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Testing modified stability analysis with biophysical process models

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Testing modified stability analysis with biophysical process models
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Hildebrand, Peter E.
Bowen, W. T.
Kelly, T. C.
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OOP
TESTING MODIFIED STABILITY ANALYSIS
WITH BIOPHYSICAL PROCESS MODELS '

P.E. Hildebrand, W.T. Bowen and T.C. Kelly 2


Crop growth computer simulation models have been developed over the past decade and are increasingly being used in agricultural research and management. Their ability to accurately predict crop
growth in a wide range of environments has, improved markedly in the past few years. These models use crop physiological
characteristics, related to the genetic make-up of specific lines, to predict responses of these lines to various environmental situations. The models can predict the response of crops to numerous combinations of planting dates, soils, climatic conditions, fertilizations, and plant populations.

Selected maize cultivars, for example, could be evaluated for many
different conditions, representing various soils, weather years and management practices, in a matter of minutes or hours. To do the same under real field conditions would require several months for each year's data, and two or more years to match the output of the
computer models. While, theoretically, recommendations for farmers could be made directly on the basis of simulated experiments, the
prospects for this application are limited, at least until greater experience with this approach has been gained, and the range of environments in which the crop models are valid is known (Harrison et al., 1990).

Meanwhile, these models still have many limitations. Their data requirements are high, and their application, particularly in
environments for which they have not been validated, requires a high degree of familiarity with the internal workings of the models. Perhaps the most severely limiting characteristic at the present time is the incapability of current models to account for
stresses due to pests, diseases, and inter-species competition, although these deficiencies are currently being addressed.
Computer solutions closely simulate observed f ield results under near optimum production conditions (such as f ound on experiment stations or on high technology farms) but overestimate production
rather severely when pest or disease problems negatively affect field yield (Gilbert, 1992).

Under the auspices of the International Benchmark Sites Network f or Agrotechnology Transfer (IBSNAT), scientists at the University of Florida, and elsewhere, have been involved with crop growth model
development and applications (IBSNAT, 1989). Recent work has begun at the University of Florida and elsewhere to analyze the utility


1 For presentation at the 12th annual Farming Systems Symposium, Michigan State University, September 13-18, 1992.
2 University of Florida, Gainesville, FL 32611-0240










of these models to complement on-f arm research. Validated crop models have the ability to screen agricultural technology options so that only the most promising go to on-farm trials. Technology packages can be designed and assessed efficiently using computer simulation. Management practices and genetic characteristics can be pretested in other locations by changing model parameters and inputs. Finally, by using historical or generated weather
sequences, variability associated with particular practices can be assessed through space and time (Dent and Thornton, 1988; Thornton, 1991).

One potentially useful aspect of crop models is to generate data which simulate an on-farm research program. Results could then be used to help understand on-farm research analysis and design and could provide an efficient source of data for training purposes.
This paper explores this use. Specifically, the CERES-maize model (Jones and Kiniry, 1986) and the Strategy Evaluation in the DSSAT (IBSNAT, 1989) were used to generate maize response to a number of
soils conditions representative of those in the southeastern United States and recorded climatic conditions over a f our year period f or 10 locations in this area. The data thus generated are analyzed by Modified Stability Analysis (Hildebrand, 1984).

Methods and materials

Crop growth simulation model

The DSSAT suite of crop models takes historical or generated weather data and user-supplied soils and management practices to simulate crop growth. For this research, historical weather data
were available for four years, 1984-87, at ten different locations, three in southern Alabama, four in southern Georgia, and three in northern Florida. At each site, a representative soil type was selected such that all ten soils were dif f erent. Soil types ranged from silty clay to silt loam to sandy loam to deep fine sands.

From a wide array of possibilities, the DSSAT model was used to generate simulated trial data over four years and for 10 soils for
two maize cultivars (PIO 3382 and CESDA-28), one planting date (March 15), two plant populations (30,000 and 60,000 plants ha-1), and two fertilizer levels (50 and 150 kg N ha-1). Fertilizer was
applied in two equal applications at planting and 30 days after planting. Ultimately, because the two cultivars were very similar, except that one consistently out yielded the other, only one cultivar (PIO 3382) is used in the analysis. Hence, the simulated on-farm trial represents a 2x2 factorial with two plant populations and two fertilizer levels. only one complete block was planted for each environment each year.










Modified Stability Analysis

Modified Stability Analysis (MSA) has been suggested as an
efficient and effective method for designing and analyzing on-farm research data when the purpose is to make recommendations f or specific environmental conditions and for farmers' varying evaluation criteria (Stroup, et al., 1991). The procedure uses an environmental index (EI) as a continuous, quantifiable measure of
the quality of the environment f or producing the crop or crops being evaluated (Hildebrand, 1984). Responses of the individual
treatments are regressed across environments and the treatments are compared in order to select those which excel in dif f erent kinds of environments and for different evaluation criteria.

It has been observed that if the data generated by on-f arm research meet three criteria, relationships among treatments and environments found in one year will be stable over years (Stroup, et al., 1991). These three criteria are:

1) The range of environments, as measured by the difference
between the highest and lowest EI, is as least as large as the overall mean yield for the set of environments included in the
on-farm trial;

2) The range of yields obtained represents approximately what would be expected over a period of years for the environments
included in the trial; and

3) The EIs are reasonably well distributed.

If this observation proves sound, it can materially shorten the amount of time between technology evaluation and diffusion. Rather than the wait of several years suggested by conventional wisdom, recommendations confidently can be made to farmers after only one year of on-farm trials.


Results

Mg ha-1 as a criterion

Data for all four years are shown in Table 1. The question to be
examined is whether the data from the first year (1984), or for that matter, any year that might have been first, represent the same relationships among treatments and between treatments and environment that would occur after several years of trials. Linear regression results for 1984 are in Figure 1. Do the data satisfy the three criteria above? For 1984, the range of EIs was very











4

Table 1. Simulated annual on-farm maize research results,
southeastern United States, 1984 - 1987.
.--- TREATMEMTS --------YEAR "STE P1N1 P2N1 P1N2 P2N2 El


1984 LFU 1984 GAHO 1984 GATI 1984 AUHE 1984 UFQU 1984 GABL 1984 AUGE 1984 AUDO 1984 UFGA 1984 GAAB

AVERAGE, 1984

1985 GAAB 1985 UFU 1985 GAHO 1985 GAT] 1985 AUHE 1985 AUGE 1985 UFQU 1985 UFGA 1985 AUDO 1985 GABL

AVERAGE, 1985

1988 GAAB 1986 GAHO 1986 UFU 1986 GAT] 1986 UFQU 1988 AUGE 1986 GABL 1988 AUHE 1986 UFGA 1986 AUDO

AVERAGE, 1986

1987 AUGE 1987 AUHE 1987 GABL 1987 GATI 1987 GAHO 1987 GAAB 1987 AUDO 1987 UPU 1987 UFQU 1987 UFGA

AVERAGE, 1987


2.09 2.50 1.94 2.33 2.22
1.52 1.35 3.29 3.53 2.42 3.88 2.55 5.68 7.35 4.87
4.75 3.64 5.95 9.47 5.95
5.38 4.09 6.25 8.88 6.15
5.50 7.22 5.48 7.47 6.42
3.28 5.66 6.20 10.79 6.48 5.78 8.49 5.78 8.52 7.14
8.29 8.65 6.29 9.24 7.82 5.95 9.44 5.95 11.33 8.17

4.44 5.36 5.28 7.80 5.74

0.00 0.00 0.00 0.00 0.00
1.94 1.88 1.84 2.07 1.93 3.42 2.46 3.36 3.96 3.30
3.93 2.55 4.87 8.21 4.89
5.68 4.32 5.94 9.44 6.35
5.88 5.15 6.22 11.09 7.09 5.95 8.03 5.95 10.75 7.17 5.91 8.42 5.91 8.97 7.30
5.98 10.39 5.98 10.62 8.24 6.18 10.39 6.18 11.14 8.47

4.49 5.16 4.63 7.63 5.47

0.00 0.00 0.00 0.00 0.00
2.47 2.48 2.51 2.61 2.52 2.5 2.93 2.67 2.93 2.80
2.00 1.32 5.12 4.01 3.11 3.30 2.30 3.99 4.49 3.52 3.91 2.89 4.72 5.72 4.31
4.38 4.32 4.38 4.71 4.45
4.89 3.11 5.59 7.35 5.24
5.81 4.99 5.81 7.72 6.08
5.57 9.62 5.57 9.78 7.64

3.50 3.40 4.04 4.93 3.97

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.94 2.23 3.85 3.94 3.24 5.33 4.07 5.94 9.04 6.10
5.78 8.19 5.78 10.00 7.44

1.41 1.45 1.56 2.30 1.68





&c) 62/
/ ,, j.'., ,


1c, 371


SIMULATED MAIZE TRIAL
1984


ENVIRONMENTAL INDEX, El


Figure 1. measured by


Response of four maize treatments to environment as the environmental index, EI, 1984.


-p


P1IN1 P2N1

P1 N2 P2N2










near the overall mean (range/overall mean = 1.0), so the first criterion is met. Because they are simulated data, it is somewhat difficult to decide the second. However, except for years when
frost kills early-planted maize, particularly in the more northern part of this geographic area as happened in 1987, these yield levels are fairly representative. Therefore, the second criterion
can be accepted for 1984 as well. The distribution of observations for 1984 was quite acceptable, given the nature of on-farm research, so this criterion was also fulfilled, even if one or two additional low observations would be helpful. Because all three criteria were at least marginally satisfied, we are hypothesizing that the relationships among the treatments (population and
fertilizer) and between the treatments and environment obtained the first year (1984) will be stable over time.

The 1985 data included frost loss in the most northern site, so the range of EIs was greater than for 1984, and greater than the overall mean (range/overall mean = 1.5), Table 1 and Figure 2. The appearance of the figure is somewhat different from Figure 1, but the relationships are quite similar. In 1985, the P2N2 treatment was superior overall, as it was in 1984. In both 1984 and 1985,
the higher population combined with the lower fertilizer rate (P2N1) was superior to either of the lower population treatments in the better environments. However, the relationships were reversed in the poorer environments which were unable to support the higher
plant populations. In 1984, UFLI, GAHO and GATI were in the poorer environments. In 1985, UFLI and GAHO, and possibly GATI were there as well as GAAB where there was frost kill. In 1984, the response
of the lower population to fertilizer was slightly stronger than it was in 1985, but this response was considerably stronger with the higher population in either year.

Had either 1984 or 1985 been the first year, the conclusions would have been essentially the same. The combined 1984-85 regression relationships (Figure 3), had they been combined, showed a great
deal of similarity to the relationships from the individual years. The 1986 data were also very similar to the previous two years, Figure 4.

Only 1987 data resulted in somewhat different relationships, Figure 5, resulting from a widespread freeze which affected seven of the
10 environments, Table 1. In 1987, treatment P2N2 was still superior overall, but there was no difference among the other three treatments, contrary to results in the other years. What if 1987
had been the first year rather than the last? The f irst two
criteria would have been met, but the third definitely was violated. Hence, there would have been limited confidence in the
resulting relationships from 1987 data if it had been the f irst year of the trial, so the criteria would have provided sufficient warning.
















SIMULATED MAIZE TRIAL 1985
12

Pl Nl
10 - . . . . . . . . . . . . H
P2N1
co 8 . . - - - -------- . . . . . . . . . . . . . . E3
Pl N2
6 . . . . . . . . . . --- -- - -------------- - - - - . . . . . . . X
w
4 . . . . . . . . . . . . . . . . . . . . . .


2 - ------------------ - ----- - - - - ---------- . . . . . . . . . . . . . . . . . . . . .


0 1 1 1
0 1 2 5 6 7 8 9
ENVIRONMENTAL INDEX, El







Figure 2. Response of four maize treatments to environment as
measured by the environmental index, EI, 1985.















SIMULATED MAIZE TRIAL 1984-85
12

P1 N1
1 0 - ------------------------- - ---------------------- - - --------- - - - -- - - - - - - -- - - --------- - ------------- - - -- - -- - -- - ---------- - - ---------- - -------- . 1H
P2N1
co 8 . . - --- - ---- - - - - - - ---- - ---------- ---------------------------- - --- - --- - - -- ------------- - - - - - - -- B
P1 N2
6 - --------------------------------------------- . . . . . . . . . . X
P2N2
w
57- 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . .


2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


0 ---1- 1 1 1 i
0 1 2 4 5 8 V
ENVIRONMENTAL INDEX, El







Figure 3. Response of four maize treatments to environment as
measured by the environmental index, EI, 1984 - 1985.
















SIMULATED MAIZE TRIAL 1986
12

P1 N1
1 0 - -- - --- - ----- - - - - ---- - ----- - - ----- - ----- -- - --- - ---- - ------ . . . . . .
H
P2N1
cz 8 - ------------ - - - - - - - - - . . . . . . . . B
P1 N2
6 . . . . . . . . - - - --- - ------------ - ---- . . . --------------------------- ------- -------- X
P2N2
w
4 - ------------------- - ------- - - - - - - -------- - - -- - - ----- . . . . . . . .


2 . . . ------ . . . . . . . . . . .


0
0 1 2 3 4 8 9
ENVIRONMENTAL INDEX, El







Figure 4. Response of f our maize treatments to environment as
measured by the environmental index, EI, 1986.















SIMULATED MAIZE TRIAL
1987


P2N1

P1 N2 P2N2


ENVIRONMENTAL INDEX, El


Figure 5. Response of four maize treatments to environment as measured by the environmental index, EI, 1987.










stability of the individual treatment regressions over years

Figures 6 - 9 show the linear regression relationships f or each year and for the four year period for the individual treatments. At least for this set of data, these relationships are remarkably
stable, even though the range of Us in the individual years is different from the range over the four year period.

If the four year period, Figure 10, is considered to provide the
real range of possibilities from which to make a decision regarding recommendations, these would have been little different from any of the individual years, even possibly 1987 when a widespread freeze killed 70 percent of the trial. The poorer environments over the four year period, Table 2, were UFLI, GAHO and GATI, the same as appeared in both 1984 and 1985.


Discussion

As the data for these simulated on-farm trials were being generated, the crop growth simulation model used demonstrated one
of the still-existing weaknesses of these models -- overestimation because of the incapability of the model to account for stresses
due to pests, diseases and weeds. When usual maize planting dates f or the region were used in the models, yields inevitably were uncharacteristically high with few low yields included. The
results violated both the first and second confidence criteria. Only by moving to earlier and earlier planting dates could a realistic range of yields be achieved.

Having achieved realistic results from the crop simulation model, the analysis demonstrated that relationships among treatments and between treatments and environment as measured by Modified Stability Analysis, do stabilize in one year if the three confidence criteria are met.

Finally, it would appear that there is potential f or using crop simulation models for training purposes, and to simulate on-farm research results. However, these models need to be more realistic in their responses to insect pests, diseases and weeds before they can begin to provide the same kind of information available from well designed and conducted on-farm research.










12

Table 2. Simulated four year on-farm maize research results,
southeastern United States, 1984 - 1987.
. TREATMEMTS . YEAR 'SITE" PIN1 P2N1 P1N2 P2N2 El ===== === = == ==-= ==== ==== === -=


1987 GATI 1987 AUGE 1985 GAAB 1987 GABL 1986 GAAB 1987 GAAB 1987 AUDO 1987 AUHE 1987 GAHO 1985 UFLI
1984 UFU 1984 GAHO 1986 GAHO 1986 UFU 1988 GATI 1987 UFU 1985 GAHO 1986 UFOU 1986 AUGE 1986 GABL
1984 GATI 1985 GATI 1986 AUHE 1984 AUHE 1986 UFGA 1987 UFQU 1984 UFQU 1985 AUHE
1984 GABL 1984 AUGE
1985 AUGE 1984 AUDO 1985 UFOU 1985 UFGA 1987 UFGA 1984 UFGA 1986 AUDO 1984 GAAB 1985 AUDO 1985 GABL


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.94 1.88 1.84 2.07 1.93 2.09 2.50 1.94 2.33 2.22 1.52 1.35 3.29 3.53 2.42 2.47 2.48 2.51 2.61 2.52 2.65 2.93 2.67 2.93 2.80 2.00 1.32 5.12 4.01 3.11 2.94 2.23 3.85 3.94 3.24 3.42 2.46 3.36 3.96 3.30 3.30 2.30 3.99 4.49 3.52 3.91 2.89 4.72 5.72 4.31 4.38 4.32 4.38 4.71 4.45 3.88 2.55 5.68 7.35 4.87 3.93 2.55 4.87 8.21 4.89 4.89 3.11 5.59 7.35 5.24 4.75 3.64 5.95 9.47 5.95 5.81 4.99 5.81 7.72 6.08 5.33 4.07 5.94 9.04 6.10 5.38 4.09 6.25 8.88 6.15 5.68 4.32 5.94 9.44 6.35 5.50 7.22 5.48 7.47 6.42 3.28 5.68 6.20 10.79 6.48 5.88 5.15 6.22 11.09 7.09 5.78 8.49 5.78 8.52 7.14 5.95 6.03 5.95 10.75 7.17 5.91 8.42 5.91 8.97 7.30 5.78 8.19 5.78 10.00 7.44 6.29 8.5 6.29 9.24 7.62 5.57 9.62 5.57 9.78 7.64 5.95 9.44 5.95 11.33 8.17 5.98 10.39 5.98 10.62 8.24 6.18 10.39 6.18 11.14 8.47





(/4 /0/ ( ~)~


SIMULATED MAIZE TRIAL P1 N1 ,INDIVIDUAL YEARS & ALL YEARS


1984

1985


1987

ALL


ENVIRONMENTAL INDEX, El


Figure 6. Response of PiNi to environment as measured by the environmental index, El, individual years (1984-87) and overall.





CuP


9
~& 1<


SIMULATED MAIZE TRIAL P2N1 , INDIVIDUAL YEARS & ALL YEARS


ENVIRONMENTAL INDEX, El


Figure 7. Response of P2Nl to environment as measured by the environmental index, El, individual years (1984-87) and overall.


1984 1985 1986 1987 ALL





1984 1985 1986 1987 ALL


ENVIRONMENTAL INDEX, El


Figure 8. Response of PIN2 to environment as measured by the environmental index, EI, individual years (1984-87) and overall.


SIMULATED MAIZE TRIAL P1 N2, INDIVIDUAL YEARS & ALL YEARS






16


SIMULATED MAIZE TRIAL P2N2, INDIVIDUAL YEARS & ALL YEARS


3 4 5 6
ENVIRONMENTAL INDEX, El


1984 1985 1986 1987 ALL


Figure 9. Response of P2N2 to environment as measured by the environmental index, El, individual years (1984-87) and overall.




/ ,
Kf~
(
/ ' L /
17


SIMULATED MAIZE TRIAL
1984-1987


P1iN1 P2N1 P1 N2 P2N2


ENVIRONMENTAL INDEX, El


Figure 10. Response of four maize treatments to environment as measured by the environmental index, El, 1984 - 1987.










REFERENCES

Dent, J. B. and P. K. Thornton. 1988. The role of biological
simulation models in farming systems research. Agricultural
Administration and Extension, 29:111-122.

Gilbert, R.A. 1992. On-farm testing of the Pnutgro crop model in
Florida. Unpublished M.S. thesis, Department of Agronomy,
University of Florida.

Harrison, S.R., P.K. Thornton and J.B. Dent. 1990. The IBSNAT
project and agricultural experimentation in developing
countries. Experimental Agriculture, 26:369-380.

Hildebrand, P.E. 1984. Modified stability analysis of farmermanaged on-farm trials. Agronomy Journal, 76:271-274.

IBSNAT. 1989. Decision Support System for Agrotechnology Transfer
(DSSAT) Version 2.1. Dept. of Agronomy and Soils, University
of Hawaii, Honolulu, HI 96822.

Jones, C.A., and J.R. Kiniry. 1986. CERES-Maize: A simulation model
of growth and development. College Station, TX: Texas A&M
University Press.

Stroup, W.W., P.E. Hildebrand and C.A. Francis. 1991. Farmer
participation for more effective research in sustainable agriculture. Staff Paper SP91-32. Food and Resource
Economics Department, University of Florida.

Thornton, Phillip. 1991. Application of crop simulation models in
agricultural research and development in the tropics and subtropics. International Fertilizer Development Center,
Muscle Shoals, Alabama.




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