On-farm testing of the PNUTGRO crop model in Florida

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Material Information

Title:
On-farm testing of the PNUTGRO crop model in Florida
Physical Description:
xv, 199 leaves : ill. ; 29 cm.
Language:
English
Creator:
Gilbert, Robert A., 1963-
Publication Date:

Subjects

Subjects / Keywords:
Peanuts -- Field experiments -- Florida   ( lcsh )
Peanuts -- Computer simulation   ( lcsh )
Genre:
bibliography   ( marcgt )
non-fiction   ( marcgt )

Notes

Thesis:
Thesis (M.S.)--University of Florida, 1992.
Bibliography:
Includes bibliographical references (leaves 191-198)
Statement of Responsibility:
by Robert A. Gilbert.
General Note:
Typescript.
General Note:
Vita.
Funding:
Florida Historical Agriculture and Rural Life

Record Information

Source Institution:
Marston Science Library, George A. Smathers Libraries, University of Florida
Holding Location:
Florida Agricultural Experiment Station, Florida Cooperative Extension Service, Florida Department of Agriculture and Consumer Services, and the Engineering and Industrial Experiment Station; Institute for Food and Agricultural Services (IFAS), University of Florida
Rights Management:
All rights reserved, Board of Trustees of the University of Florida
Resource Identifier:
aleph - 001799507
oclc - 27634855
notis - AJM3252
System ID:
UF00054859:00001

Table of Contents
    Front Cover
        A 2
    Title Page
        Page i
    Acknowledgement
        Page ii
    Table of Contents
        Page iii
        Page iv
    List of Tables
        Page v
        Page vi
    List of Figures
        Page vii
        Page viii
        Page ix
        Page x
        Page xi
        Page xii
        Page xiii
    Abstract
        Page xiv
        Page xv
    Introduction
        Page 1
        Page 2
        Page 3
    Review of literature
        Page 4
        Peanut growth dynamics
            Page 4
            Page 5
    Materials and methods
        Page 26
        Site selection
            Page 26
            Page 27
        Environmental influences on peanut growth and yield
            Page 6
            Page 7
            Page 8
            Page 9
            Page 10
            Page 11
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            Page 15
            Page 16
            Page 17
            Page 18
            Page 19
        Crop growth modelling
            Page 20
            Page 21
            Page 22
            Page 23
            Page 24
            Page 25
        Soil water sampling
            Page 29
        Weather data collection
            Page 30
        Plant growth sampling
            Page 30
            Page 31
        Final harvest sampling
            Page 32
        Soil fertility and nematode tests
            Page 28
    Results and discussions
        Page 36
        1990 on-farm experiments
            Page 36
            Page 37
            Page 38
            Page 39
            Page 40
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            Page 82
        Data entry
            Page 33
            Page 34
            Page 35
        1991 On-farm experiments
            Page 83
            Page 84
            Page 85
            Page 86
            Page 87
            Page 88
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            Page 121
            Page 122
            Page 123
            Page 124
        Accounting for pest and disease effects in PNUTGRO
            Page 125
            Page 126
            Page 127
            Page 128
            Page 129
            Page 130
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            Page 154
    Summary and conclusions
        Page 155
        Page 156
        Page 157
    Soil fertility, genetic coefficients, soil profile properties and weather data
        Page 158
        Page 159
        Page 160
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    Bibliography
        Page 191
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        Page 198
    Biographical sketch
        Page 199
    Signature page
        Page 200
Full Text












ON-FARM TESTING OF THE PNUTGRO
CROP MODEL IN FLORIDA














By

ROBERT A. GILBERT


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

UNIVERSITY OF FLORIDA


1992















ON-FARM TESTING OF THE PNUTGRO
CROP MODEL IN FLORIDA














By

ROBERT A. GILBERT


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

UNIVERSITY OF FLORIDA


1992















ACKNOWLEDGEMENTS

I would like to give my heartfelt thanks to Dr. Kenneth

J. Boote, my advisor and chairman, and Dr. Jerry M. Bennett

for their help both in the lab and in the field. I doubt

that I would have completed this study without their

knowledge and encouragement.

I am deeply grateful to Dr. Clif Hiebsch and Dr. Peter

Hildebrand for their constructive suggestions as my

committee members and for the knowledge I acquired in their

classes.

Special thanks to Dr. Ken Buhr for his friendship.

I am indebted to Anthony Drew and Ed Jowers, who took

time out of their busy extension schedules to liaise with

farmers and drive me around their counties.

Diane Hornby, Ed Blazey and Niel Hill provided

invaluable technical help (and sweat) in the field for two

years.

Finally, to my wife Ami, I give my deepest gratitude.

Without her inspiration, I would not have the good fortune

to be a graduating Gator.















TABLE OF CONTENTS


page

ACKNOWLEDGEMENTS . . . .. ii

LIST OF TABLES . . . . ... v

LIST OF FIGURES . . . . ... vii

ABSTRACT . . . . . xiv

INTRODUCTION . . . 1

REVIEW OF LITERATURE . . . . 4

Peanut Growth Dynamics .... . . 4
Environmental Influences on Peanut Growth and Yield 6
Abiotic Factors. .. . . . 6
Water, solar radiation and temperature 6
Soil fertility .. . . 11
Biotic Factors . . . . 13
Diseases . . . 13
Nematodes . . . . 17
Insects . . .. 19
Crop Growth Modelling. . ... .. .20
Purpose of Crop Modelling . . 20
Peanut Model Development . . 20
Model Calibration . . . 23
Model Validation . . .. ... 24

MATERIALS AND METHODS .. . . . .. 26

Site Selection . ... . . 26
Soil Fertility and Nematode Tests. . . 28
Soil Water Sampling . . . . 29
Weather Data Collection. . . .. .30
Plant Growth Sampling . . . .. 30
Final Harvest Sampling ............ .32
Data Entry . . . . 33

RESULTS AND DISCUSSION . . . . 36

1990 On-Farm Experiments . . . 36
Levy County ............. 36
Jackson County .. . . . .. 58


iii









1991 On-Farm Experiments . . . .. .83
Levy County . . . ... .83
Jackson County . . 102
Accounting for Pest and Disease Effects
in PNUTGRO . . . . 125

SUMMARY AND CONCLUSIONS . . . .155

APPENDIX . . . . 158

BIBLIOGRAPHY . . . ... .. 191

BIOGRAPHICAL SKETCH . . . ... .199














LIST OF TABLES


Table page

1. Growth stage descriptions for peanut
(from Boote, 1982) . . . 5

2. Field sites selected for sampling in
Levy and Jackson Counties in 1990 and
1991 . . . . . 27

3. Seasonal water received, and average daily
solar radiation and temperature for all
field sites in 1990 and 1991 . ... .39

4. Field-observed (OBS.) vs. PNUTGRO-simulated
(SIM.) growth and yield data from Levy County
in 1990 . . . . .. .47

5. Field-observed (OBS.) vs. PNUTGRO-simulated
(SIM.) growth and yield data from Jackson County
in 1990 . . . . . 68

6. Field-observed (OBS.) vs. PNUTGRO-simulated
(SIM.) growth and yield data from Levy County
in 1991 . . . ... 93

7. Field-observed (OBS.) vs. PNUTGRO-simulated
(SIM.) growth and yield data from Jackson County
in 1991 . .. . . . 113

8. Observed and simulated pod yields, root-knot
nematode levels, and other pest and disease
incidence in Levy and Jackson County in
1991 . . . .... .. 114

9. The 1991 observed (OBS.) and PNUTGRO-simulated
(SIM.) root-knot nematode levels, and adjusted
pod yields based on the 1979, 1980 and 1981
Rodriguez-Kabana (1982c) regression equations.
The loss functions were either normalized
(NORM.), or the loss function slope was
assumed to be correct (SLOPE) . ... 130








10. PNUTGRO-simulated pod yields, observed pod
yields, root-knot nematode levels, and
adjusted pod yields obtained by covariate
analysis, for all sites in 1991 . .. ..134

11. PNUTGRO-simulated (SIM.) and observed (OBS.) pod
yields, root-knot nematode levels, and adjusted
Rodriguez-Kabana regression equations and
PNUTGRO pod yields for all 1991 sites with
high root-knot nematode levels. Estimated
assimilate removed by nematodes was used to
calculate cumulative carbohydrate consumed 140

A-1. Soil fertility test results for all field sites
in Jackson and Levy Counties in 1991 . 159

A-2. Genetic coefficients used for peanut genotypes
at all field sites in Levy and Jackson Counties
in 1990 and 1991. Coefficients are defined in
Boote et al. (1989a) . . .. 163

A-3. Soil profile properties at all field sites in
Levy and Jackson Counties in 1990 and 1991.
Soil profile properties are defined in
IBSNAT (1990) . . . . 164

A-4. Daily water received at all operating raingauge
sites in Levy and Jackson Counties in 1990 166

A-5. Daily solar radiation and temperature data
from the Graham farm weather station in Levy
County in 1990 . . . .. 170

A-6. Daily solar radiation and temperature data from
the Morgan rainfed farm weather station in
Jackson County in 1990 . . .. 174

A-7. Daily water received at all operating rain-
gauge sites in Levy and Jackson Counties
in 1991 . . . . .. 178

A-8. Daily solar radiation and temperature data
from the Sandlin and Graham farm weather
stations in Levy County in 1991 ...... 182

A-9. Daily solar radiation and temperature data
from the Morgan rainfed farm weather
station in Jackson County in 1991 . .. 187















LIST OF FIGURES


Figure


page


1. Cumulative water received at all
Levy County field sites in 1990 . ... .38

2. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from 5-15 and 15-30 cm soil layers at
the Brookins farm in Levy County in 1990 . 40

3. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from 5-15 and 15-30 cm soil layers at the
Graham farm in Levy County in 1990 ...... 41

4. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from 5-15 and 15-30 cm soil layers
at the Lowman farm in Levy County in 1990 42


5. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from 5-15 and 15-30 cm soil layers
at the Sandlin farm in Levy County in 1990

6. Maximum and minimum daily temperature
recorded at the weather station on the
Graham farm in Levy County in 1990 .

7. Daily solar radiation recorded at
the weather station on the Graham farm
in Levy County in 1990 . . .


* 43



. 45



. 46


8. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the
Sandlin farm in Levy County in 1990 . .

9. Measured (points) and PNUTGRO-simulated (line)
pod harvest index at the Sandlin
farm in Levy County in 1990 . . .

10. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the
Sandlin farm in Levy County in 1990 . .


vii








11. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the
Lowman farm in Levy County in 1990 . ... .52

12. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the
Lowman farm in Levy County in 1990 . ... .53

13. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the
Brookins farm in Levy County in 1990 . 54

14. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the
Graham farm in Levy County in 1990 . ... 55

15. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the
Brookins farm in Levy County in 1990 . .. .56

16. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the
Graham farm in Levy County in 1990 . .. 57

17. Cumulative water received at the Crawford
and Morgan rainfed sites in Jackson County
in 1990 . . . .... .. 60

18. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from 5-15 and 15-30 cm soil layers
at the Crawford rainfed farm in
Jackson County in 1990 . . ... 61

19. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from 5-15 and 15-30 cm soil layers
at the Morgan rainfed farm in
Jackson County in 1990 . . . 62

20. Simulated total extractable soil water
for the Brookins (BR), Crawford rainfed (CR R)
and Morgan rainfed (MO R) farms in 1990 . 64

21. Maximum and minimum daily temperature
recorded at the weather station on the
Morgan farm in Jackson County in 1990 . 65

22. Daily solar radiation recorded at
the weather station on the Morgan rainfed
farm in Jackson County in 1990 . ... .66


viii








23. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Crawford
irrigated farm in Jackson County in 1990 . 69

24. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Crawford
irrigated farm in Jackson County in 1990 . 70

25. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Crawford
rainfed farm in Jackson County in 1990 ... .71

26. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Crawford
rainfed farm in Jackson County in 1990 ... .72

27. Measured (points) and PNUTGRO-simulated (line)
pod harvest index at the Crawford
rainfed farm in Jackson County in 1990 ... .74

28. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Morgan
irrigated farm in Jackson County in 1990 . 75

29. Measured (points) and PNUTGRO-simulated (line)
pod harvest index at the Morgan
irrigated farm in Jackson County in 1990 . 76

30. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Morgan
irrigated farm in Jackson County in 1990 . 77

31. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Morgan
rainfed farm in Jackson County in 1990 ... .78

32. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Morgan
rainfed farm in Jackson County in 1990 . 79

33. Relationship between observed and simulated pod
yield for all sites in 1990 . ... .81

34. Relationship between observed and simulated
biomass at harvest maturity (R8)
for all sites in 1990 . . ... .82

35. Cumulative water received (rainfall plus
irrigation) at all Levy County field sites
in 1991 . . . .... .. 84








36. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from the 5-15 and 15-30 cm soil layers
at the Graham farm in Levy County in 1991 85

37. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from the 5-15 and 15-30 cm soil layers
at the Lowman farm in Levy County in 1991 86

38. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from the 5-15 and 15-30 cm soil layers
at the Sandlin farm in Levy County in 1991 87

39. Daily solar radiation recorded at
the weather station on the Sandlin farm
in Levy County in 1991 . . ... .89

40. Daily solar radiation recorded at
the weather station on the Graham farm
in Levy County in 1991 . . ... .90

41. Maximum and minimum daily temperature
recorded at the weather station on the
Sandlin farm in Levy County in 1991 . .. .91

42. Maximum and minimum daily temperature
recorded at the weather station on the
Graham farm in Levy County in 1991 ...... 92

43. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the
Graham farm in Levy County in 1991 ...... 95

44. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Graham
farm in Levy County in 1991 . . 96

45. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Lowman
farm in Levy County in 1991 . ... .97

46. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Lowman
farm in Levy County in 1991 . ... .98

47. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Sandlin
farm in Levy County in 1991 . . .. 100








48. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Sandlin
farm in Levy County in 1991 . . .


. 101


49. Cumulative water received at the Crawford
rainfed, Crawford irrigated, and Morgan
rainfed sites in Jackson County in 1991 . 103

50. Cumulative water received at the Graham
and Morgan rainfed sites in 1990 and 1991 104

51. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from 5-15 and 15-30 cm soil layers
at the Crawford rainfed farm in
Jackson County in 1991. . . ... 106

52. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from 5-15 and 15-30 cm soil layers
at the Crawford irrigated farm in
Jackson County in 1991 . . .. 107

53. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from 5-15 and 15-30 cm soil layers
at the Morgan rainfed farm in
Jackson County in 1991 ... . . 108

54. Observed (points) and PNUTGRO-simulated
(lines) volumetric soil water content
from 5-15 and 15-30 cm soil layers
at the Morgan irrigated farm in
Jackson County in 1991 . . ... .109

55. Daily solar radiation recorded at
the weather station on the Morgan
rainfed farm in Jackson County in 1991 . 110

56. Maximum and minimum daily temperature
recorded at the weather station on the
Morgan rainfed farm in Jackson County in
1991 . . . ... . 111

57. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Crawford
irrigated farm in Jackson County in 1991 . 115

58. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Crawford
irrigated farm in Jackson County in 1991 . 116









59. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Crawford
rainfed farm in Jackson County in 1991 .... .118

60. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Crawford
rainfed farm in Jackson County in 1991 .... .119

61. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Morgan
irrigated farm in Jackson County in 1991 . 120

62. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Morgan
irrigated farm in Jackson County in 1991 . 122

63. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Morgan
rainfed farm in Jackson County in 1991 .... .123

64. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Morgan
rainfed farm in Jackson County in 1991 .... .124

65. Relationship between observed and simulated pod
yield for all sites in 1991 . ... 126

66. Relationship between observed and simulated
biomass at harvest (R8) for all sites
in 1991 . . . 127

67. Measured (points) and PNUTGRO-simulated (lines)
leaf area index (LAI) with and without
simulated leaf senescence at the Graham farm
in Levy County in 1991 . . ... .136

68. Measured (points) and PNUTGRO-simulated (lines)
leaf weight with and without simulated leaf
senescence at the Graham farm in Levy
County in 1991 . . . ... 137

69. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights with and without simulated
nematode damage at the Lowman farm in Levy
County in 1991 . . . ... 142

70. Measured (points) and PNUTGRO-simulated (lines)
leaf area index (LAI) with and without simulated
nematode damage at the Lowman farm in Levy
County in 1991 . . . ... 143


xii








71. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights with and without simulated
nematode damage at the Morgan rainfed farm in
Jackson County in 1991 . . .. 144

72. Measured (points) and PNUTGRO-simulated (lines)
leaf area index (LAI) with and without simulated
nematode damage at the Morgan rainfed farm in
Jackson County in 1991 . . ... .145

73. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights with and without simulated
nematode damage at the Morgan irrigated farm in
Jackson County in 1991 . . ... .146

74. Measured (points) and PNUTGRO-simulated (lines)
leaf area index (LAI) with and without simulated
nematode damage at the Morgan irrigated farm in
Jackson County in 1991 . . ... .147

75. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights with and without simulated
nematode damage at the Crawford rainfed farm in
Jackson County in 1991 . . ... .148

76. Measured (points) and PNUTGRO-simulated (lines)
leaf area index (LAI) with and without simulated
nematode damage at the Crawford rainfed farm in
Jackson County in 1991 . . .. 149

77. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights with and without simulated
nematode damage at the Crawford irrigated farm in
Jackson County in 1991 . . ... .150

78. Measured (points) and PNUTGRO-simulated (lines)
leaf area index (LAI) with and without simulated
nematode damage at the Crawford irrigated farm in
Jackson County in 1991 . . ... .151

79. Relationship between observed and simulated pod
yield adjusted for pest and disease damage for
all sites in 1991 . . . .. .154


xiii














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

ON-FARM TESTING OF THE PNUTGRO
CROP MODEL IN FLORIDA

By

ROBERT A. GILBERT

August, 1992

Chairman: Kenneth J. Boote
Major Department: Agronomy

The PNUTGRO crop growth model simulates peanut growth

and yield using weather, soil and genetic inputs. PNUTGRO

was developed from growth measurements taken in research

plots. This thesis research was conducted to evaluate the

PNUTGRO model under actual farm conditions, in order to

determine PNUTGRO's utility for farmers and extension

agents, and to identify areas for model improvement.

Field testing of the PNUTGRO model was conducted at a

total of 15 farm sites in Jackson and Levy Counties in 1990

and 1991. Rainfall and irrigation were recorded in each

field daily, as were solar radiation and air temperatures in

each county. Plant growth and soil water samples were

collected every 3 to 4 weeks, and used with final harvest

samples to evaluate PNUTGRO simulations.

The accuracy of the PNUTGRO model simulations varied


xiv








between years and locations. The production system of Levy

County growers included long-term bahiagrass rotations of 2-

8 years between peanut crops. The farms studied in Levy

County achieved the yield potential predicted by PNUTGRO in

1990 (8.9% average difference from observed). The farming

system of Jackson County included row-crop rotations between

peanut crops. The yields on these farms did not reach the

yield potential predicted by PNUTGRO (29.5% difference in

1990, 43.8% in 1991).

PNUTGRO model simulations were more accurate in 1990

than in 1991 in both counties. Greater rainfall in 1991 led

to higher pest and disease incidence (especially root-knot

nematode, stem rot and leafspot) which the PNUTGRO model

does not account for.

It appears that the present version of PNUTGRO is most

useful as a predictor of potential peanut yield under given

weather conditions. The greater the level of pest and

disease pressure on peanut growth, the less accurate the

PNUTGRO predictions became (e.g., Jackson County, 1991).

The use of regression equations or pest-coupling models

to account for pest and disease effects on peanut growth

improved PNUTGRO model fit. Further research on the effects

of pests and diseases on peanut growth and yield is needed.

Incorporation of pest and disease damage into the PNUTGRO

model would increase its predictive capability on farms in

which potential peanut yield is not being attained.














INTRODUCTION

Peanut (Arachis hvpocaea L.) is a native South American

legume, which was probably introduced to the United States

via Africa (Hammons, 1982). Commercialization of peanut

production did not begin until the onset of European oil

shortages in the 1800s, with U.S. production increasing with

oil needs during the Civil War. Increasing boll weevil

pressure on cotton (Gossypium hirsutum) production in the

early 1900s, and the introduction of a fixed production area

and the peanut combine in 1948, helped contribute to

intensification of peanut production and increasing yields

per acre in the U.S. (Hammons, 1982).

However, peanut yields have fluctuated greatly in the

southeastern U.S. during the last decade due to drought.

Average pod yields from Florida, Georgia and Alabama have

ranged from 2110 kg ha'1 in 1990 to 3560 kg ha1' in 1984

(USDA, 1988; USDA, 1991). Computer growth simulation models

may be able to help account for yield fluctuations due to

weather variation (Boote and Jones, 1988). These models may

also be useful to the grower in making management decisions

such as irrigation scheduling or the acquisition of

irrigation equipment.










Peanut growth is influenced by many complex

environmental factors. Abiotic influences include weather

(water, solar radiation and temperature) and soil fertility.

Biotic factors which influence peanut growth include

diseases, insects and nematodes.

The PNUTGRO crop growth simulation model simulates

peanut growth and yield in response to weather, soil and

genetic inputs (Boote et al., 1986). It does not presently

respond to soil fertility or the biotic factors mentioned

above.

Crop simulation models such as PNUTGRO can serve as

aids in agricultural research or crop management (Whisler et

al., 1986). However, they must be evaluated using data sets

independent of their development or calibration (Burk,

1986). This testing requires growth analysis throughout the

crop growing season; final yield data alone are inadequate

for model validation (Acock and Acock, 1991).

The PNUTGRO model was first evaluated on peanut farms

in North Florida in 1988 (Boote et al., 1988). The model

tended to overpredict peanut growth and yield on farms with

disease and nematode problems. A version of the PNUTGRO

model calibrated to the 1988 North Florida data predicted

growth and yield reasonably well in the 1989 growing season.

The objectives of this study were to: 1) evaluate the

PNUTGRO model under actual farm conditions in two Florida

counties, 2) determine the utility of PNUTGRO to farmers and









3

extension agents, and 3) identify areas for model

improvement. Independent crop and soil data sets from

Florida peanut fields were gathered to compare with PNUTGRO

simulated values.














REVIEW OF LITERATURE

Peanut Growth Dynamics

Boote (1982) developed a uniform growth stage

designation for peanut based on visually observed vegetative

(V) and reproductive (R) events (see Table 1). Peanut dry

matter accumulation follows a sigmoidal pattern with maximum

crop growth rate (CGR) occurring at 60-90 days after

planting (DAP). Maximum CGR from 25 experiments was 20 + 4

g m'2 day'1 (Ketring et al., 1982). Leaf and stem weights

increase until 90-100 DAP, when leaf weights may begin to

decline while stem weight remains constant. Pod number and

weight become measurable 60-70 DAP. After a lag phase, pod

weight increases linearly during pod filling until harvest.

Pod growth rates for 24 experiments averaged 8 + 2 g m-2

day '1 (Ketring et al., 1982).

Peanut genetic yield potential has doubled over the

last 40 years due to an increase in the partitioning

coefficient, greater length of pod filling period and more

rapid fruit establishment (Duncan et al., 1978). Crop

growth rate has not changed significantly, but partitioning

of assimilate to reproductive parts increased from 41% in

old cultivars to 98% for more recent cultivars. Duncan et

al. (1978) presented data on four cultivars introduced














REVIEW OF LITERATURE

Peanut Growth Dynamics

Boote (1982) developed a uniform growth stage

designation for peanut based on visually observed vegetative

(V) and reproductive (R) events (see Table 1). Peanut dry

matter accumulation follows a sigmoidal pattern with maximum

crop growth rate (CGR) occurring at 60-90 days after

planting (DAP). Maximum CGR from 25 experiments was 20 + 4

g m'2 day'1 (Ketring et al., 1982). Leaf and stem weights

increase until 90-100 DAP, when leaf weights may begin to

decline while stem weight remains constant. Pod number and

weight become measurable 60-70 DAP. After a lag phase, pod

weight increases linearly during pod filling until harvest.

Pod growth rates for 24 experiments averaged 8 + 2 g m-2

day '1 (Ketring et al., 1982).

Peanut genetic yield potential has doubled over the

last 40 years due to an increase in the partitioning

coefficient, greater length of pod filling period and more

rapid fruit establishment (Duncan et al., 1978). Crop

growth rate has not changed significantly, but partitioning

of assimilate to reproductive parts increased from 41% in

old cultivars to 98% for more recent cultivars. Duncan et

al. (1978) presented data on four cultivars introduced












Table 1. Growth stage descriptions for peanut (from Boote,
1982).


Typical
Stage Stage DAPi for
Number Name Florunner#

VE Emergence
V1 First tetrafoliate to
Vn nth tetrafoliate -
R1 Beginning bloom 31
R2 Beginning peg 42
R3 Beginning pod 51
R4 Full pod 60
R5 Beginning seed 62
R6 Full seed 74
R7 Beginning maturity 93
R8 Harvest maturity 129
R9 Over-mature pod 123


:Days after planting
#In southeastern United States














MATERIALS AND METHODS

Site Selection

Field testing of the PNUTGRO model was conducted in two

Florida counties in 1990 and 1991. Jackson County sites

were chosen near Marianna and Greenwood, and Levy County

sites were chosen near Bronson. The agricultural extension

agents in both counties were asked to recommend peanut

farmers with representative management practices willing to

collaborate with University of Florida researchers.

Extension agents Mr. Anthony Drew of Levy County and Mr. Ed

Jowers of Jackson County initiated contact and liaised with

the peanut farmers whose fields were used.

A total of 15 field sites were used during the two year

study. In 1990, four sites were selected in Jackson County

and four in Levy County, while in 1991 there were four field

sites in Jackson County and three in Levy County. Table 2

identifies the field sites, and shows the cultivar, soil

type, planting date and irrigation use at each site. The

Crawford farm in Jackson County in 1990 and 1991 used a

center pivot irrigation system. Sites were selected under

the pivot and in a dry corner of the field, providing

rainfed and irrigated treatments in one field. The Morgan














MATERIALS AND METHODS

Site Selection

Field testing of the PNUTGRO model was conducted in two

Florida counties in 1990 and 1991. Jackson County sites

were chosen near Marianna and Greenwood, and Levy County

sites were chosen near Bronson. The agricultural extension

agents in both counties were asked to recommend peanut

farmers with representative management practices willing to

collaborate with University of Florida researchers.

Extension agents Mr. Anthony Drew of Levy County and Mr. Ed

Jowers of Jackson County initiated contact and liaised with

the peanut farmers whose fields were used.

A total of 15 field sites were used during the two year

study. In 1990, four sites were selected in Jackson County

and four in Levy County, while in 1991 there were four field

sites in Jackson County and three in Levy County. Table 2

identifies the field sites, and shows the cultivar, soil

type, planting date and irrigation use at each site. The

Crawford farm in Jackson County in 1990 and 1991 used a

center pivot irrigation system. Sites were selected under

the pivot and in a dry corner of the field, providing

rainfed and irrigated treatments in one field. The Morgan








Table 2. Field sites selected for study in Levy


YEAR

1990















1991


COUNTY FARM

LEVY Brookins

Graham

Lowman

Sandlin

JACKSON Crawford

Crawford

Morgan

Morgan

LEVY Graham

Lowman

Sandlin

JACKSON Crawford

Crawford

Morgan

Morgan


I.D.

BR 90

GR 90

LO 90

SA 90

CR R 90

CR I 90

MO R 90

MO I 90

GR 91

LO 91

SA 91

CR R 91

CR I 91

MO R 91

MO I 91


CULTIVAR

Florunner

Florunner

Sunrunner

Sunrunner

Agritech-127

Agritech-127

Florunner

Florunner

Marc I

Florunner

Sunrunner

Agritech-127

Agritech-127

Florunner

Florunner


and Jackson Counties in 1990 and 1991.
PLANTING
IRRIGATION SOIL SUBGROUP DATE

Yes Grossarenic May 5
Paleudalfs
Yes Grossarenic May 7
Paleudalfs
No Arenic April 18
Hapludalfs
Yes Typic April 14
Quartzipsamments
No Grossarenic April 27
Paleudults
Yes Grossarenic April 27
Paleudults
No Typic April 26
Paleudults
Yes Typic April 25
Paleudults
No Grossarenic May 26
Paleudalfs
No Arenic May 3
Hapludalfs
No Typic May 1
Quartzipsamments
No Grossarenic April 20
Paleudults
Yes Grossarenic April 20
Paleudults
No Typic April 20
Paleudults
Yes Typic April 30
Paleudults









6

sequentially in the Southeast. Partitioning percent and pod

yield (kg ha'1) were: Dixie Runner (41%/2472), Early Runner

(76%/3843), Florunner (85%/4642) and Early Bunch (98%/5378).

Increasing partitioning to reproductive parts in newer

cultivars has led to higher yields, but also to a smaller

vegetative canopy at harvest, requiring increased attention

to insect and disease control (Duncan et al., 1978; McGraw,

1979).

Environmental Influences on Peanut Growth and Yield

Peanut growth is influenced by many complex

environmental factors. Abiotic influences include weather

(water, solar radiation and temperature) and soil fertility.

Biotic factors which influence peanut growth include

diseases, nematodes and insects.

Abiotic Factors

Water, solar radiation and temperature

The drought tolerance of peanut is well documented. A

comparison of four grain legumes under season-long moisture

stress at the International Rice Research Institute (IRRI)

in the Phillipines showed that peanut had greater water use

and smaller seed yield reduction (46%) than cowpea (Vicna

unquiculata, 65%), soybean (Glycine max, 66%) or mungbean

(Vigna radiata, 83%). Water use was positively correlated

to yield (Pandey et al., 1984a). Peanut also had the

highest leaf water potential at 60 DAP (-0.67 MPa), and

maintained a lower canopy temperature (Pandey et al.,










1984b). Higher peanut root densities at 0.4 and 0.8 m soil

depth were found to be an adaptive mechanism leading to

greater water extraction and leaf water potential (Pandey et

al., 1984c). Maliro (1987) also found that peanut yield was

greater than that of soybean, pigeonpea (Caianus caian) and

cowpea in both rainfed and irrigated conditions. Peanut had

the highest crop growth rate and longest effective seed

filling period of the four legumes.

Further experiments conducted at IRRI with peanut,

cowpea, soybean, mungbean and pigeon pea compared responses

under soil water deficit imposed during the reproductive

growth phase (Senthong and Pandey, 1989). Peanut had the

highest overall yield in both irrigated and rainfed

conditions. Seed yield increased linearly with water

applied (3.5-5.7 kg ha'' mm'1 for peanut) for all legumes

except pigeon pea. Peanut again had the greatest water

extraction. Differences in drought susceptibility were

thought to be primarily associated with the ability of

peanut to sustain root growth into deeper soil layers under

drying soil conditions.

Maximum daily water use for peanut ranges from 0.5 to

0.6 cm day"' during maximum LAI. Sixty cm of well

distributed water is generally sufficient for optimal peanut

production. Evapotranspiration will increase with

increasing solar radiation, temperature and wind speed, and










decreasing relative humidity and soil water deficit (Boote

et al., 1982).

The effect of timing of drought on peanut has been

studied extensively. Peanut appears more sensitive to

drought after pegging than during early vegetative growth

(Boote et al., 1982; Nagaswara-Rao et al., 1985; Nageswara-

Rao et al., 1988; Pallas et al., 1979; Stirling et al.,

1989a, 1989b; Stirling and Black, 1991). Florunner yields

were decreased 65% with the imposition of a 70 day late

season drought (Pallas et al., 1979). Early season drought

decreased yields only 16-33%.

Peanut pod yields were four times greater under late

irrigation (after pod initiation) versus early irrigation

only (sowing to pod initiation) (Stirling et al., 1989a).

Peanut's "plasticity of development" allowed growth rates to

resume to non-stressed levels in late irrigated stands.

Apical sink activity, leaf expansion and peg turgor were all

maintained during early drought (Stirling et al., 1989b).

Late season moisture deficits: 1) induced flower abortion,

2) reduced peg turgor and ability to penetrate soil, and 3)

restricted pod growth with inadequate assimilate production.

Timing of peg initiation was found to be sensitive to air

temperature, and thermal time was suggested as an

appropriate means to schedule initial irrigations (Stirling

et al., 1989a; Stirling and Black, 1991).










Pod initiation was delayed 16 to 33 days under severe

water stress in India. This delay between peg initiation

and pod growth was the cause of differences in harvest index

and pod yield (Stirling and Black, 1991). Peanut leaf area,

flower number, total biomass, pod yield, root weight and

leaf osmotic potential decreased as soil water potential

decreased (Patel et al., 1983).

Stansell and Pallas (1985) reported lower Florunner

yields when drought was imposed 35-105 DAP (1387 kg ha'1),

than when drought was imposed at 70-140 DAP (2592 kg ha'").

They hypothesized that the peanut roots were able to reach

deeper soil profiles after 70 days to withstand drought.

Peanut drought tolerance differs among cultivars.

Comet, a spanish type cultivar, showed a greater tolerance

to dehydration and higher pod yield under rainfed conditions

than Florunner, while Florunner had greater yield potential

under irrigated conditions (Erickson and Ketring, 1985).

Percent yield loss from drought was associated with yield

potential for 22 varieties when water deficit was applied

during seed fill in India (Nageswara-Rao et al., 1989),

implying a conflict between yield potential and earliness to

escape drought.

Robut 33 grown on an Alfisol in India showed a yield

increase of 12-18% when drought was applied from emergence

to pegging. "Greater synchrony of pod set in moderately

stressed plots resulted in a greater proportion of mature









10
pods at final harvest" (Nageswara-Rao et al., 1988, p. 431).

Soil water deficits during pod fill reduced yield 28 to 68%,

due to decreased initiation and development of pods caused

by increased soil temperature and decreased soil moisture

(Nageswara-Rao et al., 1985).

In summary, soil water deficits have been shown to:

increase water extraction from lower rooting depths,

decrease dry matter and crop growth rate, decrease flower

number and pod number, and inhibit pegging and pod

development through decreased peg turgor, high soil

temperatures and decreased Ca uptake. Water stress reduces

photosynthesis and transpiration while increasing diffusive

resistance (stomatal closure), and reducing leaf water

potential (Boote et al., 1982; Ketring et al., 1982).

Seedling growth is optimal when photosynthetically

active radiation (PAR) reaches 25 mol m-2 day'1. Shading

decreases peanut shoot weight, flower number, peg number,

pod weight, and pod number (Hang et al., 1984; Ketring et

al., 1982). Hang et al. (1984) found that Florunner peanut

was most sensitive to shading during pod filling. A 75%

reduction in light intensity from 83 to 104 DAP reduced pod

yield 30%. Twenty-one days of shade at flowering, however,

did not significantly reduce final yield.

Optimal temperature for peanut dry matter production is

approximately 30 OC, but flowering has a lower optimal

temperature at 20-25 OC. Photosynthetic rate decreases 25%











at 40 oC and 65% at 10 oC as air temperatures vary from the

30 oC optimum (Ketring et al, 1982). Ong (1982) compared 14

genotypes for thermal time requirements and reported base,

optimal and maximum temperatures to be 8.0-11.5, 29.0-36.5

and 41.0-47.0 oC, respectively. Peanut fruit development is

also sensitive to temperature. An increase in soil

temperature from 23 to 37 OC decreased fruit number, yield

and filling period (Dreyer, 1980; Dreyer et al., 1981).

Fruiting zone temperature is influenced by canopy and air

temperature as well as soil temperature (Sanders et al.,

1985).

Soil fertility

Peanut response to liming of soils has been found to be

caused mainly by increased calcium levels. Peanut was

tolerant of acid soils of pH 4.3 in Malawi (Cox et al.,

1982). Fertilization with K, Mg and N is not usually

necessary, although K might be needed following bahiagrass

(Paspalum notatum) (Cox et al., 1982). Phosphorous

deficiency is probably the most common nutrient deficiency

worldwide, correctable with P fertilization to levels

greater than 7 ppm (Cox et al., 1982).

Calcium is the most yield limiting nutrient in the

southeastern U.S. (Cox et al., 1982). Calcium is necessary

in the fruiting zone for peanut pod development. Three

separate "critical" levels of solution Ca levels, expressed

as the activity ratio of Ca/total cations, were 0.10 for









12
vegetative growth, 0.15 for fruit load and 0.25 for pod fill

(Wolt and Adams, 1979). Recommended levels of extractable

soil calcium vary from 335 kg ha'1 in Alabama to 540-720 kg

ha-1 in North Carolina. Ca in excess of 600 kg ha'1 showed

no yield response in Florida, nor did additions of Mo or B

(Nagel, 1981). Increase in calcium sulfate applications

from 0 to 2240 kg ha'1 did not decrease Pythium or

Rhizoctonia pod rot (Filonow et al., 1988).

Twenty-four peanut cultivars were grown under low soil

fertility on a Tifton loamy sand, pH 5.3, with 10, 25, 69,

and 19 mg kg'1 of P, K, Ca and Mg respectively (Branch and

Gascho, 1985). Significant differences were found among

cultivars for peanut grade and yield, although no

significant relationship was found for a single nutrient and

yield. University of Florida breeders should be

congratulated, because Florunner had the highest average pod

yield (3215 kg ha"1) of the 24 cultivars, while Early Runner

had the highest total SMK yield (1700 kg ha''). It should

come as no surprise that 98% of all southeastern peanut

acreage was planted to Florunner in 1982 (Henning et al.,

1982), although the proportion planted to Florunner has been

decreasing in recent years.

A study of large-seeded virginia type cultivars showed

significant differences among cultivars and nutrient

treatments for nutrient content of petioles, leaflets and

seeds (Coffelt and Hallock, 1986). Seed yield, market grade










and germination percentage were also affected. Results

indicate cultivars differed in nutrient requirement,

nutrient uptake and redistribution of nutrients.

A five year field trial in northern Ghana established

that the inclusion of peanuts in crop rotations led to

significantly higher levels of soil organic N (Tiessen,

1988).

Biotic Factors

Diseases

During its early cultivation, peanut was regarded as

relatively free of disease. However, rust infection was

identified in 1884, leafspot in 1914, and stem rot in 1917

(Porter et al., 1982).

Early and late leafspots (Cercospora arachidicola and

Cercospora personatum). Peanut yield losses up to 50% are

common in fields where fungicides are not used for leafspot

control (Porter et al., 1982). Early leafspot infection on

the adaxial leaf surface, or late leafspot infection on the

abaxial surface, results in small necrotic flecks which

enlarge and become light brown to black circular spots

ranging from 1-10 mm in diameter (Porter et al., 1982;

Smith, 1984). Optimal temperature for leafspot infection is

25-32 C, and infection is more severe when crop rotation is

not practiced (Porter et al., 1982; Smith, 1984). Severe

leafspot infection decreases leaf area index (LAI) and

canopy carbon exchange rate (Porter et al., 1982).









14
Bourgeois et al. (1991) found significant differences in LAI

and canopy weight between fungicide treated and leafspot-

infected plots, beginning at 80-90 DAP.

A screening of 16 genotypes showed similar V-stage

measurements but reduced vegetative weights and partitioning

coefficients under early and late leafspot pressure (Knauft

and Gorbet, 1990). Both Florunner and Sunrunner had high

percentage necrotic leaf area (9.3-10.9%) and disease

ratings (8.5-8.8 on a scale of 1.0-9.0). Pod yield of

Florunner and Sunrunner was high until 120 DAP, then

declined rapidly without disease control. Florunner was

also found highly susceptible to early leafspot in Oklahoma

(Melouk and Banks, 1984). Florunner's high assimilate

partitioning to pod growth (92%) was found to limit

Florunner's leaf production during pod fill and preclude

replacement of diseased leaves (Pixley et al., 1990).

Partially resistant genotypes compensated for leafspot

induced defoliation by lower partitioning to pods, thus

allowing greater leaf area growth during pod fill.

A survey of 35 peanut fields in seven Florida counties

revealed that late leafspot predominated, accounting for 88%

of the leafspots counted and 66% of the leafspot area

(Jackson, 1981). Leafspot infection levels were low in

Levy and Marion County fields following bahiagrass (Paspalum

notatum) rotations.











Rust (Puccinia arachidis). Before 1969, rust was

confined to South and Central America, but it is now spread

worldwide. Orange colored rust pustules first appear on the

abaxial leaf surface. Infection is favored by optimal

temperatures of 20-30 oC, and available water on the leaf

surface (Mallaiah and Rao, 1979; Porter et al., 1982;

Subrahmanyam et al., 1984a).

Rust infection early in the peanut life cycle leads to

reduced leaf size and premature leaf senescence. Shoot

growth is slowed, and the crop life cycle is reduced by 15

to 20 days. Seed size and oil content are also reduced.

Rust uredial pustules rupture the leaf epidermis, and are

more concentrated on the lower leaf. Rupture leads to

increased transpiration, increased water stress and leaf

senescence. Leaf fall may provide organic matter for

secondary white mold (Sclerotia) attack (Bromfield, 1971).

Rust can cause yield losses up to 70% for genotypes

without disease resistance. Pod yield is correlated to

green leaf area remaining at maturity, indicating that the

disease does not affect the photosynthetic efficiency of the

remaining green leaf area (Subrahmanyam et al., 1984b). Rust

infection in Africa is favored under "optimal temperature

and rainfall" conditions, while weather requirements for

leafspot infection are more flexible. Frequent fungal

applications are not "a realistic alternative" for most










African farmers, and more rust resistant varieties are

needed (Savary et al., 1988).

Stem rot (Sclerotium rolfsii). Sclerotium rolfsii

infection is known as stem rot, white mold, southern stem

rot, southern blight or Sclerotium rot. The disease is

found worldwide, with yield losses up to 80% in severe

cases. Symptoms start with yellowing and wilting of a lower

branch followed by the development of sheaths of white

mycelium near or at the base of the plant (Backman, 1984;

Porter et al., 1982). The disease is favored by moist, warm

conditions which usually coincide with increased canopy

cover and relative humidity at pegging and pod formation.

The use of transparent plastic mulch which increased soil

temperature beyond the stem rot optimum of 30-35 oC, caused

lower disease incidence (Porter et al., 1982). Pod yield

has been found to be negatively correlated to number of

disease loci, decreasing an average of 49 kg ha'' per locus

(Backman, 1984).

"Accumulation of dried leaves around the base of peanut

plants partially defoliated by any stress factor (e.g.

leafspot, insects or drought) creates optimal conditions for

initiating an epidemic if sclerotia are present" (Beute and

Rodriguez-Kabana, 1979, p. 872). High moisture and

temperature conditions then influence the severity of the

epidemic (Beute and Rodriguez-Kabana, 1979; Bromfield,

1971). Biological control using Pseudomonas fluorescens











bacterial strains decreased Sclerotium mycelial growth in

vitro and protected 99% of inoculated greenhouse peanut

plants from infection. Non-inoculated greenhouse plants

died within 10 days (Ganesan and Gnanamanickam, 1987).

Nematodes

All nematode pests decrease root growth, leading to

increased nutrient deficiencies and susceptibility to

drought. However, root-knot nematodes (Meloidoqyne arenaria

Chitwood) are the most important nematode pest of peanut,

causing yield losses of 20-90% (Porter et al., 1982;

Rodriquez-Kabana, 1984). Root-knot nematodes are

distributed worldwide between 350 N and 350 S latitude.

Infected plants develop galls of various sizes on the roots.

The galls are internal to peanut tissue, not appended to the

root like Rhizobium nodules. Infected tissue spread by

farming operations or running water greatly expand the range

of nematode infection (Rodriguez-Kabana, 1984; Porter et

al., 1982).

At present, there are no peanut cultivars resistant to

root-knot nematode. Control of root-knot consists of the

use of systemic nematicides along with crop rotations. A

study of four systemic root-knot nematicides (aldicarb,

phenamiphos, oxamyl and carbofuran) on Florunner revealed

they were most effective when applied during the first 2

weeks of crop growth. Yield enhancement was significantly

less if nematicides were applied at blooming, 8 weeks after










planting. Early applications delayed the exponential

population growth characteristic of nematodes and prevented

early damage to the peanut main tap root (Rodriguez-Kabana

et al., 1982a). Banded placement of aldicarb or phenamiphos

was superior to in-furrow or combined placement (Rodriguez-

Kabana et al., 1982b). Early bunch-type genotype UF77118

inoculated at seeding with root-knot nematodes had only 50%

pod yield of the control, and had lower LAI, stem weight,

leaf weight, peg and pod number (Senthong, 1979). Plants

inoculated at pegging and complete ground cover were not

significantly different from control plants.

Florunner yields from 16 experiments over 3 years were

negatively correlated to root-knot nematode larval levels

(number of larvae per 100 cm soil), with a statistically

significant quadratic regression fit (Rodriguez-Kabana et

al., 1982c). However, there were differences in the

equations among seasons. At average larval levels, average

yield loss was 427-539 kg ha"'. Yield was reduced even

with low (<50 larvae 100 cm-3 soil) nematode numbers.

Unfortunately, larval levels from pre-season assays are not

reliable for prediction.

Root-knot nematodes do not infect the forage crop

bahiagrass (Paspalum notatum). Crop rotation of peanut with

bahiagrass was found to be as effective as using aldicarb in

reducing population densities of M. arenaria and increasing

peanut yields (Rodriguez-Kabana et al., 1988). Peanut









19

yields were 27% higher, and M. arenaria population densities

were 41% lower, in plots following one year of bahiagrass

than in plots under peanut monoculture.

Insects

Smith and Barfield (1982) identified 360 species of

peanut insect pests worldwide. Peanut was ranked 10th out

of 77 plant species for number of pests. Smith and Barfield

elucidated a relationship between peanut growth stage and

tolerance to insect attack. For example, as peanuts age,

the crop becomes less tolerant to nut feeders, while

tolerance to leaf feeders is lowest at maximum LAI and rises

thereafter. They proposed an emphasis on Integrated Pest

Management (IPM) as opposed to chemical pesticides, but

admitted that economic threshold data and sampling

methodology were lacking.

Wilkerson (1980) found that early season defoliated

peanut plants suffered a decline in all plant part weights.

Late season defoliated plants lost stem and pod weight, but

used stem reserves to grow new leaves.

Peanut insect pests in the semi-arid tropics include

both above ground foliage feeders such as leaf miners

(Aproaerema modicella) and armyworms (Spodoptera spp.), and

below ground root and pod attackers such as termites

(Termitadae) and white grubs (Holotrichia spp.). Aphids

(Aphis craccivora) and thrips (Thripidae) are vectors of

groundnut rosette and tomato spotted wilt virus. More











research needs to be done on plant resistance to these

pests, and the interaction of microclimate, genotype and

natural enemies within traditional cropping systems of the

tropics (Wightman and Amin, 1988).

Crop Growth Modelling

Purpose of Crop Modelling

Whisler et al. (1986) catalogued use of crop simulation

models into three broad categories: (1) aids in interpreting

experimental results, (2) agronomic research tools, or (3)

agronomic management tools. The level of detail used in the

model is related to these objectives and to the available

data needed to build and run the model.

Crop growth models have the potential to simulate

crops, crop varieties and cropping practices over a wide

range of climatic conditions. The model selected should be

evaluated previously, be sensitive to the factors of

interest, use available inputs, and be easy to use (Boote

and Jones, 1988). Boote and Jones (1988) used PNUTGRO

simulations to choose optimal planting dates for 21 years of

weather data for Gainesville, FL. and for four years for

Niamey, Niger. The best planting dates for Florida

coincided with recommended practices, while simulated peanut

yield in semi-arid Niger declined as planting date was

delayed after the start of the rainy season.

Peanut Model Development










The PNUTGRO crop growth simulation model is a

physiologically based model which considers crop carbon

balance, nitrogen balance and water balance at the process

level. Daily canopy photosynthesis, respiration, growth,

phenology, and partitioning are predicted given weather,

soil and varietal inputs (Boote et al., 1986; Boote et al.,

1989a). Photosynthesis is a function of solar radiation,

temperature, available soil water, LAI and leaf nitrogen.

Maintenance respiration depends on temperature, crop

photosynthesis rate and current crop biomass. Growth

respiration is calculated using conversion equations (in

glucose equivalents) for synthesis costs of different plant

tissues. Rates of crop development and phenology are based

on heat unit accumulation with an optimal temperature range

of 28-320C, declining to zero at 11 and 450C (Boote et al.,

1989a).

Partitioning varies with growth stage, drought stress

and among cultivars. As pods and seeds develop, increasing

assimilate is partitioned to reproductive parts until a

varietal limit is reached, e.g. 0.85 for Florunner (Boote et

al., 1989b). Crop maturity occurs when a given heat unit

accumulation occurs, maximum shelling percentage is reached,

or photosynthetic capacity is lost through crop aging or

drought.

The PNUTGRO model uses the Ritchie soil water balance

model (Ritchie, 1985), which divides the soil into up to 10










layers. Each soil layer contains soil water and root

densities which change over time due to weather effects,

plant growth dynamics and soil characteristics.

In addition to PNUTGRO, other peanut models have been

developed. AUNUTS, an expert system for managing pests in

peanuts, is designed to be a "planting to harvest decision

aid" (Davis et al., 1989). The expert system questions

users on their changing problems and provides expert

knowledge from a stored database. AUNUTS, for example, will

predict yield loss from root-knot nematode larval numbers

and advise growers whether to grow peanuts on a given field.

Bourgeois (1989) created a simulation model of the

progression of late leafspot (LATESPOT) and coupled it to

PNUTGRO. LATESPOT predicts conidia dissemination, disease

development and plant damage based on daily environmental

conditions. Canopy photosynthesis is reduced by disease-

induced defoliation. The model predicted dry weight losses

adequately on Florunner field data in Florida from 1983,

1985 and 1987. Knudsen et al. (1987) also generated a

computer simulation model that predicts Cercospora leafspot

infection rate as a function of relative humidity, minimum

air temperature and amount of infected tissue. The model

was validated using independent weather and disease data

from the Florigiant cultivar.

A regression model for predicting yield loss as a

function of Cvlindrocladium black rot incidence has been










developed (Pataky et al., 1983). However, multiple

regression analysis models do not adequately describe

interactions between plant growth factors (Acock and Acock,

1991). Crop simulation models have greater potential than

empirical models for making extrapolations. Models

developed from growth chamber data, however, need to be

calibrated and validated with field data. Long term field

research can be especially useful because: 1) some

environmental factors would be stable over time, and 2)

effects of previous unknown treatments would be minimized

(Acock and Acock, 1991).

Model Calibration

Crop growth model development includes the process of

calibration of model parameters to improve model performance

over a range of environments. An alfalfa (Medicago sativa)

growth simulation model, ALF2LP was evaluated under Quebec

conditions by Bourgeois (1990). He calibrated the specific

leaf area and maximum growth rate, after which the model

accurately simulated dry matter yield and crude fiber

percentage when validated with independent data sets.

However, the ALF2LP model "can be considered as a potential

yield estimator because it does not include functions for

soil fertility, insect damage and disease effects"

(Bourgeois, 1990, p. 10).

Modellers must be careful not to make changes that only

serve to fit data from one site. A potato (Solanum









24

tuberosum) model developed in Idaho was used in New York and

underpredicted biomass by 17-40% (Ewing et al., 1990). When

the model was calibrated to fit New York data, it then

overestimated Idaho yields by 51%.

A limited geographical model for corn (Zea mays L.)

yield response to water stress and heat units was developed

and calibrated for the deep loessial lands of the upper

Mississippi valley (Swan et al., 1990). Limited range of

soil types and climate, no soil nutrient deficiencies or

serious pest damage, and continuous corn cropping led to

ease of model development.

Model Validation

As Burk (1986, p. 35) has noted, "modelers often spend

too little time on validation or provide results of limited

significance to the user." However, caution must be

exercised when discussing validation experiments.

Validation is not testing the truth of a model; every model

fails at some level of resolution. Neither is validation a

statistical analysis of an individual model component.

Validation is "an evaluation of the usefulness of a model,

as a whole, in providing reliable information for a specific

problem" (Burk, 1986, p. 35). Data sets independent of

those used in model development and calibration are used to

compare model predicted and observed values. Both model

error and data variability are sources of error in this

process (Whisler et al., 1986).










Final yield data alone are inadequate for model

validation. Model error in predicting final yield can not

be corrected without knowledge of crop growth dynamics

during the season. Alternatively, model results may match

final yields without correctly modelling crop growth

patterns. Acock and Acock (1991) show a wide range of

weather, soil and plant parameters needed for crop

simulation. Regression analysis may be used to compare

simulated results to observed values at different points

during the growing season (Huda, 1988).

Computer models are being tested in a wide array of

agronomic fields today, from planning planting dates in

single-truss tomato (Lycopersicon esculentum) cropping

systems (McAvoy et al., 1989) to predicting ammonia

volatilization in flooded soil systems (Jayaweera et al.,

1990) or for predicting early and late blight outbreaks on

potato (Solonum tuberosum) (Shtienberg et al., 1989).

This thesis research was conducted to evaluate the

PNUTGRO peanut crop growth model under actual farm

conditions in order to determine PNUTGRO's utility for

farmers and extension agents, and to identify areas for

model improvement. Independent crop and soil data sets from

Florida peanut fields were gathered to compare with

simulated PNUTGRO values.









29

replicates to the IFAS Nematode Assay Laboratory, where the

soil and roots were analyzed for Meloidoqyne root-knot and

other nematode populations. Another similar nematode assay

was taken in 1991 after approximately three months (August

14 and August 19) at each site to obtain population

estimates on mature plants. Late-season nematode assays

were not collected in 1990.

Soil Water Sampling

Gravimetric soil water content was sampled in two

replications per site every 3 weeks in Levy County farms and

every 4 weeks in Jackson County farms. Soil water and plant

growth samples were taken at 4 week intervals in Jackson

County due to its greater distance from Gainesville and

corresponding logistical requirements. Aluminum tubing

marked at 15, 30, 45, 60, 90 and 120 cm was manually driven

into the soil and cores from these six depths were placed in

ziplockTM freezer storage bags.

The ziplockTM bags and soil were weighed immediately

upon return to the field laboratory in Gainesville. The

bags were opened and oven dried at 90 OC for 48 hours, then

reweighed. The gravimetric water content was obtained using

the equation:

% Gravimetric soil water =

(Wet wt. (g) Dry wt. (g))/(Dry wt. (g) Ziplock wt. (g)).

Bulk density (BD) values for the six soil layers at

each site were obtained using Soil Conservation Service










(SCS) maps. Volumetric water content was calculated as:

% Volumetric soil water = % Gravimetric soil water x BD.

Weather Data Collection

In order to simulate peanut growth and yield the

PNUTGRO model requires several weather inputs: solar

radiation, minimum and maximum daily air temperature, and

daily rainfall or irrigation. Automated tipping bucket

raingauges were installed at each field site to record

rainfall and/or irrigation in each field. LICOR LI-1200

weather stations were placed in each county to obtain daily

solar radiation and maximum and minimum air temperatures.

In 1990, weather stations were placed on the Morgan farm (MO

R) in Jackson County and the Graham farm in Levy County. In

1991, LI-1200 weather stations were placed at the Morgan

farm (MO R) in Jackson County and the Sandlin farm in Levy

County. In addition, a LI-1000 data logger was installed to

record weather data at the Graham farm in Levy County.

Solar radiation and air temperatures from weather

stations in each county were used to create weather files

for each site in that county, combined with raingauge data

obtained at each field site.

Plant Growth Sampling

Plant growth samples were taken every 3 weeks at Levy

County sites and every 4 weeks at Jackson County sites

throughout the growing season. One meter of row (0.914 m2

area) was sampled from each of the four replications. Care










(SCS) maps. Volumetric water content was calculated as:

% Volumetric soil water = % Gravimetric soil water x BD.

Weather Data Collection

In order to simulate peanut growth and yield the

PNUTGRO model requires several weather inputs: solar

radiation, minimum and maximum daily air temperature, and

daily rainfall or irrigation. Automated tipping bucket

raingauges were installed at each field site to record

rainfall and/or irrigation in each field. LICOR LI-1200

weather stations were placed in each county to obtain daily

solar radiation and maximum and minimum air temperatures.

In 1990, weather stations were placed on the Morgan farm (MO

R) in Jackson County and the Graham farm in Levy County. In

1991, LI-1200 weather stations were placed at the Morgan

farm (MO R) in Jackson County and the Sandlin farm in Levy

County. In addition, a LI-1000 data logger was installed to

record weather data at the Graham farm in Levy County.

Solar radiation and air temperatures from weather

stations in each county were used to create weather files

for each site in that county, combined with raingauge data

obtained at each field site.

Plant Growth Sampling

Plant growth samples were taken every 3 weeks at Levy

County sites and every 4 weeks at Jackson County sites

throughout the growing season. One meter of row (0.914 m2

area) was sampled from each of the four replications. Care










was taken to ensure that the meter-row sample was measured

from peanut plant midgap to midgap, and that border plants

were adequate on all sides of the sample.

Plant height, width and number were measured in the

field and the plants uprooted (root samples were not taken)

and transported in plastic garbage bags to the field

laboratory in Gainesville. Reproductive stages were

determined for all the plants in the sample, and total

biomass dry weight was determined after drying at 70 oC for

3 days. A three plant subsample of representative plants

within each replication was used to calculate the following

growth data: vegetative stage, specific leaf area, leaf

area, stem dry weight, leaf dry weight, number of pegging

sites, number of pods less than and greater than R4, pod dry

weight less than and greater than R4, number of seeds, and

seed dry weight.

The principle of allometry was used to multiply total

biomass by subsample fraction leaf, stem, and pod values to

obtain total plant component dry matter per unit land area.

For example, leaf area index (LAI) was calculated by

computing a leaf area to leaf mass ratio (specific leaf

area) and a leaf to total plant mass ratio (fraction leaf)

from the three plant subsample, and multiplying these ratios

by the total biomass dry weight (g m2):



LAI = m2 leaf/g leaf x g leaf/g plant x g biomass/m2 soil.










The same approach was used to compute leaf, stem, seed and

pod biomass; i.e. fraction leaf, stem, pod, etc. was

multiplied by total biomass (including the three plant

subsample) to express dry matter partitioning to plant parts

on a land area basis. This technique assumes that the ratio

of leaf (or stem, pod, etc.) weight to total biomass is

similar for neighboring plants of the same age and genotype

(Pixley et al., 1990).

Final Harvest Sampling

A final harvest sample of 8.94 m2/replication was taken

as close as possible to the grower harvest date. Two rows,

each 4.9 m long, were dug manually in each plot with

pitchforks and the peanut plants placed on a tobacco sheet.

Dropped pods were then recovered from the soil. Pods with

healthy peg attachments that had been accidently pulled off

by the pitchfork were combined with the harvested pod

sample. Dropped pods with rotting peg attachments due to

disease and/or age were dried and weighed separately.

Harvested plants were transported to Gainesville and

dried outdoors on the tobacco sheets for 3 to 4 days and

then threshed mechanically. The remaining pegs and pods

were dried at 35-40 oC for 1 week in field dryers, then the

pegs were removed mechanically. Total pod dry weight was

measured, and a 500 g subsample was removed. Loose shelled

kernels (LSK) were extracted from the subsample and weighed

separately. The remaining pods were sized and shelled










farms in Jackson County and all the Levy County sites were

in different fields.

Farmers managed the research plots exactly the same as

the rest of the field. Survey forms on management practices

were completed by each farmer to establish crop history and

agrichemical use.

Soil Fertility and Nematode Tests

Four plots of eight rows were marked at each site to

create four replications 66.8 m2 in area. The four plots

were adjacent to one another in one block selected within a

uniform area of the field. The plots were staked just after

seedling emergence to allow evaluation of adequate plant

population. Care was taken to ensure that the research

plots were not close to the field borders and that the soil

type within the plots was as uniform as possible.

Initial soil fertility tests and nematode assays were

collected soon after plant emergence. The top 20 cm of soil

was sampled randomly and mixed in a plastic bucket.

Chemical analyses of Mehlich I extractable P, K, Ca, Mg, Zn,

Mn, Cu, Na and Fe, and water extractable pH, NH4 and NO3

were conducted at the IFAS Soils Testing Laboratory (see

Table A-l). Although PNUTGRO does not presently respond to

soil fertility factors, this information was collected to

allow comparisons and for future reference.

Nematode assays were conducted by submitting seedling

tap roots and surrounding soil from each of the four














RESULTS AND DISCUSSION

1990 On-Farm Experiments

Levy County

The Levy County farmers used peanut crop rotations with

livestock-grazed bahiagrass (Paspalum notatum) in their

farming system. Bahiagrass was grown for 2 to 8 years

between peanut plantings. Bahiagrass rotations have been

shown to reduce nematode populations (Rodriguez-Kabana et

al., 1988) and leafspot infection rates (Jackson, 1981) in

subsequent peanut crops.

The soils in Levy County were all Arenic or Grossarenic

paleudalfs except for the Typic quartzipsamment on the

Sandlin farm. Table A-1 shows soil fertility test results.

Soil pH ranged from 5.30-6.83 on the Levy County farms. At

the beginning of the growing season (after N-P-K

fertilization), the Sandlin farm had the lowest measured

Mehlich I extractable levels of Ca, Mg, K, and P of 78.2,

11.0, 13.8 and 12.5 mg kg'1, respectively. These levels are

comparable to the low values in the Branch and Gascho (1985)

study on low soil fertility effects on peanut growth.

However, nutrient deficiencies did not limit peanut growth

at the Sandlin farm. Despite having the lowest soil

fertility, the peanut pod yield was greater at the SA farm














RESULTS AND DISCUSSION

1990 On-Farm Experiments

Levy County

The Levy County farmers used peanut crop rotations with

livestock-grazed bahiagrass (Paspalum notatum) in their

farming system. Bahiagrass was grown for 2 to 8 years

between peanut plantings. Bahiagrass rotations have been

shown to reduce nematode populations (Rodriguez-Kabana et

al., 1988) and leafspot infection rates (Jackson, 1981) in

subsequent peanut crops.

The soils in Levy County were all Arenic or Grossarenic

paleudalfs except for the Typic quartzipsamment on the

Sandlin farm. Table A-1 shows soil fertility test results.

Soil pH ranged from 5.30-6.83 on the Levy County farms. At

the beginning of the growing season (after N-P-K

fertilization), the Sandlin farm had the lowest measured

Mehlich I extractable levels of Ca, Mg, K, and P of 78.2,

11.0, 13.8 and 12.5 mg kg'1, respectively. These levels are

comparable to the low values in the Branch and Gascho (1985)

study on low soil fertility effects on peanut growth.

However, nutrient deficiencies did not limit peanut growth

at the Sandlin farm. Despite having the lowest soil

fertility, the peanut pod yield was greater at the SA farm










than any other farm in this study. PNUTGRO does not

presently account for soil fertility factors, and soil

fertility was not considered a limiting growth factor in

this experiment.

Levy County farms received enough combined rainfall and

irrigation (see Figure 1) to exceed the 500-600 mm of well-

distributed water needed for adequate peanut growth and

development (Boote et al., 1982). Table A-4 lists water

received at all raingauges in 1990. Table 3 shows

cumulative water received at all sites, and average seasonal

solar radiation and temperature values.

Volumetric soil water percentages were measured for 15,

30, 45, 60, 90, and 120 cm soil layers at 3-week intervals

throughout the growing season for all farms. The PNUTGRO

model tended to overestimate volumetric soil water content,

but followed the general trend of wetting and drying cycles

observed in the individual fields (Figures 2-5). The

Brookins soil water percentages varied from 2.9 to 14.7% at

the 15 and 30 cm soil layers throughout the growing season

(Figure 2). Similarly, soil water at the Graham farm varied

from 6.1 to 13.0% at the 30 cm layer (Figure 3). The

PNUTGRO model tended to overestimate volumetric soil water

when observed values dropped below 5% on the Lowman farm

(Figure 4). Also, the measurements on the Sandlin farm were

extremely low (usually between 2.0 and 5.0% for all soil









LEVY COUNTY, 1990


E 800


w 600
0
w

w 400
I-

w
> 200


D 0
0 100


125 150 175 200 225 250


DAY OF YEAR


Figure 1. Cumulative water received at all Levy County field
sites in 1990.











Table 3. Seasonal water received, and average daily solar


radiation and temperature for all field
1991.


sites in 1990 and


Cum. Average Cum. Average
Water Solar Solar Daily
County Farm Year Received Rad. Rad. Temp.
--mm-- -MJ m"2 d'-- -MJ m-?- -C-

Levy GR 90 583 21.04 2632 25.9
BR 773 2726 -
SA 783 3214
LO 645 2859 -
Jackson MO R 90 421 22.29 3028 26.1
MO I @ 3051 -
CR R 407 2656
CR I @ 2656 -
Levy GR 91 1184 19.18 2419 27.0
SA 875 19.26 2603 26.2
LO 626 2374 -
Jackson MO R 91 808 19.07 2651 26.6
MO I @ 2758 -
CR R 675 2354
CR I 732 2354

@ Indicates missing data due to raingauge malfunction.










.240



.160




S \


0 0




24 14 81 158 15
DAYS AFTER PLANTING
-.- a SMC 15-38 :BR 9
I SC 5-15 c:BR 90

Figure 2. Observed (points) and PNUTGRO-simulated (lines)
volumetric soil water content from 5-15 and 15-30
cm soil layers at the Brookins farm in Levy County
in 1990.










.240

.200

S1690




S..


9,848


i 216 2 7? 104 13
DAYS AFTER PLANTING
-*- SWC 15-30 :GR 90
SMC 5-15 c:GR 99

Figure 3. Observed (points) and PNUTGRO-simulated (lines)
volumetric soil water content from 5-15 and 15-30
cm soil layers at the Graham farm in Levy County
in 1990.









.300

.250 t

.200






.050


6 29 58 81 116 141
DAYS AFTER PLANTING
--- o SUC 15-30 :LO 90
SC 5-15 c:LO 98

Figure 4. Observed (points) and PNUTGRO-simulated (lines)
volumetric soil water content from 5-15 and 15-30
cm soil layers at the Lowman farm in Levy County
in 1990.









.180

,1508



,098 i"'"* "






S 32 64 96 18

DAYS AFTER PLANTING
--o SMC 15-38 :SA 9
SMC 5-15 c:SA 98
Figure 5. Observed (points) and PNUTGRO-simulated (lines)
volumetric soil water content from 5-15 and 15-30
cm soil layers at the Sandlin farm in Levy County
in 1990.










layers), despite receiving 783 mm of rainfall, indicating

the coarse texture of the soil. PNUTGRO overestimated

volumetric percentages throughout the season on the Sandlin

farm (Figure 5), except for the sampling date 140 DAP when a

seasonal high of 11.1% was measured.

Figure 6 shows daily maximum and minimum temperatures

from the 1990 Graham farm weather station in Levy County.

Early season minimum temperatures were unusually low (7 days

below 10 oC), but average seasonal temperatures were

adequate for peanut growth. Average maximum and minimum

temperatures were 33.1 and 18.6 oC, respectively. Daily

mean temperature for the entire growing season was 25.9 oC.

Solar radiation at the Graham weather station is shown

in Figure 7 and Table A-5. Intermittent cloud cover and

Florida's convectional thunderstorms caused the fluctuating

daily values. The average seasonal value was 21.04 MJ m'2

day"1.

Levy County growers in 1990 generally reached the

growth and yield potential predicted by PNUTGRO. Table 4

shows observed versus simulated growth and yield parameters

for the Sandlin (SA), Lowman (LO), Brookins (BR), and Graham

(GR) farms in 1990.

Peanut growth on the Sandlin farm was vigorous despite

low native soil fertility of the Typic quartzipsamments.

Figure 8 shows PNUTGRO simulated growth (lines) versus field

measured values (points) for the SA farm. Both canopy









LEVY COUNTY, 1990


125 150 175 200 225 250
DAY OF YEAR


Figure 6. Maximum and minimum daily temperature recorded at
the weather station on the Graham farm in Levy
County in 1990.


40



S30

0
LU

120

UIl
a-


OL-
100









30

E 25

20
z

S15





CO
0
100


LEVY COUNTY, 1990


125 150 175 200 225 250


DAY OF YEAR


Figure 7. Daily solar radiation recorded at the weather
station on the Graham farm in Levy County in 1990.









Table 4. Field-observed (OBS.) vs. PNUTGRO simulated (SIM.) growth and yield data
from Levy County in 1990.




Maximum LAI Biomass at R8 Pod Yield Pod Harvest Index
Farm OBS. SIM. OBS. SIM. OBS. SIM. OBS. SIM.
----------------kg ha'--------------- -----fraction----

SA 5.5 5.0 11851 10894 5681 5470 0.48 0.50

LO 3.7 5.9 8525 11622 5009 5478 0.59 0.47

BR 5.0 5.5 12070 11273 5367 4661 0.45 0.41

GR 5.1 5.2 12190 10974 4981 4522 0.41 0.41











15900

12900

9088

6000
I ,,e *e9 **
3000 T....



3 64 96 18 160
DAYS AFTER PLANTING
--a POD-kg/ha :SA 90
CANOPY HT :SA 98
Figure 8. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Sandlin farm in Levy
County in 1990.










weight and pod weight (kg ha'1) showed close agreement to

model simulations throughout the season, as did partitioning

of assimilate to pods (Figure 9). However, the model ended

growth simulations earlier than plant harvest, indicating an

underestimate of the length of the Sunrunner growth cycle.

Leaf area index (LAI) on the Sandlin farm (Figure 10) peaked

at approximately 90 DAP and declined thereafter, while

PNUTGRO simulated a less rapid decline. PNUTGRO pod yield

estimates were within 3.7% of observed (Table 4).

Peanut growth at the Lowman farm was less vigorous than

at Sandlin's. PNUTGRO overestimated canopy weight, pod

weight and LAI (see Figures 11 and 12) during seasonal

growth. Final pod yield and biomass at maturity were

overestimated by 9.4 and 36.3% as well (Table 4).

Peanut growth and yield at the Brookins and Graham

farms were both underpredicted by PNUTGRO. Figures 13 and

14 show that measured canopy and pod weights were

consistently greater than simulated. PNUTGRO predicted that

drought stress at the Graham field would reduce

photosynthetic rates by 9.2% from July 3 to July 25, but no

drought stress was observed in the field or in the growth

measurements. LAI (Figures 15 and 16) on both farms was

higher than PNUTGRO predictions early in the season but

tended to decline faster than PNUTGRO predicted.

Overall, peanut growth and yield predictions were very

good for Levy County in 1990. PNUTGRO-simulated pod yields









.600

.500

.400 9

.300

.200

.199

.100
132 64 9 1h8 110
DAYS AFTER PLANTING

-aFRAC POD :SA 90
Figure 9. Measured (points) and PNUTGRO-simulated (line) pod
harvest index at the Sandlin farm in Levy County
in 1990.









12.9

10.0

8.00

6.00

4.00

2.00

400
S32 64 95 128 168
DAYS AFTER PLANTING

LAI :SA 90
Figure 10. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Sandlin farm in Levy
County in 1990.









12008






12000 ~ ~ --- --------------------
8000

6888
6000







82 8 8 16 11i
DAYS AFTER PLANTING
-.- a POD-kg/ha : LO 9
I CANOPY WT :LO 90

Figure 11. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Lowman farm in
Levy County in 1990.









6.00 -

5.00

4.00

3.00

2.00

1.00

.000 -



LAI
Figure 12.


DAYS AFTER PLANTING


:LO 90
Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Lowman farm in Levy
County in 1990.









18000

15000

12000




6000



6999
S2 54 8f 18
DAYS AFTER PLANTING
..- POD-kg/ha :BR 99
CANOPY HT :BR 90
Figure 13. Measured (points) and PNUTGRO-simulated
(lines) pod and canopy weights at the Brookins
farm in Levy County in 1990.









15000

12500




7500


2500


t 26

-*-o POD-kg/ha :GR 90
CANOPY MT :GR 98


Figure 14.


DAYS AFTER PLANTING


Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Graham farm in
Levy County in 1990.









12.8

10.0

8.00



4,00
4.2,00

2.00


1 2 4 81 18
DAYS AFTER PLANTING

LAI :BR 90
Figure 15. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Brookins farm in
Levy County in 1990.









12.09

10.9

8.09

6.00

4.00

2,00

.000 -



LAI

Figure 16.


DAYS AFTER PLANTING


:GR 90

Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Graham farm in Levy
County in 1990.










differed by an average of only 8.9% from those measured.

Similarly, canopy weights at R8 differed by 15.3%. PNUTGRO

seemed to predict peanut growth and yield reasonably well in

peanut growers' fields that followed long-term bahiagrass

crop rotations that reduce pest and disease pressures.

Jackson County

Crop rotations practiced by the growers in Jackson

County were different from those in Levy County. Bahiagrass

rotations with livestock grazing were not practiced on the

four field sites in Jackson County. Crawford (CR R and CR I

sites) planted peanut in 1990 following soybean (Glycine

max) in 1989 and a soybean-wheat (Triticum aestivum)

rotation in 1988. Morgan's irrigated peanut field (MO I)

followed wheat, corn (Zea mays) and cotton (Gossypium

hirsutum) planted the previous two years. Morgan's rainfed

field (MO R), however, had been in bermuda grass (Cynodon

dactylon) since 1982.

The soils on the fields studied in Jackson County were

both Paleudults, but the Morgan fields were red clayey Typic

paleudults, while the Crawford fields were sandy Grossarenic

paleudults. Soil pH was higher than in Levy County,

averaging 6.57 on the Morgan fields and 7.18 on the Crawford

fields. Soil fertility results are shown in Table A-1.

Jackson County farmers applied gypsum (CaS04) and N-P-K

fertilizers to ensure adequate plant nutrition, and nutrient

deficiencies were not observed in the field.










Rainfall, however, was a serious constraint to peanut

growth in rainfed Jackson County fields in 1990. Figure 17

and Table A-4 show cumulative rainfall in the Crawford and

Morgan fields of only 407 and 421 mm, respectively (Table

3). This value is below the minimum 500-600 mm of water

required for adequate peanut growth (Boote et al., 1982).

Peanut plants were wilted at 11 A.M. on July 17 at the MO R

and CR R farms, and on the August 15 visit, the peanut

leaves were so dry as to feel crispy on the MO R farm.

Unfortunately, raingauge malfunction at the MO I and CR I

fields occurred in mid-season. Thus, it was necessary to

assume for the PNUTGRO simulation that the plants in these

fields were not under water stress.

The drought at the CR R site caused volumetric soil

water content to gradually decrease throughout the season at

all soil layers, as did the simulations from the PNUTGRO

soil water balance model (see Figure 18).

Similarly, volumetric soil water percent declined

throughout the season at the MO R field (Figure 19),

although the absolute percentage was always higher than at

the CR R field due to the higher clay content and water

holding capacity of the MO R field. The clayey MO R and MO

I fields caused some difficulty in driving the aluminum

tubes into the dry soil to obtain soil samples. Three tubes

were broken in attempting to sample soil water on August 15

when the soil was exceptionally dry (the upper 15 cm of the










E
E 800
0
LUI

I 600
O
CC

w 400



> 200



0 1
0 1


125


150


175 200
DAY OF YEAR


225


250


Figure 17. Cumulative water received at the Crawford and
Morgan rainfed sites in Jackson County in 1990.


JACKSON COUNTY, 1990


Crawford

-"""




............


- .- *l
- 4..-*. ,,*-** **
4********

----
/ i i iii _


I


00










.249

.200

.160

o129
,0* .4t ,
.884




.000 ----------i- -
S26 52 73 1A4 13
DAYS AFTER PLANTING
-.- SWC 15-30 :CR R 90
-i SWC 5-15 c:CR R 90

Figure 18. Observed (points) and PNUTGRO-simulated (lines)
volumetric soil water content from 5-15 and 15-
30 cm soil layers at the Crawford rainfed farm
in Jackson County in 1990.










.390

.259


.150




.100 |

.000----


-- oSWC 15-30 :O R
SIC 5-15 c:0O R


Figure 19.


DAYS AFTER PLANTING


Observed (points) and PNUTGRO-simulated (lines)
volumetric soil water content from 5-15 and 15-
30 cm soil layers at the Morgan rainfed farm in
Jackson County in 1990.









63

MO R and CR R fields were only 3.3% and 1.4% volumetric soil

water), causing missing data for the 30, 45, 60, 90 and 120

cm soil layers. PNUTGRO consistently overestimated

volumetric soil water percent at MO R, although the model

did follow the general seasonal drying trend (Figure 19).

This discrepancy may indicate deficiencies in the Decision

Support System for Agrotechnology Transfer (DSSAT) method of

computing water holding capacities of the soil from percent

sand, silt clay and organic carbon. The latter percentages

were taken from SCS soil descriptions, which may have not

been accurate for individual fields. Furthermore, assuming

a full initial soil water profile may also have

overestimated soil water available.

Total extractable soil water, computed by considering

the 120 cm rooting zone, also shows the general drying trend

throughout the season for the MO R and CR R farms (Figure

20), contrasted with the adequate soil water during the

latter part of the season at the Brookins farm in Levy

County.

Figure 21 shows daily maximum and minimum temperatures

at the Morgan rainfed farm in 1990. The seasonal average

was 26.1 oC (Table 3). Solar radiation (Figure 22 and Table

3) averaged 22.3 MJ m-2 day-' over the growing season. The

complete weather files for the 1990 Morgan weather station

can be found in Table A-6.

The 1990 drought in Jackson County caused large growth










289.

249.




169,.

120.

80.0

40,0 -

...... PES
u- PES
Figure 20.S

Figure 20.


S- MM :MO R J#YS AFTER PLANTING
H nM CR R 90
; n :BR 90


Simulated total extractable soil water for the
Brookins (BR), Crawford rainfed (CR R) and
Morgan rainfed (MO R) farms in 1990.









JACKSON COUNTY, 1990


40



o30



! 20
W
C--


1io

0O
100


125 150 175 200 225


DAY OF YEAR


Figure 21. Maximum and minimum daily temperature recorded
at the weather station on the Morgan farm in
Jackson County in 1990.


250










30

25 -

Z 20
0
15 -

S10 -



0 5
100 125 150 175 200 225 250
DAY OF YEAR


Figure 22. Daily solar radiation recorded at the weather
station on the Morgan rainfed farm in Jackson
County in 1990.










and yield differences between the rainfed and irrigated

fields on both the Crawford and Morgan farms. PNUTGRO also

responded to these weather effects by creating drought

stress on the rainfed crops: simulated photosynthesis was

decreased by 45% on the MO R field and 55% on the CR R site

from end of pod addition to physiological maturity. Table 5

shows observed versus simulated growth and yield parameters

for the Jackson County farms in 1990.

The cultivar Agritech-127 was grown on the Crawford

farm. This is an earlier maturing variety than Florunner,

with lower yield potential, primarily because of its shorter

life cycle (Table A-2). Figure 23 shows that seasonal pod

and canopy growth were consistent with PNUTGRO predictions

on the CR I field. However, measured LAI for the CR I field

was significantly lower than predicted after the R4 stage

(Figure 24). Simulated maximum LAI was two units higher

than observed (5.49 to 3.49). Crawford's peanuts had been

planted following two years of soybeans, and several

diseases (leafspot and white mold) were noted in his field.

PNUTGRO does not account for pest or disease pressures and

overestimated total biomass at R8 and pod yield by 14.2 and

40.5% for the CR I field.

The Crawford rainfed corner (CR R) showed the effects

of drought as biomass was reduced and pod yield declined

(Table 5). PNUTGRO underestimated canopy weight and LAI

during the season (see Figures 25 and 26), while simulating









Table 5. Field-observed (OBS.) vs. PNUTGRO simulated (SIM.) growth and yield data
from Jackson County in 1990.


Maximum LAI Biomass at R8 Pod Yield Pod Harvest Index
Farm OBS. SIM. OBS. SIM. OBS. SIM. OBS. SIM.
----------------kg ha-1-------------- -----fraction----

CR R 3.8 3.1 7214 5385 1486 2266 0.21 0.42

CR I 3.5 5.5 9744 11127 3916 5502 0.40 0.49

MO R 3.1 5.0 8365 7744 3611 2822 0.43 0.36

MO I 5.1 5.9 12240 12206 5034 5195 0.41 0.43









15000

12500

10000

7500



2500 .

.000 "---- *"
i 26 2 7 104 1
DAYS AFTER PLANTING
-*- POD-kg/ha :CR I 90
CANOPY WT :CR I 90
Figure 23. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Crawford
irrigated farm in Jackson County in 1990.









6.00

5.00

4.00

3.00

2.00

1.00

.000


DAYS AFTER PLANTING


:CR I 90


Figure 24.


Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Crawford irrigated
farm in Jackson County in 1990.









128000

61000


8800





logo


S26 2 7 104 13(
DAYS AFTER PLANTING
--o POD-kg/ha :CR R 90
CANOPY MT :CRR 98

Figure 25. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Crawford
rainfed farm in Jackson County in 1990.



























DAYS AFTER PLANTING

:CR R 90


Figure 26.


Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Crawford rainfed
farm in Jackson County in 1990.


4.80

4.00

3.20

2.46

1.60


.000


- LAI


I-


104


130










the drought effects. Contrast the standard sigmoidal LAI

curve of CR I in Figure 24 with the drought-affected LAI

curve of CR R in Figure 26. However, PNUTGRO overestimated

fractional partitioning to pods (Figure 27). Final pod

yield estimates were 52.5% higher than observed (Table 5).

The measured final harvest index was only 0.206, compared to

0.402 in the CR I field. This value is very low compared to

typical Florunner plants, indicating a problem with drought

or pests in the CR R field.

Florunner was grown on both the irrigated and rainfed

fields on the Morgan farm. Canopy and pod growth were both

simulated well by PNUTGRO on the MO I site (Figure 28), as

was seasonal partitioning of assimilate to pods (Figure 29).

PNUTGRO estimated final biomass and pod yield within 0.3 and

3.2% of observed (Table 5). LAI was again overestimated

after the R3 stage (Figure 30). The growth and yield of

peanut on the Morgan rainfed (MO R) farm was remarkable

considering the amount of drought stress (crispy leaves on

August 15). PNUTGRO underestimated pod and canopy growth

around the R6 stage, as predicted drought stress began to

decrease photosynthesis (Figure 31). However, PNUTGRO

overestimated LAI again (Figure 32). Final biomass and

yield were underestimated by 7.4 and 21.8% (Table 5).

Overall, PNUTGRO's yield predictions were not as

accurate in Jackson County (29.5% difference from observed)

as they were in Levy County (8.9% difference) in 1990. The









.480

.400

.320

.240

.160

.080

.000
S26 52 7 154 1
DAYS AFTER PLANTING

FMC POD :CR R 90
Figure 27. Measured (points) and PNUTGRO-simulated (line)
pod harvest index at the Crawford rainfed farm
in Jackson County in 1990.









15000

12500

10000

7500


2500 .8




1 2 4 8 1 8 13
DAYS AFTER PLANTING
--o POD-kg/ha :O I 90
CANOPY WT :MO I 90
Figure 28. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Morgan irrigated
farm in Jackson County in 1990.









.488

.40099

.320

.240

.160

.080


1 2 54 81 1i8 L
DAYS AFTER PLANTING

FRAC POD :MO I 99
Figure 29. Measured (points) and PNUTGRO-simulated (line)
pod harvest index at the Morgan irrigated farm
in Jackson County in 1990.









12.9

10.0



8.00
6.00




2.00





LAI
Figure 30.


DAYS AFTER PLANTING


:NO I 90
Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Morgan irrigated
farm in Jackson County in 1990.









12000

10000

8000 -
8909


6000 1

40690

2 0 9 0 -"-' '- -
.,-..-.-.-,


S2 54 81 108 1
DAYS AFTER PLANTING
-.- POD-kg/ha :MO R 90
CANOPY MT :NO R 99
Figure 31. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Morgan rainfed
farm in Jackson County in 1990.









5.40

4.50

3,60

2.70

1.80


,000



-I LAI

Figure 32.


DAYS AFTER PLANTING


:MO R 90

Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Morgan rainfed farm
in Jackson County in 1990.









80
row crop rotations followed in three of the Jackson County

fields most likely led to higher pest levels than the

bahiagrass rotations of Levy County, causing growth limiting

factors unaccounted for in PNUTGRO. However, PNUTGRO did

predict the relative decrease in pod yield due to drought in

Jackson County. The 1990 observed yield decrease was 62.1

and 28.3% compared to irrigated fields at the Crawford and

Morgan farms. PNUTGRO predicted declines of 58.8 and 45.7%

respectively on the CR R and MO R farms. The assumption of

full irrigation compared with uncertainty on actual

irrigation may have caused some of the overestimation of the

irrigated fields.

In 1990, pod yields and crop biomass at maturity (R8)

were reasonably close to PNUTGRO simulated values in both

Jackson and Levy County combined, as shown by the proximity

of the data points to the 1:1 line on Figures 33 and 34.

Points above the 1:1 line indicate PNUTGRO underestimates

(simulated less than observed) while points below the line

are overestimates (simulated greater than observed).

Weather (especially rainfall) and cultivar effects were the

primary factors influencing growth and yield in 1990, and

PNUTGRO accounted for these factors well.














1990 POD YIELD


LEVY AND JACKSON


COUNTIES


0
O
O







1J
CO

0)

Uj




0
CL

0
W
rCr


SIMULATED POD YIELD (kg/ha x 1000)




Figure 33. Relationship between observed and simulated pod
yield for all sites in 1990.


0 2 4 6


LU
C)

0












O
O
, 16

S14

- 12

*4 A


Cl)
C/)


0
O


m


CO
0


1990 BIOMASS AT HARVEST

LEVY AND JACKSON COUNTIES


0 2 4 6 8 10 12 14
SIMULATED BIOMASS (kg/ha x 1000)


Figure 34. Relationship between observed and simulated
biomass at harvest maturity (R8) for all sites
in 1990.










mechanically. Peanut seeds were shaken over two different

size screens. Seeds riding a 21.5/64" screen were

classified as extra large kernels (ELK), and seeds riding a

16/64" screen were classified as sound mature kernels (SMK).

The seeds falling through both screens were separated into

sound seeds split in half (SS) and other kernels (OK).

Shelling percentage and SMK percentage were calculated as:

Shelling % = Total seed wt. (g)/Total pod wt. (g)

SMK % = (ELK + SMK + SS (g))/(Total pod wt. LSK (g)).

Data Entry

The PNUTGRO crop growth model predicts peanut growth

and yield in response to soil, weather and genetic inputs.

These input files were created for each site to allow

PNUTGRO to run properly. PNUTGRO's predictive capability

under actual farm conditions was then evaluated by comparing

field-observed growth and yield data to PNUTGRO-simulated

values.

PNUTGRO version 1.02 was used; this version had been

tested in North Florida in 1988 (Boote et al., 1989b), and

found to overestimate peanut growth on farms with disease

and nematode problems. The genetics file used in this study

was calibrated to 1988 on-farm studies in Jackson County,

and shown to work reasonably well in the 1989 season in

Jackson County. Table A-2 shows the genetic coefficients

used for the standard Florunner file in PNUTGRO V1.02, and

the 1988-calibrated version of the Florunner genetics file