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Effects of N and K applied to two maizesoybean no-tillage cropping systems on yields, profitability, growth, and soil acidity

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 Front Cover
 Title Page
 Acknowledgement
 Table of Contents
 Abstract
 1. Literature review
 2. Response of dry matter in a...
 3. Profitability of a maize/soybean...
 4. Relationship of growth parameters...
 5. Relation of soil pH to ammonium...
 Literature cited
 Appendix
 Biographical sketch
University of Florida






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In reference to the following dissertation:

Author: Douglas Meredith Fraiser


Title: Effects of N and K applied to two maize/soybean no-tillage cropping
systems on yields, profitability, growth, and soil aciditl


Publication Date: 1983


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Material Information

Title: Effects of N and K applied to two maizesoybean no-tillage cropping systems on yields, profitability, growth, and soil acidity
Added title page title: Maizesoybean no-tillage cropping systems
Physical Description: vi, 253 leaves : ill. ; 28 cm.
Language: English
Creator: Fraiser, Douglas Meredith, 1955- ( Dissertant )
Gallaher, Raymond N. ( Thesis advisor )
Rhue, R. Dean ( Reviewer )
Jones, James W. ( Reviewer )
Fry, Jack L. ( Degree grantor )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 1983
Copyright Date: 1983

Subjects

Subjects / Keywords: Corn -- Fertilizers   ( lcsh )
Soybean -- Fertilizers   ( lcsh )
Double cropping   ( lcsh )
No-tillage   ( lcsh )
Agronomy thesis M.S
Dissertations, Academic -- Agronomy -- UF
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )

Notes

Abstract: Maize (Zea mays L.)/soybean (Glycine max (L.) Merr.) no-tillage double cropping has shown good promise in the Southeastern United States. This experiment investigated in the effects of N and K on yields, net profits, and crop growth, and the effect of N rate on soil pH. The experiment was begun in 1977 with a split-plot design. Main treatments were cropping systems (maize for forage followed by soybeans (F/S), and maize for grain followed by soybeans (G/S)); the subtreatments were NH4NO3 rates (0, 168, and 280 kg N/ha). The pH study was conducted with this design. The data for the remainder of the studies were collected in 1981. That year, each subtreatment was divided into split-split-plots to receive 4 rates of KC1 (0, 45, 134, and 404 kg K/ha). Maximum maize total and ear dry mass were predicted to be greater in the G/S system, but the predicted maximum combined yield of normally harvested parts of both crops was higher in the F/S system. Maize crop growth rate (CGR) in both systems increased linearly with K and quadratically with N. For maize, maximum and basal CGR were both predicted to be higher in the G/S system. Maize CGR responded more to K and less to N in the G/S system. The difference in behavior between systems may be due to a greater quantity of N in the crop residues in the G/S system. In both systems, maize ear growth rate (EGR) was unaffected by K, and increased quadratically with N. The response to N may be due to the stimulation of greater grain sink size by N. Both EGR with no N or K applied and its response to N were greater in the G/S system. This may be due to the greater quantity of residues in that system causing less moisture stress, and thus decreasing sink size less. Despite the advantage of the G/S system in maize yields and growth, net profit was predicted to be much higher in the F/S system. The equations relating partitioning coefficient (PC) to applied N and K had low R^2 values in both crops and systems, as did the equations relating soybean total and seed dry mass, CGR, and SGR to N and K in both systems, indicating that other factors caused the observed variation in these parameters. Soil K at planting had no significant effect on maize total and ear yields, and on soybean total and seed yields. The equations relating soil pH to applied N are useful if the results for the top N rate are excluded. For at least one third of the pH experiment, soil pH and base content were inversely related. Throughout the experiment, the base content greatly exceeded the reported cation exchange capacity (CEC). These two results indicate that the Mehlich I procedure extracts not only the plant-available bases, but also a portion of the unreacted lime.
Thesis: Thesis (M.S.)--University of Florida, 1983.
Bibliography: Bibliography: leaves 154-161.
General Note: Typescript.
General Note: Vita.
Statement of Responsibility: by Douglas Meredith Fraiser.

Record Information

Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: aleph - 000427119
oclc - 11096240
notis - ACH5861
System ID: UF00074945:00001

Permanent Link: http://ufdc.ufl.edu/UF00074945/00001

Material Information

Title: Effects of N and K applied to two maizesoybean no-tillage cropping systems on yields, profitability, growth, and soil acidity
Added title page title: Maizesoybean no-tillage cropping systems
Physical Description: vi, 253 leaves : ill. ; 28 cm.
Language: English
Creator: Fraiser, Douglas Meredith, 1955- ( Dissertant )
Gallaher, Raymond N. ( Thesis advisor )
Rhue, R. Dean ( Reviewer )
Jones, James W. ( Reviewer )
Fry, Jack L. ( Degree grantor )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 1983
Copyright Date: 1983

Subjects

Subjects / Keywords: Corn -- Fertilizers   ( lcsh )
Soybean -- Fertilizers   ( lcsh )
Double cropping   ( lcsh )
No-tillage   ( lcsh )
Agronomy thesis M.S
Dissertations, Academic -- Agronomy -- UF
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )

Notes

Abstract: Maize (Zea mays L.)/soybean (Glycine max (L.) Merr.) no-tillage double cropping has shown good promise in the Southeastern United States. This experiment investigated in the effects of N and K on yields, net profits, and crop growth, and the effect of N rate on soil pH. The experiment was begun in 1977 with a split-plot design. Main treatments were cropping systems (maize for forage followed by soybeans (F/S), and maize for grain followed by soybeans (G/S)); the subtreatments were NH4NO3 rates (0, 168, and 280 kg N/ha). The pH study was conducted with this design. The data for the remainder of the studies were collected in 1981. That year, each subtreatment was divided into split-split-plots to receive 4 rates of KC1 (0, 45, 134, and 404 kg K/ha). Maximum maize total and ear dry mass were predicted to be greater in the G/S system, but the predicted maximum combined yield of normally harvested parts of both crops was higher in the F/S system. Maize crop growth rate (CGR) in both systems increased linearly with K and quadratically with N. For maize, maximum and basal CGR were both predicted to be higher in the G/S system. Maize CGR responded more to K and less to N in the G/S system. The difference in behavior between systems may be due to a greater quantity of N in the crop residues in the G/S system. In both systems, maize ear growth rate (EGR) was unaffected by K, and increased quadratically with N. The response to N may be due to the stimulation of greater grain sink size by N. Both EGR with no N or K applied and its response to N were greater in the G/S system. This may be due to the greater quantity of residues in that system causing less moisture stress, and thus decreasing sink size less. Despite the advantage of the G/S system in maize yields and growth, net profit was predicted to be much higher in the F/S system. The equations relating partitioning coefficient (PC) to applied N and K had low R^2 values in both crops and systems, as did the equations relating soybean total and seed dry mass, CGR, and SGR to N and K in both systems, indicating that other factors caused the observed variation in these parameters. Soil K at planting had no significant effect on maize total and ear yields, and on soybean total and seed yields. The equations relating soil pH to applied N are useful if the results for the top N rate are excluded. For at least one third of the pH experiment, soil pH and base content were inversely related. Throughout the experiment, the base content greatly exceeded the reported cation exchange capacity (CEC). These two results indicate that the Mehlich I procedure extracts not only the plant-available bases, but also a portion of the unreacted lime.
Thesis: Thesis (M.S.)--University of Florida, 1983.
Bibliography: Bibliography: leaves 154-161.
General Note: Typescript.
General Note: Vita.
Statement of Responsibility: by Douglas Meredith Fraiser.

Record Information

Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: aleph - 000427119
oclc - 11096240
notis - ACH5861
System ID: UF00074945:00001

Table of Contents
    Front Cover
        Front Cover
    Title Page
        Page i
    Acknowledgement
        Page ii
    Table of Contents
        Page iii
        Page iv
    Abstract
        Page v
        Page vi
    1. Literature review
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
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        Page 50
        Page 51
        Page 52
    2. Response of dry matter in a maize/soybean double cropping system to applied rates of nitrogen and potassium
        Page 53
        Page 54
        Page 55
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        Page 82
    3. Profitability of a maize/soybean double cropping system as affected by applied rates of nitrogen and potassium
        Page 83
        Page 84
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    4. Relationship of growth parameters in two maize/soybean double cropping systems to applied nitrogen and potassium
        Page 110
        Page 111
        Page 112
        Page 113
        Page 114
        Page 115
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        Page 132
        Page 133
        Page 134
        Page 135
        Page 136
        Page 137
        Page 138
    5. Relation of soil pH to ammonium nitrate applied to maize/soybean successions
        Page 139
        Page 140
        Page 141
        Page 142
        Page 143
        Page 144
        Page 145
        Page 146
        Page 147
        Page 148
        Page 149
        Page 150
        Page 151
        Page 152
        Page 153
    Literature cited
        Page 154
        Page 155
        Page 156
        Page 157
        Page 158
        Page 159
        Page 160
        Page 161
    Appendix
        Page 162
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    Biographical sketch
        Page 253
        Page 254
Full Text












EFFECTS OF N AND K APPLIED TO TWO MAIZE/SOYBEAN
NO-TILLAGE CROPPING SYSTEMS ON YIELDS,
PROFITABILITY, GROWTH, AND SOIL ACIDITY










BY

DOUGLAS MEREDITH FRAISER


A THESIS PRESENTED TO THE GRADUATE COUNCIL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF MASTER OF SCIENCE


UNIVERSITY OF FLORIDA


1983
















EFFECTS OF N AND K APPLIED TO TWO MAIZE/SOYBEAN
NO-TILLAGE CROPPING SYSTEMS ON YIELDS,
PROFITABILITY, GROWTH, AND SOIL ACIDITY










BY

DOUGLAS MEREDITH FRAISER


A THESIS PRESENTED TO THE GRADUATE COUNCIL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF MASTER OF SCIENCE


UNIVERSITY OF FLORIDA


1983















ACKNOWLEDGEMENTS

I am grateful to my wife, Meg, for the support and encouragement

she has given me, for her typing of my thesis, and for her assistance in

the lab and field. I would like to thank my major professor, Dr. Ray-

mond Gallaher, for the guidance and assistance he has provided me in my

research and education. Dr. Dean Rhue and Dr. James Jones, members of

my committee, are appreciated for the time and advice they have given.

I would like to thank Thomas Post, for his help and for the stimulation

he was to my thinking, and his wife Melva, for her assistance with the

analyses. I would like to thank Bruce Fritz for his encouragement and

taking care of so many details in the laboratory. I am grateful to sev-

eral people for their assistance with lab work: Ronald Kern, Deogracio

Castillo, Loretta Tennant, and David Burden. Sonny Tompkins, Rolland

Weeks, and Timmy Summers are appreciated for their assistance in the

field. I would also like to thank Dr. Ramon Littell for his assistance

in the statistical analyses. The assistance of the Potash and Phosphate

Institute, and of the International Minerals and Chemical Corporation,

is especially appreciated, as they helped to support both my research

and education.

Lastly, I am grateful to my parents, M. Meredith and Regina B.

Frasher, for their support and encouragement, and for their laying the

foundation of my education and being themselves patient teachers.
















TABLE OF CONTENTS
PAGE

ACKNOWLEGEMENTS......................................................ii

ABSTRACT................................................................ v

CHAPTER 1. LITERATURE REVIEW.......................................... 1
Overview of Maize and Soybeans.................................... 1
Maize .................................................. 1
Soybeans...................................................... 4
Multicropping and Minimum Tillage.................................8
Multicropping ................... ................... ........... 8
Minimum Tillage............................................... 9
Production Functions............................................13
Economic Analysis............................................... 20
Growth Analysis............................. ...................22
Theory of Yield Determination................................23
Limits ... ....................................................37
Effects of Plant Nutrition on Yields.............................44
Soil pH vs. Tillage........................................50

CHAPTER 2. RESPONSE OF DRY MATTER IN A MAIZE/SOYBEAN DOUBLE CROPPING
SYSTEM TO APPLIED RATES OF NITROGEN AND POTASSIUM................... 53
Introduction.....................................................53
Materials and Methods.......................................... 60
Experiment Description......................................60
Experiment History....................................... 61
Statistics...................................................63
Results and Discussion..........................................65
Conclusions.......................................................82

CHAPTER 3. PROFITABILITY OF A MAIZE/SOYBEAN DOUBLE CROPPING SYSTEM AS
AFFECTED BY APPLIED RATES OF NITROGEN AND POTASSIUM................. 83
Introduction.....................................................83
Materials and Methods...........................................87
Experiment Description......................................87
Experiment History.............................. ......... 88
Prices and Costs.............................................89
Profit Maximization.........................................95
Determination of Net Profit.................................101
Results and Discussion.........................................101
Conclusions........... ........................................108

CHAPTER 4. RELATIONSHIP OF GROWTH PARAMETERS IN TWO MAIZE/SOYBEAN
DOUBLE CROPPING SYSTEMS TO APPLIED NITROGEN AND POTASSIUM..........110
Introduction. ...... ................. ....... ..................110
Materials and Methods..........................................118









Experiment Description.....................................118
Experiment History.........................................118
Growth Rate Equations .......................................122
Statistics.................................................. 124
Results and Discussion..........................................126
Maize Growth Rate......................................... 129
Soybean Growth Rate...................................... 137
Partitioning................................................137
Conclusions .....................................................138

CHAPTER 5. RELATION OF SOIL pH TO AMMONIUM NITRATE APPLIED TO
MAIZE/SOYBEAN SUCCESSIONS.........................................139
Introduction................................................. .. 139
Materials and Methods..........................................141
Description of Experiment..................................141
Data Analysis..............................................143
Results and Discussion..........................................146
Suitability of the Equations ................................ 146
Soil Acidity........................ ........................ 146
Relationship of pH to Basic Cation Level....................150
Soil Basic Cations..........................................152
Conclusions......................................................153

LITERATURE CITED................................................... 154

APPENDIX ............................................................. 162

BIOGRAPHICAL SKETCH.................................................253















Abstract of Thesis Presented to the Graduate Council
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science


EFFECTS OF N AND K APPLIED TO TWO MAIZE/SOYBEAN NO-TILLAGE DOUBLE
CROPPING SYSTEMS ON YIELDS, PROFITABILITY, GROWTH, AND SOIL ACIDITY

by

Douglas Meredith Fraiser

December, 1983

Chairman: Raymond N. Gallaher
Major Department: Agronomy


Maize (Zea mays L.)/soybean (Glycine max (L.) Merr.) no-tillage

double cropping has shown good promise in the Southeastern United

States. This experiment investigated the effects of N and K on yields,

net profits, and crop growth, and the effect of N rate on soil pH.

The experiment was begun in 1977 with a split-plot design. Main

treatments were cropping systems (maize for forage followed by soybeans

(F/S), and maize for grain followed by soybeans (G/S)); the subtreat-

ments were NH NO3 rates (0, 168, and 280 kg N/ha). The pH study was

conducted with this design. The data for the remainder of the studies

were collected in 1981. That year, each subtreatment was divided into

split-split-plots to receive 4 rates of KC1 (0, 45, 134, and 404 kg

K/ha).

Maximum maize total and ear dry mass were predicted to be greater

in the G/S system, but the predicted maximum combined yield of normally

harvested parts of both crops was higher in the F/S system. Maize crop









growth rate (CGR) in both systems increased linearly with K and quad-

ratically with N. For maize, maximum and basal CGR were both predicted

to be higher in the G/S system. Maize CGR responded more to K and less

to N in the G/S system. The difference in behavior between systems may

be due to a greater quantity of N in the crop residues in the G/S sys-

tem. In both systems, maize ear growth rate (EGR) was unaffected by K,

and increased quadratically with N. The response to N may be due to the

stimulation of greater grain sink size by N. Both EGR with no N or K

applied and its response to N were greater in the G/S system. This may

be due to the greater quantity of residues in that system causing less

moisture stress, and thus decreasing sink size less. Despite the advan-

tage of the G/S system in maize yields and growth, net profit was pre-

dicted to be much higher in the F/S system. The equations relating par-

titioning coefficient (PC) to applied N and K had low R2 values in both

crops and.systems, as did the equations relating soybean total and seed

dry mass, CGR, and SGR to N and K in both systems, indicating that other

factors caused the observed variation in these parameters. Soil K at

planting had no significant effect on maize total and ear yields, and on

soybean total and seed yields. The equations relating soil pH to ap-

plied N are useful if the results for the top N rate are excluded. For

at least one third of the pH experiment, soil pH and base content were

inversely related. Throughout the experiment, the base content greatly

exceeded the reported cation exchange capacity (CEC). These two results

indicate that the Mehlich I procedure extracts not only the plant-avail-

able bases, but also a portion of the unreacted lime.




Chairman















CHAPTER 1
LITERATURE REVIEW

Overview of Maize and Soybeans

Maize

Maize (Zea mays L.) is the third most important crop in the world,

following wheat (Triticum aestivum) and rice (Oryza sativa) (Duncan,

1975). The United States produces about 45% of the world's maize, with

other major producers being China, the U.S.S.R., Brazil, Mexico, Roma-

nia, Argentina, and Yugoslavia. The crop is used both for human con-

sumption and as an animal feed. In the U.S., maize provides three-quar-

ters of the nutrients obtained from feed grains, and over 80% of those

obtained from silage. It is conjectured to have originated in Mexico or

Central America, with a possible secondary center of origin in South

America. Specimens of an early type of maize date to 5200 B.C. The

crop was distributed throughout the New World by the beginning of the

1700's, explorers in the sixteenth and seventeeth centuries finding it

grown from Chile to the Great Lakes (Martin et al., 1976).

Maize has become an important crop because of its high productivity

and great adaptability. It can be grown without irrigation with as lit-

tle as 0.025 m annual precipitation to as much as 5.0 m, and at alti-

tudes of from 0 to 4000 m. The wide range in its physical makeup adds

to its usefulness. Mature plants can vary in height from less than 0.6

m to 7 m, while kernel size varies more than fiftyfold. The time be-

tween planting and harvest can range from as little as three months to

over a year. There is also a wide range in grain quality, with some









varieties of maize being hard, others soft and floury, and still others

sweet (Duncan, 1975). Maize grown for forage is managed in much the

same way as that grown for grain (a little more N is applied and the

population slightly increased, and sometimes less irrigation is used),

except that it is harvested earlier, between the milk and the early

dough stages (growth stages six and seven, respectively (Hanway, 1971)).

The ears are attractive to livestock at this time, and the plant is

green and succulent with a high sugar content, so the plant in its en-

tirety is readily accepted (Arnon, 1975).

Perhaps the major drawback to maize is that, when grown by conven-

tional techniques without large fertilizer applications and high plant

populations, even the return of all the stalks to the land where they

were grown does not maintain the soil organic matter (Martin et al.,

1976).. However, the use of no-tillage and a winter cover crop in con-

junction with the return of maize residues caused an increase in soil

organic matter over five years of continuous corn (Blevins et al.,

1977). The tendency of maize to deplete the soil organic matter may

therefore be counteracted by the adoption of no-tillage production.

Maize is a highly productive grain crop, as can be seen from Table

1.1. More recent reports by the U.S. Department of Agriculture (1981,

1982) place the U.S. average even higher, at 6.9 mt/ha. The best U.S.

farmers average 14.1 mt grain/ha, while the world record is 22.2 mt

grain/ha (Wittwer, 1980). In a survey of several maize forage studies

(Cummins and Dobson, 1973; Whitaker et al., 1969; Bryant and Blaser,

1968; Doss et al., 1970; Mishra et al., 1963; Rumawas et al., 1971; Rut-

ger and Crowder, 1967), the highest yield of total dry mass recorded










Table 1.1. Average and record crop yields.
U.S. average, Best U.S. World
Crop Scientific name 1975 farmers record
-------------------mt/ha--------------------
maize Zea mays L. 5.4 14.1 22.2
grain sorghum Sorghum bicolor 3.3 16.8 21.5
rice Oryza sativa 2.5 8.0 14.4
wheat Triticum aestivum 2.1 6.7 14.5
oats Avena sativa 1.7 5.4 10.6
soybeans Glycine max (L.) Merr. 1.9 3.4 5.6
Adapted from Wittwer, 1980.









was 28.7 mt/ha, while the average of the best treatments in the seven

studies was 18.3 mt/ha.

Soybeans

Soybeans (Glycine max (L.) Merr.) are one of the major pulse crops

worldwide and the leader by far in the United States (Tables 1.2 and

1.3). Production has increased significantly since about 1960, when the

average U.S. production from 1957 through 1961 was 15.4 x 106 mt (Martin

et al., 1976). The total worldwide production in 1979 1980 was 93.6 x

106 mt (USDA, 1981). Of that, 66% was produced by the U.S., making it

the largest producer, followed by Brazil and the People's Republic of

China (see Table 1.3). The soybean is utilized for its oil (human con-

sumption as oil, shortening, and margarine, and industrial use as a dry-

ing oil), lecithin (various food products), and meal (protein supplement

for livestock, and industrial uses); as flour; and as the starting point

for a number of oriental foods (Martin et al., 1976). As such it, like

maize, is used for both human and animal consumption. Soybean meal is

the major high protein supplement used in mixed livestock rations (Mar-

tin et al., 1976).

The first record of the soybean in China is in 2838 B.C. (Morse and

Cartter, 1937). Hymowitz (1970) suggests that the crop became a domes-

ticated crop in the North China Plains during the eleventh century B.C.,

and passed from there to Manchuria. It was known of in the 1600's in

Europe, and by the early 1800's in the U.S. (Morse and Cartter, 1937).

However, it was a crop of minor importance until 1889, when several ex-

periment stations developed an interest in it. The crop gained some

importance after that, but remained primarily a forage crop until the

late 1930's (Shibles et al., 1975).









Table 1.2. World and U.S. production of major pulse crops, 1969 to 1971
average.


Crop
soybeans
peanuts (in shell)
dry beans
dry peas
broad beans
cowpeas
pigeonpeas
lentils
vetch
lupines


Scientific name
Glycine max (L.) Merr.
Arachis hypogaea
Phaseolus vulgaris L.
Pisum sativum
Vicia faba
Vigna sinensis
Cajanus indicus
Lens esculenta
Vicia spp.
Lupinus spp.


Production,
World
45.5
17.6
11.4
10.8
5.1
1.1
2.0
1.1
2.3
0.8


Adapted from Martin et al., 1976.


106 mt
U.S.
31.0
1.3
0.8
0.2
trace
0.2
trace
1.3
0.008
0.0005






6


Table 1.3. World soybean production, 1979 to 1981 average.
Country Production, 106 mt
United States 55.25
Brazil 15.17
China (P.R.C.) 7.67
Argentina 3.58
Other countries 5.49
World 87.15
Note: Calculated from the data of USDA (1981, 1982).









It can be seen from Table 1.1 that soybean yields are considerably

lower than those of maize. There are a number of factors which may con-

tribute to this. The first is that the rate of net photosynthesis is

higher in maize than in soybeans. Eagles and Wilson (1982) reviewed

studies on the photosyunthetic rates of several crop species, and report

that net photosynthesis on a whole crop basis ranges from 94 to 144 mg
2 2
002/dm ground area/hr in maize, and from 55 to 94 mg CO2/dm /hr in soy-

beans. Another factor is the difference in the chemical composition of

the seeds, and the consequent difference in their energy content per

unit mass. Additionally, the synthesis of high-energy substances such

as protein and carbohydrates from carbohydrate, the initial product of

photosynthesis, requires the expenditure of energy. A study by Howell

(1961) reports that the production of 1.0 kg soybean dry mass requires

approximately 2.7 times as much energy as the production of 1.0 kg maize

grain (assuming moisture contents of 6.4% and 15.5%, respectively).

Multiplying soybean yields by 2.7 brings them closer to, but still far

short of, the maize yields, indicating that there are other factors in-

volved. One of these factors may be the length of the seed filling pe-

riod. One study with soybeans in Florida (Boote et al., 1979) reported

filling periods of 32 to 52 days, while maize in Iowa requires about 50

to 63 days for grain fill (Shaw and Thorn, 1951; Hallauer and Russell,

1962). Finally, soybeans may be particularly susceptible to drought

stress. Boyer (1971) found that the soybean has a plant water resist-

ance (resistance from root surface to leaf surface) which is very high

(1.6 x 106 sec/cm) compared to other broadleaves, requiring the leaf

water tension to be about twice that of either beans (Phaseolus vulgaris

L.) or sunflower (Helianthus annus L.) to maintain the same water flow.









Shibles et al. (1975) concluded from this study that soybeans may under-

go drought stress more often and more severely than many crops. Since

soybeans tend to lose their yield potential and never regain it if

stressed between late flowering and fairly late bean fill (Shaw and

Laing, 1966) (from stages R4 to R7 (Fehr and Caviness, 1977)), periodic

drought stess could result in yields significantly below the maximum

possible.

Multicropping and Minimum Tillage

Multicropping

There have been several terms developed to describe the numerous

variations of multicropping. The definitions below have been adapted

from Andrews and Kassam (1976):

multiple cropping -- the production of two or more crops per
year on the same field.
intercropping -- the production of two or more crops at the
same time on the same land.
mixed intercropping -- the production of two or more crops
simultaneously on the same land without a distinct row
arrangement.
row intercropping -- intercropping with the separation of one
or more crops into rows.
relay intercropping -- the production of two or more crops on
the same land during a portion of each crop's life cycle.
sequential cropping -- the production of two or more crops on
the same land per year without the overlapping of life
cycles. Each crop is planted after the previous one has
been harvested.
double cropping -- the sequential cropping of two crops.

The types of multicropping best suited to mechanized agriculture are

those which permit the use of large, motorized harvesting equipment

without introducing complicated harvesting patterns, namely, relay in-

tercropping and sequential multicropping. One of the major advantages

of multicropping in industrialized agriculture is that fixed costs such

as land and machinery are spread over at least one additional crop, mak-

ing better use of fixed farm resources (Gallaher, 1978).









The contribution of crop residues in the maize/soybean succession

to the succeeding crop's nutrient supply can be seen by examining the

nutrient content of the crops and their residues. The nutrient content

of a crop depends, of course, on the fertilizer received; several find-

ings for maize and soybeans are given in Table 1.4. One of the advan-

tages of including soybeans in a cropping system is its fixation of at-

mospheric N. Shibles et al. (1975) report that estimates of the amount

of N fixed by soybeans vary greatly, but cite one study by Weber (1966)

which found a crop yielding 2800 kg seed/ha could fix 160 kg N/ha; it

took up another 45 N/ha from the soil. Shroder and Hinson (1975),

studying a rye (Secale cereale)/soybean succession, suggested that the

nodules and roots of soybeans could supply approximately 20 kg N/ha to

the following crop of rye. In an experiment using nodulating and non-

nodulating lines, Weber (1966) found that nodulating soybeans provided

37 kg/ha of residual fixed N to a succeeding crop of nonnodulating soy-

beans. The nutrients left in the crop residues should become available

for crop uptake, if they are not lost by volatilization, soil fixation,

microbial fixation, or leaching.

Mimimum Tillage

A number of terms have been used for the various types of tillage

management. The three types of tillage management, with synonyms in

common use, are defined below (adapted from Gallaher, 1980):

no-tillage (slot planting, coulter planting) -- a technique in
which only a slot sufficiently wide and deep to properly
deposit and cover the seed is opened, and in which no other
tillage operations are performed.
conventional tillage -- seedbed preparation with tillage
implements, usually involving the combined use of plows,
harrows, and other tillage equipment.
minimum tillage (conservation tillage) -- production of a crop
with as little disturbance of the soil as possible, but
involving more than the simple opening of a planting slot.












Table 1.4. Nutrient content of
Crop Part


maize









soybeans


grain
stover
total
grain
stover
total
total
total
total
seed
remainder
total
seed
remainder
total
seed
remainder
total


maize and soybeans.


N P K
----------kg/ha----------
63 12 30
37 6 38
100 18 68
128 20 37
72 14 93
200 34 130
154 24 91
187 37 191
232 34 225
164 16 45
42 3 17
206 19 62
96 7 26
23 1 13
119 8 39
149
70
219


Reference

Sanchez, 1976


Sanchez, 1976


Arnon, 1975 (Krantz and Chandler, 1954)
Arnon, 1975 (Barber and Olson, 1968)
Arnon, 1975 (Benne et al., 1964)
Hammond et al., 1951


Hammond et al., 1951


Post, 1983









The use of no-tillage has a variety of advantages, including re-

duced energy and equipment costs, conservation of soil moisture, and the

reduction of soil erosion. When used in conjunction with multicropping,

no-tillage shortens the time required between harvest and planting.

This permits the timely planting of a succeeding crop, an important fac-

tor in multiple cropping (Phillips et al., 1980).

The effects of crop residues in no-tillage systems are particularly

interesting. These residues form a natural mulch, which has a number of

benefits:

1. reduced water runoff
2. increased water infiltration
3. reduction of evaporation from the soil surface
4. reduced soil erosion
5. suppression of weeds
6. decreased infestation by the lesser cornstalk borer
(Elasmopalpus lignosellus).

Other benefits arising from those listed above are decreases in agricul-

tural pollution, the conservation of soil moisture, and improved crop

yields (Gallaher, 1978). An additional benefit is an increase in soil

organic matter and organic N. Over one season in a rye/soybean double-

cropping system, Post (1983) observed that organic matter in the 0-30 cm

soil layer increased under no-tillage by 4.4 mt/ha, while that in plots

tilled to 15 cm depth decreased by 6.1 mt/ha. Nitrogen in the no-till-

age plots decreased by 141 kg/ha, but the decrease in the plots tilled

to 15 cm was even greater, 271 kg/ha. The organic matter and N levels

in the no-tillage and tilled plots were initially about equal. After

five years of no-tillage and conventionally tilled continuous maize re-

ceiving 336 kg N/ha/yr, Blevins et al. (1977) found that organic matter

in the 0-30 cm soil layer decreased 3% under no-tillage, vs. 16% under

conventional tillage. Organic N in the 0-30 cm layer increased about 6%









under conventional tillage. However, degree of the buildup appears to

be influenced by climate and crop. Hargrove et al. (1982) double-crop-

ped wheat and soybeans for five years with various tillage combinations

and then measured soil properties. While organic C and organic N did

increase in the 0-7.5 cm layer of soil, they note that the increases

were not as great as in other studies conducted farther north, and that

there were no significant differences between organic C or organic N in

the surface soil between no-tillage plots and those conventionally

tilled twice a year. They attributed the nonsignificant increases in

organic C and N being due to the crops employed and the climate. Rye

and maize, they note, produce substantially more residue than does wheat

straw. This difference was noted to be particularly great in the case

of no-tillage maize, which received substantially more N than did the

wheat in their experiment. They add that most studies on the effect on

no-tillage on soil organic N and C have been conducted with maize. The

longer and warmer growing season in the Southeast was also seen as

speeding residue decomposition. It thus appears that while no-tillage

may increase soil N and organic matter levels, cooler climates or crops

producing large quantities of residues are needed to ensure this.

There are a few drawbacks to no-tillage (Robertson et al., 1980):

1. there is a risk that weed control will be ineffective.
2. the herbicides required are expensive.
3. no-tillage requires more careful management on
fine-textured soils.
4. the added mulch from crop residues can increase certain
pest problems.

The weed and pest problems require that a producer be more observant of

the buildup of weeds or harmful insects. However, in this area, as in

the case of soil texture, more careful management can overcome the dif-

ficulties of no-tillage. It is also expected that as no-tillage









production becomes more widespread, the herbicides used will be sold in

greater volume, so their cost should decrease.

Production Functions

A production function (or yield curve, or yield response surface)

is a mathematical equation relating crop yield to one or more production

inputs (e.g., N, P, K, seeding rate, amount of irrigation received,

etc.). A beginning in understanding these sorts of relationships was

the discovery by Sprengel of the "Law of the Minimum" in the early nine-

teenth century, and its subsequent promulgation by von Liebig (Mengel

and Kirkby, 1982). The Law of the Minimum states that growth will be

limited by whichever of the necessary inputs is in the least adequate

supply (Stoskopf, 1981).

This concept, that yield is limited by various growth factors, led

Mitscherlich to hypothesize that the response of yield to any one par-

ticular growth factor is asymptotic (Mengel and Kirkby, 1982). He con-

cluded from a number of pot and field experiments that the increase in

yield due to the addition of one unit of a production input was propor-

tional to the difference between the current yield and the maximum ob-

tainable. This relationship can be expressed mathematically as

dY
S= k(Y Y)
dX m

where

Y = yield

X = growth factor

k = constant

Y = maximum yield
m








Upon integration, we obtain

In(Y Y) = C kX

where C is the constant of integration. If X is the quantity of nutri-

ent absorbed, or if the soil contains none of the nutrient before its

application, then when X = 0, yield is also zero. Hence

InY = C
m
and therefore

In(Y Y) = InY kX
m m
This equation may also be written as

Y = Y [1 exp(-kX)]

A later modification, the Mitscherlich-Baule equation (Bray, 1961), con-

tained the soil level of the nutrient as well, eliminating the need to

assume that the soil contains none of the nutrient. The equation is

In(Ym Y) = InYm k(S + F)

where

S = the quantity of the nutrient present in the soil

F = the quantity of the nutrient applied in fertilizer

Bray objected, however, that this equation implies that a quantity of

nutrient present in the soil produces the same yield response as an

equal quantity applied as fertilizer. He proposed an equation which

allows for different "efficiency factors" for the soil and fertilizer

forms of the nutrient:

In(Y Y) = InY k S k F
m m 1 2
where k1 and k2 are constants.

While Bray's equation is an improvement over the original Mitscher-

lich equation, there remain several weaknesses to the approach. The

most serious, pointed out by Mengel and Kirkby (1982), is that the






15

"constants" in the Mitscherlich and Mitscherlich-Baule equations are

not, in fact, constant. This is due to the interaction of nutrients

with each other and with other growth factors, such as moisture supply.

Van der Paauw (1958) and Barber (1959) found the yield response of pota-

toes (Solanum tuberosum), wheat (Triticum aestivum), and maize to depend

upon the precipitation received during the growing season. Beer et al.

(1967) reported that the yield response of maize to N was affected by

the soil water content. Another weakness which Mengel and Kirkby note

is that the application of high levels of a nutrient may actually de-

crease yields, an effect which cannot be accounted for by an asymptotic

equation. They state that because of this second defect, some authors

prefer to use quadratic equations when relating yield response to a sin-

gle input.

There are two other weaknesses to the Mitscherlich-Baule equations,

namely, that they are difficult to handle for regression analysis, and

that they do not allow for the interaction of growth factors in deter-

mining yields. Doll et al. (1958) considered the square root and qua-

dratic functions the most satisfactory for regression analysis, perhaps

for these reasons. In describing yield response to a single variable,

these equations are

square root model: Y = a + bX1/2 + X

quadratic model: Y = a + bX + cX2

These equations can be easily expanded to include two or more growth

factors, and interactions between them. An example is the "second order

model," discussed by Montgomery (1976)






16

Z = i + aX2 + bX + cXY + dY + eY2

where

Z = dependent variable

X, Y = independent variables

i = intercept

a, b, c, d, e = constants

This model allows for a linear interaction between the independent vari-
2 2 2 2
ables. Higher order interactions (X Y, XY X Y ) can be included in

the model, but that shown is usually adequate. All of these polynomial

functions have the usefulness that their derivatives can be easily

taken, the advantage of which will be seen later. Production functions

for maize and soybean by a number of authors are given in Table 1.5.

The production function is arrived at by the process of elimina-

tion. An analysis of variance is performed on each term in the model

proposed, and those terms which are significant at the 0.25 level of

probability are retained. A term is also retained, whether significant

or not, if a higher order term in the same factor is significant, or the

term shows a significant interaction with another term in the model (R.

Littell, Statistics Department, University of Florida, personal communi-

cation; Englehorn et al., 1964). The coefficient of determination R2 is

also useful for evaluating a regression equation. An equation with the

R2 greater than 0.66 is considered to have good fit; between 0.45 and

0.66, moderate fit; and with less than 0.45, poor fit (Dev et al.,

1980).

The technique of finding the point of maximum yield is taken from

calculus. A point of maximum yield with respect to all the input varia-

bles exists if the partial derivatives of yield with respect to each











Table 1.5. Production functions for maize and soybeans.


Product
maize forage
maize forage
maize forage
maize grain




maize grain




maize grain


soybean seed


Equation
Y = -110 0.534N2 + 190N
Y = 30 0.324N2 + 154N
Y = 30 0.386N2 + 162N
Y = 4908 0.0583N2 0.0723P2 +
14.6N + 6.17P + 3.16K + 0.0390NP
+ 0.00763NK 0.00973PK
Y = 2146 0.134N2 + 51.3N -
11.5K + 0.212NK


Y = 3962 0.0773N2 + 30.5N -
1.97K + 0.0883NK
Y = 2102 + 32.03K 0.2147K2
Y = 1972 + 41.55K 0.2173K2
Y = 1299 + 37.40K 0.2127K2
Y = 1410 + 42.97K 0.2028K2


a
aEquations calculated from their data.
Note: Yield and nutrients are both expressed in kg/ha.


Independent
Variable(s)
N taken up
N taken up
N taken up
N, P, K applied




N, K applied
(each kg K accom-
panied by 0.52 kg P)
as above


K applied
K applied
K applied
K applied


R2
0.94
0.98
0.92
0.96


Reference
Bandel et al., 1980
Bandel et al., 1980
Bandel et al., 1980
Heady et al., 1966


0.99 Engelhorn et al., 1964




0.94 Engelhorn et al., 1964


0.91
0.96
0.91
0.96


Jones et al.,
Jones et al.,
Jones et al.,
Jones et al.,


1977a
1977a
1977a
1977a






18

independent variable are all equal to zero at some point, and the second

partial derivatives are all negative. The levels of the inputs required

for the maximum yield are determined by setting each partial derivative

equal to zero, and using a technique for the solution of simultaneous

equations such as Cramer's solution (for an explanation, see Perrin,

1970).

There are three terms particularly useful in discussing production

functions, namely, isoquant, marginal product, and ridge line. Illus-

trations of these appear in Figure 1.1. Production functions with two

independent variables can be represented as "contour maps," with the

independent variable axes lying within the plane of the paper, and the

yield axis being perpendicular to the paper, and rising out of it.

Points on the response surface having the same yield are joined by lines

called isoquants (Heady et al., 1966), in the same way that lines on a

contour map join points of equal elevation. Marginal product is the

increase in yield per unit increase in a given input (Castle et al.,

1972). This can be expressed mathematically as

aY
MP =--

where MP is the marginal product, Y is yield, and X is an input. A

ridge line is that line along which the marginal product with respect to

a particular input is equal to zero (Heady et al., 1966). It is thus

defined by the equation DY/IX = 0.

Marginal rate of substitution, another useful term, is the rate at

which one input will substitute for another while maintaining a constant

yield (Castle et al., 1972). In mathematical terms

















ISOQUANTS


/ /_j^31C 0 -N
C----- RIDGE
S:01 --^ ---fu LINES











l 150 300 450
Fig. 1.1. Illustration of production function with isoquants and ridge







lines. The isoquants represent dry mass produced vs. rates of N and
K applied, and are labeled in mt/ha.
S----_ _--------^


-------, ______ 5____,_-


S159) 39913 45'

K1 K6/HA


Fig. 1.1. Illustration of production function with isoquants and ridge
lines. The isoquants represent dry mass produced vs. rates of N and
K applied, and are labeled in mt/ha.










X2\
MRS = X- 2
1) Y

where MRS is the marginal rate of substitution of X1 for X2. When there

are two independent variables and both ridge lines exist, as in Figure

1.1, the marginal rates of substitution are negative within the ridge

lines (an increase in X1 allows a decrease in X2, and vice versa), and

equal zero at the ridge line. Hence, the inputs substitute for each

other only within the ridge lines (Heady et al., 1966).

Economic Analysis

If one considers only one crop and one input, the maximum profit

occurs when the increase in the total value of the product due to the

addition of the input exactly equals the increase in expenditure for the

input (Castle et al., 1972). The increase in total product value per

unit of additional input is known as marginal value product (MVP), and

is defined as

d(YP )
MVP= Y
dX

where

Y = yield of product

P = unit price of product

X = amount of input

(The equation above employs the derivative, rather than the partial de-

rivative, as Y is a function of X only. Were Y to be a function of sev-

eral variables, the partial derivative would be used.) The increase in

expenditure for an input per unit of additional input is known as mar-

ginal input cost (MIC), which is defined as









d(XP )
MIC =
dX

where:

Px = unit cost of input

If the unit cost of the input does not vary with the quantity of input

purchased, MIC = P Similarly, if the price received for the product

does not vary with the quantity produced, MVP = P dY/dX. Under these

conditions, profit is maximized when

dY P
dX P
y
The derivative dY/dX is obtained by taking the derivative of the produc-

tion function. This procedure can be expanded to account for any number

of inputs, as explained in the Materials and Methods of Chapter 3.

It is common practice to calculate interest on the variable costs

and add this to the cost of producing the crop. There are three princi-

ple techniques employed: flat interest rate, interest on the unpaid bal-

ance only, and discount (Castle et al., 1972). The simplest of these is

the flat interest rate, which can be expressed mathematically by the

equation

I = Prt

where

I = interest paid

P = amount of money borrowed

r = interest rate

t = time elapsed since borrowing

This method has been used in a number of economic analyses (West-

berry and Gallaher, 1980a, 1980b; Gallaher et al., 1980; Gallaher and

Weaver, undated), and is the technique used in this study.









Growth Analysis

There are a few terms which are particularly useful in growth anal-

ysis:

crop growth rate (CGR) -- the instantaneous rate of plant dry
matter accumulation per unit area per unit time (Radford,
1967). Usually averaged over the linear phase.
Seed growth rate (SGR) -- the instantaneous rate of seed dry
matter accumulation per unit area per unit time. Usually
averaged over the linear phase.
Partitioning coefficient (PC) -- the ratio of dry matter
accumulation in the storage organ to the crop growth rate,
i.e., SGR/CGR. Usually calculated for that period when CGR
and SGR are both linear.
effective filling period duration (EFPD) -- the time required
for seed development were it to occur at the linear rate of
growth from pollination on (Duncan, 1975).

In mathematical terms:

CGR = dWt /dt (during linear crop growth)

SGR = dW /dt (during linear seed growth)
s
PF = SGR/CGR

EFPD = Ws(at harvest)/SGR

where

Wt = total plant dry mass per unit area

Ws = seed dry mass per unit area

t = time

While there are a number of other terms used in growth analysis

(e.g., net assimilation rate, relative growth rate, and leaf area ra-

tio), Radford (1967) has pointed out that it is the basic relationships

of crop mass and crop leaf area to time which are most in need of study.

Crop growth rate and SGR are two of these basic relationships, while

the partitioning factor is derived from them.

Some researchers consider not only the passage of time in growth

analysis, but also the rate at which the crop passes through various









stages of physiological development (Hanway and Weber, 1971a; Boote et

al., 1979). In order to compare results from different experiments it is

necessary to have a common terminology for the growth stages. Two sys-

tems in fairly wide use for maize and soybeans are given in Table 1.6.

A number of experiments have been conducted to find CGR and SGR

values for maize and soybeans. Results from these are given in Table

1.7. For maize, CGR ranges from 84 to 339, while SGR ranges from 210 to

259. Crop growth rate in soybeans ranges from 88 to 172, and SGR ranges

from 79 to 106.

Theory of Yield Determination

Duncan (1975) gives a synopsis of the growth analysis perspective

on yield determination by stating "it is convenient and approximately

correct to think of maize grain yield as a product of the rate of photo-

synthesis during the grain filling period multiplied by the duration of

the grain filling period, plus the change in labile reserves, with grain

sink capacity as an upper limit." We will examine each of the factors

which contributes to the validity of this statement, as well as look at

the process of yield adjustment (the mechanisms by which yield is re-

duced below the potential) and at the effect a crop's "investment strat-

egy" (the timing and degree of partitioning of photosynthate between

vegetative and reproductive growth) has on the final yield.

Figure 1.2 is an example of the "typical" growth curve. It has a

short period of slow early development followed by a relatively long

period of approximately linear accumulation of dry matter over time, and

ends with a short period of slowing dry matter accumulation until the

maximum is reached. The linearity of CGR over most of the crop's life

is a consequence of the relationships of light interception, NAR, and











Table 1.6.
Crop
maize











soybeans


ybean physiological growth stages.
Description


Maize and so
Stage
0
1
2
3
4
5
6
7
8
9
10
VE
VC
V1
V2
V3
Vn
R1
R2


Reference
Hanway, 1971











Fehr and Caviness, 1977


a presumably equivalent to black layer formation.
Presumably equivalent to black layer formation.


emergence
fourth leaf fully emerged
eighth leaf fully emerged
twelveth leaf fully emerged
sixteenth leaf fully emerged
tassel fully emerged and pollen shedding; silks emerging
grain in blister stage
grain in dough stage
grain beginning to dent
grain fully dented
physiological maturity
emergence
cotyledons unrolled so leaf edges do not touch
fully developed leaves at cotyledonary (first) node
fully developed leaves at second node
fully developed leaves at third node
fully developed leaves at nth node
beginning bloom (one flower open on main stem)
full bloom (one flower open at either of the two topmost
nodes with fully developed leaves)
beginning pod (one pod 5 mm long at one of the four top-
most nodes with fully developed leaves)
full pod (one pod 2cm long at one of the four topmost
nodes with fully developed leaves)
beginning seed (one seed 3 mm long at one of the four top-
most nodes with fully developed leaves)
full seed (one pod containing a green seed which complete-
ly fill the seed cavity at one of the four topmost nodes
with fully developed leaves)
beginning maturity (one normal pod on main stage reaches
its mature color)
full maturity (95% of pods have reached their mature color)









Table 1.7. Maize and soybean crop growth rates
Crop CGRa EGR(SGR)b PCc
----kg/ha/day---
maize 84 -- -
250 --
204
208 --
339 259 0.76
319 210 0.66
soybeans 172 -- -
88 99 1.13
149 99 0.66
94de 91d 0.97
100ef 106 1.06
--- 79 --
--- 98


and seed growth rates.
Reference


Hanway, 1962
Hanway, 1962
Hanway, 1962
Hanway, 1962
Valle, 1978
Valle, 1978
Buttery, 1970
Hanway and Weber, 1971a
Hanway and Weber, 1971a
Egli and Leggett, 1973
Egli and Leggett, 1973
Kaplan and Koller, 1974
Kaplan and Koller, 1974


aCrop growth rate.
Ear growth rate, in maize; seed growth rate, in soybeans.
c Partitioning coefficient; calculated from CGR and SGR.
One year data only.

e Obtained by integrating the equations reported over the apparently
linear period (40 to 70 days after planting for the variety "Kent",
40 to 80 days after planting of the variety "D66-5566") and dividing
by the length of that period.
fTwo year average.
Two year average.

















10




8




6









2




0
0 28 56 84 112
days after emergence


Fig. 1.2. Typical growth curve.
Source: Hanway and Weber (1971b).









CGR with the leaf area index (LAI; leaf area per land area). These re-

lationships, and the mechanisms through which linear CGR occurs, are

described below.

The proportion of insolation intercepted by the crop canopy is a

function of LAI and the leaf inclination (Loomis and Williams, 1969;

Trenbath and Angus, 1975). Williams et al. (1965a) found that the pro-

portion of light intercepted by a maize canopy asymptotically approached

100% as the LAI increased. Interception with an LAI equal to 1, 2, and

12 was approximately 35%, 60%, and 100%, respectively.

In contrast to light interception, NAR decreases as LAI increases.

Watson (1958) found that NAR decreased linearly with LAI in kale

(Brassica oleracea var. acephala) and sugar beet (Beta vulgaris). In

kale, NAR decreased from 5.5 to 10.8 g/m2/wk per unit increase in LAI;

in sugar beet, from 2.2 to 3.9 g/m2/wk per unit increase in LAI. In the

same study, Watson uses data from another experiment with potatoes to

show that NAR in that species decreased 47 g/m2/wk for each unit in-

crease in LAI. Williams et al. (1965b) reported similar results in two

experiments with maize. In both experiments, harvests for total dry

mass per land area and LAI were taken on three dates, which were 35, 49,

and 61 days after planting. The first experiment (Experiment A) was

thinned to several populations 15 days after planting, which they pre-

sumed was before intraspecific competition became significant. The sec-

ond experiment (Experiment B) was thinned 20 days later, concurrent with

the first harvest. Mean NAR from harvest 1 to harvest 2 declined lin-

early in Experiment B at 0.99 g/m2/day. Mean NAR in Experiment A de-

creased quadratically at a diminishing rate comparable to that in Exper-

iment B. Mean NAR from harvest 2 to harvest 3 declined logarithmically









at a diminishing rate in both experiments, with nearly identical curves.

The average decrease was approximately 1.4 g/m2/day. Watson (1958)

attributed the decrease in NAR with increasing LAI to the increased mu-

tual shading of leaves at higher populations. Williams et al. (1965b)

add that the diminishing decrease they observed suggests that canopy

respiration increased less than linearly with LAI. They noted that LAI

increased faster than leaf mass as the population became denser, and

suggested that leaves became thinner as LAI was increased. Because of

the diminishing decrease in NAR with LAI, they suggested that CGR in

maize may not reach a maximum as LAI increases, but instead approaches

an asymptote.

The response of CGR to LAI is a function of the response of light

interception and NAR to LAI. The CGR of a newly-emerged crop is limited

by the leaf area of the crop (Eagles and Wilson, 1982). Because of

this, the relative growth rate (RGR, defined as CGR per unit plant mass)

of the crop is constant, and crop growth is logarithmic (Evans, 1972).

This is based on the assumption that the leaves comprise a constant

fraction of the total plant mass during this time:

1 dW
j x k
W dt


= fk dt


Wt = W exp(kt)

where

Wt = total plant mass

t = time

k = a constant









Eagles and Wilson (1982) explain that as the crop canopy continues to

develop and LAI increases, the mutual and self shading of leaves causes

NAR to drop, but CGR continues to increase due to increased light inter-

ception. Eventually a balance is obtained between decreasing NAR and

increasing light interception, and CGR reaches a maximum. In cases such

as maize, further increases in LAI result in thinner leaves; respiration

thus increases at about the same rate as gross photosynthesis due to

enhanced light interception, and CGR remains constant (Williams et al.,

1965b). Yoshida (1972) reviews many other cases where CGR increases

with LAI and then plateaus. In the case of crops where respiration is

proportional to plant dry mass and the leaves do not change their mor-

phology, respiration increases faster than light interception, and NAR

decreases (Watson, 1958; Eagles and Wilson, 1982). The maximum CGR in

these cases occurs with LAI sufficient to intercept ~95% of the insola-

tion (Loomis and Williams, 1969). If LAI remains at this point and fur-

ther photosynthate produced is deposited in storage organs, or if the

crop has an asymptotic relationship between CGR and LAI, light becomes

the factor limiting CGR. Hanway (1962) asserted that insolation fluctu-

ated to only a minor degree on a weekly basis in Iowa. Assuming the

supply of photosynthetically active radiation is constant over the sea-

son, the rate of photosynthesis, and hence the CGR, is also constant

until the plant begins to senesce.

That this linear growth phase occurs in maize is borne out by ex-

periment (Hanway, 1962; Duncan, 1975; Valle, 1978). Duncan (1975) cites

work by Sayre and by Chandler which indicates that, excepting major leaf

area or climatic changes, the photosynthetic rate of a maize canopy

tends to remain relatively constant over a long period of time. Hanway









(1962) notes that while variations in the light and water supply should

cause variation in CGR, in most years in Iowa these variations are like-

ly minor on a weekly basis. The same linear growth phase has been ob-

served in soybeans (Hanway and Weber, 1971a; Egli and Leggett, 1973;

Shibles et al., 1975; Kaplan and Koller, 1974; Boote et al., 1979).

Seed growth rate also tends to have a linear phase. Evans (1975)

cites three studies with maize, wheat and rice to back his claim that

for many crops there is a period when SGR is relatively constant, de-

spite changes in incident radiation or leaf area. He notes that SGR is

sensitive to temperature in some crops. Duncan (1975) notes that most

of the dry matter accumulation in maize takes place during the linear

phase of seed growth. Numerous studies on maize SGR and soybean SGR

(Valle, 1978; Hanway and Weber, 1971a; Boote et al., 1979; Egli and Leg-

gett, 1973; Kaplan and Koller, 1974) are based on the assumption of a

period of constant SGR, and show good coefficients of determination.

The pattern in which a crop distributes its photosynthate among its

various organs changes during the crop's life cycle (Evans, 1975). Sink

strength plays a large part in determining the pattern of distribution

(Evans, 1975), and since the reproductive organs are not in existence

early in a crop's life, it is evident that their appearance could well

change the pattern of distribution. Evans notes that a strong bias

toward the storage sink is made possible by the division of crop life

cycles into vegetative and reproductive phases, and that this division

of the life cycle is perhaps most complete in the small grain crops, and

least complete in crops with axillary flowering (e.g., soybean and cot-

ton (Gossypium hirsutum)), where there is competition between the vege-

tative and reproductive sinks throughout the life cycle. We might note









that if partitioning of new photosynthate to the seed is 100% and the

photosynthetic rate (or CGR) is increased, we would expect to see SGR

increase, and yield as well. However, Evans studied the results of ex-

periments with potatoes, soybeans, and cotton, and concluded that the

storage rate was not limited by the rate of photosynthesis.

The period of time over which assimilate is stored in the seed is a

major factor in determining the yield. Evans (1975) cites work by Wat-

son with wheat, barley (Hordeum vulgare), potatoes, and sugar beet (Beta

vulgaris), and concluded that while there were differences in the rates

of storage, the most important determinant of yield in these crops, and

in soybeans, was the duration of the storage phase. Arnon (1975) re-

lates that the majority of grain dry mass in many cereals is formed from

photosynthate produced after the ears emerge. Hanway and Weber (1971a)

found in their study with soybeans that differences in seed yields be-

tween varieties and years were primarily a function of differences in

the seed filling period duration. Duncan (1975) notes that maize varie-

ties differ in the duration of the filling period. He cites work by

several authors showing differences in varietal filling period of nine

days in one location, four days in another, and fifteen days in a third.

Shibles et al. (1975) state that it can take 30 to 52 days from fertil-

ization to the attainment of maximum seed weight in soybeans. Boote et

al. (1979) reported filling periods in soybeans, estimated as time

elapsed between stages R5 and R8, to vary from 32 to 52 days. Yield for

this group of soybeans increased about 50 kg/ha for each additional day

in the filling period. While seed yield and total dry matter production

are not the same, deWit (1972) makes the analagous observation that dif-

ferences in total dry matter production between crops are due primarily









to the length of time that a closed canopy (and hence constant CGR) is

maintained.

While the rates of crop photosynthesis and of partitioning during

the storage period are rather important in determining yield, there is

also the possibility that photosynthate produced prior to the storage

phase can be remobilized from the tissues and translocated to the seed.

Duncan (1975) cites three studies to show that this does occur in maize.

In one case, 20% of the grain mass came from material previously stored

in the stalks. Hanway and Weber (1971a) found similar behavior in soy-

beans. In their experiment, stem dry weights decreased 11% between

growth stages R5 and R9, which they took as an indication of the trans-

location of carbohydrate from stem to seed during that period.

While growth analysis contributes greatly to our understanding of

yield formation, there is another factor without which our understanding

is incomplete: yield adjustment. Yield adjustment consists of the mech-

anisms by which the potential yield of a crop is diminished in a series

of discrete steps. It is basically a process of diminishing the storage

sink size. Duncan (1975) states that most maize varieties have a maxi-

mum kernel weight which cannot be exceeded. He notes that if stress

occurs at any time before the onset of kernel formation (e.g., the lower

ears may fail to develop, those which develop may fail to silk in time

for pollination, and the pollinated kernels may be aborted), the kernel

number per plant is decreased, and despite good conditions later, yield

cannot exceed the kernel number per unit area times the maximum kernel

weight. Yield can be depressed after kernel number has been set as

well, as by the development of late drought, leaf loss, or nutrient de-

ficiency, particularly of K. A similar process can occur in soybeans.









Shaw and Laing (1966) subjected soybean crops to water stress for short

intervals during reproductive growth and observed the effect on seed

yield. They found stress during stages R1 and R8 (Fehr and Caviness,

1977) to have no significant effect on yield, but noted that stress ear-

ly in the season can reduce the stand. Stress during R2 through R7 re-

duced pod number, the greatest effect being for R2 to R4. The number of

mature beans per pod was strongly affected by stress during R5 to R7,

while seed size was most affected at R7.

An implication of yield adjustment is that dense planting may favor

higher yields, up to a point. Duncan (1975) notes that maize plants

grown at lower planting densities have higher potential yields, as indi-

vidual plants, but Arnon (1975) notes that maximum maize yields are

achieved with average-sized ears. It appears that with large ears, the

yield potential of the individual plants has been reached and cannot be

exceeded (every kernel on the plant has developed and been filled to its

maximum capacity). This is consistent with Arnon's conclusion that

large ears indicate that a higher population should have be used.

There is one final factor in understanding crop productivity, name-

ly, the pattern of assimilate distribution among the plant organs over

time. Figure 1.3. helps to illustrate this. Reinvestment strategy

helps to explain several observations. We can see, for instance, why

crop growth is exponential so long as it is limited only by leaf area.

Letting

A = leaf area

L = leaf mass

p = A/L














































A = leaf area
k = rate of photosynthesis per unit leaf area
R = reinvestment rate. if R = 1, the entire
photosynthate pool is invested in new leaf tissue.


Fig. 1.3. Model of photosynthate distribution and reinvestment.
Note: Quantities are indicated by rectangles, mass transfer by solid
arrows, and rates of transfer by the pentagons. The dashed arrow
indicates influence without mass transfer.









R = reinvestment rate (proportion of new photosynthate

used in synthesizing new leaves)

k = a constant

t = time

M = mass of plant tissue other than leaves

W = total plant mass

We see that as long as the crop growth rate is a function of leaf area

only (see Figure 1.3)

dL
S= RkA = pRkL
dt

L = L exp(pRkt)

dL dM
S= RkA d =(1 R)kA
dt dt

dM 1 R
dL R


M M= 1 (L- L )


M = M+ (L L)

W RM+L
W = M + L


= M + 1 (L L ) + L
o R o

L- L
=M + L +
o o R

L
= W + -2[exp(pRkt) 1]
o R

Hence, we see that crop growth is exponential so long as it is limited

by leaf area. Once the canopy closes, it is no longer leaf area which

is limiting but rather insolation, and crop growth becomes linear (see









earlier explanation). We can also see that once the canopy closes, CGR

is dependent upon the rate of photosynthesis alone.

Another thing which can be deduced from Figure 1.3 is that it is to

a crop's advantage to separate the vegetative and reproductive stages of

its development (Good and Bell, 1980). From the equation relating W to

R above, it is evident that a decrease in R significantly increases the

time needed to obtain a closed canopy, and hence maximum CGR. If a

plant establishes its photosynthetic apparatus early in its life cycle,

as is the case with maize (Hanway, 1962), all of the photosynthate pro-

duced during the reproductive phase can be stored in the seed. In

plants such as soybeans, though, which have axillary inflorescences,

there is often competition between the vegetative growth and storage

throughout the reproductive phase.

Good and Bell (1980) consider the functions of the vegetative stage

to be the establishment of the photosynthetic structures, and the sup-

pression of competing plants by stealing their light and water supply.

However, excessive partitioning of photosynthate to storage can have

deleterious effects. Evans (1975) points out that excessive partition-

ing to the storage organs may prevent the maintenance of roots and

leaves, thus cutting short the filling period and diminishing crop

yield. He suggested that the heavy investment by annuals in their re-

productive organs may be the cause of their life cycles being shorter

than those of perennials. The effect in soybeans may not be as severe

as imagined, though. Egli and Leggett (1973) studied two soybean varie-

ties with similar yield potentials, one determinate and the other inde-

terminate, and found that both had accumulated at least 85% of their

vegetative dry mass by the time there was any appreciable accumulation









of dry mass in the pods and seeds. They thus concluded there was little

competition from vegetative growth during the storage phase.

Limits

A serious question for agriculturalists is the maximum potential

productivity of specific crops, and of crops in general. It has often

been asserted that agricultural crops conserve less than 1% of the total

annual insolation striking agricultural fields as phytomass (Good and

Bell, 1980; see Table 1.8). Good and Bell note that some have concluded

there is thus great room for agricultural improvement, but counter that

crop efficiency is actually much higher than apparent if compared with

that theoretically attainable. This will be gone into in detail below,

but they note that the 5% efficiency of maize grown under field condi-

tions which has been observed may be close to the maximum possible. De-

Wit (1972) suggests that the potential CGR of most crops is approximate-

ly 275 kg/ha/day, when the roots are included in the plant mass. If

each kg dry mass (deWit estimated typical crop matter to consist of 25%

protein, 6% lipid, 60% cellulose, and 10% ash) represents approximately

4.44 x 103 kcal (caloric contents from Howell, 1961) and the average

daily insolation is 3.56 x 103 kcal/m2/day (Good and Bell, 1980), this

represents an efficiency of 3.43%.

Good and Bell (1980) explain why observed crop photosynthetic effi-

ciencies may be much closer to the potential than is apparent. Each

molecule of CO2 requires eight quanta of light for its reduction to car-

bohydrate. If one takes an average wavelength of 550 nm for photosyn-

thetically active radiation (PAR), the average quantum contains 8.76 x

10-20 cal:












Table 1.8. Proportion of insolation conserved by various crops.


Crop Scientific name
sugar cane Saccharum officinale
sugar beets Beta vulgaris
rice Oryza sativa
maize Zea mays
wheat Triticum aestivum
potatoes Solanum tuberosum
sugar cane Saccharum officinale
soybeans Glycine max (L.) Merr.
rice Oryza sativa
maize Zea mays
potatoes Solanum tuberosum
soybeans Glycine max L. Merr.
wheat Triticum aestivum

a Assumes an annual insolation of 1.3 X

Source: Good and Bell (1980)


Location
Hawaii
Netherlands
Japan
U.S.
Netherlands
U.S.
Cuba
Canada
world average
world average
world average
world average
world average


Energy conserved
annually in
photosynthate, 103kcal/m2
12.3
7.3
5.3
4.5
4.4
4.1
3.7
2.4
2.3
2.4
2.2
1.4
1.4


Energy use
efficiency, %a
0.95
0.56
0.42
0.35
0.35
0.31
0.30
0.18
0.18
0.17
0.17
0.10
0.10


106 kcal/m2









E = hv

= (6.62 x 10-34 J x sec)/(550 x 10-9 m)

= 3.61 x 1019 J

= 8.76 x 10-20 cal

Each mole of CO2, when reduced to carbohydrate, conserves approximately

114 kcal, and requires 6.02 x 1023 x 8 x 8.76 x 10-20 cal = 422 kcal PAR

for its reduction. Thus the maximum efficiency when using only PAR is

but 27%. However, only about 45% of sunlight is photosynthtically ac-

tive, so the maximum theoretical efficiency is about 11%. This effi-

ciency, however, is possible only at low light levels, due to two other

factors:

1. Absorbed quanta can be stored only a very short time in
excited pigment. If quanta are received faster than they
can be used, the excited pigment returns to its ground
state and the energy is dissipated as heat.
2. Transfer of energy in a reaction occurs at 100% efficiency
only when the reaction takes place at equilibrium. The
greater the reaction rate, the farther it must be displaced
from equilibrium, and the greater the energy loss. This
type of energy loss is called irreversibility loss.

The photosynthetic efficiency of a crop can thus be expected to decrease

as the light intensity increases. Good and Bell note that at low light

intensities it is generally accepted that each molecule of CO2 requires

less than 10 quanta for reduction, indicating an efficiency greater than

80% of that theoretically possible. However, it is photosynthesis under

relatively high illumination (typical of field conditions) which is of

importance. Good and Bell note that except at the intense sunlight

around noon, maize can maintain about a 5% photosynthetic efficiency.

Given the inevitability of irreversibility losses and the necessity for

some photosynthate to be expended in respiration, they suggest that this

may closely approach the maximum attainable.









DeWit (1972) has suggested that the potential CGR of most crop

plants is around 200 kg/ha/day (275 kg/ha/day if roots are included).

He bases this upon an explanation of why the crop photosynthetic rate of

most species is about the same. He notes that the initial slope of the

photosynthetic curve varies little with species, though photosynthetic

rates at high light intensities may vary considerably. However, light

in a canopy is more attenuated than that hitting a flat, horizontal sur-

face, and the leaves actually photosynthesize at less than full light

intensity even at noonday. Duncan et al. (1967) show that the effective

light intensity due to direct radiation is equal to the intensity of

insolation multiplied by the cosine of the angle of incidence (the angle

the path of light makes with the leaf surface). This has two conse-

quences. First, photosynthesis proceeds at a higher rate on a canopy

than a leaf basis (Hesketh and Baker, 1967; Eagles and Wilson, 1982).

Given a crop with canopy architecture like that of temperate cereal

crops, photosynthesis on a clear day with the sun at 30, 60, and 90 de-

grees is calculated to give canopy photosynthetic rates which are 75%,

150%, and 175% greater, respectively, than those calculated on a single

leaf basis (deWit, 1972). Secondly, since crops are actually operating

at lower light intensities, the less efficient C3 plants produce at ef-

ficiencies closer to those of the C4 plants (C4 plants photosynthesize

using the Calvin-Benson cycle, which initially incorporates CO2 into a

three-carbon molecule, phosphonyl-3-phosphoglyceric acid. The C4 plants

utilize the Hatch-Slack pathway, which fixes CO2 at first into a four-

carbon molecule, oxaloacetic acid (Meyer et al., 1973)). The relatively

greater importance of aerodynamic than leaf resistance to CO2 diffusion

(C4 plants have lower mesophyll resistance) further decreases the









advantage of C4 over C3 plants (Evans, 1975). Evans cites work with

maize and wheat to show that when measured by aerodynamic techniques,

the highest photosynthetic rates of wheat are only slightly less than

those of maize. He states that periods of low illumination and the mu-

tual shading of crop leaves further narrows the gap between C3 and C4

plants. A summary of the maximum short-term crop growth rates observed

in C3 and C4 plants is given in Table 1.9.

It may be noted from Table 1.9 that although neither C3 nor C4 me-

tabolism conveys a generally higher CGR, the crop growth rates vary con-

siderably between species, and that many of the growth rates exceed 275

kg/ha/day. This seems to contradict the work by deWit (1972), but the

two studies can be reconciled. Firstly, deWit assumed crops to have a

fairly similar chemical composition. In cases where species differ sig-

nificantly in the energy content of their chemical compositions, as when

one crop contains more oil than another, we can expect differences in

CGR. Also, deWit limited his study to the Netherlands and the level of

insolation common there. Differences in light intensity between regions

can be expected to result in different photosynthetic rates, and hence

in varied crop growth rates. Another factor, particularly significant

in soybeans, is differing crop physiologies. Sinclair and deWit (1976)

surveyed studies on 24 different crop plants, and found that while soy-

bean had the highest rate of translocation of N to the seed, it had one

of the lowest crop growth rates. They hypothesized that soybeans might

require the translocation of N from actively photosynthesizing tissue to

support seed growth, leading to a decrease in photosynthetic activity

and eventually early plant death. They found evidence to support this

in the data of Hanway and Weber (1971c), which showed that N loss during









Table 1.9. Maximum short-term crop growth


Crop
sunflower
Napier grass
rice
maize
Sudan grass
sugarcane
soybeans


Scientific name
Helianthus annus
Pennisetum purpureum
Oryza sativa
Zea mays
Sorghum vulgare
Saccharum officinale
Glycine max


rates recorded,
Type of
metabolism
C3
04

C3
C4


CGR, kg/ha/day
680
600
550
520


510
380
170


Source: Evans (1975)









leaf senescence is approximately 2% of the combined stem and petiole

mass, and at least 3% of leaf mass. Other data which supports this is

the finding of Boote et al. (1978) that the N concentration in both the

upper and lower leaves declined almost linearly during seed-fill. The

simulation model which Sinclair and deWit constructed predicted that N

translocation rates of at least 9 kg N/ha/day were required for seed

growth, but they could find no reports of a sustained N supply from up-

take or N fixation in excess of 5 kg N/ha/day, lending further support

to their hypothesis. The SGR predicted by their model was in good

agreement with the data of Hanway and Weber (1971a). The model indi-

cated that increasing the N supply to the plant during seed fill would

increase the seed filling period and the seed yield. Significantly in-

creasing the photosynthetic rate without an increase in N supply rate

was predicted to diminish seed yield, by speeding the loss of N from the

vegetative tissue and hence cutting short the seed filling period. In-

creased photosynthetic rate was predicted to increase seed yield if it

was accompanied by a higher rate of partitioning of photosynthate to N

assimilation. Sinclair and deWit's model supports the hypothesis that N

demand exceeds supply in the soybean and leads to self-destruction of

the plant. Garcia and Hanway (1976) proposed that foliar fertilization

may minimize N losses from soybean leaves during seed fill, thus main-

taining higher photosynthetic rates and causing higher yields. Boote et

al. (1978) applied a foliar fertilizer spray (kg/ha of nutrients: N,

28.0; P, 2.9; K, 8.4; S, 12.2; N was supplied as urea) each week after

the pods had formed and the seeds began to enlarge, to see whether fo-

liar fertilization would increase the concentrations of the applied ele-

ments in the leaves and the rate of photosynthesis and duration of









pod-fill. They found that while foliar fertilization significantly in-

creased the concentrations of N, P, and K in the leaves, neither yield,

SGR, nor seed number were affected. They attributed this to several

factors. First, photosynthesis on a leaf area basis was increased only

slightly by foliar fertilization, and only toward the end of the seed-

filling period, when most of the leaves had dropped. Second, the in-

creases in the photosynthetic rate may have beem negated by losses in

leaf area, due to small fertilizer burns on the leaves and greater in-

sect feeding on the fertilized soybeans. Third, N absorbed by the

leaves appears to have simply substituted for symbiotically-fixed N,

without substantially increasing leaf N concentration or the duration of

photosynthesis. Finally, the foliarly-applied N may not have been af-

fectual because of hormone changes or the loss of anabolic enzymes in

the leaves. It thus appears that while the inability to meet seed N

demand leads to the soybean's self-destruction, foliar fertilization

with urea is not effective in disabling the mechanism.

Effects of Plant Nutrition on Yields

Having discussed the mechanisms of yield determination, the next

question is how and why plant nutrition affects crop yields. This ques-

tion will be answered by looking at the physiological functions of N and

K, and by reviewing some general information on nutrient effects.

Certain effects of nutrients can be applied equally well to any

element, as the observation that placement of high concentrations of

fertilizer salts close to seeds can reduce germination (Mengel and Kirk-

by, 1982). Other effects are less obvious. Hanway (1962) and Harshber-

ger et al. (1954) found that increases in maize grain yield due to im-

proved soil fertility were due not to a higher partitioning of









photosynthate to the grain, but to equal increases in the dry mass of

each of the plant organs. Hanway also found no relationship between

leaf concentration of N, P, and K and the total or seed dry mass pro-

duced, and concluded that the primary effect of nutrient deficiencies

was the reduction of leaf area duration, rather than of the net assimi-

lation rate (NAR; net photosynthetic rate per unit leaf area). This

second point has been contended. Peaslee and Moss (1966) found that P

and K deficiencies reduced maize NAR, and Boote et al. (1978) found that

gross photosynthesis in the upper leaves of soybeans was well correlated

to their N concentration.

One of the mechanisms through which nutrients affect grain yield is

evidently hormone production. According to Mengel and Kirkby (1982),

grain filling appears to be controlled by the sink (the seed) rather

than the source (the photosynthetic apparatus) through hormones. Figure

1.4 shows the activity of various plant hormones in barley seed from

flowering to maturity. Cytokinin, gibberellic acid (GA), and indole

acetic acid (IAA) peak about one, four, and five weeks after flowering,

respectively, while abscissic acid (ABA) concentration is highest when

the seed attains its maximum fresh weight. Cytokinin is believed to

control the formation of the grain endosperm cells, thus having a large

influence on final grain yield. Gibberellic acid and IAA are also

thought to promote seed growth, while they conjecture that ABA may bring

about seed maturity in a fashion analogous to its action in causing oth-

er tissues to senesce. Hence, the effects of nutrients on these hor-

mones could affect seed yield, as will be seen below.

Potassium has many roles in plant physiology (Mengel and Kirkby,


1982), including:

















Cytokinins


Indole acetic
acid


Fresh weight




Anthesis Maturation


Fig. 1.4. Changes in hormone activity and dry
mass during barley seed fill.
Source: Mengel and Kirkby (1982).









1. maintenance of turgor in young tissue, needed for cell
expansion. This function is the most sensitive to K
deficiency.
2. catalysis of phosphorylation.
3. involvement in the synthesis of ribulose diphosphate
carboxylase, thus increasing the CO2 assimilation rate.
4. reduction of mesophyll resistance to CO2.
5. decrease of respiration..
6. translocation of newly produced photosynthate and of
remobilized reserves.
7. hormonal control; deficiencies cause increased ABA
concentrations in seed, resulting in earlier maturity.
8. strengthening of the vascular bundles, which aids in
lodging resistance.

As a consequence of these functions, K deficiency results in decreased

CGR, SGR, and seed filling period, and increases susceptibility to lodg-

ing. It also directly results in decreased net assimilation rate (NAR;

photosynthetic rate per leaf area) (Peaslee and Moss,1966). Peaslee et

al. (1971) report that K fertilization of maize shortens the vegetative

stage, perhaps due to the enhanced rate of photosynthesis and transloca-

tion caused by adequate K. The development of black layer was, however,

delayed by K fertilization. This could be attributed to the avoidance

of high ABA levels in the grain by adequate K supply.

Other effects of K include

1. enhanced disease resistance. This may be due to the
promotion of thicker outer walls in the epidermal cells, or
to prevention of abnormal metabolism favoring certain
pathogens (Mengel and Kirkby, 1982).
2. an increase in the grain:husk ratio in maize (Arnon, 1975).
3. the stimulation of the total number of ears per unit area
and the number of grains per ear, in wheat (Chapman and
Keay, 1971).

We thus see that K deficiency also diminishes the partitioning factor,

promotes the incidence of disease, and diminishes the potential seed

yield by decreasing the number of seed per unit area which may be

filled. In summary, K deficiency can decrease both the total and seed









dry mass production, and decrease CGR, SGR, PF, and the seed filling

period.

Mengel and Kirby (1982) mention three physiological functions for

N, which have far-reaching consequences:

1. Nitrogen is required for protein and nucleic acid
synthesis.
2. It enhances the synthesis of cytokinin.
3. Its deficiency causes increased ABA concentrations in the
seed.

Since protein and nucleic acids are required for the production of new

tissue, Mengel and Kirkby credit N deficiency with causing decreased

CGR. Boote et al. (1978) found the NAR of the upper leves of soybean to

be well correlated with their N concentration. They attributed the NAR

depression due to N deficiency to the loss of photosynthetic enzymes

(and hence proteins) from the leaves. Decreased CGR may be the reason

why N deficiency tends to delay crop maturity (Hanway, 1962; Arnon,

1975), and why remedial N applications advance maturity (Arnon, 1975).

Hanway also noticed slower growth of the cob, shank, and grain of maize

in N-deficient plants (i.e., lower SGR). Decreases in SGR may also be

credited with the observation of decreased seed size in N-deficient

plants (Arnon, 1975; Mengel and Kirkby, 1982). Nitrogen excess, on the

other hand, results in the conversion of a high proportion of photosyn-

thate into protein, leaving too little carbohydrate for the formation of

structural tissue and thus inducing lodging (Arnon, 1975). Excess N

also delays maturity, due to causing nutrient imbalance, but when ap-

plied in proper balance with other nutrients, N generally advances ma-

turity (Arnon, 1975).

As stated above, cytokinin is believed to control the formation of

endosperm cells in the grain, and thus strongly influence yield. We may









note that this would influence primarily seed size. Mengel and Kirkby

(1982) also state that cytokinin stimulates the formation of tillers and

buds, and delays plant senescence. They thus credit the stimulatory

effect of N on cytokinin production with the early seed maturity and

leaf senescence and the decrease in tillering observed with N deficien-

cy. The effect of N on tiller and bud formation is supported by other

research. Arnon (1975) notes the stimulation of tillering in wheat by

N. He cites several experiments showing that N applications to maize

result mainly in an increase in the number and weight of the kernels.

Nitrogen applied early in the season (maize <20 cm tall) caused an in-

crease in the number of kernels per ear, but had no such effect later in

the season. High ABA concentrations, as mentioned earlier, result in

earlier grain maturity and hence in a shortened seed filling period.

Nitrogen deficiency thus decreases seed yields by shortening the seed

filling period duration (Mengel and Kirkby, 1982). Interestingly, Arnon

notes that N applications to maize result in a higher grain:stover ra-

tio, which contrasts with Hanway's (1962) findings.

One other area which should be mentioned is the interaction between

N and K. Application of N without adequate K is a well-known cause of

lodging. As noted earlier, the overabundance of N results in the pro-

duction of protein at the expense of structural tissue, without the

counteracting influence of K to strengthen that tissue. There is,

though, another not so obvious interaction, namely, that the absorption

of N and K by the plant are linked. Several authors have pointed out

the necessity for an electrostatic balance between cations and anions in

plant cells, so that large electric potential gradients across all mem-

branes do not occur (Russell, 1977; Arnon, 1975; Nye and Tinker, 1977;









Bowling, 1976). Bowling (1976) and Barber (1968) suggest that K+ uptake

is due to K+ moving into the plant in response to an electric potential

gradient caused by active anion uptake. Barber suggested that repeated

absorption and desorption along a series of absorption sites, each se-

lective for K+ over other cations, could explain the great selectivity

plants show in the absorption of K over other cations. While some au-

thors have reported that an uptake of excess cations over anions may be

compensated for by the secretion of H+, and excess absorption of anions

by HCO3~ (Russell, 1977; Arnon, 1975; Nye and Tinker, 1977), others have

reported that cations may compete for uptake, and that their uptake can

be enhanced by increased anion uptake. Increased potassium supply, for

instance, decreases the quantities of Ca2+ and Mg2+ absorbed (Nye and

Tinker, 1977; Russell, 1977; Barber, 1968). Arnon (1975) reports that

the absorption of NH4 depresses K+ uptake, but that the absorption of

NO3 enhances K+ uptake.

Another effect of K is the stimulation of nodulation (both number

of nodules per plant, and individual nodule weight) in soybeans (Jones

et al., 1977). We might thus expect K deficiency to induce N deficiency

in soybeans as well. On the subject of the relative efficacy of K in

increasing yields between maize and soybeans, findings differ. Barber

(1967, 1971) and deMooy et al. (1973) found that maize responded more,

while Voss (cited by Sartain et al., 1979) found no difference between

the two species.

Soil pH vs. Tillage

The use of ammonium fertilizers has been reported to cause soil

acidification of the surface layer of soil in no-tillage (Tripplett and

Van Doren, 1969; Blevins et al., 1977; Blevins et al., 1978). The









reason for this is that the ammonium ion is oxidized by the nitrifying

soil bacteria to produce nitric acid. Urea, cyanamide, and the nitrog-

enous substances in barnyard manure are all converted in the soil first

to ammonium, which is then subsequently oxidized (Alexander, 1961).

Hence, these fertilizers can also be expected to depress pH.

Since fertilizer in no-tillage production is typically surface-ap-

plied, the acidification which might be spread through the plow-layer in

conventional tillage tends to concentrate in the surface layer (Blevins

et al., 1978). Low pH contributes to both deficiencies and toxicities

of various nutrients (Brady, 1974; Mengel aand Kirkby, 1982). It also

causes faster degradation of the s-triazine herbicides. Kells et al.

(1980) report faster atrazine (2-chloro-4-(ethylamino)-6-(isopropyl-

amino)-s-triazine) decomposition with lower pH, and cite other work

showing similar results with atrazine or simazine (2-chloro-4,6-bis-

(ethylamino)-s-triazine). They point out the importance of the problem

by noting that these two herbicides are applied to over 80% of the maize

produced in the U.S. Under no-tillage, they reported 19% weed control

when 18% of the applied triazine remained undegraded, vs. 76% weed con-

trol with 52% of the atrazine remaining. The percent of original atra-

zine applied at any time between 10 and 80 days after application was

about 12% higher in limed than unlimed no-tillage plots (lime was broad-

cast). Kells et al. (undated) suggest that s-triazine herbicides are

degraded by nonbiological hydrolysis, and that low pH favors s-triazine

inactivation by causing their conversion to the less-stable protonated

form, and by increasing the number of negatively-charged adsorption

sites in the soil to which the herbicides can be bound.









Problems with soil acidification could possibly be greater in the

Southeastern U.S. than farther north. Hargrove et al. (1982), in a

Georgia study, report that while the organic carbon level did increase

in the surface soil under no-tillage, it did so to a lesser degree than

in previous studies conducted farther north. They speculated that this

was due to the higher temperature and humidity at their location. They

also questioned whether there might be more severe effects from soil

acidification in areas with rapid organic matter decomposition, since

studies had shown the higher organic matter levels in the surface soil

under no-tillage to mitigate the effects of soil acidity. An example is

the work of Blevins et al. (1977), who found that the high surface soil

organic matter levels in their no-tillage study resulted in relatively

low exchangeable Al levels (about 1 meq/100 g soil) in soils of pH 4.0.

The difficulty of lower organic matter levels, though, can likely be

alleviated by the surface application of lime, since the soil acidifica-

tion occurs near the surface (Blevins et al., 1978; Moschler et al.,

1973).















CHAPTER 2
RESPONSE OF DRY MATTER IN A MAIZE/SOYBEAN
DOUBLE CROPPING SYSTEM TO APPLIED RATES
OF NITROGEN AND POTASSIUM

Introduction

The response of crop yields to fertilizer inputs is of prime con-

cern to both researchers and producers. The data from fertility experi-

ments can be analyzed by regression to give production functions, which

enable the researcher to determine the maximum yield obtainable under

the experimental conditions, and provide farmers the information neces-

sary to maximize profits.

The maize (Zea mays L.)/soybean (Glycine max (L.) Merr.) double

cropping system has shown good potential in the Southeastern United

States (R.N. Gallaher, Agronomy Department, University of Florida, per-

sonal communication), and can likely be used in many parts of the trop-

ics and subtropics. One of the major advantages of multicropping in

industrialized agriculture is that fixed costs such as land and machin-

ery are spread over at least one additional crop, making better use of

fixed farm resources (Gallaher, 1978). However, crops in multiple crop-

ping systems may not respond to fertilization in the same manner as when

monocropped. As crops occupy the land for a larger portion of the year,

there is the opportunity for greater utilization of residual nutrients

in double cropping systems (Sanchez, 1976). The contribution of crop

residues in the maize/soybean succession to the succeeding crop's nutri-

ent supply can be seen by examining the nutrient content of the crops

and their residues. Arnon (1975) reviewed the results of several









studies with maize, and reported nutrient contents of the whole plant

ranged from 36 to 241 kg N/ha, 6 to 39 kg P/ha, and 28 to 239 kg K/ha.

Sanchez (1976) reports maize stover nutrient contents of N, P, and K

ranging from 37 to 72, 6 to 14, and 38 to 93 kg/ha, respectively. Total

N content of a soybean crop can range from 119 to 219 kg/ha, while N

content of the non-seed portion can range from 23 to 70 kg/ha (Hammond

et al., 1951; Post, 1982). Additionally, crops grown in succession with

legumes receive a portion of their N needs from the legumes. One of the

advantages of including soybeans in a cropping system is its fixation

of atmospheric N. Shibles et al. (1975) report that estimates of the

amount of N fixed by soybeans vary greatly, but cite one study by Weber

(1966) which found a crop yielding 2800 kg seed/ha could fix 160 kg

N/ha. It should be noted that this is N fixed; the crop took up another

45 kg N/ha from the soil. Shroder and Hinson (1975), studying a rye

(Secale cereale)/soybean succession, suggested that the nodules and

roots of soybeans could supply approximately 20 kg N/ha to the following

crop of rye. In an experiment using nodulating and nonnodulating lines,

Weber (1966) found that nodulating soybeans provided 37 kg/ha of residu-

al fixed N to a succeeding crop of nonnodulating soybeans. The nutri-

ents left in the crop residues should become available for crop uptake,

if they are not lost by volatilization, soil fixation, microbial fixa-

tion, or leaching.

The use of no-tillage has a variety of advantages, including re-

duced energy and equipment costs, conservation of soil moisture, and the

reduction of soil erosion. When used in conjunction with multicropping,

no-tillage shortens the time required between harvest and planting.









This permits the timely planting of a succeeding crop, an important fac-

tor in multiple cropping (Phillips et al., 1980).

The effects of crop residues in no-tillage systems are particularly

interesting. These residues form a natural mulch, which has a number of

benefits:

1. reduced water runoff
2. increased water infiltration
3. reduction of evaporation from the soil surface
4. reduced soil erosion
5. suppression of weeds
6. decreased infestation by the lesser cornstalk borer
(Elasmopalpus lignosellus).

Other benefits arising from those listed above are decreases in agricul-

tural pollution, the conservation of soil moisture, and improved crop

yields (Gallaher, 1978). An additional benefit is an increase in soil

organic matter and organic N. Over one season in a rye/soybean double

cropping system, Post (1982) observed that organic matter in the 0-30 cm

soil layer increased under no-tillage by 4.4 mt/ha, while that in plots

tilled to 15 cm depth decreased by 6.1 mt/ha. Nitrogen in the no-till-

age plots decreased by 141 kg/ha, but the decrease in the plots tilled

to 15 cm was even greater, 271 kg/ha. The organic matter and N levels

in the no-tillage and tilled plots were initially about equal. After 5

years of no-tillage and conventionally tilled continuous maize receiving

336 kg N/ha/yr, Blevins et al. (1977) found that organic matter in the

0-30 cm soil layer decreased 3% under no-tillage, vs. 16% under conven-

tional tillage. Organic N in the 0-30 cm layer increased about 6% under

conventional tillage. However, degree of the buildup appears to be in-

fluenced by climate and crop. Hargrove et al. (1982) double cropped

wheat (Triticum aestivum) and soybeans for 5 years with various tillage

combinations and then measured soil properties. While organic C and









organic N did increase in the 0-7.5 cm layer of soil, they noted that

the increases were not as great as in other studies conducted farther

north, and that there were no significant differences between organic C

or organic N in the surface soil between no-tillage plots and those con-

ventionally tilled twice a year. They attributed the nonsignificant

increases in organic C and N being due to the crops employed and the

climate. Rye and maize, they noted, produce substantially more residue

than does wheat straw. This difference was noted to be particularly

great in the case of no-tillage maize, which received substantially more

N than did the wheat in their experiment. They added that most studies

on the effect on no-tillage on soil organic N and C have been conducted

with maize. The longer and warmer growing season in the Southeast was

also seen as speeding residue decomposition. It thus appears that while

no-tillage may increase soil N and organic matter levels, cooler cli-

mates or crops producing large quantities of residues are needed to en-

sure this.

A production function (or yield curve, or yield response surface)

is a mathematical equation relating crop yield to one or more production

inputs (e.g., N, P, K, seeding rate, amount of irrigation received,

etc.). A beginning in understanding these sorts of relationships was

the discovery by Sprengel of the "Law of the Minimum" in the early nine-

teenth century, and its subsequent promulgation by von Liebig (Mengel

and Kirkby, 1982). The Law of the Minimum states that growth will be

limited by whichever of the necessary inputs is in the least adequate

supply (Stoskopf, 1981).

This concept, that yield is limited by various growth factors, led

Mitscherlich to hypothesize that the response of yield to any one









particular growth factor is asymptotic (Mengel and Kirkby, 1982). He

concluded from a number of pot and field experiments that the increase

in yield due to the addition of one unit of a production input was pro-

portional to the difference between the current yield and the maximum

obtainable. This relationship can be expressed mathematically as

dY
d = k(Y Y)
dX m

where

Y = yield

X = growth factor

k = constant

Ym = maximum yield

Upon integration, we obtain

In(Y Y) = C kX
m
where C is the constant of integration. If X is the quantity of nutri-

ent absorbed, or if the soil contains none of the nutrient before its

application, then when X = 0, yield is also zero. Hence

InY = C
m
and therefore

ln(Y Y) = InY kX
m m
This equation may also be written as

Y = Y [1 exp(-kX)]
m
A later modification, the Mitscherlich-Baule equation (Bray, 1961), con-

tained the soil level of the nutrient as well, eliminating the need to

assume that the soil contains none of the nutrient. The equation is









In(Y Y) = InY k(S + F)
m m
where

S = the quantity of the nutrient present in the soil

F = the quantity of the nutrient applied in fertilizer

Bray objected, however, that this equation implies that a quantity of

nutrient present in the soil produces the same yield response as an

equal quantity applied as fertilizer. He proposed an equation which

allows for different "efficiency factors" for the soil and fertilizer

forms of the nutrient:

In(Y Y) = InY klS k2F

where k1 and k2 are constants.

There remain several weaknesses to the Mitscherlich-type equations,

the most serious being that the "constants" in the Mitscherlich and Mit-

scherlich-Baule equations are not, in fact, constant (Mengel and Kirkby,

1982). This is due to the interaction of nutrients with each other and

with other growth factors, such as moisture supply. Van der Paauw

(1958) and Barber (1959) found the yield response of potatoes (Solanum

tuberosum), wheat (Triticum aestivum), and maize to depend upon the pre-

cipitation received during the growing season. Beer et al. (1967) re-

ported that the yield response of maize to N was affected by the soil

water content. Another weakness which Mengel and Kirkby note is that

the application of high levels of a nutrient may actually decrease

yields, an effect which cannot be accounted for by an asymptotic equa-

tion. They state that because of this second defect, some authors pre-

fer to use quadratic equations when relating yield response to a single

input.









There are two other weaknesses to the Mitscherlich-Baule equations,

namely, that they are difficult to handle for regression analysis, and

that they do not allow for the interaction of growth factors in deter-

mining yields. Doll et al. (1958) considered the square root and qua-

dratic functions the most satisfactory for regression analysis, perhaps

for these reasons. In describing yield response to a single variable,

these equations are
1/2
square root model: Y = a + bX/2 + cX

quadratic model: Y = a + bX + cX2

These equations can be easily expanded to include two or more growth

factors, and interactions between them. An example is the "second order

model," discussed by Montgomery (1976)

Z = i + aX2 + bX + cXY + dY + eY2

where

Z = dependent variable

X, Y = independent variables

i = intercept

a, b, c, d, e = constants

This model allows for a linear interaction between the independent vari-

ables. Higher order interactions (X2 Y, XY X2 Y ) can be included in

the model, but that shown is usually adequate. All of these polynomial

functions have the advantage that their partial derivatives can be easi-

ly taken, as they are needed for finding the production function maxi-

mum.

The maize/soybean double cropping system has good potential, and

crop yields may respond differently to applied rates of N and K than

when the crops are monocropped. This experiment was conducted to obtain









production functions for the system relating total plant and grain

yields to two major nutrients, N and K.

Materials and Methods

Experiment Description

The experiment was conducted in 1981 at the University of Florida

Agronomy Farm (Gainesville, Fla.), on an Arrendondo fine sand (member of

the loamy silicious hyperthermic family of the grossarenic Paleudults

(Soil Survey Staff, 1982)). A randomized complete split-split block

design was used, with four replications. Main treatments were cropping

systems, split-plot treatments were N rates, and split-split plot treat-

ments were K rates. The two systems were maize for forage followed by

soybeans, and maize for grain followed by soybeans. In maize for for-

age, the entire above-ground portion of the plant was removed at har-

vest. The maize ear (husk, cob, and grain) alone was removed in maize

for grain, while the soybeans were harvested for their seed only. In

all cases the remaining plant matter was mown with a rotary mower and

left on the plots. The maize (variety "DeKalb XL71") was planted at

110,000 plants/ha 8 March and harvested 17 July, while the soybeans

(variety "Cobb") were planted at 500,000 plants/ha 6 August and har-

vested 24 November. Both crops were produced using no-tillage, and were

planted using the Tye drill, set for 25 cm rows. Nitrogen was applied

as NH4NO3 at three rates, 0, 168, and 280 kg N/ha/yr, in three equal

increments. The first came preplant (28 February), the second when the

maize was about 15 cm high (17 April), and the third when the maize was

about 55 cm high (15 May). Potassium was applied preplant (28 February)

as KC1 at four rates: 0, 45, 135, and 404 kg K/ha. Hence, only the

maize was fertilized, the soybeans scavenging nutrients from the maize









crop. Other soil amendments were added to all the plots to ensure that

N and K were the only limiting nutrients. Magnesium (17 kg Mg/ha as

MgSO4 7H120), P (56 kg P/ha as concentrated super phosphate), and micro-

nutrients (22.5 kg/ha of Fritted Trace Element 503) were all applied

broadcast preplant of the maize. Fritted Trace Element 503 is manufac-

tured by Ferro Corporation, with 3.0% B, 3.0% Cu, 18.0% Fe, 75.% Mn,

0.2% Mo, and 7.0% Zn. To ensure good stands, the crops received a vari-

ety of herbicides and insecticides, detailed in Table 2.1. The maize

also received 30 cm irrigation, and the soybeans 20 cm, to supplement

rainfall.

Plant samples were dug up using shovels, and dried at 70C before

being weighed. For yields of total plant and seed (or ear) dry mass at

harvest, samples of 914 cm row were taken. Total plant dry mass was

determined, and the seed (or ears) then separated out and weighed.

Maize harvest samples were taken on 17 July, soybean harvest samples, on

24 November.

The soil in the plots was also sampled to 45 cm. Soil samples were

taken three times in 1981, on 17 February, 27 July, and 10 December. In

all cases, the soil was air-dried and ground through a 60-mesh stainless

steel screen before analysis. Extracts were obtained by mixing 5 g soil

with 20 ml Mehlich I extractant (0.05 N HCI + 0.025 N H SO ) and shaking

5 minutes before filtration. The K concentration was determined by

flame emission.

Experiment history

The experiment was performed on plots which were part of a previous

fertility experiment, conducted from 1977 through 1980. This experiment

was organized as a randomized complete split-plot experiment, with main









Table 2.1. Herbicides and pesticides used.
Quantity used
Crop Input per ha
maize carbofurana 2.24 kg a.i.b
glyphosatec 4.68 1
atrazined 2.24 kg a.i.
metolachlore 1.68 kg a.i.
2, 4-D aminef 1.17 1
soybeans glyphosatec 2.33 1
alachlorg 2.24 kg a.i.
metribuzinh 0.45 kg a.i.
methomyli 0.13 kg a.i.
toxaphene' 4.67 1
a 2,3-dihydro-2,2-dimethyl-7-benzofuranyl methylcarbamate
bkg active ingredient
c N-(phosphonomethyl)glycine
d2-chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine

e 2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-l-
methylethyl)acetamide
f (2,4-dichlorophenoxy)acetic acid, amine salt
g 2-chloro-2',6'-diethyl-N-(methoxymethyl)acetanilide
h 4-amino-6-tert-butyl-3-(methylthio)-as-triazine-5(4H)one
SS-methyl-n-((methylcarbanoyl)oxy)thioacetimidate

Chlorinated camphene containing 67-69% Cl.









plot and split-plot treatments as described above. None of the plots

received any other nutrients. (The split-split plot (i.e., K) treat-

ments were imposed in 1981.) Dolomitic lime was applied once to all

treatments at 4480 kg/ha, three months prior to planting the first maize

crop. The maize variety "DeKalb XL395" was planted in the forage sys-

tem, "Funks G4507" in the grain system, at 110,000 plants/ha. "Cobb"

soybeans were planted at 500,000 plants/ha. The maize was usually

planted in early March and harvested in mid-July, while soybeans were

usually planted in late July and harvested in late November. The maize

was planted using the Brown-Harden Superseeder with subsoiling, while

the soybeans were planted using the Tye drill.

Statistics

The statistical analysis was conducted using both analyses of vari-

ance (AOV's) and regression analysis. The experimental results were

analyzed separately for each system. Hence, the appropriate analysis is

for a split-plot design organized as a randomized complete block with

three N rates as main treatments and four K rates as subtreatments.

The regression analysis was conducted using the second order model,

Y = B0 + B1N2 + B2N + B3NK + B K + B5K2, where Y = yield, N = N applied,

and K = K applied (Montgomery, 1976). Montgomery notes that while

higher-order interactions than the linear term can be included, the

model as shown is usually adequate. A summary of the initial AOV's ob-

tained from the regression analyses is given in Table 2.2. Sums of

squares not accounted for by the algebraic terms comprise the lack of

fit (LOF; only two of the three possible K terms were used and only one

of the six possible N x K interaction terms). A description of how the

total sums of squares are divided among the various sources of variation











Table 2.2. Probability levels at which terms in the initial regression analyses
of variance were significant.


Maize


Total


Source
R
N
N2
K
K2
NxK
LOF


Forage
0.025 a
0.01
0.01
0.01
0.10
0.01
0.25


Grain
0.25b
0.025
0.025
0.01
0.25
0.01
0.05


Ear


Forage
NS
0.01
0.01
0.01
0.05
0.01
0.10


Grain
0.05
0.01
0.01
0.01
0.10
0.01
0.25


Soybeans


Total


Forage
0.01
0.10
NS
0.10
0.05
NS
NS


Seed


Grain
0.01
NS
0.05
0.25
NS
NS
NS


Forage
0.01
0.025
NS
0.025
0.025
NS
NS


Grain
0.01
NS
0.01
NS
NS
NS
NS


a Probability level at which the term is significant by the F-test.
bNS indicates nonsignificance at the 0.10 level of probability.
NS indicates nonsignificance at the 0.10 level of probability.









is given in the Appendix. In the case of regression, terms were kept if

judged significant at the 0.25 level by the F-test, or if a higher order

term in the same variable was judged significant (e.g., N would be kept

if N2 or N x K was significant) (R. Littell, Statistics Dept., Univ. of

Fla., personal communication; Englehorn et al., 1964). The regression

analysis was performed at the Northeast Regional Data Center (University

of Florida, Gainesville, Florida), using the General Linear Model (GLM)

procedure of the Statistical Analysis System (SAS) program (Helwig and

Council, 1979). Subsequent calculations (F-values to test significance

of the regression terms; Tables 2.5 and 2.7; predicted maximum yields,

and corresponding fertilization rates; and the construction of Figures

2.1 to 2.8) were performed using the TRS-80 Model III (48,000 bit random

access memory) microcomputer, by Tandy-Radio Shack (Fort Worth, Texas).

Programs used for SAS and with the TRS-80 are recorded in the Appendix.

Results and Discussion

The production functions are presented in Table 2.3. In cases

where the surface is an elliptic paraboloid opening downward, the func-

tion's maximum can be obtained by setting aY/8N and aY/DK equal to zero.

If the paraboloid opens upward, these conditions yield the function's

minimum. In the case of a parabolic hyperboloid, setting 8Y/aN and

YY/aK equal to zero locates the "saddle point." At this point, the

response of Y to both N and to K is zero, but the response of Y to N is

a local minimum and of Y to K is a local maximum (or vice versa). The

production functions are graphed in Figures 2.1 through 2.8. Each graph

consists of three sets of lines: isoquants, ridge lines, and the limits

of experimentation. The isoquants represent the combinations of N and K

which will result in a given yield (Heady et al., 1966). Each graph is











Table 2.3. Production functions for harvested parts.
Values


Crop Part System m
maize total forage 4049.86
grain 9276.99
ear forage -121.794
grain 938.194
soybeans total forage 2390.81
grain 3287.93
seed forage 967.076
grain 1346.17


a
-0.539136
-0.495503
-0.234157
-0.222554
0
-0.0194926
0
-0.00650717


b
183.715
156.616
86.1355
87.5304
-2.29241
5.62163
-1.44825
1.56027


c
0.152296
0.121732
0.0755100
0.0570671
0
0
0
0


d
29.0065
38.4702
15.1266
14.8909
10.0923
0.678497
5.27967
0


e
-0.0655969
-0.613143
-0.0408735
-0.0309846 4
-0.0201456
0
-0.0101609
0























'1l''
261 CI


100-


' %
-sL




"- ,..


a
5,


-a


a-


'a

U


156


K, KG,/HI


Fig. 2.1. Maize total dry matter vs. N and K applied to a maize forage/soybean succession. Isoquants
labeled in mt/ha.


I


451


300


- ~ -------


-- i I I


i--


I


I
------k-


Li|6 Ai .


.-


*""




/'


../'"
."

,.P
/"'


/I


,-"

i


t
,,
I




'I,
4


Illell


I


t
j


II











aI -
a a"
- a

-UU


I -

i -1 U~
* *
-a1
S S1


S.


C--a
O a55


~aaa U
S~U -
a
a S
a -- S
-~i U


1.1gR I


150


25


308
Q, I


K, KG/H,


Fig. 2.2. Maize total dry matter vs. N and K applied to a maize grain/soybean succession. Isoquants
labeled in mt/ha.


380-


100-


v.


I' '- -


-I o l I w m


I .


n


__.-
ram
/"'


/
IJ
."I


I
.'
J
.r
j


r

.

.'
J


458
4.650






*W, 7Z = 1 12,5

I .1 1.......1... I 1
/ / U~
1i
S.,..
'
..... 208 --
.....-,-5---- .
::;,..: /








S15" 3 4512,
KiI/
















Fig. 2.3. Maize ear dry matter vs. N and K applied to a maize forage/soybean succession. Isoquants
labeled in mt/ha.
-S---- ------.S.IS-- --..U .--- ---

-5-





K., KiGHR


Fig. 2.3. Maize ear dry matter vs. N and K applied to a maize forage/soybean succession. Isoquants
labeled in mt/ha.













0 0 0 0- 15 0
**. .,. .

' "'--
la: beled in
-----* 12,.5
IS, U
1.11.1f 10U
F I 2 ima v ma







p 150 300 450


K, KKG.."HK

Fig. 2.4. Maize ear dry matter vs. N and K applied to a maize grain/soybean succession. Isoquants
labeled in mt/ha.


















,1:3::I:
200-
3::j: ,

*

i' r'''

*l *,
*
i iiinI


ItB-


I '


a


a'



'a


a a


3,8 i,


I








I' a


.'
,'



r
;


a'
1

I
aa

1

/


I


ta. I


1 58


K7 KG/HR


Fig. 2.5. Soybean total dry matter
Isoquants labeled in mt/ha.


vs. N and K applied to a maize forage/soybean succession.


i


- -- I


t'
J
i''
.'


i
i
z


%I
-- .
'a a





,





"% ,
'a
sms









',.
"a






'a ,
..,,
,\




\\
i .,.,

a t,


u,


-a
a"


/
.**
.**


I,


I
I
I


4
45-O"






7 ,

;-,- 28-'~ --._.--.__.:-- 3.





.. K ,," ,K
ii a l in m
18 -"1, -,. .- a .

M --~ ~ ~_~___~"----""-- -i
-
as1l enm-










I I I


150 300 450
K K G'/HF
Fig. 2.6. Soybean total dry matter vs. N and K applied to a maize grain/soybean succession.
Isoquants labeled in mt/ha.





300-1 ,8 q 1 1 1 6,2)
,.. iij


i,:I:: 2
n io




. I *
ss| 1ss


K, K'G/HA
Fig. 2.7. Soybean seed dry matter vs. N and K applied to a maize forage/soybean succession.
Isoquants labeled in mt/ha.


450








il .! l
1,35


'--------- ---------------------1 4 0











I- I i
100-



1,40



0 150 300 450
K, KG/HR
Fig. 2.8. Soybean seed dry matter vs. N and K applied to a maize grain/soybean succession.
Isoquants labeled in mt/ha.









bounded by four lines (N = 0, N = 280.2, K = 0, and k = 403.5) repre-

senting the lowest and highest rates of N and K applied, to indicate

that the productions functions are not to be used to predict responses

to N and K rates outside of these limits. The ridge lines are defined

by the equations DK/8N = 0 and 3N/@K = 0. They cross the isoquants

where the marginal substitution rates of K for N and N for K equal zero

(Heady et al., 1966). Heady et al. point out that inputs (N and K, in

this case) substitute for each other only within the ridge lines. The

ridge lines also represent the local maxima to N and K, respectively, as

shown below:

aY _Y aK _Y
3-- D- x A = x 0 = 0
@N K X N 5X0


Y nY K 3Y
x xx = 0
3K 7N TK PN

In the case of an elliptic paraboloid opening downward, the point of

maximum dry matter production occurs where the ridge lines intersect,

i.e., where 3Y/3N = @Y/8K = 0.

The production functions can be used to predict the rates of N and

K which will give the greatest yield of each crop in a specified system.

Alternatively, the functions for the individual crops in each system

may be combined to arrive at a production function used to obtain a

point of maximum combined yield for the system. The data were analyzed

using both approaches in order to understand the response of both the

individual crops and of the systems of N and K.

A summary of the AOV's from the final regression analyses is given

in Table 2.4. Lack of fit (LOF) was significant at the 0.05 level in

only one of the maize regressions, and in none of the soybean regression

equations. Hence, the regression equations obtained, with one exception,












Table 2.4. Regression coefficients of determination, and probability levels at which
lack of fit and terms included in the final regressions are significant.
Maize Soybeans


Total
Forage Grain


0.01a
0.01
0.01
0.01
0.01


0.25
0.90
level


0.025
0.025
0.01
0.25
0.01


0.05
0.72
at which


Ear
Forage Grain


0.01
0.01
0.01
0.05
0.01


0.10
0.84
the term


Total
Forage Grain


0.01 0.10

0.01 -
0.01 0.10
0.10 0.05
0.01 ---
-- NS

0.25 NS
0.88 0.25
is significant by the


NSb
0.05
0.25
n----


Factor
Source
N
N2

K
K2

Nx K
LOF(a)c
LOF(b) d
R2

a Probably


Seed
Forage Grain


0.025


0.025
0.025


NS
NS
0.36


NS
0.01


NS
0.06


NS indicates nonsignificance at the 0.10 level of probability.
Lack of fit at the main treatment level.
Lack of fit at the subtreatment level.


NS
0.14
F-test.


lity


~









adequately describe the response of dry mass to applied N and K. The

coefficient of determination for the one exception was good (Dev et al.,

1980), and the second order model is widely used in yield literature

(Heady et al., 1963; Englehorn et al., 1964; Pesek et al., 1959; Doll et

al., 1958). Hence, it was decided to use the second order rather than a

more complicated model not so readily comparable with other studies.

Examination of Table 2.5 reveals that the predicted maximum yields

of maize total dry matter and ear dry mass were higher in the grain than

in the forage system. It was thought this might be due to greater re-

moval of nutrients in the forage system, as the quantities of N, P, and

K contained in the above-ground portion of the maize plant are about

57%, 60%, and 189% greater, respectively, than those removed in the

grain alone (Sanchez, 1976). However, soil K was found to have a non-

significant effect (Table 2.6), indicating the probability of another

cause, which has not been identified. In both systems, maize total dry

mass and ear dry mass responded more to N than to K, which is common

behavior for the crop (Heady et al., 1966; Englehorn et al., 1964).

Results for the soybeans are less regular. The maximum predicted

yield of total dry matter was higher in the grain system, while that of

seed dry mass was greater in the forage system. Additionally, the re-

sponse to N and K varied with the system. Soybean total dry mass and

seed dry mass in the forage system responded negatively to N, with a

greater response to K than to N. In contrast, maximum soybean total dry

matter and seed yields in the grain system were predicted to occur with

over 100 kg N/ha. Soybean dry matter in the grain system responded more

to N than to K, while soybean seed mass did not respond at all to K. It

was thought that with the soybeans, too, previous soil fertility









Table 2.5. Crop yields and N and
maximum production.


K requirements at the point of


Corresponding
System Crop Part(s) N K Dry mass figure
--------kg/ha -----
forage maize total 241 501 33,467 3.1
ear 252 418 13,901 3.3
soybeans total 0 250 3,655a 3.5
seed 0 260 1,653a 3.7
grain maize total 224 536 37,117 3.2
ear 258 478 15,783 3.4
soybeans total 143 404 3,965 3.6
seed 120 0 1,440c 3.8
a Yield with 0 kg N/ha, as yield was predicted to decline linearly
with N.
b
Yield with 403.5 kg K/ha, as yield was predicted to increase
linearly with K.
Yield with 0 kg K/ha, as K was predicted to have no effect on yield.













Table 2.6. Summary of analyses of variance with the inclusion of soil K terms.
Maize Soybeans


Total
Forage Grain
0.025a NSb
0.01 0.025
0.01 0.01
0.01 0.01
NS NS
NS NS
NS NS
NS NS


Ear
Forage Grain
NS 0.05
0.01 0.01
0.01 0.01
0.01 0.05
0.10 NS
0.05 NS
NS NS
NS NS


Total
Forage Grain
0.01 0.01
NS 0.10
0.10 NS
NS NS
NS NS
NS NS
NS NS
NS NS


Seed

Forage Grain
0.01 0.01
0.05 NS
0.05 NS
NS NS
NS NS
NS NS
NS NS
NS NS


Source
R
N
K
Nx K
SK
SK2
N x SK
K x SK


a Probability level at which the term is significant by the F-test.
b NS indicates nonsignificance at the 0.10 level of probability.









differences due to the system might be causing the yield differences.

However, soil K was found to have a nonsignificant effect on soybean

total and seed yields, indicating another cause is likely involved. It

may be noted, though, that the coefficients of determination of the soy-

bean regressions are all low. Hence, the small differences in soybean

yields seen are not of concern, the coefficients of determination re-

vealing there is much remaining to explain the yield differences.

An examination of Table 2.7 reveals that the predicted maximum

yield of total dry mass is higher in the grain than in the forage sys-

tem. The greater dry matter accumulation by the grain system indicates

that that system may maintain a more healthy environment for the crops,

and that adoption of the forage system may reduce the maximum potential

yield of total dry matter. However, not all the dry matter in the grain

system can be harvested, or it too becomes a forage system. Likewise,

removal of the total soybean plant would result in a greater removal of

nutrients than was the case in the experiment, rendering the experimen-

tally obtained production functions useless. Furthermore, producers are

usually not interested in the total yield of dry matter by soybeans, but

only in the yield of seed. The portions of the crops harvested in the

experimental systems are maize forage in the forage system, maize grain

in the grain system, and soybean seed, and it is with these portions

that we must be concerned. Maximum combined yields of maize forage and

soybean seed in the forage system are predicted to be 34,281 kg dry

mass/ha, with 234 kg N/ha and 462 kg K/ha. In the grain system, the

maximum combined yield of 17,137 kg dry mass/ha of maize grain and soy-

bean seed is predicted to occur with 253 kg N/ha and 466 kg K/ha. The

maximum combined yield of the typically harvested crop portions is thus






81


Table 2.7. System yields and N and K requirements at the point of
maximum production.
Maize Soybean Maize Soybean Total
System part part N K dry mass dry mass dry mass
-------------- kg/ha------------------
forage total total 229 432 33,200 2,469 35,669
total seed 234 462 33,382 899 34,281
grain total total 221 539 37,112 3,140 41,052
ear seed 253 474 15,779 1,324 17,103









greater in the forage than in the grain system. The significance of

this, though, must be judged from an economic viewpoint, since the prod-

ucts considered (maize grain, maize forage, and soybeans) do not have

the same value.

Conclusions

The experiment was conducted in 1981 to determine the yield re-

sponse of no-tilled maize and soybeans to N and K. Two cropping systems

were used: maize for forage followed by soybeans, and maize for grain

followed by soybeans. Nitrogen was applied at 0, 168, and 280 kg/ha, K

at 0, 45, 135, and 404 kg/ha. Variables regressed on N and K were maize

total dry mass, ear dry mass, soybean total dry mass, and soybean seed

dry mass. The second order model was found acceptable for all the re-

gressions. Maize total and ear dry mass responded more to N than to K.

The maxima of these variables were predicted to be higher in the grain

system. It was thought that the level of K in the soil at planting

could be responsible, but statistical tests showed soil K to have a non-

significant effect. The regression equations for the soybeans all had

low coefficients of determination, indicating that factors other than

applied N and K caused most of the variation in both total and seed

yield. Soil K at planting was also found to be nonsignificant for the

soybeans. The production functions were used to predict the maximum

total yield of normally harvested portions in the two systems. On a dry

weight basis, maximum production in the forage system was predicted to

occur with 234 kg N and 462 kg K/ha, giving 33,400 kg maize forage and

900 kg soybeans/ha. In the grain system, maximum production was pre-

dicted to occur with 253 kg N and 466 kg K/ha, yielding 15,800 kg maize

ears and 1300 kg soybeans/ha.















CHAPTER 3
PROFITABILITY OF A MAIZE/SOYBEAN DOUBLE CROPPING
SYSTEM AS AFFECTED BY APPLIED RATES OF
NITROGEN AND POTASSIUM

Introduction

The response of crop yields to fertilizer inputs is of prime eco-

nomic concern. The data from fertility experiments can be analyzed by

regression to give production functions, which provide the information

necessary for maximizing profits.

The maize (Zea mays L.)/soybean (Glycine max (L.) Merr.) double

cropping system has shown good potential in the Southeastern United

States (R.N. Gallaher, Agronomy Department, University of Florida, per-

sonal communication), and can likely be used in many parts of the trop-

ics and subtropics. One of the major advantages of multicropping in

industrialized agriculture is that fixed costs such as land and machin-

ery are spread over at least one additional crop, making better use of

fixed farm resources (Gallaher, 1978). The use of no-tillage has a var-

iety of advantages, including reduced energy and equipment costs, con-

servation of soil moisture, and the reduction of soil erosion. When

used in conjunction with multicropping, no-tillage shortens the time

required between harvest and planting. This permits the timely planting

of a succeeding crop, an important factor in multiple cropping (Phillips

et al., 1980). Multiple cropping systems present a need for further

research, as the crops may not respond to fertilization in the same man-

ner as when monocropped. As crops occupy the land for a larger portion

of the year, there is the opportunity for greater utilization of









residual nutrients in double cropping systems (Sanchez, 1976). The con-

tribution of crop residues in the maize/soybean succession to the suc-

ceeding crop's nutrient supply can be seen by examining the nutrient

content of the crops and their residues. Arnon (1975) reviewed the re-

sults of several studies with maize, and reported nutrient contents of

the whole plant ranged from 36 to 241 kg N/ha, 6 to 39 kg P/ha, and 28

to 239 kg K/ha. Sanchez (1976) reports maize stover nutrient contents

of N, P, and K ranging from 37 to 72, 6 to 14, and 38 to 93 kg/ha, re-

spectively. Total N content of a soybean crop can range from 119 to 219

kg/ha, while N content of the non-seed portion can range from 23 to 70

kg/ha (Hammond et al., 1951; Post, 1982). Additionally, crops grown in

succession with legumes receive a portion of their N needs from the leg-

umes. One of the advantages of including soybeans in a cropping system

is its fixation of atmospheric N. Shibles et al. (1975) report that

estimates of the amount of N fixed by soybeans vary greatly, but cite

one study by Weber (1966) which found a crop yielding 2800 kg seed/ha

could fix 160 kg N/ha. It should be noted that this is N fixed; the

crop took up another 45 kg N/ha from the soil. Shroder and Hinson

(1975), studying a rye (Secale cereale)/soybean succession, suggested

that the nodules and roots of soybeans could supply approximately 20 kg

N/ha to the following crop of rye. In an experiment using nodulating

and nonnodulating lines, Weber (1966) found that nodulating soybeans

provided 37 kg/ha of residual fixed N to a succeeding crop of nonnodu-

lating soybeans. The nutrients left in the crop residues should become

available for crop uptake, if they are not lost by volatilization, soil

fixation, microbial fixation, or leaching.









A variety of production functions have been used. The Mitscherlich

equation postulated that yield asymptotically approached a maximum as a

given input is added, and assumed that the soil contains none of the

nutrient considered (Mengel and Kirkby, 1982). The Mitscherlich-Baule

equation (Bray, 1961) allowed for the soil nutrient content to be great-

er than zero, but assumed the effect of nutrients in the soil and those

applied as fertilizer on yield to be the same. Bray (1961) proposed a

modified equation which allowed for the soil and applied nutrients to

have different "activities." Written in natural logarithm form:

In(Y Y) = InYm kS k2F

where Y = maximum yield, Y = yield, S = concentration of the nutrient

in the soil, F = rate of nutrient applied, and kI and k2 are constants.

While Bray's equation is an improvement over the original Mitscher-

lich equation, there remain several weaknesses to the approach. The

most serious, pointed out by Mengel and Kirkby (1982), is that the "con-

stants" in the Mitscherlich and Mitscherlich-Baule equations are not, in

fact, constant. This is due to the interaction of nutrients with each

other and with other growth factors, such as moisture supply. Van der

Paauw (1958) and Barber (1959) found the yield response of potatoes (So-

lanum tuberosum), wheat (Triticum aestivum), and maize to depend upon

the precipitation received during the growing season. Beer et al.

(1967) reported that the yield response of maize to N was affected by

the soil water content. Another weakness which Mengel and Kirkby note

is that the application of high levels of a nutrient may actually de-

crease yields, an effect which cannot be accounted for by an asymptotic

equation. They state that because of this second defect, some authors









prefer to use quadratic equations when relating yield response to a sin-

gle input.

There are two other weaknesses to the Mitscherlich-Baule equations,

namely, that they are difficult to handle for regression analysis, and

that they do not allow for the interaction of growth factors in deter-

mining yields. Doll et al. (1958) considered the square root and qua-

dratic functions the most satisfactory for regression analysis, perhaps

for these reasons. In describing yield response to a single variable,

these equations are
1/2
square root model: Y = a + bX/2 + cX

quadratic model: Y = a + bX + cX2

These equations can be easily expanded to include two or more growth

factors, and interactions between them. An example is the "second order

model," discussed by Montgomery (1976)

Z = i + aX2 + bX + cXY + dY + eY2

where

Z = dependent variable

X, Y = independent variables

i = intercept

a, b, c, d, e = constants

This model allows for a linear interaction between the independent vari-

ables. Higher order interactions (X2Y, XY2, X2y2) can be included in

the model, but that shown is usually adequate. All of these polynomial

functions have the advantage that their partial derivatives can be easi-

ly taken, as the derivatives are needed to determine the point of maxi-

mum profitability.









The purpose of this study was to predict the points of maximum

profitability and the amounts of N and K required to reach those prof-

its.

Materials and Methods

Experiment Description

Experiment 1 was conducted in 1981 at the University of Florida

Agronomy Farm (Gainesville, Fla.), on an Arrendondo fine sand (member of

the loamy silicious hyperthermic family of the grossarenic Paleudults

(Soil Survey Staff, 1982)). A randomized complete split-split block

design was used, with four replications. Main treatments were cropping

systems, split-plot treatments were N rates, and split-split plot treat-

ments were K rates. The two systems were maize for forage followed by

soybeans, and maize for grain followed by soybeans. In maize for for-

age, the entire above-ground portion of the plant was removed at har-

vest. The maize ear (husk, cob, and grain) alone was removed in maize

for grain, while the soybeans were harvested for their seed only. In

all cases the remaining plant matter was mown with a rotary mower and

left on the plots. The maize (variety "DeKalb XL71") was planted at

110,000 plants/ha 8 March and harvested 17 July, while the soybeans

(variety "Cobb") were planted at 500,000 plants/ha 6 August and har-

vested 24 November. Both crops were produced using no-tillage, and

planted using the Tye drill, set for 25 cm rows. Nitrogen was applied

as NH NO3 at three rates, 0, 168, and 280 kg N/ha/yr, in three equal in-

crements. The first came preplant (28 February), the second when the

maize was about 15 cm high (17 April), and the third when the maize was

about 55 cm high (15 May). Potassium was applied preplant (28 February)

as KC1 at four rates: 0, 45, 135, and 404 kg K/ha. Hence, only the









maize was fertilized, the soybeans scavenging nutrients from the maize

crop. Other soil amendments were added to all the plots to ensure that

N and K were the only limiting nutrients. Magnesium (17 kg Mg/ha as

MgSO4 7H20), P (56 kg P/ha as concentrated super phosphate), and micro-

nutrients (22.5 kg/ha of Fritted Trace Element 503) were all applied

broadcast preplant of the maize. Fritted Trace Element 503 is manufac-

tured by Ferro Corporation, with 3.0% B, 3.0% Cu, 18.0% Fe, 75.% Mn,

0.2% Mo, and 7.0% Zn. To ensure good stands, the crops received a vari-

ety of herbicides and insecticides, detailed in Tables 3.1 to 3.3. The

maize also received 30 cm irrigation, and the soybeans 20 cm, to supple-

ment rainfall.

The plant samples were dug up using shovels, and dried at 700C be-

fore being weighed. For yields of total plant and seed (or ear) dry

mass at harvest, samples of 914 cm row were taken. Total plant dry mass

was determined, and the seed (or ears) then separated out and weighed.

Maize harvest samples were taken on 17 July, soybean harvest samples, on

24 November.

Experiment History

Experiment 1 was performed on plots which were part of a previous

fertility experiment, conducted from 1977 through 1980. This experiment

was organized as a randomized complete split-plot experiment, with main

plot and split-plot treatments as described above. None of the plots

received any other nutrients. (The split-split plot (i.e., K) treat-

ments were imposed in 1981.) Dolomitic lime was applied once to all

treatments at 4480 kg/ha, three months prior to planting the first maize

crop. The maize variety "DeKalb XL395" was planted in the forage sys-

tem, "Funks G4507" in the grain system, at 110,000 plants/ha. "Cobb"









soybeans were planted at 500,000 plants/ha.. The maize was usually

planted in early March and harvested in mid-July, while soybeans were

usually planted in late July and harvested in late November. The maize

was planted using the Brown-Harden Superseeder with subsoiling, while

the soybeans were planted using the Tye drill.

Experiments 2 and 3 were conducted by R.N. Gallaher (Agronomy

Dept., Univ. of Fla., personal communication), and the data from them

are used for the purpose of verifying the economic analyses. Both ex-

periments were conducted in Williston, Florida, from 1980 through 1982.

Experiment 2 occupied 0.81 ha, and Experiment 3, 20.2 ha. Both were

planted to "DeKalb XL71" maize and "Cobb" soybeans. The maize in both

experiments received 448 kg/ha of 6-11-34-3-4 (percentages of N, P, K,

S, and Mg, respectively). The other inputs are listed in Tables 3.4 and

3.5.

Prices and Costs

The prices and quantities of inputs used for each crop are given in

Tables 3.1 through 3.5. Ammonium nitrate and KC1 were applied at vary-

ing rates in Experiment 1, so the quantities and cost per hectare of

these inputs are listed as nonconstant. Likewise, the charge for cut-

ting maize forage varies with the maize yield, so this cost is listed as

nonconstant in Table 3.1. Interest is charged on the variable costs,

including the nonconstant costs. It was calculated as a flat 15% per

annum on the entire principal; i.e:

I = Prt

where

I = interest paid

P = amount of money borrowed















Table 3.1. Production costs for maize forage, Experiment 1.

Input Unit Unit cost, $
seed 1000 seed 0.6625
NH4NO3 kg N 0.5627a
KC1 kg K 0.3828b
triple super phosphate kg P 1.437b
MgSO4 7H20 kg 0.5236b
FTE 503 kg 0.5897b
irrigation ha-cm 7.783
carbofuranc kg a.i.d 15.43
glyphosatee 1 19.55
atrazinef kg a.i. 4.740
metolachlorg kg a.i. 16.53
surfactant 1 2.774
2,4-D amineh 1 2.536
planting ha 14.82
spreading, post-directed ha 14.82
spraying, ground ha 7.413
cutting kg dry mass 0.01147
mowing ha 7.413
truck charge ha 2.471
land rent ha 74.10
taxes ha 24.70
interest on variable
constant costs --


Source for cost
price paid
local stores
local stores
price paid
local stores
price paid
estimated
local stores
local stores
local stores
local stores
local stores
local stores
Gallaher and Weaver, 1982b
estimated
Gallaher and Weaver, 1982a
Taylor, 1983i
estimated
Gallaher and Weaver, 1982a
Gallaher and Weaver, 1982a
Callaher and Weaver, 1982a


Gallaher and Weaver, 1982a


TOTAL CONSTANT 1017.60
a Includes the price of ammonium nitrate ($0.5462/kg N) plus a charge for broadcasting one-third of the ammonium nitrate at
planting ($0.04935/kg N broadcast).
b Includes a charge of $0.01653/kg fertilizer for broadcasting.
c 2,3-dihydro-2,2-dimethyl-7-benzofuranyl methylcarbamate
d kg active ingredient
e N-(phosphonomethyl)glycine
f 2-chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine
g 2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-l-methylethyl)acetamide
h (2,4-dichlorophenoxy)acetic acid, amine salt
i If ammonium nitrate is applied at all, it is post-directed twice.
j Personal communication with Clifton Taylor, Program Evaluation, University of Florida, March, 1983.
k 15% per annum, for 168 days (Feb 14 through July 31) on $859.46/ha. The variable costs were comprised of all the other
costs except land rent and taxes.


Units/ha
271.7
nonconstant
nonconstant
56.02
224.2
33.63
29.85
2.240
4.677
2.242
1.681
0.1949
1.169
1
2 or 0
nncnstan
nonconstant
1
1
1
1


Cost, $/ha
180.00
nonconstant
nonconstant
80.50
117.33
19.82
232.18
34.58
91.39
10.62
27.79
0.54
2.96
14.82
29.64
7.41
nonconstant
7.41
2.47
74.10
24.70


59.34















Table 3.2. Production costs for
Input
seed
NH NO3
KC1
triple super phosphate
MgSO4 7H20
FTE 503
irrigation
carbofuran
glyphosate
atrazine
metolachlor
surfactant
2,4-D amine
planting
spreading, post-directed
spraying, ground
combining
mowing
truck charge
land rent
taxes
interest on variable
constant costs


maize grain, Experiment 1.
Unit Unit cost, $


1000 seed
kg N
kg K
kg P
kg
kg
ha-cm
kg a.i. c
1
kg a.i.
kg a.i.
1
1

ha
ha
ha
ha
ha
ha
ha
ha


0.6625
0.5627
0.3828
1.437 b
0.5236b
0.5897 b
7.783
15.43
19.55
4.740
16.53
2.774
2.536
14.82
14.82
7.413
44.46
7.413
2.471
74.10
24.70


Source for cost

price paid
local stores
local stores
price paid
local stores
price paid
estimated
local stores
local stores
local stores
local stores
local stores
local stores
Gallaher and Weaver, 1982b
estimated
Gallaher and Weaver, 1982a
Gallaher and Weaver, 1982a


estimated
Gallaher and
Gallaher and
Gallaher and


Units/ha Cost, $/ha


271.7
nonconstant
nonconstant
56.02
224.2
33.63
29.85
2.240
4.677
2.242
1.681
0.1949
1.169
1
2 or 0d
1
1
1
1
1
1


Weaver, 1982a
Weaver, 1982a
Werver, 1982a


Gallaher and Weaver, 1982a


180.00
nonconstant
nonconstant
80.50
117.33
19.82
232.18
34.58
91.39
10.62
27.79
0.54
2.96
14.82
29.64
7.41
44.46
7.41
2.47
74.10
24.70


62.41


TOTAL


1065.13


a Includes the price of ammonium nitrate ($0.5462/kg N) plus a charge for broadcasting one-third of the ammonium nitrate
planting ($0.04935/kg N broadcast).
b Includes a charge of $0.01653/kg fertilizer for broadcasting.
c kg active ingredient
d If ammonium nitrate is applied at all, it is post-directed twice.
e 15% per annum, for 168 days (Feb 14 through July 31) on $903.92/ha. The variable costs were comprised of all the other
costs except land rent and taxes.


at











Table 3.3. Production costs
Input
seed
Rhizobium inoculum
irrigation

glyphosate
alachlorb
metribuzind
surfactant
methomyl e
toxaphene
planting
spraying, ground
spraying, air
combining
truck charge
interest on variable costsg


for soybeans, Experiment 1.


Unit
kg
ha
ha-cm
1
kg a.i.c
kg a.i.
1
kg a.i.
1
ha
ha
ha
ha
ha
--


Unit cost, $
0.3307
1.85
7.783
19.55
9.811
50.49
2.774
32.46
2.351
14.82
7.413
7.413
44.46
2.471


Source for cost
estimated
Baldwin, 1983b
estimated
local stores
local stores


local stores
local stores
local stores
local stores
Gallaher and
Gallaher and
Gallaher and
Gallaher and
Gallaher and
Gallaher and


Weaver,
Weaver,
Weaver,
Weaver,
Weaver,
Weaver,


1982b
1982a
1982a
1982a
1982a
1982a


Units/ha
112.1
1
19.69
2.338
2.242
0.4483
0.1949
0.1261
4.667
1
1
1
1
1


Cost, $/ha
37.13
1.85
153.14
45.70
21.98
22.63
0.54
4.09
10.99
14.82
7.41
7.41
44.46
2.47
18.63


TOTAL
a Personal communication with John Baldwin, Extension
b 2-chloro-2',6'-diethyl-N-(methoxymethyl)acetanilide
c kg active ingredient


393.25


Director, Levy County, Florida, June, 1983.


d 4-amino-6-tert-butyl-3-(methylthio)-as-triazine-5(4H)one
e S-methyl-n-((methylcarbanoyl)oxy)thioacetimidate
f chlorinated camphene containing 67-69% Cl.
g 15% per annum for 121 days (Aug 1 through Nov 30), on $374.62/ha. The variable costs were comprised
of all other listed costs.

















Table 3.4. Production costs for
Input
seed
N
P
KC1
Mg
FTE 503
irrigation
carbofuran
paraquatc
atrazine
surfactant
2,4-D amine
planting (planter)
spraying, ground
combining
mowing
truck charge
land rent
taxes
interest on variable
constant costs


maize grain, Experiments 2 and 3.
Unit Unit cost, $
1000 seed 0.6625
kg N 0.5956a
kg P 1.437a
kg K 0.3828a
kg Mg 0.5236a
kg 0.5897a
ha-cm 7.783
kg a.i.6 15.43
1 11.83
kg a.i. 4.740
1 2.774
1 2.536
ha 19.76
ha 7.413
ha 44.46
ha 7.413
ha 2.47.1
ha 74.10
ha 24.70


TOTAL CONSTANT
aIncludes a charge of $0.01653/kg fertilizer for broadcasting.
kg active ingredient.
c 1,1'-dimethyl-4,4'-bipyridinium ion
d15% per annum, for 168 days, on $560.83/ha. The variable costs

and taxes.


698.35


were comprised of all the other costs except land rent


Source for cost

price paid
local stores
price paid
local stores
local stores
price paid
estimated
local stores
local stores
local stores
local stores
local stores
estimated
Gallaher and Weaver, 1982
Gallaher and Weaver, 1982
estimated
Gallaher and Weaver, 1982
Gallaher and Weaver, 1982
Gallaher and Weaver, 1982


Gallaher and Weaver, 1982


Units/ha
86.50
336.1
49.30
152.4
17.93
6.722
nonconstant
2.241
2.337
2.241
0.1949
2.337
1
1
1
1
1
1
1


38.72


Cost, $/ha
57.31
200.19
70.84
58.33
9.39
3.96
nonconstant
34.56
27.65
10.62
0.54
5.93
19.76
7.41
44.46
7.41
2.47
74.10
24.70