1
United Soybean Board Domestic Programs
Report Form
Project # 1520-832-8280
Title: Decision-support Tools and Economics of Maturity Group Selection for Planting-Date and
Latitude Combinations throughout the Mid-South Soybean Production Region
Reporting Period: June 16, 2015 to September 15, 2015
Project Status:
OVERVIEW:
U.S. soybean farmers lack well defined management practices, which hinders the application of
more efficient soybean production systems and reduces the value of the soybean crop to farmers.
Fundamental production questions that all farmers face include selection of soybean maturity
group (MG) for planting at their location on any given date and how yield potential may change
for various MGs from an early to a late planting date at their location. Because soybean
development responds to daylength, the planting date and latitude have a large effect on crop
performance. Additionally, climatologists predict that weather variability will become more
extreme in the coming years, which may bring more uncertainty about when farmers will be able
to plant. A thorough understanding of how crop performance and phenology change for different
MGs across a wide range of latitudes is needed to provide farmers with tools necessary for
making appropriate contingency plans should the anticipated planting date be delayed. For the
past 3 years we have evaluated the response of yield, crop development, seed grade and test
weight, standard and accelerated-aging germination, and oil and protein concentrations to a wide
range of planting dates with varieties representative of MGs 3, 4, 5, and 6. These experiments
include 10 locations stretching from College Station, Texas to Columbia, Missouri. This data set
represents the most thorough and extensive experiments ever conducted evaluating responses of
different MGs to planting date over a large latitudinal range.
The proposed one-year extension of the project starting in January of 2015 will provide the
opportunity to fully document the results of the original 3-year research and develop practical
information and tools to assist farmers in determining agronomic and economic risks for
different production choices at different locations throughout the Midsouth.
Our activities for this project can be categorized in four main areas: (1) data analysis, (2)
modeling yield, crop development, and irrigation requirements using long-term weather data, (3)
economic evaluation, and (4) developing a decision-support tool that can assist farmers in
selecting the best MG for their situation.
WHO ARE THE PARTICIPANTS?
Larry Purcell is the Principal Investigator and is a professor at the University of Arkansas at
Fayetteville, and he holds the Altheimer Chair for Soybean Research. Dr. Purcell will coordinate
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the overall research effort along with the postdoctoral associate hired for this purpose, Dr.
Montserrat Salmeron. Dr. Purcell has been at the University of Arkansas for 19 years. Dr.
Purcell’s research interests include optimizing the efficiency with which crops use essential
resources of light, water, and nutrients through management and genetic strategies.
Montserrat Salmeron is a postdoctoral associate at the University of Arkansas at Fayetteville.
She graduated and obtained her PhD from the University of Lleida, Spain and has experience in
agronomy and crop modeling. Dr. Salmeron research focus is on investigating soybean maturity
groups and planting dates that can maximize soybean yields and seed quality in the range of
latitudes in the US Midsouth, and developing and calibrating crop models and predictive tools
and that can aid in best recommendation practices.
Michael Popp is an agricultural economist and will coordinate the economic analysis with Dr.
Purcell and Dr. Salmeron. Dr. Popp’s research focus is on the evaluation of alternative farm
enterprises involving innovative and sustainable production methods by analyzing risk-return
tradeoffs. Recent efforts have involved evaluation of soybean production practices, modeling of
crop agriculture for the state with a view to estimate spatial land use changes with the
introduction of switchgrass, energy sorghum and pine (for carbon sequestration), logistics
associated with cellulosic energy crops, modeling of pasture and development of decision
support software for beef production from a net return and net greenhouse gas emissions
perspective.
Larry Earnest is Station Director at the Southeast Branch Experiment Station near Rohwer, Ar.
Mr. Earnest has an MS degree in weed science and has been at the Southeast Branch Station for
a number of years. Mr. Earnest has supervised the seed grading of samples collected from all
locations from this project, and he has agreed to supervise this activity for samples collected
from the 2014 harvest during the first 3 months of 2015. Mr. Earnest’s interests include
optimizing management strategies for agricultural production in the Mississippi Delta, especially
with regards to the production of cotton, rice, soybean, wheat, grain sorghum, corn, and catfish.
Ed Gbur is a Professor of Agricultural Statistics at the University of Arkansas, Fayetteville, AR.
His expertise is on experimental design, regression, stochastic modeling, spatial statistics, survey
sampling, and agricultural applications of statistics. Dr. Gbur will work closely with Dr.
Salmeron and Dr. Popp in the development of advanced statistical models for the agronomic and
economic analyses.
Progress Report 15 September 2015
Project highlights:



Project update presented at the MSSB meeting on August 25.
Completion of all seed quality analyses (germination, AA, oil, protein concentration,
and seed grade) and preparation of a results dataset for the 3-yr study.
Submission of a research manuscript evaluating the yield response to planting date.
3




Preparation of a production guide for Arkansas that describes the optimum MG for a
range of planting dates. Data for similar analyses have been prepared for all
locations in the study.
Recalibration of DSSAT-Cropgro with data from 2012-2013 model evaluation for
yield prediction with data from 2014.
Preparation of a draft research manuscript that identifies the profit-maximizing MG
by planting date choice for each location and also quantifies production risk.
Preliminary analysis of seed quality variables.
Key Performance Indicators:
1- Complete seed grading, seed germination/accelerated aging germination, and
oil/protein analysis from samples collected from our third year.
March 15:
 90% of seed grade analysis completed
 40% of tests for germination and accelerated aging completed
 80% of oil and protein analysis completed
June 15:
 Seed grade analysis completed
 Tests for germination and accelerated aging completed
 Oil and protein analysis completed
September 15:
 All seed quality analyses have been completed.
2- Enter and statistically analyze data for yield, crop phenology, seed grade, oil and
protein concentrations, and standard and accelerated aging germination rates for 3
years of research for all locations.
March 15:
 Preparation of dataset with yield, during season notes, and phenology data for the
3-yr study.
 Calculate environmental variables during key developmental stages and
incorporation into dataset.
 Study of the relationship between yield and cumulative intercepted
photosynthetically active radiation
June 15:
 Preparation of dataset from 3-yr study with oil and protein, accelerated aging and
germination rates.
 Preparation of dataset for economic analysis including yield, irrigation applied,
phenology, and oil and protein.
 Analysis of the yield response to planting date by location and MG.
September 15:
 Analysis of yield components as a function of environmental variables across the
3-yr of data.
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


Analysis of variance for seed quality variables: oil and protein, germination and
accelerated aging, and seed grade.
Stability analysis of oil and protein concentration in seeds, and for germination
and accelerated aging.
Preliminary analysis of seed quality variables as a function of temperature.
3- Calibrate and validate DSSAT-CropGro model using weather data and field
observations for our 10 locations and experimental observations from 3 years of
research.
March 15:
 Evaluation of model performance by location and treatment.
 Preparation of DSSAT-GLUE to perform calibrations using the high
performance computing facilities at the University of Arkansas.
June15:
 Calibration of DSSAT-CROPGRO cultivar coefficients for each cultivar and
across environments in 2012 and 2013.
 Evaluation of model performance after calibration with dataset from 2012 and
2013.
September 15:
 Recalibration of DSSAT-CROPGRO cultivars for treatments without water
stress with data from 2012 and 2013.
 Generation of cultivar coefficients from soybean cultivars in 2014 based on rMG
and the previous cultivar coefficient calibration.
 Evaluation of yield predictions with DSSAT-CROPGRO for treatments in 2014
and preparation of results and statistics for evaluation of model performance.
4- For each location, determine the most profitable MG to produce for particular planting
dates and the risk associated with production.
March 15:
 Study of MG and planting dates that maximize the fraction of light interception
and total cumulative photosynthetically active radiation at two contrasting
locations in Arkansas.
June 15:
 The analysis of the yield response to planting date allowed determining the
highest yielding MG choices at any planting date and at each location.
 Estimation of optimum planting windows that can maximize yield of each MG at
each location.
 Estimation of the yield decline with delay in planting date.
 Prepared a draft of a paper that analyzes risk-return relationships between MG
and planting date. At the Rohwer location, for example risk can be cut in half
with only minimal reduction in profitability when choosing a combination of
MG and PD rather than a single planting date using the most profitable MG
choice.
 Oil and protein content data were converted to soybean price premiums and
discounts using a methodology published in the literature.
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September 15:
 Draft of a paper analyzing risk-return tradeoffs has been completed and will be
submitted to a journal in the last quarter.
 This paper identifies the profit-maximizing MG by planting date choice for each
location and also quantifies production risk.
5- Develop ‘MG-risk portfolios’ for different locations by diversifying planting dates and
MG choices that offer the best returns at the lowest risk.
June 15:
 Modeling of risk-return relationships and a draft of a paper are nearing
completion.
September 15:
 The same paper as for KPI #4, not only highlights the profit-maximizing choice
but showcases how planting over a range of planting dates and range of MGs,
can lower risk and the cost associated with lowering the risk.
 A proposal for a presentation of this topic at the Southern Agricultural
Economics Association meetings in San Antonio, TX has been submitted. The
meeting will be held in February, 2016.
6- Collect and develop long-term weather-data sets at sites throughout the Midsouth that
are representative of each 1o of latitude between 29 and 39oN.
March 15:
 Long term weather datasets were generated for a total of 11 locations.
7- Create a database with DSSAT-CropGro using long-term weather data for combinations
of possible dates of planting from March 15 to June 30 at weekly intervals, MG
selections from 3 to 6 in one-half relative MG intervals, and for each 1 degree in
latitude between 29 and 39oN (16 weeks x 8 MGs x 10 latitude ranges = 1280
combinations) that includes predictions of yield, crop development (R1, R5, R7, R8),
seasonal irrigation amounts over the 30-year simulation.
March 15:
 Preparation of necessary simulation files to simulate the complete range of
scenarios.
 Generation of a preliminary dataset that contains all necessary information for
the development of the decision tool.
8- Determine probability of profitability from long-term simulations from DSSAT-CropGro
that take into account yield, seasonal price premiums and discounts for different harvest
dates, and irrigation costs.
9- Incorporate agronomic and economic databases into a spreadsheet interface that can be
used to query different scenarios of latitude, planting date, and MG to agronomically
and economically compare different MG.
March 15:
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

Development of the Excel interface for the decision tool.
Incorporation of preliminary agronomic and economic databases to the decision
tool.
June 15:
 Changes to the sequence of input and output screens have been discussed.
Further changes to the model are envisioned once calibrated simulation data are
available.
September 15:
 Further changes to the model have not been pursued to date. Updating with the
verified simulated data is reserved for the last quarter. Quantification and
representation of risk and returns by planting date and MG will be available.
OBJECTIVE 1: Data collection and analysis
-
Yield response to planting date
The draft manuscript analyzing the yield response to planting date was finalized during
this period and submitted to Crop Science. The manuscript is currently under review.
A proof of a production guide with results from Keiser and Rohwer, AR has been
prepared with the help of Osborn-Barr. The guide was reviewed by the Midsouth Soybean
Board on August 25th, and their suggestions are being incorporated into a revision. Results for
the other locations in the study have been prepared and will be sent to project collaborators for
preparation of similar publications for the other states.
-
Yield components response to environmental variables
The relationship between the two main yield components (seed number and seed weight)
with main environmental variables was analyzed across all locations, PD and MG combinations
in the 3-yr study. An analysis of covariance was used to take into account the MG and cultivar
effect in the yield-component response to main environmental variables during critical periods
of soybean development. The analysis identified key environmental variables with a significant
effect on seed number and seed weight. The models explained 44 and 59% of the variability of
seed number and weight, respectively (Figure 1). The environmental factors with a significant
effect on seed number and seed weight were:
-
Seed number: length of the flowering period (R1 to R5), average temperature, average
solar radiation, and average photoperiod during the flowering developmental stage.
Seed weight: average temperature during both the flowering and the seedfill (R5 to R7)
period, seedfill duration, and solar radiation and photoperiod during seedfill.
The individual effect of one environmental variable at a time on the variation of seed
number and seed weight was studied with the models obtained. The response of seed number to
average temperature during flowering is presented in Figure 2. For the conditions in our study,
the model indicates that seed number would be largest with average temperatures during
flowering of 77 °F and that seed number tends to decrease with temperatures above or below
this temperature. In particular, results indicate that seed number can decrease with high
temperatures during flowering.
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The response of seed weight to temperature during flowering showed a similar response
as seed number but with an optimum temperature that ranged from 75.5 to 79 °F depending on
the MG (Figure 3). Average temperatures during seedfill maximized seed size in our experiment
at about 70.7 °F.
Figure 1. Relationship between observed seed number (left) and seed weight (right) across all
treatments in the 3-yr study and predicted by an analysis of covariance that includes main
environmental variables and the MG and cultivar effect. Different color symbols indicate data
from a different growing season (2012, 2013, and 2014). The coefficient of determination (R2)
of the relationship and the model efficiency (ME) are shown to evaluate the model performance.
Figure 2. Seed number response
to average temperature during
flowering (R1 to R5) by maturity
group (MG) derived from the
analysis of covariance.
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Figure 3. Seed weight response to average temperature during flowering (R1 to R5)
(left) and to average temperature during seedfill (right). Results shown by maturity
group (MG) were derived from the analysis of covariance.
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Oil and protein concentration in seeds
The stability of oil and protein concentration across all the environments (year x location
x planting date combinations) was evaluated with data from the 3-yr study. Planting dates were
grouped in early planting dates within a location and year (1st and 2nd planting date), and late
planting dates (3rd and 4th planting dates). Both average concentration and the stability across
environments were affected by the soybean rMG and the planting system (early vs. late).
Oil concentration was greater for MG 3 and 4 cultivars compared to MG 5 and 6
cultivars (Figure 4). For a late planting system, oil concentration was 0.5% lower compared to
an early planting system.
Protein concentration also was dependent on the MG and planting system (Figure 5).
Protein concentrations were greater for late planting systems compared to earlier ones. Protein
concentration was significantly affected by the MG; however, there was not a clear trend of the
MG effect.
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37
Protein concentration (%)
Oil concentration (%)
21.0
20.5
20.0
19.5
19.0
Early Planting
Late planting
18.5
18.0
36
35
34
Early Planting
Late planting
33
3.0
3.5
4.0
4.5
5.0
5.5
6.0
Relative Maturity Group
6.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
Relative Maturity Group
Figure 4: Average oil (left) and protein (right) concentration by soybean relative maturity
group (rMG) and planting system (early vs. late).
The analysis of variance indicated that a large percentage of the variation in oil
concentration in seed was explained by location, as well as by planting date and their interaction
with MG and cultivar (data not shown). These results suggest that oil concentration could be
dependent in large part by the location and/or environment. The relationship between average
oil concentration by location and year with latitude (Figure 5) shows that oil concentration was
greater on average at the most southern locations and decreased at more northern latitudes.
Oil concentration was explained reasonably well with average temperature during the
seedfill period (R5 to R7) across all treatments in the 3-yr study (R2 = 0.33, Figure 6). The
relationship improved when analyzed individually by soybean cultivar (data not shown). Oil
concentration increased with increasing average temperature during seedfill up to 26.6 °C (79.9
°F), and reached a plateau with higher temperatures.
Figure 5: Relationship
between average oil
concentration in seed (%) by
year and location and
latitude. Different color
symbols indicate a different
growing season (2012, 2013,
and 2014).
10
24
22
Oil concentration (%)
Figure 6: Relationship
between oil concentration
(%) and average temperature
during seedfill (R5 to R7).
2012
2013
2014
if T < 26.6 °C: y = 0.23 T + 13.8
R2 = 0.32
20
18
16
14
16
18
20
22
24
26
28
30
32
Average temperature during seedfill (°C)
-
Germination and accelerated aging
Figure 7 shows the relationship between accelerated aging (AA) and standard
germination over all locations and over the 3 years. This relationship shows that the maximum
value of AA is similar to the standard germination for a particular seed lot. At standard
germination values below 95%, there can be large differences in AA. For example, at a standard
germination of 80%, AA values may range from a maximum of 80% to as low as 20%.
Figure 7. Accelerated aging versus standard germination over all locations from 2012 to 2014.
The solid line represents the 1-to-1 relationship.
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A preliminary analysis of the stability of standard germination and accelerated aging
germination (AA) was conducted with data from the 3-yr study (Figure 8), following a similar
approach as the one presented for the seed oil and protein concentration.
Average germination and accelerated aging were dependent on both the rMG and the
planting system (early vs. late). Germination and accelerated aging improved with later MG
cultivars, in particular for early planting systems. Planting system appeared to have a larger
effect on germination and accelerated aging than MG. These results suggest that germination
could be largely explained by environmental conditions during the seedfill period.
100
Accelerated aging (%)
Standard germination (%)
100
80
60
40
Early Planting
Late planting
20
80
60
40
20
Early Planting
Late planting
0
0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
Relative Maturity Group
6.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
Relative Maturity Group
Figure 8: Average standard germination (left) and accelerated aging (right) percentage by
soybean relative maturity group (rMG) and planting system (early vs. late).
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Seed grade
Results from seed grade from the 2014 growing season indicated that the
percentage of damaged seed had a large impact on the US seed grade classification of
the seed samples. These results are consistent with results obtained for the 2012 and
2013 growing seasons.
The US seed grade averaged by location, planting date, and MG is shown in
Figure 8. In general, seed quality had a tendency to be higher at the most northern
locations, under late planting dates, and with relatively late soybean MG. However,
there was large variability observed across locations.
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Figure 9: Average US seed grade by location, planting date, and MG for data from the 2014
growing season. Different colored cells indicate a different US seed grade number (1 to 4) or
low quality grade seed in red.
OBJECTIVE 2: Modeling of soybean yield and phenology
During this period, the cultivar coefficients for simulation with DSSAT-CROPGRO
were recalibrated with data from 2012 and 2013 and avoiding treatments where there was yield
limitation due to water stress. Water stress was defined as a reduction in yield potential of more
than 10% when comparing a simulation with no water limitation (water balance was not
activated in the simulation) with a simulation that included the actual soil and water
management for a given treatment. After cultivars were calibrated, a set of cultivar coefficients
were estimated for the cultivars used in 2014 according to their rMG.
Treatments during the 2014 growing season were simulated with the obtained cultivar
coefficients and their appropriate management and weather data. The model was efficient
simulating dates of main developmental stages in soybean (data not shown). Figure 9 shows the
observed and predicted soybean yields averaged by MG for all locations and planting dates in
2014. Data symbols located above the 1:1 line indicate that the model over predicted observed
yields, whereas data points below the line indicate a model under prediction.
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Figure 10. Observed and predicted yields averaged across varieties within a MG for
each year, location, planting date, and MG combination.
The model was evaluated for the prediction of yield by planting system (early vs. late)
and by MG (Table 1). The model was more accurate simulating yields of MG 3 and 4 cultivars
compared to later maturities. Average predicted yield by MG and planting system was close to
the observed (mean difference ranged from -30 to 228 kg/ha) for MG 3 and 4 cultivars, and the
normalized root mean square error (NRSE) was 20% or lower. On the other hand, yields were
on average over predicted for MG 5 and more so for MG 6 cultivars (mean difference from -512
to -1186). The NRSME was relatively high in MG 5 cultivars (26 and 31 %) and very high for
MG 6 cultivars (56 and 45%).
The results indicate that the model was accurate simulating yield differences due to MG
and planting system for MG 3 and 4. However, there was an over prediction of yield with later
soybean MG, in particular with MG 6.
This yield over prediction could be due to abiotic (nutrient stress) and biotic stresses
(insect and disease pressure) that the model does not take into account. The model will simulate
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yield based on planting and water management and weather data and does not take into account
other factors that might be reducing yield under field conditions.
Table 1. Observed yields, difference between observed and predicted, and normalized root
mean square error (NRMSE) of the prediction of soybean yield across treatments in 2014.
Treatments considered were each location x planting date x MG x cultivar combination.
Planting date
MG 3
MG 4
MG 5
MG 6
Observed
Early
Late
Observed - Predicted
Early
Late
NRMSE
Early
Late
-------------------------- kg /ha ------------------------- ------------ % ------------3851
3355
84
117
20
19
4085
3329
228
-30
16
17
3463
2852
-512
-565
26
31
2633
2582
-1186
-819
56
45
OBJECTIVE 3: Economic evaluation
A draft paper has been prepared that analyses risk-return tradeoffs when choosing
among planting date and MG choices across the various locations of our experiment. Included
are price premiums and discounts associated with oil and protein concentrations. These
premiums/discounts allow the inclusion of MG to be analyzed. Preliminary results suggest that
the choice of planting date and MG can have significant implications for returns and risks as
shown in Figure 11 below. Note that the analysis develops estimates of cash returns that do not
include ownership charges associated with equipment use and land charges as these costs are
fixed in nature and hence do not affect return variance across MG or planting date. In contrast,
variable irrigation costs change with MG and planting date and therefore affect return variance.
The data points in Figure 11 represent the expected returns (y-axis) plotted against the
standard deviation of particular MG-planting-date combination (x-axis) for Rohwer, AR over
the 3 years of the experiment. The standard deviation represents the variability of the expected
returns, and can be thought of as the risk associated with planting a particular MG at a particular
planting date. In general, Figure 11 shows that as expected returns increase there is also an
increase in the risk of obtaining those particular expected returns.
In the example shown in Figure 11, the highest expected returns are obtained by planting
MG 4 soybean at the first planting date, which also results in the highest return risk for this
location. The solid line in Figure 7 represents possible combinations of MG and PD that
maximize returns for a given level of risk. The data point labeled ‘C’ on the solid line in Figure
11 represents the point where risk is decreased by 50%. At this point, less than 10% of returns
are given up compared to the maximum yields that could be obtained by planting a MG 4
cultivar at the first planting date.
Table 2 shows different combinations of planting dates and MG that could be used to
lower the risk to 50% of the maximum. Using the example shown in Figure 7 for Rohwer, risk
would be lowered by 50% by planting 29% MG 3 at PD1, 54% MG 4 at PD1, 2% MG 5 at PD1,
and 14% MG 4 at PD2 compared with planting 100% MG 4 at PD1. Similar responses of
expected returns versus risk are shown in Figures 12 and 13 for other locations, and the MG/PD
combinations that decrease risk by 50% are shown in Table 2.
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Figure 11†. Expected Returns vs. Return Risk , E-V Frontier and Median Risk Portfolio Choice
(C) by Maturity Group (MG) and Planting date (PD) combinations at Rohwer, AR, 2012-14‡.
Notes:
†
MG × PD Combinations with a V (variance or risk) more than two times greater than the combination with the
highest Ei (Returns) were omitted from the graph.
‡ Point “E” represents the aggregate average Ei and average Va of all observations in Rohwer.
§ The dashed line represents the portion of the E-V frontier typically not analyzed as higher returns are available
at the same level of risk.
For the other sites analyzed, the same EV frontier but with less detail is shown in Figures 12
and 13. Significant differences in risk return tradeoffs appear across locations as shown (Table
2).
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Figure 12† Expected Net Returns in $ per ha vs. Return Standard Deviation across Maturity Group (MG) and Planting date (PD)
combinations at Four Locations, 2012-14. The E-V Frontier‡ represents combinations of MG and PD that maximizes returns (E) for a given
level of Return Variance (V) or risk.
Columbia, MO
$600
$400
$200
$$400
$600
$800
$(200)
$600
$400
$200
$$$(400)
Standard Deviation of E in $ ha-1
Milan, TN
$1,000
$200
$400
$600
$800
$600
$400
$200
$$-
$200
$400
Standard Deviation of E in $ ha-1
Keiser, AR
$1,000
$800
$600
$(200)
$(400)
$800
$(200)
$(400)
Expected Returns (E) in $ ha-1
$200
Expected Returns (E)
$800
$-
Portageville, MO
$1,000
Standard Deviation of E in $ ha-1
$800
Expected Returns (E) in $ ha-1
Expected Returns (E) in $ ha-1
$1,000
$800
$600
$400
$200
$$-
$200
$400
$600
$800
$(200)
$(400)
Standard Deviation of E in $ ha-1
Notes: The dashed line represents minimized profit at a given level of risk.
†
MG PD Combinations with a Va more than two times greater than the combination with the highest E were omitted from the graph.
‡
VMid is represented by the marker on the efficient frontier. For information on the different VMid portfolios see Table 8.
17
$800
$600
$400
$200
$$(200)
$-
Expected Returns (E) in $ ha-1
$(400)
$200
$400
$600
$800
St. Joseph, LA
$800
$600
$400
$200
$-
$(400)
$-
$200
$400
$600
Standard Deviation of E in $ ha-1
Stoneville, MS
$1,000
$800
$600
$400
$200
$$(200)
$-
$(400)
Standard Deviation of E in $ ha-1
$1,000
$(200)
Expected Returns (E) in $ ha-1
Verona, MS
$1,000
$800
Expected Returns (E) in $ ha-1
Expected Returns (E) in $ ha-1
Figure 13† Expected Net Returns in $ per ha vs. Return Standard Deviation across Maturity Group (MG) and Planting date (PD)
combinations at Four Locations, 2012-14. The E-V Frontier‡ represents combinations of MG and PD that maximizes returns (E) for a given
level of Return Variance (V) or risk.
$200
$400
$600
$800
Standard Deviation of E in $ ha-1
College Station, TX
$1,000
$800
$600
$400
$200
$$(200)
$(400)
$-
$200
$400
$600
$800
Standard Deviation of E in $ ha-1
Notes: The dashed line represents minimized profit at a given level of risk.
†
MG PD Combinations with a Va more than two times greater than the combination with the highest E were omitted from the graph.
‡
VMid is represented by the marker on the efficient frontier. For information on the different VMid portfolios see Table 8.
18
Table 2. MG×PD Planting Portfolios for VMid in Nine Locations
MG
PD
Location
III
1
IV
1
V
VI
III
IV
V
VI
1
1
2
2
2
2
% of land allocated to a MG × PD choice
III
3
IV
3
̅†
𝑬
%
Change
̅‡
𝑬
√𝑽§
%
Change
√𝑽
IRa¶
Columbia, MO
3
2
0
0
68
16
0
0
11
0
$ 639.78
-9.8%
$ 87.06
-32.5%
16
Portageville, MO
33
67
0
0
0
0
0
0
0
0
$ 493.87
-2.0%
$ 177.62
-27.7%
32
Milan, TN
24
56
20
0
0
0
0
0
0
0
$ 553.52
-9.7%
$ 145.45
-38.6%
19
Keiser, Ar
10
60
30
0
0
0
0
0
0
0
$ 399.96
-10.5%
$ 312.61
-40.6%
17
Verona, MS
0
52
0
0
23
0
0
0
25
0
$ 387.43
-14.4%
$ 117.95
-42.3%
35
Stoneville, MS
0
0
20
0
0
24
56
0
0
0
$ 688.10
-1.3%
$ 310.41
-40.3%
27
Rohwer, AR
29
54
2
0
0
14
0
0
0
0
$ 707.31
-9.4%
$ 262.63
-40.4%
33
St. Joseph, LA
0
0
10
0
0
65
12
0
13
0
$ 803.93
-8.4%
$ 174.98
-34.36%
10
College St, TX
0
0
0
0
61
26
0
0
13
0
$ 304.54
-7.1%
$ 151.14
-39.6%
15
% Change IRa
0%
-8.6%
0%
0%
0%
0%
-2.9%
0
7.1%
Notes: The model did not call for any soybeans to be planted in PD 4 or MG 5 or MG 6 varieties planted in PD 3.
†
The 𝐸̅ in $ ha-1 associated with the given VMid planting portfolio can for each location can be found in the column below
‡
This column contains the percentage change in Ei associated with a producer selecting the listed portfolio for planting rather than planting all available land in the
most profitable MG × PD combination for a given location.
§
This column contains the percentage change in Va associated with a producer selecting the listed portfolio for planting rather than planting all available land in
the most profitable MG × PD location for a given location.
¶
IRa represents the expected amount of irrigation used in ha cm-1 by the described planting portfolio. The amount of IRa listed in this column is then compared to
the amount of irrigation required by the return-maximizing MG × PD combinations.
19
OBJECTIVE 4: Development of a decision-support tool
The program and logo were developed during previous periods. The tool is ready for the final
simulation dataset.
Activities planned between now and the next reporting period
The main goal for the next period will be generation of the final simulation dataset to be used in
the decision tool, and the testing and evaluation of the tool.
Moreover, the analysis of seed quality data will continue during the next period, as well as the
preparation of research manuscript.
During the next reporting period we will also be preparing production guides, similar to the one
prepared for Arkansas, for all the locations/states in the study. These guides will be prepared in
collaboration with the participants in the project at each location.
Problems, obstacles, new developments or market/industry/research changes that impacted
or may impact the completion date, cost or scope of the project.
There are no major problems anticipated at the present time.
Message, questions, comments or requests.
The researchers on this project are all grateful for the opportunity to be involved in this
important and ambitious project. We appreciate the financial support from the USB Production
Committee and the MSSB for the development of resource materials that will help ensure
profitable soybean production in the Midsouth. If USB or MSSB members have comments on
how we can better address their needs and improve our reporting, please let Larry Purcell know.
Presentations and Publications during this reporting period
Salmerón, M., E.E. Gbur, F.M. Bourland, N.W. Buehring, L. Earnest, F.B. Fritschi, B.R.
Golden, D. Hathcoat, J. Lofton, A. McClure, T.D. Miller, C. Neely, G. Shannon, T.K. Udeigwe,
D.A. Verbree, E.D. Vories, W.J. Wiebold and L.C. Purcell. 2015. Yield response to planting
date among soybean maturity groups for irrigated production in the US Midsouth. Crop
Science. (in review).
Purcell, L.C, and M. Salmerón. 2015. Project update: Planting date x Maturity group regional
project – US Midsouth (2012 – 2015). MSSB meeting. Pine Tree Branch Experiment Station,
Colt, AR. August 25, 2015.
Salmerón, M., E.E. Gbur, F.M. Bourland, B.R. Golden, and L.C. Purcell. 2015. Soybean
maturity group choices for maximizing light interception across planting dates in the U.S.
Midsouth. Agron. J. 107:2132-2142.
20
Progress Report 15 June 2015
Project highlights:






Completion of seed grade, germination and accelerated aging, oil and protein
analysis from 2014.
Manuscript accepted in Agronomy Journal about MG choices to maximize light
interception.
Analysis of the yield response to planting date by location and MG, preparation of
an extension publication for Arkansas, and a research paper draft with data across all
locations (sent to collaborators).
Calibration of DSSAT-CROPGRO cultivar coefficients with data from 2012 and
2013 and evaluation of model simulations after calibration.
The logo for the program was developed and added to the decision support tool. A
change in the sequence of screens and an added figure on irrigation use by planting
date is planned.
A draft of a paper analyzing risk-return relationships across location, MG and
planting date has been developed and shows significant potential for lowering risk
by modifying planting date and maturity group. The paper uses conceptual premiums
and discounts for oil and protein content as well as seasonal cash price forecasts to
differentiate among MGs and planting dates. This paper will be prepared for
submission to a journal by the end of the upcoming reporting period.
Specific comments for the main areas of work in this project are presented below:
OBJECTIVE 1: Data collection and analysis
-
Analysis of the yield response to planting date
The relationship between soybean relative yield and planting day was evaluated across
data from the 3-yrs. Prior to data analysis, yields were transformed to relative yield by first
correcting for the year and cultivar effect within a location, and then dividing by the maximum
yielding MG at each location. Soybean relative yield was modeled for each location and MG
combination with either a quadratic or a linear model as a function of planting date with an
analysis of covariance using the proc MIXED procedure (SAS, v.9.4, SAS Institute, Inc., Cary,
NC.). Location and MG were included in the model as fixed variables, planting day and its
square were included as independent variables, and all the combination of interactions between
the previous were initially allowed in the model and removed when non-significant. The
analysis revealed that the relationship was dependent on the location and the MG within a
21
location (Table 3). Therefore, relative yield of MG 3 to 6 cultivars did not respond to planting
day similarly among locations or within a location. Overall, the analysis of covariance
explained 61% of the variation in relative yield.
Table 3: Analysis of covariance of relative yield with location (L), maturity group (MG) and
their interaction as covariant main effects, and planting day of year (PDOY) as an independent
variable.
Source of variation
Degrees
Sum of
Mean
F Value Pr > F
of
Squares Square
freedom
7
2.56
0.366
49.6
<.0001
Location (L)
3
0.41
0.136
18.5
<.0001
Maturity group (MG)
21
2.13
0.101
13.7
<.0001
L x MG
1
1.34
1.338
181.5
<.0001
Planting day (PDOY)
7
3.08
0.440
59.7
<.0001
PDOY x L
3
0.47
0.158
21.4
<.0001
PDOY x MG
21
2.00
0.095
12.9
<.0001
PDOY x L x MG
PDOY2
PDOY2 x L x MG
Residual
1
14
2.20
0.38
2.203
0.027
298.8
3.7
<.0001
<.0001
1256
9.26
0.007
.
.
As a graphic example of the model fits obtained from the analysis of covariance, Figure
1 shows results from Verona, MS with relative yields as a function of planting date by MG. The
graphs include yield data from the 3-yr study at this location and from all cultivars within each
MG (open symbols). The model fit obtained from the analysis of covariance is shown in dark
blue, whereas the lighter shaded area represents the 95% confidence interval in the model
prediction of relative yield. At Verona, the quadratic model was significant for the MG 3
cultivars according to the analysis of covariance. On the other hand, for MG 4 to 6 cultivars, the
relationship followed a linear model.
22
1.0
0.8
0.6
Relative yield
0.4
Measured
Predicted
95% CI
0.2
Measured
Predicted
95% CI
MG 3
MG 4
0.0
1.0
0.8
0.6
0.4
Measured
Predicted
95% CI
0.2
Measured
Predicted
95% CI
MG 5
MG 6
0.0
75
90
105
120
135
150
165
75
90
105
120
135
150
165
Planting day of year (PDOY)
Figure 1: Soybean relative yield as a function of planting day of year for MG 3 to 6 at Verona,
MS with regression and quadratic model fits as a function of planting day of year (PDOY). The
shaded are indicates the 95% confidence interval in the model predictions.
Figures 2 and 3 show the model fits obtained for each location and MG combination
from the analysis of covariance. Figure 2 includes the northern-most locations in the study
(Columbia, MO; Portageville, MO; Keiser, AR; and Milan, TN), whereas Figure 3 includes the
southernmost locations (Verona, MS; Rohwer, AR; St. Joseph, LA; and College Station, TX).
In all cases, the models obtained were significant, except for MG 6 cultivars at Rohwer and
Milan, where the analysis showed that relative yields did not respond to day of planting.
23
Figure 2: Modeled
relative yield as a
function of planting
date for MG 3 to 6 at
Columbia, MO,
Portageville, MO,
Milan, TN, and Keiser,
AR with 95%
confidence interval
(shaded area).
Figure 3: Modeled
relative yield as a
function of planting
date for MG 3 to 6 at
Verona, MS, Rohwer,
AR, St Joseph, LA, and
College Station, TX
with 95% confidence
interval (shaded area).
24
Table 1: Maximum relative yield (RelYmax), planting date to obtain maximum relative yield,
and estimated relative yield (RelY) on different planting dates (PD) for each soybean maturity
group (MG).
Estimated RelY for different PD††
Day of
Location
MG RelYmax
RelYmax
Apr 1 Apr 15 May 1 May 15 Jun 1 Jun 15
1.00
3
16-May
0.97 a
1.00 a
0.97 a 0.89 a
0.92
4
14-May
0.91 a
0.92 b
0.89 a 0.84 a
0.84
5
13-May
0.82 b
0.84 c
0.79 b 0.68 b
0.66
6
17-May
0.62 c
0.66 d
0.62 c 0.53 c
1.00
Portageville 3
3-Apr†
1.01 a 0.96 a
0.91 a
0.86 a
0.80 a 0.75 a
0.98
4
3-Apr†
0.99 a 0.94 a
0.89 a
0.84 a
0.78 a 0.73 a
0.87
5
3-Apr†
0.87 b 0.85 b
0.82 b
0.79 b
0.76 a 0.73 a
0.71
6
3-Apr†
0.71 c 0.70 c
0.69 c
0.67 c
0.66 b 0.65 b
0.96
Milan
3
22-Apr†
0.94 a
0.90 a
0.86 a 0.83 ab
1.00
4
22-Apr†
0.98 a
0.94 a
0.90 a 0.86 a
0.99
5
22-Apr†
0.96 a
0.91 a
0.85 a 0.80 bc
0.80
6
0.79 b
0.78 b
0.77 b 0.76 c
0.88
Keiser
3
14-May
0.70 b 0.80 b
0.86 b
0.88 c
0.84 b 0.77 b
1.00
4
5-May
0.88 a 0.96 a
1.00 a
0.99 a
0.93 a 0.83 a
0.96
5
24-Apr
0.92 a 0.95 a
0.95 a
0.93 b
0.85 b 0.76 b
0.87
6
17-May
0.63 b 0.76 b
0.84 b
0.87 c
0.84 b 0.77 b
0.89
Verona
3
4-May
0.77 b 0.85 b
0.89 a
0.88 a
0.81 a 0.70 b
1.00
4
22-Mar†
0.97 a 0.93 a
0.89 a
0.85 a
0.81 a 0.77 a
0.94
5
22-Mar†
0.91 a 0.87 b
0.82 b
0.78 b
0.73 b 0.69 b
0.80
6
22-Mar†
0.78 b 0.74 c
0.71 c
0.68 c
0.64 c 0.61 c
0.98
Rohwer
3
11-Apr
0.97 a 0.98 a
0.95 a
0.91 a
0.82 a 0.72 b
1.00
4
9-Apr
1.00 a 1.00 a
0.98 a
0.94 a
0.87 a 0.78 a
0.97
5
30-Mar†
0.96 a 0.92 b
0.86 b
0.82 b
0.76 b 0.71 b
0.79
6
0.74 b 0.75 c
0.76 c
0.77 c
0.77 b 0.78 a
0.92
St Joseph
3
14-May
0.74 b
0.88 b
0.92 a
0.84 a 0.69 a
0.99
4
1-May
0.95 a
0.99 a
0.96 a
0.84 a 0.68 a
0.92
5
7-Apr†
0.89 a
0.84 b
0.80 b
0.74 b 0.69 a
0.93
6
7-Apr†
0.88 a
0.79 c
0.70 c
0.60 c 0.52 b
0.79
College St.
3
21-Apr
0.74 b 0.79 b
0.78 a
0.72 a
0.57 a
0.91
4
4-Apr
0.91 a 0.89 a
0.81 a
0.69 a
0.47 b
1.00
5
27-Mar†
0.95 a 0.80 b
0.62 b
0.47 b
0.29 c
0.52
6
27-Mar†
0.48 c 0.39 c
0.28 c
0.19 c
0.07 d
†Earliest planting date at the location.
††Means followed by different letters indicate significant differences between MG cultivars
within a location and planting date.
Columbia
25
Optimum planting dates can be defined as those that would maximize relative yield of a
given MG within a location. The planting dates that maximized yield varied depending on the
location and MG combination and ranged from March 22 to 17 May (Table 1). For example, at
Columbia, all MG had optimum planting dates around mid-May; however, the maximum
yielding were MG 3 cultivars (relative yield = 1), followed by 4, 5, and 6 (relative yields of
0.92, 0.84, and 0.66, respectively). At Keiser, MG 4 cultivars had the highest yields with an
optimum planting date on May 5, followed by MG 5 planted earlier (2 April). MG 3 and 6 had
optimum planting dates in mid-May but reached lower yields than the other MG choices.
A range of optimum planting dates, or the optimum planting window, was estimated as
the range of planting dates for achieving 1 to 0.95 of the maximum relative yield within each
MG. Figure 4 shows the optimum planting window for Verona as an example. At this location,
MG 4 cultivars had the highest yields with a planting window from late March to early-mid
April. The planting window was similar for MG 5 and 6, but yields were lower than those of
MG 4 cultivars. MG 3 cultivars had a later planting window, ranging from mid-April to late
May, and with yields less than those of MG 4 and 5 cultivars.
1.0
Figure 4: Optimum planting window
by maturity group (MG). MG 4
cultivars had the highest relative yield
at the optimum planting dates and other
MGs had relative yields less than those
of MG 4 cultivars at the optimum
planting dates.
MG 4
Relative yield
MG 5
0.9
MG 3
0.8
MG 6
0.7
01 Mar
01 Apr
01 May
01 Jun
01 Jul
Optimum planting window
When planting date was delayed after the optimum, yields declined following a linear or
quadratic response depending on the relationship obtained in the analysis of covariance. In order
to compare the yield decline across treatments, the rate of yield decline with planting date was
calculated from May 17 to June 2 and expressed as the percentage per day by dividing by the
time period (2 weeks). When the relationship obtained was linear, the rate of yield decline was
equivalent to the slope of the relationship. Results were expressed on a percent basis of the
maximum relative yield for each MG by dividing the rates of yield decline by the maximum
relative yield of that MG. Delaying planting dates from mid-May to early June reduced yields
by 0.09 to 1.69 % per day, with the rate of yield decline being greatest at the southern-most
locations (Figure 5).
26
Figure 5: Rate of yield decline
per day when delaying planting
date for two weeks after May 17
and expressed as % of yield
reduction from the maximum
RelY within a location and MG.
Statistical differences among MG within a location were assessed at different planting
dates in 15 days intervals from April 1 to June 15 (Table 1). Overall trends from this analysis
can be summarized as follow:
- MG 4 cultivars maximized yield or were not statistically different than the highest
yielding MG at most locations and planting dates.
- MG 3 cultivars had the highest average yields at the two northern-most locations
(Columbia and Portageville). However, yields of MG 4 cultivars were not different from
those of MG 3 in all cases except for plantings in May 15 at Columbia.
- MG 5 achieved higher yields with earlier plantings, similar to those of MG 4. However,
yields decreased when planting date was delayed.
- MG 3 had yields similar to MG 4 as planting date was delayed.
- MG 6 had higher yields with the earliest planting dates, but yields of this MG were low
in general.
The analysis presented tested the yield response to planting date as affected by location
and MG. These results provide answers to one of the main questions and objectives in this
project, which is to identify the best MG choice at any planting date and location. Moreover,
optimum planting windows that will maximize yields were obtained for each location and MG,
as well as the rate of yield decline when planting is delayed after the optimum.
A research manuscript draft summarizing these results has been prepared and sent to
collaborators for review. Furthermore, an article for an UA extension bulletin with results from
Keiser and Rohwer, AR, has been prepared and is pending publication after revision. Similar
sets of results are planned for the six states represented in this project.
27
-
Seed quality:
Results from seed quality from the 2014 growing season were successfully completed
during this reporting period. Results from seed quality included: (i) US seed grade with test
weight, seed size, foreign material, and percentage of damaged seeds, estimated at Rohwer, AR
by our collaborator Larry Earnest, (ii) standard germination and accelerated aging performed by
the Arkansas Plant Board, and (iii) analysis of oil and protein concentration in seeds, conducted
in Portageville, MO by a collaborator in the previous project, Earl Vories.
Samples from the last location processed, at Stoneville, MS, had a storage problem due
to animal infestation that compromised the seed quality analysis of 59 % of samples. Therefore,
only the remaining samples at this location were sent for seed quality analysis.
The seed quality dataset for the 3-yr period was combined and results from 2014 are
being reviewed before further data analysis.
OBJECTIVE 2: Modeling of soybean yield and phenology
The Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation
software uses the CROPGRO-Soybean module for the simulation of soybean growth and
development. CROPGRO requires a set of genetic coefficients that are specific for each species
and cultivar and that should be stable across locations. We used data from our regional study in
2012 and 2013 to calibrate the genetic coefficients for each cultivar and across all the
environments studied. Observed data on phenology (day of flowering or R1; day of first R5
seed, and day of physiological maturity or R7), and growth (unit seed weight, yield, node
number in the main stem at R7) were used to find coefficients that improved model predictions.
The GLUE calibration tool in DSSAT was set to produce 10,000 sets of simulations with
different coefficients for phenology and then for growth determination for each cultivar and
across all environments. After the simulations, GLUE produced a set of cultivar coefficients
that can optimize model predictions across all experiments.
After the calibration, model predictions of main developmental stages were accurate,
with root mean square error (RMSE) across all treatments of 5.9, 7.6, and 8.3 days for the
prediction of R1, first R5 day, and R7 (data not shown).
Yield prediction after the calibration showed a larger variability compared to the
simulation of phenology. Observed and simulated yields were averaged across varieties within a
MG in Figure 6, where each data point represents data for a year (2012 and 2013), location,
planting date, and MG combination. Different symbols show data grouped by planting system
(early or late according to Salmeron et al. 2014). Under an optimum situation, the data would
fall close to the 1:1 line. Overall, simulations were more accurate for MG 3 to 5 compared to
MG 6. Under the early planting systems (in blue), the model underpredicted the highest
observed yields for MG 3 and 4 cultivars. Simulation of MG 5 had a tendency to overpredict
yields under late planting systems (in red). For the MG 6 cultivars, yields were overpredicted in
both early and late planting dates.
28
Figure 6: Observed and predicted yields averaged across varieties within a MG for each year,
location, planting date, and MG combination. Different symbols represent planting dates
grouped in an early or late planting system.
The normalized RMSE (NRMSE) in the prediction of yield was calculated by location,
MG, and by planting system (early vs. late) in order to identify treatments where the model
simulated with more or less accuracy (Table 2). Values of NRMSE as low as possible are
desirable. The NRMSE in yield predictions ranged from 9 to 85%. The highest NRMSE were
obtained with MG 6 cultivars and to a lesser extent with MG 5, in particular under a late
planting system. There was large variability in the accuracy of model predictions across
locations, especially for MG 6. When considering only prediction of MG 3 to 5, results at
Fayetteville, Milan, Rohwer, and St. Joseph, had acceptable yield predictions with NRMSE
lower or close to a reasonable threshold of 20%.
29
Table 7: Normalized root mean square error (NRMSE) in the prediction of yield, calculated by
location, MG, and planting system.
Location
Columbia, MO (38.9°N)
Portageville, MO (36.4°N)
Fayetteville, AR (36.1°N)
Milan, TN (35.9°N)
Keiser, AR (35.4°N)
Verona, MS (34.6°N)
Stoneville, MS (33.4°N)
Rohwer, AR (33.4°N)
St Joseph (32.0°N)
College Station, TX (30.6°N)
Early planting system
Late planting system
MG 3 MG 4 MG 5 MG 6
MG 3
MG 4
MG 5
MG 6
NRMSE (%)
28
26
19
27
28
22
26
21
14
12
34
73
18
30
30
60
14
16
15
29
14
20
16
50
12
16
16
16
23
27
29
14
26
23
18
23
13
16
30
32
9
11
31
43
43
19
32
25
27
33
20
21
25
22
21
29
23
17
23
17
21
20
21
20
16
10
20
69
22
22
34
24
22
17
60
85
The evaluation of model accuracy by location, MG and planting type, can allow
detecting possible problems associated to treatments and/or locations. Some of the yield
overpredictions could be associated with a higher sensitivity of some cultivars to disease and
insect pressure that was more pronounced at some locations (e.g. high stem canker and/or frog
eye at Verona and Portageville for some of the MG 5 and 6 cultivars). On the another hand,
modifications of the model, such as including an effect of high temperature stress during
flowering and seed set on seed size determination, could potentially improve model predictions.
The preparation of the DSSAT-GLUE for the High Performance Computing facilities at
the University of Arkansas (HPCC) showed some problems during this period and the cultivar
coefficients were calibrated in a set of 7 computers with a windows operating system instead. A
PhD student, Eric Johnston, has been hired now for preparing the calibration tool to work
correctly using the Linux operating system for the HPCC. This will allow us to perform new
cultivar calibration for different hypotheses that could improve simulations during the next
period, and that would be very difficult otherwise because of the long computation times.
OBJECTIVE 3: Economic evaluation
A draft of a paper has been created to analyze risk-return tradeoffs when choosing
among planting date and MG choices across the experimental locations for which data exist.
Included are impacts of oil and protein content derived price premiums and discounts so that the
effect of inclusion of such premiums and/or discounts on MG selection can be analyzed.
Preliminary results suggest that the choice of planting date and MG can have significant
implications for returns and risks as shown in Figure 7 below. Note that the analysis develops
estimates of cash returns that do not include ownership charges associated with equipment use
and land charges as these costs are fixed in nature and hence do not affect return variance across
MG or planting date. In contrast, variable irrigation cost change by MG and planting date and
therefore affect return variance. It can be seen from the graph that maximum cash returns are
achieved with the combination of planting MG 4 soybean on the earliest planting date with
30
highest return risk at that location. The line represents combinations of MG and PD that
maximizes returns for a given level of risk. It can be seen that risk can be cut in half or by 50%
at the cost of approx. $50 per ha or by giving up less than 5% of returns shown for the MG 4 by
PD 1 choice (see the donut shaped circle on the EV frontier).
Figure 16. Expected Cash Returns in $ per ha vs. Return Variance across Maturity Group (MG)
and Planting date (PD) combinations at Rohwer, AR, 2012-14. The EV Frontier represents
combinations of MG and PD that maximizes returns (E) for a given level of Return Variance
(V) or risk.
Expected Cash Returns (E) in $ ha-1
1,400
1,200
1,000
800
600
400
200
0
0
20,000
40,000
60,000
80,000
100,000
120,000
Return Variance (V) in $
140,000
160,000
180,000
200,000
ha-1
Frontier
PD1 MG3
PD1 MG4
PD1 MG5
PD1 MG6
PD2 MG3
PD2 MG4
PD2 MG5
PD2 MG6
PD3 MG3
PD3 MG4
PD3 MG5
PD3 MG6
PD4 MG3
PD4 MG4
PD4 MG5
PD4 MG6
OBJECTIVE 4: Development of a decision-support tool
Other than the development of the logo, the only a change to the support tool at this time
is in the sequence of input screens and the addition of an irrigation-water-use-by-planting-date
graph that are envisioned for future versions.
Presentations and Publications during this reporting period
Salmerón M, Gbur EE, Bourland FM, Golden BR, Purcell LC. 2015. Soybean maturity group
choices for maximizing light interception across planting dates in the U.S. Midsouth. Agron. J.
(in press).
Purcell LC, Van Roekel RJ, Salmerón M. 2015. Physiological and management factors
contributing to soybean potential yield. Field Crops Res. (in press).
31
Salmerón M, Purcell LC, Earnest L, Ross JR. 2015. Yield response to planting date for soybean
MG choices in Arkansas. Univ. Arkansas Ext. Service (in review).
Salmerón M, Gbur EE, Bourland FM, Buehring NW, Earnest L, Fritschi FB, Golden BR,
Hathcoat D, Lofton J, McClure A, Miller TD, Neely C, Shannon G, Udeigwe TK, Verbree DA,
Vories ED, Wiebold WJ, Purcell LC. 2015. Yield response to planting date among soybean
maturity groups for irrigated production in the Midsouth. Crop Sci. (in co-author review).
Progress Report 15 March 2015
Project highlights:








Completion of seed grade analysis in samples from the 2014 growing season in
samples from 9 out of 10 locations.
Completion of germination and accelerated aging analyses in 40% of samples.
Completion of oil and protein analyses from most of the samples.
Development of a prototype program for the decision-support tool.
Development of a logo for the decision-support tool.
Generation of a simulation dataset for testing of the decision tool (preliminary noncalibrated dataset).
Assembled 30-yr historical data for 11 locations in the Midsouth that will be used for
simulations.
Submission of a research manuscript to Agronomy Journal.
OBJECTIVE 1: Data collection and analysis
-
Yield and yield components
Collection of 2014 data from each location, including during season notes, yield, and
irrigation management was completed during this reporting period. Figure 1 shows the box-plot
distribution of soybean yields by location for 2014. The median yield at each location ranged
from 2.59 to 4.41 Mg ha-1. On average, yields were slightly above those obtained in 2012 (7%
higher), and slightly below the yields obtained in 2013 (6% lower).
The analysis of variance for soybean yield showed that all sources of variation were
significant at the P<0.0001 level (Table 1). Grouping sources of variation related to
environment, genotype, and genotype x environment showed similar results to those obtained in
2012 and 2013. The largest percentage of the yield variation was explained by the environment
(36%), followed by the interaction of genotype with environment (28%), and with the least
extent explained by genotype (22%). However, genotype explained a greater percentage of yield
variability in 2014 compared to 2012 (10%) and 2013 (9%).
32
-1
Soybean yield 13% moist (kg ha )
Overall, results indicate that by selection of optimum environmental conditions (e.g. planting
dates within a location) and genotypes (soybean MG or potentially relative MG) for a given
location and planting date, growers would be able to achieve yields in the higher range of the
yields obtained in our study (Figure 1).
7000
2014
6000
5000
4000
3000
2000
1000
0
Colu Port Faye Mila Keis Vero Ston Rohw St J C St
38.9°
36.4° 36.1°
35.9° 35.4°
34.6°
33.4° 33.4°
32.0°
30.6°
Figure1: Box-plot distribution of soybean yield by location for 2014. The boundary of the box closest to
zero indicates the 25th percentile, the line within the box indicates the median, and the upper line in the
box indicates the 75th percentile. The error bars below and above the box indicate the 10th and 90th
percentiles, respectively. The round symbols indicate the 5th and 95th percentiles, respectively.
Table 1: Four-factor analysis of variance of soybean yield for 2014. Degrees of freedom, sum of
squares, mean square, F, probability, and % of total variability explained by each source of variation (%
of the total sum of squares of the model). Error sum of squares not shown in the table but is used for
calculating the % of total variability.
Source 2014
DF
Location (L)
Planting Date (PD)
L*PD
Maturity Group (MG)
L*MG
PD*MG
L*PD*MG
Cultivar(MG)
L*Cultivar(MG)
PD*Cultivar(MG)
L*PD*Cultivar(MG)
9
3
24
3
27
9
71
12
108
36
285
Combined sources of variation
Sum of
squares
(x107)
36.2
22.8
11.1
35.7
15.2
4.4
11.4
6.2
11.9
1.7
9.1
Mean
square
(x106)
40.18
75.94
4.61
119.08
5.63
4.88
1.61
5.15
1.10
0.48
0.32
F
P-value
% of total
variability
308.06
582.22
35.32
912.97
43.17
37.42
12.31
39.48
8.43
3.68
2.45
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
19
12
6
18
8
2
6
3
6
1
5
33
Environment†
Genotype‡
GxE§
36
22
28
†Environmental effect estimated as: L + PD + L*PD
‡Genotypic effect estimated as: MG + Cultivar(MG)
§G x E effect estimated as: L *MG + PD*MG + L* PD*MG + L*Cultivar(MG) + PD*Cultivar(MG) + L*PD*Cultivar(MG)
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Seed quality:
The analysis of seed quality includes oil and protein concentration in seed, germination and
accelerated aging, and the US seed grade classification. Seed grade involves estimation of the %
of damaged seeds, test weight, unit seed weight, and foreign material.
Samples are being analyzed for seed grade at Rohwer, AR by our collaborator Larry Earnest.
Seed grading has been completed in samples from 9 out of 10 locations. Results from analysis
of germination and accelerated aging performed at the Arkansas Plant Board are already
available for 40% of the samples. The analysis of oil and protein concentration in seeds,
conducted by a collaborator in the previous project in Portageville, MO, Earl Vories, has been
completed in most of the samples and results will be available soon.
Results from seed quality are expected to be completed during the next reporting period.
OBJECTIVE 2: Modeling of soybean yield and phenology
The Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation software
with the CROPGRO-Soybean simulation model is used to predict soybean phenology, yield,
and irrigation needs as inputs for the decision-support tool developed in this project.
A complete preliminary dataset was generated during this period conducting simulations at 11
locations in the Midsouth with 30-yr of historical weather data (1985-2014), planting dates from
late March to early July at weekly intervals, and MG 3, 4, 5 and 6. Simulations were conducted
for a shallow silty clay soil with 2 inches of allowable water deficit, and for a shallow silty loam
soil with 1.5 inches of allowable water deficit. This dataset is being used for the development of
the decision-support tool, however, simulations will be re-generated after the model simulations
have been improved for further model accuracy. The list of locations and latitude that have been
included in the model is listed in Table 3.
Table 2: Location name, latitude and longitude for the source of historical weather data used to
generate the simulation dataset for the decision-tool.
Location
Columbia, MO
Carbondale, IL
Sikeston, MO
Jackson, TN
Yazoo, City, AR
Latitude
Longitude
39.0
37.7
36.9
35.6
35.1
-92.3
-89.2
-89.6
-88.8
-90.1
34
Mariana, AR
Tupelo, MS
Winnsboro, LA
Alexandria, LA
College Station, TX
Baton Rouge, LA
34.8
34.3
32.2
31.3
30.6
30.5
-90.8
-88.7
-91.7
-92.4
-96.3
-91.1
The calibration of cultivar coefficients using the High Performance Computing facilities will
continue during the next period. After all genotypes have been calibrated, a set of cultivars will
be generated based on soybean relative maturity group, and the accuracy of the model using this
approach will be evaluated with data from 2014 in our 3-yr study.
OBJECTIVE 3: Economic evaluation
After collection of results from seed quality analysis during the next reporting period, a
complete dataset will be available from the 3-yr study. This dataset will include information on
soybean yield, phenology, irrigation applied, oil and protein concentration in seed, germination
and accelerated aging, and the US seed grade classification. This dataset will be used to conduct
an economic analysis based on agronomic performance, irrigation requirements, soybean
market price at the time of harvest, and seed price discounts due to low seed quality.
OBJECTIVE 4: Development of a decision-support tool
A prototype of the decision support software was developed and demonstrated on Feb. 9 for a
meeting with the MidSouth Soybean Promotion Board. At that time the tool had non-calibrated
yield, irrigation and phenology data for one location (Tupelo, MS). This allowed for
presentation of the workings of the model.
The tool starts off with a screen of credits (Figure 2).
35
Figure 2: Screen of credits.
The first page of the tool acquires information about the location to be analyzed, the soil texture
and water deficit schedule to follow, planting date and the MG (ranging from 3.2 to 6.7) to
compare (Figure 3).
Figure 3: Model inputs.
The next page provides information about the likelihood of achieving various yield levels as
well as the risk of freezing post planting (Figure 4).
Figure 4: Yield output.
36
Additional yield information is provided next (Figure 5), and allows a view of all 30 years of
yield observations in the left hand graph, while providing an assessment of planting date
impacts on yields as well as an annual comparison of yields in the right hand side panels.
Figure 5: Yield output (continuation).
The following pages highlight phenological and irrigation information (not shown as we are in
the process of revising how this information will be presented.
With yield, phenological and irrigation information provided the user now turns their attention
to economics. The user is asked to provide an estimate of irrigation costs per acre-inch for
which assistance is provided to develop an estimate based on fuel or electricity use, type of
irrigation (center pivot vs. furrow or flood) and depth of well. The user then has a choice of
several cash markets to choose from and different weekly seasonal sale price patterns as shown
below. Depending on MG, the model determines seasonal deviations and price risk from a user
specified price expectation. Finally expected partial returns defined as revenue minus irrigation
costs are shown. Green highlighting shows results for the MG with higher sale price, lesser
price risk and higher partial returns (Figure 6).
37
Figure 6: Economic analysis.
The seasonal price index page provides a glimpse at seasonal price patterns over the course of
the whole marketing year depending on market chosen and can be toggled to focus on just the
harvest season (Figure 7).
Figure 7: Seasonal price index.
The final two screens provide sensitivity-analysis results on the range of soybean prices that
will not change the most profitable MG choice highlighted on the previous page. A similar
analysis is provided for the range of irrigation costs for which the most profitable MG choice
stays the same. Finally, the tool allows the user to specify additional costs of soybean
production to assess economic return risk. Using the 30 year yield risk, the specified price,
irrigation and other costs, the model develops estimates of the likelihood of earning a profit and
the likelihood that one MG will outperform the other using annual return comparisons (Figure
8).
38
Figure 8: Sensitivity analysis.
This information his highlighted further graphically in another output screen (Figure 9). This
page can be printed and exhibits the level of yields simulated by MG along with a probability
density function of annual return comparisons for the MGs selected.
Figure 9: Sensitivity analysis (continuation).
39
Finally a summary of all information is provided as a one-page summary as shown in Figure 10.
Figure 10: Summary.
We have identified a logo for the tool to replace the MIDSOUTH SOYBEAN GENOTYPE
SELECTION DECISION TOOL heading throughout the tool.
We have also developed data for the remaining sites to more fully test ramifications of
calculation speeds as more data is handled.
Minor modifications to sequence of information are also envisioned to provide a smooth
transition from screen to screen.
Presentations and Publications March 2015
Salmerón, M. Genotype x Environment x Management strategies for improving crop
performance in agriculture. Invited speaker. March 9-10, 2015. Lexington, KY.
Salmerón, M., E.E. Gbur, F.M. Bourland, B.R. Golden, and L.C. Purcell. 2015. Soybean
maturity group choices for maximizing light interception across planting dates in the U.S.
Midsouth. Agron. J. (in review)
Purcell, L.C., M. Salmeron, and M. Popp. Decision-support tool for soybean maturity group
choices in the US Midsouth. MidSouth Soybean Promotion Board meeting. Savannah, GA.
February 9, 2015.
40
Salmeron, M and L.C. Purcell. 2015. Soybean planting date and latitude on choice of maturity
group in Mid-South soybean production – Results summary. Handout for 58th Annual Tri-State
Soybean Forum.