Vol.2, No.3, 167-174 (2011)
opyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/AS/
Agricultural Scienc es
Measuring and simulating maize (Zea mays L.) yield
responses to reduced tillage and mulching under
semi-arid conditions
Walter Mupangwa1,2,a*, John Dimes1, Sue Walker2, Stephen Twomlow1,b
1CRISAT, Matopos Research Station, Bulawayo, Zimbabwe; *Corresponding Author: w.mupangwa@cgiar.org,
2Department of Soil, Crop and Climate Sciences, University of Free State, Bloemfontein, South Africa;
aPresent address: CIMMYT Regional Office, Mount Pleasant, Harare, Zimbabwe;
bPresent address: United Nations Environment Programme, Nairobi, Kenya.
Received 26 April 2011; revised 23 June 2011; accepted 21 July 2011.
Rainfed smallholder agriculture in semi-arid
environments of sub-Saharan Africa faces many
challenges. Productivity of the smallholder ag-
ricultural systems has been on the decline in
recent y ears. Cons erva tion agricult ure prac tices
have a potential of steering the small holder agri-
cultural systems of sub-Saharan Africa to grea-
ter and more sustainable levels. This study was
designed to calibrate the APSIM model so that it
could be used as a tool for understanding the
long term impact of conservation agriculture
techniques (mulching, tine ripping and planting
basins) on the productivity of smallholder sys-
tems under semi-arid conditions. The APSIM
model predicted reasonably well the seasonal
and mulching effects on maize production on
sand and clay soils. Under these semi-arid
conditions the use of 10 kg·N·ha–1 is preferable
under both conventional and basin tillage sys-
tems. Planting basins offer a better chance of
getting maize grain yield than the conventional
system in southern Zimbabwe at N quantities
ranging from 0 kg·ha–1 to 52 kg·ha–1. This mod-
elling exercise suggested that smallholder
farmers are still prone to complete crop failure
in some years despite the use of available con-
servation agriculture sy stems.
Keywords: Nitrogen; Modelling; Planting
Basins; Semi-Arid; Variable Rainfall; Zimbabwe
Conservation agriculture has the potential to steer the
productivity of smallholder systems to greater levels.
The advent of conservation agriculture tillage techniques
such as planting basins brought a ray of hope to small-
holder farmers in the semi-arid regions of sub-Saharan
Africa [1]. The planting basin tillage system being pro-
moted throughout Zimbabwe enables farmers to prepare
land early, spread the limited farm labour and plant on
time with respect to the effective planting rain [2,3]. The
planting basins dug by hand in a grid of 0.9 m x 0.6 m
spacing harvest rainwater and reduce surface runoff from
cropping fields [4], increase crop yields substantially [2,
Although planting basins have been in use on small-
holder farms for less than 10 growing seasons, the use of
simulation modeling can help understand the long-term
impact of the tillage system under semi-arid conditions.
The Agricultural Production Simulator Model (APSIM),
a deterministic and process based model, has been used
for simulating crop production in smallholder cropping
systems. The APSIM model has performed well in pre-
dicting crop production and its interaction with climate,
soil and management factors [6]. In smallholder farming
systems, APSIM has been used with success to simulate
nitrogen (N) dynamics of manure inputs [7], maize re-
sponse to N [8], water use efficiency [9], and N and wa-
ter dynamics in cereal-legume rotations [10]. However,
no description of the effects reduced tillage systems
(ripper tine and planting basin tillage systems) and
mulch, which are the corner stone of conservation agri-
culture, on crop yields and soil water dynamics has been
reported in any of the previous studies.
This study was designed to evaluate the capability of
APSIM cropping systems model (version 6.0) to simu-
late maize (Zea mays L.) yield responses to different
rainfall seasons, mulch levels and three tillage systems
on two soil types, Soil 1 was a granitic sand (Eutric
arenosol) and Soil 2 a clay (Chromic-Leptic cambisol)
W. Mupangwa et al. / Agricultural Science 2 (2011) 167-174
Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/AS/
[11]. Data obtained from on-station experiments [5,12]
were used to verify the model performance. The vali-
dated APSIM model was then used to assess the long
term interaction effects of N and two tillage systems,
conventional ploughing and planting basins, on maize
yield and selected components of the soil water balance
for a granitic sand soil under the semi-arid conditions of
southern Zimbabwe. The specific objectives were 1) to
evaluate APSIM capability in predicting the seasonal
and mulching effects on maize grain and total biomass
yields and 2) to use the validated APSIM model to assess
the long term interaction effects of N fertilizer, and con-
ventional and planting basin tillage systems on maize
2.1. Experimental Sites
The experiment was run at the International Crops Re-
search Institute for Semi-Arid Tropics (ICRISAT), Ma-
topos Research Station from 2004/05 through to the
2007/08 cropping seasons on two soil types, a clay and a
granitic sand. The clay soil is located at the main Mato-
pos experimental site (28˚30E, 20˚23S, and 1344 m
above sea level) and is classified as a shallow siallitic
soil (4E.1) and Chromic-Leptic Cambisol according to
the Zimbabwean and FAO systems respectively [11].
The internal drainage of Matopos clay soil indicates
saturation for short periods during the rainy season and
external drainage is characterized by slow runoff [11].
The granitic sand is located at the Lucydale experimental
site (28˚24E, 20˚25S, and 1378 m above sea level) and
is classified in the Zimbabwean system as moderately
deep to deep well-drained fersiallitic soil (5G.2). This is
classified as Eutric Arenosol [13]. Internal drainage of
Lucydale sand is rapid to very rapid and external drain-
age is characterized by slow runoff [11]. The chemical
and physical properties of the two soil types have been
described by [5]. Matopos Research Station receives
annual rainfall ranging between 450 and 650 mm with a
long-term average rainfall 573 mm.
2.2. Summary of the Field Experiment
The experiment was set up with a factorial treatment
structure consisting of three tillage methods (conven-
tional ploughing, ripping and planting basins) and seven
rates of mulch cover (0, 0.5, 1, 2, 4, 8 and 10 t·ha–1). The
treatments were arranged in a split-plot design with three
replications at each field location. The main plot factor
was tillage (63 m 6 m) and seven mulch levels were
randomly allocated in sub-plots (8 m 6 m) on each
tillage treatment. Basins were dug at 0.9 m 0.6 m
spacing using a hand hoe and each basin measured 0.15
m (length) 0.15 m (width) 0.15 m (depth). Rip lines
were opened at 0.9 m inter-row spacing using a com-
mercially available ripper tine (Zim Plow type) attached
to the beam of a donkey-drawn mouldboard plough (VS
100). The ripping depth achieved on both soils, with a
single pass of the implement, varied between 0.15 and
0.18 m. Cattle manure (8% organic carbon, 0.32% N)
was applied in October each year at a rate of 3 t·ha–1 in
all plots as basal soil fertility amendment. Conventional
ploughing was done soon after the first effective rain (30
to 50 mm) in December each year using a donkey-drawn
VS 100 mouldboard plough. Ammonium nitrate (34.5%
N) was applied to all plots at 20 kg·N·ha–1 as topdressing
six weeks after planting.
2.3. Set up of the Model
The simulation was run from 1 October 2004 to 30
June 2008 and the model was reset every 1 July to initial
soil nitrogen and water content. Soil parameters used for
calibrating the APSIM model are given in Ta bl es 1 and
2. As the experiment had a new field established in each
season at the Matopos site, the plant available water ca-
pacity (PAWC) for 2004/05 field was 116 mm, 84 mm
for 2005/06, 61 mm for 2006/07 and 84 mm for 2007/08
in the 0 - 0.85 m soil profile. The drained upper limit
(DUL), saturation (SAT) and lower limit (LL) were de-
rived from soil water measurements made in the planting
basins with no mulch cover. For Lucydale site the same
field was used for the two seasons (2004/05 and 2005/06)
and the PAWC in the 0 - 0.70 m soil profile was 54 mm.
Ta b l e 1 . Soil chemical and physical properties of the clay soil used for Matopos Research Station experimental site (adapted from
ICRISAT unpublished data).
Depth (cm) pH NO3-N (ppm) Organic carbon
Bulk density
(g·cm3) DUL (mm/mm) LL (mm/mm)
0 - 15 6.0 6.50 1.20 1.4 0.20 0.10
15 - 25 6.0 2.10 1.00 1.4 0.24 0.10
25 - 35 6.0 2.10 0.86 1.4 0.26 0.13
35 - 45 6.0 1.70 0.83 1.4 0.27 0.16
45 - 55 6.0 1.70 0.58 1.4 0.29 0.20
55 - 65 6.0 1.70 0.54 1.4 0.29 0.21
65 - 75 6.0 1.70 0.54 1.4 0.30 0.23
75 - 85 6.0 1.70 0.50 1.4 0.31 0.25
W. Mupangwa et al. / Agricultural Science 2 (2011) 167-174
Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/AS/
Table 2. Soil chemical and physical properties of the sand soil used for Lucydale experimental site (adapted from Masikati, 2006 and
Ncube et al., 2009).
Depth (cm) pH NO3-N (ppm) Organic carbon
Bulk density
(g/cm3) DUL (mm/mm) LL (mm/mm)
0 - 20 6.3 1.41 0.8 1.66 0.15 0.05
20 - 30 6.3 1.41 0.7 1.65 0.22 0.13
30 - 40 6.9 0.77 0.7 1.60 0.28 0.20
40 - 50 6.9 0.31 0.7 1.55 0.34 0.27
50 - 60 6.9 0.31 0.7 1.51 0.37 0.32
60 -70 6.3 0.24 0.6 1.34 0.41 0.36
Table 3. Dates for field activities carried out at Matopos Research Station during the four seasons of experimentation.
Season Tillage method Mulch application Manur e applicationSowing date Topdressing date
2004/05 Plough 10/11/2004 12/12/2004 13/12/2004 21/1/2005
Ripper 10/11/2004 26/10/2004 13/12/2004 21/1/2005
Basins 10/11/2004 20/10/2004 13/12/2005 21/1/2005
2005/06 Plough 15/9/2005 13/12/2005 13/12/2005 24/1/2006
Ripper 15/9/2005 18/9/2005 13/12/2005 24/1/2006
Basins 15/9/2005 17/9/2005 13/12/2005 24/1/2006
2006/07 Plough 28/7/2006 7/12/2006 8/12/2006 2/1/2007
Ripper 28/7/2006 30/8/2006 21/11/2006 2/1/2007
Basins 28/7/2006 28/8/2006 21/11/2006 2/1/2007
2007/08 Plough 25/7/2007 12/12/2007 12/12/2007 10/1/2008
Ripper 25/7/2007 5/8/2007 12/12/2007 10/1/2008
Basins 25/7/2007 27/9/2007 12/12/2008 10/1/2008
Table 4. Dates for field activities carried out at Lucydale experimental site during the two seasons of experimentation.
Season Tillage method Mulch application Manur e applicationSowing Topdressing
2004/05 Plough 17/10/2004 13/12/2004 14/12/2004 21/1/2005
Ripper 17/10/2004 25/10/2004 14/12/2004 21/1/2005
Basins 17/10/2004 26/10/2004 14/12/2004 21/1/2005
2005/06 Plough * 12/12/2005 13/12/2005 24/1/2006
Ripper * 8/9/2005 13/12/2005 24/1/2006
Basins * 14/9/2005 13/12/2005 24/1/2006
*No fresh mulch was applied
The Lucydale soil parameters were adapted from [14]
and [10] (Table 2). The fields used by [14] and [10]
were adjacent to our experimental field used in the 2004/
05 and 2005/06 growing seasons.
Total soil N for Matopos clay soil was set at 25
kg·ha–1 (20 kg 3
O and 5 kg 4
H). Soil N for the
Lucydale sandy soil was adapted from Ncube et al.
(2009) and set at 10 kg·ha–1 (5 kg 3
O and 5 kg
H). Soil water was reset to zero on the first of July
each year while N was reset to 25 kg·ha–1 on the same
date. Soil organic matter was not reset every first of July
to allow for accumulation of organic matter in the soil. It
was assumed that the 3 t·ha–1 of manure used in our ex-
periment supplied 9.6 kg·N·ha–1. Runoff curve number
for bare soil across the three tillage treatments was set at
80 because the tillage techniques created surface rough-
ness of varying degrees. For the conventional plough
and ripper tillage systems the curve number was adjusted
downwards by 10 units which were lost after 50 mm of
rainfall, so it then reverted to 80. For the planting basins
the curve number was adjusted downwards by 20 units
which were lost after 250 mm of rainfall was received.
The C:N ratio of mulching material was set at 60 (0.67%
N) and a 10% incorporation of the mulching material in
the CP system was assumed. For the planting basins and
ripper tillage systems a 0% incorporation of the mulch-
ing material was assumed. The C:N ratio of all soils was
set at 0.15. The first and second stage evaporation coef-
ficients were set at 3 and 6 mm·day–0.5 for the heavy tex-
tured Matopos soil, and 1 and 6 mm·day–0.5 for the light
textured Lucydale soil.
Daily rainfall, temperature and radiation data were
collected from Matopos Research Station weather station
which is located 3 km from Matopos experimental site
and up to 10 km from the Lucydale site. The climate
record used for APSIM calibration stretched from 1 Oc-
tober 2004 to 30 June 2008. Experimental management
in the model was according to the field experimental
procedures [12]. Sowing, manure application and top-
dressing dates for Matopos and Lucydale experimental
sites are given in Ta b les 3 and 4. For the conventional
plough treatment at Lucydale manure was applied a day
before sowing in each season, sowing being 14 and 13
December for 2004/05 and 2005/06 seasons according to
W. Mupangwa et al. / Agricultural Science 2 (2011) 167-174
Copyright © 2011 SciRes. Openly accessi ble at http://www.scirp.org/journal/AS/
the rain received. For planting basins treatment at Lucy-
dale manure was applied on 26 October 2004 and 14
September 2005 for 2004/05 and 2005/06 growing sea-
sons. A sowing depth of 50 mm was used in the simula-
tion for each tillage system. Average plant stands of 1.8
and 3.1 plants per m2 were used for Lucydale in 2004/05
and 2005/06 seasons. For Matopos a plant density of 3.0
plants per m2 was used for the four growing seasons. All
plots were kept weed free during period of experimenta-
tion. The APSIM model simulated maize yield and soil
water balance until the crop was mature.
APSIM crop module contains a description of the
short season hybrid variety SC401 used in Zimbabwe. In
our experiment a short seasoned hybrid variety SC403
was planted at Matopos in all seasons and at Lucydale in
2005/06. An open pollinated variety ZM421 was planted
at Lucydale in 2004/05 season because there was a
maize breeding experiment close to our research field.
The three varieties are drought tolerant, have similar
duration and are recommended for semi-arid areas of
Zimbabwe. Hence APSIM crop parameters for SC401
were selected to describe both SC403 and ZM421 used
in the study.
2.4. Long Term Simulation
The long term simulation was run using soil properties
of the Lucydale granitic sandy soil. The 69 year climate
record (1939-2008) derived from Matopos Research
Station weather station was used in the long term simu-
lation. The following scenarios were used in long term
Conventional ploughing plus four N rates (0, 10, 20
and 52 kg·ha–1)
Planting basins plus four N rates (0, 10, 20 and 52
The N rates of 0, 10 and 20 kg·ha–1 were similar to N
levels used in the on-farm experiments conducted in
Gwanda and Insiza districts of southern Zimbabwe dur-
ing the 2005/06, 2006/07 and 2007/08 growing seasons.
The 52 kg·N·ha–1 is the national recommendation for
smallholder cropping systems of Zimbabwe [15] so it
was included to provide a comparison with what may be
considered as providing optimal yields under semi-arid
conditions. Topdressing with ammonium nitrate (34.5%
N) was done at 40 days after sowing in both tillage sys-
tems. A sowing window stretching from 20 November to
31 December and a plant density of 3.0 plants per m2
were used for the 69 year simulation.
2.5. Reporting Frequency
For the on-station experiments the model was set to
report selected variables on a daily time step. The re-
ported variables for the on-station experiments were
total biomass and grain yield, soil water content in the 0
- 0.25 m layer, surface runoff and deep drainage. Total
biomass and grain yields were reported at 0% moisture
content and are compared to observed yields at this
moisture content. In the long term simulation the model
was set up to report variables at harvest stage of the
maize crop. In the long term simulation the reported
variables were grain yield, pre-sowing and in-crop sur-
face runoff, and in-crop deep drainage.
The root mean square deviation (RMSD) and model-
ing efficiency (ME) values were calculated for compari-
son of observed and predicted data. The RMSD was
calculated as follows:
RMSD = [1/n (xiyi)2]0.5 (1)
where xi is the observed yield or soil water content, yi is
the predicted yield or soil water content and n is the
number of observations.
Modeling Efficiency (ME) was calculated as follows:
 
where Pi and O are predicted and observed values re-
spectively, Ō is observed mean value [15].
The total seasonal rainfall was 320 mm for 2004/05,
915 mm for 2005/06, 467 mm for 2006/07 and 364 mm
for 2007/08. The predicted seasonal effects on maize
grain and biomass production at Matopos are shown in
Figures 1 and 2. Seasonal effects on grain (ME = 0.86)
and biomass (ME = 0.84) production were simulated
reasonably well for the four growing seasons with dif-
ferent rainfall patterns at Matopos. The model gave a
good prediction of grain yield in 2004/05 which was a
below average season in terms of rainfall received (320
mm). For the other below average rainfall seasons, 2006/
07 and 2007/08, the predicted yields did not really match
the observed values.
The predicted mulching effect on grain and biomass
yields in the four seasons at Matopos is also shown in
Figures 1 and 2. For the 2004/05, 2005/06 and 2006/07
seasons the model over predicted grain yield at 0 and 0.5
t·ha–1 mulch cover (Figure 1). The model below pre-
dicted grain production at 8 and 10 t·ha–1 mulch cover in
the same seasons. In the 2007/08 season the model under
predicted grain production at low mulch levels (<4 t·ha–1)
while over predicting it at 8 and 10 t·ha–1 (Figure 1). For
the wetter 2005/06 season the model under predicted
rain production and indicated a decrease in yield g
W. Mupangwa et al. / Agricultural Science 2 (2011) 167-174
Copyright © 2011 SciRes. http://www.scirp.org/journal/AS/
Figure 1. Observed and predicted grain yield from different mulch levels over four growing seasons at Matopos
Research Station. Error bars stand for standard error of means for the different mulch levels in each season across
three replications.
Figure 2. Observed and predicted total biomass yields from different mulch levels over four growing seasons at
Matopos Research Station. Error bars stand for standard error of means for the different mulch levels in each
season across three replications.
with increase in mulch cover on the clay soil (Figure 1).
This is in contrast to what was observed in the field ex-
periment at Matopos [5,12]. The modeling results sug-
gest that mulch cover could have promoted immobiliza-
tion of N given the better supply of soil water during the
2005/06 season. To be able to decompose the maize re-
sidue mulch soil micro-organisms need energy and there-
fore out-compete the maize plants in extracting soil N.
The 29.6 kg·N·ha–1 was probably not enough to meet
microbial and crop requirements thereby resulting in
inadequate N supply to the maize crop and thus a lower
predicted yield at higher mulch levels. Yellowing of
maize foliage indicating N deficiency was observed at 8
and 10 t·ha–1 mulch level particularly at the Matopos
experimental site resulting in lower yield being achieved
at 8 t·ha–1 mulch (Figure 1).
Openly accessible at
W. Mupangwa et al. / Agricultural Science 2 (2011) 167-174
Copyright © 2011 SciRes. Openly accessi ble at http://www.scirp.org/journal/AS/
The model under predicted maize biomass production
at higher mulch levels during the relatively wet 2005/06
growing season at Matopos (Figure 2). In the 2007/08
season the model predicted an increase in biomass yield
with higher mulch cover at Matopos (Figure 2). The
model is probably indicating soil water benefits derived
from mulching in 2007/08 season that was characterized
by an abrupt end of rain in January 2008. The model is
indicating that higher mulch cover conserve soil water
allowing the maize crop to reach maturity. However,
observed maize yield data did not show any significant
influence of mulch cover on either the total biomass or
grain yields in the 2007/08 season. The lack of yield
response to mulching in the 2007/08 season could be
attributed to the fact that some experimental plots were
waterlogged between the end of December 2007 and
mid-January 2008. This was observed at higher mulch
levels (>2 t·ha–1) particularly in the planting basin and
ripper tillage systems. Waterlogging promotes poor soil
aeration and uptake of nutrients by plant roots [16]. At
the Lucydale experimental site, the model predicted no
significant grain yield responses to freshly applied
mulch in 2004/05 and residual mulch cover in 2005/06
seasons (Figure 3). Lack of grain yield responses to
freshly applied and residual mulch in 2004/05 and
2005/06 is consistent with observed results. Field obser-
vations made in all seasons showed that maize residue
applied as mulch decomposed quite fast particularly in a
season with a lot of rain like 2005/06. At the end of
growing season (April/May) there was hardly any maize
residue left at the surface in the experimental plots espe-
cially where 0.5, 1 and 2 t·ha–1 had been applied. An
estimated 30% - 40% mulch cover would be remaining
in the 8 and 10 t·ha-1 treatments. Degradation of the
mulching material could have been driven by termites
which, unlike soil micro-organisms, do not need to take
up N from the soil in order to degrade the plant residues
[17]. Maize residue, with a C:N ratio averaging 52 [18],
decomposes fast when conditions of drivers of decom-
position such as rainfall, temperature and soil micro-
organisms are ideal [19]. The APSIM model predicted a
decrease in biomass yield with increase in mulch cover
in the 2005/06 growing season (Figure 4). This suggests
a suppression of biomass production owing to N immo-
bilization as some maize residues carried over from the
2004/05 season were still being decomposed during the
2005/06 growing season.
The basin system has higher chances of giving grain
yield than the conventional system regardless of N level
used in semi-arid environment of southern Zimbabwe
(Figure 5). There is a 48 % chance of getting grain yield
without N fertilizer in the basin system compared with
31% in the conventional system. At 10 kg·N·ha–1 the
chances of getting higher grain yield from the basin sys-
tem than the conventional system increases to 52%. The
predicted grain yield suggests that the use of 10
kg·N·ha–1 in both the conventional and planting basin
systems is a good entry point for improving productivity
in the cereal dominated semi-arid cropping systems of
Zimbabwe (Figure 5). This confirms earlier results from
the wide scale promotion of inorganic fertilizer which
was con ducted in semi-arid districts of Zimbabwe [20].
Figure 3. Observed and predicted grain yields for different mulch levels on a sand soil over two growing sea-
sons at Lucydale experimental site. Error bars stand for standard error of means for the different mulch levels in
each season across three replications.
W. Mupangwa et al. / Agricultural Science 2 (2011) 167-174
Copyright © 2011 SciRes. Openly accessi ble at http://www.scirp.org/journal/AS/
Figure 4. Observed and predicted total biomass yields for different mulch levels on a sand soil over two grow-
ing seasons at Lucydale experimental site. Error bars stand for standard error of means for the different mulch
levels in each season across three replications.
Figure 5. Probability of getting maize grain yield under two tillage systems (conventional ploughing and planting
basins) and four N application rates (0. 10, 20 and 52 kg·N·ha–1) over a 69 year period on a sandy soil under
semi-arid conditions.
The chances of getting similar maize yields from the
conventional and basin tillage systems increase at N ap-
plication levels greater than the microdosing rate (10
The APSIM model was used in this study to predict
the observed crop yield and give a long term impact of
the planting basin system and N fertilizer on maize yield.
For most of the seasons there was reasonable agreement
between observed and predicted maize yield data sets for
the Matopos (clay soil) and Lucydale (sandy soil) ex-
perimental sites. Maize yield under 4 - 10 t·ha–1 mulch
treatments could be suppressed in relatively wet seasons
as result of inadequate N supply. This suggests that more
N has to be applied in growing seasons with above av-
erage rainfall. Smallholder farmers need to get the sea-
sonal climate forecasts well in time so that they can ac-
quire adequate inorganic fertilizer for the wetter seasons.
Long term simulations showed that maize productivity
in both the conventional and planting basin tillage sys-
tems under semi-arid conditions can be improved sub-
stantially through addition of N. The predicted maize
yield indicated that 0, 10, 20 and 52 kg·N·ha–1 give no
W. Mupangwa et al. / Agricultural Science 2 (2011) 167-174
Copyright © 2011 SciRes. Openly accessi ble at http://www.scirp.org/journal/AS/
significant yield differences below 1250 kg·ha-1 regard-
less of the tillage system used under these semi-arid
conditions. The use of 10 kg·N·ha–1 is more favourable
in both the conventional and planting basin tillage sys-
tems under semi-arid conditions because there are better
chances of getting grain yield with the use of 10
kg·N·ha–1 in both tillage systems. It is less risky to use
10, 20 and 52 kg·N·ha–1 in the planting basin system
than the conventional system under the semi-arid condi-
tions of southern Zimbabwe.
This paper is a contribution to WaterNet Challenge Program Project
17 ‘Integrated Water Resource Management for Improved Rural Live-
lihoods: Managing risk, mitigating drought and improving water pro-
ductivity in the water scarce Limpopo Basin.
[1] Rockström, J., Kaumbutho, P., Mwalley, J., Nzabi, A.W.,
Temesgen, M., Mawenya, L., Barron, J., Mutua, J. and
Damgaard-Larsen, S. (2008) Conservation farming stra-
tegies in East and Southern Africa: Yields and rain water
productivity from on-farm action research. Soil and Till-
age Research, 103, 23-32.
[2] Nyagumbo, I. (2007) A review of experiences and de-
velopments towards conservation agriculture and related
systems in Zimbabwe. In: Goddard, T., Zoebisch, M.,
Gan, Y., Ellis, W., Watson, A. and Somatpanit, S., Eds.,
No-Till Farming Systems. Special Publication Number 3,
World Association of Soil and Water Conservation,
[3] Twomlow, S., Hove, L., Mupangwa, W., Masikati, P. and
Mashingaidze, N. (2008a) Precision conservation agri-
culture for vulnerable farmers in low potential zones.
Papers Presented at the Challenge Program on Water
and Food Theme 1 Rainfed Topic Workshop, Tamale,
22-25 September.
[4] Mupangwa, W., Twomlow, S. and Walker, S. (2008) The
influence of conservation tillage methods on soil water
regimes in semi-arid southern Zimbabwe. Physics and
Chemistry of the Earth, 33, 762-767.
[5] Mupangwa, W., Twomlow, S., Walker, S. and Hove, L.
(2007) Effect of minimum tillage and mulching on maize
(Zea mays L.) yield and water content of clayey and
sandy soils. Physics and Chemistry of the Earth, 32,
[6] Keating, B.A., Carberry, P., Hammer, G.L., Probert, M.E.,
Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves,
J.N.G., Meinke, H., Hochman, Z., McLean, G., Verbur, K.,
Snow, V., Dimes, J., Silburn, M., Wang, E., Brown, S.,
Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L.,
Freebairn, D.M. and Smith, C.J. (2003) An overview of
APSIM, a model designed for farming systems simula-
tion. European Journal of Agronomy, 18, 267-288.
[7] Delve, R.J. and Probert, M.E. (2004) Modeling nutrient
management in tropical cropping systems. Australian
Centre for International Agricultural Research (ACIAR),
Canberra, ACIAR Proceedings, No. 114.
[8] Shamudzarira, Z. and Robertson, M.J. (2002) Simulating
the response of maize to nitrogen fertilizer in semi-arid
Zimbabwe. Experimental Agriculture, 38, 79-96.
[9] Dimes, J. and Malherbe, J. (2006) Climate variability and
simulation modeling—challenges and opportunities. In:
Mgonja, M.A., Waddington, S., Rollin, D., Masenya, M.,
Eds., Livelihoods in the Limpopo: CGIAR Challenge
Program on Water and Food Project No. 1. Increased
Food Security and Income in the Limpopo Basin through
Integrated Crops, Soil Fertility and Water Management
Options and Links to Markets, Proceedings of the
CPWFPN 1 Inception Workshop, Polokwane, 25-27
January 2005.
[10] Ncube, B., Dimes, J.P., van Wijk, M., Twomlow, S. and
Giller, K. (2009) Productivity and residual benefits of
grain legumes to sorghum under semi-arid conditions in
south-western Zimbabwe: Unravelling the effects of wa-
ter and nitrogen using a simulation model. Field Crops
Research, 11 0, 173-184. doi:10.1016/j.fcr.2008.08.001
[11] Moyo, M. (2001) Representative soil profiles of ICRI-
SAT research sites. Chemistry and Soil Research Institute.
Soils Report No. A666, AREX, Harare, Zimbabwe.
[12] Mupangwa, W. (2008) Water and nitrogen management
for risk mitigation in semi-arid cropping systems. Ph.D
Thesis (Unpublished), University of the Free State, South
[13] FAO (1998) World reference base for soil resources.
World Soil Resources Report 84, Rome.
[14] Masikati, P. (2006) Tillage and manure interactions under
dryland cropping in semi-arid Zimbabwe. Ph.M Thesis,
Department of Soil Science and Agricultural Engineering,
University of Zimbabwe, Zimbabwe.
[15] Rinaldi, M., Losavio, N. and Flagella, Z. (2003) Evalua-
tion and application of CROPGRO-soybean model for
improving soybean management under rainfed conditions.
Agricultural Systems, 78, 17-30.
[16] Mengel, K. and Kirkby, E.A. (1987) Principles of plant
nutrition. 4th Edition. International Potash Institute, Wor-
blaufen-Bern, Switzerland.
[17] Konig, H. and Varma, A. (2006) Intestinal Micro-orga-
nisms of termites and other invertebrates. Springer Pub-
lishers, Dordrecht.
[18] Nhamo, N. (2007) The contribution of different fauna
communities to improved soil health: A case of Zimbab-
wean soils under conservation agriculture. Ph.D. Thesis,
University of Bonn.
[19] Heal, O.W., Anderson, J.M. and Swift, M.J. (1997) Plant
litter quality and decomposition: A historical overview. In:
Cadisch, G. and Giller, K., Eds., Driven by Nature: Plant
Litter Quality and Decomposition, CAB Publishing, Wal-
lingford, 3-30.
[20] Twomlow, S., Rohrbach, D., Dimes, J., Rusike, J., Mu-
pangwa, W., Ncube, B., Hove, L., Moyo, M., Mashin-
gaidze, N. and Maphosa, P. (2008b) Micro-dosing as a
pathway to Africa’s Green Revolution: Evidence from
broad-scale on-farm trials. Nutrient Cycling in Agroeco-
systems, 88, 3-15. doi:10.1007/s10705-008-9200-4