can detach particles from
the surface and carry them away. When the energy drops below the particles’
terminal velocity, the element settles. Three main erosive processes are usually
considered in accelerated erosion: splash erosion, run off erosion, and wind

erosion.

Splash erosion

Water falling on the soil surface reaches it with an energy that is a function
of the storm intensity or the sprinkler irrigation characteristics. The physical

impact of a water drop on a soil aggregate of fine sand and silt particles may

3

break it apart because of the particles’ light weight and low binding forces. This
creates a muddy water that clogs soil pores and forms a crust at the soil surface
causing greatly reduced infiltration rates and increased run-off (McIntyre, 1958,
Al-Durrah and Bradford, 1982). Aggregate stability and the energy of water drops

reaching the soil surface determine the extent of splash erosion.

82.1mm

When precipitation rate is higher than soil infiltration rate, water that
cannot infiltrate stays at the soil surface and ponds. The amount of water that
can pond is determined by the slope, soil micro-relief created by tillage, and
physical barriers such as plants and surface residues. When no more water can be
stored and the precipitation rate is still higher than the infiltration, water runs off
(Brakensiek and Rawls, 1982).

Due to their bipolarity, water molecules bond with soil elements weakening
the bond between soil particles. Thus at high water content, a soil aggregate is
more likely to be broken by a disruptive force of low energy. When water runs
off, the soil surface is saturated and thus more erodible. Water detaches soil
particles and moves them, increasing its abrasive effect on other aggregates, thus
accelerating the erosion process (Foster, 1982). When the surface water slows
down, its energy decreases and the particles settle. Runoff erosion is hence
dependent on soil water infiltrability, soil surface ponding capacity, pathways of

surface water, and aggregate stability.

 

mm

Winds with sufficient energy can detach soil particles from the soil surface.
When the soil surface is dry, soil particles are lighter and easier to detach, making
the blowing wind more erosive. The distance detached soil particles travels
depends on their weight and the wind energy or velocity (Chepil and Woodruff,
1963; Lyles et al., 1985). Three types of soil particle movement are considered:
surface creep of large particles (0.5 to 1.0 mm), saltation of medium size particles
(0.05 mm to 0.5 mm), and suspension of small particles (less then 0.05 mm).
Larger particles travel less in distance but have a higher abrasive role on other
particles. When they move at the soil surface they bump into other soil aggregate
particles and tend to break them into smaller particles. Small soil particles picked
up by wind can reach higher winds and thus travel longer distances than larger
particles. Wind erosion magnitude is linked to wind velocity at the soil surface

and soil aggregate stability

Prevention of soil erosion
Soil erosion can be decreased by diminishing particle detachment that

depends on soil binding forces and the magnitude of disruptive forces on the soil

surface.

Soil aggregate stabilig

As seen above, the strength of soil aggregate binding is important as it
defines how easily a soil particle can be detached from the soil surface. Aggregate
stability depends on the quantity and quality of cementing agents in the soil layer.
The major cementing agents are clay particles, ion oxides (primarily CaCO3,

F e203, A1203) (Rdmsken et al., 1987), and soil organic matter (Chaney and Swift,
1984). These agents increase the bonding between soil particles and create peds
or aggregates more resistant to erosive forces.

Whereas clay particles and ion levels depend mostly on the weathering of
the parent material, organic matter depends more on agricultural soil management
and climate. Because soil organic matter decomposes, agricultural practices such
as burning crop residues or deep plowing that remove new additions of fresh
organic matter at the surface will decrease aggregate stability, making particle
detachment more likely.

Hence keeping soil organic matter in the surface layer at sufficient levels
(up to 7% of organic carbon) provides better aggregate stability reducing soil
erosion risks. A complementary approach to soil erosion prevention is to decrease
the disruptive forces reaching the soil surface. Disruptive forces can be arranged
in three classes: water drop impact energy from rainfall or irrigation, water

movement at the surface, and wind velocity.

spasm

A drop of water strikes the soil surface with energy that is decreased when
a plant canopy or surface residues intercepts it. Water then trickles down to the
soil surface and reaches it with minimal energy. The decrease in raindrop splash
erosion depends then on how many drops are intercepted. Thus surface cover by
a crop or residue cover provides a major control to splash erosion (Mannering

and Meyer, 1963; Foster et al., 1985).

Runoff erosion

Keeping the incoming water rate lower than the infiltrability will prevent
runoff erosion. While this is possible with irrigation, we do not have any control
on storms. Hence we need to keep soil infiltrability in a satisfactory range. Soil
infiltrability is dynamic, and is a function of the matrix conductivity, and quantity
and continuity of macropores. If during a storm or irrigation splash erosion
occurs, soil infiltrability decreases as macropores clog. Consequently, preventing
splash erosion is a first step in runoff erosion control (Mannering and Meyer,
1963). Furthermore, if the soil has a high ponding capacity, it is able to store
water that will infiltrate later; less water will run off. Tillage increases ponding
capacity by increasing surface micro-relief and cutting slopes (terracing, ridge till,
contour tillage). Barriers to water movement such as plants and surface residues

also increase ponding capacity (Wischmeier and Smith, 1978).

Wind erosion

To control wind erosion, it is necessary to decrease wind velocity at the soil
surface, by natural wind breaks (hills, forests, ...), man-made barriers (edges, walls,
buildings,...), and tillage that increases surface roughness. A residue cover
provides a barrier that decreases wind velocity at the soil surface.

Erosive forces, kinetic energy and travelling distance determine how far soil
particles are translocated. Thus soil surface topography and characteristics are
important to reduce soil displacement as they control water and wind velocity, and

travelling distance.

Reducing soil displacement

The distance water travels depends on soil topography and water velocity;
both are a function of land slope. Terraces that cut slopes and contour tillage
have been used to modify natural topography to limit water movements. Natural
barriers to water movement, such as plants and crop residues, reduce water
velocity decreasing its erosive power and increasing soil particles settlement (Stein
et al., 1986). In some agricultural systems, grass strips have also been installed to
intercept soil particles in their stems and roots, thereby limiting soil displacement

(Langdale et al., 1979).

8

Residue cover as means to control soil erosion

Residue cover plays three important roles in erosion control: it furnishes
organic matter to the top layer increasing its aggregate stability; it intercepts
erosive forces of water and wind; and it limits soil displacement (Meyer et al.,
1970). Soil surface cover can be obtained by construction, natural vegetation,
cropping, or mulching. In some traditional agriculture systems, soil is left fallow
during a certain period to conserve and increase soil water or because climate is
improper for cropping. Because soil surface cover is low during that period, soil
erosion probability is more likely to occur. Surface residue from previous crops
and planted sods can provide cover to help control erosion during fallow and early

crop development.

Tillage systems creating a soil cover

In the United States, the 1930’s dust bowl pushed farm land management
in a new direction. Conservation tillage became a recommended practice that
leaves sufficient cover to protect the soil from erosion. Conservation tillage
includes a large range of systems ranging from no-till systems where seeds are
drilled through the crop residues, to systems where only one or two tillage
operations are done before planting. Recent innovations in machinery design
allows farmers to till the surface layers to break pans and alleviate compaction,
and to prepare seed beds without burying the crop residues. As a comparison,

traditional moldboard plowing buries up to 90% of surface residues whereas chisel

9
plow buries only up to 50% and a direct drill buries only 5% (Shelton et al., 1990).

Agricultural advisors often recommend a minimum soil cover of 30% at
planting to control soil erosion (Dickey et al., 1990). Small amounts of residue
cover at the soil surface decreases soil erosion. Laflen et al. (1980) reported that
a coverage of 10% could result in a decrease in soil erosion of 20%. Knowledge
of coverage throughout the season for several types of residue and climate will
assist in decisions regarding residue management for soil conservation. This is
important on highly erosive soils as erosion usually happens in one event of large
magnitude, for example, a storm or strong winds. If an erosion event happens
after the residue is decomposed and before crops develop, soil coverage will be
insufficient to prevent erosion.

In 1975, the USDA (United States Department of Agriculture, 1975) Office
of Planning and Evaluation predicted that minimum tillage would be used on 95%
of the US. planted cropland by the year 2010; however, Mannering et al. (1987)
estimated that because of economic factors, farmers would use conservation tillage
on only 50% to 60% of the cropland. These economic factors include cost of
switching to new machinery, learning curves, and also changes in yields.
Conservation tillage reports show that residue cover enhances crop yields in the
southeast region by increasing the amount of water available for transpiration
(Reicosky et al., 1977), but has detrimental effects by decreasing soil temperature
and delaying crop development in the northern Corn Belt (Amemiya, 1977;

Griffith et al., 1977; Bennett, 1977). Better understanding of residue impact on

10

crop development and economic evaluation with the help of simulation models is

needed to help developing appropriate technologies for erosion control.

Residue management and crops

In the Corn Belt, residue cover is required to control soil water erosion. It
reduces runoff, increases infiltration, and decreases soil water evaporation, thus
leaving more water for plant uptake (Griffith et al.,” 1977). The northern Corn
Belt is characterized by a cool spring with slowly rising temperatures. Soil cover
slows soil warming by intercepting incoming solar energy and keeping soil water
content higher. Lower soil temperatures delay plant’s early vegetative
development and, in a cool temperate climate, may cause a shorter maturity
period before autumn frost occurs.

In the northern Corn Belt and regions with cool wet springs, there is a
trade-off between increased water resources and delayed plant development.
Disparity between maize yields from conventional to conservation tillage are not
evident every year. Because of complex interactions between many effects of
conservation tillage on the plant’s environment, deciding whether switching to

conservation tillage would be beneficial is difficult.

11
Simulation of crop production system

Crop models can help farmers make decisions because they can give
information on crop response for a wide variety of environments and management
inputs. Crop models have been used to evaluate new technologies or practices
(Fischer et al., 1990) and for technology transfer (Ritchie, 1986). However the
values of information given is limited by the assumptions of the models. The use
of a model to decide if conservation tillage will be beneficial or not is constrained
by the model ability to predict infiltration, runoff, surface cover, and plant growth
and development.

The NTRM (Nitrogen, Tillage, and Crop-Residue Management; Shaffer
and Larson, 1987) model predicts soil conditions and water balance under several
tillage treatments. A limitation to the use of the model is that the input data
required such as soil temperature and soil hydraulic properties are quite extensive
and not readily available.

The EPIC (Erosion-Productivity Impact Calculator) was developed to
estimate soil erosion and crop productivity (Williams and Renard, 1985). Even
though the model step is one day, prediction of erosion during a storm event has
questionable reliability because soil erosion is predicted using the runoff curve
number.

Furthermore EPIC and NTRM are not development oriented crop models.

Hence, delay in crop development induced by conservation tillage cannot be

12

accommodated. The consequences of the crop development delay induced by
residue cover cannot be studied with those models without modification.

The CERES (Crop Estimation through Resource and Environment
Syntheses) family of models facilitates quantitative determination of growth and
yield of maize (Zea Mays, L.) and other cereals under a wide range of soil and
weather conditions (Jones and Kiniry, 1986; Ritchie et al., 1989). The CERES
models predict plant development through thermal time calculation in connection
with genetic specific details. This approach allows us to better understand the
consequences of conservation tillage on development and growth for several crops
and varieties, and for different climates if the difference in plant growing point
temperature is used for plant development prediction. The current CERES
models (Version 2.1) assume constant soil physical properties over time. Crop
residues are assumed to be incorporated in the soil profile at the beginning of a
simulation. These constraints limit the use of CERES models for evaluation of
different tillage practices, especially conservation tillage. If the CERES models
could be modified to consider the impact of tillage on both the soil environment
and the dynamics of soil surface properties, they would become more valuable for

assessing tillage impact on the plant and soil system.

Objectives of this thesis
A better understanding of a crop residue cover impact on the soil surface is

necessary to develop an improved simulation of residue cover consequences on

13

crop development and growth and soil environmental conditions. An experiment
was designed to study maize response to soil temperature and water content
affected by surface residue. Soil temperature patterns and soil water dynamics
were studied. Crop response was measured through leaf development rates.

A model of residue management and crop response was developed from
both the experimental data collected and the existing literature. The approach
adopted was to alter the CERES-Maize model, version 2.1, (Jones and Kiniry,
1986; Ritchie et al., 1989) to account for residue management and tillage effects
on soil properties. This imposed some constraints in the modeling approach. The
time increment of the CERES model is one day. This implies that mechanistic
numerical models with small time increments are not appropriate or must be
modified for inclusion in CERES models. Input variables should be readily
available or easily measurable. Adding tillage and residue management will
require new input variables and routines, but these should conform to the general
CERES philosophy, inputs and operational structure. Specific modeling objectives
were:

- To model the amount of crop residue at the soil surface resulting from tillage
management, and its decomposition to predict soil cover.

- Simulate the consequences of a crop residue mulch on soil evaporation, ponding
capacity, infiltration and soil temperature.

- Evaluate the effects of residue cover on crop development and growth.

14

Bibliography

Al-Durrah, M. M. and J. M. Bradford. 1982. The mechanism of raindrop splash on
soil surfaces. Soil Sci. Soc. Am. J. 46(5): 1086-1090.

Amemiya, M. 1977. Conservation tillage in the Western Corn Belt. J. Soil Water
Conserv. 29-38.

Bennett, 0. L. 1977. Conservation tillage in the Northeast. J. Soil Water Conserv.
9-13.

Brakensiek, D. L. and W. J. Rawls. 1982. An infiltration based rainfall runofi
model for SCS Type 2 distribution. Am. Soc. Agric. Eng. Trans.
25(9):]607-1611.

Chaney, K. and R. S. Swift. 1984. The influence of organic matter on aggregate
stability in some British soils. J. of Soil Science 35:223-230.

Chepil, W. S. and N. P. Woodruff. 1963. The physics of wind erosion and its
control. Adv. in Agron. 15:211-307.

Dickey, E. C., P. J. Jasa, D. P. Shelton and A. J. Jones. 1990. Conservation tillage
and planting systems for water-induced soil erosion control. In E. C.
Dickey and P. J. Jasa. (ed.) Conservation tillage proceeding no.9. University
of Nebraska, Cooperative extension, Nebraska, USA.

Fisher, R. A., J. S. Armstrong and M. Stapper. 1990. Simulation of water storage
and sowing day probabilities with fallow and no- fallow in southern New

South Wales: 1. Model and long term mean effects. Agricultural Systems
33:215-240.

Foster. 1982. Modeling the erosion process. In Hydrologic modeling of small
watersheds. St. Joseph, MI, ASAE.

Foster, G. R., R. A. Young, M. J. M. Rdmkens and C. A. Onstad. 1985. Processes
of spoil erosion by water. p. 137-162. In R. F. Follett and B. A. Stewart
(ed.) Soil erosion and crop productivity. ASA,CSSA,SSSA, Madison,
Wisconsin, USA.

15

Frere, M. H. 1976. Nutrient aspects of pollution from cropland. In Control of
water pollution from cropland: An overview. ARS-H- 5-2. 11 (4):59-90.
USDA.

Griffith, D. R., J. V. Mannering and W. C. Moldenhauer. 1977. Conservation
tillage in the Eastern Corn Belt. J. Soil Water Conserv. 32:20-29.

Jones, C. A. and J. R. Kiniry. 1986. CERES-Maize a simulation model of maize
growth and development. Texas A&M Univ. Press, College Station, Texas,
USA.

Laflen, J. M., W. C. Moldenhauer and T. S. Colvin. 1980. Conservation tillage and
soil erosion on continuously row cropped land. Crop production with
conservation in the 80s. Am. Soc. Agr. Eng., St. Joseph, Michigan, USA.

Langdale, G. W., A. P. Barnett, R. A. Leonard and W. G. Fleming. 1979.
Reduction of soil erosion by the no-till system in the southern piedmont.
Trans. ASAE 22 (1):82-86,92.

Lyles, L., G. W. Cole and L. J. Hagen. 1985. Wind erosion: processes and
prediction. p. 163-172. In R. F. Follett and B. A. Stewart (ed.) Soil erosion
and crop productivity. ASA,CSSA, SSSA, Madison, Wisconsin, USA.

Mannering, J. V. and L. D. Meyer. 1963. The effects of various rates of surface
mulch on infiltration and erosion. Soil Sci. Soc. Am. J. 27:84-86.

Mannering, J. V., D. L. Schertz and B. A. Julian. 1987. Overview of conservation
tillage. T. J. Logan, J. M. Davidson, J. L. Baker and M. R. Overcash (ed.)
Effects of conservation tillage on groundwater quality: Nitrates and
pesticides. Lewis Publishers, Chelsea, Michigan, USA.

McIntyre, D. S. 1958. Soil splash and the formation of surface crusts by raindrop
impact. Soil Sci. 85:261-266.

Meyer, L. D., W. H. Wischmeier and G. R. Foster. 1970. Mulch rates required for
erosion control on steep slopes. Soil Sci. Soc. Am. J. 34:928-931.

Miller, R. W. and R. L. Donahue. 1990. Soils, an introduction to soils and plant
growth. Prentice Hall Inc., Englewoods cliffs, NJ, USA.

Reicosky, D. C., D. K. Cassel, R. L. Blevins, W. R. Gill and G. C. Naderman.
1977. Conservation tillage in the Southeast. J. Soil Water Conserv. 32:13-20.

16

Ritchie, J. T. 1986. Using simulation models for predicting crop performance.
Symposium on the role of soils systems analysis for technology transfer.
August 1986. ISSS Congress,Hamburg, FRG.

Ritchie, J. T., U. Singh, D. Godwin and L. Hunt. 1989. A user’s guide to
CERES-Maize-V2.1. IFDC, Alabama, USA.

Rdmskens, M. J. M., C. B. Roth and D. W. Nelson. 1987. Erodibility of selected
clay subsoil in relation to physical and chemical properties. Soil Sci. Soc.
Am. J. 41(5):954-960.

Shaffer, M. J ., and W. E. Larson. 1987. NTRM, a soil-crop simulation model, for
nitrogen, tillage, and crop-residue management. US. Department of

Agriculture conservation report 34-2. National Technical information.
Springfield, VA, USA.

Shelton, D. P., E. C. Dickey and P. J. Jasa. 1990. Estimating percent residue
cover. In E. C. Dickey and P. J. J asa. (ed.) Conservation tillage proceeding
no.9. University of Nebraska, Cooperative extension,Nebraska, USA.

Stein, 0. R., W. H. Neibling, T. J. Logan and W. C. Moldenhauer. 1986. Runoff
and soil loss as influenced by tillage and residue cover. Soil Sci. Soc. Am. J.
50:1527-1531.

Williams, J. R. and K. G. Renard. 1985. Assessment of soil erosion and crop
productivity with process model (EPIC). p. 137-162. In R. F. Follett and B.
A. Stewart (ed.) Soil erosion and crop productivity. ASA,CSSA,SSSA,
Madison, Wisconsin, USA.

Wischmeier, W. H., and D. D. Smith. 1978. Predicting rainfall erosion- a guide to
conservation planning. USDA Agriculture Handbook 537.

Wolman, M. G. 1985. Soil erosion and crop productivity: A worldwide perspective.
p. 137-162. In R. F. Follett and B. A. Stewart (ed.) Soil erosion and crop
productivity. ASA,CSSA, SSSA, Madison, Wisconsin, USA.

Chapter 2: Crop residue influence on thermal time and maize (Zea Mays, L.)

development

Abstract

Soil residue cover delays maize development while its meristem is below
ground by decreasing soil surface temperature. A field experiment was conducted
in 1990 and 1991 at East Lansing, Michigan, on a Conover loam, to study the
effect of four crop residue loading rates on maize development. The residue cover
decreased the amplitude of changes in soil temperature inducing a delay in maize
development. The delay was of 2 leaves in 1990 which had a cooler spring than in
1991 which only had a delay of 0.5 leaves because the spring weather was warmer.
The delay in plant development was highest with a 100% residue cover and
smallest with a 30% residue cover. The longer the meristem was below ground,
the larger the delay in plant development. Prediction of plant development using
thermal time was best using soil temperature at 2.5 cm with a base temperature of
8 0C while the meristem was below ground and using air temperature when the
meristem was above ground. Air temperature gave reasonable predictions but with
different correlations between treatments. The use of soil temperature is essential
to predict plant development especially in a cool spring climate or when the soil

temperature differs from air temperature.

17

18
Introduction

In order to control soil erosion, new tillage systems provide increased crop
residue coverage on the soil surface. Under a temperate, cool climate, this
increase in soil residue coverage can delay plant development of important
agricultural crops by decreasing soil temperature in spring. The delay in plant
development may decrease crop yield when the maturity stage is shortened due to
cold temperature. Since plant development responds to crop residue at the soil
surface, we should account for the effect of crop residue cover on thermal time
calculations to predict the length of phenological stages.

Early work by Réaumur in the 1730’s as cited in Wang (1960) showed that
the sum of daily mean temperatures was nearly a constant to reach a given
maturity stage for any plant. Since then, several methods of temperature
summations to predict maturity stages have been developed (Gilmore and Rogers,
1958; Brown, 1975; Hunter et al., 1977; Bauer et al., 1984). Because of the wide
variety of terms for this concept, Gallagher (1979) recommends using the term
thermal time as a generic terminology for temperature summation methods.

Wang (1960) criticized summation methods, partly because plants do not
respond to air temperature the same way during various developmental stages.
Response differences are due to differences in minimum physiological temperature

and differences in location of the response. Thus, using the same base

19

temperature for all stages and a recorded temperature that is not always the
temperature of the location of the response leads to prediction errors.

Ritchie and NeSmith (1991) describe different thermal time calculations for
several stages of plant development with different base temperatures below which
development stops, and different maximum temperatures, above which only the
maximum value is added, or in the case of reversflole processes, a lower value is
added. They report several possible errors in the thermal time calculations:
reading errors due to time of manual recording of minimum and maximum values,
value errors due to the location of sensors compared to the plant, and average
temperature calculation errors when the maximum and minimum temperature are
not sufficient to calculate the mean value. These errors can be overcome by using
automated recording. However, such equipment is not available at all locations.
Also, some thermal time calculations may require data such as soil temperature or
solar radiation, that are not recorded.

For most crops, attainment of phenological stages are predicted from
thermal time calculated from air temperature. Watts (1972) showed that when
meristem temperature was modified but leaf and root temperature were kept
constant, rapid changes in plant development were observed. Because biological
activity is a function of temperature, and leaves, stem, and reproductive organs are
differentiated in the meristem, meristematic temperature should be used in
thermal time calculation to more accurately predict attainment of phenological

stages.

20

Cellier et al. (1992) measured maize meristem temperature by inserting a
thin thermocouple near the meristem. They showed differences between meristem
and air temperature ranging from - 6 °C at night to + 7 °C during the day. They
proposed a simple functional model to calculate the temperature difference from
solar radiation, air temperature, and dew point. The empirical coefficients used by
the model are climate dependent. Thus, new calibration is needed to use this
model in a different environment.

During the early developmental stages of several crops such as maize (Zea
Mays, L.) or wheat (Iiiticum aestivum, L.), the meristem of the plant is 1 to 2 cm
below the soil surface. Therefore, meristematic temperature is closer to soil
temperature at 2 cm than air temperature and using soil temperature would give a
better estimation of meristematic temperature than air temperature. Fortin and
Pierce (1991) showed better correlation in plant development prediction using soil
temperature at 2.5 cm to calculate thermal time than using air temperature.

Soil temperature appears to be a better indicator of plant development
than air temperature while the meristem is below ground, and it is easier to
measure than meristematic temperature.

As seen in Chapter 1, residue cover has been reported to delay plant
development by decreasing soil surface temperature. Soil temperature differs from
air temperature measured at 2 m where the temperature sensors are usually
positioned in a weather station. In the humid, temperate climate of North

America, average soil surface temperature is higher than average air temperature

21

due to incoming solar radiation throughout the year (Bouyoucos, 1913). However,
in the early spring, when cool, wet weather is common, soil temperature is often
decreased by high water content (Swan et al., 1987). Residue cover on the soil
surface reflects solar radiation and acts as an insulator, slowing soil warming
during the spring. This effect is more noticeable in a temperate, cool climate with
wet and cool springs because high soil water content maintained by residue cover
is combined with low energy income (Van Wijk, 1963; Allmaras et al., 1977). Thus,
a better understanding of the consequences of a mulch cover on soil temperature
is critical to better predict plant development rates.

An experiment was designed to evaluate the use of soil temperature under

a residue cover to improve maize (Zea Mays, L.) development prediction.

Material and methods

A field experiment to measure the impact of surface crop residue on crop
development was performed at the Michigan State University farm, East Lansing,
Michigan (42° 42’N, 84° 28’W) during the years 1990 and 1991. The soil at the
site was a Conover loam (Fine-loamy, Mixed, Mesic, Udollic Ochraqualf, soil
description is given in Appendix 1).

Four crop residue levels were evaluated under fallow and a growing maize
crop. The experimental units were 12 m2 in size in the fallow treatment and 24 m2

in size where maize was planted. Cropped units were larger to eliminate border

22

effects. Fallow units were installed south of the cropped units to avoid any shading
from the maize canopy.

All plots were moldboard plowed in the fall and received secondary tillage
prior to planting in the spring. Maize (hybrid ’Pioneer 3751’) was planted on the
northern plots at a density of 7.25 plants m'2 on 8 May 1990, and 14 May 1991.
Plots were raked after planting to level the surface. Plots were fertilized with urea
at a rate of 142 kg N ha'l.

Maize stalks, saved from the previous harvest, were chopped with a grinder
and applied at the soil surface after planting. Loading rates were estimated from

the equation given by Van Doren and Allmaras (1978):

Load = - Ln(1 - cover) / Am [2.1]

The residue density coefficient (Am) was estimated by measuring the area
covered by small weighed samples of chopped maize residue using a leaf area
meter. The coefficient found was 0.00029 ha kg’l :t 0.00003. In 1990, loading rates
were 11.6 Mg ha‘l, 1.75 Mg ha'l, and 0.92 Mg ha'1 to reach a soil cover of 100%,
40%, and 25%. In 1991, loading rates were 13.3 Mg ha'l, 4.1 Mg ha'l, and 1.2 Mg
ha“1 to reach a soil cover of 100%, 70%, and 30 %. Soil cover was checked by the
photographic method (mimeo prepared by J .V. Mannering, Agronomy
department, Purdue University, "Estimating surface cover photographically"). A

control plot was left without residue cover.

23

Soil temperatures were measured at depths of 2.5 cm, 10 cm, and 30 cm on
each plot using copper-constantan thermocouples. Three 1 cm long thermocouples
were connected in parallel to get an average reading and avoid failure (Culik et
al., 1982). Thermocouples were connected to a datalogger for automated
recording (Easy-Logger, OMNIDATA) through a copper-constantan extension
wire (ANSI type TX, EXPP-T-ZO, OMEGA Engineering). Soil temperatures were
measured every 5 minutes.

For the no residue and the 100% cover treatments it was assumed that the
surface condition was uniform and therefore, soil temperature was uniform. The
temperature below a residue, for the intermediate coverage treatments, was
expected to be lower than under a bare surface because the random distribution
of crop residues at the soil surface affects the energy balance. Therefore, surface
sensor location was important. If the sensor was located below a residue, the
temperature was closer to a 100% cover temperature. To better estimate the
importance of sensor location, a grid of sensors covering a large surface was
necessary. In this experiment, a single point measurement was done and
temperatures were found intermediate between full covered and bare surface, but
closer to bare surface. Since the plant showed a proportional response to the
change in temperature, the assumption was made that the sensors were properly
placed.

Maximum and minimum soil temperature at 2.5 cm, 10 cm, 30 cm soil

depth were recorded hourly as well as the hourly average temperature at 2.5 cm.

24

To reduce the amount of data saved, the average temperature of the 10 cm and
30 cm sensors was not recorded. Because variations are of less amplitude at these
depths, the calculated average from the daily minimum and maximum value at
those depths was assumed to be an accurate representation. The variation at 2.5
cm was expected to be high, hence no insulation was put on the surface of the
thermocouple and the hourly average value was recorded by the datalogger. Data
were aggregated over the day, calculating the daily maximum, minimum, and
average values from the hourly maximum, minimum, and average recording. For
the bare treatment, and intermediate covered treatment, calculated average at
2.5 cm was 1 °C lower than the recorded average temperature, but only 0.5 °C
lower for the fully covered treatment. Hence, using the maximum and minimum
values of surface temperature to calculate the mean soil temperature would
underestimate average temperature especially when daily variations were high.

Air minimum and maximum temperature and rainfall were collected at the
nearest weather station (East Lansing 4S station). Because solar radiation was not
available at a near location, it was estimated from air temperature and
precipitation by a weather generator (Richardson and Wright, 1984). Weather
data are listed in Appendix 1.

Vegetative plant development was studied using leaf appearance rate. Fully
expanded leaves (FEL) were recorded when the collar appeared and the total

number of leaves (TLN) were recorded when the leaf tip appeared. Ten

25

contiguous plants away from the plots’ borders were monitored in 1990. Five
plants were monitored in 1991.

Soil thermal time calculated for four residue cover treatments from soil
temperature measured at 2.5 cm on the fallow plots was compared to air thermal
time. Soil temperature was summed from planting to stage V8. After stage V8, air
temperature was used. When the average daily temperature was below the base
temperature (T base), no value was added to the summation. When the average
daily temperature was above the maximum temperature, the maximum
temperature minus the base temperature was added. When the average daily
temperature was between the base temperature and the maximum temperature,

the average daily temperature minus the base temperature was added.

11‘ = 2:(Tsoil " Tbase) [2'2]

The base temperature used was 8 °C for the soil and the air (Ritchie and
NeSmith, 1991).

Because thermocouples were installed late in 1990, no soil temperature
data were available for the first days of the juvenile stage. Based on average soil
temperature differences to air temperature, from the date of thermocouple
insertion (18 June 1990) until 15 July 1990, soil thermal time for no cover, 25%,
40% and full residue cover treatments was estimated by adding 22 0C, 10 0C, 7

oC, and -2 °C to air degree day from planting to 18 June 1990.

26

Results and discussion

Thermal time calculations from daily average soil temperature at 2.5 cm
under a maize crop for 1990 and 1991 are given in Figures 2.1 and 2.2.
Accumulation of thermal time was faster using soil temperature than air
temperature for all cover treatments. Intermediate cover treatments had higher
increase rates than the fully covered treatment but lower than the no cover.
Fortin and Pierce (1991) showed less difference between air thermal time and
fully covered plots than between air thermal time and bare plots. Thermal time
for the fully covered treatment was similar to air thermal time especially in 1991
where differences were small.

The longest time required to reach 200 degree days which corresponds to
the end of the juvenile stage for most maize varieties of the northern Corn Belt
(Ritchie et al., 1986), was 27 days in 1990 and only 15 days in 1991. Differences
between the years was due to lower air temperature in 1990 compared to 1991.
Bare plots reached 200 degree days five days earlier than the 100% covered plots
in 1990 and only 2.5 days earlier in 1991. Because soil temperature was lower in
1990, differences were summed over a longer time, increasing the difference
between treatments in 1990.

Differences in thermal time are due to differences in soil temperature
patterns compared to air temperature. Soil temperature variation for three
continuous days was studied to show the differences between treatments to better

explain the consequences on thermal time calculation.

27

 

180-

160‘

140'

120‘

100‘

Thermal Time (Degree Celcius)

 

 

 

 

1:15 140 135 15'0 1'35 160 165
Day of Year 1990

—l— Air --+-- 100% cover "It" 50% cover
«13- 30% cover -)(— 0% cover

Figure 2-1: Estimated thermal time for Spring 1990 calculated using air temperature
and soil temperature under four soil cover treatment with a base temperature
of 8 oC.

 

Thermal Time (Degree Celcius)
é

 

 

 

 

”-
w.
40‘
20-1

0" I I I I I

135 140 145 150 155 160 165

Day of Year 1991
+Air ~+~0$cover "lie-Mower

--Ei-- 70% cover 44—1001. cover

Figure 2-2: Thermal time for Spring 1991 calculated using air temperature and soil
temperature under four residue covers with a base temperature of 8 °C.

29
Soil temperature

Soil temperature average at 2.5 cm on the bare plots was always higher
than air average temperature due to a rapid increase in soil temperature after
sunrise (Figure 2.3). Due to the soil buffering effect, soil temperature variation
was delayed with depth and its magnitude decreased (Table 2.1). Locating the
sensor at 2.5 cm below-ground where the maize meristem is located is then
important. Therefore, the meristem, while in the soil, experiences higher

temperature than air temperature.

Table 2.1: Minimum, maximum, average and amplitude of soil temperature at 2.5 cm
under four residue covers and air temperature on 24 June 1990 and 26 June
1990.

 

Day: June 24 1990 June 26 1990

 

Cover MIN MAX AVG AMP MIN MAX AVG AMP
Treat. °C °c °c °c °c °c °c °c

 

Air 10.6 16.1 13.4 5.5 12.8 27.2 20.0 14.4
0% 13.0 17.8 15.5 4.8 8.4 33.2 20.6 24.8
30% 13.9 17.7 16.0 3.8 9.9 29.4 19.8 19.5
50% 14.1 17.7 16. 1 3.6 10.3 28.5 19.4 18.2
100% 14.9 17.7 16.4 2.8 12.5 24.6 18.6 12. 1

 

Temperature (Degree Celcius)

30

 

35TH. - 16.3 MJ/m2

”-

 

 

24 June

is: - 23.93 MJlm2

25 June

Re - 27.3 MJlm2
.9" “r.

at is
:' ‘-.

I
.5.
I

 

E ...rit'i"

 

26 June

 

 

[ITIUéTIITTIIIIIIWI'II
12 18

— Air

 

ITIIFETIIIITITIIIBTUIII
12 1

Day in June 1990

«4» 2.5 cm "It!" 10 cm

IIIUIéUIIII1ETIIIIéIIIII

"Er-30cm

Figure 2-3: Variation of air temperature and soil temperature under a bare surface

at 2.5, 10, and 30 cm, between 24 June and 26 June 1990.

Temperature (Degree Celcius)

31

 

35PM - 18.3 MJlrn2

mm

254

 

15-

5-

24 June

in: - 26.93 MJlm2

 

 

:r \‘s:
l

i

i

l 25June

 

 

F18 - 27.3 MJIm2

26 June

 

 

18

— Air
“El" 50% Cover

6 IIIUIéUIlIIiIéIIIIIIITT‘dIIIllblllII1I2'llII116'I‘TT

Day In June 1990

~+-0$cover
*100%Cover

fiom 24 June to 26 June 1990.

 

OIIITIéIIIII1I2YI'qéUIIIT

nut-- 30% Cover

Figure 2-4: Hourly soil temperature variation at 2.5 cm under four residue covers

 

32

Soil temperature increased more rapidly from sunrise to 3 pm. EDT, which
roughly corresponded to solar zenith, on the no-cover treatment due to higher
incoming solar radiation and reached a higher maximum temperature than the
covered treatments (Figure 2.4). The maximum temperature reached on the fully
covered plot was closer to maximum air temperature, partially covered plots were
intermediate (Table 2.2). Hence, it appears that the major effect of the residue
cover on maximum soil temperature is through solar radiation reflection and
interception (Allmaras et al., 1977).

Minimum soil temperature was also affected by the residue treatments.
Residue coverage acted as an insulating layer and decreased the loss of energy
from the top layer. Soil with a higher cover had a higher minimum temperature.
The bare soil minimum temperature was 8.4 °C on 26 June 1990, 4 °C above air
temperature, while the fully covered soil minimum temperature was 12.5 °C
(Table 2.2).

Because air temperature variation and solar radiation were low on 24 June
1990, differences between treatments were small and soil maximum temperatures
for all treatments at 2.5 cm were similar (Table 2.2). On 26 June 1990, however
the difference was much greater.

Overall, residue coverage decreased soil temperature average and
amplitude, at least during the spring. The beneficial effect of higher minimum
temperature was dampened by slow rising temperatures and lower maximum

temperatures. Nevertheless, when there was a cold front passage, the differences

 

33

Table 2.2: Minimum, maximum, average and amplitude of air and soil temperature
at 2.5 cm, 10 cm, and 30 cm on June 24, 1990 and June 26, 1990.

 

 

24 June 1990 26 June 1991
Sensor MIN MAX AVG AMP MIN MAX AVG AMP
Incation °C °C °C °C °C °C °C °C
Air 10.6 16.1 13.4 5.5 12.8 27.1 20.0 14.4
-2.5 cm 13.0 17.8 15.5 4.8 8.4 33.2 20.6 24.8
-10 cm 15.9 19.6 17.8 3.7 13.0 26.7 19.9 13.7
-30 cm 18.0 20.1 19.1 2.1 16.9 19.9 18.4 3.0

 

in average soil temperature between treatments decreased and even reversed at
the end of the front. Then, covered soil was warmer than bare soil. Over the
recording period, on the average, soil temperature under a full residue cover was
cooler by 1.5°C than an uncovered soil. It tended to bring soil temperature closer
to air temperature but slightly warmer by 0.18°C.

Differences in soil temperature patterns between air and at 2.5 cm
below-ground where the maize meristem is, affected thermal time accumulation.
The presence of surface crop residue affected soil temperature and hence the
thermal time experienced by the meristem. Difference between air and soil
temperature at 2.5 cm was larger at low crop residue levels and when the daily

amplitude of air temperature was large. Because there is a difference in thermal

34

time when air temperature is used instead of soil temperature, the consequences
of using one thermal time calculation versus the other on plant development

prediction needs to be studied.

Plant development

Plant development was delayed by residue treatment in 1990 and 1991
(Figure 2.5 and 2.6). Because of a cooler spring in 1990 compared to 1991, the
number of days before the meristem emerged above the ground was larger
(Figure 2.1 and 2.2). Therefore, differences between treatments were higher in
1990 compared to 1991 as the differences between treatments were cumulated
over a longer interval. The rate of fully expanded leaves appearance and leaf tip
appearance were both delayed by crop residue. The number of visible tips of
growing leaves was smaller on the residue treatment than on the no-cover
treatment (Figure 2.7). The differences diminished after tasselling and all plants
reached the same final number of leaves and height. Therefore, crop residues
delayed plant development, but did not modify final development.

Leaf development is better correlated to soil thermal time than air thermal
time for both 1990 (Figure 2.8) and 1991 (Figure 2.9). Although correlations were
good between air temperature and plant development for each treatment, they
were different among treatments.

The correlations between leaf tip number and thermal time calculated from

air temperature and from soil temperature recorded on each plot were high in

35

 

10"

Total Leaf Number

 

2. ,x' 1m 0.01

 

 

 

0 I I I I I I I I
150 155 160 165 170 175 180 185 190 195

Day of Year 1990

+0$cover ~~+~~30$cover --Jit--50%cover -Ei--100%cover

Figure 2-5: Maize early leaf development under four residue covers versus day of the
year 1990.

36

 

18

 

16‘

14-

12M

10"

Total Leaf Number

 

i M

a
1 I I j I 1 I I I

so 165 170 175 130 135 160 195 200 205 210
Day of Year 1991

 

 

 

+0$cover +30%cover -*°50%cover -EI--100%cover

Figure 2-6: Maize leaf development under four residue covers versus day of the year
1991.

37

 

Number of leaf tips in the whorl
(D

 

 

 

 

150 155 130 155 1io 1i5 160 135 190 195
Day of year 1990

-I- 0% cover ~+--- 30% cover "It” 50% cover 43- 100% cover

Figure 2-7: Number of leaf tips in the whorl (total leaf tip number minus total collar
number) versus day of the year 1990.

 

38

 

18

14"

12"

10-

 

Totel Leaf Number
O

 

 

 

 

 

 

2' Air Thoma! Time

c I I I I I I I

19'

t I

16-1 . 5‘

144 -
_ '1’:
3 121
E a:
3 d t I
z 10 .-
3 a "

‘ I

3 x
.2 6*

4.4

2"
24 :1
Soil Themm Tlme
c I I I I I j T
0 100 200 300 400 500 800 700 800

Thermal Time

I 0%oover '0 30%oover I! 60%cover X 10096ri

Figure 2-8: 1990 early leaf development versus thermal time from planting calculated
from air and soil temperature at 2.5 cm under four residue covers using a base
temperature of 8 °C.

 

39

 

 

 

 

 

 

 

 

10 fl
0
16- I
I
e- D
3 14-
5
- 12- :
5 .
3
,2 10-
I
a.
D
AirThermalTime
6 1 r
18 I:
I: III.-
18" D ~.-
“e-
j 14- ‘3
E
3 12- ,. '
D O
3
o 10.
Ed
D SoilThernanlme
‘300 400 500 500 700 300 960 1000

 

 

Thermal Time

I 0%oover 4- 30360011» 3“ soxoover D 10016ri

Figure 2-9: 1991 leaf development versus thermal time from planting calculated from
air and soil temperature at 2.5 cm under four residue covers using a base
temperature of 8 °C.

40

Table 2.3: Correlation coefficient for leaf tip number prediction from thermal time.

 

 

 

Treatment Thermal Constant STDE Slope R2
Time

0% cover Air 1.62 0.73 1/37.12 0.982
30% cover Air 1.51 0.63 1/37.98 0.986
50% cover Air 0.95 0.65 1/36.80 0.986
100% cover Air -0.53 0.32 1/37.68 0.997
All Air 0.89 1.06 1/37.39 0.950
0% cover Soil 0.32 0.47 1/42.07 0.993
30% cover Soil 0.50 0.38 1/42.09 0.995
50% cover Soil -.004 0.38 1/40.68 0.995
100% cover Soil -0.75 0.36 1/39.9 0.995
All Soil -0.05 0.45 1/40.91 0.991

 

 

both cases but improved when soil temperature was used to calculate thermal time
(Table 2.3). For better understanding, the inverse of the slope is given in Table
2.3 as it represent the phyllochron. Slopes were similar among treatments but
higher, 1/40 compared to 1/37, when soil temperature was used. When all data
were aggregated the correlation was higher using soil temperature for the early
plant development than using air temperature. The intercept was much smaller
(-0.05 leaves) compared to air thermal time correlation 0.886. The intercept can
be interpreted as the delay in the plant development. When air temperature was
used the intercept increased from -0.53 leaves on the fully covered plots to 1.62

leaves on the uncovered plot. Variation among treatments was smaller when soil

41

temperature was used from -0.75 leaves on the fully covered plots to 0.32 on the
bare plots. Differences in plant development may exist due to delayed germination
which may have occurred due to lower soil temperature. Furthermore, the error

on leaf number prediction was always smaller when soil temperature was used.

Errors in prediction

These data indicate that air temperature to predict plant development gives
less error when the soil is covered but larger error when the surface is bare. Using
a base temperature of 6 °C, as used in France to calculate thermal time (Bloc et
al., 1983), would bring the air thermal time in the same range of values as soil
thermal time calculated with a base temperature of 8 °C and would diminish the
prediction errors (Figure 2.3). Thermal time curves were in the same range of
values and slopes. Therefore, using air thermal time with a base temperature of
6 °C gave better estimation of plant development for some locations. Although
this may be true for the East Lansing location, where the bare soil surface was
warmer than the air on a average value of 2 °C, it may not hold for other
locations or for residue covered soils. The AGPM (Association Générale des
Producteurs de Mais, France) decided to offset the thermal time by a constant for
some of the study sites where the soil temperature was constantly low (F. Ruget,
1992, personal communication) to overcome the error in development prediction.

Although changing the base temperature in thermal time calculation gives

better prediction, there would still be an error in the prediction of the plant

42

 

Thermal Time (Degree Celcius)
i
I
i

 

 

 

G I I 1 I
135 1710 145 150 155 100 105

Day of Year 1990

-l- Air «Ow “Cover «lit-- 30%Cover
via-5011001!" —N— 100%Cover

Figure 2-10: Thermal time using air temperature with a base temperature of 6 °C
and soil temperature with a base temperature of 8 °C.

43

development but it would be smaller than using a base temperature of 8 °C. This
change does not hold for all climate conditions or soil conditions. Hence, soil
temperature provides the most appropriate value for the thermal time calculation.

The choice of the base temperature is important because it affects the
value of thermal time especially when the temperature is close to the base
temperature which is often the case in the early development stages in temperate
zones. It may slightly affect the correlation between development and thermal
time but it changes the calculated development rate by changing the temperature
accumulation rate.

Base temperature was evaluated from minimum temperature required to
induce a response in maize development. Hesketh and Warrington (1989)
reported different base temperatures for different physiological processes: base
temperature varied from 5.85 °C for leaf primordia calculation to 8.9 °C for
germination and emergence. Kiniry (1991) reported a base temperature of 8 °C
for leaf tip appearance and 10 °C for germination and elongation determined in
controlled environment. Hesketh and Warrington (1989) reported different rates
of leaf tip appearance for different base temperatures.

Base temperature is based on the physiological response of the plant at
different developmental stages, therefore we shouldn’t change the base
temperature but should change the parameters used in thermal time calculation.

Using soil temperature with a phenological sound base temperature will give more

44

accuracy to the prediction of plant development as it will be valid in all
environments.

Furthermore, we should be cautious in the use and definition of thermal
time needed to attain a developmental stage. Most of the values are derived from
field data and are usually calculated from air temperature. Detail on the
calculation of thermal time, temperature used, and values of base temperature
must be known to be certain that the value recorded is accurate. Correction
should be needed to obtain the true value of thermal time experienced by the

meristem.

45

Conclusion

This experiment demonstrated that maize development is sensitive to crop
residue mulches. The surface crop residues slowed plant development by
decreasing soil surface temperature. The delay in maize development and growth
was longer during the cooler year (1990) than the warmer year (1991). To
accurately predict plant development, soil temperature should be used to calculate
thermal time while the plant meristem is below ground. The base temperature to
be used is 8 °C as it represents the minimum physiological response of maize
development. Adding a model of crop residue to the thermal time based crop
models provides a more powerful tool to study the consequences of residue

management on final yield for several climatic conditions.

46

Bibliography

Allmaras, R. R., E. A. Hallauer, W. W. Nelson, and S. D. Evans. 1977. Agric. Ext.
Stn. Techn. Bull. no. 306. St Paul, Minn.

Bauer, A., C. Fanning, J. W. Enz, and C. V. Eberlein. 1984. Use of
growing-degree days to determine spring wheat growth stages. Bull. EB-37.
North Dakota, USA. North Dakota State Univ. Agric. Ext.

Bloc, D., GP Gay and J. P. Gouet. 1983. Durée des phases végétatives et
reproductrices du mais, influence de la temperature. In
INRA-CNRS-AGPM. (ed.) Physiologic du mais. Royan, France,
INRA,Paris, France.

Bouyoucos. 1913. An investigation of soil temperature factors and some of the
most important factors influencing it. Mich. Ag. Expt. Sta. Tech. Bull.
17:1-196.

Brown, D. M. 1975. Heat units for corn in the southern Ontario. Factsheet 75-007.
Ontario, Canada. Ontario Agric. College, Ministry of Agriculture and food.

Cellier, P., F. Ruget, R. Bonhomme and M. Chartier. 1992. A model to estimate
the temperature of maize apex during the early growth stages. pp 168-169
In A. Scaife. (ed.)Proceedings of the Second Congress of the European
Society for Agronomy. Warwick University, England, 23-28 August 1992.
ESA,Colmar, France.

Culik, M. N., J. W. Doran and K. A. Richards. 1982. Construction of soil
thermocouples for the novice. Soil Sci. Soc. Am. J. 46: 882-884.

Fortin, M. C. and F. J. Pierce. 1991. Timing and nature of mulch retardation of
corn vegetative development. Agron. J. 83-1:258- 263.

Gallagher, J. N. 1979. Field studies of cereal leaf growth. J. Exp. Bot.
30(117):625-636.

Gilmore, E. C. ,. Jr and J. S. Rogers. 1958. Heat units as a method of measuring
maturity in corn. Agron. J. 50:611-615.

47

Hesketh, J. D. and I. J. Warrington. 1989. Corn growth response to temperature:
rate and duration of leaf emergence. Agron. J. 81:696-701.

Hunter, R. B., M. Tollenaar and C. M. Breuer. 1977. Effects of photoperiod and
temperature on vegetative and reproductive growth of maize (Zea Mays)
hybrid. Can. J. Plant Sci. 57:1127-1133.

Kiniry, J. R. 1991. Maize phasic development. In J. Hanks and J .T. Ritchie (ed.)
Modeling plant and soil systems. Agronomy 31: 55-70.

Richardson, C. W., and D. A Wright. 1984. WGEN: A model for generating daily
weather variables. USDA, ARS-8. Springfield, VA, USA. National
Technical Information Service.

Ritchie, J. T., J. R. Kiniry, C. A. Jones and P. T. Dyke. 1986. Models input.
p. 37-48 In C. A. Jones and J. R. Kiniry (ed.) CERES-Maize a simulation
model of maize growth and development. Texas A&M Univ. Press, College
Station, Texas, USA.

Ritchie, J. T. and D. S. NeSmith. 1991. Temperature and crop development. J.
Hanks and J. T. Ritchie (ed.) Modeling plant and soil systems. Agronomy
31: 5-29.

Swan, J. B., S. C. Schneider, J. F. Moncrief, W. H. Paulson and A. E. Peterson.
1987. Estimating corn growth, yield, and grain moisture from air growing
degree days and residue cover. Agron. J. 79:53-60.

Van Doren Jr, D. M. and R. R. Allmaras. 1978. Effect of residue management
practices on the soil physical environment, microclimate, and plant growth.
In Oschwald W.R. (ed.)Crop residue management systems. Houston,
Texas, USA, Nov. 28-Dec 3, 1976. ASA-CSSA-SSSAMadison, Wisconsin,
USA.

Van Wijk, W. R. 1963. Physics of plant environment. North Holland, Amsterdam.

Wang, J. Y. 1960. A critique of the heat unit approach to plant response studies.
Ecology 41:785-789.

Watts, W. R. 1972. Leaf extension in Zea Mays. Journal of Experimental Bot.
23-76:713-721.

Chapter 3: Surface crop residue influence on maize (Zea Mays, L.) growth and

soil water content.

Abstract

Residue cover decreases soil surface temperature and increases soil surface
water content. A field experiment was conducted in 1990 and 1991 at East
Lansing, Mich., on a Conover loam, to study the effect of four crop residue loads
on soil water content, maize leaf surface area, and final biomass. The residue
covered plots had delay in plant development induced by reduced soil
temperature. The development delay lasted until maturity. Plant water content was
higher on the covered plots especially when the temperature were lower in 1990.
Plants on the covered plots took up water from the upper layers and for a longer
period than plants on the bare plots. Leaves grew slower on the residue covered
plots, but their maximum size was not affected. Leaf surface area was similar
among plots when compared at identical development stages. Leaves senesced
faster on the 100% cover plots than on others. Further work is needed to evaluate
the effect of a residue cover on the root growth and distribution, and the

consequences for the plant throughout its development.

48

49
Introduction

The experiment described in Chapter 2 showed a delay in plant
development due to decreased soil surface temperature induced by surface residue
cover at the East Lansing location. The effect of delayed development on the final
yield, which is of interest to the farmer, is not consistently different. Several
authors (Jasa and Dickey, 1990; Reicosky et al., 1977; Griffith et al., 1977;
Amemiya 1977; Bennett 1977) reported that yieldswere about the same for
conservation tillage and conventional tillage. Because of improved water
infiltration and water conservation resulting from a residue cover, yields are
improved in lower rainfall years and dry locations (Griffith et al., 1977). Reicosky
et a1. (1977) reported that on poorly drained soils, maize yields were decreased
because poorly drained soils are usually colder due to higher water content. When
vegetative maize development is delayed by lower soil temperature created by a
residue cover, maize yield loss due to short maturity period is more noticeable.
Hence, residue cover often decreases maize yield under cool temperate climates
(Griffith and Mannering, 1985).

Residue cover increases soil water availability to plants by keeping soil
water infiltrability high (Freebairn and Gupta, 1990) and reducing soil water
evaporation (Griffith et al., 1977). Residue cover has been reported beneficial in
the southern states because the delay in development is lower due to higher

temperatures in the spring. Increased water content benefits the crop (Reicosky et

50

al., 1977). Residue cover decreases the probability of water deficit by increasing
water infiltration and reducing evaporation.

Residue cover at the soil surface decreased soil temperature at 10 and
30 cm depth. The difference among treatments varied with the change in air
temperature. Most of the experimental work on the effect of soil temperature on
root morphOIOgy has been done in controlled environments. The minimum
temperature for maize root growth is 10 °C and the optimum temperature is
30 0C (Cooper, 1973). At 17 °C, maize root growth is less than half the maximum
value. Mackay and Barber (1984) found that maize root length at 18 °C was half
that at 25 °C when maize was grown with a constant air temperature of 25 °C.
Pahlavanian and Silk (1988) found the relative length increase and root biomass
deposition to be strongly temperature dependent. Gregory (1983) found that
lateral root development was correlated to meristem temperature development.
As seen in Chapter 2, plant development was delayed by a residue cover. The
delay in plant development probably caused a decrease in root length due to
lower soil temperature at 10 and 30 cm depths. Temperature effect on root
distribution can not be separated from soil water distribution. High water content
at the soil surface maintains more roots in the surface layers (Barber, 1971; Unger
et al., 1981). Plants with roots in the top soil layers take up more water from small
rains. If a severe water stress occurs later in the season, this rooting pattern may
hinder water uptake. The optimum temperature for maize root elongation is

30 °C (Anderson and Kemper 1964). Low soil temperature impairs root growth by

51

and increasing water viscosity. Maize root nutrient uptake, especially nitrogen, is
decreased when soil temperature decreases (Voorhes et al., 1981).

This study was designed to evaluate the consequences of a residue cover on
soil water content, maize leaf surface area, plant water content at harvest, and

final biomass and yield.

Material and methods

Experimental design is detailed in Chapter 2. Soil volumetric water content
was measured using a neutron probe. One polyvinyl access tube was installed on
each experimental plot. The neutron probe was calibrated when the access tubes
were installed by measuring gravimetric water content and bulk density at several
depths. The linear regression between neutron counts and soil volumetric water

content was:

= 0.286 * (COUNT/Standard count) - 0.021 R2 = 0.68 [3.1]

Neutron probe readings were taken at depths of 10, 30, 50, 70, 90, and
110 cm in 1991, on each plot starting 8 July 1991 to 1 September 1991. No
neutron probe readings were taken in 1990.

Surface water content was measured using time domain reflectometry
(TDR) in which soil dielectric conductivity is measured by sending an

electromagnetic wave through a stainless steel parallel line buried in the soil. The

52

electromagnetic wave through a stainless steel parallel line buried in the soil. The
dielectric constant of the soil is correlated with the average volumetric water
content along the transmission line (T opp and Davis, 1982). Correlation has been
proved valid for most of the soil types as long as organic matter content is not too
high. The calibration curve given by Topp and Davis (1982) was used for the
Conover loam soil. The TDR waves were saved on a portable computer and
analyzed in the laboratory on a more powerful computer using an algorithm
written in BASIC (D. Knezek and RI. Pierce, 1990, personal communication).
Duplicate set of parallel lines made of two 0.47 cm diameter stainless steel rods,
distant by 5 cm, were installed vertically from the surface to depths of 10, 20 and
40 cm in 1990. To measure the water content in the 2.5 cm surface layer, 10 cm
long transmission lines were installed diagonally in order to have a sufficient
length of transmission line. In 1991, depths of measurement were changed to 7,
15, 26, 40 cm.

Leaf area was measured using a correlation that exists between leaf length
and width. At the same time that plant development was monitored, leaf length
from collar to tip and leaf maximum width were measured using a meter stick on
the development monitored plants in 1990 and 1991. For the growing leaves, leaf
length was measured from the whorl to the tip. Percent of leaf senesced was
visually estimated. These measurements were used to estimate total and
photosynthetically active leaf area using the equation (Sanderson et al., 1981):

Area = (Length * Width) * 0.75 [3.2]

53

Maximum leaf length and width over the season were recorded on 12 July 1990
and 26 July 1991.

The development and growth monitored plants were harvested on 13
October 1990 and 17 September 1991. Leaf, stern, and cob were weighed at
harvest and then dried at 84 °C for 48 hours. Dry matter and water content of the
leaves, stem, cob, and grain were recorded. Grain number per plant was also
counted.

Analyses of variance were performed and differences between means were

tested using the least significance difference test (LSD) at an alpha level of 0.05.

Results and discussion

Residue cover on the fallow plots increased water content of the surface
layers up to a depth of 40 cm (Figure 3.1 and 3.2). Increase of water content on
the covered plots was mainly due to decreased evaporation. Jackson (1975)
observed that evaporation affects only the first 30 cm of the soil profile. The
difference between treatments was larger at the surface and decreased with depth.
The 100% cover considerably diminished soil water evaporation and kept the soil
surface layer at a water content close to the drained upper limit. Intermediate
cover decreased the rate of soil water evaporation depleting water in the surface
layer but maintaining higher water content in lower layers. Soil water content at
depths below 50 cm was not significantly affected by the residue cover on the

fallow plots. In 1991, due to frequent rainfall, the difference between treatments

Soil volumetric water content (ems/ems)

Soil volumetric water content (ems/ems)

54

 

0.45
0.4-
0.35-
0.3-
0.25-
0.2-, ,.._
0.154
0.1-

0.05“

 

0-2.5 cm

 

 

 

0.31

".
0.25“ 1’ 3.

i
0.2‘
0.15”!
0.1"

0.05

0-10 cm

0..
a...
o'.'
a...
-'

 

 

—-I— No cover

26o 265
Day of Year 1990

l
210

 

T I
21 5 220 225

-----+ 30% cover -*- 50% cover "El" 100% cover

Figure 3.1: Surface volumetric water content for the O-2.5 cm and 0-10 cm layers
under for residue cover on fallow plots during summer 1990.

55

 

0.45

0-20 cm

0,4.
0.35-
0.3:
0.25- ,
p

0.2«

0.15“

 

01*

Soil volumetric water content (cmslcma)

0.05-i

 

 

0-40 cm

 

0.15‘

0.1-4

Soil volumetric water content (cm3/cm3)

0.05‘

 

 

 

o
100 195 190 155 260 265 21'0 2i5 250 2&5 200
DayctYear1990

-l- Nooover ......... 30% cover -*- 50% cover "El" 100% cover

Figure 3.2: Surface volumetric water content from O to 20 cm and O to 40 cm under
four residue cover during summer 1990 on fallow plots.

56
was smaller (Figures 3.3 and 3.4).

Surface layer water content on the cropped plots followed the same trends
as on the fallow plots. Differences between treatments were noticeable at 40 cm
(Figure 3.5 and 3.6). The plants did not seem to have benefitted from higher soil
water content by taking up more water during the measurement period (9 July
1990 to 18 August 1990). This was probably because active roots were deeper. Soil
water change in the 40 cm depth was higher on the fully 100% cover and 70%
cover treatment throughout the measurement period (Figure 3.7). Soil water
content change from the surface to a depth of 1 m was similar among treatments
until the beginning of August 1991. After, soil water decrease was higher on the
70 and 100% cover plots than on the 0 and 30% cover. This difference in plant
water uptake at the end of the season is more linked to plant development than
root activity. Because plants on the covered plots were delayed, they needed more
water for their growth than the plants entering the grain maturity stage.

Plant leaf surface area was studied as an indicator of plant grth and
response to water stress. Although at time of measurement in 1990, maize was not
completely developed on the fully covered plots, study of the first 9 leaves was
possible because those leaves were fully developed. In 1990, covered plots seemed
to have larger and longer leaves although the difference was not always statistically
significant. Due to slower growth induced by lower soil temperature on the residue
covered plots, plants had more time to transport carbohydrate to the leaves.

Differences were less in 1991, and were probably due to a shorter vegetative

57

 

0.45- I tr”
0.4-

0.35-

 

Volumetric eoi water content (cm3lcm3)

 

0-7 cm

 

 

0.05

 

0.3

 

0.25“

0.2 4

 

0.15"

 

0.1d

Volumetric eoi water content (cm3/cm3)

0-15 cm

 

 

0005 I I I I I
1 90 200 21 0 220 230 240 250

Day of year 1991

 

-I— Nooov. «+--- 30% cov. --)K- 70% cov. «B- 100% cov.

Figure 3.3: Surface volumetric water content from O to 7 cm and O to 15 cm under
four residue covers on fallow plots during summer 1991.

58

 

 

 

 

 

 

(L35
A
2 m
o I ‘
3; or a ,’ in
E \ I X ‘a

t a ” ‘~
9; ‘ I
a ”.0.“ 1 h
c l‘ \I
o
‘5 0.25-
; ..-—-. W
* 0'-

§ . or"
_ 02-
2 J
.9
z:
2 0.15-
a
1,5 0-26cm

O.‘

(135

l \
fl ” \s
‘ \ In.
u I '- ~v --~‘*
o.a~ x". ..‘f‘y-V “t...

 

0.2‘

0.15"

Volumetric soul water content (cm3/cma)
5
A;
X

0-40 cm

OJ I I I I I
‘90 200 210 220 230 2‘0 250

Day of year 1991

 

 

 

+Nocov. --+-30%cov. -3K--70%cov. -B-tooxcov.

Figure 3.4: Volumetric water content from O to 26 cm and O to 40 cm under four
residue covers on fallow plots during summer 1991.

59

development time because of higher temperatures. Leaf length and width of the
final leaves were shorter in 1991, most probably for the same reasons.

To determine if leaf area was modified by residue cover, leaf area versus
day of the year was plotted (Figures 3.8, 3.10) and soil thermal time (Figure 3.9,
3.11). At all times of measurement, the leaf area of the fully residue covered plots
was usually lower than the bare plots especially in 1990. When leaf area versus
thermal time was plotted the differences lessened for the increasing section of the
curve. In 1991, more measurements allowed comparison of total leaf area to green
leaf area. As seen in Figure 3.11, green leaf area is decreased in the fully covered
plots whereas the total leaf surface area is relatively unaffected (Figure 3.12).

Residue cover had a major effect on delaying leaf surface area
development, but did not have much effect on final total surface area. The area
senesced faster in 1991 on the fully covered plots especially after anthesis.

Despite the late harvest date in 1990 water content of the cob and the
stover was still high. In both years, the higher the residue cover the higher the
water content of the grain and the stover (Table 3.1). This difference indicates
that the delay in maturity extended all the way to the end of the season.
Differences between years are explained by differences in rainfall patterns and
temperature regimes at the end of the season. Because September 1990 was
cooler and more humid than September 1991, maturity was delayed and water
content was higher. For the same reasons differences among treatments were

smaller in 1991 than in 1990.

 

 

 

 

225

 

 

' eeeeeeeeee
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o o o 0

880883 23:8 .82., 23.822, ..om

 

Day of Year 1990

~-~-+ 30% cover --)lE-- 50% cover -E}- 100% cover

-I- Nooover

Figure 3.5: Surface volumetric water content from O to 2.5 cm and 0 to 10 cm under

four residue covers on maize cropped plots during summer 1990.

61

 

0.45

0-20 cm

0.4-
0.35-
0.3-
0.25-'
o.2~

0.15“

 

 

0.1d

Soil volumetric water content (cm3/cm3)

 

 

0-40 cm

 

 

0.15‘

0.1d

Soil volumetric water content (cm3/cm3)
0
it

0.05'

 

I

00 165 190 19's 200 265 21I o 215 250 225
Day of Year 1990

 

 

.00
.1
d

+ Nooover ---+-- 3096 cover --)K-- 50% cover -E}- 100% cover

Figure 3.6: Surface volumetric water content from O to 20 cm and O to 40 cm under
four residue covers on maize cropped plots during summer 1990.

62

 

G.)

 

 

Water change from 0 to 40 cm (cm)

 

 

 

 

 

 

 

 

 

 

2.5-
A
E 2-4 : ‘\ ‘. ..."n‘
O 1 5" III \ “I
8 ' h... s .
a .. I ‘\ ‘\ '.

'- ‘ ‘b \t "u.

‘ -1 ' "a.
g “b‘\ .
E 0.5“ I I ‘\‘~-o'~‘\
2 a ‘QQ'sga ‘\
'0- 3; x ‘s

0 gas: 3 \
é ‘I: \* \‘ \

‘5‘: e -" ‘ ‘\
- ‘. .' \ \
2 “0.5-1 . ..' ‘e. v ..’ \\\
0 ..\ . °. ‘.:’ ‘:\
b I. u. . \‘ . \\\\\
f3 " ‘ 5; ‘~; 1:
3 \-
‘1 .5“ .‘E
'2 I I I T I I
1 80 1 90 200 21 0 220 230 240 250
Day of year 1991

-I— 0% cover "+30% cover -u<- 70% cover ~G- 100% cover

Figure 3.7: Soil water content variation (cm) in the top 20 cm and 100 cm under four
residue covers, on maize planted plots, during summer 1991.

63

 

ILed 0.05
4500-

4000 "‘
3500 -

3000 ~ //

2500-1

I_—'_‘I"

2000 '-

1500‘ /

1000‘

5°°‘ ../f_

C.

Leaf Surface Area (cm2)

J

 

 

I

0 I I I I
105 170 175 190 185 190 195
Day of Year 1990

 

+0%oover +30%oover +50%oover -B—100%oover

Figure 3.8: Plant leaf surface area for four surface residue cover treatments versus
day of year 1990.

 

Leaf Surface Area (cm2)

 

 

 

 

0100 200 300 400 500 600 700 600
Thermal Time 1990

+0%oover +30%oover +50%cover -B—100%oover

Figure 3.9: Plant leaf surface area versus thermal time calculated from soil
temperature in 1990.

65

 

4500
ILed 0.05

4000'

1500‘

Leaf Surface area (cm2)

1000‘

500‘

 

O I I I I T I I I
160 1 70 1 80 190 200 21 0 220 230 240 250

Day of Year 1991

 

 

 

+0%oover ~-+---30%cover --)K--50%oover -El--100%oover

Figure 3.10: Plant leaf surface area for maize plants grown under four residue covers
versus day of the year 1991.

66

 

4500

4000-

3500 a

3000 ‘

2500 a

2000-1

1500‘

Leaf Surface area (cm2)

1000‘

 

500"l

 

 

 

200 400 660 000 1000 1200 1400 1600
Thermal Time 1991

—I- o ‘5 cover ---—-+ 30 '5 cover -*- 50 ‘5 cover --X-- 100 ‘ia cover

Figure 3.11: Plant green leaf surface area for maize grown under four residue covers
in 1991.

67

 

4500-
4000: , -

3500‘

3000- «/

1500-1

1000-

Total Leaf Surface Area (cm2)

 

500.4 J

 

 

 

I I r l T I I l
100 165 170 175 190 105 190 195 200 205 210
Day of Year 1991

+O9boover +30%oover +50%cover -B—100%oover

Figure 3.12: Total leaf surface area for maize plants grown under four residue cover
treatments in 1991.

68

Table 3.1: Stover (leaves and stem) and cob water content at harvest in 1990 and
1991 under four residue cover treatments.

 

 

13 October 1990 27 September 1991
Soil water content (%) water content (%)
cover Stover Cob & Grain Stover Cob & Grain
0 % 79.3 29.2 29.1 16.3
30 % 80.6 31.7 37.5 16.0
50 % 81.4 35.9 28.2 15.2
100 % 84.1 41.1 43.2 17.8
LSD 2.5 6.5 6.8 2.9

 

Residue cover had more impacts on the grain water content at harvest in a
wet year than a dry year. Kernel weight was not affected by the treatment or
climate, but the grain number per ear was affected in both years. In 1990,
flowering occurred on the bare plots on 25 July and several days later on the
other treatments, after a rainfall of 40 mm. In 1991 flowering occurred on 18 July
after a period of 10 days without rain. Lower water supply or higher temperature
that increased the potential evaporation decreased the grain number in 1991
compared to 1990 (Table 3.2) possibly because of a difference in time of grain
formation (Grant et al., 1989).

While intermediate residue levels slightly increased grain number, the fully
covered plots had a smaller grain number. As discussed in the leaf expansion

data, time of water deficit matched grain differentiation stage and decreased the

69

number of grain in the fully covered plot. Kernel weight was unaffected by

treatments probably due to compensation growth.

Table 3.2: Grain number per plant , kernel weight (g), and total grain weight per
plant (g) for four residue cover treatments in 1990 and 1991.

 

 

1990 1991
Grain Kernel Yield Grain Kernel Yield

number weight number weight

per plant g g plant’l per plant g g plant'1
0% 608 0.251 152.3 451 0.228 103.0
30% 614 0.256 157.3 443 0.252 111.6
50% 620 0.245 151.9 379 0.243 92.1
100% 515 0.260 133.8 381 0.242 92.2

LSD 60.9 0.03 21.0 183.8 0.025 45.5

 

70

Conclusion

Residue cover increased soil surface water content by decreasing soil water
evaporation. The plant leaf growth was delayed by the residue cover but its
maximum grth was unaffected. In 1991, the plant leaves senesced faster on the
70% and 100% covered plots. The effect on the grain yield was not significant but
the grain water content was higher on the covered plots. Residue delayed the
growth up to maturity but did not affect the yield. The expected differences in
root distribution and plant water uptake might affect the yield for other climates
or rainfall distribution. Further study on the root distribution under a residue
cover for several climatic conditions would help us understand the consequences
of a residue cover on plant water and nutrient deficit. The use of crop models
which include a soil cover, will allow us to study the consequences of plant delayed

development on final yields for several soil and climate conditions.

71
Bibliography

Amemiya, M. 1977. Conservation tillage in the western Corn Belt. J. Soil Water
Conserv. 29-38.

Anderson, W. B. and W. D. Kemper. 1964. Corn growth as affected by aggregate
stability, soil temperature and soil moisture. Agron. J. 61:867-872.

Barber, S. A. 1971. Effect of tillage practice on corn root distribution and
morphology. Agron. J. 63:724-726.

Bennett, O. L. 1977. Conservation tillage in the Northeast. J. Soil Water Conserv.
9-13.

Cooper, A. J. 1973. Root temperature and plant growth-A review. Research
Review 4. Commonwealth bureau of horticulture and plantation crops.

Freebairn, D. M. and S. C. Gupta. 1990. Microrelief, rainfall and cover effects on
infiltration. Soil & Tillage Research 16:307- 327.

Grant, R. F., B. S. Jackson, J. R. Kiniry and G. F. Arkin. 1989. Water deficit
timing effects on yield component in maize. Agron. J. 81:61-65.

Gregory, P. J. 1983. Response to temperature in a stand of pearl millet
(Pennisetum typhoides S&H): 3. Root development. J. Exp. Bot. 34:744-756.

Griffith, D. R., J. V. Mannering and W. C. Moldenhauer. 1977. Conservation
tillage in the Eastern Corn Belt. J. Soil Water Conserv. 32:20-29.

Griffith, D. R. and J. V. Mannering. 1985. Differences in crop yields as a function
of tillage system, crop management and soil characteristics. In F. D’Itri

(ed.) A systems approach to conservation tillage. Lewis Publishers, inc.,
Chelsea, Mich., USA.

Jackson, R. D. 1975. Diurnal changes in soil water content during drying. In B. R.
Russell and M. Stelly (ed.) Field soil water regime. SSSA special
publication no 5. ASA-CSSSA-SSSA, Madison, Wisconsin, USA.

72

Jasa, P. J. and E. C. Dickey. 1990. Yield and conservation tillage. In E. C. Dickey
and P. J. Jasa. (ed.)Conservation tillage proceeding no.9. University of
Nebraska, Cooperative extension,Nebraska, USA.

Mackay, A. D. and S. A. Barber. 1984. Soil temperature effects on root grth
and phosphorus uptake by com. Soil Sci. Soc. Am. J. 48:818-823.

Pahlavanian, A. M. and W. K. Silk. 1988. Effect of temperature on spatial and

temporal aspects of growth in the primary maize root. Plant Physiol.
87:529-532.

Reicosky, D. C., D. K. Cassel, R. L. Blevins, W. R. Gill and G. C. Naderman.
1977. Conservation tillage in the Southeast. J. Soil Water Conserv. 32:13-20.

Sanderson, J. B., T. B. Daynard and M. Tollenaar. 1981. A mathematical model of
the shape of the corn leaves. Can. J. Plant Sci. 61:1009-1011.

Topp, G. C. and J. L. Davis. 1982. Measurement of soil water content using
time-domain-reflectometry . Can. Hydro]. Symposium. 1982. Assoc. Comm.
on Hydrol., Nat’l. Res. Council of Canada , Ottawa, Canada.

Unger, P. W., H. V. Eck and J. T. Musick. 1981. Alleviating plant water stress. In
G. F. Arkin and H. M. Taylor. (ed.) Modifying the root environment to
reduce crop stress. ASAE , St Joseph, Mich., USA.

Voorhes, W. B., R. R. Allmaras and C. E. Johnson. 1981. Alleviating temperature
stress. In G. F. Arkin and H. M. Taylor (ed.) Modifying the root
environment to reduce crop stress. ASAE , St Joseph, Michigan, USA.

Chapter 4: CERES-Till, a model to predict the influence of crop residue cover on

soil surface properties and plant development

Abstract

The complexity of the interactions between decreased soil temperature and
soil water conservation effects on plant growth and development make
generalizing the consequences of conservation tillage for a wide range of soil and
climate difficult. Crop modelling has been widely used to evaluate strategies in
different environments. This study describes the changes made in CERES
functional models to include surface residues and tillage. The processes modelled
are: surface residue cover, effect of a residue cover on soil temperature and soil
surface water balance, surface bulk density, water ponding capacity and saturated
water conductivity changes with tillage and precipitation intensities. The surface
residue decomposition model predicted measured wheat residues reasonably well.
Plant development prediction was improved as well as soil surface water content
prediction. Further work is needed to validate the surface properties modelled

(bulk density, ponding capacity and saturated water conductivity).

73

74

Introduction

Residue cover is important in soil erosion control because it keeps soil
water infiltration high and reduces soil erosion losses (Wishchmeier and Smith,
1978). Thus, it is important to know the residue cover throughout the year to
evaluate water runoff and potential soil erosion. Residue cover may affect plant
development through alteration of the soil temperature and water content near
the soil surface (Griffith er al., 1977). There are trade-offs between increased
water content and plant development delay. Reduced evaporation losses can result
in more water being available to the plant. Plant development delay may avoid a
water deficit period or push a critical development stage into a water deficit
period. Despite the negative effect of decreased soil temperatures, a residue cover
was found to improve maize yields in Indiana by extending the growing season and
increasing water conservation (Griffith et al., 1977). The increased length of the
growing season and water conservation is not always beneficial (Griffith et al.,
1977). A model that includes residue cover effect on soil erosion and maize
development and grth would enable the analysis of conservation tillage effect
on crop production for several climates and soil types. As seen in Chapter 1,
several models including soil erosion or tillage treatment already exist. Their use is
limited, however, by large input requirements or rather poor crop growth routines.
The CERES model was chosen for the easiness of use and the wide usage
throughout the world for the addition of these features that it heretofore did not

have.

75
The CERES model is a process-oriented program written in FORTRAN

and was designed to operate on IBM-compatible personal computers running on
MS-DOS. The model uses a standardized input and output structure (IBSNAT,
1989). The CERES model simulates individual plant performance and assumes
every plant in the area being simulated is homogeneous. Because field plants
compete for resources, three parts of the model require whole plant population
concepts. The first part is photosynthesis and light, interception which is a function
of leaf area index. The second part is transpiration and water uptake which is a
function of root density per unit soil volume in different layers, water availability
and potential evapo-transpiration. The third part is nitrogen uptake which is a
function of root density , nitrogen pools and microbial activity. The crop model
state variables describe the changes in crop grth and development, and soil
water balance for each day of the growing season. The variables are detailed in
three output files. One output file contains plant state variables for above- and
below-ground plant biomass, leaf number, surface and weight, stem weight, grain
weight and root depth. The second output file contains soil water state variables
such as water content for several layers, potential evaporation, and soil
evaporation. The third output file contains nitrogen state variables such as
nitrogen concentration in the top biomass, total nitrogen uptake, nitrogen
mineralization and leaching, denitrification, and nitrate and ammonium content for
several layers. A detailed description of input and output files is provided by

IBSNAT (1989).

76

Several model routines were developed to take into account the dynamics
of crop residue decomposition and how the residue decomposition affects soil
properties and subsequently, crop grth and development. The model includes
the influence of surface crop residues on soil temperature and soil evaporation
that are the two main effects of a residue cover on plant development and growth
(Griffith et al., 1977). It also accounts for soil physical characteristics such as
surface bulk density, surface water conductivity, and ponding capacity dynamics
(Black, 1973). The model is written in FORTRAN for ease of linkage to the
existing CERES group of models, or other similar daily incremental functional

models.

Model description

The model structure and components modified are illustrated in Figure 4.1.
Some routines only needed alterations (thick line boxes), while some new ones
needed to be added (double boxes). This section details the modifications made to
CERES models version 2.1. Computer codes of new and modified routines are

given in appendix 2.

Surface residue dynamics

The non-tillage version (2.1) of the CERES model incorporated residues in
the soil profile the first day of simulation. Inputs are initial amount of crop residue

(kg ha'l), depth of incorporation (cm), and crop residue C:N ratio. Residues are

 

 

77

 

Version 2,1 [ Experiment and Treatment Selectiopj

Wars. 2.1 modifiefl
I

I New eubmutineil

l
[Surface midi]
l
lSoil temperature I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Daily loop

 

   
     

 

 

 

 

 

 

 

Elitrogen Transformationj
[Water balance J
[Water and N rediatributionJ I pm! growth I

 

 

[Water uptake

 

 

 

 

 

{Plant development J'—l Sowing time

 

 

 

 

 

Figure 4.1: CERES model version 2.1 structure, and added (double line) or modified
subroutines (thick line) to include residue management and tillage.
Unmodified subroutines are boxed with a single line.

78

assumed to be uniformly distributed in the tilled layers and fresh organic matter
pools of each layer updated. To add a tillage component, crop residue must be
partitioned between soil layers and soil surface according to the tillage tool used,
residue decomposition, and soil coverage. Surface residue decomposition must be
predicted to calculate residue cover contribution to soil surface organic matter,

soil coverage and protection from erosion.

Residue incorporation in the soil profile

Shelton et al. (1990) proposed to use the product of percent of residues
remaining after each tillage operation as a way to calculate final residue biomass
at the surface. Values of percentage of remaining residue at the surface are
estimated from the tillage tools used. Low and high limits are given in Table 4.1
as the amount incorporated increases with residue breakability. Multiplying the
coefficients for each tillage operation provides an estimate of the percentage of
residue left at the surface. Sloneker and Moldenhauer (1977) gave similar values
for crop residues remaining after several types of tillage passes for three types of
soil. Residue remaining after tillage is a product of the fraction of residue buried
by a tillage operation and the initial amount of residue before tillage. Predicting
surface residue decomposition is also necessary to estimate the amount of residue

decomposed between two tillage events.

79

Table 4.1: Influence of field operations on surface residue (Shelton, 1990)

 

 

 

Tillage and Planting Implements Percent of
Remainin
Residues
Moldboard plow 3-5
Chisel plow
Straight shovel points 50-75
Twisted shovel points 3060
Knife-Type fertilizer Applicator 5080
Disk (Tandem or Offset)
7.5 cm deep 30-60
15. cm deep 40-70
Field Cultivator 50-80
Planters
No coulter or smooth coulter 90-95
Narrow ripple coulter
(less than 3.8 cm flutes) 85-90
Wide fluted coulter
(greater than 3.8 cm flutes) 80-85
Sweeps or double disk furrowers
(till-plant) 60-80
Drills
Disk openers 90-95
Hoe openers 50-80
Winter Weathering 70-90

.. e o o e O o 0
Use higher values for Irrigated maize resrdue, and lower values for fragile resrdue
such as soybean.

80

An example of a chisel-disk-planting sequence is:

0.86 x 0.75 x 0.60 x 0.95 = 0.37 x 100 = 37%
Spring Chisel Disk Planting Final

Residue Residue

Cover Cover

Surface Residue decomposition

Models of surface crop residue decomposition vary from deterministic,
where every step of the process is modelled, to statistical where the final results
are predicted from inputs by a simple relation developed on historical data.
Shelton et a1. (1990) proposed a 10% to 30 % loss due to winter weathering to
evaluate percentage of residue remaining at the surface at the time of spring
planting. This type of model is not weather dependent and would not fit the
general purpose of a generally applicable type model. Ghidey et al. (1985) and
Van Doren and Allmaras (1978) proposed a single first order model that consist
of an exponential decrease in the amount of residues at a rate that is a function of
residue type and size, water content and temperature. Andrén and Paustian
(1987) compared several models of residue decomposition, and showed that a
parallel first order model that uses two first order decomposition equations, one
for a labile pool and one for a resistant pool, gave as good an overall fit as a
single first order model, and better initial loss in the labile fraction. This parallel

first order model was chosen to model surface residue decomposition because it

81

first order model was chosen to model surface residue decomposition because
complies with CERES functional structure and daily increment.

Following the procedures of Reddy et al. (1980), the initial amount of
residue left at the surface (Mulch) was fractioned into two pools: a labile pool

(Mulchl) and resistant pool (Mulchr) depending on its C:N ratio (SCN):

D1 = 0.8664 - 0.1395*ln(SCN)
Mulchl = D1 * Mulch

Mulchr = (1 - D1) "‘ Mulch

where D1 is the fraction of the labile pool.

Each pool follows a first rate decay equation :

Mulchi ._. pmulchi e e(-Ki-MIN(TEMPFAC,WATFAC))

where i represents 1 for the labile pool and r for the resistant pool, Pmulchi is

it

[4.1]
[4.2]

[4.3]

[4.4]

the

amount in each pool the previous day and Mulchi at the end of the day, Kr is the

decomposition rate factor of the resistant pool at 25 °C and is derived from D1

(Reddy et al., 1980). Andrén and Paustian (1987) used a K1 of 2.96 for buried

--_ _A_ :f "1

82

residue at the optimum temperature of 23 OC. Because surface residue contact
with soil is less than when buried and microbial population is different, using a K1
value of 0.3 was necessary to obtain a good fit of available data. The Kr value

used was (Reddy et al., 1980):

KT = 0.035*D1 - 0.0013 [4.5]

TEMPFAC and WATFAC are relative modifying factors calculated from
air temperature and residue water content. TEMPFAC is equal to zero when air
temperature is below 0 °C or above 60 °C (Parr and Papendick, 1978). It is equal
to 1 between 25 and 40 °C, and follows a linear relationship from 0 to 1 between
0 and 25 °C, and 40 and 60 °C (Figure 4.2).

WATFAC is a factor varying from 0 (maximum reduction) to 1 (no

reduction) following the relationship (Figure 4.2):

ln(MulchSW/MulchSAT)

WATFAC-
ln(0.01/MulchSAT)

 

[4.6]

where MulchSW is the amount of water (cm) held in the surface residues,
MulchSAT the maximum amount of water (cm) that can be held and 0.01 the
minimum amount. Details regarding water held in mulches is presented in a later

section.

 

83

 

 

 

 

1 .2
‘ d
0.3“
E. 0.0-
'—
0.4“
0.2"1
G I I I I I I I
-10 O 10 20 30 40 50 60 70
Temperature (°C)
0
<
[t
<
3

 

 

 

 

I T T I
0 0.05 0.1 0.15 0.2 0.25
Residue water content (cm)
-- 300 Kgma «4» 1000 Kglha -ut- 3000 kglha

Figure 4.2: Temperature and water content factors affecting residue decomposition.

One represents optimal conditions and 0 no activity.

 

 

 

 

Fraction Decomposed

 

 

 

 

0 so 100 1&0 250 2:50 360 3510 400 460 500
Days

+35 + 75 nae-110

Figure 4.3: Remaining surface residue for three crops representing different C:N
ratio, and assuming optimal conditions.

 

85

Using the law of the minimum assumption, the minimum value of the water
or temperature factor becomes the modifier to decrease the rate of
decomposition. Figure 4.3 shows the remaining surface residue for three different
C:N ratios, assuming optimum temperature and water content (factor stresses
equal to 1). Decomposed organic matter is added to the surface layer organic
matter pools when a rain or irrigation occurs.

Incorporated residues decomposition routine follows the logic of the
CERES model version 2.1. They are uniformly incorporated in the fresh organic
matter (FOM) of each layer tilled. A pool of surface organic matter (Mulch) is
created and will follow a decomposition subroutine similar to one already

described.

Residue coverage

Gregory (1982), and Van Doren and Allmaras (1978) developed models to
predict the percentage of soil covered (Fc) from the weight of residue at the
surface (Mulch). The equation is the result of the probability that each piece of
residue to fall on an uncovered soil surface (Gregory, 1982) (Figure 4.4):

(- Am " Mulch)

PC = 1 - e [4.7]

where Am is used to convert mass of residue to equivalent area (ha kg’l) and is

residue type dependent (crop, diameter, density).

86

 

Fraction of soil covered

 

 

 

 

I I I I

0
l0-

4 s a 10 12
Residue amount (Kg/ha)
(Thousands)

—I— Maize ---+- Wheat -*- Soybean

Figure 4.4: Fraction of soil covered versus amount of residue at the surface for three
different crops.

87

Tables of values for Am are given by Gregory (1982) and Greb (1967), and

some values are given in Table 4.2. Values of Am can also be measured by

weighing crop residue samples and measuring their surface with a surface meter.

Chopped maize residue values were measured during field experiments described

in Chapter 2 and 3. Small samples of chopped maize residue were weighed and

the surface covered by those small samples without any overlapping was measured

using a leaf area meter. Values of 0.000358 1 0.00003 ha kg'1 in 1990 and

0.00029 :t 0.00003 ha kg'1 in 1991 were found and are in conformity with the

literature.

Table 4.2: Values of average mass to area conversion for residues.

 

 

Crop Am Source
(ha kg")
Maize 0.00032 Van Doren and Allmaras 1978
Maize 0.00040 Gregory, 1982
Wheat 0.00054 Gregory, 1982 (data: Wischmeier et al., 1978)
Winter wheat stem 0.00027 Greb, 1967
Wheat 0.00045 Gregory, 1982
Winter wheat stems 0.00027 Greb, 1967
Soybean 0.00032 Gregory, 1982
Grain sorghum stems 0.00006 Greb, 1967
Sunflower 0.00020 Gregory, 1982

 

 

Mulch thickness

When the residues are spread at the surface pieces overlay each other. To
determine how many layers (11) there are in a mulch cover from the average
surface covered per kilogram (Am, ha kg'l) and the total residue biomass (Mulch,
Kg), we must first calculate the amount of residue (Mi, kg) which overlays the
lower layer number i-l by subtracting from the amount M“, the biomass needed
to cover SM if the pieces were not overlaying each other. Then the fraction of the
surface (Si, ha) this would cover is calculated. These calculations are iterated until

no residues are left.

M Am Si-l
i' *Mi-I'E

-Am:r-M,- /S,'.1

Si-Si_l*(1-C )

n
MulchThick - 2 Si ... Athick
i -l

[4.8]

The thickness of the mulch is then calculated by summing the average thickness of

each layer (Si "' Athick) and used in soil evaporation predictions.

89
Impact of a residue cover on the soil water balance

The water balance subroutines of CERES have recently been improved
(Ritchie, Godwin, Baer, Gerakis, personal communication). Changes include the
tirne-to-ponding approach to better predict water infiltration and runoff, water
table movement, and water balance of relatively thin surface layers. Maximum
infiltration during a rainfall or irrigation is predicted from surface water hydraulic
conductivity, soil water content and cumulative infiltration. If the estimated or
measured precipitation intensity is higher than the maximum infiltration rate for
the time step, water ponds at the soil surface. If the total amount of water
ponding at the soil surface is higher than the ponding capacity, water runs off
(Chou, 1990). Surface water evaporation and redistribution are predicted from
soil water content and drained upper limit values.

Undisturbed soil cores (7.6 cm in diameter and 7.6 cm high) were sampled
using a double-cylinder sampler for measuring bulk density, total porosity, and
saturated hydraulic conductivity. Three cores were obtained from soil depth of 0
to 7.6 cm, 7.6 cm to 15.2 cm, and 15.2 to 22.8 cm. Cores were saturated from
bottom and then weighed so that total porosity could be measured at saturation.
Cores were then equilibrated on a tension table or pressure plate apparatus at
matric potentials of -1, -2, -4, -6, -33.3, and -100 kPa. Cores were then oven dried.

Bulk density was calculated from the above measurements.

Rainfall interception

Residue at the surface intercepts precipitation. The maximum amount of
water that can be retained in the surface residues (MulchSAT, cm) is proportional
to the amount of residue at the surface (Mulch, kg ha'l). Parr and Papendick
(1978) in the pressure plate method showed that residues could hold up to 3.8
times their weight in water.

The transformation from mass of water to centimeters of water gives:

MulchSAT = 3.8 * 10'5 * Mulch [4.9]

The amount of precipitation (rain or irrigation, IPRECIP) intercepted is a
function of the amount of water held (MulchSW, cm) and the maximum amount
that can be retained (MulchSAT, cm). The amount that reaches the soil surface
(PRECIP) is:

PRECIP = IPRECIP - (MulchSAT - MulchSW) [4.10]

Potential soil evaporation

Water withheld in the residue from previous precipitation is assumed to be
free and available for evaporation. The soil potential evaporation is decreased by
the amount of free water evaporating from the residues (AEos, cm) and the

residue water content is updated:

91
AEos = Eos and MulchSW = BMulchSW - Eos

Eos<MulchSW [4.11]

AEos = MulchSW and MulchSW = 0.0

EoszMulchSW [4.12]

where BmulchSW is the residue water content before evaporation.
Similar equations are used for the change in water ponding at the surface

(APOND, cm):

APOND = Eos and POND = BPOND - Eos

Eos<POND [4.13]
APOND = Eos and Eos = 0.0

EoszPOND [4,14]

where BPOND is the ponded water before evaporation.

Adams et al. (1976) used an exponential decreasing function of residue
biomass at the surface to model the impact of residue cover on soil evaporation.
The equation developed for oat residue (Avena Sativa, L.) would need calibration
for other types of residue. Unger and Parker (1976) studied the decrease in soil
water evaporation under several loading rates of cotton, wheat, and sorghum

residue. They show that the decrease in evaporation is closely correlated to soil

92

coverage and residue mulch thickness. Residue mulch thickness is hard to
determine unless the soil is fully covered: a rare occurrence in field situations. At
lower loading, soil coverage should be sufficient. Data published by Bond and
Willis (1969, 1970) were used to calculate the correlation between relative
decrease (Rcov) in soil water evaporation between a bare surface and a covered
surface and fraction of soil covered (Fc). Soil coverage was estimated from the
loading rate (see above). The relation found forthe two articles and for several

potential evaporation rates were similar:

Rcov = 1 - 0.807 * Fc R2 = 0.956 [4.15]

For higher residue loads which provide a full cover, thickness of the residue
layer should be used to predict the decrease in soil water evaporation. Unger and
Parker (1976) proposed three different logarithmic correlations between the
thickness (MulchThick, cm) of the residue layer and the evaporation rate for three
potential evaporation rates. The three equations can be summed up in one

equation:

-0.5‘MulchThick)

Rthick = e‘ [4.16]

The minimum of the two coefficients Rcov and Rthick is then used to calculate

the relative decrease in soil potential evaporation.

93
Rm = Minimum (RCOV’ RthiCk) [4.17]

The potential evaporation beneath a crop canopy and a residue cover is then

estimated using the algorithm of Adams et al. (1976):

Ecm = Rm*Rc"‘Eos [4.18]

where Eos is the potential evaporation of a bare soil (cm), and Ecm the potential
evaporation of a covered soil (cm). The effect of crop residue is combined with
the effect of crop canopy factor (Rc) to calculate the potential soil evaporation

(Em) of a partially covered soil surface (Adams et al., 1976).

Soil temperature
In the older version (2.1) of CERES models, surface boundary soil

temperature (TMA) is estimated daily from daily air average temperature
(Tempm), daily air maximum (T empmx), solar radiation (solrad), surface albedo

(Albedo) and surface temperature of the previous day (BTMA).

TMA-Albedo 4: BTMA+

 

(1-.euuedo)*(Tempmwrempmx-Tempm)*lsolrad*°-°3 ) [4 191

94

Fluker (1958) showed that yearly average soil temperatures at all depths
were identical to yearly average air temperature. The yearly course of soil
temperature follows a sine wave of amplitude that is identical to air yearly
amplitude for the soil surface layer and that decreases with depth. Time of yearly
maximum occurrence is delayed with depth. The CERES model (version 2.1)
calculates standard soil surface temperature (TA) from yearly air average
temperature (T AV) and yearly air temperature amplitude (AMP). The day when
maximum soil temperature occurs is calculated from the geographical position
(latitude and longitude) of the site (ALX).

TA = TAV + AMP/2 * COS(ALX) [4.20]

Subsurface layer temperature is modelled using the same algorithm.
Occurrence of soil maximum temperature is delayed by depth (ZD). The
difference between standard soil temperature and five days running average of
surface temperature (ATOT/S) is used to account for variation from year to year.
The difference is dampened with depth (ZD) and soil characteristics and added to

each layer standard temperature.

DT = ATOT/S - TA [4.21]

ST(L) = TAV + AMP/2 * (COS(ALX+ZD) + DT) * eZD [4.22]

where L represent the layer number from top to bottom.

95

The following modifications were developed to better predict soil surface
temperature in the seed zone and account for surface residue effect on soil
temperature. Soil surface albedo was modified to include soil surface residue and
soil surface wetness. The general equation of soil surface temperature was also

altered.

Soil surface albedo

The solar radiation intercepted by a soil surface is related to its albedo; the
higher the albedo the less energy is absorbed and the more reflected. The bare
soil albedo changes as water content at the surface changes. Due to low water
reflectivity, wet soils have lower albedo (Salbedo) than dry soils. Idso et al.
(1975) showed that soil albedo can be correlated to soil surface water content. A
model of soil albedo (Salbedo) was computed from soil volumetric water content
of 0 to 2 cm layer (SW(1)), saturated water content (SAT(1)) and air dry water

content (ASW) (Figure 4.5).

ff = (SW(l) - ASW)/(SAT(1) - ASW) [4.23]

1.1
1+0.0007*e’13°5"ff

 

Salbedo -Salb :1: ( 1 +
[4.24]

96

 

0.3“——-—

0.28“

0.26-

0.24-

0.22“

Soil albedo

0.2“

0.18‘

0.16‘

 

 

 

 

0.14 1 I I I I I I I ‘
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.6 0.9 1 1.1

Water content factor

I Measured — Model

Figure 4.5: Bare soil surface albedo versus soil water content factor from 0 to 2 cm.
Adapted from Idso et al., 1975, soil water content was changed to a relative
water content factor (SW(1)-0.03)/(0.3-0.03).

97

Soil albedo is a function of soil coverage. Residue cover increases its albedo
(Gaussman et al., 1975), and crops usually have higher albedo than soils.
Therefore, covered soils are usually colder than bare soils during the spring. The
same equation for canopy coverage as the ones used in the CERES model was
used. The variable name was changed to CANCOV. Because crop canopy is
above the residue cover, it intercepts the light first, then surface residues reflect
incoming light proportionally to their albedo (MulchALB) and finally bare soil
surface reflects part of the incoming energy. The following equation calculates the

resulting albedo:

ALBEDO = CANCOV*0.23 +

(1-CANCOV)*(Fc*MulchALB + (l-Fc)*Salbedo) [4.25]

Soil surface temperature

Because soil minimum temperature is close to air temperature for covered
and uncovered soils (Fortin and Pierce, 1990), the soil surface temperature
(TMA(1), 0C) is predicted from daily air minimum temperature (T empmn) rather
than air average temperature. Because a residue cover tends to delay soil
temperature changes, a factor calculated from surface residue cover and air

minimum temperature of the previous day (OTempmn) was added to improve the

prediction:

98

OTempmn -Tempmn

TMA(1)'Tcmpmn +(mulchcov+salbedo)* 2

 

 

+(1-Albedo)*(Tempmx-Tempmn)*(/§olrad*0.03 [4 26]

The damping calculations were altered to estimate the damping coefficient
from the surface to the deeper layers rather than using an average value for the

complete profile.

Plant development prediction

The CERES-maize model, version 2.1, predicts plant development from air
temperature with a base temperature of 10 °C for germination and 8 °C for the
plant development. As reported in Chapter 2, using the soil temperature to
predict plant development while the plant meristem is below ground improves the
accuracy of prediction when compared to using air temperature.

The model was modified to use soil temperature in thermal time
calculation for Stage 9 (seedling emergence), Stage 1 (emergence to end of

juvenile stage), and Stage 2 (end of juvenile stage to tassel initiation).

99

W

The extent of the modifications by tillage is highly dependent on the soil
conditions, tools used, and speed of tillage. In this model soil conditions after
tillage are used as an input and dynamically change when precipitation occurs.
The soil layering, which was user dependent in version 2.1, was modified to fixed
increments. Increments were based on several possible tillage depths and soil
conditions. Layering in the upper 40 cm is as follows: 0-2 cm surface boundary
layer, 2-7 cm surface tillage, 7-15 cm tillage depth of most Chisels, 15-26 cm
moldboard plow bottom layer, 26—40 cm and every 25 cm layer below the plow
zone as undisturbed soil.

Three surface properties are considered: soil surface ponding capacity, soil
surface saturated conductivity, and soil bulk density of the top 26 cm (layers 1 to
4). The process of change for those three properties follows the same rules; the
parameter changes from an initial value to a settled value following an exponential
curve of cumulative precipitation intensities (Green and Ampt, 1971) and is

detailed below.

Precipitation intensity

Precipitation intensities are rarely recorded. Most of the time the total
amount of precipitation is the only data available. Wischmeier and Smith (1958),
developed an equation to calculate precipitation intensities from duration and

quantity of the precipitation. Unfortunately the duration of precipitation was not

100

recorded for this study. A rainfall intensity generator developed by Baer and
Ritchie (personal communication) was used to predict the duration (TIME, hours)
and distribution (RAIN, cm) of precipitation during one event. The length of the
rainstorm (hours) is estimated from the precipitation amount. Distribution of the
rainfall is assumed to be a normal type. Combining the rainfall intensity generator
with the equation of Wischmeier and Smith (1958) allows us to calculate the

kinetic energy for a storm at the soil surface for every event (KE, J cm'2):
KE = (3.812 + 0.812 * Ln(RAIN/I‘IME» * RAIN [4.27]

Surface cover (SOILCOV) from residue (Fc) and crop canopy (CANCOV)
decreases the amount of energy received by the soil surface proportionally to the

surface covered (Wischmeier et al., 1978).
SOILCOV = CANCOV + Fc * (1 - CANCOV) [4.28]
Effect of the rainfall intensity decreases with soil depth. The decrease of
the effect was assumed exponential with depth (depth, cm) and the coefficient

used, 0.15, cancels the intensity at the bottom of the tilled layers (26 cm). Hence,

cumulative intensity is calculated for the top four layers.

SUMKE(L) = )3 (1 - SOECOV)*KE*e('°°‘5"“P“‘) [4.29]

101
SUMKE is reset to zero after each tillage.

Change in surface properties
Changes in soil surface properties follow an exponential decrease of

cumulative rainfall intensity (Van Doren and Allmaras, 1978):

-1250 : SUMKE)

Xvar = Xstl + (th1- Xstl) * e‘ [4.30]

where Xvar represents the dynamic surface property: ponding capacity
(PondMax), saturated water conductivity with macropores (KsMacro) of the four
t0p layers and bulk density BD of the top four layers.

The settled value (Xstl) for surface ponding capacity is read from the input
file. If no value is given it is set by default to 0.01 cm. The KsMacro settled
value is assumed to be the saturated conductivity without macropores (XKS) of
the four top surface layers. Bulk density settled value is read from the input file.

The rate of decrease (Rstl) for the surface property is a function of soil
water aggregate stability. Water aggregate stability is correlated to soil organic
matter. Several models are available (Tisdall and Oades, 1982). The model
chosen was the one described by Chaney and Swift (1984) that agrees with some
of the models described by Tisdall and Oades (1982). Modifications were made to
the equation to have aggregate stability vary from 1 to 100 rather than using mean

weight diameter as a representation of aggregate stability (As). Instead of using

102

soil organic matter content, soil organic carbon (OC) was used. The factor used
to convert organic carbon to organic matter was 0.5 (T isdall and Oades, 1982).

Values for As and Rstl are calculated for the top four layers:

As = 0.051 * 0c [4.31]

The rate of decrease (Rstl) was then computed from AS:

Rstl = 5 r (1 - As) [4.32]

103
Model input modifications

The CERES tillage and residue management input file (File number 4) was
altered to allow several tillage operations at different times during the simulation
rather than one tillage operation at the beginning of the simulation. The first line
was modified to include the amount of residue at the beginning of simulation (kg
ha'l), residue C:N ratio, dead root biomass (kg), and average surface coverage of
residue (Am, ha kg'l). The following lines contain new tillage inputs: day of
tillage (T ILLDOY(i)), tillage tool used (CODTILL(i)) depth of tillage
(TILLDEP(i)), fraction of residue incorporated (FRACTILL(i)), ponding capacity
(TILLPOND(i)), soil water conductivity with macropores (KSMACRO(i)), and
bulk density of the top four layers (TILLBD(i,l)) after tillage. Inputs can be
measured in the field or estimated from Table 4.1. Default values are given in
Table 4.3 and are used by the model if no value is specified in the input file.
Subscript i corresponds to the tillage operation number. It has been arbitrarily set
to a maximum of ten tillage operations. Examples of input File 4 are given in
Appendix 3. Modified codes are given in Appendix 2.

The daily loop of the model has been modified to allow the model to run
up to the end of the weather file. Resetting of the soil parameter can be skipped
and values from previous runs are used for soil properties, residue amount, and

soil water and nitrogen content.

104

Table 4.3: Default tillage input and tillage coding for the tillage version of the

 

 

CERES model.
Tillage implements Cod Dep Frac Till Ks Pond
Till Till Till BD Macro
cm % g cm'3 cm hr"l an

Moldboard plow 1 25 96 1.2 2.09 2.0
Chisel Plow
-Straight shovel point 2 15 39 1.2 2.06 1.0
-Twisted shovel point 3 15 55 1.2 2.06 1.0
Knife-Type fert. 4 5 35 1.2 2.06 1.0
Disk (Tandem or Offset)

7.5 cm deep 5 7.5 55 1.2 3.0 1.0

15. cm deep 6 15 40 1.2 3.0 1.0
Field Cultivator 7 10 35 1.2 3.0 1.0
Planters
-None or smooth coulter 8 5 8 1.2 3.5 0.5
-Narrow ripple coulter 9 5 3 1.2 3.5 0.5
-Wide fluted coulter 10 5 8 1.2 3.5 0.5
-sweeps or disk furrow 11 5 30 1.2 3.5 0.5
Drills

disk openers 12 3 1.2 4.2 0.5

Hoe openers 13 3 35 1.2 4.2 0.5

 

105
Model Validation

Web

Stott et al. (1990) studied the decomposition of wheat residue left at the
surface at five locations. Several sampling dates of amount of residue biomass
remaining at the surface make the data set quite useful to validate a model on
residue decomposition. Maximum and minimum air temperature, as well as
precipitation for the nearest weather station, were found in the report of the
weather service. Solar radiation was approximated using generated values
(Richardson and Wright, 1984). The soil data used to run the model was standard
soil input file given by IBSNAT (1989) for the soil type texture at the site.
Management files including planting dates, fertilization, and crop grown were built
from available data given in the article.

Five sites, as reported in Stott et al. (1990) were studied: site A, Pullman,
Washington, 1983; site B, Pullman, Washington, 1984; site C, Bushland, Texas,
1984; site D, West Lafayette, Indiana 1984; and site E, Pullman, Washington,
1985. Stroo et al. (1989) used the same data set to validate a mechanistic model
that calculates surface residue losses from predicted respiration losses.

Results obtained from the functional model described above are shown in
Figures 4.6 to 4.9 . Results agree in general to the ones obtained by Stroo et al.
(1989). The model did not accurately predict the loss of biomass at the end of the
season for most sites. Stott et al. (1990) reported that losses on the Pullman plots

in Washington during the summer were attnbuted to macro fauna activity and

 

106

 

 

 

Percent Decomposed
8

 

 

 

 

IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
252 280 308 34

4'1 1
Days after surface application

— Model 1983 I Obeerved1983 — Model 1984 El Observed 1984

Figure 4.6: Model predictions and observed data from the Pullman, Washington, site
(site A and B) for 1983 and 1984. Data are from Stott et al., 1990 .

107

 

 

Percent Decomposed
2
I

 

 

 

IIIIIWIIIIIIIIIIIIFIIIIIIIIIIIIIIITIII

i 108 100 224 252
Days after surface application

— Model I Observed

Figure 4.7: Model predictions and observed values for Bushland, Texas 1984. Data
are from Stott et al., 1990.

108

 

 

 

 

8° 1
70-
% 6°”
8. 50-
E
§ 40" I -
o
E
f °°‘
I
o
c. 20.
10-
0 IWIWWIIIIIIIIIIIIIIIIITIIIITIITIIITI—IIIITIIIIIIIIIIIWIIII
0 20 56 8411211581224
DayOftheYear
—Model I Observed

Figure 4.8: Model predictions and observed data from West Lafayette, Indiana, 1984
(site D). Data are from Stott et al., 1990.

109

 

 

 

Percent Decomposed

 

 

 

 

I I

III IIIIIIIIIIIITIIIIIITIIIIIIIIII

4 100 224
Days after surface application

— Model 6 T/ha I Observed 6 Tlha ....... Model 3.3 T/ha
D Obeerved 3.3 Tlhe — Model 1.3 T/ha A Observed 1.8 Tlha

Figure 4.9: Model prediction and observed data for Pullman, Washington, 1985
(Site B). Data are from Stott et al. (1990).

110

were more important when the residue biomass was larger. Stott et al. (1990)
also reported that at the end of the summer residues were broken in pieces too
small to be collected, therefore, biomass loss became difficult to measure.
Furthermore, at the end of the summer, the residues were dry and may have been
moved by winds. Most plots were surrounded by fields with identical cover,
nevertheless because site B at Pullman was not surrounded by fields with similar
cover, error estimations on field cover were higher.

Considering the errors in biomass loss estimations and the fact that the
climatologic data used to model residue losses were not the ones collected on site,
results of the model seem reasonable. Calibration and validation for residues

from other types of crop residues are needed to fully validate this model.

Soil water balance

Soil water data collected during summer 1990 and 1991 were used from the
experiment described in Chapter 3. Soil characteristics of the surface layers were
obtained from core samples collected in the fall at the same location. Drained
upper limit and water content at saturation were measured in the laboratory. The
model was run for 1990 and 1991 for fallow treatments. Soil volumetric water
content was averaged over layers to match the TDR measurements. The model
gave good prediction in the no cover treatments (Figures 4.10 and 4.11). In the
fully covered treatment, soil water measurements may have been overestimated

because of the presence of residues at the surface. Topp et al. (1980) reported

111

 

 

0.3
0-2 cm ‘ . l!

h 3': ;‘~

5: : -. :-. . : .

I. ‘0. . \e. E 3..“ .0. °~

.' o e g. ‘ \ \

s a i x . a / z. z ' . .i .

I ' ‘ '~ I ‘. . a"
g 0.2 f u... I t. :3 3/ $.J t“ O %‘
°-. :' - : ‘~ 0 ~
‘ai "a t ‘0. . \

g 3“ c a; x... .
g . “‘s ‘ .'\..‘ O ‘1
3 0.1 "' ' n "K
OI. .“s.
5 ‘~
'5 " II a
8 In
t- 03
g 0-7 cm

 

 

 

 

Soil water content (ems/ema)

 

 

 

0.1 046m
150 We fie 1&3 150 200 210
Day otthe year

—Mod.NoCov. -------Mod.1m%Cov. at Obs.Nocov. + Obe1Mcov.

Figure 4.10: Surface water balance of a bare surface and a fully covered surface on
a fallow plot for the upper four layers.

112

 

 

0.3
0-7 cm I u I
.' ‘ X. . : .'- u 5“ .
I. \ . ' ‘t \ a .\
i 0 2 'l ‘e‘. V ‘s . ‘ ‘\ z‘ . \
“a. O o "f . "x.
% ‘1' In ° ‘~ ._. ~ -.
8. 0.1 l W
E
g as, 0-15 cm
i
3

 

 

 

 

 

 

Sol water content (cmalcrna)

 

 

 

Q1 I I I I I I I
170 18) 190 210 210 220 230 240 250

Day of year 1991

-—Model.NoCover ---~Model.1m%00v I! WMco + Observednm‘lm

Figure 4.11: Surface water balance of a bare and fully covered soil for the upper four
layers on a fallow plot during summer 1991.

113

that on high organic matter content soils, decreased wave velocity induced an
overestimation in soil water content. However, the relative change between
treatments gave a good estimation of soil water evaporation. Hence, data were
corrected to alleviate the effect of residues at the surface. Considering that rainfall
and solar radiation data were from a set some distance away the model prediction
of surface evaporation seems reasonable.

Further study is needed to evaluate the error induced by the residue cover
when a residue cover is at the surface and TDR transmission lines are installed

vertically.

Soil temperature model and leaf development

Soil temperature data collected during the growing season of 1990 and
1991 (see Chapter 2) and during summer 1988 (Fortin and Pierce, 1991) were
used to calibrate and develop the model.

Data files needed to run the model are given in Appendix 3. The model
was run for the fallow treatments. The results are presented in Figures 4.12, 4.13,
4.14. Part of the prediction error may be due to the fact that air temperature was
not recorded at the plots and solar radiation was estimated. The pattern of soil
temperature variation agreed with the observed data. The model tended to
overestimate soil temperature at higher air temperatures.

Total leaf tip number recorded during the 1990 and 1991 cropping seasons

at East Lansing, Michigan (refer to Chapter 2) were used to validate the model

Soil Temperature (degree Celcius)

Figure 4.12: Observed and predicted daily average soil temperature at 2.5 cm at East
Lansing, Michigan, 1988 for an uncovered and 100% covered soil surface.

Data from Fortin (1990).

114

 

 

 

 

 

 

 

 

0' ‘\
Il ‘\ a
I" s ’ ‘ / ‘\
o, \a a :0 p. \\'l
\Jr" “‘ 'I :41: ‘ A «or:
z \ '0 : - : ’
' ‘0': l ‘\ :r: \"'”
: ‘ ll "0 ‘i. .t I!
3". f \‘ x, j.‘ E: .-
' ‘Q I a»?
" y- . K .17 ,3? .
- ' - °’ I:
a RP: ii ED
\ ._.;
V
‘0 T I I I I
140 150 160 170 180 190 21
Day of Year 1988
—Mod.NoCov. + Obe.NoCov. -------- Mod. 100%oov
El Oba.100%oov. ----- Version 2.1

115

 

 

 

 

 

 

 

 

32
A 30‘
a O
- \
8 no ' ‘\ ’7
J. 0 I‘s ' " V
8 I‘ ' '~ “ ‘- .
:27, v‘ 0’ “\ ‘3' “ :t " . .-
|‘ ; ‘ -I ‘s L'I " _
‘ -- e. ‘ ’ - a
e I ‘. : .. - 1; he}:
I, I f f ‘ g ..
2 fl ' I ‘2
a N i 2 n.'
i I. " 5
a" .. .4'
t
E y "'
2
(8 14‘
12 I I I I r j I .
”’0 180 190 200 210 220 230 240 2‘00
Day of year 1990

—Mod.Nooov 4' Obe.Nooov ........- Mod. 100%oov.
D Obe. 100% oov. ----- Ver. 2-1

Figure 4.13: Observed and predicted daily average soil temperature at 2.5 cm at East
Lansing, Michigan, 1990 for an uncovered and 100% covered soil surface.

116

 

 

 

 

35
’2‘ * -
E 30:: [£\~ ‘ I \ 5‘s“
8 .1“. of. \£\* .1 .o‘. '0“

g '0 I . g . ‘
g a I“ ‘ : . . .‘\“ . f 5 . v‘ I“ /’A‘ r -a- \‘ a
8) 25; a ‘x A "I. - ' D :9” I \‘s /’\\ c" " J D :- “sax
h "M .. ‘2 ”a $10 \‘ I, \\ ”I ".1 : . v
Q ,‘ -'\,‘ ;, ,‘vg' ' \’ \z’ :5 Oman»: v on" .
\ . 1 V "

2 - a. ' ' vs a“ “I" I CB
2 m. I. a . ‘1 \ I ‘e a ‘- ‘
2 .. 1 ‘ .I‘ \\ \v' V, ‘- -.
8. ' ca", ‘. ‘3' . .
E i a", i.
9 15- X:
- ‘.‘

10 I I I I I I I .

17'0 180 190 200 210 220 230 240 250
Day of year 1991
— Mod. NoOov. + Obs. NoCov. ----- Mod. 100% oov.
D Obe. 100% Oov. ----- Var. 2-1

Figure 4.14: Observed and predicted daily average soil temperature at East Lansing
Michigan, 1991 for an uncovered and 100% covered soil surface.

117

 

181

16"

14‘

Total Leaf Tip Number
3

 

 

 

 

2d
0 I I I I j
140 150 160 170 180 190 200
Day of Year 1990
-—ModNooov. mM0d.100%oov. 3K Obe.Nooov.
D Obe. 100% oov. ----- Vera. 2-1

Figure 4.15: Observed and predicted leaf tip number at East Lansing, Michigan,
1990.

118

 

 

 

 

 

 

 

E
B

2
E
a
Z
.9-
.—
c.-
3’
S
O
I-

G I I I I I

140 150 160 170 180 190 200

Day of Year 1991
— Mod. NoOov. .......... Mod. 100%Cov. 3K Obs. Non.
D Obe. 100% oov. ----- Ver. 2-1

Figure 4.16: Observed and predicted leaf tip number at East Lansing, Michigan,
1991.

119
(Figure 4.15, 4.16). In 1991, the leaf appearance slowed at the end of the season,

whereas the model kept a similar development for the final leaves appearance.
Predicted final leaf number was higher on the no—residue cover treatment by one

leaf and is due to a rounding error of the model.

Demonstration of the model of soil surface property dynamics g
No complete data set with rainstorm intensity and surface properties were
available to validate the model. The model was run for the 1990 data set for a
bare fallow soil surface and a 100% residue cover surface (Figure 4.17). Two
hypothetical tillage operations were considered: a moldboard plow on 11 January
(beginning of the simulation) and a secondary tillage 10 May. Surface bulk density
after tillage was assumed to be 1.2 g cm'3, ponding capacity 1.5 cm, saturated
water conductivity with macropores 10 for the first operation and 8 cm hour'1 for
the secondary tillage. The rapid changes in the curves are due to tillage operation.
The curves move then slowly toward the settled values as cumulative rainfall
intensity increases. The values are then reset back after the second tillage
operation and then decrease. The model does not account for compaction from
traffic because the CERES model does not include any spatial variability. Fast
reconsolidation due to flooding or ponding water was not considered in this

model.

120

10
8.75 -

7.50 -
6.25 -

 

 

 

 

1 .75-
1 .5“

1 .25‘
1 .-
0.75“
0.5“
0.25-

 

 

 

1.75 "
1.50 ...
1.25 -

eon)
g/cm3

Secondary Tillage

Primary Tillage

 

 

\if

15:4:

1.0

 

I I

 

 

o 56 160 150 260 2&0 300 350 400
Day of Year 1990
— No Cover .......... 100% Cover

Figure 4.17: Predicted effect of a 100% residue cover on soil surface properties. Data
set of 1990, East Lansing, Michigan, on a Conover soil.

121

Conclusion

The CERES model version 2.1 was modified to better predict residue cover
effects on plant development and growth. The soil temperature model was
modified to better predict temperature of the seed and meristem site for improved
prediction of plant emergence and early development. Surface water conductivity
and ponding capacity were dynamically modelled to account for changes of surface
conditions due to precipitation intensities. Soil water evaporation was modified to
account for surface ponding water evaporation and the impact of the residue
cover on soil evaporation. These modifications logically improved the prediction of
soil surface water balance of residue covered soil, and therefore, crop
performance differences observed when a residue cover was present. Further
validation for different crops would reinforce the model and more calibration will

probably be needed for other climatic conditions.

122
Bibliography

Adams, J. E., G. F. Arkin and J. T. Ritchie. 1976. Influence of row spacing and
straw mulch on first stage drying. Soil Sci. Soc. Am. J. 40:436-442.

Andrén, O. and K. Paustian. 1987. Barley straw decomposition in the field: a
comparison of models. Ecology 68(5):]190-1200.

Black, A. L. 1973. Soil property changes associated with crop residue management
in a wheat-fallow rotation. Soil Sci. Soc. Am. J. 37:943-946.

Bond, J. J. and W. O. Willis. 1969. Soil water evaporation: surface residue rate
and placement effects. Soil Sci. Soc. Am. J. 33:445-448.

Bond, J. J. and W. O. Willis. 1970. Soil water evaporation: first stage drying as
influenced by surface residue and evaporation potential. Soil Sci. Soc. Am.
J. 34:924-928.

Chaney, K. and R. S. Swift. 1984. The influence of organic matter on aggregate
stability in some British soils. J. of Soil Science 35:223-230.

Chou, T. 1990. Modeling infiltration using time-to-ponding and a storm generator
approach. Ph.D. diss. Michigan State Univ., East Lansing, Michigan, USA.

Fluker, B. J. 1958. Soil temperatures. Soil Sci. 86:35-46.

Fortin, M.-C. and F.J. Pierce. 1990. Developmental and growth effects of crop
residues on corn. Agron. J. 82:710-715.

Fortin M.-C. and F.J. Pierce. 1991. Timing and nature of mulch retardation of
corn vegetative development. Agron. J. 83:258-263.

Gaussman, H. W., A. H. Geberman, C. L. Wiegand, R. W. Leamer, R. R.
Rodriguez and J. R. Noriega. 1975. Reflectance differences between crop
residues and bare soils. Soil Sci. Soc. Am. Proc. 39:752-755.

Ghidey, F., J. M. Gregory, T. R. McCarty and E. E. Alberts. 1985. Residue decay
evaluation and prediction. ASAE Trans. 102-105.

Greb, B. W. 1967. Percent soil cover by six vegetative mulches. Agron. J.
59:610-611.

 

 

123

Green, W. H. and G. A. Ampt. 1971. Studies on soil physics. 1. The flow of air
and water through soils. J. Agric. Sci. 4:1-24.

Gregory, J. M. 1982. Soil cover prediction with various amounts and types of crop
residue. Trans. ASAE 1333-1337.

Griffith, D. R., J. V. Mannering and W. C. Moldenhauer. 1977. Conservation
tillage in the Eastern Corn belt. J. Soil Water Conserv. 32:20-29.

IBSNAT Project. 1989. Technical Report 5, Decision Support System for
Agrotechnology Transfer (DSSAT), Documentation for IBSNAT crop
model input and output files, version 1.1, University of Hawaii, Honolulu,
Hawaii, USA.

Idso, S. B., R. D. Jackson, R. J. Reginato, B. A. Kimball and F. S. Nakayama.
1975. The dependence of bare soil albedo on soil water content. J. of Appl.
Meteorol. 14:109-113.

Parr, J. F. and R. I. Papendick. 1978. Factors affecting the decomposition of crop
residues by microorganism. In Oschwald W.R. (ed.)Crop residue
management systems. Houston, Texas, USA, Nov. 28-Dec 3, 1976.
ASA-CSSA-SSSAMadison, Wisconsin, USA.

Reddy, K. R., R. Khaleel and M. R. Overcash. 1980. Carbon transformation in the
land areas receiving organic wastes in relation to non-point source
pollution: a conceptual model. J. Environ. Qual. 9(3):434-442.

Richardson, C. W., and D. A. Wright. 1984. WGEN: A model for generating daily
weather variables. USDA, ARS-8. Springfield, VA, USA. National
Technical Information Service.

Shelton, D. P., E. C. Dickey and P. J. J asa. 1990. Estimating percent residue
cover. In E. C. Dickey and P. J. Jasa. (ed.) Conservation tillage proceeding
no.9. University of Nebraska, Cooperative extension,Nebraska, USA.

Sloneker, L. L. and W. C. Moldenhauer. 1977. Measuring the amounts of crop
residue remaining after tillage. J. Soil Water Conserv. 32:231-236.

Stott, D. E., H. F. Stroo, L. F. Elliot, R. I. Papendick and P. W. Unger. 1990.

Wheat residue loss from fields under no-till management. Soil Sci. Soc. Am.
J. 54:92-98.

 

 

124

Stroo, H. F., K. L. Bristlow, I... F. Elliot, R. I. Papendick and G. S. Campbell.
1989. Predicting rates of wheat residue decomposition. Soil Sci. Soc. Am. J.
53:91-99.

Tisdall, J. M. and J. M. Oades. 1982. Organic matter and water stable aggregates
in soils. J. of Soil Science 33:141-163.

Topp, G. C., J. L. Davis and A. P. Annan. 1980. Electromagnetic determination of
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Resour. Res. 16:574-582.

Unger, P. W. and J. J. Parker. 1976. Evaporation reduction from soil with wheat,
sorghum, and cotton residues. Soil Sci. Soc. Am. J. 40:938-942.

Van Doren Jr, D. M. and R. R. Allmaras. 1978. Effect of residue management
practices on the soil physical environment, microclimate, and plant growth.
In Oschwald W.R. (ed.)Crop residue management systems. Houston,
Texas, USA, Nov. 28-Dec 3, 1976. ASA-CSSA-SSSA,Madison, Wisconsin,
USA.

Wischmeier, W. H., and D. D. Smith. 1978. Predicting rainfall erosion- a guide to
conservation planning. USDA Agriculture Handbook 537.

Wischmeier, W. H. and D. D. Smith. 1958. Rainfall energy and its relation to soil
loss. Trans. Am. Geophys. Union 39:285-291.

 

Chapter 5: Use of CERES-Till to evaluate conservation tillage strategies in four

areas of Michigan

Abstract
The impact of residue cover on final yields is a complex interaction E-
i
between climate, soil, and plant. Crop models including plant phenological ‘ L

development and soil environmental condition should help evaluate the
consequences of conservation tillage on crop yield. A modified version of the
CERES model that included tillage consequences on surface residue cover and
soil surface condition was run using 30 years of weather data for four sites in
Michigan. Three tillage systems were considered; conventional tillage, reduced
tillage, and no-till. Four planting dates, from 30 April to 30 May, were also
considered. The model results showed that a no-till system on rainfed maize crops
improved returns and final yields by increasing the length of the vegetative stage
and decreasing water stress at flowering and during the grain filling period. The

model showed a decrease in water runoff in the conservation tillage strategies.

125

126

Introduction

Conservation tillage was partly designed to control soil erosion by keeping a
sufficient residue cover on the soil surface. The adoption of conservation tillage
has consequences other than reducing soil erosion. The existence of residue at the
soil surface affects the soil’s environment, thus the plant development and growth.
Evaluation of conservation tillage consequences on final yield is difficult due to the
interactions between delayed plant development caused by reduced soil
temperature, increased soil surface water content, and climate. Thus,
consequences of residue cover on maize growth makes it difficult to generalize the
benefit of residue cover on yield for different climates and soil types. The use of
a crop model based on phenological development that considers residue cover on
soil environment should help assess the consequences of conservation tillage on
crop production.

CERES-Till is a version of the CERES family of models (Jones and Kiniry,
1986; Ritchie er al., 1989) that was designed to model tillage effect on residue
cover and soil environment. It includes models of time-variant soil surface
properties (hydraulic conductivity, ponding capacity, and bulk density), surface
residue dynamics, soil temperature and plant development, and soil water
evaporation. The simulation procedures are described in Chapter 4. The model
was used to determine the consequences of conservation tillage adoption on

economic return and environment in Michigan.

 

ire—“1

127
Methodology

Four study sites were chosen to contrast the spatial and temporal changes
in maize yield for three different tillage systems with variation in weather. The
sites were chosen for locations where sufficient information on soil and weather
data was available. The prominent soil of each study area was used for the
simulation. Table 5.1 gives the list of the four areas chosen, their geographical

location, and the major soil type.

Table 5.1: Geographical location of the Michigan areas and their major soil type.

 

 

County Longitude Latitude Soil Type Code

Huron 83°00’ 43°45’ Locke Sandy loam HUMI
Tuscola 84°25 ’ 43°30’ Tappan Loam TUMI
Ingham 84°44’ 42°43’ Capac Loam MSUM
Kalamazoo 85°30’ 42°20’ Kalamazoo Loam KBSM

 

Soil profile information was obtained from the National Cooperative Soil
Survey. Soil surface saturated conductivity was assumed to vary as described in
Chapter 4, from 8 cm hour'1 at time of tillage to 5 cm hr‘l for reconsolidated
loam soils and 7 cm hr'l for reconsolidated sandy loam soil. Maximum ponding
capacity varied from 2 cm for moldboard plowing and 1 cm for chisel plow to

0 cm after reconsolidation. Residue cover slowed soil reconsolidation by

 

128

intercepting rainfall kinetic energy, thus keeping soil ponding capacity and
saturated hydraulic conductivity high. Ponding capacity was predicted from soil
ponding capacity and residue cover. Residue was considered to increase soil
ponding capacity by creating barriers to water movement over the surface. The
increase in ponding capacity was correlated to the residue layer thickness
calculated as described in Chapter 3. As seen in the previous chapters, r

conservation tillage impact on final yield varies from year to year depending on

b
i

the climate. Using 30 years of weather data, the model was run for each area to
investigate whether reduced tillage or no-till would provide an advantage to
farmers with either increased revenue or decreased runoff. Air temperature and
precipitation from historical data were used except for the Kalamazoo site where
air temperature and precipitation were generated from weather at that site
(WGEN, Richardson and Wright, 1984). Solar radiation was generated for all sites
using a weather generator developed from actual weather data of the area
(Richardson and Wright, 1984).

The three tillage systems considered were conventional tillage, reduced
tillage, and no-till. Tillage sequences are detailed in Table 5.2. Time of tillage was
deliberately fixed to reduce computing time. Nevertheless, tillage was delayed
when the water content of the top five surface layers was above the drained upper
limit.

The simulated planting date was varied from 30 April to 30 May with a 10

day increment to evaluate the consequences of variable planting dates on final

129

Table 5.2: Description of the three tillage systems compared for four Michigan sites.

 

 

Tillage operation Date
Conservation tillage

Moldboard plow 20 cm deep 16 November

Secondary tillage, disk 7.5 cm deep 30 April

Fertilizer application, 150 N Kg/ha 10 May

Planting Variable
Reduced tillage:

Chisel plow 15 cm deep 30 April

Fertilizer application, 150 N Kg/ha 10 May

Planting Variable
No-Till

Fertilizer application 150 N Kg/ha 10 May

Direct drill planting Variable

 

yield for the three tillage strategies. Sowing was delayed if the minimum average
temperature of the five days prior to the planting date was less than 5 0C, or if
the water content of the top five layers was above the drained upper limit. No
irrigation was applied. Table 5.3 describes the genetic parameters used to run the
model where P1 is the thermal time (base 8 0C) from seedling emergence to the
end of juvenile phase, P2 is the photoperiod sensitivity coefficient, P5 is the
thermal time (base 8 °C) from silking to physiological maturity, G2 is the potential
kernel number, and G5 is the potential kernel growth rate (refer to Jones and

Kiniry, 1986).The three genotypes were determined for each part of Michigan’s

130

lower peninsula: south, center and north Michigan. They represent the class of

hybrid usually grown in these regions.

Table 5.3: Genetic coefficient chosen to run CERES-Till for four Michigan sites.

 

 

Variety# P1 P2 P5 G2 G5 Site
70 220 0.75 740 610 8.5 Kalamazoo, Ingham
71 154 0.3 685 560 8.5 Tuscola
72 130 0.3 685 560 8.5 Huron

 

For each strategy, total cost including tillage operation, fertilizer and
fertilizer application, seed cost, herbicide (conventional and reduced tillage
received one spray of herbicide, no-till received two sprays), and harvest, were
calculated using the program STEM (DeVuyst and Black, 1989). Cost of diesel
fuel was $ 1.00 gallon'l, wage rate and fringe benefits $ 5.00 hour'l, annual
interest rate 9%, nitrogen cost $ 0.29 N kg'l, and seed cost $ 0.94 per 1000 seeds.
Total costs for each tillage strategy are given in Table 5.4.

Net return was calculated from gross return minus the cost of drying when
the harvest was delayed. The selling price of maize was assumed to be
$ 115.38 T'l. Drying costs were estimated from the maturity date. When the

length of the grain filling period was longer than 70 days, maize grain was

131

Table 5.4: Cost associated with each tillage system ($ ha'l).

 

 

Gross cost

Tillage operation Cost ($ ha'l)
Conservation tillage
Moldboard plow 20 cm deep 24.33
Secondary tillage, disk 7.5 cm deep 11.20
Fertilizer application, 150 N Kg/ha 28.57
Planting 91.85
herbicide application 13.38
harvest 72.60
Interest cost 14.53
Gross cost 256.46
Reduced tillage:
Chisel plow 15 cm deep 11.20
Fertilizer application, 150 N Kg/ha 28.57
Planting 91.85
herbicide application 13.38
harvest 77.43
Interest cost 11.83
Gross cost 234.26
No-Till
Fertilizer application 150 N Kg/ha 28.57
Direct drill planting 88.10
herbicide application (x2) 26.76
harvest 77.43
Interest cost 11.25

232.11

 

132

assumed to be at 15% water content. When the length was between 55 days to 70
days, the grain water content was assumed to decrease linearly from 30% to 15%.
The cost to bring maize grain from 30% water content to a 15 % water content

was estimated at $ 4.65 '1"1 (Rose et al., 1980). For intermediate water content the

cost was assumed to decrease linearly.

.__1

The outputs of the multiple year runs were: number of days from sowing to

anthesis, number of days from anthesis to maturity, nitrogen and water deficit

i

factors during the end of the vegetative period and during grain filling period,
annual runoff (cm), grain yield (T ha'l), and return ($ ha'l).

For each strategy and planting date, the average, standard deviation,
minimum and maximum values were calculated for all the output variables of the
models. Comparisons among treatments and planting dates were done using
cumulative probability curves. Cumulative probability curves are interpreted by
reading the probability of getting a value lower than a certain level read on the X
axis. Strategy evaluation was done by using stochastic dominance analysis. Strategy
A dominates strategy B in the sense of stochastic dominance if, in a cumulative
probability graph, the outcomes or variables of interest are always higher than
outcomes in a cumulative probability of strategy B. Details on the theory of
stochastic dominance are available (Anderson et al. 1977). A software program
was written to calculate statistical parameters, sort data (Press er al., 1992), and
draw curves from output values of the model using the Graflib software (Graflib,

1989)

133

For each site and tillage strategy the planting date that maximized average
yield was determined. The maximum return condition for each tillage strategy was
then compared to the others using stochastic dominance principles in terms of
economic return, annual runoff, length of vegetative and reproductive stages, and

water deficit factors during those stages.

Results

Yield and net return mean, standard deviation, and minimum and
maximum values, are presented for each site in Tables 5.5, 5.7, 5.9, and 5.11. The
average length of the vegetative and reproductive stages, as well as the standard
deviation, minimum and maximum are presented for each site in Tables 5.6, 5.8,
5.10, and 5.12. The yield, return, and water deficits during vegetative growth and
grain filling cumulative probability curves for the planting date of each tillage
strategy with the highest return are plotted in Figures 5.1 to 5.8. The water deficit
factor for the vegetative and reproductive stages represent the average daily
relative water deficit factor (ratio of actual transpiration and potential

transpiration) the plant experienced during each stage.

Huron site
Yields and net return information are presented in Table 5.5. On the
conventional tillage strategy, yields were low, 3.42 T ha '1, and slightly increased

with late planting. The main reason for the yield increase was an extension of the

134

grain filling stage and a small decrease in water stress in water deficit during that
stage. Reduced tillage improved yields up to 4.21 T ha'l. Planting dates had little
effect on yield and return for the reduced tillage strategy. Planting date of 30 May
(DOY 150) increased the length of the maturity stage decreasing the drying costs
and improving the returns. The best return on the no-till strategy was reached for
the planting date of 10 May (DOY 130) due to higher mid-range yields and longer

vegetative stage.

Table 5.5: Yield (T ha'l), return (S ha'l) for three tillage strategies and four planting
dates at the Huron site.

 

 

TILLAGE YIELD 'r HA'I RETURN 8 HA"

sow AVG STD MIN MAX AVG s11) MIN MAX
Conventional 120 3.38 .98 1.93 6.19 120.32 110.27 -42.69 438.61
Conventional 130 3.42 1.02 1.93 6.19 125.74 114.43 -42.69 438.61
Conventional 140 3.43 1.03 1.93 6.19 127.10 115.91 .4269 438.61
Conventional 150 3.46 1.06 1.93 6.19 129.92 119.28 -42.69 438.61
Reduced 120 4.19 1.14 2.40 7.43 233.43 127.62 33.78 602.34
Reduced 130 4.21 1.18 240 7.43 235.80 132.62 33.78 60234
Reduced 140 4.21 1.19 2.40 7.43 235.88 132.87 33.78 602.34
Reduced 150 4.21 1.18 2.40 7.43 236.21 133.14 33.78 60234
No-Till 120 4.70 1.28 2.71 8.38 293.11 144.22 70.50 708.81
No-Till 130 4.83 1.37 272 8.38 308.75 154.46 71.62 708.81
No-Till 140 4.74 1.35 2.71 8.38 298.63 151.65 70.50 708.81
No-Till 150 4.68 1.35 2.71 8.38 29215 15243 70.50 708.81

 

135

The no-till strategy was best when compared to the two other strategies
(Figure 5.1). Residue cover maintained by reduced tillage and no-till increased
yields for all planting dates (Table 5.5) by decreasing daily average water deficit by
half during the vegetative growth. Another beneficial effect of the residue cover
was to slow the vegetative stage for the No-Till strategy without reducing the
length of the maturity stage (Table 5.6). Runoff was smaller on the reduced tillage
and no-till strategies due to improved water infiltration. The decrease in runoff
was larger on high runoff events due to increased water ponding capacity created

by the residues.

Tuscola site

Yields and net return information are presented in Table 5.7. Due to its
geographical location, north of mid-Michigan, crop failures (no yields) were
important at the Tuscola sites, occurring 37% of the time. Late planting, 20 and
30 May (DOY 140, 150), increased average yield and returns. This indicates that a
short season cultivar should be used. An alternate cr0p would likely be planted if
the crop was killed by slow emergence. The late planting date, 30 May (DOY
150), gave the best yields and return due to higher yields in the colder years.
Average yields and distribution were similar for all planting dates for the reduced
tillage strategy. Although the yield did not vary much with the planting date, the
best average return was reached for planting date of 10 May (DOY 130). No-till

had the best return for the earliest planting date, 30 April (DOY 120), due to a

Probabllltu

Cumulative

Probabilitfl

Cumlathn

 

136

'J

 

L‘
11

H

 

 

 

I I T

 

I I
154..
RETURN (Slha)

 

 

 

 

lflNOFF (on)

fiat-150 -CIuw Will-150 -lIod. flat-130 -IIoTi

Figure 5.1: Cumulative distribution of economic return (3 ha’l) and annual runoff

(cm) at the Huron site for the best planting dates for conventional tillage,
reduced tillage, and no-till.

137

Table 5.6: Number of days from sowing to anthesis and anthesis to maturity for three
tillage strategies and four planting dates at the Huron site.

 

 

TILLAGE SOWING TO ANTHESIS ANTHESIS TO MATURITY

DOY AVG STD MIN MAX AVG STD MIN MAX
Conventional 120 81.27 7.30 68.00 103.00 55.43 6.11 39.00 67.00
Conventional 130 80.47 8.04 66.00 103.00 55.57 6.27 39.00 67.00
Conventional 140 79.83 9.01 60.00 103.00 55.87 6.38 39.“) 67.00
Conventional 150 80.40 10.53 55.00 103.00 56.50 6.21 43.00 69.00
Reduwd 120 83.77 7.56 71.00 98.00 55.87 6.48 38.00 69.00
Reduwd 130 82.33 8.31 66.00 98.00 56.03 6.58 38.00 69.00
Reduwd 140 81.27 9.31 63.00 98.00 56.23 6.68 38.00 69.00
Reduced 150 80.53 10.36 56.00 98.00 56.70 6.60 38.00 69.00
No-Till 120 84.30 7.53 69.00 97.00 56.33 7.07 39.00 74.00
No-Till 130 85.37 10.00 64.00 102.00 56.87 6.52 45.00 69.00
No-Till 140 81.63 9.16 63.00 97.00 57.17 7.33 39.00 74.00
No-Till 150 81.07 10.13 57.1!) 97.00 57.03 7.08 3911') 74.00

 

better distribution of yields and a longer vegetative period in the warmer years.

Residue cover improved the average yield by decreasing the number of

crop failures to 24%. Water conservation by the soil cover decreased water deficit

during the vegetative and reproductive stages. Runoff was also decreased by the

residue cover due to increased water infiltrability and ponding capacity.

Ingham site

Yield distribution was similar for all planting dates for the conventional

tillage strategy (Table 5.9). Water deficit during the grain filling period was the

main limiting factor on yield establishment and was not affected by planting time.

Early planting, 30 April (DOY 120), improved returns on the best years by

138

 

 

 

 

1.. p
n
u
- n 0
n
u
°C.—1 r.
u
.3 n
d u
‘ r
1 .4
a O ‘ -" 3 0
2 r.
h - n
e f'
, J
a '04— 3 {9
— r' 0
- u
u
u
..3 U
u
u
“-9 I I I I I I I I L I I I I L l I I I L I
goL. °oL5 .- 0 °- 5 “a .

HATER DEFICIT DURING URGITQTIUR 8106!

 

Cumulative Probability
1

 

 

 

 

.02—
~ n
'1
n
0'. I I I I I W I l I I l I 1 I I
0.50 mks 0.110 e.Le
turn barren warm: mm nurse
+huni-ise -c6-w *hunI-Ise 416a. flail-130 -Nori

Figure 5.2: Water deficit during vegetative and reproductive stage for three tillage
strategies at optimal planting date at the Huron site.

139

Table 5.7: Yield (T ha'l), return (3 ha'l) for three tillage strategies and four planting
dates at the Tuscola site.

 

 

TILLAGE YIELD 'r HA-I RETURN 3 HA’1

sow AVG STD MIN MAX AVG STD MIN MAX
Conventional 120 3.70 2.62 .00 7.70 154.03 290.85 256.40 596.22
Conventional 130 3.37 2.80 .00 7.93 117.96 310.11 256.40 621.69
Conventional 140 3.85 2.61 .00 8.20 171.20 289.14 -256.40 654.13
Conventional 150 3.86 2.60 .00 7.85 173.33 289.52 256.40 617.70
Reduced 120 4.38 2.69 .00 7.97 25298 300.03 234.20 648.32
Reduced 130 4.39 275 .00 9.17 253.62 305.64 234.20 784.04
Reduced 140 4.37 271 .00 8.87 25210 301.92 234.20 750.72
Reduced 150 4.35 2.67 .00 8.41 250.42 297.36 234.20 699.65
No-Till 120 4.64 2.85 .00 8.61 284.20 317.38 232.10 721.29
No-Till 130 4.57 2.82 .00 8.86 276.51 314.02 232.10 751.71
No-Till 140 4.52 276 .00 8.61 270.37 307.18 23210 723.95
No-Till 150 4.59 278 .00 8.54 278.79 310.62 23210 718.83

 

increasing the length of the vegetative stage (Table 5.8). Reduced tillage had the

best return for the planting date of 10 May (DOY 130) due to a longer vegetative

period. Planting dates of 10 and 20 May (130,140) increased yields on the no-till

strategy by increasing the length of the vegetative period without decreasing the

length of the grain filling period. Planting date of 10 May (DOY 130) had slightly
higher mid-range yields.

Soil cover improved yields and returns by increasing plant water availability
and decreasing water deficit during the vegetative and reproductive stages. The
length of the grain filling period was unaffected by the tillage strategies. The

decrease in runoff was more important during the years when runoff was large.

140

 

 

 

 

 

 

 

1..
“e.-
a
0
“ u
u
‘
A
3 0.6—
2
fl -
U
9
: .e‘—
C
u
i -1
3
.ea —"
db
qu-
o'. I I I I I I I I T I I I I I
’2‘ e. 40].. 344.. ‘Je.

RETURN (C/ha)

 

Cumulative Probability

 

 

 

 

.-. I I I I I I ”If I ”I I I
3.. 12(0 23?.
RUNOFF (om)
—i—tumi-150 -Conv -)(-tumi-.l30 -Red. -B-tumi-120 -Ne‘l'i

Figure 5.3: Cumulative distrlbution of economic return (3 ha‘l) and annual runoff
(cm) at the Tuscola site for the best planting dates for conventional tillage,
reduced tillage, and no-till.

141

Table 5.8: Number of days from sowing to anthesis and anthesis to maturity for three
tillage strategies and four planting dates at the Tuscola site.

 

 

TILLAGE SOWING TO ANTHESIS ANTHESIS TO MATURITY

DOY AVG STD MIN MAX AVG STD MIN MAX
Conventional 120 79.70 5.86 70.00 92.00 42.33 19.01 12.00 64.“)
Conventional 130 80.07 7.45 65.00 95.00 39.47 19.62 12.00 65.00
Conventional 140 77.90 7.64 61.00 92.00 43.57 18.44 12.00 68.00
Conventional 150 77.03 9.09 57.00 91.00 43.73 18.53 12.00 63.00
Reduced 120 82.57 6.61 70.00 95.00 44.53 17.51 13.00 65.00
Reduced 130 84.20 8.30 68.00 98.00 ' 44.43 17.47 13.00 64.00
Reduced 140 80.60 8.70 63.00 95.00 44.90 17.84 13.00 65.00
Reduced 150 79.80 9.93 57.00 95.00 44.90 17.73 13.00 65.00
No-Till 120 83.37 6.77 72.00 96.00 44.60 17.44 13.00 62.00
No-Till 130 85.57 8.23 70.00 98.00 44.73 17.58 13.00 66.00
No-Till 140 82.67 9.00 66.00 96.00 44.63 17.63 13.00 64.00
No-Till 150 80.33 10.09 58.00 96.00 45.17 17.81 13.00 63.00

 

Kalamazoo site

Late planting decreased crop losses and improved high-range yields mostly
because of a longer grain filling stage that increased kernel weight (Table 5.10).
The best yield and return (Table 5.11) was reached for the planting date of 30
May (DOY 150). Reduced tillage had fewer crop failures. Late planting increased
the length of the grain filling period, thus improving the middle range yields. The
decrease in number low yields improved the average return for the planting date
of 30 May (DOY 150). For the no-till strategy late planting improved low yields by
increasing the length of the grain filling period. Best returns were obtained for the

latest planting date 30 May (DOY 150).

142

 

l
H

[I

 

Cumulative Probability

 

 

 

'-° I I I” I I’ L: I 1 ”I I k I I’ I I L
.0 .0 .0 o .0 s

NOTER DEFICIT DURING UEGETATIUE STOGE

 

Probability

 

Cumulative

 

 

 

.0. I I I I L I II I I' L I‘fi I I I I I I L I I
..L. .0 5 .e O O.L3 .0 O

NRTER DEFICIT DURING GRRIN FILLING

—i—tumi-15. ~Conv -X-tumi-13O -Red. -B-¢umi-130 -NoTl

Figure 5.4: Water deficit during vegetative and reproductive stage for three tillage
strategies at optimal planting date at the Tuscola site.

143

Table 5.9: Yield (T ha'l), return ($ ha'l) for three tillage strategies and four planting
dates at the Ingham site.

 

 

TILLAGE YIELD T HA-l RETURN 3 HA'1

SOW AVG STD MIN MAX AVG STD MIN MAX
Conventional 120 4.53 1.32 .00 7.59 253.01 148.16 -256.40 602.86
Conventional 130 4.52 1.31 .00 7.59 252.10 147.96 -256.40 602.86
Conventional 140 4.44 1.29 .00 7.59 243.43 145.81 -256.40 602.86
Conventional 150 4.38 1.29 .00 7.59 236.17 144.71 -256.40 602.86
Reduwd 120 4.86 1.57 .00 7.59 311.59 176.99 -234.20 627.42
Reduced 130 4.87 1.56 .00 7.59 313.49 176.16 -234.20 627.42
Reduced 140 4.88 1.56 .00 7.59 314.46 175.94 -234.20 627.42
Reduced 150 5.03 1.52 .00 7.59 333.13 172.00 -234.20 627.42
No-Till 120 5.30 1.65 .00 8.15 365.39 186.38 -232.10 670.35
No-Till 130 5.35 1.64 .00 8.14 371.37 185.72 -232.10 669.24
No-Till 140 5.35 1.66 .00 8.15 370.79 188.04 -232. 10 670.35
No-Till 150 5.25 1.57 .00 7.59 358.13 176.60 -232. 10 627.16

 

Residue cover increased the length of the vegetative stage without
decreasing the length of the grain filling period, thus increasing kernel weight and
final yield. The main effect of the soil cover was to decrease water deficit intensity
during the vegetative and reproductive period, thus increasing grain yield. Returns
were the highest for the no-till strategy. Maximum runoff was decreased by half on
the no-till strategy compared to the conventional tillage strategy. Runoff was

always smaller on the reduced tillage and no-till strategies.

144

Table 5.10: Number of days from sowing to anthesis and anthesis to maturity for
three tillage strategies and four planting dates at the Ingham site.

 

 

TILIAGE SOWING TO ANTHESIS ANTHESIS TO MATURITY
DOY AVG STD MIN MAX AVG STD MIN MAX
Conventional 120 92.59 8.52 76.00 111.00 60.72 7.15 47.00 73.00
Conventional 130 92.31 8.58 76.00 111.00 60.97 7.08 49.00 73.00
Conventional 140 89.86 9.47 69.00 111.00 60.69 6.83 51.00 74.00
Conventional 150 89.55 9.93 69.1!) 111.00 60.48 6.39 51.00 74.00
Reduced 120 84.45 10.24 71.00 102.00 59.93 6.59 48.00 74.00
Reduced 130 84.14 10.13 72.00 102.00 60.03 6.52 50.00 74.00
Reduwd 140 83.55 10.34 71.00 102.00 59.97 6.40 50.00 74.00
Reduwd 150 87.55 12.91 68.00 113.00 61.28 6.12 54.00 73.00
No-Till 120 91.24 11.87 71.00 114.00 60.83 6.56 49.00 73.00
N o-Till 130 90.69 12.14 71.00 114.00 60.83 6.67 48.00 73.00
No-Till 140 90.14 12.52 71.00 114.00 60.97 6.47 52.00 73.11)
No-Till 150 87.52 12.92 69.00 114.00 60.52 5.96 54.“) 74.“)

 

145

 

Probabilit!

Cumlative

 

 

 

 

 

 

 

 

 

I I I
494..
a
O
‘
fl
‘
I.
O
E
O
3
d
0
I
3
I I I
RUNOFF (om)
“um-150 -Conv *msum-lSO -Red. -B-msum-150 -Nol'i

Figure 5.5: Cumulative distribution of economic return (3 ha'l) and annual runoff
(cm) at the Ingham site for the best planting dates for conventional tillage,
reduced tillage, and no-till.

146

 

 

 

 

 

1e. l1“ L
-
.e.—
a
0
“ .-
u
d
2 .e‘—
E .
C
3
z .e‘_
C
u
-' 5
r-
'J
‘9
.e2—' r
u!"
0.. I I I I BI I I I L I I I I L I I I I I I I I L
a.“ .e O. 0 .e 5 O. O 0. 5

MTER DEFICIT DURING UEGETOTIUE GTOGE

 

Cumin ative Probabi l i ty

 

 

 

 

0....L' l I IO.LSI I I IG.LOI I I 1015' I I '01., j

MTER DEFICIT DURING GROIN FILLING

—+-msum-150 -Conv *msum-iso -Re¢l. -B-meum-150 -No'l'i

Figure 5.6: Water deficit during vegetative and reproductive stage for three tillage
strategies at optimal planting date at the Ingham site.

147

Table 5.11: Yield (T ha'l), return ($ ha'l) for three tillage strategies and four
planting dates at the Kalamazoo site.

 

 

 

TILIAGE YIELD '1‘ 11A" RETURN 8 HA'1

sow AVG STD MIN MAX AVG STD MIN MAX
Conventional 120 4.03 1.78 .00 7.46 191.88 198.41 -256.40 569.65
Conventional 130 4.00 1.78 .00 7.46 190.18 198.63 -256.40 569.65
Conventional 140 4.00 1.80 .00 7.46 190.55 201.20 -256.40 569.65
Conventional 150 4.17 1.70 1.30 7.64 210.78 190.30 -112.45 589.58
Reduced 120 5.38 1.92 1.48 9.09 366.69 216.24 -7032 800.51
Reduwd 130 5.34 1.90 1.48 8.98 363.00 214.09 -7032 787.99
Reduced 140 5.47 1.94 1.48 8.54 378.23 219.47 -7032 736.67
Reduced 150 5.49 1.93 1.48 8.54 380.96 217.64 -7032 733.23
No-Till 120 6.00 2.04 1.58 10.13 438.60 230.58 .57.15 921.00
No-Till 130 5.96 1.95 1.58 9.10 433.75 219.59 -57.15 775.54
No-Till 140 6.11 2.04 1.58 9.30 452.19 230.79 .57.15 840.93
No-Till 150 6.14 2.09 1.58 9.18 456.12 237.37 -57.15 793.03
Discussion

Due to important water deficit during vegetative growth, planting dates had
little effect on final yields. Late planting tended to increase final yield by
increasing the length of the grain filling period, thus allowing increased dry matter
accumulation. On the residue cover treatments, planting earlier than the
conventional tillage strategy was required to ensure sufficient growing season
before maturity. In some cases, late planting decreased the length of the
vegetative period due to higher air temperature. This caused a longer grain filling
period. Thus late planting improved yields for the reduced and no-till strategies at

the warmest location (Kalamazoo site, Table 5.12).

148

Table 5.12: Number of days from sowing to anthesis and anthesis to maturity for
three tillage strategies and four planting dates at the Kalamazoo site.

 

 

TILLAGE SOWING TO ANTHESIS ANTHESIS TO MATURITY
DOY AVG STD MIN MAX AVG STD MIN MAX
Conventional 120 89.97 12.20 70.00 120.00 52.93 10.24 7.00 69.00
Conventional 130 87.63 12.69 70.00 120.00 53.53 10.51 7.00 70.00
Conventional 140 86.40 13.20 69.00 120.00 54.93 11.50 7.00 70.00
Conventional 150 80.23 8.66 64.00 97.00 56.93 6.85 47.00 72.00
Reduced 120 90.80 10.04 72.00 111.00 55.63 5.50 48.00 70.00
Reduced 130 89.57 10.26 72.00 111.00 56.47 6.19 48.00 73.00
Reduced 140 89.53 13.97 69.00 123.00 57.20 6.90 48.00 73.00
Reduced 150 83.90 10.55 67.00 104.00 57.33 6.94 48.00 74.00
No—Till 120 92.03 9.59 73.00 112.00 56.03 5.59 48.00 70.00
No-Till 130 90.67 10.01 73.00 112.00 56.80 6.28 48.00 73.00
No-Till 140 87.73 10.88 69.00 112.00 57.80 7.14 48.00 73.00
No-Till 150 84.90 11.08 66.00 105.00 57.67 6.65 48.00 72.00

 

No-till always provided better yields and economic return for the four

locations. No-till increased yield by increasing infiltration (reduced runoff), which

diminished water deficit during the vegetative period and the grain filling period.

Reduced tillage improved economic returns compared to conventional tillage by

increasing yields and decreasing costs. The yield increase induced by the tillage

strategies was larger than that caused by planting dates. The yield increase from

conventional tillage to no-till varied from 18% at the Ingham site to 47% at the

Kalamazoo site. The increase was highest on the sandy soil where there was

higher rainfall. The Kalamazoo site was the most humid, 924 mm precipitation,

followed by the Huron site, 743 mm (Table 5.12). Yields for all strategies

increased when the temperature in the spring increased. The coldest site was the

.J

149

 

Probability

Cumulative

 

 

 

 

Cumulative Probabilit!

 

 

 

 

RHNOFF (on)

—i-'IthIIt-'.13.~ -600!) *itbsn-ISO -IUO¢I. Wat-1.3. -N01‘l

Figure 5.7: Cumulative distribution of economic return (3 ha'l) and annual runoff

(cm) at the Kalamazoo site for the best planting dates for conventional tillage,
reduced tillage, and no-till.

150

 

 

 

Mlativo Probability

 

 

o'.jllij lllt lfillfifill llll ITfi
O. O. 0.0 0.5 0.. 0.5

“In DUIGIT DURING UlGI'IR'I’IUR SIOGI

 

Mlatioa Probabi l i t9

 

 

 

 

0.0.. t t I 1.121 I limb] ' I luluj I I roil1

turn DII'IGIT DURING GROIN III-LING

Wet-18. -c.lw Mao-150 -Rod. airline-1.3. -No‘li

Figure 5.8: Water deficit during vegetative and reproductive stage for three tillage
strategies at optimal planting date.

Huron site and the temperature increased from the coast to the inland locations at

151

Tuscola, Ingham and Kalamazoo (Table 5.13).

Table 5.13: Five day average air temperature in the spring at the Huron, Tuscola,

Ingham and Kalamazoo sites.

 

 

Date April May May May May May May June June
30 05 10 15 20 25 30 04 09

Huron site

Average 10.38 11.06 12.10 13.81 14.46 14.59 15.70 17.94 17.93

STD 3.21 3.90 2.77 3.94 3.72 2.85 1.96 3.15 3.07

Tuscola site

Average 11.42 12.25 12.85 14.48 15.14 15.41 16.45 18.60 19.06

STD 3.39 4.11 2.94 3.75 3.69 2.58 1.96 2.90 2.94

Ingham site

Average 11.81 12.27 13.47 14.78 15.70 16.22 16.63 19.17 19.60

STD 3.39 4.11 2.94 3.75 3.69 2.58 1.96 2.90 2.94

Kalamazoo site

Average 13.10 14.15 14.67 16.66 17.29 17.33 17.98 19.09 20.76

STD 3.60 2.89 2.68 2.88 3.22 2.33 2.72 2.56 2.31

 

Runoff was decreased by the soil cover due to an increase in water

infiltration and ponding capacity (Table 5.14). The model did not include any
preferential flow that may happen on the conservation tillage system due to fauna
activity and root channels. Preferential flow will improve water infiltration and
move water faster downward the lower layers. Although the runoff was still high
on the no-till strategy, soil erosion should be smaller due to the decrease in

raindrop impact and the barrier to soil displacement created by the residue cover.

Table 5.14: Annual runoff (cm) at four sites for three different tillage strategies at
the optimum yield.

 

 

TILLAGE AVG STD MIN MAX DECREASE
%

Huron site

Conventional 15.90 6.26 5.30 30.80

Reduced 12.93 5.81 3.90 27.60 19

No-til] 11.06 5.25 2.40 24.90 30

Tuscola site

Conventional 13.24 5.73 5.30 28.20

Reduced 11.63 5.18 2.90 22.80 12

No-till 10.30 4.72 2.60 20.70 22

Ingham site

Conventional 15.86 6.56 4.40 29.40

Reduced 12.30 4.90 3.6 20.0 22

No—till 11.22 4.71 2.9 20.10 30

Kalamazoo site

Conventional 20.90 7.49 9.0 34.60

Reduced 14.67 5.69 5.80 26.00 30

No-till 12.60 5.18 3.60 24.00 40

 

These results are dependent on the model assumptions. The model does

not predict crop losses due to pests and disease. No-till has been reported to

increase the amount of pests in the crop and may be a constraint to no-till

adopdon.

153

Conclusion

The model showed that no-till would increase maize yield and economic
return in Michigan for rainfed crops. The yield increase was mainly due to
increased infiltration and reduced water deficit during vegetative and grain filling
stages. Planting date had a small effect on final yield because of the dominant
effect of the water deficit on yield. Comparison of model outputs to long term
yield data is necessary before using the model for a wide selection of climates and
soil types. The model showed a decrease in water runoff for the less tilled
strategies due to higher saturated water conductivity and water ponding capacity.
A model of particle detachment and preferential flow would also improve the

model by allowing prediction of soil erosion.

154

Bibliography

Anderson, J. R., J. L. Dillon and J. B. Hardaker. 1977. Agricultural decision
analysis. pp. 344. The Iowa State University, Ames, Iowa, USA.

Devuyst, and R.J. Black. Field Crop-Stem. 1989. Vers. 1. East Lansing, MI, USA:
Michigan State University, Departement of Agriculture Economics.

Graflib. 1989. Vers. 4.15. Sutrasoft, Sugar Land, TX, USA.

Jones, C. A. and J. R. Kiniry.1986. CERES-Maize a simulation model of maize
growth and development. Texas A&M Univ. Press, College Station, Texas,
USA.

Press, W. H., B. P. Flannery, S. A. Teukolsky and W. T. Vetterling. 1992.
Numerical recipes. The art of scientific computing. Cambridge University
Press, New York, N .Y., USA.

Richardson, C. W., and D. A. Wright. 1984. WGEN: A model for generating daily
weather variables. USDA, ARS-8. Springfield, VA, USA. National
Technical Information Service.

Ritchie, J. T., U. Singh, D. Godwin and L. Hunt. 1989. A user’s guide to
CERES-Maize-V2.1. IFDC, Alabama, USA.

Rose, W. T., Z. Helsel, B. Stout, and C. Myers. 1980. Energy management for
field crop production. Extension bulletin E-1407. East Lansing, MI, USA.
Cooperative Extension Service, Michigan State University.

155

Conclusion and recommendations

The complexity of the interaction of crop residue cover and climate was
illustrated in field experiments. Residue cover delayed maize development while
the meristem was below ground, and thus its growth. The delay in plant
development was still evident at harvest because of high water content of the
grain. The length of the delay was linked to the spring air temperatures. Under
high air temperatures, the delay was shorter due to rapid plant growth. Crop
residue cover maintained high water content at the surface, thus kept root
distribution near the surface. The root distribution pattern under a residue cover
needs to be further investigated to better understand the consequences of water
deficit later in the season.

Maize development prediction was improved by using soil temperature to
calculate thermal time while the plant meristem was below ground. Although air
temperature gave good correlation, phyllochron or base temperature had to be
modified to get an accurate prediction. Soil temperature gave a better prediction
because it used phyllochron and base temperature that has been confirmed by
experimental data.

A model of tillage and residue cover effects on soil surface conditions was
added to the CERES model. Surface bulk density, saturated hydraulic
conductivity, and ponding capacity were time-variant adding a dynamic dimension

to the model that is necessary to evaluate residue cover impact on water runoff.

156

Further work is needed to validate the model of surface properties and to include
initial soil conditions effect on soil properties after tillage. The model prediction of
chopped surface residue decomposition was an improvement but a standing crop
residue model would be closer to field conditions.

Cr0p residue cover effects on maize development was an essential step
toward no-till modelling. The modelling of maize growth response to no-till
conditions needs to be validated to ensure that the model accurately predicts
biomass, root distribution, and water uptake. The problems of pest damage that
are encountered in no-till conditions is not included in the CERES model. A
pest-weed model needs to be linked to the CERES models.

Model simulations of three tillage strategies, conventional, reduced tillage,
and no-till, showed that no-till was the best tillage strategy for the four studied
sites located in mid-Michigan. No-till had lower water deficit conditions during
vegetative grth and higher net return due to higher final grain yield and lower
costs. No-till considerably decreased runoff, especially in the high range where soil
erosion is most likely to happen. A model of particle detachment linked to the
water runofl model would allow prediction of soil loss and its impact on long-term
soil productivity. The results found in these simulations must be validated with

long-term yield data from several climates from cool temperate to tropical.

Appendix 1: Weather and soil data

15T7

SITE rm Solrad Att- tap. °c lain SITE rm Solrad Air tap. °c Rain
007 Max Ilin e- 001 lax Ilin c-
nsuu 9o 91 14.41 10.6 5.6 5.3 nsun 90 161 26.00 23.3 12.8 .0
nsuu 90 92 12.04 5.6 4.4 7.9 nsun 90 162 19.93 25.0 11.7 .5
nsun 90 93 10.81 3.9 -1.1 1.5 usuu 90 163 26.95 25.6 14.4 .o
8808 90 94 18.82 10.0 -1.1 .0 usuu 90 164 19.83 32.2 17.8 17.5
usun 90 95 19.12 4.4 -1.1 .0 nsun 90 165 18.02 26.7 18.9 1.0
nsun 90 96 19.33 3.3 -6.7 .0 nsun 90 166 25.36 27.2 13.9 .0
nsun 90 97 19.84 4.4 -7.2 .0 8808 90 167 26.46 29.4 14.4 .o
usun 90 98 13.95 11.7 -6.7 3.3 8808 90 168 26.62 32.2 19.4 .0
8808 90 99 14.11 17.2 7.2 11.2 nsun 90 169 25.53 26.7 20.0 .0
nsun 90 100 12.02 8.9 6.7 6.1 8808 90 170 25.22 25.0 10.6 .0
nsun 90 101 11.49 3.9 -1.1 2.5 usun 90 171 20.66 23.9 15.6 4.1
8808 90 102 20.33 5.0 -4.4 .0 8808 90 172 26.74 27.2 15.6 .o
usun 90 103 14.52 12.2 -2.2 5.8 nsun 90 173 26.60 20.0 18.3 .0
8808 90 104 14.59 8.9 4.4 .5 usun 90 174 18.51 15.6 13.9 11.4
usun 90 105 20.55 12.2 1.7 .0 nsun 90 175 26.93 23.3 10.0 .o
nsun 90 106 14.38 16.1 -1.1 3.8 nsun 90 176 27.32 27.2 6.7 .0
nsun 90 107 21.30 3.9 -1.1 .0 nsun 90 177 21.73 25.0 15.6 .8
usun 90 108 21.55 13.3 -5.0 .0 nsun 90 178 20.56 26.7 13.3 10.7
nsun 90 109 21.97 16.1 .0 .0 8808 90 179 27.04 23.9 17.8 .o
nsun 90 110 15.56 15.0 10.0 14.7 nsun 90 180 26.33 27.8 20.0 .o
nsun 90 111 20.67 18.9 7.8 .0 nsuu 90 181 25.56 30.0 18.3 .0
usuu 90 112 20.77 22.2 5.6 .0 nsuu 90 182 25.72 23.3 15.0 .0
8808 90 113 21.34 23.9 6.7 .o nsun 90 183 26.57 26.7 11.1 .0
nsun 90 114 14.18 28.3 11.7 1.3 usun 90 184 27.06 29.4 14.4 .o
nsun 90 115 20.59 30.0 16.7 .0 nsun 90 185 26.85 34.4 22.2 .0
nsun 90 116 19.58 29.4 16.1 .0 880» 90 186 25.12 28.3 20.0 .0
nsun 90 117 19.58 28.9 15.6 .0 nsuu 90 187 25.18 22.2 13.3 .0
8808 90 118 19.83 27.2 15.0 .o 8808 90 188 26.65 24.4 8.9 .0
nsun 90 119 20.15 22.2 12.2 .0 nsuu 90 189 20.97 33.3 15.6 .o 13.6
nsun 90 120 20.95 26.1 10.0 .0 nsuu 90 190 26.59 30.0 21.7 .0
8808 90 121 21.69 15.0 4.4 .o nsun 90 191 17.21 28.3 16.7 .o 13.3
8808 90 122 23.21 17.8 2.2 .0 H808 90 192 25.76 20.0 13.9 .0
nsun 90 123 23.71 17.2 4.4 .0 8808 90 193 26.41 23.3 15.6 .0
nsun 90 124 17.34 9.4 8.9 19.3 8808 90 194 19.75 22.8 12.2 7.6
nsun 90 125 23.36 16.7 5.6 .0 nsun 90 195 20.13 21.1 7.8 .8
nsun 90 126 23.91 15.6 3.9 .0 8808 90 196 20.82 24.4 13.9 8.2 10.2
nsun 90 127 24.21 23.3 7.8 .0 8808 90 197 20.03 26.7 14.4 5.1
8808 90 128 24.03 27.2 13.3 .0 nsuu 90 198 26.12 28.9 17.8 .0
nsun 90 129 15.02 25.0 15.6 3.3 nsun 90 199 18.59 30.0 17.8 2.5
8808 90 130 13.73 10.0 9.4 1.0 8808 90 200 18.06 30.0 17.2 14.0
nsuu 90 131 23.60 16.1 1.7 .0 8808 90 201 18.11 24.4 18.9 12.7
nsun 90 132 18.71 11.1 2.8 7.9 8508 90 202 24.80 27.8 17.8 .0
nsun 90 133 25.18 17.8 7.8 .o nsun 90 203 17.67 19.4 14.4 9.9
nsun 90 134 18.52 16.7 5.0 .3 nsun 90 204 18.97 24.4 11.7 1.3
nsun 90 135 18.88 21.1 10.0 14.7 8808 90 205 25.65 26.1 12.2 .0
8808 90 136 18.14 22.2 16.1 16.8 8808 90 206 25.57 28.3 12.8 .o
8908 90 137 15.35 16.1 12.8 .5 nsun 90 207 25.43 28.9 14.4 .0
8808 90 138 24.01 14.4 7.8 .o nsuu 90 208 25.19 28.9 14.4 .o
nsun 90 139 18.79 12.9 4.4 9.9 8808 90 209 25.05 30.0 16.7 .o
8808 90 140 25.81 16.1 7.5 .0 8808 90 210 24.64 30.0 17.8 .0
nsun 90 141 25.78 15.6 7.8 .o 830» 90 211 24.17 24.4 21.1 .0
nsun 90 142 25.84 17.8 5.6 .o nsun 90 212 23.31 21.7 12.2 .0
8808 90 143 26.09 20.0 5.0 .0 nsun 90 213 24.63 26.1 8.9 .o
8808 90 144 26.26 20.0 11.1 .0 nsun 90 214 24.86 27.2 11.1 .0
8808 90 145 19.40 19.8 8.9 4.1 nsun 90 215 24.66 27.2 14.4 .0
nsun 90 146 19.62 21.3 10.3 4.3 880» 90 216 24.29 21.7 18.9 .0
nsun 90 147 26.08 24.4 7.9 .o 880» 90 217 16.29 24.4 17.2 7.4
nsun 90 148 26.33 23.3 7.2 .0 nsun 90 218 23.31 17.2 13.3 .0
8808 90 149 26.50 16.1 8.9 .0 usun 90 219 17.36 22.2 10.0 8.6
noun 90 150 26.51 20.6 3.3 .0 usun 90 220 23.99 27.2 8.9 .0
neon 90 151 26.97 23.9 5.0 .0 nsun 90 221 23.98 28.3 13.3 .o
8808 90 152 27.01 27.2 8.3 .0 880» 90 222 23.57 27.8 13.3 .o
nsun 90 153 20.61 27.2 18.9 2.8 usun 90 223 16.91 26.7 14.4 26.9
usun 90 154 25.10 24.4 15.6 .0 nsun 90 224 23.12 22.2 15.0 .o
neon 90 155 25.36 17.8 5.0 .0 nsun 90 225 22.92 20.0 16.1 .0
nsun 90 156 20.90 18.9 3.9 .3 nsun 90 226 22.52 24.4 10.0 .0
usun 90 157 27.39 25.6 13.3 .0 nsun 90 227 22.92 27.2 14.4 .0
usun 90 158 20.31 25.6 12.2 15.0
880» 90 159 26.77 25.6 13.3 .0
nsuu 90 160 26.61 24.4 16.1 .0

RE

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15!?

5112 YEAR Solrad Air «up. °c Rain 5112 m sou-.6 Air tap. °c lain
our lax Min a. 007 In '10 9-
8521 91 91 19.94 3.9 -. .0 8521 91 158 27.13 27.2 10.6 .0
8521 91 92 18.93 8.3 -4.4 .0 8521 91 159 26.97 27.2 11.1 .0
8521 91 93 19.02 14.4 -2.2 .0 8521 91 160 26.94 28.9 11.1 .0
8521 91 94 12.59 19.4 1.7 12.4 8521 91 161 20.53 28.9 15.0 8.1
8521 91 95 18.78 21.1 8.3 .0 8521 91 162 19.40 25.0 17.8 30.7
8521 91 96 17.73 26.7 8.3 .0 8521 91 163 25.64 27.2 13.9 .0
8521 91 97 17.59 27.2 15.0 .0 8521 91 164 26.40 25.6 8.3 .0
8521 91 98 7.82 23.3 17.8 5.1 8521 91 165 27.26 31.1 13.3 .0
8521 91 99 6.70 15.6 11.7 17.3 8521 91 166 20.51 32.2 17.8 2.3
8521 91 100 17.12 7.8 1.1 .0 8521 91 167 25.94 26.7 19.4 .0
8521 91 101 19.41 8.3 -3.3 .0 8521 91 168 25.39 28.3 12.8 .0
8521 91 102 20.49 10.0 -1.7 .0 8521 91 169 26.78 29.4 16.1 .0
8521 91 103 14.29 11.7 1.7 5.8 8521 91 170 26.39 30.6 15.0 .0
8521 91 104 14.13 13.9 4.4 10.2 8521 91 171 26.57 31.1 15.0 .0
8521 91 105 13.40 18.3 6.1 3.8 8521 91 172 19.70 31.1 15.6 5.6
8521 91 106 20.05 13.3 5.0 .0 8521 91 173 19.47 16.1 14.4 16.5
8521 91 107 20.39 13.9 2.2 .0 8521 91 174 26.91 23.3 9.4 .0
8521 91 108 21.13 13.9 .0 .0 8521 91 175 27.37 26.7 9.4 .0
8521 91 109 15.13 7.2 3.3 21.8 8521 91 176 27.45 29.4 14.4 .0
8521 91 110 15.06 7.2 1.1 2.0 8521 91 177 27.08 32.2 16.1 .0
8521 91 111 15.43 5.6 1.7 1.0 8521 91 178 26.64 31.7 20.6 .0
8521 91 112 22.05 15.0 2.2 .0 8521 91 179 25.46 31.1 20.0 .0
8521 91 113 15.70 16.1 5.0 14.0 8521 91 180 25.31 32.2 21.7 .0
8521 91 114 22.03 15.6 5.6 .0 8521 91 181 24.88 28.9 19.4 .0
8521 91 115 22.00 18.3 1.7 .0 8521 91 182 17.29 28.9 15.6 6.1
8521 91 116 22.63 23.3 6.1 .0 8521 91 183 26.27 29.4 17.2 .0
8521 91 117 15.54 25.6 10.6 8.6 8521 91 184 18.95 31.1 17.2 16.0
8521 91 118 13.78 23.9 12.8 .5 8521 91 185 26.06 27.8 17.2 .0
8521 91 119 12.75 24.4 13.3 1.3 8521 91 186 26.07 28.3 17.8 .0
8521 91 120 12.47 18.9 12.8 .5 8521 91 187 25.91 33.3 15.6 .0
8521 91 121 12.82 14.4 8.3 1.8 8521 91 188 19.44 31.7 22.8 29.5
8521 91 122 22.30 12.8 7.2 .0 8521 91 189 24.74 25.6 17.8 .0
8521 91 123 22.97 13.9 1.7 .0 8521 91 190 18.04 25.6 12.2 .3
8521 91 124 23.88 18.3 4.4 .0 8521 91 191 26.61 27.8 13.3 .0
8521 91 125 23.94 15.6 3.9 .0 8521 91 192 26.59 27.2 15.6 .0
8521 91 126 24.10 7.8 6.1 .0 8521 91 193 26.37 24.4 16.7 .0
8521 91 127 24.13 12.8 4.4 .0 8521 91 194 19.29 23.3 16.1 1.0
8521 91 128 17.96 15.6 1.1 1.3 8521 91 195 26.04 26.1 15.6 .0
8521 91 129 24.72 23.9 5.6 .0 8521 91 196 26.09 28.9 11.1 .0
8521 91 130 24.59 27.2 9.4 .0 8521 91 197 26.37 30.6 13.9 .0
8521 91 131 24.17 28.9 10.6 .0 8521 91 198 26.16 30.6 19.4 .0
8521 91 132 23.83 30.6 15.6 .0 8521 91 199 25.17 32.2 18.9 .0
8521 91 133 22.80 28.9 15.0 .0 8521 91 200 24.87 32.8 23.3 .0
8521 91 134 22.81 29.4 16.1 .0 8521 91 201 23.77 33.3 22.2 .0
8521 91 135 22.70 32.2 11.1 .0 8521 91 202 23.68 31.1 22.8 .0
8521 91 136 16.12 31.1 13.3 7.6 8521 91 203 14.99 32.2 21.1 9.7
8521 91 137 23.74 24.4 18.9 .0 8521 91 204 23.70 31.7 21.1 .0
8521 91 138 22.71 15.6 6.1 .0 8521 91 205 23.71 26.1 14.4 .0
8521 91 139 25.37 22.2 6.1 .0 8521 91 206 25.12 23.9 14.4 .0
8521 91 140 25.67 25.6 6.7 .0 8521 91 207 25.24 24.4 10.6 .0
8521 91 141 25.78 29.4 10.0 .0 8521 91 208 25.43 25.6 9.4 .0
8521 91 142 25.64 31.1 13.9 .0 8521 91 209 19.39 25.6 13.3 .3
8521 91 143 24.73 28.3 16.1 .0 8521 91 210 18.80 20.0 15.0 28.7
8521 91 144 16.03 27.2 18.9 14.0 8521 91 211 24.84 23.3 14.4 .0
8521 91 145 14.66 27.8 18.9 9.4 8521 91 212 24.73 27.8 11.1 .0
8521 91 146 23.41 27.2 20.6 .0 8521 91 213 24.82 28.9 14.4 .0
8521 91 147 23.14 29.4 21.1 .0 8521 91 214 18.07 31.7 16.1 8.9
8521 91 148 23.03 32.2 19.4 .0 8521 91 215 24.13 22.8 18.9 .0
8521 91 149 14.26 32.8 21.1 7.6 8521 91 216 23.39 24.4 15.0 .o
8521 91 150 23.22 30.6 20.0 .0 8521 91 217 23.88 23.9 10.0 .0
8521 91 151 14.32 30.6 19.4 .8 8521 91 218 24.23 25.6 8.3 .0
8521 91 152 14.78 27.8 18.9 6.1 8521 91 219 18.20 26.7 12.2 2.3
8521 91 153 15.27 21.1 16.7 4.8 8521 91 220 17.64 16.7 16.1 43.9
8521 91 154 24.79 26.7 14.4 .0 8521 91 221 16.95 25.0 13.3 2.3
8521 91 155 25.52 20.6 8.9 .0 8521 91 222 23.46 26.7 12.8 .0
8521 91 156 26.76 23.9 9.4 .0 8521 91 223 23.37 27.2 11.1 .0
8521 91 157 26.90 26.1 7.2 .0 8521 91 224 23.35 27.8 12.2 .0

160

ISEL 91 225 23.16 28.3 12.8 .0
HSEL 91 226 22.95 27.8 13.3 .0
HSEL 91 227 22.76 29.6 13.9 .0
HSEL 91 228 22.55 30.0 16.1 .0
HSEL 91 229 15.11 25.0 19.6 2.8
MSEL 91 230 13.61 27.8 13.3 10.6
HSEL 91 231 15.16 23.3 15.6 5.1
HSEL 91 232 16.66 23.9 16.6 1.3
HSEL 91 233 21.53 26.1 8.9 .0
HSEL 91 236 21.86 28.3 13.9 .0
HSEL 91 235 21.67 25.0 13.3 .0
HSEL 91 236 21.29 27.8 13.9 .0
HSEL 91 237 20.98 30.6 12.8 .0
HSEL 91 238 20.93 32.2 12.8 .0
HSEL 91 239 20.76 31.7 17.8 .0
HSEL 91 260 19.70 33.3 17.2 .0
MSEL 91 261 19.39 33.9 19.6 .0
HSEL 91 262 11.08 32.2 19.6 2.5
ISEL 91 263 18.69 27.8 17.8 .0
HSEL 91 266 18.59 26.6 7.8 .0
HSEL 91 265 19.91 28.3 7.2 .0
HSEL 91 266 19.89 27.2 11.7 .0
HSEL 91 267 19.50 23.9 12.2 .0
HSEL 91 268 19.22 26.1 7.2 .0
HSEL 91 269 19.31 28.3 10.6 .0
HSEL 91 250 18.99 30.0 12.2 .0
HSEL 91 251 11.91 30.6 15.6 .3
HSEL 91 252 10.60 30.0 15.6 8.6
HSEL 91 253 17.38 25.0 18.9 .0
HSEL 91 256 16.67 21.1 9.6 .0
HSEL 91 255 11.12 20.0 11.1 .3
HSEL 91 256 17.68 22.2 13.9 .0
HSEL 91 257 10.10 25.6 12.8 2.0
HSEL 91 258 16.88 31.7 16.1 .0
HSEL 91 259 16.17 26.7 22.2 .0
HSEL 91 260 16.99 22.8 11.1 .0
HSEL 91 261 9.16 18.3 11.1 .5
HSEL 91 262 16.31 13.9 6.6 .0
HSEL 91 263 16.78 16.6 1.7 .0
HSEL 91 266 16.80 18.3 -1.7 .0
NSEL 91 265 16.80 18.3 -.6 .0
HSEL 91 266 16.60 16.1 6.7 .0
HSEL 91 267 9.62 10.6 2.8 .5
HSEL 91 268 9.56 13.3 .0 1.8
HSEL 91 269 15.93 11.7 3.9 .0
HSEL 91 270 15.61 12.2 .6 .0
HSEL 91 271 15.53 16.7 -3.3 .0
HSEL 91 272 15.51 17.8 -3.3 .0
HSEL 91 273 15.36 26.7 -.6 .0
HSEL 91 276 15.05 20.0 8.3 .0
HSEL 91 275 7.91 23.9 10.0 7.9
MSEL 91 276 7.05 22.2 12.8 23.1
HSEL 91 277 6.22 19.6 10.0 11.2
HSEL 91 278 6.35 16.6 10.0 3.6
HSEL 91 279 6.26 8.9 2.2 1.3
HSEL 91 280 13.65 12.2 2.2 .0
HSEL 91 281 13.56 20.6 1.7 .0
HSEL 91 282 13.38 22.2 3.9 .0
HSEL 91 283 6.57 16.7 5.0 1.0
HSEL 91 286 6.31 13.9 5.0 1.8
HSEL 91 285 12.65 12.8 5.0 .0
HSEL 91 286 5.87 12.8 -2.2 2.3
HSEL 91 287 6.07 10.6 -1.7 5.3
HSEL 91 288 12.67 7.2 3.3 .0
HSEL 91 289 12.16 13.3 -3.9 .0

161

LOCAIIOI COIOVIR HI+II "I

:1 lished Series
3e.. nus-rpw-wrr
l/50-

CDNOVZR 323128

the Conover series consists of very deep, somewhat poorly drained soils
formed in loamy glacial till on low parts of moraines and till plains.
These soils have moderate or moderately slow permeability. Slopes range
from 0 to 0 percent. seam annual precipitation is about 33 inches. and
mean annual temperature is about 40 degrees I.

IAXOIOHIC CLASS: tine-loamy, mixed. mesic Udollic Ochraqualfs

2231:33 3:003: Conover loam on a 2 percent convex slope in a cultivated
field. (Colors are for moist soil unless otherwise stated.)

Ap-0 to 9 inches: very dark grayish brown (1023 5/2) loam, grayish
brown (1023 5/2) dry: moderate fine granular structure: friable: about 5
percent gravel: many fine roots: slightly acid: abrupt smooth boundary. (7
to 10 inches thick)

5w-9 to 11 inches: dark yellowish brown (1023 4/4) loam: common medium
distinct yellowish brown (1023 5/6) and grayish brown (1023 5/2) mottles:
weak medium subangular blocky structure: friable: about 5 percent gravel:
cc-‘on fine roots: slightly acid: clear wavy boundary. (0 to 6 inches
t

5t1-11 to 15 inches: yellowish brown (1023 5/4) clay loam: common fine
distinct grayish brown (1023 5/2) mottles: moderate medium subangular
blocky structure: firm: few distinct dark grayish brown (1023 4/2) clay
films on faces of peds and in pores: about 5 percent gravel: few fine
roots: slightly acid: gradual wavy boundary. ,

5t2-15 to 27 inches: brown (7.523 4/4) silty clay loam: common medium
prominent yellowish brown (1023 5/5) and distinct gray (1023 6/1) mottles:
moderate medium subangular blocky structure: firm: common distinct dark
grayish brown (1023 0/2) clay films on faces of peds and in pores: about 5
percent gravel: few fine roots: slightly acid: gradual wavy boundary.
(Combined thickness of the It horizon ranges from I to 24 inches.)

Cg10-27 to 50 inches: light brownish gray (1023 6/2) loam: common medium
distinct yellowish brown (1023 5/4) and faint gray (1023 6/1) mottles: weak
medium subangular blocky structure: firm: about 5 percent gravel: slight
effervescence: mildly alkaline: gradual wavy boundary.

Cg2--50 to 60 inches: light brownish gray (1023 6/2) loam: common medium
prominent strong brown (7.523 5/0) and faint gray (1023 6/1) mottles:
m:;:§:e: firm: about 5 percent gravel: slight effervescence: mildly
a ne.

2"' LOCKTION: flashtenaw County. Michigan: about 3 miles south and 1 1/2
a : east of Chelsea: 1,060 feet north and 1,040 feet west of the
so-.neast corner of sec. 25. 2. 2 5.. 3. e f.

162

comm suns P890 3

3h362 IN C8h3hCTZ3ISTICS: Solum thickness ranges from 24 to 40 inches and
coincides with depth to effervescence. 3ock fragments range from 0

to 10 percent throughout the solum. The reaction generally ranges from
medium to slightly acid, but in some pedons the hp and B horizons are
neutral.

The hp or'h horizon has value of 2 or 3, and chroma of 1 or 2. It is loam,
silt loam, or sandy loam. Some pedons have t horizons. If present. it has
hue of 1023, value of 5 or 6, and chroma of 2 or 3. It is 2 to 6 inches
thick and has textures like the hp or h horizon.

The 8w horizon has hue of 1023 or 7.523, value of 4 to 6, and chrome of 3
or 4. It is loan or silt loan.

The St horizon has colors similar to the av horizon. It is clay loam.
silty clay loam, or loam. Some pedons have he horizons of loam or clay
loam up to 7 inches thick. Some pedons have secondary carbonates in the
lower 5 horizon (5k horizons) on the the underside of rock fragments.

The c horizon has value of 4 to 6, and chroma of 2 or 3. It is loam, silt
loan, or clay loam. It is mildly or moderately alkaline.

COMPETING $23125: These are the heardstown, Cantril, Dundas. hanheim,
Metamora, monitor. 3iceville, 3omulus, Schleyy and Skyberg series. Similar
soils are the Blount, Capac, Crosier, Locke, and Hacomb series. Beardstown,
Cantril, Monitor, and Schley soils have thicker sola. In addition,
Beardstown soils have c horizons formed in stratified loan or sandy loam
outwash materials and the Schley soils formed in stratified sediments
overlying glacial till at depths of 30 to 50 inches. Dundas soils have
lower chromas in the B horizon and typically 2.52 and 52 hues in the 5 and
c horizon. Hetamora soils formed in sandy materials overlying calcareous
loam till at depths of less than 40 inches. hanheim soils are darker
colored throughout the profile. 3iceville, and Skyberg soils do not have
rock fragments in the upper part of the series control section. Romulus
soils have hues redder than 7.523 in upper part of the series control
section. and finer-textured c horizons. Blount soils have lighter colored
hp horizons and are in a fine family. Capac and Crosier soils have lighter
colored hp horizons. Locke soils are coarse-loamy. hacomb soils have
gravelly layers within the control section.

crocnhphzc SETTING: Conover soils typically are on low parts of moraines
and till plains. Slopes range from 0 to about 6 percent, and dominant
slopes are from 1 to 4 percent. Kean annual precipitation is 29 to 37
inches, and mean annual temperature ranges from 46 to 51 degrees 2.

GtOGRthIChLLx hSSOCIhTID SOILS: The poorly drained hrookston and well
drained hiami soils are in a drainage sequence with the Conover soils, and
they are the most common associates. The very poorly drained organic
Carlisle soils are in depressions.

03h13h65 AND P£3thSILIT2: Somewhat poorly drained. Surface runoff ranges

163

as: him WhTION: Largely under cultivation. Corn, beans, small grain,
and legume-grass hey are the major crops. h small part is in forest.
Native vegetation was hardwood forest.

01mm! m m: Southern hichigan, southern Visconsin, and
northern Indiana. The series is of large extent.

SERIES rsraausno: hiami County. Ohio, 1915.

am: Diagnostic horizons and features recognized in this pedon are:
ochric epipedon - the zone from the surface to 5 inches (hp horizon):
argillic horizon - the zone from 11 to 27 inches (Btl and an horizons):
aguic soil moisture regime.

National Cooperative Soil Survey
0.S.h.

164

Table 1.1: Conover loam volumetric water content at 3 different depth and
different suction and bulk density at the Box Farm, East Lansing, Mich.,

USA.

 

Suction (kPa) 0.00 0.10 0.20 0.39 0.59 3.33 10.00 BD

 

cm3 cm'3 g cm"3
O-7.6 0.51 0.41 0.34 0.33 0.33 0.31 0.27 1.27
7.6-15.2 0.51 0.42 0.35 0.33 0.33 0.31 0.28 1.26

15.2-22.8 0.52 0.42 0.34 0.33 0.33 0.31 0.27 1.39

 

Appendix 2: Modified subroutines

nonnnnnnnnnnnnn

10

1(35

ProgramINAIN

C E R E s G E N E R I C N I T R O G E N N 0 D E L

This is an effort to combine all the models into one generic
model. All routines are generally the same except for some crop
specific ones (nfact,menu2,grosub).

This version includes changes for tillage simulation (in bold) developed by F. Dadoun

CERES MAIZE MODEL -

CERES NHEAT MODEL -

CERES SORGHUM MODEL

CERES PEARL MILLET MODEL

CERES BARLEY MODEL

CERES CEREAL GENERIC MODEL DEVELOPED BY SINGH,GODNIN AND HUMPRIES -IFDC
NITROGEN ROUTINES DEVELOPED BY GODNIN,JONES, ET AL

INCLwE 'gen1.blk'
INCLUDE 'gen2.blk'

INCLUDE
INCLUDE
INCLUDE

'gen3.blk'
'gen6.blk'
'Ntrc1.Blk'

INCLUDE 'Ntrc2.8lk'
INCLUDE 'comihs.blk'
INCLUDE 'predob.blk'
INCLUDE 'Nmove.Blk'
INCLUDE 'Enviro.Blk'
INCLUDE 'soilox.blk'
INCLlDE 'nwatbal.hlk'
INCLUDE 'till.blk'

LOGICAL iechon, filexist, er, Lbegin, wTSOPEN, FCROP, Update
DINENSION Store(12, 50)

CNARACTER‘1 iehvc, RESP2,ans

CNARACTER‘Z Crapl(6)

CNARACTER*12 FFilei, Filec, FileD, drainfile,calcevfile, wtfile
INTEGER ISNUT, DECTRT, UNTRT, DECEXP, UNEXP, ist, ifs1

DATA Crops/'MZ', 'NH', 'SG', 'ML', 'BA', 'FA'I

OPEN (UNII I 77, FILE I 'NBAL.0UT', STATUS I 'UNKNONN')
Update I .TRUE.

POND I 0.0

ioutpt I .TRUE.
Lbegin I .FALSE.
OUTSOPEN I .FALSE.
NSENS I 0

NREP I 0

NOUTD I 60

NOUT1 I 61

NOUTZ I 62

NOUT3 I 63

NOUT6 I 66

NoutS I 65

KOUTUA I 5

KOUTGR I 5

Koutnu I 5

DRAINFILE I 'OUT7.N2'
CALCEVFILE I 'OUT6.M2'

INOUIRE (FILE . 'CROP.DAT', EXIST . FCROP)

IF (Fence) OPEN (UNIT - 2, FILE - 'CROP.DAT', STATUS . 'OLD')
INOUIRE (FILE - 'BATCN.SSS', EXIST . filexist)

IF (filexist) OPEN (5, FILE . 'BATCH.$$S', STATUS . 'OLD')

OPEN (UNI? I NOUTO, FILE I 'SIN.DIR', STAIUS I 'UNKNONN')
CALL Clear()
write (6, 5000)
READ (5, '(a1)') a
if (a.eq.'y'.or.a.eq.'Y') then
call clear

20
30

60

50

166

open (unit-6,fileI'nul',statusI'old')
write (*,*) “ Program running, please wait, ...... "
endif
IF (FCROP) THEN
READ (2, *) NCROP
IF (ncrop .E0. 2.) THEN
Ichoice I S
ELSE IF (ncrop .E0. 5) THEN
Ichoice I 1
ELSE IF (ncrop .E0. 6) THEN
Ichoice I 6
ELSE IF (ncrop .E0. 10) THEN
Ichoice I 3
ELSE IF (ncrop .E0. 12) THEN
Ichoice I 2
ELSE
Ichoice I 7
END IF
END IF
IF (.NOT. FCRW .on. ICNOICE .E0. 7) THEN
DO UHILE (.TRUE.)
CALL cleari)
write (6, 5010)
READ (5, '(12)') Ichoice
CALL Selpro(1, 6, Ichoice, Index, Ierr)
IF (Index .NE. 2) GOTD 20
END DO
END IF
Crap I Cropa(1choice)
Runall .FALSE.
Imulti .FALSE.
Runend .FALSE.
Imoutf 1
Iphout .TRUE.
Numy I
Nrep I Nrep + 1
Iret I 0
CALL Clear()
CALL IPEXPI)
FileC I Files
URITE (FileC(12:12), 5020)
FileD I FileD
URITE (FileD(12:12), 5030)
FFile1 I File1
IF (Runend) SOTO 130
Change I .FALSE.
IF (nyr .ST. 1) THEN
leti I .TRUE.
write (6, 5060) nyr
END IF
IF (.NOT. Imulti) CALL Clear()
IF (.NOT. runall) 6010 180
nsens I 0
IF (.NOT. Change) CALL IPTRT(update)
IF (.NOT. Change) CALL IPSHIN()
IF (IswcoZ .NE. 1) THEN
IF (.NOT. Change) THEN
TCHANGE I 0.
Tchmin I 0.
Rchange I 1.
Schange I 1.
END IF
END IF
IF (Nsens .NE. 2) GOTO 70
CALL NENUIiret)
Change I .TRUE.
Ffilei I File1
IF (.NOT. Imulti) SOTO 70
DO HHILE (.TRUE.)

167

write (6, 5050)

IF (Imulti) write (6, 5060)

write (6, 5070)

READ (5, 5080, IOSTAT I ierr) NSENS
CALL Selpro(0, 3, Nsens, Index, Ierr)
IF (Index .NE. 2) THEN

IF (NSOnS .E0. 0 .AND. Imulti) TNEN
Imulti I .FALSE.
write (6, 5090)
END IF
IF (Hsens .E0. 3) CALL ipmulti(iupdate)
IF (NSENS .NE. 1) GOTO 50
CALL IPFREO()

END IF

END DO

GOTO 50

IF (change) ffile1 I f1le1t
IF (iret .E0. 1) THEN

CLOSE (11)
GOTO 60

END IF
CALL IPNTN()
IF (Runall) THEN

Titler I ' '
ELSE
write (6, 5100)
READ (5, 5110) TITLER

END IF

IF (TITLER .EO. ' ') TITLER I TITLET
ifirst I 1

IF (Nrep .LE. 1) THEN

1
2

ISHHT I 0
writ. (6, '(A,/,2(28X,A,/))')

ENTER HATER TABLE SUITCN: O I OFF', '1 I HT H/O CONTROL',

'2 I CONTROLLED NT'
READ (*, '(I1)') ISNNT

Open files to direct drain outputs from watbal and read water table depth:

IF (ISNHT .OE. 1) THEN

DECTRT I INT(NTRT110) I 68

UNTRT I NTRT - INT(NTRT/10)*10 + 68

DECEXP I INT(NFEXP/10) + 68

UNEXP I NFEXP - INT(NFEXP/10)*10 + 68

NTFILE I 'UT'I/CNAR(DECEXP)l/CHAR(UNEXP)I/CHAR(DECTRT)I/CNAR
(UNTRT)/I'.M20'

OPEN (380, FILE I DRAINFILE, STATUS I 'UNKNOHN')

OPEN (610, FILE I NTFILE, STATUS I 'UNKNOHN')

RESP2 I 'N'

URITE (380, 5120) NREP, TITLER

PRINT *

write (6, '(A \)')
' NANT TO URITE DAILY HATER TABLE RESULTS TO A FILE? (YIN)

READ (*, '(A1)') RESP2

IF (RESP2 .EO. 'Y' .OR. RESP2 .EO. 'Y') THEN
OPEN (370, FILE I CALCEVFILE, STATUS I 'UNKNONN')
HRITE (370, 5120) NREP, TITLER

END IF

else

cumdep I 0.0
do iI1,nlayr

cumdep I cumdep + dlayr(i)
enddo
depwt I cumdep

END IF
OPEN (UNIT I

OPEN (UNIT I

OPEN (UNIT I

OPEN (UNIT I NOUT6, FILE I OUT6, STATUS
IF (OUTS .NE.

NOUT1, FILE I OUT1, STATUS
NOUTZ, FILE I OUT2, STATUS
NOUT3, FILE I OUT3, STATUS

'UNKNOHN')
'UNKNOHN')
'UNKNOUN')
'UNKNOHN')

' ') TNEN

168

WEN (WIT I NGJTS, FILE I OJTS, STATUS I 'UNKNGJN', RECL I
1 270)
OUTSOPEN I .TRUE.
END IF
HRITE (NOUTO, 5130) OUTI, OUTZ, OUT3, OUT6, OUTS
END IF
IF (.NOT. OUTSOPEN .AND. OUTS .NE. ' ') OPEN (UNIT I NOUTS, FILE
1 I OUTS, STATUS I 'UNKNOUN', RECL I 270)
INOUIRE (FILE I FileC, EXIST I filexist)
IF (.NOT. filexist) FileC I ' '
INOUIRE (FILE I FileD, EXIST I filexist)
IF (.NOT. filexist) FileD I ' '
HRITE (NOUTO, 5160) TITLER, FILED, FILEC, FILED
iechon I .TRUE.
[run I runall
ihvon I .FALSE.
IF (Lrun) ihvon I .TRUE.
IF (Imulti) THEN
IF (".me .NE. 0) iechon I .FALSE.
ihvon I .FALSE.
lrun I .TRUE.
END IF
IF (.NOT. lrun) THEN
write (6, 5150)
READ (5, 5160) iehvc
IF (iehvc .EO. 'Y' .OR. iehvc .EO. 'y') ihvon I .TRUE.
END IF
DO UHILE (.TRUE.)
READ (11,5280,IOSTATIist) IYR,DON,SOLRAD,TENPNX,TEHPNN,RAIH
IF (ist .LT. 0) SOTO 90
IF (solrad.LE.0. .OR. rain.LT.0. .OR.
1 tempmx.EO.-99. .OR. tempmn.EO.-99.)
2 write (6, 5170) FFILE1, IYR, oov. SOLRAD, TENPNX, TEHPNH, RAIN
IF (DOY .NE. Isim) SOTO 110
Lbegin I .TRUE.
TENPNX I TEHPHR + TCHANGE
TEMPMN I TENPMN + TCNmin
SOLRAD I SOLRAD'Schange
RAIN I RAIN*Rchange
C***** zero out accumulators for multi~year run
IF (imulti) THEN
cet I 0.
ntill I 1
arunof I D
igsl I 0
yield I 0.0
hiomas I 0.0
ahioms I 0.0
gsrain I 0.0
train I 0.0
tlch I 0.0
tnox I 0.0
IF (nu-y .NE. 0) THEN
CALL reinitI)
nrep I nrep + 1
END IF
END IF
ifirst I 2
if (Latte) than
CALL PROORI(update)
CALL soltin()
IF (ISHSUB .NE. 0) CALL SOILRI()
IF (ISUNIT .NE. 0) THEN
CALL SOILNI()
CALL OHINITICEC)
CALL NDALII)
END IF
also

call progriupate)

169

endif
IF (Dov .EO. Isim) BACKSPACE 11
IF (numy .E0. 0) THEN
CALL echo(iechon)
nline I 0
IF (iphout) write (6, 5180)
END IF
IF (ht-y .E0. 0 .AND. .NOT. iphout) THEN
CALL ClearI)
IF (nline .E0. 0) write (6, 5260)
HRITE (nout1, 5260)
END IF
continue
READ (11,5280,IOSTATIifs1) IYR,DOY,SOLRAD,TEHPHX,TEHPHN,RAIN
IF (ifsi .E0. 0) THEN
IF ((solrsd .LE. 0.0 .OR. rain .LT. 0.0 .OR. tempmx .EO.
-99.0 .OR. tempmn .E0. -99.) .AND. iret .NE. 1) write (6,
5170) FFILEI, ITR, DOT, SOLRAD, TEHPHX, TEHPHN, RAIN
TENPNX I TEHPHM + TCHANSE
TEHPHN I TEHPHN + TCHnin
SOLRAD I SOLRAD*Schange
RAIN I RAIN*Rchange
Tempm I (Tempmx + Tempmn)*0.5
CALL mulche() ICERES-Till
CALL solt() lCERES-Till
IF (DOYX .E0. 367) CALL CALDATI)
IF (ISUSUB .NE. 0) CALL HATBAL(ISNUT)
IF (ISUNIT .NE. 0) THEN
CALL OMCYCLE(TC02)
CALL NTRANS()
END IF
IF ((DOY .gt. ISOU-5.and.doy.le.isow+20) .OR. ISTASE .NE. 7)
CALL PNENOL(iret, CUMDELAY,rat10)
IF (ISTASE .LT. 6 .AND. CROP .EO. 'HZ') THEN
CALL SROSUB(CUHDELAY,ratio)
ELSE IF (ISTASE .LT. 6 .AND. CROP .EO. 'HH') THEN
CALL HSROSUB()
ELSE IF (ISTASE .LT. 6 .AND. CROP .EO. 'SS') THEN
CALL SSROSUB()
ELSE IF (ISTAGE .LT. 6 .AND. CROP .EO. 'HL') THEN
CALL MSROSUB()
ELSE IF (ISTASE .LT. 6 .AND. CROP .EO. 'BA') THEN
CALL NSROSUB()
END IF
Train I Train + Rain
IF (istage.ge.3.and.lstage .Le. 6) THEN
Igsl I Igsl + 1
Ssrain I Ssrain + Rain
END IF
IF (iret .E0. 1 .AND. EOF(11)) SOTO 100
IF (ISHNIT .NE. 0) CALL NNRITE()
CALL URITE()
SOTO 80
END IF
IF (iret .E0. 1) SOTO 100
READ (FFile1(11:12), 5190) Kyr
Kyr I Kyr + 1
IF (Kyr .ST. 99) Kyr I 10
HRITE (FFile1(11:12), 5190) Kyr
INOUIRE (FILE I FFile1, EXIST I filcxist)
IF (filexist) THEN
OPEN (11, FILE I FFile1)
READ (11, 5200) LAT, XLONS, PARFAC, PARDAT
IF (LSESIN) SOTO 80
SOTO 110
END IF

URITE (nout1, 5290)
write (6, 5290)

 

100

110
120

130
160

170

Lbegin I .FALSE.

IF (OUTS .NE. ' ') CALL FOUTSII
IF (ISNNIT .NE. 0) CALL NBAL(3)
IF (.NOT. Imulti) @910 160
I‘lly I numy + 1

nline I nline + 1

CALL Phasei(CUHDELAT)

Iret I 0

Store(1, numy) I yield

Store(2, numy) I biomas‘lo.

Store(3, numy) I shin-3'10.
Store(6, numy) I totnup
Store(5, numy) I Tlch + Tnox
Store(6, numy) I Igsl
Store(7, numy) I gsrain
STore(8, numy) I Si1(1)
Store(9, numy) I 511(5)

Store(10, numy) I 813(1)
Store(11, numy) I Si3(5)
Store(12, nun) I nuuy
IF (nline .SE. 22) nline I 0
IF (.NOT. iphout) THEN
IF (nline .E0. 0) write (6, 5260)
write (6, 5270) numy, (Store(j. numv). j I 1, 12)
END IF
IF (Float(NYI15) .ED. 15 .AND. .NOT. iphout) THEN
CALL Clear()
write (6, 5220)
READ (5. '(81)') a
END IF
IF (fl-y .EO. nyr) SOTO 120
CONTINUE
END DO
CALL Psort(Store, 12, Numv)
CALL Clear()
write (6, 5210)
HRITE (nout1, 5210)
write (6, 5260)
HRITE (Nout1, 5260)
DO ny I 1, numy
write (6, 5270) HY. (store(i. DY). i I 1, 12)
IF (Float(Ny/15) .E0. 15) THEN
CALL Clear()
write (6, 5260)
write (6, 5220)
READ (5, '(e1)') a
END IF
wane (Nout1, 5270) W. (stored. ny). I = 1, 12)
END DO
write (6, 5220)
READ (5, '(a1)') a
SOTO 160
Nrep I Nrep - 1
DO HHILE (.NOT. Runall)
IF (FCROP) THEN
write (6, 5260)
ELSE
write (6, 5230)
END IF
READ (5, '(12)', IOSTAT I ierr) igo
CALL selpr0(0, 6, igo, index, ierr)
IF (index .E0. 2) SOTO 160
IF (igo .E0. 0) igo I 1
IF (FCROP .AND. ISO .ED. 3) ISO I 6
IF (igo .NE. 2) SOTO 150
IF (Imulti) THEN
write (6, 5250)
ELSE IF (Runcnd) THEN
write (6, 5300)

150

160
170
180
190

5000

5010

5020
5030
5060
5050

5060
5070
5080
5090
5100

5110
5120
5130
5160

171

ELSE
CALL disout()
END IF
END 00
SOTO 160
CONTINUE
IF (igo .ES. 6) SOTO 190
write (6)) ' Do You suit to reset soil punter (Default, y)‘
reed (*,'(s1)') ens
ntill I 1
if ((ans.eq.'n'.or.ans.eq.'N').or.(imulti.end.iupdate.eq.2)) then
(subte- .false.
else
update-.true.
endif
IF (igo .E0. 3) SOTO 170
CLOSE (11)
SOTO 30
CLOSE (11)
SOTO 10
file1t I ffile1
SOTO 60
ENDFILE (NOUTO)
ENDFILE (NOUT1)
ENDFILE (NOUTZ)
ENDFILE (NOUT3)
ENDFILE (NOUT6)
IF (OUTS .NE. ' ') ENDFILE (NOUTS)
CLOSE (HOUTO)
CLOSE (noun:
CLOSE (NOUTZ)
CLOSE (NOUT3)
CLOSE (NOUT6)
IF (OUTS .NE. ' ') CLOSE (HOUTS)
CLOSE (26)
STOP

FORMAT (Ill, 20x, 'Helcome to the C E R E S Model Version 1.99',
1 ll, 20x,

2 'Incorporates new menu structure, includes soil stresses', /,
3 20x, '(water, nitrogen and oxygen), and surface residue '
I. e ’0 20X,

5 'supports multi-year and multi.treatment ', I,
6 20x, 'simulation and also provides output support '
7 , I. 20x, ' for IBSNAT graphics and DSSAT', /
7 , I, 20x, ' To turn screen OFF press Y',/////,
8 ' Press Enter to Continue')
FORMAT (Ill.

' Hhich of the following models do you want to run :', //, 20x,
1. Maize ', I, 20x, ' 2. Sheet ', /, 20x,
3. Sorghum ', I, 20x, ' 6. Millet ', I, 20x,
5. Harley ', /, 20x, ' 6. Fallow ', Ill/ll,
Please enter your choice:')

FORMAT ('C')

FORMAT ('D')

FMMAT (' Multiple Year Rm ', i5, ' Years')

FORMAT (I, 2X, 'RUN-TIME OPTIONS: ', l/ZX, '0) RUN SIMULATION '
1 , /2X, '1) SELECT SImLATION wTPUT FREOUENCY'IZx,
2 '2) MODIFY SELECTED MODEL VARIABLES INTERACTIVELY.'//)

FORMAT (2X, '3) RUN MULTI’YEAR SIMULATION')

FORMAT (l/ZX, '<III CHOICE? I DEFAULT I 0 I')

FORMAT (I2)

FORMAT (' Single season simulation only')

FORMAT (I, T21, '<III ENTER UP TO HERE RUN IDENTIFIER, '
1 '<cr> FOR NONE.')

FORMAT (A20)

FORMAT (1x, 'RUN ', I2, ax, A20)

FORMAT (1X, 6(A7, ','), A7)

FORMAT (1X, A20, 21x, A12, 1x, A12, 1x, A12)

“ISIS!!!“d

5150

5160
5170

5180

5190
5200
5210
5220
5230

5260

5250
5260

C

172

FORMAT (' Do you want post harvest comparison with observed data'
1 , I, ' displayed on the screen (YIN) 7 ')

FORMAT (a)

FORMAT (2x, ' Please correct your weather file - ', A12, '.', I.
1 2x, 'Missing solar radiation, temperature or rainfall data.', /
2 I, 2X,

3 'Year Day Solar Rad. Max. Temp. Min. Temp. Rain', I,
6 3x, i2, 6x, i3, 6x, (5.2, 2(8x, f5.1), 6x, f5.1, II, 2x,
5 ' <Ctrl> <Sreak> to change missing values. ')
FORMAT (I, 15X, 'SIMULATION HAS BESUN....PLEASE HAIT.'/10X,
1 'DON"T TOUCH THE TERMINAL UNTIL IT PROMPTS YOU.')
FORMAT (I2.2)
FORMAT (6X, 2(1X, F6.2), 2(1X, F5.2))
FORMAT (' Simulation Outputs sorted according to yield')
FORMAT (' Press Enter to Continue')
FORMAT (' Simulation complete for this treatment.', I,

' Do you went to :', I,

' 1 Return to Experiment and Treatment Menu', I,

' 2 Display Detailed Outputs on Screen', I,

' 3 Choose another crop', I, ' 6 Ouit', II,

' Input a number (default is 1)')

FORMAT (' Simulation complete for this treatment.', /,

1 ' Do you went to :', /,

2 ' 1 Return to Experiment and Treatment Menu', I,

3 ' 2 Display Detailed Outputs on Screen', I, ' 3 Ouit', ll.
6 ' Input a number (default is 1)')

FORMAT (' Option not available under Multiple Year Setting')

FORMAT (2x, '3', 3X, 'SRAIN', 2X, 'MATURE', 2X, 'ANTHES', 5X, 'N'
1 , 5!, 'N', 2!, 'E-M', 3X, 'E-M', 2(3X, 'HAT'), 2(3X, 'NIT'), 1X,
2 'YR', /, 6X, 'YIELD', 2(1X, 'BIOMASS'), 1X, 'UPTAKE', 2X, 'LOSS'
3 , 1X, 'DAYS', 2!, 'RAIN', 2(1X, 'STRS1', 1x, 'STRSS'))

FORMAT (1x, i2, 3f8.0, 2f6.0, f5.0, f6.0, 6f6.1, 1x, f3.0)

FORMAT (Sx, i2, 1x, i3, f6.2, 2(1x, f5.1), 1x, f5.1, 1x, f6.2)

FORMAT (6x, 'EHD OF HEATHER DATA')

FORMAT (' Option not available under Multi-Treatment Setting')

UIO'WN-l

CALEO - Stbroutim to calculate potential ET
Subroutine CALEO

INCLUDE 'GEN2.BLK'
INCLUDE 'SEN3.BLK'
INCLUDE 'SEN6.BLK'
INCLUDE 'NTRC2.BLK'
inCIUOD 'TIIl.blk'
incllda 'Nwatbel .blk'

real mulchalb
data iidoy I0/

TO I 0.60‘TEMPMN+0.60‘TEMPMN

c calculation of albedo

CANCOV I 1 - EXP(-0.75*LAI)
mlchalb I 0.3
IF (ISTASE .LE. 6) THEN
IF (ISTASE .SE. 5) THEN
ALSEDOI0.23+(LAI-6)**2/160
ELSE
alWDJSOmlchooV‘fl -cancov)‘mlchalb*
S (1 ~mIldIcov)*(1-cancw)*salb
END IF
ELSE
E albedoI( 1-mlchoov)‘salbtmlchow'mlchalb
ND IF
EEO is in MJ/day -- AS
EEO I SOLRAD*(6.88E-6 - 6.37E-6 * ALBEDO) * (TO + 29.0)

0000

173

IF (TEMPMX.ST.35.) THEN
EO I EEO*((TEMPMX-35.)*0.05+1.1)
ELSE IF (TEMPMX.LT.5.0) THEN
E0 I EES*0.01*EXP(0.18*(TEMPMX+20.))
ELSE
EO I EEO‘IJ
END IF
Recheing factor the to canopy
IF (LAI.ST.1.) THEN
Ec I EXP('0.6*LAI )/1.1
ELSE
Ec I (1.-0.63*LAI)
END IF
Redacirg factor the to mlch
Rm I 1 - 0.80Mlchoow
mes I mlch
Athick I 1.5
eta-f I 1
Rm I min(exp(-0.5'mld|thick),1-O.807‘mlchoov)
Corrected soil potential evqaorstion. Rm is (mad only on the water that is not ponding
mdmtheldintheresidaessothsttheywillch-y '
if ((mldlswo0.01+pord).gt.Eo'Ec) than
Eos I Ec'Eo
else
Eos I (Eo'Ec - (mlchsw-D.01+pond))*h + (mlchsw -0.01+pond)
endif
RETURN
END

Stbreutine DTNTILL(wat,time)

Syn-it shaves of soil properties with tillage events and rainstorm
Tillage events reset values to default depuidirg on Tillage type
Rainfall affect parueters deperdim on intensity
reel rstl,tin,kedlge,xbd(6).xpalhx,xks.cro(6)
Include 'till.blk'
Include 'gm1.blk'
Incluie 'gelB.blR'
Incluie 'gen6.blk'
Incline 'ntn2.blk'
Incltsle 'Nwatbal.hlk'
Initialize swfaee prwerties after tillage
if (doy.eq.isim) then
”It I N“
do lI1,6
MD I blKl)
aka-cred) I Cameron)
mu I 0
smile

endif
ifl(doy.eq.doytill(ntill)) thm
I
depth I 0.0
do Idlile(&pth.le.deptill(ntill))
stncrofl) I tilloonsat(ntill)
Neutron) I stncrofl)
if (ksncro(l).lt.kamtrx(l)) then
ks-crou) I kutrxfl)
xkncrofl) I ksmtrxu)
ndif
xbd(l) I tillbd(ntill,l)
hd(l) I xhd(l)
dqath I thh + dlayr(l)
mom I 0
l I l+1
enthb

174

Xponthx I tillpond(ntill)
ponthx Xporuhx

8
endif
c CI-Ilative counter for rainfall intensity
if (wat.gt.0.0) then
cancov I 1 - exp(-.75'lai)
soilcov I cancov + mulchcov‘(1-cancov)
reinint I (3.812+0.812*log10(wat*.01l(tIIE'26)))*wet*.01
c Surface Dulk donsity
dopth I 0
do lI1,6
daPth ' depth + dlm(l)/2
As I 0.205’OC(l)
Rstl I 5. * (1 - As)
side“) I Me“) I (1-soilcov)*exp(- .15'depth)*rainint
Kechge I exp(-Rstl'stde(l))
hd(l) I stlbd(l)+(de(l)-stlhd(l))‘kechge
knew“) I ksmtrx(l)+(st-cro(l)-ksmtrx(l))'kechge
sat(l) I 0.SS*(1 - bd(l)/2.66)
if (sw(l)-St.sat(l)) than
pond I pond + (sw(l) - aat(l))*dlayr(l)
88(1) I sat(l)
endif
c Surface conductivity and ponding capacity equations
if (l.eq.1) then
panhx I stlpondr()(pol'xhx-stlpond)*kechge
if (pond.gt.pondmax) then
runoff I runoff + pond - pond-ex
pond I pond-ax
endif
endif
apth I mpth I dlayr(l)lZ
enddo
endif
return
end

 

 

Stbroutine IPNIT(Ipdate)

C This module will first read variables from File6 and
C check for existing treatment number choice and issue the
C appropriate message. Secondly, the variables from File7
C will be read and echoed, giving the user an option to
C change Fday, AFERT, DFERT or IFTYPE.
C
INCLLDE 'Comistlk'
INCLUDE 'Ntrc2.Blk'
INCLUDE 'Ntrc1.Slk'
INCLUDE 'SEN1.blk'
INCLUDE 'SEN3.blk'
INCLUDE 'till.blk'
Logical out,update
INTEGER ist, ist, ist, ifs3
INTESER Trtno, dout
REAL defpond(13), defdep(13), defbd(13), defconsat<13)
CHARACTER'T Ans
CHARACTER*2 Yr
DATA defpondl2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1/
DATA dedepI30, 15, 15, 15, 15, 15, 15, 10, 10, S, 5, 5, 5]
DATA defde1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2,1.2, 1.2]
DATA defconsotITO, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
C FILE6 SECTION
TrtNo I 0
Ierror I 0

OPEN (32, FILE I File6, STATUS I 'Old')
00 while (trtNo.ne.NTRT)
READ (32, 5050, IOSTAT I ist) Insts, Sites, Yr, Exptno,

 

175

1 Trtno, rihflch, rScn, rRoot, Am
IF (ist .LT. 0) then
SOTO 30
elseIF (ifs0 .ST. 0) then
Ierror I 1
SOTO 30
endif
if (trtno.ne.ntrt) then
dout I 0
dowhile (dout.ne.-1)
read (32,5000) dout
enddo
endif
END 00
i I 0
outI.false.
DO UHILE (.not. out)
i I i + 1
READ (32, 5000) doytill(i), codtill(i), deptill(i), fractill
1 (i), tillpond(i), tillconsat(i). (tillhd(i, j), j = 1, 6)
IF (doytill(i) .NE. -1) THEN .
DD UNILE (codtill(i) .LE. 0 .OR. codtill(i) .ST. 13)
write (6, I)
1 ' Em IN TILLASE m FILE 6, Please correct'
write (6, ') ' Enter new value and correct file later'
READ (*, '(i2)') codtill(i)
END DO
IF (tillpond(i) .E0. 0) tillpond(i) I defpond(codtill(i))
IF (tillconsat(i).E0.0) tillconsat(i)I defconsat(codtill(i))
IF (deptillIi).eq.0) deptill(i) I defdep(codtill(i))
DO I I 1,6
IF (tillbd(i,]) .E0. 0) tillhd(i,j) I defbd(codtill(i))
END DO
else
«It I .tl'ln.
END IF
END DO
sumke I 0
IF (stlxks .E0. 0) stlxks I 0.1
IF (stlpond .E0. 0) stlpond I 0.01
IF (stlbd(I) .E0. 0) stlbd I 1.6
ntill I 1
if (update) then
son I rscn
root I front
leach I rmulch
d1 I - 0.1395'alog(acn) + 0.566‘
kl I 2.96
kr I 0.035%d1 - 0.0013
mulchr I mulchr + mulch*(1 - d1)
mulchl I mulchl + mulch*(d1)
endif
I stlpond
30 IF (TrtNo .EO. Ntrt) THEN
DO NHILE (straw .LT. 0.0 .OR. sdep .LT. 0.0 .OR. scn .LE. 0.0
1 .OR. root .LE. 0.)
write (6, 5010) FILE6
READ (5, 5020) ANS
IF (.NOT. (ANS .EO. 'Y' .OR. ANS .EO. 'y')) SOTO 60
CALL menu6()
END 00
SOTO 50
60 IF (straw .LT. 0.) Straw I 800.
IF (adep .LT. 0.) Sdep I 30.
IF (Ion .LE. 0.) Son I 75.
IF (root .E0. 0.) root I 500.
50 CLOSE (32)
C FILE7 SECTION
TrtNo I 0

100

110
120

176

OPEN (33, FILE I File7, STATUS I 'Old')
00 K I 1, 10000
DO WHILE (.TRUE.)
READ (33, 5060, IOSTAT I ifs1) TrtNo, Inste, Sitee, Yr,
1 ExptNo
IF (ifs1 .LT. 0) SOTO 100
IF (ifs1 .ST. 0) SOTO 90
IF (TrtNo .EO. Ntrt) SOTO 70
DO M I 1, 10000
READ (33, 5070, IOSTAT I ifs3) Mday
IF (1fs3 .LT. 0) SOTO 100
IF (ifs3 .ST. 0) SOTO 90
IF (Hday .LT. 0) SOTO 60
END DO
SOTO 80
CONTINUE
END DO
00 J I 1, 10000
READ (33, 5070, IOSTAT I ist) Fday(J), Afert(J), Dfert
1 (J), Iftype(J)
IF (ifs2 .LT. 0) SOTO 100
IF (ist .ST. 0) SOTO 90
IF (Fday(J) .LT. 0) SOTO 100
IF (Iftypc(J) .E0. 17) Iftypc(J) I 12
IF (Crop .EO. 'SA') THEN
IF (Dfert(J) .EO. 0.0) Dfert(J) I 0.1
END IF
END DO
CONTINUE
END 00
Ierror I 1
TrtNo I - 99
IF (TrtNo .EO. Ntrt) THEN
J I J - 1
Nfert I J
DO NHILE (.TRUE.)
Icount I 0
00 K I 1, Nfert
IF (Afert(K) .LT. 0.0) Icount I Icount + 1
IF ((Afert(K) .NE. 0.0) .AND. (Dfert(K) .LE. 0.0))

1 Icount I Icount + 1

END DO
IF (Icount .LE. 0) SOTO 110
write (6, 5030) File7
READ (5, 5060) ANS
IF (.NOT. (ANS .EO. 'Y' .OR. ANS .EO. 'Y')) SOTO 120
CALL menu(iret)
END 00
CLOSE (33)
RETURN
CLOSE (11)
CLOSE (33)
ELSE IF (Ierror .E0. 1) THEN
write (6, 5130) File?
CLOSE (11)
ELSE
write (6, 5080) Ntrt, File7
CLOSE (11)
END IF
ELSE IF (Ierror .E0. 1) THEN
write (6, S130) File6
CLOSE (32)
CLOSE (11)
ELSE
write (6, 5080) Ntrt, File6
CLOSE (32)
CLOSE (11)
END IF
STOP

177

C
5000 FORMAT (5x, i3, 3(1x, i3), 7(2x, f5.2))
5010 FORMAT (I, ' Error! MISSINS VALUES OR VALUES OUT OF RANGE IN: ',
1 A12, I, 10x, 'DEFAULT VALUES HILL BE USED BY THE MODEL Unless ',
2 l, ' you type T to correct crop residue parameters ', I.
3 ' interactively for this run : ')
5020 FORMAT (1a)
5030 FORMAT (I, ' Error! Missing values found in ', A12, '.', /,
1 ' Type Y to correct it interactively for this run', /,
2 ' Or program will stop to allow modification : ')
5060 FORMAT (1a)
5050 FORMAT (6(a2),1x,12, 3(1x, F5.0),1X,F7.5)
5060 FORMAT (12, 1x, 3(A2), a2)
5070 FORMAT (I6, 2(1x, F5.1), 1x, 12)
5080 FORMAT (3x, '***** TREATMENT NO. ', I2, ' MISSINS IN FILE ', A,
1 ' ', I, ' Program will stop and allow modification of file.')
5090 FORMAT (A1)
5100 FORMAT (Ill, 15x,
1 'FERTILIZER APPLICATION DATA FOR TREATMENT NO. ', 12, ' ')
5110 FORMAT (I, 20x, 'DAY', 5X, 'AMOUNT', 5X, 'DEPTH', 5!, 'TYPE', /,
120x, l...|' 5x, I ...... I, 5x. I ..... I. 5x, I....l) .
5120 FORMAT (20x, I3, 6x, F5.0, 5x, F5.0, 6x, 12)
5130 FORMAT (3x, '**** READ ERROR ENCOUNTERED ON INPUT FILE ', A,
1 . .0 ’0 3X,
2 'Program will stop to allow checking of File Formats.')
END

Subroutine IPSOIL

c Soil Selection
INTESER ist
INCLLDE 'comibs.blk'
INCLUDE 'SEN1.blR'
INCLUDE 'SEN3.blk'
INCLUDE 'ntrc1.blk'
INCLUDE 'ntrc2.blk'
INCLUDE 'NUATBAL.SLK'
INCLUDE 'till.blk'

DO NHILE (.TRUE.)
OPEN (12, FILE I FILE2, STATUS I 'OLD')
DO HHILE (.TRUE.)
READ (12, 5000, IOSTAT I ist) IDUMSL, PEDON, TAXON
IF (ist .LT. 0) SOTO 20
READ (12, 5010) SALB, U, CN2, TAV, AMP, DMOD, SHCON1,
1 swcgn2, swoon3, RHUMX, PHFAC3, stlpond
Jm
cumdep I 0.0
00 UHILE (.NOT. (J .E0. 20 .AND. idumsl .EO. isoilt))
J I J +
READ (12, 5030) DLAYR(J), LL(J), DUL(J), SAT(J), SUINIT
1 (J), NR(J), stlSD(J), OC(J), Dnh6(j), Dn03(j), PH(J),
2 KSMACRO(j), sand(j), rok(j), KSMTRX(J), rwucon(j)
IF (SAT(J) .LE. DUL(J)) SAT(J) I DUL(J) + 0.001
cumdep I cumdep + dlayr(j)
IF (DLAYR(J) .LE. 0.0) SOTO 10
ID(J) I STLID(J)
END 00
IF (Dlayr(J) .NE. -1.0) THEN
J I 21
write (6, 5020)
END IF
10 NLAYR I J - 1
depmex I cumdep + 1
IF (IDUMSL .EO. ISOILT) SOTO 60
END DO
20 write (6, 5060) ISOILT, FILE2

178

READ (5, 5050) ANS
IF (.NOT. (ans .EO. 'Y' .OR. ans .EO. 'y')) SOTO 30
CLOSE (12)
CALL menu2()
DSFILE I - 1
END 00
30 CLOSE (12)
CLOSE (11)
STOP
60 CLOSE (12)
RETURN
5000 FORMAT (1X, 12, 1x, A12, 1x, A60)
5010 FORMAT (F6.2, 1x, F5.2, 7x, (1x, F6.2), 2(1x, F5.1), 1x, F3.1,
1 1x, E9.2, 1x, F6.1, 2(1x, F5.2), 1x, F6.2, 2(1x, f5.2))
5020 FORMAT (
1 ' Maxilmml nurber of 20 soil layers needed by model have been re
2ached.'
3 , I, ' Modification of File 2 may be required.')
5030 FORMAT (1x, FS.0, 5(1x, F6.3), 2(1x, F5.2), 6(f5.1), 2(1x, F5.2),
1 1x, F5.1, 1x, f5.2) _
5060 FNMAT (I. ' Error! SOIL NO ', I3, ' NOT FwND IN FILE :', A12,
1 ITS, 'Type Y to correct file interactively for this run', ITS,
2 'Or program will stop to allow modification : ')
5050 FORMAT (1a)
END

Subroutine MCI!

Include 'SEN1.blR'
Include 'SEN3.th'
Include 'SEN6.blR'
Include 'till.blk'
Include 'ntrc2.blk'
Include 'Nwatbel.blk'
logical tilltime
REAL '6 td,tq:fac,liecom,watfac
c Acfl residue to the soil Hm tillage occurs
mulchsat I 3.85e-Shmulch
IF (DON.EO.DOYTILL(ntill)) than
if (tilltime()) then
call mtehtruendecom)
call dyntill (0.0,0.0)
mdacomp I 0.0
pmulchl I mulchl
pmulchr I mulchr
mulch I mulchr + mulchl
ntill I ntill I 1
else
doytill(ntill‘) I doytill(ntill) + 1
endif
SNSIF
C TEMPERATURE FACTOR FOR DECOMPOSITION
TO I 0.60*TEMPMN I 0.60'TEMPMN
if (td.lt.0) then
telpmac I 0.0
elseif (td.lt.20) then
tempfac I 0.05 * TD
elseif (td.lt.35) than
tempfac I 1.0
elseif (td.lt.60) than
tqfac I 2.6 - moms
else
teapdac I 0.0
endif
C MOISTURE FACTOR FOR DECOMPOSITION
if (mulchsw.gt.0.01.and.mulchsat.gt.0.01) then

 

179

watfac I 1 - alog(mulchsmeulchsat)lalog(0.01Imulchsat)
else
watfac I 0.01
mdif
C “CH DEwI’OSITIM
pmulchr I mulchr
pmulchl I mulchl
mlchr I mlchr‘exu-OJNISS *ninfltqfacnmtfac”
mlchl I mlchl'epr.296*-in1(tq:fac,|-tfac))
IF (IIJLCHR.LT.0.5) IIILCIR I 0.0
IF (MCHL.LT.0.5) FIJLCHL I 0.0
mulch I mulchr + mulchl
C FERTILISATIG fra mlch occurs than rain flehes dour (ll (begining of day)
IF (rain .NE. 0.0) "EN
call mt«.false.,dacqo)
DEW I 0.0
EDIE
MDECOMP I MDECOMP I pmulchr I pmulchl - MULCH
c Mulch residue cover and thickness
mulchcov I 1 - exp(-Am‘!mulch)
mulchsat I 3.85a-5'mulch
Mlchthick I 0
Athick I 1.5
do mile (aurf.gt.0.01)
xmass I xmmss - surf/Am
surf I aurf'(1-exp(-AmFxmass/surf))
Mulchthich I Mlchthick + surf‘AthicR
enddo

WIT. GIININCEC)

Subroutine to initialize soil organic matter pools
and decay constants

0000

Incline 'WTTERJLK'
Include 'SEN3.BLK'
Incllxie 'NTRC2.BLK'
Real Cec(*),Nrn(21),LimmIl
c Rate constant and initial c/n ratio
data rate [.027,.00082,.0018,.00055,0.0l
data (souIcn(i),iI2,5) I150,8.S,10,10/
data Porghun I0.S6/
data Liml [0.1/
c Distribute root residue across depths and into F04 and FON pools, shoot residues are distributed
c at time of tillage in GNJPDAT
RNKSIRMT‘0.01
NSLMI0.0
DEPTHI0.0
DO 200 LI1,NLAYR
DEPTHIDEPTHIDLAYR (L)
illN(L)IE)(P(°3.0"DEPTH/DEPMAX)
mmwsum
200 CONTINUE
DO 300 LI1,NLATR
FOI(L)IROOT‘URN(L)INSLH
FON(L)IRNKS*URN(L)INSLM
300 CONTINlE
Do 700 lI1,Nlayr
TOTCIOC(L)*1.E03*SD(L)*DLATR(L)
LigfIfom(l)*0.6*lingl
presdI0.85-0.018*ligflfon(l)
soc( l , 1 )Ifom( l )*presd*0.60
soc(l,2)Ifom(l)*0.6-soc(l,1)
soc( l ,3)I0.03*Totc

700

0

0

180

soc(l,6)I0.67*Totc
soc(l,S)ITotc-soc(l,3)-soc(l,6)
son(l,3)Isoc(l,3)/somcn(3)
son(l,6)Isoc(l,6)/somcn(6)
son(l,5)Isoc(l,5)/somcn(5)
son(l,2)Isoc(l,2)/somcn(2)
son(l,1)Ifon(l)-son(l,2)
semcn(1)Isoc(l,1)/son(l,1)
ybiocn(l)Isomcn(3)
PcorgI(29.0+0.1*CEC(l))/100

CONTINUE

Return

End

Subroutine Maui l l,mferti )

Slbroutine to update soil Organic matter at time at tillage
and when the solutes from the surface residue deconposes are flushed in the profile

Include 'OMATTER.BLK'

Include 'SEN3.BLK'

Include 'NTRCZ.BLK'

Include 'till.blk'

Reel Liml,edd(21),mferti

logical till

data rate /.027,.00082,.0018,.00055,0.0/
data (somcn(i),iI2,5) [150,8.5,10,10/
data Porghun [0.56]

data Ligmul /0.1I

Distribute shoot residue across depths and into F04 and FON pools

IF (TILL) THEN
STRAU I NLCH'frectill(ntill)'0.01
SNISISTRAH'SCN‘O.01
INLCHI I IIJLCHl*(1 - fractill(ntill)*0.01)
ILCNr I IIJLCNr'fl - fractill(ntill)'0.01)
adep I deptill(ntill)
DEPTHI0.O
IOUTI1
i I 1
DO NHILE (STRAN.ST.0.0.AND.I.lc.NLAYR.and.IOUT.eq.1)
NOLDIDEPTH
DEPTHIOEPTH+DLAYR(I)
IF (SDEP.LE.DEPTH) THEN
FRI(SDEP'HOLD)/$DEP
IF(I.EO.1) FRI1
Ion-z
ELSE
FRIDLAYR(I)ISDEP
ENDIF
ADD(I)ISTRAN*FR
FOM(I)IFOM(I)+ADD(I)
FON(I)IFON(I)IADD(I)*0.60/SCN
i I i + 1
ENDDO
ENDIF
Do 700 lI1,Nlayr
TOTCIOC(L)*1.E03'SD(L)*DLAYR(L)
LigfIfom(l)*0.6*ligmul
presdIO.SS-0.018*ligf/fon(l)
if (l.eq.1.and.mferti.ne.0.0) then
soc(l,1)Isoc(l,1) + 217'mferti1'pread'0.60
soc(l,2)=soc(l,2) + 2I‘meerti'0.6*(1-presd)
Fa!” )Irmt 1 )+2I7"mferti
FGI(1)IF(NI(1)+2/7‘mferti*0.60/SCN
elseif (l.eq.2.and.mferti.ne.0.0) then
soc(l,1)Isoc(l,1) I 5/7‘mferti'preed'0.60
soC(l,2)Isoc(l,2) + 517*Iflerti*0.6*(1-presd)

700

181

FOI(2)IFGI(2)I5/‘Peferti
MIZIIMCZM/T‘mferti'o.wl8fl
endif
soc(l,1)Isoc(l,1) + edd(l)*presd*0.bo
soc(l,2)Isoc(l,2) + edd(l)*0.6*(1-presd)
edd(l) I 0.0
son(l,2)Isoc(l,2)/somcn(2)
son(l.1)Ifon(l)-son(l,2)
somcn(1)Isoc(l,1)lson(l,1)
ybiocn(l)Isomcn(3)
CONTINUE
do lI1,nlayr
add(l) I 0.0
enddo
Return
End

With PM!“ (PINF,WF)

PONDING - Program to determine ponding based on rainfall intensity and a function for hydraulic
conductivity. In the context of the model it calculates the daily amounts of infiltration
runoff and chances to the height of the ponded water

Created by: J.T. Ritchie, I. white, and B. Baer Febuary 1991

Modified by: A. Gerakis and 3. Beer June 1991

Include 'till.blk'
Include 'gen3.blk'
Include 'geni.blk'
Include 'Ntrc2.blk'
Include 'Nuatbal.blk'
INTEGER J,NUHSTEPS
REAL PINF,INFILT,HOUR,HAXRAIN,RUNOFF,
3 PIP,RAINDUR,TIHE,TINESTEP,TPAHT,PPRECIP,TRUNOFF

PARAMETER (HOUR I 1.0 I 24.0)
NUHSTEPS I 10
TIME-0.0
Create rainstorm triangle. Base I Length of the storm, Height = Maximum rain rate (CMVday). (A
RAINOUR I ((PRECIP - 1.0) * 0.5) ' HOUR + HOUR
IF (RAINDUR .GT. 1.0) THEN
RAINOUR I 1.0
ENDIF
TINESTEP I RAINDUR/NUNSTEPS
HAXRAIN I 2.0 * (PRECIP/RAINDUR)
RUNOFF I 0.0
INFILT I 0.0
IF ((HAXRAIN .LT. KSNTRXI1)) .AND. (POND .EO. 0.0)) THEN
PINF I PRECIP
ELSE IF (PRECIP.GT.0.0) THEN
PINF I 0.0
00 200, J I 1,NUNSTEPS
TIME I TINE + TINESTEP
TRUNOFF I 0.0
PPRECIP I RAINFUNC (RAINDUR,NAXRAIN,TINE,TIHESTEP)
TPANTITPFUNC (KSHTRX(1),SAT(1),SU(1),PINF,PPRECIP,TIHESTEP)
call dyntilltppncipfiineteplhou)
IF ((PPRECIP .LT. TPAHT) .AND. (POND .EO. 0.0)) THEN
INFILT I PPRECIP
ELSE
IF (TPAHT .GT. (KSMACRO(1)*TIHESTEP)) THEN
INFILT I TPANT
ELSE
INFILTI TPANT + ((KSMACRO(1)‘TIHESTEP)-TPAHT) *
1 (POND/PONDNAX)**2
ENDIF

 

200

000

10

182

POND I POND I PPRECIP - INFILT
IF (POND .GT. PONDMAX+mulchthick) THEN
TIUNOFF I POND - PONDNAX ' lulchthick
POND I PONDNAX I mulchthick
ELSE IF (POND .LT. 0.0) THEN
INFILT I INFILT + POND
POND I 0.0
ENDIF
ENDIF
PINF I PINF + INFILT
RUNOFF I RUNOFF + TRUNOFF
CONTINUE
ELSE
PINF I 0.0
ENDIF
PIP I 0.0
IF (POND .GT. 0.0) THEN
PIP I (0.2 ' KSMTRX(1) I 0.8 ’ KSNACRO(1)) ‘ (1.0 - TIME)
IF (PIP .GT. POND) THEN
PINF I PINF + POND
POND I 0.0
ELSE
PINF I PINF + PIP
POND I POND - PIP
ENDIF
ENDIF
RETURN
END

Silaroutine Pmuqabte)
*******'** Subroutine TO READ AND INITIALIZE SOIL INFORMATION ****

Include 'gen1.blk'
Include 'gen2.blk'
Include 'gen3.blk'
Include 'gen£.blk'
Include 'Ntrc1.le'
Include 'comibs.blk'
Include 'predob.blk'
Include 'soilox.blk'
PanutIO.
PTFIO.
S1ISIN<LAT*O.017%5)
CIICOS(LAT*0.017%5)
IF(IIRR.EO.6) then
ISNSUBIO
ISHNITIO
Endif
XPLANTIPLANTS
oo 10 II1,8
TMFAC(I)I0.931+0.114*I-0.O703*I**2+0.0053*I**3
CONTINUE
ISTAGEI?
IGSLIO
xstegeI0.1
IF(CROP.EO.'NH'.OR.CROP.EO.'BA') THEN
TBASEIZ.
SENTILID.
SENLFIO.
HIIO.
SNDF3I2.0
ELSE
TEASE-10.
SNDF3I1.O
ENDIF

.1

183

LNIO.
LAIIO.
SUDF1I1.0
SUDF2I1.0
TRNUIO.O
ICSDURIO
ndef1I1.0
NDEF2I1.0
NDEF3I1.D
NDEF‘I1.0
AnfacIO.
NfacII.
JHEADIO
KHEADIO
ioutuaIO
ioutngO
ioutnuIO
if (.not.update) than
SNOU-O.
AEP-D.
AETIO.
AEOID.

IF(CROP.EO.'NH'.OR.CROP.EO.'DA') THEN
TAHcI0.DA5
RANCID.0£S
LIFT-1.0
LIF2I1.0
ELSEIFICROP.EO.'SG') THEN
TANcI0.050
ENDIF
IFIISNNIT.NE.1) TANCIO.0
ATMINIO.O
TMINI0.0
ATNONIO.0
THOR-0.0
stoyNI0.O
ROOTNI0.0
GRAINNIO.0
GNPI0.0
TNUPIO.O
NHDUPIO
xganD.0
xptnI0.0
aptnupIO.0
sup-O-O
totnupI0.D
If(isusub.eq.0) KoutueIO
If(Imoutf.le.1)then
call clear
call opseas
Endif
DONXI367
CUMDTTIO.

184

SUMDTTID.
DTTID.
CRAINIO.
PRECIPIO.

Do 200 II1,6
Si1(i)I0.0
Si2(i)I0.0
Si3(i)I0.0
Si‘Ii)I0.0
Si5(i)I0.0

200 Continue

RETURN

END

Subroutine SOILNI

Include 'gen1.blk'
Include 'gen3.blkl
Include 'gen4.blk'
Include 'Ntrc1.Blk'
Include 'Ntrc2.8lk'
Include 'Nmove.8lk'
Include 'comibs.blk'
C*****Subroutine INITIALIZES SOIL NITROGEN PARAMETERS
IF(DNOD.E0.0.)DHODI1.
CTNUPI0.0
IuonI.false.
do 100 II1,nlayr
Urea(I)I0.0
IF (BOlI).E0.0.) 80(I)I1.2
IF (PH(I).E0.0.0) PH(I)IT.0
100 CONTINUE
C*****CALCULATE N CONTRIBUTIONS
600 TIFOMI0.0
TIFONI0.0
DO 700 II1,NLAYR
HUM(I)-OC(I)*1.E03*DD(I)*DLAYR(I)/0.58
TIFONITIFON+FOH(I)
TIFONITIFON+FON(I)
700 CONTINUE
DNINRI8.3E-05
IF(DNOD.NE.0.) DNINRIDMINR‘DHOD
do 800 lI1,nlayr
facIl)I1.0/(bd(l)*1.e-01*dlayr(l))
IF(N03(L).LT.0.25) NO3ILII0.25
IF(NH4(L).LT.0.50) NH6(L)I0.50
SNOBIL)IN03(L)/fac(l)
SNNkIL)INH4(L)/fac(l)
NHUM(L)IOC(L)‘0LAYR(L)*BD(L)*1.E02-(SNO3(L)+SNH6(L))
fpool(l,1)=fom(l)*0.20
fpoolIl,2)Ifom(l)*0.70
fpoolIl,3)Ifom(l)*0.10
800 CONTINUE
C********** INITIALIZE NITRIFICATION ROUTINE
00 1‘00 LI1,NLAYR
CNI(L)I0.1
HFYIL)I(SN(L)-LL(L))/DUL(L)
IF (SN(L).GT.DUL(L)) NFY(L)I1.0-((SN(L)-DUL(L))
1 /(SAT(L)-DUL(L)))
IF (NFVIL).LT.0.0) UFY(L)I0.0
TFYIL)I0.0009766*ST(L)*ST(L)
IF (ST(L).LT.S.0) TFYtL)I0.0
phn(l)I1.0
if(ph(l).lt.6.0)phn(l)=(ph(l)-4.5)l1.5
if(ph(l).gt.8.0)phn(l)I9.0-ph(l)
if(phn(l).lt.0.)phn(l)I0.

flflflflflflfl

0000

185

1400 COITIIHE

RETLRN
END

Putin TILLTIE

Fuiction to acih Idm tillage is to occur
Tillage coca-s than the top five layers are
near trained qper limit

Progr-r F. Dadoun

100

Imllfl 'BUIBJDIR'

Logical Tilltime
tilltiu I .false.
if (M1).le.¢hl(‘l)*0.01) that
if (M2).le.¢hl(2)+0.01) than
if (M3).le.ml(3)+0.01) then
if (M6).le.chl(£)+0.01) than
if (“(5).le.ml(5)I0.01) the!
tilltiu I .true.
aldif
endif
aidif
“if
mdif
retu-n
and

Subroutine SENT

Slbroutine to calculate daily average soil teuperature at the
center of each soil layer.

INclude 'comibs.blk'
INCLIDE 'gen1.blk'
INCLlDE 'gen2.blk'
INCLLDE 'gen3.blk'
INCLIDE 'genlo.blk'
INCLLDE 'ntrc2.blk'
Include 'Till.blk'

real adt‘lS),-alchalb,lthick, otq-I

III I floatwOY)
ALx I (III - IDAY) " 0.0176
ATOT I ATOT - TNMZ)
DO 1m K I 2.2.-1
TMA(K) I TMA(K-1)
CONTINIE
mlchalb I 0.3
ff I (M1) - 0.03)](dnlfl) - 0.03)
salbado I salb‘fl - 0.45‘ff)
IF (ISTAGE .LE. 6) THEN
IF (ISTASE .SE. 5) THEN
ALBEDOI0.23+(LAI-lo)“2/160
ELSE
CANCOV I 1 - EXPI-OJS'LAI)
albedo-cuWDJSI-chhcov‘fl -cancovv)*-.alchalbo

& (1 ~mldicov)*(1 -cancov)*salbedo

EON) IF
ELSE
elbow-(T-lllchcwI'salbadm-aldocov‘mlchalb
END IF

 

186

TA I TAV I AHP'COS(ALX)/2.
TNAI1) I tq-i I (mlchcoVIsalbedontq-I-tq-ivz
G I (1-albedo)"(Tm - TEIPIIIPSQNSQRAD I .03)
otq-w I tq-i
ATOT I ATOT I THA(1)
CT I tma(1) - TA
DO L I 1, NLAYI
IF (I .ST. 1) Ti.
Al I (All I (SWL) - ll(l))"DLAYR(L))
ELSE
an :- “(1)'dlayfl1)
END IF
ALL I (ALL'(L-1) I LL(L))/l
AND I (”’(L-T) I ”(Ln/L
IAKL) I ((1-Al)!2.66) - ALL'Am)
IN: I Ail/(IAKU‘ufl) I dlayr(l)*5))
F I EXP(D(L)*((1. - IND/(1. I m))"‘2)
no I F'DP(L)
IF (I .EO. 1)dfl Id!
2D(L) I - 2(L)IDD
if (l.eq.1) the:
adchk I '(z(1)I15)ldI1
STchk I (TAV I (ADP/2313““ I zdchk) I DT)'EXP(zdchk))
endif
ST(L) I (TAV I (mlznoosuu I 20(0) I DT)‘ENP(ZD(I)))
END DO
RETURN
END

Wine IPFLGI (AD,DLAYR,EOS,ES,LL,NLAYR,PINI),SU,DI1)
Subroutine to simlate Hater redistribution in the profile
following evaporation

00

Include 'Till.blk'
include 'Nmove.blk'
Real Ad(3),Dlayr(*),Dsu(3),Su(*),Ll(*),dul(*),Eos, Es,Diff,pond
Integer L,Nlayr,K,M
C Evaporate water from ponding (if exists) and alter ponding ammt
IF(POND.GT.0.0) THEN
ESIEOS
IF(POND.CT.EOS)THEN
POND-POND-EOS
ELSE
c Evmorates Inter fra resichas and top two layers
if ((mlchsu-D.01).gt.(eos-pord)) then
mlchsu I mlchsu - (EDS - pond)
pond I 0.0
else
SN")Ifl(1)-0.30*(EOS-(puidmlchsu-0.01))IDLAYR(1)
SI!(2)IW(Z)-0.70'(EOS-(Wuhan-0.01))/DLAYR(2)
mlchsu I 0.01
POD-0.0
endif
El) IF
ELSE
c We Nani-.- uater loss from top three layers as a fmction
c of their nistu-e cmtents
if (mldisu-0.01.ge.eos) then
es I see
mlcheu I mlchsu - one
else
do jI1,3
Ad(j)I(0.109I0.669*(LL(j)-0.3S)**2) *LLU)

IF (811(1).GT.DUL(1) - 0.02) THEN
0811(1) I (Si1(1) ° 0.02 - AD(1)) ‘ 0.80

ELSE

187

Dsu(1)I0.5*(0.SIEoc-lulchsuI0.01)‘(SU(1)-Ad(1))**1.4

ENDI
IF (
D
ELSE
D
ENDI
IF (
D
ELSE
D
ENDI
EsID

F
SN(2).6T.DUL(2) - 0.02) THEN
SN(2) I (59(2) ' 0.02 - A0(2)) * 0.12

su(2)I0.075*(SH(2)°Ad(2))**1.6

F

SN(3).CT.DUL(3) - 0.02) THEN

89(3) I (SN(3) - 0.02 - AD(3)) * 0.032

su(3)I0.0£*(Su(3)-Ad(3))*‘1.6
p
su(1)*2.0IDsu(2)*S.0IDsu(3)*8.0Imulchsu-0.01

Ensure ES does not exceed EOs - if so scale back DSN

If(E

s.Ct.Eos)Then

Do LI1,3

Dsu(L)IDsu(L)*(Eos-mulchsu+0.01)l(Es-mulchsu+0.01)

Enddo
EsIEos

Endi

f

mulchsu I 0.01

Compute a temporary change in water content of layer 3
Su(3)ISu(3).0su(3)

Flouu(2)I0.0

Calculate diffusivities and fluxes for layers 4 to Nlayr

if(su(
Do L

3).lt.dul(3)) then

I3,Nleyr-1

NIL-1

NILI1

DiffI0.5*Exp(£0.0*((Sw(M)-LL(M))I
(Su(L)-Flouu(K)/Dlayr(L)-LL(L)))/2.0)

If(Diff.St.50.0)DiffI50.0

Flouu(L)I(Su(M)-dul(m)-su(l)IFloHu(K)/Dleyr(L)+dul(l))

*Diff/(Dlayr(L)IDlayr(M))/2.0
if (flouu(l).lt.0.0) flouu(l) I 0.0

SH(L)ISH(L)-Flouu(K)/Dlayr(L)IFlouu(L)/Dlayr(L)
nddo

Flouu(Nlayr)I0.0
Su(Nlayr)ISu(Nlayr)-Flouu(Nlayr-1)lDlaerNlayr)

else
do I

I3,nlayr

flouu(l) I 0.0

endif

Compute moisture content and Flows in top three layers
Flouu(2)IDsu(3)*8.0

Flouu(1)IFlouu(2)IDsw(2)*5.0
flouu(0)=flouu(1)Idsu(1)*2.0

Su(2)ISu(2)-Dsu(2)

SH(1
endif
ENDIF
Return
End

Subrout

Include
Include
Include
Include
Include
Include
Include
Include
Include

)ISu(1)-Dsu(1)

ine’UATIAL (ISUUT)

'GEN1.BLK'
'GEN2.BLK'
'GEN3.BLK'
'GEN4.BLK'
'NHOME.BLK'
'NTRC1.BLK'
'NTRC2.BLK'
'CONIBS.BLK'
'ENVIRO.BLK'

 

1ۤ3

INCLUDE 'NNATBAL.BLK'

INCLUDE 'SOILOX.BLK'

Include 'till.blk'

Integer EVFLAG, K,L,M

REAL Albedo, Esq, Error, Floun(0:20), Leftuat,0verflou, Pinf, Snomlt, Td, Tprecp,
2 Tratio, Tsuy, oprecip

DEPIR-0.
DRAIN I 0.0
E0 I 0.0
EP I 0.0
ES I 0.0
ET I 0.0
EOSI 0.0
EOPI 0.0
ERRINI I 0.0
EVFLAC I 0
ICSDURIICSDURI1
IDRSN I .FALSE.
IOFFIO.
OVERFLW I 0.0
PINF I 0.0
PRECIP-0.
RAIN I RAIN I 10.0
RUNOFF I 0.0
NTLAYR I NLAYR I 1
DO 602 LI1,NLAYR I 1
FLOH)(L)I0.
FLM(L)I0.
FLWN(L)I0.
602 CONTINUE
ll If IIRR I 1, no irrigation. If IIRR I 2 or 3, call irrige. i
E If IIRR I 6, uatbal is not called (Hater non-limiting) .
IF (IIRR.EO.2.(NI.IIRR.E0.3) THEN
CALL IRRICE
ENDIF
DEPIR I DEPIR I 10.0
PRECIPIRAINIDEPIR
SIDEFIO.
IF (TEMPMX.LE.1..OR .SNGI.NE.0.) THEN
CALL SNNFALL (TEMPMX, PRECIP, RAIN, SNGILT, SNON)
ENDIF
TPRECPIPRECIP
c Adi inter fru precipitation to residues
if (precip.gt.0.0 .l'd.precip.gt.(mlchset - nudism) then
pncip I precip - (mlchsat - mlchsu)
mlchsu I mlchsat
elseif (precip.gt.0.0) TI.
mlchsu I lilchsu I precip

 

precip I 0.0
endif
H If u.t. switch is 1, call water table routine to find out 1
the depth of the water table. If switch is 2, read file. I

II
IF (ISNNT.ED.1) THEN
CALL NATABLE (05901, 0L1, 0L2, EVFLAG, NLAYR, sar, SH,
1 TRLDEPNT, NTCHANGE, mum
ELSEIF (ISUNT.E0.2) THEN
READ (510, 1325) NTLAYR
1325 tenant ( 5x, 12)
no 125 M . NLATR, NTLAYR, -1
su<n> I SAT(H)
125 CONTINUE
x-mumd
no 905 NHILE ((K.GE.1).AND.(SN(K).EO.SAT(K)) )
NTLAYR . x

189

K I K - 1
905 CONTINUE
DEPUT I DL1(HTLAYR)
TRUDEPNT I DEPNT
ENDIF
c Calculate potential evapotranspiration:
CALL CALEO

 

" If there has been any precip or if water remains in the pond 1
call the ponding routine. Hhen it rains, water either
" infiltrates, ponds or runs off. H

If(Pond.Gt.0.0.or.Precip.Ct.0.0)Then
CALL PONDING (PINF,RUNOFF)
Endif
c DRAINAGE calculates soil water content at ecpilibriun, downward flows, and backs q: the water if
there is a restricting layer or water table in the profile

Call Drainage (Dlayr,Dul,Sat,Sw,Flowd,Ksmacro,Overflow,
E Ntlayr,Nlayr,pinf,Idrsw,iswwt)
c If there has been overflow generated by backup add this to the pond
PondIPondIOverflow
If(Pond.gt.PondlaxImulchthick)Then
RunoffIRunoffIPond-Pond-ax-lulchthick
ikwitdklilaaxIauAchthick
Endif
arunof I arunof I runoff
Drain-Flowd(Amin1(wtlayr-1,Nlayr))
IF (ISNNIT.NE.0.AND.IDRSH) THEN
CALL NFLUND (Sat,Dul,Sw,Nlayr,Dlayr,Flowd,chon,SNo3,NNout)
ENDIF
IF (ISHNIT.NE.0.AND.IUON.AND.IDRSN) THEN
CALL NFLUXD (Sat,Dul,Sw,Nlayr,Dlayr,Flowd,chon,Urea,NNout)
ENDIF

 

” Call water table routine to find out the new depth of the w.t. H H
L 11

 

IF (ISWT.CE.1) men

CALL NATAOLE (DEPNT, 0L1, DL2, EVFLAG,NLAYR, SAT, SH,
1 TRLDEPNT, NTCHANGE, mum)
ENDIF

 

fil

fl Calculate soil evaporation and water redistribution: H
II
Call Upflow (Ad,Dlayr,Eos,Es,LL,Nlayr,pond,Sw,dul)
IF (ISHNIT.NE.0) THEN
CALL NFLUXU (Nlayr,Sw,Dlayr,Sno3,Flowu,NNout)
ENDIF
IF (ISUNIT.NE.0.AND.IUON) THEN
CALL NFLUXU (Nlayr,Sw,Dlayr,Urea,Flowu,NNout)
ENDIF
CESICESIES
TLCH I TLCH I NNOUT(NLAYR)
IF (ISNNT.GE.1) THEN

 

H Call water table routine to find out the new depth of the w.t.

 

‘i II
II

CALL NATADLE (DEPNT, 0L1, 0L2, EVFLAG, NLAYR, SAT, SN,
1 TRUDEPNT, NTCHANGE, NTLAYR)
ENDIF
IF (ISNNT.EO.2) THEN
IF (RNTLAYR.LT.NTLAYR) THEN
DO 1000 L I RNTLAYR,NTLAYR
SUDIRR I SUBIRRI(SAT(L)-SU(L))*DLAYR(L)
IF ((SAT(L)'SH(L)) .LT. 0.0) THEN
write (6,*) 'Problem with NT in NATBAL(266)'
ENDIF
SN(L)ISAT(L)

15K)

1000 CONTINUE
ELSEIF (RNTLAYR.GT.NTLAYR) THEN
CALL DRAINACE(Dlayr,Dul,Sat,Sw,Flowd,Ksmacro,Overflow,
& Utlayr,Nlayr,Ninf,Idrsw,iswwt)
DRAIN I FLOND(RNTLAYR-1)
ENDIF
NTLAYR I RNTLAYR
IF (SUBIRR .SE. DRAIN) THEN
SUOIRR I SUEIRR - DRAIN
DRAIN I 0.0
ELSE
DRAIN I DRAIN . SUBIRR
SUDIRR I 0.0
ENDIF
ENDIF
DEPNT I DL1(NTLAYR)
IF (RESP2.E0.'Y'.OR.RESP2.E0.'y') THEN
IF (ISUNT.E0.0) THEN
NRITE (370, '(/,A, I3,A, F9.6,A, F9.6)' ) ' DOY I ', DOY,
1' DEPNT I ',DEPNT, ' TRUDEPNT I ', TRUDEPNT
ENDIF
NRITE (370, 1210)'DEPTH','FLOND','FLONU','SN(L)','SAT(L)'
no 1150 L . 2, NLAYR
IF (ISNUT.E0.0) THEN
NRITE(370, 1201) DL1(L), FLOND(L), FLOUU(L), SN(L), SAT(L)
ELSE
NRITE(370, 1200) DL1(L), FLOND(L), FLONU(L). SN(L), SAT(L)
ENDIF
1150 CONTINUE
1210 FORMAT (AS,A9,1X,A9,6X,A6,3X,A8)
1201 FORMAT (F6.0, 4(1X, F9.4) )
1200 FORMAT (Fb.0, b(1X, F9.b))
ENDIF

 

II

E Calculate water deficit for automatic irrigation. "
I

 

If(Iirr.eq.3)Then
CALL HATDEF (ATHETA, CUNDEP, DLAYR, DSOIL, DUL, LL,
1 NLAYR,SH,SNDEF)
Endif
PESN I 0.0
DO 300, L I 1,NLAYR
PESN I PESU I ((SU(L) ° LL(L)) * DLAYRCL))
300 CONTINUE
IF (ISTASE.GE.6) THEN
ETIES
CETICETIET
CRAINICRAINIPRECIP
ELSE
2800 IF (GRORT.GT.0.0) THEN
CALL ROOTGRON (csos, ISTAGE, DTT, snout, ISNNIT, L1, PHINT,
2 PLANTS, RNFAC, SUDF, ISHNT)
ENDIF
3300 Continue

 

flEstimate plant water uptake,water deficit and water filled porn
II

Call Nuptake(Eop,tratio,potrwut)
CALL USTRSS (CSD1, CSDZ, EP, EOP, POTRUUT, SNDF1, SHDFZ)
ET I ES I EP
3800 CEPICEPIEP

CETICETIET
CRAINICRAINIPRECIP

ENDIF

Do LI1,Nlayr
Flown(L)IFlowd(L)-Flowu(L)

Enddo

RETURN

END

 

Appendix 3: Input files to run CERES

191

Input file structure is described in:

IBSNAT Project. 1989. Technical report 5, Decision Support System for
Agrotechnology Transfer (DSSAT), Documentation for IBSNAT crop model input
and outputs files, version 1.1, University of Hawaii, Honolulu, Hawaii, USA.

MZEXP.DIR: Directory of files for each experiment

NSEL8801.NZA NSEL8801.H28 HS91.NZ HS92.NZ NS93.NZ HS94.HZ

MSEL9001 MSU East-Lansing, Michigan 1990 HSEL0112.N90 SPROFILE.mzZ
MSEL9001.HZ4 NSEL0000.H25 HSEL9001.H26 HSEL9001.H27 HSEL9001.NZ8 GENETICS.HZ9
HSEL9001.HZA HSEL9001.NZB OUT1.HZ OUT2.HZ OUT3.NZ OUT4.HZ OUT5.H2 OUT5.NZ
MSEL9101 MSU East-Lansing, Michigan 1991 MSEL0110.N91 SPROFILE.m22
HSEL9001.MZ4 HSEL0000.M25 HSEL9001.H26 HSEL9001.HZ7 HSEL9101.HZ8 GENETICS.HZ9
NSEL9001.NZA NSEL9001.NZB OUT1.N2 OUT2.N2 OUT3.HZ OUT4.M2 OUTS.HZ OUT5.NZ

UTH.DIR: Daily weather data

MSEL 1988 MSU East~Lansing, Michigan 01/01/88 12/31/88 MSEL0112.1188
MSEL 1990 MSU EastoLansing, Michigan 01/01/90 12/31/90 MSEL0112.N90
MSEL 1991 MSU East-Lansing, Michigan 01/01/91 10/01/91 MSEL0110.N91

SPROFILEJIZZ: File 2, soil profile properties

42 CONONER

.13 9.00 .15 78.00 9.9 27.5 1.0 .27E'02 58.0 6.68 .03 01.53 02.55

2. .072 .211 .356 .211 1.000 1.54 1.24 2.4 3.1 5.6 5.5 .10 .00 5.0 0.40
5. .072 .211 .356 .211 1.000 1.54 1.24 2.4 3.1 5.6 5.5 .10 .00 5.0 0.40
8. .072 .211 .356 .211 .950 1.54 1.24 2.4 3.1 5.6 5.5 .10 .00 5.0 0.40
11. .103 .237 .347 .237 .791 1.57 1.01 2.4 3.1 6.0 5.4 .10 .00 5.0 0.35
14. .181 .302 .317 .302 .371 1.68 .33 2.3 3.1 7.7 5.2 .10 .00 5.0 0.30
17. .178 .298 .313 .298 .222 1.70 .27 2.3 3.0 8.1 5.2 .10 .00 5.0 0.20
20. .176 .295 .310 .295 .062 1.71 .21 2.4 3.1 8.1 5.2 .10 .00 5.0 0.15
23. .150 .272 .303 .272 .019 1.71 .20 2.3 3.1 8.2 5.2 .10 .00 5.0 0.15
25. .149 .270 .302 .270 .006 1.71 .20 2.3 3.1 8.2 5.2 .10 .00 5.0 0.10
-1. .000 .000 .000 .000 .000 .00 .00 .0 .0 .0 5.0

FILE 4: Soil nitrogui dyn-ics
MECP9001 01 9999. 75 500.

11 01 20 100 15.00 15.00 1.2 1.2
130 01 10 0 10.00 8.00 1.2 1.2
-1 -1 -1 -1
NECP9001 02 9999. 75 500.
11 01 20 73 15.00 15.00 1.2 1.2
130 01 10 0 10.00 8.00 1.2 1.2
-1 -1 -1 -1
MECP9001 03 9999. 75 500.
11 01 20 47 15.00 15.00 1.2 1.2
130 01 10 0 10.00 8.00 1.2 1.2
-1 -1 -1 -1
NECP9001 04 9999. 75 500.
11 01 20 0 15.00 15.00 1.2 1.2
130 01 10 0 10.00 8.00 1.2 1.2

'1 '1 -1 -1

 

 

HECP9005 05 9999. 75 500.

11
130
-1

11
130
-1

11 01 20 47 15.00 1 .
130 01 10 0 10.00
-1 -1 -1 -1
NECP9008 08 9999. 75 500.
11 01 20 0 15.00 1
130 01 10 0 10.00
-1 '1 -1 -1

File 5: Soil profile initial condition

01 20 100 15.00 15.00
0 10.00 8.00

01 10

-1 '1 -1
MECP9006 06 9999.

01 20 73 15.00 15.00
0 10.00 8.00

01 10

75 500.

-1 -1 -1
MECP9007 07 9999.

75 500.

.e—e
NN
...a-e
NN

_a...e
I 0
MN
_a_a
NN

..e-a
NN
..s..a
NN

_a_a
NN
..e—e
NN

01 NSEL9001
2. .174 2.9
5. .174 2.9
8. .174 2.9
11. .174 2.4
14. .169 1.7
17. .164 1.9
20. .154 1.3
23. .131 .9
34. .116 .8
'1. .000 .0

02 HSEL9001
2. .174 2.9
5. .174 2.9
8. .174 2.9
11. .174 2.4
14. .169 1.7
17. .164 1.9
20. .154 1.3
23. .131 .9
34. .116 .8
'1. .000 .0

03 MSEL9001
2. .174 2.9
5. .174 2.9
8. .174 2.9
11. .174 2.4
14. .169 1.7
17. .164 1.9
20. .154 1.3
23. .131 .9
34. .116 .8
-1. .000 .0

04 MSEL9001
2. .174 2.9
5. .174 2.9
8. .174 2.9
11. .174 2.4
14. .169 1.7
17. .164 1.9
20. .154 1.3
23. .131 .9
34. .116 .8
-1. .000 .0

05 HSEL9001
2. .174 2.9
5. .174 2.9
8. .174 2.9
11. .174 2.4
14. .169 1.7

ddd
. . C C C . . C .

d-b—D dd... add
. C . C . C C . O C O . . . . C C . . . . . U . C . C .

‘d‘
. C C C

00°00 OUMONOOOOO ouuoNuocoo OMUOVOOOOO OMUONOOOOO

rssrrooso

. NOOO?OOOO
dOOOO COIO-I-I-IOOOO ObOd-I-IOOOO OJIO-I-IdOOOG O5O-I-I-IOOOO

399990000

flPPPP°°°°

O0900

192

17. .164
20. .154
23. .131
34. .116
-1. .000
06 NSEL900
2. .174
5. .174
8. .174
11. .174
14. .169
17. .164
20. .154
23. .131
34. .116
-1. .000
07 NSEL9001
2. .174
5. .174
8. .174
11. .174
14. .169
17. .164
20. .154
23. .131
34. .116
-1. .000
08 HSEL9001
2. .174
5. .174
8. .174
11. .174
14. .169
17. .164
20. .154
23. .131
34. .116
-1. .000
08 HSEL9001
2. .174
5. .174
8. .174
11. .174
14. .174
17. .172
20. .166
23. .159
34. .142
-1. .000

File 6: Irrigation mt «to

01 HECP9001

05.HECP9001
06.HECP9001
or'lecgom
08.éECP%001

dd
C . . .

.f?????? ...f??--
oaoaawoooo ooouoweooo oaououbooo ooououeooo ooouo

dd-PNNNNNN fl-P-‘UUNNN
eeeeeeeee eeeeeeeee

add
e e e e e e e e
OWWOVQOOOO OUTUTOV

Add add

ONOOOOOO O

dddd‘
. . . . .

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193

Fi la 7: Nitrogen Fertilizer data

01 NECP9001
146 183.5
-1 -1.0
02 MECP9001
146 183.5
-1 -1.0
03 NECP9001
146 183.5
-1 -1.0
04 NECP9001
146 183.5
'1 -1.0
05 NECP9001
146 183.5
-1 -1.0
06 NECP9001
146 183.5
-1 -1.0
07 NECP9001
146 183.5
-1 -1.0
08 NECP9001
146 183.5
-1 -1.0

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5
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5
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5
-1

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5
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File 8: Crop mung-ant chta

NSEL9001 01 FALLOU , NO RESIDUE COVER

001 270 7.25 0.711 5.00 01 01 0.95
NSEL9001 02 FALLOU , 30% RESIDUE COVER

001 270 7.25 0.711 5.00 01 01 0.95
MSEL9001 03 FALLON , 50X RESIDUE COVER.

001 270 7.25 0.711 5.00 01 01 0.95
MSEL9001 04 FALLON , 100% RESIDUE COVER.

001 270 7.25 0.711 5.00 01 01 0.95
HSEL9001 05 CROP, NO RESIDUES COVER

001 128 7.25 0.711 5.00 01 01 0.95
NSEL9001 06 CROP, 30% RESIDUE COVER

001 128 7.25 0.711 5.00 01 01 0.95
HSEL9001 07 CROP, 50% RESIDUE COVER.

001 128 7.25 0.711 5.00 01 01 0.95
MSEL9001 08 CROP, 100 I RESIDUE COVER.

001 128 7.25 0.711 5.00 01 01 0.95

File 9: Cetutic coeffici-its
70 PIO 3475 180.00 0.5000 685.0

194

42

70

1.50 0.40 95.00 00

42

70

1.50 0.40 95.00 00

42

70

1.50 0.40 95.00 00
70

42

1.50 0.40 95.00 00

42

70

1.50 0.40 95.00 00

42

70

1.50 0.40 95.00 00

42

70

1.50 0.40 95.00 00

42

70

1.50 0.40 95.00 00

584.00 9.300