Field Crops Research 100 (2007) 200–209
www.elsevier.com/locate/fcr
Predicting on-farm soybean yields in the pampas
using CROPGRO-soybean
J.L. Mercau a,*, J.L. Dardanelli b, D.J. Collino c, J.M. Andriani d,
A. Irigoyen e, E.H. Satorre a
a
Cátedra de Cerealicultura, Facultad de Agronomı́a, Universidad de Buenos Aires, FAUBA,
Av. San Martı́n 4453, C1417DSE Buenos Aires, Argentina
b
Estación Experimental Manfredi, INTA, Manfredi, Córdoba, Argentina
c
Instituto de Fitopatologı́a y Fisiologı́a Vegetal, INTA, Córdoba, Argentina
d
Estación Experimental Oliveros, INTA, Oliveros, Santa Fe, Argentina
e
INTA Balcarce-Universidad Nacional de Mar del Plata, Balcarce, Buenos Aires, Argentina
Received 23 December 2005; received in revised form 27 June 2006; accepted 15 July 2006
Abstract
Soybean is the main rainfed crop in a wide range of latitudes and sowing dates of the Argentine Pampas. It is sown alone or as a second crop after
other winter and summer crops. Modelling approaches have proved to be helpful in the decision making process. The on-farm evaluation of
CROPGRO is rather difficult since input data are scarce and frequently of worse quality than those from experimental works. Moreover,
CROPGRO simulation of water dynamic processes and their relation with biomass production has not been comprehensively evaluated in soybean
crops. The aims of this study were (i) to evaluate the CROPGRO-soybean performance, with emphasis on water demand and supply and biomass
production under water limited conditions, (ii) to generate a revised CROPGRO model improving those aspects, and (iii) to compare simulations
outputs using the original and the revised CROPGRO models, with on-farm crop data set. In the revised model, we multiplied potential
evapotranspiration by 1–1.22 when LAI increased from 0 to 4.0. We set a root extension rate of 4.0 cm/thermal day and a maximum rooting depth
of 2.5 m. Finally, we included a nonlinear equation to simulate the relationship between relative transpiration and relative gross photosynthesis.
The ability of the revised CROPGRO-soybean to simulate water content depletion and biomass production was tested against several experiments
with an imposed drought period. We also calibrated cultivar parameters using ‘‘ad hoc’’ tests in a range of environments (combinations of sowing
dates and locations). The models were evaluated with data from 155 commercial farms. V (%) (root mean square error as percentage of the observed
mean) for the total cycle length, vegetative period, and reproductive phase simulations were 7, 13 and 15%, respectively. The revised CROPGRO
was more accurate in simulating crop yield, biomass, harvest index and yield numeric components. V (%) values ranged from 11 to 17% (revised
version) and from 13 to 22% (original version). Besides, V (%) values for yield were 16% with the revised model versus 32% with the original one,
considering only paddocks with higher water stress level. The robust prediction of phenology, biomass and yield components obtained with the
revised model across different environmental conditions, support its use in the decision making process of the soybean crop at the farm scale.
# 2006 Published by Elsevier B.V.
Keywords: Soybean; Crop simulation; Water stress; CROPGRO; On-farm evaluation
1. Introduction
Soybean is grown across the Argentinean Pampas, where it
constitutes the main rainfed crop in a wide range of ecological,
technological and management environments (Hall et al.,
1992). Management alternatives for this crop involve sowing
date, growth season duration, crop density, spatial arrangement
* Corresponding author. Tel.: +54 11 4524 8053; fax: +54 11 4524 8053.
E-mail address: [email protected] (J.L. Mercau).
0378-4290/$ – see front matter # 2006 Published by Elsevier B.V.
doi:10.1016/j.fcr.2006.07.006
and fertilization level, among others (Calviño and Sadras, 1999;
Campos, 2003; Gonzalez Venzano, 2003; Torrent, 2003). In
almost every region of the Pampas, soybean can be annually
cropped alone or as a second crop after wheat. In the northern
Pampas, soybean can also be sown as part of a double summer
crop system, with maize or another soybean as a preceding crop
in the same year.
Soybean crop systems are in part responsible of a large
transformation of the pampean landscape (Satorre, 2001). The
area cropped with soybean increased from 1.8 to 13.5 million ha
in the 1980/1981–2004/2005 period (http://www.secyt.gov.ar).
J.L. Mercau et al. / Field Crops Research 100 (2007) 200–209
Innovations in crop management have been also performed both
under the leadership of farmers and research centres. Farmers are
organized in associations such as AACREA (Argentine
Association of Agricultural Experimentation Consortia) or
AAPRESID (Argentine No-till Farmers Association), which
conduct pioneering experiences and perform local experiments
to improve management skills.
In such a dynamic and changing scenario, modelling
approaches can be a valuable tool to evaluate new technologies
under previously unexplored conditions (i.e. Savin et al., 1995;
Ferreyra et al., 2001; Carberry et al., 2002). The use of an
accurate model along with soil spatial variability and long-term
weather data may allow farmers to explore the effects of
different cropping strategies (Sadras and Hall, 1989; Meinke
et al., 2001). The model should be simple enough as to
minimize structure-linked errors and data requirements, and
should also include the main sources of yield variability
(Passioura, 1996). Up to date, simple agronomic models have
proven to be useful for the analysis of soybean yield in
particular regions of the Pampas (Calviño and Sadras, 1999;
Calviño et al., 2003). However, being developed for specific
cropping systems, these models lack of flexibility and only
evaluate a reduced set of management alternatives (Calviño and
Sadras, 1999). Agronomic simulation models such as
CROPGRO – included in DSSAT (Jones et al., 2003) –
APSIM (Keating et al., 2003) and CropSyst (Stockle et al.,
1994), offer a better balance between complexity, data
requirements and management variables to be simulated.
Minimum data sets, always required by any model included in
DSSAT at the farm level scale, are usually available or can be
acceptably estimated (Hunt et al., 2001). These models are
sensitive to soil and weather variations and management
decisions (i.e. genotype characteristics, sowing date, population
density and its spatial arrangement, fallow).
CROPGRO is a generic growth model that simulates
soybean as well as other crops (Boote et al., 1998). CROPGROsoybean, and its precursor SOYGRO, have been used to
simulate phenology (Grimm et al., 1993, 1994; Piper et al.,
1996a) and yield (Colson et al., 1995; Mavromatis et al., 2001;
Calviño et al., 2003). CROPGRO has demonstrated to be a
valuable tool for scientific research, crop management and
policy-making (Boote et al., 1996), but, at present, its
performance has been scarcely evaluated. In addition, model
calibration was often needed to achieve adequate performances
(i.e. Colson et al., 1995; Sau et al., 1999; Calmon et al., 1999).
The water balance component of a process-oriented model
may be crucial to ensure its accuracy under rainfed production
conditions. However, the on-farm evaluation of CROPGRO is
difficult since input data are scarce and frequently of worse
quality than in experimental works. CROPGRO simulation of
water dynamic processes and their relation with biomass
production has not been comprehensively evaluated in soybean
crops. The objectives of this work were (i) to evaluate the
CROPGRO-soybean performance, with emphasis on water
demand and supply and biomass production under water
limited conditions, (ii) to generate a revised CROPGRO model
improving these aspects, and (iii) to compare different
201
simulations outputs using the original versus the revised
CROPGRO models, with an on-farm crop data set.
2. Materials and methods
2.1. The CROPGRO-soybean v3.5 model
CROPGRO-soybean is a process-based model with a
simulation time step of 1 day. CROPGRO-soybean simulates
phenology and growth of soybean under a wide range of
conditions. Phenology is simulated by allowing developmental
phases to be differentially sensible to temperature, photoperiod,
water and nutrient stress (Boote et al., 1998). The soybean
species file describes cardinal temperatures of phenological
processes. In v3.5, vegetative, early and late reproductive
development base temperatures are set to 7.0, 6.0 and 48.0 8C,
respectively. However, in the early reproductive phase, we used
2.5 8C, according to Grimm et al. (1993). CROPGRO simulates
genotype differences for any phase duration and its photoperiodic sensitivity, although genetic coefficients need to be
calibrated. Water balance in CROPGRO is described in detail
in Ritchie (1998). Briefly, the model calculates the crop water
demand and the root system ability to supply water on a daily
basis. To establish soybean demand, the model can use two
equations to calculate potential evaporation: (i) Penman–FAO
(Doorenbos and Kassam, 1979) and, (ii) Priestley–Taylor as
modified by Ritchie (1998). Widespread use of Penman–FAO in
the Pampas is limited because required dew point and wind speed
data are scarce. The second approach is simpler, but could require
crop specific calibration (Villalobos et al., 1996; Ferreyra, 1998).
To calculate crop water supply, CROPGRO combines a
cascading layer model for soil water movement and a root water
uptake model. Water infiltration is calculated from daily rainfall
using a modification of the USDA Soil Conservation Service
curve number method (Williams et al., 1984). When not
measured, this parameter is assigned to each soil considering
texture, depth, slope, land cover, etc. (SCS, USDA, 1972). Soil
water availability for the crop is defined by (i) soil water upper
and lower limits, (ii) root growth rate until maximum rooting
depth is achieved, and (iii) the potential root water uptake
within each layer. Water limits are usually estimated from soil
texture and organic carbon content by using pedotransfer
equations. In soybean species file root growth rates (YRTFAC)
are set to 2.5 cm TD 1 (TD: thermal day) for the entire crop
growing period.
Soil water availability, soil water lower limit, crop root
density, and maximum daily water uptake rate by roots determine
the potential for water uptake. When soil water content is near
field capacity, root water uptake rate (cm3 cm root 1 d 1) is
maximized. When the soil dries, uptake rate decreases
exponentially from different water content thresholds and with
different slopes as a function of root density and soil lower limit
as described in Fig. 1. Higher soil lower limits correspond to fine
textured soil horizons, where water absorption rates are reduced
as consequence of non-uniform root distribution (Dardanelli
et al., 2004). Root density distribution is modulated by soil layer
using root growth factor (SRGF) and it is further modified when
202
J.L. Mercau et al. / Field Crops Research 100 (2007) 200–209
Fig. 1. Relationship used to calculate maximum root water absorption
(cm3 cm root 1 d 1) as related to the volumetric water content above the lower
limit (cm3 cm 3), root length density (4, 1 and 0.1 cm root 1 cm3 soil, from
thicker to thinner line), and the magnitude of the lower limit (at right: a clayey
soil with a 0.24 cm3 cm 3 lower limit; at left: a sandy soil with a 0.04 cm3 cm 3
lower limit).
there is poor aeration or low soil water content. Usually SRGF
values are user-defined as a function of soil depth and qualitative
description of soil root presence, in order to consider constraints
to root proliferation (Ritchie and Godwin, 1989; Calmon et al.,
1999; Wang et al., 2003). We assessed SRGF combining a soil
depth function with an exponent value of 2.0 (Jones et al., 1991)
and a root proliferation constraint factor increasing with fine
texture soils (Dardanelli et al., 2003).
2.2. A revised CROPGRO model
We proposed three modifications to the water balance
procedures of CROPGRO v3.5 model. (i) Crop water demand:
We evaluated the agreement between the Ritchie (1998)
equation and the soybean crop potential evaporation using 32
water use estimates from irrigated experiments at three sites:
Balcarce ( 37.758 latitude, 58.258 longitude), Oliveros
( 32.558, 60.838), and Manfredi ( 31.828, 63.808). Water
use was calculated once full crop cover was reached (LAI
higher than 4.0) by soil water storage differences between two
neutron probe measurements during periods (duration range 3–
17 days) without rainfall and irrigation. Average measured crop
water use (range 4.0–8.6 mm d 1) was 22% higher than
Ritchie’s (1998) equation prediction. Therefore, we introduced
an empirical correction factor in the model code that multiplies
potential evapotranspiration by 1.22 when LAI is 4.0. The
correction factor decreases linearly to 1 when LAI = 0. A
roughly similar approach was described for sunflower
(Villalobos et al., 1996) and peanut (Ferreyra, 1998). (ii) Crop
Water uptake: From previous research we established that field
measurements of rate of root extension were better predicted by
setting a 4.5 cm TD 1 value (Dardanelli et al., 2003). We found
that 4.0 cm TD 1 appeared to be a good substitute when the soil
impedance factor is not considered. For the revised model, we
assumed 2.5 m as a parsimonious estimation of maximum
rooting depth with no physical constraint throughout the soil
profile (Dardanelli et al., 1997; Dardanelli and Andriani, 2003).
(iii) Crop growth under water limitation: CROPGRO assumes a
linear relationship between actual to potential transpiration
(SWFAC) and actual to potential gross photosynthesis (PG/
PGMAX) (Ritchie, 1998). However, there is evidence that
under water supply limitations, gross photosynthesis decreases
at a lesser rate than transpiration in peanut (Ferreyra et al.,
2003) and wheat (Abbate et al., 2004).
To evaluate the ability of the revised CROPGRO-soybean to
simulate water content depletion under supply-limited conditions, we used five experiments located at Balcarce, Oliveros
and Manfredi. Soils included horizons with contrasting
textures: loam, silty loam, silty clay loam, clay loam, and
silty clay. For methodological details see Dardanelli et al.
(2003). Briefly, soybean crops were grown without water
limitation until full cover was reached. Thereafter, drought was
imposed by excluding rainfall using mobile shelters. Soil water
content was regularly measured within the whole profile (0–
2.5 m) using the gravimetric procedure in the upper 30 cm and
the neutron probe technique from this depth to 2.5 m depth. In
the same experiments, we measured total aerial biomass at
physiological maturity (range 3112–5976 kg ha 1) in order to
evaluate the relationship between SWFAC and PG/PGMAX for
soybean crops. To perform CROPGRO simulations for these
experiments, we used specific genetic and soil parameters
defined in a previous study (Dardanelli et al., 2003).
2.3. Calibration of genetic coefficients
We calibrated cultivar parameters using ‘‘ad hoc’’ trials
exploring a wide range of sowing dates (October–January) in
three localities: Marcos Juárez ( 32.688 latitude, 62.108
longitude), Pergamino ( 33.908, 60.588) and 25 de Mayo
( 35.438, 60.178). Cultivar genetic coefficients were adjusted
to reported agronomic ranges as suggested by Boote et al.
(2003). Dates of the phenological stages R1, R3, R5 and R7,
yield and unit grain weight were measured. R1 and R7 were
assessed according to Fehr and Caviness (1977), while R3 and
R5 were measured as the first pod and first seed at any node
(Boote et al., 1998). Iterative simulation runs in two steps were
performed with different coefficients values. Firstly, phenology
coefficients (photothermal days, PTD, between R stages,
critical photoperiod before and after R1 and response to day
length) for each genotype, was changed until the model
acceptably predicted the observed values for all data sets.
Secondly, a subset of experiments where water was not limiting
was used to adjust the coefficients involved in yield
determination (maximum leaf photosynthesis, maximum
weight per seed, and individual seed filling duration), until
an acceptable model error was achieved. Experiments with
moderate to severe water stress were excluded in order to
reduce simulation errors under water limited condition.
2.4. CROPGRO simulation with on-farm data
To evaluate simulations for on-farm conditions, we collected
data from AACREA commercial farms. AACREA (Argentine
J.L. Mercau et al. / Field Crops Research 100 (2007) 200–209
203
Table 1
Variables assessed in commercial farms to characterize key aspects of the crop,
its environment and management
Features of the cropping
system
Variable
Crop
Phenological development Date of sowing, date of first flowera (R1),
date of first seeda (R5), date of
physiological maturitya (R7)
Structural and functional Variety, plant population densityb
aspects
(plants m 2)
Yield and its components Aboveground biomass at maturityc (kg ha 1),
dry yieldc (kg ha 1), individual grain
massd (mg), grain numberd (# m 2)
Environment
Weather
Soil
Biological
Management
On-farm daily rainfall (mm), on-farm hail
storms, daily maximum and minimum
temperaturee (8C), daily solar radiatione
(MJ m 2)
Soil type (USDA classification), depth to the
duripan (m)
Visual estimation of weed cover at
flowering (%), visual estimation of diseases
incidencef
Previous crop, tillage system, date of harvest
a
Paddocks were scouted once every 7–15 days. R1 and R7 according to Fehr
and Caviness (1977) scale. R5 as the first seed in any node (Boote et al., 1998) in
the most of paddocks but in some of them it was scouted according to Fehr and
Caviness (1977).
b
Assessed in 10 randomly distributed linear plots of 14.3 m length each.
c
Measured in three randomly chosen rows (7 m length each) before mechanical harvest (0% humidity).
d
Measured in five sub-samples of 200 grain each (0% humidity).
e
Measured on nearby meteorological stations.
f
Main diseases included Sclerotinia stem rot (Sclerotinia sclerotiorum (Lib.)
de Bary), Sudden death syndrome (Fusarium solanii f. sp. glycines), Root rot
(Phytophthora sojae) Brown stem rot (Phialofora gregata), and Stem cancer
(Diaporthe phaseolorum f. sp. meridionalis).
Association of Agricultural Experimentation Consortia) is a
nongovernmental association of farmers. In AACREA, professional consultants advise groups of 8–12 growers on the basis of
both on-farm trials and records of crop, soil, weather and
economic data. In Argentina, this association is one of the main
sources of information on major cropping systems. We assessed a
wide number of variables (Table 1) for 186 paddocks from 1997/
1998 to 2000/2001 growing seasons across the Pampas (Fig. 2).
Thirty-one fields were discarded from the analysis due to the
severe intensity of stresses they suffered or to the lack of data to
run the model. In the remaining 155 fields, sowing dates ranged
from 1 October to 10 January and were sown with 14 varieties
from II to VII maturity groups. Only 23% of the fields were sown
with determinate growing habit varieties (six varieties), the
remaining 77% fields were sown with eight indeterminate
varieties mainly from maturity groups III and IV. Plant
population was 18–80 plants m 2, and rows were 0.26–0.70 m
apart. Harvest dates ranged from 12 March to 2 June. Previous
crops were maize (39%), soybean (39%), other summer crops
(sunflower, peanut and sorghum; 12%), and perennial pastures
(10%). In 25% of paddocks, spring wheat was sown before the
soybean crop in the same calendar year. In all cases seed was
Fig. 2. Mean differences between precipitation and potential evapotranspiration for the period November–March in the Pampas. Dashed circles represent
the distribution of soybean crop paddocks evaluated, numbers inside are the
number of paddocks in each area.
inoculated with Bradyrhizobium japonicum immediately before
sowing. Recommended pesticides were used to control weeds,
diseases and insects. Mean differences between precipitation and
potential evapotranspiration for the period November to March
in the explored area ranged from 250 to 125 mm (Fig. 2),
suggesting a frequent occurrence of water limitation during the
soybean season.
Soil profile characteristics were assigned to each paddock
according to soil survey data and maps provided by the
National Institute of Agricultural Technology (INTA) at
1:50,000 scale. Soil nitrogen initial condition was set
according to expert assessment in each zone. Soil water limits
and soil surface condition parameters were estimated according to IBSNAT methods (Ritchie and Godwin, 1989). The
initial soil water contents were roughly estimated to each
growing season and zone according to DSSAT water balance
simulation from early March to each field soybean sowing date,
including wheat growth simulation when necessary. Daily
precipitations were measured on-farm, while temperature and
radiation data were extrapolated from meteorological stations
10–130 km far-away from the fields. To simulate radiation
interception and biomass production, we used hourly photosynthesis calculations and the hedgerow photosynthesis model
options of CROPGRO (Boote et al., 1998). The phenological
stages R1 and R5 were scouted in more than the 80% of
paddocks, 53% of paddocks had R7 assessment; only in 45% of
paddocks were the three stages recorded. Soybean grain yield
(0% humidity), crop biomass, grain number and unit grain
weight were assessed in 100, 74 and 85% of paddocks,
204
J.L. Mercau et al. / Field Crops Research 100 (2007) 200–209
respectively. For each variety, simulation was performed using
specific genetic coefficients (see Section 2.3). These inputs
were used to run the model independently from sowing date to
maturity for each of the 155 fields.
3. Results
crop potential evaporation. The use of the revised CROPGRO
under supply-limited conditions in soils without physical
constraints resulted in a RMSE of 15 mm between measured
and simulated total available soil water content during the
imposed drought period. This RMSE is 14% of average
measured values (range from 27 to 293 mm). The RMSE in
soils with argillic horizons, which constrain root development
and water uptake, was slightly higher, achieving 26 mm. This is
18% of average available water measured values (range from 31
to 330 mm).
Simulation of crop biomass production under water stress
was compared considering: (i) the original model with a linear
relationship between SWFAC and PG/PGMAX, and (ii) a
revised model code with a nonlinear relationship; i.e. PG/
PGMAX = 1 (1 SWFAC)WSFEXP, where WSFEXP is an
empirical constant (Ferreyra et al., 2003). RMSE of the
observed/simulated soybean biomass with the linear model
reached 1871 kg ha 1 (39% of the measured mean biomass
values). For the non-linear version, after iterating several
values, WSFEXP was set to 4.0, reducing RMSE to
625 kg ha 1 (13% of the measured mean biomass values).
The WSFEXP value is higher than the 2.5 value obtained by
optimization in peanut (Ferreyra et al., 2003). However, the use
of 2.5 for WSFEXP increased RMSE to 1009 kg ha 1 (21% of
the observed mean biomass values).
3.1. Evaluation of CROPGRO water balance procedures
3.2. On-farm evaluation of CROPGRO-soybean
We used five experimental data sources with detailed
measurements of water dynamic and biomass production to
evaluate the modifications introduced to CROPGRO-soybean.
The RMSE for predicted versus observed crop water use under
full cover and no water limitation decreased from 1.6 to
1.2 mm d 1 after calibrating soybean water use from the
agreement between the Ritchie (1998) equation and soybean
For on-farm conditions, weather and crop management data
were available, and soil parameters were estimated from local
surveys (see Section 2.4). Most of the modified genetic
coefficients used to simulate the 14 cultivars are shown in
Table 2. The first six coefficients averaged 3.6, 6.7 and 7.9 V
(%) values for the periods from sowing date until R1, R5 and
R7, respectively. These coefficients were calibrated on the basis
2.5. Statistical methods
To evaluate the agreement between observed and simulated
data, we used the root mean square error (RMSE) and its
components (Kobayashi and Salam, 2000). The RMSE is the
square root of the average square biases between each observed
and simulated values. The square of RMSE, the mean square
deviation, is the sum of three components: (i) the disagreement
between the observed and simulated average (squared bias,
SB), (ii) the squared difference between the observed and
simulated standard deviations (SDSD), and (iii) lack of
correlation weighted by the standard deviations (LCS). In
our study, RMSE values are expressed also as a percentage of
the observed mean (V (%)), and the SB, SDSD and LCS as a
percentage of the total mean square deviations (i.e., SB (%),
SDSD (%) and LCS (%)). Finally, we also calculated the
determination coefficient (R2) between the simulated and the
observed data (Steel and Torrie, 1985).
Table 2
Genetic coefficients values calibrated for the 14 cultivars used to evaluate CROPGRO-soybean for on-farm conditions
Cultivar
DM 2800
P 9396
DM 3800
A 4456
DM 4700
DM 4800
A 5435
A 5409
A 6401
A 6445
RA 702
A 7409
Mercedes 70
Fogata 72
a
MGa
II
III
III
IV
IV
IV
V
V
VI
VI
VII
VII
VII
VII
CSDL
(h)
PPSEN
(d h 1)
R1PPO
(h)
EMFL
(PTDb)
FLSD
(PTD)
SDPM
(PTD)
LFMAX
(mg CO2 m
13.50
13.40
13.45
13.00
13.10
13.00
12.55
12.40
12.35
12.35
12.00
12.00
12.05
12.20
0.280
0.300
0.300
0.300
0.325
0.315
0.300
0.310
0.335
0.315
0.260
0.260
0.300
0.320
0.500
0.324
0.400
0.369
0.369
0.369
0.400
0.500
0.300
0.459
0.504
0.504
0.504
0.504
18.5
20.5
22.0
22.5
20.5
20.0
25.0
23.0
20.0
24.0
23.0
23.0
23.0
24.0
17.5
15.5
18.0
11.0
14.0
13.5
11.0
10.5
11.0
12.0
12.5
12.5
12.0
11.5
31.5
37.0
33.0
38.0
39.0
38.0
37.0
35.0
38.5
36.0
34.0
34.0
32.0
36.5
0.95
0.95
0.95
0.85
0.90
1.05
0.85
0.80
1.10
0.85
1.00
0.85
0.80
0.80
2
s 1)
WTPSD
(G)
SFDUR
(PTD)
0.180
0.190
0.190
0.175
0.170
0.190
0.165
0.150
0.150
0.155
0.175
0.150
0.140
0.155
20.0
26.0
22.0
26.0
29.0
26.0
28.0
24.0
29.0
25.0
27.0
27.0
23.0
27.0
MG: maturity group; CSDL: critical short day length below which reproductive development progresses with no daylength effect; PPSEN: slope of the relative
response of development to photoperiod with time; R1PPO = increase in daylength sensitivity after anaesthesis; EMFL: time between plant emergence and flower
appearance; FLSD: time between first flower and first seed; SDPM: time between first seed and physiological maturity; LFMAX: light saturated leaf photosynthesis
rate at 30 8C, 350 vpm CO2 and high light; WTPSD: maximum weight per seed; SFDUR: seed filling duration for pod cohort at standard growth conditions.
b
Photothermal days.
J.L. Mercau et al. / Field Crops Research 100 (2007) 200–209
205
Table 3
Comparison of observed and simulated durations (days) for developmental phases defined by sowing date (Sd), first flower R1, first seed (R5, see text for details), and
physiological maturity (R7)
Period
N
Rangea (days)
Observed (days)
Simulated (days)
RMSE (days)
V (%)
SB (%)
SDSD (%)
LCS (%)
R2 (%)
Sd–R1
R1–R5
R5–R7
R1–R7
Sd–R7
139
126
70
82
82
28–83
13–55
17–59
47–91
94–158
54 12
30 8
39 9
66 9
125 14
54 9
29 6
45 4
70 8
127 12
7
9
10
10
9
13
30
25
15
7
1
1
35
16
8
10
7
20
2
5
89
92
45
82
87
63
4
16
16
63
Root mean square error (RMSE) and its percentage of observed mean (V). Squared bias (SB), squared difference between the observed and simulated standard
deviations (SDSD), lack of correlation weighted by the standard deviations (LCS), are expressed as a percentage of mean square deviation. Determination coefficient
(R2).
a
Observed values.
of phenological stages dates from experiments performed along
an ample range of sowing dates and locations (see Section 2.3),
The remaining three parameters averaged 12.8, 12.1 and 9.5 V
(%) for yield, grain number and, unit grain weight, respectively.
These parameters were calibrated with yield components
obtained from experiments in which water was not a limiting
factor (see Section 2.3). Thereafter, we used a unique set of
genetic coefficients for each genotype, avoiding the use of local
coefficients (Boote et al., 2003).
The amount of rainfall accumulated from sowing to
maturity, calculated on the basis of commercial farms data
sets, ranged from 207 to 1098 mm. Total cycle length (sowing
to R7) varied from 94 to 158 days. This combination of factors
provided ample variation both in on-farm soybean aerial
biomass at R8 (3985–9495 kg ha 1) and in crop dry grain yield
(1326–5078 kg ha 1).
Flowering (R1) occurred on 6 January 25 days, first seed
(R5) on 6 February 23 days and physiological maturity (R7)
on 27 March 20 days. CROPGRO-soybean simulation of the
sowing-R1 period was similar in average to the observed value
and the RMSE was 7 days (Table 3). Physiological maturity
(R7) was correctly simulated resulting in a V (%) = 7 on the
estimation of total cycle length (Sd–R7, Table 3). A 2 days
overestimation of cycle length average and variability
contributed with only 13% to the RMSE (SB (%) plus SDSD
(%)). The model output accounted for the 63% of observed
cycle length variability until both R1 and R7 (R2, Table 3). An
adequate simulation of reproductive phase (R1–R7, Table 3)
complemented the correct estimation of the vegetative period
(Sd–R1). However, R1–R5 and R5–R7 were simulated with
less precision, mainly because in some paddocks R5 estimation
date was performed considering the four uppermost nodes (Fehr
and Caviness, 1977), instead of any node of the plant, the event
which CROPGRO model predicts (Boote et al., 1998). These
two criteria produced R5 date discrepancies and increased the
prediction errors especially for indeterminate varieties. Consequently, the model over-predicted in 6 days the average
registered value (SB (%) = 35) and under-predicted in 5 days
the observed variability (SDSD (%) = 20).
The revised CROPGRO-soybean model more accurately
simulated crop yield, biomass, harvest index and yield numeric
components than the original v.35 model (Table 4). RMSE
ranged from 11 to 17% of average measured values (V (%),
Table 4). The revised model simulated on-farm biomass and
yield across a wide range of observed values, being most of
observed deviations lesser than 15% of simulated values (Fig. 3).
The simulation accounted for the 41% of observed biomass and
yield variability (R2, Table 4). Average and variability of crop
yield and biomass simulation only accounted for less than 8% of
simulation error, reflecting similar distributions of simulated and
on-farm observed values (Table 4; Fig. 3).
To assess the impact of the water process simulation in onfarm model accuracy, we evaluated the trend of the difference
between simulated and observed yields across its corresponding
Table 4
Observed and simulated values of crop biomass, yield, harvest index (HI), grain number and unit grain weight (UGW) using original (O) and revised (R) CROPGRO
model (M)
N
Range
Observed
Ma
Simulated
RMSE
V (%)
SB (%)
SDSD (%)
LCS (%)
R2 (%)
Biomass (kg ha 1)
115
3985–9495
6778 1188
Dry yield (kg ha 1)
155
1326–5078
3351 685
HI (%)
115
33–60
50 5
Grain number (# m 2)
132
924–3348
2360 415
UGW (mg)
132
98–186
146 16
O
R
O
R
O
R
O
R
O
R
6284 1434
6532 1218
3046 787
3200 616
49 6
50 5
2349 402
2395 328
132 21
136 16
1264
1054
743
584
6.3
6
382
372
27
22
19
16
22
17
13
11
16
16
19
15
15
5
17
7
2
0
0
1
27
18
4
1
2
1
1
1
0
5
3
0
81
94
81
92
97
99
100
94
70
82
38
41
34
41
10
12
32
28
6
8
RMSE, V, SB, SDSD and LCS, as in Table 3.
a
CROPGRO model versions, original v3.5 (O) and revised (R).
206
J.L. Mercau et al. / Field Crops Research 100 (2007) 200–209
simulated water stress level for R5–R7, the period in which water
was most limiting (Fig. 4). Using the revised model, the linear
equation (solid line) simulated that, at 50% of water stress level,
the yield difference reached 612 kg ha 1 (R2 = 0.08, Fig. 4).
However, the original model resulted in 1626 kg ha 1 of yield
difference (R2 = 0.37, Fig. 4). The RMSE of yield simulation
decreased from 743 kg ha 1, with the original model, to
584 kg ha 1, with the revised one (Table 4). If only the paddocks
with water stress level higher than 10% were considered, the
RMSE obtained using the revised model (n = 73) would reach
531 kg ha 1 (16% of average observed values), whereas the
original model (n = 50) would result in a RMSE of 1007 kg ha 1
(32% of average observed values). In both the revised and the
original models, when water stress level was less than 10%, the
RMSE was near 580 kg ha 1 (17% of average observed values),
without bias in the average yield estimation.
4. Discussion
Fig. 3. Simulated and observed values of crop yield (kg DM ha 1), and
biomass (kg DM ha 1). Lines showed 1:0.85; 1:1.15 and 1:0.85 relationships
between simulated and observed values.
Fig. 4. Relationship between simulated and observed yield difference and its
corresponding simulated water stress level (SWFAC) for R5–R7 with the
original (triangles, dashed line) and revised (circles, solid line) model versions.
Model evaluation with on-farm data sets could lead to
higher errors compared to controlled experimental data sets
because of higher variability of observed data, lower quality
inputs and greater number of parameter estimations. However,
the performance of the CROPGRO-soybean model proved to
be successful in 155 paddocks conducted under standard farm
practices and contrasting environmental conditions in the
Argentine Pampas. The model accurately simulated the onfarm observed phenology in a wide range of sowing dates,
genotypes and sites (Table 3). Whereas CROPGRO v.35
simulations of crop biomass yield, harvest index and yield
numeric components were good, the proposed modifications
significantly enhanced the agreement between observed and
simulated data (Table 4). This improvement was more evident
when rainfed crops experienced increasing levels of water
stress (Fig. 4). In coincidence with this result, lower model
accuracy was also assessed in France using the SOYGRO
model under rainfed versus irrigated crops (Colson et al.,
1995).
The assessed 17% inaccuracy in yield prediction (RMSE,
Table 4) is a fairly low error when using the revised CROPGRO
to support decision making at an on-farm scale. In a more
controlled on-farm experiment in the southern Argentinean
Pampas, the original CROPGRO obtained higher simulation
accuracy, although roughly similar compared to simpler local
developed models (Calviño et al., 2003). Moreover, as in our
study only the 8% of the RMSE was attributed to the simulation
of average and standard deviation of paddocks yield (SB and
SDSD, respectively, Table 4), the actual inaccuracy introduced
will be reduced to less than 2% if the goal is centred in assessing
the average and risk of yield for a set of paddocks and crop
seasons (Kobayashi and Salam, 2000).
Water balances of the soybean crop are frequently negative in
the Pampas (Fig. 2). The modifications introduced to CROPGRO
increased plant potential evaporation and downward root
movement rates, achieving a good simulation of soil water
dynamics either under supply or demand-limited conditions.
Better biomass simulations under water stress were obtained by
J.L. Mercau et al. / Field Crops Research 100 (2007) 200–209
incorporating a non-linear relationship between water stress and
actual to potential gross photosynthesis. In the 155 crops
simulated with the revised model, the water supply satisfied on
average the 99.9% (range 95–100, median 100), 98% (range 62–
100, median 100) and 89% (range 50–100, median 92) of plant
water demand from emergence to R1, R1–R5, and R5 to R7,
respectively.
The improvement of water routines has consequences for the
prediction of water use efficiency (WUE) of soybean crops,
expressed as the seasonal ratio of biomass production to
evapotranspiration (WUE (B, T, s), Sinclair et al., 1984). WUE
in CROPGRO is not a parameter of the model. It can be
calculated from the crop evapotranspiration and biomass
production, which, in turn, are simulated by complex model
subroutines (Ritchie, 1998; Boote et al., 1998). Because of the
increase in plant potential evaporation and downward root
movement rate, the revised model tended to simulate a greater
water use (557 79 mm) than the original one (499 72 mm).
Besides, biomass prediction tended to be higher
(6591 1201 kg ha 1, see Table 4) than in the original model
(6277 1432 kg ha 1, see Table 4) as an effect of non-linearity
between water stress and actual to potential gross photosynthesis. The average water use efficiency for the 155 simulated
paddocks was lower when using the revised model
(11.8 1.2 kg ha 1 m 1), compared to the output of the
original model (12.5 2.0 kg ha 1 m 1). However, considering only those paddocks with simulated water stress higher than
10%, WUE were 6% higher with the revised model. Whereas
the increase in potential plant evaporation reduces WUE when
water supply is non-limiting, the non-linearity effect shifts it
under limiting water supply conditions. Higher WUE values
with increased water stress were assessed in wheat (Abbate
et al., 2004) and alfalfa (Collino et al., 2005) by using an
empirical regression model between relative water use and
relative dry matter production. Ferreyra et al. (2003) found
similar results using the PNUTGRO model in peanut with
simultaneous optimization of water use and dry matter
production. Liu et al. (2005) also obtained comparable
outcomes in soybean during progressive soil drying at the
whole plant level.
Agronomic models can be used to complement historical
weather data in order to assess the frequency distribution of
expected yields for various cropping strategies (Ferreyra et al.,
2001; Savin et al., 1995; Carberry et al., 2002). The revised
CROPGRO resulted suitable for simulating the effects of
different environments and management practices on soybean
because of its high precision to predict on-farm yield and the
acceptable simulation of crop phenology, biomass production
and crop yield components (Tables 3 and 4, Figs. 3 and 4).
Changes made to the water routines of the model, and the
enhanced precision got in yield simulations, had important
impacts on the evaluation of crop strategies as well. As an
example, we simulated two different crop structures: (i) an
early sowing date (10 October) with a short season genotype
(DM 4800, MG IV) and (ii) a mid-season sowing date (10
November) with a full-season genotype (A6445, MG VI), at
Oliveros ( 32.558 latitude, 60.858 longitude) in 33 climatic
207
Fig. 5. Cumulated probabilities of yield of an early sowing date (10 October,
circles) with a short season genotype (Don Mario 4800, MG IV) and a midseason sowing date (10 November, triangles) with a full season genotype
(A6445, MG VI), at Oliveros ( 32.558 latitude, 60.858 longitude) in 33
climatic scenarios corresponding to the 1971–2003 growing seasons, on a Typic
Argiudoll soil with a heavy textural B horizon (for details see Mercau et al.,
2004) simulated with the original (open symbols) and the revised model (closed
symbols).
scenarios corresponding to the growing seasons of the 1971–
2003 period, on a Typic Argiudoll soil with a heavy textural B
horizon (see Mercau et al., 2004). When using the revised
model, the distribution of soybean yields were similar in the
lower 70%, in both scenarios (Fig. 5), with the early sowing
date yields being clearly higher in the upper 30%. The original
model simulated lower yields than the revised model simulation
in most of the scenarios. These yield reductions were greater for
the early season sowing date. Simulation with the original
model could lead stakeholders to avoid early season sowing
practices, whereas the revised model results support a
combination of both strategies. Experimental results across
three growing seasons (Bodrero et al., 2003; Méndez et al.,
2004) support good performances of short season soybean in
October sowing dates. In our study, after grouping paddocks by
sowing dates, the revised model performance had similar yield
prediction accuracy in October (RMSE = 491 kg ha 1, n = 53)
and November (RMSE = 566 kg ha 1, n = 84) sowing dates. A
lower risk in crop system strategy was attained by mixing both,
early and mid-season sowing crops. Although the two
individual strategies had similar risk levels, the poorer years
were not the same (data not shown).
The use of a model is only important when the simulated
processes are the main determinants of crops yield variability.
At present, extensive crops get out of the scope of the
CROPGRO model when weeds, pests and diseases become an
important on-farm yield determinant. Although we did not
simulate 17 paddocks where scouting reports a moderate to
severe disease intensity, they were less than 10% of the total
sites scouted. The proliferation of soybean rust in the Pampas
(SENASA, 2005) may preclude the use of CROPGRO in some
crops if protection technologies do not adequately control
208
J.L. Mercau et al. / Field Crops Research 100 (2007) 200–209
disease proliferation or if a model of disease proliferation and
damage like SOYRUST (Yang et al., 1991) is not included in
crop simulation. The application of CERES maize (Jones and
Ritchie, 1991) and Oilcrop-Sun (Villalobos et al., 1996) to
simulate on-farm yield of maize (Mercau et al., 2001) and
sunflower (Mercau et al., 2000) in the Pampas showed a similar
level of accuracy as the revised CROPGRO-soybean.
Agronomic simulation models like CROPGRO are designed
to be sensitive to soil and weather variations and different
management decisions (like genotype, sowing date and density,
spatial arrangement fallow, etc.). Considering that the revised
model provided robust predictions of phenology, biomass and
yield components along different environments, including
important levels of water deficits, it is possible to acceptably
use CROPGRO to support the decision making process for the
soybean crop at the farm level.
Acknowledgements
The authors would like to acknowledge the help of
AACREA Farmers and consultants, their support were crucial
for the realization of this work. This work was partially funded
by AACREA, Facultad de Agronomı́a-UBA, INTA, and an
NSF Project (USA No. 0410348). EHS is researcher from the
National Research Council (CONICET).
References
Abbate, P.E., Dardanelli, J.L., Cantarero, M.G., Maturano, M., Melchiori, R.J.,
Suero, E.E., 2004. Climatic and water availability effects on water-use
efficiency in wheat. Crop Sci. 44, 474–483.
Bodrero, M., Enrico, J.M., Martignone, R., Nicola, P., 2003. Comportamiento
de cultivares de soja en fechas de siembra tempranas y normal Campañas 02
y 2002–2003. Para mejorar la producción 24, 55–61.
Boote, K.J., Jones, J.W., Pickering, N.B., 1996. Potential uses and limitations of
crop models. Agron. J. 88, 704–716.
Boote, K.J., Jones, J.W., Hoogenboom, G., Pickering, N.B., 1998. The CROPGRO model for grain legumes. In: Tsuji, G.Y., Hoogenboom, G., Thorton,
P.K. (Eds.), Understanding Options for Agriculture Production. Kluwer
Academic Publishers, London, UK, pp. 99–128.
Boote, K.J., Jones, J.W., Batchelor, W.D., Nafziger, E.D., Myers, O., 2003.
Genetic coefficients in the CROPGRO-soybean model: links to field
performance and genomics. Agron. J. 95, 32–51.
Calmon, M.A., Jones, J.W., Shinde, D., Specht, J.E., 1999. Estimating parameters for soil water balance models using adaptive simulated annealing.
Appl. Eng. Agric. 15, 706–713.
Calviño, P.A., Sadras, V.O., 1999. Interannual variation in soybean yield:
interaction among rainfall, soil depth, and crop management. Field Crops
Res. 63, 237–246.
Calviño, P.A., Sadras, V.O., Andrade, F.H., 2003. Quantification of environmental and management effects on the yield of late-sown soybean. Field
Crops Res. 83, 67–77.
Campos, M., 2003. El cultivo de soja en la zona núcleo. Región norte de Buenos
Aires. In: Satorre, E.H. (Ed.), El libro de la soja. Cuaderno de actualización
técnica No. 66 de AACREA, Revista Técnica de AAPRESID, Soja en
siembra directa, pp. 219–225.
Carberry, P.S., Hochman, Z., McCown, R.L., Dalgliesh, N.P., Foale, M.A.,
Poulton, P.L., Hargreaves, J.N.G., Hargreaves, D.M.G., Cawthray, S., Hillcoat, N., Robertson, M.J., 2002. The FARMSCAPE approach to decision
support: farmers’, advisors’, researchers’ monitoring, simulation, communication and performance evaluation. Agric. Syst. 74, 141–177.
Collino, D.J., Dardanelli, J.L., De Luca, M.J., Racca, R.W., 2005. Temperature
and water availability effects on radiation and water use efficiencies in
alfalfa. Aust. J. Exp. Agric. 45, 383–390.
Colson, J., Wallach, D., Bouniols, A., Denis, J.B., Jones, J.W., 1995. Mean
squared error of yield prediction by SOYGRO. Agron. J. 87, 397–402.
Dardanelli, J.L., Bachmeier, O.A., Sereno, R., Gil, R., 1997. Rooting depth and
soil water extraction patterns of different crops in a silty loam Haplustoll.
Field Crops Res. 54, 29–38.
Dardanelli, J:L., Andriani, J., 2003. La disponibilidad hı́drica y la respuesta del
cultivo de soja. In: Satorre, E.H. (Ed.), El libro de la soja. Cuaderno de
actualización técnica No. 66 de AACREA, Revista Técnica de AAPRESID,
Soja en siembra directa, pp. 107–118.
Dardanelli, J.L., Calmon, M.A., Jones, J.W., Andriani, J.M., Dı́az, M.P.,
Collino, D.J., 2003. Use of a crop model to evaluate soil impedance and
root clumping effects on soil water extraction in three Argentine soils.
Trans. ASAE 46 (4), 1265–1275.
Dardanelli, J.L., Ritchie, J.T., Calmon, M., Andriani, J.M., Collino, D.J., 2004.
An empirical model for root water uptake. Field Crops Res. 87 (1), 59–71.
Doorenbos, J., Kassam, A.H., 1979. Yield response to water. In: FAO. Irrig.
Drain, Paper No. 33, FAO, Rome, Italy.
Fehr, W.R., Caviness, C.E., 1977. Stages of soybean development. Iowa Agric.
Exp. Station Special Report 80. Iowa State University, Ames, IA.
Ferreyra, R.A., 1998. Development of a methodology for the quantitative
identification and description of zones suitable for the peanut (Arachis
hypogaea L.) crop using a crop simulation model. MS Thesis. Escuela para
Graduados, Fac. de Cs. Agropecuarias, Universidad Nacional de Cordoba,
Argentina (in Spanish).
Ferreyra, R.A., Podestá, G.P., Messina, C.D., Letson, D., Dardanelli, J.,
Guevara, E., Meira, S., 2001. A linked-modeling framework to estimate
maize production risk associated with ENSO-related climate variability in
Argentina. Agric. For. Met. 107, 177–192.
Ferreyra, R.A., Dardanelli, J.L., Pachepsky, L.B., Collino, D.J., Faustinelli,
P.C., Giambastiani, G., Reddy, V.R., Jones, J.W., 2003. Nonlinear effects of
water stress on peanut photosynthesis at crop and leaf scales. Ecol. Model.
168, 57–76.
Gonzalez Venzano, S., 2003. La soja en la zona del Oeste. El cultivo de los
grupos CREA. In: Satorre, E.H. (Ed.), El libro de la soja. Cuaderno de
actualización técnica No. 66 de AACREA, Revista Técnica de AAPRESID,
Soja en siembra directa, pp. 227–231.
Grimm, S.S., Jones, J.W., Boote, K.J., Herzog, D.C., 1994. Modeling the
occurrence of reproductive stages after flowering for four soybean cultivars.
Agron. J. 86, 31–38.
Grimm, S.S., Jones, J.W., Boote, K.J., Hesketh, J.D., 1993. Parameter estimation for predicting flowering date of soybean cultivars. Crop Sci. 33, 137–
144.
Hall, A.J., Rebella, C.M., Ghersa, C.M., Culot, J.P., 1992. Field–crop systems of
the Pampas. In: Pearson, C.J. (Ed.), Ecosystems of the World: Field Crop
Ecosystems. Elsevier, Amsterdam, pp. 413–449.
Hunt, L.A., White, J.W., Hoogenboom, G., 2001. Agronomic data: advances in
documentation and protocols for exchange and use. Agric. Syst. 70, 477–
492.
Jones, C.A., Bland, W.L., Ritchie, J.T., Williams, J.R., 1991. Simulation of root
growth. In: Hanks, J., Ritchie, J.T. (Eds.), Modeling Plant and Soil Systems.
Agron. Monogr. 31, ASA, CSA, and SSSA, Madison, pp. 91–124.
Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt,
L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., Ritchie, J.T., 2003. DSSAT
Cropping System Model. Eur. J. Agron. 18, 235–265.
Jones, J.W., Ritchie, J.T., 1991. Crop growth models. In: Hoffman, G.J., Howell, T.A., Solomon, K.H. (Eds.), Management of Farm Irrigation Systems.
American Society for Agricultural Engineering, pp. 63–89.
Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J.,
Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z.,
McLean, G., Verburg, K., Snow, V., Dimes, J.P., Silburn, M., Wang, E.,
Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn, D.M., Smith, C.J., 2003. An overview of APSIM, a model designed
for farming systems simulation. Eur. J. Agron. 18, 267–288.
Kobayashi, K., Salam, M.U., 2000. Comparing simulated and measured values
using mean squared deviation and its components. Agron. J. 92, 345–352.
J.L. Mercau et al. / Field Crops Research 100 (2007) 200–209
Liu, F., Andersenb, M.N., Jacobsena, S., Jensen, C.R., 2005. Stomatal control
and water use efficiency of soybean (Glycine max L. Merr.) during
progressive soil drying. Environ. Exp. Bot. 54, 33–40.
Mavromatis, T., Boote, K.J., Jones, J.W., Irmak, A., Shinde, D., Hoogenboom,
G., 2001. Developing genetic coefficients for crop simulation models with
data from crop performance trials. Crop Sci. 41, 40–51.
Meinke, H., Baethgen, W.E., Carberry, P.S., Donatelli, M., Hammer, G.L.,
Selvaraju, R., Stöckle, C.O., 2001. Increasing profits and reducing risks in
crop production using participatory systems simulation approaches. Agric.
Syst. 70, 493–513.
Méndez, J.M., Gentili, O., Bodrero, M.L., Enrico, J., Andriani, J., Bacigalupo, S., Manlla, A., 2004. Evaluación de cultivares de soja III y IV en
siembra temprana Campaña 2003–2004. Para mejorar la producción 27,
34–39.
Mercau, J.L., Sadras, V.O., Satorre, E.H., Messina, C.D., Hall, A.J., 2000. Onfarm Testing of Oilcrop-Sun in the Pampas of Argentina. ASA, CSSA y
SSSA (Agronomy Abstracts).
Mercau, J.L., Satorre, E.H., Otegui, M.E., Maddoni, G.A., Cárcova, J., Ruiz, R.,
Uribelarrea, M., Menéndez, F.J., 2001. Evaluación a campo del comportamiento del modelo Ceres en cultivos de maı́z del norte de la provincia de
Buenos Aires. In: VII Congreso Nacional de Maı́z.
Mercau, J.L., Dardanelli, J., Andriani, J., Collino, D., Belligoi, J., 2004. Diseño
de estrategias de producción de soja: Utilización de un modelo de simulación agronómico. Para mejorar la producción 27, 122–129.
Passioura, J.B., 1996. Simulation models: science, snake oil, education, or
engineering? Agron. J. 88, 690–716.
Piper, E.L., Boote, K.J., Jones, J.W., Grimm, S.S., 1996a. Comparison of two
phenology models for predicting flowering and maturity date of soybean.
Crop Sci. 36, 1606–1614.
Ritchie, J.T., 1998. Soil water balance and plant stress. In: Tsuji, G.Y., Hoogenboom, G., Thornton, P.K. (Eds.), Understanding Options for Agricultural Production. Kluwer Academic Publishers, Dordrecht, The
Netherlands, pp. 41–54.
Ritchie, J.T., Godwin, D.C., 1989. Obtaining soil input for the CERES model.
In: Virmani, S.M., Tandon, H.L.S., Alagarswamy, G. (Eds.), Modeling the
Growth and Development of Sorghum and Pearl Millet, Research Bulletin
No. 12. ICRISAT, Andhra Pradesh, India, pp. 16–18.
Sadras, V.O., Hall, A.J., 1989. Patterns of water availability for sunflower crops
in semi-arid central Argentina. A simulation-based evaluation of their
209
interactions with cropping strategies and cultivar traits. Agric. Syst. 31,
221–238.
Satorre, E.H., 2001. Production systems in the argentine pampas and their
ecological impact. In: Solbrig, O., Paalberg, R., Di Castri, F. (Eds.), Globalization and the Rural Environment. Harvard University Press, Cambridge, MA, pp. 81–102.
Sau, F., Boote, K.J., Ruiz-Nogueira, B., 1999. Evaluation and improvement of
CROPGRO-soybean model for a cool environment in Galicia, northwest
Spain. Field Crops Res. 61 (3), 273–291.
Savin, R., Satorre, E.H., Hall, A.J., Slafer, G.A., 1995. Assessing strategies for
wheat cropping in the monsoonal climate of the Pampas using the CERESwheat simulation model. Field Crops Res. 42, 81–91.
SENASA, 2005. Programa Nacional de Roya de la Soja. Componente de
Vigilancia Fitosanitaria-Informe Final de Evaluación Campaña 2004/
2005. http://www.sinavimo.gov.ar/fito/asp_public/new_index.htm.
Sinclair, T.R., Tanner, C.B., Bennett, J.M., 1984. Water-use efficiency in crop
production. BioScience 34, 36–40.
Soil Conservation Service, U.S. Department of Agriculture, 1972. Hydrology.
In: National Engineering Handbook, Soil Conservation Service, U.S.
Department of Agriculture, Washington, DC, USA.
Steel, R.G.D., Torrie, J.H., 1985. Principles and Procedures of Statistics—A
Biometrical Approach. McGraw-Hill Book Co., Mexico, p. 622.
Stockle, C.O., Martin, S., Campbell, G.S., 1994. CropSyst, a cropping systems
model: water/nitrogen budgets and crop yield. Agric. Syst. 46, 335–359.
Torrent, M., 2003. La soja en los planteos de producción del norte de Córdoba.
El cultivo de los grupos CREA, In: Satorre, E.H. (Ed.), El libro de la soja.
Cuaderno de actualización técnica No. 66 de AACREA. Revista Técnica de
AAPRESID, Soja en siembra directa, pp. 203–210.
Villalobos, F.J., Hall, A.J., Ritchie, J.T., Orgaz, F., 1996. OILCROP-SUN: a
development, growth and yield model of the sunflower crop. Agron. J. 88,
403–415.
Wang, F., Fraisse, C.W., Kitchen, N.R., Sudduth, K.A., 2003. Site-specific
evaluation of the CROPGRO-soybean model on Missouri claypan soils.
Agric. Syst. 76, 985–1005.
Williams, J.R., Jones, C.A., Dyke, P.T., 1984. A modeling approach to
determining the relationships between erosion and soil productivity. Trans.
ASAE 27, 129–144.
Yang, X.W., Dowler, W.M., Roger, M.H., 1991. Assessing the risk and potential
impact of an exotic plant disease. Plant Dis. 75, 976–982.