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Soil & Tillage Research 87 (2006) 119–130
Estimating impact of puddling, tillage and residue management
on wheat (Triticum aestivum, L.) seedling emergence and growth
in a rice–wheat system using nonlinear regression models
M. Mohanty *, D.K. Painuli, A.K. Misra, K.K. Bandyopadhyaya, P.K. Ghosh
Indian Institute of Soil Science, Indian Council of Agricultural Research, Nabi Bagh,
Berasia Road, Bhopal 462038, Madhya Pradesh, India
Received 3 November 2004; received in revised form 2 March 2005; accepted 14 March 2005
Abstract
Puddling is known to increase the yield of rice due to the creation of suitable physical environment that favours growth of the
crop. However, in rice–wheat system, wheat yield has been reported to decrease due to the deterioration of soil structure caused
by puddling in rice. This affects seedling emergence in wheat. Seedling emergence model that predicts seedling emergence and
early growth of wheat can be used to estimate the major effects of different tillage and residue management practices on seedling
shoot and root growth. Output from such a model can be used to initialize crop growth models under diverse soil and climatic
conditions in which the variations in crop establishment are often poorly taken into account The information on predicting wheat
(Triticum aestivum, L.) seedling emergence and growth using nonlinear regression models under rice–wheat cropping system is
essential as early emergence and growth of seedlings affect the grain yield. A study was undertaken to assess the residual effect
of puddling in rice (no puddling i.e. direct seeding, puddling by four passes of 5 hp power tiller and puddling by eight passes of
5 hp power tiller) and direct effect of different tillage (conventional and zero tillage) and residue (residue-retained and removed)
management practices on wheat seedling emergence and growth in rice–wheat system on a Vertisol of Central India. Wheat
seedling emergence was maximum where rice was direct seeded, and wheat was grown under conventional tillage with residue
retained at the surface. Prediction of wheat seedling emergence by the [France, J., Thornley, J.H.M., 1984. Mathematical Models
in Agriculture and Related Sciences. Butterworth, London] model, shoot growth by the Logistic and Gompertz models, and root
growth by Monomolecular model was attempted. Time to reach 50% emergence as predicted by the [France, J., Thornley,
J.H.M., 1984. Mathematical Models in Agriculture and Related Sciences. Butterworth, London] model was closer to the
observed emergence data. The nonlinear regression models study indicated that the Logistic model predicted the shoot growth of
wheat under different tillage and residue management practices better than the Gompertz model. Whereas, for root growth the
Monomolecular model fitted well with the experimental data.
# 2005 Elsevier B.V. All rights reserved.
Keywords: Puddling; Zero tillage; Wheat seedling; Seedling growth and emergence; Model; Vertisol; Rice–wheat cropping system
* Corresponding author. Tel.: +91 755 2730970; fax: +91 755 2733310.
E-mail address: mmohanty@iiss.ernet.in (M. Mohanty).
0167-1987/$ – see front matter # 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.still.2005.03.002
M. Mohanty et al. / Soil & Tillage Research 87 (2006) 119–130120
1. Introduction
Tillage is practiced in soils for controlling weeds,
breaking crusts (improving water entry), increasing
surface roughness (assisting water storage) and
preparing a seedbed. The type of tillage method to
be practiced, however, depends upon the soil type and
the climate of the area. For Vertisols and vertic soils
preparation of seedbed, improvement of soil structure
and conservation of soil and water are of great concern
(Coughlan et al., 1989). It is suggested that to enhance
the productivity of these soils best tillage systems need
to be practiced. Thomas et al. (1990) reported that for
improving soil water storage (0–1.8 m) in a Vertisol,
use of crop residue mulch was effective. Various
techniques viz., use of crop residue mulch, deep
tillage, raised bed in the form of ridges or broad beds
and furrow systems have been recommended for these
soils (Anonymous, 1995). However, zero tillage (ZT)
has been widely acclaimed as highly effective practice
for conservation of soil and water in Vertisols as,
compared with conventional tillage (CT), it enhances
water conservation through improved infiltration and
reduced evaporation (Unger and Ordief, 1989). Tillage
affects mechanical characteristics of the seedbed
considerably and so can influence crop emergence.
According to Leslie (1965), crop emergence is
commonly a major problem in Vertisols due to
inadequate soil moisture and poor structure of the
seedbed. Maintenance of residue on soil surface
through ZT has been shown to benefit some Vertisols
due to improved soil physical properties in north-
eastern Australia (Freebairn et al., 1986a, 1986b;
Sallaway et al., 1990). While Radford and Nielsen
(1983) have reported that stubble retention may
improve seedling emergence in Vertisols whereas
Thomas et al. (1990) have reported that zero and
reduce tillage with stubble retention decreased
seedling emergence at the same site. Chastain et al.
(1994) reported that high levels of residue cover
reduced the emergence rate of wheat probably by
reducing seed soil contact. Swan et al. (1996) reported
that removing excessive plant residue from the seed
row increased the germination and emergence rate of
maize. There is a great need to predict the effect of soil
tillage, seedbed preparation, and sowing techniques on
crop establishment. These technical changes are costly
for growers, but their effects are not easy to predict.
They cause changes in physical condition of the
seedbed and in seed placement (Aubertot et al., 1999),
which interact with seed treatment, seed character-
istics, and cultivars to determine the features of the
crop stand.
Initial plant population has direct bearing on crop
yield and seedling emergence influences the initial
plant population. Gan et al. (1992) reported that the
wheat plants that emerged early contribute more
towards crop yield than those that emerged later.
Thus, desirable crop yields are achieved by providing
seeds with an environment that encourages early
germination and emergence. Several authors have
emphasized the importance of analyzing the stand
establishment process and have shown that the main
factors affecting germination, seedling emergence
and plant establishment are associated with the
mechanical characteristics of the seedbed (Jensen,
1971; Blacklow, 1972; Schneider and Gupta, 1985).
Tillage influences bulk density, penetration resis-
tance, aggregate mean weight diameter and surface
roughness (Carman, 1996).
Normally the crop stand of wheat in rice–wheat
cropping system is lower after rice because of the
destruction of soil structure during puddling in rice
(Sharma et al., 1995). Puddling can be defined as the
process of breaking down soil aggregates into uniform
mud, accomplished by applying mechanical force to the
soil at high moisture content for transplanting of rice
seedlings. Conventional experiments comparing the
plant populations and final yields of different manage-
ment techniques are time consuming and expensive. A
seedling emergence model that predicts seedling
emergence and early growth of wheat could be used
to estimate the major effects of different tillage and
residue management practices on seedling shoot and
root growth. Output from such a model could be used to
initialize crop growth models under diverse soil and
climatic conditions in which the variations in crop
establishment are often poorly taken into account
(Brisson et al., 1998; Guerif et al., 1998). Mohanty and
Painuli (2004) have reported the effect of tillage and
residue on emergence and growth rice seedlings using
nonlinear regression models in a Vertisol. However,
such information on wheat seedling emergence and
growth is very limited. Though some models have been
developed for wheat and other crops for emergence
based on nonlinear regression models (Bouaziz and
M. Mohanty et al. / Soil & Tillage Research 87 (2006) 119–130 121
Fig. 1. Rainfall distribution during the 3 years of experimentation.
Bruckler, 1989a, 1989b, 1989c; Finch Savage and
Phelps, 1993; Mullins et al., 1996), the effects of tillage
and residue on seedling growth are not taken into
account in these models. Forcella et al. (2000), has
reported a comparison of empirical and mechanistic
model on seedling emergence. They reported that
though mechanistic models takes into account soil, seed
and climatic environment into consideration, but they
are the most difficult models to develop. Creation and
use of simpler empirical models, which also employ
microclimate and soil factors for predictions, may
provide satisfactory predictions of seedling emergence
until better mechanistic models are developed. A
model predicting seedling emergence in sugarbeet
has been developed taking into account the soil tillage
and sowing operations by Durr et al. (2001). They
predicted the emergence time in sugarbeet by using
Weibull function and early growth by exponential
function (Durr and Boiffin, 1995). Gompertz, Weilbull,
Richards, and Logistic functions have been used
successfully in predicting the cumulative relative seed-
ling emergence (CRE) of some weed species (Brown
and Mayer, 1988). Some researchers have used compli-
cated extensions of these functions, such as double
Gompertz curve (Kremer and Lotz, 1998). Most of the
emergence model studies are confined to weeds only.
Use of thermal time in CRE prediction of weeds
includes Digitaria ischmaeum (Fidanza et al., 1996), S.
halepense (Benech Arnold et al., 1990) and 15 other
species in computer software called WeedCast (For-
cella, 1998). Some researchers have used calendar days
as time variable in CRE models (Cussans et al., 1996;
Vleeshouwers, 1997). An empirical model, represent-
ing emergence over time (calendar days) under field
observation of Digitaria Sanguinalis was reported by
King and Oliver (1994). The effective empirical models
that combine soil temperature and soil water potential
(Finch Savage and Phelps, 1993) are supportive to
increasing the information-richness of agronomic
management decisions, which have been proven to
enhance the timeliness and cost effectiveness of
standard management operations (Forcella, 1998).
The present study was undertaken to determine the
effect of puddling, stillage and residue management on
emergence and growth of wheat seedling shoot and root
in a rice–wheat system on a Vertisol as predicted by
suitable nonlinear regression models. In this study some
pertinent nonlinear regression models were evaluated to
predict the wheat seedling emergence and growth in
field condition. The shoot height/length and rooting
depth denote the growth of wheat seedlings in the
present study.
2. Materials and methods
2.1. Site and soil
The field experiment was conducted during the
winter seasons of the years 2000–2002 at the experi-
mental farm of the Indian Institute of Soil Science,
Bhopal, Madhya Pradesh, India, located at 238180Nlatitude, 778240E longitude, 485 m above mean sea
level. The soil is an Entic Chromusterts with 52% clay,
30% silt, 18% sand, pH 7.8, cation exchange capacity
49 cmol (p+) kg�1 and organic carbon 4.6 g kg�1 in the
0–15 cm layer. Bhopal receives on an average 1200 mm
annual rainfall. Most of it is received during the month
of June–September (Fig. 1). However, the distribution
of rainfall is very erratic in this region.
2.2. Tillage and residue levels
The treatment details for rice (Oryza sativa, L.) and
wheat (Triticum aestivum, L.) are presented as follows.
In the first year, Rice (cv IR 36) was grown during the
M. Mohanty et al. / Soil & Tillage Research 87 (2006) 119–130122
Table 1
Empirical constants a, b, coefficient of determinations (R2), T0.5 and MER for emergence of wheat seedlings as estimated from Logistic model
(France and Thornley, 1984) for various treatments
Treatments a b R2 T0.5 MER
Predicted Observed
P0 3.87 0.92 0.99 4.20 3.75 10.60
P1 3.85 0.90 0.98 4.27 4.00 5.23
P2 3.55 0.72 0.99 4.93 4.50 5.04
LSD (P = 0.05) 0.05 0.08 0.11 0.25 2.20
Rr 4.13 0.90 0.98 4.58 4.50 4.43
R0 3.60 0.84 0.98 4.28 4.00 5.69
LSD (P = 0.05) 0.10 0.04 NS 0.15 0.55
CT 3.81 0.90 0.99 4.23 4.50 7.39
ZT 3.77 0.84 0.98 4.48 4.00 4.94
LSD (P = 0.05) NS NS NS 0.15 0.39
a: constant of integration; b: emergence rate constant; T0.5: time for 50% emergence; MER: maximum emergence rate.
wet season under three puddling intensities viz., no
puddling i.e. direct seeding (P0), puddling by four
passes of 5 hp power tiller (P1) and puddling by eight
passes of 5 hp power tiller (P2). Power tiller is a semi-
automatic manually operated device with cage wheels
attached, which helps in puddling operation. The
depth of puddled layers was 10.9 cm for P1 and
14.5 cm for P2, respectively. Rice seedlings (25 days
old) were transplanted in P1 and P2 at a row spacing of
20 cm. Fertilizers applied were urea (50% at sowing
and 50% at flowering stage) to provide 90 kg N ha�1,
super phosphate (100% at sowing) to provide
30 kg P ha�1 and muriate of potash (100% at sowing
time) to provide 40 kg K ha�1. At harvesting of rice,
residue of 30 cm height was retained in residue-
retained plots (Rr) simulating mechanical harvesting
and in other plots it was removed (R0) before the
following spring wheat crop. The wheat crop (cv. C
306) was grown under conventional tillage (CT; one
pass of a disc harrow followed by two passes of a duck
foot cultivator) as well as under zero tillage (ZT;
sowing by Pantnagar zero till seed drill in the untilled
soil) with the same levels of residue management
(residue-retained and residue removed). For conven-
tional tillage, conventional seed drill was used
whereas for zero tillage, Pantnagar zero till seed drill
was used. The sowing of wheat was carried out prior to
6 cm of presowing irrigation in dry soil. At wheat
harvest also, residue of 30 cm height (approximately
6000 kg ha�1) was retained in residue-retained plots
(Rr) and removed from other plots (R0) for the
succeeding rice crop. The wheat was sown with a seed
drill at the seed rate of 100 kg ha�1 both for
conventional and zero tillage.
2.3. Plant observations
The observations on wheat seedlings were taken
during third year of experimentation i.e. year 2002.
Seedling emergence was determined by counting the
number of newly emerged seedlings in 1 m length of
row daily with three replications. Relative emergence
(RE) was calculated directly from the final emergence
count.
The RE data was fitted using nonlinear regression
procedures to the Logistic growth model (France and
Thornley, 1984) of the following form
RE ¼ 1
½1 þ expðaþ btÞ� (1)
where a is the constant of integration, b the emergence
rate constant and t the time since sowing. The Logistic
growth model has been used successfully to assess the
effect of environmental conditions on seed germina-
tion (Schimpf et al., 1977). The Logistic growth model
has a point of inflection, at which the rate of emer-
gence reaches a maximum and this occurs at a time
when RE = 0.5M, where M is a parameter describing
the maximum number of seedlings that eventually
emerged (France and Thornley, 1984). The time at
which the point of inflection occurs, also called the
median emergence time (T0.5), is given by:
T0:5 ¼ a
b(2)
M. Mohanty et al. / Soil & Tillage Research 87 (2006) 119–130 123
The maximum emergence rate (MER) is given by:
MER ¼ Mb
4(3)
For shoot and root growth, two rows were selected
for each treatment. The soil was dug up and the
seedlings were removed daily from day 1 after
sowing till the final observation. After thorough
washing with running tap water for removal of soil,
the length of the shoots and roots were measured
using a scale. For this plants from the 50 cm length
of row were taken out from each treatment with
three replications. Observations were recorded
after 3 years of experimentation in the studied
Vertisol.
2.4. Modeling growth of wheat seedlings
In the absence of a general theoretical equation to
describe seedling growth, three empirical curvilinear
growth functions historically used for analyzing dry
matter accumulation versus time (namely the
Logistic, Gompertz and Monomolecular equations)
were examined for applicability. Since Monomole-
cular model did not fit to the shoot growth of the
experimental curve, this was not discussed for shoot
growth in this study. Similarly, Logistic and
Gompertz models did not predict the rooting depth
well, hence, was not mentioned in Section 3. In this
study only Logistic and Gompertz models were used
for shoot growth and Monomolecular model for root
growth. A common property of these models is that
the length (L) approaches a constant (Lf) as time
approaches infinity. In fact the seedling temporarily
stops increasing in height and increases its number of
leaves just after emergence (2- to 3-leaf stage)
because internodes elongation does not occur until
later and only leaves are appearing as the plant stays
in a rosette form.
2.5. Parameter estimation
On the basis of experimental data related to shoot
and root growth of wheat in different tillage and residue
management practices at a constant temperature, a
nonlinear regression fitting procedure was used to
estimate the parameters of the three functions given
below.
2.5.1. The Logistic model
This is a symmetrical logistic function with an
inflection point:
LðtÞ ¼ L0Lf
L0 þ ½Lf � L0�expð�G 0tÞ(4)
where, L(t) is the length in millimeter of shoot at any
time (t), L0 the length at the onset of growth (t = 0),
and G0 the relative growth rate at time 0 (d�1).
Assuming that L0 = 1 mm just after germination,
the above equation can be written as
LðtÞ ¼ Lf
1 þ ½Lf � 1�expð�G 0tÞ(5)
Thus, only two parameters Lf and G0 were to be
estimated.
2.5.2. The Gompertz model
This is an asymmetrical function with an inflection
point; in this case RGR (relative growth rate) defined
by G, decreases exponentially with time.
LðtÞ ¼ Lf exp½f�lnðLfÞgfexpð�KgtÞg� (6)
where, Kg is the relative growth rate. Only two para-
meters Lf and Kg were to be estimated.
2.5.3. The Monomolecular model
In this mathematical function there is no inflection
point:
LðtÞ ¼ Lf ½1 � expð�KmtÞ� (7)
where Km (d�1) is the proportionality coefficient and
the equation implies that L0 = 0 when t = 0. Two
parameters Lf and Km were to be estimated.
2.6. Model evaluation
Seedling emergence rate and growth of shoot and
root were the variables on which the model predictions
were compared with the observed values. The Logistic
model by France and Thornley (1984) was used for
emergence. Where as both the Logistic and Gompertz
model were used for the growth of shoots of wheat
seedlings and the Monomolecular model was used for
growth of roots. The statistical criteria used to
compare the predicted (Pi) and observed (Oi) values
for growth models have been depicted in Eqs. (8)–(10)
as suggested by Smith et al. (1996).
M. Mohanty et al. / Soil & Tillage Research 87 (2006) 119–130124
2.6.1. Modeling efficiency (EF)
It shows the closeness between the observed and
predicted values. The narrower the margin between
them, the better the prediction.
EF ¼Pi¼n
i¼1ðPi � OiÞ2
Pi¼ni¼1ðOi � OÞ2
(8)
Root mean square error (RMSE):
RMSE ¼ 100
O
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXi¼n
i¼1
ðPi � OiÞn
vuut (9)
2.6.2. The coefficient of residual mass (CRM)
It represents the modeling biasness. CRM describes
whether the model over predict or under predicts the
observed values.
CRM ¼Pi¼n
i¼1 Oi �Pi¼n
i¼1 Pi
� �Pi¼n
i¼1 Oi
(10)
In the above equations, n is the number of times
heights of the seedlings were observed and O is the
mean observed values.
Fig. 2. Observed vs. predicted values for the relative emergence of
wheat in puddling treatments as predicted by Logistic growth model
(year 2002): (A) direct seeded rice; (B) puddling with four passes
power tiller; (C) puddling with eight passes power tiller.
3. Results and discussion
3.1. Seedling emergence parameters
Seedling emergence parameters as influenced by
tillage and residue management practices were
estimated using the Logistic model (France and
Thornley, 1984). The Logistic model fitted well with
the emergence data as evident from the R2 value
(Figs. 2 and 3). Fitting of the relative emergence data
of each treatment to the Logistic model showed that
the parameters a and b were different for all the
treatments (Table 1). There was significant difference
in the values of a and b under different puddling
intensity. P2 registered lower a (3.55) and b (0.72) than
P1 (3.85 and 0.90) and P0 (3.87 and 0.92),
respectively. This showed that puddling in rice had
a significant residual effect depressing the emergence
of the subsequent wheat crop. The values of a and b for
R0 (4.13, 0.90) were significantly higher than Rr (3.60,
0.84). There was no significant difference between CT
and ZT with respect to a and b. The time to reach 50%
emergence as predicted by the model was higher in P2
(4.93 day), R0 (4.58 day) and ZT (4.48 day) than in P1,
Rr and CT, respectively. The higher T0.5 value in P2
was due to delayed emergence (Fig. 4). The number of
seedlings emerged was higher in P0 followed by P1
and P2. This showed puddling in rice significantly
M. Mohanty et al. / Soil & Tillage Research 87 (2006) 119–130 125
Fig. 3. Observed vs. predicted values for the relative emergence of wheat in different tillage and residue treatments as predicted by Logistic
model (year 2002): (A) conventional tillage; (B) zero tillage; (C) rice residue removed; (D) rice residue retained.
Table 2
Values of statistical criteria for growth models on all tillage and
residue treatments
Treatment Models EF RMSE CRM
P0 Logistic 0.988 7.888 0.018
Gompertz 0.986 7.103 �0.016
P1 Logistic 0.988 6.681 0.015
Gompertz 0.981 7.646 �0.017
P2 Logistic 0.988 6.071 0.014
Gompertz 0.985 8.231 �0.019
CT Logistic 0.989 5.574 0.013
Gompertz 0.985 6.717 �0.015
ZT Logistic 0.993 1.451 0.003
Gompertz 0.985 8.555 �0.020
R0 Logistic 0.994 6.422 0.015
Gompertz 0.986 7.678 �0.018
Rr Logistic 0.995 3.308 0.007
Gompertz 0.980 10.945 �0.025
EF: efficiency of the model; RMSE: root mean square error; CRM:
coefficient of residual mass
decreased the emergence of seedlings in the subse-
quent wheat crop. Similarly, CT and Rr registered
higher seedling numbers than under ZT and R0. The
time to reach T0.5 value was early in case of Rr due to
better soil aeration and moisture content provided by
the residue environment. The earlier attainment of T0.5
in ZT was due to lower ultimate emergence (describ-
ing the maximum number of seedlings that eventually
emerged out). This is in agreement with the results
reported respectively by Radford and Nielsen (1983)
and Thomas et al. (1990). Among the puddling
treatments, the maximum emergence rate (MER) was
obtained under P0 (10.60) as compared to P1 (5.23)
and P2 (5.04) (Table 1). There was no significant
difference in MER between P1 and P2. The reason is
that ploughing of puddle soils after rice results in the
formation of large clods, having high breaking
strength (Sharma et al., 1995) and thus, reduces the
seed–soil contact. The residue retention treatment (Rr)
registered significantly higher MER (5.69) than
residue removed (R0) (4.43). Similarly CT showed
significantly higher MER than ZT. Thus, the results
revealed that tillage and residue management after rice
significantly affected the emergence of wheat seed-
lings. The lower T0.5 value predicted in case of CT
might be due to early emergence, which may be
attributed to better seedbed condition leading to better
seed–soil contact (Thomas et al., 1990; Chastain et al.,
1994).
M. Mohanty et al. / Soil & Tillage Research 87 (2006) 119–130126
Fig. 4. Seedlings counts in wheat as influenced by residual effect of puddling (A) and direct effect of tillage (B) and residue of rice (C) (year
2002). Vertical bar represents LSD (P = 0.05) and ns = nonsignificant.
3.2. Comparison of growth models for seedling shoot
and root growth
Three empirical growth functions were compared
to arrive at an appropriate model for predicting shoot
and root growth. For shoot growth curve fitting
generally gave efficiency >0.90 in both the Logistic
and the Gompertz model (Table 2). Comparing the two
models, the Logistic model was considered to be the
best of the two because of the close agreement
between the predicted (EF > 0.98 in most of the cases
and RMSE < 7.89) and the observed values from the
experiment. The Gompertz model was considered to
be inferior because of the higher RMSE values (>6.72
in all the cases). The growth of wheat seedlings as
predicted by predicted by the Gompertz model was
slightly greater than the observed values (CRM having
negative values in all cases) although the shape of the
M. Mohanty et al. / Soil & Tillage Research 87 (2006) 119–130 127
Fig. 5. Shoot height in wheat seedlings as influenced by residual
effect of puddling (A) and direct effect of tillage (B) and residue of
rice (C) (year 2002). Vertical bar represents LSD (P = 0.05) and
ns = nonsignificant.
Fig. 6. Observed vs. predicted values for the root length of wheat in
puddling treatments as predicted by Monomolecular model (year
2002): (A) direct seeded rice; (B) puddling with four passes power
tiller; (C) puddling with eight passes power tiller.
curve matched the shape of the experimental data
(Fig. 5). Because of this slight over prediction by the
Gompertz model, the Logistic model was considered
best for shoot growth of wheat seedlings. The
Monomolecular model was not taken into account
in the present study, as the shape of the curve did not
match the growth of the shoot. Bouaziz and Bruckler
(1989a) also reported the unsuitability of this model
for predicting the growth of wheat seedlings in the
laboratory. However, for the root growth of wheat
seedlings this model was used instead of the Logistic
and Gompertz model as it fitted well with the
experimental observations (Figs. 6 and 7).
3.3. Estimated parameters of the growth models as
influenced by tillage and residue
Puddling, tillage and residue had different effects
on the Logistic parameters Lf and G0 and the
Monomolecular parameters Lf and Km (Table 3) for
M. Mohanty et al. / Soil & Tillage Research 87 (2006) 119–130128
Fig. 7. Observed vs. predicted values for the root length of wheat under tillage and residue treatments as predicted by Monomolecular model
(year 2002): (A) conventional tillage; (B) zero tillage; (C) rice residue removed; (D) rice residue retained.
both shoot and root growth. The Gompertz parameters
Lf and Kg are not reported here as the Logistic
parameters fitted better than the Gompertz model for
shoot growth. The residual effect of puddling had a
significant effect on Lf of shoots in the Logistic model.
The Lf decreased significantly under the Logistic
model with puddling. It also decreased but non-
significantly under ZT and R0 treatments compared
with the CT and Rr treatments. The G0 was
significantly affected by puddling and residue
Table 3
Empirical constants: final length (Lf), G0 and proportional (Km) of the tillag
models
Treatment Shoot
Logistic model
Lf G0 R
P0 284 0.660 0
P1 281 0.588 0
P2 274 0.572 0
LSD (P = 0.05) 2.55 0.010
CT 285 0.578 0
ZT 282 0.566 0
LSD (P = 0.05) NS NS
R0 281 0.536 0
Rr 283 0.583 0
LSD (P = 0.05) NS 0.013
management practices as predicted by Logistic model.
However, the direct effect of tillage on G0 as predicted
by the model was non-significant. Puddling and
residue management practices had significant effect
on Km under the Monomolecular model for root
whereas the effect of tillage was non-significant
(Table 3). Km was affected more by puddling and
residue than tillage as predicted by the models. The
decrease in wheat root growth due to puddling in rice
was due to poor soil physical environment (Oussible
e and residue treatments for shoot and root of wheat under different
Root
Monomolecular model
2 Lf Km R2
.99 47.25 0.090 0.94
.98 48.23 0.082 0.95
.98 47.80 0.075 0.95
NS 0.006
.98 46.12 0.096 0.94
.99 51.58 0.070 0.96
3.25 0.009
.99 57.39 0.083 0.96
.98 51.39 0.083 0.95
2.35 NS
M. Mohanty et al. / Soil & Tillage Research 87 (2006) 119–130 129
et al., 1992). The parameters G0 and Km were also
significantly affected by puddling, tillage and residue
management. These parameters showed nothing but
the relative growth rates for the models. Thus, it may
be inferred that the residual effect of puddling in rice
and direct effect of tillage and residue management
has significant effect on the shoot and root growth of
wheat seedling.
4. Conclusion
The study showed that puddling in rice had
significant depressing effect on emergence, shoot
and root growth of subsequent wheat seedlings.
Conventional tillage and rice residue retention as
tillage option in wheat favoured seedling emergence
and growth. The nonlinear regression models study
indicated that the Logistic model predicted the
emergence and shoot growth of wheat under different
tillage and residue management practices better than
the Gompertz model. Whereas, for root growth the
Monomolecular model fitted well with the experi-
mental data. These models can further be used as tools
for simulating some important effects of soil tillage
and sowing operations on seedling emergence, which
can be, used as submodel in crop growth simulation
models.
Acknowledgement
The fund provided by National Agricultural
Technology Project PSR No. 31 for this study is duly
acknowledged.
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