Recent advances in the CSM-CERES-Sorghum model
Poster material presented at ASA-CSSA-SSSA annual meeting. Nov 4-8,
2007, New Orleans, LA
Jeff White1*, Gerrit Hoogenboom2, Samsul Huda3, Bruce Kimball1, Mike Ottman4,
P. V. Prasad5, Wes Rosenthal6, Moussa Sanon7, Scott Staggenborg5, Sibiry
Traore8, Michel Vaksmann9, Richard Vanderlip5
1USDA-ARS,
4Univ.
ALARC, Maricopa, AZ; 2Univ. Georgia, Griffin, GA 30223-1797; 3Univ. W. Sydney, Penrith South DC, Australia;
Arizona, Tucson, AZ; 5Kansas State Univ., Manhattan, KS; 6TAES Blackland Res. Center, Temple, TX 76502; 7INERA,
Ouagadougou, Burkina Faso; 8ICRISAT, Bamako, Mali; 9CIRAD/IER, Bamako, Mali.
150
130
110
70
50
30
30
60
90
120
150
Simulated (DAP)
US hybrids
Maturity isolines
Mali cvs.
1:1
Burkina Faso cvs.
Table 1. Parameters used to distinguish cultivars in CSM-CERES-Sorghum
Thermal time from seedling emergence to the end of the juvenile phase. In this
phase, plants show no photoperiod response.
The longest photoperiod at which development occurs at a maximum rate. At
values higher than P2O, the rate of development is reduced.
P2R
Extent to which development is delayed for each hour increase in photoperiod
above P2O.
P3
Thermal time from panicle initiation to end of leaf growth.
P4
Thermal time from end leaf growth to onset of grain filling.
P5
Thermal time from onset of grain filling (3-4 days after flowering) to
† Unitless parameter.
physiological maturity.
PHINT
Phyllochron interval; the interval in thermal time (°d) between the appearance
of successive leaf tips.
G1
Scalar for relative leaf size.
G2
Scalar for partitioning of assimilates to the panicle.
Units
Fig. 1 Comparison of measured vs. simulated days
to anthesis for four groups of germplasm.
ºd
h
20000
ºd h-1
16000
Anthesis
-1
Definition
P1
Dry weight (kg ha )
Parameter
P2O
Model Description
CSM-CERES-Sorghum is based on the CERES-Sorghum model described
by Alagarswamy et al. (1988). Components involving soil processes,
evapotranspiration, input/output functions, and simulation controls are shared
with other CSM models (Jones et al., 2003; Hoogenboom et al., 2004).
Crop phenology is simulated by integrating developmental rates for
individual phases over time until threshold values are attained, corresponding to
the thermal or photothermal duration required for a given phase. Rates vary with
temperature and photoperiod, depending on the corresponding phases, which are
delimited by growth stages. Temperature is estimated on an hourly basis from
daily maximum and minimum temperatures. Two responses, are assumed, both
having a base temperature (Tbase) of 8°C. During vegetative development, the
optimum temperature (Topt) is assumed to be 50°C. For reproductive
development, Topt is 32°C, and above this temperature, development proceeds at
the maximum rate.
From the end of the juvenile phase to panicle initiation, photoperiods longer
than the critical short daylength (P2O) slow development. Photoperiod sensitivity
depends on two cultivar parameters, P2O and P2R. The latter specifies the degree
to which development is slowed for photoperiods greater than P2O. Stress effects
are also considered.
90
Measured (DAP)
The growth and development routines of the model are similar to those of early versions of
CERES-Maize as described by Jones and Kiniry (1986) and Ritchie et al. (1998). Vegetative
growth is modeled through potential radiation use efficiency (RUE), reduced by temperature or
water or nitrogen deficits, and enhanced by elevated CO2. The temperature effect is trapezoidal
with Tbase of 6°C, Topt of 20°C, Topt2 of 40°C, and Tmax of 50°C, where the temperature involves
a weighted mean of the daily minimum and maximum temperatures. The respective cardinal
temperatures for grain filling are 7°, 22°, 48°, and 50°C. Light interception is estimated
assuming a homogeneous canopy.
Cultivars are parameterized via durations of specific phases, degree of photoperiod
sensitivity, relative leaf size, and partitioning to panicle (Table 1). Ecotypic traits are constant
over a set of germplasm (e.g., modern US hybrids) and include RUE and canopy light
extinction.
ºd
ºd
ºd
Basic Plant Growth
Simulations of a well-managed crop at Maricopa, AZ indicated problems with how CSMCERES-Sorghum partitions assimilate (Fig. 2). Stem growth did not start until end of the
juvenile phase (40 days after planting), much later than observed in the field. Unallocated
assimilate accumulated in roots. Although the stage “end of leaf growth” occurred on day 63,
simulation of leaf mass halted after day 51.
The simulated drop in stem weight at day 78 was due to re-allocation of stem mass to
panicle mass. Arguably, the allocation to panicle mass should start earlier to better represent
plant processes. This sharp transition also is linked to the end of partitioning to root growth.
The difficulties are readily detectable in other data sets such as for two years of rainfed
sorghum at Manhattan, KS (Fig. 3). As for Maricopa, simulated leaf growth ceases well before
the end dates suggested by field observations.
Panicle
initiation
8000
End juvenile
0
--†
--
Sorghum Phenology
Simulations of days to anthesis (Fig. 1) showed good prediction for 17 US hybrids grown
in Arizona (r2 = .95**, RMSE = 3 d) but poor agreement for photoperiod sensitive cultivars
from Mali (r2 = .40**, RMSE = 22 d). Eight maturity isolines varied in photoperiod sensitivity
(Quinby, 1973), and simulations of the most sensitive lines also were problematic (r2 = .63**,
RMSE = 8 d). However, germplasm from Burkina Faso, which presumably would also be
highly sensitive, were simulated reasonably well (r2 = .73**, RMSE = 7 d).
Further work is needed in order to compare alternative models for photoperiod response
(e.g., as proposed by Folliard et al., 2004). Attention is also needed to sequencing of anthesis
relative to onset of grainfilling.
"End of leaf
growth"
12000
4000
ºd leaf-1
20
40
60
80
100
120
Days after planting
Above ground tot.
Leaf
Stem
Panicle
Root
Tops - measured
Leaf - measured
Stem - measured
Pan. - measured
Fig. 2. Simulated and measured dry weights over a
single season at Mariocpa, AZ.
4500
4000
Leaf weight (kg ha -1)
Introduction
Sorghum (Sorghum bicolor [L.] Moench) is grown in a wide range of
environments and is considered exceptionally heat and drought tolerant. Cultivars
differ greatly in photoperiod response, degree of tillering, and other traits. This
diversity makes the crop attractive as a flexible component in crop rotations and
as a potential bioenergy stock.
The process-based model CSM-CERES-Sorghum shows promise as a tool
for sorghum research as well as for guiding management decisions for sorghum
production. The model is compatible with the DSSAT4 shell (Hoogenboom et al.,
2004) and associated tools, allowing users to access a wide range software to
facilitate preparation of data and model application. Initial testing, however,
suggested that the model had difficulty with simulating phenology, leaf area
development, and partitioning to stem mass, especially for tropical germplasm.
We briefly describe the model and provide an assessment based on comparisons
with field data and use of sensitivity analysis.
3500
3000
2500
2000
1500
1000
500
0
20
40
60
80
100
120
140
Days after planting
Maricopa, AZ 1998
Measured
Manhattan, KS 1965
Measured
Manhattan, KS 1966
Measured
Fig. 3. Simulated and measured leaf dry weights
for well-managed trials at Maricopa, AZ and
Manhattan, KS.
160
Climate Risk
The availability of sorghum cultivars differing in phenology allows their
flexible incorporation into crop rotations. For high-input irrigated systems, the
expected duration of a cultivar for a given planting date is of prime interest. To
examine the potential for estimating sorghum phenology, effects of planting date on
Cargill-577 were simulated for Maricopa, AZ, considering weekly planting dates
from March to October for a 10 year set of observed weather. No water or nitrogen
stress was allowed.
The results suggest that if planted in June or July, this cultivar would mature
in about 80 days (Fig. 6) with little risk of extreme early or late maturation.
Planting outside of this range is possible but lengthens the growth period and the
uncertainty of the expected duration, especially for plantings from mid-August
onward.
160
140
Days after planting
Response to Population and Row Spacing
The main effects of population is through the number of seedlings creating
initial biomass. Light interception is adjusted for population and row spacing
according to a factor that reduces the effective LAI:
LIFAC =1.5 - 0.768 * ((ROWSPC * 0.01)2 * PLTPOP)0.1
where ROWSPC is row spacing (cm) and PLTPOP is the population (pl m-2).
A sensitivity analysis was conducted for the hybrid RS-610 grown under
rainfed conditions at Manhattan, KS, varying population from 1 to 19 pl m-2, and
using 25 cm, 51 cm and 102 cm row widths (Fig. 4). Grain yield increased with
population, but there was minimal effect of row spacing. The irregular response of
grain yield reflected effects of water and nitrogen deficits, including stress-induced
early maturation above 8 pl m-2.
80
Anthesis
40
29-Feb
19-Apr
8-Jun
28-Jul
16-Sep
5-Nov
Date of planting
Fig. 6. Simulated variation in anthesis and maturity for
Cargill-577 planted at weekly intervals over 10 years at
Maricopa, AZ. No water or N limitations.
4000
2000
1000
0
5
10
15
20
-2
Population (plants m )
25 cm
51 cm
102 cm
Fig. 4. Effect of plant population and row width on grain yield for a hypothetical
crop at Manhattan, KS.
The interaction of population with stress was explored further for a 1965 trial
at Manhattan, KS with RS-610 sown at four populations in rows 102 cm wide.
Measured grain yield showed only a small effect of population from 3.5 pl m-2 to
19.4 pl m-2 (Fig. 5). With no water or nitrogen stress, the modeled response to
population is much stronger. If water and nitrogen deficits are allowed, slope of the
response is similar to the measured data, but the mean yields are about 50% lower.
12000
1600
44000
1400
+1 SD
1200
1000
800
600
400
11 SD
42000
40000
38000
36000
34000
32000
200
0
30000
0
4
8
12
16
Year
20
24
28
32
0
4
8
12
16
Year
20
24
28
32
Fig. 7. Simulated change in grain yield and soil organic C for a 30 year rainfed
sorghum-fallow rotation near Goiania, Brazil.
Conclusions
The CSM-CERES-Sorghum model currently appears adequate for applications involving phenology of temperate germplasm as affected by planting
date and latitude. Applications in tropical environments require caution since the current photoperiod response may not handle all photoperiod sensitive
germplasm adequately. Simulations of growth and grain yield appeared unreliable due to incorrect timing of partitioning to stem and leaf growth. These
problems appear readily addressable, and their resolution would greatly enhance the utility of the model for examining questions such as what are likely
effects of maturity type and water use over planting dates in temperate systems.
This assessment highlights the value of using relatively large numbers of genotypes, locations and treatments in examining model performance. The
datasets are organized following the ICASA format (Hunt et al., 2006), and our intention is to make data available for other models or applications.
-1 )
10000
Crop Rotations
Many potential applications for sorghum modeling
require predicting production trends over multiple years
while accounting for carryover effects of previous
crops. The CSM models include an option for
simulating crop rotations whereby soil conditions at the
end of one crop are used as initial conditions for the
subsequent crop or fallow. To assess the compatibility
of the sorghum model with other CSM models, a
hypothetical rainfed sorghum crop was grown near
Goiania, Brazil on an oxisol receiving no additional
nitrogen. A sorghum-fallow rotation was run for 30
years using generated daily weather (replicated 30
times). The results suggest a modest yield decline from
low yields around 900 kg ha-1 (Fig. 7) but a loss of over
30% of total soil carbon.
Soil organic C (kg ha -1)
3000
Grain yield (kg ha-1)
-1
Maturity
100
60
5000
Grain yield (kg ha )
120
8000
Grain yield (kg ha
6000
4000
References
2000
0
0
5
10
Population (pl m
15
20
-2 )
Measured
Rainfed, 200 kg N/ha
Rainfed, no N stress
No water or N stress
Fig. 5. Simulated and measured effects of plant population on grain yield for RS610 grown at Manhattan, KS under rainfed conditions.
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