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This is to certify that the

thesis entitled

ZHE AVAILIBILITY 0F CROP RESIDUE AND

ITS POTENTIAL AS A FUEL

presented by

John Henry Posselius, Jr.

has been accepted towards fulfillment
of the requirements for

M S

July 20, 1981

 

0-7639

 

degree in AC EGR 'r

6112m-

Major professor

 

 

 

 

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MSU

LIBRARIES

 

 

RETURNING MATERIALS:
Place in book drop to
remove this checkout from
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THE AVAILABILITY OF CROP RESIDUE AND

ITS POTENTIAL AS A FUEL

BY

John Henry Posselius, Jr.

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

MASTER OF SCIENCE

Department of Agricultural Engineering

1982

Cepyright by
JOHN HENRY POSSELIUS, JR.
1982

ABSTRACT

THE AVAILABILITY OF CROP RESIDUE AND
ITS POTENTIAL AS A FUEL

By

John Henry Posselius, Jr.

The problems which could arise if too much crop
residue is removed from crOp production land has prompted
the development of a computer program that provides
scientific guidelines for residue removal. Used with a
number of figures and tables the computer program not only
determines the amount of crop residue available for removal
without putting undue stress on the 3011's productivity but
the program also performs an energy balance on the crOp
residue removal system. All energy inputs into crap produc-
tion, harvesting, post-harvest processes, transportation,
nutrient replacement and conversion are accounted for.

Through numerous sample runs it has been determined
that each field proposed for crop residue removal should be
considered on a case-by-case basis. It has also been deter-
mined that crop residue from some soils and locations can
safely be removed. The major concerns are potential
increases in wind and water erosion and damage to the soil

SCIUCCUIB .

ACKNOWLEDGMENTS

I wish to thank all my committee members, Dr. Bill
Stout, Dr. George Merva, Dr. "Bus" Robertson, and Mr. Dwight
Quisenberry for all the time, support, guidance, and counsel
they gave. Also, my thanks go to my colleagues, friends,

and family.

ii

Chapter
I.

II.

III.

TABLE OF CONTENTS

INTRODUCTION 0 O O O O O C O O O O O O O

Dangers in Removing Crop Residue . . . .
Erosion . . . . . . . . . . . . . .
Soil Compaction . . . . . . . . .
Nutrient Maintenance . . . . . . . . .

Objectives . . . . . . . . . . . .
Scientifically Determined Guide—

lines for Residue Removal . . . . .

Large Area Analysis . . . . . . . . .

Limitations . . . . . . . . . . . .
Single Field Analysis . . . . . . . .
Large Area Analysis . . . . . . . .

LITERATURE REVIEW . . . . . . . .q. . .

Soil Requirements for Crop Residue for
Continued Crop Production . . . .
Water Erosion . . . . . . . . . . . .
Wind Erosion . . . . . . . . . . . . .
Nutrient Maintenance . . . . . . .
Soil Physical Properties . .

Energy Inputs to 0.8. Agriculture .

Crop Residue Availability . . . . . .
Residue Estimates and Collection . . .

PROGRAM DEVELOPMENT . . . . . . . . . .

Theory and Assumptions . . . . . . . .
Residue Needed to Prevent Wind Erosion
Residue Needed to Prevent Water Erosion
Total Biomass in the Field . . . . . .
Residue Available for Uses Other

Than Soil Management . . . . . . . .
Determining the Net Energy Gain . . .
The Transportation System . . . . .

iii

Nth m c~bw»ra H

WV“

10

10
11
12
14
15

18

19
21

24

24
24
29
31

33
35
37

IV. SINGLE FIELD SAMPLE RUNS .
Purpose of Sample Runs .
Input Data for Sample Runs .
Results of the Sample Runs
Usage of Results . . . . .

V. RESULTS AND DISCUSSION OF CROP
AVAILABILITY PROGRAM . . . .

Comparisons with Other Works
Versatility of the Program .
Wind Erosion Section . .
Water Erosion Section . .
The Energy Balance . . . . .
Limitation of the Program .
VI. SUMMARY AND CONCLUSIONS

Summary . . . .

Conclusions . . . . . . . . .

VII. RECOMMENDATIONS FOR FURTHER RESEARCH

Program Development
Verification of Program

APPENDIX A--TABLES . . . . . .

RESIDUE

TABLE 1--THE EFFECT OF MOISTURE,

0N CORN PLANT GROWTH

TABLE 2--SOIL ERODIBILITY INDEX (I) FOR SOILS
WITH DIFFERENT PERCENTAGES OF NON-
ERODIBLE FRACTIONS AS DETERMINED BY

STANDARD DRY SIEVING

FERTILITY
LEVEL, AND DEGREE OF SOIL COMPACTION

TABLE 3--SOIL ERODIBILITY INDEX (I).

iv

39

39

39

42

43

46
46
47
48
48
49
50
51
51
52
54
54
55

S7

S7

58

59

APPENDIX

TABLE

TABLE

TABLE

TABLE

TABLE

TABLE

TABLE

TABLE

TABLE

TABLE

TABLE

TABLE

TABLE

TABLE17

10 -AVERAGE N,

l3-SOIL ERODIBILITY

14

15 _llmfl

16

4--PREVAILING WIND EROSION DIRECTION

5--MONTHLY CLIMATIC FACTORS "C" FOR
EACH COUNTY IN MICHIGAN . . .

6--CROPPING-MANAGEMENT VALUES FOR
CONSERVATION TILLAGE, MICHIGAN,
LOWER PENINSULA . . . . . . .

7--CROP SPECIFICS o o o o o o o o o o o

8--ESTIMATED RANGE OF FUEL REQUIRE-
MENTS FOR SELECTED FARMING
OPERATIONS I C C O O C O I O O O O O

9--FUEL CONSUMPTION OF FIELD
OPERATIONS IN DIESEL EQUIVALENTS .

P, AND K CONCENTRATIONS
IN CROP RESIDUE . . . . . . . . . . .

Il-INPUT DATA FOR SAMPLE RUNS

lZ-RESULTS OF SAMPLE ANALYSIS . . . .

"K"
TOLERABLE SOIL LOSS

VALUES AND
"T" VALUES . .

-COMPUTED K AND T VALUES FOR SOILS
ON EROSION RESEARCH STATIONS . . . .

FACTORS FOR DETERMINING LS . . .

-EROSION PREVENTION PRACTICE
FACTOR "P" O O C O C O O O C O O O O

-BIOCONVERSION EFFICIENCIES . . .

B--FIGURES . O O O O O O O O I O O O O O O O 0

FIGURE l-Potential soil loss from knolls

factor, expressed as percentage of
that on level ground: (a) from top
of knoll, (b) from that portion of
windward slope where drag velocity
and wind drag are the same as on top
of knoll (from about the upper third

of the lepe) .

60

61

65

66

67

7O

71
72

74

75

77

77

78
79

80

80

FIGURE 2-Soil ridge roughness factor chart
determines soil ridge roughness
factor "K" from the soil ridge rough-
ness . . . . . . . . . . . . . . . . 81

FIGURE 3-Annual climatic factor "C" (percen-
tage) based on the average wind
velocity and on the precipitation
evaporation index . . . . . . . . . . 82

FIGURE 4-Chart to determine soil loss E4.
E4 - I'K'C'L' from soil loss
E2 - I'K' and E - I'K'C' and from
unsheltered distance L' across the
field . . . . . . . . . . . . . . . . 83

FIGURE S-New method of determining E4
from E2, E4, and E21 . . . . . . . . 84

FIGURE 6-Vegetative equivalent chart to
determine soil loss E - I'K'C'L'V
from soil loss E - I'K'C’L' and
from the vegetative cover factor, V.
The chart can be used in reverse
to determine V needed to reduce soil
loss to any degree . . . . . . . . . 85

FIGURE 7-Residue for small grain stubble‘
including stover. Chart to determine
V from R' or R' from V of standing and
flat anchored small grain stubble with
any row width up to 10 in., including
stover . . . . . . . . . . . . . . . 86

FIGURE 8-Residue for grain sorghum and corn.
Chart to determine V from R' or R'
from V of standing and flat grain sor-
ghum and corn stubble of average stalk
thickness, leafiness, and quantity of
tops on the ground . . . . . . . . . 87

FIGURE 9-Method for determining the actual
soil erodibility factor . . . . . . . 88

FTCURElO-Average annual values of rainfall
erosion factor . . . . . . . . . . . 89

vi

FIGURE ll-Three-dimensional view of
Figure 6 . . . . . . . . . . . . . . 90

APPENDIX C--CROP RESIDUE AVAILABILITY PROGRAM . . . 91

APPENDIX D--CROP RESIDUE AVAILABILITY PROGRAM,
SECTIONS TWO-SIX . . . . . . . . . . . .106

BIBLIOGRAPHY O O O O O O O C O O O O O O O O O O O O 118

vii

CHAPTER ONE

INTRODUCTION

Dangers in Removing Crop Residue

There are few natural resources of greater importance
to mankind than the soil. In fact, it has been said that
". . . human vanity can best be served by a reminder that
whatever his accomplishments, sophistications or artistic
pretensions, mankind owes his very existence to a six inch
layer of topsoil". The six- to twelve-inch layer of topsoil
contains the nutrients which feed the crap, and its proper
maintenance determines the success of the entire agricultural
endeavor. While it is a very slow process for nature to build
up the topsoil, it is often destroyed very rapidly. The de-
terioration of productivity is usually a result of agricultural
mismanagement. One of the agricultural practices which most
severely affects that top layer of soil is the removal of too
much of the above-ground crop.

The removal of too much of the total crop is becoming a
more and more serious concern. In the past few years crop
residue has received much attention as a potential energy
source (Alich and Inman, 1974; Lipinsky, 1978; Steffgen, 1974).
It has been shown that crop residue is a good source of energy,
either burned directly for heat or converted into gaseous or

liquid form for fuel. However because of low density and wide

distribution the temptation of collecting too much residue

from one location must be resisted. If not, depletion of
the topsoil's productivity may occur.

Scientists generally do not advocate total removal of
crOp residue from the soil, for it is recognized that it is
essential for soil erosion control and maintenance of pro-
ductive capacity. Therefore, when utilization of crop
residue for purposes other than soil maintenance is proposed,
the first question scientists confront is, "To what extent
can crop residue be removedeithout adversely affecting soil
conservation and reducing productivity?" In addition, when
crop residue is proposed as an alternate source of energy,
it must be determined whether it can be grown, harvested,
collected, transported, converted to a more useful form,
and utilized while maintaining a positive energy balance.

In addressing the question, "How much residue can be
safely removed?" the following theoretical primary functions
of residue are recognized:

-- provide surface protection from erosion

-- act as a storehouse of nutrients

-- stabilize structure and improve tilth

-- reduce bulk density

-- enhance water infiltration and moisture

retention
-- provide energy for microorganism activity
-- increase cation exchange capacity

-- release carbon dioxide.

Because commercial fertilizers are readily available to
perform some of these functions, above-ground residue
primarily provides surface protection, helps maintain the
soil structure, improves water infiltration and reduces
evaporation. The crOp roots also play a role in fulfilling

the soil requirements.

Erosion

Erosion is a process whereby, under the forces of wind
and water, topsoil particles are detached from the surface
and transported to a new location. While some tapsoil loss
from erosion in unavoidable, at tolerable levels it will
permit crop production to proceed and the 3011's productivity
to be maintained, or perhaps increased, over time. The amount
of soil loss tolerance denotes the maximum level of soil
erosion that will permit crop productivity to be sustained
indefinitely. Those factors which determine the soil loss
tolerance include soil depth, physical properties and other
characteristics affecting root deve10pment, gully prevention,
on-field sediment problems, seeding losses, soil organic matter
deductions and plant nutrient losses (Wischmeier and Smith,
1978).

One of the elements whichkeeps both water and wind
erosion within the tolerable soil loss limit is the amount of
crop residue on the surface. Crop residue has the tendency

to trap detached soil particles and significantly reduce their

transport. The residue also breaks the impact from raindrops

and prevents wind from disloding soil particles.

Soil Compaction

Besides erosion, soil compaction is a major concern
when residue removal is proposed. Soil compaction is an
ever-increasing problem with introduction of larger and
heavier agricultural machinery.

As the soil is compacted, the bulk density increases,
root growth becomes inhibited, which reduces tap growth. The
higher the bulk density the less defined the structure and
the smaller the pore space. This decreases the amount of
oxygen and water infiltration, which in turn increases water
runoff. With the resulting compaction more power is required
to prepare the seed bed. Plant roots are the prime source of
residue that combat poor soil structure. When roots alone

are insufficient, above-ground residue is also needed.

Nutrient Maintenance

As a result of increasing use of commercial fertilizers,
the relative importance of crop residue as a nutrient has
been de-emphasized. Where crop residues and manure are the
primary sources of plant nutrients, through microbial action,
the nutrients are released and utilized by the crops. When
commercial fertilizers are used, the crOp residue, particularly

those high in carbon and low in nitrogen that are left on the

field have a tendency to tie-up the nutrients through micro-
bial decomposition. The resulting nitrogen deficiencies
occur mostly during the spring and early summer when the
previous year's residue are decomposing (Allison, 1973). It
should be noted that if not enough residue is left on the
soil and erosion therefore increases, the commercial fer-

tilizers will be lost with the soil.

Objectives

The problems which could arise if too much residue is
removed have prompted the development of a computer program
that will provide scientific guidelines for residue removal.
To make this program accessible to the group of people most
likely to be harmed by excessive removal of crop residue --
that is, farmers, it was designed to be used on a Texas
Instrument's TI-59 programmable calculator. Many cooperative
extension offices now have this equipment and trained personnel
who can apply it to specific farms. Used in the same fashion
as an ordinary calculator, it can be programmed to perform
the necessary calculations to determine how much residue can

be removed without exceeding soil loss tolerance.

Scientifically Determined Guidelines
for Residue Removal
The program consists of the six following sections:
Section 1 - Wind Erosion Analysis;

Section 2 — Water Erosion Analysis;

Section 3 Total Biomass in the Field;

Section 4 - Residue Available for Removal;
Section 5 - Energy Balance Analysis;
Section 6 - The Transportation System.

The Wind Erosion Section is based on the wind erosion
equation, developed and verified by Woodruff and Siddoway.
The Water Erosion Section is based on the water erosion
equation developed and verified by Wischmeier and Smith.
The other sections determine the total above-ground residue
that may be removed based on crop yields, the nutritive
value of the residue and a total energy balance. With the
use of the water and wind equations the computer program cal-
culates the amount of above-ground crop residue needed to
keep erosion within tolerable limits. It is through an
intuitive knowledge of the 3011's structure that one deter-
mines the amount of residue required to maintain optimum
bulk density. All these data, manipulated within the program,
determine how much excess above-ground crop residue exists.
By knowing the amount of excess residue and the current agri-
cultural practices, the net energy can then be determined as
can the amount of nutrient being removed with the crop
residue.

The program was designed primarily for individual
field analysis, with the best scientific guidelines available.
It is simple to use and will give relatively conservative

tolerable removal rates for actual crop residue removal.

LargegArea Analysis

Although the system can be used for areas larger
than single fields, when used for areas much larger than
a 65 ha (160 ac) field it should be noted that the output
data are rather general.

The program works well for estimating the residue
available from larger pieces of land, i.e., counties, land
resource areas, and so on. However, the larger the area,
the more averaging and generalization of the input data must

be made.

Limitations

Single Field Analysis

The model developed by the computer program is
limited by how closely the data in the tables and figures
represents actual field conditions. An example of the error-
margin inherent in these input data would be with regard to
the slope and length of slope factors used in the water ero-
sion equation. This factor is a function of the gradient
and length of the slope. The problem is one of uniformity.
If the slope is uniform there will be no variance between the
computer-determined LS and the actual field condition. If
the slape is not uniform, however, which is generally the
case, the LS factor will differ from the actual field

conditions.

Another limitation is the energy data. The energy
data used in this program has been determined either by
energy audit (Myers et al., 1980) or by calculation (White,
1974). The figures represent the average energy requirement
for specific tasks (for example, 14.0 l/ha to combine corn).
The problem with this data is that the conditions for the
field being analyzed and those of the input data in most
cases will be different, in terms of yields, equipment used
and condition of the equipment and/or field.

Another limitation is residue-to-grain ratios. The
residue-to-grain ratio is used to determine the amount of
above-ground residue based on the established yield per
unit of land. Though these figures are averages it is very
unlikely that a corn yield of 1235 kg/ha (120 bu/ac)_grown
in Northern Michigan with a particular hybrid will have
exactly the same amount of above-ground residue as a crop of
corn with a similar yield grown in lower Michigan. There-
fore, when using this program, interpolating on the conserva-
tive side is advisable. For example, if a slope in a parti-
cular field is not uniform, use of an L8 factor that is a
little steeper than the average slope of the field being
analyzed is recommended. Being on the conservative side

should help avoid future problems with the 3011's productivity.

Large Area Analysis
When using this program for areas much larger than

65 ha these limitations are compounded. Not only is it

difficult to estimate an LS factor for a field one square
kilometer in size, but even harder to come up with an
average soil type or agricultural system that is uniform for
the total area.

The method of minimizing these problems will be dis-
cussed in the "user'sgmides" in the Appendix. A full ex-
planation of all assumptions and interpretation of the

results will also be discussed, as will sample runs.

10

CHAPTER TWO

LITERATURE REVIEW

Soil Requirements of Crop Residue

for Continued Crop Production

The potential of crOp residue as an alternate source
of energy is immense (Alich and Inman, 1974; Lipinsky, 1978;
Steffgen, 1974). In these reports, however, it has been
implied that the crop residue are waste products of agri-
cultural production. This is not the case.

As reported by Lindstrom et al., (1979) crop residue
influences soil properties, both physically and chemically,
as either stable or unstable soil organic matter. This is
an important factor in maintaining soil productivity. Crop
residue retains plant nutrients and helps maintain soil
porosity and tilth for easy soil tillage and good plant
growth. When removed, residue takes with it large amounts
of nutrients that must be replaced by mineral fertilizers or
other sources, such as animal manure (Larson, 1979). Residue
removal also inhibits water infiltration, and affects soil
water storage and plant use (Larson, 1977). Left on the soil
surface, residue curtails soil detachment by raindrOp impact
and reduces the velocity of runoff, which reduces the

runoff's potential to detach and transport soil (Wischmeier,

1975).

11

Water Erosion

In a 1960 report, Wischmeier states that a "highly
significant inverse correlation between crOp yields and
erosion losses was found. This report represents the results
of a series of more or less independent studies of specific
phases of soil and water management at 37 locations in 21
states over a 30-year period. This 30-year study, along with
the previous works of other soil scientists and engineers,
led to the development of a universal soil loss equation
(USLE), which reflects the effects of locality differences in
rainfall patterns.

Over time, the USLE has been improved and verified,
the variables that make up the equation being modified and
improved. "Predicting rainfall erosion loSses, a guide to
conservation planning" (Wischmeier and Smith, 1978) describes
the USLE's current use. The USLE is "an erosion model de-
signed to predict the longtime average soil losses in runoff
from specific field areas in specified cropping and manage-
ment systems." Given an accurate selection of its factors,
the equation will compute the average soil loss for a multi-
crop system, or for a particular crop year in a rotation.
Wischmeier and Smith (1978) indicate that widespread field
use has substantiated its value and validity for this purpose.

Even though the USLE has been validated work is con-
tinuing to increase the equation's usefulness and accuracy.

Rawls et al. (1979) studied the effects of conservation

12

tillage on SCS runoff curve numbers. The study did not
generate enough data to derive an equation for predicting
the effects of conservation tillage on runoff. It was
established however, that the use of conservation tillage
will affect the cropping-management factors by reducing run-
off.

Other work on the USLE has been done in Iowa. Taylor
and Amemiya (1980) developed a computer program that can be
used on the TI-59 programmable calculator, which solves the
USLE as it is given in Agricultural Handbook Number 537,
USDA 1978. This program not only calculates the annual soil
loss but it also determines the cropping-management factor
that is required to keep yearly soil loss within tolerable

limits.

Wind Erosion

The problem of soil erosion is not limited to rain and
water runoff as the detachment and transport medium. As re-
ported by Hill (1966), wind erosion on upland crop soils is
occurring at an increasing rate in Michigan. In 1965, based
on nearly 30 years of research, and equations developed by
various soil scientists and engineers, Woodruff and Siddoway
developed what is the basis for the wind erosion equation
(WEE) now used by the Soil Conservation Service (SCS). Since
Woodruff and Siddoway's work, much has been done to simplify,

verify, and extend the WEE, not all of it entirely successful.

13

lisliderule was developed by the SCS, the Agricultural Re-
search Service, and the Graphic Calculator Company, which

was easier to use than the original equation. This sliderule
method of determining soil loss by wind erosion has been in
use since the early 19703. Leon Lyles, USDA, SEA-AR and
Dwight Quisenberry, SCS, report that as of June 1981 the
sliderule system should not be used (personal communication).
It seems that when the sliderule was developed, a particular
scale required to determine the E4 factor was assumed to be
logarithmic. The scale was not logarithmic nor was it linear.
Rather, it was based on actual field data developed by Wood-
ruff and Siddoway.

Other attempts to improve the WEE have been more suc-
cessful. Lyles and Allison (1980) were able to develop the
equivalent residue factors for a number of crops that will
work in the WEE. As mentioned in "How to Control Wind
Erosion" (Woodruff et al., 1977), "Good vegetative cover on
the land is the most permanent and effective way to control
wind erosion." Living or dead, standing or flat, the vege-
tative matter protects the soil surface from wind action by
reducing wind speed and by preventing much of the direct
wind forces from reaching erosive soil particles. The crop
residue will also trap soil particles that are being trans-
ported, which in turn prevent the normal avalanching of soil
material downwind.

Soil erosion by wind was generally considered to be

limited to semi-arid and arid regions. It has been now found

14

to be a problem wherever soil, vegetative, and climate condi-
tions are conducive. Some such conditions are as follows:
(1) the soil is loose, dry, and reasonably finely divided;
(2) the soil surface is smooth, bare or sparsely covered with
crop residue; (3) the field is sufficiently large; and,
(4) the wind is strong enough to move the soil (Skidmore and
Siddoway, 1978).

One computerization of the wind erosion equation, that
of Skidmore et a1. (1970), contains a program to be used on
a mainframe computer system in Fortran IV. The solution is
similar to the manual method developed by Woodruff and Siddo-
way (1965). A problem with this program as well as one
developed by Lyles, is that the wind erosion analysis of a
particular field cannot be performed by the field worker on
the initial visit to the site under investigation. However,
the elimination of a nomograph with a movable scale makes
these programs easier to use than the manual method and
increases the accuracy of the computations.

The method of-analyzing wind erosion used by the SOS
has also been improved over the original WEE. Instead of
graphs and nomographs with movable slides, the 803 method

depends on numerous tables (SOS-Mich, 1978).

Nutrient Maintenance
Larson et al (1976) conclude that the nutritive value

of the residue represents an appreciable portion of the total

15

commercial fertilizers applied. However, when considering
2311 the nutrient value of the residue it is generally more
economical to provide necessary nutrients via commercial
fertilizers. Normally, if a leguminous crOp is turned under,
about 45 kg/ha (4O lb/A) of nitrogen is made available to the
succeeding crop. This seldom provides the total nitrogen re-
quirement. And, when straw, corn stover or other crop residue
low in nitrogen are incorporated into the soil, microorganism
activity ties up most of the available soil nitrogen. If the
roots constitute the only new residue source for humus main-
tenance, few problems exist. But where large amounts of both
tops and roots are present a sufficiently wide carbon-nitrogen
ratio may cause nitrogen deficiencies during rapid decay in
spring and early summer (Allison, 1973).

Removing all above-ground organic matter and increasing
the fertilizer rate will not only maintain soil fertility
but, in many cases, increase it (Anon., 1964; Allison, 1973;
Barber, 1978; Larson et al., 1971; Tisdale and Nelson, 1975).
This does not mean residue are not required for total soil
maintenance, rather just not necessary for maintaining soil

fertility.

Soil Physical Properties
Crop residue functions in soil maintenance as more than
just erosion control and nutrient supplement. The residue

also reduce the bulk density of the soil, enhancing infiltration,

moisture retention and respiration. Cation exchange capacity,

16

aggregation and tilth maintenance are also increased by its
presence. Unlike residue used for soil protection or nutrient
maintenance, no easy equation or multiplier factor exists for
determining the exact requirements needed to maintain ideal
physical soil properties.

Many studies substantiate the necessity of residue for
soil maintenance but figures vary significantly for each soil
type and management practice. According to Allison (1973),
root residue represent a major source of organic matter
available for humus maintenance for a large portion of Ameri-
ca's farming areas. The amount is usually inadequate to
maintain humus content at high levels but will maintain the
level commonly reached after 50 or more years of continuous
farming. By this time, the humus level stabilizes at 30 to 50%
below virgin levels. It is still adequate for many soils,
especially with fertilizer supplements available. The in-
creased plant growth due to fertilization increases the amount
of root residue which, in turn, keeps humus at an acceptable
level. Soil organic matter has increased where abundant plant
food has been added under proper conditions.

After 12 years of experiments on a field near Lafayette,
Indiana, Barber (1979) reached similar conclusions. He stated
that, "soil productivity as measured by average corn yield,
in years 6 through 11 was not affected by removal of residue
. . . hence, we conclude that the plant roots materially con-

tribute to maintenance of organic matter level of the soil."

17

After 13 years of field experiments in which five
different types of biomass and amounts of O to 16 t/ha/yr
were applied to a Marshall silty loam, Morachan et a1.
(1971) reported that "it was not visually evident that sig-
nificant changes occurred in soil tilth because of treatment

differences.‘ Although "wet-aggregate stability and water
retention were significantly increased with increasing residue
content of the soil, and bulk density was significantly
decreased," this soil type is a "medium texture, highly aggre-
gated soil that seldom exhibits soil physical problems in the
field". The fact that tilth was not apparently improved could
be due to natural physical soil properties.

Increased bulk density of the soil due to soil compac-
tion is well documented. Foth (1978) reported that root
extension is inhibited when bulk density exceeds 1.6 g/cc.

The higher the bulk density the more poorly defined the struc-
ture, and the smaller the soil space. This is usually re-
flected in restricted plant growth. Reduced top and root

corn plant growth resulting from soil compaction is documented
in Table l (Bertrand and Rohnke, 1957).

Resistance to root penetration is only one aspect
limiting growth in high bulk density soils. Of equal or
greater importance is the reduced amount of oxygen in these
soils (Tisdale and Nelson, 1975; Foth, 1978). Tisdale and

Nelson (1975) found 1.4 to 1.7 g/cc to inhibit seedling

emergence; personal communication with Dr. Robertson in 1980

18

indicated he thought the threshold bulk density is about 1.3
g/cc. Furthermore, when bulk density increases from 0.90 to
1.30 g/cc, corn root growth decreases linearly (Phillips and
Kirkham, 1962).

Compaction due to low organic matter adversely affects
farming income as well. A $175/ha and $150/ha reduction in
income was reported for no-till and spring moldboard plowing,
respectively, when 502 of the stover from a continuous corn
operation was harvested (Holtman et al., 1979). It was con-
cluded that low organic matter and more trips over the field
increased the bulk density of the soil. Lucas and Vitosh
(1978) also reported significant changes in crop yield and
other physical properties due to changes in soil organic
matter content based on different manure applications, crop-
ping systems, soil texture, erosion and tillage practices.
Often, under revised cropping systems, soil structure and
yields are improved while farm energy requirements may be
reduced (Robertson, 1952; Anderson et al., 1975; and Robertson

and Mokma, 1978).

Energy Inputs to U.S. Agriculture

As the fuel and energy situation became more critical,
farmers, fuel suppliers, and others concerned with agricultural
production needed a more complete report of information esti-
mating fuel requirements for specific farming operations and

overall operations of the total farm enterprise. White (1974)

19

has compiled such a report. This report mentions that the

fuel requirements for a specific operation vary widely from
one section of a state to another, and even from one farm to
another. This is due to such factors as weather, soil struc-
ture, topography, depth of tillage, and condition of machinery.
White's data are substantiated by Berge (1974) and by Hunt
(1977).

Berge (1974) has not indicated how his data was ob-
tained but Hunt (1977) states that the data presented is
"compiled from many sources, including estimates." Hunt
thereby attempts to provide a range within which 90% of all
actual operations fall.

Two farm energy audits which substantiates the above
study were performed by Myers et a1. (1980) and Kramer and
Shelton (1978). The Myers et al.(1980) report was based on
over 50 farm years of data for over 30 different field

operations.

Crop Residue Availability

Lipinsky (1978) predicted the energy potential of bio-
mass for the U.S. at 10% or more of current usage. This
estimate, however, includes crops grown specifically for
energy. An average of seven dry metric tons/ha of corn stover
removed from half of America's corn crop (about 13 million
hectares) could produce one quad of energy. Lipinsky indicates
that it would be detrimental to the soil to remove more than

half of these residues (Lipinsky, 1978).

20

About 360 to 590 million metric tons (400 to 650
million tons) of residue are produced annually from the nine
leading crops in the U.S. (Larson, 1979; Alich and Inman,
1974). The majority of these residue come from corn, wheat
and soybeans. If all of these residue were available for
fuel, the potential energy content would be about 8 x 1014
kcal (4 x 1015 Btu) or only 52 of the energy used in the
United States in 1977 (75 quads).

Larson (1979) suggested that realistically crop residue
could provide 1 or 2% of the U.S. energy demand. Larson's
estimate was based on current cropping practices and technology.
Larson appears to include additional fertilizer, but other
energy inputs into the systemare not mentioned. The study
is based primarily on the water erosion equation and computed
over 100,000 times to fit various conditions. Larson also
pointed out the necessity of crop residue for preventing wind
erosion and maintaining nutritive value, soil porosity, tilth
maintenance and water utilization. Soil scientists do not
agree, however, that the residue required to reduce erosion
to a tolerable level will adequately maintain the 3011's
physical properties.

In addition to Larson (1979) there are several updated
and comprehensive reports in this area (Gupta et al., 1979;
Lindstrom et al., 1979; Campbell et al., 1979; Allmaras et
al., 1979, Skidmore et al., 1979; Dusted and Otterby, 1979;

and Holt, 1979). After reviewing the computer program used

21

for most of this work (Larson, 1979b), this writer found it
was the only practical way to obtain an overview of the
potential of crop residue. Larson et al., concluded that
soil maintenance should be a prime consideration. Then,

"if soil needs can be met with partial or near full removal
of crop residue (along with adequate fertilization and other
feasible chemical practices), there should be no objection
by agriculture to their removal" (Larson, 1979). However,
these predictions do not offer a practical guide for residue

removal or for determining net energy gain for the farmer.

Residue Estimates and Collection

The concept of removing crop residue from the soil
according to specific guidelines implies a need to determine
the actual amount of residue a field requires for its
surface to be protected. The SCS has three ways of making
these estimates: sightings by experienced personnel, field
measurements, and computations using crop yield.

Ditson (1980) has compiled a "packet" that explains
how to perform two in-the-field methods of measuring crop
residue, and has included over a half dozen photographs of
fields containing known amounts of residue for reference in
making visual estimates. Ditson describes two methods of
measuring crop residue: (1) collecting, drying, and weighing

crop residue from three sample plots that are one square

yard each. The total weight in ounces ismultiplied by 100

22

to determine the pounds of dry residue per acre. (2) The
line-point technique. This method consists of observing

100 equally-spaced points along a 50- or lOO-foot line or
tape at three random locations in the field. Each point
where the line touches a leaf, stem, or stalk from the
previous crap is counted. The average number of points
touching crop residue is equal to the "percent cover" which
is translated into pounds of residue per acre with the aid
of Figure 10 in the Agricultural Handbook #537 (Wischmeier
and Smith, 1978). The method used to estimate the amount of
crop residue left on the surface by use of the crop yields
is not effective when residue are to be removed. In the
yield method it is assumed that all residue remains in the
field. The tillage practices and type of equipment used
will determine how much crop residue will remain on the sur-
face. (In his Technical Guide, Ditson does not cite sources
for these methods.)

Very little analysis exists of the percentage of loss
from harvesting equipment in crop residue removal for the
simple reason that there is virtually no machinery designed
specifically for the removal of crop residue, such as corn
stover. Small grain straws can be harvested with close to
equal efficiencies as hay but there is more shatter loss.

As reported in AG Energy (1981), work by C.B. Ritchey and
others at Purdue have analyzed the performance of both a

big round baler and a hay stacker in collecting corn stover.

23

The residue were windrowed after being cut with a flail pick-
up. The researchers found that they could not chop the

stalks too close to the ground because the machinery picked

up too much soil. The optimum setting was about 7.6 cm above
the ground. At the 7.6 cm height in a field with a grain
yield of 8800 kg/ha the windrow yields were 775 kg/ha (dry).
The material left in the field was 380 kg/ha (dry). Applying
the "rule of thumb" for corn that for each pound of grain
there is a pound of stover, then about 275 kg/ha disappeared.
It was assumed that this loss was caused by shatter losses
from the combining and windrowing. On the average, about
two-thirds of the material was collected in the windrow.

This varied from 82 to 34 percent, depending on the amount

the residue were trampled during combining. When the

material was bailed an unexpectedly high loss was encountered.
With either of the packaging systems only half of the material
in the Windrow was accounted for in the bales, and when trans-

ported and stored, another 23% loss resulted.

24

CHAPTER THREE

PROGRAM DEVELOPMENT

Theory and Assumptions

The computer model developed to be used in a TI-59
programmable calculator is actually comprised of two
programs, both consisting of two magnetic cards. The first
program is used to determine the amount of residue required
to keep soil loss due to wind erosion within tolerable
limits. The second program analyzes the water erosion con-
straints to residue removal, measures the minimum above-
ground residue which must remain on the soil after the
combined loss from wind and water erosion in order to
satisfy the 3011's residue requirements, and performs an
energy balance on the total system. What follows is a des-
cription of each section of the program, including assump-
tions made during development and limitations or inaccuracies

introduced with the input data.

Residue Needed to Prevent Wind Erosion

The first section of the program, Section 1, the Wind
Erosion Section, is based strictly on "A Wind Erosion Equation"
(Woodruff and Siddoway, 1965), and "Wind Erosion Control"

(SCS, 1978). The Soil Conservation Services (SCS) publication

25

"Wind Erosion Control" is a technical guide prepared to
make the wind erosion equation easier to use. In the SCS
system there are many tables and much interpolation. There-
fore, starting with the original wind erosion equation which
is E - f(IKCLV) where;

E - predicted average annual soil loss (in

tons per acre per year)

HI
I

a function of

I - soil erodibility index

K 8 soil ridge roughness factor

C = climate factor

L - unsheltered field length in the direction

of the prevailing wind

V a vegetative cover
The program has been developed to do most of the calculations.
This reduces the time required to perform a field analysis
and lessens the number of charts and tables.

The initial portion of the program is very straight-
forward. The soil erodibility index I (Tables 2 and 3) is
multipliedby the knoll factor (Figure 1), and the soil
ridge roughness (Figure 2) factor K. These three components
are what comprise the E2 factor, which is multiplied by the
climate factor C (Figure 3 or Table 4) to determine E3. The
E2 and E3 factors are intermediate numbersused in the pro-
gram. The computer then recalls the appropriate segment of

Figure 4,finds the two curves on either side of the E2 and

26

through two interpolations approximates the appropriate
curve for the E2.

By breaking down curves 30 through 310, by 20's,
i.e., 30, 50, 70, 90, and into four segments, based on the
calculated distance, 10 to 60, 61 to 500, 501 to 1500, 1501
to 5000, a straight line was used to approximate these
sections (Figure A). With the curves broken down into four
sections the accuracy of the line segment replacing the
curve is well within half the last significant figure. That
is, the numbers calculated via the interpolation of line
segments is more accurate than a visual reading of the
movable scale in Figure 4. Due to limited capacity, the
TI-59 calculator stores the slope and intercept of the line
segments for distances 10 to 60 and 61 to 500 in one memory
for each of the curves and the slopes and intercepts for
segments 501 to 1500 and 1501 to 5000 have been stored in
a separate memory.

The slopes and intercepts are stored as follows:
each memory can hold a ten digit number. For the line
segment from 10 to 60 and 501 to 1500 the intercept and
slope are the five digits to the left of the decimal. The
intercept and slope of the line segment 61 to 500 and 1501
to 5000 are stored to the right of the decimal. Using a
"B" to denote the intercept and an "M" to denote the slope,
a memory location would appear as follows: BBMMM. BBMMM.

The slope and intercepts for the two line segments that

27

surround the line segment E2 are then called and the line
segment depicting the curve E2 is then calculated through
an interpolation.

Though the scale that represents E2 is not linear nor
logarithmic, an interpolation between two curves within 20
units from each other will be more accurate than can be
read off the scale. The calculated distance that determines
the E2 on the E2 curve is calculated in the computer program
automatically by dividing the length of the field by the
cosine of the deviation of the erosive wind. Then a series
of "if, then" statements establishes the correct portion of
the curve to be on. At this point the calculator has pre-
dicted the E2, E3, and the E2 which was dependent on the
calculated distances. With the aid of Figures 5 through 8,
the amount and condition of crop residue required for pro-
tection is predicted (for actual user-instructions, program
listing, and explanation, see Appendix 2).

Some of the larger errors in predicting the amount of
above-ground residue required to reduce wind erosion to an
acceptable level are the environmental factors that are
entered into the program. Starting with the soil erodibility
index ("I" factor), there are generally only six different
numerical values used to describe virtually any soil in
Michigan (Table 3). A more accurate "1" factor can be
determined by finding the dry soil fractions that are

greater than 0.84 mm through a standard dry sieving process.

28

The percentage of soil fractions greater than 0.84 mm is
then used with Table 2. The knoll erodibility factor "Is"
is used to account for or analyze the erosiveness of knolls
150 meters long (500 ft) or less. The slope of the knoll
and Figure 1 is used. The greater the slope the more effect
it will have on the "Is" factor. As can be seen in Figure
l, the relationship is not linear. There is not too much
error introduced with the soil roughness factor "K" if the
field surface is very uniform. By measuring the ridges or
small undulations, and with the use of Figure 2, the "K"
factor is predicted.

The climatic factor "C" is given in Figure 3 (or Table
5), which has been determined by a relationship between the
mean annual wind velocity for a particular location corrected
to a standard height and the effective moisture. Because of
the relatively few locations where the climatic data is
collected, the predicted soil loss, using this "C" factor,
will be less accurate for yearly predictions than it will for
the ten year predictions. It is also for ten year periods
that the allowable soil loss has been determined.

The amount of residue needed to keep the wind erosion
under control is very sensitive to the calculated field
distance for fields less than 30 meters long. Fields much
greater than 30 meters do affect the amount of soil eroded
but the actual field length does not have to be measured

very'accurately.

29

Residue Needed to Prevent Water Erosion

The water erosion equation, better known as the
Universal Soil Loss Equation (USLE), (Wischmeier and Smith,
1978) is a much simpler equation to compute, but as is the
case in the wind erosion equation, the variables and
tolerable soil loss figures are for predicting long range
trends and are not a means of analyzing the effects of
individual weather phenomena. The equation is as follows:

A - RKLSCP, where

A - computed soil loss per unit of land

R - rainfall and runoff factor

K a soil erodibility factor

L - slope-length factor

S - slope-steepness factor

C a cover and management factor

F a erosion prevention practice factor

Because these factors are multiplied by each other
the programming of this particular equation is simple. The
"L" and "S" factors combine to become the "LS" factor by the
following relationship (Wischmeier and Smith, 1978):

LS - (x/72.6)In (65.41 sinze + 4.56 sine + 0.065)

A - slope length in feet

6 = angle of slope a arctan of slope steepness

m - an exponent that varies with gradient (Figure 15)

The program multiplies all the variables andstores

the first four. This is done so that different "C" and "P"

30

factors can be tried in an attempt to lower the predicted
water erosion below the allowable limit. It should be
noted that to be safe when a particular field is being
analyzed the predicted soil loss due to water erosion com-
bined with the predicted soil loss due to wind erosion must
be lower than the tolerable soil loss.

The variable that will introduce the most error in
this equation will be the "C" factor. If a "C" factor is
used from Table 6, it must be recognized that the value will
be an approximation of conditions in the field. The reason
is that the "C" factor is based on the relationship of
several factors: (1) crop stage (in terms of its maturity
and canopy cover); (2) crap rotation; (3) quality and quan-
tity of the crop residue; (4) the tillage practices and,

(5) climatic conditions. The "R" factor, like the wind
erosion climatic factor is based on long-term weather
averages. Data are collected throughout the state and inter-
polations are performed to find the "isoerodents" -- that is,
plotted lines on a map that connect locations with equal
rainfall erosivity.

The soil erodibility factor "K" is similar to the
soil erodibility factor for the wind erosion equation in
that very few "K" factors are used to depict different soil
types. The "K" factor is based on the percentage of silts

and very find sands, organic matter, soil structure and soil

permeability. If the soil breakdown is determined, the "K"

31

factor for the specific soil can be calculated with the
following equation (Wischmeier and Smith, 1978):
100 K - 2.1 Ml-l4(10'4)(12-a) + 3.25 (b-Z) + 2.5(c-3)

where the silt fraction does not exceed 70%:

M = (silt Z + very fine sand Z) (100 - clay Z)
a a percent organic matter

b - structure code (Figure 9)

c - profile permeability (Figure 9)

The "P" factor applies if the field slope is great
enough to necessitate either contour farming or strip crop
practices (Table 16). Terracing is also a practice that can
be used to reduce the total amount of soil loss due to
water erosion. However, the practice is used very little in
this part of the country. If there is no special erosion
prevention practice "P" - 1. It should be noted that both
the water erosion and wind erosion sections of the program

can be used independently of the rest of the program.

Total Biomass in the Field

In order for the computer program to accurately pre-
dict the annual amount of removable residue over the long
run, the residue-to-grain ratios (Table 7) must be accurate
for the field being analyzed. A small deviation from the
actual residue—to-grain ratio for a specific hybrid will

make a large difference whenanalyzing a 60 hectare field

with yields of 6300 kg/ha per hectare. It is understood

32

that these ratios are just "rules of thumb," and that the
actual relationship between the above-ground residue and
grain is not a linear one.

The effect this assumption has on the total agricul-
tural system being analyzed is minimal as far as the soil's
maintenance is concerned. But it has major implications in
regard to the overall energy balance. To minimize the ill
effects to the soil's productive capacity in using the
results of this program, the amount of above-ground residue
in the field which is determined to be necessary by the
water and wind erosion analysis should be left, instead of
removing the amount of crop residue this program predicts
will be available. In following this procedure it is assured
that an adequate amount of residue is left in the field.
Because the residue-to-grain ratios are based on national
averages, this procedure becomes especially advisable when
the crop yields are much higher than the average and when the
growing season is shorter than average.

In this section the program reduces the grain yield
to a dry weight basis, with the input of either estimated
or actual crop yield, and moisture content at the time of
reporting. The dry weight is then used with the above-ground
residue-to-grain ratio to calculate the total dry above-
ground residue. Using the total amount of dry residue the
energy content of the residue is determined by assuming an

energy content of 1.60 x 104 kJ/kg (7000 Btu/lb).

33

Residue Available for Use§ chgr

Than Soil Management

In determining the amount of residue available there
are three options: the first will predict how much above-
ground residue is available after soil maintenance require-
ments are met. The total amount of above-ground dry matter
is recalled from Section 3, the grain component is subtracted,
and the amount of residue required for soil maintenance is
subtracted. This calculated amount of residue is then used
in the following section to determine the energy balance of
the system.

The second option of this section determines what the
grain portion of the total yield is on a dry weight basis
and then proceeds to the following section for the energy
balance of the grain production. For this option either the
total expected or actual yield is entered or the portion of
the yield to be used for alternate energy and the amount of
dry matter is calculated by removing the percentage of mois-
ture at crop yield reporting. For example, if a field has
a corn yield of 6300 kg/ha (100 bu/ac) and half of the crop
will go toward alcohol production, then 3150 kg/ha (50 bu/ac)
would be entered. The program would recall that moisture
content for corn at yield reporting is 15.52 and from this
determine the total dry matter available. The third option
is used when just a portion of the total above-ground residue

is to be used. One instance would be utilizing just the corn

34

cobs. The weight of cobs per bushel of grain is entered,
the reported yield is recalled from Section 3, and the pro-
gram then determines the weight of cobs on a dry weight
basis. The total dry weight is then carried on to the next
section to calculate the energy balance.

The only places where significant errors are intro-
duced in this section of the program are:

In option one of Section 3 the possibility of some
error is present with the residue-to-grain ratio, and with
how close the entered yield is to the actual yield. If
both the residue-to-grain ratio and the entered yield are
realistic, then the predicted amount of residue available
should be very close to what can actually be removed annually.
In option two, where only the grain is being analyzed, the
most important input data is the crop yield. The closer this
figure is to the actual yield the more realistic the calcu-
lated amount of grain will be. In this option it is assumed
that all above-ground residue will be returned to the soil.
Therefore, as long as there is a sufficient amount of residue
being produced at the given yield to satisfy the soil's needs,
there will be very little to no error introduced from the
soil analysis. In option three, where just a portion of the
residue are to be used, the same problems exists that affect
option one. In particular when corn cobs are used the "rule
of thumb" suggests that there are 4.5 kilograms (9.94 lb) of
dry corn cobs per bushel of shelled corn. As in the residue-

to-grain ratio, this figure is not failsafe.

35

Determininggthe Net Energy Gain

The first step in performing an energy balance is to
establish a boundary. The boundary here has been selected
to cover all primary energy input to the agricultural system
used in the field being analyzed, excluding the sun. There-
fore, the energy balance starts with seed-bed preparation,
crop planting, chemical applications, harvesting, post-harvest
processing and transportation to the edge of the field
(Tables 8 and 9). Other aspects included in this section are
the increased amount of fertilizers which are applied to
replace those nutrients that are removed with the residue
(Table 10), loss of residue due to handling and storage, and
the bioconversion efficiency (the efficiency of converting
the residue to a more useful energy source, such as converting
corn to ethanol) (Table 17).

The basis on which this section will charge energy
costs is dry weight. With corn, for example, where the
residue-to-grain ratio is 1.0, half the energy costs should
be charged to the grain production and half to the above-
ground residue. Whatever amount of residue is being removed,
a proportionate amount of energy inputs should be charged to
the production. This accounting procedure does not hold
true for the energy required to harvest the cr0p component
that is being used for alternate use, or the post-harvesting
processes. The energy inputs to the harvesting and post-
harvest processing should be charged directly to the residue

being converted for alternate use.

36

The program allows the charge of energy inputs
according to any breakdown desired. 80, if one wishes to
charge all energy inputs to the grain production up to the
additional nutrients, harvesting and post-harvesting then
a zero is entered. Conversely, if all energy inputs are
charged to the residue the total energy inputs from Table 8
or 9 would be entered. It is advised to charge the
energy inputs to the various crop components on a dry weight
basis.

The assumptions made in this section by the program
are few, and they introduce only minimal error. The
assumptions relate to the increased amount of energy applied
to the system due to the nutrient removal. It is assumed
that the amount of energy is 63500, 11340, and 9070 kJ/kg
for the nitrogen, phosphoruszum.potassium, respectively
(Myers et al., 1980). The percentage of the residue that
the nitrogen, phosphorus and potassium comprises can be
obtained from Table 10. The total energy charge to the
residue for nutrient removal is minimal.

Assuming that the data supplied up to Section 5 is
accurate,errors still will be introduced with the energy
accounting system, owing to the fact that the data used
for energy input are in the form of averages based on the
"Michigan Farm Energy Audit" (Myers et al., 1980). Unless
a farmer keeps unusually accurate records on total fuel
and fertilizers used, however, a better energy input figure

will not be easily found.

37

Two other factors that will significantly affect the
final output are the amount of field loss from handling,
processing, and storage, and the bioconversion efficiency

for the particular conversion process.

The Transportation System

The last section of the program predicts how far the
residue can be transported and still maintain a postive
net energy yield, as well as the total number of loads it
will take to get the crop residue to a bioconversion
facility, taking several energy factors into account.

First, the net energy for transport is determined.
If all the energy was used for tranSport then nothing would
be available after conversion. Once the Operator has de-
termined the amount of energy available (from harvest of
the residue) for transport, the type of fuel and the fuel
consumption and the cargo space of the transport vehicle
is entered. The bulk density of the crap residue along
with the moisture content at the time of transport is also
entered. In determining the total distance the residue
can be transported the program assumes the empty return
trip of the transport vehicle.

The problem in this phase of the program is that the
energy potential of the transport fuel is compared with

that of the alternate fuel being produced at the conversion

facility. If ethanol is being produced at the facility

38

and the transport vehicle is operating on that same fuel
then there is no problem. However, if, for example, corn
cobs are being transported by a diesel rig to a gasifica-
tion facility, the comparison becomes complicated. Even
though the conversion of diesel fuel to work is accounted
for in the fuel consumption and the conversion efficiency
(Table 17) of cobs to producer gas has been accounted for,
it is not an accurate assumption that the producer gas
would perform similarly to the diesel fuel. In current
practice energy balance is computed strictly on the heat-
ing value of the energy inputs and outputs. To be even
more realistic, the quality of the alternate fuel should

be accounted for.

39

CHAPTER FOUR

SINGLE FIELD SAMPLE RUNS

Purpose of Sample Runs

In an attempt to demonstrate what factors are used
in the program, how certain variants will affect the
amount of residue available and how the program is used,
two hypothetical fields in Ingham County have been
analyzed. In this field analysis of two different soil
types, two different craps (corn and wheat), have been
used, as well as two different slopes (22 and 62). The
input data, explanation of assumptions, and a discussion

of the results follow.

Input Data for Sample Runs

The two fields which were run in the sample are
each 32 hectares (80 ac). One of the fields in an 0wosso
sandy loam soil. The second field is a Spinks loamy sand
soil. The yield for each field is: 7540 kg/ha (120 bu/A)
for corn and 2700 kg/ha (40 bu/A) for wheat. There were
four runs per field. The first run assumed a slope of 2%
and corn as the crap. The second run assumed a 6% slope
with corn as the crop. The third and fourth runs assumed

slapes of 2% and 6% with the crap being winter wheat. All

40

the other inputs were held constant. The data used in the
order it is entered into the program are:

(1) the wind erosion analysis (Section 1 of the program);
soil erodibility index for field #1 is 86, for field #2,
134; knoll factor, 1; soil roughness factor, 1; climatic
factor, 82; field length, 400 meters (1320 ft); wind
deviation, 45°; no wind breaks.

(2) the water erosion analysis (Section 2 of the program);
rainfall factor, 75; soil erodibility factor for field #1
is 0.28,with atolerable soil loss of 6.7 t/ha,for field #2
the sail erodibility factor and tolerable soil loss are
0.17 and 11.1 t/ha respectively; the slope length factor
for the 22 slope is 0.46 and 2.5 for the 6% slope; the
support practice factor is l.

(3) prediction of total crop and residue and the energy
potential in the field (Section 3 of the program); expected
yields are 7540 kg/ha (120 bu/ac) and 2700 kg/ha (40 bu/ac)
for corn and wheat, respectively; residue-to-grain factors
are 1.0 and 1.7 for corn and wheat, respectively; moisture
content at yield reporting is 15.5 and 14 percent for corn
and wheat, respectively.

(4) determination of the amount of excess residue available
for removal (Section 4 of the program); option 1 (see
Chapter 3) was chosen because all available residue is

desired.

41

(5) determination of the energy balance (Section 5 of the
program); handling losses 50%; bioconversion efficiency,
100% (it is assumed that the residue will be burned in a
furnace. This does not indicate that the furnace is 100%
efficient, rather than 100% of the heat made available by
the burning of the residue is available for the furnace).
The energy inputs in crop production for both corn and
wheat are porportional to the residue-to-grain ratio. They
are 2,500,000 kJ/ha (950,000 btu/ac) and 1,400,000 kJ/ha
(540,000 Btu/ac) for corn and wheat. The energy to harvest
these residue was (assuming windrowing the residue and
baling in small rectangular bales) 1,700,000 kJ/ha

(650,000 Btu/ac) and 850,000 kJ/ha (325,000 Btu/ac) for corn
and wheat. No post-harvest processing was performed. The
nitrogen, phosphorus and potassium factors were 0.0111,
0.0018, and 0.0133 for corn and 0.0067, 0.0007 and 0.0097
for wheat, respectively.

(6) the transportation system (Section 6 of the program);
it is assumed that 50% of the equivalent energy is wanted
for work; the fuel used for transport is diesel; fuel
consumption is 2 km/l (5 mpg); hauling capacity of the
truck is 68 m3 (2400 ft3); the bulk density of the residue
baled is 160 kg/m3 (10 1b/ft3), and the moisture content

at harvest is 20% (see Table 11 for list of input data).

42

Results of the Sample Runs

Table 12 contains all the results generated for the
sample fields in the six sections of the program. In the
first two sections the soil loss for wind and water
erosion are found for the particular cropping and manage-
ment system. The combined losses must be less than the
tolerable soil loss for the field in question. One can
see that for field #1 it is only when the slope is 2% that
the calculated and combined soil loss is less than the
tolerable limit. If the cropping rotation was changed and
a year or two of meadow was introduced then it is possible
that residue would be available from field #1 with a 6%
slope. The cropping rotation assumed that only corn and
wheat were being produced in the examples. To be within
the tolerable soil loss of 6.7 t/ha for field #1, 2800 kg/ha
of residue was required to hold wind erosion to 2.2 t/ha.
2200 kg/ha was required to keep water erosion to 4.2 t/ha.

Section 3 calculated that the total above-ground
matter, grain and residue, for the entire field #1 was
412,800 kg (dry weight basis). Section 4 then subtracts
the grain portion and the amount of residue that must re-
main for soil maintenance and computes a total of 116,000
kg (dry weight) available. Based on 1.63 x 104 kJ/kg
(7000 Btu/lb) the gross energy potential is determined and

then all the appropriate energy inputs are subtracted.

43

The result is the net energy available for use and/or for
transport. The last section (Section 6), for field #1
with a 2% slope planted in corn determines what the great-
est distance is that the residue can be transported and
the required number of loads. With 50% of the energy
allocated for transport, the maximum distance is 1550 km
for each of the seven loads.

When compared to the corn grown in field #2 with a
2% slope, one sees that not aetmnfl1residue is available.
At first this seemed strange because the soil of field #2
is more susceptible to wind erosion, as can be seen by
comparing the soil losses. Field #1, with 2800 kg/ha left
in the field, has losses of about 2.2 t/ha, whereas field
#2 with 2500 kg/ha of residue left in the field will have
losses of about 7.8 t/ha. The reason more residue can be
removed from field #2 is because the tolerable soil loss
is 11.1 t/ha as compared to 6.7 t/ha for field #1. It is
this higher tolerable soil loss limit that allows residue
to be removed from field #2 with a 6% slope whereas in
field #1, with a 6% slope no residue can be removed due to

water erosion.

Usage of Results

Once a field has been analyzed, as have fields #1
and #2, the results can be applied to a situation where

the crop residue is to be used for some purpose other than

44

returning it to the soil. The first step in applying the
results is to plan on leaving the residue required to
maintain the soil's productive capacity. It must be noted
that the amount of residue required for soil maintenance
was calculated for a given management system. For the sake
of the soil, adherence to the management system is impera-
tive. In the case of field #1, in order to keep the water
erosion level down to 4.2 t/has the field must be strip
tilled with 2200 kg/ha (dry weight) of residue remaining.
If the energy balance is positive, as it was in all the
sample runs where residue could be removed, one can assume
that there will be more energy available than that used in
producing the residue. To keep the energy balance positive,
the residue cannot be transported farther than determined
in Section 6. It is unlikely the residue would be trans-
ported further than that, because of the economics in-
volved. The economics of the total system are not con-
sidered in the program at all. If the results indicate
that a system for residue removal and conversion is viable,
it cannot be assumed that the economics will create a positive
return.

When using the results of an area study to plan an
energy conversion center one must be careful. A realistic
amount of field, handling and storage losses of the residue

must be used to determine how much residue the facility

will have to process. Knowing the amount of residue
available at the site of the conversion center will help

in determining the apprOpriate size of the facility.

45

46

CHAPTER FIVE
RESULTS AND DISCUSSION OF CROP RESIDUE

AVAILABILITY PROGRAM

Using the tables and figures contained in this
paper, together with the computer program, much of the
agricultural land east of the Rockies can be analyzed.
The author is aware of no other program that will analyze
a single field's potential for alternate energy. Nor is
the author aware of any large-scale program that is
capable of analyzing both water and wind erosion constraints
to residue removal, along with an energy balance of all
primary energy inputs including increased fertilization,

for the total system.

Comparisons With Other Works

There are computer programs available that will
perform sections of the crop residue availability program
presented here. Dr. Bill Larson has a program that es-
timated the amount of residue available in the twelve
states comprising the North Central section of the United
States. Larson's program, however, analyzed only the
eastern part of this area for water erosion constraints
and only the western section of the area for wind erosion.

There is no energy balance or economics involved in his

47

program. The program developed by Larson was intended
only to approximate the amount of crop residue available,
with future planning and policy-making in mind. It can

be seen from the results on the two fields in Ingham
County that one cannot assume water erosion is the limiting
factor to residue removal for some states or regions and
that wind erosion is the limiting factor for others. From
Table 12 it is seen that the 0wosso sandy loam was limited
in residue removal by water erosion whereas the Spinks
loamy sand was limited by wind erosion. With 2200 kg/ha
of residue (dry weight) left in each of these fields with
a 2% slope the water erosion soil loss was 4.2 t/ha for
the 0wosso, whereas on the Spinks the soil loss due to
water erosion was only 2.7 t/ha. Wind erosion caused soil
loss of 2.1 t/ha and 7.8 t/ha for the 0wosso and Spinks
soils, respectively, when 2500 kg/ha of residue (dry

weight) was left in the field.

Versatility of the Program

In an effort to enhance its flexibility, the program
was developed so that various sections could be used
completely independently. The two most readily usable
sections of the program are Sections 1 and 2, the wind

erosion equation and the water erosion equation.

48

Wind Erosion Section

The wind erosion section is a vast improvement over
the manual method first developed by Woodruff and Smith
(1965) and the later version now used by the Soil Conser-
vation Service for analyzing the soil loss for a particular
field. The major improvement over the earlier system is
the computerization of a nomograph with a sliding scale.
Once the scale has been cut out of the figure, the possi-
bility of losing the sliding scale and the probability of
poor interpolation creates a much higher potential incidence
of error and inconvenience. The improvement over the system
now used by the Soil Conservation Service consists primarily
of the reduction in the number of tables that are used. The

possibility of faulty interpolation is also minimized.

Water Erosion Section

In the water erosion section there are three elements
which improve the use of the USLE. The first is the speed
with which a field can be analyzed, and, once run, how
rapidly changes in the annual soil loss relative to changes
either in the cropping-management or erosion prevention
practices can be predicted. This capability is important
mostly to a field agent working directly with a farmer. The
agent can determine in a matter of seconds what the pre-
dicted soil loss will be given that farmer's agricultural
practices,and how the loss would change with a change in

the agricultural practices.

49

Another improvement over the manual system is the
direct computation of the slope length (LS) factor as
opposed to obtaining the LS factor from a graph. This
accomplishes two things: first, the total time is reduced
for determining the various factors that represent the
field being analyzed; secondly, the possibility of mis-
reading the figure is eliminated. The most significant
improvement is the reduction of the number of tables and
the probability of faulty interpolation of the various
factors and end results. Section 2, compared either to the
original manual method developed by Wischmeier and Smith
or the system used by the Soil Conservation Service, pro-
vides greater accuracy than can be gotten by reading
factors off the graphs or taking interpolations from the

tables.

The Energy Balance

Like the two sections that analyze erosion phenomena,
Sections 5 and 6 can be used independently of the rest
of the program. The two sections can be used for studying
the energy balance associated with crops, or portions there-
of. Though it is recognized that this section does not
have the potentially wide-spread use as does Sections 1 and
2, it will be useful in analyzing community size alcohol
stills or other bioconversion facilities that utilize the

grain or residue component of a crop. This feature of the

50

program enables the operator to determine whether more
premium fuels, such as diesel or LP gas, are being consumed
in the production of the crop than the portion of grain or
residue being converted to other forms will yield. To
perform this analysis one must know how much energy is going
into the bioconversion facility, and must have on record

how many liters of fuel were used during total crop produc-

tion.

Limitation of the Program

 

With the crop residue availability program caution
must be used in selecting the variables that match the
agricultural system being analyzed. At best, the data
generated will be only an estimate of actual conditions.
Because of this when choosing input data it is advisable
to use approximations on the conservative side.

Because of the approximations in much of the input
data (see Chapters 1 and 3) caution must be used in inter-
preting the results of the program. The soil structure
should be examined after a year or two and the agricultural
practices modified as indicated by the results. If the soil
structure is deteriorating, more of the crop residue must
remain on the field, with the possible addition of manure;
if the soil structure remains the same, or improves, then

present practices may continue.

51

CHAPTER SIX

SUMMARY AND CONCLUSIONS

Summary

Where crop residue removal is proposed, the field
in question should first be studied to determine the
possible ill effects on the soil's productivity from such
removal. The premise is that, in terms of commonly accepted
principles of soil management, only the residue can be
safely removed which is in excess of that required to main-
tain adequate soil conservation practices and promote the
soil's productivity. It is necessary, therefore, to devise
a means of measuring the variables involved in soil main-
tenance to determine how much residue can be removed, for
any given combination of variables, and whether the result-
ing energy balance will be positive or negative.

To accomplish the above a computer program was
develOped for use on the TI-59 programmable calculator.
This program using previously developed wind (Woodruff and
Siddoway, 1965) and water (Wischmeier and Smith, 1978)
erosion equations and knowledge of the soil's physical
characteristics, determines two pieces of information.
First, the amount of residue which exists in a field in

excess of that required for soil preservation is estimated.

52

Taking into account the current agricultural practices in
that field, the second aspect the program addresses is an
energy balance. The energy balance portion of the program
considers the process from seed-bed preparation through
post-harvest processes, and current levels of fertilizer
application, and analyzes the transportation system in-
volved in moving the residue to a bioconversion center.
The final output of the program is the net energy gain

(or loss) from residue removal from a given field. Based
on the transportation system involved, the distance the
residue can be transported and how many loads of residue
there will be is also calculated. Economic considerations

involved in the total system are not addressed.

Conclusions

After a review of the literature and an analysis of
the sample runs described in this writing it was determined
that:

1) Each field proposed for crop residue removal should

be considered on a case-by-case basis, analyzing

as accurately as possible the conditions of that

particular field, and avoiding generalizations;
2) Such a field should be analyzed in terms of the

potential increase in wind and water erosion and

soil compaction from the removal of residue, and

53

whether the residue to be removed constitutes
an excess of that required for proper soil
maintenance;

3) This "tool" is the best available means of calculating
how much crop residue can be removed. It also gives
insight into the energy balance and distance the
residue can be transported;

4) Crop residue from some soils and locations can safely

be removed.

54

CHAPTER SEVEN

RECOMMENDATIONS FOR FURTHER RESEARCH

Program DevelOpment

The following recommendations are made to increase
the ease of operation and the accuracy of the program.

In Section 1, the Wind Erosion Section, determining
a mathematical relationship for Figure 5 would reduce the
inaccuracies introduced when reading the E4 factor. The
problem encountered with this nomograph is that the scale
is not logarithmic, though it closely resembles a logarith-
mic scale. When an approximate logarithmic interpretation
of this scale is attempted, the lower end, that is, from
an E4 of ten downward, is unacceptable. The error increases
as the field size is reduced. It was found that a straight
interpolation between the other segments of E4 will be well
within allowable tolerances, however, this option is
limited by computer space. Either some further curve fit-
ting techniques or a large computer should be tried.

Figure 6 should also be reduced to a mathematical
expression. With three coordinates this figure becomes a
relatively uniform surface (see Figure 11). The axes for

both the "E" and the "E4" are logarithmic.

55

In Section "2" the Water Erosion Section, com-
puterizing the cropping-management (C) factor would
greatly increase the resemblance of the model to actual
field conditions. The manner in which the C factor is
calculated is described in Wischmeier and Smith (1978),
owing to limited computer space, this procedure was not
incorporated into this program.

Another major change in the program that would
increase its accuracy relates to the manner in which the
energy inputs are charged against that portion of the crap
being studied. How this should be accomplished is not
clear. A comprehensive and widely accepted method should

be developed.

Verification of Program

A long-term study of residue removal, and the actual
effects it has on the soil's condition, is recommended.
Actual long-term (10 years or more) data collected on
erosion, compaction, and energy inputs into the system as
related to residue removal would enable researchers to
determine the extent to which the model actually represents
the field that is being analyzed. With this information,
changes could be made in the various input factors which
would be more representative of actual conditions. Also,

an indepth study of the affects of residue removal in small

56

increments, say,by 100 kg/ha, rather than by 500 kg/ha
would be of interest. If increments this smallmake signi-
ficant differences in soil structure, fertility, and soil
loss, then methods of determining the amounts of residue

which should remain in the field need to be refined.

APPENDICES

THE EFFECT OF MOISTURE, FERTILITY LEVEL, AND

APPENDIX A

TABLES

TABLE 1

DEGREE OF SOIL COMPACTION 0N CORN
PLANT GROWTH

57

 

 

 

Weight of Weight of Topzroot Weight of
t0ps roots ratio total plant

Treatments (8) (g) (g) (3)
Loose, wet, '
fertilized 39.4 14.8 1 0.38 54.2
Loose, wet,

unfertilized 23.5 10.1 1 0.43 33.7
Loose, dry,

fertilized 27.5 9.3 1 0.34 36.8
Loose, dry

unfertilized 20.3 9.3 1 0.46 29.6
Compact, wet,

fertilized 16.0 6.5 1 0.40 22.5
Compact, wet,

unfertilized 17.0 7.7 1:0.45 24.7
Compact, dry,

fertilized 20.1 11.3 1 0.56 31.4
Compact, dry,

unfertilized 19.3 9.9 1:0.51 29.2

 

Source: Bertrand and Kohnke, 1957.

58

TABLE 2

SOIL ERODIBILITY INDEX (I) FOR SOILS WITH DIFFERENT
PERCENTAGES OF NONERODIBLE FRACTIONS AS
DETERMINED BY STANDARD DRY SIEVINGl

 

 

 

 

 

 

 

Units

Percentage

of dry soil

fractions 0 1 2 3 4 5 6 7 8 9
>0.84 mm

tens t/A
0 - 310 250 220 195 180 170 160 150 140
10 134 131 128 125 121 117 113 109 106 102
20 98 95 92 9O 88 86 83 81 79 76
30 74 72 71 69 67 65 63 62 6O 58
4O 56 54 52 51 50 48’ 47 45 43 41
50 38 36 33 31 29 27 25 24 23 22
60 21 20 19 18 17 16 16 15 14 13
70 12 11 10 8 7 6 4 3 3 2
80 2 - - - - - - - - -

1

For a fully crusted soil surface, regardless of soil
texture, the erodibility "I" is, on the average, about 1/6 of
that shown.

Source: Woodruff and Siddoway, 1965.

 

TABLE 3

SOIL ERODIBILITY INDEX (I)

59

 

 

Percent of Dry

Soil Aggregates

Over 0.84 mm
Soil Textural Classes Percent

Soil
Erodibility
Index (I)

 

Very fine sand, fine sand, 1
or coarse sand.

Loamy very fine sand, loamy
fine sand, loam sand, loamy
coarse sand, or sapric
organic soil materials. 10

Very fine sandy loam, fine
sandy loam, sandy loam, or
coarse sandy loam 25

Clay, silty clay, non-
calcareous clay loam, or
silty clay loam with more
than 35% clay content. 25

Calcareous loam and silt loam,
or calcareous clay loam and
silty clay loam. 25

Noncalcareous loam and silt
loam with less than 20% clay
content, or sandy clay loam,
sandy clay, and hemic organic
soil materials. 40

Noncalcareous loam and silt loam
with more than 20% clay content,
or noncalcareous clay loam with
less than 35% clay content. 45

Silt, noncalcareous silty clay
loam with less than 35% clay
content and fibric organic soil
material. 50»

Soils not suitable for cultivation
due to coarse fragments or wetness,
wind erosion not a problem. --

310

134

86

86

86

56

48

38

 

Source: Soil Conservation Service, 1978.

TABLE 4

1

PREVAILING WIND EROSION DIRECTION ’

2

6O

 

 

Location

 

 

Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.
Battle Creek,MI 248 248 248 270 247 248 248 270 270 225 225 225
Cadillac,MI 248 248 292 292 225 225 247 225 246 203 203 247
Duluth,MN 292 270 293 90 90 248 270 68 270 248 293 293
F1int,MI 225 270 248 248 247 248 248 225 225 225 225 225
Green Bay,WI 292 228 225 247 225 225 225 225 225 225 270 227
Marquette,MI 0 338 0 0 0 180 202 0 180 180 180 180
Mt. C1emens,MI 225 225 225 203 180 201 202 180 180 202 203 225
Muskegon,MI 248 270 248 225 205 225 225 203 203 203 225 270
Oscoda,MI 338 315 270 239 227 270 202 225 248 224 226 315
Pellston,MI 270 270 270 270 248 248 248 248 248 248. 292 270
Sault St. Marie,

MI 292 293 293 293 293 293 293 293 293 293 293 292
South Bend,IN 225 270 90 315 338 338 338 0 180 180 225 225
Toledo,0H 247 247 248 247 247 225 204 225 248 225 220 225
Traverse City,MI 203 202 202 202 203 225 203 203 202 202 180 225
Ypsilanti,MI 248 270 270 270 270 270 270 270 270 248 248 248

1Prevailing wind erosion direction -- direction of winds over

12 mph 1 ft above ground surface.

2

Source: U.S. Soil Conservation Service, 1973.

"Direction" means degrees, measured in a clockwise direction
from north which is 0°.

61

TABLE 5

MONTHLY CLIMATIC FACTORS "C" FOR
EACH COUNTY IN MICHIGAN

 

 

Monthly Value of "C"

 

 

County (Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Alcona 7 7 8 7 6 3 3 3 4 S 7 7
Alger 5 4 6 6 2 3 2 2 4 4 S 3
Allegan 7 10 12 9 7 5 4 4 4 5 8 7
Alpena 7 7 8 8 6 4 3 3 5 6 7 7
Antrim 6 7 8 8 7 4 3 3 S S 8 8
Arenac 8 7 8 7 5 3 3 2 4 S 7 7
Baraga S 4 7 S 2 3 3 2 4 S S 3
Barry 7 9 10 7 S 4 3 3 4 5 7 7
Bay 7 7 8 7 S 3 3 '2 4 S 7 7
Benzie 7 8 10 9 8 S 4 3 5 6 9 8
Berrien 7 10 10 9 8 5 4 3 S 5 8 8
Branch 7 8 9 8 5 4 3 2 4 S 7 7
Calhoun 7 8 9 7 S 4 3 2 4 S 7 7
Cass 7 9 9 8 7 4 3 3 5 5 7 7
Charlevoix 6 7 9 9 7 5 3 3 5 S 7 7
Cheboygan 6 7 8 8 6 4 3 3 S S 7 7
Chippewa 6 5 7 7 7 4 3 2 4 S 6 4
Clare 7 7 8 7 S 3 3 2 4 5 7 7
Clinton 6 6 8 7 4 2 2 2 2 3 7 4
Crawford 7 7 8 7 6 4 3 2 4 5 7 7
Delta 7 5 7 7 S 4 2 4 S S 7 5

Dickinson 6 S 7 7 S 4 2 3 5 S 7 S

 

Count

Eaton

 

Emmet
Gene

Glad:
Gogel

Gran
Tra

Grat
Hill
Houg
Hurc
Ing]
Ion
Ios
Iro
Isa
Jag
Ka]
Kaj
Ker
Kex
La]
La}
LE(

TABLE

5 ..

- (Cont'd.)

_62

 

 

Monthly Value of "C"

 

 

County Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Eaton 6 8 9 7 5 3 3 2 3 4 7 7
Emmet 6 7 8 9 7 S 3 3 5 5 7 7
Genesee 7 7 8 7 4 3 2 2 3 5 7 7
Gladwin 7 7 8 7 S 3 3 2 4 S 7 7
Gogebic 5 5 8 9 6 4 5 4 5 8 9 5
Grand

Traverse 7 7 8 8 7 5 3 2 4 5 7 7
Gratiot 6 7 8 7 5 3 2 2 2 4 7 6
Hillsdale 7 8 9 8 S 4 3 2 4 S 7 7
Houghton 5 4 7 5 2 3 2 3 5 6 6 3
Huron 8 7 8 8 5 3 3 2 5 5 8 7
Ingham 6 8 8 7 S 3 3 2 2 4 7 7
Ionia 6 7 8 8 S 3 2 2 3 3 7 7
Iosco 7 7 8 7 5 3 3 2 S 7 7
Iron 5 5 7 7 6 4 3 3 S 7 8 4
Isabella 7 7 8 7 5 3 3 2 3 S 7 7
Jackson 7 8 9 7 S 4 3 2 4 S 7 7
Kalamazoo 7 8 9 8 6 4 3 3 4 5 7 7
Kalkaska 7 7 8 7 6 4 3 2 4 S 7 7
Kent 7 8 10 10 6 4 3 3 4 4 7 7
Keweenaw S 4 7 5 3 3 2 3 S 6 5 3
Lake 7 8 10 7 6 5 3 3 4 S 7 7
Lapeer 8 7 8 7 S 3 3 2 4 S 7 7
Leelanau 7 8 10 9 8 S 4 3 S 6 10 9

 

TABLE 5 --

(Cont'd.)

~63

 

 

Monthly Value of "C"

 

 

County Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Lenawee 8 9 10 7 5 4 3 2 4 5 8 7
Livingston 7 7 8 7 S 3 3 2 3 4 7 7
Luce S S 6 7 4 3 2 4 5 5 5
Mackinac 6 6 7 8 6 4 3 2 4 S 5 6
Macomb 10 10 10 10 6 5 4 3 5 6 10 10
Manistee 7 8 10 8 8 5 4 3 S 6 8 8
Marquette 4 4 6 S 2 3 Z 2 4 4 S 3
Mason 7 9 12 8 8 5 4 5 6 8 7
Mecosta 7 7 9 7 5 4 3 2 4 S 7 7
Menominee 7 6 8 9 7 5 4 3 6 8 9 8
Midland 7 7 8 7 5 3 3 2 4 5 7 7
Missaukee 7 7 8 7 S 4 3 2 4 5 7 7
Monroe 10 10 10. 9 S 4 3 3 4 S 8 7
Montcalm 7 7 8 7 S 3 3 2 3 4 7 6
Montmorency 7 7 8 7 6 4 3 3 5 6 7 7
Muskegon 8 10 12 12 6 S 3 S 4 5 8 7
Newaygo 7 8 10 8 6 S 3 3 4 S 7 7
Oakland 9 7 10 7 S 4 3 2 4 S 8 9
Oceana 8 10 12 12 7 S 3 S 5 S 8 7
Ogemaw 7 7 8 7 5 3 3 2 4 S 7 7
Ontonagon S 4 8 7 6 4 4 3 5 8 7 4
Osceola 7 7 8 7 S 4 3 3 4 5 7 7
Oscoda 7 7 8 7 6 4 3 2 4 5 7 7

 

\L

 

Count

 

Otseg
Ottaw
Presq
Rosco
Sagin
St. C
St. J
Sanil
Schoc
Shian
Tuscc
Van
Wash:

Waynu

Wexf.

Sour

 

TABLE 5

(Cont'd.)

64

 

 

Monthly Value of "C"

 

 

 

 

County Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Otsego 6 7 8 7 6 4 3 3 S 5 7 7
Ottawa 7 10 12 12 7 S 3 S 4 5 8 7
Presque Isle 6 7 8 8 6 4 3 3 S 6 7 7
Roscommon 7 7 8 7 5 3 3 2 4 S 7 7
Saginaw 7 7 8 7 S 3 2 2 3 S 7 6
St. Clair 10 10 10 10 S 4 3 3 5 6 10 10
St. Joseph 7 8 9 8 6 4 3 3 4 5 7 7
Sanilac 8 9 9 8 S 3 3 2 5 S 8 7
Schoolcraft 6 S 6 7 5 4 3 3 4 4 6 4
Shiawassee 7 7 8 7 4 2 2 2 2 3 7 4
Tuscola 7 7 8 7 S 3 3 2 4 S 7 6
Van Buren 7 10 10 9 7 5 4 3 5 S 8 8
Washtenaw 7 8 10 7 S 4 3 2 4 5 7 7
Wayne 9 8 10 10 6 4 3 3 5 S 8 9
Wexford 7 7 8 7 5 S 3 2 4 5 7 7
Source: SCS, 1978.

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O U._=<.—.

65

.nned .oo«>uom coaua>uunnoo anon

.m.=

.3ovooalx .uo>ou young: and: sauna masseuse .eouu swan» anaemic .eouu hoe-mm

we on oono adage and: shoe we so can .ouououonh .soxou on unsure Avonnosmv euou
away» we canoe some sun: «Janus we on A we and: sauna unsu masons .oseaoou auoo

"NUNDOm

.oaouuou

.ooosvoue
mauuaelou

torso .uon he acuuuu :o: unsouoawlleouo a no cumsunoso and he Heaven :0: unmouoculueouu m we
unmstoso nunNIeN no Heaven :0: ouoouoculumnmsoasuucoo each» one usooohoe sense

.osemeH cuou we
on uno~m>wsco mm mceooxom can .meouu so: .:mmuw Hanan scum snowmen mo canon mac

n

N

oomwusm “MOm on» we once so «oo no mosewmou o>mo~ noes: mEoumxm ommnnwu mononucen

.mn~ N

.wcwuceae poems
oumwuzm meow use mo oboe no ”em :0 mosewmoe o>eo~ sows: mEoumxm mundane moeanocm

.mewucmue nouwa

 

 

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ene. nNe. 22:0: .na
nNe. ewe. 2:20: .Nn
e~9. nne. :20: .-
one. Nee. 20x .en

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nNe. eNe. ene. eve. voe. mne. Nee. fine. eoe. one. «220:: .e
ene. . mne. nee. eoe. one. nee. Nne. noe. ene. wee. «Some .5
one. one. mne. one. Nen. nee. ene. nee. Nee. mnu. ezzommz .o
ewe. nee. moe. eee. NNH. Hme. moe. oee. een. evH. ozoomm .n
one. nee. Noe. one. neg. one. eoe. nee. nee. nNH. «some .e
one. Nme. ewe. nee. end. ~oe. nee. gnu. onH. me“. «some: .n

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e:uoo .ueou .N
ene. ene. and. nag. ve~. eee. nna. men. eon. mnn. ecuou .ucou .H
eeeo eeeo eeen eeen eeeo eeev eeen eeeN nonomfiuw>

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n n

meflueenq omegamu emuum xuauou
acnm no nnmu-o=-ouennwu open w wean nommgu .ucane -mh

 

 

 

<A=mz~zmm dmzoa .z<o~=u~= .mo<44~h zo~h<>¢mmzou mom wm=q<> hzm2m0<z<z-oz—mmomu

o mqm<b

66

.man ..Hm um nausea

 

 

.nmea .emoma
A.H es Ao.mee m.mea Aoov ~.A~ as “when:
n.H «H Ae.wov e.mow Aeov ~.n~ so uoaasm mamas
m.H ms Ac.meo m.mea Aoeo ~.a~ an memmpsom
m.H on Am.oov n.n~m Aonv «.mu on wax
e.~ ea Ao.n~v e.eHo Ammo n.oH so muse
Aeov ~.ooo I I mumnaom
Aoanmo ~.o~N I H.e~H I I eoaamm
Anv H.mo I smooa
I I I I u mom
e.H «a Ae.eoo w.oovn.eoo_on.uam Aem o omv n.- oo.n~ so asnwuom cameo
e.H m.nH Am.oov m.nnm Aonv «.mm so once
m.~ «a Ae.mmv c.mee Amee «a an smegma
onus“ N Amoe\oae Anee we use: acne
samuw acousoo ma\wx unwaos uoz
\oaefimom ouaumfioz munmaon xaom

H

 

 

mUHmHUmmm memo

m m4m¢a

67

 

 

 

 

he“ x oeH x veg x e.m wen x oo: xumuom
sea x we“ x meg x m.H veg x :ofiue>wufiso mono onomuomo>
men x men x wen x n.e veu x canoe>wunso ecooom .meono 3oz
ace x has x was x H.H cos x eoeos>sua=o amuse .maouu so:
”sewum>wu~:o mono
was x has x me“ x v.m mos x easemenmeeee
men x men x men x n.~ me“ x muoonwee housedm onoeuomo>
wen x men x me“ x n.~ men x Housman cumuoe
hen x men x veg x n.e we“ x agape cease
men x sea x me“ x n.~ we“ x mzou :en
meg x neg x men x n.~ veg x mzou :ev
a.ouo .sonnawusom
geese Housman monouzom
"m:0wumsomow:wucmne
e x wen x o.~ men x m.m wen x ~.o noum>fiunsu onoflm
n x men x ~.~ veH x m.e veH x n.~ zones: encepuoxwem
n x me“ x o.~ men x m.H «ea x n.n 369nm: :uoouumcfiemm
A x nee x H.~ neg x e.~ we“ x e.m .opo .mxfieum choc
n x men x e.H men x n.~ veg x n.e uo>o mafia ecooom .Heom eozone
u x me“ x v.~ men x ~.~ «on x o.n eo>o mafia umnwm .Hfiom eozofie
xmwe Scene» eeeoceum
n x we“ x e.~ men x n.~ ve~ x e.o xmwo seesaw x>mom
m x nod x m.m mos x o.e vol x s.s saga Homage
m use x m.6 mos x «.6 mo” x 6.H acne eeeonesoz
um:0wueaomo oumanwb
Q£\H. Um\aum COMHMHOQO

 

 

 

 

mzomb<mmeo 02H2m<m ombumqmm mam mbzmzmzmsdmm Juan mo muz<x nmb<zmhmm

m mqm<H

TABLE 8 (Cont'd.)

'68

 

 

 

 

 

 

Ixrxtx IxIxrx coco coco coco 000600 on cocoon
ooo ooo oo oo oo oooo oo ooo
HF‘H HHH HH HH 1-40-4 v-OI-tv-iv-d HH par-4H
xxx xxx xx xx xx xxxx xx xxx
oovo I-Ioo no [\N was valx OH NQB
£0 onus v—Imv NH NH NN HHHI—l HI—I o—Io-Io-I
.:
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l\l\l\ new 0000 coco co (\hhh [\h Ixhtx
ooo ooo oo oo oo oooo oo ooo
v-OI-Ov-i HHH HF! HH Hv-O HHHH Hui-4 o-OI-lo-O
xxx xxx xx xx xx xxxx xx xxx
v-Ivo I-Ioo No- (\N Hm move me Mfl'h
Nmm o—Imv HH Ne-I v-I—I munch em men
In In vmm mm mm In mmmm mm mmm
ooo ooo oo oo oo oooo oo ooo
HHv-O HHI-t HH HH HH Fit-0H!" HH Fit-0H
xxx xxx xx xx xx xxxx xx xxx
u-IO‘IO \OOO MN moo Ch Inc—Ina: V‘O ch-I
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m
\
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HHH HHH Hv-I HH HHI HHHH HH HHH
xxx xxx xx xx xx xxxx xx xxx
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-I-| 44 «040054 00:69 NHNHO .DEQOOEOOXHOOQ
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a o s 0:2 a m m o o o o -H o
s : LJE: ::¢:¢Iu. u. L) L) a. (L

came .ouegz "mumeom

 

69

 

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H\e sex x m.n aee\:ue eee.ev~ mm .oeo Hose Humane
H\s use x m.m Hem\=am ooo.e~a economso
:o\e oeH x e.e =o\:ue new x e.e ewe on eem
=o\n sex x 5.x :o\:ue vex x o.~ eve on ewe
=o\e med x ~.~ :o\:ue vex x H.~ ecu on eem
:o\e nee x o.m =o\=ue vex x m.m .u.: we” on won
one no new: ecwxne choc
e;\e men x m.~ «\som see x «.4 . canoeMeeeH
o\s ace x m.m has x e.e u\=um no“ x H.m - woe x w.m gonzo as:
wee x H.H use x c.m ace x o.~ Vmoa x ~.H omeeem eeeo no cusses:
ace x e.o use x a.~ mod x m.a see x e.e xenon no as; age
sozono oemuom
nee x e.v hen x ~.N meH x H.H vex x e.m peaches“: one noeflse some «on
was x e.~ ace x v.» me" x m.m .moe x a.a amooumuoe .muoono nuance oee>
eeH x n.H en x o.m en x e.n en x n.H noueoenmea «finesse maouexee<
A m m Anoccwom
sea x o.~ oeH x e.~ veg x n.n we“ x m.H .xflsoo noesoeem souwxwunom
sex x ~.e nee x ~.n meH x A.H eeH x v.n neoconnm xHeum
en x H.o nee x o.~ eH x v.~ eH x ~.o fieowumuoeo somoo eoxmuem enmeouo
Meg x n.H oeH x n.m wen x ~.n wen x ~.H meanneuoeo cameo hexagon mono 30a
"meannenomo msoocmnnoomwz
eeH x ~.n eeH x v.~ me“ x v.u me“ x n.n neoxeemo nopmo>eee ensue cone
eefi x m.~ sex x m.e meH x m.v meH x e.~ Houmo>uen ofioeuoeo>
eeH x o.~ N.eH x «.5 nee x e.n men x 5.x Houmo>uen moon geese
A.e.u=ouo meowumuoeo ecwumo>nox
mn\e om\:um :owumnomo

 

 

 

A.e.u:ooo e msm<e

70

TABLE 9

FUEL CONSUMPTION OF FIELD OPERATIONS IN DIESEL EQUIVALENTS

 

 

 

 

 

Michigan Energy Audit Data from other states2
Study1 Gal/ac
Gal/ac l/ha
Operation Low Average High
Tillage
Chisel Plow 1.2 11.2 0.5 1.1 2.3
Moldboard Plow 1.7 15.9 0.6 1.9 4.3
Secondary Tillage 0.9 8. 0.1 0. 1.7
Planting
Conventional Planter 0.5 4.7 0.2 0.5 0.9
Grain Dril 0.7 6.6 0.2 0.4 0.4
Potato Planter 1.0 9.4 0.4 0.9 1.5
Seeding Broadcast 0.4 3.7 0.2
Vegetable Planter 1.0 9.4 0.4 0.9 1.5
Crop Cultivation
Cultivation 0.5 4 7 0.2 O 4 0 6
Harvesting
Baler or Stacker 1.1 10.3 0.4 0.6 1.5
Beet Harvester 1.3 12.2 0.8 1.4 2.1
Chop Dry Material 0.8 7.5 0.4 0.9 2.4
Chop Wet Material 3.0 28.1 1.0 2.1 3.3
Combine or Picker 1.5 14.0 0.6 1.3 1.8
Conditioning 0.2 1.9 0.2 0.3 0.4
Mower - Conditioner 0.8 7.5 0.5 0.7 1.5
Pull and Windrow Dry Beans 0.4 3.7 0.2 0.4 0.6
Rake 0.4 3.7 0.2 0.3 0.5
Eggcellaneous
Chemical Application 0.3 2.8 0.1 0.3 0.5
Chemical Incorporation 1.1 10.3 0.3 0.3 1.4
Knife in Fertilizer 0.6 5.6 0.8 0.9 1.5
Stone Removal 0.2 1.9
Surface Apply Fertilizer 0.2 1.9 0.1 0.2 0.2
Top Sugar Beets 0.7 6.6 1.0 1.5 2.1

 

 

 

1Myers et al., (1980).

2Ayres, C.B. (1976).

Berge, 0.1. (1974).

Kramer, J.A., M.D. Schrock, and D.P. Shelton (1978).

White, R.G. (1974).

TABLE 10

AVERAGE N, P, AND K CONCENTRATIONS
IN CROP RESIDUE

 

 

 

 

(In percent)

Crop P

Barley 0.75 0.11 .25
Corn 1.11 0.18 .33
Cotton 1.75 0.22 .45
Oats 0.63 0.16 .65
Rice 0.60 0.09 .16
Rye 0.50 0.12 .70
Sorghum 1.08 0.15 .32
Soybeans 2.25 0.22 .05
Wheat 0.67 0.07 .97

 

 

SOURCE: Larson, W.E.,

1976.

72

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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H H H H H H H H a
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me me me me me me me me a eeHmoem Hose:
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N .62 eHoHa H .62 eHoHe

 

 

 

 

 

HH mae<b

mzom momz<m mom <h<o Panza

73

 

 

 

 

 

 

 

 

 

 

 

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xuemeoe ste
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N .02 eHoHe H .02 oHowm

 

 

 

 

 

H.e.eeoee HH mHm<e

74

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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mHm>A<z< maoz<m mo meqommm

Na mom<b

 

 

 

Soil

Ahmet
Alcol
AllC'
Alpe
Amas
Arkt
Bare
Earl
Blot
Blui
Bohi'
Boyi
Brerl
Brozl
C301
Cas
Cel
Cha
Cha
Che
C01
Cov

 

SOIL ERODIBILITY "K" VALUES AND TOLERABLE SOIL LOSS

TABLE 13

"T" VALUES (t/ac)

75

 

 

 

 

Soil Series "K" "T" Soil Series "K" "T"
Ahmeek .37 4 Michigamme .32 1
Alcona .32 2 Dighton .43 2
Allouez .24 3 Dowagiac .32 2
Alpena .24 l Dresden .32 2
Amasa .32 2 Dryburg* .24 3
Arkport -32 2 Dryden .32 2
Baraga .24 1 DUCI .17 2
Barker .37 2 East Lake .17 5
Blount .43 2 Eastport .17 5
Blue Lake .24 2 E1mdale* .32 2
Bohemian .32 3 510 .37 2
Boyer .24 3 Emmet .28 2
Brems* .17 S Fairport .37 2
Bronson .24 2 Fence* .32 3
Cadmus .37 2 Fox .32 2
Casco .24 2 Froberg .49 2
Celina .37 2 Fulton .49 2
Champion .37 2 Gagetown .37 3
Chatham .32 2 Gilchrist .17 5
Chelsea .17 S Gogebic .32 2
Coloma .17 S G-odman* .37 3
Coventry .37 2 Graycalm .17 5
Crivitz .17 s Grayling .17 5
Croswell .20 5 Guelph .37 2
Deer Park .17 5 Hillsdale .32 3
Deerton .17 2 Huron .49 2
Del Rey .43 2 Ionia .32 2
Johnswood .37 2 Iron River .32 2
Kalamazoo .32 2 Oakville .17 5
Klakaska .17 5 Oakley .37 3
Karlin .24 2 OcqueOC* .24 2
Kendallville .37 2 Omega .17 5
Kent .49 2 Omena* .32 2
Keweenaw .24 2 Onaway .32 2
Kibbie .37 3 Onota .32 1
Kiva .24 1 Ontonagon .43 2
Lapeer .28 2 Oshtemo .24 2
Leelanau .24 2 Ottawa .17 s
Longrie .32 2 Ottokee .17 5
Mancelona .24 2 OWOSSO .28 3
Manistee .28 3 Padus* .32 2
Marlette -37 2 Parma .32 l
McBride .32 2 Pence .24 l
Melita .17 S Perrin .24 2
Menominee .28 3 Plainfield .17 S
Metea .28 3 Rimer .24 3
Miami .37 2 Roselms .49 1

TABLE 13 (Cont'd.)

76

 

 

 

Soil Series "K" "T" Soil Series "K" "T"
Rousseau .24 4 Shelldrake .17 5
Rubicon .17 S Sisson .37 3
Montcalm .24 2 Sparta .17 5
Morley .43 2 Spinks .17 5
Munising .32 2 Stambaugh .37 2
Nappanee .49 2 Steuben .32 2
Nester .43 2 Ubly .28 3
Newaygo .32 2 Vilas .17 S
Nunica* .37 2 Volinia .32 2
St. Clair .49 2 Waiska .24 1
St. Ignace .32 1 Wakefie1d* .37 2
Summerville .32 1 Wallace .17 5
Sunfield* .32 2 Watton .43 2
Superior .32 2 Yalmer .17 5
Trenary .32 2

Tuscola .37 3

Seward .24 3

 

SOURCE: U.S. Soil Conservation Service, 1973.

*
Tentative series.

77

TABLE 14

COMPUTED K AND T VALUES FOR SOILS ON EROSION RESEARCH STATIONS

 

 

 

Computed

Soil Source of Data K "T"
Dunkirk silt loam Geneva, NY *0.69 2
Keene silt loam Zanesville, OH .48 2
Shelby loam Bethany, MO .41 3
Lodi loam Blacksburg, VA .39 3
Fayette silt loam LaCrosse, WI * .38 3
Cecil sandy clay loam Watkinsville, GA .36 3
Marshall slit loam Clarinda, IA .33 2
Ida silt loam Castana, IA .33 2
Mansic clay loam Hays, KS .32 3
Hagerstown silty clay loam State College, PA * .31 3
Austin clay Temple, TX .29 3
Mexico silt loam McCredie, MO .28 2
Honeoye silt loam Marcellus, NY * .28 2
Cecil sandy loam Clemson, SC * .28 4
Ontario loam Geneva, NY * .27 3
Cecil clay loam Watkinsville, GA .26 3
Boswell fine sandy loam Tyler, TX .25 4
Cecil sandy loam Watkinsville, GA .23 4
Zaneis fine sandy loam Guthrie, OK .22 4
Tifton loam sand Tifton, GA .10 3
Freehold loamy sand Marlboro, NJ .08 3
Bath flaggy silt loam with

surface stones 2" removed Arnot, NY * .05 2
Albia gravelly loam Beemerville, NJ .03 3

 

*
Evaluated from continuous fallow. All others were computed from
rowcrop data.

SOURCE: Wischmeier and Smith, 1978.

TABLE 15

"m" FACTORS FOR DETERMINING LS

 

 

 

Percent Slope of Gradient m Factor
0 - 1 0.2
1 - 3 0.3

3 5 - 4.5 0.4
5 - up 0.5

 

SOURCE: Wischmeier and Smith, 1978.

78

TABLE 16

EROSION PREVENTION PRACTICE FACTORl "P"

 

 

Percentage Contouring Strip Terracing
Slope cropping2 and
contouring

 

Parallel to

 

 

field boundary 0.83 -- --
1.1 - 2 0.6 0.30 --
2.1 - 4 0.5 0.25 0.10
401 - 7 0.5 0025 0010
7.1 - 12 0.6 0.30 0.12
12.1 - 18 0.8 0.40 0.16
18.1 + 0.9 0.45 --
1

If no prevention practice P - l.

N

A system using 4-year rotation of corn, small
grain, meadow, meadow.

to

For slope up to 12% only.

SOURCE: Schwab et al., 1966.

TABLE 17

BIOCONVERSION EFFICIENCIES

79

 

 

 

Conversion Percent
Thermo-chemical
Direct combusion1 100
Pyrolysis2 70 - 95
Gasification 60 - 95
Biological
Alcoholic fermentation4 50 - 85
Anaerobic fermentation 4o - 7o

 

1Assumes all biomaterial can be burned.

2Boyd, M. et a1. (1979).

3R.O. Williams and B. Horsfield.

4R. Ofoli (1980).

Sw. Rose et a1. (1979).

80

APPENDIX B

FIGURES

Soil Loss, IS

 

l 1.5 2 2.5 3 4 5 6 8 10
WINDWARD KNOLL SLOPE, 5, (Percent)

FIGURE 1.-—Potential soil loss from knolls factor, expressed
as percentage of that on level ground: (a) from top of knoll,
(b) from that portion of windward slope where drag velocity
and wind drag are the same as on top of knoll (from about the
upper third of the slope).

SOURCE: Woodruff and Siddoway, 1965.

 

 

 

 

 

 

/

 

 

SOIL RIDGE ROUGHNESS FACTOR, K1

 

 

 

 

 

 

 

 

 

 

 

 

0 1 2 3 4 5 6 7 8 9 10
SOIL RIDGE ROUGHNESS (In.)
FIGURE 2.--Soil ridge roughness factor chart determines
soil ridge roughness factor "K” from the soil ridge

roughness.

SOURCE: Woodruff and Siddoway, 1965.

81

82

 

 

FIGURE 3. -- Annual climatic factor "C" (percentage) based
on the average wind velocity and on the precipitation
evaporation index (when using use Decimal Form).

SOURCE: U.S. Soil Conservation Service, 1973.

 

CUT HERE
E
1

PLACE MOVABLE SCALE HERE

5
O
l

u
o
l

SOIL LOSS E2= I'K' (t/A/yr)
8
l

 

§§§.§ 388388 2

I0,000

3% 3%?-

[.000

UNSHELTERED DISTANCE, L, ALONG PREVAILING I _
WIND EROSION DIRECTION (FT)

 

 

 

FIGURE 4.—-Chart to determine soil loss E4. E4 = I'K‘C'L'
from soil loss E2 = I'K' and E3 = I'K'C' and from unsheltered
distance L' across the field.

SOURCE: Woodruff and Siddoway, 1965.

84

420 420

400 j {400

380 ' :380

:32:

320 ‘ .

: 320

300 ; 5300

280 - :280

260 i :260

240 I :240

220 l ’220

200 1 £200

180 1 :180

160 I -160

1 b

140 I #140
o I I
'3 1204 .120 m
{g I p N
g, 100 . -100 g
m l ’ g.
,0 80 l £80 ['11
a I b N
‘9 I—I
m 60 4 ’60 m
m 50 I .50 8

40 . »40 y;

30 ‘ P30

25 i ’25

20 I '20

15 ‘ ‘ b15

10 4 4'10

5 4 IS

2 4 r2

1 I 51

0.5.. I005

 

 

FIGURE 5.--New method of determining E4 from E2, E4, and E21.

(Draw a line from E2 to E3; next draw a line parallel to line
E2-E3 starting at E21. The point of interception of the E3
and E4 scale is E4.

—-l

E = I'K'C'L'V (TONS/ACRE/YEAR),

  
  

0.
8.
0.5 0.81 2 3 4 56 810 20 3040 60&)100 200 300

E4 = I'K'C'L' (TONS/ACRE/YEAR)

    

FIGURE 6.--Vegetative equivalent chart to determine soil

1055 E = I'K'C'L'V from soil 1055 E4 = I'K'C'L' and from

the vegetative cover factor, V. The chart can be used in

reverse to determine V needed to reduce soil loss to any
degree.

SOURCE: Woodruff and Siddoway, 1965.

85

86

(THOUSANDS OF lb/ac)

R!

 

O 246810121416182022242628

v (THOUSANDS OF EQUIVALENT 1b/ac)

FIGURE 7.--Residue for small grain stubble including stover.

Chart to determine V from R' or R' from V of standing and

flat anchored small grain stubble with any row width up to
10 in., including stover.

SOURCE: Woodruff and Siddoway, 1965.

 

87

N

R' (THOUSANDS OF lb/ac)

 

V (THOUSANDS OF EQUIVALENT lb/ac)

FIGURE 8.—-Residue for grain sorghum and corn. Chart to

determine V from R' or R' from V of standing and flat

grain sorghum and corn stubble of average stalk thickness,
leafiness, and quantity of tops on the ground.

SOURCE: Woodruff and Siddoway, 1965.

88

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FIGURE 11.--Three-dimensional view of Figure 6.

APPENDIX C

CROP RESIDUE AVAILABILITY PROGRAM

91

This part of the total program contains Section 1, the wind

erosion section.

listings are listed below.

The Operating instructions and program

 

Comment Load Press Print
1. Initial Run
a. Repartition computer 5 2nd op 17 --
b. Load cards 1 and 2, all four sides
2. Section 1 (Wind Erosion) -- A Key --
a. Soil erodibility index ”I", Tables
2 or 3 "I" R/S "I"
b. Knoll factor "Is", Figure 1 "Is" R/S "Is”
c. Soil roughness factor "K”, Figure 2 "K" R/S "K"
d. Climatic factor "C" in decimal form
Table 5 or Figure 3. "C" R/S "C"
e. Field length Length R/S Length
f. Wind deviation (in Degrees) from
Field length Table 4 Deviation R/S Deviation
g. Wind Break height Height R/S Height
The calculator then computes and prints
"E2," "53," and ӣ21." __ __ E2
-- -- E3
-- -- E2'

These numbers are used with Figure 5 to

determine E4.

With E4,"T"

loss, Table 13 or 14) and Figure 6, the

vegetative cover is determined.

With

Figure 7 and 8 the actual amount of crop
residue required in the field is determined.

(tolerable soil

L
'!
.
--
3‘3.
::-._:
*‘nf:
"9|.
‘4':

--, ‘-'
z ' 8-
.. -‘

Listing

Sample Print Out (English Units)

I

Is

K

C

Dist. Ft.

Dev. Degrees
Breaker Height

E2

B3

E2'

of Subroutines and Program Sections

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l

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92

93

Computer Program

 

Wind Erosion Data Stored in Memory

Data

51031..

Memorx

3’ ii? Data stored in locations 03 to
5; £3; 17 house the intercept and slope
.!_?LW;§ gag for line segment. Dn the left
paugcpq-;y§;353 5:. Slde are data for field distances
“'TI;.§ T fifiéi ii: of 10 to 60 feet; the right side
‘Z'ff:f:;:_ -3333: $33 of the decimal stores the inter-
,_“3?T:;:fiffjf1 idg cept and slope of line segments
l.“5tdi.h3flfii*$ U: for field distances of 61 to 500
23493.34El? 03
2?430.36213 0?
3 Cl 3 E: T , 3313:1233 _; {I
33355.4013? ii
35349.4316? 12
3?343.45l$4 13
40315.4?133 24
41352.4?11 i5
43239.51003 16
46354.530?6 l?

_ tiEEL 2231351 15 Data stored in locations 18 to 32
lifiSFEu 31i395! 19 house the intercepts and slopes
3313135. 334094 730 for the line segments for
133159.23'3136? :51 distances 501 to 1500 feet on
13:31:39. 45132? 3;}: the left side of the decimal and
130121 430123 2:}: for distances of 1501 to 5000
'SIZIFIB. 5 24 feet on the right side.
450?3.51 25
49049.53 25
C“! :0"?

J -..'= i‘
53013.54 3
55004.55 9
56000.56 0
5?000.5? 1
57000.57 3

91.

Computer Program (Cont'd)

Subroutines

 

Steps Code Operation

:jflfil T5. ;E&_ Subroutine SUM locates
2301 44 :38?! memory location for
13L-‘ SE; +~ distances 10 to 500 feet
HQ? fl: 1

032 53 3

065 25 =

006 59 ZHT

WM? 9 +

659 6 1

009 9 =

Ijiil S +

011 0 2

012 2 =

FMEWMJHWIUNMLHHLH

013 5 ’HT

D 1 4 E: +

015 0 l

016 9 =

Li? 2 RIM

013 F5 LEL .

1315' 523 { Subroutine C determines the
:72?! RF} . upper and lower segment numbers
{BET fig: 2 to be interpolated between
322 as =

I33 35 +

024 01 1

025 95 =

026 65 K

02? 01 1

023 DD 0

029 95 =

030 42 STD

031 35 36

032 35 +

033 02' 3

D34 00 0

L35 95 :

036 42 3T0

D3? 3? 37

033 93 2TH

95

Comppter Program (Cont'd.)
Subroutines (Cont'd.)

Steps Code Operation

 

C339 TE- LEW. Subroutine l/X obtains
CNN} :35 1’}: the intercept of the line
[H11 ?2: HERE segment for distances 10 to
23%;: 31 U} 60 and 501 to 1500

H43 H? INT

6:43, £19: +

045 01 1

34h 33 D

04? DU 0

1:1 -', E! 2:! 1:1 1:]

049 95 =

050 59 EMT

051 55 +

052 01 1

053 00 0

054 95 =

055 92 2TH

Subroutine CE determines the

056- 236 LSL
ijfi? ;34 I35 intercept fOr distances 61 to
flfifi; 7:2; 91-:‘31‘3' 500 and 1501 CO 5000
059 01 01
fléfi 23 IN“
53* INT

_I I: I
I'T'. ILT'I '1'

Ln 4:. {-0.3 ['03 0-“5 I“

r-o-L ’:.:'

I I If!-
ya- I'_l'| .

:Jl
....'....
I
I

'I IT'I '1'.

3.3 I
I (fl I

-t- 9

DE? 59 INT
063 55 +
069 01 1
UFO DD 0
'ZI?1 as =
022 92 RYH

Steps

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Subroutines (Cont'd.)

96

Computer Program (Cont'd.)

Operation

1 ['1
LL)!—
LILa
FWTi
u}
110$
~r~ ~1-
1H:
1
b
L!
0

.... ..-. .....
II II IO-"*

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41
FVFH

r303
r*r*

cgornrr
...-4 [I] tr]

:0

I'_I'_I
I I ‘

-4 4:.

up
‘—

Subroutine LNX determines the slope of
the line for distances 61 to 500 and
1501 to 5000

Subroutine Yx determines the slope of the
line for distances 10 to 60 and 501 to
1500

Subroutine GTO interpolates the slope of
the desired line segment.

Subroutine RCL interpolates the intercept
of the desired line segment.

97

Computer Program (Cont'd.)
Subroutines (Cont'd.)

Steps Code Operation

3? 2 176 110L. Subroutine > in conjunction with sub-
: 13 S;- ; routine SUM locates the memory
33:; :35 + locations for distances 501 to 5000
12*} :3: feet-
121 053 5
1223 ‘25 =
12.3: —2 ‘._.
12%— 01 01
135 92 3T0
EI . 22%. Subroutine X2 performs the actual
fl ':63 interpolation.

..|I:.. I'J] I'_I_:I "‘-.j

"I'I I'I'I I'.) ILD] IT.

:37."

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i I I 2
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9 8

Computer Program (Cont'd.)

 

Subroutines (Cont'd.)

Steps Code Operation

 

l
T

L Subroutine EE changes the sign for the
intercepts of various line segments.

n,
l- ._n
‘-.Z= IF." I
-J In I
i"? m '3

J
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A. " ~". I
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4%,; Vs; g g; Subroutine V 2 rounds off the data
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l

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Operation

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7:551
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ID

..
....-
a-

Computer Program (Cont'd.)

99

This section of the program is where all
the input data are loaded.
calculated field length equivalent is
determined.

Also, the

Steps

m m m m I"-." m In) In) M m m In: m l"-." m m m 1"." ru m M In."

..r.:.. 4;. ..lii 41:. 4:. 41:. 4'2. 41:. 41:. -12.. IL.) ILeLI I'_._'I I'_._’I I'.'I I'.’I I'

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\

100

Computer Program (Cont'd.)

Operation

Depending on the calculated field
length, this section takes the program
to the location needed for interpolating
for the line segment associated with the

{:1 I:l I'J]

-._.- I T

I"I 'I i

9y} distance.
3'! ’-

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.... -

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101

Computer Program (Cont'd.)

This section will compute the intercept and slope for line segments
associated with distances of 10 to 60 feet.

350 ?5 ;£% 355 flé E:

351 1? 3' 235 J? 9

353 43 951 23? 32 HIT
353 33 33 333 43 REL
354 T1 3&9 339 33 33
355 44 SUN £90 7? CE
256 42 iffl 291 ii f'

35? 01 01 ESE ?1 SEE
353 ?S 293 52 EE

359 DE 2 294 ?6 LSL
250 as = 2?? 16 H'

341 21 332 296 43 REL
352 53 C P9? 3? 3?
263 43 QCL 293 ?1 SEE
264 3? 3? 299 44 SUM
355 21 332 390 ‘2 STD
355 44 gum 331 ul 01
222 42 STD 302 71 SB?
353 81 01 383 45 TX

269 F1 SSE 304 42 STD
320 35 1=g 305 33 33
3?: 42 STE 336 23 REL
2?2 33 33 30? 35 35
2?3 %3 REL 303 F1 389
2?4 35 35 30? 44 SUN
222 44 sum 311 01 01
2?3 01 01 3:3 45 I”

3?? ?1 388 314 42 STD
£30 35 142 315 3? 39
331 43 ETD 316 ?1 SEE
333 39 3? 31? 61 BTU
333 $1 339 3:3 61 ETD
284 43 38L 319 15 E

102

 

Computer Program (Cont'd.)

ted with distances of 61 to 500 feet.

This section will compute the intercept and slope for the line segment
assoc1a

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103

Computer Program (Cont'd.)

 

Steps 386 to 400 determine whether the program should go to step 401 or

Section D' which is dependent on the distance.

Steps 401 through 458 calculate the intercept and slope for the desired

line segment associated with distances 501 to 1500 feet.

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104

 

Computer Program (Cont'd.)

This section computes the intercept and slope for the desired line

segment associated with distances 1501 to 5000 feet.

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....... ..:... C... n.... ma...“ run C... ..... ...... ...... L .....n.. ...h... ...... .H._... .H...._ ...... L .... ......_ ..bu

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i

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......“ ...—H. ..._.... ....H. in. ...n. 7.. ...... q... a..- .....- «..-. .....- _...I 7.. .....- Du. .H... D”. On. 0.... On. .....H. .H. .....u. .....H. Q... D... .u...

.....- 4 ..H... 4. d. 4 .4 1... 4 a. 4. a- a- A- ...u. 4 a. 4 4 a- 4 a- a. 4 4 a- 4 4 a.

105

 

Computer Program (Cont'd.)

This section computes E2' and prints 82, E3, and E2'.

-L ....— .i. I._ .

B E ...... 4 H”... ...... m. H .. ..

-L C...” Cu u...

...H... Cl; .....H. a-.. ”._:... _H...... ....I.. . _ ..I.....

a... .... .4 3.. ...D :1. .....- .w... ......

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......_ ......_ ......_ ...; ......_ ...; a... ...; .......
:4. c... c... c... c... 5.... c... r... ......

L n... w- ”A D ...... u- L ...... T- w- L 4 T w- L ...... T D c... T R
-. + .1.. 4 __ H H ...- 4 ...... .... ...... p... B ...... ...... P. D ...... 4 .... ...... T ...... u.” R.

R ILS HE FHE FHR % S ESE-

".2: T

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...... ...... ...... ......_ ...... ...... ...... 3 ...... 4 4 4 ...... 4 4 4 4 4 4 ...... c... c... .... .... .... c... c... ......
...... ...... c... cl. =1.. c... C... :.... ...... ...... c... ....H. c... c... c... :J. ...... :|.. :J. :J. :J. E... =|.. c... ...... :.... ”...”. c...

APPENDIX D

CROP RESIDUE AVAILABILITY PROGRAM, SECTIONS TWO - SIX

106

This part of the program contains sections two through six; the operating
procedures and various options are explained below.

 

Comment Area Press Print
1. Initial run
a. Load card 1 and 2, all four sides
b. Store area of field in memory 00 Area Sto OO --
2. Section 2 (Water Erosion) -- B key ~-
a. Rainfall factor "R" from Figure 10 "R" R/S "R”
b. Soil erodibility factor ”K" from
Tables 13 and 14 or Figure 9 "K" R/S ”K"
c. Field length "L" "L" R/S "L"
d. Factor for slope "M", Table 15 "M" R/S ”M"
e. Slope gradient "S" (%) "S" R/S "S"
f. Cropping management factor "C",
Table 6 "C" R/S "C"
g. Erosion prevention practice ”P”,
Table 16 "P" R/S "P"
Calculator now computes annual soil loss "A"
if "A" + ”E" (from Sectionl) >”T" (tolerable
soil loss, Table 13 and 14, then try a
different "C" and "P"
h. To try different crOpping management
factor and erosion prevention
practice 2nd 8' --
i. New "C" factor, Table 6 "C" R/S "C"
j. "P" factor, Table 16 "P" R/S "P"
New annual soil loss is computed; if "A"
+ "E" (Section 1) is :_”T", Tables 13 and
14, then note the amount of residue
required to prevent erosion.
3. Section 3, total crop and residue yield A key --
a. Estimated or actual yield Yield R/S Yield
b. Residue-to-grain ratio, Table 7 Res./Grain R/S Res./Grain
c. Weight per unit of yield,
Table 7 Weight R/S Weight
d. Moisture content in decimal form
(i.e., 15% me = .15) at cr0p
yield reporting, Table 7 me R/S mc

Calculator determines total above-ground
yield of grain and residue (dry weight
basis); also calculates in-field energy
potential.

Above ground
yield

Energy po-
tential in-
field

Computer Program (Cont'd.)

 

107

 

Comment Load Press Print
4. Section 4, Available Residue
This section has three Options: Option 1 is
used to determine amount of crop residue
available. Press "C". Option 2 determines
amount of grain that is available, press
2nd C'; option 3 will calculate the
amount of a portion of cr0p residue available
i.e., corn cobs, press 0'
a. Determine Option -- Option --
key. C,
C', or D'
b. Depending on option load; for C Residue Net
total amount of residue needed for needed amount
soil protection (dry weight) for soil R/S of resi-
due
available
or
for C', total amount of grain to be total dry
used for conversion to fuel Grain R/S weight of
grain avail-
able.
for 0', enter weight component of total
desired residue as compared to units Residue dry
of yield, i.e., 9 1b. of cobs per component amount
buschel of grain wanted R/S available
S. Section 5, the energy balance D --
3. Estimated field, handling and storage
losses in decimal form Losses R/S Losses
b. Bioconversion efficiency, Table 17,
in decimal form ”Eff” R/S "Eff"
c. Energy input into production of crOp
component, Tables 8 or 9 Produc- R/S Produc-
tion tion
d. Energy input into component harvest-
ing, Tables 8 or 9 Harvest R/S Harvest
e. Energy input into post-harvest
processing, Tables 8 or 9 Post Har. R/S Post Har.
f. Nitrogen component factor for residue
Table 10 "N" R/S "N"
g. Phosphorus component factor for
residue, Table 10 "Ph” R/S "Ph"
g. Potassium component factor for
residue, Table 10 "K” R/S ”K"
Calculator determines net energy available -- -- Net energy

available

Computer Program (Cont'd.)

 

108

 

Comment Load Press Print
6. Section 6, TranSport Section E
3. Enter, in decimal form, amount of
net energy wanted for work, not
transport, i.e., 0.75 for work
0.25 for transport Work R/S Work
b. Enter energy content of fuel used
in transport vehicle, Table 9 Fuel R/S Fuel
c. Fuel consumption of transport
vehicle Consump. R/S Cons.
d. Volumetric space of transport
vehicle Space R/S Space
e. Bulk density of crop residue,
Table 7 Bulk R/S Bulk
f. Moisture content of crop component
at harvest not at crop yield
reporting MC R/S MC
Calculator then determines maximum
radius each load can be transported. -- -- Radius
and total number of loads -- —— Loads

END OF PROGRAM.

Sample Print Out (English

Units)

 

: _, :2 3' "RH
-- o :2: "K"
- . Dist.
Ll. 4 "M"
Slope
"c"
"P"

1’“. '... "All

3,7“ Yield (bu/Ac)

‘- Residue/Grain
Lbs/Bushel

L Moisture content

‘ at crop reporting

1 lflrhfifiri TOFal dry pounds of
‘ --3‘_-- crop above ground

77HPPHWW“H
! E _ _. _‘ _. 4- :_l L] 2-- '

In-field energy
potential (Btu)

Grain used (Bushels/Acre)
lUCL

fifidfiflffil Total dry pounds of
"""" grain available

' Losses
Lu u Conversion efficiency
?4EHMJUCL Crop production energy
EEJNJUCL Harvest energy
213000130. Post-harvest energy
0.131] 1 N factors
I], (”31:- P factors

El, 0123?}: K factors

Net energy available

....
Ill
-

IT
I
1:":
0:0

Portion of energy for
~ c- work
Energy content of fuel
Fuel consumption (mpg)
Space, ft3
Bulk Density
Moisture content at

harvest

,.
l||

o
I
! I] 0:! '

Mile Radius

E” No. of Loads

Section 2

Section 3

Section 4

Option 2

Section 5

Section 6

Hl‘)

 

Listingiof

.....

”I in”;
I ‘ ‘

oa
—-

".j w-J- I-m‘ o

- r r;

..

n

Sections and Subroutines

T

.v' i_T.I.I-
1
U

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. . ”I :' '2', : I
.-- . - - .. _

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.' ' x ' . :
3.. .. ' '-‘ z... x s ‘5 :
~‘ I ' - .1 .> 1-

- '._ . . : ‘3 I I
'... ‘...‘ z .1. H :—
‘: ‘I' "‘I i 9:" C
. 2 . . l
- . .. - ..

' ‘ z- .' a a
a. -. : -; ... f‘ .
fl ..' .' - '. '. s l

110

111

Computer Program, Section 3

This is Section 3 of the total program. The total amount of above-ground
-crop and residue are determined here as is the in-field energy potential.
This will work for both English and metric units. However, for metric
steps 65 through 68 must be changed to 16,000 KJ/kg instead of 7000 Btu/lb.

I.‘

'.-I .....L i-" .....I. .....q. ...... 5...; .....t ...; .....L 5.4. I-..| 9......
I l l
.

anwucnr

c l -9 Q
u a U u: 9 HI
H 5 a S l 1

I 4 n 2 ETD

gt- 'fi. ; E"! I" ‘ "le "1':
I- (._I :_: ,- l_-l L— :- .... - -'. .-5 L .-:
-~. z“: : 9 r - - ~~ “ - ’-
uu. -; fl uég +3 an?
- :- .-, .* IL' :-. r- - . .° "' -
23th; ;KJ pi.hf LH+;: ‘fit. 2
.- .‘g-j. .-" 4 r. v.:- - _. .-—, .- 5..
Ll a__u ._: -.' i F_ .~ ._3 3-! s: .1, :, _-: {5: I T
-- {- -_. 3:' ran-'- -_ . -
UJ~ xfl rat U45 U1 3
5‘ {'1 CI .‘.- '1' I "" r .4 ' .4. " -- ‘2'
glut: 'rr. -- I [l szh; -;-,;' 3:. g D
5‘: F ,5 3’ 1 2' 1 r ,1 *7 -‘_. r -';. .-
UJ, J, J, 4,, ;5 _fl
-- - ‘P -- L. - .4 n -' -' I..- r T
=.! LJ ! t. '-! :_l 5+ :: ...: 5'1: 1"" 1 l
- - a .- r- - - _
U13 +5 ELL u43 T1 38R
{-0 u: g- l- - ' - c - .- . I__o
l_. 1 :4 :_I a I '_l I-' !_I _-| :_' :: 4 I. ..‘l
- l- f. - - c .- -. T
U I i .‘I "' 3_l._cl ._:: 3:. g D
- '1 ' ’f' C '-I "P '- q.
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C
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3'3 L":
--| I'_|"_|

"”333

'J 03] l_n J¢ £03 ['0

..n
nu '4] £13 I
11:. I-4

-.j (.211 [‘._'| -L. l2! f...) 1:: 417.

u 5 . ' q pg
0 6 '5 = 05 2 KIT
0 F 43 STD 053 1 38R
0 3 33 [fi: 359 34 {g
D 9 35 + 360 9? PET
OLD 43 REL 95; 93 HEW
021 02 02 053 43 REL
032 95 = 063 O? O.
023 42 STD 064 65 K
024 04 04 065 B? ?
035 55 a 055 00 U
326 91 RES 96? 00 D
027 9? PET 063 00 D
023 42 STD 069 95 =
as? 24 34 070 42 ST
030 95 = 3?} 03 US
031 42 STD 073 33 3:T
033 35 US DFB 02 3
033 65 K U?4 43 STD
334 53 i era 39 29
835 01 1 fl?& 23 3:?
036 F5 - 077 71 33F
037 91 R b n72 74 f3
usé 99 PP* 1"« We as?
OB” 34 . ORR 1% HOV
new :* are . : '2 are

llZ

Computer Program, Section 2

This is Section 2 of the total program, the Water Erosion Section. The
amount of soil loss due to water erosion is calculated here. Metric units
or English units can be used. The input data presented in the tables and
figures for this section are only given in English units.

333 ?g LBL :15 22 EN? 149 94

533 :2 g 115 35 TFH 11% ga x

034 35 CLR 117 33 31H 150 59 PfiL
035 91 R'S 113 43 STD 151 1; '14
525 59 PET 119 15 15 152 55 ='
03? as 2 120 99 :3 152 13 27

033 91 923 121 55 5 151 a; -na
555 95 PET 123 05 5 155 5; Léi
090 95 = 133 DE 5 155 12 :-

591 42 515 124 93 . 15? 25 :12
552 14 14 125 L4 4 152 4% ant
093 53 125 01 1 159 mi '51
094 51 9.5 12? 95 = 15m $9 E-
595 95 PET 125 35 + 151 21 are
595 55 + 129 53 i 152 99 91%
09? g? 2 130 94 4 153 as 5

093 02 2 131 93 . 124 91 2 2
555 92 . 132 95 S 155 as 221
135 55 a 133 55 5 155 22 anw
151 51 154 55 5 :27 5% L'
103 55 9 135 43 REL 1&3 5i ETD
103 21 923 135 15 15 125 n5 -n9
154 99 PRT 13? S4 “ 125 22 93%
105 95 = 125 95 = 121 a; '1

155 52 STD 139 95 + 122 32 9TB
107 15 15 140 93 . 122 22 '29
1:5 51 225 141 00 D 12% E2 211
ID? 9? PET 142 DE 6 EVE ?I 98?
11a 55 w 143 US 5 125 24 f2.
111 93 . 144 95 = 1?? 99 PR.
113 an 0 145 65 1?3 93 FD?
:13 a} 1 145 49 REL 179 91 2'2
:14 95 = 147 15 15 ' ’

113

Computer Program, Section 4

It is comprised of 3 options, which can use either
The dry weight amount of the crop component is

This is Section 4.
metric or English units.
determined.

Option 1 determines total crOp residue available (dry weight) by entering
residue needed for the soil's maintenance.

Option 2 determines total grain available (dry weight) by entering amount
of grain/hectare or acre to be used.

Option 3 determines portion of residue available, i.e., corn cobs by
entering amount of portion compared to yield, i.e., 10 lbs. of cobs
per bushel of grain.

Option 3

[8
H
p
O
D
H
O
f'?
p
O
D
N

1 1:0'- 7 ' ! r-I' -‘: ‘ '-l q. ' I
lud .0 LDL a); to LEL $2? 99 1P!
_ ._ _. - ‘ .- _ l.— '_ l_- : - I... b. t.—
131 ii L Hgfi 1H fi' man «a W.
— a - - - .‘ .-‘ .-. 3 .2 :
129 95 RIP 91: 95 212 2:? i; Z
z: :" ;‘“ *** “5 52“ 229 25 LL?
I l ‘ ; .' "° .-‘. ° 1" . _~ .-. - I
1 .z. .:. :3 1 F. ... ::. _..' 1 t. :3 1 F -:' -':. 8:. C. I: 1 Fl .' I:
:-I .' l-' I" '- l '7' -‘ 4 a .- - w- L. .-l ..- J ‘ -, O. -.
104 ?fi FE; 9:: 49 PFi "9’ QQ DH
‘ l-I: ..- c' . . T ‘ - '. r; ‘ L ._I 3;! -. .. ' F_ T
104 94 -. :13 b: ' fig? 9; w
- - - _ ' ._U , -l .- .".
1 |_I:'_ ..1 '_I :0! I -‘. 1 .3 .‘I -"I :5 — I T - - -
xwu *2 nwL :12 9o “Lu any 24 PFL
+07 r‘ 15 wr- n" -- h“ “ ““
1U; 4U Lb ggu UU UU Qqq n1 q4
100 q: m mm1 x: - “‘“ “ ‘
um :9 ‘ ;;. OJ n 24H 95 2
«Hr afi m P err q a - ”_ M
103 9; 0TH AH? 45 RLL 9&4 no mm.
__ - _ --~ -- ~- ;%i 92 tuL
39H 1H In 292 on in ~.- n— --
“‘ ‘3 '- LLJ 4* 9* 5%? NH NH
191 49 .RL 994 :5 2 a I 3; 3'
.l- 7; I ~-- *5 ” :49 b» 5
I ‘ I . o q. - - - " -
1,! L; fl- #9: 'U ‘ 9.. .r a
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1 l4 '.l . r." _ --| -_| -' '-I I: '-I C' _
‘ _. ._: g ._ ;;f—. L. .' .g '3 .4 C' ":1 l'.’ "I:
I _'4 1 —. r L ‘ " "" " g. ‘1’._I g_ ._I .L ._3
u- -u ' 917 q: _ , , -

y: 73 -;- ~5é #4 r 242 95 =
1&4 1U ;U 3:3 4: STD $37 4? QVD
1:. -‘ :2: .. .- .fi - _ .- — h "f. D L. .-. 3
12b ‘4 — igg 3d 1: 54m ,5 1m

- _ a O .c =
12? 42 TD :95 21 Pro 5~q 9. -“
4rn 1 1‘ it“ :: J L%; (D LEL
1&0 11 i1 :9” IfiJ'f'T n:- ma an
1__ c 2-- -- ~« 25m 52 a;
I14 ._J T . I _ .- ,— .- — .. ,-
::: 3: ’l :31 33 Hi?
9”” 3? aN- , , r a
:z- i: -:: 252 42 ELL
LU} M; U; 955 1? a?
firm cc . Lac 2; 5L
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_ _ _ _ _ L...‘_I‘? '..'I.'_. I": ‘l 3
---.' I... ‘1... '.L
_-u :2 ; Qfiq fl? 9
23"“; {.4 -:| .1 :- ._ __ - :- - ‘—
5- M. “W wc- 1w “T
- ;Jh a; HID
a c we m °‘ '
L: 3‘3 . 3 1:! . I "a C '7 "I I- "’0 -
--: ‘ aJ' ;4 ;H
a 3-3 3:- '4' :3: . 3.: L ”‘0 C' 2" ‘0 -;2 l 0 O :r
- 31.: _t '- '
20? 35 33 i2; $7 :gt
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,'_°_ .- I_x "3' ._l "" "t ‘- “0 .4 r" ' '
whH a :
9H9 4? TR 7i? 1? 4?
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91 n g 3 -‘- TS '. f L 1 i: E ‘
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Li: :3 'fl
T1? 9? 'T
s... A 5.. ‘-'(.. 4 i

114

Computer Program, Section 5

This is Section 5, the Energy Balance. It is set up for English units
now. To change into the metric units change:

Steps 279 to 282 from 7,000 to 16,000.
Steps 316 to 320 from 27,778 to 63,500.
Steps 330 to 333 from 4,960 to 11,340.
Steps 344 to 347 from 3,968 to 9,070.

322 72 :31 939 9?

g3$9 14 II .335 43 FIL.
365 35 CLR 306 U3 US
223 53 {j 39? 95 =
32? Q1 1 333 43 EYE
2&3 ?S - 33? 14 14
269 91 RES BED 43 REL
EFD 9? PET 311 12 12
2?1 54 B 312 65 E
EFE 65 x 313 91 RPS
3?3 43 RC 314 99 ”RT
374 13 13 315 65 H
295 95 : 316 02 2
276 43 ST 31? 0? 7
927 19 19 313 3* 7
-...I: as— h. --.._ ..l l
3?3 65 i 319 0? ?
37? 07 ? 330 33 3
238 80 D 321 35 =
331 30 D 322 42 STU
233 00 U 323 15 15
333 95 = 324 43 REL
334 43 5TB 325 12 12
235 13 13 336 65 I
236 55 2 32? 91 Rf?
23? 91 R23 323 3? PET
333 99 PR 329 65 K
339 75 _ 330 04 4
392 91 222 331 39 9
291 9? PET 333 96 9
393 65 x 333 DD 0
293 43 REL 334 95 =
294 DH 00 335 42 STD
295 F5 - 336 16 16
296 T E’S 337 43 REL
22? 99 PET :12: :2 12
393 RS 339 $5

399 43 ”CL 342 91 293
300 30 03 392 9? PP?
221 25 - 34? 93 an”
__ - - a ._ .-.
322 91 2 5 343 i5

303 9? PPT

11.5

 

Computer Program, Section 5 (Cont'd.)

m...“— a.-. :L A... IL :1. .L ..h. L 7.. mu 0.... T... nu Q. T1 Du ..u-
__... O... .H... .....H. ... ..:... ..I. ..:... ...... _ ..ul.. 4.... . .. . 1... _ ....i.. 41. : Ti. 41 pi ......_ v ..... ..:... r4 Du H.HH C.— : fin;
....... “up. mm” mm“ D...” ....... H...” C... H...” ...... .54 CL ..HH

...... 9 g... 3 c... 2 T. ...... .. 5 3 a... a... 3 ...... ...-.. ......_ ...... ...... ...... 3 2 2 2 a... 2 1 ...- 2 c... 3

Nu. Wu. ....H. ..W. .4... «g. .... ..:... 1... T..; ....H. .. A... ...; T ..._. 1... Q... A”... 1-.. .4... ....H. A”... ..:... ..:... _...... .....H. :8. ”HT. Q...
i. C... ..r... T.. ....H. m? ...... _...._ ....... .....- c... ..h. T... .H. a... .H. 1.. ...... ....... .4. .... ....n. T.. ....n. G... .H. ..:. ......_ ....x.. ...._-
.4. A... d- ..:- ...w ...... a... c... :I. ...... at. to. E... “...... .21.. ”...... .2”. ..h. ..h. ..h. ..h. ...—.... ...—H. ...n. ...“. ...n. T.- T: T.. T.. T.-

.-..,. .-.... .-.... ..... ..... .... .. . .-.-. ... .....H. ..:... 9... ...... .... ...... .....u.. ...... ..:... ..:... ....... .1.... ..x... ..:... ....-. .....H. ..:... .0... ........ ....... .H..u.. ..:...

 

1111:
Eng
tot

116

ither

This section calculates maximum radius and

total number of loads of crop residue.

1ne

It will work

Computer Program, Section 6

 

is Section 6, the Transportation Section.

English or metric units.

This

...... ..-. H..- L 1 D 2 L .H. L 2 D 3 T. D 9 T F. T w. L 2 T D .4. T F. T H..- ......
1 . P. ...... ...... ... ...... 2 _. T 2 ...... 2 ... ...... 2 _. T 2 I 2 T 2 .. E H...” F. D ...... 2 .. ...... T 2 I o... H P. . ....
...... P. .H P... ...... F. p... ...... “....” ...... H ...... .1 F .H P. ”...... ...... u...” ...... .... P

4-.. ...... 1.-. O... OH. .4. a... .....H. 1.. ...... ......— _....._ .1... ...H. C... .....H. .w..._ :3. ......_ ...... ......_ ...... ...... Q... .1.. 1.. 4 ...... ...-. ....... ...... ...... ..I. ...... Q; ...... ...... ...”... ..4..
n... ._- ...... .4. .4. .... c... ...4 2 .4. ..4 2 ...... 2. .... .4 2 ..: ...... 2 ...... ...... .4 2 3 .... ...... . .4. ...4 2 ...... .... ...... 2. 3 ...... .4.

1::
z I
in
J
-

H- ...... ..:... 1.... ....... ....H. a... O”. ....T. ......u.... ...... ......_ 5...”. ...... C... ...n. «..: ......... HT. .H. ...... ...... ........ 4.». C... ..h. «.... .u..... ..u... ...... 1... .H... ......_ 4...... C... ....H.
4... .... ......_ .... ...... ...... ...... ...... ._.... ...... ...... ...... .....U. ...... ....... ...... ...... ...... ........ ...... ...... ........ .....- ...... ...... a..- .4. d. 4.... d. d. a... c... c... cu. ...... c... c... ....-.
.4 .4 .4 .4..4..4..4..4..4 .4 .4 .4 .4 .4 .4 44 44 .4 .4 .4 .4.....4 .4 .4 .4 .4 .4 .4 44 .4 44 .4 .4 .4 .4 .4 .4 .4

._-

-... .....- 4.4..“ --.. ......... ...... T.. ...... T flu Q... C... T.. D .H. ....... T- ...... T- H...” D .... -... ......
...... ...: “mu ...... !._ ..:... .... ...... 1... . Du ...... ._. ..n z T.- 1... H...” Du ._. ...... : _.! a... m...” ._. D"... : ...: ....... ....... ..-.
.. .... ....-. ...... D”... ....4.... D... ...»... n... C... ......u D: ....... D“... n..- P... ... 1.. ...... D...”

.... ...... ...... 2 3 3 .... 3 1 c... 1 ...r .4 ._.. 1 3 ...... 2 .4. ...... 1 .4. ...... 2 .... 2 .H. 1 .4. .... 1 3 a... .... 2. 3 2

. ..
.... ... .... ...... ....._ i; .1.... ...m. E.... ...H. ....-. ....T. ....T. ...I.. :5. ...“... Q... .HT. .4“... .1.... ...“. ...? _..._... Eu... ..I.... Q... 44.- ...... 0... .HT. C... ...H... O... ........ ....T. J: ....._ A..- 4....

3 o... .H. 2 3 .4 ...... ...“. ...- 3 3 .H. 1 2 3 .....- a... ...H. ...... 3 ...... ...... 1 2 3 .4 ...... ...... ...; 3 ...... .H. 1 ...... 3 .4 ...... .
T.. _.... .....- .....m. .H. .H... ...... .H. .H. O... 0.... ......x.. .....u. .u... ....T. ....T. Q... .u... ....T. ....T. O... Q... Q. ......H. .....H. ..H. .H. ....u. .H. .H. .Hu. .H... ......... 4-.. l 1... 1... .. . 4....

...... 3 3 3 3 .-.... 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 ..4 ..4 ...... ..4 .4 ..4 ...- ..4 ...... ..4 ..4 ..4 ..4 ..4 ..4 ..4 ...-

Computer Program (Cont'd.)

Subroutine

Subroutine V X is used to round off the various output data generated
throughout the program.

~v-
.AL.
..-
-

Im-h l__'l a1") l_|_l

.J. C. I“! ~-’ .' .' Ell
‘1‘ -_1 T' T C1 ._ L.' L—
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—.' 2.! :_1 ._: -. .; .- -.
4. -' -: .: ' '— C
“I? T.“ i -1 .3. 2T. 2.
.Q ." '-l " C -—
‘45; fi-‘ ~
.4. -'-'-. ::=-. z— r»-
4 t- ._'= --_| O 1" _ . .
.4. " .4 .4 -‘ ’ 1». T
4b4 4U 1HJ
.<l .1' r: n‘o -‘_. a’
5+ f:- .__i :1 ‘— .-
.. ~~ --- c:
“‘z‘ 13 52‘ L-

.-' 'T

C 5‘

i:

1'-

....

up .11 {a} U] f'-.'| 'LJ'I Ta"

[3:] I:I:I r'I-II r'l;| r'l:' r'-

:0 "n .-. m .. m

....4 ...... :2: n1

4;. .13.. ..ll'. 42'. .[a

"‘J -'-J I

BIBLIOGRAPHY

118

BIBLIOGRAPHY

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119

Boyd, J.S. (1973). Practical Farm Buildings. Interstate
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