Citation
On-farm testing of the PNUTGRO crop model in Florida

Material Information

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

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)
General Note:
Typescript.
General Note:
Vita.
Funding:
Florida Historical Agriculture and Rural Life
Statement of Responsibility:
by Robert A. Gilbert.

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Marston Science Library, George A. Smathers Libraries, University of Florida
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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
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Resource Identifier:
001799507 ( ALEPH )
27634855 ( OCLC )
AJM3252 ( NOTIS )

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.
ii




TABLE OF CONTENTS
page
ACKNOWLEDGEMENTS......................i
LIST OF TABLES......................v
LIST OF FIGURES .....................vii
ABSTRACT... .......................xiv
INTRODUCTION ............ ...o.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.o..............11
Biotic Factors ................. 13
Diseases o... ... ....... .o . 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.o..................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
iv




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
v




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 raingauge 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
vi




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 . 43
6. Maximum and minimum daily temperature
recorded at the weather station on the
Graham farm in Levy County in 1990 ........ ..45
7. Daily solar radiation recorded at
the weather station on the Graham farm
in Levy County in 1990 .... .............. 46
8. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the
Sandlin farm in Levy County in 1990 ...... 48
9. Measured (points) and PNUTGRO-simulated (line)
pod harvest index at the Sandlin
farm in Levy County in 1990 ... .......... 50
10. Measured (points) and PNUTGRO-simulated (line) leaf area index (LAI) at the
Sandlin farm in Levy County in 1990 ...... 51
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 (RB)
for all sites in 1990 .... ............. 82
35. Cumulative water received (rainfall plus
irrigation) at all Levy County field sites
in 1991 ......... ..................... 84
ix




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
x




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 . . . . . . . . . . I i
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
xi




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 28 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.
xv




INTRODUCTION
Peanut (Arachis hypogaea 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 ha"1 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.
1




2
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




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-' (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
4




5
Table 1. Growth stage descriptions for peanut (from Boote,
1982).
Typical
Stage Stage DAP> for
Number Name Florunner#
VE Emergence
Vl 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




6
sequentially in the Southeast. Partitioning percent and pod yield (kg ha"') 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 (Vigna unguiculata, 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.,




7
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 (Calanus calan) 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"I 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




8
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; NageswaraRao 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).




9
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'l). 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"'. 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 0C, but flowering has a lower optimal temperature at 20-25 0C. Photosynthetic rate decreases 25%




at 40 0C and 65% at 10 OC as air temperatures vary from the 30 0C 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 0C, respectively. Peanut fruit development is also sensitive to temperature. An increase in soil temperature from 23 to 37 0C 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"' 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") 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




13
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 0C, 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 leafspotinfected 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 0C, 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




16
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"I 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




17
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 (Meloidogyne 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




18
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 (RodriguezKabana 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




20
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 PNTJTGRO 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




21
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




22
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 diseaseinduced 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 Cylindrocladium black rot incidence has been




23
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).




25
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.




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
26




Table 2. Field sites selected for study in Levy and Jackson Counties in 1990 and 1991.
PLANTING
YEAR COUNTY FARM I.D. CULTIVAR IRRIGATION SOIL SUBGROUP DATE
1990 LEVY Brookins BR 90 Florunner Yes Grossarenic May 5
Paleudalfs
Graham GR 90 Florunner Yes Grossarenic May 7
Paleudalfs
Lowman LO 90 Sunrunner No Arenic April 18
Hapludalfs
Sandlin SA 90 Sunrunner Yes Typic April 14
Quartzipsamments
JACKSON Crawford CR R 90 Agritech-127 No Grossarenic April 27
Paleudults
Crawford CR I 90 Agritech-127 Yes Grossarenic April 27
Paleudults
Morgan MO R 90 Florunner No Typic April 26
Paleudults
Morgan MO I 90 Florunner Yes Typic April 25
Paleudults
1991 LEVY Graham GR 91 Marc I No Grossarenic May 26
Paleudalfs
Lowman LO 91 Florunner No Arenic May 3
Hapludalfs
Sandlin SA 91 Sunrunner No Typic May 1
Quartzipsamments
JACKSON Crawford CR R 91 Agritech-127 No Grossarenic April 20
Paleudults
Crawford CR I 91 Agritech-127 Yes Grossarenic April 20
Paleudults
Morgan MO R 91 Florunner No Typic April 20
Paleudults
Morgan MO I 91 Florunner Yes Typic April 30
Paleudults




28
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 NO 3 were conducted at the IFAS Soils Testing Laboratory (see Table A-1). 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




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 SamplinQ
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 0C 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




30
(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




31
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 0C 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 m-2):
LAI = m2 leaf/g leaf x g leaf/g plant x g biomass/m2 soil.




32
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 0C 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




33
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 Vl.02, and the 1988-calibrated version of the Florunner genetics file




34
used in this study. In addition, the genetic coefficients for Sunrunner, Agritech-127 and Marc I are shown. The genetic coefficients are defined in the PNUTGRO Vl.02 user's guide (Boote et al., 1989a).
The main changes made to the genetics file during the calibration reduced leaf photosynthesis (PGLF) by 11% to reduce dry matter accumulation, and reduced length of Florunner reproductive period (VARTHR(10)) by 10% to shorten the life cycle to match the observed data of 1988 (Table A2). Specific leaf area (SLAVAR) and trifoliate production rate (TRIFOL) were also reduced by 9 and 3% respectively to decrease simulated LAI and match observed specific leaf area and V stage measurements. Pod addition rate (PODVAR), and maximum assimilate percentage partitioned to pods (XFRUIT) were lowered to reduce simulated harvest index. Finally, shell (SHVAR) and seed (SDVAR) maximum growth rates were changed, as well as duration of shell growth (LNGSH) and the lag to seed growth (LAGSD) to match observed final seed size and delay the start of seed growth (Table A-2).
Soil files were created based on Soil Conservation
Service data and maps for each field. Table A-3 shows the soil profile properties obtained using the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) soil retrieval program based on percent sand, silt, clay and organic carbon from the SCS maps at each site. Note that the soils in the Morgan field, with the highest clay




35
content, have the highest lower limit (LL) and drained upper limit (DUL) of extractable soil water. Soil profile properties are defined in IBSNAT (1990).
Weather files were compiled with weather station and raingauge data (see Tables A4-A9 for weather data).
Plant growth sampling and yield data were used to
calculate peanut growth stage, LAI, and dry matter weights in seeds, pods, shells, leaf and stem throughout the season, as well as pod yield at harvest. PNUTGRO files were created with these field-observed values, and used for graphical comparison purposes to PNUTGRO simulations.
A 95% confidence interval (C.I.) about the observed plant growth sampling mean was calculated using the following formula:
C.I. = y ta Sy
where y is the sampling mean, t. is the two-tailed t value with a = 0.05 and n-1 degrees of freedom, and sy is the standard error of the sampling mean (s/fn). The 95% confidence interval was expressed as an "error" bar centered over the field-observed values on PNUTGRO simulation graphs. Confidence intervals for the volumetric soil water measurements are not shown due to the wide range caused by having only two replications (one degree of freedom).
Simple linear regression was performed following the procedure of Huda (1988) to compare PNUTGRO-simulated and observed final pod yield and biomass at all field sites.




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
36




37
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 welldistributed 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




38
LEVY COUNTY, 1990 E800
Q Lowman
> Brookins .......
n 1 b o o . . . . . .
w 600 Sandlin
Graham
w 400
UI
>200
D . .......... i
o 100 125 150 175 200 225 250
DAY OF YEAR
Figure 1. Cumulative water received at all Levy County field
sites in 1990.




39
Table 3. Seasonal water received, and average daily solar radiation and temperature for all field sites in 1990 and 1991.
Cum. Average Cum. Average
Water Solar Solar Daily
County Farm Year Received Rad. Rad. Temp.
--mm-- -MJ m-2 d-1- -MJ m-?- -CLevy 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 1 732 2354
@ Indicates missing data due to raingauge malfunction.




40
.240
.200
tt
.120 .*1i
*120 4
.080 U
0 0
I
.040 a
I
.0 2 14 81~8 15
DAYS AFTER PLANTING
-*- a SC 15-30 :BR 90 e SHC 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.




41
.249
.299
0*
.169
.129 .
a .
,989
.949
,999
S .2 7 104 119
DAYS AFTER PLANTING
-- a SWC 15-39 :GR 99 SHC 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.




42
.300
.250 t
.200
a ** 150 t.. ,44
050
I 0
.100q* fit
.850
829 18 81 116 145
DAYS AFTER PLANTING
-*- a SUC 15-30 :LO 90 i 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.




43
.180
.150
1290
0
, 990 ******
I I I
.0$ 00 16
132 64 94 128 110
DAYS AFTER PLANTING
-.- a SHC 15-30 :SA 90 u SWC 5-15 c:SA 99
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.




44
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 0C), 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 C.
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'.
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




45
LEVY COUNTY, 1990 40
030
0
1-20

Maximum Minimum
100 125 150 175 200 225 250
DAY OF YEAR
Figure 6. Maximum and minimum daily temperature recorded at
the weather station on the Graham farm in Levy
County in 1990.




46
LEVY COUNTY, 1990 30
E 25
~20
0
~15
5
0 Graham
100 125 150 175 200 225 250
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"1 --------------- ------ 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




48
18000
15000
1200
6000
S,,.*9*****t" 3000 ,...*'T'
,*q40
, 3 64 9 18 169
DAYS AFTER PLANTING
--a POD-kg/ha :SA 90 -I CANOPY HT :SA 99
Figure 8. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Sandlin farm in Levy
County in 1990.




49
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




50
.600
.50099
.400 a
.300
.200
I
.199
I
009
1 32 64 9 18 110
DAYS AFTER PLANTING
-a FRAC POD :SA 90
Figure 9. Measured (points) and PNUTGRO-simulated (line) pod
harvest index at the Sandlin farm in Levy County
in 1990.




51 12,9 109.0 8.00 6.00
4,9900
2.90 99 3 64 9 18
DAYS AFTER PLANTING
- LAI :SA 99
Figure 10. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Sandlin farm in Levy
County in 1990.




52
12008
10000
8000
690
2 8 8 16 145
DAYS AFTER PLANTING
-- a POD-kg/ha : LO 99 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.




53
6.00
5.00 4.900 3.00
2.90 1.900 000
.999
29 18 8 16 145
DAYS AFTER PLANTING
- LAI :LO 90
Figure 12. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Lowman farm in Levy
County in 1990.




54
189000
15000
12000 /
9000
6000
1 2 54 8 18 15
DAYS AFTER PLANTING ..- a POD-kg/ha :BR 99 a CANOPY HT :BR 99
Figure 13. Measured (points) and PNUTGRO-simulated
(lines) pod and canopy weights at the Brookins
farm in Levy County in 1990.




55
1500
12500
500
1 25 2 7 14 130
DAYS AFTER PLANTING
-*-a POD-kg/ha :GR 90
5 CANOPY T :GR 90
Figure 14. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Graham farm in
Levy County in 1990.




56
12,9 19.9 8.00 6,99 4,99
2,00
2.000
2 14 81 108 15
DAYS AFTER PLANTING
- a LAI :BR 90
Figure 15. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Brookins farm in
Levy County in 1990.




57
12.9
1.99 8.99 6.909
12,9
4. 00 2,00
26 2 7 14 10
DAYS AFTER PLANTING
- Ln :GR 90
Figure 16. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Graham farm in Levy
County in 1990.




58
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 mavs) 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.




59
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




60
-- JACKSON COUNTY, 1990
E 800
o Crawford
> Mor an
w 600
0
w 400
>200 ............
...... ...........
.1.
I I I
o 100 125 150 175 200 225 250
DAY OF YEAR
Figure 17. cumulative water received at the Crawford and
Morgan rainfed sites in Jackson county in 1990.




61
.249
.200
.160
120
.089
.4 *e
.049 *
,900
S2 2 7 4 19
DAYS AFTER PLANTING
-.- a SWC 15-30 :CR R 90 -i SIC 5-15 c:CR R 90
Figure 18. Observed (points) and PNUTGRO-simulated (lines)
volumetric soil water content from 5-15 and 1530 cm soil layers at the Crawford rainfed farm
in Jackson County in 1990.




62
.300
.259
159 X R 90
S.1 S 15 *R 9
.959 I
12 481 168 135
DAYS AFTER PLANTING
-*- aSWC 15-30 :MO R 99 -e SC 5-15 c:MO R 90
Figure 19. Observed (points) and PNUTGRO-simulated (lines)
volumetric soil water content from 5-15 and 1530 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




64
289.
249. ,
19.9
12** I
200.. ,..~
v '
21 i4 81 108 15
...... PESH a :NoR HYS AFTER PLANTING
PESH n:CR R 90
- PESW a :BR 90
Figure 20. Simulated total extractable soil water for the
Brookins (BR), Crawford rainfed (CR R) and
Morgan rainfed (MO R) farms in 1990.




65
JACKSON COUNTY, 1990
40
030
w
a
!; 20
Maximum Minimum
0 I I I I
100 125 150 175 200 225 250
DAY OF YEAR
Figure 21. Maximum and minimum daily temperature recorded
at the weather station on the Morgan farm in
Jackson County in 1990.




66
JACKSON COUNTY, 1990 0--30
C\J
25
Z 20
0
15
0
~10
O 5
D Morqan
0 1 i 1 I
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.




67
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" .--------------- -----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
CO




69
15000
12500
10000
1989
?590
5900
2599 .,
000 -----"------ --- #
i26 2 7 104 1
DAYS AFTER PLANTING
*- a POD-kg/ha :CR I 90 -i 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.




70
6.00 5.00
4.00 3.900 2.09
1.00
,00
26 $2 7 14 140
DAYS AFTER PLANTING
- a LAI :CR 1 99
Figure 24. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Crawford irrigated
farm in Jackson County in 1990.




71
12000
19900
8000
60099
499
4000 ,, 1
126 2 7 1 4 190
DAYS AFTER PLANTING
-+-o POD-kg/ha :CR R 90 -n CANOPY WT :CRR 99
Figure 25. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Crawford rainfed farm in Jackson County in 1990.




72
4.89 4.0 3.29
2.40 1.60 .89900 ,099
1 8 114 9
DAYS AFTER PLANTING
- LAI :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.




73
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




74
.489 .400 .320
.240 .160
I
.080 .989
.000
1 26 $2 7 164 130
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.




75
15000
12500
10000
7500
500 *#
, 2 4 8 1 8 15
DAYS AFTER PLANTING a POD-kg/ha :NO I 90 CANOPY WT :NO 1 90
Figure 28. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Morgan irrigated
farm in Jackson County in 1990.




76
.489
I
.499 .329
.240 .160 .080
.99
1 2 4 81 18 15
DAYS AFTER PLANTING
- FRAC POD :MO 1 99
Figure 29. Measured (points) and PNUTGRO-simulated (line)
pod harvest index at the Morgan irrigated farm
in Jackson County in 1990.




77
129,0
10.0
8.99
6.900
2 4 81 115
DAYS AFTER PLANTING
-- LAI :NO I 90
Figure 30. Measured (points) and PNUTGRO-simulated (line)
leaf area index (LAI) at the Morgan irrigated
farm in Jackson County in 1990.




78
12000
8999
1000
8000
6999 409
*.....,,
2 4 8 18 15
DAYS AFTER PLANTING
0-.- POD-kg/ha :NO R 90 CANOPY WT :NO H 99
Figure 31. Measured (points) and PNUTGRO-simulated (lines)
pod and canopy weights at the Morgan rainfed
farm in Jackson County in 1990.




79
5,40
4.50
3.60
I I
2.78 i
1.80
2 14 8 18 15
DAYS AFTER PLANTING
- LAI :NO R 99
Figure 32. 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.




81
0" 1990 POD YIELD
0
8- LEVY AND JACKSON COUNTIES
8
cz<
U!4
o Y =0.87X + 465
0 2 -," R SQUARE = 0.66
Wi 1 :1 LINE
> 0
rr 0 8
c/) SIMULATED POD YIELD (kg/ha x 1000)
m
0
Figure 33. Relationship between observed and simulated pod yield for all sites in 1990.




82
1990 BIOMASS AT HARVEST
o LEVY AND JACKSON COUNTIES
,...16
14
.12 Y = 0.64X + 3682
R SQUARE = 0.55 cl)
S6
48
0
0 2 46 8 10 12 14 16
c/,
Mn SIMULATED BIOMASS (kg/ha x 1000)
Figure 34. Relationship between observed and simulated
biomass at harvest maturity (R8) for all sites
in 1990.




83
1991 On-Farm Experiments
Levy County
Growers in Levy County followed the same farming system of bahiagrass rotations with peanut described previously. Three field sites were sampled in 1991: Sandlin (SA), Graham
(GR) and Lowman (LO). Sandlin planted peanut in the same field as 1990, while Graham and Lowman switched to adjacent fields previously in bahiagrass. The soil types, fertility levels and pH values (Table A-l) of the 1991 fields were all similar to 1990 Levy County farms.
However, the 1991 weather was very different from 1990. All three Levy County farms received more than adequate rainfall for peanut growth, with an average of 895 mm per site, 200 mm greater than 1990 (Table 3 and Figure 35). Table A-7 lists water received at all sites in 1991.
Volumetric soil water measurements reflected the wetter weather of 1991. The measured Graham farm volumetric percentages were an average of 9.3 and 10.8% for the 15 and 30 cm soil layers in 1991, compared to 8.5 and 9.3% in 1990. PNUTGRO-simulated values were close to observed except for the June 25 sampling date (Figure 36). However, PNUTGRO still overestimated soil water throughout the season on the coarser Lowman and Sandlin soils (Figures 37 and 38), although average soil water percentages in 1991 were higher than 1990 at both the 15 cm (LO 6.6 to 7.3%, SA 5.1 to 5.9%) and 30 cm layers (LO 5.8 to 6.6%, SA 4.5 to 6.9%).




84
LEVY COUNTY, 1991
_ 1,200 ...
> Lowman
Sandlin
800 ...... .
200 ...
200 ._ ........
. I" -'
0
100 175 200 225 250 275
DAY OF YEAR
Figure 35. Cumulative water received (rainfall plus
irrigation) at all Levy County field sites in
1991.




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